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2023 7th International Conference on Electrical, Mechanical and Computer engineering (ICEMCE)

作者： Wenxue Li Chi Cheng Peihua Xu Renfeng Liu Yun Zhang Huabing Zhou College of Computer Science and Engineering Wuhan Institute of Technology Wuhan Hubei Prov. China Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan Hubei Prov. China Hubei Meteorological Service Center Wuhan China Faculty of Artificial Intelligence Education School of Educational Information Technology Central China Normal University Wuhan China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan China

Given the substantial load fluctuations, pronounced stochasticity, and non-linearity influenced by factors like weather and temperature in power load forecasting, we present a short-term load forecasting model based on TDCI. This model introduces several enhancements to boost forecasting accuracy. Firstly, TDCI employs temporal decomposition, separating time-series data into seasonal and trend components, enabling the capture of inherent seasonal patterns and long-term trends that significantly impact load forecasting accuracy. Additionally, the incorporation of locality-sensitive hashing attention efficiently processes relevant temporal patterns, enhancing model performance while reducing computational complexity. Lastly, a channel independence approach is applied to each variable, allowing tailored processing based on individual feature characteristics. This adaptive feature handling ensures effective capture of distinct properties, thereby enhancing forecasting accuracy. Experimental evaluations on a real-world power load dataset demonstrate the superior forecasting precision of our proposed model compared to existing models.

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Tech-Enabled Banking Revolt:The Transformational Era of IT in the Financial Sector

Tech-Enabled Banking Revolt:The Transformational Era of IT i...

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International Conference on Image Information Processing

作者： G S Pradeep Ghantasala Anjaneyulu Kunchala Pellakuri Vidyullatha Shaik Rehana Banu Amiya Bhaumik Shaik Balkhis Banu Gaurav Gupta Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Science and engineering line 3: nKoneru Lakshmaiah Education Foundation Vaddeswaram Guntur AP India Dept of Business Management Lincoln University College Malaysia Dept of Business Management Faculty of Busniess and Accountancy Lincoln University College Malaysia Dept of Physiotherapy Fatima College of Heaith Sciences Al Ain UAE Yogananda School of Artificial Intelligence Computers and Data science Shoolini University of Biotechnology and Management Sciences Solan HP India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Information technology is been widely spread all over the world. The benefits that the IT sector provides are unmeasurable. Though there are numerous benefits there are scams going around with the software developed under IT. The growing need for IT has made IT one of the necessary systems that need to be provided in an organization to enhance productivity and progress. The developed countries are making full use of IT in all sectors. The banking sector plays a vital role in the growth of the nation’s economy working along the side of IT will only result in the enhancement of the banking sector in many perspectives. The present paper views on the impact of IT on the banking sector.

关键词： Productivity Costs Digital transformation Banking Organizations Information processing Software

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Ensuring the data Security and Integrity over Cloud Computing Environment using Novel Cipher Strategy

Ensuring the Data Security and Integrity over Cloud Computin...

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IEEE International Conference on engineering Education: Innovative Practices and Future Trends (AICERA)

作者： Praveen Kumar E B. Yasotha Narmadha PG P. Chandrakala C. Ushapriya G Chamundeeswari Department of CSE (IoT) Sri Krishna College of Technology Coimbatore Tamil Nadu India Department of Data science and Business systems SRMIST kattankulathur Tamil Nadu India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning St. Martin's Engineering College Telangana India Department of Electronics and Communication Engineering Saveetha Engineering college Chennai Tamil Nadu India

