Reviewerly/exHarmony huggingface dataset:
A User Evaluation Study of Augmented and Virtual Reality Tools for Training and Knowledge Transfer

Modern augmented and virtual reality (AR/VR) technology open multiple new capabilities in the way people interact, collaborate and deliver or receive information via digital interfaces. Industry workers and operators may especially benefit from such solutions that allow them to get trained, onsite or remotely, using digital copies of the workplace environment. This paper presents the development of AR/VR-based collaborative and telepresence tools, along with a web-based platform for knowledge exchange and interaction, as part of the Ageing@Work project, that enable easy transfer of know-how from experienced (possibly older) to novice workers. The remote collaboration tools facilitate variability in location and working hours, and thus promote age-neutralizing means of participation and cooperation, whereas the knowledge transfer tools support the ageing worker’s leadership characteristics within the workplace community, allowing the company or work site to capitalize on her/his experience and expertise gained through the years. The tools were developed in accordance to a user-centred design process and were evaluated by 126 participants in total through online surveys in two pilot sites with regard to core Industry 4.0 processes of mining and machines production, as well as through off-pilot studies. This paper presents analytic outcomes of the assessment of knowledge transfer tools illustrating very positive results on the level of technology acceptance, and the overall potential of the developed solutions to support healthy and productive ageing of workers with enhanced workability.
Dynamically Adaptive Switching Based Median Mean Filter for Removal of High Density Salt and Pepper Noise

This paper proposes a novel dynamic adaptive switching based median mean filter (DASBMMF) to remove salt and pepper noise (SAP) of high noise density (ND). The algorithm detects and flags noisy pixels in a separate array and replaces the corrupt pixel with the median of neighbouring noise free pixels. Further, if all the pixels present in the sliding selection window are corrupted, the size of the window is increased up to the threshold window size, to accommodate un-corrupted pixels. Lastly, values of pre-processed pixels are also taken into consideration since they give a better result in place of a corrupt pixel. Comparative analysis reveals that the proposed scheme has improved results in Peak Signal to Noise Ratio (PSNR) and Image Enhancement Factor (IEF) without any additional computational exertion (CPU Time) on the system, due to the simplicity of the algorithm and hence giving faster results.
Taking Away Both Model and Data: Remember Training Data by Parameter Combinations

Machine Learning (ML) model hatcheries have emerged to help ML model producers. The only thing that the ML model producer needs to do is upload the untrained ML model to the hatchery with a specific task and deploy the returned trained ML model into real-world applications. Although the local private data of the hatchery are not directly accessed by the ML model producer, some backdoor attacks can still steal the private data. These attacks add malicious backdoor codes into the untrained benign ML model and recover the private data in some specific operations after training. However, existing attacks more or less have some disadvantages, such as the limited quality of the stolen private data, seriously affecting the original model performance, and being easy to defend. To address these disadvantages, we propose a novel efficient white-box backdoor attack method called Parameter Combination Encoding Attack (PCEA), which leverages the linear combinations of parameters to remember the private data during training. We evaluate the performance of the proposed method on stolen image quality, testing accuracy, and sensitivity. The experimental results show that PCEA has a much higher quality of the stolen data and robustness while keeping the testing accuracy.
Pyramid Convolution and Multi-Frequency Spatial Attention for Fine-Grained Visual Categorization

Learning diverse detailed feature is crucial for fine-grained visual categorization (FGVC). However, most of existing methods for FGVC use the standard convolution for feature extraction which leads to the loss of many subtle but important features. Besides, in the existing attention models for FGVC, the features are aggregated by a simple global average pooling operation, which is unable to characterize complex feature information. To address these problems, we propose pyramid convolution and multi-frequency spatial attention (PCMFSA) for FGVC. To capture spatial context dependencies of different levels and avoid the loss of the subtle features, pyramid convolution, which uses convolution kernels with various sizes, is introduced in the backbone network. To reduce the influence of background noise and obtain diverse fine-grained feature information, we propose multi-frequency spatial attention (MFSA) module, in which two channel-wise grouping operations are designed and discrete cosine transform (DCT) in the frequency domain is adopted to enhance features of different spatial positions. Moreover, to mine more valuable regions, peak suppression is used to suppress high-response regions after MFSA. A large number of experimental results show that our method achieves state-of-the-art results on four popularly FGVC datasets.
On Nonparametric Density Estimation for Circular Data: An Overview

