1,829 research outputs found

    Deep Learning Techniques for Electroencephalography Analysis

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    In this thesis we design deep learning techniques for training deep neural networks on electroencephalography (EEG) data and in particular on two problems, namely EEG-based motor imagery decoding and EEG-based affect recognition, addressing challenges associated with them. Regarding the problem of motor imagery (MI) decoding, we first consider the various kinds of domain shifts in the EEG signals, caused by inter-individual differences (e.g. brain anatomy, personality and cognitive profile). These domain shifts render multi-subject training a challenging task and impede robust cross-subject generalization. We build a two-stage model ensemble architecture and propose two objectives to train it, combining the strengths of curriculum learning and collaborative training. Our subject-independent experiments on the large datasets of Physionet and OpenBMI, verify the effectiveness of our approach. Next, we explore the utilization of the spatial covariance of EEG signals through alignment techniques, with the goal of learning domain-invariant representations. We introduce a Riemannian framework that concurrently performs covariance-based signal alignment and data augmentation, while training a convolutional neural network (CNN) on EEG time-series. Experiments on the BCI IV-2a dataset show that our method performs superiorly over traditional alignment, by inducing regularization to the weights of the CNN. We also study the problem of EEG-based affect recognition, inspired by works suggesting that emotions can be expressed in relative terms, i.e. through ordinal comparisons between different affective state levels. We propose treating data samples in a pairwise manner to infer the ordinal relation between their corresponding affective state labels, as an auxiliary training objective. We incorporate our objective in a deep network architecture which we jointly train on the tasks of sample-wise classification and pairwise ordinal ranking. We evaluate our method on the affective datasets of DEAP and SEED and obtain performance improvements over deep networks trained without the additional ranking objective

    Linking Representations with Multimodal Contrastive Learning

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    Many applications require grouping instances contained in diverse document datasets into classes. Most widely used methods do not employ deep learning and do not exploit the inherently multimodal nature of documents. Notably, record linkage is typically conceptualized as a string-matching problem. This study develops CLIPPINGS, (Contrastively Linking Pooled Pre-trained Embeddings), a multimodal framework for record linkage. CLIPPINGS employs end-to-end training of symmetric vision and language bi-encoders, aligned through contrastive language-image pre-training, to learn a metric space where the pooled image-text representation for a given instance is close to representations in the same class and distant from representations in different classes. At inference time, instances can be linked by retrieving their nearest neighbor from an offline exemplar embedding index or by clustering their representations. The study examines two challenging applications: constructing comprehensive supply chains for mid-20th century Japan through linking firm level financial records - with each firm name represented by its crop in the document image and the corresponding OCR - and detecting which image-caption pairs in a massive corpus of historical U.S. newspapers came from the same underlying photo wire source. CLIPPINGS outperforms widely used string matching methods by a wide margin and also outperforms unimodal methods. Moreover, a purely self-supervised model trained on only image-OCR pairs also outperforms popular string-matching methods without requiring any labels

    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic

    The Individual And Their World

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    Constructing a profile for proactive career self-management in public higher education institutions in Ghana

