Reviewerly/exHarmony huggingface dataset:
SELECTION OF THE DISTANCE LEARNING MANAGEMENT SYSTEM FOR PEDAGOGICAL HIGHER EDUCATION INSTITUTIONS

The article deals with various distance learning management system for pedagogical higher education institution. The most prominent distance learning management systems are mostly focused on: Moodle, WebCT, BlackBoard, eFront, Claroline, Metacoon, SharePoint LMS and Virtual University. Having analyzed the best platforms for creating of distance learning system in pedagogical higher education institution, it is stated that Moodle is leading according to the number of users. Moodle platform is a software suite for creating distance learning courses and websites that works with open source IMS and SCORM. As a result of the analysis and comparisons of the abovementioned platforms, based on pedagogical, didactic, organizational, technical, financial distance learning features, the distance learning management system in State Higher Educational Institution «Pereiaslav-Khmelnytskyi Hryhorii Skovoroda State Pedagogical University» on the basis of Moodle platform is developed. The platform works in different web browsers. There are no special requirements for hardware and operating system. Moodle platform can be installed on any server with any operating system using arbitrary databases. The platform is free for users. Moodle distance learning management system consists of 35 modules: Chat module, Task module, Forum module, Log module, Test module, Resource module, Research module, Seminar module and so on. Moodle distance learning management system enables distance learning in higher education at an affordable innovation level, creates a calendar of learning process events, creates formal algorithms for the teacher's activity in the educational process, thereby achieving a high level of interactivity and achievement of the required level of quality, skills.
A Domain-Adapting Word Representation Method for Word Clustering

Extracting key information from texts is a goal of natural language processing (NLP) field. A few keywords could prompt the main idea of the text, and the complete vocabulary information is richer, but not easy to organize. This paper proposes a word representation method based on frequently co-occurring entropy (FCE) and fuzzy bag-of-words model (FBoW), named frequently co-occurring entropy and fuzzy bag-of-words model (FCE-FBW). This method is used to cluster the words of different domains and integrate similar words together. These word clusters can be useful for tasks such as building knowledge-based domain repositories. FCE is used to pick out the generalizable features. FBoW supports the description of the same word by multiple dimensions. This paper combines the two models and proposes FCE-FBW method. It provides good performance.
Assessment of Digital Implementation in India and Challenges

The Digital India program is envisioned to transform the way business is carried out and how the government services are delivered. Under this programme, government offices to maintain, publish, and store records in a digital form. It is believed that promoting digital India will lead to an ease of information access, at the same time a swiftness in government function. This work argues with the help of secondary data collected from internet. Progress and gaps in the initial investment planning and the one covered is presented. Based on the data gathered, present implementation of this programme is deeply assessed, and wherever gaps exist are highlighted. In addition, challenges to various pillars of Digital India are explored. Finally, this work suggested methods to address the identified challenges in order to achieve the effective implementation of this scheme.
GAN Path Finder: Preliminary Results

2D path planning in static environment is a well-known problem and one of the common ways to solve it is to (1) represent the environment as a grid and (2) perform a heuristic search for a path on it. At the same time 2D grid resembles much a digital image, thus an appealing idea comes to being – to treat the problem as an image generation task and to solve it utilizing the recent advances in deep learning. In this work we make an attempt to apply a generative neural network as a path finder and report preliminary results, convincing enough to claim that this direction of research is worth further exploration.
Frational Order Inverse Filters Based on CCII Family

This paper proposes two generalized topologies of fractional order inverse filters (FOIF). All possible realizations of each topology are investigated using the second generation current conveyor (CCII) family. Inverse fractional highpass (IFHPF), inverse fractional bandpass (IFBPF), and inverse fractional lowpass (IFLPF) filters are realized using the same topology based on the generalized admittances. Numerical and P-Spice simulation results are presented for selected cases to approve the theoretical findings. The fractional order parameters increase the design flexibility and controllability which is validated experimentally.
An Investigation of Graph Theory Application on Portfolio Investment

In this paper, we discussed the possibility to generate diversified portfolio with graph theory. To model the performance and relationship between pairs of stocks in the market with graph theory, it is possible to give a quantitate description to the traditional concepts such as sector. Adjacency matrix was used to describe the influence between stocks, and threshold was used to adjust the outcome of portfolio generation to achieve our expected return. Further investigation includes using weighted graph to evaluate the degree of influence, using directed graph to describe the time dimension of reaction of stocks in the market and using degree matrix to find more critical assets in the market to achieve the aggressive investment strategy.
Research Progress on Key Technologies of Radar Signal Sorting

