Satisfactory User Experience Research Articles

From i-doc to VR experience: New forms of user engagement in immersive digital documentaries

Audience research proves that possibilities of interaction in i-docs are often not fulfilled by the user, who is not really part of a ‘work in progress’ (as intended by the makers). With the shift and development of new digital formats (360-degree-films, nonfictional VR experiences, AR apps), the question of the possible interactive potential should be addressed once again. Since VR projects are fully immersive (mostly using head-mounted displays), there is no possible distraction from outside on the one hand. On the other hand, there is a shift from the computer game style aesthetic of early i-docs, with their pure spatial arrangement of events, to a more inclusive digital storytelling modality with the user experiencing his own world-building. This will be discussed with taking into consideration the non-fictional VR experience as a mode of actively combining immersion and storytelling for a satisfactory user experience. Afterwards two very different examples of nonfictional VR production will be presented, and their modalities will be briefly touched; the utilized approach and its user response will be discussed. A look at the future of possible developments concludes the essay.

Developing a Model of Video Game Play: Motivations, Satisfactions, and Continuance Intentions

Background. As video game usage continues to rise, it is important to understand why people choose and continue playing a game. Purpose. This research presents a theoretical framework to explore the relationships between gameplay motivations, satisfaction, continuance intention and gameplay. Methods. To examine these relationships, survey data was collected from 353 participants who played different types of online games, including League of Legends, World of Warcraft, Overwatch, and Hearthstone, for at least 10 hours in the past three months. A series of structural equation models were tested to identify the model with the best validity and fit. All constructs were from previously validated measures. Motivations were measured by the Trojan Player Typology, which assessed story-driven, completionist, competitor, escapist, smarty-pants, and socializer motives. Satisfaction was measured using the game user experience satisfaction scale (GUESS), which assessed satisfaction with usability/playability, narratives, play engrossment, enjoyment, creative freedom, audio aesthetic, personal gratification, social connectivity, and visual aesthetics. Continuance intention was measured using a 4-item scale. Results. The final model suggested that motivations were positively related to satisfaction, while satisfaction was positively related to continuance intention and weekly play time. Motivations accounted for 20% of the variance in satisfaction, and the story-driven motivation was the strongest predictor. Further, satisfaction accounted for 47% of the variance in continuance intention and 8% of the variance in weekly play time. Conclusion. Individual differences in gameplay motivation are an important component of a player’s satisfaction. Further, satisfaction appears to be central to a player’s intention to continue using a game.

The Use of Augmented Reality to Raise Awareness of the Differences Between Osteoarthritis and Rheumatoid Arthritis.

Arthritis is one of the most common disease states worldwide but is still publicly misunderstood and lacks engaging public awareness materials. Within the UK, the most prevalent types of arthritis are osteoarthritis (OA) and rheumatoid arthritis (RA). The two are commonly mistaken as the same disease but, in fact, have very different pathogenesis, symptoms and treatments. This chapter describes a study which aimed to assess whether an augmented reality (AR) application could be used to raise awareness about the difference between OA and RA.An application was created for Android tablets that included labelled 3D models, animations and AR scenes triggered from a poster. In total 11 adult participants tested the application taking part in a pretest and posttest which aim to measure the usability of the application and the acquisition of knowledge on OA and RA. A T-test was performed to assess the effectiveness of the application from the pretest and posttest questionnaire outcomes. Overall results were encouraging reporting a very significant acquisition of knowledge and a highly satisfactory user experience.

Development and feasibility testing of a back extension-glide virtual reality game for low-back pain

Development and feasibility testing of a Kinect Xbox operated non-immersive back extension-glide virtual reality game (BE-G VRG) was carried out among 42 patients with chronic low-back pain. Feasibility of the BE-G VRG was explored using system usability scale and gaming experience questionnaire and game user experience satisfaction scale (GUESS). The visual analogue scale (VAS) was used to assess pain intensity. Participants' VAS score was 5.02 ± 1.94. The GUESS' game user experience of the BE-G VRG on a scale of 0 to 7, was 5.66 ± 0.86, 5.41 ± 1.03, 5.40 ± 0.957 and 5.37 ± 0.970 for game audio aesthetics, engrossment, game usability, personal gratification and visual aesthetics respectively. Audio level (77.5%) and visual aesthetics (62.5%) were the top rated game usability experience; and system usability and enjoyment scores were 54.9 ± 7.76 (out of 100) and 4.23 ± 0.53 (out of 5). The BE-G VRG has moderate to high usability, satisfaction and game experience among patients with moderate intensity chronic low-back pain.

