Reviewerly/exHarmony huggingface dataset:
Digital Service Design Based on Social Welfare - Taking Pet shelter as an Example

Most pet shelters are organizations established in the name of individuals or a few people out of love. PETA is a pet adoption system based on the shelter scenario, aiming to use digital means to reduce the burden of shelters in information management and dissemination, call for more people to accept pet adoption, and alleviate the social problem of stray pets to a certain extent. This study uses a mixed method of questionnaires, field observations, and semi-structured interviews to understand better shelters' real-life dilemmas and adopters' concerns. The final design results are presented through user needs summarization, design strategy refinement, and functional framework sorting.
The Effectiveness of the Attention Guidance mechanisms in VR Museum

The COVID-19 pandemic has led to a shift towards digital exhibitions on virtual platforms and the increasing use of VR devices. In most exhibitions, audiences are free to explore and understand the content. However, in themed museums, this freedom may result in missing important exhibits.In this study, we examine the effectiveness of various attention guidance mechanisms and user satisfaction in virtual museums. We designed an immersive VR experiment in a simulated virtual museum environment with four mechanisms: no guide, arrows, characters, and characters that acknowledge the user and environment. There were two guiding directions (left and right) and three museum spacial compositions (60-degree angle, 90-degree angle, and 180-degree angle).Through mixed-method analysis, we found that all three attention guidance mechanisms were effective in guiding users in the three museum spacial compositions. Among them, the virtual arrow had the longest visibility time, and the acknowledging character had the highest user satisfaction.In conclusion, attention guidance mechanisms in VR museums significantly improve user attention to the target. The acknowledging character and arrow have the best guidance efficacy, and spacial composition also has a significant impact on attention effectiveness.
Effects of Field-of-View Expansion Using a Wide-Field HMD on Sensibility During Straight-Line Video Viewing

Visual information is considered particularly important among the information that humans receive from the external world, because of its significant impact on human mental activity, including self-position and self-motion perception, impressions, moods, situational judgment, and decision-making. Therefore, we conducted a study to investigate the effects of expanding the field of view (FOV) that receives visual information on human mental activity. In the study, we created three types of visual environments in a virtual space and used image processing to expand the FOV of videos that move straight through each environment. We then conducted an experiment to explore how expanding the FOV affected viewers' impressions of the visual environment and their mood. The results showed that expanding the FOV increased the sense of dynamism and spaciousness of the visual environment, and led to improvements in viewers' activation and arousal levels. These findings suggest that expanding the FOV not only enhances the impression of the visual environment but also has the potential to change viewers' mood to a more energetic state.
Integrated Personalized and Diversified Search Based on Search Logs

Personalized search and search result diversification are two possible solutions to cope with the query ambiguity problem in search engines. In most existing studies, they have been investigated separately, but intuitively, they address the problem from two complementary perspectives and should be combined. Some recent work tried to combine them by restricting result diversification to the subtopics corresponding to the user's personal profile. However, diversification can be required even when the subtopics are outside the user's profile. In this paper, we propose a more general approach to integrate them based on users' implicit feedback in query logs. The proposed approach PER+DIV aggregates a document's novelty score and personal relevance score dynamically according to how much the query falls into the user's interests. To train the model based on user clicks in the logs, we consider user click as a result of both personal relevance and result diversity and a new method is proposed to isolate and model these two factors. To evaluate the model, we design several diversified and personalized metrics in addition to the traditional click-based metrics. Experimental results on a large-scale query log dataset show that the proposed integrated method significantly outperforms the existing personalization and diversification approaches.
Predicting a model of agile supply chain in the service provider enterprises by factor analysis method and fuzzy cognitive map

Organisations nowadays face high competition and turbulent environments, which have been intensified due to technology innovations and changing needs of the customers. In such circumstances, the fastest way to adapt is being agile. The service provider organisations are not excluded from this rule. In this paper, with the purpose of predicting a conceptual model of the agile supply chain in the service provider enterprises, an extensive study was conducted in the literature and the factors affecting agility of the supply chain in the service provider organisations are identified. Then, the importance of each factor is examined through 100 questionnaires completed by experts in the Iranian service providing enterprises. The 11 critical success factors were derived from the exploratory factor analysis technique and finally, the causal relationships among these factors is studied by fuzzy cognitive mapping method and the conceptual model is explored. This model can help service provider managers to make appropriate decisions based on the causal relationships between the critical success factors of agile supply chains to respond more rapidly than their market competitors to the environmental turbulence.
A Study on the Perception of Pre-service Music Teachers for the Preparation of the Education Practice Semester System

