Reviewerly/exHarmony huggingface dataset:
Learning to write chinese characters among non-native chinese learners / Ting Hie-Ling...[et al.]

Learning to write Chinese characters is often challenging for non-native Chinese learners mainly because the writing system is completely different from the alphabetical system. Traditionally, rote learning is commonly used in the teaching and learning of Chinese characters but it is often considered as laborious. Studies indicate mobile applications (MAs) can facilitate the learning process better (Shinagawa, 2012; Wong et al, 2010) especially for non-native Chinese learners. This study is designed to compare the use of both learning methods to promote the correct order of writing Chinese characters. A hundred Level 2 Mandarin undergraduates participated in this study. They were given a Pre-Test before they were divided equally into Treatment group (TG) and Control group (CG). The CG used traditional method of rote learning to practice writing, while TG used three differrent MAs to practice for three weeks. Later, all of them were required to complete a Post-Test. The scores for pre-test and post-test were analyzed and compared. The results suggest that traditional and MA method give significant and positive impacts on the improvement of writing order. Futher studies are needed to seek participants’ preferences in learning writing order as a way to cater for their personal needs in writing Chinese characters.
A service-based software architecture for enabling the electronic health record storage using Blockchain

The healthcare sector requires computational solutions with reliable authenticity features for the storage and retrieval of electronic health record data. To address this important issue, this article proposes a service-based software architecture to extract data from different legacy databases, standardise the patient's clinical data requirements and store data using different Blockchain technologies. To achieve this, a software architecture has been designed to guarantee the independence of data storage technology. In addition, a data meta-schema and a set of mapping rules have been specified to store and organise clinical data following an international health standard. To validate the solution presented, the real world scenario of a Brazilian healthcare institution has been used to evaluate the data extraction, standardisation and storage capabilities in two Blockchain platforms widely used in the information technology market.
Approximate Novelty Search

Width-based search algorithms seek plans by prioritizing states according to a suitably defined measure of novelty, that maps states into a set of novelty categories. Space and time complexity to evaluate state novelty is known to be exponential on the cardinality of the set. We present novel methods to obtain polynomial approximations of novelty and width-based search. First, we approximate novelty computation via random sampling and Bloom filters, reducing the runtime and memory footprint. Second, we approximate the best-first search using an adaptive policy that decides whether to forgo the expansion of nodes in the open list. These two techniques are integrated into existing width-based algorithms, resulting in new planners that perform significantly better than other state-of-the-art planners over benchmarks from the International Planning Competitions.
Role of college librarians in promoting n-list e-resources usage in academic colleges of Pune district affiliated to Savitribai Phule Pune University

In the present digital era consortia is a great example of libraries working together to take positive long-term action to acquire. Participation in these activities enables the librarians to develop skills and knowledge and be very efficient, increasing their ability to withstand the next rounds of budget challenges. The Project N-LIST provides access to more than 6,000 + e-journals and 31,35,000 + e-books to students, researchers and faculty members from colleges (covered under section 12B of UGC Act) and other beneficiary institutions. In an academic library, the librarian is involved in the evaluation, imparting training, promotion, monitoring all activities in providing better access to e-resources to the users of colleges. So the present study is to discuss the role of the librarian in promoting N-List e-resources usage in academic colleges of Pune affiliated to Savitribai Phule Pune University. To promote the use of N-list e-resources in academic libraries the library authorities should conduct awareness and orientation programmes about N-list e-resources for the faculty members and students separately.
A Parametrised Functional Interpretation of Affine Logic

This paper presents an abstract parametrised functional interpretation of Affine Logic. It is based on families of parameters allowing for different degrees of freedom on the design of the interpretation. In this way we are able to generalise previous work on unifying functional interpretations, by including in the unification the more recent bounded and Herbrandized functional interpretations.
Meet Them Where They Are: Social Work Informed Considerations for Youth Inclusion in AI Violence Prevention Systems

To address the challenges posed by social media, school districts, law enforcement, and criminal justice organizations are leveraging artificial intelligence (AI) to identify and predict harmful content online. Due to lack of understanding of language, cultural nuances, and social context, there are numerous impacts when AI technologies wrongly interpret youths’ posts as violent. As such, AI systems may create and reinforce new systems of marginalization and oppression, even put youth at risk by creating digital pathways to incarceration (Patton et al., 2017). We suggest social work ethical principles be integrated broadly as a framing guide for the development of AI technologies to prevent violence against and among youth and marginalized groups.
Pemilihan Metode dan Algoritma dalam Analisis Sentimen di Media Sosial : Sistematic Literature Review

