Reviewerly/exHarmony huggingface dataset:
Distance learning in a pandemic: the experience of sociological monitoring of students in Russia and Kazakhstan

The article is analysing the experience of using distance learning technologies in higher educational institutions of Russia and Kazakhstan in the context of a coronavirus pandemic. Before the start of the pandemic, in both Russian and Kazakhstan systems of higher education, the practice of using distance learning was limited despite the digitalization trend. The challenges of supporting the continuity of the educational process have led to the fact that, in record time, teachers and students have mastered a number of platforms that ensure their online interaction. Several months of a full-scale distance learning allowed students to form a stable opinion on the new form of education. Today we have empirical material for the period of the coronavirus pandemic and we may assess the possibilities of distance learning. The authors refer to specific case studies in which students from Ufa (Russian Federation) and Karaganda (Republic of Kazakhstan) took part. In general, it can be stated that the majority of students in both Ufa and Karaganda, at the beginning of the pandemic, were not satisfied with distance learning. However, the data from 2021 revealed the opposite trend and indicated an increase in respondents’ satisfaction with distance learning. The complete transition to distance education is not a pertinent question, since the potential of the traditional form of education will remain in demand. The use of proven teaching technologies will guarantee the quality and efficiency of the educational process as a whole. It doesn’t matter if we’re talking about digital classes or traditional ones.
Enhance while protecting: privacy preserving image filtering

Privacy is an important issue raised from the diffusion of deep learning models. These models are able to extract unauthorized information from our data, especially from the images shared on Social Networks. In this work we present a nested evolutionary algorithm able to optimize sequences of Instagram-style image filters that, when applied to an image, are able to protect it by fooling classification systems: we turn adversarial attacks into a defence form. Differently from other adversarial techniques adding small perturbations that cannot be easily detected by human eyes but can be easily recognized by softwares, our filter composition cannot be distinguished from any other filter composition used extensively every day to enhance photos and images.
Knowledge Graph Based Recommender System for an Academic Domain – A Proposal

Recommendation systems has simplified decision making phase in many instances. The paper seeks to help the interested candidate looking to incorporate semantics, knowledge-graph, and ontology. Literature reviews were presented deriving the research question which seeks answers on the question how can we fit-in ensemble learning to ontology in mapping the research scholars with common interest areas of research? It has been determined that creating the ontology of the researchers or experts with the highest acceptance and frequency of engagement is the most acceptable approach. As a result, we propose a system that first establishes the cluster, then discovers the highest-ranking member in the cluster, and then creates ontologies based on that to recommend to new users, and then exposes them to the public domain as RDF using the neo4j plugin. This is a proposal only and further exploration is required.
Divergent Global Views on Social Media, Free Speech, and Platform Regulation: Findings from the United Kingdom, South Korea, Mexico, and the United States

Citizens and policymakers in many countries are voicing frustration with social media platform companies, which are, increasingly, host to much of the world’s public discourse. Many societies have considered regulation to address issues such as misinformation and hate speech. However, there is relatively little data on how countries compare precisely in terms of public attitudes toward social media regulation. This report provides an overview of public opinion across four diverse democracies – the United Kingdom, South Korea, Mexico, and the United States – furnishing comparative perspectives on issues such as online censorship, free speech, and social media regulation. We gathered nationally representative samples of 1,758 (South Korea), 1,415 (U.S.), 1,435 (U.K.), and 784 (Mexico) adults in the respective countries. Across multiple measures, respondents from the United States and Mexico are, on the face of it, more supportive of freedoms of expression than respondents from the United Kingdom and South Korea. Additionally, the United Kingdom, South Korea, and Mexico are more supportive of stricter content moderation than the United States, particularly if the content causes harm or distress for others. The data add to our understanding of the global dynamics of content moderation policy and speak to civil society efforts, such as the Santa Clara Principles, to articulate standards for companies that are fair to users and their communities. The findings underscore how different democracies may have varying needs and translate and apply their values in nuanced ways.
Research on the Construction of Innovation and Entrepreneurship Evaluation System under the Background of "Internet Plus"

