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3rd international conference on Computer technology, information Engineering, and Electron Materials, CTIEEM 2023

作者： Song, Yichen Bai, Wenle Wei, Qing Li, Xintong School of Information Science and Technology North China University of Technology Beijing China

ISBN: (纸本)9781510672925

With the continuous development of modern communication technologies, many computation-intensive and delaysensitive applications have emerged. Unmanned Aerial Vehicle (UAV) can perform excellent and unique in emergency communication systems by virtue of its flexibility. In this paper, a UAV-assisted fog computing emergency communication system is designed, wherein a UAV that has computing capabilities can act as a UAV Fog Access Point (UAV-FAP) and offer surrounding mobile devices (MDs) computational offloading services. The objective is to minimize the maximum processing delay of the system. The optimization problem of jointly optimizing MD scheduling, distribution of resources and UAV flight direction is proposed under energy consumption constraints. Considering that the problem is a mixedinteger nonlinear programming (MINLP) problem, a computational offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) is proposed to solve the non-convex problem. And the Switches from Adam to SGD (SWATS) algorithm is also invoked to update the neural network parameters with the optimizer to avoid falling into local optima to some extent. Simulation results show that the DDPG-SWATS joint algorithm converges quickly and has a small processing delay compared with the benchmark algorithm. © 2024 SPIE.

关键词： Reinforcement learning

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MULTIMODAL HATE SPEECH DETECTION: LEVERAGING TEXTUAL AND VISUAL information (IN PORTUGUESE) 21

MULTIMODAL HATE SPEECH DETECTION: LEVERAGING TEXTUAL AND VIS...

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21st international conference on applied computing 2024, AC 2024 and 23rd international conference on WWW/Internet 2024, ICWI 2024

作者： Alves, Thayná Weitzel, Leila Computer Science Department Fluminense Federal University Rio de Janeiro Brazil

ISBN: (纸本)9789898704627

The rapid growth of social media has amplified the dissemination of hate speech, posing a substantial threat to online communities. While traditional text-based approaches have limitations, e.g., they often fall short in capturing the rich contextual information conveyed through visual cues. To tackle this issue, the multimodal analysis offers potential improvements. This paper proposes a multimodal model that integrates textual and visual cues for hate speech detection specifically focusing on Brazilian Portuguese. By combining CNNs for facial expression analysis and BERT for text processing. Multimodal classification allows for a more comprehensive understanding of the context in which hate speech occurs. Visual cues, such as symbols or gestures in images, can be strong indicators of hate speech that textual analysis alone might miss. This research suggest that multimodal approaches can reduce this ambiguity by providing additional context through images or videos, making it easier to discern the true intent behind a post. While multimodal classification holds great promise, several challenges remain. One major issue is the computational complexity associated with processing and integrating multiple modalities, which can require substantial computational resources. © 2024 Proceedings of the international conferences on applied computing and WWW/Internet 2024. All rights reserved.

关键词： Speech recognition

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Methodology Metadata-driven Knowledge Production Charac-teristics Identification by Knowledge Retrieval and Mining 7

Methodology Metadata-driven Knowledge Production Charac-teri...

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7th international conference on Data Science and information technology, DSIT 2024

作者： Xu, Hao Ge, Linlin Zhang, Yan Jin, Zhenqiang Wang, Mulan Feng, Ke Kang, Zhenyuan Nanjing Institute of Technology School of Management Engineering Nanjing China Nanjing University School of Information Management Nanjing China Nanjing Institute of Technology School of Electrical Power Engineering Shen Guorong College Nanjing China Jiangsu Power Transmission & Transformation Co. Ltd Nanjing China

ISBN: (纸本)9798350384093

The progress of science, technology, and innovation is greatly aided by the advancement, improvement, and use of methodology. Our work developed a Characteristic Recognition Algorithm based on text mining to extract methodology metadata from full-text documents and identify characteristics that are methodologically driven by time trend, dissemination speed, and co-drive. A sample of 5564 publications from the economic and management (E&M) discipline were chosen, from which 9,995 methodological metadata were extracted and classified into 4 categories: model, software & tool (S&T), data, and method & theory (M&T). The co-occurrence trait was revealed using the social network approach. Results indicate that over the past 20 years, the methodology's usage rate has increased, reaching 78.49% in 2019 alone. While S&T, model, and data have slowed down, M&T has the highest usage rate, which is reflected in a continually rising diffusion feature. The outcomes of knowledge creation in E&M exhibit the features of multi-methodology that is frequently driven, including M&T (hypotheses, questionnaire surveys), data (China Statistical Yearbook, wind, and CSMAR), model (double difference and logit), and S&T (SPSS, Amos, LISREL). The process of defining characteristics of approach metadata-driven knowledge production will be applied to different areas as a preliminary investigation, as well as used as training material for the automatic extraction of large-scale corpora. © 2024 IEEE.

