Miracle-dz/UniFAR-FTUs huggingface dataset:
Resources for Named Entity Recognition and Resolution in News Wires

In the applicative context of news wire enrichment with metadata, named entity recognition plays an important role, but requires to be followed by a resolution module that maps named entity mentions to entries in a reference database. In this paper, we describe NP, the named entity module embedded in the SXPipe shallow processing chain, that we used for extracting information from French news wires from the Agence France-Presse. We describe the construction of our reference database from freely available external resources, as well as our named entity detection, disambiguation and resolution modules. We also introduce a freely available and manually developped annotated corpus designed for the evaluation of named entity recognition and resolution tools, and provide evaluation figures for NP.
A comparative, cross-cultural criminal career analysis

For over 30 years, the criminal career paradigm in criminology has raised important theoretical and policy questions as well as research on the ‘dimensions’ of the criminal career (for example, onset, duration, lambda, persistence, chronicity, desistance). Yet few studies have examined criminal career dimensions using a cross-national comparative approach. In this paper, we use an international sample of students (aged 12–15 years) from 30 countries (International Self-Report Delinquency Study-2): (1) to determine the extent of cross-national variation in the prevalence and correlates of high-frequency, serious offenders; and (2) to explore cross-national variation in offending patterns and selected correlates of offense specialization (for example, gender, self-control, delinquent peer association). Although we find several factors are correlated with criminal career dimensions across context, important differences emerged as well that have implications for developing context-specific theories of crime and effective offender programming.
Contextual Conditional Discriminations

Three experiments used a discriminated operant procedure to study conditional discrimination learning in rats. The first experiment showed that rats were capable of learning a biconditional discrimination in which two contexts served as conditional cues signalling the reinforcement contingencies associated with two discriminative stimuli. The discrimination was learned equally well when one discriminative stimulus signalled food, the other its absence, and when one stimulus signalled food, the other extinction plus mild footshock. In Experiment 2 it was shown that prior training on such a conditional discrimination enhanced the subsequent context specificity of simple conditioning relative to control groups of animals for whom the prior training had not been conditional. Experiment 3 showed that a reversal of the significance of one pair of discriminative stimuli produced no spontaneous reversal in performance to a second, target, pair. The pattern of results is best accounted for by an analysis of contextual conditional discrimination learning in terms of stimulus configurations and offers no support for the notion that rats may learn a general conditional rule or set.
Collaborative visualization: Definition, challenges, and research agenda

The conflux of two growing areas of technology – collaboration and visualization – into a new research direction, collaborative visualization, provides new research challenges. Technology now allows us to easily connect and collaborate with one another – in settings as diverse as over networked computers, across mobile devices, or using shared displays such as interactive walls and tabletop surfaces. Digital information is now regularly accessed by multiple people in order to share information, to view it together, to analyze it, or to form decisions. Visualizations are used to deal more effectively with large amounts of information while interactive visualizations allow users to explore the underlying data. While researchers face many challenges in collaboration and in visualization, the emergence of collaborative visualization poses additional challenges, but it is also an exciting opportunity to reach new audiences and applications for visualization tools and techniques. The purpose of this article is (1) to provide a definition, clear scope, and overview of the evolving field of collaborative visualization, (2) to help pinpoint the unique focus of collaborative visualization with its specific aspects, challenges, and requirements within the intersection of general computer-supported cooperative work and visualization research, and (3) to draw attention to important future research questions to be addressed by the community. We conclude by discussing a research agenda for future work on collaborative visualization and urge for a new generation of visualization tools that are designed with collaboration in mind from their very inception.
Virtual Identity Creation by Fingerprint Combination for Privacy Protection Ms

Now a days both forensic and civilian applications makes use of biometric data as concerns about privacy and misuse of data increases. Thus protecting biometric data is one of the prime issues. Although, over the years many template protection schemes have been discovered and much research and progress have been made on fingerprint authentication system, performance of even state-of-the-art matchers is still much low. In addition to this, securing a stored fingerprint template is of paramount importance because compromised fingerprint cannot be easily revoked. Therefore, to tackle these problems, much effort is still needed to improve the performance, security and speed of fingerprint authentication systems. In this project a biometric authentication system for protecting fingerprint privacy is proposed. The idea is to combine two different fingerprints, pertaining to two different fingers into a new identity. The proposed work of this project is carried out in two phases. In the first phase combined minutiae template will be generated based on the information extracted from two different fingerprints. And in the next phase a Fingerprint-Matching process is proposed for matching the query fingerprints against a combined minutiae template by using minutiae matching algorithm. Furthermore, the proposed work is more robust to attacks in sense that by storing combined minutiae template into the database, the complete minutiae feature of a single fingerprint will not be compromised when database is stolen. The experimental results show that our system can achieve a very low error rate with FRR = 0.1% at FAR = 0.1%. Compared with the state-of-the-art technique, our work has the advantage in creating a better new virtual identity. Keywords—Fingerprint, Biometric templates, Virtual identity, Authentication, Minutiae
Ju n 20 05 Doubly Special Relativity as a Limit of Gravity

