Towards Recommender Systems for Police Photo Lineup

Photo lineups play a significant role in the eyewitness identification process. This method is used to provide evidence in the prosecution and subsequent conviction of suspects. Unfortunately, there are many cases where lineups have led to the conviction of an innocent suspect. One of the key factors affecting the incorrect identification of a suspect is the lack of lineup fairness, i.e. that the suspect differs significantly from all other candidates. Although the process of assembling fair lineup is both highly important and time-consuming, only a handful of tools are available to simplify the task. In this paper, we describe our work towards using recommender systems for the photo lineup assembling task. We propose and evaluate two complementary methods for item-based recommendation: one based on the visual descriptors of the deep neural network, the other based on the content-based attributes of persons. The initial evaluation made by forensic technicians shows that although results favored visual descriptors over attribute-based similarity, both approaches are functional and highly diverse in terms of recommended objects. Thus, future work should involve incorporating both approaches in a single prediction method, preference learning based on the feedback from forensic technicians and recommendation of assembled lineups instead of single candidates

Prospectus, December 4, 2013

TIPS FOR GETTING THROUGH FINAL EXAMS; Texting at stoplight an accident waiting to happen; Transfer tips for students; Tips to earn extra money for the holidays; Winter weather preparedness tips; Public Safety on kitchen safety tips; Gender discrimination in collegiate athletics; \u27Selfie\u27 as word of the year is a fitting self-portrait; Safety shifting to automatic; Volleyball team ends season stronghttps://spark.parkland.edu/prospectus_2013/1023/thumbnail.jp

A game theoretical model for a collaborative e-learning platform on privacy awareness

De nos jours, avec l'utilisation croissante des technologies numériques, l'éducation à la préservation de la vie privée joue un rôle important en particulier pour les adolescents. Bien que plusieurs plateformes d'apprentissage en ligne à la sensibilisation à la vie privée aient été mises en œuvre, elles sont généralement basées sur des techniques traditionnelles d'apprentissage. Plus particulièrement, ces plateformes ne permettent pas aux étudiants de coopérer et de partager leurs connaissances afin d’améliorer leur apprentissage ensemble. En d'autres termes, elles manquent d'interactions élève-élève. Des recherches récentes sur les méthodes d'apprentissage montrent que la collaboration entre élèves peut entraîner de meilleurs résultats d'apprentissage par rapport à d'autres approches. De plus, le domaine de la vie privée étant fortement lié à la vie sociale des adolescents, il est préférable de fournir un environnement d'apprentissage collaboratif où l’on peut enseigner la préservation de la vie privée, et en même temps, permettre aux étudiants de partager leurs connaissances. Il serait souhaitable que ces derniers puissent interagir les uns avec les autres, résoudre des questionnaires en collaboration et discuter de problèmes et de situations de confidentialité. À cet effet, ce travail propose « Teens-online », une plateforme d'apprentissage en ligne collaborative pour la sensibilisation à la vie privée. Le programme d'études fourni dans cette plateforme est basé sur le Référentiel de formation des élèves à la protection des données personnelles. De plus, la plateforme proposée est équipée d'un mécanisme d'appariement de partenaires basé sur la théorie des jeux. Ce mécanisme garantit un appariement élève-élève stable en fonction des besoins de l'élève (comportement et / ou connaissances). Ainsi, des avantages mutuels seront obtenus en minimisant les chances de coopérer avec des pairs incompatibles. Les résultats expérimentaux montrent que l'utilité moyenne obtenue en appliquant l'algorithme proposé est beaucoup plus élevée que celle obtenue en utilisant d'autres mécanismes d'appariement. Les résultats suggèrent qu'en adoptant l'approche proposée, chaque élève peut être jumelé avec des partenaires optimaux, qui obtiennent également en retour des résultats d'apprentissage plus élevés.Nowadays, with the increasing use of digital technologies, especially for teenagers, privacy education plays an important role in their lives. While several e-learning platforms for privacy awareness training have been implemented, they are typically based on traditional learning techniques. In particular, these platforms do not allow students to cooperate and share knowledge with each other in order to achieve mutual benefits and improve learning outcomes. In other words, they lack student-student interaction. Recent research on learning methods shows that the collaboration among students can result in better learning outcomes compared to other learning approaches. Motivated by the above-mentioned facts, and since privacy domain is strongly linked to the social lives of teens, there is a pressing need for providing a collaborative learning platform for teaching privacy, and at the same time, allows students to share knowledge, interact with each other, solve quizzes collaboratively, and discuss privacy issues and situations. For this purpose, this work proposes “Teens-online”, a collaborative e-learning platform for privacy awareness. The curriculum provided in this platform is based on the Personal Data Protection Competency Framework for School Students. Moreover, the proposed platform is equipped with a partner-matching mechanism based on matching game theory. This mechanism guarantees a stable student-student matching according to a student's need (behavior and/or knowledge). Thus, mutual benefits will be attained by minimizing the chances of cooperating with incompatible students. Experimental results show that the average learning-related utility obtained by applying the proposed partner-matching algorithm is much higher than the average utility obtained using other matching mechanisms. The results also suggest that by adopting the proposed approach, each student can be paired with their optimal partners, which in turn helps them reach their highest learning outcomes

