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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Tashman, Deemah H. Cherkaoui, Soumaya Hamouda, Walaa Senouci, Sidi Mohammed Polytechnique Montreal Department of Computer and Software Engineering Montreal Canada Concordia-University Department of Electrical and Computer Engineering Montreal Canada Univ. Bourgogne Franche Comté Drive Laboratory EA1859 NeversF58000 France

ISBN: (纸本)9798350370218

This paper investigates the physical-layer security (PLS) for an overlay cognitive radio network (CRN). Due to the unreliability of direct communication between the primary users (PUs), secondary users (SUs) are enlisted to facilitate communication between two PUs. As several SUs are available, a particular one is chosen to transmit the PUs' messages in addition to its own. This SU uses the time switching protocol to harvest power from the PU's signal. Additionally, an eavesdropper is available to capture the messages conveyed by the PUs. Given this, the SU receiver collects energy from the relayed messages through the power splitting protocol and utilizes this energy to transmit jamming signals in order to degrade the wiretap channel and improve the privacy of the PUs' communication. The signals' transmission is modeled more realistically under the assumption of cascaded Rayleigh channels. The performance of PLS is evaluated using the probability of a non-zero secrecy capacity and the intercept probability. Moreover, the SUs' Ergodic rate is studied to analyze the impact of this assistance on their transmission reliability. © 2023 IEEE.

关键词： Cascaded fading channels cooperative relay energy harvesting overlay cognitive radio networks physical-layer security

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An Empirical Study on Small-Sized Datasets Based on Eubank’s Optimal Spacing Theorem

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SN computer Science 2025年第1期6卷 1-19页

作者： Abedu, Samuel Mensah, Solomon Boafo, Frederick Department of Computer Science and Software Engineering Concordia University Montreal Canada Department of Computer Science University of Ghana Accra Ghana Department of Computer Science Lancaster University Ghana Accra Ghana

Conventional machine learning methods for software effort estimation (SEE) have seen an increase in research interest. Conversely, there are few research that try to evaluate how well deep learning techniques work in SEE. This can be attributed to the relatively small sizes of SEE datasets. The goal of the study is to establish a threshold for small-sized datasets in SEE. Additionally, it looks into how well certain deep learning and traditional machine learning models perform on small-sized datasets. From the body of existing literature, plausible SEE datasets are extracted and ranked along with their attributes and number of project cases. The ranking of the project instances is discretized into three classes (small, medium, and large) using Eubank’s optimal spacing theory. Using the leave-one-out cross-validation, each small-sized dataset is used to train two deep learning models and five conventional machine learning models. Each model’s ability to make predictions is evaluated using its mean absolute error. Results show that, on small-scale datasets, deep learning models outperform traditional machine learning models in terms of prediction accuracy, which contradicts what is previously known. Regularisation techniques can be used in conjunction with deep learning to address SEE. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Eubank’s optimal spacing theory Small-sized software effort estimation Traditional Machine learning

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Exploring the Potential of Llama Models in Automated Code Refinement: A Replication Study

Exploring the Potential of Llama Models in Automated Code Re...

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

作者： Genevieve Caumartin Qiaolin Qin Sharon Chatragadda Janmitsinh Panjrolia Heng Li Diego Elias Costa Dept of Computer Science and Software Engineering Concordia University Montreal Canada Dept of Computer Engineering and Software Engineering Polytechnique Montreal Montreal Canada

ISBN: (数字)9798331535100

ISBN: (纸本)9798331535117

Code reviews are an integral part of software development and have been recognized as a crucial practice for minimizing bugs and favouring higher code quality. They serve as an important checkpoint before committing code and play an essential role in knowledge transfer between developers. However, code reviews can be time-consuming and can stale the development of large software projects. In a recent study, Guo et al. assessed how ChatGPT3.5 can help the code review process. They evaluated the effectiveness of ChatGPT in automating the code refinement tasks, where developers recommend small changes in the submitted code. While Guo et al.'s study showed promising results, proprietary models like ChatGPT pose risks to data privacy and incur extra costs for software projects. In this study, we explore alternatives to ChatGPT in code refinement tasks by including two open-source, smaller-scale large language models: CodeLlama and Llama 2 (7B parameters). Our results show that, if properly tuned, the Llama models, particularly CodeLlama, can achieve reasonable performance, often comparable to ChatGPT in auto-mated code refinement. However, not all code refinement tasks are equally successful: tasks that require changing existing code (e.g., refactoring) are more manageable for models to automate than tasks that demand new code. Our study highlights the potential of open-source models for code refinement, offering cost-effective, privacy-conscious solutions for real-world software development.