关键词： Intelligent computing Knowledge Production Characteristics Knowledge Retrieval and Mining Methodology Driven Social Network Analysis

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Cloud computing in Industry 4.0: Enabling Real-Time Data Processing for Smart Factories 15

Cloud Computing in Industry 4.0: Enabling Real-Time Data Pro...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Thangarasu, N. Choudhary, Sunila Basha, Umi Salma Otiaf, Jana Ibrahim Nanduri, Sireesha Gaikwad, V.S. Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Jazan University Saudi Arabia Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

With Industry 4.0 leading the charge, cloud computing has revolutionized how industries work today. The cloud computing feature added in Industry 4.0 paved the way for real-time data processing that seeped right through intelligent factories and reflected the most significant improvements in efficiency, production, and cost reduction, all playing an important role today. It is a predictive maintenance technology that contributes to the concept of IoT in manufacturing, with proper machine-to-machine and system communication channels for data exchange. Cloud computing provides a scalable and adaptable data storage/analysis environment for large amounts of information to be maintained in predictive maintenance mode with optimization processes based on real-time applications. A data layer standing in between, rendering relevant information when needed to oversee the remote operation of plants can facilitate smart manufacturing over time. The cloud also allows collaboration and visibility at every point in your supply chain, significantly boosting overall SCM to Industry 4.0. © 2024 IEEE.

关键词： Smart manufacturing

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Analysing Paralinguistic information from Human Speech and its Applications in Medicine 1

Analysing Paralinguistic Information from Human Speech and i...

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1st IEEE international conference on Advances in Electronics, Communication, computing and Intelligent information Systems, ICAECIS 2023

作者： Kaushik, Palak Sharma, Ashish Chandigarh University Department of Computer Science Engineering Punjab Mohali India Department of Electrical Engineering Chandigarh University Punjab Mohali India

ISBN: (纸本)9798350348057

The recent advances in Speech Emotion Recognition (SER) technology are explored in this work. Two of these include emotion detection using excitation features of speech and emotion recognition using Zero Shot Learning (ZSL). Both methods are speaker specific in nature. The recent research is evaluated on the basis on Geneva Minimalistic Acoustic Parameter Set, which is a proposed standard set of acoustic parameters. SER is heavily based on kernelization for classification. The acoustic parameters are, however, not optimized for computing in a high dimensional form. Dimensionality reduction methods are applied to overcome this. This work also explores how SER can enhance the existing technologies in medicine to detect various vocal pathologies and other disorders that may affect speech production and analyze their progression over time. © 2023 IEEE.

关键词： Emotion Recognition

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Quantum Machine Learning Techniques based on Nurturing Agri-Ontology Framework in Agricultural Science 3

Quantum Machine Learning Techniques based on Nurturing Agri-...

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3rd international conference on applied Artificial Intelligence and computing, ICAAIC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Bennet Prabhu, A. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Drbccc Hindu College Department of Computer Applications Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Kone Elevator India Technology & Engineering Centre Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

The absence of a consistent ontological framework often impedes efficient knowledge organization and sharing within the agricultural community, despite the quantity of data created in agricultural research. The lack of defined vocabulary and conceptual frameworks hinders data integration, agricultural research, and innovation by preventing interoperability between databases and systems. To address these issues, this research work proposes the Nurturing Agri-Ontology Framework (NAOF), which offers a systematic approach to creating and updating agricultural ontologies. Ontologies that faithfully portray agricultural ideas and connections can be more easily created with the help of NAOF, which combines ideas from knowledge management, agricultural science, and ontology engineering. NAOF aims to ensure that the ontologies produced reflect the wide range of viewpoints and terminology used in agricultural research by encouraging cooperation among stakeholders, ontology engineers, and domain specialists. In this study, the improvement of knowledge organization, sharing and integration in the agricultural area is shown with the application of NAOF. Using this research study, scientists can build ontologies that improve agricultural decision-making, data interoperability, and the discovery of new information. Using quantum machine learning techniques, a strong foundation of knowledge can be laid for several agricultural applications, such as precision agriculture, crop management, and the formulation of agricultural policies. Ontology coverage, interoperability, scalability, and usability are established evaluation parameters that are used to assess the efficacy of NAOF in this research work. The analysis shows that NAOF effectively solves the problems and creates a better agricultural knowledge ecosystem by thoroughly evaluating it using these standards. © 2024 IEEE.

