Reviewerly/exHarmony huggingface dataset:
Milestones of RNPLS&amp;T scholarly activities: Historical survey

The historical background of the vectors and development of scholarly activities of RNPLS&amp;T since its foundation till 2020 is discussed. Based on the studied materials, the milestones of the Library’s scholarly activities are described decade by decade, beginning in 1950s. The Library, the flagship for Russian libraries, has been always meeting the challenges of the historical epochs. Interesting facts about the studies of previous decades are cited; the logic of their development is demonstrated. The value of RNPLS&amp;T’s scientific and methodological legacy is emphasized.
A Neural Ordinary Differential Equations (ODE)-Based Model with Attention Mechanism for Tumor Segmentation in Breast Ultrasound Images

Breast cancer is fairly common among the women worldwide, and ultrasound for screening and assessing breast masses is one of the most frequently used approaches due to its advantages like portable, noninvasive and easy to operate. The automatic tumor segmentation in breast ultrasound images is conducive to clinical diagnosis and treatment. Deep neural network for segmentation has become a popular tool nowadays to assist doctors and provide the references but it still faces some challenges such as large amounts of parameters, lack of interpretability, overfitting problem, etc. In this paper, we propose a segmentation model named Att-U-Node which combines a neural ODE-based framework U-Node and an attention-based module CBAM, trying to alleviate the problems mentioned above. Three public available breast ultrasound image datasets including BUSI, BUS and OASBUD as well as a private breast DCE-MRI dataset are used to measure the performance of the proposed model and the related neural networks and then we further extend experiments to 3D scenes for tumor segmentation with the data selected from QIN Breast DCE-MRI. The experimental results show that our proposed Att-U-Node outperforms the comparison methods with respect to the most of the metrics while mitigates the common problems of deep neural network.
Generating a Multimodal Dataset Using a Feature Extraction Toolkit for Wearable and Machine Learning: A pilot study

Studies for stress and student performance with multimodal sensor measurements have been a recent topic of discussion among research educators. With the advances in computational hardware and the use of Machine learning strategies, scholars can now deal with data of high dimensionality and provide a way to predict new estimates for future research designs. In this paper, the process to generate and obtain a multimodal dataset including physiological measurements (e.g., electrodermal activity- EDA) from wearable devices is presented. Through the use of a Feature Generation Toolkit for Wearable Data, the time to extract clean and generate the data was reduced. A machine learning model from an openly available multimodal dataset was developed and results were compared against previous studies to evaluate the utility of these approaches and tools. Keywords: Engineering Education, Physiological Sensing, Student Performance, Machine Learning, Multimodal, FLIRT, WESAD
Learning Consistent Gt-Proposal Assignment for Pedestrian Detection

Accurate pedestrian recognition has received considerable attention due to its wide applications such as security monitoring, autonomous driving, etc. We discover that the widely used IoU metric in most pedestrian detectors is easy to suffer from inconsistent GT-Proposal assignment problem. It means spatially adjacent proposals with similar features may be assigned to different ground-truth boxes. It therefore confuses the training procedure and brings more false positives when predicting results. In this paper, we put forward one novel algorithm, namely Directional Assignment Srategy (DAS), to tackle with the inconsistent problem. First, combined with depth estimation, our work transforms the assignment metric from 2D view into 3D space, by which the regression head would be optimized under the constraint of depth direction. Second, different from the previous IoU-based one-to-one assignment of one proposal to one ground-truth box, the proposed method attempts to seek a reasonable matching between the sets of proposals and ground-truth boxes. In this way, our method relies less on the setting of the threshold. Furthermore, by incorporating this strategy as a plug-in module into the state-of-the-art pedestrian detectors, we show a brand new performance improvement.
Libraries’ contributing to the sustainable development goals: The SDG library web-portal

The significance of library educational activities in the area of sustainable development within the context of Russia’s national goals, UN recommendations and International Federation of Library Associations (IFLA), is substantiated. The efforts of federal and regional libraries –members of the Russian Library Association (RLA) are analyzed on the basis of their websites (e.g. website sections “Main page”, “Information resources” “Library structure”, etc.). The author argues that the information on library activities in the area is insufficient. The new thematic portal “Sustainable development in libraries” is introduced; its goals and tasks are specified; the structure and contents are described: general information on sustainable development links to external resources, useful information. The future vectors of portal and its ecology section development are proposed. The article will be useful for library managers and librarians interested in promoting sustainable development goals and ideas, etc.
An Optimized Hybrid Fuzzy Weighted k-Nearest Neighbor with the Presence of Data Imbalance