본 연구는 시범운영 중인 교육실습학기제에 대한 의미 있는 정보를 제공하고자 현행 교육실습에 대한 예비 음악교사의 인식을 조사하였다. 이를 위해 2022년 12월부터 2023년 2월까지 사범대학과 교육대학원에 재학중인 예비 음악교사 118명을 대상으로 설문을 시행하였고, SPSS 23을 활용하여 기술통계 분석, 교차 분석, t 검정을 통해 설문을 분석하였다. 연구 결과 예비 교사는 현행 4주의 교육실습 기간이 부족하다고 인식하고 있었으며, 7~8주로 교육실습 기간을 변경하고 5학점으로 실습 학점을 상향해야 한다고 인식하고 있었다. 또한 수업 운영, 학교 행정 체계 등 다양한 활동에 참여하고 싶어 하였으며, 교육실습을 위한 학교 혹은 학과의 지원에 대해서도 긍정적으로 인식하고 있었다. 본 연구는 예비 교사들의 준비와 전문성 발달에 대한 통찰력을 제공하고, 향후 교육 실습 개편에 있어 의미 있는 자료로 활용되길 기대한다.The purpose of this study was to examine pre-service music teachers’ perceptions of the education practice system for the preparation of the education practice semester system, which is being piloted. A survey was conducted on 118 pre-service music teachers enrolled in college and graduate schools of education. The questionnaire was analyzed using descriptive statistical analysis, cross-analysis, and t-test with SPSS 23. The study found that pre-service teachers preferred 7 to 8 weeks of education practice and suggested that it should be increased to 5 credits. In addition, pre-service teachers want to participate in various activities such as class management and the school administration system. They also had a positive view of the support provided by the schools or departments they were involved with during their education practice. The study provides valuable insight into the impact of the education practice system on the preparation and professional development of future teachers. The findings can be used to make decisions about the implementation and potential improvements of the system.
Forecasting Bitcoin Prices: An LSTM Deep-Learning Approach Using On-Chain Data

Over the past decade, Bitcoin's unprecedented performance has underscored its position as the premier asset class.Starting from an insignificant value and reaching an astounding high of around 65,000 U.S dollars in 2021 -all without a central controlling authority -Bitcoin's trajectory is undoubtedly a historical feat.Its intangible nature, initially a subject of skepticism, has turned into an attractive quality, leading many investors to allocate a significant portion of their portfolios to Bitcoin.The traditional banking and investment sectors have also turned their attention to Bitcoin's exponential growth.Concurrently, research on macro-economic variables and investor sentiment explaining Bitcoin's price fluctuations has seen considerable development.However, there is a notable absence of studies leveraging On-Chain Data, information derived from transaction data in Bitcoin's blockchain network.This paper fills this gap by using LSTM (Long Short-Term Memory), a technique widely utilized for time-series data prediction, in conjunction with On-Chain Data, to predict Bitcoin prices.
Handling Imbalanced Data through Re-sampling: Systematic Review

Handling imbalanced data is an important issue that can affect the validity and reliability of the results. One common approach to addressing this issue is through re-sampling the data. Re-sampling is a technique that allows researchers to balance the class distribution of their dataset by either over-sampling the minority class or under-sampling the majority class. Over-sampling involves adding more copies of the minority class examples to the dataset in order to balance out the class distribution. On the other hand, under-sampling involves removing some of the majority class examples from the dataset in order to balance out the class distribution. It's also common to combine both techniques, usually called hybrid sampling. It is important to note that re-sampling techniques can have an impact on the model's performance, and it is essential to evaluate the model using different evaluation metrics and to consider other techniques such as cost-sensitive learning and anomaly detection. In addition, it is important to keep in mind that increasing the sample size is always a good idea to improve the performance of the model. In this systematic review, we aim to provide an overview of existing methods for re-sampling imbalanced data. We will focus on methods that have been proposed in the literature and evaluate their effectiveness through a thorough examination of experimental results. The goal of this review is to provide practitioners with a comprehensive understanding of the different re-sampling methods available, as well as their strengths and weaknesses, to help them make informed decisions when dealing with imbalanced data.
Handwritten Bengali Alphabets, Compound Characters and Numerals Recognition Using CNN-based Approach

