Miracle-dz/UniFAR-FTUs huggingface dataset:
The social networks' nodes grouping algorithm for the analysis of implicit communities

Communication in social networking has become an important part of life for most modern people. Sometimes people do not even think that interacting with other users, as well as sharing their views and interests online, they create a virtual circle of friends, uniting people with similar interests. Information about these user groups is very important, for example in the statistical analysis. The info of this analysis can be used to assist managers in the preparation of social targeting advertising of some new brand to consider the most important groups that may be potentially interested party. This article discusses methods and algorithms for the revealing of such groups - implicit communities. We used natural language processing methods and Page Rank algorithm to develop the methodology. The module has been successfully implemented by SMM team to search potential listeners for Vigel music project among similar style project Amersy.
Adding Multi-Threaded Decoding to Moses

Adding Multi-Threaded Decoding to Moses The phrase-based translation system Moses has been extended to take advantage of multi-core systems by using multi-threaded decoding. This paper describes how these extensions were implemented and how they can be used, as well as offering some experimental measurements of the potential speed-ups available. Details are also provided of how the multi-threaded Moses library is used to create the Moses server, a platform for building online translation systems.
Fast and robust video foreground segmentation for indoor surveillance

This work describes a method of background updating and shadow removal for indoor surveillance. Moving objects can be precisely extracted for various further process procedures such as recognition. Single-Gaussian model which has high computational speed is usually applied to the indoor environment with motionless backgrounds. The pixels of an image are classified as background pixels, moving foreground pixels and motionless foreground pixels, and the Single-Gaussian background model is updated according to the classification of a pixel. The proposed scheme makes the background model respond to environmental changes in time. With the ratio between the foreground pixel value and the background pixel value, pixels are distinguished among foreground, background and shadow. The effectiveness of the proposed method is demonstrated with experiments in an indoor environment.
Automatic Gender Identification for Hindi Speech Recognition

paper presents the preliminary work done towards the development of a Gender Recognition System that can be incorporated into the Hindi Automatic Speech Recognition (ASR) System. Gender Recognition (GR) can help in the development of speaker-independent speech recognition systems. This paper presents a general approach to identifying feature vectors that effectively distinguish gender of a speaker from Hindi phoneme utterances. 10 vowels and 5 nasals of the Hindi language were studied for their effectiveness in identifying gender of the speaker. All the 10 vowel Phonemes performed well, while b) bZ) A) ,) ,s) vks and vkS showed excellent gender distinction performance. All five nasals ³) ´) . k) u and e which were tested, showed a recognition accuracy of almost 100%. The Mel Frequency Cepstral Coefficients (MFCC) are widely used in ASR. The choice of MFCC as features in Gender Recognition will avoid additional computation. The effect of the MFCC feature vector dimension on the GR accuracy was studied and the findings presented.
Chaotic PSO for computing DOPF of power systems with VSCFGs wind farms

The deterioration of natural environment and exhaustion of conventional energy resources necessitate the use of clean renewable energy. Environmentally-friendly and renewable, wind energy is the world's fastest growing energy. Variable speed constant frequency generators(VSCFGs) are widely used in power systems with wind farms. Regarding valve-point effect of conventional generators, dynamic optimal power flow (DOPF) in VSCFGs wind farms integrated power systems is a typical complex multi-constrained non-convex non-linear programming problem. DOPF mode is established. The use of chaotic sequences in the traditional particle swarm optimization (PSO) algorithm can be helpful to escape more easily from local minima than can be done through PSO. This paper provides chaotic particle swarm optimization (CPSO) approaches for computing the established DOPF model. Simulation result showed that the proposed method is capable of yielding higher quality solutions.
2 tangible technologies are part of a wider body of developing technology known as ' ubiquitous computing ’ EXECUTIVE SUMMARY

representations REPORT 12 LITERATURE REVIEW IN LEARNING WITH TANGIBLE TECHNOLOGIES CLAIRE O’MALLEY, LEARNING SCIENCES RESEARCH INSTITUTE, SCHOOL OF PSYCHOLOGY, UNIVERSITY OF NOTTINGHAM DANAE STANTON FRASER, DEPARTMENT OF PSYCHOLOGY, UNIVERSITY OF BATH
Exploration of a distributed approach for simulating spectrum sensing in cognitive radio

