Publications
The 50 most recent publications from the Division of Software and Computer Systems:
[1]
Y. Liu et al.,
"Detecting and removing bloated dependencies in CommonJS packages,"
Journal of Systems and Software, vol. 230, 2025.
[2]
M. Siavashi et al.,
"Priority-Aware Preemptive Scheduling for Mixed-Priority Workloads in MoE Inference,"
in Proceedings of the 2025 The 5th Workshop On Machine Learning And Systems, EUROMLSYS 2025, 2025, pp. 132-138.
[3]
H. Jin and P. Papadimitratos,
"Accountable, Scalable and DoS-resilient Secure Vehicular Communication,"
Computers & Security, vol. 156, 2025.
[4]
G. Dong et al.,
"Obtaining Example-Based Explanations from Deep Neural Networks,"
in Advances in Intelligent Data Analysis XXIII - 23rd International Symposium on Intelligent Data Analysis, IDA 2025, Proceedings, 2025, pp. 432-443.
[5]
A. Rao,
"Practical Machine Learning for Predictions in Mobile Networks,"
Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2025:64, 2025.
[6]
G. Verardo et al.,
"Reducing the Number of Leads for ECG Imaging with Graph Neural Networks and Meaningful Latent Space,"
in Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers. - 15th International Workshop, STACOM 2024, Held in Conjunction with MICCAI 2024, Revised Selected Papers, 2025, pp. 301-312.
[7]
S. Ennadir et al.,
"Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review,"
Transactions on Machine Learning Research, vol. 2025-April, 2025.
[8]
C. Ottaviani et al.,
"Advanced Routing Strategies for LEO and VLEO Constellations : Ensuring Polar Coverage,"
in 2025 12th Advanced Satellite Multimedia Systems Conference and the 18th Signal Processing for Space Communications Workshop, ASMS/SPSC 2025, 2025.
[9]
M. I. Al Hafiz et al.,
"A Reconfigurable Stream-Based FPGA Accelerator for Bayesian Confidence Propagation Neural Networks,"
in Applied Reconfigurable Computing. Architectures, Tools, and Applications - 21st International Symposium, ARC 2025, Proceedings, 2025, pp. 196-213.
[10]
L. Bahri, B. Carminati and E. Ferrari,
"Privacy-Aware Access Control in Decentralized Online Social Networks,"
in Encyclopedia of Cryptography, Security and Privacy, Third Edition, : Springer Nature, 2025, pp. 1924-1927.
[11]
P. Papadimitratos,
"Mix-Zones in Wireless Mobile Networks,"
in Encyclopedia of Cryptography, Security and Privacy, Sushil Jajodia, Pierangela Samarati, Moti Yung Ed., 3rd ed. : Springer Nature, 2025, pp. 1555-1559.
[12]
R. Yadav et al.,
"Automatic Tracing in Task-Based Runtime Systems,"
in ASPLOS 2025 - Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2025, pp. 84-99.
[13]
V. Andersson et al.,
"UPPERCASE IS ALL YOU NEED,"
in SIGBOVIK 2025, Carnegie Mellon University, Pittsburgh, PA, USA, April 4, 2025, 2025.
[14]
G. Shang et al.,
"Atlas-Chat : Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect,"
in LoResLM 2025 - 1st Workshop on Language Models for Low-Resource Languages, Proceedings of the Workshop, 2025, pp. 9-30.
[15]
A. E. Samy, Z. T. Kefato and Š. Girdzijauskas,
"Leap : Inductive Link Prediction via Learnable Topology Augmentation,"
in Machine Learning, Optimization, and Data Science - 10th International Conference, LOD 2024, Revised Selected Papers, 2025, pp. 448-463.
[16]
V. Komini and S. Girdzijauskas,
"Integrating Logit Space Embeddings for Reliable Out-of-Distribution Detection,"
in Machine Learning, Optimization, and Data Science - 10th International Conference, LOD 2024, Revised Selected Papers, 2025, pp. 255-269.
[17]
R. Bresson et al.,
"KAGNNs : Kolmogorov-Arnold Networks meet Graph Learning,"
Transactions on Machine Learning Research, vol. 2025-March, pp. 1-29, 2025.
[18]
M. Spanghero,
"Data verification for GNSS systems and protection of GNSS services,"
Doctoral thesis Stockholm, Sweden : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2025:36, 2025.
