2,204 research outputs found
Emotion Quantification Using Variational Quantum State Fidelity Estimation
Sentiment analysis has been instrumental in developing artificial intelligence when applied to various domains. However, most sentiments and emotions are temporal and often exist in a complex manner. Several emotions can be experienced at the same time. Instead of recognizing only categorical information about emotions, there is a need to understand and quantify the intensity of emotions. The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime. The inspiration comes from manifesting human cognition and decision-making capabilities, which may adopt a brief explanation through quantum theory. Quantum state fidelity was used to characterize states and estimate emotion intensities rendered by subjects from the Amsterdam Dynamic Facial Expression Set (ADFES) dataset. The Quantum variational classifier technique was used to perform this experiment on the IBM Quantum Experience platform. The proposed method successfully quantifies the intensities of joy, sadness, contempt, anger, surprise, and fear emotions of labelled subjects from the ADFES dataset
Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis : Principles and Recent Advances
This work was supported in part by the National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT) under Grant NRF 2020R1A2B5B02002478, and in part by Sejong University through its Faculty Research Program under Grant 20212023.Peer reviewedPublisher PD
Context-Dependent Diffusion Network for Visual Relationship Detection
Visual relationship detection can bridge the gap between computer vision and
natural language for scene understanding of images. Different from pure object
recognition tasks, the relation triplets of subject-predicate-object lie on an
extreme diversity space, such as \textit{person-behind-person} and
\textit{car-behind-building}, while suffering from the problem of combinatorial
explosion. In this paper, we propose a context-dependent diffusion network
(CDDN) framework to deal with visual relationship detection. To capture the
interactions of different object instances, two types of graphs, word semantic
graph and visual scene graph, are constructed to encode global context
interdependency. The semantic graph is built through language priors to model
semantic correlations across objects, whilst the visual scene graph defines the
connections of scene objects so as to utilize the surrounding scene
information. For the graph-structured data, we design a diffusion network to
adaptively aggregate information from contexts, which can effectively learn
latent representations of visual relationships and well cater to visual
relationship detection in view of its isomorphic invariance to graphs.
Experiments on two widely-used datasets demonstrate that our proposed method is
more effective and achieves the state-of-the-art performance.Comment: 8 pages, 3 figures, 2018 ACM Multimedia Conference (MM'18
Deep-Gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning
Mobile crowdsourcing has become easier thanks to the widespread of
smartphones capable of seamlessly collecting and pushing the desired data to
cloud services. However, the success of mobile crowdsourcing relies on
balancing the supply and demand by first accurately forecasting spatially and
temporally the supply-demand gap, and then providing efficient incentives to
encourage participant movements to maintain the desired balance. In this paper,
we propose Deep-Gap, a deep learning approach based on residual learning to
predict the gap between mobile crowdsourced service supply and demand at a
given time and space. The prediction can drive the incentive model to achieve a
geographically balanced service coverage in order to avoid the case where some
areas are over-supplied while other areas are under-supplied. This allows
anticipating the supply-demand gap and redirecting crowdsourced service
providers towards target areas. Deep-Gap relies on historical supply-demand
time series data as well as available external data such as weather conditions
and day type (e.g., weekday, weekend, holiday). First, we roll and encode the
time series of supply-demand as images using the Gramian Angular Summation
Field (GASF), Gramian Angular Difference Field (GADF) and the Recurrence Plot
(REC). These images are then used to train deep Convolutional Neural Networks
(CNN) to extract the low and high-level features and forecast the crowdsourced
services gap. We conduct comprehensive comparative study by establishing two
supply-demand gap forecasting scenarios: with and without external data.
Compared to state-of-art approaches, Deep-Gap achieves the lowest forecasting
errors in both scenarios.Comment: Accepted at CloudCom 2019 Conferenc
Breaking Down the Barriers To Operator Workload Estimation: Advancing Algorithmic Handling of Temporal Non-Stationarity and Cross-Participant Differences for EEG Analysis Using Deep Learning
This research focuses on two barriers to using EEG data for workload assessment: day-to-day variability, and cross- participant applicability. Several signal processing techniques and deep learning approaches are evaluated in multi-task environments. These methods account for temporal, spatial, and frequential data dependencies. Variance of frequency- domain power distributions for cross-day workload classification is statistically significant. Skewness and kurtosis are not significant in an environment absent workload transitions, but are salient with transitions present. LSTMs improve day- to-day feature stationarity, decreasing error by 59% compared to previous best results. A multi-path convolutional recurrent model using bi-directional, residual recurrent layers significantly increases predictive accuracy and decreases cross-participant variance. Deep learning regression approaches are applied to a multi-task environment with workload transitions. Accounting for temporal dependence significantly reduces error and increases correlation compared to baselines. Visualization techniques for LSTM feature saliency are developed to understand EEG analysis model biases
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