Reviewerly/exHarmony huggingface dataset:
Comparison between FLC and PID controller for speed control of DC motor

The conventional PID controller is very good when all parameters of the system are Known, that’s mean the system is linear, but when the system is nonlinear because of the friction, the saturation, the disturbance, or the load the dynamic model of the system will be the change, in this case, the conventional PID controller is not good, therefore we used the FLC because it is considered as intelligent controller and it is very suitable when parameter changed or not known, FLC does not need to now the all dynamic model of the system, that’s mean FLC is good with nonlinear system. In this paper, we presented the modeling of DC Motor, and how to compute the full dynamic model of DC Motor, after that we built a subsystem for DC Motor using MATLAB / Simulink, then we applied the PID controller and fuzzy logic controller for controlling the speed of DC Motor, the parameters of PID controller proportional (P), integral (I), derivative (D), tuned by using PID tuning tool in Simulink, we designed FLC for speed control, then we compared the simulation results between PID and FLC, in characteristics rise time, settling time, steady-state error and overshoot. From the simulation results and table compared we found that the performance of FLC is better than the performance of the PID controller in terms of speed control in case of no disturbance or load and with disturbance or load.
Analysis of the relationship between satisfaction and use value of the amount of construction technology information provided

The Construction Technology Information System collects, processes, and provides the public with free of charge 16 types of construction technology-related texts or index information, such as construction standards, construction practices, and construction reports, required at construction sites to enhance domestic construction technology. This study judged that user satisfaction could be increased if the budget was allocated first to the most effective construction technology information DB establishment. For each type of construction technology information, the level of satisfaction felt by the population for the quantity provided and the value of use according to the acquisition and utilization of information were investigated. Reliability between independent variables and dependent variables through a multiple regression analysis process to identify how the demographic characteristics of the population affect user satisfaction and the factors that affect the use value of information for each type of construction technology information. and the correlation was analyzed. The correlation between the demographic characteristics of respondents and users' satisfaction with the provided quantity was generally low, but construction experience, age, etc. were found to have an effect on satisfaction with the provided quantity. In particular, among the demographic measurement items of respondents, the more construction experience they had, the higher their satisfaction level with the quantity provided of low-level technical standards, and it was analyzed that older age had an impact on satisfaction with the quantity provided for small and medium-sized business support information.
Research on Intelligent Recognition Technology of Cigarette Laser Code Based on Deep-Learning

Aiming to address the low processing efficiency problem that arises with the massive image data generated in tobacco-related commercial terminals, a deep learning technology-based laser code text recognition solution for commercial purposes is developed. During identification, the YOLO deep learning network is used to initially determine the position of the laser code. Afterwards, the brightness of the light source is adjusted via the light adaptive algorithm. Finally, the laser code results undergo a filtering and interference process. Then, the RANSAC algorithm is utilized for classification, and post-processing is performed, including interaction with the UI interface. The final result is analyzed and displayed. Due to the multiple variations of laser codes, the article suggests utilizing code value adaptive mutation technology to train and distinguish diverse forms of identical characters. This process enhances the model's capacity to learn. Overall, the findings suggest varying levels of recognition among the tested cigarettes. Finally, the study examined various cigarette backgrounds, revealing that 63% of cigarettes had a recognition rate of 90-100%, while 22% had a recognition rate of 80-90%. Additionally, 10% of cigarettes with a recognition rate of 70-80% were accounted for, and cigarettes with a recognition rate of 60-70% represented 5%. The algorithm developed in this study was used in a cigarette laser code recognition system, resulting in a successful recognition outcome.
Interdisciplinary Prerequisites for a Digital Textbook. Testing Digital Textbook Fragments at the Educational Platform Learn to Learn

This research is particularly relevant due to the need to supply educational resources in electronic form, including digital textbooks (DTs) and their fragments on digital educational platforms. As the issue is multidisciplinary, the research analyses the conditions for DT development, including psychodidactic, ecopsychological, and cybernetic prerequisites.&#x0D; The article’s goal is to share experience in psychodidactic development of DT fragments and DT testing on a digital platform. The idea of creating an interactive Russian language textbook serves as the study’s psychodidactic foundation (G.G. Granik et al.). The authors employed an ecopsychological agency development model as well as an ecopsychological typology of agent–media interactions (V.I. Panov). Learn to Learn, a digital learning platform, was used to implement the cybernetic component (D.D. Rubashkin, I.N. Kondratieva).&#x0D; It has been demonstrated that this trial digital platform fosters student agency and the activation of their communicative connections. The feedback (digital footprint) enables the instructor to regulate the learning process interactively. As a result, the triad student–teacher–digital platform becomes a separate agent in the educational process.&#x0D; The authors also report on the results of DT fragment testing, which evaluated students’ comprehension of an electronic learning text. Thus, digital platforms may become more than just a handy DT storage solution; they can also be a pedagogical instrument for the development of student agency and overall support for the educational process.
Improved Interpolation Algorithm Accounting for Multiple Switching Actions and Reinitialization

