Reviewerly/exHarmony huggingface dataset:
Effects of Multimodal Exercise With Augmented Reality on Cognition in Community-Dwelling Older Adults

This study aims to investigate the effects of an exercise intervention using multimodal exercise with augmented reality and multimodal exercise-only on cognitive function in older adults living in a community dwelling.Quasi-experimental research study.In this control study, 78 participants were divided into 2 experimental groups (with sessions 3 times a week for 12 weeks) and a control group (CG).EG1 participated in a multimodal exercise-only intervention program, EG2 participated in a multimodal exercise program with augmented reality exergames, and CG continued its usual activities. Participants were assessed at baseline and postintervention after 12 weeks.Comparison between baseline and postintervention at 12 weeks showed significant improvements in executive functions, verbal fluency, choice reaction time, and dual task in EG1, whereas there were improvements in general cognition, executive functions, verbal fluency, discrimination reaction time, and depression in EG2 (P ≤ .05). The clinical effect sizes of the interventions were large for overall cognition, executive functions, and reaction time on single- and dual-task reaction time in EG1 and for overall cognition, executive functions, and verbal fluency in EG2.The intervention programs showed significant improvements in several cognitive domains. The multimodal exercise-only showed improvements in more variables than the multimodal exercise with augmented reality, but the augmented reality group showed greater changes between baseline and postintervention.
Bayesian hypernetwork collaborates with time-difference evolutional network for temporal knowledge prediction

A Temporal Knowledge Graph (TKG) is a sequence of Knowledge Graphs (KGs) attached with time information, in which each KG contains the facts that co-occur at the same timestamp. Temporal knowledge prediction (TKP) aims to predict future events given observed historical KGs in TKGs, which is essential for many applications to provide intelligent analysis services. However, most existing TKP methods focus on entity and relation prediction tasks but ignore the importance of time prediction tasks. Furthermore, there is uncertainty in time prediction, and it is difficult for prediction models to model it completely. In this work, we propose a collaboration framework with Bayesian Hypernetwork and Time-Difference Evolutional Network (BH-TDEN) to address these problems. First, we begin with the time prediction task, and we present a Bayesian hypernetwork to model the uncertainty of events time. For the input of Bayesian hypernetwork, we design a novel time-difference evolutional network to obtain the entities and relations embedding. Specifically, we propose an auto-regressive time gate parameterized by the time difference of adjacent KGs in entity and relation encoder to learn the time-sensitive TKG embedding, which not only learns the relationship between the given time information and TKG embedding but also provides more expressive TKG embedding for Bayesian hypernetwork to accurately predict the time of future events. Furthermore, we also present a novel relation updating mechanism that employs the neighbor relations of the subject corresponding to the current relation to learn more adaptive relation embedding. Extensive experiments demonstrate that the proposed method obtains considerable time prediction and link prediction performance on four TKG benchmark datasets.
Magneto-optic interferometric system: Exploring building blocks and subcircuits

Recent developments in the standardization of magnetic field generators have been considered for optoelectronic industrial-scale applications. In this work, a strategy to incorporate all optical components that can be used to implement a broad range of functions for communications applications is presented. We propose a circuit design for a magneto-optic interferometric system based on building blocks and subcircuits consisting of an interferometer with magneto-optic phase shifters and a magnetic field generator. The paper shows the building blocks based on elementary 2 × 2 photonics coupling components. We demonstrate that such a design can be configured as a tunable system based on the phase shift experienced by the incoming signals from two input waveguides and their transitions.
Enhancing <scp>scene‐text</scp> visual question answering with relational reasoning, attention and dynamic vocabulary integration

Abstract Visual question answering (VQA) is a challenging task in computer vision. Recently, there has been a growing interest in text‐based VQA tasks, emphasizing the important role of textual information for better understanding of images. Effectively utilizing text information within the image is crucial for achieving success in this task. However, existing approaches often overlook the contextual information and neglect to utilize the relationships between scene‐text tokens and image objects. They simply incorporate the scene‐text tokens mined from the image into the VQA model without considering these important factors. In this paper, the proposed model initially analyzes the image to extract text and identify scene objects. It then comprehends the question and mines relationships among the question, OCRed text, and scene objects, ultimately generating an answer through relational reasoning by conducting semantic and positional attention. Our decoder with attention map loss enables prediction of complex answers and handles dynamic vocabularies, reducing decoding space. It outperforms softmax‐based cross entropy loss in accuracy and efficiency by accommodating varying vocabulary sizes. We evaluated our model's performance on the TextVQA dataset and achieved an accuracy of 53.91% on the validation set and 53.98% on the test set. Moreover, on the ST‐VQA dataset, our model obtained ANLS scores of 0.699 on the validation set and 0.692 on the test set.
Exploring variables explaining elementary school student’s cognitive, social and emotional competencies using random forest

