Reviewerly/exHarmony huggingface dataset:
Analyzing and Utilizing Thematic Stocks based on Text Mining Techniques and Information Flow-Based Networks: An Example of the Republic of Korea’s Mask-Themed Stocks

In this study, based on the theme sentiment index (TSI) derived from text mining techniques, we measured the causal relationship from the theme sentiment index to the stock price’s abnormal returns for detecting thematic stocks. We selected mask-themed stocks as the experiment subject and set 20 candidate stocks as candidates, considering the frequency of appearance as the associated search term for a mask. Then, we collected search volumes for the keyword “mask” and related keywords from December 1, 2016, to November 30, 2020, to construct the TSI. In addition, we scraped 15,337 text data, such as articles, editorials, and economic broadcast scripts. We also used the abnormal return data of selected 20 stocks derived from the market model. Results show that 19 stocks have statistically significant causal relationships from the TSI to the abnormal returns of their stock prices when the effective transfer entropy is used. We constructed a thematic stock network using the 19 stocks to detect their inner causal relationships. Network-and node-level measures were measured for the constructed thematic stock network for selecting core stocks in the thematic stock network. In addition, two experiments were conducted using the configured thematic stock network. Results confirmed the thematic stock network’s change in behavior and interconnectivity and confirmed that abnormalities such as listing stock misinformation could be detected and empirically analyzed.
Embedding Chinese Face Painting Into the StyleGAN Latent Space

We propose an efficient algorithm to embed China&#x2019;s traditional Chinese painting into the latent space of StyleGAN for the first time in the presence of noise. This embedding allows us to complete the pluralistic image editing of Chinese painting style faces by using only the StyleGAN pretraining face model without the need to retrain new models. Compared to the previous reverse network StyleGAN-Encoder, the new training model can improve the image generation speed under noise by 10% and FID by approximately 21%. We for the first time propose the application of the deep residual shrinkage networks to the image generation problem and verify the effectiveness of the proposed method through experiments on various noises.
Development of creative thinking of a student by means of staging activities at universities of culture and arts

The issues of the development of creative thinking of a student by means of staging activities in the universities of culture and arts are considered. A number of prerequisites for the development of creative thinking of a student are revealed. The features of the structure of creative thinking in foreign and domestic studies are analyzed. The staging activity of a student in the universities of culture and arts, implemented as part of the study of a number of disciplines “Composition and Staging of Dance”, “Staging Workshop”, “Skill of a Choreographer”, etc., is characterized. As a result of the study, the types of thinking that, in the context of various approaches, are decisive, that is, dominate in the process of creating choreographic compositions, are considered, among them: figurative, spatial, associative, kinesthetic thinking. A brief description of the block of creative problem tasks, etude works aimed at the development of creative thinking of a student at universities of culture and arts.
Emotion Classification for Amharic Social Media Text Comments Using Deep Learning

Text emotion classification is one of the methods used to organize massively available texts into a predefined category to maximize the utilization of information in social media. From previous research it is noted that a lot of work has been done for different languages. However, to the best of our knowledge, there is no work done for the Amharic language. This work aimed to develop the data and emotion classification models for Amharic social media text comments using deep learning approaches. We have annotated 14,716 emotion text comments into seven different emotion classes. We apply deep learning approaches namely: LSTM, BiLSTM, CNN-BiLSTM, BiGRU, and CNN models with Word2vec and RoBERTa pretrained embedding models. After experimentation and evaluation, the result shows that the RoBERTa embedding with CNN model performs better with 86.0% accuracy and 85.9% f1 score. For the future, we recommend increasing the data and adapting different contextualized pre-trained embeddings for the effectiveness of the proposed deep learning approaches.
Virtual Criminality: Examining the Successes and Pitfalls of the Fight against Sexual Abuse of Children on the Internet through a Select American, European and African Nations

The world has become a global village where activities hosted in one jurisdiction may be accessed by a visitor on the internet site from another jurisdiction from where it is hosted. However, one of the downsides of this technological advancement is its adoption for disseminating information and pictures that are inimical in respect for children, namely sexual abuse of children of which child pornography is a key element. This is an online business where perpetrators make humongous profits at the detriment of the children depicted in the pornographic materials. The key objective of this paper therefore is to interrogate the efforts of the United States, Canada, United Kingdom, Republic of Ireland, Ghana and Nigeria as well as the international efforts made so far at curbing the rise in such online activities and to find out the success or otherwise of such efforts. The paper adopts the doctrinal methodology to discover the efforts made so far and the factors, if any, militating against the success of the efforts. The study finds that there have been significant interventions through regional conventions and domestic legislation to arrest the scourge but, the impacts of these efforts have been greatly eroded by jurisdictional issues and absence of uniformity in the definition and scope of child sexual abuse. The paper finally recommends the synergy of enforcement mechanisms among nations where the crime is being perpetrated. It also recommends a comprehensive national campaign as well as parental control of their children’s activities on the internet.
A Hybrid Automatic Text Summarization Model for Judgment Documents

