Reviewerly/exHarmony huggingface dataset:
Principles of Formation of Transliteration Rules for the Azerbaijani Language using Expert Systems

The paper explores available automated transliteration systems and various approaches to their implementation. The authors propose the principles of building a knowledge base of an expert system, which includes transformation rules for Azerbaijani transliteration in a multilingual transliteration system. The experimental implementation of the knowledge base is supposed to be carried out on the example of the rules for the transliteration of names in various writing systems.
Extracting Drug Names from Medical Reports

In this paper, we present new results on drug names and dosage extraction from text-format medical reports written in a low-resource language. We locate and collect drug dosage information in continuous texts to enable the statistical processing of this information. As a first step, we use rule-based text processing methods to gain a base model and generate learning data for language model creation. This rule-based annotation part component includes tokenization, tagging, and chunk parsing of tags, using domain-specific grammar to locate and connect related tokens. Then, to improve the extraction of the drug and dosage-related text elements, we introduce a semi-supervised learning process to train a BERT-based language model. This deep learning-based approach is able to handle the rigidity presented in the data to be processed. To evaluate our approach, we consider several standard performance metrics. Based on our experiments, which show a 3.5% improvement in extraction, we conclude that these combinations of the rule-based and deep learning-based natural language processing approaches are a valid approach for this task to improve the recognition of the drug and dosage entities in medical texts.
A Deep Survey on Human Activity Recognition Using Mobile and Wearable Sensors

Activity-based wellness management is thought to be a powerful application for mobile health. It is possible to provide context-aware wellness services and track human activity thanks to accessing for multiple devices as well as gadgets that we use every day. Generally in smart gadgets like phones, watches, rings etc., the embedded sensors having a wealth data that can be incorporated to person task tracking identification. In a real-world setting, all researchers shown effective boosting algorithms can extract information in person task identification. Identifying basic person tasks such as talk, walk, sit along sleep. Our findings demonstrate that boosting classifiers perform better than conventional machine learning classifiers. Moreover, the feature engineering for differentiating an activity detection capability for smart phones and smart watches. For the purpose of improving the classification of fundamental human activities, upcoming mechanisms give the guidelines for identification for various sensors and wearable devices.
Machine learning code snippets semantic classification

Program code has recently become a valuable active data source for training various data science models, from code classification to controlled code synthesis. Annotating code snippets play an essential role in such tasks. This article presents a novel approach that leverages CodeBERT, a powerful transformer-based model, to classify code snippets extracted from Code4ML automatically. Code4ML is a comprehensive machine learning code corpus compiled from Kaggle, a renowned data science competition platform. The corpus includes code snippets and information about the respective kernels and competitions, but it is limited in the quality of the tagged data, which is ~0.2%. Our method addresses the lack of labeled snippets for supervised model training by exploiting the internal ambiguity in particular labeled snippets where multiple class labels are combined. Using a specially designed algorithm, we effectively separate these ambiguous fragments, thereby expanding the pool of training data. This data augmentation approach greatly increases the amount of labeled data and improves the overall quality of the trained models. The experimental results demonstrate the prowess of the proposed code classifier, achieving an impressive F1 test score of ~89%. This achievement not only enhances the practicality of CodeBERT for classifying code snippets but also highlights the importance of enriching large-scale annotated machine learning code datasets such as Code4ML. With a significant increase in accurately annotated code snippets, Code4ML is becoming an even more valuable resource for learning and improving various data processing models.
Proposal and Evaluation of a Robot to Improve the Cognitive Abilities of Novice Baseball Spectators Using a Method for Selecting Utterances Based on the Game Situation

Herein, an engineering method is developed to improve spectators’ sports-watching abilities. We present an interactive robot that supports the cognitive abilities of baseball novices in sports spectating. The robot watches a game with a person and utters words applicable to the game situation. We propose two methods to support cognitive watching: five categories of utterances (confirmation, information, prediction, emotion, and evaluation) and utterance rules for player participation and game scenes. We also propose a method for generating utterances for each category. To evaluate the generated utterances, we conducted an experiment whereby spectators watched baseball footage with the robot. The results of the experiment showed that the robot’s utterances could support the cognitive ability sub-factor of individual game intelligence, specifically “Spectating while knowing the player’s strengths.” In addition, the feeling of heaviness that occurs when watching alone could potentially be reduced by watching with a robot. This study is the first to attempt to support and improve spectators’ abilities to watch sports using the human–robot interaction approach. We anticipate that this approach will be used in the future to build a relationship (increase intimacy) with an agent (robot) and to support and improve cognitive abilities regardless of the type of sport.
Central Bank Digital Currencies: Evolution and Implications to the Monetary System

