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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Fahimeh Hajizadeh Loïc Alavoine Tarek Ould-Bachir Frédéric Sirois Jean Pierre David Department of Electrical Engineering Polytechnique Montréal Montreal QC Canada Department of Computer and Software Engineering MOTCE Laboratory Polytechnique Montréal Montreal QC Canada

The advancements in aircraft technology, particularly the increased electrification of aircraft, have introduced complexities in designing and integrating electrical systems. Hardware-in-the-loop (HIL) simulation is recognized as a crucial tool in the aerospace industry, utilizing Field-Programmable Gate Arrays (FPGAs) for their high-speed data processing and low latency. This paper presents a method for accurate real-time simulation of cable harnesses in modern aircrafts, employing Frequency-Dependent Network Equivalent (FDNE) models and FPGAs. The implementation methodology and results of the FPGA-based HIL simulation are presented and discussed. Various equivalent models with different data types are evaluated to determine achievable time-step and resource consumption. Results show the effectiveness of the proposed method in achieving sub-microsecond time-step.

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Question Selection for Multi-Modal Code Search Synthesis using Probabilistic Version Spaces

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IEEE Transactions on software engineering 2025年

作者： Wu, Jiarong Jiang, Yanyan Wei, Lili Xu, Congying Cheung, Shing-Chi Xu, Chang The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong McGill University Department of Electrical and Computer Engineering Montreal Canada Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing China

Searching the occurrences of specific code patterns (code search) is a common task in software engineering, and programming by example (PBE) techniques have been applied to ease customizing code patterns. However, previous PBE tools only synthesize programs meeting the input-output examples, which may not always align with the user intent. To bridge this gap, this paper proposes Excalibur, a multi-modal (example and natural language description) and interactive synthesizer for code search. Excalibur ensures that the generated programs are correct for the provided examples (soundness) and include the user-intended program (bounded completeness). Furthermore, Excalibur helps the user identify the user-intended program through question-answer interaction. To minimize the required interaction efforts, question selection is crucial. To improve question selection for code search, we propose probabilistic version spaces (ProbVS), in which the user-intended program’s probability is high and others are low. ProbVS combines traditional version spaces for compactly representing extensive programs and large language models (on the user-provided natural language description) for adjusting programs’ probabilities to align with users’ intents. Extensive experiments on a benchmark of 44 tasks demonstrated the effectiveness of Excalibur and ProbVS and demystified how ProbVS affects probability distributions and how the configurable parameters affect ProbVS. © 1976-2012 IEEE.

关键词： Normal distribution

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I-Police-An Intelligent Policing system Through Public Area Surveillance 12

I-Police-An Intelligent Policing System Through Public Area ...

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12th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2021

作者： Jayakody, Anuradha Lokuliyana, Shashika Dasanayaka, Kolitha Iddamalgoda, Ashvinda Ganepola, Isuri Dissanayake, Anupama Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665400664

Technology and law enforcement are now commonly used hand in hand to improve public safety. Most police departments only use CCTV cameras at a few major intersections for remote surveillance. The public is waiting too long for emergency response lines, therefore using new technologies to improve the current policing system has become one of the police's main goals. The paper presents a coordinated framework that could identify the subtleties of violations via an automated public area surveillance system, specifically the weapon-related crimes and vehicle accidents, which are then disassembled, analyzed, and stored for future inspections. The trained models are aimed to reduce the false positives of incident detection. The weapon detection system had the best average precision (93.8%) by using YOLOv5 while the vehicle accident detection system resulted in the best average precision (94.9%) by using YOLOv4. The system is tested against the collected set of CCTV footage and tested how long it takes to create a notification which is the main goal of this system. Notification is generated in less than 5 seconds after an incident is detected. The evidence collection engine developed with MATLAB delivered the expected with an accuracy of 97% making the extracted evidence reliable to both vehicle accidents and crime scenes. Additionally, the framework provides an effective and efficient communication channel through which the residents can report crimes to regular parties. © 2021 IEEE.

关键词： Crime

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Spatial Consistency and Feature Diversity Regularization in Transfer Learning for Fine-Grained Visual Categorization

Spatial Consistency and Feature Diversity Regularization in ...

