title: Surface-Based Registration of Airborne and Terrestrial Mobile LiDAR Point Clouds

abstract: Light Detection and Ranging (LiDAR) is an active sensor that can effectively acquire a large number of three-dimensional (3-D) points. LiDAR systems can be equipped on different platforms for different applications, but to integrate the data, point cloud registration is needed to improve geometric consistency. The registration of airborne and terrestrial mobile LiDAR is a challenging task because the point densities and scanning directions differ. We proposed a scheme for the registration of airborne and terrestrial mobile LiDAR using the least squares 3-D surface registration technique to minimize the surfaces between two datasets. To analyze the effect of point density in registration, the simulation data simulated different conditions and estimated the theoretical errors. The test data were the point clouds of the airborne LiDAR system (ALS) and the mobile LiDAR system (MLS), which were acquired by Optech ALTM 3070 and Lynx, respectively. The resulting simulation analysis indicated that the accuracy of registration improved as the density increased. For the test dataset, the registration error of mobile LiDAR between different trajectories improved from 40 cm to 4 cm, and the registration error between ALS and MLS improved from 84 cm to 4 cm. These results indicate that the proposed methods can obtain 5 cm accuracy between ALS and MLS.


title: Gleaner: Creating ensembles of first-order clauses to improve recall-precision curves

abstract: Many domains in the field of Inductive Logic Programming (ILP) involve highly unbalanced data. A common way to measure performance in these domains is to use precision and recall instead of simply using accuracy. The goal of our research is to find new approaches within ILP particularly suited for large, highly-skewed domains. We propose Gleaner, a randomized search method that collects good clauses from a broad spectrum of points along the recall dimension in recall-precision curves and employs an “at least L of these K clauses” thresholding method to combine sets of selected clauses. Our research focuses on Multi-Slot Information Extraction (IE), a task that typically involves many more negative examples than positive examples. We formulate this problem into a relational domain, using two large testbeds involving the extraction of important relations from the abstracts of biomedical journal articles. We compare Gleaner to ensembles of standard theories learned by Aleph, finding that Gleaner produces comparable testset results in a fraction of the training time.


title: A computationally fast variable importance test for random forests for high-dimensional data

abstract: Random forests are a commonly used tool for classification and for ranking candidate predictors based on the so-called variable importance measures. These measures attribute scores to the variables reflecting their importance. A drawback of variable importance measures is that there is no natural cutoff that can be used to discriminate between important and non-important variables. Several approaches, for example approaches based on hypothesis testing, were developed for addressing this problem. The existing testing approaches require the repeated computation of random forests. While for low-dimensional settings those approaches might be computationally tractable, for high-dimensional settings typically including thousands of candidate predictors, computing time is enormous. In this article a computationally fast heuristic variable importance test is proposed that is appropriate for high-dimensional data where many variables do not carry any information. The testing approach is based on a modified version of the permutation variable importance, which is inspired by cross-validation procedures. The new approach is tested and compared to the approach of Altmann and colleagues using simulation studies, which are based on real data from high-dimensional binary classification settings. The new approach controls the type I error and has at least comparable power at a substantially smaller computation time in the studies. Thus, it might be used as a computationally fast alternative to existing procedures for high-dimensional data settings where many variables do not carry any information. The new approach is implemented in the R package vita.


title: A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology

abstract: Nuclear segmentation in digital microscopic tissue images can enable extraction of high-quality features for nuclear morphometrics and other analysis in computational pathology. Conventional image processing techniques, such as Otsu thresholding and watershed segmentation, do not work effectively on challenging cases, such as chromatin-sparse and crowded nuclei. In contrast, machine learning-based segmentation can generalize across various nuclear appearances. However, training machine learning algorithms requires data sets of images, in which a vast number of nuclei have been annotated. Publicly accessible and annotated data sets, along with widely agreed upon metrics to compare techniques, have catalyzed tremendous innovation and progress on other image classification problems, particularly in object recognition. Inspired by their success, we introduce a large publicly accessible data set of hematoxylin and eosin (H&E)-stained tissue images with more than 21000 painstakingly annotated nuclear boundaries, whose quality was validated by a medical doctor. Because our data set is taken from multiple hospitals and includes a diversity of nuclear appearances from several patients, disease states, and organs, techniques trained on it are likely to generalize well and work right out-of-the-box on other H&E-stained images. We also propose a new metric to evaluate nuclear segmentation results that penalizes object- and pixel-level errors in a unified manner, unlike previous metrics that penalize only one type of error. We also propose a segmentation technique based on deep learning that lays a special emphasis on identifying the nuclear boundaries, including those between the touching or overlapping nuclei, and works well on a diverse set of test images.


title: 4chan and /b/: An Analysis of Anonymity and Ephemerality in a Large Online Community

abstract: We present two studies of online ephemerality and anonymity based on the popular discussion board /b/ at 4chan.org: a website with over 7 million users that plays an influential role in Internet culture. Although researchers and practitioners often assume that user identity and data permanence are central tools in the design of online communities, we explore how /b/ succeeds despite being almost entirely anonymous and extremely ephemeral. We begin by describing /b/ and performing a content analysis that suggests the community is dominated by playful exchanges of images and links. Our first study uses a large dataset of more than five million posts to quantify ephemerality in /b/. We find that most threads spend just five seconds on the first page and less than five minutes on the site before expiring. Our second study is an analysis of identity signals on 4chan, finding that over 90% of posts are made by fully anonymous users, with other identity signals adopted and discarded at will. We describe alternative mechanisms that /b/ participants use to establish status and frame their interactions.


title: Reducing Offline BCI Calibration Effort Using Weighted Adaptation Regularization with Source Domain Selection

abstract: Single-trial classification of Event-Related Potentials (ERPs) is needed in many real-world brain-computer interface (BCI) applications. However, because of individual differences, the classifier needs to be calibrated by using some labeled subject specific training samples, which may be inconvenient to obtain. In this paper we propose a weighted adaptation regularization (wAR) approach for offline BCI calibration, which uses data from other subjects to reduce the amount of labeled data required in offline single-trial classification of ERPs. Our proposed model explicitly handles class-imbalance problems which are common in many real-world BCI applications. War can improve the classification performance, given the same number of labeled subject-specific training samples, or, equivalently, it can reduce the number of labeled subject-specific training samples, given a desired classification accuracy. To reduce the computational cost of wAR, we also propose a source domain selection (SDS) approach. Our experiments show that wARSDS can achieve comparable performance with wAR but is much less computationally intensive. We expect wARSDS to find broad applications in offline BCI calibration.


Music and Dyslexia: A New Musical Training Method to Improve Reading and Related Disorders


Study of Single-Arm Electrode for ECG Measurement Using Flexible Print Circuit


title: Real-time stream processing for Big Data

abstract: Abstract With the rise of the web 2.0 and the Internet of things, it has become feasible to track all kinds of information over time, in particular fine-grained user activities and sensor data on their environment and even their biometrics. However, while efficiency remains mandatory for any application trying to cope with huge amounts of data, only part of the potential of today's Big Data repositories can be exploited using traditional batch-oriented approaches as the value of data often decays quickly and high latency becomes unacceptable in some applications. In the last couple of years, several distributed data processing systems have emerged that deviate from the batch-oriented approach and tackle data items as they arrive, thus acknowledging the growing importance of timeliness and velocity in Big Data analytics. In this article, we give an overview over the state of the art of stream processors for low-latency Big Data analytics and conduct a qualitative comparison of the most popular contenders, namely Storm and its abstraction layer Trident, Samza and Spark Streaming. We describe their respective underlying rationales, the guarantees they provide and discuss the trade-offs that come with selecting one of them for a particular task.


title: Machine learning in protein engineering

abstract: Machine learning-guided protein engineering is a new paradigm that enables the optimization of complex protein functions. Machine-learning methods use data to predict protein function without requiring a detailed model of the underlying physics or biological pathways. They accelerate protein engineering by learning from information contained in all measured variants and using it to select variants that are likely to be improved. In this review, we introduce the steps required to collect protein data, train machine-learning models, and use trained models to guide engineering. We make recommendations at each stage and look to future opportunities for machine learning to enable the discovery of new protein functions and uncover the relationship between protein sequence and function.


title: Job performance prediction in a call center using a naive Bayes classifier

abstract: This study presents an approach to predict the performance of sales agents of a call center dedicated exclusively to sales and telemarketing activities. This approach is based on a naive Bayesian classifier. The objective is to know what levels of the attributes are indicative of individuals who perform well. A sample of 1037 sales agents was taken during the period between March and September of 2009 on campaigns related to insurance sales and service pre-paid phone services, to build the naive Bayes network. It has been shown that, socio-demographic attributes are not suitable for predicting performance. Alternatively, operational records were used to predict production of sales agents, achieving satisfactory results. In this case, the classifier training and testing is done through a stratified tenfold cross-validation. It classified the instances correctly 80.60% of times, with the proportion of false positives of 18.1% for class no (does not achieve minimum) and 20.8% for the class yes (achieves equal or above minimum acceptable). These results suggest that socio-demographic attributes has no predictive power on performance, while the operational information of the activities of the sale agent can predict the future performance of the agent.


title: Short Text Understanding by Leveraging Knowledge into Topic Model

abstract: In this paper, we investigate the challenging task of understanding short text (STU task) by jointly considering topic modeling and knowledge incorporation. Knowledge incorporation can solve the content sparsity problem effectively for topic modeling. Specifically, the phrase topic model is proposed to leverage the auto-mined knowledge, i.e., the phrases, to guide the generative process of short text. Experimental results illustrate the effectiveness of the mechanism that utilizes knowledge to improve topic modeling’s performance.


