Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of Ideas about Young Scientist in the Russian and Foreign Academic Practice

The purpose of the study is to determine portrait characteristics of a young scientist in the foreign and Russian academic practice in order to design pedagogical conditions for their development in the university environment. The article presents an overview of research papers on the issue of determining specifics of the “young scientist” and “young scientist’s competence” categories, as well as legal documents regulating requirements for participants of grant and competitive programmes for young scientists. The study is novel in that it defines the category “young scientist” as a specific socio-professional group of scientists distinguished within the academic community on the basis of a number of criteria (among them are age; degree of involvement in a research project; qualification requirements and others) and the category “young scientist’s competence” as a person’s integrative dynamic quality expressed in their awareness of social and personal significance of research activities with the purpose of obtaining new knowledge, in internal motive for its implementation revealed in the process of productive solution of research tasks on the basis of the mastered system of methodological knowledge, methods and ways of research. The author has also determined the range of the pedagogical conditions contributing to young scientists’ academic career development in higher education institutions of the Russian Federation (actualising young scientists’ motivation for research work, enriching the educational environment of a university with event-based practices, involving a young scientist in the activity and reflexive process of competence development). As a result, based on a comparative analysis of the Russian and foreign experience, practical recommendations for specialised structural divisions of the Russian universities in terms of organising young scientists’ effective research work have been formulated.
Event-Based American Sign Language Recognition Using Dynamic Vision Sensor

American Sign language (ASL) is one of the most effective communication tools for people with hearing difficulties. However, most of people do not understand ASL. To bridge this gap, we propose EV-ASL, an automatic ASL interpretation system based on dynamic vision sensor (DVS). Compared to the traditional RGB-based approach, DVS consumes significantly less resources (energy, computation, bandwidth) and it outputs the moving objects only without the need of background subtraction due to its event-based nature. At last, because of its wide dynamic response range, it enables the EV-ASL to work under a variety of lighting conditions. EV-ASL proposes novel representation of event streams and facilitates deep convolutional neural network for sign recognition. In order to evaluate the performance of EV-ASL, we recruited 10 participants and collected 11,200 samples from 56 different ASL words. The evaluation shows that EV-ASL achieves a recognition accuracy of 93.25%.
CATNet: Scene Text Recognition Guided by Concatenating Augmented Text Features

In this paper, we propose an end-to-end trainable text recognition model that consists of an auxiliary augmentation module and a text recognizer. Well-established traditional methods in computer vision (such as binarization, sharpening and morphological operations) play critical roles in image preprocessing. These operations are proven to be particularly useful for downstream computer vision tasks. In order to achieve better results, case-by-case hyperparameter adjustment is often required. Inspired by traditional CV methods, we propose an auxiliary network to mimic traditional CV operations. The auxiliary network acts like an image preprocessing module to extract rich augmented features from the input image to ease the downstream recognition difficulty. We studied three types of augmentation modules with parameters that can be learned directly via gradient back-propagation. This way, our method combines traditional CV techniques and deep neural network by joint learning. The proposed method is extensively tested on major benchmark datasets to show that it can boost the performance of the recognizers, especially for degraded text images in various challenging conditions.
Research on STEM Curriculum Integration Technology

Interdisciplinary integrated education is the inevitable trend of future education development, and how to use and develop relevant educational resources to promote disciplinary integration is bound to become a new practical challenge. This paper summarizes and refines the STEM curriculum integration from a technical perspective, and finds that the current curriculum integration has a narrow vision, a plane, an imperfect mechanism and a lack of resources, and concludes that the research on technical integration is also very scattered. In view of this, this paper takes one famous STEM discipline integration model as a case, analyzes and puts forward the corresponding technical means, and provides a technical reference for the teaching implementer.
Evaluating and Presenting the Test Results

The individual test performance is determined on the basis of open complex test tasks with a processing time of up to 120 min. The evaluation of the solutions is each carried out by two teachers independent of one another. Each of the eight criteria is operationalised by five items each. The evaluators must first decide which of the eight criteria or which of the 40 items can be applied to the respective task. Test practice has shown that the test items were formulated in such a way that almost all forty items are used. It was specified that
ThamizhiUDp: A Dependency Parser for Tamil.

