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This chapter describes issues related to teaching music appreciation online and presents experiences with online music appreciation instruction in narratives provided by professors teaching music appreciation online. It provides background on music appreciation pedagogy, including significant conferences on music in general studies. It describes the nature of music appreciation study as rooted in the disciplines of music theory and musicology, lists accreditation standards for music in general studies, and explains its signature pedagogy. It reviews the state of the practice, including pedagogical innovations as reported in the literature and the status of online music appreciation instruction as reported in a national survey. It features two professors who describe their online teaching experiences: music appreciation online for a diverse audience and music appreciation online for adult learners. Each professor offers suggestions for prospective online music appreciation teachers, and the chapter concludes with some lessons drawn from the field.

This paper presents a framework for telepresence operation by touching live video on a touchscreen. Our goal is to enable users to use a smartpad to teleoperate everyday objects by touching the objects live video they are watching. To this end, we coined the term teleinteractive device to describe such an object with an identity, an actuator, and a communication network. We developed a touchable live video image-based user interface (TIUI) that empowers users to teleoperate any teleinteractive device by touching its live video with touchscreen gestures. The TIUI contains four modules touch, control, recognition, and knowledge to perform live video understanding, communication, and control for telepresence operation. We implemented a telepresence operation system that consists of a telepresence robot and teleinteractive devices at a local site, a smartpad with the TIUI at a remote site, and communication networks connecting the two sites. We demonstrated potential applications of the system in remotely controlling telepresence robots, opening doors with access control panels, and pushing power wheelchairs. We conducted user studies to show the effectiveness of the proposed framework.

‘Touch’, and the return of the body to digital music-making in general, has taken on a central importance in electronic instrument design and theory in recent decades. ‘Touch’ and lack thereof has been considered central to the challenge of a ‘disconnect’ between musicians’ bodily actions and digital instruments, and as a solution to it that instruments ‘of the future’ can provide. But ‘touch’ is not a universal quantity; it is a construction that varies widely in its application and invocation, across academic and commercial contexts of instrument design. This article examines the complex history of the contemporary concern with ‘touch’, and its role in the framing and reception of instruments such as ROLI’s Seaboard, JazzMutant’s Lemur, Sensel’s Morph and the Joué. With reference to writings on embodied cognition, the recent material turn in the humanities and to Mills and Sterne’s concept of dismediation it seeks to historicise, interrogate, and denaturalise ‘touch’ within instrument design and its surrounding discourse.

Participatory art allows for the spectator to be a participant or a viewer able to engage actively with interactive art. Real-time technologies offer new ways to create participative artworks. We hereby investigate how to engage participation through movement in interactive digital art, and what this engagement can awaken, focusing on the ways to elicit improvisation and letting go. We analyze two Virtual Reality installations, ''InterACTE'' and ''Eve, dance is an unplaceable place,'' involving body movement, dance, creativity and the presence of an observing audience. We evaluate the premises, the setup, and the feedback of the spectators in the two installations. We propose a model following three different perspectives of resonance: 1. Inter Resonance between Spectator and Artwork, which involves curiosity, imitation, playfulness and improvisation. 2. Inner Resonance of Spectator him/herself, where embodiment and creativity contribute to the sense of being present and letting go. 3. Collective Resonance between Spectator/Artwork and Audience, which is stimulated by curiosity, and triggers motor contagion, engagement and gathering. The two analyzed examples seek to awaken open-minded communicative possibilities through the use of interactive digital artworks. Moreover, the need to recognize and develop the idea of resonance becomes increasingly important in this time of urgency to communicate, understand and support collectivity.

Mid-air haptics create a new mode of feedback to allow people to feel tactile sensations in the air. Ultrasonic arrays focus acoustic radiation pressure in space, to induce tactile sensation from the resulting skin deflection. In this work, we present a low-cost tactile robot to test mid-air haptics. By combining a desktop robot arm with a 3D-printed biomimetic tactile sensor, we developed a system that can sense, map, and visualize mid-air haptic sensations created by an ultrasonic transducer array. We evaluate our tactile robot by testing it on a variety of mid-air haptic stimuli, including unmodulated and modulated focal points that create a range of haptic shapes. We compare the mapping of the stimuli to another method used to test mid-air haptics: Laser Doppler Vibrometry, highlighting the advantages of the tactile robot including far lower cost, a small lightweight form-factor, and ease-of-use. Overall, these findings indicate our method has multiple benefits for sensing mid-air haptics and opens up new possibilities for expanding the testing to better emulate human haptic perception., Comment: 8 pages. Accepted in RA-L and IROS 2022

With the fast advancement of science and technology, numerous music systems have emerged. However, there are few folk music in the music industry, for example, pop music, which leads to the inability of folk music lovers to find their favorite music. For this problem, this paper develops a national system of smart concert hall based on a neural network and wireless Internet of things (IoT). It establishes the architecture model of the wireless IoT system of the concert hall by describing the neural network, neuron model, and BP neural network model in detail. Besides, this paper develops the smart music national system based on this model, completes the functional module design of the national music system, and uses the music attribute recommendation model. Finally, it analyzes the scoring performance of the national music system. The results show that when the number of recommended folk music songs is less than 20, the lowest MAE is 0.73.

