Yanping Lv

Particle swarm optimization based on information diffusion and clonal selection

A novel PSO algorithm called InformPSO is introduced in this paper. The premature convergence problem is a deficiency of PSOs. First, we analyze the causes of premature convergence for conventional PSO. Second, the principles of information diffusion and clonal selection are incorporated into the proposed PSO algorithm to achieve a better diversity and break away from local optima. Finally, when compared with several other PSO variants, it yields better performance on optimization of unimodal and multimodal benchmark functions.

Chinese Character CAPTCHA Recognition and performance estimation via deep neural network

Abstract To identify machine and human, Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is increasingly used in many web applications. The classical English and digital characters based CAPTCHAs are recognized with high accuracy. Due to the complication of Chinese characters which greatly enhance the difficulty of automatic recognition, an increasing number of Chinese web sites use Chinese Character CAPTCHAs. To recognize Chinese Character CAPTCHAs, we propose a Convolution Neural Network (CNN) based approach to learn strokes, radicals and character features of Chinese characters, and prove that our network structure is superior to LENET-5 in this task. Furthermore, we formulate the relation among accuracy, the number of training samples and iterations, which is used to estimate the performance of our approach. Firstly, this approach greatly improves the recognition accuracy of Chinese Character CAPTCHAs with distortion, rotation and background noise. Our experiments results show that this approach achieves over 95% accuracy for single Chinese character and 84% accuracy for three types of Chinese Character CAPTCHAs with four Chinese characters. Secondly, our experiment results and theoretical analysis show that the accuracy of recognition has the exponential relationship with the product of the number of training samples and iterations in the condition of enough and representative training samples. Therefore, we can estimate the training time for a certain accuracy. Finally, we certify that our approach is superior to the most famous Chinese Optical Character Recognition (OCR) software, Hanvon, in Chinese Character CAPTCHAs recognition.

GIF Video Sentiment Detection Using Semantic Sequence

With the development of social media, an increasing number of people use short videos in social media applications to express their opinions and sentiments. However, sentiment detection of short videos is a very challenging task because of the semantic gap problem and sequence based sentiment understanding problem. In this context, we propose a SentiPair Sequence based GIF video sentiment detection approach with two contributions. First, we propose a Synset Forest method to extract sentiment related semantic concepts from WordNet to build a robust SentiPair label set. This approach considers the semantic gap between label words and selects a robust label subset which is related to sentiment. Secondly, we propose a SentiPair Sequence based GIF video sentiment detection approach that learns the semantic sequence to understand the sentiment from GIF videos. Our experiment results on GSO-2016 (GIF Sentiment Ontology) data show that our approach not only outperforms four state-of-the-art classification methods but also shows better performance than the state-of-the-art middle level sentiment ontology features, Adjective Noun Pairs (ANPs).

Improved genetic algorithm for multiple sequence alignment using segment profiles (GASP)

This paper presents a novel genetic algorithm (GA) for multiple sequence alignment in protein analysis. The most significant improvement afforded by this algorithm results from its use of segment profiles to generate the diversified initial population and prevent the destruction of conserved regions by crossover and mutation operations. Segment profiles contain rich local information, thereby speeding up convergence. Secondly, it introduces the use of the norMD function in a genetic algorithm to measure multiple alignment Finally, as an approach to the premature problem, an improved progressive method is used to optimize the highest-scoring individual of each new generation. The new algorithm is compared with the ClustalX and T-Coffee programs on several data cases from the BAliBASE benchmark alignment database. The experimental results show that it can yield better performance on data sets with long sequences, regardless of similarity.

Multi-modality weakly labeled sentiment learning based on Explicit Emotion Signal for Chinese microblog

Abstract Understanding the sentiments of users from cross media contents which contain texts and images is an important task for many social network applications. However, due to the semantic gap between cross media features and sentiments, machine learning methods need a lot of human labeled samples. Furthermore, for each kind of media content, it is necessary to constantly add a lot of new human labeled samples because of new expressions of sentiments. Fortunately, there are some emotion signals, like emoticons, which denote users’ emotions in cross media contents. In order to use these weakly labels to build a unified multi-modality sentiment learning framework, we propose an Explicit Emotion Signal (EES) based multi-modality sentiment learning approach which uses huge number of weakly labeled samples in sentiment learning. There are three advantages in our approach. Firstly, only a few human labeled samples are needed to reach the same performance which can be obtained by the traditional machine learning based sentiment prediction approaches. Secondly, this approach is flexible and can easily combine text and vision based sentiment learning through deep neural networks. Thirdly, because a lot of weakly labeled samples can be used in EES, trained model is more robust in different domain transfer. In this paper, firstly, we investigate the correlation between sentiments and emoticons and choose emoticons as the Explicit Emotion Signals in our approach; secondly, we build a two stages multi-modality sentiment learning framework based on Explicit Emotion Signals. Our experiment results show that our approach not only achieves the best performance but also only needs 3% and 43% training samples to obtain the same performance of Visual Geometry Group (VGG) model and Long Short-Term Memory (LSTM) model in images and texts, respectively.

Chinese character CAPTCHA recognition based on convolution neural network

CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are increasingly used in many applications for machine and human identification. Compared with traditional English and digital characters based CAPTCHAs, Chinese characters contain more complicated characters which greatly enhance difficulty of automatic recognition. To solve that problem, we proposed a Convolution Neural Network (CNN) based approach. This approach greatly improves the recognition accuracy of Chinese Character CAPTCHAs with distortion, rotation and background noise. Our experiment results show that this approach achieves more than 95% accuracy for single character and 84% accuracy for three types of Chinese Character CAPTCHAs with four characters. This encouraging result indicates that deep neural network is useful in complicated structure perception of Chinese Character CAPTCHAs.

Public opinion analysis based on geographical location

With the development of social network applications, an increasing number of public opinion analysis systems focus on virtual network space. However, many events in virtual network space connect strongly with the events in real physical space. To solve that problem, we propose a graphical location based topic analysis framework, which combines the geographical location information and Latent Dirichlet Allocation (LDA) based topic analysis to visually analysis the connection between visual network space and real physical space. Our experiments show that this framework is useful for emergent events including natural disaster and social unrest.

Minimum Information Quantity Partition based fast semantic feature subset selection

Processing of web text clustering data usually results in more than ten thousand features. The traditional dimensionality reduction methods and optimal feature subset selection methods increase the time complexity. To reduce the time complexity, we propose a Minimum Information Quantity Partition (MIQP) method. First, MIQP selects a useful feature subset by determining the best partition according to the diminishing trend of feature weight curve. Second, to remove the feature independent assumption and compute the semantic relation between selected features, Latent Semantic Indexing (LSI) is used to eliminate noisy data and extend the missed semantic of each sample. This approach reduces the time complexity from O(mn3) to O(mn2). The experimental results show that the performance of MIQP is close to the best clustering results of selecting top k features, and the speed of MIQP is much faster than clustering with all features in our experiment data.

ECG codebook model for Myocardial Infarction detection

ECG is a kind of high dimensional dataset and the useful information of illness only exists in few heartbeats. To achieve a good classification performance, most existing approaches used features proposed by human experts, and there is no approach for automatic useful feature extraction. To solve that problem, we propose an ECG Codebook Model (ECGCM) which automatically builds a small number of codes to represent the high dimension ECG data. ECGCM not only greatly reduces the dimension of ECG, but also contains more meaningful semantic information for Myocardial Infarction detection. Our experiment results show that ECGCM achieves 2% and 20.5% improvement in sensitivity and specificity respectively in Myocardial Infarction detection.