Specific Usage Contexts Research Articles

Unlocking the Potential of Digital, Situation-Aware Nudging for Promoting Sustainable Mobility

The context ultimately decides on mobility options and thus shapes mobility behavior. Nudges are an increasingly used strategy for promoting sustainable modes of everyday mobility. However, in most cases, the design of nudges and the triggers for issuing these interventions neglect the user's specific context and are thus less relevant to the recipient. Digital nudges communicated through mobile devices offer situation awareness, which is facilitated by geographic information systems (GIS). Using the geographic reference as the “primary key” allows for connecting the current location information of recipients with static and real-time environmental data that define the contextual situation. We describe a framework for triggering situation-aware nudges and provide a functional proof-of-concept. Through linking concepts from behavioral economics and psychology with methods from GIS science and Human-Computer-Interaction (HCI), we illuminate new opportunities for promoting sustainable mobility.

Power profiling and monitoring in embedded systems: A comparative study and a novel methodology based on NARX neural networks

Power consumption in electronic systems is an essential feature for the management of energy autonomy, performance analysis, and the aging monitoring of components. Thus, several research studies have been devoted to the development of power models and profilers for embedded systems. Each of these models is designed to fit a specific usage context. This paper is a part of a series of works dedicated to modeling and monitoring embedded systems in airborne equipment. The objective of this paper is twofold. Firstly, it presents an overview of the most used models in the literature. Then, it offers a comparative analysis of these models according to a set of criteria, such as the modeling assumptions, the necessary instrumentation necessary, the accuracy, and the complexity of implementation. Secondly, we introduce a new power estimator for ARM-Based embedded systems, with component-level granularity. The estimator is based on NARX neural networks and used to monitor power for diagnosis purposes. The obtained experimental results highlight the advantages and limitations of the models presented in the literature and demonstrate the effectiveness of the proposed NARX, having obtained the best results in its class for a smartphone (An online Mean Absolute Percentage Error = 2.2%).

Interactive 3D Time-Integrated Solar Shadow Maps

Abstract. Urban areas are characterized by a complex topography of buildings, terrain, vegetation and temporary structures, which, depending on their extent, geometry, geographic location and daytime, cast shadow on their surroundings. Given the importance of sunlight for various groups of interest and tasks, we argue that a comprehensive, accessible, usable and intuitive way of predicting its availability for specific usage contexts is surprisingly lacking. In our research, we are investigating how to improve the visual communication of urban solar conditions for various real-world usage scenarios like having a coffee in the sun, parking a car in the shade, or taking a photograph of a particular building in a favorable light. All of these activities take place over a period of time, not in a temporal instant, causing solar shadows to move. Hence, a static representation of the light situation at a distinct point in time, such as offered by available 3D urban maps or GIS systems, is often not sufficient for planning above mentioned recreational or professional activities.This factor is incorporated in our system by integrating shadow motion into a shadow map that covers arbitrary timeframes within a given day (Figure 1). Shadows are accumulated by projecting shadow maps from astronomically precise sun positions spanning over the defined timeframe. The number of sampling points directly influences the quality and speed of the rendering. All provided figures currently sample and integrate nine different sun positions.Using our system, questions in a manner of “Given a specific point in space, at which time will this point be in sun/shade again” can be answered. E.g., one would like to spend time in the sun at a specific Viennese “Kaffeehaus” (coffee place), from which time on (and for how long) will this space be sunlit (Figure 2). In the future, this process might even be automated.

WindowWall

As architects usually decide on the shape and look of windows during the design of buildings, opportunities for interactive windows have not been systematically explored yet. In this work, we extend the vision of sustainable and comfortable adaptive buildings using interactive smart windows. We systematically explore the design space of interactive windows to chart requirements, constraints, and challenges. To that end, we built proof-of-concept prototypes of smart windows with fine-grained control of transparency. In two studies, we explored user attitudes towards interactive windows and elicited control methods. We found that users understand and see potential for interactive windows at home. We provide specific usage contexts and specify interactions that may facilitate domestic applications. Our work illustrates the concept of interactive smart windows and provides insights regarding their design, development, and user controls for adaptive walls. We identify design dimensions and challenges to stimulate further development in the domain of adaptive buildings.

