Image usefulness of compressed surveillance footage with different scene contents

The police use both subjective (i.e. police staff) and automated (e.g. face recognition systems) methods for the completion of visual tasks (e.g person identification). Image quality for police tasks has been defined as the image usefulness, or image suitability of the visual material to satisfy a visual task. It is not necessarily affected by any artefact that may affect the visual image quality (i.e. decrease fidelity), as long as these artefacts do not affect the relevant useful information for the task. The capture of useful information will be affected by the unconstrained conditions commonly encountered by CCTV systems such as variations in illumination and high compression levels. The main aim of this thesis is to investigate aspects of image quality and video compression that may affect the completion of police visual tasks/applications with respect to CCTV imagery. This is accomplished by investigating 3 specific police areas/tasks utilising: 1) the human visual system (HVS) for a face recognition task, 2) automated face recognition systems, and 3) automated human detection systems. These systems (HVS and automated) were assessed with defined scene content properties, and video compression, i.e. H.264/MPEG-4 AVC. The performance of imaging systems/processes (e.g. subjective investigations, performance of compression algorithms) are affected by scene content properties. No other investigation has been identified that takes into consideration scene content properties to the same extend. Results have shown that the HVS is more sensitive to compression effects in comparison to the automated systems. In automated face recognition systems, `mixed lightness' scenes were the most affected and `low lightness' scenes were the least affected by compression. In contrast the HVS for the face recognition task, `low lightness' scenes were the most affected and `medium lightness' scenes the least affected. For the automated human detection systems, `close distance' and `run approach' are some of the most commonly affected scenes. Findings have the potential to broaden the methods used for testing imaging systems for security applications.

Los conocimientos acerca del maltrato infantil de los futuros profesionales de la educación: un estudio exploratorio

Los profesionales de la educación se encuentran, en su ejercicio profesional, en una posición privilegiada para realizar una detección precoz del maltrato infantil y para identificar posibles casos de riesgo. Sin embargo, en ocasiones, maestros y educadores en general aducen falta de conocimiento y formación para realizar dichas tareas. Es por ello que, en este trabajo deseamos insistir en la necesidad de analizar la formación de los futuros profesionales de la educación en torno al maltrato infantil, tanto en el seno de la familia como fuera de ella, y ya sea ejercido por un adulto o por otros menores. No olvidemos que la identificación temprana de comportamientos violentos y, por supuesto, la puesta en marcha de estrategias sólidas para su prevención requieren disponer de una buena capacitación. Por esta razón, hemos realizado un estudio piloto que nos permitiera conocer la formación que los estudiantes del Grado de Pedagogía tienen sobre el maltrato infantil, utilizando un cuestionario que hemos diseñado específicamente para alcanzar tal propósito. En la realización de un estudio piloto contamos con una muestra de 24 alumnos y alumnas del 4º curso del Grado de Pedagogía. Entre las conclusiones alcanzadas destacamos que, tras analizar los datos derivados del pase piloto, podemos concluir que los futuros pedagogos consideran necesario tener formación específica al respecto, una preparación que, mayoritariamente, consideran insuficiente y muy limitada para poder afrontar sus responsabilidades profesionales en el futuro.

A comparison of the estrous behavior of Holstein-Friesian cows when cubicle-housed and at pasture

