Since 2014, Microservice Architecture (MSA) has been widely applied and deployed by big companies such as Google, Netflix and Twitter. This is a way of architecting software systems in which the services of a single application are decomposed then deployed and executed separately. This research examines the possibility of applying Spring Security Framework and OAuth2 to secure microservice APIs which are built on top of Spring Framework. By developing a Proof of Concept (POC) of an Inventory Management System using MSA on top of Spring Framework, Spring Security Framework and OAuth2. we have conducted security tests over the POC using unit testing and manual testing techniques to examine if there are any vulnerabilities and we were able to show and confirm the effectiveness of the Spring Security Framework and OAuth2 in securing Spring-based APIs.

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Recently, a number of voice conversion methods have been developed. These methods attempt to improve conversion performance by using diverse mapping techniques in various acoustic domains, e.g. high-resolution spectra and low-resolution Mel-cepstral coefficients. Each individual method has its own pros and cons. In this paper, we introduce a system fusion framework, which leverages and synergizes the merits of these state-of-the-art and even potential future conversion methods. For instance, methods delivering high speech quality are fused with methods capturing speaker characteristics, bringing another level of performance gain. To examine the feasibility of the proposed framework, we select two state-of-the-art methods, Gaussian mixture model and frequency warping based systems, as a case study. Experimental results reveal that the fusion system outperforms each individual method in both objective and subjective evaluation, and demonstrate the effectiveness of the proposed fusion framework.

An automatic voice conversion evaluation strategy based on perceptual background noise distortion and speaker similarity

With the rapid growth of users' data in SaaS (Software-as-a-service) platforms using micro-services, it becomes essential to detect duplicated entities for ensuring the integrity and consistency of data in many companies and businesses (primarily multinational corporations). Due to the large volume of databases today, the expected duplicate detection algorithms need to be not only accurate but also practical, which means that it can release the detection results as fast as possible for a given request. Among existing algorithms for the deduplicate detection problem, using Siamese neural networks with the triplet loss has become one of the robust ways to measure the similarity of two entities (texts, paragraphs, or documents) for identifying all possible duplicated items. In this paper, we first propose a practical framework for building a duplicate detection system in a SaaS platform. Second, we present a new active learning schema for training and updating duplicate detection algorithms. In this schema, we not only allow the crowd to provide more annotated data for enhancing the chosen learning model but also use the Siamese neural networks as well as the triplet loss to construct an efficient model for the problem. Finally, we design a user interface of our proposed deduplicate detection system, which can easily apply for empirical applications in different companies.