title: Orange: From Experimental Machine Learning to Interactive Data Mining

text: Orange (www.ailab.si/orange) is a suite for machine learning and data mining. It can be used though scripting in Python or with visual programming in Orange Canvas using GUI components called widgets. In the demonstration we will show how to easily prototype stateof-the-art machine learning algorithms through Orange scripting, and design powerful and interactive data exploration applications through visual programming. 1 Orange, a Component-Based Framework Orange is a comprehensive, component-based framework for machine learning and data mining. It is intended for both experienced users and researchers in machine learning who want to prototype new algorithms while reusing as much of the code as possible, and for those just entering the field who can either write short Python scripts for data analysis or enjoy in the powerful while easy-to-use visual programming environment. Orange includes a range of techniques, such as data management and preprocessing, supervised and unsupervised learning, performance analysis, and a range of data and model visualization techniques. 1.1 Scripting in Python As a framework, Orange is comprised of several layers. The core design principle was to use C++ to code the basic data representation and manipulation and all time-complex procedures, such as most learning algorithms and data preprocessing. Tasks that are less time consuming are coded in Python. Python is a popular object-oriented scripting language known for its simplicity and power, and often used for prototyping and as a ”glue-language” for components written in other languages. The interface between C++ and Python provides a tight integration: Python scripts can access and manipulate Orange objects as if they were implemented in Python. On the other hand, components defined in Python can be used by the C++ core. For instance, one can use classification tree as implemented within Orange (in C++) but prototype a component for attribute selection in Python. For Orange, we took special care to implement machine learning methods so that they are assembled from a set of reusable components one can either use in the new algorithms, or replace them with prototypes written in Python. Just for a taste, here is a simple Python script, which, using Orange, reads the data, reports on the number of instances and attributes, builds two classifiers and outputs predicted and true class of the first five instances. import orange data = orange.ExampleTable(’voting.tab’) print ’Instances:’, len(data), ’Attributes:’, len(data.domain.attributes) nbc = orange.BayesLearner(data) knn = orange.kNNLearner(data, k=10) for i in range(5): print nbc(data[i]), knn(data[i]), ’vs. true class’, data[i].getClass() Another, a bit more complicated script below, implements a classification tree learner where node attributes that split the data are chosen at random by a function randomChoice, which is used in place of data splitting component of Orange’s classification tree inducer. The script builds a standard and random tree from the data, and reports on their sizes. import orange, random def randomChoice(instances, *args): attr = random.choice(instances.domain.attributes) cl = orange.ClassifierFromVar(whichVar=attr, classVar=attr) return cl, attr.values, None, 1 treeLearner = orange.TreeLearner() rndLearner = orange.TreeLearner() rndLearner.split = randomChoice data = orange.ExampleTable(’voting.tab’) tree = treeLearner(data) rndtree = rndLearner(data) print tree.treesize(), ’vs.’, rndtree.treesize() 1.2 Visual Programming Component-based approach was also used for graphical user’s interface (GUI). Orange’s GUI is made of widgets, which are essentially a GUI wrappers around data analysis algorithms implemented in Orange and Python. Widgets communicate through channels, and a particular set of connected widgets is called a schema. Orange schemas can be either set in Python scripts, or, preferably, are put together by means of visual programming in an application called Orange Canvas. Besides ease-of-use and flexibility, data exploration widgets were carefully design to support interaction. Clicking on a classification tree node in the tree visualization widget, for example, outputs the corresponding data instances making them available for further analysis. Any visualization of predictive or visualization models where their elements are associated with particular subsets of instances, attributes, data domains, etc., behave in the similar way. A snapshot of Orange Canvas with an example schema is shown in Fig. 1. Fig. 1. Snapshot of Orange Canvas with a schema that takes a microarray data, performs k-means clustering, and evaluates the performance of two different supervised learning methods when predicting the cluster label. Clustered data is visualized in the Heat Map widget, which sends any selected data subset to the Scatterplot widget. 2 Orange Demonstration at ECML-PKDD The demonstration at ECML-PKDD will feature an introduction to Orange, give a tutorial on how to do machine learning by scripting, and show how to use component-based programming to design a new induction algorithm. The second part of the demonstration will demonstrate Orange widgets and visual programming, and will show hot to use these to build classifiers, find interaction between attributes, and do a range of interesting data visualizations. Demonstration will be live, e.g. we will write scripts and visually program at the stage rather than show transparencies. The demonstration will use the data from medicine (predictive models) and functional genomics (data mining of microarray and sequence data). CD’s with installations and copies of the white-papers will be handed out. 3 On Significance and Contribution Orange is an open-source framework that features both scripting and visual programming. Because of component-based design in C++ and integration with Python, Orange should appeal to machine learning researchers for the speed of execution and ease of prototyping of new methods. Graphical user’s interface is provided through visual programming and carefully designed widgets that support interactive data exploration. Component-based design, both on the level of procedural and visual programming, flexibility in combining components to design new machine learning methods and data mining applications, and user and developer-friendly environment are also the most significant attributes of Orange and those where Orange can make its contribution to the community.


title: Analytics-driven data ingestion and derivation in the AWESOME polystore

text: Polystores, i.e., data management systems that use multiple stores for different data models, are gaining popularity. We are developing a polystore-based system called AWESOME to support social data analytics. The AWESOME polystore can support relational, semistructured, graph and text data and houses a Spark computation engine to produce derived data during ingestion. ADIL, the data ingestion language of AWESOME allows a user to flexibly specify the placement of original and derived data into and across component stores and the computation engine. The paper also outlines a number of optimization strategies for managing data placement in AWESOME.


title: Good Word Attacks on Statistical Spam Filters

text: Unsolicited commercial email is a significant problem for users and providers of email services. While statistical spam filters have proven useful, senders of spam are learning to bypass these filters by systematically modifying their email messages. In a good word attack, one of the most common techniques, a spammer modifies a spam message by inserting or appending words indicative of legitimate email. In this paper, we describe and evaluate the effectiveness of active and passive good word attacks against two types of statistical spam filters: naive Bayes and maximum entropy filters. We find that in passive attacks without any filter feedback, an attacker can get 50% of currently blocked spam past either filter by adding 150 words or fewer. In active attacks allowing test queries to the target filter, 30 words will get half of blocked spam past either filter.


title: Social Networking Sites and Addiction: Ten Lessons Learned

text: Online social networking sites (SNSs) have gained increasing popularity in the last decade, with individuals engaging in SNSs to connect with others who share similar interests. The perceived need to be online may result in compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning online social networking sites and addiction based on the insights derived from recent empirical research will be presented. These are: (i) social networking and social media use are not the same; (ii) social networking is eclectic; (iii) social networking is a way of being; (iv) individuals can become addicted to using social networking sites; (v) Facebook addiction is only one example of SNS addiction; (vi) fear of missing out (FOMO) may be part of SNS addiction; (vii) smartphone addiction may be part of SNS addiction; (viii) nomophobia may be part of SNS addiction; (ix) there are sociodemographic differences in SNS addiction; and (x) there are methodological problems with research to date. These are discussed in turn. Recommendations for research and clinical applications are provided.


title: Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

text: Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model environmental systems. We review some of them and their environmental applicability, with examples and a reference list. The techniques covered are case-based reasoning, rule-based systems, artificial neural networks, fuzzy models, genetic algorithms, cellular automata, multi-agent systems, swarm intelligence, reinforcement learning and hybrid systems. © 2008 IMACS. Published by Elsevier B.V. All rights reserved.


title: Suitable MLP Network Activation Functions for Breast Cancer and Thyroid Disease Detection

text: This paper compared various MLP activation functions for classification problems. The most well-known (Artificial Neural Network) ANN architecture is the Multilayer Perceptron (MLP) network which is widely used for solving problems related to data classifications. Selection of the activation functions in the MLP network plays an essential role on the network performance. A lot of studies have been conducted by reseachers to investigate special activation function to solve different kind of problems. Therefore, this paper intends to investigate the activation functions in MLP networks in terms of the accuracy performances. The activation functions under investigation are sigmoid, hyperbolic tangent, neuronal, logarithmic, sinusoidal and exponential. Medical diagnosis data from two case studies, thyroid disease classification and breast cancer classification, have been used to test the performance of the MLP network. The MLP networks are trained using Back Propagation learning algorithm. The performance of the MLP networks are calculated based on the percentage of correct classificition. The results show that the hyperbolic tangent function in MLP network had the capability to produce the highest accuracy for classifying breast cancer data. Meanwhile, for thyroid disease classification, neuronal function is the most suitable function that performed the highest accuracy in MLP network.


title: Monolithic Low Cost Ka-Band Wilkinson Power Dividers on Flexible Organic Substrates

text: A millimeter wave Wilkinson power divider is presented on a thin and durable organic substrate, along with an analysis of bend topologies to reduce coupling between the output paths. The divider provides excellent performance across the entire Ka-band from 27 to 40 GHz. Insertion loss is approximately 0.3 dB and isolation between the output ports is around 30 dB at the design frequency.


