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Publikationer

De 50 senaste publikationerna från avdelningen för programvaruteknik och datorsystem:

[1]
L. Bahri, B. Carminati och E. Ferrari, "Privacy-Aware Access Control in Decentralized Online Social Networks," i Encyclopedia of Cryptography, Security and Privacy, Third Edition, : Springer Nature, 2025, s. 1924-1927.
[2]
V. Andersson et al., "UPPERCASE IS ALL YOU NEED," i SIGBOVIK 2025, Carnegie Mellon University, Pittsburgh, PA, USA, April 4, 2025, 2025.
[3]
G. Shang et al., "Atlas-Chat : Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect," i LoResLM 2025 - 1st Workshop on Language Models for Low-Resource Languages, Proceedings of the Workshop, 2025, s. 9-30.
[4]
A. E. Samy, Z. T. Kefato och Š. Girdzijauskas, "Leap : Inductive Link Prediction via Learnable Topology Augmentation," i Machine Learning, Optimization, and Data Science - 10th International Conference, LOD 2024, Revised Selected Papers, 2025, s. 448-463.
[5]
V. Komini och S. Girdzijauskas, "Integrating Logit Space Embeddings for Reliable Out-of-Distribution Detection," i Machine Learning, Optimization, and Data Science - 10th International Conference, LOD 2024, Revised Selected Papers, 2025, s. 255-269.
[6]
R. Bresson et al., "KAGNNs : Kolmogorov-Arnold Networks meet Graph Learning," Transactions on Machine Learning Research, vol. 2025-March, s. 1-29, 2025.
[7]
M. Spanghero, "Data verification for GNSS systems and protection of GNSS services," Doktorsavhandling Stockholm, Sweden : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2025:36, 2025.
[8]
M. Spanghero et al., "GNSS jammer localization and identification with airborne commercial GNSS receivers," IEEE Transactions on Information Forensics and Security, vol. 20, s. 3550-3565, 2025.
[9]
M. Spanghero och P. Papadimitratos, "UnReference: analysis of the effect of spoofing on RTK reference stations for connected rovers," i Proceedings of the 2025 IEEE/ION Position, Localization and Navigation Symposium (PLANS), Salt Lake City, UT, USA, 2025, s. 1-12.
[10]
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, s. 1-23, 2025.
[11]
G. Verardo et al., "FMM-Head: Enhancing Autoencoder-Based ECG Anomaly Detection with Prior Knowledge," i Pattern Recognition and Artificial Intelligence - 4th International Conference, ICPRAI 2024, Proceedings, 2025, s. 18-32.
[12]
S. Sheikholeslami, "Tools and Methods for Distributed and Large-Scale Training of Deep Neural Networks," Doktorsavhandling Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2025:28, 2025.
[13]
S. Sheikholeslami et al., "Utilizing Large Language Models for Ablation Studies in Machine Learning and Deep Learning," i The 5th Workshop on Machine Learning and Systems (EuroMLSys), co-located with the 20th European Conference on Computer Systems (EuroSys), 2025.
[14]
A. H. Akhavan Rahnama, "Evaluating the Faithfulness of Local Feature Attribution Explanations : Can We Trust Explainable AI?," Doktorsavhandling Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2025:23, 2025.
[15]
M. Spanghero och P. Papadimitratos, "Consumer INS coupled with carrier phase measurements for GNSS spoofing detection," i ION ITM/PTTI, International Technical Meeting January 27 - 30, 2025 Long Beach, CA, 2025.
[16]
F. Cornell et al., "Unsupervised Ontology- and Taxonomy Construction Through Hyperbolic Relational Domains and Ranges," i Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2023, Revised Selected Papers, 2025, s. 339-348.
[17]
A. Alkhatib, "Addressing Shortcomings of Explainable Machine Learning Methods," Doktorsavhandling Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2025:11, 2025.
[18]
E. Listo Zec, "Decentralized deep learning in statistically heterogeneous environments," Doktorsavhandling Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2025:4, 2025.
[19]
S. Sheikholeslami et al., "Deep Neural Network Weight Initialization from Hyperparameter Tuning Trials," i Neural Information Processing, 2024.
[20]
M. Scazzariello, T. Caiazzi och M. Chiesa, "Deliberately Congesting a Switch for Better Network Functions Performance," i 2024 IEEE 32nd International Conference on Network Protocols, ICNP 2024, 2024.
[21]
A. H. Akhavan Rahnama et al., "Can local explanation techniques explain linear additive models?," Data mining and knowledge discovery, vol. 38, no. 1, s. 237-280, 2024.
[22]
Z. Kharazian et al., "CoPAL: Conformal Prediction for Active Learning with Application to Remaining Useful Life Estimation in Predictive Maintenance," i Proceedings of the 13th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2024, 2024, s. 195-217.
