Reviewerly/exHarmony huggingface dataset:
Students Collaboration Skills in Science Learning

Collaboration skills are skills that students must possess to face the challenges of the 21st century.Collaboration skills do not only play a role in learning but are indispensable for solving life problems.The purpose of this study was to identify the collaboration skills of high school students in science learning.This non-experimental survey research involved 226 high school students.Data collection was carried out by observation with observation guidelines.The aspects of collaboration skills that we observe are work productively, demonstrate respect, compromise, and shared responsibility.The results showed that the highest level of productive collaboration skills in working at level 2 basic category was 55.71%.shown the highest award at level 2 basic category of 54.29%.The highest compromise was at level 1 for the novice category at 55.91%.the highest share responsibility at level 1 for the novice category of 51.43%.In general, collaboration skills in student learning need to be improved.Based on the results of this study, a learning model is needed that can empower students' collaboration skills.
An Energy-efficient Non-volatile Silicon Photonic Accelerator for Convolutional Neural Networks (NVSP-CNNs)

In this paper, we proposed a non-volatile silicon photonic (NVSP) CNN accelerator architecture. Simulation results demonstrate that compared with traditional photonic CNN accelerator DEAP-CNNs, our architecture can reduce the power loss by 29.47%.
The Analysis and Implication of Data Deduplication in Digital Forensics

Data deduplication is a file storage system method that is available on various operating systems such as Windows Server, MacOS, and Linux distributions. However, rehydration of deduplicated files is not yet a functionality supported in forensic tools. With the increasing cost of cybercrimes each year, and more users looking for ways to save storage space on ever growing file sizes, developing forensic tools to support detection and recovery of deduplicated files is more important than ever. To address this issue, in this paper, we first give a comprehensive analysis of data deduplication and its implementation in modern Operating Systems. Then, we examine how data deduplication techniques affect digital forensic investigation tools, particularly, TSK, a widely used open-source forensic tool for volume and file system analysis. We then propose a solution to restore deduplicated files from an acquired deduplicated file system volume and implement it into TSK to extend TSK for supporting data deduplication. Furthermore, we also study the positive forensic implications of data deduplication techniques, which are seldom considered in existing studies. Specially, we also investigate new sources of evidence or new artifacts generated during data deduplication.
Benefits of applying virtual reality in pelvic movement training through a Wii Fit: a randomized controlled trial

Pelvic movement training has become compulsory for part of medical students. An increasing amount of research has focused on the influence of virtual reality (VR) on learning effectiveness. However, its application to pelvic floor muscles or pelvic movement training is still in its infancy. We compared the effectiveness of conventional pelvic movement training with or without VR-assisted pelvic movement training for student learning.We recruited 44 university students (16 male and 28 female participants; average age = 19.7 ± 0.31 years) who had not previously received pelvic movement education or training. The participants were randomly assigned into traditional and experimental groups to acquire pelvic movements and relevant knowledge. The traditional group received conventional classes (about 15 min), whereas the experimental group received both conventional classes and VR-assisted teaching (additional VR session took approximately 25-45 min depending on the speed of movement of each participant). The participants were asked to control the trajectory of the centre of pressure on the Wii Fit balance board and build-in games to learn pelvic movements. We conducted evaluations before, immediately after, and 2 weeks after the experiment, based on the scores of written and practical examinations. The experimental group was also asked to complete a questionnaire during the posttest.We carried out two-way repeated measures ANOVA and discovered that the written examination scores indicated a significant Time × Group interaction (p=0.015). In each group, the written and practical examinations in the posttest and follow-up test exhibited significantly improved results compared with the baseline value (p <0.001, except for traditional group of written exam in follow up test vs. baseline p=0.001). The written examination in the follow-up test did not decline significantly compared with those in the posttest, but the practical examination in the follow-up test was decline significantly compared with those in the posttest (p=0.033). The experimental group had superior overall performance in the practical examinations than the traditional group (experimental group: mean = 76.27, 95% confidence level [CI] = 70.84-81.71; traditional group: mean = 64.21, 95% CI = 58.78-69.65). No significant difference in the written examination between two groups. The percentage for agreement ratio on the usefulness, ease of use, users' intention to continue using the VR-assisted teaching is high (95.5-100%).The results of this study suggested that conventional and conventional + VR teaching were both effective. However, the incorporation of VR stimulated learning motivation and facilitated precise performance of pelvic movements. It is recommended that pelvic floor muscles training could be supplemented with VR or games to increase students' motivation and understanding how to perform pelvic movements.
A Binary Translator to Accelerate Development of Deep Learning Processing Library for AArch64 CPU

