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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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Dynamic Artificial Neural Network-Assisted GPS-Less Navigation for IoT-Enabled Drones

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IEEE Internet of Things Magazine 2024年第3期7卷 92-99页

作者： Simsek, Murat Boukerche, Azzedine Kantarci, Burak Bitirgen, Rahman Hancer, Muhsin Bayezit, Ismail School of Electrical Engineering and Computer Science The University of Ottawa Ottawa Canada Istanbul Technical University Faculty of Aeronautics and Astronautics Turkey

Uncrewed Aerial Vehicles (UAVs) have enabled key duties in emergency preparedness, traffic monitoring, environmental monitoring, and public safety. Since the presence of GPS-enabled contexts is not always guaranteed, a grand challenge with the UAVs is the lack of accomplishing their tasks without the presence of GPS coordinates (latitude, longitude, and altitude). Hence, the performance of UAVs in GPS-denied environments is expected to degrade dramatically when compared to the UAVs employed in GPS-enabled environments. In this article, an alternative approach to the state-of-the-art, Dynamic Artificial Neural Network (D-ANN)-based solution is proposed to assist UAV navigation without GPS positions during a mission. Besides accelerometer and gyroscope data, Pulse Width Modulation (PWM) signals, which have been traditionally used in the design of UAV flight controllers, are proposed to be a part of the input for D-ANN-assisted UAV navigation without GPS data. Since the latitude, longitude, and altitude values of the UAV are not correlated, each position is obtained through a separate D-ANN system. The proposed D-ANN location of a quadrotor UAV assisted by D-ANN has less than 3m average destination error at the end of the testing trajectory and also less than 0.12 average normalized mean square error during the testing trajectory in terms of the 3D GPS coordinates. © 2018 IEEE.

关键词： Global positioning system

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Zero-dynamics attack detection based on data association in feedback pathway

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Cognitive Robotics 2025年 5卷 126-139页

作者： Zhang, Zeyu Li, Hongran Todo, Yuki Division of Electrical Engineering and Computer Science Kanazawa University Ishikawa Kanazawa9201192 Japan School of Computer Engineering Jiangsu Ocean University Jiangsu Lianyungang222005 China Faculty of Electrical Information and Communication Engineering Kanazawa University Ishikawa Kanazawa9201192 Japan

This paper considers the security of non-minimum phase systems, a typical kind of cyber-physical systems. Non-minimum phase systems are characterized by unstable zeros in their transfer functions, making them particularly susceptible to disturbances and attacks. The non-minimum phase systems are more vulnerable to zero-dynamics attack (ZDA) than minimum phase systems. ZDA is a stealthy attack strategy that exploits the internal dynamics of a system, remaining undetectable while causing gradual system destabilization. Recent cyber incidents have demonstrated the increasing risk of such hidden attacks in critical infrastructures, such as power grids and transportation systems. This paper first demonstrates that the existing ZDA has the limitation of falling into local convergence, and then proposes an enhanced zero-dynamics attack (EZDA), which overcomes local convergence by diverging system data. Furthermore, this paper presents an autoregressive model which can build the data association between the original data and the forged data. By observing the fluctuations in state values, the presented model can detect not only ZDA, but also EZDA. Finally, numerical simulations and an application example are provided to verify the theoretical results. © 2025

关键词： Cyber attacks

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Many-Objective Evolutionary Optimization Algorithm based on the Conflict Measurement Grouping Strategy 4

Many-Objective Evolutionary Optimization Algorithm based on ...

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4th International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Zhang, Jianzhong Shi, Xuhua Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331531225

In order to address the convergence issues faced by traditional multi-objective optimization algorithms as the number of objectives increases, this paper proposes a new optimization algorithm, MaOEA-SPC, based on the conflict measurement grouping strategy. First, due to the inherent correlation between subproblems and many-objective optimization problems, we propose a conflict measurement grouping strategy to facilitate knowledge transfer. Next, we extract the distribution characteristics of Pareto solutions during the evolution process of subproblems to establish a Gaussian distribution model that serves as knowledge information. Subsequently, the new individuals generated by the distribution model are added to the population of MaOPs. Finally, the simulation results indicate that the MaOEA-SPC algorithm effectively enhances the convergence speed of MaOPs without lowering the quality of the solution. © 2024 IEEE.

关键词： Optimization algorithms

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A Sub-nanosecond Delay Floating-voltage Level Shifter for SiC MOSFETs Gate Driver Application 6

A Sub-nanosecond Delay Floating-voltage Level Shifter for Si...

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6th International Conference on Energy Systems and electrical Power, ICESEP 2024

作者： Wan, Yue Xia, Yinshui Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350353563

In order to meet the requirements for the design of SiC gate driver in terms of speed and dv/dt immunity, a sub-nanosecond delay level shifter based on a high-voltage (HV) digital level shifter circuit is proposed. It can decrease the response time compared with the existing level-shifting circuitry, enabling its application in high-voltage scenarios such as closed-loop drive circuits for silicon carbide. The edge detection circuit and simple pull-up branches are utilized to reduce the transmission delay and power consumption. The circuit is simulated at a frequency of 10MHz in a 018um BCD process with Virtuoso, and the results demonstrate that the average transmission delay is about 380ps and the charging response time of the slow path is reduced to 3.3ns. © 2024 IEEE.

关键词： Delay circuits

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Research on API Sequence Recommendation Based on Code Property Graph 5

Research on API Sequence Recommendation Based on Code Proper...

