Reviewerly/exHarmony huggingface dataset:
Sistem Informasi Administrasi Kependudukan (SIASIK) Pada Kelurahan Berbasis Web

Sub-district in general acts as a community service center in certain areas. One of the sub-district services is to administer population administration letters. The main problem in sub-district in general there is no system that helps in storing Population Administration data. The Demography Administration Information System aims to carry out administration such as (Transfer Letter, Death Certificate, etc.). This system is also for handling a lot of documents so that the making of application letters can be faster and more efficient. In gathering information, the authors made direct observations and direct interviews with parties related to the administration of the village. making using the Demography Administration Information System with SDLC Waterfall method. In its development, planning is made starting from collecting all data and making designs to build system requirements. The programming language used is web-based. The advantages obtained in this application are made to make it easier for officers in handling a lot of documents, such as difficulty finding and printing data. And for the sake of data security to avoid data loss, data duplication and data manipulation. The system using the Sublime application with the programming language PHP, jquery, and Enterprise Architect 7.0. The result of this research is a village administration program that can assist the administrative activities of the village.
Wavelet-Supervision Convolutional Neural Network for Restoration of JPEG-LS Near Lossless Compression Image

JPEG-LS near lossless compression algorithm is widely used in remote sensing image compression. However, the run-length coding of the algorithm results in horizontal stripe distortion of the decompressed image, which will greatly affect the quality of remote sensing image. In order to solve such distortion and restore the image, a wavelet-supervision convolutional neural network (WSCNN) with large receptive field is proposed. WSCNN can make full use of the information both in spatial and frequency domains. With translation invariance, convolutional neural network (CNN) is very adept at extracting features in pixel space. To further explore spatial information, we enlarge the receptive field of our WSCNN. Alternatively, wavelet coefficients show promising of frequency information digging, we adopt them to supervise our WSCNN. With this wavelet-supervision, WSCNN can focus on the horizontal stripe distortion in frequency domain. Besides, we have collected a dataset, it consists of original remote sensing images at a resolution of 4096×4096 and corresponding JPEG-LS near-lossless compression images as data pairs. Subjective and objective experiments have verified the effectiveness of our WSCNN for the restoration of LPEG-LS near-lossless compression images.
ThriftyNets: Convolutional Neural Networks with Tiny Parameter Budget

Deep Neural Networks are state-of-the-art in a large number of challenges in machine learning. However, to reach the best performance they require a huge pool of parameters. Indeed, typical deep convolutional architectures present an increasing number of feature maps as we go deeper in the network, whereas spatial resolution of inputs is decreased through downsampling operations. This means that most of the parameters lay in the final layers, while a large portion of the computations are performed by a small fraction of the total parameters in the first layers. In an effort to use every parameter of a network at its maximum, we propose a new convolutional neural network architecture, called ThriftyNet. In ThriftyNet, only one convolutional layer is defined and used recursively, leading to a maximal parameter factorization. In complement, normalization, non-linearities, downsamplings and shortcut ensure sufficient expressivity of the model. ThriftyNet achieves competitive performance on a tiny parameters budget, exceeding 91% accuracy on CIFAR-10 with less than 40 k parameters in total, 74.3% on CIFAR-100 with less than 600 k parameters, and 67.1% On ImageNet ILSVRC 2012 with no more than 4.15 M parameters. However, the proposed method typically requires more computations than existing counterparts.
3D Vessel Segmentation in CT for Augmented and Virtual Reality

Blood vessels 3D rendering has numerous applications, ranging from diagnosis to pre-procedural and surgical approaches as it enriches vessels visualization for the clinician. In this paper, we propose a 3D blood vessels segmentation method designed for use with a cone beam computed tomography (CBCT). The algorithm constitutes a module in the development of an angio-CT visualization system, based on augmented or virtual reality as instruments supporting and improving medical decisions. The proposed segmentation tool exploits a bone segmentation step for easing the extraction of blood vessels. Both steps are based on region growing technique. For each subject are used two CBCT acquisitions, where the first one is acquired with a traditional CT scan and is used for bone extraction, while the second one is acquired after contrast medium administration and is used for vessels reconstruction. This novel segmentation algorithm provides an automatic and accurate tool to segment and render blood vessels tree. 3D anatomical models were viewed through a virtual reality environment in order to validate the visualization system.
Examining Malicious Online Comments from the Bystander Effect Perspective

