title: Athlete burnout in elite sport: A self-determination perspective

abstract: Abstract Using self-determination theory (Deci & Ryan, 1985) as the theoretical framework, we examined potential antecedents of athlete burnout in 201 elite Canadian athletes (121 females, 80 males; mean age 22.9 years). Employing a cross-sectional design, our primary aims were to investigate the relationships between behavioural regulations and athlete burnout and to examine whether self-determined motivation mediated relationships between basic needs satisfaction and athlete burnout. Our self-determination theory-derived hypotheses were largely supported. Relationships among athlete burnout and behavioural regulations mostly varied according to their rank on the self-determination continuum, with less self-determined motives showing positive associations and more self-determined motives showing negative correlations with burnout. The basic needs of competence and autonomy, plus self-determined motivation, accounted for significant amounts of variance in athlete burnout symptoms (exhaustion, R 2 = 0.31; ...


title: Novel Modeling and Damping Technique for Hybrid Stepper Motor

abstract: It is well known that commercial hybrid stepper system has one or more low-speed resonant points. However, this characteristic cannot be accurately modeled without high-order equations or complicated measurement of motor parameters. In this paper, a novel approach is proposed to model the behavior of a commercial 1.8deg hybrid stepper motor accurately and efficiently. Also, model-based damping algorithms for both open-loop control and servo control are proposed. Simulation and experimental results show that the proposed algorithms can effectively eliminate low-speed resonance and vibration of the stepper system. The algorithms are efficient enough to be implemented on commercial DSP-based controllers without sacrificing motion control performance.


title: Parsing as AbstractInterpretation

abstract: Earley's parsing algorithm is shown to be an abstract interpretation of a refinement of the derivation semantics of context-free grammars.


title: Developing professional judgement with the aid of a ‘low-profile’ group support system

abstract: This paper reports positive results from an application of one type of group support system (GSS) to a training application. Reviews of the findings of other trials of GSS have been mixed, and inconclusive. We describe the results of a series of seven training sessions in a field-based application of group process support. The subjects were professionals working in various agencies concerned with the welfare of older people. A ‘low-profile’ type of group support system, based on wireless handsets, was used. This design enabled responses from each participant to be input and displayed anonymously. Each session was aimed at stimulating a dialogue focused on the reasons for differences of judgement, as displayed on a single projected feedback screen. Changes of individual judgements were recorded for subsequent analysis and comparison with already known ‘expert judgements’. Frequent changes of judgement were recorded. A significant proportion of these were related to an improvement, which could not be explained as simply the result of conforming behaviour. We propose that the mode of operation and design of a ‘low-profile’ GSS have the potential to create a learning environment by reducing personal anxieties while encouraging group-based learning with focussed conversation. We conclude that this type of GSS design is particularly suited to ‘selective’-type tasks in groups.


title: CNN using memristors for neighborhood connections

abstract: In this paper we consider using memristors to implement the neighborhood connections of a CNN. First the benefits and drawbacks of using memristors as programmable CNN weights are described. Then, an existing memristor model is improved to allow full-scale simulation of the design. The new model is implemented in the SPICE simulation environment and is not restricted to CNN applications. Then, the CNN cell design is presented and simulations describing memristor programming are performed.


title: Control of distributed generation systems-Part I: Voltages and currents control

abstract: This paper discusses a digital control strategy for three-phase pulse-width modulation voltage inverters used in a single stand-alone ac distributed generation system. The proposed control strategy utilizes the perfect robust servomechanism problem control theory to allow elimination of specified unwanted voltage harmonics from the output voltages under severe nonlinear load and to achieve fast recovery performance on load transient. This technique is combined with a discrete sliding mode current controller that provides fast current limiting capability necessary under overload or short circuit conditions. The proposed control strategy has been implemented on a digital signal processor system and experimentally tested on an 80-kVA prototype unit. The results showed the effectiveness of the proposed control algorithm.


title: Predictive coding under the free-energy principle

abstract: This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain. We assume that the brain models the world as a hierarchy or cascade of dynamical systems that encode causal structure in the sensorium. Perception is equated with the optimization or inversion of these internal models, to explain sensory data. Given a model of how sensory data are generated, we can invoke a generic approach to model inversion, based on a free energy bound on the model's evidence. The ensuing free-energy formulation furnishes equations that prescribe the process of recognition, i.e. the dynamics of neuronal activity that represent the causes of sensory input. Here, we focus on a very general model, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states. We first review hierarchical dynamical models and their inversion. We then show that the brain has the necessary infrastructure to implement this inversion and illustrate this point using synthetic birds that can recognize and categorize birdsongs.


title: A robust EM clustering algorithm for Gaussian mixture models

abstract: Clustering is a useful tool for finding structure in a data set. The mixture likelihood approach to clustering is a popular clustering method, in which the EM algorithm is the most used method. However, the EM algorithm for Gaussian mixture models is quite sensitive to initial values and the number of its components needs to be given a priori. To resolve these drawbacks of the EM, we develop a robust EM clustering algorithm for Gaussian mixture models, first creating a new way to solve these initialization problems. We then construct a schema to automatically obtain an optimal number of clusters. Therefore, the proposed robust EM algorithm is robust to initialization and also different cluster volumes with automatically obtaining an optimal number of clusters. Some experimental examples are used to compare our robust EM algorithm with existing clustering methods. The results demonstrate the superiority and usefulness of our proposed method.


title: Trajectory pattern mining: Exploring semantic and time information

abstract: With the development of GPS and the popularity of smart phones and wearable devices, users can easily log their daily trajectories. Prior works have elaborated on mining trajectory patterns from raw trajectories. Trajectory patterns consist of hot regions and the sequential relationships among them, where hot regions refer the spatial regions with a higher density of data points. Note that some hot regions do not have any meaning for users. Moreover, trajectory patterns do not have explicit time information or semantic information. To enrich trajectory patterns, we propose semantic trajectory patterns which are referred to as the moving patterns with spatial, temporal, and semantic attributes. Given a user trajectory, we aim at mining frequent semantic trajectory patterns. Explicitly, we extract the three attributes from a raw trajectory, and convert it into a semantic mobility sequence. Given such a semantic mobility sequence, we propose two algorithms to discover frequent semantic trajectory patterns. The first algorithm, MB (standing for matching-based algorithm), is a naive method to find frequent semantic trajectory patterns. It generates all possible patterns and extracts the occurrence of the patterns from the semantic mobility sequence. The second algorithm, PS (standing for PrefixSpan-based algorithm), is developed to efficiently mine semantic trajectory patterns. Due to the good efficiency of PrefixSpan, algorithm PS will fully utilize the advantage of PrefixSpan. Since the semantic mobility sequence contains three attributes, we need to further transform it into a raw sequence before using algorithm PrefixSpan. Therefore, we propose the SS algorithm (standing for sequence symbolization algorithm) to achieve this purpose. To evaluate our proposed algorithms, we conducted experiments on the real datasets of Google Location History, and the experimental results show the effectiveness and efficiency of our proposed algorithms.


title: Redressing First Nations historical trauma: Theorizing mechanisms for indigenous culture as mental health treatment

abstract: Indigenous “First Nations” communities have consistently associated their disproportionate rates of psychiatric distress with historical experiences of European colonization. This emphasis on the socio-psychological legacy of colonization within tribal communities has occasioned increasingly widespread consideration of what has been termed historical trauma within First Nations contexts. In contrast to personal experiences of a traumatic nature, the concept of historical trauma calls attention to the complex, collective, cumulative, and intergenerational psychosocial impacts that resulted from the depredations of past colonial subjugation. One oft-cited exemplar of this subjugation—particularly in Canada—is the Indian residential school. Such schools were overtly designed to “kill the Indian and save the man.” This was institutionally achieved by sequestering First Nations children from family and community while forbidding participation in Native cultural practices in order to assimilate them into the lo...


title: A historical review of robust control

abstract: A historical review of recent results in robust control is presented. The robust control problem, i.e., the problem of designing accurate control systems in the presence of significant plant uncertainties, is classical. However, over the past 15 years, significant new theory has been developed for the solution of this problem, especially with respect to linear multivariable systems characterized in the frequency domain, and the term robust control for this classical problem is only of recent vintage (1972). Some of the major contributions of modern robust control theory include the development of synthesis techniques for robust stabilization, and H2and H^{\infty} sensitivity optimization of multivariable systems. In this review, we confine the term robust control to the design of fixed controllers. Thus, only nonadaptive or nonself-tuning solutions to the problem of controlling uncertain systems are reviewed. Finally, it should be noted that the review is largely limited to the literature published in IEEE journals and conference proceedings, and some English-language journals. It is, of course, recognized that many significant related contributions have appeared elsewhere.


