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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Monica Li Bruno Belzile Ali Imran Lionel Birglen Giovanni Beltrame David St-Onge Department of Software and Computer Engineering Polytechnique Montreal 2900 boul. Edouard Montpetit Montreal Quebec Canada Kinova Robotics 4333 Bd de la Grande-Allée Boisbriand Quebec Canada Department of Mechanical Engineering Ecole de technologie superieure 1100 Notre-Dame W. Montreal Quebec Canada Department of Mechanical Engineering Polytechnique Montreal 2900 boul. Edouard Montpetit Montreal Quebec Canada

This paper provides an overview of the latest trends in robotics research and development, with a particular focus on applications in manufacturing and industrial settings. We highlight recent advances in robot design, including cutting-edge collaborative robot mechanics and advanced safety features, as well as exciting developments in perception and human-swarm interaction. By examining recent contributions from Kinova, a leading robotics company, we illustrate the differences between industry and academia in their approaches to developing innovative robotic systems and technologies that enhance productivity and safety in the workplace. Ultimately, this paper demonstrates the tremendous potential of robotics to revolutionize manufacturing and industrial operations, and underscores the crucial role of companies like Kinova in driving this transformation forward.

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Classification of Anomalies in Telecommunication Network KPI Time Series

arXiv

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arXiv 2023年

作者： Bordeau–Aubert, Korantin Whatley, Justin Nadeau, Sylvain Glatard, Tristan Jaumard, Brigitte Department of Computer Science and Software Engineering Concordia University MontrealQC Canada EXFO Inc. MontrealQC Canada

The increasing complexity and scale of telecommunication networks have led to a growing interest in automated anomaly detection systems. However, the classification of anomalies detected on network Key Performance Indicators (KPI) has received less attention, resulting in a lack of information about anomaly characteristics and classification processes. To address this gap, this paper proposes a modular anomaly classification framework. The framework assumes separate entities for the anomaly classifier and the detector, allowing for a distinct treatment of anomaly detection and classification tasks on time series. The objectives of this study are (1) to develop a time series simulator that generates synthetic time series resembling real-world network KPI behavior, (2) to build a detection model to identify anomalies in the time series, (3) to build classification models that accurately categorize detected anomalies into predefined classes (4) to evaluate the classification framework performance on simulated and real-world network KPI time series. This study has demonstrated the good performance of the anomaly classification models trained on simulated anomalies when applied to real-world network time series data. © 2023, CC BY.

关键词： Time series

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Predicting software Functional Size Using Natural Language Processing: An Exploratory Case Study

Predicting Software Functional Size Using Natural Language P...

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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Hüseyin Ünlü Samet Tenekeci Can Çiftçi İbrahim Baran Oral Tunahan Atalay Tuna Hacaloğlu Burcu Musaoğlu Onur DemirǶrs Department of Computer Engineering Izmir Institute of Technology Izmir Turkiye Bilgi Grubu Izmir Turkiye Department of Information Systems Engineering Atilim University Ankara Turkiye Department of Software and IT Engineering Ecole de Technologie Superieure Montreal Canada Siskon Software & Automation Izmir Turkiye

ISBN: (数字)9798350380262

ISBN: (纸本)9798350380279

software Size Measurement (SSM) plays an essential role in software project management as it enables the acquisition of software size, which is the primary input for development effort and schedule estimation. However, many small and medium-sized companies cannot perform objective SSM and software Effort Estimation (SEE) due to the lack of resources and an expert workforce. This results in inadequate estimates and projects exceeding the planned time and budget. Therefore, organizations need to perform objective SSM and SEE using minimal resources without an expert workforce. In this research, we conducted an exploratory case study to predict the functional size of software project requirements using state-of-the-art large language models (LLMs). For this aim, we fine-tuned BERT and BERT_SE with a set of user stories and their respective functional size in COSMIC Function Points (CFP). We gathered the user stories included in different project requirement documents. In total size prediction, we achieved 72.8% accuracy with BERT and 74.4% accuracy with BERT_SE. In data movement-based size prediction, we achieved 87.5% average accuracy with BERT and 88.1% average accuracy with BERT_SE. Although we use relatively small datasets in model training, these results are promising and hold significant value as they demonstrate the practical utility of language models in SSM.