title: Operation and Control of a Direct-Driven PMSG-Based Wind Turbine System with an Auxiliary Parallel Grid-Side Converter

abstract: In this paper, based on the similarity, in structure and principle, between a grid-connected converter for a direct-driven permanent magnet synchronous generator (D-PMSG) and an active power filter (APF), a new D-PMSG-based wind turbine (WT) system configuration that includes not only an auxiliary converter in parallel with the grid-side converter, but also a coordinated control strategy, is proposed to enhance the low voltage ride through (LVRT) capability and improve power quality. During normal operation, the main grid-side converter maintains the DC-link voltage constant, whereas the auxiliary grid-side converter functions as an APF with harmonic suppression and reactive power compensation to improve the power quality. During grid faults, a hierarchical coordinated control scheme for the generator-side converter, main grid-side converter and auxiliary grid-side converter, depending on the grid voltage sags, is presented to enhance the LVRT capability of the direct-driven PMSG WT. The feasibility and the effectiveness of the proposed system’s topology and hierarchical coordinated control strategy were verified using MATLAB/Simulink simulations.


title: A 1.62 µW 8-channel ultra-high input impedance EEG amplifier for dry and non-contact biopotential recording applications

abstract: In this paper, a low-power electroencephalogram (EEG) analog front-end (AFE) with ultra-high input impedance is presented. This AFE can be utilized for both dry and non-contact biomedical applications due to its very high input impedance along with DC signal suppression. The amplifier achieves an input impedance of 45 ƒF ∥1 TΩ and a common mode rejection ratio (CMRR) higher than 110 dB. The power consumption of the proposed 8-channel AFE and its input referred noise over the 0.5–100 Hz bandwidth are 1.62 µW and 2.62 µV rms , respectively. This yields a noise efficiency factor (NEF) of 3.11. The simulations are carried out in the 180 nm standard CMOS technology using a ± 0.5 V power supply.


title: Saudi Students' Flying the Nest of "Knowledge": Reality and Prospects

abstract: The paper investigates Saudi university students' traditional and predominant learning strategies of "remembering" and the prospects of persuading them to adopt higher learning strategies like "understand", "apply", "analyse", "evaluate", and "create". The study was conducted with 33 fourth year students of the Department of English of King Khalid University, Saudi Arabia, who were never formally trained for critical thinking. The researcher tried to instil critical thinking skills in one of their courses for one semester (42 contact hours) by adopting heuristic teaching method and challenging students' cognitive skills in the tests. Statistical data analysis of their final examination scores confirms the widely acknowledged view that they are very good in memorization. However, after completing the course, they were found to be able to reduce their dependence on "remembering" as they had developed their learning domains of "understand", "apply" and "analyse" if not those of "evaluate" and "create". The paper concludes that if the students' critical thinking skills can be developed to this extent in a stand- alone course in such a short time, a combined and synchronized effort of all the course teachers throughout the students' academic career would be able to develop all higher order cognitive skills, including "evaluate" and "create", in a much better way.


title: Sclera Recognition - A Survey

abstract: This paper presents a survey on sclera-based biometric recognition. Among the various biometric methods, sclera is one of the novel and promising biometric techniques. The sclera, a white region of connective tissue and blood vessels, surrounds the iris. A survey of the techniques available in the area of sclera biometrics will be of great assistance to researchers, and hence a comprehensive effort is made in this article to discuss the advancements reported in this regard during the past few decades. As a limited number of publications are found in the literature, an attempt is made in this paper to increase awareness of this area so that the topic gains popularity and interest among researchers. In this survey, a brief introduction is given initially about the sclera biometric, which is subsequently followed by background concepts, various pre-processing techniques, feature extraction and finally classification techniques associated with the sclera biometric. Benchmarking databases are very important for any pattern recognition related research, so the databases related with this work is also discussed. Finally, our observations, future scope and existing difficulties, which are unsolved in sclera biometrics, are discussed. We hope that this survey will serve to focus more researcher attention towards the emerging sclera biometric.


title: Real-time Upper Body 3 D Pose Estimation from a Single Uncalibrated Camera

abstract: Abstract This paper outlines a method of estimating the 3D pose of the upper human body from a single uncalibratedcamera. The objective application lies in 3D Human Computer Interaction where hand depth information offersextended functionality when interacting with a 3D virtual environment, but it is equally suitable to animation andmotion capture. A database of 3D body conﬁgurations is built from a variety of human movements using motioncapture data. A hierarchical structure consisting of three subsidiary databases, namely the frontal-view HandPosition (top-level), Silhouette and Edge Map Databases, are pre-extracted from the 3D body conﬁguration data-base. Using this hierarchy, subsets of the subsidiary databases are then matched to the subject in real-time. Theexamples of the subsidiary databases that yield the highest matching score are used to extract the corresponding3D conﬁguration from the motion capture data, thereby estimating the upper body 3D pose. Categories and SubjectDescriptors


title: A surgical parallel continuum manipulator with a cable-driven grasper

abstract: In this paper, we present the design, construction, and control of a six-degree-of-freedom (DOF), 12 mm diameter, parallel continuum manipulator with a 2-DOF, cable-driven grasper. This work is a proof-of-concept first step towards miniaturization of this type of manipulator design to provide increased dexterity and stability in confined-space surgical applications, particularly for endoscopic procedures. Our robotic system consists of six superelastic NiTi (Nitinol) tubes in a standard Stewart-Gough configuration and an end effector with 180 degree motion of its two jaws. Two Kevlar cables pass through the centers of the tube legs to actuate the end effector. A computationally efficient inverse kinematics model provides low-level control inputs to ten independent linear actuators, which drive the Stewart-Gough platform and end-effector actuation cables. We demonstrate the performance and feasibility of this design by conducting open-loop range-of-motion tests for our system.


The Real-Time Driver Model and First Applications


title: Learning Cross-Media Joint Representation With Sparse and Semisupervised Regularization

abstract: Cross-media retrieval has become a key problem in both research and application, in which users can search results across all of the media types (text, image, audio, video, and 3-D) by submitting a query of any media type. How to measure the content similarity among different media is the key challenge. Existing cross-media retrieval methods usually focus on modeling the pairwise correlation or semantic information separately. In fact, these two kinds of information are complementary to each other and optimizing them simultaneously can further improve the accuracy. In this paper, we propose a novel feature learning algorithm for cross-media data, called joint representation learning (JRL), which is able to explore jointly the correlation and semantic information in a unified optimization framework. JRL integrates the sparse and semisupervised regularization for different media types into one unified optimization problem, while existing feature learning methods generally focus on a single media type. On one hand, JRL learns sparse projection matrix for different media simultaneously, so different media can align with each other, which is robust to the noise. On the other hand, both the labeled data and unlabeled data of different media types are explored. Unlabeled examples of different media types increase the diversity of training data and boost the performance of joint representation learning. Furthermore, JRL can not only reduce the dimension of the original features, but also incorporate the cross-media correlation into the final representation, which further improves the performance of both cross-media retrieval and single-media retrieval. Experiments on two datasets with up to five media types show the effectiveness of our proposed approach, as compared with the state-of-the-art methods.


title: Studying and modelling dynamic biological processes using time-series gene expression data

abstract: Biological processes are inherently dynamic and therefore capturing data about gene expression at multiple time points can provide valuable insights into biological systems. This Review discusses experimental and analytical considerations for studies of gene expression dynamics, and the possibilities for integration with other data sets.


title: Coded exposure deblurring: Optimized codes for PSF estimation and invertibility

abstract: We consider the problem of single image object motion deblurring from a static camera. It is well-known that deblurring of moving objects using a traditional camera is ill-posed, due to the loss of high spatial frequencies in the captured blurred image. A coded exposure camera modulates the integration pattern of light by opening and closing the shutter within the exposure time using a binary code. The code is chosen to make the resulting point spread function (PSF) invertible, for best deconvolution performance. However, for a successful deconvolution algorithm, PSF estimation is as important as PSF invertibility. We show that PSF estimation is easier if the resulting motion blur is smooth and the optimal code for PSF invertibility could worsen PSF estimation, since it leads to non-smooth blur. We show that both criterions of PSF invertibility and PSF estimation can be simultaneously met, albeit with a slight increase in the deconvolution noise. We propose design rules for a code to have good PSF estimation capability and outline two search criteria for finding the optimal code for a given length. We present theoretical analysis comparing the performance of the proposed code with the code optimized solely for PSF invertibility. We also show how to easily implement coded exposure on a consumer grade machine vision camera with no additional hardware. Real experimental results demonstrate the effectiveness of the proposed codes for motion deblurring.


title: A systematic approach for resource allocation in software projects

abstract: The completion of reliable software products within their expected time frame represents a major problem for companies that develop software applications. Today, the software industry continues to struggle with delivering products in a timely manner. A major cause for delays is the training time required for engineers and other personnel to acquire the necessary skills to complete software tasks. Therefore, it is important to develop systematic personnel assignment processes that consider complete skill sets of candidates to provide solutions that reduce training time. This paper presents a novel methodology to assign resources to tasks when optimum skill sets are not available. The methodology takes into account existing capabilities of candidates, required levels of expertise, and priorities of required skills for the task. A sample case is used to show the model capabilities, and the results are compared with the current resource assignment approach.


