On online shopping platforms， concise， authentic and effective product summarizations are crucial to improving the shopping experience. In addition， online shopping cannot touch the actual product， and the information contained in the product image is important visual information except the product text description， so product summarization that fuses multimodal information including product text and product image is of great significance for online shopping. Aiming at fusing product text descriptions and product images， a product summarization extraction model with multimodal information fusion was proposed. Different from the general product summarization task whose input only contains the product text description， the proposed model introduces product image as an additional source of information to make the extracted summary richer. Specifically， first the pre-trained model was used to represent the features of the product text description and product image by which the text feature representation of each sentence was extracted from the product text description， and the overall visual feature representation of the product was extracted from the product image. Then the low-rank tensor-based multimodal fusion method was used to modally fuse the text features and overall visual features to obtain the multimodal feature representation for each sentence. Finally， the multimodal feature representations of all sentences were fed into the summary generator to generate the final product summarization. Comparative experiments were conducted on CEPSUM 2.0 （Chinese E-commerce Product SUMmarization 2.0） dataset. On the three subsets of CEPSUM 2.0， the average ROUGE-1 （Recall-Oriented Understudy for Gisting Evaluation 1） of this model is 3.12 percentage points higher than that of TextRank and 1.75 percentage points higher than that of BERTSUMExt （BERT SUMmarization Extractive）. Experimental results show that the proposed model is effective in fusing product text and image information， which performs well on ROUGE evaluation index.

It is necessary to pre-define a cluster number in classical Fuzzy C-means (FCM) algorithm. Otherwise, FCM algorithm can not work normally, which limits the applications of this algorithm. Aiming at the problem of pre-assigning cluster number for FCM algorithm, a new fuzzy cluster validity index was presented. Firstly, the membership matrix was got by running the FCM algorithm. Secondly, the intra class compactness and the inter class overlap were computed by the membership matrix. Finally, a new cluster validity index was defined by using the intra class compactness and the inter class overlap. The proposal overcomes the shortcomings of FCM that the cluster number must be pre-assigned. The optimal cluster number can be effectively found by the proposed index. The experimental results on artificial and real data sets show the validity of the proposed index. It also can be seen that the optimal cluster number are obtained for three different fuzzy factor values of 1.8, 2.0 and 2.2 which are general used in FCM algorithm.

A transmission rate aware candidate forwarder selection and ordering algorithm based on expected transmission delay of nodes was proposed. It first separated opportunistic route forwarding into two components: the anycast forwarding from source node to its candidate forwarders set, and the remaining forwarding from that candidate forwarders set to destination, and then the shortest expected transmission delay of opportunistic routing was computed iteratively. Finally, candidate forwarders were selected and ordered according to the shortest expected transmission delay of nodes. The simulation results indicate that the proposed algorithm can improve the performance of opportunistic routing obviously.

This paper aims at reducing the influence of ranging error on localization accuracy. A Wireless Sensor Network (WSN) localization algorithm was proposed, which was based on Kernel Locality Preserving Projection (KLPP). The algorithm took transmission time vector between nodes as input, and a model was established by KLPP, which can reflect partial information network topology structure. The simulation results indicate that compared with Kernel Principle Component Analysis (KPCA), the algorithm can achieve higher localization accuracy in Time of Arrival (TOA) localization and reduce the influence of ranging error on localization accuracy effectively in complex environment.