Reviewerly/exHarmony huggingface dataset:
Borders and Bridges – Boundaries as an Opportunity

The boundary is an ephemeral concept, an imaginary line that marks the terms of a territory. The term boundary is related to the concept of limit that indicates both a sign not to be exceeded and, by extension, a value that conditions a behavior or a performance. The Italian word "confine" that means boundary comes from the Latin cum-finis; the etymology of the term indicates something that separates but, at the same time, that unites, something that has an end but that creates the presuppositions for something else. The border, therefore, is not only a limit but an opportunity; crossing the border is a gesture that gives us the awareness that nothing is taken for granted, immutable and that everything can change in the relationship with otherness. Cultural wealth is built first in a direct relationship with the material and immaterial resources of a place but also through "contaminations" that come from external knowledge and are acquired and made their own through a local reinterpretation. Globalization has cut the threads of such contaminations, drying the sources of diversity in the project and giving us back the shared universal languages that guide the aesthetics of objects. The recent pandemic has shown us how the creation of virtual connections has contributed to enrich the scenarios of research by giving it a multiplicity of views. The same mode can be used for applied research. Borders and Bridges is a pilot project of exchange between international universities that is born with the aim of developing practices of transdisciplinary cultural contamination in the context of an exchange through the internet between international universities. The design discipline has in its D.N.A the ability to connect and develop proposals that create synthesis between the vision of project activities and that of social sciences. The idea of the project is to work on borders as lines of opportunity for the elaboration of elements of innovation through the tools of the design discipline. The dual objective is, on the one hand, to promote new methods of exchange in the educational field, and on the other to promote the encounter between cultural systems. The design, in fact, is free from borders, "has always had the ability to look at different fields and disciplines favoring cross-contamination" (Cappellieri A. Tenuta L. 2019).
Long short-term memory (LSTM) deep neural networks for sentiment classification

Recurrent neural networks (RNNs) are useful for text data classification problems. However, when a sequence of words in text data has long-term dependencies, RNNs suffer from “vanishing gradient problem” that makes network training difficult for long sequence of words or integers. Long short-term memory (LSTM) neural networks are a special type of RNNs that help overcome this problem and make them possible to capture long-term dependencies between keywords or integers in a sequence that are separated by a large distance. This chapter provides an application example and illustrates steps for using LSTM deep neural network for movie review sentiment classification. The steps include text data preparation, creating LSTM model, training the model, and then assessing the model performance.
A forward and backward secure conjunctive keywords dynamic searchable encryption scheme

Dynamic searchable symmetric encryption (DSSE) allows a user to carryout search and update operations on the outsourced encrypted data on cloud. The recent research work in DSSE exposed many flaws in existing strategies by introducing leakage abuse attacks. To protect DSSE schemes from leakage, forward and backward privacy notions were introduced. In this paper, we propose a forward and backward secure chained dictionary-based indexing mechanism with support for conjunctive keyword queries. The proposed scheme uses a minimal leakage index design, requiring compact storage at client, easier update operations at server and removes entries of deleted files from index to ensure backward privacy resulting in reduced index size. It ensures that the searches followed by update operation do not violate query privacy. Rigorous security analysis and experimental evaluations show that our scheme achieves sub-linear search time and supports efficient update operations ensuring forward and backward privacy.
Interactive Motor Design System Using 2-D Finite Element Analysis With Fast Mesh Modification Method

In this article, we propose an interactive visualization method for motor design. We have proposed a visualization system for education using augmented reality (AR) and virtual reality (VR). As a further development of this, we are working on motor design as an industrial application. There are two issues in achieving high-speed interactivity. One is a fast mesh modification method in finite element analysis, and the other is a fast solving method. We propose a high-speed mesh modification method that utilizes regularly arranged grid points and coordinate transformation. In the proposed design system, the user can immediately observe the analysis result of the design change. Furthermore, if the proposed system is executed in cooperation with the projector, designers can change the design of the motor with their fingers and discuss it on the board. Furthermore, since this system can be executed on a Web browser, it can also be executed on a smartphone or tablet PC.
Necessity of distance learning implementation during the pandemic: modern reality of Ukrainian universities

