Social Public Security Research Articles

Research on Residents' Willingness to Protect Privacy in the Context of the Personal Information Protection Law: A Survey Based on Foshan Residents' Data

The Personal Information Protection Law, as the frst law on personal information protection in China, hits the people’s most concerned, realistic and direct privacy and information security issues, and plays an extremely important role in promoting the development of the digital economy, the legalization of socialism with Chinese characteristics and social public security, and marks a new historical development stage in the protection of personal information in China. However, the awareness of privacy protection and privacy protection behavior of the public in personal information privacy protection is weak. Based on the literature review and in-depth understanding of current legal regulations, this study integrates the relevant literature and theoretical knowledge of the Personal Protection Law to construct a conceptual model of “privacy information protection willingness—privacy information protection behavior”. Taking the residents of Foshan City as an example, this paper conducts a questionnaire survey on their attitudes toward the Personal Protection Law, analyzes the factors infuencing their willingness to protect their privacy and their behaviors, and explores the mechanisms of their infuencing variables, to provide advice and suggestions for promoting the protection of privacy information and building a security barrier for the high-quality development of public information security.

Extensible Continuum Manipulator Toward In-situ Explosive Ordnance Disposal

Abstract Homemade bombs made by terrorists threaten seriously the social public security. Now, it is still a challenge to guarantee safety without destroying bombs. Compared with the destructive explosive disposal approach employing rigid robots, we proposed an in-situ explosive disposal method, which takes advantage of an extensible continuum manipulator to pass through obstacles and cut wires by end-effector. The spring-based continuum manipulator has six-degrees-of-freedom and is 400 mm in length but only 15 mm in diameter. Also, we have created only a 15 g end-effector that can cut 1-mm-diameter copper wire and embed a miniature camera. According to the experiment, a single continuum manipulator has around 2 mm repeatability accuracy and can carry a 50 g weight. Without special training, the operator can control the integrated explosive ordnance disposal (EOD) robot to complete a simulation mission in approximately five minutes. This paper broadens the application of the continuum manipulator and gives a unique solution for EOD robots.

Spatial Pattern and Influencing Factors of Basic Education Resources in Rural Areas around Metropolises—A Case Study of Wuhan City’s New Urban Districts

Basic education resources are basic urban and rural social public security resources, and their spatial distribution is an important issue related to people’s livelihoods and social justice. Taking Wuhan as a case study, this paper analyzed the spatial distribution characteristics of rural basic education resources based on the methods of the average nearest neighbor index, imbalance index, kernel density analysis and two-step floating catchment area and then used geographic detector analysis to detect its influencing factors. The following findings were obtained: (1) Rural kindergartens and elementary schools in Wuhan City’s new urban districts showed a clustered distribution pattern, while secondary schools showed a uniform distribution trend. The spatial distribution of rural basic education resources is poorly balanced, with a tendency to cluster in Huangpi District, Xinzhou District and Caidian District; the overall spatial distribution density of rural basic education resources showed the distribution characteristics of “block-like clustering and multicenter development”. (2) The spatial accessibility of kindergartens showed a spatial pattern of “large dispersion and small clustering”, with multiple high-value clustering areas; and the accessibility of elementary and secondary schools showed a spatial pattern of high in the south and low in the north. (3) The population, economy and education development level are the main factors affecting the spatial distribution of rural basic education resources, while the influence of infrastructure construction is weak. The core influencing factors of the spatial distribution of each type of basic education resource are both consistent and different. According to the interaction factor detection, the spatial distribution of rural basic education resources in Wuhan City’s new urban districts is the result of the combined effect of multiple factors.

Analysis of Network Public Opinion in New Media Based on BP Neural Network Algorithm

With the rapid development of Internet technology, new media is more and more favored by people and has become an important medium to control online public opinion. Social public opinion caused by new media is also more and more concerned by all walks of life. New media has the characteristics of fast information dissemination, wide dissemination range, and strong arbitrariness of news release. Positive online public voice or negative online public voice will have a very different impact on people’s lives. Some negative online public voice may even constitute a social crisis and seriously affect social public security. In order to analyze and predict the development trend of new media network public opinion, this paper presents a design of improved BP neural network model based on genetic algorithm, which is used to analyze public opinion in new media network. Experimental results show that this way has stronger processing ability and higher warning accuracy for online public opinion event index data. It can provide certain theoretical basis and data support for relevant departments to effectively prevent and manage new media network public opinion events.

Structure Reversal of Online Public Opinion for the Heterogeneous Health Concerns under NIMBY Conflict Environmental Mass Events in China.

