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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Imran Khan Fazlullah Khan Asad Ali Bandar Alshawi Ryan Alturki Mohammad Wedyan Computer Science Department Abdul Wali Khan University Mardan KPK Pakistan Faculty of Science and Engineering School of Computer Science University of Nottingham Ningbo China Zhejiang China Department of Computer Science University of Engineering and Technology Peshawar KPK Pakistan Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Sciences Faculty of Information Technology and Computer Sciences Yarmouk University (YU) Irbid Jordan

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Vehicular ad hoc networks (VANETs) take vital role in intelligent transportation systems, but they face challenges due to dynamic network topology, impacting communication efficiency, especially with increasing active nodes. Security and privacy concerns are paramount, necessitating authentication to ensure only legitimate vehicles access the network. Secure vehicular clustering offers a solution, yet the number of clusters affects network dependability and efficiency. To tackle this, we present a secure clustering mechanism using Bat optimization algorithm, mimicking echolocation behavior to optimize clusters. We introduce an authentication technique for cluster heads to admit approved members, enhancing vehicular authentication. Simulations using MATLAB evaluate our approach, showing improved performance in end-to-end delay, overhead, and cluster head count compared to similar methods.

关键词： Privacy Network topology Authentication Vehicular ad hoc networks Security Vehicle dynamics Optimization Intelligent transportation systems Faces Matlab

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Lessons Learned from Persona Usage in Requirements engineering Practice

Lessons Learned from Persona Usage in Requirements Engineeri...

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IEEE International Conference on Requirements engineering

作者： Devi Karolita John C. Grundy Tanjila Kanij Jennifer McIntosh Humphrey O. Obie Department Informatics Engineering Faculty of Engineering Palangka Raya University Palangka Raya Indonesia Department of Software Systems and Cybersecurity Faculty of IT Monash University Melbourne Australia School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia Melbourne School of Population and Global Health University of Melbourne Melbourne Australia Telstra Health Melbourne Australia

ISBN: (数字)9798350395112

ISBN: (纸本)9798350395129

Personas, as a tool for characterising end-users, are widely utilised in requirements engineering (RE), primarily to enhance the understanding of end-users and their needs. However, the efficacy of persona usage in RE practice remains inadequately explored. To bridge this gap, we conducted an interview-based study with 22 international RE experts. These experts shared their experiences in utilising personas for RE-related tasks. Through thematic analysis of the collected insights, we propose recommendations for the effective creation and integration of personas in RE tasks. Our suggestions for efficient persona usage in RE emphasise the necessity of direct human interaction throughout persona development, while also con-sidering project constraints. We recommend creating personas that reflect the needs and preferences of persona consumers regarding the information included and the presentation style. Additionally, fostering organisational awareness of the benefits of personas in understanding targeted end-users is crucial. Lastly, we highlight the importance of the interpersonal skills required by requirements engineers to support the successful incorporation of personas in RE.

关键词： Bridges Requirements engineering Task analysis

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Online Dynamic Cyber-Attack Diagnosis in Power Electronics Systems Based on Few-Shot Learning

Online Dynamic Cyber-Attack Diagnosis in Power Electronics S...

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IEEE General Meeting Power& Energy Society

作者： Qi Li Jinan Zhang Jin Ye Liang Zhao Tianqi Hong Jamie Lian Beshoy Morkos Hongyue Sun Feraidoon Zahiri Chris Farnell Alan Mantooth WenZhan Song Center for Cyber-Physical Systems University of Georgia Athens Georgia College of Computing and Software Engineering Kennesaw State University Marietta GA 402 CMXG/MXDEO Robins Air Force Base Warner Robins GA Department of Electrical Engineering University Arkansas Fayetteville AR

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

With increasing exposure to software-based sensing and control, power electronics systems are facing higher risks of cyber-physical attacks. To ensure system stability and minimize potential economic losses, it is critical to monitor the operating states and detect those attacks at the early stage. However, anomaly detection and diagnosis of attacks are still challenging, especially when labeled anomaly data is difficult or even infeasible to obtain. To overcome this problem, we propose a Few-Shot Learning (FSL) based approach for cyber-attack diagnosis leveraging the waveform data. To the best of our knowledge, this work is the first attempt at leveraging FSL for cyber-attack diagnosis in power electronics systems. Extensive experimental results demonstrate that our proposed approach can achieve comparable diagnosis accuracy with the state-of-the-art data-driven methods using less than 0.04% of the training samples.

