Publications
The 50 most recent publications from the Division of Software and Computer Systems:
[1]
A. Layegh, A. H. Payberah and M. Matskin,
"REA: Refine-Estimate-Answer Prompting for Zero-Shot Relation Extraction,"
in Natural Language Processing and Information Systems - 29th International Conference on Applications of Natural Language to Information Systems, NLDB 2024, Proceedings, 2024, pp. 301-316.
[2]
L. Cao et al.,
"Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems,"
in Artificial Neural Networks and Machine Learning – ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Proceedings, 2024, pp. 373-388.
[3]
A. Rao et al.,
"Generalizable One-Way Delay Prediction Models for Heterogeneous UEs in 5G Networks,"
in Proceedings of 2024 IEEE/IFIP Network Operations and Management Symposium, NOMS 2024, 2024.
[4]
N. Atienza et al.,
"Cutting the Black Box: Conceptual Interpretation of a Deep Neural Net with Multi-Modal Embeddings and Multi-Criteria Decision Aid,"
in Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024, 2024, pp. 3669-3678.
[5]
H. Fu et al.,
"In industrial embedded software, are some compilation errors easier to localize and fix than others?,"
in Proceedings - 2024 IEEE Conference on Software Testing, Verification and Validation, ICST 2024, 2024, pp. 383-394.
[6]
T. Wang,
"Representation Learning and Parallelization for Machine Learning Applications with Graph, Tabular, and Time-Series Data,"
Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:72, 2024.
[7]
E. Gogoulou et al.,
"Continual Learning Under Language Shift,"
in Text, Speech, and Dialogue - 27th International Conference, TSD 2024, Proceedings, 2024, pp. 71-84.
[8]
G. Çaylak,
"Automated Optimizations for Inference in Probabilistic Programming Languages,"
Licentiate thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:73, 2024.
[9]
V. Palmkvist,
"Abstraction, Composition, and Resolvable Ambiguity in Programming Language Implementation,"
Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:69, 2024.
[10]
F. Schmidt et al.,
"A Scalable System Architecture for Composition and Deployment of Machine Learning Models in Cognitive Behavioral Therapy,"
in 2024 IEEE International Conference on Digital Health (ICDH), 2024, pp. 79-86.
[11]
A. H. Akhavan Rahnama, J. Butepage and H. Boström,
"Local List-Wise Explanations of LambdaMART,"
in Explainable Artificial Intelligence - Second World Conference, xAI 2024, Proceedings, 2024, pp. 369-392.
[12]
Y. Abbahaddou et al.,
"Bounding The Expected Robustness Of Graph Neural Networks Subject To Node Feature Attacks,"
in 12th International Conference on Learning Representations, ICLR 2024, 2024.
[13]
G. Siachamis et al.,
"CheckMate : Evaluating Checkpointing Protocols for Streaming Dataflows,"
in Proceedings - 2024 IEEE 40th International Conference on Data Engineering, ICDE 2024, 2024, pp. 4030-4043.
[14]
K. Segeljakt, S. Haridi and P. Carbone,
"AquaLang : A Dataflow Programming Language,"
in DEBS 2024 - Proceedings of the 18th ACM International Conference on Distributed and Event-Based Systems, 2024, pp. 42-53.
[15]
H. Jin, Z. Zhou and P. Papadimitratos,
"Future-proofing Secure V2V Communication against Clogging DoS Attacks,"
in ARES 2024 - 19th International Conference on Availability, Reliability and Security, Proceedings, 2024.
[16]
F. Reyes García et al.,
"BUMP : A Benchmark of Reproducible Breaking Dependency Updates,"
in Proceedings - 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024, 2024, pp. 159-170.
[17]
M. Polverini et al.,
"Achieving Best-path Selection at Line Rate through the SRv6 Live-Live Behavior,"
in Proceedings of IEEE/IFIP Network Operations and Management Symposium 2024, NOMS 2024, 2024.
[18]
T. C. Tavares et al.,
"TimeGAN as a Simulator for Reinforcement Learning Training in Programmable Data Planes,"
in Proceedings of IEEE/IFIP Network Operations and Management Symposium 2024, NOMS 2024, 2024.
[19]
B. Baudry and M. Monperrus,
"Programming Art With Drawing Machines,"
Computer, vol. 57, no. 7, pp. 104-108, 2024.
[20]
E. L. Zec et al.,
"Efficient Node Selection in Private Personalized Decentralized Learning,"
in Proceedings of the 5th Northern Lights Deep Learning Conference, NLDL 2024, 2024.
[21]
A. Q. Khan et al.,
"A Taxonomy for Cloud Storage Cost,"
in Management of Digital EcoSystems - 15th International Conference, MEDES 2023, Revised Selected Papers, 2024, pp. 317-330.
[22]
H. Jin and P. Papadimitratos,
"Over-the-Air Runtime Wi-Fi MAC Address Re-randomization,"
in WiSec 2024 - Proceedings of the 17th ACM Conference on Security and Privacy in Wireless and Mobile Networks, 2024, pp. 8-13.
[23]
W. Liu and P. Papadimitratos,
"Extending RAIM with a Gaussian Mixture of Opportunistic Information,"
in Proceedings of the 2024 International Technical Meeting of The Institute of Navigation, 2024, pp. 454-466.
