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arXiv 2023年

作者： Mlika, Zoubeir Cherkaoui, Soumaya Laprade, Jean Frédéric Corbeil-Letourneau, Simon The Department of Electrical and Computer Engineering Université de Sherbrooke SherbrookeQC Canada Department of Computer and Software Engineering Polytechnique Montreal QC Canada The Insitut Quantique Université de Sherbrooke QC Canada Thales Digital Solutions MontrealQC Canada

This paper applies a quantum machine learning technique to predict mobile users’ trajectories in mobile wireless networks using an approach called quantum reservoir computing (QRC). Mobile users’ trajectories prediction belongs to the task of temporal information processing and it is a mobility management problem that is essential for self-organizing and autonomous 6G networks. Our aim is to accurately predict the future positions of mobile users in wireless networks using QRC. To do so, we use a real-world time series dataset to model mobile users’ trajectories. The QRC approach has two components: reservoir computing (RC) and quantum computing (QC). In RC, the training is more computational-efficient than the training of simple recurrent neural networks (RNN) since, in RC, only the weights of the output layer are trainable. The internal part of RC is what is called the reservoir. For the RC to perform well, the weights of the reservoir should be chosen carefully to create highly complex and nonlinear dynamics. The QC is used to create such dynamical reservoir that maps the input time series into higher dimensional computational space composed of dynamical states. After obtaining the high-dimensional dynamical states, a simple linear regression is performed to train the output weights and thus the prediction of the mobile users’ trajectories can be performed efficiently. In this paper, we apply a QRC approach based on the Hamiltonian time evolution of a quantum system. We simulate the time evolution using IBM gate-based quantum computers and we show in the experimental results that the use of QRC to predict the mobile users’ trajectories with only a few qubits is efficient and is able to outperform the classical approaches such as the long short-term memory (LSTM) approach and the echo-state networks (ESN) approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Hamiltonians

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Research and Development of WRD600: Innovations in High-Power Double-Ridge Waveguide Combiners for Ultra-Wideband Applications

Research and Development of WRD600: Innovations in High-Powe...

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Microwave, MTT-S International Symposium

作者： Mohamed Mamdouh M. Ali Mostafa O. Shady Mahmoud Elsaadany Shoukry I. Shams Ghyslain Gagnon W. Ke Scientific Microwave Corporation Canada Department of Electrical Engineering Polytechnique Montreal Canada Department of Electrical Engineering Ecole de Technologie Superieure Canada Department of Electrical and Computer Engineering Concordia University Canada

ISBN: (数字)9798350375046

ISBN: (纸本)9798350375053

This paper proposes for the first time a novel double-ridge waveguide standard, the WRD600, tailored for high-power applications across a wide frequency range. Overcoming the operational constraints of existing standards, the WRD600 offers a 3:1 bandwidth (6–18 GHz) with a cutoff frequency at 4.45 GHz. Optimized dimensions ensure non-dispersive transmission behavior, which is essential for achieving a promising performance. In addition, a complete ultra-wideband 4-way power combining assembly is presented as an application for the proposed double-ridge standard. This includes building the test setup and experimentally validating the combining network from 6 to 18 GHz, where an agreement has been shown between the simulated and measured responses. The assembly achieves and demonstrates an output return loss and isolation levels better than 12.5 dB with a 0.8 dB insertion loss and power handling up to 6 KW.

关键词： Microwave measurement Time-frequency analysis Technological innovation Waveguide components Bandwidth Standards Assembly

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Comprehensive review of reinforcement learning for medical ultrasound imaging

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Artificial Intelligence Review 2025年第9期58卷 1-90页

作者： Elmekki, Hanae Islam, Saidul Alagha, Ahmed Sami, Hani Spilkin, Amanda Zakeri, Ehsan Zanuttini, Antonela Mariel Bentahar, Jamal Kadem, Lyes Xie, Wen-Fang Pibarot, Philippe Mizouni, Rabeb Otrok, Hadi Singh, Shakti Mourad, Azzam Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Software and IT engineering Ecole de Technologie Superieure (ETS) Montreal Canada Department of CSM Artificial Intelligence & Cyber Systems Research Center Lebanese American University Beirut Lebanon Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Canada Department of Medicine Laval University Quebec Canada Department of Computer Science Khalifa University Abu Dhabi UAE Gina Cody School of Engineering and Computer Science Concordia University Montreal Canada

