2024

Huang, X., Ruan, W., Huang, W., Jin, G., Dong, Y., Wu, C., Mustafa, M. A.: A Survey of Safety and Trustworthiness of Large Language Models Through the Lens of Verification and Validation. in Artificial Intelligence Review, 57(7), 175, 2024.

Matos, J. B. P., de Lima Filho, E. B., Bessa, I., Manino, E., Song, X., & Cordeiro, L. C.: Counterexample Guided Neural Network Quantization Refinement. in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2023.

Dantas, P. V., Sabino da Silva Jr, W., Cordeiro, L. C., & Carvalho, C. B.: A Comprehensive Review of Model Compression Techniques in Machine Learning. in Applied Intelligence, 2024.

Ferrag, M. A., Ndhlovu, M., Tihanyi, N., Cordeiro, L. C., Debbah, M., Lestable, T., & Thandi, N. S.: Revolutionizing Cyber Threat Detection With Large Language Models: A Privacy-Preserving BERT-Based Lightweight Model for IoT/IIoT Devices. in IEEE Access, 2024.

Menezes, R. S., Aldughaim, M., Farias, B., Li, X., Manino, E., Shmarov, F., Cordeiro, L. C.: ESBMC v7. 4: Harnessing the Power of Intervals: (Competition Contribution). In proceedings of the International Conference on Tools and Algorithms for the Construction and Analysis of Systems, 2024.

Jain, R., Tihanyi, N., Ndhlovu, M., Ferrag, M. A., & Cordeiro, L. C.: Rapid Taint Assisted Concolic Execution (TACE). in Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering, 2024.

Farias, B., Menezes, R., de Lima Filho, E. B., Sun, Y., & Cordeiro, L. C.: ESBMC-Python: A Bounded Model Checker for Python Programs. in Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, 2024.

Cheng, Z., Wu, T., Schrammel, P., Tihanyi, N., de Lima Filho, E. B., & Cordeiro, L. C.: JCWIT: A Correctness-Witness Validator for Java Programs Based on Bounded Model Checking. in Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, 2024.

Wu, T., Xiong, S., Manino, E., Stockwell, G., Cordeiro, L. C.: Verifying components of Arm(R) Confidential Computing Architecture with ESBMC. CoRR, abs/2406.04375, 2024.

Charalambous, Y., Manino, E., Cordeiro, L. C.: Automated Repair of AI Code with Large Language Models and Formal Verification. CoRR, abs/2405.08848, 2024.

Tihanyi, N., Bisztray, T., Ferrag, M. A., Jain, R., & Cordeiro, L. C.: Do Neutral Prompts Produce Insecure Code? FormAI-v2 Dataset: Labelling Vulnerabilities in Code Generated by Large Language Models. arXiv preprint arXiv:2404.18353, 2024.

Braberman, V. A., Bonomo-Braberman, F., Charalambous, Y., Colonna, J. G., Cordeiro, L. C., & de Freitas, R.: Tasks People Prompt: A Taxonomy of LLM Downstream Tasks in Software Verification and Falsification Approaches. arXiv preprint arXiv:2404.09384, 2024.

Alshmrany, K. M., Aldughaim, M., Wei, C., Sweet, T., Allmendinger, R., & Cordeiro, L. C.: FuSeBMC AI: Acceleration of Hybrid Approach through Machine Learning. arXiv preprint arXiv:2404.06031, 2024.

Zhang, Y., Valentino, M., Carvalho, D. S., Pratt-Hartmann, I., Freitas, A.: Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders. arXiv preprint, 2023.

Rauf, H. T., Freitas, A., Paton, N. W.: Deep Clustering for Data Cleaning and Integration. arXiv preprint, 2023.

Ranaldi, L., Freitas, A.: Aligning Large and Small Language Models via Chain-of-Thought Reasoning. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), 2024.

Quan, X., Valentino, M., Dennis, L. A., Freitas, A.: Enhancing Ethical Explanations of Large Language Models through Iterative Symbolic Refinement. arXiv preprint, 2024.

