Publikationer
De 50 senaste publikationerna från avdelningen för programvaruteknik och datorsystem:
[1]
W. Liu och P. Papadimitratos,
"Position-based Rogue Access Point Detection,"
i IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), 2024.
[2]
W. Liu och P. Papadimitratos,
"Extending RAIM with a Gaussian Mixture of Opportunistic Information,"
i ION International Technical Meeting (ITM), 2024.
[3]
J. Lindén et al.,
"Autonomous Realization of Safety- and Time-Critical Embedded Artificial Intelligence,"
i 2024 Design, Automation and Test in Europe Conference and Exhibition, DATE 2024 - Proceedings, 2024.
[4]
D. Tiwari et al.,
"With Great Humor Comes Great Developer Engagement,"
i Proceedings - 2024 ACM/IEEE 46th International Conference on Software Engineering: Software Engineering in Society, ICSE-SEIS 2024, 2024, s. 1-11.
[5]
I. Evdaimon et al.,
"GreekBART: The First Pretrained Greek Sequence-to-Sequence Model,"
i 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings, 2024, s. 7949-7962.
[6]
J. de la Rua Martinez et al.,
"The Hopsworks Feature Store for Machine Learning,"
i SIGMOD-Companion 2024 - Companion of the 2024 International Conferaence on Management of Data, 2024, s. 135-147.
[7]
A. Q. Khan et al.,
"Cloud storage cost: a taxonomy and survey,"
World wide web (Bussum), vol. 27, no. 4, 2024.
[8]
A. Ågren Thuné, K. Matsuda och M. Wang,
"Reconciling Partial and Local Invertibility,"
i Programming Languages and Systems - 33rd European Symposium on Programming, ESOP 2024, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2024, Proceedings, 2024, s. 59-89.
[9]
A. Karlsson et al.,
"Mind the Data, Measuring the Performance Gap Between Tree Ensembles and Deep Learning on Tabular Data,"
i Advances in Intelligent Data Analysis XXII - 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Proceedings, 2024, s. 65-76.
[10]
D. Lundén et al.,
"Suspension Analysis and Selective Continuation-Passing Style for Universal Probabilistic Programming Languages,"
i Programming Languages and Systems - 33rd European Symposium on Programming, ESOP 2024, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2024, Proceedings, 2024, s. 302-330.
[11]
Y. He, A. Podobas och S. Markidis,
"Leveraging MLIR for Loop Vectorization and GPU Porting of FFT Libraries,"
i Euro-Par 2023: Parallel Processing Workshops - Euro-Par 2023 International Workshops, Limassol, Cyprus, August 28 – September 1, 2023, Revised Selected Papers, 2024, s. 207-218.
[12]
H. Boström,
"Example-Based Explanations of Random Forest Predictions,"
i Advances in Intelligent Data Analysis XXII - 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Proceedings, 2024, s. 185-196.
[13]
H. Ghasemirahni et al.,
"Deploying Stateful Network Functions Efficiently using Large Language Models,"
i EuroMLSys 2024 - Proceedings of the 2024 4th Workshop on Machine Learning and Systems, 2024, s. 28-38.
[14]
M. García Lozano,
"Toward automated veracity assessment of data from open sources using features and indicators,"
Doktorsavhandling Stockholm, Sweden : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:47, 2024.
[15]
M. Spanghero et al.,
"Uncovering GNSS Interference with Aerial Mapping UAV,"
i Uncovering GNSS Interference with Aerial Mapping UAV, 2024.
[16]
S.-F. Horchidan et al.,
"Crayfish: Navigating the Labyrinth of Machine Learning Inference in Stream Processing Systems,"
i Advances in Database Technology - EDBT, 2024, s. 676-689.
[17]
G. Verardo,
"Optimizing Neural Network Models for Healthcare and Federated Learning,"
Licentiatavhandling Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:39, 2024.
[18]
S. Ennadir et al.,
"A Simple and Yet Fairly Effective Defense for Graph Neural Networks,"
i AAAI Technical Track on Safe, Robust and Responsible AI Track, 2024, s. 21063-21071.
[19]
M. Girondi et al.,
"Toward GPU-centric Networking on Commodity Hardware,"
i 7th International Workshop on Edge Systems, Analytics and Networking (EdgeSys 2024), April 22, 2024, Athens, Greece, 2024.
[20]
A. Rauniyar et al.,
"Federated Learning for Medical Applications : A Taxonomy, Current Trends, Challenges, and Future Research Directions,"
IEEE Internet of Things Journal, vol. 11, no. 5, s. 7374-7398, 2024.
[21]
A. Hasselberg et al.,
"Cliffhanger : An Experimental Evaluation of Stateful Serverless at the Edge,"
i 2024 19th Wireless On-Demand Network Systems and Services Conference, 2024, s. 41-48.
[22]
N. Jansson et al.,
"Neko: A modern, portable, and scalable framework for high-fidelity computational fluid dynamics,"
Computers & Fluids, vol. 275, s. 106243-106243, 2024.
