title: Implementation of Fuzzy and PID Controller to Water Level System using LabView

abstract: paper is about the water level control system which is required in many industrial processes like water treatment plant, the idea of the process is to keep the tank water level at the desired set point. First we implemented the conventional proportional - integral - derivative (PID) controller to eliminate the steady state error, then it was used Fuzzy logic controllers which gives more performance and stability. It is noted from the results that the Fuzzy Logic controller is more enhanced than PID controller in which its no overshoot, faster settling time, better set point tracking and produced lower performances like integral of time and absolute error (ITAE) integral of time and squared error (ITSE),and integral absolute error (IAE), integral squared error (ISE). Both PID and fuzzy controller are implemented using LabView software Keywordsroportional-integral-derivative (PID) controller, Fuzzy Controller, LabView


title: Critical Success Factors In Implementing ITIL in the Ministry of Education in Saudi Arabia: An Exploratory Study

abstract: This paper engages with the ITIL framework for IT service delivery within the specific context of the Ministry of Education in the Kingdom of Saudi Arabia (KSA). A literature review process is used to develop a critical success factors (CSFs) for the implementation of the ITIL framework in an organisation, based on a series of models like TAM and UTAUT, and then put into an overall conceptual model of use behaviour towards ITIL described by [1]. The conceptual model is then deployed in the field through a series of interviews with IT professionals within the Ministry of Education in the KSA. The interviews are semi-structured, and were intended to draw out the corresponding factors for success and the factors that have hindered the implementation of ITIL within this organisation. The data confirm the view of the literature that strong leadership and management involvement is essential both as a success factor in its own right, and as the means by which other success factors are enabled. The findings of paper make two observations. First, the literature sets out a series of quite precise success factors that relate to project management, communication, and quality control. The data presented in this research project demonstrate that in practice it is hard to make clear all of these factors to such a level of detail within the Ministry of Education in Saudi. Second, the implementation of ITIL is more reflexive than the literature and the conceptual model would initially demonstrate.


title: Real time motion generation and control for biped robot -1st report: Walking gait pattern generation-

abstract: Generating stable dynamic motions for a biped robot in real time is difficult due to the unstable nature of biped systems and their high degrees of freedom. We propose an approximate dynamics model for biped robots with three masses and no kinematic constraints. We also propose a relaxed boundary condition called “the divergent component of motion”. These techniques allow us to generate walking gait patterns with large margin from the edges of support polygon in real time.


Vacant Parking Space Detection in Static Images


title: Chi2: feature selection and discretization of numeric attributes

abstract: Discretization can turn numeric attributes into discrete ones. Feature selection can eliminate some irrelevant attributes. This paper describes Chi2 a simple and general algorithm that uses the /spl chi//sup 2/ statistic to discretize numeric attributes repeatedly until some inconsistencies are found in the data, and achieves feature selection via discretization. The empirical results demonstrate that Chi/sup 2/ is effective in feature selection and discretization of numeric and ordinal attributes.


title: Learning composable models of parameterized skills

abstract: There has been a great deal of work on learning new robot skills, but very little consideration of how these newly acquired skills can be integrated into an overall intelligent system. A key aspect of such a system is compositionality: newly learned abilities have to be characterized in a form that will allow them to be flexibly combined with existing abilities, affording a (good!) combinatorial explosion in the robot's abilities. In this paper, we focus on learning models of the preconditions and effects of new parameterized skills, in a form that allows those actions to be combined with existing abilities by a generative planning and execution system.


title: Testing Multivariate Adaptive Regression Splines (MARS) as a Method of Land Cover Classification of TERRA-ASTER Satellite Images

abstract: This work proposes a new method to classify multi-spectral satellite images based on multivariate adaptive regression splines (MARS) and compares this classification system with the more common parallelepiped and maximum likelihood (ML) methods. We apply the classification methods to the land cover classification of a test zone located in southwestern Spain. The basis of the MARS method and its associated procedures are explained in detail, and the area under the ROC curve (AUC) is compared for the three methods. The results show that the MARS method provides better results than the parallelepiped method in all cases, and it provides better results than the maximum likelihood method in 13 cases out of 17. These results demonstrate that the MARS method can be used in isolation or in combination with other methods to improve the accuracy of soil cover classification. The improvement is statistically significant according to the Wilcoxon signed rank test.


title: A hierarchical global path planning based on multi-objective particle swarm optimization

abstract: In this study, a novel hierarchical global path planning approach for mobile robot navigation in a clutter environment is proposed. This approach has a three — level structure to obtain a feasible, optimal and safe path. In the first level, the triangular decomposition method is used to quickly establish a geometric free configuration space of the robot. In the second level, Dijkstra's algorithm is applied to find a collision — free path used as input reference for the next level. Lastly, a proposed particle swarm optimization called constrained multi-objective particle swarm optimization (CMOPSO) with an accelerated update methodology is employed to generate the global optimal path with the focus on minimizing the path length and maximizing path smoothness. The simulations illustrates the superiority of this method in terms of solution quality and actual execution time.


title: Deep Networks with Internal Selective Attention through Feedback Connections

abstract: Traditional convolutional neural networks (CNN) are stationary and feedforward. They neither change their parameters during evaluation nor use feedback from higher to lower layers. Real brains, however, do. So does our Deep Attention Selective Network (dasNet) architecture. DasNets feedback structure can dynamically alter its convolutional filter sensitivities during classification. It harnesses the power of sequential processing to improve classification performance, by allowing the network to iteratively focus its internal attention on some of its convolutional filters. Feedback is trained through direct policy search in a huge million-dimensional parameter space, through scalable natural evolution strategies (SNES). On the CIFAR-10 and CIFAR-100 datasets, dasNet outperforms the previous state-of-the-art model.


title: Environmental concerns of desalinating seawater using reverse osmosis

abstract: This Critical Review on environmental concerns of desalination plants suggests that planning and monitoring stages are critical aspects of successful management and operation of plants. The site for the desalination plants should be selected carefully and should be away from residential areas particularly for forward planning for possible future expansions. The concerning issues identified are noise pollution, visual pollution, reduction in recreational fishing and swimming areas, emission of materials into the atmosphere, the brine discharge and types of disposal methods used are the main cause of pollution. The reverse osmosis (RO) method is the preferred option in modern times especially when fossil fuels are becoming expensive. The RO has other positives such as better efficiency (30-50%) when compared with distillation type plants (10-30%). However, the RO membranes are susceptible to fouling and scaling and as such they need to be cleaned with chemicals regularly that may be toxic to receiving waters. The input and output water in desalination plants have to be pre and post treated, respectively. This involves treating for pH, coagulants, Cl, Cu, organics, CO(2), H(2)S and hypoxia. The by-product of the plant is mainly brine with concentration at times twice that of seawater. This discharge also includes traces of various chemicals used in cleaning including any anticorrosion products used in the plant and has to be treated to acceptable levels of each chemical before discharge but acceptable levels vary depending on receiving waters and state regulations. The discharge of the brine is usually done by a long pipe far into the sea or at the coastline. Either way the high density of the discharge reaches the bottom layers of receiving waters and may affect marine life particularly at the bottom layers or boundaries. The longer term effects of such discharge concentrate has not been documented but it is possible that small traces of toxic substances used in the cleaning of RO membranes may be harmful to marine life and ecosystem. The plants require saline water and thus the construction of input and discharge output piping is vital. The piping are often lengthy and underground as it is in Tugun (QLD, Australia), passing below the ground. Leakage of the concentrate via cracks in rocks to aquifers is a concern and therefore appropriate monitoring quality is needed. Leakage monitoring devices ought to be attached to such piping during installation. The initial environment impact assessment should identify key parameters for monitoring during discharge processes and should recommend ongoing monitoring with devices attached to structures installed during construction of plants.


title: A Compression Based Distance Measure for Texture

abstract: The analysis of texture is an important subroutine in application areas as diverse as biology, medicine, robotics, and forensic science. While the last three decades have seen extensive research in algorithms to measure texture similarity, almost all existing methods require the careful setting of many parameters. There are many problems associated with a lot of parameters, the most obvious of which is that with many parameters to fit, it is very difficult to avoid overfitting. In this work, we propose to extend recent advances in Kolmogorov complexity‐based similarity measures to texture matching problems. These Kolmogorov‐based methods have been shown to be very useful in intrinsically discrete domains such as DNA, protein sequences, MIDI music, and natural languages; however, they are not well defined for real‐valued data. To address this, we introduce a very simple idea, the Campana‐Keogh (CK) video compression‐based method for texture measures. These measures utilize video compressors to approximate the Kolmogorov complexity. Using the parameter‐free CK method, we novely utilize lossy compression to create an efficient and robust parameter‐lite texture similarity measure: the CK‐1 distance measure. We demonstrate the utility of our measure with extensive empirical evaluations on real‐world case studies drawn from nematology, arachnology, entomology, medicine, forensics, texture analysis benchmarks, and many other domains. Copyright © 2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 3: 381‐398, 2010 © 2010 Wiley Periodicals, Inc.