Thayaparan, M., Valentino, M., Freitas, A.: A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference. arXiv preprint, 2024.

Dalal, D., Valentino, M., Freitas, A., Buitelaar, P.: Inference to the Best Explanation in Large Language Models. arXiv preprint, 2024.

Garcia-Constantino, M., Konios, A., Ekerete, I., Mustafa, M. A., Hussein Lopez-Nava, I., Altamirano-Flores, Y. V.: Using Thermal and Contact Sensors for Mood Detection in Smart Living Environments. In proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments, 2024.

Xu, M., Dennis, L. A., Mustafa, M. A.: Safeguard Privacy for Minimal Data Collection with Trustworthy Autonomous Agents. In proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024.

Huang, W., Zhao, X., Banks, A., Cox, V., Huang, X.: Hierarchical Distribution-aware Testing of Deep Learning. in ACM Transactions on Software Engineering and Methodology, 2023.

Hu, Y., Dong, B., Huang, K., Ding, L., Wang, W., Huang, X., Wang, Q. F.: Scene Text Recognition via Dual-path Network with Shape-driven Attention Alignment. in ACM Transactions on Multimedia Computing, Communications and Applications, 2024.

Yang, Y., Lee, S., Zhang, H., Huang, X., Pedrycz, W.: Negative Hesitation Fuzzy Sets and Their Application to Pattern Recognition. in IEEE Transactions on Fuzzy Systems, 2023.

Chen, Z., Wang, F., Mu, R., Xu, P., Huang, X., Ruan, W.: NRAT: Towards Adversarial Training with Inherent Label Noise. in Machine Learning, 2024.

Bensalem, S., Huang, X., Ruan, W., Tang, Q., Wu, C., Zhao, X.: Bridging Formal Methods and Machine Learning with Global Optimisation. in Journal of Logical and Algebraic Methods in Programming, 2024.

Huang, W., Zhou, Y., Jin, G., Sun, Y., Meng, J., Zhang, F., Huang, X.: Formal Verification of Robustness and Resilience of Learning-Enabled State Estimation Systems. in Neurocomputing, 2024.

Liu, Z., Yang, P., Zhang, L., Huang, X.: DeepCDCL: A CDCL-based Neural Network Verification Framework. In International Symposium on Theoretical Aspects of Software Engineering, July 2024.

Liu, J., Yi, X., Wu, S., Yin, X., Zhang, T., Huang, X., Jin, S.: Continuous Geometry-Aware Graph Diffusion via Hyperbolic Neural PDE. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, August 2024.

Wu, W., Dai, T., Huang, X., Ma, F., Xiao, J.: Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing, April 2024.

Zhang, Y., Zhang, T., Mu, R., Huang, X., Ruan, W.: Towards Fairness-Aware Adversarial Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024.

Yin, X., Wu, S., Liu, J., Fang, M., Zhao, X., Huang, X., Ruan, W.: Representation-Based Robustness in Goal-Conditioned Reinforcement Learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, No. 19, March 2024.

Mu, R., Marcolino, L. S., Zhang, Y., Zhang, T., Huang, X., Ruan, W.: Reward Certification for Policy Smoothed Reinforcement Learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, No. 19, March 2024.

Zhou, Z., Wang, Q., Jin, M., Yao, J., Ye, J., Liu, W., Huang, K.: Mathattack: Attacking large language models towards math solving ability. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, No. 17, March 2024.

Brown, G., & Ali, R.: Bias/Variance is not the same as Approximation/Estimation. in Transactions on Machine Learning Research, 2024.

Wodiany, I., Pop, A., Luján, M.: LeanBin: Harnessing Lifting and Recompilation to Debloat Binaries. arXiv preprint, 2024.

Wright, C. J., Luján, M., Petoumenos, P., Goodacre, J.: Quff: A Dynamically Typed Hybrid Quantum-Classical Programming Language. in Proceedings of the 21st ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes, September 2024.