In today’s society, children’s furniture is an important piece of furniture for every household. Consumers and even companies may ignore its environmental merits since it is not a regular item of home furniture. This research focuses on the use of environmentally friendly materials in the design of children’s furniture and combines fuzzy technology with structured design technology to build a fuzzy technology-based children’s furniture design system. This research also introduces two intelligent systems for the design of children’s furniture, analyzes the structure of children’s furniture via structured design, and produces an interactive system with user experience. Finally, this component conducts a system test study based on real-world needs. The experimental research results show that the children’s furniture design system based on fuzzy technology proposed in this paper has a good user experience and can play an important role in the application of environmentally friendly materials in the design of children’s furniture.

International audience; We study tangible touch tablets combined with Augmented Reality Head-Mounted Displays (AR-HMDs) to perform spatial 3D selections. We are primarily interested in the exploration of 3D unstructured datasets such as cloud points or volumetric datasets. AR-HMDs immerse users by showing datasets stereoscopically, and tablets provide a set of 2D exploration tools. Because AR-HMDs merge the visualization, interaction, and the users’ physical spaces, users can also use the tablets as tangible objects in their 3D space. Nonetheless, the tablets’ touch displays provide their own visualization and interaction spaces, separated from those of the AR-HMD. This raises several research questions compared to traditional setups. In this paper, we theorize, discuss, and study different available mappings for manual spatial selections using a tangible tablet within an AR-HMD space. We then study the use of this tablet within a 3D AR environment, compared to its use with a 2D external screen.

Eye gaze is an important natural behavior in social interaction as it delivers complex exchanges between observer and observed, by building up the geometric constraints and relation of the exchanges. These inter-person exchanges can be modeled based on gaze direction estimated using computer vision. Despite significant progresses in vision-based gaze estimation in last 10 years, it is still nontrivial since the accuracy of gaze estimation is significantly affected by such intrinsic factors as head pose variance, individual bias between optical axis and visual axis, eye blink, occlusion and image blur, degrade gaze features, lead to inaccurate gaze-involved human social interaction analysis. This paper aims to review and discuss existing methods addressing above-mentioned problems, gaze involved applications and datasets against the state-of-the-arts in vision-based gaze estimation. It also points out future research directions and challenges of gaze estimation in terms of meta learning, causal inference, disentangled representation, and social gaze behaviour for unconstrained gaze estimation.

Accurate gaze estimation that frees from glints and the slippage problem is challenging. Pupil-contour-based gaze estimation methods can meet this challenge, except that the gaze accuracy is low due to neglecting the pupil's corneal refrac-tion. This paper proposes a refraction-aware gaze estimation approach using the real pupil axis, which is calculated from the virtual pupil image based on the derived function between the real pupil and the refracted virtual pupil. We present a 2D gaze estimation method that regresses the real pupil normal's spherical coordinates to the gaze point. The noise and outliers of calibration data are removed by aggregation filtering and Random Sample Consensus, respectively. Moreover, we propose a 3D gaze estimation method that transforms the real pupil axis to the gaze direction. Experimental results show that the proposed gaze estimation approach has comparable accuracy to state-of-the-art pupil-center-based gaze estimation methods, which suffer from the slippage problem.

This study examined arousal and engagement during video game play for interactive players and passive spectators who watched a recording of gameplay. We examined interactive (on-line) and non-interactive (off-line) events in the game design. We collected psychophysiological data (eye movements, pupil dilation, heart rate) and self-reported measures (general anxiety, game experience, levels of fear). Overall, playing the game was associated with general increases in heart rate and a significant increase in the mean fixations distance. During Off-line events, players and spectators showed similar levels of arousal as indicated by pupil dilation. During on-line events, however, players showed increased pupil dilation more so than someone who is watching the exact event without interactive control. These findings suggest that both players and spectators show increases in arousal and engagement, but there is a unique level of arousal and engagement for interactive play, as indicated by changes in heart rate and eye tracking metrics. We discuss the application of the psychophysiological findings to recent trends in consumer media such as Twitch and YouTube, as well as the importance for game design, as it relates to human behavior.

Taxonomies and ontologies are often merged. Ontologists might look upon taxonomies as the component or extension of ontologies to include hierarchies of subclasses. But it also goes the other way. Taxonomies can be semantically enriched to become ontologies with the addition of a high-level ontology model. This bottom-up approach to ontology creation leverages existing taxonomies. While stakeholder input and analysis of data are standard inputs into ontology design, taxonomies can serve as an additional source of input into designing an ontology. This session also addresses the difference of taxonomies and ontologies, presents benefits of taxonomy-based ontology design, and shows how an example taxonomy excerpt can serve as the basis for classes, object properties (semantic relations), and datatype properties (attributes).

