title: Athena: A Framework for Scalable Anomaly Detection in Software-Defined Networks

abstract: Network-based anomaly detection is a well-mined area of research, with many projects that have produced algorithms to detect suspicious and anomalous activities at strategic points in a network. In this paper, we examine how to integrate an anomaly detection development framework into existing software-defined network (SDN) infrastructures to support sophisticated anomaly detection services across the entire network data plane, not just at network egress boundaries. We present Athena as a new SDN-based software solution that exports a well-structured development interface and provides general purpose functions for rapidly synthesizing a wide range of anomaly detection services and network monitoring functions with minimal programming effort. Athena is a fully distributed application hosting architecture, enabling a unique degree of scalability from prior SDN security monitoring and analysis projects. We discuss example use-case scenarios with Athena's development libraries, and evaluate system performance with respect to usability, scalability, and overhead in real world environments.


title: OctoMap: An Efficient Probabilistic 3D Mapping Framework Based on Octrees

abstract: Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environ- ment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly repre- sents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The re- sults demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.


title: A hybrid islanding detection technique for inverter based distributed generations

abstract: This paper proposes a new hybrid islanding detection method for grid-connected inverter based distributed generation units. The proposed method combines passive and active islanding detection techniques to aggregate their individual advantages and reduce or eliminate their drawbacks. The passive method is based on extraction of critical system parameters from local voltage measurements at target DG location and application of decision tree based classifiers for extraction of decision rules in order to detect islanding events. The active method is based on Sandia Frequency Shift technique and is initiated only when the passive method is unable to properly classify between islanding and other transient generating events, thus minimizing the power quality degradation introduced in the system. A detailed case study on a grid-connected PV system is performed to evaluate the performance of the proposed technique. Test results demonstrate the effectiveness of the proposed method in detection of islanding events in grid-connected distributed generations.


title: Classification of tweets for sentiment and trend analysis

abstract: Education and work-related reading have probably gone up and on-screen reading also increased considerably. A survey by the Pew Research Center in the year 2012 proved that the increase in ownership of tablet computers and electronic book reading devices, such as ipad, kindle fires, Nook, etc., the online book reading also increases considerably. A survey by National Literacy Trust during the year 2013 reported that more than 52% of students prefer to read electronic devices compared to 32% who preferred printed materials. The survey made by the Wall Street Journal, USA today and United Parcel Service (UPS) says that there is a rapid growth in online shopping during recent years. As online reading and online purchase have increased over the period, recommendation becomes mandatory for today's online world, an especially recommendation of books become most important for the student community, because of the availability of huge volume of books online. In order to gain the trusted recommendation people today depends on social networks. Twitter a social network is a popular microblogging service where users create tweets that sometimes express opinions about different topics which helps them to identify the current trends. The work proposed in this paper move towards recommending books based on trending topics from the trusted users obtained through provenance. The proposed work utilizes Naive Bayesian technique and Moving Average (MA) method to identify the sentiments from tweets and trend respectively.


title: Motion Planning for Humanoid Robots

abstract: Humanoid robotics hardware and control techniques have advanced rapidly during the last five years. Presently, several companies have announced the commercial availability of various humanoid robot prototypes. In order to improve the autonomy and overall functionality of these robots, reliable sensors, safety mechanisms, and general integrated software tools and techniques are needed. We believe that the development of practical motion planning algorithms and obstacle avoidance software for humanoid robots represents an important enabling technology. This paper gives an overview of some of our recent efforts to develop motion planning methods for humanoid robots for application tasks involving navigation, object grasping and manipulation, footstep placement, and dynamically-stable full-body motions. We show experimental results obtained by implementations running within a simulation environment as well as on actual humanoid robot hardware.


Effectiveness and Efficiency in Information Technology Projects : SWOT Analysis and Theory of Constraint


title: Episodic Training for Domain Generalization

abstract: Domain generalization (DG) is the challenging and topical problem of learning models that generalize to novel testing domains with different statistics than a set of known training domains. The simple approach of aggregating data from all source domains and training a single deep neural network end-to-end on all the data provides a surprisingly strong baseline that surpasses many prior published methods. In this paper, we build on this strong baseline by designing an episodic training procedure that trains a single deep network in a way that exposes it to the domain shift that characterises a novel domain at runtime. Specifically, we decompose a deep network into feature extractor and classifier components, and then train each component by simulating it interacting with a partner who is badly tuned for the current domain. This makes both components more robust, ultimately leading to our networks producing state-of-the-art performance on three DG benchmarks. Furthermore, we consider the pervasive workflow of using an ImageNet trained CNN as a fixed feature extractor for downstream recognition tasks. Using the Visual Decathlon benchmark, we demonstrate that our episodic-DG training improves the performance of such a general-purpose feature extractor by explicitly training a feature for robustness to novel problems. This shows that DG training can benefit standard practice in computer vision.


title: Learning Action Representations for Reinforcement Learning

abstract: Most model-free reinforcement learning methods leverage state representations (embeddings) for generalization, but either ignore structure in the space of actions or assume the structure is provided a priori. We show how a policy can be decomposed into a component that acts in a low-dimensional space of action representations and a component that transforms these representations into actual actions. These representations improve generalization over large, finite action sets by allowing the agent to infer the outcomes of actions similar to actions already taken. We provide an algorithm to both learn and use action representations and provide conditions for its convergence. The efficacy of the proposed method is demonstrated on large-scale real-world problems.


title: Minimization of Cogging Torque in Axial-Flux Permanent-Magnet Machines: Design Concepts

abstract: Various techniques exist for reducing the cogging torque in radial-flux permanent-magnet (RFPM) machines. However, although some of these can be applied to axial-flux PM (AFPM) machines, the additional manufacturing complexity and cost may be prohibitive. Therefore, alternative low-cost techniques are desirable for use in AFPM machines. In this paper, the utility of employing cogging torque minimization techniques that have been developed principally for use in RFPM machines is examined by 3-D finite-element analysis, and several alternative cogging torque minimization techniques for AFPM machines are proposed.


