Reviewerly/exHarmony huggingface dataset:
Bayesian Hierarchical Pointing Models

Bayesian hierarchical models are probabilistic models that have hierarchical structures and use Bayesian methods for inferences. In this paper, we extend Fitts’ law to be a Bayesian hierarchical pointing model and compare it with the typical pooled pointing models (i.e., treating all observations as the same pool), and the individual pointing models (i.e., building an individual model for each user separately). The Bayesian hierarchical pointing models outperform pooled and individual pointing models in predicting the distribution and the mean of pointing movement time, especially when the training data are sparse. Our investigation also shows that both noninformative and weakly informative priors are adequate for modeling pointing actions, although the weakly informative prior performs slightly better than the noninformative prior when the training data size is small. Overall, we conclude that the expected advantages of Bayesian hierarchical models hold for the pointing tasks. Bayesian hierarchical modeling should be adopted a more principled and effective approach of building pointing models than the current common practices in HCI which use pooled or individual models.
Rapid person re-identification retraining strategy for flexible deployment in new environments

Person re-identification (Re-ID) can be used to find the owner of lost luggage, to find suspects after a terrorist attack, or to fuse multiple sensors. Common state-of-the-art deep-learning technology performs well on a large public dataset but it does not generalize well to other environments, which makes it less suitable for practical applications. In this paper, we present and evaluate a new strategy for rapid Re-ID retraining to increase flexibility for deployment in new environments. In addition, we pay special attention to make our method work with anonymized data due to the sensitive nature of the collected data. A training set with anonymized snippets is automatically collected using additional cameras and person tracking. The evaluation results show that this rapid training approach obtains high performance scores.
Chinese sign language recognition based on multiview deep neural network for millimeter wave radar

People in the deaf-mute community benefit a lot from Chinese sign language (CSL) recognition, which can promote communication between sign language users and non-users. Recently, some studies have been made on sign language recognition with the millimeter-wave radar because of its advantages of non-contact measurements and privacy controls. The millimeter-wave radar acquires the motion characteristics based on the micro-Doppler images, which can be used for CSL recognition. Existing recognition methods measure the micro-Doppler image in a certain direction, which cannot reflect all the motion information of CSL and leads to the failure of recognition of the CSL with similar actions. In order to improve the recognition accuracy, this paper proposes a multi-view deep neural network (MV-DNN), which fuses micro-Doppler features measured in different directions. The simulation results show that the recognition accuracy of the proposed method reaches 96% for eight CSLs, which is 8% higher than that of the traditional single-view method.
ICMiF: Interactive cascade microformers for cross-domain person re-identification

Person re-identification (ReID) arises in many applications, such as video surveillance and intelligent security. A challenge of cross-domain person ReID is the notorious distribution drift problem. Improving the accuracy of pseudo labels can promote the model to fit the target domain, so it is crucial to extract discriminative person features. The transformer can extract discriminative person features due to its ability to capture long distance dependencies, but its poor generalization ability may aggravate the influence of noise pseudo labels. The isolation between local information modeling and global information modeling further prevents the extraction of the discriminative person features. Therefore, this paper proposes an interactive cascade transformer framework. Firstly, a lightweight transformer structure (microformer) with better generalization is proposed. It maintains the advantages of traditional transformers on extracting discriminative features, and avoids the aggravation of the influence of noise labels. Secondly, an interactive cascade microformer framework (ICMiF) is proposed, which promotes the interaction between local information modeling and global information modeling to enhance the person feature representation. Local information facilitates mining global attribute dependencies, and global information improves the accuracy of local information. The experimental results demonstrate that the proposed ICMiF outperforms state-of-the-art methods for the cross-domain person ReID tasks.
Towards using Involuntary Body Gestures for Measuring the User Engagement in VR Gaming

Understanding the degree of user engagement in a VR game is vital to provide a better gaming experience. While prior work has suggested self-reports, and biological signal-based methods, measuring game engagement remains a challenge due to its complex nature. In this work, we provide a preliminary exploration of using involuntary body gestures to measure user engagement in VR gaming. Based on data collected from 27 participants performing multiple VR games, we demonstrate a relationship between foot gesture-based models for measuring arousal and physiological responses while engaging in VR games. Our findings show the possibility of using involuntary body gestures to measure engagement.
Bodyweight Exercise based Exergame to Induce High Intensity Interval Training

