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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： Uma Yadav Shweta Bondre Bhakti Thakre Kiran Likhar Computer Science & Engineering (Data Science) Shri Ramdeobaba College of Engineering and Management Nagpur India Information Technology Department Shri Ramdeobaba College of Engineering and Management Nagpur India Cyber Security Department St. Vincent Pallotti College of Engineering and Technology Nagpur India Computer Science & Engineering G H Raisoni Institute of Engineering and Technology Nagpur India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

Emotions have a tremendous impact on human experiences and decision-making processes. People are frequently motivated to repeat happy behaviors while avoiding depressing ones. Emotions may spread like wildfire in the modern digital era due to the quick broadcast of information and the heavy use of emotive language in communication. The identification of these emotional undercurrents in a variety of literary forms has been made easier by the analysis of indigenous languages, allowing for prompt interventions when needed. This study presents an algorithm that can analyze statements and then categorize them into sentiments in order to determine a person's emotional state. An analysis of the algorithm's performance shows that BERT performs better than SVM and CNN in a variety of emotional categories, showing better recall, accuracy, and F1-scores in the identification of emotions including Joy, Sadness, Fear, and Anger. For example, BERT outperforms SVM and CNN by large margins, achieving an average F1-score of 0.85 across all emotions. CNN performs competitively, especially when it comes to identifying emotions such as Fear and Sadness, but SVM performs somewhat worse overall than BERT and CNN. Interestingly, all models are good at recognizing Neutral emotions, with BERT and CNN doing somewhat better than SVM. As a result, BERT stands out as the best model for classifying emotions, demonstrating its effectiveness in identifying complex emotional states.

关键词： Support vector machines Emotion recognition Renewable energy sources Wildfires Accuracy Power control Decision making

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Not All Pairs are Equal: Hierarchical Learning for Average-Precision-Oriented Video Retrieval 24

Not All Pairs are Equal: Hierarchical Learning for Average-P...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Yang Xu, Qianqian Wen, Peisong Dai, Siran Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences BDKM University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400706868

The rapid growth of online video resources has significantly promoted the development of video retrieval methods. As a standard evaluation metric for video retrieval, Average Precision (AP) assesses the overall rankings of relevant videos at the top list, making the predicted scores a reliable reference for the users. However, recent video retrieval methods utilize pair-wise losses that treat all sample pairs equally, leading to an evident gap between the training objective and evaluation metric. To effectively bridge this gap, in this work, we aim to address two primary challenges: a) The current similarity measure and AP-based loss are suboptimal for video retrieval;b) The noticeable noise from frame-to-frame matching introduces ambiguity in estimating the AP loss. In response to these challenges, we propose the Hierarchical learning framework for Average-Precision-oriented Video Retrieval (HAP-VR). For the former challenge, we develop the TopK-Chamfer Similarity and QuadLinear-AP loss to measure and optimize video-level similarities in terms of AP. For the latter challenge, we suggest constraining the frame-level similarities to achieve an accurate AP loss estimation. Experimental results present that HAP-VR outperforms existing methods on several benchmark datasets, providing a feasible solution for video retrieval tasks and thus offering potential benefits for the multi-media application. © 2024 Owner/Author.

关键词： Self-supervised learning

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Fingerprint Authentication Method using Two Steps Verification Algorithm 25

Fingerprint Authentication Method using Two Steps Verificati...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Bahy, Ramy M. Ababneh, Jafar Attar, Hani Al-Hihi, Mohammad Hafez, Mohamed Solyman, Ahmed Technical Research Center Cairo Egypt Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Zarqa University Faculty of Engineering Zarqa Jordan Erlangen Germany INTI-IU-University Faculty of Engineering Feqs Nilai Malaysia Glasgow Caledonian University School of Computing Engineering and Built Environment Glasgow United Kingdom

ISBN: (纸本)9798331540012

This paper presents a new Fingerprint Authentication method based on a steps verification algorithm. The first verification step depends on feature extraction of contours, edges, and lines in both the source fingerprint image and the stored database images, followed by matching the same edges and lines by measuring the Euclidean distance between pixels of the matched lines. This step indicates realization and recognition between the source fingerprint image and its matched corresponding image in database images. The second verification step is performed to give a final decision of recognition using mutual information as a similarity measure between the source fingerprint image and its initially matched image in database *** experimental results proved that the proposed fingerprint authentication method gives an accurate and high recognition rate. © 2024 IEEE.