This paper describes how we developed a neural-based dependency parser, namely ThamizhiUDp, which provides a complete pipeline for the dependency parsing of the Tamil language text using Universal Dependency formalism. We have considered the phases of the dependency parsing pipeline and identified tools and resources in each of these phases to improve the accuracy and to tackle data scarcity. ThamizhiUDp uses Stanza for tokenisation and lemmatisation, ThamizhiPOSt and ThamizhiMorph for generating Part of Speech (POS) and Morphological annotations, and uuparser with multilingual training for dependency parsing. ThamizhiPOSt is our POS tagger, which is based on the Stanza, trained with Amrita POS-tagged corpus. It is the current state-of-the-art in Tamil POS tagging with an F1 score of 93.27. Our morphological analyzer, ThamizhiMorph is a rule-based system with a very good coverage of Tamil. Our dependency parser ThamizhiUDp was trained using multilingual data. It shows a Labelled Assigned Score (LAS) of 62.39, 4 points higher than the current best achieved for Tamil dependency parsing. Therefore, we show that breaking up the dependency parsing pipeline to accommodate existing tools and resources is a viable approach for low-resource languages.
Integration of morphological features and contextual weightage using monotonic chunk attention for part of speech tagging

Part-of-Speech (POS) tagging is a fundamental sequence labeling problem in Natural Language Processing. Recent deep learning sequential models combine the forward and backward word informatio for POS tagging. The information of contextual words to the current word play a vital role in capturing the non-continuous relationship. We have proposed Monotonic chunk-wise attention with CNN-GRU-Softmax (MCCGS), a deep learning architecture that adheres to these essential information. This architecture consists of Input Encoder (IE), encodes word and character-level, Contextual Encoder (CE), assigns the weightage to adjacent word and Disambiguator (D), which resolves intra-label dependencies as core components. Moreover, different morphological features have been integrated into the core components of MCCGS architecture as MCCGS-IE, MCCGS-CE and MCCGS-D. The MCCGS architecture is validated on the 21 languages from Universal Dependency (UD) treebank. The state-of-the-art models, Type constraints, Retrofitting, Distant Supervision from Disparate Sources and Position-aware Self Attention, MCCGS and its variants such as MCCGS-IE, MCCGS-CE and MCCGS-D are obtained mean accuracy 83.65%, 81.29%, 84.10%, 90.18%, 90.40%, 91.40%, 90.90%, 92.30%, respectively. The proposed model architecture provides state-of-the-art accuracy on the low resource languages as Marathi (93.58%), Tamil (87.50%), Telugu (96.69%) and Sanskrit (97.28%) from UD treebank and Hindi (95.64%) and Urdu (87.47%) from Hindi-Urdu multi-representational treebank.
Employee’s Performance as a Consequence of Effective Communication and Working Environment: The Moderating Role of Organizational Learning Culture

Purpose: FMCG (Fast Moving Consumer Goods) sector has a significant role in the economic development of Pakistan. According to Pakistan Bureau of Statistics, the retail sector contributes 18.6% to the GDP of Pakistan. This sector can use effective communication with a learning work environment as a tool to increase their productivity and at the same time can build customer relations. This is because communication is an essential element used to deal with customers and employees. Whereas, at the same time comfortable work environment is helpful to boost the performance of this sector. The present study aims to examine the influence of effective communication and working environment on employee’s performance with the moderating role of organizational learning culture in FMCG sector of South Punjab Region.&#x0D; Design/Methodology/Approach: For the said purpose structured questionnaires were developed by using online google forms for collecting data. The sample population was selected from the FMCG sector of Pakistan. Then the collected data was analyzed by using regression analysis and Pearson Correlation via SPSS whilst Structural Equation Modeling (SEM) via Smart PLS.&#x0D; Findings: The results indicates that effective communication and work environment has a significant influence on employee’s performance and organizational learning culture plays a moderating role between effective communication and work environment.&#x0D; Implications/Value: This research has used FMCG sector specifically to examine the impact of effective communication hence, further research can be done by using other sectors i.e., Education, Agriculture, IT etc. In addition to that this study is based on the impact of effective communication and working environment on the employee’s performance through the moderating role of organizational culture further research can be made on the ways or methods improve working conditions of this sector.
Mobile Applications for COVID-19: Benefits, Technologies and Future Research Opportunities

The SARS-CoV-2 (COVID-19) pandemic has emerged as one of the greatest problems of the 21st century worldwide. Efforts to fight this pandemic require a global co-operation and a multidisciplinary approach. An application of information and communication technologies (ICT) to a great degree contributes to fighting the pandemic as these technologies are one of the key services that assist patients, researchers, health institutions and other interested parties in different activities in an effort to fight the pandemic and its consequences. The present paper presents the features of certain mobile applications (apps) that are being used for different purposes such as: tracking patients, COVID-19-related warnings, keeping tracks of statistical data, organising life and business, etc. Aside from presenting the features of a certain number of applications, a review of technologies used for the development of these applications will also be presented. Furthermore, the paper addresses certain challenges that come along with the mobile technologies applications and offers suggestions for future research.
Distributed cloud resources allocation for fair utilization using multi-objective bin packing algorithm

