Miracle-dz/UniFAR-FTUs huggingface dataset:
Public sector leaders and institutional change: a study of the antecedents of leaders´ adaptive behaviour in Dutch arts and cultural education

Despite the increasing awareness that leadership behaviour is not only influenced by leaders’ personal characteristics but also by contextual influences, empirical evidence on this topic stays behind (Yukl, 2010; Silvia and McGuire, 2010; Currie et al, 2009; Porter and McLaughlin, 2006). This article fills this contextual gap by looking at the factors influencing the way in which public sector leaders adapt to the changing institutional environment. Since the environment of public sector leaders is often very uncertain and ever-changing, it is interesting to study the leaders’ adaptive behaviour toward environmental demands and constraints. Following several authors (Pulakos et al, 2002), this study sees adaptive behaviour as a multidimensional concept and therefore operationalized it into adaptive behaviour on an interpersonal and on a functional level. In this study, leaders in the Dutch arts and cultural education sector are examined. Regression analyses show that the more selfefficacious and motivated to change leaders are, the more they behave adaptively on an interpersonal level. Experience with changing institutional demands and the perceived degree of constraints coming from the institutional environment appear to influence adaptive behaviour on a functional level.
NUMERICAL SIMULATION OF A MOTORCYCLE-RIDER SYSTEM WITH LASER SENSOR FOR COLLISION AVOIDANCE

The paper illustrates the research outcomes of the integration of sensor system and control logic into a multibody simulation environment for motorcycle accidental dynamics analysis. One of the main objectives is to propose feasible scenarios for intelligent collision avoidance scheme for powered two wheeler (PTW) systems. A multibody model of a motorcycle that comprises a simplified model of the rider is studied with a conceptual virtual laser sensor placed on the front fork and beaming forward. The sensor is modelled as a device that is able to detect objects within its beam that is characterized by sweeping, beaming and sampling parameters. The virtual environment is populated with moving obstacles placed along the nominal trajectory of the motorcycle. Several control logics are then investigated, from a simple warning system that will result in an action taken by the rider, towards an automatic collision avoidance manoeuvre system that includes controlled steering and braking compatible with the model dynamics and limitations. It is necessary to take into account the fact that the active control system may introduce additional discomfort to the rider due to the sense of limiting his control of the vehicle. Through virtual prototyping techniques which involves a complex model of the motorcycle and the behaviour of the rider, a conceptual study is conducted in order to demonstrate that a proper sensor system together with an active control scheme could, in principle, help the rider to take appropriate actions to avoid the crash against an obstacle or reduce the energy of the impact. Moreover this method is used also to investigate realistic motorcycle manoeuvres especially when the accident is unavoidable leading to a pre-crash scenario that is suitable for a more viable crash and post- crash simulation. This work has been developed within the framework of the EC Marie Curie project MYMOSA which focuses on the research of the integration of active safety systems in motorcycles to reduce the fatalities and injuries on European roads.
Estimation of R= P [Y < X] for Two-parameter Burr Type XII Distribution

In this article, we consider the estimation of P[Y < X], when strength, X and stress, Y are two independent variables of Burr Type XII distribution. The MLE of the R based on one simple iterative procedure is obtained. Assuming that the common parameter is known, the maximum likelihood estimator, uniformly minimum variance unbiased estimator and Bayes estimator of P[Y < X] are discussed. The exact confidence interval of the R is also obtained. Monte Carlo simulations are performed to compare the different proposed methods. Keywords—Stress-Strength model; Maximum likelihood estimator; Bayes estimator; Burr type XII distribution.
A case-based reasoning approach to local-scale and short-term weather forecast

As an intelligent computing paradigm Case-Based Reasoning (CBR) techniques have been studied and adopted in weather forecast applications recently. Nonetheless, many subtle issues remain to be resolved for CBR to be a practical problem solver. This paper establishes a CBR system for local-scale and short-term weather forecast at an airport. We develop a strategy of knowledge guidance combined with nearest-similarity and design a heuristic similarity function accordingly. Real data based tests have shown the effectiveness and comparative advantages of the proposed method.
TRIP8b Is Required for Maximal Expression of HCN1 in the Mouse Retina

