Reviewerly/exHarmony huggingface dataset:
Research on Museum Online User Classification and Evaluation Model Based on the Fogg’s Behavior Model

The main purpose of this paper is to construct the evaluation index of the system and study the degree of influence of the main factors affecting the design of mobile music app from the perspective of user experience. By means of literature research and questionnaire survey, the index system of influencing factors of user experience was established, and the fuzzy DEMATEL model was established. This paper constructs an index system of influencing factors of mobile phone music app user experience, which includes five dimensions (reliability, usability, ease of use, interaction, security) and 19 influencing factors. The 10 key factors affecting the mobile music App user experience were identified, namely, music richness, personalized service, listening function, copyright information protection, interpersonal interaction, system performance, personal information protection, audio quality, interface design and payment protection. Finally, the index system of influencing factors of mobile music App user experience is constructed, and the importance of influencing factors of mobile music App user experience is analyzed.
Towards a Customizable Usage Requirements Cycle

Usage orientation is an essential success factor in development processes. Especially in the mobility sector, development processes concern product combinations of software, hardware and services. These processes require interdisciplinary teams, but both the procedure and the results are shaped by the respective disciplines, such as software development, product design or marketing, and are difficult to access for outsiders. Thus, the flexible application of user-oriented methods remains difficult. The aim of this paper is a scalable process model that flexibly supports the integration of user-oriented methods in development processes and is oriented towards the needs of interdisciplinary development teams. The findings conclude in a concept of a usage requirements cycle supporting the integration of usage requirements especially in the mobility sector.
Giving Alexa a Face - Implementing a New Research Prototype and Examining the Influences of Different Human-Like Visualizations on the Perception of Voice Assistants

Since the introduction of the Amazon Echo, smart speakers have increasingly found their way into private households. What if the voice assistant could not only be heard but also seen? How would people then evaluate smart speakers? Based on the trend that smart speakers will start to integrate or even become displays, this article (1) presents a research prototype of a visualized smart speaker and (2) investigates how people perceive a visualized voice assistant (VA) by comparing three different human-like visualizations of the prototype. A software solution using Unity combined with a commercial smart speaker makes it possible to visualize the speech assistant. The prototype can record the interaction with the VA without sending sensitive data to the VA provider. We created three visualizations of a VA differing in their amount of human-like facial features based on this prototype. The online study with 51 participants reveals that visualizations with more facial features were perceived significantly more human-like than visualizations with fewer features. Furthermore, our results indicate that perceived anthropomorphism significantly influences how other human-like characteristics are attributed to the visualizations. Overall, our study gives initial insights into the growing segment of visualized VAs with implications for future cases of use and design.
The Effect of Academic Stress, Learning Modes, Learning Motivation on Student Learning Achievement

This study aims to find out the relationship between the influence of academic stress, learning methods, learning motivation on student achievement. The method used is a quantitative method. The population used is students from various schools. Data was collected by online questionnaire. Data analysis using Smart PLS Version 3.2.9 (Partial Least Square) application.There are several objectives to be achieved in this study, such as knowing how the influence of academic stress on students' motivation to learn, then how the influence of academic stress on student achievement, then how also the influence of learning methods on student motivation and how the influence of learning methods on achievement results students, and the last is the purpose of this study, namely how learning motivation affects student achievement.
Episodic and Semantic Memory for Interactions with Voice-Based Conversational Agents: Developing an Integrative Model of Technology Engagement and Cognitive Elaboration

An integrative perspective is developed on memory performance in the context of voice-based conversational agents (VCAs). Memory, as an outcome of human-technology interaction, matters where technology is designed to enhance knowledge and to function as a cognitive support tool and aide. To date, this has meant focusing on the evaluation of specialist applications such as assistive and educational technologies. The increased use of VCAs in everyday life, in the form of smart speakers or as assistants for mobile phones, however, makes effects on memory more relevant to a large user base. Adding VCAs to multi-functional devices such as smartphones increases their potential to be taken as digital companions and to make interactions with them more meaningful. This has implications for different types of memory, semantic and episodic/autobiographical. Since memory is not stable, but malleable and context-dependent, its performance is likely to be influenced by the wide range of social cues that VCAs can convey. Further, VCAs can be expected to contribute to conversational engagement, the general state of involvement in a conversational setting, which in turn influences cognitive outcomes.
Chatbot Based Human Interaction Model for Food Ordering System

