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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 1475-1491页

作者： Mohamed, Ramy Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada École de technologie supérieure Department of Software and IT Engineering MontrealQCH3C 1K3 Canada

In the 6G wireless era, the strategical deployment of Virtual Network Functions (VNFs) within a network infrastructure that optimizes resource utilization while fulfilling performance criteria is critical for successfully implementing the Network Function Virtualization (NFV) paradigm across the Edge-to-Cloud continuum. This is especially prominent when resource fragmentation -where available resources become isolated and underutilized- becomes an issue due to the frequent reallocations of VNFs. However, traditional optimization methods often struggle to deal with the dynamic and complex nature of the VNF placement problem when fragmentation is considered. This study proposes a novel online VNF placement approach for Edge/Cloud infrastructures that utilizes Deep Reinforcement Learning (DRL) and Reward Constrained Policy Optimization (RCPO) to address this problem. We combine DRL's adaptability with RCPO's constraint incorporation capabilities to ensure that the learned policies satisfy the performance and resource constraints while minimizing resource fragmentation. Specifically, the VNF placement problem is first formulated as an offline-constrained optimization problem, and then we devise an online solver using Neural Combinatorial Optimization (NCO). Our method incorporates a metric called Resource Fragmentation Degree (RFD) to quantify fragmentation in the network. Using this metric and RCPO, our NCO agent is trained to make intelligent placement decisions that reduce fragmentation and optimize resource utilization. An error correction heuristic complements the robustness of the proposed framework. Through extensive testing in a simulated environment, the proposed approach is shown to outperform state-of-the-art VNF placement techniques when it comes to minimizing resource fragmentation under constraint satisfaction guarantees. © 2024 2024 The Authors.

关键词： Constrained optimization

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Fundamental Limits to Enhancing the Local Density of States Through Nanostructuring

Fundamental Limits to Enhancing the Local Density of States ...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Chao, Pengning Defo, Rodrick Kuate Molesky, Sean Rodriguez, Alejandro Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States Department of Engineering Physics Polytechnique Montreal MontrealQCH3T 1J4 Canada

We analyze bounds to the electromagnetic local density of states achievable via material structuring, deriving novel results including finite bound saturation in the infinite susceptibility limit and sub-linear bandwi...

ISBN: (纸本)9781957171258

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The Bifurcation Method: White-Box Observation Perturbation Attacks on Reinforcement Learning Agents on a Cyber Physical System

The Bifurcation Method: White-Box Observation Perturbation A...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： KIERNAN BRODA-MILIAN HANANE DAGDOUGUI RANWA AL MALLAH Royal Military College of Canada Electrical and Computer Engineering Kingston Canada Polytechnique Montreal Computer and Software Engineering Quebec Canada

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

Components of cyber physical systems, which affect real-world processes, are often exposed to the internet. Replacing conventional control methods with Deep Reinforcement Learning (DRL) in energy systems is an active area of research, as these systems become increasingly complex with the advent of renewable energy sources and the desire to improve their efficiency. Artificial Neural Networks (ANN) are vulnerable to specific perturbations of their inputs or features, called adversarial examples. These perturbations are difficult to detect when properly regularized, but have significant effects on the ANN's output. Because DRL uses ANN to map optimal actions to observations, they are similarly vulnerable to adversarial examples. This work proposes a novel attack technique for continuous control using Group Difference Logits loss with a bifurcation layer. By combining aspects of targeted and untargeted attacks, the attack significantly increases the impact compared to an untargeted attack, with drastically smaller distortions than an optimally targeted attack. We demonstrate the impacts of powerful gradient-based attacks in a realistic smart energy environment, show how the impacts change with different DRL agents and training procedures, and use statistical and time-series analysis to evaluate attacks' stealth. The results show that adversarial attacks can have significant impacts on DRL controllers, and constraining an attack's perturbations makes it difficult to detect. However, certain DRL architectures are far more robust, and robust training methods can further reduce the impact.

关键词： Training Renewable energy sources Perturbation methods Time series analysis Artificial neural networks Bifurcation Deep reinforcement learning Distortion Robustness Glass box

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Virtual Receiver Matrix and Combinatory Analog Operations for Future Multifunction Reconfigurable Sensing and Communication Wireless Systems

Virtual Receiver Matrix and Combinatory Analog Operations fo...