This article use Systematic Literature Review (SLR) to classify sentiment analysis based on case studies, methods, social media, and platforms. The coding stage is divided into three stages, namely the open, selective and axial coding. The literature study on sentiment analysis is divided into two parts: identifying gaps based on case studies and data sources and identifying gaps based on the methods or algorithms used. The gap identification results based on case studies and data sources show that popular review topics are synonymous with entertainment, economic and political content. Therefore, the quantity of research with review topics related to the implementation of education, the dynamics of the bureaucracy, health facilities and services, and non-governmental organization’s activities need to be increased. Meanwhile, the most dominant platforms used as data sources are website and mobile-based applications. The results of the gap identification based on the method and algorithm show that the quantity of research with the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM) method or algorithm is more dominant than the k-Nearest Neighbor (k-NN) and Lexicon-based. Thus, it is necessary to increase the number of other classification methods such as Particle Swarm Optimization, BM25, Decision Tree, K-Means, and Neural Networks.
Machine Learning for Data Science Applications

Machine Learning (ML) involves use of various mathematical and statistical methods to draw useful conclusions and insights from the given data. With data growing exponentially, its analysis becomes extremely crucial. In this chapter, the theory and utility of various machine learning algorithms for data science applications is discussed. The data can be distinguished based on whether it is labeled or not. The major supervised and unsupervised algorithms considered in this chapter are linear regression, decision trees, naïve Bayes, support vector machines (SVMs), and clustering techniques like K-means. For the sake of completeness, this chapter also presents techniques for making better predictions on the new (unseen) data with performance metrics used for evaluating machine learning algorithms. These include concepts like splitting the given data into train/test/validation datasets, cross-validation, precision, recall, F/F1 measures, purity of clustering, and so on. Feature engineering and dimensionality reduction is a very important step for successfully modeling a data science problem. The chapter concludes with some discussion on practical issues and how to resolve them using ensemble learning and meta-learning techniques like bagging, boosting, and stacking.
Quantum robotics: A scientometrics assessment of global publications during 1993-2020

The study examines global research on Quantum Robotics on quantitative and qualitative metrics to understand the status of research in the subject at the global, national, institutional, and individual level. The analysis is based on 650 publications as indexed in the Scopus database for the period 1993-2020. The study provides a window to most productive countries, organizations and authors in the subject, provides a distribution of publications across broad subjects, identifies top modes of research communication, and describes the characteristics of highly-cited papers. The quantum robotics research registered a 33.92% annual average growth, received an average of 9.47 citations per paper (CPP) since publication during 1993-2020, which was 16.31 CPP in 1993-2006 and 8.53 CPP in 2007-2020. Only 12 of the 650 publications (1.85% share) have registered as highly cited papers. China and the USA lead the world ranking with a global publication share of 21.38% and 17.38% respectively during the period.
Цифрова компетентність і технології для освіти: принципи та інструменти

The article analyzes such concepts as «digital competence» and «digital consumption». The conditions that become decisive factors for the comprehensive development of digital competence are also analyzed. Issues related to students' digital competencies and the organization of education in a post-industrial society are discussed. The role of digital education in the processes of a country's economic transformation and the formation of a single digital space is considered. The goals and objectives of educational process participants under reforming education are determined. The article reveals the importance of digital technologies for the organization of joint activities of an educator and students. The possibilities of using modem educational tools to increase a teacher’s level of digital competence are characterized.In the current conditions a teacher is obliged to constantly develop and improve informational and communicational competence by the introduction of digital tools of educational activities; to promote the acquisition of students' skills of critical perception of information, to learn to identify inaccurate information, to distinguish facts from judgments, to protect himself from information pressure dangers, to use media opportunities, to carry out systematic work on media literacy.
Sensei: Self-Supervised Sensor Name Segmentation

Sensor names as alphanumeric strings typically encode their key contextual information such as their function or physical location.We focus here on sensors used in smart building applications.In these applications, sensor names are curated in a building vendorspecific manner using different structures and esoteric vocabularies.Tremendous manual effort is needed to annotate sensor nodes for each building or even to just segment these sensor names into meaningful chunks for intelligent operation of buildings.We propose here a fully automated self-supervised framework, Sensei, that can learn to segment sensor names without any human annotation.We employ a neural language model to capture the underlying structure in sensor names and then induce self-supervision based on information from the language model to build the segmentation model.Extensive experiments on five real-world buildings comprising thousands of sensors demonstrate the superiority of Sensei over baseline methods.
Sistem Informasi Logbook Pegawai Politeknik Negeri Cilacap Sebagai Absensi Online Pada Masa Pandemi COVID-19

