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IEEE International Conference on Big Data

作者： Chengyu Liu Bo Zhang Wendong Wang Beijing Univ. of Posts and Telecom Beijing China State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications

Motion retargeting technology plays a vital role in fields such as computer animation, virtual reality, and gaming industries. Users can save a lot of costs in art design and animation production. However, the current motion retargeting methods still has many strict requirements. For example, some methods require the source and target skeletons need to have the same number of joints or share the same topology. In addition, we find many methods currently use the Mixamo dataset as the training set and test set, and use joint position errors optimize retargeting results, but ignore the source-target differences at the shape geometry level. This may result in interpenetration or loss of contact. Therefore, we introduce a novel framework and use evaluation metrics such as position error related to skeleton and interpenetration ratio related to shape, which makes the retargeting result more realistic.

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Incomplete Multi-View Representation Learning Through Anchor Graph-Based GCN and Information Bottleneck

Incomplete Multi-View Representation Learning Through Anchor...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhenjiao Liu Xiao Wang Xiaodi Huang Guanlin Li Ke Sun Zhikui Chen School of Software Technology Dalian University of Technology Dalian China Samovar Telecom SudParis Institut Polytechnique de Paris Palaiseau France School of Computing Mathematics and Engineering Charles Sturt University Albury Australia School of Computer Science and Technology Dalian University of Technology

Real-world data often contain incomplete views with varying degrees of missing information. While there are existing methods for learning representations from such data, effectively utilizing all incomplete view data and ensuring robustness to different levels of completeness remains a challenging task. To address this problem, we propose a novel framework named IMRL-AGI. IMRL-AGI combines the anchor graph-based Graph Convolutional Network (GCN) and information bottleneck. Specifically, the framework starts by constructing an anchor graph to effectively captures the nonlinear information between instances. Next, an anchor graph-based GCN is designed to extract feature information from various views. IMRL-AGI maximizes the mutual information between the views obtained by the common representation and the anchor-graph-based GCN, ensuring the accurate extraction of view information. Furthermore, the minimization of mutual information is applied to promote diversity and reduce redundancy in the multi-view representation. Extensive experiments are conducted on several real-world datasets, and the results demonstrate the superiority of IMRL-AGI.

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Liquefied Petroleum Gas Concentration Prediction Based on Regression Model

Liquefied Petroleum Gas Concentration Prediction Based on Re...

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International Conference on Intelligent System and Knowledge engineering, ISKE

作者： Siting Chen Yi Song Yunchuan Li Hongjun Wang School of Computer Science And Engineering Northeastern University Shenyang Liaoning School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu Sichuan Sichuan Branch of China Telecom Corporation Limited Chengdu Sichuan

Liquefied Petroleum Gas (LPG) is an important energy source widely used in industry, agriculture and households. However, high concentrations of LPG in the air can easily cause explosions, and its widespread use has extremely high safety risks for our lives. Among the many air quality prediction methods, LPG is only one of the attribute indicators for comprehensive air quality prediction. Therefore, the prediction accuracy of these methods for LPG is not high. In this paper, we propose to use regression models to predict LPG concentrations, taking LPG as the study object and other factors in the air as reference influencing factors, and using multiple regression models and deep learning models to compare the prediction errors. Extensive experimental results show that the prediction of LPG concentration using regression models can achieve good prediction performance. Among them, a comprehensive comparison of linear regression performs the best, which verifies the validity of our idea.

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Contactless palm-vein recognition for e-commerce

Contactless palm-vein recognition for e-commerce

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IET International Conference on engineering Technologies and Applications (ICETA 2023)

作者： P. -H. Chen C. Liu C. -H. Hsia Intelligent Internet of Things Institute Chunghwa Telecom Laboratories Taiwan Kang Chiao International School Taiwan Department of Computer Science and Information Engineering National llan University Taiwan

This study proposed a palm vein recognition system for credit card transactions. When the transaction amount exceeds the predefined signature exemption limit, palm vein recognition is triggered for user recognition. The system utilizes CNN to verify the user's palm vein image, achieving highly accurate identity authentication, which provides an Equal Error Rate (EER) of 0.239%. This system also has the potential to be applied on edge devices and achieve efficient and fast identity verification.

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A DNN Operation Scheme Based on IPSO for UAV-Assisted MEC Networks 22

A DNN Operation Scheme Based on IPSO for UAV-Assisted MEC Ne...

