Reviewerly/exHarmony huggingface dataset:
Ethical Aspects of Biometric Identification

The term biometrics derives from Greek (bio=life and metrics=measure) and implies the measurement of biological signs. Biometrics is the science of recognizing people based on their physical, behavioral, and physiological attributes, such as fingerprint, face scan, iris, retina, and voice. The present paper aims to develop a study on biometric identification. The major objective of the study is to conduct a survey among the Romanian population on the importance and knowledge of biometric identification methods. This objective was achieved by assessing the knowledge held by the general population of Romania regarding biometric indicators and the degree of adaptability and openness of citizens related to the widest possible implementation of biometrics. The study was based on conducting a quantitative analysis using a questionnaire. Due to the high degree of accessibility, the online environment was chosen as a method of application, distribution being made through social networks. A biometric template digitizes the human body, it has been argued that the collection of biometric identifiers not only interferes with the privacy and right to protection of a person's data, but also with the integrity of an individual's body. In conclusion, the creation and storage of a unique biometric template must be seen in relation to the purpose of the operation. The protection of citizens from criminal activities is a primary obligation of the state. However, it must be exercised with due respect for a number of fundamental ethical values and in the light of modern human rights law.
Analisis User Experience (UX) Pada Aplikasi Game Clash Of Clans Menggunakan Metode User Experience Questionnaire (UEQ)

Information technology is currently developing very rapidly and meets people's needs to make work easier, fulfill entertainment needs, and even fulfill sports needs among people in the era of the industrial revolution generation 4.0. What is currently popular is Clash of clans or better known as "CoC". One game that is quite popular among young people. Based on 54.9 million reviews on Android devices and 2.3 million reviews on iOS devices, it still gets negative reviews from CoC users themselves who are not satisfied with the features or security of the game itself. From the problems above, a User Experience (UX) evaluation can be carried out using the User Experience Questionnaire (UEQ) method, which is an analysis of user experience reviews in order to improve the quality of the game based on six different variables. The review was carried out by distributing an online questionnaire in the form of a Google form consisting of 26 questions and succeeded in collecting 105 respondents. Then, after the analysis was carried out, it was concluded that all aspects of the Clash Of Clans game application received positive evaluation values, namely with a mean value &gt; 0.8 starting from the aspects of Attractiveness, Perspicuity, Efficiency, Dependabilit(Accuracy), Stimulation (Stimulation), and Novelty (Novelty). However, in the Dependability aspect, one of the indicators has a neutral evaluation value, namely the indicator can be predicted or cannot be predicted. This is influential because the resulting mean value is based on the benchmark results graph, namely "below avarege". Based on the analysis that has been carried out on the Clash of Clans game, it can be concluded that in using this application some users are satisfied, but there needs to be improvements to this game by providing more sophisticated security features and providing continuous innovation.
An Algorithm for Face Recognition Taking Into Account the Human Brain Peculiaritie

This article presents an algorithm for face recognition, based on the human brain activity pe-culiarities during image recognition. The difference of this approach from the existing ones is that the face is recognized as a single graphic image, without paying attention to the attributes of the face (geometry of eyebrows, eyes, nose, lips, etc.). At the same time, the arrangement of facial attributes is subject to the rules of golden ratio. The results of the algorithm are presented.
Data Ethics in Practice: Rethinking Scale, Trust, and Autonomy

Ethics as practice in data-driven contexts refers to ways of organizing, acting with, relating to, or contesting data. The use of data within urban settings provides a number of specific contexts and practices, intersecting and transcending what might be considered ‘top-down’ or ‘bottom-up’ dynamics. Data-based governance, management, and civic engagement are deeply embedded into the function and experience of cities, raising issues of justice that require considerations of meaning that cut across scale, since issues of justice are temporally dispersed and contextually specific. Ethnographic methods can surface a range of possibilities for understanding issues of data justice across these contexts and scales. Areas of practice with ethical and justice implications include: commercial practices of data-based companies; participatory and civic data-gathering and engagement processes, including data activism; and community- or commons-based data governance strategies. A future data ethics can move away from responsive actions set within frameworks set by existing powerful actors and towards attention to implications across scale and time, producing and drawing from dynamics of resistance, resilience, and community strength. This chapter outlines a multiscalar data ethics in practice, using examples to illustrate the processes of trust and autonomy modelled through practices of ethics including governance, management, and civic engagement.
THE CONTENT OF TEACHING THE SUBJECT "INFORMATICS" IN THE SCHOOLS OF KYRGYZSTAN: THEORETICAL AND PRACTICAL ANALYSIS