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    Text in English with abstracts and keywords in English, isiXhosa, and isiZuluThe principal focus of this research study was to investigate the relationship among psychosocial career pre-occupation, social connectedness, and organisational commitment, and to determine whether an overall proactive career management profile can be constructed to inform career self-management practices in public higher education institutions (HEIs) in Ghana. Again, the research study intended to provide a background for testing hypotheses and theories as well as moderating the effect of socio-demographic characteristics (age, gender, marital status and job level) on the relationship between psychosocial career pre-occupations, social connectedness and organisational commitment. A cross-sectional quantitative approach was conducted on a purposively selected sample of senior staff comprising academic and administrative staff (n = 288), from a single public higher educational institution in Ghana. The measuring instruments include a career pre-occupations inventory workplace friendship scale and an organisational commitment scale. Descriptive statistics (mean, standard deviation, skewness and kurtosis), bi-variate correlation analysis (Pearson product moment correlation coefficient), and inferential and multivariate statistics (SEM analysis, moderated regression analysis, ANOVAs and independent sample t-test) were used for the study. Descriptive, bi-variate correlation and inferential statistics revealed that individual psychosocial career pre-occupations, social connectedness and organisational commitment can be used as elements within a proactive career self-management framework within the Ghanaian higher education institutions. The results of the moderated analysis showed that respondents’ gender and job level moderated employees’ level of psychosocial career pre-occupations in predicting organisational commitment. Furthermore, the test for significant mean differences revealed that gender, marital status and job level differed marginally in their psychosocial career pre-occupations, social connectedness and commitment to the organisation. Theoretically and empirically, the results advanced the career construction theory by empirically validating the core elements of career self-management. Practically, a recommendation was made to inform human resource (HR) managers and HR practitioners in public higher education institutions in Ghana and the consequences indicated in the study offer the opportunity to monitor and provide strategies and interventions for employees in their quest for career choices.Ugqaliselo oluphambili kolu phononongo lophando yayikukuphanda ubudlelwane phakathi komsebenzi wangaphambi kwekhondo ngokwesimo sengqondo nangokwezentlalo (psychosocial career pre-occupation), ukuziphatha, nokunxulumana kwezinto zokuziphatha nezentlalo (social connectedness), kunye nokuzibophelela kulungelelaniso /kucwangciso lombutho (organisational commitment), nokuqonda ukuba ingaba iprofayili yeendlela zokulawula ikhondo elisebenzayo elipheleleyo inokwakhelwa ukwazisa izenzo zekhondo lomsebenzi lokuzilawula kumaziko emfundo ephakamileyo karhulumente/oluntu (HEIs) eGhana. Kwakhona , uphononongo lophando lwaluzimisele ukunikezela ngomhlaba osisiseko wokuvavanya ingcinga ethathwa njengeyinyaniso engekaqinisekiswa (hypothesis) neethiyori kwakunye nokumodereyitha impembelelo yeempawu zedemografi yoluntu, i-socio-demographic characteristics (iminyaka, isini, imeko yomtshato,kunye nenqanaba lomsebenzi) kubudlelwane phakathi kwemisebenzi yangaphambi kwekhondo nemeko yezengqondo nabantu (psychosocial career pre-occupations), unxulumano lwabantu kunye nokuzibophelela kumbutho (organizational commitment). Inkqubo yophando ngokobungakanani enqamlezileyo ngokwamacandelo ahlukeneyo (cross sectional quantitative approach) yenziwe kwisampulu ekhethwe ngenjongo kujoliswe kubasebenzi abaphezulu/abadala ababandakanya izifundiswa nabasebenzi bezolawulo (n = 288), besuka kwiziko elinye loluntu lemfundo ephakamileyo eGhana. Izixhobo zokulinganisa zibandakanya uluhlu lwezinto kwisikali semisebenzi yangaphambi kwekhondo ubudlelwane kunye nokuzibophelela kumbutho kwindawo yokusebenzela. Iinkcukacha-manani ezichazayo (i-avareji, ukutenxa kumgangatho (standard deviation), ubugoso (skewness) kunye nomlinganiselo weenkcukacha-manani osetyenziselwa ukuchaza ukuhanjiswa kwedatha ephawulweyo malunga nentsingiselo (kurtosis), uhlalutyo lolungelelwaniso oluphindwe kabini kunye neenkcukacha-manani ezinokuthelelelekwa (i- Pearson product moment correlation coefficient), kunye neenkcukacha manani ezizintlobo-ntlobo ezininzi