Aiming at the ambiguous and unclarity logical relationship among disposal text information in gas emergencies, a two-stage method focusing on gas accident case text is proposed for constructing the event knowledge graph of gas emergency disposal. First, a gas text joint extraction method (GTJM) for gas accident cases is proposed at the first stage. The multigranularity training tasks in the enhanced representation through knowledge integration (ERNIE) pretrained model are used to address the issue of insufficient context understanding when processing long texts in gas cases while combining multihead attention mechanism to obtain richer semantic information. The cascade binary tagging framework (CasRel) is then utilized to extract entities and relationships contained in the background and disposal information of accident cases, to tackle the problem of overlapping entity extraction in gas accident case texts. Second, in the second stage, the bidirectional encoder representation from transformers (BERT) is used to capture the contextual features of compressed events, while a novel Incept-text convolutional neural network (TextCNN) focuses on the global semantic information within compressed events. The combination of both models could resolve the problem of sequential relationship identification between long events much better. Finally, a clear event logic knowledge graph is formed for gas accident disposal in emergencies. The experimental results indicate that in the first phase, the GTJM model has an average improvement of 7% in F1 value compared to other baseline models. In the second phase, the accuracy of the sequential relationship identification reaches 92%. The feasibility of the proposed method is verified through real cases. The study could provide auxiliary decision support for emergency disposal in gas accidents.

(Source) Code summarization aims to automatically generate summaries/comments for given code snippets in the form of natural language. Such summaries play a key role in helping developers understand and maintain source code. Existing code summarization techniques can be categorized into extractive methods and abstractive methods . The extractive methods extract a subset of important statements and keywords from the code snippet using retrieval techniques and generate a summary that preserves factual details in important statements and keywords. However, such a subset may miss identifier or entity naming, and consequently, the naturalness of the generated summary is usually poor. The abstractive methods can generate human-written-like summaries leveraging encoder-decoder models. However, the generated summaries often miss important factual details. To generate human-written-like summaries with preserved factual details, we propose a novel extractive-and-abstractive framework. The extractive module in the framework performs the task of extractive code summarization, which takes in the code snippet and predicts important statements containing key factual details. The abstractive module in the framework performs the task of abstractive code summarization, which takes in the code snippet and important statements in parallel and generates a succinct and human-written-like natural language summary. We evaluate the effectiveness of our technique, called EACS, by conducting extensive experiments on three datasets involving six programming languages. Experimental results show that EACS significantly outperforms state-of-the-art techniques for all three widely used metrics, including BLEU, METEOR, and ROUGH-L. In addition, the human evaluation demonstrates that the summaries generated by EACS have higher naturalness and informativeness and are more relevant to given code snippets.

(Source) code search is widely concerned by software engineering researchers because it can improve the productivity and quality of software development. Given a functionality requirement usually described in a natural language sentence, a code search system can retrieve code snippets that satisfy the requirement from a large-scale code corpus, e.g., GitHub. To realize effective and efficient code search, many techniques have been proposed successively. These techniques improve code search performance mainly by optimizing three core components, including query understanding component, code understanding component, and query-code matching component. In this article, we provide a 3-dimensional perspective survey for code search. Specifically, we categorize existing code search studies into query-end optimization techniques, code-end optimization techniques, and match-end optimization techniques according to the specific components they optimize. These optimization techniques are proposed to enhance the performance of specific components, and thus the overall performance of code search. Considering that each end can be optimized independently and contributes to the code search performance, we treat each end as a dimension. Therefore, this survey is 3-dimensional in nature, and it provides a comprehensive summary of each dimension in detail. To understand the research trends of the three dimensions in existing code search studies, we systematically review 68 relevant literatures. Different from existing code search surveys that only focus on the query end or code end or introduce various aspects shallowly (including codebase, evaluation metrics, modeling technique, etc.), our survey provides a more nuanced analysis and review of the evolution and development of the underlying techniques used in the three ends. Based on a systematic review and summary of existing work, we outline several open challenges and opportunities at the three ends that remain to be addressed in future work.

