Reviewerly/exHarmony huggingface dataset:
The Relationship between Organizational Culture and Knowledge Management: A Case Study of Bam University of Medical Sciences

Background: Knowledge management creates value for the organization by turning human capital into intellectual assets. Organizational culture affects knowledge management by influencing these assets. The present study aimed to examine the relationship between the components of organizational culture and knowledge management at Bam University of Medical Sciences. Methods: This descriptive-analytical study was conducted using a cross-sectional design in 2017 on 190 staff members of Bam University of Medical Sciences who were selected through stratified sampling. The data were collected using Probst’s Knowledge Management Questionnaire and Denison Organizational Culture Questionnaire. Data analysis was performed using Pearson’s correlation test and linear regression analysis with SPSS-26 software. Results: Most of the staff (81%) had a positive evaluation of the state of organizational culture. The highest and lowest mean scores for the components of organizational culture were related to consistency (2.88) and adaptability (2.67) and also knowledge identification (3.30) and knowledge use (2.53) as the components of knowledge management. The results showed a statistically significant relationship (P&lt;0.005) between organizational culture and knowledge management. Conclusion: The findings confirmed a significant relationship between organizational culture and knowledge management, indicating that the stronger the organizational culture, the more successful the implementation of knowledge management will be. Thus, to improve knowledge management and sharing in the organization, managers can focus on the organizational culture and improve its various dimensions.
Meningkatkan Kemampuan Berbicara Bahasa Inggris Peserta Didik dengan Menggunakan Metode Window Shopping

Being proficient in English is a necessity in the modern and evolving era, involving the use of English in various aspects of life. Many children have already become proficient in understanding and reading in English, but their speaking abilities remain an unresolved issue. There are many factors that contribute to the lack of speaking proficiency in English, one of which is the limited opportunity to practice and a lack of confidence. In this article, the researcher conducted a classroom action research focusing on improving the speaking skills of students, particularly at the high school level. The researcher employed the window shopping or gallery walk method to assist students in honing their speaking abilities. Thirty-six students were divided into several groups, with 1-2 individuals as presenters and other group members serving as evaluators who would engage in gallery walk/window shopping with other groups, providing questions, opinions, and suggestions. The results of the researcher's observations during the presentations indicated an improvement in students' English speaking abilities from the first to the second cycle of implementation.
Pelatihan Aplikasi E-Learning “MojadiPro” Berbasis Android untuk Siswa SMK IT Raflesia Depok

SMK IT Raflesia Depok is one of the high schools in the Depok City area which currently has a website that is used as a medium of information and communication, but does not yet provide teaching material services online. Apart from getting subject matter from school, students are usually still looking for additional material by attending private classes. With the development of technology that has developed in the field of education, access to subject matter can be done online, or what is commonly called e-learning. In order to carry out the Tri Dharma of Higher Education, Nusa Mandiri University held an android-based "MojadiPro" e-learning application training for students of SMK IT Raflesia Depok which aims to provide information regarding the existence of the MojadiPro e-learning application, which later can be used by students as a companion application learning that can be accessed via a smartphone. The training was held on January 14 2023 at UNM Margonda Campus with 15 participants. The research results can increase participants' understanding of the development of e-learning from time to time and the technology used in the development of e-learning platforms
Workshop and Assistance on the Utilization of E-Learning at the Universitas Muhammadiyah Buton

Improving the learning system chosen by the Universitas Muhammadiyah Buton (UM Buton) in order to compete in the era of disruption with advances in information and communication technology (ICT), particularly electronic-based learning systems, one of which is known as the e-learning learning system. Several professors at UM Buton had difficulties in adding learning material in the form of text, and video, visualizing quiz results and semester examinations, and building forums for question-and-answer sessions while utilizing e-learning. Based on these issues, the service team developed training and mentoring for UM Buton lecturers. It is hoped that with this training, UM Buton will be able to produce quality lecturers and highly educated skilled professionals who can keep up with the times and ICT-based technology while carrying out Catur Dharma tasks and responsibilities as a lecturer at Muhammadiyah Higher Education.
DevOps and continuous integration/continuous deployment (CI/CD) automation

The rapid integration of DevOps and Continuous Integration/Continuous Deployment (CI/CD) has ushered in a transformative era in software development, streamlining processes and bolstering release efficiency. The amalgamation of development (Dev) and IT operations (Ops) into a cohesive DevOps model facilitates a more synergistic and responsive approach to the software lifecycle. Simultaneously, CI/CD automation accelerates the software release cadence, promising quicker feedback loops and more frequent feature releases. This paper delves into the underlying principles of DevOps and CI/CD, elucidating their collaborative benefits while also highlighting the associated challenges. Furthermore, the study assesses the impact of CI/CD on software quality, providing insights into best practices and potential future trends in the landscape of DevOps-driven software engineering.
Rancang Bangun Aplikasi pengelola Data Pekerja Berbasis Website Di BRI Kendari Cabang Samratulangi

