7,774 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Exploring cognitive mechanisms involved in self-face recognition
Due to the own face being a significant stimulus that is critical to one’s identity, the own face is suggested to be processed in a quantitatively different (i.e., faster and better recognition) and qualitatively different (i.e., processed in a more featural manner) manner compared to other faces. This thesis further explored the cognitive mechanisms (perceptual and attentional systems) involved in the processing of the own face.
Chapter 2 explored the role of holistic and featural processing involved in the processing of self-face (and other faces) with eye-tracking measures in a passive-viewing paradigm and a face identification task. In the passive-viewing paradigm, the own face was sampled in a more featural manner compared to other faces whereas when asked to identify faces, all faces were sampled in a more holistic manner. Chapter 3 further explored the role of holistic and featural processing in the identification of the own face using the three standard measures of holistic face processing: The face inversion task, the composite face task, and the part-whole task. Compared to other faces, individuals showed a smaller “holistic interference” by a task irrelevant bottom half for the own face in the composite face task and a stronger feature advantage for the own face, but inversion impaired the identification of all faces. These findings suggest that self-face is processed in a more featural manner, but the findings do not deny the role of holistic processing.
The final experimental chapter, Chapter 4, explored the modulation effects of cultural differences in one’s self-concept (i.e., independent vs. interdependent self-concept) and a negative self-concept (i.e., depressive traits) on the attentional prioritization for the own face with a visual search paradigm. Findings showed that the attentional prioritization for the own face over an unfamiliar face is not modulated by cultural differences of one’s self-concept nor one’s level of depressive traits, and individuals showed no difference in the attentional prioritization for both the own face and friend’s face, demonstrating no processing advantage for the own face over a personally familiar face. These findings suggests that the attentional prioritization for the own face is better explained by a familiar face advantage.
Altogether, the findings of this thesis suggest that the own face is processed qualitatively different compared to both personally familiar and unfamiliar face, with the own face being processed in a more featural manner. However, in terms of quantitative differences, the self-face is processed differently compared to an unfamiliar face, but not to a familiar face. Although the specific face processing strategies for the own face may be due to the distinct visual experience that one has with their face, the attentional prioritization of the own face is however, better explained by a familiar face advantage rather than a self-specificity effect
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
Component Segmentation of Engineering Drawings Using Graph Convolutional Networks
We present a data-driven framework to automate the vectorization and machine
interpretation of 2D engineering part drawings. In industrial settings, most
manufacturing engineers still rely on manual reads to identify the topological
and manufacturing requirements from drawings submitted by designers. The
interpretation process is laborious and time-consuming, which severely inhibits
the efficiency of part quotation and manufacturing tasks. While recent advances
in image-based computer vision methods have demonstrated great potential in
interpreting natural images through semantic segmentation approaches, the
application of such methods in parsing engineering technical drawings into
semantically accurate components remains a significant challenge. The severe
pixel sparsity in engineering drawings also restricts the effective
featurization of image-based data-driven methods. To overcome these challenges,
we propose a deep learning based framework that predicts the semantic type of
each vectorized component. Taking a raster image as input, we vectorize all
components through thinning, stroke tracing, and cubic bezier fitting. Then a
graph of such components is generated based on the connectivity between the
components. Finally, a graph convolutional neural network is trained on this
graph data to identify the semantic type of each component. We test our
framework in the context of semantic segmentation of text, dimension and,
contour components in engineering drawings. Results show that our method yields
the best performance compared to recent image, and graph-based segmentation
methods.Comment: Preprint accepted to Computers in Industr
Reframing museum epistemology for the information age: a discursive design approach to revealing complexity
This practice-based research inquiry examines the impact of an epistemic shift, brought about by the dawning of the information age and advances in networked communication technologies, on physical knowledge institutions - focusing on museums. The research charts adapting knowledge schemas used in museum knowledge organisation and discusses the potential for a new knowledge schema, the network, to establish a new epistemology for museums that reflects contemporary hyperlinked and networked knowledge. The research investigates the potential for networked and shared virtual reality spaces to reveal new ‘knowledge monuments’ reflecting the epistemic values of the network society and the space of flows.
