Reviewerly/exHarmony huggingface dataset:
MODERN TECHNOLOGIES OF DISTANCE LEARNING

The article highlights the principles of formation and development of distance learning, which involves the physical separation of teachers and students during the educational process using various technologies to facilitate and simplify communication between teacher and student and between students. It is established that the successful implementation of distance learning is highly dependent on the orderly implementation and consistent application of a set of methods and processes that provide online access to educational materials on the basis of interactive interaction of participants in the educational process. The study of the development of distance learning in Ukraine allowed us to identify the following stages: in 2002 an experiment on distance learning was introduced; in 2003 the Program of development of the distance learning system for 20004–2006 was adopted; in 2004 the first Regulation on distance learning was developed; in 2013 a new Regulation on distance learning taking into account the development of modern technologies and the Concept for the development of distance education in Ukraine were approved. In accordance with these regulations, the definition of the term "distance learning" is formulated. It is determined that distance learning technologies are a set of methods, forms and means of interaction of participants in the educational process in the process of independent assimilation of a certain array of knowledge while exercising appropriate control over their assimilation. Information technologies of distance learning are systematized with their further separation into presentation forms of educational materials, methods of delivery of educational materials, types of interaction of participants of educational process. The structure of distance learning with the use of such online services as Google Classroom or Khan Academy is described; video conferencing platforms such as Zoom or Skype. The characteristic features and advantages of using the online platform Moodle are identified. The world practice of using modern distance learning technologies with the definition of dominant online platforms (Canvas, Blackboard, Brightspace and Moodle) and a high level of perception of distance learning has been studied. The advantages of using modern distance learning technologies and the prospects of further exploration in the context of the development of innovative technologies for informatization and intellectualization of society are outlined.
INTEGRATIVE COMPETENCE OF FUTURE SPECIALISTS: PSYCHOLOGICAL AND PEDAGOGICAL ASPECTS OF FORMATION IN HIGHER EDUCATION INSTITUTIONS

The article outlines the relevance of psychological and pedagogical aspects of the formation of integrative competence of future professionals in higher education institutions. It is substantiated that in modern conditions integrativeness as a principle of development of society, science, production, education, which provides interdisciplinary complexity, generalization, the density of knowledge, methods and means of cognition, as well as a new development in educational structures, necessitates the formation of integrative competence. Within the article, integrative competence is considered from the standpoint of psychophysiological, activity, personal, personal-functional approaches. The article aims to determine the content and features of the formation of integrative competence based on the analysis of psychological and pedagogical aspects of the process of training future professionals in higher education institutions. The psychological and pedagogical aspects of the formation of integrative competence of future specialists are determined, the implementation of which should be carried out in the following areas: functional-target, problem-cognitive, procedural-methodical and integrative-effective. It is substantiated that the formation of integrative competence of future specialists is facilitated by the use of some types of project activities of students (simulation-professional game projects, information-analytical projects, specialized practice-oriented projects), as well as the use of multifunctional integrated portfolio, case sets, contextual educational technologies, individual multifunctional integrative case set off a specialist. It is generalized that the implementation of the selected psychological and pedagogical aspects of the formation of integrative competence of future professionals scientifically and methodologically provides interdisciplinary links, content and logical unity of different special disciplines; the relationship between theoretical training courses and various workshops, types of industrial practice and quasi-professional activities. Further research is identified in the development of forms of interaction between the university and employers to improve the professional and practical competence of future professionals; development of a self-educational culture of students in the process of mastering the technology of professional activity; training of teachers of higher education institutions to work on the formation of integrative competence of future specialists in various fields of training.
Гуманистические ценности в педагогической концепции Василия Сухомлинского: опыт и ориентиры

In the article the ideas of humanism and innovative pedagogic of V. Sukhomlynsky, grounded him pedagogical conception which has common to all mankind character. The peculiarities of becoming a teacher-educator in the pedagogical heritage of V. Sukhomlynsky through the disclosure of the components of the pedagogical profession are explored. The article highlights the relevant competencies that a modern teacher should possess: high civic, professional and personal qualities, their balance, harmony of manifestation, confirmed by confidence in their own knowledge, attitude to children, respect for their feelings, which is a mandatory component of professionalism.
Introducing Scholarly Research: Ready-To-Use Lesson Plans and Activities for Undergraduates

"Introducing Scholarly Research: Ready-To-Use Lesson Plans and Activities for Undergraduates." Technical Services Quarterly, 39(3), pp. 346–347
A Novel Motion Accumulation and Selection Network for Rgb-Based Action Recognition