title: Universal Robotic Gripper based on the Jamming of Granular Material

abstract: Gripping and holding of objects are key tasks for robotic manipulators. The development of universal grippers able to pick up unfamiliar objects of widely varying shape and surface properties remains, however, challenging. Most current designs are based on the multifingered hand, but this approach introduces hardware and software complexities. These include large numbers of controllable joints, the need for force sensing if objects are to be handled securely without crushing them, and the computational overhead to decide how much stress each finger should apply and where. Here we demonstrate a completely different approach to a universal gripper. Individual fingers are replaced by a single mass of granular material that, when pressed onto a target object, flows around it and conforms to its shape. Upon application of a vacuum the granular material contracts and hardens quickly to pinch and hold the object without requiring sensory feedback. We find that volume changes of less than 0.5% suffice to grip objects reliably and hold them with forces exceeding many times their weight. We show that the operating principle is the ability of granular materials to transition between an unjammed, deformable state and a jammed state with solid-like rigidity. We delineate three separate mechanisms, friction, suction, and interlocking, that contribute to the gripping force. Using a simple model we relate each of them to the mechanical strength of the jammed state. This advance opens up new possibilities for the design of simple, yet highly adaptive systems that excel at fast gripping of complex objects.


title: Document classification through image-based character embedding and wildcard training

abstract: Languages such as Chinese and Japanese have a significantly large number (several thousands) of alphabets as compared to other languages, and each of their sentences consists of several concatenated words with wide varieties of inflected forms; thus appropriate word segmentation is quite difficult. Therefore, recently proposed sophisticated language-processing methods designed for languages such as English cannot be applied. In this paper, we address those issues and propose a new and efficient document classification technique for such languages. The proposed method is characterized into a new “image-based character embedding” method and character-level convolutional neural networks method with “wildcard training.” The first method encodes each character based on its pictorial structures and preserves them. Further, the second method treats some of the input characters as wildcards in the classification stage and functions as efficient data augmentation. We confirmed that our proposed method showed superior performance when compared conventional methods for Japanese document classification problems.


title: Outlier Detection on Semantic Space for Sentiment Analysis With Convolutional Neural Networks

abstract: Sentiment analysis is a text categorization problem that consists in automatically assigning text documents to pre- defined classes that represent sentiments or a positive/negative opinion about a subject. To solve this task, machine learning techniques can be used. However, in order to achieve good gen- eralization, these techniques require a thorough pre-processing and an apropriate data representation. To deal with these fundamental issues, this work proposes the use of convolutional neural networks and density-based clustering algorithms. The word representations used in this work were obtained from vectors previously trained in an unsupervised way, denominated word embeddings. These representations are able to capture syntactic and semantic information of words, which leads to similar words to be projected closer together in the semantic space. In this scenario, in order to improve the performance of the convolutional neural network, the use of a clustering algorithm in the semantic space to extract additional information from the data is proposed. A density-based clustering algorithm was used to detect and remove outliers from the documents to be classified before these documents were used to train the con- volutional neural network. We conducted experiments with two different embeddings across three datasets in order to validate the effectiveness of our method. Results show that removing outliers from documents is capable of slightly improving the accuracy of the model and reducing computational cost for the non-static training approach. (0)


title: Evaluation of Convolutional Neural Network Architectures for Chart Image Classification

abstract: Many information visualization techniques map abstract data into visual representations to present the underlying information in a more understandable way. However, when data is not available, automatic methods are necessary to extract the underlying data from the chart images. This paper focuses on chart image classification, which is a fundamental step of chart analysis and data extraction. The performance of most conventional classifiers depends on the feature extraction technique applied, usually requiring a domain-specific design for creating the features. In this context, Convolutional Neural Network (CNN) has gained attention not only because it has achieved good results in many computer vision tasks but also because it can learn representations of images without the design of a feature extractor specific for a domain problem. Thereby, this paper proposes an evaluation of CNN architectures (VGG19, Resnet-50, and Inception-V3), comparing them with conventional classifiers (HOG features combined with KNN, Naive Bayes, Random Forest and Support Vector Machine). An artificially generated dataset of 10 classes of chart images was used for training and testing. Furthermore, another dataset composed of chart images collected from the internet was used for validation. The results show that Resnet-50 and Inception-V3 performed best achieving an accuracy of 77.76% and 76.77% respectively on the validation set. The CNN models outperformed the conventional methods, where the best combination was HOG+SVM, achieving 45.03 %. Moreover, some similar chart types were expected to be misclassified, and they were discussed based on the confusion matrix.


title: Discriminant analysis of principal components: a new method for the analysis of genetically structured populations

abstract: BackgroundThe dramatic progress in sequencing technologies offers unprecedented prospects for deciphering the organization of natural populations in space and time. However, the size of the datasets generated also poses some daunting challenges. In particular, Bayesian clustering algorithms based on pre-defined population genetics models such as the STRUCTURE or BAPS software may not be able to cope with this unprecedented amount of data. Thus, there is a need for less computer-intensive approaches. Multivariate analyses seem particularly appealing as they are specifically devoted to extracting information from large datasets. Unfortunately, currently available multivariate methods still lack some essential features needed to study the genetic structure of natural populations.ResultsWe introduce the Discriminant Analysis of Principal Components (DAPC), a multivariate method designed to identify and describe clusters of genetically related individuals. When group priors are lacking, DAPC uses sequential K-means and model selection to infer genetic clusters. Our approach allows extracting rich information from genetic data, providing assignment of individuals to groups, a visual assessment of between-population differentiation, and contribution of individual alleles to population structuring. We evaluate the performance of our method using simulated data, which were also analyzed using STRUCTURE as a benchmark. Additionally, we illustrate the method by analyzing microsatellite polymorphism in worldwide human populations and hemagglutinin gene sequence variation in seasonal influenza.ConclusionsAnalysis of simulated data revealed that our approach performs generally better than STRUCTURE at characterizing population subdivision. The tools implemented in DAPC for the identification of clusters and graphical representation of between-group structures allow to unravel complex population structures. Our approach is also faster than Bayesian clustering algorithms by several orders of magnitude, and may be applicable to a wider range of datasets.


title: Hydrophobic−Electrostatic Balance Driving the LCST Offset Aggregation−Redissolution Behavior ofN-Alkylacrylamide-Based Ionic Terpolymers

abstract: A series of random terpolymers composed of N-isopropylacrylamide (NIPAAm), 2-acrylamido-2-methyl-1-propanesulfonic acid (AMPS), and N-tert-butylacrylamide (NTBAAm) monomers were synthesized by free radical polymerization. The molar fraction of the negatively charged monomer (AMPS) was maintained constant (0.05) for all studied terpolymer compositions. Turbidity measurements were used to evaluate the influence of the relative amount of NIPAAm and NTBAAm, polymer concentration, and solution ionic strength on the cloud point and redissolution temperatures (macroscopic phase separation). Dynamic light scattering (DLS) was employed to elucidate some aspects regarding the molecular scale mechanism of the temperature-induced phase separation and to determine the low critical solution temperature (LCST). The aqueous solutions of terpolymers remained clear at all studied temperatures; turbidity was only observed in the presence of NaCl. The cloud point temperature (CPT) determined by turbidimetry was found to be s...


title: Short-circuit behavior of the three-level advanced-active-neutral-point-clamped converter

abstract: The Advanced-ANPC converter is designed to withstand almost every possible short-circuit failure of the semiconductors in comparison to state-of-the-art (A)NPC converters. The idea behind this concept is an additional short-circuit inductor to improve the robustness against faults. The presented investigations within this paper examine all the possible short-circuit situations and their effects for an (A)ANPC converter with high-voltage IGBTs.


title: Pedagogy-Based-Technology and Chemistry Students' Performance in Higher Institutions: A Case of Debre Berhan University.

abstract: Many students have difficulty in learning abstract and complex lessons of chemistry. This study investigated how students develop their understandings of abstract and complex lessons in chemistry with the aid of visualizing tools: animation, simulation and video that allow them to build clear concepts. Animation, simulation and video enable learning by doing and provide opportunity to explore the abstract and complex lessons of chemistry. They also enable to present information in more dynamic, compelling and interactive way with engaging environment. The study applied animation, simulation and video supporting, on student-centered learning activities, in electrochemistry for second year students organized with state of art technology flash and micro media player and evaluated for its effectiveness. A particular focus of this investigation was to what extent the uses of animation, simulation and video influenced student-centered learning. Pre- and post- tests were administrated on the target group and concurrent embedded mixed case study design was employed. Total quality of the developed package was evaluated by administrating a separate schedule containing open and closed ended question. The comments and ratings obtained in the learners’ insights provided the basis for the learning impact of the study. The result obtained from the experiment and responses of the schedule showed that technology integrated with appropriate pedagogy improves the performance of students.


title: Empirical Activation Function Effects on Unsupervised Convolutional LSTM Learning

abstract: This paper empirically evaluates and analyzes the effect of the choice of recurrent activation and unit activation functions on the unsupervised convolutional LSTM learning process. The goal of this work is to provide guidance for selecting the optimal non-linear activation function for the convolutional LSTM models which target the video prediction problem. This paper shows an empirical analysis of different non-linear activation functions that are commonly implemented in different deep learning APIs. We used the moving MNIST dataset as the most common benchmark for video prediction problems.


title: Heat shock protein 27 is involved in neurite extension and branching of dorsal root ganglion neurons in vitro.

abstract: Alteration of the cytoskeleton in response to growth factors and extracellular matrix proteins is necessary for neurite growth. The cytoskeletal components, such as actin and tubulin, can be modified through interaction with other cellular proteins, including the small heat shock protein Hsp27. Our previous work suggested that Hsp27 influences neurite growth, potentially via its phosphorylation state interactions with actin. To investigate further the role of Hsp27 in neurite outgrowth of adult dorsal root ganglion (DRG) neurons, we have both down-regulated endogenous Hsp27 and expressed exogenous Hsp27. Down-regulation of Hsp27 with Hsp27 siRNA resulted in a decrease of neuritic tree length and complexity. In contrast, expression of exogenous Hsp27 in these neurons resulted in an increase in neuritic tree length and branching. Collectively, these results demonstrate that Hsp27 may play a role in neuritic growth via modulation of the actin cytoskeleton.


