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8th International Symposium on Digital Forensics and Security, ISDFS 2020

作者： Ali, Chawan Taha Hama Varol, Asaf University of Sulaimani Computer Science Sulaimani Iraq Firat University Department of Software Engineering Elazig Turkey

ISBN: (数字)9781728169392

ISBN: (纸本)9781728169392

The main purpose of this research is to create a new application in Android Operating System for a complainant who cannot visit his/her lawyer regularly or unable to go out due to fear of his/her life. Thanks to this research, a new application was established to make a bridge between a complainant and his/her lawyer. Everything is done automatically after necessary data entered into the application. So, costs are reduced and wasting of the time is diminished. © 2020 IEEE.

关键词： Android (operating system)

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PTSC: an efficient random boolean consensus algorithm for distributed systems with the probabilistic propagation model

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Peer-to-Peer Networking and Applications 2025年第4期18卷 1-12页

作者： Cao, Yuke She, Kun Xiao, Yuxuan Wu, Yongyue Ren, Yuntao Chen, Ruidong School of Information and Software Engineering University of Electronic Science and Technology of China Xiyuan Avenue SiChuan Province Chengdu611731 China Information Technology Co. LTD Hele 2nd Street SiChuan Province Chengdu610041 China Frontier Research Department Chengdu Anheng Information Technology Co. LTD Hele 2nd Street SiChuan Province Chengdu610041 China School of Computer Science and Engineering University of Electronic Science and Technology of China Xiyuan Avenue SiChuan Province Chengdu611731 China

As the foundation of distributed systems, consensus mechanisms play a crucial role in ensuring the proper operation of the system. In a distributed network composed of trusted peer nodes, nodes may need to perform operations on random Boolean values (0 or 1) during their execution. To address this, this paper, inspired by Paxos, Raft, and their derivative algorithms, proposes a random Boolean consensus algorithm based on the principle of the Probabilistic Propagation Model. In each consensus round, a Boolean value is selected with a smaller probability in the first round of communication, and the final decision is made with a larger probability in the second round, ensuring that the probabilities of the system obtaining 0 or 1 are approximately equal. The running time is further reduced by minimizing the participation of nodes. Experimental results show that this method enables nodes in the system to quickly reach a consensus on a random Boolean value, and as the number of nodes increases, the savings in system overhead become more apparent. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Parallel processing systems

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Citizen Engagement in Smart Government: Content Analysis of Mohammed Bin Rashid Tweets 14

Citizen Engagement in Smart Government: Content Analysis of ...

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14th International Conference on Innovations in Information Technology, IIT 2020

作者： Sawafi, Aysha M. Al Awad, Mamoun A. UAE University College of IT Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9781728181844

Citizen engagement is one of the main concepts of smart governments. In this paper we will measure the citizen engagement in different topics within the official Twitter account of Mohammed Bin Rashid. We have streamed the account tweets and classified them into five categories. The categories were identified after descriptive analysis of the total tweets. We used Retweetability rate to measure citizen engagement and Pearson Chi-Square test to measure the relationship between citizen retweets in response to Mohammed Bin Rashid tweets in four categories, namely, political engagement, achievements, development, and share values. Our results showed that there is a correlation between tweet contents and number of retweets in the development category only. Additionally, we found that other categories did not motivate citizen engagement. © 2020 IEEE.

关键词： Statistical tests

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Innovative, Integrated and Interactive (3I) LMS for Learners and Trainers 2

Innovative, Integrated and Interactive (3I) LMS for Learners...

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2nd International Conference on Advancements in Computing, ICAC 2020

作者： Fernando, K.J.L. Jayalath, W.J.D.L.D.D. Ranasinghe, A.D.R.N. Bandara, P.K.B.P.S. De Silva, Hansi Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781728184128

3I-LMS is meant to conquer the insurmountable restraints to class/lecture room education. What are insurmountable restraints to physical classroom education and how does 3I-LMS conquer them. Firstly, the lockdown and physical distancing warranted by the spread of Covid-19. In order to overcome the restraints of the pandemic 3I-LMS would be the obvious answer. 3I-LMS, the medium for delivery and reception, is online for both learners and trainers. Secondly, ineffectiveness resulting in frail in pass rates, fast diminishing memory, faulty study techniques are overcome. 3I-LMS makes studying satisfied, gamified, engaged and enjoying. The outcome would be lasting memory, effective study techniques, higher recall rates leading to improved pass rates. Thirdly, inefficiencies such as slow access to relevant lessons, notes, slow answer evaluation, feedback, clarification to routine questions are also resolved by 3I-LMS. 3I-LMS provides for keyword search, relevant subject wise notes, instant answers for routine questions thus contributing to improved efficiencies. Additionally, 3I-LMS has unique and innovative features to assist assignment completion, emanate milestone alerts, monitoring emotions and time utilization. 3I-LMS provides for utmost security. Thus, the solution deploys face recognition and keystroke dynamics to combat impersonation, copying and unauthorized referencing. © 2020 IEEE.