关键词： Training Adaptation models Electric potential Power electronics Data models Stability analysis Sensors Few shot learning Cyberattack Monitoring

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Reinforced Learning for Label-Efficient 3D Face Reconstruction

Reinforced Learning for Label-Efficient 3D Face Reconstructi...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hoda Mohaghegh Hossein Rahmani Hamid Laga Farid Boussaid Mohammed Bennamoun School of Computer Science and Software Engineering University of Western Australia Australia Department of Computing and Communications Lancaster University England Information Technology Discipline Murdoch University Australia School of Engineering Electrical Electronic and Computer Engineering University of Western Australia Australia

3D face reconstruction plays a major role in many human-robot interaction systems, from automatic face authentication to human-computer interface-based entertainment. To improve robustness against occlusions and noise, 3D face reconstruction networks are often trained on a set of in-the-wild face images preferably captured along different viewpoints of the subject. However, collecting the required large amounts of 3D annotated face data is expensive and time-consuming. To address the high annotation cost and due to the importance of training on a useful set, we propose an Active Learning (AL) framework that actively selects the most informative and representative samples to be labeled. To the best of our knowledge, this paper is the first work on tackling active learning for 3D face reconstruction to enable a label-efficient training strategy. In particular, we propose a Reinforcement Active Learning approach in conjunction with a clustering-based pooling strategy to select informative view-points of the subjects. Experimental results on 300W-LP and AFLW2000 datasets demonstrate that our proposed method is able to 1) efficiently select the most influencing view-points for labeling and outperforms several baseline AL techniques and 2) further improve the performance of a 3D Face Reconstruction network trained on the full dataset.

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A Framework for Analyzing Context-Oriented Programming Languages

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SSRN 2022年

作者： Elyasaf, Achiya Cardozo, Nicolás Sturm, Arnon Software and Information Systems Engineering Department Ben-Gurion University of the Negev Beer-Sheva Israel Systems and Computing Engineering Department Universidad de los Andes Bogota Colombia

Context-aware systems keep on emerging as an intrinsic part of everyday activities. To cope with such situations, programming languages are extended to support the notion of context developing the context-oriented programming paradigm. Although context-oriented programming languages exist for over 15 years, they were tested for their suitability in developing context-aware systems only to a limited extent. In this paper, we propose a framework for analyzing context-oriented languages from that viewpoint. Using this framework, we are able to examine context definition and activation, reasoning capabilities, process aspects of how to work with the language, and other pragmatic considerations. To demonstrate the use of the framework, we apply it to analyze three context-oriented programming languages: ServalCJ, Subjective-C, and COBPjs which represent the major Context-Oriented Programming themes. We evaluate the capabilities of each language using the purposed framework. Developers of context-oriented programming languages can use the framework to improve their languages and the associated development and supporting tools. Furthermore. such analysis can support users of context-oriented programming languages in deciding the language that best suits their needs. © 2022, The Authors. All rights reserved.

关键词： C (programming language)

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Improved Traffic Filtering for IoT Security 5

Improved Traffic Filtering for IoT Security

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5th IEEE International Conference on Electro-computing Technologies for Humanity, NIGERCON 2024

作者： Ovili, Henry Peter Okpor, Margaret Nwanze, N.E. Olayinka, Tosin Ojei, Emmanuel Nwankwo, Wilson Delta State University of Science and Technology Department of Information Systems & Technology Ozoro Nigeria Delta State University of Science and Technology Department of Cyber Security Ozoro Nigeria Delta State University of Science and Technology Faculty of Engineering Marine Engineering Department Ozoro Nigeria Wellspring University Department of Computing Benin City Nigeria Delta State University of Science and Technology Department of Software Engineering Ozoro Nigeria

ISBN: (纸本)9798331542559

This research presents an advanced traffic filtering technique using a Support Vector Machine (SVM) model to enhance IoT network security, achieving 85% accuracy, with high precision (87%) and recall (96%) in identifying legitimate traffic. However, the model struggles with neutral traffic, highlighting the need for further refinement, and offers a scalable solution for real-time dynamic analysis, with future work focusing on improving neutral traffic classification and integrating additional features to enhance performance. © 2024 IEEE.

