Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized average detection probability of centralized cooperative-MIMO cognitive radio network over β — μ faded reporting channel

In this paper a generalized closed form expression for the average detection probability of an energy detector is derived in terms of computable Meijer G-function. Where the reporting channel in the centralized cooperative spectrum sensing is experiencing a n - μ fading. The reporting network is modeled as secondary users and fusion center equipped with multiple antenna for reporting. The fusion center combines the received data using maximal ratio combining and performs energy detection to make decision. The arrived generalized low complex expression offers the different fading conditions of reporting channel. Also, it is generalized for multiple antenna scenario. The arrived expression is validated by analytical results over different n - μ values with various multiple antenna scenario.
Biomedical Data Mining Using RBF Neural Networks

Accurate diagnosis of cancers is of great importance for doctors to choose a proper treatment. Furthermore, it also plays a key role in the searching for the pathology of cancers and drug discovery. Recently, this problem attracts great attention in the context of microarray technology. Here, we apply radial basis function (RBF) neural networks to this pattern recognition problem. Our experimental results in some well-known microarray data sets indicate that our method can obtain very high accuracy with a small number of genes. baCkgROUND
What should I comment: Recommending posts for commenting

Nowadays, with the appearance of the Internet and personal computers, Web becomes one of the most important vehicles to convey information. There are many new forms of information on the Web, including websites, blogs, wikis, social networks, and Internet forums. The explosion of user-generated content poses challenges to browsing and finding valuable information on the Web. In this paper, we present a study on the task of recommending, for a given user, a short list of suitable forum posts for commenting. We propose a collaborative filtering method which exploits the co-commenting patterns of the users to generate recommendations, and compare the method with traditional content-based filtering approaches. Experimental results on two types of forums show that the proposed collaborative filtering method achieved substantial improvements in terms of accuracy over a baseline and the content-based filtering methods. The results also demonstrate the stability of our method in handling new posts with small number of comments.
Ieee Transactions on Evolutionary Computation 1 Functions with Noise-induced Multi-modality: a Test for Evolutionary Robust Optimization – Properties and Performance Analysis

— This paper proposes and analyzes a class of test functions for evolutionary robust optimization, the so-called " Functions with Noise-Induced Multi-Modality " (FNIMs). After a motivational introduction gleaned from a real-world optimization problem, the robust optimizer properties of this test class are investigated w.r. t. different robustness measures. The steady state behavior of Evolution Strategies (ES) on FNIMs will be investigated empirically. Being based on the empirical results, a subclass of FNIMs is identified which is amenable to an asymptotical performance analysis. The results of this analysis will be used to derive recommendations for the choice of strategy-specific parameters such as population size and truncation ratio.
Sex classification of face areas: How well can a linear neural network predict human performance?

Human subjects and an artificial neural network, composed of an autoassociative memory and a perceptron, gender classified the same 160 frontal face images (80 male and 80 female). All 160 face images were presented under three conditions (1) full face image with the hair cropped (2) top portion only of the Condition 1 image (3) bottom portion only of the Condition 1 image. Predictions from simulations using Condition 1 stimuli for training and testing novel stimuli in Conditions 1, 2, and 3, were compared to human subject performance. Although the network showed a fair ability to generalize learning to new stimuli under the three conditions, performing from 66 to 78% correctly on novel faces, and predicted main effects, a more detailed comparison with the human data was not as promising. As expected, human accuracy declined with decreased image area, but showed a surprising interaction between the sex of the face and the partial image conditions. The network failed to predict this interaction, or the likelihood of correct human classification for a particular face. This analysis on an item level raises concern about the psychological relevance of the model.
Predicting Vulnerable Software Components via Text Mining

This paper presents an approach based on machine learning to predict which components of a software application contain security vulnerabilities. The approach is based on text mining the source code of the components. Namely, each component is characterized as a series of terms contained in its source code, with the associated frequencies. These features are used to forecast whether each component is likely to contain vulnerabilities. In an exploratory validation with 20 Android applications, we discovered that a dependable prediction model can be built. Such model could be useful to prioritize the validation activities, e.g., to identify the components needing special scrutiny.
Social network analysis, methods and measurements calculations

In recent years, the term of social network has become a very interesting field for research. It requires a specific approach in order to research what happened on behind of social network. This goal is based on scientific analysis, typically on measurements that are defined on 60's and the today's developed methods. Measurements and relations between them will be analyzed, using the modern software for social network analysis such as UCINET and ORA. This related work is completed on many steps, while for data analysis is used DBLP dataset. The main propose of this paper was the analysis of co-authorship in a specific conference and relation of these co-authors with paper proceedings.
Effective sampling and learning for mallows models with pairwise-preference data

