Introduction. Libraries have traditionally protected the privacy of their users. It is an ambition that is becoming increasingly difficult, with the introduction of new information technologies in libraries. This thesis consists of an examination of an incipient interest in digital security among Swedish libraries and librarians. It also consists of an examination to what degree Swedish municipality libraries are using social plugins, an information practice with potential privacy concerns, on their websites. Method. Key actors were interviewed to shed light on why issues on privacy and digital security have moved into the foreground. 59 municipality libraries were randomly selected, and their websites surveyed. Analysis. Contextual integrity (CI), a privacy concept developed by Helen Nissenbaum, was used as a theoreti-cal framework. According to the concept, privacy is linked to information norms in a specific context. The con-cept has both a descriptive and a prescriptive aspect. In this thesis I used CI to evaluate libraries’ information practice of using social plugins on their websites. Results. The study showed that one out of three municipality libraries in Sweden are using social plugins on their website. The interest in digital security among Swedish libraries is linked to privacy concerns for groups of people with the need of high digital protection. The interest was sparked by Edward Snowden revelations of mass surveillance and the refugee crisis in 2015. Conclusion. Contextual integrity was used to evaluate libraries’ practice of using social plugins on their web-sites. The evaluation showed that the practice encroaches on information norms normally taken for granted in the library. The use of social plugins should be cancelled. Libraries have a responsibility to secure the privacy of their users, and CI is a useful concept to evaluate different information practices.This is a two years master’s thesis in library and information science.

Yu, Tsz Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 95-100). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.2 / Chapter 1.1 --- Surveillance and Computer Vision --- p.3 / Chapter 1.2 --- The Need for Abnormal Behavior Detection --- p.3 / Chapter 1.2.1 --- The Motivation --- p.3 / Chapter 1.2.2 --- Choosing the Right Surveillance Target --- p.5 / Chapter 1.3 --- Abnormal Behavior Detection: An Overview --- p.6 / Chapter 1.3.1 --- Challenges in Detecting Abnormal Behaviors --- p.6 / Chapter 1.3.2 --- Limitations of Existing Approaches --- p.8 / Chapter 1.3.3 --- New Design Concepts --- p.9 / Chapter 1.3.4 --- Requirements for Abnormal Behavior Detection --- p.10 / Chapter 1.4 --- Contributions --- p.11 / Chapter 1.4.1 --- An Unsupervised Experience-based Approach for Abnormal Behavior Detection --- p.11 / Chapter 1.4.2 --- Motion Histogram Transform: A Novel Feature Descriptors --- p.12 / Chapter 1.4.3 --- Real-time Algorithm for Abnormal Behavior Detection --- p.12 / Chapter 1.5 --- Thesis Organization --- p.13 / Chapter 2 --- Literature Review --- p.14 / Chapter 2.1 --- From Segmentation to Visual Tracking --- p.14 / Chapter 2.1.1 --- Environment Modeling and Segmentation --- p.15 / Chapter 2.1.2 --- Spatial-temporal Feature Extraction --- p.18 / Chapter 2.2 --- Detecting Irregularities in Videos --- p.21 / Chapter 2.2.1 --- Model-based Method --- p.22 / Chapter 2.2.2 --- Non Model-based Method --- p.26 / Chapter 3 --- Design Framework --- p.29 / Chapter 3.1 --- Dynamic Scene and Behavior Model --- p.30 / Chapter 3.1.1 --- Images Sequences and Video --- p.30 / Chapter 3.1.2 --- Motions and Behaviors in Video --- p.31 / Chapter 3.1.3 --- Discovering Abnormal Behavior --- p.32 / Chapter 3.1.4 --- Problem Definition --- p.33 / Chapter 3.1.5 --- System Assumption --- p.34 / Chapter 3.2 --- Methodology --- p.35 / Chapter 3.2.1 --- Potential Improvements --- p.35 / Chapter 3.2.2 --- The Design Framework --- p.36 / Chapter 4 --- Implementation --- p.40 / Chapter 4.1 --- Preprocessing --- p.40 / Chapter 4.1.1 --- Data Input --- p.41 / Chapter 4.1.2 --- Motion Detection --- p.41 / Chapter 4.1.3 --- The Gaussian Mixture Background Model --- p.43 / Chapter 4.2 --- Feature Extraction --- p.46 / Chapter 4.2.1 --- Optical Flow Estimation --- p.47 / Chapter 4.2.2 --- Motion Histogram Transforms --- p.53 / Chapter 4.3 --- Feedback Learning --- p.56 / Chapter 4.3.1 --- The Observation Matrix --- p.58 / Chapter 4.3.2 --- Eigenspace Transformation --- p.58 / Chapter 4.3.3 --- Self-adaptive Update Scheme --- p.61 / Chapter 4.3.4 --- Summary --- p.62 / Chapter 4.4 --- Classification --- p.63 / Chapter 4.4.1 --- Detecting Abnormal Behavior via Statistical Saliencies --- p.64 / Chapter 4.4.2 --- Determining Feedback --- p.65 / Chapter 4.5 --- Localization and Output --- p.66 / Chapter 4.6 --- Conclusion --- p.69 / Chapter 5 --- Experiments --- p.71 / Chapter 5.1 --- Experiment Setup --- p.72 / Chapter 5.2 --- A Summary of Experiments --- p.74 / Chapter 5.3 --- Experiment Results: Part 1 --- p.78 / Chapter 5.4 --- Experiment Results: Part 2 --- p.81 / Chapter 5.5 --- Experiment Results: Part 3 --- p.83 / Chapter 5.6 --- Experiment Results: Part 4 --- p.86 / Chapter 5.7 --- Analysis and Conclusion --- p.86 / Chapter 6 --- Conclusions --- p.88 / Chapter 6.1 --- Application Extensions --- p.88 / Chapter 6.2 --- Limitations --- p.89 / Chapter 6.2.1 --- Surveillance Range --- p.89 / Chapter 6.2.2 --- Preparation Time for the System --- p.89 / Chapter 6.2.3 --- Calibration of Background Model --- p.90 / Chapter 6.2.4 --- Instability of Optical Flow Feature Extraction --- p.91 / Chapter 6.2.5 --- Lack of 3D information --- p.91 / Chapter 6.2.6 --- Dealing with Complex Behavior Patterns --- p.92 / Chapter 6.2.7 --- Potential Improvements --- p.92 / Chapter 6.2.8 --- New Method for Classification --- p.93 / Chapter 6.2.9 --- Introduction of Dynamic Texture as a Feature --- p.93 / Chapter 6.2.10 --- Using Multiple-camera System --- p.93 / Chapter 6.3 --- Summary --- p.94 / Bibliography --- p.95