title: Residual Convolutional CTC Networks for Automatic Speech Recognition

abstract: Deep learning approaches have been widely used in Automatic Speech Recognition (ASR) and they have achieved a significant accuracy improvement. Especially, Convolutional Neural Networks (CNNs) have been revisited in ASR recently. However, most CNNs used in existing work have less than 10 layers which may not be deep enough to capture all human speech signal information. In this paper, we propose a novel deep and wide CNN architecture denoted as RCNN-CTC, which has residual connections and Connectionist Temporal Classification (CTC) loss function. RCNN-CTC is an end-to-end system which can exploit temporal and spectral structures of speech signals simultaneously. Furthermore, we introduce a CTC-based system combination, which is different from the conventional frame-wise senone-based one. The basic subsystems adopted in the combination are different types and thus mutually complementary to each other. Experimental results show that our proposed single system RCNN-CTC can achieve the lowest word error rate (WER) on WSJ and Tencent Chat data sets, compared to several widely used neural network systems in ASR. In addition, the proposed system combination can offer a further error reduction on these two data sets, resulting in relative WER reductions of $14.91\%$ and $6.52\%$ on WSJ dev93 and Tencent Chat data sets respectively.


title: FastShrinkage: Perceptually-aware Retargeting Toward Mobile Platforms

abstract: Retargeting aims at adapting an original high-resolution photo/video to a low-resolution screen with an arbitrary aspect ratio. Conventional approaches are generally based on desktop PCs, since the computation might be intolerable for mobile platforms (especially when retargeting videos). Besides, only low-level visual features are exploited typically, whereas human visual perception is not well encoded. In this paper, we propose a novel retargeting framework which fast shrinks photo/video by leveraging human gaze behavior. Specifically, we first derive a geometry-preserved graph ranking algorithm, which efficiently selects a few salient object patches to mimic human gaze shifting path (GSP) when viewing each scenery. Afterward, an aggregation-based CNN is developed to hierarchically learn the deep representation for each GSP. Based on this, a probabilistic model is developed to learn the priors of the training photos which are marked as aesthetically-pleasing by professional photographers. We utilize the learned priors to efficiently shrink the corresponding GSP of a retargeted photo/video to be maximally similar to those from the training photos. Extensive experiments have demonstrated that: 1) our method consumes less than 35ms to retarget a 1024 × 768 photo (or a 1280 × 720 video frame) on popular iOS/Android devices, which is orders of magnitude faster than the conventional retargeting algorithms; 2) the retargeted photos/videos produced by our method outperform its competitors significantly based on the paired-comparison-based user study; and 3) the learned GSPs are highly indicative of human visual attention according to the human eye tracking experiments.


Code Division Multiple Access


Music and Language 1 Running head: THE USE OF MUSIC FOR LEARNING LANGUAGES: A REVIEW The Use of Music for Learning Languages: A Review of the Literature


title: A Study of 28 GHz, Planar, Multilayered, Electrically Small, Broadside Radiating, Huygens Source Antennas

abstract: Two 28 GHz, planar, electrically small Huygens source antennas are presented that are broadside radiating and are based on multilayer PCB technology. The designs seamlessly integrate electric Egyptian axe dipole and magnetic capacitively loaded loop near-field resonant parasitic elements with a coax-fed dipole radiator. Both linearly polarized (LP) and circularly polarized (CP) systems are demonstrated. The simulations of the LP system indicate that it is electrically small: ka = 0.961; has peak realized gains and front-to-back ratios (FTBRs) in the range from, respectively, 3.77 to 4.54 dBi and 7.16 to 33.92 dB; and radiation efficiencies higher than 81.14% over its entire 2.14%, −10-dB fractional impedance bandwidth (FBW−10dB). A prototype was fabricated and tested; the measured and simulated results are in good agreement. The CP system exhibits similar properties: ka = 0.942; 1.41% FBW−10dB with a 0.47% 3-dB axial ratio fractional bandwidth; and peak realized gain, FTBR, and radiation efficiency values equal to 2.03 dBi, 26.72 dB, and 73.4%, respectively. To confirm their efficacy for on-body applications, the specific absorption rate values of both the LP and CP Huygens source antennas were evaluated and found to be very low.


title: An approach to detecting duplicate bug reports using natural language and execution information

abstract: An open source project typically maintains an open bug repository so that bug reports from all over the world can be gathered. When a new bug report is submitted to the repository, a person, called a triager, examines whether it is a duplicate of an existing bug report. If it is, the triager marks it as duplicate and the bug report is removed from consideration for further work. In the literature, there are approaches exploiting only natural language information to detect duplicate bug reports. In this paper we present a new approach that further involves execution information. In our approach, when a new bug report arrives, its natural language information and execution information are compared with those of the existing bug reports. Then, a small number of existing bug reports are suggested to the triager as the most similar bug reports to the new bug report. Finally, the triager examines the suggested bug reports to determine whether the new bug report duplicates an existing bug report. We calibrated our approach on a subset of the Eclipse bug repository and evaluated our approach on a subset of the Firefox bug repository. The experimental results show that our approach can detect 67%-93% of duplicate bug reports in the Firefox bug repository, compared to 43%-72% using natural language information alone.


title: Family story play: reading with young children (and elmo) over a distance

abstract: We introduce Family Story Play, a system that supports grandparents to read books together with their grandchildren over the Internet. Family Story Play is designed to improve communication across generations and over a distance, and to support parents and grandparents in fostering the literacy development of young children. The interface encourages active child participation in the book reading experience by combining a paper book, a sensor-enhanced frame, video conferencing technology, and video content of a Sesame Street Muppet (Elmo). Results with users indicate that Family Story Play improves child engagement in long-distance communication and increases the quality of interaction between young children and distant grandparents. Additionally, Family Story Play encourages dialogic reading styles that are linked with literacy development. Ultimately, reading with Family Story Play becomes a creative shared activity that suggests a new kind of collaborative story telling.


title: Automatic detection of pain intensity

abstract: Previous efforts suggest that occurrence of pain can be detected from the face. Can intensity of pain be detected as well? The Prkachin and Solomon Pain Intensity (PSPI) metric was used to classify four levels of pain intensity (none, trace, weak, and strong) in 25 participants with previous shoulder injury (McMaster-UNBC Pain Archive). Participants were recorded while they completed a series of movements of their affected and unaffected shoulders. From the video recordings, canonical normalized appearance of the face (CAPP) was extracted using active appearance modeling. To control for variation in face size, all CAPP were rescaled to 96x96 pixels. CAPP then was passed through a set of Log-Normal filters consisting of 7 frequencies and 15 orientations to extract 9216 features. To detect pain level, 4 support vector machines (SVMs) were separately trained for the automatic measurement of pain intensity on a frame-by-frame level using both 5-folds cross-validation and leave-one-subject-out cross-validation. F1 for each level of pain intensity ranged from 91% to 96% and from 40% to 67% for 5-folds and leave-one-subject-out cross-validation, respectively. Intra-class correlation, which assesses the consistency of continuous pain intensity between manual and automatic PSPI was 0.85 and 0.55 for 5-folds and leave-one-subject-out cross-validation, respectively, which suggests moderate to high consistency. These findings show that pain intensity can be reliably measured from facial expression in participants with orthopedic injury.


Depth from a Single Image Through User Interaction


title: Recurrent Neural Network for Learning DenseDepth and Ego-Motion from Video

abstract: Learning-based, single-view depth estimation often generalizes poorly to unseen datasets. While learning-based, two-frame depth estimation solves this problem to some extent by learning to match features across frames, it performs poorly at large depth where the uncertainty is high. There exists few learning-based, multi-view depth estimation methods. In this paper, we present a learning-based, multi-view dense depth map and ego-motion estimation method that uses Recurrent Neural Networks (RNN). Our model is designed for 3D reconstruction from video where the input frames are temporally correlated. It is generalizable to single- or two-view dense depth estimation. Compared to recent single- or two-view CNN-based depth estimation methods, our model leverages more views and achieves more accurate results, especially at large distances. Our method produces superior results to the state-of-the-art learning-based, single- or two-view depth estimation methods on both indoor and outdoor benchmark datasets. We also demonstrate that our method can even work on extremely difficult sequences, such as endoscopic video, where none of the assumptions (static scene, constant lighting, Lambertian reflection, etc.) from traditional 3D reconstruction methods hold.


title: A robotics simulator platform for RADOE

abstract: This paper presents a new simulation platform for the industrial project of Robot Application Development and Operating Environment (RADOE). The simulator displays the robot models in Gazebo and Rviz interfaces concurrently starting from the bare bone framework. A Rviz interface plugin is developed for users to easily load the launch files or parameter files to the simulation platform. Experiments are designed to show some typical industrial robots integrated with their end-effectors.


title: Geoscience Data Provenance: An Overview

abstract: The advancement of Earth observing sensors, data, and information systems enhances significantly the capabilities to access and process large volumes of geoscience data, which are often consumed by scientific workflows and processed in a distributed information environment. Consequently, data provenance becomes important since it allows users to determine the usability and reliability of data products. Motivation for capturing and sharing provenance also comes from the distributed data and information infrastructure that has been benefiting the Earth science community in the past decade, such as spatial data and information infrastructure, e-Science, and cyberinfrastructure. This paper provides an overview of geoscience data provenance in supporting provenance-aware geoscience data and information systems by summarizing state-of-the-art technologies and methodologies of geoscience data provenance and highlighting key considerations and possible solutions for geoscience data provenance.


