Miracle-dz/UniFAR-FTUs huggingface dataset:
Search for optimality in traffic matrix estimation: a rational approach by Cramer-Rao lower bounds

In this paper we study the problem of traffic matrix estimation. The problem is ill-posed and thus some additional information has to be brought in to obtain an estimate. One common approach is to use the second moment statistics through a functional mean-variance relationship. We derive analytically the Fisher information matrix under this framework and obtain the Cramer-Rao lower bound (CRLB) for the variance of an estimator of the traffic matrix. Applications for the use of the CRLB are then demonstrated. From the bounds we can directly obtain confidence intervals for maximum likelihood estimates. Another use for the CRLB is the possibility to evaluate the efficiency of an estimator against the lower bound. A third possible application is to utilize the bounds in an approach to find the best placement for direct measurements of OD flows, so that it is optimal with regard to the traffic matrix estimation problem
E-Learning Takes the Stage: From La Dama Boba to a Serious Game

In Spain, young people are losing their interest in classical drama. We believe that educational games are an excellent medium for addressing this issue. This article describes the design and implementation of a videogame based on La Dama Boba, Lope de Vega's stage play. The aim of the videogame is to motivate young people to go to classical theater plays and to familiarize students with the plot and the characters before they go to the theater. This goal is achieved by making avatars interesting to players, by incorporating different theater techniques to generate characters' personalities and by including the elements that can be used as audiovisual content such as music, scenery, and costume design. In this paper, we also present the results of an initial evaluation of the game with master's students from the E-learning group. In order to improve the game, we have studied its effects in terms of improvement of knowledge, learning outcomes, and student motivation.
Alignment of Lyrics With Accompanied Singing Audio Based on Acoustic-Phonetic Vowel Likelihood Modeling

This study addresses the task of aligning lyrics with accompanied singing recordings. With a vowel-only representation of lyric syllables, our approach evaluates likelihood scores of vowel types with glottal pulse shapes and formant frequencies extracted from a small set of singing examples. The proposed vowel likelihood model is used in conjunction with a prior model of frame-wise syllable sequence in determining an optimal evolution of syllabic position. In lyrics alignment experiments, we optimized numerical parameters on two independent development sets and then tested the optimized system on two other datasets. New objective performance measures are introduced in the evaluation to provide further insight into the quality of alignment. Use of glottal pulse shapes and formant frequencies is shown by a controlled experiment to account for a 0.07 difference in average normalized alignment error. Another controlled experiment demonstrates that, with a difference of 0.03, F0-invariant glottal pulse shape gives a lower average normalized alignment error than does F0-invariant spectrum envelope, the latter being assumed by MFCC-based timbre models.
Monitoring Classification Blindspots to Detect Drifts from Unlabeled Data

Machine learning models deployed in real world applications, operate in a dynamic environment where the data distribution can change constantly. These changes, called concept drifts, cause the performance of the learned model to degrade over time. As such it is essential to detect and adapt to changes in the data, for the model to be of any real use. While, model adaptation requires labeled data (for retraining), the detection process does not have to. Labeling data is time consuming and expensive, and if data changes are infrequent, most of the labeling effort, spent on verification, is wasted. In this paper, an ensemble based detection method is proposed, which tracks the number of samples in the critical disagreement regions of the ensemble, to detect concept drift from unlabeled data. The proposed algorithm, is-distribution and model independent, unsupervised and can be used in an online incremental fashion. Experimental analysis on 4 real world concept drift datasets shows that the proposed methodology gives high prediction performance, low false alarm rate and uses only 11.3% overall labeling, on average.
Noninvasive Brain-Computer Interface-based control of humanoid navigation

This study proposes an asynchronous noninvasive Brain Computer Interface (BCI) -based navigation system for a humanoid robot, which can behave similarly to a human. In the experimental procedure, each subject is asked to undertake three different sessions: offline training, an online feedback test, and real-time control of a humanoid robot in an indoor maze. During the offline training session, amplitude features from the EEG are extracted using auto-regressive frequency analysis with a Laplacian filter. The optimal feature components are selected by using the Fisher ratio and the linear discriminant analysis (LDA) distance metric. Two classifiers are hierarchically set to build the asynchronous BCI system. During the online test session, the trained BCI system translates a subject's ongoing EEG into four mental states: rest, left-hand imagery, right-hand imagery, and foot imagery. Event-by-event analysis is applied to evaluate the performance of the BCI system. If the test performance is consistently satisfactory, the subject executes the real-time control experiments. During the navigation experiments, the subject controls the robot in an indoor maze using the BCI system while surveying the environment through visual feedback. The results show that BCI control was comparable to manual control with a performance ratio of 81%. The evaluation of the results validates the feasibility and power of the proposed system.
Maintenance Tools : Some Empirical Research

