Satisfactory User Experience Research Articles

CrowdDBS: A Crowdsourced Brightness Scaling Optimization for Display Energy Reduction in Mobile Video

Mobile display has become one of the most power-hungry components in mobile video viewing. Currently, mobile devices can reduce the display energy by performing dynamic brightness scaling (DBS) under the distortion constraint of video signals. We observe that there is a pitfall preventing current practice from systematic display energy reduction. In particular, existing objective DBS schemes lack direct connection to the subjective human perception on DBS-enabled videos, which is the key to achieving human-centered energy-experience optimization. To overcome this pitfall, we present CrowdDBS, a crowdsourced display energy reduction framework for mobile video viewing. CrowdDBS is empowered by a set of crowdsourcing studies that uncover the relationship between human perception and DBS frequency, magnitude, and temporal consistency, respectively. Motivated by the insights obtained from these studies, CrowdDBS employs a suit of designs and a DBS optimization framework to optimize the energy-experience tradeoff in mobile video viewing. Comprehensive experimental results and user evaluations under a variety of practical settings show that CrowdDBS can achieve 37 percent device energy reduction on average while guaranteeing satisfactory user experience in mobile video viewing.

Efficient query-by-example spoken document retrieval combining phone multigram representation and dynamic time warping

Query-by-example spoken document retrieval (QbESDR) aims at finding those documents in a set that include a given spoken query. Current approaches are, in general, not valid for real-world applications, since they are mostly focused on being effective (i.e. reliably detecting in which documents the query is present) but practical implementations must also be efficient (i.e. the search must be performed in a limited time) in order to allow for a satisfactory user experience. In addition, systems usually search for exact matches of the query, which limits the number of relevant documents retrieved by the search. This paper proposes a representation of the documents and queries for QbESDR based on combining different-sized phone n-grams obtained from automatic transcriptions, namely phone multigram representation. Since phone transcriptions usually have errors, several hypotheses for the query transcriptions are combined in order to ease the impact of these errors. The proposed system stores the document in inverted indices, which leads to fast and efficient search. Different combinations of the phone multigram strategy with a state-of-art system based on pattern matching using dynamic time warping (DTW) are proposed: one consists in a two-stage system that intends to be as effective but more efficient than a DTW-based system, while the other aims at improving the performance achieved by these two systems by combining their output scores. Experiments performed on the MediaEval 2014 Query-by-Example Search on Speech (QUESST 2014) evaluation framework suggest that the phone multigram representation for QbESDR is a successful approach, and the assessed combinations with a DTW-based strategy lead to more efficient and effective QbESDR systems. In addition, the phone multigram approach succeeded in increasing the detection of non-exact matches of the queries.

Real Time ML-Based QoE Adaptive Approach in SDN Context for HTTP Video Services

Due to the high dynamism of network conditions, operators and service providers are facing the challenge of providing satisfactory user experience during a real-time video streaming session where clients are often suffering from frequent interruptions and significant visual quality degradation. Video parameters such as playback quality, rate switching amplitude/frequency, occupancy, overflow/underflow buffer are the main key factors responsible for affecting the user experience’s quality. Recently, adaptive streaming protocols over HTTP have become widely adopted for providing continuous video streaming services to users with their different heterogeneous devices under dynamic network conditions. In this paper, we leverage the emerging paradigm of software defined networking SDN. Our contribution consists in developing some scenarios on SDN helping to adapt video streaming to the network state. The current work proposes to experience ML algorithms in order to predict user QoE over SDN networks. We present an approach that collects MOS score from users under varying network parameters as well as objective parameters such as SSIM, VQM and PSNR. The MOS scores are collected by playing videos to actual users in an SDN environment. We design an architecture which could use the measured MOS values under varying network conditions to predict the expected MOS based on machine learning algorithms. This work provides an outlook of experiments done for demonstration, by describing SDN environment deployment, detailing the realized scenarios and finally giving the results and values. We highlight, at the end of this paper, the perspectives of our proposition.

