Virtual sensor tracking using byzantine fault tolerance and predictive outlier model for complex tasks recognition

The problem of volume averaging in quantitating CSF, gray-matter, and white-matter fractions in the brain is solved using a three-compartment model and a simple graphical analysis of a multispectral MR feature space. Compartmentalization is achieved without the ambiguities of thresholding techniques or the need to assume that the underlying pixel probability distributions have a particular form. A 2D feature space is formed by double SE (proton density- and T2-weighted) MR data with image nonuniformity removed by a novel technique in which the brain itself serves as a uniformity reference. Compartments other than the basic three were rejected by the tailoring of limits in feature space. Phantom scans substantiate this approach, and the importance of the careful selection and standardization of pure tissue reference signals is demonstrated. Compartmental profiles from standardized subvolumes of three normal brains, based on a 3D (Talairach) coordinate system, demonstrate slice-by-slice detail; longitudinal studies confirm reproducibility. Compartmentalization may be described graphically and algebraically, complementing data displays in feature space and images of compartmentalized brain scans. These studies anticipate the application of our compartmentalization technique to patients with neurological disorders.

Prompt learning automates the manual crafting of prompts for adapting vision-and-language models, to downstream tasks, particularly in few-shot scenarios. This paper addresses two key challenges in prompt learning: limited performance in one-shot settings and inefficient dataset construction from unlabeled data. To tackle these challenges, we visualize and compare CLIP's feature spaces after prompt learning under one-shot and 16-shot conditions, identifying necessary characteristics of feature spaces that yield better prompts. We propose two novel loss functions—Inclusive Loss and Exclusive Loss—that enhance accuracy in one-shot scenarios by encouraging the feature space to resemble those trained with sufficient data. Additionally, we investigate the distribution of image features within CLIP's feature space and introduce a sampling method called Cluster-Centroid Sampling (CCS). CCS constructs a more category-balanced dataset by selecting samples closest to cluster centroids. To validate our approaches, we conducted extensive experiments. First, we demonstrate the effectiveness of our proposed loss functions across multiple datasets, showing accuracy improvements in one-shot conditions. Second, we evaluate CCS using an unlabeled data pool, confirming its superiority over existing sampling methods in downstream task accuracy due to the construction of more balanced dataset.

Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P < 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer.

Data sets resulting from physical simulations typically contain a multitude of physical variables. So, visualization methods should take into account the entire multifield volume data rather than concentrate on one variable. We have developed a visualization approach based on surface extraction from multifield volume data. The extracted surfaces segment the data with respect to an underlying multivariate function. Decisions on segmentation properties are based on the analysis of a multidimensional feature space. We perform feature space exploration using automated multidimensional hierarchical clustering. The hierarchical clusters appear as a cluster tree in a 2D radial layout. In this layout, the user can select clusters of interest. A selected cluster in feature space corresponds to a segmenting surface in object space. On the basis of the segmentation property induced by the cluster membership, we extract surfaces from the volume data.

This paper shows the impact of speaker normalization techniques, such as vocal tract length normalization (VTLN) and speaker-adaptive training (SAT), prior to discriminant feature space transforms, such as LDA (linear discriminant analysis). We demonstrate that removing the inter-speaker variability by using speaker compensation methods results in improved discrimination as measured by the LDA eigenvalues and also in improved classification accuracy (as measured by the word error rate). Experimental results on the SPINE (speech in noisy environments) database indicate an improvement of up to 5% relative over the standard case where speaker adaptation (during testing and training) is applied after the LDA transform which is trained in a speaker independent manner. We conjecture that performing linear discriminant analysis in a canonical feature space (or speaker normalized space) is more effective than LDA in a speaker independent space because the eigenvectors carve a subspace of maximum intra-speaker phonetic separability whereas in the latter case this subspace is also defined by the inter-speaker variability. Indeed, we show that the more normalization is performed (first VTLN, then SAT), the higher the LDA eigenvalues become.

While some image textures can be changed with scale, others cannot. We focus on a multi-scale features of determing the sensitivity of the texture intensity to change. This paper presents a new method of texture structure classification and depth estimation using multi-scale features extracted from a higher order of the local autocorrelation functions. Multi-scale features consist of the meansand variances of distributions, which are extracted from theautocorrelation feature vectors according to multi-level scale. In order to reduce dimensional feature vectors, we employ the Principal Component Analysis (PCA) in the autocorrelation feature space. Each training image texture makes its own multi-scale model in a reduced PCA feature space, and the test of the texture image is projected in the homogeneous PCA space of the training data. The experimental results show that the proposed multi-scale feature can be utilized notonly for texture classification, but also depth estimation in two dimensional images with texture gradients.