Cyberbullying has become a social problem as malicious text messages and online comments among teenagers have increased in the late 2000s. Some serious reporting has attempted to impress on us the need to pay more attention to reducing malicious online content as a typical type of cyberbullying. Meanwhile, despite environmental changes that have made it easier to report perpetrators of such messages, it is often the case that the crime occurs in a public place and is tolerated. However, there is a growing tendency for people to exhibit the bystander effect, the problem of personal indifference to witnessing or knowing about crimes, but individuals do not offer any means of help to a victim when other people are present. This effect is rampant in the case of cybercrimes. This study aims to extract the motivations behind posting malicious comments through in-depth interviews and to suggest recommendations for relative issues by demonstrating how the bystander effect can be reduced using causal relationship diagrams of the system dynamics methodology. Hopefully, this work will contribute to a better understanding of factors that could cause a decrease in malicious online comments.
A Fault Modeling Based Runtime Diagnostic Mechanism for Vehicular Distributed Control Systems

This paper presents a study about a runtime mechanism to monitor the performance degradation in intra-vehicular networks. The proposed mechanism focuses on the integration of fault modeling in communication protocols, as non-functional requirements (NFR), using aspect-oriented modeling (AOM), to model the performance degradation generated by faults, linking test and design phases of distributed control systems. A case study analyzing the mechanism performance in both CAN and CAN-FD protocols was conducted considering the NFR specification related to fault disturbances. In order to evaluate and simulate the mechanism under real fault scenarios, an active suspension control system was considered as an example of a critical control system and faults were injected using the hardware Vector VH6501 (CAN disturbance interface). The network performance analysis was made based on the software Vector CANoe considering different network busloads and CAN/CAN-FD rates between 1 to 4 Mbps. Results show that the mechanism efficiently detects anomalous events on performance with short response time with busloads up to 30%. The performed experiments show better performance on CAN-FD registering lower average jitter during fault injection in all busloads tested scenarios.
Advanced Collaborative Robots for the Factory of the Future

This paper presents an integrated robotic platform for advanced collaborative robots and demonstrates an application of multiple robots collaboratively transporting an object to different positions in a factory environment. The proposed platform integrates a drone, a mobile manipulator robot, and a dual-arm robot to work autonomously, while also collaborating with a human worker.
Selection of online shopping with the electree method

E-Commerce stands for Electronic Commerce which means a marketing system by means of electronic media. E-Commerce is defined as the process of buying and selling of products, services and information that is done electronically using a computer network. One form of e-commerce is online shopping. The purpose of this study is to determine the best online shopping based on predetermined criteria. The criteria used in E-Commerce are the price of goods, completeness of goods, shipping costs, quality of goods, and length of delivery time. The Electre method is a multi-criteria decision-making method based on the concept of outranking using pairwise comparisons of alternatives based on each appropriate criterion. Based on the analysis using the Electric method, it is recommended that the best online shopping is TOKOPEDIA which dominates other alternatives.
Analysis of the Effect of Hidden Layer Units on Coronary Heart Prediction Using the Radial Basis Functions Algorithm

Heart disease is a disease that occurs due to disturbances in the heart, especially when pumping blood so that it can cause death. Nearly half of deaths in the United States and other developed countries are caused by heart disease. Therefore, an early prognosis of heart disease is needed to prevent the risk of coronary heart disease. One thing that can be done is to predict coronary heart disease sufferers using the neural network method. This study conducted an analysis of the effect of hidden layer units on the neural network radial basis functions algorithm to predict coronary heart disease sufferers. This study obtained the highest accuracy at 10 hidden layers, namely 85.08%.
ANALYSIS OF CURRENT TRENDS IN DISTANCE EDUCATION DURING COVID-19: A SOUTH AFRICAN HIGHER EDUCATION CONTEXT

This research explores current trends in distance education (DE) within the higher education sector. The first aspect of the study acknowledges the evolving state of distance education by adopting a definitional approach of what has been covered by previous scholars. By presenting a document analysis of the relevant studies in this field, the paper contextualizes distance education from a South African contextual perspective. Such a methodology was necessary in order to not only highlight the gaps but to also present specific up-to-date trends of distance education. This has helped to identify the key research question on what the nature of the trends are and what practical steps are necessary in enhancing quality education within the current COVID-19 situation in South Africa. The conclusion is drawn from a discussion of the major trends that have emerged to characterize the South African distance education context as a result of COVID-19 and to highlight areas for future research endeavours.
Maximum Spanning Tree Graph Model: National Examination Data Analysis of Junior High School in Lampung Province

