How to find negative emotion burst topic in time from massive and noisy microblog text stream is essential for emergency response and handling of emergencies. However, the traditional burst topic detection methods often ignore the differences between negative emotion burst topic and non-negative emotion burst topic. Therefore, a Negative Emotion Burst Topic Detection (NE-BTD) algorithm for microblog text stream was proposed. Firstly, the accelerations of keyword pairs in microblog and the change rate of negative emotion intensity were used as the basis for judging the topics of negative emotion. Secondly, the speeds of burst word pairs were used to determine the window range of negative emotion burst topics. Finally, a Gibbs Sampling Dirichlet Multinomial Mixture model (GSDMM) clustering algorithm was used to obtain the topic structures of the negative emotion burst topics in the window. In the experiments, the proposed NE-BTD algorithm was compared with an existing Emotion-Based Method of Topic Detection (EBM-TD) algorithm. The results show that the NE-BTD algorithm was at least 20% higher in accuracy and recall than the EBM-TD algorithm, and it can detect negative emotion burst topic at least 40 minutes earlier.

In view of the problem that conventional incremental learning models may go through a way of performance degradation during the update stage, a kernelized incremental learning method was proposed based on objective equilibrium measurement. By setting the optimization term of "empirical risk minimization", an optimization objective function fulfilling the equilibrium measurement with respect to training data size was designed. The optimal solution was given under the condition of incremental learning training, and a lightweight incremental learning classification model was finally constructed based on the effective selection strategy of new data. Experimental results on a publicly available fall detection dataset show that, when the recognition accuracy of representative methods falls below 60%, the proposed method can still maintain the recognition accuracy more than 95%, while the computational consumption of the model update is only 3 milliseconds. In conclusion, the proposed method contributes to achieving a stable growth of recognition performance as well as efficiently decreasing the time consumptions, which can effectively realize wearable devices based intellectual applications in the cloud service platform.

Clustering algorithm is used to preprocess personal privacy information in order to achieve differential privacy protection, which can reduce the reconstruction error caused by directly distributing histogram data, and the reconstruction error caused by different combining methods of histogram. Aiming at the problem of sensitivity to input data parameters in DP-DBSCAN (Differential Privacy-Density-Based Spatial Clustering of Applications with Noise) differential privacy algorithm, the OPTICS (Ordering Points To Identify Clustering Structure) algorithm based on density clustering was applied to differential privacy protection. And an improved differential privacy protection algorithm, called DP-OPTICS (Differential Privacy-Ordering Points To Identify Clustering Structure) was introduced, the sparse dataset was compressed, the same variance noise and different variance noise were used as two noise-adding ways by comparison, considering the probability of privacy information's being broken by the attacker, the upper bound of privacy parameter ε was determined, which effectively balanced the relationship between the privacy of sensitive information and the usability of data. The DP-OPTICS algorithm was compared with the differential privacy protection algorithm based on OPTICS clustering and DP-DBSCAN algorithm. The DP-OPTICS algorithm is between the other two in time consumption. However, in the case of having the same parameters, the stability of the DP-OPTICS algorithm is the best among them, so the improved OP-OPTICS differential privacy protection algorithm is generally feasible.

When the traditional genetic algorithm is used to solve the computation-intensive task, the execution time of the fitness function increases rapidly, and the convergence rate of the algorithm is very low when the population size or generation increases. A "coarse-grained combined with master-slave" HyBrid Parallel Genetic Algorithm (HBPGA) was designed and implemented on Sunway "TaihuLight" supercomputer which is ranked first in the latest TOP500 list. Two-level parallel architecture was used and two different programming models, MPI and Athread were combined. Compared with the traditional genetic algorithm implemented on single-core or multi-core cluster with single-level parallel architecture, the algorithm using two-level parallel architecture was implemented on the Sunway many-core processors, better performance and higher speedup ratio were achieved. In the experiment, when using 16×64 CPEs (Computing Processing Elements), the maximum speedup can reach 544, and the CPE speedup ratio is more than 31.

Aiming at new sentiment word identification, an automatic extraction of new words about microblog was proposed based on the word association. Firstly, a new word, which was incorrectly separated into several words using the Chinese auto-segmentation system, should be assembled as the candidate word. In addition, to make full use of the semantic information of word context, the spatial representation vector of the candidate words was obtained by training a neural network. Finally, using the existing emotional vocabulary as a guide, combining the association-sort algorithm based on vocabulary list and the max association-sort algorithm, the final new emotional word was selected from candidate words. The experimental results on the task No. 3 of COAE2014 show that the precision of the proposed method increases at least 22%, compared to Pointwise Mutual Information (PMI), Enhanced Mutual Information (EMI), Normalized Multi-word Expression Distance (NMED), New Word Probability (NWP), and identification of new sentiment word based on word embedding, which proves the effectiveness of the proposed method.

Focusing on the issue that the label kernel functions do not take the correlation between labels into consideration in the multi-label feature extraction method, two construction methods of new label kernel functions were proposed. In the first method, the multi-label data were transformed into single-label data, and thus the correlation between labels could be characterized by the label set; then a new label kernel function was defined from the perspective of loss function of single-label data. In the second method, mutual information was used to characterize the correlation between labels, and a new label kernel function was proposed from the perspective of mutual information. Experiments on three real-life data sets using two multi-label classifiers demonstrated that the best method of all measures was feature extraction method with label kernel function based on loss function and the performance of five evaluation measures on average increased by 10%; especially on the data set Yeast, the evaluation measure Coverage reached a decline of about 30%. Closely followed by feature extraction method with label kernel function based on mutual information and the performance of five evaluation measures on average increased by 5%. The theoretical analysis and simulation results show that the feature extraction methods based on new output kernel functions can effectively extract features, simplify learning process of multi-label classifiers and, moreover, improve the performance of multi-label classification.

This paper proposed a mobile cloud computing architecture based on mobile Agent paradigm concerning the problems faced by the mobile cloud computing, such as application migration on network, network latency and non-persistent connection issues caused by execution on the remote device, cross-cloud service problems, and security risks and privacy issues. In this architecture, break-point saving ideas and events replay mechanism were introduced in application migration issues, optimized contract net protocol was used in the synergy between the mobile Agents, and mobile Agent exchange keys for authentication. What's more, this paper described the workflow of this architecture using colored nested Petri nets, and designed a system of mobile e-book sales based on the architecture.

An automatic segmentation for pollen microscopic images was proposed in this paper, which was useful to develop a recognition system of airborne pollen. First, the image patch classifier was trained with normalized color component. Then, conditional random field was employed to model pollen images and Maximum A Posterior (MAP) was used to segment the pollen areas in microscopic images, with graph cut algorithm for optimization. In the experiments, the respective average values of mean distance error was 7.3 pixels and the true positive rate was 87% on 133 images. The experimental results show that image patch classifier and conditional random field model can be used to accomplish segmentation of the pollen microscopic images.

By analyzing the deficiency of the existing network virus models, referring to the reality and considering the infectious disease models in biology, an improved Susceptible-Infected-Removed (SIR) computer virus propagation model with pre-immune measures was put forward. The authors considered the varying number of nodes and analyzed its impact on the spread of the virus in the network. In addition, several related dynamic properties were analyzed. The numerical simulation results demonstrate that improving pre-immune rate and controlling the nodes flow in the network can effectively constrain virus prevalence. And this model has inspiration to predict and prevent from computer virus propagation.