Reviewerly/exHarmony huggingface dataset:
Real-time Swimming Posture Image Correction Framework based on Novel Visual Action Recognition Algorithm

Human action recognition has important applications in the motion detection, timely tracking, and comprehensive behavior analysis. It is a direction of the great scientific research significance in the field of computer vision. This study focuses on analyzing the real-time swimming posture image correction framework based on novel visual action recognition algorithm. To begin with, the body image digitization process is applied. First, this study divides the video into frames, extract the coordinates of the human body frame through the pre-trained CNN and get the annotated images for digitalization, then the marked frontal posture data is preprocessed through the translation and normalization operations. After this, the proposed OF-STH network is applied to finalize the swimming posture image correction framework. In the experiment section, the different angles are tested and the pseudo color image recognition and labeling is also tested.
Face Detection and Recognition for Criminal Identification

Face detection and recognition have become essential tools in criminal identification and investigation. Face detection involves identifying individuals by comparing their facial features to a database of known faces. In criminal identification, face detection and recognition are used to identify suspects and match them to surveillance footage or images from crime scenes. It has the potential to significantly improve the accuracy and speed of criminal investigations and to increase public safety by enabling law enforcement to identify and apprehend dangerous criminals more quickly. However, the use of face detection and recognition technology also raises ethical and privacy concerns, as it can potentially be used to track individuals without their consent or to discriminate against certain groups. It is therefore important to use these tools responsibly and transparently and to ensure that appropriate safeguards are in place to protect individual rights and freedoms.
Movie Endorsement to Deliver Top-N Recommendation for User using CoFiTor Framework

The objective of top N recommendation is to recommend to each user a minor set of N items from a huge group of items based on past dataset on the user's preferences. It provides appropriate recommendations for a user to improve the shopping experience. With the help of this approach users can choose items quickly from a shorter list of suitable products. A lot of current video streaming website recommendation systems do not take individual user's shopping behavior into account. In this research study, a transfer to rank approach that mockups a user's behaviors by simulating the user's shopping processes is considered because this approach has been shown to be better than other state-of-the-art methods. A novel and comprehensive recommendation framework termed coarse-to-fine transfer to rank (CoFiToR), which is a major extension of the most recent work known as transfer to rank, is used to achieve this method (ToR). The proposed approach able to produce more relevant product recommendations for each and every user. As an improvement, the product description can correlate the behavior data in all three stages to generate the recommendation list.
Multisignature Crypto Wallet Paper

Multi signatures technology is used in digital wallets to increase the security of cryptography transactions. It requires the approval of multiple parties before the transaction can be processed, reducing the risk of fraud or theft. Multi signature technology is particularly useful for large scale transactions, where multiple parties may be involved. It can be used by individuals or organizations to manage their digital assets securely, multi signature technology provides an additional layer of security to protect against hacking or theft. It can be used in cryptocurrency exchanges, initial coin offerings and estate planning. Multi signature wallets offer greater transparency and accountability in the crypto world. With the continued growth of crypto industry multi signature technology is likely to become even more prevalent in the future.
FSTRE: Fuzzy Spatiotemporal RDF Knowledge Graph Embedding Using Uncertain Dynamic Vector Projection and Rotation

Knowledge Graphs (KGs) use Resource Description Framework (RDF) triples to model various crisp and static resources in the world. Meanwhile, knowledge embedded into vector space can imply more meanings. Much real-world information, however, is often uncertain and dynamic. Existing knowledge graph embedding (KGE) models are insufficient to deal with uncertain dynamic knowledge in vector spaces. To overcomethisdrawback, thispaperconcentrates on anembedding module for the distributed representation of uncertain dynamic knowledge and proposes a strongly adaptive fuzzy spatiotemporal RDF embedding model (FSTRE). Specifically, we first propose a fine-grained fuzzy spatiotemporal RDF model, which provides the underlying representation framework for FSTRE. Then, within the complex vector space, spatial and temporal information is embedded by projection and rotation, respectively. Fine-grained fuzziness penetrates each element of the spatiotemporal 5-tuples by a modal length of the anisotropic vectors. By using geometric operations as its transformation operator, FSTRE can capture the rich interaction between crisp and static knowledge and fuzzy spatiotemporal knowledge. We performed an experimental evaluation of FSTRE based on the built fuzzy spatiotemporal KG. It was shown that our FSTRE model is superior to state-of-the-art methods and can handle complex fuzzy spatiotemporal knowledge.
Implicaciones de la cirugía de catarata para prevenir la demencia en España