As we know that time is money, and each company needs to deploy an Artificially Intelligent system in order to interact with the customers. The same functionality is served by the Chatbot. It is a human designed but artificially trained system which is used to give an ease to the human interaction. By deploying this Chatbot to the website main purpose served would be to collect the database of maximum customers visiting our website every day. This database thus received would serve as the medium of interaction between the food outlet and the customers. Customers will also feel convenient in ordering with a more interactive platform rather than manual ordering. Thus, this will lead to generate an enhanced number of customers and thus we could lead to get details example phone number and email id of various customers and directly interact with them with a decreased human assistance
Automatic Classification of Working Activities for Risk Assessment in Large-Scale Retail Distribution by Using Wearable Sensors: A Preliminary Analysis

Providing reliable information on human activities and behaviors is an extremely important goal in various application areas such as healthcare, entertainment, and security. Within the working environment, a correct identification of the actual performed tasks can provide an effective support in the assessment of the risk associated to the execution of the task itself, and thus preventing the development of work-related musculoskeletal diseases. In this perspective, wearable-based Human Activity Recognition systems have been representing a prominent application. This study aimed to compare three different classification approaches appointed from supervised learning techniques, namely k-Nearest Neighbors, Support Vector Machine and Decision Tree. Motion data, related to several working activities realized in the large-scale retail distribution, were collected by using a full-body system based on 17 Inertial Measurement Units (MVN Analyze, XSens). Reliable features in both time- and frequency-domain were first extracted from raw 3D accelerations and angular rates data, and further processed by Principal Component Analysis, with 95% threshold. The classification models were validated via 10-fold cross-validation on a defined training dataset. k-Nearest Neighbors classifier, which provide the best results on the training session, was eventually tested for generalization on additional data acquired on few specific tasks. As a result, considering 5 main macro activities, k-Nearest Neighbors provided a classification accuracy of 80.1% and a computational time of 1865.5 s. To test the whole assessment process, the activities labelled by the classification model as handling of low loads at high frequency were automatically evaluated for risk exposure via OCRA Checklist method.
A Study on Projection-Based Public Displays that Attract People with Peripheral Vision

In this paper, we propose a presentation position determination method to improve the noticeability of information presentation in floor and wall projection public displays. Two types of information presentation methods: fixed position presentation and moving presentation, and four parameters: distance to the person, distance from the gazing point, difference in position, and direction of movement from other moving information, were considered and evaluated in this study. The experimental results showed that the method using the distance to the person was the most effective in both presentation methods.
Ease of Use and Preferences Across Virtual Reality Displays

Head mounted displays have become popular, but it is uncertain whether the interactive quality of these systems is sufficient for educational and training applications. This work is a longitudinal study into a variety of VR systems, which examines interface restrictions, ease of use, and user preferences with an emphasis on educational settings. Four different systems were examined with a range of interaction elements. Certain interactions failed in some systems and users did not necessarily prefer the highest-end systems. Overlapping interaction elements were also discovered, which may direct future work in later interaction test suites.
Dance Through Visual Media: The Influence of COVID-19 on Dance Artists

Among the arts, dance is regarded as a “dynamic spatiotemporal art using the body as a medium,” and it is considered better to appreciate it live [10]. By appreciating dance work live, the theme, movement, and impressions of the work are communicated [12]. However, because of the spread of COVID-19, the first emergency state was declared in Japan in March 2020. Under these circumstances, theaters were closed because of the risk of infection, and all dance performances were cancelled. Live dance appreciation is no longer possible, and dance performances using visual media have soared. Therefore, to clarify how Japanese dance artists have shifted to video distribution in response to the spread of COVID-19 and how this shift has been perceived, we first conducted semi-structured interviews with dance artists who have engaged in video distribution of dance owing to the spread of COVID-19. The interview revealed the merits and demerits of video-delivering dance, problems that emerged, points particular to video-delivering dance, and new physical sensations obtained by video-delivering dance. Based on these results, we suggest room for improvement and then discuss how to provide better computer support.
Algorithmic Fairness and Service Failures: Why Firms Should Want Algorithmic Accountability

