title: Neural Multi-task Learning in Automated Assessment

abstract: Grammatical error detection and automated essay scoring are two tasks in the area of automated assessment. Traditionally these tasks have been treated independently with different machine learning models and features used for each task. In this paper, we develop a multi-task neural network model that jointly optimises for both tasks, and in particular we show that neural automated essay scoring can be significantly improved. We show that while the essay score provides little evidence to inform grammatical error detection, the essay score is highly influenced by error detection.


title: Functional Brain Correlates of Social and Nonsocial Processes in Autism Spectrum Disorders: An Activation Likelihood Estimation Meta-Analysis

abstract: Background Functional neuroimaging studies of autism spectrum disorders (ASD) have examined social and nonsocial paradigms, although rarely in the same study. Here, we provide an objective, unbiased survey of functional brain abnormalities in ASD, related to both social and nonsocial processing. Methods We conducted two separate voxel-wise activation likelihood estimation meta-analyses of 39 functional neuroimaging studies consisting of 24 studies examining social processes (e.g., theory of mind, face perception) and 15 studies examining nonsocial processes (e.g., attention control, working memory). Voxel-wise significance threshold was p Results Compared with neurotypical control (NC) subjects, ASD showed greater likelihood of hypoactivation in two medial wall regions: perigenual anterior cingulate cortex (ACC) in social tasks only and dorsal ACC in nonsocial studies. Further, right anterior insula, recently linked to social cognition, was more likely to be hypoactivated in ASD in the analyses of social studies. In nonsocial studies, group comparisons showed greater likelihood of activation for the ASD group in the rostral ACC region that is typically suppressed during attentionally demanding tasks. Conclusions Despite substantial heterogeneity of tasks, the rapidly increasing functional imaging literature showed ASD-related patterns of hypofunction and aberrant activation that depended on the specific cognitive domain, i.e., social versus nonsocial. These results provide a basis for targeted extensions of these findings with younger subjects and a range of paradigms, including analyses of default mode network regulation in ASD.


title: Network intrusion detection system using various data mining techniques

abstract: There are many risk of network attacks in the Internet environment. Nowdays, Security on the internet is a vital issue and therefore, the intrusion detection is one of the major research problem for business and personal networks which resist external attacks. A Network Intrusion Detection System (NIDS) is a software application that monitors the network or system activities for malicious activities and unauthorized access to devices. The goal of designing NIDS is to protect the data's confidentiality and integrity. Our project focuses on these issues with the help of Data Mining. This research paper includes the implementation of different data mining algorithms including Linear regression and K-Means Clustering to automatically generate the rules for classify network activities. A comparative analysis of these techniques to detect intrusions has also been made. To learn the patterns of the attacks, NSL-KDD dataset has been used.


title: Establishing Fraud Detection Patterns Based on

abstract: All over the world we have been assisting to a significant increase of the telecommunication systems usage. People are faced day after day with strong marketing campaigns seeking their attention to new telecommunication products and services. Telecommunication companies struggle in a high competitive business arena. It seems that their efforts were well done, because customers are strongly adopting the new trends and use (and abuse) systematically communication services in their quotidian. Although fraud situations are rare, they are increasing and they correspond to a large amount of money that telecommunication companies lose every year. In this work, we studied the problem of fraud detection in telecommunication systems, especially the cases of superimposed fraud, providing an anomaly detection technique, supported by a signature schema. Our main goal is to detect deviate behaviors in useful time, giving better basis to fraud analysts to be more accurate in their decisions in the establishment of potential fraud situations.


Rapid eye movements during sleep in dark-reared kittens.


title: A distributed network intrusion detection system architecture based on computer stations using GPGPU

abstract: This article deals with the proposal of distributed parallel network system architecture for intrusion detection. The aim is to design model of architecture that will be used for searching, detecting and analyzing intrusions. This proposal of detection system in network will use enormous performance of the GPGPU high-parallel data computing technology which can substitute the most complicated part of the intrusion detection process in NIDS. The model is highly scalable and allows the usage of several most recent trends in the IDS field with GPGPU support. Due to the fast attacks reaction system analyzes all the data in different formats in whole set of attacks and system alerts.


title: Skin Segmentation Using YCBCR and RGB Color Models

abstract: Face detection is one of the challenging problems in image processing. This report proposes a novel technique for detecting faces in color images using skin color model algorithm combined with skin likely-hood, skin Segmentation, Morphological operation and Template matching. Color images with skin color in the chromatic and pure color space YCrCb, which separates luminance and chrominance components. A Gaussian probability density is estimated from skin samples, collected from different ethnic groups, via the maximum-likelihood criterion. Adaptive thresholding for segmentation to localize the faces within the detected skin regions. Further, mathematical morphological operators are used to remove noisy regions and fill holes in the skin-color region, so that candidate human face regions can be extracted. These systems can achieve high detection accuracy, high detection speed and reduce the false detecting rate. The two methodology used for the Skin Segmentation is YCbCr and RGB Model. In YCbCr model both the skin colour and texture of the image can be used to identify the particular object in the image,where as in RGB model only the skin color has to be used for identification of the person.Hence the YCbCr model is better than the RGB model . From our analysis we conclude that the new approach in modeling skin color can achieve good detection success rate. The algorithm gives computationally a very efficient as well as an accurate approach for skin detection. The performance of different color space may be dependent on the method used to model the color for skin pixel.


title: Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior

abstract: Motivation for reward drives adaptive behaviors, whereas impairment of reward perception and experience (anhedonia) can contribute to psychiatric diseases, including depression and schizophrenia. We sought to test the hypothesis that the medial prefrontal cortex (mPFC) controls interactions among specific subcortical regions that govern hedonic responses. By using optogenetic functional magnetic resonance imaging to locally manipulate but globally visualize neural activity in rats, we found that dopamine neuron stimulation drives striatal activity, whereas locally increased mPFC excitability reduces this striatal response and inhibits the behavioral drive for dopaminergic stimulation. This chronic mPFC overactivity also stably suppresses natural reward-motivated behaviors and induces specific new brainwide functional interactions, which predict the degree of anhedonia in individuals. These findings describe a mechanism by which mPFC modulates expression of reward-seeking behavior, by regulating the dynamical interactions between specific distant subcortical regions.


title: Personality , attitudes , and intentions : Predicting initial adoption of information security behavior

abstract: Investigations of computer user behavior become especially important when behaviors like security software adoption affect organizational information resource security, but adoption antecedents remain elusive. Technology adoption studies typically predict behavioral outcomes by investigating the relationship between attitudes and intentions, though intention may not be the best predictor of actual behavior. Personality constructs have recently been found to explain even more variance in behavior, thus providing insights into user behavior. This research incorporates conscientiousness and agreeableness into a conceptual model of security software use. Attitudinal constructs perceived ease of use and perceived usefulness were linked with behavioral intent, while the relationship between intent and actual use was found to be moderated by conscientiousness and agreeableness. The results that the moderating effect of personality greatly increases the amount of variance explained in actual use. Display Omitted


title: Research Article A New Switching-Based Median Filtering Scheme and Algorithm for Removal of High-Density Salt and Pepper Noise in Images

abstract: A new switching-based median filtering scheme for restoration of images that are highly corrupted by salt and pepper noise is proposed. An algorithm based on the scheme is developed. The new scheme introduces the concept of substitution of noisy pixels by linear prediction prior to estimation. A novel simplified linear predictor is developed for this purpose. The objective of the scheme and algorithm is the removal of high-density salt and pepper noise in images. The new algorithm shows significantly better image quality with good PSNR, reduced MSE, good edge preservation, and reduced streaking. The good performance is achieved with reduced computational complexity. A comparison of the performance is made with several existing algorithms in terms of visual and quantitative results. The performance of the proposed scheme and algorithm is demonstrated.


title: Combinatorica: A System for Exploring Combinatorics and Graph Theory in Mathematica

abstract: This invention pertains to compounds which specifically inhibit the adenosine A1 receptor and the use of these compounds to treat a disease associated with A1 adenosine receptors in a subject.


title: Deep clustering: On the link between discriminative models and K-means

abstract: In the context of recent deep clustering studies, discriminative models dominate the literature and report the most competitive performances. These models learn a deep discriminative neural network classifier in which the labels are latent. Typically, they use multinomial logistic regression posteriors and parameter regularization, as is very common in supervised learning. It is generally acknowledged that discriminative objective functions (e.g., those based on the mutual information or the KL divergence) are more flexible than generative approaches (e.g., K-means) in the sense that they make fewer assumptions about the data distributions and, typically, yield much better unsupervised deep learning results. On the surface, several recent discriminative models may seem unrelated to K-means. This study shows that these models are, in fact, equivalent to K-means under mild conditions and common posterior models and parameter regularization. We prove that, for the commonly used logistic regression posteriors, maximizing the L2-regularized mutual information via an approximate alternating direction method (ADM) is equivalent to a soft and regularized K-means loss. Our theoretical analysis not only connects directly several recent state-of-the-art discriminative models to K-means, but also leads to a new soft and regularized deep K-means algorithm, which yields competitive performance on several image clustering benchmarks.