Meta-learning-based methods prevail in few-shot text classification. Current methods perform meta-training and meta-testing on two parts of a dataset in the same or similar domains. This results in a significant limit in model performance when faced with data from different domains, limiting the generalization of few-shot models. To solve this problem, this study proposes a new setting, namely, domain-generalized few-shot text classification. First, meta-training is conducted on a multi-domain dataset to learn a generalizable model. Subsequently, the model is meta-tested on a target dataset. In addition, a domain-generalized model, namely, a dual adversarial network, is designed to improve the meta-learning-based methods under domain drift between different datasets and domains. Unlike previous meta-learning methods, two N-way-K-shot tasks were input from different domains for a dual adversarial network at each episode. Dual adversarial networks leverage the features from two different domains for adversarial training to improve the domain adaptability of the model. The proposed model utilizes a domain-knowledge generator during adversarial training to produce domain-specific knowledge, and a domain discriminator to recognize the domain label of the produced knowledge. Extensive experiments are conducted to verify the effectiveness of the proposed settings and model. The experimental results show that the model performance in our proposed setting is improved by an average of 3.84% compared to that in cross-domain few-shot text classification. Furthermore, the dual adversarial network significantly outperforms the five competitive baseline models, with an average improvement of 7.20%. The proposed model achieves an average performance improvement of 2.69% compared with the best baseline method.
Java Implementation of the All-round Collaborative Education Platform Based on Real-time Data Analysis Algorithm

This paper proposes a relatively complete multi-dimensional dynamic data mining system theoretical framework, constructs a multi-dimensional dynamic information representation model, establishes a time series mining model based on support vector regression machine, and a continuous input and output process neural network mining model.The new information teaching system with "multi-dimensional information processing and application in professional research" as the main structure has shown its advantages and characteristics in the current innovative education platform of colleges and universities in my country.This new information teaching system has played a positive supporting role in the development of relevant courses, innovative education platforms and students' innovative ability in colleges and universities.
LifeSeeker: an interactive concept-based retrieval system for lifelog data

Lifelogging was introduced as the process of passively capturing personal daily events via wearable devices. It ultimately creates a visual diary encoding every aspect of one's life with the aim of future sharing or recollecting. In this paper, we present LifeSeeker, a lifelog image retrieval system participating in the Lifelog Search Challenge (LSC) for 3 years, since 2019. Our objective is to support users to seek specific life moments using a combination of textual descriptions, spatial relationships, location information, and image similarities. In addition to the LSC challenge results, a further experiment was conducted in order to evaluate the power retrieval of our system on both expert and novice users. This experiment informed us about the effectiveness of the user's interaction with the system when involving non-experts.
Chronotope of Engineering and Pedagogical Thinking

The article is devoted to engineering and pedagogical thinking as a kind of professional thinking that exists due to specific reasons and necessary and sufficient conditions. The “assembly” of these causes and conditions takes place in a certain time-and-place (chronotope), the conceptual reconstruction of which opens the way to overcoming the traditions of haphazard enumerative descriptions of various properties that have developed in the literature and to reaching the construction of the theory of engineering and pedagogical thinking, which, in turn, will help to reasonably update the content and organization of training engineers-teachers, on whom a lot depends in the near technological future of Russia. A review of the characteristics of engineering-pedagogical and related engineering and pedagogical thinking presented in scientific publications is proposed. It shows the need for greater certainty in understanding the nature, foundations, essence and features of heterogeneous engineering and pedagogical thinking. Heterogeneity requires actualization of the potential not only of pedagogy and technical sciences, but also of philosophy, cultural studies, sociology, and psychology. Such actualization shows the heuristic of the communicative interpretation of the nature and essence of thinking. In its light, engineering-pedagogical thinking appears simultaneously as an articulated and unarticulated, discrete and continuous communication of engineers-teachers with students, within which the competencies required by the profession and the impulse developing engineering and technical flair and ingenuity are transmitted, “infecting” (V. Shklovsky2) and charging them, thanks to which communication overcomes the boundaries of the chronotope, gaining continuum features, demonstrating its pulsating situational-suprasituative (corpuscular-wave) dualism. The scientific novelty lies in the justification, by the example of advancing to the theory of engineering and pedagogical thinking, of supplementing the competence approach with consideration of no less significant cultural and subcultural factors, including latent ones, which sometimes manifest themselves in very weak signals about the features of the chronotope of training engineers and technicians by engineers–teachers, not so much by virtue of official regulations as by virtue of singular acts of everyday life communication.
Research on Situational Teaching Method of Ancient Chinese Poetry in Primary School

