Miracle-dz/UniFAR-FTUs huggingface dataset:
A new method for image segmentation

On the basis of analyzing the blur images with noise, this paper presents a new segmentation method which is based on the morphology method, fuzzy K-means algorithm and some parts operator of the Canny algorithm. Because of the Canny's good performance on good detection, good localization and only one response to a single edge, we introduce the course of Canny operator that calculating the value and direction of grads, non-maxima suppression to the grad value and lag threshold process into our post-treatment process. Through experiments, it is demonstrated that the image segmentation method in this paper is very effective.
Properties of cooperatively induced phases in sensing models.

A large number of eukaryotic cells are able to directly detect external chemical gradients with great accuracy and the ultimate limit to their sensitivity has been a topic of debate for many years. Previous work has been done to understand many aspects of this process but little attention has been paid to the possibility of emergent sensing states. Here we examine how cooperation between sensors existing in a two-dimensional network, as they do on the cell's surface, can both enhance and fundamentally alter the response of the cell to a spatially varying signal. We show that weakly interacting sensors linearly amplify the cell's response to an external gradient while a network of strongly interacting sensors form a collective nonlinear response with two separate domains of active and inactive sensors forming what have called a "1/2-state. " In our analysis we examine the cell's ability to sense the direction of a signal and pay special attention to the substantially different behavior realized in the strongly interacting regime.
Trip similarity computation for context-aware travel recommendation exploiting geotagged photos

The popularity of GPS-enabled digital cameras, smart phones, and photo sharing web sites, e.g. Flickr and Panoramio, has led to huge volumes of community-contributed geotagged photos (CCGPs) available on the Internet, which could be regarded as digital footprints of photo takers. In this paper, we propose a method to make context-aware and trip similarity based travel recommendations by mining CCGPs. We obtain user-specific travel preferences from the travel history of user in one city, and use these to recommend tourist locations in another city. The season and weather context are considered during the mining and the recommendation processes. The similarity of users is computed by the modified longest common subsequence and a user-location graph is built from their travel histories in one city, which is then exploited to make travel recommendations. Our method is evaluated on a Flickr dataset, which contains photos taken in four cities of China. Experimental results show the effectiveness of the proposed method.
tucSage: Grammar Rule Induction for Spoken Dialogue Systems via Probabilistic Candidate Selection

We describe the grammar induction system for Spoken Dialogue Systems (SDS) submitted to SemEval’14: Task 2. A statistical model is trained with a rich feature set and used for the selection of candidate rule fragments. Posterior probabilities produced by the fragment selection model are fused with estimates of phraselevel similarity based on lexical and contextual information. Domain and language portability are among the advantages of the proposed system that was experimentally validated for three thematically different domains in two languages.
A Cross-Sectional Survey of Seroprevalence to P. Falciparum Antigens in Children Living Along the Border of Thailand and Myanmar and its Lack of Correlation with Nutritional Status and Anaemia.

Background: In Thailand, the prevalence of malaria was highest in the northern region and most infected cases were school-age children. This study aimed to determine the seroprevalence of antibodies against malaria antigens among school-age children living on the Thailand-Myanmar border and its correlation with nutritional status and anaemia. Methods: One-hundred and forty eight children in Tak Province were enrolled. Brief histories were taken, and dried blood spots collected on filter paper were used to determine antibodies to malaria antigens. Weight and height were measured, and body mass index (BMI) was calculated to assess health status. Results: The mean BMI was 16.2 kg/m2 (SD 2.6) and 116 (78%) children had normal BMI-for-age. The mean haematocrit was 38 % (SD 3) and 12 (9%) children had anaemia. Seroprevalence of antibodies to Plasmodium falciparum schizont extracts, the 19kDa of merozoite surface protein-1 and the apical membrane antigen-1 were 13.5%, 8.1% and 5.4%, respectively. The mean haematocrit and BMI-for-age were not statistically different between children with and without antibodies to malaria antigens. Conclusion: We report an overall rate of 17.6% of antibodies to malaria antigens in schoolage children living in an endemic area. No significant differences in nutritional status or anaemia were found between children who had and did not have the antibodies. The data from this study may contribute to understanding the relationship between health status and specific anti-P. falciparum antibody response in an area of low malaria transmission.
Optimal information diffusion in stochastic block models

We use the linear threshold model to study the diffusion of information on a network generated by the stochastic block model. We focus our analysis on a two-community structure where the initial set of informed nodes lies only in one of the two communities and we look for optimal network structures, i.e., those maximizing the asymptotic extent of the diffusion. We find that, constraining the mean degree and the fraction of initially informed nodes, the optimal structure can be assortative (modular), core-periphery, or even disassortative. We then look for minimal cost structures, i.e., those for which a minimal fraction of initially informed nodes is needed to trigger a global cascade. We find that the optimal networks are assortative but with a structure very close to a core-periphery graph, i.e., a very dense community linked to a much more sparsely connected periphery.
Fingerprint - Iris Fusion Based Identification System Using a Single Hamming Distance Matcher