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作者： Keivaan, Seyed Ali Burasa, Pascal Wu, Ke Polytechnique Montréal Department of Electrical and Computer Engineering MontrealH3T 1J4 Canada

This article proposes and presents a topological receiver architecture, named virtual receiver matrix (VRM), suitable for future smart multifunction wireless systems. This concept is devised and benefits from using combinatory analog operations with multiple distributed units in a receiver matrix or array. This mechanism of receiving data through spatially 'floating' distributed virtual receiver channels offers an unprecedented solution of providing unparalleled degrees of freedom to implement multiple functions such as data reception, angle-of-arrival (AoA) detection, radar, and imaging operations among many others in a single receiver architecture. Interestingly, the total number of possible virtual receivers from different combinations of unit cells in a matrix is also significantly increased compared with a conventional 'fixed' receiver array. Each virtual receiver, made of a combinatory set, depends on the characteristics of incoming signals and their illumination angle. A mathematical model is established and investigated for the design of unit cells. Although a prototype of choice is studied and designed for the fifth-generation (5G) wireless systems, it is anticipated that the VRM concept is applicable to the sixth-generation (6G) and future wireless systems for enhancing their functionality, capacity, agility, and speed. In this article, a multiport interferometric technique is used for each unit cell of VRM as a proof-of-concept;however, any other receiver type that uses the phase difference of incoming signals can be deployed as the unit cell for the realization of the VRM concept. First, the experimental results of the fabricated proof-of-concept prototype for various modulation schemes including QPSK, 16-quadrature amplitude modulation (QAM), and 32-QAM are shown with data rates up to 1 MS/s, having a maximum error vector magnitude (EVM) of 9%. Finally, a systematic scheme for 2-D AoA detection using a special combination of unit cells is proposed and d

关键词： Topology

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AutoMESC: Automatic Framework for Mining and Classifying Ethereum Smart Contract Vulnerabilities and Their Fixes

AutoMESC: Automatic Framework for Mining and Classifying Eth...

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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Majd Soud Ilham Qasse Grischa Liebel Mohammad Hamdaqa Department of Computer Science Reykjavik University Reykjavik Iceland Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

Due to the risks associated with vulnerabilities in smart contracts, their security has gained significant attention in recent years. However, there is a lack of open datasets on smart contract vulnerabilities and their fixes that allows for data-driven research. Towards this end, we propose an automated framework for mining and classifying Ethereum’s smart contract vulnerabilities and their corresponding fixes from GitHub and from the Common Vulnerabilities and Exposures (CVE) records in the National Vulnerability Database. We implemented the proposed method in a fully automated framework, which we call AutoMESC. AutoMESC uses seven of the most well-known smart contract security tools to classify and label the collected vulnerabilities based on vulnerability types. Furthermore, it collects metadata that can be used in data-intensive smart contract security research (e.g., vulnerability detection, vulnerability classification, severity prediction, and automated repair). We used AutoMESC to construct a sample dataset and made it publicly available. Currently, the dataset contains 6.7K smart contract vulnerability-fix pairs written in Solidity. We assess the quality of the constructed dataset in terms of accuracy, provenance, and relevance, and compare it with existing datasets. AutoMESC is designed to collect data continuously and keep the corresponding dataset up-to-date with newly discovered smart contract vulnerabilities and their fixes from GitHub and CVE records.

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"It’s Sink or Swim": Exploring Patients’ Challenges and Tool Needs for Self-Management of Postoperative Acute Pain

arXiv

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arXiv 2024年

作者： Zghab, Souleima Pagé, Gabrielle Lussier, Mélanie Bédard, Sylvain Cheng, Jinghui Department of Computer and Software Engineering Polytechnique Montreal MontrealQC Canada Department of Anesthesiology and Pain Medicine University of Montreal MontrealQC Canada Montreal QC Canada

Poorly managed postoperative acute pain can have long-lasting negative impacts and pose a major healthcare issue. There is limited investigation to understand and address the unique needs of patients experiencing acute pain. In this paper, we tackle this gap through an interview study with 14 patients who recently underwent postoperative acute pain to understand their challenges in pain self-management and their need for supportive tools. Our analysis identified various factors associated with the major aspects of acute pain self-management. Together, our findings indicated that tools for supporting these patients need to carefully consider information and support delivery to adapt to rapid changes in pain experiences, offer personalized and dynamic assistance that adapts to individual situations in context, and monitor emotion when promoting motivation. Overall, our work provided valuable knowledge to address the less-investigated but highly-needed problem of designing technology for the self-management of acute pain and similar health conditions. Copyright © 2024, The Authors. All rights reserved.

