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2023 China Automation Congress, CAC 2023

作者： Zhao, Xiaoyu Sun, Xiaobo Zhong, Shaobo Wei, Shuangfeng School of Geomatics and Urban Spatial Information Beijing University of Civil Engineering and Architecture Beijing China Natural Resources Big Data Center Hebei Geographic Information Group Co. Ltd. Hebei China Institute of Urban Systems Engineering Beijing Academy of Science and Technology Beijing China

ISBN: (纸本)9798350303759

Large-scale skiing events are commonly conducted in complex outdoor terrains and weather conditions, where adverse weather conditions can significantly impede the smooth progress of these events. The occurrence of adverse weather conditions increases the risk of domino chains caused by meteorological disasters during large-scale skiing events. To promptly identify the cascading risks caused by adverse weather, this paper proposes a solution to promptly identify cascading risks caused by adverse weather by integrating information related to adverse weather risks of skiing events and constructing a domain knowledge ontology. The paper analyzes and identifies the risk transmission paths of adverse weather, and constructs the risk identification rule base of related event chains to enable global risk chain reasoning driven by spatiotemporal semantics. The proposed approach is demonstrated through the skiing events of the 2022 Beijing Winter Olympics, from which the study correctly identifies the risk events caused by adverse weather encountered in multiple skiing events through reasoning. The results show the accuracy and feasibility of the event chain inference model. © 2023 IEEE.

关键词： Meteorology

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Reevaluating data Partitioning for Emotion Detection in EmoWOZ

arXiv

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arXiv 2023年

作者： Mostafavi, Moeen Porter, Michael D. Systems and Information Engineering University of Virginia CharlottesvilleVA United States Data Science & Systems and Information Engineering University of Virginia CharlottesvilleVA United States

This paper focuses on the EmoWoz dataset, an extension of MultiWOZ that provides emotion labels for the dialogues. MultiWOZ was partitioned initially for another purpose, resulting in a distributional shift when considering the new purpose of emotion recognition. The emotion tags in EmoWoz are highly imbalanced and unevenly distributed across the partitions, which causes sub-optimal performance and poor comparison of models. We propose a stratified sampling scheme based on emotion tags to address this issue, improve the dataset's distribution, and reduce dataset shift. We also introduce a special technique to handle conversation (sequential) data with many emotional tags. Using our proposed sampling method, models built upon EmoWoz can perform better, making it a more reliable resource for training conversational agents with emotional intelligence. We recommend that future researchers use this new partitioning to ensure consistent and accurate performance evaluations. Copyright © 2023, The Authors. All rights reserved.

关键词： Emotion Recognition

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WHAT USERS WANT: A NATURAL LANGUAGE PROCESSING APPROACH TO DISCOVER USERS' NEEDS FROM ONLINE REVIEWS 24

WHAT USERS WANT: A NATURAL LANGUAGE PROCESSING APPROACH TO D...

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24th International Conference on engineering Design, ICED 2023

作者： Spada, Irene Barandoni, Simone Giordano, Vito Chiarello, Filippo Fantoni, Gualtiero Martini, Antonella School of Engineering Department of Information Engineering University of Pisa Italy Department of Computer Science University of Pisa Italy School of Engineering Department of Energy Systems Land and Construction Engineering University of Pisa Italy School of Engineering Department of Civil and Industrial Engineering University of Pisa Italy B4DS - Business Engineering for Data Science Lab University of Pisa Italy

Digital media are a means to deliver products and services, but also a channel to interact with consumers and a source of information on users' preferences. data shared by customers on the web, the User-Generated Content (UGC), can give entrepreneurs a detailed perspective of the market. This work examines an application of Natural Language Processing techniques on UGC to discover insights on users' opinions. We collected more than 13.000 reviews of software from digital stores and review website to gather information on the customers' perspective and their response to a given marketing strategy in two case studies on digital product's launch. The objective is to give support to two Italian companies in the process of business model development through data-driven evidence. We aim to discover who are the users and which are their needs using a lexicon-based approach to mine unstructured text. The results provide qualitative and quantitative descriptions of the market segments. We propose a method to examine UGC and to explore customers' behavior on social media. The findings helped managers for the development of their business model, enhancing an informed decision-making process. © The Author(s), 2023. Published by Cambridge University Press.

关键词： Digital storage

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A New Tree Structure for Local Diagnosis

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Journal of information science and engineering 2024年第2期40卷 231-244页

作者： Chen, Meirun Li, Xiao-Yan Lin, Cheng-Kuan Pai, Kung-Jui School of Mathematics and Statistics Xiamen University of Technology Xiamen361024 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology National Defense University Taoyuan30010 Taiwan Department of Industrial Engineering and Management Ming Chi University of Technology New Taipei City243303 Taiwan

Diagnosability is an important parameter to measure the fault tolerance of a multiprocessor system. If we only care about the state of a node, instead of doing the global diagnosis, Hsu and Tan proposed the idea of local diagnosis. Chiang and Tan provided an extended star structure to diagnose a node under comparison model. In this work, we evaluate the local diagnosability better by proposing a tree structure around this node. We provide the corresponding algorithm to diagnose the node. Simulation results are presented for different failure probability of a node in the tree and different percentage of faulty nodes in the tree, showing the performance of our algorithm. © 2024 Institute of information science. All rights reserved.