We present an optimized hybrid fuzzy Weighted k-Nearest Neighbor classification model in the presence of imbalanced data.More attention is placed on data points in the boundary area between two classes.Finding greater results in the general classification of imbalanced data for both the minority and the majority classes.The fuzzy weighted approach assigns large weights to small classes and small weights to large classes.It improves the classification performance for the minority class.Experimental results show a higher average performance than other relevant algorithms, e.g., the variants of kNN with SMOTE such as Weighted kNN alone and Fuzzy kNN alone.The results also signify that the proposed approach makes the overall solution more robust.At the same time, the overall classification performance on the complete dataset is also increased, thereby improving the overall solution.
Optimization of photonic reservoir computing using short internal cavity laser

With the advent of the high-speed information age and the explosive growth of the information, higher requirements have been placed on the information processing speed. In recent years, the delay-based reservoir computing (RC) systems have been extensively investigated. Meanwhile, the information processing rate is improved mainly around the replacement of nonlinear nodes in the system. Nevertheless, as the most commonly used distributed feedback semiconductor (DFB) laser, many researchers only use ordinary commercial DFB products for research, and they have not noticed the improvement of RC performance caused by changes in internal parameters of laser. With the development of photonic integration technology, the processing technology of DFB turns more mature, so that the size of DFB can be fabricated in a range of 100 μm–1 mm when it still generates laser, and the photon lifetime of the laser will also change. The shorter photon lifetime in the laser leads to a faster dynamic response, which has the potential to process the information at a higher rate in the RC system. According to the laser rate equation (Lang-Kobayashi), changing the internal cavity length will affect the feedback strength, injection strength and other parameters required for the laser to enter into each dynamic state, which in turn affects the parameter space required for the RC system to exhibit high performance. According to this, we study the relationship between the internal cavity length (120 μm–900 μm) and the information processing rate of the RC system. In addition, the influences of different internal cavity lengths on the parameter space of the RC system are analyzed. The results show that when the internal cavity length is in a range from 120 μm to 171 μm, the system can achieve 20-Gbps low-error information processing. It is worth noting that when the internal cavity length decreases from 600 μm to 128 μm, the parameter space with better prediction performance of the RC system is greatly improved. When performing the Santa-Fe chaotic time series prediction task, the normalized mean square error (NMSE) is less than 0.01, and the parameter range of the injection strength is increased by about 22%. The range of parameter with NMSE no more than 0.1 is improved by nearly 40% for the 10&lt;sup&gt;th&lt;/sup&gt; order nonlinear auto-regressive moving average (NARMA-10) task. When the number of virtual nodes is 50, the system can achieve a high-precision prediction for the above two tasks. This is of great significance for the practical development of the system.
The formation of a theater school in Yaroslavl: the soviet period

The article describes the process of formation of a theater school in Yaroslavl during the Soviet period. State theatrical education in Yaroslavl is for the first time presented in detail in the chronological sequence of origin, formation, development. Development is understood as a process of regular change aimed at creating a new qualitative state of theater education: from a theater technical school and a theater studio to a secondary theater school and a higher educational institution. Analyzing the process of formation and development of theater education in Yaroslavl in the context of a certain historical time (the Soviet period), the author relies on the principle of historicism. The study uses documents that record key events: government decrees, decisions and orders of various levels. The results of the study allow us to reasonably assert that the policy implemented by the Soviet authorities in the field of creative and, in particular, theater education contributed to the formation of a theater school in Yaroslavl. Many methodological practices tested and developed in Soviet times have not lost their relevance at the present time. At the same time, the formation and development of the Yaroslavl theater school, which has pronounced individual features, goes beyond the rationalistic and politicized educational policy of the Soviet state. An exceptional role in the development of theater education and the formation of the Yaroslavl theater school was played by F. E. Shishigin, artistic leader of the Theater named after F. Volkova. He was the only one in the Soviet theatrical province who combined artistic, staging and pedagogical work, supplementing these activities with organizational and creative work. The problem under consideration will be of interest to specialists in the field of the history of theater education, as well as culturologists involved in the study of the culture of the Soviet period.
REDUCING CYBER INCIDENTS THROUGH GOOD ONLINE BEHAVIORAL NORMS: LESSONS FROM SOUTH AFRICA