title: Coordinate Noun Phrase Disambiguation in a Generative Parsing Model

abstract: In this paper we present methods for improving the disambiguation of noun phrase (NP) coordination within the framework of a lexicalised history-based parsing model. As well as reducing noise in the data, we look at modelling two main sources of information for disambiguation: symmetry in conjunct structure, and the dependency between conjunct lexical heads. Our changes to the baseline model result in an increase in NP coordination dependency f-score from 69.9% to 73.8%, which represents a relative reduction in f-score error of 13%.


title: Reliable Human Detection and Tracking in Top-View Depth Images

abstract: The paper presents a method for human detection and tracking in depth images captured by a top-view camera system. We introduce a new feature descriptor which outperforms state-of-the-art features like Simplified Local Ternary Patterns in the given scenario. We use this feature descriptor to train a head-shoulder detector using a discriminative class scheme. A separate processing step ensures that only a minimal but sufficient number of head-shoulder candidates is evaluated. This contributes to an excellent runtime performance. A final tracking step reliably propagates detections in time and provides stable tracking results. The quality of the presented method allows us to recognize many challenging situations with humans tailgating and piggybacking.


title: Three-dimensional solid texture analysis in biomedical imaging: review and opportunities.

abstract: Three-dimensional computerized characterization of biomedical solid textures is key to large-scale and high-throughput screening of imaging data. Such data increasingly become available in the clinical and research environments with an ever increasing spatial resolution. In this text we exhaustively analyze the state-of-the-art in 3-D biomedical texture analysis to identify the specific needs of the application domains and extract promising trends in image processing algorithms. The geometrical properties of biomedical textures are studied both in their natural space and on digitized lattices. It is found that most of the tissue types have strong multi-scale directional properties, that are well captured by imaging protocols with high resolutions and spherical spatial transfer functions. The information modeled by the various image processing techniques is analyzed and visualized by displaying their 3-D texture primitives. We demonstrate that non-convolutional approaches are expected to provide best results when the size of structures are inferior to five voxels. For larger structures, it is shown that only multi-scale directional convolutional approaches that are non-separable allow for an unbiased modeling of 3-D biomedical textures. With the increase of high-resolution isotropic imaging protocols in clinical routine and research, these models are expected to best leverage the wealth of 3-D biomedical texture analysis in the future. Future research directions and opportunities are proposed to efficiently model personalized image-based phenotypes of normal biomedical tissue and its alterations. The integration of the clinical and genomic context is expected to better explain the intra class variation of healthy biomedical textures. Using texture synthesis, this provides the exciting opportunity to simulate and visualize texture atlases of normal ageing process and disease progression for enhanced treatment planning and clinical care management.


The Established and the Outsiders: A Relational Analysis of Political representations in the trilogy Shrek


title: Multi View Facial Action Unit Detection Based on CNN and BLSTM-RNN

abstract: This paper presents our work in the FG 2017 Facial Expression Recognition and Analysis challenge (FERA 2017) and we participate in the AU occurrence sub-challenge. Our work of AU occurrence recognition is based on deep learning, and we design convolution neural network (CNN) models for two types of work: facial view recognition and AU occurrence recognition. For facial view recognition, our model could achieve 97.7% accuracy on validation dataset about 9 facial views. For AU occurrence recognition, we use both visual features and temporal information of dataset. We use CNN models to get deep visual feature and then use BLSTM-RNN to learn the high-level feature in the time domain. When training models, we divide dataset into 9 parts based on 9 facial views, and each model is trained in a specific view. When recognizing AUs, we recognize facial view first and then choose the corresponding model for AU occurrence recognition. Finally, our method shows good performance, the F1 score of test data is 0.507 and the accuracy is 0.735.


title: Expansion of C9ORF72 in amyotrophic lateral sclerosis correlates with brain-computer interface performance

abstract: Abnormal expansion of hexanucleotide GGGGCC (G4C2) in the C9ORF72 gene has been associated with multiple neurodegenerative disorders, with particularly high prevalence in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Repeat expansions of this type have been associated with altered pathology, symptom rate and severity, as well as psychological changes. In this study, we enrolled twenty-five patients with ALS and fifteen neurologically healthy controls in a P300 brain-computer interface (BCI) training procedure. Four of the patients were found to possess an expanded allele, which was associated with a reduction in the quality of evoked potentials that led to reduced performance on the BCI task. Our findings warrant further exploration of the relationship between brain function and G4C2 repeat length. Such a relationship suggests that personalized assessment of suitability of BCI as a communication device in patients with ALS may be feasible.


title: A 10-b 320-MS/s Stage-Gain-Error Self-Calibration Pipeline ADC

abstract: A 10-b 320-MS/s pipeline analog-to-digital converter (ADC) with low dc gain opamps, as low as 30.6 dB based on simulations, in its multiplying digital-to-analog converters (MDACs) is presented. A foreground self-calibration technique is proposed to reduce stage gain error by adjusting feedback factor with a calibration capacitor array. The prototype in 90-nm low-power CMOS technology achieves conversion rate of 320 MS/s with peak SFDR and SNDR of 66.7 and 54.2 dB, respectively. The total power dissipation is 42 mW, and it occupies an active chip area of 0.21 mm2 including the calibration circuit. It results in a figure-of-merit (FOM) of 442 fJ/conversion-step. Only 168 clock cycles are used, and no external precise reference sources are needed for the calibration.


title: A Fully Integrated 1 . 9-GHz CMOS Low-Noise Amplifier

abstract: A fully integrated 1.9 GHz CMOS low-noise amplifer (LNA) has been implemented in a 0.8 /spl mu/m CMOS technology. For low-noise performance, the amplifier employs high-quality spiral inductors with a duality factor of 8.5-12.5, and device layout and bias condition of the active devices were optimized for low-noise conditions. This amplifier showed a noise figure of 2.8 dB with a forward gain of 15 dB at current consumption of 15 mA. To the authors' knowledge, this represents the lowest noise figure reported to date for a fully integrated CMOS LNA operating at 1.9 GHz.


title: Sub-A resolution electron density analysis of the surface of organic rubrene crystals.

abstract: The electron density distribution of an organic semiconductor is observed as a function of the depth from the crystal surface or interface. The surface x-ray scattering technique combined with a recently developed analyzing technique, coherent Bragg rod analysis, enables us to observe the electron density profile. The obtained near-surface electron density profile of a single crystal of rubrene, which is known as a high-mobility organic transistor material, shows not only a large positional distribution of the molecules at the surface, but also a sub-A molecular deformation that affects the molecular orbital.


title: ESD Protection Design With On-Chip ESD Bus and High-Voltage-Tolerant ESD Clamp Circuit for Mixed-Voltage I/O Buffers

abstract: Considering gate-oxide reliability, a new electrostatic discharge (ESD) protection scheme with an on-chip ESD bus (ESD_BUS) and a high-voltage-tolerant ESD clamp circuit for 1.2/2.5 V mixed-voltage I/O interfaces is proposed. The devices used in the high-voltage-tolerant ESD clamp circuit are all 1.2 V low-voltage N- and P-type MOS devices that can be safely operated under the 2.5-V bias conditions without suffering from the gate-oxide reliability issue. The four-mode (positive-to-VSS, negative-to-VSS, positive-to-VDD, and negative-to-VDD) ESD stresses on the mixed-voltage I/O pad and pin-to-pin ESD stresses can be effectively discharged by the proposed ESD protection scheme. The experimental results verified in a 0.13-mum CMOS process have confirmed that the proposed new ESD protection scheme has high human-body model (HBM) and machine-model (MM) ESD robustness with a fast turn-on speed. The proposed new ESD protection scheme, which is designed with only low- voltage devices, is an excellent and cost-efficient solution to protect mixed-voltage I/O interfaces.


title: Implications of a Public Smoking

abstract: OBJECTIVES ::: Legislation to ban smoking in public places is currently a major area of interest across Canada. The main objectives of the study were to 1) determine the effect of the smoking ban on incidence of acute myocardial infarction, 2) determine if the new legislation altered population-based smoking prevalence, and 3) measure public support for the public smoking ban. ::: ::: ::: METHODS ::: The city of Saskatoon initiated a public smoking ban on July 1, 2004. We retrospectively reviewed all hospital discharges for acute MI from July 2000 to June 2005. We reviewed CCHS survey information on smoking prevalence for Saskatoon, Saskatchewan and Canada from 2003 to 2005. We prospectively contacted 1,255 Saskatoon residents by telephone to determine support for the public smoking ban. ::: ::: ::: RESULTS ::: The age-standardized incidence rate of acute MI fell from 176.1 (95% CI 165.3-186.8) cases per 100,000 population (July 1, 2000 to June 30, 2004) to 152.4 (95% CI 135.3-169.3) cases per 100,000 population (July 1, 2004 to June 30, 2005). Smoking prevalence in Saskatoon fell from 24.1% in 2003 (95% CI 20.4-27.7) to 18.2% in 2005 (95% CI 15.7-20.9) while smoking prevalence in Saskatchewan remained unchanged at 23.8% (95% CI 22.6-25.3) and Canada reduced from 22.9% (95% CI 22.5-23.3) to 21.3% (95% CI 20.8-21.8). Seventy-nine percent of Saskatoon residents believed the smoking ban was a good idea. ::: ::: ::: INTERPRETATION ::: The public smoking ban in Saskatoon, Canada, is associated with reduced incidence rates of acute MI, lower smoking prevalence and high levels of public support.