A large number of existing methods have demonstrated the significance of motion information for action recognition in video. In the literature, most of the previous methods rely heavily on the temporal differences of features extracted by convolutional networks (CNN) to represent the motion. However, this type of motion representation approach may have two potential drawbacks: 1) The difference operation may cause the incompleteness of the extracted moving target contour; 2) Treating all the extracted motion features equally may lead to situations where some motion features will not contribute to the classification or even produce negative incentives. Inspired by the fact that the human eye is often a cumulative and selective process of visual attention when observing a video sequence, we propose a motion accumulation and selection network (MAS-Net) based on a novel embedded 2D CNN. According to the experimental results on typical video datasets such as Something-Something V1&V2 and Kinetics-400, it is shown that MAS-Net has achieved the state-of-the-arts on Something-Something V1&V2 and the competitive results on Kinetics-400, while the computational load is kept at a relatively low level.
An Effective Moving Cast Shadow Detection Framework for Surveillance Video Analysis

Moving object detection is crucial in intelligent surveillance system, such as object tracking, object classification, and so on. However, moving cast shadows seriously degrade the accuracy of moving object detection and are often detected as objects, resulting in object merging, object loss or shape distortion. To overcome the drawbacks, we propose an original moving shadow detection approach by incorporating the broad learning system. Firstly, according to the color constancy and texture consistency of shadows, the color, texture and gradient features are extracted by input frames and a background. Then, the broad learning system is introduced, which can map input samples into a more appropriate feature space. Meanwhile, the BLS offers an alternative way, which makes it classify moving cast shadows effectively and efficiently. Finally, a post-processing is designed for keeping complete moving objects. Extensive experiments verify the proposed method’s effectiveness and generalization ability on outdoor and indoor surveillance scenes.
Field Complete Coverage Path Planning Based on Improved Genetic Algorithm

The Complete Coverage Path Planning (CCPP) is a key technology in the field of agricultural machinery automatic driving, and has great significance for improving the efficiency and quality of tillage, fertilization, harvesting and other agricultural machinery operations, as well as reducing the operation energy consumption. The traditional boustrophedon or heuristic search algorithm based CCPP methods, when coping with the field with irregular boundaries, obstacles and other complex environments, still face many problems and challenges, such as large repeated work areas, multiple turns or U-turns, low operation efficiency and prone to local optimum. In order to solve the above problems, an improved-genetic-algorithm-based CCPP method was proposed in this paper, which introduces the idea of chromosome pairs and multi-points mutation to improve its global optimization ability in complex environments, reduce the repeated work areas and the number of turns and U-turns, and thereby improve the operation efficiency. The simulation and experimental results on simple regular fields showed that the proposed improved genetic algorithm-based CCPP method achieved the comparable performance with the traditional boustrophedon-based CCPP method. However, on the complex irregular fields, although the proposed CCPP method increased the number of turns by 13.76\%, the area of repeated operation and the number of U-turns were decreased by 38.54\% and 35.00\% respectively, and saves the battery voltage by 7.82\% on average compared to the boustrophedon-based CCPP method. This proved that the proposed CCPP method has strong adaptive capacity to the environment, and has practical application value in improving the efficiency and quality of agricultural machinery operations, and reducing the energy consumption.
Applications of Natural Language Processing in Software Traceability: A Systematic Mapping Study

A key part of software evolution and maintenance is the continuous integration from collaborative efforts, often resulting in complex traceability challenges between software artifacts: features and modules remain scattered in the source code, and traceability links become harder to recover. In this paper, we perform a systematic mapping study dealing with recent research recovering these links through information retrieval, with a particular focus on natural language processing (NLP). Our search strategy gathered a total of 96 papers in focus of our study, covering a period of 2013 to 2021. We conducted trend analysis on NLP techniques and tools involved, and traceability efforts (applying NLP) across the software development life cycle (SDLC). Based on our study, we have identified the following key issues, barriers and setbacks: syntax convention, configuration, translation, explainability, properties representation, tacit knowledge dependency, scalability and data availability. Based on these, we consolidated the following open challenges: representation similarity across artifacts, effectiveness of NLP for traceability and achieving scalable, adaptive and explainable models. To address these challenges, we recommend a holistic framework for NLP solutions to achieve effective traceability, and efforts in achieving interoperability and explainability in NLP models for traceability.
Impact of Gender as a Confounding Factor in Perceived Usability: A Family of Observational Studies