This study was designed to explore major predictors related to cognitive, social and emotional competencies of elementary school students to discuss the implication for competencies development. To do this, random forest was applied to 1st wave data(4th graders) of the Seoul Educational Longitudinal Study of Students(SELS) 2020, and Shapley addictive explanations (SHAP) analysis was conducted to explore the importance of major predictors and the relationship between competencies and the predictors. The main result are as follows. First, the predictors commonly derived from three competencies were 'class attitude' and 'ICT competency'. The more positive class attitude and higher ICT competency, the higher cognitive and social-emotional competencies. Second, 'Anxiety' was found to major predictor of three competencies, but it was found to have positive relationship with cognitive competency and negative relationship with social-emotional competency. Third, there were the most common predictors explaining social and emotional competencies. Based on these findings, implications for developing competencies and suggestions for future research are discussed.
Potensi Konsep Matematika dalam Penggunaan Aperture di Fotografi

This study aims to help understand how the use of aperture works with mathematical concepts so that the use of this method is applied to photography activities and as a medium for learning mathematics in schools. The method used in this study is descriptive with the form of action research, because this study describes the mathematical concepts contained in the use of aperture in photography. From the interview, researchers collected information on mathematical concepts contained in the sequence located behind the F-stop number and explored the relationship between the F-stop number and the diameter of the circular aperture opening area in the lens, there are several mathematical concepts such as fractional numbers, geometric series, scales, comparisons, magnitudes &amp; units, space builds, to the area of a circle. In this case, it proves that in the use of Aperture in photography, there are mathematical concepts related to school mathematics learning
Analysis of the Readability Characteristics of Educational Text: based on Texts in Korean Language, Social Studies, and Science

In this study, an educational text database of Korean language, social studies, and science departments was established, and a qualitative checklist for measuring readability was developed. After setting the level of readability through the teacher
Análisis de accesibilidad y visibilidad para los portales académicos de las universidades de Cartagena

Introduction: With the growing demand for applications in the market, companies have increasingly focused on meeting the technological needs of the average user, often overlooking those with functional diversity. Consequently, accessibility has emerged as a critical challenge across various contexts, particularly within the educational sphere, where the diverse range of individuals accessing web content is particularly pronounced. Objective: This paper presents a study centered on the accessibility inspection of six university websites located in the city of Cartagena. The primary aim is to ascertain the relationship between the integration of accessibility guidelines and web positioning factors as determined by Webometrics. Methodology: The methodology comprises five distinct phases: website selection, tool selection for inspection, execution of accessibility inspection, analysis of obtained results, and formulation of conclusions and recommendations. Results: The study revealed a correlation exceeding 0.8 between the local ranking of universities according to Webometrics and the ranking of accessibility issues identified through automated inspection tools. Conclusions: It is recommended that educational institutions adopt automatic web evaluation tools to enhance the accessibility of their institutional websites. This regular review and rectification of issues represent best practices for improving both local and international visibility and usability.
CT-GCN+: a high-performance cryptocurrency transaction graph convolutional model for phishing node classification

Abstract Due to the anonymous and contract transfer nature of blockchain cryptocurrencies, they are susceptible to fraudulent incidents such as phishing. This poses a threat to the property security of users and hinders the healthy development of the entire blockchain community. While numerous studies have been conducted on identifying cryptocurrency phishing users, there is a lack of research that integrates class imbalance and transaction time characteristics. This paper introduces a novel graph neural network-based account identification model called CT-GCN+, which utilizes blockchain cryptocurrency phishing data. It incorporates an imbalanced data processing module for graphs to consider cryptocurrency transaction time. The model initially extracts time characteristics from the transaction graph using LSTM and Attention mechanisms. These time characteristics are then fused with underlying features, which are subsequently inputted into a combined SMOTE and GCN model for phishing user classification. Experimental results demonstrate that the CT-GCN+ model achieves a phishing user identification accuracy of 97.22% and a phishing user identification area under the curve of 96.67%. This paper presents a valuable approach to phishing detection research within the blockchain and cryptocurrency ecosystems.
Easy Ontology and Undecidable Sentences

