Miracle-dz/UniFAR-FTUs huggingface dataset:
On the choice of similarity measures for image retrieval by example

In image retrieval systems, a variety of simple similarity measures are used. The choice for one similarity measure or another is generally driven by an experimental comparison on a labeled database. The drawback of such an approach is that, while a large number of possible similarity measures can be tested, we do not know how to extend from the obtained results. However, the choice of a good similarity measure leads to noticeable better results. It is known that this choice is related to the variability of the images within the same class. Therefore, we propose a model of image retrieval systems and deduce a scheme for deriving the best similarity measure in a set of similarity measures, assuming a parametric model of the variability of feature vectors within the same class. An experimental validation of the model and the derived similarity measures is performed on synthetic ground-truth databases. Finally, from our experiments, we give several rules to follow for the design of ground-truth databases allowing reliable conclusions on the search of better similarity measures.
Toward a Semantic Indexing Based on Images Color Features Similarity

Considering that similarity of images is an underlying issue in many developed algorithms in the eld of automatic video indexing, we wondered about which kind of information is retrieved through the usually extracted color features and associated distances. In this paper, we tried to understand through CBIR experiments which information one can expect to retrieve. As a rst step, we proposed in our work to build a database close to real-world images and to lead experiments on semantic examples in order to evaluate and understand color features and measures.
Mining models of exceptional objects through rule learning

A new technique, SNIPER, is proposed for learning a model that deals with continuous values of exceptionality. Specifically, given some training objects associated with a continuous attribute F, SNIPER induces a rule-based model for the identification of those objects likely to score the maximum values for F. The purpose of SNIPER differs from the one pursued in regression problems, since its main objective is to retrieve those objects more likely to score the highest values of F. Although there are opportunities for improvement, the results of a preliminary evaluation are encouraging. SNIPER is competitive in the quality of the attained results with respect to some established competitors, while outperforming them when the exceptional objects are very rare. Additionally, SNIPER is much faster in the induction of a model of object exceptionality.
Serum Leptin and Adiponectin Levels in Obese and Nonobese Asthmatic School Children in relation to Asthma Control

There is growing evidence of a positive correlation between asthma and obesity in children and adults. Leptin and adiponectin regulate several metabolic and inflammatory functions. This study aims to evaluate serum leptin and adiponectin concentrations in asthmatic school children to investigate their association with obesity and the degree of asthma control. Obese asthmatic (OA) and nonobese asthmatic (NOA) children, aged 7 to 14, were randomly enrolled in this prospective study. Data on demographic, anthropometric, serum lipids, and spirometric measures and allergy status were collected and analyzed. Serum leptin was significantly higher (25.8 ± 11.1 versus 8.7 ± 11.1; P < 0.0001) and adiponectin levels were lower (2.5 ± 1.2 versus 5.4 ± 2.9; P < 0.0001) in OA compared to NOA children. The uncontrolled group had higher leptin and lower adiponectin levels compared to well and partially controlled asthma. BMI was positively correlated with leptin (r = 0.79; P < 0.001) and negatively with adiponectin (r = −0.73; P < 0.001). Mean BMI and leptin levels were observed to be higher in girls compared to boys. Stepwise multiple linear regression analysis showed that higher BMI and female gender had significant effect on serum leptin levels. Among asthmatic children higher serum leptin and lower adiponectin levels were significantly associated with obesity and showed no significant association with degree of asthma controls.
RRAM refresh circuit: A proposed solution to resolve the soft-error failures for HfO2/Hf 1T1R RRAM memory cell

RRAM-based memory is a promising emerging technology for both on-chip and stand-alone non-volatile data storage in advanced technologies. In addition to its small dimensions, the RRAM device has many technological advantages including its low-programming voltages, high speed, low power, CMOS-compatible fabrication process, and potentially monolithic 3D integration. However, one of the critical challenges for RRAM technology is the reliability concerns due to retention and endurance failures. In this paper, we propose a novel Refresh circuit for 1T1R RRAM array which detects and distinguishes soft and hard errors from retention and endurance failures, as well as corrects the soft errors through refreshing. Using the HfO2/ Hf RRAM array, our simulation results show that the proposed solution increases the resilience to soft-error of an 8Gb RRAM-based memory by 80% with a small penalty on the energy and delay of the Read operations (6% and 0.4% respectively). The proposed methodology can be used for other RRAM arrays with minor modifications to the design parameters depending on the characteristics of RRAM cell.
Hierarchical graph partitioning