We present an automatic query expansion approach by generalizing the vector space model of information retrieval. In this framework, the images are presented by vectors of weighted concepts similar to the keyword-based representation in the text retrieval domain. The concepts comprise of color and texture patches from local image regions in a multi-dimensional feature space. To generate the concept vocabularies and represent the images, statistical model is built by utilizing a multi-class Support Vector Machine (SVM)-based classification technique. For automatic query expansion, the correlations between concepts are analyzed based on the neighborhood proximity between the concepts in encoded images by considering the local feedback information. The experimental results on a photographic image collection demonstrate the effectiveness of the proposed query expansion approaches.

This paper presents a method for recognizing sign language using hand movement trajectory. By applying Kernel Principal Component Analysis (KPCA), the motion trajectory data are firstly mapped into a higher-dimensional feature space for analysis. The advantage of using high dimensionality is that it allows a more flexible decision boundary and thus helps us to achieve better classification accuracy. Then we perform the Nonparametric Discriminant Analysis (NDA) in the higher-dimensional feature space, so that the most helpful information can be extracted. We have tested the proposed method using the Australian Sign Language (ASL) data set. The results demonstrate that our approach outperforms the current state-of-the-art for trajectory-based sign language recognition.

Optical flow calculation from feature space analysis through an automatic segmentation process

Convolutional neural networks (CNNs) are powerful tools that can be trained on image classification tasks and share many structural and functional similarities with biological visual systems and mechanisms of learning. In addition to serving as a model of biological systems, CNNs possess the convenient feature of transfer learning where a network trained on one task may be repurposed for training on another, potentially unrelated, task. In this retrospective study of public domain MRI data, we investigate the ability of neural network models to be trained on brain cancer imaging data while introducing a unique camouflage animal detection transfer learning step as a means of enhancing the networks' tumor detection ability. Training on glioma and normal brain MRI data, post-contrast T1-weighted and T2-weighted, we demonstrate the potential success of this training strategy for improving neural network classification accuracy. Qualitative metrics such as feature space and DeepDreamImage analysis of the internal states of trained models were also employed, which showed improved generalization ability by the models following camouflage animal transfer learning. Image saliency maps further this investigation by allowing us to visualize the most important image regions from a network's perspective while learning. Such methods demonstrate that the networks not only 'look' at the tumor itself when deciding, but also at the impact on the surrounding tissue in terms of compressions and midline shifts. These results suggest an approach to brain tumor MRIs that is comparable to that of trained radiologists while also exhibiting a high sensitivity to subtle structural changes resulting from the presence of a tumor.

Over than three_ forth of lands in Iran are located in arid or semi arid environment. There are 60 playa in Iran which covers about 67250 square kilometers. Major part of human settlements in such a areas are subjected to the problems such as desertification, sand blowing, salinization, vegetation deterioration and erosion. Sustainable management of such lands especially for areas which are near the playa due to inaccessibility and limitations in resources is a challenging task which is need up-to-data and precise information for land covers situations. In this context, remotely sensed data, especially because of clear sky, shortage of rainfall and thus low moisture contents, has been showed very promising and valuable tools in arid lands. In most of studies, due to lower resolution (60 m) of thermal bands comparing to other SWIR and VIR bands (30 m), this band, despite of bearing valuable information are being eliminated from processing and classifications. In this research, the data of Landsat 7 (ETM+) Path/Row 163-35, dated 20 July 2000 acquired. Using Visual _ Digital Analysis (VDA) such as color composite, feature space and spectral signature analysis, homogeneous classes are defined. Then based on field works, auxiliary data and GPS points, 19 classes were defined. In order to study the role of thermal bands and contribution to accuracy improvement, four combination bands feed to maximum likelihood classifiers. The result showed, for such a studies in arid lands, thermal bands despite of lower resolution, is essential which including valuable information and could improve accuracy.