This study compared estrous behavior of dairy cows kept in cubicle housing and fed a total mixed ration diet (HOUSED treatment) with that of cows kept at pasture with concentrate supplementation (PASTURE treatment). Behavior was compared both in the 48 h around standing estrus and during the standing estrus period. The 23 spring-calving Holstein-Friesians in each treatment were observed directly three times per day for nine weeks. The occurrence of nine selected behaviors associated with estrus was recorded during 20 min observation sessions. Twelve standing estrus events from each treatment were selected for analysis of the frequency of these nine behaviours over the 48 h around standing estrus. Milk progesterone profiles were used to confirm the dates of standing estrus events. Attempting to mount other cows, sniffing the anogenital region of other cows, resting the chin on other cows, receiving chin rests and head-to-head butts all showed significant changes in frequency in the 48 h around standing estrus in both treatments, reaching a peak during standing estrus (P ≤ 0.05). Mounting other cows increased significantly in the PASTURE treatment around standing estrus (P <0.001), but not in the HOUSED treatment. The frequency of ano-genital sniffs received by the animals in the PASTURE treatment also increased significantly around standing estrus (P <0.01) but not in the HOUSED treatment. When the animals were in standing estrus there was a significantly higher frequency of standing to be mounted in PASTURE than in HOUSED cows (median (q1, q3) PASTURE = 2.5 (1.0, 3.0), HOUSED = 0.0 (0.0, 1.0)) (P <0.01), but no difference in the frequency of the other eight sexual behaviors recorded. HOUSED cows did not exhibit the same increase in mounting during the standing estrus period as PASTURE cows and received fewer mounts in observation sessions during standing estrus. These results have implications for the use of estrus detection systems that rely solely on mounting behavior in cubicle-housed dairy cows. © 2012 Elsevier Inc.

Techniques en appui des formats de modulation avancés pour les futurs réseaux optiques

Les systèmes de communication optique avec des formats de modulation avancés sont actuellement l’un des sujets de recherche les plus importants dans le domaine de communication optique. Cette recherche est stimulée par les exigences pour des débits de transmission de donnée plus élevés. Dans cette thèse, on examinera les techniques efficaces pour la modulation avancée avec une détection cohérente, et multiplexage par répartition en fréquence orthogonale (OFDM) et multiples tonalités discrètes (DMT) pour la détection directe et la détection cohérente afin d’améliorer la performance de réseaux optiques. Dans la première partie, nous examinons la rétropropagation avec filtre numérique (DFBP) comme une simple technique d’atténuation de nonlinéarité d’amplificateur optique semiconducteur (SOA) dans le système de détection cohérente. Pour la première fois, nous démontrons expérimentalement l’efficacité de DFBP pour compenser les nonlinéarités générées par SOA dans un système de détection cohérente porteur unique 16-QAM. Nous comparons la performance de DFBP avec la méthode de Runge-Kutta quatrième ordre. Nous examinons la sensibilité de performance de DFBP par rapport à ses paramètres. Par la suite, nous proposons une nouvelle méthode d’estimation de paramètre pour DFBP. Finalement, nous démontrons la transmission de signaux de 16-QAM aux taux de 22 Gbaud sur 80km de fibre optique avec la technique d’estimation de paramètre proposée pour DFBP. Dans la deuxième partie, nous nous concentrons sur les techniques afin d’améliorer la performance des systèmes OFDM optiques en examinent OFDM optiques cohérente (CO-OFDM) ainsi que OFDM optiques détection directe (DDO-OFDM). Premièrement, nous proposons une combinaison de coupure et prédistorsion pour compenser les distorsions nonlinéaires d’émetteur de CO-OFDM. Nous utilisons une interpolation linéaire par morceaux (PLI) pour charactériser la nonlinéarité d’émetteur. Dans l’émetteur nous utilisons l’inverse de l’estimation de PLI pour compenser les nonlinéarités induites à l’émetteur de CO-OFDM. Deuxièmement, nous concevons des constellations irrégulières optimisées pour les systèmes DDO-OFDM courte distance en considérant deux modèles de bruit de canal. Nous démontrons expérimentalement 100Gb/s+ OFDM/DMT avec la détection directe en utilisant les constellations QAM optimisées. Dans la troisième partie, nous proposons une architecture réseaux optiques passifs (PON) avec DDO-OFDM pour la liaison descendante et CO-OFDM pour la liaison montante. Nous examinons deux scénarios pour l’allocations de fréquence et le format de modulation des signaux. Nous identifions la détérioration limitante principale du PON bidirectionnelle et offrons des solutions pour minimiser ses effets.