The result of a typical web search is often overwhelming. It is very difficult to explore the textual listing of the resulting documents, which may be in the thousands. In order to improve the utility of the search experience, we explore presenting search results through clustering and a zoomable two-dimensional map (zoomable treemap). Furthermore, we apply the fisheye view technique to this map of web search clusters to provide details in context. In this study, we report on our evaluation of these presentation features. The particular interfaces evaluated were: (1) a textual list, (2) a zoomable two-dimensional map of the clustered results, and (3) a fisheye version of the zoomable two dimensional map where the results were clustered. We found that subjects completed search tasks faster with the visual interfaces than with the textual interface, and faster with the fisheye interface than just the zoomable interface. Based on the findings, we conclude that there is promise in the use of clustering and visualization with a fisheye zooming capability in the exploration of web search results.

With the rapid development of music streaming media service industry, users can easily hear any song on mobile devices. Internet has become a huge music storage platform. With the development of network and large-scale digital music industry, the acquisition and listening of music are presented to users in a more convenient way. How to find the music loved by users from the massive Internet digital music data has become the key problem and main goal to be solved in the field of music information retrieval. Personalized music recommendation system can accurately find and push songs that users may be interested in from tens of millions of huge music libraries according to users’ information under the condition that users only have vague demand for listening to songs. Relying on the traditional search method to find the music that you are interested in can no longer meet the needs of users, so the current music recommendation system needs to dig out the music that has no clear needs in the long tail to help people find their favorite songs.

An important sign of the accessibility of Braille information is the realization of the mutual translation between Chinese and the Braille. Due to the irregularity and uncertainty of the Prevailing Mandarin Braille, coupled with the lack of a large-scale Braille corpus, the quality of Chinese-Braille translation seems to be poor. In July 2018, the National Language Commission released the “Chinese Common Braille Scheme” and advocated replacing the “Prevailing Mandarin Braille.” Aimed at improving translation accuracy, this research, which is based on the self-built Chinese Common Braille corpus and combined with the HanLP (Han Language Processing) dictionary and the Chinese-Braille word corpus (a Braille word segmentation and concatenation dictionary for generating a unigram language model), uses the n-gram language model to design and implement a Chinese-Braille intertranslation system that integrates Chinese and Braille Word Segmentation and Concatenation Rules. More importantly, this research proposes an experimental plan for improving the Braille Word Segmentation and Concatenation Rules using a Chinese-Braille word corpus. Experiments show that in the field of educational literature, the accuracy rate of translation from Chinese to Chinese Common Braille has reached 95.01%, and the accuracy of Chinese Common Braille to Chinese translation has reached 90.15%.

This paper's goal is to give a broad overview of the field of Human-Computer Interaction. The basic definitions and terminology are covered, as well as a study of existing technologies and recent breakthroughs in the field, popular architectures used in the design of HCI systems, including unimodal and multimodal configurations, and HCI applications. A major goal of human-computer interaction research is to make systems more usable, valuable, and to give users with experiences tailored to their specific knowledge and goals. In an information-rich environment, the challenge is not just to make information available to individuals at any time, in any place, and in any form, but also to keep it up to date as per current trends and demands. Keywords: HCI - Human Computer Interaction, Multimodal HCI, Unimodal HCI, MMHCI - Multimodal HCI, UX - User Experience.

Vocal music teaching is a professional, technical, and practical subject. It is also an important part of music and art education and has certain social and educational significance. In vocal music teaching, recording equipment is an essential teaching tool. It plays a pivotal role in vocal music teaching. In recent years, with the rapid development of China’s social economy, education has also achieved great development, and it has also brought development opportunities to vocal music education. More and more advanced technologies and equipment have appeared to assist the smooth progress of vocal music teaching, especially recording equipment. This paper is aimed at studying the application of IoT technology in vocal music teaching recording equipment assisted by machine learning. Combined with machine learning and Internet of Things technology, the experiment of recognition effect of vocal music teaching recording equipment was carried out. It designs an end-to-end vocal music teaching recording device recognition model based on the Internet of Things technology assisted by machine learning. The experimental results show that the use of this model improves the recording recognition accuracy of vocal music teaching recording equipment by 20%.

This research aims to test a concept of individual dexterity in improving individual performance for the digital workplace. Individual dexterity is the ability of individuals to explore fingers and other senses when in contact with information technology devices (finger dexterity) and exploit the idea or ideas to complete the work independently (manual dexterity). A number of 150 respondents who work in creative industries and using information technology-based communication tools to finish their daily job involved in this research. Data was collected using a questionnaire and analyzed using SmartPLS. The results show that individual dexterity is able to predict the performance of individuals who work in a digital environment. In addition, psychological empowering leadership and individual readiness to change play a role as a significant trigger to the formation of individual dexterity which then increases individual performance