When does comparability better enhance relevance? Policy implications from empirical evidence

This study investigates the user-specific contexts under which comparability better enhances relevance of accounting information. We first confirm the intuition in the FASB’s (2010) current conceptual framework by documenting that the short-window earnings response coefficient (ERC) is positively associated with the pre-determined level of comparability. Using the cross-sectional variation in the positive relation between ERC and comparability, we show that the link between ERC and comparability is more pronounced for firms with higher investor sophistication and lower information asymmetry among investors. We further support our predictions using analysts’ earnings forecast revisions and various alternative measures of earnings informativeness. In sum, our paper shows that comparability improves information users’ ability to identify similarities and differences across different firms to a greater extent when investor base is more sophisticated and private information is less prevalent. These results suggest that standard setters, regulators, and practitioners should devote more attention to the role of comparability in firms whose investors are less sophisticated and information environment is more opaque.

Naturalgwas: An R package for evaluating genomewide association methods with empirical data.

Association studies of polygenic traits are notoriously difficult when those studies are conducted at large geographic scales. The difficulty arises as genotype frequencies often vary in geographic space and across distinct environments. Those large-scale variations are known to yield false positives in standard association testing approaches. Although several methods alleviate this problem, no tools have been proposed to evaluate the power that association tests could achieve for a specific study design and set of genotypes. Our goal here is to present an R program fulfilling this objective, by allowing users to simulate phenotypes from observed genotypes and to estimate upper bounds on achievable power. The simulation model can incorporate realistic features such as population structure and gene-by-environment interactions, and the package implements a gold-standard test that evaluates power using information on confounders. We illustrated the use of the program with example studies based on data for the plant species Arabidopsis thaliana. Simulated phenotypes were used to compare the ability of two recent association methods to correctly remove confounding factors, to evaluate power to detect causal variants, and to assess the influence various parameters. For the simulated data, the new tests reached performances close to the gold-standard test and could be reasonably used with measured phenotypes. Power to detect causal variants was influenced by the number of variants and by the strength of their effect sizes, and specific thresholds were obtained from the simulation study. In conclusion, our program provides guidance on methodological choice of association tests, as well as useful knowledge on test performances in a user-specific context.

Coarse-to-fine online learning for hand segmentation in egocentric video

Hand segmentation is one of the most fundamental and crucial steps for egocentric human-computer interaction. The special egocentric view brings new challenges to hand segmentation tasks, such as the unpredictable environmental conditions. The performance of traditional hand segmentation methods depend on abundant manually labeled training data. However, these approaches do not appropriately capture the whole properties of egocentric human-computer interaction for neglecting the user-specific context. It is only necessary to build a personalized hand model of the active user. Based on this observation, we propose an online-learning hand segmentation approach without using manually labeled data for training. Our approach consists of top-down classifications and bottom-up optimizations. More specifically, we divide the segmentation task into three parts, a frame-level hand detection which detects the presence of the interactive hand using motion saliency and initializes hand masks for online learning, a superpixel-level hand classification which coarsely segments hand regions from which stable samples are selected for next level, and a pixel-level hand classification which produces a fine-grained hand segmentation. Based on the pixel-level classification result, we update the hand appearance model and optimize the upper layer classifier and detector. This online-learning strategy makes our approach robust to varying illumination conditions and hand appearances. Experimental results demonstrate the robustness of our approach.

