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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Liao, Guiqiu Jogan, Matjaz Koushik, Sai Eaton, Eric Hashimoto, Daniel A. University of Pennsylvania Pcaso Laboratory Department of Surgery United States University of Pennsylvania Department of Electrical and Systems Engineering United States University of Pennsylvania Department of Computer and Information Science United States

ISBN: (纸本)9798331510831

Weakly supervised video object segmentation (WSVOS) enables the identification of segmentation maps without requiring extensive annotations of object masks, relying instead on coarse video labels indicating object presence. WSVOS in surgical videos is, however, more challenging due to the complex interaction of multiple transient objects, such as surgical tools moving in and out of the surgical field. In this scenario, state-of-the-art WSVOS methods struggle to learn accurate segmentation maps. We address this problem by introducing ViDeo Spatio-Temporal disentanglement Networks (VDST-Net), a framework to disentangle complex spatio-temporal object interactions using semi-decoupled knowledge distillation to predict high-quality class activation maps (CAMs). A teacher network is designed to help a temporal-reasoning student network resolve activation conflicts, as the student leverages temporal dependencies when specifics about object location and timing in the video are not provided. We demonstrate the efficacy of our framework on a challenging surgical video dataset where objects are, on average, present in less than 60% of annotated frames, and compare our method to state-of-the-art methods on surgical data and on a public dataset commonly used to benchmark WSVOS. Our method outperforms state-of-the-art techniques and generates accurate segmentation masks under video-level weak supervision. Our code is available at: https://***/PCASOlab/VDST-net. © 2025 IEEE.

关键词： Image segmentation

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A Stealthy Backdoor Attack Method Based on Generative Adversarial Networks 5

A Stealthy Backdoor Attack Method Based on Generative Advers...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： Tan, Yayue Yu, Gao Zhang, Yaqi Tang, Yongqing Southwest University College of Computer and Information Science School of Software Chongqing China Payment core platform Ant Group Co. Ltd Department of Payment Platform Technology Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350374407

Deep neural networks have been widely and successfully applied to many critical tasks, such as image pattern recognition, natural language processing and autonomous driving, and large amounts of training data and increasing computational power are key factors in their success. Many studies in recent years have shown that deep neural network models face many practical security threats, such as adversarial attacks and backdoor attacks, which can have very serious consequences in the real world. Aiming at the problems of stealth, backdoor trigger design and universality of current backdoor attack methods, a novel method of invisible backdoor attack based on generative adversarial network is proposed. As a backdoor attack method based on data poisoning, inspired by image steganography, this method trains an encoder to generate poisoned samples through generative adversarial network to implement backdoor attack. The secret image used to guide the generation of backdoor triggers does not need to be carefully designed, and the encoder has good generalization ability across datasets. These features not only make its attack effective and stealthy, but also greatly reduce the cost of backdoor. Extensive experimental and analytical results show that the proposed backdoor attack method is effective, stealthy, sustainable and universal. © 2024 IEEE.

关键词： Generative adversarial networks

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Poster: Towards Accurate and Fast Federated Learning in End-Edge-Cloud Orchestrated Networks 43

Poster: Towards Accurate and Fast Federated Learning in End-...

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43rd IEEE International Conference on Distributed Computing Systems, ICDCS 2023

作者： Li, Mingze Sun, Peng Zhou, Huan Zhao, Liang Liu, Xuxun Leung, Victor C. M. College of Computer and Information Technology China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang China College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electronic and Information Engineering South China University of Technology Guangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350339864

This work proposes a novel three-layer federated learning (FL) framework with parameter selection and pre-synchronization (PSPFL) to achieve fast and accurate model training. The basic idea of PSPFL is that clients select partial model parameters for transmission and then base stations aggregate them cooperatively (i.e., pre-synchronization) and send the aggregated results to the server for global model update periodically. However, there is an intrinsic trade-off between parameter transmission overhead and model training loss. To strike a desirable balance between them, we investigate the optimal parameter pre-synchronization round and local training round under PSPFL. Specifically, we propose a Deep Q-Network (DQN)-based method to obtain the local training round and parameter pre-synchronization round. Finally, extensive experiments are conducted to evaluate the performance of the proposed method on commonly used datasets. The results show that the proposed method can reduce the sum of FL completion time and training loss by an average of 8.17%-18.82% compared to benchmarks. © 2023 IEEE.