title: Classification of sim box fraud detection using support vector machine and artificial neural network

abstract: SIM box fraud is classified as one of the dominant types of fraud instead of subscription and superimposed types of fraud. This fraud activity has been increasing dramatically each year due to the new modern technologies and the global superhighways of communication, resulting the decreasing of the revenue and quality of service in telecommunication providers especially in Africa and Asia. This paper outlines the Artificial Neural Network (ANN) and Support Vector Machine (SVM) to detect Global System for Mobile communication (GSM) gateway bypass in SIM Box fraud. The suitable features of data obtained from the extraction process of Customer Database Record (CDR) are used for classification in the development of ANN and SVM models. The performance of ANN is compared with SVM to find which model gives the best performance. From the experiments, it is found that SVM model gives higher accuracy compared to ANN by giving the classification accuracy of 99.06% compared with ANN model, 98.71% accuracy. Normal 0 false false false EN-AU X-NONE X-NONE


title: Look Wider to Match Image Patches with Convolutional Neural Networks

abstract: When a human matches two images, the viewer has a natural tendency to view the wide area around the target pixel to obtain clues of right correspondence. However, designing a matching cost function that works on a large window in the same way is difficult. The cost function is typically not intelligent enough to discard the information irrelevant to the target pixel, resulting in undesirable artifacts. In this letter, we propose a novel convolutional neural network (CNN) module to learn a stereo matching cost with a large-sized window. Unlike conventional pooling layers with strides, the proposed per-pixel pyramid-pooling layer can cover a large area without a loss of resolution and detail. Therefore, the learned matching cost function can successfully utilize the information from a large area without introducing the fattening effect. The proposed method is robust despite the presence of weak textures, depth discontinuity, illumination, and exposure difference. The proposed method achieves near-peak performance on the Middlebury benchmark.


title: 3D printing: Basic concepts mathematics and technologies

abstract: 3D printing is the process of being able to print any object layer by layer. But if we question this proposition, can we find any three dimensional objects that can't be printed layer by layer? To banish any disbeliefs we walked together through the mathematics that prove 3d printing is feasible for any real life object. 3d printers create three dimensional objects by building them up layer by layer. The current generation of 3d printers typically requires input from a CAD program in the form of an STL file, which defines a shape by a list of triangle vertices. The vast majority of 3d printers use two techniques, FDM (Fused Deposition Modelling) and PBP (Powder Binder Printing). One advanced form of 3d printing that has been an area of increasing scientific interest the recent years is bioprinting. Cell printers utilizing techniques similar to FDM were developed for bioprinting. These printers give us the ability to place cells in positions that mimic their respective positions in organs. Finally through series of case studies we show that 3d printers in medicine have made a massive breakthrough lately.


title: Efficient Processing of Encrypted Data in Honest-but-Curious Clouds

abstract: Encrypted data stored in clouds usually cannot be processed. To address this limitation, we design a practically efficient fully homomorphic encryption (FHE) scheme, which allows encrypted data to be directly processed by the clouds. Our scheme assumes that the clouds are curious to derive information from encrypted data, but not performing any nondeterministic brute-force attacks. We implemented a prototype of our scheme and evaluated its concrete performance by evaluating high-degree polynomials over encrypted data and calculating inner product of high-dimensional encrypted vectors.


title: The Psychology of Close Relationships: Fourteen Core Principles

abstract: Relationship science is a theory-rich discipline, but there have been no attempts to articulate the broader themes or principles that cut across the theories themselves. We have sought to fill that void by reviewing the psychological literature on close relationships, particularly romantic relationships, to extract its core principles. This review reveals 14 principles, which collectively address four central questions: (a) What is a relationship? (b) How do relationships operate? (c) What tendencies do people bring to their relationships? (d) How does the context affect relationships? The 14 principles paint a cohesive and unified picture of romantic relationships that reflects a strong and maturing discipline. However, the principles afford few of the sorts of conflicting predictions that can be especially helpful in fostering novel theory development. We conclude that relationship science is likely to benefit from simultaneous pushes toward both greater integration across theories (to reduce redundancy...


title: Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey

abstract: Interest in automatic action and gesture recognition has grown considerably in the last few years. This is due in part to the large number of application domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gesture recognition in sequences of images. The survey reviews both fundamental and cutting edge methodologies reported in the last few years. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. Details of the proposed architectures, fusion strategies, main datasets, and competitions are reviewed. Also, we summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, their highlighting features, and opportunities and challenges for future research. To the best of our knowledge this is the first survey in the topic. We foresee this survey will become a reference in this ever dynamic field of research.


title: LoRea: A Backscatter Reader for Everyone!

abstract: Computational RFID (CRFID) platforms have enabled reconfigurable, battery-free applications for close to a decade. However, several factors have impeded their wide-spread adoption: low communication range, low throughput, and expensive infrastructure - CRFID readers usually cost upwards of $1000. This paper presents LoRea, a backscatter reader that achieves an order of magnitude higher range than existing CRFID readers, while costing a fraction of their price. LoRea achieves this by diverging from existing designs of CRFID readers and more specifically, how self-interference is tackled. LoRea builds on recent work that frequency-shifts backscatter transmissions away from the carrier signal to reduce self- interference. LoRea also decouples the carrier generation from the reader, helping to further decrease self-interference. Decoupling carrier generation also enables the use of deployed infrastructure of smartphones and sensor nodes to provide the carrier signal. Together these methods reduce cost and complexity of the reader. LoRea also purposefully operates at lower bi- trates than recent backscatter systems which enables high sensitivity and longer range. We evaluate LoRea experimentally and find that it achieves a communication range of up to 225 m in line-of-sight scenarios. In indoor environments, where the signal traverses several walls separating the reader and the backscatter tag, LoRea achieves a range of 30 m. These results illustrate how LoRea outperforms state- of-art backscatter systems and CRFID platforms.


title: Aff2Vec: Affect--Enriched Distributional Word Representations

abstract: Human communication includes information, opinions, and reactions. Reactions are often captured by the affective-messages in written as well as verbal communications. While there has been work in affect modeling and to some extent affective content generation, the area of affective word distributions in not well studied. Synsets and lexica capture semantic relationships across words. These models however lack in encoding affective or emotional word interpretations. Our proposed model, Aff2Vec provides a method for enriched word embeddings that are representative of affective interpretations of words. Aff2Vec outperforms the state--of--the--art in intrinsic word-similarity tasks. Further, the use of Aff2Vec representations outperforms baseline embeddings in downstream natural language understanding tasks including sentiment analysis, personality detection, and frustration prediction.


title: Financial Entity Identification and Information Integration (FEIII) Challenge: The Report of the Organizing Committee

abstract: This report presents the goals and outcomes of the 2019 Financial Entity Identification and Information Integration (FEIII) Challenge. We describe two challenge datasets and tasks. FEIII SHIP was a bill of lading dataset for incoming shipments to the United States and the task was to identify the major shippers for some product, from some country. FEIII CALI included state and local regulatory data from California, and the task was entity linkage for San Francisco restaurants. The report summarizes plans for the 2020 Challenge and the Business Open Knowledge Network (BOKN).


title: (α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing

abstract: Privacy preservation is an important issue in the release of data for mining purposes. The k-anonymity model has been introduced for protecting individual identification. Recent studies show that a more sophisticated model is necessary to protect the association of individuals to sensitive information. In this paper, we propose an (α, k)-anonymity model to protect both identifications and relationships to sensitive information in data. We discuss the properties of (α, k)-anonymity model. We prove that the optimal (α, k)-anonymity problem is NP-hard. We first presentan optimal global-recoding method for the (α, k)-anonymity problem. Next we propose a local-recoding algorithm which is more scalable and result in less data distortion. The effectiveness and efficiency are shown by experiments. We also describe how the model can be extended to more general case.


Fuzzy rough topological entropy structure spaces


title: Sensory impairment in hip-fracture patients 65 years or older and effects of hearing/vision interventions on fall frequency

abstract: Aim: ::: Examine the effect of nursing interventions to improve vision and hearing, systematic assessment, and referral to sensory specialists on falling.


title: A wearable mobihealth care system supporting real-time diagnosis and alarm

abstract: This paper describes a wearable mobihealth care system aiming at providing long-term continuous monitoring of vital signs for high-risk cardiovascular patients. We use a portable patient unit (PPU) and a wearable shirt (WS) to monitor electrocardiogram (ECG), respiration (acquired with respiratory inductive plethysmography, RIP), and activity. Owing to integrating fabric sensors and electrodes endowed with electro-physical properties into the WS, long-term continuous monitoring can be realized without making patients feel uncomfortable and restricting their mobility. The PPU analyzes physiological signals in real time and determines whether the patient is in danger or needs external help. The PPU will alert the patient and an emergency call will be automatically established with a medical service center (MSC) when life-threatening arrhythmias or falls are detected. With advanced gpsOne technology, the patient can be located and rescued immediately whether he/she is indoors or outdoors in case of emergency.


title: Methodologies for Ontology Development

abstract: It is now widely recognised that constructing a domain model, or ontology, is an important step in the development of knowledge based systems. What is lacking, however, is a clear understanding of how to build ontologies. In this paper we survey the work which has been done so far in beginning to provide a methodology for building ontologies. This work is still formative, and relies heavily on particular experiences. We also provide some discussion of this work, and identify the key issues that must be addressed if we are to move on from ontology construction being an art and to make it an understood engineering process.