关键词： Training Schedules Accuracy Estimation Project management Companies Predictive models Size measurement software Natural language processing

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Generative Adversarial Networks for Robust Anomaly Detection in Noisy IoT Environments

Generative Adversarial Networks for Robust Anomaly Detection...

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IEEE International Conference on Communications (ICC)

作者： Adel Abusitta Talal Halabi Ahmed Saleh Bataineh Mohammad Zulkernine Department of Computer and Software Engineering Poly technique Montreal Montreal QC Canada Department of Computer Science Université Laval Québec QC Canada School of Computing Queen's University Kingston ON Canada

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The Internet of Things (IoT) enables us to collect and process vast amounts of data in real time. However, the security of IoT devices and networks is highly susceptible to cyber attacks that threaten data integrity and service availability. Furthermore, due to the diverse nature of data collected from numerous nodes in IoT systems and the disturbances occurring within them, detecting anomalous activities and compromised nodes is considerably more challenging than in conventional computer systems. Therefore, it is crucial to develop robust and dependable anomaly detection methods to identify and remove malicious and/or unwanted data, which ensures their exclusion from IoT-powered applications and data analytics. To achieve this, this paper proposes a Generative Adverserial Networks (GAN)-based anomaly detection for IoT systems. The proposed model enables the autoencoder - using the adversarial training of GAN - to learn a better representation of IoT data, making it robust against noisy and changing environments. Based on experiments with real-world IoT datasets, the proposed framework has shown to improve the accuracy of detecting malicious traffic in IoT and surpass state-of-the-art anomaly detection models.

关键词： Training Data analysis Data integrity Generative adversarial networks Real-time systems Data models Internet of Things

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Understanding Quantum software engineering Challenges An Empirical Study on Stack Exchange Forums and GitHub Issues

arXiv

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arXiv 2022年

作者： El aoun, Mohamed Raed Li, Heng Khomh, Foutse Openja, Moses Department of Computer Engineering and Software Engineering Polytechnique Montréal MontréalQC Canada

With the advance of quantum computing, quantum software becomes critical for exploring the full potential of quantum computing systems. Recently, quantum software engineering (QSE) becomes an emerging area attracting more and more attention. However, it is not clear what are the challenges and opportunities of quantum computing facing the software engineering community. This work aims to understand the QSE-related challenges perceived by developers. We perform an empirical study on Stack Exchange forums where developers post-QSE-related questions & answers and Github issue reports where developers raise QSE-related issues in practical quantum computing projects. Based on an existing taxonomy of question types on Stack Overflow, we first perform a qualitative analysis of the types of QSE-related questions asked on Stack Exchange forums. We then use automated topic modeling to uncover the topics in QSE-related Stack Exchange posts and GitHub issue reports. Our study highlights some particularly challenging areas of QSE that are different from that of traditional software engineering, such as explaining the theory behind quantum computing code, interpreting quantum program outputs, and bridging the knowledge gap between quantum computing and classical computing, as well as their associated opportunities. Copyright © 2022, The Authors. All rights reserved.

关键词： software engineering

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Classification Method for Malware Detection on Android Devices

Classification Method for Malware Detection on Android Devic...

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Future Technologies Conference, FTC 2020

作者： Fournier, Arthur El Khoury, Franjieh Pierre, Samuel Department of Computer and Software Engineering Polytechnique Montreal Station Centre-Ville P.O. Box 6079 MontrealQCH3C 3A7 Canada

ISBN: (纸本)9783030630911

Due to its popularity and open-source nature, Android is the mobile platform that has been targeted the most by malware. Android allows downloading and installation of apps from other unofficial market places. This aims to steal personal information or to control the users’ devices. More specifically, malware attacks private and financial information on mobile payment applications and networks, and thus is very sensitive. In this paper, we propose an efficient malware detection model for Android devices centered on mobile payment applications. This model is based on client/server architecture to reduce the heavy computations of data on the mobile device and doing the processing remotely on the server. Our approach aims to develop an optimized algorithm based on machine learning models to extract the permissions and for better classification of the new installed applications on Android devices. The Random Forest regression algorithm with the numerical ranging from −100 (benign) to 100 (malware) gives good results and an accuracy close to 100%. Therefore, the proposed model is suitable to secure the Android devices in the mobile commerce context. © 2021, Springer Nature Switzerland AG.