In this position paper we discuss research into tools for software maintenance, where tools include everything from large scale integrated CASE products down to simple one-function commands or features. The objective of this research is to learn more about software engineers’ work with and needs for tools, so that they can become more productive. We discuss several categories of questions to be asked in this research, and various methods for gathering data. We present some data from our research to date which indicates, among other things the need for efficient software exploration mechanisms. Finally we propose some directions for collaborative research.
Understanding the resource-based view : implications of methodological choice and a new creative context

Over the past two decades, the resource-based view of the firm (RBV) has emerged as one of the more influential paradigms from the field of strategic management. However, the theory has been subjected to a number of criticisms, particularly related to the use of methodologies in past research. Many RBV studies have tended to use averaged findings across broad industry samples. Approaches reliant on "averaging" methods will only uncover what is the case for the average, "representative" firm, and will not identify those unique, firm-specific assets that can result in sustained profitability. In order to examine the implications of methodological choice and the RBV, the subjective approach of Q methodology was used in a sample of music industry firms to identify a key resource set for the context of interest, identify strategic groups within the sample based on resource emphasis, and explore the ways in which managers use their resources to generate firm profits. A comparative approach examined resource outcomes by performance group, over multiple levels of analysis. The findings revealed (i) a number of relevant and new, context-specific resources from the music industry, (ii) the identification of three distinct clusters of firms that emerged from the sample based on resource preferences, firm characteristics, and managerial perceptions (iii) key resource findings that varied by level of analysis and by firm performance, and (iv) distinct processes through which the resources become valuable at the level of the firm--even when the same resources are considered. The outcomes of this thesis illustrate how methodological choice can affect findings when using the RBV to uncover important sources of advantage. Furthermore, the outcomes in this thesis point to the weaknesses of many past RBV studies that investigate the impact of resources and capabilities on firm performance, and remind scholars that a defining feature of the RBV is that its intention was to identify sources of advantage at the level of the firm. Moreover, the findings show that past RBV research using aggregated data across multi-industry samples can be misleading in its prescription to managers.
M ay 2 00 3 On the dynamics of Gowdy space times

We study the behavior near the singularity t=0 of Gowdy metrics. We prove existence of an open dense set of boundary points near which the solution is smoothly “asymptotically velocity term dominated” (AVTD). We show that the set of AVTD solutions satisfying a uniformity condition is open in the set of all solutions. We analyse in detail the asymptotic behavior of “power law” solutions at the (hitherto unchartered) points at which the asymptotic velocity equals zero or one. Several other related results are established.
Ensembles of Multilayer Perceptron and Modular Neural Networks for Fast and Accurate Learning

In this paper, we analyze the performance of a classifier ensemble consisting of small-sized neural networks for accurate and fast learning. To this end, three topologies of neural networks are evaluated: the multilayer perceptron with two different configurations in the hidden layer, and the modular neural network. The experiments here carried out illustrate the effectiveness of the ensembles of neural networks in terms of average predictive accuracy and processing time, as compared to the single classifiers.
Employing fuzzy trust levels to improve wireless network reliability

Wireless Ad-hoc networks consist of wireless devices that communicate with each other without the need for a management infrastructure. As such, the very basic operation of Ad-hoc networks is dependent on the cooperation of their nodes to provide connectivity and communication routes. In practice though, such cooperation may not always be achieved. Cases where some nodes behave in a selfish manner or maliciously are of particular interest and consequence. Such cases normally result in degradation of the performance of the network or even disruption of its operation altogether. To mitigate the effect of such nodes and to achieve higher levels of reliability, the communications paths should try to avoid using them. In other words, the rout should be based on reliable and trustworthy nodes. In turn, that does necessitate evaluation of trust and trustworthiness of the nodes. This paper proposes an approach that is based on soft computing to establish quantifiable trust levels between the nodes of an Ad- hoc network. The trust levels are then used in the routing decision making process. Extensive simulator studies that utilize the proposed approach are carried out, analyzed, and reported in this paper. The resulting relatively significant improvements in the performance of typical Ad-hoc networks are also reported here.
BlastMultAl, a Blast Extension for Similarity Searching with Alignment Graphs

We describe a new method of processing similarity queries of a proteic multiple alignment with a set (database) of protein sequences, or similarity queries of a protein sequence with a set of protein alignments. We use a representation of multiple alignments as alignment-graphs. Comparisons with different classical methods is made. This new method allows the detection of subtle similarities which are not found by the other methods. It has direct applications for similarities querying with the database of protein domains ProDom.
LEARNING BY TEACHING: A NEW AGENT PARADIGM FOR EDUCATIONAL SOFTWARE