This article first clarifies the definition of situational teaching method of ancient Chinese poetry in primary school language education. Then, from the perspectives of catering to the cognitive development characteristics of primary school students and meeting the requirements of the new curriculum standard, it elaborates on the implementation basis of situational teaching of ancient Chinese poetry in primary school language education. It specifically analyzes three important significances of situational teaching of ancient Chinese poetry, namely, its benefits in stimulating learning interest, facilitating in-depth understanding of texts, and contributing to the cultivation of emotional education and aesthetic abilities. This article believes that there are three problems in the current practice of situational teaching of ancient Chinese poetry in primary school language education, namely, situational teaching is single and patterned, the superficiality of situational teaching methods, and excessive reliance on technology in context creation. This paper argues that in teaching ancient Chinese poetry, it is necessary to properly use the situational teaching method, which integrates performance, music, daily life, multimedia and other means to create a context.
Speaking Challenge, Does Pronunciation as the Attribute of Fluency?

Speaking skill is a difficult aspect for language students. In speaking, there are things that need to be considered such as pronunciation of vocabulary. However, students still have difficulty in articulating many words as the indicator of fluency. This case also brings many assumptions in vocabulary pronunciation. The variety of speaking difficulties faced by students of grade VIII in MTs S Ummi Lubuk Pakam was being the main aim. A qualitative descriptive method that presents findings and research results qualitatively was implemented. The voice recordings used as the data and the supporting finding was gotten from the observation result, then it was clarified through the interviews transcript. This analysis found that in speaking English accurately, students had not mastered the phonological rules and they did not know the various sounds or pronunciations. The evidence also proven that the stress, intonation and pitch become the big problems in English utterances. The low quality of pronunciation brings conclusion about students’ factors in low speaking competency.
Lingua Franca and Information Technology in Foreign Language Acquisition: Competitive Advantages of Multilingual Instruction

The paper describes digital strategies helping to boost the competitiveness of Russian as a foreign language. The authors argue that it is impossible to deny the universal popularity of the English language. Knowledge of English is important to learners of Russian as a foreign language since it functions as a social language facilitating their transition to multilingualism. Thus, to improve the competitiveness of Russian courses, it is beneficial to supplement them with instructions in English and modern online tools with English interfaces. The paper offers two scenarios of in-class activities for beginner and intermediate-to-advanced learners of Russian, featuring key strategies utilized for students from Kazakhstan and the People’s Republic of China. The authors argue that the described activities are also useful for students from other countries. The scenarios are included in the common framework of the “Working for a Russian Digital Company” business game and enhance the competitive advantages of Russian studies programs expanding the limits of teaching language and culture in the field of online technologies and Java programming basics.
Audiobook-Mediated Learning: Investigating the Effects of ‘Storynory’ on Listening Skill

Audiobooks have received attention as learning media, and have been studied for language-learning benefits. However, Storynory audiobooks’ effects on listening skills are poorly studied. Understanding its effect can help educators and policymakers create effective instructional approaches. So, the objective of this study was to assess the potential impact of ‘Storynory’ audiobooks on the listening abilities. This study was carried out at SMA Kartini Batam on the odd semester of 2022/2023 academic year, employing a quantitative with quasi-experimental design. This study used two tests in collecting the data, namely a pre-test and a post-test. The study’s control group was comprised of 27 students, whereas the experimental group was composed of 19 students. The process of analyzing the data entailed the use of the Shapiro-Wilk technique to examine normality, the Levene Statistics test to evaluate homogeneity, and the t-test formula for the purpose of hypothesis testing. Based on the hypothesis testing, it was found that the t-value was 5.006, which exceeded the critical t-value of 2.101 (5.006 &gt; 2.101). Therefore, the null hypothesis (H0) was rejected and the alternative hypothesis (Ha) was accepted. The findings indicate a moderate and statistically significant impact of Storynory implementation on the auditory aptitude of tenth-grade students at SMA Kartini Batam. This study provides evidence-based for teachers to improve listening instruction using Storynory Audiobook. Since this study was only carried out on a limited population, it is recommended that further researchers expand the population to provide a broader understanding of its effectiveness and generalization.
Blockchain-Enabled Internet of Things (IoT) Applications in Healthcare: A Systematic Review of Current Trends and Future Opportunities

