A fuzzy model for reasoning about reputation in web services

Reputation systems are typically based on ratings given by the users. When there are no mechanisms in place to de-tect collusion and deception, combining user testimonies as such to form a provider’s reputation may not give an ac-curate assessment, especially if the context of the ratings is not known. Moreover, such systems are vulnerable to manipulations by malicious users. Hence it becomes essen-tial to establish the validity of the ratings prior to using them in formulating reputation based on such ratings. It is important to identify the rationale behind the ratings so that similar ratings (or ratings pertaining to a context) can be aggregated to obtain a reputation value meaningful in that context. We propose a fuzzy approach to analyze user rating behavior to infer the rationale for ratings in a web services environment. This inference of rationale facilitates the system to validate ratings, detect deception and collu-sion, identify user preferences and provide recommendations to users

Cognitive Compliance: Assessing Regulatory Risk in Financial Advice Documents

This paper describes Cognitive Compliance - a solution that automates the complex manual process of assessing regulatory compliance of personal financial advice. The solution uses natural language processing (NLP), machine learning and deep learning to characterise the regulatory risk status of personal financial advice documents with traffic light rating for various risk factors. This enables comprehensive coverage of the review and rapid identification of documents at high risk of non-compliance with government regulations

End-to-End Service Support for Mashups

We propose a service-oriented approach to generate and manage mashups. The proposed approach is realized using the Mashup Services System (MSS), a novel platform to support users to create, use, and manage mashups with little or no programming effort. The proposed approach relieves users from programming-intensive, error-prone, and largely nonreusable output process for creating and maintaining mashups. We describe the overall design of MSS and discuss and evaluate its main enabling technologies