The Impact of Employment Web Sites' Traffic on Unemployment: A Cross Country Comparison

Although employment web sites have recently become the main source for re- cruitment and selection process, the relation between those sites and unemploy- ment rates is seldom addressed. Deriving data from 32 countries and 427 web sites, this study explores the correlation between unemployment rates of European countries and the attractiveness of country specific employment web sites. It also compares the changes in unemployment rates and traffic on all the aforementioned web sites. The results showed that there is a strong correlation between web sites traffic and unemployment rates.Comment: 9 page

Estimating Material Properties of Interacting Objects Using Sum-GP-UCB

Robots need to estimate the material and dynamic properties of objects from observations in order to simulate them accurately. We present a Bayesian optimization approach to identifying the material property parameters of objects based on a set of observations. Our focus is on estimating these properties based on observations of scenes with different sets of interacting objects. We propose an approach that exploits the structure of the reward function by modeling the reward for each observation separately and using only the parameters of the objects in that scene as inputs. The resulting lower-dimensional models generalize better over the parameter space, which in turn results in a faster optimization. To speed up the optimization process further, and reduce the number of simulation runs needed to find good parameter values, we also propose partial evaluations of the reward function, wherein the selected parameters are only evaluated on a subset of real world evaluations. The approach was successfully evaluated on a set of scenes with a wide range of object interactions, and we showed that our method can effectively perform incremental learning without resetting the rewards of the gathered observations

High Frequency Scattering from Arbitrarily Oriented Dielectric Disks

Calculations have been made of electromagnetic wave scattering from dielectric disks of arbitrary shape and orientation in the high frequency (physical optics) regime. The solution is obtained by approximating the fields inside the disk with the fields induced inside an identically oriented slab (i.e. infinite parallel planes) with the same thickness and dielectric properties. The fields inside the disk excite conduction and polarization currents which are used to calculate the scattered fields by integrating the radiation from these sources over the volume of the disk. This computation has been executed for observers in the far field of the disk in the case of disks with arbitrary orientation and for arbitrary polarization of the incident radiation. The results have been expressed in the form of a dyadic scattering amplitude for the disk. The results apply to disks whose diameter is large compared to wavelength and whose thickness is small compared to diameter, but the thickness need not be small compared to wavelength. Examples of the dependence of the scattering amplitude on frequency, dielectric properties of the disk and disk orientation are presented for disks of circular cross section

Entropy-based fault detection approach for motor vibration signals under accelerated aging process

The purpose of this study is to analyze motor vibration signals due to the bearing fault, which is artificially generated by aging process. Vibration signal data recorded by the experimental setup has been conditioned by a high-pass filter (Butterworth type) to reach the regarding frequency components of the bearing failure. Spectral analysis has been applied to realize the degradation on the bearing and the power spectral density figures revealed that the magnitudes of frequency components between 1.5-4 kHz bandwidth increased after every aging cycle. Vibration signals were investigated statistically by examining four main statistical parameters: mean value, standard deviation, skewness and kurtosis. Evaluation of these parameters indicated that significant variance occurred on standard deviation. At this point Shannon entropy became an approach to analyze the variance on the standard deviation. The probability of the aging cycles has been defined as a function of standard deviation values for each aging cycle. Entropy definition, which is a function of probability, determines the uncertainty level on the data and it has been examined to identify the effect of the aging progress on the bearing by examining the transferred entropy amount between aging cycles

A Quantisation of Cognitive Learning Process by Computer Graphics-Games: Towards More Efficient Learning Models

Research group of Computer Sciences at DMU, Psychology Research Group at University of Birmingham and Reseach Group of Computer Science at University of Northumbria.With the latest developments in computer technologies and artificial intelligence (AI) techniques, more opportunities of cognitive data acquisition and stimulation via game-based systems have become available for computer scientists and psychologists. This may lead to more efficient cognitive learning model developments to be used in different fields of cognitive psychology than in the past. The increasing popularity of computer games among a broad range of age groups leads scientists and experts to seek game domain solutions to cognitive based learning abnormalities, especially for younger age groups and children. One of the major advantages of computer graphics and using game-based techniques over the traditional face-to-face therapies is that individuals, especially children immerse in the game’s virtual environment and consequently feel more open to share their cognitive behavioural characteristics naturally. The aim of this work is to investigate the effects of graphical agents on cognitive behaviours to generate more efficient cognitive models