Judgment documents are the final carrier of judicial trial activities, and they are an indispensable component for assisting sentencing decision-making and standardizing the scale of judgment. At present, the number of public judgment documents in China has reached 120 million, and it does not stop there, which brings a significant challenge for users to obtain useful information. To solve this problem, this paper proposes a hybrid automatic text summarization model for judgment documents. The method is divided into two stages. The first stage is, through extracting abstract technology, to extract key sentences from the original text to form a set of them. In the second stage, the set of key sentences extracted in the previous stage will be copied or rewritten to generate the final summary using the sequence generation model. The ROUGE indexes of this method in the automatic summarization experiment of judgment documents are 59.79, 37.71, and 52.67, which are 5.26, 7.25, and 10.41 higher than the benchmark model UniLM, respectively. The method proposed in this paper can effectively be applied in the automatic summarization service of judgment documents and solve the problem of information overload thereof and, finally, provide a new way for users to gain smooth access to judgment documents and information.
Architectural Insight of Neural Information Extraction, Retrieval, and Processing for Multimodal Neural Search

In the growing world of digitization, digital media is engendered in abundance. With the ascension of the utilization of the Internet, there has been a prodigious increase in the engendering of digital content which includes images, audio, video, and documents such as pdf and text data. Information is free and more accessible than in any other era of humanity. Due to such a cognizance explosion, there is a vigorous need to make it more accessible. This can be achieved with semantic search. The quandary of processing, indexing, and storing such content has grown exponentially. At the same time, the infrastructure to handle such length has to be efficient and scalable. The current scenario of erudition explosion resulted in sizably voluminous data having a high performant scalable and resilient architecture which can parallelly process this multimodal binary file, can be gamely transmuting, and is becoming a requirement of the future. Different from the subsisting approaches that design handcrafted and task-concrete architectures for neural search to address only a single task, our architecture is tuned to handle multimodality which fundamentally denotes those data types (modalities) that can be audio, video, documents, images. This paper discusses the solution available to make digital content more accessible which is engendered as a result of the cognizance explosion. The proposed architecture will explore the domains of information extraction from this digital media securely and efficiently with various deep learning approaches for some categorical use cases.
The Use of a Robotic Module for Monitoring and Diagnostic Systems for Various Applications

This article describes the solution of scientific and technical problems that arise when integrating a robotic module into monitoring and diagnostic systems when solving problems of various directions and varying complexity, which ensures an increase in the efficiency of processing and transmission of information, an increase in reliability and survivability, and an expansion of interaction opportunities (“openness”) monitoring systems through the use of “digital economy” technologies (“fog” and “edge” computing, distributed registry, big data) and resource reconfiguration.
Smart and Intelligent Health Monitoring System

The new era of smart devices and mobile health care has brought several opportunities to the public. Alongside, the worldwide urbanization process is bringing formidable challenges and issues in the same field. In this research work, we have proposed a system that the elder people or the person with a chronic disease can wear a health tracker wearable device equipped with a number of sensors for cardiovascular activity rate, blood pressure measurement, and temperature degree. This device will also be equipped with a small LED display module that will show immediately the detected values. Furthermore, the signals collected will be sent in real time to a back end through the Internet. The back end is a Web application having few artificial intelligence features in order to detect as soon as possible the eventually infected people. Machine learning will process these data and generate predictions and the risk level.
Two-stage pricing strategy for personal cloud storage: free trial and the cloud security risk

A common approach to attract potential cloud users is to offer a two-stage pricing strategy to enhance the perceived value. This value is based on the willingness of diverse cloud users to pay and the market stages of cloud storage providers (CSPs). However, the risk of breaching cloud security in the free trial stage due to the sharing of personal cloud storage products (PCSPs) may decrease the number of cloud users. Consequently, the study proposes a two-stage pricing strategy to make a trade-off between the cloud security risk and the perceived value. Furthermore, it aims to uncover the conditions under different security risk coefficients. It selects the optimal pricing strategy to help CSPs determine the free trial time and optimal price. Our findings can provide helpful insight in devising an optimal pricing strategy by CSPs considering security risks, which assures the safety of PCSPs and benefits CSPs by increasing profits.
Study of the quality of the municipal environment for the development of pedagogical personnel: qualimetric approach