Kyparissas, N., Brown, G., Luján, M.: FINESSD: Near-Storage Feature Selection with Mutual Information for Resource-Limited FPGAs. in 2024 IEEE 32nd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), May 2024.

Skordalakis, E., Attwood, A., Goodacre, J.: AccProf: Increasing the Accuracy of Embedded Application Profiling Using. in Architecture of Computing Systems: 37th International Conference, ARCS 2024, Potsdam, Germany, May 14-16, 2024.

Navaridas, J., Kynigos, M., Pascual, J. A., Luján, M., Miguel-Alonso, J., Goodacre, J.: Understanding the Impact of Arbitration in MZI-Based Beneš Switching Fabrics. in IEEE Transactions on Parallel and Distributed Systems, 2023.

Skapars, A., Manino, E., Sun, Y., Cordeiro, L. C.: Was it Slander? Towards Exact Inversion of Generative Language Models. CoRR, abs/2407.11059, 2024.

Menezes, R. S., Manino, E., Shmarov, F., Aldughaim, M., de Freitas, R., Cordeiro, L. C.: Interval Analysis in Industrial-Scale BMC Software Verifiers: A Case Study. CoRR, abs/2406.15281, 2024.

Zhang, Y., Carvalho, D., Freitas, A.: Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networks. In proceedings of the ACL, 2024.

Wysocki, O., Wysocka, M., Carvalho, D., Bogatu, A.T., Gusicuma, D.M., Delmas, M., Unsworth, H., Freitas, A.: An LLM-based Knowledge Synthesis and Scientific Reasoning Framework for Biomedical Discovery. CoRR, 2024.

Dong, Y., Mu, R., Jin, G., Qi, Y., Hu, J., Zhao, X., Huang, X.: Building guardrails for large language models. in The 41st International Conference on Machine Learning (ICML 2024), 2024.

Dong, Y., Zhao, X., Wang, S., Huang, X.: Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems. in IEEE Robotics and Automation Letters, 2024.

Zhao, H., Tang, Z., Li, Z., Dong, Y., Si, Y., Lu, M., Panoutsos, G.: Real-time object detection and robotic manipulation for agriculture using a yolo-based learning approach. in 2024 IEEE International Conference on Industrial Technology (ICIT) (pp. 1-6). IEEE, 2024.

Tang, Z., Rossiter, J. A., Dong, Y., Panoutsos, G.: Reinforcement learning-based output stabilization control for nonlinear systems with generalized disturbances. in 2024 IEEE International Conference on Industrial Technology (ICIT) (pp. 1-6). IEEE, 2024.

Rozanova, J., Valentino, M., & Freitas, A.: Estimating the Causal Effects of Natural Logic Features in Transformer-Based NLI Models. in Proceedings of the Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC/COLING), 2024.

Yingjie Wang, Fairouz Zobiri, Mustafa A Mustafa, James Nightingale, Geert Deconinck: Consumption prediction with privacy concern: Application and evaluation of Federated Learning. in Sustainable Energy, Grids and Networks.

Eman Alqahtani, Mustafa Mustafa: Poster: Privacy-Preserving Billing for Local Energy Markets. in Proceedings of the 2024 Poster Session of the 8th IEEE European Symposium on Security and Privacy.

Khlood Jastaniah, Ning Zhang, Mustafa A Mustafa: Efficient User-Centric Privacy-Friendly and Flexible Wearable Data Aggregation and Sharing. in IEEE Transactions on Cloud Computing.

Ruichang Zhang, Youcheng Sun, Mustafa A Mustafa: Proactive Load-Shaping Strategies with Privacy-Cost Trade-offs in Residential Households based on Deep Reinforcement Learning. in arXiv preprint arXiv:2405.18888.

Akash Madhusudan, Mustafa A Mustafa, Hilder VL Pereira, Erik Takke: Fully Privacy-preserving Billing Models for Peer-to-Peer Electricity Trading Markets. in Cryptology ePrint Archive.