Improved Methods on PCA Based Human Face Recognition for Distorted Images


title: Online Model-Based Testing under Uncertainty

abstract: Modern software systems are required to operate in a highly uncertain and changing environment. They have to control the satisfaction of their requirements at run-time, and possibly adapt and cope with situations that have not been completely addressed at design-time. Software engineering methods and techniques are, more than ever, forced to deal with change and uncertainty (lack of knowledge) explicitly. For tackling the challenge posed by uncertainty in delivering more reliable systems, this paper proposes a novel online Model-based Testing technique that complements classic test case generation based on pseudo-random sampling strategies with an uncertainty-aware sampling strategy. To deal with system uncertainty during testing, the proposed strategy builds on an Inverse Uncertainty Quantification approach that is related to the discrepancy between the measured data at run-time (while the system executes) and a Markov Decision Process model describing the behavior of the system under test. To this purpose, a conformance game approach is adopted in which tests feed a Bayesian inference calibrator that continuously learns from test data to tune the system model and the system itself. A comparative evaluation between the proposed uncertainty-aware sampling policy and classical pseudo-random sampling policies is also presented using the Tele Assistance System running example, showing the differences in achieved accuracy and efficiency.


title: A Focus on Throughput: Lean Improvement of Nurse Scheduling in the Operating Theatre

abstract: The utilization of operating theatres and efficiency of nurse scheduling has an impact on patient outcomes, hospital finances and clinical effectiveness. To date, research has tended to focus on the output rather than on the process of nurse scheduling. We report on use of the Lean mapping tool, Makigami, in operating room (OR) nurse scheduling. This study was conducted at a large surgical department in the Netherlands. A multidisciplinary team of nine health-care professionals used Makigami, includingfive steps: (1) mapping the current state of OR nurse scheduling, (2) setting the ideal state and the first target condition, (3) performing a fishbone analysis, (4) conducting actions and (5) mapping the revised process of OR nurse scheduling. The current OR nurse scheduling process showed 44 transfers, 42 documents and 64 types of waste, which collectively led to 35 errors in 7 days. The first target condition was to guarantee quality of care:the right employees, at the right time, in the right place without errors. The revised process, as a result of the action plans that emerged through the fishbone analysis, led to an outcome of two errors in 7 days with a reduction in waste of 41%. The use of Makigami led to an optimized OR nurse scheduling process: the right OR nurses, with the right qualifications, were scheduled for the right surgical procedures. A focus on process led to a reduction in transfers and errors, which indicate a higher quality of care.


title: Infant-Directed Speech Facilitates Word Segmentation

abstract: There are reasons to believe that infant-directed (ID) speech may make language acquisition easier for infants. However, the effects of ID speech on infants' learning remain poorly understood. The experiments reported here assess whether ID speech facilitates word segmentation from fluent speech. One group of infants heard a set of nonsense sentences spoken with intonation contours characteristic of adult-directed (AD) speech, and the other group heard the same sentences spoken with intonation contours characteristic of ID speech. In both cases, the only cue to word boundaries was the statistical structure of the speech. Infants were able to distinguish words from syllable sequences spanning word boundaries after exposure to ID speech but not after hearing AD speech. These results suggest that ID speech facilitates word segmentation and may be useful for other aspects of language acquisition as well. Issues of direction of preference in preferential listening paradigms are also considered.


A Survey on Security Attacks in Wireless Sensor Network


title: A VLSI Implementation of an Analog Neural Network Suited for Genetic Algorithms

abstract: The usefulness of an artificial analog neural network is closely bound to its trainability. This paper introduces a new analog neural network architecture using weights determined by a genetic algorithm. The first VLSI implementation presented in this paper achieves 200 giga connections per second with 4096synapses on less than 1 mm2 silicon area. Since the training can be done at the full speed of the network, several hundred individuals per second can be tested by the genetic algorithm. This makes it feasible to tackle problems that require large multi-layered networks.


title: Architecture for B2B2C Business Model by Spinning off End-to-End Communications from Applications

abstract: We propose an architecture that frees application providers from designing direct end-to-end connectivity between end clients by spinning off end-to-end communications from applications. The business model of telecom carriers has been shifting from "business to consumer" to "business to business to consumer" (B2B2C), and the proposed architecture fits the B2B2C business model well. The proposed architecture also reduces unnecessary processing loads placed on application servers, and this feature is attractive to application providers. The effectiveness of the proposed architecture is also discussed along with implemented test results.


title: Cache-oblivious priority queue and graph algorithm applications

abstract: (MATH) In this paper we develop an optimal cache-oblivious priority queue data structure, supporting insertion, deletion, and deletemin operations in O(1 \over B logM/BN \over B) amortized memory transfers, where M and B are the memory and block transfer sizes of any two consecutive levels of a multilevel memory hierarchy. In a cache-oblivious data structure, M and B are not used in the description of the structure. The bounds match the bounds of several previously developed external-memory (cache-aware) priority queue data structures, which all rely crucially on knowledge about M and B. Priority queues are a critical component in many of the best known external- memory graph algorithms, and using our cache-oblivious priority queue we develop several cache- oblivious graph algorithms.


