Reviewerly/exHarmony huggingface dataset:
Pengelompokkan Penyakit Pasien Menggunakan Algoritma K-Means

Health is one of the most important factors besides education and income. Everyone has the same human rights to get good health services. A government agency that functions to serve all people who need medical services in Indonesia, namely the puskesmas. Ujung Batu Health Center which is located in Ujung Batu sub-district, Rokan Hulu Regency as one of the government agencies. The Ujung Batu health center stores patient medical record data, only sorting out the disease. Therefore, the medical record data needs to be processed using clustering or grouping using the K-Means method. This algorithm partitions the data into clusters so that data with the same characteristics are grouped into the same cluster and data with different characteristics are grouped. into another cluster. The data used consisted of 3875 records and 5 attributes, namely Gender, Participant Type, Diagnosis, Return Status, Address. From the test using the K-means algorithm, the clustering results show that cluster 1 has 710 data while cluster 2 has 3165 data. The results of the study show that the use of 2 clusters is the best cluster with a Silhouette Coefficient value showing results with a SC value of 0.646.
Implementasi User Centered Design Pada Rancangan Pengembangan UX Aplikasi Pendataan Kegiatan Marketing

The Roadshow application is an application used by the Marketing Unit of the Universitas Muhammadiyah Magelang to help collect data on their activities. In the process of using it, users often experience problems when operating the Roadshow application. This can also be seen from the preliminary research results which show that the results of the User Experience assessment are still low. To overcome this, development is needed to improve the user experience of the roadshow application so that it can improve the performance of the Marketing Unit. User-Centered Design (UCD) was chosen as the design development method. The UCD approach was chosen because it emphasizes specific user needs without changing their behavior in using the system or application. User testing is done by using the prototype made. The results of Usability Concept testing generate positive feedback from users. The evaluation was carried out on the same user using the System Usability Scale (SUS) and the User Experience Questionnaire (UEQ). From the results of SUS, a score of 88 out of 100 was obtained and UEQ was ranked Good and above with an average score of 1.8. From the process of testing and evaluating the user experience design of the Roadshow application, it can be said to be good and can be a recommendation for the development of the Roadshow application in the future.
Jointly Texture Enhanced and Stereo Captured Network for Stereo Image Super-Resolution

Stereo image super-resolution (StereoISR) aims at recovering high resolution (HR) details and realistic textures from low-resolution (LR) left-right pairs with stereo correspondence. Recently, some advanced 2D methods have achieved good performance in realistic texture reconstruction via texture attention mechanism in a reference-based training way. However, the existing 3D super-resolution (SR) datasets lack the HR reference to obtain the LR-to-HR reference pair for the reference-based training. To overcome this problem, we propose a novel StereoISR network with a joint texture and parallax attention module (JTPAM), where the down-sampled LR (named super-low-resolution (SLR)) image and the LR image are taken as the SLR-to-LR reference pair, to simulate the texture conversion relationship between the LR and HR images. Additionally, the parallax attention mechanism is utilized to maintain the stereo correspondence by calculating the feature similarity along the Epipolar line. Experiment results on the Flickr1024, Middlebury, KITTI 2012 and KITTI 2015 datasets show that our network achieves the state-of-the-art (SOTA) performance over the compared methods, and the stereo SR images generated by our network have abundant binocular information and clear texture details.
Theoretical Research on the Construction of PE Teaching Evaluation System Under the Background of Information Technology

Under the background of the era of big data, the physical education teaching evaluation system of colleges and universities has more characteristics and manifestations of the new era. At the same time, big data also brings more requirements for the improvement of the physical education teaching evaluation system of colleges and universities. Under such an environment and trend, colleges and universities need to make certain reform measures to meet the challenges calmly according to the requirements of the new era. This paper makes a systematic study on the construction of the evaluation system of physical education teaching in colleges and universities, expounds the theoretical basis of building an efficient evaluation system of physical education teaching, analyzes the components of the evaluation system, and gives the construction path of the evaluation system of physical education teaching in colleges and universities. The purpose of this paper is to provide some reference information for physical education in higher education and the cultivation of talents' comprehensive quality.
Effects of Aging Simulation Activity on College Students’ Perception of Physical Changes in Older Adulthood

Objectives The purposes of this study were to develop an aging simulation activity as a part of gerontology class and to examine its effects on college students’ perception of physical changes in older adulthood.&#x0D; Methods The participants were 14 students who signed up for a gerontology class during fall semester in 2022 at J university. They participated in the aging simulation activity. Before and after the participation, the students completed a questionnaire composed of five questions. At the end of the activity, they were also asked to list three words associated with their experiences. The data were analyzed by Wilcoxon signed rank test and content analysis.&#x0D; Results After doing the aging simulation activity, the participants perceived the effects of aging-related physical changes on the elderly more seriously. They described aging-related physical changes with the word of ‘frustrated’ and ‘hard.’ The analysis of the meanings of free association words contained 19 sub-themes, and they were categorized into three themes: ‘experience of physical functioning limitations or decline,’ ‘chargers accompanying the physical functioning limitations or decline,’ and ‘empathetic understanding of the elderly and the realization of needs of behavioral chargers for older adults.’&#x0D; Conclusions Aging simulation activity is effective in that it helps college students experience physical, psychological and emotional, social, and behavioral chargers with aging in older adulthood and understand the elderly in the empathetic way, and realize the needs of behavioral changes. These results reflect that the aging simulation activity proposed in this study would be an effective teaching method in operating gerontology-related classes. Based on the limitations, the directions for future research and aging simulation activity programs are suggested.
Generating scientific inquiry questions and changes in scientific concepts through data-based modeling activities