The large number of workers managed makes it difficult for BRI officers at the Samratulangi branch to manage them. Management of employee data at the Samratulangi branch of BRI was previously managed via Microsoft Excel. Management carried out manually causes several problems, including the difficulty of identifying workers whose work terms have expired and the number of workers being managed is quite large and takes a long time. This problem can be solved by building a worker data management application. Application development is carried out using the prototype method. In this research, the application is designed to be able to manage workers' data well, be able to manage workers' attendance, know the active status of workers, and be able to issue Specified Time Work Agreement Decree in a timely manner. Based on the prototype method, researchers carry out the communication stage to obtain information about the problems faced by users. At the Quick plan and Modeling quick design stage, researchers began to design the UML and display in prototype form to then create a User Interface. Next, at the Feedback and Delivery stage, researchers ask for feedback and suggestions regarding the application that has been created which will then be developed and improved according to user input. So the system is created so that users can run the system according to their needs. Based on the test results using a blackbox, the system is designed according to its function and use.
Combining mathematical and statistical algorithms for objectivity in the use of ogive for determining rainfall onset, peak, and retreat dates over Nigeria

&lt;p&gt;&lt;span lang="EN-GB"&gt;The study combined the second-order polynomial and some elements of algebra and trigonometry with the ogive to objectively (mathematically) locate the rainfall onset, peak, and retreat on the ogive, which was visually done. The data used for the investigation are the daily rainfalls of 16 synoptic stations (Ikeja, Calabar, Port-Harcourt, Benin, Ondo, Enugu, Ilorin, Lokoja, Jos, Kaduna, Yola, Kano, Sokoto, Maiduguri, Potiskum, and Nguru) across all the ecological zones of Nigeria. The datasets spanning 50 years (1971–2020) were collected from the Archives of the Nigerian Meteorological Services, Abuja, Nigeria. The ogives were derived from the frequency of rainy days. The peak periods were best detected from the pentad graphs of the rainy-day frequency. The results showed that rainy-day frequency is better than rainfall amount in determining the various rainfall parameters over Nigeria. The second-order polynomial modeled the two curvatures of the rainfall perfectly. The rainy-day frequency in the southern part of the country exhibited double rainfall maxima, while those in the northern part showed a single rainfall maximum. The double rainfall maxima are not peculiar to the southwestern region of Nigeria, as previously widely asserted; they cut across the whole southern region, although the number of days and the depth of the trough between the peaks dimmish from the largest values in the extreme southwest to the least in the extreme southeast. The first rainfall peaks were attained in southern Nigeria in July, except at Ikeja, which was in June. The second rainfall peak was reached in September in all the southern stations. The single peaks were attained in all the extreme northern stations in August and in the other stations south of them in early September. The rainfall onset begins at Ikeja in the extreme southwestern corner of Nigeria around March ending. It spreads eastwards and northwards to cover the entire country by mid-June, reaching Nguru in the northeastern corner. It is generally earlier on the western flank than the eastern flank. Rainfall begins to retreat from the northernmost stations by the third week in September to reach the extreme southern stations between October and early November.&lt;/span&gt;&lt;/p&gt;
Fuzzy Controller for Rainfall Prediction

This paper uses fuzzy logic principles applied in Matlab to present the occurrence of rainfall. The rain gauge station on the Vjosa River in Albania is where the data are collected. Initially, the input variables in this application were divided into five linguistic ranges as high, medium, low, very low, very high. It makes use of the triangular membership functions. The primary goal of the author in this particular work is to lessen drought and flood conditions. The author's primary concern is using temperature, speed, and wind variation to predict when it will rain. The study illustrates an STLF method based on fuzzy logic. Mamdani's implication is used to prepare the fuzzy rule foundation. Fuzzy Tool Box and Matlab Simulink are the programs used for this by utilizing triangles.
The Involvement of Local Binary Pattern to Improve the Accuracy of Multi Support Vector-Based Javanese Handwriting Character Recognition