关键词： Decision trees

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Work in Progress: Congestion Control in mmWave Fluctuating Scenarios in 5G-A/6G

Work in Progress: Congestion Control in mmWave Fluctuating S...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Jorge Ignacio Sandoval Sandra Céspedes Department of Electrical Engineering University of Chile Santiago Chile Dept. of Computer Science & Software Eng Concordia University Montreal Canada

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

The development of the next generation of cellular networks, 5G-Advanced and 6G (5G-A/6G), offers higher speeds, sub-millisecond latency, and providing wider coverage, which are key to meeting the requirements of new and demanding applications. As such, there are several challenges in managing and leveraging this opportunity to ensure that channel conditions do not impact the application due to mobility and obstructions. 5G introduces the use of millimeter wave (mmWave), and the commercial deployment of this technology brings to light the issues related to propagation and mobility in high-frequency bands, and their impact on applications has begun to be studied by exploring the response of the congestion control algorithms (CCAs) in representative scenarios. However, the highly variable channel conditions in mmWave require that they be defined as fluctuating bandwidth scenarios. It is necessary to determine how the transport layer can take advantage of the maximum available capacity and not affect the applications, and whether the network promises are fulfilled. This work aims to design and evaluate a CCA capable of efficiently adapting to the fluctuating bandwidth of extreme condition scenarios of 5G-A/6G mobile networks and to compare it with the state-of-the-art algorithms. As a first result, a set of simulation-based evaluation using 5GLENA was discussed. A combination of CCAs and different radio link control (RLC) buffer sizes allows us to highlight the importance of defining appropriate metrics and including convergence times as part of the evaluation performance of the algorithms. Algorithms such as HighSpeed and New Reno are among the slowest to converge, while BBR is the fastest. The analysis and characterization of the algorithms will allow us to define the requirements for designing an Machine Learning (ML) based algorithm with optimal performance and better convergence times.

关键词： Measurement Machine learning algorithms Millimeter wave technology Bandwidth Machine learning Radio links Millimeter wave propagation Millimeter wave communication Next generation networking Convergence

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Fpa-GCN: enhancing aspect sentiment triplet extraction with feature-rich prediction-aware graph convolutional networks

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Applied Intelligence 2025年第9期55卷 1-29页

作者： Jiang, Haoyu Chen, Xiaoliang Miao, Duoqian Zhang, Hongyun Qin, Xiaolin Gu, Xu Lu, Peng Li, Xianyong School of Computer and Software Engineering Xihua University Chengdu P. R. China College of Electronic and Information Engineering Tongji University Shanghai P. R. China Department of Computer Science and Operations Research University of Montreal Montreal Canada Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu P. R. China

Aspect Sentiment Triplet Extraction (ASTE) is a critical yet challenging task within Aspect-Based Sentiment Analysis (ABSA), which is dedicated to discerning aspect terms, their corresponding opinion terms, and sentiment polarities. The applicability of ASTE spans a broad array of sectors, from e-commerce to social media analytics and customer feedback analysis, highlighting its versatility and significance across various industries. However, the prevailing research methodologies frequently fail to capture the slight interplay among the three essential sentiment elements, often resulting in a misalignment that hampers the establishment of accurate contextual relationships. These misalignments often occur as discrepancies in the model’s understanding of contextually related content, deviating from expected associations. To address these challenges, this paper introduces the Feature-Rich Prediction-Aware Graph Convolutional Network (Fpa-GCN) model, meticulously designed for the ASTE task. By harnessing the power of BERT for superior semantic feature extraction and utilizing these features through Biaffine attention, the Fpa-GCN model employs a multi-branch graph convolutional network to effectively encapsulate contextual relations. Additionally, the model incorporates Information Fusion and an advanced Gating mechanism, further enhancing triplet extraction precision. Rigorous evaluations on benchmark datasets affirm the Fpa-GCN model’s supremacy over existing state-of-the-art models, achieving significant F1 score improvements. Specifically, the model achieves gains of 1.31%, 0.11%, 3.7%, and 2.15% on the Restaurant14, Laptop14, Restaurant15, and Restaurant16 datasets, respectively, in experiment group $$ \varvec{D_{1}} $$ , and 1.66%, 0.08%, 4%, and 3.33% on the same datasets in experiment group $$ \varvec{D_{2}} $$ . These results underscore the Fpa-GCN model’s efficacy and its potential to set a new benchmark in the field of ABSA.