Purpose Cloud computing services gained huge attention in recent years and many organizations started moving their business data traditional server to the cloud storage providers. However, increased data storage introduces challenges like inefficient usage of resources in the cloud storage, in order to meet the demands of users and maintain the service level agreement with the clients, the cloud server has to allocate the physical machine to the virtual machines as requested, but the random resource allocations procedures lead to inefficient utilization of resources. Design/methodology/approach This thesis focuses on resource allocation for reasonable utilization of resources. The overall framework comprises of cloudlets, broker, cloud information system, virtual machines, virtual machine manager, and data center. Existing first fit and best fit algorithms consider the minimization of the number of bins but do not consider leftover bins. Findings The proposed algorithm effectively utilizes the resources compared to first, best and worst fit algorithms. The effect of this utilization efficiency can be seen in metrics where central processing unit (CPU), bandwidth (BW), random access memory (RAM) and power consumption outperformed very well than other algorithms by saving 15 kHz of CPU, 92.6kbps of BW, 6GB of RAM and saved 3kW of power compared to first and best fit algorithms. Originality/value The proposed multi-objective bin packing algorithm is better for packing VMs on physical servers in order to better utilize different parameters such as memory availability, CPU speed, power and bandwidth availability in the physical machine.
Preferential Mixture-of-Experts: Interpretable Models that Rely on Human Expertise As Much As Possible.

We propose Preferential MoE, a novel human-ML mixture-of-experts model that augments human expertise in decision making with a data-based classifier only when necessary for predictive performance. Our model exhibits an interpretable gating function that provides information on when human rules should be followed or avoided. The gating function is maximized for using human-based rules, and classification errors are minimized. We propose solving a coupled multi-objective problem with convex subproblems. We develop approximate algorithms and study their performance and convergence. Finally, we demonstrate the utility of Preferential MoE on two clinical applications for the treatment of Human Immunodeficiency Virus (HIV) and management of Major Depressive Disorder (MDD).
Global Horizontal Irradiance Forecast at Kanto Region in Japan by Qunatile Regression of Support Vector Machine

In the interests of the stable operation of the transmission system, transmission system operators (TSOs) procure regulating power supplies to cope with significant deviations from renewable energy forecasts. Therefore, it becomes important to improve the average precision of the one-day ahead forecast and to decrease the maximum error of the forecast in a power transmission system with a large number of photovoltaic systems. In this paper, the quantile regression using support vector machines is applied to the prediction of the previous day’s solar radiation, and it is confirmed that maximum width of the error can be reduced while suppressing the minimum length of the prediction error.
Unsupervised learning-based registration for T1 and T2 breast MRI images

The traditional image registration methods use the iterative optimization to search the most optimal transformation parameter. However, they are all time-consuming. Therefore, we proposed a fast and unsupervised T1/T2 weighted breast images registration model based on the convolutional neural network. The proposed model estimate the spatial transformations from pairs of T1/T2 weighted breast images , and used the transformations to warp the moving images. There is no supervised information such as the ground truth of the deformation fields should be provided in this model. The registration of T1/T2 weighted breast MRI images can assist doctors to get better diagnosis results and provide better data resource for lesion detection and image prediction. We have evaluated the proposed model and the experimental results show that the model is robust and effective.
Interaction with IoT Comfort technologies for Deaf and Dumb People

Sign language is usually spoken by the deaf and dumb community. However, there are many available constraints in this. The first being that there are around 138 to 300 total sign languages that are currently in use around the world so there is no cohesiveness between them all. This elongates the already huge communication gap not only between the non-sign language speakers but also the people who are part of this community. Secondly, not many people or rather the majority of the people capable of speaking and listening, don't bother to pick up and learn sign language. This distances people and prohibits them from communicating and connecting. People using sign languages also are unfortunate when it comes to modern and evolving technologies. What we hear today is “Voice-controlled”, “Voice-activated”, “Speak to control”, these are the new buzzwords of this century. The entire development of comfort technologies revolves around this idea of ease of use, replacing the old capacitive buttons with the voice as the trigger. It is uncommon for developers to think about those who don't share the luxury of speaking and rely on their hands for communication. Pondering and researching about the feasibility of this concept of bringing the Internet of things for the people who talk through or whose voice is the Sign language, a new model is being discussed.
A Novel Attention-guided Network for Deep High Dynamic Range Imaging