title: Comparing Touchscreen and Mouse Input Performance by People With and Without Upper Body Motor Impairments

abstract: Controlled studies of touchscreen input performance for users with upper body motor impairments remain relatively sparse. To address this gap, we present a controlled lab study of mouse vs. touchscreen performance with 32 participants (16 with upper body motor impairments and 16 without). Our study examines: (1) how touch input compares to an indirect pointing device (a mouse); (2) how performance compares across a range of standard interaction techniques; and (3) how these answers differ for users with and without motor impairments. While the touchscreen was faster than the mouse overall, only participants without motor impairments benefited from a lower error rate on the touchscreen. Indeed, participants with motor impairments had a three-fold increase in pointing (tapping) errors on the touchscreen compared to the mouse. Our findings also highlight the high frequency of spurious touches for users with motor impairments and update past accessibility recommendations for minimum touchscreen target sizes to at least 18mm.


title: Early skin-to-skin contact for mothers and their healthy newborn infants.

abstract: BACKGROUND ::: Mother-infant separation postbirth is common in Western culture. Early skin-to-skin contact (SSC) begins ideally at birth and involves placing the naked baby, covered across the back with a warm blanket, prone on the mother's bare chest. According to mammalian neuroscience, the intimate contact inherent in this place (habitat) evokes neurobehaviors ensuring fulfillment of basic biological needs. This time may represent a psychophysiologically 'sensitive period' for programming future behavior. ::: ::: ::: OBJECTIVES ::: To assess the effects of early SSC on breastfeeding, behavior, and physiological adaptation in healthy mother-newborn dyads. ::: ::: ::: SEARCH STRATEGY ::: Cochrane Pregnancy and Childbirth Group's and Neonatal Group's Trials Registers (August 2006), Cochrane Central Register of Controlled Trials (The Cochrane Library 2006, Issue 2), MEDLINE (1976 to 2006). ::: ::: ::: SELECTION CRITERIA ::: Randomized and quasi-randomized clinical trials comparing early SSC with usual hospital care. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: We independently assessed trial quality and extracted data. Study authors were contacted for additional information. ::: ::: ::: MAIN RESULTS ::: Thirty studies involving 1925 participants (mother-infant dyads), were included. Data from more than two trials were available for only 8-of-64 outcome measures. We found statistically significant and positive effects of early SSC on breastfeeding at one to four months postbirth (10 trials; 552 participants) (odds ratio (OR) 1.82, 95% confidence interval (CI) 1.08 to 3.07), and breastfeeding duration (seven trials; 324 participants) (weighted mean difference (WMD) 42.55, 95% CI -1.69 to 86.79). Trends were found for improved summary scores for maternal affectionate love/touch during observed breastfeeding (four trials; 314 participants) (standardized mean difference (SMD) 0.52, 95% CI 0.07 to 0.98) and maternal attachment behavior (six trials; 396 participants) (SMD 0.52, 95% CI 0.31 to 0.72) with early SSC. SSC infants cried for a shorter length of time (one trial; 44 participants) (WMD -8.01, 95% CI -8.98 to -7.04). Late preterm infants had better cardio-respiratory stability with early SSC (one trial; 35 participants) (WMD 2.88, 95% CI 0.53 to 5.23). No adverse effects were found. ::: ::: ::: AUTHORS' CONCLUSIONS ::: Limitations included methodological quality, variations in intervention implementation, and outcome variability. The intervention may benefit breastfeeding outcomes, early mother-infant attachment, infant crying and cardio-respiratory stability, and has no apparent short or long-term negative effects. Further investigation is recommended. To facilitate meta-analysis, future research should be done using outcome measures consistent with those in the studies included here. Published reports should clearly indicate if the intervention was SSC and include means, standard deviations, exact probability values, and data to measure intervention dose.


title: AsicBoost - A Speedup for Bitcoin Mining

abstract: AsicBoost is a method to speed up Bitcoin mining by a factor of approximately 20%. The performance gain is achieved through a high-level optimization of the Bitcoin mining algorithm which allows for drastic reduction in gate count on the mining chip. AsicBoost is applicable to all types of mining hardware and chip designs. This paper presents the idea behind the method and describes the information flow in implementations of AsicBoost.


title: Big Data For Development: Applications and Techniques

abstract: With the explosion of social media sites and proliferation of digital computing devices and Internet access, massive amounts of public data is being generated on a daily basis. Efficient techniques/algorithms to analyze this massive amount of data can provide near real-time information about emerging trends and provide early warning in case of an imminent emergency (such as the outbreak of a viral disease). In addition, careful mining of these data can reveal many useful indicators of socioeconomic and political events, which can help in establishing effective public policies. The focus of this study is to review the application of big data analytics for the purpose of human development. The emerging ability to use big data techniques for development (BD4D) promises to revolutionalize healthcare, education, and agriculture; facilitate the alleviation of poverty; and help to deal with humanitarian crises and violent conflicts. Besides all the benefits, the large-scale deployment of BD4D is beset with several challenges due to the massive size, fast-changing and diverse nature of big data. The most pressing concerns relate to efficient data acquisition and sharing, establishing of context (e.g., geolocation and time) and veracity of a dataset, and ensuring appropriate privacy. In this study, we provide a review of existing BD4D work to study the impact of big data on the development of society. In addition to reviewing the important works, we also highlight important challenges and open issues.