This paper provides a short review of modern smoothing methods for density and distribution functions dealing with circular data. We highlight the usefulness of circular kernels for smooth density estimation in this context and contrast it with smooth density estimation based on orthogonal series. It is seen that the wrapped Cauchy kernel as a choice of circular kernel appears as a natural candidate as it has a close connection to orthogonal series density estimation on a unit circle. In the literature, the use of von Mises circular kernel is investigated, which requires the numerical computation of the Bessel function. On the other hand, the wrapped Cauchy kernel is much simpler to use. This adds further weight to the considerable role of the wrapped Cauchy distribution in circular statistics. We also investigate some transformation-based methods in adapting the linear kernel density estimator to the circular data. This is very useful in practice as widely available software on linear kernel density estimation can easily be adapted to the circular case. These simpler methods are illustrated using some real data.
Bivariate Cardioid Distributions

Bivariate generalizations of the cardioid distribution have been constructed by the mixture method. Two types of mixtures have been considered. These mixture models allow the joint probability density functions to be represented as Fourier series easily, and this facilitates parameter estimation. A test of uniformity or isotropy of a given set of directions for one bivariate model is developed. For another bivariate model, a test of independence is derived. A data set on orientation of nests of 50 scrub birds and creek directions has also been analysed.
TAME: A Method of Teachable Agent Modeling for Error-Visualization

In the context of learning, the effectiveness of learning-by-teaching (LbT) is widely recognized. LbT is a method through which learners deepen their understanding by teaching others what they have learned. In order to realize LbT, it is necessary to have the behavior of the teachable person, such as the knowledge of other and an attitude towards receiving instruction, as well as the existence of the teachable presence. One technology for approaching this challenge is called a teachable agent. A teachable agent is an agent that behaves as if it is learning what is taught by the learner. Another effective learning method is error-visualization. Error-visualization is a feedback-based approach to simulating the answers by the learner as (abnormal) behavior. However, no modeling methods have been proposed that combine teachable agents with error-visualization. In this paper, we propose teachable agent modeling for error-visualization (TAME) as a teachable agent modeling method that aims to combine teachable agent with error-visualization. As a case study, we realize a teachable agent using the TAME modeling method based on a conventional learning support system called Monsakun.
A Comparative Study: Influence on Real-World Consumer Perception of Products Presented in Augmented Reality

With the development of the mobile device, augmented reality (AR) is moving from the laboratory into the consumer market. Product presentation in the augmented environment has become a compelling function that helps consumers have a better perception of the product. In this article, we proposed a comparative study to figure out how users perceive the product model in the AR environment and the difference compared with the real-world product. Through this research, we will further understand which attributes of the product can be better perceived in AR. The product semantic differential method was used here to build the product evaluation metrics and compare the user perception of 3 types of product presentation based on product semantic.
An Audience Data-Driven Alternate Reality Storytelling Design

This case study, @LAs, made audiences the authors of exhibits as a way to unite audiences and artworks through a method of collecting their data and using artificial intelligence to process it. Three types of audience data were shared with this prototype exhibit system—identity data, real-time interaction data, and real-time location data. Tagged by folksonomy, audience data became a novel element for the AI system to process to yield media content. Thus, the system selected and generated real-time multimedia content with audiences’ personal features and allowed real-time interaction between audiences and the system, which realized dynamic control in the alternate reality. Developed and built during the 2019 University of California, Los Angeles, School of Theater, Film and Television Future Storytelling Summer Institute program, @LAs contained three prototype exhibits for a multimedia alternate reality storytelling pavilion, created as a prototype for a hypothetical mixed reality pop-up installation run throughout Los Angeles during the 2028 Summer Olympic Games. The three exhibits—i.e., three different virtual interaction spaces—were thematically based on murals, foods, and public transportation development in Los Angeles. This paper 1) introduces the theoretical and practical bases of the case study for new forms of alternate reality, 2) describes the data processing method used and the audiences’ experiences, and 3) shares the prototype’s design lessons based on the questionnaire responses from designers.
Localizing Climate-Smart Applications Through Participatory Design: A Case Study of the Beekeeper’s Companion

This paper details the need for more localization and customization of climate-smart applications in development contexts and demonstrates how practitioners’ commitment to participatory design methods can improve uptake and use of technological interventions that support environmental justice and indigenous knowledge.
Generating Hand Posture and Motion Dataset for Hand Pose Estimation in Egocentric View

