Reviewerly/exHarmony huggingface dataset:
Blockchain Technology in Procurement - A Systematic Literature Mapping

Procurement processes are deemed to lack supporting digital technologies that raise efficiency and automation.Blockchain solutions are piloted in procurement in order to offer a decentralized IT infrastructure covering these needs. This paper aims at identifying current blockchain approaches in the field of procurement and presenting affected business processes. In order to get an overview of the current state of the art, a systematic literature mapping is conducted.Moreover, the out-comes are gathered and categorized in a classification scheme. Based on the analysis, systematic maps are presented to showcase relevant findings. Within the findings, several blockchain use cases in the field of procurement are identified and information about addressed challenges, utilized blockchain frameworks and affected business processes are extracted.
CYBER CRIMES IN INDIA: TRENDS AND PREVENTION

The COVID-19 virus has affected most countries in the world, India being one of them with over 19000 people infected till date. The purpose of the paper to analyse the increasing cases of cyber violations, the urgency for more robust and comprehensive cyber security measures, among other issues. Between March and April 2020, India has witnessed a staggering 86% increase in cyber-attacks. According to the UN Special Reporter, women are both disproportionately targeted by online violence and suffer disproportionately serious consequences as a result. Cybercrime has real consequences and costs. It undermines women’s wellbeing, their rights, and their progress in all aspects of life. Cyber violence results in psychological, physical, sexual or economic harm to women. Given the push towards digitisation, amongst the ongoing pandemic, more women and girls are using the internet for varied purposes including education, work, and financial transactions, amongst others. Many of these women and girls could be first-time users and/or may have a limited understanding of good practices when interacting with others in cyberspace and could be subjected to cybercrimes. No doubt the crime rate has subsided as people are staying back but online frauds have seen an upsurge. Apart from being interaction/communication interfaces, sometimes these also serve as platforms for criminal elements and eventually end up being the epicentres of immeasurable security concerns. This working from home has now become an opportunity for cybercriminals to exploit the people through e-mail scams, hacking passwords, phishing, ransom attacks, online sexual harassment, etc.
Comment on “Separability of completely symmetric states in a multipartite system”

In their interesting paper [Phys. Rev. A 99, 032312 (2019)], Chen et al. consider the completely symmetric (CS) states, which are invariant under any index permutation. They proved that the CS states are separable if and only if they are a convex combination of real symmetric pure product states in Theorem 14, and claimed that any CS state in a two-qutrit system is separable in Theorem 20. We point out that their proof of Theorem 20 is flawed because of invalid assumptions. Furthermore, we present a sufficient and necessary condition for the separability of the CS states in two-qutrit systems.
Novel Class Detection Using Hybrid Ensemble

In this research, we propose a hybrid meta-classifier for novel class detection. It is able to efficiently detect the arrival of novel unseen classes as well as tackle real-time data stream classification. Specifically, the proposed hybrid meta-classifler includes three ensemble models, i.e. class-specific, cluster-specific and complementary boosting ensemble classifiers. Distinctive training strategies are also proposed for the generation of effective and diversified ensemble classifiers. The weights of the above ensemble models and the threshold of the novel class confidence are subsequently optimized using a modified Firefly Algorithm, to enhance performance. The above proposed ensemble and optimization algorithms cooperate with each other to conduct the detection of novel unseen classes. Several UCI databases are employed for evaluation, i.e. The KDD Cup, Image Segmentation, Soybean Large, Glass and Iris databases. In comparison with the baseline meta-algorithms such as Boosting, Bagging and Stacking, our approach shows significantly enhanced performance with the increment of the number of novel classes for all the test data sets, which poses great challenges to the existing baseline ensemble methods.
Adding and removing elements of the proportionality and necessity test to achieve desired outcomes. Breyer and the necessity to end anonymity of cell phone users

