Reviewerly/exHarmony huggingface dataset:
Broadcast Proxy Reencryption Based on Certificateless Public Key Cryptography for Secure Data Sharing

Broadcast proxy reencryption (BPRE), which combines broadcast encryption (BE) and proxy reencryption (PRE), is a technology used for the redistribution of data uploaded on the cloud to multiple users. BPRE reencrypts data encrypted by the distributor and then uploads it to the cloud into a ciphertext that at a later stage targets multiple recipients. As a result of this, flexible data sharing is possible for multiple recipients. However, various inefficiencies and vulnerabilities of the BE, such as the recipient anonymity problem and the key escrow problem, also creep into BPRE. Our aim in this study was to address this problem of the existing BPRE technology. The partial key verification problem that appeared in the process of solving the key escrow problem was solved, and the computational efficiency was improved by not using bilinear pairing, which requires a lot of computation time.
Disability’s rights to health: an obligation triggered by corona virus pandemic

This paper examines the right to health and disabilities rights in the wake of Corona virus pandemic. The objective of this paper is to examine the applicable legal and policy frameworks on the rights of persons with disabilities and how it has adequately protected such persons in the face of Corona virus pandemic. The study adopts analytical, qualitative approach and builds its argument on existing literatures. The paper recommends the existing laws and policies on disability’s rights to health be enforced by relevant agencies, whilst Article 25 of the Convention on the Rights of Persons with Disabilities 2006 should be made proactive.
Evaluation of Adaptive Interaction Systems for Virtual Museum Development

Virtual Museum (VM) is an application of Virtual Reality (VR) technology generating realistic visualization and sensation to convince museum visitors to interact with digital content. There are many immersive VR devices that support interactive VM applications. We investigate appropriate devices for interaction within VM. We proposed a Storytelling platform to achieve device organization without modification, the story and interaction were self-adapted to the selected device. Three types of interactive content were designed on our Storytelling platform to be applied on different interaction systems: a 2D standard display, a 3D stereoscopic display and a full immersive CAVE. The results showed different performances of each system supporting VM developers to select an appropriate interaction system. The evaluation contributes to the design of content and interaction of VM development with more efficiency based on user requirements.&#x0D; HIGHLIGHTS&#x0D; &#x0D; Three types of interactive content were designed on our Storytelling platform to be applied on different interaction systems: A 2D standard display, a 3D stereoscopic display, and a full immersive CAVE&#x0D; The 2D Powerwall system with a wide range of views provides immersion. However, with two-dimensional displays, users lack depth perception&#x0D; Users spent more time in selection and manipulation in the 3D stereoscopic system because depth perception is added&#x0D; The CAVE system has user attraction or holding power, users spent more interacting time&#x0D; &#x0D; GRAPHICAL ABSTRACT
Human Capital Management of Government Internal Supervisory at the Ministry of Defense of the Republic Indonesia

The results of the study found that the government internal supervisory performance was not yet optimal in supporting the achievement of the vision and mission as well as organizational goals. This study aims to provide an overview of human capital management practices of government internal supervisory at the Ministry of Defense by using a balanced scorecard perspective approach from a stakeholder perspective, a business internal process perspective, a learning and growth perspective and a financial perspective. This study used a qualitative method with a case study approach and used purposive and snowball sampling. There are 19 informants including a structural officer, the auditor, and personnel from the National Financial and Development Supervisory Board. It is noted that the quality of the auditors does not fulfill the required quality. The recommendations include the importance of improving aspects of human resource competence through education and training for certification of the Functional Auditor (JFA) according to the level of the position, improving the quality of the code of ethics, improving the recruitment system, and increasing the budget for providing the operational budget and enhancing the quality of the government internal supervisory.
Multi-Level Fusion Temporal–Spatial Co-Attention for Video-Based Person Re-Identification