In modern conditions of modernization of educational processes improvement of the quality of education, the requirements for ensuring the conditions for the development of human resources in the field of education are significantly increasing. This task is especially relevant for municipal territories that have different opportunities and conditions to support and accompany teaching staff. To analyze the potential of such territories, the classification of educational systems proposed by L.V. Rezinkina was used. An essential link has been established between the comprehensive capabilities and potential of existing educational systems (centers, methodological services, branches of institutes, etc.) and their effectiveness in the organization of continuing education of teachers. One of the mechanisms for evaluating this effectiveness is the implementation of the qualimetric approach in the system of assessing the quality of the municipal environment of teacher’s development. The article shows that the qualimetric approach as the leading direction of building scientific knowledge and practice of organizing evaluation activities is based on the identification of criteria, indicators and levels of development of a specific pedagogical system. Designing a system of continuing education of teachers based on the results of the qualimetric approach allows identifying gaps in the structure of municipal education, strengthening or introducing new components, and foreseeing possible risks.
PTAC: Privacy-Preserving Time and Attribute Factors Combined Cloud Data Access Control with Computation Outsourcing

Cloud storage service has significant advantages on both cost reduction and convenient data sharing. It frees data owners from technical management. However, it poses new challenges on privacy and security protection. To protect data confidentiality and privacy of users against malicious entities in the cloud, fine-grained data access control in cloud storage has become a challenging issue and draws considerable investigation. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptographic technique to address the above issue. In many scenarios, access policies are associated with privacy and sensitive information of users which needs to be preserved from disclosure. However, existing schemes cannot simultaneously support time-sensitive data publishing and attribute information preservation. In this paper, we propose a privacy-preserving time and attribute factors combined cloud data access control with computation outsourcing scheme (named PTAC). To preserve attribute privacy, we design a dual access policy tree mechanism where one access policy tree is public and another is sensitive and hidden. Moreover, time-sensitive data publishing can be achieved by combining CP-ABE with timed-release encryption. By using edge computing and cloud computing, we also outsource partitive computational cost of encryption and decryption to third parties. Extensive security and performance analysis demonstrate the security and efficiency of our proposed scheme in cloud storage. As a result, valuable attribute information in the access policy can be preserved in case of disclosing to unauthorized recipients.
Interview With Professor Yaakov Bar-Shalom

I am guessing that many of our readers know our subject in this interview very well. Yaakov Bar-Shalom is my University of Connecticut colleague, my collaborator, my mentor, and my friend. One cannot overstate his impact on dynamic estimation, especially that which labors under the burden of measurement-origin-uncertainty (MOU). These may be unusual terms to some readers, but dynamic estimation means filtering (like Kalman) and tracking for prediction and fire-control, and MOU refers to having a “bag” of measurements (like radar plots) and having no idea a priori which are target-generated and which false; and, among the latter, which come from which objects.
A Method of Automatic Social Tension Detection on the Internet Based on Cognitive Analysis and Individual’s Emotional State

The purpose of this study is to develop a method for automatic detection of hotbeds of social tension on the Internet. The main problem with such detection is that currently there is no general methodology for detecting signs of social tension in the digital space, while more and more communication processes are moving to the network. Within the framework of this study, based on the analysis of sources, attributes of social tension were formed that are characteristic of expression in the digital space. Cognitive models and an ontology of attributes were developed, on the basis of which attributes of social tension, characteristic of the initial stages of the development of the situation, were formed. The formed complex of attributes is based on the methods of automatic detection of psychoemotional states of the individual. The developed method of automatic social tension detection on the Internet makes it possible to detect hotbeds of information dissemination that are suspicious from the point of view of intensifying social tension with the aim of further information countermeasures.
Digital Environments to Enable High-Quality Education for Disadvantaged and Disabled Learners in Bulgaria

