Reviewerly/exHarmony huggingface dataset:
Evaluation of the moodle-based learning system applying the end user computing satisfaction method

Abstrak: Pembelajaran elektronik dengan memanfaatkan perangkat teknologi seperti komputer, tablet maupun telefon pintar telah berkembang pesat pasca pandemic COVID-19 namun belum menghasilkan temuan yang konklusif. Maka, tujuan penelitian ini untuk menginvestigasi minat siswa terhadap sistem pembelajaran elektronik berbasis Moodle. Metode yang digunakan End User Computing Satisfaction (EUCS) dengan pengujian sistem pada Moodle dengan sampel penelitian 70 siswa. Moodle merupakan sistem pengelolaan pembelajaran elektronik dimana guru dapat menyusun mata pelajaran, membuat group kelas, memberikan materi (mengunggah materi), memberikan tugas baik berbentuk ujian, kuis, tes, mengumpulkan tugas, menilai tugas yang dapat digunakan secara sinkronus maupun asinkronus. Hasil uji EUCS menunjukkan bahwa setelah menggunakan pembelajaran elektronik minat siswa mengalami peningkatan sebesar 86,34%. Selain itu capaian hasil belajar siswa juga mengalami peningkatan signifikan. Penerapan e-learning berbasis Moodle meningkatkan minat siswa dalam belajar karena siswa dapat mengakses kapanpun dan dimanapun. Abstract: Electronic learning using is utilizing technological devices such as computers, tablets, and smartphones has developed rapidly after the COVID-19 pandemic but has not produced conclusive findings. Thus, the purpose of this learning is to investigate students' interest in Moodle-based electronic learning systems. The method used is End User Computing Satisfaction (EUCS) by testing the system on Moodle with a research sample of 70 students. Moodle is an electronic learning management system where teachers can arrange subjects, create class groups, provide material (upload material), give assignments in the form of exams, quizzes, and tests, collect evaluations, and assess assignments that can be used synchronously or asynchronously. EUCS test results show that after using electronic learning, students’ interest has increased by 86.34%. In addition, student achievement also experienced a significant increase. The application of Moodle-based e-learning increases students' interest in learning because students can access it anytime and anywhere.
PENERAPAN METODE ROLE PLAYING UNTUK MENINGKATKAN KETERAMPILAN BERBICARA PESERTA DIDIK KELAS II SDN 013 PASIR KALIKI

This research is motivated by learning which is dominated by smart students in class, another problem found is when students are asked to speak in front of the class and share experiences, ideas, and ideas students tend to be shy, less expressive, confused about what to do. convey. The solution to this problem is the use of the Role Playing method. This study aims to determine the effect of using the Role Playing method on the speaking skills of students in class II-B SDN 013 Pasir Kaliki. This type of research is Classroom Action Research (CAR). Based on the analysis of performance test scores there was an increase in students' speaking skills. The results of the research on student activities experienced an increase from cycle I obtaining a percentage of 72.20% in the medium category and in cycle II obtaining a percentage of 86.10% in the high category. The ability to speak in cycle I obtained a percentage of 40% in the low category and increased to 80% in the high category. Based on the problems that have been described, it can be said that the application of the Role Playing method can improve the speaking skills of class II students at SDN 013 Pasir Kaliki.
Movable Page-Based Interactive Books on Numbers in Elementary Schools

The development of a Movable Page-Based Interactive Book on Numbers in Elementary Schools aims to determine the feasibility and effectiveness of the product being developed. The use of teaching materials has an important role including the role for students, teachers in classical learning, groups and individuals. An interactive book based on Movable Pages is a teaching material that is systematically arranged to help students understand the concept of numbers in elementary schools. This study uses a development research approach by applying the ADDIE Model (Analyze, Design, Development, Implementation, Evaluation). Research that has been carried out up to the Development stage. Based on the results of the research that the development of movable page-based interactive books on number material of n elementary schools was developed declared and valid by 4 (four) validators, with a percentage of 90.4% in the very valid cate-gory. In the implementation stage, namely the product trial stage, namely one-to-one and small group trials which were carried out in class V students at SD Negeri 11 Sungai Pinang as many as 21 students, the results of the student questionnaire recapitulation were 89.7% with the very good category. Thus, from the several stages that have been passed in this study, it was found that the Movable Page-Based Interactive Book on Numbers Material in Elementary Schools is valid and practical. This interactive book can help elementary school teachers so that students can easily understand number material in learning mathematics and fur-ther researchers can continue this research to determine the effectiveness of interactive book products and can develop interactive books for other materials in learning mathematics in elementary schools.
Equilibrium or Studying Attractors to Upgrade Educational Suitability, Teaching Presence, Social Presence and Learning Results in MOOCs

