Reviewerly/exHarmony huggingface dataset:
Influence of Color Spaces for Deep Learning Image Colorization

Colorization is a process that converts a grayscale image into a colored one that looks as natural as possible. Over the years this task has received a lot of attention. Existing colorization methods rely on different color spaces: RGB, YUV, Lab, etc. In this chapter, we aim to study their influence on the results obtained by training a deep neural network, to answer the following question: “Is it crucial to correctly choose the right color space in deep learning-based colorization?” First, we briefly summarize the literature and, in particular, deep learning-based methods. We then compare the results obtained with the same deep neural network architecture with RGB, YUV, and Lab color spaces. Qualitative and quantitative analysis do not conclude similarly on which color space is better. We then show the importance of carefully designing the architecture and evaluation protocols depending on the types of images that are being processed and their specificities: strong/small contours, few/many objects, recent/archive images.
PRODUCTION OF PRE-INTERVIEW CHECK IN CASES OF FRAUD: TACTICAL FEATURES, PROBLEMS AND WAYS TO SOLVE THEM

Technological progress, modernization of entrepreneurial activity, applying new ways of earning, business, the development of economic relations has led to increased interest of fraudsters. Traditional types of fraud, such as the promise of employment of the victim, did not assume such a geography of the crime and the circle of victims, as with modern types of fraud using information and communication technologies, the Internet, software, social messengers. This article discusses the procedural procedure for conducting pre-investigation checks on modern types of fraud, as well as the tactical features of its production. To solve the difficulties in the investigation of modern forms of fraud, such as masquerading as civil law torts, criteria for their delineation have been developed. It also provides a list of issues and circumstances to be established during the pre-investigation inspection, as well as the procedure for the seizure, inspection and registration of new types of evidence. Despite the computerization of all spheres of society, applying new types of fraud, the identification of new types of evidence and traces (electronic), the procedure for the seizure, inspection and registration of electronic evidence is not regulated procedurally (there is no concept of «electronic evidence»), as well as there is no unified approach to the production of certain investigative actions for detection and the withdrawal of such evidence. The article presents the opinions of scientists regarding the conceptual apparatus of «electronic evidence», as well as on the tactics of seizure, inspection and fixation of electronic evidence using modern technologies and software. In conclusion, proposals for amendments and additions to the criminal procedure legislation were put forward.
ISSUES OF CRIMINAL LAW REGULATION OF LEGALIZATION OF PROCEEDS FROM CRIMINAL ACTIVITY AND IMPROVEMENT OF LAW ENFORCEMENT ACTIVITIES OF CUSTOMS AUTHORITIES IN THE FIGHT AGAINST THESE CRIMES

The article analyzes the economic reforms carried out in the Republic of Uzbekistan, in particular, aimed at liberalizing the foreign exchange market, issues of responsibility in the criminal legislation of the Republic of Uzbekistan and foreign countries for crimes related to the legalization of proceeds from criminal activity. The criminalistic characteristics of this crime are also considered. In particular, the legalization of proceeds from criminal activity is widespread and refers mainly to latent crimes. As the analysis has shown, the methods of committing these crimes are constantly changing, becoming more complicated, using loopholes in legislation and committed through various criminal schemes. This criminal activity is constantly connected with regional, national and global markets, including those markets that are prohibited for sale. Criminal groups are actively diversifying their activities and creating their own circle. This means that they are difficult to identify and expose. In addition, foreign experience and existing international standards for combating this crime are analyzed, some proposals are also presented on the role of customs authorities in countering the legalization of proceeds from criminal activity, as well as on improving the effectiveness of criminal law regulation of this act and improving the law enforcement activities of customs authorities.
Fast algorithm for computation the parameters of s-boxes that determine the security of SNOW 2.0-like stream ciphers against correlation attacks over extension fields

The security of SNOW 2.0-like stream ciphers against a wide class of correlation attacks can be evaluated by values of some numerical parameters of s-boxes used in these ciphers. We propose a fast algorithm that computes the values of these parameters. The proposed algorithm is based on the fast Hadamard transform and has significantly lower time complexity compared to the previously known ones. We also show experimentally, using the Monte Carlo method, that for most random 8 x 8 s-boxes the values of the considered parameters ensure an appropriate security level of SNOW 2.0-like stream ciphers against known correlation attacks.
ACADEMIC CHEATING: CORONAVIRUS PANDEMIC NEGATIVE IMPACT ON HIGHER EDUCATION