title: An L=S criterion for optimal multiprogramming

abstract: Balancing interpagefault lifetime (L) against page swap time (S) has always been a performance criterion of great intuitive appeal. This paper shows that, under normal conditions, controlling the memory policy parameter to enforce the constraint L ≥ S, and allowing the multiprogramming load to rise as high as demand warrants without violating this constraint, will produce a load slightly higher than optimum. Equivalently, using the criterion L = uS for some u slightly larger than 1 will approximate an optimal load. Using simulations, this criterion is compared with two others reported in the literature, namely the “knee criterion” (operate with L at the knee of the lifetime curve) and the 50% criterion (operate with the paging device at 50% utilization).The knee criterion produced optimal loads more often than the L=S criterion, which in turn produced optimal loads more often than the 50% criterion. Since no practical implementation of the knee criterion is known, the L=S criterion is the most attractive of the three.


title: Field test of bistatic forward-looking synthetic aperture radar

abstract: Monostatic radar systems reach their limits if a two-dimensional resolution is desired for a forward-looking geometry. Bistatic systems offer an alternative to handle this problem if the transmitter operates as a separated illuminator while the receiver is arranged in a forward-looking mode. A newly arranged field test presented in this paper successfully showed the applicability of the chosen approach. By this test the idea to realize a two-dimensional image in a forward-looking mode with a bistatic geometry could be verified. Furthermore the adjustment of collected pulses by the use of timestamps from the received signals was introduced successfully. In addition, a new calibration method for bistatic measurements with the help of spheres was presented. Further steps are intended in order to determine the correct dimensioning of designed SAR images.


title: Analytical modeling of a double-sided flux concentrating E-Core Transverse Flux Machine with pole windings

abstract: In this paper, a nonlinear analytical model based on the Magnetic Equivalent Circuit (MEC) method is developed for a double-sided E-Core Transverse Flux Machine (TFM). The proposed TFM has a cylindrical rotor, sandwiched between E-core stators on both sides. Ferrite magnets are used in the rotor with flux concentrating design to attain high airgap flux density, better magnet utilization, and higher torque density. The MEC model was developed using a series-parallel combination of flux tubes to estimate the reluctance network for different parts of the machine including air gaps, permanent magnets, and the stator and rotor ferromagnetic materials, in a two-dimensional (2-D) frame. An iterative Gauss-Siedel method is integrated with the MEC model to capture the effects of magnetic saturation. A single phase, 1 kW, 400 rpm E-Core TFM is analytically modeled and its results for flux linkage, no-load EMF, and generated torque, are verified with Finite Element Analysis (FEA). The analytical model significantly reduces the computation time while estimating results with less than 10 percent error.


title: Self-reported physical activity compared with maximal oxygen uptake in adults

abstract: BackgroundPhysical activity level and cardiorespiratory fitness are both inversely associated with the risk of cardiovascular diseases and with all-cause mortality. Physical activity questionnaires are often validated against objectively measured maximal oxygen uptake (Vo2max).AimTo validate a self-report physical activity questionnaire against Vo2max and furthermore to establish whether a simple question on self-rated physical fitness could predict objectively measured Vo2max.MethodsA total of 102 men and women aged between 35 and 65 years were recruited from an ongoing population-based intervention study, the Inter99 Study. Participants reported their self-rated fitness and daily physical activity using a new questionnaire based on metabolic equivalents (MET). Vo2max (ml/kg per min) was determined using a graded bicycle test with increasing workload until exhaustion and with simultaneous measurement of breath-to-breath oxygen uptake in 15-s periods. Statistical analyses were performed by linear regressi...


title: Topic evolution and social interactions: how authors effect research

abstract: We propose a method for discovering the dependency relationships between the topics of documents shared in social networks using the latent social interactions, attempting to answer the question: given a seemingly new topic, from where does this topic evolve? In particular, we seek to discover the pair-wise probabilistic dependency in topics of documents which associate social actors from a latent social network, where these documents are being shared. By viewing the evolution of topics as a Markov chain, we estimate a Markov transition matrix of topics by leveraging social interactions and topic semantics. Metastable states in a Markov chain are applied to the clustering of topics. Applied to the CiteSeer dataset, a collection of documents in academia, we show the trends of research topics, how research topics are related and which are stable. We also show how certain social actors, authors, impact these topics and propose new ways for evaluating author impact.


title: Graph-Based Multi-Modality Learning for Topic-Focused Multi-Document Summarization

abstract: Graph-based manifold-ranking methods have been successfully applied to topic-focused multi-document summarization. This paper further proposes to use the multi-modality manifold-ranking algorithm for extracting topic-focused summary from multiple documents by considering the within-document sentence relationships and the cross-document sentence relationships as two separate modalities (graphs). Three different fusion schemes, namely linear form, sequential form and score combination form, are exploited in the algorithm. Experimental results on the DUC benchmark datasets demonstrate the effectiveness of the proposed multi-modality learning algorithms with all the three fusion schemes.


uHelp : intelligent volunteer search for mutual help communities


title: EL-E : an assistive mobile manipulator that autonomously fetches objects from flat surfaces

abstract: Assistive mobile robots that autonomously manipulate objects within everyday settings have the potential to improve the lives of the elderly, injured, and disabled. Within this paper, we present the most recent version of the assistive mobile manipulator EL-E with a focus on the subsystem that enables the robot to retrieve objects from and deliver objects to flat surfaces. Once provided with a 3D location via brief illumination with a laser pointer, the robot autonomously approaches the location and then either grasps the nearest object or places an object. We describe our implementation in detail, while highlighting design principles and themes, including the use of specialized behaviors, task-relevant features, and low-dimensional representations. ::: ::: We also present evaluations of EL-E's performance relative to common forms of variation. We tested EL-E's ability to approach and grasp objects from the 25 object categories that were ranked most important for robotic retrieval by motor-impaired patients from the Emory ALS Center. Although reliability varied, EL-E succeeded at least once with objects from 21 out of 25 of these categories. EL-E also approached and grasped a cordless telephone on 12 different surfaces including floors, tables, and counter tops with 100% success. The same test using a vitamin pill (ca. 15 mm × 5 mm × 5 mm) resulted in 58% success.


title: Design of PCB RF probe landing pads for measurements up to 90 GHz

abstract: The characterization of active and passive mm-wave components on printed circuit boards becomes more and more important in the design of today's RF systems. The performance of the transition between a coaxial or a waveguide port of a mm-wave measurement device and a microstrip interface on the board plays a crucial role for the overall performance of the measurement system. This paper addresses the design and optimization of landing pads for RF probes on printed circuit boards for measurements up to 90 GHz.


title: A triple band arc-shaped slot patch antenna for UAV GPS/Wi-Fi applications

abstract: In this paper, a triple band capacitive-fed circular patch antenna with arc-shaped slots is proposed for 1.575 GHz GPS and Wi-Fi 2.4/5.2 GHz communications on unmanned aerial vehicle (UAV) applications. In order to enhance the impedance bandwidth of the antenna, a double-layered geometry is applied in this design with a circular feeding disk placed between two layers. The antenna covers 2380 - 2508 MHz and 5100 - 6030 MHz for full support of the Wi-Fi communication between UAV and ground base station. The foam-Duroid stacked geometry can further enhance the bandwidths for both GPS and Wi-Fi bands when compared to purely Duroid form. The simulation and measurement results are reported in this paper.


title: HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition

abstract: Existing techniques for 3D action recognition are sensitive to viewpoint variations because they extract features from depth images which change significantly with viewpoint. In contrast, we directly process the pointclouds and propose a new technique for action recognition which is more robust to noise, action speed and viewpoint variations. Our technique consists of a novel descriptor and keypoint detection algorithm. The proposed descriptor is extracted at a point by encoding the Histogram of Oriented Principal Components (HOPC) within an adaptive spatio-temporal support volume around that point. Based on this descriptor, we present a novel method to detect Spatio-Temporal Key-Points (STKPs) in 3D pointcloud sequences. Experimental results show that the proposed descriptor and STKP detector outperform state-of-the-art algorithms on three benchmark human activity datasets. We also introduce a new multiview public dataset and show the robustness of our proposed method to viewpoint variations.


title: JA-BE-JA: A Distributed Algorithm for Balanced Graph Partitioning

abstract: Balanced graph partitioning is a well known NP-complete problem with a wide range of applications. These applications include many large-scale distributed problems including the optimal storage of large sets of graph-structured data over several hosts-a key problem in today's Cloud infrastructure. However, in very large-scale distributed scenarios, state-of-the-art algorithms are not directly applicable, because they typically involve frequent global operations over the entire graph. In this paper, we propose a fully distributed algorithm, called JA-BE-JA, that uses local search and simulated annealing techniques for graph partitioning. The algorithm is massively parallel: there is no central coordination, each node is processed independently, and only the direct neighbors of the node, and a small subset of random nodes in the graph need to be known locally. Strict synchronization is not required. These features allow JA-BE-JA to be easily adapted to any distributed graph-processing system from data centers to fully distributed networks. We perform a thorough experimental analysis, which shows that the minimal edge-cut value achieved by JA-BE-JA is comparable to state-of-the-art centralized algorithms such as METIS. In particular, on large social networks JA-BEJA outperforms METIS, which makes JA-BE-JA-a bottom-up, self-organizing algorithm-a highly competitive practical solution for graph partitioning.


title: Adaptive document image binarization

abstract: Abstract A new method is presented for adaptive document image binarization, where the page is considered as a collection of subcomponents such as text, background and picture. The problems caused by noise, illumination and many source type-related degradations are addressed. Two new algorithms are applied to determine a local threshold for each pixel. The performance evaluation of the algorithm utilizes test images with ground-truth, evaluation metrics for binarization of textual and synthetic images, and a weight-based ranking procedure for the final result presentation. The proposed algorithms were tested with images including different types of document components and degradations. The results were compared with a number of known techniques in the literature. The benchmarking results show that the method adapts and performs well in each case qualitatively and quantitatively.


