Reviewerly/exHarmony huggingface dataset:
Designing data interaction in exhibitions contexts

Nowadays, the primary purpose of information representation is to support users in understanding complex phenomena.Avoiding the 'black hole between data and knowledge" means research tools and methods that help people experience and benefit from this data.Through the analysis of case studies, the contribution aims to provide an overview of existing data visualization and physicalization techniques that enable participatory processes with users.This contribution focuses on interactive data visualizations, particularly on installations in museums, exhibitions or events that involve the user in an active and participatory way.These types of activities offer a simple but effective way to make complex data understandable.It will explore processes of collaborative creation of input from visitors, processes of direct interaction of the public with data that are not easily accessible by traditional methods, and finally, some visualizations that, through this powerful representational medium, promote renewed needs for engagement.
Making-with the environment through more-than-human design

Current climate and ecological crises require questioning currently dominant understandings and relations to nonhumans.While design is a human-centered field and practice, many intruders or competing theories challenge human-centered approaches and propose ways to include nonhumans in design.This article explores different perspectives for post-anthropocentric design approaches and focuses on how design can approach the notion more-than-human as an intruder to human-centered design.Proposing practice-based studies of making-with the environment as an alternative to human-centered design, it explores how to design beyond ideas of "human progress".Firstly, more-than-human and related concepts are introduced.Secondly, how human-centered design can be challenged is explained through the concept of core theories and intruders, relating it with "more-than-human" and posthuman theories.Afterwards, traditional knowledge is introduced as a concept to explore morethan-human approaches, and a case study is introduced as a post-anthropocentric making activity.The case study demonstrates that designers should acknowledge and listen to traditional and indigenous knowledges, while shifting to a more-than-human design approach.
Participatory IoT policies: A case study of designing governance at a local level

As IoT devices proliferate in public spaces, it is vital that adequate governance structures and policies are designed and implemented in order to enhance trust, and protect privacy and security of citizens.At a local level, smaller towns and cities that are not part of the 'smart city' movement, but instead are connected through IoT devices, also need to consider how these devices are governed.This research explores how two novel methods (design fiction and walkshops) can be combined and embedded in the design of policy for IoT governance at a local level.The contribution of the work lies in wider discussions of design methods in policy making and offers a case study of how these methods can be used at a local level.
An End-to-End Methodology for Semi-Supervised HAR Data Collection, Labeling, and Classification Using a Wristband

This work presents a semi-automated labeling system for Human Activity Recognition (HAR) datasets. A 1D-CNN trained on scripted activities is used to generate predicted labels for free-form walking segments. Labels below a confidence threshold are marked for review using closed captioned video reducing the time required to label the unscripted sequences. The publicly available TWristAR dataset collected for this work includes Empatica e4 wristband data recorded during six HAR activities and free-form walks. A complete time-stamped video record is provided which we believe makes this dataset unique and useful. Our 1D-CNN model achieves a subject-dependent accuracy of 96.4% and a subject-independent accuracy of 77.4% for six scripted activities. The source code for this work is available at https://github.com/imics-lab/Semi-Supervised-HAR-e4-Wristband.
Calibration of chaos medichain by non-functional testing approaches

The evolution of blockchain networks extends with the proliferation of the Internet of Medical Things devices. These devices require stupendous storage space which enlarges the maintenance cost. It raises undermined data security &amp; Privacy, risk of failure, dishonest interlopes access to the centralized system. The propitious solution against these challenges of an extensive centralized system is decentralized-based blockchain technology. This paper implements decentralized-based blockchain architecture deployed with chaotic encryption and proof of consensus mechanism for mining. The disintermediate architecture as an enchanting target for Denial of Service (DoS) attacks. To covenant the trust, testers must certify that all the components of the blockchain network are employing flawlessly. The performance of non-functional testing assured the quality assurance services is a decisive aspect of the proposed blockchain architecture. The proposed decentralized-based blockchain framework is calibrated by injecting various malicious attacks. Moreover, the attacks are injected with other encryption-based blockchains to correlate the high effective scheme for medical data security. The evaluation reveals that the Proposed chaos medicine architecture shows 14% lower latency than SHA and 18 % lower latency than AES on all attacks and deliberate to be safe from malicious attack.
Research Information Systems and Ethics relating to Open Science