The article says that a theoretical and practical analysis was carried out in order to find answers to the questions of how the content of the subject "Informatics" in secondary schools in Kyrgyzstan is compatible with the content of education in other countries, and whether students in Kyrgyz schools differ from students in other countries. The author analyzed the questions about which countries of the world teach the subject "Informatics" in their schools, and what content of the subject "Informatics" is mastered by students of foreign schools. As a result, it is noted that in the schools of Kyrgyzstan the subject "Informatics" is taught in accordance with the social conditions and economic situation in Kyrgyzstan, keeping up with the world education system. But even so, he writes that in the era of global development in the modern world, it is necessary to periodically update the content of this subject.
Effects of Self-Regulation, Goal Orientation, and Anxiety on EFL Speaking in Metaverse and Face-to-Face Contexts

This study aimed to compare the effects of self-regulation, goal orientation, and speaking anxiety on speaking performance between metaverse and face-to-face contexts. We randomly assigned 253 Korean middle school students to either metaverse or face-toface groups for 12 weeks of English-speaking lessons. Before and after the experiment, students completed speaking tests and submitted a post-questionnaire. Structural equation modeling and multi-group analysis revealed that student attributes had a similar impact on speaking performance in both settings. Specifically, self-regulation and mastery goal orientation positively influenced speaking performance, while speaking anxiety had a negative effect, regardless of the context. Furthermore, self-regulation played a mediating role in reducing speaking anxiety in both settings. This implies that metaverse-mediated and face-to-face classes offer similar learning environments, where students can leverage their goal orientation and self-regulation skills to manage speaking anxiety. Ultimately, students can enhance their speaking performance by employing selfregulation strategies and nurturing a mastery goal orientation, irrespective of the learning context.
Analisis Quality of Service (QOS) pada Akses Game Online Menggunakan Standar Tiphon

The quality of internet service plays a crucial role in online gaming experiences. This study focuses on assessing the quality of internet services provided by 6 cellular cards in Indonesia using the TIPHON standard. The objective is to determine the suitability of these services for online gaming on a website. Data packets generated during online gaming sessions were captured using the Wireshark application in three different locations. The captured data was processed using the TIPHON standard formula to evaluate the quality of internet service provided by the 6 cellular cards. The analysis of the captured data revealed that three cellular cards, namely Telkomsel, XL, and Tri, demonstrated good performance in terms of providing a quality online gaming experience. The results were consistent across the three tested locations. Based on the TIPHON standard, the evaluation of 6 cellular cards in three different locations indicated that three of them (Telkomsel, XL, and Tri) offer suitable internet service for playing online games on the website.
Cat Disease Detection Expert System Application Using WML on Mobile Phone

This research aims to find out how to implement the forward chaining method in an expert system. This research used observation methods, questionnaires, literature studies, and interviews conducted with experts, namely veterinarians. This interview was conducted in the form of an unstructured interview. The researcher used a PC (personal computer) as the hardware. The software used in this research is Notepad++ 6.1.6. The operating system that the author uses in research is Windows XP SP2. The research results show that using this expert system helps detect disease in animals by helping users consult by answering a series of questions based on visible disease symptoms, which in turn will lead to a series of answers leading to a particular disease. In the solutions section, you can find first aid measures and follow-up actions that pet owners are expected to take to help the animal recover. Adding knowledge, documentation, and development to this expert system can be done via the mobile phone available on the admin page, so there is no need to overhaul the existing system; just insert, delete, and update. Development can be carried out by people with authority to add symptoms, diseases, and solutions to diseases.
Rekomendasi Posting Promosi pada Sosial Media Berdasarkan Pengelompokan Hasil Penjualan Produk (Studi Kasus: Maula Hijab)

Maula Hijab is an MSME (Small and Micro Medium Enterprises) located in Sidomoyo, Godean District, Sleman Regency, Yogyakarta Special Region Province that sells Muslim clothing products. Maula Hijab sells its products directly and through marketplace platforms such as Shopee, Lazada, and Tokopedia. In addition, Maula Hijab promotes its products through social media, one of which is Instagram. Social media is used to promote Maula Hijab products, but there is a decrease in the number of viewers reached by the Maula Hijab Instagram account. In addition, a decline in sales of Maula Hijab was found. Therefore, it is necessary to analyze the level of product promotion performance on Instagram on product sales. To analyze the two data, the Data Mining technique used in this study is K-Means Clustering. The K-Means Clustering algorithm is used to group, classify, or group a set of objects based on their attributes or features into a number of similar groups called clusters. This study aims to provide recommendations for promotion of Maula Hijab products using the K-Means Clustering algorithm. This study uses the K-Means Clustering method. The final result of this research is that 3 product clusters are produced, namely product clusters that are recommended to be promoted more often, product clusters that can be re-promoted, and product clusters that have good promotions. The recommendation system built can run to retrieve Instagram data and process the data to produce output in the form of product promotion recommendations.
Impact of Technology Revolution on Economic Development Over the Past Decade

