Reviewerly/exHarmony huggingface dataset:
PELATIHAN PEMBUATAN ECO ENZYME SEBAGAI ALTERNATIF PENGELOLAAN SAMPAH ORGANIK DI KOTA BATU

This community service activity aims to obtain alternative natural products as cleaning fluids, disinfectants, and organic fertilizers as well as reduce the impact of environmental pollution due to the accumulation of organic waste.The implementation method begins through the preparation, pre-test, training and post-test stages.Evaluation is carried out on the process which includes the attendance, enthusiasm, and responses of participants to the training activities, as well as to the eco-enzyme products produced by the participants.The results of the service show that the training activities have been able to improve the knowledge and skills of the community in processing organic waste into eco-enzymes.The results of the evaluation showed that the response, enthusiasm, acceptance, presence and participation of the community in training activities were very good.The eco-enzyme products produced by the participants met several indicators, namely brownish color, distinctive fruity odor with a slight alcohol aroma, acidic pH (3-4), and not overgrown with fungus or maggots.
The Effect of Knowledge Management System and Knowledge Sharing on Employee Performance and Loyalty

The study aimed to determine the effect of knowledge management and knowledge sharing on employee performance and loyalty. It is quantitative research. The respondents were 50 employees of the West Kalimantan Regional Police Operations Bureau. Data analysis uses a structural approach to the Equation Model (SEM) assisted by a smart PLS application. The study found that knowledge management and knowledge sharing have a significant positive effect on employee performance variables. Knowledge management and knowledge sharing have a significant positive effect on employee loyalty variables. The ability to manage knowledge and share knowledge has become an essential factor in encouraging a competitive business, and this will also be able to improve employee performance and loyalty.
Cyber Security Challenges in Georgia

The XXI century turned out to be a turning point in terms of technological development. Rapid technical progress along with many other benefits has posed significant challenges to the world. The growing reliance on digital technology in almost every field has increased the risks and dangers of cybercrime. Cyberspace is widely used by both state and non-state actors -terrorist organizations not to mention cyberattacks carried out by individual individuals. Georgia could not avoid this global threat either. This paper addresses the challenges of cyber security in Georgia. The paper discusses the methods of cybercrime and its impact on the concept of national security. The paper is based on the theory of realism and discusses the key characteristics through which states and non-state actors pursue their own interests and demonstrate or use force.The study reflects the main threats and challenges that exist in cyber and information in Georgia, relevant conclusions are drawn and some kind of recommendations are given at the end of the study.
Non-Contact 3D Characterization System of Scratch-Induced Surface Damage on Monolithic Glass Panel

Glass material has been widely used in modern architecture. Scratch-induced surface damage of aged monolithic glass panel leads to the strength degradation of material and thus threatens the glass safety. Therefore, in order to accurately evaluate the strength of aged glass elements, it is crucial to extract key damage features including the damage location and depth in a precise way. This study aims to develop a non-contact stage-wise scanning method to extract 3D damage characteristics on glass surface, which can further facilitate the investigation into the associated influences on the flexural strength of glass. Coaxial double ring tests on annealed glass specimens under various magnitudes of applied loads were performed, which aimed to explore the influence of the surface damage on the flexural strength. Monocular microscope equipped with an industrial camera was used to detect damage area throughout the glass panel in the first stage rapidly. It was then followed by a chromatic confocal scanner to precisely measure the damage depth within local damage area. The results via confocal microscope scanning were considered as the reference values. It shows that the proposed method can be a potentially alternative solution instead of confocal microscope for damage quantification.
A heuristic-based package-aware function scheduling approach for creating a trade-off between cold-start time and cost in FaaS computing environments

Abstract With the migration of the enterprise applications to micro-services and containers, cloud service providers, starting with Amazon in 2014, announced a new computational model called function-as-a-service. In these platforms, developers create a set of fine-grained functions with shorter execution times instead of developing coarse-grained software. In addition, the management of system resources and servers is entrusted to cloud service providers. This model has many benefits, such as reducing costs, but still faces many challenges such as balancing cost, balancing performance, programming models, using current dev tools, containers’ cold start problem, saving data in caches, security issues, privacy concerns, and scheduling challenges like execution time prediction. In this paper, we focus on scheduling and cold start problems. Compromise occurs when keeping warm operating environments which can reduce cold start times but increase costs. Here, we aim to create a trade-off by using four different types of decisions at runtime using a heuristic method and analyzing the function dependency graph, functions’ invocation frequency, and other environmental parameters. The proposed method shows a 32% improvement over the fixed-time method (i.e. the method used by Amazon). This comparison is made from the cumulative measurement viewpoint which is a combination of response time, turnaround time, cost, and utilization.
Genetic Algorithms in a Visual Declarative Programming

