Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian Neighbourhood Component Analysis

Learning a good distance metric in feature space potentially improves the performance of the KNN classifier and is useful in many real-world applications. Many metric learning algorithms are however based on the point estimation of a quadratic optimization problem, which is time-consuming, susceptible to overfitting, and lack a natural mechanism to reason with parameter uncertainty, an important property useful especially when the training set is small and/or noisy. To deal with these issues, we present a novel Bayesian metric learning method, called Bayesian NCA, based on the well-known Neighbourhood Component Analysis method, in which the metric posterior is characterized by the local label consistency constraints of observations, encoded with a similarity graph instead of independent pairwise constraints. For efficient Bayesian optimization, we explore the variational lower bound over the log-likelihood of the original NCA objective. Experiments on several publicly available datasets demonstrate that the proposed method is able to learn robust metric measures from small size dataset and/or from challenging training set with labels contaminated by errors. The proposed method is also shown to outperform a previous pairwise constrained Bayesian metric learning method.
Resistência à Mudança no Sistema de Informação Gerencial: uma análise da institucionalização de estruturas e processos de TI em uma organização do Poder Judiciário Brasileiro

This study aimed to analyze the resistance factors to change the Management Information System - MIS, in an organization of the Brazilian judiciary, describing how the actions of the actors can inhibit the institutionalization of management control practices. We use the case study method, with the framework of Burns and Scapens (2000) to understand the process in which the rules become habits and routines and are institutionalized. Huy’s framework (2001) were used to explain how the ideal types of planned change to analyzing can explain resistance factors. The cut chosen for analysis was the IT structures and processes recommended by law and supported by the literature in the field. We conducted interviews, document analysis and field notes for the collection and discourse analysis elements - DA for data analysis. The results indicate that the structures and processes IT should form the MIS of organization are not fully institutionalized, although the coercive pressure to change. Thus, we observe a purely ceremonial use of various processes of MIS and concluded that if senior management does not exercise its coordinating role and support the process of change and if there is no proper training of servers, hardly the rules become will habits and patterns, or they will be institutionalized. The study contributes to the field to describe how failures in planning the change and / or the lack of interest of senior management, coupled with the lack of training of staff inhibits the institutionalization of new rules, even when these are established coercively.
AN IMPROVED DECISION MAKING ALGORITHM FOR ON LINE DISCRIMINATION BETWEEN INRUSH AND FAULT CURRENT IN A TRANSFORMER : DWT APPROACH

Transformer protection is critical issue in power system and the issue lies in the accurate and rapid discrimination of magnetizing inrush current from internal fault current. This paper describes a new method to discriminate the magnetizing inrush current and interturn fault using the wavelet transform. This method is independent of setting any threshold for discrimination amongst these. A discriminating function and feature extraction is defined in terms of difference of two-peak amplitude of wavelet coefficients in a specific frequency band. Interturn faults were staged at various time instants and it was observed that the strategy holds good for all instants i.e. it is independent of instant of occurrence of fault also. This discrimination will aid in development of an automatic detection method and shall give information to predict the failure ahead of time so that the necessary corrective actions are taken to prevent outages and reduce down time. This paper also presents a comparison of two algorithms to discriminate magnetizing inrush current from interturn fault current, based on wavelet coefficients as a discriminating function, A) where two peak values corresponding to the |d5| level following the fault instant is used as a discriminating feature, and B) where difference of two peak amplitude of wavelet coefficients of fast cross-correlated signal at |d4| level is used as a discriminating feature. The performance of this algorithm is demonstrated on custom-built mains feed single-phase transformer, used in the laboratory to collect the data from controlled experiments. The proposed on line detection scheme is also discussed.
Interactive search results for Digital Libraries

In this paper, we propose an architecture that enables interactive ranking and filtering of search results in a digital library by taking under consideration their importance. The importance of a document is measured according to its popularity among the users of the digital library. Further search results manipulation can be performed after the creation of the corresponding list from the search engine and preferably in a different context, without any interaction with the server. A prototype implementation is also presented that demonstrates the functionality of the proposed architecture.
Performance Evaluation of J2EE Web Applications with Queueing Networks

The prediction of systems response time for different intensity of users requests, esp. identifying requests loads causing performance degradation becomes an important issue in the design of a e-commerce system. A method for modeling application server environment by the use of queueing networks has been proposed. The physical components of the environment are represented as service centers which form a separable queueing network. Model parameters, i.e. average service times and average visit counts can be experimentally measured in a test environment. Model output are response times for different request classes as well as other parameters describing systems performance. Tools supporting the measurements and model calculations have also been developed. Experiments carried out on a J2EE web application show that the model predicts real systems performance with satisfactory accuracy.
Applying falsity input to neural networks to solve single output regression problems