关键词： Anomaly detection

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching 39

DAMPER: A Dual-Stage Medical Report Generation Framework wit...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Diagnosis

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Integrating Mobile Edge computing into Unmanned Aerial Vehicle Networks: An Sdn-Enabled Architecture

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IEEE Internet of Things Magazine 2021年第4期4卷 18-23页

作者： Lin, Chuan Han, Guangjie Shah, Syed Bilal Hussain Zou, Yan Gou, Linfeng Software College Northeastern University China Hohai University Department of Internet of Things Engineering China School of Computing and Mathematics Manchester Metropolitan University United Kingdom School of Power and Energy Shenyang Aircraft Design and Research Institute China Northwestern Polytechnical University China

Over the past few years, we have witnessed an exponential increase in demand for seamless and continuous Internet access to online services - multimedia services, online business, gaming, scientific analysis, and so on. With the significant advances in computing and communication techniques of edge devices, the mobile edge computing (MEC) paradigm has emerged and has been demonstrated to be more feasible and efficient in providing wireless Internet access or computing in some scenarios compared to the traditional cloud computing platform. Furthermore, the promotion and popularization of unmanned aerial vehicles (UAVs) in recent years provides the promis of ubiquitous data sensing, leading to an era of aerial MEC. In this article, we first present four representative architectures for the UAV-based MEC system. Then we survey some recent research products in this area. In particular, we argue that the UAV can be regarded as a MEC unit and propose the paradigm of a multi-UAV-based MEC network. Furthermore, we employ software-defined networking to improve the network's scalability and controllability and present the framework of the software-defined UAV network. Based on the proposed framework, we introduce the concept of a 'smart function table' that can be applied to execute the network operation in the proposed framework. After evaluating our proposed framework, especially concerning the cooperative flying ability and control delay, several challenging issues and future research suggestions are presented to guide this direction. © 2018 IEEE.

关键词： Mobile edge computing

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PolyLite: A Lightweight Transformer With Multi-Scale Attention for Colorectal Polyp Segmentation

PolyLite: A Lightweight Transformer With Multi-Scale Attenti...

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International Conference on software, Knowledge Information, Industrial Management and Applications (SKIMA)

作者： Khaled ELKarazle Valliappan Raman Caslon Chua Patrick Then Faculty of Engineering Computing and Science Swinburne University of Technology (Sarawak Campus) Sarawak Malaysia Department of Artificial Intelligence and Data Science Coimbatore Institute of Technology Tamil Nadu India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia

The urgent need for early detection and intervention in colorectal cancer highlights the importance of automating colorectal polyp detection. Despite the use of deep learning techniques for polyp detection from medical images, these methods often struggle with issues such as skewed data distribution and class imbalances. In this study, we introduce PolyLite, a lightweight transformer-based model designed to balance performance and efficiency. PolyLite features a custom SegFormer encoder that takes advantage of its lightweight design, and a decoder that uses a multi-scale attention network (MA-NET) to achieve an optimal balance between accuracy and efficiency. To improve accuracy and reduce false negatives, we incorporate a reverse attention layer that effectively counteracts input mask attention during training. This refocuses the model's efforts to better learn non-polyp regions. PolyLite undergoes a two-stage training process, beginning with pre-training on the ImageNet dataset and then fine-tuning on the Kvasir-SEG dataset. We conduct rigorous evaluations on three complex datasets: CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB. Empirical results demonstrate PolyLite's superiority over existing techniques, as evidenced by its performance in terms of Intersection over Union and Dice scores. This validates its effectiveness and scalability in polyp segmentation, making complex and computationally intensive architectures unnecessary.

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Research on perimeter intrusion monitoring technology of high-speed railway based on multi-source heterogeneous data fusion 24

Research on perimeter intrusion monitoring technology of hig...

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4th International Conference on Big Data, Artificial Intelligence and Risk Management, ICBAR 2024

作者： Li, Wentao Yu, Tao Zhang, Bo Fu, Huijin Graduate Department China Academy of Railway Sciences Beijing China School of Automation and Software Engineering Shanxi University Shanxi Taiyuan China Institute of Computing Technologies China Academy of Railway Sciences Group Co. Ltd. Beijing China

ISBN: (纸本)9798400709753

Aiming at the defects of a single protection technology for high-speed railway perimeter intrusion, such as poor environmental adaptability and difficult technical shortcomings to improve, this paper conducted an in-depth study on railway perimeter intrusion monitoring technology based on multi-source heterogeneous data fusion, and designed a monitoring technology scheme of system coupling and collaborative linkage integration. In order to solve the technical shortcomings of a single sensor, the whole scene high-precision monitoring under bad weather is realized. Due to their geographical characteristics, the tunnel entrance and the road bridge over the railway are prone to foreign body intrusion caused by geological disasters or human factors, so they are the key protection areas for the perimeter intrusion of high-speed railway. In this paper, a high-speed rail perimeter intrusion monitoring technology based on multi-source heterogeneous data fusion is studied for two typical running scenarios of tunnel entrance and upper span railway bridge. The test results show that the proposed technology can meet the demand of accurate and timely railway perimeter intrusion alarm. © 2024 Copyright held by the owner/author(s).

关键词： Data fusion High-speed railway Multi-source heterogeneous Object detection Perimeter intrusion

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