title: Memory scaling: A systems architecture perspective

abstract: The memory system is a fundamental performance and energy bottleneck in almost all computing systems. Recent system design, application, and technology trends that require more capacity, bandwidth, efficiency, and predictability out of the memory system make it an even more important system bottleneck. At the same time, DRAM technology is experiencing difficult technology scaling challenges that make the maintenance and enhancement of its capacity, energy-efficiency, and reliability significantly more costly with conventional techniques. In this paper, after describing the demands and challenges faced by the memory system, we examine some promising research and design directions to overcome challenges posed by memory scaling. Specifically, we survey three key solution directions: 1) enabling new DRAM architectures, functions, interfaces, and better integration of the DRAM and the rest of the system, 2) designing a memory system that employs emerging memory technologies and takes advantage of multiple different technologies, 3) providing predictable performance and QoS to applications sharing the memory system. We also briefly describe our ongoing related work in combating scaling challenges of NAND flash memory.


title: LVDT demodulator based on amplitude detector

abstract: A circuit technique to demodulate a signal from a linear variable differential transducer (LVDT) is introduced in this paper. The proposed LVDT demodulator consists of the frequency doubler and the sample and hold circuit (SHC). The sum of two output signals of the LVDT is used as the reference signal instead of the excitation signal used in the traditional synchronous demodulators. The reference frequency is provided for the frequency doubler obtained by the phase-locked loop (PLL) to generate the control signal for the SHC. The frequency doubler generates the control signal for the SHC to sample the LVDT signal at the peak position. As a result, the output signal from the SHC is accurately achieved and linearly proportional to the moving core of the LVDT. The realization technique determines the LVDT signal at one-fourth period of the excitation signal. Therefore, the response time of the proposed circuit provides in a quarter of the excitation signal. The circuit configuration of the proposed scheme is simple and low cost. Performances of the proposed demodulator confirmed by experimental results are also included.


title: Chaos control and hybrid projective synchronization for a class of new chaotic systems

abstract: The problems on chaos control and hybrid projective synchronization for a class of new chaotic systems are considered. First, new 4D chaotic systems are proposed by introducing an additional state into a 3D quadratic chaotic system and the states of the systems corresponding to the different ranges of parameter b are exhibited. Second, a single scalar adaptive feedback controller for chaos control of the systems is presented. Third, hybrid projective synchronization (HPS) of two of the chaotic systems with parameters in different conditions are investigated by presenting adaptive feedback control strategies with adaptive parameter update laws and considering controller simplification to achieve complete synchronization. Finally, numerical simulations are demonstrated to verify the effectiveness of the strategies.


title: Supporting Cancer Patients in Illness Management: Usability Evaluation of a Mobile App

abstract: Background: Mobile phones and tablets currently represent a significant presence in people’s everyday lives. They enable access to different information and services independent of current place and time. Such widespread connectivity offers significant potential in different app areas including health care. Objective: Our goal was to evaluate the usability of the Connect Mobile app. The mobile app enables mobile access to the Connect system, an online system that supports cancer patients in managing health-related issues. Along with symptom management, the system promotes better patient-provider communication, collaboration, and shared decision making. The Connect Mobile app enables access to the Connect system over both mobile phones and tablets. Methods: The study consisted of usability tests of a high fidelity prototype with 7 cancer patients where the objectives were to identify existing design and functionality issues and to provide patients with a real look-and-feel of the mobile system. In addition, we conducted semistructured interviews to obtain participants’ feedback about app usefulness, identify the need for new system features and design requirements, and measure the acceptance of the mobile app and its features within everyday health management. Results: The study revealed a total of 27 design issues (13 for mobile apps and 14 for tablet apps), which were mapped to source events (ie, errors, requests for help, participants' concurrent feedback, and moderator observation). We also applied usability heuristics to identify violations of usability principles. The majority of violations were related to enabling ease of input, screen readability, and glanceability (15 issues), as well as supporting an appropriate match between systems and the real world (7 issues) and consistent mapping of system functions and interactions (4 issues). Feedback from participants also showed the cancer patients’ requirements for support systems and how these needs are influenced by different context-related factors, such as type of access terminal (eg, desktop computer, tablet, mobile phone) and phases of illness. Based on the observed results, we proposed design and functionality recommendations that can be used for the development of mobile apps for cancer patients to support their health management process. Conclusions: Understanding and addressing users’ requirements is one of the main prerequisites for developing useful and effective technology-based health interventions. The results of this study outline different user requirements related to the design of the mobile patient support app for cancer patients. The results will be used in the iterative development of the Connect Mobile app and can also inform other developers and researchers in development, integration, and evaluation of mobile health apps and services that support cancer patients in managing their health-related issues. [JMIR Mhealth Uhealth 2014;2(3):e33]


title: Mindfulness starts with the body: somatosensory attention and top-down modulation of cortical alpha rhythms in mindfulness meditation

