Publications
The 50 most recent publications from the Division of Software and Computer Systems:
[1]
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.
[2]
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.
[3]
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.
[4]
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.
[5]
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.
[6]
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.
[7]
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.
[8]
M. Girondi,
"Toward Highly-efficient GPU-centric Networking,"
Licentiate thesis : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:30, 2024.
[9]
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.
[10]
D. Roy,
"Towards Trustworthy Machine Learning For Human Activity Recognition,"
Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:12, 2024.
[11]
J. Cabrera Arteaga,
"Software Diversification for WebAssembly,"
Doctoral thesis : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:10, 2024.
[12]
J. Cabrera-Arteaga et al.,
"Wasm-Mutate : Fast and effective binary diversification for WebAssembly,"
Computers & security (Print), vol. 139, pp. 103731-103731, 2024.
[13]
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.
[14]
T. Wang, A. H. Payberah and V. Vlassov,
"Graph Representation Learning with Graph Transformers in Neural Combinatorial Optimization,"
in 2023 International Conference on Machine Learning and Applications (ICMLA), 2023, pp. 488-495.
[15]
A. E. Samy and S. Girdzijauskas,
"Mitigating Sybil Attacks in Federated Learning,"
in INFORMATION SECURITY PRACTICE AND EXPERIENCE, ISPEC 2023, 2023, pp. 36-51.
[16]
M. Balliu et al.,
"Challenges of Producing Software Bill of Materials for Java,"
IEEE Security and Privacy, vol. 21, no. 6, pp. 12-23, 2023.
[17]
Y. Yang et al.,
"Controller Sensitivity-Based Shaping Method for Grid Forming Inverter,"
in 2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023, 2023, pp. 6273-6278.
[18]
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.
[19]
U. Johansson et al.,
"Confidence Classifiers with Guaranteed Accuracy or Precision,"
in Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, pp. 513-533.
[20]
H. Boström, H. Linusson and A. Vesterberg,
"Mondrian Predictive Systems for Censored Data,"
in Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, pp. 399-412.
[21]
S. Ennadir et al.,
"Conformalized Adversarial Attack Detection for Graph Neural Networks,"
in Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, pp. 311-323.
[22]
A. Alkhatib et al.,
"Approximating Score-based Explanation Techniques Using Conformal Regression,"
in Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, pp. 450-469.
[23]
N. Gauraha and H. Boström,
"Investigating the Contribution of Privileged Information in Knowledge Transfer LUPI by Explainable Machine Learning,"
in Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, pp. 470-484.
[24]
A. Samy, Z. T. Kefato and S. Girdzijauskas,
"Data-Driven Self-Supervised Graph Representation Learning,"
in ECAI 2023 : 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings, 2023, pp. 629-636.
[25]
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, pp. 71-104, 2023.
[26]
D. Roy, V. Komini and S. Girdzijauskas,
"Classifying falls using out-of-distribution detection in human activity recognition,"
AI Communications, vol. 36, no. 4, pp. 251-267, 2023.
[27]
N. Nikolov et al.,
"Container-Based Data Pipelines on the Computing Continuum for Remote Patient Monitoring,"
Computer, vol. 56, no. 10, pp. 40-48, 2023.
[28]
S. W.D. Chien et al.,
"Improving Cloud Storage Network Bandwidth Utilization of Scientific Applications,"
in Proceedings of the 7th Asia-Pacific Workshop on Networking, APNET 2023, 2023, pp. 172-173.
[29]
M. Lindholm, F. Lindskog and J. Palmquist,
"Local bias adjustment, duration-weighted probabilities, and automatic construction of tariff cells,"
Scandinavian Actuarial Journal, vol. 2023, no. 10, pp. 946-973, 2023.
[30]
D. F. Perez Ramirez et al.,
"DeepGANTT : A Scalable Deep Learning Scheduler for Backscatter Networks,"
in IPSN 2023 - Proceedings of the 2023 22nd International Conference on Information Processing in Sensor Networks, 2023, pp. 163-176.
[31]
M. Isaksson et al.,
"mmWave Beam Selection in Analog Beamforming Using Personalized Federated Learning,"
in IEEE Future Networks World Forum, 13–15 November 2023, Baltimore, MD, USA, 2023.
[32]
A. Layegh et al.,
"ContrastNER : Contrastive-based Prompt Tuning for Few-shot NER,"
in Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023, 2023, pp. 241-249.
[33]
Y. Jin et al.,
"Learning Cellular Coverage from Real Network Configurations using GNNs,"
in 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings, 2023.
[34]
G. Tu, Y. Liu and V. Vlassov,
"AIC-AB NET : A Neural Network for Image Captioning with Spatial Attention and Text Attributes,"
(Manuscript).
[35]
[36]
T. Caiazzi et al.,
"Nesting Containers for Faithful Datacenters Emulations,"
in Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023, 2023.
[37]
M. Bonvalet, Z. T. Kefato and S. Girdzijauskas,
"Graph2Feat : Inductive Link Prediction via Knowledge Distillation,"
in ACM Web Conference 2023 : Companion of the World Wide Web Conference, WWW 2023, 2023, pp. 805-812.
[38]
M. Lohstroh et al.,
"Logical Time for Reactive Software,"
in Proceedings of 2023 Cyber-Physical Systems and Internet-of-Things Week, CPS-IoT Week 2023 : Workshops, 2023, pp. 313-318.
[39]
B. Carminati and L. Bahri,
"Cyber Pandemics,"
IEEE Internet Computing, vol. 27, no. 2, pp. 5-6, 2023.
[40]
G. Verardo et al.,
"Fast Server Learning Rate Tuning for Coded Federated Dropout,"
in FL 2022 : Trustworthy Federated Learning, 2023, pp. 84-99.
[41]
A. Alkhatib, H. Boström and M. Vazirgiannis,
"Explaining Predictions by Characteristic Rules,"
in Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, Part I, 2023, pp. 389-403.
[42]
E. Listo Zec et al.,
"Decentralized Adaptive Clustering of Deep Nets is Beneficial for Client Collaboration,"
in FL 2022 : Trustworthy Federated Learning, 2023, pp. 59-71.
[43]
M. Isaksson et al.,
"Adaptive Expert Models for Federated Learning,"
in Trustworthy Federated Learning : First International Workshop, FL 2022, 2023, pp. 1-16.
[44]
R. M. Tsoupidi,
"Generating Optimized and Secure Binary Code,"
Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2023:44, 2023.
[45]
M. Spanghero and P. Papadimitratos,
"Detecting GNSS misbehavior leveraging secure heterogeneous time sources,"
in IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, California, April 24-27, 2023, 2023.
[46]
C. Soto Valero,
"Debloating Java Dependencies,"
Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2023:36, 2023.
[47]
M. Scazzariello et al.,
"A High-Speed Stateful Packet Processing Approach for Tbps Programmable Switches,"
in 20th USENIX Symposium on Networked Systems Designand Implementation (NSDI ’23), 2023, pp. 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, pp. 4501-4530, 2023.
[49]
D. Lundén,
"Correct and Efficient Monte Carlo Inference for Universal Probabilistic Programming Languages,"
Doctoral thesis 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,"
in Programming Languages and Systems, 2023.