The central practice for this thesis focuses on two main elements. The first is applying networks and visual complexity to reveal multi-linearity and adapting perspectives in relational knowledge networks. This concept was explored through two discursive design projects, the Museum Collection Engine, which uses data visualisation, cloud data, and image recognition within an immersive projection dome to create a dynamic and searchable museum collection that returns new and interlinking constellations of museum objects and knowledge. The second discursive design project was Shared Pasts: Decoding Complexity, an AR app with a unique ‘anti-personalisation’ recommendation system designed to reveal complex narratives around historic objects and places. The second element is folksonomy and co-design in developing new community-focused archives using the community's language to build the dataset and socially tagged metadata. This was tested by developing two discursive prototypes, Women Reclaiming AI and Sanctuary Stories
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure
PERSONALIZED POINT OF INTEREST RECOMMENDATIONS WITH PRIVACY-PRESERVING TECHNIQUES
Location-based services (LBS) have become increasingly popular, with millions of people using mobile devices to access information about nearby points of interest (POIs). Personalized POI recommender systems have been developed to assist users in discovering and navigating these POIs. However, these systems typically require large amounts of user data, including location history and preferences, to provide personalized recommendations.
The collection and use of such data can pose significant privacy concerns. This dissertation proposes a privacy-preserving approach to POI recommendations that address these privacy concerns. The proposed approach uses clustering, tabular generative adversarial networks, and differential privacy to generate synthetic user data, allowing for personalized recommendations without revealing individual user data. Specifically, the approach clusters users based on their fuzzy locations, generates synthetic user data using a tabular generative adversarial network and perturbs user data with differential privacy before it is used for recommendation.
The proposed approaches achieve well-balanced trade-offs between accuracy and privacy preservation and can be applied to different recommender systems. The approach is evaluated through extensive experiments on real-world POI datasets, demonstrating that it is effective in providing personalized recommendations while preserving user privacy. The results show that the proposed approach achieves comparable accuracy to traditional POI recommender systems that do not consider privacy while providing significant privacy guarantees for users.
The research\u27s contribution is twofold: it compares different methods for synthesizing user data specifically for POI recommender systems and offers a general privacy-preserving framework for different recommender systems. The proposed approach provides a novel solution to the privacy concerns of POI recommender systems, contributes to the development of more trustworthy and user-friendly LBS applications, and can enhance the trust of users in these systems
Anime Studies: media-specific approaches to neon genesis evangelion
Anime Studies: Media-Specific Approaches to Neon Genesis Evangelion aims at advancing the study of anime, understood as largely TV-based genre fiction rendered in cel, or cel-look, animation with a strong affinity to participatory cultures and media convergence. Making Neon Genesis Evangelion (Shin Seiki Evangerion, 1995-96) its central case and nodal point, this volumen forground anime as a media with clearly recognizable aesthetic properties, (sub)cultural affordances and situated discourses
Crystallographic fragment screening - improvement of workflow, tools and procedures, and application for the development of enzyme and protein-protein interaction modulators
One of the great societal challenges of today is the fight against diseases which reduce
life expectancy and lead to high economic losses. Both the understanding and the
addressing of these diseases need research activities at all levels. One aspect of this is
the discovery and development of tool compounds and drugs. Tool compounds support
disease research and the development of drugs. For about 20 years, the discovery of new
compounds has been attempted by screening small organic molecules by high-throughput
methods. More recently, X-ray crystallography has emerged as the most promising method
to conduct such screening. Crystallographic fragment-screening (CFS) generates binding
information as well as 3D-structural information of the target protein in complex with the
bound fragment. This doctoral research project is focused primarily on the optimization of
the crystallographic fragment screening workflow. Investigated were the requirements for
more successful screening campaigns with respect to the crystal system studied, the
fragment libraries, the handling of the crystalline samples, as well as the handling of the
data associated with a screening campaign. The improved CFS workflow was presented
as a detailed protocol and as an accompanying video to train future CFS users in a
streamlined and accessible way. Together, these improvements make CFS campaigns a
more high-throughput method, offering the ability to screen larger fragment libraries and
allowing higher numbers of campaigns performed per year. The protein targets throughout
the project were two enzymes and a spliceosomal protein-protein complex. The enzymes
comprised the aspartic protease Endothiapepsin and the SARS-Cov-2 main protease. The
protein-protein complex was the RNaseH-like domain of Prp8, a vital structural protein in
the spliceosome, together with its nuclear shuttling factor Aar2. By performing the CFS
campaigns against disease-relevant targets, the resulting fragment hits could be used
directly to develop tool compounds or drugs. The first steps of optimization of fragment
hits into higher affinity binders were also investigated for improvements. In summary, a
plethora of novel starting points for tool compound and drug development was identified
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