Reviewerly/exHarmony huggingface dataset:
Uplatnenie QR kódov vo vyučovaní biológie

The paper describes one of the progressive forms of innovation of the educational process through the implementation of a QR code on worksheets in biology. QR code as a new technology is already accepted and fully used in biology. A part of mobile education (m-learning) is also the application of innovative methods and tools that develop students' key competencies. Emphasis is placed on the real work of students, the use of digital technologies, Internet literacy, the strengthening of interdisciplinary relationships and the elements of informal science education. The educational application of QR codes increases the attractiveness and demostrativeness of Biology lessons.
WiP: A Distributed Approach for Statistical Disclosure Control Technologies

In the current era of data driven governance and businesses, data sharing and opening become an essential growth factor. Data intensive organizations are eager to share their data with the public, research institutions and private enterprises. However, data sharing must adhere to data protection laws and regulations, particularly respect the principle of personal data minimization. Statistical Disclosure Control (SDC) is a major technology that aims at minimizing personal information in a data set while retaining the data utility at an acceptable level for data consumers. Despite having many tools developed to automate the process of applying SDC technology, still the majority of organizations are struggling to adapt it. In this contribution, first, we mention the common challenges that data intensive organizations are facing for employing existing SDC tools. Then, we propose a SDC tool set-up, whereby organizations can outsource the anonymization of their microdata sets to a central party safely (i.e., without sharing their raw data). Finally, we present the current status of the study together with a few questions for future research.
Combination of Local Feature Extraction for Image Retrieval

Image feature extraction can be effectively realized using local binary pattern (LBP). This paper proposes an image retrieval method using directional image features and an adaptive threshold. There are two steps: a. encoding with the standard deviation of the neighbors’ intensity values in the 3 × 3 pattern for threshold in center pixel. The threshold is adaptively updated concerning the intensity of the neighbor pixels. b. Estimation of the local directional pattern using the changes in the intensity of the neighborhood pixels in different directions along with binary encoding of neighboring pixels. These two features are combined and used for image retrieval. The proposed method is evaluated on Corel-1k data set and compared with other standard local feature representation methods.
베트남인의 식품소비가치에 따른 가정간편식 소비태도와 인식

Purpose: The HMR market of Vietnam is expected to expand due to the economic growth and urbanization of the country. The purpose of this study was to provide basic data for directing product development and establishing a marketing strategy for HMR in Vietnam. Methods: A survey was conducted targeting Vietnamese consumers who usually use HMR and those who have had the experience of using it earlier. Food consumption value was used for the market segmentation of Vietnamese consumers, and differences in purchasing behavior, such as the reasons for using HMR, preference for HMR, and the importance-performance of HMR selection attributes, were analyzed for each segment. Results: Vietnamese consumers were classified into the sub-markets of ‘Pursuing Simplicity and Convenience’, ‘Pursuing Family Health’, and ‘Pursuing Safety and Simplicity’ types. As a result of analyzing the characteristics of the segmented market, it was observed that the influence of family values reflecting Vietnam’s family-oriented culture and situational values reflecting climate characteristics existed. Moreover, when purchasing HMR, quality, hygiene, safety, taste, nutrition, price, and freshness were found to be key attributes, and there were differences in HMR consumption attitude and perception for each market segment. Conclusion: This study is meaningful in that it was conducted on the Vietnamese HMR market, where limited research has been performed, and that it reflects the cultural and climate characteristics of Vietnam. Such segmented market characteristics can be utilized for formulating market tailored HMR development and marketing strategies.
Comparative Analysis on Diverse Heuristic-Based Joint Probabilistic Data Association for Multi-target Tracking in a Cluttered Environment

The target tracking using the passive multi-static radar system produces various detections via distinct signal propagation paths. Trackers solve the uncertainties that arise from the measurement path as well as the measurement origin. The existing multi-target tracking algorithms suffer from high computational loads, because they require the entire probable joint measurement-to-track assignments. This paper proposes to develop a comparative analysis on diverse heuristic algorithms for implementing the optimized JDPA model for tracking multiple targets using multi-static passive radar system in the presence of clutter. Here, the nature-inspired algorithms like Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) are used for analyzing the optimized JDPA model for tracking multiple targets in multi-static passive scenario. This paper further, aims to tune the position and velocity of the tracker towards the target using two heuristic algorithms, and intends to analyze the effect of those algorithms on improving the performance of multiple target tracking. The key objective of the proposed model is to minimize the Mean Absolute Error (MAE) between the estimated trajectory of the track and the true target state.
Modules

