Rui Wang

Shaobo Liu, Guiran Liu, Binrong Zhu et al.

This research addresses privacy protection in Natural Language Processing (NLP) by introducing a novel algorithm based on differential privacy, aimed at safeguarding user data in common applications such as chatbots, sentiment analysis, and machine translation. With the widespread application of NLP technology, the security and privacy protection of user data have become important issues that need to be solved urgently. This paper proposes a new privacy protection algorithm designed to effectively prevent the leakage of user sensitive information. By introducing a differential privacy mechanism, our model ensures the accuracy and reliability of data analysis results while adding random noise. This method not only reduces the risk caused by data leakage but also achieves effective processing of data while protecting user privacy. Compared to traditional privacy methods like data anonymization and homomorphic encryption, our approach offers significant advantages in terms of computational efficiency and scalability while maintaining high accuracy in data analysis. The proposed algorithm's efficacy is demonstrated through performance metrics such as accuracy (0.89), precision (0.85), and recall (0.88), outperforming other methods in balancing privacy and utility. As privacy protection regulations become increasingly stringent, enterprises and developers must take effective measures to deal with privacy risks. Our research provides an important reference for the application of privacy protection technology in the field of NLP, emphasizing the need to achieve a balance between technological innovation and user privacy. In the future, with the continuous advancement of technology, privacy protection will become a core element of data-driven applications and promote the healthy development of the entire industry.

Haocheng Ren, Hao Zhang, Jia Zheng et al.

With the rapid development of data-driven techniques, data has played an essential role in various computer vision tasks. Many realistic and synthetic datasets have been proposed to address different problems. However, there are lots of unresolved challenges: (1) the creation of dataset is usually a tedious process with manual annotations, (2) most datasets are only designed for a single specific task, (3) the modification or randomization of the 3D scene is difficult, and (4) the release of commercial 3D data may encounter copyright issue. This paper presents MINERVAS, a Massive INterior EnviRonments VirtuAl Synthesis system, to facilitate the 3D scene modification and the 2D image synthesis for various vision tasks. In particular, we design a programmable pipeline with Domain-Specific Language, allowing users to (1) select scenes from the commercial indoor scene database, (2) synthesize scenes for different tasks with customized rules, and (3) render various imagery data, such as visual color, geometric structures, semantic label. Our system eases the difficulty of customizing massive numbers of scenes for different tasks and relieves users from manipulating fine-grained scene configurations by providing user-controllable randomness using multi-level samplers. Most importantly, it empowers users to access commercial scene databases with millions of indoor scenes and protects the copyright of core data assets, e.g., 3D CAD models. We demonstrate the validity and flexibility of our system by using our synthesized data to improve the performance on different kinds of computer vision tasks.

Colleen E. Crangle, Rui Wang, Marcos Perreau-Guimaraes et al.

The automatic recognition of emotion in speech can inform our understanding of language, emotion, and the brain. It also has practical application to human-machine interactive systems. This paper examines the recognition of emotion in naturally occurring speech, where there are no constraints on what is said or the emotions expressed. This task is more difficult than that using data collected in scripted, experimentally controlled settings, and fewer results are published. Our data come from couples in psychotherapy. Video and audio recordings were made of three couples (A, B, C) over 18 hour-long therapy sessions. This paper describes the method used to code the audio recordings for the four emotions of Anger, Sadness, Joy and Tension, plus Neutral, also covering our approach to managing the unbalanced samples that a naturally occurring emotional speech dataset produces. Three groups of acoustic features were used in our analysis: filter-bank, frequency, and voice-quality features. The random forests model classified the features. Recognition rates are reported for each individual, the result of the speaker-dependent models that we built. In each case, the best recognition rates were achieved using the filter-bank features alone. For Couple A, these rates were 90% for the female and 87% for the male for the recognition of three emotions plus Neutral. For Couple B, the rates were 84% for the female and 78% for the male for the recognition of all four emotions plus Neutral. For Couple C, a rate of 88% was achieved for the female for the recognition of the four emotions plus Neutral and 95% for the male for three emotions plus Neutral. For pairwise recognition, the rates ranged from 76% to 99% across the three couples. Our results show that couple therapy is a rich context for the study of emotion in naturally occurring speech.