Miracle-dz/UniFAR-FTUs huggingface dataset:
Product Traceability Information : An Approach for Information Linking

Traceability is a concept relating to all types of products and it is strictly linked to supply chain management, aiming to keep trace of the products along their entire lifecycle. In this work a new methodological approach to realize a traceability system is presented. The system developed has been structured with the aim to give a model that can be applied to the most common production realities. The information needful to reconstruct a product history have been represented in the way of information linking and organized through the well known Entity-Relationship (ER) models. The approach presented can be considered as the evolution of the actual ISO9001 Quality Management Systems that at present are not able to give practical tools and/or methodologies to realize a traceability system.
State-Space Algorithms for Estimating Spike Rate Functions

The accurate characterization of spike firing rates including the determination of when changes in activity occur is a fundamental issue in the analysis of neurophysiological data. Here we describe a state-space model for estimating the spike rate function that provides a maximum likelihood estimate of the spike rate, model goodness-of-fit assessments, as well as confidence intervals for the spike rate function and any other associated quantities of interest. Using simulated spike data, we first compare the performance of the state-space approach with that of Bayesian adaptive regression splines (BARS) and a simple cubic spline smoothing algorithm. We show that the state-space model is computationally efficient and comparable with other spline approaches. Our results suggest both a theoretically sound and practical approach for estimating spike rate functions that is applicable to a wide range of neurophysiological data.
SEM2 suite — Towards a tool suite for supporting knowledge management in situation awareness systems

Human operators in today's control centers, such as air or road traffic control, need to monitor a plethora of information obtained from diverse sources. To support them in detecting critical situations within this information flood and taking timely actions, operators thus need adequate information fusion and decision support systems. Research efforts on such dedicated Situation Awareness (SAW) systems have concentrated on assisting the operator in managing the current situations. However, little focus has been so far on integratively supporting the different phases of knowledge management in SAW systems, which encompasses the acquisition, representation, validation, maintenance and reuse of knowledge gathered for and during the use of these systems, such as configuring and maintaining suitable situation templates and exploiting already assessed situations. If operators and domain experts are not supported in these tasks, however, this may discourage them from a successful adoption of such systems in real-world control center applications, as user studies revealed. Based on these, and the lessons learned from the application of our SAW system implementations BeAware! and CSI to the domain of road traffic control, we therefore propose a first step towards a tool suite fostering knowledge management in SAW systems, which stretches from the configuration phase of the system to its runtime maintenance in the light of evolving environments and user needs.
PaxosStore: High-availability Storage Made Practical in WeChat

In this paper, we present PaxosStore, a high-availability storage system developed to support the comprehensive business of WeChat. It employs a combinational design in the storage layer to engage multiple storage engines constructed for different storage models. PaxosStore is characteristic of extracting the Paxos-based distributed consensus protocol as a middleware that is universally accessible to the underlying multi-model storage engines. This facilitates tuning, maintaining, scaling and extending the storage engines. According to our experience in engineering practice, to achieve a practical consistent read/write protocol is far more complex than its theory. To tackle such engineering complexity, we propose a layered design of the Paxos-based storage protocol stack, where PaxosLog, the key data structure used in the protocol, is devised to bridge the programming-oriented consistent read/write to the storage-oriented Paxos procedure. Additionally, we present optimizations based on Paxos that made fault-tolerance more efficient. Discussion throughout the paper primarily focuses on pragmatic solutions that could be insightful for building practical distributed storage systems.
Joint source and Turbo Trellis Coded Hierarchical Modulation for context-aware medical image transmission

An iterative Joint Source and Turbo Trellis Coded Hierarchical Modulation is introduced for robust context-aware medical image transmission. Lossless source compression as well as Quality of Service (QoS) might be considered as the main constraints in the telemedicine field. Our proposed scheme advocated was design to exploit both the joint source-and-channel iterative decoding and the cooperative structure in order for tackling these requirements. The Source Node (SN) is constituted by a lossless Variable Length Code (VLC) and Turbo Trellis-Coded Modulation (TTCM) which relies on Hierarchical Modulation (HM). The Relay Node (RN) is used to support the transmission of the most important content of the image. Our proposed scheme exhibits a robustness performance over a realistic uncorrelated Rayleigh fading channel, while it outperforms the non-cooperative scheme by 3 dB at asymptotic (error-free) Peak Signal to Noise Ratio (PSNR) value.
The role of semantic memory in short-term recall: Effect of strategic retrieval ability in an elderly population