In the past decade, the exponential development of ICT has generated a fast economic development. So, in order to better understand this phenomenon, a systematic literature review based on the Scopus database was carried out. The majority of the studies was developed in Europe and was secondary (82.4%) and qualitative (64.7%) research. Regarding the microeconomic level, the evidences are: greater efficiency of businesses and companies, improved productivity; reduction of transaction costs; improved profit/economic performance of businesses; creation of closer ties between companies and customers, suppliers and partners/collaborators; changes in the functioning of markets that become more dynamic, challenging and competitive; new ways of production organization, changes in the structure of demand and supply for goods and services; and, the emergence of new products. With regard to the macroeconomic level, the results show that ICT and emerging technologies have positive impacts on innovation, employment, economic and financial development, the human development index, social development, with greater civic participation of citizens in Society, greater social well-being, improvement of living conditions especially in the poorest rural areas, better quality of life, reduction of poverty, although there is also the risk of exclusion of significant parts of Society if not adhered to technologies. There are also researches that highlight the role of ICT in sustainable development (economic, social and environmental). It should be noted that existing studies confirm that the impacts are greater on economic growth when compared to economic development and the human development index.
Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation

Accurate uncertainty quantification is necessary to enhance the reliability of deep learning (DL) models in real-world applications. In the case of regression tasks, prediction intervals (PIs) should be provided along with the deterministic predictions of DL models. Such PIs are useful or "high-quality (HQ)" as long as they are sufficiently narrow and capture most of the probability density. In this article, we present a method to learn PIs for regression-based neural networks (NNs) automatically in addition to the conventional target predictions. In particular, we train two companion NNs: one that uses one output, the target estimate, and another that uses two outputs, the upper and lower bounds of the corresponding PI. Our main contribution is the design of a novel loss function for the PI-generation network that takes into account the output of the target-estimation network and has two optimization objectives: minimizing the mean PI width and ensuring the PI integrity using constraints that maximize the PI probability coverage implicitly. Furthermore, we introduce a self-adaptive coefficient that balances both objectives within the loss function, which alleviates the task of fine-tuning. Experiments using a synthetic dataset, eight benchmark datasets, and a real-world crop yield prediction dataset showed that our method was able to maintain a nominal probability coverage and produce significantly narrower PIs without detriment to its target estimation accuracy when compared to those PIs generated by three state-of-the-art neural-network-based methods. In other words, our method was shown to produce higher quality PIs.
A Survey on Decentralization and Virtualization of Medical Trials: An approach through Ensemble learning models and Convolutional Neural Networks

- In sectors such as medicine where accuracy and time are the most prioritized factors the use of technology and its tools have led to huge advancements and contributed to saving millions of lives. Technology has been bifurcated into various categories to suit multiple requirements of the medical sector. The use of Machine Learning along with well-designed models and sister technologies such as Computer Vision has opened up a whole new dimension in medical sciences. Machine learning deals with the building of algorithms that can predict using fed patterns which further specializes in Deep Learning. Deep learning's success in a variety of pattern recognition applications has sparked excitement and raised expectations that deep learning, or Artificial Intelligence (AI), could revolutionize the health care that we know today. Right from diagnosis to treatment it can revolutionize the way we construct and operate the medical sector. Over the last decade, Convolutional Neural Networks have achieved breakthroughs in a range of pattern recognition domains. CNN's are generally employed to solve complex image-driven pattern recognition tasks. They have proven to be a great tool in image recognition and classification and are tuned according to the needs of the user. Ensemble learning, as a research area, strives to bring data fusion, data modeling, and data mining together in a single framework. Ensemble learning combines the informative knowledge gained from the previous outcomes to produce knowledge, discovery, and improved predictive performance using adaptive voting methods. In this study, the methodologies of numerous models of image recognition and classification, as well as ensemble learning from multiple authors, are evaluated. In addition, this study shall discuss any feasible enhancements that could be implemented to the existing models as well as how the ideologies of the models could be used for efficient diagnosis in the field of medicine.
Polarity Prediction in Tourism Cuban Reviews Using Transformer with Estimation of Distribution Algorithms

