Bayesian inferential reasoning model for crime investigation

Forensic inferential reasoning is a “fact-finding” journey for crime investigation and evidence presentation. In complex legal practices involving various forms of evidence, conventional decision making processes based on human intuition and piece-to-piece evidence explanation often fail to reconstruct meaningful and convincing legal hypothesis. It is necessary to develop logical system for evidence management and relationship evaluations. In this paper, a forensic application-oriented inferential reasoning model has been devised base on Bayesian Networks. It provides an effective approach to identify and evaluate possible relationships among different evidence. The model has been developed into an adaptive framework than can be further extended to support information visualisation and interaction. Based on the system experiments, the model has been successfully used in verifying the logical relationships between DNA testing results and confessions acquired from the suspect in a simulated criminal investigation, which provided a firm foundation for the future developments

Automatic object classification for surveillance videos.

PhDThe recent popularity of surveillance video systems, specially located in urban scenarios, demands the development of visual techniques for monitoring purposes. A primary step towards intelligent surveillance video systems consists on automatic object classification, which still remains an open research problem and the keystone for the development of more specific applications. Typically, object representation is based on the inherent visual features. However, psychological studies have demonstrated that human beings can routinely categorise objects according to their behaviour. The existing gap in the understanding between the features automatically extracted by a computer, such as appearance-based features, and the concepts unconsciously perceived by human beings but unattainable for machines, or the behaviour features, is most commonly known as semantic gap. Consequently, this thesis proposes to narrow the semantic gap and bring together machine and human understanding towards object classification. Thus, a Surveillance Media Management is proposed to automatically detect and classify objects by analysing the physical properties inherent in their appearance (machine understanding) and the behaviour patterns which require a higher level of understanding (human understanding). Finally, a probabilistic multimodal fusion algorithm bridges the gap performing an automatic classification considering both machine and human understanding. The performance of the proposed Surveillance Media Management framework has been thoroughly evaluated on outdoor surveillance datasets. The experiments conducted demonstrated that the combination of machine and human understanding substantially enhanced the object classification performance. Finally, the inclusion of human reasoning and understanding provides the essential information to bridge the semantic gap towards smart surveillance video systems

Detecting Riots with Uncertain Information on the Semantic Web

PhDThe ubiquitous nature of CCTV Surveillance cameras means substantial amounts of data being generated. In case of an investigation, this data must be manually browsed and analysed in search of relevant information for the case. As an example, it took more than 450 detectives to examine the hundreds of thousands of hours of videos in the investigation of the 2011 London Riots: one of the largest the London's MET police has ever seen. Anything that can help the security forces save resources in investigations such as this, is valuable. Consequently, automatic analysis of surveillance scenes is a growing research area. One of the research fronts tackling this issue, is the semantic understanding of the scene. In this, the output of computer vision algorithms is fed into Semantic Frameworks, which combine all the information from different sources and try to reach a better knowledge of the scene. However, representing and reasoning with imprecise and uncertain information remains an outstanding issue in current implementations. The Demspter-Sha er (DS) Theory of Evidence has been proposed as a way to deal with imprecise and uncertain information. In this thesis we use it for the main contributions. In our rst contribution, we propose the use of the DS theory and its Transferable Belief Model (TBM) realisation as a way to combine Bayesian priors, using the subjectivist view of the Bayes' Theorem, where the probabilities are beliefs. We rst compute the a priori probabilities of all the pair of events in the model. Then a global potential is created for each event using the TBM. This global potential will encode all the prior knowledge for that particular concept. This has the bene t that when this potential is included in a knowledge base because it has been learned, all the knowledge it entails comes with it. We also propose a semantic web reasoner based on the TBM. This reasoner consists of an ontology to model any domain knowledge using the TBM constructs of Potentials, Focal Elements, and Con gurations. The reasoner also consists of the implementations of the TBM operations in a semantic web framework. The goal is that after the model has been created, the TBM operations can be applied and the knowledge combined and queried. These operations are computationally complex, so we also propose parallel heuristics to the TBM operations. This allows us to apply this paradigm on problems of thousands of records. The nal contribution, is the use of the TBM semantic framework with the method to combine the prior knowledge to detect riots on CCTV footage from the 2011 London riots. We use around a million and a half manually annotated frames with 6 di erent concepts related to the riot detection task, train the system, and infer the presence of riots in the test dataset. Tests show that the system yields a high recall, but a low precision, meaning that there are a lot of false positives. We also show that the framework scales well as more compute power becomes available

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining

Développement d une méthodologie d'exploitation des images témoins en science forensique