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are cation-selective channels present in retina, brain and heart. The activity of HCN channels contributes to signal integration, cell excitability and pacemaker activity. HCN1 channels expressed in photoreceptors participate in keeping light responses transient and are required for normal mesopic vision. The subcellular localization of HCN1 varies among cell types. In photoreceptors HCN1 is concentrated in the inner segments while in other retinal neurons, HCN1 is evenly distributed though the cell. This is in contrast to hippocampal neurons where HCN1 is concentrated in a subset of dendrites. A key regulator of HCN1 trafficking and activity is tetratricopeptide repeat-containing Rab8b interacting protein (TRIP8b). Multiple splice isoforms of TRIP8b are expressed throughout the brain and can differentially regulate the surface expression and activity of HCN1. The purpose of the present study was to determine which isoforms of TRIP8b are expressed in the retina and to test if loss of TRIP8b alters HCN1 expression or trafficking. We found that TRIP8b colocalizes with HCN1 in multiple retina neurons and all major splice isoforms of TRIP8b are expressed in the retina. Photoreceptors express three different isoforms. In TRIP8b knockout mice, the ability of HCN1 to traffic to the surface of retinal neurons is unaffected. However, there is a large decrease in the total amount of HCN1. We conclude that TRIP8b in the retina is needed to achieve maximal expression of HCN1.
THE EFFECTIVENESS OF FOREST CARBON SEQUESTRATION STRATEGIES WITH SYSTEM-WIDE ADJUSTMENTS Draft : May 13 , 1998

This paper addresses the effectiveness of tree planting and forest conservation strategies to increase the sink of carbon in global forests. Because forests are expected to sequester additional carbon without explicit human intervention, a baseline case is presented. The baseline predicts that forests will sequester an additional 17.9 Pg (10 15 grams) of carbon over the next 150 years, with nearly 95% of this accruing to storage in marketed forest products. The paper then compares strategies which assume markets adjust to changes in future timber supply to an optimistic regional planner case in which no market adjustment occurs. The resulting predictions show that system wide market interactions may lead to substantial leakage of carbon from the forest system.
Chandra Discovers a Very High Density X-Ray Plasma on the O Star ζ Orionis

We report on a Chandra line spectrum observation of the O supergiant ζ Orionis (O9.7 Ib). A 73.4 ks High-Energy Transmission Grating Spectrometer observation shows a wide range of ionization stages and line strengths over the wavelength range of 5-26 Å. The observed emission lines indicate a range in temperature of 2-10 MK, which is consistent with earlier X-ray observations of ζ Ori. Many lines are spectrally resolved showing Doppler broadening of 900 ± 200 km s-1. The observed He-like ions (O VII, Ne IX, Mg XI, and Si XIII) provide information about the spatial distribution of the X-ray emission. Although the observations support a wind distribution of X-ray sources, we find three conflicting results. First, line diagnostics for Si XIII indicate that this line emission forms very close to the stellar surface, where the density is of order 1012 cm-3, but the velocity there is too small to produce the shock jump required for the observed ionization level. Second, the strong X-ray line profiles are symmetric and do not show any evidence of Doppler-blueshifted line centroids, which are expected to accompany an outwardly moving source in a high-density wind. Third, the observed velocity dispersions do not appear to correlate with the associated X-ray source radii velocities, contrary to expectations of wind-distributed source models. A composite source model involving wind shocks and some magnetic confinement of turbulent hot plasma in a highly nonsymmetric wind appears to be needed to explain the line diagnostic anomalies.
Traffic sign recognition using a novel permutation-based local image feature