To accelerate deep learning (DL) processes on the supercomputer Fugaku, the authors have ported and optimized oneDNN for Fugaku's CPU, the Fujitsu A64FX. oneDNN is an open-source DL processing library developed by Intel for the x86_64 architecture. The A64FX CPU is based on the Armv8-A architecture. oneDNN dynamically creates the execution code for the computation kernels, which are implemented at the granularity of x86_64 instructions using Xbyak, the Just-In-Time (JIT) assembler for x86_64 architecture. To port oneDNN to A64FX, it must be rewritten into Armv8-A instructions using Xbyak_aarch64, the JIT assembler for the Armv8-A architecture. This is challenging because the number of steps to be rewritten exceeds several tens of thousands of lines. This study presents the Xbyak_translator_aarch64. Xbyak_translator_aarch64 is a binary translator that at runtime converts dynamically produced executable codes for the x86_64 architecture into executable codes for the Armv8-A architecture. Xbyak_translator_aarch64 eliminates the need to rewrite the source code for porting oneDNN to A64FX and allows us to port oneDNN to A64FX quickly.
Modularity

This chapter discusses the classical modular model of the mind introduced by Fodor, detailing each of its characteristic features. It focuses on contemporary issues, comparing Fodorian modest modularity with the post-Fodorian massive modularity hypothesis. One of the fundamental principles of cognitive science is that cognition is information processing. The internal structure of the mind, which involves a model of how the mind is organized into different cognitive systems and how information is processed within those cognitive systems, has been studied in philosophy of mind, psychology, and cognitive science for more than 50 years. The fact that the assumed modularity of processing within the brain does not translate to modularity of mental architecture suggests that more interdisciplinary collaboration between cognitive neuroscientists and philosophers is needed to progress on this topic, that is, to provide decisive empirical evidence for one of the theoretical modular approaches.
A Ditherless Bias Control Technique for IQ Mach-Zehnder Modulator Based on Partial Derivative and Neural Network

We proposed a ditherless automatic bias control algorithm based on partial derivative estimation and neural network. Simulation results show the precision for bias IQ and P can achieve 0.5% and 0.1% of 21 V π respectively.
SYSTEM FOR CORRELATION ANALYSIS OF STATISTICAL INFORMATION ON CORONAVIRUS INCIDENCE

The article is devoted to conducting a study on the influence of various sociological, economic, environmental and other factors on the state of the incidence and spread of coronavirus in the world. The authors proposed a scheme for obtaining information from Internet resources with the possibility of conducting a correlation analysis of data on the causes, rates and scale of the pandemic, and the factors affecting its spread. The introduction shows the relevance of the topic, carried out a detailed analysis of Internet resources. The systematization of the data placed in them has been carried out, the necessary conclusions and conclusions have been drawn. The website coronavirus-monitor.ru was selected as a source of statistical information. As a toolkit, it is proposed to develop the Coronavirus Stat program, which is necessary to view statistical data on a PC offline and carry out calculations to test hypotheses about the influence of external factors on the spread and course of COVID-19. The authors considered in detail the methods of obtaining information from Internet sources, their advantages and disadvantages, the method of automatic search with the development of their own version of the parser was chosen. Requirements for the selection of factors for correlation analysis and testable hypotheses are formulated. Examples of testing hypotheses with the presentation of graphs of dependences of the number of cases on various factors and correlation fields are given. A detailed description of the developed program, consisting of the frontend part of the program, a parser for obtaining new information, a database for storing old information, files for storing static information, is carried out. Requirements for the parser are formulated, a block diagram of its algorithm is presented. The requirements taken into account in the development of the program are shown and examples of its work are given. Testing of the program was carried out by conducting experiments to test the hypotheses put forward. The results of the experiments are summarized in the table. In conclusion, conclusions are drawn on the further use of the developed program.
Drone-Based Tower Survey by Multi-Task Learning