ezinokuthelekelelwa (uhlalutyo lwe-SEM, uhlalutyo oluhlehlayo olonganyelweyo/ olumodareyithiweyo, i-ANOVA kunye novavanyo oluzimeleyo lwe-t-test) zasetyenziswa kolu phando/phononongo. Inkcukacha-manani ezichazayo, ulungelelwaniso/unxulumaniso oluphindwe kabini kunye nezinokuthelekelelwa ezinentsingiselo zibonise ukuba imisebenzi yangaphambi kwekhondo,ngokwemeko yengqondo neyentlalo, unxulumano lwentlalo yoluntu kunye nokuzibophelela kumbutho kunokusetyenziswa njengezinto ezingaphakathi kwesakhelo solawulo esisebenzayo ngaphakathi kumaziko emfundo ephakamileyo yaseGhana.Iziphumo zohlalutyo olumodareyithiweyo zibonise ukuba isini sabaphenduli nenqanaba lomsebenzi samodareyitha inqanaba labasebenzi kwimisebenzi yangaphambi kwekhondo kwimeko yengqondo nezentlolontle ekuqikeleleni ukuzibophelela kumbutho. Ngaphaya koko, uvavanyo olubalulekileyo lweeyantlukwano lubonise ukuba isini, meko yomtshato kunye nenqanaba lomsebenzi lahlukile kancinci ngokwe-avareji kwimisebenzi yaphambi kwekhondo labo yengqondo nentlalontle, unxibelelwano lwentlalo kunye nokuzinikela embuthweni. Ngokwethiyori nangobungqina, iziphumo ziqhubele phambili ithiyori yolwakhiwo lwekhondo lomsebenzi ngokuqinisekisa ngobuchule izinto ezingundoqo zokuzilawula kwekhondo lomsebenzi.Ngokwenene, kwenziwa isindululo sokwazisa abaphathi bezabasebenzi neengcali ze-HR kumaziko emfundo ephakamileyo yoluntu eGhana kwaye iziphumo ezibonakaliswe kuphononongo zinika ithuba lokubeka esweni nokubonelela ngezicwangciso-qhinga nongenelelo ngoncedo kubaqeshwa kwiphulo labo lokukhetha amakhondo omsebenzi.Lesi sifundo socwaningo sasigxile kakhulu ekuphenyeni ngobudlelwano phakathi kokucabanga ngomkhakha womsebenzi komuntu, ukuxhumana nabantu, ukuzibophezela komuntu emsebenzini, kanye nokunquma ukuthi iphrofayili yokukwazi ukuzilawula ngokuzenzela ekukhetheni umkhakha womsebenzi kungahlelwa yini ngendlela yokuthi kudlale indima ekuzilawuleleni maqondana nomkhakha womsebenzi ezikhungweni zezemfundo ephakeme zomphakathi (ama-HEI) eGhana. Kanti futhi, isifundo socwaningo sihlose ukunikeza umlando maqondana nokuhlola imicabango kanye nemibono yezinjulalwazi kanjalo nokulinganisa umphumela wezici zokuhleleka kwabantu emphakathini (iminyaka yobudala, ubulili, isimo somshado kanye nezinga lomsebenzi) ebudlelwaneni phakathi kokucabanga ngomkhakha womsebenzi komuntu, ukuxhumana nabantu, kanye nokuzibophezela emsebenzini. Kwalandelwa indlela yokubheka isibalo samaqoqo abantu emphakathini ngokukhetha ngokuqaphela isampula labasebenzi abasezikhundleni eziphezulu elibandakanya abasebenza ngezemfundo kanye nezokuphatha (n = 288), esikhungweni sezemfundo ephakeme somphakathi esisodwa eGhana. Amathuluzi okukala afaka kuwo isilinganiso sobungane basemsebenzini ekukhetheni ezinhlobeni zemikhakha ecatshangwayo kanye nesilinganiso maqondana nokuzibophezela kwabantu emsebenzini. Kulolu cwaningo kwasetshenziswa izibalo ezichazayo (okujwayelekile, ukuphambuka okuvamile, ukungalingani kanye nobukhali), ukuhlaziywa kokuhlobana okukhona phakathi kwezinombolo ezimbili (isilinganiso sokuxhumana kwezinombolo ezimbili ngokukaPearson), kanye nezibalo eziveza izimbangela nezibalo ezahlukahlukene (ukuhlaziywa kwe-SEM, ukuhlaziywa kokubuyelamuva okulingene, ama-ANOVA kanye nesampula elizimele lokuhlola). Izibalo ezichazayo, eziveza ukuhlobana phakathi kwezinombolo ezimbili kanye nezibalo eziveza izimbangela ziveze ukuthi ukucabanga ngomkhakha womsebenzi komuntu ngamunye, ukuxhumana nabantu nokuzibophezela emsebenzini kungasetshenziswa njengezinto ezikhona ohlakeni lokukwazi ukuzilawula ngokuzenzela ekukhetheni umkhakha ezikhungweni zezemfundo ephakeme zaseGhana. Imiphumela yokuhlaziywa okulingene yakhombisa ukuthi ubulili bababambiqhaza kanye nezinga labo ngokwezikhundla zomsebenzi kuhambisana nezinga abasebenzi abacabanga ngalo nomkhakha womsebenzi ekuqageleni indlela abazozibophezela ngayo emsebenzini. Ngaphezu kwalokho, ukuhlolwa komehluko omkhulu ojwayelekile kwaveza ukuthi ubulili, isimo somshado kanye nezinga ngokwesikhundla somsebenzi kwahluka kakhulu nendlela abacabanga ngayo ngomkhakha womsebenzi, ukuxhumana nabantu nokuzibophezela emsebenzini. Ngokombono wenjulalwazi nocwaningo olufakazelwe, imiphumela ithuthukise umbono wenjulalwazi wokuhlela umkhakha womsebenzi okuqinisekiswe ngokocwaningo olufakazelwe izinto ezinqala zokuzilawulela umkhakha womsebenzi. Ekusebenzeni, kwenziwa izincomo zokufundisa abaphathi babasebenzi (ba-HR) kanye nabasebenza ngezabasebenzi (ngezakwa-HR) ezikhungweni zezemfundo ephakeme zomphakathi eGhana, kanti nemiphumela evezwe ocwaningweni inikeza ithuba lokuqapha nokuhlinzeka amasu kanye nokungenelela kwabasebenzi ekwenzeni kwabo izinqumo zomsebenzi.Business ManagementPh. D. (Business Management