The advent of cloud computing has revolutionized the Internet. Users may effortlessly collaborate, back up, and access their information from any location thanks to cloud computing. When it comes to providing IT enabled services via the internet, cloud computing is the clear winner. Cloud computing allows a large number of people to share resources like computers, programs, and servers. Users of cloud services often have no idea where their data actually resides or where it came from. Users of cloud computing services are understandably concerned about the safety of their data once it has been uploaded to the cloud with respect to service providers. Privilege control, comprehensive encryption algorithms, management of keys, provenance instances and global consistency verification are only some of the numerous data privacy and security issues that occur as a result. This study introduces a novel cloud security strategy, Cloud based Novel Cipher Strategy (CNCS), and analyzes issues related to the safety and integrity of data stored in the cloud. We're looking into many data safety related factors here. In this article, we'll look closely at a selection of data protection strategies now in use throughout the globe to guarantee the highest level of security possible. Later, the study delves into the various methods of cloud based data security. To prevent unwanted access, alteration, or deletion of dynamic as well as static information, a procedure for validating their integrity is outlined. The proposed system CNCS efficiency is evaluated in the resulting section through the following metrics such as: integrity level, data storage efficiency and security level. All these metrics are evaluated based on the size of data, in which it is tested with respect to number of days the proposed scheme is tested over the industrial environment to prove the performance and efficiency of the proposed approach.

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Myoelectric Prosthetic Hand with Adjustable Tightening Socket for Varied Residual Limb Shapes*

Myoelectric Prosthetic Hand with Adjustable Tightening Socke...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Yuki Inoue Yuki Kuroda Yoshiko Yabuki Yusuke Yamanoi Xu Yong Xiaobei Jing Hiroshi Yokoi Department of Mechanical and Intelligent Engineering The University of Electro-Communications Chofu Tokyo JAPAN OMRON SINIC X Corporation Bunkyo-ku Tokyo JAPAN department of Electrical Engineering Faculty of Engineering Tokyo University of Science Katushika-ku Tokyo JAPAN CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology (SIAT) Chinese Academy of Sciences (CAS) and the SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen CHINA Department of Mechanical and Intelligent Engineering Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Chofu Tokyo JAPAN Mu-BORG K.K. Chofu JAPAN

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

The prosthetic socket serves as an interface between the body and the machine. If the socket is not well-fitted to the residual limb, pain can occur. The shape of the residual limb changes over time as surgical scars stabilize or the patient’s body weight fluctuates. A socket with a cutout on the side can be tightened from the outside to adjust its circumference and accommodate these changes. However, when the circumference of the rigid socket is adjusted to fit, soft skin can become trapped due to friction between the skin and the socket. This phenomenon causes pain due to pinching. Consequently, this mechanism cannot be used for myoelectric prosthetic hands, where direct contact between the socket and the skin is necessary. In this study, we developed a socket with a variable circumference that suppresses skin pinching. We applied it to participants with different residual limb shapes. A Box and Block Test (BBT) was performed using two types of sockets. The first used a conventional socket designed to fit their residual limb shape. The second used the proposed socket, which was designed to fit the residual limb’s shape of one out of two participants and adapted to the residual limb’s changing shape. The results of the BBT showed not much difference in the number of blocks moved between the conventional socket and the proposed socket. Therefore, the performance of the proposed socket was comparable to that of conventional sockets and could be applied to different residual limbs. In other words, the proposed socket was shown to adapt to changes in the residual limb without causing pain. Sockets with the proposed functionality could be used in a variety of bio-interfaces, allowing for flexible connections between the body and machines.

关键词： Limbs Shape Pain Sockets Human-machine systems Friction Surgery Skin Interviews Prosthetic hand

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Ontology Knowledge Based Multimodal Service Tasking Planning

Ontology Knowledge Based Multimodal Service Tasking Planning

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International Conference on Electronic engineering and Informatics (EEI)

作者： Xiaochen Zhang Guoliang Liu Wei Li Zhenhua Liu Shanmei Wang Jiwei Zhang Bin Wang Guohui Tian School of Control Science and Engineering Shandong University Jinan China Big Data and Artificial Intelligence Institute China Telecom Research Institute Beijing China Research Department Inspur Intelligent Technology Jinan China Department of Development Ji'nan Power Supply Company Jinan China Department of Investment and Development State Nuclear Electric Power Planning Design & Research Institute Co. LTD. Beijing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Considering smart device and robot having entered the home environment to provide human with convenient services, this paper proposes a multimodal service system to enhance the system dynamic understanding of the environment and dynamic task planning capabilities. The proposed system introduces a family ontology to record information about the object, device, and user. The multimodal information can be acquired and updated automatically to the family ontology. The task planning and execution are introduced to dynamically generate task. Finally, the experiments in a family environment prove that the system has outstanding task processing capabilities and environmental understanding.