Abstract According to Thomasson’s Easy Ontology, all existential questions have straightforward answers and are solvable by conceptual and empirical work. So there is no need for traditional metaphysics to solve them. First, I give some counterexamples to this thesis from incomplete and undecidable theories. Then I discuss some possible responses, I consider a wider sense of conceptual analysis and argue that even in this sense Easy ontology is not able to resolve the problem and must sacrifice either easiness or answerability. Finally, I will argue that although traditional metaphysics does not make the undecidable sentences answerable, it can still make sense of them and helps us to understand why there are unanswerable questions at all.
Persona/scenario (P/S) toolkit enhancing inclusive fashion design education

Abstract This study aimed to address the diversity issue in fashion design education by developing two prototypes for Inclusive Fashion Design (IFD) education. The prototypes were constructed based on the 3C3R model of Project-Based Learning (PBL) and incorporated the Persona/Scenario (P/S) toolkit for IFD. Additionally, the study examined the effectiveness of the FEA (Function, Expressive, and Aesthetics) self-checklist and the P/S toolkit in the IFD process. The experiments involved two design instructors, two teaching assistants, and 12 senior-level students divided into four teams. Thematic analysis was conducted on the interview data collected from the participants. The results revealed that FEA factors were highly considered in both education prototypes, regardless of the presence of the P/S toolkit. Maintaining consistency of the IFD concept throughout each stage of the process was found to be crucial, and the use of the P/S toolkit played a significant role in achieving this consistency. Team communication emerged as an essential factor in IFD education, as team collaboration using the P/S toolkit triggered diverse perspectives on targets, facilitated design expansion, and extended individual competences. Overall, this study contributes to the understanding of the diversity issue in fashion design education and promotes the adoption of the IFD education methodology, emphasizing the significance of consistent concept development, effective toolkits, and team collaboration in achieving inclusive design practices.
Convolutional neural network based data interpretable framework for Alzheimer’s treatment planning

Alzheimer's disease (AD) is a neurological disorder that predominantly affects the brain. In the coming years, it is expected to spread rapidly, with limited progress in diagnostic techniques. Various machine learning (ML) and artificial intelligence (AI) algorithms have been employed to detect AD using single-modality data. However, recent developments in ML have enabled the application of these methods to multiple data sources and input modalities for AD prediction. In this study, we developed a framework that utilizes multimodal data (tabular data, magnetic resonance imaging (MRI) images, and genetic information) to classify AD. As part of the pre-processing phase, we generated a knowledge graph from the tabular data and MRI images. We employed graph neural networks for knowledge graph creation, and region-based convolutional neural network approach for image-to-knowledge graph generation. Additionally, we integrated various explainable AI (XAI) techniques to interpret and elucidate the prediction outcomes derived from multimodal data. Layer-wise relevance propagation was used to explain the layer-wise outcomes in the MRI images. We also incorporated submodular pick local interpretable model-agnostic explanations to interpret the decision-making process based on the tabular data provided. Genetic expression values play a crucial role in AD analysis. We used a graphical gene tree to identify genes associated with the disease. Moreover, a dashboard was designed to display XAI outcomes, enabling experts and medical professionals to easily comprehend the prediction results.
Evaluation of Governance in Information Systems Security to Minimize Information Technology Risks

Information system security within XYZ University constitutes a vital component of its IT framework, exerting significant influence over security levels across all facets of the information systems. Among the numerous implemented information system services at the university, a considerable portion lacks active security measures within operational systems. In pursuit of achieving uniform governance, this study adopts the most recent COBIT 2019 framework. The primary objective of this research is to evaluate the degree to which current information system security management aligns with the process achievement values stipulated in the COBIT 2019 standard. This evaluation entails the calculation of maturity level values that gauge performance levels in managing information system security. Findings from the COBIT 2019 Design assessment conducted at XYZ University's LTIK reveal that individuals scoring above 80 or those requiring Capability Level 4 include APO12 and BAI10. Moreover, the calculation outcomes for each subdomain reveal the presence of 2 subdomains at Level 4, 4 subdomains at Level 3, 15 subdomains at Level 2, and 19 subdomains at Level 1. The identification outcomes underscore the existence of gaps within each domain. Particularly, the APO12 and BAI10 domains exhibit a gap spanning 2 levels.
The Empirical Study On Algorithm Optimization In Expert Systems For Diagnosing Rice Plant Diseases

