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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xuefei Chen, Mingzhe Liu, Danpu Zhang, Zhilong Quek, Tony Q. S. Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Information Systems Technology and Design Pillar Singapore University of Technology and Design 487372 Singapore

ISBN: (纸本)9798350351255

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs). © 2024 IEEE.

关键词： Reinforcement learning

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CAT: A Collaborative Annotation Tool for Chinese Genealogy Textual Documents 26

CAT: A Collaborative Annotation Tool for Chinese Genealogy T...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Jiang, Huan Wang, Zihao Li, Rongrong Peng, Yuwei Peng, Zhiyong Xu, Bin School of Computer Science Wuhan University Wuhan430072 China School of History Wuhan University Wuhan430072 China The Big Data Institute Wuhan University Wuhan430072 China Cultural Heritage Intelligent Computing Laboratory Wuhan University Wuhan430072 China

ISBN: (纸本)9798350331684

The annotation for Chinese genealogy textual documents is helpful for constructing genealogy knowledge graph, training effective machine learning models for knowledge extraction, etc. However, this kind of documents is difficult to annotate. The primary reason is that the texts are written in both classical and vernacular Chinese. These texts also contain numerous ancient characters and are usually without punctuation. Understanding genealogy texts requires sufficient expertise. When multiple users labeling the same text, conflicts may occur. Existing annotation tools are inappropriate for this work. In this paper, we propose a novel interactive labeling tool, which provides text segmenting, entity and relationship tagging etc. With the annotated information, it is convenient to construct knowledge graph from textual documents, which can be used to analyze Chinese genealogy texts. Furthermore, we introduce a weak supervised mechanism with Hidden Markov Model for collaborative annotating with crowdsourcing. The practice shows that our approach is effective for collaborative annotation. It also facilitates the construction of knowledge graph and obtains more high-quality data sets. At present, this annotation tool has been applied into service. © 2023 IEEE.

关键词： Hidden Markov models

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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Cyber attack prediction on IoT and IIOT devices using ensemble based predictive analytic model

Cyber attack prediction on IoT and IIOT devices using ensemb...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Priyan Malarvizhi Kumar Tayyaba Shahwar C Gokulnath Jeeva Selvaraj Department of Data Science University of North Texas Denton USA Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of ISE Jain University Global Campus Bangalore India

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

The accelerated connectivity across almost all entities of the globe has eventually escalated the number of Internet of Things (IoT) and Industrial IoT (IIoT) devices. These objects need seamless internet access which makes them vulnerable to various types of cyber-attacks at various layers. Many innovative and efficient solutions are developed for preventing these attacks and protecting the devices and data from malicious access. However, new forms of attacks continue to surge every year, which necessitates the development of more adaptive solutions by harnessing the attack profile data. The proposed research work constructs a heterogeneous ensemble model consisting of different types of base machine learning algorithms. These models explore the network profile data of the IoT and IIoT devices to extract inherent and latent features from it and predicts the network activity. Later, the predictions of the individual base learners are aggregated using a meta learner model to give the final prediction. The performance of the proposed ensemble model is validated using standard prediction metrics to assess its performance. As future research extension many base learners could be integrated to form a more efficient and powerful ensemble model with novel aggregation techniques.

关键词： Performance evaluation Analytical models Adaptation models Surge protection Predictive models Feature extraction Surges Cyberattack Standards Industrial Internet of Things

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Analysis of Activity Logs of GakuNin LMS in the Use of the Information Security Course at Gunma University 27

Analysis of Activity Logs of GakuNin LMS in the Use of the I...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Hamamoto, Nobukuni Ogawa, Koichi Ueda, Hiroshi Furukawa, Masako Nakamura, Motonori Yamaji, Kazutsuna Library and IT center Gunma University 4-2 Aramaki-machi Gunma Maebashi371-8510 Japan Research Center for Computing and Multimedia Studies Hosei University Japan Research Center for Open Science and Data Platform National Institute of Informatics Japan Institute for Information Management and Communication Kyoto University Japan