Learning preference distributions is a critical problem in many areas (e.g., recommender systems, IR, social choice). However, many existing learning and inference methods impose restrictive assumptions on the form of user preferences that can be admitted as evidence. We relax these restrictions by considering as data arbitrary pairwise comparisons of alternatives, which represent the fundamental building blocks of ordinal rankings. We develop the first algorithms for learning Mallows models (and mixtures thereof) from pairwise comparison data. At the heart of our technique is a new algorithm, the generalized repeated insertion model (GRIM), which allows sampling from arbitrary ranking distributions, and conditional Mallows models in particular. While we show that sampling from a Mallows model with pairwise evidence is computationally difficult in general, we develop approximate samplers that are exact for many important special cases|and have provable bounds with pairwise evidence--and derive algorithms for evaluating log-likelihood, learning Mallows mixtures, and non-parametric estimation. Experiments on real-world data sets demonstrate the effectiveness of our approach.
Autonomous Learning of Object-specific Grasp Affordance Densities

In this paper, we address the issue of learning and representing object grasp affordances. Our first aim is to organize and memorize, independently of grasp information sources, the whole knowledge that an agent has about the grasping of an object, in order to facilitate reasoning on grasping solutions and their likelihood of success. By grasp affordance, we refer to the the different ways to place a hand or a gripper near an object so that closing the gripper will produce a stable grip. The grasps we consider are parametrized by a 6D gripper pose and a grasp (preshape) type. The gripper pose is composed of a 3D position and a 3D orientation, defined within an object-relative reference frame. We represent the affordance of an object for a certain grasp type through a continuous probability density function defined on the 6D object-relative gripper pose space SE(3), similar to the approach of de Granville et al. [2]. The computational encoding is nonparametric: A density is simply represented by the samples we see from it. The samples supporting a density are called particles; the probabilistic density in a region of space is given by the local density of the particles in that region. The underlying continuous density is accessed by assigning a kernel function to each particle ‐ a technique generally known as kernel density estimation [6].
The Relationship between Cortical Magnification Factor and Population Receptive Field Size in Human Visual Cortex: Constancies in Cortical Architecture

Receptive field (RF) sizes and cortical magnification factor (CMF) are fundamental organization properties of the visual cortex. At increasing visual eccentricity, RF sizes increase and CMF decreases. A relationship between RF size and CMF suggests constancies in cortical architecture, as their product, the cortical representation of an RF (point image), may be constant. Previous animal neurophysiology studies of this question yield conflicting results. Here, we use fMRI to determine the relationship between the population RF (pRF) and CMF in humans. In average and individual data, the product of CMF and pRF size, the population point image, is near constant, decreasing slightly with eccentricity in V1. Interhemisphere and subject variations in CMF, pRF size, and V1 surface area are correlated, and the population point image varies less than these properties. These results suggest a V1 cortical processing architecture of approximately constant size between humans. Up the visual hierarchy, to V2, V3, hV4, and LO1, the population point image decreases with eccentricity, and both the absolute values and rate of change increase. PRF sizes increase between visual areas and with eccentricity, but when expressed in V1 cortical surface area (i.e., corticocortical pRFs), they are constant across eccentricity in V2/V3. Thus, V2/V3, and to some degree hV4, sample from a constant extent of V1. This may explain population point image changes in later areas. Consequently, the constant factor determining pRF size may not be the relationship to the local CMF, but rather pRF sizes and CMFs in visual areas from which the pRF samples.
Two-Component Signal Transduction System SaeRS Positively Regulates Staphylococcus epidermidis Glucose Metabolism

Staphylococcus epidermidis, which is a causative pathogen of nosocomial infection, expresses its virulent traits such as biofilm and autolysis regulated by two-component signal transduction system SaeRS. In this study, we performed a proteomic analysis of differences in expression between the S. epidermidis 1457 wild-type and saeRS mutant to identify candidates regulated by saeRS using two-dimensional gel electrophoresis (2-DE) combined with matrix-assisted laser desorption/lonization mass spectrometry (MALDI-TOF-MS). Of 55 identified proteins that significantly differed in expression between the two strains, 15 were upregulated and 40 were downregulated. The downregulated proteins included enzymes related to glycolysis and TCA cycle, suggesting that glucose is not properly utilized in S. epidermidis when saeRS was deleted. The study will be helpful for treatment of S. epidermidis infection from the viewpoint of metabolic modulation dependent on two-component signal transduction system SaeRS.
Is the diagnosis of metabolic syndrome useful for predicting cardiovascular disease in asian diabetic patients? Analysis from the Japan Diabetes Complications Study.