title: Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation

abstract: Deep learning models for semantic segmentation of images require large amounts of data. In the medical imaging domain, acquiring sufficient data is a significant challenge. Labeling medical image data requires expert knowledge. Collaboration between institutions could address this challenge, but sharing medical data to a centralized location faces various legal, privacy, technical, and data-ownership challenges, especially among international institutions. In this study, we introduce the first use of federated learning for multi-institutional collaboration, enabling deep learning modeling without sharing patient data. Our quantitative results demonstrate that the performance of federated semantic segmentation models (Dice=0.852) on multimodal brain scans is similar to that of models trained by sharing data (Dice=0.862). We compare federated learning with two alternative collaborative learning methods and find that they fail to match the performance of federated learning.


title: Object, Scene and Actions: Combining Multiple Features for Human Action Recognition

abstract: In many cases, human actions can be identified not only by the singular observation of the human body in motion, but also properties of the surrounding scene and the related objects. In this paper, we look into this problem and propose an approach for human action recognition that integrates multiple feature channels from several entities such as objects, scenes and people. We formulate the problem in a multiple instance learning (MIL) framework, based on multiple feature channels. By using a discriminative approach, we join multiple feature channels embedded to the MIL space. Our experiments over the large YouTube dataset show that scene and object information can be used to complement person features for human action recognition.


title: Half step position sensorless control of a Linear Switched Reluctance Motor based on back EMF

abstract: This paper presents a position sensorless closed loop control of a switched reluctance linear motor. The aim of the proposed control is to damp the position of the studied motor. Indeed, the position oscillations can harm some applications requiring high position precision. Moreover, they can induce the linear switched reluctance motor to an erratic working. The proposed control solution is based on back Electromotive Forces which give information about the oscillatory behaviour of the studied motor and avoid the use of a cumbersome and expensive position linear sensor. The determination of the designed control law parameters was based on the singular perturbation theory. The efficiency of the proposed control solution was proven by simulations and experimental tests.


title: MACE prediction of acute coronary syndrome via boosted resampling classification using electronic medical records

abstract: Display Omitted To predict MACE for ACS patients at the early stage of their hospitalizations.A novel classification algorithm is proposed for MACE prediction from EMRs.To alleviate the imbalance of MACE of ACS patients using a boosting resampling strategy.Evaluation is done using a real datasetcollected from a Chinese hospital. ObjectivesMajor adverse cardiac events (MACE) of acute coronary syndrome (ACS) often occur suddenly resulting in high mortality and morbidity. Recently, the rapid development of electronic medical records (EMR) provides the opportunity to utilize the potential of EMR to improve the performance of MACE prediction. In this study, we present a novel data-mining based approach specialized for MACE prediction from a large volume of EMR data. MethodsThe proposed approach presents a new classification algorithm by applying both over-sampling and under-sampling on minority-class and majority-class samples, respectively, and integrating the resampling strategy into a boosting framework so that it can effectively handle imbalance of MACE of ACS patients analogous to domain practice. The method learns a new and stronger MACE prediction model each iteration from a more difficult subset of EMR data with wrongly predicted MACEs of ACS patients by a previous weak model. ResultsWe verify the effectiveness of the proposed approach on a clinical dataset containing 2930 ACS patient samples with 268 feature types. While the imbalanced ratio does not seem extreme (25.7%), MACE prediction targets pose great challenge to traditional methods. As these methods degenerate dramatically with increasing imbalanced ratios, the performance of our approach for predicting MACE remains robust and reaches 0.672 in terms of AUC. On average, the proposed approach improves the performance of MACE prediction by 4.8%, 4.5%, 8.6% and 4.8% over the standard SVM, Adaboost, SMOTE, and the conventional GRACE risk scoring system for MACE prediction, respectively. ConclusionsWe consider that the proposed iterative boosting approach has demonstrated great potential to meet the challenge of MACE prediction for ACS patients using a large volume of EMR.


title: On the stability of some second order numerical methods for weak approximation of Itô SDEs

abstract: In this paper, we first investigate the stability of two weak second order methods introduced by Debrabant and Roβler (Appl Numer Math 59:582---594, 2009) and Platen (Math Comput Simulation 38:69---76, 1995). We then propose a new weak second order predictor-corrector method, with an improved stability properties, based on the Roβler's method as the predictor and the implicit method of Platen as the corrector. The stability functions of these methods, applied to a scalar linear test equation with multiplicative noise, are determined and their regions of stability are then compared with the corresponding stability regions of the test equation. Furthermore, we also investigate mean square stability (MS-stability) of these methods applied to a linear Ito 2-dimensional stochastic differential test equation. Numerical examples will be presented to support the theoretical results.


title: Tuberculosis Disease Diagnosis Using Artificial Neural Networks

abstract: Tuberculosis is an infectious disease, caused in most cases by microorganisms called Mycobacterium tuberculosis. Tuberculosis is a great problem in most low income countries; it is the single most frequent cause of death in individuals aged fifteen to forty-nine years. Tuberculosis is important health problem in Turkey also. In this study, a study on tuberculosis diagnosis was realized by using multilayer neural networks (MLNN). For this purpose, two different MLNN structures were used. One of the structures was the MLNN with one hidden layer and the other was the MLNN with two hidden layers. A general regression neural network (GRNN) was also performed to realize tuberculosis diagnosis for the comparison. Levenberg-Marquardt algorithms were used for the training of the multilayer neural networks. The results of the study were compared with the results of the pervious similar studies reported focusing on tuberculosis diseases diagnosis. The tuberculosis dataset were taken from a state hospital's database using patient's epicrisis reports.


title: Scuddle: Generating Movement Catalysts for Computer-Aided Choreography

abstract: We present a tool for computer aided choreography titled Scuddle. Scuddle uses a Genetic Algorithm to generate movement catalysts for contemporary choreography. The use of movement catalysts challenge choreographers to distance themselves from habits to better explore creative movement. Scuddle was designed as a method for both facilitating creativity and supporting active reflection on creativity through awareness of the decision making process. The system is successful in generating complex catalysts that result in creative approaches to movement and support articulation of decisions. We detail the design motivation, implementation, analysis and results of a qualitative evaluation by choreo-


title: Design of a Human-Like Range of Motion Hip Joint for Humanoid Robots

abstract: For a humanoid robot to have the versatility of humans, it needs to have similar motion capabilities. This paper presents the design of the hip joint of the Tactical Hazardous Operations Robot (THOR), which was created to perform disaster response duties in human-structured environments. The lower body of THOR was designed to have a similar range of motion to the average human. To accommodate the large range of motion requirements of the hip, it was divided into a parallel-actuated universal joint and a linkage-driven pin joint. The yaw and roll degrees of freedom are driven cooperatively by a pair of parallel series elastic linear actuators to provide high joint torques and low leg inertia. In yaw, the left hip can produce a peak of 115.02 [Nm] of torque with a range of motion of −20° to 45°. In roll, it can produce a peak of 174.72 [Nm] of torque with a range of motion of −30° to 45°. The pitch degree of freedom uses a Hoeken’s linkage mechanism to produce 100 [Nm] of torque with a range of motion of −120° to 30°.Copyright © 2014 by ASME


title: Optimized Broadcast for Deep Learning Workloads on Dense-GPU InfiniBand Clusters: MPI or NCCL?

abstract: Traditionally, MPI runtimes have been designed for clusters with a large number of nodes. However, with the advent of MPI+CUDA applications and dense multi-GPU systems, it has become important to design efficient communication schemes. This coupled with new application workloads brought forward by Deep Learning frameworks like Caffe and Microsoft CNTK pose additional design constraints due to very large message communication of GPU buffers during the training phase. In this context, special-purpose libraries like NCCL have been proposed. In this paper, we propose a pipelined chain (ring) design for the MPI_Bcast collective operation along with an enhanced collective tuning framework in MVAPICH2-GDR that enables efficient intra-/internode multi-GPU communication. We present an in-depth performance landscape for the proposed MPI_Bcast schemes along with a comparative analysis of NCCL Broadcast and NCCL-based MPI_Bcast. The proposed designs for MVAPICH2-GDR enable up to 14X and 16.6X improvement, compared to NCCL-based solutions, for intra- and internode broadcast latency, respectively. In addition, the proposed designs provide up to 7% improvement over NCCL-based solutions for data parallel training of the VGG network on 128 GPUs using Microsoft CNTK. The proposed solutions outperform the recently introduced NCCL2 library for small and medium message sizes and offer comparable/better performance for very large message sizes.


title: Cyber Wars: A Paradigm Shift from Means to Ends

abstract: Abstract The last couple of decades have seen a colossal change in terms of the influence that computers can have on the battlefield, with defence pundits claiming it to be the dawn of a new era in warfare. Under these circumstances, there has been a gradual paradigm shift in military thinking and strategies from the strategic aspect to the tactical aspect of cyber warfare, laying more emphasis on it being a potent force multiplier. The author believes this is wrong and rather than cyber warfare being an enhancement of traditional operations, the latter will be force multipliers of cyber warfare. This article tries to shatter myths woven around cyber warfare so as to illuminate the strategic aspects of this relatively misinterpreted notion, thus identifying a paradigm shift, making cyber war the primary means of achieving grand strategic objectives in the contemporary world order.


title: What is new in computational stereo since 1989: A survey on current stereo papers

abstract: Only a few problems in computer vision have been investigated more vigorously than stereo. Consequently, more than 200 different stereo vision methods have been published and their number is increasing month by month. Although the principle of computational stereo has been known for more than 20 years, new directions in stereo research are still being under development. The objective of this report is to show and to categorize what is new in stereo research since 1989. This overview is supplemented in an appendix by a survey on 86 current stereo papers.