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The care of individuals with concerns about development, health, and wellness is often a difficult, complicated task and may rely on a team of diverse caregivers. There are many decisions that caregivers must make to help ensure that the best care and health monitoring are administered. For my dissertation work, I have explored the use of embedded capture and access to support decision-making for caregivers. Embedded capture and access integrates simple and unobtrusive capture and useful access, including trending information and rich data, into existing work practices. I hypothesized that this technology encourages more frequent access to evidence, increased collaboration amongst caregivers, and decisions made with higher confidence. I have explored this technology through real world deployments of new embedded capture and access applications in two domains. For the first domain, I have developed two applications to support decision-making for caregivers administering therapy to children with autism. The first application, Abaris, supports therapists working with a single child in a home setting, and the second application, Abaris for Schools, extends the ideas of Abaris for use in a school setting for many teachers working with multiple children. The second domain I have explored is decision-making for parents of newborn children. In particular, I developed and evaluated embedded capture and access technology to support parents, pediatricians, and secondary childcare providers in making decisions about whether a child s development is progressing normally in order to promote the earlier detection of developmental delays.

Cette thèse s’intéresse aux méthodes de classification par Machines à Vecteurs de Support (SVM) partiellement supervisées permettant la détection de nouveauté (One-Class SVM). Celles-ci ont été étudiées dans le but de réaliser la détection d’événements audio anormaux pour la surveillance d’infrastructures publiques, en particulier dans les transports. Dans ce contexte, l’hypothèse « ambiance normale » est relativement bien connue (même si les signaux correspondants peuvent être très non stationnaires). En revanche, tout signal « anormal » doit pouvoir être détecté et, si possible, regroupé avec les signaux de même nature. Ainsi, un système de référence s’appuyant sur une modélisation unique de l’ambiance normale est présenté, puis nous proposons d’utiliser plusieurs SVM de type One Class mis en concurrence. La masse de données à traiter a impliqué l’étude de solveurs adaptés à ces problèmes. Les algorithmes devant fonctionner en temps réel, nous avons également investi le terrain de l’algorithmie pour proposer des solveurs capables de démarrer à chaud. Par l’étude de ces solveurs, nous proposons une formulation unifiée des problèmes à une et deux classes, avec et sans biais. Les approches proposées ont été validées sur un ensemble de signaux réels. Par ailleurs, un démonstrateur intégrant la détection d’événements anormaux pour la surveillance de station de métro en temps réel a également été présenté dans le cadre du projet Européen VANAHEIM / This thesis addresses partially supervised Support Vector Machines for novelty detection (One-Class SVM). These have been studied to design abnormal audio events detection for supervision of public infrastructures, in particular public transportation systems. In this context, the null hypothesis (“normal” audio signals) is relatively well known (even though corresponding signals can be notably non stationary). Conversely, every “abnormal” signal should be detected and, if possible, clustered with similar signals. Thus, a reference system based on a single model of normal signals is presented, then we propose to use several concurrent One-Class SVM to cluster new data. Regarding the amount of data to process, special solvers have been studied. The proposed algorithms must be real time. This is the reason why we have also investigated algorithms with warm start capabilities. By the study of these algorithms, we have proposed a unified framework for One Class and Binary SVMs, with and without bias. The proposed approach has been validated on a database of real signals. The whole process applied to the monitoring of a subway station has been presented during the final review of the European Project VANAHEIM

The purpose of this exploratory study was to examine resident, family member and staff perceptions of electronic monitoring and their effect on resident rights. The sample consisted of 53 nursing home residents, 104 staff and 25 family members, in the Dallas Fort Worth metroplex, from a nursing facility in which residents utilize video cameras in their rooms (Nursing Facility 1), two nursing facilities that have video cameras in their common rooms areas (Nursing Facility 2 and 3) and a nursing facility that does not utilize video cameras (Nursing Facility 4). The interview questions and self-administered surveys were in regard to the participant's perceptions of electronic monitoring, perceived risks and benefits of video cameras, awareness of resident rights and consciousness of potential risks to resident rights. Data were analyzed using a mixed methods approach using both ATLAS t.i and SAS. Study findings revealed that residents, family members and staff are aware of the potential benefits of electronic monitoring in nursing facilities. While respondents are hesitant to have electronic monitoring in resident rooms, they are interested in utilizing electronic monitoring in common areas. While residents and staff believe that electronic monitoring compromises resident rights, family members believe resident rights are protected. Different types of staff have different perceptions of electronic monitoring. Those staff members that are more directly involved in resident care are less accepting of electronic monitoring compared to staff that have episodic visits with residents. Among staff members, nursing facilities with prior experience with electronic monitoring are less accepting of electronic monitoring. Further studies are needed to enhance this research.

Recently, there is a great interest in moving object tracking in the fields of security and surveillance. Object recognition under partial occlusion is the core of any object tracking system. This thesis presents an automatic and real-time color object-recognition system which is not only robust but also occlusion tolerant. The intended use of the system is to recognize and track external vehicles entered inside a secured area like a school campus or any army base. Statistical morphological skeleton is used to represent the visible shape of the vehicle. Simple curve matching and different feature based matching techniques are used to recognize the segmented vehicle. Features of the vehicle are extracted upon entering the secured area. The vehicle is recognized from either a digital video frame or a static digital image when needed. The recognition engine will help the design of a high performance tracking system meant for remote video surveillance.

