Reviewerly/exHarmony huggingface dataset:
The Concept of Cybercrime in the Russian Federation and the Republic of Kazakhstan

Relevance. The article is devoted to one of the current topical issues - the concept of cybercrime in the Russian Federation and the Republic of Kazakhstan. The concept of cybercrime is widely used in criminology and practical activities of the police of the two countries. However, an accurate and complete understanding of cybercrime, as well as its legislative consolidation, remains open to this day. The essence of this problem lies in the fact that the effectiveness of law enforcement agencies in preventing such crimes depends on the correct understanding of cybercrime. Currently, during the rapid development of information technology, the number of crimes committed in cyberspace is growing in proportion to the number of users of computer networks. The purpose is to give an accurate and complete definition of the concept of cybercrime in the Russian Federation and the Republic of Kazakhstan. Objectives: based on the available definitions in the legal literature, to identify the main signs of cybercrime and to systematize the concept of "cybercrime". Methodology. In the process of working on the study, methods of theoretical research (analysis and synthesis, induction and deduction, mental modeling), a comparative legal approach were used. Results. The study suggests an understanding of cybercrime as a historically volatile, latent, social and criminal-legal negative phenomenon, which is a system of crimes committed virtually in the information space using information and communication technologies. Conclusions. The study concluded that the analysis of doctrinal approaches does not show a consensus among scientists in the definition of cybercrime. This is due to different interpretations of cyberspace and ways of using computer systems when committing illegal actions. Despite the differences, scientists raise the question of the relationship between international legislation and Russian legislation, regarding the list of illegal actions that are attributed to crimes committed in the cybersphere. In our opinion, the concept of "cybercrime" can be applied to crimes that are committed using any means of in-formation and communication technologies. Cybercrime is a broader concept than "Internet crime", as it includes the possibility of committing crimes using any information or telecommunications networks.
DESIGN AND IMPLEMENT VIDEO AND CHAT APPLICATION USING MESH NETWORK

Web Real-Time Communication (WebRTC) technology permits real-time media and data exchange between browsers. A connection is proven via a detection procedure named signalling. However, signalling has no exact definition in WebRTC. This paper aims to design and implement WebRTC chat, video communication, and recording between peers (browserto-browser) in the real application using Chrome and Firefox based on mesh topology. Thus, a signalling channel between two peers for chat and video conferencing using the following: Socket.io mechanism, Node.JS platform, and Express.JS has been produced and performed. Including, Axios (HTTP JSON), and JavaScript as the main programing language utilised. The results of this work have ensured that a signalling channel has been built and implemented. Keywords: Web Real-Time Communication (Webrtc); Session Description Protocol (SDP); Socket.Io Protocol; Node. JS, Javascript (JS); Local Area Network (LAN)
Combining and Fusion of Multispectral and RADAR Data for Better Identification of Geological Features

Combining and Fusion of Multispectral and RADAR Data for Better Identification of Geological Features
Qualitative and Quantitative Evaluation of Geophysical Data Accordance with Assay Data in Yazd Darreh-Zereshk Copper Deposit to Propose Optimal Location for new Exploration Boreholes

Qualitative and Quantitative Evaluation of Geophysical Data Accordance with Assay Data in Yazd Darreh-Zereshk Copper Deposit to Propose Optimal Location for new Exploration Boreholes
Petrography, geothermobarometry and tectonomagmatic setting of intrusive rocks in Khuni (Northeast of Anarak, Central Iran)

Petrography, geothermobarometry and tectonomagmatic setting of intrusive rocks in Khuni (Northeast of Anarak, Central Iran)
Inaccessible data sharing: Link rot in the Data Availability Statement

Data Availability Statement, an account within research articles about whether the data underlying the research is shared and if so, how it can be accessed, has been increasingly embraced by academic communities as a venue to share research data. The Public Library of Science (PLOS) is one of the first major publishers to implement this policy from the beginning of 2014 and reported that 20% of statements linking to external websites in 2016. However, does this policy greatly improve the availability of research data? Few studies have examined the effectiveness of these statements in data sharing. In this study, we examined to what extent the shared data can be accessed from the links specified in the statements. 39,444 URLs from all PLOS ONE publications between 2014 and 2020 were extracted. In this poster, we present preliminary findings on the types of URLs specified in the statements and their access rates. Our study aims to draw attention from data publishers and researchers on the issues of inaccessible data sharing and highlight the inconsistent practices of linking certain data repositories in the data availability statements. Finally, we propose three recommendations for those dedicated to data sharing and publishing to improve their data policy so as to promote the availability of datasets. The implications of our findings and the next step of the study are also discussed in this presentation.
NISO SSOS (Standards-Specific Ontology Standard): A New Resource for Standards Publishers

