A stacked convolutional neural network for detecting the resource tweets during a disaster

Social media platform like Twitter is one of the primary sources for sharing real-time information at the time of events such as disasters, political events, etc. Detecting the resource tweets during a disaster is an essential task because tweets contain different types of information such as infrastructure damage, resources, opinions and sympathies of disaster events, etc. Tweets are posted related to Need and Availability of Resources (NAR) by humanitarian organizations and victims. Hence, reliable methodologies are required for detecting the NAR tweets during a disaster. The existing works don’t focus well on NAR tweets detection and also had poor performance. Hence, this paper focus on detection of NAR tweets during a disaster. Existing works often use features and appropriate machine learning algorithms on several Natural Language Processing (NLP) tasks. Recently, there is a wide use of Convolutional Neural Networks (CNN) in text classification problems. However, it requires a large amount of manual labeled data. There is no such large labeled data is available for NAR tweets during a disaster. To overcome this problem, stacking of Convolutional Neural Networks with traditional feature based classifiers is proposed for detecting the NAR tweets. In our approach, we propose several informative features such as aid, need, food, packets, earthquake, etc. are used in the classifier and CNN. The learned features (output of CNN and classifier with informative features) are utilized in another classifier (meta-classifier) for detection of NAR tweets. The classifiers such as SVM, KNN, Decision tree, and Naive Bayes are used in the proposed model. From the experiments, we found that the usage of KNN (base classifier) and SVM (meta classifier) with the combination of CNN in the proposed model outperform the other algorithms. This paper uses 2015 and 2016 Nepal and Italy earthquake datasets for experimentation. The experimental results proved that the proposed model achieves the best accuracy compared to baseline methods.

     

High-level feature recognition using feature relationship graphs

In this paper, we present a method to extract user-specific features from common features. This contrasts with other approaches which work directly off B-rep geometric models. Here, user-specific features are called high-level features which are a set of common features combined in a user-specific manner. A feature relationship graph is used to organize common features in a part and to define high-level feature patterns. The research presented in this paper focuses mainly on feature relationship graph construction and high-level feature recognition using subgraph isomorphic techniques.     

Passive and active navigation of virtual environments vs. traditional printed evacuation maps: A comparative evaluation in the aviation domain

Printed maps are the most common tool to prepare people for emergency evacuation in contexts such as public buildings or transportation. Unfortunately, they are poorly understood and often ignored by people. Virtual environments (VEs) could be a more effective method to support people in acquiring spatial knowledge about the real-world environment to evacuate. This paper pursues three main goals. First, we propose a VE-based tool to support spatial knowledge acquisition for evacuation purposes, using aviation as a real-world domain in which such knowledge is crucial for passengers’ safety. Second, we study in detail one of the VE design choices (active or passive navigation), comparing a version of our tool in which users navigate by actively controlling their position with another version in which users are passively led along pre-defined routes. Third, we contrast the two versions of the tool with the traditional, printed diagrammatic map provided to passengers by airlines. Results of our study show that the VE-based approach produces objectively better spatial knowledge when users are asked to pinpoint their assigned position in the environment, and that active navigation produces a performance improvement in a subsequent virtual evacuation. Moreover, the VE-based approach is perceived as more enjoyable, easier to comprehend and more effective than printed maps when active navigation is available.     

PalmHashing: a novel approach for dual-factor authentication

Many systems require a reliable personal authentication infrastructure to recognise the identity of a claimant before granting access to him/her. Conventional secure measures include the possession of an identity card or special knowledge like password and personal identification numbers (PINs). These methods are insecure as they can be lost, forgotten and potentially be shared among a group of co-workers for a long time without change. The fact that biometric authentication is convenient and non-refutable makes it a popular approach for a personal identification system. Nevertheless, biometric methods suffer from some inherent limitations and security threats. A more practical approach is to combine two-factor or more authenticators to achieve a higher level of security. This paper proposes a novel dual-factor authenticator based on the iterated inner product between tokenised pseudo-random numbers and user-specific palmprint features. This process generates a set of user-specific compact code called PalmHash, which is highly tolerant of data offset. There is no deterministic way to get the user-specific code without having both PalmHash and the user palmprint feature. This offers strong protection against biometric fabrication. Furthermore, the proposed PalmHashing technique is able to produce zero equal error rate (EER) and yields clean separation of the genuine and imposter populations. Hence, the false acceptance rate (FAR) can be eliminated without suffering from the increased occurrence of the false rejection rate (FRR).This revised version was published online in August 2004 with corrections to the section numbers.     

