366 research outputs found
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
2023- The Twenty-seventh Annual Symposium of Student Scholars
The full program book from the Twenty-seventh Annual Symposium of Student Scholars, held on April 18-21, 2023. Includes abstracts from the presentations and posters.https://digitalcommons.kennesaw.edu/sssprograms/1027/thumbnail.jp
2023 SOARS Conference Program
Program for the 2023 Showcase of Osprey Advancements in Research and Scholarship (SOARS
Visual Preference, Sensitivity, Perceived Complexity and Similarity of Images Varying in Natural Scene Statistics
Introduction: Our visual system is optimised to process natural scenes, but it is still unclear which properties of natural scenes drive these adaptations. Natural scenes are characterised by distance-dependent regularities in their spatial structure such that nearby regions are more similar in their spatial properties, compared to more distal regions. These regularities have been linked to the notions of scale invariance and self-similarity, commonly indexed by the two different scaling techniques: the slope alpha of the Fourier amplitude spectrum (1/f^alpha) and the box-counting fractal dimension (D). The two measures capture either more photometric (amplitude) or more geometric (density) of contrast variations in an image. Aims: The current study aims to examine the role of photometric (spectral contrast amplitude) and geometric (density of spatial contrast variations) properties in visual preference, sensitivity, perceived complexity and similarity in synthetic noise images. We also examine the effects of prolonged exposure (visual adaptation) to natural scene statistics on preference, discrimination sensitivity and perceived complexity. Methods: Visual preference, sensitivity, perceived complexity and similarity were studied separately. The stimuli varied in their amplitude spectral slope (alpha = 0.25, 0.75, 1.25,1.75,2.25) and image type (Greyscale, GS; Threshold, TH; Edges, ED). Stimuli systematically varied in their photometric and geometric properties, thus allowing analyses of their relative contributions. We used a traditional visual adaptation paradigm where participants were exposed to an initial adaptation period (150s, 300s) followed by test trials (AFC, rating tasks) with top-up adaptation periods (5s, 10s). Results & Discussion: Visual sensitivity, preference, perceived complexity and similarity were strongly modulated by the variations in the amplitude spectra of synthetic noise images. The visual preference and perceived complexity were similar, if not identical for different image types, suggesting that these effects are driven mostly by the geometric image properties. Visual preferences were influenced by adaptation, with post-adaptation preference shifting towards the adaptor. Slightly enhanced discrimination performance was observed after adapting to and testing at alpha = 2.25, albeit inconsistently. Overall, the effects of adaptation on sensitivity was neither strong nor robust requiring at least 300s of adaptation for the effects to occur. There were no effects of adaptation on perceived complexity. There were also considerable individual differences in the effects of adaptation, particularly in the case of visual preference and sensitivity. Overall, the results suggest that visual sensitivity, preference, perceived complexity and similarity are affected by variations in natural scene statistics
Applied Mathematics to Mechanisms and Machines
This book brings together all 16 articles published in the Special Issue "Applied Mathematics to Mechanisms and Machines" of the MDPI Mathematics journal, in the section “Engineering Mathematics”. The subject matter covered by these works is varied, but they all have mechanisms as the object of study and mathematics as the basis of the methodology used. In fact, the synthesis, design and optimization of mechanisms, robotics, automotives, maintenance 4.0, machine vibrations, control, biomechanics and medical devices are among the topics covered in this book. This volume may be of interest to all who work in the field of mechanism and machine science and we hope that it will contribute to the development of both mechanical engineering and applied mathematics
Seeing affect: knowledge infrastructures in facial expression recognition systems
Efforts to process and simulate human affect have come to occupy a prominent role in
Human-Computer Interaction as well as developments in machine learning systems.
Affective computing applications promise to decode human affective experience and
provide objective insights into usersʼ affective behaviors, ranging from frustration and
boredom to states of clinical relevance such as depression and anxiety. While these
projects are often grounded in psychological theories that have been contested both
within scholarly and public domains, practitioners have remained largely agnostic to
this debate, focusing instead on the development of either applicable technical systems
or advancements of the fieldʼs state of the art. I take this controversy as an entry point
to investigate the tensions related to the classification of affective behaviors and how
practitioners validate these classification choices.