Medical Ultrasound (US) imaging has seen increasing demands over the past years, becoming one of the most preferred imaging modalities in clinical practice due to its affordability, portability, and real-time capabilities. However, it faces several challenges that limit its applicability, such as operator dependency, variability in interpretation, and limited resolution, which are amplified by the low availability of trained experts. This calls for the need of autonomous systems that are capable of reducing the dependency on humans for increased efficiency and throughput. Reinforcement Learning (RL) comes as a rapidly advancing field under Artificial Intelligence (AI) that allows the development of autonomous and intelligent agents through rewarded interactions with their environments. Several existing surveys on advancements in US imaging predominantly focus on partially autonomous AI solutions. However, none of these surveys explore the intersection between the stages of the US process and the recent advancements in RL solutions. To bridge this gap, this survey proposes a comprehensive taxonomy that integrates the stages of the US process with the RL development pipeline -including data preparation, problem formulation, simulation environment, RL training, validation and finetuning- and reviews current research efforts under this taxonomy. This work aims to highlight the potential of RL in building autonomous US solutions while identifying limitations and opportunities for further advancements in this field.

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A First Look at the Inheritance-Induced Redundant Test Execution

A First Look at the Inheritance-Induced Redundant Test Execu...

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International Conference on software engineering (ICSE)

作者： Dong Jae Kim Tse-Hsun Peter Chen Jinqiu Yang Software PErformance Analysis and Reliability (SPEAR) Lab Concordia University Montreal Quebec Canada Department of Computer Science and Software Engineering Concordia University Montreal Quebec Canada

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Inheritance, a fundamental aspect of object-oriented design, has been leveraged to enhance code reuse and facilitate efficient software development. However, alongside its benefits, inheritance can introduce tight coupling and complex relationships between classes, posing challenges for software maintenance. Although there are many studies on inheritance in source code, there is limited study on using inheritance in test code. In this paper, we take the first step by studying inheritance in test code, with a focus on redundant test executions caused by inherited test cases. We empirically study the prevalence of test inheritance and its characteristics. We also propose a hybrid approach that combines static and dynamic analysis to identify and locate inheritance-induced redundant test cases. Our findings reveal that (1) inheritance is widely utilized in the test code, (2) inheritance-induced redundant test executions are prevalent, accounting for 13% of all execution test cases, (3) bypassing these redundancies can help reduce 14% of the test execution time, and finally, (4) our study highlights the need for careful refactoring decisions to minimize redundant test cases and identifies the need for further research on test code quality.

关键词： Couplings software maintenance Java Codes Source coding Redundancy Maintenance

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Fourier neural operator approach to large eddy simulation of three-dimensional turbulence

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Theoretical & Applied Mechanics Letters 2022年第6期12卷 438-444页

作者： Zhijie Li Wenhui Peng Zelong Yuan Jianchun Wang Department of Mechanics and Aerospace Engineering Southern University of Science and TechnologyShenzhen 518055China Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou) 511458China Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications Southern University of Science and TechnologyShenzhen 518055China Department of Computer Engineering Polytechnique MontrealMontreal H3T1J4Canada

Fourier neural operator(FNO)model is developed for large eddy simulation(LES)of three-dimensional(3D)*** fields of isotropic turbulence generated by direct numerical simulation(DNS)are used for training the FNO model to predict the filtered velocity field at a given *** input of the FNO model is the filtered velocity fields at the previous several time-nodes with large time *** the a posteriori study of LES,the FNO model performs better than the dynamic Smagorinsky model(DSM)and the dynamic mixed model(DMM)in the prediction of the velocity spectrum,probability density functions(PDFs)of vorticity and velocity increments,and the instantaneous flow ***,the proposed model can significantly reduce the computational cost,and can be well generalized to LES of turbulence at higher Taylor-Reynolds numbers.

