Reviewerly/exHarmony huggingface dataset:
SERE: Exploring Feature Self-relation for Self-supervised Transformer

Learning representations with self-supervision for convolutional networks (CNN) has been validated to be effective for vision tasks. As an alternative to CNN, vision transformers (ViT) have strong representation ability with spatial self-attention and channel-level feedforward networks. Recent works reveal that self-supervised learning helps unleash the great potential of ViT. Still, most works follow self-supervised strategies designed for CNN, e.g., instance-level discrimination of samples, but they ignore the properties of ViT. We observe that relational modeling on spatial and channel dimensions distinguishes ViT from other networks. To enforce this property, we explore the feature SElf-RElation (SERE) for training self-supervised ViT. Specifically, instead of conducting self-supervised learning solely on feature embeddings from multiple views, we utilize the feature self-relations, i.e., spatial/channel self-relations, for self-supervised learning. Self-relation based learning further enhances the relation modeling ability of ViT, resulting in stronger representations that stably improve performance on multiple downstream tasks. Our source code is publicly available at: https://github.com/MCG-NKU/SERE.
Role of Emotions in the Development of Citizenship and Instructional Methods Utilizing Emotions

시민성의 정서적 측면을 강조하는 경합주의 시민교육론자들은 혐오와 갈등의 시대에 정치적 감정을 활용한 시민교육이 필요함을 주장한다. 본 연구에서는 시민성의 핵심요소인 의사결정 및 민주적 태도 형성에서 감정이 어떤 역할을 하는지 관련 선행연구를 살펴보았으며 이를 바탕으로 학교 시민교육에서 감정을 어떻게 활용할 수 있을지 그 대안을 모색하였다. 그 결과, 감정은 일정한 조건 하에서 신중하고 효율적인 의사결정을 촉진하는 역할을 하며 특히 자신과 타자의 감정적 상태를 파악할 수 있는 역량은 의사결정의 합리성을 제고하고 허위 정보를 판별하는 데 도움이 된다는 점을 확인했다. 또한 다양한 사회 집단에 대한 공감 능력은 혐오와 같은 부정적 감정을 억제하며 타자를 존중하는 민주적 태도를 형성하는 데 효과가 있다는 점을 살펴보았다. 이러한 선행연구 및 경합주의 시민교육론자들의 제안을 참조하여 본 연구에서는 감정을 활용한 시민교육의 대안으로 첫째, 감정의 형성과정에 대한 비판적 탐구, 둘째, 다양한 사회 집단에 대한 공감 활성화, 셋째, 의사결정의 감정적 원천 분석 및 대안의 감정적 결과 예측을 제안하였으며, 각각의 대안을 뒷받침하는 구체적인 수업 사례도 제시하였다.Agonist civic educators who emphasize the affective aspect of citizenship argue that civic education utilizing political emotions is necessary for an era of hate and conflict. This study reviewed previous studies related to the role of emotions in decision-making and attitude formation, which are key elements of citizenship. Based on the review, this study explored the alternatives to utilizing emotions in civic education classrooms. The review of research suggests that emotions play important roles in promoting prudent and efficient decision-making under certain conditions. In particular, the ability to be aware of the emotional state of oneself and others facilitates rational decision-making and fake news detection. The ability to empathize with various social groups is helpful in suppressing negative emotions such as disgust and forming a democratic attitude that respects others. Referring to these prior studies and the proposals of agonist civic educators, this study presented three suggestions to utilize emotions in the civic education classrooms with specific instructional strategies: a critical inquiry into the process of forming emotional responses; activation of empathy for various social groups; analysis of the emotional source of decisions and prediction of the emotional results of choices.
Summariser – Transforming your online meets

