Reviewerly/exHarmony huggingface dataset:
A Remote Reading Literacy for Senior High School: A Voice from Students

The pandemic Covid-19 outbreak caused reading literacy to get less attention than the normal school condition in SMA Negeri 2 Semarang. Therefore, there should be ways to run remote reading literacy strategies. This study aimed to investigate students' perception toward a remote reading literacy strategy, particularly the reading texts, learning process, advantages and disadvantages of the remote reading literacy strategy. The population of this study was 36 students of the 12th graders in SMA Negeri 2 Semarang. The study design was qualitative descriptive. The data was collected through questionnaires and interview. Then, the data was analyzed using interpretive descriptive analysis. The specific target to be achieved in this study is that by knowing the students' perception of remote reading literacy, the result may be used to reference the English teachers to have the reading literacy in English lesson. The study results were the students' perception that they needed to read an average of five times before reading aloud the texts with the tangible pronunciation and comprehend the meaning of the new vocabularies. Moreover, they informed that the learning process was unique, promoted autonomous learning. However, the video compressing and the internet connection were challenging for the students.
비행청소년의 소년원 생활경험에 관한 현상학적 연구 - 10호 처분 남자소년원생을 중심으로 -

This study was conducted with male protected boys who received No. 10 disposition, who were in the reformatory. Six participants were selected and through in-depth interviews the researchers examined what experiences they had in reformatory and what their experiences mean to them. The research method was analyzed through Giorgi’s phenomenological research procedure, a qualitative research method. As a result of the study, 4 essential elements and 15 sub-elements were derived. The four essential elements were ‘big change in living environment’, ‘conflict and adaptation’, ‘reflective reflection and change’, and ‘worries about new life’, Through these components, experiencing the process of adaptation to the reformatory, its accompanying conflicts and the need for a new start after being discharged from the reformatory were discovered as the subject of this study. Based on the results of this study, finally, it was intended to contribute to providing a more practical and systematic reformatory education program.
Network-based Trajectory Topic Interaction Map for Text Mining of COVID-19 Biomedical Literature

Since the emergence of the worldwide pandemic of COVID-19, relevant research has been published at a dazzling pace, which makes it hard to follow the research in this area without dedicated efforts. It is practically impossible to implement this task manually due to the high volume of the relevant literature. Text mining has been considered to be a powerful approach to address this challenge, especially the topic modeling, a well-known unsupervised method that aims to reveal latent topics from the literature. However, in spite of its potential utility, the results generated from this approach are often investigated manually. Hence, its application to the COVID-19 literature is not straightforward and expert knowledge is needed to make meaningful interpretations. In order to address these challenges, we propose a novel analytical framework for estimating topic interactions and effective visualization for topic interpretation. Here we assumed that topics constituting a paper can be positioned on an interaction map, which belongs to a high-dimensional Euclidean space. Based on this assumption, after summarizing topics with their topic-word distributions using the biterm topic model, we mapped these latent topics on networks to visualize relationships among the topics. Moreover, in the proposed approach, the change of relationships among topics can be traced using a trajectory plot generated with different levels of word richness. These results together provide deeply mined and intuitive representation of relationships among topics related to a specific research area. The application of this proposed framework to the PubMed literature shows that our approach facilitates understanding of the topics constituting the COVID-19 knowledge.
A neurorobotics approach to behaviour selection based on human activity recognition

Behaviour selection has been an active research topic for robotics, in particular in the field of human-robot interaction. For a robot to interact autonomously and effectively with humans, the coupling between techniques for human activity recognition and robot behaviour selection is of paramount importance. However, most approaches to date consist of deterministic associations between the recognised activities and the robot behaviours, neglecting the uncertainty inherent to sequential predictions in real-time applications. In this paper, we address this gap by presenting an initial neurorobotics model that embeds, in a simulated robot, computational models of parts of the mammalian brain that resembles neurophysiological aspects of the basal ganglia-thalamus-cortex (BG-T-C) circuit, coupled with human activity recognition techniques. A robotics simulation environment was developed for assessing the model, where a mobile robot accomplished tasks by using behaviour selection in accordance with the activity being performed by the inhabitant of an intelligent home. Initial results revealed that the initial neurorobotics model is advantageous, especially considering the coupling between the most accurate activity recognition approaches and the computational models of more complex animals.
General and Specific Security Services, Risks, and Their Modeling

