Defending Face-Recognition Technology (And Defending Against It)

This Article looks beneath the surface of attacks on face-recognition technology and explains how it can be an exceptionally useful tool for law enforcement, complementing traditional forensic evidence such as fingerprints and DNA. It punctures myths about the technology and explains how existing rules of criminal procedure, developed for other kinds of forensic evidence, are readily adaptable to face-recognition. It opposes across-the-board restrictions on use of face-recognition technologies and advocates a more sophisticated set of guarantees of defendant access to the information necessary to probe reliability of computerized face-matches. Defendants must have reasonable access to the details of the technology and how it was used so that they have a meaningful opportunity to inform the factfinder of doubts about reliability. Part II explains the technology, starting with machine learning, which enables a computer to represent faces digitally based on their physical characteristics, so that they can be matched with other faces. This part also explains how shortcomings in the algorithms or training database of faces can produce errors, both positive and negative, in identification. Part III explores existing and potential uses of face-recognition in law enforcement, placing the technology into the context of traditional police investigations. Part IV summarizes the relatively sparse caselaw and the much fuller literature on face-recognition technology, in particular, evaluates claims of threats to privacy, and analyzes legal principles developed for analogous conventional criminal investigative and proof methods. Part V constructs a legal framework for evaluating the probativeness of face-recognition technology in criminal prosecutions, develops strategies, and offers actual cross examination questions to guide defense counsel in challenging face-recognition technology. Part VI acknowledges that some specific uses of the technology to scan crowds and streams of people may need judicial control and suggests a draft statute to assure such control

Schooling and Civic Behavior: A Global Perspective

National governments confront different challenges to the goal of creating model citizens, as well as different ambitions in the type of citizen that they wish to create. The United States government faces a tension in determining the role of education in shaping the social order. As a liberal democracy that extols the virtue of individual liberty, the United States should allow educational pluralism to flourish. Paradoxically, however, a nation of immigrants might require an education system that turns students into “proper Americans” who honor the precepts of liberty, equality, and self-government. I draw from domestic and international studies to inform some of the drawbacks, strengths, and limitations of homogenizing centralized education versus decentralized pluralistic education. The chapters that follow feature studies from regions in which a majority ascribes to a different Abrahamic religion: The United States, the Arab World, and Israel. In chapter one, I empirically examine whether non-government (i.e. private) schools undermine American civic health. Specifically, I examine how attending private school affects American voting behavior. I observe that private schooling has no association with the likelihood of voting, but that each additional year of private schooling is associated with a decreased likelihood of supporting Donald Trump in the 2016 election. In chapter two, I examine the root cause of low private returns to education in the Arab World, where education is highly centralized. I find suggestive evidence that common characteristics of Arab world political economy, including poor academic performance, economic reliance on natural resources, and corruption suppress private returns to education. I hypothesize that low returns to education might contribute to frequent waves of social unrest and upheaval. In chapter three, I examine how Israel’s pluralistic education system allows Haredi (i.e. ultra-Orthodox) Jews to teach values at odds with much of Israeli society. I further explain that other segments of the population express frustration over the subsidization of an education sector that provides no discernible benefit for a society with secular, materialistic visions of progress. Finally, I explain how Israel’s parliamentary system limits the likelihood of meaningful reform to address the grievances of secular Israeli society