Case of Breyer v Germany Application no 50001/12 (ECtHR, 30 January 2020) The Breyer judgment concerns the storage of subscriber data by telecommunications service providers. To the Court, the collection and storage of such data amounted to interference of a rather limited nature. Additional safeguards were provided in the relevant German laws and there was independent supervision by the data protection authorities. The German lawmaker had not exceeded the margin of appreciation. There had been no violation of Article 8 of the European Convention on Human Rights.
SCHOOL LIBRARY AS TOOLS FOR INCULCATING AND ENHANCING READING CULTURE AMONG SCHOOL PUPILS IN GIWA LOCAL GOVERNMENT, KADUNA STATE

The study investigate the roles of the school library in inculcating and enhancing their reading culture in three selected secondary school in Giwa local government Kaduna State. Survey research method was used and questionnaire was used as the instrument of collecting the required information from the schools libraries. The study revealed that in those libraries, they render Current Awareness Services, Translation Services, Reference Services, Story Telling, and Book Talk. The study also revealed that information resources and services are efficient and effective while there are some challenges such as; inadequate finds unqualified man power among others. Moreover, the research recommends the way forward by employing qualified staff, provision of adequacy funds, and extension of library hours.
Modelled Neural Networks for Multiple Object Tracking

Recent development in artificial intelligence brought deep learning and neural networks that are applied in various areas, e.g.robotics, surveillance, autonomous driving, automation and medicine.Multiple Object Tracking very commonly utilises those architectures and there are many different approaches for this task.Those solutions are based on different kinds of neural network structures and this paper provides comparison of the corresponding algorithms that could improve further research.The paper investigates the performance of the Faster R-CNN, the VITAL and the RetinaNet methods with practical results and examines their different architectures used for object detection.The requirements for models are the detection of objects' position and their classification.For tracking the instances, we use algorithms that are based on object detection systems.For registering the location of items Neural Networks use the IOU (Intersection of Union) in order to determine which bounding boxes should be examined and according to the IOU we distinguish positive and negative proposed bounding boxes.The negative predictions impact the performance and negatively contribute to the wanted signal.The results of the Faster R-CNN method present those challenges.The object classification could become difficult in the event of occlusion.The RetinaNet method provides distinguished detection and classification results that could be applied for the Faster R-CNN and the VITAL computations.There are many evolving implementations for object tracking.The VITAL detector that uses the GAN for the motion prediction was evaluated on the custom set of image sequences, that are used for deep neural network adaptive parameter regulation..
Random Valued Impulse Noise Detection Using Fuzzy c-Means Clustering Technique

Noise detection is critical facet in eliminating impulse noise from digital images. Faulty detection can have deeper consequences in the ultimate outcome of the image filtering procedure. Established detection methods while distinguishing corrupted and non-corrupted pixels, have a tendency to upsurge errors while judging the edge points. In this paper, a detection technique, based on fuzzy c-means clustering algorithm is proposed to deal with above-mentioned issues. Fuzzy c-means clustering is used here to group similar non-corrupted pixels intensities by partitioning them into separate fuzzy clusters. Here, a pixel intensity value belongs to each cluster with certain membership strength. Depending on the maximum membership strength of a pixel element in a certain cluster among all the clusters, the most suitable belongingness of that pixel to that cluster is determined. After this operation, maximum element holding cluster is selected to be the cluster having the detected non-corrupted pixel elements, since there is a plausible tendency of similar non-corrupted pixel elements to group together. Standard measures like “miss” and “false-hit” have been used to compare the proposed method against the prevailing state-of-art methods to determine the noise detection accuracy of the proposed method. Being evaluated upon the aforesaid standard measures, the proposed detector is found superior to all the others in terms of its detection performance.
A Case Study on Physical Education Class in Elementary School with Blended Learning

