scholarly journals User-Adaptive Key Click Vibration on Virtual Keyboard

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Seokhee Jeon ◽  
Hongchae Lee ◽  
Jiyoung Jung ◽  
Jin Ryong Kim
Keyword(s):  
Mobile Device ◽  
Tactile Feedback ◽  
User Preferences ◽  
User Preference ◽  
Performance Study ◽  
Mobile Environment ◽  
Virtual Keyboard ◽  
User Groups ◽  
Environment Study ◽  
Future Work

This study focuses on design of user-adaptive tactile keyboard on mobile device. We are particularly interested in its feasibility of user-adaptive keyboard in mobile environment. Study 1 investigates how tactile feedback intensity of the virtual keyboard in mobile devices affects typing speed and user preference. We report how different levels of feedback intensity affect user preferences in terms of typing speed and accuracy in different user groups with different typing performance. Study 2 investigates different tactile feedback modes (i.e., whether feedback intensity is linearly increased, linearly decreased, or constant from the centroid of the key, and whether tactile feedback is delivered when a key is pressed, released, or both pressed and released). We finally design and implement user-adaptive tactile keyboards on mobile device to explore the design space of our keyboards. We close by discussing the benefits of our design along with its future work.

Usability testing for a community college library website

2019 ◽  
Vol 36 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Alyssa M. Valenti
Keyword(s):  
Web Design ◽  
Usability Testing ◽  
User Preferences ◽  
User Preference ◽  
Full Time ◽  
Content Type ◽  
Usability Tests ◽  
User Groups ◽  
Testing Case ◽  
First Time

Purpose This paper details a usability testing case study on a simplified homepage for [Library]. The usability testing was completed in Spring 2017 to meet the needs of diverse user groups and shifting trends in Web design and development. At the conclusion of the usability testing, recommendations for change informed the design decisions and a new homepage was implemented in October 2018. Design/methodology/approach The researcher performed eight usability tests with a combination of the different library user types; full-time faculty, students, an administrator and members of the public. The usability test consisted of 13 specific tasks. After testers completed the tasks, users filled out a 30-question Likert-scale questionnaire and answered a set of 8 open-ended questions. Findings This paper discusses the recommendations for change which the researcher discovered at the conclusion of the usability testing period. The research found the need to improve and include specific navigational, visual and easy-to-use elements to best meet the needs of the users in the usability tests. Changes were ranked and implemented on a scale of catastrophic to cosmetic. Research limitations/implications As websites, technology and user preferences continually evolve, the homepage will need to be tested for usability again in the next several years. Researchers are encouraged to adapt the methods to their own institutions. Practical implications This paper discusses findings specific to [Library], which in turn has proved to increase usage of certain features and functions by the user community. Originality/value This is the first time usability testing has been done for the [Library’s] website. It was the first time the design of the homepage was informed by real user preference. This paper is valuable to those looking to create a simple, easy-to-use homepage that best benefits their own unique community of users.

scholarly journals Predicting Implicit User Preferences with Multimodal Feature Fusion for Similar User Recommendation in Social Media

2021 ◽  
Vol 11 (3) ◽  
pp. 1064
Author(s):  
Jenq-Haur Wang ◽  
Yen-Tsang Wu ◽  
Long Wang
Keyword(s):  
Social Media ◽  
Feature Fusion ◽  
Relevant Information ◽  
Image Features ◽  
User Preferences ◽  
User Preference ◽  
Late Fusion ◽  
Multimodal Features ◽  
Fusion Methods ◽  
Text Features

In social networks, users can easily share information and express their opinions. Given the huge amount of data posted by many users, it is difficult to search for relevant information. In addition to individual posts, it would be useful if we can recommend groups of people with similar interests. Past studies on user preference learning focused on single-modal features such as review contents or demographic information of users. However, such information is usually not easy to obtain in most social media without explicit user feedback. In this paper, we propose a multimodal feature fusion approach to implicit user preference prediction which combines text and image features from user posts for recommending similar users in social media. First, we use the convolutional neural network (CNN) and TextCNN models to extract image and text features, respectively. Then, these features are combined using early and late fusion methods as a representation of user preferences. Lastly, a list of users with the most similar preferences are recommended. The experimental results on real-world Instagram data show that the best performance can be achieved when we apply late fusion of individual classification results for images and texts, with the best average top-k accuracy of 0.491. This validates the effectiveness of utilizing deep learning methods for fusing multimodal features to represent social user preferences. Further investigation is needed to verify the performance in different types of social media.

