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Fundamental Trends and Hot-Spots in Public Governance and Artificial intelligence Research: A Bibliometric Study Analysis From 2013 to 2023 24

Fundamental Trends and Hot-Spots in Public Governance and Ar...

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5th international conference on Computer Information and big data Applications, CIBDA 2024

作者： Lee, Siyuan National University of Defense Technology ChangSha China

ISBN: (纸本)9798400718106

Background: AI has become an important theoretical and practical hotspot in public governance. this study aims to analyses the publication characteristics of AI in public governance research in terms of institutions, journals and partnerships, and to analyses the development trend of AI in public governance research. Methods: Publications regarding AI in public governance were retrieved from Web of Science core collection. Microsoft Excel 2010, VOSviewer were used to characterize the contributions of the authors, journals, and institutions. the trends, hot-spots and knowledge network were analyzed by Citespace and VOSviewer. Results: We identified 2525 papers between 2013 and 2023. the number of papers grows gradually until 2019, followed by a sharp increase between 2020 and 2023. the University of Oxford is the most active institution. Most of the papers in this field are published in Sustainability and ieee Access. Luciano is the most productive author, and EUROPEAN COMMISSION is the most frequently co-cited author. the frontier topics are public sector and smart city. the basic themes of this field are "application scenarios, operation characteristics, algorithm characteristics, risk governance". Conclusion: In the past three years, artificial intelligence and public governance research have attracted widespread attention. the research hotspots are gradually shifting to public service, decision-making and risk. © 2024 ACM.

关键词： Artificial intelligence

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Extraction of Actionable threat intelligence from Dark Web data 5

Extraction of Actionable Threat Intelligence from Dark Web D...

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5th international conference on Control, Communication and computing, ICCC 2023

作者： Varghese, Varsha Mahalakshmi, S. Senthilkumar, K.B. ER&DCI Institute of Technology Cyber Forensics and Information Security Department of Computer Science & Engineering Thiruvananthapuram India Cyber Security Group Centre for Development of Advanced Computing Thiruvananthapuram India

ISBN: (纸本)9798350334128

Darknet has emerged as an excellent platform for cybercriminals to conduct various illicit activities such as operating a fully functional crypto-currency-based marketplace or maintaining a highly anonymous communication channel. However, it also acts as a source of Cyber threat intelligence which is the information about the techniques, tactics, or motives of an emerging threat. the life cycle of a Darknet-based Cyber threat intelligence solution mainly consists of the Collection, Processing, Analysis, and Production of data from the Darknet. the Collection and Processing stages deal with accumulating information from various dark web pages and transforming the data into some format using a big data system. the Analysis part is integral to the threat intelligence schema since it constitutes the extraction of named entities like Organization, Offensive Activity, Tools, etc., and the determination of relationships between these entities. the production stage is like data mining where some knowledge is extracted that can be relevant to an organization under consideration. Such knowledge can mitigate risks and disrupt any targeted cyber-attack campaigns, making Cyber threat intelligence actionable. In this research, an open-source-intelligence toolset is used to scan and collect data from the dark web forums through crawling and scrapping. the collected data is then ingested into a state-of-the-art NLP model that extracts actionable threat intelligence using Named Entity Recognition. the experimental results indicate that the model could identify HackerIds, tools, software, organizations, and other entities in the discussions of dark web forums with better efficiency and accuracy. this could be used to identify the source of a data leak, the release of new malware, evidence of a new exploit, and other offensive activities. © 2023 ieee.

关键词： Extraction

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Research on Intelligent Community 'Housekeeper' System Based on Artificial intelligence 5

Research on Intelligent Community 'Housekeeper' System Based...

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5th ieee international conference on Electronics Technology, ICET 2022

作者： Zeng, Yuting Wu, Qinglin Wuhan Business University School of Information Engineering Wuhan China

ISBN: (纸本)9781665485081

'intelligence' is a new trend of the development of science and technology in the 21st century. With the progress of the society and the acceleration of the pace of life as well as the continuous improvement of people's requirements for themselves, the traditional community has been unable to meet the needs and dependence of modern residents. In the current situation and background of the intelligent community to carry out a 'keeper' system design and system implementation and operation. By using the advantages and technologies of big data and combining the advanced information technology means such as Internet of things and cloud computing, it provides a good platform environment for the development of smart community, maximizes its functions, improves work efficiency, reduces the waste of manpower and material resources, and becomes an intelligent equipment developed by city and community property managers for more convenient work. © 2022 ieee.

关键词： Systems analysis

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Design of Campus Behavior Management Architecture Using IoT Technology 5

Design of Campus Behavior Management Architecture Using IoT ...