In researching social network data and depression, it is often necessary to manually label depressed and non-depressed users, which is time-consuming and labor-intensive. The aim of this study is that it explores the relationship between social network data and depression. It can also contribute to detecting and identifying depression. Through collecting and analyzing college students’ microblog social data, a preliminary screening algorithm for college students’ suspected depression microblogs based on depression keywords, and semantic expansion is researched; a comprehensive lexical grammar was proposed. This research provided has a preliminary screening method based on depression keywords and semantic expansion for college students’ suspected depression microblogs, with a screening accuracy. This method forms a depression keyword table by constructing the basic keyword table and the semantic expansion based on the word embedding learning model Word2Vec. Finally, the word table is used to calculate the semantic similarity of the tested microblogs and then identify whether it is a suspected depression microblog. The experimental results on the microblog dataset of college students show that the comprehensive lexical method is better than the SDS questionnaire segmentation method and the expert lexical method in terms of screening accuracy; the comprehensive lexical approach can quickly and automatically screen out a tiny proportion of suspected doubts from a large number of college students’ microblogs. Depression Weibo can reduce the workload of experts’ annotation, improve annotation efficiency, and provide a suitable data processing basis for the subsequent accurate identification (classification problem) of patients with depression.

In face-to-face interaction, speakers spontaneously produce manual gestures that can facilitate listeners' comprehension of spoken language. The present study explores the factors affecting the uptake and influence of gesture cues in situations where a speaker is referring to objects visible to the listener. In this context, the listener's attention must be distributed across various scene regions, potentially reducing the ability to draw on and apply gesture cues in real time. In two experiments, the instruction provided by a speaker (e.g., "pick up the candy") was accompanied by an iconic grasp gesture (produced alongside the verb) that reflected the size/shape of the intended target. Effects on listeners' comprehension were compared with a no-gesture condition. Experiment 1 (audiovisual gating task) showed that, under simplified processing circumstances, gesture cues allowed earlier identification of intended targets. Experiment 2 (eye tracking) explored whether this facilitation is found in real-time comprehension, and whether attention to gesture information is influenced by the acoustic environment (quiet vs. background noise). Measures of gaze position showed that although the speaker's gesturing hand was rarely fixated directly, gestures did facilitate comprehension, particularly when the target object was smaller relative to alternatives. The magnitude of the gesture effect was greater in quiet than in noise, suggesting that the latter did not provoke listeners to increase attention to gesture to compensate for the challenging auditory signal. Together, the findings clarify how situational factors influence listeners' attention to visual information during real-time comprehension. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

An amazing invention in Computer Technology is the mouse. Nowadays, A Bluetooth mouse or wireless mouse still has some limitations as it requires a battery for power and a dongle to connect to a PC. This issue may be solved in the proposed gesture-based AI virtual mouse by capturing hand motions and revealing hand tips with a webcam or integrated camera, as gestures are a powerful means of communication between people. Based on hand gestures, the computer can be almost controlled and can perform right- clicking, left-clicking, scrolling and computer cursor functions without using the physical mouse. Therefore, the proposed system will help avoid the dissemination of COVID19 by hindering the deadly intervention and confidence of bias in the control of the computer. Keywords: AI, Virtual Mouse, OpenCV, Computer Vision, Mediapipe

Music is used in one’s everyday life to modulate, enhance, and plummet undesirable emotional states like stress, fatigue, or anxiety. Even though in today's modern world, with the ever-increasing advancement in the area of multimedia content and usage, various intuitive music players with various options have been developed, the user still has to manually browse through the list of songs and select songs that complement and suit his or her current mood. This paper aims to solve this issue of the manual finding of songs to suit one’s mood along with creating a high accuracy CNN model for Facial emotion recognition. Through the webcam, the emotional state can be deduced from facial expressions. To create a neural network model, the CNN classifier was used. This model is trained and tested using OpenCV to detect mood from facial expressions. A playlist will be generated through the system according to the mood predicted by the model. The model used for predicting the mood obtained an accuracy of 97.42% with 0.09 loss on the training data from the FER2013 Dataset. Keywords: Convolutional neural networks, Facial emotion recognition, FER2013, Music, OpenCV.

The National Core Music Standards, a set of common educational goals in music formulated in the United States in 2014, state that the ultimate goal of music education is the acquisition of "music literacy. This study focuses on the "creative" domain of the Washington State Music Standards, which has the same structure as the National Core Music Standards and also includes specific instructional content and analyzes it to clarify music literacy skills and their learning content. Music literacy in the “creative” domain was defined as "the ability to create and perform musical ideas and compositions that are connected to specific purposes and contexts (social, cultural, and historical). In addition, by acquiring musical knowledge and skills along with the process of creating music, competency development was secured while adding the ability to truly engage with music. Therefore, it was suggested that in the U.S., the idea is not to use music to develop competency but to refine competency by learning music itself. This presents one way of thinking about how music studies should be in a competency-based curriculum.

