Reviewerly/exHarmony huggingface dataset:
Predictive Analysis of Resource Usage Data in Academic Libraries using the VADER Sentiment Algorithm

In any educational campus, a well-equipped library is the most essential requirement, as it is a complete storehouse of information - comprising books, magazines, articles, research papers, and other important documents; not only in print but also on digital media. The most prominent change observed in the library’s design of educational institutions in recent days is automation. Library automation allows the librarians easy cataloging of books and maintaining proper records. In big institutions, a pool of resource usage data is generated. This data can be productively used to understand the learning approach of students by professors. Hence, the purpose is to apply sentiment analysis on it for predicting the use of books and resources that would help the qualitative up-gradation of the library. This paper presents the analysis of the data for the renewal of books and resources in the existing work. The progress of the work to date is discussed in the paper.
Economical and User-Friendly Design of Vision-Based Natural-User Interface via Dynamic Hand Gestures

A vision-based hand gesture recognition technology can bring a revolutionary and beneficial change in the field of human-machine interaction for elderly and special people living at home. Nevertheless, continuous detection and localization of the moving hand region in true-color images are strenuous tasks because the hand is a non-rigid object and occupies a small area in the whole frame. True-color images are also sensitive to light variation, camera-view, and background conditions. To ease the process of hand detection and tracking, researchers prefer advanced cameras equipped with costly sensors. This increases the overall cost of interfaces and also requires technical knowledge to operate them. The second issue in dynamic hand gesture recognition is the unpredictability of hand pose used by the user and the random behavior of the hand movement while performing the hand gesture. Therefore, the use of dynamic hand gestures in vision-based human-machine interaction is limited. The goal of this paper is to propose an economical, and user-friendly, technique for vision-based human-machine interaction via dynamic hand gestures that is user-friendly and affordable by all. The proposed technique can be integrated with any day-to-day machines, for example, washing machines, radio, fans, automated doors, etc.
A Foreground-Aware Framework for Local Face Attribute Transfer

In the context of social media, large amounts of headshot photos are taken everyday. Unfortunately, in addition to laborious editing and modification, creating a visually compelling photographic masterpiece for sharing requires advanced professional skills, which are difficult for ordinary Internet users. Though there are many algorithms automatically and globally transferring the style from one image to another, they fail to respect the semantics of the scene and are unable to allow users to merely transfer the attributes of one or two face organs in the foreground region leaving the background region unchanged. To overcome this problem, we developed a novel framework for semantically meaningful local face attribute transfer, which can flexibly transfer the local attribute of a face organ from the reference image to a semantically equivalent organ in the input image, while preserving the background. Our method involves warping the reference photo to match the shape, pose, location, and expression of the input image. The fusion of the warped reference image and input image is then taken as the initialized image for a neural style transfer algorithm. Our method achieves better performance in terms of inception score (3.81) and Fréchet inception distance (80.31), which is about 10% higher than those of competitors, indicating that our framework is capable of producing high-quality and photorealistic attribute transfer results. Both theoretical findings and experimental results are provided to demonstrate the efficacy of the proposed framework, reveal its superiority over other state-of-the-art alternatives.
fabsam @ AMI: A Convolutional Neural Network Approach

The presence of misogynistic contents is one of the most crucial problems of social networks. In this paper we present our system for misogyny identification on Twitter. Our approach is based on a convolutional neural network that exploits pre-trained word embeddings. We also experimented a comparison among different architectures to understand the effectiveness of our method. The paper also described our submissions to both subtasks A and B to Automatic Misogyny Identification competition at Evalita 2020.
Characterful Data

Our project was a ‘Big Data’ project in that it used all available digital sources on English archaeology. This chapter introduces our data and the notion that data are characterful, which is to say that they are shaped and influenced by the ways they are created, curated, and used. We outline our approach to making data compatible, and some of the influences on how evidence is preserved and then discovered and recovered. We aimed to experiment with the data, rather than provide any final or definitive interpretation of them. Sources of information on this scale are susceptible to many interpretations and approaches. However, we also hope to have brought out crucial aspects of the archaeological evidence from England and followed many of the main topic areas currently of interest. We end with an evaluation of our working practices and some influences on our results.
Concept Of Preparing Future Teachers To Strengthen Moral And Spiritual Education