关键词： Fourier neural operator Large eddy simulation Data-driven simulation Incompressible turbulence

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Using Convolutional Neural Network to Handle Word Shape Similarities in Handwritten Cursive Arabic Scripts of Pashto Language 3rd

Using Convolutional Neural Network to Handle Word Shape Simi...

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3rd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Shah, Muhammad Ismail Suen, Ching Y. Computer Science and Software Engineering Department Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9783031090363

The Inter-Class Word Similarities in combination with Intra-Class Variations make it a difficult task for an OCR or any other machine learning system to recognize the handwritten characters and words with high accuracy, especially in the domain of cursive Arabic scripts. Convolutional Neural Network was originally designed to handle the problems of shape resemblance, position shift and distortion in the domain of handwritten characters and digit recognition. In this paper, we have used Convolutional Neural Network (CNN) to recognize multi class handwritten words written in cursive Arabic scripts of Pashto Language. The handwritten word images in Pashto Database [3] contain a high level of shape resemblance, and position shift in terms of diacritic marks. Hence it has been proved to be a good source for properly analyzing the performance of any CNN based recognition system. The model has successfully handled the problem and has not been affected by the level of complexity of Inter-class resemblances. The CNN model has been well tested on three sub data sets of Pashto Database. In each data set the number of classes and level of complexity, i.e., Shape Similarity, increase from one data set to another, i.e., 25, 40 and 68 classes of handwritten words. The average accuracies for the three Data Sets are 97.26%, 96.25% and 95.84%, respectively. © 2022, Springer Nature Switzerland AG.

关键词： Convolution

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Frequency-Agile WLAN Notch UWB Antenna for URLLC Applications

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computers, Materials & Continua 2021年第5期67卷 2243-2254页

作者： Amir Haider MuhibUr Rahman Hamza Ahmad Mahdi NaghshvarianJahromi Muhammad Tabish Niaz Hyung Seok Kim Department of Intelligent Mechatronics Engineering Sejong UniversitySeoul05006Korea Department of Electrical Engineering Polytechnique MontrealMontrealQC H3T 1J4Canada Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Electrical and Computer Engineering McMaster UniversityHamilton ONL8S 4L8Canada

This paper introduces a compact dual notched UWB antenna with an independently controllable WLAN notched band integrated with fixed WiMAX *** proposed antenna utilizes a slot resonator placed in the main radiator of the antenna for fixed WiMAX band notch,while an inverted L-shaped resonator in the partial ground plane for achieving frequency agility within WLAN notched *** inverted L-shaped resonator is also loaded with fixed and variable capacitors to control and adjust the WLAN *** WLAN notched band can be controlled independently with a wide range of tunability without disturbing the WiMAX bandnotch *** by step design approach of the proposed antenna is discussed and the corresponding mathematical analysis of the proposed resonators are provided in both *** of the proposed antenna is performed utilizing commercially available 3D-EM simulator,Ansoft High Frequency Structure Simulator(HFSS).The proposed antenna has high selectivity with experimental validation in terms of reflection coefficient,radiation characteristics,antenna gain,and percentage radiation *** corresponding measured frequency response of the input port corresponds quite well with the calculations and simulations in both *** proposed antenna is advantageous and can adjust according to the device requirements and be one of the attractive candidates for overlay cognitive radio UWB applications and URLLC service in 5G tactile *** proposed multifunctional antenna can also be used for wireless vital signs monitoring,sensing applications,and microwave imaging techniques.

关键词： WiMAX UWB lower and upper WLAN single/dual notch behavior independent controlling notched behavior tunable band-notch behavior overlay cognitive radio applications frequency-agile WLAN notched antennas 5G URLLC tactile internet

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Detecting Free-Riders in Federated Learning Using an Ensemble of Similarity Distance Metrics 4

Detecting Free-Riders in Federated Learning Using an Ensembl...