In this paper, both truth and falsity inputs are used to trained neural networks. Falsity input is the complement of the truth input. Two pairs of neural networks are created. The first pair of neural networks are trained using the truth input whereas the second pair of neural networks are trained using the falsity input. Each pair of neural networks are trained to predict degree of truth and degree of falsity outputs based on the truth and falsity targets, respectively. Two novel techniques are proposed based on these two pairs of neural network. We experiment our proposed techniques to three classical benchmark data sets, which are housing, concrete compressive strength, and computer hardware from the UCI machine learning repository. It is found that our proposed techniques improve the prediction performance when compared to backpropagation neural network and complementary neural networks.
BDNF Genotype Interacts with Motor Function to Influence Rehabilitation Responsiveness Poststroke

Background Persistent motor impairment is common but highly heterogeneous poststroke. Genetic polymorphisms, including those identified on the brain-derived neurotrophic factor (BDNF) and apolipoprotein E (APOE) genes, may contribute to this variability by limiting the capacity for use-dependent neuroplasticity, and hence rehabilitation responsiveness. Objective To determine whether BDNF and APOE genotypes influence motor improvement facilitated by poststroke upper-limb rehabilitation. Methods BDNF-Val66Met and APOE isoform genotypes were determined using leukocyte DNA for 55 community-dwelling patients 2–123 months poststroke. All patients completed a dose-matched upper-limb rehabilitation program of either Wii-based Movement Therapy or Constraint-induced Movement Therapy. Upper-limb motor function was assessed pre- and post-therapy using a suite of functional measures. Results Motor function improved for all patients post-therapy, with no difference between therapy groups. In the pooled data, there was no significant effect of BDNF or APOE genotype on motor function at baseline, or following the intervention. However, a significant interaction between the level of residual motor function and BDNF genotype was identified (p = 0.029), whereby post-therapy improvement was significantly less for Met allele carriers with moderate and high, but not low motor function. There was no significant association between APOE genotype and therapy outcomes. Conclusion This study identified a novel interaction between the BDNF-Val66Met polymorphism, motor-function status, and the magnitude of improvement with rehabilitation in chronic stroke. This polymorphism does not preclude, but may reduce, the magnitude of motor improvement with therapy, particularly for patients with higher, but not lower residual motor function. BDNF genotype should be considered in the design and interpretation of clinical trials.
A Method for Storing Historical Stream Data Based on Degree of Interest

The query time was shortened efficiently by adopting sampling method for storing historical stream data. However, important information was discarded in a large possibility because all data were treated non-discriminately with same sampling probability. In this paper, we addressed the problem by introducing a method for storing historical stream data based on degree of interest. The method used parameter Q to indicate the importance of each data, based on which a sampling algorithm had better effect on maintaining the important information. The experimental results showed that the degree of interest based method had fine performance in the query accuracy, especially for simple query on the historical stream data.
Color image restoration using compound Gauss-Markov Random Fields

In this work we extend the use of Compound Gauss Markov Random Fields to the restoration of color images. While most of the work in color image restoration is concentrated on enforcing similarity between the intensity values of the pixels in the image bands, we propose combining information by means of the line process. In order to find the multichannel restoration modified versions of ICM and SA are proposed. The methods are finally tested on real images.
Qubit-induced phonon blockade as a signature of quantum behavior in nanomechanical resonators

The observation of quantized nanomechanical oscillations by detecting femtometer-scale displacements is a significant challenge for experimentalists. We propose that a phonon blockade can serve as a signature of quantum behavior in nanomechanical resonators. In analogy to the photon blockade and Coulomb blockade for electrons, the main idea for phonon blockade is that the second phonon cannot be excited when there is one phonon in the nonlinear oscillator. To realize phonon blockade, a superconducting quantum two-level system is coupled to the nanomechanical resonator and is used to induce the phonon self-interaction. Using Monte Carlo simulations, the dynamics of the induced nonlinear oscillator is studied via the Cahill-Glauber s-parametrized quasiprobability distributions. We show how the oscillation of the resonator can occur in the quantum regime and demonstrate how the phonon blockade can be observed with the currently accessible experimental parameters.
Robust filtering for 2-D state-delayed systems with NFT uncertainties

