Forensic identification of students outsourcing assignment projects from freelancer.com

This paper reports the increasing popularity of outsourcing academic works by university students motivated by the lure of lucrative dividends and visa opportunities. Due to a lack of formal methods in detecting such transactions, freelance websites are thriving in facilitating the trade of outsourced assignments. This is compounded by the fact that many university staff have neither the time nor training to perform complex media analysis and forensic investigations. This paper proposes a method to aid in the identification of those who outsource assignment works on the most popular site freelancer.com. We include a recent real-world case study to demonstrate the relevancy and applicability of our methodology. In this case study, a suspect attempts to evade detection via use of anti-forensics which demonstrates the capability and awareness of evasion techniques used by students.

Adopting global virtual engineering teams in AEC Projects: a qualitative meta-analysis of innovation diffusion studies

Purpose - This study aims to present an integrated conceptual model in order to highlight the major aspects of diffusion of innovations in the architecture, engineering and construction (AEC) context. To this end, a critical review of literature is conducted, accompaniedbysynthesising the findings of previous studies. The driving force behind this study is stemmed from the fragmentation of literature on innovation diffusion, and paucity of research on diffusion of Global Virtual Engineering Teams (GVETs) as the platform formany technological innovations in relevant literature. Thus, the present study is intended to facilitate filling the gap in GVETs literature. That is, the proposed model will offer a foundation for academia for grounding studies on any innovation including GVETs in the literature on innovation diffusion in the AEC context. Design/methodology/approach - This paper draws upon the qualitative meta-analysis approach encompassing a critical review of the relevant literature. To this end, the review builds upon studies found within 15 prestigious journals in AEC. The domain of this review was confined to areas described as "innovation", "innovation diffusion" and "innovation adoption", along with keywords used within a broad review of recently published GVETs literature. The rigour of review is augmented by incorporating 35 authoritative works from other disciplines published in 21 well-known journals in the manufacturing, business and management fields. Moreover, the study deploys the peer-debriefing approach through conducting unstructured interviews with five Australian scholars to verify a model presenting an aggregated summary of previous studies. Findings - The key findings of the study include the following items: Synthesising the fragmented studies on innovation diffusion in the AEC context. In doing so, a model capturing the major aspects affecting diffusion of an innovation in AEC projects is presented; providing a foundation to address the drawbacks of previous studies within the sphere of GVETs, based on the developed model. Research limitations/implications - The developed model was only enhanced using a small sample size of academics, as such not empirically validated. Originality/value - As possibly, the first literature review of innovation in the AEC context, this paper contributes to the sphere by sensitising the AEC body of knowledge on innovation diffusion as a concise conceptual model, albeit verified through the peer-debriefing approach. This study will also further establish the research field in AEC on GVETs along with other methods reliant on virtual working such as building information modelling (BIM) through providing an expanded foundation for future inquiries and creation of knowledge.

Characterization and prediction of issue-related risks in software projects

Identifying risks relevant to a software project and planning measures to deal with them are critical to the success of the project. Current practices in risk assessment mostly rely on high-level, generic guidance or the subjective judgements of experts. In this paper, we propose a novel approach to risk assessment using historical data associated with a software project. Specifically, our approach identifies patterns of past events that caused project delays, and uses this knowledge to identify risks in the current state of the project. A set of risk factors characterizing “risky” software tasks (in the form of issues) were extracted from five open source projects: Apache, Duraspace, JBoss, Moodle, and Spring. In addition, we performed feature selection using a sparse logistic regression model to select risk factors with good discriminative power. Based on these risk factors, we built predictive models to predict if an issue will cause a project delay. Our predictive models are able to predict both the risk impact (i.e. the extend of the delay) and the likelihood of a risk occurring. The evaluation results demonstrate the effectiveness of our predictive models, achieving on average 48%-81% precision, 23%-90% recall, 29%-71% F-measure, and 70%-92% Area Under the ROC Curve. Our predictive models also have low error rates: 0.39-0.75 for Macro-averaged Mean Cost-Error and 0.7-1.2 for Macro-averaged Mean Absolute Error.