The phenomenon of academic cheating/e-cheating is not new, but due to the massive growth of higher education and development of ICT in recent years, the problem is still relevant for the higher education system. In the context of distance learning, during the pandemic, academic cheating/e-cheating among university students has significantly increased, the range of forms of IT manifestation has expanded, and there is also a tendency to increase students' tolerance for these practices in the learning process. The probability that cheating/e-cheating is widely used by students during their studies raises doubts about the students' academic performance, the quality of training for professional activities, devalues investments into the education by students and their families, undermines the image of the educational institution, etc.The aim of the study is to work out mechanisms (approaches, tools and methods) to prevent the decline in the quality of education and the validity of the assessment of knowledge caused by academic cheating/e-cheating of students, including in the short-and long-term pandemic consequences. Methodology. The paper uses the analysis of empirical and theoretical studies of foreign and Russian authors devoted to the study of the factors of unfair behaviour of students. The empirical basis of the study is the data of surveys of students and teachers of Russian universities. As a result of the study, it was found that academic cheating/e-cheating among students increased in the context of the pandemic; factors that encourage students to resort to unfair means to achieve educational goals were identified. The mechanisms of effective measures to prevent student cheating/e-cheating are proposed, based on both the individual (personal) characteristics of students and the features (characteristics) of the educational environment, including the role of the teacher, fellow students, the educational environment, software and technical support, etc.
ARE COMPANY SOFTWARE MANAGEMENT PRACTICES SUITED FOR VOLUNTARY STUDENT ORGANIZATIONS?

Developing software inside an organization can be a valuable experience for students in their early careers as software engineers. Delivering code written by more than one person means extra work on organizing, communicating, tracking progress, and assessing results. As skills like team management, leadership, teamwork, and communication are not taught as a main course in computer science or engineering universities, a good question to ask is how students learn to be effective? An adequate assumption would be that they see successful companies as models. If a corporation developing software projects uses a certain set of tools, schemes, and concepts to organize their work, then the same things should work for student projects as well. However, even if programs and strategies designed for good teamwork and management have the best intentions in mind, this assumption is not always true. This could result in a poor organization with limited performance. As such consequences are to be avoided, a study focusing on identifying good practices for students could establish the needs of voluntary student organizations in order to meet them without the unnecessary corporate overhead. To correctly assess student voluntary organization status, data describing several team management strategies have been collected both via forms and interviews. The following paper presents a research conducted on student voluntary organizations. This work identifies key tools and management practices that are commonly used by teams of students and how they compare to the tools used inside a company with paid employees. The final purpose is to identify good management practices inside student voluntary organizations that can be used as a reference by students when establishing new teams. The paper starts by analyzing each side's motivation for working inside a company/organization, continues with setting key performance metrics for collecting data, and ends with a comparative discussion. This study's contribution represents a series of recommendations of tools and approaches that suit student voluntary organizations the most.
THE EDUCATIONAL CHALLENGES OF DIGITAL TECHNOLOGIES

It is a feature of the last years the proliferation of virtual communities due to the convergence of digital technologies and the use of the computer, respectively of the Internet. The global village promoted by McLuhan more than half a century ago, has undergone transformations in the world class and social relations. This paper proposes an epistemological incursion in the field of virtual communities and the impact of digital technologies on the educational space, in the sense of identifying certain utilities / benefits, respectively limits from the perspective of the teachers as some of the main actors affected. The web has become an indispensable working tool for all educational actors, both in the classroom and outside. Modern technologies offer a wide range of applications, these being the foundation for a new system of education provision and knowledge building. With the development of information and communication technology, the Internet in this case, the classical way of learning undergoes rapid and profound transformations. More and more voices express the fact that we are heading for a revolution in education; the current role of the teacher, that of intermediary of knowledge, loses much of its importance. The training is oriented to the learner, allowing them to choose the content and tools corresponding to their own interests, needs and skill levels. The information society represents a new stage of human civilization, a new way of life, of superior quality, which involves the intensive use of information in all spheres of human activity and existence, with a major economic and social impact. It allows wide access to information of its members, a new way of working and of knowledge, amplifies the possibility of economic globalization and of increasing social cohesion. The information society is the company in which the production and consumption of information is the most important type of activity, the information is recognized as the main resource, the information and communications technologies are the basic technologies, and the information environment, together with the social and the ecological one - an environment of human existence.
DATA SCIENCE AND MACHINE LEARNING TECHNIQUES FOR CASE-BASED LEARNING IN MEDICAL BIOENGINEERING EDUCATION