关键词： comparison model diagnosis algorithm fault diagnosis local diagnosability tree

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English Offensive Text Detection using CNN based Bi-GRU model 2

English Offensive Text Detection using CNN based Bi-GRU mode...

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2nd International Conference on information and Communication Technology, ICICT 2024

作者： Roy, Tonmoy Islam, Md Robiul Miazee, Asif Ahammad Antara, Anika Al Amin Hossain, Sunjim Data Analytics & Information Systems Utah State University Utah United States Computer Science William & Mary Virginia United States Computer Science Maharishi International University Iowa United States Electrical and Electronics Engineering Brac University Dhaka Bangladesh Computer Science Uttara University Dhaka Bangladesh Computer Science Northern University Dhaka Bangladesh

ISBN: (纸本)9798331508227

Over the years, the number of users of social media has increased drastically. People frequently share their thoughts through social platforms, and this leads to an increase in hate content. In this virtual community, individuals share their views, express their feelings, and post photos, videos, blogs, and more. Social networking sites like Facebook and Twitter provide platforms to share vast amounts of content with a single click. However, these platforms do not impose restrictions on the uploaded content, which may include abusive language and explicit images unsuitable for social media. To resolve this issue, a new idea must be implemented to divide the inappropriate content. Numerous studies have been done to automate the process. In this paper, we propose a new Bi-GRU-CNN model to classify whether the text is offensive or not. The combination of the Bi-GRU and CNN models outperforms the existing models. ©2024 IEEE.

关键词： Tweets

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Mobility and Behavior based Trustable Routing in Mobile Wireless Sensor Network 7

Mobility and Behavior based Trustable Routing in Mobile Wire...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Praveen Kumar, B. Umamageswaran, J. Kalpana, A.V. Dhanalakshmi, R. Department of Computer Science and Engineering KL University Andhra Pradesh Vijayawada India Department of Information Technology RMK Engineering College Tamil Nadu Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology 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)9781665464086

Mobile wireless sensor networks (MWSNs) allow the sensor nodes to move freely and transmit to each other without needing a fixed infrastructure. Usually, the routing process is very complex, and it becomes even more complicated as the sensor nodes are low-power, cost-effective mobile devices with minimum resources. MWSNs raises deployed in security-critical applications due to their resource-constrained features;they are prone to several security attacks that critically affect data transmission. The most significant innovation of trust is that it evades untrustable nodes by activating several detection routes to detect and obtain node trust, enhancing security quickly. Mobility and Behavior based Trustable Routing (MBTR) is proposed to solve these issues. This approach measures sensor node mobility based on the Received Signal Strength (RSS) between the two sensor nodes. The trust manager computes the node trust ability by the node behavior. Here, the node behavior is checked through the node energy, supportive account, and received packet account. The trust manager selects the security algorithm based on distance during data transmission. The simulation examination demonstrates lesser delay and lesser packet loss in the network. Furthermore, it improves the network throughput in the MWSN. © 2023 IEEE.

关键词： Sensor nodes

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Perceptual Video Summarization Using Keyframes Extraction Technique 3

Perceptual Video Summarization Using Keyframes Extraction Te...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Rajitha Jasmine, R. Nimmagadda, Padmaja Sudhakar, K. Benitha Christinal, J. Rajasekar, P. Syed Musthafa, A. R. M. K. Engineering College Department of Information Technology Kavaraipettai India Mohan Babu University Department of Electronics and Communication Tirupati India Madanapalle Institute of Technology Department of Computer Science Madanapalle India Panimalar Engineering College Department of Information Technology Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu India M. Kumarasamy College of Engineering Department of Information Technology Karur India

ISBN: (纸本)9798350336238

The growth of video content in recent years is a challenging problem due to increased memory storage and time consuming for analyzing content of the video. Therefore, there is a need to reduce the content for human usage. This paper presents a keyframes technique that uses co-occurrence matrix and permutation computation for perceptual video summarization (PVS). The Human Visual System (HVS) needs to incorporate the PVS. It helps to allow for the importance of perceptually significant contents. The proposed method uses different kinds of videos to evaluate the effectiveness of the work. The subjective evaluation scores have evaluated the proposed work. © 2023 IEEE.

