There is an on-going battle in the Department of Defense between reason and the faith in technology. Those ascribing to technological fundamentalism are blind to the empirical evidence that their faith in technology is obscuring the technological limitations that are evident. The desire for information dominance to reach the state of total transparency of the opponent in order to win the war is untenable. The reasoning voiced by skeptics should be heeded but the technological fundamentalists are deaf to their views. The use of UAVs have provided for limited visibility of the opponent and not the perfect Panopticon as envisioned. / Master of Public and International Affairs

This paper makes an attempt to explain the construction of a newly developed genre called revenge porn flourishing in new media. The study analyzes the patterns of production and display of revenge porn content as well as the mechanisms of the site that archives such materials. The results of this study suggest that the development of such a genre cannot be attributed only to liberatory and/or victimizing effects of the electronic space. Rather, social power structures based on discourses like gender, heterosexuality and capitalist patriarchy that exploit the surveillance mechanism of the internet are significantly influencing both individual uses of the internet as well as its apparatus and technologies. These are the major forces contributing to the institutionalization and commercialization of revenge porn in new media. This is a case study based investigation that uses both content analysis and discourse analysis as methods to interpret the revenge porn genre in new media.

This thesis aims to understand the connexions between the ethical practices associated with suffering a chronic illness and possibilities of truth, subjectivity, governmentality and freedom. This is attempted via an analysis of the specific case of HIV/AIDS. In the 1980s there emerged a variety of competing ways to construct the truth of HIV/AIDS. By the early 1990s, however, one particular way of thinking about and problematizing the syndrome - an account which reflected less the repressive intentions and perspectives of recently ascendant neo-liberal governments than the efforts and world-views of grass-roots community activism - had achieved ascendancy. This approach to HIV/AIDS remains today the authoritative one, and that from which expertise on the subject is derived. The emergence to pre-eminence of this way of thinking about HIV/AIDS is mapped, and three of its principal manifestations are examined in detail, using techniques of textual analysis. It is argued that within these texts, through the use of various forms of textual management, ethical subject relations of the sort discussed by Foucault are constructed, which delimit the possibilities of being for those who are touched by the disease, and which comprise elements of an ethico-panoptic regulatory technology. The parallels and differences between the technologies of government articulated via these 'community' based discourses and those of recent neo-liberal discourses are explored, with consideration being given to their implications for the practising of resistance and of freedom by people infected or affected by HIV or AIDS. Engagement with the field in this fashion is uncommon within sociology of HIV/AIDS, and to do so raises a variety of conceptual and methodological issues. Hence, within this thesis the task of interrogating HIV/AIDS discourse is radically linked to the construction of a distinct form of sociology, derived from the Foucauldian project of the 'history of the present'.

<p>    </p> <p>Meeting the nutritional requirements of an increasing population in a changing climate is the foremost concern of agricultural research in recent years. A solution to some of the many questions posed by this existential threat is breeding crops that more efficiently produce food with respect to land and water use. A key aspect to this optimization is geometric aspects of plant physiology such as canopy architecture that, while based in the actual 3D structure of the organism, does not necessarily require such a representation to measure. Although deep learning is a powerful tool to answer phenotyping questions that do not require an explicit intermediate 3D representation, training a network traditionally requires a large number of hand-segmented ground truth images. To bypass the enormous time and expense of hand- labeling datasets, we utilized a procedural sorghum image pipeline from another student in our group that produces images similar enough to the ground truth images from the phenotyping facility that the network can be directly used on real data while training only on automatically generated data. The synthetic data was used to train a deep segmentation network to identify which pixels correspond to which leaves. The segmentations were then processed to find the number of leaves identified in each image to use for the leaf-counting task in high-throughput phenotyping. Overall, our method performs comparably with human annotation accuracy by correctly predicting within a 90% confidence interval of the true leaf count in 97% of images while being faster and cheaper. This helps to add another expensive- to-collect phenotypic trait to the list of those that can be automatically collected. </p>

