Archipelagos of Narratives: (Re)constructing National and Diasporic Identities in Filipino Art, From Martial Law to the Present

As an archipelagic nation numbering over 7,000 islands, the Philippines is home to a vast number of cultures and peoples. This diversity means that one’s identity hinged on region, language, or locale, despite the kinship these peoples have historically shared. Spanish and American colonization have only interfered in the Philippines’ heterogenous cultural landscape, which has resulted in a Westernized sense of self fractured from its precolonial and indigenous roots. The Philippines’ exposure to the global market, with foreign powers entering and overseas labourers departing, has rendered it a hotbed of cultural influences that also invites conflict and further dispersion of the country’s sense of identity. As a nation whose cultural identity has been hegemonized by Western society, Filipinx abroad and island-side struggle to piece together elements of what it means to be ‘Filipino’. Through personal, social, and cultural excavation, Filipinx artists articulate national and diasporic qualities of Filipino identity that subvert the essentialist, colonial narratives historically imposed upon by Western powers. Anchored between martial law (1970s) and the present, I cite the works of Roberto Villanueva, Cian Dayrit, and Club Ate, who use their relationships to place in their work to critique grand narratives to unearth the stories, mythologies, and cultural practices eclipsed by history

Unraveling the Socioeconomic Layers Leading to Materialism: Exploring the Mediating Role of Happiness and Moderating Influence of Gratitude on Materialism

In a world where consumption is becoming increasingly accessible with online retailers, where social media influencers equate material possessions to happiness, convenience, or success, and where shopping has been proven to boost our mood, it is no wonder that consumers are struggling to control their buying behaviour. This research examines the impact of social classes on materialism, focusing on the mediating role of happiness and the moderating role of gratitude. An experimental research design was implemented, and two online surveys were utilized to collect data. In study 1, an unexpected, though interesting, effect of perceived social class on materialism was found. It was also found, as predicted, an effect of perceived social class on happiness, which in turn, predicted materialistic values; however, the mediating effect of happiness was not found to be significant. In study 2, as predicted, an effect between perceived happiness and gratitude was found. Additionally, gratitude had an effect on materialism. However, the moderating effect of gratitude was not found to be significant. The findings of this study will provide valuable insights into how different social classes impact happiness levels, how happiness plays a role in materialism, and how practicing gratitude can influence your materialism levels. This study also offers practical implications for marketers to effectively target and position marketing messages, and for consumers to make more thoughtful purchase decisions that may enhance their well-being, as well as society’s

The Last Knight's End

What if Ancient American civilizations developed into an advanced technological stage before European ones? What if these advanced civilizations then sailed across the ocean and discovered new lands ruled by monarchs and protected by knights? And what if they invaded these new-found lands to extract crucial resources for their survival, slaughtering anyone who stood in their way, including every single knight except one? Set in a fictional continent based on medieval Europe, fifteen years after the invasion of a modern Ancient-American-like nation, this novel follows Angmar, a seventeen-year-old hunter who’s obsessed with tales about knights and their adventures to slay monsters and save those in need—stories within the story based on classic medieval-fantasy epics such as Beowulf, Arthurian legends, and Tolkien’s novels. His father having died during the invasion, Angmar was adopted by Henry, an old hunter and war hero who takes care of the town’s orphans. Once Angmar learns that the outlaw known as the gun-breaker, who has the highest bounty ever recorded and is rumoured to be the last knight, was sighted near his town, he becomes determined to go after the terrible bounty

Ideological Narratives in Contemporary Russian War Genre Cinema

This thesis titled “Ideological Narratives in Contemporary Russian War Genre Cinema” explores popular commercial films about the Second World War produced between 2018 and 2022. Through analyzing popular culture and media this study aims to build a framework of contemporary Russian war genre cinema as a tool for the promotion of Vladimir Putin’s government’s ideological rhetoric. This thesis will focus on three case studies: war genre films T-34 [T-34] (2019, dir. Alekseĭ Sidorov), Zoya [Zoi͡a] (2020, dir. Leonid Pli͡askin and Maksim Brius), and To Paris! [Na Parizh!] (2019, dir. Sergeĭ Sarkisov). Each case study will be put in conversation with other media, such as music, literature, and television, as well as genres, such as comedy, melodrama, and documentary. Building on the works of Denise J. Youngblood and Nancy Condee, I will explore how these films sponsored by the Russian government through organizations such as the Russian Military and Historical Society, Culture Fund, and state-owned TV channels represent the current imperial and nationalist ideology in the country that has contributed to the military invasion of Ukraine in 2022. The feeling of patriotism manifested through continuous reproduction of the Second World War military history and glorified representation of the Russian characters is capitalized upon in this political context. The goal of this research is to demonstrate how these films restructure the memory of the Second World War into a commercial enterprise that actively contributes to the construction of the new Russian identity and rapid militarization of Russian society prior to and after the invasion of Ukraine