mperative languages like Java, C++, and Python are mostly used for the implementation of Genetic algorithms (GA). Other programming paradigms are far from being an object of study. The paper explores the advantages of a new non-mainstream programming paradigm, with declarative and nondeterministic features, in the implementation of GA. Control Network Programming (CNP) is a visual declarative style of programming in which the program is a set of recursive graphs, that are graphically visualized and developed. The paper demonstrates how the GA can be implemented in an automatic, i.e. non-procedural (declarative) way, using the built-in CNP inference mechanism and tools for its control. The CNP programs are easy to develop and comprehend, thus, CNP can be considered a convenient programming paradigm for efficient teaching and learning of nondeterministic, heuristic, and stochastic algorithms, and in particular GA. The outcomes of using CNP in delivering a course on Advanced Algorithm Design are shown and analyzed, and they strongly support the positive results in teaching when CNP is applied.
Analysis of Students' Attitudes and Difficulties in Studying Computational Physics

Ideally, students who study Computational Physics are required to think computationally. However, student learning outcomes tend to be low. Low learning outcomes are suspected by students having difficulties. One of the causes of learning difficulties is students' attitude in responding to learning. This study aims to determine student attitudes in studying Computational Physics and the factors influencing student learning difficulties. This research is descriptive research with a quantitative approach. The population in this study were students of Physics FMIPA UNP. The sample in this research is students who take Computational Physics courses in January-June 2021. The data analysis technique used is the multivariate analysis based on factor loading testing with Confirmatory Factor Analysis (CFA) using Lisrel 8.80. The results showed that students' attitudes toward Computational Physics were good, with a percentage of student responses of 67.16%. Factors that influence learning difficulties are internal factors in the form of psychological factors in the aspect of interest (65%), motivational aspects (58%), and aspects of study habits (49%). Meanwhile, external factors do not affect students' difficulties in studying Computational Physics. Further research, it is necessary to carry out a similar analysis by taking into account other factors that are thought to influence the attitudes and difficulties of students in studying Computational Physics, both internal and external factors, so that they are better in determining the next steps to overcome student difficulties in studying Computational Physics.
Mobile E-Module In Teaching Computer Hardware Servicing (CHS)

The K to 12 Basic Education Program of the Philippines emphasizes the importance of the integration of ICT in the teaching and learning process.Teachers are encouraged to utilize all available ICT resources for the betterment of lesson delivery.Relatively, this study was intended to develop, validate and determine the effectiveness of a mobile e-module in Computer Hardware Servicing for Grade 10 ICT students.The study tried to determine the level of acceptability of the parts of the developed mobile e-module in terms of its objectives, contents, activities, and assessments as well as the level of its validity in terms of usability, consistency, adaptability, and aesthetic value.The difference between the evaluators' ratings on the acceptability of the module parts and the validity of its characteristics was also determined.Selected ICT teachers validated and evaluated the developed mobile e-module.Selected Grade 10 computer students were also utilized in the conduct of the study.The statistical treatment of data revealed that the overall weighted mean rating for the acceptability of the module parts as rated by the groups of evaluators was extremely acceptable.It also revealed that the overall mean rating on its characteristics validity as rated by the groups of evaluators was extremely valid.The evaluation of the components and characteristics of the mobile e-module bore no implication on how the students accomplished the practical test given.Based on the foregoing findings presented, the following conclusion was made: There was no significant difference in the evaluators' ratings on the acceptability of the developed mobile e-module parts and the validity of its characteristics.The evaluators' ratings on the components and characteristics of the developed mobile e-module bore no implications on how the students accomplished the practical test given.Therefore, the hypothesis are accepted as there were not enough evidence to reject the claim.Based on the findings and conclusion made, it is hereby recommended that ICT teachers may use this mobile e-module as supplementary material in teaching computer subjects that will lead the students to have knowledge and skills that they can extend in their daily living.
ARCS: A model to foster motivation in times of the new normal

