Sentiment Analysis on Online Videos by Time-Sync Comments.

Video highlights are welcomed by audiences, and are composed of interesting or meaningful shots, such as funny shots. However, video shots of highlights are currently edited manually by video editors, which is inconvenient and consumes an enormous amount of time. A way to help video editors locate video highlights more efficiently is essential. Since interesting or meaningful highlights in videos usually imply strong sentiments, a sentiment analysis model is proposed to automatically recognize sentiments of video highlights by time-sync comments. As the comments are synchronized with video playback time, the model detects sentiment information in time series of user comments. Moreover, in the model, a sentimental intensity calculation method is designed to compute sentiments of shots quantitatively. The experiments show that our approach improves the F1 score by 12.8% and overlapped number by 8.0% compared with the best existing method in extracting sentiments of highlights and obtaining sentimental intensities, which provides assistance for video editors in editing video highlights efficiently.

Chinese diabetes datasets for data-driven machine learning.

Data of the diabetes mellitus patients is essential in the study of diabetes management, especially when employing the data-driven machine learning methods into the management. To promote and facilitate the research in diabetes management, we have developed the ShanghaiT1DM and ShanghaiT2DM Datasets and made them publicly available for research purposes. This paper describes the datasets, which was acquired on Type 1 (n = 12) and Type 2 (n = 100) diabetic patients in Shanghai, China. The acquisition has been made in real-life conditions. The datasets contain the clinical characteristics, laboratory measurements and medications of the patients. Moreover, the continuous glucose monitoring readings with 3 to 14 days as a period together with the daily dietary information are also provided. The datasets can contribute to the development of data-driven algorithms/models and diabetes monitoring/managing technologies.

A Dual-Stage Attention Model for Tool Wear Prediction in Dry Milling Operation.

The intelligent monitoring of tool wear status and wear prediction are important factors affecting the intelligent development of the modern machinery industry. Many scholars have used deep learning methods to achieve certain results in tool wear prediction. However, due to the instability and variability of the signal data, some neural network models may have gradient decay between layers. Most methods mainly focus on feature selection of the input data but ignore the influence degree of different features to tool wear. In order to solve these problems, this paper proposes a dual-stage attention model for tool wear prediction. A CNN-BiGRU-attention network model is designed, which introduces the self-attention to extract deep features and embody more important features. The IndyLSTM is used to construct a stable network to solve the gradient decay problem between layers. Moreover, the attention mechanism is added to the network to obtain the important information of output sequence, which can improve the accuracy of the prediction. Experimental study is carried out for tool wear prediction in a dry milling operation to demonstrate the viability of this method. Through the experimental comparison and analysis with regression prediction evaluation indexes, it proves the proposed method can effectively characterize the degree of tool wear, reduce the prediction errors, and achieve good prediction results.

Inter- and Intra-Modal Contrastive Hybrid Learning Framework for Multimodal Abstractive Summarization.

Internet users are benefiting from technologies of abstractive summarization enabling them to view articles on the internet by reading article summaries only instead of an entire article. However, there are disadvantages to technologies for analyzing articles with texts and images due to the semantic gap between vision and language. These technologies focus more on aggregating features and neglect the heterogeneity of each modality. At the same time, the lack of consideration of intrinsic data properties within each modality and semantic information from cross-modal correlations result in the poor quality of learned representations. Therefore, we propose a novel Inter- and Intra-modal Contrastive Hybrid learning framework which learns to automatically align the multimodal information and maintains the semantic consistency of input/output flows. Moreover, ITCH can be taken as a component to make the model suitable for both supervised and unsupervised learning approaches. Experiments on two public datasets, MMS and MSMO, show that the ITCH performances are better than the current baselines.

Secure Obfuscation for Encrypted Group Signatures.

In recent years, group signature techniques are widely used in constructing privacy-preserving security schemes for various information systems. However, conventional techniques keep the schemes secure only in normal black-box attack contexts. In other words, these schemes suppose that (the implementation of) the group signature generation algorithm is running in a platform that is perfectly protected from various intrusions and attacks. As a complementary to existing studies, how to generate group signatures securely in a more austere security context, such as a white-box attack context, is studied in this paper. We use obfuscation as an approach to acquire a higher level of security. Concretely, we introduce a special group signature functionality-an encrypted group signature, and then provide an obfuscator for the proposed functionality. A series of new security notions for both the functionality and its obfuscator has been introduced. The most important one is the average-case secure virtual black-box property w.r.t. dependent oracles and restricted dependent oracles which captures the requirement of protecting the output of the proposed obfuscator against collision attacks from group members. The security notions fit for many other specialized obfuscators, such as obfuscators for identity-based signatures, threshold signatures and key-insulated signatures. Finally, the correctness and security of the proposed obfuscator have been proven. Thereby, the obfuscated encrypted group signature functionality can be applied to variants of privacy-preserving security schemes and enhance the security level of these schemes.