title: Using Machine Learning to Refine Black-Box Test Specifications and Test Suites

abstract: In the context of open source development or software evolution, developers often face test suites which have been developed with no apparent rationale and which may need to be augmented or refined to ensure sufficient dependability, or even reduced to meet tight deadlines. We refer to this process as the re-engineering of test suites. It is important to provide both methodological and tool support to help people understand the limitations of test suites and their possible redundancies, so as to be able to refine them in a cost effective manner. To address this problem in the case of black-box testing, we propose a methodology based on machine learning that has shown promising results on a case study.


title: Big Data Processing Architecture for Radio Signals Empowered by Deep Learning: Concept, Experiment, Applications and Challenges

abstract: In modern society, the demand for radio spectrum resources is increasing. As the information carriers of wireless transmission data, radio signals exhibit the characteristics of big data in terms of volume, variety, value, and velocity. How to uniformly handle these radio signals and obtain value from them is a problem that needs to be studied. In this paper, a big data processing architecture for radio signals is presented and a new approach of end-to-end signal processing based on deep learning is discussed in detail. The radio signal intelligent search engine is used as an example to verify the architecture, and the system components and experimental results are introduced. In addition, the applications of the architecture in cognitive radio, spectrum monitoring, and cyberspace security are introduced. Finally, challenges are discussed, such as unified representation of radio signal features, distortionless compression of wideband sampled data, and deep neural networks for radio signals.


title: Joint Rate-Distortion Optimization for Simultaneous Texture and Deep Feature Compression of Facial Images

abstract: The explosion of surveillance cameras in smart cites and the increasing demand of low latency visual analysis have pushed the horizon from the traditional image/video compression to feature compression. Due to the recent advances of face recognition, we investigate the simultaneous compression of facial images and deep features, which is demonstrated to be beneficial in terms of the whole system performance including visual quality and recognition accuracy. Herein, we propose the Texture-Feature-Quality-Index (TFQI) to measure the ultimate utility of the facial images based on automatic visual analysis and monitoring. Furthermore, based on TFQI, a bit allocation scheme is proposed to optimally allocate the given bits for images and features, such that the overall coding performance can be optimized. The proposed scheme is validated using the standard face verification benchmark, Labeled Faces in the Wild (LFW). Better rate-TFQI and rate-Accuracy performance compared to the traditional texture coding can be achieved, especially in the scenario of low bit-rate coding.


title: Coordination of surge arresters in DC 3 kV railway traction system-field tests

abstract: This paper presents field tests of surge protection devices used at railway lines in Poland. Two types of arresters have been investigated: a horn air spark gap and a varistor. The aim of the field tests was to verify the possibility of coordination of both types of arresters at railway line under normal operating conditions of the electric traction system 3 kV DC.


title: ORB-ODOM: Stereo and odometer sensor fusion for simultaneous localization and mapping

abstract: ORB-SLAM2 is one of the better-known open source SLAM implementations available. However, the dependence of visual features causes it to fail in featureless environments. With the present work, we propose a new technique to improve visual odometry results given by ORB-SLAM2 using a tightly Sensor Fusion approach to integrate camera and odometer data. In this work, we use odometer readings to improve the tracking results by adding graph constraints between frames and introduce a new method for preventing the tracking loss. We test our method using three different datasets, and show an improvement in the estimated trajectory, allowing a continuous tracking without losses.


title: Paraphrasing and Translation

abstract: Paraphrasing and translation have previously been treated as unconnected natural language processing tasks. Whereas translation represents the preservation of meaning when an idea is rendered in the words in a different language, paraphrasing represents the preservation of meaning when an idea is expressed using different words in the same language. We show that the two are intimately related. The major contributions of this thesis are as follows: • We define a novel technique for automatically generating paraphrases using bilingual parallel corpora, which are more commonly used as training data for statistical models of translation. • We show that paraphrases can be used to improve the quality of statistical machine translation by addressing the problem of coverage and introducing a degree of generalization into the models. • We explore the topic of automatic evaluation of translation quality, and show that the current standard evaluation methodology cannot be guaranteed to correlate with human judgments of translation quality. Whereas previous data-driven approaches to paraphrasing were dependent upon either data sources which were uncommon such as multiple translation of the same source text, or language specific resources such as parsers, our approach is able to harness more widely parallel corpora and can be applied to any language which has a parallel corpus. The technique was evaluated by replacing phrases with their paraphrases, and asking judges whether the meaning of the original phrase was retained and whether the resulting sentence remained grammatical. Paraphrases extracted from a parallel corpus with manual alignments are judged to be accurate (both meaningful and grammatical) 75% of the time, retaining the meaning of the original phrase 85% of the time. Using automatic alignments, meaning can be retained at a rate of 70%. Being a language independent and probabilistic approach allows our method to be easily integrated into statistical machine translation. A paraphrase model derived from parallel corpora other than the one used to train the translation model can be used to increase the coverage of statistical machine translation by adding translations of previously unseen words and phrases. If the translation of a word was not learned, but a translation of a synonymous word has been learned, then the word is paraphrased


title: A Wideband Dual-Polarized Omnidirectional Antenna for Base Station/WLAN

abstract: A wideband dual-polarized omnidirectional antenna is proposed for mobile communication base station and 2.4 GHz wireless local area network applications. An integrated design is achieved by combining an inverted-cone monopole for vertical polarization (VP) and a modified cross bow-tie dipole for horizontal polarization (HP). The proposed antenna has a compact size because the HP element acts as the HP radiating element and the ground plane for the VP element simultaneously. The proposed VP and HP antennas are excited by a $50~\Omega $ Sub-Miniature-A connector and a broadband feeding network, respectively. The overall volume of the proposed antenna is only $0.35\lambda _{0}\times 0.35\lambda _{0}\times 0.25\lambda _{0}$ (with $\lambda _{0}$ being the wavelength of the lowest frequency). Simulation results show that the dual-polarized omnidirectional antenna achieves a bandwidth (for $\vert \text{S}_{11}\vert dB) of about 41.5% (1.64–2.5 GHz) with an isolation of at least 25 dB and the gain variations at the center frequency in the horizontal plane are 0.7 dB for VP and 2.3 dB for HP. The good agreements between the simulation and measured results validate the proposed design.


title: CREAM: A Concurrent-Refresh-Aware DRAM Memory architecture

abstract: As DRAM density keeps increasing, more rows need to be protected in a single refresh with the constant refresh number. Since no memory access is allowed during a refresh, the refresh penalty is no longer trivial and can result in significant performance degradation. To mitigate the refresh penalty, a Concurrent-REfresh-Aware Memory system (CREAM) is proposed in this work so that memory access and refresh can be served in parallel. The proposed CREAM architecture distinguishes itself with the following key contributions: (1) Under a given DRAM power budget, sub-rank-level refresh (SRLR) is developed to reduce refresh power and the saved power is used to enable concurrent memory access; (2) sub-array-level refresh (SALR) is also devised to effectively lower the probability of the conflict between memory access and refresh; (3) In addition, novel sub-array level refresh scheduling schemes, such as sub-array round-robin and dynamic scheduling, are designed to further improve the performance. A quasi-ROR interface protocol is proposed so that CREAM is fully compatible with JEDEC-DDR standard with negligible hardware overhead and no extra pin-out. The experimental results show that CREAM can improve the performance by 12.9% and 7.1% over the conventional DRAM and the Elastic-Refresh DRAM memory, respectively.


title: Crowdsourcing for Enterprises

abstract: Crowdsourcing is emerging as the new on-line distributed problem solving and production model in which networked people collaborate to complete a task. Enterprises are increasingly employing crowdsourcing to access scalable workforce on-line. In parallel, cloud computing has emerged as a new paradigm for delivering computational services, which seamlessly interweave physical and digital worlds through a common infrastructure.This paper presents a sample crowdsourcing scenario in software development domain to derive the requirements for delivering a general-purpose crowdsourcing service in the Cloud. It proposes taxonomy for categorization of crowdsourcing platforms, and evaluates a number of existing systems against the set of identified features. Finally, the paper outlines a research agenda for enhancing crowdsourcing capabilities, with focus on virtual team building and task-based service provisioning, whose lack has been a barrier to the realization of a peer-production model that engages providers from around the world.


title: On the Human–Machine Interaction of Unmanned Aerial System Mission Specialists

abstract: This paper surveys the human-machine interaction technologies supporting the Mission Specialist role in unmanned aerial systems (UASs). The Mission Specialist role is one of three formal human team member roles extracted from the UAS-related literature (the others are Flight Director and Pilot), but unlike the Pilot role, the interface needs have not been established. The interfaces used by 17 micro, small, medium altitude long endurance (MALE) and high altitude long endurance (HALE) platforms are examined to determine (1) what type of user interface technologies are present and/or available; (2) how the Mission Specialist currently or could interact with the user interface technology; and (3) what are the perceived positive and negative aspects of this user interface technology in the context of the UAS human-robot team roles. Micro and small UAVs pose significant user interface limitations for the Mission Specialist role and may produce unintentional interaction conflicts between the Mission Specialist role and the Pilot, potentially resulting in suboptimal performance and loss of robustness. The survey is expected to serve as a reference for future design and refinement of user interfaces for UAS and a foundation for better understanding human-robot interaction in UAS.