Current research information systems (CRIS) evaluate research performance and are intended to contribute to the continuous improvement of research. Based on former research on the ethical dimensions of CRIS, our paper presents the results of a survey with a small sample of representatives of ethics committees from different European countries on ethical aspects of CRIS. Ethics committees and experts are rarely associated with CRIS-related projects. However, their opinion on ethical indicators and the implementation and use of a CRIS is undoubtedly essential for the future development and management of these systems. Against this background, our purpose is to provide a deeper understanding of the ethical aspects in the field of research information management, to show how CRIS represent ethical dimensions of scientific research and to suggest some adjustment of their development, implementation and use.
Towards a Legal Methodology of Digitalisation

Digital technologies have not only changed the foundations for our economic, social and cultural life. They have also challenged our basic notions about “law”. This contribution argues that in order to deal with the fundamental challenges from digitalisation a new framework for a legal methodology of digitalisation needs to be developed to guide regulators, decision-making bodies and private legal actors in their adoption, application and governance of rules of information law. Using EU copyright law as an exemplary example, four methodological shifts of general importance and rising out of digitalisation are identified: (1) from substantive law to procedural law; (2) towards globalisation; (3) towards horizontally based law; and (4) from state-enacted law to contract and code. Moving forward, these shifts should inform discussions about a legal methodology of digitalisation.
Universities innovative development under the globalization conditions

The article systematizes main directions of improving organizational and economic support of higher education innovative development in the globalization context. One of the main intensification directions of innovative activity in the higher education field is the creation of a national strategy for innovative higher education development, synchronized with existing programs for the development of domestic higher education system and national economy in general. Authors emphasize that the policy of targeted financing of higher education system innovative development requires optimization. Prerequisites for university innovative development are the availability of state support, adequate financial support, lack of bureaucracy, presence of a developed innovation infrastructure, developed innovation culture in society and more. Promising areas for further universities’ development are also identified: deepening the relationship between higher education, science and industry, particularly in the direction of scientific research commercialization; ensuring university autonomy (including financial autonomy), flexibility and digitalization of higher education; use of distance learning; regular educational material updating in accordance with business demands and scientific-technological progress latest achievements.
Online Voting System

Abstract: The main moto of this project is to focus extremely on handling voting, and its candidates, voters, etc. Also, this project shows all the accessible data of voting like number of votes, voters, and non-voters. Also, it is designed to function in a way such that it permits candidates by submitting their name, script and picture. The project is parted into two parts: Admin Panel and Voters Panel. In regard with this web application, the admin can totally use the system. The admin has the authority to test every particular aspect of the project. To conduct the process smoothly, an admin should to provide a title and opt possible candidates. Here, the system contains certain prerequisites such as the admin must opt more candidates to participate. According with voters account, the admin should arrange them by providing unique name and username respectively
RESERVOIR COMPUTING WITH TRUNCATED NORMAL DISTRIBUTION FOR SPEECH EMOTION RECOGNITION

Speech is an effective, quick, and important way for communicating and exchanging complex information between humans. Emotions have always been a part of normal human conversation which makes the speech more attractive. Because of this major role of both speech and emotion, many researchers are inspired by studying Speech Emotion Recognition (SER) which still has plenty of challenges. In this study, we proposed a novel reservoir computing approach with the initialization of random connection weights for the input weight by the truncated normal distribution. Furthermore, Population-Based Training (PBT) is adopted to optimize the hyperparameters of the whole Echo State Network (ESN) model which have a significant impact on the model performance. The proposed model has adopted bidirectional reservoir input to increase the memorization capability, and Sparse Random Projection (SRP) was applied for dimensional reduction as a simple, unsupervised, and low complexity approach. The speaker-independent strategy was employed on EMODB and SAVEE datasets as an acted speech emotion dataset and Aibo as a non-acted dataset. The model achieved 84.8%, 65.95%, and 45.99% unweighted average recalls on the EMODB, SAVEE, and Aibo datasets respectively. The results show that the proposed model outperforms the recent state-of-the-art studies with a cheaper computational cost.
Self-Replication in Neural Networks

A key element of biological structures is self-replication. Neural networks are the prime structure used for the emergent construction of complex behavior in computers. We analyze how various network types lend themselves to self-replication. Backpropagation turns out to be the natural way to navigate the space of network weights and allows non-trivial self-replicators to arise naturally. We perform an in-depth analysis to show the self-replicators' robustness to noise. We then introduce artificial chemistry environments consisting of several neural networks and examine their emergent behavior. In extension to this work's previous version (Gabor et al., 2019), we provide an extensive analysis of the occurrence of fixpoint weight configurations within the weight space and an approximation of their respective attractor basins.
Practicability of Sports Goods in the Sports Field Based on Artificial Intelligence Technology