Visually Abstracting Event Sequences as Double Trees Enriched with Category-Based Comparison

Event sequence visualization aids analysts in many domains to better understand and infer new insights from event data. Analysing behaviour before or after a certain event of interest is a common task in many scenarios. In this paper, we introduce, formally define, and position double trees as a domain-agnostic tree visualization approach for this task. The visualization shows the sequences that led to the event of interest as a tree on the left, and those that followed on the right. Moreover, our approach enables users to create selections based on event attributes to interactively compare the events and sequences along colour-coded categories. We integrate the double tree and category-based comparison into a user interface for event sequence analysis. In three application examples, we show a diverse set of scenarios, covering short and long time spans, non-spatial and spatial events, human and artificial actors, to demonstrate the general applicability of the approach.     

Modeling pedestrian mobility in disaster areas

Realistic mobility modeling is necessary for testing disaster management strategies as well as performance of disaster–resilient networks. Evacuation of the people from a disaster area depends on the environment and type of the hazard which cause certain changes in the pedestrian flows. Although most models focus on the building evacuations or city-scale evacuation planning, there is a need for a mobility model that captures the pedestrians’ movement behavior during evacuation from large and crowded disaster areas such as theme parks.In this paper, we propose a mobility model of the pedestrians in disaster areas. In our application scenario of theme parks, the main mission of the operators is the evacuation of the visitors and providing access to transportation vehicles such as ambulances. We use real maps to generate theme park models with obstacles, roads, and disaster events. We incorporate macro and micro mobility decisions of the visitors, considering their local knowledge and the social interactions among the visitors. We analyze the outcomes of the simulation of our theme park disaster (TP-D) mobility model with simulations of currently used models and real-world GPS traces. Moreover, using the proposed model as a baseline, we analyze the performance of an opportunistic network application.     

A time decoupling approach for studying forum dynamics

Online forums are rich sources of information about user communication activity over time. Finding temporal patterns in online forum communication threads can advance our understanding of the dynamics of conversations. The main challenge of temporal analysis in this context is the complexity of forum data. There can be thousands of interacting users, who can be numerically described in many different ways. Moreover, user characteristics can evolve over time. We propose an approach that decouples temporal information about users into sequences of user events and inter-event times. We develop a new feature space to represent the event sequences as paths, and we model the distribution of the inter-event times. We study over 30,000 users across four Internet forums, and discover novel patterns in user communication. We find that users tend to exhibit consistency over time. Furthermore, in our feature space, we observe regions that represent unlikely user behaviors. Finally, we show how to derive a numerical representation for each forum, and we then use this representation to derive a novel clustering of multiple forums.     

A BIM-based visualization and warning system for fire rescue

Structural fires are common disasters. In Taiwan, about 100 firefighters die during fire rescues each year, primarily because they are unaware of the causes of the fire and unfamiliar with the location’s environment. Meanwhile, evacuees often die in the panic of evacuation. To solve these problems, this research proposes a Building Information Modeling (BIM)-based visualization and warning system for fire rescue. A fire dynamics simulator (FDS) simulates various conditions of structural fires in conjunction with the visualization and integration properties of BIM, and the simulation results for temperature, carbon monoxide, and visibility can be integrated and presented in the BIM model for briefing purposes before rescue operations begin. In addition, this research integrates Internet of Things (IoT) technology, which allows real-time situation monitoring. In the event of a fire, the BIM model will immediately display the situation of the fire scene and control LED escape route pointers according to the actual situation. The primary objective of this system is to provide useful information to firefighters such that they can be aware of the fire’s environment and create an effective rescue plan. Moreover, the automated LED escape route pointer may assist the building’s occupants to escape, provide the firefighters with valuable information, and allow them quickly to discover hazards so that the number of casualties can be minimized.     