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2022 IEEE International Conference on systems, Man, and Cybernetics, SMC 2022

作者： Dai, Zhigang Chen, Junying Mian, Ajmal South China University of Technology Key Laboratory of Big Data and Intelligent Robot Ministry of Education School of Software Engineering Guangzhou China The University of Western Australia Department of Computer Science Perth Australia

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Fine-grained visual categorization is challenged by limited training data by localizing discriminative regions and learning diverse features. We propose an effective regularization method that simultaneously imposes spatial consistency and feature diversity on CNN feature maps from a unified perspective. The former guides different feature map channels to concentrate collaboratively on the discriminative areas while the latter ensures that the feature maps are diverse. The proposed method does not require additional supervision, and leverages the covariance matrix of multi-channel feature maps to regularize the loss at the last convolutional layer where the semantic information is the richest. This allows the influence to be backpropagated to update all convolutional layers. We perform experiments using four network architectures for transfer learning from two source domains to three target domains, and demonstrate that our regularization method improves accuracy in all different settings. The proposed regularization method achieves state-of-the-art performance on CUB-200-2011, Stanford-Cars, and Stanford-Dogs datasets with 89.8%, 94.6%, and 88.5% accuracy, respectively. © 2022 IEEE.

关键词： Covariance matrix

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Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition

arXiv

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arXiv 2024年

作者： Wang, Zihan Song, Siyang Luo, Cheng Deng, Songhe Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China Shenzhen Institute of Artificial Intelligence and Robotics for Society China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China University of Leicester United Kingdom Monash University Australia

Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions. While none of existing approach thoroughly model such hierarchical inter-dependencies among AUs, this paper proposes to comprehensively model multi-scale AU-related dynamic and hierarchical spatio-temporal relationship among AUs for their occurrences recognition. Specifically, we first propose a novel multi-scale temporal differencing network with an adaptive weighting block to explicitly capture facial dynamics across frames at different spatial scales, which specifically considers the heterogeneity of range and magnitude in different AUs’ activation. Then, a two-stage strategy is introduced to hierarchically model the relationship among AUs based on their spatial distribution (i.e., local and cross-region AU relationship modelling). Experimental results achieved on BP4D and DISFA show that our approach is the new state-of-the-art in the field of AU occurrence recognition. Our code is publicly available at https://***/CVI-SZU/MDHR. © 2024, CC BY.

关键词： Face recognition

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PLAME: Piecewise-Linear Approximate Measure for Additive Kernel SVM (Extended abstract)

PLAME: Piecewise-Linear Approximate Measure for Additive Ker...

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International Conference on Data engineering

作者： Tsz Nam Chan Zhe Li Leong Hou U Reynold Cheng Department of Computer Science and Software Engineering Shenzhen University Alibaba Cloud Department of Computer and Information Science State Key Laboratory of Internet of Things for Smart City University of Macau Department of Computer Science and Musketeers Foundation Institute of Data Science The University of Hong Kong

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Additive Kernel SVM has been extensively used in many applications, including human activity detection and pedestrian detection. Since training an additive kernel SVM model is very time-consuming, which is not scalable to largescale datasets, many efficient solutions have been developed in the past few years. However, most of the existing methods normally fail to achieve one of these three important conditions which are (1) low classification error, (2) low memory space, and (3) low training time. In order to simultaneously fulfill these three conditions, we develop the new piecewise-linear approximate measure (PLAME) for training additive kernel SVM models. Experimental results verify that this approach can achieve the best trade-off between accuracy, memory space, and training time compared with different types of state-of-the-art methods.

关键词： Training Support vector machines Additives Pedestrians Accuracy Data engineering Kernel

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Unlearnable 3D point clouds: class-wise transformation is all you need 24

Unlearnable 3D point clouds: class-wise transformation is al...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Xianlong Wang Minghui Li Wei Liu Hangtao Zhang Shengshan Hu Yechao Zhang Ziqi Zhou Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (纸本)9798331314385

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples; (ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC.