Imitation and Mirror Systems in Robots through Deep Modality Blending Networks

Learning to interact with the environment not only empowers the agent with manipulation capability but also generates information to facilitate building of action understanding and imitation capabilities. This seems to be a strategy adopted by biological systems, in particular primates, as evidenced by the existence of mirror neurons that seem to be involved in multi-modal action understanding. How to benefit from the interaction experience of the robots to enable understanding actions and goals of other agents is still a challenging question. In this study, we propose a novel method, deep modality blending networks (DMBN), that creates a common latent space from multi-modal experience of a robot by blending multi-modal signals with a stochastic weighting mechanism. We show for the first time that deep learning, when combined with a novel modality blending scheme, can facilitate action recognition and produce structures to sustain anatomical and effect-based imitation capabilities. Our proposed system, can be conditioned on any desired sensory/motor value at any time-step, and can generate a complete multi-modal trajectory consistent with the desired conditioning in parallel avoiding accumulation of prediction errors. We further showed that given desired images from different perspectives, i.e. images generated by the observation of other robots placed on different sides of the table, our system could generate image and joint angle sequences that correspond to either anatomical or effect based imitation behavior. Overall, the proposed DMBN architecture not only serves as a computational model for sustaining mirror neuron-like capabilities, but also stands as a powerful machine learning architecture for high-dimensional multi-modal temporal data with robust retrieval capabilities operating with partial information in one or multiple modalities

DeepSym: Deep Symbol Generation and Rule Learning from Unsupervised Continuous Robot Interaction for Planning

Autonomous discovery of discrete symbols and rules from continuous interaction experience is a crucial building block of robot AI, but remains a challenging problem. Solving it will overcome the limitations in scalability, flexibility, and robustness of manually-designed symbols and rules, and will constitute a substantial advance towards autonomous robots that can learn and reason at abstract levels in open-ended environments. Towards this goal, we propose a novel and general method that finds action-grounded, discrete object and effect categories and builds probabilistic rules over them that can be used in complex action planning. Our robot interacts with single and multiple objects using a given action repertoire and observes the effects created in the environment. In order to form action-grounded object, effect, and relational categories, we employ a binarized bottleneck layer of a predictive, deep encoder-decoder network that takes as input the image of the scene and the action applied, and generates the resulting object displacements in the scene (action effects) in pixel coordinates. The binary latent vector represents a learned, action-driven categorization of objects. To distill the knowledge represented by the neural network into rules useful for symbolic reasoning, we train a decision tree to reproduce its decoder function. From its branches we extract probabilistic rules and represent them in PPDDL, allowing off-the-shelf planners to operate on the robot's sensorimotor experience. Our system is verified in a physics-based 3d simulation environment where a robot arm-hand system learned symbols that can be interpreted as 'rollable', 'insertable', 'larger-than' from its push and stack actions; and generated effective plans to achieve goals such as building towers from given cubes, balls, and cups using off-the-shelf probabilistic planners

Twenty-seven mutations with three novel pathologenic variants causing biotinidase deficiency: a report of 203 patients from the southeastern part of Turkey

BACKGROUND: Biotinidase deficiency (BD) is an autosomal recessive inborn error of metabolism characterized by neurologic and cutaneous symptoms and can be detected by newborn screening. Newborn screening for BD was implemented in Turkey at the end of 2008. METHODS: In total, 203 patients who were identified among the infants detected by the newborn screening were later confirmed to have BD through measurement of serum biotinidase activity. We also performed BTD mutation analysis to characterize the genetic profile. RESULTS: Twenty-seven mutations were identified. The most commonly found variants were c.1330G>C (p.D444H), c.1595C>T (p.T532M), c.470G>A (p.R157H), and c.198_104delGCGGCTGinsTCC (p.C33Ffs ) with allele frequencies of 0.387, 0.175, 0.165 and 0.049, respectively. Three novel pathogenic and likely pathogenic variants were identified: p.W140* (c.419G>A), p.S319F (c.956C>T) and p.L69Hfs*24 (c.192_193insCATC). We also identified three mutations reported in just one patient in the past (p.V442Sfs*59 [c.1324delG], p.H447R [c.1340A>G] and p.198delV [c.592_594delGTC]). Although all of the patients were asymptomatic under the treatment of biotin, only one patient, who had the novel c.419G>A homozygous mutation became symptomatic during an episode of acute gastroenteritis with a presentation of ketosis and metabolic acidosis. Among the screened patients, 156 had partial and 47 had profound BD. CONCLUSIONS: We determined the mutation spectra of BD from the southeastern part of Turkey. The results of this study add three more mutations to the total number of mutations described as causing BD

FEPDS: A Proposal for the Extraction of Fuzzy Emerging Patterns in Data Streams

Nowadays, most data is generated by devices that produce data continuously. These kinds of data can be categorised as data streams and valuable insights can be extracted from them. In particular, the insights extracted by emerging patterns are interesting in a data stream context as easy, fast, reliable decisions can be made. However, their extraction is a challenge due to the necessary response time, memory and continuous model updates. In this paper, an approach for the extraction of emerging patterns in data streams is presented. It processes the instances by means of batches following an adaptive approach. The learning algorithm is an evolutionary fuzzy system where previous knowledge is employed in order to adapt to concept drift. A wide experimental study has been performed in order to show both the suitability of the approach in combating concept drift and the quality of the knowledge extracted. Finally, the proposal is applied to a case study related to the continuous determination of the profiles of New York City cab customers according to their fare amount, in order to show its potential