Effect of Mobile Device–Supported Single-Patient Multi-crossover Trials on Treatment of Chronic Musculoskeletal Pain

Individually designed single-patient multi-crossover (n-of-1) trials can facilitate tailoring of treatments directed at various conditions, including chronic musculoskeletal pain (CMSP) but are potentially burdensome, which may limit uptake in research and practice. To determine whether patients randomized to participate in an n-of-1 trial supported by a mobile health (mHealth) app would experience less pain and improved global health, adherence, satisfaction, and shared decision making compared with patients assigned to usual care. This randomized clinical trial compared participation in an individualized, mHealth-supported n-of-1 trial vs usual care. The participating 215 patients had CMSP for at least 6 weeks, had a smartphone or tablet with a data plan, were enrolled in northern California from July 2014 through July 2016, and were followed for up to 1 year by 48 clinicians in academic, community, Veterans Affairs, and military settings. Intervention patients met with their clinicians and used a desktop interface to select treatments and trial parameters for an n-of-1 trial comparing 2 pain-management regimens. The mHealth app provided reminders to take designated treatments on assigned days and to upload responses to daily questions on pain and treatment-associated adverse effects. Control patients received care as usual. The primary outcome was change in the PROMIS (Patient-Reported Outcomes Measurement Information System) pain-related interference 8-item short-form scale (full scale range, 41-78) from baseline to 6 months. Secondary outcomes included patient-reported pain intensity, overall health, analgesic adherence, trust in clinician, satisfaction with care, medication-related shared decision making, and, for the n-of-1 group only, participant engagement and experience. Among 215 patients (108 randomized to the n-of-1 intervention and 107 to control), 102 (47%) were women, and the mean (SD) age was 55.5 (11.1) years. At the 6-month follow-up, pain interference was reduced in both groups, though there was no difference between the intervention and control groups (-1.36 points; 95% CI, -2.91 to 0.19 points; P = .09). There were no advantages in secondary outcomes for intervention patients vs control patients except for higher medication-related shared decision making at 6 months (between-group difference, 11.9 points; 95% CI, 2.6-21.2 points; P = .01). Among patients assigned to the n-of-1 group, 88% (n = 86) affirmed that the mHealth app could help people like them manage their pain. In this population of patients with CMSP, mHealth-supported n-of-1 trials were feasible and associated with a satisfactory user experience, but n-of-1 trial participation did not significantly improve pain interference at 6 months vs usual care. ClinicalTrials.gov identifier: NCT02116621.

A self-scalable distributed network simulation environment based on cloud computing

While parameter sweep simulations can help undergraduate students and researchers to understand computer networks, their usage in the academia is hindered by the significant computational load they convey. This paper proposes DNSE3, a service oriented computer network simulator that, deployed in a cloud computing infrastructure, leverages its elasticity and pay-per-use features to compute parameter sweeps. The performance and cost of using this application is evaluated in several experiments applying different scalability policies, with results that meet the demands of users in educational institutions. Additionally, the usability of the application has been measured following industry standards with real students, yielding a very satisfactory user experience.

On the Goodness of Using Orthogonal Channels in WLAN IEEE 802.11 in Realistic Scenarios

Due to the high density of Wi‐Fi networks, especially in the unlicensed 2.4 GHz frequency band, channel assignment has become a critical duty for achieving a satisfactory user experience. Probably, the main peculiarity of Wi‐Fi networks is the partial overlap of the radio channels that can be used by access points. For that reason, a number of works avoid cochannel interferences by using only channels which are far enough from each other to have no interferences, the so‐called orthogonal channels. However, there is a range of choices between using the whole spectrum and using only orthogonal channels. In this work we evaluate the influence of the choice of channel set in realistic settings, using both optimization and heuristic approaches. Results show that the optimizer is not able to achieve better results when using the whole spectrum instead of restricting to only the orthogonal channels. In fact, the optimizer uses mainly the orthogonal channels when they are available, while the heuristics considered lose performance when more channels are available. We believe this insight will be useful to design new heuristics for Wi‐Fi channel assignment.

T2FA: Transparent Two-Factor Authentication

Traditional username and password-based single-factor authentication is easy to deploy but vulnerable to dictionary attacks, snooping, and brute force attacks. Two-factor authentication (2FA) has been proposed to improve the security, where smart devices are used as the second authentication factor. However, the interaction between human and the smart device is required, which is inconvenient to users. In addition, an attacker is able to get the second authentication factor through fraud, thus invalidating current 2FA mechanisms. In order to solve these problems, we propose a transparent two-factor authentication (T2FA) based on physical unclonable function (PUF) and voiceprint. The second authentication authenticates the user's mobile phone through the PUF. The third one is to determine whether the login terminal and the user's mobile phone are in the same environment with the environment voiceprint. The second and third authentication in the second factor is completely transparent to users. Therefore, T2FA avoids the tedious interaction and provides the same high user experience satisfaction as the single-factor authentication and exhibits high security simultaneously. Moreover, the fraud is eliminated technically due to the transparency of the authentication.