[pt] Robôs moveis se tornaram populares nos últimos anos devido a sua capacidade de operar de forma autônoma e performar tarefas que são perigosas, repetitivas ou tediosas para seres humanos. O robô necessita ter um mapa de seus arredores e uma estimativa de sua localização dentro desse mapa para alcançar navegação autônoma. O problema de Localização e Mapeamento Simultâneos (SLAM) está relacionado com a determinação simultânea do mapa e da localização usando medidas de sensores. SLAM visual diz respeito a estimar a localização e o mapa de um robô móvel usando apenas informações visuais capturadas por câmeras. O uso de câmeras para o sensoriamento proporciona uma vantagem significativa, pois permite resolver tarefas de visão computacional que fornecem informações de alto nível sobre a cena, incluindo detecção, segmentação e reconhecimento de objetos. A maioria dos sistemas de SLAM visuais não são robustos a ambientes dinâmicos. Os sistemas que lidam com conteúdo dinâmico normalmente contem com métodos de aprendizado profundo para detectar e filtrar objetos dinâmicos. Existem vários sistemas de SLAM visual na literatura com alta acurácia e desempenho, porem a maioria desses métodos não englobam objetos desconhecidos. Este trabalho apresenta um novo sistema de SLAM visual robusto a ambientes dinâmicos, mesmo na presença de objetos desconhecidos. Este método utiliza segmentação panóptica para filtrar objetos dinâmicos de uma cena durante o processo de estimação de estado. A metodologia proposta é baseada em ORB-SLAM3, um sistema de SLAM estado-da-arte em ambientes estáticos. A implementação foi testada usando dados reais e comparado com diversos sistemas da literatura, incluindo DynaSLAM, DS-SLAM e SaD-SLAM. Além disso, o sistema proposto supera os resultados do ORB-SLAM3 em um conjunto de dados personalizado composto por ambientes dinâmicos e objetos desconhecidos em movimento. / [en] Mobile robots have become popular in recent years due to their ability to operate autonomously and accomplish tasks that would otherwise be too dangerous, repetitive, or tedious for humans. The robot must have a map of its surroundings and an estimate of its location within this map to achieve full autonomy in navigation. The Simultaneous Localization and Mapping (SLAM) problem is concerned with determining both the map and localization concurrently using sensor measurements. Visual SLAM involves estimating the location and map of a mobile robot using only visual information captured by cameras. Utilizing cameras for sensing provides a significant advantage, as they enable solving computer vision tasks that offer high-level information about the scene, including object detection, segmentation, and recognition. There are several visual SLAM systems in the literature with high accuracy and performance, but the majority of them are not robust in dynamic scenarios. The ones that deal with dynamic content in the scenes usually rely on deep learning-based methods to detect and filter dynamic objects. However, these methods cannot deal with unknown objects. This work presents a new visual SLAM system robust to dynamic environments, even in the presence of unknown moving objects. It uses Panoptic Segmentation to filter dynamic objects from the scene during the state estimation process. The proposed methodology is based on ORB-SLAM3, a state-of-the-art SLAM system for static environments. The implementation was tested using real-world datasets and compared with several systems from the literature, including DynaSLAM, DS-SLAM and SaD-SLAM. Also, the proposed system surpasses ORB-SLAM3 results in a custom dataset composed of dynamic environments with unknown moving objects.

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George Orwell’s Nineteen Eighty-Four, first published in 1949, is a vision of socialism gone wrong. The setting of Oceania is a world ruled over by an oligarchical collective, “The Party,” which wields absolute power through a formidable combination of surveillance technology and the operation of the principles of “panoptic control,” a concept drawn from Jeremy Bentham’s model prison design of the late 1700s and revived by Foucault in the mid 1970s. The combination of surveillance technology and panoptic control is central to the functioning of power in Orwell’s novel, a union which has created a self-sustaining form of totalitarianism dependent on the oppression of individual identity for its automatic perpetuation. This essay offers a reading of Nineteen Eighty-Four as an implicit critique of Bentham’s Panopticon which in many ways foreshadowed the later work of Michel Foucault on the functioning of power within this specific type of physical and social architecture. / George Orwells roman 1984, vilken publicerades första gången 1949, är en framtidsvision om socialism som gått fel. Romanen utspelas i Oceania, en värld som styrs av ett oligarkiskt kollektiv, “Partiet,” vilket utövar absolut makt genom en utstuderad kombination av övervakningsteknik och teorin om “panoptisk” kontroll, ett begrepp sprunget ur av Jeremy Benthams fängelsemodell från sent 1700-tal, vilket återskapades av Michel Foucault i mitten av 1970-talet. Kombinationen av övervakningsteknologi och panopticism har i Oceanien skapat en totalitarianism som fungerar med automatik och förtrycker individuell identitet för att befästa statens makt. Denna uppsats närmar sig Orwells 1984 som en underförstådd kritik av Benthams arbete. Vidare identifier i romanen 1984 många av Foucault’s idéer om hur makt fungerar i en panoptisk struktur.

