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EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN INDONESIAN PRISONS 22

EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN IN...

22nd International Conference on e-Society 2024, ES 2024 and 20th International Conference on Mobile Learning 2024, ML 2024

作者： Imandeka, Ejo Hidayanto, Achmad Nizar Suhartanto, Heru Pidanic, Jan Department of Computer Science Faculty of Computer Science University of Indonesia Depok Indonesia Department of Electrical Engineering Faculty of Electrical Engineering and Informatics Pardubice University Pardubice Czech Republic

The exchange of knowledge is widely recognized as a crucial aspect of effective knowledge management. When it comes to sharing knowledge within Prison settings, things get complicated due to various challenges such as limited resources, social stigma, isolation, communication barriers, and security concerns. Additionally, there's a scarcity of resources dedicated to promoting knowledge sharing in secure environments. This study aims to dig deep into the key factors that influence how knowledge is shared in Indonesian prisons. To navigate this intricate terrain, the study uses questionnaires distributed among Prison officers to understand the complex relationship between the identified critical factors and knowledge sharing in Prison. Leadership support, organizational culture, organizational structure, incentives, and individual beliefs emerge as pivotal elements in knowledge sharing. It's also noted that the connection between these critical factors and the intention to share knowledge is significantly influenced by organizational structure. Recognizing and addressing critical factors in knowledge sharing within prisons not only improves rehabilitation within prison walls but also transforms the broader criminal justice system. This approach fosters evidence-based practices, better communication, and a comprehensive understanding of factors leading to successful rehabilitation and reduced recidivism, ultimately creating a more just and effective system. © 2024 International Conferences e-Society 2024 and Mobile Learning 2024. All rights reserved.

关键词： Critical Factors Indonesia Knowledge Knowledge Sharing Prison

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Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

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computers, Materials & Continua 2024年第1期78卷 357-374页

作者： Ch Avais Hanif Muhammad Ali Mughal Muhammad Attique Khan Nouf Abdullah Almujally Taerang Kim Jae-Hyuk Cha Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

The demand for a non-contact biometric approach for candidate identification has grown over the past ten *** on the most important biometric application,human gait analysis is a significant research topic in computer *** have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far *** recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access *** systems require a complex combination of technical,operational,and definitional *** employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked *** proposes a novel deep learning-based framework for human gait classification in video *** framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing *** proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer ***,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)***,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization *** algorithm chooses the best features,combined in a novel correlation-based fusion ***,the fused best features are categorized using medium,bi-layer,and tri-layered neural *** the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was *** achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this wor

关键词： Gait recognition covariant factors biometric deep learning fusion feature selection

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Fair Probabilistic Multi-Armed Bandit With Applications to Network Optimization

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 994-1016页

作者： Guo, Zhiwu Zhang, Chicheng Li, Ming Krunz, Marwan The University of Arizona Department of Electrical and Computer Engineering TucsonAZ87521 United States The University of Arizona Department of Computer Science TucsonAZ85721 United States

Online learning, particularly Multi-Armed Bandit (MAB) algorithms, has been extensively adopted in various real-world networking applications. In certain applications, such as fair heterogeneous networks coexistence, multiple links (individual arms) are selected in each round, and the throughputs (rewards) of these arms depend on the chosen set of links. Additionally, ensuring fairness among individual arms is a critical objective. However, existing MAB algorithms are unsuitable for these applications due to different models and assumptions. In this paper, we introduce a new fair probabilistic MAB (FP-MAB) problem aimed at either maximizing the minimum reward for all arms or maximizing the total reward while imposing a fairness constraint that guarantees a minimum selection fraction for each arm. In FP-MAB, the learning agent probabilistically selects a meta-arm, which is associated with one or multiple individual arms in each decision round. To address the FP-MAB problem, we propose two algorithms: Fair Probabilistic Explore-Then-Commit (FP-ETC) and Fair Probabilistic Optimism In the Face of Uncertainty (FP-OFU). We also introduce a novel concept of regret in the context of the max-min fairness objective. We analyze the performance of FP-ETC and FP-OFU in terms of the upper bound of average regret and average constraint violation. Simulation results demonstrate that FP-ETC and FP-OFU achieve lower regrets (or higher objective values) under the same fairness requirements compared to existing MAB algorithms. © 2024 The Authors.