초록·키워드 목차 오류제보하기 The purpose of this study was to examine teachers" perceptions and present a direction as a teaching method for educational effects, satisfaction, and substantiality in elementary school physical education classes to which blended running was applied. Five fifth grade elementary school teachers headquartered in Seoul were selected as research participants and had a deep interview. After recording and transcribing the interview materials, the concept was derived by iterative checking, and categorized and themed. In the research, the teachers who participated in the research had a low level of understanding of blended running, and the community of teachers was utilized, and the theory and utilization method of blended learning was made with reasonable efforts such as participation in training and discussions between fellow teachers. Was mastered. The study participants expressed a positive opinion that the quality of the lesson could be improved by structuring the lesson through the physical education lesson to which the blended running was applied, and striving to achieve the learning goal and stimulate it. However, there were also side effects. Due to the characteristics of blended learning in parallel with online lessons and school attendance lessons, it was not uncommon for students to accept lessons in a passive and passive manner, and it became clear that feedback and evaluation for learning were difficult. Based on this, we proposed the direction of a follow-up survey of physical education classes using blended running. #blended learning #physical education #teacher"s perception AbstractⅠ. 서론Ⅱ. 연구방법Ⅲ. 결과Ⅳ. 논의Ⅴ. 결론 및 제언참고문헌
Markers of Ælfric's Style

This study is a stylometric analysis of the works authored by Ælfric, a well-known Old English (OE) writer and translator. While the basic aim of the investigation is to identify the most typical linguistic features of his texts, the general idea behind the study is to show the extent of inter-textual variation of OE prose works. Ælfric, a very prolific writer, is responsible for ca. 25% of all prose records of this language. We employ stylometric techniques to show that Ælfric's style involves relatively frequent use of specific phrases initiated by function words (e.g., the prepositions mid and on) and grammatical constructions (e.g., adverbial clauses of reason). Our analysis demonstrates that there are some striking discrepancies between Ælfric and other (mostly anonymous) OE authors. This finding leads to the more general conclusion that individual stylistic preferences are an important aspect of research on Old English syntax and phraseology.
Analysis of vocational interests and student’s perception of work-based on society 5.0 towards learning outcomes

The challenge of education in Indonesia with the entry of the era of society 5.0 requires efforts to improve student learning outcomes, especially engineering education students. This paper focuses on looking at the influence of vocational interest on learning outcomes, perceptions of the world of work based on society 5.0 on learning outcomes, and vocational interests and perceptions of the world of work together on learning outcomes. The results of the study found that (1) there was a significant influence between vocational interest on student learning outcomes by 10.3%, (2) there was a significant influence between perceptions of the world of work based on society 5.0 on student learning outcomes by 11.3% and (3) there is a joint influence of vocational interest and perceptions of the world of work based on society 5.0 on student learning outcomes by 16%. Thus, it can be concluded that vocational interest and perceptions about the world of work based on society 5.0 are two factors that contribute to student learning outcomes. This means that the higher the vocational interest and perception of the world of work based on society 5.0, the student learning outcomes will tend to be higher.
Application of educational robotics in rural students of the Atacama region

Rurality has traditionally been a factor of marginalization in education, and it persists as a barrier that affects the progression of students in the educational system. Educational Robotics (ER) has its origins in Papert's constructionism, it is not simply assembling robots, it is a very versatile learning tool that allows working in different areas of knowledge. The objective of the article is to present information regarding the use and acceptance of ER by students from rural establishments in the Región de Atacama; The work methodology used was proposed sequentially and divided into groups of activities: i) Hypothesis Approach, ii) Definition of Methodology and Assessment Instruments, iii) Development of ER Activities, iv) Documentation and v) Analysis of Results. The information obtained after the results shows that the efficient and effective use of ER depends to a great extent on the level of ownership that the participants present in this regard, quickly becoming accustomed to the robot, carrying out participatory interactions between peers and oriented towards learning and solving problems.
Pedagogical conditions of civic education of senior high school students on the basis of national traditions