Ruichang Zhang, Kaiyue Wu, Youcheng Sun, Mustafa Mustafa: Privacy-Preserving Load-Shaping Strategies for Smart Meters using Deep Reinforcement Learning. in IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe).

Yi Dong, Yingjie Wang, Mariana Gama, Mustafa A Mustafa, Geert Deconinck, Xiaowei Huang: Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting. in IEEE Internet of Things Journal.

Jastaniah, K., Zhang, N., Mustafa, M. A.: Efficient Privacy-Friendly and Flexible Wearable Data Processing With User-Centric Access Control. in IEEE Access, 2024.

Abacha, F., Teo, S.G., Cordeiro, L.C., Mustafa, M.A.: Synthetic Data Aided Federated Learning Using Foundation Models. in arXiv preprint arXiv:2407.05174.

Abacha, F., Teo, S.G., Cordeiro, L.C., Mustafa, M.A.: Poster: Improved Federated Learning with Non-IID Data Using Foundation Models. in Advance online publication.

Li, X., Song, K., Gadelha, M.R., Brauße, F. Menezes, R.F., Korovin, K. Cordeiro, L.C.: ESBMC v7. 6: Enhanced Model Checking of C++ Programs with Clang AST. in arXiv preprint arXiv:2406.17862.

Erdayandi K., Mustafa, M.A.: PP-LEM: Efficient and Privacy-Preserving Clearance Mechanism for Local Energy Markets. Sustainable Energy, Grids and Networks.

Erdayandi K., Cordeiro, L., Mustafa, M.A.: Privacy-Preserving and Accountable Billing in Peer-to-Peer Energy Trading Markets with Homomorphic Encryption and Blockchain. in SSRN 4839765.

Sihao Wu, Xingyu Zhao, Xiaowei Huang: Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory. inarXiv preprint arXiv:2408.13452.

Jiaxu Liu, Xinping Yi, Xiaowei Huang: DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional Networks. in IEEE Transactions on Artificial Intelligence.

Wangyu Wu, Tianhong Dai, Zhenhong Chen, Xiaowei Huang, Fei Ma, Jimin Xiao: APC: Adaptive Patch Contrast for Weakly Supervised Semantic Segmentation. in arXiv preprint arXiv:2407.10649.

Gaojie Jin, Ronghui Mu, Xinping Yi, Xiaowei Huang, Lijun Zhang: Invariant Correlation of Representation with Label. in arXiv preprint arXiv:2407.01749.

Dong Yi, Ronghui Mu, Gaojie Jin, Yi Qi, Jinwei Hu, Xingyu Zhao, Jie Meng, Wenjie Ruan, Xiaowei Huang: Position: Building Guardrails for Large Language Models Requires Systematic Design. in Forty-first International Conference on Machine Learning.

Zihan Ye, Shreyank N Gowda, Xiaobo Jin, Xiaowei Huang, Haotian Xu, Yaochu Jin, Kaizhu Huang: Exploring Data Efficiency in Zero-Shot Learning with Diffusion Models. in arXiv preprint arXiv:2406.02929.

Yi Dong, Ronghui Mu, Yanghao Zhang, Siqi Sun, Tianle Zhang, Changshun Wu, Gaojie Jin, Yi Qi, Jinwei Hu, Jie Meng, Saddek Bensalem, Xiaowei Huang: Safeguarding Large Language Models: A Survey. in arXiv preprint arXiv:2406.02622.

Jiaxu Liu, Xiangyu Yin, Sihao Wu, Jianhong Wang, Meng Fang, Xinping Yi, Xiaowei Huang: Tiny Refinements Elicit Resilience: Toward Efficient Prefix-Model Against LLM Red-Teaming. in arXiv preprint arXiv:2405.12604.

Dengyu Wu, Yi Qi, Kaiwen Cai, Gaojie Jin, Xinping Yi, Xiaowei Huang: Direct Training Needs Regularisation: Anytime Optimal Inference Spiking Neural Network. in arXiv preprint arXiv:2405.00699.

