Reviewerly/exHarmony huggingface dataset:
E-Jobsheet Based on Mobile Pocket Book as Digital Learning Resources (DLRs)

This study aims to develop an E-jobsheet based on a mobile pocket book in the form of a digital pocket book on algorithms and basic programming courses. This research is a Research &amp; Development (R&amp;D) research with the Borg &amp; Gall development model which consists of 10 stages, but in this research, the researcher limits it to producing an initial product in the form of an initial product or to the 5th stage, namely main product revision. The research instrument in collecting data used validation questionnaires, response questionnaires, and unstructured interviews to users during small-scale trials. The data analysis used in this research is descriptive data analysis with a quantitative approach. Based on the validation results from the media and material aspects, the overall product validity level is 85.46% with a valid category, the student response rate in a small-scale trial of the E-jobsheet is 86% with a very good category. So, it can be stated that the E-jobsheet based on the mobile pocket book that was developed is valid and suitable for further use on a wider scale.
The Role of WhatsApp for Madrasah Ibtidaiyah Teachers in Online Learning during the Covid-19 Pandemic

This research aims to determine the role of the WhatsApp application in online learning during COVID-19 pandemic. This research uses a descriptive qualitative approach. The analysis uses the Miles Huberman and Saldana model, researchers explore data using google form to 27 respondents consisting of MI teachers in Soko-Tuban. The results of this research indicate that the use of the WhatsApp application can act as an alternative solution to online learning problems, although not all problems can be solved by using WhatsApp application, however, WhatsApp application plays a role in 6 of the 16 online learning problems in MI teachers in Soko Tuban. The problems that can be solved by using WhatsApp application are 1) the implementation of the distance learning process is not yet ready; 2) lack of mastery of technology for parents, educators, and students; 3) lack of student interest in online learning; 4) Less than optimal delivery of material using online media; 5) limited time for teaching and learning process; and 6) lack of communication between students, educators and parents. These results are expected to be an evaluation of the extent to which the use of WhatsApp application plays a role in problems online learning, besides that teachers and all related components can maximize the use of the WhatsApp application as an alternative solution to some of the existing problems.
Usability Evaluation of a Virtual Reality Interactive Music Appreciation Module (E-Marz) for Secondary School

Advancement in Virtual Reality (VR) technology can be benefited for educational purposes.This article reports on a study undertaken to evaluate the usability of a Virtual Reality (VR) Interactive Music Appreciation Module (E-Marz) for the Lower Secondary School.The study has employed the Design and Development Research (DDR) which comprised of three different phases as its methodology.E-Marz was developed systematically based on the m-ADDIE Model which include the Evaluation Phase.The objective of the evaluation phase was to measure the level of usability of the module by using the System Usability Scale Questionnaire given to the field experts and the Post-Study System Usability Questionnaire given to students as the end users.The findings of the evaluation phase showed that E-Marz had obtained a high level of usability based on the expert views and the surveys on students as end users.The outcome of this study has contributed in terms of the development of an educational media which incorparated elements of VR for the teaching and learning of music appreciation in secondary schools.The module has the potential to assist teachers and students in facilitating for a more active and fun learning environment in the classroom through the application of the VR technology.The E-Marz module also served as a medium of early exposure for students to be familiar with the VR technology which will be widely used in the era of the 4.0 Industrial Revolution.
Copyright Concerns for Educators: Online Learning Post Pandemic Effect

Online distance education was once a process that was not easily been accepted by students, even by the educators, but when the pandemic strikes, they had to adopt and adapt the process in order to gain knowledge. The COVID-19 has resulted in shutting down schools, including tertiary institutions, all across the world. Consequently, education changed dramatically, and the mode of teaching was done remotely and on a digital platform. One of the adoptions of online learning involves using numerous online platforms and inserting interactive programs, music, animated graphics, photos in the teaching material to attract the interest of students. These types of works are, more often than not, copyrighted works that belong to someone. Generally, a license or permission must be sought before these works can be used by anyone. The permission or license, once granted, would involve a licensing fee or royalty payments to the copyright owner. However, this article looks at the law relating to the copyright exploitation awareness in the context of the law of intellectual property and the exceptions to this law, in particular, the scope of the hybrid fair dealing defence for education. This paper employs a doctrinal analysis using secondary data from academic journals, books, and online databases. The findings will respond to the legal framework for the understanding of copyright exploitation and its exception in the post-pandemic era.
Data Set Preparation Using the Method of Data Augmentation for Classification of Skin Diseases

