Fire situation monitoring is critical for protecting lives and property, offering real-time data on fire location, intensity, and progression. Traditional vision-based methods often treat fires as rigid objects, overlooking their fuzzy boundaries. To address this, we propose the subtractive counterfactual-based fire segmentation (SCF) method, which enhances edge detail focus for precise fire profiling. The approach constructs a counterfactual image by masking flames. Factual and counterfactual features are extracted and hypothetical counterfactual features are introduced during inference. Subtracting factual features from hypothetical counterfactual features yields unbiased fire features for segmentation. Experiments on five datasets demonstrate SCF's superior accuracy and reliability in fire monitoring.

Emotion motivates behavior. Investigating the correlation between behavior and emotion, an often overlooked perspective, plays a significant role in uncovering the underlying motives behind behaviors and the intrinsic cause-effects of social events. This article proposes a methodology for mining the correlation between public behavior and emotion using daily news data. Initially, aspect-emotion-reaction (A-E-R) triplets are extracted and generalized, encompassing both explicit and implicit patterns. Then, a knowledge representation model based on hypothetical context (KRHC) with a self-reflection mechanism is proposed to uncover implicit relationships between emotion and behavior through attention mechanisms. By combining rule-based methods for explicit relationships and deep learning for implicit ones, an understanding of emotion-behavior patterns is achieved. In this study, the behaviors are divided into three categories of prosocial, antisocial, and normal behaviors with ten secondary types. Seven categories of emotions are adopted. The proposed deep learning model KRHC is validated on A-E-R datasets and public KINSHIP datasets. The experiment results are concluded; for example, when "fear," "sad," and "surprise" emotions appear, it drives behavior "panic" with most probability. These findings could provide insights for both human-computer interaction and public safety management applications.

In the contemporary social environment, social crisis events occur frequently with significant impacts. Effective management of these events requires comprehensive group intention mining, which encompasses intention detection and intention attribution. Knowledge graph inference facilitates the detection of group intention in crisis events. This is supported by the construction of crisis knowledge graphs, which organize crisis elements and inter-element relations into structured semantic information. This paper provides a comprehensive overview of the research about knowledge graph in social crisis management, focusing on three key areas: knowledge graph construction and inference, knowledge graph-based interpretable crisis attribution, and risk management. Specifically, the interpretable semantics in crisis knowledge graphs enables attribution of intention. To illustrate the significance of knowledge graphs in group intention mining, the COVID-19 and China–US game events are selected as two case studies. Finally, the paper proposes future research directions to solve the limitations of existing knowledge graph-related methods in social crises.

In the current social environment, social crisis events occur frequently with significant impacts.Group intention mining through automatic technologies for managing social crises has gained extensive attention.This paper presents an overview of research on group intention mining in social crisis events, covering three areas: knowledge graph inference, intention attribution, and risk management.Knowledge graph inference facilitates the detection of group intention in crisis events.It is supported by the construction of crisis knowledge graphs, which organize crisis elements and inter-element relations into structured semantic knowledge.The interpretable semantics in the crisis knowledge graphs enables attribution of intention.Group intention mining consists of intention detection and intention attribution, serving the risk management of social crisis events.To gain insights into the process of group intention mining in social crises, the Covid-19 event is selected as a case study.Finally, the paper proposes future research directions to solve the limitations of existing intention mining methods in social crises.

Recent years have witnessed an increasing popularity of wireless body area network (WBAN), with which continuous collection of physiological signals can be conveniently performed for healthcare monitoring. Energy consumption is a critical issue because it directly affects the duration of the equipped sensors. In this article, we propose a low-cost and confidential electrocardiogram (ECG) acquisition approach for WBAN. The compressed sensing (CS) is employed for low-cost signal acquisition, and its cryptographic features are exploited for promoting the framework's confidentiality. In particular, the RIPless measurement matrix is used to give CS the resistance against plaintext attack, while the first-order Σ∆ quantizer is employed to embed the cryptographic diffusion feature into the whole system. Two chaotic systems are employed for generating the required secret elements for the acquisition and encryption. Experiment results well demonstrate the signal reconstruction and security performance of the proposed framework.

