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As a popular data representation technique, Nonnegative matrix factorization (NMF) has been widely applied in edge computing, information retrieval and pattern recognition. Although it can learn parts-based data representations, existing NMF-based algorithms fail to integrate local and global structures of data to steer matrix factorization. Meanwhile, semi-supervised ones ignore the important role of instances from different classes in learning the representation. To solve such an issue, we propose a novel semi-supervised NMF approach via joint graph regularization and constraint propagation for edge computing, called robust constrained nonnegative matrix factorization (RCNMF), which learns robust discriminative representations by leveraging the power of both L2, 1-norm NMF and constraint propagation. Specifically, RCNMF explicitly exploits global and local structures of data to make latent representations of instances involved by the same class closer and those of instances involved by different classes farther. Furthermore, RCNMF introduces the L2, 1-norm cost function for addressing the problems of noise and outliers. Moreover, L2, 1-norm constraints on the factorial matrix are used to ensure the new representation sparse in rows. Finally, we exploit an optimization algorithm to solve the proposed framework. The convergence of such an optimization algorithm has been proven theoretically and empirically. Empirical experiments show that the proposed RCNMF is superior to other state-of-the-art algorithms.

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.

Community detection is a complex and meaningful process, which plays an important role in studying the characteristics of complex networks. In recent years, the discovery and analysis of community structures in complex networks has attracted the attention of many scholars, and many community discovery algorithms have been proposed. Many existing algorithms are only suitable for small-scale data, not for large-scale data, so it is necessary to establish a stable and efficient label propagation algorithm to deal with massive data and complex social networks. In this paper, we propose a novel label propagation algorithm, called WRWPLPA (Parallel Label Propagation Algorithm based on Weight and Random Walk). WRWPLPA proposes a new similarity calculation method combining weights and random walks. It uses weights and similarities to update labels in the process of label propagation, improving the accuracy and stability of community detection. First, weight is calculated by combining the neighborhood index and the position index, and the weight is used to distinguish the importance of the nodes in the network. Then, use random walk strategy to describe the similarity between nodes, and the label of nodes are updated by combining the weight and similarity. Finally, parallel propagation is comprehensively proposed to utilize label probability efficiently. Experiment results on artificial network datasets and real network datasets show that our algorithm has improved accuracy and stability compared with other label propagation algorithms.

The Internet of Medical Things (IoMT) is a brand new technology of combining medical devices and other wireless devices to access to the healthcare management systems. This article has sought the possibilities of aiding the current Corona Virus Disease 2019 (COVID-19) pandemic by implementing machine learning algorithms while offering emotional treatment suggestion to the doctors and patients. The cognitive model with respect to IoMT is best suited to this pandemic as every person is to be connected and monitored through a cognitive network. However, this COVID-19 pandemic still remain some challenges about emotional solicitude for infants and young children, elderly, and mentally ill persons during pandemic. Confronting these challenges, this article proposes an emotion-aware and intelligent IoMT system, which contains information sharing, information supervision, patients tracking, data gathering and analysis, healthcare, etc. Intelligent IoMT devices are connected to collect multimodal data of patients in a surveillance environments. The latest data and inputs from official websites and reports are tested for further investigation and analysis of the emotion analysis. The proposed novel IoMT platform enables remote health monitoring and decision-making about the emotion, therefore greatly contribute convenient and continuous emotion-aware healthcare services during COVID-19 pandemic. Experimental results on some emotion data indicate that the proposed framework achieves significant advantage when compared with the some mainstream models. The proposed cognition-based dynamic technology is an effective solution way for accommodating a big number of devices and this COVID-19 pandemic application. The controversy and future development trend are also discussed.

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.

With XML becoming a promising standard for data storage, describing, transferring and exchanging information on the Internet, data security and privacy protection of XML become the focus of research in recent years. In order to achieve the authorization of legitimate user and ensure the secure access to sensitive information, in this paper, in the context of cloud storage, with the purpose of sharing sensitive XML information, a polynomial authorization scheme with Kerberos authentication was proposed, which was based on the users' access purpose and privacy policy. In this scheme, first, Kerberos authentication was used to identify the user, and then the polynomial whose coefficients were from the leaf node address was used to complete the authorization of user. For the legitimate user, under the interaction of authorization polynomials and the global structure view, authorization matrix is generated dynamically, its temporary and dynamic characteristics greatly improves the security of the system. Finally, with the help of authorization matrix and auxiliary information tables, security queries were successfully completed. The experimental results show that the scheme not only effectively protects XML sensitive data, but also reduces the server's storage pressure, at the same time it is beneficial to the rapid search and information positioning.

This paper presents an edge detection algorithm based on singular value eigenvector and gradient operator. In the proposed algorithm, the singular values of image blocks are first calculated, and the Sobel gradient template is extended to eight other directions. Then the gradient values of image pixels are determined according to the stability of the singular values of image blocks. The determination of gradient threshold is considered from both global and local aspects. After calculating the global and local gradient thresholds of the original image, the gradient threshold of the whole image is determined by weighting function. Then the edge pixels of the image are filtered according to the gradient threshold, and the edge information image of the original image is obtained. The experimental data show that the proposed algorithm can resist a certain degree of noise interference, and the accuracy and efficiency of edge extraction are better than other similar algorithms.

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.

Location-based Service has become the fastest growing activity related service that people use in their daily life due to the boom of location-aware mobile devices. In edge computing along with the benefits brought by LBS, privacy preservation becomes a more challenging issue because of the nature of the paradigm, in which peers may cooperate with each other to collect and analyze user's location data. To avoid potential information leakage and usage, user's exact location should not be exposed to the edge node. In this paper, we propose a stochastic location privacy protection scheme for edge computing, in which the geographical distribution of surrounding users is obtained by analyzing proposed long-term density map and short-term density map. The cloaking scheme transfers user's exact location to a cloaked location to satisfy predefined probability of having k-users in that area. Our scheme does not reveal any exact location information, thus it is practicable for the real scenario when edge computing is honest but curious. Extensive experimental results are conducted to verify the efficiency and effectiveness of our method. By varying the privacy protection requirements, the corresponding performance have been examined and discussed.

In the last few decades, urban areas across the world have experienced rapid growth in transportation technology with a subsequent increase in transport-related challenges. These challenges have increased our need to employ technology for creating more intelligent solutions. One of the essential tools used to address challenges in traffic is providing vehicles with information about traffic conditions in nearby areas. Vehicle ad-hoc networks (VANETs) allow vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication with the aim of providing safe and efficient transportation. Since drivers might make life-critical decisions based on information provided by other vehicles, dealing with rogue vehicles that send invalid data or breach users' privacy is an essential security issue in VANETs. This paper proposes a novel privacy-preserving vehicular rogue node detection scheme using fog computing. The proposed scheme improves vehicle privacy, communication between vehicles, and computation efficiency by avoiding the exchange of traffic data between vehicles, allowing communication only through roadside units (RSUs). This scheme also proposes an RSU authentication mechanism, along with a mechanism that would allow RSUs to detect and eliminate vehicles providing false traffic data, which will improve the accuracy and efficiency of VANETs. The proposed scheme is analyzed and evaluated using simulation, which presents significant improvements for data processing, accurately detecting rogue vehicles, minimizing overhead, and immunizing the system against colluding vehicles.