Politeknik Negeri Cilacap as an educational institution under The Ministry of Education of The Republic of Indonesia has adopted the Work From Home (WFH) policy to suppress the spread of COVID-19. In the implementation of WFH, lecturers and employees are required to make reports. The mechanism of this report has problems, including a lack of standardized report forms, obstructed work report (logbook) approval processes by the unit’s leader, and reports that are still in Microsoft Excel files, making it difficult for the staffing department to map and calculate the employee's attendance. The purpose of this research is to design and build a logbook information system for Politeknik Negeri Cilacap that is used for online employee attendance during the COVID-19 pandemic. This research uses the Waterfall method as a system development method, with Unified Modeling Language (UML) as a design model. The result of this research is a logbook information system that was built as a tool for lecturers and employees to report their activities, and for staffing department to monitor and appraise employee performance. Based on the usability test result, this system has a 78.61% value and a “Good” predicate.
UPAYA MENINGKATKAN KETERAMPILAN BERBICARA MENCERITAKAN PENGALAMAN MELALUI METODE TIME TOKEN ARENDS DAN MEDIA GAMBAR PADA ANAK TK DESA SIWAL KECAMATAN BAKI TAHUN PELAJARAN 2018/2019

Classroom Action Research aims to improve speaking skills to tell experiences in kindergarten children Siwal Village 2018/2019 Academic Year through the Time Token Arends method and image media. The design of this study used the Classroom Action Research (CAR) which was carried out in 2 (two) cycles, carried out in the Siwal Village Kindergarten with research subjects for all kindergartens in the Siwal Village 2018/2019 Academic Year, totaling 22 children. Data collection is done through observation, documentation and tests. Data analysis was carried out with 3 (three) stages including; data reduction, data presentation, and data verification. The results of the study stated that through the Time Token Arends method and media images can improve speaking skills to tell the experience of the initial conditions of children who get a Good grade only 5 children (22.7%), 7 children (31.8%) get a Fair value, and as many as 10 children (45.5%) received a grade of Less., Increased to the end of the second cycle to be a child with a Good value of 21 children (95.5%), the value of which is Enough 1 child (4.5%), and no children whose grades are Less than a total of 22 kindergarten children Siwal Village 2018/2019 Academic Year.
An Empirical Analysis of College Students’ English Learning under the Background of “Internet ”

In order to understand the English learning situation of Chinese college students in the context of Internet +, college students as our survey subjects are used to collect data by stratified classification and cluster random sampling.So we collected the needs of college students in the online education market for online English education and teaching.We strive to have a more in-depth understanding of the problems that contemporary college students have in English learning, as well as their opinions and suggestions on English learning apps.Only in this way can we develop a software development path and distribution strategy that is more suitable for the public to learn English and provide guidance and data support.
A comprehensive Study for Different Types of CAPTCHA Methods and Various Attacks

Abstract –Due to the growth of the internet in our life and its importance in all our everyday activities , including occupational and educational ones .We should also keep in mind that it harbours a negative aspect , as it brings security problems. Hence CAPTCHAs are used for making systems more secure. The major role of CAPTCHA is to prevent roboticbot (spam) form surrender or prove user personality. Using a pattern-matching algorithm is necessary for the user for comparison by receiving signs founded in similar matches. This method makes it difficult to get a forbidden access to data,since malicious bot is to spy out signs in the image. This mechanism implements actions on the attitude of user gesticulation which makes it distinguishable and secure. Human-Computer Interaction i.e. interaction between computer and individual is made to make it easy in understand human language .On other words, Human-computer Interaction focuses on the study of interfaces between computer and human. This paper presents a comprehensive study of various different types of CAPTCHA, the ones good in securing webpages. It also discusses the strengths and weaknesses of each one. Moreover, its importance in discerning the user’s individuality, the behavior of humans, and bots and the paper will illustrate some CAPTCHAs mechaniits and impairments which allows hackers to break .In addition, is necessary to take into consideration the approaches of building a good CAPTCHA.
An Efficient Trust Computing Scheme for Implementing a Trustworthy and Collaborative Cloud Service

