The past few years have witnessed some public safety incidents occurring around the world. With the advent of the big data era, effectively extracting public security information from the internet has become of great significance. Up to hundreds of TBs of data are injected into the network every second, and thus it is impossible to process them manually. Natural Language Processing (NLP) is dedicated to the development of an intelligent system for effective text information mining. By analysing the text and quickly extracting the relationships between the relevant entities, NLP can establish the knowledge graph (KG) of public security, which lays the foundation for safety case analysis, information monitoring, and activity tracking and locating. One of the current pre-training relation extraction models is the Word2Vec model. The Word2vec model is single mapped, and it produces a static, single representation of the words in sentences. Then, the BERT model considers contextual information and provides more dynamic, richer vector representations of generated words. Therefore, in this paper, we propose a Bidirectional Encoder Representation from Transformers (BERT) based on the Chinese relation extraction algorithm for public security, which can effectively mine security information. The BERT model is obtained by training the Masked Language Model and predicting the next sentence task, which is based on the Transformer Encoder and the main model structure is the stacked Transformers. Extensive simulations are conducted to evaluate our proposed algorithm in comparison to some state-of-the-art schemes.

In history research, cohort analysis seeks to identify social structures and figure mobilities by studying the group-based behavior of historical figures. Prior works mainly employ automatic data mining approaches, lacking effective visual explanation. In this paper, we present CohortVA, an interactive visual analytic approach that enables historians to incorporate expertise and insight into the iterative exploration process. The kernel of CohortVA is a novel identification model that generates candidate cohorts and constructs cohort features by means of pre-built knowledge graphs constructed from large-scale history databases. We propose a set of coordinated views to illustrate identified cohorts and features coupled with historical events and figure profiles. Two case studies and interviews with historians demonstrate that CohortVA can greatly enhance the capabilities of cohort identifications, figure authentications, and hypothesis generation.

The chemical terrorist attack is an unconventional form of terrorism with vast scope of influence, strong concealment, high technical means and severe consequences. Chemical terrorism risk refers to the uncertainty of the effects of terrorist organisations using toxic industrial chemicals/drugs and classic chemical weapons to attack the population. There are multiple risk factors infecting chemical terrorism risk, such as the threat degree of terrorist organisations, attraction of targets, city emergency response capabilities, and police defense capabilities. We have constructed a Bayesian network of chemical terrorist attacks to conduct risk analysis. The scenario analysis and sensitivity analysis are applied to validate the model and analyse the impact of the vital factor on the risk of chemical terrorist attacks. The results show that the model can be used for simulation and risk analysis of chemical terrorist attacks. In terms of controlling the risk of chemical terrorist attack, patrol and surveillance are less critical than security checks and police investigations. Security check is the most effective approach to decrease the probability of successful attacks. Different terrorist organisations have different degrees of threat, but the impacts of which are limited to the success of the attack. Weapon types and doses are sensitive to casualties, but it is the level of emergency response capabilities that dominates the changes in casualties. Due to the limited number of defensive resources, to get the best consequence, the priority of the deployment of defensive sources should be firstly given to governmental buildings, followed by commercial areas. These findings may provide the theoretical basis and method support for the combat of the public security department and the safety prevention decision of the risk management department.

Many chemical plant explosion accidents occur along with the development of the chemical industry. Meanwhile, the interaction and influence of various factors significantly increase the uncertainty of the evolution process of such accidents. This paper presents a framework to dynamically evaluate chemical plant explosion accidents. We propose twelve nodes to represent accident evolution and establish a Bayesian network model for chemical plant explosion accidents, combining historical data with expert experience to support the prevention, management, and real-time warning. Hypothetical scenarios and catastrophic explosion scenarios were analyzed by setting different combinations of states for nodes. Moreover, the impacts of factors such as factory type, material form, accident equipment, the emergency response on casualty and property loss are evaluated. We find that sensitivity of property loss and casualties to factory type and ongoing work are less significant; the equipment factors result in more casualties than that from personnel factors; the impact of emergency response on the accident results is significant; equipment safety management and personnel safety training are the most important measures to prevent chemical plant explosion risks.

The chemical terrorist attack is a type of unconventional terrorism that threatens the safety of cities. This kind of terrorist attack is highly concealed and difficult to be detected. Once the attack is successful, the consequences will be severe and the scope of impact will be enormous. Therefore, public security and emergency departments need to perform risk analysis and dynamic knowledge update to reduce risk or mitigate the effects of accidents. In order to quickly and effectively analyze the risk of chemical terrorist attacks, this article proposed a hybrid approach (B-R model) to analyze the risk of chemical terrorist attacks. First, a modular and customizable Bayesian network (BN) model library was built, which can satisfy users to select multi-dimensional risk factors. Based on the personalized BN, a risk knowledge graph (RKG) is constructed with multi-source data to realize the combination of risk analysis and knowledge acquisition. Then the threat degree of terrorist organizations, the strength of defensive forces, and the risk value of targets is calculated and displayed. The BN-RKG method provides data and theoretical support for defenders’ resource allocation and emergency decision-making. Finally, a case study was conducted for a hypothetical scenario analysis. The result shows that the hybrid method can help with risk control and have the potential to support practical policymaking.

Natural language processing (NLP) is the best solution to extensive, unstructured, complex, and diverse network big data for counter-terrorism. Through the text analysis, it is the basis and the most critical step to quickly extract the relationship between the relevant entities pairs in terrorism. Relation extraction lays a foundation for constructing a knowledge graph (KG) of terrorism and provides technical support for intelligence analysis and prediction. This paper takes the distant-supervised relation extraction as the starting point, breaks the limitation of artificial data annotation. Combining the Bidirectional Encoder Representation from Transformers (BERT) pre-training model and the sentence-level attention over multiple instances, we proposed the relation extraction model named BERT-att. Experiments show that our model is more efficient and better than the current leading baseline model over each evaluative metrics. Our model applied to the construction of anti-terrorism knowledge map, it used in regional security risk assessment, terrorist event prediction and other scenarios.