Traffic sign recognition (TSR) is an essential research issue in the design of driving support system and smart vehicles. In this paper, we propose a permutation-based image feature to describe traffic signs, which has an inherent advantage of illumination invariance and fast implementation. Our proposed feature LIPID (local image permutation interval descriptor) employs interval division and zone number assignment on order permutation of pixel intensities, and takes the zone numbers as the descriptor. A comprehensive performance evaluation on German Traffic Sign Recognition Benchmark (GTSRB) dataset is carried out, which reveals the great performance of our proposed method. Experiment results exhibit that our feature outperforms some state-of-the-art descriptors, showing a potential prospect in TSR applications.
Haptic-Based Sensible Graphical Password

Authentication, the process of identifying an individual, currently plays a critical role in security systems. Graphical password schemes are believed to be more secure and resilient to dictionary attacks than traditional textual passwords. We designed a graphical password system with the novelty of incorporating the sense of touch via haptic technologies. Using haptics, the system utilizes the physical attributes captured during human-haptic-computer interactions. Parameters such as pressure and velocity can be handled as ‘hidden’ features to increase the resiliency of the system. Initial testing showed that the users do not follow the conventional password’s guidelines, thus making the system easy to be flawed. Consequently, we proposed two solutions: enforce stringent conditions during password creation, or modify the verification algorithm. The use of haptics for password creation lends itself ideally to the latter solution and to a subsequent modification of the authentication algorithm. Therefore, we adapted the initial pressure recording system to authenticate individuals by using a two-class classification Artificial Neural Network (ANN) and Nearest-Neighbor’ (NN) algorithms. On average, NN approach allowed for a (92.0 ± 0.3) % probability of successful verification. On the other hand, by classifying the ‘hidden-feature’ vectors using an ANN approach, the probability of successful password verification becomes (90.09 ± 3.8) %. Overall, both methods offer similar Probability of Verification (PV) rates.
Two-stage series-based neural network approach to nonlinear independent component analysis

Linear independent component analysis (ICA) played an important role in the development of various signal processing techniques due to the inherent simplicity. However, the assumption of linear mixture is always violated in real life, which narrows down its applications. In this paper, the problem of nonlinear independent component analysis is considered. Based on a new type of nonlinear mixing model, we propose a two-stage series-based approach to recover the original source signals. The two-stage series-based algorithm offers significant advantages in terms of reduced computational complexity and better learning dynamics of the trajectory. Simulations have also been carried out to verify the efficacy of the proposed method
The use of discriminative belief tracking in POMDP-based dialogue systems

Statistical spoken dialogue systems based on Partially Observable Markov Decision Processes (POMDPs) have been shown to be more robust to speech recognition errors by maintaining a belief distribution over multiple dialogue states and making policy decisions based on the entire distribution rather than the single most likely hypothesis. To date most POMDP-based systems have used generative trackers. However, concerns about modelling accuracy have created interest in discriminative methods, and recent results from the second Dialog State Tracking Challenge (DSTC2) have shown that discriminative trackers can significantly outperform generative models in terms of tracking accuracy. The aim of this paper is to investigate the extent to which these improvements translate into improved task completion rates when incorporated into a spoken dialogue system. To do this, the Recurrent Neural Network (RNN) tracker described by Henderson et al in DSTC2 was integrated into the Cambridge statistical dialogue system and compared with the existing generative Bayesian network tracker. Using a Gaussian Process (GP) based policy, the experimental results indicate that the system using the RNN tracker performs significantly better than the system with the original Bayesian network tracker.
Simulating an Informal Contact's Network in the Context of Organizational Change

In this work we examine the impact of informal contacts network in the context of organizational change. We use a cellular automata model with majority rule and movement but supplemented with additional mechanism which generates much more unbalanced network between agents. The structures of the emerged networks are briefly described and the outcomes of two variants of simulation experiments are presented. The main results are that the presence of informal contacts network leads to (1) stronger effects when the two groups of agents (those in favor of the change, and those against it) are not equal and (2) to more unpredictable outcome for large density of agents.
Collaborative Platform for Universities, Foster Clubs and Scientists in Aerospace Research