The purpose of this article is to explain the importance of using ARCS model for motivation among students who have returned to face-to-face classes. Therefore, a bibliographical review was carried out by reviewing different articles related to motivation and problems in education in times of COVID-19 pandemic. Thus, the information presented in this paper is to help teachers realize the benefits of implementing ARCS model to make students more motivated, engaged, collaborative, and dynamic through a variety of strategies that can be implemented in the classroom. Finally, the results show that ARCS is a suitable model because it is cross-curricular and helps teachers to motivate students and tackle the problems left by the covid-19 pandemic in different contexts, especially in education.
Analisis Perbandingan Metode K-Nn Dan Decision Tree Dalam Klasifikasi Kenyamanan Thermal Bangunan

Thermal comfort and good air quality can have a positive influence on a person's health and activities. Human knowledge will determine the right quality for a building or room that can only be used at a minimum first if it is based on the value of temperature and humidity standard measuring instruments that display raw data so that a system is needed that makes it easy for humans to determine thermal building comfort. Most systems that discuss thermal comfort revolve around temperature and humidity monitoring still do not use a machine learning model to maximize data analysis. In this study, a comparison of machine learning models is designed that is able to provide a classification to predict the category or label given to a data set consisting of the thermal comfort variable. The output produced in this comparison is the result of labeling predictions from 3 temperature comfort levels by testing 30% praise data and 70% training data. From the results of the segmentation accuracy level using K-Nearest Neighbor, the accuracy reaches 100% with the highest accuracy at a value of K = 1, while the Decision tree comparison method also gets an accuracy value that reaches 100%, this shows that thermal comfort can be applied in the classification machine learning method
The Role of Fidelity in Goal-Oriented Semantic Communication: A Rate Distortion Approach

&lt;p&gt; We study a variant of a robust description source coding framework via its corresponding characterization, which is a relevant model for goal-oriented semantic information transmission. Considering two individual single-letter separable distortion constraints and input and output data acting as the intrinsic and extrinsic message, respectively, we first derive bounds on the optimal rates of the problem, as well as necessary and sufficient conditions for these bounds to be tight. Subsequently, we prove a general result that provides in parametric form the optimal solution of the characterization of this problem. Capitalizing on these results, we examine the structure of the solution for one case study of general binary alphabets under Hamming distortions and solve in closed form a special case. We also solve another general binary alphabet case where a Hamming and an erasure distortion coexist, as a means to highlight the importance of selecting the type of the distortion constraint in goal-oriented semantic communication. We also develop a semantic-aware Blahut-Arimoto (BA) algorithm, which can be used for the computation of any finite alphabet intrinsic or extrinsic message under individual distortion criteria. Finally, we revisit the problem for multidimensional independent and identically distributed (IID) jointly Gaussian processes with individual mean-square error (MSE) distortion constraints, providing new insights that have previously been overlooked. This work reveals the cardinal role of context-dependent fidelity criteria in goal-oriented semantic communication. &lt;/p&gt;
Harnessing Distributed Acoustic Sensing for Earthquake Early Warning: Magnitude Estimation and Ground Motion Prediction

Earthquake Early Warning (EEW) systems provide seconds to tens of seconds of warning time before potentially-damaging ground motions are felt. For optimal warning times, seismic sensors should be installed as close as possible to expected earthquake sources. However, while the most hazardous earthquakes on Earth occur underwater, most seismological stations are located on-land; precious seconds may go by before these earthquakes are detected. In this work, we harness available optical fiber infrastructure for EEW using the novel approach of Distributed Acoustic Sensing (DAS). DAS strain measurements of earthquakes from different regions are converted to ground motions using a real-time slant-stack approach, magnitudes are estimated using a theoretical earthquake source model, and ground shaking intensities are predicted via ground motion prediction equations. The results demonstrate the robustness of DAS-based EEW and the significant time-gains that can be achieved compared to the use of standard seismometers, in particular for offshore earthquakes.
SYSTEM OF CONTROL OF KNOWLEDGE, SKILLS OF STUDENTS IN STUDYING THE DISCIPLINE "ECONOMICS"

In the article the problem of the control of knowledge, abilities, of trainees is considered. Own the variant of the monitoring system of knowledge, abilities of pupils on a subject «Economy» is offered (S. V. Stepanenko s curriculum). The periodic (thematic) control of knowledge, abilities of pupils to the fourth section «National economy» is developed (the program S.V. Stepanenko's «Economy»).
Digital and e-Learning Accessibility for People with Special Educational Needs: A Robotic Perspective