(Main)streaming Hate: Analyzing White Supremacist Content and Framing Devices on YouTube

The emboldening of white supremacist groups, as well as their increased mainstream presence in online circles, necessitates the creation of studies that dissect their tactics and rhetoric, while offering platform-specific insights. This study seeks to address these needs by analyzing white supremacist content and framing devices on the video hosting website, YouTube. Data were collected through a multi-stage sampling technique, designed to capture a \u27snapshot\u27 of white supremacist content on the platform during a 45-day period in 2019. After line-by-line coding and qualitative thematic analysis, results showed that sampled channels varied between different levels of color-blindness and overt racialization in their framing. Furthermore, channels containing more color-blind approaches yielded higher subscriber counts than their counterparts. What this indicates is that sampled channels use framing to both activate racial threat and minimize race, attempting to reproduce racism while avoiding coming off as racist in the color-blind, mainstream political climate. Secondary findings also show how sampled channels (a) rhetorically bridge the gap between fascism, nationalism, hegemonic gender roles, and mainstream conservative thought; (b) reconcile the idea of political action within a perilous and conspiratorial worldview; (c) leverage interactive, visual media to engage, manage, and collect funding from their audiences. This study is unique because it unpacks the discursive intricacies of white supremacist messaging, while showing the processes by which a racist society is reproduced in the cosmopolitan, digital hub that is YouTube. It sets precedent and opens doors for future inquiry into how social media platforms are used as tools to mainstream white supremacist ideas

Redefining the anthology : forms and affordances in digital culture

Alors que le modèle économique de la télévision américaine, longtemps dominant, a été mis au défi de diverses manières par les changements industriels et technologiques de ces dernières années, des formes narratives de plus en plus hétérogènes sont apparues, qui se sont ajoutées aux structures sérielles originaires. La diversité des formes télévisuelles est devenue particulièrement évidente depuis que les paysages télévisuels nationaux et locaux ont commencé à s’ouvrir aux marchés étrangers situés en dehors des États-Unis, pour finalement adopter une perspective transnationale et globale. La transition vers la télévision distribuée sur Internet a joué un rôle central dans cette fragmentation formelle et la nouvelle dynamique de la diffusion en ligne a ouvert une different perspective pour comprendre le flux mondial de contenus télévisuels, qui reflète aujourd'hui un environnement multimédia et numérique hautement interconnecté et mis en réseau. En effet, la multiplication des services de vidéo à la demande oblige la sérialité à s’adapter au paysage médiatique contemporain, donnant naissance à des produits audiovisuels pouvant être transférés en ligne et présentant des spécificités de production, de distribution et de réception. L’un des résultats de tels changements dans les séries télévisées américaines à l’aube du XXIe siècle est la série anthologique divisée en différentes saisons avec des histoires distinctes, et pourtant liées par le ton et le style. Ma recherche se situe dans un tel contexte technologique, industriel et culturel, où le contenu télévisuel est de plus en plus fragmenté. Compte tenu de cette fragmentation des contenus, cette thèse examine la manière dont les contenus télévisuels contemporains sont distribués, dans l'interaction entre les processus de recommandation basés sur des algorithmes et les pratiques éditoriales plus traditionnelles. L’objectif de ce projet est donc d’étudier la manière dont certaines structures narratives typiques de la forme de l’anthologie apparaissent dans le contexte de la sérialité de la télévision nord-américaine, à partir de conditions spécifiques de production, de distribution et de consommation dans l’industrie des médias. En se concentrant sur l'évolution (dimension temporelle et historique) et sur la circulation numérique (dimension spatiale, géographique) des séries d'anthologies américaines, et en observant les particularités de leur production et de leur style, ainsi que leurs réseaux de distribution et les modes de consommation qu'elles favorisent, cette thèse s’inscrit finalement dans une conversation plus vaste sur les études culturelles et numériques. L’objectif final est d’étudier la relation entre les formes anthologiques, les plateformes de distribution et les modèles de consommation, en proposant une approche comparative de l’anthologie qui soit à la fois cross-culturelle, crosshistorique, cross-genre et qui prenne en consideration les pratiques pre- et post-numériques pour l’organisation de contenus culturels.As the longtime dominant U.S. television business model has been challenged in various ways by industrial and technological changes in recent years, more heterogeneous narrative forms have emerged in addition to original serial structures. The diversity of televisual forms became particularly evident since national, local television landscapes started opening up to foreign markets outside of the U.S., finally embracing a transnational, global perspective and tracing alternative value-chains. The transition to internet-distributed television played a pivotal role in this formal fragmentation and new dynamics of online streaming opened up another path for understanding the flow of television content, which today reflects a highly interconnected, networked media and digital environment. Indeed, the proliferation of video-on-demand services is forcing seriality to adapt to the contemporary mediascape, giving rise to audiovisual products that can be transferred online and present specificities in production, distribution and reception. One of the outcomes of such changes in U.S. television series at the dawn of the twenty-first century is the anthology series divided in different seasons with separate stories, yet linked by tone and style. My research positions itself in such a technological, industrial and cultural context, where television content is increasingly fragmented. Given such a fragmentation, this thesis considers the ways contemporary television content is distributed in the interaction between algorithmic-driven recommendation processes and more traditional editorial practices. The aim of the project is to investigate the way certain narrative structures typical of the anthology form emerge in the context of U.S. television seriality, starting from specific conditions of production, distribution and consumption in the media industry. By focusing on the evolution (temporal, historical dimension) and on the digital circulation (spatial, geographic dimension) of U.S. anthology series, and observing the peculiarities in their production and style, as well as their distributional networks and the consumption patterns they foster, this thesis ultimately insert itself into a larger conversation on digital-cultural studies. The final purpose is to examine the relation between anthological forms, distribution platforms and consumption models, by proposing a comparative approach to the anthology that is at the same time cross-cultural, cross-historical, cross-genre and accounting for both pre- and post-digital practices for cultural content organization

