Miracle-dz/UniFAR-FTUs huggingface dataset:
Primary Principals' Leadership Styles, School Organizational Health and Workplace Bullying.

Purpose – The purpose of this paper is to determine the relationships between leadership styles of primary school principals and organizational health and bullying.Design/methodology/approach – Two hypotheses were formulated in relation to the research. Three instruments were used – a multi‐level questionnaire for measuring leadership, an Organizational Health Inventory (OHI‐S), and a negative acts questionnaire (NAQ) for measuring workplace bullying. The questionnaires were administered to 500 teachers working in primary schools in Turkey. Path analysis was conducted using the AMOS programs; other statistical analyses were conducted via LISREL and EQS 6.1 programs.Findings – There is a positive relationship between transformational leadership acts of principals and organizational health and a negative relationship between the transformational leadership acts of principals and workplace bullying. While a reverse relationship between organizational health and workplace bullying is identified, there is a re.
Aortic Input Impedance in Normal Man: Relationship to Pressure Wave Forms

SUMMARYThe relationship between the shape of the ascending aortic pressure wave form and aortic input impedance was studied in 18 patients who underwent elective cardiac catheterization but in whom no heart disease was found. Ascending aortic flow velocity and pressure were simultaneously recorded from a multisensor catheter with an electromagnetic velocity probe and a pressure sensor mounted at the same location. Another pressure sensor at the catheter tip provided left ventricular pressure or a second aortic pressure to determine pulse-wave velocity. Fick cardiac outputs were used to scale the velocity signal to instantaneous volumetric flow. Input impedance was calculated from 10 harmonics of aortic pressure and flow. For each patient, impedance moduli and phases from a minimum of 15 beats during a steady state were averaged. Peripheral resistance was 1137 ± 39 dyn-sec-cm-5 (± SEM) and characteristic impedance was 47 ±4 dyn-sec-cm-5; pulsewave velocity was 6.68 ± 0.32 m-sec-1. In all patients, a well-defined systolic inflection point divided the aortic pressure wave form into an early and late systolic phase. The patients were classified into three groups: group A (n = 7) patients whose late systolic pressure exceeded early systolic pressure; group B (n = 7) patients whose early and late systolic pressures were nearly equal; group C (n = 4) - patients whose early systolic pressure exceeded late systolic pressure. Group A and B patients all demonstrated oscillations of the impedance moduli about the characteristic impedance. Group C patients demonstrated flatter impedance spectra. Thus, a larger secondary rise in pressure was associated with a more oscillatory impedance spectrum. These results suggest that the differences in pressure wave forms are due to differences in reflections in the arterial tree and not secondary to differences in cardiac function. Using pulse-wave velocity, the "effective" reflection site distance was determined from both pressure (48 cm) and impedance (44 cm) data, implying that the region of the terminal abdominal aorta acts as the major reflection site in normal adult man.
ERP, e-Commerce, Social Media and Absorptive Capacity of Greek Firms: An Empirical Investigation

The absorptive capacity (ACAP), defined as firm's ability to identify useful knowledge in its external environment, assimilate it, transform it and finally apply it for achieving firm's objectives, has become quite important for the achievement of high financial performance of firms, and even their survival, and in the rapidly changing, competitive and 'knowledge intensive' modern econo-my. It has been recognized that information and communication technologies (ICT) can be quite useful for the enhancement of firm's ACAP. However, there has been no empirical investigation of the effects of the main types of information systems (IS) current-ly used by firms, aiming at efficiency improvements, on their ACAP. This paper makes a contribution towards filling this research gap, by empirically investigating and comparing the effects of the use of three important types of IS, the ERP and e-commerce ones, and also a recently emerged one, the social media (SM), on the ACAP of Greek firms. Our study is based on data collected through a survey from 122 Greek firms from both manufacturing and services sectors. It has been concluded that the use of ERP systems affects positively firms' ACAP, while this does not hold for the e-commerce ones. Furthermore, the use of external and internal SM both affect positively the ACAP, with the effect of the former being the strongest among the three examined IS types.
Combining information from multiple sources for the diagnosis of autism spectrum disorders for toddlers and young preschoolers from 12 to 47 months of age.

