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Dan Peng

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3 papers

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AIIM Journal 2022 Journal Article

The interactive fuzzy linguistic term set and its application in multi-attribute decision making

Dan Peng
Jie Wang
Donghai Liu
Yu Cheng

In multi-attribute decision making problems, some decision information interact with each other. The paper proposes an interactive fuzzy linguistic term set to describe the interactive information in multi-attribute decision making problems. The properties of the interactive fuzzy linguistic term set and its advantages of improving the consistency of decision information are discussed, which are also interpreted from the geometric point of view. Meanwhile, some numerical examples are given to illustrate its application in dealing with the interactive information in multi-attribute decision making problems, which can improve the effectiveness of the decision results and promote the development of artificial intelligence.

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ECAI Conference 2020 Conference Paper

Structure Matters: Towards Generating Transferable Adversarial Images

Dan Peng
Zizhan Zheng
Linhao Luo
Xiaofeng Zhang 0002

Recent works on adversarial examples for image classification focus on directly modifying pixels with minor perturbations. The small perturbation requirement is imposed to ensure the generated adversarial examples being natural and realistic to humans, which, however, puts a curb on the attack space thus limiting the attack ability and transferability especially for systems protected by a defense mechanism. In this paper, we propose the novel concepts of structure patterns and structure-aware perturbations that relax the small perturbation constraint while still keeping images natural. The key idea of our approach is to allow perceptible deviation in adversarial examples while keeping structure patterns that are central to a human classifier. Built upon these concepts, we propose a structure-preserving attack (SPA) for generating natural adversarial examples with extremely high transferability. Empirical results on the MNIST and the CIFAR10 datasets show that SPA exhibits strong attack ability in both the white-box and black-box setting even defenses are applied. Moreover, with the integration of PGD or CW attack, its attack ability escalates sharply under the white-box setting, without losing the outstanding transferability inherited from SPA.

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AAAI Conference 2019 Conference Paper

Incorporating Semantic Similarity with Geographic Correlation for Query-POI Relevance Learning

Ji Zhao
Dan Peng
Chuhan Wu
Huan Chen
Meiyu Yu
Wanji Zheng
Li Ma
Hua Chai

Point-of-interest (POI) retrieval that searches for relevant destination locations plays a significant role in on-demand ridehailing services. Existing solutions to POI retrieval mainly retrieve and rank POIs based on their semantic similarity scores. Although intuitive, quantifying the relevance of a Query-POI pair by single-field semantic similarity is subject to inherent limitations. In this paper, we propose a novel Query-POI relevance model for effective POI retrieval for ondemand ride-hailing services. Different from existing relevance models, we capture and represent multi-field and local&global semantic features of a Query-POI pair to measure the semantic similarity. Besides, we observe a hidden correlation between origin-destination locations in ride-hailing scenarios, and propose two location embeddings to characterize the specific correlation. By incorporating the geographic correlation with the semantic similarity, our model achieves better performance in POI ranking. Experimental results on two real-world click-through datasets demonstrate the improvements of our model over state-of-the-art methods.

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