关键词： Temperature measurement Data privacy Codes Reviews Large language models Documentation Chatbots software Data models software development management

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Dense Error Map Estimation for MRI-Ultrasound Registration in Brain Tumor Surgery Using Swin UNETR

Dense Error Map Estimation for MRI-Ultrasound Registration i...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Salari, Soorena Rasoulian, Amirhossein Rivaz, Hassan Xiao, Yiming Concordia University Department of Computer Science and Software Engineering MontrealQC Canada Concordia University Department of Electrical and Computer Engineering MontrealQC Canada

ISBN: (纸本)9798350346459

Early surgical treatment of brain tumors is crucial in reducing patient mortality rates. However, brain tissue deformation (called brain shift) occurs during the surgery, rendering pre-operative images invalid. As a cost-effective and portable tool, intra-operative ultrasound (iUS) can track brain shift, and accurate MRI-iUS registration techniques can update pre-surgical plans and facilitate the interpretation of iUS. This can boost surgical safety and outcomes by maximizing tumor removal while avoiding eloquent regions. However, manual assessment of MRI-iUS registration results in real-time is difficult and prone to errors due to the 3D nature of the data. Automatic algorithms that can quantify the quality of inter-modal medical image registration outcomes can be highly beneficial. Therefore, we propose a novel deep-learning (DL) based framework with the Swin UNETR to automatically assess 3D-patch-wise dense error maps for MRI-iUS registration in iUS-guided brain tumor resection and show its performance with real clinical data for the first time. © 2023 IEEE.

关键词： Grading

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Federated Learning-based MARL for Strengthening Physical-Layer Security in B5G Networks

Federated Learning-based MARL for Strengthening Physical-Lay...

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IEEE International Conference on Communications (ICC)

作者： Deemah H. Tashman Soumaya Cherkaoui Walaa Hamouda Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada Department of Electrical and Computer Engineering Concordia University Montreal Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

This paper explores the application of a federated learning-based multi-agent reinforcement learning (MARL) strategy to enhance physical-layer security (PLS) in a multi-cellular network within the context of beyond 5G networks. At each cell, a base station (BS) operates as a deep reinforcement learning (DRL) agent that interacts with the surrounding environment to maximize the secrecy rate of legitimate users in the presence of an eavesdropper. This eavesdropper attempts to intercept the confidential information shared between the BS and its authorized users. The DRL agents are deemed to be federated since they only share their network parameters with a central server and not the private data of their legitimate users. Two DRL approaches, deep Q-network (DQN) and Reinforce deep policy gradient (RDPG), are explored and compared. The results demonstrate that RDPG converges more rapidly than DQN. In addition, we demonstrate that the proposed method outperforms the distributed DRL approach. Furthermore, the outcomes illustrate the trade-off between security and complexity.

关键词： Privacy Base stations 5G mobile communication Aggregates Interference Deep reinforcement learning Complexity theory

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Deep Learning-Based Anomaly Detection in 5G Cellular Networks 15

Deep Learning-Based Anomaly Detection in 5G Cellular Network...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Farooq, Umar Hameed, Aroosa Leivadeas, Aris Lambadaris, Ioannis École de Technologie Supérieure Department of Software and It Engineering Montreal Canada Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350368833

The telecommunication industry saw a dramatic shift with the introduction of 5G technologies. This new cellular generation brought lightening speed, massive capacity, and better connectivity. Despite the many introduced opportunities, several challenges emerge at the same time, especially in the field of performance assurance. An example of such challenges is the rapid identification of network anomalies, which is critical for maintaining network performance and ensuring user satisfaction. Traditional techniques of detecting anomalies have fallen short given the unique operating requirements of 5G networks. To address this issue, this paper presents a novel technique applying deep learning for the detection of cellular network anomalies. Specifically, we leverage and exploit the capabilities of several Long-Short-Term-Memory (LSTM) and Artificial Neural Networks (ANN) flavors, such as LSTM with ANN and Bidirection-LSTM (BiLSTM) with ANN, to excel at identifying network anomalies for the preservation and efficiency of 5G networks. Our work includes extensive experimentation and the attained results show that our proposed models are overwhelmingly adapting to preserving the integrity of the network in the fast-paced, ever-changing realm of cellular networks. © 2024 IEEE.

关键词： 5G mobile communication systems

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A First Look at the Inheritance-Induced Redundant Test Execution 24

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

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44th ACM/IEEE International Conference on software engineering, ICSE 2024

作者： Kim, Dong Jae Chen, Tse-Hsun Peter Yang, Jinqiu Lab Concordia University MontrealQC Canada Concordia University Department of Computer Science and Software Engineering MontrealQC Canada

ISBN: (纸本)9798400702174

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. © 2024 ACM.

