Reviewerly/exHarmony huggingface dataset:
cGAN-based Sketched Image Art Generator Using Deep Learning

Art is a creative way of expressing human emotions through sketches. Sketches can be black and white or grey or sometimes coloured. Coloured sketches express a human emotion effectively. Not all human beings can express their emotions through sketch but with advancements in technology machines can generate sketch images and add colours to them. Art Model is one such proposed method which helps humans to generate colourful sketch images which can be used in magazines or newspapers and save the time for sketch artists. The proposed model consists of three stages: data pre-processing, art generation and colour generation stages to obtain a beautiful coloured sketch. Generated sketch when compared with existing methods gives a better quality image. Experimental results for proposed methodology is evaluated using popularity index metric. Better sketch to image conversion can be seen as a part of the result.
Creating Collaborative Data Representations Using Matrix Manifold Optimal Computation and Automated Hyperparameter Tuning

Balancing performance and privacy in centralized machine learning, especially in sensitive domains such as medicine and finance, remains a significant issue. Concerns about data confidentiality result in a reluctance to share confidential data even among related entities, hindering integrated analysis despite its acknowledged benefits. Recent advancements in distributed data analysis have provided a solution to the concern, particularly through the framework of federated learning. This framework enables multiple clients to train models collaboratively while preserving the confidentiality of their data. Fed-DR-Filter extends federated learning by introducing the Privacy-Preserving Data Representation (PPDR) transformation algorithm that converts private datasets into a Global Data Representation (GDR) across multiple clients [4]. The GDR retains the correlation of the original data while ensuring its confidentiality. The PPDR is obtained by adding noise to the GDR, which is then used to filter noisy labels in federated learning environments based on the preserved correlation. Another recent development in distributed data analysis is Data Collaboration Analysis (DCA) [5]. It employs Collaborative Data Representation (CDR), a data representation almost identical to GDR, to directly generate high-performance models without the need for model sharing or iterative communication among clients. The present study builds the crucial role of CDRs and GDRs in DCA and Fed-DR-Filter by proposing a novel method for CDR creation with a solid theoretical foundation. The purpose is to enhance the performance of these techniques by providing an alternative and more effective algorithm for CDR and GDR creation. The primary contributions of this study include a novel optimization for CDR creation utilizing matrix manifold optimization and automated hyperparameter tuning. An algorithm for solving this problem is proposed with its empirical evaluation of artificial and real-world datasets within the DCA setting. The result shows substantial improvement in mean recognition performance.
Empowering Labor Education Based on Construction of Digital Information Platform

Labor education is essential in implementing moral education and cultivating talents. Traditional labor education has drawbacks such as a lack of process management, non-standard input of basic information, poor uniqueness, and duplicate data. In order to effectively overcome these drawbacks, we built an information management platform and established a labor education assessment subsystem and a labor education comprehensive management subsystem. In the digital empowerment of labor education, we used the role of virtual reality technology and labor education resources to connect and create virtual simulation scene data portraits. A student labor practice information database was established with online task allocation management and information inspection. Online dynamic result notification and feedback effectively solved the problems existing in traditional labor education. As a result, the labor spirit and quality of college students were cultivated, which improved the effectiveness of labor education and enhance the evaluation effect of labor education.
COVID-19 Vaccine Adverse Event Detection Based on Multi-Label Classification With Various Label Selection Strategies

Analyzing massive VAERS reports without medical context may lead to incorrect conclusions about vaccine adverse events (VAE). Facilitating VAE detection promotes continual safety improvement for new vaccines. This study proposes a multi-label classification method with various term-and topic-based label selection strategies to improve the accuracy and efficiency of VAE detection. Topic modeling methods are first used to generate rule-based label dependencies from Medical Dictionary for Regulatory Activities terms in VAE reports with two hyper-parameters. Multiple label selection strategies, namely one-vs-rest (OvsR), problem transformation (PT), algorithm adaption (AA), and deep learning (DL) methods, are used in multi-label classification to examine the model performance, respectively. Experimental results indicated that the topic-based PT methods improve the accuracy by up to 33.69% using a COVID-19 VAE reporting data set, which improves the robustness and interpretability of our models. In addition, the topic-based OvsR methods achieve an optimal accuracy of up to 98.88%. The accuracy of the AA methods with topic-based labels increased by up to 87.36%. By contrast, the state-of-art LSTM- and BERT-based DL methods have relatively poor performance with accuracy rates of 71.89% and 64.63%, respectively. Our findings reveal that the proposed method effectively improves the model accuracy and strengthens VAE interpretability by using different label selection strategies and domain knowledge in multi-label classification for VAE detection.
Exploring the Preparation Process for the Secondary School Teacher Qualifying Examination of Librarian Education Major Students at the Graduate School of Education