Programming involves creating layers of abstraction, and programming languages provide ways of creating interlocking and stackable layers of classes and functions, so that the programmer can think in higher-level constructs and get the job done as fast as possible. These module applications can be implemented in many different ways in Raku. Once they are implemented, we encourage you to release your modules to the ever-growing (in size and awesomeness) Raku ecosystem, so that others can benefit from what you have done.
Dimensionality Reduction for Ordinal Classification

Many unsupervised and supervised dimension reduction techniques are available for visualization and interpretation of high-dimensional data for classification tasks. While the unsupervised techniques do not employ the class information at all, most supervised algorithms are blind to the order of classes in ordinal classification problems. In this paper, we propose a novel and intuitive dimension reduction technique specifically designed for visualization of high-dimensional features in ordinal classification tasks. The technique is an iterative process, where at each iteration a search is conducted in the high-dimensional space to find the viewpoint from which the centers of adjacent classes are seen most distant from each other. The data is then projected to the lower dimensional space defined by the optimum viewpoint. The iteration is terminated when the desired dimensionality is achieved. Experimental results on various ordinal datasets demonstrate that our technique can be used as a complementary tool to the classical dimensionality reduction methods.
Real-Time Agent-Based Load-Balancing Algorithm for Internet of Drone (IoD) in Cloud Computing

Cloud computing (CC) is the most recent computing standard in IT where stages, applications, programming, and several other IT administrations are given over the web. CC is used to maintain the large amount of data generated by Internet of Drones (IoD) devices. The data generated by Internet of Things (IoT) devices are stored at a base station in the form of unstructured and duplicated data. For proficient asset use, the load-balancing system issue needs more consideration in CC. This chapter proposes an operator-based calculation for load balancing in appropriate conditions. In the proposed approach, a versatile specialist assumes a significant job, which is a product element and generally characterized as an autonomous programming program that suddenly spikes the demand for the sake of a system manager. It can learn. After contrasting the proposed technique and customary load-balancing plan, we reasoned that the agent baseload balancing plan incredibly diminishes the correspondence cost of hubs and quickens the pace of load balancing, which by implication improves the throughput and response time of the cloud.
21st Century Experts Required!

The article outlines the foundations and key ideas of new research projects and educational programs of Lomonosov Moscow State University, implemented in the form of Interdisciplinary scientific and educational schools. The Faculty of Philosophy of Moscow University participates in two schools: “Preservation of the World Cultural and Historical Heritage” and “Brain, Cognitive Systems and Artificial Intelligence”, which include several research areas and corresponding master’s programs that respond to present challenges and form new students’ competencies (“Digital Practices and Digital Culture”, “Sociocultural Origins of the Present and Forecasting the Future”, “Experimental Philosophy of Neurocog­nitive Sciences”). The article examines the context of the emergence of modern theoretical and practical problems, which require the participation of specialists in the field of philosophical sciences – in particular, the growing role of digital technologies in education and culture, the uncertainty of future scenarios and the importance of forecasting, development of the experimental project in the field of philosophy of neurocognitive sciences. In addition, the article reveals some aspects of applied ethics – due to the fact that it is planned to open the Bioethics program on the basis of the Faculty of Philosophy of Moscow State University, for the development of a complex interdisciplinary human study, social and hu­manitarian expertise of scientific projects and technological solutions.
Research and Model Construction of Teacher Competency Based on K-Means Clustering Algorithm

Based on the design of senior high school English education function evaluation system, a constraint parameter standard analysis model is established to evaluate the position ability of big data information model, extract the characteristic information of position ability limitation, and use k-means fusion algorithm to evaluate the position ability in the workplace. Integrate the evaluation and ability index parameters, complete the educational resource allocation plan, realize the scientificity of English education post ability evaluation, and design the information fusion analysis ability evaluation index system according to the experimental results, so as to improve the measurement accuracy and high practical application value.
ENDO-GDC: Projetando Jogos Educacionais

