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Human-autonomy teaming using physiological sensors poses a novel sensor fusion problem due to the dynamic nature of the sensor models and the difficulty of modeling their temporal and inter-subject variability. Developing analytical models therefore requires defining objective criteria for selection and weighting of sensors under an appropriate fusion paradigm. We investigate a selection methodology...
In robotics, non-linear least squares estimation is a common technique for simultaneous localization and mapping. One of the remaining challenges are measurement outliers leading to inconsistency or even divergence within the optimization process. Recently, several approaches for robust state estimation dealing with outliers inside the optimization back-end were presented, but all of them include...
Digital watermarking is a technique that is widely used to protect rightful ownership of digital images. In this paper, we proposed a hybrid watermarking scheme for stereo images copyright protection. According to the property of stereo images, similar block pairs are located by using a certain key. Then, each block pair is transformed into DCT domain and DCT coefficients are extracted from the anti-diagonal...
Robust finite-time (FT) stabilization of uncertain continuous-time singular systems is of concern in this paper. By adopting a Lyapunov-like functional (LLF) and the finite-time stability (FTS) technique, the linear matrix inequality (LMI)-based conditions are derived for uncertain continuous-time singular systems to be stabilizable. Then, based on solving these LMIs, a FT H-infinite state feedback...
After studying the node degree distribution and average path length of the kernel module call graphs of 223 Linux kernels (V1.1.0 to V2.4.35), we have found that the call graphs of the file system, drivers, kernel, and memory management modules are scale-free small-world complex networks that exhibit strong clustering tendency. Using the random error case and attack case methods, we investigated the...
This paper addresses the robust non-fragile leader-following consensus problem for multi-agent systems against state-dependent uncertainties and controller coefficient variations. Without the requirement of knowledge of uncertainties of agents and controllers, adaptive distributed controllers are constructed to guarantee the follower agents tracking the leader agent. Asymptotic consensus results of...
Context: Recent studies have shown that performance of defect prediction models can be affected when data sampling approaches are applied to imbalanced training data for building defect prediction models. However, the magnitude (degree and power) of the effect of these sampling methods on the classification and prioritization performances of defect prediction models is still unknown. Goal: To investigate...
Due to variations in pose and illumination condition, the appearance of can be significantly different in different cameras and the performance of person re-identification is degraded. In this paper, a person re-identification based on multi-level and multi-feature fusion for this phenomenon is proposed. Firstly, we divided each sample into three parts and multi-layer sampling. Secondly, we extracted...
A novel robust video watermarking scheme is proposed in this paper, in which crowdsourcing technique is used to extract the most important regions from the original video. In fact, these regions are obtained by interacting with users and analyzing their behaviors while using an interactive interface where the summaries of the videos are given. The obtained regions are then detected in the mosaic frame...
A near-light perspective shape from shading (SfS) technique applied to endoscopy for 3D visualizations of the gastrointestinal tract regions is presented. By utilizing an extensible reflectance model, we study a robust Huber regularization function based variational SfS model. A balancing parameter is used for weighting the irradiance ad smoothness/regularization terms. Experimental results on different...
High-dimensionality of single-cell RNA sequencing (scRNA-Seq) data needs methods or heuristics to reduce the feature space (genes) prior to using as inputs for machine learning methods to analyze the data. Using an unsupervised learning approach, mixture-model based single cell analyses (MiMoSA) were proposed to infer single-cell subpopulations induced after drug treatment. In this method, a threshold...
Big data analysis has been pervasively adopted as a method to analyze the tremendous amount of daily generated high throughput data in an efficient and accurate manner. Among the series of tools available in the field of big biomedical data, correlation networks are one of the most powerful tools for modelling gene expression, which is important in the study of disease and ageing. With the help of...
Non-negative Matrix Factorization (NMF) is widely used as a data dimensionality reduction tool. However, the assumption of most conventional NMF-based methods is that the gene expression data are only destroyed by Gaussian noise. In practice, the gene expression data are unavoidably destroyed by sparse noise. Although Sparsity-Regularized Robust NMF by using L1/2 constraint (L1/2-RNMF) can achieve...
In molecular biology, the selection of feature genes and tumor clustering are the hotspots and difficulties in bioinformatics research. The traditional PCA method based on the minimization of the squares of the loss function is sensitive to the outliers and noise. Therefore, it is necessary to design a new method to weaken the effects of errors and noise. In this paper, we propose a novel PCA method...
Opinion mining and demographic attribute inference have many applications in social science. In this paper, we propose models to infer daily joint probabilities of multiple latent attributes from Twitter data, such as political sentiment and demographic attributes. Since it is costly and time-consuming to annotate data for traditional supervised classification, we instead propose scalable Learning...
In today's era of big data, robust least-squares regression becomes a more challenging problem when considering the adversarial corruption along with explosive growth of datasets. Traditional robust methods can handle the noise but suffer from several challenges when applied in huge dataset including 1) computational infeasibility of handling an entire dataset at once, 2) existence of heterogeneously...
Since their introduction over a decade ago, time series motifs have become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. In this work we introduce Time Series Chains, which are related to, but distinct from, time series motifs. Informally, time series chains are a temporally ordered set of subsequence patterns, such that each pattern is similar to the pattern...
Detecting fraudulent users in online social networks is a fundamental and urgent research problem as adversaries can use them to perform various malicious activities. Global social structure based methods, which are known as guilt-by-association, have been shown to be promising at detecting fraudulent users. However, existing guilt-by-association methods either assume symmetric (i.e., undirected)...
Time series motifs are approximately repeating patterns in real-valued time series data. They are useful for exploratory data mining and are often used as inputs for various time series clustering, classification, segmentation, rule discovery, and visualization algorithms. Since the introduction of the first motif discovery algorithm for univariate time series in 2002, multiple efforts have been made...
Local community detection (or local clustering) is of fundamental importance in large network analysis. Random walk based methods have been routinely used in this task. Most existing random walk methods are based on the single-walker model. However, without any guidance, a single-walker may not be adequate to effectively capture the local cluster. In this paper, we study a multi-walker chain (MWC)...
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