Skin disease are mostly ignored and provided less importance at the early stages. Some ignorance among people might lead to skin cancer. In existing approach, the increased skin disease are identified at the later stage using biopsy only. The inspection is performed manually by considering many histopathological features. This paper explains the development of a model which helps to detect the various skin diseases at early stage using neural networks. For classification and prediction purpose this paper using Convolutional Neural Network. For improving the accuracy of prediction we are going to do dataset preparation. Data augmentation is using for increasing the count size of the training data. So that the accuracy of the classification will increase and will get 97% result.
Enabling Game-Theoretical Analysis of Social Rules

In the field of normative multiagent systems, the relationship between a game structure and its underpinning agent interaction rules is hardly ever addressed in a systematic manner. In this work, we introduce the Action Situation Language (ASL), inspired by Elinor Ostrom’s Institutional Analysis and Development framework, to bridge the gap between games and rules. The ASL provides a syntax for the description of agent interactions, and is complemented by an engine that automatically provides semantics for them as extensive-form games. The resulting games can then be analysed using standard game-theoretical solution concepts, hence allowing any community of agents to automatically perform what-if analysis of potential new interaction rules.
Multi-Temporal Prediction of Contaminating Mineral Abundance Using Hyperspectral Spectroscopy

Mine tailings, charged with toxic minerals, of the abandoned mining of Hammam Zriba in the north of Tunisia are continually exposed to erosion and thus destroys the normal function of soils and endangers human health. Characterization, estimate and efficient monitoring of the abundance of minerals with high pollution potential in a quick and timely fashion is crucial for protecting the natural environment. Particularly, detecting changes in abundance of mineral concentrations measured in the same collection points at different time series is of special interest in this application domain. In this context, field Hyperspectral spectroscopy and X-ray diffraction (XRD) method were used to characterize of both several minerals. The partial least squares regression (PLSR) method was processed for the prediction of mineral contents.
Pengembangan E-Book Interaktif Berbasis Android Mata Pelajaran Layanan Lembaga Keuangan Syariah

The changing of times have reduced the boundary between digital world and physical world in many fields of human life including education. The revised edition of Kurikulum 2013, which was the last change in the education curriculum in Indonesia, must always be balanced with the development of learning tools including teaching materials. This research and development is aimed to develop teaching materials in interactive e-books form based on android devices with a scientific approach to the subject of Islamic financial services in class XII semester 1. This development uses the 4D model belonging to Thiagarjan, Semmel, and Semmel which consists of the stage define, design, develop, and disseminate. However, due to the limitations of the researcher, the research focused on the development stage and implemented the dissemination on a limited basis. The result of this study in feasibility test by a material, linguist, graphical, and stundents’ responses to the use of e-book. From those results, it is known that android based interactive e-book with scientific approach are very suitable for use for learning activities.
Strategic Abilities of Asynchronous Agents: Semantic Side Effects and How to Tame Them

Recently, we have proposed a framework for verification of agents' abilities in asynchronous multi-agent systems (MAS), together with an algorithm for automated reduction of models. The semantics was built on the modeling tradition of distributed systems. As we show here, this can sometimes lead to counterintuitive interpretation of formulas when reasoning about the outcome of strategies. First, the semantics disregards finite paths, and yields unnatural evaluation of strategies with deadlocks. Secondly, the semantic representations do not allow to capture the asymmetry between proactive agents and the recipients of their choices. We propose how to avoid the problems by a suitable extension of the representations and change of the execution semantics for asynchronous MAS. We also prove that the model reduction scheme still works in the modified framework.
Evaluation of risk factors for fall in elderly using Bayesian networks: A case study

Background: Falls in the elderly are the number one cause of traumatic death in this population. Prevention of falls requires to evaluate which risk factors for fall are present for a person on the basis of available health information. Our objective is to predict the presence or the absence of 12 risk factors for fall in elderly people based on partial observations. Methods: A data set of 1810 patients of the multidisciplinary falls consultation of Lille University Hospital covering fourteen years admissions were used to learn and evaluate a Bayesian network and four usual machine learning classifiers. Variable selection and data pre-processing were achieved on the basis of an ontology and interviews of the experts. The prediction of each target risk factor using the complete set of observations is first compared with the prediction based on a specific subset of variables, and second based on partial observation, from 10 to 90% of the variables. Results: For 7 out of 12 target risk factors, the f1-score of classifiers using complete set of variables is slightly better than the specific subset of variables, with a difference of less than 3%. Bayesian Networks and other classiers perform equivalently in terms of accuracy and f1-score. The best prediction were obtained for the loss of autonomy and osteoporosis with a f1-score from 15 to 20% better than the baseline classifier when using the Bayesian network. At the opposite, for 3 risk factors, no classifier allows to improve the f1-score or the accuracy of more than 1% compared to the baseline classifier. Conclusion: Our results show that the use of specific subsets of variables does not improve the prediction of risk factors, and that no classifier outperform the others. However Bayesian networks perform well and are interesting due to their explainability.
Developing Materials For Special Academic Purposes For Islamic Education Using Google Apps For Education/ تطوير المادة الدراسية الأكاديمية لقسم التربية الإسلامية عبر تطبيقات جوجل التربوية