Introduction to Security Onion

Security Onion is a Network Security Manager (NSM) platform that provides multiple Intrusion Detection Systems (IDS) including Host IDS (HIDS) and Network IDS (NIDS). Many types of data can be acquired using Security Onion for analysis. This includes data related to: Host, Network, Session, Asset, Alert and Protocols. Security Onion can be implemented as a standalone deployment with server and sensor included or with a master server and multiple sensors allowing for the system to be scaled as required. Many interfaces and tools are available for management of the system and analysis of data such as Sguil, Snorby, Squert and Enterprise Log Search and Archive (ELSA). These interfaces can be used for analysis of alerts and captured events and then can be further exported for analysis in Network Forensic Analysis Tools (NFAT) such as NetworkMiner, CapME or Xplico. The Security Onion platform also provides various methods of management such as Secure SHell (SSH) for management of server and sensors and Web client remote access. All of this with the ability to replay and analyse example malicious traffic makes the Security Onion a suitable low cost alternative for Network Security Management. In this paper, we have a feature and functionality review for the Security Onion in terms of: types of data, configuration, interface, tools and system management.

Implementação de um Módulo de Supervisão para um Sistema de Detecção de Vazamentos em Dutos de Petróleo

The transport of fluids through pipes is used in the oil industry, being the pipelines an important link in the logistics flow of fluids. However, the pipelines suffer deterioration in their walls caused by several factors which may cause loss of fluids to the environment, justifying the investment in techniques and methods of leak detection to minimize fluid loss and environmental damage. This work presents the development of a supervisory module in order to inform to the operator the leakage in the pipeline monitored in the shortest time possible, in order that the operator log procedure that entails the end of the leak. This module is a component of a system designed to detect leaks in oil pipelines using sonic technology, wavelets and neural networks. The plant used in the development and testing of the module presented here was the system of tanks of LAMP, and its LAN, as monitoring network. The proposal consists of, basically, two stages. Initially, assess the performance of the communication infrastructure of the supervisory module. Later, simulate leaks so that the DSP sends information to the supervisory performs the calculation of the location of leaks and indicate to which sensor the leak is closer, and using the system of tanks of LAMP, capture the pressure in the pipeline monitored by piezoresistive sensors, this information being processed by the DSP and sent to the supervisory to be presented to the user in real time

Preparation of an Immunosorbent and its use in the Solid Phase Extraction of Benzodiazepines

The use of capillary electrophoresis (CE) has been restricted to applications having high sample concentrations because of its low sensitivity caused by small injection volumes and, when ultraviolet (UV) detection is used, the short optical path length. Sensitivity in CE can be improved by using more sensitive detection systems, or by preconcentration techniques which are based on chromatographic and/or electrophoretic principles. One of the promising strategies to improve sensitivity is solid phase extraction (SPE). Solid Phase Extraction utilizes high sample volumes and a variety of complex matrixes to facilitate trace detection. To increase the specificity of the SPE a selective solid phase must be chosen. Immunosorbents, which are a combination of an antibody and a solid support, have proven to be an excellent option because of high selectivity of the antibody. This thesis is an exploratory study of the application of immunosorbent-SPE combined with CE for trace concentration of benzodiazepines. This research describes the immobilization and performance evaluation of an immunosorbent prepared by immobilizing a benzodiazepine-specific antibody on aminopropyl silica. The binding capacity of the immunosorbent, measured as µg of benzodiazepine/ gram of immunosorbent, was 39 ± 10. The long term stability of the prepared immunosorbent has been improved by capping the remaining aminopropyl groups by reaction with acetic anhydride. The capped immunosorbent retained its binding capacity after several uses.

A multiagent based strategy for detecting attacks in databases in a distributed mode

This paper presents a distributed hierarchical multiagent architecture for detecting SQL injection attacks against databases. It uses a novel strategy, which is supported by a Case-Based Reasoning mechanism, which provides to the classifier agents with a great capacity of learning and adaptation to face this type of attack. The architecture combines strategies of intrusion detection systems such as misuse detection and anomaly detection. It has been tested and the results are presented in this paper.

