Till innehåll på sidan
Till KTH:s startsida Till KTH:s startsida

Publikationer

De 50 senaste publikationerna från avdelningen för programvaruteknik och datorsystem:

[1]
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.
[2]
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.
[3]
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.
[4]
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.
[5]
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.
[6]
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.
[7]
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.
[8]
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.
[9]
M. Girondi, "Toward Highly-efficient GPU-centric Networking," Licentiatavhandling : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:30, 2024.
[10]
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.
[11]
D. Roy, "Towards Trustworthy Machine Learning For Human Activity Recognition," Doktorsavhandling Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:12, 2024.
[12]
J. Cabrera Arteaga, "Software Diversification for WebAssembly," Doktorsavhandling : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:10, 2024.
[13]
J. Cabrera-Arteaga et al., "Wasm-Mutate : Fast and effective binary diversification for WebAssembly," Computers & security (Print), vol. 139, s. 103731-103731, 2024.
[14]
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.
[15]
T. Wang, A. H. Payberah och V. Vlassov, "Graph Representation Learning with Graph Transformers in Neural Combinatorial Optimization," i 2023 International Conference on Machine Learning and Applications (ICMLA), 2023, s. 488-495.
[16]
A. E. Samy och S. Girdzijauskas, "Mitigating Sybil Attacks in Federated Learning," i INFORMATION SECURITY PRACTICE AND EXPERIENCE, ISPEC 2023, 2023, s. 36-51.
[17]
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.
[18]
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.
[19]
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.
[20]
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.
[21]
H. Boström, H. Linusson och A. Vesterberg, "Mondrian Predictive Systems for Censored Data," i Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, s. 399-412.
[22]
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.
[23]
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.
[24]
N. Gauraha och H. Boström, "Investigating the Contribution of Privileged Information in Knowledge Transfer LUPI by Explainable Machine Learning," i Proceedings of the 12th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2023, 2023, s. 470-484.
[25]
A. Samy, Z. T. Kefato och S. Girdzijauskas, "Data-Driven Self-Supervised Graph Representation Learning," i ECAI 2023 : 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings, 2023, s. 629-636.
[26]
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.
[27]
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.
[28]
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.
[29]
S. W.D. Chien et al., "Improving Cloud Storage Network Bandwidth Utilization of Scientific Applications," i Proceedings of the 7th Asia-Pacific Workshop on Networking, APNET 2023, 2023, s. 172-173.
[30]
M. Lindholm, F. Lindskog och J. Palmquist, "Local bias adjustment, duration-weighted probabilities, and automatic construction of tariff cells," Scandinavian Actuarial Journal, vol. 2023, no. 10, s. 946-973, 2023.
[31]
D. F. Perez Ramirez et al., "DeepGANTT : A Scalable Deep Learning Scheduler for Backscatter Networks," i IPSN 2023 - Proceedings of the 2023 22nd International Conference on Information Processing in Sensor Networks, 2023, s. 163-176.
[32]
M. Isaksson et al., "mmWave Beam Selection in Analog Beamforming Using Personalized Federated Learning," i IEEE Future Networks World Forum, 13–15 November 2023, Baltimore, MD, USA, 2023.
[33]
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.
[34]
Y. Jin et al., "Learning Cellular Coverage from Real Network Configurations using GNNs," i 2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings, 2023.
[35]
G. Tu, Y. Liu och V. Vlassov, "AIC-AB NET : A Neural Network for Image Captioning with Spatial Attention and Text Attributes," (Manuskript).
[36]
G. Chikafa et al., "Cloud-native RStudio on Kubernetes for Hopsworks," (Manuskript).
[37]
M. Bonvalet, Z. T. Kefato och S. Girdzijauskas, "Graph2Feat : Inductive Link Prediction via Knowledge Distillation," i ACM Web Conference 2023 : Companion of the World Wide Web Conference, WWW 2023, 2023, s. 805-812.
[38]
M. Lohstroh et al., "Logical Time for Reactive Software," i Proceedings of 2023 Cyber-Physical Systems and Internet-of-Things Week, CPS-IoT Week 2023 : Workshops, 2023, s. 313-318.
[39]
B. Carminati och L. Bahri, "Cyber Pandemics," IEEE Internet Computing, vol. 27, no. 2, s. 5-6, 2023.
[40]
G. Verardo et al., "Fast Server Learning Rate Tuning for Coded Federated Dropout," i FL 2022 : Trustworthy Federated Learning, 2023, s. 84-99.
[41]
A. Alkhatib, H. Boström och M. Vazirgiannis, "Explaining Predictions by Characteristic Rules," i Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, Part I, 2023, s. 389-403.
[42]
E. Listo Zec et al., "Decentralized Adaptive Clustering of Deep Nets is Beneficial for Client Collaboration," i FL 2022 : Trustworthy Federated Learning, 2023, s. 59-71.
[43]
M. Isaksson et al., "Adaptive Expert Models for Federated Learning," i Trustworthy Federated Learning : First International Workshop, FL 2022, 2023, s. 1-16.
[44]
R. M. Tsoupidi, "Generating Optimized and Secure Binary Code," Doktorsavhandling Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2023:44, 2023.
[45]
M. Spanghero och P. Papadimitratos, "Detecting GNSS misbehavior leveraging secure heterogeneous time sources," i IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, California, April 24-27, 2023, 2023.
[46]
C. Soto Valero, "Debloating Java Dependencies," Doktorsavhandling 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," 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.
Fullständig lista i KTH:s publikationsportal