关键词： Wireless sensor networks

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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Shrink: Data Compression by Semantic Extraction and Residuals Encoding

Shrink: Data Compression by Semantic Extraction and Residual...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Sun, Guoyou Karras, Panagiotis Zhang, Qi Aarhus University Digit Department of Electrical and Computer Engineering Denmark Aarhus University Digit Department of Computer Science Denmark University of Copenhagen Department of Computer Science Denmark

ISBN: (纸本)9798350362480

The distributed data infrastructure in Internet of Things (IoT) ecosystems requires efficient data-series compression methods, as well as the capability to meet different accuracy demands. However, the compression performance of existing compression methods degrades sharply when calling for ultra-accurate data recovery. In this paper, we introduce Shrink, a novel highly accurate data compression method that offers a higher compression ratio and lower runtime than prior compressors. Shrink extracts data semantics in the form of linear segments to construct a compact knowledge base, using a dynamic error threshold which can adapt to data characteristics. Then, it captures the remaining data details as residuals to support lossy compression at diverse resolutions as well as lossless compression. As Shrink effectively identifies repeated semantics, its compression ratio increases with data size. Our experimental evaluation demonstrates that Shrink outperforms state-of-art methods, achieving a twofold to fivefold improvement in compression ratio depending on the dataset. © 2024 IEEE.

关键词： Data accuracy

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Tourism Route Recommendation Based on A Multi-Objective Evolutionary Algorithm Using Two-Stage Decomposition and Pareto Layering

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 486-500页

作者： Xiaoyao Zheng Baoting Han Zhen Ni IEEE the School of Computer and Information Anhui Normal UniversityWuhu 241002China the Department of Electrical Engineering and Computer Science Florida Atlantic UniversityBoca Raton FL33431 USA

Tourism route planning is widely applied in the smart tourism *** Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism *** by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on *** method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage *** crowding degree mechanism between extreme and intermediate populations is used in the two-stage *** neighborhood is determined according to the weight of the subproblem for crossover ***,Pareto layering is used to improve the updating efficiency and population diversity of the *** two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same *** with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)*** the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better *** proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.

关键词： Evolutionary algorithm multi-objective optimization Pareto optimization tourism route recommendation two-stage decomposition

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Secure information embedding in images with hybrid firefly algorithm

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Neural Computing and Applications 2024年 1-14页

作者： Nokhwal, Sahil Chandrasekharan, Manoj Chaudhary, Ankit Department of Computer Science University of Memphis MemphisTN United States Department of Electrical and Computer Engineering University of Memphis MemphisTN United States School of Engineering Jawaharlal Nehru University Delhi New Delhi India

Various methods have been proposed to secure access to sensitive information over time, such as many cryptographic methods in use to facilitate secure communications on the internet. But other methods like steganography have been overlooked which may be more suitable in cases where the act of transmission of sensitive information itself should remain a secret. Multiple techniques that are commonly discussed for such scenarios suffer from low capacity and high distortion in the output signal. This research introduces a novel steganographic approach for concealing a confidential portable document format (PDF) document within a host image by employing the hybrid firefly algorithm (HFA) proposed to select the pixel arrangement. This algorithm combines two widely used optimization algorithms to improve their performance. The suggested methodology utilizes the HFA algorithm to conduct a search for optimal pixel placements in the spatial domain. The purpose of this search is to accomplish two main goals: increasing the host image’s capacity and reducing distortion. Moreover, the proposed approach intends to reduce the time required for the embedding procedure. The findings indicate a decrease in image distortion and an accelerated rate of convergence in the search process. The resultant embeddings exhibit robustness against steganalytic assaults, hence rendering the identification of the embedded data, a formidable undertaking. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Steganography

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Dynamic Evolutionary Game-based Modeling,Analysis and Performance Enhancement of Blockchain Channels