The Regional School of Information Systems of the state of Rio de Janeiro (ERSI-RJ) is an event that brings together professionals, teachers, and students interested in learning and discussing problems, solutions, and concepts related to information systems. ERSI-RJ mini-courses are short-term activities (four hours long) that are part of the ERSI-RJ program. They address issues related to information systems with the aim of providing the public an environment of learning and discussion of issues such as trends and challenges in the information systems area. In 2020, the ERSI-RJ was canceled because of the SAR-COV-2 pandemic. From all of the submissions for this year (2021), six mini-courses were chosen. Besides, two other submitted proposals were selected to be added as chapters of this book. A group of professionals and professors reviewed the mini-course proposals and selected the best ones. The committee members weighed in on the proposals and chose the finest ones. The selection criteria were: relevance to the event, audience expectation, timeliness and content. This book covers: the concepts and ways of detecting fake news in digital news media; an organizational modeling language that addresses the topic of competency-based human resource strategy and alignment; the use of simple language to support transparency and the development of best practices for digital transformation; the practice of data management in the field of data journalism; the introduction to game projects with educational purposes, which includes a canvas for Endogenous Educational Game Design; the concepts, definitions, applications, and research on Ethics in the information systems area; the main protocols of the Internet of Things' application layer, with a practical emphasis on the CoAP and MQTT protocols; and an example of treatment, correlation, and reducement of trends and seasonality of data by using R to analyze data about COVID-19 and public transport in Rio de Janeiro city. We believe this material will be useful for information systems classes and for supporting discussions, research, and professional practice. We congratulate and thank the authors of the chapters. We also thank the Selection Committee for their dedication and efficiency; the Editorial Committee for their effort; and the CESI (Special Committee on Information Systems) of SBC for this publication's support. Finally, the book's cover was designed by Matheus Nunes Ritton, a graduate student in information systems at the Universidade Federal Rural do Rio de Janeiro. This is his second time working on the ERSI-RJ minicourse book covers.
Launch of cybercrime-fighting group

The US Cybersecurity & Infrastructure Security Agency (CISA) is teaming up with a number of major players in the information security industry in a bid to fight cybercrime.
PowerCell boosts link-up with Hitachi ABB

PowerCell Sweden and Hitachi ABB Power Grids have signed an agreement to deepen their collaboration on fuel cell-based stationary power solutions.
Technical Writing as Embodiment

Our electronic devices indirectly assert a claim to power over humans, particularly when their usefulness ends due to planned obsolescence or entropy. Discarded devices—from cell phone batteries to laptops—quietly and insidiously morph from innocuous possessions that enhance our existence to dangerous e-waste whose toxic contents of heavy metals are mined for sustenance by marginalized people half a world away. Calling on Actor Network Theory (ANT), this project examines the right-to-repair movement and its pedagogical value in technical writing instruction to demonstrate global citizenry, ethics, and ecological activism as a rhetorical response to the agency of commonplaces. The learning objective for this chapter is for technical writers of all experience levels to be challenged to consider the role of technical writing as a means of perpetuating social justice and effective risk communication.
Monitoring Social Distancing for Industries and in Public Areas Using Machine Learning

The deadly coronavirus disease 2019 (COVID-19) has been spreading vigorously and has led to a global crisis, with its spread to more than 220 countries and territories. About 153,504,608 confirmed cases of the coronavirus COVID-19 that originated from Wuhan, China, and a death toll of 3,216,577 deaths as on 3 May 2021. At the time of writing, India is the worst affected country by COVID-19 and the death ratio is increasing day by day. To date we have more than four vaccines available, but social distancing has been identified as the best way to overcome and fight against this disease. In order to ensure social distancing protocols in overcrowded places and workplace, this tool can monitor whether or not people are ensuring a safe distancing protocol from each other by analyzing real-time video streams with the help of a constant camera feed. To keep track of people in various workplaces, factories, and shops we can use this tool to their security camera systems and can monitor whether or not people are keeping a secured distance from one another. This chapter proposes a Machine Learning and Python-based framework for monitoring social distancing using surveillance video with the help of a camera. In this proposed framework, we are utilizing the YOLO v3, an object detection model for separating the foreground details from the background details and OpenCV for tracking the humans by using the bounded boxes and assigning IDs to them.
Implementation of Automatic Detection of Text from Complex Images and Converting to Semantic Speech

The state of the art to recognize the printed English text character from any captured images containing misspelled text-to-speech signal which produces a voice with the correct pronunciation. The work uses an efficient preprocessing method to identify the region of interest (ROI) to detect the location of the text from the complex images having less resolution. The post-processing uses a Symspell correction model which provides a semantic text efficiently. The spell correction algorithms take minutes to process 127,000 words. The proposed model Symspell takes less than a minute to give the result. The innovation in the proposed algorithm produces an accuracy of 95.21%. The work is a cost-effective and truly efficient system that will be able to automatically identify and recite text aloud to visually challenged user.
The Effects of Immersive Learning for Poetry Writing via a VR GAME for Generation Z Students’ Creativity: Focusing on “Forum VR: Artist of Oz”