Public opinions play an important role in the formation of Not in My Back Yard (NIMBY) conflict environmental mass events. Due to the continual interactions between affected groups and the corresponding government responses surrounding the public interests related to health, online public opinion structure reversal arises frequently in NIMBY conflict events, which pose a serious threat to social public security. To explore the underlying mechanism, this paper introduces an improved dynamic model which considers multiple heterogeneities in health concerns and social power of individuals and in government’s ability. The experimental results indicate that the proposed model can provide an accurate description of the entire process of online public opinion structure reversal in NIMBY conflict environmental mass incidents on the Internet. In particular, the proportion of the individual agents without health interest appeals will delay the online public opinion structure reversal, and the upper threshold remains within regulatory limits from 0.4 to 0.5. Unlike some previous results that show that the guiding powers of the opinion leaders varied over its ratio in a fixed-sized group, our results suggest that the impact of opinion leaders is of no significant difference for the time of structure reversal after it increased to about 6%. Furthermore, a double threshold effect of online structure reversal during the government’s response process was observed. The findings are beneficial for understanding and explaining the process of online public opinion structure reversal in NIMBY conflict environmental mass incidents, and provides theoretical and practical implications for guiding public or personal health opinions on the Internet and for a governments’ effective response to them.

High‐quality tweet generation for online behavior security management based on semantics measurement

AbstractBehavior security management refers to monitoring and guiding the user's opinions in online social networks to reduce their harmful influence to social public security. Pushing designed tweets with specific contains to them is one of the promising ways to solve this problem. In this paper, we developed a new method for high‐quality supervision tweet generation, which not only considers the semantics of the tweets but also includes the supervision requirement of different aspects. Firstly, we collect millions of tweets of six typical events. Following, we construct a sentiment lexicon suitable for online behavior analysis, and we also construct a lexicon for tweet preprocessing and emotional score calculation. Secondly, to include the semantics during sentence similarity calculation, we employ the tweets collected to train the word2vec model and employ the sum of the word vectors in specific sentences to form the sentence vector. Finally, we employ the TextRank to generate the supervision tweet. Experimental results based on data collected showed that the proposed methods outperform other related traditional methods, which can be used for effective social security management.

Deep feature representation and multiple metric ensembles for person re-identification in security surveillance system

With the increasing concern about social public security and the development of large scale data storage technology, person re-identification in security surveillance system becomes a hot topic. Large variations in viewpoint and lighting across different camera views could change the appearance of the person a lot, which makes person re-identification still a challenging problem. Therefore, developing robust feature descriptors and designing discriminative distance metrics to measure the similarity between pedestrian images are two key aspects in person re-identification. In this paper, we propose a method using both deep learning and multiple metric ensembles to improve the performance of the re-identification. Firstly, we jointly use the various datasets to train a general Convolutional Neural Network (CNN) which is employed to extract the deep features of training and testing set afterwards. The deep architecture makes it possible to learn more abstract and internal features which are robust against the variations in viewpoint and lighting. Then we utilize the deep features of the training set to learn the specific distance metric of different datasets and combine it with Cosine distance metric together, multiple metric ensembles can measure the similarity between different images in a more comprehensive way. Finally, extensive experiments demonstrate that our method can improve the recognition performance effectively when compared with the state-of-the-art methods.

A classification method based on streak flow for abnormal crowd behaviors

An abnormal crowd behavior would harm social public security. Different abnormal crowd behaviors would bring about different harms and subject to different attentions of social public. The higher the harm of the abnormal crowd behavior was, the higher the attention of the social public would be. Therefore, in this paper, a classification method, using crowd density estimation, is proposed for abnormal crowd behaviors. First, in order to enhance the accuracy of the abnormal crowd behavior detection, streak flow, based on fluid mechanics, is introduced to improve the algorithm proposed by Hassner et al. Second, the abnormal crowd behavior in a video scene is detected, the crowd density is estimated and the abnormal behavior is classified on the basis of the crowd density. Lastly, many challenging real-world surveillance videos are used to validate the effectiveness and feasibility of our method for abnormal behavior classification.

A Classification Method Based on Multi-Features for Abnormal Crowd Behaviors

An abnormal crowd behavior would harm social public security. Different abnormal crowd behaviors would bring about different harms and subject to different attentions of social public. The higher the harm of the abnormal crowd behavior was, the higher the attention of the social public would be. Therefore, in this paper, a classification method, using crowd density estimation and crowd intensity, is proposed for abnormal crowd behaviors. In order to describe the abnormal crowd behavior more reasonably, we shoot some videos. Finally, the effectiveness and accuracy of our method for abnormal behavior classification is validated by the experimental results.

Analysis of major risks associated with hydrocarbon storage caverns in bedded salt rock

Salt rock is internationally accepted as an ideal medium for energy storage. As an energy storage structure, the safety of hydrocarbon storage caverns in salt rock is related to the national economy and to social public security. Risk analysis is an important method of engineering safety evaluation. In this paper the major risks associated with hydrocarbon storage caverns in bedded salt rock are defined. The major risks are classified under the headings of ‘oil and gas leakage’, ‘ground subsidence’, and ‘cavern failure’, and are discussed under these topical titles. The factors leading to the major risks associated with storage caverns are identified by reviewing descriptions of major accidents of salt storage caverns around the world. Fault tree models for the three major risks are established and analyzed. Basic paths of the risk and their occurrence probability ranking are derived. The risk factors which contribute greatly to the risk are identified by calculating the importance degree of all the basic events. Finally, a comprehensive evaluation methodology for major risk loss is generated based on the analytic hierarchy process. This provides a theoretical foundation for the evaluation and prevention of major risks in the construction and operation of storage caverns in bedded salt rock.