Hand interaction is one of the main input modalities for augmented reality glasses. Vision-based approaches using deep learning have been applied to hand tracking and shown good results. To train a deep neural network, a large dataset of hand information is required. However, obtaining real hand data is painful due to a large number of annotations and lack of diversities such as skins, lighting conditions, and backgrounds. In this paper, we propose a method to generate a synthetic hand dataset that includes diverse human and environmental parameters. By applying constraints of a human hand, we can get realistic hand poses for hand dataset. We also generate dynamic hand animations which can be used for hand gesture recognition.
Future Convergences: Time Matters

While in the history of design, spatial qualities have been central to the search for techniques and tools, temporal qualities have come to take part in design, with the advent of the digital revolution, as qualities capable of deforming, compressing, reconfiguring spaces, and supporting new ways of living. The essay investigates various time-based approaches, developed by scholars and designers from different disciplines, to build tools that can identify convergences capable of generating various forms of future. The time-based design approach will allow future studies to explore: Spaces: Digital algorithmic design/production technologies will make spaces and components adaptable and able to govern kinetic and sensory performance. Experiences: Time-based technologies will make perception and emotions more adaptable, through a continuous dialogue between humans and inhabited spaces and by employing machines and computer systems capable of formulating personalized proposals. Behaviors: Media technologies, have changed people’s behaviors and their interaction with spaces, with people, with objects. Through temporal analysis we could understand parameters such as presence, speed, proximity, to redesign spaces and services. The essay explores the directions taken by design that can be considered time-based, to identify the temporal and convergence tools that can prefigure future spaces and ensure coherent and congruent visions based on collaboration rather than competition, presence rather than absence, optimization of space rather than unsustainable waste of resources. The essay aims to demonstrate the relevance of temporal dimensions, which offer increasingly reliable tools, called chronotopes, available to Future Studies to identify trajectories and possible configurations of the world in which future generations will live.
Status and Trend: The Application of Educational VR Games in Teaching Chinese as a Foreign Language

With more frequent international exchanges, the value of studying a foreign language is becoming increasingly apparent, and Chinese is becoming the language of choice for an increasing number of people. Virtual reality technology, with its ability to create fully immersive environments, has given foreign language learners a new lease of life, and academics believe that games have the potential to create virtual worlds rich of cultural and social value. As a result, the purpose of this work is to examine the current status and limitations of virtual reality games for Chinese language learning from three perspectives: academic literature, game applications, and online resources, as well as to recommend future research areas. According to the findings, the virtual reality games have shown a positive impact on Chinese learning, especially those adopting immersive VR system; most of the VR games are used to assist adult beginners in integrated language learning and cultural experience, with the impact on higher levels to be explored more; the suggestions on designing a better VR Chinese game are also given, including combining the characteristics of Chinese language, fully presenting the textbook content, and providing regular and timely corrective feedback. Limitations found in the previous studies are small sample size, single interaction mode, no placement test and so on. In the future, we expect to see more interdisciplinary experts work together and more VR games created to display characteristics of the Chinese language well, to completely exploit the capabilities of VR technology, and to produce lots of pleasure.
A Circular Distribution Constructed from the Product of Cardioid-Type Densities with (Hyper-) Toroidal Extension

This study defines the cardioid-type product distribution which is a possibly asymmetric and multimodal distribution on the circle. A parsimonious distribution as a special case, which is denoted by CTP $$_2$$ , includes sine-skewed cardioid and cosine-perturbed cardioid distributions. Although the cardioid-type product distribution is a special case of the nonnegative trigonometric sums distribution, the nonnegativity and the role of the parameters are clear. The CTP $$_2$$ has a simple normalizing constant and is sufficiently flexible with respect to fitting purposes. A cardioid-type product distribution on the hypertorus is defined by utilizing an approach analogous to the construction of CTP $$_2$$ . The properties of a toroidal distribution, denoted by TCTP $$_2$$ , are investigated in detail. Both the marginal and conditional distributions of the TCTP $$_2$$ belong to the CTP $$_2$$ family. Joint trigonometric moments, correlation coefficients, random number generation, estimation, test for independence and discretization were also studied. Thunder and circular genome data are used as illustrative examples.
Analyzing Change on Emotion Scores of Tweets Before and After Machine Translation

Many of the texts posted on Twitter are broken sentences, and the translated sentences may not be accurate. An inaccurate translation may spoil the meaning of the original text and induce miscommunication between the poster and the reader who uses the machine translation. Since many sentences tweeted on Twitter contain emotional expressions, this study uses sentiment analysis to calculate and compare the sentiment scores of the original and translated sentences to investigate the change in sentiment before and after machine translation. As a result of using dictionaries to classify tweets before and after translation, it was found that the classification of positive sentences tended to be more likely the same before and after translation. In addition, the results of the sentiment analysis of "joy", "like", "relief" and "excitement" by machine learning showed that the sentiment of "joy" was particularly increased when translated from Japanese into English.
Recommendation Model for Tourism by Personality Type Using Mass Diffusion Method