The paper justifies the relevance of the research problem, formulates the object, subject, purpose, tasks, hypothesis, methodological foundations and methods of study.The state of development of the problem is considered, for which a short analysis of sources and various approaches to the problem are carried out.The theoretical and methodological foundations and pedagogical conditions of the preparation of a future teacher for strengthening moral and spiritual education are revealed.The following factors are put forward as pedagogical conditions: integration of educational programs in the compulsory educational discipline Pedagogy and the special course Strengthening moral and spiritual education in the educational process; ensuring that a future teacher learns the criteria of moral and spiritual education and methods of diagnosing its level; inclusion of a future teacher in such types of vocational and pedagogical activities as: designing, modeling, conducting and analyzing a lesson on specific topics within a special discipline from the point of view of strengthening moral and spiritual education.The concept of preparing a future teacher to strengthen moral and spiritual education in the educational process is proposed, as well as the methodology for organizing the experimental part of the study aimed at verifying the effectiveness of the proposed concept.Criteria and indicators of readiness of a future teacher to strengthen moral and spiritual education in the educational process, including cognitive, operational-activity and motivationalattitude components, are disclosed.In conclusion, the fidelity of the hypothesis put forward is confirmed.
Topical issues of practice and science

The content and reliability of the articles are the responsibility of the authors. When using and borrowing materials reference to the publication is required. Collection of scientific articles published is the scientific and practical publication, which contains scientific articles of students, graduate students, Candidates and Doctors of Sciences, research workers and practitioners from Europe, Ukraine, Russia and from neighboring countries and beyond. The articles contain the study, reflecting the processes and changes in the structure of modern science. The collection of scientific articles is for students, postgraduate students, doctoral candidates, teachers, researchers, practitioners and people interested in the trends of modern science development.
Research status of depression related to virtual reality technology in China

This article aims to study the current research status of the application of virtual reality technology in depression in my country. Entering the precise search of virtual reality and depression through the advanced search topic of CNKI, there are 12 results. Read and study the research results of others carefully, and this paper selects 4 more prominent research results for analysis, although the current reality Virtual technology still has some shortcomings, but with the development of science and technology, the application of virtual reality technology in depression and other mental illnesses has been significantly accelerated. Virtual reality technology can improve the depression of patients and improve the coordination and enthusiasm of patients' limbs; can make patients get more psychological satisfaction and joy of life, thereby improving the quality of life.
Facial Recognition: Why We Should Worry About the Use of Big Tech for Law Enforcement

The terrain of the privacy battle in India is changing. What was once a subject of cinematic fiction in Minority Report has now become reality, powered by improvements in computational power and artificial intelligence. Facial Recognition Technology (FRT) and Live Facial Recognition Technology have entered our lives, and are here to stay. This essay argues that rather than improving transparency or efficiency, FRTs end up threatening democracy. It sketches out the privacy and fundamental rights concerns, which arise due to the use of FRT, including free speech and free assembly concerns, and necessitate the application of the proportionality test. The essay adopts the four-pronged proportionality test to evaluate the constitutionality of the use of FRT. This involves a specific consideration of the legality, suitability, necessity, and procedural guarantees surrounding the deployment of FRT by law enforcement agencies. The essay concludes with certain recommendations on the way forward for the use of FRT in India.
Waiter–Client triangle-factor game on the edges of the complete graph

Consider the following game played by two players, called Waiter and Client, on the edges of Kn (where n is divisible by 3). Initially, all the edges are unclaimed. In each round, Waiter picks two yet unclaimed edges. Client then chooses one of these two edges to be added to Waiter's graph and one to be added to Client's graph. Waiter wins if she forces Client to create a K3-factor in Client's graph at some point, while if she does not manage to do that, Client wins. It is not difficult to see that for large enough n, Waiter has a winning strategy. The question considered by Clemens et al. is how long the game will last if Waiter aims to win as soon as possible, Client aims to delay her as much as possible, and both players play optimally. Denote this optimal number of rounds by τWC(Fn,K3−fac,1). Clemens et al. proved that 1312n≤τWC(Fn,K3−fac,1)≤76n+o(n), and conjectured that τWC(Fn,K3−fac,1)=76n+o(n). In this note, we verify their conjecture.
A Framework Based on Latent Neighbourhood and Tensor Based Method for Recommender System