Cette thèse de doctorat propose une méthodologie qui met en valeur le potentiel latent de l'image témoin pour la reconstruction forensique d'un évènement. Les images témoins englobent toutes les images liées à un évènement, qu'elles proviennent de systèmes de surveillance, de témoins, de protagonistes ou des premier s intervenants. De nature ambigüe, elles constituent une mémoire de ce qui se déroule avant, pendant et après un évènement. Elles sont décomposées en traces visuelles et auditives de l'évènement ainsi qu'en traces du système d'enregistrement. Omniprésentes dans les enquêtes actuelles, leur utilisation pose plusieurs défis : les intégrer à l'enquête pour obtenir des indices et des moyens de preuve ; exploiter tout leur potentiel lorsque l'évènement a une certaine durée et qu'une masse d'images doit être gérée ; combiner des photographies et des vidéos enregistrées par plusieurs appareils, fixes et mobiles. En vue d'amener des solutions concrètes à ces défis, cette recherche s'applique à formaliser l'exploitation des images témoins pour reconstruire des activités criminelles de manière efficiente. La première étape de recherche consiste en une revue de la littérature sur l'utilisation des images en science forensique et les bonnes pratiques, avec une réflexion sur le rôle des images. La seconde étape formalise une méthodologie appliquée, construite sur la base des cas pratiques traités par le chercheur . La troisième étape explore les expériences de praticiens suisses et européens à l'aide d'entretiens individuels et de groupe pour co-construire une méthodologie partagée. La quatrième étape confronte ces manières d'exploiter les images témoins pour proposer une méthodologie consolidée. Cette méthodologie amène une solution structurée pour gérer les questions, les informations et le matériel d'une affaire litigieuse. Elle intègre graduellement les images qui proviennent de différents appareils et permet d'obtenir des informations mesurables sur la réalité spatiale et le déroulement chronologique. La recherche a permis de mettre en avant plusieurs niveaux d'observation, d'exploitation et de communication qui combinent des traces fragmentaires, illisibles ou ambigües pour révéler des informations nouvelles. Ces nouveaux indices peuvent amener les enquêteurs et les magistrats à ajuster le périmètre et la fenêtre temporelle de 1'affaire, générer des hypothèses sur le déroulement des actions et interactions des protagonistes ou des témoins et modifier la direction de 1'enquête. La plus-value amenée par la reconstruction 3D, la chronologie ou les indices sonores devient plus explicite pour les praticiens et les parties prenantes de l'enquête. Le fait de percevoir ce potentiel latent des images constitue un nouveau paradigm e. Le potentiel de reconstruction des images est anticipé ; l'observation des images change. Les enjeux pour l'avenir sont de renforcer l'intégration des nouvelles technologies de l'image tout en développant l'accès à une reconstruction ouverte lors de l'enquête et du procès. Un tel accès permettrait aux parties de formuler et d'apprécier leurs propres hypothèses sur le déroulement de l'évènement

Machinic Eyes: New and Post-Digital Aesthetics, Surveillance, and Resistance

This work concerns the rise of the New Aesthetic, an art project developed by James Bridle in 2012. The New Aesthetic, as envisioned by Bridle, was chiefly concerned with the overlapping of physical and digital realities through both the artifacts produced by this overlapping and the systems involved therein. I introduce the advent of the New Aesthetic and present the major criticisms: the lack of a robust theoretical and scholarly framework, the lack of a historical framework, the privileging of artifacts over systems as new Aesthetic, and the fragmented scholarly outlook on the New Aesthetic. Upon further examination, I discovered that the New Aesthetic is less of an art project but a metaphor for a global surveillance apparatus that is the result of clandestine partnerships between multinational technology corporations and intelligence agencies associated the Five Eyes consortium. In this dissertation, I critique the New Aesthetic from a scholarly viewpoint, offer a historical precedent of how the New Aesthetic came to be from cultural and technological perspectives, examine the rise of the global surveillance apparatus within the New Aesthetic, and offer ideas of how to resist surveillance as a result of our reliance upon computational technologies

Active Residues

My PhD studies the aftermath of the museum collection to show how the removal of the object leaves behind the multiplicity of its conditions. As an entry point, I probe a set of questions that arise from a sequence of events that happen in the autumn of 2018. It's a story that begins with an error: in six short hours in September, a disastrous fire brought an end to two centuries' worth of treasures held in Brazil's National Museum. Only a handful of artifacts of the 20 million items that were housed at the museum survived the fire. At the age of algorithmic reproduction, it feels almost unimaginable that so many valuable objects were simply wiped off the face of the earth without leaving any digital trace. I propose that although the museum's objects no longer operate within their inherited institutional orders or colonial indexes, some of their constitutions, temperaments, and affordances are "dragged" with them from their original matter to the digital and information realm. The residues are unordered strata of matter, bio-form, and digital information that remained unclaimed by the institution. The museum's residues do not have form, like objects. Instead, they are the surplus of affects, tools, and affordances that arrive with the objects. They enunciate the futurity of the museum apparatus in its state of afterness. Museum afterness applies to the incomplete state between the "no longer" and the "not yet". Afterness is the state that comes after an event or an institutional structure has ended but the orders and relations that conditioned its existence are still active. I argue that the state of afterness not only stands for what comes after the institution but can potentially represent knowledge based on continuity of transformation between technical systems, matter formations, and biological life forms. Active Residues is a practice-theory research project where I use theoretical frameworks and performance-based methods to speculate on several "modes of afterness," which is how I define a set of modalities and practices stirred up in the wake of the museum that can become active sites for unlearning it

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe