Miracle-dz/UniFAR-FTUs huggingface dataset:
A multi-modular approach for design and evaluation of context information interactive systems

In the pervasive environment, the context, the data/information processing system and the human being comprise an information chain, along which the information from the context is conducted to the user in a comprehensive way. However, current context-aware systems and methodologies are more concerned about the processing systems without sufficient consideration about the relationships between all three parties on the chain, causing a lack of comprehensiveness. Therefore, a high level design and evaluation approach is proposed in this paper targeted towards providing an abstract design guideline for context-aware systems development and an evaluation method assessing eight selected parameters related to all three parties on the information chain and their relationships. Three typical modular systems are demonstrated, which present more intuitive and comprehensive representation by following the proposed design guideline. These three systems are also evaluated with an overall consideration of the whole information lifecycle, using the proposed method.
Altered resting-state amygdala functional connectivity in men with posttraumatic stress disorder.

BACKGROUND Converging neuroimaging research suggests altered emotion neurocircuitry in individuals with posttraumatic stress disorder (PTSD). Emotion activation studies in these individuals have shown hyperactivation in emotion-related regions, including the amygdala and insula, and hypoactivation in emotion-regulation regions, including the medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC). However, few studies have examined patterns of connectivity at rest in individuals with PTSD, a potentially powerful method for illuminating brain network structure. METHODS Using the amygdala as a seed region, we measured resting-state brain connectivity using 3 T functional magnetic resonance imaging in returning male veterans with PTSD and combat controls without PTSD. RESULTS Fifteen veterans with PTSD and 14 combat controls enrolled in our study. Compared with controls, veterans with PTSD showed greater positive connectivity between the amygdala and insula, reduced positive connectivity between the amygdala and hippocampus, and reduced anticorrelation between the amygdala and dorsal ACC and rostral ACC. LIMITATIONS Only male veterans with combat exposure were tested, thus our findings cannot be generalized to women or to individuals with non-combat related PTSD. CONCLUSION These results demonstrate that studies of functional connectivity during resting state can discern aberrant patterns of coupling within emotion circuits and suggest a possible brain basis for emotion-processing and emotion-regulation deficits in individuals with PTSD.
On the Efficiency of Support Vector Classifiers for intrusion detection

We implement multi-class SVMs (by one-versus-rest, one-versus-rest method and a new decision tree (DT) SVM) for intrusion detection. None of these methods show advantages over two-class method wherever in detection accuracy or time cost in our experiments. We also apply a support vector (SV) reduction algorithm and find that it decreases the training time dramatically while improves the detection rate
THE PRIVACY FEATURE OF TRUSTED COMPUTING TECHNOLOGY USING THE CONCEPT OF DIRECT ANONYMOUS ATTESTATION WITH CLOUD AS A TECHNIQUE

The Trusted Platform Module (TPM) is a hardware chip designed to enable computers to achieve a greater level of security. In the Distributed Environment all the systems are connected together with a limited bandwidth security feature. The TPM module will increase the security feature by the concept of DAA. Direct anonymous attestation (DAA) is a special digital signature primitive, which provides a balance between signer authentication and privacy. A DAA scheme involves a set of issuers, signers, and verifiers. An issuer is in charge of verifying the legitimation of signers and of issuing a DAA credential to each signer. The TPM is the real signer and holds the secret signing key. Authentication can be applied at the level of the virtual private network (VPN). As a technique of cloud service the authentication to the distributed environment can be modeled with the Trusted Platform Technology.
An efficient carrier frequency offset estimate for MIMO-OFDM in LTE system

The problem of pilot assisted carrier frequency offset (CFO) estimation in LTE system is investigated in this paper. Since both the pilot design and the number of pilot symbols are already fixed in LTE technical standards, it is proposed to employ multiple successive pilot symbols to improve the CFO estimation performance under the LTE standard constraints. We firstly present the generalized CRLB (GCRLB) analysis based on multiple successive pilot symbols. It is revealed that, better CFO estimate can be achieved by jointly using multiple successive pilot symbols than by using only one pilot symbol. The enhanced maximum likelihood (ML) carrier frequency offset estimate problem is formulated and simulation results are presented to validate that the proposed CFO estimate scheme offers an effective mechanism to improve the CFO estimate in practical LTE system.
A novel general structure of tunable frequency selective surface without bias grid

A novel, general structure of tunable frequency-selective surface that does not require additional bias networks is presented. Varactor diodes are positioned in the middle of each cell, and no via is required. Inductors between the cells provide DC connections, which also choke high frequency signal. Two FSS patterns are applied in the tunable structure. Simulation results of the two FSS patterns both show that by altering the capacitance of the varactors from 0.3 to 3.0 pF, a wide frequency tunability can be achieved, and at the same time the bandwidth becomes obviously wider.
Real-time head pose classification in uncontrolled environments with Spatio-Temporal Active Appearance Models