Communication is an essential element in our lives. The Internet has become part of our everyday life connecting everybody, everywhere and anytime. This is obviously changing the way people work and interact. People want to collaborate with team partners from wherever and whenever necessary. This paper presents the collaborative platform for an innovating project (called PERSEUS - Student Project in European Space Research for Universities and Scientists) in the domain of small size satellites. The participants of this project come from a large variety of technical and nontechnical domains. Thus, the need for a tool to ease their cooperation and coordination of multiple disciplinary analyses is obvious.
Predicting recovery of dextrous hand function in acute stroke

Purpose. To determine the clinical characteristics during acute stroke that predicted dextrous function in the paretic hand at 6 months post-stroke. Method. Fifty-seven patients within 5 days post-stroke were recruited in stroke wards. Recovery of dextrous hand function, indicated by a score of ≥35 of Action Research Arm Test, was assessed weekly in the first 4 weeks then monthly till 6 months post-stroke. The seven predictor candidates evaluated included side and site of brain infarct, stroke severity, cognition, spatial neglect, two-point discrimination (2-PD), muscle tone and muscle strength of the paretic upper extremity (UE). Results. Site of infarct, stroke severity, 2-PD and UE muscle strength had independent association with dextrous hand function at 6 months post-stroke. Stepwise multiple logistic regressions showed that the best early predictor was 2-PD in week 1 to 3 (Odds ratio [OR] ranged from 0.51–0.83) and UE muscle strength during the first 2 months post-stroke (OR ≥ 1.04). The strongest predictor was muscle strength at week 4 post-stroke, followed by combined 2-PD and muscle strength at week 2 post-stroke. Conclusions. Muscle strength and 2-PD in the paretic UE during the first month post-stroke were the best predictors of dextrous hand function recovery at 6 months.
Preliminary study of pneumonia symptoms detection method using Cellular Neural Network

Medical diagnosis is one of the most important procedure in which image processing are usefully applied. In this paper, a pneumonia symptoms detection method based on cellular neural networks (CNNs) is proposed. The CNN design is characterized by a virtual template expansion obtained through a multistep operation. It is based on linear space invariant 3 × 3 templates. The proposed design is capable of performing pneumonia symptoms detection within a short time. The main idea in Cellular Neural Network is that connection is allowed between adjacent units only. There are few rules in Cellular Neural Network that has to be implemented when designing the templates, such as state equation, output equation, boundary equation, and also the initial value. These templates are combined to create the most ideal algorithm to detect the pneumonia symptoms in an image. Candy software is used as a CNN simulator to detect the pneumonia symptoms area. It was tested on the 23 grayscale pneumonia symptoms CT image obtained from the diagnostic imaging department. The simulation results show good performance based on the difference grayscale color and segmentation between the normal area and lung region area.
Mathematical Symbol Indexing Using Topologically Ordered Clusters of Shape Contexts

This paper addresses the indexing and retrieval of mathematical symbols from digitized documents. The proposed approach exploits Shape Contexts (SC) to describe the shape of mathematical symbols. Starting from the vector space method, that is based on SC clustering, we explore the use of topological ordered clusters to improve the retrieval performance. The clustering is computed by means of Self-Organizing Maps that organize the clusters in two dimensional topologically ordered feature maps. The retrieval performance are compared with those obtained using the K-means clustering on a large collection of mathematical symbols gathered from the widely used INFTY database.
Representation of a Voiced Speech Signal Model With Wigner- Ville Transform

Wigner Ville Transform is introduced as a suitable tool for the analysis of the speech signal, as it allows making a correct representation of the signal energy. First, we give a mathematical formulation of the Wigner Ville Transform of a stationary model of voiced speech. The speech model is expressed as a linear combination of the harmonic frequencies of fundamental frequency of the voiced speech signal. Identification of frequency coefficients from a stationary signal of real speech is operated. Then WVT term is compared with the transform of a real WVT voiced speech signal. Finally, identification of frequency coefficients from a stationary signal of real speech is operated.
A novel pruned-based algorithm for energy-efficient SATD operation in the HEVC coding

