Reviewerly/exHarmony huggingface dataset:
The Effect of Personal Pronouns on Users’ Emotional Experience in Voice Interaction

There is a growing tendency that users expect voice assistants can recognize and follow some principles in interpersonal communication to enhance emotional experience. Therefore, we study how personal pronouns should be used in the response of voice assistants for Chinese users. We conducted a quantitative experiment. The independent variable is the use of personal pronouns in the intelligent voice assistants, including three levels: no personal pronouns, singular first-person pronouns and singular second person pronouns. 24 participants listened to dialogues between users and the voice assistant and evaluated the perception of emotional experience. It is found in the results that the use of personal pronouns by voice assistants can affect users’ emotional experience. Compared with non-use of personal pronouns and use of first-person pronouns, users would have more trust in the voice assistants when it responded in the second person pronouns, and users were more satisfied with the response of the voice assistants in the second person pronouns. These results can inform the design of voice interaction and it is possible to design response strategies for machines based on the theory of interpersonal communication and pragmatics.
Transformation of Non-Euclidean Space to Euclidean Space for Efficient Learning of Singular Vectors

Singular value decomposition (SVD) is a popular technique to extract essential information by reducing the dimension of a feature set. SVD is able to analyze a vast matrix in spite of a relatively low computational cost. However, singular vectors produced by SVD have been seldom used in convolutional neural networks (CNNs). This is because the inherent properties of singular vectors such as sign ambiguity and manifold features make CNNs difficult to learn singular vectors. In order to overcome the limitations, this paper analyzes the undesirable properties of singular vectors and presents the transformation of singular vectors into Euclidean space as a smart solution. If the singular vectors are transformed to follow Euclidean geometry, SVD can be used for pooling to maintain the feature information well, which is called singular vector pooling (SVP). Since SVP can extract essential information from a feature map, it is robust against adversarial attacks in comparison to global average pooling. Thus, SVP shows a quantitative performance improvement of about 36% for the CIFAR10 dataset. In addition, we applied SVP to a knowledge distillation scheme that uses singular vectors in a restricted manner. As a result, SVP improved the performance by up to 1.7% for the CIFAR100 dataset.
Improving the Training Materials of Information Security Based on Cybersecurity Framework

The shortage of human resources related to information security has been a problem in Japan since the last decade. To encourage self-learning, we created training materials revised based on the guidelines of information security published by the Information-technology Promotion Agency, Japan. The materials were partly designed using a text-mining analysis based on the framework core of the cybersecurity framework published by the National Institute of Standards and Technology. In this extended abstract, we consider the accuracy of the text-mining approach and a rough design of the materials.
Memory, Expectation and the Temporal Flux of Acousmatic Music

This article concerns the temporal experience of acousmatic music, how the music can impact a listener’s sense of time passing and the implications of memory and expectations of auditory events and their perceived connections to one another. It will outline how memory and schemas lead to predictions in the immediate future and larger expectations of a work’s form. An overview of the temporal listening framework for acousmatic music will be provided to show the interrelationship between memory and expectations and how they influence one’s listening focus in the present. Trevor Wishart’s Imago will be used to illustrate how one might compose an acousmatic work to promote active listening using compositional techniques that engage personal schemas and those built through the course of experiencing the piece.
Frequency, Dispersion and Abstractness in the Lexical Sophistication Analysis of A Learner-Based Word Bank: Dimensionality Reduction and Identification

The current study incorporated a number of lexical sophistication indices including frequency, dispersion and abstractness of words. A learner-based word bank (inclusive of a Chinese middle-school vocabulary list, a Chinese high-school vocabulary list and a Chinese college-English-test vocabulary list) was manually coded based on two existing corpora: Corpus of Contemporary American English (COCA) and British National Corpus (BNC). Indices of frequency, dispersion and abstractness of the word bank were analysed to shed light on the predetermined categorization of lexical sophistication among second language learners. Based on the principal component analysis, the results demonstrated that dispersion was a unique factor loaded on all entered eight variables while word frequency and abstractness were extracted by the same factor in the learner-based word bank. Moreover, a follow-up MANOVA analysis with post hoc comparisons showed that lexical sophistication indices in general produced pronounced differences among the three levels of word lists. More critically, dispersion was found to be the only significant indicator to differentiate the three levels of word lists. Discussion centred on the uniqueness of dispersion in lexical sophistication and the shared algorithm in frequency and abstractness.
Towards the Specification of an Integrated Measurement Model for Evaluating VR Cybersickness in Real Time

