Edge Computing to Solve Security Issues for Infectious Disease Intelligence Prevention

Nowadays, with the rapid development of intelligent technology, it is urgent to effectively prevent infectious diseases and ensure people's privacy. The present work constructs the intelligent prevention system of infectious diseases based on edge computing by using the edge computing algorithm, and further deploys and optimizes the privacy information security defense strategy of users in the system, controls the cost, constructs the optimal conditions of the system security defense, and finally analyzes the performance of the model. The results show that the system delay decreases with the increase of power in the downlink. In the analysis of the security performance of personal privacy information, it is found that six different nodes can maintain the optimal strategy when the cost is minimized in the finite time domain and infinite time domain. In comparison with other classical algorithms in the communication field, when the intelligent prevention system of infectious diseases constructed adopts the best defense strategy, it can effectively reduce the consumption of computing resources of edge network equipment, and the prediction accuracy is obviously better than that of other algorithms, reaching 83%. Hence, the results demonstrate that the model constructed can ensure the safety performance and forecast accuracy, and achieve the best defense strategy at low cost, which provides experimental reference for the prevention and detection of infectious diseases in the later period.

Exploring the Risky Travel Area and Behavior of Car-hailing Service

Recent years have witnessed the rapid development of car-hailing services, which provide a convenient approach for connecting passengers and local drivers using their personal vehicles. At the same time, the concern on passenger safety has gradually emerged and attracted more and more attention. While car-hailing service providers have made considerable efforts on developing real-time trajectory tracking systems and alarm mechanisms, most of them only focus on providing rescue-supporting information rather than preventing potential crimes. Recently, the newly available large-scale car-hailing order data have provided an unparalleled chance for researchers to explore the risky travel area and behavior of car-hailing services, which can be used for building an intelligent crime early warning system. To this end, in this article, we propose a Risky Area and Risky Behavior Evaluation System (RARBEs) based on the real-world car-hailing order data. In RARBEs, we first mine massive multi-source urban data and train an effective area risk prediction model, which estimates area risk at the urban block level. Then, we propose a transverse and longitudinal double detection method, which estimates behavior risk based on two aspects, including fraud trajectory recognition and fraud patterns mining. In particular, we creatively propose a bipartite graph-based algorithm to model the implicit relationship between areas and behaviors, which collaboratively adjusts area risk and behavior risk estimation based on random walk regularization. Finally, extensive experiments on multi-source real-world urban data clearly validate the effectiveness and efficiency of our system.

Multi-Graph Heterogeneous Interaction Fusion for Social Recommendation

With the rapid development of online social recommendation system, substantial methods have been proposed. Unlike traditional recommendation system, social recommendation performs by integrating social relationship features, where there are two major challenges, i.e., early summarization and data sparsity. Thus far, they have not been solved effectively. In this article, we propose a novel social recommendation approach, namely Multi-Graph Heterogeneous Interaction Fusion (MG-HIF), to solve these two problems. Our basic idea is to fuse heterogeneous interaction features from multi-graphs, i.e., user–item bipartite graph and social relation network, to improve the vertex representation learning. A meta-path cross-fusion model is proposed to fuse multi-hop heterogeneous interaction features via discrete cross-correlations. Based on that, a social relation GAN is developed to explore latent friendships of each user. We further fuse representations from two graphs by a novel multi-graph information fusion strategy with attention mechanism. To the best of our knowledge, this is the first work to combine meta-path with social relation representation. To evaluate the performance of MG-HIF, we compare MG-HIF with seven states of the art over four benchmark datasets. The experimental results show that MG-HIF achieves better performance.

Designing and Evaluating an Automatic Forensic Model for Fast Response of Cross-border E-commerce Security Incidents

The rapid development of cross-border e-commerce over the past decade has accelerated the integration of the global economy. At the same time, cross-border e-commerce has increased the prevalence of cybercrime, and the future success of e-commerce depends on enhanced online privacy and security. However, investigating security incidents is time- and cost-intensive as identifying telltale anomalies and the source of attacks requires the use of multiple forensic tools and technologies and security domain knowledge. Prompt responses to cyber-attacks are important to reduce damage and loss and to improve the security of cross-border e-commerce. This article proposes a digital forensic model for first incident responders to identify suspicious system behaviors. A prototype system is developed and evaluated by incident response handlers. The model and system are proven to help reduce time and effort in investigating cyberattacks. The proposed model is expected to enhance security incident handling efficiency for cross-border e-commerce.