title: Liquid crystal polymer (LCP) for microwave/millimeter wave multilayer packaging

text: This paper presents characterization and analysis of liquid crystal polymer materials for microwave and millimeter multi-layer packaging. Processing techniques have been developed to fabricate interconnects on this new LCP material. The experimental results demonstrate that an interconnect on LCP achieves a measured insertion loss of less than 0.1 dB/mm up to 50 GHz. This material is highly suitable for microwave and millimeter wave packaging.


title: Wideband coplanar waveguide RF probe pad to microstrip transitions without via holes

text: A novel via-less coplanar waveguide (CPW) to microstrip transition is discussed and design rules based on simulations and experimental results are presented. This transition demonstrates a maximum insertion loss of 1 dB over the frequency range from 10 GHz to 40 GHz with a value of 0.4 dB at 20 GHz. This transition could find a variety of applications due to its compatibility with RF systems-on-a chip, low loss performance, low cost and its ease of fabrication.


title: A broadband Wilkinson balun using microstrip metamaterial lines

text: A metamaterial balun that converts a single-ended input to a differential output over a large bandwidth is presented. The device also exhibits excellent return loss, isolation, and through characteristics over the same frequency band. The balun comprises a Wilkinson divider, followed by a +90/spl deg/ negative-refractive-index (NRI) metamaterial (MM) phase-shifting line along the top branch, and a -90/spl deg/ MM phase-shifting line along the bottom branch. Utilizing MM lines for both the +90/spl deg/ and -90/spl deg/ branches allows the slopes of their phase responses to be matched, resulting in a broadband differential output signal. The theoretical performance of the balun is verified through circuit simulations and measurements of a fabricated prototype at 1.5 GHz. The MM balun exhibits a measured differential output phase bandwidth (180/spl deg//spl plusmn/10/spl deg/) of 1.16 GHz (77%), from 1.17 to 2.33 GHz. The measured isolation and return loss for all three ports remain below -10 dB over a bandwidth in excess of 2 GHz, while the output quantities |S/sub 21/| and |S/sub 31/| remain above -4 dB from 0.5 to 2.5 GHz.


title: Mop: an efficient and generic runtime verification framework

text:
Monitoring-Oriented Programming (MOP1) [21, 18, 22, 19] is a formal framework for software development and analysis, in which the developer specifies desired properties using definable specification formalisms, along with code to execute when properties are violated or validated. The MOP framework automatically generates monitors from the specified properties and then integrates them together with the user-defined code into the original system.
 The previous design of MOP only allowed specifications without parameters, so it could not be used to state and monitor safety properties referring to two or more related objects. In this paper we propose a parametric specification formalism-independent extension of MOP, together with an implementation of JavaMOP that supports parameters. In our current implementation, parametric specifications are translated into AspectJ code and then weaved into the application using off-the-shelf AspectJ compilers; hence, MOP specifications can be seen as formal or logical aspects.
 Our JavaMOP implementation was extensively evaluated on two benchmarks, Dacapo [14] and Tracematches [8], showing that runtime verification in general and MOP in particular are feasible. In some of the examples, millions of monitor instances are generated, each observing a set of related objects. To keep the runtime overhead of monitoring and event observation low, we devised and implemented a decentralized indexing optimization. Less than 8% of the experiments showed more than 10% runtime overhead; in most cases our tool generates monitoring code as efficient as the hand-optimized code. Despite its genericity, JavaMOP is empirically shown to be more efficient than runtime verification systems specialized and optimized for particular specification formalisms. Many property violations were detected during our experiments; some of them are benign, others indicate defects in programs. Many of these are subtle and hard to find by ordinary testing.


title: A Low Voltage Delta-Sigma Fractional Frequency Divider for Multi-band WSN Frequency Synthesizers

text: A 1 V low voltage delta-sigma fractional-N frequency divider for multi-band (780/868/915 MHz and 2.4 GHz) WSN frequency synthesizers is presented. The frequency divider consists of a dual-modulus prescaler, a pulse-swallow counter and a delta-sigma modulator. The high-speed and low-voltage phase-switching dualmodulus prescaler is used in the frequency divider. Low threshold voltage transistors are applied to overcome low voltage supply and forward phase-switching technique is adopted to prevent glitches. The modified deltasigma modulator with long output sequence length and less spurs is adopted to minimize the fractional spurs. The frequency divider is designed in 0.18 μm TSMC RF CMOS technology under 1 V supply instead of the standard 1.8 V supply. The total chip area is 1190 μm × 485 μm including I/O pads. The post simulation results show the frequency divider operates normally over a wide range of 1.3-5.0 GHz and the core circuit (without test buffers) consumes 2.3 mW. Copyright © 2013 IFSA.


title: A high-accuracy method for fine registration of overlapping point clouds

text: This paper presents a high-accuracy method for fine registration of two partially overlapping point clouds that have been coarsely registered. The proposed algorithm, which is named dual interpolating point-tosurface method, is principally a modified variant of point-to-surface Iterative Closest Point (ICP) algorithm. The original correspondences are established by adopting a dual surface fitting approach using B-spline interpolation. A novel auxiliary pair constraint based on the surface fitting approach, together with surface curvature information, is employed to remove unreliable point matches. The combined constraint directly utilizes global rigid motion consistency in conjunction with local geometric invariant to reject false correspondences precisely and efficiently. The experimental results involving a number of realistic point clouds demonstrate that the new method can obtain accurate and robust fine registration for pairwise 3D point clouds. This method addresses highest accuracy alignment with less focus on recovery from poor coarse registrations. 2009 Elsevier B.V. All rights reserved.


title: Rethink energy accounting with cooperative game theory

text:
Energy accounting determines how much a software principal contributes to the total system energy consumption. It is the foundation for evaluating software and for operating system based energy management. While various energy accounting policies have been tried, there is no known way to evaluate them directly simply because it is hard to track all hardware usage by software in a heterogeneous multicore system like modern smartphones and tablets.
 In this work, we argue that energy accounting should be formulated as a cooperative game and that the Shapley value provides the ultimate ground truth for energy accounting policies. We reveal the important flaws of existing energy accounting policies based on the Shapley value theory and provide Shapley value-based energy accounting, a practical approximation of the Shapley value, for battery-powered mobile systems. We evaluate this approximation against existing energy accounting policies in two ways: (i) how well they identify the top energy consuming applications, and (ii) how effective they are in system energy management. Using a prototype based on Texas Instruments Pandaboard and smartphone workload, we experimentally demonstrate existing energy accounting policies can deviate by 400% in attributing energy consumption to running applications and can be up to 25% less effective in system energy management when compared to Shapley value-based energy accounting.


title: A Controlled Pilot-Outcome Study of Sensory Integration ( SI ) in the Treatment of Complex Adaptation to Traumatic Stress

text: This study tested whether sensory integration (SI) treatment combined with psychotherapy would improve symptom outcome over psychotherapy alone in the treatment of complex posttraumatic stress, as measured by the Disorders of Extreme Stress Not Otherwise Specified (DESNOS) clinical construct in a group of 10 adult patients with histories of childhood abuse. DESNOS symptoms were assessed at three time periods (T1, baseline; T2, after experimental group SI treatment; and T3, after wait-list control group SI treatment) using the Structured Interview for Disorders of Extreme Stress (SIDES). The Sensory Learning Program (SLP), developed by the Sensory Learning Institute of Boulder, Colorado, was used as the SI treatment modality. Results indicated significant differential improvement for the group treated with SLP in SIDES Total Score (T1/T2 and T2/T3), Self Perception (T1/T2 and T2/T3), Affect Regulation (T2/T3), and Alterations in Meaning (T1/T2).


title: B2C web site quality and emotions during online shopping episodes: An empirical study

text: This paper explores the impact of the quality of a web site on the cognitive process leading to consumers’ emotions—considered as direct antecedents to shopping behaviors and operationalized as mental states of readiness arising from the appraisal of events. A parsimonious theoretical model was defined and tested with data collected from 215 web-shopping episodes during which consumers were shopping for low-touch products. Analysis of the results showed that web site quality had a positive impact on the cognitive appraisal of situational state, which in turn influenced five of the six emotions of the proposed model: liking, joy, pride, dislike, and frustration. Results also showed that a substantial number of shoppers experienced intensively the emotions of liking and joy. Moreover, this paper highlights several implications that could help managers and webmasters improve the quality of their web sites. # 2006 Elsevier B.V. All rights reserved.


title: An Efficient Character-Level Neural Machine Translation

text: Neural machine translation aims at building a single large neural network that can be trained to maximize translation performance. The encoder-decoder architecture with an attention mechanism achieves a translation performance comparable to the existing state-of-the-art phrase-based systems on the task of English-to-French translation. However, the use of large vocabulary becomes the bottleneck in both training and improving the performance. In this paper, we propose an efficient architecture to train a deep character-level neural machine translation by introducing a decimator and an interpolator. The decimator is used to sample the source sequence before encoding while the interpolator is used to resample after decoding. Such a deep model has two major advantages. It avoids the large vocabulary issue radically; at the same time, it is much faster and more memory-efficient in training than conventional character-based models. More interestingly, our model is able to translate the misspelled word like human beings.


title: Naturalizing consciousness: a theoretical framework.

text: Consciousness has a number of apparently disparate properties, some of which seem to be highly complex and even inaccessible to outside observation. To place these properties within a biological framework requires a theory based on a set of evolutionary and developmental principles. This paper describes such a theory, which aims to provide a unifying account of conscious phenomena.