Indonesia is a country that is rich in cultural diversity. An example of one such variety is the Javanese language. The letters that are usually used in Javanese are non-Latin letters or are usually known as Javanese script. However, along with advances in technology, the Javanese language is increasingly being forgotten. In the past, the Javanese script was used as a subject in schools, aiming for Indonesian students to continue to gain knowledge about the Javanese script. The initial step in the introduction of the Javanese script starts with the preprocessing process by changing the image of the Javanese script from the RGB image to a grayscale image which is then performed feature extraction, where the feature extraction used in this script recognition is texture extraction with the Local Binary Pattern (LBP) algorithm. The results of this processing are obtained information that can be used as a parameter in the Multi Support Vector Machine (SVM) classification to predict Javanese script images. In this study using the LBP method with the Multi SVM Algorithm as a classification algorithm produces a high accuracy of 90% in the recognition of Javanese script, better than using only Multi SVM with an accuracy of 80%.
Research on the Effectiveness of Qinglan Project in Promoting the Professional Growth of Secondary Vocational Teachers

The purpose of this paper is to explore the effectiveness of the Qinglan Project in promoting the professional growth of young secondary vocational teachers. Through the empirical analysis of the implementation process and effect, this paper finds that the Qinglan Project has a positive impact on the professional development of secondary vocational teachers, with a view to providing a guideline for the development of secondary vocational education.
The Impact of IoT Supply Chain Integrated Blockchain Technology Advancements in the Healthcare Sector: A Literature Review

This literature review article focuses on an in-depth analysis of the Influence of Blockchain Technology Integrated with IoT in Healthcare Supply Chain. The purpose of this scholarly article is to formulate a strong hypothesis on how the advancement of blockchain technology affects the efficiency and effectiveness of supply chains in the healthcare sector. In order to achieve this goal, the research applied a meticulous desk research method, collecting references from various reliable online sources such as Google Scholar, IEEE, and other academic online media. The results outline the significant impact that the integration of blockchain technology and IoT can have in the context of the healthcare sector. The discussion in this article addresses how blockchain technology connected with the Internet of Things (IoT) can provide concrete benefits in optimizing the healthcare supply chain. The practical implications of the integration of these technologies are revealed in the form of improved operational efficiency, information transparency, more accurate product tracking, and reduced risk of human error.
Hierarchical Detailed Intermediate Supervision for Image-to-Image Translation

The image-to-image translation aims to learn a mapping between the source and target domains. For improving visual quality, the majority of previous works adopt multi-stage techniques to refine coarse results in a progressive manner. In this work, we present a novel approach for generating plausible details by only introducing a group of intermediate supervisions without cascading multiple stages. Specifically, we propose a Laplacian Pyramid Transformation Generative Adversarial Network (LapTransGAN) to simultaneously transform components in different frequencies from the source domain to the target domain within only one stage. Hierarchical perceptual and gradient penalization are utilized for learning consistent semantic structures and details at each pyramid level. The proposed model is evaluated based on various metrics, including the similarity in feature maps, reconstruction quality, segmentation accuracy, similarity in details, and qualitative appearances. Our experiments show that LapTransGAN can achieve a much better quantitative performance than both the supervised pix2pix model and the unsupervised CycleGAN model. Comprehensive ablation experiments are conducted to study the contribution of each component.
Leveraging Quantum Dynamics for Physical Computing Applications

Quantum reservoir computing uses genuine quantum properties to process temporal information. It combines machine learning with physical computing in a novel quantum computing paradigm. Unlike gate-based quantum computing, which requires precise qubit control and decoherence suppression, quantum reservoir computing utilizes the dynamics of disordered quantum networks, with moderate noise even benefiting the reservoir's memory capacity. In this study, we explore how quantum properties affect a system's suitability as a reservoir computer. We investigate the relationship between entanglement in the reservoir and phase space dimension. Our findings show higher entanglement increases the percentage of the exponentially large phase space used for computation. Additionally, we discuss methods to quantify information spread throughout the network. Network symmetries, accidental or intentional, significantly influence dynamics and the reservoir's memory capacity. Lastly, we highlight the potential and limitations of using arrays of semiconductor-based coupled microcavities for hybrid quantum-photonic implementations of reservoir computing.
C³: Code Clone-Based Identification of Duplicated Components

Reinventing the wheel is a detrimental programming practice in software development that frequently results in the introduction of duplicated components. This practice not only leads to increased maintenance and labor costs but also poses a higher risk of propagating bugs throughout the system. Despite numerous issues introduced by duplicated components in software, the identification of component-level clones remains a significant challenge that existing studies struggle to effectively tackle. Specifically, existing methods face two primary limitations that are challenging to overcome: 1) Measuring the similarity between different components presents a challenge due to the significant size differences among them; 2) Identifying functional clones is a complex task as determining the primary functionality of components proves to be difficult.
Design and Implementation of an On-Line Quality Control System for Latch-Based True Random Number Generator