title: Data-driven modeling for boiling heat transfer: using deep neural networks and high-fidelity simulation results

abstract: Boiling heat transfer occurs in many situations and can be used for thermal management in various engineered systems with high energy density, from power electronics to heat exchangers in power plants and nuclear reactors. Essentially, boiling is a complex physical process that involves interactions between heating surface, liquid, and vapor. For engineering applications, the boiling heat transfer is usually predicted by empirical correlations or semi-empirical models, which has relatively large uncertainty. In this paper, a data-driven approach based on deep feedforward neural networks is studied. The proposed networks use near wall local features to predict the boiling heat transfer. The inputs of networks include the local momentum and energy convective transport, pressure gradients, turbulent viscosity, and surface information. The outputs of the networks are the quantities of interest of a typical boiling system, including heat transfer components, wall superheat, and near wall void fraction. The networks are trained by the high-fidelity data processed from first principle simulation of pool boiling under varying input heat fluxes. State-of-the-art algorithms are applied to prevent the overfitting issue when training the deep networks. The trained networks are tested in interpolation cases and extrapolation cases which both demonstrate good agreement with the original high-fidelity simulation results.


title: Smart city technologies and architectures: A literature review

abstract: The main goal of smart cities is to improve the traditional services that are provided to the citizens and also create new and more challenging ones. This vision aims not only to citizens' prosperity, but also to economic progress and sustainability of the city. It is feasible to achieve this goal through the use of technologies and architectures which purpose is to integrate the various elements of the city and help them interact in an effective manner. In this paper, we discuss the key technologies and architectures that have already been proposed in the literature in order to find the appropriate ones to be implemented for the development of smart cities.


Heterogeneous agent models in economics and finance


title: A visual tracker based on improved kernel correlation filter

abstract: Recently, Kernel Correlation Filter (KCF) is introduced into visual tracking, which has shown to provide the highest speed among the top ten trackers. However, the KCF tracker could not perfectly handle the occlusion during target's moving. Leading to the failure of tracking. A robust occlusion detection scheme was proposed based on the KCF framework. When the object was under partially and fully occlusion, the kalman filter was used to predict the state information and stop the classifier model to update at the same time, which improved the ability of occlusion handling. The experimental results on comparison with some state-of-the-art trackers such as Struck, KCF, TLD and MIL demonstrate that the proposed method could deal with the occlusion without any additional complex computing consumption.


title: X-ray diffraction, electron microscopy, and Fourier transform infrared spectroscopy of apatite crystals isolated from chicken and bovine calcified cartilage

abstract: Apatite crystals of the calcified zone of the subarticular cartilaginous growth plates of the long bones of young growing chickens and calves were isolated by low temperature reaction with hydrazine and plasma ashing and examined by electron microscopy, electron diffraction and microprobe analysis, and computer-generated deconvolution of the spectra obtained by Fourier transform infrared spectroscopy. The crystal habit was that of wide, very thin, relatively long rectangular plates, which tended to form small clusters of crystals, possibly because reaction with hydrazine alone did not remove all of the organic matrix constituents. Further reaction with low power plasma ashing released more of the isolated crystals although to a lesser extent than was possible with bone. Stereograms of the small clusters showed that many of the crystals in the small isolated aggregates of crystals were bent and/or curved. Together with the resultant overlap of individual adjacent crystals, they also produced images of sharp, very dense lines, reminiscent of the electron-dense needle or rod-like appearances frequently observed by transmission electron microscopy of thin sections of calcified cartilage and thought to represent the habit of the apatite crystals. No true rod or needle-like crystals were observed in the isolated crystals. Although the overall general apatitic structure of the apatite crystals was similar to that of the apatitic crystals of bone, the individual crystals were significantly larger than those of bone from the same specimen, and there were small but significant differences in the concentrations of acid phosphate and carbonate groups and in their short range order.


title: Pest Control Expert System for Tomato (PCEST)

abstract: This paper presents a real-life pest control expert system for tomato. The system involves two main subtasks, namely: ‘diagnose’ and ‘treat’. The ‘diagnose’ subtask finds out the causes of the growers' complaints, while the ‘treat’ subtask finds out a treatment plan for these causes. CommonKADS methodology has been used to develop the system. Dependency network is used as one of our knowledge representation schemes in both subtasks. An expert system evaluation methodology has been suggested and applied to the developed system.


title: Speech Recognition using SVMs

abstract: An important issue in applying SVMs to speech recognition is the ability to classify variable length sequences. This paper presents extensions to a standard scheme for handling this variable length data, the Fisher score. A more useful mapping is introduced based on the likelihood-ratio. The score-space defined by this mapping avoids some limitations of the Fisher score. Class-conditional generative models are directly incorporated into the definition of the score-space. The mapping, and appropriate normalisation schemes, are evaluated on a speaker-independent isolated letter task where the new mapping outperforms both the Fisher score and HMMs trained to maximise likelihood.


title: A BLAS-3 version of the QR factorization with column pivoting

abstract: The QR factorization with column pivoting (QRP), originally suggested by Golub [Numer. Math., 7 (1965), 206--216], is a popular approach to computing rank-revealing factorizations. Using Level 1 BLAS, it was implemented in LINPACK, and, using Level 2 BLAS, in LAPACK. While the Level 2 BLAS version delivers superior performance in general, it may result in worse performance for large matrix sizes due to cache effects. We introduce a modification of the QRP algorithm which allows the use of Level 3 BLAS kernels while maintaining the numerical behavior of the LINPACK and LAPACK implementations. Experimental comparisons of this approach with the LINPACK and LAPACK implementations on IBM RS/6000, SGI R8000, and DEC AXP platforms show considerable performance improvements.


title: Epileptiform spike detection via convolutional neural networks

abstract: The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated fashion. The CNN has a convolutional architecture with filters of various sizes applied to the input layer, leaky ReLUs as activation functions, and a sigmoid output layer. Balanced mini-batches were applied to handle the imbalance in the data set. Leave-one-patient-out cross-validation was carried out to test the CNN and benchmark models on EEG data of five epilepsy patients. We achieved 0.947 AUC for the CNN, while the best performing benchmark model, Support Vector Machines with Gaussian kernel, achieved an AUC of 0.912.


title: Energy-Saving Offloading by Jointly Allocating Radio and Computational Resources for Mobile Edge Computing

abstract: Mobile edge computing (MEC) providing information technology and cloud-computing capabilities within the radio access network is an emerging technique in fifth-generation networks. MEC can extend the computational capacity of smart mobile devices (SMDs) and economize SMDs’ energy consumption by migrating the computation-intensive task to the MEC server. In this paper, we consider a multi-mobile-users MEC system, where multiple SMDs ask for computation offloading to a MEC server. In order to minimize the energy consumption on SMDs, we jointly optimize the offloading selection, radio resource allocation, and computational resource allocation coordinately. We formulate the energy consumption minimization problem as a mixed interger nonlinear programming (MINLP) problem, which is subject to specific application latency constraints. In order to solve the problem, we propose a reformulation-linearization-technique-based Branch-and-Bound (RLTBB) method, which can obtain the optimal result or a suboptimal result by setting the solving accuracy. Considering the complexity of RTLBB cannot be guaranteed, we further design a Gini coefficient-based greedy heuristic (GCGH) to solve the MINLP problem in polynomial complexity by degrading the MINLP problem into the convex problem. Many simulation results demonstrate the energy saving enhancements of RLTBB and GCGH.


title: A quantitative approach for evaluating software maintenance services

abstract: The ever-increasing representativeness of software maintenance in the daily effort of software team requires initiatives for enhancing the activities accomplished to provide a good service for users who request a software improvement. This article presents a quantitative approach for evaluating software maintenance services based on cluster analysis techniques. The proposed approach provides a compact characterization of the services delivered by a maintenance organization, including characteristics such as service, waiting, and queue time. The ultimate goal is to help organizations to better understand, manage, and improve their current software maintenance process. We also report in this paper the usage of the proposed approach in a medium-sized organization throughout 2010. This case study shows that 72 software maintenance requests can be grouped in seven distinct clusters containing requests with similar characteristics. The in-depth analysis of the clusters found with our approach can foster the understanding of the nature of the requests and, consequently, it may improve the process followed by the software maintenance team.


title: Autonomously Generating Hints by Inferring Problem Solving Policies

abstract: Exploring the whole sequence of steps a student takes to produce work, and the patterns that emerge from thousands of such sequences is fertile ground for a richer understanding of learning. In this paper we autonomously generate hints for the Code.org `Hour of Code,' (which is to the best of our knowledge the largest online course to date) using historical student data. We first develop a family of algorithms that can predict the way an expert teacher would encourage a student to make forward progress. Such predictions can form the basis for effective hint generation systems. The algorithms are more accurate than current state-of-the-art methods at recreating expert suggestions, are easy to implement and scale well. We then show that the same framework which motivated the hint generating algorithms suggests a sequence-based statistic that can be measured for each learner. We discover that this statistic is highly predictive of a student's future success.