title: Image-based Calorie Content Estimation for Dietary Assessment

text: In this paper, we present an image-analysis based approach to calorie content estimation for dietary assessment. We make use of daily food images captured and stored by multiple users in a public Web service called Food Log. The images are taken without any control or markers. We build a dictionary dataset of 6512 images contained in Food Log the calorie content of which have been estimated by experts in nutrition. An image is compared to the ground truth data from the point of views of multiple image features such as color histograms, color correlograms and SURF fetures, and the ground truth images are ranked by similarities. Finally, calorie content of the input food image is computed by linear estimation using the top n ranked calories in multiple features. The distribution of the estimation shows that 79% of the estimations are correct within ±40% error and 35% correct within ±20% error.


title: A religious upbringing reduces the influence of genetic factors on disinhibition: evidence for interaction between genotype and environment on personality.

text: Information on personality, on anxiety and depression and on several aspects of religion was collected in 1974 Dutch families consisting of adolescent and young adult twins and their parents. Analyses of these data showed that differences between individuals in religious upbringing, in religious affiliation and in participation in church activities are not influenced by genetic factors. The familial resemblance for different aspects of religion is high, but can be explained entirely by environmental influences common to family members. Shared genes do not contribute to familial resemblances in religion. The absence of genetic influences on variation in several dimensions of religion is in contrast to findings of genetic influences on a large number of other traits that were studied in these twin families. Differences in religious background are associated with differences in personality, especially in Sensation Seeking. Subjects with a religious upbringing, who are currently religious and who engage in church activities score lower on the scales of the Sensation Seeking Questionnaire. The most pronounced effect is on the Disinhibition scale. The resemblances between twins for the Disinhibition scale differ according to their religious upbringing. Receiving a religious upbringing seems to reduce the influence of genetic factors on Disinhibition, especially in males.


title: A shape and image merging technique to solve jigsaw puzzles

text: This paper proposes an algorithm for solving subsets of typical (canonical) jigsaw puzzles. This algorithm combines shape and image matching with a cyclic ‘‘growth’’ process that tries to place pieces in their correct positions. First, the jigsaw pieces are extracted from the input image. Then, the corner points of the jigsaw pieces are detected. Next, piece classification and recognition are performed based on jigsaw piece models. Connection relationships between pieces are calculated and finally recovered by using boundary shape matching and image merging. We tested this algorithm by employing real-world images containing dozens of jigsaw pieces. The experiment s results show this algorithm is efficient and effective. 2003 Elsevier Science B.V. All rights reserved.


title: Adaptive Droop Control Applied to Voltage-Source Inverters Operating in Grid-Connected and Islanded Modes

text: This paper proposes a novel control for voltage-source inverters with the capability to flexibly operate in grid-connected and islanded modes. The control scheme is based on the droop method, which uses some estimated grid parameters such as the voltage and frequency and the magnitude and angle of the grid impedance. Hence, the inverter is able to inject independently active and reactive power to the grid. The controller provides a proper dynamics decoupled from the grid-impedance magnitude and phase. The system is also able to control active and reactive power flows independently for a large range of impedance grid values. Simulation and experimental results are provided in order to show the feasibility of the control proposed.


title: Manipulating Attributes of Natural Scenes via Hallucination

text: Fig. 1. Given a natural image, the proposed approach can hallucinate different versions of the same scene in a wide range of conditions, e.g.night, sunset, winter, spring, rain, fog or even a combination of those. First, our method utilizes a single generator network to imagine the scene with respect to its semantic layout and the desired set of attributes. Then, it directly transfers the look from the hallucinated output to the input image, without a need to have access to a reference style image.


title: Reflection Removal Using Low-Rank Matrix Completion

text: The images taken through glass often capture a target transmitted scene as well as undesired reflected scenes. In this paper, we propose a low-rank matrix completion algorithm to remove reflection artifacts automatically from multiple glass images taken at slightly different camera locations. We assume that the transmitted scenes are more dominant than the reflected scenes in typical glass images. We first warp the multiple glass images to a reference image, where the gradients are consistent in the transmission images while the gradients are varying across the reflection images. Based on this observation, we compute a gradient reliability such that the pixels belonging to the salient edges of the transmission image are assigned high reliability. Then we suppress the gradients of the reflection images and recover the gradients of the transmission images only, by solving a low-rank matrix completion problem in gradient domain. We reconstruct an original transmission image using the resulting optimal gradient map. Experimental results show that the proposed algorithm removes the reflection artifacts from glass images faithfully and outperforms the existing algorithms on typical glass images.


title: Unveiling clusters of events for alert and incident management in large-scale enterprise it

text: Large enterprise IT (Information Technology) infrastructure components generate large volumes of alerts and incident tickets. These are manually screened, but it is otherwise difficult to extract information automatically from them to gain insights in order to improve operational efficiency. We propose a framework to cluster alerts and incident tickets based on the text in them, using unsupervised machine learning. This would be a step towards eliminating manual classification of the alerts and incidents, which is very labor intense and costly. Our framework can handle the semi-structured text in alerts generated by IT infrastructure components such as storage devices, network devices, servers etc., as well as the unstructured text in incident tickets created manually by operations support personnel. After text pre-processing and application of appropriate distance metrics, we apply different graph-theoretic approaches to cluster the alerts and incident tickets, based on their semi-structured and unstructured text respectively. For automated interpretation and read-ability on semi-structured text clusters, we propose a method to visualize clusters that preserves the structure and human-readability of the text data as compared to traditional word clouds where the text structure is not preserved; for unstructured text clusters, we find a simple way to define prototypes of clusters for easy interpretation. This framework for clustering and visualization will enable enterprises to prioritize the issues in their IT infrastructure and improve the reliability and availability of their services.


title: Deep Unsupervised Learning of Visual Similarities

text: Exemplar learning of visual similarities in an unsupervised manner is a problem of paramount importance to Computer Vision. In this context, however, the recent breakthrough in deep learning could not yet unfold its full potential. With only a single positive sample, a great imbalance between one positive and many negatives, and unreliable relationships between most samples, training of Convolutional Neural networks is impaired. In this paper we use weak estimates of local similarities and propose a single optimization problem to extract batches of samples with mutually consistent relations. Conflicting relations are distributed over different batches and similar samples are grouped into compact groups. Learning visual similarities is then framed as a sequence of categorization tasks. The CNN then consolidates transitivity relations within and between groups and learns a single representation for all samples without the need for labels. The proposed unsupervised approach has shown competitive performance on detailed posture analysis and object classification.


title: Overuse lower extremity injuries in sports.

text: When athletes train harder the risk of injury increases, and there are several common overuse injuries to the lower extremity. Three of the most common lower extremity overuse injuries in sports are discussed including the diagnosis and treatments: medial tibal stress syndrome, iliotibial band syndrome, and stress fractures. The charge of sports medicine professionals is to identify and treat the cause of the injuries and not just treat the symptoms. Symptomatology is an excellent guide to healing and often the patient leads the physician to the proper diagnosis through an investigation of the athlete's training program, past injury history, dietary habits, choice of footwear, and training surface.


title: Tree Adjoining Grammar Based Parser for a Hindi Text-to-Scene Conversion System

text: We have developed Preksha: a Hindi text visualizer. Preksha generates a 3D scene by understanding a given Hindi text. We propose the Earley's parsing algorithm using Tree adjoining grammar (TAG) for Hindi language understanding. A brief overview of the TAG formalism and the Earley's parsing algorithm is presented. Preksha's Hindi language parsing is explainedwith the Lisp notation. The maincontribution of this paper is to apply TAG parsing for morphologically-richfree-order language Hindi for automatic text visualization (ATV).


title: Autonomous Battery Exchange of UAVs with a Mobile Ground Base

text: This paper presents the autonomous battery exchange operation for small scale UAVs, using a mobile ground base that carries a robotic arm and a service station containing the battery exchange mechanism. The goal of this work is to demonstrate the means to increase the autonomy and persistence of robotic systems without requiring human intervention. The design and control of the system and its components are presented in detail, as well as the collaborative software framework used to plan and execute complex missions. Finally, the results of autonomous outdoor experiments are presented, in which the ground rover successfully localizes, retrieves, services, and deploys the landed UAV, proving its capacity to extend and enhance autonomous operations.


title: A Survey Paper on PID Control System

text: This paper concentrates on the work done on the PID control system specially on time delaying systems like network control systems. The different works which have already been done are summarised here like gain margin, phase margin methods. The absolute focal point on time delayed systemare the delays induced by network and the packet losses. This survey paper covers the study on such methods that compensates these


title: Flying Swarm of Drones Over Circulant Digraph

text: Drones are critical to military collaborative engagement, surveillance, and monitoring of areas of interest. They also play an increasingly influential role in commercial applications. We describe a new innovative method for collisionless, survivable, and persistent flying of given <inline-formula><tex-math notation="LaTeX">$k$</tex-math> </inline-formula> drones over <inline-formula><tex-math notation="LaTeX">$2n$</tex-math></inline-formula> waypoints that results in dispersed coverage of a given area of interest, where <inline-formula><tex-math notation="LaTeX">$n > k \geq 3$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$k$</tex-math> </inline-formula> divides <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>. Our method is fundamentally different than any known to date since it does not need sensors to avoid collisions between drones. In addition, the flight of drones does not have to be coordinated.