The purpose of this study is to determine the tendencies of learning in Lampung Province especially at junior high school level. Subjects of this study are all result of national examination data for junior high school in 2017 and 2018 in Lampung Province. The method of this study using model analysis of maximum spanning tree (MST) graph assisted by descriptive and inferential statistics technique. The data obtained are analysed using descriptive statistical analysis to find out the basic information of the data and inferential statistical analysis to get the value of coefficient correlation. Then, the MST graph is formed using Kruskal’s algorithm by interpreting the vertices as a subject and the edges as the value of coefficient correlation. The subject that have the closest relationship to any subject is a graph vertex with the greatest degree. Based on the MST graph model, the result of this study shows that the subject which has closest relationship with another subject is Science in 2017, while in 2018 are English and Mathematics. Based on these result and relevant research, the conclusion shows that Science and Mathematics to be the centre for the national examination of junior high school in 2017 and 2018 in Lampung Province.
High-Order Sliding Modes Based On-Line Training Algorithm for Recurrent High-Order Neural Networks

This work presents a discrete on-line training algorithm for recurrent high-order neural networks (RHONN). The proposed training algorithm is based on the arbitrary order differentiators of high-order sliding modes (HOSM) theory. Due to HOSM-based differentiators can approximate derivatives in finite time, the proposed training algorithm avoids the compute of the derivatives, unlike conventional training algorithms. The proposed HOSM-based algorithm is implemented for the training of a RHONN identifier, and its performance is compared with the results using the extended Kalman filter (EKF) training algorithm. Results of a implementation of the identifier for the Lorenz system and an implementation of the identifier for a tracked robot using experimental data are presented.
Promoting Financial and Cultural Citizenship Literacy as Multiliteracy in Tourism Village

Literacy skills provide opportunities to build society to face the global changes. This study aims to promoting the community members of the tourist village according to financial and cultural citizenship literacy as a part of multiliteracy in tourism village. This research applied descriptive qualitative approach. The subjects are 10 villagers in Jojogan Village, Wonosobo Regency. The method of data collecting use interview, observation, and documentation. Data analysis begins during the data collection process, performs data reduction, data presentation, and ends with drawing conclusions and verification. The findings describe promoting financial and cultural citizenship literacy as multiliteracy in Tourism Village is carried out in the community to have a financial and cultural literacy in their daily lives as a part of tourism village community. This activity promotes literacy cultur e by adjusting their free time because the learning schedule was coming from the community member's needs. With this condition, the manager and facilitator continuously try to adjust the conditions of the community members. These literacy learning activities include reading, writing, numerating, speaking, and listening. The benefit of this research is to provide an explanation that financial and cultural citizenship literacy as a part of multiliteracy in tourism village must be done.
Influence of training programme design on employee education

The design of training programmes involves the process of planning the content and methods of training and assessing the learners’ prior knowledge. Andragogy principles play an important role in the design of employee training. They refer to adult learning strategies that involve using adequate content, relevant examples and technical terms in the design and delivery of training, as well as incorporating learners’ previous knowledge and experience. They can be used as the basis for the selection of the appropriate content and training methods. This paper seeks to investigate whether the inclusion of andragogy principles in the design of employee training affects the training outcomes, in particular their motivation for training and the transfer of training as one of the main goals of employee training.Quantitative research was conducted using the statistical methods of correlation coefficient and regression. A correlation coefficient of 0.485 was found between andragogy principles and motivation for training, which indicates a small but statistically significant correlation (p<0.01). The results of regression analysis show that andragogy principles explain 25.7% of the variance. For each unit of change in andragogy principles, motivation increases by 0.424 (B = 0.424) of the unit (p<0.01).A correlation coefficient of 0.5889 was found between andragogy principles and the transfer of training, which means that the increase of andragogy principles also increases the transfer of knowledge to work tasks. The regression analysis results show that the application of andragogy principles explains 34% of the variance in training transfer. For each unit of change in the application of andragogy principles, motivation increases by 0.306 (B = 0.306) of the unit (p<0.01).
Developing a Model for Managing the Quality of Education in the Republic of Dagestan