MUIT: A Domain-Specific Language and its Middleware for Adaptive Mobile Web-Based User Interfaces in WS-BPEL

In enterprise organizations, the Bring-Your-Own-Device (BYOD) requirement has become prevalent as employees use their own mobile devices to process the workflow-oriented tasks. Consequently, it calls for approaches that can quickly develop and integrate mobile user interactions into existing business processes, and adapt to various contexts. However, designing, developing, and deploying adaptive and mobile-oriented user interfaces for existing process engines are non-trivial, and require significant systematic efforts. To address this issue, we present a novel middleware-based approach, called MUIT, to developing and deploying the Mobility, User Interactions and Tasks into WS-BPEL engines. MUIT provides a Domain-Specific Language (DSL) that provides some intuitive facilities to support the declarative development of adaptive, mobile-oriented, and Web-based user interfaces in WS-BPEL. The DSL can significantly reduce developers' manual efforts of developing user interactions by preventing arbitrarily mixed code, and its runtime supports satisfactory user experiences. Additionally, MUIT can be seamlessly integrated into WS-BPEL without intrusions of existing process instances. We implement a proof-of-concept prototype by integrating MUIT into the commodity WS-BPEL-based Apusic Platform, and evaluate the performance and usability of MUIT platform.

Heuristic Evaluation on Usability of Educational Games: A Systematic Review

This work presents a systematic review whose objective was to identify heuristics applicable to the evaluation of the usability of educational games. Heuristics are usability engineering methods that aim to detect problems in the use of a system during its development and / or when its interface is in interaction with the user. Therefore, applying heuristics is an essential part of developing digital educational games. Search sources were articles available in all the databases present in the Capes / MEC / Brazil periodicals portal, in the available languages. The descriptors adopted were "educational games", "heuristic" and "usability" in Boolean search in titles, abstracts and keywords, with AND operator, for publications starting in 2014. The inclusion criteria were: (a) articles with a clear description of the methodology used in the usability analysis; (b) studies presenting primary data and (c) articles whose focus corresponds to the investigated question. Two examiners conducted the searches in the databases and a third the evaluation and general review of the data. Initially, 93 articles were identified, of which 19 were repeated, 5 were literature reviews. Of the 69 that remained, 57 were elected as not eligible with only 12 selected for full studies, of which 6 entered the final review. With this review we can deduce that the field of heuristics and usability for educational games is still little explored, with few specific evaluations validated or in the process of validation, requiring greater investment in the area. Through this review, we found at least one heuristic that meets the usability evaluation of educational software: Game User Experience Satisfaction Scale (GUESS).

Improving Cache Performance for Large-Scale Photo Stores via Heuristic Prefetching Scheme

Photo service providers are facing critical challenges of dealing with the huge amount of photo storage, typically in a magnitude of billions of photos, while ensuring national-wide or world-wide satisfactory user experiences. Distributed photo caching architecture is widely deployed to meet high performance expectations, where efficient still mysterious caching policies play essential roles. In this work, we present a comprehensive study on internet-scale photo caching algorithms in the case of QQPhoto from Tencent Inc., the largest social network service company in China. We unveil that even advanced cache algorithms can only perform at a similar level as simple baseline algorithms and there still exists a large performance gap between these cache algorithms and the theoretically optimal algorithm due to the complicated access behaviors in such a large multi-tenant environment. We then expound the reasons behind this phenomenon via extensively investigating the characteristics of QQPhoto workloads. Finally, in order to realistically further improve QQPhoto cache efficiency, we propose to incorporate a prefetcher in the cache stack based on the observed immediacy feature that is unique to the QQPhoto workload. The prefetcher proactively prefetches selected photos into cache before they are requested for the first time to eliminate compulsory misses and promote hit ratios. Our extensive evaluation results show that with appropriate prefetching we improve the cache hit ratio by up to 7.4 percent, while reducing the average access latency by 6.9 percent at a marginal cost of 4.14 percent backend network traffic compared to the original system that performs no prefetching.

SSPA-LBS: Scalable and Social-Friendly Privacy-Aware Location-Based Services

Privacy-aware location-based service (PA-LBS) preserves LBS users’ privacy but undesirably sacrifices service quality. In order to balance the two factors with satisfactory user experience, existing frameworks are faced with two barriers, that is, scalability and social-friendliness. First, existing schemes do not enable LBS users to flexibly scale their privacy level on service provision. Such a lack of scalability easily results in either unacceptable service-quality degradation or insufficient privacy protection and fails to meet dynamic user requirements. Second, existing schemes handle privacy protection by merely considering the trust relationship between users and servers but ignore the complex trust relationships among users. As a result, users cannot preserve privacy in location-based social services that involve user-to-user interactions. In this paper, we present the first scalable and social-friendly PA-LBS system. In particular, we propose a novel camouflage algorithm with a formal privacy guarantee that enables LBS users to expose their location information by scaling two privacy related factors, that is, camouflage range and place type. Furthermore, we apply the scalable ciphertext policy attribute-based encryption algorithm to enable LBS users to effectively control the access from other users to their location information. Moreover, we also demonstrated the operational efficiency of the proposed system through successful implementations on Android devices.