One of the important optimization questions in highly parallel systems is the problem of assigning computational resources to communicating tasks. While scheduling tasks/operators, tasks assigned to nearby resources (e.g. on the same CPU core) have low communication costs, whereas tasks assigned to distant resources (e.g. on different server racks) have high communication costs. An optimal solution of task to resource assignment minimizes the communication cost of the task ensemble while satisfying the load balancing requirements. We model such an optimization question of minimizing communication cost as a new class of graph partitioning problems called hierarchical graph partitioning. In hierarchical graph partitioning we are given a graph G=(V,E), vertices representing the tasks and edges representing the communication among the vertices. We are also given vertex demands d: V(G) → R+ denoting the processing load of each task and edge weights w: E(G) → R+ denoting the amount of communication and our goal is to decompose G into k parts/servers of nearly equal weight (for load balancing) and minimize the total cost of the edges being cut (communication cost). However, unlike traditional k-balanced graph partitioning where the cost of an edge cut is independent of the parts containing the two respective end vertices, here the cost varies with the distance of the servers corresponding to the two parts. Since, the servers are generally arranged in a hierarchy, distance is given by a tree metric. In this paper, we initiate the study of hierarchical graph partitioning problem and give efficient algorithms with approximation guarantee. Hierarchical graph partitioning is a significant generalization of graph partitioning problem and faithfully captures several practical scenarios that have served as major motivating applications for graph partitioning.
The Tapenade automatic differentiation tool: Principles, model, and specification

Tapenade is an Automatic Differentiation (AD) tool which, given a Fortran or C code that computes a function, creates a new code that computes its tangent or adjoint derivatives. Tapenade puts particular emphasis on adjoint differentiation, which computes gradients at a remarkably low cost. This article describes the principles of Tapenade, a subset of the general principles of AD. We motivate and illustrate with examples the AD model of Tapenade, that is, the structure of differentiated codes and the strategies used to make them more efficient. Along with this informal description, we formally specify this model by means of data-flow equations and rules of Operational Semantics, making this the reference specification of the tangent and adjoint modes of Tapenade. One benefit we expect from this formal specification is the capacity to formally study the AD model itself, especially for the adjoint mode and its sophisticated strategies. This article also describes the architectural choices of the implementation of Tapenade. We describe the current performance of Tapenade on a set of codes that include industrial-size applications. We present the extensions of the tool that are planned in a foreseeable future, deriving from our ongoing research on AD.
Central European Journal of Operational Research , 2003 Mining Inspire Data for the Determinants of Successful Internet Negotiations

1525 bilateral negotiations were conducted over the Internet via a negotiation support system Inspire. In 53% cases the negotiations were successful and the parties achieved a compromise. The data about these negotiations is studied here in order to determine the underlying reasons for successful negotiations. Because of the size of the dataset the knowledge discovery process is modified allowing also testing and comparing the methods’ predictive accuracy. * This work has been supported with grants from the Social Sciences and Humanities Research Council of Canada and the Natural Sciences and Engineering Research Council of Canada. http: / / interneg.org/
A Framework for Alternate Queueing: Towards Traffic Management by PC-UNIX Based Routers

Queueing is an essential element of traffic management, but the only queueing discipline used in traditional UNIX systems is simple FIFO queueing. This paper describes ALTQ, a queueing framework that allows the use of a set of queueing disciplines. We investigate the issues involved in designing a generic queueing framework as well as the issues involved in implementing various queueing disciplines. ALTQ is implemented as simple extension to the FreeBSD kernel including minor fixes to the device drivers. Several queueing disciplines including CBQ, RED, and WFQ are implemented onto the framework to demonstrate the design of ALTQ. The traffic management performance of a PC is presented to show the feasibility of traffic management by PC-UNIX based routers.
Image segmentation based on modified information cut in wavelet domain

To solve the mis-clusters caused by the traditional information cut algorithm when it is applied to segment images with gray changes, modified information cut in wavelet domain (W-MIC) algorithm is proposed. First, using the gray relevance and space relevance between image pixels, a modified information cut (MIC) is presented, which utilizes a new Parzen windowing function to evaluate probability density functions, and reduces the effect of gray changes to image segmentation; further, considering the difficulties of selecting the optimal parameter in MIC, the proposed W-MIC can reduce the complexity of parameter selection via the smoothing role of wavelet, and improve segmentation results by fusing low frequency information and high frequency information derived from undecimated wavelet decomposition. Segmentation experiments demonstrate that it effectively decreases the influence of parameters selection, and it can not only avoid the mis-clusters caused by gray changes, but also keep image edges.
Using neural networks to estimate wind turbine power generation