Objectives The purpose of this study was to investigate the development of ‘asking scientific inquiry questions’ for pre-service teachers through engaging in data-based modeling activities and to examine their understanding of relevant science concepts.&#x0D; Methods 23 university students in Chungcheong-do participated in a 4 week of data modeling-based experiment. A design-based research method was used for this study. Changes in scientific concepts and scientific inquiry questions generated from modeling activities were analyzed.&#x0D; Results Through data modeling activities, pre-service teachers showed development in generating inquiry questions from simple information questions to thinking questions, and in this process, scientific concepts such as boiling point rise in heating phenomena were also improved.&#x0D; Conclusions A science practice, ‘asking scientific inquiry question, testable and investigatable, contributes to the development of scientific conceptual knowledge through the process of ‘problemization’.
Vocabulary Instruction In Improving Students' Reading Comprehension In SD Negeri 8 Menteng Palangka Raya

One of the most important lessons in elementary school is understanding what they read. While reading comprehension is essential to academic success, not all students demonstrate adequate proficiency. The researcher interviewed and observed classes in SD Negeri 8 Menteng Palangka Raya, notably sixth grade, and found this issue. The researcher utilized one of the instructions to promote reading comprehension from that issue. Vocabulary instruction could increase English text comprehension, according to the study. This semester, students studied Describing People and Objects and picked descriptive text. The researcher solved the challenge with a quasi-experimental non-equivalent control group design. This study had two groups: 6A (control) and 6C (experimental). The experimental group randomly sampled vocabulary training, while the control group received non-vocabulary education. There were also two variables in this study: vocabulary teaching served as the independent variable, and the mean scores of both groups' tests served as the dependent variable. Pre- and post-test results determined the class's vocabulary instruction effectiveness. Finally, the data showed that vocabulary instruction improves reading comprehension more than non-vocabulary instruction. Scores were less than 0.05, rejecting H0 and accepting Ha. Data less than 0.05 indicates that independent variables only partially impact the dependent variable. Vocabulary education improves students' reading comprehension in SD Negeri 8 Menteng Palangka Raya.
FUNDAMENTALS OF THE INNOVATICS OF HIGHER EDUCATION

The purpose of the article is to develop the concept and principles of a new scientific direction – the higher education innovatics. Higher education innovatics studies and summarizes the theoretical issues and practice of implementing and managing innovative activities of higher education institutions, scientific institutions, organizations, and management bodies related to the higher education system. The article analyzes innovative changes in the system of higher education caused by the environment of academic capitalism. Innovative activity in the field of higher education and science leads to serious innovative transformations both in the entire field of higher education and science and in its subjects - universities, research institutes, related organizations, and institutions. Academic entrepreneurship is considered as an economic category in the system of socio-economic ties and relations of an integrated economic system in the modern knowledge society.
PERSONAL-PROFESSIONAL CHARACTERISTICS OF FUTURE SPECIALISTS IN THE PROCESS OF PROFESSIONAL TRAINING IN THE POST-CLASSICAL PARADIGM

The aim of this article is to investigate the personal and professional characteristics of future specialists in the process of their preparation in the post-classical paradigm in science. The contribution is in solving general problems related to the person in the complex self-organizing system: higher education-business-management, with a perspective of constructing adequate principles, methods, and realistic adequate measures of its new quality. Historical, interdisciplinary, and synergetic approaches of analysis reveal that personal-professional characteristics change, determine the type of presence (position) of the specialist that higher education prepares. Their change is related to new requirements imposed by each new era in the development of society in all its spheres. The analysis of different theories of management reveals the transition in the development of the personal-professional characteristics of the person, which goes from the "silent" (passive) subject in the classical era through the prepared, administrative-"orderly", creative non-classical subject to the self-organizing, self-learning, charged and giving energy to others post-classical subject-student and specialist. The future specialist with its characteristics is a necessary equal complement to the personal-professional characteristics of both the university teacher and the manager in the workplace.
TYPES OF INNOVATIONS IN THE HIGHER EDUCATION SPHERE

Innovative information technologies, information and innovation management, as well as innovative marketing are widely used in the management of innovation in education and uniting in the group of means and tools for innovation management in educationThey can take place in almost all types of the innovations in the education system, which can lead to innovative changes to achieve new quantitative and qualitative parameters of education.They can take place in almost all types of the innovations in the education system, which can lead to innovative changes to achieve new quantitative and qualitative parameters of education.This article analyzestypes of innovations in the higher education system that can lead to innovative changes: innovations of organizational type in the higher education sphere; innovations of educational and pedagogical type in the system of higher education; means and tools for innovation management in education; problems of the forced or emergency distance learning, etc.
Product configuration of a software product line using a Domain Specific Language