Accurately classifying user-independent handwritten Bengali characters and numerals presents a formidable challenge in their recognition. This task becomes more complicated due to the inclusion of numerous complex-shaped compound characters and the fact that different authors employ diverse writing styles. Researchers have recently conducted significant researches using individual approaches to recognize handwritten Bangla digits, alphabets, and slightly compound characters. To address this, we propose a straightforward and lightweight convolutional neural network (CNN) framework to accurately categorize handwritten Bangla simple characters, compound characters, and numerals. The suggested approach exhibits outperformance in terms of performance when compared too many previously developed procedures, with faster execution times and requiring fewer epochs. Furthermore, this model applies to more than three datasets. Our proposed CNN-based model has achieved impressive validation accuracies on three datasets. Specifically, for the BanglaLekha isolated dataset, which includes 84-character classes, the validation accuracy was 92.48%. On the Ekush dataset, which includes 60-character classes, the model achieved a validation accuracy of 97.24%, while on the customized dataset, which includes 50-character classes, the validation accuracy was 97.03%. Our model has demonstrated high accuracy and outperformed several prominent existing frameworks.
PENGEMBANGAN MEDIA PEMBELAJARAN “PETUAH KIMIA” (KOMIK DIGITAL MATERI IKATAN KIMIA)

The Indonesian children's literacy is still relatively low, and it has become a severe problem that the government, teachers, parents, and librarians should face. The data shows that only 10% of Indonesian children like to read a book, while most chemistry material is written in textbooks. This study aims to produce digital comics with chemical bonding material, determine comics' feasibility, and respond to readers. The research method develops the Borg and Gall model with seven stages of development without compromising the essence of the model. Meanwhile, data collection techniques are interviews, validation, and questionnaires using a Likert scale. The media and material validator has validated the product developed in this study. As a result, the media aspect has an overall score of 48 or 100%; the score belongs to the very decent criteria. The material aspect has got a score of 26 or 93% from the basic chemistry lecturers; Also, the chemistry teachers give 100%. In addition, the digital comic readers have an extensive test. Conversely, 48 respondents have been assessed with an overall score of 2753 or 90%. Hence, this percentage value relates to the very feasible category. Thus, the characteristics of this digital comic have fulfilled the material, media, language, and visual aspects; since it is included in the worthy category as an independent learning medium without replacing the role of educators.
Cross-view scene image localization with Triplet Network integrating NetVLAD and Fully Connected Layers

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Continuous knowledge contribution in social Q&amp;A communities: the moderation effects of self-presentation and motivational affordances

Purpose Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&amp;A) communities. Design/methodology/approach The authors collect the panel data of 10,193 users from a popular social Q&amp;A community in China. Then, a negative binomial regression model is adopted to analyze the collected data. Findings The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance. Originality/value First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&amp;A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&amp;A communities to optimize community norms and incentive mechanisms.
Supplementary material to "RoGeR v3.0.3 – a process-based hydrological toolbox model in Python"

The supplement contains: 5 -Test cases for continuous development (Sect.S1) -Energy usage of a notebook (Sect.S2) -Spatial distribution of model parameters, soil water content and median travel time of percolation (Sect.S3) S1 Test cases for continuous developmentThe test cases are simulated with vertical processes only (i.e.no lateral flow and no lateral transfer between cells).The input 10 data covers an entire hydrologic year (from 1 st November 2010 to 31 st October 2011).We selected an entire hydrologic year to cover different meteorological conditions and keep computation time little.Generally, the two test cases could be modified to test other model structures, for example, considering vertical and lateral processes without lateral transfer between cells.
Development of Quantum Teaching-Based Shorof Materials at Madrasah Aliyah

