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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Deng, Xiongwen Tang, Haoyu Jiang, Han Zheng, Qinghai Zhu, Jihua School of Software Engineering Xi’an Jiaotong University China School of Software Shandong University China College of Computer and Data Science Fuzhou University China

ISBN: (纸本)157735897X

Zero-shot Natural Language Video Localization (NLVL) aims to automatically generate moments and corresponding pseudo queries from raw videos for the training of the localization model without any manual annotations. Existing approaches typically produce pseudo queries as simple words, which overlook the complexity of queries in real-world scenarios. Considering the powerful text modeling capabilities of large language models (LLMs), leveraging LLMs to generate complete queries that are closer to human descriptions is a potential solution. However, directly integrating LLMs into existing approaches introduces several issues, including insensitivity, isolation, and lack of regulation, which prevent the full exploitation of LLMs to enhance zero-shot NLVL performance. To address these issues, we propose BTDP, an innovative framework for Boundary-aware Temporal Dynamic Pseudo-supervision pairs generation. Our method contains two crucial operations: 1) Boundary Segmentation that identifies both visual boundaries and semantic boundaries to generate the atomic segments and activity descriptions, tackling the issue of insensitivity. 2) Context Aggregation that employs the LLMs with a self-evaluation process to aggregate and summarize global video information for optimized pseudo moment-query pairs, tackling the issue of isolation and lack of regulation. Comprehensive experimental results on the Charades-STA and ActivityNet Captions datasets demonstrate the effectiveness of our BTDP method. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1476-1489页

作者： Shoufei Han Kun Zhu MengChu Zhou Xiaojing Liu Haoyue Liu Yusuf Al-Turki Abdullah Abusorrah College of Computer Science and Technology Nanjing University of Aeronautics and Astronauticsand also with the Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjing 211106China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand also with the K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing *** goal is to find a Pareto front and as many equivalent Pareto optimal solutions as *** some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions *** this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision *** first extend a latest single-objective fireworks algorithm to handle *** we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special ***,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization *** results show that the proposed algorithm is superior to compared algorithms in solving ***,its runtime is less than its peers'.

关键词： Adaptive strategy fireworks algorithm multimodal multiobjective optimization problems(MMOP)

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Class-attention video transformer for engagement prediction

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Multimedia Tools and Applications 2024年 1-20页

作者： Ai, Xusheng Sheng, Victor Li, Chunhua Yang, Han Cui, Zhiming Software and Service Outsourcing College Suzhou Vocational Institute of Industrial Technology 1 Zhineng Avenue Jiangsu Suzhou215104 China Department of Computer Science Texas Tech University 2500 Broadway LubbockTX79409 United States School of Electronics and Information Engineering Suzhou University of Science and Technology No.1 Kerui Road Jiangsu Suzhou215009 China

In this paper, we propose the Class Attention in Video Transformer (CavT), an end-to-end method designed to process both long and short variant-length videos for student engagement prediction. CavT introduces a single vector for class embedding and incorporates the Binary-Order Representatives Sampling (BorS) technique to augment the dataset by adding multiple video sequences. Our method outperforms the state-of-the-art with MSE values of 0.0495 on the EmotiW-EP and 0.0377 on the DAiSEE datasets, providing a robust and scalable solution for engagement prediction. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

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Detecting Deepfakes: Using CNN to Identify Manipulated Visual Media 8

Detecting Deepfakes: Using CNN to Identify Manipulated Visua...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sunanda, N. Shailaja, K. Babu, Jampani Satish Vamsi Krishna, K. Lakshmanpratap, N. Motupalli, Ravi Kanth and Ai&ds Hyderabad India Vasavi College of Engineering & Technology Department of Cse Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Vaddeswaram India Prowess Software Services Hyderabad India Mallareddy Engineering College Department of Ece Hyderabad India Vnr Vignana Jyothi Institute of Engineering & Technology Department of Cse Hyderabad India

ISBN: (纸本)9798350376425

Deepfake detection is a rapidly evolving field with significant implications for the integrity of visual media. This review explores techniques, challenges, and future directions in deepfake detection. Traditional image forensics techniques, deep learning models, and multi-modal fusion approaches are employed to differentiate real content from deepfakes. Addressing the challenges of evolving deepfake algorithms and the need for real-time detection, future research should focus on novel architectures, training strategies, temporal and contextual information, and ethical considerations. By advancing deepfake detection methods, researchers can contribute to combating the proliferation of synthetic content and preserving the authenticity of visual media in the digital age. © 2024 IEEE.