title: Dynamic-Hash-Table Based Public Auditing for Secure Cloud Storage

abstract: Cloud storage is an increasingly popular application of cloud computing, which can provide on-demand outsourcing data services for both organizations and individuals. However, users may not fully trust the cloud service providers (CSPs) in that it is difficult to determine whether the CSPs meet their legal expectations for data security. Therefore, it is critical to develop efficient auditing techniques to strengthen data owners’ trust and confidence in cloud storage. In this paper, we present a novel public auditing scheme for secure cloud storage based on dynamic hash table (DHT), which is a new two-dimensional data structure located at a third parity auditor (TPA) to record the data property information for dynamic auditing. Differing from the existing works, the proposed scheme migrates the authorized information from the CSP to the TPA, and thereby significantly reduces the computational cost and communication overhead. Meanwhile, exploiting the structural advantages of the DHT, our scheme can also achieve higher updating efficiency than the state-of-the-art schemes. In addition, we extend our scheme to support privacy preservation by combining the homomorphic authenticator based on the public key with the random masking generated by the TPA, and achieve batch auditing by employing the aggregate BLS signature technique. We formally prove the security of the proposed scheme, and evaluate the auditing performance by detailed experiments and comparisons with the existing ones. The results demonstrate that the proposed scheme can effectively achieve secure auditing for cloud storage, and outperforms the previous schemes in computation complexity, storage costs and communication overhead.


title: Complex Adaptive Systems: An Introduction to Computational Models of Social Life: An Introduction to Computational Models of Social Life

abstract: Theoretical physics is replete with models. When equations of motion are not available, or not amenable to perturbation theory, or just too hard from which to extract useful information, then physicists turn to models and computation. The Ising model of ferromagnetism is a classic example. A simple nearest neighbor temperature dependent interaction, in two or more dimensions, leads to long-range order and a phase transition at a finite temperature. This model has many locally interacting parts and an emergent behavior (ferromagnetism) at a critical temperature. However, the system never adapts. It does not change the phase transition to a higher temperature or avoid a phase transition altogether. Social systems are always adapting, and this interesting twist produces a vast array of possibilities and forms the basis of much of the discussion in Miller and Page’s book. This book is not a textbook, but rather an essay on complex adaptive systems. The discussions and insights will be better appreciated by readers who have already tried their hand at investigating complex adaptive systems. These systems can be so complex that the best method to discover their properties is to dispatch many computer agents to experience the system’s possibilities. The study becomes more interesting when the agents can alter their actions and the rules of the game. Miller and Page give the simple, but instructive example of forest growth and lightning induced forest fires. If trees grow too rapidly they will cover the allowable space and a fire started anywhere in the forest will spread and destroy the entire forest. A very slow growth will only produce a sparse forest. The authors find a tree growth rate to achieve an optimal stable high forest coverage. Their solution is trumped when altruistic agents are introduced, one for each tree. Some of the agents adapt by not growing a tree in their plot of land (to their personal disadvantage) but the overall global organization is one of firebreaks preventing large scale fires. Adaptation wins! Another model discussed is what physicists call the minority game, that is, making a choice that puts you in the minority. This is perhaps best known through the El Faro example of choosing whether or not to go to Santa Fe’s El Faro bar tonight based on whether it was


title: A shortest augmenting path algorithm for dense and sparse linear assignment problems

abstract: We develop a shortest augmenting path algorithm for the linear assignment problem. It contains new initialization routines and a special implementation of Dijkstra's shortest path method. For both dense and sparse problems computational experiments show this algorithm to be uniformly faster than the best algorithms from the literature. A Pascal implementation is presented.


title: Support Vector Machine Active Learning with Applications to Text Classification

abstract: Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using pool-based active learning. Instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels for some number of them. We introduce a new algorithm for performing active learning with support vector machines, i.e., an algorithm for choosing which instances to request next. We provide a theoretical motivation for the algorithm using the notion of a version space. We present experimental results showing that employing our active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.


title: Defeating Software Mitigations Against Rowhammer: A Surgical Precision Hammer

abstract: With software becoming harder to compromise due to modern defenses, attackers are increasingly looking at exploiting hardware vulnerabilities such as Rowhammer. In response, the research community has developed several software defenses to protect existing hardware against this threat. In this paper, we show that the assumptions existing software defenses make about memory addressing are inaccurate. Specifically, we show that physical address space is often not contiguously mapped to DRAM address space, allowing attackers to trigger Rowhammer corruptions despite active software defenses. We develop RAMSES, a software library modeling end-to-end memory addressing, relying on public documentation, where available, and reverse-engineered models otherwise. RAMSES improves existing software-only Rowhammer defenses and also improves attacks by orders of magnitude, as we show in our evaluation. We use RAMSES to build Hammertime, an open-source suite of tools for studying Rowhammer properties affecting attacks and defenses, which we release as open-source software.


title: An easy-to-use evaluation framework for benchmarking entity recognition and disambiguation systems

abstract: Entity recognition and disambiguation (ERD) is a crucial technique for knowledge base population and information extraction. In recent years, numerous papers have been published on this subject, and various ERD systems have been developed. However, there are still some confusions over the ERD field for a fair and complete comparison of these systems. Therefore, it is of emerging interest to develop a unified evaluation framework. In this paper, we present an easy-to-use evaluation framework (EUEF), which aims at facilitating the evaluation process and giving a fair comparison of ERD systems. EUEF is well designed and released to the public as an open source, and thus could be easily extended with novel ERD systems, datasets, and evaluation metrics. It is easy to discover the advantages and disadvantages of a specific ERD system and its components based on EUEF. We perform a comparison of several popular and publicly available ERD systems by using EUEF, and draw some interesting conclusions after a detailed analysis.


title: Perspectives on Live Streaming: Apps, Users, and Research

abstract: The emergence of mobile live streaming as a popular app over the past couple years provides a new platform for socio-technical interaction in the world. This panel brings together several different perspectives on live streaming: companies who are producing commercial apps, researchers studying how people are using live streaming, and users. We hope the discussion identifies design and research implications for the future.


title: An empirical investigation of employee portal success

abstract: Employee portals are utilized by many companies to improve companies' information exchange, communication, and employee collaboration, as well as to better support their business processes. Owing to limited IT budgets and the need to justify investments in employee portals, assessing the benefits of these is an important field in research and practice. Thus, the purpose of this study is to gain a better understanding of employee portal success. We introduce a theoretical model for this that is based on the DeLone and McLean IS Success Model. Furthermore, we develop hypotheses regarding the associations between different models' success dimensions and test them using more than 10,000 employees' responses collected in a survey of 22 companies. Our results indicate that besides the factors contributing to IS success in general, other success dimensions - like the quality of the collaboration and process support - have to be considered when aiming for a successful employee portal. The study's findings make it possible for practitioners to understand the levers with which to improve their employee portals. By empirically validating a comprehensive success model for employee portals, the study's results advance theoretical development in the area of collaboration-centered systems and present a basis for further research in this field.


title: Learning Local Feature Descriptors Using Convex Optimisation

abstract: The objective of this work is to learn descriptors suitable for the sparse feature detectors used in viewpoint invariant matching. We make a number of novel contributions towards this goal. First, it is shown that learning the pooling regions for the descriptor can be formulated as a convex optimisation problem selecting the regions using sparsity. Second, it is shown that descriptor dimensionality reduction can also be formulated as a convex optimisation problem, using Mahalanobis matrix nuclear norm regularisation. Both formulations are based on discriminative large margin learning constraints. As the third contribution, we evaluate the performance of the compressed descriptors, obtained from the learnt real-valued descriptors by binarisation. Finally, we propose an extension of our learning formulations to a weakly supervised case, which allows us to learn the descriptors from unannotated image collections. It is demonstrated that the new learning methods improve over the state of the art in descriptor learning on the annotated local patches data set of Brown et al. and unannotated photo collections of Philbin et al. .


title: Measuring the Latency of Depression Detection in Social Media

abstract: Detecting depression is a key public health challenge, as almost 12% of all disabilities can be attributed to depression. Computational models for depression detection must prove not only that can they detect depression, but that they can do it early enough for an intervention to be plausible. However, current evaluations of depression detection are poor at measuring model latency. We identify several issues with the currently popular ERDE metric, and propose a latency-weighted F1 metric that addresses these concerns. We then apply this evaluation to several models from the recent eRisk 2017 shared task on depression detection, and show how our proposed measure can better capture system differences.


title: Content-adaptive image downscaling

abstract: This paper introduces a novel content-adaptive image downscaling method. The key idea is to optimize the shape and locations of the downsampling kernels to better align with local image features. Our content-adaptive kernels are formed as a bilateral combination of two Gaussian kernels defined over space and color, respectively. This yields a continuum ranging from smoothing to edge/detail preserving kernels driven by image content. We optimize these kernels to represent the input image well, by finding an output image from which the input can be well reconstructed. This is technically realized as an iterative maximum-likelihood optimization using a constrained variation of the Expectation-Maximization algorithm. In comparison to previous downscaling algorithms, our results remain crisper without suffering from ringing artifacts. Besides natural images, our algorithm is also effective for creating pixel art images from vector graphics inputs, due to its ability to keep linear features sharp and connected.


title: Avoiding spectral efficiency loss in unipolar OFDM for optical wireless communication

abstract: Unipolar orthogonal frequency division multiplexing (U-OFDM) has recently been introduced for intensity modulation and direct detection (IM/DD) systems. The scheme achieves higher power efficiency than the conventional direct-current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) at the expense of half the spectral efficiency for the same M-ary quadrature amplitude modulation (M-QAM) order. This paper presents a modulation approach which doubles the spectral efficiency of U-OFDM and still allows it to achieve better performance in terms of both electrical power and optical power dissipation compared to DCO-OFDM. The simulation results and the theoretical analysis suggest that the performance improvement of the proposed scheme over DCO-OFDM increases with the modulation order. This trend is different from the inherently unipolar state-of-the-art techniques such as U-OFDM, asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) and pulse-amplitude-modulated discrete multitone modulation (PAM-DMT). It is typical for these schemes to exhibit a loss in the power efficiency as the spectral efficiency is increased. The novel approach is very promising for the achievement of high data rates in IM/DD systems. To the best of the authors' knowledge, this is the first design of a strictly unipolar orthogonal frequency division multiplexing (OFDM) scheme which requires no biasing and is able to demonstrate significant energy advantage over DCO-OFDM without sacrificing spectral efficiency.


title: Multiphysical lithium-based battery pack modeling for simulation purposes

abstract: Hybrid and electric vehicle simulation requires battery models capable of predicting important macroscopic quantities such as state-of-charge, temperature and voltage. This paper presents a multiphysical battery pack model and describes the test procedure that must be followed to identify the different model parameters. Two high-energy density cells types have been modeled with this method and a comparison between measured and simulated values has been carried out. The model shows an error below than 3% compared to experimental results.


