2023

Agiakatsikas, D., Foutris, N., Sari, A., Vlagkoulis, V., Souvatzoglou, I., Psarakis, M., Ye, R., Goodacre, J., Luján, M., Kastriotou, M., Cazzaniga, C., Frost, C.: Single Event Effects Assessment of UltraScale+ MPSoC Systems Under Atmospheric Radiation. in IEEE Transactions on Reliability, 2023.

Ye, R., Iordanou, K., Riley, G., Luján, M.: Exploring Sparse Visual Odometry Acceleration With High-Level Synthesis. in IEEE Access, 2023.

Erdayandi K., Cordeiro, L., Mustafa, M.A.: A Privacy-Preserving and Accountable Billing Protocol for Peer-to-Peer Energy Trading Markets. In proceedings of the International Conference on Smart Energy Systems and Technologies (SEST 2023).

Manino, E., Magri, B., Mustafa, M.A., Cordeiro, L.: Certified Private Inference on Neural Networks via Lipschitz-Guided Abstraction Refinement. In proceedings of the 6th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS 2023).

Manino, E., Bessa, I., Cordeiro, L.: Towards global neural network abstractions with locally-exact reconstruction. In Neural Networks, 2023.

Carvalho, D.S., Mercatali, G., Zhang, Y., Freitas, A.: Learning Disentangled Representations for Natural Language Definitions. In Findings of the European chapter of Association for Computational Linguistics (Findings of EACL), 2023.

Rozanova, J., Valentino, M., Cordeiro, L., Freitas, A.: Interventional Probing in High Dimensions: An NLI Case Study. In Findings of the European chapter of Association for Computational Linguistics (Findings of EACL), 2023 [to appear].

Dong, Y., Li, Z., Zhao, X., Ding, Z., Huang, X.: Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation. In proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023) [to appear].

Song, S., Sun, Y., Mustafa, M.A., Cordeiro, L.: AIREPAIR: A Repair Platform for Neural Networks. In proceedings of the 45th IEEE/ACM International Conference on Software Engineering (ICSE 2023).

2022

Nightingale, J.S., Wang, Y., Zobiri, F., Mustafa, M.A.: Effect of Clustering in Federated Learning on Non-IID Electricity Consumption Prediction. In proceedings of IEEE ISGT-Europe 2022.

Madhusudan, A., Zobiri F., Mustafa M.A.: Billing Models for Peer-to-Peer Electricity Trading Markets with Imperfect Bid-Offer Fulfillment. In proceedings of IEEE ISC2.

Dong, Y., Huang W., Bharti, V., Cox, V., Banks, A., Wang, S., Zhao, X., Schewe, S., Huang X.: Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance. ACM Transactions on Embedding Computing Systems.

Dong, Y., Chen, Y., Zhao, X., Huang X.: Short-term Load Forecasting with Distributed Long Short-Term Memory. In proceedings of 2023 IEEE ISGT North America.

Matos Jr, J.B.P, Bessa, I., Manino, E., Song, X., Cordeiro, L.: CEG4N: Counter-Example Guided Neural Network Quantization Refinement. In proceedings of 5th Workshop on Formal Methods for ML-Enabled Autonomous Systems, affiliated with FLoC 2022.

Dong, Y., Zhao, X., Huang X.: Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022).

Capper, T., Gorbatcheva, A., Mustafa, M.A., Bahloul, M., Schwidtal, J.M., Chitchyan, R., Andoni, M., Robu, V., Montakhabi, M., Scott, I.J., Francis, C., Mbavarira, T., Espana, J.M., Kiesling, L.: Peer-to-peer, community self-consumption, and transactive energy: A systematic literature review of local energy market models. In Renewable and Sustainable Energy Reviews, 2022.

Manino, E., Rozanova, J., Carvalho, D., Freitas, A., Cordeiro, L.: Systematicity, Compositionality and Transitivity of Deep NLP Models: a Metamorphic Testing Perspective. In Findings of the Association for Computational Linguistics (Findings of ACL), 2022, pp. 2355-2366.

Manino, E., Carvalho, D., Dong, Y., Rozanova, J., Song, X., Mustafa, M., Freitas, A., Brown, G., Lujan, M., Huang, X., Cordeiro, L.: EnnCore: End-to-End Conceptual Guarding of Neural Architectures. In AAAI's Workshops on Artificial Intelligence Safety (SafeAI), pp. 1-8, 2022 (to appear).

Capper, T., Gorbatcheva, A., Schwidtal, J.M., Mustafa, M.A., Andoni, M., Chitchyan, R., Robu, V., Montakhabi, M., Piccini, P., Mohamed, B., Mbavarira, T., Kiesling, L., Scott, I.J., Francis, C., Espana, J.M., Troncia, M.: Peer-to-Peer, Self-Consumption and Transactive Energy Literature Review Data Extraction Table

Alshmrany, K., Aldughaim, M., Bhayat, A., Cordeiro, L.: FuSeBMC v4: Smart Seed Generation for Hybrid Fuzzing (Competition Contribution)”. In 24th International Conference on Fundamental Approaches to Software Engineering (FASE), LNCS 13241, pp. 336-340, 2022.

Thandi, R. and Mustafa, M.A.: Privacy-Enhancing Settlements Protocol in Peer-to-Peer Energy Trading Markets. In the 13th International Conference on Innovative Smart Grid Technologies (ISGT 2022), pp. 1-5, 2022 (to appear).

Erdayandi, Kamil, Amrit Paudel, Lucas Cordeiro, and Mustafa A. Mustafa. Privacy-Friendly Peer-to-Peer Energy Trading: A Game Theoretical Approach. In IEEE Power & Energy Society General Meeting (GM), pp. 1-5, 2022 (to appear).