Central Bank Digital Currencies (CBDCs) are the newest entrants to the financial landscape. Widely backed by Central Banks around the globe, CBDCs offer unique characteristics and novel solutions to the existing gaps in the monetary system. Nevertheless, potential dangers like infringement on personal liberties cannot be overlooked while adopting a new system with broad implications. Introduction of CBDCs exposes the government's necessity to retain the 'trust' in its institutions amidst emerging financial innovations in the private space. An honest scrutiny of the positives and negatives of the proposed system would contribute to designing a better monetary policy tool while mitigating risks. Although individuals stand to reap some benefits, governments would gain excessively from a mass CBDC adoption, if implemented. Therefore, civil society must play an active role in designing the technical specifications of the proposed CBDCs. A CBDC should be designed to maximise the policy goals without parting with individual privacy and personal liberties.
Evaluating the Utility and Privacy of Synthetic Breast Cancer Clinical Trial Data Sets

PURPOSE There is strong interest from patients, researchers, the pharmaceutical industry, medical journal editors, funders of research, and regulators in sharing clinical trial data for secondary analysis. However, data access remains a challenge because of concerns about patient privacy. It has been argued that synthetic data generation (SDG) is an effective way to address these privacy concerns. There is a dearth of evidence supporting this on oncology clinical trial data sets, and on the utility of privacy-preserving synthetic data. The objective of the proposed study is to validate the utility and privacy risks of synthetic clinical trial data sets across multiple SDG techniques. METHODS We synthesized data sets from eight breast cancer clinical trial data sets using three types of generative models: sequential synthesis, conditional generative adversarial network, and variational autoencoder. Synthetic data utility was evaluated by replicating the published analyses on the synthetic data and assessing concordance of effect estimates and CIs between real and synthetic data. Privacy was evaluated by measuring attribution disclosure risk and membership disclosure risk. RESULTS Utility was highest using the sequential synthesis method where all results were replicable and the CI overlap most similar or higher for seven of eight data sets. Both types of privacy risks were low across all three types of generative models. DISCUSSION Synthetic data using sequential synthesis methods can act as a proxy for real clinical trial data sets, and simultaneously have low privacy risks. This type of generative model can be one way to enable broader sharing of clinical trial data.
Dual discursive articulation: languages of persuasion and resistance in street library community

ABSTRACTThis study examines the dual discursive articulation in the Instagram posts of the street library community, Literasi Trotoar (LIAR), in Purwakarta, Indonesia. The study focuses on two groups of posts: those directed towards the LIAR community and the general public, and those aimed at the Indonesian government. To analyse the posts, this study adopts ‘Multimodal Critical Discourse Analysis (MCDA),’ which focuses on both the language and visual elements of the posts. The findings reveal the presence of two integrated and inseparable types of articulation within the posts. Firstly, articulation serves as a discursive tool to persuade the public to embrace the ideology of anarchism within the context of in-group power relations. This articulation is strategically employed for positive in-group representation, in-group ideological glorification, subject interpellation, ideological legitimation, and power formation. Secondly, articulation functions as a discursive mechanism to challenge the authority of the government within the context of out-group power relations. This articulation is strategically utilized for negative out-group representation using a narrative model of populism, out-group ideological debasement, subject resistance, ideological delegitimization, and power exertion. This study proposes a model termed ‘dual discursive articulation,’ which integrates two discursive objectives: persuasion and resistance.KEYWORDS: Street librarymultimodal critical discourse analysisarticulationpower relationideology AcknowledgementAs this work is an expert-based work, we would like to thank Jejen Jaelani and Yani Suryani for warm discussions and insights.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Research, Community Services and Innovation Programs (PPMI), Institute for Research and Community Services (LPPM), Institute of Technology Bandung (ITB) (PPMI Batch 1, 2021).Notes on contributorsYasraf Amir PiliangYasraf A Piliang is a Professor of Design and Culture at the Postgraduate Program of Art and Design Sciences, Institute of Technology Bandung (ITB). His research focuses on semiotics, art and design discourses and cultural studies.Tri SulistyaningtyasTri Sulistyaningtyas is a lecturer and researcher at literacy, media, and culture research group, Faculty of Fine Art and Design, Institute of Technology Bandung (ITB). Her research focuses on discourse, socio-pragmatics, and cultural studies.Ghina Zoraya AzharGhina Zoraya Azhar is a postgraduate student at the Postgraduate Program of Art and Design Sciences, Institute of Technology Bandung (ITB). Her research focuses on discourse analysis and cultural studies.
Infusing a Convolutional Neural Network with Encoded Joint Node Image Data to Recognize 25 Daily Human Activities