&lt;p&gt;The paper presents the currently developed framework for analysis of accessibility in digital and e-Learning systems, which extends the subject-object relation by including&lt;/p&gt; &lt;p&gt;robots in the interaction process. A mainstream experimental framework in special education – false-belief task - is analyzed from a Piagetian perspective and modified to account for the intermediate role of the robot in supporting the learner acquire new knowledge or access the digital and e-Learning information available in the World. &lt;/p&gt; &lt;p&gt;Published as: M. Dimitrova, A. Krastev, N. Sabev and J. David Nunez-Gonzalez, "Digital and e-Learning Accessibility for People with Special Educational Needs: A Robotic Perspective," &lt;em&gt;2021 19th International Conference on Information Technology Based Higher Education and Training (ITHET)&lt;/em&gt;, 2021, pp. 1-5, doi: 10.1109/ITHET50392.2021.9759704.&lt;/p&gt;
Data anomaly detection based on improved kernel density estimation

Abstract With the advent of the era of big data, while machines process data instead of humans, timely detection of abnormal conditions in data through algorithms has become the focus of research, and the importance of data detection accuracy has also increased. Therefore, based on the traditional kernel density estimation theory, this paper adds adaptive diffusion equation theory to improve it, selects the best window width value to achieve a high degree of data distribution fitting, and further formulates detection rules. Finally, compared with other traditional detection methods, the results show that the method proposed in this paper can improve the accuracy and stability of abnormal data detection.
Meaningful Learning through Virtual Reality and Augmented Reality Technological Environments

Abstract: Augmented reality (AR) overlies manually made materials directly over the real-world materials. Virtual and augmented reality industry committed innovative technologies that can be explored in the field of entertainment, education, training, medical and industrial innovation, and the development are explored. Augmented reality (AR) allows the physical world to be enhanced by incorporating digital knowledge in real time created by virtual machine. Few applications that have used augmented and virtual reality in real-world applications are discussed. VR will make it conceivable to educate in virtual situations that are difficult to picture in physical study halls, such as getting into virtual research facilities, imagining machines, mechanical plants, or even clinical cases. The gigantic prospects of open virtual advancements will make it conceivable to break the limits of formal instruction.
DEVELOPMENT OF A MARKERLESS OPTICAL MOTION CAPTURE SYSTEM BY AN ACTION SPORTS CAMERA FOR RUNNING MOTION

A marker-based optical motion capture system is often used to obtain the kinematics parameters of a running analysis. However, the attached marker could affect the participant's movement, and the system is costly because of the exclusive cameras. Due to its drawbacks, the present research aimed to develop an affordable markerless optical motion capture system for running motion. The proposed system used an action sports camera to acquire the running images of the participant. The images were segmented to get the silhouette of the participant. Then, a human body model was generated to provide a priori information to track participants' segment position. The subsequent procedure was image registration to estimate the pose of the participant's silhouette. The transformation parameters were estimated by particle swarm optimization. The optimization output in the form of the rotation angle of the body segment was then employed to identify right or left lower limbs. To validate the results of the optimization, a manual matching was conducted to obtain the actual rotation angle for all body segments. The correlation coefficient between the rotation angle from image registration and the actual rotation angle was then evaluated. It was found that the lowest correlation coefficient was 0.977 for the left foot. It implies that the accuracy of the developed system in the present work is acceptable. Furthermore, the results of the kinematics analysis have good agreement with the literature. Therefore, the developed system, not only yields acceptable running parameters, but also affordable since it uses an action sports camera and easy to use.
Blended System for Data-Driven Learning of English for Specific Purposes

English for Specific Purposes (ESP) and Data-Driven Learning (DDL) are two constructivist and student-centered approaches to language pedagogy that are well-established in second language acquisition. Despite recent fruitful attempts to unify the two approaches for enhanced pedagogical effectiveness, current technological bottlenecks, characterized by a lack of specialized functionality and accessible interfaces for ESP learning, have limited their application in a broader range of scenarios such as blended language learning.&#x0D; To address these issues, the present paper proposes the design and development of a new DDL system tailored to the needs of ESP pedagogy, with functions specifically designed to foster student-centered learning and develop genre awareness at different stages of the ESP learning process. Built on a flexible plug-in architecture, the system utilizes state-of-the-art NLP tools for efficient multilayered linguistic annotation and indexing, made query-able through a user-friendly web interface that minimizes expertise required for DDL activities. We demonstrate the usefulness of the system with a case study showcasing its key functions for data-driven ESP learning, and discuss its potential use in an integrative blended learning environment.