Tag #named
12583 papers:
PADL-2020-BasuSG #visual notation- AQuA: ASP-Based Visual Question Answering (KB0, FS, GG), pp. 57–72.
- PADL-2020-NguyenZJXD #artificial reality #learning #programming #set
- VRASP: A Virtual Reality Environment for Learning Answer Set Programming (VTN, YZ0, KJ, WX, TD), pp. 82–91.
- PADL-2020-ShivkumarMZ #realtime #runtime
- RTMLton: An SML Runtime for Real-Time Systems (BS, JCM, LZ), pp. 113–130.
POPL-2020-HinrichsenBK #logic #reasoning- Actris: session-type based reasoning in separation logic (JKH, JB, RK), p. 30.
- POPL-2020-Jaber #automation #equivalence #higher-order #source code
- SyTeCi: automating contextual equivalence for higher-order programs with references (GJ), p. 28.
- POPL-2020-SongCKKKH #composition #lightweight #verification
- CompCertM: CompCert with C-assembly linking and lightweight modular verification (YS, MC, DK, YK, JK, CKH), p. 31.
- POPL-2020-VandenbrouckeS #functional #probability
- PλωNK: functional probabilistic NetKAT (AV, TS), p. 27.
ASPLOS-2020-0004JE #hybrid #multi- HSM: A Hybrid Slowdown Model for Multitasking GPUs (XZ0, MJ, LE), pp. 1371–1385.
- ASPLOS-2020-AbeydeeraS #parallel #performance
- Chronos: Efficient Speculative Parallelism for Accelerators (MA, DS0), pp. 1247–1262.
- ASPLOS-2020-AchermannPBRG #self
- Mitosis: Transparently Self-Replicating Page-Tables for Large-Memory Machines (RA, AP, AB, TR, JG), pp. 283–300.
- ASPLOS-2020-ApostolakisXCCA #approach #automation #parallel
- Perspective: A Sensible Approach to Speculative Automatic Parallelization (SA, ZX, GC, SC, DIA), pp. 351–367.
- ASPLOS-2020-BalkindLSGCLLNF
- BYOC: A (JB, KL, MS, FG, GC, AL, AL, TMN0, YF, FZ, KG, LB, DW), pp. 699–714.
- ASPLOS-2020-BindschaedlerGZ #database #distributed
- Hailstorm: Disaggregated Compute and Storage for Distributed LSM-based Databases (LB, AG, WZ), pp. 301–316.
- ASPLOS-2020-ChenLYWWS #memory management #performance #persistent
- FlatStore: An Efficient Log-Structured Key-Value Storage Engine for Persistent Memory (YC, YL, FY, QW, YW, JS), pp. 1077–1091.
- ASPLOS-2020-DeLozierLPD #reuse
- Hurdle: Securing Jump Instructions Against Code Reuse Attacks (CD, KL, GP, JD), pp. 653–666.
- ASPLOS-2020-DuYXZYQWC
- Catalyzer: Sub-millisecond Startup for Serverless Computing with Initialization-less Booting (DD, TY, YX, BZ, GY, CQ, QW, HC), pp. 467–481.
- ASPLOS-2020-GhasemazarNL #clustering #performance
- Thesaurus: Efficient Cache Compression via Dynamic Clustering (AG, PN, ML), pp. 527–540.
- ASPLOS-2020-HanelAXKL #abstraction #data-driven #memory management #streaming
- Vortex: Extreme-Performance Memory Abstractions for Data-Intensive Streaming Applications (CH, AA, DX, JK, DL), pp. 623–638.
- ASPLOS-2020-HariaHS #memory management #order #persistent
- MOD: Minimally Ordered Durable Datastructures for Persistent Memory (SH, MDH, MMS), pp. 775–788.
- ASPLOS-2020-HeoWLBJHL #architecture
- IIU: Specialized Architecture for Inverted Index Search (JH, JW, YL, SB, JJ, TJH, JWL), pp. 1233–1245.
- ASPLOS-2020-HildebrandKTLA #automation #integer #linear #memory management #programming #using
- AutoTM: Automatic Tensor Movement in Heterogeneous Memory Systems using Integer Linear Programming (MH, JK, ST, JLP, VA), pp. 875–890.
- ASPLOS-2020-HuangJ0 #gpu #learning #memory management
- SwapAdvisor: Pushing Deep Learning Beyond the GPU Memory Limit via Smart Swapping (CCH, GJ, JL0), pp. 1341–1355.
- ASPLOS-2020-HuLL0Z0XDLSX #architecture #framework #learning
- DeepSniffer: A DNN Model Extraction Framework Based on Learning Architectural Hints (XH, LL, SL, LD0, PZ, YJ0, XX, YD, CL, TS, YX), pp. 385–399.
- ASPLOS-2020-HyunKCKR #architecture #performance
- NeuMMU: Architectural Support for Efficient Address Translations in Neural Processing Units (BH, YK, YC, JK, MR), pp. 1109–1124.
- ASPLOS-2020-KarandikarOAMKN #co-evolution #design #hardware #performance #profiling
- FirePerf: FPGA-Accelerated Full-System Hardware/Software Performance Profiling and Co-Design (SK, AJO, AA, HM, RHK, BN, KA), pp. 715–731.
- ASPLOS-2020-KatsarakisGKJDG #fault tolerance #performance #protocol #replication
- Hermes: A Fast, Fault-Tolerant and Linearizable Replication Protocol (AK, VG, MRSK, AJ, AD, BG, VN), pp. 201–217.
- ASPLOS-2020-KimPCKOMK #architecture #performance
- Evanesco: Architectural Support for Efficient Data Sanitization in Modern Flash-Based Storage Systems (MK, JP, GC, YK, LO, OM, JK), pp. 1311–1326.
- ASPLOS-2020-Kokologiannakis #hardware #memory management #model checking #modelling
- HMC: Model Checking for Hardware Memory Models (MK, VV), pp. 1157–1171.
- ASPLOS-2020-LiHNGGPZBGB #performance
- LeapIO: Efficient and Portable Virtual NVMe Storage on ARM SoCs (HL, MH, SN, VG, SG, DRKP, IZ, RB, HSG, AB), pp. 591–605.
- ASPLOS-2020-LockermanFBSG0B #memory management
- Livia: Data-Centric Computing Throughout the Memory Hierarchy (EL, AF, MB, AS, SG, DS0, NB), pp. 417–433.
- ASPLOS-2020-LuoHZQ #distributed
- Prague: High-Performance Heterogeneity-Aware Asynchronous Decentralized Training (QL, JH, YZ, XQ), pp. 401–416.
- ASPLOS-2020-MaZCSWQ #architecture #data type #framework #implementation #performance #persistent #symmetry
- AsymNVM: An Efficient Framework for Implementing Persistent Data Structures on Asymmetric NVM Architecture (TM, MZ, KC, ZS, YW, XQ), pp. 757–773.
- ASPLOS-2020-MemaripourIS #data type #performance #persistent
- Pronto: Easy and Fast Persistence for Volatile Data Structures (AM, JI, SS), pp. 789–806.
- ASPLOS-2020-MengPH #mobile #multi #similarity
- Coterie: Exploiting Frame Similarity to Enable High-Quality Multiplayer VR on Commodity Mobile Devices (JM, SP, YCH), pp. 923–937.
- ASPLOS-2020-MinBLNK #architecture #effectiveness #performance #scalability
- CryoCache: A Fast, Large, and Cost-Effective Cache Architecture for Cryogenic Computing (DM, IB, GhL, SN, JK), pp. 449–464.
- ASPLOS-2020-MireshghallahTR #learning #privacy
- Shredder: Learning Noise Distributions to Protect Inference Privacy (FM, MT, PR, AJ, DMT, HE), pp. 3–18.
- ASPLOS-2020-NiuMLWQLWR #execution #mobile #realtime
- PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning (WN, XM, SL, SW, XQ, XL, YW, BR), pp. 907–922.
- ASPLOS-2020-PengSD0MXYQ #gpu #learning #memory management
- Capuchin: Tensor-based GPU Memory Management for Deep Learning (XP, XS, HD, HJ0, WM, QX, FY, XQ), pp. 891–905.
- ASPLOS-2020-RiaziLPD #architecture
- HEAX: An Architecture for Computing on Encrypted Data (MSR, KL, BP, WD), pp. 1295–1309.
- ASPLOS-2020-SadrediniRLSS #adaptation #automaton #performance
- FlexAmata: A Universal and Efficient Adaption of Applications to Spatial Automata Processing Accelerators (ES, RR, ML, MS, KS), pp. 219–234.
- ASPLOS-2020-ShenTCCWXXY #multi #performance
- Occlum: Secure and Efficient Multitasking Inside a Single Enclave of Intel SGX (YS, HT, YC, KC, RW, YX, YX, SY), pp. 955–970.
- ASPLOS-2020-SmolyarMPEZBL0T
- IOctopus: Outsmarting Nonuniform DMA (IS, AM, BP, HE, GZ, AB, LL, AM0, DT), pp. 101–115.
- ASPLOS-2020-SriramanD #comprehension
- Accelerometer: Understanding Acceleration Opportunities for Data Center Overheads at Hyperscale (AS, AD), pp. 733–750.
- ASPLOS-2020-SunC #algorithm #co-evolution #design
- SAC: A Co-Design Cache Algorithm for Emerging SMR-based High-Density Disks (DS, YC), pp. 1047–1061.
- ASPLOS-2020-ThomasHZ #framework #parallel #streaming
- Fleet: A Framework for Massively Parallel Streaming on FPGAs (JJT, PH, MZ), pp. 639–651.
- ASPLOS-2020-TorkMS #architecture #network
- Lynx: A SmartNIC-driven Accelerator-centric Architecture for Network Servers (MT, LM, MS), pp. 117–131.
- ASPLOS-2020-TovletoglouMNK #energy #memory management
- HaRMony: Heterogeneous-Reliability Memory and QoS-Aware Energy Management on Virtualized Servers (KT, LM, DSN, GK), pp. 575–590.
- ASPLOS-2020-TsaiSFS
- Safecracker: Leaking Secrets through Compressed Caches (PAT, AS, CWF, DS), pp. 1125–1140.
- ASPLOS-2020-WangHZYZMQ #architecture
- DNNGuard: An Elastic Heterogeneous DNN Accelerator Architecture against Adversarial Attacks (XW, RH, BZ, FY, JZ, DM, XQ), pp. 19–34.
- ASPLOS-2020-Williams-KingKW
- Egalito: Layout-Agnostic Binary Recompilation (DWK, HK, KWK, GP, FS, YJW, JY, VPK), pp. 133–147.
- ASPLOS-2020-XuSS #memory management #performance #persistent #reduction #security
- MERR: Improving Security of Persistent Memory Objects via Efficient Memory Exposure Reduction and Randomization (YX, YS, XS), pp. 987–1000.
- ASPLOS-2020-YangGLSPNBCHRKH #scheduling #using
- Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators (XY, MG, QL, JS, JP, AN, SB, KC, HH, PR, CK, MH), pp. 369–383.
- ASPLOS-2020-YuPAR
- AvA: Accelerated Virtualization of Accelerators (HY, AMP, AA, CJR), pp. 807–825.
- ASPLOS-2020-ZhangBBBL #architecture #consistency #memory management
- Peacenik: Architecture Support for Not Failing under Fail-Stop Memory Consistency (RZ, SB, VB, MDB, BL), pp. 317–333.
- ASPLOS-2020-ZhangSYZS0 #execution #obfuscation #performance
- Klotski: Efficient Obfuscated Execution against Controlled-Channel Attacks (PZ, CS, HY, DZ, ES, HJ0), pp. 1263–1276.
- ASPLOS-2020-Zheng0WCS #automation #framework #optimisation
- FlexTensor: An Automatic Schedule Exploration and Optimization Framework for Tensor Computation on Heterogeneous System (SZ, YL0, SW, RC, KS), pp. 859–873.
CGO-2020-ChenPPLR #adaptation #graph- ATMem: adaptive data placement in graph applications on heterogeneous memories (YC, IBP, ZP, XL, BR), pp. 293–304.
- CGO-2020-Haj-AliAWSAS #learning
- NeuroVectorizer: end-to-end vectorization with deep reinforcement learning (AHA, NKA, TLW, YSS, KA, IS), pp. 242–255.
- CGO-2020-JoshiFM #approximate #reliability #source code #verification
- Aloe: verifying reliability of approximate programs in the presence of recovery mechanisms (KJ, VF, SM), pp. 56–67.
- CGO-2020-KangCP #framework #performance #precise #scalability
- PreScaler: an efficient system-aware precision scaling framework on heterogeneous systems (SK, KC, YP), pp. 280–292.
- CGO-2020-KrugerAB #api #generative
- CogniCryptGEN: generating code for the secure usage of crypto APIs (SK, KA0, EB), pp. 185–198.
- CGO-2020-MatsumuraZWEM #automation #framework
- AN5D: automated stencil framework for high-degree temporal blocking on GPUs (KM, HRZ, MW, TE, SM), pp. 199–211.
- CGO-2020-SavageJ #optimisation
- HALO: post-link heap-layout optimisation (JS, TMJ0), pp. 94–106.
- CGO-2020-YuPJLT #collaboration #manycore #symmetry
- COLAB: a collaborative multi-factor scheduler for asymmetric multicore processors (TY, PP, VJ, HL, JT), pp. 268–279.
ECSA-2019-MattosBO #deployment #optimisation- ACE: Easy Deployment of Field Optimization Experiments (DIM, JB, HHO), pp. 264–279.
EDM-2019-ChoffinPBV #distributed #learning #modelling #scheduling #student- DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills (BC, FP, YB, JJV).
- EDM-2019-Yeung #learning #using
- Deep-IRT: Make Deep Learning Based Knowledge Tracing Explainable Using Item Response Theory (CKY).
ICPC-2019-BauerSPHA #comprehension #matter #question- Indentation: simply a matter of style or support for program comprehension? (JB, JS, NP, JCH, SA), pp. 154–164.
- ICPC-2019-HondaTYCYI #evolution #maintenance #visualisation
- CCEvovis: a clone evolution visualization system for software maintenance (HH, ST, KY, EC, NY, KI), pp. 122–125.
- ICPC-2019-MizouchiSII #adaptation #detection #online #using
- PADLA: a dynamic log level adapter using online phase detection (TM, KS, TI, KI), pp. 135–138.
- ICPC-2019-PeitekABPS #data analysis #multi
- CodersMUSE: multi-modal data exploration of program-comprehension experiments (NP, SA, AB, CP, JS), pp. 126–129.
- ICPC-2019-PetersonLD #correctness #specification
- CCSpec: a correctness condition specification tool (CLP, PL, DD), pp. 220–230.
- ICPC-2019-XieQMZ #learning #programming #visual notation
- DeepVisual: a visual programming tool for deep learning systems (CX, HQ, LM0, JZ), pp. 130–134.
- ICPC-2019-XuDM #api #migration
- Meditor: inference and application of API migration edits (SX, ZD, NM), pp. 335–346.
- ICPC-2019-ZyrianovNGCM #analysis #framework #implementation #pointer
- srcPtr: a framework for implementing static pointer analysis approaches (VZ, CDN, DTG, MLC, JIM), pp. 144–147.
ICSME-2019-AlmeidaTK #framework #technical debt- Tracy: A Business-Driven Technical Debt Prioritization Framework (RRdA, CT, UK), pp. 181–185.
- ICSME-2019-AlrubayeM0 #automation #detection #java #library #migration
- MigrationMiner: An Automated Detection Tool of Third-Party Java Library Migration at the Method Level (HA, MWM, AO0), pp. 414–417.
- ICSME-2019-Bello-JimenezML #android #testing
- OPIA: A Tool for On-Device Testing of Vulnerabilities in Android Applications (LBJ, AMR, MLV, GB), pp. 418–421.
- ICSME-2019-BraiekK #approach #network #search-based #testing
- DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks (HBB, FK), pp. 454–458.
- ICSME-2019-EdwardsJS #architecture #issue tracking
- Sciit: Aligning Source Control Management and Issue Tracking Architectures (NE, DJ, TS), pp. 402–405.
- ICSME-2019-GaoJJ00G #contract #debugging #detection
- SmartEmbed: A Tool for Clone and Bug Detection in Smart Contracts through Structural Code Embedding (ZG, VJ, LJ, XX0, DL0, JCG), pp. 394–397.
- ICSME-2019-GaoWLYSC #approach #clone detection #detection
- TECCD: A Tree Embedding Approach for Code Clone Detection (YG, ZW, SL, LY, WS, YC), pp. 145–156.
- ICSME-2019-HataNNKH #android #source code
- OSAIFU: A Source Code Factorizer on Android Studio (MH, MN, KN, HK, TH), pp. 422–425.
- ICSME-2019-Kashiwa #optimisation
- RAPTOR: Release-Aware and Prioritized Bug-Fixing Task Assignment Optimization (YK), pp. 629–633.
- ICSME-2019-NeupaneCW #approach #development
- EmoD: An End-to-End Approach for Investigating Emotion Dynamics in Software Development (KN, KC, YW), pp. 252–256.
- ICSME-2019-Ravelo-MendezEL #android #testing
- Kraken-Mobile: Cross-Device Interaction-Based Testing of Android Apps (WRM, CEV, MLV), pp. 410–413.
- ICSME-2019-WangYQJM #ad hoc
- BARRIERFINDER: Recognizing Ad Hoc Barriers (TW, XY, ZQ, GJ, FM), pp. 323–327.
- ICSME-2019-Zhang00HM19a #automation
- AutoMR: Automatic Discovery and Cleansing of Numerical Metamorphic Relations (BZ, HZ0, JC0, DH, PM), p. 246.
MSR-2019-AhluwaliaFP #dataset #fault #predict- Snoring: a noise in defect prediction datasets (AA, DF, MDP), pp. 63–67.
- MSR-2019-AkbarK #order #retrieval #semantics #source code
- SCOR: source code retrieval with semantics and order (SAA, ACK), pp. 1–12.
- MSR-2019-BaltesT0 #evolution #stack overflow
- SOTorrent: studying the origin, evolution, and usage of stack overflow code snippets (SB, CT, SD0), pp. 191–194.
- MSR-2019-ChrenMB0 #analysis #automation #reliability
- STRAIT: a tool for automated software reliability growth analysis (SC, RM, BB, BR0), pp. 105–110.
- MSR-2019-GoteSS #git #mining #network #repository #scalability
- git2net: mining time-stamped co-editing networks from large git repositories (CG, IS, FS), pp. 433–444.
- MSR-2019-HoangDK0U #fault #framework #learning #predict
- DeepJIT: an end-to-end deep learning framework for just-in-time defect prediction (TH, HKD, YK, DL0, NU), pp. 34–45.
- MSR-2019-JoshiC #agile #dataset #git
- RapidRelease: a dataset of projects and issues on github with rapid releases (SDJ, SC), pp. 587–591.
- MSR-2019-KampKP #java #semantics #set
- SeSaMe: a data set of semantically similar Java methods (MK, PK, MP), pp. 529–533.
- MSR-2019-KolovosNBMP #distributed #framework #mining #repository
- Crossflow: a framework for distributed mining of software repositories (DSK, PN, KB, NM, RFP), pp. 155–159.
- MSR-2019-KovalenkoBBB #library #mining
- PathMiner: a library for mining of path-based representations of code (VK, EB, TB, AB), pp. 13–17.
- MSR-2019-MarkovtsevLMSB #algorithm #consistency #nondeterminism
- STYLE-ANALYZER: fixing code style inconsistencies with interpretable unsupervised algorithms (VM, WL, HM, KS, EB), pp. 468–478.
- MSR-2019-Rua0S #android #energy #metric #scalability #testing
- GreenSource: a large-scale collection of Android code, tests and energy metrics (RR, MC0, JS), pp. 176–180.
- MSR-2019-WangSL0 #android #dataset #metadata #reliability #towards
- RmvDroid: towards a reliable Android malware dataset with app metadata (HW, JS, HL, YG0), pp. 404–408.
- MSR-2019-ZeroualiCRGM
- ConPan: a tool to analyze packages in software containers (AZ, VC, GR, JMGB, TM), pp. 592–596.
SANER-2019-BritoBBV - GoCity: Code City for Go (RB, AB, GB, MTV), pp. 649–653.
- SANER-2019-DelfimUMM #automation #benchmark #debugging #java #metric #program repair
- BEARS: An Extensible Java Bug Benchmark for Automatic Program Repair Studies (FM, SU, MdAM, MM), pp. 468–478.
- SANER-2019-JbaraAAJY #comprehension #interactive #visual notation
- ICSD: Interactive Visual Support for Understanding Code Control Structure (AJ, MA, AA, MJ, AY), pp. 644–648.
- SANER-2019-KirinukiTN #recommendation #testing #using #web
- COLOR: Correct Locator Recommender for Broken Test Scripts using Various Clues in Web Application (HK, HT, KN), pp. 310–320.
- SANER-2019-LiuK0B #debugging #semantics #static analysis
- AVATAR: Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations (KL0, AK, DK0, TFB), pp. 456–467.
- SANER-2019-LiZLFG #android #automation #library
- APPCOMMUNE: Automated Third-Party Libraries De-duplicating and Updating for Android Apps (BL, YZ0, JL, RF, DG), pp. 344–354.
- SANER-2019-MaJXLLLZ #combinator #learning #testing
- DeepCT: Tomographic Combinatorial Testing for Deep Learning Systems (LM0, FJX, MX, BL0, LL0, YL0, JZ), pp. 614–618.
- SANER-2019-MatsuoMHK #volunteer #web
- Madoop: Improving Browser-Based Volunteer Computing Based on Modern Web Technologies (HM, SM, YH, SK), pp. 634–638.
- SANER-2019-PizzolottoFC #android #java #obfuscation #source code
- OBLIVE: Seamless Code Obfuscation for Java Programs and Android Apps (DP, RF, MC), pp. 629–633.
- SANER-2019-PupoNENRB #data flow #information management #machine learning
- GUARDIAML: Machine Learning-Assisted Dynamic Information Flow Control (ALSP, JN, KE, AN, CDR, EGB), pp. 624–628.
- SANER-2019-XieCYLHDZ #approach #graph #learning
- DeepLink: A Code Knowledge Graph Based Deep Learning Approach for Issue-Commit Link Recovery (RX, LC, WY0, ZL, TH, DD, SZ), pp. 434–444.
- SANER-2019-ZhangLML #approach #effectiveness #fault #network #using
- CNN-FL: An Effective Approach for Localizing Faults using Convolutional Neural Networks (ZZ0, YL, XM, PL), pp. 445–455.
SCAM-2019-LeeBFGY #approximate #dependence #modelling- MOAD: Modeling Observation-Based Approximate Dependency (SL, DWB, RF, NG, SY), pp. 12–22.
- SCAM-2019-TeotonioGMM #evolution #legacy #runtime #web
- WAL: A Tool for Diagnosing Accessibility Issues and Evolving Legacy Web Systems at Runtime (WT, PG, PM, PM), pp. 175–180.
- SCAM-2019-TiwariPG0 #analysis #android #scalability #web
- LUDroid: A Large Scale Analysis of Android - Web Hybridization (AT, JP, SG, CH0), pp. 256–267.
FM-2019-GomesB19a #csp #model checking #using- Circus2CSP: A Tool for Model-Checking Circus Using FDR (AOG, AB), pp. 235–242.
- FM-2019-KasampalisGMSZF #ecosystem
- IELE: A Rigorously Designed Language and Tool Ecosystem for the Blockchain (TK, DG, BMM, TFS, YZ, DF, VNS, RJ, GR), pp. 593–610.
- FM-2019-MarmsolerB #architecture #modelling #proving
- APML: An Architecture Proof Modeling Language (DM, GB), pp. 611–630.
- FM-2019-SogokonMTCP #framework #generative #invariant
- Pegasus: A Framework for Sound Continuous Invariant Generation (AS, SM, YKT, KC, AP), pp. 138–157.
- IFM-2019-CamilliGMS #nondeterminism #testing #tool support #web
- HYPpOTesT: Hypothesis Testing Toolkit for Uncertain Service-Based Web Applications (MC, AG, RM, PS), pp. 495–503.
- IFM-2019-HsiehM #automaton #protocol #verification
- Dione: A Protocol Verification System Built with Dafny for I/O Automata (CH, SM), pp. 227–245.
- IFM-2019-Nemouchi0GK #assurance #formal method
- Isabelle/SACM: Computer-Assisted Assurance Cases with Integrated Formal Methods (YN, SF0, MG, TK), pp. 379–398.
- IFM-2019-OsamaW #gpu #satisfiability
- SIGmA: GPU Accelerated Simplification of SAT Formulas (MO, AW), pp. 514–522.
SEFM-2019-BruckerW #design #implementation- Isabelle/DOF: Design and Implementation (ADB, BW), pp. 275–292.
Haskell-2019-ErtelARGC #composition #concurrent #data flow #monad #parallel #thread- STCLang: state thread composition as a foundation for monadic dataflow parallelism (SE, JA, NAR, AG, JC), pp. 146–161.
- Haskell-2019-HallahanXP #constraints #haskell #theorem proving
- G2Q: Haskell constraint solving (WTH, AX, RP), pp. 44–57.
- ICFP-2019-DelawareSPYC
- Narcissus: correct-by-construction derivation of decoders and encoders from binary formats (BD, SS, CPC, QY, AC), p. 29.
- ICFP-2019-YallopW #case study #experience
- Lambda: the ultimate sublanguage (experience report) (JY, LW), p. 17.
- ICFP-2019-ZhangRHP0 #difference #logic #privacy
- Fuzzi: a three-level logic for differential privacy (HZ, ER, AH, BCP, AR0), p. 28.
AIIDE-2019-AzadM #simulation- Lyra: Simulating Believable Opinionated Virtual Characters (SA, CM), pp. 108–115.
- AIIDE-2019-BisbergC #parametricity
- SCOPE: Selective Cross-Validation over Parameters for Elo (AJB, RECR), pp. 116–122.
- AIIDE-2019-LinXR #learning #semantics
- GenerationMania: Learning to Semantically Choreograph (ZL, KX, MR), pp. 52–58.
CHI-PLAY-2019-CmentowskiKK #approach #artificial reality #game studies #multi- Outstanding: A Multi-Perspective Travel Approach for Virtual Reality Games (SC, AK, JHK), pp. 287–299.
- CHI-PLAY-2019-GrafBWCLNK #game studies #interactive
- iGYM: An Interactive Floor Projection System for Inclusive Exergame Environments (RG, PB, AEW, DC, ZL, MN, HSK), pp. 31–43.
- CHI-PLAY-2019-LesselAB #framework #game studies #platform
- Crowdjump: Investigating a Player-Driven Platform Game (PL, MA, NB), pp. 149–159.
- CHI-PLAY-2019-ParanthamanC #crowdsourcing #development #game studies #towards
- ARAPID: Towards Integrating Crowdsourced Playtesting into the Game Development Environment (PKP, SC), pp. 121–133.
- CHI-PLAY-2019-ZhouSDJI #artificial reality #collaboration #game studies
- Astaire: A Collaborative Mixed Reality Dance Game for Collocated Players (ZZ, EMS, JD, MJ, KI), pp. 5–18.
CoG-2019-AbuzuraiqFP #clustering #framework #generative #graph- Taksim: A Constrained Graph Partitioning Framework for Procedural Content Generation (AMA, AF, PP), pp. 1–8.
- CoG-2019-BeigKCM #game studies #graph
- G-SpAR: GPU-Based Voxel Graph Pathfinding for Spatial Audio Rendering in Games and VR (MB, BK, KC, PMB), pp. 1–8.
- CoG-2019-BraviLL0 #game studies #optimisation #statistics
- Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor (IB, DPL, SML, JL0), pp. 1–8.
- CoG-2019-ChangAS #assurance #automation #quality #testing #using
- Reveal-More: Amplifying Human Effort in Quality Assurance Testing Using Automated Exploration (KC, BA, AMS), pp. 1–8.
- CoG-2019-GomesDM #game studies #interactive #multi
- GIMME: Group Interactions Manager for Multiplayer sErious games (SG, JD, CM), pp. 1–8.
- CoG-2019-HarriesLRHD #3d #benchmark #learning #metric
- MazeExplorer: A Customisable 3D Benchmark for Assessing Generalisation in Reinforcement Learning (LH, SL, JR, KH, SD), pp. 1–4.
- CoG-2019-KanagawaK #challenge #learning
- Rogue-Gym: A New Challenge for Generalization in Reinforcement Learning (YK, TK), pp. 1–8.
- CoG-2019-KanervistoH #learning
- ToriLLE: Learning Environment for Hand-to-Hand Combat (AK, VH), pp. 1–8.
- CoG-2019-PalenciaO #framework #platform #web
- PsyRTS: a Web Platform for Experiments in Human Decision-Making in RTS Environments (DOVP, MO), pp. 1–4.
- CoG-2019-Salvucci
- ToonToy: a node-based NPR solution for UnrealEngine4 (ES), pp. 1–4.
- CoG-2019-VolkovasFWL #2d #game studies #prototype
- Mek: Mechanics Prototyping Tool for 2D Tile-Based Turn-Based Deterministic Games (RV, MF, JRW, SML), pp. 1–8.
- CoG-2019-WarriarUWT #design #framework #game studies #platform
- PlayMapper: Illuminating Design Spaces of Platform Games (VRW, CU, JRW, LT), pp. 1–4.
FDG-2019-GarbeKSWM #generative- StoryAssembler: an engine for generating dynamic choice-driven narratives (JG, MK, BS, NWF, MM), p. 10.
- FDG-2019-KaltmanOA #analysis #game studies #visualisation
- S4LVE: shareable videogame analysis and visualization (EK, JCO, JA), p. 5.
- FDG-2019-Kao #embedded #java #programming
- JavaStrike: a Java programming engine embedded in virtual worlds (DK), p. 5.
- FDG-2019-KicikogluBCP #approach #gamification
- Wormingo: a 'true gamification' approach to anaphoric annotation (DK, RB, JC, MP), p. 7.
- FDG-2019-LaiEKL #game studies
- Introducing: the game jam license (GL, KE, FK, WHL), p. 6.
- FDG-2019-MacCormickZ #development #game studies #programming #using #visual notation
- SuBViS: the use of subjunctive visual programming environments for exploring alternatives in game development (DM, LZ), p. 11.
- FDG-2019-MasonSW #generative
- Lume: a system for procedural story generation (SM, CS, NWF), p. 9.
- FDG-2019-Mawhorter #incremental #library
- Anarchy: a library for incremental chaos (PM), p. 8.
- FDG-2019-MillerMPK
- Panoptyk: information driven MMO engine (MM, SM, NP, FK), p. 4.
- FDG-2019-SongW #evolution #generative #network
- TownSim: agent-based city evolution for naturalistic road network generation (AS, JW), p. 9.
- FDG-2019-SteinbockLKPK #communication #education #game studies
- Cologon: a game to foster communication skills in inclusive education (MS, NL, FK, MP, GK), p. 5.
ICGJ-2019-AryaGFM #game studies- GGJ-Next: The Global Game Jam for Youth (AA, SG, MF, KM), p. 4.
- ICGJ-2019-FrenchBGWZ #design #experience
- ZooJamming: Designing Beyond Human Experience (FF, SB, RG, SW, AZ), p. 8.
- ICGJ-2019-Kultima #case study #experience #game studies
- Superjammers: Motivations and Experiences of Exceptional Game Jammers in Finland (AK), p. 7.
- VS-Games-2019-GardeliV #approach #artificial reality
- ARQuest: A Tangible Augmented Reality Approach to Developing Computational Thinking Skills (AG, SV), pp. 1–8.
- VS-Games-2019-GiariskanisPM #3d #architecture #design #multimodal #music
- ARCHIMUSIC3D: Multimodal Playful Transformations between Music and Refined Urban Architectural Design (FG, PP, KM), pp. 1–4.
- VS-Games-2019-RodriguesFPPS #game studies #problem
- Festarola: a Game for Improving Problem Solving Strategies (RR0, PCF, RP, PP, AMVS), pp. 1–8.
- VS-Games-2019-TahaiWEP #adaptation #design #evaluation #game studies #reasoning
- Scalebridge: Design and Evaluation of Adaptive Difficulty Proportional Reasoning Game for Children (LT, JRW, CE, KP), pp. 1–4.
CIKM-2019-0001SKJ #recommendation- Rehab-Path: Recommending Alcohol and Drug-free Routes (YZ0, PS, YK, AJ), pp. 2929–2932.
- CIKM-2019-0002CZTZG #classification #graph
- Meta-GNN: On Few-shot Node Classification in Graph Meta-learning (FZ0, CC, KZ, GT, TZ, JG), pp. 2357–2360.
- CIKM-2019-AhmadvandSCA #classification #topic
- ConCET: Entity-Aware Topic Classification for Open-Domain Conversational Agents (AA, HS, JIC, EA), pp. 1371–1380.
- CIKM-2019-AliARR #identification
- EPA: Exoneration and Prominence based Age for Infection Source Identification (SSA, TA, AR, SAMR), pp. 891–900.
- CIKM-2019-BernhauerSHPS #modelling #similarity
- SIMILANT: An Analytic Tool for Similarity Modeling (DB, TS, IH, LP, MS), pp. 2889–2892.
- CIKM-2019-BespinyowongT #graph
- kBrowse: kNN Graph Browser (RB, AKHT), pp. 2933–2936.
- CIKM-2019-CaoDGMT #network #query
- BeLink: Querying Networks of Facts, Statements and Beliefs (TDC, LD, FG, IM, XT), pp. 2941–2944.
- CIKM-2019-CaoZSX #modelling #network #sequence
- HiCAN: Hierarchical Convolutional Attention Network for Sequence Modeling (YC, WZ, BS, CX), pp. 1723–1732.
- CIKM-2019-ChengLCHHCMH #learning
- DIRT: Deep Learning Enhanced Item Response Theory for Cognitive Diagnosis (SC, QL0, EC, ZH, ZH, YC, HM, GH), pp. 2397–2400.
- CIKM-2019-ChenMLZM #dataset #scalability #web
- TianGong-ST: A New Dataset with Large-scale Refined Real-world Web Search Sessions (JC, JM, YL, MZ0, SM), pp. 2485–2488.
- CIKM-2019-Cohen-ShapiraRS #dataset #recommendation #representation #visual notation
- AutoGRD: Model Recommendation Through Graphical Dataset Representation (NCS, LR, BS, GK, RV), pp. 821–830.
- CIKM-2019-CuiSW0L #detection
- dEFEND: A System for Explainable Fake News Detection (LC, KS, SW, DL0, HL0), pp. 2961–2964.
- CIKM-2019-DengLSDFYL #approach #multi
- MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data (YD, YL, YS, ND, WF, MY0, KL), pp. 719–728.
- CIKM-2019-DuT #multi
- MrMine: Multi-resolution Multi-network Embedding (BD, HT), pp. 479–488.
- CIKM-2019-ElMS #data analysis #learning
- ATENA: An Autonomous System for Data Exploration Based on Deep Reinforcement Learning (OBE, TM, AS), pp. 2873–2876.
- CIKM-2019-FanHZLLW #learning #scalability
- MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data (MF, JH, AZ, YL, PL0, HW), pp. 2655–2663.
- CIKM-2019-FuL #estimation #network
- DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation (TYF, WCL), pp. 69–78.
- CIKM-2019-GaoHWWWPC #recommendation
- STAR: Spatio-Temporal Taxonomy-Aware Tag Recommendation for Citizen Complaints (JG, YH, YW, XW, JW, GP, XC), pp. 1903–1912.
- CIKM-2019-GershteinMN #e-commerce #effectiveness #reduction
- ReducE-Comm: Effective Inventory Reduction System for E-Commerce (SG, TM, SN), pp. 2957–2960.
- CIKM-2019-GuHDM #analysis #learning
- LinkRadar: Assisting the Analysis of Inter-app Page Links via Transfer Learning (DG, ZH, SD, YM0), pp. 2077–2080.
- CIKM-2019-GuillyPSI #database #interactive #sql
- ExplIQuE: Interactive Databases Exploration with SQL (MLG, JMP, VMS, IFI), pp. 2877–2880.
- CIKM-2019-HaoYLLJL #distributed #pattern matching
- PatMat: A Distributed Pattern Matching Engine with Cypher (KH, ZY, LL, ZL, XJ, XL0), pp. 2921–2924.
- CIKM-2019-HeSLJPP #network #random
- HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding (YH, YS, JL, CJ, JP, HP), pp. 639–648.
- CIKM-2019-HouFZYLWWXS #android #detection #graph #robust
- αCyber: Enhancing Robustness of Android Malware Detection System against Adversarial Attacks on Heterogeneous Graph based Model (SH, YF, YZ, YY, JL, WW, JW, QX, FS), pp. 609–618.
- CIKM-2019-HuangLCKQ
- MiCRon: Making Sense of News via Relationship Subgraphs (ZH, SL, GC0, EK, YQ), pp. 2901–2904.
- CIKM-2019-HuangWWT #multi #network #self
- DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting (SH, DW, XW, AT), pp. 2129–2132.
- CIKM-2019-IosifidisN #adaptation #cumulative
- AdaFair: Cumulative Fairness Adaptive Boosting (VI, EN), pp. 781–790.