Recommendation systems are highly used in the online shopping websites, movie websites etc. Many techniques are developed for the item recommendation system for the user based on their interest. These techniques are suffering from the cold start and data sparsity problem and have low efficiency. This paper aims to overcome the problem of cold start and sparsity based on the framework of Latent Neighbourhood and Tensor based methods. The latent value combines then neighbourhood similarity value for item and user to solve the cold start and sparsity problem. Secondly, this approach provides effective recommendation for the user with sufficient data. The method is evaluated with error value such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) for analysing the closeness of the prediction. The MovieLen and Epinion benchmark datasets are used to evaluate the performance of the proposed work.
Lifelong Education in Business: Role of Simulation Games in the Teaching Process

The significance of business simulation games (BSG) rapidly increases. And this quite understandable. Because simulation games are based on the model playback mode realistic processes, events, locations or situations. The necessity of BSG in the educational process can be explained on the one hand by the growing demand of the labor market in specialists with practical skills, and on the other hand by growing needs of students to be able to navigate themselves through unfamiliar situations and find their decision in a proper and responsible way, instead of blind copying teachers instructions. BSG offers a broad scope of the forms of competencies students such as an ability to work in group teamwork, find a common language and understand that is globally relevant and informed while providing space to adapt the framework to local contexts.&#x0D; Business simulation games train students create risk-free spaces where everyone can work out specific skills and feel the effects of own decision-making, requiring a certain level of risk.&#x0D; Entrepreneurial activity is pivotal to the continued dynamism of the private sector, as the generation of new businesses fosters competition and economic growth. This is particularly relevant for Azerbaijan, whose entrepreneurship and business innovation levels are low, and it faces a central challenge to create conditions that will facilitate growth in nonoil tradable sectors. Therefore the proposed paper has three objectives: development of teaching resources and tools for delivery of entrepreneurship education. BSG will facilitate strategic problem-solving in life-like simulated business environments and it will be attached to the existing UNEC incubator. BSG should become an integral part of the educational process. Simulation games allow students to practice and improve their entrepreneurial skills in a ‘virtual’ environment by making informed decisions and applying the knowledge and skills acquired in the class.&#x0D; According to OECD LEARNING COMPASS 2030, the educational process should be focused on evolving learning framework that sets out an aspirational vision for the future of education. The wider goals of education open new horizons of orientation towards the tomorrow we want to see. Thinking globally acting locally we will create collective well-being for every society’s member.
Teaching multivariable calculus and tensor calculus with computer algebra software

To go from calculus of scalar functions of one variable to multivariate calculus of vector-valued functions is a steep learning curve for many students. It takes a lot of practice to get used to the new concepts such as the directional derivatives, the di.erentiability, the many types of first order di.erential operators, parameterization of surfaces and the fundamental theorems of integrals, e.g. the Divergence Theorem and the Stokes Theorem. Along the learning process of mastering the skills, the students often need to check whether the intermediate steps in the tedious calculations are correct. Unfortunately, this is beyond the capability of an ordinary calculator and the answers provided at the end of the books. This is where computer algebra software, such as Mathematica, can come to students' help. With the developed symbolic computation tools, the students can tweak a given problem, solve a new one by hand and then check the answer against the result obtained by using the computer algebra software.
Multi-Attribute Learning With Highly Imbalanced Data