The NISO Working Group for the Standards Specific Ontology Standard (SSOS) has developed and standardized a high-level standards ontology that describes a limited set of core concepts and relationships, starting with a component to define standards’ lifecycle states. This will facilitate use, create deeper and more consistent discovery and navigation, and set a foundation for other semantic application, such as linked data, within the standards ecosystem. This poster will outline the process undertaken to create the ontology standard, and show next steps for the project and its adoption by standards publishers.
A Changing Culture? An Exploration of Data Sharing in the Humanities

This poster will present preliminary results from a survey of humanities authors, and interviews with editors of humanities journals, which aimed to explore their attitudes towards, and knowledge of research data sharing. Although academic publishers are increasingly adopting data sharing policies across their journal portfolios, many policies are drafted to reflect scientific research outputs rather than those from the humanities or social sciences. Researchers in the humanities may find compliance with such policies challenging, and struggle to identify what constitutes their “research data” and how it can be shared appropriately. As part of the STM Association’s Research Data Program, a group representing seven academic publishers (Taylor &amp; Francis, SAGE, BRILL, Oxford University Press, Wiley, F1000 and Cambridge University Press) have come together to discuss how data sharing by authors in humanities journals can be supported and encouraged. The group developed an online survey to identify which aspects of data policy compliance researchers find most challenging; and in parallel, conducted a series of interviews with editors to discuss the impacts of data sharing policies on their journals. The results presented will address questions including “What is humanities data?” and “Do humanities researchers perceive journal data sharing policies negatively, or positively?”
Persona Profiles for CRediT roles

CRediT is the most frequently used contributor role data model, adopted extensively by key scholarly stakeholders including funders (e.g., Wellcome Trust), publishers (e.g., Elsevier, Sage, Springer), and repositories (e.g., Zenodo/InvenioRDM), and is expected to play an increasingly critical role in the context of data sharing and open science workflows. By providing a standard list of fourteen roles to specify individual contributions to scholarly publications, CRediT enhances transparency and consistency about reporting diverse contributions to scholarly works, thereby improving the attribution of credit and responsibilities. However, CRediT can be limited, as it oversimplifies complex research activities and overlooks the rich range of responsibilities and requirements in today’s interdisciplinary research. Likewise, ethical application of CRediT suffers from inconsistent and sometimes inaccurate understanding of roles, resulting in the misattribution of credit and responsibilities due to brief descriptive definitions of roles that are difficult to apply across disciplines. In response, we have developed a strategy to articulate CRediT roles with persona profiles to facilitate better communication of these roles and reduce the likelihood of misunderstanding. This poster will present our rationale and activities to create persona profiles for CRediT roles, providing an opportunity to gather feedback on our understanding of these roles across various research domains to improve the quality of our persona profiles. This project is supported by a Mission Advancement Award from Northwestern University Department of Preventive Medicine and the Northwestern University Clinical and Translational Sciences Institute (NUCATS, UL1TR001422).
Perceptual Contrastive Generative Adversarial Network Based on Image Warping for Unsupervised Image-to-Image Translation

This paper proposes an unsupervised image-to-image (UI2I) translation model, called Perceptual Contrastive Generative Adversarial Network (PCGAN), which can solve the distortion problem to enhance performance of the traditional UI2I methods. The PCGAN is designed with a two-stage UI2I model. In the first stage of the PCGAN, it leverages a novel image warping to transform shapes of objects in input (source) images. In the second stage of the PCGAN, the residual prediction is devised in refinements of the outputs of the first stage of the PCGAN. To promote performance of the image warping, a loss function, called Perceptual Patch-Wise InfoNCE, is developed in the PCGAN to effectively memorize the visual correspondences between warped images and refined images. Experimental results on quantitative evaluation and visualization comparison for UI2I benchmarks show that the PCGAN definitely is superior to other existing methods considered here.
DEVELOPMENT OF DIGITAL COMIC LEARNING MEDIA BASED ON SCIENTIFIC APPROACH AS CHARACTER STRENGTHENING IN 2nd GRADE OF ELEMENTARY SCHOOL