The phenomenon of cyber incidents has grown commonplace in schools throughout the world, including South Africa. Cyber mishaps are becoming more common, affecting both learners and parents, and expecting parents to supervise their children's online activity 24 hours a day is unrealistic. Several studies have highlighted several remedies, however even with such solutions, cyber incidents are still on the rise. As a result, the study aims to use a technical tool to investigate how cyber incidents can be reduced through good online behavioral norms which is an alternative strategy for reducing cyber occurrences among learners. Using the experimental action approach, the findings revealed that if learners are given alert messages that encourage them to consider appropriate behavioral standards, the number of learners who send hurtful messages may be lower than the number of learners who wish to send hurtful messages. As a result, the study suggests that educational institutions should step up their efforts to ensure that learners receive alarm messages that encourage them to consider appropriate behavior norms.
Formation of the communication culture of the future specialist in the international business

Nowadays, the growth of economic cooperation requires improving of the professional training system for future international business specialist, which will help increase his level of professional competence, effective communication with partners and build a positive image of the Ukrainian economic sector.Disciplines of the socio-liberal cycle of the educational program "International Business" specialty 292 "International Economic Relations" at the Simon Kuznets Kharkiv National University of Economics allow to acquaint future professionals with the peculiarities of business relations, to form worldview values, to master the techniques and methods of effective cooperation, to master the ethics and culture of communication, which involves the formation of experience of cultural interaction for further future professional activity.One of such disciplines is the "Culture of Communication", which was introduced into the educational process of S. Kuznets KhNEU.The relevance of teaching this discipline for future specialists in international business is based on the communicative nature of the professional activities of workers in the economic sector.The article considers the content of the discipline "Culture of Communication" in accordance with pedagogical technology, which allows to streamline the structure of the discipline, which aims to motivate, acquire knowledge, acquire qualities and professional skills to communicate.The stages of pedagogical technology introduced into the educational process are analyzed, namely: motivational, cognitive, communicative and reflexive.In accordance with these stages of pedagogical technology, students form a culture of communication that provides fruitful activities in cooperation with other colleagues and business partners.
Down and Across: Introducing Crossword-Solving as a New NLP Benchmark

Solving crossword puzzles requires diverse reasoning capabilities, access to a vast amount of knowledge about language and the world, and the ability to satisfy the constraints imposed by the structure of the puzzle.In this work, we introduce solving crossword puzzles as a new natural language understanding task.We release a corpus of crossword puzzles collected from the New York Times daily crossword spanning 25 years and comprised of a total of around nine thousand puzzles.These puzzles include a diverse set of clues: historic, factual, word meaning, synonyms/antonyms, fill-in-the-blank, abbreviations, prefixes/suffixes, wordplay, and crosslingual, as well as clues that depend on the answers to other clues.We separately release the clue-answer pairs from these puzzles as an open-domain question answering dataset containing over half a million unique clueanswer pairs.For the question answering task, our baselines include several sequence-tosequence and retrieval-based generative models.We also introduce a non-parametric constraint satisfaction baseline for solving the entire crossword puzzle.Finally, we propose an evaluation framework which consists of several complementary performance metrics.
Self-Potential Survey at the Yuhuang Hydrothermal Field on the Southwest Indian Ridge

Summary In this study, we report a self-potential survey at the Yuhuang hydrothermal field (YHF) on the ultraslow-spreading Southwest Indian Ridge, and the purpose of this survey is to understand the concentration and its volume of the mineral resource of seafloor massive sulfide deposits at the YHF. The survey lines are located at the outside around the known ore body of YHF. Negative self-potential anomalies up to −22mV were acquired along with the survey line and it is suggested that the scale range of the ore body under the seafloor maybe significantly larger than what we currently estimated, and the volume deposit of mineral resources is much more larger. Meanwhile, the self-potential method is economically efficient to carry out along with other geophysical methods, and it will play an essential role in the exploration and resource evaluations of seafloor massive sulfide deposits over large areas in the future.
Towards Human Evaluation of Mutual Understanding in Human-Computer Spontaneous Conversation: An Empirical Study of Word Sense Disambiguation for Naturalistic Social Dialogs in American English

Current evaluation practices for social dialog systems, dedicated to human-computer spontaneous conversation, exclusively focus on the quality of system-generated surface text, but not human-verifiable aspects of mutual understanding between the systems and their interlocutors.This work proposes Word Sense Disambiguation (WSD) as an essential component of a valid and reliable human evaluation framework, whose long-term goal is to radically improve the usability of dialog systems in real-life human-computer collaboration.The practicality of this proposal is proved via experimentally investigating (1) the WordNet 3.0 sense inventory coverage of lexical meanings in spontaneous conversation between humans in American English, assumed as an upper bound of lexical diversity of human-computer communication, and (2) the effectiveness of state-of-the-art WSD models and pretrained transformer-based contextual embeddings on this type of data. 1
Program for Development of Communication, Perception, Sound, and Rhythm to Build Self Esteem of Students with Special Needs