The purpose of the article is to check the effectiveness of pedagogical conditions of civic education of senior high school students on the basis of national traditions. Various diagnostic methods were introduced, which made it possible to identify the effectiveness of applying the developed pedagogical conditions, to adjust the methodology for their further implementation. Attention was paid to comparing the results in the control group and the experimental one both on each criterion and on the array of criteria. The authors conclude that it is important to properly combine different research methods based on the research findings on checking the effectiveness of pedagogical conditions of civic education of senior high school students on the basis of national traditions. The significance of the article is that the criteria of the proposed pedagogical conditions in secondary schools will help to improve the process of civic education of senior high school students.
Tactowel: A Subtle Sports Performance Display for Giving Real-Time Performance Feedback in Tennis

Sports technology enhances athletes’ performance by providing feedback. However, interaction techniques of current devices may overwhelm athletes with excessive information or distract them from their performance. Despite previous research, design knowledge on how to interact with these devices to prevent such occasions are scarce. To address this gap, we introduce subtle displays as real-time sports performance feedback output devices that unobtrusively present low-resolution information. In this paper, we conceptualize and apply subtle displays to tennis by designing Tactowel, a texture changing sports towel. We evaluate Tactowel through a remote user study with 8 professional tennis players, in which they experience, compare and discuss Tactowel. Our results suggest subtle displays could prevent overwhelming and distracting athletes through three distinct design strategies: (1) Restricting the use excluding duration of performance, (2) using the available routines and interactions, and (3) giving an overall abstraction through tangible interaction. We discuss these results to present design implications and future considerations for designing subtle displays.
On the Development of the Database “ArchaeoNomos” to Support Archaeological Research

Archeology is the only branch of human study that relies more on material remains left by people than on direct observation of human behavior or on written evidence. This focus on material evidence compels archaeologists to improve the methods and techniques of collecting and interpreting data that ensure the extraction of maximum information from available sources. The article provides an analysis of existing national and foreign information and geoinformation systems representing data on archaeological finds, cultural sites, and research documentation. A description of the existing standards, which regulate the list of attributes of geoarchaeological objects, is provided. The article also describes the stages of conceptual and logical modeling of the “ArchaeoNomos” database, which represents a unified repository for all data accompanying archaeological investigations, archaeological research and design, and documentation of research activities of the Scientific Research Center “Baikal Region” of Irkutsk State University. The problem of automating the processes of storage, processing, modification, and interpretation of geoarchaeological data is formulated. The system analysis of the subject area, system specification of an application that works with a database, the surveying specialists of the subject area to identify associations between database objects are presented. The infological and physical models of data presentation in the database are described, which allows the integration of the accumulated archaeological data into larger scientific associations. Finally, the advantages and disadvantages of using relational databases as repositories for archaeological data are outlined. The purpose of creating the “ArchaeoNomos” database is to implement measures for the preservation of the archaeological heritage, including the introduction and the use of the database by scientists that systematize and automate access to accumulated scientific data, including literary sources. The sub-goals include solving the problem of organizing and storing archaeological data, implementing multilingual interfaces in the information system, organizing information support for field and laboratory archaeological research, transferring the physical (nondigital) historical data storage system to a digital system. “ArchaeoNomos” database allows to keep records, analyze, visualize, and interpret data on geoarchaeological sites of Irkutsk and the Irkutsk region.
A Vision Based approach for Fall Detection System for Elderly People