The GakuNin LMS operated by the National institute of Informatics (NII) provides an information security teaching course called "Princess Rin-Rin's security column". Gunma University extensively encourages students, faculty members, and staff to take this course. In this paper, we have classified the participants who took this course at Gunma University in the first semester of 2022 according to their course status and the results of the final test. Hierarchical clustering and K-means clustering, which are unsupervised learning methods, were used for the classification. The course status was classified into eight clusters, and the result of the final test was into four clusters. We have identified the most excellent cluster of the final test by investigating the clusters where the participants complete the final test with fewer attempts and in a shorter period of time. Furthermore, we have identified clusters of course status that include relatively good participants compared to the overall average using the relation between the clusters of course status and the clusters of final tests. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Clustering Infomation Security e-learing Learing Analytics Moodle

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InsTex: Indoor Scenes Stylized Texture Synthesis

arXiv

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arXiv 2025年

作者： Zhang, Yunfan Xiong, Zhiwei Shen, Zhiqi Lin, Guosheng Wang, Hao Vun, Nicolas College of Computing and Data Science Nanyang Technological University Singapore Alibaba-NTU Singapore Joint Research Institute Nanyang Technological University Singapore The Hong Kong University of Science and Technology Guangzhou China

—Generating high-quality textures for 3D scenes is crucial for applications in interior design, gaming, and augmented/virtual reality (AR/VR). Although recent advancements in 3D generative models have enhanced content creation, significant challenges remain in achieving broad generalization and maintaining style consistency across multiple viewpoints. Current methods, such as 2D diffusion models adapted for 3D texturing, suffer from lengthy processing times and visual artifacts, while approaches driven by 3D data often fail to generalize effectively. To overcome these challenges, we introduce InsTex, a two-stage architecture designed to generate high-quality, style-consistent textures for 3D indoor scenes. InsTex utilizes depth-to-image diffusion priors in a coarse-to-fine pipeline, first generating multi-view images with a pre-trained 2D diffusion model and subsequently refining the textures for consistency. Our method supports both textual and visual prompts, achieving state-of-the-art results in visual quality and quantitative metrics, and demonstrates its effectiveness across various 3D texturing applications. © 2025, CC BY-NC-ND.

关键词： Image texture

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An Extensive Study on Pretrained Models for Natural Language Processing Based on Transformers

An Extensive Study on Pretrained Models for Natural Language...

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Ramprasath, M. Dhanasekaran, K. Karthick, T. Velumani, R. Sudhakaran, P. Srm Institute of Science and Technology Department of Data Science and Business Systems School of Computing TamilNadu Chennai India Department of Computer Science and Engineering Andhra Pradesh Madhrawada India Srm Trp Enginerring College Department of Computer Science and Engineering Tamil Nadu Trichy India

ISBN: (纸本)9781665484251

In recent years, Pretraining Language Models based on Transformers -Natural Language Processing (PLMT-NLP) have been highly successful in nearly every NLP task. In the beginning, Generative Pre-trained model-based Transformer, BERT- Bidirectional Encoder model Representations using Transformers was used to develop these models. Models constructed on transformers, Self-supervise knowledge acquiring, and transfer learning establish the foundation of these designs. Transformed-based pre-trained models acquire common linguistic illustrations from vast amounts of textual information through self-supervised model and apply this information to downstream tasks. To eliminate the need to retrain downstream models, these models provide a solid foundation of knowledge. In this paper, the enhanced learning on PLMT-NLP has been discussed. Initially, a quick introduction to self-supervised learning is presented, then diverse core concepts used in PLMT-NLP are explained. Furthermore, a list of relevant libraries for working with PLMT-NLP has been provided. Lastly, the paper discusses about the upcoming research directions that will further improve these models. Because of its thoroughness and relevance to current PLMT-NLP developments, this survey study will positively serve as a valuable resource for those seeking to understand both basic ideas and new developments better. © 2022 IEEE.