SWAROVsky: Optimizing Resource Loading for Mobile Web Browsing

Imperfect Web resource loading prevents mobile Web browsing from providing satisfactory user experience. In this article, we design and implement the SWAROVsky system to address three main issues of current inefficient Web resource loading: (1) on-demand and thus slow loading of sub-resources of webpages ; (2) duplicated loading of resources with different URLs but the same content ; and (3) redundant loading of the same resource due to improper cache configurations . SWAROVsky employs a dual-proxy architecture that comprises a remote cloud-side proxy and a local proxy on mobile devices. The remote proxy proactively loads webpages from their original Web servers and maintains a resource loading graph for every single webpage. Based on the graph, the remote proxy is capable of deciding which resources are “really” needed for the webpage and their loading orders, and thus can synchronize these needed resources with the local proxy of a client efficiently and timely. The local proxy also runs an intelligent and light-weight algorithm to identify resources with different URLs but the same content, and thus can avoid duplicated downloading of the same content via network. Our system can be used with existing Web browsers and Web servers, and does not break the normal semantics of a webpage. Evaluations with 50 websites show that on average our system can reduce the page load time by 43.1 percent and the network data transmission by 57.6 percent, while imposing marginal system overhead.

User-aware partitioning algorithm for mobile cloud computing based on maximum graph cuts

Partitioning and offloading of mobile applications have been demonstrated as a promising approach that not only enhances the performance but also extends the battery life of Smart Mobile Devices (SMDs) effectively. Researchers have proposed four factors that affect the partitioning decision: device (hardware), application (software), developer and user. However, most existing research efforts focus on the first three factors and pay little attention to the influence of user preferences on the partitioning decision. Among these factors, user preference usually affects user experience most. Moreover, previous work which took into account the other factors cannot generate adaptive partitioning results. In this work, we propose a user-aware partitioning algorithm to offer a personalized and precise partitioning plan for better user experience. Based on machine learning methods, we first propose a user profile model for characterizing the preferences of different phone users. In addition, a novel cost evaluation model (called CMET model) is proposed to evaluate the comprehensive offloading costs in terms of CPU & memory utilization, time cost and energy consumption. Finally, we propose a Max-Cuts partitioning algorithm based on Branch-and-Bound search to obtain the optimal partitioning plan. Experimental results demonstrate that for different types of phone users, our partitioning algorithm could effectively improve corresponding performances that they concern about, and achieve the most satisfactory user experience compared with state-of-the-art approaches.

Gaming on the Rift: How Virtual Reality Affects Game User Satisfaction

Virtual reality (VR) headsets like the Oculus Rift, HTC Vive, and PlayStation VR can provide a unique experience different from traditional computer monitors. Research demonstrates some support for VR resulting in more immersive gaming than traditional games using a computer or TV monitor. This study investigates how VR technology impacts game user satisfaction. Participants played the same strategy video game using the Oculus Rift, a VR headset, and a computer monitor. Game user satisfaction was measured by the psychometrically validated Game User Experience Satisfaction Scale (GUESS) which consists of nine constructs. Results from this study showed that VR enhanced overall satisfaction, enjoyment, engrossment, creativity, sound, and graphics quality.

Neighboring vehicle-assisted fast handoff for vehicular fog communications

With the fast development of vehicular technology and the emergence of mobile applications, it has become urgent to connect moving vehicles to Internet due to the ever-growing user demands. As an emerging paradigm of Internet of vehicles (IoV), vehicular fog communication (VFC) can not only efficiently enable drivers to access traditional Internet multimedia services, but also possible to enjoy new Internet applications dedicated to intelligent transportations. However, in VFC, travelling vehicles move with a high velocity and change their points of attachment frequently, which results in frequent network handoffs and even connection interruptions. In order to provide a satisfactory user experience, and enable vehicles to keep always-on-line connections when they are moving along urban roads, this paper proposes a cross-layer and neighboring vehicle-aided fast handoff (CVFH) scheme. A vehicle in CVFH utilizes the neighboring vehicle’s assistance to find a qualified vehicle to determine the target AP, and get related information before the vehicle moves into the coverage of the target AP. Simulation results show that the proposed scheme can improve the handoff performance in terms of handoff delay and throughput.