Doubly Special Relativity (DSR) is a theory with two observerindependent scales, of velocity and mass, which is expected to replace Special Relativity at ultra-high energies. In these notes we first discuss the postulates of DSR, and then turn to presenting arguments supporting the hypothesis that DSR can be regarded as a flat space, semiclassical limit of gravity. The notes are based on the talk presented at the conference “Special Relativity – Will it Survive the Next 100 Years? ”
Zeolite Impregnated with Ag as Catalysts for Glycerol Conversion to Ethanol Assisted by Ultrasonic

The objectives of this study are to investigate the effectiveness of activated zeolite with and without impregnated Ag on the conversion of glycerol to ethanol. Zeolite catalyst used in this study was synthesized zeolite from silica extracted from local sand and glycerol conversion using zeolite with and without impregnated Ag done at various temperatures of sonication. Product conversion analyzed using Gas Chromatography. The results showed that the impregnation of g on the activated zeolite catalyst increase the acidity from 0.0774 to 3.6253 mmol/g. The surface area slightly decreases from 1339 becomes 1279 m2/g. The process of converting glycerol to ethanol, the use of ultrasonic waves increases the effectiveness of glycerol conversion. The higher the sonication temperature, the greater ethanol produced. The highest ethanol product with a yield of 13.6% was obtained at a sonication temperature of 60 °C when the active Ag/zeolite catalyst was used. The use of Ag/zeolite catalyst makes glycerol conversion can be carried out at relatively low temperatures and ambient pressures so that it has more promising future prospects.
POLITICAL CONDITIONS AND FEMALE LABOUR FORCE PARTICIPATION

This paper examines the impact of political conditions on female labour force participation in case of Pakistan. The study covers the period of 1980-2010. We found the existence of cointegration between the variables. Democracy and government spending lowers labour female labour participation. Development spending increases female labour participation. This study opens up new insights for policy makers in Pakistan.
Adaptive Channel Equalization for Wireless Personal Communications

In this paper, a new adaptiveH1 filtering algorithm is developed to recursively update the tap-coefficient vector of a decision feedback equalizer (DFE) in order to adaptively equalize the time-variant dispersive fading channel of a high-rate indoor wireless personal communication system. Different from conventional L2 [such as the recursive least squares (RLS)] filtering algorithms which minimize the squared equalization error, the adaptiveH1 filtering algorithm is a worst case optimization. It minimizes the effect of the worst disturbances (including input noise and modeling error) on the equalization error. Hence, the DFE with the adaptive H1 filtering algorithm is more robust to the disturbances than that with the RLS algorithm. Computer simulation demonstrates that better transmission performance can be achieved using the adaptiveH1 algorithm when the received signal-to-noise ratio (SNR) is larger than 20 dB.
Aligning development tools with the way programmers think about code changes

Software developers must modify their programs to keepup with changing requirements and designs. Often, aconceptually simple change can require numerous editsthat are similar but not identical, leading to errors andomissions. Researchers have designed programming environmentsto address this problem, but most of thesesystems are counter-intuitive and difficult to use.By applying a task-centered design process, we developeda visual tool that allows programmers to makecomplex code transformations in an intuitive manner.This approach uses a representation that aligns wellwith programmers' mental models of programming structures.The visual language combines textual and graphicalelements and is expressive enough to support a broadrange of code-changing tasks. To simplify learning thesystem, its user interface scaffolds construction and executionof transformations. An evaluation with Java programmerssuggests that the interface is intuitive, easyto learn, and effective on a representative editing task.
On the uses of variable rules

The introduction of variable rules ten years ago has provoked a variety of critical reactions; among these Kay & McDaniel's (1979) review appears as a clear and penetrating study of many issues neglected or unresolved in earlier discussions. It is refreshing to observe an approach to the problems of variation and sociolinguistics relatively free from the ideological constraints that other critiques have inherited from formal linguistics (e.g. Bickerton 1971, 1973; Gazdar 1976). They bring to the discussion of variable rules a clarity gained by several years' reflection on the early statistical approaches, together with a certain distance from current sociolinguistic methodological developments and problems. At the same time, there are some attendant disadvantages of such a distance; these appear in their treatment of the work that preceded probabilistic models, in their lack of attention to the interaction between the practical aspects of linguistic data analysis and the evolution of theoretical concerns, in their misunderstanding of certain mathematical facts, and in their neglect of the more recent developments over the past five years. The K & M analysis may best be evaluated as a reaction to the stage in variable rule analysis around 1971–4 when the first probabilistic models were being proposed and tested.
Transmembrane proteoglycans syndecan-2, 4, receptor candidates for the impact of HGF and FGF2 on semaphorin 3A expression in early-differentiated myoblasts