With the increasing penetration of new energy and power electronic equipment in the power grid, the operation of the power system is characterized by power electronics, which puts forward new requirements for the acceleration algorithm of electromagnetic transient simulation. This paper proposes an interpolation algorithm considering multiple switching actions and reinitialization, which has higher simulation accuracy at simulation steps of 10 μs or above. Firstly, the electromagnetic transient switch action positioning method is studied, and the algorithmic characteristics of the existing interpolation algorithm are analyzed. Secondly, to address the limitations of the existing interpolation algorithms, an interpolation algorithm that can take into account the simulation efficiency and accuracy is proposed. Furthermore, it is considered to improve the interpolation algorithm by using the reinitialization method to deal with the instant of the switch action, which can reduce the switching error and ensure the simulation accuracy at the instant of the switch action. Finally, a simulation example of the proposed interpolation algorithm is constructed, and the advantages of the proposed interpolation algorithm are verified by comparing with the simulation constructed in PSCAD/EMTDC.
Secure Text Encryption Based on Clifford Attractors

Due to the numerous ways that confidential information can be compromised, the security of data exchanged online requires increasingly diverse methods of protection. In this research, we will demonstrate a secure text encryption algorithm on the base of two Clifford attractors to improve security of message transfer. The method is subjected to different security tests. The strong analysis show that the algorithm featuring improved characteristics against other similar text encryption algorithms.
How to Design a R-D Model for UAV Video Transmission?

Unmanned aerial vehicle (UAV) video transmission has been extensively applied in many crucial fields. However, the problem of designing a rate-distortion (R-D) model for UAV video transmission, which is essential for theoretical analysis of video coding and optimization of video transmission quality, is under-studied. The designed R-D model is desired to be simple, accurate, and generic owing to the limited capability of the UAV. Observing the key role of transformed residuals in the R-D model, this paper elaborately discusses the modeling of the transformed residual distribution and proposes an estimation algorithm to estimate the statistical parameter of the distribution. Specifically, considering the stringent requirements for low complexity and high generalization capability, we first design a linear parameter estimator by mining and analyzing UAV video statistical characteristics. Further, we develop a bias update scheme to improve the accuracy of the estimator. Test results on multiple real video sequences taken by UAVs show that the proposed estimation algorithm is more accurate than benchmarks.
Quantum Network Coding Based on Quantum Steering

Quantum steering is a physical resource that has important application value in quantum information. Quantum network coding provides an alternative solution for quantum-state distribution, especially when the bottleneck problems must be considered and high communication speed is required. In this paper, we introduce quantum steering into quantum network coding, and propose a feasible quantum network coding scheme to transmit any of the four quantum states in butterfly networks. By only using the steerable property, we make an attempt to relax the restriction on entangled quantum pairs to quantum steering.
Deep-Learning Based Gaussian Mixture Likelihoods Joint Source-Channel Coding for Image Transmission

Recent research on joint source channel coding (JSCC) for wireless image communications has achieved great success owing to the employment of deep learning (DL). However, most of the current DL-based JSCC methods improve the model distortion performance under different signal-to-noise ratios (SNRs) and neglect the joint optimization of the model's rate-distortion performance in the encoding and decoding process. Meanwhile, existing JSCC models typically use traditional channel coding for side information transmission, suffering from cliff-effect at low SNRs as well. To overcome these limitations, this paper proposes a new method called GMLJSCC, which utilizes Gaussian Mixture Likelihoods to parameterize the distributions of latent codes for code rate estimation, saving rate in the process of signal compression and transmission. The model also leverages DL-JSCC-based design to transmit side information, which provides accurate estimation for source statistical probability. The reconstruction performance of the model is also improved.
Full Quantum One-way Function Based on Quantum Hypergraph State