Conventional multimodal biometric identification systems tend to have larger memory footprint, slower processing speeds and a higher implementation and operational cost. In this paper we propose a state of the art framework for multimodal biometric identification system which can be adapted for any type of biometrics to provide smaller memory footprint and faster implementation than the conventional multimodal biometrics systems. The proposed framework is verified by development of a fingerprint and iris fusion system which utilizes a single Hamming Distance based matcher to provide higher accuracy than the individual unimodal system.
Functional Remodeling of Gap Junction-Mediated Electrical Communication between Adrenal Chromaffin Cells in Stressed Rats

An increase in circulating catecholamine levels represents one of the mechanisms whereby organisms cope with stress. In the periphery, catecholamines mainly originate from the sympathoadrenal system. As we reported, in addition to the central control through cholinergic innervation, a local gap junction-delineated route between adrenal chromaffin cells contributes to catecholamine exocytosis. Here, we investigated whether this intercellular communication is modified when the hormonal demand is increased as observed during cold stress. Our results show that in cold exposed rats, gap-junctional communication undergoes a functional plasticity, as evidenced by an increased number of dye-coupled cells. Of a physiological interest is that this upregulation of gap-junctional coupling results in the appearance of a robust electrical coupling between chromaffin cells that allows the transmission of action potentials between coupled cells. This enhancement of gap-junctional communication parallels an increase in expression levels of connexin36 (Cx36) and connexin43 (Cx43) proteins. Both transcriptional and posttranslational mechanisms are involved because Cx36 transcripts are increased in stressed rats and the expression of the scaffolding protein zonula occludens-1, known to interact with both Cx36 and Cx43, is also upregulated. Consistent with an upregulated coupling extent in stressed rats, the cytosolic Ca2+ concentration rises triggered in a single cell by an iontophoretic application of nicotine occur simultaneously in several neighboring cells. These results describe for the first time a functional plasticity of junctional coupling between adult chromaffin cells that should be crucial for adaptation to stress or sensitization to subsequent stressors.
The Blind Separation of Noisy Mixing Image Based on FASTICA and Wavelet Transform

Blind source separation problem have recently drawn a lot of attention in unsupervised neural learning. In the current approaches, the additive noise is negligible so that it can be omitted from the consideration. To be applicable in realistic scenarios, blind source separation approaches should deal evenly with the presence of noise. In this paper, a method is proposed of combining wavelet threshold de-noising and independent component analysis to the blind source separation problem for mixing images corrupter with white noise. We first use wavelet threshold to de-noise and then use a new blind separation algorithm of FASTICA to separate the wavelet de-noised images. The result shows that this method may reduce the affect of noise and improve the signal-noise ratio (SNR) of separation images, accordingly renew the original images.
Organizational ontologies to support semantic business process management

The Semantic Business Process Management (SBPM) aims at automation of the Business Process Management life cycle with use of semantics and Semantic Web services technology. The key issue to fulfil this aim is to provide an adequate machine-processable representation of processes. In this article we present one of the most important elements of process description, namely organizational ontologies. Moreover, we discuss their role in the early phases of SBPM and illustrate it with a set of application scenarios.
LOW MEMORY DECISION TREE METH MAPPING

Pronunciation models are commonly used in Automatic Speech Recognition (ASR) as well as in Text-to-Speech (TTS) applications. Decision Tree (DT) and Neural Network (NN) methods have been used for modeling the languages with irregular pronunciation. The DT based methods are usually more accurate and therefore they provide better recognition accuracy than the NN based methods. The main drawback of the DT based methods is their large memory footprint. In the paper, we propose three methods, i.e. 1) a clipping approach to reduce the pronunciation variability in the aligned dictionary, 2) a revised DT structure, and 3) a Huffman coding scheme for efficient DT parameter storing that can jointly be applied to minimize the memory footprint of the DT models. The results obtained in the simulation experiments indicate that the memory requirements of the DT models can significantly be reduced without degrading the mapping accuracy. The applicability of the approach is also verified in the speech recognition experiments.
A Novel Robust Scene Change Detection Algorithm for Autonomous Robots Using Mixtures of Gaussians