title: Characterizing the memory behavior of Java workloads: a structured view and opportunities for optimizations

abstract: This paper studies the memory behavior of important Java workloads used in benchmarking Java Virtual Machines (JVMs), based on instrumentation of both application and library code in a state-of-the-art JVM, and provides structured information about these workloads to help guide systems' design. We begin by characterizing the inherent memory behavior of the benchmarks, such as information on the breakup of heap accesses among different categories and on the hotness of references to fields and methods. We then provide detailed information about misses in the data TLB and caches, including the distribution of misses over different kinds of accesses and over different methods. In the process, we make interesting discoveries about TLB behavior and limitations of data prefetching schemes discussed in the literature in dealing with pointer-intensive Java codes. Throughout this paper, we develop a set of recommendations to computer architects and compiler writers on how to optimize computer systems and system software to run Java programs more efficiently. This paper also makes the first attempt to compare the characteristics of SPECjvm98 to those of a server-oriented benchmark, pBOB, and explain why the current set of SPECjvm98 benchmarks may not be adequate for a comprehensive and objective evaluation of JVMs and just-in-time (JIT) compilers.We discover that the fraction of accesses to array elements is quite significant, demonstrate that the number of "hot spots" in the benchmarks is small, and show that field reordering cannot yield significant performance gains. We also show that even a fairly large L2 data cache is not effective for many Java benchmarks. We observe that instructions used to prefetch data into the L2 data cache are often squashed because of high TLB miss rates and because the TLB does not usually have the translation information needed to prefetch the data into the L2 data cache. We also find that co-allocation of frequently used method tables can reduce the number of TLB misses and lower the cost of accessing type information block entries in virtual method calls and runtime type checking.


title: Multistage SFM: Revisiting Incremental Structure from Motion

abstract: In this paper, we present a new multistage approach for SfM reconstruction of a single component. Our method begins with building a coarse 3D reconstruction using high-scale features of given images. This step uses only a fraction of features and is fast. We enrich the model in stages by localizing remaining images to it and matching and triangulating remaining features. Unlike traditional incremental SfM, localization and triangulation steps in our approach are made efficient and embarrassingly parallel using geometry of the coarse model. The coarse model allows us to use 3D-2D correspondences based direct localization techniques to register remaining images. We further utilize the geometry of the coarse model to reduce the pair-wise image matching effort as well as to perform fast guided feature matching for majority of features. Our method produces similar quality models as compared to incremental SfM methods while being notably fast and parallel. Our algorithm can reconstruct a 1000 images dataset in 15 hours using a single core, in about 2 hours using 8 cores and in a few minutes by utilizing full parallelism of about 200 cores.


title: Column sampling based discrete supervised hashing

abstract: By leveraging semantic (label) information, supervised hashing has demonstrated better accuracy than unsupervised hashing in many real applications. Because the hashing-code learning problem is essentially a discrete optimization problem which is hard to solve, most existing supervised hashing methods try to solve a relaxed continuous optimization problem by dropping the discrete constraints. However, these methods typically suffer from poor performance due to the errors caused by the relaxation. Some other methods try to directly solve the discrete optimization problem. However, they are typically time-consuming and unscalable. In this paper, we propose a novel method, called column sampling based discrete supervised hashing (COSDISH), to directly learn the discrete hashing code from semantic information. COSDISH is an iterative method, in each iteration of which several columns are sampled from the semantic similarity matrix and then the hashing code is decomposed into two parts which can be alternately optimized in a discrete way. Theoretical analysis shows that the learning (optimization) algorithm of COSDISH has a constant-approximation bound in each step of the alternating optimization procedure. Empirical results on datasets with semantic labels illustrate that COSDISH can outperform the state-of-the-art methods in real applications like image retrieval.


title: A-PNR: Automatic Plate Number Recognition

abstract: Automatic Plate Number Recognition (APNR) has important applications in traffic surveillance, toll booth, protected parking lot, No parking zone, etc. It is a challenging problem, especially when the license plates have varying sizes, the number of lines, fonts, background diversity etc. This work aims to address APNR using deep learning method for real-time traffic images. We first extract license plate candidates from each frame using edge information and geometrical properties, ensuring high recall using one class SVM. The verified candidates are used for NP recognition purpose along with a spatial transformer network (STN) for character recognition. Our system is evaluated on several traffic images with vehicles having different license plate formats in terms of tilt, distances, colors, illumination, character size, thickness etc. Also, the background was very challenging. Results demonstrate robustness to such variations and impressive performance in both the localization and recognition.


title: Routing Centralization Across Domains via SDN: A Model and Emulation Framework for BGP Evolution

abstract: The Border Gateway Protocol (BGP) was designed almost three decades ago and has many limitations relating to its fully distributed nature, policy enforcement capabilities, scalability, security and complexity. For example, the control plane can take several minutes to converge after a routing change; this may be unacceptable for real-time network services. Despite many research proposals for incremental improvements and clean-slate redesigns of how inter-domain routing should work, BGP is likely one of the most ossified protocols of the Internet architecture and it has not retrofitted the proposed ideas. In this work, we propose a radical, incrementally deployable Internet routing paradigm in which the control plane of multiple networks is logically centralized. This follows the Software Defined Networking (SDN) paradigm, although at the inter-domain level involving multiple Autonomous Systems (AS). Multi-domain SDN centralization can be realized by outsourcing routing functions to an external contractor, which provides inter-domain routing services facilitated through a multi-AS network controller. The proposed model promises to become a vehicle for evolving BGP and uses the bird's eye view over several networks to benefit aspects of inter-domain routing, such as convergence properties, policy conflict resolution, inter-domain troubleshooting, and collaborative security. In addition to the proposed paradigm, we introduce a publicly available emulation platform built on top of Mininet and the Quagga routing software, for experimenting on hybrid BGP-SDN AS-level networks. As a proof of concept, we focus specifically on exploiting multi-domain centralization to improve BGP's slow convergence. We build and make publicly available a first multi-AS controller tailored to this use case and demonstrate experimentally that SDN centralization helps to linearly reduce BGP convergence times and churn rates with expanding SDN deployments.


title: Nutrition Therapy during Initiation of Refeeding in Underweight Children and Adolescent Inpatients with Anorexia Nervosa: A Systematic Review of the Evidence

abstract: Abstract Restoration of weight and nutritional rehabilitation are recognized as fundamental steps in the therapeutic treatment of children and adolescent inpatients with anorexia nervosa (AN). However, current recommendations on initial energy requirements for this population are inconsistent, with a clear lack of empirical evidence. Thus, the aim of our study was to systematically review, assess, and summarize the available evidence on the effect of differing nutrition therapies prescribed during refeeding on weight restoration in hospitalized children and adolescents (aged 19 years and younger) with diagnosed AN. Searches were conducted in Scopus, Web of Science, Global Health (CABI), PubMed, and the Cochrane database for articles published in English up to May 2012, and complemented by a search of the reference lists of key publications. Seven observational studies investigating a total of 403 inpatients satisfied the inclusion criteria. The range of prescribed energy intakes varied from 1,000 kcal to >1,900 kcal/day with a progressive increase during the course of hospitalization. It appeared that additional tube feeding increased the maximum energy intake and led to greater interim or discharge weight; however, this was also associated with a higher incidence of adverse effects. Overall, the level of available evidence was poor, and therefore consensus on the most effective and safe treatment for weight restoration in inpatient children and adolescents with AN is not currently feasible. Further research on refeeding methods is crucial to establish the best practice approach to treatment of this population.


ALK : L EARNING TO W ALK IN G RAPH WITH M ONTE C ARLO T REE S EARCH


title: Interactively test driving an object detector: Estimating performance on unlabeled data

abstract: In this paper, we study the problem of ‘test-driving’ a detector, i.e. allowing a human user to get a quick sense of how well the detector generalizes to their specific requirement. To this end, we present the first system that estimates detector performance interactively without extensive ground truthing using a human in the loop. We approach this as a problem of estimating proportions and show that it is possible to make accurate inferences on the proportion of classes or groups within a large data collection by observing only 5 – 10% of samples from the data. In estimating the false detections (for precision), the samples are chosen carefully such that the overall characteristics of the data collection are preserved. Next, inspired by its use in estimating disease propagation we apply pooled testing approaches to estimate missed detections (for recall) from the dataset. The estimates thus obtained are close to the ones obtained using ground truth, thus reducing the need for extensive labeling which is expensive and time consuming.