Deep learning-based brain ventricle segmentation in Computed Tomography using domain adaptation

Accurate segmentation of brain ventricles from CT scans is crucial for clinical procedures such as ventriculostomy, which involves draining excess fluid from the ventricles to control intracranial pressure. Ventriculostomy is often performed in acute settings, making CT imaging the most commonly available modality. Unlike MRI, there is a lack of publicly available, well-annotated databases for developing CT-based brain segmentation algorithms. Furthermore, there is a need for intuitive confidence measures for segmentation results produced by automated algorithms, such as deep learning methods, which can potentially improve the confidence and accuracy of clinical tasks. To address these needs, we propose an end-to-end uncertainty-aware domain adaptation technique for CT ventricle segmentation. This technique is based on the joint training of translation models and anatomical segmentation, leveraging unpaired MRI and CT scans without segmentation ground truths. For the translation task, we experimented with three different generative models: Cycle-Consistent Adversarial Networks (CycleGAN), Contrastive Learning for Unpaired Image-to-Image Translation (CUT) from GANs, and the Unpaired Neural Schrödinger Bridge (UNSB) from diffusion models, and compared their results. For the segmentation phase, we employed an attention-based residual recurrent U-Net architecture to compare with U-Net and ResNet. Also, considering CycleGAN's challenges with stability and structural consistency, we assessed various methods to understand their impact on translation and segmentation during our end-to-end training process. Additionally, we incorporated Monte Carlo dropouts in both MRI-to-CT translation and CT segmentation to provide an intuitive interpretation of the segmentation results

Adaptive Priors in Probabilistic Topic Models for Bursty Discovery in Textual Data

In the field of natural language processing, topic modeling plays an important role in detecting latent topics in large amounts of text. Models that use traditional methods of representation, however, often fail to capture the 'burstiness' characteristic of natural language - the tendency for previously occurring words to recur within the same document. In order to address this limitation, we introduce two innovative topic modeling frameworks: the Generalized Dirichlet Compound Multinomial Latent Dirichlet Allocation (GDCMLDA) and the Beta-Liouville Dirichlet Compound Multinomial Latent Dirichlet Allocation (BLDCMLDA). Using Dirichlet Compound Multinomial distribution together with Generalized Dirichlet and Beta-Liouville distributions, both frameworks integrate advanced distribution methods. By integrating these concepts, it is possible to model the burstiness phenomenon while maintaining a variety of topic proportion patterns that can be varied and flexible. As a result of our comprehensive evaluations across multiple benchmark text datasets, we conclude that GDCMLDA and BLDCMLDA are superior to existing models. The evidence for this is found in the improved performance metrics, including the scores for perplexity and coherence. Our results confirm that the proposed models are able to capture the complexities of word usage dynamics, thus contributing to a significant advancement in topic modeling

Multi-Valued Model Checking IoT and Intelligent Systems with Trust and Commitment Protocols

Abstract Multi-Valued Model Checking IoT and Intelligent Systems with Trust and Commitment Protocols Ghalya Alwhishi, Ph.D. Concordia University, 2024 In the era of connectivity, numerous domains utilize multi-sensor Internet of Things (IoT) and Intelligent Systems (IS) applications, which involve complex interactions among numerous components in open environments. This complexity challenges the verification of these systems’ reliability and efficiency. This study pioneers the verification of IoT applications and intelligent systems within multi-source data environments, employing multi-agent commitment and trust protocols, particularly in uncertain and inconsistent settings. Our research introduces efficient frameworks to model and verify these systems, incorporating commitment and trust protocols in settings characterized by uncertainty and inconsistency. We extend existing logics of commitment CTLcc and CTLc and the logic of trust TCTL to multi-valued cases for reasoning about uncertainty and inconsistency. We introduce 3v-CTLc and 3v-CTLcc, three-valued logics of commitment for reasoning about uncertainty, and 4v-CTLc and 4v-CTLcc, four-valued logics of commitment for reasoning about inconsistency. In the context of trust, we introduce 3v-TCTL and 4v-TCTL, multivalued logics for reasoning about uncertainty and inconsistency over systems with trust protocols. To address the complexity and time needed for developing direct algorithms, coupled with the scarcity of multi-valued model checking tools, we developed reduction algorithms. These algorithms transform the introduced multi-valued logics of commitment and trust into their classical case or into CTL, facilitating interaction with efficient model checkers such as MCMAS+ and MCMASt, and NuSMV, respectively. To demonstrate the practicality and applicability of the tool in real settings, we presented and reported experimental results over multiple IoT and IS applications in healthcare, finance, and smart buildings. Our findings indicate that the proposed approaches and the MV-Checker tool are highly efficient and scalable, providing accurate results under varying conditions