Teaching reading skills based on interactive construction theory requires more complex teaching materials because the process involves the interaction between students and teaching materials in the form of direct information and latent information. In addition, the facts on the field show that the characteristics of adult learners are more likely to choose to study in comfortable places and more appropriate times. Therefore, teaching language using Google Apps is one of the best solutions to answer this problem. This research aims to develop a model of reading materials for special academic purposes for Islamic Education using the Google Apps for Education. The method used in this research is Research and Development method. The result of the research is as follows; (1) Using reading materials according to the learner’s need made them easier to understand how to determine their function in various model of sentences and the material is more in line with the needs of the learners. (2) This research reinforces Krashen’s input hypothesis idea, while the characteristics of the resulting material in this research reinforce Stravens’ theory of elements language programs for a special purpose. At last, using Google Application for Education in developing materials can encourage students to study more since it is more interactive and in line with students’ need
Distinguishing and key recovery attacks on the reduced-round SNOW-V and SNOW-Vi

This paper presents distinguishing and key recovery attacks on the reduced-round SNOW-V and SNOW-Vi, which are stream ciphers proposed for standard encryption schemes for the 5G mobile communication system. First, we construct a Mixed-Integer Linear Programming (MILP) model to search for integral characteristics using the division property, and find the best integral distinguisher in the 3-, 4-, 5-round SNOW-V, and 5-round SNOW-Vi with time complexities of 28, 216, 248, and 216, respectively. Next, we construct a bit-level MILP model to efficiently search for differential characteristics, and find the best differential characteristics in the 3- and 4-round versions. These characteristics lead to the 3-round differential distinguishers for SNOW-V and SNOW-Vi with time complexities of 217 and 212 and the 4-round differential distinguishers for SNOW-V and SNOW-Vi with time complexities of 297 and 239, respectively. Then, we consider single-bit and dual-bit differential cryptanalysis, which is inspired by the existing study on Salsa and ChaCha. By carefully choosing the IV values and differences, we can construct practical bit-wise differential distinguishers for the 4-round SNOW-V, 4-, and 5-round SNOW-Vi with time complexities of 24.466, 21.000, and 214.670, respectively. Finally, we improve the existing differential attack based on probabilistic neutral bits, which is also inspired by the existing study on Salsa and ChaCha. As a result, we present the best key recovery attack on the 4-round SNOW-V and SNOW-Vi with time complexities of 2153.97 and 2233.99 and data complexities of 226.96 and 219.19, respectively. Consequently, we significantly improve the existing best key recovery attack in the initialization phase by the designers.
Emergence and Development of Bullet Comments in China

Danmu denotes user-generated dynamic and contextualised comments scrolling across the screen. Owing to its resemblance to a bullet curtain, such a text-over-screen technology is referred to as ‘bullet comment/message’ or ‘barrage subtitling’. Since being adopted from Japan, danmu has expeditiously developed from a niche subcultural entity into a prominent property of Chinese online video culture. Danmu obtains popularity among young audiences, in that it establishes social media co-viewing, creates a sense of belonging, allows self-expression and facilitates social connectedness. Moreover, danmu enables creators to demonstrate a sense of existence, especially via informative, alerting and spoiler comments. The popularity of danmu in China is ascribed to diversified and concise literacy practice. More significantly, it is attributed to linguistic and cultural prerequisites: the Chinese written language is featured by a high information density and robust parafoveal preview effects; the Chinese culture is marked by a high level of polychronicity and collectivism, as well as conspicuous social and peer pressure.
An efficient metrics based <scp>self‐adaptive</scp> design model by multiobjective gray wolf optimization with extreme learning machine for autonomic computing system application

