Integrating Social Media with Ontologies for Real-Time Crowd Monitoring and Decision Support in Mass Gatherings

Situation awareness plays an essential role in making real-time decisions in mass gatherings. In the last few years, social media data analysis has been proved to be an effective approach to enable and enhance situation awareness. Mass gathering events are dynamic and critical environments where thousands of people attend. During the event, there is a potential for injuries and other health hazards, and thus it is critical for emergency medical services to access real-time and situational awareness information, especially concerning the nature of the crowd. It has been well recognized in the literature that crowd mood and behaviour can have a direct impact on the crowd safety and patient presentation rates. We describe a mobile social media-enabled crowd monitoring architecture that aims to improve emergency management decision-making by analysing the data from social networks in real-time. The proposed architecture incorporates a crowd behaviour classification model, which facilitates real-time situation awareness and provides a better understanding of analysis results. Awareness and perception of crowd mood and behaviour during the event can significantly improve prediction of patient presentation rates; leading to timely and effective medical care provision. The implementation and evaluation of the proposed framework on an Android mobile phone is described

MobiGroup: Enabling Lifecycle Support to Social Activity Organization and Suggestion with Mobile Crowd Sensing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a group-aware mobile crowd sensing system called MobiGroup, which supports group activity organization in real-world settings. Acknowledging the complexity and diversity of group activities, this paper introduces a formal concept model to characterize group activities and classifies them into four organizational stages. We then present an intelligent approach to support group activity preparation, including a heuristic rule-based mechanism for advertising public activity and a context-based method for private group formation. In addition, we leverage features extracted from both online and offline communities to recommend ongoing events to attendees with different needs. Compared with the baseline method, people preferred public activities suggested by our heuristic rule-based method. Using a dataset collected from 45 participants, we found that the context-based approach for private group formation can attain a precision and recall of over 80%, and the usage of spatial-temporal contexts and group computing can have more than a 30% performance improvement over considering the interaction frequency between a user and related groups. A case study revealed that, by extracting the features such as dynamic intimacy and static intimacy, our cross-community approach for ongoing event recommendation can meet different user needs

The use of cellular TMSI/Bluetooth technology for tracking pedestrian movement at a mass event: a pilot-study undertaken at the Cape Town Stadium

Paper presented at the 31st Annual Southern African Transport Conference 9-12 July 2012 "Getting Southern Africa to Work", CSIR International Convention Centre, Pretoria, South Africa.In this paper, proximity-based Temporary Mobile Subscriber Identity (TMSI) tagging and Bluetooth tracking is postulated as an efficient and effective methodology for analyzing the routing dynamics of spectator movements around the Cape Town stadium both before and after a mass participation event. A case pilot-study of the “Coldplay” music concert event undertaken by Path Intelligence Ltd (a UK-based company) on 5 October 2011 (with 60,000 spectators attending the concert) is described in detail. The results of this study will give an indication of the added value of the methodology for the various stakeholders hosting and managing the event and provides valuable input towards the feasibility study for provision of a proposed new pedestrian bridge across the Western Boulevard at Portswood Road. By covering seven locations within the stadium study area with receiver units with a further two units located at the BRT and rail station in the Cape Town CBD, the study was able to extract individual pathway trajectories generated by detected spectators. Apart from generating clear statistics such as pedestrian routing, the analysis revealed other valuable outputs such as pedestrian counts, travel times, fan-walk versus BRT modal split etc. The paper concludes that TMSI/Bluetooth tracking offers significant advantages for tracking pedestrians at mass participation events and outlines some shortcomings and remaining deficiencies identified during the pilot-project experience.This paper was transferred from the original CD ROM created for this conference. The material was published using Adobe Acrobat 10.1.0 Technology. The original CD ROM was produced by Document Transformation Technologies Postal Address: PO Box 560 Irene 0062 South Africa. Tel.: +27 12 667 2074 Fax: +27 12 667 2766 E-mail: nigel@doctech URL: http://www.doctech.co.zadm201

What makes the city pulse

The topics of this thesis are event detection and social network analysis in social media. Our work centres on Geo-tagged User Generated Content (UGC) in Twitter, such as Twitter data generated from the metropolitan area of Dublin Ireland over a one month period of time. In this thesis we address the problem of how to detect small scale unexpected events using UGC both in real-time and retrospectively. We proposed a language-text joint modeling algorithm to cope with the large volume and unstructured nature of UGC. We also demonstrate our discovery of interesting correlations between a Twitter user’s social communities and their mobility patterns. Finally a set of features are proposed for carrying out Twitter user’s account type classification, for the purpose of irrelevant contents filtering. This thesis includes several experimental evaluations using real data from users and shows the performance of our algorithms in event detection and provide evidence for our discoveries