[19]
M. Spanghero et al.,
"GNSS jammer localization and identification with airborne commercial GNSS receivers,"
IEEE Transactions on Information Forensics and Security, vol. 20, pp. 3550-3565, 2025.
[20]
M. Spanghero and P. Papadimitratos,
"UnReference: analysis of the effect of spoofing on RTK reference stations for connected rovers,"
in Proceedings of the 2025 IEEE/ION Position, Localization and Navigation Symposium (PLANS), Salt Lake City, UT, USA, 2025, pp. 1-12.
[21]
E. Listo Zec et al.,
"On the effects of similarity metrics in decentralized deep learning under distributional shift,"
Transactions on Machine Learning Research, vol. 2025-January, pp. 1-23, 2025.
[22]
G. Verardo et al.,
"FMM-Head: Enhancing Autoencoder-Based ECG Anomaly Detection with Prior Knowledge,"
in Pattern Recognition and Artificial Intelligence - 4th International Conference, ICPRAI 2024, Proceedings, 2025, pp. 18-32.
[23]
S. Sheikholeslami,
"Tools and Methods for Distributed and Large-Scale Training of Deep Neural Networks,"
Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2025:28, 2025.
[24]
S. Sheikholeslami et al.,
"Utilizing Large Language Models for Ablation Studies in Machine Learning and Deep Learning,"
in The 5th Workshop on Machine Learning and Systems (EuroMLSys), co-located with the 20th European Conference on Computer Systems (EuroSys), 2025.
[25]
A. H. Akhavan Rahnama,
"Evaluating the Faithfulness of Local Feature Attribution Explanations : Can We Trust Explainable AI?,"
Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2025:23, 2025.
[26]
M. Spanghero and P. Papadimitratos,
"Consumer INS coupled with carrier phase measurements for GNSS spoofing detection,"
in ION ITM/PTTI, International Technical Meeting January 27 - 30, 2025 Long Beach, CA, 2025.
[27]
F. Cornell et al.,
"Unsupervised Ontology- and Taxonomy Construction Through Hyperbolic Relational Domains and Ranges,"
in Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers, 2025, pp. 339-348.
[28]
A. Alkhatib,
"Addressing Shortcomings of Explainable Machine Learning Methods,"
Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2025:11, 2025.
[29]
E. Listo Zec,
"Decentralized deep learning in statistically heterogeneous environments,"
Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2025:4, 2025.
[30]
M. Lindholm and J. Palmquist,
"Black-box guided generalised linear model building with non-life pricing applications,"
Annals of Actuarial Science, vol. 18, no. 3, pp. 675-691, 2024.
[31]
J. Spenger, P. Carbone and P. Haller,
"A Survey of Actor-Like Programming Models for Serverless Computing,"
in Active Object Languages: Current Research Trends, : Springer, 2024.
[32]
A. H. Akhavan Rahnama et al.,
"Can local explanation techniques explain linear additive models?,"
Data mining and knowledge discovery, vol. 38, no. 1, pp. 237-280, 2024.
[33]
Z. Xu et al.,
"A Semi-Supervised Model for Non-Cellular Elements Segmentation in Microscopy Images of Wood,"
in 2024 IEEE International Conference on Big Data (BigData), 2024, pp. 2049-2056.
[34]
D. Tiwari,
"Augmenting Test Oracles with Production Observations,"
Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:87, 2024.
[35]
B. Lindqvist and A. Podobas,
"Algorithms for Fast Spiking Neural Network Simulation on FPGAs,"
IEEE Access, vol. 12, pp. 150334-150353, 2024.
[36]
S. Karimi, S. Asadi and A. H. Payberah,
"BaziGooshi : A Hybrid Model of Reinforcement Learning for Generalization in Gameplay,"
IEEE Transactions on Games, vol. 16, no. 3, pp. 722-734, 2024.
[37]
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.
[38]
G. Çaylak,
"Automated Optimizations for Inference in Probabilistic Programming Languages,"
Licentiate thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:73, 2024.
[39]
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.
[40]
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.
[41]
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.
[42]
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.
[43]
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.
[44]
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.
[45]
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.
[46]
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.
[47]
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.
[48]
A. Q. Khan et al.,
"Cloud storage cost: a taxonomy and survey,"
World wide web (Bussum), vol. 27, no. 4, 2024.
[49]
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.
[50]
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.