title: High-voltage pulse waveform modulator based on solid-state Marx generator

text: Many research fields need special high-voltage pulses in pulsed power applications, which are different from typical rectangular pulses. In this paper, a high-voltage waveform modulator of two methods using a solid-state Marx generator has been developed and tested. Instead of manufacturing a generator with a specialized structure, the modulator can control a general Marx generator by low-voltage programmable logic devices to produce an irregular but controllable high-voltage triangular pulse with pulse length of ~4.5 μs and peak voltage of ~123 kV.


title: Treevis.net: A Tree Visualization Reference

text: Tree visualization is one of the best-studied areas of information visualization; researchers have developed more than 200 visualization and layout techniques for trees. The treevis.net project aims to provide a hand-curated bibliographical reference to this ever-growing wealth of techniques. It offers a visual overview that users can filter to a desired subset along the design criteria of dimensionality, edge representation, and node alignment. Details, including links to the original publications, can be brought up on demand. Treevis.net has become a community effort, with researchers sending in preprints of their tree visualization techniques to be published or pointing out additional information.


title: Thinking Aloud : Reconciling Theory and Practice —

text: —M. TED BOREN AND JUDITH RAMEY, ASSOCIATE MEMBER, IEEE Abstract—Thinking-aloud protocols may be the most widely used method in usability testing, but the descriptions of this practice in the usability literature and the work habits of practitioners do not conform to the theoretical basis most often cited for it: Ericsson and Simon’s seminal work PROTOCOL ANALYSIS: VERBAL REPORTS AS DATA [1]. After reviewing Ericsson and Simon’s theoretical basis for thinking aloud, we review the ways in which actual usability practice diverges from this model. We then explore the concept of SPEECH GENRE as an alternative theoretical framework. We first consider uses of this new framework that are consistent with Simon and Ericsson’s goal of eliciting a verbal report that is as undirected, undisturbed, and constant as possible. We then go on to consider how the proposed new approach might handle problems that arise in usability testing that appear to require interventions not supported in the older model.


title: Cone Trees: animated 3D visualizations of hierarchical information

text: The task of managing and accessing large information spaces is a problem in large scale cognition. Emerging technologies for 3D visualization and interactive animation offer potential solutions to this problem, especially when the structure of the information can be visualized. We describe one of these Information Visualization techniques, called the Cone Tree, which is used for visualizing hierarchical information structures. The hierarchy is presented in 3D to maximize effective use of available screen space and enable visualization of the whole structure. Interactive animation is used to shift some of the user's cognitive load to the human perceptual system.


title: Stacked Graphs – Geometry & Aesthetics

text: In February 2008, the New York Times published an unusual chart of box office revenues for 7500 movies over 21 years. The chart was based on a similar visualization, developed by the first author, that displayed trends in music listening. This paper describes the design decisions and algorithms behind these graphics, and discusses the reaction on the Web. We suggest that this type of complex layered graph is effective for displaying large data sets to a mass audience. We provide a mathematical analysis of how this layered graph relates to traditional stacked graphs and to techniques such as ThemeRiver, showing how each method is optimizing a different ldquoenergy functionrdquo. Finally, we discuss techniques for coloring and ordering the layers of such graphs. Throughout the paper, we emphasize the interplay between considerations of aesthetics and legibility.


title: Readings in information visualization - using vision to think

text: Visualization: The use of computer-supported, interactive, visual representations of data to amplify cognition. (6) Cognition is the acquisition or use of knowledge. This definition has the virtue of focusing as much on the purpose of visualization as the means. Hamming (1973) saud, “the purpose of computation is insight, not numbers.” Likewise for visualization, “the purpose of visualization is insight not pictures.” The main goals of this insight are discovery, decision  making, and explanation. (6)


title: Managing information sharing within an organizational setting: A social network perspective

text: In the current information-driven and technologically based global economy, organizations are becoming increasingly dependent on the cumulative knowledge of their employees, suppliers, customers, and other key stakeholders. An organization’s ability to share this knowledge among organizational members is key to its competitive advantage (Bock, Zmud, Kim, & Lee, 2007; Brown & Duguid, 2000; Small & Sage, 2006). Information sharing is critical to an organization’s competitiveness and requires a free flow of information among members that is undistorted and up-to-date (Childhouse & Towill, 2003; Li & Lin, 2006; Moberg, Cutler, Gross, & Speh, 2002; Rahman, 2004; Tan, Lyman, & Wisner, 2002). However, extensive information sharing within organizations still appears to be the exception rather than the rule (Bock et al., 2007; Davenport & Prusack, 1998; Li & Lin, 2006). According to Li and Lin (2006), intensified competition and globalized markets are some of the challenges associated with getting products and services to the right place at the right time and at the lowest cost. These challenges, for instance, have forced organizations to realize that it is not enough to improve their efficiencies; rather, their entire supply chains have to be made competitive. One way for organizations to do this is to support information sharing among their work groups (Li & Lin, 2006). This article explores organizational information sharing as it relates to individuals within and between work groups. A review of the literature on organizational structure and information sharing was conducted to examine the research in this area. A case example then illustrates how a social network approach (SNA) was used to explore the process of measuring the social


title: A SECURE ONLINE PAYMENT SYSTEM USING STEGANOGRAPHY AND VISUAL CRYPTOGRAPHY

text: In This paper we presents a new methodology which provides limited information only that is important in transferring money while shopping online provides security for customers data and increasing in confidence of customer and preventing identity stealing. Here we used cryptography technique for image encryption. Data is encrypted in images shares which are meaningless images. These shares are then transferred over an untrusted communication channel. By combining only these shares give the original secret image. This images may contain ATM card details, credit card information etc. At merchants side merchant have to pay some amount per transaction so it’s merchant’s responsibility to protect customers information from being misused. So here is solution for such operations.


title: A novel method for eye tracking and blink detection in video frames

text: This paper presents a novel method for eye tracking and blink detection in the video frames obtained from low resolution consumer grade web cameras. It uses a method involving Haar based cascade classifier for eye tracking and a combination of HOG features with SVM classifier for eye blink detection. The presented method is non intrusive and hence provides a comfortable user interaction. The eye tracking method has an accuracy of 92.3% and the blink detection method has an accuracy of 92.5% when tested using standard databases and a combined accuracy of 86% when tested under real world conditions of a normal room.


title: THE INSTITUTIONAL FOUNDATIONS OF PUBLIC POLICY : A TRANSACTIONS APPROACH WITH APPLICATION TO ARGENTINA *

text: Public policies are the outcomes of complex intertemporal exchanges among politicians. The basic institutional characteristics of a country constitute the framework within which those transactions are accomplished. We develop a transactions theory to understand the ways in which political institutions affect the transactions that political actors are able to undertake, and hence the policies that emerge. We argue that Argentina is a case in which the functioning of political institutions has been such that it prevented the capacity to undertake efficient intertemporal political exchanges. We use positive political theory and transaction cost economics to explain the workings of Argentine political institutions, and to show how that maps into low-quality policies.


title: Functional cartography of complex metabolic networks

text: High-throughput techniques are leading to an explosive growth in the size of biological databases and creating the opportunity to revolutionize our understanding of life and disease. Interpretation of these data remains, however, a major scientific challenge. Here, we propose a methodology that enables us to extract and display information contained in complex networks. Specifically, we demonstrate that we can find functional modules in complex networks, and classify nodes into universal roles according to their pattern of intra- and inter-module connections. The method thus yields a ‘cartographic representation’ of complex networks. Metabolic networks are among the most challenging biological networks and, arguably, the ones with most potential for immediate applicability. We use our method to analyse the metabolic networks of twelve organisms from three different superkingdoms. We find that, typically, 80% of the nodes are only connected to other nodes within their respective modules, and that nodes with different roles are affected by different evolutionary constraints and pressures. Remarkably, we find that metabolites that participate in only a few reactions but that connect different modules are more conserved than hubs whose links are mostly within a single module.


title: Dengue fever prediction using K-means clustering algorithm

text: Dengue fever is a virus infection which is transmitted to humans by mosquitoes that living in tropical and subtropical climates and carries the virus. The dengue viruses occur in 4 serotypes (DENV-1 to DENV-4). A dengue disease ranges from mild febrile disease to severe hemorrhagic fever. Predicting the relationship between the dengue serotypes will surely help the biotechnologists and bioinformaticians to move one step forward to discover antibiotic for dengue. This paper has been focused four stages namely preprocessing, attribute selection, clustering and predicting the dengue fever. R 3.3.2 Tool is used for preprocessing the household of dengue dataset. D win's method has been applied to generate filled dataset by substituting all missing values for nominal and numeric attributes with mode and mean value. Dengue virus can be predicted by applying different data mining techniques. The main goal of research work is to predict the people who are affected by dengue depending upon categorization of age group using K-means clustering algorithm has been implemented.