Quantum one-way function (QOWF) was proposed for quantum cryptography, which was computable but almost irreversible in quantum polynomial time. Full quantum one-way function (F-QOWF) is one of the most important models of QOWF, which deals with quantum states. Although the universal construction of full quantum one-way function has been proposed, there is still little further discussion on the entangled input, specific function instantiation and application scenarios of F-QOWF. In this paper, we introduce quantum hypergraph states into the universal construction of F-QOWF. Quantum hypergraph states are highly entangled multi-party states constructed from the hypergraph. By applying quantum hypergraph states to the new F-QOWF, we generalize the universal construction of F-QOWF to entangled states. The application to quantum aggregate signature shows that quantum hypergraph states help the new F-QOWF be well applied to quantum multi-party cryptographic protocols.
MITIGASI RISIKO DALAM MEREDAM KONFLIK DI SEKOLAH [STUDI KASUS PADA TK &amp; SD RABBANI BANDUNG]

This research aims to identify and analyze risk mitigation efforts that are effective in reducing conflict in the school environment, with a focus on case studies at Rabbani Kindergarten &amp; Elementary School in Bandung. Conflict in schools can be detrimental to both students and teaching staff, so there is a need for measurable and targeted strategies to reduce risks and overcome these conflicts. The research method used involved observation, interviews, and document analysis to understand the context of conflict at Rabbani Kindergarten &amp; Elementary School in Bandung. The research results show that effective risk mitigation involves a combination of conflict prevention, quick conflict handling, and the creation of an inclusive school environment. In addition, risk mitigation involves various related parties such as students, parents, and school staff in the handling process which is also a key factor in reducing potential conflicts. The school manages to create a harmonious environment by effectively managing conflicts and implementing positive initiatives. Stakeholder involvement and regular evaluations can maintain the sustainability of risk mitigation policies that have been implemented.
WORKLOAD QUALIFICATION FRAMEWORK FOR DATA STORES

Data Store migration involves data transfer among data storage systems, data formats &amp;/or computer systems. DataStore migration is often considered a difficult subject as it requires careful adaptation of data structures &amp; data modelsthat are appropriate with the target data store without affecting the functionality of application, &amp; correctness andintegrity of data. With rapid evolution of cloud services, migrating from legacy data stores to cloud native, or open-sourcedata stores is gaining wide attention. However, there is no standardized approach which addresses the problem ofworkload categorization, &amp; simplifying the choice of target data stores. This paper presents a comprehensive workloadqualification framework &amp; shares an example of choosing between relational &amp; non-relational (key value) data stores.The paper addresses the pervasive challenges and lack of standardization in migrating traditional data stores to opensource, cloud native data stores by proposing a unified framework. This framework encompasses algorithm migration,model migration, and migration schemes, tailored to enhance the data migration process across various environments.Through comparative analysis and integration of existing frameworks, it aims to offer a robust solution that aidsorganizations and developers in navigating the complexities of data migration, thereby contributing to more efficient andeffective database management and transition strategies
Prototype Of DPO Search Information System (People Search List) On CCTV Cameras Using Face Recognition

The efficiency aspect of the housing security sector is the main consideration in creating information technology in a CCTV security development, because so far CCTV has not guaranteed maximum housing security, because CCTV can only record not analyze. This makes CCTV only a recording device for the point of occurrence, so this activity is less effective considering CCTV cannot analyze the face of a person who has committed a DPO action. So on that basis, researchers are developing existing technologies with facial recognition systems using the face recognition method for housing security. This method works by detecting a person's facial features. The face recognition apps provide information to the authorities by notifying the notification on the user's smartphone (security guard). So face recognition apps have been applied to the Green Cakra Housing, to help security guards monitor the entrance and exit of guests in the housing. This system can recognize the face of DPOs by obtaining a percentage of the assessment as much as agreeing that the application is effective enough.
The Mentoring Role for Maritime Cadets' Guidance in Seafaring Carrier

This article is a result of a joint research conducted by the international team of SEAMENTORS project ”SEAfarers Experiential Knowledge Based MENTORS”, project no. 2021-1-RO01-KA220-VET-000029622, (www.seamentors.eu), implemented under Erasmus+ KA220 Vocational Education and Training, by Romanian Naval Academy (coordinator), Lithuanian Maritime Academy, Bulgarian Naval Academy, Polish Naval Academy, Spinaker Co and Maritime Innovators. According to the project objectives, the research team has concluded a survey study, applied on the cadets’ and professionals’ perception, aiming to disclose the importance of the mentorship programs during the cadetship training stages at sea. For this purpose, the authors have developed a KAP Survey Model (Knowledge, Attitudes, and Practices questionnaire), seeking to collect the opinions and to analyse the impact of mentorship practices onboard the commercial and military ships. In compliance with the STCW standards, STW 44/3/2 Leadership and Teamwork, HTW 5/3/2 Leadership and Managerial Skills, correlated with HTW 5.3.5 Crisis management and human behaviour training, as developed under the supervision of the Sub-Committee on Human Element, Training and Watchkeeping, the study is revealing in its concluding remarks, the role of mentoring for assuring an effective and smooth insertion of seafaring professionals onboard the ships, in balance with educational, training and coaching operational dimensions.
WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network