- CIKM-2019-IslamMR #graph #network #predict #social #using
- NActSeer: Predicting User Actions in Social Network using Graph Augmented Neural Network (MRI, SM, NR), pp. 1793–1802.
- CIKM-2019-JinCCHV #interactive #music #recommendation
- MusicBot: Evaluating Critiquing-Based Music Recommenders with Conversational Interaction (YJ, WC, LC, NNH, KV), pp. 951–960.
- CIKM-2019-JinZ0LGQJTWWWY #multi #order #platform
- CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms (JJ, MZ, WZ0, ML, ZG, ZQ, YJ, XT, CW, JW0, GW, JY), pp. 1983–1992.
- CIKM-2019-JungLPP #framework #using
- PRIVATA: Differentially Private Data Market Framework using Negotiation-based Pricing Mechanism (KJ, JL, KP, SP), pp. 2897–2900.
- CIKM-2019-KangT #mining #network
- N2N: Network Derivative Mining (JK, HT), pp. 861–870.
- CIKM-2019-LeePY #network
- BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network (SL, CP, HY), pp. 619–628.
- CIKM-2019-LiCWZW #feature model #graph #interactive #modelling #network #predict
- Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction (ZL, ZC, SW, XZ, LW0), pp. 539–548.
- CIKM-2019-LiHLDZ #detection #network
- SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks (YL, XH, JL, MD, NZ), pp. 2233–2236.
- CIKM-2019-LiJC0W #approach #hybrid #recommendation
- HAES: A New Hybrid Approach for Movie Recommendation with Elastic Serendipity (XL, WJ, WC, JW0, GW0), pp. 1503–1512.
- CIKM-2019-Liu0PLZZ
- SkyRec: Finding Pareto Optimal Groups (JL, LX0, JP, JL, HZ, SZ), pp. 2913–2916.
- CIKM-2019-LiuNX0Y #framework #self
- MoBoost: A Self-improvement Framework for Linear-based Hashing (XL, XN, XX, LZ0, YY), pp. 871–880.
- CIKM-2019-LiuWJYZZ #recommendation
- In2Rec: Influence-based Interpretable Recommendation (HL, JW, LJ, JY, XZ0, MZ), pp. 1803–1812.
- CIKM-2019-LuoZWZ #framework #learning #representation
- ResumeGAN: An Optimized Deep Representation Learning Framework for Talent-Job Fit via Adversarial Learning (YL, HZ, YW, XZ), pp. 1101–1110.
- CIKM-2019-LvJYSLYN #online #recommendation #scalability
- SDM: Sequential Deep Matching Model for Online Large-scale Recommender System (FL, TJ, CY, FS, QL, KY, WN), pp. 2635–2643.
- CIKM-2019-MaWZLLCYT0 #recommendation
- DBRec: Dual-Bridging Recommendation via Discovering Latent Groups (JM, JW, MZ, LL, CL, WC, YY, HT, XL0), pp. 1513–1522.
- CIKM-2019-MiloMY #analysis #datalog #probability
- PODIUM: Probabilistic Datalog Analysis via Contribution Maximization (TM, YM, BY), pp. 2865–2868.
- CIKM-2019-MohantyR #effectiveness #graph #towards
- Insta-Search: Towards Effective Exploration of Knowledge Graphs (MM, MR), pp. 2909–2912.
- CIKM-2019-NeutatzMA #detection #fault #learning
- ED2: A Case for Active Learning in Error Detection (FN, MM, ZA), pp. 2249–2252.
- CIKM-2019-OppoldH #data-driven #personalisation
- LuPe: A System for Personalized and Transparent Data-driven Decisions (SO, MH), pp. 2905–2908.
- CIKM-2019-PaganelliS0V #rule-based
- TuneR: Fine Tuning of Rule-based Entity Matchers (MP, PS, FG0, YV), pp. 2945–2948.
- CIKM-2019-PangWZG0 #design #generative #network
- NAD: Neural Network Aided Design for Textile Pattern Generation (ZP, SW, DZ, YG, GC0), pp. 2081–2084.
- CIKM-2019-PratamaCXL0 #atl #information management #process #streaming
- ATL: Autonomous Knowledge Transfer from Many Streaming Processes (MP, MdC, RX, EL, JL0), pp. 269–278.
- CIKM-2019-QianW0 #behaviour #comprehension #modelling #process
- BePT: A Behavior-based Process Translator for Interpreting and Understanding Process Models (CQ, LW, AK0), pp. 1873–1882.
- CIKM-2019-RekatsinasDP #adaptation #crowdsourcing #performance #query
- CRUX: Adaptive Querying for Efficient Crowdsourced Data Extraction (TR, AD, AGP), pp. 841–850.
- CIKM-2019-RoySMSG #information retrieval #plugin
- I-REX: A Lucene Plugin for EXplainable IR (DR, SS, MM, BS, DG), pp. 2949–2952.
- CIKM-2019-Sanei-MehriZST #estimation #graph
- FLEET: Butterfly Estimation from a Bipartite Graph Stream (SVSM, YZ, AES, ST), pp. 1201–1210.
- CIKM-2019-SiddiqueeAM #detection
- SeiSMo: Semi-supervised Time Series Motif Discovery for Seismic Signal Detection (MAS, ZA, AM), pp. 99–108.
- CIKM-2019-SongS0DX0T #automation #feature model #interactive #learning #network #self
- AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks (WS, CS, ZX0, ZD, YX, MZ0, JT), pp. 1161–1170.
- CIKM-2019-SunAJHS #dataset #flexibility
- MithraLabel: Flexible Dataset Nutritional Labels for Responsible Data Science (CS, AA, HVJ, BH, JS), pp. 2893–2896.
- CIKM-2019-SunLWPLOJ #bidirectional #recommendation
- BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer (FS, JL, JW, CP, XL, WO, PJ), pp. 1441–1450.
- CIKM-2019-TanMYYDWTYWCCY #network #predict #risk management
- UA-CRNN: Uncertainty-Aware Convolutional Recurrent Neural Network for Mortality Risk Prediction (QT, AJM, MY, BY, HD, VWSW, YKT, TCFY, GLHW, JYLC, FKLC, PCY), pp. 109–118.
- CIKM-2019-TaoGFCYZ #game studies #learning #multi #online #predict
- GMTL: A GART Based Multi-task Learning Model for Multi-Social-Temporal Prediction in Online Games (JT, LG, CF, LC, DY, SZ), pp. 2841–2849.
- CIKM-2019-WangCCCHHLC #bound #interactive #mining #mobile #process #visualisation
- TraVis: An Interactive Visualization System for Mining Inbound Traveler Activities by Leveraging Mobile Ad Request Data (PXW, HC, WQC, CCC, YHH, THH, YL, CHC), pp. 2881–2884.
- CIKM-2019-WangLZHG #e-commerce #interactive #network #recommendation
- QPIN: A Quantum-inspired Preference Interactive Network for E-commerce Recommendation (PW, ZL, YZ, YH, LG), pp. 2329–2332.
- CIKM-2019-WangWLL #network
- CamDrop: A New Explanation of Dropout and A Guided Regularization Method for Deep Neural Networks (HW, GW, GL, LL), pp. 1141–1149.
- CIKM-2019-WeiXZZZC0ZXL #learning
- CoLight: Learning Network-level Cooperation for Traffic Signal Control (HW, NX, HZ, GZ, XZ, CC, WZ0, YZ, KX, ZL), pp. 1913–1922.
- CIKM-2019-WuWHX #estimation
- NICE: Neural In-Hospital Cost Estimation from Medical Records (CW, FW, YH, XX0), pp. 2409–2412.
- CIKM-2019-WuWLH019a #overview #predict #rating
- ARP: Aspect-aware Neural Review Rating Prediction (CW, FW, JL, YH, XX0), pp. 2169–2172.
- CIKM-2019-YanCKWM #2d #network #recommendation
- CosRec: 2D Convolutional Neural Networks for Sequential Recommendation (AY, SC, WCK, MW, JJM), pp. 2173–2176.
- CIKM-2019-YiDLLZZ #approach #modelling
- CityTraffic: Modeling Citywide Traffic via Neural Memorization and Generalization Approach (XY, ZD, TL, TL, JZ, YZ), pp. 2665–2671.
- CIKM-2019-ZhangH #multi #nearest neighbour
- GRIP: Multi-Store Capacity-Optimized High-Performance Nearest Neighbor Search for Vector Search Engine (MZ, YH), pp. 1673–1682.
- CIKM-2019-ZhangLZLWWX #benchmark #learning #metric #multi #representation
- Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning (DZ, JL, HZ, YL, LW, PW, HX), pp. 2763–2771.
- CIKM-2019-ZhangRZYZ #network
- PRNet: Outdoor Position Recovery for Heterogenous Telco Data by Deep Neural Network (YZ, WR, KZ, MY, JZ), pp. 1933–1942.
- CIKM-2019-ZhangYWH #automation #e-commerce #learning #ranking #realtime
- Autor3: Automated Real-time Ranking with Reinforcement Learning in E-commerce Sponsored Search Advertising (YZ, ZY, LW, LH), pp. 2499–2507.
- CIKM-2019-ZhaoSSW #graph #learning #precise #retrieval
- GRAPHENE: A Precise Biomedical Literature Retrieval Engine with Graph Augmented Deep Learning and External Knowledge Empowerment (SZ, CS, AS, FW), pp. 149–158.
- CIKM-2019-ZhaoZXQJ0 #predict
- AIBox: CTR Prediction Model Training on a Single Node (WZ, JZ, DX, YQ, RJ, PL0), pp. 319–328.
- CIKM-2019-ZhengQJCM #interactive
- CurrentClean: Interactive Change Exploration and Cleaning of Stale Data (ZZ, TMQ, ZJ, FC, MM), pp. 2917–2920.
- CIKM-2019-ZhuC0LZ #framework #recommendation
- DTCDR: A Framework for Dual-Target Cross-Domain Recommendation (FZ, CC, YW0, GL, XZ), pp. 1533–1542.
- CIKM-2019-ZouLAWZ #learning #multi #rank
- MarlRank: Multi-agent Reinforced Learning to Rank (SZ, ZL, MA, JW0, PZ), pp. 2073–2076.
ECIR-p1-2019-DavilaJSGZ #graph #image #using- Tangent-V: Math Formula Image Search Using Line-of-Sight Graphs (KD, RJ, SS, VG, RZ), pp. 681–695.
- ECIR-p1-2019-GhaderyMSFS #approach #detection #independence
- LICD: A Language-Independent Approach for Aspect Category Detection (EG, SM, MJS, HF, AS), pp. 575–589.
- ECIR-p1-2019-LandinVPB #modelling #probability #recommendation
- PRIN: A Probabilistic Recommender with Item Priors and Neural Models (AL, DV, JP, ÁB), pp. 133–147.
- ECIR-p1-2019-OscheCB #multi #recommendation
- AntRS: Recommending Lists Through a Multi-objective Ant Colony System (PEO, SC, AB), pp. 229–243.
- ECIR-p1-2019-VakulenkoRCR #data-driven
- QRFA: A Data-Driven Model of Information-Seeking Dialogues (SV, KR, CDC, MdR), pp. 541–557.
- ECIR-p2-2019-FarberSJ
- PaperHunter: A System for Exploring Papers and Citation Contexts (MF0, AS, AJ), pp. 246–250.
- ECIR-p2-2019-HalkidiK #assessment #clustering #graph #quality
- QGraph: A Quality Assessment Index for Graph Clustering (MH, IK), pp. 70–77.
- ECIR-p2-2019-MaityPGBGM #framework #recommendation #stack overflow
- DeepTagRec: A Content-cum-User Based Tag Recommendation Framework for Stack Overflow (SKM, AP, SG0, AB, PG, AM0), pp. 125–131.
- ECIR-p2-2019-MeloSMC
- Contender: Leveraging User Opinions for Purchase Decision-Making (TdM, ASdS, ESdM, PC), pp. 230–235.
- ECIR-p2-2019-MoraesH #stack #tool support #web
- node-indri: Moving the Indri Toolkit to the Modern Web Stack (FM, CH), pp. 241–245.
- ECIR-p2-2019-NikolenkoTMSA #predict #rating
- AspeRa: Aspect-Based Rating Prediction Model (SIN, ET, VM, IS, AA), pp. 163–171.
- ECIR-p2-2019-SahinBEZY
- MedSpecSearch: Medical Specialty Search (MUS, EB, CE, EZ, RY), pp. 225–229.
- ECIR-p2-2019-TschuggnallGS #tool support #visualisation
- StyleExplorer: A Toolkit for Textual Writing Style Visualization (MT, TG, GS), pp. 220–224.
ICML-2019-0001HLZZ #clustering #multi #parametricity- COMIC: Multi-view Clustering Without Parameter Selection (XP0, ZH, JL, HZ, JTZ), pp. 5092–5101.
- ICML-2019-Abu-El-HaijaPKA #architecture #graph #higher-order
- MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing (SAEH, BP, AK, NA, KL, HH, GVS, AG), pp. 21–29.
- ICML-2019-AdelW #approach #learning #visual notation
- TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning (TA, AW), pp. 71–81.
- ICML-2019-AivodjiAFGHT
- Fairwashing: the risk of rationalization (UA, HA, OF, SG, SH, AT), pp. 161–170.
- ICML-2019-BansalLRSW #higher-order #logic #machine learning #proving #theorem proving
- HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving (KB, SML, MNR, CS, SW), pp. 454–463.
- ICML-2019-BatsonR #self
- Noise2Self: Blind Denoising by Self-Supervision (JB, LR), pp. 524–533.
- ICML-2019-ChenXHY #problem #scalability
- Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number (ZC, YX, HH, TY), pp. 1102–1111.
- ICML-2019-ChenZWMH
- RaFM: Rank-Aware Factorization Machines (XC, YZ, JW, WM, JH), pp. 1132–1140.
- ICML-2019-ChuL #multi #summary
- MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization (EC, PJL), pp. 1223–1232.
- ICML-2019-ColasOSFC #composition #learning #motivation #multi
- CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning (CC, PYO, OS, PF, MC), pp. 1331–1340.
- ICML-2019-DasGRBPRP #communication #multi
- TarMAC: Targeted Multi-Agent Communication (AD, TG, JR, DB, DP, MR, JP), pp. 1538–1546.
- ICML-2019-DiakonikolasKK0 #optimisation #probability #robust
- Sever: A Robust Meta-Algorithm for Stochastic Optimization (ID, GK0, DK, JL0, JS, AS), pp. 1596–1606.
- ICML-2019-ElfekiCRE #learning #process #using
- GDPP: Learning Diverse Generations using Determinantal Point Processes (ME, CC, MR, ME), pp. 1774–1783.
- ICML-2019-FischerBDGZV #logic #network #query
- DL2: Training and Querying Neural Networks with Logic (MF, MB, DDC, TG, CZ, MTV), pp. 1931–1941.
- ICML-2019-FuLTL #black box #generative #metric #network #optimisation #speech
- MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement (SWF, CFL, YT0, SDL), pp. 2031–2041.
- ICML-2019-GeifmanE #network
- SelectiveNet: A Deep Neural Network with an Integrated Reject Option (YG, REY), pp. 2151–2159.
- ICML-2019-GeladaKBNB #learning #modelling #representation
- DeepMDP: Learning Continuous Latent Space Models for Representation Learning (CG, SK, JB, ON, MGB), pp. 2170–2179.
- ICML-2019-GroverZE #generative #graph #modelling
- Graphite: Iterative Generative Modeling of Graphs (AG, AZ, SE), pp. 2434–2444.
- ICML-2019-HwangJY #classification #generative
- HexaGAN: Generative Adversarial Nets for Real World Classification (UH, DJ, SY), pp. 2921–2930.
- ICML-2019-JohnHS #difference #equation #off the shelf
- GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver (DJ, VH, MS), pp. 3152–3162.
- ICML-2019-KenterWCCV #network #speech #synthesis
- CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network (TK, VW, CaC, RC, JV), pp. 3331–3340.
- ICML-2019-KimKJLS
- EMI: Exploration with Mutual Information (HK, JK, YJ, SL, HOS), pp. 3360–3369.
- ICML-2019-KimNKKK
- Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty (YK, WN, HK, JHK, GK), pp. 3379–3388.
- ICML-2019-KingmaAH #recursion
- Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables (FHK, PA, JH), pp. 3408–3417.
- ICML-2019-KipfLDZSGKB #composition #execution #learning
- CompILE: Compositional Imitation Learning and Execution (TK, YL, HD, VFZ, ASG, EG, PK, PWB), pp. 3418–3428.
- ICML-2019-KleimanP #machine learning #metric #modelling #multi #performance
- AUCμ: A Performance Metric for Multi-Class Machine Learning Models (RK, DP), pp. 3439–3447.
- ICML-2019-KoLWDWL #network #robust
- POPQORN: Quantifying Robustness of Recurrent Neural Networks (CYK, ZL, LW, LD, NW, DL), pp. 3468–3477.
- ICML-2019-KoratanaKBZ #representation
- LIT: Learned Intermediate Representation Training for Model Compression (AK, DK, PB, MZ), pp. 3509–3518.
- ICML-2019-LawrenceEC #dependence #learning #multi #parametricity
- DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures (ARL, CHE, NDFC), pp. 3682–3691.
- ICML-2019-LiDMMHH #learning #network
- LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning (HYL, WD, XM, CM, FH, BGH), pp. 3825–3834.
- ICML-2019-LiLWZG #black box #learning #network
- NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks (YL, LL, LW, TZ, BG), pp. 3866–3876.
- ICML-2019-LuHW #convergence #higher-order #optimisation
- PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization (SL, MH, ZW), pp. 4134–4143.
- ICML-2019-LuYFZ0 #generative #modelling
- CoT: Cooperative Training for Generative Modeling of Discrete Data (SL, LY, SF, YZ, WZ0), pp. 4164–4172.
- ICML-2019-MaTPHNZ #performance
- EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE (CM, ST, KP, JMHL, SN, CZ), pp. 4234–4243.
- ICML-2019-MatteiF #generative #modelling #semistructured data #set
- MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets (PAM, JF), pp. 4413–4423.
- ICML-2019-MendisRAC #estimation #network #performance #throughput #using
- Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks (CM, AR, SPA, MC), pp. 4505–4515.
- ICML-2019-OdenaOAG #debugging #fuzzing #network
- TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing (AO, CO, DA, IJG), pp. 4901–4911.
- ICML-2019-QianZCYH
- AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss (KQ, YZ, SC, XY, MHJ), pp. 5210–5219.
- ICML-2019-QuBT #graph #markov #network
- GMNN: Graph Markov Neural Networks (MQ, YB, JT0), pp. 5241–5250.
- ICML-2019-RatzlaffL #generative #network
- HyperGAN: A Generative Model for Diverse, Performant Neural Networks (NR, FL), pp. 5361–5369.
- ICML-2019-SlimCAV #framework #kernel
- kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection (LS, CC0, CAA, JPV), pp. 5857–5865.
- ICML-2019-SmithFRM #geometry
- GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects (EJS, SF, AR, DM), pp. 5866–5876.
- ICML-2019-SongK0 #learning #robust
- SELFIE: Refurbishing Unclean Samples for Robust Deep Learning (HS, MK, JGL0), pp. 5907–5915.
- ICML-2019-SongTQLL #generative #sequence
- MASS: Masked Sequence to Sequence Pre-training for Language Generation (KS, XT, TQ, JL, TYL), pp. 5926–5936.
- ICML-2019-SonKKHY #learning #multi
- QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning (KS, DK, WJK, DH, YY), pp. 5887–5896.
- ICML-2019-SuWSJ #adaptation #evaluation #learning #policy
- CAB: Continuous Adaptive Blending for Policy Evaluation and Learning (YS, LW, MS, TJ), pp. 6005–6014.
- ICML-2019-TangYLZL #parallel #probability
- DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression (HT, CY, XL, TZ, JL0), pp. 6155–6165.
- ICML-2019-TanL #network #scalability
- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (MT, QVL), pp. 6105–6114.
- ICML-2019-TranKSK #network #using
- DeepNose: Using artificial neural networks to represent the space of odorants (NBT, DRK, SS, AAK), pp. 6305–6314.
- ICML-2019-TrippeHAB #approximate #rank #using
- LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations (BLT, JHH, RA, TB), pp. 6315–6324.
- ICML-2019-WangDWK #learning #logic #reasoning #satisfiability #using
- SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver (PWW, PLD, BW, JZK), pp. 6545–6554.
- ICML-2019-WangGFZ
- EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis (CW, RBG, SF, GZ), pp. 6566–6575.
- ICML-2019-WeiszGS #algorithm #approximate
- CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration (GW, AG, CS), pp. 6707–6715.
- ICML-2019-WengCNSBOD #approach #network #probability #robust #verification
- PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach (LW, PYC, LMN, MSS, AB, IVO, LD), pp. 6727–6736.
- ICML-2019-X #bound #policy #predict #using
- POLITEX: Regret Bounds for Policy Iteration using Expert Prediction, pp. 3692–3702.
- ICML-2019-XieKG #distributed #fault tolerance #probability
- Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance (CX, SK, IG), pp. 6893–6901.
- ICML-2019-YangWLCXS0X #network #performance
- LegoNet: Efficient Convolutional Neural Networks with Lego Filters (ZY, YW, CL, HC, CX, BS, CX0, CX0), pp. 7005–7014.
- ICML-2019-YangZXK #effectiveness #estimation #matrix #robust #towards
- ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation (YY, GZ, ZX, DK), pp. 7025–7034.
- ICML-2019-YingKCR0H #architecture #towards
- NAS-Bench-101: Towards Reproducible Neural Architecture Search (CY, AK, EC, ER, KM0, FH), pp. 7105–7114.
- ICML-2019-YinYZ #category theory
- ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables (MY, YY, MZ), pp. 7095–7104.
- ICML-2019-YoonSM #adaptation #learning #network
- TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning (SWY, JS, JM), pp. 7115–7123.
- ICML-2019-YuCGY #graph #learning #network
- DAG-GNN: DAG Structure Learning with Graph Neural Networks (YY, JC, TG, MY), pp. 7154–7163.
- ICML-2019-ZhangHK #design #distributed #graph #network
- Circuit-GNN: Graph Neural Networks for Distributed Circuit Design (GZ, HH, DK), pp. 7364–7373.
- ICML-2019-ZhangHY #learning #performance #recognition #visual notation
- LatentGNN: Learning Efficient Non-local Relations for Visual Recognition (SZ, XH, SY), pp. 7374–7383.
- ICML-2019-ZhangVSA0L #learning #modelling
- SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning (MZ, SV, LS, PA, MJJ0, SL), pp. 7444–7453.
- ICML-2019-ZhouYWP #approach #architecture
- BayesNAS: A Bayesian Approach for Neural Architecture Search (HZ, MY, JW0, WP), pp. 7603–7613.
KDD-2019-0009ZGZNQH #framework #graph #recommendation #scalability- IntentGC: A Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation (JZ0, ZZ, ZG, WZ0, WN, GQ, XH), pp. 2347–2357.
- KDD-2019-AdhikariXRP
- EpiDeep: Exploiting Embeddings for Epidemic Forecasting (BA, XX, NR, BAP), pp. 577–586.
- KDD-2019-AkibaSYOK #framework #optimisation
- Optuna: A Next-generation Hyperparameter Optimization Framework (TA, SS, TY, TO, MK), pp. 2623–2631.
- KDD-2019-AnastasiuRT #order #problem #tutorial
- Tutorial: Are You My Neighbor?: Bringing Order to Neighbor Computing Problems (DCA, HR, AT), pp. 3241–3242.
- KDD-2019-BiswasPVSN #interactive #realtime
- Seeker: Real-Time Interactive Search (AB, TTP, MV, BS, HN), pp. 2867–2875.
- KDD-2019-ChenC0GLLW #modelling #recommendation
- λOpt: Learn to Regularize Recommender Models in Finer Levels (YC, BC, XH0, CG, YL0, JGL, YW), pp. 978–986.
- KDD-2019-ChenHXGGSLPZZ #generative #personalisation #recommendation
- POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion (WC, PH, JX, XG, CG, FS, CL, AP, HZ, BZ), pp. 2662–2670.
- KDD-2019-ChenreddyPNCA #learning #optimisation
- SMOILE: A Shopper Marketing Optimization and Inverse Learning Engine (ARC, PP, SN, RC, RA), pp. 2934–2942.
- KDD-2019-ChiangLSLBH #algorithm #clustering #graph #network #performance #scalability
- Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks (WLC, XL, SS, YL0, SB, CJH), pp. 257–266.
- KDD-2019-DeyZSN #effectiveness #personalisation #predict
- PerDREP: Personalized Drug Effectiveness Prediction from Longitudinal Observational Data (SD, PZ, DS, KN), pp. 1258–1268.
- KDD-2019-FanGZMSL #generative #towards
- MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search (MF, JG, SZ, SM, MS, PL0), pp. 2509–2517.
- KDD-2019-FuZCR #gpu #optimisation #performance #robust #visualisation
- AtSNE: Efficient and Robust Visualization on GPU through Hierarchical Optimization (CF, YZ, DC, XR), pp. 176–186.
- KDD-2019-GaoPH19a #generative #network #proximity
- ProGAN: Network Embedding via Proximity Generative Adversarial Network (HG, JP, HH), pp. 1308–1316.
- KDD-2019-GengLLJXZYLZ #network #predict
- LightNet: A Dual Spatiotemporal Encoder Network Model for Lightning Prediction (YaG, QL, TL, LJ, LX, DZ, WY, WL, YZ), pp. 2439–2447.
- KDD-2019-HanYZSLZ0K #graph #identification #matrix #network
- GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorization (PH, PY, PZ, SS, YL0, JZ, XG0, PK), pp. 705–713.
- KDD-2019-HuH #learning #network #set
- Sets2Sets: Learning from Sequential Sets with Neural Networks (HH, XH0), pp. 1491–1499.
- KDD-2019-HulsebosHBZSKDH #approach #data type #detection #learning #semantics
- Sherlock: A Deep Learning Approach to Semantic Data Type Detection (MH, KZH, MAB, EZ, AS, TK, ÇD, CAH), pp. 1500–1508.
- KDD-2019-HuNYZ #collaboration #distributed #framework #machine learning
- FDML: A Collaborative Machine Learning Framework for Distributed Features (YH, DN, JY, SZ), pp. 2232–2240.
- KDD-2019-JhaWYWFLCA #framework
- IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery (DJ, LTW, ZY, CW, ITF, WkL, ANC, AA), pp. 2385–2393.
- KDD-2019-JiangSHSXCWKS #predict
- DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events (RJ, XS, DH, XS, TX, ZC, ZW, KSK, RS), pp. 2114–2122.
- KDD-2019-JinSH #architecture #performance
- Auto-Keras: An Efficient Neural Architecture Search System (HJ, QS, XH), pp. 1946–1956.
- KDD-2019-KapoorLK #3d #image #modelling
- Nostalgin: Extracting 3D City Models from Historical Image Data (AK, HL, RK), pp. 2565–2575.
- KDD-2019-KeXZBL #framework #learning #online #predict
- DeepGBM: A Deep Learning Framework Distilled by GBDT for Online Prediction Tasks (GK, ZX, JZ, JB0, TYL), pp. 384–394.
- KDD-2019-LeeIFSM #approach #data-driven #estimation #using
- DeepRoof: A Data-driven Approach For Solar Potential Estimation Using Rooftop Imagery (SL, SI, MF, PJS, SM), pp. 2105–2113.
- KDD-2019-LeeIJCC #recommendation
- MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation (HL, JI, SJ, HC, SC), pp. 1073–1082.
- KDD-2019-LiangOJRLZRZ #fine-grained
- UrbanFM: Inferring Fine-Grained Urban Flows (YL, KO, LJ, SR, YL0, JZ, DSR, YZ0), pp. 3132–3142.
- KDD-2019-LiHWL #approach #community #detection
- EdMot: An Edge Enhancement Approach for Motif-aware Community Detection (PZL, LH, CDW, JHL), pp. 479–487.
- KDD-2019-LiuLDCG #learning #recommendation
- DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation (DL, JL0, BD, JC, RG), pp. 344–352.
- KDD-2019-LiuLTL
- CoSTCo: A Neural Tensor Completion Model for Sparse Tensors (HL, YL, MT, YL0), pp. 324–334.
- KDD-2019-LiuTZLDX #multi #personalisation #recommendation
- Hydra: A Personalized and Context-Aware Multi-Modal Transportation Recommendation System (HL, YT, PZ, XL, JD, HX), pp. 2314–2324.
- KDD-2019-LiX #data mining #mining #privacy #scalability
- PrivPy: General and Scalable Privacy-Preserving Data Mining (YL, WX), pp. 1299–1307.
- KDD-2019-LuoHHLZ #predict #quality
- AccuAir: Winning Solution to Air Quality Prediction for KDD Cup 2018 (ZL, JH, KH, XL, PZ), pp. 1842–1850.
- KDD-2019-LuoWZYTCDY #automation
- AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications (YL, MW, HZ, QY, WWT, YC, WD, QY0), pp. 1936–1945.
- KDD-2019-MengY0N #framework #induction
- HATS: A Hierarchical Sequence-Attention Framework for Inductive Set-of-Sets Embeddings (CM, JY, BR0, JN), pp. 783–792.
- KDD-2019-NieWL #clustering #multi
- K-Multiple-Means: A Multiple-Means Clustering Method with Specified K Clusters (FN, CLW, XL), pp. 959–967.
- KDD-2019-Pan0Z00
- TrajGuard: A Comprehensive Trajectory Copyright Protection Scheme (ZP, JB0, WZ0, YY0, YZ0), pp. 3060–3070.
- KDD-2019-PasumarthiBWLBN #library #ranking #scalability
- TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank (RKP, SB, XW, CL0, MB, MN, JP, NG, RA, SW), pp. 2970–2978.
- KDD-2019-PellegrinaRV #mining #testing
- SPuManTE: Significant Pattern Mining with Unconditional Testing (LP, MR, FV), pp. 1528–1538.
- KDD-2019-QinZZXZMZX #personalisation #perspective #recommendation
- DuerQuiz: A Personalized Question Recommender System for Intelligent Job Interview (CQ, HZ, CZ, TX, FZ, CM, JZ, HX), pp. 2165–2173.
- KDD-2019-SahooHKWLALH #image #learning #recognition
- FoodAI: Food Image Recognition via Deep Learning for Smart Food Logging (DS, WH, SK, XW, HL, PA, EPL, SCHH), pp. 2260–2268.
- KDD-2019-ShuCW0L #detection
- dEFEND: Explainable Fake News Detection (KS, LC, SW, DL0, HL0), pp. 395–405.
- KDD-2019-SiciliaPG #using
- DeepHoops: Evaluating Micro-Actions in Basketball Using Deep Feature Representations of Spatio-Temporal Data (AS, KP, KG), pp. 2096–2104.
- KDD-2019-SvyatkovskiyZFS #code completion
- Pythia: AI-assisted Code Completion System (AS, YZ, SF, NS), pp. 2727–2735.
- KDD-2019-TangBMLLK #classification #e-commerce #image #scalability
- MSURU: Large Scale E-commerce Image Classification with Weakly Supervised Search Data (YT, FB, SM, YL, YL, SK), pp. 2518–2526.
- KDD-2019-TangWYS #recommendation
- AKUPM: Attention-Enhanced Knowledge-Aware User Preference Model for Recommendation (XT, TW, HY, HS), pp. 1891–1899.
- KDD-2019-Tao0WFYZ0 #memory management #modelling #semantics #towards
- Log2Intent: Towards Interpretable User Modeling via Recurrent Semantics Memory Unit (ZT, SL0, ZW, CF, LY, HZ, YF0), pp. 1055–1063.
- KDD-2019-TaoLZZWFC #detection #game studies #multi #network #online
- MVAN: Multi-view Attention Networks for Real Money Trading Detection in Online Games (JT, JL, SZ, SZ, RW, CF, PC), pp. 2536–2546.
- KDD-2019-TokuiOANOSSUVV #framework #learning #research
- Chainer: A Deep Learning Framework for Accelerating the Research Cycle (ST, RO, TA, YN, TO, SS, SS, KU, BV, HYV), pp. 2002–2011.
- KDD-2019-TuM0P0 #network #optimisation
- AutoNE: Hyperparameter Optimization for Massive Network Embedding (KT, JM, PC0, JP, WZ0), pp. 216–225.
- KDD-2019-Wang00LC #graph #network #recommendation
- KGAT: Knowledge Graph Attention Network for Recommendation (XW, XH0, YC0, ML0, TSC), pp. 950–958.
- KDD-2019-WangJCJ #behaviour #predict
- TUBE: Embedding Behavior Outcomes for Predicting Success (DW, TJ, NVC, MJ0), pp. 1682–1690.
- KDD-2019-WangXLLCDWS #framework #learning #multi #network #social
- MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network (HW, TX, QL0, DL, EC, DD, HW, WS), pp. 1064–1072.
- KDD-2019-WangYMHLS #privacy
- SurfCon: Synonym Discovery on Privacy-Aware Clinical Data (ZW, XY, SM, YH, SML, HS), pp. 1578–1586.
- KDD-2019-WangZTWX #network #using
- AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks (JW, YZ0, KT, JW, ZX), pp. 1900–1908.
- KDD-2019-WeiCZWGXL #coordination #learning #network
- PressLight: Learning Max Pressure Control to Coordinate Traffic Signals in Arterial Network (HW, CC, GZ, KW, VVG, KX, ZL), pp. 1290–1298.
- KDD-2019-WengZBT #performance
- OCC: A Smart Reply System for Efficient In-App Communications (YW, HZ, FB, GT), pp. 2596–2603.
- KDD-2019-WuHX #classification #graph #network
- DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification (JW, JH, JX), pp. 406–415.
- KDD-2019-WuWAHHX #personalisation #recommendation
- NPA: Neural News Recommendation with Personalized Attention (CW, FW, MA, JH, YH, XX0), pp. 2576–2584.
- KDD-2019-XiaoS #data mining #development #mining #tutorial
- Tutorial: Data Mining Methods for Drug Discovery and Development (CX, JS), pp. 3195–3196.
- KDD-2019-Yang #framework #graph #network #platform
- AliGraph: A Comprehensive Graph Neural Network Platform (HY), pp. 3165–3166.
- KDD-2019-YangAKU #collaboration
- OBOE: Collaborative Filtering for AutoML Model Selection (CY, YA, DWK, MU), pp. 1173–1183.
- KDD-2019-YangRLC #graph #recursion #sketching
- NodeSketch: Highly-Efficient Graph Embeddings via Recursive Sketching (DY, PR, BL, PCM), pp. 1162–1172.
- KDD-2019-YangSZDGDQZ #automation
- AiAds: Automated and Intelligent Advertising System for Sponsored Search (XY, DS, RZ, TD, ZG, ZD, SQ, YZ0), pp. 1881–1890.
- KDD-2019-YangWZK #performance
- ET-Lasso: A New Efficient Tuning of Lasso-type Regularization for High-Dimensional Data (SY, JW, XZ, DK), pp. 607–616.
- KDD-2019-YanZDSSK #classification #network
- GroupINN: Grouping-based Interpretable Neural Network for Classification of Limited, Noisy Brain Data (YY, JZ, MD, ES, CSS, DK), pp. 772–782.
- KDD-2019-Ye #approach #data-driven
- Transportation: A Data Driven Approach (JY), p. 3183.
- KDD-2019-YeS #online #ranking
- MediaRank: Computational Ranking of Online News Sources (JY, SS), pp. 2469–2477.
- KDD-2019-YinLHCTW0 #representation
- QuesNet: A Unified Representation for Heterogeneous Test Questions (YY, QL0, ZH, EC, WT, SW, YS0), pp. 1328–1336.
- KDD-2019-Zhang0 #performance #similarity
- MinJoin: Efficient Edit Similarity Joins via Local Hash Minima (HZ, QZ0), pp. 1093–1103.
- KDD-2019-ZhangLTDYZGWSLW #graph #scalability #towards
- OAG: Toward Linking Large-scale Heterogeneous Entity Graphs (FZ, XL, JT, YD, PY, JZ, XG, YW, BS, RL, KW), pp. 2585–2595.
- KDD-2019-ZhangTDZW #health #predict #risk management
- MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records (XSZ, FT, HHD, JZ, FW), pp. 2487–2495.
- KDD-2019-ZhaoGS #keyword #mining
- Riker: Mining Rich Keyword Representations for Interpretable Product Question Answering (JZ, ZG, HS), pp. 1389–1398.
- KDD-2019-ZhuangL #multi
- PinText: A Multitask Text Embedding System in Pinterest (JZ, YL), pp. 2653–2661.
MoDELS-2019-BencomoP #learning #modelling #ram #runtime #using- RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning (NB, LHGP), pp. 216–226.
- MoDELS-2019-SantosSSN #contract #design #synthesis
- CONDEnSe: Contract Based Design Synthesis (CARdS, AHS, TS, MN), pp. 250–260.
ECOOP-2019-Fernandez-Reyes - Godot: All the Benefits of Implicit and Explicit Futures (KFR, DC0, LH, EBJ, TW), p. 28.