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Middlebox Detection: Evasion and Effectiveness

Middlebox Detection: Evasion and Effectiveness

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International Black Sea Conference on Communications and Networking (BlackSeaCom)

作者： Vahab Pournaghshband Hassan Pournaghshband Eric Vance Computer Science Department University of San Francisco San Francisco USA Software Engineering Department Kennesaw State University Atlanta USA Jet Propulsion Laboratory NASA Pasadena USA

ISBN: (数字)9798350351859

ISBN: (纸本)9798350351866

Network middleboxes are crucial for the security and efficiency of modern Internet infrastructure, performing various tasks that necessitate robust detection and localization tools. Traditional detection strategies often overlook the adaptability of these middleboxes, which can actively avoid detection. To address this, our paper examines how adversaries can manipulate network traffic to undermine detection tools, focusing on strict priority queueing (SPQ) as an example. We introduce an adversary model that uses SPQ to mask discriminatory traffic handling from detection tools. Simulations show that under specific conditions, a middlebox can successfully evade detection by standard SPQ detection tools.

关键词： Location awareness Adaptation models Focusing Telecommunication traffic Middleboxes Security Task analysis

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Enabling Generalized Zero-Shot Vulnerability Classification

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Hu, Jinghao Guo, Jinsong Luo, Chen Hu, Yang Lanzinger, Matthias Li, Zhanshan Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Software Changchun130012 China University College London Department of Computer Science United Kingdom Amazon United States United Kingdom University of Oxford Department of Computer Science United Kingdom Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Changchun130012 China

Regarding computer security, the growth of code vulnerability types presents a persistent challenge. These vulnerabilities, which may cause severe consequences, necessitate precise classification for effective mitigation. However, the rapid emergence of new vulnerability types complicates the classification process. Traditional methodologies, which often involve human expertise and the manual labeling or generation of example instances, are not only resource-intensive but also struggle to adapt to the dynamic nature of these vulnerabilities. This paper introduces VulnSense, an innovative method that harnesses the capabilities of Generalized Zero-Shot Learning (GZSL) to address the vulnerability classification problem. VulnSense learns about unseen vulnerability classes from the descriptions of these unseen classes, while not requiring to see any instances of these unseen classes. Specifically, VulnSense learns from three main resources: (1) seen classes with labeled code instances;(2) descriptions of these seen classes;and (3) descriptions of "unseen"classes which have no labeled instances. Our experiments underscore VulnSense's superiority over existing GZSL methods in classifying instances of unseen classes. Concurrently, it maintains a performance parity with traditional labeled-example based learning methods in classifying instances of seen vulnerabilities. VulnSense demonstrates the potential of using GZSL for vulnerability classification, while also highlighting challenges that inspire future work. © 2004-2012 IEEE.

关键词： Zero-shot learning

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AI Dynamics: Revolutionizing Organizational Skills and Contributing to Enhanced Business in Morocco 9th

AI Dynamics: Revolutionizing Organizational Skills and Contr...

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9th International Conference on Smart City Applications, SCA 2024

作者： Chegri, Badre Eddine Aiche, Karim Berhich, Asmae Mettiti, Abderrahman El Rhajbal, Zineb Saadane, Rachid Chehri, Abdellah Mohammed V University in Rabat Rabat Morocco AMIPS Research Team E3S Research Center Computer Science Department EMI Mohammed V University in Rabat Rabat Morocco LRIT-GSCM Faculty of Sciences University Mohammed V Rabat Morocco Laboratory Engineering System SIRC/LAGeS EHTP Casablanca Morocco Department of Mathematics and Computer Science Royal Military College Kingston Canada

ISBN: (纸本)9783031886522

This study investigates the transformative effects of Artificial Intelligence (AI) on organizational skills within the Moroccan context. Utilizing a mixed-methods approach, combining qualitative interviews and a quantitative survey across diverse industries, the research explores AI's impact on crucial competencies like adaptability, communication, decision-making, and collaboration. The findings reveal a nuanced picture of both positive and challenging aspects of AI adoption, emphasizing the need for strategic planning and skill development. Focused on Morocco, this research contributes unique insights into the socio-economic implications of AI in a developing economy. The study's outcomes provide actionable recommendations for policymakers, business leaders, and educators, highlighting the imperative of fostering an environment that maximizes the potential of AI for organizational skills enhancement, ultimately bolstering overall competitiveness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Strategic planning

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