E-HEALTH BIOSENSOR PLATFORM FOR NONINVASIVE HEALTH MONITORING FOR THE ELDERLY IN LOW RESOURCE SETTING

New technologies in the field of tele-health using biosensor systems for non-invasive vital signs monitoring of patients, especially elderly people who need long-term care, and marginalized areas with hard to reach health care services are emerging. A study involving a self-care approach within the cardiac domain, where late detection increases the likelihood of patient disability or of premature death is proposed. In the study the application of e-health biosensors platform in medical services is experimented. The study resulted into the synthesis of vital signs from various body positions with biosensors that does not require a full coupled system. A model for the prevention of cardiovascular disease management based on noninvasive personal health monitoring systems with easy access for everybody, at any time or location is designed. A personal vital sign system such as ECG sensor which contain the functionality, allows recording anywhere and at any time a diagnostic quality ECG and analyzing it “on-board” by comparing it to a reference ECG, is modelled. The model called Mobile Health for the Elderly Persons (MOHELP) which relies on with application in estimation and control of boolean processes based on noisy and incomplete measurements is designed. This enabled a reliable recommendation from a digital artificial intelligence-based diagnosis, which can support an elderly person to take timely and correct decisions upon his (her) health status. In a case of urgency, the assistant puts the elderly person in a contact with healthcare providers. The signal pattern sensitivity related to sensors placement is one of the issues this study addressed using e-sensor platform. Sensors displacement errors have a direct impact on the medical diagnosis, especially if the diagnostic procedure is automated. The study resulted into the formulation of a methodology for e-Health Sensor Platform, in software architecture terms, that permits use of system biosensors to adapt to the user-specific context for self-healthcare.

Personalized app recommendation using spatio-temporal app usage log

This paper proposes a probabilistic method to recommend apps appropriate to current time and location of a user. The proposed method regards an app as a distribution of topics discovered from a large number of app descriptions. A user preference is then modeled, using spatio-temporal app usage log of a user, as a topic distribution that is affected by time and location. Since time and location can be regarded as two continuous random variables that are independent of each other, the proposed method is in contrast to conventional methods in that the conventional methods are based on a limited number of discrete contexts and assume that locations are dependent on time. Therefore, the proposed method captures user-specific contexts and is robust even in unseen time and location. Our experiments show that the proposed method outperforms two baseline methods in NDCG, which implies that the proposed method is effective in personalized app recommendation.

Cross-cultural variation in deliberate metaphor interpretation

The distinction between ‘deliberate’ and ‘non deliberate’ metaphors has been developed within a five-step framework (Steen) of metaphor production. Deliberate metaphors invite the addressee to pay special attention to their cross-domain structure mapping rather than focusing primarily on the topical proposition. This paper presents results of a pilot survey eliciting interpretations for the metaphors a nation is a body/a nation is a person from an international sample of respondents in 10 different countries. ESL/EFL users from diverse cultural and/or linguistic backgrounds were asked to apply the metaphorical idiombody politicto their home nations. The responses show systematic variation in preferred metaphor interpretations, some of which can be linked to dominant cultural traditions, as well as evidence of polemical and/or ironic elaboration. Neither of these findings is predicted by classic conceptualist models that describe metaphor understanding as an automatic and unconscious process. Instead, when paying special attention to metaphoricity, informants seem to have chosen between diverse interpretation versions and in some cases to have elaborated them further to achieve social pragmatic effects. These findings provide new supporting evidence for Deliberate Metaphor Theory by highlighting deliberateness in metaphor interpretation and outlining perspectives for further empirical testing of metaphor understanding in specific registers and usage contexts (e.g., political discourse, EFL/ESL acquisition).

A critical incident approach to consumer response in the smartphone market: product, service and contents

Multitudes of theory-based models have been developed and applied to identify drivers behind user behaviors in various information systems (IS) markets. Although these theory-based models have been successful in identifying and explaining consumer behavior, their fixed nature necessitates customization efforts when applied to a specific usage context. In this paper, we utilized critical incident technique (CIT) as a complementary approach to IS user behavior study to traditional theory-based models in the context of the smartphone market. Through a two-stage survey, both positive and negative critical incidents are collected, which are categorized into device, device-related services, network services and content services. Then, their impacts on user intention variables of loyalty and recommendation are derived by a structural equation analysis of 795 smartphone users’ responses. The results can be summarized as follows: Positive critical incidents improve user intentions whereas negative ones worsen them; among the positive incidents, the events related to network services are the most influential; Critical incidents related to application contents are the most influential among the negative; Mobile device manufacturers are the most strongly affected by critical incidents among the smartphone supply chain parties. Merits of CIT in IS user behavior study have been demonstrated: CIT’s flexible nature provides convenience in adapting to a particular IS usage context; Both positive and negative incidents are examined, yielding a complementary implication to other methods. We also revealed that a conventional wisdom of CIT to focus mainly on negative critical incidents might have to be adjusted since positive ones might be more influential depending on the particular context of IS usage.