Creating a summary of a given online meet, online lecture or any video having giant content could be a terribly abstract method that everybody participates in. Automating such a method will facilitate dissecting through heaps of information and facilitate human higher use their time to form crucial selections. Not only making the minutes of the meeting but also generating questions and answers from it and that so automatically will help us save our time. With the huge volume of data and media out there, one will be economical by reducing the fluff around the most crucial data. We’ve already started seeing text summaries across the net that square measure mechanically generated. Automatic video summarisation is the method of reducing a video or online meet into a little text outline and generating question answers from it with a computer program so as to make an outline that retains the foremost details of the initial video. Because the drawback of data overload has grown-up, and as the amount of knowledge has augmented, therefore the interest in the auto-summarisation. It’s terribly tough for individuals to manually summarise hours of videos having dunes of data. As everything went into online mode and changed a lot, so does our mental health. People are having stress, depression and other mental health issues. These textual summaries of online meets can be utilised to comment on the mental state of the speakers in the meet and overall vibe of the meet using emotional analysis which can help the companies/colleges to create a better environment for employees/students in order to boost the productivity and enhance the work environment accordingly. Hence, it will not only summarise the meet for you and generate question answers but also summarise the overall emotion in that online meet for enhanced productivity.
Are We Training with The Right Data? Evaluating Collective Confidence in Training Data using Dempster Shafer Theory

The latest trend of incorporating various data-centric machine learning (ML) models in software-intensive systems has posed new challenges in the quality assurance practice of software engineering, especially in a high-risk environment. ML experts are now focusing on explaining ML models to assure the safe behavior of ML-based systems. However, not enough attention has been paid to explain the inherent uncertainty of the training data. The current practice of ML-based system engineering lacks transparency in the systematic fitness assessment process of the training data before engaging in the rigorous ML model training. We propose a method of assessing the collective confidence in the quality of a training dataset by using Dempster Shafer theory and its modified combination rule (Yager’s rule). With the example of training datasets for pedestrian detection of autonomous vehicles, we demonstrate how the proposed approach can be used by the stakeholders with diverse expertise to combine their beliefs in the quality arguments and evidences about the data. Our results open up a scope of future research on data requirements engineering that can facilitate evidence-based data assurance for ML-based safety-critical systems. CCS CONCEPTS•Software and its engineering $\rightarrow$Risk management; Collaboration in software development;•Mathematics of computing $\rightarrow$ Hypothesis testing and confidence interval computation.
Materializing risk in the pregnant athlete: Using material-semiotic tools to examine black-boxed and dis-qualified “facts” in the IOC evidence summaries

In this chapter, I examine the difficulties associated with establishing risk “facts” specific to pregnant elite athletes, using as my empirical site the International Olympic Committee (IOC) evidence summaries on exercise and pregnancy in recreational and elite athletes. I argue that, in the case of the pregnant elite athlete, scientists have been unable to assemble sufficient entities (human and non-human) to enact evidence-based risk “facts” about how much and how far is safe. To support my argument, I employ theoretical and methodological insights from the field of Science and Technology Studies (STS), namely a material-semiotic approach. Material-semiotics provides the tools and sensibilities to explore how a scientific “fact” is assembled into a dense network of human and non-human entities that are simultaneously semiotic (they are relational and carry meanings) and material (human and non-human entities are caught up and given meaning in these relations) (Law, 2019). I mobilize the concepts of black-boxes and patterning to elucidate how knowledge networks – and risk “facts” – about elite pregnant athletes and high-intensity exercise have largely been “dis-qualified” when judged by the scientific standard of evidence-based medicine (EBM). Future directions for research utilizing a material-semiotic approach are suggested, including a study of a proposed international registry of elite athletes and a bibliometric examination of the IOC evidence summaries.
Exploring the Utility of Anonymized EHR Datasets in Machine Learning Experiments in the Context of the MODELHealth Project