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5th ieee international conference on Artificial intelligence and big data, ICAIBD 2022

作者： Xu, Gang MacHica, Ivy Kim Su, Weibing He, Zhengfang University of Southeastern Philippines College of Information and Computing Davao Philippines Yunnan Technology and Business University Edge Computing Network Center Kunming China

ISBN: (纸本)9781665499132

Student management has always been one of the key tasks of colleges and universities. the use of intelligent management is an indispensable tool for college student management. Especially in the current epidemic situation, colleges and universities, as densely populated places, are the key areas of epidemic prevention and control. However, relying on the traditional manual method, they can not be completed efficiently, nor can they accurately grasp the latest trends in real time, and the investment time and management cost are high. At present, Internet of things technology has gradually entered all aspects of our life and work, changing our way of life. It is a very popular phenomenon to use Internet of things technology to realize campus student management. However, at present, campus Internet of things is mostly used in all-in-one card consumption, and only a few places use all-in-one card to manage students' business. this paper will study the use of Internet of things technology to design a behavior management architecture of colleges and universities, use the technologies such as sensors, face recognition and Bluetooth positioning integrated in wearable devices to upgrade only simple business management, and realize the information collection of students' health status and action track in the school, so as to realize the accurate monitoring of students' behavior, Provide reference for campus students' intelligent management. © 2022 ieee.

关键词： Students

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Exploring the Role of Prosodic Features to Perform Personality Classification from Labelled Speech data 5

Exploring the Role of Prosodic Features to Perform Personali...

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5th international conference on Inventive Research in computing Applications, ICIRCA 2023

作者： Rangra, Kalpana Kadyan, Virender Kapoor, Monit School of Computer Science Machine Intelligence Research Centre Upes Uttrakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350321425

Personality is an essential element to determine human behavior. Personality detection refers to identifying how people interact with each other. the work in personality classification has been intensively carried out with speech to text conversion but voice being an important aspect still needs attention and analysis. So, the present research work focuses on speech based automatic personality classification from audio features like Mel Frequency Cepstral Constant (MFCC), Linear Predictive Coding (LPC), Pitch, Zero Crossing Rate (ZCR), Root Mean Square Error (RMS), amplitude using Machine Learning (ML) algorithms. the work proposes a personality classification system using prosodic speech features and compares the classification performance of multiple ML techniques. the classification system uses big five model with K- Nearest Neighbor (KNN), Support Vector Machine (SVM), Trees, Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR), AdaBoost, and Stochastic Gradient Decent (SGD). It is observed that the RF & NN outperform all the classifiers when implemented on integrated feature set. the AUC score of 91.7 % and 92.3% is achieved with RF & NN respectively. © 2023 ieee.

关键词： Classification (of information)

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the design of a mechanical force feedback VR data glove 21

The design of a mechanical force feedback VR data glove

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21st ieee international conference on Ubiquitous computing and Communications / 5th ieee international conference on data Science and Computational intelligence / 21st ieee international conference on Computer and Information Technology

作者： Lin, Jiliang Hu, Bin Pang, Jiahao Liao, Jingjie Xiamen Univ Technol Sch Int Educ Xiamen 361000 Peoples R China Soochow Univ Sch Arts Suzhou Peoples R China Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou 543002 Peoples R China Wuzhou Univ Sch Big Data & Software Engn Wuzhou 543002 Peoples R China

ISBN: (纸本)9781665477260

With the development of virtual reality technology, data gloves began to widely popularize various fields. Some of the force feedback data gloves at this stage still have problems such as high cost and complex structure. the mechanical force feedback data glove proposed in this paper uses the principle of mechanical rotation, using a potentiometer as the rotating shaft and a nylon rope to drive the potentiometer to rotate, so that it records the relevant data instead of sensors, and uses a servo to bring force feedback experience. Experiments indicate that the force feedback glove in this paper has both the force feedback effect of the existing force feedback data gloves and is lower in cost than the existing data gloves and has good generalizability.

关键词： data gloves virtual reality machine vision mechanical design force feedback gloves

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Impact of Mobility Speed and Network Density on the Performance of Vehicular Ad Hoc Network Routing Protocols 5

Impact of Mobility Speed and Network Density on the Performa...