본 연구는 교육대학원 사서교육전공에 재학 중이거나 졸업한 예비사서교사들에게 중등교사 임용시험을 준비하는 과정을 탐색하여 경험적 자료를 제공하고, 더불어 사서교육전공 교육 과정을 개선하는데 필요한 근거를 도출하는데 목적이 있다. 연구 목적 달성을 위해 175명의 예비사서교사들에게 개방형 질문이 포함된 설문조사를 실시한 뒤 응답 내용을 분석하였다. 그 결과 교육대학원 사서교육전공에 진학한 가장 큰 이유는 ‘사서교사 자격 취득을 위해서’가 165명(94.3%)으로, 교육대학원 교육 과정과 중등교사 임용시험과의 관련성을 묻는 질문에는 ‘80% 이상’ 연관되어 있다는 답변이 50명(28.6%)으로 가장 많았다. 또한 중등교사 임용시험을 준비하고 있는 방법으로는 ‘학원 과정 수강을 통해’가 111명(63.4%)으로 가장 많았고, ‘중등교사 임용시험 준비 과정에서의 어려움’에서는 6개의 주제, 8개의 범주, 168개의 개념이 도출되었다.The purpose of this study is to provide empirical data by exploring the preparation process for the secondary school teacher qualifying examination for those who are currently enrolled or graduated from the librarian education major at the graduate school of education, and to derive the necessary basis for improving the librarian education major curriculum. To achieve the purpose of the study, 175 prospective librarians were surveyed including open-ended questions and the responses were analyzed as follows. 165 (94.3%) of respondents stated that the ‘biggest reason for entering the graduate school of librarian education major’ was ‘to acquire qualification as a librarian teacher’. And 50 (28.6%) respondents the ‘more than 80%’ about question the ‘relationship between the graduate school of education curriculum and the secondary school teacher qualifying examination’. In addition, when asked the question of ‘how to prepare for the secondary school teacher qualifying examination’, 111 (63.4%) of the respondents ‘indicated that taking academy courses’ was the preferred methods, and 6 topics, 8 categories, 168 concepts were derived from ‘difficulties in preparing for the secondary school teacher qualifying examination’.
PENGARUH PENGGUNAAN TEKNOLOGI INFORMASI DALAM MANAJEMEN PERENCANAAN PENDIDIKAN UNTUK MENINGKATKAN EFISIENSI PENDIDIKAN

The use of information technology has changed the way we carry out various aspects of life, including in the education sector. In this digital era, the use of news technology in educational planning management has brought considerable changes and various benefits. This study aims to determine how the influence of the use of information technology in the management of educational planning to increase educational efficiency. The research method used is the library research method, which involves collecting data and information from various sources of literature that are relevant to the research topic. This research will identify and analyze various challenges and obstacles in the use of information technology in educational planning management and the solutions offered to overcome them. In addition, this study will also analyze the impact of using information technology in increasing educational efficiency. The results of this study are expected to provide a better understanding of the potential use of information technology in increasing educational efficiency and its benefits. The findings of this study are expected to be the basis for decision making and strategic planning in the implementation of information technology in education management, with the ultimate goal of improving the overall quality of education.
A survey of algorithm and hardware architecture for bilateral filter and application

In original images/videos, disturbance and noise are inevitable. Therefore, image and video filtering technology has been proposed and developed rapidly. In recent years, bilateral filtering has been widely utilized as an efficient filtering method that can effectively preserve edge information while reducing noise. Researchers have extensively studied the bilateral filtering algorithm and its hardware structures, generating several excellent optimization methods. This paper will comprehensively analyze typical bilateral filtering algorithms and hardware structure optimization design. Some directions for future bilateral filtering optimization will be provided, including algorithms and hardware structures, with analysis and discussion.
Introduction to Design Education: Theory, Research, and Practical Applications for Educators, Steven Faerm (2023)

Review of: Introduction to Design Education: Theory, Research, and Practical Applications for Educators , Steven Faerm (2023) New York: Routledge, 301 pp., ISBN 978-0-36750-230-0, h/bk, $39.95
Critical Separation Hashing for Cross-Modal Retrieval