关键词： Ontology

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information technologies of the Russian-Cuban GNSS service

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international Journal of information and Communication technology 2024年第2期24卷 156-164页

作者： Bezrukov, Ilia Yakovlev, Vladislav Marshalov, Dmitry Bondarenko, Yuri Salnikov, Alexander Rodríguez, Omar Pons Institute of Applied Astronomy of the Russian Academy of Science 10 Kutuzova Emb. St. Petersburg191187 Russia Institute of Geophysics and Astronomy 212 St. 2906 La Lisa Havana11600 Cuba

We present the hardware and software implementation of the information technologies for the GNSS service of the Russian-Cuban co-located geodetic station. The service is equipped with geodetic and meteorological instruments with data acquisition and transmission systems and allows to conduct high-precision observations of the GPS and GLONASS global navigation satellite systems (GNSS) as well as meteorological measurements. The coordinates of the station are refined while processing GNSS observations and according to the change in coordinates, the movement of Cuban tectonic plates is estimated. information technologies of the GNSS service is one of the key elements required for conducting regular automated geodetic and meteorological measurements. information technologies is a group of computing and networking equipment that provides preliminary processing and transfer of observations to the data processing centre at the Institute of applied Astronomy in St. Petersburg. It also allows the remote monitoring, control, and maintenance of GNSS service scientific instruments. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Virtual private networks

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Fashion Parsing and Identification for E-Commerce Using Semantic Segmentation 12

Fashion Parsing and Identification for E-Commerce Using Sema...

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12th international conference on information and Communication technology, ICoICT 2024

作者： Farhan Audianto, Muhammad Dwi Sulistiyo, Mahmud Rachmawati, Ema Hadiyoso, Sugondo School of Computing Telkom University Bandung Indonesia School of Applied Science Telkom University Bandung Indonesia

ISBN: (纸本)9798350363432

The rapid expansion of the Internet and its infrastructure has significantly impacted various sectors, particularly e-commerce. With the rise of platforms like Instagram Shopping, where product labels are often missing, there is a growing need for automated fashion image labeling to enhance user experience and facilitate better product identification. This study addresses the challenge of labeling fashion images using semantic segmentation techniques. We propose a method employing the U-Net architecture, which was trained and evaluated on two different epochs: 10 and 50. We evaluate semantic segmentation models using the average of Intersection over Union (IoU) metric with several thresholds for class IoU. In addition, for the fashion objects recognized through segmentation results, we evaluate them using the weighted F1-Score metric. The quantitative results are reported in this paper. Furthermore, a pixel-count threshold was applied to the label outputs to minimize the incorrectly detected objects. This approach provides a significant step towards more accurate and efficient fashion item identification in e-commerce. © 2024 IEEE.

关键词： Semantic Segmentation

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Numerical Simulation of Outdoor Edge Server Cooling Based on Radiation Cooling Materials 8

Numerical Simulation of Outdoor Edge Server Cooling Based on...

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8th international conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2024

作者： Wang, Yongguang Sun, Bo Fu, Changzhao Guo, Fen Sun, Yi Peng, Chunyang Inspur Electronic Information Industry Co. Ltd Jinan China Inspur Cisco Network Technology Co. Ltd Jinan China Yunhai Guochuang Cloud Computing Innovation Co. Ltd Jinan China

ISBN: (纸本)9798331506230

With the rapid development of edge computing technology, edge servers are being extensively deployed in outdoor environments. However, when outdoor environments experience high temperatures, the heat dissipation of servers becomes a critical factor limiting their performance. Radiative cooling materials, as a new type of cooling material, have highly selective and efficient radiative capabilities, which can significantly reduce the surface temperature of objects in windless outdoor conditions. In this study, numerical simulations were used to investigate the cooling effects of radiative cooling materials applied to edge server enclosures, and the cooling performance of the enclosures under different orientations was analyzed. The results show that, compared to conventional coating materials, radiative cooling materials can significantly lower the surface temperature of server enclosures, and the cooling performance varies depending on the server's orientation. © 2024 IEEE.

关键词： Surface temperature

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Complex scene fall detection method based on improved YOLOv5 24

Complex scene fall detection method based on improved YOLOv5

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13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Li, Yifan Liu, Pengyu Kong, Lili Deng, Yiyang Liu, Weilong Chen, Shanji Chen, Jiali Yuan, Bowen School of Information Science and Technology Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China Hebei Hongyi Environmental Protection Technology Co. Ltd Dingzhou Hebei China Beijing-Dublin International College at BJUT Beijing University of Technology Beijing China Information Technology Co. Ltd Dingzhou Hebei China College of Physics and Electronic Information Engineering Qinghai Minzu University Qinghai China

ISBN: (纸本)9798400717482

Aiming at the existing deep learning-based fall detection model in multiple complex scenes such as multi-person scenes, long-distance scenes, and occlusion scenes, there are problems such as leakage detection and false detection. In this paper, we take the falling person as the research object, construct a dataset containing multiple complex scenes such as multi-person scenes, long-distance scenes, and occlusion scenes, and propose a fall detection model based on improved YOLOv5. By improving the backbone network and neck network structure of the detection model, the problem of low accuracy of fall detection due to incomplete target deformation features and target detail features in complex scenes is solved. The proposed algorithm in this paper achieves an average accuracy of 92.78% for fall detection in complex scenes, and the detection speed reaches 36 frames/second. © 2024 Copyright held by the owner/author(s).

关键词： Fall detection

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