Regenerative mechanisms that regulate intramuscular motor innervation are thought to reside in the spatiotemporal expression of axon‐guidance molecules. Our previous studies proposed an unexplored role of resident myogenic stem cell (satellite cell)‐derived myoblasts as a key presenter of a secreted neural chemorepellent semaphorin 3A (Sema3A); hepatocyte growth factor (HGF) and basic fibroblast growth factor (FGF2) triggered its expression exclusively at the early differentiation phase. In order to advance this concept, the present study described that transmembrane heparan/chondroitin sulfate proteoglycans syndecan‐2, 4 may be the plausible receptor candidates for HGF and FGF2 to signal Sema3A expression. Results showed that mRNA expression of syndecan‐2, 4 was abundant (two magnitudes higher than syndecan‐1, 3) in early‐differentiated myoblasts and their in vitro knockdown diminished the HGF/FGF2‐induced expression of Sema3A down to a baseline level. Pretreatment with heparitinase and chondroitinase ABC decreased the HGF and FGF2 responses, respectively, in non–knockdown cultures, supporting a possible model that HGF and FGF2 may bind to heparan and chondroitin sulfate chains of syndecan‐2, 4 to signal Sema3A expression. The findings, therefore, extend our understanding that HGF/FGF2‐syndecan‐2, 4 association may stimulate a burst of Sema3A secretion by myoblasts recruited to the site of muscle injury; this would ensure a coordinated delay in the attachment of motoneuron terminals onto fibers early in muscle regeneration, and thus synchronize the recovery of muscle fiber integrity and the early resolution of inflammation after injury with reinnervation toward functional recovery.
A Data Driven Neuromuscular Model of the Leg During Walking

Biological neuromuscular systems typically employ redundancy in joint actuation, using several muscle tendon units to rotate a joint and produce motion. Resolution of this redundancy is of fundamental importance for the fields of biomechanics and neuroscience, as well as for developing control schemes for robotic prosthetic limbs. Determination of the roles of individual muscles in a given task requires knowledge of both the neural control of the acting muscles and the morphology of the muscle-tendon unit. Krishnaswamy et al [1] recently proposed a framework for resolving the roles of individual muscles in the actuation of the human ankle while walking at self-selected speed. Here we extend that method to evaluate the ankle, knee, and hip joints during walking, elucidating the workings of the major muscle groups of the leg as well as the interaction among its joints during locomotion.
Anemonin improves intestinal barrier restoration and influences TGF-β1 and EGFR signaling pathways in LPS-challenged piglets

The present study was aimed at investigating whether dietary anemonin could alleviate LPS-induced intestinal injury and improve intestinal barrier restoration in a piglet model. Eighteen 35-d-old pigs were randomly assigned to three treatment groups (control, LPS and LPS+anemonin). The control and LPS groups were fed a basal diet, and the LPS + anemonin group received the basal diet + 100 mg anemonin/kg diet. After 21 d of feeding, the LPS- and anemonin-treated piglets received i.p. administration of LPS; the control group received saline. At 4 h post-injection, jejunum samples were collected. The results showed that supplemental anemonin increased villus height and transepithelial electrical resistance, and decreased crypt depth and paracellular flux of dextran (4 kDa) compared with the LPS group. Moreover, anemonin increased tight junction claudin-1, occludin and ZO-1 expression in the jejunal mucosa, compared with LPS group. Anemonin also decreased TNF-α, IL-6, IL-8 and IL-1β mRNA expression. Supplementation with anemonin also increased TGF-β1 mRNA and protein expression, Smad4 and Smad7 mRNA expressions, and epidermal growth factor and epidermal growth factor receptor (EGFR) mRNA expression in the jejunal mucosa. These findings suggest that dietary anemonin attenuates LPS-induced intestinal injury by improving mucosa restoration, alleviating intestinal inflammation and influencing TGF-β1 canonical Smads and EGFR signaling pathways.
Relationship of Stress on the Eating Behaviors of Science Undergraduates in