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5th International Conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Luo, Xianchao Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331533991

The utilization of APIs not only enhances software reusability but also improves development efficiency and reduces costs. However, with the continuous increase in the number of APIs, developers face difficulties in quickly recalling and comprehending the appropriate API call sequences and their constraints. Thus, there is a need for an efficient API sequence recommendation tool. The key task in achieving efficient API sequence recommendation is to understand the semantic structure of the code context. However, most researchers currently lack an analysis of program structure in API sequence recommendation studies, leading to a decrease in the accuracy of these tools. To achieve a deeper understanding of the semantic context of code, this paper employs semantic syntax information and incorporates control structure information to improve the accuracy of API sequence recommendations. Specifically, this study first employs early fusion techniques based on the abstract syntax tree to incorporate control flow edges and data dependency edges - two types of heterogeneous edges - to construct a new representation of code known as a code property graph. This graph is then used to analyze the semantic syntax information and control structures of the code. Subsequently, a relational graph attention network model is employed to analyze the code property graph. Experimental results indicate that the accuracy of API sequence recommendations significantly improves with the incorporation of code control structures. © 2024 IEEE.

关键词： computer software reusability

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Graph Collaborative Filtering and Data Augmentation Strategies in Dual-Target CDR 29th

Graph Collaborative Filtering and Data Augmentation Strateg...

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29th International Conference on Cooperative Information Systems, CoopIS 2023

作者： Shao, Xiaowen Liu, Baisong Zhang, Xueyuan Li, Junru Xu, Ercong Wu, Shiqi Faculty of Electrical Engineering and Computer Science NingBo University Ningbo China

ISBN: (纸本)9783031468452

Current dual-target recommendation methods focus on efficient feature fusion but neglect the inherent noise issues in the domains. However, noise negatively affects the fusion of domains. To tackle this issue, we introduce an improvement and noise reduction strategy named DA-DCDR(Data Augmentation-Dual Target Cross Domain Recommendation), for domain fusion. By refining and reducing noise in each domain’s subgraphs, we not only enhance the accuracy of interaction data but also ensure consistency in data scales. To establish associations between distinct domains, we implement a graph co-training strategy. Key procedures of DA-DCDR include interaction refinement and noise reduction, domain fusion, and correlation expansion. We use graph encoders to acquire user/item embeddings for both domains before domain fusion, followed by enhancement and noise reduction in interactions via top-k sampling and re-prediction. Additionally, we amplify user-user and item-item correlation elements after the domain fusion. Experimental results validate the noteworthy performance enhancement of our proposed strategy in the dual-target recommendation, mitigating the noise effects and boosting the accuracy of the dual-target recommendation system. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Collaborative filtering

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An Efficient Closed-Form Method For Underwater Source Localization With Isogradient Sound Speed Profile 16

An Efficient Closed-Form Method For Underwater Source Locali...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Chen, Guanxu Wang, Gang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331506322

In this paper, we address the underwater localization problem with isogradient sound speed profile (SSP) using time-of-arrival (TOA) measurements. The sound speed is related to the unknown source depth in the isogradient SSP model, which is highly nonlinear. Owing to this nature of the isogradient SSP model, the maximum likelihood (ML) problem for estimating the source position is non-convex such that local convergence may occur without sufficiently good initialization. To address this difficulty, we first approximate the isogradient SSP model, and then propose a closed-form two-step weighted least squares (TSWLS) method for estimating the source position based on the approximate model. The solution method is accurate since the approximate model takes the source depth into consideration, and can be used to solve the ML problem as an initialization. Moreover, the proposed TSWLS solution is simple and computationally attractive since it is in closed form. Simulation results show that the proposed TSWLS solution is sufficiently accurate and can be used as the initial estimate for solving the non-convex ML problem, such that the mean square error (MSE) of the ML solution can reach the Cramér-Rao lower bound (CRLB). Index Terms-Underwater localization, time-of-arrival, isogradient sound speed profile, two-step weighted least squares. © 2024 IEEE.

关键词： Mean square error

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Real-Time Spectrum Sensing for TDD-LTE Signals Using Deep Learning 16

Real-Time Spectrum Sensing for TDD-LTE Signals Using Deep Le...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Xu, Xiang Jin, Ming Fang, Xiangqian Jiang, Tao Yu, Yanghang Li, Yaming Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331506322

Existing spectrum sensing techniques for Time Division Duplex-Long Term Evolution (TDD-LTE) signals primarily focus on large time-scale signals. However, the spectrum holes that exist in the small time-scale special subframes of TDD-LTE are often ignored. This paper proposes a method for spectrum sensing within TDD-LTE special subframes by applying Parallel Convolutional Neural Networks (ParallelCNN). The effective feature extraction capability of ParallelCNN enhances the spectrum sensing performance. Simulation results demonstrate superior sensing performance under various signal-to-noise ratio (SNR) conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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Millimeter-Wave Radar-Based Vital Sign Signal Extraction Using Multiple Distance Bin

Millimeter-Wave Radar-Based Vital Sign Signal Extraction Usi...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Zheng, Zhiwei Huo, Jianjian Wang, Jian Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798350355581

This paper introduces a multi-distance non-contact vital sign signal reconstruction and extraction algorithm based on frequency modulated continuous wave (FMCW) millimeter wave radar. The technology of measuring vital signs with millimeter wave radar has significant medical value. However, issues such as environmental noise, unconscious micro-movements of the human body, and the extremely subtle vibrations of the thoracic cavity caused by heartbeats greatly reduce the signal-to-noise ratio of the vital sign signals. To address these issues, considering the characteristic that the human body is perceived as a point cloud target under millimeter wave radar, this paper proposes a technique to enhance the signal-to-noise ratio of weak heartbeat signals by utilizing multi-distance vital sign signals. Furthermore, this paper employs the variational mode decomposition (VMD) algorithm for the reconstruction of vital sign signals, achieving high-precision extraction of the human heart rate signal. © 2024 IEEE.

关键词： Signal to noise ratio

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