title: Designing and Prototyping a Novel Five-Phase Pancake-Shaped Axial-Flux SRM for Electric Vehicle Application Through Dynamic FEA Incorporating Flux-Tube Modeling

abstract: Switched reluctance motors (SRMs) show crucial attributes to applications where light weight, high-temperature adaptability, fault-tolerance capability, ruggedness, and simplicity are strongly required. The axial-flux configuration of SRM has additional features over the radial-flux configuration. This paper presents the design and analysis of a novel axial-flux SRM. Detailed procedures of deriving the output power equation as a function of the motor dimensions and parameters are provided. A modified phase winding design approach is thoroughly explained, a flowchart describing the design algorithm is presented, and the inductance determination by different methods is verified experimentally. The 3-D finite-element analysis (FEA) unveils the excessive end core and radial-flux fringing effects in the axial-flux configuration. An exclusive pole-shape design is also proposed. The operation of the motion model using 3-D dynamic FEA is analyzed, and its prototype development process and static testing are demonstrated.


title: Solving portfolio optimization problem through differential evolution

abstract: Portfolio theories best describes the asset management. Several investment offers are available to a customer and the task is to select the most appropriate option to gain the maximum financial benefit. The investor requires a thorough comparative study to decide the best possible option where the return is maximum and the risk is minimum. Portfolio optimization can help in the process of decision making by bringing out the selected options beneficial for the investor. In this paper we have considered the Markovitz's Mean Variance portfolio optimization model and the data is considered from National Stock Exchange (NSE), Mumbai. Differential Evolution, a population based metaheuristics is applied for obtaining the optimal portfolio.


title: Bi-Directional Vectors from Apex in CNN for Micro-Expression Recognition

abstract: The impressive performance of utilizing deep learning or neural network has attracted much attention in both the industry and research communities, especially towards computer vision aspect related applications. Despite its superior capability of learning, generalization and interpretation on various form of input, micro-expression analysis field is yet remains new in applying this kind of computing system in automated expression recognition system. A new feature extractor, BiVACNN is presented in this paper, where it first estimates the optical flow fields from the apex frame, then encode the flow fields features using CNN. Concretely, the proposed method consists of three stages: apex frame acquisition, multivariate features formation and feature learning using CNN. In the multivariate features formation stage, we attempt to derive six distinct features from the apex details, which include: the apex itself, difference between the apex and onset frames, horizontal optical flow, vertical optical flow, magnitude and orientation. It is demonstrated that utilizing the horizontal and vertical optical flow capable to achieve 80% recognition accuracy in CASME II and SMIC-HS databases.


title: A Rugged LDMOS for LBC5 Technology

abstract: This paper presents a new method of enhancing the SOA of n-channel Ldmos transistors. Attention is focused on those applications where “Electrical SOA” is important and where the power pulse time is typically a few µs or less. Typical applications include gate drives, H-bridge commutation, and self-protection against ESD pulses.


title: Anomalous cell detection with kernel density-based local outlier factor

abstract: Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor (KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOF is applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators (KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.


title: PHENIX: a comprehensive Python-based system for macromolecular structure solution

abstract: Macromolecular X-ray crystallography is routinely applied to understand biological processes at a molecular level. However, significant time and effort are still required to solve and complete many of these structures because of the need for manual interpretation of complex numerical data using many software packages and the repeated use of interactive three-dimensional graphics. PHENIX has been developed to provide a comprehensive system for macromolecular crystallographic structure solution with an emphasis on the automation of all procedures. This has relied on the development of algorithms that minimize or eliminate subjective input, the development of algorithms that automate procedures that are traditionally performed by hand and, finally, the development of a framework that allows a tight integration between the algorithms.


title: EEG Mu (&mgr;) rhythm spectra and oscillatory activity differentiate stuttering from non‐stuttering adults

abstract: Stuttering is linked to sensorimotor deficits related to internal modeling mechanisms. This study compared spectral power and oscillatory activity of EEG mu (μ) rhythms between persons who stutter (PWS) and controls in listening and auditory discrimination tasks. EEG data were analyzed from passive listening in noise and accurate (same/different) discrimination of tones or syllables in quiet and noisy backgrounds. Independent component analysis identified left and/or right μ rhythms with characteristic alpha (α) and beta (β) peaks localized to premotor/motor regions in 23 of 27 people who stutter (PWS) and 24 of 27 controls. PWS produced μ spectra with reduced β amplitudes across conditions, suggesting reduced forward modeling capacity. Group time-frequency differences were associated with noisy conditions only. PWS showed increased μ-β desynchronization when listening to noise and early in discrimination events, suggesting evidence of heightened motor activity that might be related to forward modeling deficits. PWS also showed reduced μ-α synchronization in discrimination conditions, indicating reduced sensory gating. Together these findings indicate spectral and oscillatory analyses of μ rhythms are sensitive to stuttering. More specifically, they can reveal stuttering-related sensorimotor processing differences in listening and auditory discrimination that also may be influenced by basal ganglia deficits.


title: Explaining and Forecasting Online Auction Prices and Their Dynamics Using Functional Data Analysis

abstract: Online auctions have become increasingly popular in recent years, and as a consequence there is a growing body of empirical research on this topic. Most of that research treats data from online auctions as cross-sectional, and consequently ignores the changing dynamics that occur during an auction. In this article we take a different look at online auctions and propose to study an auction's price evolution and associated price dynamics. Specifically, we develop a dynamic forecasting system to predict the price of an ongoing auction. By dynamic, we mean that the model can predict the price of an auction “in progress” and can update its prediction based on newly arriving information. Forecasting price in online auctions is challenging because traditional forecasting methods cannot adequately account for two features of online auction data: (1) the unequal spacing of bids and (2) the changing dynamics of price and bidding throughout the auction. Our dynamic forecasting model accounts for these special featur...


Linguistic, personal and social development of immigrant children


Intrusion Detection System using Genetic-Fuzzy Classification


Overview of the 1st Classification of Spanish Election Tweets Task at IberEval 2017


title: Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets

abstract: Stock price prediction is an important issue in the financial world, as it contributes to the development of effective strategies for stock exchange transactions. In this paper, we propose a generic framework employing Long Short-Term Memory (LSTM) and convolutional neural network (CNN) for adversarial training to forecast high-frequency stock market. This model takes the publicly available index provided by trading software as input to avoid complex financial theory research and difficult technical analysis, which provides the convenience for the ordinary trader of nonfinancial specialty. Our study simulates the trading mode of the actual trader and uses the method of rolling partition training set and testing set to analyze the effect of the model update cycle on the prediction performance. Extensive experiments show that our proposed approach can effectively improve stock price direction prediction accuracy and reduce forecast error.


title: Performance analysis of elliptic curve cryptography for SSL

abstract: Elliptic Curve Cryptography (ECC) is emerging as an attractive public-key cryptosystem for mobile/wireless environments. Compared to traditional cryptosystems like RSA, ECC offers equivalent security with smaller key sizes, which results in faster computations, lower power consumption, as well as memory and bandwidth savings. This is especially useful for mobile devices which are typically limited in terms of their CPU, power and network connectivity. However, the true impact of any public-key cryptosystem can only be evaluated in the context of a security protocol. This paper presents a first estimate of the performance improvements that can be expected in SSL (Secure Socket Layer), the dominant security protocol on the Web today, by adding ECC support.


title: Will they catch up? The role of age at cochlear implantation in the spoken language development of children with severe to profound hearing loss.

abstract: PURPOSE ::: The authors examined the benefits of younger cochlear implantation, longer cochlear implant use, and greater pre-implant aided hearing to spoken language at 3.5 and 4.5 years of age. ::: ::: ::: METHOD ::: Language samples were obtained at ages 3.5 and 4.5 years from 76 children who received an implant by their 3rd birthday. Hierarchical linear modeling was used to identify characteristics associated with spoken language outcomes at the 2 test ages. The Preschool Language Scale (I. L. Zimmerman, V. G. Steiner, & R. E. Pond, 1992) was used to compare the participants' skills with those of hearing age-mates at age 4.5 years. ::: ::: ::: RESULTS ::: Expected language scores increased with younger age at implant and lower pre-implant thresholds, even when compared at the same duration of implant use. Expected Preschool Language Scale scores of the children who received the implant at the youngest ages reached those of hearing age-mates by 4.5 years, but those children implanted after 24 months of age did not catch up with hearing peers. ::: ::: ::: CONCLUSION ::: Children who received a cochlear implant before a substantial delay in spoken language developed (i.e., between 12 and 16 months) were more likely to achieve age-appropriate spoken language. These results favor cochlear implantation before 24 months of age, especially for children with aided pure-tone average thresholds greater than 65 dB prior to surgery.


title: Towards a Secure SCRUM Process for Agile Web Application Development

abstract: Agile development such as Scrum and Extreme Programming deliver software in short iterations for quick response to rapid business requirement and market changes. However, established secure software development methodologies are mostly based on linear models such as waterfall and V-model, making them unsuitable for direct application in an agile environment. This paper presents a proposal for integrating security activities into Scrum process for developing secure Web applications. We identify gaps in existing approaches to secure agile development and analyze established security engineering activities. We then adapt these activities and orchestrate them into Scrum development process to achieve both security and agility. Our proposal is evaluated by a Scrum team developing commercial JAVA EE applications in an opinion survey.