Motivated by the problem of multiple unlicensed cognitive radio users seek to access unused frequency channels licensed to the primary users in a wireless communication scenario, this paper presents a simulation environment that is able to simulate various effects of radio wave propagation and shadow fading in a software model. A cognitive radio designer's intent, knowledge about the critical issues and trade-offs underlying in a particular design are implicit in the simulations set up. However, this knowledge is tightly conjoined with specific features and a thorough simulation analysis is essential. Towards this, an algorithm was developed for performing this distributed sensing method between neighboring terminals in a network. VHF TV Broadcast FM Broadcast and GSM systems were investigated as possible candidates of primary users. It was found that a high degree of detection probability could be obtained for the TV and FM broadcast systems than for GSM systems under the same environmental parameters, largely due to the higher transmit powers involved in broadcast networks. The probability of false detection cannot be completely negated without risking an increase in the probability of non-detection of a present primary system. Hence a trade-off can be shown to exist between these two probabilities. It can be concluded that minimizing the possibility of interference to primary users should take priority in this scenario.
Perceptual illusion in virtual reality using haptic interface

Recently, the haptic interface system (one of a variety of force display devices) has attracted great interest as a new virtual reality technology, stimulating vigorous research. Most reports deal with methods of constructing a haptic interface system or the development of new devices having with a particular mechanism, and the various methods suitable for force display. However, it is also necessary to clarify the relationship between the virtual dynamics characteristics of a system and a human being's feelings in order to present the accurate force sensation from virtual environment. In this paper, we evaluate the force sensations experienced by human beings with the psychological experiments that examine perceptual illusion phenomena, including the horizontal-vertical illusion and the size-weight illusion. These results show that perceptual illusions occurring in a real environment can be also reappeared in a virtual environment. Therefore, perceptual illusions may have the potential for application to virtual reality. Moreover, the characteristics of perceptual illusions can be examined by changing the virtual dynamics parameters of the haptic interface. Finally, this paper shows that the haptic interface is a very effective tool for the investigation of perceptual illusions.
PHOSPHODIESTERASE 3 A EXPRESSION IS MODULATED BY NITRIC OXIDE IN RAT PULMONARY ARTERY SMOOTH MUSCLE CELLS

Nitric oxide (NO) increases intracellular levels of cyclic guanosine monophosphate (cGMP) in vascular smooth muscle cells. NO acts, in part, by stimulating soluble guanylate cyclase (sGC) to synthesize cGMP leading to relaxation (1). Cyclic GMP is inactivated by cyclic nucleotide phosphodiesterases (PDEs), a complex family of enzymes which catalyze the hydrolysis of 3’,5’-cyclic nucleotides to their corresponding nucleoside 5’-monophosphates. PDEs are multi-domain proteins with distinct catalytic and regulatory sites (2). Eleven classes of PDEs are categorized on the basis of substrate specificity, kinetics, regulatory and immunological properties, and selective inhibition (3). In vascular smooth muscle cells, PDEs limit vasodilation induced by a variety of signaling cascades by decreasing intracellular concentrations of cAMP and cGMP. Of the eleven classes of PDE isozymes described, at least eight have been identified in the pulmonary vasculature including PDEs 1, 2, 3, 4, 5, 7, 9, 11 (4-8). Expression of PDE6 is restricted to the eye and the pineal gland (9). Some classes of phosphodiesterases can metabolize both cAMP and cGMP, whereas PDEs 5, 6, and 9 selectively hydrolyze cGMP, and PDE4, PDE7 and PDE8 metabolize cAMP selectively (10). PDE3 contributes most of total cAMP-specific PDE activity in rat pulmonary artery smooth muscle cells (11) and bovine pulmonary arteries (12). Since PDE3 has a higher affinity for cGMP than for cAMP but hydrolyzes cAMP at a 10-fold greater rate than cGMP, cGMP behaves as a competitive inhibitor of PDE3 (13, 14). As a result, increasing intracellular cGMP can subsequently alter the metabolism of intracellular cAMP by inhibiting the activity of PDE3. There are two PDE3 genes (PDE3A and PDE3B) (15, 16). PDE3A and PDE3B are structurally similar, containing a catalytic domain at the carboxy terminus and an amino-terminal domain which is important for the intracellular localization of the enzyme. PDE3A is expressed in vascular smooth muscle cells (17), cardiomyocytes (15), lung parenchyma [18], and platelets (19), whereas PDE3B is expressed in adipocytes, hepatocytes, pancreatic cells, and vascular smooth muscle cells (17). There are 3 isoforms of PDE3A that are generated by transcription from alternative start sites in the PDE3A gene (14). The aim of the current study was to characterize the influence of NO on the expression of PDE3A in rat pulmonary arterial smooth muscle cells (rPASMC). We hypothesized that NO induces PDE3A via the NO-cGMP pathway and may therefore be inhibitable by administration of ODQ. We observed that NOdonor compounds markedly induced PDE3A gene and protein JOURNAL OF PHYSIOLOGY AND PHARMACOLOGY 2010, 61, 6, 663-669 www.jpp. krakow.pl
Randomized , Placebo-Controlled Comparison of Amitriptyline , Duloxetine , and Pregabalin in Patients With Chronic Diabetic Peripheral