Adopting the methods of multimodal conversation analysis, this study demonstrates that adult second language (L2) users with emergent literacy formulate requests as ‘complex multimodal Gestalts’ consisting prototypically of deictic or depictive gestures combined with gaze, situationally relevant material, and vocal or linguistic resources. The data obtained for this study are from classroom interactions and out-of-class service encounters that were conducted as part of pedagogical tasks in the context of integration training for adult L2 students with emergent literacy. The analysis shows how co-constructed request sequences typically involve multimodal negotiation in which the co-participants support L2 users to formulate requests that are understandable and that contribute to the progressivity of the interaction. The findings contribute to an understanding of practical requests as multimodal, context-sensitive, collaborative actions and underline the importance of this perspective especially from the view of L2 users with emergent literacy. Adopting the methods of multimodal conversation analysis, this study demonstrates that adult second language (L2) users with emergent literacy formulate requests as ‘complex multimodal Gestalts’ consisting prototypically of deictic or depictive gestures combined with gaze, situationally relevant material, and vocal or linguistic resources. The data obtained for this study are from classroom interactions and out-of-class service encounters that were conducted as part of pedagogical tasks in the context of integration training for adult L2 students with emergent literacy. The analysis shows how co-constructed request sequences typically involve multimodal negotiation in which the co-participants support L2 users to formulate requests that are understandable and that contribute to the progressivity of the interaction. The findings contribute to an understanding of practical requests as multimodal, context-sensitive, collaborative actions and underline the importance of this perspective especially from the view of L2 users with emergent literacy. publishedVersion

With the rapid development of information technology, digital music is subsequently increasing in large quantities, and how a good integration of vocal input and recognition technology can be transformed into digital music can greatly improve the efficiency of music production while ensuring the quality and effect of music. This paper focuses on the implementation and application of human voice input and recognition technology in digital music creation, enabling users to generate digital music forms by simply humming a melodic fragment of a piece of music into a microphone. The paper begins with an introduction to digital music and speech recognition technology and goes on to describe the respective characteristics of various audio formats, which are selected as data sources for digital music creation based on the advantages of the files in terms of retrieval. Following that, the method of extracting musical information from music is described, and the main melody is successfully extracted from the multitrack file to extract the corresponding musical performance information. The feature extraction of humming input melody is further described in detail. The traditional speech recognition method of using short-time energy and short-time overzero rate features for speech endpoint detection is analyzed. Combining the characteristics of humming music, the method of cutting notes by two-stage cutting mode, i.e., combining energy saliency index, overzero rate, and pitch change, is adopted to cut notes, which leads to a substantial improvement in performance. The algorithm uses the melody extraction algorithm to obtain the melody line, merges the short-time segments of the melody line to reduce the error rate of emotion recognition, uses the melody line to segment the music signal to generate segmented segments, then abstracts the features of the segmented segments through a CNN-based structural model, and inputs the output of the model to the regressor in cascade with the melody contour features of the corresponding segmented segments to finally obtain the emotion V / A value of the segmented segments.

Based on the material of several languages, semantic transitions from gestural nominations to gestural phraseological units, as well as the development of figurative meanings of gestural units are considered. The subject of the study is emblems — communicative gestures containing information that a person intentionally conveys to the addressee, and symptomatic gestures — gestures that express the emotional state of the gesticulator. The study showed that the transition from gestural nomination to gestural phraseology is often accompanied by a change in the actant structure of the original unit. One of the signs of the transition from gesture to gesture phraseology is the generalization of the situation — the generalization of one of the actants of the situation, which is expressed by the presence in the sentence of quantifier units that form universal noun phrases with nouns. Idiomatization of gestural units in different languages has both similarities and differences: differences in the meaning of phraseological units built on the basis of gestural units of the same semantics, or differences in the derivational potential of similar gestural units in different languages. The relevance of the study is due to the fact that gestures play a huge role in oral communication, and the fact that it was carried out in line with such areas as the construction of explanatory models of language and semantic typology.

Music rhythm detection and tracking is an important part of music understanding system and music visualization system. Based on the important position of rhythm in music expression and the wide range of multimedia applications, rhythm extraction has become an important hotspot in computer music analysis. In the field of audio recognition research, deep learning can automatically learn the features of audio and extract the rhythm of music. This paper takes music audio rhythm recognition as the main research object and carries out a series of researches with deep learning GRU neural network as the main technical support. A residual network is introduced into the GRU network model, and it is found that when the residual network is at 50 layers, the model has the highest accuracy for audio rhythm extraction. After adjusting the model parameters through experiments, this paper concludes that the average recognition accuracy of the ResNet_50-GRU model for recognizing the rhythm of the music audio in the MSD, AudioSet, and FMA data sets is 92.5%.