- ECOOP-2019-LuoDB #analysis #approach #editing #ide
- MagpieBridge: A General Approach to Integrating Static Analyses into IDEs and Editors (Tool Insights Paper) (LL, JD, EB), p. 25.
- ECOOP-2019-MakwanaK #algebra #linear
- NumLin: Linear Types for Linear Algebra (DCM, NRK), p. 25.
- ECOOP-2019-MeierMC #control flow #protocol
- Lifestate: Event-Driven Protocols and Callback Control Flow (SM, SM, BYEC), p. 29.
- ECOOP-2019-SpringerM #memory management #object-oriented #parallel #performance #programming
- DynaSOAr: A Parallel Memory Allocator for Object-Oriented Programming on GPUs with Efficient Memory Access (MS, HM), p. 37.
Onward-2019-AcharDL #debugging #distributed #interactive- GoTcha: an interactive debugger for GoT-based distributed systems (RA, PD, CVL), pp. 94–110.
- Onward-2019-Gariano0S
- CallƐ: an effect system for method calls (IOG, JN0, MS), pp. 32–45.
- Onward-2019-StoelSV #bound #relational
- AlleAlle: bounded relational model finding with unbounded data (JS, TvdS, JJV), pp. 46–61.
OOPSLA-2019-BaderSP0 #automation #debugging #learning- Getafix: learning to fix bugs automatically (JB, AS, MP, SC0), p. 27.
- OOPSLA-2019-BavishiLFSS #generative #synthesis
- AutoPandas: neural-backed generators for program synthesis (RB, CL, RF, KS, IS), p. 27.
- OOPSLA-2019-BenavidesV0 #distributed #profiling
- DProf: distributed profiler with strong guarantees (ZB, KV, RG0), p. 24.
- OOPSLA-2019-CambroneroR #learning #source code
- AL: autogenerating supervised learning programs (JPC, MCR), p. 28.
- OOPSLA-2019-LuanYBS0 #code search #recommendation
- Aroma: code recommendation via structural code search (SL, DY, CB, KS, SC0), p. 28.
- OOPSLA-2019-NearDASGWSZSSS #difference #higher-order #linear #privacy #type system
- Duet: an expressive higher-order language and linear type system for statically enforcing differential privacy (JPN, DD, CA, TS, PG, LW, NS, MZ0, NS, AS, DS), p. 30.
- OOPSLA-2019-PadhyeLSSV #fuzzing
- FuzzFactory: domain-specific fuzzing with waypoints (RP, CL, KS, LS, HV), p. 29.
- OOPSLA-2019-RahmaniNDJ #consistency #database #generative #testing
- CLOTHO: directed test generation for weakly consistent database systems (KR, KN, BD, SJ), p. 28.
- OOPSLA-2019-ShajiiNBBA
- Seq: a high-performance language for bioinformatics (AS, IN, RB, BB, SPA), p. 29.
- OOPSLA-2019-SharifSHKJSZAMA #compilation #information retrieval #optimisation
- ApproxHPVM: a portable compiler IR for accuracy-aware optimizations (HS, PS, MH, MK, KJ, YS, NZ, VSA, SM, SVA), p. 30.
- OOPSLA-2019-ShenZZLJW #refactoring
- IntelliMerge: a refactoring-aware software merging technique (BS, WZ, HZ, GL, ZJ, QW), p. 28.
- OOPSLA-2019-SjobergSWS
- DeepSEA: a language for certified system software (VS, YS, SCW, ZS), p. 27.
- OOPSLA-2019-ToschBBJM #online
- PlanAlyzer: assessing threats to the validity of online experiments (ET, EB, EDB, DDJ, JEBM), p. 30.
- OOPSLA-2019-VukoticRV #fault
- Asphalion: trustworthy shielding against Byzantine faults (IV, VR, PJEV), p. 32.
- OOPSLA-2019-WangHZTT #performance #polymorphism #type system
- IVT: an efficient method for sharing subtype polymorphic objects (YPW, XQH, ZXZ, WT, GT), p. 22.
- OOPSLA-2019-ZhangYTWK0 #abstract interpretation #analysis #bytecode #dependence
- BDA: practical dependence analysis for binary executables by unbiased whole-program path sampling and per-path abstract interpretation (ZZ0, WY, GT, GW, YK, XZ0), p. 31.
PLDI-2019-CampagnaXMSL #generative #natural language #parsing #semantics- Genie: a generator of natural language semantic parsers for virtual assistant commands (GC, SX, MM, RS, MSL), pp. 394–410.
- PLDI-2019-CauligiSJBWRGBJ #domain-specific language
- FaCT: a DSL for timing-sensitive computation (SC, GS, BJ, FB, RSW, JR, BG, GB, RJ, DS), pp. 174–189.
- PLDI-2019-ChajedTKZ #refinement #verification
- Argosy: verifying layered storage systems with recovery refinement (TC, JT, MFK, NZ), pp. 1054–1068.
- PLDI-2019-Cusumano-Towner #probability #programmable #programming
- Gen: a general-purpose probabilistic programming system with programmable inference (MFCT, FAS, AKL, VKM), pp. 221–236.
- PLDI-2019-DathathriS0LLMM #compilation #optimisation
- CHET: an optimizing compiler for fully-homomorphic neural-network inferencing (RD, OS, HC0, KL, KEL, SM, MM, TM), pp. 142–156.
- PLDI-2019-FremontDGYSS #generative #specification
- Scenic: a language for scenario specification and scene generation (DJF, TD, SG, XY, ALSV, SAS), pp. 63–78.
- PLDI-2019-GuanSL #framework #performance
- Wootz: a compiler-based framework for fast CNN pruning via composability (HG, XS, SHL), pp. 717–730.
- PLDI-2019-KhanZPMK #detection #hybrid
- Huron: hybrid false sharing detection and repair (TAK, YZ, GP, BM, BK), pp. 453–468.
- PLDI-2019-LeZN #dynamic analysis #invariant #logic #using
- SLING: using dynamic analysis to infer program invariants in separation logic (TCL, GZ, TN), pp. 788–801.
- PLDI-2019-LiaoHM #calculus #composition #encryption
- ILC: a calculus for composable, computational cryptography (KL, MAH, AM0), pp. 640–654.
- PLDI-2019-LiLCGL #automation #debugging #distributed
- DFix: automatically fixing timing bugs in distributed systems (GL, HL, XC, HSG, SL), pp. 994–1009.
- PLDI-2019-MercadierD
- Usuba: high-throughput and constant-time ciphers, by construction (DM, PÉD), pp. 157–173.
- PLDI-2019-PerryKSZ #clustering #imperative #programming #semantics
- SemCluster: clustering of imperative programming assignments based on quantitative semantic features (DMP, DK, RS, XZ), pp. 860–873.
- PLDI-2019-PowersTB0 #c #c++ #memory management
- Mesh: compacting memory management for C/C++ applications (BP, DT, EDB, AM0), pp. 333–346.
- PLDI-2019-ProkopecRLD0SBZ #benchmark #metric #parallel #virtual machine
- Renaissance: benchmarking suite for parallel applications on the JVM (AP, AR, DL, GD, PT0, MS, LB, YZ, AV, DS, TW, WB), pp. 31–47.
- PLDI-2019-PultePKLH #concurrent #performance
- Promising-ARM/RISC-V: a simpler and faster operational concurrency model (CP, JPP, JK, SHL, CKH), pp. 1–15.
- PLDI-2019-ShullHT #framework #java #reachability
- AutoPersist: an easy-to-use Java NVM framework based on reachability (TS, JH0, JT), pp. 316–332.
- PLDI-2019-VasilakisKPSDS #scalability
- Ignis: scaling distribution-oblivious systems with light-touch distribution (NV, BK, YP, JS, AD, JMS), pp. 1010–1026.
- PLDI-2019-VollmerKRS0N #source code
- LoCal: a language for programs operating on serialized data (MV0, CK, MR, LS, MK0, RRN), pp. 48–62.
- PLDI-2019-WangCCZVML0X #big data #hybrid #memory management
- Panthera: holistic memory management for big data processing over hybrid memories (CW, HC, TC, JZ, HV0, OM, FL, XF0, GHX), pp. 347–362.
- POPL-2019-AlonZLY #distributed #learning
- code2vec: learning distributed representations of code (UA0, MZ, OL, EY), p. 29.
- POPL-2019-BizjakGKB #concurrent #higher-order #logic
- Iron: managing obligations in higher-order concurrent separation logic (AB, DG, RK, LB), p. 30.
- POPL-2019-CousotGR
- A²I: abstract² interpretation (PC, RG, FR), p. 31.
- POPL-2019-HoushmandL #analysis #coordination #replication #synthesis
- Hamsaz: replication coordination analysis and synthesis (FH, ML), p. 32.
- POPL-2019-ParkerVH #data flow #multi #security #web
- LWeb: information flow security for multi-tier web applications (JP, NV, MH0), p. 30.
- POPL-2019-ShiSL #component #synthesis
- FrAngel: component-based synthesis with control structures (KS, JS, PL), p. 29.
- POPL-2019-SkorstengaardDB #control flow #encapsulation #linear #stack #using
- StkTokens: enforcing well-bracketed control flow and stack encapsulation using linear capabilities (LS, DD, LB), p. 28.
- POPL-2019-TomanG #abstract interpretation #framework
- Concerto: a framework for combined concrete and abstract interpretation (JT, DG), p. 29.
- POPL-2019-WattRPCS #ecosystem #encryption #web
- CT-wasm: type-driven secure cryptography for the web ecosystem (CW, JR, NP, SC, DS), p. 29.
PPDP-2019-SteenvoordenNK #formal method #programming- TopHat: A formal foundation for task-oriented programming (TS, NN, MK), p. 13.
SAS-2019-NeiderS0M #algorithm #invariant #learning- Sorcar: Property-Driven Algorithms for Learning Conjunctive Invariants (DN, SS, PG0, PM), pp. 323–346.
ASE-2019-AhmadiJD #state machine #testing #uml- mCUTE: A Model-Level Concolic Unit Testing Engine for UML State Machines (RA, KJ, JD), pp. 1182–1185.
- ASE-2019-AlizadehOKC #refactoring
- RefBot: Intelligent Software Refactoring Bot (VA, MAO, MK, MC), pp. 823–834.
- ASE-2019-BagherzadehJKD #execution #modelling #uml
- PMExec: An Execution Engine of Partial UML-RT Models (MB, KJ, NK, JD), pp. 1178–1181.
- ASE-2019-BaoLWF #automation #generative #network
- ACTGAN: Automatic Configuration Tuning for Software Systems with Generative Adversarial Networks (LB, XL, FW, BF), pp. 465–476.
- ASE-2019-BeyerL #execution #metric #robust #testing
- TestCov: Robust Test-Suite Execution and Coverage Measurement (DB0, TL0), pp. 1074–1077.
- ASE-2019-BuiYJ #network
- AutoFocus: Interpreting Attention-Based Neural Networks by Code Perturbation (NDQB, YY, LJ), pp. 38–41.
- ASE-2019-CastroPA #locality #tool support
- Pangolin: An SFL-Based Toolset for Feature Localization (BC, AP, RA), pp. 1130–1133.
- ASE-2019-ChittimalliAPMP #framework #verification
- BuRRiTo: A Framework to Extract, Specify, Verify and Analyze Business Rules (PKC, KA, SP, SM, CP, RS, RN), pp. 1190–1193.
- ASE-2019-DuCWLSC #graph
- CocoQa: Question Answering for Coding Conventions Over Knowledge Graphs (TD, JC, QW, WL, BS, YC), pp. 1086–1089.
- ASE-2019-EndresSCJW #automation
- InFix: Automatically Repairing Novice Program Inputs (ME, GS, BC, RJ, WW), pp. 399–410.
- ASE-2019-Escobar-Velasquez #android #generative
- MutAPK: Source-Codeless Mutant Generation for Android Apps (CEV, MOR, MLV), pp. 1090–1093.
- ASE-2019-FengYLBWTSYXPXH #detection #off the shelf #open source #reuse
- B2SFinder: Detecting Open-Source Software Reuse in COTS Software (MF, ZY, FL, GB, SW, QT, HS, CY, JX, AP, JX, WH), pp. 1038–1049.
- ASE-2019-GhanbariZ #bytecode #program repair
- PraPR: Practical Program Repair via Bytecode Mutation (AG, LZ), pp. 1118–1121.
- ASE-2019-Gu00 #api #approach #graph #kernel
- CodeKernel: A Graph Kernel Based Approach to the Selection of API Usage Examples (XG, HZ0, SK0), pp. 590–601.
- ASE-2019-HortonP #detection #performance #python
- V2: Fast Detection of Configuration Drift in Python (EH, CP), pp. 477–488.
- ASE-2019-HuangFZZWJMP #analysis #editing #modelling #precise #requirements
- Prema: A Tool for Precise Requirements Editing, Modeling and Analysis (YH, JF, HZ, JZ, SW, SJ, WM, GP), pp. 1166–1169.
- ASE-2019-LacomisYSAGNV #approach #identifier
- DIRE: A Neural Approach to Decompiled Identifier Naming (JL, PY, EJS, MA, CLG, GN, BV), pp. 628–639.
- ASE-2019-Laguna #detection #exception #float #gpu
- FPChecker: Detecting Floating-Point Exceptions in GPU Applications (IL), pp. 1126–1129.
- ASE-2019-Li0G00 #fault #specification #static analysis
- Ares: Inferring Error Specifications through Static Analysis (CL, MZ0, ZG, MG0, HZ0), pp. 1174–1177.
- ASE-2019-LiuFXLGGY #automation #evaluation #performance #simulation #tool support #workflow
- FogWorkflowSim: An Automated Simulation Toolkit for Workflow Performance Evaluation in Fog Computing (XL, LF, JX, XL, LG, JCG, YY), pp. 1114–1117.
- ASE-2019-LiuW0B0X #android #detection
- DaPanda: Detecting Aggressive Push Notifications in Android Apps (TL, HW, LL0, GB, YG0, GX), pp. 66–78.
- ASE-2019-LiuZHHZL #clustering
- Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression (JL, JZ, SH, PH, ZZ, MRL), pp. 863–873.
- ASE-2019-LiW0ZCM #clustering #detection #effectiveness #fault #spreadsheet
- SGUARD: A Feature-Based Clustering Tool for Effective Spreadsheet Defect Detection (DL, HW, CX0, RZ, SCC, XM), pp. 1142–1145.
- ASE-2019-LiWXWZ0 #contract #mutation testing #testing
- MuSC: A Tool for Mutation Testing of Ethereum Smart Contract (ZL, HW, JX, XW, LZ, ZC0), pp. 1198–1201.
- ASE-2019-LiY0C #android #approach #automation #black box #testing
- Humanoid: A Deep Learning-Based Approach to Automated Black-box Android App Testing (YL, ZY, YG0, XC), pp. 1070–1073.
- ASE-2019-MaYLYZ #c++ #fault #pointer #source code #static analysis
- SPrinter: A Static Checker for Finding Smart Pointer Errors in C++ Programs (XM, JY, YL, JY0, JZ), pp. 1122–1125.
- ASE-2019-MehraSKP #artificial reality #towards #using
- XRaSE: Towards Virtually Tangible Software using Augmented Reality (RM, VSS, VK, SP), pp. 1194–1197.
- ASE-2019-MengZYL0Y #concurrent #detection #effectiveness
- CONVUL: An Effective Tool for Detecting Concurrency Vulnerabilities (RM, BZ, HY, HL, YC0, ZY), pp. 1154–1157.
- ASE-2019-MossbergMHGGFBD #contract #execution #framework #symbolic computation
- Manticore: A User-Friendly Symbolic Execution Framework for Binaries and Smart Contracts (MM, FM, EH, AG, GG, JF, TB, AD), pp. 1186–1189.
- ASE-2019-MuGCCGXMS #alias #analysis #execution #performance
- RENN: Efficient Reverse Execution with Neural-Network-Assisted Alias Analysis (DM, WG, AC, YC, JG, XX, BM, CS), pp. 924–935.
- ASE-2019-NafiKRRS #api #clone detection #detection #documentation #using
- CLCDSA: Cross Language Code Clone Detection using Syntactical Features and API Documentation (KWN, TSK, BR, CKR, KAS), pp. 1026–1037.
- ASE-2019-NamHMMV #api #identification #mining #problem #usability
- MARBLE: Mining for Boilerplate Code to Identify API Usability Problems (DN, AH, AM, BAM, BV), pp. 615–627.
- ASE-2019-PaulsenSPW #static analysis
- Debreach: Mitigating Compression Side Channels via Static Analysis and Transformation (BP, CS, PAHP, CW), pp. 899–911.
- ASE-2019-PiskachevDJB #automation #detection
- SWAN_ASSIST: Semi-Automated Detection of Code-Specific, Security-Relevant Methods (GP, LNQD, OJ, EB), pp. 1094–1097.
- ASE-2019-RahatFT #android #debugging #empirical
- OAUTHLINT: An Empirical Study on OAuth Bugs in Android Applications (TAR, YF, YT), pp. 293–304.
- ASE-2019-ReicheltKH #identification #performance
- PeASS: A Tool for Identifying Performance Changes at Code Level (DGR, SK, WH), pp. 1146–1149.
- ASE-2019-SadiqLLAL #contract #java #source code
- Sip4J: Statically Inferring Access Permission Contracts for Parallelising Sequential Java Programs (AS, LL, YFL, IA, SL), pp. 1098–1101.
- ASE-2019-SahinAMCE #android #runtime
- RANDR: Record and Replay for Android Applications via Targeted Runtime Instrumentation (OS, AA, HM, AKC, ME), pp. 128–138.
- ASE-2019-SondhiP #consistency #nondeterminism #semantics #specification #string
- SEGATE: Unveiling Semantic Inconsistencies between Code and Specification of String Inputs (DS, RP), pp. 200–212.
- ASE-2019-StepanovAB #compilation #debugging #how
- ReduKtor: How We Stopped Worrying About Bugs in Kotlin Compiler (DS, MA, MAB), pp. 317–326.
- ASE-2019-TankovGB #framework
- Kotless: A Serverless Framework for Kotlin (VT, YG, TB), pp. 1110–1113.
- ASE-2019-TokumotoT #development #metric #quality
- PHANTA: Diversified Test Code Quality Measurement for Modern Software Development (ST, KT), pp. 1206–1207.
- ASE-2019-WangC00S #analysis #fault #memory management #pointer
- TsmartGP: A Tool for Finding Memory Defects with Pointer Analysis (YW, GC, MZ0, MG0, JS), pp. 1170–1173.
- ASE-2019-WangLZX #co-evolution
- CoRA: Decomposing and Describing Tangled Code Changes for Reviewer (MW, ZL, YZ, BX), pp. 1050–1061.
- ASE-2019-WangZL00L #novel #test coverage #testing #thread
- MAP-Coverage: A Novel Coverage Criterion for Testing Thread-Safe Classes (ZW, YZ, SL, JS0, XC0, HL), pp. 722–734.
- ASE-2019-WenCC #kernel #linux
- PTracer: A Linux Kernel Patch Trace Bot (YW, JC, SC), pp. 1210–1211.
- ASE-2019-WuLZYZ0 #analysis #mobile #performance
- MalScan: Fast Market-Wide Mobile Malware Scanning by Social-Network Centrality Analysis (YW, XL, DZ, WY0, XZ, HJ0), pp. 139–150.
- ASE-2019-YuF0Z0 #automation #image #layout #mobile #platform #recognition #testing
- LIRAT: Layout and Image Recognition Driving Automated Mobile Testing of Cross-Platform (SY, CF, YF0, WZ, ZC0), pp. 1066–1069.
- ASE-2019-ZamanHY #concurrent #fault #scalability
- SCMiner: Localizing System-Level Concurrency Faults from Large System Call Traces (TSZ, XH, TY), pp. 515–526.
- ASE-2019-ZhangC #adaptation #approach #learning #modelling
- Apricot: A Weight-Adaptation Approach to Fixing Deep Learning Models (HZ, WKC), pp. 376–387.
- ASE-2019-ZhangYFSL0 #learning #modelling #visualisation
- NeuralVis: Visualizing and Interpreting Deep Learning Models (XZ, ZY, YF0, QS, JL, ZC0), pp. 1106–1109.
- ASE-2019-ZhengFXS0HMLSC #automation #game studies #learning #online #testing #using
- Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning (YZ, CF, XX, TS, LM0, JH, ZM, YL0, RS, YC), pp. 772–784.
- ASE-2019-ZhengLZLZD #detection #feedback #online #realtime #scalability
- iFeedback: Exploiting User Feedback for Real-Time Issue Detection in Large-Scale Online Service Systems (WZ, HL, YZ, JL, HZ, YD), pp. 352–363.
- ASE-2019-ZhouSZ #what
- Lancer: Your Code Tell Me What You Need (SZ, BS, HZ), pp. 1202–1205.
- ASE-2019-ZhouWLLS0 #comprehension #fuzzing #interactive #visualisation
- VisFuzz: Understanding and Intervening Fuzzing with Interactive Visualization (CZ, MW, JL, ZL, CS, YJ0), pp. 1078–1081.
ESEC-FSE-2019-AsthanaKBBBMMA #automation #scalability- WhoDo: automating reviewer suggestions at scale (SA, RK0, RB, CB, CB, CSM, SM, BA), pp. 937–945.
- ESEC-FSE-2019-AwadhutkarSHK #algorithm #complexity #detection
- DISCOVER: detecting algorithmic complexity vulnerabilities (PA, GRS, BH, SK), pp. 1129–1133.
- ESEC-FSE-2019-BabicBCIKKLSW #generative #scalability
- FUDGE: fuzz driver generation at scale (DB, SB, YC, FI, TK, MK, CL, LS, WW), pp. 975–985.
- ESEC-FSE-2019-BanerjeeCS #java #null #safety #type system
- NullAway: practical type-based null safety for Java (SB, LC, MS), pp. 740–750.
- ESEC-FSE-2019-BavishiYP #automation #data-driven #static analysis #synthesis
- Phoenix: automated data-driven synthesis of repairs for static analysis violations (RB, HY, MRP), pp. 613–624.
- ESEC-FSE-2019-BuiYJ #api #learning
- SAR: learning cross-language API mappings with little knowledge (NDQB, YY, LJ), pp. 796–806.
- ESEC-FSE-2019-CaiWH0X0 #api #recommendation
- BIKER: a tool for Bi-information source based API method recommendation (LC, HW, QH, XX0, ZX, DL0), pp. 1075–1079.
- ESEC-FSE-2019-CaiWXH00X #generative #stack overflow #summary
- AnswerBot: an answer summary generation tool based on stack overflow (LC, HW, BX, QH, XX0, DL0, ZX), pp. 1134–1138.
- ESEC-FSE-2019-ChekamPT #generative
- Mart: a mutant generation tool for LLVM (TTC, MP, YLT), pp. 1080–1084.
- ESEC-FSE-2019-ChenCLML #analysis #approach #learning #re-engineering #sentiment
- SEntiMoji: an emoji-powered learning approach for sentiment analysis in software engineering (ZC, YC, XL, QM, XL), pp. 841–852.
- ESEC-FSE-2019-CorreiaASN #multi #testing #using
- MOTSD: a multi-objective test selection tool using test suite diagnosability (DC, RA, PS, JN), pp. 1070–1074.
- ESEC-FSE-2019-DuttaZHM #debugging #probability #programming #reduction #testing
- Storm: program reduction for testing and debugging probabilistic programming systems (SD, WZ, ZH, SM), pp. 729–739.
- ESEC-FSE-2019-DuXLM0Z #analysis #learning #modelling
- DeepStellar: model-based quantitative analysis of stateful deep learning systems (XD, XX, YL0, LM0, YL0, JZ), pp. 477–487.
- ESEC-FSE-2019-FuRMSYJLS #detection #testing
- EVMFuzzer: detect EVM vulnerabilities via fuzz testing (YF, MR, FM, HS, XY, YJ0, HL, XS), pp. 1110–1114.
- ESEC-FSE-2019-GuerreroFJF0MR #agile #development #framework
- Eagle: a team practices audit framework for agile software development (AG, RF, AJ, AF, PF0, CM, ARC), pp. 1139–1143.
- ESEC-FSE-2019-JinWXPDQ0X #generative #testing
- FinExpert: domain-specific test generation for FinTech systems (TJ, QW, LX, CP, LD, HQ, LH0, TX), pp. 853–862.
- ESEC-FSE-2019-KoyuncuLB0MKT #debugging #program repair
- iFixR: bug report driven program repair (AK, KL0, TFB, DK0, MM, JK, YLT), pp. 314–325.
- ESEC-FSE-2019-LiXCWZXWL #adaptation #detection #effectiveness #fuzzing
- Cerebro: context-aware adaptive fuzzing for effective vulnerability detection (YL, YX, HC, XW, CZ, XX, HW, YL0), pp. 533–544.
- ESEC-FSE-2019-MesbahRJGA #compilation #fault #learning
- DeepDelta: learning to repair compilation errors (AM, AR, EJ, NG, EA), pp. 925–936.
- ESEC-FSE-2019-MostaeenSRRS #machine learning #validation
- CloneCognition: machine learning based code clone validation tool (GM, JS, BR, CKR, KAS), pp. 1105–1109.
- ESEC-FSE-2019-NielsenHG #static analysis
- Nodest: feedback-driven static analysis of Node.js applications (BBN, BH, FG), pp. 455–465.
- ESEC-FSE-2019-PobeeC #concurrent #multi #performance #source code #thread
- AggrePlay: efficient record and replay of multi-threaded programs (EBP, WKC), pp. 567–577.
- ESEC-FSE-2019-ShiLOXM #automation #framework #testing
- iFixFlakies: a framework for automatically fixing order-dependent flaky tests (AS, WL, RO, TX, DM), pp. 545–555.
- ESEC-FSE-2019-SongZH #android #detection
- ServDroid: detecting service usage inefficiencies in Android applications (WS0, JZ, JH0), pp. 362–373.
- ESEC-FSE-2019-StallenbergP #detection #injection #search-based #web #xml
- JCOMIX: a search-based tool to detect XML injection vulnerabilities in web applications (DMS, AP), pp. 1090–1094.
- ESEC-FSE-2019-TundoMORGM #as a service #framework #modelling
- VARYS: an agnostic model-driven monitoring-as-a-service framework for the cloud (AT, MM, MO, OR, MG, LM), pp. 1085–1089.
- ESEC-FSE-2019-WuJYBSPX #grammar inference #learning
- REINAM: reinforcement learning for input-grammar inference (ZW, EJ, WY0, OB, DS, JP, TX0), pp. 488–498.
- ESEC-FSE-2019-YuFMRPC #automation #testing #user interface
- TERMINATOR: better automated UI test case prioritization (ZY0, FMF, TM, GR, KP, SC), pp. 883–894.
- ICSE-2019-AtlidakisGP #api #fuzzing #rest
- RESTler: stateful REST API fuzzing (VA, PG, MP), pp. 748–758.
- ICSE-2019-ChenFCSLLX #android #automation #generative
- StoryDroid: automated generation of storyboard for Android apps (SC, LF, CC, TS, WL, YL0, LX), pp. 596–607.
- ICSE-2019-ChowdhuryHKSMK #empirical #energy
- GreenBundle: an empirical study on the energy impact of bundled processing (SAC, AH, RK, TS, KM, YK), pp. 1107–1118.
- ICSE-2019-DmeiriTWBLDVR #dataset #mining
- BugSwarm: mining and continuously growing a dataset of reproducible failures and fixes (DAT, ND, YW, AB, YCL, PTD, BV, CRG), pp. 339–349.
- ICSE-2019-Du0LGZLJ #assessment #identification #metric
- Leopard: identifying vulnerable code for vulnerability assessment through program metrics (XD, BC0, YL, JG, YZ, YL0, YJ0), pp. 60–71.
- ICSE-2019-FanWS0ZZ #detection #memory management #scalability
- Smoke: scalable path-sensitive memory leak detection for millions of lines of code (GF, RW, QS, XX0, JZ, CZ), pp. 72–82.
- ICSE-2019-GrechBSS #contract #declarative #decompiler
- Gigahorse: thorough, declarative decompilation of smart contracts (NG, LB, BS, YS), pp. 1176–1186.
- ICSE-2019-HaoFJL0 #crowdsourcing #summary
- CTRAS: crowdsourced test report aggregation and summarization (RH, YF0, JAJ, YL, ZC0), pp. 900–910.
- ICSE-2019-HaZ #configuration management #network #performance #predict
- DeepPerf: performance prediction for configurable software with deep sparse neural network (HH, HZ0), pp. 1095–1106.
- ICSE-2019-HortonP #automation #dependence #python
- DockerizeMe: automatic inference of environment dependencies for python code snippets (EH, CP), pp. 328–338.
- ICSE-2019-HuangGLLQC0 #api #java #safety
- SafeCheck: safety enhancement of Java unsafe API (SH, JG, SL, XL, YQ, KC, JH0), pp. 889–899.
- ICSE-2019-KiPDKZ #android #testing #user interface
- Mimic: UI compatibility testing system for Android apps (TK, CMP, KD, SYK, LZ), pp. 246–256.
- ICSE-2019-LiC0S #detection #smell
- Dlfinder: characterizing and detecting duplicate logging code smells (ZL, TH(C, JY0, WS), pp. 152–163.
- ICSE-2019-MalikPP #javascript #natural language
- NL2Type: inferring JavaScript function types from natural language information (RSM, JP, MP), pp. 304–315.
- ICSE-2019-NguyenRRODP #api #mining #recommendation
- FOCUS: a recommender system for mining API function calls and usage patterns (PTN, JDR, DDR, LO, TD, MDP), pp. 1050–1060.
- ICSE-2019-NilizadehNP #analysis #difference #fuzzing
- DifFuzz: differential fuzzing for side-channel analysis (SN, YN, CSP), pp. 176–187.
- ICSE-2019-PhamLQT #debugging #detection #learning #library #locality #validation
- CRADLE: cross-backend validation to detect and localize bugs in deep learning libraries (HVP, TL, WQ, LT0), pp. 1027–1038.
- ICSE-2019-PhilipBKMN #online #scalability
- FastLane: test minimization for rapidly deployed large-scale online services (AAP, RB, RK0, CSM, NN), pp. 408–418.
- ICSE-2019-Wang0WL #fuzzing
- Superion: grammar-aware greybox fuzzing (JW0, BC0, LW, YL0), pp. 724–735.
- ICSE-2019-WangKZ #online #runtime
- Adjust: runtime mitigation of resource abusing third-party online ads (WW, ILK, YZ), pp. 1005–1015.
- ICSE-2019-WangYKMW
- iSENSE: completion-aware crowdtesting management (JW0, YY, RK, TM, QW0), pp. 912–923.
- ICSE-2019-WeiLC #android #correlation #detection #learning
- Pivot: learning API-device correlations to facilitate Android compatibility issue detection (LW, YL, SCC), pp. 878–888.
- ICSE-2019-XiaoWCWG #android #automation #classification #identification #user interface
- IconIntent: automatic identification of sensitive UI widgets based on icon classification for Android apps (XX, XW, ZC, HW, PG), pp. 257–268.
- ICSE-2019-XuSYX #null #pointer #precise #program repair
- VFix: value-flow-guided precise program repair for null pointer dereferences (XX, YS, HY, JX), pp. 512–523.
- ICSE-2019-YouLMPZ0 #fuzzing
- SLF: fuzzing without valid seed inputs (WY, XL, SM, DMP, XZ0, BL0), pp. 712–723.
- ICSE-2019-ZhangHMBLU #ltl #source code #using
- AutoTap: synthesizing and repairing trigger-action programs using LTL properties (LZ, WH, JM, NB, SL, BU), pp. 281–291.
- ICSE-2019-ZhaoXC00 #programming #recognition #workflow
- ActionNet: vision-based workflow action recognition from programming screencasts (DZ, ZX, CC, XX0, GL0), pp. 350–361.
- ICSE-2019-ZhaoYSLZZH #android #automation #debugging
- ReCDroid: automatically reproducing Android application crashes from bug reports (YZ, TY, TS, YL0, WZ, JZ, WGJH), pp. 128–139.
GPCE-2019-CronburgG #in memory #layout #memory management- Floorplan: spatial layout in memory management systems (KC, SZG), pp. 81–93.
- ASPLOS-2019-0001WZKK #flexibility #framework #memory management #performance #persistent #source code #testing
- PMTest: A Fast and Flexible Testing Framework for Persistent Memory Programs (SL0, YW, JZ, AK, SMK), pp. 411–425.
- ASPLOS-2019-AbulilaMQHKXH
- FlatFlash: Exploiting the Byte-Accessibility of SSDs within a Unified Memory-Storage Hierarchy (AHMOA, VSM, ZQ, JH0, NSK, JX, WMWH), pp. 971–985.
- ASPLOS-2019-AnkitHCNFWFHS0M #machine learning #programmable
- PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference (AA, IEH, SRC, GN, MF, RSW, PF, WmWH, JPS, KR0, DSM), pp. 715–731.
- ASPLOS-2019-BaiLTH #automation #detection #fault #kernel #linux
- DCNS: Automated Detection Of Conservative Non-Sleep Defects in the Linux Kernel (JJB, JL, WT, SMH0), pp. 287–299.
- ASPLOS-2019-BhatKBG #program transformation
- ProbeGuard: Mitigating Probing Attacks Through Reactive Program Transformations (KB, EvdK, HB, CG), pp. 545–558.
- ASPLOS-2019-ChenDM #clustering #interactive #multi
- PARTIES: QoS-Aware Resource Partitioning for Multiple Interactive Services (SC, CD, JFM), pp. 107–120.
- ASPLOS-2019-ChoOPJL #learning
- FA3C: FPGA-Accelerated Deep Reinforcement Learning (HC, PO, JP, WJ, JL), pp. 499–513.
- ASPLOS-2019-ChungKIAL
- LightStore: Software-defined Network-attached Key-value Drives (CC, JK, JI, A, SL), pp. 939–953.
- ASPLOS-2019-DaglisSF
- RPCValet: NI-Driven Tail-Aware Balancing of µs-Scale RPCs (AD, MS, BF), pp. 35–48.
- ASPLOS-2019-DathathriGHP #distributed #graph
- Phoenix: A Substrate for Resilient Distributed Graph Analytics (RD, GG, LH, KP), pp. 615–630.
- ASPLOS-2019-DavisWRNMBCCFGJ #c #pointer #runtime
- CheriABI: Enforcing Valid Pointer Provenance and Minimizing Pointer Privilege in the POSIX C Run-time Environment (BD, RNMW, AR, PGN, SWM, JB, DC, JC, NWF, KG, AJ, BL, ATM, JEM, AM, ETN, RMN, MR, PS, SDS, JW), pp. 379–393.
- ASPLOS-2019-EsfedenKJWA
- CORF: Coalescing Operand Register File for GPUs (HAE, FK, HJ, DW0, NBAG), pp. 701–714.
- ASPLOS-2019-FernandoFAMT #approximate #manycore
- Replica: A Wireless Manycore for Communication-Intensive and Approximate Data (VF, AF, SA, SM, JT), pp. 849–863.
- ASPLOS-2019-GallagherBCAYAH #architecture
- Morpheus: A Vulnerability-Tolerant Secure Architecture Based on Ensembles of Moving Target Defenses with Churn (MG, LB, SC, ZBA, SFY, MTA, AH, ZX, BK, VB, SM, MT, TMA), pp. 469–484.
- ASPLOS-2019-GanZHCHPD #big data #complexity #debugging #performance
- Seer: Leveraging Big Data to Navigate the Complexity of Performance Debugging in Cloud Microservices (YG0, YZ, KH, DC, YH, MP, CD), pp. 19–33.
- ASPLOS-2019-GaoYPHK #data flow #scalability
- TANGRAM: Optimized Coarse-Grained Dataflow for Scalable NN Accelerators (MG, XY, JP, MH, CK), pp. 807–820.
- ASPLOS-2019-JinH #memory management #network #optimisation
- Split-CNN: Splitting Window-based Operations in Convolutional Neural Networks for Memory System Optimization (TJ, SH), pp. 835–847.
- ASPLOS-2019-JiZXLWHZX #architecture #configuration management #stack
- FPSA: A Full System Stack Solution for Reconfigurable ReRAM-based NN Accelerator Architecture (YJ0, YZ, XX, SL, PW0, XH0, YZ, YX0), pp. 733–747.
- ASPLOS-2019-KimMKRM #multi #scalability
- MV-RLU: Scaling Read-Log-Update with Multi-Versioning (JK, AM, SK, MKR, CM), pp. 779–792.
- ASPLOS-2019-KondguliH #hardware #performance #smt #thread #using
- Bootstrapping: Using SMT Hardware to Improve Single-Thread Performance (SK, MH), pp. 687–700.
- ASPLOS-2019-LascorzJSPMSNSM #approach #hardware #network
- Bit-Tactical: A Software/Hardware Approach to Exploiting Value and Bit Sparsity in Neural Networks (ADL, PJ, DMS, ZP, MM, SS, MN, KS, AM), pp. 749–763.
- ASPLOS-2019-LehmannP #framework
- Wasabi: A Framework for Dynamically Analyzing WebAssembly (DL0, MP), pp. 1045–1058.