The tourism sector has benefited from recent research in the area of natural language processing, where digital platforms on the web offer the opportunity for people to express their opinions about the services and places they visit. The texts of the reviews are unstructured data characterized by high dimensionality, variable size, and complex semantic relationships between words, which has led to the development of neural architectures with a larger number of parameters to optimize. The training of deep neural networks has been approached by methods based on partial derivatives of the objective function and presents several theoretical and practical limitations, such as the probability of convergence to local minima. In this paper, a hybrid method based on Distribution Estimation Algorithms is proposed for fine-tuning an mT5-based Transformer for polarity prediction. For this purpose, a new Spanish dataset is proposed for polarity classification compiled from TripAdvisor reviews of Cuba. Different preprocessing variants are applied and compared in the solution and data imbalance is treated by back translation. The proposed method combined with back translation decreases the mean of the absolute error in the mT5-based Transformer for polarity prediction.
Enhancing Spanish Aspect-Based Sentiment Analysis Through Deep Learning Approach

Aspect-based sentiment analysis is the task of monitoring user sentiment on textual opinions about the characteristics of a given entity. Recognizing the aspects present in the opinion and determining its sentimental orientation (positive or negative) in a similar way as if it were done by a human being continues to be a challenging task, but at the same time necessary. Achieving quality improvement of existing aspect-based sentiment analysis solutions remains a challenge and the vast majority of solutions reported in this task are focused on the English language, so further progress is needed in languages like Spanish. This paper presents an aspect-based sentiment analysis method which uses language models based on Transformers (BERT models) with a linear layer to extract aspects from opinions in Spanish and a similar model to perform Aspect Sentiment Classification, demonstrating that its use allows us to surpass the state of the art for said language. The proposed solution was evaluated using the Semeval2016 Task 5 dataset achieving promising results, respect those reported by other solutions.
MEDICAL IMAGING ENHANCEMENT WITH AI MODELS FOR AUTOMATIC DISEASE DETECTION AND CLASSIFICATION BASED ON MEDICAL IMAGES

The development of artificial intelligence (AI) in the healthcare industry has recently been of utmost importance. Early discovery, diagnosis, categorization, analysis, and treatment options are always positive medical breakthroughs. In making diagnoses and strategic decisions for healthcare, precise and consistent picture classification is essential. The semantic gap has emerged as the main problem with picture sorting. To bridge the gap, traditional machine learning techniques for classification mainly depend on low-level features rather than high-level ones, use some hand-crafted features, but compel intensive feature extraction and classification procedures. Deep convolution neural networks (CNNs), a potent technique that has significantly advanced in recent years, are successful in classifying images. A thorough assessment of pertinent studies is required to further assist readers in understanding the research and its main concepts captured. This study highlights the various AI models for automatic disease detection and classification based on medical images. This can include identifying tumors, lesions, fractures, or other abnormalities. The models discussed in this article include Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), One-Class Learning Models, Recurrent Neural Networks (RNNs), and 3D and Multimodal Models. Since these models work with various modalities, there are numerous instances where computed tomography (CT), positron emission computed tomography (PET), X-ray, magnetic resonance imaging (MRI), and ultrasound (US). Are mentioned to help complement the research.
CNN-based Occluded Person Re-identification in a Multi Camera Environment

In the context of rising global urban security concerns and the growing use of surveillance cameras, this study aims to enhance individual identification accuracy in occlusion scenarios using deep learning. Four CNN-based models for person re-identification are analyzed and put into practice. Additionally, comparative studies are conducted, and the model’s performance is assessed using the Market-1501 and Occluded-Reid datasets. We propose the use of ensemble learning and convolutional neural networks (CNNs) to address occlusion issues. Our results show that the ensemble approach performs better in re-identification tasks than traditional deep learning algorithms with an improvement of 1%–2% in mAP and Rank-1 scores, respectively.
A Semantic Enhanced Course Recommender System via Knowledge Graphs for Limited User Information Scenarios

Abstract The Universal Declaration of Human Rights states that “everyone has the right to education” and it must be “equally accessible to all”. Aligned with these principles are online learning platforms that offer free online courses that require no or minimal information from their students to democratize learning. Despite the benefits, the lack of user information challenges traditional recommendation systems and, when applied to education, impacts student experiences. This paper addresses the problem of limited student information in course recommendation systems. We tackle the problem by generating a semantically enriched vector-based representation of course content using an Open Knowledge Graph (DBpedia) and Topic Modeling methods, such as LDA and LSA and by collecting tracking events from log systems (Google Analytics) to create a hybrid recommendation system using content-based and collaborative filtering strategies for low-information scenarios. Our experiments use GCFGlobal.org, an online learning platform offering free self-paced online courses to 100 million people, to validate our approach. Results indicate that the proposed approach outperforms the previous works in the field contributing to the creation of fairer recommendation systems.