BACKGROUND Purpose of this study was to systematically examine combined use of the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) for children under age 4 using newly developed and revised diagnostic algorithms. METHODS Single and combined use of the ADI-R and ADOS algorithms were compared to clinical best estimate diagnoses for 435 children with autism spectrum disorders (ASD), 113 children with nonspectrum disorders, and 47 children with typical development from 12 to 47 months of age. Sequential strategies to reach a diagnostic decision by prioritizing administrations of instruments were also evaluated. RESULTS Well-balanced sensitivities and specificities above 80% were obtained for ASD diagnoses using both instruments. Specificities significantly improved when both instruments were used compared to one. Scores that can be used to systematically prioritize administrations of instruments were identified. CONCLUSIONS The ADI-R and ADOS make independent, additive contributions to more accurate diagnostic decisions for clinicians evaluating toddlers and young preschoolers with ASD. Sequential assessment strategies using the scores identified may be appropriate for some children.
Single-trial EEG-based emotion recognition using kernel Eigen-emotion pattern and adaptive support vector machine

Single-trial electroencephalography (EEG)-based emotion recognition enables us to perform fast and direct assessments of human emotional states. However, previous works suggest that a great improvement on the classification accuracy of valence and arousal levels is still needed. To address this, we propose a novel emotional EEG feature extraction method: kernel Eigen-emotion pattern (KEEP). An adaptive SVM is also proposed to deal with the problem of learning from imbalanced emotional EEG data sets. In this study, a set of pictures from IAPS are used for emotion induction. Results based on seven participants show that KEEP gives much better classification results than the widely-used EEG frequency band power features. Also, the adaptive SVM greatly improves classification performance of commonly-adopted SVM classifier. Combined use of KEEP and adaptive SVM can achieve high average valence and arousal classification rates of 73.42% and 73.57%. The highest classification rates for valence and arousal are 80% and 79%, respectively. The results are very promising.
A robust minimum volume enclosing simplex algorithm for hyperspectral unmixing

Hyperspectral unmixing is a process of extracting hidden spectral signatures (or endmembers) and the corresponding proportions (or abundances) of a scene, from its hyperspectral observations. Motivated by Craig's belief, we recently proposed an alternating linear programming based hyperspectral unmixing algorithm called minimum volume enclosing simplex (MVES) algorithm, which can yield good unmixing performance even for instances of highly mixed data. In this paper, we propose a robust MVES algorithm called RMVES algorithm, which involves probabilistic reformulation of the MVES algorithm, so as to account for the presence of noise in the observations. The problem formulation for RMVES algorithm is manifested as a chance constrained program, which can be suitably implemented using sequential quadratic programming (SQP) solvers in an alternating fashion. Monte Carlo simulations are presented to demonstrate the efficacy of the proposed RMVES algorithm over several existing benchmark hyperspectral unmixing methods, including the original MVES algorithm.
Towards Privacy-Preserving Query Log Publishing

It’s an open secret that search engines collect detailed query logs, and sometimes release these data to third parties. While making this wealth of information available provides enormous opportunities for information retrieval and web mining research, it also raises serious concerns about the privacy of individuals. We strongly believe that this data should be published to allow researchers to develop new information access algorithms, however, it is desirable to anonymize these logs, so that they are still usable for research but do not contain sensitive information. The most important need is to define in a principled way the notion of privacy for query logs. This paper attempts to lay out some dimensions for defining privacy guidelines for query log publishing. We focus on the central issue of how to strike a balance between protecting the sensitive information and maintaining useful data for analysis. This work is within the overall vision of developing anonymization techniques to allow construction of IR algorithms (e.g., spelling correction) that maintain state-of-the-art performance over the anonymized data. We first describe some important applications of query log analysis and discuss their requirements on the degree of granularity of query logs. We then analyze the sensitive information in query logs and classify them from the privacy perspective. We lay out two orthogonal dimensions for anonymizing query logs and present a spectrum of approaches along those dimensions. We discuss whether existing privacy guidelines such as HIPAA can apply to query logs directly, or whether these guidelines require significant adaptation. For each of the approaches, we discuss the implications on query log utility regarding the important applications as well as the privacy of the anonymized query logs. More generally, our goal is to bring up questions and suggest challenges for privacy-preserving query log publishing.
Implementation of Distance Based Semi Supervised Clustering and Probabilistic Assignment Technique for Network Traffic Classification

Network Traffic Classification is an important process in various network management activities like network planning, designing, workload characterization etc. Network traffic classification using traditional techniques such as well known port number based and payload analysis based techniques are no more effective because various applications uses port hopping and encryption technique to avoid detection. Recently machine learning techniques such as supervised, unsupervised and semi supervised techniques are used to overcome the problems of traditional techniques. In this work we use semi supervised machine learning approach and proposed distance based semi supervised clustering and probabilistic assignment technique for network traffic classification. This technique used only flow statistics to classify network traffic. It permits to build the classifier using both labeled and unlabeled instances in training dataset.
Achieving near-capacity on a multiple-antenna channel