The complexity and computational complexity of the radar receiver signal processing are mainly concentrated on the signal sorting process. The precondition of radar signal sorting is to extract signal features and then to select key features for sorting. This paper discusses several aspects from feature extraction technology and feature selection technology.
Critical Design in Librarianship: Visual and Narrative Exploration for Critical Praxis

In a politically charged social climate, many disciplines are inwardly grappling with historic ideologies and systems of oppression. In information science, critical librarianship seeks to disrupt normative approaches to library work by confronting these ideologies and systems, empowering both library workers and users to understand how these structures affect access and dissemination of information. In industrial design, critical design challenges the status quo by posing troublesome design problems that encourage reflection and discourse. Design works that fall under this category are reactions against consumerism, systemic biases, and heedless scientific and technological development. In librarianship, critical design may be employed to help us explore current paradigms and approaches to library spaces, services, and technologies, as well as to uncover assumptions about what a library is and what a librarian does. Prototypes and narratives derived from critical design can make visible the normative constructions and problems heartedly debated in librarianship.
A Modified Fuzzy Sentiment Analysis Approach Based on User Ranking Suitable for Online Social Networks

Processing Sentiment Analysis (SA) in social networks has lead decision makers to value opinion leaders who can sway people’s impressions concerning certain business or commodity. Yet, a tremendous scarceness of considering perspectivism, while computing text polarity, has been spotted. Considering perspectivism in SA can help in the production of polarity scores that represent the perceptible sentiment within the content. This emphasizes the necessity for integrating social behavior (user influence factor) with SA (text polarity scores), providing a more pragmatic portrayal of how text-recipients comprehend the message. In this paper, a novel model is proposed to intensify SA process in Twitter. In the achievement of such, UCINET tool and Artificial Neural Networks (ANN) are used for social network analysis (SNA) and users ranking respectively. For sentiment classification, a hybrid approach is presented—lexicon-based technique (TextBlob) along with fuzzy classification technique—to handle language vagueness as well as for an inclusive analysis of tweets into seven classes; for the purpose of enhancing final results. The proposed model is practiced on data collected from Twitter. Results show a significant enhancement in the final polarity scores, associated with the analyzed tweets, representing more realistic sentiments.
Transfer Learning via Feature Selection Based Nonnegative Matrix Factorization

Transfer learning has been successfully used in recommender systems to deal with the data sparsity problem. Existing techniques assume that the source and target domains share the same feature space. This paper proposes a new direction in transfer learning where the source and target domains can have different feature space. The proposed technique, Feature Selection based Nonnegative Matrix Factorization (FSNMF), selects the useful features that can minimize the cost function of the target domain. The features of the source domain are learned using NMF and their importance is measured using the gradient principle. Experiments with real-world datasets show the effectiveness of FSNMF in comparison to state-of-the-art relevant transfer learning techniques.
Selective End-To-End Data-Sharing in the Cloud

Cloud-based services enable easy-to-use data-sharing between multiple parties, and, therefore, have been widely adopted over the last decade. Storage services by large cloud providers such as Dropbox or Google Drive as well as federated solutions such as Nextcloud have amassed millions of users. Nevertheless, privacy challenges hamper the adoption of such services for sensitive data: Firstly, rather than exposing their private data to a cloud service, users desire end-to-end confidentiality of the shared files without sacrificing usability, e.g., without repeatedly encrypting when sharing the same data set with multiple receivers. Secondly, only being able to expose complete (authenticated) files may force users to expose overmuch information. The receivers, as well as the requirements, might be unknown at issue-time, and thus the issued data set does not exactly match those requirements. This mismatch can be bridged by enabling cloud services to selectively disclose only relevant parts of a file without breaking the parts’ authenticity. While both challenges have been solved individually, it is not trivial to combine these solutions and maintain their security intentions. In this paper, we tackle this issue and introduce selective end-to-end data-sharing by combining ideas from proxy re-encryption and redactable signature schemes. Proxy re-encryption provides us with the basis for end-to-end encrypted data-sharing, while redactable signatures enable to redact parts and selectively disclose only the remaining still authenticated parts. We overcome the issues encountered when naively combining these two concepts, introduce a security model, and present a modular instantiation together with implementations based on a selection of various building blocks. We conclude with an extensive performance evaluation of our instantiation.
Generate Desired Images from Trained Generative Adversarial Networks

