Reviewerly/exHarmony huggingface dataset:
Evaluating Seniors’ Virtual Reality Experience Performed at a Local Community Event in Japan

Head-mounted display has become one of the most known hardware and best practice experience for virtual reality. However, it still lacks of opportunities for many people who may not have much interest in gaming such as seniors. While many seniors have some interest to virtual reality through watching television program, the opportunity to actually experience is still limited. Therefore, this paper focused first to provide an opportunity for seniors to experience virtual reality, and then investigate about their impression and acceptance. An event booth to experience virtual reality contents was provided at a local community event held for senior. Voluntary participants were asked to answer questionnaires after the virtual reality experience. The questionnaire was gathered from 11 seniors over the age 65. Evaluation of acceptance was done using the System Usability Scale, indicated not so high acceptance of usability though positive outcomes delivering higher satisfaction than anticipated.
Increase Electricity Supply to the Community to Support Sustainable Development by Minimizing House Load in Geothermal Power Plant

Abstract Recently, electricity consumption in Indonesia continue to increase rapidly along with the increase in development, economic growth and also the increase in the population. One potential source of electrical energy comes from geothermal energy, where Indonesia has enormous potential. According to data from the Geological Agency, Ministry of Energy and Mineral Resources, until the end of 2016 there were at least 331 geothermal energy source locations throughout Indonesia, where most of the potential was on a volcanic track with a potential of 28,579 MW and this is the largest geothermal energy in the world. In its operation, power plants need energy to supply their own needs (house load) which amounts up to 10 % of the electrical energy produced. Own electricity needs are used to drive brine and condensate pumps, fan drive motors in cooling towers, lamps and electronic appliances in well pad and offices. The use of this energy can be minimized to increase electricity production which is distributed to users. The methodology used in this research is lean sigma process through the Define-Measure-Analyze-Improve (DMAI) steps. The first step is to define the problems regarding opportunity from electricity use for own needs that can be saved and constraints faced, the next step is to measure the electrical usage as baseline data, then analyze to find the causes of inefficiencies in electricity use and the last make improvement programs. From a case study conducted at the Gunung Salak geothermal power plant in West Java, the energy efficiency of house load can be minimized by up to 7.5%-10% in 2018-2019. Greater energy supply to the community will have an impact on improving the economy of the community to support production activities that require electrical energy.
Proposal of Character Input Method for Smartphone Using Hand Movement

Character input in smartphones, such as erroneous input due to the small size of each button or the small size of the information presentation screen due to the large size of the input screen, has been considered a problem. To solve these issues, in our previous research, a multi-choice input method with a low degree of freedom was designed, which could input up to sixty-four characters by flicking four buttons in two directions: up and down and repeating it twice. In addition, it was possible to input not only by touch but also by gaze and gestures, thereby proposing four alternatives and a method of dividing these alternatives into two and inputting twice. In this research, the aim is to develop a multi-choice input interface with a low degree of freedom, in which four actions are taken by moving the two corners of the device closer and far from the user’s body, and eight actions are performed by tapping the back of the device before performing these actions. The three-axis acceleration sensor of the smartphone was used to determine the movement. Based on the analysis of the four movements, it was suggested that it was possible to determine whether the movement was approaching or moving away by the acceleration sensor’s z-axis or y-axis and whether to tilt right or left from the camera depending on the phase of the x-axis and y-axis. Accordingly, we could determine with 100% accuracy for the three movements and 95% accuracy for the remaining movement.
Leveraging Hierarchical Features for HateSpeech Identification in Short Message Texts

This study espouses that quick gains in hate speech identification can be achieved by using a simple hierarchical structure of high-level features that map into low level features e.g. hate lexical terms mapped to term frequency-inverse document frequency features. The study implements this approach and uses supervised machine learning to train a classifier on 48k human annotated tweets to automatically identify hate speech generated during the 2012 and 2017 presidential elections in Kenya. Preliminary results indicate an accuracy of 0.74, which is higher than the baseline for the same data set labeled by human annotators.
Did You Say Buttonless? Exploring Alternative Modes of Sensory Engagement for Augmented Reality Storytelling Experiences