abstract: Using a common set of mindfulness exercises, mindfulness based stress reduction (MBSR) and mindfulness based cognitive therapy (MBCT) have been shown to reduce distress in chronic pain and decrease risk of depression relapse. These standardized mindfulness (ST-Mindfulness) practices predominantly require attending to breath and body sensations. Here, we offer a novel view of ST-Mindfulness's somatic focus as a form of training for optimizing attentional modulation of 7-14 Hz alpha rhythms that play a key role in filtering inputs to primary sensory neocortex and organizing the flow of sensory information in the brain. In support of the framework, we describe our previous finding that ST-Mindfulness enhanced attentional regulation of alpha in primary somatosensory cortex (SI). The framework allows us to make several predictions. In chronic pain, we predict somatic attention in ST-Mindfulness "de-biases" alpha in SI, freeing up pain-focused attentional resources. In depression relapse, we predict ST-Mindfulness's somatic attention competes with internally focused rumination, as internally focused cognitive processes (including working memory) rely on alpha filtering of sensory input. Our computational model predicts ST-Mindfulness enhances top-down modulation of alpha by facilitating precise alterations in timing and efficacy of SI thalamocortical inputs. We conclude by considering how the framework aligns with Buddhist teachings that mindfulness starts with "mindfulness of the body." Translating this theory into neurophysiology, we hypothesize that with its somatic focus, mindfulness' top-down alpha rhythm modulation in SI enhances gain control which, in turn, sensitizes practitioners to better detect and regulate when the mind wanders from its somatic focus. This enhanced regulation of somatic mind-wandering may be an important early stage of mindfulness training that leads to enhanced cognitive regulation and metacognition.


title: RNA expression profiling at the single molecule level

abstract: We developed a microarray platform for PCR amplification-independent expression profiling of minute samples. A novel scanning system combined with specialized biochips enables detection down to individual fluorescent oligonucleotide molecules specifically hybridized to their complementary sequence over the entire biochip surface of cm2 size. A detection limit of 1.3 fM target oligonucleotide concentration—corresponding to only 39,000 molecules in the sample solution—and a dynamic range of 4.7 orders of magnitude have been achieved. The applicability of the system to PCR amplification-independent gene-expression profiling of minute samples was demonstrated by complex hybridization of cDNA derived from the equivalent of only 104 cells, which matches results obtained in ensemble studies on large samples. By counting each hybridized molecule on the microarray, the method is insusceptible to gene-specific variations of the labeling, thereby representing a principle advance to conventional ensemble-based microarray analysis.


title: Robust Lane Detection and Tracking in Challenging Scenarios

abstract: A lane-detection system is an important component of many intelligent transportation systems. We present a robust lane-detection-and-tracking algorithm to deal with challenging scenarios such as a lane curvature, worn lane markings, lane changes, and emerging, ending, merging, and splitting lanes. We first present a comparative study to find a good real-time lane-marking classifier. Once detection is done, the lane markings are grouped into lane-boundary hypotheses. We group left and right lane boundaries separately to effectively handle merging and splitting lanes. A fast and robust algorithm, based on random-sample consensus and particle filtering, is proposed to generate a large number of hypotheses in real time. The generated hypotheses are evaluated and grouped based on a probabilistic framework. The suggested framework effectively combines a likelihood-based object-recognition algorithm with a Markov-style process (tracking) and can also be applied to general-part-based object-tracking problems. An experimental result on local streets and highways shows that the suggested algorithm is very reliable.


title: The great intimidators.

abstract: After Disney's Michael Eisner, Miramax's Harvey Weinstein, and Hewlett-Packard's Carly Fiorina fell from their heights of power, the business media quickly proclaimed thatthe reign of abrasive, intimidating leaders was over. However, it's premature to proclaim their extinction. Many great intimidators have done fine for a long time and continue to thrive. Their modus operandi runs counter to a lot of preconceptions about what it takes to be a good leader. They're rough, loud, and in your face. Their tactics include invading others' personal space, staging tantrums, keeping people guessing, and possessing an indisputable command of facts. But make no mistake--great intimidators are not your typical bullies. They're driven by vision, not by sheer ego or malice. Beneath their tough exteriors and sharp edges are some genuine, deep insights into human motivation and organizational behavior. Indeed, these leaders possess political intelligence, which can make the difference between paralysis and successful--if sometimes wrenching--organizational change. Like socially intelligent leaders, politically intelligent leaders are adept at sizing up others, but they notice different things. Those with social intelligence assess people's strengths and figure out how to leverage them; those with political intelligence exploit people's weaknesses and insecurities. Despite all the obvious drawbacks of working under them, great intimidators often attract the best and brightest. And their appeal goes beyond their ability to inspire high performance. Many accomplished professionals who gravitate toward these leaders want to cultivate a little "inner intimidator" of their own. In the author's research, quite a few individuals reported having positive relationships with intimidating leaders. In fact, some described these relationships as profoundly educational and even transformational. So before we throw out all the great intimidators, the author argues, we should stop to consider what we would lose.


Cost-Effectiveness of an Internet-Based Treatment Program for Stress Urinary Incontinence


title: Static gate control for charge-transfer switches in DC-DC conversion

abstract: A gate controller was designed to improve the operation of switches in the static charge-transfer switch (CTS) converters. In this study, the gate controller was called as voltage level controller (VC) which would eliminate the reverse charge sharing effect in the static CTS. The proposed VC circuit operates with the higher voltage at the later stage to enhance the pumped voltage in the Dickson circuit without losing charges in the auxiliary path. Since the VC needs little current to control the transistor gates, compact layout with minimal sizes for the VC circuit can be achieved without consuming too much area in the chip. The function of the VC was verified both by HSPICE and chip testing. In the simulation, a CTS converter of 4 pumping stages would generate an output near 8 V with the input of 1.5 V. The generated power was estimated as 5 mW for a load of 10 KΩ. Based on the 0.35-μm CMOS technology of TSMC, a chip of 125*160 μ2 was obtained for testing. Due to the voltage tolerance of the transistors, only 3 stages of pumping worked correctly for the 1.5-V input and the highest output in the measurement was 5.4 V.