- ASPLOS-2019-Liskov #manycore #programming
- Keynote: Multicore Programming (BL), p. 1.
- ASPLOS-2019-LuoLZQ #distributed
- Hop: Heterogeneity-aware Decentralized Training (QL, JL, YZ, XQ), pp. 893–907.
- ASPLOS-2019-MahmoudVAMMFA #adaptation #fault #hardware #testing
- Minotaur: Adapting Software Testing Techniques for Hardware Errors (AM, RV, KA, SM, DM, CWF, SVA), pp. 1087–1103.
- ASPLOS-2019-MiaoJPML #hybrid #memory management
- StreamBox-HBM: Stream Analytics on High Bandwidth Hybrid Memory (HM, MJ, GP, KSM, FXL), pp. 167–181.
- ASPLOS-2019-NagarajanSBT
- ρ: Relaxed Hierarchical ORAM (CN, AS, RB, MT), pp. 659–671.
- ASPLOS-2019-OsterlundKOBBG #detection #execution #kernel #multi
- kMVX: Detecting Kernel Information Leaks with Multi-variant Execution (SÖ, KK, PO, AB, HB, CG), pp. 559–572.
- ASPLOS-2019-PanwarBG #fine-grained #performance
- HawkEye: Efficient Fine-grained OS Support for Huge Pages (AP, SB, KG), pp. 347–360.
- ASPLOS-2019-PellauerSCCHVKF #composition #distributed #performance
- Buffets: An Efficient and Composable Storage Idiom for Explicit Decoupled Data Orchestration (MP, YSS, JC, NCC, KH, RV, SWK, CWF, JSE), pp. 137–151.
- ASPLOS-2019-PinaA0C #execution #multi
- MVEDSUA: Higher Availability Dynamic Software Updates via Multi-Version Execution (LP, AA, MH0, CC), pp. 573–585.
- ASPLOS-2019-RenZYLXQLW #co-evolution #design #framework #multi #using
- ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Methods of Multipliers (AR, TZ, SY, JL, WX, XQ, XL, YW), pp. 925–938.
- ASPLOS-2019-RouhaniCK #framework #network
- DeepSigns: An End-to-End Watermarking Framework for Ownership Protection of Deep Neural Networks (BDR, HC, FK), pp. 485–497.
- ASPLOS-2019-ShenSSBDRW #performance
- X-Containers: Breaking Down Barriers to Improve Performance and Isolation of Cloud-Native Containers (ZS, ZS, GES, EB, CD, RvR, HW), pp. 121–135.
- ASPLOS-2019-SivathanuCSZ #learning #predict
- Astra: Exploiting Predictability to Optimize Deep Learning (MS, TC, SSS, LZ), pp. 909–923.
- ASPLOS-2019-Svore #quantum
- Keynote: Developing our Quantum Future (KMS), p. 2.
- ASPLOS-2019-TangZQC #architecture #message passing #performance
- pLock: A Fast Lock for Architectures with Explicit Inter-core Message Passing (XT, JZ, XQ, WC), pp. 765–778.
- ASPLOS-2019-WillseySTVNPBNJ #framework #platform
- Puddle: A Dynamic, Error-Correcting, Full-Stack Microfluidics Platform (MW, APS, CT, PV, BHN, MP, CB, SN, SJ, KS, LC), pp. 183–197.
- ASPLOS-2019-XuV0 #graph #predict
- PnP: Pruning and Prediction for Point-To-Point Iterative Graph Analytics (CX, KV, RG0), pp. 587–600.
- ASPLOS-2019-ZhangL0HLG #graph #multi #performance
- DiGraph: An Efficient Path-based Iterative Directed Graph Processing System on Multiple GPUs (YZ0, XL, HJ0, BH, HL, LG0), pp. 601–614.
- ASPLOS-2019-ZhangLJ #memory management #safety
- BOGO: Buy Spatial Memory Safety, Get Temporal Memory Safety (Almost) Free (TZ, DL, CJ), pp. 631–644.
- ASPLOS-2019-ZhengOZSYC #framework #performance #pipes and filters
- HiWayLib: A Software Framework for Enabling High Performance Communications for Heterogeneous Pipeline Computations (ZZ, CO, JZ, XS, YY, WC), pp. 153–166.
CASE-2019-AhmedG0 #approach #detection #generative #online- O-LoMST: An Online Anomaly Detection Approach And Its Application In A Hydropower Generation Plant (IA, TG, YD0), pp. 762–767.
- CASE-2019-ChenGL0
- InSPeCT: Iterated Local Search for Solving Path Conditions (FC, AG, DL0, SK0), pp. 1724–1729.
- CASE-2019-NiuLNLW #detection #fault #generative #network #using
- DefectGAN: Weakly-Supervised Defect Detection using Generative Adversarial Network (SN, HL, TN, BL, XW), pp. 127–132.
- CASE-2019-PerdomoPMMKP #2d #detection #performance
- c-M2DP: A Fast Point Cloud Descriptor with Color Information to Perform Loop Closure Detection (LP, DP, MM, RM, MLK, EP), pp. 1145–1150.
- CASE-2019-SoniGAS #hybrid #learning
- HMC: A Hybrid Reinforcement Learning Based Model Compression for Healthcare Applications (RS, JG, GA, VRS), pp. 146–151.
- CASE-2019-WiggertABCVVG #delivery #low cost #open source #precise
- RAPID-MOLT: A Meso-scale, Open-source, Low-cost Testbed for Robot Assisted Precision Irrigation and Delivery (MW, LA, RB, SC, JV, SV, KG), pp. 1489–1496.
- CASE-2019-YamadaFWMUT
- WInBot: A Disc Cutter Wear Inspection Robot for a Tunnel Boring Machine (YY, RF, SW, EM, MU, ST), pp. 1837–1843.
CC-2019-LavaeeCD #interprocedural #layout #optimisation- Codestitcher: inter-procedural basic block layout optimization (RL, JC, CD), pp. 65–75.
- CC-2019-LiuHWCL0X #compilation #concurrent #kernel #thread
- PPOpenCL: a performance-portable OpenCL compiler with host and kernel thread code fusion (YL, LH, MW, HC, FL, XF0, JX), pp. 2–16.
- CC-2019-MendisJJA
- Revec: program rejuvenation through revectorization (CM, AJ, PJ, SPA), pp. 29–41.
- CC-2019-ZhouJKDS #encoding
- Valence: variable length calling context encoding (TZ, MRJ, PAK, KAD, VS), pp. 147–158.
- CGO-2019-AgaA #layout #runtime #stack
- Smokestack: Thwarting DOP Attacks with Runtime Stack Layout Randomization (MTA, TMA), pp. 26–36.
- CGO-2019-BaghdadiRRSAZSK #compilation #performance
- Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code (RB, JR, MBR, EDS, AA, YZ, PS, SK, SPA), pp. 193–205.
- CGO-2019-ChandrasekharCC #compilation #open source
- IGC: The Open Source Intel Graphics Compiler (AC, GC, PYC, WYC, JG, PG, SHPK, GYL, PM, WP, TR, KT), pp. 254–265.
- CGO-2019-LiuSWDL #detection
- CSOD: Context-Sensitive Overflow Detection (HL, SS, XW, LD, TL), pp. 50–60.
- CGO-2019-PanchenkoANO
- BOLT: A Practical Binary Optimizer for Data Centers and Beyond (MP, RA, BN, GO), pp. 2–14.
- CGO-2019-TeixeiraAPG #optimisation
- Locus: A System and a Language for Program Optimization (TSFXT, CA, DAP, WG), pp. 217–228.
- CGO-2019-Zhou0 #automation #parallel
- Janus: Statically-Driven and Profile-Guided Automatic Dynamic Binary Parallelisation (RZ, TMJ0), pp. 15–25.
FASE-2019-BeyerJ #testing- CoVeriTest: Cooperative Verifier-Based Testing (DB0, MCJ), pp. 389–408.
- FASE-2019-EniserGS #fault #locality #network
- DeepFault: Fault Localization for Deep Neural Networks (HFE, SG, AS0), pp. 171–191.
- FASE-2019-PengR #effectiveness #gpu #kernel
- CLTestCheck: Measuring Test Effectiveness for GPU Kernels (CP, AR), pp. 315–331.
- FASE-2019-QianZWO #c #modelling #source code #verification
- KupC: A Formal Tool for Modeling and Verifying Dynamic Updating of C Programs (JQ, MZ0, YW, KO0), pp. 299–305.
- FASE-2019-Wynn-WilliamsDP #refactoring
- SL2SF: Refactoring Simulink to Stateflow (SWW, ZD, VP, ML, GMKS, CM, MD, FW), pp. 264–281.
- FASE-2019-ZweihoffNS #collaboration #generative #modelling #online
- Pyro: Generating Domain-Specific Collaborative Online Modeling Environments (PZ, SN, BS), pp. 101–115.
CADE-2019-BohrerFP #difference #logic- dLι: Definite Descriptions in Differential Dynamic Logic (BB, MF, AP), pp. 94–110.
- CADE-2019-BrownGKSU #challenge
- GRUNGE: A Grand Unified ATP Challenge (CEB, TG, CK, GS, JU), pp. 123–141.
- CADE-2019-ChenWAZKZ #learning
- NIL: Learning Nonlinear Interpolants (MC, JW0, JA, BZ, DK, NZ), pp. 178–196.
- CADE-2019-ChvalovskyJ0U #performance
- ENIGMA-NG: Efficient Neural and Gradient-Boosted Inference Guidance for E (KC, JJ, MS0, JU), pp. 197–215.
- CADE-2019-SutcliffeP #logic
- JGXYZ: An ATP System for Gap and Glut Logics (GS, FJP), pp. 526–537.
- CADE-2019-Tammet #knowledge base #reasoning #scalability
- GKC: A Reasoning System for Large Knowledge Bases (TT), pp. 538–549.
CAV-2019-DreossiFGKRVS #analysis #design #tool support- VerifAI: A Toolkit for the Formal Design and Analysis of Artificial Intelligence-Based Systems (TD, DJF, SG, EK, HR, MVC, SAS), pp. 432–442.
- CAV-2019-FaymonvilleFSSS #cyber-physical #monitoring
- StreamLAB: Stream-based Monitoring of Cyber-Physical Systems (PF, BF, MS, MS, MS, LT, HT), pp. 421–431.
- CAV-2019-LauGMPS #c #concurrent #semantics
- Cerberus-BMC: A Principled Reference Semantics and Exploration Tool for Concurrent and Sequential C (SL, VBFG, KM, JPP, PS), pp. 387–397.
- CAV-2019-NeupaneMM00 #analysis #approximate #infinity #model checking #probability
- STAMINA: STochastic Approximate Model-Checker for INfinite-State Analysis (TN, CJM, CM, HZ0, ZZ0), pp. 540–549.
- CAV-2019-BeckerDMT #compilation #optimisation
- Icing: Supporting Fast-Math Style Optimizations in a Verified Compiler (HB, ED, MOM, ZT), pp. 155–173.
- CAV-2019-EmmiE #concurrent #generative #refinement #testing
- Violat: Generating Tests of Observational Refinement for Concurrent Objects (ME, CE), pp. 534–546.
- CAV-2019-ErnstM #concurrent #logic #security
- SecCSL: Security Concurrent Separation Logic (GE, TM), pp. 208–230.
- CAV-2019-JonasS #performance #quantifier #smt
- Q3B: An Efficient BDD-based SMT Solver for Quantified Bit-Vectors (MJ, JS), pp. 64–73.
- CAV-2019-LeeHL #optimisation #verification
- AliveInLean: A Verified LLVM Peephole Optimization Verifier (JL, CKH, NPL), pp. 445–455.
- CAV-2019-ReynoldsBNBT #performance #synthesis
- cvc4sy: Smart and Fast Term Enumeration for Syntax-Guided Synthesis (AR, HB, AN, CWB, CT), pp. 74–83.
ICST-2019-AkramQL - VCIPR: Vulnerable Code is Identifiable When a Patch is Released (Hacker's Perspective) (JA, LQ, PL0), pp. 402–413.
- ICST-2019-BaloghHB #developer #eclipse #fault #ide #interactive #java #locality
- Poster: Aiding Java Developers with Interactive Fault Localization in Eclipse IDE (GB, FH, ÁB), pp. 371–374.
- ICST-2019-CoppikSS #fuzzing #memory management #using
- MemFuzz: Using Memory Accesses to Guide Fuzzing (NC, OS, NS), pp. 48–58.
- ICST-2019-GyimesiVSMBF0 #benchmark #debugging #javascript #metric
- BugsJS: a Benchmark of JavaScript Bugs (PG, BV, AS0, DM, ÁB, RF, AM0), pp. 90–101.
- ICST-2019-JebbarSKT #testing
- Poster: Re-Testing Configured Instances in the Production Environment - A Method for Reducing the Test Suite (OJ, MAS, FK, MT), pp. 367–370.
- ICST-2019-KooS0B #automation #generative #learning #testing #worst-case
- PySE: Automatic Worst-Case Test Generation by Reinforcement Learning (JK, CS, MK0, SB), pp. 136–147.
- ICST-2019-LamOSM0 #detection #framework #testing
- iDFlakies: A Framework for Detecting and Partially Classifying Flaky Tests (WL, RO, AS, DM, TX0), pp. 312–322.
- ICST-2019-MuscoYN #approach #clustering #implementation #testing
- SmokeOut: An Approach for Testing Clustering Implementations (VM, XY, IN), pp. 473–480.
- ICST-2019-PanarinBIZZMRGT #automation #framework #testing
- Poster: ClearTH Test Automation Framework: A Running Example of a DLT-Based Post-Trade System (VP, AB, II, AZ, KZ, MM, AR, AG, ET, ST, RY), pp. 358–362.
- ICST-2019-VancsicsGSMBF0 #javascript
- Poster: Supporting JavaScript Experimentation with BugsJS (BV, PG, AS0, DM, ÁB, RF, AM0), pp. 375–378.
- ICST-2019-VuottoNPT #automation #consistency #requirements
- Poster: Automatic Consistency Checking of Requirements with ReqV (SV, MN, LP, AT), pp. 363–366.
- ICST-2019-ZhaoLWSH #framework #fuzzing #industrial #learning #perspective #protocol
- SeqFuzzer: An Industrial Protocol Fuzzing Framework from a Deep Learning Perspective (HZ, ZL, HW, JS, YH), pp. 59–67.
- ICST-2019-ZhongZK #scalability #testing #web #web service
- TestSage: Regression Test Selection for Large-Scale Web Service Testing (HZ, LZ, SK), pp. 430–440.
ICTSS-2019-AragaoASCLD #process #type system- TestDCat: Catalog of Test Debt Subtypes and Management Activities (BSA, RMCA, ISS, RNSC, VL, TGRD), pp. 279–295.
- ICTSS-2019-Huerta-GuevaraA19a #adaptation #performance #testing #using
- DYNAMOJM: A JMeter Tool for Performance Testing Using Dynamic Workload Adaptation (OHG, VAR, LM0, AOPD), pp. 234–241.
TAP-2019-OliveiraMDM #modelling #testing- BTestBox: A Tool for Testing B Translators and Coverage of B Models (DdAO, VMJ, DD, MAM), pp. 83–92.
VMCAI-2019-BuenoS #model checking- euforia: Complete Software Model Checking with Uninterpreted Functions (DB, KAS), pp. 363–385.
- ECSA-2018-AlkhabbasSD #approach #architecture #internet
- ECo-IoT: An Architectural Approach for Realizing Emergent Configurations in the Internet of Things (FA, RS, PD), pp. 86–102.
- ECSA-2018-BarbosaGMRM #distributed #framework #lightweight #platform
- Beethoven: An Event-Driven Lightweight Platform for Microservice Orchestration (DMB, RG, PHMM, LSR, NCM), pp. 191–199.
- ECSA-2018-Christensen #architecture #assessment #automation #feedback
- Crunch: Automated Assessment of Microservice Architecture Assignments with Formative Feedback (HBC), pp. 175–190.
ICSA-2018-ArcelliCDP #approach #architecture #multi #refactoring- EASIER: An Evolutionary Approach for Multi-objective Software ArchItecturE Refactoring (DA, VC, MD, DDP), pp. 105–114.
- ICSA-2018-Chen #architecture #delivery
- Microservices: Architecting for Continuous Delivery and DevOps (LC), pp. 39–46.
JCDL-2018-BunkK #modelling #topic #word- WELDA: Enhancing Topic Models by Incorporating Local Word Context (SB, RK), pp. 293–302.
- JCDL-2018-SalatinoOM #research #topic
- AUGUR: Forecasting the Emergence of New Research Topics (AAS, FO, EM), pp. 303–312.
- EDM-2018-AbhinavSDBV #framework #modelling
- LeCoRe: A Framework for Modeling Learner's preference (KA, VS, AD, PB, ADV).
- EDM-2018-DuDP #analysis #behaviour #learning
- ELBA: Exceptional Learning Behavior Analysis (XD, WD, MP).
- EDM-2018-KimVG #learning #performance #predict #student
- GritNet: Student Performance Prediction with Deep Learning (BHK, EV, VG).
- ICPC-2018-BlasiG #clone detection #identification
- Replicomment: identifying clones in code comments (AB, AG), pp. 320–323.
- ICPC-2018-GaoMSSZMMDDZC #c #c++ #debugging #detection
- CoBOT: static C/C++ bug detection in the presence of incomplete code (QG, SM, SS, YS, GZ, LM, XM, FD, XD, SZ, XC), pp. 385–388.
- ICPC-2018-LiNJWHW #behaviour #evolution #learning
- Logtracker: learning log revision behaviors proactively from software evolution history (SL, XN, ZJ, JW, HH, TW0), pp. 178–188.
- ICPC-2018-LyuNK #diagrams #sequence chart #tool support
- SDexplorer: a generic toolkit for smoothly exploring massive-scale sequence diagram (KL, KN, TK), pp. 380–384.
- ICPC-2018-MaL0T0L #android #detection
- LESdroid: a tool for detecting exported service leaks of Android applications (JM0, SL, YJ0, XT, CX0, JL0), pp. 244–254.
- ICPC-2018-PorkolabBKC #comprehension #framework #industrial
- Codecompass: an open software comprehension framework for industrial usage (ZP, TB, DK, MC), pp. 361–369.
- ICPC-2018-ScarsbrookKR0 #debugging #javascript #scalability #visualisation
- MetropolJS: visualizing and debugging large-scale javascript program structure with treemaps (JDS, RKLK, BR, DB0), pp. 389–392.
- ICSME-2018-ArmalyRM
- AudioHighlight: Code Skimming for Blind Programmers (AA, PR, CM), pp. 206–216.
- ICSME-2018-ChenDZGH #automation #implementation #testing #verification
- DRLgencert: Deep Learning-Based Automated Testing of Certificate Verification in SSL/TLS Implementations (CC, WD, YZ, SG, CH), pp. 48–58.
- ICSME-2018-CleareI #ruby
- GemChecker: Reporting on the Status of Gems in Ruby on Rails Projects (JC, CI), pp. 700–704.
- ICSME-2018-FrickWP #algorithm #independence #visualisation
- DiffViz: A Diff Algorithm Independent Visualization Tool for Edit Scripts (VF, CW, MP0), pp. 705–709.
- ICSME-2018-HortonP #execution #git #python
- Gistable: Evaluating the Executability of Python Code Snippets on GitHub (EH, CP), pp. 217–227.
- ICSME-2018-HuZLWLG #analysis #approach #hybrid #semantics
- BinMatch: A Semantics-Based Hybrid Approach on Binary Code Clone Analysis (YH, YZ0, JL, HW, BL, DG), pp. 104–114.
- ICSME-2018-JiarpakdeeTT #automation #correlation #fault #metric #modelling
- AutoSpearman: Automatically Mitigating Correlated Software Metrics for Interpreting Defect Models (JJ, CT, CT), pp. 92–103.
- ICSME-2018-JiarpakdeeTT18a #implementation
- Artefact: An R Implementation of the AutoSpearman Function (JJ, CT, CT), p. 711.
- ICSME-2018-JimenezCTP18a #analysis
- TUNA: TUning Naturalness-Based Analysis (MJ, MC, YLT, MP), p. 715.
- ICSME-2018-RahmanR18a #api #code search #crowdsourcing #data analysis #query #using
- NLP2API: Query Reformulation for Code Search Using Crowdsourced Knowledge and Extra-Large Data Analytics (MMR0, CKR), p. 714.
- ICSME-2018-Volanschi #code generation #editing
- Stereo: Editing Clones Refactored as Code Generators (NV), pp. 595–604.
- ICSME-2018-YueSMT00 #android #detection #runtime #user interface
- RegionDroid: A Tool for Detecting Android Application Repackaging Based on Runtime UI Region Features (SY, QS, JM0, XT, CX0, JL0), pp. 323–333.
- MSR-2018-BaltesDT008 #evolution #stack overflow
- SOTorrent: reconstructing and analyzing the evolution of stack overflow posts (SB, LD, CT, SD0), pp. 319–330.
- MSR-2018-Chatzidimitriou #framework #quality
- npm-miner: an infrastructure for measuring the quality of the npm registry (KCC, MDP, TGD, MT, ALS), pp. 42–45.
- MSR-2018-GaoYJLYZ #concurrent #dataset #testing
- Jbench: a dataset of data races for concurrency testing (JG, XY, YJ0, HL0, WY, XZ), pp. 6–9.
- MSR-2018-GkortzisMS #dataset #open source #security
- VulinOSS: a dataset of security vulnerabilities in open-source systems (AG, DM, DS), pp. 18–21.
- MSR-2018-MartinsAL #dataset #java
- 50K-C: a dataset of compilable, and compiled, Java projects (PM0, RA, CVL), pp. 1–5.
- MSR-2018-NayrollesH #clone detection #detection #fault #industrial #metric #scalability
- CLEVER: combining code metrics with clone detection for just-in-time fault prevention and resolution in large industrial projects (MN, AHL), pp. 153–164.
- MSR-2018-PaixaoKHH #code review #source code
- CROP: linking code reviews to source code changes (MP, JK, DH, MH), pp. 46–49.
- MSR-2018-SanchezBNPK08 #api
- Restmule: enabling resilient clients for remote APIs (BAS, KB, PN, RFP, DSK), pp. 537–541.
- SANER-2018-BritoXHV18a #api #detection
- APIDiff: Detecting API breaking changes (AB, LX, ACH, MTV), pp. 507–511.
- SANER-2018-BurgerG #identification #locality
- FINALIsT2: Feature identification, localization, and tracing tool (AB, SG), pp. 532–537.
- SANER-2018-ChatleyJ #automation #code review #mining #overview #repository
- Diggit: Automated code review via software repository mining (RC, LJ), pp. 567–571.
- SANER-2018-Franzio0LB #assessment #quality #realtime
- RETICULA: Real-time code quality assessment (LF, BL0, ML, GB), pp. 542–546.
- SANER-2018-HanLLXF #graph #reasoning
- DeepWeak: Reasoning common software weaknesses via knowledge graph embedding (ZH, XL0, HL, ZX, ZF0), pp. 456–466.
- SANER-2018-JiaLLLL #comprehension #fault
- SMARTLOG: Place error log statement by deep understanding of log intention (ZJ, SL, XL, XL, YL), pp. 61–71.
- SANER-2018-JolakLSC #concurrent #interactive #modelling #multi #uml #visualisation
- OctoBubbles: A Multi-view interactive environment for concurrent visualization and synchronization of UML models and code (RJ, KDL, KBS, MRVC), pp. 482–486.
- SANER-2018-MaruyamaHO #fine-grained #source code
- ChangeMacroRecorder: Recording fine-grained textual changes of source code (KM, SH, TO), pp. 537–541.
- SANER-2018-PelloniGCPPG #overview #stack
- BECLoMA: Augmenting stack traces with user review information (LP, GG, AC, SP, FP, HCG), pp. 522–526.
- SANER-2018-Salem #agile #identification #mining
- GoldRusher: A miner for rapid identification of hidden code (AS), pp. 517–521.
- SANER-2018-VislavskiRCB #clone detection #detection
- LICCA: A tool for cross-language clone detection (TV, GR, NC, ZB), pp. 512–516.
- FM-2018-AlbertGRS0 #modelling #source code #verification
- SDN-Actors: Modeling and Verification of SDN Programs (EA, MGZ, AR, MS, AS0), pp. 550–567.
- FM-2018-Porncharoenwase
- CompoSAT: Specification-Guided Coverage for Model Finding (SP, TN, SK), pp. 568–587.
- FM-2018-RuchkinSISG #cyber-physical #integration #multi
- IPL: An Integration Property Language for Multi-model Cyber-physical Systems (IR, JS, GI, BRS, DG), pp. 165–184.
- FM-2018-VandinBLL #analysis #configuration management
- QFLan: A Tool for the Quantitative Analysis of Highly Reconfigurable Systems (AV, MHtB, AL, ALL), pp. 329–337.
FSCD-2018-KohlM #proving- ProTeM: A Proof Term Manipulator (System Description) (CK, AM), p. 8.
- Haskell-2018-Barenz0
- Rhine: FRP with type-level clocks (MB, IP0), pp. 145–157.
- Haskell-2018-HandleyH #haskell #performance #source code
- AutoBench: comparing the time performance of Haskell programs (MATH, GH), pp. 26–37.
- ICFP-2018-BourRS #case study #experience #ml
- Merlin: a language server for OCaml (experience report) (FB, TR, GS), p. 15.
- ICFP-2018-KaiserZKRD #coq #reasoning
- Mtac2: typed tactics for backward reasoning in Coq (JOK, BZ, RK, YRG, DD), p. 31.
- ICFP-2018-KrebbersJ0TKTCD #framework #interactive #logic #proving
- MoSeL: a general, extensible modal framework for interactive proofs in separation logic (RK, JHJ, RJ0, JT, JOK, AT, AC, DD), p. 30.
- AIIDE-2018-AytemizKHSW #framework
- Talin: A Framework for Dynamic Tutorials Based on the Skill Atoms Theory (BA, IK, JH, AMS, JW), pp. 138–144.
- AIIDE-2018-ChenG #domain-specific language #generative
- GIGL: A Domain Specific Language for Procedural Content Generation with Grammatical Representations (TC, SJG), pp. 9–16.
- AIIDE-2018-GainaLP #algorithm #visualisation
- VERTIGØ: Visualisation of Rolling Horizon Evolutionary Algorithms in GVGAI (RDG, SML, DPL), pp. 265–267.
- AIIDE-2018-Horswill #embedded #runtime #satisfiability
- CatSAT: A Practical, Embedded, SAT Language for Runtime PCG (IDH), pp. 38–44.
- AIIDE-2018-Horswill18a #game studies
- Postmortem: MKULTRA, An Experimental AI-Based Game (IDH), pp. 45–51.
- AIIDE-2018-MathewsonM #game studies #using
- Improbotics: Exploring the Imitation Game Using Machine Intelligence in Improvised Theatre (KWM, PM), pp. 59–66.
- AIIDE-2018-Ontanon #comprehension #game studies #natural language #prototype
- SHRDLU: A Game Prototype Inspired by Winograd's Natural Language Understanding Work (SO), pp. 268–270.
- AIIDE-2018-StefnissonT #authoring #interactive
- Mimisbrunnur: AI-Assisted Authoring for Interactive Storytelling (ISS, DT), pp. 236–242.
- AIIDE-2018-SummervilleMSOW #analysis #bidirectional #game studies #generative
- Gemini: Bidirectional Generation and Analysis of Games via ASP (AS, CM, BS, JCO, NWF, MM), pp. 123–129.
- CHI-PLAY-2018-FrohlichASDM #artificial reality
- VRBox: A Virtual Reality Augmented Sandbox for Immersive Playfulness, Creativity and Exploration (TF, DA, TS, TD, RM), pp. 153–162.
- CHI-PLAY-2018-IntharahB #2d
- DeepLogger: Extracting User Input Logs From 2D Gameplay Videos (TI, GJB), pp. 221–230.
- CHI-PLAY-2018-KrekhovCEMK #artificial reality #game studies #navigation
- GulliVR: A Walking-Oriented Technique for Navigation in Virtual Reality Games Based on Virtual Body Resizing (AK, SC, KE, MM, JHK), pp. 243–256.
- CHI-PLAY-2018-MehtaKPM #case study #comprehension #design #experience #towards
- Arm-A-Dine: Towards Understanding the Design of Playful Embodied Eating Experiences (YDM, RAK, RP, F'M), pp. 299–313.
- CHI-PLAY-2018-MuscatD #ambiguity #design #game studies
- WORLD4: Designing Ambiguity for First-Person Exploration Games (AM, JD), pp. 341–351.
- CHI-PLAY-2018-WeiszAA #education #game studies #quantum
- Entanglion: A Board Game for Teaching the Principles of Quantum Computing (JDW, MA, ZA), pp. 523–534.
- CIG-2018-ChenANCSE #game studies #performance #recommendation
- Q-DeckRec: A Fast Deck Recommendation System for Collectible Card Games (ZC, CA, THDN, SC, YS, MSEN), pp. 1–8.
- CIG-2018-KosmidesDAKOL #game studies #using
- InLife: Combining Real Life with Serious Games using IoT (PK, KPD, EFA, NK, YO, VDL), pp. 1–7.
DiGRA-2018-Genovesi #game studies #video- Fragmentation: between expansion packs and episodic video games (MG).
- FDG-2018-AzadvarC #evaluation #experience #game studies #self #video
- UPEQ: ubisoft perceived experience questionnaire: a self-determination evaluation tool for video games (AA, AC), p. 7.
- FDG-2018-GreenKBMNT #automation #design #game studies
- AtDELFI: automatically designing legible, full instructions for games (MCG, AK, GABB, TM, AN, JT), p. 10.
- FDG-2018-LankesRDM #3d #collaboration #game studies #social
- Socialeyes: social gaze in collaborative 3D games (ML, MR, OD, BM), p. 10.
- FDG-2018-Maureira #game studies #learning #tool support
- CURIO: a game-based learning toolkit for fostering curiosity (MAGM), p. 6.
- ICGJ-2018-Grace #game studies
- Newsjam: Making Games at the Pace of News (LDG), pp. 17–20.
- VS-Games-2018-RappMBM #artificial reality #game studies #social
- Pathomon: A Social Augmented Reality Serious Game (DR, JM, KB, SvM), pp. 1–4.
- VS-Games-2018-SarupuriHWL #artificial reality #testing #usability
- LUTE: A Locomotion Usability Test Environmentfor Virtual Reality (BS, SH, MCW, RWL), pp. 1–4.
- CIKM-2018-0001CWCLJSZ #network #social
- MEgo2Vec: Embedding Matched Ego Networks for User Alignment Across Social Networks (JZ0, BC, XW, HC0, CL, FJ, GS, YZ), pp. 327–336.
- CIKM-2018-0002B #framework
- GYANI: An Indexing Infrastructure for Knowledge-Centric Tasks (DG0, KB), pp. 487–496.
- CIKM-2018-0002WYTZZ #predict #semistructured data #social
- vec2Link: Unifying Heterogeneous Data for Social Link Prediction (FZ0, BW, YY, GT, KZ, TZ), pp. 1843–1846.
- CIKM-2018-AbelKPFK0AE #multi
- SOURCERY: User Driven Multi-Criteria Source Selection (EA, JAK, NWP, AAAF, MK, NK0, NAA, SME), pp. 1947–1950.
- CIKM-2018-AfsharPPSHS #dataset #scalability
- COPA: Constrained PARAFAC2 for Sparse & Large Datasets (AA, IP, EEP, ES, JCH, JS), pp. 793–802.
- CIKM-2018-AminiNPGYK #authentication #framework #mobile
- DeepAuth: A Framework for Continuous User Re-authentication in Mobile Apps (SA, VN, AP, SG, PSY, CK), pp. 2027–2035.
- CIKM-2018-BorisovKMR #modelling
- Calibration: A Simple Way to Improve Click Models (AB, JK, IM, MdR), pp. 1503–1506.
- CIKM-2018-ChaeKKL #collaboration #framework #generative #network
- CFGAN: A Generic Collaborative Filtering Framework based on Generative Adversarial Networks (DKC, JSK, SWK, JTL), pp. 137–146.
- CIKM-2018-ChinZJC #recommendation
- ANR: Aspect-based Neural Recommender (JYC, KZ0, SRJ, GC), pp. 147–156.
- CIKM-2018-DasGZ #datalog #reasoning
- ASTRO: A Datalog System for Advanced Stream Reasoning (AD, SMG, CZ), pp. 1863–1866.
- CIKM-2018-DeutchF #classification #constraints
- CEC: Constraints based Explanation for Classifications (DD, NF), pp. 1879–1882.
- CIKM-2018-FangZYMZ #benchmark #metric #speech #video
- TED-KISS: A Known-Item Speech Video Search Benchmark (FF, BWZ, XCY, HXM, FZ), pp. 1803–1806.
- CIKM-2018-GarigliottiB #knowledge base
- IntentsKB: A Knowledge Base of Entity-Oriented Search Intents (DG, KB), pp. 1659–1662.
- CIKM-2018-HeimannSSK #graph #representation
- REGAL: Representation Learning-based Graph Alignment (MH, HS, TS, DK), pp. 117–126.
- CIKM-2018-HoangVN #benchmark #dataset #detection #metric #topic
- W2E: A Worldwide-Event Benchmark Dataset for Topic Detection and Tracking (TAH, KDV, WN), pp. 1847–1850.
- CIKM-2018-HooiAEPJPF #detection #graph #locality #online
- ChangeDAR: Online Localized Change Detection for Sensor Data on a Graph (BH, LA, DE, AP, MJ, LTP, CF), pp. 507–516.
- CIKM-2018-HosseiniCWSS #network
- HeteroMed: Heterogeneous Information Network for Medical Diagnosis (AH, TC0, WW, YS, MS), pp. 763–772.
- CIKM-2018-HuangZZC #network #predict
- DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction (CH0, JZ, YZ, NVC), pp. 1423–1432.
- CIKM-2018-HuZY #collaboration #network #recommendation
- CoNet: Collaborative Cross Networks for Cross-Domain Recommendation (GH, YZ, QY), pp. 667–676.
- CIKM-2018-JangCJK #memory management #performance
- Zoom-SVD: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range (JGJ, DC, JJ, UK), pp. 1083–1092.
- CIKM-2018-JiaARSW #knowledge base
- TEQUILA: Temporal Question Answering over Knowledge Bases (ZJ, AA, RSR, JS, GW), pp. 1807–1810.
- CIKM-2018-JiangW #network #query
- RIN: Reformulation Inference Network for Context-Aware Query Suggestion (JYJ, WW0), pp. 197–206.
- CIKM-2018-Kakar0SMTZRHSD #interactive #safety
- DEVES: Interactive Signal Analytics for Drug Safety (TK, XQ0, AS, BM, HQT, BZ, EAR, LH, SKS, SD), pp. 1891–1894.
- CIKM-2018-KalayciXRKC #ontology #query
- Ontop-temporal: A Tool for Ontology-based Query Answering over Temporal Data (EGK, GX0, VR, TEK, DC), pp. 1927–1930.
- CIKM-2018-KalokyriBM #integration
- YourDigitalSelf: A Personal Digital Trace Integration Tool (VK, AB, AM), pp. 1963–1966.
- CIKM-2018-KangFYXCT #multi #network #ranking
- X-Rank: Explainable Ranking in Complex Multi-Layered Networks (JK, SF, HY, YX, NC, HT), pp. 1959–1962.
- CIKM-2018-KenthapadiT #framework #privacy
- PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn (KK, TTLT), pp. 2183–2191.
- CIKM-2018-KhattabHS #in memory #manycore #paradigm #polymorphism
- PolyHJ: A Polymorphic Main-Memory Hash Join Paradigm for Multi-Core Machines (OK, MH, OS), pp. 1323–1332.
- CIKM-2018-KhattarKV0 #hybrid #recommendation
- HRAM: A Hybrid Recurrent Attention Machine for News Recommendation (DK, VK, VV, MG0), pp. 1619–1622.
- CIKM-2018-KweeCPL #lifecycle #mining #using #visualisation
- Traffic-Cascade: Mining and Visualizing Lifecycles of Traffic Congestion Events Using Public Bus Trajectories (ATK, MFC, PKP, EPL), pp. 1955–1958.
- CIKM-2018-LeiXWP #behaviour #mining
- CAPatternMiner: Mining Ship Collision Avoidance Behavior from AIS Trajectory Data (PRL, LPX, YTW, WCP), pp. 1875–1878.
- CIKM-2018-LevchenkoYAMKS #dataset #distributed #sketching
- Spark-parSketch: A Massively Distributed Indexing of Time Series Datasets (OL, DEY, RA, FM, BK, DES), pp. 1951–1954.
- CIKM-2018-LiGC #network
- VTeller: Telling the Values Somewhere, Sometime in a Dynamic Network of Urban Systems (YL, TG, CXC), pp. 577–586.
- CIKM-2018-LiLAL #industrial #multi #scalability
- CUSNTF: A Scalable Sparse Non-negative Tensor Factorization Model for Large-scale Industrial Applications on Multi-GPU (HL0, KL, JyA, KL0), pp. 1113–1122.
- CIKM-2018-LosterNEF #graph
- CurEx: A System for Extracting, Curating, and Exploring Domain-Specific Knowledge Graphs from Text (ML, FN, JE, BF), pp. 1883–1886.