Research on the Impact of Information Security Certification and Concealment onFinancial Performance

With the rapid development of information technology, information security has been gaining attention. The International Organization for Standardization (ISO) has issued international standards and technical reports related to information security, which are gradually being adopted by enterprises. This study analyzes the relationship between information security certification (ISO 27001) and corporate financial performance using data from Chinese publicly listed companies. The study focusses on the impact of corporate decisions such as whether to obtain certification, how long to hold certification, and whether to publicize information regarding certification. The results show that there is a positive correlation between ISO 27001 and financial performance. Moreover, the positive impact of ISO 27001 on financial performance gradually increases with time. In addition, choosing not to publicize ISO 27001 certification can negatively affect enterprise performance.

Aesthetic Evaluation of Interior Design Based on Visual Features

In the modern context, interior design has inevitably become a part of social culture. All kinds of modeling, decoration and furnishings in modern interior space show people's pursuit and desire for a better life. These different styles of modern interior design rely on science and technology, utilize culture and art as the connotation. Its development often reflects the cultural spirit of a nation. The aesthetic evaluation plays an important role in the modern interior design. With development of derivative digital devices, a large number of digital images have been emerged. The rapid development of computer vision and artificial intelligence makes aesthetic evaluation for interior design become automatic. This paper implements an intelligent aesthetic evaluation of interior design framework to help people choose the appropriate and effective interior design from collected images or mobile digital devices.

A Survey of Deep Active Learning

Active learning (AL) attempts to maximize a model’s performance gain while annotating the fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize a massive number of parameters if the model is to learn how to extract high-quality features. In recent years, due to the rapid development of internet technology, we have entered an era of information abundance characterized by massive amounts of available data. As a result, DL has attracted significant attention from researchers and has been rapidly developed. Compared with DL, however, researchers have a relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples, meaning that early AL is rarely according the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to a large number of publicly available annotated datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, making it unfeasible in fields that require high levels of expertise (such as speech recognition, information extraction, medical images, etc.). Therefore, AL is gradually coming to receive the attention it is due. It is therefore natural to investigate whether AL can be used to reduce the cost of sample annotation while retaining the powerful learning capabilities of DL. As a result of such investigations, deep active learning (DeepAL) has emerged. Although research on this topic is quite abundant, there has not yet been a comprehensive survey of DeepAL-related works; accordingly, this article aims to fill this gap. We provide a formal classification method for the existing work, along with a comprehensive and systematic overview. In addition, we also analyze and summarize the development of DeepAL from an application perspective. Finally, we discuss the confusion and problems associated with DeepAL and provide some possible development directions.

Designing and Evaluating an Automatic Forensic Model for Fast Response of Cross-Border E-Commerce Security Incidents

The rapid development of cross-border e-commerce over the past decade has accelerated the integration of the global economy. At the same time, cross-border e-commerce has increased the prevalence of cybercrime, and the future success of e-commerce depends on enhanced online privacy and security. However, investigating security incidents is time- and cost-intensive as identifying telltale anomalies and the source of attacks requires the use of multiple forensic tools and technologies and security domain knowledge. Prompt responses to cyber-attacks are important to reduce damage and loss and to improve the security of cross-border e-commerce. This article proposes a digital forensic model for first incident responders to identify suspicious system behaviors. A prototype system is developed and evaluated by incident response handlers. The model and system are proven to help reduce time and effort in investigating cyberattacks. The proposed model is expected to enhance security incident handling efficiency for cross-border e-commerce.

Prediction of Football Match Results Based on Edge Computing and Machine Learning Technology

With the rapid development of artificial intelligence, various machine learning algorithms have been widely used in the task of football match result prediction and have achieved certain results. However, traditional machine learning methods usually upload the results of previous competitions to the cloud server in a centralized manner, which brings problems such as network congestion, server computing pressure and computing delay. This paper proposes a football match result prediction method based on edge computing and machine learning technology. Specifically, we first extract some game data from the results of the previous games to construct the common features and characteristic features, respectively. Then, the feature extraction and classification task are deployed to multiple edge nodes.Finally, the results in all the edge nodes are uploaded to the cloud server and fused to make a decision. Experimental results have demonstrated the effectiveness of the proposed method.

Mobile Edge Computing to Assist the Online Ideological and Political Education

With the rapid development of mobile Internet technology, mobile network data traffic presents an explosive growth trend. Especially, the proportion of mobile video business has become a large proportion in mobile Internet business. Mobile video business is considered as a typical business in the 5G network, such as in online education. The growth of video traffic poses a great challenge to mobile network. In order to provide users with better quality of experience (QoE), it requires mobile network to provide higher data transmission rate and lower network delay. This paper adopts a combined optimization to minimize total cost and maximize QoE simultaneously. The optimization problem is solved by ant colony algorithm. The effectiveness is verified on experiment.