关键词： Video recording

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UAV-Assisted data Synchronization for Digital-Twin-Enabled Vehicular Networks

UAV-Assisted Data Synchronization for Digital-Twin-Enabled V...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wu, Guoquan Nie, Jiangtian Tang, Jianhang Zhang, Yang Xiao, Jiang Xiong, Zehui Yanshan University School of Information Science and Engineering China Nanyang Technological University School of Computer Science and Engineering Singapore Guizhou University State Key Laboratory of Public Big Data China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China Huazhong University of Science and Technology School of Computer Science and Technology China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore

ISBN: (纸本)9798350345384

Digital twin (DT) is becoming a promising solution for vehicular networks to improve the interoperability of distributed autonomous driving systems. Mobile edge computing (MEC) has been introduced to provide low-latency services for DT-enabled vehicular networks at the edge of the network. However, it is hard to obtain the dynamic network topology for moving vehicles by the ground-based MEC system, which may deteriorate the service quality for DT synchronization. In this paper, we propose a novel unmanned aerial vehicle (UAV)-assisted synchronization framework for DT-enabled vehicular networks. With the proposed framework, an intelligent resource allocation algorithm is developed to improve UAV resource utilization and maximize the synchronization completion ratio. By leveraging an advantage actor-critic (A2C) algorithm, the synchronization decisions are obtained with low time complexity. Experiment results demonstrate that the proposed algorithm can reduce the synchronization latency and improve the synchronization completion ratio effectively. © 2023 IEEE.

关键词： Resource allocation

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The statistical observation localized equivalent-weights particle filter in a simple nonlinear model

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Acta Oceanologica Sinica 2022年第2期41卷 80-90页

作者： Yuxin Zhao Shuo Yang Renfeng Jia Di Zhou Xiong Deng Chang Liu Xinrong Wu College of Intelligent Systems Science and Engineering Harbin Engineering UniversityHarbin 150001China Harbin Marine Boiler&Turbine Research Institute Harbin 150078China China Ship Development and Design Center Wuhan 430064China Key Laboratory of Marine Environmental Information Technology National Marine Data and Information ServiceState Oceanic AdministrationTianjin 300171China

This paper presents an improved approach based on the equivalent-weights particle filter(EWPF)that uses the proposal density to effectively improve the traditional particle *** proposed approach uses historical data to calculate statistical observations instead of the future observations used in the EWPF’s proposal density and draws on the localization scheme used in the localized PF(LPF)to construct the localized *** new approach is called the statistical observation localized EWPF(LEWPF-Sobs);it uses statistical observations that are better adapted to the requirements of real-time assimilation and the localization function is used to calculate weights to reduce the effect of missing observations on the *** approach not only retains the advantages of the EWPF,but also improves the assimilation quality when using sparse *** experiments performed with the Lorenz 96 model show that the statistical observation EWPF is better than the EWPF and EAKF when the model uses standard distribution *** of the statistical observation localized EWPF and LPF reveal the advantages of the new method,with fewer particles giving better *** particular,the new improved filter performs better than the traditional algorithms when the observation network contains densely spaced measurements associated with model state nonlinearities.

关键词： data assimilation particle filter equivalent weights particle filter localization methods

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GPR B-scan data Classification through Deep Learning Approach 9

GPR B-scan Data Classification through Deep Learning Approac...

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9th International Conference on Big data and information Analytics, BigDIA 2023

作者： Zhang, Yaohong Xiao, Kaiming Liu, Kehui Ge, Bin Chen, Haiwen Li, Donghui National University of Defense Technology Laboratory for Big Data and Decision Changsha China Beijing Academy of Science and Technology Institute of Urban Systems Engineering Beijing China Hunan Agricultural University College of Information and Intelligence Changsha China

ISBN: (纸本)9798350330076

Ground Penetrating Radar (GPR) has proven indispensable in non-destructive geophysical investigations, offering a window into subsurface structures using electromagnetic waves. Its prowess in identifying underground utilities and hazards, like cavities, has been universally recognized. However, the intricate B-scan data generated by GPR poses significant interpretation challenges. Traditional methodologies, especially those reliant on dielectric constants and earlier machine learning models, often necessitate prior subsurface knowledge, inhibiting their universal applicability. Enter deep learning, which, in recent years, has changed the landscape of data interpretation across domains. Among the various architectures, Convolutional Neural Networks (CNNs) have shown immense potential in deciphering GPR data. This paper champions a novel methodology that harnesses the power of the ResNet18 architecture. Uniquely, our approach begins with a model pre-trained on the WiderFace dataset, renowned for facial recognition, and subsequently fine-tuned for GPR data. This merger of facial pattern recognition with geophysical data interpretation creates a synergy that promises unprecedented accuracy in subsurface detection. In summation, our research showcases how deep learning, especially the fusion of facial recognition insights with geophysical data, can redefine the boundaries of GPR data interpretation, offering enhanced accuracy and adaptability over conventional methods. © 2023 IEEE.

关键词： Deep learning

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