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22nd IEEE International Conference on Communication Technology, ICCT 2022

作者： Wang, Zhenqian Yang, Wei Zhang, Bo Zhuo, Linlin Rong, Huigui College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Guangdong Southern Planning & Designing Institute of Telecom Consultation Co. Ltd. Shenzhen518038 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325035 China

ISBN: (纸本)9781665470674

Applications' demands on computing resources have increased in recent years. One potential option is to offload tasks with high computational demands to edge servers. Therefore, Mobile Edge Computing (MEC) deployed on Unmanned Aerial Vehicles (UAVs) emerges as the need arises to offer task offloading for ground User Devices (UDs). However, due to the changing environment, how to offer quick decision-making solutions is still a popular research topic. In this paper, we provide a Deep Neural Network (DNN) operation scheme based on Improved Particle Swarm Optimization (IPSO), which combines a conventional heuristic approach with neural networks to give an effective operation scheme. We take into account the requirement to decrease latency and conserve energy while maintaining equity in offloading, which is formulated as a Mixed Integer Non-Linear Programming (MINLP) issue. We first use the IPSO approach to generate labeled data and then train the neural network. The well-trained neural network is utilized to generate quick decisions. Finally, simulation results reveal our scheme's advantage in terms of fast environment adaptation. © 2022 IEEE.

关键词： Energy utilization

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Movable Antennas Aided Multicast MISO Communication Systems

arXiv

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arXiv 2024年

作者： Cheng, Zhenqiao Li, Nanxi Long, Ruizhe Zhu, Jianchi Ouyang, Chongjun Chen, Peng 6G Research Centre China Telecom Beijing Research Institute Beijing102209 China University of Electronic Science and Technology of China Chengdu611731 China School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom School of Electrical and Electronic Engineering University College Dublin Ireland

A novel multicast communication system with movable antennas (MAs) is proposed, where the antenna position optimization is exploited to enhance the transmission rate. Specifically, an MA-assisted two-user multicast multiple-input single-input system is considered. The joint optimization of the transmit beamforming vector and transmit MA positions is studied by modeling the motion of the MA elements as discrete movements. A low-complexity greedy search-based algorithm is proposed to tackle this non-convex inter-programming problem. A branch- and-bound (BAB)-based method is proposed to achieve the optimal multicast rate with a reduced time complexity than the brute-force search by assuming the two users suffer similar line-of-sight path losses. Numerical results reveal that the proposed MA systems significantly improve the multicast rate compared to conventional fixed-position antennas (FPAs)-based systems. © 2024, CC BY.

关键词： Beamforming

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Blockchain and Edge Computing Integration Boosts Scalability and Reduces Latency

Blockchain and Edge Computing Integration Boosts Scalability...

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Mahmoud Mahfuri Ahmad Enad Alnawaiseh Umber Asad Muhammad Yousif Shoukat Aslam Zakariya Chabani Muhammad Fiaz Faculty of Engineering - Computer and Information Technolgy Department International University for Science and Technolgy Orange-Jordan Telecom Company Jordan School of Computer Computer NCBA&E Lahore Pakistan Faculty of Management Canadian University Dubai Dubai UAE School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan

Nowadays, the integration of blockchain and edge computing has become important in computer science due to the significant advantages it can bring to a range of real-world applications. By combining the strengths of both technologies, it is Combining the strengths of both technologies makes it possible to create a highly secure and reliable network with reduced latency and efficient transaction processing. This article explores the challenges and opportunities of combining these two technologies, including the scalability barrier of blockchain and the management and security challenges of edge computing. This paper discusses some of the theories and research challenges associated with this integration, including developing efficient consensus algorithms and enabling data sharing and provenance. this paper concludes by highlighting the potential benefits of this technology in the field of medicine and healthcare, finance, supply chain management, and Internet of Things.