Various industries use towers as part of their daily operations, such as transmission towers (aka. electricity pylons), telecommunications towers and water towers. These towers re- quire regular maintenance, and before the maintenance work can be done, a preliminary survey must be conducted to determine where to work. More and more, such surveys are being conducted via drones. This work develops a detection model to help locate tower issues from the video frames of drones. However, it does not provide satisfactory performance to directly train such an object detection model with the annotated problem locations from domain experts. Therefore, we propose to improve the quality of the extracted image features with the help of another separate task which detects the various parts that are involved in the tower issues, such as bolts, nuts, washers and pins, the annotations of which can be done without the need of domain expertise. Through this multi-task learning scheme, we improved the problem detection recall from 59.6% to 71.5%, providing much more effective recommendations of potential issues for inspectors to examine further. Also, the average number of problem detections in each image is merely 5.54 so inspectors are not overwhelmed by the recommended locations.
PENGARUH ICE BREAKING TERHADAP HASIL BELAJAR PESERTA DIDIK PADA PEMBELAJARAN TEMA 8 SUB TEMA 2 KELAS II SEKOLAH DASAR

This research is motivated by the low student learning outcomes in class II thematic learning. An effort to overcome these problems is to use Ice Breaking in thematic learning. This study aims to see the effect of applying Ice Breaking on thematic learning outcomes of second grade students of SD Negeri 21 Pulau Punjung. This study uses a True Experimental Design in the form of a posttest-only control design. The population in this study were all second grade students of SD Negeri 21 Pulau Punjung. The sample in this study was taken using systematic sampling. The data collection technique was carried out by giving a posttest in the form of a written test consisting of 17 multiple choice questions and 3 description questions. The results of the t-test analysis technique showed a significance level of 0.017 &lt;0.05 so that H0 was rejected and Ha was accepted. This means that there is an effect of applying ice breaking to the thematic learning outcomes of second grade students of SD Negeri 21 Pulau Punjung, Dharmasraya Regency.
THE ROLE OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN THE PROFESSIONAL TRAINING OF SOCIAL EDUCATORS FOR WORK WITH REFUGEES IN GERMANY

The authors lighted out German practice of social work with refugees and the role of ITC during this process. In the article the role of different international organizations such as UNESCO, United Nations Office of the High Commissioner for Refugees, International Red Cross Committee, European Council on Refugees and Exiles, International Organization for Migration, etc. was pointed out. These organizations develop and approve international standards of work with refugees, as well as propose modern approaches to solving problems with this category of the world community. The authors identified the main forms and trends of social work with refugees in Germany. They also highlighted the characteristic features in the work of German universities during the training of future social educators to work with migrants and the development of their competencies (namely, ICT competencies). In this article, the authors concluded that the process of training social educators to work with different groups of clients (including work with refugees) in Germany is multilevel. The principles of offensiveness, consistency, persistence in the implementation of content and technology of education are the main ones in the training of future social workers. Other features of the training process for social educators in Germany are also the practical use of an interdisciplinary approach and close cooperation with various state and non-state social organizations. The authors emphasized also that considerable attention during the professional training of future social pedagogues is paid to scientific and practical aspects of their training to work with refugees and immigrants. The formation of ICT competence and mastery of technologies of social "intervention" in the life and situation of the studied category of clients plays the great role for social educators in the preparing for their future profession. In the article is also stated that one of the main requirements for a social educator working with refugees is the ability to use in practice the acquired knowledge and skills to get the best results.
Integrated Development Frameworks Based on Linked Data for Web- Based Interactive Educational Materials

This paper treats one of the activities of ICER (Innovation Centre for Educational Resources) in Kyushu University Library of Kyushu University, Japan.It is the development of attractive educational materials using recent ICT (Information Communication Technologies) like 3D CG (Computer Graphics) and VR (Virtual Reality) for enhancing the educational efficiency in the university.The authors have already proposed a framework dedicated for the development of web-based interactive 3D educational materials.Firstly, this paper introduces this development framework and its educational material example of Japanese history education.For developing attractive educational materials, such materials need multimedia contents like images and videos related to the teaching target.If the multimedia contents can be retrieved from the Internet, the development cost can be reduced.To do so, the authors focused on Linked Data and have already proposed an authoring tool of educational materials based on Linked Data.Furthermore, the authors integrated the authoring tool into the development framework.Secondly, this paper introduces the integrated development framework based on Lined Data and its educational materials of two teaching targets, i.e., IoT security education and Japanese history education.Furthermore, the authors have proposed another educational material development framework that supports 360VR images/videos and integrated the authoring tool with it.Finally, this paper introduces this integrated development framework based on Linked Data and its example educational material for IoT security education.
Can You Do Real-Time Gesture Recognition with 5 Watts?