- CIKM-2018-MafrurSK #data analysis #recommendation #visual notation
- DiVE: Diversifying View Recommendation for Visual Data Exploration (RM, MAS, HAK), pp. 1123–1132.
- CIKM-2018-MaYXCZG #knowledge-based #predict
- KAME: Knowledge-based Attention Model for Diagnosis Prediction in Healthcare (FM, QY, HX, RC, JZ, JG0), pp. 743–752.
- CIKM-2018-NagarkarC #execution #performance #query #set
- PSLSH: An Index Structure for Efficient Execution of Set Queries in High-Dimensional Spaces (PN, KSC), pp. 477–486.
- CIKM-2018-NishidaSOAT #comprehension #information retrieval #learning #multi
- Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension (KN, IS, AO, HA, JT), pp. 647–656.
- CIKM-2018-PalottiZH #evaluation #framework #multi
- MM: A new Framework for Multidimensional Evaluation of Search Engines (JRMP, GZ, AH), pp. 1699–1702.
- CIKM-2018-PapalexakisP #framework #multi
- tHoops: A Multi-Aspect Analytical Framework for Spatio-Temporal Basketball Data (EEP, KP), pp. 2223–2232.
- CIKM-2018-PathakGNK #query #retrieval
- AQuPR: Attention based Query Passage Retrieval (PP, MDG, NN, HK), pp. 1495–1498.
- CIKM-2018-PrasojoKN #fine-grained #information management #semantics #using
- StuffIE: Semantic Tagging of Unlabeled Facets Using Fine-Grained Information Extraction (REP, MK, WN), pp. 467–476.
- CIKM-2018-RaynaudSL #navigation #topic
- Fouilla: Navigating DBpedia by Topic (TR, JS, FL), pp. 1907–1910.
- CIKM-2018-SahaSGD #information management #modelling #process
- CRPP: Competing Recurrent Point Process for Modeling Visibility Dynamics in Information Diffusion (AS, BS, NG, AD), pp. 537–546.
- CIKM-2018-SantuLCZ #behaviour #modelling
- JIM: Joint Influence Modeling for Collective Search Behavior (SKKS, LL, YC, CZ), pp. 637–646.
- CIKM-2018-ScellsZ #comprehension #query #visualisation
- searchrefiner: A Query Visualisation and Understanding Tool for Systematic Reviews (HS, GZ), pp. 1939–1942.
- CIKM-2018-SojkaRN #library #retrieval
- MIaS: Math-Aware Retrieval in Digital Mathematical Libraries (PS, MR, VN), pp. 1923–1926.
- CIKM-2018-SongZWTZJC #graph #learning #rank
- TGNet: Learning to Rank Nodes in Temporal Graphs (QS, BZ, YW, LAT, HZ, GJ, HC), pp. 97–106.
- CIKM-2018-SyedRN #rdf #using #validation
- FactCheck: Validating RDF Triples Using Textual Evidence (ZHS, MR, ACNN), pp. 1599–1602.
- CIKM-2018-VadapalliSPSV #automation #towards
- Sci-Blogger: A Step Towards Automated Science Journalism (RV, BS, NP, BVS, VV), pp. 1787–1790.
- CIKM-2018-VazirgiannisMN #graph #information retrieval #mining
- GraphRep: Boosting Text Mining, NLP and Information Retrieval with Graphs (MV, FDM, GN), pp. 2295–2296.
- CIKM-2018-VillalobosDIGB #industrial #reduction
- I4TSRS: A System to Assist a Data Engineer in Time-Series Dimensionality Reduction in Industry 4.0 Scenarios (KV, BD, AI, AG, JMB), pp. 1915–1918.
- CIKM-2018-WangXWCC #classification
- DualBoost: Handling Missing Values with Feature Weights and Weak Classifiers that Abstain (WW, JX0, YW0, CC, FC0), pp. 1543–1546.
- CIKM-2018-WangYWJZZW #data mining #graph #mining #scalability
- AceKG: A Large-scale Knowledge Graph for Academic Data Mining (RW, YY, JW, YJ, YZ, WZ0, XW), pp. 1487–1490.
- CIKM-2018-WangZWZLXG #graph #recommendation
- RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (HW, FZ, JW, MZ, WL, XX, MG), pp. 417–426.
- CIKM-2018-WuQLJ #what
- PARL: Let Strangers Speak Out What You Like (LW, CQ, CL, DJ), pp. 677–686.
- CIKM-2018-WuSDHFC #behaviour #rest
- RESTFul: Resolution-Aware Forecasting of Behavioral Time Series Data (XW, BS, YD, CH0, LF, NVC), pp. 1073–1082.
- CIKM-2018-XuLHLXYW #corpus #multi #type system
- METIC: Multi-Instance Entity Typing from Corpus (BX, ZL, LH, BL, YX, DY, WW0), pp. 903–912.
- CIKM-2018-YangS #learning #multi #performance
- FALCON: A Fast Drop-In Replacement of Citation KNN for Multiple Instance Learning (SY, XS), pp. 67–76.
- CIKM-2018-ZhangWFSL #mobile #performance
- FastInput: Improving Input Efficiency on Mobile Devices (JZ, XW, YF, MS, PL0), pp. 2057–2065.
- CIKM-2018-ZhangZNWH #knowledge base #multi
- MultiE: Multi-Task Embedding for Knowledge Base Completion (ZZ, FZ, ZYN, DW, QH), pp. 1715–1718.
- CIKM-2018-ZhouYWBEYZW #network #personalisation #ranking
- PRRE: Personalized Relation Ranking Embedding for Attributed Networks (SZ, HY, XW0, JB, ME, PY, JZ, CW0), pp. 823–832.
- ECIR-2018-0001TJ18a #recommendation
- CITEWERTs: A System Combining Cite-Worthiness with Citation Recommendation (MF0, AT, AJ), pp. 815–819.
- ECIR-2018-CharbonnierSRRW #image #reuse
- NOA: A Search Engine for Reusable Scientific Images Beyond the Life Sciences (JC, LS, JR, BR, CW), pp. 797–800.
- ECIR-2018-MansouriZ0FR #normalisation #rule-based
- ParsTime: Rule-Based Extraction and Normalization of Persian Temporal Expressions (BM, MSZ, RC0, MF, MR), pp. 715–721.
- ECIR-2018-VogelsGE #web
- Web2Text: Deep Structured Boilerplate Removal (TV, OEG, CE), pp. 167–179.
- ECIR-2018-ZangerleTWS #analysis #multimodal #music #towards
- ALF-200k: Towards Extensive Multimodal Analyses of Music Tracks and Playlists (EZ, MT, SW, GS), pp. 584–590.
- ICML-2018-0001KS #optimisation #probability
- Shampoo: Preconditioned Stochastic Tensor Optimization (VG0, TK, YS), pp. 1837–1845.
- ICML-2018-AcharyaKSZ
- INSPECTRE: Privately Estimating the Unseen (JA, GK0, ZS, HZ), pp. 30–39.
- ICML-2018-AghazadehSLDSB #feature model #scalability #sketching #using
- MISSION: Ultra Large-Scale Feature Selection using Count-Sketches (AA, RS, DL, GD, AS, RGB), pp. 80–88.
- ICML-2018-AinsworthFLF #analysis
- oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis (SKA, NJF, AKCL, EBF), pp. 119–128.
- ICML-2018-AlaaS18a #automation #kernel #learning #modelling #optimisation
- AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning (AMA, MvdS), pp. 139–148.
- ICML-2018-AmodioK #biology
- MAGAN: Aligning Biological Manifolds (MA, SK), pp. 215–223.
- ICML-2018-BajajGHHL #clustering #using
- SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions (CB, TG, ZH, QH, ZL), pp. 334–343.
- ICML-2018-BernsteinWAA #optimisation #problem
- SIGNSGD: Compressed Optimisation for Non-Convex Problems (JB, YXW, KA, AA), pp. 559–568.
- ICML-2018-BojchevskiSZG #generative #graph #random
- NetGAN: Generating Graphs via Random Walks (AB, OS, DZ, SG), pp. 609–618.
- ICML-2018-BoracchiCCM #data type #detection #multi
- QuantTree: Histograms for Change Detection in Multivariate Data Streams (GB, DC, CC, DM), pp. 638–647.
- ICML-2018-ChenBLR #adaptation #multi #network #normalisation
- GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks (ZC0, VB, CYL, AR), pp. 793–802.
- ICML-2018-ChenMRA #generative
- PixelSNAIL: An Improved Autoregressive Generative Model (XC0, NM, MR, PA), pp. 863–871.
- ICML-2018-ChenWCP #distributed
- DRACO: Byzantine-resilient Distributed Training via Redundant Gradients (LC, HW, ZBC, DSP), pp. 902–911.
- ICML-2018-ChenXCY #adaptation #probability
- SADAGRAD: Strongly Adaptive Stochastic Gradient Methods (ZC, YX, EC, TY), pp. 912–920.
- ICML-2018-ColasSO #algorithm #learning
- GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms (CC, OS, PYO), pp. 1038–1047.
- ICML-2018-DaiS0XHLCS #approximate #convergence #learning
- SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation (BD, AS, LL0, LX, NH, ZL0, JC, LS), pp. 1133–1142.
- ICML-2018-DiengRAB #network
- Noisin: Unbiased Regularization for Recurrent Neural Networks (ABD, RR, JA, DMB), pp. 1251–1260.
- ICML-2018-EspeholtSMSMWDF #architecture #distributed #scalability
- IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures (LE, HS, RM, KS, VM, TW, YD, VF, TH, ID, SL, KK), pp. 1406–1415.
- ICML-2018-FalknerKH #optimisation #performance #robust #scalability
- BOHB: Robust and Efficient Hyperparameter Optimization at Scale (SF, AK, FH), pp. 1436–1445.
- ICML-2018-FazelniaP
- CRVI: Convex Relaxation for Variational Inference (GF, JWP), pp. 1476–1484.
- ICML-2018-FoersterFARXW #infinity #monte carlo
- DiCE: The Infinitely Differentiable Monte Carlo Estimator (JNF, GF, MAS, TR, EPX, SW), pp. 1524–1533.
- ICML-2018-GaoCL #network #optimisation
- Spotlight: Optimizing Device Placement for Training Deep Neural Networks (YG, LC0, BL), pp. 1662–1670.
- ICML-2018-Hebert-JohnsonK #multi
- Multicalibration: Calibration for the (Computationally-Identifiable) Masses (ÚHJ, MPK, OR, GNR), pp. 1944–1953.
- ICML-2018-HoffmanTPZISED #adaptation
- CyCADA: Cycle-Consistent Adversarial Domain Adaptation (JH, ET, TP, JYZ, PI, KS, AAE, TD), pp. 1994–2003.
- ICML-2018-JiangZLLF #data-driven #education #learning #network
- MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels (LJ0, ZZ, TL, LJL, LFF0), pp. 2309–2318.
- ICML-2018-JinYXYJFY #network #performance
- WSNet: Compact and Efficient Networks Through Weight Sampling (XJ, YY, NX0, JY, NJ, JF, SY), pp. 2357–2366.
- ICML-2018-KarmonZG #locality
- LaVAN: Localized and Visible Adversarial Noise (DK, DZ, YG), pp. 2512–2520.
- ICML-2018-KennamerKIS #classification #learning
- ContextNet: Deep learning for Star Galaxy Classification (NK, DK, ATI, FJSL), pp. 2587–2595.
- ICML-2018-LehtinenMHLKAA #image #learning
- Noise2Noise: Learning Image Restoration without Clean Data (JL, JM, JH, SL, TK, MA, TA), pp. 2971–2980.
- ICML-2018-LiangLNMFGGJS #abstraction #distributed #learning
- RLlib: Abstractions for Distributed Reinforcement Learning (EL, RL, RN, PM, RF, KG, JG, MIJ, IS), pp. 3059–3068.
- ICML-2018-LongLMD #learning
- PDE-Net: Learning PDEs from Data (ZL, YL, XM, BD0), pp. 3214–3222.
- ICML-2018-MassiasSG #performance
- Celer: a Fast Solver for the Lasso with Dual Extrapolation (MM, JS, AG), pp. 3321–3330.
- ICML-2018-MetzlerSVB #flexibility #network #retrieval #robust
- prDeep: Robust Phase Retrieval with a Flexible Deep Network (CAM, PS, AV, RGB), pp. 3498–3507.
- ICML-2018-MoreauOV #coordination #distributed
- DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding (TM, LO, NV), pp. 3623–3631.
- ICML-2018-MorvanV #algorithm #higher-order #interactive #modelling #set
- WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models (MLM, JPV), pp. 3632–3641.
- ICML-2018-NiculaeMBC
- SparseMAP: Differentiable Sparse Structured Inference (VN, AFTM, MB, CC), pp. 3796–3805.
- ICML-2018-ParmasR0D #flexibility #modelling #policy #robust
- PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos (PP, CER, JP0, KD), pp. 4062–4071.
- ICML-2018-PuDGWWZHC #generative #learning #multi
- JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets (YP, SD, ZG, WW, GW0, YZ, RH, LC), pp. 4148–4157.
- ICML-2018-QiuCCS #network
- DCFNet: Deep Neural Network with Decomposed Convolutional Filters (QQ, XC, ARC, GS), pp. 4195–4204.
- ICML-2018-RamdasZWJ #adaptation #algorithm #online
- SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate (AR, TZ, MJW, MIJ), pp. 4283–4291.
- ICML-2018-RashidSWFFW #learning #multi
- QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning (TR, MS, CSdW, GF, JNF, SW), pp. 4292–4301.
- ICML-2018-ReagenGAMRWB #encoding #network
- Weightless: Lossy weight encoding for deep neural network compression (BR, UG, BA, MM, AMR, GYW, DB0), pp. 4321–4330.
- ICML-2018-SanyalKGK #as a service #predict
- TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service (AS, MJK, AG, VK), pp. 4497–4506.
- ICML-2018-ShazeerS #adaptation #learning #memory management #sublinear
- Adafactor: Adaptive Learning Rates with Sublinear Memory Cost (NS, MS), pp. 4603–4611.
- ICML-2018-ShiarlisWSWP #composition #learning
- TACO: Learning Task Decomposition via Temporal Alignment for Control (KS, MW, SS, SW, IP), pp. 4661–4670.
- ICML-2018-TangLYZL #distributed
- D2: Decentralized Training over Decentralized Data (HT, XL, MY0, CZ, JL0), pp. 4855–4863.
- ICML-2018-TianZZ #learning
- CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions (KT, TZ, JZ), pp. 4933–4942.
- ICML-2018-TschannenKA #learning #multi
- StrassenNets: Deep Learning with a Multiplication Budget (MT, AK, AA), pp. 4992–5001.
- ICML-2018-WeiszGS #algorithm #approximate #bound
- LEAPSANDBOUNDS: A Method for Approximately Optimal Algorithm Configuration (GW, AG, CS), pp. 5254–5262.
- ICML-2018-WuHS #approach #collaboration #ranking
- SQL-Rank: A Listwise Approach to Collaborative Ranking (LW, CJH, JS), pp. 5311–5320.
- ICML-2018-YoonJS #generative #using
- GAIN: Missing Data Imputation using Generative Adversarial Nets (JY, JJ, MvdS), pp. 5675–5684.
- ICML-2018-YoonJS18a #dataset #generative #modelling #multi #network #predict #using
- RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks (JY, JJ, MvdS), pp. 5685–5693.
- ICML-2018-YouYRHL #generative #graph #modelling
- GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models (JY, RY, XR, WLH, JL), pp. 5694–5703.
ICPR-2018-0002ZT0 #visual notation- OWP: Objectness Weighted Patch Descriptor for Visual Tracking (BJ0, YZ, JT, BL0), pp. 2118–2123.
- ICPR-2018-0004YW #kernel
- Spindle-Net: CNNs for Monocular Depth Inference with Dilation Kernel Method (LH0, MY, GW), pp. 2504–2509.
- ICPR-2018-AiZYH #algorithm #identification
- iCushion: A Pressure Map Algorithm for High Accuracy Human Identification (HA, LZ, ZY, HH), pp. 3483–3488.
- ICPR-2018-BandyopadhyayND #approach
- DivGroup: A Diversified Approach to Divide Collection of Patterns into Uniform Groups (SB, SN, RD, NMM), pp. 964–969.
- ICPR-2018-CaoLZSLS #detection #network #performance
- ThinNet: An Efficient Convolutional Neural Network for Object Detection (SC, YL, CZ, QSS, PL, SS), pp. 836–841.
- ICPR-2018-ChengLYT #image #network #recursion
- SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks (XC, XL0, JY0, YT), pp. 147–152.
- ICPR-2018-ChenSSZ #evaluation #modelling
- WiTT: Modeling and the evaluation of table tennis actions based on WIFI signals (CC, YS, KIS, HZ0), pp. 3100–3107.
- ICPR-2018-CuradoELH #ranking
- net4Lap: Neural Laplacian Regularization for Ranking and Re-Ranking (MC, FE, MAL, ERH), pp. 1366–1371.
- ICPR-2018-DiSP #gender
- GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks (XD, VAS, VMP), pp. 1079–1084.
- ICPR-2018-DuCWP #distributed #learning #representation
- Zone2Vec: Distributed Representation Learning of Urban Zones (JD, YC, YW0, JP), pp. 880–885.
- ICPR-2018-GuoDGB #detection #multi
- MSFD: Multi-Scale Receptive Field Face Detector (QG, YD, YG, HB), pp. 1869–1874.
- ICPR-2018-HaoDWT #fine-grained #learning #representation #retrieval
- DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval (JH, JD0, WW0, TT), pp. 3335–3340.
- ICPR-2018-HuKLFLZD #learning #representation
- FV-Net: learning a finger-vein feature representation based on a CNN (HH, WK, YL, YF, HL, JZ, FD), pp. 3489–3494.
- ICPR-2018-LakraTKV0 #segmentation
- SegDenseNet: Iris Segmentation for Pre-and-Post Cataract Surgery (AL, PT, RK, MV, RS0), pp. 3150–3155.
- ICPR-2018-LiangLJXL #benchmark #dataset #metric #multi #predict
- SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction (LL, LL, LJ, DX, ML), pp. 1598–1603.
- ICPR-2018-LiaoZWN #detection #network #recognition
- Uniface: A Unified Network for Face Detection and Recognition (ZL, PZ, QW, BN), pp. 3531–3536.
- ICPR-2018-LiLXD
- CascadeNet: Modified ResNet with Cascade Blocks (XL, WL0, XX, QD), pp. 483–488.
- ICPR-2018-LinLJ #predict #ranking
- R2-ResNeXt: A ResNeXt-Based Regression Model with Relative Ranking for Facial Beauty Prediction (LL, LL, LJ), pp. 85–90.
- ICPR-2018-LiuMZ #performance #quality
- Artsy-GAN: A style transfer system with improved quality, diversity and performance (HL, PNM, DZ), pp. 79–84.
- ICPR-2018-LiZQ #assessment #documentation #image #quality
- CG-DIQA: No-Reference Document Image Quality Assessment Based on Character Gradient (HL, FZ, JQ), pp. 3622–3626.
- ICPR-2018-MaGLLZ #3d #multi #network #segmentation #semantics
- 3DMAX-Net: A Multi-Scale Spatial Contextual Network for 3D Point Cloud Semantic Segmentation (YM, YG, YL, ML, JZ), pp. 1560–1566.
- ICPR-2018-MaGWW #kernel #symmetry
- RotateConv: Making Asymmetric Convolutional Kernels Rotatable (JM, WG, WW0, LW0), pp. 55–60.
- ICPR-2018-MandalSVM #policy #robust
- CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction (MM, PS, SKV, SM), pp. 2468–2473.
- ICPR-2018-MaWWW #detection #multi #network #performance
- MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection (WM, YW, ZW, GW), pp. 2510–2515.
- ICPR-2018-MingCLVB #detection #interactive #liveness #network #verification
- FaceLiveNet: End-to-End Networks Combining Face Verification with Interactive Facial Expression-Based Liveness Detection (ZM, JC, MML, MV, JCB), pp. 3507–3512.
- ICPR-2018-NiuHSC #learning
- SynRhythm: Learning a Deep Heart Rate Estimator from General to Specific (XN, HH, SS, XC), pp. 3580–3585.
- ICPR-2018-ParkAMLCP0PK #generative #network
- MMGAN: Manifold-Matching Generative Adversarial Networks (NP, AA, JRAM, KL, JC, DKP, TC0, HP, YK), pp. 1343–1348.
- ICPR-2018-PereraAP #generative #multi #network #using
- In2I: Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks (PP, MA, VMP), pp. 140–146.
- ICPR-2018-QiuLL #clustering #nearest neighbour
- D-NND: A Hierarchical Density Clustering Method via Nearest Neighbor Descent (TQ, CL, YL), pp. 1414–1419.
- ICPR-2018-QiuXC #flexibility #linear #network
- FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks (SQ, XX, BC), pp. 1223–1228.
- ICPR-2018-RazinkovSM #algorithm #detection
- ALFA: Agglomerative Late Fusion Algorithm for Object Detection (ER, IS, JM), pp. 2594–2599.
- ICPR-2018-SahaVJ #learning
- Class2Str: End to End Latent Hierarchy Learning (SS, GV, CVJ), pp. 1000–1005.
- ICPR-2018-SaitohK #database #recognition #smarttech #speech #visual notation
- SSSD: Speech Scene database by Smart Device for Visual Speech Recognition (TS, MK), pp. 3228–3232.
- ICPR-2018-SwamiD #image #network
- CANDY: Conditional Adversarial Networks based End-to-End System for Single Image Haze Removal (KS, SKD), pp. 3061–3067.
- ICPR-2018-TheagarajanBC #automation
- DeepDriver: Automated System For measuring Valence and Arousal in Car Driver Videos (RT, BB, ACC), pp. 2546–2551.
- ICPR-2018-TheagarajanGB #automation #classification #generative
- DeephESC: An Automated System for Generating and Classification of Human Embryonic Stem Cells (RT, BXG, BB), pp. 3826–3831.
- ICPR-2018-Wang #network
- ReNN: Rule-embedded Neural Networks (HW), pp. 824–829.
- ICPR-2018-WangGZXH #correlation #network #visual notation
- SPCNet: Scale Position Correlation Network for End-to-End Visual Tracking (QW, JG, MZ, JX, WH), pp. 1803–1808.
- ICPR-2018-WuLPL #embedded #performance #robust
- Em-SLAM: a Fast and Robust Monocular SLAM Method for Embedded Systems (YW, ZL, SP, TL), pp. 1882–1887.
- ICPR-2018-XuZL #feature model #multi #recognition
- MSSVT: Multi-scale feature extraction for single face recognition (XX, LZ, FL), pp. 1996–2001.
- ICPR-2018-YangYZT #challenge #self
- Self-Talk: Responses to Users' Opinions and Challenges in Human Computer Dialog (MY, NSRY, KZ, JT), pp. 2839–2844.
- ICPR-2018-ZhangCSZWHJL #assessment #network #performance #quality
- SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks (TZ, JC, DFS, KZ, AW, PH, AJ, BCL), pp. 2314–2319.
- ICPR-2018-ZhangJZZSLH #multi #segmentation
- SequentialSegNet: Combination with Sequential Feature for Multi-Organ Segmentation (YZ, XJ, CZ, YZ, ZS, ZL, ZH), pp. 3947–3952.
- ICPR-2018-ZhangLXQ #network
- LHONE: Label Homophily Oriented Network Embedding (LZ, XL, JX, YQ), pp. 665–670.
- ICPR-2018-ZhangZW #classification #lightweight #network
- LD-CNN: A Lightweight Dilated Convolutional Neural Network for Environmental Sound Classification (XZ, YZ, WW), pp. 373–378.
- ICPR-2018-ZhaoMS #classification #image #network
- Retraining: A Simple Way to Improve the Ensemble Accuracy of Deep Neural Networks for Image Classification (KZ, TM, ES), pp. 860–867.
- ICPR-2018-ZhuangTYMZJX #image #learning #representation #segmentation #semantics
- RelationNet: Learning Deep-Aligned Representation for Semantic Image Segmentation (YZ, LT, FY, CM, ZZ, HJ, XX), pp. 1506–1511.
- KDD-2018-0003GZZSL #data type
- HeavyGuardian: Separate and Guard Hot Items in Data Streams (TY0, JG, HZ, LZ0, LS, XL), pp. 2584–2593.
- KDD-2018-AbernethyCFSW #pipes and filters
- ActiveRemediation: The Search for Lead Pipes in Flint, Michigan (JDA, AC, AF, EMS, JW), pp. 5–14.
- KDD-2018-BorisyukGS #detection #image #recognition #scalability
- Rosetta: Large Scale System for Text Detection and Recognition in Images (FB, AG, VS), pp. 71–79.
- KDD-2018-ChenCDLW0C #information management
- Learning-to-Ask: Knowledge Acquisition via 20 Questions (YC, BC, XD, JGL, YW, WZ0, YC), pp. 1216–1225.
- KDD-2018-ChenHNLLWX #multi
- MIX: Multi-Channel Information Crossing for Text Matching (HC, FXH, DN, DL, KL, CW, YX), pp. 110–119.
- KDD-2018-ChenYWWNL #metric #network #predict
- PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction (HC, HY, WW0, HW0, QVHN, XL), pp. 1177–1186.
- KDD-2018-ConoverHBSS #performance
- Pangloss: Fast Entity Linking in Noisy Text Environments (MDC, MH, SB, PS, SS), pp. 168–176.
- KDD-2018-ConteMSGMV #community #detection #network #scalability
- D2K: Scalable Community Detection in Massive Networks via Small-Diameter k-Plexes (AC, TDM, DDS, RG, AM, LV), pp. 1272–1281.
- KDD-2018-DasSCHLCKC #learning #performance #using
- SHIELD: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression (ND, MS, STC, FH, SL, LC, MEK, DHC), pp. 196–204.
- KDD-2018-DingLBZL #framework
- Investor-Imitator: A Framework for Trading Knowledge Extraction (YD, WL, JB0, DZ, TYL), pp. 1310–1319.
- KDD-2018-DuT #equation #graph #mining #performance
- FASTEN: Fast Sylvester Equation Solver for Graph Mining (BD, HT), pp. 1339–1347.
- KDD-2018-EswaranFGM #detection #graph #streaming
- SpotLight: Detecting Anomalies in Streaming Graphs (DE, CF, SG, NM), pp. 1378–1386.
- KDD-2018-GorovitsGPB #community #learning
- LARC: Learning Activity-Regularized Overlapping Communities Across Time (AG, EG, EEP, PB), pp. 1465–1474.
- KDD-2018-IyengarLISW #approach #data-driven #energy #performance
- WattHome: A Data-driven Approach for Energy Efficiency Analytics at City-scale (SI, SL, DEI, PJS, BW), pp. 396–405.
- KDD-2018-JhaXWGZ #concept #evolution
- Concepts-Bridges: Uncovering Conceptual Bridges Based on Biomedical Concept Evolution (KJ, GX, YW, VG, AZ), pp. 1599–1607.
- KDD-2018-KochGGWGX #framework #optimisation
- Autotune: A Derivative-free Optimization Framework for Hyperparameter Tuning (PK, OG, SG, BW, JG, YX), pp. 443–452.
- KDD-2018-KoptelovZBBC #identification
- PrePeP: A Tool for the Identification and Characterization of Pan Assay Interference Compounds (MK, AZ, PB, RB, BC), pp. 462–471.
- KDD-2018-Li0ZQHG0 #information management #reliability
- TruePIE: Discovering Reliable Patterns in Pattern-Based Information Extraction (QL0, MJ0, XZ, MQ, TPH, JG0, JH0), pp. 1675–1684.
- KDD-2018-LianZZCXS #feature model #interactive #recommendation
- xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems (JL, XZ, FZ, ZC, XX0, GS), pp. 1754–1763.
- KDD-2018-LinKLZSXZQZ #big data #identification #interactive #multi
- BigIN4: Instant, Interactive Insight Identification for Multi-Dimensional Big Data (QL, WK, JGL, HZ0, KS, YX, ZZ, BQ, DZ), pp. 547–555.
- KDD-2018-LiRMLKC #predict
- TATC: Predicting Alzheimer's Disease with Actigraphy Data (JL, YR, HM, ZL0, TK, HC), pp. 509–518.
- KDD-2018-LiuZMZ #memory management #recommendation
- STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation (QL, YZ, RM, HZ), pp. 1831–1839.
- KDD-2018-LuJZDZW #learning #semantics #visual notation
- R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering (PL, LJ, WZ0, ND, MZ0, JW), pp. 1880–1889.
- KDD-2018-LuoCTSLCY #information management #invariant #learning #network
- TINET: Learning Invariant Networks via Knowledge Transfer (CL, ZC, LAT, AS, ZL, HC, JY), pp. 1890–1899.
- KDD-2018-MolinoZW #network #ranking
- COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks (PM, HZ, YCW), pp. 586–595.
- KDD-2018-NaKY #data type #detection #effectiveness #memory management #performance
- DILOF: Effective and Memory Efficient Local Outlier Detection in Data Streams (GSN, DHK, HY), pp. 1993–2002.
- KDD-2018-Park0 #effectiveness #graph #performance #scalability
- EvoGraph: An Effective and Efficient Graph Upscaling Method for Preserving Graph Properties (HP, MSK0), pp. 2051–2059.
- KDD-2018-PerrosPPVYdSS #scalability
- SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping (IP, EEP, HP, RWV, XY, Cd, WFS, JS), pp. 2080–2089.
- KDD-2018-QahtanEFO0 #detection #robust
- FAHES: A Robust Disguised Missing Values Detector (AAQ, AKE, RCF, MO, NT0), pp. 2100–2109.
- KDD-2018-QiuTMDW0 #learning #predict #social
- DeepInf: Social Influence Prediction with Deep Learning (JQ, JT, HM, YD, KW, JT0), pp. 2110–2119.
- KDD-2018-RiondatoV #mining #pseudo
- MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension (MR, FV), pp. 2130–2139.
- KDD-2018-RongXYM #big data #realtime
- Du-Parking: Spatio-Temporal Big Data Tells You Realtime Parking Availability (YR, ZX, RY, XM), pp. 646–654.
- KDD-2018-ShenLOLZC #framework #network #novel #predict
- StepDeep: A Novel Spatial-temporal Mobility Event Prediction Framework based on Deep Neural Network (BS, XL, YO, ML, WZ, KMC), pp. 724–733.
- KDD-2018-ShenWLZRVS0 #taxonomy
- HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion (JS, ZW, DL, CZ0, XR, MTV, BMS, JH0), pp. 2180–2189.
- KDD-2018-SilvisSL #student
- PittGrub: A Frustration-Free System to Reduce Food Waste by Notifying Hungry College Students (MS, AS, AL), pp. 754–763.
- KDD-2018-TaoXGLFZ #detection #framework #game studies
- NGUARD: A Game Bot Detection Framework for NetEase MMORPGs (JT, JX, LG, YL, CF, ZZ), pp. 811–820.
- KDD-2018-Ting #bound #empirical #estimation #fault #using
- Count-Min: Optimal Estimation and Tight Error Bounds using Empirical Error Distributions (DT), pp. 2319–2328.
- KDD-2018-TsitsulinMKBM #graph
- NetLSD: Hearing the Shape of a Graph (AT, DM, PK, AMB, EM), pp. 2347–2356.
- KDD-2018-WangMJYXJSG #detection #multi #network
- EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection (YW, FM, ZJ, YY0, GX, KJ, LS, JG0), pp. 849–857.
- KDD-2018-WeiZYL #approach #learning
- IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control (HW, GZ, HY, ZL), pp. 2496–2505.
- KDD-2018-WongPKFJ #biology #community #network #performance
- SDREGION: Fast Spotting of Changing Communities in Biological Networks (SWHW, CP, MK, CF, IJ), pp. 867–875.
- KDD-2018-Xing #algorithm #co-evolution #design #machine learning
- SysML: On System and Algorithm Co-design for Practical Machine Learning (EPX), p. 2880.
- KDD-2018-XuBDMS #monitoring #multimodal
- RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data (YX, SB, SRD, KOM, JS), pp. 2565–2573.
- KDD-2018-XuDH #online #quality
- SQR: Balancing Speed, Quality and Risk in Online Experiments (YX, WD, SH), pp. 895–904.
- KDD-2018-YeZZGZ #evaluation #parallel #performance
- RapidScorer: Fast Tree Ensemble Evaluation by Maximizing Compactness in Data Level Parallelization (TY, HZ, WYZ, BG, RZ), pp. 941–950.
- KDD-2018-YuanZY #approach #learning #predict
- Hetero-ConvLSTM: A Deep Learning Approach to Traffic Accident Prediction on Heterogeneous Spatio-Temporal Data (ZY, XZ, TY), pp. 984–992.
- KDD-2018-YuCAZCW #approach #detection #flexibility #network
- NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks (WY, WC, CCA, KZ0, HC, WW0), pp. 2672–2681.
- KDD-2018-ZhangLMGS #approach #multi
- TextTruth: An Unsupervised Approach to Discover Trustworthy Information from Multi-Sourced Text Data (HZ, YL, FM, JG0, LS), pp. 2729–2737.
- KDD-2018-ZhangTCSJSV0 #adaptation #clustering #taxonomy #topic
- TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering (CZ0, FT, XC, JS, MJ0, BMS, MV, JH0), pp. 2701–2709.
- KDD-2018-ZhangWCDYW #modelling #reliability
- StockAssIstant: A Stock AI Assistant for Reliability Modeling of Stock Comments (CZ, YW, CC, CD, HY, HW), pp. 2710–2719.
- KDD-2018-ZhaoSWZN0 #framework #rest
- REST: A Reference-based Framework for Spatio-temporal Trajectory Compression (YZ0, SS, YW, BZ, QVHN, KZ0), pp. 2797–2806.
- KDD-2018-ZhengMD0
- OpenTag: Open Attribute Value Extraction from Product Profiles (GZ, SM, XLD, FL0), pp. 1049–1058.
- KDD-2018-ZhouHYF #network #representation #self
- SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization (DZ, JH, HY, WF), pp. 2807–2816.
ECMFA-2018-MustafizDKT #ide #modelling #process- MAPLE: An Integrated Environment for Process Modelling and Enactment for NFV Systems (SM, GD, FK, MT), pp. 164–178.
ICMT-2018-Hinkel #framework #modelling #multi #platform- NMF: A Multi-platform Modeling Framework (GH), pp. 184–194.
- ICMT-2018-TisiC #coq #domain-specific language #model transformation
- CoqTL: An Internal DSL for Model Transformation in Coq (MT, ZC), pp. 142–156.
- MoDELS-2018-ShinCNSBZ #analysis #framework #physics #testing #uml
- HITECS: A UML Profile and Analysis Framework for Hardware-in-the-Loop Testing of Cyber Physical Systems (SYS, KC, SN, MS, LCB, FZ), pp. 357–367.
- ECOOP-2018-BellP #lightweight #traversal
- CROCHET: Checkpoint and Rollback via Lightweight Heap Traversal on Stock JVMs (JB0, LP), p. 31.
- ECOOP-2018-ChungLNV #type system
- KafKa: Gradual Typing for Objects (BC, PL, FZN, JV), p. 24.
- ECOOP-2018-Gascon-SamsonJG #independence #javascript #migration #platform
- ThingsMigrate: Platform-Independent Migration of Stateful JavaScript IoT Applications (JGS, KJ, SG, ARA, KP), p. 33.
- ECOOP-2018-InoueAI #domain-specific language #monad #workflow
- ContextWorkflow: A Monadic DSL for Compensable and Interruptible Executions (HI, TA, AI), p. 33.
- ECOOP-2018-KrugerS0BM #api #approach #encryption #validation
- CrySL: An Extensible Approach to Validating the Correct Usage of Cryptographic APIs (SK, JS, KA0, EB, MM), p. 27.
- ECOOP-2018-TomanG #analysis
- Legato: An At-Most-Once Analysis with Applications to Dynamic Configuration Updates (JT, DG), p. 32.
- ECOOP-2018-WangZOS #formal method
- FHJ: A Formal Model for Hierarchical Dispatching and Overriding (YW, HZ, BCdSO, MS), p. 30.
- Onward-2018-GavranMMPZ #automation #collaboration #reasoning
- Tᴏᴏʟ: accessible automated reasoning for human robot collaboration (IG, OM, RM, RP, DZ), pp. 44–56.
- Onward-2018-InostrozaS
- JEff: objects for effect (PI, TvdS), pp. 111–124.
- OOPSLA-2018-BarowyBZ #automation #fault #spreadsheet
- ExceLint: automatically finding spreadsheet formula errors (DWB, EDB, BGZ), p. 26.
- OOPSLA-2018-BezansonCCKSVZ #design #performance
- Julia: dynamism and performance reconciled by design (JB, JC, BC, SK, VBS, JV, LZ), p. 23.
- OOPSLA-2018-BlackshearGOS #composition #concurrent #detection
- RacerD: compositional static race detection (SB, NG, PWO, IS), p. 28.