    Less is More: Restricted Representations for Better Interpretability and Generalizability

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    Deep neural networks are prevalent in supervised learning for large amounts of tasks such as image classification, machine translation and even scientific discovery. Their success is often at the sacrifice of interpretability and generalizability. The increasing complexity of models and involvement of the pre-training process make the inexplicability more imminent. The outstanding performance when labeled data are abundant while prone to overfit when labeled data are limited demonstrates the difficulty of deep neural networks' generalizability to different datasets. This thesis aims to improve interpretability and generalizability by restricting representations. We choose to approach interpretability by focusing on attribution analysis to understand which features contribute to prediction on BERT, and to approach generalizability by focusing on effective methods in a low-data regime. We consider two strategies of restricting representations: (1) adding bottleneck, and (2) introducing compression. Given input x, suppose we want to learn y with the latent representation z (i.e. x→z→y), adding bottleneck means adding function R such that L(R(z)) < L(z) and introducing compression means adding function R so that L(R(y)) < L(y) where L refers to the number of bits. In other words, the restriction is added either in the middle of the pipeline or at the end of it. We first introduce how adding information bottleneck can help attribution analysis and apply it to investigate BERT's behavior on text classification in Chapter 3. We then extend this attribution method to analyze passage reranking in Chapter 4, where we conduct a detailed analysis to understand cross-layer and cross-passage behavior. Adding bottleneck can not only provide insight to understand deep neural networks but can also be used to increase generalizability. In Chapter 5, we demonstrate the equivalence between adding bottleneck and doing neural compression. We then leverage this finding with a framework called Non-Parametric learning by Compression with Latent Variables (NPC-LV), and show how optimizing neural compressors can be used in the non-parametric image classification with few labeled data. To further investigate how compression alone helps non-parametric learning without latent variables (NPC), we carry out experiments with a universal compressor gzip on text classification in Chapter 6. In Chapter 7, we elucidate methods of adopting the perspective of doing compression but without the actual process of compression using T5. Using experimental results in passage reranking, we show that our method is highly effective in a low-data regime when only one thousand query-passage pairs are available. In addition to the weakly supervised scenario, we also extend our method to large language models like GPT under almost no supervision --- in one-shot and zero-shot settings. The experiments show that without extra parameters or in-context learning, GPT can be used for semantic similarity, text classification, and text ranking and outperform strong baselines, which is presented in Chapter 8. The thesis proposes to tackle two big challenges in machine learning --- "interpretability" and "generalizability" through restricting representation. We provide both theoretical derivation and empirical results to show the effectiveness of using information-theoretic approaches. We not only design new algorithms but also provide numerous insights on why and how "compression" is so important in understanding deep neural networks and improving generalizability