Rice is one of the most important cultivated plants for human survival. The activity of cultivating rice plants becomes a livelihood for most of these residents, so the success rate of the amount of rice harvested becomes very important because they depend on how much rice can be harvested, disease diagnosis is very important for farmers, this is very important to reduce economic losses due to diseases that cause crop failure. Therefore, when dealing with rice diseases, an expert is needed to make diagnoses or solutions to rice diseases. However, an expert does not know when to come to the village, and farmers also do not understand all rice diseases. Therefore, a web-based expert system application using the forward chaining method is proposed to represent an expert to help farmers diagnose and solve diseases of rice plants with existing symptoms.
A multimodal dialogue system for customer service based on user personality adaptation and dialogue strategies

This paper presents a multimodal dialogue system with user personality adaptation and multiple nonverbal behavior generation for a conversational android robot that plays a customer service role in recommending travel plans. It is useful to estimate the user's personality to realize a dialogue system that adapts the dialogue strategy to individual users. To improve the user's impression of the conversation robot as a customer service agent, we must control the nonverbal behavior of the robot, such as its facial expressions and motions. Moreover, we need to design the dialogue strategy to provide information and make users enjoy the conversation by adding an ice-breaking step. Against this background, we develop a dialogue system prototype that adjusts the dialogue strategy based on the user's personality, as estimated by a pre-trained multimodal sensing model. Furthermore, we implement appropriate nonverbal behavior patterns for each situation, including the voice, motion, and facial expressions of the android robot, to provide it appropriate and polite service agent behaviors. A personality assessment game is introduced into the dialogue to prevent the user from becoming bored. The game plays an ice-breaking role in the dialogue. To let the user engage with the robot, we implement a complement to the user as a dialogue act. We evaluate the proposed multimodal dialogue system for customer service by means of participation in a dialogue robot competition. The fair evaluation results from 26 dialogue users who conversed with multiple systems show that the proposed dialogue system achieved the best score among 13 teams participating in the preliminary round of the competition, indicating that the user personality adaptation and various elements implemented in our dialogue system improve the dialogue experience of general users. The results of a third-party evaluation and multiple regression analysis show that natural response is the most important factor contributing to user impression.
Motivating Readers: A Tale of Teacher Leadership and Retention

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Usage of Multiplane Image Information SEI Message for Distribution of Lightweight 3DoF+ Immersive Content with Conventional 2D Decoder

In this presentation, we describe a new SEI message under consideration for VSEI/H.274 - the multiplane image information SEI message (MPII SEI) [1]. The motivation to design this MPI is to establish open non-proprietary standards to enable distribution of lightweight 3DoF+ immersive consumer experiences encoded in a single bitstream compatible with already deployed smartphones, laptops, etc. equipped with a conventional 2D decoder.
Overview of Visual Signal Compression towards Machine Vision

Machine analysis of visual signals has been providing fundamental infrastructure support for artificial intelligence (AI) applications. As such, the compact representation of visual signal towards machine analysis is critical in current AI systems. A straightforward and general pipeline of visual signal compression for machines is to compress the visual signal, then perform the machine analysis on the reconstructed visual signal for ultimate results. This pipeline naturally supports multiple machine vision tasks and human perception, and is denoted as visual signal compression towards machines (VSCM). This paper provides an overview to summarize the VSCM related technologies in terms of various modules, and envision the future development of VSCM.
No-Reference Quality of Experience Model for Dynamic Point Clouds in Augmented Reality

Point cloud streaming is becoming increasingly popular due to its ability to provide six degrees of freedom (6DOF) for immersive media. Measuring the quality of experience (QoE) is essential to evaluate the performance of point cloud applications. However, most existing QoE models for point cloud streaming are complicated and/or not open source. Therefore, it is desirable to provide an open-source QoE model for point cloud streaming.
Accelerated and Adaptive Non-local Mean Temporal Filtering for Improved HEVC Coding Efficiency

As modern video codecs exploit at their basis temporal and spatial correlations in video signal for compression, and such correlations are corrupted by noise that is present in the video signal, temporal filtering has been shown to be effective for improving coding efficiency in various encoder implementations [1], [2], [3]. In particular, the non-local mean approach in [1] has been applied in AV1, an open video coding format competing with HEVC (aka H.265), which includes built-in mechanisms that allow effective temporal filtering using non-displayable reference frames (aka ALTREF frames).