Cybersecurity has been a mainstream challenge over the years for security analysts and architects alike. The changes in the scenario for virtually accomplishing almost every regular activity, due to the coronavirus pandemic, have only increased the use of systems around the globe. With the increase in such usage of systems, it is essential to better design systems concerning their security aspects. It is essential to understand the basic security services that a system must comply with, to avoid scenarios of becoming compromised in terms of a breach or an attack. This chapter covers general security services and the modeling aspects associated with any system. It also looks at the latest research done on such services and security models in recent times. We explore the risks associated with a system and how, for a specific use-case of virtual reality implementation, we task the reader to see what risks can be mapped out and tackled. This chapter acts as a baseline for an individual to understand security services, the risks associated with a system, and the security models available that can be used while designing any system.
Correlation of national and international in modern Russian science

The article examines the issues that determine the ratio of national and international elements in modern Russian science. The need for this study is caused by the role of science in modern society and the culture of the entire nation. Russian scientific culture is currently experiencing a certain crisis, globalization, informatization and alienation from its roots adversely affect its development and functioning. It is necessary to study the process of such influence in order to prevent the loss of the identity of the national scientific culture.
Image to Image Content Generation

In days of old, before cameras captured everything, if a crime were committed and it was particularly heinous, the police would send a sketch artist around to interview witnesses. The purpose of this was to build an image or likeness of the criminal and expedite an arrest.
Error-Aware Interactive Semantic Parsing of OpenStreetMap

In semantic parsing of geographical queries against real-world databases such as Open-StreetMap (OSM), unique correct answers do not necessarily exist.Instead, the truth might be lying in the eye of the user, who needs to enter an interactive setup where ambiguities can be resolved and parsing mistakes can be corrected.Our work presents an approach to interactive semantic parsing where an explicit error detection is performed, and a clarification question is generated that pinpoints the suspected source of ambiguity or error and communicates it to the human user.Our experimental results show that a combination of entropy-based uncertainty detection and beam search, together with multi-source training on clarification question, initial parse, and user answer, results in improvements of 1.2% F1 score on a parser that already performs at 90.26% on the NLMaps dataset for OSM semantic parsing.
Credit Card Fraud Prediction

Fraud detection by credit companies is essential in this digital era where the majority of financial transactions are made online. Fraudsters use loopholes in the payment systems to their benefit. Such problems can be solved to a large extent if the companies add an extra layer of security before confirming the transactions using machine learning algorithms. This project intends to use the Isolation Forest algorithm to enhance the security of credit card transactions by predicting the credibility of the transaction before authorization. Detecting 100% of the fraudulent transaction, minimizing the incorrect fraud classifications, and making the process automated is our objective.
So You Want to Analyze Scheme Programs With Datalog?

Static analysis approximates the results of a program by examining only its syntax. For example, control-flow analysis (CFA) determines which syntactic lambdas (for functional languages) or (for object-oriented) methods may be invoked at each call site within a program. Rich theoretical results exist studying control flow analysis for Scheme-like languages, but implementations are often complex and specialized. By contrast, object-oriented languages (Java in particular) enjoy high-precision control-flow analyses that scale to thousands (or more) of lines of code. State-of-the-art implementations (such as DOOP on Soufflé) structure the analysis using Horn-SAT (Datalog) to enable compilation of the analysis to efficient implementations such as high-performance relational algebra kernels. In this paper, we present an implementation of control-flow analysis for a significant subset of Scheme (including set!, call/cc, and primitive operations) using the Soufflé Datalog engine. We present an evaluation on a worst-case term demonstrating the polynomial complexity of our m-CFA and remark upon scalability results using Soufflé.
A review of phishing email detection approaches with deep learning algorithm implementation

Phishing email is designed to mimics the legitimate emails to fool the victim into revealing their confidentialinformation for the phisher's benefit. There have been many approaches in detecting phishing emails butthe whole solution is still needed as the weaknesses of the previous and current approaches are beingmanipulated by phishers to make phishing attack works. This paper provides an organized guide to presentthe wide state of phishing attack generally and phishing email specifically. This paper also categorizesmachine learning into shallow learning and deep learning, followed by related works in each category withtheir contributions and drawbacks. The main objective of this review is to uncover the utility of machinelearning in general, and deep learning in particular, in order to detect phishing email by studying theliterature. This will provide an insight of the phishing issue, the alternatives prior to the phishing emaildetection and the contrast of machine learning and deep learning approaches in detecting phishing emails.
IIIT Hyderabad Submission To WAT 2021: Efficient Multilingual NMT systems for Indian languages