This work offers an empirical examination of the discursive and material
repertoires ‒ the infrastructures of knowledge ‒ that affective computing practitioners
mobilize to legitimize and validate their practice. I build on feminist studies of science
and technology to interrogate and challenge the claims of objectivity on which affective
computing applications rest. By looking at research practices and commercial
developments of Facial Expression Recognition (FER) systems, the findings unpack
the interplay of knowledge, vision, and power underpinning the development of
machine learning applications of affective computing.
The thesis begins with an analysis of historical efforts to quantify affective
behaviors and how these are reflected in modern affective computing practice. Here,
three main themes emerge that will guide and orient the empirical findings: 1) the role
that framings of science and scientific practice play in constructing affective behaviors
as “objective” scientific facts, 2) the role of human interpretation and mediation
required to make sense of affective data, and 3) the prescriptive and performative
dimensions of these quantification efforts. This analysis forms the historical backdrop
for the empirical core of the thesis: semi-structured interviews with affective
computing practitioners across the academic and industry sectors, including the data
annotators labelling the modelsʼ training datasets.
My findings reveal the discursive and material strategies that participants adopt
to validate affective classification, including forms of boundary work to establish
credibility as well as the local and contingent work of human interpretation and
standardization involved in the process of making sense of affective data. Here, I show
how, despite their professed agnosticism, practitioners must make normative choices
in order to ʻseeʼ (and teach machines how to see) affect. I apply the notion of knowledge
infrastructures to conceptualize the scaffolding of data practices, norms and routines,
psychological theories, and historical and epistemological assumptions that shape
practitionersʼ vision and inform FER design.
Finally, I return to the problem of agnosticism and its socio-ethical relevance to
the broader field of machine learning. Here, I argue that agnosticism can make it
difficult to locate the technologyʼs historical and epistemological lineages and,
therefore, obscure accountability. I conclude by arguing that both policy and practice
would benefit from a nuanced examination of the plurality of visions and forms of
knowledge involved in the automation of affect
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
Microfluidics for Investigation of Electric-Induced Behaviors of Zebrafish Larvae
Zebrafish has emerged as a model organism for studying the genetic, neuronal and behavioral bases of diseases and for drug screening. Being a vertebrate, they are phylogenetically closer to humans than invertebrates, possess complex organs and the overall organization of their brain shows structural similarities with human. They are small at larval stages, optically transparent and easy to culture. In addition, zebrafish models of human diseases and genetic mutants are widely available. These characteristics make this vertebrate model an ideal organism for neurodegeneration study and drug screening from the molecule to whole organism level. Despite these attractive features, the conventional zebrafish screening methods used for movement-based behavioral tests are mostly time-consuming, uncontrollable, qualitative, low-throughput and inaccurate. Zebrafish larvae behavioral response to various stimulations including optical and chemical stimuli, have been already investigated. However, zebrafish sensory-motor responses to electrical signals, a controllable stimulus which its potential in inducing locomotion response was proven in research done before, have not been broadly studied. Examples of research questions remaining to be answered are if zebrafish electric induced response is sensitive to different electric current intensities, voltage drops, multiple electrical stimulation, and the electric field direction. The involvement of different pathways and genes in this response and its potential for utilization in disease studies and chemical screening, and drug discovery can also be investigated. This research aims to enhance our understanding of zebrafish electric-induced response via presenting novel microfluidic devices that address the challenges associated with monitoring the behavioral activities of zebrafish larvae in response to various electrical signals. In Objective 1 of the thesis, we designed a microfluidic device to deliver electrical stimuli to the awake and partially immobilized zebrafish larvae, screen and study their phenotypic behavioral responses and analyze the outputs. Behavioral response was characterized in terms of response duration and tail beat frequency. A multi-phenotypic microfluidic device was also developed to study the effect of electric stimulation on the heartrate. In Objective 2, attention was given to investigate the effect of electric current, voltage, and field direction on the zebrafish larvae’s response to find an optimized setting which can induce a traceable response in zebrafish. Using different habituation-dishabituation strategies, we also investigated if the zebrafish larvae show adaptation towards repeated exposures to electric stimuli. In Objective 3, we developed a quadruple-fish device to enhance the behavioral throughput of our microfluidic platform and showed the technique's effectiveness for larger sample size and faster behavioral assay. In Objective 4, our quadruple-fish device was employed to investigate the involvement of dopaminergic neurons in electric-induced movement response of zebrafish larvae. Lastly, since we could monitor the electric-induced behavioral responses of zebrafish larvae, in Objective 5, the applicability of our proposed technique in chemical toxicity and gene screening assays was investigated.
