Zhipeng Xue

Zhipeng Xue, Yan Zhang, Ming Li et al.

Existing multi-view classification and clustering methods typically improve task accuracy by leveraging and fusing information from different views. However, ensuring the reliability of multi-view integration and final decisions is crucial, particularly when dealing with noisy or corrupted data. Current methods often rely on Kullback-Leibler (KL) divergence to estimate uncertainty of network predictions, ignoring domain gaps between different modalities. To address this issue, KPHD-Net, based on Hölder divergence, is proposed for multi-view classification and clustering tasks. Generally, our KPHD-Net employs a variational Dirichlet distribution to represent class probability distributions, models evidences from different views, and then integrates it with Dempster-Shafer evidence theory (DST) to improve uncertainty estimation effects. Our theoretical analysis demonstrates that Proper Hölder divergence offers a more effective measure of distribution discrepancies, ensuring enhanced performance in multi-view learning. Moreover, Dempster-Shafer evidence theory, recognized for its superior performance in multi-view fusion tasks, is introduced and combined with the Kalman filter to provide future state estimations. This integration further enhances the reliability of the final fusion results. Extensive experiments show that the proposed KPHD-Net outperforms the current state-of-the-art methods in both classification and clustering tasks regarding accuracy, robustness, and reliability, with theoretical guarantees.

Zhipeng Xue, Yuanliang Zhang, Rulin Xu

When programmers retrieve a code method and want to reuse it, they need to understand the usage patterns of the retrieved method. However, it is difficult to obtain usage information of the retrieved method since this method may only have a brief comment and few available usage examples. In this paper, we propose an approach, called LUPIN (cLone-based Usage Pattern mIniNg), to mine the usage patterns of these methods, which do not widely appeared in the code repository. The key idea of LUPIN is that the cloned code of the target method may have a similar usage pattern, and we can collect more usage information of the target method from cloned code usage examples. From the amplified usage examples, we mine the usage pattern of the target method by frequent subsequence mining after program slicing and code normalization. Our evaluation shows that LUPIN can mine four categories of usage patterns with an average precision of 0.65.

Zhipeng Xue, Zhijie Jiang, Chenlin Huang et al.

Type-4 clones refer to a pair of code snippets with similar semantics but written in different syntax, which challenges the existing code clone detection techniques. Previous studies, however, highly rely on syntactic structures and textual tokens, which cannot precisely represent the semantic information of code and might introduce non-negligible noise into the detection models. To overcome these limitations, we design a novel semantic graph-based deep detection approach, called SEED. For a pair of code snippets, SEED constructs a semantic graph of each code snippet based on intermediate representation to represent the code semantic more precisely compared to the representations based on lexical and syntactic analysis. To accommodate the characteristics of Type-4 clones, a semantic graph is constructed focusing on the operators and API calls instead of all tokens. Then, SEED generates the feature vectors by using the graph match network and performs clone detection based on the similarity among the vectors. Extensive experiments show that our approach significantly outperforms two baseline approaches over two public datasets and one customized dataset. Especially, SEED outperforms other baseline methods by an average of 25.2% in the form of F1-Score. Our experiments demonstrate that SEED can reach state-of-the-art and be useful for Type-4 clone detection in practice.

Chenhao Wang, Yubo Chen, Zhipeng Xue et al.

In this paper, we present CogNet, a knowledge base (KB) dedicated to integrating three types of knowledge: (1) linguistic knowledge from FrameNet, which schematically describes situations, objects and events. (2) world knowledge from YAGO, Freebase, DBpedia and Wikidata, which provides explicit knowledge about specific instances. (3) commonsense knowledge from ConceptNet, which describes implicit general facts. To model these different types of knowledge consistently, we introduce a three-level unified frame-styled representation architecture. To integrate free-form commonsense knowledge with other structured knowledge, we propose a strategy that combines automated labeling and crowdsourced annotation. At present, CogNet integrates 1,000+ semantic frames from linguistic KBs, 20,000,000+ frame instances from world KBs, as well as 90,000+ commonsense assertions from commonsense KBs. All these data can be easily queried and explored on our online platform, and free to download in RDF format for utilization under a CC-BY-SA 4.0 license. The demo and data are available at http://cognet.top/.

Zhipeng Xue, Xiaojun Yuan, Yang Yang

In this paper, we consider the compressed video background subtraction problem that separates the background and foreground of a video from its compressed measurements. The background of a video usually lies in a low dimensional space and the foreground is usually sparse. More importantly, each video frame is a natural image that has textural patterns. By exploiting these properties, we develop a message passing algorithm termed offline denoising-based turbo message passing (DTMP). We show that these structural properties can be efficiently handled by the existing denoising techniques under the turbo message passing framework. We further extend the DTMP algorithm to the online scenario where the video data is collected in an online manner. The extension is based on the similarity/continuity between adjacent video frames. We adopt the optical flow method to refine the estimation of the foreground. We also adopt the sliding window based background estimation to reduce complexity. By exploiting the Gaussianity of messages, we develop the state evolution to characterize the per-iteration performance of offline and online DTMP. Comparing to the existing algorithms, DTMP can work at much lower compression rates, and can subtract the background successfully with a lower mean squared error and better visual quality for both offline and online compressed video background subtraction.