Interest in change detection techniques has considerably increased during recent years in the field of autonomous robotics. This is partly because changes in a robot's working environment are useful for several robotic skills (e.g., spatial cognition, modelling or navigation) and applications (e.g., surveillance or guidance robots). Changes are usually detected by comparing current data provided by the robot's sensors with a previously known map or model of the environment. When the data consists of a large point cloud, dealing with it is a computationally expensive task, mainly due to the amount of points and the redundancy. Using Gaussian Mixture Models (GMM) instead of raw point clouds leads to a more compact feature space that can be used to efficiently process the input data. This allows us to successfully segment the set of 3D points acquired by the sensor and reduce the computational load of the change detection algorithm. However, the segmentation of the environment as a Mixture of Gaussians has some problems that need to be properly addressed. In this paper, a novel change detection algorithm is described in order to improve the robustness and computational cost of previous approaches. The proposal is based on the classic Expectation Maximization (EM) algorithm, for which different selection criteria are evaluated. As demonstrated in the experimental results section, the proposed change detection algorithm achieves the detection of changes in the robot's working environment faster and more accurately than similar approaches.
Fuzzy Discretization for Rough Set Based Gene Selection Algorithm

Selection of reliable genes from micro array gene expression data is essential to carry out a diagnostic test and successful treatment. In this regard, a rough set based gene selection algorithm is developed recently to select genes from micro array data. In this paper, a fuzzy discretization method is proposed for rough set based gene selection algorithm to compute relevance and significance of continuous valued genes directly. The performance of the proposed fuzzy discretization method, along with a comparison with crisp counterpart, is presented in terms of classification accuracy of K-nearest neighbor rule and support vector machine on seven micro array data sets. An important finding is that the proposed discretization method is shown to be effective in selecting relevant and significant genes from micro array data.
Traffic classification and verification using unsupervised learning of Gaussian Mixture Models

This paper presents the use of unsupervised Gaussian Mixture Models (GMMs) for the production of per-application models using their flows' statistics in order to be exploited in two different scenarios: (i) traffic classification, where the goal is to classify traffic flows by application (ii) traffic verification or traffic anomaly detection, where the aim is to confirm whether or not traffic flow generated by the claimed application conforms to its expected model. Unlike the first scenario, the second one is a new research path that has received less attention in the scope of Intrusion Detection System (IDS) research. The term “unsupervised” refers to the method ability to select the optimal number of components automatically without the need of careful initialization. Experiments are carried out using a public dataset collected from a real network. Favorable results indicate the effectiveness of unsupervised GMMs.
A eficácia das estratégias de coping após um evento traumático: uma revisão sistemática

A resiliencia apos a exposicao a um evento traumatico esta associada a eficacia das estrategias de coping. Todavia, nao e claro o que se pode revelar eficaz apos a exposicao ao trauma. Com a presente revisao sistematica da literatura, o nosso objetivo foi compreender, perante os resultados psicologicos, que estrategias especificas de coping surgem como eficazes para o ajustamento psicologico posterior. Foram pesquisadas bases de dados eletronicas (Medline, PsycINFO, Scopus e Google Scholar) para estudos publicados em ingles, entre 2000 e 2012, que avaliassem estrategias de coping e os resultados psicologicos em adultos vitimas de um evento traumatico nos dois anos anteriores a avaliacao. Das 60 publicacoes encontradas inicialmente, apenas cinco foram incluidas nesta revisao. Nao encontramos estudos sobre a relacao entre coping social e ajustamento psicologico. Existem controversias quanto as estrategias de evitamento serem mais prejudiciais do que a verbalizacao de sentimentos negativos, sugerindo a necessidade de se refletir acerca dos beneficios de encorajar as vitimas a expressarem pensamentos e sentimentos apos a exposicao a um evento traumatico.
Identification of essential amino acid residues in the nisin dehydratase NisB

Nisin is a posttranslationally-modified antimicrobial peptide that has the ability to induce its own biosynthesis. Serines and threonines in the modifiable core peptide part of precursor nisin are dehydrated to dehydroalanines and dehydrobutyrines by the dehydratase NisB, and subsequently cysteines are coupled to the dehydroamino acids by the cyclase NisC. In this study, we applied extensive site-directed mutagenesis, together with direct binding studies, to investigate the molecular mechanism of the dehydratase NisB. We use a natural nisin-producing strain as a host to probe mutant-NisB functionality. Importantly, we are able to differentiate between intracellular and secreted fully dehydrated precursor nisin, enabling investigation of the NisB properties needed for the release of dehydrated precursor nisin to its devoted secretion system NisT. We report that single amino acid substitutions of conserved residues, i.e., R83A, R83M, and R87A result in incomplete dehydration of precursor nisin and prevention of secretion. Single point NisB mutants Y80F and H961A, result in a complete lack of dehydration of precursor nisin, but do not abrogate precursor nisin binding. The data indicate that residues Y80 and H961 are directly involved in catalysis, fitting well with their position in the recently published 3D-structure of NisB. We confirm, by in vivo studies, results that were previously obtained from in vitro experiments and NisB structure elucidation and show that previous findings translate well to effects seen in the original production host.
Gaze-Based Annotations for Reading Comprehension

