High-level User Research Articles

MichiGAN

Despite the recent success of face image generation with GANs, conditional hair editing remains challenging due to the under-explored complexity of its geometry and appearance. In this paper, we present MichiGAN (Multi-Input-Conditioned Hair Image GAN), a novel conditional image generation method for interactive portrait hair manipulation. To provide user control over every major hair visual factor, we explicitly disentangle hair into four orthogonal attributes, including shape, structure, appearance, and background. For each of them, we design a corresponding condition module to represent, process, and convert user inputs, and modulate the image generation pipeline in ways that respect the natures of different visual attributes. All these condition modules are integrated with the backbone generator to form the final end-to-end network, which allows fully-conditioned hair generation from multiple user inputs. Upon it, we also build an interactive portrait hair editing system that enables straightforward manipulation of hair by projecting intuitive and high-level user inputs such as painted masks, guiding strokes, or reference photos to well-defined condition representations. Through extensive experiments and evaluations, we demonstrate the superiority of our method regarding both result quality and user controllability.

Building Science Gateways for Analysing Molecular Docking Results Using a Generic Framework and Methodology

Molecular docking and virtual screening experiments require large computational and data resources and high-level user interfaces in the form of science gateways. While science gateways supporting such experiments are relatively common, there is a clearly identified need to design and implement more complex environments for further analysis of docking results. This paper describes a generic framework and a related methodology that supports the efficient development of such environments. The framework is modular enabling the reuse of already existing components. The methodology, which proposes three techniques that the development team can use, is agile and encourages active participation of end-users. Based on the framework and methodology, two prototype implementations of science-gateway-based docking environments are presented and evaluated. The first system recommends a receptor-ligand pair for the next docking experiment, and the second filters docking results based on ligand properties.

SecNetworkCloudSim: An Extensible Simulation Tool for Secure Distributed Mobile Applications

Fueled by the wide interest for achieving rich-storage services with the lowest possible cost, cloud computing has emerged into a highly desired service paradigm extending well beyond Virtualization technology. The next generation of mobile cloud services is now manipulated more and more sensitive data on VM-based distributed applications. Therefore, the need to secure sensitive data over mobile cloud computing is more evident than ever. However, despite the widespread release of several cloud simulators, controlling user’s access and protecting data exchanges in distributed mobile applications over the cloud is considered a major challenge. This paper introduces a new NetworkCloudSim extension named SecNetworkCloudSim, a secure mobile simulation tool which is deliberately designed to ensure the preservation of confidential access to data hosted on mobile device and distributed cloud’s servers. Through high-level mobile users’ requests, users connect to an underlying proxy which is considered an important layer in this new simulator, where users perform secure authentication access to cloud services, allocate their tasks in secure VM-based policy, manage automatically the data confidentiality among VMs and derive high efficiency and coverage rates. Most importantly, due to the secure nature of proxy, user’s distributed tasks can be executed without alterations on different underlying proxy’s security policies. We implement a scenario of follow-up healthcare distributed application using the new extension.

Integrated Groundwater Resources Management: Spatially-Nested Modelling Approach for Water Cycle Simulation

Groundwater resources is one of the key water resources mobilized to support water uses of modern societies. The growing competition among water uses and complexity of faced situations requests a holistic approach able to ensure efficient answer to demands and at the same time satisfied water security. The current demands for integrated groundwater resources management drive the groundwater modelling research to focus on exploring an effective approach to simulate the full water-cycle at catchment scale. Referencing the existing modelling approaches, this paper presents an innovative approach to setting up a spatially-nested modelling system combining fully-distributed deterministic models for catchment hydrology, surface hydraulic and groundwater. The application to the low Var valley locates in the French Rivera is presented in order to validate performance of the approach and reproducibility. Confirmed by the high-quality model outputs (less than 7% difference in average) and high-level users’ satisfaction, the spatially-nested modelling approach demonstrates obvious advantages in assessing complex water processes such as stream-aquifer exchanges. Moreover, the proposed approach can be also considered as one of the promising strategy for the implementation of deterministic modelling tools in decision support systems.

A comparison of methods to measure daily cigarillo consumption among adolescents and young adults

ObjectiveCigarillo use is widespread among young people. Accurate assessment of cigarillo consumption is necessary to inform and evaluate tobacco research, but is complicated by product sharing and irregular use. This...