title: Vaginal speculum examinations without stirrups.

abstract: The pelvic examination using a vaginal speculum is one of the most common medical procedures performed by doctors and experienced by women. About 55 million smear tests are performed in the United States every year.1 The article in this issue by Seehusen and colleagues is a provocative one for US clinicians, raising basic questions about how pelvic examinations are performed.2 American medical schools uniformly teach a single way to position a woman for pelvic examinations—in the dorsal lithotomy position with feet in stirrups.3,4 The article raises the possibility that the standard use of stirrups may not be best for patients, and that other positioning options should be considered. Most clinicians I spoke with (admittedly, an unscientific sample) shared my initial reaction to the article's suggestion of performing a speculum exam without stirrups—how would that work? Given this reaction among providers of family medicine in the northeastern part of the United States, it is interesting—and somewhat surprising—to find that speculum examinations without stirrups are routine in the United Kingdom, Australia, and New Zealand.2 ::: ::: Seehusen and colleagues' well conducted randomised controlled trial shows that women who have speculum examinations without stirrups report less discomfort and feelings of vulnerability than do women who have examinations with stirrups. Overall, the study is of high quality with adequate randomisation, reasonable inclusion and exclusion criteria, adequate follow-up, and appropriate blinding of the investigators who recorded and reported the outcome measures. ::: ::: Despite this strong methodology, the study has limitations. It does not fully consider the effect these alternative positions might have on clinical outcomes. Although the study was adequately powered to look for differences in patients' experiences, it was underpowered for assessing the adequacy of cervical specimens. The authors would have needed 382 patients (191 patients in each group) to have 80% power to detect a 50% difference in rates of cervical specimen adequacy. When this is combined with a slight trend towards an increase in inadequate cervical specimens in the no stirrups group, questions arise as to whether the use of the no stirrups position is as effective as the current US practice. In addition, the patients studied came from a somewhat homogeneous US military population, potentially limiting external validity for a more general and diverse population. ::: ::: Even with these limitations, this article raises the question of whether US doctors who routinely perform speculum examinations should consider changing their practice to offer women a more patient centered option without stirrups. On the basis of my experience in another area of women's health, obstetrics, I am sceptical as to whether US doctors will change their behaviour to position patients in a way that is more comfortable for the patient, but perhaps less convenient for the doctor. Several studies, including a Cochrane review, indicate that there is clinical benefit and improved patient comfort if patients are allowed or encouraged to be in non-supine positions during the second stage of labour.5,6 Yet it is rare to see a US doctor use a non-supine position, both because these positions are often less comfortable for the doctor and because doctors are trained to deliver with the woman in a classic dorsal lithotomy position with the “bed broken” and her feet in stirrups. Many US obstetricians are concerned that these “difficult” non-supine positions without stirrups could become problematic if a shoulder dystocia develops or if an assisted delivery with forceps or vacuum is required, even though no medical evidence supports this belief. ::: ::: When I discussed the article by Seehusen and colleagues with family medicine colleagues, their reactions were similar to those of many obstetricians on the issue of non-supine labour positions and keeping the birthing bed intact. Their concerns included ensuring sufficient space for the handle of the speculum and whether the speculum could be opened sufficiently for adequate visualisation of the cervix. And in contrast to the evidence of improved clinical outcomes with non-supine obstetric positioning, the study does not definitively answer similar questions for speculum examinations. ::: ::: For US physicians to change patient positioning during pelvic examinations, additional randomised controlled trials will probably be necessary, both to determine if the quality of cervical specimens is different and to measure doctors' acceptance of this new technique. The trials would also need to provide more data supporting this study's outcomes of decreased patient discomfort and vulnerability in diverse patient populations. In addition, articles describing the examination techniques in detail will be needed—the article is unclear, for example, on how to avoid the speculum handle hitting the table when smaller patients are examined. (The video used by the investigators to train clinicians, available on bmj.com, will go some way towards meeting this.) Finally, this evidence will need to be published not only in primary care literature but also in gynaecology literature, since the vast majority of doctors learn their speculum exam techniques in medical school during their obstetrics and gynaecology rotations.


title: Challenges in automotive software engineering

abstract: The amount of software in cars grows exponentially. Driving forces of this development are cheaper and more powerful hardware and the demand for innovations by new functions. The rapid increase of software and software based functionality brings various challenges (see [21], [23], [25], [26]) for the automotive industries, for their organization, key competencies, processes, methods, tools, models, product structures, division of work, logistics, maintenance, and long term strategies. From a software engineering perspective, the automotive industry is an ideal and fascinating application domain for advanced techniques. Although the automotive industry may adopt general results and solutions from the software engineering body of knowledge gained in other domains, the specific constraints and domain specific requirements in the automotive industry ask for individual solutions and bring various challenges for automotive software engineering. In cars we find literally all interesting problems and challenging issues of software and systems engineering.


title: Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates.

abstract: The most widely used task functional magnetic resonance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumptions. In this work, we use real resting-state data and a total of 3 million random task group analyses to compute empirical familywise error rates for the fMRI software packages SPM, FSL, and AFNI, as well as a nonparametric permutation method. For a nominal familywise error rate of 5%, the parametric statistical methods are shown to be conservative for voxelwise inference and invalid for clusterwise inference. Our results suggest that the principal cause of the invalid cluster inferences is spatial autocorrelation functions that do not follow the assumed Gaussian shape. By comparison, the nonparametric permutation test is found to produce nominal results for voxelwise as well as clusterwise inference. These findings speak to the need of validating the statistical methods being used in the field of neuroimaging.


title: The logical primitives of thought: Empirical foundations for compositional cognitive models

abstract: The notion of a compositional language of thought (LOT) has been central in computational accounts of cognition from earliest attempts (Boole, 1854; Fodor, 1975) to the present day (Feldman, 2000; Penn, Holyoak, & Povinelli, 2008; Fodor, 2008; Kemp, 2012; Goodman, Tenenbaum, & Gerstenberg, 2015). Recent modeling work shows how statistical inferences over compositionally structured hypothesis spaces might explain learning and development across a variety of domains. However, the primitive components of such representations are typically assumed a priori by modelers and theoreticians rather than determined empirically. We show how different sets of LOT primitives, embedded in a psychologically realistic approximate Bayesian inference framework, systematically predict distinct learning curves in rule-based concept learning experiments. We use this feature of LOT models to design a set of large-scale concept learning experiments that can determine the most likely primitives for psychological concepts involving Boolean connectives and quantification. Subjects' inferences are most consistent with a rich (nonminimal) set of Boolean operations, including first-order, but not second-order, quantification. Our results more generally show how specific LOT theories can be distinguished empirically. (PsycINFO Database Record


title: Unexpectedness as a Measure of Interestingness in Knowledge Discovery

abstract: Organizations are taking advantage of "data-mining" techniques to leverage the vast amounts ofdata captured as they process routine transactions. Data-mining is the process of discoveringhidden structure or patterns in data. However several of the pattern discovery methods in dataminingsystems have the drawbacks that they discover too many obvious or irrelevant patternsand that they do not leverage to a full extent valuable prior domain knowledge that managershave. This research addresses these drawbacks by developing ways to generate interestingpatterns by incorporating managers' prior knowledge in the process of searching for patterns indata. Specifically we focus on providing methods that generate unexpected patterns with respectto managerial intuition by eliciting managers' beliefs about the domain and using these beliefs toseed the search for unexpected patterns in data. Our approach should lead to the development ofdecision support systems that provide managers with more relevant patterns from data and aid ineffective decision making.


title: Deploying Dense Networks for Maximal Energy Efficiency: Small Cells Meet Massive MIMO

abstract: What would a cellular network designed for maximal energy efficiency look like? To answer this fundamental question, tools from stochastic geometry are used in this paper to model future cellular networks and obtain a new lower bound on the average uplink spectral efficiency. This enables us to formulate a tractable uplink energy efficiency (EE) maximization problem and solve it analytically with respect to the density of base stations (BSs), the transmit power levels, the number of BS antennas and users per cell, and the pilot reuse factor. The closed-form expressions obtained from this general EE maximization framework provide valuable insights on the interplay between the optimization variables, hardware characteristics, and propagation environment. Small cells are proved to give high EE, but the EE improvement saturates quickly with the BS density. Interestingly, the maximal EE is achieved by also equipping the BSs with multiple antennas and operate in a “massive MIMO” fashion, where the array gain from coherent detection mitigates interference and the multiplexing of many users reduces the energy cost per user.


title: Design of a novel antipodal vivaldi antenna with further modifications for notch characteristics

abstract: In this literature a new design of printed antipodal UWB vivaldi antenna is proposed. The design is further modified for acquiring notch characteristics in the WLAN band and high front to backlobe ratio (F/B). The modifications are done on the ground plane of the antenna. Previous literatures have shown that the incorporation of planar meta-material structures on the CPW plane along the feed can produce notch characteristics. Here, a novel concept is introduced regarding antipodal vivaldi antenna. In the ground plane of the antenna, square ring resonator (SRR) structure slot and circular ring resonator (CRR) structure slot are cut to produce the notch characteristic on the WLAN band. The designed antenna covers a bandwidth of 6.8 GHz (2.7 GHz–9.5 GHz) and it can be useful for a large range of wireless applications like satellite communication applications and biomedical applications where directional radiation characteristic is needed. The designed antenna shows better impedance matching in the above said band. A parametric study is also performed on the antenna design to optimize the performance of the antenna. The size of the antenna is 40×44×1.57 mm3. It is designed and simulated using HFSS. The presented prototype offers well directive radiation characteristics, good gain and efficiency.