Recent advancements in iterative processing of channel codes and the development of turbo codes have allowed the communications industry to achieve near-capacity on a single-antenna Gaussian or fading channel with low complexity. We show how these iterative techniques can also be used to achieve near-capacity on a multiple-antenna system where the receiver knows the channel. Combining iterative processing with multiple-antenna channels is particularly challenging because the channel capacities can be a factor of ten or more higher than their single-antenna counterparts. Using a "list" version of the sphere decoder, we provide a simple method to iteratively detect and decode any linear space-time mapping combined with any channel code that can be decoded using so-called "soft" inputs and outputs. We exemplify our technique by directly transmitting symbols that are coded with a channel code; we show that iterative processing with even this simple scheme can achieve near-capacity. We consider both simple convolutional and powerful turbo channel codes and show that excellent performance at very high data rates can be attained with either. We compare our simulation results with Shannon capacity limits for ergodic multiple-antenna channel.
Comparative Optimal Designs of Non-Uniformly Excited Concentric Circular Antenna Array Using Evolutionary Optimization Techniques

In this paper the comparative optimal designs for maximum sidelobe level (SLL) reduction of three-ring concentric circular antenna array (CCAA) are determined using two novel Particle Swarm Optimization (PSO) techniques namely Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach (PSOCFIWA), Craziness based Particle Swarm Optimization (CRPSO) and Binary coded Genetic Algorithm (BGA). Apart from physical construction of an array, the CCAA design may be classified into two major categories: uniformly excited arrays and non-uniformly excited arrays. The present paper assumes non-uniform excitation and a design problem of maximizing sidelobe level reduction. So, the design problem is modeled as an optimization problem and subsequently solved using three different evolutionary optimization techniques, BGA, PSOCFIWA and CRPSO. Each technique helps to determine an optimal set of normalized excitation weights for CCAA elements, which, when incorporated, results in a radiation pattern with maximum sidelobe level reduction. Among all the designs, the three-ring structure containing (N1=4, N2=6, N3=8) elements proves to be the optimal design owing to the highest SLL reduction achieved by each technique. However, while comparing the optimization efficiencies among the three techniques the newly proposed CRPSO technique yields the best optimum sets of normalized excitation weights with least "Misfitness" objective function values and maximum achievable SLL reductions for all sets of three-ring CCAA designs. CRPSO yields grand maximum SLL reduction (-14.94 dB) for the above optimal set. Further, a reduction of major lobe beamwidth is also observed when compared to a uniformly excited CCAA having equal number of elements and same radii.
Adaptive Energy-Based Updating Algorithm in Opportunistic Routing

The opportunistic routing is popularly employed in the wireless multi-hop network. To obtain higher delivery rate and longer lifetime of network, we propose an adaptive energy-based updating opportunistic routing (AEUOR). We firstly introduce a novel priority metric of the routing selection in order to guarantee delivery rate. Then, an adaptive update algorithm is realized by efficiently detecting the dump energy of the node. In addition, we discusse the candidates including the primary candidate and the secondary candidate in the paper, the latter of which plays a key role in extending the lifetime of the network. Moreover, we test AEUOR and compare it with ExOR in NS2.34, and observe significant performance improvement.
Calculating joint queue-length distributions in product-form queuing networks

A new computational algorithm called distribution analysis by chain (DAC) is developed. This algorithm computes joint queue-length distributions for product-form queuing networks with single-server fixed rate, infinite server, and queue-dependent service centers. Joint distributions are essential in problems such as the calculation of availability measures using queuing network models. The algorithm is efficient since the cost to evaluate joint queue-length probabilities is of the same order as the number of these probabilities. This contrasts with the cost of evaluating these probabilities using previous algorithms. The DAC algorithm also computes mean queue lengths and throughputs more efficiently than the recently proposed RECAL and MVAC algorithms. Furthermore, the algorithm is numerically stable and its recursion is surprisingly simple.
Self-Organized Exploration and Automatic Sensor Integration From the Homeokinetic Principle

Starting from the homeokinetic principle introduced earlier the present paper presents simple learning rules for the neurons of a closed loop robot controller. These learning rules are shown in a simple application case to realize a self-learning autonomous robot which can survive in a sufﬁciently simple world without any further external help. In particular we demonstrate that sensors are automatically integrated according to their response strength as soon as they deliver a signal to the controller. Moreover the system also can deal with the problem of a rapid change in the properties of the sensors. The basic effect observed is that the learning rule drives the robot in the sensorimotor loop into an explorative mode of behavior which however is sensitive to the reactions of the environment by way of the model error. From a dynamic systems point of view we have a closed loop control system with a pitchfork or a Hopf bifurcation (if the learning of the threshold is included) and the effect of the learning is to drive the system to a regime slightly above the bifurcation point where such systems are known to be particularly sensitive.
Are high drug prices for hematologic malignancies justified? A critical analysis

