Guang-Gang Geng

Yi-Xiang Lu, Xiao-Bo Jin, Jian Chen et al.

With the development of society, time series anomaly detection plays an important role in network and IoT services. However, most existing anomaly detection methods directly analyze time series in the time domain and cannot distinguish some relatively hidden anomaly sequences. We attempt to analyze the impact of frequency on time series from a frequency domain perspective, thus proposing a new time series anomaly detection method called F-SE-LSTM. This method utilizes two sliding windows and fast Fourier transform (FFT) to construct a frequency matrix. Simultaneously, Squeeze-and-Excitation Networks (SENet) and Long Short-Term Memory (LSTM) are employed to extract frequency-related features within and between periods. Through comparative experiments on multiple datasets such as Yahoo Webscope S5 and Numenta Anomaly Benchmark, the results demonstrate that the frequency matrix constructed by F-SE-LSTM exhibits better discriminative ability than ordinary time domain and frequency domain data. Furthermore, F-SE-LSTM outperforms existing state-of-the-art deep learning anomaly detection methods in terms of anomaly detection capability and execution efficiency.

Xiao-Bo Jin, Xu-Yao Zhang, Kaizhu Huang et al.

Conjugate gradient (CG) methods are a class of important methods for solving linear equations and nonlinear optimization problems. In this paper, we propose a new stochastic CG algorithm with variance reduction and we prove its linear convergence with the Fletcher and Reeves method for strongly convex and smooth functions. We experimentally demonstrate that the CG with variance reduction algorithm converges faster than its counterparts for four learning models, which may be convex, nonconvex or nonsmooth. In addition, its area under the curve performance on six large-scale data sets is comparable to that of the LIBLINEAR solver for the L2-regularized L2-loss but with a significant improvement in computational efficiency

Xiao-Bo Jin, Guang-Gang Geng, Kaizhu Huang et al.

Entity search is a new application meeting either precise or vague requirements from the search engines users. Baidu Cup 2016 Challenge just provided such a chance to tackle the problem of the entity search. We achieved the first place with the average MAP scores on 4 tasks including movie, tvShow, celebrity and restaurant. In this paper, we propose a series of similarity features based on both of the word frequency features and the word semantic features and describe our ranking architecture and experiment details.

Xiao-Bo Jin, Guang-Gang Geng, Dexian Zhang

Web content quality estimation is crucial to various web content processing applications. Our previous work applied Bagging + C4.5 to achive the best results on the ECML/PKDD Discovery Challenge 2010, which is the comibination of many point-wise rankinig models. In this paper, we combine multiple pair-wise bipartite ranking learner to solve the multi-partite ranking problems for the web quality estimation. In encoding stage, we present the ternary encoding and the binary coding extending each rank value to $L - 1$ (L is the number of the different ranking value). For the decoding, we discuss the combination of multiple ranking results from multiple bipartite ranking models with the predefined weighting and the adaptive weighting. The experiments on ECML/PKDD 2010 Discovery Challenge datasets show that \textit{binary coding} + \textit{predefined weighting} yields the highest performance in all four combinations and furthermore it is better than the best results reported in ECML/PKDD 2010 Discovery Challenge competition.

Guang-Gang Geng, Xiao-Bo Jin, Xin-Chang Zhang et al.

Web content quality measurement is crucial to various web content processing applications. This paper will explore multi-scale features which may affect the quality of a host, and develop automatic statistical methods to evaluate the Web content quality. The extracted properties include statistical content features, page and host level link features and TFIDF features. The experiments on ECML/PKDD 2010 Discovery Challenge data set show that the algorithm is effective and feasible for the quality tasks of multiple languages, and the multi-scale features have different identification ability and provide good complement to each other for most tasks.

Guang-Gang Geng, Xiu-Tao Yang, Wei Wang et al.

Hidden links are designed solely for search engines rather than visitors. To get high search engine rankings, link hiding techniques are usually used for the profitability of black industries, such as illicit game servers, false medical services, illegal gambling, and less attractive high-profit industry, etc. This paper investigates hyperlink hiding techniques on the Web, and gives a detailed taxonomy. We believe the taxonomy can help develop appropriate countermeasures. Study on 5,583,451 Chinese sites' home pages indicate that link hidden techniques are very prevalent on the Web. We also tried to explore the attitude of Google towards link hiding spam by analyzing the PageRank values of relative links. The results show that more should be done to punish the hidden link spam.

Xiao-Bo Jin, Guang-Gang Geng

Linear NDCG is used for measuring the performance of the Web content quality assessment in ECML/PKDD Discovery Challenge 2010. In this paper, we will prove that the DCG error equals a new pair-wise loss.