The past years have witnessed growing consumer concern about the fairness implications of the widespread adoption of AI in business contexts. To protect consumers against bias from algorithmic service decisions, regulators have introduced legislation holding firms accountable for the fairness of their algorithmic decisions. However, regulators have yet to invest in the systematic monitoring of algorithmic fairness. Our research reveals the unintended consequences of this policy: we show that the resulting lack of algorithmic accountability inhibits consumers’ ability to assess firms’ degree of compliance with algorithmic fairness criteria, thereby reducing firms’ ability to manage consumer expectations. We posit that consumers gather information about firm actions from their immediate social network. We model how beliefs of bias may propagate within the market – even if the firm is using a fair algorithm. We show that, paradoxically, the lack of algorithmic accountability may lead to a divergence between consumer perceptions and the judicial view regarding the fairness of firm actions. Under certain conditions, a firm with a fair algorithm can be perceived by the population as less fair than a firm with an unfair algorithm. We also demonstrate how a watchdog institution may correct these misperceptions of bias.
FLAS: A Platform for Studying Attacks on Federated Learning

Smartphones have become a part of everyday life, and users are contributing to Machine Learning with a simple touch (ML). Federated Learning (FL) is a new collaborative learning technique that preserves privacy and addresses the problem of traditional ML. Despite this, it has a large attack surface area and is vulnerable to privacy attacks. Studying the impact of such attacks on the resulting FL models is an important research topic. Currently, there is a lack of an experimental platform to conduct such studies. We attempt to bridge this gap in this paper by proposing the Federated Learning Attack Simulation (FLAS) platform. It is a web-based application designed with an easy-to-use workflow for non-experts and the ability to accelerate testing and analysis for Federated Learning (FL) professionals. Preliminary evaluations have demonstrated the effectiveness of FLAS in supporting the study of common privacy attacks on FL.
Complex Multiplication Model for Circular Regression

In this chapter, we treat circular data as unit complex numbers and model their correlation by multiplying them in the complex plane. We propose a general complex multiplication framework for circular regression. The proposed framework is very flexible in the sense that several different models can be included as special cases. Based on the linear relationship, a linear complex multiplication model is introduced and discussed in detail. Our estimation and inference results are presented together with a simulation study and two real data examples and are compared with those of two existing methods.
Learning Robot Manipulation Skills From Human Demonstration Videos Using Two-Stream 2-D/3-D Residual Networks With Self-Attention

Learning manipulation skills from observing human demonstration video is a promising aspect for intelligent robotic systems. Recent advances in Video-to-Command (V2C) provide an end-to-end approach to translate a video into robot plans. However, simultaneous V2C and action segmentation remain a major challenge for bimanual manipulations with fine-grained actions. Another concern is the generalization capability of end-to-end approaches in dealing with varied task parameters as well as environmental changes between the learned skills and the one-shot task demonstration for the robot to replay. In this article, we propose a two-stream network for robots to learn and segment manipulation subactions from human demonstration videos. Our framework with the self-attention mechanism can segment learned skills and generate action commands simultaneously. To arrive at refined plans in situations of underspecified or redundant human demonstrations, we utilize PDDL-based skill scripts to model the semantics of demonstrated activities and infer latent movements. Experimental results on the extended manipulation data set indicate that our approach generates more accurate commands than the state-of-the-art methods. Real-world experiment results on a Baxter robotic arm also demonstrated the feasibility of our method in reproducing fine-grained actions from video demonstrations.
Quantitative Progress Evaluation for Open Source Project with Application to Bullseye Chart

Open source software (OSS) are used under the various situations, because OSS are useful for many users to make cost reduction, standardization, and quick delivery. While OSS have many advantages, it is difficult for us to quantitatively grasp the progress of development such as general software development because OSS are developed by many different people around the world. In general software development, Earned Value Management (EVM) is often used to quantitatively evaluate the progress of a project. However, it is difficult to apply EVM directly in terms of the characteristic of the open source project. Therefore, EVM in open source projects has not been sufficiently researched. In this paper, we apply the progress data of open source projects to EVM. We also try to derive the bullseye chart by using the Cost Performance Index (CPI) and Schedule Performance Index (SPI) known as the indexes of EVM. Also, we apply the derivation method discussed in this paper to actual open source project data in order to quantitatively evaluate the project.
Hybrid Dark Channel Prior for Image Dehazing Based on Transmittance Estimation by Variant Genetic Algorithm

Image dehazing has always been one of the main researches in image processing. In order to obtain haze-free images with high quality, a Hybrid Dark Channel Prior (HDCP) algorithm is proposed in this paper. HDCP first employs Retinex to remove the interference of the illumination component. The is then used to obtain the guidance image required by the guided filter to optimize the atmospheric transmittance. Finally, the Modified Dark Channel Prior algorithm is used to obtain the dehazed image. Compared with other 3 modified DCP algorithms, HDCP has the best subjective visual effects of haze removal and color fidelity. HDCP also shows the superior objective indexes in the mean square error ( MSE ), peak signal-to-noise ratio ( PSNR ), structural similarity index ( SSIM ) and information entropy ( E ) for different haze degrees.
Modelling Circular Time Series with Applications

