229,686 research outputs found
Culture in the design of mHealth UI:An effort to increase acceptance among culturally specific groups
Purpose: Designers of mobile applications have long understood the importance of usersâ preferences in making the user experience easier, convenient and therefore valuable. The cultural aspects of groups of users are among the key features of usersâ design preferences, because each groupâs preferences depend on various features that are culturally compatible. The process of integrating culture into the design of a system has always been an important ingredient for effective and interactive human computer interface. This study aims to investigate the design of a mobile health (mHealth) application user interface (UI) based on Arabic culture. It was argued that integrating certain cultural values of specific groups of users into the design of UI would increase their acceptance of the technology. Design/methodology/approach: A total of 135 users responded to an online survey about their acceptance of a culturally designed mHealth. Findings: The findings showed that culturally based language, colours, layout and images had a significant relationship with usersâ behavioural intention to use the culturally based mHealth UI. Research limitations/implications: First, the sample and the data collected of this study were restricted to Arab users and Arab culture; therefore, the results cannot be generalized to other cultures and users. Second, the adapted unified theory of acceptance and use of technology model was used in this study instead of the new version, which may expose new perceptions. Third, the cultural aspects of UI design in this study were limited to the images, colours, language and layout. Practical implications: It encourages UI designers to implement the relevant cultural aspects while developing mobile applications. Originality/value: Embedding Arab cultural aspects in designing UI for mobile applications to satisfy Arab users and enhance their acceptance toward using mobile applications, which will reflect positively on their lives.</p
Quantitative Safety: Linking Proof-Based Verification with Model Checking for Probabilistic Systems
This paper presents a novel approach for augmenting proof-based verification
with performance-style analysis of the kind employed in state-of-the-art model
checking tools for probabilistic systems. Quantitative safety properties
usually specified as probabilistic system invariants and modeled in proof-based
environments are evaluated using bounded model checking techniques.
Our specific contributions include the statement of a theorem that is central
to model checking safety properties of proof-based systems, the establishment
of a procedure; and its full implementation in a prototype system (YAGA) which
readily transforms a probabilistic model specified in a proof-based environment
to its equivalent verifiable PRISM model equipped with reward structures. The
reward structures capture the exact interpretation of the probabilistic
invariants and can reveal succinct information about the model during
experimental investigations. Finally, we demonstrate the novelty of the
technique on a probabilistic library case study
Sensemaking Practices in the Everyday Work of AI/ML Software Engineering
This paper considers sensemaking as it relates to everyday software engineering (SE) work practices and draws on a multi-year ethnographic study of SE projects at a large, global technology company building digital services infused with artificial intelligence (AI) and machine learning (ML) capabilities. Our findings highlight the breadth of sensemaking practices in AI/ML projects, noting developers' efforts to make sense of AI/ML environments (e.g., algorithms/methods and libraries), of AI/ML model ecosystems (e.g., pre-trained models and "upstream"models), and of business-AI relations (e.g., how the AI/ML service relates to the domain context and business problem at hand). This paper builds on recent scholarship drawing attention to the integral role of sensemaking in everyday SE practices by empirically investigating how and in what ways AI/ML projects present software teams with emergent sensemaking requirements and opportunities
Validating specifications of dynamic systems using automated reasoning techniques
In this paper, we propose a new approach to validating formal specifications of observable behavior of discrete dynamic systems. By observable behavior we mean system behavior as observed by users or other systems in the environment of the system. Validation of a formal specification of an informal domain tries to answer the question whether the specification actually describes the intended domain. This differs from the verification problem, which deals with the correspondence between formal objects, e.g. between a formal specification of a system and an implementation of it. We consider formal specifications of object-oriented dynamic systems that are subject to static and dynamic integrity constraints. To validate that such a specification expresses the intended behavior, we propose to use a tool that can answer reachability queries. In a reachability query we ask whether the system can evolve from one state into another without violating the integrity constraints. If the query is answered positively, the system should exhibit an example path between the states; if the answer is negative, the system should explain why this is so. An example path produced by the tool can be used to produce scenarios for presentations of system behavior, but can also be used as a basis for acceptance testing. In this paper, we discuss the use of planning and theoremproving techniques to answer such queries, and illustrate the use of reachability queries in the context of information system development
Modelling of Multi-Agent Systems: Experiences with Membrane Computing and Future Challenges
Formal modelling of Multi-Agent Systems (MAS) is a challenging task due to
high complexity, interaction, parallelism and continuous change of roles and
organisation between agents. In this paper we record our research experience on
formal modelling of MAS. We review our research throughout the last decade, by
describing the problems we have encountered and the decisions we have made
towards resolving them and providing solutions. Much of this work involved
membrane computing and classes of P Systems, such as Tissue and Population P
Systems, targeted to the modelling of MAS whose dynamic structure is a
prominent characteristic. More particularly, social insects (such as colonies
of ants, bees, etc.), biology inspired swarms and systems with emergent
behaviour are indicative examples for which we developed formal MAS models.
Here, we aim to review our work and disseminate our findings to fellow
researchers who might face similar challenges and, furthermore, to discuss
important issues for advancing research on the application of membrane
computing in MAS modelling.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314
Software Engineers' Information Seeking Behavior in Change Impact Analysis - An Interview Study
Software engineers working in large projects must navigate complex
information landscapes. Change Impact Analysis (CIA) is a task that relies on
engineers' successful information seeking in databases storing, e.g., source
code, requirements, design descriptions, and test case specifications. Several
previous approaches to support information seeking are task-specific, thus
understanding engineers' seeking behavior in specific tasks is fundamental. We
present an industrial case study on how engineers seek information in CIA, with
a particular focus on traceability and development artifacts that are not
source code. We show that engineers have different information seeking
behavior, and that some do not consider traceability particularly useful when
conducting CIA. Furthermore, we observe a tendency for engineers to prefer less
rigid types of support rather than formal approaches, i.e., engineers value
support that allows flexibility in how to practically conduct CIA. Finally, due
to diverse information seeking behavior, we argue that future CIA support
should embrace individual preferences to identify change impact by empowering
several seeking alternatives, including searching, browsing, and tracing.Comment: Accepted for publication in the proceedings of the 25th International
Conference on Program Comprehensio
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