This paper describes the work and the systems submitted by the IIIT-Hyderbad team (Id: IIIT-H) in the WAT 2021(Nakazawa et al., 2021) MultiIndicMT shared task.The task covers 10 major languages of the Indian subcontinent.For the scope of this task, we have built multilingual systems for 20 translation directions namely English-Indic (one-to-many) and Indic-English (many-to-one).Individually, Indian languages are resource poor which hampers translation quality but by leveraging multilingualism and abundant monolingual corpora, the translation quality can be substantially boosted.But the multilingual systems are highly complex in terms of time as well as computational resources.Therefore, we are training our systems by efficiently selecting data that will actually contribute to most of the learning process.Furthermore, we are also exploiting the language relatedness found in between Indian languages.All the comparisons were made using BLEU score and we found that our final multilingual system significantly outperforms the baselines by an average of 11.3 and 19.6 BLEU points for English-Indic (en-xx) and Indic-English (xx-en) directions, respectively.
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty

Multi-Agent Reinforcement Learning (MARL) is a challenging subarea of Reinforcement Learning due to the non-stationarity of the environments and the large dimensionality of the combined action space. Deep MARL algorithms have been applied to solve different task offloading problems. However, in real-world applications, information required by the agents (i.e. rewards and states) are subject to noise and alterations. The stability and the robustness of deep MARL to practical challenges is still an open research problem. In this work, we apply state-of-the art MARL algorithms to solve task offloading with reward uncertainty. We show that perturbations in the reward signal can induce decrease in the performance compared to learning with perfect rewards. We expect this paper to stimulate more research in studying and addressing the practical challenges of deploying deep MARL solutions in wireless communications systems.
DIGITAL LIBRARY FOR RESEARCH AND EDUCATION: A CASE OF THE URAL REGION

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 10018-10023ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.2058Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference
How do different factors Impact the Inter-language Similarity? A Case Study on Indian languages

India is one of the most linguistically diverse nations of the world and is culturally very rich.Most of these languages are somewhat similar to each other on account of sharing a common ancestry or being in contact for a long period of time (Bhattacharyya et al., 2016).Nowadays, researchers are constantly putting efforts in utilizing the language relatedness to improve the performance of various NLP systems such as cross lingual semantic search, machine translation (Kunchukuttan and Bhattacharyya, 2020), sentiment analysis systems, etc.So in this paper, we performed an extensive case study on similarity involving languages of the Indian subcontinent.Language similarity prediction is defined as the task of measuring how similar the two languages are on the basis of their lexical, morphological and syntactic features.In this study, we concentrate only on the approach to calculate lexical similarity between Indian languages by looking at various factors such as size and type of corpus, similarity algorithms, subword segmentation, etc.The main takeaways from our work are: (i) Relative order of the language similarities largely remain the same, regardless of the factors mentioned above, (ii) Similarity within the same language family is higher, (iii) Languages share more lexical features at the subword level.
ERGO FACE SHIELD MEMBRANE: INTERDISCIPLINARY PPE DESIGN EXPERIENCE, ENCOURAGING UNDERGRADUATE STUDENTS INTO REAL RESEARCHER CHALLENGES IN THE SOUTH OF CHILE

Information about the paper titled "ERGO FACE SHIELD MEMBRANE: INTERDISCIPLINARY PPE DESIGN EXPERIENCE, ENCOURAGING UNDERGRADUATE STUDENTS INTO REAL RESEARCHER CHALLENGES IN THE SOUTH OF CHILE" at IATED Digital Library
THE CONCEPT OF THE LMS SYSTEM FOR TEACHING SUBJECTS FROM THE FIELD OF INDUSTRY 4.0 BASED ON AUGMENTED REALITY

THE CONCEPT OF THE LMS SYSTEM FOR TEACHING SUBJECTS FROM THE FIELD OF INDUSTRY 4.0 BASED ON AUGMENTED REALITY
Product Design of Portable Nail Puller for Practical Work

Failure to pull the nail properly without damaging the material is a major problem for the user. In this regard, this study was developed to develop a portable nail pull tool that can be used to remove nails without wasting material. This study was conducted to develop a portable nail pull tool for practical training to replace existing tools manually. Through this manual action, users encounter problems when removing nails without damaging the workpiece and materials used. This product was developed using the ADDIE model as a development model. There are five phases in the ADDIE model namely the analysis, design, development, design implementation and evaluation phases. The development of this simulation uses Solidwork's designing application to develop it. Three experts have been selected to evaluate the design and functionality of the Portable Nail Puller Simulation using questionnaires and expert endorsements. Data analysis is done using percentage method. All experts agree on the development of this Nail Puller Simulation where the functionality level is 100%. Therefore, the development of these simulations can provide a clear and detailed picture to the user to develop the actual tool.
THE UKULELE AS A DIDACTICAL TOOL IN PRIMARY EDUCATION: DESIGN, IMPLEMENTATION, AND EVALUATION OF A PEDAGOGICAL PROJECT IN FACE-TO-FACE AND VIRTUAL ENVIRONMENTS, DURING THE FORMATION OF FUTURE EDUCATORS