The article deals with online platforms through which blended learning takes place at Pavlo Tychyna Uman State Pedagogical University on the example of the discipline “Foreign Language” taught to students of the speciality Tourism and Hotel and Restaurant Business. The mechanism of e-learning courses creating in the Moodle information environment system has been analyzed. The advantages of the platform are indicated, in particular free access, ease of use, a large number of activities that increase the motivation and interest of students. At the same time, the difficulties of work in the Moodle information environment system for both students and teachers are highlighted. It is determined that technical and computer training of both teachers and students is important to achieve high positive results. It is recommended to conduct pieces of training and seminars to improve the professional level of foreign language teachers who administer Moodle courses and students who use them.
Principles of Design and Construction

This chapter deals with the design and construction of keyboard instruments and covers topics such as Mersenne’s Law, keyboard compass, scaling, plucking and striking points, string tension, case and soundboard proportion and structure, as well as technical descriptions of harpsichord, clavichord, and piano mechanisms, including the so-called “English” and “Viennese” hammer actions. Early English, Nuremberg, Berlin, and Vienna wire gauges are compared.
Investigating the effect of targets’ spatial distribution on the performance of gesture interaction in virtual reality environment

The primary task of human-computer interaction is to point to graphical elements. Selecting the targets precisely is an important part of interactive tasks such as virtual simulation and virtual assembly. In actual application conditions, especially in virtual reality(VR) environment, how to accurately and effectively select target objects has already been a popular research topic in recent years. As one of the most crucial interaction technologies in human-computer interaction, gesture interaction is widely used in VR environment. Based on the gesture interaction with ray-casting feedback, this paper conducted a multi-factor experiment in VR environment to explore the effects on the performance of target selection tasks under different depths and different perspectives. Three depth levels of 1 m, 2.5 m, and 6 m were set in the experiment. The positions of 9 circular target objects were determined at 20° intervals in the horizontal and vertical viewing angle, and the vertical plane in front of the participants was divided into nine areas. The size of the target at different depth levels remains the same. Participants need to click on circular target objects in different viewing angle areas at different depth levels. We used the HTC-Vive head-mounted display and Noitom HI5 gloves as the experimental equipment and recruited 14 participants with normal vision in the experiment. The results show that the higher the depth level, the higher the pointing accuracy of gesture interaction with ray-casting feedback, and the closer the target object is to the visual center, the higher the pointing accuracy. In addition, the results show that the spatial distribution of the ray-casting interactive pointing deviation, the participants’ actual point position is lower than the target point position, and the deviation of the actual point position on the right side of the viewing angle is significantly higher than that on the left and middle positions. The research results have reference value for the spatial distribution of targets for gesture interactive tasks in VR environment.
STRUCTURE FOR SOCIAL SUCCESS OF ADOLESCENTS WITH DISABILITIES

We consider the author's definition of the social success among adolescents with disabilities as an integrative personality quality. This integrative personality quality is considered taking into account the psychological and developmental challenges of this category of adolescents, reflecting the purposeful achievement of their goals and the ability to use the achieved result for further development, contributing to successful socialization in society based on productive interaction with others. The presented structure of social success is a unity of interrelated components: value-motivational, cognitive-active, personal-creative. The value-motivational component includes achievement motivation that promotes activity, overcoming risk factors and full-fledged socialization; value orientations that determine the moral character, character and direction of behavior of adolescents with disabilities; cognitive interest that helps to reveal abilities, develop imagination, imagination and creative activity. The cognitive-activity component includes theoretical knowledge in the field of personal success, knowledge of social norms and behavioral rules that help adolescents with disabilities to establish social contact and socially successful adaptation; mastering practical skills of conscious, independent activity; interpersonal relationships and social experience that expand social horizons for adaptation in society. The personal-creative component provides for communicative skills that ensure a successful existence in society and help to build full-fledged constructive relationships; creative self-presentation skills that are indicators of personal changes and achievements; project skills and creative self-development that contribute to a positive change in personal qualities and self-expression. The presented structure of social success of adolescents with disabilities is based on the leading ideas of personal-activity, environmental, inclusive and integrative approaches.
Target Characteristics of Creative Young Professionals in the Field of Science and Higher Education