Cybersickness (CS) is an affliction that limits the use of virtual reality (VR) applications. For decades, the measurement of cybersickness has presented one of the most challenges that have aroused the interest of VR research community. Having strong effects on users’ health, cybersickness causes several symptoms relating to different factors. In most cases, the literature studies for VR cybersickness evaluation adopt the questionnaire-based approaches. Some studies have focused on physiological and postural instability-based approaches, while others support the VR content. Despite the attention paid to define measurements for assessing cybersickness, there is still a need for a more complete evaluation model that allows measuring cybersickness in real time. This paper defines a conceptual model that integrates subjective and objective evaluation of CS in real time. The proposed model considers three CS factors (i.e. individual, software and hardware). The aim is to consider the heterogeneous findings (subjective and objective measures) related to the selected CS factors that define integrated indicators. The theoretical part of the model was initially validated by researchers who have comprehensive knowledge and skills in VR domain. As a research perspective, we intend to evaluate the proposed model through a practical case study.
IMU-based Smart Knee Pad for Walking Distance and Stride Count Measurement

To calculate the knee angle, stride counts, and walking distance, we propose a system, iKneePad, fusing two 9-axis sensors with Bluetooth equipped on the thigh and shank segments. The changing rates of hip and knee angles are used to determine the beginning and the ending of a stride. The thigh length, shank length, hip angle, and knee angle are used to calculate the walking distance. The experimental results show that the accuracy of stride count is 100%, the absolute mean errors of knee angle are 2.99° and 1.42° for the maximum and minimum flexion angles, respectively. For walking distance, the mean error rates are −2.40% and −2.26% for short (10m) and long (33m) distances, respectively. The proposed system also instantly provides feedback to users by showing on an Android smartphone when conducting rehabilitation or exercise with iKneePad.
Experimental Study on Improvement of Sign Language Motion Classification Performance Using Pre-trained Network Models

Sign language is a major means of communication for people with hearing disabilities. However, there are very few hearing people who have learned sign language, and this is a great barrier to communication between hearing-impaired and hearing people. While automatic speech interpretation has already been put to practical use in some fields, there remains a lot of difficulties in putting sign language interpretation into practical use. Considering the variety of sign languages, the complexity of their motions and many subtle differences, it seems that there is bound to be a limit to any method of artificially extracting feature elements from motions and inputting them to a classifier to decide sign language motions. The authors are now investigating a method for automatically extracting feature elements using a pre-trained network model that has been trained by deep learning, and for classifying each motion. The problem of using deep learning is that it requires a large amount of training data to create a trained model. The acquisition of sign language motion data in order to satisfy this requirement seems to be difficult in practice. This paper presents a method of artificially creating data by data augmentation under conditions where the number of data items that can be collected is limited, and improving classification accuracy. It is shown that the proposed method has obtained a result of about 10% improvement in classification accuracy. In addition, the application of ensemble learning, which is another techniques for improving accuracy, is also described. The author shows that classification performance after integrating the results using a plurality of trained models built on the feature elements obtained from each pre-trained network model, resulted in a significant improvement in accuracy of more than about 10%.
LUISA: Decoupling the Frequency Model From the Context Model in Prediction-Based Compression

Abstract Prediction-based compression methods, like prediction by partial matching, achieve a remarkable compression ratio, especially for texts written in natural language. However, they are not efficient in terms of speed. Part of the problem concerns the usage of dynamic entropy encoding, which is considerably slower than the static alternatives. In this paper, we propose a prediction-based compression method that decouples the context model from the frequency model. The separation allows static entropy encoding to be used without a significant overhead in the meta-data embedded in the compressed data. The result is a reasonably efficient algorithm that is particularly suited for small textual files, as the experiments show. We also show it is relatively easy to built strategies designed to handle specific cases, like the compression of files whose symbols are only locally frequent.
Research on a Washout Algorithm for 2-DOF Motion Platforms

This paper proposes a new washout algorithm optimized from those designed for 6-DOF Stewart platforms using the Human Vestibular Based Strategy. Its actual effect, from the perspective of user experience, was verified via a within-subject design experiment using a 2-DOF platform with limited rotation angles. The result showed that compared with the common 2-DOF algorithms using the Linear Shrinkage Ratio Strategy, our algorithm provided users with better immersion, presence and overall satisfaction, though no significant difference was found regarding simulator sickness. This positive result demonstrated the potential of the algorithm proposed here and may help to enhance the user experience of various kinds of motion platforms.
Research on Positioning of Robot based on Stereo Vision