关键词： System dynamics Knowledge based systems Ontologies Planning Smart devices Informatics Robots

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Smart Surface-Aided Wireless Networks with Joint Active-Passive Beamforming For Enhanced Signal Coverage

Smart Surface-Aided Wireless Networks with Joint Active-Pass...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Neeraj Kaushik Ritika Mehra Shruti Aggarwal N. Thangarasu K. S. Bhuvaneshwari Deepika Dongre Department of ECE Teerthankar Mahaveer University Moradabad Uttar Pradesh India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India SOCSA IIMT University Meerut Uttar Pradesh India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Tamilnadu India Department of Artificial Intelligence and Data science Karpagam College of Engineering Coimbatore Tamilnadu India AI & Ds Vishwakarma Institute of Information Technology Pune Maharashtra India

Clever mirrored surfaces are a ground-breaking and innovative technique for future value frequency and environmentally friendly cellular transmission. In particular, an is made up of lots of inexpensive inductor, each of which is capable of autonomously reflecting the received signal with a programmable phase change to jointly accomplish multi detached subcarrier without the assistance of radio broadcast networks. In this article, we investigate a separate wirelessly system which employs an IRS to help with transmission between one number of co ap and a handful of single-antenna users. By good agreements the send modulation schemes by effective antenna elements and reflect spatial multiplexing by silent power switches, pursuant to users' particular transmission ratio restrictions, we devise and resolve new issues to reduce the total system throughput. In addition, we examine the dynamical properties of passive directional antennas with indefinitely many expressing components and contrast it with the behavior of the conventional active modulation schemes. According to the findings of the experiment, a Genuineness (openness and self-system is capable of achieving so same class performance as a baseline large network without the use of IRS while using a great deal fewer active signal boosters' network. We also gain information into how to best implement in upcoming wireless networks.

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LMVD: A Large-Scale Multimodal Vlog dataset for Depression Detection in the Wild

arXiv

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arXiv 2024年

作者： He, Lang Chen, Kai Zhao, Junnan Wang, Yimeng Pei, Ercheng Chen, Haifeng Jiang, Jiewei Zhang, Shiqing Zhang, Jie Wang, Zhongmin He, Tao Tiwari, Prayag The School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China The Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China The Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China The School of Electronic Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China The College of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology Xi’an710016 China The Institute of Intelligent Information Processing Taizhou University Zhejiang Taizhou318000 China The School of Education Shenzhen University Shenzhen518060 China The School of Information Technology Halmstad University Halmstad Sweden

Depression can significantly impact many aspects of an individual’s life, including their personal and social functioning, academic and work performance, and overall quality of life. Many researchers within the field of affective computing are adopting deep learning technology to explore potential patterns related to the detection of depression. However, because of subjects’ privacy protection concerns, that data in this area is still scarce, presenting a challenge for the deep discriminative models used in detecting depression. To navigate these obstacles, a large-scale multimodal vlog dataset (LMVD), for depression recognition in the wild is built. In LMVD, which has 1823 samples with 214 hours of the 1475 participants captured from four multimedia platforms (Sina Weibo, Bilibili, Tiktok, and YouTube). A novel architecture termed MDDformer to learn the non-verbal behaviors of individuals is proposed. Extensive validations are performed on the LMVD dataset, demonstrating superior performance for depression detection. We anticipate that the LMVD will contribute a valuable function to the depression detection community. The data and code will released at the link: https://***/helang818/LMVD/. © 2024, CC BY-NC-ND.