Zhiming Chi, Jianan Ma, Pengfei Yang, Cheng-Chao Huang, Renjue Li, Xiaowei Huang, Lijun Zhang: ADVREPAIR: Provable Repair of Adversarial Attack. in arXiv preprint arXiv:2404.01642.

Yi Zhang, Yun Tang, Wenjie Ruan, Xiaowei Huang, Siddartha Khastgir, Paul Jennings, Xingyu Zhao: ProTIP: Probabilistic Robustness Verification on Text-to-Image Diffusion Models against Stochastic Perturbation. in 2024 European Conference on Computer Vision (ECCV).

Xiaowei Huang, Wenjie Ruan, Wei Huang, Gaojie Jin, Yi Dong, Changshun Wu, Saddek Bensalem, Ronghui Mu, Yi Qi, Xingyu Zhao, Kaiwen Cai, Yanghao Zhang, Sihao Wu, Peipei Xu, Dengyu Wu, Andre Freitas, Mustafa A Mustafa: A survey of safety and trustworthiness of large language models through the lens of verification and validation. in Artificial Intelligence Review.

Hafiz Tayyab Rauf, Alex Bogatu, Norman W Paton, Andre Freitas: Gem: Gaussian Mixture Model Embeddings for Numerical Feature Distributions. in arXiv preprint arXiv:2410.07485.

Lan Zhang, Xin Quan, Andre Freitas: Consistent Autoformalization for Constructing Mathematical Libraries. in arXiv preprint arXiv:2410.04194.

Marco Valentino, André Freitas: Reasoning with Natural Language Explanations. in arXiv preprint arXiv:2410.04148.

M Jullien, A Bogatu, H Unsworth, A Freitas: Controlled LLM-based Reasoning for Clinical Trial Retrieval. in arXiv preprint arXiv:2409.18998.

Magdalena Wysocka, Oskar Wysocki, Maxime Delmas, Vincent Mutel, André Freitas: Large Language Models, scientific knowledge and factuality: A framework to streamline human expert evaluation. in Journal of Biomedical Informatics.

Marco Valentino, André Freitas: On the Nature of Explanation: An Epistemological-Linguistic Perspective for Explanation-Based Natural Language Inference. in Philosophy & Technology.

Geonhee Kim, Marco Valentino, André Freitas: A Mechanistic Interpretation of Syllogistic Reasoning in Auto-Regressive Language Models. in arXiv preprint arXiv:2408.08590.

João Pedro Gandarela, Danilo S. Carvalho, André Freitas: Inductive Learning of Logical Theories with LLMs: A Complexity-graded Analysis. in arXiv:2408.16779.

Nura Aljaafari, Danilo S Carvalho, André Freitas: The Mechanics of Conceptual Interpretation in GPT Models: Interpretative Insights. in arXiv preprint arXiv:2408.11827.

Stephen Menary, Samuel Kaski, Andre Freitas: Transformer Normalisation Layers and the Independence of Semantic Subspaces. in arXiv preprint arXiv:2406.17837.

Hafiz Tayyab Rauf, Andre Freitas, Norman W Paton: TableDC: Deep Clustering for Tabular Data. in arXiv preprint arXiv:2405.17723.

Maxime Delmas, Magdalena Wysocka, André Freitas: Relation Extraction in underexplored biomedical domains: A diversity-optimised sampling and synthetic data generation approach. in Computational Linguistics.

Xin Quan, Marco Valentino, Louise A Dennis, André Freitas: Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem Proving. in arXiv preprint arXiv:2405.01379.

Danilo Silva De Carvalho, Yingji Zhang, Andre Freitas: Formal Semantic Controls over Language Models. in Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024): Tutorial Summaries.

Leonardo Ranaldi, Andrè Freitas: Self-Refine Instruction-Tuning for Aligning Reasoning in Language Models. in arXiv preprint arXiv:2405.00402.