OBJECTIVEdChronic diabetic peripheral neuropathic pain (DPNP) is difficult to treat, with treatment regimens often inadequate at controlling pain and limited by side effects and drug tolerance. Secondary parameters, such as quality of sleep and mood, may also be important for successful DPNP management. The objectives of this study were to compare the analgesic efficacy of pregabalin, amitriptyline, and duloxetine, and their effect on polysomnographic sleep, daytime functioning, and quality of life in patients with DPNP.
Object Tracking by Maximizing Classification Score of Detector Based on Rectangle Features

In this paper, we propose a novel classifier-based object tracker. Our tracker is the combination of Rectangle Feature (RF) based detector [17], [18] and optical-flow based tracking method [1]. We show that the gradient of extended RFs can be calculated rapidly by using Integral Image method. The proposed tracker was tested on real video sequences. We applied our tracker for face tracking and car tracking experiments. Our tracker worked over 100 fps while maintaining comparable accuracy to RF based detector. Our tracking routine that does not contain image I/O processing can be performed about 500 to 2,500 fps with sufficient tracking accuracy.
GPU-Accelerated High-Speed Eye Pupil Tracking System

Pupil tracking under infrared illumination is an important tool for many researchers in physiological visual optics and ophthalmology. It is also a relevant topic for gaze tracking which is used in psychological and medical research, marketing, human-computer interaction, virtual reality and other areas. A typical setup can be either a low-cost webcam with some infrared LEDs or glasses with mounted cameras and infrared illumination. In this work, we evaluate and parallelize several pupil tracking algorithms with the aim of estimating the pupil's position and size with high accuracy in order to develop a high-speed pupil tracking system. To achieve high processing speed the original non-parallel algorithms have been parallelized by using CUDA and OpenMP. Graphics cards are designed to process images at very high frequencies and resolutions, and CUDA enables them to be used for general purpose computing. Our experimental results show that pupil tracking can be efficiently performed at high speeds with high-resolution images (up to 530 Hz with images of 1280x1024 pixels) using a state-of-the-art GP-GPU.
Layered HMM for Motion Intention Recognition

Acquiring, representing and modeling human skills is one of the key research areas in teleoperation, programming-by-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several subtask in order to provide manageable modeling. In this paper we consider the use of a layered hidden Markov model (LHMM) to model human skills. We evaluate a gestem classifier that classifies motions into basic action-primitives, or gestems. The gestem classifiers are then used in a LHMM to model a simulated teleoperated task. We investigate the online and offline classification performance with respect to noise, number of gestems, type of HMM and the available number of training sequences. We also apply the LHMM to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the LHMM is suitable for modeling teleoperative trajectory-tracking tasks and that the difference in classification performance between one and multi-dimensional HMMs for gestem classification are small. It can also be seen that the LHMM is robust w.r. t misclassifications in the underlying gestem classifiers
Feature selection for intrusion detection: an evolutionary wrapper approach

With the ongoing growth of the Internet, intrusion detection systems (IDS) play an increasing role in securing communication networks. Particularly, where sensitive and confidential information is stored or transmitted, there is a vital importance of security. In the past few years, soft-computing techniques (especially neural networks) found their way more and more into the research area of intrusion detection and are now an inherent part of it. Although, feature selection is an important task for almost all neural network applications, only very few investigations dealing with any type of automated feature selection are known in the area of intrusion detection. This article sets out an evolutionary algorithm (EA) that performs the tasks of feature selection and architecture optimization for radial basis function (RBF) networks automatically. With the feature selection process proposed, it is possible to reduce the number of input features significantly, which is very important due to the fact that the neural networks can effectively be prevented from overfitting. Furthermore, reducing the number of input features also reduces the time needed for feature extraction as well as the execution time for the trained RBF networks and allows the extraction of well interpretable rules. The results (depending on the attack type, about 2 up to 5 features are needed for attack detection on an average) are demonstrated for seven attack types using the DARPA 1998 intrusion detection evaluation data.
What is decreased by the max-sum arc consistency algorithm?