Data science and artificial intelligence (AI) are the main factors driving a new technological revolution. Just recently (November 2019), key U.S. policymakers have announced intentions to create an agency that would invest $100 billion over 5 years on basic research in AI, with a focus on quantum computing, robotics, cybersecurity, and synthetic biology. The need for well educated people in these areas is growing exponentially, and this is more stringent than ever for medical bioengineering professionals who are expected to play a leading role in the promotion of advanced algorithms and methods to advance health care in fields like diagnosis, monitoring, and therapy. In a recent study on the current research areas of big data analytics and AI in health care, the authors have performed a systematic review of literature and found that out the primary interest area proved to be medical image processing and analysis (587 entries out of 2421 articles analysed) followed by decision-support systems and text mining and analysis. Case-based learning is an instructional design model that is learner-centered and intensively used across a variety of disciplines. In this paper, we present a set of tools and a case study that would help medical bioengineering students to grasp both theoretical concepts (both medical, such as gynecological disorders and technological, such as deep learning, neural network architectures, learning algorithms) and delve into practical applications of these techniques in medical image processing. The case study concerns the automated diagnosis of cervigrams (also called cervicographic images), that are colposcopy images used by the gynecologist for cervical cancer diagnosis, study and training. The tools described in this paper are based on using PyTorch, Keras and Tensor Flow. They allow image segmentation, automated detection of cervix, and cervical cancer classification, while also sustaining an intense interaction between participants to the case study. Based on these tools (for which we describe their distinctive advantages and provide comparisons in terms of accuracy and speed), we describe in full details different teaching strategies.
PROVIDING SEMANTICALLY-ENABLED INFORMATION FOR SMES KNOWLEDGE WORKERS: MULTI-AGENT-BASED MIDDLEWARE

The objective of this research is to present a Multi-agent-based middleware that provides semantically-enabled information for SmES knowledge workers. Market currently offers several instruments for enabling knowledge management such as database management systems, data warehouse, intranet and extranet knowledge portals. However, these technological solutions do not take into account that knowledge management practices in small and medium companies are more congruous with apprenticeship-based learning rather than the formal training typical of big companies. This software is based on the European project E! 9770 PrEmISES and it helps small and medium enterprises to better exploit their information spaces. One central piece is the ontology component. In our days, ontologies are playing an important role. Many computer science domains, including software engineering, online learning, education and knowledge extraction are using ontologies in order to organize and share information in a semantic way. PrEmISES was the capability to couple with the existing data systems that are used by small and medium companies and in this way, to enhance them with a semantic layer/engine. The engine is used to find organizational documents within companies and make the searches more accurate by the use of ontologies. Premises is an improved software with educational purposes, based on the ubiquitous learning paradigm, that uses ontologies in order to find and organize relevant organizational documents, based on the employees work profile. In this way, employees can benefit of a fast-educational process (i.e. online learning) based on their individual work profiles The engine is semantically enriched, meaning that it is searching for the specified words/query plus for semantically related concepts. In this paper we present PrEmISES architecture. We will present the main components and the main steps that were followed in order to develop the ontologies that were used for the ontology component.
Cybercrime Landscape and Changes During the Pandemic in Division 4 ( West North Central

Since its existence in December 2019, COVID-19 has significantly impacted different sectors of the economy in both the physical and digital worlds. In cyberspace, the Covid-19 pandemic has changed the cybercrime landscape. In this study, we focus on data collected by the Federal Bureau of Investigation (FBI) in the West North Central (Division 4) region. Data was collected for each state for different types of crimes and was divided into various age groups. Non-payment and non-delivery have the highest number of victims compared to other types of crimes. Moreover, over the past five years, the over-60 age group had the highest number of victims in Kansas, Missouri, South Dakota, and Minnesota. However, the number of elderly cybercrime victims ranked second compared to other age groups in Iowa and Nebraska and third in North Dakota.
Comparison of Text Classification Methods Using Deep Learning Neural Networks