title: Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making

text: Research on next generation agricultural systems models shows that the most important current limitation is data, both for on-farm decision support and for research investment and policy decision making. One of the greatest data challenges is to obtain reliable data on farm management decision making, both for current conditions and under scenarios of changed bio-physical and socio-economic conditions. This paper presents a framework for the use of farm-level and landscape-scale models and data to provide analysis that could be used in NextGen knowledge products, such as mobile applications or personal computer data analysis and visualization software. We describe two analytical tools - AgBiz Logic and TOA-MD - that demonstrate the current capability of farmlevel and landscape-scale models. The use of these tools is explored with a case study of an oilseed crop, Camelina sativa, which could be used to produce jet aviation fuel. We conclude with a discussion of innovations needed to facilitate the use of farm and policy-level models to generate data and analysis for improved knowledge products.


title: A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems

text: Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria.


title: Review on Printed Log Periodic and Yagi MSA

text: The log periodic antenna and Yagi-Uda antenna are used in the applications where very high directivity is required. They also give very high gain in the range of 17-20dBi. This paper presents a review on various configurations of log periodic and Yagi antennas, their advantages and problems. One problem encountered with Yagi-Uda antenna is relatively less bandwidth. This problem is solved by log periodic antenna which can operate over high bandwidth and providing high gain at the same time. In this paper, a review of various techniques to realize printed Yagi-Uda and log periodic antenna is discussed. They are realized by using different feeding techniques like microstrip feeding, co-axial feeding etc. They are also realized by using modifying the shape of directors and reflectors. The high bandwidth (log periodic antenna) has also been realized by increasing the number of reflectors.


title: EMG analysis of the scapular muscles during a shoulder rehabilitation program.

text: The purpose of this study was to determine which exercises most effectively use the scapular muscles. Eight muscles in 9 healthy subjects were studied with indwelling electromyographic electrodes and cinematography while performing 16 exercises. The 8 muscles studied were the upper, middle, and lower trapezius; levator scapula; rhomboids; pectoralis minor; and the middle and lower serratus anterior. Each exercise was divided into arcs of motion and the electromyographic activity was quantified as a percentage of the maximal manual muscle test. The optimal exercises for each muscle were identified based on intensity (greater than 50% maximal manual muscle test) and duration (over at least 3 consecutive arcs of motion) of the muscle activity. Twelve of the exercises qualified as top exercises for all of the muscles. On further analysis, a group of 4 exercises was shown to make up the core of a scapular muscle strengthening program. Those 4 exercises include scaption (scapular plane elevation), rowing, push-up with a plus, and press-up.


title: Research on autonomous stairs climbing for the shape-shifting robot

text: In this paper, we present an algorithm for a shape-shifting robot climbing stairs autonomously. The robot has been used several times for search and rescue missions. Because of the special environment of tasks, the robot must have the ability to climb stairs autonomously to help the operators. Due to the characters of robot in "T" configuration, the mathematical model of force is established and the whole process of climbing stairs is analyzed. Grouser-tread hooking, track-stair edge frictional force et al. are all considered during the process of climbing. A description of its dynamic model is also presented which is involved in the controller. The controller is comprised of center control and heading control, which can help the robot climb stairs safely and quickly. The effectiveness of the algorithms proposed are verified by experiment.


title: A framework based on deep learning and mathematical morphology for cabin door detection in an automated aerobridge docking system

text: In this paper, a cabin door detection framework based on deep learning and mathematical morphology is proposed. It is applied to an automated docking system for airplane cabin door. This system needs to work under any weather condition like rain, shine, day and night. Limited by the number of videos, just a small dataset based on actual airport operation is established for aerobridge docking process. As the training dataset is small, the trained detector cannot identify all the cabin doors in this dataset. Some of the cabin doors, which are not detected, can be identified with the combination of deep learning and mathematical morphology. Experimental results show that the integration of deep learning and mathematical morphology performs better than the simple deep learning method.


title: Enhancements in UltraScale CLB Architecture

text: Each generation of FPGA architecture benefits from optimizations around its technology node and target usage. In this paper, we discuss some of the changes made to the CLB for Xilinx's 20nm UltraScale product family. We motivate those changes and demonstrate better results than previous CLB architectures on a variety of metrics. We show that, in demanding scenarios, logic placed in an UltraScale device requires 16% less wirelength than 7-series. Designs mapped to UltraScale devices also require fewer logic tiles. In this paper, we demonstrate the utilization benefits of the UltraScale CLB attributed to certain CLB enhancements. The enhancements described herein result in an average packing improvement of 3% for the example design suite. We also show that the UltraScale architecture handles aggressive, tighter packing more gracefully than previous generations of FPGA. These significant reductions in wirelength and CLB counts translate directly into power, performance and ease-of-use benefits.


title: PPDDL 1 . 0 : An Extension to PDDL for Expressing Planning Domains with Probabilistic Effects

text: We desribe a variation of the planning domain definition language, PDDL, that permits the modeling of probabilistic planning problems with rewards. This language, PPDDL1.0, was used as the input language for the probabilistic track of the 4th International Planning Competition. We provide the complete syntax for PPDDL1.0 and give a semantics of PPDDL1.0 planning problems in terms of Markov decision processes. Department of Computer Science, Rutgers University, Piscataway, NJ 08854, USA


title: Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

text: To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic and its hardware components are not energy-proportional, since it cannot precisely scale its usage to match its supply. Instead, the system must choose when to satisfy its energy demands based on its current energy reserves and predictions of its future energy supply. In this paper, we explore the use of weather forecasts to improve a system's ability to satisfy demand by improving its predictions. We analyze weather forecast, observational, and energy harvesting data to formulate a model that translates a weather forecast to a wind or solar energy harvesting prediction, and quantify its accuracy. We evaluate our model for both energy sources in the context of two different energy harvesting sensor systems with inelastic demands: a sensor testbed that leases sensors to external users and a lexicographically fair sensor network that maintains steady node sensing rates. We show that using weather forecasts in both wind- and solar-powered sensor systems increases each system's ability to satisfy its demands compared with existing prediction strategies.


title: Fuzzy Ontology and LSTM-Based Text Mining: A Transportation Network Monitoring System for Assisting Travel

text: Intelligent Transportation Systems (ITSs) utilize a sensor network-based system to gather and interpret traffic information. In addition, mobility users utilize mobile applications to collect transport information for safe traveling. However, these types of information are not sufficient to examine all aspects of the transportation networks. Therefore, both ITSs and mobility users need a smart approach and social media data, which can help ITSs examine transport services, support traffic and control management, and help mobility users travel safely. People utilize social networks to share their thoughts and opinions regarding transportation, which are useful for ITSs and travelers. However, user-generated text on social media is short in length, unstructured, and covers a broad range of dynamic topics. The application of recent Machine Learning (ML) approach is inefficient for extracting relevant features from unstructured data, detecting word polarity of features, and classifying the sentiment of features correctly. In addition, ML classifiers consistently miss the semantic feature of the word meaning. A novel fuzzy ontology-based semantic knowledge with Word2vec model is proposed to improve the task of transportation features extraction and text classification using the Bi-directional Long Short-Term Memory (Bi-LSTM) approach. The proposed fuzzy ontology describes semantic knowledge about entities and features and their relation in the transportation domain. Fuzzy ontology and smart methodology are developed in Web Ontology Language and Java, respectively. By utilizing word embedding with fuzzy ontology as a representation of text, Bi-LSTM shows satisfactory improvement in both the extraction of features and the classification of the unstructured text of social media.


title: Single-structure 3-axis lorentz force magnetometer with sub-30 nT/√HZ resolution

text: This work demonstrates a 3-axis Lorentz force magnetometer for electronic compass purposes. The magnetometer measures magnetic flux in 3 axes using a single structure. With 1 mW power consumption, the sensor achieves sub-30 nT/√Hz resolution in each of the 3 axes. Compared to the 3-axis Hall sensors currently used in smartphones, the 3-axis magnetometer shown here has the advantages of 10× lower noise floor and the ability to be co-fabricated with MEMS inertial sensors.


title: FrameBase: Representing N-Ary Relations Using Semantic Frames

text: Large-scale knowledge graphs such as those in the Linked Data cloud are typically represented as subject-predicate-object triples. However, many facts about the world involve more than two entities. While n-ary relations can be converted to triples in a number of ways, unfortunately, the structurally different choices made in different knowledge sources significantly impede our ability to connect them. They also make it impossible to query the data concisely and without prior knowledge of each individual source. We present FrameBase, a wide-coverage knowledge-base schema that uses linguistic frames to seamlessly represent and query n-ary relations from other knowledge bases, at different levels of granularity connected by logical entailment. It also opens possibilities to draw on natural language processing techniques for querying and data mining.


title: The Role of Deliberate Practice in the Acquisition of Expert Performance

text: The theoretical framework presented in this article explains expert performance as the end result of individuals' prolonged efforts to improve performance while negotiating motivational and external constraints. In most domains of expertise, individuals begin in their childhood a regimen of effortful activities (deliberate practice) designed to optimize improvement. Individual differences, even among elite performers, are closely related to assessed amounts of deliberate practice. Many characteristics once believed to reflect innate talent are actually the result of intense practice extended for a minimum of 10 years. Analysis of expert performance provides unique evidence on the potential and limits of extreme environmental adaptation and learning.