ABSTRACT This paper discusses Betty's Brain, a teachable agent in the domain of river ecosystems that combines learning by teaching with self-regulation mentoring to promote deep learning and understanding. Two studies demonstrate the effectiveness of this system. The first study focused on components that define student-teacher interactions in the learning by teaching task. The second study examined the value of adding meta-cognitive strategies that governed Betty's behavior and self-regulation hints provided by a mentor agent. The study compared three versions: a system where the student was tutored by a pedagogical agent, a learning by teaching system, where students taught a baseline version of Betty, and received tutoring help from the mentor, and a learning by teaching system, where Betty was enhanced to include self-regulation strategies, and the mentor provided help on domain material on how to become better learners and better teachers. Results indicate that the addition of the self-regulated Betty and the self-regulation mentor better prepared students to learn new concepts later, even when they no longer had access to the SRL environment.
Non-linear parametric Bayesian regression for robust background subtraction

This paper presents a novel statistical method for background subtraction aimed at robustness with regards to common disturbance factors such as sudden illumination changes, variations of the camera parameters, noise. The proposed approach relies on a novel non-linear parametric model for the local effect of disturbance factors on a neighbourhood of pixel intensities. Assuming additive gaussian noise, we also propose Bayesian estimation of model parameters by means of a maximum-a-posteriori regression and a statistical change detection test. Experimental results demonstrate that the proposed approach is state-of-the-art in sequences where disturbance factors yield linear as well as non-linear intensity transformations.
Tunnel construction workers’ well-being: the role of job control and supervisor support

Abstract The construction industry is one of the most dangerous work environments, since it is characterized by numerous physical and psycho-social risk factors. Nevertheless, only a few studies have been conducted to analyze workers’ well-being within this sector. Drawing on the Job Demands-Resources model, this study aimed to investigate whether job resources (i.e. job control and supervisor support) could independently buffer the relationship between job demands (i.e. workload, emotional demands and work pace) and psycho-physical health among alpine transit tunnel construction employees. 96 workers employed in an Italian construction company filled in anonymous self-report questionnaires investigating: job demands, job resources and psycho-physical health. Descriptive analyses, analyses of variance and structural equation models were carried out. Workers who perceived higher job control and supervisor support were less likely to experience psycho-physical malaise, even in presence of high workload and work pace. Fostering job resources is crucial to design interventions aimed at maintaining or improving construction workers’ well-being.
Hybrid approach for optimizing onthe-fly web map generalization

Abstract The creation of the map on-the-fly becomes today an important domain in cartography. Thousands of the users need access to spatial data on the web specific to their needs. This requires generating a map in real time. Different approaches appeared for optimizing the on-the-fly generation maps, but those not suffice for guiding a powerful and efficient process. Thus, a new approach must be developed for optimizing the on-thefly generalization process according to the user needs. This paper describe a new approach that combines the multi agent system and the techniques of genetic algorithm which improved by Tabu search method (GATS). We introduced the TS into the genetic algorithm to refine the solutions space found by GA toward the optimum solution. This approach uses the multiple representation and cartographic generalization. KeywordsOn-the-fly maps generalization, Agent, Genetic Algorithm, Tabu Search, cartographic constraints, multiple representations.
Expression of Rod-Derived Cone Viability Factor: Dual Role of CRX in Regulating Promoter Activity and Cell-Type Specificity

Background RdCVF and RdCVF2, encoded by the nucleoredoxin-like genes NXNL1 and NXNL2, are trophic factors with therapeutic potential that are involved in cone photoreceptor survival. Studying how their expression is regulated in the retina has implications for understanding both their activity and the mechanisms determining cell-type specificity within the retina. Methodology/Principal Findings In order to define and characterize their promoters, a series of luciferase/GFP reporter constructs that contain various fragments of the 5′-upstream region of each gene, both murine and human, were tested in photoreceptor-like and non-photoreceptor cell lines and also in a biologically more relevant mouse retinal explant system. For NXNL1, 5′-deletion analysis identified the human −205/+57 bp and murine −351/+51 bp regions as having promoter activity. Moreover, in the retinal explants these constructs drove expression specifically to photoreceptor cells. For NXNL2, the human −393/+27 bp and murine −195/+70 bp regions were found to be sufficient for promoter activity. However, despite the fact that endogenous NXNL2 expression is photoreceptor-specific within the retina, neither of these DNA sequences nor larger upstream regions demonstrated photoreceptor-specific expression. Further analysis showed that a 79 bp NXNL2 positive regulatory sequence (−393 to 315 bp) combined with a 134 bp inactive minimal NXNL1 promoter fragment (−77 to +57 bp) was able to drive photoreceptor-specific expression, suggesting that the minimal NXNL1 fragment contains latent elements that encode cell-type specificity. Finally, based on bioinformatic analysis that suggested the importance of a CRX binding site within the minimal NXNL1 fragment, we found by mutation analysis that, depending on the context, the CRX site can play a dual role. Conclusions/Significance The regulation of the Nucleoredoxin-like genes involves a CRX responsive element that can act as both as a positive regulator of promoter activity and as a modulator of cell-type specificity.
Application of Collagen Scaffold in Tissue Engineering: Recent Advances and New Perspectives