CAFE: Adaptive VDI Workload Prediction with Multi-Grained Features

Virtual desktop infrastructure (VDI) is a virtualization technology that hosts desktop operating system on centralized server in a data center of private or public cloud. Effective resource management is of crucial importance for VDI customers, where maintaining sufficient virtual machines helps guarantee satisfactory user experience while turning off spare virtual machines helps save running cost. Generally, existing techniques work in passive manner by either driving available capacity reactively or configuring management schedules manually. In this paper, a novel proactive resource management approach is proposed which aims to predict VDI pool workload adaptively by utilizing CoArse to Fine historical dEscriptive (CAFE) features. Specifically, aggregate session count from pool end users serves as the basis for workload measurement and predictive model induction. Extensive experiments on real VDI customers data sets clearly validate the effectiveness of multi-grained features for VDI workload prediction. Furthermore, practical insights identified in our VDI data analytics are also discussed.

Visualisation and analysis of multi-resolution raster geodatasets in the cloud

Abstract. Raster geodatasets are an important data source for map-related web applications. However, existing differences in how those datasets are georeferenced, formatted and made available complicate their integration for analysis and visualisation purposes. Furthermore, the inconsistent cell resolutions make it difficult to perform efficient and reliable display of the datasets over a range of scales. In particular, it is challenging to achieve satisfactory user experience in visual exploration of interactive analysis operations carried out on these datasets.Dataset harmonisation carried out as a pre-processing procedure seems to be necessary for resolving the raster geodata inconsistency challenge. After the pre-processing step, the harmonized datasets have to be stored into an easily accessible data storage, preferably in close connection with the computing platform where the analysis operations are to be performed.Multidimensional datacube has emerged as a conceptual framework for organising a repository of harmonised raster geodatasets. Satellite images have so far been the most popular geospatial application area for datacubes. The set of dimensions of such geodatacube typically consist of two or three coordinate axes, time, and the content theme. While being ingested into the datacube, the involved datasets are brought into a common georeferencing frame, series of cell resolutions, and storage format. The selected harmonised resolution levels can actually be seen as a set of individual geodatacubes.An initiative has launched in Finland to build a harmonised multi-resolution geodatacube, called GeoCubes Finland, containing some of the most important national geodatasets. The datasets currently incorporated into the geodatacube include digital elevation models together with land cover, superficial deposit, forest inventory and administrative unit datasets. GeoCubes Finland is being built as part of the Finnish Open Geospatial Information Infrastructure for Research (oGIIR) programme, aimed at developing the geospatial research infrastructure of the country. The data storage and the related computing facilities have been implemented on a cloud service platform provided by CSC - IT Center for Science that serves Finnish universities and research institutes.

A Wearable Daily Respiration Monitoring System Using PDMS-graphene Compound Tensile Sensor for Adult.

A wearable respiration monitoring system based on Respiratory induction plethysmography (RIP) using a new Polydimethylsiloxane-graphene (PDMS-graphene) compound tensile sensor is proposed. The manufacture procedure of this novel resistance-based tensile sensor is presented together with a wireless acquisition system. The proposed sensor shows a high sensitivity during stretching and a promising cyclic stability for continuous 3,600 cycles. Statistical analysis confirms a high correlation of respiratory rate monitoring between the proposed system and a medical-level instrument. This proposed system based on RIP, using a new PDMS-graphene compound tensile sensor can acquire respiratory signal unobtrusively with high accuracy and satisfactory user experience, and thus has great potential in home monitoring scenarios.

Research on the Attenuation Law of User Experience Satisfaction of Mobile Phone

With the continuous development and widespread popularity of smart phones, people have been deeply studying mobile phones, and pay more attention to user experience. Based on literature research, this paper establishes a hierarchical chart of influencing factors of user experience satisfaction of mobile phone. A calculation method of user experience satisfaction is proposed. User interviews are used to determine the key factors, and user satisfaction model is constructed. Using questionnaire survey method to collect data and explore the process and rules of satisfaction changes in the process of purchasing, using and replacing mobile phones. The results show that user experience satisfaction of mobile phone decreases with the increase of mobile phone usage time, and battery loss, running stuck, damage and insufficient storage space are all key factors affecting the satisfaction of mobile phone users. Through data analysis, the change rule of user experience satisfaction of each key factor is obtained, and then guide relevant enterprises to upgrade and improve products pertinently.