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PrAIoritize: Automated Early Prediction and Prioritization of Vulnerabilities in Smart Contracts

arXiv

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arXiv 2023年

作者： Soud, Majd Liebel, Grischa Hamdaqa, Mohammad Department of Computer Science Reykjavik University Reykjavik Iceland Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada

Context: Smart contracts are prone to numerous security threats due to undisclosed vulnerabilities and code weaknesses. In Ethereum smart contracts, the challenges of timely addressing these code weaknesses highlight the critical need for automated early prediction and prioritization during the code review process. Efficient prioritization is crucial for smart contract security. Objective: Toward this end, our research aims to provide an automated approach, PrAIoritize, for prioritizing and predicting critical code weaknesses in Ethereum smart contracts during the code review process. Method: To do so, we collected smart contract code reviews sourced from Open Source software (OSS) on GitHub and the Common Vulnerabilities and Exposures (CVE) database. Subsequently, we developed PrAIoritize, an innovative automated prioritization approach. PrAIoritize integrates advanced Large Language Models (LLMs) with sophisticated natural language processing (NLP) techniques. PrAIoritize automates code review labeling by employing a domain-specific lexicon of smart contract weaknesses and their impacts. Following this, feature engineering is conducted for code reviews, and a pre-trained DistilBERT model is utilized for priority classification. Finally, the model is trained and evaluated using code reviews of smart contracts. Results: Our evaluation demonstrates significant improvement over state-of-the-art baselines and commonly used pre-trained models (e.g. T5) for similar classification tasks, with 4.82%-27.94% increase in F-measure, precision, and recall. Conclusion: This paper introduces PrAIoritize, an automated approach that effectively prioritizes smart contract code weaknesses during the review process. By leveraging PrAIoritize, practitioners can efficiently prioritize smart contract code weaknesses, addressing critical code weaknesses promptly and reducing the time and effort required for manual triage. © 2023, CC BY-NC-SA.

关键词： Smart contract

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Scalable Low-Complexity Implementation of Constant Matrix Multiplication Circuits

Scalable Low-Complexity Implementation of Constant Matrix Mu...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Aymen-Alaeddine Zeghaida Dinesh Daultani J.M. Pierre Langlois Jean Pierre David Department of Electrical Engineering Polytechnique Montréal Canada Department of Computer and Software Engineering Polytechnique Montréal Canada

ISBN: (数字)9798350387179

ISBN: (纸本)9798350387186

Modern neural network models consume a large proportion of power in matrix-vector multiplications. At inference time, the trained network parameters are constant and organized in matrices. Knowing the constant matrices in advance can significantly benefit hardware utilization, throughput and energy consumption of neural network implementations. This paper seeks to achieve hardware optimizations and reduce the computational cost of constant matrix multiplication by exploiting redundancies in sub-expressions common to several products and outputs. For FPGA implementation, our results show that the number of lookup tables can be decreased by up to 62% compared to a standard baseline version already optimized by vendor tools.

关键词： Tensors Redundancy Throughput Hardware Table lookup Iterative methods Kernel

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Model Checking in Medical Imaging for Tumor Detection and Segmentation

arXiv

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arXiv 2025年

作者： Elfatimi, Elhoucine Fatimi, Lahcen El Department of Pathology Laboratory Medicine University of California IrvineCA United States Department of Computer and Software Engineering Polytechnique Montreal University of Montreal MontrealQC Canada

Recent advancements in model checking have demonstrated significant potential across diverse applications, particularly in signal and image analysis. Medical imaging stands out as a critical domain where model checking can be effectively applied to design and evaluate robust frameworks. These frameworks facilitate automatic and semi-automatic delineation of regions of interest within images, aiding in accurate segmentation. This paper provides a comprehensive analysis of recent works leveraging spatial logic to develop operators and tools for identifying regions of interest, including tumorous and non-tumorous areas. Additionally, we examine the challenges inherent to spatial model-checking techniques, such as variability in ground truth data and the need for streamlined procedures suitable for routine clinical practice. © 2025, CC BY.

关键词： Image segmentation

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Broadcasting and Three List Subtraction 17th

Broadcasting and Three List Subtraction

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17th International Conference on Combinatorial Optimization and Applications, COCOA 2024

作者： Harutyunyan, Hovhannes A. Hovhannisyan, Narek Department of Computer Science and Software Engineering Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9789819644476

Broadcasting is an information dissemination primitive where a message is passed from one node (called originator) to all other nodes in the network. In the scope of this paper, we will mainly focus on determining the broadcast time and the optimal broadcasting scheme for graphs. Determination of the broadcast time of a node in an arbitrary network is known to be NP-hard. Polynomial time solutions are known only for a few classes of networks. In this paper, we will consider networks that can be represented as k-path graphs. We will pose a new problem, called 3 list subtraction, and discuss its relation to the broadcast time problem on k-path graphs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Interconnection networks (circuit switching)

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