With the development of Internet technology, unimodal retrieval techniques are no longer suitable for the current environment, and mutual retrieval between multiple modalities is needed to obtain more complete information. Deep hashing has clearly become a simpler and faster method in cross-modal hashing. In recent years, unsupervised cross-modal hashing has received increasing attention. However, existing methods fail to exploit the common information across modalities, thus resulting in information wastage. In this paper, we propose a new critical separation cross-modal hashing (CSCH) for unsupervised cross-modal retrieval, which explores the similarity information across modalities by highlighting the similarity between instances to help the network learn the hash function, and we carefully design the loss function by introducing the likelihood loss commonly used in supervised learning into the loss function. Extensive experiments on two cross-modal retrieval datasets show that CSCH has better performance.
A Novel Function Fusion Approach for Serverless Cold Start

Serverless computing is a new cloud technology that is popular due to its fast reaction time and scale-to-zero features, where users are only charged for the actual quantity of resource utilization and the code can work closer to the users to minimize the delay. The code that can run independently on a different container serves as the processing element in serverless computing. A container needs to be set up and prepared each time a request for function implementation is made. This is referred to as a "cold start," and it takes some time before the actual execution starts.The cold start issue substantially impacts the overall response time of a workflow that consists of functions because it can happen in any of the functions. One method for reducing a workflow’s cold start delay is function fusion. When two functions are combined to form one function, the cold start of the second function is removed. However, if parallel functions are fused, the workflow reaction time can be increased because the parallel functions run sequentially even though the second function’s cold start is eliminated. This study provides a method for using function fusion and taking into account a parallel run to reduce the cold start latency of a workflow. We first identify the three latencies that have an impact on response time, then we show a workflow response time model that takes the latency into account. Finally, we effectively locate a fusion solution that can improve response time on a cold start.
Personality Traits in the Emotion-Performance-Relationship in Intelligent Tutoring Systems

Affective Tutoring Systems (ATS) detect and mitigate critical emotional learner states with the aim of providing individualized support. In tutoring systems for safety-critical work environments, students are trained to achieve and maintain high performance, therefore an ATS should be capable of identifying critical emotional states hindering performance. Interindividual differences in the emotion-performance-relationship can be considered by using the ARC categorization system. The present contribution aims at developing a questionnaire-based method of classifying new learners to the categories. To that end, we investigated differences in personality traits between the different categories. In an airspace surveillance task, we measured performance, emotional valence, emotional arousal, and personality traits in N = 50 subjects. Results showed that a positive valence-performance-relationship, compared to a negative valence-performance-relationship, is associated with higher Neuroticism, lower Conscientiousness, and lower Openness to experience. There were no significant differences in the traits Agreeableness and Extraversion. Based on these results, a future ATS for safety-critical work environments could classify new learners in the ARCs using self-report data and thus dispense with physiological sensors. Thereby, user state diagnosis and evaluation for high performance is possible, setting the ground for an ATS adapting to critical emotional learner states.
A Trustworthy Decentralized System for Health Data Integration and Sharing: Design and Experimental Validation

Personal health data collected via wearable devices can be used for sharing and utilization to provide smart healthcare services. Since personal health data involves sensitive information, it is necessary to require a secure way to manage and use data with the consent of each individual. To integrate and share health data securely, many frameworks using federated learning and blockchain-based system have been proposed. However, the issues of ensuring data ownership and enhancing privacy protection remain to be solved. In this paper, we propose a trustworthy system for health data integration and sharing enabled by decentralized federated learning. We describe the major functions and features, including health data integration, doubling data ownership, data analysis via decentralized federated learning, and incentive mechanisms. We further introduce the experiment and assume two application scenarios for sharing and utilization of personal health data and visualization feedback to users. Various types of health data are collected and integrated into the system with decentralized data analysis while sharing results and models and reducing data transmission for privacy-preserving. The proposed system can be expected to provide an effective way to integrate and analyze personal health data for personalized smart healthcare.
The Aesthetic Education Function of Digital Information Resources in the Teaching of Ethnic Vocal Music in Colleges and Universities Based on Big Data

As a carrier of culture, folk music is the embodiment of social economy, nature and culture, and reflects the social, historical and aesthetic conditions at that time.This can understand history, aesthetics, values and so on from another perspective.Folk music has a certain role in promoting people's self-cultivation.Ethnic music is the carrier of a country's beliefs and values.Its prosperity promotes national self-confidence and enhances the country's patriotism.However, in the era of big data, due to the proliferation of popular music, people tend to ignore the importance of ethnic music, including some textbooks and courses, and even ignore ethnic music directly.Therefore, this paper mainly studies the aesthetic education function of digital information resources in the teaching of ethnic vocal music in colleges and universities under big data.This paper proposes how to solve this situation, and conducts market research and analysis.The interviews and questionnaires were conducted on all the first-year students of a middle school in Xiamen and the high school music teachers, and the clustering algorithm was used for data analysis, which also made the experimental results more accurate.The test results show that in the case of dissatisfaction with the teaching materials, students believe that the proportion of popular music should be increased, and the proportion of film and television animation music is as high as 99% and 89%.The proportion of choosing to increase Chinese folk music and opera music is pitifully small, only 2% and 1%.This fully shows that the teaching of ethnic music appreciation in colleges and universities has not achieved the desired effect at all, and the inheritance of ethnic music is in a dangerous situation.Therefore, after the research of this experiment, it is also necessary to call on everyone to protect Chinese national vocal music.
The Application of the Teaching Model of the Course "Overview of Chinese Culture"