A convolutional neural network can easily fall into local minima for insufficient data, and the needed training is unstable. Many current methods are used to solve these problems by adding pedestrian attributes, pedestrian postures, and other auxiliary information, but they require additional collection, which is time-consuming and laborious. Every video sequence frame has a different degree of similarity. In this paper, multi-level fusion temporal-spatial co-attention is adopted to improve person re-identification (reID). For a small dataset, the improved network can better prevent over-fitting and reduce the dataset limit. Specifically, the concept of knowledge evolution is introduced into video-based person re-identification to improve the backbone residual neural network (ResNet). The global branch, local branch, and attention branch are used in parallel for feature extraction. Three high-level features are embedded in the metric learning network to improve the network's generalization ability and the accuracy of video-based person re-identification. Simulation experiments are implemented on small datasets PRID2011 and iLIDS-VID, and the improved network can better prevent over-fitting. Experiments are also implemented on MARS and DukeMTMC-VideoReID, and the proposed method can be used to extract more feature information and improve the network's generalization ability. The results show that our method achieves better performance. The model achieves 90.15% Rank1 and 81.91% mAP on MARS.
Digital Pedagogy: Trends Of The Modern School

At the present stage of development of society, digital technologies play a significant role in all areas of life, production, social and other activities.The sphere of education is one of the leading social institutions contributing to the formation of personality and the training of competent specialists capable of ensuring the development of society, the country, its economy, science, etc.This article discusses the issues of digital pedagogy, the features of its organization in modern socio-economic conditions, the prospects for the development of digital educational technologies.The digitalization of education is a new industry and a requirement within the modernization of the education and training system for schoolchildren.The article presents not only the results of an empirical study of the current state of implementation of the principles of digital pedagogy in schools, but also the main directions and examples of solving this problem at the level of Russian and foreign educational institutions.The prospects for the massive introduction of digital educational resources into a modern school are analyzed, the main methodological and didactic methods of organizing the digital educational process are proposed, the features of the Russian educational space are indicated, which determine the need to adapt existing technologies to the realities of domestic education.
Textual-based DSL for Conceptual Database Modeling: A Controlled Experiment

The variety of database system technologies that have become available in recent years makes it difficult to select tools for entity-relationship modeling (ER) in the teaching-learning context. This paper reports a replicated controlled experiment carried out with 33 subjects in order to compare effort spent (time) and quality, using the harmonic average between precision and recall, of the models produced with two different approaches. The models were produced in a proposed tool (ERtext) with a textual-based DSL and in another tool with a graphical approach (brModelo). Briefly, the data obtained indicate: i) both approaches present similar performance in relation to associated effort, and; ii) that there is a statistically significant difference in relation to the quality of the generated models, with a slightly advantage for the textual approach. Therefore, we conclude that the use of a textual-based DSL is feasible and our proposal is an acceptable solution in the context of conceptual database modeling.
Kebutuhan Pengembangan Media Pembelajaran iSpring Suite Berbasis Android Pada Mata Pelajaran Morfologi Bahasa Arab

Learning media is an important component so that the process of teaching and learning activities in class is carried out properly. Learning media as a means or intermediary tool in order to convey messages in the form of learning. This study aims to determine the needs of students in learning media Shorof. Where the effective media is the media according to the needs of students. The researcher uses the R&amp;D method with a 4D Thiagarajan model in this study which consists of defining, planning, developing and disseminating. In collecting data, the researchers conducted observations and gave questionnaires to students' media needs. The average results show that students need learning media that can be accessed at any time to increase enthusiasm for learning in class VIII A with a percentage value of 79.00% (Required) and class VIII B with a percentage value of 83.00% (Required). The results of the percentage value state that students need an Android-based ispring suite learning media on shorof subjects because it can be accessed anytime.
Embedding the Principles and Practices of DevOps into the IT Education

With the rapid advancements of Information Technology (IT) in enterprises, the existing IT educational methods in universities are struggling to meet the new functional and technical requirements.This paper offers an overview of DevOps principles and practices and proposes an effective method for augmenting IT education with these concepts.To achieve this, a conceptual framework needs to be developed where the principles and practice of DevOps can be easily embedded into existing IT education courses.In addition to this framework, an end-to-end pipeline and toolchains for efficient execution of selected IT education programs need to be developed.These pipelines must support the working culture and enhance the productivity of students, professors, and teaching boards while still being in line with the requirements of enterprises.The proposed model is developed from six aspects of DevOps which are continuous development, continuous integration, continuous testing, packaging and staging, release automation, and performance/relevance monitoring.Through the embedding of DevOps culture and practices into existing IT education, we expect to advance the relevance of the education-industry flow while providing a platform for continual learning and experimentation.The proposed framework was implemented and evaluated at two IT courses at the Faculty of Organizational Sciences, University of Belgrade.
Strain analysis in diametral compression test through digital image correlation