title: Design of Compact Dual Band High Directive Electromagnetic Bandgap (EBG) Resonator Antenna Using Artificial Magnetic Conductor

text: A compact high directive EBG resonator antenna operating in two frequency bands is described. Two major contributions to this compact design are using single layer superstrate and using artificial surface as ground plane. To obtain only the lower operating frequency band using superstrate layer is enough, but to extract the upper operating frequency band both superstrate layer and artificial surface as ground plane are necessary. Therefore, design of a superstrate to work in two frequency bands is very important. Initially, using appropriate frequency selective surface (FSS) structure with square loop elements, we design an optimum superstrate layer for each frequency band separately to achieve maximum directivity. Also, to design an artificial surface to work in the upper frequency band we use a suitable FSS structure over dielectric layer backed by PEC. Next, by using the idea of FSS structure with double square loop elements we propose FSS structure with modified double square loop elements, so that it operates in both of the desired operating frequency bands simultaneously. Finally, the simulation results for two operating frequency bands are shown to have good agreement with measurements.


title: Functional Brain Connectivity Using fMRI in Aging and Alzheimer’s Disease

text: Normal aging and Alzheimer’s disease (AD) cause profound changes in the brain’s structure and function. AD in particular is accompanied by widespread cortical neuronal loss, and loss of connections between brain systems. This degeneration of neural pathways disrupts the functional coherence of brain activation. Recent innovations in brain imaging have detected characteristic disruptions in functional networks. Here we review studies examining changes in functional connectivity, measured through fMRI (functional magnetic resonance imaging), starting with healthy aging and then Alzheimer’s disease. We cover studies that employ the three primary methods to analyze functional connectivity—seed-based, ICA (independent components analysis), and graph theory. At the end we include a brief discussion of other methodologies, such as EEG (electroencephalography), MEG (magnetoencephalography), and PET (positron emission tomography). We also describe multi-modal studies that combine rsfMRI (resting state fMRI) with PET imaging, as well as studies examining the effects of medications. Overall, connectivity and network integrity appear to decrease in healthy aging, but this decrease is accelerated in AD, with specific systems hit hardest, such as the default mode network (DMN). Functional connectivity is a relatively new topic of research, but it holds great promise in revealing how brain network dynamics change across the lifespan and in disease.


title: An End-to-End Signal Strength Model for Underwater Optical Communications

text: In this paper, we present a generic model of signal strength in underwater optical communications. The model includes light sources, detectors, amplifier and detector circuitry, optics, as well as a simple extinction model of the water channel. The end-to-end model provides insights into optimization approaches for underwater optical modems and enables relative pose estimation between underwater optical transmitters and receivers. We instantiate our model to the AquaOptical model by determining its parameters and verifying the model prediction in a suite of pool experiments.


title: A dynamic bayesian network click model for web search ranking

text: As with any application of machine learning, web search ranking requires labeled data. The labels usually come in the form of relevance assessments made by editors. Click logs can also provide an important source of implicit feedback and can be used as a cheap proxy for editorial labels. The main difficulty however comes from the so called position bias - urls appearing in lower positions are less likely to be clicked even if they are relevant. In this paper, we propose a Dynamic Bayesian Network which aims at providing us with unbiased estimation of the relevance from the click logs. Experiments show that the proposed click model outperforms other existing click models in predicting both click-through rate and relevance.


title: Implementation of SMS based Heartbeat monitoring system using PSoC Microcontroller

text: The rapidly growing VLSI technology could reduce the cost of the embedded systems considerably which has motivated a new era of low cost personal health care monitoring systems which meeting common man requirements. Such systems are dedicated to one person and provide the diagnostic information through remotely. This paper proposes a PSoC microcontroller and GSM modules we are eliminating the cables attached to patient. The patient has a freedom of doing daily activities and still be under continuous monitoring (very suitable for old age people). PSoC has inbuilt ADC and Programmable Gain amplifier which enabled single chip implementation. The hardware complexity is also simple and reduces cost. The basic principle of this system is to read the bio medical signals from the biomedical sensor modules and perform the tasks of data conversion, sending SMS using GSM, as well as providing the ability of simple preprocessing such as waveform averaging or rectification. GSM based modem interface through AT commands to PSoC. The Microcontroller will be able to receive and send text messages Heart beat is sensed by using a high intensity type LED and LDR. The finger is placed between the LED and LDR. The skin may be illuminated with visible (red) using transmitted or reflected light for detection. The concept of this paper is builds upon the integration of wireless communications into medical applications to revolutionize personal healthcare. Under this concept, patients are no longer bound to a specific healthcare location where they are monitored by medical instruments—wireless communications would not only provide them with safe and accurate monitoring but also the freedom of movement. Remote patient monitoring will not only redefine hospital care but also work, home, and recreational activities. Imagine the home of the future: elderly individuals no longer have to travel to the hospital, a mother stuck at work will be able to receive email updates of her sick child's temperature, a doctor on the go will have the ability to turn on his cell phone to check the status of his patients. The objective of this project is to build a wireless heart beat monitoring system using GSM Technology, which could potentially be an integral part of a suite of personal healthcare appliances for a large-scale remote patient monitoring system. In this paper a low cost Digital Heart rate meter using PSoC Microcontroller is designed, and also this unit is connecting to GSM Modem for send …


title: Unsupervised domain adaptation for robust speech recognition via variational autoencoder-based data augmentation

text: Domain mismatch between training and testing can lead to significant degradation in performance in many machine learning scenarios. Unfortunately, this is not a rare situation for automatic speech recognition deployments in real-world applications. Research on robust speech recognition can be regarded as trying to overcome this domain mismatch issue. In this paper, we address the unsupervised domain adaptation problem for robust speech recognition, where both source and target domain speech are available, but word transcripts are only available for the source domain speech. We present novel augmentation-based methods that transform speech in a way that does not change the transcripts. Specifically, we first train a variational autoencoder on both source and target domain data (without supervision) to learn a latent representation of speech. We then transform nuisance attributes of speech that are irrelevant to recognition by modifying the latent representations, in order to augment labeled training data with additional data whose distribution is more similar to the target domain. The proposed method is evaluated on the CHiME-4 dataset and reduces the absolute word error rate (WER) by as much as 35% compared to the non-adapted baseline.


title: MagNet: A Two-Pronged Defense against Adversarial Examples

text:
Deep learning has shown impressive performance on hard perceptual problems. However, researchers found deep learning systems to be vulnerable to small, specially crafted perturbations that are imperceptible to humans. Such perturbations cause deep learning systems to mis-classify adversarial examples, with potentially disastrous consequences where safety or security is crucial. Prior defenses against adversarial examples either targeted specific attacks or were shown to be ineffective.
 We propose MagNet, a framework for defending neural network classifiers against adversarial examples. MagNet neither modifies the protected classifier nor requires knowledge of the process for generating adversarial examples. MagNet includes one or more separate detector networks and a reformer network. The detector networks learn to differentiate between normal and adversarial examples by approximating the manifold of normal examples. Since they assume no specific process for generating adversarial examples, they generalize well. The reformer network moves adversarial examples towards the manifold of normal examples, which is effective for correctly classifying adversarial examples with small perturbation. We discuss the intrinsic difficulties in defending against whitebox attack and propose a mechanism to defend against graybox attack. Inspired by the use of randomness in cryptography, we use diversity to strengthen MagNet. We show empirically that MagNet is effective against the most advanced state-of-the-art attacks in blackbox and graybox scenarios without sacrificing false positive rate on normal examples.


title: Delving into Transferable Adversarial Examples and Black-box Attacks

text: An intriguing property of deep neural networks is the existence of adversarial examples, which can transfer among different architectures. These transferable adversarial examples may severely hinder deep neural network-based applications. Previous works mostly study the transferability using small scale datasets. In this work, we are the first to conduct an extensive study of the transferability over large models and a large scale dataset, and we are also the first to study the transferability of targeted adversarial examples with their target labels. We study both non-targeted and targeted adversarial examples, and show that while transferable non-targeted adversarial examples are easy to find, targeted adversarial examples generated using existing approaches almost never transfer with their target labels. Therefore, we propose novel ensemble-based approaches to generating transferable adversarial examples. Using such approaches, we observe a large proportion of targeted adversarial examples that are able to transfer with their target labels for the first time. We also present some geometric studies to help understanding the transferable adversarial examples. Finally, we show that the adversarial examples generated using ensemble-based approaches can successfully attack Clarifai.com, which is a black-box image classification system.


title: Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong

text: Ongoing research has proposed several methods to defend neural networks against adversarial examples, many of which researchers have shown to be ineffective. We ask whether a strong defense can be created by combining multiple (possibly weak) defenses. To answer this question, we study three defenses that follow this approach. Two of these are recently proposed defenses that intentionally combine components designed to work well together. A third defense combines three independent defenses. For all the components of these defenses and the combined defenses themselves, we show that an adaptive adversary can create adversarial examples successfully with low distortion. Thus, our work implies that ensemble of weak defenses is not sufficient to provide strong defense against adversarial examples.


title: Sentiment Predictability for Stocks

text: In this work, we present our findings and experiments for stock-market prediction using various textual sentiment analysis tools, such as mood analysis and event extraction, as well as prediction models, such as LSTMs and specific convolutional architectures.


title: Control of microglial neurotoxicity by the fractalkine receptor

text: Microglia, the resident inflammatory cells of the CNS, are the only CNS cells that express the fractalkine receptor (CX3CR1). Using three different in vivo models, we show that CX3CR1 deficiency dysregulates microglial responses, resulting in neurotoxicity. Following peripheral lipopolysaccharide injections, Cx3cr1−/− mice showed cell-autonomous microglial neurotoxicity. In a toxic model of Parkinson disease and a transgenic model of amyotrophic lateral sclerosis, Cx3cr1−/− mice showed more extensive neuronal cell loss than Cx3cr1+ littermate controls. Augmenting CX3CR1 signaling may protect against microglial neurotoxicity, whereas CNS penetration by pharmaceutical CX3CR1 antagonists could increase neuronal vulnerability.


title: A valid two-item food security questionnaire for screening HIV-1 infected patients in a clinical setting.

text:
OBJECTIVE
To validate a two-item food security questionnaire (FSQ) for use in a clinical setting to screen HIV-1 infected patients for food insecurity.