Pornography Addiction and Mental Health Disorders


title: Network attribute selection, classification and accuracy (NASCA) procedure for intrusion detection systems

abstract: With the progressive development of network applications and software dependency, we need to discover more advanced methods for protecting our systems. Each industry is equally affected, and regardless of whether we consider the vulnerability of the government or each individual household or company, we have to find a sophisticated and secure way to defend our systems. The starting point is to create a reliable intrusion detection mechanism that will help us to identify the attack at a very early stage; otherwise in the cyber security space the intrusion can affect the system negatively, which can cause enormous consequences and damage the system's privacy, security or financial stability. This paper proposes a concise, and easy to use statistical learning procedure, abbreviated NASCA, which is a four-stage intrusion detection method that can successfully detect unwanted intrusion to our systems. The model is static, but it can be adapted to a dynamic set up.


title: A Fast Search Algorithm for a Large Fuzzy Database

abstract: In this paper, we propose a fast search algorithm for a large fuzzy database that stores iris codes or data with a similar binary structure. The fuzzy nature of iris codes and their high dimensionality render many modern search algorithms, mainly relying on sorting and hashing, inadequate. The algorithm that is used in all current public deployments of iris recognition is based on a brute force exhaustive search through a database of iris codes, looking for a match that is close enough. Our new technique, Beacon Guided Search (BGS), tackles this problem by dispersing a multitude of ldquobeaconsrdquo in the search space. Despite random bit errors, iris codes from the same eye are more likely to collide with the same beacons than those from different eyes. By counting the number of collisions, BGS shrinks the search range dramatically with a negligible loss of precision. We evaluate this technique using 632,500 iris codes enrolled in the United Arab Emirates (UAE) border control system, showing a substantial improvement in search speed with a negligible loss of accuracy. In addition, we demonstrate that the empirical results match theoretical predictions.


title: An 80.4% Peak Power Efficiency Adaptive Supply Class H Power Amplifier for Audio Applications

abstract: This paper presents a novel class H power amplifier aiming for audio applications on battery-powered electronic devices. The power supply of the amplifier is adaptively adjusted to track the instantaneous input signal amplitude for higher power efficiency. By embedding audio input signal amplitude information into the class AB amplifier’s output common-mode voltage level, the amplifier is able to operate with only single-rail power supply and demonstrates more smooth transitions between the light load and heavy load mode, hence achieves lower total harmonic distortion (THD) for a wide load range. Fabricated in the AMS 0.18- $\mu \text{m}$ CMOS process, the chip consumes 3.52 mW quiescent power and is able to deliver 526 mW peak output power to a $16~\Omega $ load. The measurement results indicate that the amplifier incurs no deterioration on THD when entering the supplytracking mode and achieves a lowest THD+N ratio of −80 dB. The peak power efficiency of the system is 80.4%; moreover, it demonstrates significant higher efficiency for medium load range compared to other linear mode amplifiers.


title: The effects of technology use in postsecondary education: A meta-analysis of classroom applications

abstract: This meta-analysis is a study of the experimental literature of technology use in postsecondary education from 1990 up to 2010 exclusive of studies of online or distance education previously reviewed by Bernard et al. (2004). It reports the overall weighted average effects of technology use on achievement and attitude outcomes and explores moderator variables in an attempt to explain how technology treatments lead to positive or negative effects. Out of an initial pool of 11,957 study abstracts, 1105 were chosen for analysis, yielding 879 achievement and 181 attitude effect sizes after pre-experimental designs and studies with obvious methodological confounds were removed. The random effects weighted average effect size for achievement was g+ = 0.27, k = 879, p < .05, and for attitude outcomes it was g+ = 0.20, k = 181, p < .05. The collection of achievement outcomes was divided into two sub-collections, according to the amount of technology integration in the control condition. These were no technology in the control condition (k = 479) and some technology in the control condition (k = 400). Random effects multiple meta-regression analysis was run on each sub-collection revealing three significant predictors (subject matter, degree of difference in technology use between the treatment and the control and pedagogical uses of technology). The set of predictors for each sub-collection was both significant and homogeneous. Differences were found among the levels of all three moderators, but particularly in favor of cognitive support applications. There were no significant predictors for attitude outcomes.


title: Percutaneous kyphoplasty and pedicle screw fixation for the management of thoraco-lumbar burst fractures

abstract: The study design includes prospective evaluation of percutaneous osteosynthesis associated with cement kyphoplasty on 18 patients. The objective of the study is to assess the efficacy of a percutaneous method of treating burst vertebral fractures in patients without neurological deficits. Even if burst fractures are frequent, no therapeutic agreement is available at the moment. We report in this study the results at 2 years with a percutaneous approach for the treatment of burst fractures. 18 patients were included in this study. All the patients had burst vertebral fractures classified type A3 on the Magerl scale, between levels T9 and L2. The patients' mean age was 53 years (range 22-78 years) and the neurological examination was normal. A percutaneous approach was systematically used and a kyphoplasty was performed via the transpedicular pathway associated with percutaneous short-segment pedicle screw osteosynthesis. The patients' follow-up included CT scan analysis, measurement of vertebral height recovery and local kyphosis, and clinical pain assessments. With this surgical approach, the mean vertebral height was improved by 25% and a mean improvement of 11.28 degrees in the local kyphotic angle was obtained. 3 months after the operation, none of the patients were taking class II analgesics. The mean duration of their hospital stay was 4.5 days (range 3-7 days) and the mean follow-up period was 26 months (range 17-30 months). No significant changes in the results obtained were observed at the end of the follow-up period. Minimally invasive methods of treating burst vertebral fractures can be performed via the percutaneous pathway. This approach gives similar vertebral height recovery and kyphosis correction rates to those obtained with open surgery. It provides a short hospital stay, however, and might therefore constitute a useful alternative to open surgical methods.


title: Drone Path Planning for Secure Positioning and Secure Position Verification

abstract: Many dependable systems rely on the integrity of the position of their components. In such systems, two key problems are secure localization and secure location verification of the components. Researchers proposed several solutions, which generally require expensive infrastructures of several fixed stations ( anchors ) with trusted positions. In this paper, we explore the approach of replacing all the fixed anchors with a single drone that flies through a sequence of waypoints. At each waypoint, the drone acts as an anchor and securely determines the positions. This approach completely eliminates the need for many expensive anchors. The main challenge becomes how to find a convenient path for the drone to do this for all the devices. The problem presents novel aspects, which make existing path planning algorithms unsuitable. We propose LocalizerBee , VerifierBee , and PreciseVerifierBee : three path planning algorithms that allow a drone to respectively measure, verify, and verify with a guaranteed precision a set of positions in a secure manner. They are able to securely localize all the positions in a generic deployment area, even in the presence of drone control errors. Moreover, they produce short path lengths and they run in a reasonable processing time.


title: Data augmentation-assisted deep learning of hand-drawn partially colored sketches for visual search

abstract: In recent years, image databases are growing at exponential rates, making their management, indexing, and retrieval, very challenging. Typical image retrieval systems rely on sample images as queries. However, in the absence of sample query images, hand-drawn sketches are also used. The recent adoption of touch screen input devices makes it very convenient to quickly draw shaded sketches of objects to be used for querying image databases. This paper presents a mechanism to provide access to visual information based on users’ hand-drawn partially colored sketches using touch screen devices. A key challenge for sketch-based image retrieval systems is to cope with the inherent ambiguity in sketches due to the lack of colors, textures, shading, and drawing imperfections. To cope with these issues, we propose to fine-tune a deep convolutional neural network (CNN) using augmented dataset to extract features from partially colored hand-drawn sketches for query specification in a sketch-based image retrieval framework. The large augmented dataset contains natural images, edge maps, hand-drawn sketches, de-colorized, and de-texturized images which allow CNN to effectively model visual contents presented to it in a variety of forms. The deep features extracted from CNN allow retrieval of images using both sketches and full color images as queries. We also evaluated the role of partial coloring or shading in sketches to improve the retrieval performance. The proposed method is tested on two large datasets for sketch recognition and sketch-based image retrieval and achieved better classification and retrieval performance than many existing methods.


title: RUDDER: Return Decomposition for Delayed Rewards

abstract: We propose RUDDER, a novel reinforcement learning approach for delayed rewards in finite Markov decision processes (MDPs). In MDPs the Q-values are equal to the expected immediate reward plus the expected future rewards. The latter are related to bias problems in temporal difference (TD) learning and to high variance problems in Monte Carlo (MC) learning. Both problems are even more severe when rewards are delayed. RUDDER aims at making the expected future rewards zero, which simplifies Q-value estimation to computing the mean of the immediate reward. We propose the following two new concepts to push the expected future rewards toward zero. (i) Reward redistribution that leads to return-equivalent decision processes with the same optimal policies and, when optimal, zero expected future rewards. (ii) Return decomposition via contribution analysis which transforms the reinforcement learning task into a regression task at which deep learning excels. On artificial tasks with delayed rewards, RUDDER is significantly faster than MC and exponentially faster than Monte Carlo Tree Search (MCTS), TD(λ), and reward shaping approaches. At Atari games, RUDDER on top of a Proximal Policy Optimization (PPO) baseline improves the scores, which is most prominent at games with delayed rewards.


title: White Matter Integrity and Episodic Memory Performance in Mild Cognitive Impairment: A Diffusion Tensor Imaging Study

abstract: White matter (WM) integrity in the medial temporal lobes and episodic memory performance were examined in patients with mild cognitive impairment (MCI) and age-matched cognitively intact controls. Material specific associations between WM in the left versus right hemisphere and verbal versus visual memory performance were examined as well. Fourteen right-handed amnestic MCI patients underwent diffusion tensor imaging (DTI) and received verbal (words, story) and visual (designs) memory tests. Delayed verbal memory was significantly correlated with loss of WM integrity in the medial temporal lobe. This finding was associated with both the left and right temporal regions. Immediate visual memory performance was significantly correlated with the loss of WM integrity in the left temporal region. The results indicate that WM integrity in the medial temporal lobe is associated with objective memory functioning in MCI. However, strong material specific relationships were not observed, possibly reflecting diverse encoding strategies used by participants such as imagery of verbal material and verbal encoding of designs.