Ashkan Tousi, Mikel Luján.: Comparative Analysis of Machine Learning Models for Performance Prediction of the SPEC Benchmarks. IEEE Access, 2022, DOI: 10.1109/ACCESS.2022.3142240

Meadows, J., Zhou, Z., & Freitas, A.: PhysNLU: A Language Resource for Evaluating Natural Language Understanding and Explanation Coherence in Physics. arXiv preprint arXiv:2201.04275, 2022.

Izumi, F., Filho, E., Cordeiro, L., Maia, O., Fabricio, R., Farias, B., Silva, A. A Fuzzing-Based Test-Creation Approach for Evaluating Digital TV Receivers via Transport Streams. In Software Testing, Verification and Reliability, v32(1), pp. 1-30, 2022. DOI

2021

Rozanova, J., Ferreira, D., Valentino, M., Thayaparan, M., & Freitas, A.: Decomposing Natural Logic Inferences in Neural NLI. arXiv preprint arXiv:2112.08289, 2021.

Thayaparan, M., Valentino, M., & Freitas, A.: Explainable Inference Over Grounding-Abstract Chains for Science Questions. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (pp. 1-12).

Omar M. Alhawi, Herbert Rocha, Mikhail R. Gadelha, Lucas C. Cordeiro, Eddie Batista de Lima Filho: Verification and refutation of C programs based on k-induction and invariant inference. Int. J. Softw. Tools Technol. Transf. 23(2): 115-135 (2021). DOI: 10.1007/s10009-020-00564-1

Song, X., Manino, E., Sena, L., Alves, E., de Lima Filho, E., Bessa, I., Lujan, M., Cordeiro, L.: QNNVerifier: A Tool for Verifying Neural Networks using SMT-Based Model Checking. CoRR abs/2111.13110 (2021) (Technical Report).

Garofalo, G., Preuveneers, D., Joosen, W., Abidin, A. and Mustafa, M.A., 2021. PIVOT: PrIVate and effective cOntact Tracing. IEEE Internet of Things Journal. DOI: 10.1109/JIOT.2021.3138694

Zhao, X.*, Huang, W., Banks, A., Cox, V., Flynn, D., Schewe, S., and Huang, X. (2021a).: Assessing the reliability of deep learning classifiers through robustness evaluation and operational profiles. In AISafety’21 Workshop at IJCAI’21.

Huang, W., Zhao, X., and Huang, X. (2021b).: Embedding and extraction of knowledge intree ensemble classifiers. Machine Learning. Springer. DOI: 10.1007/s10994-021-06068-6

Mustafa, M., Konios, A., Garcia-Constantino, M.: IoT-Based Activities of Daily Living for Abnormal Behavior Detection: Privacy Issues and Potential Countermeasures. In IEEE Internet of Things Magazine v4(3), pp. 90-95, 2021.

Symeonidis, I. Rotaru, D. Mustafa, M. Mennink, B. Preneel, B. Papadimitratos, P.: HERMES: Scalable, Secure, and Privacy-Enhancing Vehicular Sharing-Access System. In IEEE Internet of Things Journal (Early Access), pp. 1-1, 2021.

Mercatali, G., Freitas, A.: Disentangling Generative Factors in Natural Language with Discrete Variational Autoencoders. EMNLP Findings, 2021.

Meadows, J., Freitas, A.: Similarity-based equational inference in physics, Physical Review Research, 2021.

Huang, W., Sun, Y., Zhao, X., Sharp, J., Ruan, W., Meng, J. and Huang, X.: Coverage Guided Testing for Recurrent Neural Networks. IEEE Tran. on Reliability, 2021.

Zhao, X., Huang, W., Huang, X., Robu, V. and Flynn, D.: BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations. UAI, 2021.

Monteiro, F., Gadelha, M., Cordeiro, L. Model Checking C++ Programs. In Software Testing, Verification and Reliability, 2021.

Alshmrany, K., Aldughaim, M., Bhayat, A., Cordeiro, L.: FuSeBMC: An Energy-Efficient Test Generator for Finding Security Vulnerabilities in C Programs. In 15th International Conference on Tests and Proofs (TAP), LNCS 12740, pp. 85-105, 2021. DOI: 10.1007/978-3-030-79379-1_6

Alshmrany, K., Menezes, R., Gadelha, M., Cordeiro, L.: FuSeBMC: A White-Box Fuzzer for Finding Security Vulnerabilities in C Programs (Competition Contribution). In 24th International Conference on Fundamental Approaches to Software Engineering (FASE), LNCS 12649, pp. 363-367, 2021. DOI: 10.1007/978-3-030-71500-7_19

Nicolas Berthier, Amany Alshareef, James Sharp, Sven Schewe, Xiaowei Huang: Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features. CoRR abs/2103.03704 (2021) (Technical Report).

Luiz H. Sena, Xidan Song, Erickson H. da S. Alves, Iury Bessa, Edoardo Manino, Lucas C. Cordeiro: Verifying Quantized Neural Networks using SMT-Based Model Checking. CoRR abs/2106.05997 (2021) (Technical Report)

Thayaparan, M., Valentino, M., Ferreira, D., Rozanova, J., & Freitas, A.: ∂-Explainer: Abductive Natural Language Inference via Differentiable Convex Optimization. arXiv preprint arXiv:2105.03417, 2021.

Rozanova, J., Ferreira, D., Thayaparan, M., Valentino, M., & Freitas, A.: Supporting Context Monotonicity Abstractions in Neural NLI Models. arXiv preprint arXiv:2105.08008, 2021.