A Semantic Collaboration Method Based on Uniform Knowledge Graph

The Semantic Internet of Things is the extension of the Internet of Things and the Semantic Web, which aims to build an interoperable collaborative system to solve the heterogeneous problems in the Internet of Things. However, the Semantic Internet of Things has the characteristics of both the Internet of Things and the Semantic Web environment, and the corresponding semantic data presents many new data features. In this study, we analyze the characteristics of semantic data and propose the concept of a uniform knowledge graph, allowing us to be applied to the environment of the Semantic Internet of Things better. Here, we design a semantic collaboration method based on a uniform knowledge graph. It can take the uniform knowledge graph as the form of knowledge organization and representation, and provide a useful data basis for semantic collaboration by constructing semantic links to complete semantic relation between different data sets, to achieve the semantic collaboration in the Semantic Internet of Things. Our experiments show that the proposed method can analyze and understand the semantics of user requirements better and provide more satisfactory outcomes

Developing a migraine attack prediction system using resting-state EEG

University of Technology Sydney. Faculty of Engineering and Information Technology.Migraine is a common episodic neurological disorder with complex pathophysiology that is characterised by recurrent headaches during a set period, such as one month. A small group of migraine patients (13-31%) experience transient neurological symptoms (most frequently visual aura) prior to headache onset, while the majority of patients do not possess any premonitory symptoms. This study explored neurophysiological evidence of the resting-state electroencephalogram (EEG) power, coherence and entropy to support the cortical signals that relate to different migraine phases, and then used this to develop an EEG-based system for predicting migraine attacks. First, we investigated EEG devices, pre-processing and artefact removal methods, and feature extraction technologies, including power, coherence and entropy analysis. Next, we discovered the cyclic EEG dynamics of migraine on a cross-sectional basis. The results indicated that EEG power spectral and coherence were significantly increased in the pre-ictal group, relative to EEG data obtained from the inter-ictal group. Inter-ictal patients had decreased EEG power and connectivity relative to healthy controls, which were “normalised” in the pre-ictal patients. Furthermore, using longitudinal design, we utilised a wearable EEG device to estimate brain dynamics before migraine attacks. The results showed the EEG entropy of individual patients in the pre-ictal phase, resembling normal control subjects, was significantly higher than that in their inter-ictal phase in prefrontal area. That is, the entropy measures identified enhancement or “normalisation” of frontal EEG complexity in pre-ictal phase. Finally, based on these neuroscience discovery of inter- and pre-ictal EEG entropy in individuals, this study proposed a support vector machine (SVM) based system with 76% accuracy to predict migraine attacks. The prediction system characterised the EEG entropy of a single (prefrontal) area and favoured the application of brain-computer interface in migraine

Tensor Decomposition for EEG Signal Retrieval

Prior studies have proposed methods to recover multi-channel electroencephalography (EEG) signal ensembles from their partially sampled entries. These methods depend on spatial scenarios, yet few approaches aiming to a temporal reconstruction with lower loss. The goal of this study is to retrieve the temporal EEG signals independently which was overlooked in data pre-processing. We considered EEG signals are impinging on tensor-based approach, named nonlinear Canonical Polyadic Decomposition (CPD). In this study, we collected EEG signals during a resting-state task. Then, we defined that the source signals are original EEG signals and the generated tensor is perturbed by Gaussian noise with a signal-to-noise ratio of 0 dB. The sources are separated using a basic non-negative CPD and the relative errors on the estimates of the factor matrices. Comparing the similarities between the source signals and their recovered versions, the results showed significantly high correlation over 95%. Our findings reveal the possibility of recoverable temporal signals in EEG applications

Kernelized Similarity Learning and Embedding for Dynamic Texture Synthesis

Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess a high similarity correlation that is critical prior knowledge. However, existing methods cannot effectively learn a promising synthesis model for high-dimensional DT from a small number of training data. In this paper, we propose a novel DT synthesis method, which makes full use of similarity prior knowledge to address this issue. Our method bases on the proposed kernel similarity embedding, which not only can mitigate the high-dimensionality and small sample issues, but also has the advantage of modeling nonlinear feature relationship. Specifically, we first raise two hypotheses that are essential for DT model to generate new frames using similarity correlation. Then, we integrate kernel learning and extreme learning machine into a unified synthesis model to learn kernel similarity embedding for representing DT. Extensive experiments on DT videos collected from the internet and two benchmark datasets, i.e., Gatech Graphcut Textures and Dyntex, demonstrate that the learned kernel similarity embedding can effectively exhibit the discriminative representation for DT. Accordingly, our method is capable of preserving the long-term temporal continuity of the synthesized DT sequences with excellent sustainability and generalization. Meanwhile, it effectively generates realistic DT videos with fast speed and low computation, compared with the state-of-the-art methods. The code and more synthesis videos are available at our project page https://shiming-chen.github.io/Similarity-page/Similarit.html.Comment: 13 pages, 12 figures, 2 table

Distributed Semi-supervised Fuzzy Regression with Interpolation Consistency Regularization

Recently, distributed semi-supervised learning (DSSL) algorithms have shown their effectiveness in leveraging unlabeled samples over interconnected networks, where agents cannot share their original data with each other and can only communicate non-sensitive information with their neighbors. However, existing DSSL algorithms cannot cope with data uncertainties and may suffer from high computation and communication overhead problems. To handle these issues, we propose a distributed semi-supervised fuzzy regression (DSFR) model with fuzzy if-then rules and interpolation consistency regularization (ICR). The ICR, which was proposed recently for semi-supervised problem, can force decision boundaries to pass through sparse data areas, thus increasing model robustness. However, its application in distributed scenarios has not been considered yet. In this work, we proposed a distributed Fuzzy C-means (DFCM) method and a distributed interpolation consistency regularization (DICR) built on the well-known alternating direction method of multipliers to respectively locate parameters in antecedent and consequent components of DSFR. Notably, the DSFR model converges very fast since it does not involve back-propagation procedure and is scalable to large-scale datasets benefiting from the utilization of DFCM and DICR. Experiments results on both artificial and real-world datasets show that the proposed DSFR model can achieve much better performance than the state-of-the-art DSSL algorithm in terms of both loss value and computational cost