In the search for alternatives to obtain more information from rock tests and to make them more accessible, this study applies the Digital Image Correlation technique (DIC 2D) in a diametral compression test called Brazilian Test, comparing the result measured by an extensometer with the result obtained by DIC.Rock tests provide information about rock characteristics applicable to several engineering areas, such as mining and construction.In tests using strain gauges to measure strain, the equipment is expensive and there is a risk of breaking the instrument due to the contact with the specimen.However, tests measuring strain through the DIC technique may be more accessible, using open source software and lower cost equipment when compared to the cost of the extensometers.The results of this study demonstrate that the DIC technique enables: 1) complementing the information of diametrical tests obtained using strain gauges, for example, supporting the visualization of crack propagation; and 2) in some cases, replacing the use of extensometers, as far as a variation is tolerated.
COMPARISON OF SEGMENTATION METHODS USED FOR BONE FRACTURE IMAGES

Abstract. The usage of computers and software in the biomedical field has been increasing and applications for doctors, clinicians, scientists and other users have been developed in the recent times. Manual, semi-automatic and fully automatic applications developed for bone fracture detection are one of the important studies in this field. Image segmentation, which is one of the image preprocessing steps in bone fracture detection, is an important step to obtain successful results with high accuracy. In this study, Otsu thresholding method, active contour method, k-means method, fuzzy c-mean method, Niblack thresholding method and max min thresholding range (MMTR) method are used on bone images obtained by Karabük University Training and Research Hospital. When any filters are not applied on images to remove noises, the most successful method is obtained by K-means method based on specificity and accuracy as 89,55% and 83,31% respectively. Niblack thresholding method has the highest sensitivity result as 92,45%.
Functional And Semantic Features Of Hypertext Links In Online Informational Newspaper Texts

The semantic and functional features of intertextual hyperlinks in news and informational and analytical articles of online newspapers are considered in this article.The relevance of the research is due to the insufficient study of the features of semantic relations between the content of hypertext link sources and texts acting as link objects, as well as the functioning of hyperlinks in this type of text.The purpose of the study is to identify the role of hypertext links in shaping the semantic structure of a text based on the potential possibility of developing a particular semantic perspective, as well as to analyze the functional load of hypertext links in terms of implementing the main functions of texts belonging to the genre under consideration.As a result of the analysis, the equivalent and partitive semantic types of hypertext links are identified, the role of partitive hypertext links in the development of the semantic perspective of the source text is revealed; the role of hypertext links in the implementation of the main functions of newspaper texts of the informational type is distinguished.The informational and persuasive messages of this type of texts are produced under the influence of the following functions in hypertext links: the function of delivering additional information, the constructive function, the expressive function, the function of reducing epistemic responsibility, the function of multimedialization.The functional features of hypertext links also include their polyfunctionality and correlation with some functions of newspaper intertextual inclusions.
An IoT Photovoltaic Sensing System

This paper follows the development of an IoT photovoltaic (PV) sensing system built around a pyranometer from Kipp and Zonen. Two Raspberry Pi act as IoT nodes connected to this system with the purpose of collecting time series values, of Global Horizontal Irradiance (GHI) from the pyranometer, and of electrical current from a reference cell. The latter will be then calibrated with the pyranometer based on numerical methods. These time series values are sent to the Influx database cluster for storage. To ensure continuous operations for an extended period and to eliminate single points of failure, a High Availability (HA) architecture was employed. Web based Grafana acts as the monitoring solution for the collected values, stored inside the InfluxDB cluster. After obtaining a somewhat considerable dataset of irradiance-current values from the two nodes, numerical methods are then used to calibrate the reference cell. Linear regression, polynomial regression and interpolation are the methods tested. Based on observations, the numerous iterations of the models and the model performance indicators, interpolation proved to be a sound fit for calibration. The reference cell, after having learnt the values from the pyranometer will be able to swiftly and reliably determine the GHI from the current variation of the solar cell thus acting as a standalone irradiance sensing instrument. An array of such reference cells can then be calibrated to expand the IoT solar sensing network.
The Expression of Moral Values in the Twitter Debate: a Corpus of Conversations