title: Annotation and Classification of Light Verbs and Light Verb Variations in Mandarin Chinese

abstract: Light verbs pose an a challenge in linguistics because of its syntactic and semantic versatility and its unique distribution different from regular verbs with higher semantic content and selectional resrictions. Due to its light grammatical content, earlier natural language processing studies typically put light verbs in a stop word list and ignore them. Recently, however, classification and identification of light verbs and light verb construction have become a focus of study in computational linguistics, especially in the context of multi-word expression, information retrieval, disambiguation, and parsing. Past linguistic and computational studies on light verbs had very different foci. Linguistic studies tend to focus on the status of light verbs and its various selectional constraints. While NLP studies have focused on light verbs in the context of either a multi-word expression (MWE) or a construction to be identified, classified, or translated, trying to overcome the apparent poverty of semantic content of light verbs. There has been nearly no work attempting to bridge these two lines of research. This paper takes this challenge by proposing a corpus-bases study which classifies and captures syntactic-semantic difference among all light verbs. In this study, we first incorporate results from past linguistic studies to create annotated light verb corpora with syntactic-semantics features. We next adopt a statistic method for automatic identification of light verbs based on this annotated corpora. Our results show that a language resource based methodology optimally incorporating linguistic information can resolve challenges posed by light verbs in NLP.


title: A Riemannian approach to graph embedding

abstract: In this paper, we make use of the relationship between the Laplace-Beltrami operator and the graph Laplacian, for the purposes of embedding a graph onto a Riemannian manifold. To embark on this study, we review some of the basics of Riemannian geometry and explain the relationship between the Laplace-Beltrami operator and the graph Laplacian. Using the properties of Jacobi fields, we show how to compute an edge-weight matrix in which the elements reflect the sectional curvatures associated with the geodesic paths on the manifold between nodes. For the particular case of a constant sectional curvature surface, we use the Kruskal coordinates to compute edge weights that are proportional to the geodesic distance between points. We use the resulting edge-weight matrix to embed the nodes of the graph onto a Riemannian manifold. To do this, we develop a method that can be used to perform double centring on the Laplacian matrix computed from the edge-weights. The embedding coordinates are given by the eigenvectors of the centred Laplacian. With the set of embedding coordinates at hand, a number of graph manipulation tasks can be performed. In this paper, we are primarily interested in graph-matching. We recast the graph-matching problem as that of aligning pairs of manifolds subject to a geometric transformation. We show that this transformation is Pro-crustean in nature. We illustrate the utility of the method on image matching using the COIL database.


title: A review of analytical techniques for gait data. Part 2: neural network and wavelet methods

abstract: Abstract Multivariate gait data have traditionally been challenging to analyze. Part 1 of this review explored applications of fuzzy, multivariate statistical and fractal methods to gait data analysis. Part 2 extends this critical review to the applications of artificial neural networks and wavelets to gait data analysis. The review concludes with a practical guide to the selection of alternative gait data analysis methods. Neural networks are found to be the most prevalent non-traditional methodology for gait data analysis in the last 10 years. Interpretation of multiple gait signal interactions and quantitative comparisons of gait waveforms are identified as important data analysis topics in need of further research.


title: Hierarchical Co-Attention for Visual Question Answering

abstract: A number of recent works have proposed attention models for Visual Question Answering (VQA) that generate spatial maps highlighting image regions relevant to answering the question. In this paper, we argue that in addition to modeling "where to look" or visual attention, it is equally important to model "what words to listen to" or question attention. We present a novel co-attention model for VQA that jointly reasons about image and question attention. In addition, our model reasons about the question and consequently the image via the co-attention mechanism in a hierarchical fashion via a novel 1-dimensional convolution neural networks (CNN) model. Our final model outperforms all reported methods, improving the state-of-the-art on the VQA dataset from 60.4% to 62.1%, and from 61.6% to 65.4% on the COCO-QA dataset.


title: The Neuroscience of Learning: Beyond the Hebbian Synapse

abstract: From the traditional perspective of associative learning theory, the hypothesis linking modifications of synaptic transmission to learning and memory is plausible. It is less so from an information-processing perspective, in which learning is mediated by computations that make implicit commitments to physical and mathematical principles governing the domains where domain-specific cognitive mechanisms operate. We compare the properties of associative learning and memory to the properties of long-term potentiation, concluding that the properties of the latter do not explain the fundamental properties of the former. We briefly review the neuroscience of reinforcement learning, emphasizing the representational implications of the neuroscientific findings. We then review more extensively findings that confirm the existence of complex computations in three information-processing domains: probabilistic inference, the representation of uncertainty, and the representation of space. We argue for a change in the conceptual framework within which neuroscientists approach the study of learning mechanisms in the brain.


title: HYBRIDIZING GMDH AND LEAST SQUARES SVM SUPPORT VECTOR MACHINE FOR FORECASTING TOURISM DEMAND

abstract: In this paper, we proposed a novel hybrid group method of data handling least squares support vector machine (GLSSVM) algorithm, which combines the theory a group method of data handling (GMDH) with the least squares support vector machine (LSSVM). With the GMDH is used to determine the inputs of LSSVM method and the LSSVM model which works as time series forecasting. The aim of this study is to examine the feasibility of the hybrid model in tourism demand forecasting by comparing it with GMDH and LSSVM model. The tourist arrivals to Johor Malaysia during 1970 to 2008 were employed as the data set. The comparison of modeling results demonstrate that the hybrid model outperforms than two other nonlinear approaches GMDH and LSSVM models.