    Video Summarization Using Unsupervised Deep Learning

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    In this thesis, we address the task of video summarization using unsupervised deep-learning architectures. Video summarization aims to generate a short summary by selecting the most informative and important frames (key-frames) or fragments (key-fragments) of the full-length video, and presenting them in temporally-ordered fashion. Our objective is to overcome observed weaknesses of existing video summarization approaches that utilize RNNs for modeling the temporal dependence of frames, related to: i) the small influence of the estimated frame-level importance scores in the created video summary, ii) the insufficiency of RNNs to model long-range frames' dependence, and iii) the small amount of parallelizable operations during the training of RNNs. To address the first weakness, we propose a new unsupervised network architecture, called AC-SUM-GAN, which formulates the selection of important video fragments as a sequence generation task and learns this task by embedding an Actor-Critic model in a Generative Adversarial Network. The feedback of a trainable Discriminator is used as a reward by the Actor-Critic model in order to explore a space of actions and learn a value function (Critic) and a policy (Actor) for video fragment selection. To tackle the remaining weaknesses, we investigate the use of attention mechanisms for video summarization and propose a new supervised network architecture, called PGL-SUM, that combines global and local multi-head attention mechanisms which take into account the temporal position of the video frames, in order to discover different modelings of the frames' dependencies at different levels of granularity. Based on the acquired experience, we then propose a new unsupervised network architecture, called CA-SUM, which estimates the frames' importance using a novel concentrated attention mechanism that focuses on non-overlapping blocks in the main diagonal of the attention matrix and takes into account the attentive uniqueness and diversity of the associated frames of the video. All the proposed architectures have been extensively evaluated on the most commonly-used benchmark datasets, demonstrating their competitiveness against other approaches and documenting the contribution of our proposals on advancing the current state-of-the-art on video summarization. Finally, we make a first attempt on producing explanations for the video summarization results. Inspired by relevant works in the Natural Language Processing domain, we propose an attention-based method for explainable video summarization and we evaluate the performance of various explanation signals using our CA-SUM architecture and two benchmark datasets for video summarization. The experimental results indicate the advanced performance of explanation signals formed using the inherent attention weights, and demonstrate the ability of the proposed method to explain the video summarization results using clues about the focus of the attention mechanism

    Evaluating automated and hybrid neural disambiguation for African historical named entities

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    Documents detailing South African history contain ambiguous names. Ambiguous names may be due to people having the same name or the same person being referred to by multiple different names. Thus when searching for or attempting to extract information about a particular person, the name used may affect the results. This problem may be alleviated by using a Named Entity Disambiguation (NED) system to disambiguate names by linking them to a knowledge base. In recent years, transformer-based language models have led to improvements in NED systems. Furthermore, multilingual language models have shown the ability to learn concepts across languages, reducing the amount of training data required in low-resource languages. Thus a multilingual language model-based NED system was developed to disambiguate people's names within a historical South African context using documents written in English and isiZulu from the 500 Year Archive (FHYA). The multilingual language model-based system substantially improved on a probability-based baseline and achieved a micro F1-score of 0.726. At the same time, the entity linking component was able to link 81.9% of the mentions to the correct entity. However, the system's performance on documents written in isiZulu was significantly lower than on the documents written in English. Thus the system was augmented with handcrafted rules to improve its performance. The addition of handcrafted rules resulted in a small but significant improvement in performance when compared to the unaugmented NED system

    Second-Person Surveillance: Politics of User Implication in Digital Documentaries

