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检索条件"任意字段=Proceedings of the 2023 5th International Conference on Big Data Engineering and Technology"

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Design of capacitor explosion-proof warning system based on STM32F103C8T6 single chip microcomputer 3

Design of capacitor explosion-proof warning system based on ...

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3rd international conference on Mechanical, Electronics, and Electrical and Automation Control, METMS 2023

作者： Yan, Jian-Hua You, Guang-Hao Zheng, Hai-Tao Zhao, Xu-Rui Yang, Cai-Wei Yu, Lan School of Energy and Power Changchun Institute of Technology Changchun130012 China Jilin Construction Energy Supply and Indoor Environment Control Engineering Research Center Changchun130012 China

ISBN: (纸本)9781510666689

this paper takes the STM32F103C8T6 single chip microcomputer as the core, adopts the combination of software and hardware, collects the field data through the partial pressure measurement module and temperature, pressure and other sensors set on the equipment site, uses the STM32F103C8T6 single chip microcomputer for intelligent data monitoring and real-Time data processing, ESP8266 Internet of things WIFI chip to transmit the field monitoring information, and the Internet of things platform for remote monitoring. the system can intelligently monitor all kinds of abnormal conditions. When there are abnormal conditions, the danger degree can be determined by defining the data collected on the site, and the intelligent decision-making system can conduct comprehensive analysis to realize real-Time early warning and alarm of capacitor fault. © 2023 SPIE.

关键词： Microcomputers

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Exploring the Fintech Adoption among Indonesian Gen Z: An Analysis using the theory of Planned Behavior among Digital Literacy Users 5

Exploring the Fintech Adoption among Indonesian Gen Z: An An...

引用

5th international conference on Advancement in data Science, E-Learning and Information Systems, ICADEIS 2023

作者： Widiharlina, Regina Tasya Saputra, Muhardi Wegman, Dirk Jan Puspitasari, Warih Telkom University School of Industrial and System Engineering Bandung Indonesia Business and Technology Saxion University of Applied Sciences School of Building Netherlands

ISBN: (纸本)9798350303414

One of the most significant financial sector breakthroughs, fintech is expanding quickly thanks in part to the sharing economy, regulations, and information technology. the next use of fintech services is generation Z, but generation z is still not ready to receive fintech services due to free-oriented thinking. And there are also other factors that influence generation z, namely technostress and social influence. To ascertain the impact of social influence and technostress on generation Z's adoption of fintech services, With regard to attitudes, subjective norms, and perceived behavior control, the TPB model is utilized to forecast behavioral intentions, and uses a quantitative approach using simple random sampling and slovin techniques to calculate a large number of samples. there are 13 hypotheses where social influence and technostress have an impact on how people use fintech services, based on the hypothesis has positive and significant effect. © 2023 IEEE.

关键词： Economic and social effects

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Initialization with Description Information and Incorporating Structure Learning for Efficient Dynamic Knowledge Graph Embedding 4

Initialization with Description Information and Incorporatin...

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4th international conference on Computer, big data and Artificial Intelligence, ICCBD+AI 2023

作者： Liu, Yao Liu, Ye Wang, Xin Beihang University Beijing Key Laboratory of Digital Media Beijing China Beihang University The School of Instrumentation and Optoelectronic Engineering Beijing China Tsinghua University Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350373233

Knowledge graphs (KGs) are an efficient method for representing heterogeneous knowledge. In KGs, knowledge is represented as triples and transformed into a low-dimensional space using graph embedding techniques, benefiting downstream tasks such as recommender systems and question answering. In real-world scenarios, knowledge graphs are continuously updated with new facts. However, existing KG embedding models only support static graph embeddings, requiring the re-training of the entire graph whenever knowledge updates occur, resulting in time-consuming. there are limited models that support dynamic embedding, which utilizes already trained embeddings to generate representations of new data to improve embedding efficiency. However, these models often ignore the semantic information inherent in the new data itself and the unique structural information in the KG, resulting in low embedding quality. To address this issue, we propose an embedding method specifically designed for dynamic knowledge graphs. Initially, we obtain the semantic representation by quantifying the descriptive information of the added nodes as an initialization vector. Next, to ensure compliance with the graph structure in the KG, we leverage the triple structure of neighboring nodes to the new nodes, and iteratively train the semantic representation to satisfy the structural constraints of KG, resulting in an embedding vector that incorporates both semantic and structural information. We compare our model with existing KG embedding models on two benchmark datasets in link prediction task. the experimental results demonstrate that our model achieves higher efficiency while maintaining the same level of accuracy, thereby validating its effectiveness. © 2023 IEEE.

关键词： Knowledge graph

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the application of spatial big data in urban real estate industry-A case study of Xi'an city 22

The application of spatial big data in urban real estate ind...