The 800 Pound Python in the Machine Learning Room


title: Oxytocin Modulates Neural Circuitry for Social Cognition and Fear in Humans

abstract: In non-human mammals, the neuropeptide oxytocin is a key mediator of complex emotional and social behaviors, including attachment, social recognition, and aggression. Oxytocin reduces anxiety and impacts on fear conditioning and extinction. Recently, oxytocin administration in humans was shown to increase trust, suggesting involvement of the amygdala, a central component of the neurocircuitry of fear and social cognition that has been linked to trust and highly expresses oxytocin receptors in many mammals. However, no human data on the effects of this peptide on brain function were available. Here, we show that human amygdala function is strongly modulated by oxytocin. We used functional magnetic resonance imaging to image amygdala activation by fear-inducing visual stimuli in 15 healthy males after double-blind crossover intranasal application of placebo or oxytocin. Compared with placebo, oxytocin potently reduced activation of the amygdala and reduced coupling of the amygdala to brainstem regions implicated in autonomic and behavioral manifestations of fear. Our results indicate a neural mechanism for the effects of oxytocin in social cognition in the human brain and provide a methodology and rationale for exploring therapeutic strategies in disorders in which abnormal amygdala function has been implicated, such as social phobia or autism.


title: Cross-language context-aware citation recommendation in scientific articles

abstract: Adequacy of citations is very important for a scientific paper. However, it is not an easy job to find appropriate citations for a given context, especially for citations in different languages. In this paper, we define a novel task of cross-language context-aware citation recommendation, which aims at recommending English citations for a given context of the place where a citation is made in a Chinese paper. This task is very challenging because the contexts and citations are written in different languages and there exists a language gap when matching them. To tackle this problem, we propose the bilingual context-citation embedding algorithm (i.e. BLSRec-I), which can learn a low-dimensional joint embedding space for both contexts and citations. Moreover, two advanced algorithms named BLSRec-II and BLSRec-III are proposed by enhancing BLSRec-I with translation results and abstract information, respectively. We evaluate the proposed methods based on a real dataset that contains Chinese contexts and English citations. The results demonstrate that our proposed algorithms can outperform a few baselines and the BLSRec-II and BLSRec-III methods can outperform the BLSRec-I method.


How much of driving is pre-attentive ?


title: Efficient Rotation-Scaling-Translation Parameters Estimation in Multimodal Settings Based on Fractal Image Model

abstract: This paper deals with area-based subpixel image registration under the rotation-isometric scaling-translation transformation hypothesis. Our approach is based on parametrical modeling of geometrically transformed textural image fragments and maximum-likelihood estimation of the transformation vector between them. Due to the parametrical approach based on the fractional Brownian motion modeling of the local fragments' texture, the proposed estimator $\mbox{ML}_\mathrm{fBm}$ (ML stands for “maximum likelihood” and fBm stands for “fractal Brownian motion”) has the ability to better adapt to real image texture content compared with other methods relying on universal similarity measures such as mutual information or normalized correlation. The main benefits are observed when assumptions underlying the fBm model are fully satisfied, e.g., for isotropic normally distributed textures with stationary increments. Experiments on both simulated and real images and for high and weak correlations between registered images show that the $\mbox{ML}_\mathrm{fBm}$ estimator offers significant improvement compared with other state-of-the-art methods. It reduces translation vector, rotation angle, and scaling factor estimation errors by a factor of about 1.75–2, and it decreases the probability of false match by up to five times. In addition, an accurate confidence interval for $\mbox{ML}_\mathrm{fBm}$ estimates can be obtained from the Cramer–Rao lower bound on rotation-scaling-translation parameter estimation error. This bound depends on texture roughness, noise level in reference and template images, correlation between these images, and geometrical transformation parameters.


title: Minimally invasive treatment of pancreatic pseudocysts

abstract: A pancreatic pseudocyst (PPC) is typically a complication of acute and chronic pancreatitis, trauma or pancreatic duct obstruction. The diagnosis of PPC can be made if an acute fluid collection persists for 4 to 6 wk and is enveloped by a distinct wall. Most PPCs regress spontaneously and require no treatment, whereas some may persist and progress until complications occur. The decision whether to treat a patient who has a PPC, as well as when and with what treatment modalities, is a difficult one. PPCs can be treated with a variety of methods: percutaneous catheter drainage (PCD), endoscopic transpapillary or transmural drainage, laparoscopic surgery, or open pseudocystoenterostomy. The recent trend in the management of symptomatic PPC has moved toward less invasive approaches such as endoscopic- and image-guided PCD. The endoscopic approach is suitable because most PPCs lie adjacent to the stomach. The major advantage of the endoscopic approach is that it creates a permanent pseudocysto-gastric track with no spillage of pancreatic enzymes. However, given the drainage problems, the monitoring, catheter manipulation and the analysis of cystic content are very difficult or impossible to perform endoscopically, unlike in the PCD approach. Several conditions must be met to achieve the complete obliteration of the cyst cavity. ::: ::: Pancreatic duct anatomy is an important factor in the prognosis of the treatment outcome, and the recovery of disrupted pancreatic ducts is the main prognostic factor for successful treatment of PPC, regardless of the treatment method used. In this article, we review and evaluate the minimally invasive approaches in the management of PPCs.


title: Article Bounded Memory, Inertia, Sampling and Weighting Model for Market Entry Games

abstract: This paper describes the “Bounded Memory, Inertia, Sampling and Weighting” (BI-SAW) model, which won the http://sites.google.com/site/gpredcomp/Market Entry Prediction Competition in 2010. The BI-SAW model refines the I-SAW Model (Erev et al. [1]) by adding the assumption of limited memory span. In particular, we assume when players draw a small sample to weight against the average payoff of all past experience, they can only recall 6 trials of past experience. On the other hand, we keep all other key features of the I-SAW model: (1) Reliance on a small sample of past experiences, (2) Strong inertia and recency effects, and (3) Surprise triggers change. We estimate this model using the first set of experimental results run by the competition organizers, and use it to predict results of a second set of similar experiments later ran by the organizers. We find significant improvement in out-of-sample predictability (against the I-SAW model) in terms of smaller mean normalized MSD, and such result is robust to resampling the predicted game set and reversing the role of the sets of experimental results. Our model’s performance is the best among all the participants.


Gaze Estimation in the 3 D Space Using RGB-D sensors Towards Head-Pose And User Invariance


title: An introduction to total variation for image analysis

abstract: These are the lecture notes of a course taught in Linz in Sept., 2009, at the school "summer school on sparsity", organized by Massimo Fornasier and Ronny Romlau. They address various theoretical and practical topics related to Total Variation-based image reconstruction. They focu first on some theoretical results on functions which minimize the total variation, and in a second part, describe a few standard and less standard algorithms to minimize the total variation in a finite-differences setting, with a series of applications from simple denoising to stereo, or deconvolution issues, and even more exotic uses like the minimization of minimal partition problems.


title: A Large-Scale Hidden Semi-Markov Model for Anomaly Detection on User Browsing Behaviors

abstract: Many methods designed to create defenses against distributed denial of service (DDoS) attacks are focused on the IP and TCP layers instead of the high layer. They are not suitable for handling the new type of attack which is based on the application layer. In this paper, we introduce a new scheme to achieve early attack detection and filtering for the application-layer-based DDoS attack. An extended hidden semi-Markov model is proposed to describe the browsing behaviors of web surfers. In order to reduce the computational amount introduced by the model's large state space, a novel forward algorithm is derived for the online implementation of the model based on the M-algorithm. Entropy of the user's HTTP request sequence fitting to the model is used as a criterion to measure the user's normality. Finally, experiments are conducted to validate our model and algorithm.