This paper is concerned with the robust filtering problem for two-dimensional (2-D) state-delayed systems with uncertainties represented by nonlinear fraction transformation. The authors first establish the stability H/sub /spl infin// performance and generalized H/sub 2/ performance criteria for the system. Based on the results, the authors propose efficient methods to solve the robust H/sub /spl infin// filtering, generalized H/sub 2/ filtering, and mixed generalized H/sub 2//H/sub /spl infin// filtering problems by using a parameter-dependent Lyapunov function approach. The methods involve solving linear matrix inequalities. Two examples are given to show the effectiveness of the proposed approach.
Scaling laws for the resistivity increase of sub-100 nm interconnects

A physically based model is used to describe the resistivity increase of sub-100 nm copper interconnect structures. The main factors determining the increase are additional scattering of electrons at the surface as well as at the grain boundaries of the conductor. The model has been applied to several sets of experimental data. The parameters of the model, each of them with physical meaning, are appropriate to fit the model to the experimental data very well. A compact model for the surface contribution of the model has been derived capturing the essentials in a simple, analytical expression.
NDM‐1, the ultimate promiscuous enzyme: substrate recognition and catalytic mechanism

The specter of a return to an era in which infectious disease looms as a significant threat to human health is not just hyperbole; there are serious concerns about the widespread overuse and misuse of antibiotics contributing to increased antibiotic resistance in pathogens. The recent discovery of a new enzyme, first identified in Klebsiella pneumoniae from a patient from New Delhi and denoted as NDM‐1, represents an example of extreme promiscuity: It hydrolyzes and inactivates nearly all known β‐lactam‐based antibiotics with startling efficiency. NDM‐1 can utilize different metal cofactors and seems to exploit an alternative mechanism based on the reaction conditions. Here we report the results of a combined experimental and theoretical study that examines the substrate, metal binding, and catalytic mechanism of the enzyme. We utilize structures obtained through X‐ray crystallography, biochemical assays, and numerical simulation to construct a model of the enzyme catalytic pathway. The NDM‐1 enzyme interacts with the substrate solely through zinc, or other metals, bound in the active site, explaining the observed lack of specificity against a broad range of β‐lactam antibiotic agents. The zinc ions also serve to activate a water molecule that hydrolyzes the β‐lactam ring through a proton shuttle. —Kim, Y. , Cunningham, M. A. ; Mire, J., Tesar, C. , Sacchettini, J., Joachimiak, A. NDM‐1, the ultimate promiscuous enzyme: substrate recognition and catalytic mechanism. FASEB J. 27, 1917–1927 (2013). www.fasebj. org
Kicking Maturity Down the Road: Early Refinancing and Maturity Management in the Corporate Bond Market

This paper examines debt maturity management through early refinancing, where firms retire their outstanding bonds before the due date and simultaneously issue new ones as replacements. Speculative-grade firms frequently refinance their corporate bonds early to extend maturity, particularly under accommodating credit supply conditions, leading to a procyclical maturity structure. In contrast, investment-grade firms do not manage their maturity in the same manner. I exploit the protection period of callable bonds to show that the maturity extension is not driven by unobservable confounding factors. The evidence is consistent with speculative-grade firms dynamically managing maturity to mitigate refinancing risk. Received June 6, 2016; editorial decision September 21, 2017 by Editor Philip Strahan.
Knowledge Games: How Playing Games Can Solve Problems, Create Insight, and Make Change

The first section of this book is devoted to working through the complex definition of what constitutes a “knowledge game,” and more specifically, what does not. Schrier’s book is a substantial literature review of the vast – and rapidly growing – field of games that contribute to knowledge production. “Knowledge games,” by her definition, are “the set of practices, contexts, designs, and relationships that emerge from and around those games with a goal or sub-goal of generating new knowledge about humanity, society, the universe, and any previously unknown phenomena” (26). In contrast to games such as “citizen science games,“ “crowd games,” “collective games,” “participatory games,” and “human games,” Schrier takes great care to delineate that she is exploring only those games which seek to produce knowledge, solve authentic, applicable problems, and/or “generate new ideas and possibilities for real world change” (25).
Adaptive Searching in One and Two Dimensions