Sparse models for positive definite matrices

University of Minnesota Ph.D. dissertation. Febrauary 2015. Major: Electrical Engineering. Advisor: Nikolaos P. Papanikolopoulos. 1 computer file (PDF); ix, 141 pages.Sparse models have proven to be extremely successful in image processing, computer vision and machine learning. However, a majority of the effort has been focused on vector-valued signals. Higher-order signals like matrices are usually vectorized as a pre-processing step, and treated like vectors thereafter for sparse modeling. Symmetric positive definite (SPD) matrices arise in probability and statistics and the many domains built upon them. In computer vision, a certain type of feature descriptor called the region covariance descriptor, used to characterize an object or image region, belongs to this class of matrices. Region covariances are immensely popular in object detection, tracking, and classification. Human detection and recognition, texture classification, face recognition, and action recognition are some of the problems tackled using this powerful class of descriptors. They have also caught on as useful features for speech processing and recognition.Due to the popularity of sparse modeling in the vector domain, it is enticing to apply sparse representation techniques to SPD matrices as well. However, SPD matrices cannot be directly vectorized for sparse modeling, since their implicit structure is lost in the process, and the resulting vectors do not adhere to the positive definite manifold geometry. Therefore, to extend the benefits of sparse modeling to the space of positive definite matrices, we must develop dedicated sparse algorithms that respect the positive definite structure and the geometry of the manifold. The primary goal of this thesis is to develop sparse modeling techniques for symmetric positive definite matrices. First, we propose a novel sparse coding technique for representing SPD matrices using sparse linear combinations of a dictionary of atomic SPD matrices. Next, we present a dictionary learning approach wherein these atoms are themselves learned from the given data, in a task-driven manner. The sparse coding and dictionary learning approaches are then specialized to the case of rank-1 positive semi-definite matrices. A discriminative dictionary learning approach from vector sparse modeling is extended to the scenario of positive definite dictionaries. We present efficient algorithms and implementations, with practical applications in image processing and computer vision for the proposed techniques

Data Collection and Analysis in Urban Scenarios

The United Nations estimates that the world population will continue to grow, with a projection indicating a world population of up to approximately 8.5 billion people in 2030, 9.7 billion in 2050 and 10.9 billion in 2100. In addition to the phenomenon of population growth, the United Nations also estimates that in 2050 about 70% of the total world population will live in cities. These conditions increase the complexity of the services that public administrations and private companies must provide to citizens with the aim of optimising resources and increasing the level of quality of life. For an adequate design, implementation and management of these services, an extensive effort is required towards the design of effective solutions for data collection and analysis, applying Data Science and Artificial Intelligence techniques. Several approaches were addressed during the development of this research thesis. Furthermore, different real-world use cases are introduced where the presented work was tested and validated. The first thesis part focuses on data analysis on data collected using crowdsourcing. A real case study used for the analyses was a study conducted in Sheffield in which the goal was to understand people’s interaction with green areas and their wellbeing. In this study, an app with a chatbot was used to ask questions targeted to the study and collected not only the subjective answers but also objective data like users’ location. Through the analysis of this data, it was possible to extract insights that otherwise would not be easily reachable in other ways. Some limitations have arisen for less frequented areas, in fact, not enough information has been collected to have a statistical significance of the insights found. Conversely, more information than necessary was collected in the most frequented areas. For this reason, a framework that analyses the amount of information and its statistical significance in real-time has been developed. It increases the efficiency of the study and reduces intrusiveness towards the study participants. The limit that this approach presents is certainly the low sample of data that can be acquired. In the second part of this thesis, a move on to passive data collection is done, where the user does not have to interact in any way. Any data acquired is pseudonymised upon capture so that the dictates of the privacy legislation are respected. A system is then presented that collects probe requests generated by Wi-Fi devices while scanning radio channels to detect Access Points. The system processes the collected data to extract key information on people’s mobility, such as crowd density by area of interest, people flow, permanence time, return time, heat maps, origin-destination matrix and estimate of the locations of the people. The main novelty with respect to the state of the art is related to new powerful indicators necessary for some key services of the city, such as safety management and passenger transport services, and to experimental activities carried out in real scenarios. Furthermore, a de-randomisation algorithm to solve the problem of MAC address randomisation is presented.N/

Inducing sparsity in deep neural networks through unstructured pruning for lower computational footprint

Deep learning has revolutionised the way we deal with media analytics, opening up and improving many fields such as machine language translation, autonomous driver assistant systems, smart cities and medical imaging to only cite a few. But to handle complex decision making, neural networks are getting bigger and bigger resulting in heavy compute loads. This has significant implications for universal accessibility of the technology with high costs, the potential environmental impact of increasing energy consumption and the inability to use the models on low-power devices. A simple way to cut down the size of a neural network is to remove parameters that are not useful to the model prediction. In unstructured pruning, the goal is to remove parameters (ie. set them to 0) based on some importance heuristic while maintaining good prediction accuracy, resulting in a high-performing network with a smaller computational footprint. Many pruning methods seek to find the optimal capacity for which the network is the most compute efficient while reaching better generalisation. The action of inducing sparsity – setting zero-weights – in a neural network greatly contributes to reducing over-parametrisation, lowering the cost for running inference, but also leveraging complexity at training time. Moreover, it can help us better understand what parts of the network account the most for learning, to design more efficient architectures and training procedures. This thesis assesses the integrity of unstructured pruning criteria. After presenting a use-case application for the deployment of an AI application in a real-world setting, this thesis demonstrates that unstructured pruning criteria are ill-defined and not adapted to large scale networks due to the over-parametrisation regime during training, resulting in sparse networks lacking regularisation. Furthermore, beyond solely looking at the performance accuracy, the fairness of different unstructured pruning networks is evaluated highlighting the need to rethink how we design unstructured pruning