In this paper, we present a full-automatic real-time system for recognizing the head pose in uncontrolled environments over a cotinouos spatio-temporal behavior by the subject . The method is based on tracking facial features through Active Appearance Models. To differentiate and identify the different head pose we use a multiclassifier composed of different binary Support Vector Machines. Finally, we propose a continuous solution to the problem using the Tait-Bryan angles, addressing the problem of the head pose as an object that performs a rotary motion in dimensional space.
A SAT-Based Implication Engine for Efficient Derivation of Indirect Impli cations

This paper presents a flexible and efficient approach to deriv ing indirect implications in logic circuits. Indirect implica tions are essential in ATPG, equivalence checking, and netlist optimizati on. Contrary to other methods, our approach is based on a graph model f a circuit’s clause description called the implication grap h. It combines both the flexibility of SAT-based techniques and high e fficiency of structure based methods. As the proposed algorithms oper ate only on the implication graph, they are independent of the chosen logic. Computation of indirect implications is performed by simpl e and efficient graph algorithms. Experimental results for various a pplications relying on indirect implications demonstrate the efficienc y of our approach.
Brain machine interface: Motor imagery recognition with different signal length representations

This work investigates how signal representations affect the performance of a motor imagery recognition system, specifically we investigate on recognition accuracy and computational time of a brain machine interface designed using motor imagery. Experiments show that the signal length should not be larger than a critical range for good recognition accuracy. The results presented here is a part of our work on the design and development of a brain machine interface to operate a wheelchair. EEG motor imagery signals recorded from the motor cortex area using non-invasive electrodes, are used for recognition of four tasks namely, left, right, forward and stop. Experiments are conducted for 12 signal representations from signal lengths varying from 3s to 0.25s. From the results it is observed that good recognition accuracies (93.2% –94.2%) are obtainable for 2s to 3s signal representations
Robust combining methods in committee neural networks

Combining a set of suitable experts can improve the generalization performance of the group when compared to single experts alone. The classical problem in this area is to answer the question about how to combine the ensemble members or the individuals. Different methods for combining the outputs of the experts in a committee machine (ensemble) are reported in the literature. The popular method to determine the error in every prediction is Mean Square Error (MSE), which is heavily influenced by outliers that can be found in many real data such as geosciences data. In this paper we introduce Robust Committee Neural Networks (RCNNs). Our proposed approach is the Huber and Bisquare function to determine the error between measured and predicted value which is less influenced by outliers. Therefore, we have used a Genetic Algorithm (GA) method to combine the individuals with the Huber and Bisquare as the fitness functions. The results show that the Root Mean Square Error (RMSE) and R-square values for these two functions are improved compared to the MSE as the fitness function and the proposed combiner outperformed other five existing training algorithms.
Verifying Linked Data Structure Properties using Roles

Introduction: Types capture important properties of the objects that programs manipulate, increasing both the reliability and the maintainability of the program. Traditional type systems capture properties (such as the format of data items stored in the fields of the object) that are invariant over the lifetime of the object. But in many cases, properties that do change are as important as properties that do not. In a typestate system (first introduced in [1]) the “type” of the object changes as the values stored in its fields change.
Edge Preserving Image Denoising Using Wavelet Approach and Local Linear SURE Method

In this paper, we propose a new approach for performing edgepreserving image filtering. This paper deals with wavelet decomposition, local linear Stein’s unbiased risk estimate as an estimator for the mean squared error from the noisy image only, we derive a simple explicit image filter which can filter out noise while preserving edges and fine-scale details. Moreover, this filter has a 6 db better PSNR and minimum Standard Deviation algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks. The experimental results demonstrate the effectiveness of the new filter for various applications, including noise reduction.
Siegel automorphic form corrections of some Lorentzian Kac-Moody Lie algebras

We find automorphic form corrections which are generalized Lorentzian Kac-Moody superalgebras without odd real simple roots for two elliptic Lorentzian Kac-Moody algebras of rank 3 with a lattice Weyl vector, and calculate multiplicities of their simple and arbitrary roots. These Kac-Moody algebras are defined by hyperbolic symmetrized generalized Cartan matrices [inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" xlink:href="01i" /] of rank 3. Both these algebras have elliptic type (i.e., their Weyl groups have fundamental polyhedra of finite volume in corresponding hyperbolic spaces) and have a lattice Weyl vector. The correcting automorphic forms are Siegel modular forms. The form corresponding to G 1 is the classical Siegel cusp form of weight 5 which is the product of ten even theta-constants. In particular we find an infinite product formula for this modular form.
Combined ART started during acute HIV infection protects central memory CD4+ T cells and can induce remission.