Searching in a geometric space is an active area of research, predating computer technology. The applications are varied ranging from robotics, to search-andrescue operations in the high seas [24, 23] as well as in land, such as in an avalanche [5] or an office space [12, 7, 13], to scheduling of heuristic algorithms for solvers searching an abstract solution space for a specific solution [16, 17, 22, 2, 19]. Within academia, the field has seen two marked boosts in activity. The first was motivated by the loss of weaponry off the coast of Spain in 1966 in what is known as the Palomares incident and of the USS Thresher and Scorpion submarines in 1963 and 1966 respectively [24, 26]. A second renewed thrust took place in the late 1980s when the applications for autonomous robots became apparent. Geometric searching has proved a fertile ground within computational geometry for the design and analysis of search and recognition strategies under various initial conditions [14, 12, 6, 7, 8, 18, 20]. The basic search scenarios consist of exploring a one dimensional object, such as a path or office corridor, usually modeled as the real line, and of exploring a two dimensional scene, such as a room or a factory floor, usually modelled as a polygonal scene. However, in spite of numerous advances in the theoretical understanding of both of these scenarios, so far such solutions have generally had a limited impact in practice. Over the years various efforts have been made to address this situation, both in terms of isolated research papers attempting to narrow the gap, as well as in organized efforts such as the Algorithmic Foundations of Robotics conference and the Dagstuhl seminars on on-line robotics which bring together theoreticians and practitioners. From these it is apparent that the cost model and hence the solutions obtained from theoretical analysis do not fully reflect real life constraints. Several efforts have been made to resolve this, such as including the turn cost, the scanning cost, and error in navigation and reckoning [9, 10, 15, 20, 18]. In this paper we address one more shortcoming of the standard model. Consider for example a vacuuming robot—such as Roomba(TM). Such a robot explores the environment using sophisticated motion planning algorithms with the goal of attaining complete coverage of
Spider-based Stixel object segmentation

Stereo vision has established in the field of driver assistance and vehicular safety systems. Next steps along the road towards accident free driving aim to assist the driver in increasingly complex situations such as inner-city traffic. In order to achieve these goals, it is desirable to incorporate higher-order object knowledge in the stereo vision-based understanding of traffic scenes. In particular, object shape and dimension information can help to achieve correct interpretations. However, typically this kind of higher-order information results in a difficult energy minimization problem since large areas of the input image have to be constrained. In this contribution, an efficient global optimization approach based on dynamic programming is proposed that is able to take into account such higher-order object knowledge. The approach is built upon a simple tree representation of the Dynamic Stixel World, an efficient super-pixel object representation. Experiments show that object segmentation can be improved significantly by means of the higher-order object information.
Approach the Good, Withdraw from the Bad—A Review on Frontal Alpha Asymmetry Measures in Applied Psychological Research

Basic research has established a strong relationship between stimulus induced human motivation for approach-related behavior and left-frontal electrophysiological activity in the alpha band, i.e. frontal alpha asymmetry (FAA). Since approach motivation is also of interest for various fields of applied research, several recent studies investigated the usefulness of FAA as a diagnostic tool of stimulus induced motivational changes. The present review introduces the theory and the methods commonly used in approach/ withdrawal motivation research, and summarizes work on applied FAA with a focus on product design, marketing, brain-computer communication and mental health studies, where approach motivation is of interest. Studies investigating and developing the application of FAA training in the treatment of affective disorders such as major depressive disorder and anxiety disorder are also introduced, highlighting some of the future possibilities.
Maternal beliefs about television and parental mediation in a low-income United States sample

Abstract Low-income children are at greater risk for excess screen time and negative correlates associated with screen media use. The goal of this study is to increase our understanding of low-income mothers’ beliefs and practices around their children’s television (TV) use (parental mediation). We administered semi-structured interviews to 296 low-income mothers of children ages four–eight years old in the United States. Five themes emerged from the qualitative analysis: (1) mothers are confident in restriction of TV content; (2) time limits are not as important as TV content and are only necessary in extreme situations; (3) mothers make meaning of child learning from TV content; (4) mothers identified individual differences in child TV overuse; and (5) mothers’ policy on TV during mealtime depends on how they believe TV to affect child mealtime behaviors and mothers’ mealtime goals. We discuss the implications of these themes for promoting parental mediation in low-income families.
INVARIANCE PROPERTIES OF TIME-VARYING LINEAR SYSTEMS

This paper formulates and proves two types of necessary and sufficient conditions for the characterization of positively (flow) invariant sets with respect to the state-space trajectories of the time-varying (non-autonomous) linear systems in both continuousand discrete-time case. These conditions are expressed in terms of inequalities involving the matrix function that defines the system dynamics and a constant matrix that defines the shape of the invariant set. The first type of results refers to contractive invariant sets which decrease exponentially, and the second one considers invariant sets that remain constant. Our approach to non-autonomous systems accommodates, as particular cases, the elements of the invariant set analysis already elaborated for autonomous systems.
Effective single image dehazing by fusion

This papers introduces a new single image dehazing approach. The method employs a fusion-based strategy that takes as inputs two adapted versions of the original image that are weighted by speci? c maps in order to yield accurate hazefree results. The method computes in a per-pixel fashion being straightforward to be implemented. Our effective method demonstrates to yield comparative and even better results than the more complex state-of-the-art techniques but has the advantage to be appropriate for real-time applications.