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21st International Workshop on Satisfiability Modulo Theories, SMT 2023

作者： Soureshjani, Fatemeh Heidari Hall-Andersen, Mathias Jahanara, MohammadMahdi Kam, Jeffrey Gorzny, Jan Ahmadvand, Mohsen Computer Engineering and Software Engineering Department Polytechnique Montréal MontrealQC Canada Quantstamp Inc. United States Department of Computer Science Aarhus University Denmark

Zero-knowledge proof systems are becoming increasingly prevalent and being widely used to secure decentralized financial systems and protect the privacy of users. Given the sensitivity of these applications, zero-knowledge proof systems are a natural target for formal verification methods. We describe methods for checking one such proof system: Halo2. We use abstract interpretation and an SMT solver to check various properties of Halo2 circuits. Using abstract interpretation, we can detect unused gates, unconstrained cells, and unused columns. Using an SMT solver, we can detect under-constrained circuits (in the sense that for the same public input they have two efficiently computable satisfying assignments). This is the first work we are aware of that applies lightweight formal methods to PLONKish arithmetization and Halo2 circuits. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Timing circuits

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Exploration of the Possibility for a Wider Range of the Discount Factor for Reinforcement Learning

Exploration of the Possibility for a Wider Range of the Disc...

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International Conference on Communications, Information System and computer engineering (CISCE)

作者： Monica M.Q. Li Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

The discount factor for reinforcement learning has been constrained to the range [0, 1] to avoid overestimation of state values during training. Although theoretical studies have indicated that discount factors greater than 1 could be valid for certain tasks, experimental work has generally maintained the [0, 1] limit in flexible discount factor optimization. Our study explores the impact of allowing discount factors to exceed 1 across a variety of reinforcement learning algorithms and classic training environments. Our findings demonstrate that in some grid-based environments, reinforcement learning algorithms with discount factors above 1 can achieve comparable performance to those with discount factors within the [0, 1] range. However, performance tends to be more variable when the discount factor exceeds 1.

关键词： Training Reinforcement learning Task analysis Optimization Information systems

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Securing Next-Generation Networks against Eavesdroppers: FL-Enabled DRL Approach

Securing Next-Generation Networks against Eavesdroppers: FL-...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Deemah H. Tashman Soumaya Cherkaoui Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Anticipated advancements in 5G wireless networks and beyond would necessitate an increased emphasis on security measures to accommodate the projected rise in demand for connections and services. Therefore, this paper aims to investigate the physical layer security (PLS) to evaluate the privacy of authorized users in multi-cellular networks, which represent a fundamental architecture in next-generation networks. Each cell is assumed to include a base station (BS) that serves multiple users. This scenario also takes into account the presence of several eavesdroppers. Every BS functions as a reinforcement learning (RL) agent that must undergo training in order to optimize security. To enhance the safety and speed of training, a federated learning (FL) technique is utilized. In this approach, a central unit regularly receives the neural network (NN) weights from the agents, updates them, and then transfers the result back to the agents to update their model. We examine and compare two deep RL methodologies, specifically deep Q-network, and Reinforce deep policy gradient. The findings of our research demonstrate the influence of the number of eavesdroppers on security, as well as the impact of the number of cells and the aggregation frequency of neural network parameters.

关键词： Training Privacy Federated learning Microprocessors computer architecture Reinforcement learning Physical layer security

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LiDAR-based Real-Time Object Detection and Tracking in Dynamic Environments

arXiv

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arXiv 2024年

作者： Du, Wenqiang Beltrame, Giovanni MISTLab the Department of Computer Engineering and Software Engineering Polytechnique Montreal Montreal Canada