There is a surge in the aging population, it is important for doctors and guardians to be conscious of issues related to health, which could impact majorly to the health of the elderly. Falling is one of the significant concerns that lead to fatal health problems in the case of the older population.The proposed system is especially for elderly people who are living alone in their homes or for old age homes. A vision-based fall detection approach has been used below in a home environment. A UR dataset of kinetic sensor’s output videos is used which classify into two categories i.e. fall and activities of daily living which are considered for experimentation. The MOG2 algorithm is used for background subtraction to focus on the human object and ignore the rest of the surrounding environment. The Shi Tomasi algorithm is used for finding interest points that are tracked using the optical flow method: Lucas-Kanade algorithm. Using interest points we compute its maximum displacement along with direction and speed of motion. For fall activity the last positions of interest points were observed. So the system detects a person in the environment, keeps tracking the person, and calculates the optical flow in case of a fall. If the fall is above the threshold point, an alert is sent to the concerned people and medical authorities.
ICS Code Automatic Classification(ICAC) in Standard Documents Using Machine Learning Algorithms

4차 산업혁명 이후, 표준의 범위가 확대되어 가고 있다. 새로운 용어나 유사한 용어들이 표준의 제목에 사용됨에 따라, 표준문서의 분류 및 검색이 복잡해지고 있다. 현재, 표준문서들의 효율적인 검색 및 관리를 위해 국제표준화기구인 ISO에서 운영하는 국제표준분류체계(ICS) 코드가 있으나, 아직도 ISO와 IEC를 제외한 다른 표준 개발 기구들이 ICS 코드체계를 채택하지 않은 설정이다. 더욱이 ICS 코드가 필요에 따라 새로이 수정되면, 이에 따라 ICS코드를 할당받은 기존 표준들도 새로이 ICS 코드를 재할당 받아야 한다. 그러므로 수정되는 ICS 코드 체계에 따라 자동적으로 표준문서를 분류할 수 있는 시스템이 필요한 실정이다. 본 연구는 이러한 표준 문서 분류 시스템을 구축하기 위하여 연구단계를 두 단계로 나누었다. 첫 번째 단계로, 현재의 ICS가 있는 표준들을 가지고 ICS 할당 시스템을 만들어 정확도를 검증하는 것이고, 두 번째 단계로, ICS 코드를 채택하지 않은 표준문서들에 ICS 코드를 할당하도록 하는 것이다. 특별히, 본 논문은 위에서 언급한 첫 번째 단계에 해당하는ICS 분류 및 할당 시스템을 머신러닝 알고리즘을 이용한 구조를 제안하고 이를 구현한 것을 보이는 것이다. 최선의 알고리즘을 선택하여 표준문서들의 ICS 코드를 분류하기 위해 잘 알려진 GRU 알고리즘을 비롯한 여러 알고리즘을 가장 최적의 결과를 보이는 파라미터를 사용하여 시험하였다. 결과적으로 머신러닝 알고리즘 보다 단어의 종속성을 학습하여 분류를 진행하는 딥러닝 알고리즘이 높은 성능을 보였으며, 딥러닝 알고리즘 중 GRU 알고리즘이 가장 높은 성능을 보였다.Since the Fourth Industrial Revolution, the scope of standards has been expanding. Classification and retrieval of standard documents are becoming complicated as new terms or similar terms are used in the title of the standard. Currently, there is an International Classification for Standards (ICS) code operated by ISO, the International Organization for Standardization, for the efficient search and management of standard documents, but it is still not adopted by other standard development organizations except ISO and IEC. Furthermore, if the ICS code is newly modified as needed, existing standards will also be required to reallocate the new ICS code accordingly. Therefore, a system is needed to automatically classify standard documents according to the ICS code scheme being modified. This work has been divided into two steps to build this standard document classification system. The first step is to create an ICS allocation system with the current ICS standards to verify accuracy, and the second step is to assign ICS codes to standard documents that do not adopt ICS codes. Specifically, this paper proposes a structure using machine learning algorithms and performs ICS classification and allocation systems corresponding to the first step mentioned above. To classify the ICS code of standard documents by selecting the best algorithm, several algorithms, including the well-known GRU algorithm, were tested using parameters with the best results. As a result, deep learning algorithms that learn the dependencies of words to proceed with classification showed higher performance than machine learning algorithms, and GRU algorithms showed the highest performance among deep learning algorithms.