title: Layered Ensemble Architecture for Time Series Forecasting

abstract: Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.


title: Segmentation of the Blood Vessels and Optic Disk in Retinal Images

abstract: Retinal image analysis is increasingly prominent as a nonintrusive diagnosis method in modern ophthalmology. In this paper, we present a novel method to segment blood vessels and optic disk in the fundus retinal images. The method could be used to support nonintrusive diagnosis in modern ophthalmology since the morphology of the blood vessel and the optic disk is an impor- tant indicator for diseases like diabetic retinopathy, glaucoma, and hypertension. Our method takes as first step the extraction of the retina vascular tree using the graph cut technique. The blood vessel information is then used to estimate the location of the optic disk. The optic disk segmentation is performed using two alternative methods. The Markov random field (MRF) image reconstruction method segments the optic disk by removing vessels from the optic disk region, and the compensation factor method segments the op- tic disk using the prior local intensity knowledge of the vessels. The proposed method is tested on three public datasets, DIARETDB1, DRIVE, and STARE. The results and comparison with alternative methods show that our method achieved exceptional performance in segmenting the blood vessel and optic disk.


title: On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration

abstract: Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network. MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks. This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies. It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options. In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges.


Power Quality Improvement By Using D-STATCOM : Review


title: Subungual melanoma: a deceptive disorder.

abstract: Subungual melanoma is an uncommon form of acral melanoma that arises within the nail bed. The incidence for acral melanomas is similar worldwide, but the proportion is higher in dark-skinned individuals. The subungual form represents about 2% of cutaneous non-sun induced melanomas in the western world, and up to 75% in Africans, 10% in Japanese, and 25% in the Chinese of Hong Kong. Up to 33% of subungual melanomas are amelanotic. Black pigmentation of the adjacent nail fold, termed Hutchinson's sign, may be a diagnostic clue. Non-specific features and symptoms along with a high incidence of amelanosis often lead to delayed diagnosis, disease progression, and a poor prognosis with challenging treatment options.


title: Depression and cardiovascular disease: a clinical review.

abstract: Cardiovascular disease (CVD) and depression are common. Patients with CVD have more depression than the general population. Persons with depression are more likely to eventually develop CVD and also have a higher mortality rate than the general population. Patients with CVD, who are also depressed, have a worse outcome than those patients who are not depressed. There is a graded relationship: the more severe the depression, the higher the subsequent risk of mortality and other cardiovascular events. It is possible that depression is only a marker for more severe CVD which so far cannot be detected using our currently available investigations. However, given the increased prevalence of depression in patients with CVD, a causal relationship with either CVD causing more depression or depression causing more CVD and a worse prognosis for CVD is probable. There are many possible pathogenetic mechanisms that have been described, which are plausible and that might well be important. However, whether or not there is a causal relationship, depression is the main driver of quality of life and requires prevention, detection, and management in its own right. Depression after an acute cardiac event is commonly an adjustment disorder than can improve spontaneously with comprehensive cardiac management. Additional management strategies for depressed cardiac patients include cardiac rehabilitation and exercise programmes, general support, cognitive behavioural therapy, antidepressant medication, combined approaches, and probably disease management programmes.


title: Comprehensible Visualization for Augmented Reality

abstract: This article presents interactive visualizations to support the comprehension of spatial relationships between virtual and real world objects for augmented reality (AR) applications. To enhance the clarity of such relationships we discuss visualization techniques and their suitability for AR. We apply them on different AR applications with different goals, e.g. in X-Ray vision or in applications which draw a user's attention to an object of interest. We demonstrate how Focus and Context (F+C) visualizations are used to affect the user's perception of hidden or nearby objects by presenting contextual information in the area of augmentation. We discuss the organization and the possible sources of data for visualizations in augmented reality and present cascaded and multi level F+C visualizations to address complex, cluttered scenes that are inevitable in real environments. This article also shows filters and tools to interactively control the amount of augmentation. It compares the impact of real world context preserving to a pure virtual and uniform enhancement of these structures for augmentations of real world imagery. Finally this paper discusses the stylization of sparse object representations for AR to improve x-ray vision.


title: Decision-Driven Execution: A Distributed Resource Management Paradigm for the Age of IoT

abstract: This paper introduces a novel paradigm for resource management in distributed systems, called decision-driven execution. The paradigm is appropriate for mission-driven systems, where the goal is to enable faster, leaner, and more effective decision making. All resource consumption, in this paradigm, is tied to the needs of making decisions on alternative courses of action. A point of departure from traditional architectures lies in interfaces that allow applications to specify their underlying decision logic. This specification, in turn, allows the system to reason about most effective means to meet information needs of decisions, resulting in simultaneous optimization of decision accuracy, cost, and speed. The paper discusses the overall vision of decision-driven execution, outlining preliminary work and novel challenges.


The Puzzle: Exploring the Evolutionary Puzzle of Male Homosexuality


title: Design and implementation of a high-performance distributed Web crawler

abstract: Broad Web search engines as well as many more specialized search tools rely on Web crawlers to acquire large collections of pages for indexing and analysis. Such a Web crawler may interact with millions of hosts over a period of weeks or months, and thus issues of robustness, flexibility, and manageability are of major importance. In addition, I/O performance, network resources, and OS limits must be taken into account in order to achieve high performance at a reasonable cost. In this paper, we describe the design and implementation of a distributed Web crawler that runs on a network of workstations. The crawler scales to (at least) several hundred pages per second, is resilient against system crashes and other events, and can be adapted to various crawling applications. We present the software architecture of the system, discuss the, performance bottlenecks, and describe efficient techniques for achieving high performance. We also report preliminary experimental results based on a crawl of 120 million pages on 5 million hosts.


Deep Heuristic-learning in the Rubik's Cube Domain: An Experimental Evaluation


title: Digital forensics research on cloud computing: An investigation of cloud forensics solutions

abstract: Digital forensics has been utilized in computer crime investigation for last thirty years. It has evolved and progressed around the technical revolutions, and is now facing yet another new era due to the emergence of cloud computing. Cloud computing is a new computing model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). The innovative nature of cloud computing has created unique challenges in the field of digital forensics. In this research paper, we present detail study to investigate the forensic issues in cloud computing and provide possible solutions, guidelines, including existing case studies. The basics of traditional digital forensics and cloud computing are also discussed.


title: Anomaly detection in temperature data using DBSCAN algorithm

abstract: Anomaly detection is a problem of finding unexpected patterns in a dataset. Unexpected patterns can be defined as those that do not conform to the general behavior of the dataset. Anomaly detection is important for several application domains such as financial and communication services, public health, and climate studies. In this paper, we focus on discovery of anomalies in monthly temperature data using DBSCAN algorithm. DBSCAN algorithm is a density-based clustering algorithm that has the capability of discovering anomalous data. In the experimental evaluation, we compared the results of DBSCAN algorithm with the results of a statistical method. The analysis showed that DBSCAN has several advantages over the statistical approach on discovering anomalies.


title: A framework for evaluating pseudonym strategies in vehicular ad-hoc networks

abstract: The standard approach to privacy-friendly authentication in vehicular ad-hoc networks is the use of pseudonym certificates. The level of location privacy users can enjoy under the threat of an attacker depends on the attacker's coverage and strategy as well as on the users' strategy for changing their pseudonym certificates. With this paper, we propose a generic framework for evaluation and comparison of different pseudonym change strategies with respect to the privacy level they provide under the threat of a realistic, local, passive attacker. To illustrate the applicability of this framework, we propose a new tracking strategy that achieves unprecedented success in vehicle tracking and thus lowers the achievable location privacy significantly. We use this attacker as a means to evaluate different pseudonym change strategies and highlight the need for more research in this direction.


title: Analysis of Big Data and Quality-of-Experience in High-Density Wireless Network

abstract: The proliferation of smart devices, along with the availability of bandwidth-intensive applications are generating huge volumes of data that create challenges to IT industries. Data handling becomes more troublesome when mobile user's gather in tens and thousands of quantity at confine locations and generates Big Data. Analysis and storing of this huge, varied and complex data put great challenges to the Network Service Providers (NSP). Thus, service providers are facing problems in managing big data in dense environment and maintaining user's Quality of Experience (QoE). However, big data also provide great opportunities to NSP. The accurate analysis of big data in real-time reveals the user experience of network services which helps the service providers to take timely action to improve user QoE. Thus, this paper presents an overview of Big Data and QoE in High-Density Wireless Network (HDWN) environment.


