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International Mining software Repositories, MSR

作者： Samuel Abedu Laurine Menneron SayedHassan Khatoonabadi Emad Shihab Department of Computer Science and Software Engineering Concordia University Montreal Canada CESI Graduate School of Engineering Saint-Nazaire France

ISBN: (数字)9798331501839

ISBN: (纸本)9798331501846

software repositories contain a wealth of data about the software development process, such as source code, documentation, issue tracking, and commit histories. However, accessing and extracting meaningful insights from these data is timeconsuming and requires technical expertise, posing challenges for software practitioners, especially non-technical stakeholders like project managers. Existing solutions, such as software engineering chatbots leveraging LLMs, have demonstrated significant limitations in retrieving relevant data to answer user questions. In this paper, we introduce RepoChat, a web-based tool designed to answer repository-related questions by synergizing LLMs with knowledge graphs. RepoChat operates in two steps: (1) the Data Ingestion step, where it collects and constructs a knowledge graph from repository metadata, such as commits, issues, files and users; and (2) the Interaction step, where it takes the users natural language question, translates it into graph queries using an LLM, executes these queries against the knowledge graph, and generates a user-friendly response to the question using the query results as context. We evaluate RepoChat by conducting a user study in which participants asked a series of repository-related questions representing common developer intents. RepoChat achieved an accuracy of $90 \%$, correctly answering 36 out of 40 questions, demonstrating its effectiveness in accurately retrieving relevant information to answer user’s questions. RepoChat is available at https://***/, and its source code is accessible on Zenodo [1].

关键词： Translation Source coding Knowledge graphs Metadata Chatbots software Data mining Stakeholders software development management software engineering

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Delay-Doppler Domain Pulse Design for OTFS-NOMA

Delay-Doppler Domain Pulse Design for OTFS-NOMA

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IEEE International Conference on Communications Workshops, ICC

作者： Michel Kulhandjian Hovannes Kulhandjian Gunes Karabulut Kurt Halim Yanikomeroglu Department of Electrical and Computer Engineering Rice University Houston TX USA Department of Electrical and Computer Engineering California State University Fresno CA USA Department of Electrical Engineering Polytechnique Montreal Montreal QC Canada Systems and Computer Engineering Carleton University Ottawa ON Canada

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

We address the challenge of developing an orthogonal time-frequency space (OTFS)-based non-orthogonal multiple access (NOMA) system where each user is modulated using orthogonal pulses in the delay-Doppler domain. Building upon the concept of the sufficient (bi)orthogonality train-pulse [1], we extend this idea by introducing Hermite functions, known for their orthogonality properties. Simulation results demonstrate that our proposed Hermite functions outperform the traditional OTFS-NOMA schemes, including power-domain (PDM) NOMA and code-domain (CDM) NOMA, in terms of bit error rate (BER) over a high-mobility channel. The algorithm's complexity is minimal, primarily involving the demodulation of OTFS. The spectrum efficiency of Hermite-based OTFS-NOMA is $K$ times that of OTFS-CDM-NOMA scheme, where $K$ is the spreading length of the NOMA waveform.

关键词： NOMA Time-frequency analysis Conferences Simulation Bit error rate Buildings Complexity theory

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Rapid Thermal Modeling and Discharge Characterization for Accurate Lithium-ion Battery Core Temperature Estimation 48

Rapid Thermal Modeling and Discharge Characterization for Ac...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Samanta, Akash Huynh, Alvin Rutovic, Emmanuel Williamson, Sheldon Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada AESIM. Tech Montreal Canada

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

The increasing events of fire and catastrophic failure of lithium-ion batteries (LIBs) due to inaccurate thermal information or improper thermal management based on surface temperature data only, once again indicates the necessity of accurate core temperature information. In view of this, a rapid, more convenient but accurate thermal modeling technique for LIB is introduced in this paper using SIMBA. SIMBA is a powerful new generation power electronics simulation software powered by Python. A second-order electro-thermal model-based core temperature estimation scheme is developed in SIMBA. A wide range of battery test data is used for experimental validation of the model. Further, four standard drive cycle profiles are used to assess the impact of discharge current on the core temperature of LIB. The electro-thermal model allows estimating the core temperature from external measurements including voltage, current, and surface temperature without installing a physical core temperature sensor which is practically challenging. The proposed modeling technique is extremely convenient and the model is computationally efficient and simple enough to be implemented in practical purpose lithium-ion battery management systems. © 2022 IEEE.