This is a research note investigating the MIDI data processing function of the Mido library on Python. MIDI (Musical Instrument Digital Interface) is a standard for transmitting and receiving music performance information between electronic musical instruments and computers. Mido is a library that allows MIDI data to be handled as an object on the programming language Python. By using a library such as Mido, it can be expected to process the control and performance expression of various electronic musical instruments efficiently and flexibly. The author has made various attempts, such as converting information from various sensors into MIDI data based on certain rules, using a board computer such as Arduino or a personal computer. As a result of investigating the MIDI processing function of Mido this time, it was found to be sufficiently useful for the author's research. In the future, I would like to further explore the Python and Mido libraries and deepen my research activities., 本稿はPython上のMidoライブラリによる、MIDIデータ処理機能について調べた研究ノートである。MIDI（Musical Instrument Digital Interface）とは，電子楽器やコンピュータ間で音楽の演奏情報を送受信するための規格である。またMidoは，MIDIデータをプログラム言語Python上のオブジェクトとして扱うことができるようにするためのライブラリで，Midoのようなライブラリを利用することで，様々な電子楽器の制御や演奏表現を効率よく，かつ柔軟に処理することが期待できる。著者はこれまでArduinoなどのボードコンピュータやパソコンを使い，各種センサーからの情報を一定ルールに基づきMIDIデータに変換するなど、様々な試みを行ってきたが，今回MidoによるMIDI処理機能を調べた結果、著者の研究に十分役立つことが分かった。今後はPythonやMidoライブラリをさらに探求し、より研究活動を深めて行きたい。

Ethnography valuably informs design when applied in a variety of contexts, from medical and government to community, non-profit organizations. Ethno-graphic methods in conjunction with anthropologically contextualized analyses are the ultimate grounding elements for innovative design solutions. In this paper, I conceptualize design anthropology/ethnography within the process of design thinking. I present three distinct case studies of design ethnography conducted by undergraduates studying graphic design to highlight how designers incorporate ethnographic research and anthropological analysis in solving real-life problems. I showcase anthropology’s strength at synthesizing multiple data points to find patterns and draw conclusions about human behavior. Lastly, I reflect on the insights that ethnography and the empathy of anthropology bring to contemporary design and problem solving in general.

With selective auditory attention (SAA), the human brain can cope with dynamic natural environments efficiently by prioritizing relevant auditory information while filtering out the rest. Auditory saliency describes how bottom-up auditory attention is attracted by local acoustic properties and plays an important role in SAA. In existing neuroimaging studies, the neural processing of auditory saliency has usually been explored via strictly controlled laboratory paradigms with abstract stimuli, overlooking the intrinsic functional interactions among spatially distributed brain networks underlying auditory saliency. In addition, it is unclear how auditory saliency related neural responses vary in auditory information in different semantic categories. In this study, we explored functional brain networks underlying auditory saliency via a multivariate brain decoding approach, in which naturalistic auditory excerpts in three semantic categories (pop music, classic music and speech) were used as stimuli in functional magnetic resonance imaging (fMRI). Our experimental results demonstrate that: 1) Dynamic auditory saliency can be decoded with relatively high accuracy from fMRI brain activities; 2) The neural processing of auditory saliency in different types of auditory information on the one hand shares some common brain regions (e.g., the primary auditory cortex), on the other hand recruits distinct brain regions/networks, e.g., the sensorimotor network and working memory network for classic music and the auditory-language network for speech. Our study provides complementary evidence to the neural processing of auditory saliency under naturalistic experience.

The aim of the study in highlight the practical experience of organizing distance learning of choreographers at the Kyiv Municipal Ukrainian Academy of Dance named after Serge Lifar. Practical Significance lies in using the research results in the educational process in teaching disciplines of the choreographic cycle “Theory and methods of working with the choreographic team”, “History of choreographic art”, “Theory and methods of teaching choreographic disciplines at higher educational institutions” for students of the educational program “Choreography" at the Kyiv Municipal Ukrainian Academy of Dance named after Serge Lifar. The approbation of modern forms of organizing distance learning and its daily analysis in conditions dictated by real events makes it possible to state that it is convenient to work with students-choreographers in practical classes using video communication in the mobile applications Viber, Messenger on Facebook and Instagram, as well as in the mode of video conferences in the Zoom program.

This systematic review analyzes the differential use of gestures in learning by children with intellectual disability (ID) compared to typically developing ones (TD). Eleven studies published between 2000 and 2020 fulfilled the inclusion criteria (N = 364 participants). The results identify three key elements: (1) Children with ID accompany their spoken language with more gestures than TD children; (2) Specifically, they produce more iconic gestures that provide access to the conceptualization process and understanding in students with ID; (3) Children with ID rely on gesture more than TD children to carry meaning (i.e., produce unimodal gestural utterances without accompanying speech). Possible implications for teaching and guidelines for future research are proposed.

Managing classroom activities in children has become a challenge for English as a Foreign Language (EFL) teachers. Teachers usually use gestures in class activities, such as giving directions and pointing at students. This study explores the multimodal representation of the teacher's gestures when managing classroom activities in the EYL context. Data were collected through non-participant observation and analyzed by Systemic Functional Multimodal Discourse Analysis (SFMDA). The findings show that four meanings are multimodally represented in teacher gestures, namely (1) building student-teacher relationships through clapping and thumbs up, (2) emphasizing instruction through raising hands, pointing and lifting class objects, (3) encouraging involvement of students in academic tasks through finger pointing and counting down, and (4) warning of disruptive behavior of students through pulling gestures. Pedagogically, this research provides a paradigm shift that classroom-based communication does not have to use a single semiotic source but also a combination of other semiotic sources to help students understand teacher instructions easily.