关键词： Face recognition

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ML Techniques for Employee Performance Prediction

ML Techniques for Employee Performance Prediction

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Brajesh Kumar Prateek Agrawal Dipesh Uike Melanie Lourens Indira Bhati M. Chandrakumar Peter Department of Computer Science & Engineering Dr. APJ Abdul Kalam Women's Institute of Technology Darbhanga Bihar Gurukula Kangri (Deemed to be University) Dr. Ambedkar Institute of management studies and research Nagpur Maharashtra Faculty of Management Sciences Durban University of Technology South Africa Department of Management MMIM Maharishi Markandeshwar (Deemed to be University) Department of Software Engineering PeriyarManiammai Institute of Science and Technology Thanjavur Tamilnadu

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

When it comes to maximizing the effectiveness of a business and promoting professional growth, employee performance prediction is an extremely important factor. This research article investigates the use of machine learning (ML) approaches to forecast employee performance, with a particular emphasis on the incorporation of different algorithms from different sources to improve accuracy and dependability. Methods that have been used for a long time, such as linear regression and decision trees, are evaluated alongside more recent approaches, such as ensemble methods and deep learning. To refine the prediction models, our research makes use of approaches such as feature selection and dimensionality reduction. These techniques are utilized by utilizing previous performance data, demographic information, and behavioral indicators. It has been demonstrated through the analysis that sophisticated machine learning techniques, in particular ensemble and deep learning models, are superior to traditional methods when it comes to anticipating employee performance. Our findings offer HR departments concrete insights that can be used to adopt data-driven methods for performance management, which will eventually contribute to improved organizational outcomes and increased employee satisfaction. This paper offers opportunities for future development in predictive analytics within the field of human resources and emphasizes the potential of machine learning to alter performance evaluation systems.

关键词： Deep learning Performance evaluation Accuracy Machine learning algorithms Linear regression Predictive models Prediction algorithms Feature extraction Ensemble learning Predictive analytics

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Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networks

arXiv

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arXiv 2022年

作者： Yazdinejad, Abbas Dehghantanha, Ali Parizi, Reza M. Hammoudeh, Mohammad Karimipour, Hadis Srivastava, Gautam The Cyber Science Lab University of Guelph Canada The College of Computing and Software Engineering Kennesaw State University GA United States Information & Computer Science Department King Fahd University of Petroleum & Minerals Saudi Arabia The School of Engineering Department of Electrical and Software Engineering The University of Calgary Alberta Canada The Department of Math and Computer Science Brandon University Manitoba Canada The Research Centre for Interneural Computing China Medical University Taichung Taiwan

Nowadays, blockchain-based technologies are being developed in various industries to improve data security. In the context of the Industrial Internet of Things (IIoT), a chain-based network is one of the most notable applications of blockchain technology. IIoT devices have become increasingly prevalent in our digital world, especially in support of developing smart factories. Although blockchain is a powerful tool, it is vulnerable to cyber attacks. Detecting anomalies in blockchain-based IIoT networks in smart factories is crucial in protecting networks and systems from unexpected attacks. In this paper, we use Federated Learning (FL) to build a threat hunting framework called Block Hunter to automatically hunt for attacks in blockchain-based IIoT networks. Block Hunter utilizes a cluster-based architecture for anomaly detection combined with several machine learning models in a federated environment. To the best of our knowledge, Block Hunter is the first federated threat hunting model in IIoT networks that identifies anomalous behavior while preserving privacy. Our results prove the efficiency of the Block Hunter in detecting anomalous activities with high accuracy and minimum required bandwidth. © 2022, CC BY.

关键词： Blockchain

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GLIB: Ameliorated English Skills Development with Artificial Intelligence 1

GLIB: Ameliorated English Skills Development with Artificial...