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4th Intelligent Cybersecurity Conference, ICSC 2024

作者： Arisdakessian, Sarhad Wahab, Omar Abdel Wehbi, Osama Mourad, Azzam Otrok, Hadi Polytechnique Montréal Computer and Software Engineering Montréal Canada Khalifa University KU 6G Research Center Department of CS Abu Dhabi United Arab Emirates Khalifa University Center of Cyber-Physical Systems Department of EECS Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350354775

Federated learning (FL) has witnessed an increase in adoption mainly because of its practical implications and ability to leverage large amounts of data while safeguarding their privacy. However, at its core, FL highly relies on the resources of its participants (i.e., clients), that directly influence the accuracy of the global model in any given training task. A significant challenge arises from the presence of clients that do not fully engage in the training process, yet still benefit from the updated models provided by the server. This lack of active participation has the potential to undermine the overall performance of the global model. Furthermore, free-riders create an unfair scenario where honest participants contribute more while receiving the same overall benefit. In this work, we propose a strategy that relies on an ensemble of several distance measures to mitigate the impact of free-riders in FL environments. By integrating multiple distance metrics into a unified ensemble approach, our objective is to detect and identify free-riders effectively. Extensive simulations and experimental results highlight the robustness of our approach. © 2024 IEEE.

关键词： Federated learning

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Fairness and Sparsity within Rashomon sets: Enumeration-Free Exploration and Characterization

arXiv

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arXiv 2025年

作者： Langlade, Lucas Ferry, Julien Laberge, Gabriel Vidal, Thibaut Ecole nationale des ponts et chaussées Paris France CIRRELT & SCALE-AI Chair in Data-Driven Supply Chains Department of Mathematics and Industrial Engineering Polytechnique Montréal Canada Department of Computer Engineering and Software Engineering Polytechnique Montréal Canada

We introduce an enumeration-free method based on mathematical programming to precisely characterize various properties such as fairness or sparsity within the set of "good models", known as Rashomon set. This approach is generically applicable to any hypothesis class, provided that a mathematical formulation of the model learning task exists. It offers a structured framework to define the notion of business necessity and evaluate how fairness can be improved or degraded towards a specific protected group, while remaining within the Rashomon set and maintaining any desired sparsity level. We apply our approach to two hypothesis classes: scoring systems and decision diagrams, leveraging recent mathematical programming formulations for training such models. As seen in our experiments, the method comprehensively and certifiably quantifies trade-offs between predictive performance, sparsity, and fairness. We observe that a wide range of fairness values are attainable, ranging from highly favorable to significantly unfavorable for a protected group, while staying within less than 1% of the best possible training accuracy for the hypothesis class. Additionally, we observe that sparsity constraints limit these trade-offs and may disproportionately harm specific subgroups. As we evidenced, thoroughly characterizing the tensions between these key aspects is critical for an informed and accountable selection of models. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Assessing the Security of GitHub Copilot's Generated Code - A Targeted Replication Study

Assessing the Security of GitHub Copilot's Generated Code - ...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Vahid Majdinasab Michael Joshua Bishop Shawn Rasheed Arghavan Moradidakhel Amjed Tahir Foutse Khomh Department of Computer and Software Engineering Polytechnique Montreal Canada School of Mathematical and Computational Sciences Massey University New Zealand Information & Communication Technology Group UCOL - Te Pukenga New Zealand

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

AI-powered code generation models have been developing rapidly, allowing developers to expedite code generation and thus improve their productivity. These models are trained on large corpora of code (primarily sourced from public repositories), which may contain bugs and vulnerabilities. Several concerns have been raised about the security of the code generated by these models. Recent studies have investigated security issues in AI-powered code generation tools such as GitHub Copilot and Amazon Code Whisperer, revealing several security weaknesses in the code generated by these tools. As these tools evolve, it is expected that they will improve their security protocols to prevent the suggestion of insecure code to developers. This paper replicates the study of Pearce et al., which investigated security weaknesses in Copilot and uncovered several weaknesses in the code suggested by Copilot across diverse scenarios and languages (Python, C, and Verilog). Our replication examines Copilot's security weaknesses using newer versions of Copilot and CodeQL (the security analysis framework). The replication focused on the presence of security vulnerabilities in Python code. Our results indicate that, with the improvements in newer versions of Copilot, the percentage of vulnerable code suggestions has reduced from 36.54% to 27.25%. Nonetheless, it remains evident that the model still suggests insecure code.

关键词： Productivity Codes Protocols Reviews Filtering software Security

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