[23]
U. Johansson, C. Sönströd och H. Boström, "Conformal Regression with Reject Option," i Proceedings of the 13th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2024, 2024, s. 277-294.
[24]
H. Boström, "Conformal Prediction in Python with crepes," i Proceedings of the 13th Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2024, 2024, s. 236-249.
[25]
C. Eryonucu och P. Papadimitratos, "Detecting Mobile Crowdsensing Sybil Attackers via Presence Verification," i CPSIoTSec 2024 - Proceedings of the 6th Workshop on CPS and IoT Security and Privacy, Co-Located with: CCS 2024, 2024, s. 118-124.
[26]
Z. Xu et al., "A Semi-Supervised Model for Non-Cellular Elements Segmentation in Microscopy Images of Wood," i 2024 IEEE International Conference on Big Data (BigData), 2024, s. 2049-2056.
[27]
M. Roy et al., "Colour Channel Separation and Recombination of Images to Improve Object Detection of Diffuse Characters," i 28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024, Seville, Spain, Nov 11 2022 - Nov 12 2022, 2024, s. 3457-3466.
[28]
D. Tiwari, "Augmenting Test Oracles with Production Observations," Doktorsavhandling Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:87, 2024.
[29]
B. Lindqvist och A. Podobas, "Algorithms for Fast Spiking Neural Network Simulation on FPGAs," IEEE Access, vol. 12, s. 150334-150353, 2024.
[30]
S. Karimi, S. Asadi och A. H. Payberah, "BaziGooshi : A Hybrid Model of Reinforcement Learning for Generalization in Gameplay," IEEE Transactions on Games, vol. 16, no. 3, s. 722-734, 2024.
[31]
A. Q. Khan et al., "Cost modelling and optimisation for cloud : a graph-based approach," Journal of Cloud Computing : Advances, Systems and Applications, vol. 13, no. 1, 2024.
[32]
L. Cao et al., "Beyond Gut Feel: Using Time Series Transformers to Find Investment Gems," i Artificial Neural Networks and Machine Learning – ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Proceedings, 2024, s. 373-388.
[33]
N. Atienza et al., "Cutting the Black Box: Conceptual Interpretation of a Deep Neural Net with Multi-Modal Embeddings and Multi-Criteria Decision Aid," i Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024, 2024, s. 3669-3678.
[34]
E. Gogoulou et al., "Continual Learning Under Language Shift," i Text, Speech, and Dialogue - 27th International Conference, TSD 2024, Proceedings, 2024, s. 71-84.
[35]
G. Çaylak, "Automated Optimizations for Inference in Probabilistic Programming Languages," Licentiatavhandling Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:73, 2024.
[36]
V. Palmkvist, "Abstraction, Composition, and Resolvable Ambiguity in Programming Language Implementation," Doktorsavhandling Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2024:69, 2024.
[37]
F. Schmidt et al., "A Scalable System Architecture for Composition and Deployment of Machine Learning Models in Cognitive Behavioral Therapy," i 2024 IEEE International Conference on Digital Health (ICDH), 2024, s. 79-86.
[38]
Y. Abbahaddou et al., "Bounding The Expected Robustness Of Graph Neural Networks Subject To Node Feature Attacks," i 12th International Conference on Learning Representations, ICLR 2024, 2024.
[39]
G. Siachamis et al., "CheckMate : Evaluating Checkpointing Protocols for Streaming Dataflows," i Proceedings - 2024 IEEE 40th International Conference on Data Engineering, ICDE 2024, 2024, s. 4030-4043.
[40]
K. Segeljakt, S. Haridi och P. Carbone, "AquaLang : A Dataflow Programming Language," i DEBS 2024 - Proceedings of the 18th ACM International Conference on Distributed and Event-Based Systems, 2024, s. 42-53.
[41]
F. Reyes García et al., "BUMP : A Benchmark of Reproducible Breaking Dependency Updates," i Proceedings - 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024, 2024, s. 159-170.
[42]
M. Polverini et al., "Achieving Best-path Selection at Line Rate through the SRv6 Live-Live Behavior," i Proceedings of IEEE/IFIP Network Operations and Management Symposium 2024, NOMS 2024, 2024.
[43]
A. Q. Khan et al., "A Taxonomy for Cloud Storage Cost," i Management of Digital EcoSystems - 15th International Conference, MEDES 2023, Revised Selected Papers, 2024, s. 317-330.
[44]
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.
[45]
A. Q. Khan et al., "Cloud storage cost: a taxonomy and survey," World wide web (Bussum), vol. 27, no. 4, 2024.
[46]
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.
[47]
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.
[48]
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.
[49]
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.
[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.
Fullständig lista i KTH:s publikationsportal