Aiming at the problem that Artificial Neural Network (ANN) has limited memory capability for knowledge reasoning oriented to Knowledge Graph (KG) and the KG cannot deal with uncertain knowledge, a reasoning method named DNC-BN was propsed based on Differentiable Neural Computer (DNC) and Bayesian Network. Firstly, using Long Short-Term Memory (LSTM) network as the controller, the output vector and the interface vector of network were obtained by processing the input vector and the read vector obtained from the memory at each moment. Then, the read and write heads were used to realize the interaction between the controller with the memory, the read weights were used to calculate the weighted average of data to obtain the read vector, and the write operation was performed by combining the erase vector and write vector with the write weights, so as to modify the memory matrix. Finally, based on the probabilistic inference mechanism, the BN was used to judge the inference relationship between the nodes, and the KG was completed. In the experiments, on the WN18RR dataset, DNC-BN has the Mean Rank of 2 615 and the Hits@10 of 0.528; on the FB15k-237 dataset, DNC-BN has the Mean Rank of 202, and the Hits@10 of 0.519. Experimental results show that the proposed method has good application effect on knowledge reasoning oriented to KG.

Aiming at the problems that simple Recurrent Neural Network (RNN) cannot memorize information for a long time and single Convolutional Neural Network (CNN) lacks the ability to capture the semantics of text context, in order to improve the accuracy of text classification, a sentiment analysis model G-Caps (Gated Recurrent Unit-Capsule) was proposed, which combines Gated Recurrent Unit (GRU) and capsule features. First, the contextual global features of the text were captured through GRU in order to obtain the global scalar information. Second, the captured information was iterated through the dynamic routing algorithm at the initial capsule layer to obtain the vectorized feature information representing the overall attributes of the text. Finally, the features were combined in the main capsule part to obtain more accurate text attributes, and the sentiment polarity of the text was analyzed according to the intensity of each feature. Experimental results on the benchmark dataset MR (Movie Reviews) showed that compared with the CNN + INI (Convolutional Neural Network + Initializing convolutional filters) and CL_CNN (Critic Learning_Convolutional Neural Network) methods, G-Caps had the classification accuracy increased by 3.1 percentage points and 0.5 percentage points respectively. It can be seen that the G-Caps model effectively improves the accuracy of text sentiment analysis in practice.

The Fuzzy C-Means clustering (FCM) algorithm has the defects of being sensitive to initial clustering center,being susceptible to noise point interference and poor robustness in solving the problem of speech underdetermined mixing matrix estimation. An improved WEighted FCM algorithm based on evolutionary programming (WE-FCM) was proposed to eliminate the defects. Firstly, the powerful search ability of Evolutionary Programming (EP) algorithm was used to optimize FCM for obtaining FCM algorithm based on EP (EP-FCM), in order to obtain a better initial clustering center. Then, the Local Outlier Factor (LOF) algorithm was used to perform weighting to reduce the effects of noise points. The simulation experiment results show that, the normalized mean square error value and the deviation angle value of the proposed algorithm were both much smaller than those of the classical K -means clustering, K -Hough, FCM algorithm based on Genetic Algorithm (GAFCM) and FCM algorithm based on Find Density Peaks (FDP-FCM) when the number of source signals were 3 and 4. The experimental results show that, the proposed algorithm significantly improves the robustness of FCM algorithm and the accuracy of mixing matrix estimation.

The temporal relation or temporal links (denoted by the TLink tag) in Chinese electronic medical records includes temporal relations within a sentence (hereafter referred to as "within-sentence TLinks"), and between-sentence TLinks. Among them, within-sentence TLinks include event/event TLinks and event/time TLinks, and between-sentence TLinks include event/event TLinks. The recognition of temporal relation in Chinese electronic medical record was transformed into classification problem on entity pairs. Heuristic rules with high accuracy were developed and two different classifiers with basic features, phrase syntax, dependency features, and other features were trained to determine within-sentence TLinks. Apart from heuristic rules with high accuracy, basic features, phrase syntax, and other features were used to train the classifiers to determine between-sentence TLinks. The experimental results show that Support Vector Machine (SVM), SVM and Random Forest (RF) algorithms achieve the best performance of recognition on within-sentence event/event TLinks, within-sentence event/time TLinks and between-sentence event/event TLinks, with F ₁-scores of 84.0%, 85.6% and 63.5% respectively.

For balancing transparency and robustness of watermark, this paper proposed wavelet-domain digital watermarking method based on Fruit Fly Optimization Algorithm (FOA). The algorithm used Discrete Wavelet Transform (DWT) by FOA to watermarking technology and solved the contradiction between transparency and robustness in the watermark by swarm intelligence algorithm. In order to protect the copyright information of digital image, the selected original image was decomposed through a two-dimensional discrete wavelet transform, and watermark image through Arnold transformation was better embedded into wavelet coefficients of vertical sub-band, which guaranteed image quality. In the optimization process, the scaling factor was continuously being trained and updated by FOA. In addition, a new algorithm framework was proposed, which evaluated the scaling factor by prediction feasibility of DWT domain. The experimental results show that, the proposed algorithm has higher transparency and robustness against attacks, with watermarking similarity above 0.95, and 10% higher under geometric attacks such as rotation and shearing compared to some existing watermarking methods based on swarm intelligence.

According to the driving effect of people's social relations and interests on the social activities of nodes,a mobility model based on user interest similarity was presented.The interest degree of node to the activities was described with a interest probability matrix,and Pearson correlation coefficient was used to calculate the similar interest groups of nodes.Simulation results show that,the complementary cumulative density function of inter-contact time and contact duration in a certain time approximately follows power-law distribution,which is more consistent with the curve obtained from statistical results of real data set.Additionally, strong space-time regularity is observed when nodes are involved in the activities in the evening.

Handover procedure of modern cellular wireless networks depends on IP-based technology. IETF Proxy Mobile IPv6 (PMIPv6) protocol guarantees the Quality of Service (Qos) in fast handover moving while it does not support realtime communications between two mobile nodes. Therefore Cluster Mobile Node (CMN) algorithm was proposed to reduce handover delay in the system by applied Media Independent Handover (MIH) Technology. Also, the algorithm extended PMIPv6 protocol with an Aggregated Proxy Binding Update (A-PBU) scheme in the paper. Finally, the network model and mobile model were simulated and the effectiveness of handover delay was analyzed. Quantitative results show a significant reduction in handover delay compared with the original handover algorithm.

An artificial neural net(ANN) model was established for considering multi physical factors’ effects on reading environment.Typical back-propagation net(BP net) with three layers was selected with input parameters of illuminance and desktop’s brightness,outputing relative reading gross and reading accuracy.The ANN model could consider several factors’ effects on reading working at the same time,figuring out multi-parameters and strong coupling in reading environment.Using the ANN model,illuminance and brightness were analyzed,and relative reading gross and reading accuracy were calculated under different reading environments.

A lease model was cited into mobile agent system.With the model,there was a lease relationship between mobile agent and its run-time environment.Taking advantage of lease negotiation and a limited time,mobile agent run-time environment could restrict mobile agent’s lifecycle without changing mobile agent’s behaviors.This mechanism can enhance security of mobile agent system,and make it self-heal and self-management.

An approach to Web Service Composition based on a kind of Object-Oriented Petri Net-OOPN was proposed in this paper. By means of this approach, not only the formal semantics of Web Service and its composition could be definitely described, but also the control flow of composed Web Service could be graphically modeled. Furthermore, this approach could be used to validate the correctness of Web Serviced composition. 更