ABSTRACT The present paper examines the relationship between two classic phenomena: semantic effects in short-term recall (STR) tasks, which are interpreted as indicating the involvement of long-term memory (LTM) in the functioning of short-term memory, on the one hand, and the existence of individual differences amongst elderly people in strategic retrieval ability (i.e., the ability to activate representations in LTM in a controlled way) on the other hand. Forty elderly participants completed a STR task under four different conditions which were thought to differentially involve LTM representations. Several executive functions, among which the strategic retrieval ability, were evaluated. The results showed that the participants who obtained the best performances in terms of strategic retrieval ability, and only in this executive ability, also exhibited better performances in the STR task, in particular when this task was performed under conditions which favored the use of LTM.
An Ecological Perspective on Health Promotion Programs

During the past 20 years there has been a dramatic increase in societal interest in preventing disability and death in the United States by changing individual behaviors linked to the risk of contracting chronic diseases. This renewed interest in health pro motion and disease prevention has not been without its critics. Some critics have accused proponents of life-style interventions of promoting a victim-blaming ideology by neglecting the importance of social influences on health and disease. This article proposes an ecological model for health promotion which focuses atten tion on both individual and social environmental factors as targets for health promo tion interventions. It addresses the importance of interventions directed at changing interpersonal, organizational, community, and public policy, factors which support and maintain unhealthy behaviors. The model assumes that appropriate changes in the social environment will produce changes in individuals, and that the support of individ uals in the population is essential for implementing environmental changes.
Cooperation preferences and framing effects

This paper presents the results from an experiment investigating whether framing affects the elicitation and predictive power of preferences for cooperation, i.e., the willingness to cooperate with others. Cooperation preferences are elicited in three treatments using the method of Fischbacher, GA¤chter and Fehr (2001). The treatments vary two features of their method: the sequence and order in which the contributions of other group members are presented. The predictive power of the elicited preferences is evaluated in a one-shot and a finitely-repeated public-good game. I find that the order in which the contributions of others are presented, by and large, has no impact on the elicited preferences and their predictive power. In contrast, presenting the contributions of others in a sequence has a pronounced effect on the elicited preferences and reduces substantially their predictive power. Overall, elicited preferences are more accurate at predicting behavior when others contributions are presented simultaneously and in ascending order, like in Fischbacher, GA¤chter and Fehr (2001).
Simulating The Effect Of Therapies And Traumas On Emotion Regulation

Previous research suggests that therapies can help people to regulate their emotions, and on the other hand, traumas can cause impaired emotion regulation. This paper introduces a computational model for simulating the effects of events like traumas or therapies on emotion regulation. This model is an extension of an existing computational model of emotion regulation (Bosse et al. 2007c). A number of simulation experiments has been performed and evaluated.
Joint uncertainty decoding with unscented transform for noise robust subspace Gaussian mixture models

Common noise compensation techniques use vector Taylor series (VTS) to approximate the mismatch function. Recent work shows that the approximation accuracy may be improved by sampling. One such sampling technique is the unscented transform (UT), which draws samples deterministically from clean speech and noise model to derive the noise corrupted speech parameters. This paper applies UT to noise compensation of the subspace Gaussian mixture model (SGMM). Since UT requires relatively smaller number of samples for accurate estimation, it has significantly lower computational cost compared to other random sampling techniques. However, the number of surface Gaussians in an SGMM is typically very large, making the direct application of UT, for compensating individual Gaussian components, computationally impractical. In this paper, we avoid the computational burden by employing UT in the framework of joint uncertainty decoding (JUD), which groups all the Gaussian components into small number of classes, sharing the compensation parameters by class. We evaluate the JUD-UT technique for an SGMM system using the Aurora 4 corpus. Experimental results indicate that UT can lead to increased accuracy compared to VTS approximation if the JUD phase factor is untuned, and to similar accuracy if the phase factor is tuned empirically.
2 1/2 D Visual Servoing

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. 2 1/2 D Visual Servoing E. Malis, François Chaumette, S. Boudet
Radical embodied cognitive neuroscience: addressing “grand challenges” of the mind sciences

