Query: A generative layout approach for rooted tree drawings

Relevant passages:
Treevis.net: A Tree Visualization Reference <=SEP=> 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.

Thinking Aloud : Reconciling Theory and Practice — <=SEP=> —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.

Cone Trees: animated 3D visualizations of hierarchical information <=SEP=> 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.

Stacked Graphs – Geometry & Aesthetics <=SEP=> 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.

Readings in information visualization - using vision to think <=SEP=> 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)

Managing information sharing within an organizational setting: A social network perspective <=SEP=> 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

A SECURE ONLINE PAYMENT SYSTEM USING STEGANOGRAPHY AND VISUAL CRYPTOGRAPHY <=SEP=> 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.

A novel method for eye tracking and blink detection in video frames <=SEP=> 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.

THE INSTITUTIONAL FOUNDATIONS OF PUBLIC POLICY : A TRANSACTIONS APPROACH WITH APPLICATION TO ARGENTINA * <=SEP=> 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.

Functional cartography of complex metabolic networks <=SEP=> 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.

Dengue fever prediction using K-means clustering algorithm <=SEP=> 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.

Next generation data systems and knowledge products to support agricultural producers and science-based policy decision making <=SEP=> 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.

A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems <=SEP=> 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.

Review on Printed Log Periodic and Yagi MSA <=SEP=> 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.

EMG analysis of the scapular muscles during a shoulder rehabilitation program. <=SEP=> 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.

Research on autonomous stairs climbing for the shape-shifting robot <=SEP=> 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.

A framework based on deep learning and mathematical morphology for cabin door detection in an automated aerobridge docking system <=SEP=> 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.

Enhancements in UltraScale CLB Architecture <=SEP=> 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.

"An Odd Kind of Pleasure": Differentiating Emotional Challenge in Digital Games <=SEP=> Recent work introduced the notion of emotional challenge as a means to afford more unique and diverse gaming experiences. However, players' experience of emotional challenge has received little empirical attention. It remains unclear whether players enjoy it and what exactly constitutes the challenge thereof. We surveyed 171 players about a challenging or an emotionally challenging experience, and analyzed their responses with regards to what made the experience challenging, their emotional response, and the relation to core player experience constructs. We found that emotional challenge manifested itself in different ways, by confronting players with difficult themes or decisions, as well as having them deal with intense emotions. In contrast to more'conventional' challenge, emotional challenge evoked a wider range of negative emotions and was appreciated significantly more by players. Our findings showcase the appeal of uncomfortable gaming experiences, and extend current conceptualizations of challenge in games.

PPDDL 1 . 0 : An Extension to PDDL for Expressing Planning Domains with Probabilistic Effects <=SEP=> 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

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems <=SEP=> 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.

Fuzzy Ontology and LSTM-Based Text Mining: A Transportation Network Monitoring System for Assisting Travel <=SEP=> 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.

Single-structure 3-axis lorentz force magnetometer with sub-30 nT/√HZ resolution <=SEP=> 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.

FrameBase: Representing N-Ary Relations Using Semantic Frames <=SEP=> 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.

The Role of Deliberate Practice in the Acquisition of Expert Performance <=SEP=> 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.

Design of Compact Dual Band High Directive Electromagnetic Bandgap (EBG) Resonator Antenna Using Artificial Magnetic Conductor <=SEP=> 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.

Functional Brain Connectivity Using fMRI in Aging and Alzheimer’s Disease <=SEP=> 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.

An End-to-End Signal Strength Model for Underwater Optical Communications <=SEP=> 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.

A dynamic bayesian network click model for web search ranking <=SEP=> 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.

Implementation of SMS based Heartbeat monitoring system using PSoC Microcontroller <=SEP=> 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 …