Optimizing Multi-Item Lot-Sizing Problem: A Study on Aggregate Service Levels, Piecewise Linear Approximations, and Fix-and-Optimize Heuristics

In this research thesis, we address the intricate challenges presented by multi-item lot-sizing problems in production environments, considering stochastic demand, capacity constraints, and inventory limitations. Our objective is to formulate an optimized production schedule, drawing inspiration from existing literature models. We introduce two mathematical models for the lotsizing problem, incorporating aggregate service levels β and γ, and employ novel piece-wise linear approximations to address and extend existing formulations. Our research presents an iterative optimization-based solution approach for the piecewise linear approximation of the stochastic lotsizing problem. This process involves breaking down the overall planning horizon into smaller intervals, creating a more manageable planning horizon, and iteratively addressing a series of subproblems. Extensive computational experiments explore the implications of aggregate service levels, comparing the solution quality of the actual piecewise linear approximation model and the Fix-and-Optimize heuristic using four different interval lengths. Results highlight the nuanced relationship between interval lengths and computational efficiency, emphasizing the strategic importance of selecting intervals aligned with operational objectives. For instance, solving the piecewise linear approximation model for the β service level with a higher interval length (9) reduces computational time by 60% on average, with a corresponding average increase of 4.5% in the relative gap (cost). Similarly, for the γ service level, computational time decreases by 38% on average, with an average relative gap increase of 3.7%

Predicting User Performance for the Evaluation of User Interfaces in Immersive Augmented Reality

Augmented Reality (AR) is defined as the enhancement of the user's view with interactive, computer-generated content, and arises in several forms, including Mobile AR, Spatial AR, and notably, Immersive AR. Among these, immersive AR has piqued considerable attention from both academia and industry. It enables users to immerse themselves in virtual content registered to real-world objects through head-mounted displays (HMDs) and optics technology. Providing robust interaction with virtual objects has necessitated immersive headset designers and producers to develop a broad array of interaction techniques and input modalities, enabling users to experience both a natural interaction with virtual objects and a profound sense of presence. Well-known companies in this domain, such as Microsoft, Meta, Apple, and Magic Leap, have pioneered various interactions and input modalities such as hand gestures, head pointing, eye tracking, and voice commands. The current state of the art indicates that specific input modalities, like hand gestures and head pointing, can increase both the physical and mental workload on users, subsequently increasing the error margins in object selection and manipulation within immersive environments. In response, this research adeptly applies statistical and contemporary machine learning techniques to establish guidelines for immersive AR environment designers and developers, aiming to mitigate the effects of physical and mental workload on users, particularly during hierarchical menu selection tasks, a commonplace activity in various computer applications, including immersive AR. This thesis, embodied by four research papers, extends guidelines and recommendations for designers and developers of immersive AR headsets, endeavoring to alleviate workload and error rates induced by natural interactions such as hand gestures and head pointing while performing hierarchical menu selection. The initial research focused on identifying the most efficient combination of hierarchical menu types, like radial and drop-down menus, and input modalities, such as hand gestures and head pointing in terms of workload and performance measures. The subsequent study deployed a machine learning approach, leveraging semantic encoders and the standard cognitive performance test, WAIS-IV, to predict human performance in an immersive AR environment during hierarchical menu selection tasks. The third research introduces an analysis and index for error rate in hierarchical menu selection, utilizing both subjective and objective data derived from the users. The final research is an analysis of head pointing and hand gesture path data during menu selection tasks, elucidating an index and its relationship with both subjective and objective methods for calculating workload and error rate in immersive AR

Behavioural Consultation in Preschool Settings for Children with Behavioural Difficulties: Two Case Studies

Approximately 5%-20% of preschool children exhibit significant challenging behaviours within the classroom, stemming from underlying behavioural or emotional difficulties (Campbell, 1995; Charach et al., 2020). However, most early childhood educators do not feel supported or equipped to handle these challenging behaviours due to a lack of training and resources, low confidence, and a growing pressure to perform (Arnold et al., 2006; Moore et al., 2017). This qualitative research study investigated the impact of implementing Behavioural Consultation (Kratochwill & Bergan, 1990) in preschool classrooms to support educators who intervened with children with behaviour challenges. Collaborative meetings with educators over the course of 10 weeks were implemented to investigate changes in educators’ knowledge of children’s behavioural needs, educators’ knowledge of appropriate strategies, educators’ self-efficacy, and children’s display of challenging behaviours. Findings from pre- and post- intervention interviews, questionnaires, and child observations were analysed. Thematic coding revealed that preschool educators’ experiences with the consultation process partially improved their self-efficacy when they used more developmentally appropriate behaviour strategies that positively impacted the children’s behaviours. Educators’ use of positive behavioural strategies matched the children’s developmental and individual needs and served to improve the educators’ perceptions of the children. These findings have implications for designing professional development programs in preschool settings to support educators who intervene with children with behavioural difficulties