Human Activity Recognition In article number 2300266, Yuliang Zhao, Tianang Sun, Chao Lian, Wen Jung Li, and their collaborators propose a human-pose encoding algorithm. This algorithm encodes human body nodes into RGB images, which are then input into an improved neural network for the purpose of monitoring daily human activities. With this lightweight, reliable, and highly accurate human activity recognition solution, it can accurately identify 25 common activities.
Analyzing Information System Quality and Financial Information Quality: An Evidence of Local Government Organizations in Indonesia

End-user satisfaction is a potential substitute for utility in decision-making, user satisfaction is a measure of the extent to which an information system meets the information needs of its users. The purpose of this study was to determine the effect of the regional management information system quality and the financial information quality on end-user satisfaction. The method used in this research is descriptive and verification with a quantitative approach. The sample used in this study was 50 people in Local Government Organizations of Bandung City Government in Indonesia. The analytical tools used are Multiple Linear Regression, Correlation Analysis Test, Determination Analysis Test, and Partial Hypothesis Test. The results showed that the regional management information system quality had a positive and significant effect on the end-user satisfaction and the financial information quality had a positive and significant effect on end-user satisfaction at the Local Government Organizations of Bandung City Government. This study will contribute both to practitioners and academics related to end-user satisfaction in implementing the quality of information systems and the quality of financial information used. This study provides additional knowledge about financial information quality which is used as the basis for making decisions by local government organizations.
Conversation Echo: Communication in virtual environments that reflects conversation contents

In this study, we propose Conversation Echo, a system that reflects the topics of conversation in a VR environment in real time.This method uses AI to convert speech data into text, extract conversation topics, and generate panoramic images to generate a VR environment, which dynamically changes the environment in real-time. This method aims to realize an experience that generates conversation topics and inspiration.
Recovering Detailed Neural Implicit Surfaces from Blurry Images

In this poster, we present a method for recovering surface details from blurry images. We achieve this by transforming input position features of the neural implicit surface and radiance field using a blur kernel and simulating the motion blur process through weighted averaging of different transformations. Then, we can obtain more clear surface reconstruction results by discarding the blur kernel during the testing phase. Experimental results on the blurred DTU dataset demonstrate that our approach exhibits robustness to blurry inputs and effectively reconstructs surface details.
A Method of Printmaking Image Generation Based on Convolutional Neural Network

In this paper, a novel image reconstruction method for printmaking is proposed via the combination of nonlinear diffusion filtering and convolution neural network. Nonlinear diffusion filtering based on a partial differential equation abstracts the input image to extract color features and texture features of images by nonlinear structured tensors. The image color features and texture features are combined to obtain a feature vector, and the feature vector is fed into the model as the input data. Then, the pre-trained deep learning model VGG-19 is utilized as the backbone network for further feature representation, and the extracted feature maps of each layer of the VGG19 model can be visualized for image reconstruction. The quality of the reconstructed images of prints is improved with the powerful feature extraction and combination ability of neural networks, which has the advantages of fast reconstruction speed and high quality of reconstructed images. Finally, the simulation experiments can demonstrate that the proposed reconstruction approach can achieve relatively ideal reconstruction performance in scenes of printmaking.
Comparison of Different Classification Techniques to Predict Student Graduation

Every year, the number of students accepted at the Maulana Malik Ibrahim State Islamic University of Malang continues to increase. Still, not all students can graduate on time according to the specified study period, resulting in a buildup of students who have not graduated according to their graduation period. One of the aspects evaluated in the Study Program accreditation process is the student graduation rate. Apart from that, for each semester, Study Programs are also required to report educational data to DIKTI, and student graduation is one of the factors considered in the report. There is an imbalance between the number of students graduating each year and the number of new students accepted. To overcome this problem, it is necessary to predict student graduation to determine whether they will graduate on time. In science and data analysis, predictions are often used to make predictions based on existing data and information. Classification models in predicting student graduation include the Nave Bayes method, Support Vector Machine SVM, and Random Forest, as well as the level of accuracy of these three methods. From the results of experiments and model evaluations carried out, with data from 458 Informatics Engineering Study Program students with details of 366 training data and 92 testing data, it was obtained that the SVM model had the highest accuracy, reaching around 87% and Random Forest also had good accuracy, around 82%. At the same time, the Naïve Bayes model has lower accuracy, around 76%.
A Blockchain-Based Transparent Solution for Achieving Investment for Farming