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5th international conference on Computer Science and Software engineering, CSSE 2022

作者： Yu, Haoyu Dang, Xianxian School of Computer Science and Technology Xidian University Xi'an China School of Human Settlements and Civil Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9781450397780

Real estate data analysis and visualization are important contents of urban research and urban management, while big data and computer technology provide scientific methods and tools for them. this research is based on big data technology. Firstly, the paper obtains the relevant massive data on the network through computer technology, and makes a visual analysis of the current situation. Secondly, the paper uses Kriging interpolation method to predict and analyze the trend of house prices and display them visually. Finally, the paper establishes an ordinary least squares (OLS) regression model to visualize and quantify the location factors affecting house prices, and makes a spatial differentiation *** research results of this paper explore the spatial pattern and action mechanism of house prices in Xi'an, which has a certain reference significance for optimizing urban spatial layout and rational allocation of urban resources. At the same time, this paper establishes a set of methods of big data mining, data analysis, data visualization and extracting factors affecting house prices based on computer technology, which provides an idea for the application of big data in real estate and urban planning and management. © 2022 ACM.

关键词： data visualization

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Mobile Network Monitoring technology for City Freeway Based on big data Analysis 8th

Mobile Network Monitoring Technology for City Freeway Based ...

引用

8th international conference on Signal and Information Processing, Network and Computers, ICSINC 2021

作者： Wang, Baoyou Yao, Saibin Huang, Jiucheng Pan, Hui Liu, Chao Shanghai Branch of China Unicom Shanghai200080 China

ISBN: (纸本)9789811933868

In the scene of city freeway, the driving speed is relatively fast, the flow of people is denser, and the customers have higher requirements for communication experience. Firstly, the architecture of big data platform for mobile network quality control was introduced. then, the mobile network monitoring scheme for city freeway based on big data analysis was proposed. the scheme is oriented to group perception which approaches real experience, so as to effectively improving the evaluation ability and frequency for mobile network of city freeway. It automatically identifies users who driving on freeway from the data lake of XDR by clustering algorithm. through 3W matching association, the signaling data pool of freeway users was generated, which would contribute to reduce the data volume and the consumption of computing resources greatly. through road section analysis of freeway user trajectory, KPI monitoring and warning of abnormal for road section were realized. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： big data

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Security situation awareness method of power mobile application based on big data architecture 5

Security situation awareness method of power mobile applicat...

引用

5th international conference on data Science and Information technology, DSIT 2022

作者： Yong, Li Mu, Chen Zaojian, Dai Lu, Chen State Grid Key Laboratory of Information & Network Security State Grid Smart Grid Research Enstitute Co. Ltd Nanjing210003 China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

According to the characteristics of security threats and massive users in power mobile applications, a mobile application security situational awareness method based on big data architecture is proposed. the method uses open-source big data technology frameworks such as Kafka, Flink, Elasticsearch, etc. to complete the collection, analysis, storage and visual display of massive power mobile application data, and improve the throughput of data processing. the security situation awareness method of power mobile application takes the mobile terminal threat index as the core, divides the risk level for the mobile terminal, and predicts the terminal threat index through support vector machine regression algorithm (SVR), so as to construct the security profile of the mobile application operation terminal. Finally, through visualization services, various data such as power mobile applications and terminal assets, security operation statistics, security strategies, and alarm analysis are displayed to guide security operation and maintenance personnel to carry out power mobile application security monitoring and early warning, banning disposal and traceability analysis and other decision-making work. the experimental analysis results show that the method can meet the requirements of security situation awareness for threat assessment accuracy and response speed, and the related results have been well applied in a power company. © 2022 IEEE.

关键词： big data

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A Bit-Plane Slicing Technique for the Classification of Anti-forensically Contrast-Enhanced Images 5

A Bit-Plane Slicing Technique for the Classification of Anti...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Taneja, Neeti Mishra, Gouri Sankar Bhardwaj, Dinesh Sharda University Sharda School of Engineering & Technology Department of Cse Uttar Pradesh Greater Noida India Thapar Institute of Engineering & Technology Department of Ece Punjab Patiala India

ISBN: (纸本)9798350383522

Digital images are frequently employed in many sectors, including forensic investigations, medical CT, handwriting recognition, and computer-aided diagnosis. these images are easily modifiable by fraudsters using various image editing techniques. this means that they must be kept safe since they contain a wealth of important information. Many forgery detection techniques exist, but anti-forensics acts as a barrier to these techniques in detecting forgery in a given image. Contrast enhancement operation is generally employed for blending two different regions while creating a forgery. A forgery detection technique can exploit contrast enhancement artifacts to identify the presence of a forged area. However, anti-forensics can enhance a given image without introducing contrast enhancement artifacts, which makes it difficult for forensic analysts to detect forgery. In the current digital era, identifying these enhancement processes is crucial for obtaining genuine data. this paper presented an efficient technique for classifying original, contrast-enhanced, and anti-forensically contrast-enhanced images. the analytical results show how well the suggested strategy works and attains good accuracy. © 2024 IEEE.