Circular time series modelling has posed a big challenge to many researchers due to the nature of observations. This article traces different circular time series models and the methods adopted in the development. It goes further to illustrate the use of these methods and the resultant circular time series models of wind direction data. The application involved hourly data on wind direction for the two major seasons (wet and dry seasons) in Nigeria. Training and development of the models are done using R codes and SPSS version 24. The results shows that score-driven models based on von Misses distribution is the best model using some error statistics and goodness of fit tests.
Design and Evaluation of a Programming Tutor Based on an Instant Messaging Interface

Computer programming skills are essential for a variety of disciplines in the fields of Science, Technology, Engineering and Mathematics (STEM). To support learning of programming in personalized ways, Programming Tutors (PTs) have been utilized in higher education contexts for decades. However, implementation of PTs with mobile devices has remained unexplored from both design and adoption standpoints. In this research, we designed, implemented, and trialled a PT based on Telegram Messenger. The tutor was introduced to a cohort of engineering freshmen ( $$N=227$$ ) in a computer programming course; however, it was introduced as an optional learning tool with no extrinsic incentives for fostering its adoption. Under these conditions, students' activity with the tool was monitored for 11 days. A total of 99 students ( $$44.7\%$$ ) chose to use the tutor and did so at least once. In average, students who used the tutor solved 9.2 tasks out of a maximum of 18 ( $$SD=5.49$$ ). The use of notifications was found highly influential to motivate tutor use: within 3 days of a student receiving a reminder notification, 80.6% of responses to tasks were registered. The tutor scored a mean of 73.8 ( $$SD=13.81$$ ) in the Systems Usability Scale. Students suggestions for improvement emphasized the need for more elaborate feedback after submitting their responses to problems and leveling of task difficulty. Regarding the tutor's best features, mobility and ease of use of the interface were found the most prominent.
Shopping in the Dark

Dark patterns are user interfaces designed to trick users into doing things they might not otherwise do. Human psychological insights are carefully exploited by designers to craft these patterns. This study investigates the relation between dark pattern recognition and platform choice. An experiment was designed in which 54 participants performed a shopping task. In the website different dark pattern types were implemented, such as "Sneak into Basket", "Toying with emotions" and "Trick Questions". Results showed that mobile users are twice as likely to fall for one of the patterns. In addition, a significant correlation was found between falling for that same dark pattern and the age of users. The older the user, the more chance of falling for that pattern. Lastly it showed that the higher the website's "honesty" is rated, the higher the "navigability" is rated.
Multiple Perspectives Co-creation Process and Human-Centered Scenario-Oriented Activity Design

Design is changing. The object of design has changed from users and products to customers and services, from the interface of products to the touch points of the journey, and from the interaction of digital products to activities in the digital world. In the past, the division of labor in which the designer made the design first, and then provided the design to the user after completion, will become a continuous co-creation activity between the customer and the designer in the future. Scenario was originally a design tool, but now it has become a design object. This article explores how to carry out human-centered, Scenario-Oriented Activity Design through a multi-perspective co-creation process under such changes. This paper proposes a framework for connecting activities and scenarios with narratives. The visual narrative combined with the guidance method of switching from multiple perspectives allows the co-creation team to generate multiple empathy, and conduct usage activities, design activities, and facilitating activities at the same time. This paper also proposes a co-creative narrative guiding structure O-S-M-C that echoes the dynamic sequence of activity theory to assist the design of innovative activities. This paper also illustrates with practical examples.
In Sync! Design for Social Connection

Participatory approaches are increasingly used in solving complex design challenges. Workshop facilitation is a key aspect of many co-design projects bringing together heterogeneous groups of people to connect share and co-create. Developing high quality social connections across the group and recognizing when they occur can be highly beneficial to workshop engagements and outcomes. Such connections between people are often established through temporal aspects of non-verbal communication, known as behavioral synchrony. The aim of this study was to examine whether high quality connections could be facilitated in dyads engaged in set-tasks that involved 'doing' and 'making' activities. The results showed that all activities evoked behavioral synchrony, high-quality connections, and positive emotions, but the extent and type of connection differed according to the activity. This paper proposes that non-verbal communication and behavioral synchrony should be considered in designing for social connection between co-design participants and suggests a framework for its use in design and evaluation.