title: PSYCHOLOGICAL SCIENCE Research Article DO AMNESICS EXHIBIT COGNITIVE DISSONANCE REDUCTION? The Role of Explicit Memory and Attention in Attitude Change

abstract: In two studies, we investigated the roles of explicit memory and attentional resources in the process of behavior-induced attitude change. Although most theories of attitude change (cognitive dissonance and self-perception theories) assume an important role for both mechanisms, we propose that behavior-induced attitude change can be a relatively automatic process that does not require explicit memory for, or consciously controlled processing of, the discrepancy between attitude and behavior. Using a free-choice paradigm, we found that both amnesics and normal participants under cognitive load showed as much attitude change as did control participants.


title: Expected Patch Log Likelihood with a Sparse Prior

abstract: Image priors are of great importance in image restoration tasks. These problems can be addressed by decomposing the degraded image into overlapping patches, treating the patches individually and averaging them back together. Recently, the Expected Patch Log Likelihood (EPLL) method has been introduced, arguing that the chosen model should be enforced on the final reconstructed image patches. In the context of a Gaussian Mixture Model (GMM), this idea has been shown to lead to state-of-the-art results in image denoising and debluring. In this paper we combine the EPLL with a sparse-representation prior. Our derivation leads to a close yet extended variant of the popular K-SVD image denoising algorithm, where in order to effectively maximize the EPLL the denoising process should be iterated. This concept lies at the core of the K-SVD formulation, but has not been addressed before due the need to set different denoising thresholds in the successive sparse coding stages. We present a method that intrinsically determines these thresholds in order to improve the image estimate. Our results show a notable improvement over K-SVD in image denoising and inpainting, achieving comparable performance to that of EPLL with GMM in denoising.


title: Carving research slices out of your production networks with OpenFlow

abstract: 1. SLICED PROGRAMMABLE NETWORKS OpenFlow [4] has been demonstrated as a way for researchers to run networking experiments in their production network. Last year, we demonstrated how an OpenFlow controller running on NOX [3] could move VMs seamlessly around an OpenFlow network [1]. While OpenFlow has potential [2] to open control of the network, only one researcher can innovate on the network at a time. What is required is a way to divide, or slice, network resources so that researchers and network administrators can use them in parallel. Network slicing implies that actions in one slice do not negatively affect other slices, even if they share the same underlying physical hardware. A common network slicing technique is VLANs. With VLANs, the administrator partitions the network by switch port and all traffic is mapped to a VLAN by input port or explicit tag. This coarse-grained type of network slicing complicates more interesting experiments such as IP mobility or wireless handover. Here, we demonstrate FlowVisor, a special purpose OpenFlow controller that allows multiple researchers to run experiments safely and independently on the same production OpenFlow network. To motivate FlowVisor’s flexibility, we demonstrate four network slices running in parallel: one slice for the production network and three slices running experimental code (Figure 1). Our demonstration runs on real network hardware deployed on our production network at Stanford and a wide-area test-bed with a mix of wired and wireless technologies.


title: A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system

abstract: Our bodies—especially our skin, our saliva, the lining of our mouth and our gastrointestinal tract—are home to a diverse collection of bacteria and other microorganisms called the microbiome. While the roles played by many of these microorganisms have yet to be identified, it is known that they contribute to the health and wellbeing of their host by metabolizing indigestible compounds, producing essential vitamins, and preventing the growth of harmful bacteria. They are important for nutrient and carbon cycling in the environment. The advent of advanced sequencing techniques has made it feasible to study the composition of this microbial community, and to monitor how it changes over time or how it responds to events such as antibiotic treatment. Sequencing studies have been used to highlight the significant differences between microbial communities found in different parts of the body, and to follow the evolution of the gut microbiome from birth. Most of these studies have focused on live animals, so little is known about what happens to the microbiome after its host dies. In particular, it is not known if the changes that occur after death are similar for all individuals. Moreover, the decomposing animal supplies nutrients and carbon to the surrounding ecosystem, but its influence on the microbial community of its immediate environment is not well understood. Now Metcalf et al. have used high-throughput sequencing to study the bacteria and other microorganisms (such as nematodes and fungi) in dead and decomposing mice, and also in the soil beneath them, over the course of 48 days. The changes were significant and also consistent across the corpses, with the microbial communities in the corpses influencing those in the soil, and vice versa. Metcalf et al. also showed that these measurements could be used to estimate the postmortem interval (the time since death) to within approximately 3 days, which suggests that the work could have applications in forensic science.


title: Swarm Intelligence Algorithms for Data Clustering

abstract: Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks, in these days, require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This, in turn, imposes severe computational requirements on the relevant clustering techniques. A family of bio-inspired algorithms, well-known as Swarm Intelligence (SI) has recently emerged that meets these requirements and has successfully been applied to a number of real world clustering problems. This chapter explores the role of SI in clustering different kinds of datasets. It finally describes a new SI technique for partitioning any dataset into an optimal number of groups through one run of optimization. Computer simulations undertaken in this research have also been provided to demonstrate the effectiveness of the proposed algorithm.


title: Hard Drive Side-Channel Attacks Using Smartphone Magnetic Field Sensors

abstract: In this paper we present a new class of side-channel attacks on computer hard drives. Hard drives contain one or more spinning disks made of a magnetic material. In addition, they contain different magnets which rapidly move the head to a target position on the disk to perform a write or a read. The magnetic fields from the disk’s material and head are weak and well shielded. However, we show that the magnetic field due to the moving head can be picked up by sensors outside of the hard drive. With these measurements, we are able to deduce patterns about ongoing operations. For example, we can detect what type of the operating system is booting up or what application is being started. Most importantly, no special equipment is necessary. All attacks can be performed by using an unmodified smartphone placed in proximity of a hard drive.


title: Semantic Neighbor Graph Hashing for Multimodal Retrieval

abstract: Hashing methods have been widely used for approximate nearest neighbor search in recent years due to its computational and storage effectiveness. Most existing multimodal hashing methods try to preserve the similarity relationship based on either metric distances or semantic labels in a procrustean way, while ignoring the intra-class and inter-class variations inherent in the metric space. In this paper, we propose a novel multimodal hashing method, termed as semantic neighbor graph hashing (SNGH), which aims to preserve the fine-grained similarity metric based on the semantic graph that is constructed by jointly pursuing the semantic supervision and the local neighborhood structure. Specifically, the semantic graph is constructed to capture the local similarity structure for the image modality and the text modality, respectively. Furthermore, we define a function based on the local similarity in particular to adaptively calculate multi-level similarities by encoding the intra-class and inter-class variations. After obtaining the unified hash codes, the logistic regression with kernel trick is employed to learn view-specific hash functions independently for each modality. Extensive experiments are conducted on four widely used multimodal data sets. The experimental results demonstrate the superiority of the proposed SNGH method compared with the state-of-the-art multimodal hashing methods.


title: The Relationship Between the Stomatognathic System and Body Posture

abstract: In recent years, many researchers have investigated the various factors that can influence body posture: mood states, anxiety, head and neck positions, oral functions (respiration, swallowing), oculomotor and visual systems, and the inner ear. Recent studies indicate a role for trigeminal afferents on body posture, but this has not yet been demonstrated conclusively. The present study aims to review the papers that have shown a relationship between the stomatognathic system and body posture. These studies suggest that tension in the stomatognathic system can contribute to impaired neural control of posture. Numerous anatomical connections between the stomatognathic system's proprioceptive inputs and nervous structures are implicated in posture (cerebellum, vestibular and oculomotor nuclei, superior colliculus). If the proprioceptive information of the stomatognathic system is inaccurate, then head control and body position may be affected. In addition, the present review discusses the role the myofascial system plays in posture. If confirmed by further research, these considerations can improve our understanding and treatment of muscular-skeletal disorders that are associated with temporomandibular joint disorders, occlusal changes, and tooth loss.


title: Locally Differentially Private Frequent Itemset Mining

abstract: The notion of Local Differential Privacy (LDP) enables users to respond to sensitive questions while preserving their privacy. The basic LDP frequent oracle (FO) protocol enables an aggregator to estimate the frequency of any value. But when each user has a set of values, one needs an additional padding and sampling step to find the frequent values and estimate their frequencies. In this paper, we formally define such padding and sample based frequency oracles (PSFO). We further identify the privacy amplification property in PSFO. As a result, we propose SVIM, a protocol for finding frequent items in the set-valued LDP setting. Experiments show that under the same privacy guarantee and computational cost, SVIM significantly improves over existing methods. With SVIM to find frequent items, we propose SVSM to effectively find frequent itemsets, which to our knowledge has not been done before in the LDP setting.


title: Parallel Database Recovery for Multicore Main-Memory Databases.

abstract: Main-memory database systems for multicore servers can achieve excellent performance when processing massive volume of OLTP workloads. But crash-resilience mechanism, or namely loggingand-replay, can either degrade the performance of transaction processing or slow down the process of failure recovery. In this paper, we show that, by exploiting application semantics, it is possible to achieve speedy failure recovery without introducing any costly logging overhead to the execution of online transactions. We propose TARS, a parallel log-recovery mechanism that is specifically designed for lightweight, coarse-grained command-logging approach. TARS leverages a combination of static and dynamic analyses to parallelize the log replay: at compile time, TARS decomposes stored procedures by carefully analyzing dependencies within and across programs; at recovery time, a re-execution schedule with a high degree of parallelism is attained through lightweight one-pass scans over transaction parameter values. As such, recovery latency is remarkably minimized. We evaluated TARS in a main-memory database prototype running on a 40-core server. Compared to the state-of-the-art mechanisms, TARS yields significantly higher recovery rate without compromising the efficiency of transaction processing.