Reconfiguration in Network Slicing—Optimizing the Profit and Performance

Network slicing enables diversified services to be accommodated by isolated slices in network function virtualization-enabled software-defined networks. To maintain satisfactory user experience and high profit for service providers in a dynamic environment, a slice may need to be reconfigured according to the varying traffic demand and resource availability. However, frequent reconfigurations incur certain cost and might cause service interruption. In this paper, we propose a hybrid slice reconfiguration (HSR) framework, where a fast slice reconfiguration (FSR) scheme reconfigures flows for individual slices at the time scale of flow arrival/departure, while a dimensioning slices with reconfiguration (DSR) scheme is occasionally performed to adjust allocated resources according to the time-varying traffic demand. In order to optimize the slice’s profit, i.e., the total utility minus the resource consumption and reconfiguration cost, we formulate the problems for FSR and DSR, which are difficult to solve due to the discontinuity and non-convexity of the reconfiguration cost function. Hence, we approximate the reconfiguration cost function with ${L} _{1}$ norm, which preserves the sparsity of the solution, thus facilitating restricting reconfigurations. Besides, we design an algorithm to schedule FSR and DSR, so that DSR is timely triggered according to the traffic dynamics and resource availability to improve the profit of slice. Furthermore, we extend HSR with a resource reservation mechanism, which reserves partial resources for near future traffic to reduce potential reconfigurations. Numerical results validate that our reconfiguration framework is effective in reducing reconfiguration overhead and achieving high profit for slices.

Digital Library Polona: Digitization, Technology, Cooperation

ABSTRACTThe Digital Library Polona, the digital counterpart of the National Library of Poland, is one of the leading digital libraries in Europe. Its long-term strategies are providing open access to the best quality scans, providing satisfactory user experience, and keeping up with technological progress in the field. Polona formed collaborative partnerships with several institutions to strengthen digital humanities. These include Wikipedia, Europeana (“Europeana Collections,” https://www.europeana.eu/portal/en (accessed May 2, 2019)) and the International Image Interoperability Framework Consortium (IIIF-C) (“IIIF Consortium – IIIF, International Image Interoperability Framework,” https://iiif.io/community/consortium/#the-iiif-consortium (accessed May 2, 2019)). In this article, the collections and history of Polona are discussed as well as future prospects for development.

DISDAIN: An Auto Content Generation VR Game

Objectives: To build a video game that implementing procedural content generation with a first-person shooter genre and using the newest gaming hardware technology, i.e. Virtual Reality. Methods: Some steps being incorporated in this study, starting from designing the video game, implementing the Procedural Content Generation and Grammars methods, implementing the game level generation, and evaluating the results using Game User Experience Satisfaction Scale (GUESS) and Procedural Content Generation (PCG) evaluation techniques. Findings: DISDAIN was successfully built using Grammars to generate the story and Room Generation Algorithm and Corridor Generation Algorithm to generate the maze-like game space. The game was evaluated by using the GUESS questionnaire which results on 77% satisfaction scale. Application/Improvements: The technique can be adapted to other heavy content games to reduce the production cost. Keywords: Corridor Generation, Game User Experience Satisfaction Scale (GUESS), Grammars, Room Generation, Virtual Reality

Energy-efficient computation offloading and resource allocation for delay-sensitive mobile edge computing

Mobile edge computing is an emerging computing paradigm to augment computational capabilities of mobile devices by offloading computation-intensive tasks from resource-constrained smart mobile device onto edge clouds nearby with potential computation capability. However, in general, edge clouds have limited computation resource and energy. Thus it is critical to achieve high energy efficiency while ensuring satisfactory user experience. In this paper, we first formulate the computation offloading problem into the system energy-efficiency cost minimization problem by taking into account the completion time and energy. Then, by solving the optimization problem, we propose a distributed algorithm consisting of clock frequency configuration, transmission power allocation, channel rate scheduling and offloading strategy selection. In this algorithm, the clock frequency for local execution and the transmission power in edge cloud execution are optimized jointly. Furthermore, to optimize the queue delay, we formulate an M/M/n queue model with individual capacity between different windows and propose an algorithm for optimal task offloading rate. Finally, the experimental results show that our algorithm can achieve energy-efficient offloading performance compared to other existing algorithms.