In the past 15 years, treatment outcomes for hematologic malignancies have improved substantially. However, drug prices have also increased drastically. This commentary examines the value of the treatment of hematologic malignancies at current prices in the United States through a reanalysis of a systematic review evaluating 29 studies of 9 treatments for 4 hematologic malignancies. Incremental cost‐effectiveness ratios (ICERs) were calculated on the basis of drug prices in the United States in 2014. Sixty‐three percent of the studies (15 of 24) had ICERs higher than $50,000 per quality‐adjusted life‐year (QALY), the benchmark widely used by health economists to define cost‐effectiveness. In studies evaluating the current standard‐of‐care treatments for chronic myeloid leukemia, the ICERs for tyrosine kinase inhibitors versus hydroxyurea or interferon ranged from $210,000 to $426,000/QALY. The lower ICER values were mostly obtained from 11 studies evaluating rituximab, which was approved by the Food and Drug Administration in 1997 (ICER range, $37,000‐$69,000/QALY). In conclusion, the costs of the majority of new treatments for hematologic cancers are too high to be deemed cost‐effective in the United States. Cancer 2015;121:3435–43. © 2015 American Cancer Society.
Can neurophysiological assessment improve timing of intervention in posthaemorrhagic ventricular dilatation?

Objective Intraventricular haemorrhage is still the most common cause of brain lesion in preterm infants and development of a posthaemorrhagic ventricular dilatation (PHVD) can lead to additional neurological sequelae. Flash visual evoked potentials (fVEP) and amplitude-integrated electroencephalography (aEEG) are non-invasive neurophysiological monitoring tools. The aim of the study was to evaluate fVEPs and aEEGs in preterm infants with progressive PHVD prior to and after neurosurgical intervention for cerebrospinal fluid removal and to correlate the findings with severity of ventricular dilatation. Design fVEPs and aEEGs were performed weekly in infants with developing PHVD. As soon as the ventricular index reached the 97th percentile recordings were performed twice a week. Methods 17 patients admitted to the neonatal intensive care unit of the Medical University of Vienna who developed progressive PHVD were evaluated using fVEP and aEEG until and after reduction of intracranial pressure by placement of an external ventricular drainage. Results In all 17 cases (100%) wave latencies of fVEP increased above normal range and aEEG showed increased suppression in 13 patients (76%) with increasing ventricular dilatation. Both methods showed normalisation of patterns mostly within a week of successful therapeutic intervention (mean 8.5 days). Both changes in fVEP latencies and aEEG background patterns were detected before clinical signs of elevated intracranial pressure occurred. In only 10 patients (58.8%) ventricular width exceeded the 97th percentile+4 mm. Conclusions fVEP and aEEG are useful additional tools for the evaluation of preterm infants with progressive PHVD.
Actions of cytochalasins on the organization of actin filaments and microtubules in a neuronal growth cone

Actions of cytochalasin B (CB) on cytoskeletons and motility of growth cones from cultured Aplysia neurons were studied using a rapid flow perfusion chamber and digital video light microscopy. Living growth cones were observed using differential interference contrast optics and were also fixed at various time points to assay actin filament (F- actin) and microtubule distributions. Treatment with CB reversibly blocked motility and eliminated most of the phalloidin-stainable F- actin from the leading lamella. The loss of F-actin was nearly complete within 2-3 min of CB application and was largely reversed within 5-6 min of CB removal. The loss and recovery of F-actin were found to occur with a very distinctive spatial organization. Within 20-30 s of CB application, F-actin networks receded from the entire peripheral margin of the lamella forming a band devoid of F-actin. This band widened as F- actin receded at rates of 3-6 microns/min. Upon removal of CB, F-actin began to reappear within 20-30 s. The initial reappearance of F-actin took two forms: a coarse isotropic matrix of F-actin bundles throughout the lamella, and a denser matrix along the peripheral margin. The denser peripheral matrix then expanded in width, extending centrally to replace the coarse matrix at rates again between 3-6 microns/min. These results suggest that actin normally polymerizes at the leading edge and then flows rearward at a rate between 3-6 microns/min. CB treatment was also observed to alter the distribution of microtubules, assayed by antitubulin antibody staining. Normally, microtubules are restricted to the neurite shaft and a central growth cone domain. Within approximately 5 min after CB application, however, microtubules began extending into the lamellar region, often reaching the peripheral margin. Upon removal of CB, the microtubules were restored to their former central localization. The timing of these microtubule redistributions is consistent with their being secondary to effects of CB on lamellar F-actin.
Rethinking LDA: Moment Matching for Discrete ICA

We consider moment matching techniques for estimation in latent Dirichlet allocation (LDA). By drawing explicit links between LDA and discrete versions of independent component analysis (ICA), we first derive a new set of cumulant-based tensors, with an improved sample complexity. Moreover, we reuse standard ICA techniques such as joint diagonalization of tensors to improve over existing methods based on the tensor power method. In an extensive set of experiments on both synthetic and real datasets, we show that our new combination of tensors and orthogonal joint diagonalization techniques outperforms existing moment matching methods.