Self-esteem is one of the most important personal attitudes in life, high and low self-esteem which is built from language and cognition modalities will affect a person's personality.This article aims to describe the results of preliminary research on the development of self-esteem for students with special needs.The method used in this research is descriptive analysis.This research is focused on learning related to arts education in the Special School for the Deaf, namely Program Development of Communication, Perception, Sound and Rhythm.The research participants were five students with special needs for hearing impairment, one teacher, and five parents/guardians of grade 9 deaf students.The research location was at the Cicendo State Special School, Bandung.Data were collected through observation during process teaching and learning, interviews with teachers, students, and parents/guardians of students.Data analysis was carried out by triangulation.This study found that the self-esteem of deaf students grew positively through art activities in the program for development of communication, perception, sound and rhythm.Teachers and the school environment provide comfort and space for students to grow self-confidence by optimizing the abilities of deaf students in various fields, including the arts.
Making terminology dictionary as an important element of library research activities

The authors review the digital terminological dictionaries published by RNPLS&amp;T. The publishing story is told; the importance of this work is substantiated, the publications are characterized. The terminological dictionaries, i.e. the English-Russian dictionary of machine-readable cataloguing, e-dictionary of SIBID terminology, e-dictionary of standardized abbreviations in Russian and 25 European languages for bibliographic records, and the union terminology dictionary of e-librarianship, are described in brief. In conclusion, the further plan for the Library’s linguistic support group is discussed as related to creating terminological dictionaries. The article is prepared within the framework of the State Order No. 730000F99.1 BV09AA00006 to RNPLS&amp;T.
Learning Dendrite Morphological Neurons Using Linkage Trees for Pattern Classification

This article presents a Dendrite Morphological Neuron model learned by Linkage Trees (LT-DMN). It is presented as an alternative to modern DMN model training approaches based on k-means clustering that must tune the number of dendrites per class by defining a k-value. Also, the k-means based methods have a problem of non-reproducibility and, for each potential solution, they may present the risk of falling into local minima. The LT-DMN algorithm selects the centroids from a deterministic hierarchical clustering, which builds a linkage tree for each class of patterns. In addition, the simulated annealing algorithm is used to automatically fit a suitable cut-off point in the structure of each tree that minimizes the classification error and the number of dendrites. The proposed method is evaluated on nine synthetic data sets and 17 real-world problems. The results reveal that the proposed method is competitive or even better than seven DMN models from the literature. Furthermore, LT-DMN achieves low architectural complexity by using few dendrites.
Robot Movement Based on Color Detection

The robot is similar to a human in that it detects environmental changes with the various sensors, considers what procedure must be implemented with the assistance of the logic circuits & electronic components, and then perform the necessary acts by activating its mechanical portions. Skills of several fields of engineering are need to construct a robot for example mechanical engineers distribute with the designs construction and use of suitable materials for the design of a robot. This project is split up into three sections. The first section is to capture the live video stream from the cam-mounted on the three-wheeled robot. The second section is to program the robot to move automatically. The third section is to detect the colour of the obstacle, make the robot move accordingly based on the colour detection. The primary objectives of this project are to capture the live video, to be skilled to sense the color of the object and move the robot accordingly, to vary the speed of the robot and to analyze for various colour combinations.
Need Analysis of Augmented Reality Based Virtual Laboratory for Chemistry Practicum Purposes

To keep following the Covid-19 protocol, students are not recommended to take courses on campus.As a result, they are not able to do essential lab work as usual.In this paper, we address the early stage of the developing process of an Augmented Reality run on Android devices to help students understand the theoretical framework of atoms.The need analysis was conducted to acquire the teachers' and students' thoughts toward the development of the AR.The questionnaire was developed based on the Likert scale.113 respondents including students and teachers volunteered and filled the online questionnaire.The results show that most respondents agree that the AR application is needed for online practicum and they are willing to support and use the application.Android platform is prefered over other platforms, and the concept of atom and molecule was chosen by the vast majority of respondents.Therefore, the AR application about atom will be developed on the Android platform.
Influence of Structure Information for Cross-Domain Person Re-identification

Cross-domain person re-identification (re-ID) is a challenging task in computer vision research. We intent to learn a discriminative model when given labeled source and unlabeled target datasets, in order to obtain good results on the target test datasets. In this paper, we add structure information for pedestrian images. To this end, we evaluate the influence of structure information for cross-domain re-ID in the deep networks and a series of experiments with different settings on Market-1501 and DukeMTMC-reID are conducted. The influence of structure information on the effectiveness in the optimized deep models is shown in the experimental results.