The general expectations toward User Experience (UX) have increased massively over the last 20 years. In order to meet the new users’ demands, developers need to become proficient with UX evaluation techniques. Among these, usability questionnaires are widely used in usability studies to measure UX. These questionnaires may be sensitive to different factors, such as gender or culture, and ignoring them may cause the developers to make decisions based on biased assumptions. Unfortunately, there is a scarcity of empirical data that allows establishing under which circumstances this bias occurs. In this paper, we have analyzed data from 20 formative usability studies involving advanced mobile prototypes. The study design was shared between the 20 studies, and all of them included five usability questionnaires (ACSI, ASQ, ER, NPS, and SUS), in order to assess the impact of gender on their perceived usability ratings. A meta-analysis with a fixed effect model was planned to analyze the data due to the expected clinical and methodological homogeneity of the data in the included studies. Results indicate that none of the studied questionnaires are affected by gender and that scores on advanced prototypes are consistently lower than the norm established for some of these measures.
Static Graph Convolution with Temporal and Channel-Wise Graph Topology Generation for Skeleton-Based Action Recognition

Graph convolutional networks (GCNs) are widely used in skeleton based action recognition. It is known that the graph topology is a vital part in GCNs, and different kinds of graph topologies have been proposed for skeleton based action recognition, mostly based on a predefined topology and a dynamically learned one. The predefined topology is based on the human intuition for skeleton (the connectivity of joints) and has not been investigated whether it is optimal. In this paper, we focus on investigating this static graph topology and propose to generate a learned static graph topology for skeleton. To be specific, a temporal frame-wise and channel-wise topology based GCNs (TC-GCNs) are developed, where, instead of using a predefined topology by human, a topology is learned for skeleton based action recognition. The TC-GCNs consist of generating a temporal frame-wise topology and a channel-wise topology to formulate the relationship of skeleton joints in the temporal dimension and channel dimension, respectively. The proposed method can be integrated with the conventional dynamic topology by replacing the predefined graph topology with our generated one. Experimental results show that our method achieves the state-of-the-art performance on two widely used benchmarks, namely the NTU-RGB+D, NTU-RGB+D 120 and UAV-Human.
Designing Solar Power Generation Output Forecasting Methods Using Time Series Algorithms: Global Warming Affected Weather Conditions

The present photovoltaic (PV) power generation systems are globally facing the irregularity problem in the distribution of PV generation. Time series analysis is one of the most important aspects of PV output prediction, especially in South Korea where past solar radiation data or other weather parameters have not been recorded. In this paper, various time-series methods including deep-learning algorithm and machine learning algorithms was used to predict the PV power generation output for quick respond to equipment and panel defects. For designing AI models, the input data were characterized by dividing seasons and choosing the multiple parameters from seasons. PV power generation data was collected from Ansan city, South Korea during January 2017 to June 2021 and the weather data was collected from Suwon city, South Korea during January 2017 to June 2021. In totality, approx. 40,000 hours of operation data from 1.5 MW grid-connected PV system in South Korea was used in this work. PV power generation forecasting was carried out on an hourly basis to test efficacy of various models. LSTM model presented the lowest error rate as compared to other models for quick PV power generation forecasting.
World Map of Low-Layer Atmospheric Extinction Values. Overview of its Impact on Solar Power Tower Plants Projects

In this paper, we present a world map with annual average values of low-layer atmospheric extinction. It is the first of its kind and contains helpful information for the improvement and evaluation of concentrating solar power tower plant projects. To reduce the computational time for world mapping, the methodology developed and validated in 2021 for the elaboration of the map of Chile has been improved. The improvement presents a 2% error in comparison with the original methodology. The world map has been elaborated processing the 30 years hourly values of atmospheric parameters provided by MERRA-II in each of the 31,560 pixels existing between the latitudes of 35N and 35S. Global daily averages of atmospheric extinction are between approximately 3% and 30%, implying annual energy losses in solar power tower plants of between 3% and 30%. The southern hemisphere is characterised by low extinction values, while hight attenuation values are found in the northern hemisphere, reaching values of 30% in some places.Low-layer atmospheric extinction has a high impact on the economic evaluation of solar tower projects. In the case of the LCOE, it is necessary to correct upwards the calculated values without considering the energy losses due to atmospheric extinction.
Modelling emotion, perfecting heart: disassembling technologies of affect with an android bodhisattva in Japan