Exergames have been proposed as an attractive way of making exercise fun; however, most of them do not reach the recommended intensity. Although HCI research has explored how exergame can be designed to follow High Intensity Interval Training (HIIT) that is effective exercise consisting of intermittent vigorous activity and short rest or low-intensity exercise, there are limited studies on designing bodyweight exercise (BWE) based exergame to follow HIIT. In this paper, we propose BWE based exergame to encourage users to maintain high intensity exercise. Our initial study (n=10) showed that the exergame had a significant effect on enjoyment, while the ratio of incorrect posture (ex., squat) also increased due to participants’ concentration on the exergame, which imply future design implications of BWE based exergames.
SomaFlatables: Supporting Embodied Cognition through Pneumatic Bladders

Applying the theory of Embodied Cognition through design allows us to create computational interactions that engage our bodies by modifying our body schema. However, in HCI, most of these interactive experiences have been stationed around creating sensing-based systems that leverage our body’s position and movement to offer an experience, such as games using Nintendo Wii and Xbox Kinect. In this work, we created two pneumatic inflatables-based prototypes that actuate our body to support embodied cognition in two scenarios by altering the user’s body schema. We call these ”SomaFlatables” and demonstrate the design and implementation of these inflatables based prototypes that can move and even extend our bodies, allowing for novel bodily experiences. Furthermore, we discuss the future work and limitations of the current implementation.
LUNAChair: Remote Wheelchair System that Links Up a Remote Caregiver and Wheelchair Surroundings

We introduce LUNAChair, a remote control and communication system that uses omnidirectional video to connect a remote caregiver to a wheelchair user and a third person around the wheelchair. With the recent growing need for wheelchairs, much of the wheelchair research has focused on wheelchair control, such as fully automatic driving and remote operation. For wheelchair users, conversations with caregivers and third persons around them are also important. Therefore, we propose a system that connects a wheelchair user and a remote caregiver using omnidirectional cameras, which allows the remote caregiver to control the wheelchair while observing both the wheelchair user and his/her surroundings. Moreover, the system facilitates communication by using gaze and hand pointing estimation from an omnidirectional video.
On digitalization issues in radioactive waste management

The article considers some topical issues associated with the digitalization of preparatory, design development and support processes in nuclear decommissioning and radioactive waste management, as well as the development of an appropriate knowledge management system.
Systems of Grounding

In this chapter we propose and develop a class of formal systems of grounding.
Space and Place in Cyberspace

This chapter deals with the key issue, of creating a meaningful sense of place. This chapter outlines limitations of the ‘cyberspace’ theorists’ notions of place and suggests how these limitations adversely affect virtual heritage environments. A fivefold description of different features of place that may be appropriate for virtual environments is proposed. These five features of place are summarized as: Combing literatureLiterature and various creative arts suggest various components that help create the above place-experiences. Embodiment and dynamic attenuating environmental features as well as phobic triggersTriggers; social embedding and cultural agency; place as an inscribable artifact; and causal feedback are all suggested. I note here that there is a danger in automatically simulating all of the above elements digitally. From the point of view of the designer, a roadmap for designing for three distinct audiences and intentions is instead suggested. The three types of environmentsHermeneuticenvironment are categorized as visualization-based, activity affording, or hermeneutically enriched. The last type of virtual environment is a new addition to the literatureLiterature of place and cyberspace, and will be focused on, for the importance of place as a cultural site is a central concern in this book. For virtual heritage environments in particular, we need to have a clear and distinct idea of what place as a cultural site and the related sense of ‘cultural presence’ entails.
Detecting Group Shilling Profiles in Recommender Systems: A Hybrid Clustering and Grey Wolf Optimizer Technique

Collaborative filtering, though a success in the field of recommender systems, is vulnerable to shilling attacks. These attacks add shilling profiles in the dataset and thus manipulate the recommendation process, resulting in bias output. To curtail the bias generated in the system, several machine learning techniques exist; however, such methods either suffer from low precision or require prerequisites, such as prior knowledge of attack profiles or hand-designed features. To address the above limitations, a novel method, namely, Grey Wolf Optimization Technique for Detecting Shilling Attacks (GWODS), based on the grey wolf optimizer (GWO) and k-means clustering to detect and thus nullify/diminish the effect of group shilling profiles has been proposed. First, k-means algorithm is adapted to partition users into clusters by exploiting their rating behavior. The users’ preferences are then used to compute the intra-cluster correlation, resulting in the most suspicious cluster being returned. Finally, the GWO is modified to separate shilling profiles from genuine profiles. This process is culminated using the introduced fitness function and on lines of hunting behavior of grey wolves. GWODS has been assessed on the MovieLens dataset of three different sizes. Further, it has been compared with six state-of-the-art approaches on the basis of precision. Investigation delineates that GWODS outperformed all state-of-the-art approaches with a mean precision of 0.986, 0.991, and 0.992 in case of power item attack, average attack, and average over popular items attack respectively.
Open, Distance, and Digital Education (ODDE): An Equity View