关键词： Contourlet transform

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Traceable ring signature schemes based on SM2 digital signature algorithm and its applications in the data sharing scheme

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Frontiers of computer Science 2024年第2期18卷 251-253页

作者： Yongxin ZHANG Hong LEI Bin WANG Qinghao WANG Ning LU Wenbo SHI Bangdao CHEN Qiuling YUE R&D Department SSC Holding CompanyLtd.Chengmai 571924China School of Cyberspace Security(School of Cryptography) Hainan UniversityHaikou 570228China College of Computer Science and Engineering Northeastern UniversityShenyang 110819China School of Computer Science and Technology Xidian UniversityXi'an 710071China Blockchain Research Group Oxford-Hainan Blockchain Research InstituteChengmai 571924China

1 Introduction Identity privacy concerns hinder data sharing by casting doubt on the safeguarding of personal information,eroding trust,and impeding the willingness of individuals and organizations to exchange their data[1,2].The traceable ring signatures(TRSs)addresses the contradiction between identity privacy and regulation[3],no scheme has been developed thus far that is based on SM2,the Chinese cryptographic public key algorithm standard,without relying on centralized trust.

关键词： SM2 signature scheme

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Dual Branch PnP Based Network for Monocular 6D Pose Estimation

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Intelligent Automation & Soft Computing 2023年第6期36卷 3243-3256页

作者： Jia-Yu Liang Hong-Bo Zhang Qing Lei Ji-Xiang Du Tian-Liang Lin Department of Computer Science and Technology Huaqiao UniversityXiamen361000China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao UniversityXiamen361000China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao UniversityXiamen361000China College of Mechanical Engineering and Automation Xiamen361000China

Monocular 6D pose estimation is a functional task in the field of com-puter vision and *** recent years,2D-3D correspondence-based methods have achieved improved performance in multiview and depth data-based ***,for monocular 6D pose estimation,these methods are affected by the prediction results of the 2D-3D correspondences and the robustness of the per-spective-n-point(PnP)*** is still a difference in the distance from the expected estimation *** obtain a more effective feature representation result,edge enhancement is proposed to increase the shape information of the object by analyzing the influence of inaccurate 2D-3D matching on 6D pose regression and comparing the effectiveness of the intermediate ***,although the transformation matrix is composed of rotation and translation matrices from 3D model points to 2D pixel points,the two variables are essentially different and the same network cannot be used for both variables in the regression ***,to improve the effectiveness of the PnP algo-rithm,this paper designs a dual-branch PnP network to predict rotation and trans-lation ***,the proposed method is verified on the public LM,LM-O and YCB-Video *** ADD(S)values of the proposed method are 94.2 and 62.84 on the LM and LM-O datasets,*** AUC of ADD(-S)value on YCB-Video is *** experimental results show that the performance of the proposed method is superior to that of similar methods.

关键词： 6D pose monocular RGB edge enhancement dual-branch PnP 2D-3D correspondence

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Personalized Educational Video Evaluation Combining Student's Cognitive and Teaching Style

Personalized Educational Video Evaluation Combining Student'...