关键词： Supply chain management Scalability Process control Organizations Reliability theory Blockchains Security

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Aspect-Based Sentiment Analysis on Amharic Text for Evaluating Ethio-telecom Services

Aspect-Based Sentiment Analysis on Amharic Text for Evaluati...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Tarikwa Tesfa Befikadu Belete Samuel Abera Sudhir Kumar Mohapatra Tapan Kumar Das Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Ethio Telecom Services Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

People are increasingly expressing their views and opinions about a company's goods or service on social media these days. For all types of businesses and organizations, sentiment analysis in text can be used to find prospective customers and obtain quick feedback on the services or goods they provided. As a result, the organization has a better chance and opportunity to meet the needs of the consumer. Using sentiment analysis, we may extract important information from the thoughts and emotions of an organization's supporters and clients. It examines natural language content produced about services and goods. Detailed opinion mining at the aspect/feature level to serve consumer and organizational needs. In this study, three different machine learning algorithms, these are Naïve Bayes (NB), Support Vector Machine (SVM), and K- Nearest Neighbor (KNN) are used to build the proposed aspect-based sentiment analysis model from the customer comments in Amharic text which is collected from Ethio-telecom official Facebook page. The collected data is annotated into sentiment polarity and aspect-based sentiment classes by the help of five annotators. After data annotation, different preprocessing tasks were implemented. Bag of words and TF-IDF word representation techniques were used to represent each unique word to their vector representation. The experiment was conducted on those three models and the result was validated. The labeled aspects were not balanced so that a SMOTE class imbalance handling technique was employed to the dataset. Finally, SVM model trained with TF-IDF word representation technique with SMOTE oversampled dataset has achieved the highest accuracy result of 98% as compared to NB and KNN.

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Adversarial Masking Contrastive Learning for vein recognition

arXiv

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arXiv 2024年

作者： Qin, Huafeng Wu, Yiquan El-Yacoubi, Mounim A. Wang, Jun Yang, Guangxiang Chongqing Engineering Laboratory of intelligent perception and block-chain School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China SAMOVAR Telecom SudParis CNRS University Paris-Saclay Paris91011 France School of Information and Control Engineering China University of Mining and Technology Xuzhou China

Vein recognition has received increasing attention due to its high security and privacy. Recently, deep neural networks such as Convolutional neural networks (CNN) and Transformers have been introduced for vein recognition and achieved state-of-the-art performance. Despite the recent advances, however, existing solutions for finger-vein feature extraction are still not optimal due to scarce training image samples. To overcome this problem, in this paper, we propose an adversarial masking contrastive learning (AMCL) approach, that generates challenging samples to train a more robust contrastive learning model for the downstream palm-vein recognition task, by alternatively optimizing the encoder in the contrastive learning model and a set of latent variables. First, a huge number of masks are generated to train a robust generative adversarial network (GAN). The trained generator transforms a latent variable from the latent variable space into a mask space. Then, we combine the trained generator with a contrastive learning model to obtain our AMCL, where the generator produces challenging masking images to increase the contrastive loss and the contrastive learning model is trained based on the harder images to learn a more robust feature representation. After training, the trained encoder in the contrastive learning model is combined with a classification layer to build a classifier, which is further fine-tuned on labeled training data for vein recognition. The experimental results on three databases demonstrate that our approach outperforms existing contrastive learning approaches in terms of improving identification accuracy of vein classifiers and achieves state-of-the-art recognition results. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Link Adaptive Approach for Federated Learning aided UAV Networks

A Link Adaptive Approach for Federated Learning aided UAV Ne...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Hongming Zhang Xi Meng Yiran Xian Pengpeng Li School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China China Telecom. Research Institute Beijing China School of Computer Science & Technology Beijing Institute of Technology Beijing China State Radio Regulation of China Beijing China

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

Federated learning (FL) assisted unmanned aerial vehicle (UAV) networks have been considered as a promising technology for the future Internet of Things (IoT) scenarios, where both the communication cost and computational cost can be reduced. In this paper, a FL assisted UAV networks are considered for performing image classification tasks in IoT, where an aerial base station (ABS) is deployed as the aggregation center and UAVs are deployed as the local entities. Firstly, the system performance is analyzed in terms of algorithm convergence and energy consumption. Then, we propose a link adaptive approach for the FL assisted UAV networks, where a link quality selection scheme and a model parameter selection scheme are conceived at the ABS and each UAV, respectively. Finally, the system performance is evaluated by simulation results, showing that the proposed link adaptive approach is capable of attaining a faster convergence performance and a higher classification accuracy compared to the conventional FL scheme.

关键词： Wireless communication Energy consumption Adaptation models Adaptive systems Accuracy Federated learning System performance

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