- OOPSLA-2018-BostonGC #execution #fault tolerance #hardware #modelling #programmable #verification
- Leto: verifying application-specific hardware fault tolerance with programmable execution models (BB, ZG, MC), p. 30.
- OOPSLA-2018-GrechKJBSS #contract
- MadMax: surviving out-of-gas conditions in Ethereum smart contracts (NG, MK, AJ, LB, BS, YS), p. 27.
- OOPSLA-2018-LeissaBHPMSMS #framework #library #partial evaluation #programming
- AnyDSL: a partial evaluation framework for programming high-performance libraries (RL, KB, SH, APG, RM, PS, AM, BS), p. 30.
- OOPSLA-2018-MendisA #framework #parallel
- goSLP: globally optimized superword level parallelism framework (CM, SPA), p. 28.
- OOPSLA-2018-OttLCLB #programming
- BioScript: programming safe chemistry on laboratories-on-a-chip (JO, TL, CC, ML, PB), p. 31.
- OOPSLA-2018-PadhiJPPGM #framework
- FlashProfile: a framework for synthesizing data profiles (SP, PJ0, DP, OP, SG, TDM), p. 28.
- OOPSLA-2018-PradelS #approach #debugging #detection #learning
- DeepBugs: a learning approach to name-based bug detection (MP, KS), p. 25.
- OOPSLA-2018-XuCBZY #implementation #jit #process
- ShareJIT: JIT code cache sharing across processes and its practical implementation (XX, KC, JB, YZ, HY), p. 23.
- OOPSLA-2018-ZhangYBKSA #domain-specific language #graph
- GraphIt: a high-performance graph DSL (YZ, MY, RB, SK, JS, SPA), p. 30.
PLATEAU-2018-HuangM #algorithm #quantum #source code #towards- QDB: From Quantum Algorithms Towards Correct Quantum Programs (YH0, MM), p. 14.
- PADL-2018-GermanoCP #ide #logic programming
- LoIDE: A Web-Based IDE for Logic Programming Preliminary Report (SG, FC, EP), pp. 152–160.
- PADL-2018-StewartML #declarative #haskell
- Snårkl: Somewhat Practical, Pretty Much Declarative Verifiable Computing in Haskell (GS0, SM, LL), pp. 36–52.
- PLDI-2018-Adams #float #performance #string
- Ryū: fast float-to-string conversion (UA), pp. 270–282.
- PLDI-2018-BohrerTMMP #bytecode #cyber-physical #modelling
- VeriPhy: verified controller executables from verified cyber-physical system models (BB, YKT, SM, MOM, AP), pp. 617–630.
- PLDI-2018-DathathriGHDBDS #distributed #graph
- Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics (RD, GG, LH, HVD, AB, ND, MS, KP), pp. 752–768.
- PLDI-2018-DuckY #c #c++ #detection #effectiveness #fault #memory management #using
- EffectiveSan: type and memory error detection using dynamically typed C/C++ (GJD, RHCY), pp. 181–195.
- PLDI-2018-GehrMTVWV #network #probability
- Bayonet: probabilistic inference for networks (TG, SM, PT, LV, PW, MTV), pp. 586–602.
- PLDI-2018-KangKSLPSKCCHY #compilation
- Crellvm: verified credible compilation for LLVM (JK, YK, YS, JL, SP, MDS, YK, SC, JC, CKH, KY), pp. 631–645.
- PLDI-2018-KoeplingerFPZHF #compilation
- Spatial: a language and compiler for application accelerators (DK, MF, RP, YZ, SH, RF, TZ, LN, AP, CK, KO), pp. 296–311.
- PLDI-2018-LiuH #analysis #concurrent #debugging #difference #parallel #performance
- D4: fast concurrency debugging with parallel differential analysis (BL, JH0), pp. 359–373.
- PLDI-2018-LiuSWTL #parallel #thread
- iReplayer: in-situ and identical record-and-replay for multithreaded applications (HL, SS, WW, CT0, TL), pp. 344–358.
- PLDI-2018-LiuW #analysis #bound #concurrent #interprocedural #source code
- CUBA: interprocedural Context-UnBounded Analysis of concurrent programs (PL, TW), pp. 105–119.
- PLDI-2018-MilanoM #consistency #transaction
- MixT: a language for mixing consistency in geodistributed transactions (MM, ACM), pp. 226–241.
- PLDI-2018-PengGD #detection
- CURD: a dynamic CUDA race detector (YP, VG, JD), pp. 390–403.
- PLDI-2018-ShiXWZFZ #analysis #performance #precise
- Pinpoint: fast and precise sparse value flow analysis for million lines of code (QS, XX0, RW, JZ, GF, CZ), pp. 693–706.
- PLDI-2018-TranJCKSK #co-evolution #design
- SWOOP: software-hardware co-design for non-speculative, execute-ahead, in-order cores (KAT, AJ, TEC, KK, MS, SK), pp. 328–343.
- PLDI-2018-VilkB #automation #debugging #memory management #web
- BLeak: automatically debugging memory leaks in web applications (JV, EDB), pp. 15–29.
- PLDI-2018-WangHR #algebra #framework #probability #source code #static analysis
- PMAF: an algebraic framework for static analysis of probabilistic programs (DW, JH0, TWR), pp. 513–528.
- POPL-2018-0002JKD #programming language #rust
- RustBelt: securing the foundations of the rust programming language (RJ0, JHJ, RK, DD), p. 34.
- POPL-2018-ChandraB #reasoning #type system
- Bonsai: synthesis-based reasoning for type systems (KC, RB), p. 34.
- POPL-2018-InalaS #spreadsheet #using #web
- WebRelate: integrating web data with spreadsheets using examples (JPI, RS), p. 28.
- POPL-2018-OderskyBLBMS
- Simplicitly: foundations and applications of implicit function types (MO, OB, FL, AB, HM, SS), p. 29.
- POPL-2018-SantosMNWG #javascript #verification
- JaVerT: JavaScript verification toolchain (JFS, PM, DN, TW0, PG), p. 33.
- PPDP-2018-DangK #encryption
- Verse: An EDSL for Cryptographic Primitives (AD, PPK), p. 9.
- PPDP-2018-Gardner #framework #javascript #testing #verification
- JaVerT: JavaScript Verification and Testing Framework: Invited Talk (PG), p. 4.
- SAS-2018-Albarghouthi #perspective
- Fairness: A Formal-Methods Perspective (AA), pp. 1–4.
- ASE-2018-BaoLXF #automation #distributed
- AutoConfig: automatic configuration tuning for distributed message systems (LB, XL0, ZX, BF), pp. 29–40.
- ASE-2018-BeyerL #execution #performance #symbolic computation
- CPA-SymExec: efficient symbolic execution in CPAchecker (DB, TL), pp. 900–903.
- ASE-2018-BorgesHZ #android #framework #generative #platform #testing
- DroidMate-2: a platform for Android test generation (NPBJ, JH, AZ), pp. 916–919.
- ASE-2018-CaoLP #automation #verification
- L-CMP: an automatic learning-based parameterized verification tool (JC, YL, JP0), pp. 892–895.
- ASE-2018-DustmannWC #execution #multi #symbolic computation
- PARTI: a multi-interval theory solver for symbolic execution (OSD, KW, CC), pp. 430–440.
- ASE-2018-GaoYFJS #learning #platform #semantics
- VulSeeker: a semantic learning based vulnerability seeker for cross-platform binary (JG, XY, YF, YJ0, JS), pp. 896–899.
- ASE-2018-GharibiTL #automation #generative #graph #python #source code
- Code2graph: automatic generation of static call graphs for Python source code (GG, RT, YL), pp. 880–883.
- ASE-2018-HanYL #debugging #learning #performance
- PerfLearner: learning from bug reports to understand and generate performance test frames (XH, TY, DL0), pp. 17–28.
- ASE-2018-HaririS #c #mutation testing #representation #source code #testing #tool support
- SRCIROR: a toolset for mutation testing of C source code and LLVM intermediate representation (FH, AS), pp. 860–863.
- ASE-2018-HassanRW #analysis #recommendation
- RUDSEA: recommending updates of Dockerfiles via software environment analysis (FH, RR, XW), pp. 796–801.
- ASE-2018-HuangCPZWLZ #difference #generative
- ClDiff: generating concise linked code differences (KH, BC0, XP0, DZ, YW, YL0, WZ), pp. 679–690.
- ASE-2018-JanesMR #developer #lens
- code_call_lens: raising the developer awareness of critical code (AJ, MM, BR), pp. 876–879.
- ASE-2018-JiangLC #contract #detection #fuzzing
- ContractFuzzer: fuzzing smart contracts for vulnerability detection (BJ, YL, WKC), pp. 259–269.
- ASE-2018-LemieuxS #testing
- FairFuzz: a targeted mutation strategy for increasing greybox fuzz testing coverage (CL, KS), pp. 475–485.
- ASE-2018-LinWLSZW #concurrent #data access #debugging #memory management
- PFix: fixing concurrency bugs based on memory access patterns (HL, ZW, SL, JS0, DZ, GW), pp. 589–600.
- ASE-2018-LiuHZLLPZ #detection #similarity
- αDiff: cross-version binary code similarity detection with DNN (BL, WH, CZ0, WL, FL, AP, WZ), pp. 667–678.
- ASE-2018-LiuLZJS #contract #security #semantics #towards
- S-gram: towards semantic-aware security auditing for Ethereum smart contracts (HL, CL, WZ, YJ0, JS), pp. 814–819.
- ASE-2018-LiuWX #android #control flow
- TDroid: exposing app switching attacks in Android with control flow specialization (JL0, DW, JX), pp. 236–247.
- ASE-2018-MaJZSXLCSLLZW #learning #multi #testing
- DeepGauge: multi-granularity testing criteria for deep learning systems (LM0, FJX, FZ, JS, MX, BL0, CC, TS, LL0, YL0, JZ, YW), pp. 120–131.
- ASE-2018-NagashimaH #higher-order #proving #recommendation
- PaMpeR: proof method recommendation system for Isabelle/HOL (YN, YH), pp. 362–372.
- ASE-2018-PereiraSCS #java
- jStanley: placing a green thumb on Java collections (RP, PS, JC, JS), pp. 856–859.
- ASE-2018-QuLCJCHZ #2d #fault #network #predict #using
- node2defect: using network embedding to improve software defect prediction (YQ, TL0, JC, YJ, DC, AH, QZ), pp. 844–849.
- ASE-2018-ReissXH #development #editing #execution
- SEEDE: simultaneous execution and editing in a development environment (SPR, QX, JH0), pp. 270–281.
- ASE-2018-RoohitavafK #agile #distributed #framework #prototype
- DKVF: a framework for rapid prototyping and evaluating distributed key-value stores (MR, SSK), pp. 912–915.
- ASE-2018-ScalabrinoGNGLG #c #generative #search-based #testing
- OCELOT: a search-based test-data generation tool for C (SS, GG, DDN, MG, ADL, HCG, RO), pp. 868–871.
- ASE-2018-SharifAGZ
- TRIMMER: application specialization for code debloating (HS, MA, AG, FZ), pp. 329–339.
- ASE-2018-Shen000ML #regular expression
- ReScue: crafting regular expression DoS attacks (YS, YJ0, CX0, PY0, XM, JL0), pp. 225–235.
- ASE-2018-SungPW #analysis #framework
- CANAL: a cache timing analysis framework via LLVM transformation (CS, BP, CW0), pp. 904–907.
- ASE-2018-TangZPAM0Z #comprehension #execution
- Dual-force: understanding WebView malware via cross-language forced execution (ZT, JZ, MP, YA, SM, XZ0, JZ), pp. 714–725.
- ASE-2018-Vera-PerezMB #detection #pseudo #tool support
- Descartes: a PITest engine to detect pseudo-tested methods: tool demonstration (OLVP, MM, BB), pp. 908–911.
- ASE-2018-ZhangZZ0K #framework #testing #validation
- DeepRoad: GAN-based metamorphic testing and input validation framework for autonomous driving systems (MZ, YZ, LZ, CL0, SK), pp. 132–142.
- ESEC-FSE-2018-Anand #distributed #hybrid #model checking
- Dara: hybrid model checking of distributed systems (VA), pp. 977–979.
- ESEC-FSE-2018-AngellJBM #automation #testing
- Themis: automatically testing software for discrimination (RA, BJ, YB, AM), pp. 871–875.
- ESEC-FSE-2018-BaoXX0L #interactive #programming
- VT-revolution: interactive programming tutorials made possible (LB, ZX, XX0, DL0, SL), pp. 924–927.
- ESEC-FSE-2018-BianLSH0 #debugging #detection
- NAR-miner: discovering negative association rules from code for bug detection (PB, BL0, WS, JH0, YC0), pp. 411–422.
- ESEC-FSE-2018-ChenLCXL #configuration management #framework #fuzzing
- FOT: a versatile, configurable, extensible fuzzing framework (HC, YL, BC0, YX, YL0), pp. 867–870.
- ESEC-FSE-2018-DashAB #using
- RefiNym: using names to refine types (SKD, MA, ETB), pp. 107–117.
- ESEC-FSE-2018-DongWLGBLXK #android #automation #detection
- FraudDroid: automated ad fraud detection for Android apps (FD, HW, LL0, YG0, TFB, TL, GX, JK), pp. 257–268.
- ESEC-FSE-2018-ErataGKT #automation #first-order #logic #reasoning #relational
- AlloyInEcore: embedding of first-order relational logic into meta-object facility for automated model reasoning (FE, AG, IK, BT), pp. 920–923.
- ESEC-FSE-2018-GaoYFJSS #learning #semantics
- VulSeeker-pro: enhanced semantic learning based binary vulnerability seeker with emulation (JG, XY, YF, YJ0, HS, JS), pp. 803–808.
- ESEC-FSE-2018-GaoZ0LLK
- INFAR: insight extraction from app reviews (CG, JZ, DL0, CYL, MRL, IK), pp. 904–907.
- ESEC-FSE-2018-GulzarWK #automation #big data #data analysis #data-driven #debugging #scalability
- BigSift: automated debugging of big data analytics in data-intensive scalable computing (MAG, SW, MK), pp. 863–866.
- ESEC-FSE-2018-GuoJZCS #difference #fuzzing #learning #testing
- DLFuzz: differential fuzzing testing of deep learning systems (JG, YJ0, YZ, QC, JS), pp. 739–743.
- ESEC-FSE-2018-HuaZWK #approach #automation #generative #lazy evaluation #program repair #sketching #using
- SketchFix: a tool for automated program repair approach using lazy candidate generation (JH, MZ, KW, SK), pp. 888–891.
- ESEC-FSE-2018-HuZY #machine learning #reuse #robust #testing #user interface #using
- AppFlow: using machine learning to synthesize robust, reusable UI tests (GH, LZ, JY), pp. 269–282.
- ESEC-FSE-2018-KateOZEX #consistency #detection #nondeterminism #physics #probability
- Phys: probabilistic physical unit assignment and inconsistency detection (SK, JPO, XZ, SGE, ZX), pp. 563–573.
- ESEC-FSE-2018-LeB0 #mining #network #specification #using
- DSM: a specification mining tool using recurrent neural network based language model (TDBL, LB, DL0), pp. 896–899.
- ESEC-FSE-2018-LeeHO #c #fault #memory management #static analysis
- MemFix: static analysis-based repair of memory deallocation errors for C (JL, SH, HO), pp. 95–106.
- ESEC-FSE-2018-LiangJCWZS #fuzzing #industrial #optimisation #parallel
- PAFL: extend fuzzing optimizations of single mode to industrial parallel mode (JL, YJ0, YC, MW, CZ, JS), pp. 809–814.
- ESEC-FSE-2018-LiuYLJZS #clone detection #detection #semantics #sketching #transaction
- EClone: detect semantic clones in Ethereum via symbolic transaction sketch (HL, ZY, CL, YJ0, WZ, JS), pp. 900–903.
- ESEC-FSE-2018-LuLL0 #concurrent #debugging #in the cloud
- CloudRaid: hunting concurrency bugs in the cloud via log-mining (JL, FL, LL0, XF0), pp. 3–14.
- ESEC-FSE-2018-MaLLZG #analysis #automation #debugging #difference #network
- MODE: automated neural network model debugging via state differential analysis and input selection (SM, YL, WCL, XZ, AG), pp. 175–186.
- ESEC-FSE-2018-QuerelR #debugging #modelling #static analysis #statistics
- WarningsGuru: integrating statistical bug models with static analysis to provide timely and specific bug warnings (LPQ, PCR), pp. 892–895.
- ESEC-FSE-2018-SainiFLBL #detection
- Oreo: detection of clones in the twilight zone (VS, FF, YL, PB, CVL), pp. 354–365.
- ESEC-FSE-2018-SpadiniAB #framework #mining #python #repository
- PyDriller: Python framework for mining software repositories (DS, MFA, AB), pp. 908–911.
- ESEC-FSE-2018-StoccoYM18a #using #web
- Vista: web test repair using computer vision (AS0, RY, AM0), pp. 876–879.
- ESEC-FSE-2018-WangGXXNDHX #automation #black box #testing
- FACTS: automated black-box testing of FinTech systems (QW, LG, MX, LX, WN, LD, LH0, TX), pp. 839–844.
- ESEC-FSE-2018-WangSMK #alloy #framework #sketching
- ASketch: a sketching framework for Alloy (KW, AS, DM, SK), pp. 916–919.
- ESEC-FSE-2018-WeiCFFD #complexity #fuzzing
- Singularity: pattern fuzzing for worst case complexity (JW, JC, YF, KF, ID), pp. 213–223.
- ESEC-FSE-2018-YangYSLC #automation #detection #ide #web
- PowerStation: automatically detecting and fixing inefficiencies of database-backed web applications in IDE (JY, CY, PS, SL, AC), pp. 884–887.
- ESEC-FSE-2018-ZhaoH #functional #learning #similarity
- DeepSim: deep learning code functional similarity (GZ, JH0), pp. 141–151.
- ICSE-2018-0013P0 #analysis #mobile
- EnMobile: entity-based characterization and analysis of mobile malware (WY0, MRP, TX0), pp. 384–394.
- ICSE-2018-BellLHEYM #automation #detection #testing
- DeFlaker: automatically detecting flaky tests (JB0, OL, MH, LE, TY, DM), pp. 433–444.
- ICSE-2018-BenninKPMM #approach #fault #predict
- MAHAKIL: diversity based oversampling approach to alleviate the class imbalance issue in software defect prediction (KEB, JK, PP, AM, SM), p. 699.
- ICSE-2018-CalinescuWGIHK #adaptation #assurance #self
- ENTRUST: engineering trustworthy self-adaptive software with dynamic assurance cases (RC, DW, SG, MUI, IH, TK), p. 495.
- ICSE-2018-ChenCXWCLX #optimisation #performance
- Speedoo: prioritizing performance optimization opportunities (ZC, BC0, LX0, XW, LC0, YL0, BX), pp. 811–821.
- ICSE-2018-ChoiSNW #android #testing #user interface
- DetReduce: minimizing Android GUI test suites for regression testing (WC, KS, GCN, WW), pp. 445–455.
- ICSE-2018-GaoW #debugging #parallel
- MSeer: an advanced technique for locating multiple bugs in parallel (RG, WEW), p. 1064.
- ICSE-2018-HassanW #approach #automation
- HireBuild: an automatic approach to history-driven repair of build scripts (FH, XW), pp. 1078–1089.
- ICSE-2018-HempelLLC #editing #lightweight #user interface
- Deuce: a lightweight user interface for structured editing (BH, JL, GL, RC), pp. 654–664.
- ICSE-2018-KimKBC0KT #code search
- FaCoY: a code-to-code search engine (KK, DK0, TFB, EC, LL0, JK, YLT), pp. 946–957.
- ICSE-2018-LinJGM #integer #multi #programming #testing
- Nemo: multi-criteria test-suite minimization with integer nonlinear programming (JWL, RJ, JG, SM), pp. 1039–1049.
- ICSE-2018-MarianiPZ #generative #semantics #testing #user interface
- Augusto: exploiting popular functionalities for the generation of semantic GUI tests with Oracles (LM, MP, DZ), pp. 280–290.
- ICSE-2018-MoralesSKCA #approach #energy #mobile #refactoring
- EARMO: an energy-aware refactoring approach for mobile apps (RM, RS, FK, FC, GA), p. 59.
- ICSE-2018-PatraDP #comprehension #javascript #library
- ConflictJS: finding and understanding conflicts between JavaScript libraries (JP, PND, MP), pp. 741–751.
- ICSE-2018-RadhakrishnaLMM #android #type system
- DroidStar: callback typestates for Android classes (AR, NVL, SM, SM, KCS, DZ, BYEC, PC), pp. 1160–1170.
- ICSE-2018-SunLZGS #reduction
- Perses: syntax-guided program reduction (CS, YL, QZ, TG, ZS), pp. 361–371.
- ICSE-2018-TianPJR #automation #testing
- DeepTest: automated testing of deep-neural-network-driven autonomous cars (YT, KP, SJ, BR), pp. 303–314.
- ICSE-2018-WangQHSB0 #android #policy #privacy
- GUILeak: tracing privacy policy claims on user input data for Android applications (XW, XQ, MBH, RS, TDB, JN0), pp. 37–47.
- ICSE-2018-WangSWXR #clone detection #detection
- CCAligner: a token based large-gap clone detector (PW, JS, YW, YX, CKR), pp. 1066–1077.
- ICSE-2018-WuWCZ
- ChangeLocator: locate crash-inducing changes based on crash reports (RW, MW, SCC, HZ0), p. 536.
- GPCE-2018-HatchF #interactive #reliability #source code
- Rash: from reckless interactions to reliable programs (WGH, MF), pp. 28–39.
- GPCE-2018-LiuAT #automation #constraints #execution #realtime #refactoring
- RT-trust: automated refactoring for trusted execution under real-time constraints (YL, KA, ET), pp. 175–187.
- GPCE-2018-RadanneT #generative #regular expression
- Regenerate: a language generator for extended regular expressions (GR, PT), pp. 202–214.
SLE-2018-MerinoVS #generative #parametricity- Bacatá: a language parametric notebook generator (MVM, JJV, TvdS), pp. 210–214.
- SLE-2018-Regis-GianasJT #parsing
- Morbig: a static parser for POSIX shell (YRG, NJ, RT), pp. 29–41.
- SLE-2018-RiesCG #approach #requirements #uml
- Messir: a text-first DSL-based approach for UML requirements engineering (BR, AC, NG), pp. 103–107.
- SLE-2018-Stromback #framework #platform
- Storm: a language platform for interacting and extensible languages (FS), pp. 60–64.
- ASPLOS-2018-0004MTC #lightweight
- Minnow: Lightweight Offload Engines for Worklist Management and Worklist-Directed Prefetching (DZ0, XM, MT, DC), pp. 593–607.
- ASPLOS-2018-Ausavarungnirun #concurrent #gpu #memory management #multi
- MASK: Redesigning the GPU Memory Hierarchy to Support Multi-Application Concurrency (RA, VM, JL, SG, JG, AJ, CJR, OM), pp. 503–518.
- ASPLOS-2018-CaiRLDWQPW #hardware #network
- VIBNN: Hardware Acceleration of Bayesian Neural Networks (RC, AR, NL0, CD, LW, XQ, MP, YW), pp. 476–488.
- ASPLOS-2018-DavidPY #detection #precise #static analysis
- FirmUp: Precise Static Detection of Common Vulnerabilities in Firmware (YD, NP, EY), pp. 392–404.
- ASPLOS-2018-DeLozierELD #concurrent #thread
- SOFRITAS: Serializable Ordering-Free Regions for Increasing Thread Atomicity Scalably (CD, AE, BL, JD), pp. 286–300.
- ASPLOS-2018-DickensGFH #encryption #performance #using
- StrongBox: Confidentiality, Integrity, and Performance using Stream Ciphers for Full Drive Encryption (BDI, HSG, AJF, HH), pp. 708–721.
- ASPLOS-2018-EvtyushkinRAP #branch #predict
- BranchScope: A New Side-Channel Attack on Directional Branch Predictor (DE, RR, NBAG, DP), pp. 693–707.
- ASPLOS-2018-GuoH #approximate #mobile
- Potluck: Cross-Application Approximate Deduplication for Computation-Intensive Mobile Applications (PG, WH), pp. 271–284.
- ASPLOS-2018-HsuDMT #scalability
- SmoothOperator: Reducing Power Fragmentation and Improving Power Utilization in Large-scale Datacenters (CHH, QD, JM, LT), pp. 535–548.
- ASPLOS-2018-KumarMKVYKBK #lazy evaluation
- LATR: Lazy Translation Coherence (MK, SM, SK, JV, ZY, TK, AB, TK), pp. 651–664.
- ASPLOS-2018-KwonSK #configuration management #data flow #flexibility
- MAERI: Enabling Flexible Dataflow Mapping over DNN Accelerators via Reconfigurable Interconnects (HK, AS, TK), pp. 461–475.
- ASPLOS-2018-LeeKLCGK #evaluation #performance
- WSMeter: A Performance Evaluation Methodology for Google's Production Warehouse-Scale Computers (JL, CK, KL, LC, RG, JK), pp. 549–563.
- ASPLOS-2018-LiuWLLYT #automation #debugging #detection
- FCatch: Automatically Detecting Time-of-fault Bugs in Cloud Systems (HL, XW, GL, SL, FY, CT0), pp. 419–431.
- ASPLOS-2018-LottariniRCKRSW #benchmark #in the cloud #metric #video
- vbench: Benchmarking Video Transcoding in the Cloud (AL, AR, JC, MAK, PR, DS, MW), pp. 797–809.
- ASPLOS-2018-MaLKSN0WWL0 #optimisation
- NEOFog: Nonvolatility-Exploiting Optimizations for Fog Computing (KM, XL, MTK, JS, VN, JL0, TW, ZW, YL, YX0), pp. 782–796.
- ASPLOS-2018-MallonGJ #performance
- DLibOS: Performance and Protection with a Network-on-Chip (SM, VG, GJ), pp. 737–750.
- ASPLOS-2018-MarkuzeSMT #network
- DAMN: Overhead-Free IOMMU Protection for Networking (AM, IS, AM0, DT), pp. 301–315.
- ASPLOS-2018-MishraILH #energy #latency #learning #predict
- CALOREE: Learning Control for Predictable Latency and Low Energy (NM, CI, JDL, HH), pp. 184–198.
- ASPLOS-2018-NguyenFNXDL #big data #distributed
- Skyway: Connecting Managed Heaps in Distributed Big Data Systems (KN, LF, CN, G(X, BD, SL), pp. 56–69.
- ASPLOS-2018-RahmaniDMMJMD #coordination #manycore #resource management
- SPECTR: Formal Supervisory Control and Coordination for Many-core Systems Resource Management (AMR, BD, TM, KM, AJ, OM, NDD), pp. 169–183.
- ASPLOS-2018-RajaduraiBWA #configuration management #source code
- Gloss: Seamless Live Reconfiguration and Reoptimization of Stream Programs (SR, JB, WFW, SPA), pp. 98–112.
- ASPLOS-2018-SabetQ0 #graph
- Tigr: Transforming Irregular Graphs for GPU-Friendly Graph Processing (AHNS, JQ, ZZ0), pp. 622–636.
- ASPLOS-2018-SadrosadatiMESD #hardware
- LTRF: Enabling High-Capacity Register Files for GPUs via Hardware/Software Cooperative Register Prefetching (MS, AM, SBE, HSA, MD, BF, RA, OM), pp. 489–502.
- ASPLOS-2018-TaassoriSB #performance #verification
- VAULT: Reducing Paging Overheads in SGX with Efficient Integrity Verification Structures (MT, AS, RB), pp. 665–678.
- ASPLOS-2018-TurakhiaBD #assembly
- Darwin: A Genomics Co-processor Provides up to 15, 000X Acceleration on Long Read Assembly (YT, GB, WJD), pp. 199–213.
- ASPLOS-2018-WuZLLCZG #java #memory management
- Espresso: Brewing Java For More Non-Volatility with Non-volatile Memory (MW, ZZ0, HL, HL, HC, BZ, HG), pp. 70–83.
- ASPLOS-2018-YaoMLSC #gpu #web
- Sugar: Secure GPU Acceleration in Web Browsers (ZY, ZM, YL, AAS, AC), pp. 519–534.
- ASPLOS-2018-ZhangWZQHC #graph #novel
- Wonderland: A Novel Abstraction-Based Out-Of-Core Graph Processing System (MZ, YW, YZ, XQ, CH, KC), pp. 608–621.
- CASE-2018-GuldeLC #2d #estimation #industrial
- RoPose: CNN-based 2D Pose Estimation of Industrial Robots (TG, DL, CC), pp. 463–470.
- CASE-2018-KattiPRS #semantics
- SA-OPC-UA: Introducing Semantics to OPC-UA Application Methods (BK, CP, MR, MS), pp. 1189–1196.
- CASE-2018-TangWWWLT #framework
- RobCutt: A Framework of Underwater Biomimetic Vehicle-Manipulator System for Autonomous Interventions (CT0, RW0, YW0, SW0, UFvL, MT0), pp. 477–482.
- CASE-2018-ZhaoCCPLG #first-order #lightweight
- FLUIDS: A First-Order Lightweight Urban Intersection Driving Simulator (HZ, AC, SAC, BP, ML, KG), pp. 697–704.
- CC-2018-BrockDXZ #compilation #implementation #jit
- PAYJIT: space-optimal JIT compilation and its practical implementation (JB, CD, XX, YZ), pp. 71–81.
- CC-2018-GinsbachCO #analysis #compilation #domain-specific language
- CAnDL: a domain specific language for compiler analysis (PG, LC, MFPO), pp. 151–162.
- CGO-2018-BaiWH #automation #generative
- AutoPA: automatically generating active driver from original passive driver code (JJB, YPW0, SMH0), pp. 288–299.
- CGO-2018-BaumanWZL #self
- SGXElide: enabling enclave code secrecy via self-modification (EB, HW, MZ, ZL), pp. 75–86.
- CGO-2018-CostaA #framework #performance
- CollectionSwitch: a framework for efficient and dynamic collection selection (DC0, AA0), pp. 16–26.
- CGO-2018-FuLJ #android #detection
- nAdroid: statically detecting ordering violations in Android applications (XF, DL, CJ), pp. 62–74.
- CGO-2018-KruseG #dependence #memory management
- DeLICM: scalar dependence removal at zero memory cost (MK, TG), pp. 241–253.
- CGO-2018-ShenSLL #profiling #runtime
- CUDAAdvisor: LLVM-based runtime profiling for modern GPUs (DS, SLS, AL, XL0), pp. 214–227.
- CGO-2018-XieZLGJHZ #performance
- CVR: efficient vectorization of SpMV on x86 processors (BX, JZ, XL0, WG, ZJ, XH, LZ0), pp. 149–162.
- CGO-2018-ZhangX #encoding #execution
- Poker: permutation-based SIMD execution of intensive tree search by path encoding (FZ0, JX), pp. 87–99.
ESOP-2018-Matsuda0 #combinator #lens #programming #using- HOBiT: Programming Lenses Without Using Lens Combinators (KM, MW0), pp. 31–59.
- ESOP-2018-RahliVVV #coq #fault tolerance #protocol
- Velisarios: Byzantine Fault-Tolerant Protocols Powered by Coq (VR, IV, MV, PJEV), pp. 619–650.
- FASE-2018-ChechikSDR #analysis #commutative #performance
- FPH: Efficient Non-commutativity Analysis of Feature-Based Systems (MC, IS, CD, JR), pp. 319–336.
- FASE-2018-ChenHYCKX #framework #testing
- CRETE: A Versatile Binary-Level Concolic Testing Framework (BC0, CH, ZY, KC, RK, FX), pp. 281–298.
- FASE-2018-KorogluS #android #detection #testing
- TCM: Test Case Mutation to Improve Crash Detection in Android (YK, AS0), pp. 264–280.
- FASE-2018-LiuOSWGM #analysis #distributed #formal method #protocol #transaction
- ROLA: A New Distributed Transaction Protocol and Its Formal Analysis (SL0, PCÖ, KS, QW0, IG, JM), pp. 77–93.
- CAV-2018-BlondinEJ #analysis #protocol
- Peregrine: A Tool for the Analysis of Population Protocols (MB, JE, SJ), pp. 604–611.
- CAV-2018-CeskaMMSVV #approximate #automation #design
- ADAC: Automated Design of Approximate Circuits (MC0, JM, VM, LS, ZV, TV), pp. 612–620.
- CAV-2018-CordeiroKKST #bound #bytecode #java #model checking #verification
- JBMC: A Bounded Model Checking Tool for Verifying Java Bytecode (LCC, PK, DK, PS, MT), pp. 183–190.
- CAV-2018-Eilers0 #python #verification
- Nagini: A Static Verifier for Python (ME, PM0), pp. 596–603.
- CAV-2018-MeyerSL #exclamation #synthesis
- Strix: Explicit Reactive Synthesis Strikes Back! (PJM, SS, ML), pp. 578–586.
- CAV-2018-BlotskyMBZKG #fuzzing #string
- StringFuzz: A Fuzzer for String Solvers (DB, FM, MB, YZ, IK, VG), pp. 45–51.
- CAV-2018-LiDPRV #approximate #performance #reachability
- SimpleCAR: An Efficient Bug-Finding Tool Based on Approximate Reachability (JL, RD, GP, KYR, MYV), pp. 37–44.
- CAV-2018-ZhangGSW #verification
- SCInfer: Refinement-Based Verification of Software Countermeasures Against Side-Channel Attacks (JZ, PG, FS, CW0), pp. 157–177.
- ICST-2018-AlsharifKM #database #effectiveness #generative #performance #relational #testing
- DOMINO: Fast and Effective Test Data Generation for Relational Database Schemas (AA, GMK, PM), pp. 12–22.
- ICST-2018-Arcuri #automation #generative #multi #testing
- EvoMaster: Evolutionary Multi-context Automated System Test Generation (AA), pp. 394–397.
- ICST-2018-HodovanK #composition #framework #open source #random testing #testing
- Fuzzinator: An Open-Source Modular Random Testing Framework (RH, ÁK0), pp. 416–421.
- ICST-2018-KorogluSMMUTD #android
- QBE: QLearning-Based Exploration of Android Applications (YK, AS0, OM, YM, CU, TT, YD), pp. 105–115.
- ICST-2018-PradhanW00L #mining #multi #testing #using
- REMAP: Using Rule Mining and Multi-objective Search for Dynamic Test Case Prioritization (DP, SW0, SA0, TY0, ML), pp. 46–57.
- ICST-2018-RaposC #evolution #maintenance #tool support
- SimEvo: A Toolset for Simulink Test Evolution & Maintenance (EJR, JRC), pp. 410–415.
- ICST-2018-RibeiroSACK #fault #locality
- Jaguar: A Spectrum-Based Fault Localization Tool for Real-World Software (HLR, HAdS, RPAdA, MLC, FK), pp. 404–409.
- ICST-2018-SullivanWK #alloy #automation #testing
- AUnit: A Test Automation Tool for Alloy (AS, KW, SK), pp. 398–403.
- ICST-2018-YangHWK #api #sequence
- EdSynth: Synthesizing API Sequences with Conditionals and Loops (ZY, JH, KW, SK), pp. 161–171.
- ICTSS-2018-BordingHJW #automation #contract #java #testing
- JMCTest: Automatically Testing Inter-Method Contracts in Java (PB, JH, MCJ, HW), pp. 39–55.
IJCAR-2018-PiotrowskiU #feedback #learning- ATPboost: Learning Premise Selection in Binary Setting with ATP Feedback (BP, JU), pp. 566–574.
- IJCAR-2018-WinklerM #order
- MædMax: A Maximal Ordered Completion Tool (SW, GM), pp. 472–480.
- IJCAR-2018-ZhaoS #automation #logic #semantics
- FAME: An Automated Tool for Semantic Forgetting in Expressive Description Logics (YZ, RAS), pp. 19–27.
- ICSA-2017-MucciniS #architecture #physics
- CAPS: Architecture Description of Situational Aware Cyber Physical Systems (HM, MS), pp. 211–220.
- JCDL-2017-ColeAM #case study #framework
- Quill: A Framework for Constructing Negotiated Texts - With a Case Study on the US Constitutional Convention of 1787 (NC, AAR, GM), pp. 79–88.
- JCDL-2017-GippBMB #feedback #overview #using
- CryptSubmit: Introducing Securely Timestamped Manuscript Submission and Peer Review Feedback Using the Blockchain (BG, CB, NM, JB), pp. 273–276.
- JCDL-2017-JanaMMG
- WikiM: Metapaths Based Wikification of Scientific Abstracts (AJ, SM, AM0, PG), pp. 21–30.
- JCDL-2017-WuCCLG #approach #hybrid
- HESDK: A Hybrid Approach to Extracting Scientific Domain Knowledge Entities (JW0, SRC, AC, CL, CLG), pp. 241–244.
CSEET-2017-RongGZS #education- DevOpsEnvy: An Education Support System for DevOps (GR, SG, HZ, DS), pp. 37–46.
- EDM-2017-KhosraviCK #recommendation
- RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests (HK, KMLC, KK).
- EDM-2017-Sales #tutorial
- Tutorial: Principal Stratification for EDM Experiments (AS).
- EDM-2017-Yukita #evaluation
- Whitebox: A Device To Assist Group Work Evaluation (DY).