This paper proposes a novel approximate computing algorithm for the Sum of Absolute Transformed Differences (SATD) to meet energy efficiency in CMOS accelerator circuits. It is based on the pruning of least significant coefficients in the 2-D Hadamard Transform (HT) which is the most compute intensive kernel in the SATD. The SATD is a metric for block matching that is used in video coding standards like the new High Efficiency Video Coding (HEVC). This metric is used to provide better results in mode decision when compared to the Sum of Absolute Differences (SAD) at the expense of larger amount of arithmetic operations as well as higher energy consumption. We present 6 different approximate SATD 4×4 architectures that were synthesized for a 45 nm PDK. Results for the approximate architecture with 10 discarded HT coefficients show energy per operation reduction of 70.7% and BD-PSNR reduction of just -0.008 dB, for a 1080p video sequence.
SAABCOT : Secure Application-Agnostic Bandwidth COnservation Techniques ( Invited Paper )

High speed modern networks are tasked with moving large amounts of data to diverse groups of interested parties. Often under heavy loads, a significant portion of the data exhibits large amounts of redundancy on short and/or long-term time scales. As a result, a large body of work has emerged offering bandwidth conservation exemplified by the work in caching and multicast. The majority of the techniques that have experienced widespread adoption rely on parsing / reacting to applicationspecific data. With the advent of simplified end-to-end security, as introduced by IPv6, these techniques will no longer have access to the plaintext data. We present a novel technique for preserving security while allowing in-network devices to identify redundant data flows in order to apply bandwidth conservation techniques. Our communication protocol does not require modifications to existing applications nor does it inflict a significant amount of overhead to the existing network infrastructure.
Protective Effect of Gallotannin-Enriched Extract Isolated from Galla Rhois against CCl4-Induced Hepatotoxicity in ICR Mice

To investigate the toxicity, protective effects, and action mechanism of gallotannin-enriched extracts isolated from Galla Rhois (GEGR) against carbon tetrachloride (CCl4)-induced hepatotoxicity in Institute for Cancer Research (ICR) mice, alterations in serum biochemical indicators, histopathological structure, antioxidative status, hepatic apoptosis-related proteins, and liver fibrosis regulating factors were measured in mice pretreated with GEGR for five days before CCl4 injection. The GEGR/CCl4 treated group showed decreased levels of three serum marker enzymes (ALP, AST, and ALT) representing liver toxicity, although LDH levels remained constant. Necrotic area indicating hepatic cell death significantly inhibited, while malondialdehyde (MDA) concentration and superoxide dismutase (SOD) expression were dramatically recovered in the GEGR preadministrated group. In mechanism analyses of GEGR, the formation of active caspase-3 and enhancement of Bax/Bcl-2 expression was effectively inhibited in the GEGR/CCl4 treated group. The level of pro-inflammatory cytokines, TNF-α and IL-6, as well as the phosphorylation of p38 and JNK in the TNF-α downstream signaling pathway was rapidly recovered in the GEGR/CCl4 treated group, while anti-inflammatory cytokine (IL-10) increased slightly in the same group. Furthermore, the GEGR/CCl4 treated group showed a significant decrease in collagen accumulation results from alleviation of MMP-2 expression, TGF-β1 secretion and the phosphorylation of Smad2/3. Taken together, these results suggest that GEGR may induce remarkable protective effects against hepatic injury induced by CCl4 treatment through upregulation of the anti-inflammatory and antioxidant system.
Enforcement and Compliance with Labor Regulations in Argentina

The author analyzes the effect of government enforcement on compliance with labor regulations in Argentina, a country where only half of the workforce receives all the benefits to which they are legally entitled. Constructing panel data for the period 1995–2002 across provinces and using the number of labor inspectors per capita as a proxy for enforcement, he estimates the effect of enforcement on compliance with six employment and social security regulations: minimum wage, maximum hours, paid vacation time, annual extra monthly wage, workers compensation insurance, and health insurance. Because of potential simultaneity between enforcement and compliance, the author explores instrumenting enforcement by electoral years. Two-stage least squares estimates suggest enforcement increases compliance.