Reviewerly/exHarmony huggingface dataset:
Fitted avatars: automatic skeleton adjustment for self-avatars in virtual reality

Abstract In the era of the metaverse, self-avatars are gaining popularity, as they can enhance presence and provide embodiment when a user is immersed in Virtual Reality. They are also very important in collaborative Virtual Reality to improve communication through gestures. Whether we are using a complex motion capture solution or a few trackers with inverse kinematics (IK), it is essential to have a good match in size between the avatar and the user, as otherwise mismatches in self-avatar posture could be noticeable for the user. To achieve such a correct match in dimensions, a manual process is often required, with the need for a second person to take measurements of body limbs and introduce them into the system. This process can be time-consuming, and prone to errors. In this paper, we propose an automatic measuring method that simply requires the user to do a small set of exercises while wearing a Head-Mounted Display (HMD), two hand controllers, and three trackers. Our work provides an affordable and quick method to automatically extract user measurements and adjust the virtual humanoid skeleton to the exact dimensions. Our results show that our method can reduce the misalignment produced by the IK system when compared to other solutions that simply apply a uniform scaling to an avatar based on the height of the HMD, and make assumptions about the locations of joints with respect to the trackers.
ASTNN-Based System for Auditing PHP Code

As PHP language is widely used in web applications, effectively detecting and repairing security vulnerabilities in PHP code has become crucial in research. Thus, we developed an ASTNN-based system for auditing PHP code that transforms input source code into its corresponding Abstract Syntax Tree (AST) to learn and extract features by encoding and decoding at each layer. The system outputs the classification results. We assessed the effectiveness of the proposed model using the test dataset and obtained an accuracy rate of 90%, surpassing other methods based on PHP vulnerability libraries and rules. Moreover, the proposed model exhibits a low false-positive rate and a high recall rate, providing greater advantages than traditional rule-based methods and enhancing the efficiency and precision of vulnerability detection.
Iterative pseudo-labelling with SoftMax probability in text classification

Semi-supervised learning is one of the potential research fields in text classification. In this paper, semi-supervised pseudo-label training experiments are conducted using the BERT model that has been pre-trained as a baseline. Only 20% of the original dataset is used for the new training set after segmenting the training set. The raw corpus used for pseudo-label training consists of the remaining 80% of data after labels are removed, while the original test set is still utilized. The results indicate that the key to the semi-supervised pseudo-labelling method is the performance of the original model and reasonable data filtering techniques. Even though the SoftMax value used for data filtering is not precisely equivalent to model prediction accuracy, experimental results show it can somewhat reduce the error propagation problem of the model. This is consistent with earlier research. However, using SoftMax as the threshold for data screening can't bring enough benefits to the model training and make it surpass the training performance of the original data set. As a result, future studies will focus on improving the accuracy of pseudo-labelling with a more suitable data selection method to better the model's performance.
Virtual home for college couple in long-distance romantic relationships

College students form a significant portion of all couples that are engaging in long-distance romantic relationships. The physical separation has made maintaining Long-Distance Romantic Relationships especially difficult for college couples. In order to provide design solutions to help college couples in Long-Distance Romantic Relationships better overcome challenges in their relationships, this paper concludes through surveys that the most pressing issue facing Long-Distance Romantic Relationships is feeling and understanding their significant other's emotions. Based on the unique offerings of Virtual Reality (VR) technologies and the limitations of existing social VR technologies, the author proposes the design solution of a virtual HOME that emphasizes co-presence, empathy promotion, and collaborative activities.
Path planning based on reinforcement learning

With the wide application of mobile robots in industry, path planning has always been a difficult problem for mobile robots. Reinforcement learning algorithms such as Q-learning play a huge role in path planning. Traditional Q-learning algorithm mainly uses - greedy search policy. But for a fixed search factor -greedy. For example, the problems of slow convergence speed, time-consuming and many continuous action transformations (such as the number of turns during robot movement) are not conducive to the stability requirements of mobile robots in industrial transportation. Especially for the transportation of dangerous chemicals, continuous transformation of turns will increase the risk of objects toppling. This paper proposes a new method based on - greedy 's improved dynamic search strategy is used to improve the stability of mobile robots in motion planning. The experiment shows that the dynamic search strategy converges faster, consumes less time, has less continuous transformation times of action, and has higher motion stability in the test environment.
Application of Convolutional Neural Networks for the Classification of Human Emotions