The use of advanced computer technology in the healthcare industry has the potential to improve patient care and therapeutic results. The goal of this project is to improve data security, privacy, and decentralisation in healthcare by integrating blockchain and Internet of Things (IoT) technologies. The adoption of IoT devices makes it possible to gather and analyse patient sensory data in real–time; however centralised processing and storage present problems such as data manipulation and privacy issues. The study investigates the creation of a decentralised IoT-based e-healthcare system that takes these issues into account by utilising blockchain technology. In addition, the paper also emphasises how blockchain use has advanced smart contract technologies. Smart contracts provide safe user authentication for IoT device access, assuring responsibility, traceability, and data integrity. The study investigates the potentially game-changing applications of blockchain technology in healthcare, such as enhanced data interoperability, patient-cantered care, reduced administrative procedures, and increased transaction transparency. The report also highlights the significance of blockchain in managing pharmaceutical supply chains, considering the essential influence on patient welfare and safety. Effective management is essential in the healthcare business because supply chain interruptions or breaches can have serious implications. The present level of research in blockchain-enabled IoT applications for healthcare is examined comprehensively using the PRISMA framework and records from the Scopus database. The three most important research topics are cloud computing, fog computing, and medical services. The results highlight the important role that blockchain-enabled IoT applications have played in enhancing data security and privacy in the healthcare industry. Real-time data gathering, precise diagnoses, individualised treatments, and simplified administrative procedures are all made possible by the integration of blockchain and IoT. Additionally, scalable solutions and insightful data for healthcare decision-making are provided via fog computing, cloud computing, machine learning, and smart contracts.
Artificial Intelligence in Subject-Specific Library Work

The general implications of AI for libraries are much discussed in library literature. But while this discussion takes place at the library-wide level, there are also important implications for subject librarians due to the specific uses of AI in different professions and areas of study. These are often overlooked as these specializations tend to publish in subject-specific journals. This article aims to address this research gap by providing a comparison and thematic analysis of this literature. Subject-specific library journals in the areas of law, health sciences, business, and humanities and social sciences were searched to identify relevant journal articles that discussed AI. 131 articles were identified and tagged with at least one category that reflected the nature of the discussion around AI. The following analysis showed that literature related to law had the greatest number of articles by far, though the publishing activity in all disciplines has increased significantly in the last 10 years. This article explores these trends to gain a more comprehensive understanding of the implications for subject-specific library work.
Effect of Daily Class Assignment on Academic Achievement and Academic Anxiety of Senior Secondary School Students

This paper spotlighted findings of the Effect of Daily Class Assignments as a formative assessment technique on the Academic Achievement and Academic Anxiety of Senior Secondary Students. This assessment strategy was designed to revamp assessment, remove constraints, and make teaching learning more powerful. The experiment was conducted on students of 11th standard; a pre-test was taken to equalize both the experimental and control group. After equalization, economics was taught for 15 days to both groups, but DCA evaluation through DCA was done only for the experimental group after teaching, while the other was left uninterrupted after teaching. Post-test of Academic achievement and anxiety was administered to both groups to determine the effect of DCA. Results pictured the positive impact of DCA on Academic Achievement, but no difference was seen in the Academic Anxiety of students after the experiment.
Image caption extraction to aid visual learning

An image caption generator is essential for social media enthusiasts or visually impaired individuals. It can be used as a plugin in popular social media platforms to recommend suitable captions or to assist visually impaired people in comprehending the image content on the web, thereby eliminating ambiguity in image meaning and ensuring accurate knowledge acquisition. This research describes an image caption generator that utilizes a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) model to generate natural language descriptions of images. The CNN was employed to extract features from the input image, which were then fed into the LSTM to generate the corresponding caption. The model is trained on a large dataset of image-caption pairs, using a combination of supervised and reinforcement learning techniques. The model's performance is evaluated using several metrics, and the results demonstrate that the proposed CNN LSTM model outperforms existing state-of-the-art approaches in generating accurate and diverse image captions. This model has the potential to be used in various applications, including image retrieval, content-based image search, and assisting visually impaired individuals to understanding their surroundings. It also discusses about the structure and functions of the various neural networks involved.