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IEEE International Conference on Systems, Man and cybernetics

作者： Jinta Weng Haoyu Dong Yifan Deng Yue Hu Hao Wu Heyan Huang School of Cyber Security University of Chinese Academy of Sciences China School of Computer Science and Cyber Engineering Guangzhou University China School of Computer Science and Technology Southeast Academy of Information Technology Beijing Institute of Technology China

AI-powered technologies, like ChatGPT and learning analytic technologies, have encouraged the sharing of online teaching resources and the transformation of teaching methods and learning pathways. However, the mixed resources and the result-oriented video evaluation repeatedly let students fall into an information trap and only appeal to students' attention to unsuitable resources. Inspired by human-computer interaction, a novel online video assessment LPSA(Linguistic- Presentative-scientific-Artistic) is proposed, integrated by cognitive style and teaching style, to realize more precise learning detection and teaching quality assessment. The LPSA evaluation consists of a four-level classification and eight secondary indexes quantified by machine learning algorithms. By automatically searching for an appropriate threshold of all secondary indexes, a real-time video assessment system is developed to certify its technical feasibility and pedagogical availability. The results show that the proposed AI-assisted assessment could realize practical pedagogical recommendations and real-time supervision.

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Mutual information oriented deep skill chaining for multi‐agent reinforcement learning

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CAAI Transactions on Intelligence technology 2024年第4期9卷 1014-1030页

作者： Zaipeng Xie Cheng Ji Chentai Qiao WenZhan Song Zewen Li Yufeng Zhang Yujing Zhang Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjingChina College of Computer and Information Hohai UniversityNanjingChina Center for Cyber‐Physical Systems University of GeorgiaAthensGeorgiaUSA Information Networking Institute Carnegie Mellon UniversityPittsburghPennsylvaniaUSA Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPennsylvaniaUSA

Multi‐agent reinforcement learning relies on reward signals to guide the policy networks of individual ***,in high‐dimensional continuous spaces,the non‐stationary environment can provide outdated experiences that hinder convergence,resulting in ineffective training performance for multi‐agent *** tackle this issue,a novel reinforcement learning scheme,Mutual Information Oriented Deep Skill Chaining(MioDSC),is proposed that generates an optimised cooperative policy by incorporating intrinsic rewards based on mutual information to improve exploration *** rewards encourage agents to diversify their learning process by engaging in actions that increase the mutual information between their actions and the environment *** addition,MioDSC can generate cooperative policies using the options framework,allowing agents to learn and reuse complex action sequences and accelerating the convergence speed of multi‐agent *** was evaluated in the multi‐agent particle environment and the StarCraft multi‐agent challenge at varying difficulty *** experimental results demonstrate that MioDSC outperforms state‐of‐the‐art methods and is robust across various multi‐agent system tasks with high stability.

关键词： artificial intelligence techniques decision making intelligent multi‐agent systems

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A Bibliometric and Social Media Analysis of the Nexus Between Blockchain technology and Finance 2nd

A Bibliometric and Social Media Analysis of the Nexus Betwee...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Ayodele, Olamide Emmanuel Ajibade, Temiloluwa Iyanuoluwa Oyebode, Oluwadare Joshua Akintoye, Kayode Akinlekan Ajayi, Babatunde Adedotun Adediran, Anthonia Oluwatosin Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Management Scholar School System Birmingham United Kingdom Civil and Environmental Engineering Afe Babalola University Ekiti State Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria Department of Cyber Security Faculty of Computing and Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya KL Kuala Lumpur Malaysia Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This study explores Blockchain technology in the global financial industry using trends analysis, bibliometric analysis, and a literature appraisal using Elsevier Scopus data from 2016 to 2023. The Blockchain in Finance (BCF) study produced 185 publications: 53.5% papers, 45.4% conference proceedings, and 1.1% reviews. Analysis of the subject area revealed three important computer Science, engineering, and Decision Sciences articles. However, source title analysis showed that ACM International Conference Proceeding Series, Sustainability Switzerland, and E3S Web of Conferences publish BCF research most often. According to the stakeholders study, Jon M. Truby and Qatar University (Qatar) are the top BCFin researchers and affiliations with 4% and % publications, respectively. China produces the most research on the topic. Due to active support from funding sources like the Chinese National Natural Science Foundation (NNSF), 18 publications have been funded. Co-occurrence of keywords The investigation indicated that BCFin’s top keywords include Blockchain, Finance, and Supply Chain Finance. Three clusters with 4–8 keywords, 132 linkages, and 866 TLS were found via cluster analysis. Keywords classify the clusters as Integrated Business Solutions (Cluster 1), Digital Commerce Infrastructure (Cluster 2), and Decentralized Financial Network (Cluster 3). The findings indicate that BCFin is a dynamic and influential multidisciplinary study field. BCFin has transformed the global financial sector by improving trust, transaction security, and communication. It still struggles with interoperability, system integration, and sustainability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Turmeric leaf disease detection using optimized deep learning model