Abstract Understanding how equity manifests in open, distance, and digital education (ODDE) requires us to grapple with several coexisting trends, including the changing forms of teaching and learning provision, the advent of a post-digital society and education, the datafication of education, inequality in society at large, and digital inequities. Most of these trends are social in nature, yet they shape, and are shaped by, the educational sector. It is at the intersection of these coexisting trends that equity issues in ODDE are raised and become apparent, reinforced by the uneven distribution of technology in society, and with deep roots in economic and social inequities. Current scholarship foregrounds these nested relationships and entanglements, as well as their intersection with power relations and contestations which play out across ODDE at macro, meso, and micro levels.
Analysis on Syllable-Based Intonational Features of Assamese Speech Signals

For identifying the emotion from any speech sound, speech analysis is required. Speech sound consists of different kinds of parameters such as fundamental frequency, formant, intensity, duration. The main concept of this speech synthesis system is analysis on syllabification rules which varies all kind of languages and also syllable based intonation analysis. The main concept of this paper is the syllable-based intonation analysis. In this paper, we made some syllable structured Assamese word dataset from recorded emotional speech of both male and female of same age group in Assamese language and to establish the significant consequence of emotions on intonational features such as F0 contour using PRAAT and MATLAB. Experimental outcomes of the analysis significantly show that emotions have effect on pitch level of different types of syllable structured word in both male and female voices dissimilar.
A Study on Medical Diagnostic Approach to Investigate Cardiac Diseases

The present research article deals with design and development of a user-friendly Soft Computing Model to solve real-life problems related with medical sciences with specific reference to cardiac problem. In order to achieve the aim, we made fusion of Saaty’s pairwise comparison matrices and fuzzy relational equations.
Nonclassical Evidences of Class of Number States Filtered Coherent States for Quantum Information

Hack-proof communication systems are much anticipated by society. In the current scenario, the conveyance of information is employed within the worldwide realm without a hitch. However, there is a need for a secured transformation against eavesdropping. Quantum information science commences a track for genuine communication systems. Nonclassical states, states with a lack of classical description, are the key elements of quantum information processes, such as quantum teleportation and quantum cryptography. This work is intended to examine the nonclassicality of the quantum states generated via filtration from the coherent state (CS). The CS borders the classical quantum transition and it can be expressed as the infinite superposition of photon number states (NS). There is a possibility to create an array of nonclassical states by removing one or a combination of NSs from the CS. Three nonclassical criteria have been chosen as the witness of nonclassicality, namely Vogel’s, Klyshko’s and Agarwal and Tara criteria. The states tailored by the filtration process violate all the three nonclassical criteria, thus exhibit their nonclassicality.
Extracting Narrative Patterns in Different Textual Genres: A Multilevel Feature Discourse Analysis

We present a data-driven approach to discover and extract patterns in textual genres with the aim of identifying whether there is an interesting variation of linguistic features among different narrative genres depending on their respective communicative purposes. We want to achieve this goal by performing a multilevel discourse analysis according to (1) the type of feature studied (shallow, syntactic, semantic, and discourse-related); (2) the texts at a document level; and (3) the textual genres of news, reviews, and children’s tales. To accomplish this, several corpora from the three textual genres were gathered from different sources to ensure a heterogeneous representation, paying attention to the presence and frequency of a series of features extracted with computational tools. This deep analysis aims at obtaining more detailed knowledge of the different linguistic phenomena that directly shape each of the genres included in the study, therefore showing the particularities that make them be considered as individual genres but also comprise them inside the narrative typology. The findings suggest that this type of multilevel linguistic analysis could be of great help for areas of research within natural language processing such as computational narratology, as they allow a better understanding of the fundamental features that define each genre and its communicative purpose. Likewise, this approach could also boost the creation of more consistent automatic story generation tools in areas of language generation.
Case Study on Class to Strengthen Interaction in a Non-face-to-face Basic Science Course in University