The article deals with the problem of rejuvenation of the staff potential in scientific organizations and higher educational institutions through the selection and motivation of talented young specialists to build their career trajectory in the field of science, high technology and higher education. The authors provide a detailed description of the target characteristics necessary for successful work in a creative environment. Based on original methods and building on the basic provisions of the theory of motivational complex work activities and use of the MMPI diagnostic test, they define the reference variant personality, and motivational profile of the scientist and compare it with the real profiles of undergraduate and graduate students wishing or not wishing to continue their graduate education with subsequent employment in a higher school or scientific organization. By logical comparison of the reference and real profiles, the article came to the paradoxical conclusion that people who are predisposed to scientific activity do not have aspirations to enter graduate school. Those who are professionally unfit for scientific activity, on the contrary, tend towards graduate school. Based on the analysis of the causes of this situation, a number of proposals for improving the system of professional selection and motivation of talented young professionals are formulated and justified. They are mainly related to the development of diagnostic tests for career guidance and the use of the postdoctoral institute. The authors suggest that the implementation of these proposals will contribute to the rejuvenation and strengthening of the human resources of higher educational institutions and scientific organizations in the Russian Federation.
Tuning Supervised Learners

As we saw in the previous chapter, there are multiple suites of supervised learning algorithms that can be used to model prediction systems through a labelled training data which might predict a real number (in regression) or one or more discrete classes (in classification). Each method provides a set of features that can be modified or tuned to manipulate the capabilities of the model – which might have a significant effect on the qualities of results thus achieved.
A General Divergence Modeling Strategy for Salient Object Detection

Salient object detection is subjective in nature, which implies that multiple estimations should be related to the same input image. Most existing salient object detection models are deterministic following a point to point estimation learning pipeline, making them incapable of estimating the predictive distribution. Although latent variable model based stochastic prediction networks exist to model the prediction variants, the latent space based on the single clean saliency annotation is less reliable in exploring the subjective nature of saliency, leading to less effective saliency “divergence modeling”. Given multiple saliency annotations, we introduce a general divergence modeling strategy via random sampling, and apply our strategy to an ensemble based framework and three latent variable model based solutions to explore the “subjective nature” of saliency. Experimental results prove the superior performance of our general divergence modeling strategy.
A Mathematical Method with Word Frequency Sorting for Mining Web Content Outliers

The Internet is a massive collection of information that makes it extremely difficult to search and retrieve the required and valuable information. So, Search engine became an important tool for searching various data from the web. The primary evaluation of search engine is effectiveness and efficiency. While searching for information through search engines, always users retrieve redundant and irrelevant information. This replicated and uninteresting information affects both the effectiveness and efficiency of search engine by wasting users’ time by browsing the uninterested documents and its accessing time. Web content outlier mining plays a significant role in identifying and removing these redundant document (outliers) which is an important issue among the information retrieval and web mining research communities since most of the people rely on search engines to get the required information. Most existing algorithms for web content outlier mining focuses attention on applying weightage only to the common terms in the documents whereas in this research work, a mathematical approach based on term frequency ranking to identify the duplicates, and uses the domain dictionary to check for relevant document has been carried out to improve the effectiveness and efficiency of the search engine.
Named Entity Recognition and Event Extraction in Chinese Electronic Medical Records

The China Conference on Knowledge Graph and Semantic Computing (CCKS) 2021 Evaluation Task 4 presented clinical named entity recognition and event extraction for the Chinese electronic medical records. Two annotated data sets for the two subtasks were provided for participators. Our model on the test dataset achieves the strict F1-Measure of 0.7684 which ranked the first place.
Vision-Based Facial Activity Recognition

This chapter introduces the facial-related explicit activities using the low-cost camera, including head pose estimation and tracking, gaze direction estimation, and gaze fixation tracking. The facial activities are important cues that can indicate human attention and intention, which are the key technique to construct the harmonious human–machine system. The corresponding data-driven DL-based approaches are introduced and analysed. The corresponding available datasets are introduced for the related researcher reference. Leveraging the advanced deep learning approach, the existing studies have achieved outstanding performance. In the future, we encourage the researchers can put attention to the collection of large and accurate datasets that can unify the facial states.
MEDIASI PERAN ORANG TUA DAN MOTIVASI BELAJAR TERHADAP PRESTASI BELAJAR SISWA MASA PANDEMI COVID-19

