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arXiv 2024年

作者： Liu, Jiao Sun, Zhu Feng, Shanshan Chen, Caishun Ong, Yew-Soon College of Computing & Data Science Nanyang Technological University Singapore Information Systems Technology and Design Singapore University of Technology and Design Singapore Centre for Frontier AI Research A*STAR The Institute of High Performance Computing A*STAR Singapore

In the evolutionary computing community, the remarkable language-handling capabilities and reasoning power of large language models (LLMs) have significantly enhanced the functionality of evolutionary algorithms (EAs), enabling them to tackle optimization problems involving structured language or program code. Although this field is still in its early stages, its impressive potential has led to the development of various LLM-based EAs. To effectively evaluate the performance and practical applicability of these LLM-based EAs, benchmarks with real-world relevance are essential. In this paper, we focus on LLM-based recommender systems (RSs) and introduce a benchmark problem set, named RSBench, specifically designed to assess the performance of LLM-based EAs in recommendation prompt optimization. RSBench emphasizes session-based recommendations, aiming to discover a set of Pareto optimal prompts that guide the recommendation process, providing accurate, diverse, and fair recommendations. We develop three LLM-based EAs based on established EA frameworks and experimentally evaluate their performance using RSBench. Our study offers valuable insights into the application of EAs in LLM-based RSs. Additionally, we explore key components that may influence the overall performance of the RS, providing meaningful guidance for future research on the development of LLM-based EAs in RSs. © 2024, CC BY.

关键词： Algorithmic languages

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A Provably Secure and Lightweight Access Control Protocol for EI-Based Vehicle to Grid Environment

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IEEE Internet of Things Journal 2023年第18期10卷 16650-16657页

作者： Shamshad, Salman Mahmood, Khalid Shamshad, Usman Hussain, Ibrar Hussain, Shafiq Das, Ashok Kumar The University of Lahore Department of Software Engineering Lahore54590 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan Riphah International University Riphah School of Computing and Innovation Lahore54000 Pakistan University of Sahiwal Department of Computer Science Sahiwal57000 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Virginia Modeling Analysis and Simulation Center SuffolkVA23435 United States

The energy Internet (EI) presents a novel paradigm for renewable energy distribution that utilizes communication and computing technologies to revolutionize the conventional intelligent transportation systems (ITSs) and power grid into a structure that provides assistance to open innovation. The EI offers sustainable and bidirectional transmission for the improvement and analysis of energy convention among electric vehicles (EVs) and service providers. To ensure efficient, reliable, and secure operation, EI must be safe from security attacks. Therefore, efficient and secure key negotiation is a significant issue for the EI-based Vehicle-to-Grid (V2G) architecture. Thus, to ensure the system's security, we have devised a robust scheme to facilitate a secure key agreement between the entities involved for the secure and efficient renewable energy distribution for the EI-based energy vehicles in V2G. The devised scheme's robustness is solicited through the widely accepted formal random or real (RoR) model. In addition, the informal security analysis is conducted on the devised scheme, which is evident that the designed scheme achieves all the required security features of EI-based ITS. Moreover, the performance evaluation results endorse that the designed scheme achieves the desired security and minimizes the communication, computation, and energy overhead by 29.33%, 27.94%, and 28.08% in comparison to the existing competitive schemes. © 2014 IEEE.

关键词： Vehicle-to-grid

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Cloud-Enhanced Machine Learning Models for Predictive Maintenance in Industrial IoT 2

Cloud-Enhanced Machine Learning Models for Predictive Mainte...

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2nd IEEE International Conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Usharani, R. Sivagami, V.M. Saravanan, K. Pushparani, S. Rekha, K. Sashi Department of Computational Intelligence School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Care College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798350384277

This study examines the implementation and impact of Cloud-Enhanced Machine Learning Models in the realm of Predictive Maintenance within Industrial Internet of Things (IIoT) settings. Emphasizing the integration of cloud computing with machine learning, the research focuses on how this synergy improves the accuracy and efficiency of maintenance predictions in industrial environments. Key metrics evaluated include prediction accuracy, operational efficiency, cost savings, and reduction in equipment downtime. The study demonstrates a significant increase in prediction accuracy, from 65% to 88%, attributed to the advanced data processing and analytical capabilities of cloud-enhanced machine learning. Operational efficiency saw marked improvements, with a 50% reduction in response times and a 25% increase in maintenance scheduling efficiency, leading to a 20% rise in asset utilization rates. Financially, the implementation led to a 37% reduction in annual maintenance costs and a 40% decrease in both the cost per downtime incident and long-term repair expenses. Most notably, the study recorded a 62.5% reduction in both the duration and frequency of downtime incidents. These results highlight the transformative potential of integrating cloud computing and machine learning in IIoT for predictive maintenance, showcasing significant advancements in predictive accuracy, operational efficiency, cost-effectiveness, and overall reduction in downtime. The study underscores the critical role of technological innovation in advancing industrial maintenance strategies, leading to more proactive, efficient, and economically viable operations. © 2024 IEEE.