Digital transformation is one of the European Commission's policy priorities in the last few years. High quality and inclusive education and training are a key part of reaching this object and ensuring that all people are prepared to live and be educated in the digital age. These priorities coincide with the crucial need to provide inclusive digital learning environments to all learners including those with special educational needs – disadvantaged and disabled learners. The paper presents a primary analysis of the results of a sociological qualitative survey conducted in the framework of the project “Inclusive digital environments to enable high-quality education and training for disadvantaged learners”. Methodology: The main method used was an in-depth interview. Thirty in-depth interviews were conducted among School Leaders and Teaching Staff in High Schools and Universities, Primary and Secondary Education Organizations in Bulgaria. The data was collected from October 2021 to January 2022. This study was provoked by inequalities in inclusive education and therefore by variations in the way of remote learning that has been delivered across EU Member States and Bulgaria, in particular. The aim of the study was provoked not only by the exceptional significance and reflection of digital education on basic human needs. It seeks to provide insights into the national special education traditions in the country and aims to establish how these have influenced current developments in inclusive education together with digital environments for disadvantaged and disabled learners. The main conclusion approached by the study is that there is a major need to raise awareness about unequal access to inclusive education for students with disabilities in Bulgarian society, as well as a need of providing а detailed information on how to implement more inclusive educational practices and at sharing promising examples of inclusive methods in education for learners with special educational needs. Keywords: Digital Education, Digital Environments, Special education needs, Disadvantaged and Disabled Learners Acknowledgments: The article has been developed in the framework of the European Funded Project ERASMUS+ EACEA/34/2019 - Social Inclusion and Common Values - Action: the contribution in the field of education and training, entitled: ‘INCLUDEME - Inclusive digital environments to enable high-quality education and training for disadvantaged learners’. The project is co-funded by the European Commission through the Erasmus+ programme.
Automatic Attendance Monitoring System (AAMS)

As We Know Marking Attendance manually is something that takes more time and also leads to distraction of class whilst taking attendance. This form of taking attendance didn’t guarantee the safety of the whole data. So, we automated this system by creating an Automated Attendance Monitoring Application that will manage all the tasks related to attendance marking. This application is based on facial recognition technology which is nothing but identification of a particular person by peculiar features of their faces and it functions as a clear identifier of anyone (Matilda and Shahin in 2019 IEEE international conference on system, computation, automation and networking (ICSCAN), pp. 1–4, 2019[1]). As computer vision (CV) is growing day by day as it is technology that takes images/videos as its input and performs classification of images, detection and recognition of given input images (Deeba et al. in Int. J. Adv. Comput. Sci. Appl. 2019[2]). Its functionality is simply we had to move our camera into room and it will detect the faces and later train the images by our pre-trained model i.e. Haar Cascade Files and then recognizes every face and marks the attendance by matching with stored images data in firebase and stores attendance in csv file and this will save time and hence will avoid fake proxies.
An Ultra-Fast Static Hand Gesture Recognition Method for Finger-Guessing Robots

High-speed processing is a prerequisite for Human-Computer Interaction (HCI). In order to improve the intellectualization and efficiency of finger-guessing robots under complex backgrounds, this paper presents an ultra-fast 2-dimensional static gesture recognition method based on deep learning. The proposed method, termed as GestureNet, adopts MobileNetV2 as the backbone followed by several strategies to promote the overall performance. Firstly, a pre-trained MobileNetV2 model is introduced and trained on an elaborate dataset to efficiently distinguish three different hand gestures. Then the model is selectively pruned and some operators are merged to be lightweight. Moreover, the weight of the optimized model is quantized to integral type to further accelerate the recognition. As a result, the proposed depth model could achieve outstanding performance in practical application. Quantitative experiments verify its ultra-fast running speed and state-of-the-art accuracy.
On the Differences between View-Based and Purchase-Based Recommender Systems

E-commerce platforms often use collaborative filtering (CF) algorithms to recommend products to consumers. What recommendations consumers receive and how they respond to the recommendations largely depend on the design of CF algorithms. However, extant empirical research on recommender systems primarily focuses on how the presence of recommendations affects product demand, without considering the underlying algorithm design. Leveraging a field experiment on a major e-commerce platform, we examine the differential impact of two widely used CF designs: view-also-view (VAV) and purchase-also-purchase (PAP). We find several striking differences between the impact of these two designs on individual products. First, VAV is about seven times more effective in generating additional product views than PAP, but only about twice more effective in generating sales due to a lower conversion rate. Second, VAV is more effective in increasing views for more expensive products, whereas PAP is more effective in increasing sales for cheaper products. Third, VAV is less effective in increasing the views but more effective in increasing the sales of products with higher purchase incidence rates (PIRs). At the aggregate level, we find that PAP generates more sales than VAV for products with low price or moderate PIRs, albeit VAV generates more sales than PAP overall. Our findings suggest that platforms may benefit from employing different CF designs for different types of products.