Augmented Reality designers and content creators continue to explore ways to engage audiences. However, studies have yet to focus on how different modes of interaction affect understanding and immersion in AR environments. To address this, a simulation and focus group was conducted to elicit feedback about five different modes of interaction: sound, touch, haptic feedback, presence, and gesture. Results identified four themes, with gesture interaction garnering more appeal and immersion than alternatives. Accessibility and self-consciousness in public settings were illuminated during the simulation, which highlighted barriers related to some modes of interaction.
Voice and Speech Training System for the Hearing-Impaired Children Using Tablet Terminal

Speech is an important human capability, especially as a learning base for expression and communication. However, it is very difficult for children with hearing disorders to obtain this basic ability while they are young. The authors have found the potential of using voice recognition in application systems for portable devices and tablet terminal for use in the speech training of children with hearing disorders since early childhood (pre-school). In this paper, we propose the creation of this application through evaluation and discussion of practicality. The application focuses on the enjoyment of a game and a sense of accomplishment so that children can keep learning with motivation while operating. A tablet is used for this system for easy operation by children. The environment includes OS: Windows 10, Game Engine: Unity, and Voice analysis: Julius. The application has voice recognition, so it can be operated through speech. This voice recognition covers from simple sounds to utterance accuracy: it recognizes whether the child is using their voice and if the utterance is correct. In this way, the application can help children with hearing disorders to acquire speech and reduce disability in the future.
Applications of Speaker Identification for Universal Access

Speaker Identification is the process of a machine identifying who is speaking automatically based solely on the voice of the person speaking. Recognising a person in a meeting room or on telephone, purely from their voice is an important and interesting research challenge. The voice is one of the human biometric properties. Recognition of a particular person's voice can be used in different applications such as: unlocking an office door, marking student or employee attendance, monitoring elderly people's health, online banking services, or helping people with dementia to be able to identify a who is speaking. This paper explores a range of such applications and discusses how emerging technologies can be used to support a variety of users in a series of different contexts of use.
Comparative Study of Extreme Learning Machine using Various Computing Platforms

This paper focuses on testing the performance of an optimized extreme learning machine (ELM) algorithm using various resources-constrained computational platforms, to be used in autonomous intelligent vehicles. The purpose of this comparative study is to find the best suited embedded computational platform for future use in an experimental autonomous mobile robot, where computing power, responsiveness and power consumption are key to further development. The aforementioned algorithm was used as a benchmarking tool in this performance review, but also as an important starting point for processing large amounts of different data types from the robots' sensorial units. For now, the conducted experiments are limited to pre-trained datasets, to have a precise output of the very same data type.
Mathematical analysis and handling of a general stochastic scheduling problem arising in heterogeneous clouds

In the large-scale online services built upon data centers from heterogeneous clouds, user requests come from all over the world and will create stochastic demands for various resources in these data centers. It is an impending need to optimally schedule resource while considering the heterogeneity of different data centers. The corresponding general scheduling problem is a highly complex non-linear programming problem, which can hardly be solved efficiently using existing algorithms. We establish a differential evolution oriented mathematical model that can help reduce the search space significantly, and then propose an improved differential evolution algorithm to handle the problem efficiently. Experiments using the emulated and realistic data show that the algorithm outperforms the existing algorithms by increasing the revenue by up to 35%.
A Big Data Semantic Driven Context Aware Recommendation Method

Classical content-based recommender systems (CB) help users to find preferred items in overloaded search spaces, comparing items descriptions with user profiles. However, classical CBs do not take into account that user preferences may change over time influenced by the user context. This paper propounds to consider context-awareness (CA) in order to improve the quality of recommendations, using contextual information obtained from streams of status updates in microblogging platforms. A novel CA-CB approach is proposed, which provides context awareness recommendations based on topic detection within the current trend interest in Twitter. Finally, some guidelines for the implementation, using the Map Reduce paradigm, are given.
Human Facial Feature Matching based on Motion-Smoothness Constraint

Abstract Face recognition is a practical and important problem. In our life, face recognition problems exist everywhere. How to identify the face in the image accurately and quickly is an important and urgent topic. In this paper, an efficient and accurate face recognition method based on keypoints feature matching is proposed to extract the keypoints features of the face image, and then the nearest neighbor method is used to obtain a large number of keypoints rough matching. Then the correct matching of the keypoints is determined by the motion smoothness constraint of the original keypoints matching. Finally, the matching of the face is determined according to the number of keypoints matching between the two input face images. Some experiments show that this method is suitable for face recognition with expression change and pose change.
Insights from the Apple Human Interface Guidelines on Intuitive Interaction