The development of digital comic media as an effort to provide innovation and education in character education in learning material on religious diversity is based on several gaps, including: 1) students' lack of understanding of religious diversity in Indonesia, 2) lack of student understanding of tolerance, 3) The way teachers provide religious diversity material is conventional and only based on books. The purpose of this research to find out digital comic media development and implementation at SDN 2 Golong. Based on the problems that have been described, the development aims to produce valid digital comic media for grade II students of SDN 2 Golong. The development of this digital comic media used the ADDIE development model. There are 5 stages in the development process that have been carried out in this research, namely 1) analysis, 2) design, 3) development, 4) implementation, and 5) evaluation. The assasement of digital comic media was carried out by material experts, media experts, educators, and second grade students at SDN 2 Golong. The results of the research showed that the development of digital comic media show that the percentage obtained from material expert validators, which is 85% with a very valid category and suitable for use without improvement, the value obtained from media expert validators, is 91.25% with a very valid category and suitable for use without improvement. but there are still revisions. Furthermore, the results of the educator's trial 91% with a very valid category and suitable for use without improvement. The results of field trials were carried out with 20 students with a percentage of 88% in the very interesting category. Based on the results of the data analysis, it can be concluded that the development of digital comic media is fit and possible to used as a media learning.
A Semantic Framework based on Domain Knowledge for Opinion Mining of Drug Reviews

Opinion mining has been attracting increasing attention in recent years. Existing approaches to opinion mining that have worked on general domains face two major challenges for polarity classification of drug reviews. Firstly, indirect opinions frequently occur in the drug domain, while the existing methods have mainly focused on direct opinions and ignored the indirect ones. Secondly, previous works are not sufficient for polarity classification of ambiguous concepts in the drug domain.&#x0D; This paper proposed a semantic framework based on domain knowledge for resource construction and exploitation for indirect opinion mining of drug reviews. Accordingly, some methods were introduced, developed, and compared for building and exploiting a combined knowledge base, polarity-tagged corpus, and context-aware resources for the polarity detection of drug reviews. The test results showed that the proposed methods reached a precision of 89.18% and 80.4% in the application of the combined knowledge base and the polarity-tagged corpus for polarity detection of indirect opinions, respectively. Also, a precision of 79.93% was achieved with the use of context-aware resources constructed for the polarity detection of ambiguous concepts. Overall, the results obtained demonstrated the performance of the proposed methods compared to the existing methods.
Clustering algorithms with prediction the optimal number of clusters

The clustering is a widely used technique for grouping of objects. The objects, which are similar to each other should be in the same cluster. One disadvantage of general clustering algorithms is that the user must specify the number of clusters in advance, as input parameter. This is a major drawback since it is possible that the user cannot specify the number of clusters correctly, and the algorithm thus creates a clustering that puts very different elements into the same cluster. The aim of this paper is to present our representation and evaluation technique to determine the optimal cluster count automatically. With this technique, the algorithms itself determine the number of clusters. In this paper first, the classical clustering algorithms are introduced, then the construction and improvement algorithms and then our representation and evaluation method are presented. Then the performance of the algorithms with test results are compared.
Strategies to Solve EFL Students’ Problems in Writing

People nowadays have realized that in learning a language, they also need to master the four language skills, including writing. Second language writing, then, is a necessity to be taught in the schools and universities. Yet, many still consider writing as a complex activity since the writers need to deal with the content, mechanical, and readers’ expectation. That makes EFL students encounter many problems in writing. This article aims to share the strategies in teaching writing to solve students’ problems in writing for all level.
Qompress: Efficient Compilation for Ququarts Exploiting Partial and Mixed Radix Operations for Communication Reduction

Quantum computing is in an era of limited resources. Current hardware lacks high fidelity gates, long coherence times, and the number of computational units required to perform meaningful computation. Contemporary quantum devices typically use a binary system, where each qubit exists in a superposition of the $\ket{0}$ and $\ket{1}$ states. However, it is often possible to access the $\ket{2}$ or even $\ket{3}$ states in the same physical unit by manipulating the system in different ways. In this work, we consider automatically encoding two qubits into one four-state qu\emph{quart} via a \emph{compression scheme}. We use quantum optimal control to design efficient proof-of-concept gates that fully replicate standard qubit computation on these encoded qubits. We extend qubit compilation schemes to efficiently route qubits on an arbitrary mixed-radix system consisting of both qubits and ququarts, reducing communication and minimizing excess circuit execution time introduced by longer-duration ququart gates. In conjunction with these compilation strategies, we introduce several methods to find beneficial compressions, reducing circuit error due to computation and communication by up to 50\%. These methods can increase the computational space available on a limited near-term machine by up to 2x while maintaining circuit fidelity.
STI: Turbocharge NLP Inference at the Edge via Elastic Pipelining