The object of this paper was the application of machine learning to a clinical dataset that was anonymized using the Mondrian algorithm. (1) Background: The preservation of patient privacy is a necessity rising from the increasing digitization of health data; however, the effect of data anonymization on the performance of machine learning models remains to be explored. (2) Methods: The original EHR derived dataset was subjected to anonymization by applying the Mondrian algorithm for various k values and quasi identifier (QI) set attributes. The logistic regression, decision trees, k-nearest neighbors, Gaussian naive Bayes and support vector machine models were applied to the different dataset versions. (3) Results: The classifiers demonstrated different degrees of resilience to the anonymization, with the decision tree and the KNN models showing remarkably stable performance, as opposed to the Gaussian naïve Bayes model. The choice of the QI set attributes and the generalized information loss value played a more important role than the size of the QI set or the k value. (4) Conclusions: Data anonymization can reduce the performance of certain machine learning models, although the appropriate selection of classifier and parameter values can mitigate this effect.
Effectiveness of Personality Education Adapting Podcast in University: Focused on liberal arts education curriculum

This study conducted model classes for 13 weeks to examine the effectiveness of the Personality Education Adapting Podcast as a teaching material in university liberal arts education curriculum. Based on teaching and learning methods, Flipped Learning and Task based Learning, the course was planned as following two stages. The first stage was listening to podcast contents and reading texts related to the main topic of the podcast as an assignment before the class; and second stage was participating in discussion and in-depth activities focusing on the topic during class. By implementing Factor Analysis on the opinions from 122 respondents collected through surveys taken twice at pre and post class, four main factors were extracted; [satisfaction with the class], [recognition of personality], [satisfaction with my personality], and [recognition of effectiveness of the podcast]. The impression essays were analyzed by applying Delphi method and repeating the process of categorization and keyword extraction through continuous discussion among three experts. Comparing the results from pre and post class surveys by taking Paired Sample t-Test, [satisfaction with the class] factor increased from 3.93 before class to 4.38 after class, and [recognition of personality] factor rose about 0.49, from 4.09 before class to 4.58 after class. [Satisfaction with my personality] factor increased about 0.66 from 3.45 before class to 4.11 after class and the last factor, [recognition of effectiveness of podcast] improved about 0.68 from 3.21 before class to 3.89 after class. Moreover, as keywords and frequency of keyword usage elicited from analyzing impression essays supported the results from quantitative data analysis, it was possible to verify effectiveness of the class with various dimensions. It is hoped that the results of this study will contribute to production and design of educational digital contents dealing with personality education and to the research on personality education utilizing digital contents in university.
Pengaruh Larutan Natrium Peroksida Terhadap Kekuatan Komposit Serat Sabut Kelapa

Abstract: The purpose of this study was to determine the effect of soaking coconut fiber in sodium hydroxide solution on the strength of the coconut fiber composite. This research was divided into several stages, namely: preparation of materials and tools, the process of soaking coconut fiber, making coconut fiber composites, testing the tensile and flexural properties of coconut fiber composites, and analyzing the test results. Coconut fiber is soaked in sodium hydroxide solution, then dried in an oven, then used as a composite reinforcement using BQTN 157 polyester resin with a composition of 5% coconut fiber. Based on the results of the tensile test according to the ASTM D638-03 standard, and the flexural according to the ASTM D790 standard, it was concluded that the process of soaking coco fiber in sodium hydroxide solution of 5%, 10%, 15%, and 20% had not been able to increase the strength of the coco fiber composite with composition of 5% coconut coir fiber compared to the strength of the resin.
Learning-based Collision Probability Prediction and Obstacle Avoidance for a Mobile robot using a Depth Image

This paper proposes a vision-based navigation algorithm in cluttered environments by using a convolutional neural network (CNN)-based deep learning and depth images. Existing approaches for robot navigation are developed based on obstacle detection or an accurate map to generate a path, which requires complex computation. To solve this issue, we propose a deep-learning-based navigation algorithm that does not require a preobtained map or a recognition of obstacles. In addition, by exploiting a collision probability prediction using a depth image, our method automatically adjusts the velocity command while passing the complex area, so we can improve the safety of a mobile robot. To validate the performance of our proposed method, we conduct a simulation with a mobile robot in a Gazebo environment. The simulation results show that our method outperforms the conventional avoidance algorithm.
Nanta education contents for the visually impaired