Collagen is the main structural protein of most hard and soft tissues in animals and the human body, which plays an important role in maintaining the biological and structural integrity of the extracellular matrix (ECM) and provides physical support to tissues. Collagen can be extracted and purified from a variety of sources and offers low immunogenicity, a porous structure, good permeability, biocompatibility and biodegradability. Collagen scaffolds have been widely used in tissue engineering due to these excellent properties. However, the poor mechanical property of collagen scaffolds limits their applications to some extent. To overcome this shortcoming, collagen scaffolds can be cross-linked by chemical or physical methods or modified with natural/synthetic polymers or inorganic materials. Biochemical factors can also be introduced to the scaffold to further improve its biological activity. This review will summarize the structure and biological characteristics of collagen and introduce the preparation methods and modification strategies of collagen scaffolds. The typical application of a collagen scaffold in tissue engineering (including nerve, bone, cartilage, tendon, ligament, blood vessel and skin) will be further provided. The prospects and challenges about their future research and application will also be pointed out.
Understanding Collaborative Working in a Facilitated Interdisciplinary Environment

Purpose This paper aims to report on a new form of project insurance in the UK construction designed to improve collaborative working among project participants. It aims to examine the interprofessional collaborative endeavour under the new insurance arrangements, drawing on a structuration model of interdisciplinary collaboration developed in the field of healthcare studies. Design/Methodology/Approach The research employed a longitudinal, action research approach with participant observation. A novel element included a project facilitator as part of the action research team. Qualitative and quantitative data were collected using semi-structured interviews with close scrutiny of the documented project record. Findings Project structure emerges as a more important element in the collaboration process than is typically recognised in practice-based studies; the analysis revealing the interplay of structure, the socialising processes of participants and how facilitation helps develop interdependence among project participants. The analysis provides a basis for understanding collaboration on the trial project. Research Limitations/Implications The project provides a unique context for the examination of collaboration under the new project insurance arrangements. The results have implications for the study of collaboration in highlighting the important relationship between project structure, collaboration and facilitation. Practical Implications Understanding how interdependency is developed through structure, the socialising processes of collaborators and facilitation has important implications for those concerned with designing project arrangements and managing collaborative processes. Originality/Value The paper reports on the first full trial of new project insurance arrangements in the UK. It highlights the significance of project structure, and the interplay between collaborators’ emerging practices and facilitation.
Effects of anaesthesia on atrial fibrillation organization during catheter ablation procedures

Effect of anaesthetic agents in restoration rhythm procedures during atrial fibrillation (AF) has not been fully investigated. Intra-atrial recordings belong to 27 patients diagnosed with AF were analyzed ldquobeforerdquo (baseline) and ldquoduringrdquo anaesthetic infusion. Organization measurements were evaluated in time-domain and frequency-domain, as well as approaches from non linear analysis. The frequency domain measurements showed that the average AF cycle length increased as an effect of anaesthesia. The results of time and frequency linear organization measurements showed a higher correlation during the anaesthetic infusion in the right atrium with an opposite effect in the left atrium. The same effects were corroborated with entropy analysis. The proposed methodology provide additional insights to the understanding of the role of the anaesthetic, by suggesting increased parasympathetic activation at atrial level.
Breakthrough Technologies High-Throughput Fluorescent Tagging of Full-Length Arabidopsis Gene Products in Planta 1

Department of Biochemistry and Cell Biology, State University of New York, Stony Brook, New York 11794–5215 (G.-W.T., B.P., C.D.K., J.L., V.C.); Watson School of Biological Sciences (C.D.K.) and Cold Spring Harbor Laboratory (A.M., D.J.), Cold Spring Harbor, New York 11724; Center for Plant Cell Biology and Department of Botany and Plant Sciences, University of California, Riverside, California 92521–0124 (N.C., G.D., N.V.R.); and The Arabidopsis Information Resource (S.L., S.Y.R.) and Carnegie Institution of Washington (S.L., D.E., S.Y.R), Stanford, California 94305