Computer vision training is implemented using deep learning.Deep learning can be applied to solve a variety of classification problems.Accordingly, the problem of recognizing human emotions based on facial images can be singled out.For this problem, the use of convolutional neural networks proved to be the best solution.Specifically, the convolutional neural network models used in this paper are ResNet50, VGG16, and VGG19.The CNN model was implemented using the Keras library and the Python programming language.As input to these models, the RAF-DB database containing images of human faces with emotions was used.Based on the results obtained from the mentioned three CNN models, VGG16 proved to be the best, achieving a precision of 83.61%.VGG19 was on the second place with an accuracy of 82.37%, and the worst was ResNet50, whose accuracy was 75.31%.
Rethinking Interaction with Conversational Agents: How to Create a Positive User Experience Utilizing Dialog Patterns

Conversational agents (CAs) are increasingly used as an additional convenient and innovative customer service channel to relieve service employees, as in the studied organization. In the process of analyzing and maintaining the present AI-based agent, however, user satisfaction is low as the CA lacks understanding and offers unsatisfactory solutions to users. Nonetheless, solving the requests and providing a positive user experience is crucial to relieve the service employees’ workload permanently. For CAs’ improvement, this study followed action design research (ADR) and used design thinking. We identified the central interaction problems (findability, welcome message, dialog control and fallback issues) with a monitoring process and analysis. Afterward, we interviewed users about their expectations and requirements and addressed these problems by creating user-centric mock-ups. Through a quantitative survey, the most popular solutions were implemented in a prototype. Finally, the resulting CA prototype was evaluated, showing a significantly improved user experience afterward, and design guidelines were discovered.
Tell Me, What Are You Most Afraid Of? Exploring the Effects of Agent Representation on Information Disclosure in Human-Chatbot Interaction

Self-disclosure counts as a key factor influencing successful health treatment, particularly when it comes to building a functioning patient-therapist-connection. To this end, the use of chatbots may be considered a promising puzzle piece that helps foster respective information provision. Several studies have shown that people disclose more information when they are interacting with a chatbot than when they are interacting with another human being. If and how the chatbot is embodied, however, seems to play an important role influencing the extent to which information is disclosed. Here, research shows that people disclose less if the chatbot is embodied with a human avatar in comparison to a chatbot without embodiment. Still, there is only little information available as to whether it is the embodiment with a human face that inhibits disclosure, or whether any type of face will reduce the amount of shared information. The study presented in this paper thus aims to investigate how the type of chatbot embodiment influences self-disclosure in human-chatbot-interaction. We conducted a quasi-experimental study in which $$n=178$$ participants were asked to interact with one of three settings of a chatbot app. In each setting, the humanness of the chatbot embodiment was different (i.e., human vs. robot vs. disembodied). A subsequent discourse analysis explored difference in the breadth and depth of self-disclosure. Results show that non-human embodiment seems to have little effect on self-disclosure. Yet, our data also shows, that, contradicting to previous work, human embodiment may have a positive effect on the breadth and depth of self-disclosure.
Epistemic Logics of Structured Intensional Groups

Epistemic logics of intensional groups lift the assumption that membership in a group of agents is common knowledge. Instead of being represented directly as a set of agents, intensional groups are represented by a property that may change its extension from world to world. Several authors have considered versions of the intensional group framework where group-specifying properties are articulated using structured terms of a language, such as the language of Boolean algebras or of description logic. In this paper we formulate a general semantic framework for epistemic logics of structured intensional groups, develop the basic theory leading to completeness-via-canonicity results, and show that several frameworks presented in the literature correspond to special cases of the general framework.
Incentive Engineering for Concurrent Games

We consider the problem of incentivising desirable behaviours in multi-agent systems by way of taxation schemes. Our study employs the concurrent games model: in this model, each agent is primarily motivated to seek the satisfaction of a goal, expressed as a Linear Temporal Logic (LTL) formula; secondarily, agents seek to minimise costs, where costs are imposed based on the actions taken by agents in different states of the game. In this setting, we consider an external principal who can influence agents' preferences by imposing taxes (additional costs) on the actions chosen by agents in different states. The principal imposes taxation schemes to motivate agents to choose a course of action that will lead to the satisfaction of their goal, also expressed as an LTL formula. However, taxation schemes are limited in their ability to influence agents' preferences: an agent will always prefer to satisfy its goal rather than otherwise, no matter what the costs. The fundamental question that we study is whether the principal can impose a taxation scheme such that, in the resulting game, the principal's goal is satisfied in at least one or all runs of the game that could arise by agents choosing to follow game-theoretic equilibrium strategies. We consider two different types of taxation schemes: in a static scheme, the same tax is imposed on a state-action profile pair in all circumstances, while in a dynamic scheme, the principal can choose to vary taxes depending on the circumstances. We investigate the main game-theoretic properties of this model as well as the computational complexity of the relevant decision problems.
An Abstract Look at Awareness Models and Their Dynamics