OBJECTIVE The metabolic syndrome (MetS) is believed to be associated with an increased risk of cardiovascular disease (CVD). Although its prevalence is extremely high among diabetic patients, its prevalence in those with no history of CVD has not been determined. Moreover, prospective studies published on the association between MetS and cardiovascular events in diabetic populations have used only the World Health Organization (WHO) definition of MetS and included only white European subjects. The aim of this study was to determine the prevalence of MetS, as defined by both the WHO and the National Cholesterol Education Program (NCEP), and its predictive value for CVD in Asian diabetic patients in a long-term, prospective setting. RESEARCH DESIGN AND METHODS The baseline characteristics and incidence/hazard ratio of cardiovascular events (coronary heart disease and stroke) were determined in 1,424 Japanese type 2 diabetic patients with and without MetS, as defined by WHO (WHO-MetS) or the NCEP. RESULTS A high prevalence (38-53%, depending on sex and definition) of MetS was found among diabetic patients, even those with no history of CVD. During the 8-year study period, only WHO-MetS was a predictor for CVD in female patients. In male patients, although both definitions of MetS were significant predictors for CVD, individual components of MetS, such as hyperlipidemia or hypertension, were equivalent or better predictors. CONCLUSIONS We found that MetS is relatively common in diabetic patients with no history of CVD. We suggest that the commonly used definitions of MetS, at least in their present forms, have limited clinical usefulness for Asian diabetic patients and may need some ethnic group-specific modifications for global use.
Language Independent Feature Extractor

We propose a new customizable tool, Language Independent Feature Extractor (LIFE), which models the inherent patterns of any language and extracts relevant features of thelanguage. There are two contributions of this work: (1) no labeled data is necessary to train LIFE (It works when a sufficient number of unlabeled documents are given), and (2) LIFE is designed to be applicable to any language. We proved the usefulness of LIFE by experimental results of time information extraction.
The effect of mobile velocity on a communication system operating over multipath fading channels

We present an analytical model to determine the probability of error for a communication system as a function of the mobile velocity. We investigate the performance of a system model with power control and error control coding. In a multipath fading environment, the rate of fading is highly dependent on the velocity of the mobile. To incorporate the effect of velocity, we apply a Markovian model to the channel that captures the correlation between successive bits. We then develop an error recursion that accounts for the power control and the channel coding. At low speeds, we find that effective power control is crucial to maintain low error rates. At higher speeds where power control is ineffective, error control coding is necessary to mitigate the channel impairments. For more intermediate speeds, the model gives the worst performance because neither power control nor error control coding can counteract the effects of the channel.
Decentralized optimal control of a car platoon with guaranteed string stability

This paper presents new decentralized optimal strategies for Cooperative Adaptive Cruise Control (CACC) of a car platoon under string-stability constraints. Two related scenarios are explored in the article: in the first one, a linear-quadratic regulator in the presence of measurable disturbances is synthesized, and the string-stability of the platoon is enforced over the controller's feedback and feedforward gains. In the second scenario, H2- and H∞-performance criteria, respectively accounting for the desired group behavior and the string-stability of the platoon, are simultaneously achieved using the recently-proposed compensator blending method. An analytical study of the impact of actuation/communication delays and uncertain model parameters on the stability of the multi-vehicle system, is also conducted. The theory is illustrated via numerical simulations.
Collider signatures of new large space dimensions

Recently, Arkani-Hamed, Dimopoulos, and Dvali have proposed that there are extra compact dimensions of space, accessible to gravity but not to ordinary matter, which could be macroscopically large. In this letter, we argue that high-energy collider processes in which gravitons are radiated into these new dimensions place significant, model-independent constraints on this picture. We present the constraints from anomalous single photon production at e{sup +}e{sup {minus}} colliders and from monojet production at hadron colliders.
A Bayesian context fear learning algorithm/automaton

Contextual fear conditioning is thought to involve the synaptic plasticity-dependent establishment in hippocampus of representations of to-be-conditioned contexts which can then become associated with USs in the amygdala. A conceptual and computational model of this process is proposed in which contextual attributes are assumed to be sampled serially and randomly during contextual exposures. Given this assumption, moment-to-moment information about such attributes will often be quite different from one exposure to another and, in particular, between exposures during which representations are created, exposures during which conditioning occurs, and during recall sessions. This presents challenges to current conceptual models of hippocampal function. In order to meet these challenges, our model's hippocampus was made to operate in different modes during representation creation and recall, and non-hippocampal machinery was constructed that controlled these hippocampal modes. This machinery uses a comparison between contextual information currently observed and information associated with existing hippocampal representations of familiar contexts to compute the Bayesian Weight of Evidence that the current context is (or is not) a known one, and it uses this value to assess the appropriateness of creation or recall modes. The model predicts a number of known phenomena such as the immediate shock deficit, spurious fear conditioning to contexts that are absent but similar to actually present ones, and modulation of conditioning by pre-familiarization with contexts. It also predicts a number of as yet unknown phenomena.
Knowing dance or knowing how to dance?