The present work introduces MoralConvITA, the first Italian corpus of conversations on Twitter about immigration whose annotation is focused on how moral beliefs shape users interactions.The corpus currently consists of a set of 1,724 tweets organized in adjacency pairs and annotated by referring to a pluralistic social psychology theory about moral values, i.e. the Moral Foundations Theory (MFT).According to the MFT, different configurations of moral values determines the stance of individuals on sensitive topics, such as immigration, civil rights, and gender equality.Moreover, an annotation of adjacency pairs' conversational dynamics is provided.Results of the analysis we applied on MoralConvITA shows that interesting patterns occur in the corpus, emerging from the intersection of moral studies and pragmatics that need to be generalized over larger corpora, and shedding some light on a novel promising perspective on the inter-user dynamics occurring in social media.
Text Summarizing Using NLP

In this era everything is digitalized we can find a large amount of digital data for different purposes on the internet and relatively it’s very hard to summarize this data manually. Automatic Text Summarization (ATS) is the subsequent big one that could simply summarize the source data and give us a short version that could preserve the content and the overall meaning. While the concept of ATS is started long back in 1950’s, this field is still struggling to give the best and efficient summaries. ATS proceeds towards 2 methods, Extractive and Abstractive Summarization. The Extractive and Abstractive methods had a process to improve text summarization technique. Text Summarization is implemented with NLP due to packages and methods in Python. Different approaches are present for summarizing the text and having few algorithms with which we can implement it. Text Rank is what to extractive text summarization and it is an unsupervised learning. Text Rank algorithm also uses undirected graphs, weighted graphs. keyword extraction, sentence extraction. So, in this paper, a model is made to get better result in text summarization with Genism library in NLP. This method improves the overall meaning of the phrase and the person reading it can understand in a better way.
A Convolutional Hierarchical Neural Network Classifier

The report presents an algorithm for constructing a convolutional hierarchical neural network classifier, which is a modification of the algorithm for constructing hierarchical neural network classifiers suggested before. The original algorithm was designed to exploit intrinsic class hierarchy to build a class tree with a neural network in each node classifying groups of initial classes (in a non-terminal node) or a subset of original classes (in a terminal node). The convolutional modification utilizes convolutional neural networks instead of regular fully connected networks in order to apply the model to image classification tasks. Use of class hierarchy for image classification should reduce the number of adjusted neural network parameters compared to deep convolutional neural networks, and therefore it should reduce training and inference time. In this context the algorithm may be compared with some pruning techniques. The convolutional hierarchical neural network classifier inherits some hyperparameters of a conventional hierarchical neural network classifier, like the activation threshold and the threshold by the share of voting patterns. The goal of this study was to explore different strategies of choosing these hyperparameters. To test these strategies, we used the CIFAR-10 dataset. Also, for demonstration purposes we apply the convolutional hierarchical neural network classifier to the CIFAR-100 dataset.
The concept of using artificial intelligence in distance learning

The article presents an analysis of possible directions of using artificial intelligence in education. It is shown that artificial intelligence in modern distance education contributes to its further development in the direction of modernization and has a significant impact, especially on the modern distance learning system. The requirements for artificial intelligence on the part of education and the negative consequences of the use of artificial intelligence and problems that may affect the quality of education are analyzed. The possible directions of work in terms of the development of artificial intelligence related to the development of knowledge representation models, the creation of knowledge bases forming the core of the expert system are considered. Attention is drawn to the fact that an intelligent learning system should be able to perform various functions of a teacher (to help in the process of solving problems, to determine the cause of students’ mistakes, to choose the optimal educational impact) almost as intelligently as a person does. Attention is also paid to such a direction as the use of intelligent chat-bots or conversational agents and their applications.
Learning from missing data with the binary latent block model

Missing data can be informative. Ignoring this information can lead to misleading conclusions when the data model does not allow information to be extracted from the missing data. We propose a co-clustering model, based on the binary Latent Block Model, that aims to take advantage of this nonignorable nonresponses, also known as Missing Not At Random data. A variational expectation–maximization algorithm is derived to perform inference and a model selection criterion is presented. We assess the proposed approach on a simulation study, before using our model on the voting records from the lower house of the French Parliament, where our analysis brings out relevant groups of MPs and texts, together with a sensible interpretation of the behavior of non-voters.
Synthetic Data Generation with Differential Privacy via Bayesian Networks

This paper describes PrivBayes, a differentially private method for generating synthetic datasets that was used in the 2018 Differential Privacy Synthetic Data Challenge organized by NIST.