Recommendation systems were applied in various fields, such as e-tailing, movies, books,…, and so on. Among them, tourism recommendation systems are also one of the widely research topics. Many tourism recommendation system studies use Collaborative Filtering (CF) method and try to add personality traits to the recommendation system methods to improve the precision. Zhou et al. (2007) suggested that Mass Diffusion (MD) method has more precision than CF method, but this method is mostly applied to recommending movie genres or books, but less often in tourism. Compared to other recommendation systems, fewer studies have considered personality traits such as Big Five Factor (BFF) and Myers-Briggs Type Indicator (MBTI) 16 (personality type). In this study, we used the MD method to establish a model of tourism attraction recommendation, and combined the personality traits commonly used in other recommendation system studies, such as BFF and MBTI 16, to achieve personalized recommendation of tourism attractions. According to the experimental results of this study, compared with the CF method combined with personality traits, the MD method combined with personality traits can recommend attractions to users more accurately.
Research on the Promotion of Excellent Sports Culture of the Chinese Nation Based on AR Technology—Take Ansai Waist Drum as an Example

Sports intangible cultural heritage is an important part of China's excellent traditional culture, and it can strengthen the development of sports culture and promote its prosperity. As an art with the characteristics of the Chinese nation and the continuation of the red revolutionary culture, Ansai waist drum has the reputation of "the first drum in the world", "victory waist drum" and "the soul drum of the Chinese nation". The Ansai waist drum was included in the first batch of national intangible cultural heritage in 2006, which made the Ansai waist drum once again enter the public eye. In order to better inherit and develop Ansai waist drum, which is an excellent sports culture of the Chinese nation, this paper aims to explore the application of AR technology to promote the spread of Ansai waist drum. While introducing AR technology and Ansai waist drum, this paper analyzes the role of AR technology in promoting the excellent sports culture of the Chinese nation, summarizes the advantages of AR technology in promoting the spread of Ansai waist drum, and proposes strategies for AR technology to accelerate the promotion of Ansai waist drum. It is hoped that AR technology can better advance the efficient popularization of a series of Chinese excellent sports culture such as Ansai waist drum.
Flick Typing: A New VR Text Input System Based on Space Gestures

Text Entry is a significant topic in human-computer interaction in Virtual Reality. Most common text entry methods require users to interact with a 2D QWERTY keyboard in 3D space using a ray emitting from their hands or controllers. This requires the user’s head and hands to be in a specific position and orientation to do text entry. We propose a new text entry input method that we call Flick Typing that is agnostic to user posture or keyboard position. Flick Typing utilizes the user’s knowledge of a QWERTY keyboard layout, but does not explicitly provide visualization of the keys. To type with Flick Typing, users will move their controller to where they think the target QWERTY key is with respect to the controller’s starting position and orientation, often with a simple flick of their wrists. We provide a manually defined gesture-key mapping model of the QWERTY keys in 3D space. Experiments containing both quantitative and qualitative measurements are conducted and discussed this paper, which shows the potential of our method.
Don’t Throw It Over the Fence! Toward Effective Handover from Conversational Agents to Service Employees

Contemporary conversational agents (CAs) are capable of reliably answering repetitive low-complexity requests in online customer service, but regularly breakdown when dealing with high content or semantic complexity. The resulting service failures have a detrimental impact on customers' satisfaction and their willingness to use CAs in the future. By aiming to avert CA breakdown in service encounters with a hybrid service recovery strategy via handover UI, we address a knowledge gap in service literature. As automated recovery strategies via conversation repair do not invariably prevent CA breakdown, real-time handover of customer interaction from CA to service employee (SE) is increasingly applied and investigated. This hybrid service recovery strategy places high demands on SEs, as they must keep waiting times short and avoid repetition of questions to customers after handover. Considering SEs limited cognitive capacities for information processing, we present a handover user interface (UI) with relevant information to support SEs after handover. Following a Design Science Research approach, we define design principles for the handover UI, based on meta-requirements derived from kernel theories and expert interviews. By evaluating the design principles via prototype instantiation, we show that the information types and their presentation in the handover UI keep cognitive efforts for SEs at a manageable level and help them initiate customer interaction quickly.