Abstract: Innovation and entrepreneurship based on "Internet Plus" is of great significance to the educational ecosystem of universities and even the national economic development system. Accurate evaluation of the innovation and entrepreneurship system in the context of "Internet Plus" is the basic guarantee for its development. Firstly, based on the actual research results, this paper constructs the resource evaluation index system with material resources, human resources, financial resources, resource allocation of service system and incubation service effect as the core. After obtaining the relevant data of each index, the analytic hierarchy process (AHP) and entropy method were used to assign subjective weight and objective weight to each index respectively. Finally, the comprehensive evaluation index is calculated based on the comprehensive weight, and the level of innovation and entrepreneurship space resources of Internet Plus is evaluated. The method proposed in this paper is used to evaluate object resources. The results show that the evaluation system can accurately evaluate the resource status of the research object. Based on the evaluation results, the development trend of the research object can be analyzed from the time dimension and space dimension, which is conducive to the development and management of the research object.
Overview of Block Chain Privacy Protection Methods

Nowadays, as a decentralized, secure and trustworthy, collectively maintained non-tamperable distributed database, blockchain has become an important means to protect private data, but at the same time, these characteristics cause privacy and security issues, so the privacy of blockchain Protection has become an important research direction in the current research on blockchain. This article summarizes some privacy protection strategies, analyzes the advantages and disadvantages of these strategies, and finally discusses the directions for future research on these privacy security protection strategies.
Indian Make Portable and Real-Time Shearography System (IMPRESS) for Non-destructive Testing (NDT)

Laser shearographic interferometry is a well-proven NDT technique for detecting defects in various structures and mechanical components. There is only a countable number of shearography systems available commercially. This paper highlights the development of an indigenous low-cost digital laser shearography system, namely, IMPRESS (Indian Make Portable and Real-time Shearography System). Herein, the details of the system are explained with its online and in situ capability. Moreover, a few specimen-level results are shown to demonstrate the application of the system. The developmental cost of this system is much lower as compared to commercial systems. Therefore, it would be beneficial for small and medium-scale Indian industries that are desirous of using shearography as an NDT tool in their production line. Currently, the system is being upgraded to make it on par with other commercial systems.
A Stakeholder-Centric Approach for Defining Metrics for Information Security Management Systems

An enterprise comprises of information processing systems that help realize its business processes. Automation of these systems is achieved with the help of IT assets like hardware, software and network devices. Assets and their interconnections may contain vulnerabilities, which can be exploited by threats, leading to breach of security of information and business processes. Such probable security risks are managed by implementing an Information Security Management System (ISMS). An important aspect of ISMS is the measurement of information security posture of the enterprise; this enables the comparison of information security status over time, and provides assurance to stakeholders about the amount of security that exists within the information processing systems. Different stakeholders have separate concerns regarding the security of an Enterprise IT System. This paper attempts to identify all such stakeholders and analyze their security concerns. A set of metrics has been defined that covers all facets of ISMS and addresses security concerns of all categories of stakeholders. This would help in the design of an effective and efficient ISMS.
3D Instance Segmentation Using Deep Learning on RGB-D Indoor Data

3D object recognition is a challenging task for intelligent and robot systems in industrial and home indoor environments.It is critical for such systems to recognize and segment the 3D object instances that they encounter on a frequent basis.The computer vision, graphics, and machine learning fields have all given it a lot of attention.Traditionally, 3D segmentation was done with hand-crafted features and designed approaches that didn't achieve acceptable performance and couldn't be generalized to large-scale data.Deep learning approaches have lately become the preferred method for 3D segmentation challenges by their great success in 2D computer vision.However, the task of instance segmentation is currently less explored.In this paper, we propose a novel approach for efficient 3D instance segmentation using red green blue and depth (RGB-D) data based on deep learning.The 2D region based convolutional neural networks (Mask R-CNN) deep learning model with point based rending module is adapted to integrate with depth information to recognize and segment 3D instances of objects.In order to generate 3D point cloud coordinates (x, y, z), segmented 2D pixels (u, v) of recognized object regions in the RGB image are merged into (u, v) points of the depth image.Moreover, we conducted an experiment and analysis to compare our proposed method from various points of view and distances.The experimentation shows the proposed 3D object recognition and instance segmentation are sufficiently beneficial to support object handling in robotic and intelligent systems.
Efficient management of files in the cloud using a desktop application