In dynamic environments, the ability to detect and track moving objects in real-time is crucial for autonomous robots to navigate safely and effectively. Traditional methods for dynamic object detection rely on high accuracy odometry and maps to detect and track moving objects. However, these methods are not suitable for long-term operation in dynamic environments where the surrounding environment is constantly changing. In order to solve this problem, we propose a novel system for detecting and tracking dynamic objects in real-time using only LiDAR data. By emphasizing the extraction of low-frequency components from LiDAR data as feature points for foreground objects, our method significantly reduces the time required for object clustering and movement analysis. Additionally, we have developed a tracking approach that employs intensity-based egomotion estimation along with a sliding window technique to assess object movements. This enables the precise identification of moving objects and enhances the system's resilience to odometry drift. Our experiments show that this system can detect and track dynamic objects in real-time with an average detection accuracy of 88.7% and a recall rate of 89.1%. Furthermore, our system demonstrates resilience against the prolonged drift typically associated with front-end only LiDAR odometry. All of the source code, labeled dataset, and the annotation tool are available at: https://***/MISTLab/lidar dynamic objects *** © 2024, CC BY.

关键词： Object detection

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Securing Cognitive IoT Networks: Reinforcement Learning for Adaptive Physical Layer Defense

Securing Cognitive IoT Networks: Reinforcement Learning for ...

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International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Deemah H. Tashman Soumaya Cherkaoui Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Single-input multiple-output (SIMO) configurations are commonly employed in applications where managing power consumption is critical. This is the case for battery-powered sensors and low-power Internet of Things (IoT) devices, which may also employ energy harvesting (EH) strategies to augment their battery longevity. These systems can further access the underutilized spectrum through cognitive radio networks (CRNs). This study focuses on mitigating eavesdropping concerns in such configurations through the application of physical layer security (PLS). More specifically, the paper proposes a PLS technique to evaluate and enhance the confidentiality of secondary users' (SUs) transmissions for SIMO underlay CRN, considering the presence of an eavesdropper. The secondary user receiver incorporates the power splitting-EH method to extract energy, subsequently utilizing it to generate jamming signals to perplex the eavesdropper. We compare the results obtained when the eavesdropper extracts energy from the SUs' transmissions and when it chooses to only decode the wiretapped messages. We implement a deep reinforcement learning (DRL) approach [1], specifically the deep Q-network, to optimize the transmission power and thereby maximize the secrecy rate of the SUs and demonstrate that the performance of the approach surpasses that of the benchmarks.

关键词： Wireless networks Surveillance Signal processing Sensors Internet of Things Energy harvesting Cognitive radio Security SIMO Eavesdropping

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Parkinson’s Disease Detection from Resting State EEG Using Multi-head Graph Structure Learning with Gradient Weighted Graph Attention Explanations 7th

Parkinson’s Disease Detection from Resting State EEG Using...

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7th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9783031787607

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Open RAN Slicing with Quantum Optimization

arXiv

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arXiv 2025年

作者： Keyela, Patatchona Cherkaoui, Soumaya Computer Engineering and Software Engineering Dept. Polytechnique Montreal Montreal Canada

RAN slicing technology is a key aspect of the Open RAN paradigm, allowing simultaneous and independent provision of various services such as ultra-reliable low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC) through virtual networks that share a single radio access infrastructure. Efficient resource allocation is crucial for RAN slicing, as each service has specific quality of service (QoS) requirements, and a balance between different services must be maintained. Although heuristic and reinforcement learning (RL) techniques have been explored to achieve efficient resource allocation, these approaches face notable limitations: heuristic algorithms face complexity issues that limit their effectiveness in large networks, RL solutions are constrained by their dependency on training data and struggle to adapt to new scenarios and environments. This paper proposes a framework that leverages quantum optimization techniques to optimize radio resource blocks allocation in Open RAN slicing for URLLC and eMBB services. We provide a classical problem formulation and the quantum implementation using the constrained quadratic model on Dwave quantum annealing platform, showcasing the potential of quantum optimization techniques to deliver in real-time optimal solutions for optimization problems in 5G and beyond networks. © 2025, CC BY.