Aiming at the problems in positioning accuracy of stereo vision guided robots. Here, we firstly calibrated two cameras separately and calculated the relative position-attitude between the two cameras. A more accurate relative position-attitude and the distortion parameters between the two cameras of each image were obtained by optimizing the projection error of the marks via Bundle Adjustment (BA). Then, we identified the world coordinates of the marker point by using the triangulation principle of the parallel binocular camera. According to the position-and-pose parameters of each image that captured by left camera and the data of corresponding robot end, Tsai hand-eye calibration method was applied to work out the relative position-and-attitude of the end of the robot to the left camera. Hence, the robot could be accurately guided to operate with the precise position of target point. The experimental result suggests that the algorithm in this study is favorably reasonable and can accurately to guide robot positioning.
Learning Latent Features with Pairwise Penalties in Low-Rank Matrix Completion

Low-rank matrix completion has achieved great success in many real-world data applications. A matrix factorization model that learns latent features is usually employed and, to improve prediction performance, the similarities between latent variables can be exploited by pairwise learning using the graph regularized matrix factorization (GRMF) method. However, existing GRMF approaches often use the squared loss to measure the pairwise differences, which may be overly influenced by dissimilar pairs and lead to inferior prediction. To fully empower pairwise learning for matrix completion, we propose a general optimization framework that allows a rich class of (non- )convex pairwise penalty functions. A new and efficient algorithm is developed to solve the proposed optimization problem. We conduct extensive experiments on real recommender datasets to demonstrate the superior performance of this general framework.
Image feature extraction algorithm in big data environment

The purpose of this article is to explore effective image feature extraction algorithms in the context of big data, and to mine their potential information from complex image data. Based on the BRISK and SIFT algorithms, this paper proposes an image feature extraction and matching algorithm based on BRISK corner points. By combining the SIFT scale space and the BRISK algorithm, a new scale space construction method is proposed. The BRISK algorithm extracts the corner invariant features. Then, by using the improved feature matching method and eliminating the mismatching algorithm, the exact matching of the images is realized. A large number of experimental verifications were performed in the standard test Mikolajczyk image database and aerial image database. The experimental results show that the improved algorithm in this paper is an effective image matching algorithm. The highest accuracy of actual aerial image matching can reach 85.19%, and it can realize the actual aerial image matching that BRISK and SIFT algorithms cannot complete. The improved algorithm in this paper has the advantages of higher matching accuracy and strong robustness.
Switchable Controlled Operations with Bent Functions

The paper presents an idea of providing a high degree of security against crypto attack in the switchable data-dependent operations (SDDOs). SDDOs is a cryptographic primitive focused on the development of ciphers appropriate for applications in limited environments. SDDO architecture is based on the block transform binary data based on the substitution-permutation networks with a control binary vector. The new design of the SDDOs is proposed using bent functions. Bent functions provide maximum non-linearity of functions and system resistance to cryptanalysis. Using a new SDDOs can be constructed the new block ciphers.
The Current State of m-Learning in Higher Education: A Survey Study of Mobile Technology Usage in the Classroom

Existing research acknowledges the proliferation of mobile devices in society, but very few studies have sought to understand the demographics of the students who actually utilize mobile technology for their learning needs. Despite the pervasiveness and attention given to m-learning, there is still a lot of unknowns about how to integrate mobile technology into learning practices. Therefore, this paper describes an exploratory survey study that examined U.S. college and university students’ responses to collect data about current mobile technology use. Novel to this study, gaining a clearer picture of mobile technology from students, educators and researchers can better apply m-learning to target desired learning behaviors and outcomes. Results suggest students have access to many mobile devices, however, they tend to be very selective in what device they choose to use for their learning. The findings indicate that their use of mobile technology is highly dependent upon the characteristics of the learning activity they are engaging in, and the subject matter. Students also reported mobile devices having a positive impact on their learning for quick, just-in-time learning activities.
The Development of Interval Data Type for Analytical Information Processing