title: Intense THz pulses down-regulate genes associated with skin cancer and psoriasis: a new therapeutic avenue?

abstract: Terahertz (THz) radiation lies between the infrared and microwave regions of the electromagnetic spectrum and is non-ionizing. We show that exposure of artificial human skin tissue to intense, picosecond-duration THz pulses affects expression levels of numerous genes associated with non-melanoma skin cancers, psoriasis and atopic dermatitis. Genes affected by intense THz pulses include nearly half of the epidermal differentiation complex (EDC) members. EDC genes, which are mapped to the chromosomal human region 1q21, encode for proteins that partake in epidermal differentiation and are often overexpressed in conditions such as psoriasis and skin cancer. In nearly all the genes differentially expressed by exposure to intense THz pulses, the induced changes in transcription levels are opposite to disease-related changes. The ability of intense THz pulses to cause concerted favorable changes in the expression of multiple genes implicated in inflammatory skin diseases and skin cancers suggests potential therapeutic applications of intense THz pulses.


title: Memory Architectures in Recurrent Neural Network Language Models

abstract: We compare and analyze sequential, random access, and stack memory architectures for recurrent neural network language models. Our experiments on the Penn Treebank and Wikitext-2 datasets show that stack-based memory architectures consistently achieve the best performance in terms of held out perplexity. We also propose a generalization to existing continuous stack models (Joulin & Mikolov,2015; Grefenstette et al., 2015) to allow a variable number of pop operations more naturally that further improves performance. We further evaluate these language models in terms of their ability to capture non-local syntactic dependencies on a subject-verb agreement dataset (Linzen et al., 2016) and establish new state of the art results using memory augmented language models. Our results demonstrate the value of stack-structured memory for explaining the distribution of words in natural language, in line with linguistic theories claiming a context-free backbone for natural language.


title: How to Pitch a Brilliant Idea

abstract: Author(s): Elsbach, Kimberly D | Abstract: Coming up with creative ideas is easy; selling them to strangers is hard. Entrepreneurs, sales executives, and marketing managers often go to great lengths to demonstrate how their new concepts are practical and profitable--only to be rejected by corporate decision makers who don't seem to understand the value of the ideas. Why does this happen? Having studied Hollywood executives who assess screenplay pitches, the author says the person on the receiving end--the "catcher"--tends to gauge the pitcher's creativity as well as the proposal itself. An impression of the pitcher's ability to come up with workable ideas can quickly and permanently overshadow the catcher's feelings about an idea's worth. To determine whether these observations apply to business settings beyond Hollywood, the author attended product design, marketing, and venture-capital pitch sessions and conducted interviews with executives responsible for judging new ideas. The results in those environments were similar to her observations in Hollywood, she says. Catchers subconsciously categorize successful pitchers as showrunners (smooth and professional), artists (quirky and unpolished), or neophytes (inexperienced and naive). The research also reveals that catchers tend to respond well when they believe they are participating in an idea's development. As Oscar-winning writer, director, and producer Oliver Stone puts it, screen-writers pitching an idea should "pull back and project what he needs onto your idea in order to make the story whole for him." To become a successful pitcher, portray yourself as one of the three creative types and engage your catchers in the creative process. By finding ways to give your catchers a chance to shine, you sell yourself as a likable collaborator.


title: On language and connectionism: Analysis of a parallel distributed processing model of language acquisition

abstract: Abstract Does knowledge of language consist of mentally-represented rules? Rumelhart and McClelland have described a connectionist (parallel distributed processing) model of the acquisition of the past tense in English which successfully maps many stems onto their past tense forms, both regular (walk/walked) and irregular (go/went), and which mimics some of the errors and sequences of development of children. Yet the model contains no explicit rules, only a set of neuronstyle units which stand for trigrams of phonetic features of the stem, a set of units which stand for trigrams of phonetic features of the past form, and an array of connections between the two sets of units whose strengths are modified during learning. Rumelhart and McClelland conclude that linguistic rules may be merely convenient approximate fictions and that the real causal processes in language use and acquisition must be characterized as the transfer of activation levels among units and the modification of the weights of their connections. We analyze both the linguistic and the developmental assumptions of the model in detail and discover that (1) it cannot represent certain words, (2) it cannot learn many rules, (3) it can learn rules found in no human language, (4) it cannot explain morphological and phonological regularities, (5) it cannot explain the differences between irregular and regular forms, (6) it fails at its assigned task of mastering the past tense of English, (7) it gives an incorrect explanation for two developmental phenomena: stages of overregularization of irregular forms such as bringed, and the appearance of doubly-marked forms such as ated and (8) it gives accounts of two others (infrequent overregularization of verbs ending in t/d, and the order of acquisition of different irregular subclasses) that are indistinguishable from those of rule-based theories. In addition, we show how many failures of the model can be attributed to its connectionist architecture. We conclude that connectionists' claims about the dispensability of rules in explanations in the psychology of language must be rejected, and that, on the contrary, the linguistic and developmental facts provide good evidence for such rules.


title: Analysis of the Bitcoin UTXO Set

abstract: Bitcoin relies on the Unspent Transaction Outputs (UTXO) set to efficiently verify new generated transactions. Every unspent output, no matter its type, age, value or length is stored in every full node. In this paper we introduce a tool to study and analyze the UTXO set, along with a detailed description of the set format and functionality. Our analysis includes a general view of the set and quantifies the difference between the two existing formats up to the date. We also provide an accurate analysis of the volume of dust and unprofitable outputs included in the set, the distribution of the block height in which the outputs where included, and the use of non-standard outputs.


title: From Modeling to Implementation of Virtual Sensors in Body Sensor Networks

abstract: Body Sensor Networks (BSNs) represent an emerging technology which has received much attention recently due to its enormous potential to enable remote, real-time, continuous and non-invasive monitoring of people in health-care, entertainment, fitness, sport, social interaction. Signal processing for BSNs usually comprises of multiple levels of data abstraction, from raw sensor data to data calculated from processing steps such as feature extraction and classification. This paper presents a multi-layer task model based on the concept of Virtual Sensors to improve architecture modularity and design reusability. Virtual Sensors are abstractions of components of BSN systems that include sensor sampling and processing tasks and provide data upon external requests. The Virtual Sensor model implementation relies on SPINE2, an open source domain-specific framework that is designed to support distributed sensing operations and signal processing for wireless sensor networks and enables code reusability, efficiency, and application interoperability. The proposed model is applied in the context of gait analysis through wearable sensors. A gait analysis system is developed according to a SPINE2-based Virtual Sensor architecture and experimentally evaluated. Obtained results confirm that great effectiveness can be achieved in designing and implementing BSN applications through the Virtual Sensor approach while maintaining high efficiency and accuracy.


title: Efficient Machine Learning Techniques for Stock Market Prediction

abstract: Stock market prediction is forever important issue for investor. Computer science plays vital role to solve this problem. From the evolution of machine learning, people from this area are busy to solve this problem effectively. Many different techniques are used to build predicting system. This research describes different state of the art techniques used for stock forecasting and compare them w.r.t. their pros and cons. We have classified different techniques categorically; Time Series, Neural Network and its different variation (RNN, ESN, MLP, LRNN etc.) and different hybrid techniques (combination of neural network with different machine learning techniques) (ANFIS, GA/ATNN, GA/TDNN, ICA-BPN). By extensive study of different techniques, it was analyzed that Neural Network is the best technique till time to predict stock prices especially when some denoising schemes are applied with neural network. We, also, have implemented and compared different neural network techniques like Layered Recurrent Neural Network (LRNN), Wsmpca-NN and Feed forward Neural Network (NN). By comparing said techniques, it was observed that LRNN performs better than feed forward NN and Wsmpca-NN performs better than LRNN and NN. We have applied said techniques on PSO (Pakistan State Oil), S&P500 data sets.


title: A Secure Cloud Backup System with Assured Deletion and Version Control

abstract: Cloud storage is an emerging service model that enables individuals and enterprises to outsource the storage of data backups to remote cloud providers at a low cost. However, cloud clients must enforce security guarantees of their outsourced data backups. We present Fade Version, a secure cloud backup system that serves as a security layer on top of today's cloud storage services. Fade Version follows the standard version-controlled backup design, which eliminates the storage of redundant data across different versions of backups. On top of this, Fade Version applies cryptographic protection to data backups. Specifically, it enables fine-grained assured deletion, that is, cloud clients can assuredly delete particular backup versions or files on the cloud and make them permanently inaccessible to anyone, while other versions that share the common data of the deleted versions or files will remain unaffected. We implement a proof-of-concept prototype of Fade Version and conduct empirical evaluation atop Amazon S3. We show that Fade Version only adds minimal performance overhead over a traditional cloud backup service that does not support assured deletion.


title: ADHD and academic performance: why does ADHD impact on academic performance and what can be done to support ADHD children in the classroom?

abstract: This paper reviews the relationship between attention deficit hyperactivity disorder (ADHD) and academic performance. First, the relationship at different developmental stages is examined, focusing on pre-schoolers, children, adolescents and adults. Second, the review examines the factors underpinning the relationship between ADHD and academic underperformance: the literature suggests that it is the symptoms of ADHD and underlying cognitive deficits not co-morbid conduct problems that are at the root of academic impairment. The review concludes with an overview of the literature examining strategies that are directed towards remediating the academic impairment of individuals with ADHD.