This study sought to develop "Nanta" education contents for the visually impaired. "Nanta" means tapping using percussion instruments. Unlike the general public, the visually impaired cannot search and use information through sight, thus suffering many limitations in education. For this reason, most people in the 21st century are participating in cultural arts education programs to improve their cultural lives, but the disabled people in the culturally marginalized class have many difficulties in enjoying cultural activities. In particular, the visually impaired have lower self-esteem and lower vitality in life due to the loss of sight. Thus, this study aims to resolve cultural needs and improve the quality of life for the visually impaired. However, Nanta education in the field is has focused on education for nondisabled people, making it unsuitable for the visually impaired. The visually impaired have developed auditory and tactile abilities, and conversely, their physical and kinesthetic senses are lowered. The Nanta education program considering the weaknesses and strengths of the visually impaired will, albeit not completely, meet the cultural needs of the visually impaired.
Typology of Undergraduate Dance Students' Perception towards Technology Education: a Q-methodological approach

This study aims to explore undergraduate dance students'' perception towards technology education by applying a Q-methodology. The analysis based on 40 P-samples and 33 Q-statements is classified into four types; 1) low interests- avoidance type, 2) practice-oriented type, 3) media(hardware) utilization-focused type, 4) dance-specialized type. Based on the results, it is expected that it would serve as the basis for further research for designing a technology curriculum in universities to adapt dance students to the digital creation platform in the 4th Industrial Revolution Era.
Extension of Critical Programs of the Computational Theory of Mind

Technological advances in computer science have secured the computer metaphor status of a heuristic methodological tool used to answer the question about the nature of mind. Nevertheless, some philosophers strongly support opposite opinions. Anti-computationalism in the philosophy of mind is a methodological program that uses extremely heterogeneous grounds for argumentation, deserving analysis and discussion. This article provides an overview and interpretation of the traditional criticism of the computational theory of mind (computationalism); its basic theses have been formed in Western philosophy in the last quarter of the 20th century. The main goal is to reveal the content of the arguments of typical anti-computationalist programs and expand their application to the framework of the semantic problems of the Classic Computational Theory of Mind. The main fault of the symbolic ap-proach in the classical computationalism is the absence of a full-fledged theory of semantic properties. The relevance of considering these seemingly outdated problems is justified by the fact that the problem of meaning (and general problems of semantics) remains in the core of the latest developments in various areas of AI and the principles of human-computer interaction.
College Ballot System

This project is Entitled as “COLLEGE BALLOT SYSTEM”. Online Ballot System is a web-based voting system that will help you to manage your elections easily and securely. This voting system can be used for casting votes during the elections held in colleges and it minimizes the errors of vote counting. The individual votes are submitted in a database which can be counted to get the highest number of votes of the candidates for a given post. The system computes the election result for all the posts and provides report for the entire election process. The main objective of this system is to design, develop and implement an efficient and interactive web based student voting system. This project is titled as “COLLEGE BALLOT SYSTEM” and was developed using PHP as front end and MYSQL as back end.
Private Set Matching Protocols

We introduce Private Set Matching (PSM) problems, in which a client aims to determine whether a collection of sets owned by a server matches her interest. Existing privacy-preserving cryptographic primitives cannot solve PSM problems efficiently without harming privacy. We propose a new modular framework that enables designers to build privacy-friendly PSM systems that output one bit: whether a server set or collection of server sets matches the client's set, while guaranteeing privacy of client and server sets. The communication cost of our protocols scales linearly with the size of the client's set and is independent of the number of server sets and their total size. We demonstrate the potential of our framework by designing and implementing novel solutions for two real-world PSM problems: determining whether a dataset has chemical compounds of interest, and determining whether a document collection has relevant documents. Our evaluation shows that our privacy gain comes at a reasonable communication and computation cost.
QUESTION ANSWERING SYSTEM FOR HOSPITALITY DOMAIN USING TRANSFORMER-BASED LANGUAGE MODELS