关键词： Constrained optimization

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Open RAN Slicing with Quantum Optimization

Open RAN Slicing with Quantum Optimization

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International Global Information Infrastructure Symposium (GIIS)

作者： Patatchona Keyela Soumaya Cherkaoui Computer Engineering and Software Engineering Dept. Polytechnique Montreal Montreal Canada

ISBN: (数字)9798331520113

ISBN: (纸本)9798331520120

关键词： Annealing Training data Open RAN Quality of service Reinforcement learning Ultra reliable low latency communication Quantum annealing Resource management Optimization Faces

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Temporal Separators with Deadlines 34

Temporal Separators with Deadlines

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34th International Symposium on Algorithms and Computation, ISAAC 2023

作者： Harutyunyan, Hovhannes A. Koupayi, Kamran Pankratov, Denis Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (纸本)9783959772891

We study temporal analogues of the Unrestricted Vertex Separator problem from the static world. An (s, z)-temporal separator is a set of vertices whose removal disconnects vertex s from vertex z for every time step in a temporal graph. The (s, z)-Temporal Separator problem asks to find the minimum size of an (s, z)-temporal separator for the given temporal graph. The (s, z)-Temporal Separator problem is known to be NP-hard in general, although some special cases (such as bounded treewidth) admit efficient algorithms [15]. We introduce a generalization of this problem called the (s, z, t)-Temporal Separator problem, where the goal is to find a smallest subset of vertices whose removal eliminates all temporal paths from s to z which take less than t time steps. Let τ denote the number of time steps over which the temporal graph is defined (we consider discrete time steps). We characterize the set of parameters τ and t when the problem is NP-hard and when it is polynomial time solvable. Then we present a τ-approximation algorithm for the (s, z)-Temporal Separator problem and convert it to a τ2-approximation algorithm for the (s, z, t)-Temporal Separator problem. We also present an inapproximability lower bound of Ω(ln(n) + ln(τ)) for the (s, z, t)-Temporal Separator problem assuming that NP Dtime(nlog log n). Then we consider three special families of graphs: (1) graphs of branchwidth at most 2, (2) graphs G such that the removal of s and z leaves a tree, and (3) graphs of bounded pathwidth. We present polynomial-time algorithms to find a minimum (s, z, t)-temporal separator for (1) and (2). As for (3), we show a polynomial-time reduction from the Discrete Segment Covering problem with bounded-length segments to the (s, z, t)-Temporal Separator problem where the temporal graph has bounded pathwidth. © Hovhannes A. Harutyunyan, Kamran Koupayi, and Denis Pankratov;licensed under Creative Commons License CC-BY 4.0.

关键词： Approximation algorithms

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Performance comparison of Dask and Apache Spark on HPC systems for neuroimaging

Performance comparison of Dask and Apache Spark on HPC syste...

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作者： Dugré, Mathieu Hayot-Sasson, Valérie Glatard, Tristan Department of Computer Science and Software Engineering Concordia University Montreal Canada

The general increase in data size and data sharing motivates the adoption of Big Data strategies in several scientific disciplines. However, while several options are available, no particular guidelines exist for selecting a Big Data engine. In this paper, we compare the runtime performance of two popular Big Data engines with Python APIs, Apache Spark, and Dask, in processing neuroimaging pipelines. Our experiments use three synthetic neuroimaging applications to process the 606 GB BigBrain image and an actual pipeline to process data from thousands of anatomical images. We benchmark these applications on a dedicated HPC cluster running the Lustre file system while using varying combinations of the number of nodes, file size, and task duration. Our results show that although there are slight differences between Dask and Spark, the performance of the engines is comparable for data-intensive applications. However, Spark requires more memory than Dask, which can lead to slower runtime depending on configuration and infrastructure. In general, the limiting factor was the data transfer time. While both engines are suitable for neuroimaging, more efforts need to be put to reduce the data transfer time and the memory footprint of applications. © 2023 John Wiley & Sons, Ltd.

关键词： Big data

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