We study eye gaze movement behavior during paper reading and generate a series of annotations from a user's reading features: gray shading to indicate reading speed, borders to indicate frequency of re-reading, and lines to indicate transitions between sections of a document. Through a user study, we validate that our SocialReading system that shares teachers' gaze data for an academic paper can improve students' reading comprehension of that paper.
Meaning in Life and Wellbeing

Much of the eff ort expended in the medical and social sciences seems to follow an implicit notion that all people desire is a life free from pain, suff ering and distress. Our collective eff ort has identifi ed unending legions of threats, from viruses and bacteria to personal depression and global recession. Without diminishing the importance of identifying and attempting to ameliorate such threats, the past few decades have seen an unprecedented eruption of research that looks less at what people might want to avoid and more at what they might truly desire in life. Perhaps people want not only an absence of sickness but also their share of health. Perhaps people desire not only relief from the sinking pull of psychological suff ering but also the buoyancy of psychological wellbeing. And perhaps people not only want to avoid the annihilation of existential emptiness but also strive for lives rich in meaning and purpose. Th is chapter wholeheartedly embraces these ideas and provides a brief overview of what we know about how fi nding and pursuing meaning in life may help ameliorate suff ering and promote wellbeing.
A COM-Based Decision Tree Model Implemented in ArcGIS: A Case Study for Assessment of Soil Pollution in Fuyang, Zhejiang, China

The problem of soil pollution by heavy metals has been receiving an increasing attention in the last few decades. Geostatistics module in ArcGIS, could not however efficiently simulate the spatial distribution of heavy metals with satisfied accuracy when the spatial autocorrelation of the study area severely destroyed by human activities. In this study, the classification and regression tree (CART) has been integrated into ArcGIS using ArcObjects and Visual Basic for Application (VBA) to predict the spatial distribution of soil heavy metals contents in the area severely polluted. It has got a great improvement compared to ordinary Kriging method in ArcGIS. The integrated approach allows for relatively easy, fast, and cost-effective estimation of spatially distributed soil heavy metals pollution. The methods and results described in this study are also valuable for understanding the relationship between heavy metals pollution risk and environmental factors.
Sound waves in the intracluster medium of the Centaurus cluster

We report the discovery of ripple-like X-ray surface brightness oscillations in the core of the Centaurus cluster of galaxies, found with 200 ks of Chandra observations. The features are between 3 and 5 per cent variations in surface brightness with a wavelength of around 9 kpc. If, as has been conjectured for the Perseus cluster, these are sound waves generated by the repetitive inflation of central radio bubbles, they represent around 5 × 1042 erg s−1 of spherical sound wave power at a radius of 30 kpc. The period of the waves would be 107 yr. If their power is dissipated in the core of the cluster, it would balance much of the radiative cooling by X-ray emission, which is around 1.3 × 1043 erg s−1 within the inner 30 kpc. The power of the sound waves would be a factor of 4 smaller that the heating power of the central radio bubbles, which means that energy is converted into sound waves efficiently.
Data-Driven Modeling of Acoustical Instruments∗

We present a framework for the analysis and synthesis of acoustical instruments based on data-driven probabilistic inference modeling. Audio time series and boundary conditions of a played instrument are recorded and the non-linear mapping from the control data into the audio space is inferred using the general inference framework of Cluster-Weighted Modeling. The resulting model is used for real-time synthesis of audio sequences from new input data.
Implementation of an Automatic Counting and Control Production System for Metal-Mechanics Industries

This paper describes the implementation of a system to have control over the production in metal-mechanics industry enterprises. Through a system based on a micro controller unit, it is possible to count automatically the number of pieces produced and keep constant communication with a computer to get important production data. It is an electronic system, economic enough for micro and small enterprises that belong to this area or with similar necessities.
Efficient management of multiple agent tracking through observation handling

Non-supervised multiple-agent tracking is a complex task which demands a structured framework in order to accomplish it. Therefore, this proposal presents a system which is modular and hierarchically organised. It consists in several levels, working in cascade, which are defined according to the different functionalities to be performed. The goal of this work is to implement and experimentally verify a novel image-based algorithm which deals with serious segmentation difficulties, thereby being able to simultaneously perform a reliable tracking of several agents. As a result, agents’ trajectories are obtained, as well as quantitative information about their state at any time, such as their speed or size.