关键词： software testing

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Exploring the Potential of Llama Models in Automated Code Refinement: A Replication Study

arXiv

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arXiv 2024年

作者： Caumartin, Genevieve Qin, Qiaolin Chatragadda, Sharon Panjrolia, Janmitsinh Li, Heng Costa, Diego Elias Dept of Computer Science and Software Engineering Concordia University Montreal Canada Dept of Computer Engineering and Software Engineering Polytechnique Montreal Montreal Canada

Code reviews are an integral part of software development and have been recognized as a crucial practice for minimizing bugs and favouring higher code quality. They serve as an important checkpoint before committing code and play an essential role in knowledge transfer between developers. However, code reviews can be time-consuming and can stale the development of large software projects. In a recent study, Guo et al. assessed how ChatGPT3.5 can help the code review process. They evaluated the effectiveness of ChatGPT in automating the code refinement tasks, where developers recommend small changes in the submitted code. While Guo et al.’s study showed promising results, proprietary models like ChatGPT pose risks to data privacy and incur extra costs for software projects. In this study, we explore alternatives to ChatGPT in code refinement tasks by including two open-source, smaller-scale large language models: CodeLlama and Llama 2 (7B parameters). Our results show that, if properly tuned, the Llama models, particularly CodeLlama, can achieve reasonable performance, often comparable to ChatGPT in automated code refinement. However, not all code refinement tasks are equally successful: tasks that require changing existing code (e.g., refactoring) are more manageable for models to automate than tasks that demand new code. Our study highlights the potential of open-source models for code refinement, offering cost-effective, privacy-conscious solutions for real-world software development. Copyright © 2024, The Authors. All rights reserved.

关键词： Reviews

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Enhancing P4 Syntax to Support Extended Finite State Machines as Native Stateful Objects

Enhancing P4 Syntax to Support Extended Finite State Machine...

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IEEE Conference on Network Softwarization (NetSoft)

作者： Florent Allard Tarek Ould-Bachir Yvon Savaria Department of Computer and Software Engineering Polytechnique Montréal Montreal Canada Department of Electrical Engineering Polytechnique Montréal Montreal Canada

ISBN: (数字)9798350369588

ISBN: (纸本)9798350369595

The P4 language has proven to be a powerful tool for programming packet processing, but its original design did not intend to handle stateful processing effectively. This shortcoming stems from the fact that the network switches for which the language was designed have restricted memory capacities, which makes it challenging to manage complex stateful objects. As a result, P4’s syntax was not optimized for handling such objects. With contemporary networks increasingly relying on stateful processing and abstractions like Extended Finite State Machines (EFSMs), we propose extending P4’s syntax through an EFSM construct. This work aims to grant developers the ability to create streamlined and productive P4 programs that can effortlessly deal with stateful objects. This improvement holds great promise for expanding P4’s functionality and refining it to support stateful processing.

关键词： Pipelines software algorithms Refining Automata Syntactics Programming software

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Normal and Resilient Mode FPGA-based Access Gateway Function Through P4-generated RTL

Normal and Resilient Mode FPGA-based Access Gateway Function...

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International Workshop on Design of Reliable Communication Networks (DRCN)

作者： Mostafa Abbasmollaei Tarek Ould-Bachir Yvon Savaria Department of Computer and Software Engineering Department of Electrical Engineering Polytechnique Montréal Montreal Canada

ISBN: (数字)9798350348972

ISBN: (纸本)9798350348989

Customizing packet processing is crucial in the evolving network landscape, especially with the rise of 5G telecommunications and beyond. software-Defined Networking and programmable data planes, powered by the P4 language and FPGA-based platforms, offer dynamic network customization that can be used to implement resilient networks. With their high performance and programmability, FPGAs present cost-effective alternatives for diverse network applications, including offloading packet processing from servers. This paper introduces a configurable FPGA-based data plane implementing the Access Gateway Function (AGF). It offers a resilient operating mode to enhance network reliability and availability. The paper leverages the P4 language and the VitisNetP4 Intellectual Property to create RTL streams, enabling AGF on a pure FPGA target. The reported experimental results demonstrate that the proposed architecture can support 50K user flows with a resource utilization lower than 15% of that available in an Ultrascale+ FPGA (xcu280-fsvh2892-21-e). This leaves massive logic resources available to incorporate fault mitigation techniques and spare streams needed to enhance resiliency. Moreover, the presented workflow maintains an average latency of approximately 9 microseconds for each downstream or upstream packet.

关键词： 5G mobile communication Logic gates Telecommunications Telecommunication network reliability Servers Resource management software defined networking

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