In this article, we tend to examine the text classification task by using various neural networks. A small number of previously classified texts can change the accuracy of the studied text classifiers. This is often vital in many text classification applications because an oversized range of uncategorized data is effortlessly reachable. However, getting an annotated text is a quite challenging task. The article additionally demonstrates that the Convolution Neural Network (CNN) does not demand semantic or syntactic knowledge and can perform in a better way on a words level. Secondly, a Recurrent Neural Network (RNN) model can effectively classify the text data (sequence type). RNN outperforms the other Neural Networks for the sequence test classification task. We used corpora of two different types from separate sources (IMDB and self-created bloggers corpus). The results of our experiments provide evidence that vector representation of the text can improve the score of the task.
An Empirical Data Selection Schema in Annotation Projection Approach

Named entity recognition (NER) system is often realized using supervised methods such as CRF and LSTM-CRF. However, supervised methods often require large training data. In some low-resource languages, annotated data is often hard to obtain. Annotation projection method obtains annotated data from high-resource languages automatically. However, the data obtained automatically contains a lot of noise. In this paper, we propose a new data selection schema to select the high-quality sentences in annotated data. The data selection schema computes the sentence score considering the occurrence number of entity-tags and the minimum scores of entity-tags in sentences. The selected sentences can be used as an auxiliary annotated data in low resource languages. Experiments show that our data selection schema outperforms previous methods.
EASY: Evaluation System for Summarization

Automatic text summarization aims at producing a shorter version of a document (or a document set). Extractive summarizers compile summaries by extracting a subset of sentences from a given text, while abstractive summarizers generate new sentences. Both types of summarizers strive to preserve the meaning of the original document as much as possible. Evaluation of summarization quality is a challenging task. Due the expense of human evaluations, many researchers prefer to evaluate their systems automatically, with help of software tools. Automatic evaluations are usually performed to provide comparisons between a system-generated summary and one or more human-written summaries, according to selected measures. However, a single metric cannot reflect all quality-related aspects of a summary. For instance, evaluation of an extractive summarizer by comparing, at word level, its summaries to the abstracts written by humans is not good enough. This is so because the summaries being compared do not necessarily use the same vocabulary. Also, considering only single words does not reflect the coherency or readability of a generated summary. Multiple tools and metrics have been proposed in literature for evaluating the quality of summarizers. However, studies show that correlations between these metrics do not always hold. In this paper we present the EvAluation SYstem for Summarization (EASY), which enables the evaluation of summaries with several quality measures. The EASY system can also compare system-generated summaries to the extractive summaries produced by the OCCAMS baseline, which is considered the best possible extractive summarizer. EASY currently supports two languages–English and French–and is freely available online for the NLP community.
Adversarial Training Based Cross-Lingual Emotion Cause Extraction

Emotion cause extraction (ECA) aims to identify the reasons behind a certain emotion expression in a text. It is a key topic in natural language processing. Existing methods relies on high-quality emotion resources and focuses on only one language. However, the public annotated corpora is fairly rare. Therefore, we propose an adversarial training based cross-lingual emotion cause extraction approach to leverage the semantic and emotion knowledge in a resource-abundant language (source language) for ECA in a resource-scarce language (target language). Instead of large-scale parallel corpora, we capture task-related but language-irrelevant features only on a small-scale Chinese corpora and an English corpora. In addition, an attention mechanism based on position and emotion expression information is designed to obtain the key parts of the clause devoting to ECA. Our proposed approach could capture rich semantic and emotion information in ECA learning process. It is demonstrated that our method can achieve better performance than the state-of-the-art results.
Sentiment Analysis Through Finite State Automata

The present research aims to demonstrate how powerful Finite State Automata (FSA) can be, into a domain in which the vagueness of the human opinions and the subjectivity of the user generated contents make the automatic “understanding” of texts extremely hard. Assuming that the semantic orientation of sentences is based on the manipulation of sentiment words, we built from scratch, for the Italian language, a network of local grammars for the annotation of sentiment expressions and electronic dictionaries for the classification of more than 15,000 opinionated words. In the paper we explain in detail how we made use of FSA for both the automatic population of sentiment lexicons and the sentiment classification of real sentences.