Time-aware cloud manufacturing service selection using unknown QoS prediction and uncertain user preferences

2021 ◽  
pp. 1063293X2110195
Author(s):  
Ying Yu ◽  
Shan Li ◽  
Jing Ma
Keyword(s):  
Time Series Prediction ◽  
Service Selection ◽  
Cloud Manufacturing ◽  
Service Evaluation ◽  
User Preferences ◽  
User Preference ◽  
Selection Methods ◽  
Qos Prediction ◽  
Qos Performance ◽  
Time Aware

Selecting the most efficient from several functionally equivalent services remains an ongoing challenge. Most manufacturing service selection methods regard static quality of service (QoS) as a major competitiveness factor. However, adaptations are difficult to achieve when variable network environment has significant impact on QoS performance stabilization in complex task processes. Therefore, dynamic temporal QoS values rather than fixed values are gaining ground for service evaluation. User preferences play an important role when service demanders select personalized services, and this aspect has been poorly investigated for temporal QoS-aware cloud manufacturing (CMfg) service selection methods. Furthermore, it is impractical to acquire all temporal QoS values, which affects evaluation validity. Therefore, this paper proposes a time-aware CMfg service selection approach to address these issues. The proposed approach first develops an unknown-QoS prediction model by utilizing similarity features from temporal QoS values. The model considers QoS attributes and service candidates integrally, helping to predict multidimensional QoS values accurately and easily. Overall QoS is then evaluated using a proposed temporal QoS measuring algorithm which can self-adapt to user preferences. Specifically, we employ the temporal QoS conflict feature to overcome one-sided user preferences, which has been largely overlooked previously. Experimental results confirmed that the proposed approach outperformed classical time series prediction methods, and can also find better service by reducing user preference misjudgments.

scholarly journals Evolutionary Social Poisson Factorizationfor Temporal Recommendation

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
ChunYan Yin ◽  
YongHeng Chen ◽  
Wanli Zuo
Keyword(s):  
Recommendation Systems ◽  
Predictive Performance ◽  
User Preferences ◽  
Social Model ◽  
User Preference ◽  
Latent Factors ◽  
Factorization Model ◽  
Real World Datasets ◽  
Item Attributes ◽  
The Impact

AbstractPreference-based recommendation systems analyze user-item interactions to reveal latent factors that explain our latent preferences for items and form personalized recommendations based on the behavior of others with similar tastes. Most of the works in the recommendation systems literature have been developed under the assumption that user preference is a static pattern, although user preferences and item attributes may be changed through time. To achieve this goal, we develop an Evolutionary Social Poisson Factorization (EPF$$\_$$ _ Social) model, a new Bayesian factorization model that can effectively model the smoothly drifting latent factors using Conjugate Gamma–Markov chains. Otherwise, EPF$$\_$$ _ Social can obtain the impact of friends on social network for user’ latent preferences. We studied our models with two large real-world datasets, and demonstrated that our model gives better predictive performance than state-of-the-art static factorization models.

scholarly journals Towards a Development Approach to Serious Games

2009 ◽  
pp. 215-231 ◽  
Author(s):  
Sara de Freitas ◽  
Steve Jarvis
Keyword(s):  
Game Design ◽  
Serious Games ◽  
Development Process ◽  
Development Project ◽  
Game Based Learning ◽  
Data Collection And Analysis ◽  
User Groups ◽  
Development Approach ◽  
Tools And Techniques ◽  
Future Work

This chapter reviews some of the key research supporting the use of serious games for training in work contexts. The review indicates why serious games should be used to support training requirements, and in particular identifies “attitudinal change” in training as a key objective for deployment of serious games demonstrators. The chapter outlines a development approach for serious games and how it is being evaluated. Demonstrating this, the chapter proposes a game-based learning approach that integrates the use of a “four-dimensional framework”, outlines some key games principles, presents tools and techniques for supporting data collection and analysis, and considers a six-stage development process. The approach is then outlined in relation to a serious game for clinical staff concerned with infection control in hospitals and ambulances, which is being developed in a current research and development project. Survey findings from the target user group are presented and the use of tools and techniques explained in the context of the development process. The chapter proposes areas for future work and concludes that it is essential to use a specific development approach for supporting consistent game design, evaluation and efficacy for particular user groups.

scholarly journals Health monitoring modular panel: Interface design and evaluation

2018 ◽  
Vol 22 (Suppl. 4) ◽  
pp. 1259-1270
Author(s):  
Milica Vujovic ◽  
Milan Ristanovic ◽  
Marko Milos ◽  
Francisco Perales-López
Keyword(s):  
Interface Design ◽  
The Elderly ◽  
Control Group ◽  
Processing Unit ◽  
Residential Unit ◽  
Lack Of Information ◽  
User Groups ◽  
Future Work ◽  
Functional Areas ◽  
Elderly Users

In this paper we present a conceptual solution of modular panel for measuring health parameters of the elderly. The conceptual solution was followed by a study that analyzed the design and evaluated interface of the system. Modular panel contains sensors, processing unit, and interface enabling data acquisition and communication between the user and the medical staff. Positioning of the panel within the residential unit was determined by the categories of actions which it should provide and functional areas of typical housing unit. Interface design is based on a specific type of users and is on the basis of the type of data that should be collected and displayed. Evaluation of interface is conducted by using two user groups, where the first is made up of people older than 60 years and represents the interest group of the study, while the second group consisted of people younger than 60 years as the control group. The collected data were analyzed and the results indicate that the simplicity of the interface suits good to the users. Elderly users need more time to conduct certain commands, but most of them understood interface completely. The limitations of the system, such as lack of information provided for the users, will be considered in the future work.