Automated Design of Robotic Hands for In-Hand Manipulation Tasks

Grasp planning and motion synthesis for dexterous manipulation tasks are traditionally done given a pre-existing kinematic model for the robotic hand. In this paper, we introduce a framework for automatically designing hand topologies best suited for manipulation tasks given high-level objectives as input. Our pipeline is capable of building custom hand designs around specific manipulation tasks based on high-level user input. Our framework comprises of a sequence of trajectory optimizations chained together to translate a sequence of objective poses into an optimized hand mechanism along with a physically feasible motion plan involving both the constructed hand and the object. We demonstrate the feasibility of this approach by synthesizing a series of hand designs optimized to perform specified in-hand manipulation tasks of varying difficulty. We extend our original pipeline 32 to accommodate the construction of hands suitable for multiple distinct manipulation tasks as well as provide an in depth discussion of the effects of each non-trivial optimization term.

Quantifiable Isovist and Graph-Based Measures for Automatic Evaluation of Different Area Types in Virtual Terrain Generation

This article describes a set of proposed measures for characterizing areas within a virtual terrain in terms of their attributes and their relationships with other areas for incorporating game designers’ intent in gameplay requirement-based terrain generation. Examples of such gameplay elements include vantage point, strongholds, chokepoints and hidden areas. Our measures are constructed on characteristics of an isovist, that is, the volume of visible space at a local area and the connectivity of areas within the terrain. The calculation of these measures is detailed, in particular we introduce two new ways to accurately and efficiently calculate the 3D isovist volume. Unlike previous research that has mainly focused on aesthetic-based terrain generation, the proposed measures address a gap in gameplay requirement-based terrain generation – the need for a flexible mechanism to automatically parameterise specified areas and their associated relationships, capturing semantic knowledge relating to high level user intent associated with specific gameplay elements within the virtual terrain. We demonstrate applications of using the measures in an evolutionary process to automatically generate terrains that include specific gameplay elements as defined by a game designer. This is significant as this shows that the measures can characterize different gameplay elements and allow gameplay elements consistent with the designers’ intents to be generated and positioned in a virtual terrain without the need to specify low-level details at a model or logic level, hence leading to higher productivity and lower cost.

Evaluating UX using a Metric Model: the Case of a Handheld Computing System in South Africa

For most experienced graphical user interface (GUI) and interactive designers, succeeding in acquiring, maintaining, and guiding potential high-level user engagement and user experience (UX) at the first attempt is often a dream. It often takes many low- and high-fidelity prototypes to obtain the desired solution, if not abandoning the entire activity altogether, due to mounting pressure and disappointment after failing to satisfy user needs while maintaining industry standards and design principles. The challenges are often due to the absence of known, agreed-upon evaluation mechanisms that are known and acceptable to interactive designers. The aim of this paper is to introduce an instrument that can be used to measure and evaluate UX, which can be used at any time during the design process and limits the pressure interactive designers too often experience.

PattRec: An easy-to-use CNV detection tool optimized for targeted NGS assays with diagnostic purposes

Genetic laboratories use custom-commercial targeted next-generation sequencing (tg-NGS) assays to identify disease-causing variants. Although the high coverage achieved with these tests allows for the detection of copy number variants (CNVs), which account for an important proportion of the genetic burden in human diseases, an easy-to-use tool for automatic CNV detection is still lacking. This article presents a new CNV detection tool optimized for tg-NGS data: PattRec. PattRec was evaluated using a wide range of data, and its performance compared with those of other CNV detection tools. The software includes features for selecting optimal controls, discarding polymorphic CNVs prior to analysis, and filtering out deletions based on SNV zygosity, and automatically creates an in-house CNV database. There is no need for high level bioinformatic expertise and users can choose color-coded xlsx output that helps to prioritize potentially pathogenic CNVs. PattRec is presented as a Java based GUI, freely available online: https://github.com/irotero/PattRec.