- ICPC-2017-JiangAMZM #api
- Docio: documenting API input/output examples (SJ, AA, CM, QZ, RAM), pp. 364–367.
- ICPC-2017-MostafaRW #android #behaviour #maintenance #network
- NetDroid: summarizing network behavior of Android apps for network code maintenance (SM, RR, XW), pp. 165–175.
- ICPC-2017-SousaSFFB #composition #detection #flexibility #smell
- FindSmells: flexible composition of bad smell detection strategies (BLS, PPS, EF, KAMF, MASB), pp. 360–363.
- ICPC-2017-YueFM0TXL #android #automation #detection
- RepDroid: an automated tool for Android application repackaging detection (SY, WF, JM0, YJ0, XT, CX0, JL0), pp. 132–142.
- ICSME-2017-CaiR17a #android #tool support
- DroidFax: A Toolkit for Systematic Characterization of Android Applications (HC, BGR), pp. 643–647.
- ICSME-2017-CampbellT #natural language
- NLP2Code: Code Snippet Content Assist via Natural Language Tasks (BAC, CT), pp. 628–632.
- ICSME-2017-GuC0SDML #android #automation #multi #testing
- AimDroid: Activity-Insulated Multi-level Automated Testing for Android Applications (TG, CC, TL0, CS, JD, XM, JL0), pp. 103–114.
- ICSME-2017-GuoWXL #adaptation #consistency #detection #nondeterminism #scheduling
- GEAS: Generic Adaptive Scheduling for High-Efficiency Context Inconsistency Detection (BG, HW, CX0, JL0), pp. 137–147.
- ICSME-2017-HuangVGSS #analysis #assembly #execution #scalability #visualisation
- Atlantis: Improving the Analysis and Visualization of Large Assembly Execution Traces (HNH, EV0, DMG, MADS, MS), pp. 623–627.
- ICSME-2017-LeuenbergerOGN #api
- KOWALSKI: Collecting API Clients in Easy Mode (ML, HO, MG, ON), pp. 653–657.
- ICSME-2017-LiFZMR #approach #clone detection #detection
- CCLearner: A Deep Learning-Based Clone Detection Approach (LL, HF, WZ, NM, BGR), pp. 249–260.
- ICSME-2017-MerinoGAN #visualisation
- CityVR: Gameful Software Visualization (LM, MG, CA, ON), pp. 633–637.
- ICSME-2017-RaposC #impact analysis #modelling
- SimPact: Impact Analysis for Simulink Models (EJR, JRC), pp. 489–493.
- ICSME-2017-VerwerH #automaton #learning
- flexfringe: A Passive Automaton Learning Package (SV, CAH), pp. 638–642.
- ICSME-2017-WangSYAN #automation #impact analysis
- RCIA: Automated Change Impact Analysis to Facilitate a Practical Cancer Registry System (SW0, TS, TY0, SA0, JFN), pp. 603–612.
- ICSME-2017-XuSHL #git #personalisation #recommendation
- REPERSP: Recommending Personalized Software Projects on GitHub (WX, XS, JH, BL0), pp. 648–652.
- ICSME-2017-Yu #evolution #multi #testing
- SimEvo: Testing Evolving Multi-process Software Systems (TY), pp. 204–215.
- ICSME-2017-ZhangZLHLG #android
- Embroidery: Patching Vulnerable Binary Code of Fragmentized Android Devices (XZ, YZ0, JL, YH, HL, DG), pp. 47–57.
- MSR-2017-BellerGZ17a #git #integration #research
- TravisTorrent: synthesizing Travis CI and GitHub for full-stack research on continuous integration (MB, GG, AZ), pp. 447–450.
- MSR-2017-BrandauerW #analysis #interactive
- Spencer: interactive heap analysis for the masses (SB, TW), pp. 113–123.
- MSR-2017-HurierSDBTKC #android #anti #unification
- Euphony: harmonious unification of cacophonous anti-virus vendor labels for Android malware (MH, GST, SKD, TFB, YLT, JK, LC), pp. 425–435.
- MSR-2017-SilvaV #detection #refactoring #version control
- RefDiff: detecting refactorings in version histories (DS, MTV), pp. 269–279.
- MSR-2017-TiwariUNR #framework #mining #platform #repository #tool support
- Candoia: a platform for building and sharing mining software repositories tools as apps (NMT, GU, HAN, HR), pp. 53–63.
- MSR-2017-XuDGWWZH #clustering #similarity #spreadsheet #version control
- SpreadCluster: recovering versioned spreadsheets through similarity-based clustering (LX, WD, CG, JW, JW0, HZ, TH0), pp. 158–169.
- SANER-2017-AmellerFFCVE #release planning
- Replan: A Release Planning Tool (DA, CF, XF, AC, DV, VE), pp. 516–520.
- SANER-2017-BartmanNCM #query #source code
- srcQL: A syntax-aware query language for source code (BB, CDN, MLC, JIM), pp. 467–471.
- SANER-2017-BuckersCDGWBZ #automation #multi #static analysis #tool support
- UAV: Warnings from multiple Automated Static Analysis Tools at a glance (TB, CC, MD, BG, SW, MB, AZ), pp. 472–476.
- SANER-2017-Ganea #dependence #developer #evolution
- Hindsight: Revealing the evolution of dependencies to developers (GG), pp. 511–515.
- SANER-2017-GoerFM #execution #learning
- scat: Learning from a single execution of a binary (FdG, CF, LM), pp. 492–496.
- SANER-2017-Kirda #approach #automation #detection #scalability
- UNVEIL: A large-scale, automated approach to detecting ransomware (keynote) (EK), p. 1.
- SANER-2017-LiuWDYZ #android
- InsDal: A safe and extensible instrumentation tool on Dalvik byte-code for Android applications (JL, TW, XD, JY0, JZ0), pp. 502–506.
- SANER-2017-MercierCJ #analysis #automation #memory management #reverse engineering
- dynStruct: An automatic reverse engineering tool for structure recovery and memory use analysis (DM, AC, RJ), pp. 497–501.
- SANER-2017-Raber
- Columbo: High perfomance unpacking (JR), pp. 507–510.
- SANER-2017-RahmanR #concept #identification #information retrieval #strict
- STRICT: Information retrieval based search term identification for concept location (MMR0, CKR), pp. 79–90.
- SANER-2017-StefanicaM #program analysis #tool support
- XCORE: Support for developing program analysis tools (AS, PFM), pp. 462–466.
- SANER-2017-TangL #approach #mining #novel #probability #using
- StiCProb: A novel feature mining approach using conditional probability (YT, HL), pp. 45–55.
- SANER-2017-Ujihara0II #identification #refactoring
- c-JRefRec: Change-based identification of Move Method refactoring opportunities (NU, AO0, TI, KI), pp. 482–486.
- SANER-2017-ZhaoXKSLL #graph
- HDSKG: Harvesting domain specific knowledge graph from content of webpages (XZ, ZX, MAK, NS, JL0, SWL), pp. 56–67.
CIAA-2017-FijalkowGKK #automaton #monad- Stamina: Stabilisation Monoids in Automata Theory (NF, HG, EK, DK), pp. 101–112.
- IFM-2017-BeckertBGHLU #automation #relational #slicing #verification
- SemSlice: Exploiting Relational Verification for Automatic Program Slicing (BB, TB, SG, MH, DL, MU), pp. 312–319.
- IFM-2017-KrishnaPS #automation #process #verification
- VBPMN: Automated Verification of BPMN Processes (Tool Paper) (AK0, PP, GS), pp. 323–331.
- IFM-2017-PereiraOFS #component #development
- BTS: A Tool for Formal Component-Based Development (DIdAP, MVMO, MSCF, SRDRS), pp. 211–226.
- IFM-2017-Wu #consistency #constraints #diagrams #uml
- MaxUSE: A Tool for Finding Achievable Constraints and Conflicts for Inconsistent UML Class Diagrams (HW0), pp. 348–356.
- SEFM-2017-LiuW #api #program analysis
- IJIT: An API for Boolean Program Analysis with Just-in-Time Translation (PL, TW), pp. 316–331.
- Haskell-2017-AlgehedCJS #lightweight
- QuickSpec: a lightweight theory exploration tool for programmers (system demonstration) (MA, KC, MJ, NS), pp. 38–39.
- Haskell-2017-BraquehaisR #black box #equation #reasoning
- Speculate: discovering conditional equations and inequalities about black-box functions by reasoning from test results (RB, CR), pp. 40–51.
- Haskell-2017-Remy #automation #parametricity #reuse
- Ornaments: exploiting parametricity for safer, more automated code refactorization and code reuse (invited talk) (DR), p. 1.
- ICFP-2017-ChoiVSCA #composition #framework #hardware #parametricity #platform #specification #verification
- Kami: a platform for high-level parametric hardware specification and its modular verification (JC, MV, BS, AC, A), p. 30.
- ICFP-2017-WayeCD #contract #higher-order
- Whip: higher-order contracts for modern services (LW, SC, CD), p. 28.
- ICFP-2017-WeitzLHTET #library #tool support #verification
- SpaceSearch: a library for building and verifying solver-aided tools (KW, SL, SH, ET, MDE, ZT), p. 28.
- AIIDE-2017-FerreiraW #composition #game studies #music
- MTG: Context-Based Music Composition for Tabletop Role-Playing Games (LNF, JW), pp. 286–288.
- AIIDE-2017-LinGKS #dataset #research
- STARDATA: A StarCraft AI Research Dataset (ZL, JG, VK, GS), pp. 50–56.
- AIIDE-2017-OsbornLM #game studies #hybrid #modelling
- HyPED: Modeling and Analyzing Action Games as Hybrid Systems (JCO, BL, MM), pp. 87–93.
- AIIDE-2017-PadovaniFL #game studies #music #recommendation
- Bardo: Emotion-Based Music Recommendation for Tabletop Role-Playing Games (RRP, LNF, LHSL), pp. 214–220.
- AIIDE-2017-PowleyNGCFSIC #design #game studies
- Wevva: Democratising Game Design (EJP, MJN, SEG, SC, BPF, RS, PI, MC0), pp. 273–276.
- AIIDE-2017-SanghrajkaHCK
- LISA: Lexically Intelligent Story Assistant (RS, DH, PPC, MK), pp. 221–227.
- CHI-PLAY-2017-KromeHG #game studies
- AutoGym: An Exertion Game for Autonomous Driving (SK, JH, SG), pp. 33–42.
- CHI-PLAY-2017-LesselVK #game studies
- CrowdChess: A System to Investigate Shared Game Control in Live-Streams (PL, AV, AK), pp. 389–400.
- CHI-PLAY-2017-MerrittNJG #design #game studies
- GlowPhones: Designing for Proxemics Play with Low-Resolution Displays in Location-based Games (TM, CLN, FLJ, JEG), pp. 69–81.
- CIG-2017-GuimaraesSJ #architecture #game studies #social
- CiF-CK: An architecture for social NPCS in commercial games (MG, PS0, AJ), pp. 126–133.
- CIG-2017-PoulsenTFR #learning #visual notation
- DLNE: A hybridization of deep learning and neuroevolution for visual control (APP, MT, MHF, SR), pp. 256–263.
- FDG-2017-Debus #game studies #ontology
- Metagames: on the ontology of games outside of games (MSD), p. 9.
- FDG-2017-DrenikowM #case study #experience #interactive #visualisation
- Vixen: interactive visualization of gameplay experiences (BD, PMB), p. 10.
- FDG-2017-KaoH #education #framework #platform
- MazeStar: a platform for studying virtual identity and computer science education (DK, DFH), p. 6.
- FDG-2017-LaPlanteNS
- PlayScope: augmented microscopy as a tool to increase STEM engagement (CL, MN, TS), p. 4.
- FDG-2017-LoparevSVWDFBS #biology #collaboration #education #game studies #video
- BacToMars: a collaborative educational video game for teaching biological engineering (AL, AS, CV, LW, JD, MF, MUB, OS), p. 3.
- FDG-2017-MazeikaW #3d #generative #graph #modelling
- Solusforge: controlling the generation of the 3D models with spatial relation graphs (JM, JW), p. 4.
- VS-Games-2017-KoskelaPHAAO #3d #modelling #scalability #web
- DRUMM: Dynamic viewing of large-scale 3D city models on the web (TK, MP, AH, TA, PA, TO), pp. 8–14.
- CIKM-2017-0001HWN
- Metacrate: Organize and Analyze Millions of Data Profiles (SK0, DH, MW, FN), pp. 2483–2486.
- CIKM-2017-0001KGR #constraints #learning
- TaCLe: Learning Constraints in Tabular Data (SP0, SK, TG, LDR), pp. 2511–2514.
- CIKM-2017-AmsterdamerG #multi
- PODIUM: Procuring Opinions from Diverse Users in a Multi-Dimensional World (YA, OG0), pp. 2443–2446.
- CIKM-2017-ArmanACA #3d #database #query #scalability
- VizQ: A System for Scalable Processing of Visibility Queries in 3D Spatial Databases (AA, MEA, FMC, KA), pp. 2447–2450.
- CIKM-2017-BastB #performance #query
- QLever: A Query Engine for Efficient SPARQL+Text Search (HB, BB), pp. 647–656.
- CIKM-2017-BeheshtiBNCXZ
- CoreDB: a Data Lake Service (AB, BB, RN, VMC, HX, XZ), pp. 2451–2454.
- CIKM-2017-CaoSCOC #comprehension #predict
- DeepHawkes: Bridging the Gap between Prediction and Understanding of Information Cascades (QC, HS, KC, WO, XC), pp. 1149–1158.
- CIKM-2017-CaoZL #approach #approximate #distributed #effectiveness #graph #mining #scalability
- PMS: an Effective Approximation Approach for Distributed Large-scale Graph Data Processing and Mining (YC, YZ, JL), pp. 1999–2002.
- CIKM-2017-ChenXLDTCP #framework #network
- HotSpots: Failure Cascades on Heterogeneous Critical Infrastructure Networks (LC, XX, SL, SD, AGT, SC, BAP), pp. 1599–1607.
- CIKM-2017-DhakadDBDKM #process #using
- SOPER: Discovering the Influence of Fashion and the Many Faces of User from Session Logs using Stick Breaking Process (LD, MKD, CB, SD, MK, VM), pp. 1609–1618.
- CIKM-2017-DingZLTCZ #network #personalisation #ranking #recommendation
- BayDNN: Friend Recommendation with Bayesian Personalized Ranking Deep Neural Network (DD, MZ0, SYL, JT0, XC, ZHZ), pp. 1479–1488.
- CIKM-2017-EkronMY #semantics
- SimMeme: Semantic-Based Meme Search (ME, TM, BY), pp. 2455–2458.
- CIKM-2017-El-RobyA #feedback #integration #web
- UFeed: Refining Web Data Integration Based on User Feedback (AER, AA), pp. 187–196.
- CIKM-2017-FeigenblatBRK #analysis #summary
- SummIt: A Tool for Extractive Summarization, Discovery and Analysis (GF, OB, HR, DK), pp. 2459–2462.
- CIKM-2017-FuLL #learning #network #representation
- HIN2Vec: Explore Meta-paths in Heterogeneous Information Networks for Representation Learning (TYF, WCL, ZL), pp. 1797–1806.
- CIKM-2017-HaqueWCDKH #classification #multi #online
- FUSION: An Online Method for Multistream Classification (AH, ZW, SC, BD, LK, KWH), pp. 919–928.
- CIKM-2017-HuangZLLZH #graph #identification
- KIEM: A Knowledge Graph based Method to Identify Entity Morphs (LH, LZ, SL, FL, YZ, SH), pp. 2111–2114.
- CIKM-2017-HubigPSVK0 #data analysis
- HyPerInsight: Data Exploration Deep Inside HyPer (NH, LP, MES, DV, AK, TN0), pp. 2467–2470.
- CIKM-2017-HuLSLL0Z #approach #information management #probability #segmentation
- CNN-IETS: A CNN-based Probabilistic Approach for Information Extraction by Text Segmentation (MH, ZL, YS, AL0, GL0, KZ0, LZ0), pp. 1159–1168.
- CIKM-2017-KharlamovGSGKH
- SemFacet: Making Hard Faceted Search Easier (EK, LG, ES, BCG, EVK, IH), pp. 2475–2478.
- CIKM-2017-KimCCJY #difference
- DiagTree: Diagnostic Tree for Differential Diagnosis (YK, JC, YC, XJ, HY), pp. 1179–1188.
- CIKM-2017-LeeL #collaboration
- IDAE: Imputation-boosted Denoising Autoencoder for Collaborative Filtering (JwL, JL), pp. 2143–2146.
- CIKM-2017-LeL17a #analysis #interactive #semantics #topic #visualisation
- SemVis: Semantic Visualization for Interactive Topical Analysis (TMVL, HWL), pp. 2487–2490.
- CIKM-2017-LiGBC #approach #correlation #modelling #online #process
- FM-Hawkes: A Hawkes Process Based Approach for Modeling Online Activity Correlations (SL, XG, WB, GC), pp. 1119–1128.
- CIKM-2017-LiHPG #detection #framework #machine learning
- DeMalC: A Feature-rich Machine Learning Framework for Malicious Call Detection (YL, DH, AP, ZG), pp. 1559–1567.
- CIKM-2017-LimLH #framework #interactive #twitter
- iFACT: An Interactive Framework to Assess Claims from Tweets (WYL, MLL, WH), pp. 787–796.
- CIKM-2017-LiuCC #approximate #nearest neighbour
- PQBF: I/O-Efficient Approximate Nearest Neighbor Search by Product Quantization (YL, HC, JC), pp. 667–676.
- CIKM-2017-LiuHF #detection
- HoloScope: Topology-and-Spike Aware Fraud Detection (SL, BH, CF), pp. 1539–1548.
- CIKM-2017-LiuJXTLN #database
- TICC: Transparent Inter-Column Compression for Column-Oriented Database Systems (HL0, YJ, JX, HT, QL0, LMN), pp. 2171–2174.
- CIKM-2017-LiuLZZYH0 #interactive #streaming
- Urbanity: A System for Interactive Exploration of Urban Dynamics from Streaming Human Sensing Data (ML, ZL, CZ0, KZ, QY0, TH, JH0), pp. 2503–2506.
- CIKM-2017-LuoHCYK #query
- SEQ: Example-based Query for Spatial Objects (SL, JH, RC, JY, BK), pp. 2179–2182.
- CIKM-2017-MaYC #predict #video
- LARM: A Lifetime Aware Regression Model for Predicting YouTube Video Popularity (CM, ZY, CWC), pp. 467–476.
- CIKM-2017-MehdiKSXKBHRR #rule-based #semantics
- SemDia: Semantic Rule-Based Equipment Diagnostics Tool (GM, EK, OS, GX0, EGK, SB0, IH, MR, TAR), pp. 2507–2510.
- CIKM-2017-NguyenTW #ambiguity
- J-REED: Joint Relation Extraction and Entity Disambiguation (DBN, MT, GW), pp. 2227–2230.
- CIKM-2017-PangLGXXC #architecture #information retrieval #ranking
- DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval (LP, YL, JG, JX0, JX, XC), pp. 257–266.
- CIKM-2017-PatwariGB #classification #detection #multi
- TATHYA: A Multi-Classifier System for Detecting Check-Worthy Statements in Political Debates (AP, DG, SB), pp. 2259–2262.
- CIKM-2017-PhanSTHL #ambiguity #semantics
- NeuPL: Attention-based Semantic Matching and Pair-Linking for Entity Disambiguation (MCP, AS, YT, JH, CL), pp. 1667–1676.
- CIKM-2017-RemusKBLB #knowledge base #personalisation #web
- Storyfinder: Personalized Knowledge Base Construction and Management by Browsing the Web (SR, MK, KB, TvL, CB), pp. 2519–2522.
- CIKM-2017-RuchanskySL #detection #hybrid
- CSI: A Hybrid Deep Model for Fake News Detection (NR, SS, YL0), pp. 797–806.
- CIKM-2017-Saleem0CXP #network #social
- IMaxer: A Unified System for Evaluating Influence Maximization in Location-based Social Networks (MAS, RK0, TC, XX, TBP), pp. 2523–2526.
- CIKM-2017-SeoK #algorithm #clustering #graph #performance #scalability
- pm-SCAN: an I/O Efficient Structural Clustering Algorithm for Large-scale Graphs (JHS, MHK), pp. 2295–2298.
- CIKM-2017-ShaikhK #analysis #framework
- StreamingCube: A Unified Framework for Stream Processing and OLAP Analysis (SAS, HK), pp. 2527–2530.
- CIKM-2017-TangHLTWYZ #documentation
- QALink: Enriching Text Documents with Relevant Q&A Site Contents (YT, WH, QL, AKHT, XW, JY, BZ), pp. 1359–1368.
- CIKM-2017-VasiloudisBM #distributed #streaming
- BoostVHT: Boosting Distributed Streaming Decision Trees (TV, FB, GDFM), pp. 899–908.
- CIKM-2017-WangDCLG #big data
- CleanCloud: Cleaning Big Data on Cloud (HW, XD, XC, JL, HG), pp. 2543–2546.
- CIKM-2017-WangPLZJ #clustering #graph
- MGAE: Marginalized Graph Autoencoder for Graph Clustering (CW, SP, GL, XZ, JJ0), pp. 889–898.
- CIKM-2017-XiongLCH #documentation #query #ranking
- JointSem: Combining Query Entity Linking and Entity based Document Ranking (CX, ZL0, JC, EHH), pp. 2391–2394.
- CIKM-2017-XuM #analysis #multimodal #network #semantics #sentiment
- MultiSentiNet: A Deep Semantic Network for Multimodal Sentiment Analysis (NX, WM), pp. 2399–2402.
- CIKM-2017-YaoZHB #predict #semantics
- SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories (DY, CZ, JHH, JB), pp. 2411–2414.
- CIKM-2017-ZhangCLG0X #multi
- MIKE: Keyphrase Extraction by Integrating Multidimensional Information (YZ, YC, XL, SDG, XL0, CX), pp. 1349–1358.
- CIKM-2017-ZhangCYL #community #detection #enterprise #learning
- BL-ECD: Broad Learning based Enterprise Community Detection via Hierarchical Structure Fusion (JZ, LC, PSY, YL), pp. 859–868.
- CIKM-2017-ZhangWHCZ #estimation #realtime
- RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation (YZ, WW0, BH, KMC, YZ), pp. 2423–2426.
- CIKM-2017-ZhiSLZ0 #database #realtime #using #verification #web
- ClaimVerif: A Real-time Claim Verification System Using the Web and Fact Databases (SZ, YS, JL, CZ0, JH0), pp. 2555–2558.
- CIKM-2017-ZhongLCWWAF #knowledge base #online #probability
- POOLSIDE: An Online Probabilistic Knowledge Base for Shopping Decision Support (PZ, ZL, QC, YW, LW, MHMA, FF), pp. 2559–2562.
- CIKM-2017-ZhuangLZF #human-computer #hybrid #knowledge base #scalability
- Hike: A Hybrid Human-Machine Method for Entity Alignment in Large-Scale Knowledge Bases (YZ, GL0, ZZ, JF), pp. 1917–1926.
- ECIR-2017-AlbahemHTE #realtime #twitter
- QweetFinder: Real-Time Finding and Filtering of Question Tweets (AA, MH, MT, TE), pp. 766–769.
- ECIR-2017-AlkhawaldehPJY #clustering #information retrieval #learning #query
- LTRo: Learning to Route Queries in Clustered P2P IR (RSA, DP0, JMJ, FY), pp. 513–519.
- ECIR-2017-Anand0D #recommendation
- FairScholar: Balancing Relevance and Diversity for Scientific Paper Recommendation (AA, TC0, AD), pp. 753–757.
- ECIR-2017-CeroniGF #constraints #corpus #crowdsourcing #strict #validation
- JustEvents: A Crowdsourced Corpus for Event Validation with Strict Temporal Constraints (AC, UG, MF), pp. 484–492.
- ECIR-2017-GangulyP #graph #representation
- Paper2vec: Combining Graph and Text Information for Scientific Paper Representation (SG, VP), pp. 383–395.
- ECIR-2017-GyselKR #interface #python
- Pyndri: A Python Interface to the Indri Search Engine (CVG, EK, MdR), pp. 744–748.
- ICML-2017-Allen-Zhu #optimisation #parametricity #performance #probability
- Natasha: Faster Non-Convex Stochastic Optimization via Strongly Non-Convex Parameter (ZAZ), pp. 89–97.
- ICML-2017-AmosK #network #optimisation
- OptNet: Differentiable Optimization as a Layer in Neural Networks (BA, JZK), pp. 136–145.
- ICML-2017-AnschelBS #learning #reduction
- Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning (OA, NB, NS), pp. 176–185.
- ICML-2017-ChoB #network
- MEC: Memory-efficient Convolution for Deep Neural Network (MC, DB), pp. 815–824.
- ICML-2017-CortesGKMY #adaptation #learning #network
- AdaNet: Adaptive Structural Learning of Artificial Neural Networks (CC, XG, VK, MM, SY), pp. 874–883.
- ICML-2017-CuturiB
- Soft-DTW: a Differentiable Loss Function for Time-Series (MC, MB), pp. 894–903.
- ICML-2017-DempseyMSDGMR #predict
- iSurvive: An Interpretable, Event-time Prediction Model for mHealth (WHD, AM, CKS, MLD, DHG, SAM, JMR), pp. 970–979.
- ICML-2017-DevlinUBSMK #learning
- RobustFill: Neural Program Learning under Noisy I/O (JD, JU, SB, RS, ArM, PK), pp. 990–998.
- ICML-2017-GuptaSGSPKGUV0
- ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices (CG, ASS, AG, HVS, BP, AK, SG, RU, MV, PJ0), pp. 1331–1340.
- ICML-2017-HadjeresPN #generative
- DeepBach: a Steerable Model for Bach Chorales Generation (GH, FP, FN), pp. 1362–1371.
- ICML-2017-HigginsPRMBPBBL #learning
- DARLA: Improving Zero-Shot Transfer in Reinforcement Learning (IH, AP, AAR, LM, CB, AP, MB, CB, AL), pp. 1480–1490.
- ICML-2017-HirayamaHK
- SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling (JH, AH, MK), pp. 1491–1500.
- ICML-2017-HongHZ #algorithm #distributed #learning #network #optimisation #performance
- Prox-PDA: The Proximal Primal-Dual Algorithm for Fast Distributed Nonconvex Optimization and Learning Over Networks (MH, DH, MMZ), pp. 1529–1538.
- ICML-2017-JohnsonG #coordination
- StingyCD: Safely Avoiding Wasteful Updates in Coordinate Descent (TBJ, CG), pp. 1752–1760.
- ICML-2017-KimPKH #learning #network #parallel #parametricity #reduction #semantics
- SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization (JK, YP, GK, SJH), pp. 1866–1874.
- ICML-2017-LiuZLS #automation #composition #sequence
- Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling (HL, ZZ, XL, SS), pp. 2188–2197.
- ICML-2017-MaystreG17a #identification #network
- ChoiceRank: Identifying Preferences from Node Traffic in Networks (LM, MG), pp. 2354–2362.
- ICML-2017-MrouehSG
- McGan: Mean and Covariance Feature Matching GAN (YM, TS, VG), pp. 2527–2535.
- ICML-2017-NguyenLST #machine learning #novel #probability #problem #recursion #using
- SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient (LMN, JL, KS, MT), pp. 2613–2621.
- ICML-2017-ShenLYM #algorithm #multi #optimisation
- GSOS: Gauss-Seidel Operator Splitting Algorithm for Multi-Term Nonsmooth Convex Composite Optimization (LS, WL0, GY, SM), pp. 3125–3134.
- ICML-2017-SunRMW #learning
- meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting (XS0, XR, SM, HW), pp. 3299–3308.
- ICML-2017-TrivediDWS #graph #reasoning
- Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs (RT, HD, YW0, LS), pp. 3462–3471.
- ICML-2017-Zhang0KALZ #learning #linear #modelling #precise
- ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning (HZ, JL0, KK, DA, JL0, CZ), pp. 4035–4043.
- KDD-2017-0001YXWY #approximate #effectiveness #personalisation #rank
- FORA: Simple and Effective Approximate Single-Source Personalized PageRank (SW0, RY, XX, ZW, YY), pp. 505–514.
- KDD-2017-AgrawalAKHLCK
- Tripoles: A New Class of Relationships in Time Series Data (SA, GA, AK, WH, SL, SC, VK), pp. 697–706.
- KDD-2017-BaylorBCFFHHIJK #framework #machine learning #platform
- TFX: A TensorFlow-Based Production-Scale Machine Learning Platform (DB, EB, HTC, NF, CYF, ZH, SH, MI, VJ, LK0, CYK, LL, CM, ANM, NP, SR, SR0, SEW, MW, JW, XZ, MZ), pp. 1387–1395.
- KDD-2017-BlalockG #data mining #mining #performance
- Bolt: Accelerated Data Mining with Fast Vector Compression (DWB, JVG), pp. 727–735.
- KDD-2017-BorisyukZK #performance #towards
- LiJAR: A System for Job Application Redistribution towards Efficient Career Marketplace (FB, LZ, KK), pp. 1397–1406.
- KDD-2017-CaoZZYPZARL #detection #mobile #modelling #type system
- DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection (BC, LZ, CZ, PSY, AP, JZ, OA, KR, ADL), pp. 747–755.
- KDD-2017-ChenZ
- KATE: K-Competitive Autoencoder for Text (YC0, MJZ), pp. 85–94.
- KDD-2017-ChoiBSSS #graph #learning #representation
- GRAM: Graph-based Attention Model for Healthcare Representation Learning (EC, MTB, LS, WFS, JS), pp. 787–795.
- KDD-2017-DebGIPVYY #automation #learning #network #policy #predict
- AESOP: Automatic Policy Learning for Predicting and Mitigating Network Service Impairments (SD, ZG, SI, SCP, SV, HY, JY), pp. 1783–1792.
- KDD-2017-DongCS #learning #network #representation #scalability
- metapath2vec: Scalable Representation Learning for Heterogeneous Networks (YD, NVC, AS), pp. 135–144.
- KDD-2017-DuZCT #interactive #performance
- FIRST: Fast Interactive Attributed Subgraph Matching (BD, SZ, NC, HT), pp. 1447–1456.
- KDD-2017-FuA0TX #automation #detection #interactive
- REMIX: Automated Exploration for Interactive Outlier Detection (YF, CCA, SP0, DST, HX), pp. 827–835.
- KDD-2017-GhoshCLMCBMR #automation #open source
- GELL: Automatic Extraction of Epidemiological Line Lists from Open Sources (SG, PC, BLL, MSM, EC, JSB, MVM, NR), pp. 1477–1485.
- KDD-2017-HouYSA #android #detection #network
- HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network (SH, YY, YS, MA), pp. 1507–1515.
- KDD-2017-HuangZ #analysis
- SPOT: Sparse Optimal Transformations for High Dimensional Variable Selection and Exploratory Regression Analysis (QH, MZ), pp. 857–865.
- KDD-2017-JiangSCRKH0 #corpus
- MetaPAD: Meta Pattern Discovery from Massive Text Corpora (MJ0, JS, TC, XR, LMK, TPH, JH0), pp. 877–886.
- KDD-2017-KoutraDBWIFB #design #performance
- PNP: Fast Path Ensemble Method for Movie Design (DK, AD, SB, UW, SI, CF, JB), pp. 1527–1536.
- KDD-2017-LiJZXLZZWZWXB #analysis #framework #platform
- FLAP: An End-to-End Event Log Analysis Platform for System Management (TL0, YJ, CZ, BX0, ZL0, WZ, XZ, WW0, LZ, JW, LX, DB), pp. 1547–1556.
- KDD-2017-LouO #performance
- BDT: Gradient Boosted Decision Tables for High Accuracy and Scoring Efficiency (YL, MO), pp. 1893–1901.
- KDD-2017-MaCZYSG #bidirectional #network #predict
- Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks (FM, RC, JZ, QY, TS, JG0), pp. 1903–1911.
- KDD-2017-PaulLTYF #analysis #exclamation #sentiment #twitter #what
- Compass: Spatio Temporal Sentiment Analysis of US Election What Twitter Says! (DP, FL0, MKT, XY, RF), pp. 1585–1594.
- KDD-2017-PerrosPWVSTS #scalability
- SPARTan: Scalable PARAFAC2 for Large & Sparse Data (IP, EEP, FW0, RWV, ES, MT, JS), pp. 375–384.
- KDD-2017-QinKWRC #multi
- MARAS: Signaling Multi-Drug Adverse Reactions (XQ0, TK, SW, EAR, LC), pp. 1615–1623.
- KDD-2017-RibeiroSF #learning
- struc2vec: Learning Node Representations from Structural Identity (LFRR, PHPS, DRF), pp. 385–394.
- KDD-2017-ShenHGC #comprehension #learning
- ReasoNet: Learning to Stop Reading in Machine Comprehension (YS, PSH, JG, WC), pp. 1047–1055.
- KDD-2017-ShiCZG0 #network #probability
- PReP: Path-Based Relevance from a Probabilistic Perspective in Heterogeneous Information Networks (YS, PWC, HZ, HG, JH0), pp. 425–434.
- KDD-2017-ShinHKF #detection #incremental
- DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams (KS, BH, JK, CF), pp. 1057–1066.
- KDD-2017-Song0H #clustering #parallel #performance
- PAMAE: Parallel k-Medoids Clustering with High Accuracy and Efficiency (HS, JGL0, WSH), pp. 1087–1096.
- KDD-2017-SybrandtSS #automation #generative
- MOLIERE: Automatic Biomedical Hypothesis Generation System (JS, MS, IS), pp. 1633–1642.
- KDD-2017-Tagami #approximate #classification #multi #nearest neighbour
- AnnexML: Approximate Nearest Neighbor Search for Extreme Multi-label Classification (YT), pp. 455–464.
- KDD-2017-VandalKGMNG #generative #image
- DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution (TV, EK, SG, ARM, RRN, ARG), pp. 1663–1672.
- KDD-2017-Xu0TTL #distance #higher-order #optimisation #rating #recommendation
- HoORaYs: High-order Optimization of Rating Distance for Recommender Systems (JX0, YY0, HT, XT, JL0), pp. 525–534.
- KDD-2017-YenHDRDX #classification #parallel
- PPDsparse: A Parallel Primal-Dual Sparse Method for Extreme Classification (IEHY, XH, WD0, PR, ISD, EPX), pp. 545–553.
- KDD-2017-YinCZ #comprehension #design #network #sequence
- DeepProbe: Information Directed Sequence Understanding and Chatbot Design via Recurrent Neural Networks (ZY, KhC, RZ), pp. 2131–2139.
- KDD-2017-Zhang0 #performance #similarity
- EmbedJoin: Efficient Edit Similarity Joins via Embeddings (HZ, QZ0), pp. 585–594.
- KDD-2017-ZhangCTSS #effectiveness #learning #multi
- LEAP: Learning to Prescribe Effective and Safe Treatment Combinations for Multimorbidity (YZ, RC, JT0, WFS, JS), pp. 1315–1324.
- KDD-2017-ZhangLLYZH0 #detection #online #twitter
- TrioVecEvent: Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams (CZ0, LL, DL, QY0, HZ, TH, JH0), pp. 595–604.
- KDD-2017-ZhouLZCLYCYCDQ #distributed #learning #parametricity
- KunPeng: Parameter Server based Distributed Learning Systems and Its Applications in Alibaba and Ant Financial (JZ, XL, PZ, CC, LL, XY, QC, JY, XC, YD, Y(Q), pp. 1693–1702.
- KDD-2017-ZhouXBWZLXLSG #analysis
- STAR: A System for Ticket Analysis and Resolution (WZ, WX, RB, QW, CZ, TL0, JX0, ZL0, LS, GYG), pp. 2181–2190.
- ECMFA-2017-KlugeSGSM #c #embedded #generative #modelling #network
- cMoflon: Model-Driven Generation of Embedded C Code for Wireless Sensor Networks (RK, MS, DG, AS, MM), pp. 109–125.
- ICMT-2017-HoWP #approach #bidirectional
- BXE2E: A Bidirectional Transformation Approach for Medical Record Exchange (JH, JHW, MP), pp. 155–170.
- MoDELS-2017-EgeaD #automation #code generation #ocl #sql
- SQL-PL4OCL: An Automatic Code Generator from OCL to SQL Procedural Language (ME, CD), p. 54.
- MoDELS-2017-KlareBKLSR #java #metamodelling #tool support
- Ecoreification: Making Arbitrary Java Code Accessible to Metamodel-Based Tools (HK, EB, MEK, ML, TS, RHR), pp. 221–228.
- ECOOP-2017-FuPAC #declarative #set
- muPuppet: A Declarative Subset of the Puppet Configuration Language (WF, RP, PA0, JC), p. 27.
- ECOOP-2017-ZhangO #framework #programming language #reuse #visitor
- EVF: An Extensible and Expressive Visitor Framework for Programming Language Reuse (WZ, BCdSO), p. 32.
- Onward-2017-HallSH #programming language #visual notation
- Infra: structure all the way down: structured data as a visual programming language (CH0, TS, TH), pp. 180–197.