The purpose of this research is to find the equilibrium or studying attractors to upgrade educational suitability, instructional existing, social existing and learning results in Korean Massive Open Online Courses (K-MOOCs). To conduct this study, learners who had taken K-MOOC were selected for the study; data were collected from 369 K-MOOC learners. The data were studied using the SPSS 26.0 and AMOS 26.0 platforms. About the methodology of analysis, descriptive statistics analysis was carried out first, followed by an examination of the correlation. The measurement model was then confirmed using confirmatory factor analysis (CFA), and structural equation modeling (SEM) was used to validate the structural links and mediating effects between the variables. Further, to verify a mediating effect on educational performance, the numerical importance of the mediating result was verified using bootstrapping. Analysis outcomes are as the following: First, this study discovered that K-MOOC educational appropriateness positively impacted learning results. Second, this study discovered that K-teaching MOOC's presence meaningfully and statistically significantly moderated the association between educational appropriateness and learning outcomes. Lastly, this research discovered that K-social MOOC's presence had a statistically significant impact on moderating the link between educational appropriateness and learning results. The conclusions that can be derived from the analyses' findings are the following: To improve the learning performance of K-MOOC, which is operated through the national budget, education authorities must first deeply understand the learning needs of learners in accordance with social changes. It is also important to secure educational suitability by preparing and providing a variety of educational courses in consideration of learners' learning motives and learning goals. Lastly, it is important to increase the satisfaction and immersion in education by expanding the components of teaching existence and social existence to derive actual learning results. This study is meaningful in that it examines the direct and indirect effects of the state-led K-MOOC's educational suitability on learning outcomes, and in that it confirms the influence of teaching existence and social existence in that they enhance education outcomes.
Geometry and convergence of natural policy gradient methods

Abstract We study the convergence of several natural policy gradient (NPG) methods in infinite-horizon discounted Markov decision processes with regular policy parametrizations. For a variety of NPGs and reward functions we show that the trajectories in state-action space are solutions of gradient flows with respect to Hessian geometries, based on which we obtain global convergence guarantees and convergence rates. In particular, we show linear convergence for unregularized and regularized NPG flows with the metrics proposed by Kakade and Morimura and co-authors by observing that these arise from the Hessian geometries of conditional entropy and entropy respectively. Further, we obtain sublinear convergence rates for Hessian geometries arising from other convex functions like log-barriers. Finally, we interpret the discrete-time NPG methods with regularized rewards as inexact Newton methods if the NPG is defined with respect to the Hessian geometry of the regularizer. This yields local quadratic convergence rates of these methods for step size equal to the inverse penalization strength.
DEVELOPMENT OF SCIENCE AND LEARNING THROUGH THE PRISM OF THE INTERNATIONAL CONFERENCE ON COMPUTER LINGUISTICS AND COLINS INTELLECTUAL SYSTEMS

The present article is devoted to the international scientific conference CoLInS, which has become a platform to exchange the experience and new knowledge in the field of computational linguistics and intelligent systems between scientists, students and graduate students. The purpose of the article is to highlight the role of the conference in improving the educational process and developing scientific thought at the Department of Intelligent Computer Systems. One of the main problems of the conference is the development of intelligent systems designed for natural language processing, which has become particularly relevant in the world of the fact that the process of information exchange between participants in the educational process takes place in a global environment using various applications and instant messengers. In most cases informal learning takes place in social networks, where the exchange of knowledge between participants with different specialized knowledge occurs spontaneously within informal groups and communities of interest. E-learning is perceived by the digital community as the main element of the social progress, which is reflected in the graduate projects of bachelors and masters, as well as masters works in the specialty "Applied and Computational Linguistics". In turn, this affects the formation of the next directions of the subsequent conferences. It is noted that the staff of the Department of ICS takes care of promoting their most significant results of research activities in the educational process and preserving the individual face that exists at the department of the scientific school.
Tighter Abstract Queries in Neural Network Verification

Neural networks have become critical components of reactive systems in various do- mains within computer science. Despite their excellent performance, using neural networks entails numerous risks that stem from our lack of ability to understand and reason about their behavior. Due to these risks, various formal methods have been proposed for verify- ing neural networks; but unfortunately, these typically struggle with scalability barriers. Recent attempts have demonstrated that abstraction-refinement approaches could play a significant role in mitigating these limitations; but these approaches can often produce net- works that are so abstract, that they become unsuitable for verification. To deal with this issue, we present CEGARETTE, a novel verification mechanism where both the system and the property are abstracted and refined simultaneously. We observe that this approach allows us to produce abstract networks which are both small and sufficiently accurate, allowing for quick verification times while avoiding a large number of refinement steps. For evaluation purposes, we implemented CEGARETTE as an extension to the recently proposed CEGAR-NN framework. Our results are highly promising, and demonstrate a significant improvement in performance over multiple benchmarks.
Refining Unification with Abstraction