关键词： Synchronization

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Artificial Intelligence-Based Centralized Resource Management Application for Distributed Systems 13

Artificial Intelligence-Based Centralized Resource Managemen...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Hettiarachchi, Lasal Sandeepa Jayadeva, Senura Vihan Bandara, Rusiru Abhisheak Vikum Palliyaguruge, Dilmi Arachchillage, Udara Srimath S. Samaratunge Kasthurirathna, Dharshana Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Due to the decentralized nature and emergence of new practices, tools, and platforms, microservices have become one of the most widely spread software architectures in the modern software industry. Furthermore, the advancement of software packaging tools like Docker and orchestration platforms such as Kubernetes enable developers and operation engineers to deploy and manage microservice applications more effectively and efficiently. However, establishing and managing microservice applications are still cumbersome due to the infrastructure configuration and array of disjoint tools that fail to understand the application's dynamic behavior. As a result, developers need to configure multiple tools and platforms to automate the deployment and monitoring process to provide the optimal deployment strategy for microservices. Even though many tools are available in the industry, the fully automated product which comprises deployment, monitoring, resiliency evaluation and optimization were not developed yet. In response to this issue, we propose an artificial intelligence (AI)-based centralized resource management tool, that provides an automated low latency container management, cluster metrics gathering, resiliency evaluation and optimal deployment strategy behave in dynamic nature. © 2022 IEEE.

关键词： Machine learning

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Attribute-Based Data Sharing Scheme with Flexible Search Functionality for Cloud-Assisted Autonomous Transportation System

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IEEE Transactions on Industrial Informatics 2023年第11期19卷 10977-10986页

作者： Xiong, Hu Wang, Hanxiao Meng, Weizhi Yeh, Kuo-Hui University of Electronic Science and Technology of China School of Information and Software Engineering Network and Data Security Key Laboratory of Sichuan Province Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark National Dong Hwa University Department of Information Management Hualien97401 Taiwan National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804201 Taiwan

The existing group public key encryption with equality test schemes could only support one-to-one data sharing and are not suitable for cloud-assisted autonomous transportation systems, which demand one-to-many data sharing. To tackle this problem efficiently, in this article, we put forward the group-attribute-based encryption with equality test (G-ABEET) scheme. The presented G-ABEET allows sensors equipped in vehicles to encrypt traffic data with an expressive access policy before sharing it. Only users with attributes required by the access policy ought to access the shared ciphertexts, thus achieving selective one-to-many data sharing. Meanwhile, the authorized cloud server could provide group users with equality tests over the ciphertexts, realizing ciphertext search ability. Furthermore, with the group mechanism, the G-ABEET scheme could resist offline message recovery attacks. Besides, in the standard model, we give rigorous security proof of the G-ABEET construction. The feasibility and efficiency of G-ABEET are demonstrated by experimental simulations. © 2005-2012 IEEE.

关键词： Cloud computing

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Leaf Guard: Detecting the Diseases in Banana Leaf 5

Leaf Guard: Detecting the Diseases in Banana Leaf

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Yathurshan, K. Barathy, S. Karini, G. Sajitha, P. Abeygunawardhna, Pradeep Rajapakse, Kaushalya Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Engineering Electrical & Electronic Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This research paper introduces 'Leaf Guard,' a mobile app designed for disease detection and management in banana trees, specifically tailored for Sri Lanka's agricultural landscape. Through the amalgamation of image processing, deep learning, and customized chemical recommendations, this app proficiently identifies early-stage diseases, offering tailored remedies. Catering to farmers and agricultural experts, 'Leaf Guard' provides an intuitive tool, revolutionizing disease management and bolstering sustainable banana cultivation practices. By seamlessly integrating these cutting-edge technologies, this research significantly advances disease detection methods, paving the way for environmentally friendly and economical interventions. The study's findings not only enrich the realm of disease detection technology but also hold promise for future innovations, fostering sustainable approaches to enhance banana cultivation. © 2023 IEEE.

关键词： Cultivation

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Enhancing Anxiety Prediction: Leveraging Machine Learning with Functional Design Metrics 21

Enhancing Anxiety Prediction: Leveraging Machine Learning wi...

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21st International Conference on Electrical engineering, Computing science and Automatic Control, CCE 2024

作者： Javed, Abqa Ajmal, Sahar Tajammal, Taliah Aslam, Muhammad Martinez-Enriquez, A.M. Sheikh, Usman Sadiq University of Engineering and Technology Department of Computer Science Lahore Pakistan National University of Computer and Emerging Sciences Department of Software Engineering Pakistan Virtual University of Pakistan Department of Computer Science and Information Technology Lahore Pakistan CINVESTAV-IPN D.F. Department of Computer Science Mexico Medicine Bronglais General Hospital Clinical Fellow in Respiratory SY23 1ER United Kingdom

ISBN: (纸本)9798350377545

Fear, nervousness, and social phobia all contribute to anxiety episodes reaching their peak in seconds. Using data collected from patient questionnaires, this study seeks to anticipate and diagnose anxiety disorders effectively and precisely using machine learning approaches. However, current research offers standardized metrics for assessing machine learning approaches throughout the design process. This work presents a systematic method for efficiently extracting literature from digital sources and emphasizes the need for data engineers to evaluate the efficiency of machine learning approaches to assure forecast quality. The International Organization for Standardization (ISO) 9126 standard is used to assess machine learning approaches for anxiety prediction to address this gap. This evaluation considers parameters such as functionality, accuracy, resilience, throughput, explainability, privacy, and correctness. These defined characteristics serve as the benchmark to measure the efficacy of machine learning approaches in predicting anxiety disorders. © 2024 IEEE.