Examining the Impact of Mobile App Features on Impulsiveness: The Moderating Role of Pay More Get More Promotion

With the exponential growth of smart phone users, mobile applications (apps) have become an essential platform for electronic retailers. Considering the research gap in the existing literature on shoppers' impulsiveness in the context of mobile medium, the present study investigates the effect of various antecedents in triggering impulsiveness in mobile apps shopping and inducing continuous purchases. The pioneer study used the structural equation modelling to test the hypothesised research model. It is observed that antecedents like effort expectancy, apps atmosphere, user experience and user satisfaction, price and discount have influence on impulse buying intention of shoppers. The study also confirms the moderating role of 'pay-more-get-more' promotional offers. The paper concludes with theoretical and managerial implications.

Enhancing the User Experience in Vehicular Edge Computing Networks: An Adaptive Resource Allocation Approach

Mobile edge computing (MEC) has been developed as a key technique to handle the explosive computation demands of vehicles. However, it is non-trivial to realize high-reliable and low-latency vehicular requirements among distributed and capacity-constrained MEC nodes. Besides, the dynamic and uncertain vehicular environments bring extra challenges to preserve the long-term satisfactory user experience. In this paper, an adaptive resource allocation approach is investigated to enhance the user experience in vehicular edge computing networks. Specifically, leveraging the idea of task scalability, a model for balancing computing quality and resource consumption is introduced to exploit the computational resources fully. Towards the goal of minimizing the long-term computing quality loss by specifying the needed resource and the expected quality of each running task, a mix-integer non-linear stochastic optimization problem is formulated to jointly optimize the allocation of radio and computing resources, as well as the task placement. Due to the unpredictable network states and the high computational complexity of the formulated problem, the long-term optimization problem is firstly decomposed into a series of one-slot problems, and then, an iterative algorithm is provided to derive a computation efficient solution. Finally, both rigorous theoretical analysis and extensive trace-driven simulations validate the efficacy of our proposed approach.

Characterizing Situations of Dock Overload in Bicycle Sharing Stations

Bicycle sharing systems are becoming increasingly popular in cities around the world as they are an inexpensive and sustainable means of transportation. Promoting the use of these systems substantially improves the quality of life in cities by reducing pollutant emissions and traffic congestion. In these systems, bikes are made available for shared use to individuals on a short-term basis. They allow people to borrow a bike in one dock and return it to any other station with free docks belonging to the same system. The occupancy level of the stations can be constantly monitored. However, to achieve a satisfactory user experience, all the stations in the system must be neither overloaded nor empty when the user needs to access the station. The aim of this paper is to analyze occupancy level data acquired from real systems to determine situations of dock overload in multiple stations which could lead to service disruption. The proposed methodology relies on a pattern mining approach. A new pattern type called Occupancy Monitoring Pattern is proposed here to detect situations of dock overload in multiple stations. Since stations are geo-referenced and their occupancy levels are periodically monitored, occupancy patterns can be filtered and evaluated by taking into consideration both the spatial and temporal correlation of the acquired measurements. The results achieved on real data highlight the potential of the proposed methodology in supporting domain experts in their maintenance activities, such as periodic re-balancing of the occupancy levels of the stations, as well as in improving user experience by suggesting alternative stations in the nearby area.

SMGuard: A Flexible and Fine-Grained Resource Management Framework for GPUs

GPUs have been becoming an indispensable computing platform in data centers, and co-locating multiple applications on the same GPU is widely used to improve resource utilization. However, performance interference due to uncontrolled resource contention severely degrades the performance of co-locating applications and fails to deliver satisfactory user experience. In this paper, we present SMGuard, a software approach to flexibly manage the GPU resource usage of multiple applications under co-location. We also propose a capacity based GPU resource model CapSM, which provisions the GPU resources in a fine-grained granularity among co-locating applications. When co-locating latency-sensitive applications with batch applications, SMGuard can prevent batch applications from occupying resources without constraint using quota based mechanism, and guarantee the resource usage of latency-sensitive applications with reservation based mechanism. In addition, SMGuard supports dynamic resource adjustment through evicting the running thread blocks of batch applications to release the occupied resources and remapping the uncompleted thread blocks to the remaining resources, which avoids the relaunch of the preempted kernel. The SMGuard is a pure software solution that does not rely on special GPU hardware or programming model, which is easy to adopt on commodity GPUs in data centers. Our evaluation shows that SMGuard improves the average performance of latency-sensitive applications by 9.8× when co-located with batch applications. In the meanwhile, the GPU utilization can be improved by 35 percent on average.