This research is motivated by the problems of students who find it difficult and feel bored in understanding learning, which is caused by the use of monotonous methods and the high language of the book used. This study aims to develop quantum teaching-based Shorof material to increase student learning effectiveness and determine how students respond to this teaching material. This study uses the Borg and Gall research and development (RD) design model with six steps, namely 1) potential and problems, 2) data collection, 3) product design, 4) design validation, 5) design revision, and 6) product trial. This teaching material product was validated by material experts and media experts and responded to by 11 Putri Azzahro Islamic Boarding School students. The results of the development of this textbook were based on the results of the textbook needs questionnaire with an average percentage of 88.44%, with valid criteria, the results of the material test expert reached a validity level of 92.5% and the results of media experts with a validity level of 86.66% . While the application in class, the pretest results of students get an average score of 38.18% and posttest with an average score of 74.54%. Thus the development of quantum teaching-based Shorof material developed is very feasible to be used to improve students' Shorof learning abilities.
Multi-Class Fall Detection Based on Machine Learning by using FallAIID dataset

The prevalence of falls among older adults is a significant concern for healthcare professionals and researchers. Preventing fall-related injuries and deaths requires accurate and efficient fall detection systems. This paper proposes a novel approach for multi-class fall detection using machine learning techniques with the FallAIID dataset, evaluates its performances, and proposes an efficient low-cost prototype hardware system. The proposed method leverages the unique characteristics of the FallAIID dataset to accurately classify different types of falls and daily activities with an accuracy of 96% for wrist-worn devices and 95% for neck and waist worn devices. The results of our evaluation demonstrate the effectiveness of our approach and its potential to improve fall detection in real-world settings.
A Bi-Directional GRU Architecture for the Self-Attention Mechanism: An Adaptable, Multi-Layered Approach with Blend of Word Embedding

Sentiment analysis (SA) has become an essential component of natural language processing (NLP) with numerous practical applications to understanding “what other people think”. Various techniques have been developed to tackle SA using deep learning (DL); however, current research lacks comprehensive strategies incorporating multiple-word embeddings. This study proposes a self-attention mechanism that leverages DL and involves the contextual integration of word embedding with a time-dispersed bidirectional gated recurrent unit (Bi-GRU). This work employs word embedding approaches GloVe, word2vec, and fastText to achieve better predictive capabilities. By integrating these techniques, the study aims to improve the classifier’s capability to precisely analyze and categorize sentiments in textual data from the domain of movies. The investigation seeks to enhance the classifier’s performance in NLP tasks by addressing the challenges of underfitting and overfitting in DL. To evaluate the model’s effectiveness, an openly available IMDb dataset was utilized, achieving a remarkable testing accuracy of 99.70%.
SACHA: Soft Actor-Critic With Heuristic-Based Attention for Partially Observable Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF) is a crucial component for many large-scale robotic systems, where agents must plan their collision-free paths to their given goal positions. Recently, multi-agent reinforcement learning has been introduced to solve the partially observable variant of MAPF by learning a decentralized single-agent policy in a centralized fashion based on each agent's partial observation. However, existing learning-based methods are ineffective in achieving complex multi-agent cooperation, especially in congested environments, due to the non-stationarity of this setting. To tackle this challenge, we propose a multi-agent actor-critic method called Soft Actor-Critic with Heuristic-Based Attention (SACHA), which employs novel heuristic-based attention mechanisms for both the actors and critics to encourage cooperation among agents. SACHA learns a neural network for each agent to selectively pay attention to the shortest path heuristic guidance from multiple agents within its field of view, thereby allowing for more scalable learning of cooperation. SACHA also extends the existing multi-agent actor-critic framework by introducing a novel critic centered on each agent to approximate $Q$-values. Compared to existing methods that use a fully observable critic, our agent-centered multi-agent actor-critic method results in more impartial credit assignment and better generalizability of the learned policy to MAPF instances with varying numbers of agents and types of environments. We also implement SACHA(C), which embeds a communication module in the agent's policy network to enable information exchange among agents. We evaluate both SACHA and SACHA(C) on a variety of MAPF instances and demonstrate decent improvements over several state-of-the-art learning-based MAPF methods with respect to success rate and solution quality.