title: Robust rendezvous for mobile autonomous agents via proximity graphs in arbitrary dimensions

abstract: This paper presents coordination algorithms for networks of mobile autonomous agents. The objective of the proposed algorithms is to achieve rendezvous, that is, agreement over the location of the agents in the network. We provide analysis and design results for multiagent networks in arbitrary dimensions under weak requirements on the switching and failing communication topology. The novel correctness proof relies on proximity graphs and their properties and on a general LaSalle invariance principle for nondeterministic discrete-time dynamical systems


title: Optimal Gabor filters for texture segmentation

abstract: Texture segmentation involves subdividing an image into differently textured regions. Many texture segmentation schemes are based on a filter-bank model, where the filters, called Gabor filters, are derived from Gabor elementary functions. The goal is to transform texture differences into detectable filter-output discontinuities at texture boundaries. By locating these discontinuities, one can segment the image into differently textured regions. Distinct discontinuities occur, however, only if the Gabor filter parameters are suitably chosen. Some previous analysis has shown how to design filters for discriminating simple textures. Designing filters for more general natural textures, though, has largely been done ad hoc. We have devised a more rigorously based method for designing Gabor filters. It assumes that an image contains two different textures and that prototype samples of the textures are given a priori. We argue that Gabor filter outputs can be modeled as Rician random variables (often approximated well as Gaussian rv's) and develop a decision-theoretic algorithm for selecting optimal filter parameters. To improve segmentations for difficult texture pairs, we also propose a multiple-filter segmentation scheme, motivated by the Rician model. Experimental results indicate that our method is superior to previous methods in providing useful Gabor filters for a wide range of texture pairs.


title: Lightweight, Mesoporous, and Highly Absorptive All-Nanofiber Aerogel for Efficient Solar Steam Generation.

abstract: The global fresh water shortage has driven enormous endeavors in seawater desalination and wastewater purification; among these, solar steam generation is effective in extracting fresh water by efficient utilization of naturally abundant solar energy. For solar steam generation, the primary focus is to design new materials that are biodegradable, sustainable, of low cost, and have high solar steam generation efficiency. Here, we designed a bilayer aerogel structure employing naturally abundant cellulose nanofibrils (CNFs) as basic building blocks to achieve sustainability and biodegradability as well as employing a carbon nanotube (CNT) layer for efficient solar utilization with over 97.5% of light absorbance from 300 to 1200 nm wavelength. The ultralow density (0.0096 g/cm3) of the aerogel ensures that minimal material is required, reducing the production cost while at the same time satisfying the water transport and thermal-insulation requirements due to its highly porous structure (99.4% porosity). Owing to its rationally designed structure and thermal-regulation performance, the bilayer CNF-CNT aerogel exhibits a high solar-energy conversion efficiency of 76.3% and 1.11 kg m-2 h-1 at 1 kW m-2 (1 Sun) solar irradiation, comparable or even higher than most of the reported solar steam generation devices. Therefore, the all-nanofiber aerogel presents a new route for designing biodegradable, sustainable, and scalable solar steam generation devices with superb performance.


title: A database for handwritten text recognition research

abstract: An image database for handwritten text recognition research is described. Digital images of approximately 5000 city names, 5000 state names, 10000 ZIP Codes, and 50000 alphanumeric characters are included. Each image was scanned from mail in a working post office at 300 pixels/in in 8-bit gray scale on a high-quality flat bed digitizer. The data were unconstrained for the writer, style, and method of preparation. These characteristics help overcome the limitations of earlier databases that contained only isolated characters or were prepared in a laboratory setting under prescribed circumstances. Also, the database is divided into explicit training and testing sets to facilitate the sharing of results among researchers as well as performance comparisons. >


title: Automated Inference on Criminality using Face Images

abstract: We study, for the first time, automated inference on criminality based solely on still face images. Via supervised machine learning, we build four classifiers (logistic regression, KNN, SVM, CNN) using facial images of 1856 real persons controlled for race, gender, age and facial expressions, nearly half of whom were convicted criminals, for discriminating between criminals and non-criminals. All four classifiers perform consistently well and produce evidence for the validity of automated face-induced inference on criminality, despite the historical controversy surrounding the topic. Also, we find some discriminating structural features for predicting criminality, such as lip curvature, eye inner corner distance, and the so-called nose-mouth angle. Above all, the most important discovery of this research is that criminal and non-criminal face images populate two quite distinctive manifolds. The variation among criminal faces is significantly greater than that of the non-criminal faces. The two manifolds consisting of criminal and non-criminal faces appear to be concentric, with the non-criminal manifold lying in the kernel with a smaller span, exhibiting a law of normality for faces of non-criminals. In other words, the faces of general law-biding public have a greater degree of resemblance compared with the faces of criminals, or criminals have a higher degree of dissimilarity in facial appearance than normal people.


title: Modeling and control for longitudinal attitude of a twin-rotor tail-sitter unmanned aerial vehicle

abstract: In this paper, the modeling and model reference adaptive control (MRAC) for longitudinal attitude of a twin-rotor tail-sitter unmanned aerial vehicle (UAV), which is highly unstable during flight, are presented. First, the attitude dynamic models are established. Linearized model for longitudinal attitude in vertical flight mode is given that is used in later derivation of controller as well as for testing the algorithms in simulation. Then, a control law based on the MRAC technique is utilized to stabilize the longitudinal attitude control system with uncertainty. Simulation results show that the MRAC of pitch angle has good trajectory tracking and the designed control law has strong adaptive ability and anti-jamming ability.


title: Semantic-based Social Media Threats Detection

abstract: The exponential growth of internet access and computing devices, has led to the growth of social media usage and changed the way of our lives in society. Due to the growth of social media usage such as Facebook, Twitter, Instagram, YouTube, etc., there are possibilities that users share information which may pose a threat to the national security. For example, the threats might be propagated by a certain group of terrorists. The threats can be written either in Malay or mixed with other languages; especially English and in a short form basis. The major issue is to extract the 'meaning' of the messages for further processing tasks (e.g. categorizing and identifying the insight of threats). Therefore, a semantic-based grammar approach helps to fill this 'knowledge gap' for threat analysis at the early stage. This research will focus on knowledge extraction (based-on Malay words) through semantic relations between words and their dependents.


title: From user requirements to UML class diagram

abstract: The transition from user requirements to UML diagrams is a difficult task for the designer especially when he handles large texts expressing these needs. Modeling class Diagram must be performed frequently, even during the development of a simple application. This paper proposes an approach to facilitate class diagram extraction from textual requirements using NLP techniques and domain ontology.


title: ADTreesLogit model for customer churn prediction

abstract: In this paper, we propose ADTreesLogit, a model that integrates the advantage of ADTrees model and the logistic regression model, to improve the predictive accuracy and interpretability of existing churn prediction models. We show that the overall predictive accuracy of ADTreesLogit model compares favorably with that of TreeNet®, a model which won the Gold Prize in the 2003 mobile customer churn prediction modeling contest (The Duke/NCR Teradata Churn Modeling Tournament). In fact, ADTreesLogit has better predictive accuracy than TreeNet® on two important observation points.


title: Coordinating Filters for Faster Deep Neural Networks

abstract: Very large-scale Deep Neural Networks (DNNs) have achieved remarkable successes in a large variety of computer vision tasks. However, the high computation intensity of DNNs makes it challenging to deploy these models on resource-limited systems. Some studies used low-rank approaches that approximate the filters by low-rank basis to accelerate the testing. Those works directly decomposed the pre-trained DNNs by Low-Rank Approximations (LRA). How to train DNNs toward lower-rank space for more efficient DNNs, however, remains as an open area. To solve the issue, in this work, we propose Force Regularization, which uses attractive forces to enforce filters so as to coordinate more weight information into lower-rank space1. We mathematically and empirically verify that after applying our technique, standard LRA methods can reconstruct filters using much lower basis and thus result in faster DNNs. The effectiveness of our approach is comprehensively evaluated in ResNets, AlexNet, and GoogLeNet. In AlexNet, for example, Force Regularization gains 2× speedup on modern GPU without accuracy loss and 4:05× speedup on CPU by paying small accuracy degradation. Moreover, Force Regularization better initializes the low-rank DNNs such that the fine-tuning can converge faster toward higher accuracy. The obtained lower-rank DNNs can be further sparsified, proving that Force Regularization can be integrated with state-of-the-art sparsity-based acceleration methods.