Applying Event-Related Potentials to Measure Perceptual Experience toward the Navigation Interface of a Mobile Game for Improving the Design

High-level user experience has become the key factor that one game can be successful in the game market. The home page of mobile games, especially the design of the navigation interface, has a significant impact on users’ initial experience, which is an important determent to users’ preferences and purchase decision. Hence, measuring users’ perceptual experiences of the navigation interface can help designers understand real demands from users. Previous studies primarily used self-report scales or interviews to measure gamers’ perceptual experiences. However, it may not reflect gamers’ real perceptions that they are feeling as most of time the feeling is short-lived and implicit. To fill this gap, the current study attempted to combine subjective evaluation with event-related potentials (ERP) to objectively measure gamers’ perceptual experience evoked by the navigation interface of the mobile game. The navigation interfaces of mobile games with low, medium, and high perceptual experience were developed and the ERP experiment was conducted to detect the differences in users’ electroencephalograph (EEG) components when subjects were exposed to the different design levels of navigation interface. The results showed that N1 reaction showed asymmetry in brain regions, and P2 and N2 showed symmetry, and relative to the navigation interface with low and medium perceptual experiences, the high level of navigation interface induced a larger amplitude of N2 in the anterior scalp and P2 in the frontal scalp. These EEG components can, therefore, be regarded as significant indicators reflecting gamers’ perceptions of the navigation interface. The findings benefit game companies of navigation interface designs.

Engaging m-commerce adopters in India

PurposeThe purpose of this paper is to identify the impact of factors derived from the unified theory of user acceptance of technology (performance expectancy, effort expectancy, social influence, facilitating conditions, age, gender) and of those drawn from literature (perceived risk, perceived enjoyment and innovativeness) on the adoption of m-commerce in India. It also suggests implications of these for the consumer behavior theory practitioners and marketers.Design/methodology/approachData were collected using an online survey from 321 respondents, split into two groups (high and low adoption level users) based on the usage scores of the four categories of m-commerce- location-based, transaction-based, entertainment and content delivery. Logistic regression technique was used to identify the prominent factors among the nine identified influencers to understand the differences between the two groups.FindingsThe findings of this paper are sample biasness, self-reported m-commerce adoption level, limited m-commerce categories and specific context.Research limitations/implicationsExcept the two factors of performance expectancy and facilitating conditions, all other variables discriminate between low and high adoption levels of m-commerce services in India. Social influence, perceived enjoyment and innovativeness were the three main factors that were found to have the most significant impact on the discrimination levels of m-commerce service users in India. Further, it was found that women and the younger generation users of m-commerce showed a greater propensity for adopting m-commerce practices.Practical implicationsMarketers need to focus on key factors like social influence, perceived enjoyment, perceived risk and effort expectancy to persuade the young and innovative consumer target groups increase their adoption of m-commerce services.Originality/valueThis is the first study of its kind to explore factors that distinguish users with low and high levels of m-commerce adoption, by taking into consideration all four categories of m-commerce (transaction-based, content delivery, location-based and entertainment). In doing so, it highlights the need for marketers to focus on factors beyond facilitating conditions and performance expectancy, to enhance the adoption of m-commerce practices.

Segmenting Hemorrhagic and Ischemic Infarct Simultaneously From Follow-Up Non-Contrast CT Images in Patients With Acute Ischemic Stroke

Cerebral infarct volume (CIV) measured from follow-up non contrast CT (NCCT) scans of acute ischemic stroke (AIS) patients is an important radiologic outcome measure of the effectiveness of ischemic stroke treatment. Post-treatment CIV in NCCT of AIS patients typically includes ischemic infarct only. In around 10% of AIS patients, however, hemorrhagic transformation or frank hemorrhage occurs along with ischemic infarction. Manual segmentation used to segment CIV into these two components in clinical practice is tedious and user dependent. Although automated segmentation methods exist, they can only segment either hemorrhage or ischemic infarct alone. In order to measure post-treatment CIV more efficiently, a novel joint segmentation approach is proposed to segment ischemic and hemorrhage infarct simultaneously. The proposed method makes use of advances in deep learning and convex optimization techniques. Specifically, convolutional neural network learned semantic information, local image context, and high-level user initialized prior are integrated into a multi-region time-implicit contour evolution scheme, which can be globally optimized by convex relaxation. The proposed segmentation approach is quantitatively evaluated using 30 patient images using Dice similarity coefficient and the mean and maximum absolute surface distance, compared to the gold standard of manual segmentation. The results show that the proposed semi-automatic segmentation is accurate and robust, outperforming some state-of-the-art semi-and automatic segmentation approaches.

