The boom in wireless networking technology has led to an exponential increase in the number of web comments. Therefore, sentiment analysis of web comments is vital, and aspect-based sentiment analysis(ABSA) is very useful for the sentiment feature extraction of web comments. Currently, context-dependent sentiment feature typically derives from recurrent neural networks (RNN), and an average target vector usually replaces the target vector. However, web comments have become increasingly complex, and RNN may lose some essential sentiment information. At the same time, the average target vector may be the wrong target feature. We propose a new Transformer based memory network (TF-MN) to correct the shortcomings of the previous method. In TF-MN, the task becomes the question-answering process, which optimizes context, question, and the memory module. We use a global self-attention mechanism and a local attention mechanism (memory network) to construct emotionally inclined web comment semantics. Since self-attention can only obtain global semantic links, words such as nouns, prepositions, and adverbs still affect the emotional extraction of comments. To shield the influence of unrelated vocabulary on classification, we propose to use improved memory networks to optimize the extraction of web comments semantics. We conduct experiments on two datasets, and experimental results show that our model exceeds the state-of-the-art model.

Opinions in complex reviews often vary on different aspects of a thing. Coarse-grained sentiment analysis on a sentence can’t capture the sentiment polarity of it accurately. Therefore, aspect-level sentiment classification is a better choice because

As a key issue of building a virtual network (VN), the VN mapping problem can be addressed by various state-of-the-art algorithms. While these algorithms are efficient for the construction of unicast service-oriented VNs, they are generally not suitable for multicast cases. In this paper, we investigate the mapping problem in the context of virtual multicast service-oriented network subject to delay and delay variation constraints (VMNDDVC). We present a novel and efficient heuristic algorithm to tackle this problem based on a sliding window approach. The primary objective of this algorithm is in two-fold: to minimize the cost of VMNDDVC request mapping, and to achieve load balancing so as to increase the acceptance ratio of virtual multicast network (VMN) requests. The numerical results obtained from extensive simulation experiments demonstrate the effectiveness of the proposed approach and superiority than existing solutions in terms of VN mapping acceptance ratio, total revenue and cost in the long term.

When describing an image, people can rapidly extract the topic from the image and find the main object, generating sentences that match the main idea of the image. However, most of the scene graph generation methods do not emphasise the importance of the topic of the image. Consequently, the captions generated by the scene graph‐based image captioning models cannot reflect the topic in the image then expressing the central idea of the image. In this paper, we propose a method for image captioning based on topic scene graphs (TSG). Firstly, we propose the structure of topic scene graphs that express images' topics and the relationships between objects. Then, combined with the topic scene graph, we utilise the salient object detection to generate the topic scene graph highlighting the salient objects of the image. Note that our framework is agnostic to any scene graph‐based image captioning model and thus can be widely applied in the community which seeks salient object predictions. We compare the performance of our topic scene graph with the state‐of‐the‐art scene graph generation models and mainstream image captioning models on MSCOCO and Visual Genome datasets, both achieving better performance.

The CNN-based crowd counting method uses image pyramid and dense connection to fuse features to solve the problems of multiscale and information loss. However, these operations lead to information redundancy and confusion between crowd and background information. In this paper, we propose a multi-scale guided attention network (MGANet) to solve the above problems. Specifically, the multilayer features of the network are fused by a top-down approach to obtain multiscale information and context information. The attention mechanism is used to guide the acquired features of each layer in space and channel so that the network pays more attention to the crowd in the image, ignores irrelevant information, and further integrates to obtain the final high-quality density map. Besides, we propose a counting loss function combining SSIM Loss, MAE Loss, and MSE Loss to achieve effective network convergence. We experiment on four major datasets and obtain good results. The effectiveness of the network modules is proved by the corresponding ablation experiments. The source code is available at https://github.com/ lpfworld/MGANet.