Automated reasoning with theories and quantifiers is a common demand in formal methods. A major challenge that arises in this respect comes with rewriting/simplifying terms that are equal with respect to a background first-order theory T , as equality reasoning in this context requires unification modulo T . We introduce a refined algorithm for unification with abstraction in T , allowing for a fine-grained control of equality constraints and substitutions introduced by standard unification with abstraction approaches. We experimentally show the benefit of our approach within first-order linear rational arithmetic.
How Much Should This Symbol Weigh? A GNN-Advised Clause Selection

Clause selection plays a crucial role in modern saturation-based automatic theorem provers. A commonly used heuristic suggests prioritizing small clauses, i.e., clauses with few symbol occurrences. More generally, we can give preference to clauses with a low weighted symbol occurrence count, where each symbol’s occurrence count is multiplied by a respective symbol weight. Traditionally, a human domain expert would supply the symbol weights. In this paper, we propose a system based on a graph neural network that learns to predict symbol weights with the aim of improving clause selection for arbitrary first-order logic problems. Our experiments demonstrate that by advising the automatic theorem prover Vampire on the first-order fragment of TPTP using a trained neural network, the prover’s problem solving capability improves by 6.6% compared to uniformly weighting symbols and by 2.1% compared to a goal-directed variant of the uniformly weighting strategy.
Keep me out of the loop: a more flexible choreographic projection

Choreographic programming is a paradigm where programmers write global descrip- tions of distributed protocols, called choreographies, and correct implementations are au- tomatically generated by a mechanism called projection. Not all choreographies are pro- jectable, because decisions made by one process must be communicated to other processes whose behaviour depends on them – a property known as knowledge of choice. The standard formulation of knowledge of choice disallows protocols such as third-party authentication with retries, where two processes iteratively interact, and other processes wait to be notified at the end of this loop. In this work we show how knowledge of choice can be weakened, extending the class of projectable choreographies with these and other interesting behaviours. The whole development is formalised in Coq. Working with a proof assistant was crucial to our development, because of the help it provided with detecting counterintuitive edge cases that would otherwise have gone unnoticed.
A Fast and Accurate ASP Counting Based Network Reliability Estimator

The quantification of system reliability is fundamental to the assessment of a system’s safety and resilience, and has been of interest to decision-makers. Since quantifying the system reliability is shown to be computationally intractable, researchers aim to find approximations. Existing approaches to approximate reliability either suffer from poor scalability or lack of correctness guarantees. Answer Set Programming (ASP) is a powerful tool for knowledge representation that can specify complex combinatorial problems. In recent years, the new applications of ASP have propelled the emergence of well-engineered ASP systems. This paper proposes a new ASP counting based framework, RelNet-ASP, to approximate or estimate the reliability of a system or network. The framework reduces the problem of reliability estimation to an approximate model counting problem on ASP programs, offering formal guarantees of the estimated reliability. The experimental evaluation demonstrates that RelNet-ASP outperforms state-of-the-art techniques in terms of both runtime performance and accuracy.
Ontology development for web services to be used within the scope of remote monitoring project

In today’s society, as the digital transformation has become widespread rapidly; information technologies also started to develop themselves quickly along with this prevalence. This rapid development and transformation bring about new and different requirements. Situations like reuse of the information, the ability to integrate the obtained information and sharing it, among others, have pushed Semantic Web to the forefront, especially scientifically. Semantic Web, which provides the communication of a machine with other machines, gets a great attention especially in today’s digital age. Probably because of this, significant works have been done on ontology development method and ontology based systems started to be advanced over the last decade. Ontology development methods or ontology based systems play a key role in the integrity, being shared and management of the data. Ontology development method, which is of vital importance in reusability and expandability of real time monitoring systems, is in a position to be accommodable to many architectural systems like Deep Learning architecture at the same time.
Global and Local Knowledge Distillation Method for Few-Shot Classification of Electrical Equipment

