2017 IEEE International Conference on Intelligence and Security Informatics (ISI), 2017
Social media has become an important platform for people to express opinions, share information a... more Social media has become an important platform for people to express opinions, share information and communicate with others. Detecting and tracking topics from social media can help people grasp essential information and facilitate many security-related applications. As social media texts are usually short, traditional topic evolution models built based on LDA or HDP often suffer from the data sparsity problem. Recently proposed topic evolution models are more suitable for short texts, but they need to manually specify topic number which is fixed during different time period. To address these issues, in this paper, we propose a nonparametric topic evolution model for social media short texts. We first propose the recurrent semantic dependent Chinese restaurant process (rsdCRP), which is a nonparametric process incorporating word embeddings to capture semantic similarity information. Then we combine rsdCRP with word co-occurrence modeling and build our short-text oriented topic evolu...
This article summarizes recent progress in developing a validated computational account of the co... more This article summarizes recent progress in developing a validated computational account of the cognitive antecedents and consequences of emotion. We describe the potential of this work to impact a variety of AI problem domains.
Deep neural networks (DNNs) have been verified to be easily attacked by well-designed adversarial... more Deep neural networks (DNNs) have been verified to be easily attacked by well-designed adversarial perturbations. Image objects with small perturbations that are imperceptible to human eyes can induce DNN-based image class classifiers towards making erroneous predictions with high probability. Adversarial perturbations can also fool real-world machine learning systems and transfer between different architectures and datasets. Recently, defense methods against adversarial perturbations have become a hot topic and attracted much attention. A large number of works have been put forward to defend against adversarial perturbations, enhancing DNN robustness against potential attacks, or interpreting the origin of adversarial perturbations. In this article, we provide a comprehensive survey on classical and state-of-the-art defense methods by illuminating their main concepts, in-depth algorithms, and fundamental hypotheses regarding the origin of adversarial perturbations. In addition, we f...
... As a member of the USC Computational Emotion Group, I have learned so much from the lively di... more ... As a member of the USC Computational Emotion Group, I have learned so much from the lively discussions in the group. I was also provided plenty of chance to present my work ii ... mistake. Living in Los Angeles, I am fortunate to learn from Bernard Weiner. Bernie has led me ...
2017 IEEE International Conference on Intelligence and Security Informatics (ISI), 2017
Social media has become an important platform for people to express opinions, share information a... more Social media has become an important platform for people to express opinions, share information and communicate with others. Detecting and tracking topics from social media can help people grasp essential information and facilitate many security-related applications. As social media texts are usually short, traditional topic evolution models built based on LDA or HDP often suffer from the data sparsity problem. Recently proposed topic evolution models are more suitable for short texts, but they need to manually specify topic number which is fixed during different time period. To address these issues, in this paper, we propose a nonparametric topic evolution model for social media short texts. We first propose the recurrent semantic dependent Chinese restaurant process (rsdCRP), which is a nonparametric process incorporating word embeddings to capture semantic similarity information. Then we combine rsdCRP with word co-occurrence modeling and build our short-text oriented topic evolu...
This article summarizes recent progress in developing a validated computational account of the co... more This article summarizes recent progress in developing a validated computational account of the cognitive antecedents and consequences of emotion. We describe the potential of this work to impact a variety of AI problem domains.
Deep neural networks (DNNs) have been verified to be easily attacked by well-designed adversarial... more Deep neural networks (DNNs) have been verified to be easily attacked by well-designed adversarial perturbations. Image objects with small perturbations that are imperceptible to human eyes can induce DNN-based image class classifiers towards making erroneous predictions with high probability. Adversarial perturbations can also fool real-world machine learning systems and transfer between different architectures and datasets. Recently, defense methods against adversarial perturbations have become a hot topic and attracted much attention. A large number of works have been put forward to defend against adversarial perturbations, enhancing DNN robustness against potential attacks, or interpreting the origin of adversarial perturbations. In this article, we provide a comprehensive survey on classical and state-of-the-art defense methods by illuminating their main concepts, in-depth algorithms, and fundamental hypotheses regarding the origin of adversarial perturbations. In addition, we f...
... As a member of the USC Computational Emotion Group, I have learned so much from the lively di... more ... As a member of the USC Computational Emotion Group, I have learned so much from the lively discussions in the group. I was also provided plenty of chance to present my work ii ... mistake. Living in Los Angeles, I am fortunate to learn from Bernard Weiner. Bernie has led me ...
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