关键词： bit-plane slicing Contrast enhancement Digital image anti-forensics Ensemble classifier Gamma Correction

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A signel-channel blind signal separation method based on signal type difference for time-frequency overlapped signal 21

A signel-channel blind signal separation method based on sig...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Pang, Lihui Tang, Yilong Liu, Yulang Yang, Bin Zhang, Wenwei Tan, Qingyi Shenzhen Technology University Sino-German College of Intelligent Manufacturing Shenzhen China University of South China School of Electrical Engineering Hengyang China

ISBN: (纸本)9798350329223

Performing blind signal separation (BSS) from the domain in which source signals have difference characteristics is an efficient method to recover sources from the recorded mixture signal(s) in a complex electromagnetic environment, especially for single-channel BSS (SCBSS) problem. this paper proposes a novel time-frequency overlapped signal separation method to rapidly divide digital communication signal and pulse repetition interval (PRI) radar signal based on signal type difference characteristics for the single-channel separation scenarios. Firstly, a robust PRI estimation is applied to single-channel record to identity the sample number in one PRI based on the power difference of two types of original signals and the periodic characteristics of radar signal. then, we transfer the one-dimensional observation mixed signal record into a multidimensional observation matrix, by setting its column number equals to the estimated sample number in one PRI. After that, we perform singular value decomposition (SVD) on the multi-dimensional observation matrix, and then map the obtained the eigenvalues and eigenvectors into original observation space, respectively, as so achieve the aim of original separation. A series of simulation experiments are carried out, which indicate our method can achieve satisfactory separation results with correlation coefficient noless than 0.95 and Signal-to-Interference Ratio (SIR) more than 28dB when signal to noise ratio (SNR) no less than 10dB. Additionally, it demonstrates performance of our method is better than that of typical separation-FastICA/JADE-and it shows that our method not sensitive to the frequency overlap level (FOL) of the source signal, even FOL as high as 50%, it still can get high-precision separation results with SIR nearly achieve 40dB when SNR ≥ 20dB. © 2023 IEEE.

关键词： Singular value decomposition

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the Future of AI in Predicting Cybersecurity threats 5th

The Future of AI in Predicting Cybersecurity Threats

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5th international conference on data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Pavan Kumar, P. Satish, Mankala Sunitha Devi, B. Prakash, A. Reddy, Kumbala Pradeep Malli Babu, S. Department of CSE CMR Institute of Technology Telangana Hyderabad India CMR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9789819780426

the swift advancement of artificial intelligence (AI) technologies has brought about a profound shift in the realm of cybersecurity threat prediction. this comprehensive literature review delves into the advancements and challenges within the realm of AI-driven cybersecurity threat prediction. By comprehensively amalgamating various scholarly sources, this analysis elucidates the present situation and anticipates the future developments of artificial intelligence (AI) in the realm of predicting cybersecurity threats. the discussion delves into pivotal topics such as machine learning algorithms, deep learning techniques, natural language processing, and anomaly detection methodologies. Additionally, ethical considerations, data privacy issues, and the integration of AI with traditional cybersecurity practices are critically examined. the review concludes by highlighting emergent trends and suggesting avenues for future research, thereby providing valuable insights for both academics and practitioners in the field of cybersecurity. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity

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Sinhala Sign Language Translation through Immersive 3D Avatars and Adaptive Learning 5

Sinhala Sign Language Translation through Immersive 3D Avata...

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5th international conference on Advancements in Computing, ICAC 2023

作者： Gamage, B.G.J. Paranagama, R.P.S.D. Ranaweera, R.M.S.H. Dilshan, A.V.R. Weerathunga, Ishara Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Faculty of Computing Department of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

this research paper addresses the challenge of bridging the communication gap between sign language users and non-users through a comprehensive study on sign language translation. the focus is on Sinhala Sign Language (SSL), and the research problem encompasses various innovative components. these include the conversion of Sinhala Sign Language (SSL) into text, emotion recognition and representation, personalized sign language learning, and accurate identification and translation of sign language gestures. By leveraging machine learning, deep learning, 3D modeling, and image/video processing techniques, the system enhances accessibility, communication, and education for the deaf and hard-of-hearing community. the outcomes of this research encompass an accurate and visually appealing sign language translation system, personalized learning interfaces, and the integration of emotions into sign language communication. the research contributes by providing insights into SSL grammar and lexicon, developing a tailored sign language learning curriculum, implementing reinforcement learning algorithms for personalized feedback, utilizing 3D components for enhanced visual quality, and optimizing deployments and models for improved performance. the proposed system has significant implications for improved accessibility, education, and inclusivity, fostering effective communication and understanding between sign language users and non-users. © 2023 IEEE.

关键词： Emotion Recognition

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