This study aimed to determine the effect of stress on eating behaviors of the university undergraduates from Kuala Lumpur. One hundred fourteen students completed the Depression, Anxiety and Stress Scale (DASS) questionnaire and the Three-Factor Eating Questionnaire-R18 (TFEQ-R18). Data analysis done using SPSS 20.0 included descriptive statistics, parametric statistics, non-parametric statistics, Spearman rho's correlation and stepwise multiple regression. Majority of the students had medium cognitive restraint (56.1%) and uncontrolled eating scores (69.3%), but low emotional eating score (43.9%). Most students had normal depression level (57.9%) and normal to moderate anxiety (86.9%) and stress level (81.6%). Male (12.92±8.83) and Malay (9.64±6.40) undergraduates had higher depression score (p<0.05). Male (18.17±2.62) and underweight (18.93±2.29) subjects obtained higher cognitive restraint score (p<0.05). There was a significant negative correlation between depression an uncontrolled eating (r s = -0.324, p<0.001, R 2 = 0.103, adjusted R 2 = 0.095, F (1, 112) = 12.80) and stress and emotional eating (r s = -0.301, p = 0.001). In conclusion depression and stress does affect the uncontrolled eating and emotional eating.
An Improved PIC Algorithm of Background Reconstruction for Detecting Moving Object

In machine vision, moving object detection and segment pay more attention to the real-time and the accuracy. Generally, familiar case is immovable camera with the fixed focus in moving object detection, however, it is difficult to detect whole and actual object because of the influence of the environment noise and others. This paper makes some improvement in PIC algorithm and presents a new method of detecting moving object. According to normalization the pixels of the chosen images series used to reconstruct the background, quantization statistic, extent the quantized range, reconstruction the background image, the improved PIC algorithm avoids to providing thresholds of the PIC algorithm manually and removes these steps of combining the approximate gray scope, which needs plenty of time and is hard to realize through programming. After acquiring the reconstructed image, the coarse-fine two steps method is suggested to confirm the object position exactly and complete the moving object detection finally. The experiment results show that the method proposed in this paper needs shorter running time of the program and provides more accurate position of the moving object.
Replication Stress and Telomere Dysfunction Are Present in Cultured Human Embryonic Stem Cells

Replication stress causes DNA damage at fragile sites in the genome. DNA damage at telomeres can initiate breakage-fusion-bridge cycles and chromosome instability, which can result in replicative senescence or tumor formation. Little is known about the extent of replication stress or telomere dysfunction in human embryonic stem cells (hESCs). hESCs are grown in culture with the expectation of being used therapeutically in humans, making it important to minimize the levels of replication stress and telomere dysfunction. Here, the hESC line UCSF4 was cultured in a defined medium with growth factor Activin A, exogenous nucleosides, or DNA polymerase inhibitor aphidicolin. We used quantitative fluorescence in situ hybridization to analyze individual telomeres for dysfunction and observed that it can be increased by aphidicolin or Activin A. In contrast, adding exogenous nucleosides relieved dysfunction, suggesting that telomere dysfunction results from replication stress. Whether these findings can be applied to other hESC lines remains to be determined. However, because the loss of telomeres can lead to chromosome instability and cancer, we conclude that hESCs grown in culture for future therapeutic purposes should be routinely checked for replication stress and telomere dysfunction.
Design of fractional order PID controller for DC motor using evolutionary optimization techniques

In this paper, a comparison of various Evolutionary Optimization (EO) techniques has been performed for controller tuning (PID and FOPID) for DC motor speed control applications. Three techniques have been considered, which are: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). A fixed structure PID and FOPID controller has been considered. Usually in literature, comparison has been carried out based on time domain performance indices; whereas, in this work, mixed sensitivity H∞ method is considered as the fitness function for the comparison of various EO techniques in order to assess the robustness of the designed system. It has been shown that GA optimized FOPID controller has better robustness with respect to model uncertainties. Key-words: Controllers, differential evolution, DC motor, genetic algorithm, mixed sensitivity function, particle swarm optimization
Material Classification in theWild: Do Synthesized Training Data Generalise Better than Real-world Training Data?

We question the dominant role of real-world training images in the field of material classification by investigating whether synthesized data can generalise more effectively than real-world data. Experimental results on three challenging real-world material databases show that the best performing pre-trained convolutional neural network (CNN) architectures can achieve up to 91.03% mean average precision when classifying materials in cross-dataset scenarios. We demonstrate that synthesized data achieve an improvement on mean average precision when used as training data and in conjunction with pre-trained CNN architectures, which spans from ~ 5% to ~ 19% across three widely used material databases of real-world images.
Scheduling a dynamic job shop production system with sequence-dependent setups : An experimental study

This paper presents the salient aspects of a simulation-based experimental study of scheduling rules for scheduling a dynamic job shop in which the setup times are sequence dependent. A discrete event simulation model of the job shop system is developed for the purpose of experimentation. Seven scheduling rules from the literature are incorporated in the simulation model. Five new setup-oriented scheduling rules are proposed and implemented. Simulation experiments have been conducted under various experimental conditions characterized by factors such as shop load, setup time ratios and due date tightness. The results indicate that setup-oriented rules provide better performance than ordinary rules. The difference in performance between these two groups of rules increases with increase in shop load and setup time ratio. One of the proposed rules performs better for mean flow time and mean tardiness measures. r 2007 Elsevier Ltd. All rights reserved.