Research on intuitive interaction in the academic community developed early in the millennium with the pioneering research of groups in Australia and Germany. Apple has produced a wide array of products and technologies which are widely proclaimed as "intuitive" leading to great commercial success. As far as can be determined Apple has never produced a white paper or any other document detailing the process or components of intuitive interaction. Apple, however, does provide Human Interface Guidelines (Guidelines) which frequently mention the "intuitive" as a desirable goal. This paper analyses the references to the "intuitive" and related key words in the Guidelines seeking to understand the context in which these references appear, and thereby identify elements that comprise "intuitive" interaction as outlined in the Guidelines. The results show broad correspondence to the aspects of intuitive interaction that has been developed by academic researchers triangulating the academic research performed. The analysis provides additional insights concerning the nature of intuitive interaction laying the basis for further research.
Research on Interaction Design of Artificial Intelligence Mock Interview Application Based on Goal-Directed Design Theory

In recent years, the employment problems confronted with the graduates become a livelihood issue in China. How to build an effective mock interview platform to provide more practical interview experience for graduates is an important direction for employment guidance. The gradual maturity of artificial intelligence technology provides technical support, but in academic research, it still focuses on system architecture design and artificial intelligence algorithm development, and few people have studied the improvement of the system user experience. This paper aims to use the goal-directed design theory to research the applications of artificial intelligence mock interview, identity user goals and needs. Propose interaction design principles and strategies to provide ideas and reference for the future design of such products. First, conduct theoretical research and elaborate related concepts to provide a basic theoretical framework for subsequent research; then, follow the design process provided by goal-directed theory to conduct research, modeling, and requirements construction of mock interview applications; and finally according to the user goals and artificial intelligence application interaction design trends, this paper proposes some principles and strategies of interaction design for artificial intelligence mock interview applications.
Improving the User Experience for Healthcare Professionals Using a Conversational Agent to Complete Business Intelligence Analysis

We explore using a conversational agent to simplify healthcare user’s access to business intelligence. We are extending an initial prototype based on the observations from a user study. As we describe, the improvements enhance the user experience and facilitate user’s completing tasks. Most basic we extended the agent to help users become familiar with using the conversational interface itself. Related to the agent’s business intelligence, we expand examples and descriptions. Finally, the improvements broaden the audience so that even non-healthcare experts can complete some tasks even if they might not fully appreciate the insights of the results. This work is part of IBM Watson Health’s initiative to leverage cognitive computing to create healthcare solutions.
Partner Agent Showing Continuous and Preceding Daily Activities for Users’ Behavior Modification

In this research, we aimed to support improvement in users' daily lives by behavior modification for people who cannot self-manage their daily activities, such as bed-making, cleaning, tidying, and sleeping. We focused on the preceding behaviors of others who get along with users to encourage them to act on daily matter. Our proposed system adopts an anthropomorphic animation agent that shows its own activities in daily life to users to stimulate their incentives to follow actions of the agent as a familiar and ambient presence. We conducted a series of four-day experiments to investigate whether the preceding behaviors of the agent were repeated and continuously affected users' spontaneous actions. From the results, we suggest that the proposed system has a possibility to induce daily activities of users that will ultimately become spontaneous.
Research on Key Technologies of Cloud Database Automatic Deployment System

Cloud database is the combination of traditional database and cloud computing technology. It can provide the virtualization of database resources, and has the advantages of on-demand use, dynamic capacity expansion, high availability and so on. In recent years, with the rapid development of cloud database technology, more and more enterprises, institutions and organizations transfer the database to the cloud, so the demand for building cloud database is also increasing. However, it is a complex and huge task to migrate the existing business to the cloud platform. The key to solve this kind of problem lies in how to realize the automatic deployment and management of each business. In this paper, the corresponding scheme is proposed to realize the automatic deployment of database module in cloud platform.
Prospective Elementary Education Teachers Hack a Digital Cash Algorithm

Prospective teachers and cryptography students use programming, statistics and guile to enrich both their courses.
Two Phase Approach to Detection of Software Projects with Similar Architecture Based on Clustering and Ontological Methods