Neural sentence embedding using only in-domain sentences for out-of-domain sentence detection in dialog systems

To ensure satisfactory user experience, dialog systems must be able to determine whether an input sentence is in-domain (ID) or out-of-domain (OOD). We assume that only ID sentences are available as training data because collecting enough OOD sentences in an unbiased way is a laborious and time-consuming job. This paper proposes a novel neural sentence embedding method that represents sentences in a low-dimensional continuous vector space that emphasizes aspects that distinguish ID cases from OOD cases. We first used a large set of unlabeled text to pre-train word representations that are used to initialize neural sentence embedding. Then we used domain-category analysis as an auxiliary task to train neural sentence embedding for OOD sentence detection. After the sentence representations were learned, we used them to train an autoencoder aimed at OOD sentence detection. We evaluated our method by experimentally comparing it to the state-of-the-art methods in an eight-domain dialog system; our proposed method achieved the highest accuracy in all tests.

Congestion Control for WebRTC: Standardization Status and Open Issues

The WebRTC initiative has achieved impressive results in terms of the gained industrial interest, the penetration of the technology in end-user devices, and the ever growing community of developers. WebRTC is today supported by major mobile platforms and Internet browsers, allowing potentially billions of users to seamlessly establish real-time communication sessions. Among all the functionalities that must be implemented by WebRTC devices, congestion control is particularly important to ensure that the network operates properly while providing a satisfactory user experience. A working group that is focusing on this issue is the IETF RTP Media Congestion Avoidance Techniques (RMCAT), which aims at defining the requirements and designing congestion control algorithms to be used for the transport of real-time media flows over RTP. This article overviews the status of the standardization efforts that are taking place in the RMCAT working group. We discuss the choices involved in the design of media congestion control, the proposed algorithms, and the issues that are considered still open.

Frameworks for Exploring the User Experience of Mobile Apps

Mobile applications have become more popular due to mobile technology innovation and the sustaining rising smartphone and tablet users. As design products comply with user experience they are able to meet customer satisfaction and improve customer loyalty. Therefore, the user interface design of mobile applications has become more important to academics and industry. The goal of this study was to improve customer satisfaction through redesign user interface of the mobile app. This study was based on Hassenzahl's Model of user experience, through three design process, improve Nike+ Running user interface. Our approaches included focus group interviews for runners to understand the needs of runners of pedometer APP, a questionnaire to measure user experience satisfaction, and product design to redesign Nike+ Running APP UI, The result showed that satisfying experience was highly salient in this study.

Research on E-book Interaction Design for a Smartphone Using the Re-mediation of Paper Book UX

Background Since their introduction in 2000, electronic book devices—or simply e-book readers, have integrated new interface and technological features. Despite their evolution, current e-book readers do not offer satisfactory user experience to readers partly due to the lack of understanding of the essence of the reading experience, which has a long cultural history. This paper investigates the differences and similarities of the reading experience between paper books and electronic books to understand the impact of the specific medium (i.e., paper and electronic books) on the reading experience. Based on our findings, we suggest some improvements in the interactivity of ‘turning of the book page’. Methods We conducted a literature survey to grasp the concept of paper books and changes in the reading method, after which we conducted a comparison with the mobile application interface, and an analysis of reading type. We then estimated changes to the mobile interface due to the application of force-touch technology. Finally, after designing a new e-book interface, we created the prototype and conducted a usability test on it. Results Based on the findings of this study, we suggest a new electronic book interface for smart phone devices. Our findings suggest that both usability and sensibility are the key elements of the reading experience of electronic books. In particular, force-touch, which is a form of haptic technology, should be applied to improve the interactivity of today’s e-book devices. Conclusions The effective use of direct contact with the content on any medium can provide a physical sense of reading that can not be achieved by existing e-book interfaces. We are now in a period of transition in which traditional printing technology coexists with digital technology, so we should continue to discover the possibility of both methods complementing each other.

Click-Search: Supporting information search with crowd-powered image-to-keyword query formulation

Information search is a common yet important task in everyday work and life. It remains challenging how to help users search for information or things they don’t know how to express with words. Also, even when people know how to express, the cognitive cost required to retrieve the concepts and formulate the queries can be excessive. In this paper, we present Click-Search, a search user interface that allows people to indicate their search intents by merely selecting and cropping segments of pictures. The system automatically converts cropped image segments to keywords based on known semantic labels at the level of image pixels that are generated by crowdsourced image tagging. Through a user study, we showed that Click-Search could support object-finding activities effectively with a satisfactory user experience.