Channel Choice Determinants of (Digital) Government Communication: A Case Study of Spatial Planning in Flanders

Governments at all levels believe the digitisation of their services and increased interaction with citizens will bring significant advantages in terms of transparency, creation of public value, and improvement of government performance (Al-Hujran, Al-Debei, Chatfield, & Migdadi, 2015). Nonetheless, this evolution towards more digital services and communication by governments raises questions in terms of inclusivity and accessibility. We conducted focus groups with a heterogeneous panel of over 80 citizens, ranging from non-users of digital technologies to high-level users, to study their choice of channel and their perception towards the evolving digitisation of communication and services, applied to the case of spatial planning in Flanders (Belgium). The results reveal that the most decisive channel choice determinants in spatial planning relate to the channel characteristics themselves, the information, the contextual aspect of the communication flow, and digital inequality mechanisms; meaning that (a) citizens opt for local communication channels when interacting with local, regional, and national governments, (b) citizens prefer to be personally informed when the communicated message has a direct impact on them, and (c) more vulnerable digital profiles consider the transition to digital communication by default as problematic.

Using Unity and Shield UI for Displaying 3D Medical Education Objects in the Web Browser

In this paper we describe how Unity and Shield UI frameworks are used for building the three-dimensional object rendering section of an online knowledge management system for human anatomy. 3D models of humans are organized in multiple levels of hierarchy and each component contains different types of related information. To allow seamless navigation, like searching for and selecting a specific organ, and viewing its medical description in several languages, developers would need to rely on an optimized and stable framework that can target web browsers as well as other platform builds. The specific requirements of this project called for trying the Unity package for building our 3D rendering scene and exporting it to the WebGL platform to make it available to its viewers on all mod-ern web browsers. To build the rich web user interface and bring high-level user experience to the viewers, the Shield UI framework is integrated on top of the Unity webgl build.

IStoryline: Effective Convergence to Hand-drawn Storylines.

Storyline visualization techniques have progressed significantly to generate illustrations of complex stories automatically. However, the visual layouts of storylines are not enhanced accordingly despite the improvement in the performance and extension of its application area. Existing methods attempt to achieve several shared optimization goals, such as reducing empty space and minimizing line crossings and wiggles. However, these goals do not always produce optimal results when compared to hand-drawn storylines. We conducted a preliminary study to learn how users translate a narrative into a hand-drawn storyline and check whether the visual elements in hand-drawn illustrations can be mapped back to appropriate narrative contexts. We also compared the hand-drawn storylines with storylines generated by the state-of-the-art methods and found they have significant differences. Our findings led to a design space that summarizes 1) how artists utilize narrative elements and 2) the sequence of actions artists follow to portray expressive and attractive storylines. We developed iStoryline, an authoring tool for integrating high-level user interactions into optimization algorithms and achieving a balance between hand-drawn storylines and automatic layouts. iStoryline allows users to create novel storyline visualizations easily according to their preferences by modifying the automatically generated layouts. The effectiveness and usability of iStoryline are studied with qualitative evaluations.

E-Store Management Using Bell-LaPadula Access Control Security Model

Generally, the existing store management system does not provide any access control mechanism in order to manage resources. All levels of user have the same right to access the store and borrow the equipment. Therefore, the E-Store management system using Bell-LaPadula access control model was proposed. The prototyping methodology was used to develop the system because methodology model is quickly constructed to test or illustrate design features and ideas, in order to gather user feedback. Moreover, the system is built using hypertext processor (PHP) language. The E-Store system has three types of users, which are known as top management of Welding Department, lecturers and students. The user’s access control is divided by high-level privilege to lower-level privilege. Therefore, each user will have different login interface according to their role and access right to the system. Through the system, high-level user manages in and out equipment flow, manages authorization, view history log in activity and verify complaint report. Lower-level user can view list of equipment, report complaint and damage equipment and borrow equipment. The E-Store management system is expected to manage the store effectively and reduced redundancy issues of equipment requested. The user access right has been assigned based on their access level.