JPE 13-1-3 Analysis , Design and Implementation of a Soft Switching DC / DC Converter


title: Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering

abstract: Feature extraction is known to be an effective way in both reducing computational complexity and increasing accuracy of hyperspectral image classification. In this paper, a simple yet quite powerful feature extraction method based on image fusion and recursive filtering (IFRF) is proposed. First, the hyperspectral image is partitioned into multiple subsets of adjacent hyperspectral bands. Then, the bands in each subset are fused together by averaging, which is one of the simplest image fusion methods. Finally, the fused bands are processed with transform domain recursive filtering to get the resulting features for classification. Experiments are performed on different hyperspectral images, with the support vector machines (SVMs) serving as the classifier. By using the proposed method, the accuracy of the SVM classifier can be improved significantly. Furthermore, compared with other hyperspectral classification methods, the proposed IFRF method shows outstanding performance in terms of classification accuracy and computational efficiency.


title: Stochastic Variance-Reduced Hamilton Monte Carlo Methods

abstract: We propose a fast stochastic Hamilton Monte Carlo (HMC) method, for sampling from a smooth and strongly log-concave distribution. At the core of our proposed method is a variance reduction technique inspired by the recent advance in stochastic optimization. We show that, to achieve $\epsilon$ accuracy in 2-Wasserstein distance, our algorithm achieves $\tilde O\big(n+\kappa^{2}d^{1/2}/\epsilon+\kappa^{4/3}d^{1/3}n^{2/3}/\epsilon^{2/3}\big)$ gradient complexity (i.e., number of component gradient evaluations), which outperforms the state-of-the-art HMC and stochastic gradient HMC methods in a wide regime. We also extend our algorithm for sampling from smooth and general log-concave distributions, and prove the corresponding gradient complexity as well. Experiments on both synthetic and real data demonstrate the superior performance of our algorithm.


title: Whatever It Takes to Win: Rivalry Increases Unethical Behavior

abstract: This research investigates the link between rivalry and unethical behavior. We propose that people will be more likely to engage in unethical behavior when competing against their rivals than when competing against non-rival competitors. Across an archival study and a series of experiments, we found that rivalry was associated with increased unsporting behavior, use of deception, and willingness to employ unethical negotiation tactics. We also explore the psychological underpinnings of rivalry in order to illuminate how it differs from general competition and why it increases unethical behavior. The data reveal a serial mediation pathway whereby rivalry heightens the psychological stakes of competition (by increasing actors’ contingency of self-worth and status concerns), which leads to the adoption of a stronger performance-approach orientation, which then increases unethical behavior. These findings highlight the importance of rivalry as a widespread, powerful, yet largely unstudied phenomenon with sign...


title: A compressive multi-kernel method for privacy-preserving machine learning

abstract: As the analytic tools become more powerful, and more data are generated on a daily basis, the issue of data privacy arises. This leads to the study of the design of privacy-preserving machine learning algorithms. Given two objectives, namely, utility maximization and privacy-loss minimization, this work is based on two previously non-intersecting regimes — Compressive Privacy and multi-kernel method. Compressive Privacy is a privacy framework that employs utility-preserving lossy-encoding scheme to protect the privacy of the data, while multi-kernel method is a kernel-based machine learning regime that explores the idea of using multiple kernels for building better predictors. In relation to the neural-network architecture, multi-kernel method can be described as a two-hidden-layered network with its width proportional to the number of kernels. The compressive multi-kernel method proposed consists of two stages — the compression stage and the multi-kernel stage. The compression stage follows the Compressive Privacy paradigm to provide the desired privacy protection. Each kernel matrix is compressed with a lossy projection matrix derived from the Discriminant Component Analysis (DCA). The multikernel stage uses the signal-to-noise ratio (SNR) score of each kernel to non-uniformly combine multiple compressive kernels. The proposed method is evaluated on two mobile-sensing datasets — MHEALTH and HAR — where activity recognition is defined as utility and person identification is defined as privacy. The results show that the compression regime is successful in privacy preservation as the privacy classification accuracies are almost at the random-guess level in all experiments. On the other hand, the novel SNR-based multi-kernel shows utility classification accuracy improvement upon the state-of-the-art in both datasets. These results indicate a promising direction for research in privacy-preserving machine learning.


title: New Library World: 109 (1/2). User experience in the library: a case study

abstract: Purpose – The purpose of this article is to describe the need for and realization of a new kind of interface for searching and obtaining library materials, an interface designed around user needs and decoupled from, though interoperating with, current library systems.Design/methodology/approach – The paper describes a product that was released in May 2007 – the Primo® system from Ex Libris – as an example of a new solution for the discovery and delivery of library collections. The paper deals with the issues involved in the design of the product, the way in which the product was built to address the needs of both information seekers and libraries, and the use of usability studies to affirm the overall design and help shape fine points of the interface.Findings – The paper demonstrates how users' expectations, which emanate from the everyday experience on the internet, can be addressed by library software in a way that corresponds to librarians' requirements and suits and libraries' technological infrastru...


title: Sentiment Analysis in Social Media Platforms: The Contribution of Social Relationships

abstract: The massive amount of data in social media platforms is a key source for companies to analyze customer sentiment and opinions. Many existing sentiment analysis approaches solely rely on textual contents of a sentence (e.g. words) for sentiment identification. Consequently, current sentiment analysis systems are ineffective for analyzing contents in social media because people may use non-standard language (e.g., abbreviations, misspellings, emoticons or multiple languages) in online platforms. Inspired by the attribution theory that is grounded in social psychology, we propose a sentiment analysis framework that considers the social relationships among users and contents. We conduct experiments to compare the proposed approach against the existing approaches on a dataset collected from Facebook. The results indicate that we can more accurately classify sentiment of sentences by utilizing social relationships. The results have important implications for companies to analyze customer opinions.


title: Automated 3D Face authentication & recognition

abstract: This paper presents a fully automated 3D face authentication (verification) and recognition (identification) method and recent results from our work in this area. The major contributions of our paper are: (a) the method can handle data with different facial expressions including hair, upper body, clothing, etc. and (b) development of weighted features for discrimination. The input to our system is a triangular mesh and it outputs a matching % against a gallery. Our method includes both surface and curve based features that are automatically extracted from a given face data. The test set for authentication consisted of 117 different people with 421 scans including different facial expressions. Our study shows equal error rate (EER) at 0.065% for normal faces and 1.13% in faces with expressions. We report verification rates of 100% in normal faces and 93.12% in faces with expressions at 0.1% FAR. For identification, our experiment shows 100% rate in normal faces and 95.6% in faces with expressions. From our experiment we conclude that combining feature points, profile curve, and partial face surface matching gives better authentication and recognition rate than any single matching method.


title: Schema Management for Document Stores

abstract: Document stores that provide the efficiency of a schema-less interface are widely used by developers in mobile and cloud applications. However, the simplicity developers achieved controversially leads to complexity for data management due to lack of a schema. In this paper, we present a schema management framework for document stores. This framework discovers and persists schemas of JSON records in a repository, and also supports queries and schema summarization. The major technical challenge comes from varied structures of records caused by the schema-less data model and schema evolution. In the discovery phase, we apply a canonical form based method and propose an algorithm based on equivalent sub-trees to group equivalent schemas efficiently. Together with the algorithm, we propose a new data structure, eSiBu-Tree, to store schemas and support queries. In order to present a single summarized representation for heterogenous schemas in records, we introduce the concept of "skeleton", and propose to use it as a relaxed form of the schema, which captures a small set of core attributes. Finally, extensive experiments based on real data sets demonstrate the efficiency of our proposed schema discovery algorithms, and practical use cases in real-world data exploration and integration scenarios are presented to illustrate the effectiveness of using skeletons in these applications.


title: Clinical spinal instability and low back pain.

abstract: Clinical instability is an important cause of low back pain. Although there is some controversy concerning its definition, it is most widely believed that the loss of normal pattern of spinal motion causes pain and/or neurologic dysfunction. The stabilizing system of the spine may be divided into three subsystems: (1) the spinal column; (2) the spinal muscles; and (3) the neural control unit. A large number of biomechanical studies of the spinal column have provided insight into the role of the various components of the spinal column in providing spinal stability. The neutral zone was found to be a more sensitive parameter than the range of motion in documenting the effects of mechanical destabilization of the spine caused by injury and restabilization of the spine by osteophyle formation, fusion or muscle stabilization. Clinical studies indicate that the application of an external fixator to the painful segment of the spine can significantly reduce the pain. Results of an in vitro simulation of the study found that it was most probably the decrease in the neutral zone, which was responsible for pain reduction. A hypothesis relating the neutral zone to pain has been presented. The spinal muscles provide significant stability to the spine as shown by both in vitro experiments and mathematical models. Concerning the role of neuromuscular control system, increased body sway has been found in patients with low back pain, indicating a less efficient muscle control system with decreased ability to provide the needed spinal stability.


title: System modeling based on machine learning for anomaly detection and predictive maintenance in industrial plants

abstract: Electricity, water or air are some Industrial energy carriers which are struggling under the prices of primary energy carriers. The European Union for example used more 20.000.000 GWh electricity in 2011 based on the IEA Report [1]. Cyber Physical Production Systems (CPPS) are able to reduce this amount, but they also help to increase the efficiency of machines above expectations which results in a more cost efficient production. Especially in the field of improving industrial plants, one of the challenges is the implementation of anomaly detection systems. For example as wear-level detection, which improves maintenance cycles and thus leads to a better energy usage. This paper presents an approach that uses timed hybrid automata of the machines normal behavior for a predictive maintenance of industrial plants. This hybrid model reduces discrete and continuous signals (e.g. energy data) to individual states, which refer to either the present condition of the machines. This allows an effective anomaly detection by implementing a combined data acquisition and anomaly detection approach, and the outlook for other applications, such as a predictive maintenance planning. Finally, this methodology is verified by three different industrial applications.


title: Representation of Events in Nerve Nets and Finite Automata

abstract: Abstract : To what kinds of events can a McCulloch-Pitts nerve net respond by firing a certain neuron? More generally, to what kinds of events can any finite automaton respond by assuming one of certain states? This memorandum is devoted to an elementary exposition of the problems and of results obtained on it during investigations in August 1951.


title: Overcurrent protection relay model using IEC 61850-9-2 sampled values

abstract: This document deals with usage of sampled values for protection functions in medium voltage networks. IEC 61850-9-2 brings solution to distribution of current and voltage values measured by sensors. Sampled values could be used for various protection functions. One of the most extensively used type of protection is overcurrent protection relay. If settings of parameters are set wrong in overcurrent protection it can lead to big catastrophe. In this document there is described overcurrent protection relay model which is programmed in LabVIEW and it is the first part of programming a central protection relay model.