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 188-202页

作者： PeiYun Zhang MengChu Zhou ChenXi Li Abdullah Abusorrah IEEE the School of Computer Science Nanjing University of Information Science&TechnologyNanjing 210044China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the School of Computer and Information Anhui Normal UniversityWuhu 241003China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

The recent development of channel technology has promised to reduce the transaction verification time in blockchain *** transactions are transmitted through the channels created by nodes,the nodes need to cooperate with each *** one party refuses to do so,the channel is unstable.A stable channel is thus *** nodes may show uncooperative behavior,they may have a negative impact on the stability of such *** order to address this issue,this work proposes a dynamic evolutionary game model based on node *** model considers various defense strategies'cost and attack success ratio under *** can dynamically adjust their strategies according to the behavior of attackers to achieve their effective *** equilibrium stability of the proposed model can be *** proposed model can be applied to general channel *** is compared with two state-of-the-art blockchain channels:Lightning network and Spirit *** experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable *** its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.

关键词： Blockchain channel network evolutionary game malicious behavior secure computing stability analysis

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Reconstruction Attacks in Template-Based ECG Biometric Recognition Systems

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IEEE Internet of Things Journal 2024年第8期11卷 14971-14984页

作者： Lee, Song-Hong Nien, Cing-Ping Wu, Shun-Chi Swindlehurst, A. Lee National Tsing Hua University Department of Engineering and System Science Hsinchu30013 Taiwan University of California Department of Electrical Engineering and Computer Science IrvineCA92697 United States

The success of Internet of Things (IoT) services will be determined by how the security of the IoT devices and the networks to which they are connected can be guaranteed. Integrating biometric recognition technology into IoT systems has gained popularity as a means of accomplishing this objective. Electrocardiograms (ECGs) have become a promising biometric tool for security because their intrinsic and dynamic nature makes them difficult to steal and forge for replay attacks. However, as with other biometric-based security approaches, attacks on ECG biometric systems have been developed. This study examines the vulnerability of template-based ECG biometric systems to reconstruction attacks. These attacks involve exposing the biometric templates of registered subjects in a hacked database and attempting to reconstruct their ECGs to spoof the system. Both deep learning models and 'heuristic' approaches are used to perform this task, depending on the intruder's level of knowledge of the template construction. Several reconstruction attack strategies are proposed and evaluated for fiducial- and PCA-based systems using ECGs from the Physikalisch-Technische Bundesanstalt database of 285 subjects. The experimental results demonstrate a reconstruction similarity of at least 0.93 and an increase in the false-positive identification-error rate of more than 73%. © 2014 IEEE.

关键词： Electrocardiograms

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Grid-Side Current Improvement With Direct Digital Control and Capacitor Voltage Feedforward to Mitigate Distorted Grid Currents for 3Φ3W LCL Grid-Connected Inverter Under Distorted Grid Voltages

IEEE Open Journal of Power Electronics

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IEEE Open Journal of Power Electronics 2024年 5卷 37-49页

作者： Wu, Tsai-Fu Chan, Ching-Chih Chang, Yun-Hsiang Chiu, Jui-Yang Hung, Chien-Chih Chuang, Tzu-Hsien National Tsing Hua University Department of Electrical Engineering Hsinchu300044 Taiwan National Tsing-Hua University College of Electrical Engineering and Computer Science Hsinchu300044 Taiwan

A three-phase three-wire LCL grid-connected inverter is usually used as an interface between renewable-energy sources and grid. However, grid voltage is always distorted and results in grid-current distortion when there is no control at the filter-capacitor current. This article presents the grid-current improvement with inverter-side-current direct digital control and capacitor voltage feedforward (CVF) for the grid-connected inverter under distorted grid voltages. With the inverter-side current feedback, the simple direct digital control can cover inductance variation and determine the control laws to track inverter-side inductor current, achieving robustness. A hysteresis repetitive predictor with finite impulse response low-pass filter and compensator term is introduced to the CVF loop which can stabilize the system and compensate the distorted grid current. The virtual impedance method is adopted to confirm the stability when the filter inductances drop and control parameters vary with the inductor current. Simulated and experimental results from 12 kW inverter are used to verify the theoretical analyses and discussions. © 2020 IEEE.

关键词： Inductance

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