Inference tasks in Markov random fields (MRFs) are closely related to the constraint satisfaction problem (CSP) and its soft generalizations. In particular, MAP inference in MRF is equivalent to the weighted (maxsum) CSP. A well-known tool to tackle CSPs are arc consistency algorithms, a.k. a. relaxation labeling. A promising approach to MAP inference in MRFs is linear programming relaxation solved by sequential treereweighted message passing (TRW-S). There is a not widely known algorithm equivalent to TRW-S, max-sum diffusion, which is slower but very simple. We give two theoretical results. First, we show that arc consistency algorithms and max-sum diffusion become the same thing if formulated in an abstractalgebraic way. Thus, we argue that max-sum arc consistency algorithm or max-sum relaxation labeling is a more suitable name for max-sum diffusion. Second, we give a criterion that strictly decreases during these algorithms. It turns out that every class of equivalent problems contains a unique problem that is minimal w.r. t. this criterion.
Lightweight protocol suite for wireless sensor networks: design and evaluation

Lots of protocols have been proposed to make sensor networks more reliable, efficient and applicable. However, a vitally important problem has been neglected, that is the feasibility to implement the protocol and compatibility with other protocols. In this paper, a layered architecture was designed to organize the sensor network protocols in a light way, with detailed description on the interactive interfaces between layers. In accordance with the architecture, a suite of lightweight protocols were designed and implemented as prototypes. Our proposed protocols are featured by their low requirements on the node resources, and high possibility to be deployed in the real world. All these protocols were implemented and evaluated in a unified simulation environment. Simulation results verified our expectation on the performance of the protocols.
Boneless motion reconstruction

With the improvement and declining costs of motion capture (Mocap) technology, modern computer graphics increasingly uses it as a major source of data for character animation. Mocap data provides trajectories for character animation by tracing the motion of a set of markers on moving subjects. Standard techniques for motion data reconstruction from such data require global knowledge of the model structure, such as a skeleton. Skeletons are difficult and quite time consuming to construct. We introduce the first, to our knowledge, automated technique that can realistically reconstruct character animation based on Mocap data alone (Figure 1(b)).
Dynamic Shape Capture via Periodical-Illumination Optical Flow Estimation and Multi-view Photometric Stereo

Multi-view photometric stereo is well established for the shape recovery of static objects. However, it is difficult to align motion images under varying illumination so as to perform photometric stereo reconstruction for dynamic objects. To tackle this issue, this paper presents an optical flow estimation approach which works under periodically varying illuminations, and in cooperation with photometric stereo, enables high-quality 3D reconstruction of dynamic objects. Firstly, multi-view images of the moving object are captured under periodically varying illumination by the multi-camera multi-light system. Then, the optical flow is estimated to synthesize images under different illuminations for each viewpoint. Finally, the multi-view photometric stereo technique is employed to get a high accurate 3D model for each time instant. Experimental results on motion actors demonstrate that temporal successive images under varying illuminations are effectively registered, permitting accurate photometric reconstruction for moving objects.
Acceleration of learning in binary choice problems

An objective of machine learning is to find a machine parameter that rxovides the best output prediction for an i~dividual input. For the binary choice problem whose target relation is stochastic, we present art incremental ,. algorithm that makes the machine parameter 6 converge rapidly to its optimal value 00 with a number of examdes t. We will consider here a one-dimensional” moblem in which for a real number z, a binary choice s = +1 or – 1 is drawn from a certain conditional distribution P(S ] z). A learning machine haa no knowledge of the conditional distribution function other than the facts that p(+l I z) = 1p(-1 I z) is an infinitely differentiable and is a monotonically incr&sing function of z. A learning machine specifies the decision boundary by a parameter O so that output prediction is s = +1 if z > 0 and s = –1 otherwise, The question we wish to address here is how fast d can approach the optimal value t90, at which alternative probabilities balance, P(+ 1 I 0.) = P(– 1 I 00). The convergence depends critically upon the two possible learnimz para&gms in the first of which-the machine ca~ specify inputs as queries to the target relation, and the second in which the inputs are drawn from a certain distribution p(z). If queries are allowed, the rnachke can achieve the fastest possible convergence, (0 – 190)2 * O(t 1). If not, O(t -‘) convergence is generally” not attainable, and the faatest achievable convergence turns -t to be O((lnt)%-l). “on leave of absence from Kyoto University to University of Oxford, Department of Experimental Psychology, Oxford, OX1 3UD, Englwnd; e-mail: shinoOpey.ox. ac.uk Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying IS by permission of the Assoclatlon for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. ACM COLT ’93 171931CA, USA a 1993 ACM 0-89791-61 I-51931000710446 . $J . 50 I II I
Classification des potentiels évoqués par corrélation de Pearson dans une interface cerveau-ordinateur

Dans cette communication, nous decrivons et evaluons les performances d'une technique d'apprentissage des coefficients d'un classifieur lineaire utilise dans une interface cerveau-ordinateur. Les signaux de l'electroencephalogramme d'un individu sont analyses au moyen de cette technique afin de mettre en evidence les reponses de ce dernier a des stimuli visuels. Le traitement et la classification des signaux sont utilises afin d'implanter un systeme de communication palliative permettant a l'individu d'epeler des mots. Les performances de la methode de classification ont ete evaluees par une experimentation sur huit personnes.