title: Femtosecond absorption bleaching in glasses with semiconductor nanoparticles

abstract: Pump-probe femtosecond transmission measurements in the vicinity of the first excitonic resonance are performed in a silicate glass embedded with Cd-S-Se semiconductor nanoparticles. In the experiment, the pump at 400 nm (duration 50 fs, energy up to 0.1 mJ) excites the sample, while the change of the optical absorption is probed by femtosecond continuum. The time-resolved spectrum of the absorption change in the wavelength range 450-650 nm is visualized using a two-channel imaging spectrometer. A strong optical nonlinearity of the glass containing Cd-S-Se nanoparticles results in the up to 50% bleaching of the first excitonic resonance via the depopulating of the ground state. The temporal evolution of the bleaching consists of fast (with relaxation time as small as 3 ps) and slow (>15 ps) components. The dependence of the nonlinear absorption on the detuning of the pump and probe wavelengths with respect to the exciton resonance is evaluated.


title: Learning to fly by crashing

abstract: How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid obstacles? One approach is to use a small dataset collected by human experts: however, high capacity learning algorithms tend to overfit when trained with little data. An alternative is to use simulation. But the gap between simulation and real world remains large especially for perception problems. The reason most research avoids using large-scale real data is the fear of crashes! In this paper, we propose to bite the bullet and collect a dataset of crashes itself! We build a drone whose sole purpose is to crash into objects: it samples naive trajectories and crashes into random objects. We crash our drone 11,500 times to create one of the biggest UAV crash dataset. This dataset captures the different ways in which a UAV can crash. We use all this negative flying data in conjunction with positive data sampled from the same trajectories to learn a simple yet powerful policy for UAV navigation. We show that this simple self-supervised model is quite effective in navigating the UAV even in extremely cluttered environments with dynamic obstacles including humans. For supplementary video see:


title: Maladaptive and adaptive emotion regulation through music: a behavioral and neuroimaging study of males and females

abstract: Music therapists use guided affect regulation in the treatment of mood disorders. However, self-directed uses of music in affect regulation are not fully understood. Some uses of music may have negative effects on mental health, as can non-music regulation strategies, such as rumination. Psychological testing and functional magnetic resonance imaging (fMRI) were used explore music listening strategies in relation to mental health. Participants (n=123) were assessed for depression, anxiety and Neuroticism, and uses of Music in Mood Regulation (MMR). Neural responses to music were measured in the medial prefrontal cortex (mPFC) in a subset of participants (n=56). Discharge, using music to express negative emotions, related to increased anxiety and Neuroticism in all participants and particularly in males. Males high in Discharge showed decreased activity of mPFC during music listening compared with those using less Discharge. Females high in Diversion, using music to distract from negative emotions, showed more mPFC activity than females using less Diversion. These results suggest that the use of Discharge strategy can be associated with maladaptive patterns of emotional regulation, and may even have long-term negative effects on mental health. This finding has real-world applications in psychotherapy and particularly in clinical music therapy.


title: Co-Transduction for Shape Retrieval

abstract: In this paper, we propose a new shape/object retrieval algorithm, namely, co-transduction. The performance of a retrieval system is critically decided by the accuracy of adopted similarity measures (distances or metrics). In shape/object retrieval, ideally, intraclass objects should have smaller distances than interclass objects. However, it is a difficult task to design an ideal metric to account for the large intraclass variation. Different types of measures may focus on different aspects of the objects: for example, measures computed based on contours and skeletons are often complementary to each other. Our goal is to develop an algorithm to fuse different similarity measures for robust shape retrieval through a semisupervised learning framework. We name our method co-transduction, which is inspired by the co-training algorithm. Given two similarity measures and a query shape, the algorithm iteratively retrieves the most similar shapes using one measure and assigns them to a pool for the other measure to do a re-ranking, and vice versa. Using co-transduction, we achieved an improved result of 97.72% (bull's-eye measure) on the MPEG-7 data set over the state-of-the-art performance. We also present an algorithm called tri-transduction to fuse multiple-input similarities, and it achieved 99.06% on the MPEG-7 data set. Our algorithm is general, and it can be directly applied on input similarity measures/metrics; it is not limited to object shape retrieval and can be applied to other tasks for ranking/retrieval.


title: A generic abstract syntax model for embedded languages

abstract: Representing a syntax tree using a data type often involves having many similar-looking constructors. Functions operating on such types often end up having many similar-looking cases. Different languages often make use of similar-looking constructions. We propose a generic model of abstract syntax trees capable of representing a wide range of typed languages. Syntactic constructs can be composed in a modular fashion enabling reuse of abstract syntax and syntactic processing within and across languages. Building on previous methods of encoding extensible data types in Haskell, our model is a pragmatic solution to Wadler's "expression problem". Its practicality has been confirmed by its use in the implementation of the embedded language Feldspar.


title: Evaluating the Effect of Style in Information Visualization

abstract: This paper reports on a between-subject, comparative online study of three information visualization demonstrators that each displayed the same dataset by way of an identical scatterplot technique, yet were different in style in terms of visual and interactive embellishment. We validated stylistic adherence and integrity through a separate experiment in which a small cohort of participants assigned our three demonstrators to predefined groups of stylistic examples, after which they described the styles with their own words. From the online study, we discovered significant differences in how participants execute specific interaction operations, and the types of insights that followed from them. However, in spite of significant differences in apparent usability, enjoyability and usefulness between the style demonstrators, no variation was found on the self-reported depth, expert-rated depth, confidence or difficulty of the resulting insights. Three different methods of insight analysis have been applied, revealing how style impacts the creation of insights, ranging from higher-level pattern seeking to a more reflective and interpretative engagement with content, which is what underlies the patterns. As this study only forms the first step in determining how the impact of style in information visualization could be best evaluated, we propose several guidelines and tips on how to gather, compare and categorize insights through an online evaluation study, particularly in terms of analyzing the concise, yet wide variety of insights and observations in a trustworthy and reproducable manner.


title: An Adversarial Neuro-Tensorial Approach For Learning Disentangled Representations

abstract: Several factors contribute to the appearance of an object in a visual scene, including pose, illumination, and deformation, among others. Each factor accounts for a source of variability in the data, while the multiplicative interactions of these factors emulate the entangled variability, giving rise to the rich structure of visual object appearance. Disentangling such unobserved factors from visual data is a challenging task, especially when the data have been captured in uncontrolled recording conditions (also referred to as “in-the-wild”) and label information is not available. In this paper, we propose a pseudo-supervised deep learning method for disentangling multiple latent factors of variation in face images captured in-the-wild. To this end, we propose a deep latent variable model, where the multiplicative interactions of multiple latent factors of variation are explicitly modelled by means of multilinear (tensor) structure. We demonstrate that the proposed approach indeed learns disentangled representations of facial expressions and pose, which can be used in various applications, including face editing, as well as 3D face reconstruction and classification of facial expression, identity and pose.


title: A Meta-Analysis of 137 Personality Subjective Well-Being

abstract: This recta-analysis used 9 literature search strategies to examine 137 distinct personality constructs as correlates of subjective well-being (SWB). Personality was found to be equally predictive of life satisfaction, happiness, and positive affect, but significantly less predictive of negative affect. The traits most closely associated with SWB were repressive-defensiveness, trust, emotional stability, locus of control-chance, desire for control, hardiness, positive affectivity, private collective selfesteem, and tension. When personality traits were grouped according to the Big Five factors, Neuroticism was the strongest predictor of life satisfaction, happiness, and negative affect. Positive affect was predicted equally well by Extraversion and Agreeableness. The relative importance of personality for predicting SWB, how personality might influence SWB, and limitations of the present review are discussed.


title: Decision Trees for Uncertain Data

abstract: Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information, which originates from measurement/quantisation errors, data staleness, multiple repeated measurements, etc. The value uncertainty is represented by multiple values forming a probability distribution function (pdf). We discover that the accuracy of a decision tree classifier can be much improved if the whole pdf, rather than a simple statistic, is taken into account. We extend classical decision tree building algorithms to handle data tuples with uncertain values. Since processing pdf's is computationally more costly, we propose a series of pruning techniques that can greatly improve the efficiency of the construction of decision trees.


title: SDN and OpenFlow for Dynamic Flex-Grid Optical Access and Aggregation Networks

abstract: We propose and discuss the extension of software-defined networking (SDN) and OpenFlow principles to optical access/aggregation networks for dynamic flex-grid wavelength circuit creation. The first experimental demonstration of an OpenFlow1.0-based flex-grid λ-flow architecture for dynamic 150 Mb/s per-cell 4 G Orthogonal Frequency Division Multiple Access (OFDMA) mobile backhaul (MBH) overlays onto 10 Gb/s passive optical networks (PON) without optical network unit (ONU)-side optical filtering, amplification, or coherent detection, over 20 km standard single mode fiber (SSMF) with a 1:64 passive split is also detailed. The proposed approach can be attractive for monetizing optical access/aggregation networks via on-demand support for high-speed, low latency, high quality of service (QoS) applications over legacy fiber infrastructure.