Transaction mining for fraud detection in ERP Systems

Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. Traditionally, organizations have focused on fraud prevention rather than detection, to combat fraud. In this paper we present a role mining inspired approach to represent user behaviour in Enterprise Resource Planning (ERP) systems, primarily aimed at detecting opportunities to commit fraud or potentially suspicious activities. We have adapted an approach which uses set theory to create transaction profiles based on analysis of user activity records. Based on these transaction profiles, we propose a set of (1) anomaly types to detect potentially suspicious user behaviour, and (2) scenarios to identify inadequate segregation of duties in an ERP environment. In addition, we present two algorithms to construct a directed acyclic graph to represent relationships between transaction profiles. Experiments were conducted using a real dataset obtained from a teaching environment and a demonstration dataset, both using SAP R/3, presently the predominant ERP system. The results of this empirical research demonstrate the effectiveness of the proposed approach.

Fraud detection in ERP systems using scenario matching

ERP systems generally implement controls to prevent certain common kinds of fraud. In addition however, there is an imperative need for detection of more sophisticated patterns of fraudulent activity as evidenced by the legal requirement for company audits and the common incidence of fraud. This paper describes the design and implementation of a framework for detecting patterns of fraudulent activity in ERP systems. We include the description of six fraud scenarios and the process of specifying and detecting the occurrence of those scenarios in ERP user log data using the prototype software which we have developed. The test results for detecting these scenarios in log data have been verified and confirm the success of our approach which can be generalized to ERP systems in general.

Robust suspicious behaviour detection for smart surveillance systems

Video surveillance technology, based on Closed Circuit Television (CCTV) cameras, is one of the fastest growing markets in the field of security technologies. However, the existing video surveillance systems are still not at a stage where they can be used for crime prevention. The systems rely heavily on human observers and are therefore limited by factors such as fatigue and monitoring capabilities over long periods of time. To overcome this limitation, it is necessary to have “intelligent” processes which are able to highlight the salient data and filter out normal conditions that do not pose a threat to security. In order to create such intelligent systems, an understanding of human behaviour, specifically, suspicious behaviour is required. One of the challenges in achieving this is that human behaviour can only be understood correctly in the context in which it appears. Although context has been exploited in the general computer vision domain, it has not been widely used in the automatic suspicious behaviour detection domain. So, it is essential that context has to be formulated, stored and used by the system in order to understand human behaviour. Finally, since surveillance systems could be modeled as largescale data stream systems, it is difficult to have a complete knowledge base. In this case, the systems need to not only continuously update their knowledge but also be able to retrieve the extracted information which is related to the given context. To address these issues, a context-based approach for detecting suspicious behaviour is proposed. In this approach, contextual information is exploited in order to make a better detection. The proposed approach utilises a data stream clustering algorithm in order to discover the behaviour classes and their frequency of occurrences from the incoming behaviour instances. Contextual information is then used in addition to the above information to detect suspicious behaviour. The proposed approach is able to detect observed, unobserved and contextual suspicious behaviour. Two case studies using video feeds taken from CAVIAR dataset and Z-block building, Queensland University of Technology are presented in order to test the proposed approach. From these experiments, it is shown that by using information about context, the proposed system is able to make a more accurate detection, especially those behaviours which are only suspicious in some contexts while being normal in the others. Moreover, this information give critical feedback to the system designers to refine the system. Finally, the proposed modified Clustream algorithm enables the system to both continuously update the system’s knowledge and to effectively retrieve the information learned in a given context. The outcomes from this research are: (a) A context-based framework for automatic detecting suspicious behaviour which can be used by an intelligent video surveillance in making decisions; (b) A modified Clustream data stream clustering algorithm which continuously updates the system knowledge and is able to retrieve contextually related information effectively; and (c) An update-describe approach which extends the capability of the existing human local motion features called interest points based features to the data stream environment.