Hierarchical multiple-image encryption based on the cascaded interference structure and vector stochastic decomposition algorithm

A new kind of hierarchical multiple-image encryption method based on the cascaded interference structure and vector stochastic decomposition algorithm is proposed. In this method, by using the nonequal modulus decomposition and vector stochastic decomposition algorithm, the Kth-level secret image modulated by a random phase distribution is analytically encoded into a Kth-level phase-only mask (POM) key and the (K−1)th-level complex amplitude field whose real amplitude is the (K−1)th-level secret image. Then, served as the input condition, the generated complex amplitude field is further encoded into a (K−1)th-level POM key and the (K−2)th-level complex amplitude, and so on, until the 1st-level complex amplitude field is decomposed into two POMs: one is the 1st-level key, and the other is the ciphertext. When decryption occurs, only when the high-level users simultaneously obtain all the phase keys, the correct sequential orders of phase keys, and the correct geometrical parameters, all the secret images are retrieved successfully. Both theoretical analysis and numerical simulations verify the feasibility of this method.

Multi-Context Integrated Deep Neural Network Model for Next Location Prediction

The prediction of next location for users in location-based social networks has become an increasing significant requirement since it can benefit both users and business. However, existing methods lack an integrated analysis of sequence context, input contexts, and user preferences in a unified way, and result in an unsatisfactory prediction. Moreover, the interaction between different kinds of input contexts has not been investigated. In this paper, we propose a multi-context integrated deep neural network model (MCI-DNN) to improve the accuracy of the next location prediction. In this model, we integrate sequence context, input contexts, and user preferences into a cohesive framework. First, we model sequence context and interaction of different kinds of input contexts jointly by extending the recurrent neural network to capture the semantic pattern of user behaviors from check-in records. After that, we design a feedforward neural network to capture high-level user preferences from check-in data and incorporate that into MCI-DNN. To deal with different kinds of input contexts in the form of multi-field categorical, we adopt embedding representation technology to automatically learn dense feature representations of input contexts. Experimental results on two typical real-world data sets show that the proposed model outperforms the current state-of-the-art approaches by about 57.12% for Foursquare and 76.4% for Gowalla on average regarding F1-score@5.

“There is Nothing Inherently Mysterious about Assistive Technology”: A Qualitative Study about Blind User Experiences in US Academic Libraries

Eighteen academic library users who are blind were interviewed about their experiences with academic libraries and the libraries’ websites using an open-ended questionnaire and recorded telephone interviews. The study approaches these topics from a user-centered perspective, with the idea that blind users themselves can provide particularly reliable insights into the issues and potential solutions that are most critical to them. Most participants used reference librarians’ assistance, and most had positive experiences. High-level screen reader users requested help with specific needs. A larger number of participants reported contacting a librarian because of feeling overwhelmed by the library website. In some cases, blind users and librarians worked verbally without the screen reader. Users were appreciative of librarians’ help but outcomes were not entirely positive. Other times, librarians worked with users to navigate with a screen reader, which sometimes led to greater independence. Some users expressed satisfaction with working with librarians verbally, particularly if websites did not seem screen reader user friendly, but many users preferred independence. Participants agreed it would be helpful if librarians knew how to use screen readers, or at least if librarians were familiar enough with screen readers to provide relevant verbal cues. Many users liked and used chat reference and many preferred Purdue Online Writing Lab (OWL) to learn citation style, though learning citation style was challenging. Questions such as reference librarians’ role when e-resources are not equally accessible deserve wider discussion in the library literature and in practice. Given the challenges described by the research participants and legal requirements for equally effective electronic and information technologies, libraries and librarians should approach reference services for blind users more proactively. Recommendations are provided.

An Efficient Image Retrieval of Digital Images Using Content Based Image Retrieval by Fuzzy Based Rule Extraction

The key idea of image retrieval system is to browse, search and retrieve images from a large distributed database of digital images in an efficient manner. Traditional method of image retrieval utilizes some methods of adding of metadata such as keywords or description to the images so that retrieval can be done using concept of annotation. Manually searching images from the large database is time-consuming and too expensive job. More over semantic gap between the low level features and high level user requirement remains an additional overhead. In order to overcome these issues we are moving towards the “Content Based Image Retrieval”, where CBIR aims at avoiding the use of textual description to retrieves image. Based on the feature similarities like shape, color, and texture & combined features the image retrieval can be done in an efficient manner. Contributing towards the image search from sensor based collected image dataset using CBIR can be helpful for various precautions from Natural disaster. Here we provide the interface between the sensors collected image dataset storage in the Google street view image repository and efficient image retrieval from the Google street view image repository using CBIR.