[24]
J. Lindén et al.,
"Autonomous Realization of Safety- and Time-Critical Embedded Artificial Intelligence,"
in 2024 Design, Automation and Test in Europe Conference and Exhibition, DATE 2024 - Proceedings, 2024.
[25]
I. Evdaimon et al.,
"GreekBART: The First Pretrained Greek Sequence-to-Sequence Model,"
in 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings, 2024, pp. 7949-7962.
[26]
J. de la Rua Martinez et al.,
"The Hopsworks Feature Store for Machine Learning,"
in SIGMOD-Companion 2024 - Companion of the 2024 International Conferaence on Management of Data, 2024, pp. 135-147.
[27]
A. Q. Khan et al.,
"Cloud storage cost: a taxonomy and survey,"
World wide web (Bussum), vol. 27, no. 4, 2024.
[28]
A. Ågren Thuné, K. Matsuda and M. Wang,
"Reconciling Partial and Local Invertibility,"
in 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, pp. 59-89.
[29]
A. Karlsson et al.,
"Mind the Data, Measuring the Performance Gap Between Tree Ensembles and Deep Learning on Tabular Data,"
in Advances in Intelligent Data Analysis XXII - 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Proceedings, 2024, pp. 65-76.
[30]
D. Lundén et al.,
"Suspension Analysis and Selective Continuation-Passing Style for Universal Probabilistic Programming Languages,"
in 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, pp. 302-330.
[31]
Y. He, A. Podobas and S. Markidis,
"Leveraging MLIR for Loop Vectorization and GPU Porting of FFT Libraries,"
in Euro-Par 2023: Parallel Processing Workshops - Euro-Par 2023 International Workshops, Limassol, Cyprus, August 28 – September 1, 2023, Revised Selected Papers, 2024, pp. 207-218.
[32]
H. Boström,
"Example-Based Explanations of Random Forest Predictions,"
in Advances in Intelligent Data Analysis XXII - 22nd International Symposium on Intelligent Data Analysis, IDA 2024, Proceedings, 2024, pp. 185-196.
[33]
H. Ghasemirahni et al.,
"Deploying Stateful Network Functions Efficiently using Large Language Models,"
in EuroMLSys 2024 - Proceedings of the 2024 4th Workshop on Machine Learning and Systems, 2024, pp. 28-38.
[34]
M. García Lozano,
"Toward automated veracity assessment of data from open sources using features and indicators,"
Doctoral thesis Stockholm, Sweden : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:47, 2024.
[35]
M. Spanghero et al.,
"Uncovering GNSS Interference with Aerial Mapping UAV,"
in Uncovering GNSS Interference with Aerial Mapping UAV, 2024.
[36]
S.-F. Horchidan et al.,
"Crayfish: Navigating the Labyrinth of Machine Learning Inference in Stream Processing Systems,"
in Advances in Database Technology - EDBT, 2024, pp. 676-689.
[37]
G. Verardo,
"Optimizing Neural Network Models for Healthcare and Federated Learning,"
Licentiate thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:39, 2024.
[38]
S. Ennadir et al.,
"A Simple and Yet Fairly Effective Defense for Graph Neural Networks,"
in AAAI Technical Track on Safe, Robust and Responsible AI Track, 2024, pp. 21063-21071.
[39]
M. Girondi et al.,
"Toward GPU-centric Networking on Commodity Hardware,"
in 7th International Workshop on Edge Systems, Analytics and Networking (EdgeSys 2024), April 22, 2024, Athens, Greece, 2024.
[40]
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, pp. 7374-7398, 2024.
[41]
A. Hasselberg et al.,
"Cliffhanger : An Experimental Evaluation of Stateful Serverless at the Edge,"
in 2024 19th Wireless On-Demand Network Systems and Services Conference, 2024, pp. 41-48.
[42]
N. Jansson et al.,
"Neko: A modern, portable, and scalable framework for high-fidelity computational fluid dynamics,"
Computers & Fluids, vol. 275, pp. 106243-106243, 2024.
[43]
C. Eryonucu and P. Papadimitratos,
"Security and Privacy for Mobile Crowdsensing: Improving User Relevance and Privacy,"
in Computer Security. ESORICS 2023 International Workshops - CyberICS, DPM, CBT, and SECPRE, 2023, Revised Selected Papers, 2024, pp. 474-493.
[44]
N. Xu, C. Kosma and M. Vazirgiannis,
"TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting,"
in Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023 Volume 1, 2024, pp. 87-99.
[45]
S. Ennadir et al.,
"UnboundAttack: Generating Unbounded Adversarial Attacks to Graph Neural Networks,"
in Complex Networks and Their Applications XII - Proceedings of The 12th International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2023 Volume 1, 2024, pp. 100-111.
[46]
M. Girondi,
"Toward Highly-efficient GPU-centric Networking,"
Licentiate thesis : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:30, 2024.
[47]
A. H. Akhavan Rahnama,
"The Blame Problem in Evaluating Local Explanations and How to Tackle It,"
in Artificial Intelligence. ECAI 2023 International Workshops - XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, 2023, Proceedings, 2024, pp. 66-86.
[48]
D. Roy,
"Towards Trustworthy Machine Learning For Human Activity Recognition,"
Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:12, 2024.
[49]
J. Cabrera Arteaga,
"Software Diversification for WebAssembly,"
Doctoral thesis : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:10, 2024.
[50]
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.