The emerging of Generative Adversarial Networks (GANs) gives rise to a significant improvement in image generation. However, a controllable way of synthesizing images with specific characteristics still is a challenging issue. Many existing methods are not efficient enough that require additional information and pre-designed attributes, and are with much more human intervention. In this paper, we propose GAGAN, an extension method to the Generative Adversarial Network, that is the first work to generate specific images from a trained GAN model. To control the characteristics of images, a DNA pool of the trained GAN model is introduced and evolved by a genetic algorithm (GA). Then, with the DNA pool, GAGAN can generate the corresponding latent vector (DNA) of target images. Furthermore, GAGAN can synthesize images containing a single specific characteristic or multiple specific attributes (including AND and OR relation). Moreover, several fitness evaluation strategies are also proposed to make GAGAN flexible to control the target characteristics. Experiments on CelebA and MNIST are conducted, and results show that the proposed method is feasible and effective in specific image generation problem.
Virtual Reality

Moving beyond the traditional K-12 classroom environment, this chapter examines the pedagogical benefits virtual learning offer gifted and talented students. The use of virtual reality as a learning tool is relatively new in the field of education. A discussion is provided how virtual reality promote and support learning theories such as Gardner's Theory of Multiple Intelligences, Constructivism, and Connectivism. The benefits of virtual reality in terms of spatial awareness and the use in STEM (science, technology, engineering, and mathematics) areas the technology offers to K-12 gifted and talented students are also discussed. On a global level, the use of three-dimensional software and computer devices can help students discover and understand abstract and complex concepts. This chapter provides an overview of current research and projects relating to the use of virtual technology devices within the field of education.
Memory-Efficient GPU-Based Exact and Parallel Triangle Counting in Large Graphs

Triangle counting is one of the basic research topics in many practical problems, such as clustering coefficients, transitivity ratio and traffic network complexity, etc., all of which can be converted into triangle counting issues. In this paper, a memory efficient GPU-based parallel triangle counting algorithm is proposed for large undirected graphs, which based on Polak's parallel forward triangle counting algorithm and improved the algorithm flow, edges sorting and triangle counting kernel function to increase the memory usage efficiency and run time performance. Algorithm comparison with the description of proposed algorithm steps and the implementation details is also introduced in the paper. Performance evaluations show that the proposed algorithm achieves 5 to 10 times speedup over the existing parallel forward algorithm, moreover, the GPU memory usage is reduced to a maximum of 50% compared to the existing algorithm.
Pruned Sets for Multi-Label Stream Classification without True Labels

In multi-label classification problems an example can be simultaneously classified into more than one class. This is also a challenging task in Data Streams (DS) classification, where unbounded and non-stationary distributed multi-label data contain multiple concepts that drift at different rates and patterns. In addition, the true labels of the examples may never become available and updating classification models in a supervised fashion is unfeasible. In this paper, we propose a Multi-Label Stream Classification (MLSC) method applying a Novelty Detection (ND) procedure task to update the classification model detecting any new patterns in the examples, which differ in some aspects from observed patterns, in an unsupervised fashion without any external feedback. Although ND is suitable for multi-class stream classification, it is still a not well-investigated task for multi-label problems. We improve a initial work proposed in [1] and extended it with a new Pruned Sets (PS) transformation strategy. The experiments showed that our method presents competitive performances over data sets with different concept drifts, and outperform, in some aspects, the baseline methods.
Towards Graphical User Interface Redefinition without Source Code Access

Nowadays several interactive computing systems (ICSs) still have Graphical User Interfaces (GUIs) that are inadequate in terms of usability and user experience. Numerous improvements were made in the development of better GUIs however, little has been done to improve existing ones. This might be explained by the fact that most ICSs do not provide source code access. In most cases, this means that only persons with source code access can (easily) enhance the respective GUI.
APML: An Architecture Proof Modeling Language