Emotion analysis has been attracting researchers' attention. Most previous works in the artificial-intelligence field focus on recognizing emotion rather than mining the reason why emotions are not or wrongly recognized. The correlation among emotions contributes to the failure of emotion recognition. In this article, we try to fill the gap between emotion recognition and emotion correlation mining through natural language text from Web news. The correlation among emotions, expressed as the confusion and evolution of emotion, is primarily caused by human emotion cognitive bias. To mine emotion correlation from emotion recognition through text, three kinds of features and two deep neural-network models are presented. The emotion confusion law is extracted through an orthogonal basis. The emotion evolution law is evaluated from three perspectives: one-step shift, limited-step shifts, and shortest path transfer. The method is validated using three datasets: 1) the titles; 2) the bodies; and 3) the comments of news articles, covering both objective and subjective texts in varying lengths (long and short). The experimental results show that in subjective comments, emotions are easily mistaken as anger. Comments tend to arouse emotion circulations of love-anger and sadness-anger. In objective news, it is easy to recognize text emotion as love and cause fear-joy circulation. These findings could provide insights for applications regarding affective interaction, such as network public sentiment, social media communication, and human-computer interaction.

Speech emotion recognition is a challenging problem because human convey emotions in subtle and complex ways.For emotion recognition on human speech, one can either extract emotion related features from audio signals or employ speech recognition techniques to generate text from speech and then apply natural language processing to analyze the sentiment.Further, emotion recognition will be beneficial from using audio-textual multimodal information, it is not trivial to build a system to learn from multimodality.One can build models for two input sources separately and combine them in a decision level, but this method ignores the interaction between speech and text in the temporal domain.In this paper, we propose to use an attention mechanism to learn the alignment between speech frames and text words, aiming to produce more accurate multimodal feature representations.The aligned multimodal features are fed into a sequential model for emotion recognition.We evaluate the approach on the IEMOCAP dataset and the experimental results show the proposed approach achieves the state-of-the-art performance on the dataset. 1

In recent years, there has been increasing interest in transparency in Deep Neural Networks. Most of the works on transparency have been done for image classification. In this paper, we report on work of transparency in Deep Reinforcement Learning Networks (DRLNs). Such networks have been extremely successful in learning action control in Atari games. In this paper, we focus on generating verbal (natural language) descriptions and explanations of deep reinforcement learning policies. Successful generation of verbal explanations would allow better understanding by people (e.g., users, debuggers) of the inner workings of DRLNs which could ultimately increase trust in these systems. We present a generation model which consists of three parts: an encoder on feature extraction, an attention structure on selecting features from the output of the encoder, and a decoder on generating the explanation in natural language. Four variants of the attention structure full attention, global attention, adaptive attention and object attention - are designed and compared. The adaptive attention structure performs the best among all the variants, even though the object attention structure is given additional information on object locations. Additionally, our experiment results showed that the proposed encoder outperforms two baseline encoders (Resnet and VGG) on the capability of distinguishing the game state images.

A public safety event is a danger and urgent event that need early detection, quick response, and accuracy recover. The efficient method for responding to a happening public safety event is to collect and describe the related data. Besides the surveillance cameras from the physical space, the social media data can also be used to collect and describe the related data of a public safety event. In this work, the proposed method focuses on the step for describing public safety events. Given a public safety event, videos from the surveillance cameras and social messages from social sensors are collected. The multi-modal information including texts, images, videos, and spatial-temporal data is mined to give a description precisely and concisely. First, the social sensors are associated to surveillance cameras by the spatial and temporal information. In the second stage, the social messages are associated to surveillance cameras by the semantic information. In the third stage, the social messages are associated to surveillance cameras by the visual feature. Besides the text, social sensors may upload images or videos. Finally, the multi-modal description step is given based on the three different associations. The experiments on the real data demonstrate the superiority of the proposed framework. Case studies on the real public safety event show the proposed model has good performance and high effectiveness.

Recent years, big data has attracted increasing interest.Sentiment analysis from microblog as one kind of big data also receive great attention.Some recent research works are not suitable for sentiment analysis as the result that users prefer to express their feelings in individual ways.In this paper, a framework is proposed to calculate sentiment for aspects of event.Based on some state of art technologies, we build up one flowchart to get sentiment for aspects of event.During the process, name entities with the same meaning are clustered and sentiment carrier are filtered.In this way sentiment can be got even user express feeling for the same object with different words.