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1st IEEE Bangalore Humanitarian Technology Conference, B-HTC 2020

作者： Srikanthan, Praveenkumar Nizar, Roshan Ravikumar, Abishaan Lalitharan, Keerthigan Harshanath, S.M.B. Alosius, Jesuthasan Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781728187945

In language learning, most of the learners can learn the theory and memorize the sound of a language. However, the ability to speak and learn a language properly requires good practice, experience and good learning strategies. This research is an approach to innovate and improve the language learning strategies along with practices that help to improve the language skills for non-native learners and children who are in the early stage of learning. In this research, the English language will be used to experiment with our solution as the source due to its demand and familiarity in the world. This research is focused on four main skills such as conversation skills, pronunciation skills, listening skills and grammatical skills. This research is done by analyzing the difficulties in each of the skills mentioned above and also discusses and provides details about the solution implemented to improve learning English in an efficient way. The implementation of this research is done by using technologies like natural language processing, machine learning, and deep learning approaches to come up with components to train the learner. The application of this research is delivered by using a cross-platform application called GLIB. The name GLIB is inspired by a library in C language which represents fluency. This mobile application provides facilities to improve all the English language skills mentioned above along with guides, tips, practices, and feedback based on an evaluation to improve the English language. © 2020 IEEE.

关键词： Learning algorithms

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Understanding Neural Network Through Neuron Level

SSRN

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SSRN 2023年

作者： Dou, Hui Zhao, Jian Shen, Furao State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China School of Electronic Science and Engineering Nanjing University Nanjing210023 China School of Artificial Intelligence Nanjing University Nanjing210023 China

Neuron is the basic unit of the neural network. In this paper, we present a method for visualizing the process of neuron learning. The features learned by different neurons can be combined flexibly to analyze the working mechanism of different components of the network. In the medical field, researchers use medical scanners that capture images of the brain to observe the human brain. Similarly, we analyze the neural network by visualizing the feature learned by the neuron. The method is applicable to the neural network composed of artificial neurons without requiring structure changes. Through experiments, we analyze the working mechanism of different network structures and evaluate the network interpretability from different perspectives. © 2023, The Authors. All rights reserved.

关键词： Neurons

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Unveiling the evolution of IoT threats: Trends, tactics, and simulation analysis

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computers & Security 2025年

作者： Kok Onn Chee Mengmeng Ge Guangdong Bai Dan Dongseong Kim School of Electrical Engineering and Computer Science The University of Queensland Brisbane QLD Australia Department of Computer Science and Software Engineering University of Canterbury New Zealand

Since the inception of Mirai in 2016, a proliferation of advanced botnets targeting Internet of Things (IoT) devices has occurred, resulting in a notable increase in large-scale cyber attacks against online services. The continual emergence of novel strategies characterises the evolving landscape of IoT botnets. Despite this, a comprehensive understanding of this evolving threat remains elusive, impeding the development of robust defence mechanisms. This paper investigated 55 instances of IoT botnets spanning from 2008 to 2021 to elucidate their evolutionary patterns based on prevalent tactics and techniques. A novel taxonomy of IoT botnets is proposed and formulated with attack tactics, techniques, types, and procedures. We augment our existing simulation framework, IoTSecSim, with enhanced functionalities to simulate novel cyber-attack scenarios incorporating diverse network configurations, evolving attack tactics, and defence strategies. Through comprehensive simulations via the extended IoTSecSim, we assessed the impact of these evolving IoT attack tactics and gauged the efficacy of traditional defence mechanisms using various security metrics.

关键词： Graphical security modelling Internet of things Botnet attacks Simulation Security evaluation Evolving attacks Taxonomy

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SiamIRPN: Siamese Visual Tracking with Improved Region Proposal Networks

SiamIRPN: Siamese Visual Tracking with Improved Region Propo...

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Frontiers of Robotics and software engineering (FRSE), International Conference on

作者： Dun Cao Renhua Dai Tianyu Zhao Fayez Alqahtani Amr Tolba R. Simon Sherratt Min Zhu School of Computer and Communications Engineering Changsha University of Science and Technology Changsha Hunan China Key Lab of Broadcast Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Computer Science Department Community College King Saud University Riyadh Saudi Arabia School of Biomedical Engineering The University of Reading United Kingdom College of Information Science and Technology Zhejiang Shuren University Zhejiang China

Though introducing the Region Proposal Network (RPN) from object detection enabled Siamese trackers’ success, RPN-based trackers still struggle in challenging scenarios. We posit that the reason comes from two major limitations of the introduced RPN, where one is that the external structure of the RPN is simple and straight, the other is that the internal components used in RPN cannot cope with complex scenes. In this paper, we propose an Improved RPN (IRPN) suitable for visual tracking. Externally, we place a Convolutional Block Attention Mechanism (CBAM) to the IRPN, and internally, we adopt the advanced component groups for the IPRN. Using multiple IRPN blocks and deep architecture, we propose a Siamese tracker (SiamIRPN) having a layer-wise structure. Comprehensive experiments and ablation studies on five benchmarks (VOT-2019, OTB100, UAV123, GOT-10k, and NFS) show our proposed SiamIRPN achieves competitive performance.

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