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    This dissertation analyzes digital documentaries that utilize second-person address and roleplay to make users feel implicated in contemporary refugee crises, mass incarceration in the U.S., and state and corporate surveillances. Digital documentaries are seemingly more interactive and participatory than linear film and video documentary as they are comprised of a variety of auditory, visual, and written media, utilize networked technologies, and turn the documentary audience into a documentary user. I draw on scholarship from documentary, game, new media, and surveillance studies to analyze how second-person address in digital documentaries is configured through user positioning and direct address within the works themselves, in how organizations and creators frame their productions, and in how users and players respond in reviews, discussion forums, and Let’s Plays. I build on Michael Rothberg’s theorization of the implicated subject to explore how these digital documentaries bring the user into complicated relationality with national and international crises. Visually and experientially implying that users bear responsibility to the subjects and subject matter, these works can, on the one hand, replicate modes of liberal empathy for suffering, distant “others” and, on the other, simulate one’s own surveillant modes of observation or behavior to mirror it back to users and open up one’s offline thoughts and actions as a site of critique. This dissertation charts how second-person address shapes and limits the political potentialities of documentary projects and connects them to a lineage of direct address from educational and propaganda films, museum exhibits, and serious games. By centralizing the user’s individual experience, the interventions that second-person digital documentaries can make into social discourse change from public, institution-based education to more privatized forms of sentimental education geared toward personal edification and self-realization. Unless tied to larger initiatives or movements, I argue that digital documentaries reaffirm a neoliberal politics of individual self-regulation and governance instead of public education or collective, social intervention. Chapter one focuses on 360-degree virtual reality (VR) documentaries that utilize the feeling of presence to position users as if among refugees and as witnesses to refugee experiences in camps outside of Europe and various dwellings in European cities. My analysis of Clouds Over Sidra (Gabo Arora and Chris Milk 2015) and The Displaced (Imraan Ismail and Ben C. Solomon 2015) shows how these VR documentaries utilize observational realism to make believable and immersive their representations of already empathetic refugees. The empathetic refugee is often young, vulnerable, depoliticized and dehistoricized and is a well-known trope in other forms of humanitarian media that continues into VR documentaries. Forced to Flee (Zahra Rasool 2017), I am Rohingya (Zahra Rasool 2017), So Leben Flüchtlinge in Berlin (Berliner Morgenpost 2017), and Limbo: A Virtual Experience of Waiting for Asylum (Shehani Fernando 2017) disrupt easy immersions into realistic-looking VR experiences of stereotyped representations and user identifications and, instead, can reflect back the user’s political inaction and surveillant modes of looking. Chapter two analyzes web- and social media messenger-based documentaries that position users as outsiders to U.S. mass incarceration. Users are noir-style co-investigators into the crime of the prison-industrial complex in Fremont County, Colorado in Prison Valley: The Prison Industry (David Dufresne and Philippe Brault 2009) and co-riders on a bus transporting prison inmates’ loved ones for visitations to correctional facilities in Upstate New York in A Temporary Contact (Nirit Peled and Sara Kolster 2017). Both projects construct an experience of carceral constraint for users to reinscribe seeming “outside” places, people, and experiences as within the continuation of the racialized and classed politics of state control through mass incarceration. These projects utilize interfaces that create a tension between replicating an exploitative hierarchy between non-incarcerated users and those subject to mass incarceration while also de-immersing users in these experiences to mirror back the user’s supposed distance from this mode of state regulation. Chapter three investigates a type of digital game I term dataveillance simulation games, which position users as surveillance agents in ambiguously dystopian nation-states and force users to use their own critical thinking and judgment to construct the criminality of state-sanctioned surveillance targets. Project Perfect Citizen (Bad Cop Studios 2016), Orwell: Keeping an Eye on You (Osmotic Studios 2016), and Papers, Please (Lucas Pope 2013) all create a dual empathy: players empathize with bureaucratic surveillance agents while empathizing with surveillance targets whose emails, text messages, documents, and social media profiles reveal them to be “normal” people. I argue that while these games show criminality to be a construct, they also utilize a racialized fear of the loss of one’s individual privacy to make players feel like they too could be surveillance targets. Chapter four examines personalized digital documentaries that turn users and their data into the subject matter. Do Not Track (Brett Gaylor 2015), A Week with Wanda (Joe Derry Hall 2019), Stealing Ur Feelings (Noah Levenson 2019), Alfred Premium (Joël Ronez, Pierre Corbinais, and Émilie F. Grenier 2019), How They Watch You (Nick Briz 2021), and Fairly Intelligent™ (A.M. Darke 2021) track, monitor, and confront users with their own online behavior to reflect back a corporate surveillance that collects, analyzes, and exploits user data for profit. These digital documentaries utilize emotional fear- and humor-based appeals to persuade users that these technologies are controlling them, shaping their desires and needs, and dehumanizing them through algorithmic surveillance
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