Recent research demonstrates significant success on a wide range of Natural Language Processing (NLP) tasks by utilizing Transformer architectures. Question answering (QA) is an important aspect of the NLP task. The systems enable users to ask a question in natural language and receive an answer accordingly. Most questions in the hospitality industry are content-based, with the expected response being accurate data rather than”yes” or ”no.” Therefore, it requires the system to understand the semantics of the questions and return relevant answers. Despite several advancements in transformer-based models for QA, we are interested in evaluating how it performs with unlabeled data using a pre-trained model, which could also define-tune. This project aims to develop a Question-Answering system for the hospitality domain, in which text will have hospitality content, and the user will be able to ask a question about them. We use an Attention mechanism to train a span-based model that predicts the position of the start and end tokens in a paragraph. By using the model, the users can directly type in their questions in the interactive user interface and receive the response. The data set for this study is created using response templates from the existing dialogue system. We use the Stanford Question and Answer (SQuAD 2.0) data structure to form the dataset, which is mostly used for QA models. During phase1, we evaluate the pre-trained QA models BERT, ROBERTa, and DistilBERT to predict answers and measure the results using Exact Match(EM) and ROUGE-LF1-Score. In Phase 2 of the project, we fine-tune the QA models and their hyper-parameters by training the model with hospitality data sets, and the results are compared. The fine-tuned ROBERTa models achieved the maximum of ROUGE-L F1-Score and EM of 71.39 and 52.17, respectively, which is a relatively 4% increase in F1-Score and 8.7% increase in EM score compared to the pre-trained model. The results of this project will be used to improve the efficiency of the dialogue system in the hospitality industry.
Analysis of software developers’ coding behavior: A survey of visualization analysis techniques using eye trackers

The coding behavior of a software developer is not easily gathered and investigated. One accurate and objective approach is to utilize eye tracking techniques to capture how developers' write code, use tools, and read natural language documents and instructions. A beneficial insight is to visualize the eye gazes that capture coding behavior. A software developer tasks, and individual actions create complexity to design eye tracking experiments and to analyze the eye gazes. Our systematic literature survey focuses on published methods for multiple types of static and dynamic changing eye tracking stimuli, especially techniques that use multiple participant-editable types of stimuli presented at once to allow for a realistic coding experience. We propose an eye tracking design and analysis framework, which break down the various stages of software coding that require using different programming skills. Our decision matrix maps objectives for software programming to analysis techniques for comparing the eye gazes among software developers'. Finally, we discuss the limitations of current visualization methods, specifically for user controlled dynamic stimuli consisting of existing source code stimuli. We propose additional visualization techniques to help researchers investigate behavior of software developers' while they work software coding tasks.
Cyber Space as Battlefield for Nationalist and Separatist Groups

Since their independence in the 1960s, most African countries have been confronted by separatist movements and violent pressure groups that have threatened their corporate existence. These separatist and pressure groups often use the media to exert their weight in the political life of their respective countries of origin as well as to draw the international community's attention to both their cause and plight. A recent trend has been that these groups (separatist movements in particular) leverage the Internet and social media to win the sympathy and support of the international community. In response, African governments have deployed aggressive online counter-strategies to generate noise and asphyxiate separatist and anti-government Internet-based propaganda. This dynamic has made the cyber space to be redefined not only as a flexible public discursive space but also as a battle ground where both separatist/anti-government movements and nationalist philosophies are seriously in strife. This chapter seeks to illustrate the above-mentioned situation in the light of Nigeria's Indigenous People of Biafra (IPOB). Based on secondary sources and critical observations, this chapter explores how IPOB leveraged social media in particular and the Internet in general in their agitation for self-determination and how the Nigerian government or pro-government entities reacted to IPOB's online activism.