[23]
C. Eryonucu och P. Papadimitratos,
"Security and Privacy for Mobile Crowdsensing: Improving User Relevance and Privacy,"
i Computer Security. ESORICS 2023 International Workshops - CyberICS, DPM, CBT, and SECPRE, 2023, Revised Selected Papers, 2024, s. 474-493.
[24]
N. Xu, C. Kosma och M. Vazirgiannis,
"TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting,"
i Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023 Volume 1, 2024, s. 87-99.
[25]
S. Ennadir et al.,
"UnboundAttack: Generating Unbounded Adversarial Attacks to Graph Neural Networks,"
i Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023 Volume 1, 2024, s. 100-111.
[26]
M. Girondi,
"Toward Highly-efficient GPU-centric Networking,"
Licentiatavhandling : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:30, 2024.
[27]
A. H. Akhavan Rahnama,
"The Blame Problem in Evaluating Local Explanations and How to Tackle It,"
i Artificial Intelligence. ECAI 2023 International Workshops - XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, 2023, Proceedings, 2024, s. 66-86.
[28]
D. Roy,
"Towards Trustworthy Machine Learning For Human Activity Recognition,"
Doktorsavhandling Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:12, 2024.
[29]
J. Cabrera Arteaga,
"Software Diversification for WebAssembly,"
Doktorsavhandling : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:10, 2024.
[30]
J. Cabrera-Arteaga et al.,
"Wasm-Mutate : Fast and effective binary diversification for WebAssembly,"
Computers & security (Print), vol. 139, s. 103731-103731, 2024.
[31]
F. J. Pena et al.,
"DEEPAQUA : Semantic segmentation of wetland water surfaces with SAR imagery using deep neural networks without manually annotated data,"
International Journal of Applied Earth Observation and Geoinformation, vol. 126, 2024.
[32]
C. Soto Valero et al.,
"Automatic Specialization of Third-Party Java Dependencies,"
IEEE Transactions on Software Engineering, vol. 49, no. 11, s. 5027-5045, 2023.
[33]
J. Brynielsson et al.,
"Comparison of Strategies for Honeypot Deployment,"
i Proceedings Of The 2023 Ieee/Acm International Conference On Advances In Social Networks Analysis And Mining, Asonam 2023, 2023, s. 612-619.
[34]
M. Balliu et al.,
"Challenges of Producing Software Bill of Materials for Java,"
IEEE Security and Privacy, vol. 21, no. 6, s. 12-23, 2023.
[35]
Y. Yang et al.,
"Controller Sensitivity-Based Shaping Method for Grid Forming Inverter,"
i 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023, 2023, s. 6273-6278.
[36]
J. Domke et al.,
"At the Locus of Performance: Quantifying the Effects of Copious 3D-Stacked Cache on HPC Workloads,"
ACM Transactions on Architecture and Code Optimization (TACO), vol. 20, no. 4, 2023.
[37]
U. Johansson et al.,
"Confidence Classifiers with Guaranteed Accuracy or Precision,"
i Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, s. 513-533.
[38]
S. Ennadir et al.,
"Conformalized Adversarial Attack Detection for Graph Neural Networks,"
i Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, s. 311-323.
[39]
A. Alkhatib et al.,
"Approximating Score-based Explanation Techniques Using Conformal Regression,"
i Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, s. 450-469.
[40]
J. Wessén et al.,
"A constraint programming model for the scheduling and workspace layout design of a dual-arm multi-tool assembly robot,"
Constraints, vol. 28, no. 2, s. 71-104, 2023.
[41]
D. Roy, V. Komini och S. Girdzijauskas,
"Classifying falls using out-of-distribution detection in human activity recognition,"
AI Communications, vol. 36, no. 4, s. 251-267, 2023.
[42]
N. Nikolov et al.,
"Container-Based Data Pipelines on the Computing Continuum for Remote Patient Monitoring,"
Computer, vol. 56, no. 10, s. 40-48, 2023.
[43]
A. Layegh et al.,
"ContrastNER : Contrastive-based Prompt Tuning for Few-shot NER,"
i Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023, 2023, s. 241-249.
[44]
G. Tu, Y. Liu och V. Vlassov,
"AIC-AB NET : A Neural Network for Image Captioning with Spatial Attention and Text Attributes,"
(Manuskript).
[45]
[46]
M. Isaksson et al.,
"Adaptive Expert Models for Federated Learning,"
i Trustworthy Federated Learning : First International Workshop, FL 2022, 2023, s. 1-16.
[47]
M. Scazzariello et al.,
"A High-Speed Stateful Packet Processing Approach for Tbps Programmable Switches,"
i 20th USENIX Symposium on Networked Systems Designand Implementation (NSDI ’23), 2023, s. 1237-1255.
[48]
H. Basloom et al.,
"A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems,"
Computers, Materials and Continua, vol. 74, no. 2, s. 4501-4530, 2023.
[49]
D. Lundén,
"Correct and Efficient Monte Carlo Inference for Universal Probabilistic Programming Languages,"
Doktorsavhandling Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2023:22, 2023.
[50]
D. Lundén et al.,
"Automatic Alignment in Higher-Order Probabilistic Programming Languages,"
i Programming Languages and Systems, 2023.