Summary form only given, as follows. This paper uses data collected at Central and South West Services Fort Davis wind farm (USA) to develop a neural network based prediction of power produced by each turbine. The power generated by electric wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to perform this prediction for diagnostic purposes-lower-than-expected wind power may be an early indicator of a need for maintenance. In this paper, characteristics of wind power generation are first evaluated in order to establish the relative importance for the neural network. A four input neural network is developed and its performance is shown to be superior to the single parameter traditional model approach.
Serum Progranulin as an Independent Marker of Liver Fibrosis in Patients with Biopsy-Proven Nonalcoholic Fatty Liver Disease

Background: Elevated progranulin levels are associated with visceral obesity, elevated plasma glucose, and dyslipidemia. Progranulin has not been previously investigated as a biomarker of nonalcoholic fatty liver disease (NAFLD). We sought to determine whether serum progranulin levels are altered in patients with biopsy-proven NAFLD and if they are associated with their clinical, biochemical, and histological characteristics. Subjects and methods: We measured serum progranulin levels in 95 patients with biopsy-proven NAFLD and 80 age- and sex-matched controls. The potential associations between progranulin and the characteristics of NAFLD patients were examined by multiple linear regression analysis. Results: Serum progranulin levels were significantly higher in NAFLD patients (34 ± 13 ng/mL) than in controls (28 ± 7 ng/mL, P < 0.001). In NAFLD patients, serum progranulin levels were associated with lipid levels and the degree of hepatic fibrosis. After adjustment for potential confounders, serum progranulin remained an independent predictor of the degree of hepatic fibrosis in NAFLD patients (β = 0.392; t =2.226, P < 0.01). Conclusions: Compared with controls, NAFLD patients have higher serum progranulin concentrations, which are closely associated with lipid values and the extent of hepatic fibrosis.
An on-line acoustic compensation technique for robust speech recognition

In this work we report on the use of an on-line acous- tic compensation technique for robust speech recognition. With this technique acoustic mismatch between training and actual conditions is reduced through acoustic mapping. At recognition stage, observation vectors deliv- ered by the acoustic front-end are mapped into a reference acoustic space, while input data are exploited to update the statistical parameters of the mapping. Experimental results, obtained for matched and unmatched training and testing environment conditions, show that the investigated technique tangibly improves the perfor- mance of a speaker independent speech recognizer based on hidden Markov models. Furthermore, recognition re- sults are close to those obtained with unsupervised incremental model adaptation based on maximum likelihood linear regression.
Nucleotide sequence and transcriptional analysis of the pl 0 gene of Spodoptera exigua nuclear polyhedrosis virus

The pl0 gene of Spodoptera exigua multiple nuclear polyhedrosis virus (SeMNPV) was localized on the XbaI fragment H (5.1 kb) of the physical map of the viral genome. The coding sequence of the SeMNPV pl0 gene is 264 nucleotides (nt) long corresponding to a predicted protein of 88 amino acids with an MuF of 9607. The SeMNPV pl0 protein showed only limited amino acid identity (39 % and 26 %, respectively) to those of Orgyia pseudotsugata MNPV (OpMNPV) and Autographa californica MNPV (AcMNPV) and thus appears less conserved than other viral proteins. The SeMNPV pl0 gene was expressed by a transcript of approximately 450 nt, which started in the conserved baculovirus late gene promoter motifTAAG. The leader of the SeMNPV pl0 transcript was AT-rich (92 %) and at 36 nt was the shortest leader of all baculovirus major late genes reported so far. The SeMPNV pl 0 transcript terminated 6nt downstream from a putative poly(A) signal sequence (AATAAA); the latter was 61 nt downstream of the translational stop codon TAA. Upstream and downstream of the p 10 gene, partial putative ORFs were found that showed significant amino acid sequence identity to the baculovirus p26 and p74 proteins. It is concluded that the region of SeMNPV DNA containing the p 10 gene is collinear with the corresponding regions in the AcMNPV and OpMNPV genomes.
Exact Solution for the Stokes Problem of an Infinite Cylinder in a Fluid with Harmonic Boundary Conditions at Infinity

We present an exact solution for the time-dependent Stokes problem of an infinite cylinder of radius r = a in a fluid with harmonic boundary conditions at infinity. This is a 3-dimensional problem but, because of translational invariance along the axis of the cylinder it effectively reduces to a 2-dimensional one. The Stokes problem being a linear reduction of the full Navier-Stokes equations, we show how to satisfy the no-slip boundary condition at the cylinder surface and the harmonic boundary condition at infinity, exhibit the full velocity field for radius r > a, and discuss the nature of the solutions for the specific case of air at sea level.
Learning EEG-based spectral-spatial patterns for attention level measurement