title: BSPAT: a fast online tool for DNA methylation co-occurrence pattern analysis based on high-throughput bisulfite sequencing data

abstract: BACKGROUND ::: Bisulfite sequencing is one of the most widely used technologies in analyzing DNA methylation patterns, which are important in understanding and characterizing the mechanism of DNA methylation and its functions in disease development. Efficient and user-friendly tools are critical in carrying out such analysis on high-throughput bisulfite sequencing data. However, existing tools are either not scalable well, or inadequate in providing visualization and other desirable functionalities. ::: ::: ::: RESULTS ::: In order to handle ultra large sequencing data and to provide additional functions and features, we have developed BSPAT, a fast online tool for bisulfite sequencing pattern analysis. With a user-friendly web interface, BSPAT seamlessly integrates read mapping/quality control/methylation calling with methylation pattern generation and visualization. BSPAT has the following important features: 1) instead of using multiple/pairwise sequence alignment methods, BSPAT adopts an efficient and widely used sequence mapping tool to provide fast alignment of sequence reads; 2) BSPAT summarizes and visualizes DNA methylation co-occurrence patterns at a single nucleotide level, which provide valuable information in understanding the mechanism and regulation of DNA methylation; 3) based on methylation co-occurrence patterns, BSPAT can automatically detect potential allele-specific methylation (ASM) patterns, which can greatly enhance the detection and analysis of ASM patterns; 4) by linking directly with other popular databases and tools, BSPAT allows users to perform integrative analysis of methylation patterns with other genomic features together within regions of interest. ::: ::: ::: CONCLUSION ::: By utilizing a real bisulfite sequencing dataset generated from prostate cancer cell lines, we have shown that BSPAT is highly efficient. It has also reported some interesting methylation co-occurrence patterns and a potential allele-specific methylation case. In conclusion, BSPAT is an efficient and convenient tool for high-throughput bisulfite sequencing data analysis that can be broadly used.


title: weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming

abstract: Selective weed treatment is a critical step in autonomous crop management as related to crop health and yield. However, a key challenge is reliable and accurate weed detection to minimize damage to surrounding plants. In this letter, we present an approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV). We use the recently developed encoder–decoder cascaded convolutional neural network, SegNet, that infers dense semantic classes while allowing any number of input image channels and class balancing with our sugar beet and weed datasets. To obtain training datasets, we established an experimental field with varying herbicide levels resulting in field plots containing only either crop or weed, enabling us to use the normalized difference vegetation index as a distinguishable feature for automatic ground truth generation. We train six models with different numbers of input channels and condition (fine tune) it to achieve $\sim$ 0.8 F1-score and 0.78 area under the curve classification metrics. For the model deployment, an embedded Graphics Processing Unit (GPU) system (Jetson TX2) is tested for MAV integration. Dataset used in this letter is released to support the community and future work.


title: Beyond Knowledge Distillation: Collaborative Learning for Bidirectional Model Assistance

abstract: Knowledge distillation (KD) is a powerful technique that enables a well-trained large model to assist a small model. However, KD is constrained in a teacher-student manner. Thus, this method may not be appropriate in general situations, where the learning abilities of two models are uncertain or not significantly different. In this paper, we propose a collaborative learning (CL) method, which is a flexible strategy to achieve bidirectional model assistance for two models using a mutual knowledge base (MKB). The MKB is used to collect mutual information and provide assistance, and it is updated along with the learning process of the two models and separately deployed when converged. We show that CL can be applied to any two deep neural networks and is easily extended to multiple networks. Compared with the teacher-student framework, CL can achieve bidirectional assistance and does not impose specific requirements on the involved models, such as pretraining and different abilities. The experimental results demonstrate that CL can efficiently improve the learning ability and convergence speed of the two models, with superior performance to a series of relevant methods, such as ensemble learning and a series of KD-based methods. More importantly, we show that the state-of-the-art models, such as DenseNet, can be greatly improved using CL along with other popular models.


title: Unsupervised Generation of Free-Form and Parameterized Avatars

abstract: We study two problems involving the task of mapping images between different domains. The first problem, transfers an image in one domain to an analog image in another domain. The second problem, extends the previous one by mapping an input image to a tied pair, consisting of a vector of parameters and an image that is created using a graphical engine from this vector of parameters. Similar to the first problem, the mapping's objective is to have the output image as similar as possible to the input image. In both cases, no supervision is given during training in the form of matching inputs and outputs. We compare the two unsupervised learning problems to the problem of unsupervised domain adaptation, define generalization bounds that are based on discrepancy, and employ a GAN to implement network solutions that correspond to these bounds. Experimentally, our methods are shown to solve the problem of automatically creating avatars.


title: Structured Neural Network Dynamics for Model-based Control

abstract: We present a structured neural network architecture that is inspired by linear time-varying dynamical systems. The network is designed to mimic the properties of linear dynamical systems which makes analysis and control simple. The architecture facilitates the integration of learned system models with gradient-based model predictive control algorithms, and removes the requirement of computing potentially costly derivatives online. We demonstrate the efficacy of this modeling technique in computing autonomous control policies through evaluation in a variety of standard continuous control domains.


title: Abstractions for network update

abstract: Configuration changes are a common source of instability in networks, leading to outages, performance disruptions, and security vulnerabilities. Even when the initial and final configurations are correct, the update process itself often steps through intermediate configurations that exhibit incorrect behaviors. This paper introduces the notion of consistent network updates---updates that are guaranteed to preserve well-defined behaviors when transitioning mbetween configurations. We identify two distinct consistency levels, per-packet and per-flow, and we present general mechanisms for implementing them in Software-Defined Networks using switch APIs like OpenFlow. We develop a formal model of OpenFlow networks, and prove that consistent updates preserve a large class of properties. We describe our prototype implementation, including several optimizations that reduce the overhead required to perform consistent updates. We present a verification tool that leverages consistent updates to significantly reduce the complexity of checking the correctness of network control software. Finally, we describe the results of some simple experiments demonstrating the effectiveness of these optimizations on example applications.


title: Temporal Data Mining for Educational Applications

abstract: Intelligent tutoring systems (ITSs) acquire rich data about studentsO behavior during learning; data mining techniques can help to describe, interpret and predict student behavior, and to evaluate progress in relation to learning outcomes. This paper surveys a variety of data mining techniques for analyzing how students interact with ITSs, including methods for handling hidden state variables, and for testing hypotheses. To illustrate these methods we draw on data from two ITSs for math instruction. Educational datasets provide new challenges to the data mining community, including inducing action patterns, designing distance metrics, and inferring unobservable states associated with learning.


title: Learning joint query interpretation and response ranking

abstract: Thanks to information extraction and semantic Web efforts, search on unstructured text is increasingly refined using semantic annotations and structured knowledge bases. However, most users cannot become familiar with the schema of knowledge bases and ask structured queries. Interpreting free-format queries into a more structured representation is of much current interest. The dominant paradigm is to segment or partition query tokens by purpose (references to types, entities, attribute names, attribute values, relations) and then launch the interpreted query on structured knowledge bases. Given that structured knowledge extraction is never complete, here we choose a less trodden path: a data representation that retains the unstructured text corpus, along with structured annotations (mentions of entities and relationships) on it. We propose two new, natural formulations for joint query interpretation and response ranking that exploit bidirectional flow of information between the knowledge base and the corpus. One, inspired by probabilistic language models, computes expected response scores over the uncertainties of query interpretation. The other is based on max-margin discriminative learning, with latent variables representing those uncertainties. In the context of typed entity search, both formulations bridge a considerable part of the accuracy gap between a generic query that does not constrain the type at all, and the upper bound where the "perfect" target entity type of each query is provided by humans. Our formulations are also superior to a two-stage approach of first choosing a target type using recent query type prediction techniques, and then launching a type-restricted entity search query.


title: Design and implementation of a biomimetic robotic fish with 3D locomotion

abstract: This paper investigates the design and implementation of a biomimetic robotic fish with 3D locomotion. The multi-joint body structure of the robotic fish is designed to simulate the carangiform locomotion and the pitching is controlled by a “two-tanks”-based diving system. A GIM-based biomimetic learning approach is adopted to learn and to regenerate carangiform fish swim patterns to the robot. By virtue of the scaling properties, the learning approach is able to generate various 3D motion patterns by tuning parameters without arduous learning or training process. Finally, the performance of the robotic fish system is analyzed through experiments.


Impact of Data Normalization on Stock Index Forecasting


Software-Defined Data Plane Enhancing SDN and NFV


title: An Evaluation of Factors Affecting Brand Awareness in the Context of Social Media in Malaysia

abstract: Living in the middle of a global communication boom with vast usage of social media, the business environment has become more complicated. So, it is more difficult for marketers to create and increase brand awareness as they have to be able to coordinate messages and efforts across all the existing media to capture customers. Therefore, marketers have to consider these communication tools on branding process in the current competitive market-space. The purpose of this study is to evaluate the factors affecting on brand awareness through social media in Malaysia. Data for this study was obtained from 391 students of Universiti Putra Malaysia. The results indicate customer engagement, brand exposure, and electronic-word-of-mouth have positive correlation with brand awareness in the context of social media and the most effective factor is customer engagement. The study recommends that brands will be profited from social media in order to create and enhance brand awareness and the benefits will be mostly increased by using this media’s interactivity features to tie customers more closely to a brand.


title: Wiener variable step size and gradient spectral variance smoothing for double-talk-robust acoustic echo cancellation and acoustic feedback cancellation

abstract: Double-talk (DT)-robust acoustic echo cancellation (AEC) and acoustic feedback cancellation (AFC) are needed in speech communication systems, e.g., in hands-free communication systems and hearing aids. In this paper, we derive a practical and computationally efficient algorithm based on the frequency-domain adaptive filter prediction error method using row operations (FDAF-PEM-AFROW) for DT-robust AEC and AFC. The proposed algorithm features two main modifications: (a) the Wiener variable step size (WVSS) and (b) the gradient spectral variance smoothing (GSVS). In AEC simulations, the WVSS-GSVS-FDAF-PEM-AFROW algorithm obtains outstanding robustness and smooth adaptation in highly adverse scenarios such as in bursting DT at high levels, and in a change of acoustic path during continuous DT. Similarly, in AFC simulations, the algorithm outperforms state-of-the-art algorithms when using a low-order near-end speech model and in colored non-stationary noise.