关键词： Natural language processing systems

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Paddy Plant Disease Detection using Hybrid Deep Learning Algorithms 8

Paddy Plant Disease Detection using Hybrid Deep Learning Alg...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Suguna, R. Deepa, S. Kanimozhi, T. Chandru, M. Arun, G. Harshini, M. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam Tamilnadu Erode638401 India Kongu Engineering College Perundurai Department of Computer Technology - Pg Tamilnadu Erode638060 India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore641202 India College Department of Computer Science Erode India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Kongu Engineering College Tamilnadu Perundurai Erode638060 India

ISBN: (纸本)9798350367904

Agriculture is the backbone of India's economy, supporting a large portion of the population and establishing the country as a key global player in food production. India ranks as the second-largest producer of both paddy and wheat. However, agricultural productivity is highly susceptible to various plant diseases, which can significantly reduce crop yields and quality. Paddy, one of the most important staple crops, is particularly vulnerable to diseases caused mainly by viral and bacterial related diseases. The climate conditions are also play a vital role in paddy plant diseases. This research work proposes a deep learning-based approach for early detection and classification of paddy diseases. The proposed research devises a hybrid model that combines the VGG19 and ResNet50 algorithms, the system analyses images of both healthy and disease-affected paddy leaves, sourced from the Kaggle repository. This research focuses on predicting four major paddy diseases that can cause yield losses of up to 80%, and in severe cases, can destroy an entire crop: (i) Paddy Blast, (ii) Bacterial Leaf Blight, (iii) Sheath Blight, and (iv) Ufra Disease. The proposed model achieves a high accuracy rate of 98% in predicting paddy diseases and offers a rapid classification process, showcasing the potential of advanced image processing and deep learning techniques to mitigate the impact of paddy plant diseases on agricultural productivity. © 2024 IEEE.

关键词： Crops

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A Survey on Usage of Multimedia databases for Emotion Elicitation: A Quantitative Report on How Content Diversity Can Improve Performance

A Survey on Usage of Multimedia Databases for Emotion Elicit...

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Proceedings of the International Convention MIPRO

作者： M. Horvat P. Jerčić Department of Applied Computing Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Graz University of Technology Institute of Interactive Systems and Data Science Graz Austria

Affective picture databases provide a standardized set of images to elicit controlled and consistent emotional responses in research participants. They are a valuable tool for studying various emotion-related phenomena across several research domains. These domains include emotion perception, emotion regulation, and the neural basis of emotion. However, affective picture databases have diverse schemas, structures, and content, making them difficult to use. Searching and retrieving optimal pictures relevant to affective stimulation may be challenging and time-consuming. In this context, we surveyed domain experts about their practices and experiences working with affective multimedia databases such as IAPS, NAPS, OASIS, GAPED, and others. The survey identified a need for novel data observatory software. This finding motivates the authors’ intention to develop and validate such software platform that relies on AI. Such a platform would describe better, retrieve, and integrate various semi-structured affective multimedia datasets. The results prominently indicate the overwhelming dissatisfaction regarding stimuli content diversity and cultural bias, specifically regarding emotional and semantic context. The main driver of satisfaction from users of existing automated retrieval software is the quality of semantic descriptors available. This points to the direction AI should take in novel data observatory software. This survey follows up on a similar survey conducted ten years ago and explores the differences in researchers’ opinions and experiences during that time. The complete aggregated results are publicly available at https://***/mhorvat/stimdbsurvey.

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An improved reconfigurable logic in resistive random access memory

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Integration 2022年 87卷 169-175页

作者： Zhao, Yi Chen, Hui Liu, Peng Wu, Jigang Luo, Dongxiang The Center for High Performance Computing and Data Science School of Computer Science and Technology Guangdong University of Technology Guangdong510006 China School of Chemistry and Chemical Engineering/Institute of Clean Energy and Materials/Guangzhou Key Laboratory for Clean Energy and Materials/Huangpu Hydrogen Innovation Center Guangzhou University Guangzhou510006 China Institute of Semiconductors South China Normal University Guangzhou510631 China

Emerging non-volatile devices have shown great potential in computing in-memory (CIM). This work proposes a logic design method based on the complementary resistance switching (CRS) structure, which is connected by two anti-serially memristors. Three logics including AND, OR, and NIMP can be implemented in one step. Moreover, XOR logic can be implemented in two steps. Based on the proposed design, a 1-bit full adder is implemented with the fewer number of memristors and operation steps compared with existing works. The feasibility and correctness of our design are verified by the SPICE simulations with the Voltage Threshold Adaptive Memristor (VTEAM) model. In addition, we evaluate the ISCAS’85 benchmark circuit based on the proposed logic. Compared with existing methods, the performance and area overhead have been improved by the proposed method. © 2022 Elsevier B.V.

关键词： Memristors

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