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5th international conference on Artificial intelligence, big data, computing and data Communication Systems (ICABCD)

作者： Osifeko, Martins O. Hancke, Gerhard P. Abu-Mahfouz, Adnan M. Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa Nanjing Univ Posts & Telecommun Coll Automat & Artificial Intelligence Nanjing Peoples R China Council Sci & Ind Res CSIR Pretoria South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Vehicular Ad Hoc Network (VANET) is a type of network that facilitates communication between vehicles usually on highways or in any environment with vehicular traffic such as a parking space. the dynamic nature of vehicular movements makes VANETs' topology highly unstable and prone to transmission unreliability. Hence, to achieve its major goal of traffic safety and enhanced traffic flow, the research community has identified the need for solutions that address efficient data transmission, ultrafast connectivity, reliable handovers, security, and privacy for vehicles. To achieve this, several routing protocols have been developed in the literature. Some of which include Ad hoc on-demand Distance Vector (AODV), Optimized Link State Routing (OLSR), Destination-Sequenced Distance-Vector (DSDV), and Dynamic Source Routing Protocol (DSR). In this paper, a performance analysis of four of these routing protocols in a realistic environment is carried out. Specifically, this work investigated the effect of mobility speed and network density on the efficiency of four VANET protocols using the metrics of packet loss rates, delivery ratio, and average end to end delay. For the evaluated cases, results reveal that OLSR had the best performance for having the lowest average delay, while the DSDV had the best performance in packet loss rates and delivery ratio.

关键词： VANET AODV OLSR DSDV Packet Delivery Rates

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Research on data Assets and their Appraisal Models 7

Research on Data Assets and Their Appraisal Models

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7th ieee international conference on big data intelligence and computing, datacom 2021

作者： Jing, Cui Tingshan, Liu Lu, Jia China Electronics Standarzation Institute Research Center for Software Applications and Services Bei Jing China

ISBN: (纸本)9781665420716

At present, the rapid development of emerging technologies such as 5G, mobile Internet and Internet of things in China and the explosive growth of data acquisition capacity and data accumulation in various fields and enterprises are prompting China to accelerate its march from a big data country to a powerful data country. In order to give full play to the maximum value of data assets, based on the in-depth study of the connotation and characteristics of data assets, the data asset appraisal model is proposed, and the appraisal basis, appraisal elements, appraisal process, appraisal method, appraisal assurance and appraisal security are given respectively, and finally the data asset appraisal ecology is constructed. © 2021 ieee.

关键词： data acquisition

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A Dimensionality Reduction and Reconstruction Method for data with Multiple Connected Components 5

A Dimensionality Reduction and Reconstruction Method for Dat...

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5th ieee international conference on big data and Artificial intelligence (BDAI)

作者： Yao, Yuqin Gao, Yang Long, Zhiguo Meng, Hua Sioutis, Michael Southwest Jiaotong Univ Sch Math Chengdu Peoples R China Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu Peoples R China Univ Bamberg Fac Informat Syst & Appl Comp Sci Bavaria Germany

ISBN: (数字)9781665470810

ISBN: (纸本)9781665470810

In the literature on dimensionality reduction, including Spectral Clustering and Laplacian Eigenmaps, one of the core ideas is to reconstruct data based on similarities between data points, which makes the choice of similarity matrices a key factor on the performance of a dimensionality reduction model. Traditional methods like K-nearest neighbor, is an element of-neighbor, and Gaussian Kernel for constructing similarity matrices based on data distribution characteristics have been extensively studied. However, these methods usually focus on only a specific level of the data when considering the similarity between data points, which might result in a great flaw in data reconstruction when data possess hierarchical and multiple groups structure. Specifically, such methods can only characterize the similarity between data within a group, but ignore the similarity between different groups. To overcome this deficiency, this paper proposes a hierarchical way of similarity matrix construction, by introducing strong, weak, and intra- and inter-cluster similarities to describe relations between multiple levels. the proposed method can better adapt to complex data with multiple connected components, and the effectiveness of it is verified in a series of experiments on synthetic and real-world datasets.

关键词： LE Dimensionality reduction Manifold learning Topological connectivity

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Research on Edge to End Collaborative computing Unloading Based on Deep reinforcement learning 5

Research on Edge to End Collaborative Computing Unloading Ba...

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5th international conference on Electronics and Communication Technologies, ECT 2023

作者： Xu, Hengyuan Wang, Zhe Lina, Ge Jing, Zou GuangXi University for Nationalities College of Electronic Information GuangXi China GuangXi University for Nationalities College of Artificial Intelligence GuangXi China

ISBN: (纸本)9798350307719

To address the limited computing resources and real-time data processing requirements of mobile devices in the mobile edge network environment, this paper proposes a deep reinforcement learning-based edge-cloud collaborative computing offloading method. Specifically, considering computational resources, bandwidth, and task offloading strategies comprehensively, we formulate the problem of minimizing task execution delay, energy consumption, and cost for all mobile devices. Based on this, we design an online coordinated offloading algorithm using deep reinforcement learning, which transforms the cost minimization problem into an optimal offloading strategy problem under Markov decision process framework. the best task offloading strategy is obtained by utilizing experience replay for solution. Experimental results demonstrate that the proposed algorithm exhibits good convergence and outperforms other methods in terms of system performance. © 2023 ieee.

关键词： Energy utilization

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