The purpose of this study was to examine the effect of mediating the role of parents and students' learning motivation on student achievement during the Covid-19 pandemic.The research method in this study used a quantitative approach with field studies andconfirmatory methods. Data analysis was carried out using the Structural EquationModeling (SEM) approach with the Partial Least Square technique. The software toolsused are Smart PLS 3.3.2. The research was conducted at SMA Negeri 02 Gorontalo with the number of research respondents 51 students with saturated sampling techniqueThe results of this study are (1) learning motivation has a positive and significant effect on learning achievement with a significance value of 0.000 and t-count t-table (4.589 2.010), (2) learning motivation has a positive and significant influence on the role of people. parents with a significance value of 0.000 and t-count t-table (8.975 2.010), (3) the role of parents has a positive and significant influence on learning achievement with a significance value of 0.001 and t-count t-table (3,405 2.010), (4) and the role of parents can mediate the effect of the relationship of learning motivation on student learning achievement with a significance value of 0.006 and t-count t-table (2.784 2.010). Learning achievement can be influenced by learning motivation and the role of parents either directly or indirectly.
Acceleration of Dynamic Web Interface Generation Through Multilingual Voice Commands

Modern web solutions require skilled efforts from professionals. This is expensive to attain and does not suit the budget and necessity of everyday people. Businesses looking to develop a website, or professional website development service providers can greatly benefit from a new approach to web development. This new approach comes in the form of voiceassisted development which allows the user of a cloud-hosted service to speak in natural language and express their needs to the system. The system follows up to enquire about necessary information and generates the desired result. The user would be able to interact in a language that they can choose themselves. Combined with the ability to enable dynamic web pages, this work aims to bring a revolution in the web development industry.
Prognostic Outcome Prediction on Patient Treatment Trajectory Data Using PSO Optimization on LTSM-RNN Model

Patient treatment trajectory data is defined as a concatenation of multiple clinical events of the patients in association with a large volume and velocity of treatment information to determine future outcomes. Predicting the future treatment plan is an important and crucial work for providing cognizance on the complete treatment trajectory of patient as a structured and dynamic service. However, it is highly cumbersome due to the existence of long-term dependencies between patient clinical events to any selected diseases, which are asymmetrically transformed along the temporal and time-varying treatment information. This remarkable characteristic inevitably obstructs the performance of determining the longitudinal risk factors of patient to the treatment using the existing machine learning approaches. To mitigate the above-mentioned challenge, a novel deep learning approach has been proposed on basis of effective feature selection and classification steps to generate effective prediction solutions using long short-term memory recurrent neural networks. This architecture is to store long dependency of the trajectory data in latent representations. Processing of large-scale data leads to computational error and irregularities in the data which can be eliminated by activating the error function with optimization of particle swarm optimization to generate the effective feature space for the prediction of the prognosis outcome. To estimate the prediction performance of the proposed deep learning approach on a real clinical dataset consisting of 2569 type 2 diabetics’ mellitus patients and nondiabetic patient with trajectories of fasting plasma glucose, lipids, body mass index, and finally, blood pressure of the patient has been used. It has been proved to be outperforming against state-of-the-art methods, against precision, recall and F-Measure, respectively.
BERT-BU12 Hate Speech Detection using Bidirectional Encoder-Decoder

In the recent times transfer learning models have known to exhibited good results in the area of text classification for question-answering, summarization, next word prediction but these learning models have not been extensively used for the problem of hate speech detection yet. We anticipate that these networks may give better results in another task of text classification i.e. hate speech detection. This paper introduces a novel method of hate speech detection based on the concept of attention networks using the BERT attention model. We have conducted exhaustive experiments and evaluation over publicly available datasets using various evaluation metrics (precision, recall and F1 score). We show that our model outperforms all the state-of-the-art methods by almost 4%. We have also discussed in detail the technical challenges faced during the implementation of the proposed model.