Garbage in, garbage out: Zero-shot detection of crime using Large Language Models

This paper proposes exploiting the common sense knowledge learned by large language models to perform zero-shot reasoning about crimes given textual descriptions of surveillance videos. We show that when video is (manually) converted to high quality textual descriptions, large language models are capable of detecting and classifying crimes with state-of-the-art performance using only zero-shot reasoning. However, existing automated video-to-text approaches are unable to generate video descriptions of sufficient quality to support reasoning (garbage video descriptions into the large language model, garbage out).Comment: 5 pages, 4 figures, 1 tabl

Toward a Social Media Usage Policy

Social media has become the archetype of technology underpinning communication and collaboration across all lifestyles, from the personal to the public. Despite its increasing deployment into corporate technology infrastructures, the encroachment of social media poses several doubts, including its business value, need for a social media strategy and its appropriate management. Although given a pressing need, there is a lack of clear guidance from IS literature around how to study these challenges, and ultimately to answer the question— is the use of social media a distraction at the work place? This research-in-progress paper lays the foundation to answer this specific question. Our work positions social media as a platform that can enable business and service value co-creation. We propose the Social Media -Beliefs, Action and Outcomes (SM-BAO) model, to help develop a framework that can inform social media use policy in the workplace

Leveraging Emerging Web Technologies for Community Engagement Project Success in Higher Education

The widespread availability of Web 2.0 technologies such as Facebook and Twitter has led to the adoption in a number of community engagement projects. Unfortunately, the breath and depth of these web technologies leads to a disjointed and incoherent adoption. In light of the above, there is a need for a model to structure its planning and execution. In this article, we present a model to assist community engagement projects. The model comprises of four crucial dimensions: functional quality, degree of psychological attachment, hedonic attitude of members and amount of social relationships. We discuss how each dimension can leverage on Web 2.0 technology capabilities in the context of uniS—the Information and Communications Faculty in a leading Australian University. The emphasis on community engagement follows for one, strategic recommendations proposed through Australian Universities Quality Agency (AUQA) reviews. Given this, we discuss two specific initiatives currently in place at UniS that attempts to improve community engagement. The implications of this article are twofold. For educators, it recommends a set of considerations for establishing and designing community engagement programs and initiatives for higher education. For managers, it proposes a tool for systematically evaluating engagement success of initiatives within a community of practice

Requirements of API Documentation: A Case Study into Computer Vision Services

Using cloud-based computer vision services is gaining traction, where developers access AI-powered components through familiar RESTful APIs, not needing to orchestrate large training and inference infrastructures or curate/label training datasets. However, while these APIs seem familiar to use, their non-deterministic run-time behaviour and evolution is not adequately communicated to developers. Therefore, improving these services' API documentation is paramount-more extensive documentation facilitates the development process of intelligent software. In a prior study, we extracted 34 API documentation artefacts from 21 seminal works, devising a taxonomy of five key requirements to produce quality API documentation. We extend this study in two ways. Firstly, by surveying 104 developers of varying experience to understand what API documentation artefacts are of most value to practitioners. Secondly, identifying which of these highly-valued artefacts are or are not well-documented through a case study in the emerging computer vision service domain. We identify: (i) several gaps in the software engineering literature, where aspects of API documentation understanding is/is not extensively investigated; and (ii) where industry vendors (in contrast) document artefacts to better serve their end-developers. We provide a set of recommendations to enhance intelligent software documentation for both vendors and the wider research community.Comment: Early Access preprint for an upcoming issue of the IEEE Transactions on Software Engineerin

How Usability Defects Defer from Non-Usability Defects? : A Case Study on Open Source Projects

Usability is one of the software qualities attributes that is subjective and often considered as a less critical defect to be fixed. One of the reasons was due to the vague defect descriptions that could not convince developers about the validity of usability issues. Producing a comprehensive usability defect description can be a challenging task, especially in reporting relevant and important information. Prior research in improving defect report comprehension has often focused on defects in general or studied various aspects of software quality improvement such as triaging defect reports, metrics and predictions, automatic defect detection and fixing.  In this paper, we studied 2241 usability and non-usability defects from three open-source projects - Mozilla Thunderbird, Firefox for Android, and Eclipse Platform. We examined the presence of eight defect attributes - steps to reproduce, impact, software context, expected output, actual output, assume cause, solution proposal, and supplementary information, and used various statistical tests to answer the research questions. In general, we found that usability defects are resolved slower than non-usability defects, even for non-usability defect reports that have less information. In terms of defect report content, usability defects often contain output details and software context while non-usability defects are preferably explained using supplementary information, such as stack traces and error logs. Our research findings extend the body of knowledge of software defect reporting, especially in understanding the characteristics of usability defects. The promising results also may be valuable to improve software development practitioners' practice

A large-scale comparative analysis of Coding Standard conformance in Open-Source Data Science projects

Background: Meeting the growing industry demand for Data Science requires cross-disciplinary teams that can translate machine learning research into production-ready code. Software engineering teams value adherence to coding standards as an indication of code readability, maintainability, and developer expertise. However, there are no large-scale empirical studies of coding standards focused specifically on Data Science projects. Aims: This study investigates the extent to which Data Science projects follow code standards. In particular, which standards are followed, which are ignored, and how does this differ to traditional software projects? Method: We compare a corpus of 1048 Open-Source Data Science projects to a reference group of 1099 non-Data Science projects with a similar level of quality and maturity. Results: Data Science projects suffer from a significantly higher rate of functions that use an excessive numbers of parameters and local variables. Data Science projects also follow different variable naming conventions to non-Data Science projects. Conclusions: The differences indicate that Data Science codebases are distinct from traditional software codebases and do not follow traditional software engineering conventions. Our conjecture is that this may be because traditional software engineering conventions are inappropriate in the context of Data Science projects.Comment: 11 pages, 7 figures. To appear in ESEM 2020. Updated based on peer revie