Re-Kindling Community Education in Neoliberal times

This is a study about community education in Ireland. It begins by problematising our current globalised neoliberal epoch interpreting this socio-political environment as built on a return to laissez-faire economics and a hegemonic imbuement of inequality as the norm. The research draws from literature to demonstrate how neoliberalism has greatly exacerbated income inequality demonstrating how this has been despite those responsible for implementing the neoliberal project as often asserting an equality-based agenda. This mixed-methods study sets out to determine the impact of neoliberalism on community education. It enhances our knowledge of community education by mapping a landscape of domestic practice. This is undertaken by drawing from the experience and insights of circa 226 practitioners who are working in a range of local settings. The study uncovers community educator characteristics, identifies where community educators work and details what their day-to-day practice often entails. Examples of community education that are uncovered locate practice within both Community Sector organisations and State (public) provision alike. This locational dispersal is congruent with a domestic history of community education most notably that which emerged since the 1970s and 1980s with much practice built from collective organisation through social movements most notably the women’s movement, the literacy movement and the wider community development/anti-poverty movement. The history of community education presented also reveals a growth in public provision which was often a response to community demands. The study upholds the view that much community education is inspired by a vision of an egalitarian society, something that is determined both through literature, and through the many practitioner insights that are shared. This research also demonstrates varying interpretations of how inequality should be addressed including, for example, a second-chance approach to dealing with educational disadvantage and a minority approach that directly links community education to community development and collective action. Across each philosophical perspective, the study reveals a harsh neoliberalisation of community education practice. This has been advanced through an EU-led policy approach that interprets all education as largely instrumentalist. In the main, neoliberalism views community education as an exercise in up-skilling a flexible workforce, a perspective which underpins a system of New Public Management. This type of managerialism focuses on the measurement of outputs and brings the logic of business into spaces where ‘education’ once related to issues of personal development and recovery or to consciousness-raising praxis is now instrumentally reduced to the vocational demands of the market. Based on the politics of free market neoliberalism, New Public Management has had a profound effect on community education with much Community Sector activism either co-opted into State structures or simply shut down. Other findings from this research demonstrate deterioration in working conditions for community educators. These include pay cuts, unstable contracts of employment and an erosion of occupational autonomy on a day-to-day basis. These problems are caused by the increased demand to accredit all (community) learning through an overly prescriptive, over-assessed model of certification that has been principally designed for Further Education (FE) settings. Given its ambition to re-kindle community education this research is undertaken as an attempt to support practitioners in upholding the equality agenda many hold firm. The study calls on community educators to expand anti-neoliberal spaces where possible and draws from the suggestions of those participating in this research to imagine how this might be done. Specifically, these suggestions are the need (a) to be more strategic in relationships with the State (b) to strengthen network relationships amongst themselves (c) to assert a stronger sectoral identity (d) to explore alternative accreditation mechanisms and (e) to expand ways in which stronger links with oppositional movements can be created. Along with drawing from practitioners suggestions, the research is underpinned by a critical realist-feminist perspective, a paradigm that brings with it an interrogation of contemporary community education practice. It particularly challenges a majority person-centered philosophy held by practitioners critiquing the potential for this philosophy to realise egalitarian change when utilised within neoliberal contexts. Person-centeredness is important for its holistic attention to participants of community education, but it is a limiting perspective in achieving egalitarianism given its lack of attention to the structural causes of inequality. It also discourages dichotomisation of radical and non-radical practice and encourages community educators to infuse a problem-posing approach within all of their interactions. Rekindling Community Education encourages community educators to reflect on their practice and to consider how best to advance the principles of equality and social justice though their work. This reflection should include consideration of practitioner relationships with the communities they purport to support encouraging authenticity in their interactions

A Multi Agent System for Flow-Based Intrusion Detection Using Reputation and Evolutionary Computation

The rising sophistication of cyber threats as well as the improvement of physical computer network properties present increasing challenges to contemporary Intrusion Detection (ID) techniques. To respond to these challenges, a multi agent system (MAS) coupled with flow-based ID techniques may effectively complement traditional ID systems. This paper develops: 1) a scalable software architecture for a new, self-organized, multi agent, flow-based ID system; and 2) a network simulation environment suitable for evaluating implementations of this MAS architecture and for other research purposes. Self-organization is achieved via 1) a reputation system that influences agent mobility in the search for effective vantage points in the network; and 2) multi objective evolutionary algorithms that seek effective operational parameter values. This paper illustrates, through quantitative and qualitative evaluation, 1) the conditions for which the reputation system provides a significant benefit; and 2) essential functionality of a complex network simulation environment supporting a broad range of malicious activity scenarios. These results establish an optimistic outlook for further research in flow-based multi agent systems for ID in computer networks