Natural Language Processing (NLP) inference is seeing increasing adoption by mobile applications, where on-device inference is desirable for crucially preserving user data privacy and avoiding network roundtrips. Yet, the unprecedented size of an NLP model stresses both latency and memory, creating a tension between the two key resources of a mobile device. To meet a target latency, holding the whole model in memory launches execution as soon as possible but increases one app’s memory footprints by several times, limiting its benefits to only a few inferences before being recycled by mobile memory management. On the other hand, loading the model from storage on demand incurs IO as long as a few seconds, far exceeding the delay range satisfying to a user; pipelining layerwise model loading and execution does not hide IO either, due to the high skewness between IO and computation delays.
Literal Reading Comprehension Ability of English Study Program Students of Politeknik Negeri Jember

This article addresses the result of study that was carried out at the English Study Program, Politeknik Negeri Jember. This research was conducted to determine reading comprehension ability especially literal reading comprehension ability. The study is a descriptive quantitative research because it describes students' literal reading comprehension ability. In this study, data was used to describe and report students' ability in understanding literal text. In this study, researcher used a purposive sampling technique so that he made all students of the English Study Program in Semester1 for the 2020/2021 Academic Year as samples to be studied in this study. The samples taken in this study were 64 students who took reading course (literal reading course). To analyse the data in this study, the researcher conducted a literal reading test to measure their literal reading comprehension ability.
On the complexity of nonsmooth automatic differentiation

Using the notion of conservative gradient, we provide a simple model to estimate the computational costs of the backward and forward modes of algorithmic differentiation for a wide class of nonsmooth programs. The overhead complexity of the backward mode turns out to be independent of the dimension when using programs with locally Lipschitz semi-algebraic or definable elementary functions. This considerably extends Baur-Strassen's smooth cheap gradient principle. We illustrate our results by establishing fast backpropagation results of conservative gradients through feedforward neural networks with standard activation and loss functions. Nonsmooth backpropagation's cheapness contrasts with concurrent forward approaches, which have, to this day, dimensional-dependent worst-case overhead estimates. We provide further results suggesting the superiority of backward propagation of conservative gradients. Indeed, we relate the complexity of computing a large number of directional derivatives to that of matrix multiplication, and we show that finding two subgradients in the Clarke subdifferential of a function is an NP-hard problem.
Perbandingan Waktu Respon Aplikasi Database NoSQL Elasticsearch dan MongoDB pada Pengujian Operasi CRUD

Currently, humans live in an era of data oceans, where the amount of data production is increasing from time to time, which is followed by severe challenges in terms of processing, storing, and analyzing data, especially big data. The increase in the number of large data production can affect the speed of access to the database, effectiveness, and speed of response time in the data processing. Relational databases have been the leading model for data storage, analysis, processing, and retrieval for more than forty years. However, due to the increasing need for large-scale data storage, the scalability and performance of a data processing system, as well as the constant growth of the amount of data, another alternative to databases emerged, namely NoSQL technology. Based on previous studies regarding the comparison of response time and database performance, the average concludes that NoSQL performance is more effective and efficient than relational databases. Based on the implementation and testing, it can be concluded that the NoSQL database application MongoDB is proven to be superior in every command of CRUD tested compared to the Elasticsearch NoSQL database application, where in testing the create data command with a JSON file, the MongoDB database application is 42.5 times faster than the Elasticsearch database application. In testing the command to create data into a database containing different amounts of data, the MongoDB database application is 333.9 times faster than the average response time of the Elasticsearch database application. In testing the read command for data in a database containing different amounts of data, the MongoDB database application is 35.5 times faster than the Elasticsearch database application. In testing the update operation of data in a database containing different amounts of data, the MongoDB database application is 9.8 times faster than the Elasticsearch database application. in testing the delete operation of data in a database containing different amounts of data, the MongoDB database application is 58.9 times faster than the Elasticsearch database application.
K-NEAREST NEIGHBOR MENGGUNAKAN FEATURE SELECTION BACKWARD ELIMINATION UNTUK PREDIKSI JUMLAH PERMINTAAN DARAH PADA PMMI KOTA GORONTALO

The importance of the availability of blood at PMI, it is expected that PMI always maintains the amount of blood supply to meet the need for blood transfusions. Prediction of blood supply is needed to overcome problems related to bloodstock supply at PMI Gorontalo. The application of predicting the number of blood requests with the K-Nearest Neighbor Algorithm can be done to overcome the existing problems. K-NN is a non-parametric algorithm that can be used for classification and regression. The last few decades have been used in prediction cases, but the K-NN algorithm is better if feature selection is applied in selecting features that are not relevant to the model, the feature selection used in this study is Backward Selection. This study aims to determine the error value in predicting the number of requests for blood at the PMI in Gorontalo City. Meanwhile, the purpose of this research is to find the error value of the K-Nearest Neighbor Algorithm and Feature Selection which can be used as a reference for PMI in making policies to make various efforts to maintainbloodstockk in the future.