Summary An emergency event is an unexceptional event that exceeds the capacity of normal resources and organization to cope and a situation that poses an immediate risk to health, life, property, or environment. Crowdsourcing connects unobtrusive and ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods, to create solutions that improve urban environment, human life quality, and city operation systems. The crowdsourcing on social media can be used to detect and analyze urban emergency events. In this paper, in order to detect and describe the real‐time urban emergency event, the knowledge base model is proposed. The crowdsourcing‐based knowledge base model is firstly introduced, which uses the information from social media. Secondly, the basic definition of the proposed knowledge base model including keywords, patterns, positive sentences, and knowledge graph is given. Thirdly, the temporal information is added to the proposed knowledge base model. The case study on real data sets shows that the proposed algorithm has good performance and high effectiveness in the analysis and detection of emergency events. Copyright © 2016 John Wiley & Sons, Ltd.

Sentiment analysis from microblog platform has received an increasing interest from web mining community in recent years. Current sentiment analysis methods are mainly based on the hypothesis that each word expresses only one sentiment. However, human sentiment are prototyped and fuzzy-confined as declared in social psychology, which is conflicting with the hypothesis. This is one of the barriers that impede the computation of complex public sentiment of web events in microblog. Therefore, how to find a reasonable computational model, combining learning technology and human sentiment cognition theory, is a novel idea in event sentiment analysis of microblog. In this paper, a new sentiment computation approach, which is defined as public sentiments discriminator (PSD), considering both fuzzy logic and sentiment complexity, is proposed. Unlike traditional machine learning methods, PSD is based on the rational hypothesis that sentiments are correlated with each other. A three-level computing structure, sentiment-term level, microblog level and public sentiment level, is employed. Experiments show that the proposed approach, PSD, can achieve similar accuracy and [Formula: see text]1-measure but more cognitive results when compared with traditional well-known machine learning methods. These experimental studies have confirmed that PSD can generate an interpretable result with no restriction among sentiments.

Detection about urban emergency events, e.g., The fires, storms, traffic jams is of great importance to protect the security of humans. While there are limited physical sensors such as surveillance cameras in a city, urban emergency events are still difficult to be detected for their real-time feature. Recently, social media feeds are rapidly emerging as a novel platform for providing and dissemination of information that is often geographic. The content from social media often includes references to urban emergency events occurring at, or affecting specific locations. In this paper, the real-time detection of urban emergency events based on social media is proposed. Firstly, users of social media are set as the target of crowd sensing. Secondly, the spatial and temporal information from the social media are extracted to detect the real-time event. Thirdly, a GIS based annotation of the detected urban emergency event is shown. The proposed method is evaluated with extensive experiments based on five real urban emergency events. The result show the accuracy and efficiency of the proposed method.

Abstract We propose a volume fraction based approach to effectively simulate the miscible and immiscible flows simultaneously. In this method, a volume fraction is introduced for each fluid component and the mutual interactions between different fluids are simulated by tracking the evolution of the volume fractions. Different techniques are employed to handle the miscible and immiscible interactions and special treatments are introduced to handle flows involving multiple fluids and different kinds of interactions at the same time. With this method, second‐order accuracy is preserved in both space and time. The experiment results show that the proposed method can well handle both immiscible and miscible interactions between fluids and much richer mixing detail can be generated. Also, the method shows good controllability. Different mixing effects can be obtained by adjusting the dynamic viscosities and diffusion coefficients. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract We present a novel pressure correction scheme for the Smoothed Particle Hydrodynamics (SPH) for fluid animation. In the conventional SPH method, equations of state (EOS) are employed to relate the pressure to the particle density. To enforce the volume conservation, high speeds of sound are usually required, which leads to very small time steps and noisy pressure distribution. The problem remains one of the main reasons of numerical instability in SPH. In the paper, a new extra pressure correction scheme is proposed to transport the local pressure disturbance to the neighboring area and no solution of the Poisson equation is required. As a result, smoother pressure distribution and more efficient simulation are achieved. The proposed method has been used to simulate free surface problems. The results demonstrate the validation of the present SPH method. Surface tension and fluid fragmentation can be well handled. Copyright © 2009 John Wiley & Sons, Ltd.