Turmeric leaf disease detection using optimized deep learnin...

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2nd International Conference on computer Vision and Internet of Things (ICCVIoT 2024)

作者： R. Selvaraj M. S. Geetha Devasena T. Satheesh K. Karpagavadivu F. Ravindaran Department of Computer Science and Engineering Dr.N.G.P. Institute of Technology Coimbatore India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Artificial Intelligence and Data Science Dr.N.G.P. Institute of Technology Coimbatore India Computer Science and Engineering (Cyber Security) Karpagam college of Engineering Coimbatore India

ISBN: (数字)9781837242535

Turmeric is popular for its medicinal, culinary, and non-culinary uses. Turmeric cultivation is affected due to disease in their leaves. Farmers conventionally identify the leaf diseases and provide pesticides to avoid further loss. However, manual identification of leaf disease over large farms is time-consuming and requires human effort. Also, in several types of leaf disease, the specific type that occurs in the plant should be distinguished; thus, proper knowledge of farming is required. To simplify this and to know the type of disease, smart agricultural practices are followed worldwide. One of the intelligent techniques adopted in farming is leaf disease detection. By providing the filed image to a learning algorithm, the type of disease can be detected accurately without manual interventions. ML and DL are used in this smart agricultural practice. To improve the performance further, an optimized deep learning model is presented in this research work. A Novel gazelle optimization algorithm is combined with deep learning MobileNetV3 network to attain enhanced disease detection performance. Experimentation on our own dataset with 5000 samples, including leaf spot, leaf blight, leaf rot, and curl disease-affected leaves, the proposed model performance is evaluated and observed with a maximum accuracy of 96.8%, a precision of 97.1% over conventional disease detection models.

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Identification and Categorization of Unusual Internet of Vehicles Events in Noisy Audio

Identification and Categorization of Unusual Internet of Veh...

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IEEE Conference on Vehicular technology (VTC)

作者： Farkhund Iqbal Ahmad Abbasi Abdul Rehman Javed Gautam Srivastava Zunera Jalil Thippa Reddy Gadekallu Department of Cyber Security Zayed University UAE Department of Cyber Security Air University Islamabad Pakistan Dept. of Math and Computer Science Brandon University Brandon Canada Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

The volume of multimedia data produced by various smart devices has increased dramatically with the advent of new digital technologies, including in the Internet of Vehicles (IoV). It has become more difficult to extract valuable insights from multimedia data due to several challenges during data analysis. The main problem is the need to quickly and precisely identify abnormalities in multimedia data. This research presents an unusual occurrence of the audio forensics database named UOAFDB and a practical method for identifying and categorizing unusual occurrences in audio files. To study the detection of abnormal audio and the classification of rare sound (e.g., car crash—machine gun, explosion) events for audio forensics, we construct a large audio dataset containing ten rare special events (anomalies) with 15 different background environmental settings (e.g., beach, restaurant, and train). The suggested method determines the optimal amount of features using the best feature extraction methodology available by extracting Mel-frequency cepstral coefficients (MFCCs) features from the audio signals of the newly formed dataset. Modern deep learning algorithms use these features as input to assess performance. Additionally, we apply deep learning methods to the most recent and best available dataset and obtain promising outcomes. The experimental findings demonstrate promising results on the UOAFDB dataset.

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