People are in the intellectual age. The use of technologies such as smart search engines, machine translation, fingerprint analysis, facial scanning, and self-driving makes people’s social and work lives happier. The intersection of artificial intelligence and other projects has also become the focus of research in the new era. This article uses comparative experimental methods and sampling methods, puts forward the evaluation method of association rules in the recommendation of sports goods, and compares the selection of sensors used by the five types of sports companies in the sports experiments. In the experiment part, according to the minimum confidence level, the two rules are met. Minimum confidence is a threshold defined by users or experts to measure confidence, which represents the lowest reliability of association rules. The conclusion is that 30% of users have bought running shoes and hiking shoes at the same time and 66.6% of users have also purchased sports suits; there are also sports suits purchased at the same time. All mountain bike users have bought sports gloves. The weight of Freescale is 0.2530, the weight of Bosch is 0.4457, the weight of ST is 0.0946, the weight of VTI is 0.0953, and the weight of Konix is 0.1114. The experimental results show that the selection result of the three-axis acceleration sensor is the SMB380 model of Bosch.
IMITATION TECHNIQUE IN LEARNING ENGLISH AT ENGLISH EDUCATION DEPARTMENT UNIVERSITAS NEGERI MANADO

English as a foreign language is something interest to learn even though it is not easy to learn. In learning English many teachers were try some media, method, strategy, and technique. The aim of this research is to describe how the use of imitation technique is effective in learning English. In this research the researcher used imitation technique in learning English to improve students’ pronunciation and listening skill. By used video the students imitate what are the speaker say, before that they must listen carefully what speaker say. In this research consists of 20 students in third semester and used three videos. This research applied in three treatments. The result showed the student receive better improvement in learning English. The findings in the analysis it showed the improvement of students’ pronunciation is increased from 22.85 to 0.95 incorrect words and the improvement of listening is increased from 28.2 to 78.7, and it concluded that imitation technique is effective technique in learning English. When the words pronounced correctly it was help to recognize the words in case listening skill. In COVID-19 pandemic it is possible to use media such as video youtube in teaching and learning process even though technology could not replace the teacher position but technology will be distributor the knowledge from teacher to student.
Improving node embedding by a compact neighborhood representation

Graph Embedding, a learning paradigm that represents graph vertices, edges, and other semantic information about a graph into low-dimensional vectors, has found wide applications in different machine learning tasks. In the past few years, we have had a plethora of methods centered on graph embedding using different techniques, such as spectral classification, matrix factorization and learning. In this context, choosing the appropriate dimension of the obtained embedding remains a fundamental issue. In this paper, we propose a compact representation of a node’s neighborhood, including attributes and structure, that can be used as an additional dimension to enrich node embedding, to ensure accuracy. This compact representation ensures that both semantic and structural properties of a node’s neighboring-hood are properly captured in a single dimension. Consequently, we improve the learned embedding from state-of-the-art models by introducing the neighborhood compact representation for each node as an additional layer of dimensionality. We leverage on this neighborhood encoding technique and compare with embedding from state-of-the-art models on two learning tasks: node classification and link prediction. The performance evaluation shows that our approach gives a better prediction and classification accuracy in both tasks.
An Empirical Study on the Relationship of Financial Sector Development, Remittances Inflows, and Income Inequality in South Asian Countries

The study explores the moderating role of financial sector development in the remittances-income inequality nexus in the six highest remittances-recipient “South-Asian economies”, “namely “Afghanistan, Bangladesh, India, Nepal, Sri Lanka, and Pakistan”” for the period 2006 to 2019. The direct channel of remittances-income inequality and the financial sector development &amp; income-inequality have been greatly explored in the literature, but the indirect channel, i.e. the moderating role of the financial sector in the remittances- income inequality nexus, is still lacking; this study tries to fill this literature gap. The study explores the indirect link of the financial sector based on financial access, financial stability, financial depth, and financial efficiency in remittances-income inequality nexus in highest remittances-recipient South-Asian economies. The study applied Fixed effects and Pooled Ordinary Least Squares (POLS) econometric techniques in order to examine the role of financial sector development in the relationship between international remittances and income inequality. The empirical findings of the study show that the financial sector development mitigates the income-inequality effects in the selected remittance-recipient South-Asian economies. The interaction term of financial sector development and remittances mitigate the negative influence of income inequality in the selected economies. Remittances abridge income inequality in the presence of a well-functioning and sound financial sector in the selected South-Asian remittance-recipient economies.
APPLICATION OF DECISION TREE AND NAIVE BAYES ON STUDENT PERFORMANCE DATASET