This paper presents an MRI feature-space image-analysis method and its application to brain tumor studies. The proposed method generates a transformed feature space in which the normal tissues (white matter, gray matter, and CSF) become orthonormal. As such, the method is expected to have site-to-site and patient-to-patient consistency, and is useful for identification of tissue types, segmentation of tissues, and quantitative measurements on tissues. The steps of the work accomplished are as follows: (1) Four T2-weighted and two T1-weighted images (before and after injection of gadolinium) were acquired for 10 tumor patients. (2) Images were analyzed by an image analyst according to the proposed algorithm. (3) Biopsy samples were extracted from each patient and were subsequently analyzed by the pathology laboratory. (4) Image-analysis results were compared with the biopsy results. Pre- and postsurgery feature spaces were also compared. The proposed method made it possible to visualize the MRI feature space and to segment the image. In all cases, the operators were able to find clusters for normal and abnormal tissues. Also, clusters for different zones of the tumor were found. The method successfully segmented the image into normal tissues (white matter, gray matter, and CSF) and different zones of the lesion (tumor, cyst, edema, radiation necrosis, necrotic core, and infiltrated tumor). The results agreed with those obtained from the biopsy samples. Comparison of pre- with postsurgery and radiation feature spaces illustrated that the original solid tumor was not present in the second study, but a new tissue component appeared in a different location of the feature space. This tissue could be radiation necrosis generated as a result of radiation.

Interests in skyline query processing have significantly increased since it can be used in many applications, such as multi-criteria decision making, data mining, and user preference queries. In this paper, we propose continuous skyline queries over dynamic data set. Since the dynamic data set constantly changes as time passes, continuous skyline queries processing over dynamic data set becomes ever more complicated compared with static data set. We propose a multiple layer grids scheme for efficiently processing continuous skyline queries over skewed data set. The proposed scheme divides the work space into multiple layer grids and creates the skyline influence regions of each layer grid based on the existing data set. The continuous skyline queries are handled only when the dynamic data points update within the skyline influence regions of each layer grid. Since the multiple layer grids are divided dynamically, it is feasible even for the highly skewed data set. Experiments based on various synthetic data sets and real data sets show that our proposed scheme outperforms the existing schemes.

With the surge of data-driven analysis techniques, there is a rising demand for enhancing the exploration of large high-dimensional data by enabling interactions for the joint analysis of features (i.e., dimensions). Such a dual analysis of the feature space and data space is characterized by three components, 1) a view visualizing feature summaries, 2) a view that visualizes the data records, and 3) a bidirectional linking of both plots triggered by human interaction in one of both visualizations, e.g., Linking & Brushing. Dual analysis approaches span many domains, e.g., medicine, crime analysis, and biology. The proposed solutions encapsulate various techniques, such as feature selection or statistical analysis. However, each approach establishes a new definition of dual analysis. To address this gap, we systematically reviewed published dual analysis methods to investigate and formalize the key elements, such as the techniques used to visualize the feature space and data space, as well as the interaction between both spaces. From the information elicited during our review, we propose a unified theoretical framework for dual analysis, encompassing all existing approaches extending the field. We apply our proposed formalization describing the interactions between each component and relate them to the addressed tasks. Additionally, we categorize the existing approaches using our framework and derive future research directions to advance dual analysis by including state-of-the-art visual analysis techniques to improve data exploration.

Fatalities due to road accidents are mainly caused by distracted driving. Driving demands continuous attention of the driver. Certain levels of distraction while driving can cause the driver lose his/her attention which might lead to a fatal accident. Thus, early detection of distraction will help reduce the number of accidents. Several researches have been conducted for automatic detection of driver distraction. Many previous approaches have employed camera based techniques. However, these methods might detect the distraction rather late to warn the drivers. Although neurophysiological signals using Electroencephalography (EEG) have shown to be another reliable indicator of distraction, EEG signals are very complex and the technology is intrusive to the drivers, which creates serious doubt for its implementation. In this study we investigate Galvanic Skin Responses (GSR) using a wrist band wearable and conduct an empirical characterization of driver GSR signals during a naturalistic driving experiment. We explored time and frequency domain to extract relevant features to capture the changes/patterns at the physiological level. Due to the fact that feature extraction is a manual process and to normalize the feature space toward the identification task, we then transform the feature space using linear discriminant dimensionality reduction to discover discriminative bases of the GSR multivariate feature space that identify distraction. That would eliminate both the computational complexity and the redundancies in the manually generated feature space. Due to multi-class nature of the identification task, there might be biases between the distracted and non-distracted categories that can bias the estimation of between- and within-class scatter matrices. Therefore, we incorporated a class dependent weight to calculate the within class scatter matrices. The proposed weight aims to increase the flexibility of the discriminative bases vectors to capture the factors that focus on eliminating the overlap between distracted versus non-distracted in the generalization phase. Our experimental results demonstrated high cross validation accuracies of distraction identification using GSR signals (i.e. 85.19%). Conducting dimensionality reduction using LDA resulted in slight improvement in accuracy (i.e. 85.94%) using only two discriminant bases. The generalization accuracy was further improved by applying our proposed weighting mechanism (i.e. 88.92%).