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Security is one of the top concerns of any enterprise. Most security practitioners in enterprises rely on correlation rules to detect potential threats. While the rules are intuitive to design, each rule is independently defined per log source, unable to collectively address heterogeneity of data from a myriad of enterprise networking and security logs. Furthermore, correlation rules do not look for...
In this paper we propose a new method to anonymize (share relevant and detailed information while not naming names) and protect data sets (minimize the utility loss) based on Factor Analysis. The method basically consists of obtaining the factors, which are uncorrelated, protecting them and undoing the transformation in order to get interpretable protected variables. We first show how to proceed when...
Mid-Infrared (MIR) spectroscopy has emerged as the most economically viable technology to determine milk values as well as to identify a set of animal phenotypes related to health, feeding, well-being and environment. However, Fourier transform-MIR spectra incurs a significant amount of redundant data. This creates critical issues such as increased learning complexity while performing Fog and Cloud...
Accurate estimation of the vehicle sideslip angle is fundamental in vehicle dynamics control and stability. In this paper two different methods for vehicle sideslip estimation, based on Principal Component Analysis (PCA) and Neural Networks (NN), are presented comparing the procedure responses with full-scale vehicle acquired test data. The estimation algorithms use driver's steering angle, lateral...
The method principal component (PCA) allows to allocate from a matrix of these several objects with a large amount of signs only 1–3 vectors containing 90–95% of information. Usually measuring problem of assessment of these main components is solved by the iterative NIPALS procedure or the algebraic SVD procedure, however both of these methods often give ambiguous estimates. For the purpose of elimination...
A Brain-Computer Interface (BCI) speller system based on the Steady-State Visually Evoked Potentials (SSVEP) paradigm is presented. The potentials are elicited through the gaze fixation at one out of the four checkerboards shown on screen, which are flickering at 5, 12, 15 and 20 Hz. After the feature extraction, two dimensionality reduction algorithms, Principal Components Analysis (PCA) and Linear...
This paper deals with an unsupervised approach for land change detection and extraction using bitemporal and multispectral remotely sensed images. It is a statistical approach based on multivariate alteration detection (MAD) transformation combined with a new ChiMerge thresholding method. As opposed to most other multivariate change detection schemes the MAD technique is invariant to affine transformations...
Clustering is a popular method to deal with the problem for mode identification of multimode processes. Unlike traditional distance-based clustering methods, in this paper, a new correlation-based bi-partition hierarchical clustering (CBHC) method is proposed, which classifies the observations according to their correlation relationships rather than their distances. Motivated by an existing correlation-based...
Available sensing measurements in modern industrial process include two significant characteristics: distribution and autocorrelation. Different types of sensing measurements exhibit different characteristics. Moreover, different feature extraction methods are suitable for data with corresponding characteristics. This paper proposes a novel dual-step subspace partition method in order to establish...
Current industrial system develops more and more complex and intelligent, whose safety and reliability relies on fault diagnosis technology. In the age of big-data, data-driven fault diagnosis becomes the state of the art, and the demand for the diagnostic toolbox also increases. In this paper, theories of data-driven fault detection models, both static model and dynamic, are revealed. And then a...
Aesthetic quality assessment plays an important role in how people organize large image collections. Many studies on aesthetic quality assessment are based on design of hand-crafted features without considering whether attributes conveyed by images can actually affect image aesthetics. This paper presents an aesthetic quality assessment method which uses new visual features. The proposed method utilizes...
Analysis of electrocardiogram and heart rate provides useful information about health condition of a patient. The North Sea Bicycle Race is an annual competition in Norway. Examination of ECG recordings collected from participants of this race may allow defining and evaluating the relationship between physical endurance exercises and heart electrophysiology. Parameters reflecting potentially alarming...
Background modelling and subtraction is an essential component in motion analysis with wide range of applications in computer vision, whereas the task becomes more challenging in context of complex scenarios such as dynamic backgrounds. In this paper, we address the problem of modelling dynamic backgrounds in online tensor leaning framework. We use Tucker decomposition to model thespatio-temporal...
Academic performance is one of the indicators to evaluate the achievement of college students, which plays an important role in individual development. However, it is worth mentioning that personality traits and thinking styles have an influence on academic performance. To further study the correlation among personality traits, thinking styles and academic performance, we carry out a series of experiments...
Feature embedding is an emerging research area which intends to transform features from the original space into a new space to support effective learning. Many feature embedding algorithms exist, but they are often designed to handle a single type of feature, or users have to clearly separate features into different feature views and supply such information for feature embedding learning. In this...
This paper tries to obtain the main factors influencing quality and safety of dairy products by using grey relation analysis model. We select 11 sub factors from four fields and confirm the system behavior character by applying principal component analysis. Finally, we find cow's milk yields, proportion of urban population, qualified rate of veterinary drug, qualified feed rate and the proportion...
Nowadays the activity recognition based on multiple wearable sensors is still a challenging task due to the diversity of human activities. The application of unsupervised classification is helpful to discovery new activity classes and improve the activity classification model. Therefore, a new multi-sensor activity recognition scheme using the two-dimensional principal component analysis (2DPCA) and...
Oral squamous cell carcinomas (OSCC) is the most common head and neck cancer worldwide, with more than 300,000 new cases being diagnosed annually. Studies have shown that miRNAs are involved in the process of growth, differentiation, apoptosis, invasion and metastasis of OSCC tumor cells. How miRNAs work together to contribute to this process is still largely unknown. The goal of our study was to...
The comprehensive index system is a prerequisite and basis for ensuring the scientific performance of the product performance evaluation, so, the screening of the index is the necessary link in the construction of the index system. The paper regards the armored vehicle PHM system as a special product, aiming at the problem that the redundancy of the primary index set of the armored vehicle PHM system...
In this paper, we propose a multiple line extraction method from multimodal data points in high dimensional space. It can sparsely represent multimodal sensor network data by utilizing high correlation among channels in the data. We exploit the idea of Color Lines, which is a model using high correlation among RGB channels in computer vision. It represents real color images as a collection of multiple...
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