Code comments play an essential role in aiding developers understand and maintain source code. Current code comment generation techniques can be classified into categories: extractive methods and abstractive methods. Extractive methods use text retrieval techniques to extract important code tokens to constitute comments. Such comments contain important factual details articulated explicitly in code tokens, but are poor in naturalness. Abstractive methods usually regard code comment generation as a neural machine translation task. By leveraging powerful deep learning-based language models, abstractive methods can generate comments that resemble human writing. However, compared with natural language, programming language code is more complex. Comments generated by abstractive methods often leave out important factual details. In this paper, we propose a novel method for code comment generation by integrating extractive and abstractive models. Our extractive model is built on the Latent Semantic Analysis (LSA) model, effectively extracting important factual details in code snippets. Meanwhile, our abstractive model is built on a deep learning-based encoder-decoder model, enabling it to generate concise and human-written-like comments. We evaluate the effectiveness of our method, called ICS, by conducting extensive experiments on the CodeSearchNet dataset involving six programming languages. The results demonstrate that ICS outperforms state-of-the-art techniques in three widely used metrics: BLEU, METEOR, and ROUGE-L. Moreover, the outcomes of the human evaluation indicate that the comments generated by ICS exhibit superior naturalness and informativeness, and closely align with the provided code snippets.

To address the continuous occurrence of safety accidents in large public buildings, emergency evacuation has been an essential means of emergency disposal. However, risks also exist in the evacuation processes. Evaluating the risk of evacuation processes can be used for improving the safety of the evacuation processes and providing additional support for evacuation decision-making, which has important practical significance. At present, because of the complexity of evacuation processes and the lack of data, the research on evacuation risk assessment is still limited. Traditional risk assessment methods have more subjective and are difficult to fulfill the requirements of timeliness in emergency evacuations. With the development of artificial intelligence, it has provided a possibility to use deep learning methods to excavate the internal relationship of complex evacuation systems and achieve rapid risk assessments. This article innovatively applies deep learning methods to the field of risk assessment of evacuation. An approach based on the convolutional neural network is proposed in this article to establish an evacuation assessment model. Two network structures, Lenet and Resnet, are selected to train the model, respectively. A real case of the large stadium is used to illustrate the assessment way, and a large number of experiments were carried out to obtain the data required for training. The result shows that the deep learning method can realize an efficient and fast risk assessment.

In order to decrease the casualties in fire disasters and to improve the efficiency of evacuation, exploring, and revealing the impact of influence factors on evacuation is of vital importance. This paper is focused on the influence of fire and human factors on evacuation processes directed by evacuation strategies in the building structure. Interactions between fire environment and evacuees are considered in a systematic view. Building artificial evacuation systems and performing computational experiments are the main research ways. A case is given to illustrate the research approach and quantitative results have been analyzed. The work in this paper can be used for optimizing occupant distribution and composition, estimating evacuation strategies, and ultimately for improving the evacuation efficiency.

Integrating artificial systems, computational experiments, and parallel execution (ACP) is an effective approach to modeling, simulating, and intervening real complex systems. Emergency response is an important issue in the operation of urban rail transport systems for ensuring the safety of people and property. Inspired by the ACP method, this paper introduces a basic framework of parallel control and management (PCM) for emergency response of urban rail transportation systems. The proposed framework is elaborated from three interdependent aspects: Points, Lines, and Networks. Points represent the modeling of urban rail stations, Lines describe the microscopic characteristics of urban rail connections between designated stations, and Networks present the macroscopic properties of all the urban rail connections. Based on the given framework, a series of parallel experiments, which were impossible to achieve in real systems, can now be conducted in the constructed artificial system. Furthermore, the constructed artificial system can be used to test and develop effective emergency control and management strategies for real rail transport systems. Therefore, this proposed framework will be able to enhance the reliability, security, robustness, and maneuverability of urban rail transport systems in case of an emergency.