Indoor Localization Using Semi-Supervised Manifold Alignment with Dimension Expansion

Location estimation plays a crucial role in Location-Based Services (LBSs) with satisfactory user experience. The Wireless Local Area Network (WLAN) localization approach is preferred as a cost-efficient solution to indoor localization on account of the widely-deployed WLAN infrastructures. In this paper, we propose a new WLAN Received Signal Strength (RSS)-based indoor localization approach using the semi-supervised manifold alignment with dimension expansion. In concrete terms, we first construct an innovative objective function based on the augmented physical coordinates and the corresponding WLAN RSS measurements. Second, the closed-form solution to the objective function is derived out according to the Lagrange multiplier equation, which results in the manifold in physical coordinate space. Third, the target location is estimated by matching the transformed newly-collected RSS against the manifold. The localization performance with noise perturbation is analyzed upon the constructed objective function, and meanwhile, the closed-form solution to the objective function with respect to multiple types of measurements is also derived out for the sake of leveraging all of the potential measurements for indoor localization. The extensive testing results show that the proposed approach performs well in localization accuracy even at low calibration load, and its performance can be further improved by using multiple types of measurements for localization.

The Development and Validation of the Game User Experience Satisfaction Scale (GUESS).

The aim of this study was to develop and psychometrically validate a new instrument that comprehensively measures video game satisfaction based on key factors. Playtesting is often conducted in the video game industry to help game developers build better games by providing insight into the players' attitudes and preferences. However, quality feedback is difficult to obtain from playtesting sessions without a quality gaming assessment tool. There is a need for a psychometrically validated and comprehensive gaming scale that is appropriate for playtesting and game evaluation purposes. The process of developing and validating this new scale followed current best practices of scale development and validation. As a result, a mixed-method design that consisted of item pool generation, expert review, questionnaire pilot study, exploratory factor analysis (N = 629), and confirmatory factor analysis (N = 729) was implemented. A new instrument measuring video game satisfaction, called the Game User Experience Satisfaction Scale (GUESS), with nine subscales emerged. The GUESS was demonstrated to have content validity, internal consistency, and convergent and discriminant validity. The GUESS was developed and validated based on the assessments of over 450 unique video game titles across many popular genres. Thus, it can be applied across many types of video games in the industry both as a way to assess what aspects of a game contribute to user satisfaction and as a tool to aid in debriefing users on their gaming experience. The GUESS can be administered to evaluate user satisfaction of different types of video games by a variety of users.

Gender differences in information quality of virtual communities: A study from an expectation-perception perspective

Knowing the effect of user characteristics on information quality is important to optimize the quality of content and provide a satisfactory user experience. In this paper, we investigate gender differences in the assessment of information quality in virtual communities. To understand the influence of gender on information quality, this paper measures information quality by the discrepancy between expectation and perception. The proposed conceptualized model is validated by 144 survey observations being collected at a public university. Then, the multivariate analysis of variance is used to analyze the data. The results show that gender could indeed have an influence on information quality through expectations or perceptions. Specifically, males assess representational data quality more highly than females. Females have higher expectations of representational data quality than males. Males regard accessible data quality more highly than females. Managers of virtual communities need to realize that the same information may be perceived differently by different genders. They need to take the gender of users into account and provide customized information accordingly.

A novel contention-on-demand design for WiFi hotspots

In widely deployed wireless "hot-spot" networks, nodes frequently join or leave, and inelastic/elastic and saturated/nonsaturated flows coexist. In such dynamic and diverse environments, it is challenging to maximize the channel utilization while providing satisfactory user experiences. In this paper, we propose a novel contention-on-demand (CoD) MAC scheme to address this problem. The CoD scheme consists of a fixed-CW algorithm, a dynamic-CW algorithm, and an admission control rule. The fixed-CW algorithm allows elastic flows to access limited system bandwidth; the dynamic-CW algorithm enables inelastic flows to contend for channel on demand and quickly adapt to network change; and the admission control rule rejects overloaded traffic for providing good user experiences. We then perform an asymptotic analysis to develop a simple and practical admission control rule for homogeneous and heterogeneous traffic; our rule can not only adapt to the change in offered loads and node number, but also maximize the system utilization . Finally, extensive simulations verify that our scheme is very effective and our theoretical result is very accurate.