关键词： Deep learning

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Metallic product recognition with dual attention and multi-branch residual blocks-based convolutional neural networks

Circular Economy

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Circular Economy 2022年第2期1卷

作者： Han, Honggui Zhang, Qiyu Li, Fangyu Du, Yongping Gu, Yifan Wu, Yufeng Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing University of Technology Beijing100124 China Beijing Artificial Intelligence Institute and Beijing Laboratory for Intelligent Environmental Protection Beijing100124 China College of Materials Science and Engineering Beijing University of Technology Beijing100124 China Institute of Circular Economy Beijing University of Technology Beijing100021 China

Visual recognition technologies based on deep learning have been gradually playing an important role in various resource recovery fields. However, in the field of metal resource recycling, there is still a lack of intelligent and accurate recognition of metallic products, which seriously hinders the operation of the metal resource recycling industry chain. In this article, a convolutional neural network with dual attention mechanism and multi-branch residual blocks is proposed to realize the recognition of metallic products with a high accuracy. First, a channel-spatial dual attention mechanism is introduced to enhance the model sensitivity on key features. The model can focus on key features even when extracting features of metallic products with too much confusing information. Second, a deep convolutional network with multi-branch residual blocks as the backbone while embedding a dual-attention mechanism module is designed to satisfy deeper and more effective feature extraction for metallic products with complex characteristic features. To evaluate the proposed model, a waste electrical and electronic equipment (WEEE) dataset containing 9266 images in 18 categories and a waste household metal appliance (WHMA) dataset containing 11,757 images in 23 categories are built. The experimental results show that the accuracy reaches 94.31% and 95.88% in WEEE and WHMA, respectively, achieving high accuracy and high quality recycling. © 2022 The Author(s)

关键词： Recycling

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NEURAL TOPIC MODEL VIA OPTIMAL TRANSPORT

arXiv

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arXiv 2020年

作者： Zhao, He Phung, Dinh Huynh, Viet Le, Trung Buntine, Wray Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Australia

Recently, Neural Topic Models (NTMs) inspired by variational autoencoders have obtained increasingly research interest due to their promising results on text analysis. However, it is usually hard for existing NTMs to achieve good document representation and coherent/diverse topics at the same time. Moreover, they often degrade their performance severely on short documents. The requirement of reparameterisation could also comprise their training quality and model flexibility. To address these shortcomings, we present a new neural topic model via the theory of optimal transport (OT). Specifically, we propose to learn the topic distribution of a document by directly minimising its OT distance to the document’s word distributions. Importantly, the cost matrix of the OT distance models the weights between topics and words, which is constructed by the distances between topics and words in an embedding space. Our proposed model can be trained efficiently with a differentiable loss. Extensive experiments show that our framework significantly outperforms the state-of-the-art NTMs on discovering more coherent and diverse topics and deriving better document representations for both regular and short texts. © 2020, CC BY-NC-SA.

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Bridging the gaps in statistical models of protein alignment

arXiv

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arXiv 2020年

作者： Sumanaweera, Dinithi Allison, Lloyd Konagurthu, Arun S. Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Australia

This work demonstrates how a complete statistical model quantifying the evolution of pairs of aligned proteins can be constructed from a time-parameterised substitution matrix and a time-parameterised 3-state alignment machine. All parameters of such a model can be inferred from any benchmark data-set of aligned protein sequences. This allows us to examine nine well-known substitution matrices on six benchmarks curated using various structural alignment methods;any matrix that does not explicitly model a "time"-dependent Markov process is converted to a corresponding base-matrix that does. In addition, a new optimal matrix is inferred for each of the six benchmarks. Using Minimum Message Length (MML) inference, all 15 matrices are compared in terms of measuring the Shannon information content of each benchmark. This has resulted in a new and clear overall best performed time-dependent Markov matrix, MMLSUM, and its associated 3-state machine, whose properties we have analysed in this work. For standard use, the MMLSUM series of (log-odds) scoring matrices derived from the above Markov matrix, are available at https://***/mmlsum. Copyright © 2020, The Authors. All rights reserved.

关键词： Markov processes

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