Abstract: This article examines neuroscience, network science, and dynamic systems research on brain-inspired intelligence. Instead of using artificial computing networks, reconstructing cortical networks with dynamic activities that nourish brain functions can replicate brain intelligence. The…
Abstract: Resting-state functional magnetic resonance imaging (rs-fMRI) functional connectivity (FC) networks can diagnose Alzheimer’s disease and its prodromal stage, mild cognitive impairment (MCI). FC is estimated as a temporal correlation of regional mean rs-fMRI signals…
Abstract: Noninvasive structural magnetic resonance imaging (MRI) can accurately predict neuropsychological test scores, which helps diagnose dementia (e.g., Alzheimer’s disease (AD)) and predict its progression. Existing machine/deep learning approaches preselect dementia-sensitive brain locations for MRI…
Abstract: In our data world, many untrusted controllers collect personal data. Giving the individual data agency protects her privacy and informational self-determination. Local anonymization allows each person to anonymize her data before giving them to…
Abstract: Kernel-based extreme learning machine (KELM), a natural extension of ELM to kernel learning, excels in regression and classification problems. Due to its random projection mechanism and lack of hidden node requirements, KELM generalizes better…
Abstract: With the maturity and popularity of Internet of Things (IoT), Social Internet of Things (SIoT) has been proposed to support novel IoT applications and networking services more effectively and efficiently. Many SIoT works design…
Abstract: Textual information helps model item concepts. Many models ignore conceptual “correlation sparsity” by not using concepts’ inferential property to capture word meanings. This paper distinguishes between word modeling and concept modeling and proposes an…
Abstract: This paper addresses static 3D cloth draping on virtual human bodies. A two-stream deep network model that extracts features from body and garment shapes drapes a template cloth on virtual 3D bodies. Our network…
Abstract: GPS and wireless technology generate massive trajectory data. LBSN activity trajectory adds user semantic activities like visiting work/home/entertainment places to traditional trajectory data. Comparing activity trajectories in time, location, and semantics measures their similarity….
Abstract: Sparse representation-based image classification is a key component of modern computer vision systems due to its robustness. On sparse learning system optimization, regularization and data augmentation are powerful but isolated. Regularization and data augmentation…
Abstract: Clustering is a classic data structure analysis method in machine learning and pattern recognition. The anchor-based graph has improved graph-based clustering accuracy recently. The progressive self-supervised clustering method with novel category discovery (PSSCNCD) uses…
Abstract: A ParametRIc MAnifold Learning (PRIMAL) algorithm for Gaussian mixture models (GMM) assumes that GMMs lie on or near a manifold of probability distributions generated from a low-dimensional hierarchical latent space through parametric mappings. Latent…
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