预案可视化与推演技术

应急决策是抗灾救灾中的关键步骤,直接决定着救灾成果,关系着人民生命财产安全。应急决策的及时、有效依赖于对灾害情况的全面了解以及对救助预案的准确分析。文章设计的预案可视化与推演系统采用了预案可视化表达技术以及基于情景分析模型的推演模拟与演播技术,为应急决策提供了可视化环境,实现了预案的可视性以及可操作性。文章从体系结构、功能设计等方面对系统内容进行了阐述,并且在此基础上介绍了一个系统应用实例,证明了技术的可行性。     

Content-trajectory approach for searching video databases

In the past few years, modeling and querying video databases have been a subject of extensive research to develop tools for effective search of videos. In this paper, we present a hierarchal approach to model videos at three levels, object level (OL), frame level (FL), and shot level (SL). The model captures the visual features of individual objects at OL, visual-spatio-temporal (VST) relationships between objects at FL, and time-varying visual features and time-varying VST relationships at SL. We call the combination of the time-varying visual features and the time-varying VST relationships a Content trajectory which is used to represent and index a shot. A novel query interface that allows users to describe the time-varying contents of complex video shots such as those of skiers, soccer players, etc., by sketch and feature specification is presented. Our experimental results prove the effectiveness of modeling and querying shots using the content trajectory approach.     

Investigating the influence of route turning angle on compliance behaviors and evacuation performance in a virtual-reality-based experiment

Route turning is one of the most essential and ubiquitous physical features in the complex building environment. Under the influence of route turning, evacuees’ approaching perspective to an emergency sign could vary, affecting their information perception and behavioral compliance during the evacuation. Although conventional simulation methods assess the effectiveness of the emergency sign in the visible region, they fail to consider evacuees’ wayfinding behaviors and interaction with the emergency sign. It remains unclear whether the route turning angle affects evacuees’ compliance for detecting and responding to the emergency sign. To investigate such an influence, a virtual-reality-based method for assessing human evacuation behaviors in building fire evacuations was proposed. In this study, two evacuation routes with different turning angles in a shopping mall were created and implemented in a virtual-reality environment, and 67 subjects participated in the immersive virtual-reality-based experiment. All participants took the two routes to find the nearest exit for evacuation in a fire event, aiming to evaluate the effect of the route turning angle on the evacuation process. The participants were asked to complete a questionnaire at the end of the experiment. Next, statistical analyses were conducted on evacuation results, information perception, and evacuation performance of the participants. The results indicated the route turning angle significantly affected participants' behavioral compliance with emergency signs. The results also suggested the route turning angle was influential on participants’ information perception and evacuation performance. Besides, a significant effect on rotation change, wayfinding pause, and speed deviation were observed. This study validates the effectiveness of investigating evacuees’ interaction with emergency signs using virtual-reality technology and has potential implications for complex building path planning and evacuation simulation modeling.     

Active Foreground Region Extraction and Tracking for Sports Video Annotation

Automatic video segmentation plays a vital role in sports videos annotation. This paper presents a fully automatic and computationally efficient algorithm for analysis of sports videos. Various methods of automatic shot boundary detection have been proposed to perform automatic video segmentation. These investigations mainly concentrate on detecting fades and dissolves for fast processing of the entire video scene without providing any additional feedback on object relativity within the shots. The goal of the proposed method is to identify regions that perform certain activities in a scene. The model uses some low-level feature video processing algorithms to extract the shot boundaries from a video scene and to identify dominant colours within these boundaries. An object classification method is used for clustering the seed distributions of the dominant colours to homogeneous regions. Using a simple tracking method a classification of these regions to active or static is performed. The efficiency of the proposed framework is demonstrated over a standard video benchmark with numerous types of sport events and the experimental results show that our algorithm can be used with high accuracy for automatic annotation of active regions for sport videos.     

Movie scene segmentation using background information

Scene extraction is the first step toward semantic understanding of a video. It also provides improved browsing and retrieval facilities to users of video database. This paper presents an effective approach to movie scene extraction based on the analysis of background images. Our approach exploits the fact that shots belonging to one particular scene often have similar backgrounds. Although part of the video frame is covered by foreground objects, the background scene can still be reconstructed by a mosaic technique. The proposed scene extraction algorithm consists of two main components: determination of the shot similarity measure and a shot grouping process. In our approach, several low-level visual features are integrated to compute the similarity measure between two shots. On the other hand, the rules of film-making are used to guide the shot grouping process. Experimental results show that our approach is promising and outperforms some existing techniques.     

Assisting comprehension of animation programs through interactive code visualization

Visual languages have been widely used to help people create animation programs. However, current programming environments lack features supporting efficient code exploration and program comprehension, particularly for understanding relationships among parts of animation programs. In this paper, we present novel interactive visualizations aimed at helping people to understand animation programs. We conducted an empirical study to evaluate the impact of these visualizations on programmer comprehension of the code, showing that our approach enabled programmers to comprehend more information with less effort and in less time. This result is potentially significant because it demonstrates an approach for helping users to explore and understand animation code. We anticipate that this approach could be applied in a wide variety of animation programming tools, which could ease common animation programming tasks that require understanding code.     