THE UKULELE AS A DIDACTICAL TOOL IN PRIMARY EDUCATION: DESIGN, IMPLEMENTATION, AND EVALUATION OF A PEDAGOGICAL PROJECT IN FACE-TO-FACE AND VIRTUAL ENVIRONMENTS, DURING THE FORMATION OF FUTURE EDUCATORS
LibGuides @ WRAIR: Effective Database Searching: Document and Save Your Searches

Explore this guide for tips on how to maximize your database search and retrieve relevant results.
Pre-training Methods for Neural Machine Translation

Mingxuan Wang, Lei Li. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts. 2021.
Analysis on the Sustainable Development of Higher Education Based on Rank-Sum Ratio and Time Series

In order to help every country to have a healthy and sustainable higher education system, this paper proposes a series of mathematical models, including Rank-Sum Ratio Model and Time Series Model to measure and assess the health of higher education system at a national level, and identify a healthy and sustainable state of higher education system and propose and analyze relevant effective policies for one country. After collecting various valid education-related data, a Rank-Sum Ratio Model is used to analyze and rank the current comprehensive education strength of the U.S.and Japan. According to the raking result, we further choose three countries, including the United States, Japan and China, for specific analysis.A Time Series Model is used to predict the missing higher education data in 2020 for the United States, Japan and China, as the data of 2020 have not been published.
UNDERGRADUATE TEACHER TRAINING FOR THE DEVELOPMENT OF DIGITAL TECHNOLOGIES OF INFORMATION AND COMMUNICATION IN THE TECHNOTEACHING CONTEXT

L. de Lima 1, R. C. Loureiro 1, B. Collyer de Aguiar 2 1Universidade Federal do Ceará (BRAZIL) 2University of Sussex (UNITED KINGDOM)
Deep Learning to Ternary Hash Codes by Continuation

Recently, it has been observed that {0,1,-1}-ternary codes which are simply generated from deep features by hard thresholding, tend to outperform {-1,1}-binary codes in image retrieval. To obtain better ternary codes, we for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non-smoothed ternary function by a continuation method. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the generated codes indeed could achieve higher retrieval accuracy.
3D 프린팅 기술로 출력된 치과 보철물의 임상적 적합도 평가

Purpose: The objectives of this study was to evaluate clinical adaptation of dental prostheses printed by 3 dimensional(3D) printing technology. Methods: Ten study models were prepared. Ten specimens of experimental group were printed by 3D printing(3DP group). As a control group, 10 specimens were fabricated by casting method on the same models. Marginal gaps of all specimens were measured to evaluate clinical adaptation. Marginal adaptations were measured using silicone replica technique and measured at 8 sites per specimen. Wilcoxon’s signed-ranks test was used for statistical analysis(α = 0.05). Results: Means of marginal adaptations were 95.1μm for 3DP group and 75.9μm for CAST group(p < 0.000). Conclusion : However, the mean of the 3DP group was within the clinical tolerance suggested by the previous researchers. Based on this, dental prosthesis fabricated by 3D printing technology is considered to be clinically acceptable.
Know Deeper: Knowledge-Conversation Cyclic Utilization Mechanism for Open-domain Dialogue Generation

End-to-End intelligent neural dialogue systems suffer from the problems of generating inconsistent and repetitive responses. Existing dialogue models pay attention to unilaterally incorporating personal knowledge into the dialog while ignoring the fact that incorporating the personality-related conversation information into personal knowledge taken as the bilateral information flow boosts the quality of the subsequent conversation. Besides, it is indispensable to control personal knowledge utilization over the conversation level. In this paper, we propose a conversation-adaption multi-view persona aware response generation model that aims at enhancing conversation consistency and alleviating the repetition from two folds. First, we consider conversation consistency from multiple views. From the view of the persona profile, we design a novel interaction module that not only iteratively incorporates personalized knowledge into each turn conversation but also captures the personality-related information from conversation to enhance personalized knowledge semantic representation. From the view of speaking style, we introduce the speaking style vector and feed it into the decoder to keep the speaking style consistency. To avoid conversation repetition, we devise a coverage mechanism to keep track of the activation of personal knowledge utilization. Experiments on both automatic and human evaluation verify the superiority of our model over previous models.