Measuring eGovernment success: a public value approach

Measuring the success of eGovernment systems depends on how citizens perceive their value. Our understanding of success has been hampered however by (i) the rapid development and complexity of Internet technologies and (ii) the lack of conceptual bases necessary to represent the ever expanding range of success dimensions. This study proposes Public Value theory to reposition the DeLone and McLean IS Success Model in order to encompass three essential success or value clusters: efficiency, effectiveness and social value. The efficacy of this approach is demonstrated by creating a Public Value-based (Net Benefits) construct to measure IS success from the citizens' perspective within the context of eGovernment 2.0 systems. Survey responses from 347 experienced users of U.S. government Web 2.0 websites confirm that the proposed success measure is reliable and valid and that the nine-factor structure (Cost, Time, Convenience, Personalisation, Communication, Ease of Information Retrieval, Trust, Well-Informedness and Participate in Decision-Making) can explain a major portion of citizens' perceptions of eGovernment success. Additionally, the nine-factor Public Value construct was applied to three identified eGovernment user groups: Passive, Active and Participatory, in order to better understand success in specific usage contexts, including Web 2.0.

Root Modal Uses of Should, Ought to and Be Supposed to in Present-Day English: From Patterns and Profiles to Proficiency Guidelines

Many non-native speakers of English find it hard to use modal auxiliaries correctly. As far as the so-called “weak” root modals of necessity are concerned, (learner) grammars fail to provide clear guidelines about the use of these modals in specific usage contexts. While differences between should and ought to are minimised, be supposed to tends to be marginalised and its semantic profile remains vague. A study of 1200 corpus examples reveals the overlapping and distinctive usage contexts of these auxiliaries and allows us to derive the parameters that play a role in their usage: source, discourse context and strength. The article offers precise definitions of each parameter and provides a clear semantic profile for each auxiliary, thus paving the way towards more useful proficiency guidelines in (learner) grammars.

Personalized tag recommendation based on generalized rules

Tag recommendation is focused on recommending useful tags to a user who is annotating a Web resource. A relevant research issue is the recommendation of additional tags to partially annotated resources, which may be based on either personalized or collective knowledge. However, since the annotation process is usually not driven by any controlled vocabulary, the collections of user-specific and collective annotations are often very sparse. Indeed, the discovery of the most significant associations among tags becomes a challenging task. This article presents a novel personalized tag recommendation system that discovers and exploits generalized association rules, that is, tag correlations holding at different abstraction levels, to identify additional pertinent tags to suggest. The use of generalized rules relevantly improves the effectiveness of traditional rule-based systems in coping with sparse tag collections, because: (i) correlations hidden at the level of individual tags may be anyhow figured out at higher abstraction levels and (ii) low-level tag associations discovered from collective data may be exploited to specialize high-level associations discovered in the user-specific context. The effectiveness of the proposed system has been validated against other personalized approaches on real-life and benchmark collections retrieved from the popular photo-sharing system Flickr.

Online Internet traffic identification algorithm based on multistage classifier

Internet traffic classification plays an important role in network management. Many approaches have been proposed to classify different categories of Internet traffic. However, these approaches have specific usage contexts that restrict their ability when they are applied in the current network environment. For example, the port based approach cannot identify network applications with dynamic ports; the deep packet inspection approach is invalid for encrypted network applications; and the statistical based approach is time-consuming. In this paper, a novel technique is proposed to classify different categories of network applications. The port based, deep packet inspection based and statistical based approaches are integrated as a multistage classifier. The experimental results demonstrate that this approach has high recognition rate which is up to 98% and good performance of real-time for traffic identification.

Beyond Usability: The Study of Functionality of the 2.0 Online Catalogue (OPAC)

A usability test of Copac was carried out to inform developments in both its usability and functionality. Participants were assigned tasks, using specific system features, and their actions and thoughts were collected through think aloud to reveal potential issues with the system. Retrospective thinking and subsequent reflection were gathered in a focus group to provide greater insight into perceived functionality (current and potential) of the system. The study demonstrated the importance of the application of usability principles in the interface design. Many of the issues associated with search, records display, and use of specific features related to the attributes of ease of use, appearance, navigation, and terminology and provided the evidence base for re-design. The study also highlighted the importance of the users’ perceptions and expectations of functionality in the specific usage contexts. Continued user testing is recommended to inform further development of functionality in the broader contexts of information use to ensure the value and use of the system features are evident in the system design and meet the expectations of its users.