Everyday in open repositories like GitHub, GitLab and others lots of software projects are created and updated. A method to determine similar source code or even better reusing project architecture could reduce an amount of wasted resources. In this paper two phase approach to detection of software projects with similar architecture based on NLP, clustering and ontological methods is proposed. This approach starts with natural language processing techniques and clustering to narrow the scope of processed software projects. And then goes stage of a novel described method for comparing software projects based on ontological representation of their architecture. This method allows to obtain project’s architecture automatically from the projects source code. In order to estimate results several similarity metrics of similarity between projects are proposed.
Gesture-Based Ιnteraction: Visual Gesture Mapping

Gesture-based interaction allows for interacting with computers, machines and robots in an intuitive way without direct physical contact. The challenge is that there are no agreed-upon interaction patterns for gesture-based interaction in VR and AR environments. In this paper we have developed a set of 10 gestures and corresponding visualizations in the following categories of gestures: (1) directional movement, (2) flow control, (3) spatial orientation, (4) multifunctional gestures, and (5) tactile gestures. One of the multifunctional gestures and its visualization were selected for usability testing (N = 18) in a 3D car track simulator. We found that the visualization made it faster and easier to understand the interaction made the interaction more precise. Further, we learned that the visualization worked well as guidance to learn to control the car but could be removed after a while as the user had learned the interaction. By combining gestures from the library, gesture-based interaction can be used to control advanced machines, robots and drones in an intuitive and non-strenuous way.
Comparison of the Purchasing Behavior for Oneself or Other Using Eye Tracking Gaze Data

In recent years, as the ratio of e-commerce is increasing, in the real store, it is necessary to use the strength of touching products. In order to be seen more products, we need to improve the layout at the real store. Therefore, it is necessary to understand the purchasing behavior of customers. Based on the idea that behavior changes depending on the purpose of purchase, we considered two types of purchasing behaviors: one for oneself and another for others. In this paper, we conducted the experiment of viewpoint observation about two kinds of the purchasing behaviors with the eye tracking device like glasses at an apparel store. In this study, we evaluate the difference of the purchasing behaviors by network analysis using gaze data obtained by experiments. Specifically, we clarify the relationships between showcases by network analysis. We analyzed the clusters between the showcases to use the average data of subjects for each scenario and we compared the two clusters. As a result, we found the differences and the features of two clusters.
The Art School and the Library

Art students interact with the library as a physical and virtual space, as well as a source of materials and services for research and inspiration. This article describes the results of a library-use survey distributed to students at a public university art school with sixteen programs. Responses challenged assumptions about awareness, frequency of use, and importance of resources. Survey results also demonstrated discipline-specific variations in the use of the library as a third space, the desirability of makerspaces, and the value of collections. Analysis suggests implications for the role of the art liaison and opportunities for library service and outreach.[The following article is an expanded version of a presentation given at the “Assessment in Art Libraries” session at the ARLIS/NA conference held in Salt Lake City, Utah, in March 2019.]
A library for constraint consistent learning

This paper introduces the first, open source software library for Constraint Consistent Learning (CCL). It implements a family of data-driven methods that are capable of (i) learning state-independent and -dependent constraints, (ii) decomposing the behaviour of redundant systems into task- and null-space parts, and (iii) uncovering the underlying null space control policy. It is a tool to analyse and decompose many everyday tasks, such as wiping, reaching and drawing. The library also includes several tutorials that demonstrate its use with both simulated and real world data in a systematic way. This paper documents the methods contained within the library, including the implementations of said methods in tutorials and associated helper methods. The software is made freely available to the community, to enable code reuse and allow users to gain in-depth experience in statistical learning in this area.
A Probabilistic Fuzzy Table Lookup Scheme with Negation Logic

The well known Fuzzy Table Lookup Scheme (FTLS) is extended here by introducing negation logic within a probabilistic fuzzy logic framework. The negation logic consists of contradictory negation, medium negation, and opposite negation. As shown here, this complements well the probabilistic framework with fuzzy logic by effectively utilizing the available information while also addressing higher aspects of uncertainty. In particular, it complements information in areas where there is either insufficient observations or the observations carry high ambiguity and inaccuracy. The probabilistic fuzzy framework further allows concurrent handling of both stochastic and deterministic uncertainties in the available information. The proposed approach is implemented on two classic function types, i.e. an increasing sinusoid and a pyramid step function, with fast versus slow changes as well as with few versus many observations. Results indicate the improved performance of the proposed PFTLSwNL by up to 60% at particularly higher noise levels.