Jordan Meadows, Tamsin James, Andre Freitas: Exploring the Limits of Fine-grained LLM-based Physics Inference via Premise Removal Interventions. in arXiv preprint arXiv:2404.18384.

Maël Jullien, Marco Valentino, André Freitas: SemEval-2024 task 2: Safe biomedical natural language inference for clinical trials. in arXiv preprint arXiv:2404.04963.

Yingji Zhang, Danilo S Carvalho, Marco Valentino, Ian Pratt-Hartmann, Andre Freitas: Improving semantic control in discrete latent spaces with transformer quantized variational autoencoders. in arXiv preprint arXiv:2402.00723.

Nikolaos Kyparissas, Gavin Brown, Mikel Luján: FINESSD: Near-Storage Feature Selection with Mutual Information for Resource-Limited FPGAs. in 2024 IEEE 32nd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM).

Konstantinos Iordanou, Timothy Atkinson, Emre Ozer, Jedrzej Kufel, Grace Aligada, John Biggs, Gavin Brown, Mikel Luján: Low-cost and efficient prediction hardware for tabular data using tiny classifier circuits. in Nature Electronics.

Oliviero Massi, Edoardo Manino, Alberto Bernardini: Wave Digital Modeling of Circuits with Multiple One-Port Nonlinearities Based on Lipschitz-Bounded Neural Networks. in International Conference on Digital Audio Effects (DAFx24).

Wang, Y., Lu, L., Dong, Z., Dong, Y.: Privacy-preserving decentralised federated learning for short-term load forecasting. in Proceedings of the 43rd Chinese Control Conference.

H Zhao, Z Tang, Z Li, Y Dong, Y Si, M Lu, G Panoutso: Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach. in the 25th IEEE International Conference on Industrial Technology.

Z Tang, J R Anthony, Y Dong, G Panoutsos: Reinforcement Learning-Based Output Stabilization Control for Nonlinear Systems With Generalized Disturbances. in the 25th IEEE International Conference on Industrial Technology .

Okechi Onuoha, Suleiman Kurawa, Zezhi Tang, Yi Dong: Discrete-Time Stress Matrix-Based Formation Control of General Linear Multi-Agent Systems. in arXiv preprint arXiv:2401.05083.

Pirzada, M.A.A., Bhayat, A., Cordeiro, L.C., Reger, G.: LLM-Generated Invariants for Bounded Model Checking Without Loop Unrolling. in proceedings of 39th IEEE/ACM International Conference on Automated Software Engineering (ASE 2024) .

2023

Montakhabi, M., Madhusudan, A., Mustafa, M. A., Vanhaverbeke, W., Almirall, E., Van Der Graaf, S.: Leveraging blockchain for energy transition in urban contexts. in Big Data & Society, 2023.

Perrett, A., Wood, D., & Brown, G.: A max-affine spline approximation of neural networks using the Legendre transform of a convex-concave representation. in arXiv preprint arXiv:2307.09602, 2023.

Iordanou, K., Atkinson, T., Ozer, E., Kufel, J., Biggs, J., Brown, G., & Lujan, M.: Tiny classifier circuits: Evolving accelerators for tabular data. in arXiv preprint arXiv:2303.00031, 2023.

Wood, D., Mu, T., Webb, A. M., Reeve, H. W., Lujan, M., & Brown, G.: A unified theory of diversity in ensemble learning. in Journal of Machine Learning Research, 2023.

Qi, Y., Dong, Y., Khastgir, S., Jennings, P., Zhao, X., & Huang, X.: STPA for learning-enabled systems: a survey and a new practice. In IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 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.

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.

Ferrag, M. A., Friha, O., Kantarci, B., Tihanyi, N., Cordeiro, L., Debbah, M., Choo, K. K. R.: Edge learning for 6G-enabled Internet of Things: A comprehensive survey of vulnerabilities, datasets, and defenses. in IEEE Communications Surveys & Tutorials, 2023.

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.