title: IOStack: Software-Defined Object Storage

abstract: As the complexity and scale of cloud storage systems grow, software-defined storage (SDS) has become a prime candidate to simplify cloud storage management. Here, the authors present IOStack, the first SDS architecture for object stores (such as OpenStack Swift). At the control plane, the provisioning of SDS services to tenants is made according to a set of policies managed via a high-level domain-specific language (DSL). Policies can target storage automation or specific service-level agreement (SLA) objectives. At the data plane, policies define the enforcement of SDS services, namely filters, on a tenant's requests. Moreover, IOStack is a framework to build a variety of filters, ranging from general-purpose computations close to the data to specialized data management mechanisms. Experiments illustrate that IOStack enables easy and effective policy-based provisioning, which can significantly improve the operation of a multitenant object store.


title: An enhanced obstacle avoidance method for the visually impaired using deformable grid

abstract: Various obstacle avoidance (OA) methods with the monocular vision have been studied to assist visually impaired people. Recently, a method that effectively locates obstacles at a risk of collision using the shape variation of a grid, called deformable grid (DG), was introduced [11]. To further improve the detection performance of the previous work, a vertex deformation function representing the displacement of each vertex in the DG is firstly defined employing the perspective projection geometry. Then, the collision risk is accurately estimated based on the shape variation of the DG that is measured using the vertex deformation function. Experimental results show that the proposed method outperforms other conventional methods as well as the previous work in terms of both the accuracy and processing time, and clearly detects obstacles that present a risk of collision in a variety of scenarios. This method is suitable for application to electronic travel aid (ETA) systems using consumer devices, such as smart-phones, tablets, and wearable devices.


title: Original article Effects of Total Extract of Dracocephalum moldavica on Ischemia/Reperfusion Induced Arrhythmias and Infarct Size in the Isolated Rat Heart

abstract: Objective(s) Dracocephalum moldavica (D. moldavica) have been traditionally used as a cardiotonic agent in the folk medicine of some regions of Iran. In the present study, effects of total extract of D. moldavica on ischemia/reperfusion induced arrhythmias and infarct size investigated in isolated rat heart. Materials and Methods The isolated hearts were mounted on a Langendorff apparatus then perfused during 30 min regional ischemia and 120 min reperfusion, either by a modified Krebs-Henseleit solution as the control group or by enriched Krebs solution with total extract of D. moldavica (25-200 µg/ml) as the treatment groups. The ECGs recorded and analyzed to determine cardiac arrhythmias. At the end of the reperfusion, the hearts stained by Evans blue solution then incubated by triphenyltetrazolium chloride. The volume of infarcted tissue and percentage of infarct size determined by computerized planimetry. Results The results demonstrated that total extract of D. moldavica caused a significant reduction in the number of ventricular tachycardia (VT), total ventricular ectopic beats (VEBs) and VT duration in ischemic and reperfusion periods. The incidence of ischemic VT reduced from 93% in the control group to 0, 50 and 50% in the treatment groups. The infarct size was 37±1% in the control group, however, perfusion of the extract (25, 50, 200 µg/ml) reduced it to 13±2, 8±1 and 18±2%, respectively (P<0.001). In addition, the extract remarkably lowered volume of infarcted tissue compared to the control group (P<0.05). Conclusion Our findings showed cardioprotective effects of total extract of D. moldavica against ischemia/reperfusion injuries in the isolated rat heart.


title: Reconfigurable Hardware Accelerators: Opportunities, Trends, and Challenges

abstract: With the emerging big data applications of Machine Learning, Speech Recognition, Artificial Intelligence, and DNA Sequencing in recent years, computer architecture research communities are facing the explosive scale of various data explosion. To achieve high efficiency of data-intensive computing, studies of heterogeneous accelerators which focus on latest applications, have become a hot issue in computer architecture domain. At present, the implementation of heterogeneous accelerators mainly relies on heterogeneous computing units such as Application-specific Integrated Circuit (ASIC), Graphics Processing Unit (GPU), and Field Programmable Gate Array (FPGA). Among the typical heterogeneous architectures above, FPGA-based reconfigurable accelerators have two merits as follows: First, FPGA architecture contains a large number of reconfigurable circuits, which satisfy requirements of high performance and low power consumption when specific applications are running. Second, the reconfigurable architectures of employing FPGA performs prototype systems rapidly and features excellent customizability and reconfigurability. Nowadays, in top-tier conferences of computer architecture, emerging a batch of accelerating works based on FPGA or other reconfigurable architectures. To better review the related work of reconfigurable computing accelerators recently, this survey reserves latest high-level research products of reconfigurable accelerator architectures and algorithm applications as the basis. In this survey, we compare hot research issues and concern domains, furthermore, analyze and illuminate advantages, disadvantages, and challenges of reconfigurable accelerators. In the end, we prospect the development tendency of accelerator architectures in the future, hoping to provide a reference for computer architecture researchers.


title: Learning Low-Dimensional Representations of Medical Concepts

abstract: We show how to learn low-dimensional representations (embeddings) of a wide range of concepts in medicine, including diseases (e.g., ICD9 codes), medications, procedures, and laboratory tests. We expect that these embeddings will be useful across medical informatics for tasks such as cohort selection and patient summarization. These embeddings are learned using a technique called neural language modeling from the natural language processing community. However, rather than learning the embeddings solely from text, we show how to learn the embeddings from claims data, which is widely available both to providers and to payers. We also show that with a simple algorithmic adjustment, it is possible to learn medical concept embeddings in a privacy preserving manner from co-occurrence counts derived from clinical narratives. Finally, we establish a methodological framework, arising from standard medical ontologies such as UMLS, NDF-RT, and CCS, to further investigate the embeddings and precisely characterize their quantitative properties.


title: 3D Mesh Reconstruction from Height map and Post Processing

abstract: Nowadays, researches for reconstructing 3D terrain from images are being widely carried out. The method of reconstruction according to the kind of the input image and the post-processing process after reconstruction are different. In this paper, we propose a 3D mesh generation and post-processing method based on a height map generated from a satellite image. First, we generate 3D mesh based on height map, then execute refinement of 3D mesh to remove stair shape on the side of building, and finally simplify them.


title: Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization

abstract: Parametric generative deep models are state-of-the-art for photo and non-photo realistic image stylization. However, learning complicated image representations requires compute-intense models parametrized by a huge number of weights, which in turn requires large datasets to make learning successful. Non-parametric exemplar-based generation is a technique that works well to reproduce style from small datasets, but is also compute-intensive. These aspects are a drawback for the practice of digital AI artists: typically one wants to use a small set of stylization images, and needs a fast flexible model in order to experiment with it. With this motivation, our work has these contributions: (i) a novel stylization method called Fully Adversarial Mosaics (FAMOS) that combines the strengths of both parametric and non-parametric approaches; (ii) multiple ablations and image examples that analyze the method and show its capabilities; (iii) source code that will empower artists and machine learning researchers to use and modify FAMOS.


title: Mindfulness-Based Stress Reduction for Urban Youth

abstract: Abstract Objectives: The objectives of this study were to assess the general acceptability and to assess domains of potential effect of a mindfulness-based stress reduction (MBSR) program for human immunodeficiency virus (HIV)–infected and at-risk urban youth. Methods: Thirteen-to twenty-one-year-old youth were recruited from the pediatric primary care clinic of an urban tertiary care hospital to participate in 4 MBSR groups. Each MBSR group consisted of nine weekly sessions of MBSR instruction. This mixed-methods evaluation consisted of quantitative data—attendance, psychologic symptoms (Symptom Checklist 90-Revised), and quality of life (Child Health and Illness Profile–Adolescent Edition)—and qualitative data—in-depth individual interviews conducted in a convenience sample of participants until interview themes were saturated. Analysis involved comparison of pre- and postintervention surveys and content analysis of interviews. Results: Thirty-three (33) youth attended at least one MBSR session. Of the ...


title: Analytical approach to parallel repetition

abstract: We propose an analytical framework for studying parallel repetition, a basic product operation for one-round two-player games. In this framework, we consider a relaxation of the value of a game, $\mathrm{val}_+$, and prove that for projection games, it is both multiplicative (under parallel repetition) and a good approximation for the true value. These two properties imply a parallel repetition bound as $$ \mathrm{val}(G^{\otimes k}) \approx \mathrm{val}_+(G^{\otimes k}) = \mathrm{val}_+(G)^{k} \approx \mathrm{val}(G)^{k}. $$ Using this framework, we can also give a short proof for the NP-hardness of Label-Cover$(1,\delta)$ for all $\delta>0$, starting from the basic PCP theorem. We prove the following new results: - A parallel repetition bound for projection games with small soundness. Previously, it was not known whether parallel repetition decreases the value of such games. This result implies stronger inapproximability bounds for Set-Cover and Label-Cover. - An improved bound for few parallel repetitions of projection games, showing that Raz's counterexample is tight even for a small number of repetitions. Our techniques also allow us to bound the value of the direct product of multiple games, namely, a bound on $\mathrm{val}(G_1\otimes ...\otimes G_k)$ for different projection games $G_1,...,G_k$.


title: Deception detection expertise.

