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With the rapid development of online social networks, text-communication has become an indispensable part of daily life. Mining the emotion hidden behind the conversation-text is of prime significance and application value when it comes to the government public-opinion supervision, enterprise decision-making, etc. Therefore, in this paper, we propose a text emotion prediction model in a multi-participant text-conversation scenario, which aims to effectively predict the emotion of the text to be posted by target speaker in the future. Specifically, first, an affective space mapping is constructed, which represents the original conversation-text as an n-dimensional affective vector so as to obtain the text representation on different emotion categories. Second, a similar scene search mechanism is adopted to seek several sub-sequences which contain similar tendency on emotion shift to that of the current conversation scene. Finally, the text emotion prediction model is constructed in a two-layer encoder-decoder structure with the emotion fusion and hybrid attention mechanism introduced at the encoder and decoder side respectively. According to the experimental results, our proposed model can achieve an overall best performance on emotion prediction due to the auxiliary features extracted from similar scenes and the adoption of emotion fusion as well as the hybrid attention mechanism. At the same time, the prediction efficiency can still be controlled at an acceptable level.

The typical aim of user matching is to detect the same individuals cross different social networks. The existing efforts in this field usually focus on the users' attributes and network embedding, but these methods often ignore the closeness between the users and their friends. To this end, we present a friend closeness based user matching algorithm (FCUM). It is a semi-supervised and end-to-end cross networks user matching algorithm. Attention mechanism is used to quantify the closeness between users and their friends. We considers both individual similarity and their close friends similarity by jointly optimize them in a single objective function. Quantification of close friends improves the generalization ability of the FCUM. Due to the expensive costs of labeling new match users for training FCUM, we also design a bi-directional matching strategy. Experiments on real datasets illustrate that FCUM outperforms other state-of-the-art methods that only consider the individual similarity.

The combination of Unmanned Aerial Vehicle (UAV) technologies and computer vision makes UAV applications more and more popular. Computer vision tasks based on deep learning usually require a large amount of task-related data to train algorithms for specific tasks. Since the commonly used datasets are not designed for specific scenarios, in order to give UAVs stronger computer vision capabilities, large enough aerial image datasets are needed to be collected to meet the training requirements. In this paper, we take low-altitude aerial image object detection as an example to propose a framework to demonstrate how to construct datasets for specific tasks. Firstly, we introduce the existing low-altitude aerial images datasets and analyze the characteristics of low-altitude aerial images. On this basis, we put forward some suggestions on data collection of low-altitude aerial images. Then, we recommend several commonly used image annotation tools and crowdsourcing platforms for data annotation to generate labeled data for model training. In addition, in order to make up the shortage of data, we introduce data augmentation techniques, including traditional data augmentation and data augmentation based on oversampling and generative adversarial networks.

The data security of fog computing is a key problem for the Internet of things. Identity-based encryption (IBE) from lattices is extremely suitable for fog computing. It is able to not only simplify certificate management, but also resist quantum attacks. In this paper, firstly, we construct a novel efficient lattice-based IBE scheme with Combined Public Key (CPK) technique by keeping from consumptive trapdoor generation algorithm and preimage sampling algorithm, which is required by the existing lattice-based IBE schemes based on learning with errors (LWE). In addition, its key storage cost is lower and it is IND-ID-CPA secure in the random oracle model. Furthermore, based on this, an enhanced lattice-based IBE scheme with IND-ID-CCA security is developed by employing strong one-time signature. Our schemes only need O(n3/log n) additions of vectors, while the existing schemes need at least O(n3) of additions and multiplications in Setup and Extract phase.

Accurate image segmentation results would show a great significance to computer vision-based manufacturing for complex helical surface. However, the image segmentation for complex helical surface is always a difficult problem because of the uneven gray distribution and non-homogeneous feature patterns of its images. Therefore, a multi-direction evolutionary segmentation model is constructed and a multi-population cooperative evolution algorithm is proposed to solve the new model. According to the characteristics of gray distribution and feature patterns of complex helical surface image, an eigenvector extraction and description strategy is researched by combining gray level co-occurrence matrix algorithm with fractal algorithm, and the complex helical surface image can be described succinctly by gray feature and shape feature. Based on the description algorithm of image features, an image segmentation strategy using cooperative evolution from different eigenvector is discussed, and the helical surface image segmentation is decomposed from a single objective optimization problem to a multi-objective optimization problem to improve the accuracy of segmentation. Meanwhile, a multi-objective particle swarm optimization algorithm based on multi-directional evolution and shared archives is presented. Due to the fact that each eigenvector segmentation corresponds to one evolution direction, the collaboration of local and global segmentation can be realized by information sharing and interaction between evolution directions and the archive set. The comprehensive quality of non-dominated solution can be improved by the selection strategy of local and global optimal solution as well as the archive set maintenance. The practical numerical experiments for complex helical surface image segmentation are carried out to prove the validity of the proposed model and algorithm.

The Industrial Internet of Things (IIoT) plays an important role in the development of smart factories. However, the existing IIoT systems are prone to suffering from single points of failure and unable to provide stable service. Meanwhile, with the increase of node scale and network quantity, the maintenance cost presents to be higher. Such a disadvantage can be effectively compensated by the features such as security, privacy, non-tamperability and distributed deployment supported by the blockchain. In this paper, first, an intelligent manufacturing security model based on blockchain was proposed. Due to the high power consumption and low throughput of the traditional blockchain, IoT devices with limited power consumption can not work independently. Therefore, in this paper, a new Merkle Patricia tree (MPT) was adopted to extend the blockchain structure and provide fast query of node status. Second, since the MPT does not support concurrent operation and the data operation performance deteriorates with high data volume, a lock-free concurrent and cache-based Merkle Patricia tree was proposed (CMPT) to support lock-free concurrent data operation, which can improve the data operation efficiency in multi-core system. The experimental results indicate that, compared with the original MPT, the CMPT proposed in this paper effectively reduced the time complexity of data insertion and data query and improved the speed of block construction and data query.

Most current automatic summarization methods are for English texts. The distinction between words in Chinese text is large, the types of parts of speech are many and complex, and polysemy or ambiguous words appear frequently. Therefore, compared with English text, Chinese text is more difficult to extract useful feature words. Due to the complex syntax of Chinese, there are currently relatively few automatic summarization methods for Chinese text. In the past, only the important sentences in the original text can be selected and simply arranged to obtain a summary with chaotic sentences and insufficient coherence. Meanwhile, because Chinese short text usually contains more redundant information and the sentence structure is not neat, we propose a topic-based automatic summary method for Chinese short text. Firstly, a key sentence selection method is proposed combining topic words and TF-IDF to obtain the score of each text corresponding to the topic in the original text data. Then the sentence with the highest score as the topic sentence of the topic is selected. Considering that the short text of Weibo may contain a lot of irrelevant information and sometimes even lack some important components of topic, three retouching mechanisms are proposed to improve the conciseness, richness and readability of topic sentence extraction results. We validate our approach on natural disaster and social hot event datasets from Sina Weibo. The experimental results show that the polished topic summary not only reflects the exact relationship between topic sentences and natural disasters or social hot events, but also has rich semantic information. More importantly, we can almost grasp the basic elements of natural disaster or social hot event from the topic sentence, so as to help the government guide disaster relief or meet the needs of users for quickly obtaining information of social hot events.