Agriculture is an important element of many societies and has played a key role in the development of civilizations. Agriculture is the cultivation of plants, animals, and other life forms for food, fiber, biofuel, medicinal plants, and other products used to sustain human life. Agriculture needs a considerable amount of cash investment on top of the effort and time of farmers. These days it is quite observable that many farmers face economic challenges, such as low crop prices, high input costs, and limited access to credit, making it difficult for them to sustain their operations. In this article, a blockchain-based solution has been proposed to meet up the gap between farmers and investors. In this article, the problems of farmers have been identified, the utility of blockchain-based solutions has been explained, and the architecture of the proposed solution has been demonstrated for finding investors, disbursement of investment, and repayment solutions.
Blockchain Technology in Construction Supply Chain Management: Enhance Transaction Speed, Cost Effectiveness and Security

The traditional construction sector encountered obstacles that prevented it from developing, including knowledge asymmetry, complicated intermediary relationships, and high transaction costs. Blockchain technology offered a more effective, secure, and convenient mode of transaction and management to the construction industry because it was a decentralized, safe, and reliable information transmission and management tool. Blockchain technology could be applied to the construction industry's supply chain to achieve traceability and supervision, enhancing the transparency and dependability of transactions. At the same time, blockchain technology could be applied to the management of the construction supply chain to achieve rapid information sharing and verification, lowering transaction costs, and increasing transaction efficiency. However, the previous study on the use of blockchain technology in the building sector was still in its infancy and required more investigation and study. This study aimed to investigate how blockchain technology could be used in supply chain management for the construction sector to address present issues. The study's goals were to examine how blockchain technology could speed up transactions and improve supply chain management for the construction industry, as well as to examine how it could lower transaction costs and increase data sharing and information transparency. This study used a semi-structured interviewing technique for the purpose of gathering and analysing data. The researcher learned more about professionals and industry experts' thoughts and experiences with blockchain technology in construction supply chain management through in-person or remote interviews with them. The advancement of the construction industry's digital transformation and information upgrading reflected the significance of this study. This study would help the supply chain management in the construction sector in enhancing the effectiveness of supply chain transactions in the construction industry and reducing the transaction cycle; lowering the price of supply chain transactions in the construction industry and boosting the industry's competitiveness; strengthening the security and reliability of supply chain transactions in the construction industry and reducing data compromise and transaction risks.
The Effect of Technology In Mathematics Learning

This study aims to describe the role of technology in the implementation of mathematics learning. This writing is based on a literature review of journal articles. It is concluded that the use of technology in learning mathematics has a positive impact. Technology can be used as a source of student learning. Technology can visualize abstract mathematical objects into concrete objects that are easily accessible to students. There are various platforms that support the learning process without time limits and various applications that can be used as a learning space for the teacher's role. However, it is undeniable that technology can have a bad effect on students, for example technology provides easy information for students in terms of instant answers available on the internet and even the use of technology can reduce algebraic abilities. Minimizing this impact requires the ability of HOTS in the implementation of mathematics learning and changing the views of teachers and students towards technology, making technology a partner in learning mathematics. The use of technology with the right use can have a better impact on learning mathematics.
The Effect of Student Team Achievement Division to Improve Students’ Reading Comprehension for the 10th Grade of SMAN 5 Kabupaten Tangerang

The purpose of this research is to find out the effect of using STAD (Student Team Achievement Division) to Improve Student’ Reading Comprehension for the tenth grade of SMAN 5 Kabupaten Tangerang. This research was quantitative and the method is quasi experimental with the qualitative data. In this research the total of population is 403 students in the tenth grade of SMAN 5 Kabupaten Tangerang. To choose the sample of this research the writer conducted by using the purposive sampling, and took 71 students of two classes, there are: the first is experimental class (X MIPA 1) and the second is control class (X MIPA 2). The STAD technique used in experimental class in teaching and learning process, while LM technique used in control class in teaching and learning process. To collect the data, the writer used the written test, and to counting the data of descriptive data of class experiment and control, normality, homogeneity, chi-square, rank, and mann whitney used SPSS 24. Then the result of this research showed the score of students’ reading comprehension that used STAD technique in the value of post-test was higher than the student’ score of reading comprehension that used LM technique in the value of post-test. In conclusion, in this research indicated that there is any significant effect of using STAD (Student Team Achievement Division) to improve student’ reading comprehension