关键词： Convolutional neural networks

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A Safety Evaluation Method and System for a High Core Wall Dam Based on Construction Data

Journal of Intelligent Construction

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Journal of Intelligent Construction 2025年第1期3卷 1-14页

作者： Zicheng Wang Yingchi Mao Yaosheng Tan Qiaoling Shen Houlei Xu College of Computer Science and Software Engineering Hohai University Nanjing China PowerChina Kunming Engineering Corporation Limited Kunming China China Three Gorges Group Corporation Beijing China

In view of the complex operational conditions and dynamically changing safety risks associated with high core wall dams in the construction period, a comprehensive safety evaluation system for high core wall dams, which utilizes fuzzy theory and emergency response methods, has been developed. Considering the material zoning, load characteristics, and layout of the safety monitoring system specific to high core wall dams and considering the impacts of flood control capacity, foundation conditions, structural safety, operational status, and slope conditions, a safety evaluation method for high core wall dams on the basis of construction data has been proposed. The model dynamically determines the weights of evaluation factors via evaluation criteria and the term frequency-inverse document frequency (TF-IDF) method. It constructs a comprehensive evaluation subordination feature vector, calculates the degree of match between the current safety evaluation result and historical safety evaluation results, and identifies the matching operational conditions. Finally, on the basis of the risk analysis matrix, the risk level of each tier of evaluation factors is determined, and emergency response measures are formulated. This system provides an online monitoring platform for the operational safety of high core wall dams. This approach enhances the capacity for safety analysis and risk emergency decision-making in hydraulic and hydroelectric engineering.

关键词： Matched filters Dams Current measurement Inspection Emergency services Vectors Floods Risk analysis Personnel Monitoring

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DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models

arXiv

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arXiv 2025年

作者： Chang, Ying Shi, Xiaohu Zhao, Xiaohui Chen, Zhaohuang Ma, Deyin College of Software Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China College of Computer Science and Engineering Changchun University of Technology Changchun130000 China

Personalized federated learning (PFL) has garnered significant attention for its ability to address heterogeneous client data distributions while preserving data privacy. However, when local client data is limited, deep learning models often suffer from insufficient training, leading to suboptimal performance. Foundation models, such as CLIP (Contrastive Language-Image Pretraining), exhibit strong feature extraction capabilities and can alleviate this issue by fine-tuning on limited local data. Despite their potential, foundation models are rarely utilized in federated learning scenarios, and challenges related to integrating new clients remain largely unresolved. To address these challenges, we propose the Dual Prompt Personalized Federated Learning (DP2FL) framework, which introduces dual prompts and an adaptive aggregation strategy. DP2FL combines global task awareness with local data-driven insights, enabling local models to achieve effective generalization while remaining adaptable to specific data distributions. Moreover, DP2FL introduces a global model that enables prediction on new data sources and seamlessly integrates newly added clients without requiring retraining. Experimental results in highly heterogeneous environments validate the effectiveness of DP2FL’s prompt design and aggregation strategy, underscoring the advantages of prediction on novel data sources and demonstrating the seamless integration of new clients into the federated learning framework. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Smart Energy Management System Using Machine Learning

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computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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DAMFE-Net: A Few-shot Crack Segmentation Model Based on Transfer Learning for Earth Dams

DAMFE-Net: A Few-shot Crack Segmentation Model Based on Tran...