According to the characteristics of the metro vehicles running on the same track leading to a similar wheel wear, a clustering method based on k-means is proposed. The weighted wheel flange height of Sh and qR, and wheel flange thickness of Sd are used as clustering parameters. This paper classifies the treads of the wheels to be repaired and obtains a typical worn wheel profile to reprofile the wheels. Under the premise of ensuring the operation safety of vehicles, the wheel profile optimization model is established for a typical worn wheel profile based on the minimum repair volume. The optimized profile is used as the reprofiling template for all wheels in the same classification. Finally, based on the reprofiling standard for the wheel diameter difference between the left and right wheels, the optimization reprofiling of the wheel profile is achieved. The research results show that the proposed optimization strategy base on cluster analysis can significantly reduce the material removal.

Current music recommendation systems can explore the general relationship between the users and songs to recommend music to the users; however, they cannot distinguish the different preferences of different users for the same song. For example, a user may like a song because of the singer, while another user will like it not for the singer but just because of the composition of the song or its melody. A recommender system that knows this difference would be more effective in recommending music to the users. To this end, this paper proposes a music recommendation model based on multilayer attention representation, which learns song representations from multidimensions using user-attribute information and song content information, and mines the preference relationship between users and songs. In order to distinguish the differences in user preferences for multidomain features of songs, a feature-dependent attention network is designed; in order to distinguish the differences in user preferences for different historical behaviors and to explore the temporal dependence of user behaviors, a song-dependent attention network is designed. Finally, the SoftMax function is used to calculate the distribution of users’ preferences for candidate songs and is used to generate recommendations. The experimental results on 30Music and MIGU datasets show that the proposed model achieves significant improvement in recall and MRR compared with the current recommendation models.

Force-feedback enhances digital touch by enabling users to share non-verbal aspects such as rhythm, poses, and so on. To achieve this, interfaces actuate the user’s to touch involuntarily (using exoskeletons or electrical-muscle-stimulation); we refer to this as computer-driven touch. Unfortunately, forcing users to touch causes a loss of their sense of agency. While researchers found that delaying the timing of computer-driven touch preserves agency, they only considered the naïve case when user-driven touch is aligned with computer-driven touch. We argue this is unlikely as it assumes we can perfectly predict user-touches. But, what about all the remainder situations: when the haptics forces the user into an outcome they did not intend or assists the user in an outcome they would not achieve alone? We unveil, via an experiment, what happens in these novel situations. From our findings, we synthesize a framework that enables researchers of digital-touch systems to trade-off between haptic-assistance vs. sense-of-agency.

The use of an experimental design technique in the development of various pharmaceutical preparations, including tablet preparations, has become the latest trend. Because of their ease of use, tablet formulations are popular among both producers and patients. To increase the usage of tablets in diverse circles and settings, researchers are working to develop a variety of tablet excipients for various functions. Fast dissolving tablets (FDT), effervescent tablets, modified-release tablets, oral mucoadhesive tablets, gastroretentive tablets, and colon targeted tablets are some of the tablet formats that have been developed in addition to traditional tablets. This review will look at how formulation optimization in tablet preparations has been done during the previous ten years using specific literacies. The articles for this review were found using the keywords tablet, excipient, matrices, formulation, and QBD in specialized databases such as Elsevier, Pubmed, and Cambridge. Other options include Springer publications, material from the Internet, and articles published online by The Lancet Respiratory Medicine, Medscape, and Statpearls. The formulation design strategy is based on the experimental design approach carried out on the kind of tablet preparation, which has distinct important quality parameters.

Despite the utility of gaze gestures as an input method, there is a lack of guidelines available regarding how to design gaze gestures, what algorithms to use for gaze gesture recognition, and how these algorithms compare in terms of performance. To facilitate the development of applications that leverage gaze gestures, we have evaluated the performance of a combination of template-based and data-driven algorithms on two custom gesture sets that can map to user actions. Template-based algorithms had consistently high accuracies but the slowest runtimes, making them best for small gesture sets or accuracy-critical applications. Data-driven algorithms run much faster and scale better to larger gesture sets, but require more training data to achieve the accuracy of the template-based methods. The main takeaways for gesture set design are 1) gestures should have distinct forms even when performed imprecisely and 2) gestures should have clear key-points for the eyes to fixate onto.