In our every day life, our brain is constantly processing information and paying attention, reacting accordingly, to all sorts of sensory inputs (auditory, visual, etc.). In some cases, there is a need to accurately measure a person's level of attention to monitor a sportsman performance, to detect Attention Deficit Hyperactivity Disorder (ADHD) in children, to evaluate the effectiveness of neuro-feedback treatment, etc.
Unsupervised learning and clustering using a random field approach

In this work we propose a random field approach to unsupervised machine learning, classifier training and pattern classification. The proposed method treats each sample as a random field and attempts to assign an optimal cluster label to it so as to partition the samples into clusters without a priori knowledge about the number of clusters and the initial centroids. To start with, the algorithm assigns each sample a unique cluster label, making it a singleton cluster. Subsequently, to update the cluster label, the similarity between the sample in question and the samples in a voting pool and their labels are involved. The clusters progressively form without the user specifying their initial centroids, as interaction among the samples continues. Due to its flexibility and adaptability, the proposed algorithm can be easily adjusted for on-line learning and is able to cope with the stability-plasticity dilemma.
Designing judicious interactions for cognitive assistance: the acts of assistance approach

The completion of complex activities of daily living (ADL) like meal preparation is a key concept for achieving autonomous living. Due to cognitive impairments, some people need to be supported when performing ADL. In this paper, we present a technological approach to guide people with cognitive impairments to complete complex activities. The assistance is provided in the form of pervasive human-machine interactions (HMI), that encourage the person to complete some actions to settle the difficulty identified by the system. These HMI are called "acts of assistance". This approach was implemented in Archipel, a cognitive orthosis developed at the DOMUS Laboratory, University of Sherbrooke (Canada). An evaluation of the prototype was conducted around meal preparation activities, involving 12 people with intellectual disabilities. This study demonstrates that our approach is promising.
Learning in knowledge team: can passion in teams' activities help?

This study investigates the antecedents of individual learning under knowledge team environments, where team members work together for a limited time period, share knowledge informally and eventually create a knowledge outcome that is beneficial for an organization. Participating in a knowledge team provides individual members with a good opportunity to learn during a short period of time, since they work on novel and creative tasks. However, because knowledge team activities are often non-routine for each member, their attitudes toward and psychological involvement with their knowledge team activities vary among different individuals. In this study, we suggest that individual passion about knowledge team activities is an important psychological input for individual members' external knowledge sourcing, internal knowledge sharing, and helping behaviors, which eventually lead to improving an individual's learning outcomes through knowledge team activities. We hypothesize that individual passion about the activities in a knowledge team positively influences both 1) knowledge management (sourcing and sharing) behavior; and 2) the organizational citizenship behavior -- helping (OCB-helping) of individuals, and these behaviors, in turn, result in their learning outcomes. Also, members' perceived psychological safety within knowledge teams positively moderates the impact of members' passion on internal knowledge sharing and helping behavior on learning outcomes. The research model will be tested with survey data from the knowledge team participants. We conclude with potential contributions of this study for the academy and practice.
Resource Optimization in Automatic web page classification using integrated feature selection and machine learning

Increasing with the number of users, the need for automatic classification techniques with good classification accuracy increases as search engines depend on previously classified web pages stored in classified directories to retrieve the relevant results. Preprocessing is the important step in web page classification problem as most of the web pages contain more irrelevant information than relevant details useful for finding its category. For the classification purpose the representative words in the web page called as features are used for classification rather than the entire web page to reduce time and space requirements. The selection of relevant features which reduces the high dimensionality and redundancy is the current research topic. In this paper selecting relevant features from the pages is treated as an optimization problem and we propose an algorithm to find the optimal features for web page classification. Machine learning techniques for automatic classification gains more interest as the classifier improves its performance with experience. In this paper we use Naive Bayes, Kstar, Random Forest and Bagging machine learning classifiers.
Constructing Cube Blocks Effectively for Stream Data Analysis

With the rapid growth of WWW, many applications based on web are generating tremendous amount of data. Analyzing and mining such data will be important for administrators and other users. Because these data are increasing continuously and rapidly, retrieving these data efficiently and effectively are challenging tasks. In this paper, we propose a new Cube Block model to organize multi-dimensional data efficiently for online analysis and mining. The main idea of Cube Block model is to split the whole data set into blocks by dividing time into exponential time frames. In each block, we omit individual time identifiers of data items and aggregate duplicate data items to one data cell. Cube Block model uses the approximating method to get historical data in any time interval. The approximation accuracy is guaranteed by an upper bound and a lower bound. For the implementation of Cube Block model, we develop the CB-Builder system to scan data and construct cube blocks. CB-Builder's algorithm complexity (runtime and space cost) is analyzed in this paper. Experiments on synthetic data sets demonstrate that Cube Block model with its implementation CB-Builder is an effective and efficient tool for web application data analysis and mining.