Indiana University-Purdue University Indianapolis (IUPUI) / Surveillance videos are recorded continually and the retrieval of such videos currently still relies on human operators. Automatic retrieval has not reached a satisfactory accuracy. As an intermediate representation, this work develops multiple original temporal profiles of video to convey accurate temporal information in the video while keeping certain spatial characteristics. These are effective methods to visualizes surveillance video contents efficiently in a 2D temporal image, suitable for indexing and retrieving a large video database. We are aiming to provide a compact index that is intuitive and preserves most of the information in the video in order to avoid browsing extensive video clips frame by frame. By considering some of the properties of static surveillance videos, we aim at accentuating the temporal dimension in our visualization. We have introduced our framework as three unique methods that visualize different aspects of a surveillance video, plus an extension to non-static surveillance videos. In our first method "Localized Temporal Profile", by knowing that most surveillance videos are monitoring specific locations, we try to emphasize the other dimension, time, in our solution. we focus on describing all the events only in critical locations of the video. In our next method "Multi-Position Temporal Profile", we generate an all-inclusive profile that covers all the events in the video field of view. In our last method "Motion Temporal Profile" we perform in-depth analysis of scene motion and try to handle targets with non-uniform, non-translational motion in our temporal profile. We then further extend our framework by loosening the constraint that the video is static and including cameras with smooth panning motion as such videos are widely used in practice. By performing motion analysis on the camera, we stabilize the camera to create a panorama-like effect for the video, allowing us to utilize all of the aforementioned methods. The resulting profiles allows temporal indexing to each video frame, and contains all spatial information in a continuous manner. It also shows the actions and progress of events in the temporal profile. Flexible browsing and effective manipulation of videos can be achieved using the resulting video profiles.

Introduction: We analyse the privacy policies of 15 private corporations to understand if the data handling practices – data collection, storage, and sharing –described in the policies are ethical or unethical. The data we leave behind when we use the Internet are crucial for corporations. The data provides valuable insights into our lives, thus helping corporations improve targeted marketing campaigns and increase their revenue. Method: Extensive literature review of peer-reviewed articles, written between1993 and 2021, to examine how theoretical perspectives and empirical findings evolved over time; combined with empirical research to analyse the privacy policies and “dark patterns” of 15 companies. The companies were chosen at random and belong to different sectors to give a broader understanding of the current privacy and data handling practices. Analysis: Discourse analysis of the privacy policies to evaluate the type of language used, if it is clear, easy to understand, and if the policy informs users about how their data are collected, shared, and stored. But also, a visual analysis to understand if the company is implementing “dark patterns”. Results: The results indicate that most privacy policies use misleading terms, are not fully transparent about the company’s data handling practices, and often implement “dark patterns” to try to influence the users’ decisions. Conclusion: Most companies have privacy policies available on their websites due to a clear influence from the GDPR legislation, however, there appears to be a conflicting relationship between wanting to comply with the GDPR and wanting to gather as much information as possible.

Much of the first generation literature on counter terrorist financing made sweeping generalisations and observations regarding these interventions based on relatively limited case study data. Given that the UK approach to counter terrorism clearly attests to the symbiosis between terrorism and money, this thesis evaluates the contemporary relevance of Cicero's aphorism that “the sinews of war are infinite money.” Drawing on a series of discussions and formal interviews with CTF practitioners into several of the most recent high profile terror attacks in the United Kingdom, it confirms a notable shift in terrorist financing methodology in recent years and underscores the trend towards increasing operational independence and financial autonomy. It thus considers the continuing centrality of money in the terrorism equation and has been framed specifically to examine the financing challenges posed by domestic terror cells in the UK, given the trend towards low cost terrorism with its emphasis on self sufficiency and the emergence of more discreet and ‘criminally sterile' funding methodologies. This thesis is primarily concerned with reviewing the efficacy of the UK counter terrorism-financing (CTF) model as perceived by practitioners, both in policy terms and in the context of operational outcomes. The increasing emphasis on new funding methodologies and the ensuing lack of visibility and opportunities for interdiction at the conspiracy phase of terrorist plots, further highlights the operational challenges posed for practitioners in confronting these ‘new' threats. As such, this research encourages several new perspectives, including a review of UK corporate knowledge on previous CTF interventions and consideration of military ‘threat finance' practice to deliver greater operational impact. In particular, it advocates a new focus on micro CTF interventions to address changes in the ‘economy of terror'. Finally, this thesis strongly attests to the continued relevance of finance or more specifically, the 'financial footprint' to inform and provide intelligence insight for counter terrorism responses generally. In doing so, it also considers the impact on privacy from increasingly intrusive financial and digital data collection and the trade-offs that inevitably emerge when liberty and security collide.