We present WSSAMNet, a weakly supervised method for medical image registration. Ours is a two step method, with the first step being the computation of segmentation masks of the fixed and moving volumes. These masks are then used to attend to the input volume, which are then provided as inputs to a registration network in the second step. The registration network computes the deformation field to perform the alignment between the fixed and the moving volumes. We study the effectiveness of our technique on the BraTSReg challenge data against ANTs and VoxelMorph, where we demonstrate that our method performs competitively.
Double Wins: Boosting Accuracy and Efficiency of Graph Neural Networks by Reliable Knowledge Distillation

The recent breakthrough achieved by graph neural networks (GNNs) with few labeled data accelerates the pace of deploying GNNs on real-world applications. While several efforts have been made to scale GNNs training for large-scale graphs, GNNs still suffer from the scalability challenge of model inference, due to the graph dependency issue incurred by the message-passing mechanism, therefore hindering its deployment in resource-constrained applications. An intuitive remedy is compressing the cumbersome GNN model into inference-friendly multi-layer perceptrons (MLPs) using knowledge distillation (KD). However, the standard KD strategy, i.e., training MLPs using the soft labels of labeled and unlabeled nodes from the teacher, is suboptimal, since the GNN teacher would inevitably make wrong predictions for unlabeled data, especially in the semi-supervised scenario. To address this, we propose a novel Reliable Knowledge Distillation framework for MLP optimization (RKDMLP), which shows strong promise in achieving a “sweet point” in co-optimizing model accuracy and efficiency. Its core insight is to use a meta-policy to filter out those unreliable soft labels. To train the meta-policy, we design a reward-driven objective based on a meta-set and adopt policy gradient to optimize the expected reward. Then we apply the meta-policy to the unlabeled nodes and select the most reliable soft labels for distillation. Extensive experiments across various GNN backbones, on 7 small graphs and 2 large-scale datasets from the challenging Open Graph Benchmark, demonstrate the superiority of our proposal. Moreover, RKD-MLP also shows good robustness iv.r.t. graph topology and node feature noises.
Privacy-Preserving Cross-Domain Sequential Recommendation

Cross-domain sequential recommendation is an important development direction of recommender systems. It combines the characteristics of sequential recommender systems and cross-domain recommender systems, which can capture the dynamic preferences of users and alleviate the problem of cold-start users. However, in recent years, people pay more and more attention to their privacy. How to protect the users’ privacy has become an urgent problem to be solved. In this paper, we propose a novel privacy-preserving cross-domain sequential recommender system (PriCDSR), which can provide users with recommendation services while preserving their privacy at the same time. Specifically, we define a new differential privacy on the data, taking into account both the ID information and the order information. Then, we design a random mechanism that satisfies this differential privacy and provide its theoretical proof. Our PriCDSR is a non-invasive method that can adopt any cross-domain sequential recommender system as a base model without any modification to it. To the best of our knowledge, our PriCDSR is the first work to investigate privacy issues in cross-domain sequential recommender systems. We conduct experiments on three domains, and the results demonstrate that our PriCDSR, despite introducing noise, still outperforms recommender systems that only use data from a single domain.
Systematic Review on the Conversion of Tacit Knowledge

Knowledge Knowledge management is a systematic process within organizations aimed at creating, sharing, and managing knowledge to improve efficiency, productivity, and competitiveness. It involves distinguishing between tacit knowledge (difficult to transfer or document) and explicit knowledge (easily communicable). Converting tacit knowledge into explicit knowledge is essential for fostering innovation and growth. The Nonaka model provides strategies for this conversion, with externalization being one of them, necessitating creative expression of tacit knowledge. This paper presents a systematic review of IT methods for converting tacit knowledge into explicit knowledge, highlighting its benefits and limitations. It further proposes a comprehensive model based on e-learning to effectively capture, codify, and disseminate tacit knowledge.