Path Planning, Mapping & Learning for Mobile Robot Navigation

Ph.DDOCTOR OF PHILOSOPH

Human machine collaboration for foreground segmentation in images and videos

Foreground segmentation is defined as the problem of generating pixel level foreground masks for all the objects in a given image or video. Accurate foreground segmentations in images and videos have several potential applications such as improving search, training richer object detectors, image synthesis and re-targeting, scene and activity understanding, video summarization, and post-production video editing. One effective way to solve this problem is human-machine collaboration. The main idea is to let humans guide the segmentation process through some partial supervision. As humans, we are extremely good at perception and can easily identify the foreground regions. Computers, on the other hand, lack this capability, but are extremely good at continuously processing large volumes of data at the lowest level of detail with great efficiency. Bringing these complementary strengths together can lead to systems which are accurate and cost-effective at the same time. However, in any such human-machine collaboration system, cost effectiveness and higher accuracy are competing goals. While more involvement from humans can certainly lead to higher accuracy, it also leads to increased cost both in terms of time and money. On the other hand, relying more on machines is cost-effective, but algorithms are still nowhere near human-level performance. Balancing this cost versus accuracy trade-off holds the key behind success for such a hybrid system. In this thesis, I develop foreground segmentation algorithms which effectively and efficiently make use of human guidance for accurately segmenting foreground objects in images and videos. The algorithms developed in this thesis actively reason about the best modalities or interactions through which a user can provide guidance to the system for generating accurate segmentations. At the same time, these algorithms are also capable of prioritizing human guidance on instances where it is most needed. Finally, when structural similarity exists within data (e.g., adjacent frames in a video or similar images in a collection), the algorithms developed in this thesis are capable of propagating information from instances which have received human guidance to the ones which did not. Together, these characteristics result in a substantial savings in human annotation cost while generating high quality foreground segmentations in images and videos. In this thesis, I consider three categories of segmentation problems all of which can greatly benefit from human-machine collaboration. First, I consider the problem of interactive image segmentation. In traditional interactive methods a human annotator provides a coarse spatial annotation (e.g., bounding box or freehand outlines) around the object of interest to obtain a segmentation. The mode of manual annotation used affects both its accuracy and ease-of-use. Whereas existing methods assume a fixed form of input no matter the image, in this thesis I propose a data-driven algorithm which learns whether an interactive segmentation method will succeed if initialized with a given annotation mode. This allows us to predict the modality that will be sufficiently strong to yield a high quality segmentation for a given image and results in large savings in annotation costs. I also propose a novel interactive segmentation algorithm called Click Carving which can accurately segment objects in images and videos using a very simple form of human interaction---point clicks. It outperforms several state-of-the-art methods and requires only a fraction of human effort in comparison. Second, I consider the problem of segmenting images in a weakly supervised image collection. Here, we are given a collection of images all belonging to the same object category and the goal is to jointly segment the common object from all the images. For this, I develop a stagewise active approach to segmentation propagation: in each stage, the images that appear most valuable for human annotation are actively determined and labeled by human annotators, then the foreground estimates are revised in all unlabeled images accordingly. In order to identify images that, once annotated, will propagate well to other examples, I introduce an active selection procedure that operates on the joint segmentation graph over all images. It prioritizes human intervention for those images that are uncertain and influential in the graph, while also mutually diverse. Building on this, I also introduce the problem of measuring compatibility between image pairs for joint segmentation. I show that restricting the joint segmentation to only compatible image pairs results in an improved joint segmentation performance. Finally, I propose a semi-supervised approach for segmentation propagation in video. Given human supervision in some frames of a video, this information can be propagated through time. The main challenge is that the foreground object may move quickly in the scene at the same time its appearance and shape evolves over time. To address this, I propose a higher order supervoxel label consistency potential which leverages bottom-up supervoxels to enforce long-range temporal consistency during propagation. I also introduce the notion of a generic pixel-level objectness in images and videos by training a deep neural network which uses appearance and motion to automatically assign a score to each pixel capturing its likelihood to be an "object" or "background". I show that the human guidance in the semi-supervised propagation algorithm can be further augmented with the generic pixel-objectness scores to obtain an even more accurate foreground segmentation in videos. Throughout, I provide extensive evaluation on challenging datasets and also compare with many state-of-the-art methods and other baselines validating the strengths of proposed algorithms. The outcomes across several different experiments show that the proposed human-machine collaboration algorithms achieve accurate segmentation of foreground objects in images and videos while saving a large amount of human annotation effort.Computer Science