This article assesses the role of education in ensuring sustainable socio-economic development of the country, region and municipalities.The authors considered approaches to the definition of the essence of the concept of "knowledge economy".Also, we have substantiated the understanding of the quality of education as the basis for reforming the education system in Russia and determined the main problematic zones of the education development in Russian Federation and Dagestan Republic.The authors of the article propose a mechanism for the formation of a unified system of education quality management in a specific region -the Republic of Dagestan, linking the regional and municipal levels of education management and educational organizations.The authors also developed a process model for managing the quality of education in the Republic of Dagestan, which provides practical recommendations for the development of the education sector in the Republic of Dagestan.The proposed model provides the creation of a unified interconnected organic system that connects both regional and municipal levels of government.
CONCERNING THE ARCHAEOGRAPHIC PURSUITS OF A. I. TURGENEV (BIOGRAPHICAL DATA) PART 1

A. I. Turgenev’s archaeographic efforts have been investigated from the point of view of their substance, as well as in biographical terms, the research covering the period from the emergence of his historical interests (the University of Göttingen) to the publication of the so-called Acts of Turgenev. The article is based on the data from the archives of the Turgenev brothers (IRLI. F. 309), including the unexplored body of correspondence between A. I. Turgenev and Prince A. N. Golitsyn.
Simon @ LT-EDI-EACL2021: Detecting Hope Speech with BERT

In today’s society, the rapid development of communication technology allows us to communicate with people from different parts of the world. In the process of communication, each person treats others differently. Some people are used to using offensive and sarcastic language to express their views. These words cause pain to others and make people feel down. Some people are used to sharing happiness with others and encouraging others. Such people bring joy and hope to others through their words. On social media platforms, these two kinds of language are all over the place. If people want to make the online world a better place, they will have to deal with both. So identifying offensive language and hope language is an essential task. There have been many assignments about offensive language. Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI 2021-EACL 2021 uses another unique perspective – to identify the language of Hope to make contributions to society. The XLM-Roberta model is an excellent multilingual model. Our team used a fine-tuned XLM-Roberta model to accomplish this task.
An Effective Deep Learning Approach for Extractive Text Summarization

Nowadays, most research on extractive text summarization uses deep learning approaches as they provide better performances than the others.However, a difficulty in these approaches is the shortage of a large dataset for training summarization systems.To deal with this problem, we take advantage of contextualized word embeddings from pre-trained BERT models to produce sentence embedding vectors.These vectors are then used as the input of a Multi-Layer Perceptron classifier for sentence selection.The outputs of the Multi-Layer Perceptron classifier are processed by a Maximal Marginal Relevance algorithm to remove redundant sentences.Finally, the selected sentences are rearranged using information about sentence position in the original document to create a summary.Our proposed system is evaluated by using both English and Vietnamese datasets.Experimental results show that our system achieves promising results comparing to existing researches in this field.
Analysing Twitter Data for Phishing Tweets Identification

Detecting threats like adult, violent, and phishing tweets on online social networks is a crucial issue in recent years. The aim of the work is to identify phishing content from the users' perspective in real-time tweets. To outline such content comprehensively, lexicon analysis with sentiments are encapsulated to investigate tweets that yield phishing dynamic keywords, while some features and parameters are altered to optimize the performance. To support the preliminary study, the approach is rigorously designed to assemble users' opinions on completely different classes of phishing content. Each direct and indirect opinions as well as recently projected opinions are listed to characterize all sorts of phishing content. The authors use word level analysis with sentiments to build keyword blacklist lexicons. High promising results and high level of accuracy and performance are obtained experimentally if compared with the alternative algorithms.
Croatian Corpus of Non‐Professional Written Language – Typical speakers and speakers with language disorders

Corpora, as annotated archives of human communication, are objective, reliable resources for language analysis. Here we present the corpus of non-professional written Croatian, based on 1-year sampling of writings by typical speakers and speakers with language disorders. This corpus provides a unique resource because it samples language used by non-professionals, in contrast to corpora based on texts by professional writers (such as journalists, scholars or novelists) sampled over more than a century. In addition, our corpus contains written language from typical and impaired speakers sampled under identical conditions, allowing detailed analyses of language use. This paper describes the language tasks (essay, story generation, non-formal and formal letter and dictation) used to elicit text production, and procedures for sampling and annotation used to generate the corpus. Its usefulness is illustrated through language productivity analyses of transcripts of different genres produced by writers of different age and language status. This corpus may prove useful for the analysis of writing skills in typical and language-impaired speakers of Croatian.