关键词： Lithium-ion batteries

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Is visual explanation with Grad-CAM more reliable for deeper neural networks? a case study with automatic pneumothorax diagnosis

arXiv

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arXiv 2023年

作者： Qiu, Zirui Rivaz, Hassan Xiao, Yiming Department of Computer Science and Software Engineering Concordia University Montreal Canada Department of Electrical and Computer Engineering Concordia University Montreal Canada

While deep learning techniques have provided the state-of-the-art performance in various clinical tasks, explainability regarding their decision-making process can greatly enhance the credence of these methods for safer and quicker clinical adoption. With high flexibility, Gradient-weighted Class Activation Mapping (Grad-CAM) has been widely adopted to offer intuitive visual interpretation of various deep learning models’ reasoning processes in computer-assisted diagnosis. However, despite the popularity of the technique, there is still a lack of systematic study on Grad-CAM’s performance on different deep learning architectures. In this study, we investigate its robustness and effectiveness across different popular deep learning models, with a focus on the impact of the networks’ depths and architecture types, by using a case study of automatic pneumothorax diagnosis in X-ray scans. Our results show that deeper neural networks do not necessarily contribute to a strong improvement of pneumothorax diagnosis accuracy, and the effectiveness of GradCAM also varies among different network architectures. Copyright © 2023, The Authors. All rights reserved.

关键词： Network architecture

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FocalErrorNet: Uncertainty-aware focal modulation network for inter-modal registration error estimation in ultrasound-guided neurosurgery

arXiv

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arXiv 2023年

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

In brain tumor resection, accurate removal of cancerous tissues while preserving eloquent regions is crucial to the safety and outcomes of the treatment. However, intra-operative tissue deformation (called brain shift) can move the surgical target and render the pre-surgical plan invalid. Intra-operative ultrasound (iUS) has been adopted to provide real-time images to track brain shift, and inter-modal (i.e., MRI-iUS) registration is often required to update the pre-surgical plan. Quality control for the registration results during surgery is important to avoid adverse outcomes, but manual verification faces great challenges due to difficult 3D visualization and the low contrast of iUS. Automatic algorithms are urgently needed to address this issue, but the problem was rarely attempted. Therefore, we propose a novel deep learning technique based on 3D focal modulation in conjunction with uncertainty estimation to accurately assess MRI-iUS registration errors for brain tumor surgery. Developed and validated with the public RESECT clinical database, the resulting algorithm can achieve an estimation error of 0.59±0.57 mm. © 2023, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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Towards multi-modal anatomical landmark detection for ultrasound-guided brain tumor resection with contrastive learning

arXiv

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arXiv 2023年

Homologous anatomical landmarks between medical scans are instrumental in quantitative assessment of image registration quality in various clinical applications, such as MRI-ultrasound registration for tissue shift correction in ultrasound-guided brain tumor resection. While manually identified landmark pairs between MRI and ultrasound (US) have greatly facilitated the validation of different registration algorithms for the task, the procedure requires significant expertise, labor, and time, and can be prone to inter- and intra-rater inconsistency. So far, many traditional and machine learning approaches have been presented for anatomical landmark detection, but they primarily focus on mono-modal applications. Unfortunately, despite the clinical needs, inter-modal/contrast landmark detection has very rarely been attempted. Therefore, we propose a novel contrastive learning framework to detect corresponding landmarks between MRI and intra-operative US scans in neurosurgery. Specifically, two convolutional neural networks were trained jointly to encode image features in MRI and US scans to help match the US image patch that contain the corresponding landmarks in the MRI. We developed and validated the technique using the public RESECT database. With a mean landmark detection accuracy of 5.88±4.79 mm against 18.78±4.77 mm with SIFT features, the proposed method offers promising results for MRI-US landmark detection in neurosurgical applications for the first time. © 2023, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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Joint Machine-Transporter Scheduling for Multistage Jobs with Adjustable Computation Time

arXiv

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arXiv 2023年

作者： Khateri, Koresh Beltrame, Giovanni Department of Computer and Software Engineering Polytechnique Montréal Chem. de Polytechnique 2500 Canada

This paper presents a scalable solution with adjustable computation time for the joint problem of scheduling and assigning machines and transporters for missions that must be completed in a fixed order of operations across multiple stages. A battery-operated multi-robot system with a maximum travel range is employed as the transporter between stages and charging them is considered as an operation. Robots are assigned to a single job until its completion. Additionally, The operation completion time is assumed to be dependent on the machine and the type of operation, but independent of the job. This work aims to minimize a weighted multi-objective goal that includes both the required time and energy consumed by the transporters. This problem is a variation of the flexible flow shop with transports, that is proven to be NP-complete. To provide a solution, time is discretized, the solution space is divided temporally, and jobs are clustered into diverse groups. Finally, an integer linear programming solver is applied within a sliding time window to determine assignments and create a schedule that minimizes the objective. The computation time can be reduced depending on the number of jobs selected at each segment, with a trade-off on optimality. The proposed algorithm finds its application in a water sampling project, where water sampling jobs are assigned to robots, sample deliveries at laboratories are scheduled, and the robots are routed to charging stations. © 2023, CC BY.