Accurate and reliable gesture recognition is a central problem in human-computer interaction (HCI). Many applications that make use of gesture recognition call for mobile devices with reduced power consumption, weight and form factors. Recent advances in computer vision were particularly brought by deep neural networks and come at the cost of high computational complexity that hinders the employment on mobile devices. In this study, we evaluate the usability of a low-cost Raspberry Pi 4B amended by a Coral USB Accelerator, or a Neural Compute Stick 2, respectively, for low power real-time gesture recognition. To this end we evaluate the accuracy, inference time and power consumption for two different deep neural network-based recognition models and compare the results to other computer systems available as standard. Our experiments show that a combination of a Raspberry Pi 4B and Coral USB Accelerator allows for hand gesture recognition at frame rates of up to 30 frames per second at a power consumption of less than 5 Watts.
Efficient Breast Cancer Classification Using Histopathological Images and a Simple VGG

Breast cancer is the second most deadly disease worldwide. This severe condition led to 627,000 people dying in 2018. Thus, early detection is critical for improving the patients' lifetime or even curing them. In this context, we can appeal to Medicine 4.0, which exploits machine learning capabilities to obtain a faster and more efficient diagnosis. Therefore, this work aims to apply a simpler convolutional neural network, called VGG-7, for classifying breast cancer in histopathological images. Results have shown that VGG-7 overcomes the performance of VGG-16 and VGG-19, showing an accuracy of 98%, a precision of 99%, a recall of 98%, and an F1 score of 98%.
Analyzing Web Search Strategy of Software Developers to Modify Source Codes

Software developers often use a web search engine to improve work efficiency. However, web search strategies (e.g., frequently changing web search keywords) may be different for each developer. In this study, we attempted to define a better web search strategy. Although many previous studies analyzed web search behavior in programming, they did not provide guidelines for web search strategies. To suggest guidelines for web search strategies, we asked 10 subjects four questions about programming which they had to solve, and analyzed their behavior. In the analysis, we focused on the subjects' task time and the web search metrics defined by us. Based on our experiment, to enhance the effectiveness of the search, we suggest (1) that one should not go through the next search result pages, (2) the number of keywords in queries should be suppressed, and (3) previously used keywords must be avoided when creating a new query.
Blended Learning: Can It Be a Solution to Improve Digital Literacy and HOTS for PGSD Students in a Pandemic Situation?

The Covid-19 pandemic in the industrial revolution 4.0 era transformed traditional learning into bold-based learning through distance learning (PJJ). However, its implementation still encounters several obstacles, such as the lack of digital literacy skills. One of the solutions offered is blended learning, which combines traditional learning with learning to complement each learning method's shortcomings. The purpose of this study is to prove whether or not blended learning affects increasing digital literacy and HOTS of PGSD students. The methodology used is experimental research with Pre-Experimental Design with One-Group Pretest-Posttest Design model. The sample in this study was 55 PGSD students. The instruments used are digital literacy questionnaire sheets and HOTS test sheets. The technique used to analyze the data is descriptive qualitative, quantitative, and inferential statistics. Test Results Paired sample t-test produces sig values. (2-tailed) 0.000 is less than 0.05. It means that blended learning affects digital student literacy skills. The results of the n-gain test for increasing digital literacy after applying blended learning obtained n-gain of 0.56 in the medium category and the results of the student HOTS n-gain of 0.55 also in the medium category, so it can be said that there is an effect of blended learning on literacy improvement. Digital and student HOTS.
Sequential Association Rule Mining Revisited: A Study Directed at Relational Pattern Mining for Multi-morbidity

Sequential rule mining is a well-established data mining technique for binary valued data. Many variations have been proposed, most approaches use the support-confidence-lift framework. Existing approaches make assumptions concerning the definition of what a sequence is. However, this definition is application dependent. In this paper we look at sequential rule mining with respect to multi-morbidity disease prediction which entails a rethink of the definition of what a sequence is, and a consequent rethink of the operation of the support-confidence-lift framework. A novel sequential rule mining algorithm is proposed designed to address the challenge of multi-morbidity disease prediction. The SEquential RElational N-DIsease Pattern (SERENDIP) algorithm.