关键词： Predictive maintenance

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Innovative Deep Learning Techniques for Solving Complex Computer Vision Challenges and Enhancing Pattern Recognition Accuracy

Innovative Deep Learning Techniques for Solving Complex Comp...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Jayachitra, T. Sathya, S. Ramesh, P.S. Jothikumar, R. Gouri, M.S. Hussain, Mohammed Waheeduddin Sharda University Department of Electrical Electronics and Communication Engineering Greater Noida India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tiruttani India Vellore Institute of Technology Department of Smart Computing Vellore India Shadan College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Baselios Mathews - Ii College of Engineering Department of Computer Science and Engineering Sasthamcotta Kollam India Shadan College of Engineering and Technology Department of Information Technology Peerancheru Hyderabad India

ISBN: (纸本)9798350375442

The proposed work provides an in-depth analysis of DL techniques to enhance pattern recognition accuracy in difficult computer vision applications. This background explains the present status of the problems in the area and motivates the development of a complex framework that blends innovative neural network topologies, state-of-the-art optimization approaches, and inventive data augmentation strategies. Among the methods are CNNs enhanced with residual blocks and attention mechanisms, which are implemented using the TensorFlow deep learning framework. The results demonstrate significant improvements over baseline models on a number of measures. Using the CIFAR-10 dataset, the proposed system achieves an impressive 92.5% accuracy and 92.1% F1-score for classification tasks. With an IoU of 78.2% and a mAP of 81.4% in object identification tasks, the system shows its robustness in accurately localizing and recognizing things within photos. These findings have practical ramifications for industries needing accurate visual identification, such autonomous driving and medical imaging, in addition to advancing theoretical knowledge. The efficacy of the architecture highlights its potential to serve as a benchmark for next deep learning studies and applications. © 2024 IEEE.

关键词： Stochastic systems

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Implementing Multi-factor Authentication in Cloud Services for Enhanced Security

Implementing Multi-factor Authentication in Cloud Services f...

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2024 IEEE International Conference on Recent Advances in science and Engineering Technology, ICRASET 2024

作者： Rekha, K. Sashi Sivagami, V.M. Usharani, R. Amutha, T. Pushparani, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Venkateswara College of Engineering Department of Information Technology Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India Care College of Engineering Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350388602

The cloud services which are now the most common data transmission and endanger organizations' confidential information, it's more and more visible that security of any data should be a main priority for companies. MFA, which consists of adopting multi factor approach to authentication and thereby provides multiple types of verification thus ensures effective security, is leveraged to ensure resource access. This paper proposes a full-fledged way of implementing MFA for cloud services in order to strengthen the security given and manage the constatoing risks. The proposed methodology consists of main stages like requirements determining, technology reviewing, risks assessment, technology design, integration with cloud services, users training, pilot deployment and testing, implementation and configuration, monitoring and maintenance as well as constant improvement. Through a systemic approach, the organizations can be equipped with the capability to personalize the MFA solutions according to their security goals, to bring the cloud environments on board, as well as to ensure that the organizations comply with the security standards as set by various governing bodies. This research adds a grain of salt to the development of cloud security good practices through offering a disciplined approach to MFA implementation, which allows partners to harden their cloud assets and protect against growing cyber threats. © 2024 IEEE.

关键词： Cloud security

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An Enhanced Model for ASR in the Medical Field

An Enhanced Model for ASR in the Medical Field

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IEEE International Conference on information Reuse and Integration (IRI)

作者： Wei-Chen Hsu Pei-Xu Lin Chi-Jou Li Hao-Yu Tien Yi-Huang Kang Pei-Ju Lee Department of Information Management National Chung Cheng University Chiayi Taiwan Department of Information Management National Sun Yat-sen University Kaohsiung Taiwan Institute of Data Science and Information Computing National Chung Hsing University Taichung Taiwan

ISBN: (数字)9798350351187

ISBN: (纸本)9798350351194

The application of speech recognition has been utilized to enhance work efficiency and even improve quality of life. Previous studies have indicated that using general-purpose speech recognition models for domain-specific recognition yields suboptimal results. Therefore, this study aims to enhance speech recognition models and optimize the correction of generated errors to improve the model’s performance in the medical field. This study employed the PubMed dataset to optimize the speech recognition language model and dictionary. Subsequently, the output results were subjected to error correction using the Bert2Bert, BioBert2BioBert, and BART model architectures. Finally, the model was tested using out-of-domain data from the MedDialog dataset to ensure its robustness. It is anticipated that in the future, the model architecture can be applied to the medical domain to enhance efficiency in scenarios such as medical consultations.