To address the increasing size and complexity of modern software systems, compositional verification separates the verification of single components from the verification of their composition. In architecture-based verification, the former is done using Model Checking, while the latter is done using interactive theorem proving (ITP). As of today, however, architects are usually not trained in using a full-fledged interactive theorem prover. Thus, to bridge the gap between ITP and the architecture domain, we developed APML: an architecture proof modeling language. APML allows one to sketch proofs about component composition at the level of architecture using notations similar to Message Sequence Charts. With this paper, we introduce APML: We describe the language, show its soundness and completeness for the verification of architecture contracts, and provide an algorithm to map an APML proof to a corresponding proof for the interactive theorem prover Isabelle. Moreover, we describe its implementation in terms of an Eclipse/EMF modeling application, demonstrate it by means of a running example, and evaluate it in terms of a larger case study. Although our results are promising, the case study also reveals some limitations, which lead to new directions for future work.
Model for Cloud Migration Cost

Actual trend today is towards a macro economy based on the OPEX costing model. This trend is the solution proposed for dealing with a large population sharing a limited number of resources on this planet. Cloud Computing is one of the consequences of this tendency. It promises access to a large number of services in an on-demand manner. This raises the problem of finding the best strategy to migrate on premise resources in the cloud. Despite the popularity of the subject, none of the existing papers treats directly the migration period when cost increases and solutions have to be found to limit the expenses and diminish the risk of service interruption. In this paper we propose a methodology of modeling a migration in the cloud that aims achieving lower costs or minimal time, depending on which constraint is needed first. Through a realistic experiment we show the migration of a web system using the proposed methodology. We also see a lot of possible developments and generalizations of the method in similar IT projects of transitioning from CAPEX to OPEX.
SE-GAN: A Swap Ensemble GAN Framework

In recent years, Generative adversial network(GAN) becomes prevalent in the research and was applied in various fields. Though GAN attracting great attention undoubtedly, it was still blamed for the difficulty of training. Training a GAN is very likely to get into undesirable results such as gradient vanishing, gradient explosion, model collapse. In order to train GAN more efficiently, this study proposes the Swap Ensemble Generative Adversial Network(SE-GAN), a framework for training an ensemble GAN. Based on the concept of information sharing, SE-GAN allows serveral GANs to be trained in parallel: one of them is the leader, while the others are the workers. The leader pair swaps with each worker periodically to collect the information of all workers. At the same time, each worker acquires the information the leader learned. Under such implementation, the leader and workers would help each other. In this paper, a toy dataset was used to describe the concept and better show the effect of swap ensemble framework. And three real-world datasets were used to validate the effect. Results of the experiments shows this framework improves the performance and efficiency in training GAN.
Organizing studies and research: Framework for science information delivery

The possibility is examined for providing open data in pursuance of the goals and objectives of sustainable development set up in the UNESCO Science Report: Towards 2030. Organization of science research work implies managing vast data arrays, availability of scientific information and its reliability in the aspect of increase of researchers and volumes of scientific publications. Focus is also made on finding sources of reliable scientific information. The role of the libraries as a source of bibliographic information, organizing the system of support of scientific research is emphasized. The possibilities for providing open data and online access to information resources are considered. The directions of the information space development in the perspective of collective management of rights are underlined. The framework and possibilities for providing access to scientific information are specified. However, the remote user authorization technologies and IP-address range access capabilities limit the openness of information resources. The author emphasizes that the terms of use of documents differentiated by right holders depend on the strategy of data management. She also argues that educational and scientific programs should include training in using of information resources.
Indonesian Protected Health Information Removal using Named Entity Recognition

The Electronic Health Record (EHR) is the implementation of a computer system to keep a history of patient health records. All information related to patients is stored as patients who have medical history, immunization status, sugar level, and blood pressure records. EHR also contains the patient protected health information such as patient name and patient id. However, before processing further any text processing method, the protected health information needs to be removed from the data. This research employs a combination of long short term memory neural networks and conditional random fields in order to remove protected health information within the data. We employ several evaluations criteria to discover the best model. Our final evaluation result yields 0.76 the proposed model best result.
Cloud Computing Adoption

The purpose of this chapter is to develop a cloud computing adoption scale for Indian manufacturing, service, and process industries. The scale development procedure has been followed from the previous studies and is refined further for clear understanding to adopt easily. In the first step, the authors have conducted a qualitative study for item selection. The second step includes pilot testing of 110 responses for measurement scale purification, and finally, they are validating the scale with 660 sample respondents through convergent validity and discriminant validity. The initial result showed very poor threshold values for the items “top management support” and “marketplace establishment,” which have strong literature support. This measurement scale will help managers to evaluate the level of cloud adoption to increase the business performance. The study is a first attempt to develop a validated scale for cloud adoption that can be used in Indian industries.