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IEEE International Conference on software engineering and Service sciences (ICSESS)

作者： Lili Zhang Zizun Wei Peng Jin College of Information Science and Engineering Hohai University Nanjing Jiangsu Province China College of Computer Science and Software Engineering Hohai University Nanjing Jiangsu Province China

ISBN: (数字)9798350389166

ISBN: (纸本)9798350389173

Some universal segmentation models have already shown good performance on public datasets, but they perform poorly in detecting cracks in earth dams. This deficiency stems from two aspects: one is the shortage of the dataset about cracks on earth dams, and the other is the complex and unstable appearance influenced by material and environmental conditions, making crack segmentation more susceptible to background noise. In this paper, we put forward a crack segmentation model based on transfer learning for earth dams with higher accuracy in few-shot conditions, named Dual Attention and Multi-scale Feature Enhancement Network (DAMFE-Net). It introduces a Spatial and Channel Squeeze and Excitation module (SCSE) to electively enhance key features for better extraction, and it incorporates the Multi-scale Feature Enhancement Module (MFEM) suppresses irrelevant features, and enhances crack feature extraction in earth dam images, reducing high background noise. The MFEM learns the weight of each scale feature map and fuses the semantic information of features at different levels, so it improves the representation of effective features. The F1-score for crack segmentation on earth dams with our model is 86.29%, surpassing DeepLab v3+ by 16.4%, which indicates that the proposed model can accurately segment cracks on earth dams.

关键词： Earth Dams Fuses Transfer learning software algorithms Semantics Feature extraction Background noise Surface cracks software engineering

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An Efficient Intrusion Detection System using Advanced Machine Learning Techniques in SDN for Healthcare System

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Asif, Muhammad Waseem Aqdus, Aqsa Amin, Rashid Chaudhry, Shehzad Ashraf Alsubaei, Faisal S. Iqbal, Sajid Department of Computer Science and IT The University of Lahore Lahore54000 Pakistan Department of Computer Science University of Chakwal Chakwal48800 Pakistan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey The Department of Cybersecurity College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

The quick advancement of healthcare systems necessitates robust and efficient network security keys to defend sensitive patient records and guarantee uninterrupted service delivery. The current IDS has many challenges, such as a high false positive rate, poor accuracy of detection, slow response to threats, and inability to scale well. This paper proposes an efficient and real-time intrusion detection system (IDS) using advanced machine learning techniques within a software-defined networking (SDN) framework specifically tailored for healthcare systems. The proposed architecture implements a Machine Learning (ML) model that combines the SVM and KNN to better identify malicious activities. Full sets of detection and mitigation capabilities are implemented to address different types of traffic in the network with the least interference. Through the different evaluation measures, the efficiency of the proposed model is assured. Network performance is determined by success rate queries, packet losses in each domain path, and the CPU being used by the system. Responsiveness is measured through delay metrics grounded on end-to-end delay, hop-to-hop packet delay, latency rate, and propagation delay. Moreover, model accuracy fidelity is reviewed via precision assessment, alpha (α) affecting the accuracy of the model, and confusion matrix with different techniques with the proposed hybrid SVM-KNN model. Last of all, a comparison of the security of the models in question strengthens the argument in favor of the proposed model. More specifically, flow and network topology diagrams are included to show how integration may be accomplished in linkage or merger with existing healthcare networks. The results also present a 30% overall advancement in detection and mitigation by presenting the hybrid SVM-KNN model to overcome other traditional models. This proposed model shows significant improvements not less than 20-30% improvement in CPU use, 30-50% reduction in end-to-end delay,

关键词： Decision trees

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Towards Quantifying Requirements Technical Debt for software Requirements concerning Veracity: A Perspective and Research Roadmap

arXiv

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arXiv 2024年

作者： Perera, Judith Tempero, Ewan Tu, Yu-Cheng Blincoe, Kelly Galster, Matthias School of Computer Science The University of Auckland Auckland1010 New Zealand Department of Electrical Computer and Software Engineering The University of Auckland Auckland1010 New Zealand Department of Computer Science and Software Engineering College of Engineering University of Canterbury Christchurch1010 New Zealand

software practitioners can make sub-optimal decisions concerning requirements during gathering, documenting, prioritizing, and implementing requirements as software features or architectural design decisions - this is captured by the metaphor 'Requirements Technical Debt (RTD).' In our prior work, we developed a conceptual model to understand the quantification of RTD and support its management. In this paper, we present our perspective and the vision to apply the lens of RTD to software requirements concerning veracity, i.e., requirements related to truth, trust, authenticity, and demonstrability in software-intensive systems. Our goal is to cultivate awareness of veracity as an important concern and eventually support the management of RTD for software requirements concerning veracity, what we term as 'Veracity Debt,' through its quantification. Copyright © 2024, The Authors. All rights reserved.

关键词： Requirements engineering

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