title: Detection of plant disease using threshold, k-mean cluster and ann algorithm

abstract: Indian economy highly depends on agricultural productivity. An important role is played by the detection of disease to obtain a perfect results in agriculture, and it is natural to have disease in plants. Proper care should be taken in this area for product quality and quantity. To reduce the large amount of monitoring in field automatic detection techniques can be used. This paper discuss different processes for segmentation technique which can be applied for different lesion disease detection. Thresholding and K-means cluster algorithms are done to detect different diseases in plant leaf.


title: Sleep, sleepiness and school start times: a preliminary study.

abstract: Background: High school students are reported to be excessively sleepy, resulting in decreased academic performance, increased psycho-social problems and increased risk of morbidity and mortality from accidents. Early school start times have been noted to contribute to this problem. This report attempts to confirm the relationship of early school start times with decreased sleep and increased sleepiness. Methods: We examined sophomore and junior students in 2 local high schools with different start times and measured the amount of time slept and sleepiness. Results: We found that students at the early start school reported reduced sleep time and more sleepiness than their counterparts at the later starting school. Conclusion: Early school start times are associated with student reports of less sleep and increased sleepiness. Further studies in larger groups are recommended in view of the potential significant impact of sleep deprivation in this age group.


title: Ambiguous intentions: a paper-like interface for creative design

abstract: Interfaces for conceptual and creative design should recognize and interpret drawings. They should also capture users’ intended ambiguity, vagueness, and imprecision and convey these qualities visually and through interactive behavior. Freehand drawing can provide this information and it is a natural input mode for design. We describe a pen-based interface that acquires information about ambiguity and precision from freehand input, represents it internally, and echoes it to users visually and through constraint based edit behavior.


title: The survival of direct composite restorations in the management of severe tooth wear including attrition and erosion: A prospective 8-year study

abstract: Abstract Objectives Survival of directly placed composite to restore worn teeth has been reported in studies with small sample sizes, short observation periods and different materials. This study aimed to estimate survival for a hybrid composite placed by one clinician up to 8-years follow-up. Methods All patients were referred and recruited for a prospective observational cohort study. One composite was used: Spectrum® (DentsplyDeTrey). Most restorations were placed on the maxillary anterior teeth using a Dahl approach. Results A total of 1010 direct composites were placed in 164 patients. Mean follow-up time was 33.8 months (s.d. 27.7). 71 of 1010 restorations failed during follow-up. The estimated failure rate in the first year was 5.4% (95% CI 3.7–7.0%). Time to failure was significantly greater in older subjects (p = 0.005) and when a lack of posterior support was present (p = 0.003). Bruxism and an increase in the occlusal vertical dimension were not associated with failure. The proportion of failures was greater in patients with a Class 3 or edge-to-edge incisal relationship than in Class 1 and Class 2 cases but this was not statistically significant. More failures occurred in the lower arch (9.6%) compared to the upper arch (6%) with the largest number of composites having been placed on the maxillary incisors (n = 519). Conclusion The worn dentition presents a restorative challenge but composite is an appropriate restorative material. Clinical significance This study shows that posterior occlusal support is necessary to optimise survival.


title: Hybrid attribute and personality based recommender system for book recommendation

abstract: In recent years, with the rapid increases of books, finding relevant books has been a problem. For that, people might need their peers' opinion to complete this task. The problem is that relevant books can be gained only if there are other users or peers have same interests with them. Otherwise, they will never get relevant books. Recommender systems can be a solution for that problem. They work on finding relevant items based on other users' experience. Although research on recommender system increases, there is still not much research that considers user personality in recommender systems, even though personal preferences are really important these days. This paper discusses our research on a hybrid-based method that combines attribute-based and user personality-based methods for book recommender system. The attribute-based method has been implemented previously. In our research, we have implemented the MSV-MSL (Most Similar Visited Material to the Most Similar Learner) method, since it is the best method among hybrid attribute-based methods. The personality factor is used to find the similarity between users when creating neighborhood relationships. The method is tested using Book-crossing and Amazon Review on book category datasets. Our experiment shows that the combined method that considers user personality gives a better result than those without user personality on Book-crossing dataset. In contrary, it resulted in a lower performance on Amazon Review dataset. It can be concluded that user personality consideration can give a better result in a certain condition depending on the dataset itself and the usage proportion.


title: Ring-theoretic properties of certain Hecke algebras

abstract: The purpose of this article is to provide a key ingredient of [W2] by establishing that certain minimal Hecke algebras considered there are complete intersections. As is recorded in [W2], a method going back to Mazur [M] allows one to show that these algebras are Gorenstein, but for the complete intersection property a new approach is required. The methods of this paper are related to those of Chapter 3 of [W2]. The methods of Section 3 of this paper are based on a previous approach of one of us (A.W.). We would like to thank Henri Darmon, Fred Diamond and Gerd Faltings for carefully reading the first version of this article. Gerd Faltings has also suggested a simplification of our argument as well as of the argument of Chapter 3 of [W2] and we would like to thank him for allowing us to reproduce these in the appendix to this paper. R. T. would like to thank A. W. for his invitation to collaborate and for sharing his many insights into the questions considered. R. T. would also like to thank Princeton University, Universite de Paris 7 and Harvard University for their hospitality during this collaboration. A. W. was supported by an NSF grant.


title: Role of surfactant type and concentration for the mean drop size during emulsification in turbulent flow.

abstract: A systematic experimental study of the effect of several factors on the mean drop diameter, d32, during emulsification, is performed with soybean oil-in-water emulsions. These factors are (1) type of used emulsifier; (2) emulsifier concentration, CS; and (3) ionic strength of the aqueous solution. Three different types of emulsifier, anionic (sodium dodecyl sulfate, SDS), nonionic (polyoxyethylene-20 cetyl ether, Brij 58), and protein (whey protein concentrate), are studied. For all of the studied systems, two well-defined regions are observed in the dependence of d32 on CS: at low surfactant concentration, d32 increases significantly with the decrease of CS (region 1), whereas d32 does not depend on CS at high surfactant concentration (region 2). The model, proposed by Tcholakova et al. (Langmuir 2003, 19, 5640), is found to describe well the dependence of d32 on CS in region 1 for the nonionic surfactant and for the protein emulsifier at high electrolyte concentration, 150 mM NaCl. According to this model, a well defined minimal surfactant adsorption (close to that of the dense adsorption monolayer) is needed for obtaining an emulsion. On the other hand, this model is found inapplicable to emulsions stabilized by the ionic surfactant, SDS, and by the nonionic surfactant, Brij 58, at low electrolyte concentration. The performed theoretical analysis of drop-drop interactions, in the emulsification equipment, shows that a strong electrostatic repulsion between the colliding drops impedes the drop-drop coalescence in the latter systems, so that smaller emulsion drops are obtained in comparison with the theoretically predicted ones. The results for SDS-stabilized emulsions in region 1 are explained by a quantitative consideration of this electrostatic repulsion. The drop size in region 2 (surfactant-rich regime) is described very well by the Kolmogorov-Hinze theory of turbulent emulsification.


title: Centroidal Voronoi Tessellation in Universal Covering Space of Manifold Surfaces 1

abstract: The centroidal Voronoi tessellation (CVT) has found versatile applications in geometric modeling, computer graphics, and visualization, etc. In this paper, we first extend the concept of CVT from Euclidean space to spherical space and hyperbolic space, and then combine all of them into a unified framework - the CVT in universal covering space. The novel spherical and hyperbolic CVT energy functions are defined, and the relationship between minimizing the energy and the CVT is proved. We also show by our experimental results that both spherical and hyperbolic CVTs have the similar property as their Euclidean counterpart where the sites are uniformly distributed with respect to given density values. As an example of the application, we utilize the CVT in universal covering space to compute uniform partitions and high-quality remeshing results for genus-0, genus-1, and high-genus (genus>1) surfaces.


title: Unbiased Estimation of the Value of an Optimized Policy

abstract: Randomized trials, also known as A/B tests, are used to select between two policies: a control and a treatment. Given a corresponding set of features, we can ideally learn an optimized policy P that maps the A/B test data features to action space and optimizes reward. However, although A/B testing provides an unbiased estimator for the value of deploying B (i.e., switching from policy A to B), direct application of those samples to learn the the optimized policy P generally does not provide an unbiased estimator of the value of P as the samples were observed when constructing P. In situations where the cost and risks associated of deploying a policy are high, such an unbiased estimator is highly desirable. We present a procedure for learning optimized policies and getting unbiased estimates for the value of deploying them. We wrap any policy learning procedure with a bagging process and obtain out-of-bag policy inclusion decisions for each sample. We then prove that inverse-propensity-weighting effect estimator is unbiased when applied to the optimized subset. Likewise, we apply the same idea to obtain out-of-bag unbiased per-sample value estimate of the measurement that is independent of the randomized treatment, and use these estimates to build an unbiased doubly-robust effect estimator. Lastly, we empirically shown that even when the average treatment effect is negative we can find a positive optimized policy.


title: GPU Enabled Serverless Computing Framework

abstract: A new form of cloud computing, serverless computing, is drawing attention as a new way to design micro-services architectures. In a serverless computing environment, services are developed as service functional units. The function development environment of all serverless computing framework at present is CPU based. In this paper, we propose a GPU-supported serverless computing framework that can deploy services faster than existing serverless computing framework using CPU. Our core approach is to integrate the open source serverless computing framework with NVIDIA-Docker and deploy services based on the GPU support container. We have developed an API that connects the open source framework to the NVIDIA-Docker and commands that enable GPU programming. In our experiments, we measured the performance of the framework in various environments. As a result, developers who want to develop services through the framework can deploy high-performance micro services and developers who want to run deep learning programs without a GPU environment can run code on remote GPUs with little performance degradation.