关键词： Dictionaries Text recognition Biological system modeling Speech recognition data science data models Robustness Rough surfaces Error correction

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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Two-Tier Clustering with Routing Protocol for IoT Assisted WSN

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Computers, Materials & Continua 2022年第5期71卷 3375-3392页

作者： AArokiaraj Jovith Mahantesh Mathapati M.Sundarrajan N.Gnanasankaran Seifedine Kadry Maytham N.Meqdad Shabnam Mohamed Aslam Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Rajarajeswari College of EngineeringBengaluru560074India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science Thiagarajar CollegeMadurai625019India Department of Applied Data Science Noroff University CollegeKristiansandNorway Al-Mustaqbal University College HillahIraq Department of Information Technology College of Computing and Information SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia

In recent times,Internet of Things(IoT)has become a hot research topic and it aims at interlinking several sensor-enabled devices mainly for data gathering and tracking *** Sensor Network(WSN)is an important component in IoT paradigm since its inception and has become the most preferred platform to deploy several smart city application areas like home automation,smart buildings,intelligent transportation,disaster management,and other such IoT-based *** methods are widely-employed energy efficient techniques with a primary purpose i.e.,to balance the energy among sensor *** and routing processes are considered as Non-Polynomial(NP)hard problems whereas bio-inspired techniques have been employed for a known time to resolve such *** current research paper designs an Energy Efficient Two-Tier Clustering with Multi-hop Routing Protocol(EETTC-MRP)for IoT *** presented EETTC-MRP technique operates on different stages namely,tentative Cluster Head(CH)selection,final CH selection,and *** first stage of the proposed EETTC-MRP technique,a type II fuzzy logic-based tentative CH(T2FL-TCH)selection is ***,Quantum Group Teaching Optimization Algorithm-based Final CH selection(QGTOA-FCH)technique is deployed to derive an optimum group of CHs in the ***,Political Optimizer based Multihop Routing(PO-MHR)technique is also employed to derive an optimal selection of routes between CHs in the *** order to validate the efficacy of EETTC-MRP method,a series of experiments was conducted and the outcomes were examined under distinct *** experimental analysis infers that the proposed EETTC-MRP technique is superior to other methods under different measures.

关键词： Wireless networks internet of things energy efficiency clustering multi-hop routing metaheuristics

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Research on intelligent penetration mechanism of netgap data based on database adaptive matching 3

Research on intelligent penetration mechanism of netgap data...

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3rd International Conference on Electronic information Engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Wu, Rutao Xu, Zhiwei Liu, Xuyang Liu, Wenjing Liu, Limin School of Data Science and Application Inner Mongolia University of Technology Huhehaote010000 China Inner Mongolia Autonomous Reg. Software Eng. Serv. Eng. Technology Research Center Based on Big Data Huhehaote010000 China Inner Mongolia Autonomous Region Product Quality Inspection and Research Institute Huhehaote010070 China Haihe Laboratory of Information Technology Application Innovation Tianjin300450 China Institute of Computing Technology of the Chinese Academy of Sciences Beijing100080 China

ISBN: (纸本)9781510680449

The purpose of data penetration is to achieve timely exchange of data information while ensuring the security of network isolation environment. The current research mainly focuses on protecting data privacy and developing basic data penetration methods for netgap, but these methods generally overlook the adaptive matching of data. Therefore, this article proposes an intelligent netgap data penetration mechanism based on database adaptive matching, which is an end-to-end cross netgap data packet adaptation mechanism. To overcome the problems of overfitting and local optima, XGBoost based on adaptive particle swarm optimization is adopted. By increasing the diversity of subgroups, the adaptive matching effect was improved, and the optimized XGBoost model was fitted to real-time data, with an accuracy increase from 72% to 94%. The experimental results show that this mechanism significantly improves the efficiency of packet adaptive matching while solving the problem of data penetration in the isolated environment of the network gate. The time cost is only 50% of the previous one, which improves the throughput of the system. The optimized XGBoost model is superior to the original model in terms of error and accuracy, and has certain practicality and easy generalization. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： data privacy

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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