title: Exact Algorithms for a Loading Problem with Bounded Clique Width

abstract: In this paper we discuss a special pallet-loading problem, which we encountered at a manufacturing company. In graph-theoretical terms, the problem is equivalent to partitioning a permutation graph into bounded-size cliques. We formulate the problem as an integer program, and present two exact algorithms for solving it. The first algorithm is a branch-and-price algorithm based on the integer-programming formulation; the second one is an algorithm based on the concept of bounded clique width. The latter algorithm was motivated by the structure present in the real-world instances. Test results are given, both for real-world instances and randomly generated instances. As far as we are aware, this is the first implementation of an algorithm based on bounded clique width.


title: Vaginal Delivery of Dicephalic Parapagus Conjoined Twins: Case Report and Literature Review

abstract: After an unsuccessful midwife-assisted delivery in which a head was born but delivery could not be advanced, episiotomy performed at Sanliurfa Maternity Hospital allowed vaginal delivery of female conjoined twins. Visual and x-ray examination showed two heads, two vertebral columns, two feet, two arms, and fusion at the level of the pelvis. The baby was born dead, but the mother made an uneventful recovery. Parapagus (anterolaterally joined) dicephalus (two-headed) twins account for only 11-13% of all conjoined twins, and they rarely survive. Complex malformations of hearts, lungs and abdominal organs, duplication of the tracheae, upper gastrointestinal tract and spinal column, and either double or single versions of other organs have been reported in parapagus dicephalus cases. The incidence, anatomical, embryological, diagnostic, prognostic, obstetrical, perinatal, and ethical aspects of conjoined twins are reviewed, with a focus on parapagus dicephalus conjoined twins.


title: Secure Computer Systems: Mathematical Foundations

abstract: This paper reports the first results of an investigation into solutions to problems of security in computer systems; it establishes the basis for rigorous investigation by providing a general descriptive model of a computer system.


title: Design of a high voltage pulse circuit for exciting ultrasonic transducers

abstract: Ultrasonic nondestructive testing with characters of fine resolution and low cost has well been developed and widely applied in modern industrial world. The generation of high voltage pulse to excite the transducer has been a critical challenge in an ultrasonic testing system. In this paper a DC/DC converting circuit with an input of 12V and an output of 300V and -300V independently is introduced, so is a pulse generator with pulse width configured by a FPGA with parameters transmitted through a PCI bus by VC++ program. With different connection of the circuit, a positive unipolar pulse, a negative unipolar pulse, a monocycle bipolar pulse and a 2-cycle bipolar pulse can be generated respectively. Finally some pulse characters which may influence the excitation of the ultrasonic transducer are analyzed, the spectrum signature of each type is presented in the later part of this paper.


title: Effects of Ordinary and Adaptive Toys on Pre-School Children with Developmental Disabilities.

abstract: Toys help children in mastering developmental tasks. This study investigated toy effect on children with developmental disabilities as they engage in using ordinary and adaptive toys. A single-subject design was used to identify the effects on their toy play abilities. Differences in toy effects between playing ordinary and adaptive toys were examined. Three special education teachers chose ordinary toys and modified ordinary toys. Modified ordinary toys, i.e., adaptive toys, were designed according to the individual disabilities of participating children, treatment goals, and the toy types. Three children with developmental disabilities from pre-schools in Taiwan were enrolled. Appropriate participation of three pre-schoolers increased dramatically in playing adaptive toys during intervention phase. The toy effects demonstrate that when using adaptive toys, children with developmental disabilities may response better during toy play sessions.


SPE 112267 Semantic web technologies for smart oil field applications


Image mining: a new approach for data mining


title: Bit-pragmatic deep neural network computing

abstract: Deep Neural Networks expose a high degree of parallelism, making them amenable to highly data parallel architectures. However, data-parallel architectures often accept inefficiency in individual computations for the sake of overall efficiency. We show that on average, activation values of convolutional layers during inference in modern Deep Convolutional Neural Networks (CNNs) contain 92% zero bits. Processing these zero bits entails ineffectual computations that could be skipped. We propose Pragmatic (PRA), a massively data-parallel architecture that eliminates most of the ineffectual computations on-the-fly, improving performance and energy efficiency compared to state-of-the-art high-performance accelerators [5]. The idea behind PRA is deceptively simple: use serial-parallel shift-and-add multiplication while skipping the zero bits of the serial input. However, a straightforward implementation based on shift-and-add multiplication yields unacceptable area, power and memory access overheads compared to a conventional bit-parallel design. PRA incorporates a set of design decisions to yield a practical, area and energy efficient design. Measurements demonstrate that for convolutional layers, PRA is 4.31$\times$ faster than DaDianNao [5] (DaDN) using a 16-bit fixed-point representation. While PRA requires 1.68$\times$ more area than DaDN, the performance gains yield a 1.70$\times$ increase in energy efficiency in a 65nm technology. With 8-bit quantized activations, PRA is 2.25$\times$ faster and 1.31$\times$ more energy efficient than an 8-bit version of DaDN. CCS CONCEPTS • Computing methodologies $\rightarrow$ Machine learning; Neural networks; • Computer systems organization $\rightarrow$ Single instruction, multiple data; • Hardware $\rightarrow$ Arithmetic and datapath circuits;