Towards Analyzing the Architectural Support Level of Usability

The user acceptance of a software product especially depends on its usability. Therefore, it is a matter that needs to be addressed early on in the software development process. In existing scenario-based software architecture analysis methods that focus on usability, the usage context is not employed to select scenarios used for analysis, although it is known that understanding a specific usage context is important to carefully design for usability. In order to address this, we propose the method Software ArchitecTural analysis of Usability Requirements realizatioN'' SATURN in this paper. It is set in the requirements context, incorporates a usage context analysis, and a simple walkthrough of a user interface prototype. The method furthermore provides a knowledge base of standardized analysis scenarios and analysis templates that are derived from general usability requirements and design patterns. By arriving at an Architectural Support Level (ASL) based on the architectural impact of analyzed scenarios, the proposed procedure model shall guide evaluators to accomplish a repeated analysis of the current architectural design for usability in a traceable way.

Search structures and algorithms for personalized ranking

As data of an unprecedented scale are becoming accessible on the Web, personalization, of narrowing down the retrieval to meet the user-specific information needs, is becoming more and more critical. For instance, while web search engines traditionally retrieve the same results for all users, they began to offer beta services to personalize the results to adapt to user-specific contexts such as prior search history or other application contexts. In a clear contrast to search engines dealing with unstructured text data, this paper studies how to enable such personalization in the context of structured data retrieval. In particular, we adopt contextual ranking model to formalize personalization as a cost-based optimization over collected contextual rankings. With this formalism, personalization can be abstracted as a cost-optimal retrieval of contextual ranking, closely matching user-specific retrieval context. With the retrieved matching context, we adopt a machine learning approach, to effectively and efficiently identify the ideal personalized ranked results for this specific user. Our empirical evaluations over synthetic and real-life data validate both the efficiency and effectiveness of our framework.

Utility-driven assessment of data quality

Data consumers assess quality within specific business contexts or decision tasks. The same data resource may have an acceptable level of quality for some contexts but this quality may be unacceptable for other contexts. However, existing data quality metrics are mostly derived impartially, disconnected from the specific contextual characteristics. This study argues for the need to revise data quality metrics and measurement techniques to incorporate and better reflect contextual assessment. It contributes to that end by developing new metrics for assessing data quality along commonly used dimensions - completeness, validity, accuracy, and currency. The metrics are driven by data utility, a conceptual measure of the business value that is associated with the data within a specific usage context. The suggested data quality measurement framework uses utility as a scaling factor for calculating quality measurements at different levels of data hierarchy. Examples are used to demonstrate the use of utility-driven assessment in real-world data management scenarios and the broader implications for data management are discussed

Designing claims for reuse in interactive systems design

Claims have been proposed as a means of expressing HCI knowledge that is associated with a specific artifact and usage context. Claims describe design trade-offs and record HCI knowledge related to a specific design, or artifact, as psychological design rationale. Claims are created in the task-artifact cycle of interactive design and evaluation. Usability evaluation establishes a claim for a specific usage context, but this can restrict subsequent reuse of claims-related knowledge. To widen the scope of reuse the knowledge contained within claims and their associated artifacts has to be classified and generalized. To address this problem a schema and method for classifying claims is introduced. The schema elaborates the description of HCI knowledge in claims and enables reuse by describing the assumptions and dependencies upon which a claim rests. Methods for generalising claims and discovering new claims from existing claims and artifacts were investigated. A factoring method for evolving child claims from parent claims and their usage scenarios is described. This employs a walkthrough technique based on Norman's model of action with questions directed at the contributions a claim makes to usability at different stages in interaction. Factoring promotes evolution of child claims that either address different aspects of task support in the same domain as the parent claim, or development of more general child claims for user-interface design. The relationships between claims are represented in maps to illustrate histories of task-artifacts investigation that lead to claims evolution either via the factoring process or by empirical investigation. The schema and method for claims evolution are illustrated by case studies of claims development in tutoring systems and claims for functional requirements for specification reuse support tools. The paper concludes with a discussion of the contribution that reusable claims can make as a repository of HCI knowledge.