The study of human movement and action has become a topic of increasing relevance over the last decade, bringing dance into the focus of the cognitive sciences. The Neurocognition of Dance brings together contributors from the worlds of psychology and dance, and discusses the relationship between dance and perception. Fully updated throughout, this edition introduces scientific perspectives on human movement, before dance professionals considering how their creative work relates to cognition and learning. Finally, researchers with personal links to the dance world demonstrate how neurocognitive methods are applied to studying different aspects related to dance.
Extended Emission Line Gas in Radio Galaxies: PKS 0349–27

PKS 0349-27 is a classical FR II radio galaxy whose active galactic nucleus host has a spectacular, spiral-like structure in its extended emission line gas (EELG). We have measured the velocity field in this gas and find that it splits into two cloud groups separated by radial velocities that at some points approach 400 km s-1. Measurements of the diagnostic emission line ratios [O III] 5007/Hβ, [S II] 6716+6731/Hα, and [N II] 6583/Hα in these clouds show no evidence for the type of H II region emission associated with starburst activity in either velocity system. The measured emission line ratios are similar to those found in the nuclei of narrow-line radio galaxies, but the extended ionization/excitation cannot be produced by continuum emission from the active nucleus alone. We present arguments that suggest that the velocity disturbances seen in the EELG are most likely the result of a galaxy-galaxy collision or merger, but we cannot completely rule out the possibility that the gas has been disrupted by the passage of a radio jet.
Integrating Positivist and Interpretive Approaches to IS Security Research

The use of a socio-organizational perspective for understanding information system security is still at a theory building stage. This is due in part to the slow progress of positivist research in a domain where is very often difficult to have access to empirical data for quantitative studies. However, even if interpretive research can be performed on a single case basis, this kind of studies are facing the lack of any prescriptive component and subsequently the lack of value to the security manager. In this paper the main positivist and interpretive concepts and assumptions will be introduced. Then, an integrated framework already proposed in the organizational literature will be presented and the possibility to apply this framework to the IS security domain will be investigated.
A novel semi-feature selection method based on hybrid feature selection mechanism

Many Semi-supervised learning applications require a feature selection method to deal with the unlabeled samples. Traditional researches deal it either with the “filter-type” feature selection mechanism, which may not work well for classification tasks or “wrapper” mechanism, which need high computational cost. Here we proposed a new semi-supervised feature selection method based on hybrid feature selection mechanism. Its principle lies in using Relief Wrapper method to explore the usage of unlabeled examples, which will help for training classifiers. In essence, it uses unlabeled examples to extend the initial labeled training set with the help of classifiers. Extensive experiments on publicly available datasets and formal analysis show its nice combination of efficiency and accuracy
Ratio of Trunk to Leg Volume as a New Body Shape Metric for Diabetes and Mortality

Background Body shape is a known risk factor for diabetes and mortality, but the methods estimating body shape, BMI and waist circumference are crude. We determined whether a novel body shape measure, trunk to leg volume ratio, was independently associated with diabetes and mortality. Methods Data from the National Health and Nutritional Examination Survey 1999–2004, a study representative of the US population, were used to generate dual-energy X-ray absorptiometry-derived trunk to leg volume ratio and determine its associations to diabetes, metabolic covariates, and mortality by BMI category, gender, and race/ethnicity group. Results The prevalence of pre-diabetes and diabetes increased with age, BMI, triglycerides, blood pressure, and decreased HDL level. After adjusting for covariates, the corresponding fourth to first quartile trunk to leg volume ratio odds ratios (OR) were 6.8 (95% confidence interval [CI], 4.9–9.6) for diabetes, 3.9 (95% CI, 3.0–5.2) for high triglycerides, 1.8 (95% CI, 1.6–2.1) for high blood pressure, 3.0 (95% CI, 2.4–3.8) for low HDL, 3.6 (95% CI, 2.8–4.7) for metabolic syndrome, and 1.76 (95% CI, 1.20–2.60) for mortality. Additionally, trunk to leg volume ratio was the strongest independent measure associated with diabetes (P<0.001), even after adjusting for BMI and waist circumference. Even among those with normal BMI, those in the highest quartile of trunk to leg volume ratio had a higher likelihood of death (5.5%) than those in the lowest quartile (0.2%). Overall, trunk to leg volume ratio is driven by competing mechanisms of changing adiposity and lean mass. Conclusions A high ratio of trunk to leg volume showed a strong association to diabetes and mortality that was independent of total and regional fat distributions. This novel body shape measure provides additional information regarding central adiposity and appendicular wasting to better stratify individuals at risk for diabetes and mortality, even among those with normal BMI.