This study designed and operated the instructional design in a basic chemistry class for the enhancement of interactions in a non-face-to-face environment. The collaboratively executed strategies were as follows: 1) Flipped learning method 2) Analyzing students’ learning results at the pre-learning stage 3) Real-time lectures and small group activities based on a diagnosis of the learning level. By the analysis of the results of class operation, positive effects were seen concerning the strengthening of interactions requested in a non-face-to-face environment. In particular, this has played an important role in the strengthening of professor-student interactions through identifying the learning needs of the student and the interactions between students through the small group activity based on the degree of understood concepts. Secondly, positive results were shown regarding learning achievement when it comes to regular exams and subject competencies. Third, this model produced a high level of lecture satisfaction. Through these findings, our study will be helpful to the designing of the BSM class for the enhancement of interactions in non face-to-face environments. In addition, we believe that this study can contribute to more effective curriculum operation through flexible linkage with the face-to-face class environment.
A Study on the Ways to Promote Interaction Between Teachers and Learners in Content Utilization-Centered Classes －Focusing on the Remote Model of J University’s xClass

Recently, after COVID-19, university distance learning has been in full swing. In addition, guidelines for systematizing distance learning management were announced along with the introduction of an accreditation system to strengthen the university's distance learning capabilities. Therefore, non-face-to-face education at universities is expected to be further strengthened. In particular, liberal arts classes centered on content utilization, unlike majors, do not have a strong desire for learning. In addition, the interaction between the instructor and the learner is not sufficient, and the interest in the liberal arts class and the quality of education can be lowered due to the difficulty of the learner's participation and management of the class on their own. As an effort to solve these problems, the xClass non-face-to-face class model was developed and liberal arts classes were operated. The xClass non-face-to-face class model is a new model for distance learning at J University based on a learning guide for the learner's leading learning and feedback and friendliness for interaction between teaching and learning in content-based classes. The detailed composition of the liberal arts xClass non-face-to-face class used in this study is as follows. 14 study guides, 1,247 minutes of video content, 540 minutes of face-to-face class, 13 quizzes, 15 worksheets, 6 bulletin boards, and 575 feedbacks. In the xClass non-face-to-face class, it was found that the learning guide was helpful to the learner by 82%, satisfaction with colloquial use was 85%, and the feedback provided by the instructor was helpful by 85%. And in the xClass non-face-to-face class, learners were satisfied with the learning guide (49%) and feedback (48%) in that order. Learners in this liberal arts class were satisfied with the learning guide (49%) and feedback (48%) in the xClass non-face-to-face class. The results of this study suggest that learning guides and feedback can be used as a strategy to increase learners' class satisfaction in future classes focused on the use of liberal arts content.
A Study on Professors' Perception on the direction for the Development of General Education

The purpose of this study was to explore the ideals, present, and the future of general education perceived the professors in A university. this study focused on drawing implications for the direction and tasks of general education by examining not only the general education viewed by professors belonging to the liberal arts but also the thoughts that professors belonging to the department. In order to do, a qualitative interview was conducted with a total of 8 faculty members. As a result of the study, interview participants agreed on the value, necessity, and importance of general education(the ideal of general education). Regarding the current status of general education, it was confirmed that professors had different perception on vision, goals, roles, operating methods, and organizational structure of general education. Lastly, for the future of general education, reevaluation, identity, and structural improvement of general education were suggested. Based on the above discussion, the direction and tasks of general education were suggested.
Conventional Implicatures in Argumentation

Despite the ubiquity of conventional implicatures in language and the critical role they play in argumentation, they have heretofore been almost entirely absent from theories of argument and the linguistic expression of reasoning. In this paper, we discuss conventional implicatures (cis) as an interesting phenomenon at the interface of semantics, pragmatics and argumentation by harnessing research in semantics and pragmatics and extending an existing account for argument diagramming with this type of implicit meaning. In particular, we show that cis are unlike enthymemes, which are extremely challenging to conceptualise and to specify precisely. Instead, cis are anchored on the linguistic surface, trigger a largely predictable discourse contribution and are therefore more apt for argument analysis. By surfacing conventionally implicated material, we can unpack a wider variety of ways in which arguments are triggered by, composed of, and demolished by implicit discourse material, in particular inferential structures, conflicts and references to ethos. This also allows us to model the complex interplay between conventional implicature and argumentation, which in turn sheds new light on the interplay of meaning and argumentation in general.