title: Thermo-Compression Bonding for fine-pitch copper-pillar flip-chip interconnect - tool features as enablers of unique technology

abstract: Thermo-Compression Bonding (TCB) is a chip attach technology under investigation and implementation in multiple platforms. This paper is introducing this technology from the equipment features aspects, as well as from the materials/process aspects. The unique characteristics of the TCB technology over the existing chip attach technology (C2 mass reflow) are discussed in detail. The development of both equipment and processes at ASMPT and Intel resulted in deployment of the technology towards high Volume Manufacturing (HVM). Quality, process and equipment controls are implemented as part of the deployment, and will be discussed in this paper.


title: Adaptive Sliding-Window Strategy for Vehicle Detection in Highway Environments

abstract: In highway traffic monitoring systems, vehicle detection is one of the most important tasks. To automatically detect vehicles in general CCTV environments, we should effectively handle the problems caused by the following three issues: severe variation in appearance, ambiguities in location, and ambiguities in size. Over the last decade, these issues have been mainly addressed by employing a sliding-window-based image search. Although it is quite effective in many applications, the manual procedure of initializing necessary size models and high computational costs impede the automation of monitoring systems. To remedy these drawbacks, we propose an adaptive sliding-window strategy, where useful size templates are continuously modeled for a given scene, and the detection is performed by adaptively deforming a sliding window based on the obtained templates. This strategy allows us not only to avoid complex user interferences but also to reduce computational costs for vehicle detection. Experiments on real-world highway environments show that the proposed strategy achieved about 23% improved detection accuracy with 43% reduced processing time over the state-of-the-art sliding-window approach without any user assistance.


title: Synthesis of charged phenyl radicals and biradicals by laser photolysis in a Fourier-transform ion cyclotron resonance mass spectrometer

abstract: The feasibility of generating substituted phenyl radicals and biradicals (with a charged substituent) in the gas phase by laser photolysis was examined by using a Fourier-transform ion cyclotron resonance mass spectrometer. The precursors were generated by ipso-substitution of a halogen atom in the radical cation of a di- or trihalobenzene by various nucleophiles. Photolytic cleavage of the remaining carbon-halogen bond(s) with 266-nm radiation was found to produce many substituted phenyl radicals in greater yields than the earlier employed method, sustained off-resonance irradiated collision-activated dissociation (SORI-CAD). Furthermore, ion generation by photolysis leads to isomerization less often than collisional activation. Finally, not only phenyl-bromine and phenyl-iodine but also certain phenyl-chlorine bonds can be cleaved by photolysis, whereas the synthetic utility of SORI-CAD appears to be largely limited to the cleavage of phenyl-iodine bonds. Hence, laser photolysis greatly expands the variety of substituted phenyl radicals and biradicals that can be synthesized inside a mass spectrometer.


Presence of Mobile Devices The iPhone Effect : The Quality of In-Person Social Interactions in the


Time Complexity of K-Means and K-Medians Clustering Algorithms in Outliers Detection


title: Tractable Queries for Lightweight Description Logics

abstract: It is a classic result in database theory that conjunctive query (CQ) answering, which is NP-complete in general, is feasible in polynomial time when restricted to acyclic queries. Subsequent results identified more general structural properties of CQs (like bounded treewidth) which ensure tractable query evaluation. In this paper, we lift these tractability results to knowledge bases formulated in the lightweight description logics DL-Lite and ELH. The proof exploits known properties of query matches in these logics and involves a query-dependent modification of the data. To obtain a more practical approach, we propose a concrete polynomial-time algorithm for answering acyclic CQs based on rewriting queries into datalog programs. A preliminary evaluation suggests the interest of our approach for handling large acyclic CQs.


title: Personality Traits, Self-Esteem and Academic Achievement in Secondary School Students in Campania, Italy.

abstract: For years educators have attempted to identify the effective predictors of scholastic achievement and several personality variables were described as significantly correlated with grade performance. Since one of the crucial practical implications of identifying the factors involved in academic achievement is to facilitate the teaching-learning process, the main variables that have been associated with achievement should be investigated simultaneously in order to provide information as to their relative merit in the population examined. In contrast with this premise, limited research has been conducted on the importance of personality traits and self-esteem on scholastic achievement. To this aim in a sample of 439 subjects (225 males) with an average age of 12.36 years (SD= .99) from three first level secondary school classes of Southern Italy, personality traits, as defined by the Five Factor Model, self-esteem and socioeconomic status were evaluated. The academic results correlated significantly both with personality traits and with some dimensions of self-esteem. Moreover, hierarchical regression analyses brought to light, in particular, the predictive value of openness to experience on academic marks. The results, stressing the multidimensional nature of academic performance, indicate a need to adopt complex approaches for undertaking action addressing students' difficulties in attaining good academic achievement.


title: Shape-Up: Shaping Discrete Geometry with Projections

abstract: We introduce a unified optimization framework for geometry processing based on shape constraints. These constraints preserve or prescribe the shape of subsets of the points of a geometric data set, such as polygons, one-ring cells, volume elements, or feature curves. Our method is based on two key concepts: a shape proximity function and shape projection operators. The proximity function encodes the distance of a desired least-squares fitted elementary target shape to the corresponding vertices of the 3D model. Projection operators are employed to minimize the proximity function by relocating vertices in a minimal way to match the imposed shape constraints. We demonstrate that this approach leads to a simple, robust, and efficient algorithm that allows implementing a variety of geometry processing applications, simply by combining suitable projection operators. We show examples for computing planar and circular meshes, shape space exploration, mesh quality improvement, shape-preserving deformation, and conformal parametrization. Our optimization framework provides a systematic way of building new solvers for geometry processing and produces similar or better results than state-of-the-art methods. © 2012 Wiley Periodicals, Inc.


title: The impact of persuasive SMS on students' self‐regulated learning

abstract: This paper presents the outcome of an experimental design to investigate the impact of persuasive short messaging service (SMS) on students' self-regulated learning strategies while attending an introductory information systems course over a 12-week trimester. The participants were undergraduate students enrolled in INFO 101 at a tertiary institution in New Zealand. The instrument used in this study was the original motivated strategies for learning questionnaires (MSLQ) developed by Pintrich. MSLQ measures three general types of strategies: cognitive, meta-cognitive and resource management. The findings of the research identified that several aspects of students' learning strategies had been improved for the experiment group while the Time and Study Environment Management dimension of MSLQ had been significantly lowered for the control group who received no SMS intervention. The study demonstrates a positive impact of persuasive SMS on students' learning and suggests that the intervention is able to improve students' self-regulated learning effort compared to the control group. Moreover the study shows that students who received SMS intervention performed better than students who did not receive SMS intervention. Most importantly, the study shows that SMS intervention enables Māori and Pacific students, who historically have a lower performance than the main cohort, to perform better than the main cohort and to a significant level higher than those Māori and Pacific students who did not receive any SMS intervention. This study suggests that practitioners should consider the adoption of the persuasive SMS intervention by using the principles of persuasive technology for sending SMS messages especially for the high risk students. Practitioner Notes What is already known about this topic Persuasive technology is the adoption of computing technology in an attempt to change attitudes and behaviour., This technology is widely used in the preventive health care industry and social organizations for their and social marketing campaigns., What this paper adds The study shows that students who received persuasive short messaging service (SMS) intervention performed better than students who did not receive any SMS intervention., The study demonstrates a positive impact of persuasive SMS on students' learning and suggests that the intervention is able to improve students' self-regulated learning effort., Implications for practice and/or policy Practitioners should consider the adoption of the persuasive SMS intervention., Practitioners should adopt the seven principles of persuasive technology for sending SMS messages., Practitioners should also target the persuasive SMS intervention to high risk students. [ABSTRACT FROM AUTHOR]


title: Characterization and Feasibility Assessment of Recycled Paper Mill Sludges for Land Application in Relation to the Environment

abstract: The disposal of industrial paper mill sludge waste is a big issue and has a great importance all over the world. A study was conducted to determine the chemical properties of recycled paper mill sludge (RPMS) and assess its possibilities for land application. RPMS samples were collected from six different paper mills in Malaysia and analyzed for physical and chemical properties, heavy metals, polycyclic aromatic hydrocarbons, 13C-NMR spectra and for the presence of dioxins/furans. The RPMS was dewatered, sticky with a strong odour, an average moisture of 65.08%, pH 7.09, cation exchange capacity (CEC) 14.43 cmol (+) kg–1, N 1.45, P 0.18, K 0.12, Ca 0.82, Mg 0.73, Na 0.76 and Al, 1.38%. The polycyclic aromatic hydrocarbons (PAHs) and heavy metals levels were below the standard Class 2 limits. The dioxin and furan were in below the standard concentration of Class 1. The most prominent peak in the 13C-NMR spectra of RPMS was centered at 31 ppm, proving the presence of methylene (-CH2) groups in long aliphatic chains, with lipids and proteins. The signal at 89 ppm and highly shielded shoulder at 83 ppm were due to presence of cellulose carbon C-4, and the peak at 63 and 65 ppm was due to the cellulose carbon spectrum. The RPMS therefore contains significant amount of nutrients with safe levels of heavy metals and PAHs for environment and can be used as a fertilizer and soil amendment for land application.