This study is expected to introduce a microfluidic platform for on-demand and phenotypic behavioral screening of zebrafish larvae with applications in chemical screening and drug discovery
25th Annual Computational Neuroscience Meeting: CNS-2016
Abstracts of the 25th Annual Computational Neuroscience
Meeting: CNS-2016
Seogwipo City, Jeju-do, South Korea. 2–7 July 201
Metacognition in decision-making: Exploring age-related changes in confidence
Metacognition is a fundamental human function that supports goal-directed behaviour. By constantly monitoring and evaluating our decisions we are able to detect errors when they occur and adjust the behaviour accordingly. Metacognitive evaluations can be expressed in ratings of decision confidence or error detection reports. Humans are generally capable of forming well-calibrated estimates of their own performance, yet metacognitive abilities have been shown to be specifically affected by healthy ageing. However, the mechanisms underlying this decline remain poorly understood. This thesis aims to investigate the cognitive processes of age-related changes in perceptual metacognitive performance by combining approaches from the fields of error monitoring and decision confidence. For this, we developed a new paradigm for studying the metacognitive evaluation of errors and correct responses that was feasible for adults of all ages. While recording an electroencephalogram (EEG) and response force, a sample of 65 healthy adults from 20 to 76 years made a series of decisions in a modified version of the Flanker task and subsequently indicated how confident they felt about their decision on a four-point scale.
Across two studies, conducted in the same large sample, I addressed three specific research questions: first, how is metacognitive performance affected by healthy ageing? Second, what are factors contributing to the observed decline in metacognitive performance? And third, how does an age-related decline in metacognitive performance affect subsequent behaviour?
The analysis of behavioural data (Study 1a) showed that metacognitive accuracy declined significantly with older age and that this decline could not be explained by the decline in task performance alone. Independent of age, however, participants adjusted their performance according to their metacognitive evaluation of their previous decision and responded more cautiously after reporting low confidence.
The analysis of electrophysiological data (Study 1b) focussed on the modulation of two correlates of error monitoring by confidence and age. The results indicated that the error/correct positivity (Pe/c), a component discussed as a marker of error detection and decision confidence, scaled with reported confidence in errors but did not show the expected modulation by age. The amplitude of the error/correct negativity (Ne/c), a marker of early error monitoring processes, also scaled with reported confidence in errors, but in contrast, was less sensitive to variations in confidence with older age.
Finally, Study 2 investigated the effect of age on the relationship between confidence and two response parameters of the initial decision: response time and response force. We replicated a widely reported negative relationship between confidence and response time. Importantly, we showed, for the first time, that confidence was also negatively related to fine-grained changes in peak force, which was intuitively exerted by the participants. Notably, these associations were dependent on the accuracy of the response and changed markedly across age: the relationship between confidence and response time was only found in correct responses and was pronounced with older age, while the relationship between confidence and peak force was only found in errors and only in younger adults.
Overall, these findings jointly provide novel insights deepening our understanding of the observed decline in metacognitive performance with older age. A similar modulation of the Pe/c by confidence across the lifespan suggests that the post-decisional process of accumulating evidence about the correctness of a prior decision might generally be intact until old age. Instead, the age-related decline in metacognitive accuracy appears to be related to a multitude of cognitive and neural changes, which might reflect increased noise and hence higher uncertainty in older adults’ computation of confidence. Moreover, I discuss how a metacognitive decline could manifest in real life and how recent findings offer a promising view regarding the effect of training on metacognitive performance
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