In natural scenes with multi-exposure image fusion (MEF), high dynamic range (HDR) imaging is often affected by moving objects or misalignments in the scene, resulting in ghosting artifacts in the final imaging results, with the help of optical flow method and deep network architecture. To avoid ghosting artifacts better, we propose a novel attention- guided neural network (ADeepHDR) to produce high-quality ghost-free HDR images. Unlike the previous methods, we use the attention module to guide the process of image merging. The attention module can detect the large motions and the notable parts of the different input features and enhance details in the results. Based on the attention module, we also try different subnetwork variants to make full use of the hierarchical features to get more ideal results. Besides, fractional-oder differential convolution is used in the subnetwork variant to extract more detailed features. The proposed ADeepHDR is an improvement method without optical flows, which can better avoid the ghosting artifacts caused by error optical flow estimation and large motions. We have conducted extensive quantitative and qualitative assessments, and show that the proposed method is superior to the most state-of-the- art approaches.
General calculation of transmission characteristics of programmable photonic integrated waveguide mesh

Programmable photonic integrated waveguide mesh (PPIWM) is similar to field programmable gate array (FPGA) in electronics, which can be programmed to form a variety of topologies to provide different functions flexibly. In mesh structure, the calculation of transmission characteristics between arbitrary input and output ports is the premise to realize its application. With the continuous development of PPIWM in scale and structure, it is particularly important to automatically and efficiently calculate the transmission characteristics between input and output ports by programming. At present, the existing calculation method is complicated, which is not conducive to programming and has the prominent problems of low computational efficiency. In this paper, the mesh structure is mapped to a graph network represented by dual node, which is easy to be calculated programmatically. On this basis, a calculation method for solving the transfer function between arbitrary input and output ports required by the mesh is proposed by successively simplifying the nodes. This method can greatly reduce the time of solving the transfer function in the large and complex mesh structure, and has the advantages of easy programming and high efficiency. A specific example of the mesh is given to verify the effectiveness of the algorithm.
Meaning of Word ‘Content’ in Linguistic Consciousness of Young People

The article is devoted to the semantic-cognitive analysis of the meaning of the word ‘content’. The scientific novelty of the work is seen in the fact that in the linguistic literature there are no studies in which a semantic-cognitive description of the meaning of the word ‘content’ is presented. The results of a comparative analysis of the formal-logical and psycholinguistic study of the semantics of the word ‘content’ are presented. In the course of the study, a discrepancy was found between the lexicographic description and the psychological representation of concepts associated with discrepancies between the data of explanatory dictionaries and the conclusions made on the associative material. It was found that the systemic meaning of the word content, containing a minimum of features recorded in dictionaries in accordance with the principles of reductionism, is reduced to the content of messages and information. The experiment showed that in the mental representation of young people, content is perceived as unconfirmed interesting information on the Internet using visuality, written in simple language and designed to consume ⅓ of the active time of the day among modern youth.
SOCIALIZATION OF DIGITAL LITERACY EDUCATION TO ANTICIPATE HOAX NEWS

Background: In the midst of the Covid-19 pandemic situation, hoax news emerged which often caused people to panic and make wrong decisions. The emergence of this hoax is because many people are not literate with information, so they trust all the information that is circulating. People also do not have social awareness to filter the information they get. Objective: This article aims to present the results of the socialization of digital literacy education to anticipate hoax news. Method: The method used in this community service is to provide socialization on the dangers of hoaxes and literacy education digital to anticipate hoax news, then explain how to search for valid and accurate information. Results: The result of this community service activity was an increase in public understanding of how to find valid and accurate Covid-19 information / news. As well as people's understanding of how to differentiate between true and false information about Covid-19. Conclusion: This community service activity has a positive impact on increasing public understanding of the spread of Covid-19 news on the internet.
Sleep Quality Evaluation System Based On Video Surveillance

Sleep quality evaluation without interference is of great importance for improving human health and reducing the occurrence of various risks. This paper simulates the 100-day sleep video data set of the observed object. Digital image processing method is used to realize the detection function of going to bed, getting up, turning over and other activities By drawing a sleep curve for one day, and then calculating the number of turns over a day, the duration of sleep, and judging the quality of sleep according to parameters such as the number of waking up, the number of turns over, the duration of sleep, and finally the abnormality detection by comparing the similarity of the curves over multiple days. This paper aims to make use of automatic detection of sleep conditions to make intelligent video surveillance play a greater role in human sleep quality.