With the increasing utilization of intelligent mobile devices for online inspection of electrical equipment in smart grids, the limited computing power and storage capacity of these devices pose challenges for deploying large algorithm models and it&amp;rsquo;s hard to obtain a substantial number of images of electrical equipment in public. In this paper, we propose a novel distillation method that compresses the knowledge of teacher networks into a compact few-shot classification network, employing a global and local knowledge distillation strategy. Central to our method is exploiting the global and local relationship between the features exacted by the backbone of the teacher network and student network. We compare our method with recent state-of-the-art (SOTA) methods on three public datasets and achieve superior performance. Additionally, we contribute a new dataset, namely EEI-100, which is specifically designed for classification of electrical equipment. We validate our method on this dataset and demonstrate its exceptional prediction accuracy of 94.12% when utilizing only 5-shot images.
ULTRASOUND VS. MAMMOGRAPHY IN EVALUATING SUSPICIOUS BREAST LESIONS

This study aimed to compare the effectiveness of ultrasound and mammography in evaluating suspicious breast lesions and determine their respective diagnostic capabilities. A retrospective analysis was conducted at Khyber Teaching Hospital Peshawar over 6 months, from March 2021 to September 2021. The study included 100 patients with suspicious breast lesions who underwent ultrasound and mammography examinations. The demographic data, including age and breast density, were recorded. Both imaging modalities' diagnostic accuracy, sensitivity, specificity, and positive predictive value were calculated. Statistical analysis was performed using appropriate tests. The analysis included 100 patients with an average age of 51.3 years. The breast density distribution was as follows: 20% dense, 35% scattered fibroglandular, 30% heterogeneously dense, and 15% extremely dense. Ultrasound demonstrated higher sensitivity (75%) than mammography (65%) in the study population. When stratified by age, ultrasound had higher sensitivity in all age groups, with the highest sensitivity observed in patients aged &gt;60 years (85%). Regarding breast density, ultrasound showed higher sensitivity across all density categories, particularly in patients with dense breasts (80%) and heterogeneously dense breasts (85%). Our study findings suggest that ultrasound is a valuable imaging modality in evaluating suspicious breast lesions, offering higher sensitivity than mammography, especially in patients with dense breast tissue. Incorporating ultrasound as an adjunct to mammography can improve diagnostic accuracy and aid in the early detection of breast cancer. These results support the growing recognition of ultrasound as an important tool in breast cancer diagnosis and highlight the need for further research and collaboration to optimize its utilization in clinical practice.
An analysis of entity normalization evaluation biases in specialized domains

Abstract Background Entity normalization is an important information extraction task which has recently gained attention, particularly in the clinical/biomedical and life science domains. On several datasets, state-of-the-art methods perform rather well on popular benchmarks. Yet, we argue that the task is far from resolved. Results We have selected two gold standard corpora and two state-of-the-art methods to highlight some evaluation biases. We present non-exhaustive initial findings on the existence of evaluation problems of the entity normalization task. Conclusions Our analysis suggests better evaluation practices to support the methodological research in this field.
Learning generative models for geostatistical facies simulation based on a single training image

Characterization of subsurface reservoirs often requires geological facies models to identify areas with favorable rock properties. With the development of computing powers, deep learning approaches, such as the generative adversarial networks (GANs), became widely used for simulating complex geological models. However, training of the GANs typically requires a large quantity of training data for updating neural parameters. This process is generally done using traditional geostatistical methods based on multiple-point statistics or process-based models to build the training data. In this study, we propose to train the GANs using one single training image, a conceptual model from which the statistics of the geological patterns can be extracted. The training image is first down-sampled to different scales, and the generator and the discriminator are trained alternately for each scale. The training process is implemented from the coarsest to the finest scale to learn the spatial statistics from the training image progressively. We apply the proposed GANs to simulate the 2D Lena river delta and 3D Descalvado aquifer analog model, in which complex geological patterns and structures from the training image are successfully learned and reproduced by GANs. The gradual deformation method is further applied to iteratively calibrate the random realizations by the generator to observed data, in an optimization workflow. The optimization scheme is implemented many times to obtain multiple independent models that all match the observed data.
Using Mixed Reality Application for Super Market: A Case Study

Abstract: Augmented Reality and Virtual reality applications are not a new thing in today’s era and are getting popular day by day. Mixed reality is where physical and virtual environments are combined to give life like experience to the customer. Due to user friendly nature of the mixed reality applications, they are mainly developed for end user’s. Supermarkets are frequently visited and crowded place many due to market competition store try to churn in customers with exciting offers and discounts but due to large variety and large quantity of the products combined with different brands there is so much information which gets lost as intended users don’t get to know products which they require due to shelf arrangement and not knowing location of the products to the user leads to loss of the super market. This paper focuses on how mixed reality can be applied in super market environment to help customers find required products and provide inherent user experience to the end user.