A Software Product Line (SPL) reuses software assets to implement products that share a significant set of their features. When a developer needs to generate a new product, the selection of features determines which components and source code are assembled together as the product. In recent years, the Database Laboratory has been working with SPL tech- nologies in the field of Geographic Information Systems (GIS). Our SPL creates products from a specification that allows, in addition to defining the data model of the application, to customize specific elements of the application such as maps and their associated layers. However, during its use with real projects we detected that this customization was insuf- ficient: since the selected features are included for the whole product, if we need a feature only for a specific element, we need to apply it to all the elements of the same type. In this paper we propose a solution that, using a Domain Specific Language, allows to associate features with specific elements of the generated application in order to achieve a greater customization of the generated GIS and to improve their quality. This way, it is possible to select a feature (e.g., clustering) for a specific element (e.g., map-viewer), thus limiting the functionalities of the application to those parts where they are really necessary.
Some experiments on the use of Natural Language Processing for sexism detection and classification in social media

As the world’s digital population grows, so does the reach and usage of social media: in 2021, 56% of the global population were social media users [1]. Social networks are now a part of our everyday life and continue to transform the way we interact with others on a global scale The downside is that negative behaviors in social interactions are also increasing their presence. For example, between March 1 and April 30, the OBERAXE (Spanish Observatory of Racism and Xenophobia) has detected a 27% increase in hate speech on social networks with respect to the previous two-month period [2]. In this paper we target the detection and classification of sexist content in social media texts. Two tasks are considered: (i) a binary classification task to decide whether a given text is sexist or not; and (ii) a multiclass classification task according to the type of sexism present in it.
Scalable In-Memory Clustered Annealer With Temporal Noise of Charge Trap Transistor for Large Scale Travelling Salesman Problems

Combinatorial optimization problems (COP) are ubiquitous and practical in our daily life, but many of them are NP-hard and intractable to solve using conventional computing. Ising model-based annealer has gained increasing attention due to its efficiency in finding fast and approximate solutions. However, such solvers are suffering from scalability problem due to quadratically increasing spins, and there are few hardware designs proposed to support large-scale COPs. In this paper, we propose scalable in-memory annealers to solve the large-scale travelling salesman problem (TSP) with crossbar arrays of FinFET based charge trap transistors. The intrinsic temporal noise of the drain current caused by trapping/detrapping is used to realize the annealing process. We apply two kinds of hardware implementations to map Ising model: 1) Hopfield neural network (HNN) based design, converting from a graph network to a regular neural network to utilize crossbar memory arrays; 2) index-based design, using index to record the order to avoid the quadratic scaling up of spins to represent all permutations. For both implementations, hierarchical clustering algorithms are adopted to enable the solving of large-scale TSP and overcome the scalability challenge. We further speed up the system convergence by updating non-adjacent clusters in parallel with equal size k-means clustering. At last, three hardware designs (HNN with conventional and equal-size clustering, index method with conventional clustering) are tested with up to 1060-city problems and benchmarked by the system-level simulation to show their trade-offs. They all show benefits on fast convergence and ultra-low energy compared with state-of-the-art annealers, and present advantages on accuracy, chip area, or latency/energy consumption, respectively.
Unsupervised Learning Feature Estimation for MISO Beamforming by Using Spiking Neural Networks

Deep learning (DL)-based beamforming frameworks have the advantage of low computational complexity. However, existing works are based on artificial neural networks (ANNs) and have high energy cost. To improve this situation, this letter proposes a feature estimation framework based on spiking neural networks (SNNs) for sum rate maximization (SRM). Specifically, we first devise a coding method at the input side to convert channel state information into binary spike sequences that can be processed by SNNs. Then, at the output side, the output binary sequences are decoded into the features that are used to generate the beamforming weights. Based on the characteristics of the decoding method, unsupervised learning can be used to train the SNNs. Simulation results show that compared to existing feature estimation frameworks using ANNs, the SNNs-based framework reduces energy cost by orders of magnitude and has comparable sum rate performance.
Proximal Operator

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Adaptive Gradient Methods

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Gaussian Distribution

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Influencing Factors of Perceived Anxiety Control of Nursing Students on COVID 19

This study is a description to explore the factors related to knowledge of COVID-19, behavioral triggers, cognition of COVID-19, and perceived anxiety and control of nursing students in one region, and to identify the influencing factors on perceived anxiety and control. It is an investigative study. The collected data were analyzed using the SPSS Program. As a result of the study, in the case of general characteristics of the subjects, there was a statistically significant difference in disaster letters and perceived health status. In addition, it was found that there was a positive correlation between the perceived sense of anxiety and control between behavioral triggers and cognition of COVID-19. The explanatory power of the variables was 17.1%. Based on the results of this study, as a way to improve the sense of control of perceived anxiety, efforts to perceive correct health status through awareness improvement and accurate information provision using SNS, portal sites, messengers, etc. I think that institutional measures are needed.