The state-of-the-art metric-learning algorithms cannot perform well for domain adaptation settings, such as cross-domain face recognition, image annotation, etc., because labeled data in the source domain and unlabeled ...

This paper presents an efficient and effective application-independent database cluster for mining multi-databases. Our experimental results show that the proposed approach is effective and promising.

Traditional association rules mining algorithm depends on two user-specified thresholds. One is the minimum support, which is statistically required. Another is the minimum confidence. Users or even experts are difficult ...

Elizabeth Mulhollann from the Library's eScholarship Unit and Luc Small from Intersect discuss national developments and initiatives in Data Management processes and policy and how these can benefit researchers at UTS.

Collaborative virtual environments are becoming an intrinsic part of professional practices. In addition to providing communication and collaboration means, they have the potential to collect tremendous amounts of data ...

In this paper we propose a formal framework for dealing with the inconsistency in secure messages with weights, WhIChtakes into account both the freshness and dynamic properties of secure messages. This enables us to ...

Abstract: In this paper we discuss problems related to the teaching of object-oriented programming (OOP). We argue that more research on how the computer science teacher understands OOP would be beneficial. ...

Debt detection is important for improving payment accuracy in social security. Since debt detection from customer transactional data can be generally modelled as a fraud detection problem, a straightforward solution is to ...