Abstract As part of a surge in technologies with so‐called ‘artificial emotional intelligence’, robotics engineers and Buddhist monks in Japan have developed an android bodhisattva to deliver teachings at a popular Zen temple. Like many recent robots in Japan, the android is designed to impact visitors’ feelings. For this reason, it can be called a ‘technology of affect’. In order to communicate how new affective technologies are facilitating intimacy in human‐machine relations in Japan, we employ the concept of ‘disassembling’. By conceptually disassembling technologies of affect and placing them in performative contexts, we show how technologies of affect also disassemble established associations between artificial agents and the feelings they evoke in popular imaginaries. We argue that identifying these disassembling processes helps demonstrate how emerging AI technologies can engender social change at the level of affect through evocative depictions of machine emotion.
A reconstruction method for cross-cut shredded documents based on the extreme learning machine algorithm

Reconstruction of cross-cut shredded text documents (RCCSTD) has important applications for information security and judicial evidence collection. The traditional method of manual construction is a very time-consuming task, so the use of computer-assisted efficient reconstruction is a crucial research topic. Fragment consensus information extraction and fragment pair compatibility measurement are two fundamental processes in RCCSTD. Due to the limitations of the existing classical methods of these two steps, only documents with specific structures or characteristics can be spliced, and pairing error is larger when the cutting is more fine-grained. In order to reconstruct the fragments more effectively, this paper improves the extraction method for consensus information and constructs a new global pairwise compatibility measurement model based on the extreme learning machine algorithm. The purpose of the algorithm’s design is to exploit all available information and computationally suggest matches to increase the algorithm’s ability to discriminate between data in various complex situations, then find the best neighbor of each fragment for splicing according to pairwise compatibility. The overall performance of our approach in several practical experiments is illustrated. The results indicate that the matching accuracy of the proposed algorithm is better than that of the previously published classical algorithms and still ensures a higher matching accuracy in the noisy datasets, which can provide a feasible method for RCCSTD intelligent systems in real scenarios.
Pendampingan Pembelajaran Kosakata Bahasa Inggris Bagi Siswa SD 2 Panjang Kudus dengan Permainan Guessing Games

Teaching English for elementary school students aims to introduce English as a language to communicate. In this case, teachers should find ways of delivering English materials easily and how to make something difficult to be interesting and challenging to learn. Playing games is one of the interesting ways in the learning process for early childhood. Through the game, knowledge and skills are delivered in a relaxed manner. This will make the classroom atmosphere fun, so it is expected to generate the spirit of learning for them. Elementary school students as early age learners should be given the opportunity to play and have fun as well as learning, so that knowledge is given in a fun way will be easily absorbed. The purpose of community service is to provide English language teaching assistance for elementary students using Guessing games. This activity is expected that students can learn with fun by using interactive games, so that learning will be more interesting and fun, and expected the students will master the English well.
Boomerang-shaped neural embeddings for NK landscapes

Understanding the landscape underlying NK models is of fundamental interest. Different representations have been proposed to better understand how the ruggedness of the landscape is influenced by the model parameters, such as the problem dimension, the degree of non-linearity and the structure of variable interactions. In this paper, we propose to use neural embedding, that is a continuous vectorial representation obtained as a result of applying a neural network to a prediction task, in order to investigate the characteristics of NK landscapes. The main assumption is that neural embeddings are able to capture important features that reflect the difficulty of the landscape. We propose a method for constructing NK embeddings, together with metrics for evaluating to what extent this embedding space encodes valuable information from the original NK landscape. Furthermore, we study how the embedding dimensionality and the parameters of the NK model influence the characteristics of the NK embedding space. Finally, we evaluate the performance of optimizers that solve the continuous representations of NK models by searching for solutions in the embedding space.
Scanner Neural Network for On-Board Segmentation of Satellite Images

Traditional Convolutional Neural Networks (CNN) for semantic segmentation of images use 2D convolution operations. While the spatial inductive bias of 2D convolutions allow CNNs to build hierarchical feature representations, they require that the whole feature maps are kept in memory until the end of the inference. This is not ideal for memory and latency-critical applications such as real-time on-board satellite image segmentation. In this paper, we propose a new neural network architecture for semantic segmentation, “ScannerNet”, based on a Recurrent 1D Convolutional architecture. Our network performs a segmentation of the input image line-by-line, and thus reduces the memory footprint and output latency. These characteristics make it ideal for on-the-fly segmentation of images on-board satellites equipped with push broom sensors such as Landsat 8, or satellites with limited compute capabilities, such as Cubesats. We perform cloud segmentation experiments on embedded hardware and show that our method offers a good compromise between accuracy, memory usage and latency.