This work builds upon a well-established research tradition on modal logics of awareness.One of its aims is to export tools and techniques to other areas within modal logic.To this end, we illustrate a number of significant bridges with abstract argumentation, justification logics, the epistemic logic of knowing-what and deontic logic, where basic notions and definitional concepts can be expressed in terms of the awareness operator combined with the ✷ modality.Furthermore, these conceptual links point to interesting properties of awareness sets beyond those standardly assumed in awareness logics -i.e.positive and negative introspection.We show that the properties we list are characterised by corresponding canonical formulas, so as to obtain a series of off-the-shelf axiomatisations for them.As a second focus, we investigate the general dynamics of this framework by means of event models.Of specific interest in this context is to know under which conditions, given a model that satisfies some property, the update with an event model keeps it within the intended class.This is known as the closure problem in general dynamic epistemic logics.As a main contribution, we prove a number of closure theorems providing sufficient conditions for the preservation of our properties.Again, these results enable us to axiomatize our dynamic logics by means of reduction axioms.
Belief Revision from Probability

In previous work ([5, 6]), we develop a question-relative, probabilistic account of belief.On this account, what someone believes relative to a given question is (i) closed under entailment, (ii) sufficiently probable given their evidence, and (iii) sensitive to the relative probabilities of the answers to the question.Here we explore the implications of this account for the dynamics of belief.We show that the principles it validates are much weaker than those of orthodox theories of belief revision like AGM [1], but still stronger than those valid according to the popular Lockean theory of belief [4], which equates belief with high subjective probability.We then consider a restricted class of models, suitable for many but not all applications, and identify some further natural principles valid on this class.We conclude by arguing that the present framework compares favorably to the rival probabilistic accounts of belief developed by Leitgeb [13,14] and Lin and Kelly [17]. Probability StructuresWe will work with the following simplification of the models in [5]: Definition 1.1.A probability structure is a tuple S, E , Q, Pr,t such that:
Knowledge-wh and False Belief Sensitivity: A Logical Study (An Extended Abstract)

In epistemic logic, a way to deal with knowledge-wh is to interpret them as a kind of mention-some knowledge (MS-knowledge).But philosophers and linguists have challenged both the sufficiency and necessity of such an account: some argue that knowledge-wh has, in addition to MS-knowledge, also a sensitivity to false belief (FS); others argue that knowledge-wh might only imply mention-some true belief (MS-true belief).In this paper, we offer a logical study for all these different accounts.We apply the technique of bundled operators, and introduce four different bundled operators -which characterize the notions of MS-true belief, MS-true belief with FS, MSknowledge and MS-knowledge with FS respectively.We axiomatize the four logics which take the above operators (as well as [K]) as primitive modalities on the class of S4.2-constant-domain models, and compare the patterns of reasoning in the obtained logics, in order to show how the four accounts of knowledge-wh differ from each other, as well as what they have in common.
Scenes, Interaction, Community and Values: A Service Experience Design Framework for Museum Public Space Based on the Theory of Experience Preference

Focus on the development mode of museum public space service, exploring a sustainable service experience system and design strategy. This paper traces the origin and development of the theory of experience preference, and analyzes its concept, elements and application. Based on the summary of experience preference, audience needs and the status quo of museum public space, service design thinking is introduced to rebuild the internal correlation among the three. A “Scenes, Interaction, Community and Values (SICV)” service experience design framework of museum public space is established. This framework can build an open and social cultural service platform for the public space of museums. It is conducive to improving the audience’s stereotype and evaluation of the museum’s public space, promoting the “museum into the audience’s life”, and providing new ideas for the transformation and development of museums.
Know What Not To Know: Users’ Perception of Abstaining Classifiers

Machine learning systems can help humans to make decisions by providing decision suggestions (i.e., a label for a datapoint). However, individual datapoints do not always provide enough clear evidence to make confident suggestions. Although methods exist that enable systems to identify those datapoints and subsequently abstain from suggesting a label, it remains unclear how users would react to such system behavior. This paper presents first findings from a user study on systems that do or do not abstain from labeling ambiguous datapoints. Our results show that label suggestions on ambiguous datapoints bear a high risk of unconsciously influencing the users' decisions, even toward incorrect ones. Furthermore, participants perceived a system that abstains from labeling uncertain datapoints as equally competent and trustworthy as a system that delivers label suggestions for all datapoints. Consequently, if abstaining does not impair a system's credibility, it can be a useful mechanism to increase decision quality.
Managing Electricity Costs in Industrial Mining and Cryptocurrency Data Centers