This paper presents a design and an implementation of an on-line quality control method for a TRNG (True Random Number Generator) on an FPGA. It is based on a TRNG with RS latches and a temporal XOR corrector, which can make a trade-off between throughput and randomness quality by changing the number of accumulations by XOR. The goal of our method is to increase the throughput within the range of keeping the quality of output random numbers. In order to detect a sign of the loss of quality from the TRNG in parallel with random number generation, our method distinguishes random bitstrings to be tested from those to be output. The test bitstring is generated with the fewer number of accumulations than that of the output bitstring. The number of accumulations will be increased if the test bitstring fails in the randomness test. We designed and evaluated a prototype of on-line quality control system, using a Zynq-7000 FPGA SoC. The results indicate that the TRNG with the proposed method achieved 1.91-2.63 Mbits/s of throughput with 16 latches, following the change of the quality of output random numbers. The total number of logic elements in the prototype system with 16 latches was comparable to an existing system with 256 latches, without quality control capabilities.
A Comprehensive Model for Securing Patient Sensitive Data in a Clinical Scenario

The clinical environment is one of the most important sources of sensitive patient data in healthcare. These data have attracted cybercriminals who pursue the theft of this information for personal gain. Therefore, protecting these data is a critical issue. This paper focuses on an analysis of the clinical environment, presents its general ecosystem and stakeholders, and inspects the main protocols implemented between the clinical components from a security and privacy perspective. Additionally, this article defines a complete use case to describe the typical workflow within a clinical setting: the life cycle of a patient sample. Moreover, we present and categorize crucial clinical information and divide it into two sensitivity levels: High and Very Sensitive, while considering the severe risks of cybercriminal access. The threat model for the use case has also been identified, in conjunction with the use case’s security and privacy needs. This work served us as basis to develop the minimum security and privacy requirements to protect the use case. Accordingly, we have defined protection mechanisms for each sensitivity level with the enabling technologies needed to satisfy each requirement. Finally, the main challenges and future steps for the use case are presented.
DTLR-CS: Deep tensor low rank channel cross fusion neural network for reproductive cell segmentation

In recent years, with the development of deep learning technology, deep neural networks have been widely used in the field of medical image segmentation. U-shaped Network(U-Net) is a segmentation network proposed for medical images based on full-convolution and is gradually becoming the most commonly used segmentation architecture in the medical field. The encoder of U-Net is mainly used to capture the context information in the image, which plays an important role in the performance of the semantic segmentation algorithm. However, it is unstable for U-Net with simple skip connection to perform unstably in global multi-scale modelling, and it is prone to semantic gaps in feature fusion. Inspired by this, in this work, we propose a Deep Tensor Low Rank Channel Cross Fusion Neural Network (DTLR-CS) to replace the simple skip connection in U-Net. To avoid space compression and to solve the high rank problem, we designed a tensor low-ranking module to generate a large number of low-rank tensors containing context features. To reduce semantic differences, we introduced a cross-fusion connection module, which consists of a channel cross-fusion sub-module and a feature connection sub-module. Based on the proposed network, experiments have shown that our network has accurate cell segmentation performance.
GTQCP: Greedy Topology-Aware Quantum Circuit Partitioning

We propose Greedy Topology-Aware Quantum Circuit Partitioning (GTQCP), a novel quantum gate circuit partitioning method which partitions circuits by applying a greedy heuristic to the qubit dependency graph of the circuit. GTQCP is compared against three other gate partitioning methods, two of which (QuickPartitioner and ScanPartitioner) are part of the Berkley Quantum Synthesis Toolkit. GTQCP is shown to have 18% run time improvement ratio over the fastest approach (QuickPartitioner), and a 96% improvement over the highest quality approach (ScanPartitioner). The algorithm also demonstrates nearly identical result quality (number of partitions) compared with ScanPartitioner, and a 38% quality improvement over QuickPartitioner.
YOLO-PAM: Parasite-Attention-Based Model for Efficient Malaria Detection

Malaria is a potentially fatal infectious disease caused by the Plasmodium parasite. The mortality rate can be significantly reduced if the condition is diagnosed and treated early. However, in many underdeveloped countries, the detection of malaria parasites from blood smears is still performed manually by experienced hematologists. This process is time-consuming and error-prone. In recent years, deep-learning-based object-detection methods have shown promising results in automating this task, which is critical to ensure diagnosis and treatment in the shortest possible time. In this paper, we propose a novel Transformer- and attention-based object-detection architecture designed to detect malaria parasites with high efficiency and precision, focusing on detecting several parasite sizes. The proposed method was tested on two public datasets, namely MP-IDB and IML. The evaluation results demonstrated a mean average precision exceeding 83.6% on distinct Plasmodium species within MP-IDB and reaching nearly 60% on IML. These findings underscore the effectiveness of our proposed architecture in automating malaria parasite detection, offering a potential breakthrough in expediting diagnosis and treatment processes.