The development of the interval analysis theory and its applications in solving applied problems led to the need to collect, store and extract efficiently interval-significant data in relational databases. Modern relational database management systems (DBMS) either allow to work with built-in data types which however are not friendly for use or provide the ability to create your own user-defined data types in several ways. The article describes the creation of a user-defined data type iInterval, functions and aggregates to work with interval data in MS SQL Server using the Common Language Runtime (CLR).
Case Studies of Developing and Using Learning Systems in a Department of Engineering

Sequential circuits are difficult for 1st year university students to understand because of the concept of state. In this research, learning systems were developed for education of FF circuits and counter circuits, and the systems have been used in experiment classes. This paper describes the development and usage of a FF circuits learning system in 2012, and describes the development and usage of a new learning system in 2019. This paper also describes the development and usage of a counter circuits learning system in 2012, and describes the new usage of the same learning system and the development of a new learning system in 2019. Students answered the questionnaire after using the systems. The evaluation of usefulness of the system is relatively high. This result indicates that the basic framework of learning part and quiz part of our system is necessary and the contents are adequate. Also, displaying multiple materials (diagram, chart, table and text) on the same page is effective for counter learning system. The results also indicate that usefulness and contentedness are related if students use the system individually, but that some students are not contented if they use the learning system with members. The evaluation of “motivated” is relatively lower than the evaluations of “useful” or “contented”. Therefore, we have to develop a new system to encourage students to study with members and to improve the motivation for study.
User Experience: How to Drive Innovation on the Fuzzy Front End

The future market and demands of users are unpredictable during the fuzzy front-end of product development. In order to save time and economic cost, user experience may be replaced directly by empathic design and empathy model for experimental and chaotic nature of the front-end. Meanwhile, with acceleration of product innovation and growth of brand, innovation has become an indispensable part of the development of enterprises. Enterprises could survive for a long time only by continuously providing innovative products to the market. Although role-playing and empathic design still has a great impact on designers, they cannot fully meet the needs of fundamental innovation due to their limitations. In recent years, more and more enterprises begin to pay attention to the end users, and explore the user demands, economic and social benefits. Designers also gradually began to study the typical emotional changes of users and their social world in order to seek new breakthroughs. This paper will start with the research of fuzzy front-end and discuss how user experience will bring innovation to fuzzy front-end. Because of the interaction with the environment, there will inevitably emerge social factors and social networks. The participation of users will not only create economic value, but also create emotional and social value, so that innovation can reach a new level.
Standard Representation for Digital Forensic Processing

This paper discusses the lack of reliability and reproducibility validation in digital forensics for a criminal trial. It is argued that this challenge can be addressed with standard data-representation for digital evidence. The representation must include reproducibility documentation on processing operations including automation, human interaction, and investigation steps. Analyzed are two blueprint articles – the CASE specification language for cyber-investigations [1] and the WANDA data standard for the documenting semi-automated hand-writing examination [2]. These two generic frameworks are studied for their granularity to support reproducibility testing by representing: (i) artefact characteristics, forensic – tool parameters and input – output logic; (ii) human and tool data interpretation; and (iii) parallel-running forensic tasks or chains of processes. Proposed is the integration of WANDA-based schema as CASE expression. The utility of such integration is demonstrated as a new module in CASE designed to meet the high standard of proof and scientific validation typically required in criminal investigations and trials. The expression ensures compliance without overburdening digital forensic practitioners.
Museum Immersion Interactive Design:

Art is the museum’s collection, and education is the heart of the museum. The management of the museum is aimed at 3E: “Educate, Entertain, Enrich”. In the 1960s, the theory of interaction was put forward. From the 1980s, museum exhibitions began to focus on audience participation. In the face of the challenges of the children’s museum in new century, how should new strategies be used to transform resources? It is the subject of contemporary museums. Through cross-disciplinary cooperation in culture, entertainment, education and technology, using storytelling and cutting-edge technology, and adding Ecstatic experience to build a 4E model for future museum operations, that is, using the technology of immersive interactive devices, can better present the new look of the exhibits, generate a new dialogue mode, and highly enhance the joyful experience of the audience in contact with the exhibits. In this study, through in-depth interviews with museum practitioners and expert consultations, the SHE curatorial design model for children’s art gallery space are put forward, and they are actually applied to project planning. Then, by observing the museum audience experience, the narrative power of immersive interactive devices and the effects of cutting-edge technology are verified. Responding to and surpassing the concerns and needs of the parent-child audience of contemporary museums, inherited and followed in museum education.