Data is one of the most important keys for success when studying a simple or a complex phenomenon. With the use of deep learning exploding and its democratization, non-computer science experts may struggle to use highly complex deep learning architectures, even when straightforward models offer them suitable performances. In this article, we study the specific and common problem of data imbalance in real databases as most of the bad performance problems are due to the data itself. We cover two keys aspects. First, we propose ways to deal with the situation when a data set contains different levels of imbalance. Classical imbalanced learning strategies cannot be directly applied when using multi-attribute deep learning models, i.e., multi-task or multi-label architectures. Therefore, one of our contributions is a proposed adaptation to face each one of the problems derived from imbalance. Second, we demonstrate that with little to no imbalance, straightforward deep learning models work well. However, for non-experts, these models can be seen as black boxes, where all efforts are invested in preprocessing the data. To simplify the problem, we perform the classification task without features that are costly to extract, such as part localization which is widely used in the state of the art of attribute classification. We make use of three widely known attribute databases, CUB-200-2011 - CUB as our main use case due to its deeply imbalanced nature, along with two balanced databases: celebA and AwA2. All of them contain multi-attribute annotations. The results of very fine-grained attribute learning demonstrate that in the presence of imbalance, our proposed strategies make it possible to have competitive results against the state of the art while taking advantage of multi-attribute deep learning models. We also noticed an increase in performance while using a specialized loss function (Focal Loss). For CUB, we have competitive results, and for CelebA and AwA2 our strategies over-perform the state of the art.
Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries.

Password security hinges on an in-depth understanding of the techniques adopted by attackers. Unfortunately, real-world adversaries resort to pragmatic guessing strategies such as dictionary attacks that are inherently difficult to model in password security studies. In order to be representative of the actual threat, dictionary attacks must be thoughtfully configured and tuned. However, this process requires a domain-knowledge and expertise that cannot be easily replicated. The consequence of inaccurately calibrating dictionary attacks is the unreliability of password security analyses, impaired by a severe measurement bias. In the present work, we introduce a new generation of dictionary attacks that is consistently more resilient to inadequate configurations. Requiring no supervision or domain-knowledge, this technique automatically approximates the advanced guessing strategies adopted by real-world attackers. To achieve this: (1) We use deep neural networks to model the proficiency of adversaries in building attack configurations. (2) Then, we introduce dynamic guessing strategies within dictionary attacks. These mimic experts' ability to adapt their guessing strategies on the fly by incorporating knowledge on their targets. Our techniques enable more robust and sound password strength estimates within dictionary attacks, eventually reducing overestimation in modeling real-world threats in password security. Code available: this https URL
Software Fault Estimation Tool Based on Object-Oriented Metrics

A fault is an error that has effects on system behaviour. A software metric is a value that represents the degree to which software processes work properly and where faults are more probable to occur. In this research, we study the effects of removing redundancy and log transformation based on threshold values for identifying faults-prone classes of software. The study also contains a comparison of the metric values of an original dataset with those after removing redundancy and log transformation. E-learning and system dataset were taken as case studies. The fault ratio ranged from 1%-31% and 0%-10% for the original dataset and 1%-10% and 0%-4% after removing redundancy and log transformation, respectively. These results impacted directly the number of classes detected, which ranged between 1-20 and 1-7 for the original dataset and 1-7 and 0-3) after removing redundancy and log transformation. The Skewness of the dataset was deceased after applying the proposed model. The classified faulty classes need more attention in the next versions in order to reduce the ratio of faults or to do refactoring to increase the quality and performance of the current version of the software.
Explainable Graph-Based Search for Lessons-Learned Documents in the Semiconductor Industry

Industrial processes produce a considerable volume of data and thus information. Whether it is structured sensory data or semi- to unstructured textual data, the knowledge that can be derived from it is critical to the sustainable development of the industrial process. A key challenge of this sustainability is the intelligent management of the generated data, as well as the knowledge extracted from it, in order to utilize this knowledge for improving future procedures. This challenge is a result of the tailored documentation methods and domain-specific requirements, which include the need for quick visibility of the documented knowledge. In this paper, we utilize the expert knowledge documented in chip-design failure reports in supporting user access to information that is relevant to a current chip design. Unstructured, free, textual data in previous failure documentations provides a valuable source of lessons-learned, which expert design-engineers have experienced, solved and documented. To achieve a sustainable utilization of knowledge within the company, not only the inherent knowledge has to be mined from unstructured textual data, but also the relations between the lessons-learned, uncovering potentially unknown links. In this research, a knowledge graph is constructed, in order to represent and use the interconnections between reported design failures. A search engine is developed and applied onto the graph to answer queries. In contrast to mere keyword-based searching, the searchability of the knowledge graph offers enhanced search results beyond direct matches and acts as a mean for generating explainable results and result recommendations. Results are provided to the design engineer through an interactive search interface, in which, the feedback from the user is used to further optimize relations for future iterations of the knowledge graph.