关键词： Economic and social effects

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Learning Multi-Agent Multi-Machine Tending by Mobile Robots

arXiv

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arXiv 2024年

作者： Abdalwhab, Abdalwhab Beltrame, Giovanni Kahou, Samira Ebrahimi St-Onge, David The Department of Mechanical Engineering ETS Montreal Canada Mila - Quebec Artificial Intelligence Institute Canada Department of Computer and Software Engineering Polytechnique Montréal Canada University of Calgary Canada CIFAR AI Canada

Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborative robots can tackle that can also highly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. We introduce a multi-agent multi-machine tending learning framework by mobile robots based on Multi-agent Reinforcement Learning (MARL) techniques with the design of a suitable observation and reward. Moreover, we integrate an attention-based encoding mechanism into the Multi-agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine tending scenarios. Our model (AB-MAPPO) outperforms MAPPO in this new challenging scenario in terms of task success, safety, and resource utilization. Furthermore, we provided an extensive ablation study to support our design decisions. © 2024, CC BY.

关键词： Mobile robots

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An Approach for Auto Generation of Labeling Functions for software engineering Chatbots

arXiv

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arXiv 2024年

作者： Alor, Ebube Abdellatif, Ahmad Khatoonabadi, SayedHassan Shihab, Emad Lab The Department of Computer Science & Software Engineering Concordia University MontrealQC Canada The Department of Electrical & Software Engineering University of Calgary CalgaryAB Canada

software engineering (SE) chatbots are increasingly gaining attention for their role in enhancing development processes. At the core of chatbots are the Natural Language Understanding platforms (NLUs), which enable them to comprehend and respond to user queries. Before deploying NLUs, there is a need to train them with labeled data. However, acquiring such labeled data for SE chatbots is challenging due to the scarcity of high-quality datasets. This challenge arises because training SE chatbots requires specialized vocabulary and phrases not found in typical language datasets. Consequently, chatbot developers often resort to manually annotating user queries to gather the data necessary for training effective chatbots, a process that is both time-consuming and resource-intensive. Previous studies propose approaches to support chatbot practitioners in annotating users’ posed queries. However, these approaches require human intervention to generate rules, called labeling functions (LFs), that identify and categorize user queries based on specific patterns in the data. To address this issue, we propose an approach to automatically generate LFs by extracting patterns from labeled user queries. We evaluate the effectiveness of our approach by applying it to the queries of four diverse SE datasets (namely AskGit, MSA, Ask Ubuntu, and Stack Overflow) and measure the performance improvement gained from training the NLU on the queries labeled by the generated LFs. We find that the generated LFs effectively label data with AUC scores of up to 85.3%, and NLU’s performance improvement of up to 27.2% across the studied datasets. Furthermore, our results show that the number of LFs used to generate LFs affects the labeling performance. We believe that our approach can save time and resources in labeling users’ queries, allowing practitioners to focus on core chatbot functionalities rather than on manually labeling queries. Copyright © 2024, The Authors. All rights reserved.

关键词： software engineering

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A Robust Preprocessing Method for Measuring Image Visual Quality Using Log-Polar FFT Features 8th

A Robust Preprocessing Method for Measuring Image Visual Qua...

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8th International Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023

作者： Chen, Guang Yi Krzyzak, Adam Valev, Ventzeslav Department of Computer Science and Software Engineering Concordia University MontrealQCH3G 1M8 Canada Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia1113 Bulgaria

ISBN: (纸本)9783031495519

To register two images, existing methods normally estimate the parameters of an affine transform, and then perform an inverse affine transform to the test image by using the estimated parameters. Any metric can then be applied to the reference image and the normalized image. In this paper we propose a new method for measuring visual image quality which circumvents the need to estimate the parameters of the affine transform. Instead, we use the log-polar transform and the fast Fourier transform (FFT) to extract features that are invariant to translation, rotation, and scaling. We apply the existing structural similarity index measure (SSIM) to the two invariant feature images, where no inverse transform is needed. Experimental results show that our proposed method outperforms the standard metric, the mean SSIM (MSSIM), significantly in terms of visual quality scores. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image visual quality quality metrics rotation invariance scale invariance translation invariance

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