BACKGROUND Therapeutic control of HIV replication reduces the size of the viral reservoir, particularly among central memory CD4+ T cells, and this effect might be accentuated by early treatment. METHODS We examined the effect of ART initiated at the time of the primary HIV infection (early ART), lasting 2 and 6 years in 11 and 10 patients, respectively, on the HIV reservoir in peripheral resting CD4+ T cells, sorted into naive (TN), central memory (TCM), transitional memory (TTM) and effector memory (TEM) cells, by comparison with 11 post-treatment controllers (PTCs). RESULTS Between baseline and 2 years, CD4+ T cell subset numbers increased markedly (P < 0.004) and HIV DNA levels decreased in all subsets (P < 0.009). TTM cells represented the majority of reservoir cells at both timepoints, T cell activation status normalized and viral diversity remained stable over time. The HIV reservoir was smaller after 6 years of early ART than after 2 years (P < 0.019), and did not differ between PTCs and patients treated for 6 years. One patient, who had low reservoir levels in all T cell subsets after 2 years of treatment similar to the levels in PTCs, spontaneously controlled viral replication during 18 months off treatment. CONCLUSIONS Early prolonged ART thus limits the size of the HIV reservoir, protects long-lived cells from persistent infection and may enhance post-treatment control.
T-S Fuzzy Direct Maximum Power Point Tracking of Wind Energy Conversion Systems

This paper presents the T-S fuzzy maximum power point tracking (MPPT) control for wind energy conversion systems. First, the T-S fuzzy model-based MPPT controller is developed to directly regulate the DC rectified voltage of the generator to the maximum power point voltage. The asymptotic MPPT control is guaranteed without measuring the wind speed and searching the maximum power point. Next, the robustness of the T-S fuzzy MPPT control is discussed under uncertainties, disturbance, and rapidly changing wind speed. The maximum power operational point of the converter is not calculated in the controller design, such that uncertainties are allowed in strict stability analysis. Finally, the satisfactory performance is shown from the simulation and experimental results.
Application Experiences with a Real-Time Java Processor

Abstract In this paper we present three different industrial real-time applications that are based on an embedded Java processor. Although from different application domains all three projects have one topic in common: communication. Today's embedded systems are networked systems. Either a proprietary protocol is used due to legacy applications or for real-time aspects or standard Internet protocols are used. We present the challenges and solutions for this variety of protocols in small, memory constraint embedded devices.
On the remitting patterns of immigrants: evidence from Mexican survey data

The information technology (IT) boom dramatically boosted the rapid growth of the U.S. economy during the 1990s, contributing 1.4 percentage points of the 4.6 percent national average real gross domestic product growth from 1996 to 2000. As the IT boom went bust in 2001, however, the IT sector’s influence on the economy dwindled. ; But a lingering effect of the IT boom may still be apparent in the wages of IT workers. This article explores the extent to which variations in wages between IT-producing and non-IT industries can be accounted for by differences in wages paid to IT-related occupations. ; Using data for 1996 to 2002 from the Current Population Survey’s Earner Study, the authors study a sample of more than 845,000 U.S. workers aged eighteen to sixty-four. The sample is categorized according to individuals’ primary job and is divided into nine industry groups—three IT-related and six non-IT-related. ; The analysis shows that the average wage of IT occupations is greater than for non-IT occupations irrespective of industry. Individual worker characteristics such as years of education may account for some of this wage differential. But even after such characteristics and occupational differences are controlled for, workers in IT-producing industries still enjoy a wage premium over workers in other sectors.
Real-time quadtree analysis using HistoPyramids

Region quadtrees are convenient tools for hierarchical image analysis. Like the related Haar wavelets, they are simple to generate within a fixed calculation time. The clustering at each resolution level requires only local data, yet they deliver intuitive classification results. Although the region quadtree partitioning is very rigid, it can be rapidly computed from arbitrary imagery. This research article demonstrates how graphics hardware can be utilized to build region quadtrees at unprecedented speeds. To achieve this, a data-structure called HistoPyramid registers the number of desired image features in a pyramidal 2D array. Then, this HistoPyramid is used as an implicit indexing data structure through quadtree traversal, creating lists of the registered image features directly in GPU memory, and virtually eliminating bus transfers between CPU and GPU. With this novel concept, quadtrees can be applied in real-time video processing on standard PC hardware. A multitude of applications in image and video processing arises, since region quadtree analysis becomes a light-weight preprocessing step for feature clustering in vision tasks, motion vector analysis, PDE calculations, or data compression. In a sidenote, we outline how this algorithm can be applied to 3D volume data, effectively generating region octrees purely on graphics hardware.
Combined Non-Local averaging and intersection of confidence intervals for image de-noising