abstract: A lively debate between Bond and Uysal (2007, Law and Human Behavior, 31, 109–115) and O’Sullivan (2007, Law and Human Behavior, 31, 117–123) concerns whether there are experts in deception detection. Two experiments sought to (a) identify expert(s) in detection and assess them twice with four tests, and (b) study their detection behavior using eye tracking. Paroled felons produced videotaped statements that were presented to students and law enforcement personnel. Two experts were identified, both female Native American BIA correctional officers. Experts were over 80% accurate in the first assessment, and scored at 90% accuracy in the second assessment. In Signal Detection analyses, experts showed high discrimination, and did not evidence biased responding. They exploited nonverbal cues to make fast, accurate decisions. These highly-accurate individuals can be characterized as experts in deception detection.


title: Analysis and design of common-gate low-noise amplifier for wideband applications

abstract: The design of a common-gate LNA for the wideband applications is discussed in this paper. First, the effect of the different components in matching network is analyzed in detail. This is followed by the design of a wideband input matching and output signal current for the input stage. A design example is given to demonstrate the effectiveness of the presented theory.


title: Trajectory prediction of spinning ball for ping-pong player robot

abstract: An analytic flying model that can well represent the physical behavior is derived, where the ball's self-rotational velocity changes along with the flying velocity. Based on the least square method, a rebound model that represents the relation between the velocities before and after rebound is established. The initial trajectory is fitted to three second order polynomials of the flying time with the measured positions of the ball. The initial velocities of the ball in the analytic flying model, including the flying velocity and the self-rotational velocity, are computed from the polynomials. The ball's landing position and velocity is predicted with the model. The velocities after rebound are determined with the rebound model. By taking the velocities after rebound as new initial ones, the flying trajectory after rebound is described with the model again. In other words, the ball's trajectory is predicted. Experimental results verify the effectiveness of the proposed method.


The Influence of Cu 3 ( BTC ) 2 metal organic framework on the permeability and perm-selectivity of PLLA-MOF mixed matrix membranes


title: Evaluating the Influence of Cultural Competence Education on Students’ Transcultural Self-Efficacy Perceptions

abstract: Guided by the cultural competence and confidence (CCC) model, the Transcultural Self-Efficacy Tool (TSET) was used to evaluate the influence of cultural competence education on the transcultural se...


title: An optimal algorithm for expanding the composition of polynomials

abstract: A runtime analysis is made of a previously published algorithm for polynomial composition. The relationship between this composition algorithm and Sablonniegre's algorithm is explored. This composition algorithm is then made optimal aby first performing a change of basis.


title: PennCOSYVIO: A challenging Visual Inertial Odometry benchmark

abstract: We present PennCOSYVIO, a new challenging Visual Inertial Odometry (VIO) benchmark with synchronized data from a VI-sensor (stereo camera and IMU), two Project Tango hand-held devices, and three GoPro Hero 4 cameras. Recorded at UPenn's Singh center, the 150m long path of the hand-held rig crosses from outdoors to indoors and includes rapid rotations, thereby testing the abilities of VIO and Simultaneous Localization and Mapping (SLAM) algorithms to handle changes in lighting, different textures, repetitive structures, and large glass surfaces. All sensors are synchronized and intrinsically and extrinsically calibrated. We demonstrate the accuracy with which ground-truth poses can be obtained via optic localization off of fiducial markers. The data set can be found at https://daniilidis-group.github.io/penncosyvio/.


title: Delivering health education programs: a model.

abstract: The OPT/EVALUATE delivery model provides a system for applying principles of organizational management to the problems of administering health education. Whether one is a new health educator, or part of a health education team, solving operational problems calls for expertise in a variety of fields. Most health educators are not prepared for program administration functions. The article includes a diagram of the OPT/EVALUATE model and an Action Step Checklist. The checklist may be for professional self-assessment, for guiding internships and on-the-job training experiences, or for systematically coordinating a health education project. Each section of the checklist is explained in the article with resources for further reading. Individuals can adapt the OPT/EVALUATE model to their own work setting and target populations. The importance of the checklist steps will vary from project to project. Some specific steps may need to be added.


title: Randomized Controlled Trial of Specific Spinal Stabilization Exercises and Conventional Physiotherapy for Recurrent Low Back Pain

abstract: Study Design. Pragmatic, multicentered randomized controlled trial, with 12-month follow-up. Objective. To evaluate the effect of adding specific spinal stabilization exercises to conventional physiotherapy for patients with recurrent low back pain (LBP) in the United Kingdom. Summary of Background Data. Spinal stabilization exercises are a popular form of physiotherapy management for LBP, and previous small-scale studies on specific LBP subgroups have identified improvement in outcomes as a result. Methods. A total of 97 patients (18–60 years old) with recurrent LBP were recruited. Stratified randomization was undertaken into 2 groups: “conventional,” physiotherapy consisting of general active exercise and manual therapy; and conventional physiotherapy plus specific spinal stabilization exercises. Stratifying variables used were laterality of symptoms, duration of symptoms, and Roland Morris Disability Questionnaire score at baseline. Both groups received The Back Book, by Roland et al. Back-specific functional disability (Roland Morris Disability Questionnaire) at 12 months was the primary outcome. Pain, quality of life, and psychologic measures were also collected at 6 and 12 months. Analysis was by intention to treat. Results. A total of 68 patients (70%) provided 12-month follow-up data. Both groups showed improved physical functioning, reduced pain intensity, and an improvement in the physical component of quality of life. Mean change in physical functioning, measured by the Roland Morris Disability Questionnaire, was 5.1 (95% confidence interval 6.3 to 3.9) for the specific spinal stabilization exercises group and 5.4 (95% confidence interval 6.5 to 4.2) for the conventional physiotherapy group. No statistically significant differences between the 2 groups were shown for any of the outcomes measured, at any time. Conclusions. Patients with LBP had improvement with both treatment packages to a similar degree. There was no additional benefit of adding specific spinal stabilization exercises to a conventional physiotherapy package for patients with recurrent LBP.


title: A Study of Apache Kafka in Big Data Stream Processing

abstract: Big data the name implies huge volume of data. Now a days streaming of data is more popular model which enables real time streaming data for data analytics. In current era Apache Kafka is most popular architecture used for processing the stream data. Kafka is scalable, distributed, and reliable result into high throughput. It also provides an API similar to messaging system.


title: Self-Imitation Learning

abstract: This paper proposes Self-Imitation Learning (SIL), a simple off-policy actor-critic algorithm that learns to reproduce the agent's past good decisions. This algorithm is designed to verify our hypothesis that exploiting past good experiences can indirectly drive deep exploration. Our empirical results show that SIL significantly improves advantage actor-critic (A2C) on several hard exploration Atari games and is competitive to the state-of-the-art count-based exploration methods. We also show that SIL improves proximal policy optimization (PPO) on MuJoCo tasks.


title: A Secure Cryptocurrency Scheme Based on Post-Quantum Blockchain

abstract: Nowadays, blockchain has become one of the most cutting-edge technologies, which has been widely concerned and researched. However, the quantum computing attack seriously threatens the security of blockchain, and related research is still less. Targeting at this issue, in this paper, we present the definition of post-quantum blockchain (PQB) and propose a secure cryptocurrency scheme based on PQB, which can resist quantum computing attacks. First, we propose a signature scheme based on lattice problem. We use lattice basis delegation algorithm to generate secret keys with selecting a random value, and sign message by preimage sampling algorithm. In addition, we design the first-signature and last-signature in our scheme, which are defined as double-signature. It is used to reduce the correlation between the message and the signature. Second, by combining the proposed signature scheme with blockchain, we construct the PQB and propose this cryptocurrency scheme. Its security can be reduced to the lattice short integer solution (SIS) problem. At last, through our analysis, the proposed cryptocurrency scheme is able to resist the quantum computing attack and its signature satisfies correctness and one-more unforgeability under the lattice SIS assumption. Furthermore, compared with previous signature schemes, the sizes of signature and secret keys are relatively shorter than that of others, which can decrease the computational complexity. These make our cryptocurrency scheme more secure and efficient.


title: Computer vision cracks the leaf code

abstract: Understanding the extremely variable, complex shape and venation characters of angiosperm leaves is one of the most challenging problems in botany. Machine learning offers opportunities to analyze large numbers of specimens, to discover novel leaf features of angiosperm clades that may have phylogenetic significance, and to use those characters to classify unknowns. Previous computer vision approaches have primarily focused on leaf identification at the species level. It remains an open question whether learning and classification are possible among major evolutionary groups such as families and orders, which usually contain hundreds to thousands of species each and exhibit many times the foliar variation of individual species. Here, we tested whether a computer vision algorithm could use a database of 7,597 leaf images from 2,001 genera to learn features of botanical families and orders, then classify novel images. The images are of cleared leaves, specimens that are chemically bleached, then stained to reveal venation. Machine learning was used to learn a codebook of visual elements representing leaf shape and venation patterns. The resulting automated system learned to classify images into families and orders with a success rate many times greater than chance. Of direct botanical interest, the responses of diagnostic features can be visualized on leaf images as heat maps, which are likely to prompt recognition and evolutionary interpretation of a wealth of novel morphological characters. With assistance from computer vision, leaves are poised to make numerous new contributions to systematic and paleobotanical studies.