Intelligent Store Agent for Mobile Shopping

2013 ◽  
pp. 322-335
Author(s):  
Ryan Anthony Brown ◽  
Suresh Sankaranarayanan
Keyword(s):  
Mobile Device ◽  
Intelligent Agent ◽  
User Preferences ◽  
Purchase Decision ◽  
Human Being ◽  
User Agent ◽  
Agent Based ◽  
Two Agents ◽  
Mobile Shopping ◽  
Performance Results

The conventional shopping process involves a human being visiting a designated store and perusing first the items available. A purchase decision is then made based on the information so gathered. However, a number of unique challenges a human shopper would face, if he/she prefers to execute this process using a mobile device, such as a phone. Taking this aspect into consideration, the authors propose the use of an Intelligent Agent for performing the Mobile Shopping on behalf of customers. In this situation, the agents gather information about the products through the use of ‘Store Coordinator Agents’ and then use them for comparing with the user preferences. The proposed agent based system is composed of two agents, viz., a User Agent and Store Coordinator Agent. The implementation of the scheme so proposed has been done using JADE-LEAP development kit and the performance results are discussed in the paper.

The Effect of 5 Mechanical Gaming Keyboard Key Switch Profiles on Typing and Gaming Muscle Activity, Performance and Preferences

2018 ◽  
Vol 62 (1) ◽  
pp. 1552-1556
Author(s):  
Charles Miller ◽  
Alan Barr ◽  
Raziel Riemer ◽  
Carisa Harris
Keyword(s):  
Task Performance ◽  
Muscle Activity ◽  
Word Processing ◽  
Tactile Feedback ◽  
User Preference ◽  
Displacement Curve ◽  
Study Task ◽  
Activity Performance ◽  
Linear Force ◽  
Force Displacement

Introduction:Single force-displacement characteristics of mechanical key switches have been shown to affect performance, fatigue and discomfort during keyboard use. This study compared the effects of mechanical key switches with differing force-displacement characteristics on forearm muscle activity, typing performance, Fitts Study task performance, subjective fatigue and user preference. Methods: Using a within subjects intervention study of crossover design, 64 subjects completed modified Fitts and typing tasks on five different mechanical key switches to mimic dual word processing and gaming keyboard use. Bilateral muscle activity was recorded using surface electromyography (EMG); typing and Fitts task performance measures were tracked. Results: The key switch with a linear force displacement curve had higher net strokes and lower net typing speed than two key switches with tactile feedback (p<0.05). The key switch with the longest tactile travel, operating travel and highest bottom force required slightly higher peak muscle activity compared to 2 other key switches with lower values (p<0.05). Key switches with shorter tactile and operating travel and lower bottom forces were preferred over other key switches.Conclusions: Among mechanical key switches, a linear force displacement curve had the worst outcomes; key switches with shorter tactile (1.2mm) and operating travel (2.0mm) and a lower bottom force (35-40g) had best outcomes overall.

Personalisation in Highly Dynamic Grid Services Environments

2009 ◽  
pp. 284-313
Author(s):  
Edgar Jembere ◽  
Matthew O. Adigun ◽  
Sibusiso S. Xulu
Keyword(s):  
User Preferences ◽  
User Preference ◽  
Grid Services ◽  
Context Aware Computing ◽  
Preference Representation ◽  
Use Of Services ◽  
Order Preference ◽  
Computing Environments ◽  
Preference Models ◽  
Intuitive Interpretation

Human Computer Interaction (HCI) challenges in highly dynamic computing environments can be solved by tailoring the access and use of services to user preferences. In this era of emerging standards for open and collaborative computing environments, the major challenge that is being addressed in this chapter is how personalisation information can be managed in order to support cross-service personalisation. The authors’ investigation of state of the art work in personalisation and context-aware computing found that user preferences are assumed to be static across different context descriptions whilst in reality some user preferences are transient and vary with changes in context. Further more, the assumed preference models do not give an intuitive interpretation of a preference and lack user expressiveness. This chapter presents a user preference model for dynamic computing environments, based on an intuitive quantitative preference measure and a strict partial order preference representation, to address these issues. The authors present an approach for mining context-based user preferences and its evaluation in a synthetic m-commerce environment. This chapter also shows how the data needed for mining context-based preferences is gathered and managed in a Grid infrastructure for mobile devices.

Interactive Product Catalog for M-Commerce

2007 ◽  
pp. 345-351
Author(s):  
S. Guan ◽  
Y. Tay
Keyword(s):  
Mobile Device ◽  
Recommender System ◽  
Personal Digital Assistant ◽  
Detection Technique ◽  
User Preference ◽  
Interactive Product ◽  
Product Appearance

We propose a product catalog where browsing is directed by an integrated recommender system. The recommender system is to take incremental feedback in return for browsing assistance. Product appearance in the catalog will be dynamically determined at runtime based on user preference detected by the recommender system. The design of our hybrid m-commerce catalog-recommender system investigated the typical constraints of m-commerce applications to conceptualize a suitable catalog interface. The scope was restricted to the case of having a personal digital assistant (PDA) as the mobile device. Thereafter, a preference detection technique was developed to serve as the recommender layer of the system.

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