It is becoming ever more accepted that investigations of mind span the brain, body, and environment. To broaden the scope of what is relevant in such investigations is to increase the amount of data scientists must reckon with. Thus, a major challenge facing scientists who study the mind is how to make big data intelligible both within and between fields. One way to face this challenge is to structure the data within a framework and to make it intelligible by means of a common theory. Radical embodied cognitive neuroscience can function as such a framework, with dynamical systems theory as its methodology, and self-organized criticality as its theory.
An associative classifier based on positive and negative rules

Associative classifiers use association rules to associate attribute values with observed class labels. This model has been recently introduced in the literature and shows good promise. The proposals so far have only concentrated on, and differ only in the way rules are ranked and selected in the model. We propose a new framework that uses different types of association rules, positive and negative. Negative association rules of interest are rules that either associate negations of attribute values to classes or negatively associate attribute values to classes. In this paper we propose a new algorithm to discover at the same time positive and negative association rules. We introduce a new associative classifier that takes advantage of these two types of rules. Moreover, we present a new way to prune irrelevant classification rules using a correlation coefficient without jeopardizing the accuracy of our associative classifier model. Our preliminary results with UCI datasets are very encouraging.
Effect of Preservative-Free Artificial Tears on the Antimicrobial Activity of Human β-Defensin-2 and Cathelicidin LL-37 In Vitro

Purpose. Human β-defensin-2 (hBD-2) and cathelicidin LL-37 are salt-sensitive cationic antimicrobial peptides expressed by ocular surface epithelia. The goal of this study was to investigate the effect of preservative-free artificial tears on hBD-2 and LL-37 antimicrobial activity against Pseudomonas aeruginosa. Methods. P. aeruginosa was incubated with hBD-2 or LL-37 in the absence or presence (70% vol/vol) of different preservative-free artificial tears-Visine Tears (300 mOsm/kg), Tears Naturale Free (261 mOsm/kg), TheraTears (185 mOsm/kg), and Refresh Plus (325 mOsm/kg)-for 2 hours at 37°C. In some experiments, P. aeruginosa was incubated with hBD-2 or LL-37 and Visine Tears or Tears Naturale Free with or without carboxymethylcellulose (0.5% vol/vol final concentration). Plates were inoculated with samples of each reaction mixture and then incubated for 24 hours at 37°C. Results. Visine Tears and Tears Naturale Free had little or no effect on the antimicrobial activity of 100 μg/mL hBD-2 or LL-37. In the presence of Refresh Plus and TheraTears, the activity of 100 μg/mL hBD-2 or LL-37 was reduced by 90% to 100%. Carboxymethylcellulose, at a concentration comparable to that present in Refresh Plus, reduced the effectiveness of hBD-2 or LL-37 by 40% to 90% in the presence of Tears Naturale Free and Visine Tears. Conclusion. Human β-defensin-2 and cathelicidin LL-37 inhibit the growth of P. aeruginosa in vitro, but this activity is markedly reduced in the presence of Refresh Plus and TheraTears. These results suggest that carboxymethylcellulose-containing artificial tears may reduce the activity of the endogenously produced antimicrobial peptides.
Combining Incremental Strategy Generation and Branch and Bound Search for Computing Maxmin Strategies in Imperfect Recall Games

Extensive-form games with imperfect recall are an important model of dynamic games where the players are allowed to forget previously known information. Often, imperfect recall games are the result of an abstraction algorithm that simplifies a large game with perfect recall. Unfortunately, solving an imperfect recall game has fundamental problems since a Nash equilibrium does not have to exist. Alternatively, we can seek maxmin strategies that guarantee an expected outcome. The only existing algorithm computing maxmin strategies in two-player imperfect recall games without absentmindedness, however, requires approximating a bilinear mathematical program that is proportional to the size of the whole game and thus has a limited scalability. We propose a novel algorithm for computing maxmin strategies in this class of games that combines this approximate algorithm with an incremental strategy-generation technique designed previously for extensive-form games with perfect recall. Experimental evaluation shows that the novel algorithm builds only a fraction of the game tree and improves the scalability by several orders of magnitude. Finally, we demonstrate that our algorithm can solve an abstracted variant of a large game faster compared to the algorithms operating on the unabstracted perfect-recall variant.
High level bilateral talks. Focus on "effect of low-frequency repetitive transcranial magnetic stimulation on interhemispheric inhibition".