title: Co-expression of PD-L1 and p-AKT is associated with poor prognosis in diffuse large B-cell lymphoma via PD-1/PD-L1 axis activating intracellular AKT/mTOR pathway in tumor cells

abstract: // Ling Dong 1, * , Huijuan Lv 1, * , Wei Li 1 , Zheng Song 1 , Lanfang Li 1 , Shiyong Zhou 1 , Lihua Qiu 1 , Zhengzi Qian 1 , Xianming Liu 1 , Lixia Feng 1 , Bin Meng 2 , Kai Fu 1, 3 , Xi Wang 4 , Qiang Pan-Hammarstrom 1, 5 , Ping Wang 6 , Xianhuo Wang 1 , Huilai Zhang 1 1 Department of Lymphoma, Sino-US Center for Lymphoma and Leukemia, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China 2 Department of Pathology, Sino-US Center for Lymphoma and Leukemia, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China 3 Department of Pathology and Microbiology and Internal Medicine, University of Nebraska Medical Center, Omaha, NE, USA 4 Department of Cellular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China 5 Department of Laboratory Medicine, Clinical Immunology, Karolinska Institutet at Karolinska University Hospital Huddinge, Stockholm, Sweden 6 Department of Radiotherapy, Sino-US Center for Lymphoma and Leukemia, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China * These authors have contributed equally to this work Correspondence to: Huilai Zhang, email: zhlwgq@126.com Xianhuo Wang, email: tjzlyy_xianhuow@163.com Keywords: PD-1, PD-L1, p-AKT, AKT/mTOR signaling, DLBCL Received: January 06, 2016 Accepted: March 31, 2016 Published: April 27, 2016 ABSTRACT Programmed death-1 (PD-1) /programmed death-ligand 1 (PD-L1) engagement usually leads to diminished antitumor T-cell responses, which mediates the immune escape of tumor cells. However, little is known whether PD-1/PD-L1 could directly activates intracellular oncogenic signaling pathways in tumor cells. The purpose of this study is to investigate whether intracellular AKT/mTOR signaling could be directly activated by PD-1/PD-L1 during the malignant progression in diffuse large B-cell lymphoma (DLBCL). Detection of the expression of PD-L1 and p-AKT by immunohistochemistry (IHC) showed that both proteins were overexpressed in 54% and 48% DLBCL cases, respectively. Spearman test showed that PD-L1 expression was correlated with p-AKT expression (R=0.244, χ 2 =5.962; P =0.017) and the expression of PD-L1 and p-AKT were also correlated with clinic-pathological characteristics. In addition, survival analysis showed that DLBCL patients who co-expressed PD-L1 and p-AKT had significantly poorer outcome than patients with single positive or both negative expression ( P <0.05). In vitro, total PD-L1 and membrane PD-L1 (mPD-L1) proteins were overexpressed in five DLBCL cell lines by western blot and flow cytometry. We observed that AKT/mTOR pathway was activated in DLBCL cells after stimulated with human recombination PD-1/Fc. Taken together, these results suggested that the combination of PD-1/PD-L1 antibodies and AKT/mTOR inhibitor might be a promising and novel therapeutic approach for DLBCL in the future.


title: A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers

abstract: This paper describes a brainmachine interface for the online control of a powered lower-limb exoskeleton based on electroencephalogram (EEG) signals recorded over the users sensorimotor cortical areas. We train a binary decoder that can distinguish two different mental states, which is applied in a cascaded manner to efficiently control the exoskeleton in three different directions: walk front, turn left and turn right. This is realized by first classifying the users intention to walk front or change the direction. If the user decides to change the direction, a subsequent classification is performed to decide turn left or right. The users mental command is conditionally executed considering the possibility of obstacle collision. All five subjects were able to successfully complete the 3-way navigation task using brain signals while mounted in the exoskeleton. We observed on average 10.2% decrease in overall task completion time compared to the baseline protocol.


title: Image inpainting via sparse representation

abstract: This paper proposes a novel patch-wise image inpainting algorithm using the image signal sparse representation over a redundant dictionary, which merits in both capabilities to deal with large holes and to preserve image details while taking less risk. Different from all existing works, we consider the problem of image inpainting from the view point of sequential incomplete signal recovery under the assumption that the every image patch admits a sparse representation over a redundant dictionary. To ensure the visually plausibility and consistency constraints between the filled hole and the surroundings, we propose to construct a redundant signal dictionary by directly sampling from the intact source region of current image. Then we sequentially compute the sparse representation for each incomplete patch at the boundary of the hole and recover it until the whole hole is filled. Experimental results show that this approach can efficiently fill in the hole with visually plausible information, and take less risk to introduce unwanted objects or artifacts.


title: A Sense-Topic Model for Word Sense Induction with Unsupervised Data Enrichment

abstract: Word sense induction (WSI) seeks to automatically discover the senses of a word in a corpus via unsupervised methods. We propose a sense-topic model for WSI, which treats sense and topic as two separate latent variables to be inferred jointly. Topics are informed by the entire document, while senses are informed by the local context surrounding the ambiguous word. We also discuss unsupervised ways of enriching the original corpus in order to improve model performance, including using neural word embeddings and external corpora to expand the context of each data instance. We demonstrate significant improvements over the previous state-of-the-art, achieving the best results reported to date on the SemEval-2013 WSI task.


title: Accurate spear phishing campaign attribution and early detection

abstract: There is growing evidence that spear phishing campaigns are increasingly pervasive, sophisticated, and remain the starting points of more advanced attacks. Current campaign identification and attribution process heavily relies on manual efforts and is inefficient in gathering intelligence in a timely manner. It is ideal that we can automatically attribute spear phishing emails to known campaigns and achieve early detection of new campaigns using limited labelled emails as the seeds. In this paper, we introduce four categories of email profiling features that capture various characteristics of spear phishing emails. Building on these features, we implement and evaluate an affinity graph based semi-supervised learning model for campaign attribution and detection. We demonstrate that our system, using only 25 labelled emails, achieves 0.9 F1 score with a 0.01 false positive rate in known campaign attribution, and is able to detect previously unknown spear phishing campaigns, achieving 100% 'darkmoon', over 97% of 'samkams' and 91% of 'bisrala' campaign detection using 246 labelled emails in our experiments.


title: Scale Adaptive Clustering of Multiple Structures

abstract: We propose the segmentation of noisy datasets into Multiple Inlier Structures with a new Robust Estimator (MISRE). The scale of each individual structure is estimated adaptively from the input data and refined by mean shift, without tuning any parameter in the process, or manually specifying thresholds for different estimation problems. Once all the data points were classified into separate structures, these structures are sorted by their densities with the strongest inlier structures coming out first. Several 2D and 3D synthetic and real examples are presented to illustrate the efficiency, robustness and the limitations of the MISRE algorithm.


title: Effect of flexible spine on stability of a passive quadruped robot: Experimental results

abstract: In rapid quadruped animals, the flexibility of spine is obviously observable while running. In this paper, we introduce a 3D passive dynamic quadruped robot, Fanari, that is capable to gallop down a slope without any external energy. We study the effect of spine flexibility on improvement of stability, adaptability and achievement of rapid leg locomotion. So a flexible spine that is capable to bend both outward and downward, a flexible spine that is only capable to bend outward and a rigid spine are designed and tested on the robot. Based on the case studies, we discuss about the challenges and the roles of spine stiffness on the gallop action. A simulation model is also made in order to corroborate the experimental results of the robot. It is concluded that the robot with the spine that is flexible in both outward and downward directions, has the most stability, adaptability and fastest velocity in its gallop action.


title: Answer Garden 2: merging organizational memory with collaborative help

abstract: This research examines a collaborative solution to a common problem, that of providing help to distributed users. The Answer Garden 2 system provides a secondgeneration architecture for organizational and community memory applications. After describing the need for Answer Garden 2’s functionality, we describe the architecture of the system and two underlying systems, the Cafe ConstructionKit and Collaborative Refinery. We also present detailed descriptions of the collaborative help and collaborative refining facilities in the Answer Garden 2 system.


title: Video as a new teaching tool to increase student motivation

abstract: The main objective of this research is to explore the results of the use of videos as an educational tool which helps increasing students' motivation in any discipline. The study is based on several streaming videos created as a support material for learning and used by 12 lecturers with 487 students in three different degrees of engineering (Mechanical, Industrial and Management, and Aeronautical) at the School of Industrial and Aeronautical Engineering of Terrassa (ETSEIAT). The paper describes the different areas and ways in which this innovative learning tool has been used and emphasizes the skills developed in each application. Finally, it presents the results of the impact of the use of videos upon students' motivation.


title: Author Age Prediction from Text using Linear Regression

abstract: While the study of the connection between discourse patterns and personal identification is decades old, the study of these patterns using language technologies is relatively recent. In that more recent tradition we frame author age prediction from text as a regression problem. We explore the same task using three very different genres of data simultaneously: blogs, telephone conversations, and online forum posts. We employ a technique from domain adaptation that allows us to train a joint model involving all three corpora together as well as separately and analyze differences in predictive features across joint and corpus-specific aspects of the model. Effective features include both stylistic ones (such as POS patterns) as well as content oriented ones. Using a linear regression model based on shallow text features, we obtain correlations up to 0.74 and mean absolute errors between 4.1 and 6.8 years.