In the domain of sound event detection (SED), Convolutional Recurrent Neural Network (CRNN) has become the most successful architecture, which adopts Recurrent Neural Network (RNN) to model temporal dependencies from the output of Convolutional Neural Network (CNN). However, CRNN does not fully use the subband dependencies that have been proved critical for human perception of sound events. In this paper, we propose a subband dependency model (SDM) to enhance the capability of CRNN in modeling subband dependencies from the input spectrogram. To select prominent subband dependencies, we propose a novel SoftSparsemax transformation. It can select the salient parts by comparing all dependencies and further strengthen them by projecting them onto a probability simplex. Furthermore, since subband dependencies of different sound events may be prominent in different timescales, multi-timescale subband dependency is considered. The experiment results demonstrate the effectiveness of our method.

Data privacy is an essential issue in publishing data visualizations. However, it is challenging to represent multiple data patterns in privacy-preserving visualizations. The prior approaches target specific chart types or perform an anonymization model uniformly without considering the importance of data patterns in visualizations. In this paper, we propose a visual analytics approach that facilitates data custodians to generate multiple private charts while maintaining user-preferred patterns. To this end, we introduce pattern constraints to model users' preferences over data patterns in the dataset and incorporate them into the proposed Bayesian network-based Differential Privacy (DP) model PriVis. A prototype system, DPVisCreator, is developed to assist data custodians in implementing our approach. The effectiveness of our approach is demonstrated with quantitative evaluation of pattern utility under the different levels of privacy protection, case studies, and semi-structured expert interviews.

Collusive fraud, in which multiple fraudsters collude to defraud health insurance funds, threatens the operation of the healthcare system. However, existing statistical and machine learning-based methods have limited ability to detect fraud in the scenario of health insurance due to the high similarity of fraudulent behaviors to normal medical visits and the lack of labeled data. To ensure the accuracy of the detection results, expert knowledge needs to be integrated with the fraud detection process. By working closely with health insurance audit experts, we propose FraudAuditor, a three-stage visual analytics approach to collusive fraud detection in health insurance. Specifically, we first allow users to interactively construct a co-visit network to holistically model the visit relationships of different patients. Second, an improved community detection algorithm that considers the strength of fraud likelihood is designed to detect suspicious fraudulent groups. Finally, through our visual interface, users can compare, investigate, and verify suspicious patient behavior with tailored visualizations that support different time scales. We conducted case studies in a real-world healthcare scenario, i.e., to help locate the actual fraud group and exclude the false positive group. The results and expert feedback proved the effectiveness and usability of the approach.

LSP has many advantages for speech representation, especially correlates well to spectrum formants as long as the LSP parameters are strictly ordered and bounded. This ordering property cannot be guaranteed during HMM-based speech synthesis when LSP is adopted as the spectrum feature, because diagonal covariance is utilized and correlation between LSP dimensions is ignored, with the result that unstable issue will be caused in synthesized speech. In this paper, we modify the parameter generation criterion to preserve ordering property of generated LSPs, by considering not only the likelihoods for HMM and GV maximized in conventional method but also a mis-orderings penalty. Experimental results show that the proposed method can alleviate the mis-orderings significantly and achieve high quality synthesizing performance when the penalty weight is selected appropriately.