The Non-Local means (NL-means) algorithm was recently introduced and shown state of the art performance in image de-noising. However the computational complexity of the NL- means is very high which can prevent its implementation into products. In this paper we propose a method which retains the excellent de-noising characteristics of the NL-means algorithm but with much less computational complexity. Comparison between our method, the NL-means algorithm and other fast NL-means based approaches is illustrated through several experiments.
Synthetic Oligosaccharide Stimulates and Stabilizes Angiogenesis: Structure-Function Relationships and Potential Mechanisms

Abstract: To determine the proangiogenesis effect of series of saccharides and a synthetic oligosaccharide and potential mechanisms, an in vitro 3-dimensional endothelial cell sprouting (3D-ECS) assay and the chick chorioallantoic membrane (CAM) model were used. We demonstrated that a sulfated oligosaccharide significantly promotes the endothelial capillary network initiated by vascular endothelial growth factor (VEGF) and basic fibroblast growth factor (b-FGF). Furthermore, although the capillary network initiated by VEGF and b-FGF lasts no more than 7 days, addition of a sulfated oligosaccharide significantly amplifies angiogenesis and stabilizes the capillary network of new blood vessels. In the CAM model, sulfated oligosaccharide also stimulated angiogenesis. In both the CAM and the 3D-ECS assay, structure-function studies reveal that increased saccharide chain length up to the hexa- to decasaccharide show optimal proangiogenesis efficacy. In addition, the sulfation and molecular shape (branched vs linear) of oligosaccharide are important for sustained proangiogenesis efficacy. Data indicate that chemically defined synthetic oligosaccharides can play an important role in regulation of capillary structure and stability, which may contribute to future advances in therapeutic angiogenesis. The proangiogenesis efficacy of an oligosaccharide is mediated via integrin αvβ3 and involves mitogen-activated protein kinase signaling mechanisms.
The government and politics of Spain

Acknowledgements - Introduction: Analysing Spanish Politics - PART 1: THE LEGACY OF HISTORY - Reconciling State and Nation: the Politics of Regional Diversity - The 1978 Constitutional Settlement - The Institutional Legacy of Francoism - PART 2: THE CONSTRUCTION OF A DEMOCRATIC STATE - Central Government: Monarchy, Core Executive and Parliament - The Judiciary and the 'State of Law' - The Public Sector: Reform and Renewal - The Autonomous Communities - The Electoral and Party Systems - Political Parties: Devalued Defenders of Democracy - PART 3: THE EMERGENCE OF A MODERN DEMOCRATIC POWER - Economic Modernisation - The Policy Process - Spain as an International Power - Appendices - Bibliography
Local edge detection of an image using weighted pixels based on their neighbourhood

In this paper, a new method of edge detection in gray scale images based on comparing a pixel's value to its eight neighbors' values is presented. Each pixel gets a new value based on such comparison. Similar pixels or smooth surfaces are then those which have a zero value resulting from the sum of all values and the edges are the set of pixels whose values are different from zero. This decision of assigning zero to a pixel is based on the use of an automatic range, being the mean, detected from the histogram of differences of all pixels' values. This leads to smoothing the pixels of the same regions and keep the contours only. The proposed method gives visually very similar results to some famous edge detection algorithms but with less complexity. The results can be used into to two forms in order to accommodate the objective usage.
KNAVE-II : A Distributed Architecture for Interactive Visualization and Intelligent Exploration of Time-Oriented Clinical Data

Interpretation and exploration of longitudinal clinical data is a major part of diagnosis, therapy, quality assessment, and clinical research, particularly for chronic patients. KNAVE-II is an intelligent interface to a distributed architecture specific to the tasks of query, knowledge-based interpretation, summarization, visualization, interactive exploration of large numbers of distributed time-oriented clinical data and dynamic sensitivity analysis of these data. The web-based architecture enables users (e.g., physicians) to query, visualize and explore clinical time-oriented databases. Both, the generation of context-sensitive interpretations (abstractions) of the time-stamped data, as well as the dynamic visual exploration of the raw data and the multiple levels of concepts abstracted from these data, are supported by runtime access to domain-specific knowledge bases, maintained by domain experts. KNAVE-II was designed according to a set of well-defined desiderata. The architecture enables exploration along both absolute (calendar-based) and relative (clinically meaningful) time-lines. The underlying architecture uses standardized vocabularies (such as a controlled dictionary for laboratory tests and physical observations), and predefined mappings to local data sources, for communication among its various components. Thus, the new framework enables users to access and explore multiple remote heterogeneous databases, without explicitly knowing their local structure and vocabulary, through a filter of a set of task -specific knowledge bases. The complete architecture has been implemented and is currently evaluated by expert clinicians in several medical domains, such as oncology, involving monitoring of chronic patients.