DESIGN
The present study was a questionnaire-based survey of forty-nine subjects attending an HIV clinic. Subjects completed a two-item questionnaire and a six-item validated FSQ contemporaneously.

RESULTS
A strong correlation was found between the two-item and six-item FSQ (rho = 0.895; 95 % CI 0.821, 0.940; P < 0.0001). Cronbach's alpha coefficient was found to be 0.94 and 0.90 for the two-item and six-item FSQ, respectively. The two-item FSQ yielded a sensitivity of 100 % (95 % CI 75, 100) and a specificity of 78 % (95 % CI 61, 90). The negative predictive value was found to be 100 % (95 % CI 88, 100).

CONCLUSIONS
The results of the present study suggest that the two-item FSQ is a valid, reliable and sensitive screening tool of food insecurity in people living with HIV in a clinical setting.


title: Clinical Information Extraction via Convolutional Neural Network

text: We report an implementation of a clinical information extraction tool that leverages deep neural network to annotate event spans and their attributes from raw clinical notes and pathology reports. Our approach uses context words and their part-of-speech tags and shape information as features. Then we hire temporal (1D) con-volutional neural network to learn hidden feature representations. Finally, we use Multilayer Perceptron (MLP) to predict event spans. The empirical evaluation demonstrates that our approach significantly outperforms baselines.


title: Playable Experiences at AIIDE 2015

text: This paper describes entries to the third Playable Experiences track to be held at the AIIDE conference. We discuss the five entries accepted to the track for 2015, as well as the ongoing development of the track as part


title: Affordable Self Driving Cars and Robots with Semantic Segmentation

text: Image semantic segmentation is of immense interest for self-driving car research. Accurate and efficient segmentation mechanisms are required. We have evaluated Intersection over Union (IoU) metric over Cityscapes and KITTI datasets. We designed baseline softmax regression and maximum likelihood estimation, which performs quite poorly for the image segmentation task. We ran fully convolutional networks (FCN), which performed almost perfectly over the KITTI dataset. We found overfitting problem for the more complex Cityscape dataset. We conducted several experiments with regularization, dropout, data augmentation, image scaling and newer architectures. We are able to successfully mitigate overfitting by data augmentation. We also generated a confusion matrix and conducted error ablative analysis to get a deeper understanding of FCNs.


title: Genetic process mining: an experimental evaluation

text: One of the aims of process mining is to retrieve a process model from an event log. The discovered models can be used as objective starting points during the deployment of process-aware information systems (Dumas et al., eds., Process-Aware Information Systems: Bridging People and Software Through Process Technology. Wiley, New York, 2005) and/or as a feedback mechanism to check prescribed models against enacted ones. However, current techniques have problems when mining processes that contain non-trivial constructs and/or when dealing with the presence of noise in the logs. Most of the problems happen because many current techniques are based on local information in the event log. To overcome these problems, we try to use genetic algorithms to mine process models. The main motivation is to benefit from the global search performed by this kind of algorithms. The non-trivial constructs are tackled by choosing an internal representation that supports them. The problem of noise is naturally tackled by the genetic algorithm because, per definition, these algorithms are robust to noise. The main challenge in a genetic approach is the definition of a good fitness measure because it guides the global search performed by the genetic algorithm. This paper explains how the genetic algorithm works. Experiments with synthetic and real-life logs show that the fitness measure indeed leads to the mining of process models that are complete (can reproduce all the behavior in the log) and precise (do not allow for extra behavior that cannot be derived from the event log). The genetic algorithm is implemented as a plug-in in the ProM framework.


title: Joint Spatial Division and Multiplexing: Opportunistic Beamforming, User Grouping and Simplified Downlink Scheduling

text: Joint Spatial Division and Multiplexing (JSDM) is a downlink multiuser MIMO scheme recently proposed by the authors in order to enable “massive MIMO” gains and simplified system operations for Frequency Division Duplexing (FDD) systems. The key idea lies in partitioning the users into groups with approximately similar channel covariance eigenvectors and serving these groups by using two-stage downlink precoding scheme obtained as the concatenation of a pre-beamforming matrix, that depends only on the channel second-order statistics, with a multiuser MIMO linear precoding matrix, which is a function of the effective channels including pre-beamforming. The role of pre-beamforming is to reduce the dimensionality of the effective channel by exploiting the near-orthogonality of the eigenspaces of the channel covariances of the different user groups. This paper is an extension of our initial work on JSDM, and addresses some important practical issues. First, we focus on the regime of finite number of antennas and large number of users and show that JSDM with simple opportunistic user selection is able to achieve the same scaling law of the system capacity with full channel state information. Next, we consider the large-system regime (both antennas and users growing large) and propose a simple scheme for user grouping in a realistic setting where users have different angles of arrival and angular spreads. Finally, we propose a low-overhead probabilistic scheduling algorithm that selects the users at random with probabilities derived from large-system random matrix analysis. Since only the pre-selected users are required to feedback their channel state information, the proposed scheme realizes important savings in the CSIT feedback.


title: Classification of Brain Cancer using Artificial Neural Network

text: A Brain Cancer Detection and Classification System has been designed and developed. The system uses computer based procedures to detect tumor blocks or lesions and classify the type of tumor using Artificial Neural Network in MRI images of different patients with Astrocytoma type of brain tumors. The image processing techniques such as histogram equalization, image segmentation, image enhancement, morphological operations and feature extraction have been developed for detection of the brain tumor in the MRI images of the cancer affected patients. The extraction of texture features in the detected tumor has been achieved by using Gray Level Co-occurrence Matrix (GLCM). These features are compared with the stored features in the Knowledge Base. Finally a Neuro Fuzzy Classifier has been developed to recognize different types of brain cancers. The whole system has been tested in two phases firstly Learning/Training Phase and secondly Recognition/Testing Phase. The known MRI images of affected brain cancer patients obtained from Radiology Department of Tata Memorial Hospital (TMH) were used to train the system. The unknown samples of brain cancer affected MRI images are also obtained from TMH and were used to test the system. The system was found efficient in classification of these samples and responds any abnormality.


title: Triple Band Annular Ring Loaded Stacked Circular Patch Microstrip Antenna

text: In this paper a novel structure of annular ring loaded stacked circular patch microstrip antenna is theoretically analysed to observe various parameters such as return loss, input impedance, gain, directivity and radiation pattern. It is found that antenna possess three band of operation which signify the compactness and multiband operation of antenna. The antenna is resonating at three operating frequencies 1.720, 2.950, 3.060 GHz. The proposed theory is verified by simulation using Ansoft’s HFSS and theoretical results are in good agreement with simulated results. The antenna is useful for multi-services operations such as WLAN, GSM, UMTS, and WiMAX services.


title: Transfer learning for children's speech recognition

text: Children's speech processing is more challenging than that of adults due to lacking of large scale children's speech corpora. With the developing of the physical speech organ, high inter speaker and intra speaker variabilities are observed in children's speech. On the other hand, data collection on children is difficult as children usually have short attention span and their language proficiency is limited. In this paper, we propose to improve children's automatic speech recognition performance with transfer learning technique. We compare two transfer learning approaches in enhancing children's speech recognition performance with adults' data. The first method is to perform acoustic model adaptation on the pre-trained adult model. The second is to train acoustic model with deep neural network based multi-task learning approach: the adults' and children's acoustic characteristics are learnt jointly in the shared hidden layers, while the output layers are optimized with different speaker groups. Our experiment results show that both transfer learning approaches are effective in transferring rich phonetic and acoustic information from adults' model to children model. The multi-task learning approach outperforms the acoustic adaptation approach. We further show that the speakers' acoustic characteristics in languages can also benefit the target language under the multi-task learning framework.


title: Process Mining and Security: Visualization in Database Intrusion Detection

text: Nowadays, more and more organizations keep their valuable and sensitive data in Database Management Systems (DBMSs). The traditional database security mechanisms such as access control mechanisms, authentication, data encryption technologies do not offer a strong enough protection against the exploitation of vulnerabilities (e.g. intrusions) in DBMSs from insiders. Intrusion detection systems recently proposed in the literature focus on statistical approaches, which are not intuitive. Our research is the first ever effort to use process mining modeling low-level event logs for database intrusion detection. We have proposed a novel approach for visualizing database intrusion detection using process mining techniques. Our experiments showed that intrusion detection visualization will be able to help security officers who might not know deeply the complex system, identify the true positive detection and eliminate the false positive results.