2022 has seen a significant decline in global cryptocurrency ratings, especially bitcoin. As it is known, one of the key components of the cryptocurrency’s cost is the amount of electrical energy spent by the computing equipment of the mining data centers. In the context of declining bitcoin rates, the management of the data centers’ energy costs becomes critical for maintaining the profitability and investment return of the mining projects. Russia is among the top-3 leading producers of cryptocurrencies, providing 11% of the global primary bitcoin transactions. In this regard, the management of the data centers’ costs related to the purchase of electricity in the Russian wholesale and retail electricity (capacity) markets presents a high scientific and practical importance. This article analyzes the pricing mechanisms for the purchase of electricity in Russia’s wholesale and retail electricity (capacity) markets on an industrial scale given the specifics of the hourly demand-based pricing. This paper suggests a new metrics system, including a capacity demand management coefficient and a transmission cost management coefficient, which allow setting specific price parameters for the different components of the electricity price based on demand analytics. Simulation of different parameters of the energy cost management in mining data centers demonstrated that the ultimate electricity price can, on average, be reduced by 70% of the initial level across all regions of the Siberian Federal District of Russia. The suggested energy cost management model takes into account both internal and external factors of industrial data centers as well as monitoring of their operations along with the price factors of the wholesale and retail electricity markets. This material may be useful to specialists in the field of management of mining data centers who are involved in the operation and/or design of such facilities across various regions of Russia.
Muwidang Jang Il-soon's Life and Educational Thought: Implications for Lifelong Education

Research exploring the value of lifelong education by connecting the life of a leader who made the living ground into a place of education and led change is significant. This study aims to realize the implications of lifelong education by examining the life and educational ideology of Muwidang Jang Il-soon, who is considered to have lived as an ordinary person all his life with a consistent attitude in Korean modern history. More specifically, the meaning of teaching and the principle of lifelong education in his life are examined, and the implications of Jang Il-soon's educational thought for today's lifelong learning society are discussed. Regarding the research method, qualitative research was conducted for interpretative research, such as exploring historical data and performing a literature analysis on Jang Il-soon. In the results of the study, Jang Il-soon's teachings are presented as 'Gaemunryuha', 'Illinillgi', 'Gyohaksangjang', and 'Gyojachaesin'. The principles of lifelong education that appeared in Jang Il-soon's educational thought are categorized into 'the principle of equality', 'the principle of practice', 'the principle of coexistence', and 'the principle of liberation'. The implications of Jang Il-soon's educational thought for a lifelong learning society are identified as 'the provision of equal lifelong education opportunities', 'practice-oriented educational activities and social transformation', 'the implementation of meaning and value of learning community', 'instructors' critical reflection and action', etc. As an educator and a learner simultaneously, Jang Il-soon's educational ideology has implications for the reality of lifelong education centered on the delivery of knowledge by instructors and for the education movement and practitioners' educational activities at the private level, suggesting means for the realization of a lifelong learning society.
THE IMPORTANCE OF ECOLOGY FIELDWORK IN ENGLISH SECONDARY SCHOOL CURRICULA

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 2789-2797ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.0793Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis

Human body-pose estimation is a complex problem in computer vision.Recent research interests have been widened specifically on the Sports, Yoga, and Dance (SYD) postures for maintaining health conditions.The SYD pose categories are regarded as a fine-grained image classification task due to the complex movement of body parts.Deep Convolutional Neural Networks (CNNs) have attained significantly improved performance in solving various human body-pose estimation problems.Though decent progress has been achieved in yoga postures recognition using deep learning techniques, fine-grained sports, and dance recognition necessitates ample research attention.However, no benchmark public image dataset with sufficient inter-class and intra-class variations is available yet to address sports and dance postures classification.To solve this limitation, we have proposed two image datasets, one for 102 sport categories and another for 12 dance styles.Two public datasets, Yoga-82 which contains 82 classes and Yoga-107 represents 107 classes are collected for yoga postures.These four SYD datasets are experimented with the proposed deep model, SYD-Net, which integrates a patch-based attention (PbA) mechanism on top of standard backbone CNNs.The PbA module leverages the self-attention mechanism that learns contextual information from a set of uniform and multi-scale patches and emphasizes discriminative features to understand the semantic correlation among patches.Moreover, random erasing data augmentation is applied to improve performance.The proposed SYD-Net has achieved stateof-the-art accuracy on Yoga-82 using five base CNNs.SYD-Net's accuracy on other datasets is remarkable, implying its efficiency.Our Sports-102 and Dance-12 datasets are publicly available at https://sites.google.