於 Zoom (2021年10月26日-10月29日), 2021年度科学研究費補助金 基盤研究(S)(課題番号 17H06127, 代表 齋藤政彦), 世話人: 田中 公(東京大), 古川 勝久(城西大), 馬 昭平(東工大)

Multi-sensory wearable haptic devices are able to encode a variety of information using multiple haptic cues. However, simultaneous cues can be misperceived due to tactile masking effects. In this paper, we investigate the effect of masking on the perception of skin stretch and squeeze. We performed three experiments measuring the just-noticeable difference (JND) and the absolute threshold of skin stretch and squeeze alone and in the presence of simultaneous haptic cues. Additionally, we investigate the relative perceptual amplitudes of these haptic cues. Results indicate that the JND for a skin stretch cue increases with a masking squeeze cue, while the JND for a squeeze cue does not change with a masking stretch cue. Also, masking has a significant effect on the absolute threshold of both skin stretch and squeeze. These results suggest that the effect of masking diminishes as haptic cues become larger in amplitude. The results from the subjective equality experiment suggest a potential nonlinear relationship between perceptual magnitudes. Further testing should be carried out to investigate this relationship. Future multi-sensory devices can use these perceptual experiment findings to ensure the delivery of salient cues to users.

於 Zoom (2021年10月26日-10月29日), 2021年度科学研究費補助金 基盤研究(S)(課題番号 17H06127, 代表 齋藤政彦), 世話人: 田中 公(東京大), 古川 勝久(城西大), 馬 昭平(東工大)

This paper focuses on the emergent importance of curiosity at work for individuals and organizations by reviewing management research on curiosity at work. We start by leveraging prior reviews on e...

於 Zoom (2021年10月26日-10月29日), 2021年度科学研究費補助金 基盤研究(S)(課題番号 17H06127, 代表 齋藤政彦), 世話人: 田中 公(東京大), 古川 勝久(城西大), 馬 昭平(東工大)

於 Zoom (2021年10月26日-10月29日), 2021年度科学研究費補助金 基盤研究(S)(課題番号 17H06127, 代表 齋藤政彦), 世話人: 田中 公(東京大), 古川 勝久(城西大), 馬 昭平(東工大)

於 Zoom (2021年10月26日-10月29日), 2021年度科学研究費補助金 基盤研究(S)(課題番号 17H06127, 代表 齋藤政彦), 世話人: 田中 公(東京大), 古川 勝久(城西大), 馬 昭平(東工大)

於 Zoom (2021年10月26日-10月29日), 2021年度科学研究費補助金 基盤研究(S)(課題番号 17H06127, 代表 齋藤政彦), 世話人: 田中 公(東京大), 古川 勝久(城西大), 馬 昭平(東工大)

於 Zoom (2021年10月26日-10月29日), 2021年度科学研究費補助金 基盤研究(S)(課題番号 17H06127, 代表 齋藤政彦), 世話人: 田中 公(東京大), 古川 勝久(城西大), 馬 昭平(東工大), 標数2は桂利行氏との共同研究

Despite a historical focus on prosthetics, the incorporation of electromyography (EMG) sensors into less obtrusive wearable designs has recently gained attention as a potential human–computer interaction scheme for general consumer use. Because consumers are more used to wrist-worn devices, this article presents a comprehensive and systematic investigation of the feasibility of hand gesture recognition using EMG signals recorded at the wrist. A direct comparison of signal and information quality is conducted between concurrently recorded wrist and forearm signals. Both signals were collected simultaneously from 21 subjects while they performed a selection of 17 different single-finger gestures, multifinger gestures, and wrist gestures. Wrist EMG signals yielded consistently higher ( $p ) signal quality metrics than forearm signals for gestures that involved fine finger movements, while maintaining comparable quality for wrist gestures. Similarly, the performance of both individual state-of-the-art EMG features and a standard feature set was found to be significantly better when using wrist signals for single and multifinger gestures, and comparable for wrist gestures. Classifiers trained and tested using wrist EMG signals achieved average accuracy levels of 92.1% for single-finger gestures, 91.2% for multifinger gestures, and 94.7% for the conventional wrist gestures. In conclusion, this article clearly demonstrates the feasibility of using wrist EMG signals for hand gesture recognition. Results highlight not only the promise of this approach, but also the viability of incorporating prior knowledge from the prosthetics field in the design of wrist-based EMG pattern recognition systems.

於 Zoom (2021年10月26日-10月29日), 2021年度科学研究費補助金 基盤研究(S)(課題番号 17H06127, 代表 齋藤政彦), 世話人: 田中 公(東京大), 古川 勝久(城西大), 馬 昭平(東工大)

Haptic feedback is envisioned to be a powerful tool in (digital) orthosis fitment procedures. In context of a larger research project on digital molding and developing a glove for orthopedic experts, we explored the use of vibrotactile feedback on the wrist for wrist angle adjustments. Five different patterns are presented on both the inside and outside of the wrist as well as crossing signals. Participants were asked to indicate whether the pattern was communicating that the wrist angle had to be increased or decreased by moving the hand up or down. The results show that the vibrotactile stimuli are being interpreted consistently by the participants, provided the patterns are presented on one side of the arm. Although the interpretations were consistent within participants, there were individual differences in the reported directions of the signals, which makes it important to take into account personal preferences and calibration when implementing haptic feedback.