The vital task of cloud computing programme is to provide trusted services to users. The user wants to deliver their critical tasks and information to cloud data centre. So that, the burden of the cloud customer is shifted to cloud data centre. Trust has a prime role between user and cloud service provider. There are many approaches available to calculate trust value in collaborative cloud computing. In the proposed system, to provide security, data uploaded to cloud will be in encrypted format. Parallel trust computing scheme will calculate the trust. A thread will be created for each user request and will be fast as it has to evaluate the trust for single user. Trust will be determined based on successful login, and response time. The response time calculated from each request is added to matrix window, from which we can predict and recommend the best performing cloud services to user. The experimental result support practicality and success of the proposed system.
An Interactive E-Learning Design for the grade 12 Tvl-Ict Programming Using Kotobee of Lagro Senior High School

The research used an open-educational resource using Kotobee software to design interactive elearning for the Grade 12 TVL-ICT programming students of the Lagro Senior High School. The researchers wanted to assess the learners' interest by using an interactive e-learning design made by the teachers. We used stratified and simple random sampling techniques to get the samples. The method and procedure of the research used are quantitative, descriptive, and developmental. The respondents of the study are selected Grade 12 TVL-ICT programming students. We used the Google form survey questionnaire to gather and collect the respondents' data because of the online distance learning implemented amidst a pandemic situation for this Academic Year 2020-2021. The statistical treatment used in the study is mean, frequency, percentage, and Likert scale. As the results, 41 out of 50 respondents were answered and retrieved the survey data. There were 33 male and 8 female participants who answered the survey through Google form. The learner's characteristics among the respondents were 76% are visual, 10% were auditory, and 7% were kinesthetic and pragmatist. Most of the learners have used gadgets for their online distance learning. 33 out of 41 respondents have used the mobile phone, and 26 responses used the tablets. When the teachers utilized e-learning through virtual online classes, the students' challenges were the image blurred and very small to view, which had 26 responses out of 41 participants. Next, the text is too much, and the content is too hard to understand, which had 22 answers and agreed. Followed-by the text used is small, and the video presentation is pixelated. When the student's losing interest talked about, the teacher has always presented a YouTube video clip related to the topic, which had the highest mean of 3.10 or agreed and placed in the rank 1. Second, using a simple PowerPoint presentation to discuss the lesson with no proper explanation had a mean of 3.05 o agree. Three (3) losing the interest of the student had the same mean of 2.80 or agreed. These are 1) hard to read the text from the teacher's PowerPoint presentation, 2)the graphics are pixelated and inappropriate to the lesson, and 3) there is no interconnection of the image, colors, text, and shape used in the presentation. Meanwhile, the level of satisfaction to utilize the teacher's interactive e-learning in terms of effectiveness shows that it is minimally satisfied or with an average mean of 2.31. The efficiency had an average of 2.46 or minimally satisfied.
Is Each Layer Non-trivial in CNN? (Student Abstract)

Convolutional neural network (CNN) models have achieved great success in many fields. With the advent of ResNet, networks used in practice are getting deeper and wider. However, is each layer non-trivial in networks? To answer this question, we trained a network on the training set, then we replace the network convolution kernels with zeros and test the result models on the test set. We compared experimental results with baseline and showed that we can reach similar or even the same performances. Although convolution kernels are the cores of networks, we demonstrate that some of them are trivial and regular in ResNet.
Sum-Product Network Decompilation

There exists a dichotomy between classical probabilistic graphical models, such as Bayesian networks (BNs), and modern tractable models, such as sum-product networks (SPNs). The former generally have intractable inference, but provide a high level of interpretability, while the latter admits a wide range of tractable inference routines, but are typically harder to interpret. Due to this dichotomy, tools to convert between BNs and SPNs are desirable. While one direction -- compiling BNs into SPNs -- is well discussed in Darwiche's seminal work on arithmetic circuit compilation, the converse direction -- decompiling SPNs into BNs -- has received surprisingly little attention. In this paper, we fill this gap by proposing SPN2BN, an algorithm that decompiles an SPN into a BN. SPN2BN has several salient features when compared to the only other two works decompiling SPNs. Most significantly, the BNs returned by SPN2BN are minimal independence-maps that are more parsimonious with respect to the introduction of latent variables. Secondly, the output BN produced by SPN2BN can be precisely characterized with respect to a compiled BN. More specifically, a certain set of directed edges will be added to the input BN, giving what we will call the moral-closure. Lastly, it is established that our compilation-decompilation process is idempotent. This has practical significance as it limits the size of the decompiled SPN.