Abstract Autonomic computing refers to the capability of the software system to monitor environmental changes by itself. The primary objective of the proposed approach is to identify the quality of software components concerning complexity metrics. In this article, extreme learning machine (ELM) supported multiobjective gray wolf optimization (MOGWO) is proposed to predict the quality products. The proposed methodology considers four component‐based system metrics such as failure response time, throughput rate, interface surface consistency (ISC) and bounded interface complexity metrics (BICM) to resolve the complexity issues. The ELM is utilized in this article to conduct a prediction in the complexity metrics on the context‐awareness self‐adaptive autonomous system application. MOGWO is then utilized to optimize the cost metric. The evaluation results are compared with the existing approaches such as ELM, ANN, and ANN‐GWO. From the simulations, the overall accuracy rate of the prediction model is obtained to be 95%.
Open Source Academic Publishing Suite (OS-APS)

The project “Open Source Academic Publishing Suite (OS-APS)” aims to enable small and university publishers to publish easily and media-neutrally in Open Access. The open source software includes a document importer (i.e. for Word documents) and an editor to edit structures and metadata. Using templates, which can either be selected from a pool of existing templates or be created with a template development kit, the design of the exported PDF can be automatically adapted according to the format requirements of the publisher. In addition, e-books in EPUB and HTML format are created quickly and reliably.
Pruning Neural Networks with Supermasks

The Lottery Ticket hypothesis by Frankle and Carbin states that a randomly initialized dense network contains a smaller subnetwork that, when trained in isolation, will match the performance of the original network.However, identifying this pruned subnetwork usually requires repeated training to determine optimal pruning thresholds.We present a novel approach to accelerate the pruning: By methodically evaluating different Supermasks, the threshold for selecting neurons as part of a pruned Lottery Ticket network can be determined without additional training.We evaluate the method on the MNIST dataset and achieve a size reduction of over 60% without a drop in performance.
Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning

Deep neural networks are increasingly being used for the analysis of medical images. However, most works neglect the uncertainty in the model’s prediction. We propose an uncertainty-aware deep kernel learning model which permits the estimation of the uncertainty in the prediction by a pipeline of a Convolutional Neural Network and a sparse Gaussian Process. Furthermore, we adapt different pre-training methods to investigate their impacts on the proposed model. We apply our approach to Bone Age Prediction and Lesion Localization. In most cases, the proposed model shows better performance compared to common architectures. More importantly, our model expresses systematically higher confidence in more accurate predictions and less confidence in less accurate ones. Our model can also be used to detect challenging and controversial test samples. Compared to related methods such as Monte-Carlo Dropout, our approach derives the uncertainty information in a purely analytical fashion and is thus computationally more efficient.
Research on Characteristics and Programming of Simulated Human Intelligence

This paper concluded that parallel storage similar to the human brain must have the following characteristics: 1) The connection mode of its information storage is scattered and parallel.2) The operating pattern of this system must be similar to that of the nervous system for memory, facilitation, and recall.Because of this similarity, we can "borrow" some concepts from neuroscience to describe Advanced intelligent basic programming the attributes of parallel storage systems.3) The excitation mode of this system must be selective excitation.4) Memory interference is one of the most important characteristics of parallel storage system.We can't eliminate recall interference completely, but there are a number of steps can take to keep within the limits of our tolerance.5) Memory interference requires only excitatory memory columns in the contact area.6) The "memory excitation" mode of the parallel storage system for information must be the fuzzy "memory excitation", which is inevitable of memory interference.Whereafter, This paper describes and discusses how and why to program on the theoretical basis of the first part.Includes an introduction to functions that enable memory, recall, stimulation, excitement.And the relationship between memory, memory, fuzzy excitement, excitement, reward and punishment, attention distribution and their significance in solving memory interference.
Human-Paper Interaction in the Digital Era: Directions for Human-Information Interaction Design

These days, we receive most information through digital mediums such as emails and social networking applications.Investigating the characteristics of human-paper interactions can help us design more meaningful interactions and better understand why people use paper documents in the age of digitalization.In this study, the interaction of people with physical documents was studied through a mixed-method of conducting a literature review, gathering expert opinions, interviewing subjects, and analysing Instagram photos.By codifying the gathered information, the human-paper interaction framework was developed.This framework articulates the advantages of physical documents compared to electronic documents and serves researchers and practitioners by providing insightful human factors about human-document interaction.Finally, we propose six design themes as the solutions to the findings of this study.These implications can provide practical foundations for future design and research.