Interhemispheric interactions are relevant to fundamental cognitive processes including perception, awareness ([Gazzaniga 2000][1]), and motor control ([Geffen et al. 1994][2]). In the motor domain, interhemispheric inhibitory interactions (IHI) operate between homonymous body part representations
Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods

We present a methodology for Bayesian analysis of software quality. We cast our research in the broader context of constructing a causal framework that can include process, product, and other diverse sources of information regarding fault introduction during the software development process. In this paper, we discuss the aspect of relating internal product metrics to external quality metrics. Specifically, we build a Bayesian network (BN) model to relate object-oriented software metrics to software fault content and fault proneness. Assuming that the relationship can be described as a generalized linear model, we derive parametric functional forms for the target node conditional distributions in the BN. These functional forms are shown to be able to represent linear, Poisson, and binomial logistic regression. The models are empirically evaluated using a public domain data set from a software subsystem. The results show that our approach produces statistically significant estimations and that our overall modeling method performs no worse than existing techniques.
Evaluation of PIMA point-of-care CD4 testing in a large UK HIV service

Objectives To evaluate the performance and patient acceptability of the PIMA point-of-care (POCT) CD4 test. Methods Parallel POCT and laboratory CD4 testing were performed in newly diagnosed HIV patients and those with chronic infection attending routine or emergency clinics. Demographics, clinical status and time taken for CD4 results to be available were recorded. Patient acceptability was assessed using a five-point Likert scale. POCT and laboratory results were compared. Results 283 patients underwent POCT and laboratory CD4 testing. Paired laboratory and POCT results were available in 269 patients. After excluding 15 patients tested during the lead-in period, the test comparison was based on 254 results. Most patients were asymptomatic, male and white British reflecting this patient cohort. 236 patients were chronically infected and 47 were newly diagnosed HIV positive. The POCT result was available within 30 min (86%). The laboratory and POCT results were strongly correlated, r=0.93 (p<0.001), but were generally lower for the POCT (201/254 (79%): p<0.001). As a percentage of the laboratory count, the median (95% range) POCT was 87% (57%–126%). The difference between the POCT and laboratory result was greater for those patients attending the emergency clinic. The sensitivity and specificity of the POCT, to identify patients with laboratory CD4 below 350, were 95% (95% CI 88% to 98%) and 88% (95% CI 82% to 93%), respectively. 235 (83%) patients completed the questionnaire and the POCT was highly acceptable. Conclusions POCT CD4 was highly correlated with laboratory CD4 testing in this cohort, provided immediate results and was highly acceptable to patients.
Adaptive estimation of residue signal for voice pathology diagnosis

The use of noninvasive techniques to evaluate the larynx and vocal tract helps the speech specialists to perform accurate diagnose of diseases. In this study, a method to distinguish among 21 different pathologies using speech signals was developed. Through inverse filtering (Kalman and Wiener filters) of the voice signal, the residue was estimated and seven acoustic features were extracted from it to evaluate the laryngeal diseases. As time-invariant inverse filtering was used, the nonstationary nature of dysphonic voices was also considered. Together with the estimation of the acoustic features using a robust statistical method, this technique also allowed us to discriminate among pathologies with very close perceptual characteristics. The results from a Mann-Whitney test indicated that the best measurement for pathological discrimination was JITTER with 54.79% ability to cluster the voice types and the worst one was spectral flatness of residue (SFR) with 36.41%.
Associations of Cumulative Family Risk With Academic Performance and Substance Involvement: Tests of Moderation by Child Reading Engagement

Abstract Background: Exposure to cumulative contextual risk within the family early in life increases children’s risk for substance involvement and related difficulties, including school failure, in adolescence and young adulthood. However, potential protective factors that buffer these risk associations are relatively untested, yet such tests are needed to improve existing preventive interventions for enhancing resilience among vulnerable children. Objectives: This study tested child reading engagement with parents at home as a moderator of cumulative family risk associations with adolescent substance use and academic performance as well as young adult substance abuse. Methods: Population register data as well as parent-report and adolescent-report data from 6,963 participants of the 1986 Northern Finland Birth Cohort study were analyzed via structural equation modeling with latent variable interactions. Results: Results showed that child reading engagement moderated the associations of cumulative family risk with both adolescent academic performance and young adult substance abuse, but not with adolescent substance use. The highest levels of academic performance were observed under conditions of low risk and high reading engagement. Interestingly, cumulative family risk had a small positive association with substance abuse when reading engagement was low and a negative association with the young adult outcome when reading engagement was high. Conclusions/Importance: Moderation tests revealed complex interaction forms that may have implications for both theory and family-based preventive interventions.