自动特征选择和加权的图像显著区域检测

视觉显著性度量是图像显著区域提取中的一个关键问题,现有的方法主要根据图像的底层视觉特征,构造相应的显著图。不同的特征对视觉显著性的贡献是不同的,为此提出一种能够自动进行特征选择和加权的图像显著区域检测方法。提取图像的亮度、颜色和方向等特征,构造相应的特征显著图。提出一种新的特征融合策略,动态计算各特征显著图的权值,整合得到最终的显著图,检测出图像中的显著区域。在多幅自然图像上进行实验,实验结果表明,该方法在运算速度和检测效果方面都取得了不错的效果。     

考虑上客点选择的公交紧急疏散时空路网模型

为了在灾难发生后及时将被疏散人员引导到合适上客点,并优化随后的公交疏散路线,建立了一个公交紧急疏散的线性混合整数规划模型。新模型不仅考虑了合理的上客点的选择和相应的建造费用,并且利用时空网络技术细致刻画了公交疏散中被疏散者从疏散集结点到上客点的运行轨迹。与已有的研究多采用非线性整数规划技术相比,新模型的线性化特征可以大大提高了模型的求解效率。在算例分析部分通过改变上客点造价和疏散集结点数量等条件,对不同条件下的疏散方案进行了比较分析,验证了模型的有效性。     

Using object and trajectory analysis to facilitate indexing and retrieval of video

This paper aims to show that by using low level feature extraction, motion and object identifying and tracking methods, features can be extracted and indexed for efficient and effective retrieval for video; such as an awards ceremony video. Video scene/shot analysis and key frame extraction are used as a foundation to identify objects in video and be able to find spatial relationships within the video. The compounding of low level features such as colour, texture and abstract object identification lead into higher level real object identification and tracking and scene detection. The main focus is on using a video style that is different to the heavily used sports and news genres. Using different video styles can open the door to creating methods that could encompass all video types instead of specialized methods for each specific style of video.     

一种基于特征跟踪的视频镜头检测算法

将视频切分为镜头是视频内容分析及基于内容的视频检索和浏览的第一步。针对视频镜头边界检测,提出了一种基于特征跟踪的新算法。该算法从镜头起始帧中提取出一组角点特征,然后在后续帧中基于Kalman滤波进行特征窗跟踪,最后根据对应特征窗内的像素特征变化规律,得到镜头边界检测所需的测度,判断镜头切换的性质和渐变镜头的起止时间。实验结果表明该算法运算复杂度低,且具有较强的鲁棒性。     

基于序列模式挖掘的田径视频分类

现有的体育视频分析方法大多都专注于重要事件的提取,而忽视了如何对这些事件进行组织和语义分析。本文提出了一种基于序列模式挖掘的田径视频镜头分类算法。本文主要围绕两个问题展开——特征提取和语义规则的定义。在特征提取阶段,自动的将田径视频镜头分割为一系列可识别的运动事件序列,然后使用机器学习的算法对每类行为事件进行识别。在语义规则定义阶段,使用序列模式挖掘方法发现其中的频繁序列,在此基础上进行。实验选用了上千段田径视频镜头进行测试,结果显示了本文算法进行田径视频镜头分类的有效性。     

Multithreaded Hybrid Feature Tracking for Markerless Augmented Reality

We describe a novel markerless camera tracking approach and user interaction methodology for augmented reality (AR) on unprepared tabletop environments. We propose a real-time system architecture that combines two types of feature tracking. Distinctive image features of the scene are detected and tracked frame-to-frame by computing optical flow. In order to achieve real-time performance, multiple operations are processed in a synchronized multi-threaded manner: capturing a video frame, tracking features using optical flow, detecting distinctive invariant features, and rendering an output frame. We also introduce user interaction methodology for establishing a global coordinate system and for placing virtual objects in the AR environment by tracking a user's outstretched hand and estimating a camera pose relative to it. We evaluate the speed and accuracy of our hybrid feature tracking approach, and demonstrate a proof-of-concept application for enabling AR in unprepared tabletop environments, using bare hands for interaction.