title: Vision-based obstacle recognition system for automated lawn mower robot development

text: Digital image processing techniques (DIP) have been widely used in various types of application recently. Classification and recognition of a specific object using vision system require some challenging tasks in the field of image processing and artificial intelligence. The ability and efficiency of vision system to capture and process the images is very important for any intelligent system such as autonomous robot. This paper gives attention to the development of a vision system that could contribute to the development of an automated vision based lawn mower robot. The works involve on the implementation of DIP techniques to detect and recognize three different types of obstacles that usually exist on a football field. The focus was given on the study on different types and sizes of obstacles, the development of vision based obstacle recognition system and the evaluation of the system’s performance. Image processing techniques such as image filtering, segmentation, enhancement and edge detection have been applied in the system. The results have shown that the developed system is able to detect and recognize various types of obstacles on a football field with recognition rate of more 80%.


title: Radio frequency identification: technologies, applications, and research issues

text: A radio frequency identification (RFID) system is a special kind of sensor network to identify an object or a person using radio frequency transmission. A typical RFID system includes transponders (tags) and interrogators (readers): tags are attached to objects/persons, and readers communicate with the tags in their transmission ranges via radio signals. RFID systems have been gaining more and more popularity in areas such as supply chain management, automated identification systems, and any place requiring identifications of products or people. RFID technology is better than barcode in many ways, and may totally replace barcode in the future if certain technologies can be achieved such as low cost and protection of personal privacy. This paper provides a technology survey of RFID systems and various RFID applications. We also discuss five critical research issues: cost control, energy efficiency, privacy issue, multiple readers’ interference, and security issue. Copyright # 2006 John Wiley & Sons, Ltd.


title: Binarized Genetic Algorithm with Neural Network for Stock Market Prediction

text: Stock market prediction is a challenging task to predict the upcoming stock values. It is very difficult to predict because of unstable nature of stock. This stock market prices are continuously changing day by day. Many Business Analysts spends more money on this stock market. Some of the persons may lose the money on this stock market because they don't have clear idea about the stock market. Estimate and analyse the stock market accurately then only get more profit. Artificial neural network is a very popular technique for forecast the stock market price, but using this technique to forecast the stock market price up to some extent. So there is a need to improve the accuracy of the system. In this paper, propose a novel system called Binarized Genetic Algorithm with Artificial Neural Network (BGANN) technique to predict and forecast future behavior of individual stocks or the upcoming stock. Binarized Genetic Algorithm is used for optimizing Neural Network weights while training. Comparative analysis shows that BGANN method performance is better compared to the Support Vector Machine (SVM), Neural Network (NN), and Auto Regressive Integrated Moving Average (ARIMA) models.


title: Modeling visual problem solving as analogical reasoning.

text: We present a computational model of visual problem solving, designed to solve problems from the Raven's Progressive Matrices intelligence test. The model builds on the claim that analogical reasoning lies at the heart of visual problem solving, and intelligence more broadly. Images are compared via structure mapping, aligning the common relational structure in 2 images to identify commonalities and differences. These commonalities or differences can themselves be reified and used as the input for future comparisons. When images fail to align, the model dynamically rerepresents them to facilitate the comparison. In our analysis, we find that the model matches adult human performance on the Standard Progressive Matrices test, and that problems which are difficult for the model are also difficult for people. Furthermore, we show that model operations involving abstraction and rerepresentation are particularly difficult for people, suggesting that these operations may be critical for performing visual problem solving, and reasoning more generally, at the highest level. (PsycINFO Database Record


title: A 71-Gb/s NRZ Modulated 850-nm VCSEL-Based Optical Link

text: We report error free (BER <; 10-12) operation of a directly non-return-to-zero modulated 850-nm vertical cavity surface-emitting laser (VCSEL) link operating to 71 Gb/s. This is the highest error free modulation rate for a directly modulated laser of any type. The optical link consists of a 130-nm BiCMOS driver IC with two-tap feed-forward equalization, a wide bandwidth 850-nm VCSEL, a surface illuminated GaAs PIN photodiode, and a 130-nm BiCMOS receiver IC.


title: Minimizing Faulty Executions of Distributed Systems

text: When troubleshooting buggy executions of distributed systems, developers typically start by manually separating out events that are responsible for triggering the bug (signal) from those that are extraneous (noise). In this paper we present DEMi, a tool for automatically performing this minimization. We apply DEMi to buggy executions of two very different distributed systems, Raft and Spark, and find that it achieves up to 97% reduction of execution length within a small time budget.


title: Stationary Frame Induction Motor Feed Forward Current Controller With Back EMF Compensation

text: In this paper, a novel induction motor (IM) stator current controller is proposed based on the stationary frame feed forward controlling structure. The proposed regulator represents an improved variant of the existing stationary and rotational frame IM stator current regulators based on the back EMF compensation. The paper novelty consists of the stationary frame controlling structure based on two feed forward actions that enable asymptotic reference tracking. Namely, the novel stationary frame regulator includes a proportional integral (PI) regulation term combined with a stator resistance voltage drop and back EMF feed forward compensations, with the back EMF compensation based on the stator flux value estimated by means of a programmable low-pass filter. The feedback term is based on a PI controller, with the corresponding parameter tuning procedure outlined in the paper. The simulation runs are included in the paper in order to examine the regulator performance in relation to the factors critical to the IM stator current regulation: variable IM model parameter values, transport delay introduced in the control loop, variable stator frequency, and rotor speed values. Also, the experimental tests are presented and carried out on an experimental setup for different operating conditions typical for an IM drive.


title: Coping with stress during childhood and adolescence: problems, progress, and potential in theory and research.

text: Progress and issues in the study of coping with stress during childhood and adolescence are reviewed. Definitions of coping are considered, and the relationship between coping and other aspects of responses to stress (e.g., temperament and stress reactivity) is described. Questionnaire, interview, and observation measures of child and adolescent coping are evaluated with regard to reliability and validity. Studies of the association of coping with symptoms of psychopathology and social and academic competence are reviewed. Initial progress has been made in the conceptualization and measurement of coping, and substantial evidence has accumulated on the association between coping and adjustment. Problems still remain in the conceptualization and measurement of coping in young people, however, and aspects of the development and correlates of coping remain to be identified. An agenda for future research on child-adolescent coping is outlined.


title: An Efficient & Secure Content Contribution and Retrieval content in Online Social Networks using Level-level Security Optimization & Content Visualization Algorithm

text: Received Nov 14, 2017 Revised Jan 26, 2018 Accepted Feb 11, 2018 Online Social Networks (OSNs) is currently popular interactive media to establish the communication, share and disseminate a considerable amount of human life data. Daily and continuous communications imply the exchange of several types of content, including free text, image, audio, and video data. Security is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). However, there are no contentbased preferences supported, and therefore it is not possible to prevent undesired messages. Providing the service is not only a matter of using previously defined web content mining and security techniques. To overcome the issues, Level-level Security Optimization & Content Visualization Algorithm is proposed to avoid the privacy issues during content sharing and data visualization. It adopts level by level privacy based on user requirement in the social network. It evaluates the privacy compatibility in the online social network environment to avoid security complexities. The mechanism divided into three parts namely like online social network platform creation, social network privacy, social network within organizational privacy and network controlling and authentication. Based on the experimental evaluation, a proposed method improves the privacy retrieval accuracy (PRA) 9.13% and reduces content retrieval time (CRT) 7 milliseconds and information loss (IL) 5.33%.


title: Probability of Random Correspondence for Fingerprints

text: Individuality of fingerprints can be quantified by computing the probabilistic metrics for measuring the degree of fingerprint individuality. In this paper, we present a novel individuality evaluation approach to estimate the probability of random correspondence (PRC). Three generative models are developed respectively to represent the distribution of fingerprint features: ridge flow, minutiae and minutiae together with ridge points. A mathematical model that computes the PRCs are derived based on the generative models. Three metrics are discussed in this paper: (i) PRC of two samples, (ii) PRC among a random set of n samples (nPRC) and (iii) PRC between a specific sample among n others (specific nPRC). Experimental results show that the theoretical estimates of fingerprint individuality using our model consistently follow the empirical values based on the NIST4 database.


title: Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing

text: We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that allows programmers to perform in-memory computations on large clusters while retaining the fault tolerance of data flow models like MapReduce. RDDs are motivated by two types of applications that current data flow systems handle inefficiently: iterative algorithms, which are common in graph applications and machine learning, and interactive data mining tools. In both cases, keeping data in memory can improve performance by an order of magnitude. To achieve fault tolerance efficiently, RDDs provide a highly restricted form of shared memory: they are read-only datasets that can only be constructed through bulk operations on other RDDs. However, we show that RDDs are expressive enough to capture a wide class of computations, including MapReduce and specialized programming models for iterative jobs such as Pregel. Our implementation of RDDs can outperform Hadoop by 20× for iterative jobs and can be used interactively to search a 1 TB dataset with latencies of 5–7 seconds.