Cloud computing is a computing service that includes servers, storage, databases, networking, software, etc., over the internet to offer faster innovation, flexible resources, and economies of scale. Utilizing dynamic resource allocation for load balancing is considered a vital optimization process in cloud computing. Here, we used a virtual machine serving maximum resource efficiency and scalability. Also, the creation of a desktop application for managing files in the cloud gives higher scope. The resource slicing and allocation problem use two-time scales, including an extensive period for inter-slice resource pre-allocation and a small-time slot for intra-slice resource scheduling. The application consists of two major modules - admin and users. The admin can upload the files, view user details, view log history, view storage, and download details. Also, the admin can approve the files for download on request. The user can register their account, view or update their details, view the files in the cloud, request and download a file from the cloud. The application uses a cryptographic algorithm for ensuring authentication and scheduling algorithm for allocating files to the cloud in an optimal way. The priority for the files is given based on the Grey Wolf Optimization Algorithm and Priority Based Optimization technique. The focus is given to the highly requested file to reduce the time of approval. For cloud storage services, the storage resources become virtualized and can form a storage pool. The cloud storage services use the internet to allow the data to be accessed and backed up on the local storage. Here, we use three cloud storage for file allocation. As a result, this application is better and efficient for write-in under low-cost circumstances.
Are the Scala Checks Effective? Evaluating Checks with Real-world Projects

Static analyzers can assist developers in detecting flaws and improving software quality. An analyzer often has numerous checkers, each of which implements a different checking rule. These checks can create a lot of warnings in real-world projects, putting a lot of pressure on programmers to examine them. Thus, it is critical to assess the effectiveness of these checkers before putting them to use. Typically, time-consuming questionnaires or human assessments of the warnings are employed to evaluate the checkers, which results in inefficiency when applied to real-world work. The significance and accuracy of checkers are the topics of this research, with the first reflecting the developers' attention to the checkers and the second reflecting the false-positive rate. We focus on Scala checkers in particular because, despite the popularity of the Scala programming language, there has been little study on them. We propose a method for tracking warnings in real-world projects and assessing the two features for each checker. We use 115 checks and six well-known Scala apps to demonstrate our approach. Based on the 191k warnings delivered by these checkers, the approach can identify 154k false positives, and it finds that only around 1/5 of the checks can benefit developers.
E-Services Cybersecurity Assessment Model

With the increasing importance of e-services in cyberspace, cybersecurity is essential for safety and trustworthiness. This paper presents a cybersecurity assessment model that can be used to determine the current level of protection of e-services and to support its future development. The model is distinguished by its comprehensiveness, flexibility, and usability. In terms of comprehensiveness, the model integrates various cybersecurity measures recommended by international and national organizations and by academic researchers. In terms of flexibility, it structures these measures into the four-domain framework of technology, organization, people, and environment. The model also structures the assessment into levels based on the dimensions of importance, strategy consideration, and practical implementation. The usability of the model is illustrated by its application to three different Saudi organizations concerned with e-services. This work will be useful to researchers for the future development of cybersecurity assessments, and to professionals for practical applications.
Handwriting Digit Generation Based on GAN Model

Over the past decades, generating pictures of handwritten digits has always been a challenging task. However, most neural networks do not have the function to imitate the real samples of the training set. By contrast, the Generative Adversarial Network (GAN) can randomly sample from the latent space and output results that mimic the real samples in the training set. In this work, we take advantage of the excellent characteristics of GAN and then realize the generation of handwritten digital pictures through the learning of the MNIST data set. The experimental results demonstrate that the GAN model achieves superior accuracy and robustness of handwritten digits generation. Furthermore, we note that the network parameters could be adjusted constantly through the formation of new adversarial data sets, which could enhance the accuracy of picture identification dramatically.
A Robust and Efficient Numeric Approach for Relational Database Watermarking

Sharing relational databases on the Internet creates the need to protect these databases. Its output in substantial losses to the data storing systems because of unauthorized access to information that could lose novelty. The research associations use the research databases to mine new information about the research works of the relational databases that are available for free. It is a great challenge to maintain authenticity because these databases are vulnerable to security issues. Watermarking is a candidate solution that fully protects databases shared with the receiver. The protection of relational database ownership that may continue to evolve against the various aquatic mechanisms shared with the recipient that arouses appetite for attacks and must continue to evolve so that they can have database knowledge to support their decision-making system is effective. The relational database based onVirtual private key Watermarking using numeric attribute) involves embedding the same watermark in the same properties in different places in the same place. Therefore, data attackers cannot remove watermarks from data. The proposed strategy is to work by inserting watermark bits in such a way that it causes minimal distortion in the data and the data usability must remain intact after the data is watermarked. The proposed strategy is to work by inserting watermark bits in such a way that it causes minimal distortion in the data and the ability to use the data after watermarking the data must remain intact. The existence of a primary key is the main feature or compulsory item for most of the strategies. Our method provides solutions no primary key feature where the integrating search system of the database remains intact after watermarking distortion.
Grouping Public Complaints in the City of Tangerang Using K-Means Clustering Method