关键词： Natural language processing Aspect-based sentiment analysis Aspect sentiment triplet extraction Graph convolutional network Biaffine attention

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Automated computer Vision-based Detection of Solar Panel Defects Using a Thermal Camera Mobile Application

Automated Computer Vision-based Detection of Solar Panel Def...

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International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Hassan Hajjdiab Gasm ELbary Mohamed Salim Alzaabi Marah Alhalabi Abdalla Gad Maha Yaghi Mohammad Alkhedher Mohammed Ghazal Department of Computer Science and Software Engineering Concordia University Montreal Canada College of Engineering Abu Dhabi University Abu Dhabi UAE

Solar energy production has risen significantly globally in recent years. Solar Panels and Photovoltaic PV modules are the key components of solar energy production. Therefore, ensuring their health and efficiency and finding technologies to facilitate this became more important. However, the PV inspection costs money, time, and human resources. Thus, many technologies and techniques have been introduced to facilitate PV cell inspection, such as using a drone with a thermal camera. Moreover, as technology evolves every day, so do mobile devices. Nowadays, smartphones have brought many conveniences to our lives. Smartphones have become smarter with applications, and now they can be used as health monitoring devices, projectors, and more. One of smartphones’ most revolutionary features and technology is that they can come with a thermal camera or be compatible with thermal cameras. Thermal camera application varies and is diverse, from detecting heat on an object to inspecting photovoltaic PV cells. This paper proposes a novel system consisting of a thermal camera mobile app to detect the defects in PV modules and estimate the defect percentage. The result of this work has shown a potential research and further development area in the field of renewable energy and PV cell inspection.

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Masked Image Self-Learning and Knowledge Distillation for Source-Free Unsupervised Domain Adaptation in Crowd Counting

Masked Image Self-Learning and Knowledge Distillation for So...

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Image Processing, computer Vision and Machine Learning (ICICML), International Conference on

作者： Jia Zeng Chaoqun Ma Penghui Shao Anyong Qing Yang Wang School of Electrical Engineering Southwest Jiaotong University Chengdu China Department of Computer Science and Software Engineering Concordia University Montreal Canada

Unsupervised Domain Adaptation (UDA) techniques leverage labeled data from a source domain to adapt to unlabeled data from a target domain, offering a primary solution for addressing cross-domain challenges in crowd counting. However, in real-world scenarios, accessing source data during the model adaptation process within the target domain can be impractical due to privacy and security concerns. This gives rise to a practical yet challenging issue known as Source-Free Unsupervised Domain Adaptation (SFUDA), where model adaptation is limited to the utilization of an existing source model and unlabeled target data. To tackle this challenge, we propose an algorithm that combines mask image self-learning and knowledge distillation to address SFUDA in crowd counting effectively. Specifically, the student model uses masked images to self-learn at both feature and output levels, exploring clues and contextual relationships around masked image blocks to predict occluded areas. Furthermore, we employ knowledge distillation to boost the model’s performance within the teacher-student framework, encompassing feature extraction and density map generation stages. To mitigate cumulative errors, we stochastically recovery a subset of parameters from the source model. We conducted extensive experiments to compare our approach with a modified SFUDA algorithm, and the results demonstrate competitive performance across multiple datasets.

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