- Onward-2017-LamZC #generative #interactive #random #testing
- ChimpCheck: property-based randomized test generation for interactive apps (ESLL, PZ, BYEC), pp. 58–77.
- Onward-2017-VeroyG #case study #how #java
- Garbology: a study of how Java objects die (RLV, SZG), pp. 168–179.
- OOPSLA-2017-AlbarghouthiDDN #probability #verification
- FairSquare: probabilistic verification of program fairness (AA, LD, SD, AVN), p. 30.
- OOPSLA-2017-ClebschFDYWV #co-evolution #design #garbage collection #type system
- Orca: GC and type system co-design for actor languages (SC, JF, SD, AMY, TW, JV), p. 28.
- OOPSLA-2017-DingS
- GLORE: generalized loop redundancy elimination upon LER-notation (YD, XS), p. 28.
- OOPSLA-2017-GrechS #analysis #points-to
- P/Taint: unified points-to and taint analysis (NG, YS), p. 28.
- OOPSLA-2017-LopesMMSYZSV #git
- DéjàVu: a map of code duplicates on GitHub (CVL, PM, PM0, VS, DY, JZ, HS, JV), p. 28.
- OOPSLA-2017-MaengCL #execution
- Alpaca: intermittent execution without checkpoints (KM, AC, BL), p. 30.
- OOPSLA-2017-PapadakisB0AH #graph
- Seam: provably safe local edits on graphs (MP, GLB, RS0, AA, PH), p. 29.
- OOPSLA-2017-SakkaS0 #framework #recursion #traversal
- TreeFuser: a framework for analyzing and fusing general recursive tree traversals (LS, KS, MK0), p. 30.
- OOPSLA-2017-SpathAB #alias #analysis #data flow #performance #precise
- IDEal: efficient and precise alias-aware dataflow analysis (JS, KA0, EB), p. 27.
- OOPSLA-2017-WangWC #analysis #complexity #functional #invariant
- TiML: a functional language for practical complexity analysis with invariants (PW0, DW, AC), p. 26.
- OOPSLA-2017-Yaghmazadeh0DD #natural language #query #synthesis
- SQLizer: query synthesis from natural language (NY, YW0, ID, TD), p. 26.
- OOPSLA-2017-ZhangM #interface #morphism #polymorphism #product line
- Familia: unifying interfaces, type classes, and family polymorphism (YZ, ACM), p. 31.
AdaEurope-2017-CarlettoV #benchmark #comparative #metric #runtime- Ravenscar-EDF: Comparative Benchmarking of an EDF Variant of a Ravenscar Runtime (PC, TV), pp. 18–33.
- AdaEurope-2017-DiazFKMHAC #analysis #bound #manycore #probability
- MC2: Multicore and Cache Analysis via Deterministic and Probabilistic Jitter Bounding (ED, MF, LK, EM, CH, JA, FJC), pp. 102–118.
- AdaEurope-2017-Mosteo #ada #api #implementation
- RxAda: An Ada implementation of the ReactiveX API (ARM), pp. 153–166.
- PLDI-2017-ChuWCS #proving #query #semantics #sql
- HoTTSQL: proving query rewrites with univalent SQL semantics (SC, KW, AC, DS), pp. 510–524.
- PLDI-2017-EizenbergPPMD #analysis #runtime #source code
- BARRACUDA: binary-level analysis of runtime RAces in CUDA programs (AE, YP, TP, WM, JD), pp. 126–140.
- PLDI-2017-HenriksenSEHO #array #functional #parallel
- Futhark: purely functional GPU-programming with nested parallelism and in-place array updates (TH, NGWS, ME, FH, CEO), pp. 556–571.
- PLDI-2017-MamourasRAIK #composition #evaluation #performance #query #specification #streaming
- StreamQRE: modular specification and efficient evaluation of quantitative queries over streaming data (KM, MR, RA, ZGI, SK), pp. 693–708.
- PLDI-2017-MenendezN #data-driven #optimisation
- Alive-Infer: data-driven precondition inference for peephole optimizations in LLVM (DM, SN), pp. 49–63.
- PLDI-2017-PattersonPDA #assembly #functional
- FunTAL: reasonably mixing a functional language with assembly (DP0, JP, CD, AA), pp. 495–509.
- PLDI-2017-PetrashkoLO #compilation #composition #performance #program transformation #using
- Miniphases: compilation using modular and efficient tree transformations (DP, OL, MO), pp. 201–216.
- PLDI-2017-RhodesFF #concurrent #detection #static analysis
- BigFoot: static check placement for dynamic race detection (DR, CF, SNF), pp. 141–156.
- PLDI-2017-YessenovKS #api
- DemoMatch: API discovery from demonstrations (KY, IK, ASL), pp. 64–78.
- POPL-2017-AminR17a #abstraction #programming
- LMS-Verify: abstraction without regret for verified systems programming (NA, TR), pp. 859–873.
- POPL-2017-KopczynskiT #semantics #syntax
- LOIS: syntax and semantics (EK, ST), pp. 586–598.
- POPL-2017-OmarVHAH #bidirectional #calculus #editing
- Hazelnut: a bidirectionally typed structure editor calculus (CO, IV, MH, JA, MAH), pp. 86–99.
- POPL-2017-Paykin0Z #quantum
- QWIRE: a core language for quantum circuits (JP, RR0, SZ), pp. 846–858.
- POPL-2017-SubramanianDA #multitenancy #network #overview
- Genesis: synthesizing forwarding tables in multi-tenant networks (KS, LD, AA), pp. 572–585.
- POPL-2017-Turon #rust
- Rust: from POPL to practice (keynote) (AT), p. 2.
- POPL-2017-ZhangK #automation #difference #privacy #proving #towards
- LightDP: towards automating differential privacy proofs (DZ, DK), pp. 888–901.
QAPL-2017-ArmingBS #parametricity- SEA-PARAM: Exploring Schedulers in Parametric MDPs (SA, EB, AS), pp. 25–38.
- QAPL-2017-KoenigKM #analysis
- PAWS: A Tool for the Analysis of Weighted Systems (BK, SK, CM), pp. 75–91.
- ASE-2017-AbateBCCCDKKP #automation #physics #synthesis
- DSSynth: an automated digital controller synthesis tool for physical plants (AA, IB, DC, LCC, LCC, CD, PK, DK, EP), pp. 919–924.
- ASE-2017-AhmedBIR #analysis #code review #interactive #overview #sentiment
- SentiCR: a customized sentiment analysis tool for code review interactions (TA, AB, AI, SR), pp. 106–111.
- ASE-2017-AsaduzzamanRSH #framework #recommendation
- FEMIR: a tool for recommending framework extension examples (MA, CKR, KAS, DH), pp. 967–972.
- ASE-2017-CelikPG #proving #scalability #verification
- iCoq: regression proof selection for large-scale verification projects (AÇ, KP, MG), pp. 171–182.
- ASE-2017-ChengZS0S #automation #fault #integer #type inference
- IntPTI: automatic integer error repair with proper-type inference (XC, MZ0, XS, MG0, JS), pp. 996–1001.
- ASE-2017-CoppikSWS #fault #kernel #operating system
- TrEKer: tracing error propagation in operating system kernels (NC, OS, SW0, NS), pp. 377–387.
- ASE-2017-CorradiniFP0TV #framework #modelling #process #verification
- BProVe: a formal verification framework for business process models (FC, FF, AP, BR0, FT, AV), pp. 217–228.
- ASE-2017-CorradiniFP0TV17a #process #tool support #verification
- BProVe: tool support for business process verification (FC, FF, AP, BR0, FT, AV), pp. 937–942.
- ASE-2017-DanielJSC #framework #model transformation #scalability
- Gremlin-ATL: a scalable model transformation framework (GD, FJ, GS, JC), pp. 462–472.
- ASE-2017-ErataGGSLTKM #documentation #framework #platform
- ModelWriter: text and model-synchronized document engineering platform (FE, CG, BG, AS, YL, BT, GK, AM), pp. 907–912.
- ASE-2017-GrigeraGR #as a service #usability #web
- Kobold: web usability as a service (JG, AG, GR), pp. 990–995.
- ASE-2017-JiangWLC #android #performance #sequence
- SimplyDroid: efficient event sequence simplification for Android application (BJ0, YW, TL, WKC0), pp. 297–307.
- ASE-2017-KjolstadCLKA #algebra #kernel
- taco: a tool to generate tensor algebra kernels (FK, SC, DL, SK, SPA), pp. 943–948.
- ASE-2017-KrugerNRAMBGGWD #developer #encryption #using
- CogniCrypt: supporting developers in using cryptography (SK, SN, MR, KA0, MM, EB, FG, FG0, CW, DD, RK), pp. 931–936.
- ASE-2017-LeeYSNM #android #detection #security #visualisation
- SEALANT: a detection and visualization tool for inter-app security vulnerabilities in Android (YKL, PY, AS, DN, NM), pp. 883–888.
- ASE-2017-LegunsenSM #testing
- STARTS: STAtic regression test selection (OL, AS, DM), pp. 949–954.
- ASE-2017-LeungL #ide #parsing #synthesis
- Parsimony: an IDE for example-guided synthesis of lexers and parsers (AL, SL), pp. 815–825.
- ASE-2017-LinL0CGLLMR #interface #natural language #query
- TiQi: a natural language interface for querying software project data (JL, YL, JG0, JCH, WG, WL, SL, NM, AR), pp. 973–977.
- ASE-2017-LinSXLSH #invariant
- FiB: squeezing loop invariants by interpolation between Forward/Backward predicate transformers (SWL, JS0, HX, YL0, DS, HH), pp. 793–803.
- ASE-2017-MeftahGRC #in the large #mobile #peer-to-peer #testing
- ANDROFLEET: testing WiFi peer-to-peer mobile apps in the large (LM, MG0, RR, IC), pp. 961–966.
- ASE-2017-NguyenDV #invariant #using
- SymInfer: inferring program invariants using symbolic states (TN, MBD, WV), pp. 804–814.
- ASE-2017-RupprechtC0BLB #c #c++ #data type #identification
- DSIbin: identifying dynamic data structures in C/C++ binaries (TR, XC0, DHW0, JHB, GL, HB), pp. 331–341.
- ASE-2017-SaddlerC #generative #testing #user interface
- EventFlowSlicer: a tool for generating realistic goal-driven GUI tests (JAS, MBC), pp. 955–960.
- ASE-2017-SahaLYP #effectiveness #object-oriented #program repair
- ELIXIR: effective object oriented program repair (RKS, YL, HY, MRP), pp. 648–659.
- ASE-2017-SchmidtNF #mining #specification
- TREM: a tool for mining timed regular specifications from system traces (LS, AN, SF), pp. 901–906.
- ASE-2017-SongQH #android #testing #user interface
- EHBDroid: beyond GUI testing for Android applications (WS0, XQ, JH0), pp. 27–37.
- ASE-2017-SulirP
- RuntimeSearch: Ctrl+F for a running program (MS, JP), pp. 388–393.
- ASE-2017-TianTSL #api #documentation
- APIBot: question answering bot for API documentation (YT0, FT, AS0, DL0), pp. 153–158.
- ASE-2017-UddinK17a #api #summary
- Opiner: an opinion search and summarization engine for APIs (GU, FK), pp. 978–983.
- ASE-2017-WangKZAKLLMZE #programming #web
- PAD: programming third-party web advertisement censorship (WW, YK, YZ, YA, ILK, WCL, YL, WM, XZ0, PE), pp. 240–251.
- ASE-2017-WeissGB #interactive
- Tortoise: interactive system configuration repair (AW, AG, YB), pp. 625–636.
- ASE-2017-XuXXL #automation #developer #generative #summary
- AnswerBot: automated generation of answer summary to developersź technical questions (BX, ZX, XX0, DL0), pp. 706–716.
- ASE-2017-ZhouSLCL #detection
- UNDEAD: detecting and preventing deadlocks in production software (JZ, SS, HL, YC0, TL), pp. 729–740.
- ESEC-FSE-2017-AliabadiKGP #cyber-physical #detection #invariant #security
- ARTINALI: dynamic invariant detection for cyber-physical system security (MRA, AAK, JGS, KP), pp. 349–361.
- ESEC-FSE-2017-DAntoniSV
- NoFAQ: synthesizing command repairs from examples (LD, RS, MV), pp. 582–592.
- ESEC-FSE-2017-Gazzillo
- Kmax: finding all configurations of Kbuild makefiles statically (PG), pp. 279–290.
- ESEC-FSE-2017-GlanzAERHLM #obfuscation
- CodeMatch: obfuscation won't conceal your repackaged app (LG, SA, ME, MR, BH, JL, MM), pp. 638–648.
- ESEC-FSE-2017-GuoCY #concurrent #detection #execution #thread
- AtexRace: across thread and execution sampling for in-house race detection (YG, YC0, ZY), pp. 315–325.
- ESEC-FSE-2017-JabbarvandM #android #energy #framework #mutation testing #testing
- µDroid: an energy-aware mutation testing framework for Android (RJ, SM), pp. 208–219.
- ESEC-FSE-2017-Kohli #android #identification
- DecisionDroid: a supervised learning-based system to identify cloned Android applications (AK), pp. 1059–1061.
- ESEC-FSE-2017-LeCLGV #programming #semantics #synthesis
- S3: syntax- and semantic-guided repair synthesis via programming by examples (XBDL, DHC, DL0, CLG, WV), pp. 593–604.
- ESEC-FSE-2017-LiCCLLT #fuzzing
- Steelix: program-state based binary fuzzing (YL, BC0, MC, SWL, YL0, AT), pp. 627–637.
- ESEC-FSE-2017-MaAXLZLZ #algorithm #graph #machine learning
- LAMP: data provenance for graph based machine learning algorithms through derivative computation (SM, YA, ZX, WCL, JZ, YL, XZ0), pp. 786–797.
- ESEC-FSE-2017-NunezMR #comprehension
- ARCC: assistant for repetitive code comprehension (WZN, VJM, CRR), pp. 999–1003.
- ESEC-FSE-2017-Oliveira #architecture #co-evolution #dependence #fine-grained #refactoring #using
- DRACO: discovering refactorings that improve architecture using fine-grained co-change dependencies (MCdO0), pp. 1018–1021.
- ESEC-FSE-2017-PastoreM #automation #detection #fault
- VART: a tool for the automatic detection of regression faults (FP, LM), pp. 964–968.
- ESEC-FSE-2017-PastoreMM #behaviour #identification
- BDCI: behavioral driven conflict identification (FP, LM, DM), pp. 570–581.
- ESEC-FSE-2017-RastogiDCJM #automation
- Cimplifier: automatically debloating containers (VR, DD, LDC, SJ, PDM), pp. 476–486.
- ESEC-FSE-2017-RegisDDA #lts
- CLTSA: labelled transition system analyser with counting fluent support (GR, RD, ND, NA), pp. 979–983.
- ESEC-FSE-2017-SadeghiJM #android #testing #user interface
- PATDroid: permission-aware GUI testing of Android (AS, RJ, SM), pp. 220–232.
- ESEC-FSE-2017-ThomeSBB #injection
- JoanAudit: a tool for auditing common injection vulnerabilities (JT, LKS, DB, LCB), pp. 1004–1008.
- ESEC-FSE-2017-WangNT #testing
- QTEP: quality-aware test case prioritization (SW0, JN, LT0), pp. 523–534.
- ESEC-FSE-2017-WeiLC #android #static analysis
- OASIS: prioritizing static analysis warnings for Android apps based on app user reviews (LW, YL, SCC), pp. 672–682.
- ESEC-FSE-2017-WuY #android #framework #kernel
- LaChouTi: kernel vulnerability responding framework for the fragmented Android devices (JW, MY), pp. 920–925.
- ESEC-FSE-2017-XieCZLLL #analysis #termination
- Loopster: static loop termination analysis (XX, BC0, LZ, SWL, YL0, XL), pp. 84–94.
- ESEC-FSE-2017-XuXXLL #retrieval
- XSearch: a domain-specific cross-language relevant question retrieval tool (BX, ZX, XX0, DL0, XBDL), pp. 1009–1013.
- ESEC-FSE-2017-YuanXXPZ #android #execution #graph
- RunDroid: recovering execution call graphs for Android applications (YY, LX, XX, AP, HZ), pp. 949–953.
- ESEC-FSE-2017-YuCZWD #java
- RGSE: a regular property guided symbolic executor for Java (HY, ZC, YZ, JW0, WD0), pp. 954–958.
- ESEC-FSE-2017-YuZW #concurrent
- DESCRY: reproducing system-level concurrency failures (TY, TSZ, CW0), pp. 694–704.
- ICSE-2017-BehringerPB #editing #product line #projectional
- PEoPL: projectional editing of product lines (BB, JP, TB), pp. 563–574.
- ICSE-2017-BusariL #analysis #architecture #lightweight #requirements
- RADAR: a lightweight tool for requirements and architecture decision analysis (SAB, EL), pp. 552–562.
- ICSE-2017-CoblenzNAMS #java #transitive
- Glacier: transitive class immutability for Java (MJC, WN, JA, BAM, JS), pp. 496–506.
- ICSE-2017-HawkinsD #detection #php
- ZenIDS: introspective intrusion detection for PHP applications (BH, BD), pp. 232–243.
- ICSE-2017-LiWWWWLXH #android #detection #library #precise #scalability
- LibD: scalable and precise third-party library detection in android markets (ML, WW, PW0, SW0, DW, JL, RX, WH), pp. 335–346.
- ICSE-2017-PadhyeS #detection #dynamic analysis #traversal
- Travioli: a dynamic analysis for detecting data-structure traversals (RP, KS), pp. 473–483.
- ICSE-2017-SuCFR #evaluation #markov #performance #runtime
- ProEva: runtime proactive performance evaluation based on continuous-time markov chains (GS, TC, YF0, DSR), pp. 484–495.
- ICSE-2017-XuCCLS #analysis #comprehension #security #towards
- SPAIN: security patch analysis for binaries towards understanding the pain and pills (ZX, BC0, MC, YL0, FS), pp. 462–472.
- ICSE-2017-ZhangW #web
- RClassify: classifying race conditions in web applications via deterministic replay (LZ0, CW0), pp. 278–288.
- GPCE-2017-NakamaruIYC #api #generative
- Silverchain: a fluent API generator (TN, KI, TY, SC), pp. 199–211.
- GPCE-2017-Perard-GayotWMS #abstraction #algorithm #performance #traversal
- RaTrace: simple and efficient abstractions for BVH ray traversal algorithms (APG, MW, RM, PS, RL, SH), pp. 157–168.
- SLE-2017-GiacomoACSM #domain-specific language
- Metacasanova: an optimized meta-compiler for domain-specific languages (FDG, MA, AC, PS, GM), pp. 232–243.
- SLE-2017-SmitsV #analysis #data flow #declarative #specification
- FlowSpec: declarative dataflow analysis specification (JS, EV), pp. 221–231.
- ASPLOS-2017-AgarwalW #in memory #memory management
- Thermostat: Application-transparent Page Management for Two-tiered Main Memory (NA, TFW), pp. 631–644.
- ASPLOS-2017-AjaySRZX #3d #analysis #energy #optimisation
- 3DGates: An Instruction-Level Energy Analysis and Optimization of 3D Printers (JA, CS0, ASR, CZ, WX), pp. 419–433.
- ASPLOS-2017-AkturkK #automation
- AMNESIAC: Amnesic Automatic Computer (IA, URK), pp. 811–824.
- ASPLOS-2017-ChenYGKMT #precise #predict
- Prophet: Precise QoS Prediction on Non-Preemptive Accelerators to Improve Utilization in Warehouse-Scale Computers (QC0, HY, MG, RSK, JM, LT), pp. 17–32.
- ASPLOS-2017-DelimitrouK #in the cloud #what
- Bolt: I Know What You Did Last Summer... In The Cloud (CD, CK), pp. 599–613.
- ASPLOS-2017-GaoPYHK #3d #memory management #network #performance #scalability
- TETRIS: Scalable and Efficient Neural Network Acceleration with 3D Memory (MG, JP, XY, MH, CK), pp. 751–764.
- ASPLOS-2017-GeCJ #control flow #using
- GRIFFIN: Guarding Control Flows Using Intel Processor Trace (XG, WC, TJ), pp. 585–598.
- ASPLOS-2017-HsiaoNKPP #scalability
- AsyncClock: Scalable Inference of Asynchronous Event Causality (CHH, SN, EMIK, CLP, GAP), pp. 193–205.
- ASPLOS-2017-HuangAZ #debugging #performance #semantics
- Pallas: Semantic-Aware Checking for Finding Deep Bugs in Fast Path (JH0, MAB, XZ), pp. 709–722.
- ASPLOS-2017-KanevXWB #memory management
- Mallacc: Accelerating Memory Allocation (SK, SLX, GYW, DMB), pp. 33–45.
- ASPLOS-2017-KangHGRMMT #collaboration #mobile
- Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge (YK, JH, CG, AR, TNM, JM, LT), pp. 615–629.
- ASPLOS-2017-KhazraeeZVT #optimisation
- Moonwalk: NRE Optimization in ASIC Clouds (MK, LZ, LV, MBT), pp. 511–526.
- ASPLOS-2017-KlimovicLK
- ReFlex: Remote Flash ≈ Local Flash (AK, HL, CK), pp. 345–359.
- ASPLOS-2017-LiCCZ #learning #modelling #topic
- SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs (KL, JC0, WC, JZ0), pp. 497–509.
- ASPLOS-2017-LiuLLLLGT #automation #concurrent #debugging #detection #distributed
- DCatch: Automatically Detecting Distributed Concurrency Bugs in Cloud Systems (HL, GL, JFL, JL, SL, HSG, CT0), pp. 677–691.
- ASPLOS-2017-LiuLNCKA #towards
- IncBricks: Toward In-Network Computation with an In-Network Cache (ML, LL, JN, LC, AK, KA), pp. 795–809.
- ASPLOS-2017-LiuZCQWZR #memory management #persistent #transaction
- DudeTM: Building Durable Transactions with Decoupling for Persistent Memory (ML, MZ, KC, XQ, YW, WZ, JR), pp. 329–343.
- ASPLOS-2017-PowersVB
- Browsix: Bridging the Gap Between Unix and the Browser (BP, JV, EDB), pp. 253–266.
- ASPLOS-2017-RenLDQWLQY #network #probability #using
- SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing (AR, ZL0, CD, QQ, YW, JL0, XQ, BY0), pp. 405–418.
- ASPLOS-2017-TrippelMLPM #hardware #memory management #verification
- TriCheck: Memory Model Verification at the Trisection of Software, Hardware, and ISA (CT, YAM, DL, MP, MM), pp. 119–133.
- ASPLOS-2017-VoraGX #approximate #graph #performance #streaming
- KickStarter: Fast and Accurate Computations on Streaming Graphs via Trimmed Approximations (KV, RG0, G(X), pp. 237–251.
- ASPLOS-2017-VoraTGH #distributed #graph
- CoRAL: Confined Recovery in Distributed Asynchronous Graph Processing (KV, CT0, RG0, ZH), pp. 223–236.
- ASPLOS-2017-WangHZXS #analysis #graph #interprocedural #scalability
- Graspan: A Single-machine Disk-based Graph System for Interprocedural Static Analyses of Large-scale Systems Code (KW0, AH0, ZZ0, G(X, AAS), pp. 389–404.
- ASPLOS-2017-WenCL #locality
- REDSPY: Exploring Value Locality in Software (SW, MC, XL0), pp. 47–61.
- ASPLOS-2017-WuLZJ #flexibility #performance
- FLEP: Enabling Flexible and Efficient Preemption on GPUs (BW0, XL0, XZ0, CJ), pp. 483–496.
- ASPLOS-2017-ZhangJL #concurrent #detection
- ProRace: Practical Data Race Detection for Production Use (TZ, CJ, DL), pp. 149–162.
- CASE-2017-KapadiaSJG #learning
- EchoBot: Facilitating data collection for robot learning with the Amazon echo (RK, SS, LJ, KG), pp. 159–165.
- CASE-2017-WakabayashiKKK #concept #process
- KitPaDy: Robot co-worker in kitting process: Basic concept and computation of working time (YW, MK, JK, KK), pp. 320–327.
- CASE-2017-ZhuJZ #database #interactive
- PPIM: A protein-protein interaction database for Maize (GZ, RJ, XMZ), p. 97.
- CC-2017-BrotherstonDL #java
- Granullar: gradual nullable types for Java (DB, WD, OL), pp. 87–97.
- CGO-2017-GongCZUK #execution #gpu #hardware #kernel #scheduling
- TwinKernels: an execution model to improve GPU hardware scheduling at compile time (XG, ZC, AKZ, RU, DRK), pp. 39–49.
- CGO-2017-JohnsonAL #incremental #scalability
- ThinLTO: scalable and incremental LTO (TJ, MA, DXL), pp. 111–121.
- CGO-2017-SenguptaCBK #bound #hardware #memory management #transaction #using
- Legato: end-to-end bounded region serializability using commodity hardware transactional memory (AS, MC, MDB, MK0), pp. 1–13.
- CGO-2017-SteuwerRD #code generation #functional #gpu #information retrieval
- Lift: a functional data-parallel IR for high-performance GPU code generation (MS, TR, CD), pp. 74–85.
- CGO-2017-TranCKSSKJ #scheduling
- Clairvoyance: look-ahead compile-time scheduling (KAT, TEC, KK, MS, VS, SK, AJ), pp. 171–184.
- CGO-2017-ZhangWZHC #architecture #clustering #fine-grained
- FinePar: irregularity-aware fine-grained workload partitioning on integrated architectures (FZ0, BW0, JZ, BH, WC), pp. 27–38.
- ESOP-2017-GeorgesMOP #framework #linear #logic
- LINCX: A Linear Logical Framework with First-Class Contexts (ALG, AM, SO, BP), pp. 530–555.
- FASE-2017-AlvinPM #diagrams #generative #sequence chart #uml
- StaticGen: Static Generation of UML Sequence Diagrams (CA, BP, SM), pp. 173–190.
- FASE-2017-DomenechGJS #agile #development #prototype #research #tool support #user interface
- EasyInterface: A Toolkit for Rapid Development of GUIs for Research Prototype Tools (JD, SG, EBJ, RS), pp. 379–383.
- FASE-2017-Henda0LNSS #analysis #security
- OpenSAW: Open Security Analysis Workbench (NBH, BJ0, PL, KN, PS, OS), pp. 321–337.
- FASE-2017-MontaghamiR
- Bordeaux: A Tool for Thinking Outside the Box (VM, DR), pp. 22–39.
- CADE-2017-NageleFM
- CSI: New Evidence - A Progress Report (JN, BF, AM), pp. 385–397.
- CADE-2017-PapapanagiotouF #composition #framework #process #specification #workflow
- WorkflowFM: A Logic-Based Framework for Formal Process Specification and Composition (PP, JDF), pp. 357–370.
- CAV-2017-AlurDLS #detection #gpu #source code
- GPUDrano: Detecting Uncoalesced Accesses in GPU Programs (RA, JD, OSNL, NS), pp. 507–525.
- CAV-2017-BenesBDPS #parallel #parametricity #synthesis
- Pithya: A Parallel Tool for Parameter Synthesis of Piecewise Multi-affine Dynamical Systems (NB, LB, MD, SP, DS), pp. 591–598.
- CAV-2017-BrihayeGHM #automaton #composition
- MightyL: A Compositional Translation from MITL to Timed Automata (TB, GG, HMH, BM), pp. 421–440.
- CAV-2017-FanQM0 #composition #data-driven #reasoning #verification
- DryVR: Data-Driven Verification and Compositional Reasoning for Automotive Systems (CF, BQ, SM, MV0), pp. 441–461.
- CAV-2017-KatzBDJK #network #performance #smt #verification
- Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks (GK, CWB, DLD, KJ, MJK), pp. 97–117.
- CAV-2017-RoehmHM #validation
- STLInspector: STL Validation with Guarantees (HR, TH0, ECM), pp. 225–232.
- CAV-2017-Ulus #monitoring #regular expression
- Montre: A Tool for Monitoring Timed Regular Expressions (DU), pp. 329–335.
- CAV-2017-WindsorDSP #concurrent #lightweight #verification
- Starling: Lightweight Concurrency Verification with Views (MW, MD, BS, MJP), pp. 544–569.
- CAV-2017-EkiciMTKKRB #coq #plugin #smt
- SMTCoq: A Plug-In for Integrating SMT Solvers into Coq (BE, AM, CT, CK, GK, AR, CWB), pp. 126–133.
- CAV-2017-FaymonvilleFT #bound #framework #synthesis
- BoSy: An Experimentation Framework for Bounded Synthesis (PF, BF, LT), pp. 325–332.
- CAV-2017-FinkbeinerHS #equivalence #satisfiability
- EAHyper: Satisfiability, Implication, and Equivalence Checking of Hyperproperties (BF, CH, MS), pp. 564–570.
- CAV-2017-SinghBM #debugging #detection #fault #formal method #locality #validation
- E-QED: Electrical Bug Localization During Post-silicon Validation Enabled by Quick Error Detection and Formal Methods (ES, CWB, SM), pp. 104–125.
CSL-2017-HeerdtS0 #automaton #category theory #framework #learning- CALF: Categorical Automata Learning Framework (GvH, MS, AS0), p. 24.
- ICST-2017-ArcainiGR17a #framework #model checking
- NuSeen: A Tool Framework for the NuSMV Model Checker (PA, AG, ER), pp. 476–483.
- ICST-2017-ChanWSPS #analysis #concurrent #fault #invariant #multi #source code #thread #using
- IPA: Error Propagation Analysis of Multi-Threaded Programs Using Likely Invariants (AC, SW0, HS, KP, NS), pp. 184–195.
- ICST-2017-Fard0 #javascript
- JavaScript: The (Un)Covered Parts (AMF, AM0), pp. 230–240.
- ICST-2017-FazziniFCO #android #encoding #framework #independence #platform #testing
- Barista: A Technique for Recording, Encoding, and Running Platform Independent Android Tests (MF, ENdAF, SRC, AO), pp. 149–160.
- ICST-2017-JeongLKKH #embedded #fault #framework #injection #kernel #linux
- FIFA: A Kernel-Level Fault Injection Framework for ARM-Based Embedded Linux System (EJ, NL, JK, DK, SH), pp. 23–34.
- ICST-2017-KleineS #combinator #testing
- Coveringcerts: Combinatorial Methods for X.509 Certificate Testing (KK, DES), pp. 69–79.
- ICST-2017-LiCWH0WL #automation #evolution #maintenance #mobile #testing #user interface
- ATOM: Automatic Maintenance of GUI Test Scripts for Evolving Mobile Applications (XL, NC, YW, HH, YP0, LW, XL), pp. 161–171.
- ICST-2017-MarijanLGSI #configuration management #optimisation #testing
- TITAN: Test Suite Optimization for Highly Configurable Software (DM, ML, AG, SS, CI), pp. 524–531.
- ICST-2017-MilewiczP #model checking #static analysis
- Ariadne: Hybridizing Directed Model Checking and Static Analysis (RM, PP), pp. 442–447.
- ICST-2017-OliveiraFDHS #effectiveness #performance #testing
- Perphecy: Performance Regression Test Selection Made Simple but Effective (ABdO, SF, AD, MH, PFS), pp. 103–113.
- ICST-2017-PradhanWAYL #algorithm #clustering #multi #optimisation #search-based
- CBGA-ES: A Cluster-Based Genetic Algorithm with Elitist Selection for Supporting Multi-Objective Test Optimization (DP, SW0, SA0, TY0, ML), pp. 367–378.
- ICST-2017-SunRJB #android #runtime #using #verification #weaving
- ADRENALIN-RV: Android Runtime Verification Using Load-Time Weaving (HS, AR, OJ, WB), pp. 532–539.
- ICST-2017-TangCZGXHBM #android #automation #detection #verification
- NIVAnalyzer: A Tool for Automatically Detecting and Verifying Next-Intent Vulnerabilities in Android Apps (JT, XC, ZZ0, SG, XSX, CH, TB, BM), pp. 492–499.
- ICTSS-2017-RenardRF #game studies #runtime
- GREP: Games for the Runtime Enforcement of Properties (MR, AR, YF), pp. 259–275.
- TAP-2017-MaalejKJ #composition #modelling #testing
- WSCLim: A Tool for Model-Based Testing of WS-BPEL Compositions Under Load Conditions (AJM, MK, MJ), pp. 139–151.
CBSE-2016-KjaergaardJSH #framework #platform #reasoning- OccuRE: An Occupancy REasoning Platform for Occupancy-Driven Applications (MBK, AJ, FCS, EH), pp. 39–48.
- CBSE-2016-SantosA #component #runtime
- VISCTE: Runtime Exploration of Component-Based Systems (ALS, IA), pp. 91–96.
- CBSE-2016-TricoireBLFSMBN #configuration management
- KevoreeJS: Enabling Dynamic Software Reconfigurations in the Browser (MT, OB, ML, FF, GS, BM, JB, GN, LM), pp. 49–58.
- ECSA-2016-BrogiCCDNGPPS #architecture #multi
- SeaClouds: An Open Reference Architecture for Multi-cloud Governance (AB, JC, JC, FD, EDN, MG, DP, EP, JS), pp. 334–338.
QoSA-2016-JohnsenLHPT #architecture #assurance #framework #quality- AQAF: An Architecture Quality Assurance Framework for Systems Modeled in AADL (AJ, KL, KH, PP, MT), pp. 31–40.
WICSA-2016-GarbaNB #multi #scalability #variability- MUSA: A Scalable Multi-touch and Multi-perspective Variability Management Tool (MG, AN, RB), pp. 299–302.
- WICSA-2016-SantosMMZ #architecture #research #traceability
- BUDGET: A Tool for Supporting Software Architecture Traceability Research (JCSS, MM, IM, WZ), pp. 303–306.
- JCDL-2016-HolzmannGA #performance #web
- ArchiveSpark: Efficient Web Archive Access, Extraction and Derivation (HH, VG, AA), pp. 83–92.
- JCDL-2016-McCainKW
- Panel: Preserving Born-digital News (EM, MK0, MW), p. 281.
- CSEET-2016-PorrasSRAK #development #pervasive
- PERCCOM: A Master Program in Pervasive Computing and COMmunications for Sustainable Development (JP, AS, ER, KA0, AK), pp. 204–212.
- EDM-2016-SharmaBGPD16a #education #multimodal #network #predict
- LIVELINET: A Multimodal Deep Recurrent Neural Network to Predict Liveliness in Educational Videos (AS, AB, AG, SP, OD), pp. 215–222.
- EDM-2016-SharmaBGPD16a_ #education #multimodal #network #predict
- LIVELINET: A Multimodal Deep Recurrent Neural Network to Predict Liveliness in Educational Videos (AS, AB, AG, SP, OD), pp. 215–222.
- EDM-2016-YadavSKSD #framework #learning #platform
- TutorSpace: Content-centric Platform for Enabling Blended Learning in Developing Countries (KY, KS, RK, SS, OD), pp. 705–706.
- ICPC-2016-AmannPN #interactive #visual notation
- FeedBaG: An interaction tracker for Visual Studio (SA, SP, SN), pp. 1–3.
- ICPC-2016-AzadmaneshH #debugging
- InfectoMeter: A tool that helps to place bug fixes (MRA, MH), pp. 1–3.
- ICPC-2016-BreckelT16a
- INLINE: Now you're coding with portals (AB, MT), pp. 1–3.
- ICPC-2016-CloutierKE #reverse engineering #web
- WAVI: A reverse engineering tool for web applications (JC, SK, GEB), pp. 1–3.
- ICPC-2016-IslamB #parallel #slicing
- PORBS: A parallel observation-based slicer (SSI, DWB), pp. 1–3.
- ICPC-2016-KhatiwadaKM #analysis
- STAC: A tool for Static Textual Analysis of Code (SK, MK, AM), pp. 1–3.
- ICPC-2016-SunSYL #source code #word
- WB4SP: A tool to build the word base for specific programs (WS, XS, HY, BL0), pp. 1–3.
- ICSME-2016-ChenXH #stack overflow
- TechLand: Assisting Technology Landscape Inquiries with Insights from Stack Overflow (CC, ZX, LH0), pp. 356–366.
- ICSME-2016-McCurdyMK #effectiveness #reduction
- mrstudyr: Retrospectively Studying the Effectiveness of Mutant Reduction Techniques (CJM, PM, GMK), pp. 591–595.
- ICSME-2016-McMinnWKMCK #database #generative #relational #search-based #testing
- SchemaAnalyst: Search-Based Test Data Generation for Relational Database Schemas (PM, CJW, CK, CJM, MC, GMK), pp. 586–590.
- ICSME-2016-MedeleanuM #design pattern #null #pseudo #refactoring
- NullTerminator: Pseudo-Automatic Refactoring to Null Object Design Pattern (SM, PFM), pp. 601–603.
- ICSME-2016-NewmanMC #performance #static typing
- srcType: A Tool for Efficient Static Type Resolution (CDN, JIM, MLC), pp. 604–606.
- ICSME-2016-PanditaTWT #api #constraints #natural language
- ICON: Inferring Temporal Constraints from Natural Language API Descriptions (RP, KT, LW, TT), pp. 378–388.
- ICSME-2016-RahimiC #feature model #matrix</