The government's efforts in developing electronic-based government services by utilizing information technology are referred to as the concept of e-government. Tangerang City is one of the cities that applies e-government in an application called Tangerang LIVE. In the Tangerang LIVE application, a LAKSA feature is used as a place for complaints from the people of Tangerang. This research was conducted to classify complaint data and determine the priority of groups of complaints received from the LAKSA feature. The technique used to conduct this research is clustering using the unsupervised learning method and the k-means algorithm, which will classify and predict the class for each document. In addition, an analysis of the priority complaint data was carried out based on the group that was received the most. The analysis carried out is to find out the class predictions for each complaint received, and then labeling will be given so that the complaint belongs to a more specific group. The results of the predictions will be displayed in the browser using web services.
Temporal Event Reasoning Using Multi-source Auxiliary Learning Objectives

Temporal event reasoning is vital in modern information-driven applications operating on news articles, social media, financial reports, etc. Recent works train deep neural nets to infer temporal events and relations from text. We improve upon the state-of-the-art by proposing an approach that injects additional temporal knowledge into the pre-trained model from two sources: (i) part-of-speech tagging and (ii) question constraints. Auxiliary learning objectives allow us to incorporate this temporal information into the training process. Our experiments show that these types of multi-source auxiliary learning objectives lead to better temporal reasoning. Our model improves over the state-of-the-art model on the TORQUE question answering benchmark by 1.1% and on the MATRES relation extraction benchmark by 2.8% in F1 score.
CORONA RUSH: A 3D Platformer Pandemic Based Runner Game

To present a platformer game for android, while opting for an optimized build for midrange devices using the Unity game engine. The game is in 3D side scrolling perspective, with tappable UI controls. The design of game is based on the 2019 Coronavirus Pandemic and aims to spread awareness about the safety protocols during the pandemic - like the use of face mask, face shields and taking vitamins through playing, especially for younger people. The development process tried a modular approach with the development of the stages and are coded with optimization in mind. Results for the evaluation received for the gameplay and visuals are positive with minor improvements in mind.
Hash-Comb: A Hierarchical Distance-Preserving Multi-Hash Data Representation for Collaborative Analytics

Data privacy regulations like the EU GDPR allow the use of hashing techniques to anonymize data that may contain personal information. However, cryptographic hashing is well-known to destroy any possibility of performing analytics. Homomorphic crypto-systems allow computing analytics over encrypted data, but cannot guarantee privacy compliance without being coupled with specific privacy-preservation provisions. In this work, we present a novel distance-preserving hashing scheme supporting both regulatory compliance and collaborative analytics. Our scheme achieves regulatory compliance by relying on standard cryptographic hashes while preserving a controllable notion of distance between data points.
Hand Written Devanagari Script Short Scale Character Recognition

India is a country of various languages right from Kashmir to Kanyakumari. The national language of India is Hindi which is also the third most popular language in the world. The script in which the Hindi language is written is known as Devanagari script which in fact is used to write many other languages such as Sanskrit, Marathi, Nepali, and Konkani languages. Neural networks are recently being used in several different ways of pattern identification. It is common knowledge that every person’s handwriting is dissimilar. Therefore, it is challenging to recognize those handwritten monograms. The sector of pattern recognition that has become a hot topic for research purposes is handwritten character recognition. This is where neural networks play an important role. The competence of a computer to take in and decipher comprehensible transcribed input whose origin is paper documents, touch screens, photographs, and alternative gadgets are termed as handwriting recognition. Handwritten recognition of words is a model which is used to convert the written text into words that are crucial in the human computer interface. The handwriting recognition area is an extensively experimented branch till date and the Devanagari script recognition is progressing area of research. The above application is used in mail sorting, office automation, cheque verification, and human computer communication, i.e. the growing age of artificial intelligence. A sample of the dataset of images which are centralized and grayscale are considered and analysed using the K-nearest neighbour classification, extremely randomized decision forest classification and random forest classification are considered.