title: AIPNet: Image-to-Image Single Image Dehazing With Atmospheric Illumination Prior

abstract: The atmospheric scattering and absorption gives rise to the natural phenomenon of haze, which severely affects the visibility of scenery. Thus, the image taken by the camera can easily lead to over brightness and ambiguity. To resolve an ill-posed and intractable problem of single image dehazing, we propose a straightforward but remarkable prior—atmospheric illumination prior in this paper. The extensive statistical experiments for different colorspaces and theoretical analyses indicate that the atmospheric illumination in hazy weather mainly has a great influence on the luminance channel in YCrCb colorspace, and has less impact on the chrominance channels. According to this prior, we try to maintain the intrinsic color of hazy scene and enhance its visual contrast. To this end, we apply the multiscale convolutional networks that can automatically identify hazy regions and restore deficient texture information. Compared with previous methods, the deep CNNs not only achieve an end-to-end trainable model, but also accomplish an easy image-to-image system architecture. The extensive comparisons and analyses with existing approaches demonstrate that the proposed approach achieves the state-of-the-art performance on several dehazing effects.


title: Visual localization within LIDAR maps for automated urban driving

abstract: This paper reports on the problem of map-based visual localization in urban environments for autonomous vehicles. Self-driving cars have become a reality on roadways and are going to be a consumer product in the near future. One of the most significant road-blocks to autonomous vehicles is the prohibitive cost of the sensor suites necessary for localization. The most common sensor on these platforms, a three-dimensional (3D) light detection and ranging (LIDAR) scanner, generates dense point clouds with measures of surface reflectivity-which other state-of-the-art localization methods have shown are capable of centimeter-level accuracy. Alternatively, we seek to obtain comparable localization accuracy with significantly cheaper, commodity cameras. We propose to localize a single monocular camera within a 3D prior ground-map, generated by a survey vehicle equipped with 3D LIDAR scanners. To do so, we exploit a graphics processing unit to generate several synthetic views of our belief environment. We then seek to maximize the normalized mutual information between our real camera measurements and these synthetic views. Results are shown for two different datasets, a 3.0 km and a 1.5 km trajectory, where we also compare against the state-of-the-art in LIDAR map-based localization.


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title: Determinants of pulse pressure.

abstract: We have searched to define the major arterial parameters that determine aortic systolic (Ps) and diastolic (Pd) pressure in the dog. Measured aortic flows were used as input to the 2-element windkessel model of the arterial system, with peripheral resistance calculated as mean pressure divided by mean flow and total arterial compliance calculated from the decay time in diastole. The windkessel model yielded an aortic pressure wave from which we obtained the predicted systolic (Ps,wk) and diastolic (Pd,wk) pressures. These predicted pressures were compared with the measured systolic and diastolic pressures. The measurements and calculations were performed for 7 dogs under control conditions during aortic occlusion at 4 locations (the trifurcation, between the trifurcation and diaphragm, the diaphragm, and the proximal descending thoracic aorta) and during occlusion of both carotid arteries. Under all conditions studied, the predicted systolic and diastolic pressures matched the experimental ones very well: Ps,wk=(1.000+/-0.0055) Ps with r=0.958 and Pd,wk=(1.024+/-0.0035) Pd with r=0.995. Linear regression for pulse pressure (PP) resulted in PPwk=(0.99+/-0.016) PP with r=0.911. We found the accuracy of prediction equally good under control conditions and in the presence of aortic or carotid artery occlusion. Multiple regression between pulse pressure and arterial resistance and total arterial compliance yielded a poor regression constant (R2=0.19), suggesting that the 2 arterial parameters alone cannot explain pulse pressure and that flow is an important determinant as well. We conclude that for a given ejection pattern (aortic flow), 2 arterial parameters, total arterial resistance and total arterial compliance, are sufficient to accurately describe systolic and diastolic aortic pressure.


title: Mycorrhizosphere interactions to improve plant fitness and soil quality

abstract: Arbuscular mycoruhizal fungi are key components of soil microbiota and obviously interact with other microorganisms in the rhizosphere, i.e. the zone of influence of plant roots on microbial populations and other soil constituents. Mycorrhiza formation changes several aspects of plant physiology and some nutritional and physical properties of the rhizospheric soil. These effects modify the colonization patterns of the root or mycovrhizas (mycorrhizosphere) by soil microorganisms. The rhizosphere of mycorrhizal plants, in practice a mycorrhizosphere, harbors a great array of microbial activities responsible for several key ecosystem processes. This paper summarizes the main conceptual principles and accepted statements on the microbial interactions between mycorrhizal fungi and other members of rhizosphere microbiota and discusses current developments and future trends concerning the following topics: (i) effect of soil microorganisms on mycorrhiza formation; (ii) mycorrhizosphere establishment; (iii) interactions involved in nutrient cycling and plant growth; (iv) interactions involved in the biological control of plant pathogens; and (v) interactions to improve soil quality. The main conclusion is that microbial interactions in the rhizosphere of mycorrhizal plants improve plant fitness and soil quality, critical issues for a sustainable agricultural development and ecosystem functioning.


title: Universal GP Care in Ireland: Potential Cost Implications

abstract: In 2011, the Government made a commitment to the introduction of universal General Practitioner (GP) care, which they equated to the removal of fees for GP care. The aim of this paper is to quantify the potential cost implications of implementing universal GP care in Ireland. The analysis finds that universal GP care would add between 2 and 3.5 per cent to overall public healthcare expenditure and up to 1.2 per cent to total healthcare expenditure. While the introduction of universal GP care in Ireland would go some way to addressing limitations of the current system, other reforms may be required before universality can be achieved.


title: A multicentre validation of the 1-min sit-to-stand test in patients with COPD.

abstract: Our aim was to comprehensively validate the 1-min sit-to-stand (STS) test in chronic obstructive pulmonary disease (COPD) patients and explore the physiological response to the test.We used data from two longitudinal studies of COPD patients who completed inpatient pulmonary rehabilitation programmes. We collected 1-min STS test, 6-min walk test (6MWT), health-related quality of life, dyspnoea and exercise cardiorespiratory data at admission and discharge. We assessed the learning effect, test-retest reliability, construct validity, responsiveness and minimal important difference of the 1-min STS test.In both studies (n=52 and n=203) the 1-min STS test was strongly correlated with the 6MWT at admission (r=0.59 and 0.64, respectively) and discharge (r=0.67 and 0.68, respectively). Intraclass correlation coefficients (95% CI) between 1-min STS tests were 0.93 (0.83-0.97) for learning effect and 0.99 (0.97-1.00) for reliability. Standardised response means (95% CI) were 0.87 (0.58-1.16) and 0.91 (0.78-1.07). The estimated minimal important difference was three repetitions. End-exercise oxygen consumption, carbon dioxide output, ventilation, breathing frequency and heart rate were similar in the 1-min STS test and 6MWT.The 1-min STS test is a reliable, valid and responsive test for measuring functional exercise capacity in COPD patients and elicited a physiological response comparable to that of the 6MWT.


title: Optimal control as a graphical model inference problem

abstract: In this paper we show the identification between stochastic optimal control computation and probabilistic inference on a graphical model for certain class of control problems. We refer to these problems as Kullback-Leibler (KL) control problems. We illustrate how KL control can be used to model a multi-agent cooperative game for which optimal control can be approximated using belief propagation when exact inference is unfeasible.


title: Nigerian Youths and Social Media : Harnessing the Potentials for Academic Excellence

abstract: One of the breakthroughs in information and communication technology in the 21st century was the discovery and emergence of the new media which have facilitated the creation of the different platforms for social interaction. The potentials of the new media are seamless and boundless in terms of interactions, interrelationships, and information sharing and exchanges. The only limitation to the utilization of the potentials of the platforms is illiteracy in terms of the hardware, software and usage. The question is, to what extent has the Nigerian youths converted the potentials and possibilities of the social media to platforms for networking academic materials for scholarship and academic excellence? The study examined the extent of usage of one of the most popular social media platforms, facebook, by the Nigerian youths in their academic pursuit. It found that about two-thirds of the Nigerian youths that use Facebook platform use it for other reasons than exchange of academic materials. And, recommends that the youths should be encouraged to harness the potentials of the platform for scholarship.


title: Analysis of Cisco and Huawei routers cooperation for MPLS network design

abstract: This paper deals with describing MPLS technology behavior on two different, but nowadays the most commonly used network components of well-known vendors Cisco and Huawei. We focused on two significant MPLS applications namely MPLS VPN and MPLS AToM. For each application of MPLS, aseparately assembled network topology was used. Compatibility and interoperability between network devices Huawei and Cisco was verified by testing the appropriate network topology. After successful validation, compatibility and functionality of the network topologies between the routers created in a laboratory environment, it was possible to provide and operate appropriate MPLS applications in real network traffic. This paper brings the verified scenario of MPLS interoperability between vendors Cisco and Huawei.


title: Modeling user search behavior

abstract: Web usage mining is a main research area in Web mining focused on learning about Web users and their interactions with Web sites. Main challenges in Web usage mining are the application of data mining techniques to Web data in an efficient way and the discovery of non trivial user behaviour patterns. In this paper we focus the attention on search engines analyzing query log data and showing several models about how users search and how users use search engine results.