VISUAL ANALYTICS FOR OPEN-ENDED TASKS IN TEXT MINING

Overview of documents using topic modeling and multidimensional scaling is helpful in understanding topic distribution. While we can spot clusters visually, it is challenging to characterize them. My research investigates an interactive method to identify clusters by assigning attributes and examining the resulting distributions. ParallelSpaces examines the understanding of topic modeling applied to Yelp business reviews, where businesses and their reviews each constitute a separate visual space. Exploring these spaces enables the characterization of each space using the other. However, the scatterplot-based approach in ParallelSpaces does not generalize to categorical variables due to overplotting. My research proposes an improved layout algorithm for those cases in our follow-up work, Gatherplots, which eliminate overplotting in scatterplots while maintaining individual objects. Another limitation in clustering methods is the fixed number of clusters as a hyperparameter. TopicLens is a Magic Lens-type interaction technique, where the documents under the lens are clustered according to topics in real time. While ParallelSpaces help characterize the clusters, the attributes are sometimes limited. To extend the analysis by creating a custom mixture of attributes, CommentIQ is a comment moderation tool where moderators can adjust model parameters according to the context or goals. To help users analyze documents semantically, we develop a technique for user-driven text mining by building a dictionary for topics or concepts in a follow-up study, ConceptVector, which uses word embedding to generate dictionaries interactively and uses those dictionaries to analyze the documents. My dissertation contributes interactive methods to overview documents to integrate the user in text mining loops that currently are non-interactive. The case studies we present in this dissertation provide concrete and operational techniques for directly improving several state-of-the-art text mining algorithms. We summarize those generalizable lessons and discuss the limitations of the visual analytics approach

Washington, D.c., A Black Aesthetic, & The Politics Of Renewal

Since 2010, Washington, D.C. has undergone major shifts in its racial demographics and property value. What was once a city that boasted a seventy percent population of black residents in the 1970s, giving it the moniker “Chocolate City,” has become an urban landscape overrun by developers, construction sites, and new residents who assist in branding the “new” Washington. What does that branding include? Not only new buildings, but an investment in an aesthetics that makes use of “Chocolate City’s” past to define its new and “authentically urban” future. I argue that despite the intentional attempt at erasure of black life and culture from the capital’s landscape, native black Washingtonians continue to produce an aesthetic birthed out of the Chocolate City of the 1970s, but decidedly invested in the contemporary focus on specificity of place. To that end, “Washington, D.C., a Black Aesthetic, & the Politics of Renewal” is an excavation of literary, film and visual artistic production as a means by which to interrogate what I identify as a new practice in black aestheticism. I articulate that black aesthetic, which I term an aesthetics of loss, as existing alongside and despite ongoing displacement and dispossession of black people and black spaces, and I enact a multi-methodological approach to get at its nuance. My work centers around black cultural curators in Washington, D.C. who use literature, film, music, and visual art to inscribe themselves and their communities as in place in the face of systemic erasure. To be clear, by “in place” I mean taking up material place, not just ideological space, on the mapping of the District of Columbia. Ultimately, this dissertation asks: What does it mean to live when you have been pronounced dead

Computational Transformation of the Public Sphere : Theories and Case Studies

This book is an edited collection of MA research paper on the digital revolution of the public and governance. It covers cyber governance in Finland, and the securitization of cyber security in Finland. It investigates the cases of Brexit, the 2016 US presidental election of Donald Trump, the 2017 presidential election of Volodymyr Zelensky, and Brexit. It examines the environmental concerns of climate change and greenwashing, and the impact of digital communication giving rise to the #MeToo and Incel movements. It considers how digitilization can serve to emancipate women through ride-sharing, and how it leads to the question of robot rights. It considers fake news and algorithmic governance with respect to case studies of the Chinese social credit system, the US FICO credit score, along with Facebook, Twitter, Cambridge Analytica and the European effort to regulate and protect data usage.Non peer reviewe