In order to spread Chinese culture on contemporary university campuses, we should start with the college English quality development course and let college students skillfully use English to spread Chinese excellent culture, which can play a role in further improving students' ability to use language.This paper takes the subject "Overview of Chinese Culture" as an example to explore how to optimize the practical teaching activities, and carry out teaching reflection on the existing problems, and try to formulate reasonable solutions, hoping to achieve ideal teaching results in the future through adjustment, so as to better build an efficient teaching mode of the college English quality expansion course "Overview of Chinese Culture", and promote the improvement of the comprehensive quality of contemporary college students. The importance of carrying out the teaching of "Overview of Chinese Culture" in English coursesSo as to improve the sense of national superiority of contemporary college students.As the basic carrier of cultural exchange, in the process of English learning, in addition to learning the unique
Using Automatic Speech Recognition to Evaluate Team Processes in Aviation - First Experiences and Open Questions

This paper reports on the application of automatic speech recognition (ASR) for the analysis of team communication. Shortening the time needed to derive literal transcripts of team communication is seen as an enabler to conduct team process analysis more often in applied research. First, the theoretical background, the connection between teamwork and communication and the state-of-the-art of ASR is described. Second, one use case of collaboration is described in detail to understand the specifics of the observed communication. Applying the nearly untrained ASR to this data yielded a word error rate of 36%. These results and required performance are discussed. The outlook discusses the use of a new generation of ASR models and their expected performance benefits.
Recognizing Emotions in the Discourse of Women in Power Positions in Social Networks via Sentiment and Content Analysis

Understanding people’s emotions is one of the biggest challenges today. From the last decades, the study of emotions has been one of the most emerging research areas from social science to computer science. Indeed, emotions are recognized as a vital element in the relationships between people and computers. Human computer interaction (HCI) is the discipline that deals with the design, evaluation, and implementation of the interaction between people and computing systems. For an adequate interaction between the computer and people, the computer must be able to have some perception of the emotional state of the human being with whom it interacts. The computer be only able to obtain the perception of people emotions through the lexical expressions communicated in his speech. Emotions are present in most forms of communication and can change the meaning of the message transmitted. In this work, we take different Instagram accounts from well-known mass media (CNN, Deutsche Welle among others) to analyze the emotions contained in the representations of women in positions of power. We combine sentiment and content analysis to help you understand sentiment in briefings, including irony and mixed language. Interesting results were obtained to understand the feelings involved in information about women in positions of power.
From Stone Age to New Age Statistics: How Neural Networks Overcome the Irreproducibility Problems in Choice Based Profile Creation

In the gaming world, as well as in assistive technologies, profile creation and further characterizations of humans are basic. Oftentimes, Likert-type scales are needed to collect responses in questionnaires. A subsequent mistake by the analyst occurs when he/she computes the composition score derived from these response categories. Because: such a score can only be computed if these categorical responses are converted into computable numbers. An AI method, namely the application of artificial neural networks, can extract information and overcome various erroneous statistical methods (such as: linearizing responses of Likert scales, computing scores and correlations, and disregarding the ‘curse of dimensionality’). We collected data from 480 respondents who were asked to specify boundaries between the colors of the blackbody spectrum (‘rainbow’). We first used an auto-encoder for dimension reduction, then searched for categories by implementing clustering algorithms, computed likelihood plots, and calculated confusion matrices based on Dirichlet distributions. We found that every respondent was a member of one of only three clusters. Each cluster is characterized by a different distribution of the color boundaries between purple-blue, blue-green, and so on. Surprisingly, the boundaries between some colors in one cluster where within the color interval for members of another cluster. Conclusion: where people see a color boundary is far from obvious even within a shared range. There are implications for several fields that have been using ‘Stone Age statistics’ involving scores, etc. as listed above, among them psychology, sociology, behavioral economy, and human-computer interaction. The modern approach, based on AI, should be adopted by researchers in these fields to ensure reproducibility and provide insight into participant/user/citizen profiles.