Student performance is the ability of students to deal with the entire academic series taken during school. Student performance produces two labels, namely successful and unsuccessful students. Successful students can graduate with excellent, excellent, and suitable performance labels. At the same time, students who have a label on average are students who get poor performance. Measurement of student performance is needed for every educational institution to take strategic steps to improve student performance. This study aimed to obtain a data mining method that worked well on student performance datasets. In this study, student performance datasets were processed, which had 11 indicators with one result label. Student performance datasets are processed using data mining methods, namely decision tree and nave Bayes, while the tool used for dataset processing is WEKA. The research results from processing student performance datasets obtained that the accuracy value for the decision tree method was 94.3132%, and the accuracy produced by the naive Bayes method was 84.8052%.
Estimation of Solar Insolation and Angstrom–Prescott Coefficients Using Sunshine Hours over Nepal

The amount of solar insolation that reaches the Earth in one hour is sufficient to fulfill its annual energy budget. One of the challenges for harvesting this energy is due to a lack of relevant data. In the least developed countries like Nepal, the number of observation stations is insufficient. This data gap can be filled by employing credible empirical models to estimate solar insolation in regions where insolation measurements are not available. In this paper, Angstrom–Prescott model parameters are estimated for fifteen different locations of Nepal. Then, correlation is developed for the prediction of solar insolation using only sunshine hour data. The different statistical parameters such as root mean square error (RMSE = 1.958), mean bias error (MBE = −0.018), mean percentage error (MPE = 2.973), coefficient of residual mass (CRM = 0.001), and correlation coefficient (r = 0.909) were used to validate the developed coefficients. The resulting Angstrom–Prescott coefficients are a = 0.239 and b = 0.508. These coefficients can be utilized for the prediction of solar energy at different parts of the country in similar weather conditions.
Sensor Data Analytics: Challenges and Methods for Data-Intensive Applications

Sensors have become a key element for the development of the Information Society [...]
Using Cryptocurrency to Increase Student Motivation and Agency: Thoughts from Vietnam

This paper will argue that cryptocurrency can be used as a tool to motivate and increase the agency of students in higher education. Although just a few cryptocurrencies, or coins, dominate the headlines (bitcoin, ether, etc.), more than 10,000 different coins currently exist. Each coin targets specific problems (inter-user payments, unlocking specific services, etc.) and two key problems in Vietnamese higher education are student motivation and student agency. The argument is that by rewarding particular behaviors (good grades, attendance, etc.) with a grant of coins to each student, the coin can bring the benefits of badging but can go even further because coins, unlike badges, can be spent. One major weakness of cryptocurrencies is that they are like money but with less utility. This paper argues that this weakness can be turned into a strength by making a new coin that is targeted specifically to education. Students with better academic performance would receive more coins than other students. These coins can then be used to purchase exclusive options such as entry to watch or even meet a guest speaker or the student could buy time from the teacher to help them improve their study plan. While teachers do offer office hours, some students are hesitant to attend as they feel they do not deserve to consume the teacher’s time, because it was their parents, not the students, who paid the tuition. However, by earning the coins to buy the teacher’s time, it is anticipated that students would become more agentic and more willing to take advantage of those resources that have been available yet remain underutilized. Students without coins may be able to buy them from other students at a market price. This can provide even greater motivation for students, especially those from disadvantaged backgrounds, to perform well because their reward (the coins) can be exchanged for real world money.
Detection of Cyber bullying on Social Media Using Machine Learning

Abstract: In the modern era, the usage of the internet has increased tremendously which in turn has led to the evolution of large amounts of data. Cyber world has its own pros and cons. One of the alarming situations in web 4.0 is cyber bullying, a type of cyber-crime. When bullying occurs online with the aid of technology it is known as cyber bullying. This research paper has surveyed the work done by 30 different researchers on cyber bullying, and elaborated on different methodologies adopted by them for the detection of bullying. Three types of features namely textual, behavioral and demographic features are extracted from the dataset as compared to earlier study over the same dataset where only textual features were considered. Textual features include certain bullying words that if exists within the text may lead to a true outcome for cyber bullying. Personality trait features are extracted for the user if it is involved once in bullying may bully in future too. While demographic features extracted from the dataset include age, gender and location. The system is evaluated through different performance measures for both classifiers used and the performance of the Support Vector Machine classifier is found better than the Bernoulli NB with overall 87.14 accuracy.