关键词： Adversarial machine learning

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Google Scholar University Ranking Algorithm to Evaluate the Quality of Institutional Research

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computers, Materials & Continua 2023年第6期75卷 4955-4972页

作者： Noor Ul Sabah Muhammad Murad Khan Ramzan Talib Muhammad Anwar Muhammad Sheraz Arshad Malik Puteri Nor Ellyza Nohuddin Department of Computer Science Government College UniversityFaisalabad 38000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Software Engineering Government College UniversityFaisalabad 38000Pakistan Institute of IR4.0 Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia

Education quality has undoubtedly become an important local and international benchmark for education,and an institute’s ranking is assessed based on the quality of education,research projects,theses,and dissertations,which has always been ***,this research paper is influenced by the institutes ranking all over the *** data of institutes are obtained through Google Scholar(GS),as input to investigate the United Kingdom’s Research Excellence Framework(UK-REF)*** this purpose,the current research used a Bespoke Program to evaluate the institutes’ranking based on their *** bespoke program requires changes to improve the results by addressing these methodological issues:Firstly,Redundant profiles,which increased their citation and rank to produce false ***,the exclusion of theses and dissertation documents to retrieve the actual publications to count for ***,the elimination of falsely owned articles from scholars’*** accomplish this task,the experimental design referred to collecting data from 120 UK-REF institutes and GS for the present year to enhance its correlation analysis in this new *** data extracted from GS is processed into structured data,and afterward,it is utilized to generate statistical computations of citations’analysis that contribute to the ranking based on their *** research promoted the predictive approach of correlational ***,experimental evaluation reported encouraging results in comparison to the previous modi-fication made by the proposed *** paper discussed the limitations of the current evaluation and suggested the potential paths to improve the research impact algorithm.

关键词： Google scholar institutes ranking research assessment exercise research excellence framework impact evaluation citation data

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Arabic Offense Text Detection in Social Networks using Collaborative Machine Learning 6

Arabic Offense Text Detection in Social Networks using Colla...

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6th International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2024

作者： Alabdulrahman, Maria Alotaibi, Lara Latif, Ghazanfar Alghazo, Jaafar Prince Mohammad Bin Fahd University Software Engineering Department Khobar Saudi Arabia Prince Mohammad bin Fahd University Department of Computer Science Al-Khobar Saudi Arabia University of Minnesota Software Engineering and Information Technology Management Departments CrookstonMN56716 United States

ISBN: (纸本)9798331529987

In an era of abundant social media use and digital communication, the classification of Arabic social media content and user comments into relevant categories poses a significant challenge. The content in social network platforms often encompasses a wide range of topics, sentiments, and societal concerns, making it essential to develop effective methods for discerning and categorizing these expressions. In this research, a new dataset for Arabic offensive text is built along with the methodology proposed for classifying Arabic content into five categories collected from different social networking sites. Leveraging a diverse array of text representation techniques, including TF-IDF, Bag of Words (BoW), custom embeddings, and pre-trained embeddings from models such as Arabert and Mbert, a comprehensive evaluation is conducted based, shedding light on the optimal approach for tackling this complex problem. The findings reveal that the TF-IDF and BoW methods stand out with an impressive 89% accuracy, emphasizing their robustness in addressing the nuances of Arabic tweet classification across these sensitive topics. This study offers valuable insights into the challenges inherent in categorizing Arabic social media content, emphasizing the significance of the TF-IDF approach as an effective tool for this task. © 2024 IEEE.

关键词： Adversarial machine learning

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K-Banhatti Invariants Empowered Topological Investigation of Bridge Networks

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computers, Materials & Continua 2022年第12期73卷 5423-5440页

作者： Khalid Hamid Muhammad Waseem Iqbal Erssa Arif Yasir Mahmood Ahmad Salman Khan Nazri Kama Azri Azmi Atif Ikram Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of Lahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Faculty of Informatics and Applied Mathematics University Malaysia TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of Lahore54000Pakistan

Any number that can be uniquely determined by a graph is called graph *** the most recent twenty years’innumerable numerical graph invariants have been described and used for correlation *** the fast and advanced environment of manufacturing of networks and other products which used different networks,no dependable assessment has been embraced to choose,how much these invariants are connected with a network graph or molecular *** this paper,it will talk about three distinct variations of bridge networks with great capability of expectation in the field of computer science,chemistry,physics,drug industry,informatics,and mathematics in setting with physical and synthetic constructions and networks,since K-Banhatti invariants are newly introduced and have various forecast characteristics for various variations of bridge graphs or *** review settled the topology of bridge graph/networks of three unique sorts with three types of K-Banhatti *** concluded outcomes can be utilized for the modeling of interconnection networks of Personal computers(PC),networks like Local area network(LAN),Metropolitan area network(MAN)and Wide area network(WAN),the spine of internet and different networks/designs of PCs,power generation interconnection,bio-informatics and chemical structures.

关键词： Bridge networks invariants k-banhatti indices maple network graph molecular graph

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