This essay is an introduction to the context and processes of ‘undefinition’ in the work of the author. Through fragmentation of the original image, the depicted image problematizes identification. Gaps in the portrayal allow for personal interpretation to bias the definition in order to make our perception visible. Definitions are the means of power, and we primarily order and define the world visually. Foucault describes this as Panoptic technology. Unless we learn to understand how we see individually, authoritarian definitions will remain the de-facto means of operating powerfully.

The rise of convolutional neural networks (CNNs) in the context of computer vision has occurred in tandem with the advancement of depth sensing technology. Depth cameras are capable of yielding two-dimensional arrays storing at each pixel the distance from objects and surfaces in a scene from a given sensor, aligned with a regular color image, obtaining so-called RGBD images. Inspired by prior models in the literature, this work develops a suite of RGBD CNN models to tackle the challenging tasks of object detection, instance segmentation, and semantic segmentation. Prominent architectures for object detection and image segmentation are modified to incorporate dual backbone approaches inputting RGB and depth images, combining features from both modalities through the use of novel fusion modules. For each task, the models developed are competitive with state-of-the-art RGBD architectures. In particular, the proposed RGBD object detection approach achieves 53.5% mAP on the SUN RGBD 19-class object detection benchmark, while the proposed RGBD semantic segmentation architecture yields 69.4% accuracy with respect to the SUN RGBD 37-class semantic segmentation benchmark. An original 13-class RGBD instance segmentation benchmark is introduced for the SUN RGBD dataset, for which the proposed model achieves 38.4% mAP. Additionally, an original depth-aware panoptic segmentation model is developed, trained, and tested for new benchmarks conceived for the NYUDv2 and SUN RGBD datasets. These benchmarks offer researchers a baseline for the task of RGBD panoptic segmentation on these datasets, where the novel depth-aware model outperforms a comparable RGB counterpart.

Environment perception is crucial to autonomous driving. Panoptic segmentation and objects tracking are two challenging tasks, and the combination of both, namely 4D panoptic segmentation draws researchers’ attention recently. In this work, we implement 4D panoptic LiDAR segmentation (4D-PLS) on Volvo datasets and provide a pipeline of data preparation, model building and model optimization. The main contributions of this work include: (1) building the Volvo datasets; (2) adopting an 4D-PLS model improved by Hyperparameter Optimization (HPO). We annotate point cloud data collected from Volvo CE, and take a supervised learning approach by employing a Deep Neural Network (DNN) to extract features from point cloud data. On the basis of the 4D-PLS model, we employ Bayesian Optimization to find the best hyperparameters for our data, and improve the model performance within a small training budget. / Miljöuppfattning är avgörande för autonom körning. Panoptisk segmentering och objektspårning är två utmanande uppgifter, och kombinationen av båda, nämligen 4D panoptisk segmentering, har nyligen uppmärksammat forskarna. I detta arbete implementerar vi 4D-PLS på Volvos datauppsättningar och tillhandahåller en pipeline av dataförberedelse, modellbyggande och modelloptimering. De huvudsakliga bidragen från detta arbete inkluderar: (1) bygga upp Volvos datauppsättningar; (2) anta en 4D-PLS-modell förbättrad av HPO. Vi kommenterar punktmolndata som samlats in från Volvo CE och använder ett övervakat lärande genom att använda en DNN för att extrahera funktioner från punktmolnsdata. På basis av 4D-PLS-modellen använder vi Bayesian Optimization för att hitta de bästa hyperparametrarna för vår data och förbättra modellens prestanda inom en liten utbildningsbudget.