Purpose: This study aims to examine and understand the effect of immersive learning in poetry writing by using VR games to enhance the creativity of Generation Z. Method: To examine and understand which emotions among the positive and negative emotions the participating students experienced with respect to playing the VR games, the PANAS scale was used for the investigation. Also, this case study was carried out where a semi-structured interview was conducted to examine and understand the effect of immersive learning for poetry writing to foster creativity via VR games. Results: As a result of the investigation conducted via the PANAS scale, it turned out that the Generation Z students experienced positive affection(PA) for the immersive learning via the VR games, and experienced nearly no negative affection(NA). Plus, a semi-structured interview was conducted to examine and understand the immersive learning effect of the participating students. Keywords were extracted from the transcribed interview details by the inducive coding. The extracted themes are 1 fun, 2)collaborative problem solving, and 3)creativity. Conclusion: As the interest in the “metaverse”, which refers to the world of virtual reality has heightened, many of Generation Z are visible as they engage in even economic activities there. It is necessary to pay more attention to designing the learning experience of Generation Z by combining the unique experiences of virtual reality with creativity education. In this respect, it would be worth noting that the opportunities for research findings that the powerful experiential learning provided through immersive learning had a positive effect on the creative poetry writing activity in this study, and such discovery opportunities are quite educationally meaningful.
Design Scheme of Shared Tower Grounding Network Based on Uniform Ground Potential

The grounding standard of mobile base station in shared tower is relatively strict, so it is necessary to transform the grounding network of traditional transmission tower to meet the grounding requirement of base station. In this paper, the method of field-circuit coupling is adopted to treat the ground conductor in sections, calculate the self-resistance and cross-resistance of each section conductor, derive the solution formula of ground potential of grounding grid of shared tower, and then judge the uniformity of ground potential of grounding grid. Referring to the existing grounding standard of power and communication, the design idea and 4 design schemes of shared tower grounding grid are put forward. The results show that the ground potential distribution is the most uniform and the maximum potential gradient is 43.83% lower than that of the simple plan of expanding the area of the ground network.
PENINGKATAN KETERAMPILAN BERHITUNG SISWA MELALUI PENERAPAN MEDIA BRAILLE BAPER DI SLB CURUP

Getting proper education is the right of every citizen, both normal students and students with special needs (disabilities). In this case, the tools and learning media needed are very important to support teaching and learning activities. However, there are more learning tools and media available for normal students than students with special needs. The results of the observations that the author saw in SLB Rejang Lebong were still lacking in learning tools in special schools, especially learning tools for counting, especially blind children. Thus encouraging the author to be able to create a learning aid, especially in numeracy skills for children with disabilities, especially blind children in SLB Rejang Lebong Bengkulu.&#x0D; Starting from the problem above, the writer tries to deal with this problem by creating learning media, especially for blind children, namely "Braile Baper" which is simultaneously used as a research on the principle of usefulness of the tool in SLB Rejang Lebong which is a tool used. to practice multiplication counting skills in a simple and fast and not boring way so that it is expected to be useful to help improve understanding of blind children in learning media for numeracy skills in mathematics, especially in arithmetic operations material. The results of the application of the Braille Baper learning media that the student activities observed during the learning process were good, seen in the student activity observation sheets as well as interviews with students applying the Braille Baper learning media were responded positively and effectively applied in learning, especially for blind children.
Abnormal Activity Detection Using Deep Learning

Identifying abnormal activity is a laborious job, and this has led to the advancement in the domain of deep learning for surveillance which assured performance gain. Abnormal detection plays a pivotal role in the field of research and application systems. Detecting real-world problems such as accidents, burglary, explosion, fighting, robbery and other critical events is crucial, so we developed a deep learning-based algorithm to reduce manual work and time. To provide an optimal solution for the same, we have designed a model that detects abnormal events. To achieve this objective, we have implemented two approaches. The first approach is the model that we have trained (auto-encoder), and the second approach is the pre-trained model (C3D feature extraction). In approach 1, we have introduced a spatiotemporal auto-encoder, which is based on a 3D convolution neural network. After training the model with auto-encoder, our model has reconstructed the frames with a minimum loss of 8% and a maximum accuracy of 71%. To improve accuracy and to reduce loss, we have exercised approach 2 (C3D extraction) that computes features from the fully connected layers of the C3D which resulted in increased accuracy. To detect the abnormal activity in banks, roads and many more crowded areas, this can be implemented through CCTV cameras to automate and simplify manual work.