title: Towards detecting anomalous user behavior in online social networks

abstract: Users increasingly rely on crowdsourced information, such as reviews on Yelp and Amazon, and liked posts and ads on Facebook. This has led to a market for blackhat promotion techniques via fake (e.g., Sybil) and compromised accounts, and collusion networks. Existing approaches to detect such behavior relies mostly on supervised (or semisupervised) learning over known (or hypothesized) attacks. They are unable to detect attacks missed by the operator while labeling, or when the attacker changes strategy. ::: ::: We propose using unsupervised anomaly detection techniques over user behavior to distinguish potentially bad behavior from normal behavior. We present a technique based on Principal Component Analysis (PCA) that models the behavior of normal users accurately and identifies significant deviations from it as anomalous. We experimentally validate that normal user behavior (e.g., categories of Facebook pages liked by a user, rate of like activity, etc.) is contained within a low-dimensional subspace amenable to the PCA technique. We demonstrate the practicality and effectiveness of our approach using extensive ground-truth data from Facebook: we successfully detect diverse attacker strategies--fake, compromised, and colluding Facebook identities--with no a priori labeling while maintaining low false-positive rates. Finally, we apply our approach to detect click-spam in Facebook ads and find that a surprisingly large fraction of clicks are from anomalous users.


title: Privacy Protection: p-Sensitive k-Anonymity Property

abstract: In this paper, we introduce a new privacy protection property called p-sensitive k-anonymity. The existing kanonymity property protects against identity disclosure, but it fails to protect against attribute disclosure. The new introduced privacy model avoids this shortcoming. Two necessary conditions to achieve p-sensitive kanonymity property are presented, and used in developing algorithms to create masked microdata with p-sensitive k-anonymity property using generalization and suppression.


title: Mindfulness-based therapy: a comprehensive meta-analysis.

abstract: Abstract Background Mindfulness-based therapy (MBT) has become a popular form of intervention. However, the existing reviews report inconsistent findings. Objective To clarify these inconsistencies in the literature, we conducted a comprehensive effect-size analysis to evaluate the efficacy of MBT. Data sources A systematic review of studies published in journals or in dissertations in PubMED or PsycINFO from the first available date until May 10, 2013. Review methods A total of 209 studies (n = 12,145) were included. Results Effect-size estimates suggested that MBT is moderately effective in pre-post comparisons (n = 72; Hedge's g = .55), in comparisons with waitlist controls (n = 67; Hedge's g = .53), and when compared with other active treatments (n = 68; Hedge's g = .33), including other psychological treatments (n = 35; Hedge's g = .22). MBT did not differ from traditional CBT or behavioral therapies (n = 9; Hedge's g = − .07) or pharmacological treatments (n = 3; Hedge's g = .13). Conclusion MBT is an effective treatment for a variety of psychological problems, and is especially effective for reducing anxiety, depression, and stress.


title: Teleoperation of humanoid baxter robot using haptic feedback

abstract: This paper presents a teleoperation strategy, which is featured by haptic feedback. The teleoperation system is composed of a SensAble® Omni haptic device, set as the master and providing haptic feedback, and an anthropomorphic robot slave, which is embodied by the 7-DOF (degrees of freedom) robotic arm of the Baxter® robot. The haptic feedback enables a bilateral manipulation of the teleoperation system. The joint angles and Cartesian position of the stylus of the Omni device are sampled and transferred to the slave, determining its motion. Meanwhile a force, proportional to the amplitude of position error of the slave manipulator, is sent back to the master and applied to the stylus. Hereby, the operator can sense the motion of the Baxter robot and adjust its manipulator accordingly. The kinematics of the master and slave have been analysed and a workspace mapping has been realized. Two methods, direct angle mapping and CLIK (closed-loop inverse kinematics), are used to implement the manipulation of the slave in position-position mode. Two experiments have been designed and tested to verify the validity of the methods provided by this paper. The results of the experiments illustrate that the designed teleoperation system is feasible and effective.


title: Supervised Descent Method and Its Applications to Face Alignment

abstract: Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved through a nonlinear optimization method. It is generally accepted that 2nd order descent methods are the most robust, fast and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, 2nd order descent methods have two main drawbacks: (1) The function might not be analytically differentiable and numerical approximations are impractical. (2) The Hessian might be large and not positive definite. To address these issues, this paper proposes a Supervised Descent Method (SDM) for minimizing a Non-linear Least Squares (NLS) function. During training, the SDM learns a sequence of descent directions that minimizes the mean of NLS functions sampled at different points. In testing, SDM minimizes the NLS objective using the learned descent directions without computing the Jacobian nor the Hessian. We illustrate the benefits of our approach in synthetic and real examples, and show how SDM achieves state-of-the-art performance in the problem of facial feature detection. The code is available at www.humansensing.cs. cmu.edu/intraface.


title: Inferring Privacy Information from Social Networks

abstract: Since privacy information can be inferred via social relations, the privacy confidentiality problem becomes increasingly challenging as online social network services are more popular. Using a Bayesian network approach to model the causal relations among people in social networks, we study the impact of prior probability, influence strength, and society openness to the inference accuracy on a real online social network. Our experimental results reveal that personal attributes can be inferred with high accuracy especially when people are connected with strong relationships. Further, even in a society where most people hide their attributes, it is still possible to infer privacy information.


title: NONCOMMUTATIVE GEOMETRY YEAR 2000

abstract: Our geometric concepts evolved first through the discovery of Non-Euclidean geometry. The discovery of quantum mechanics in the form of the noncommuting coordinates on the phase space of atomic systems entails an equally drastic evolution. We describe a basic construction which extends the familiar duality between ordinary spaces and commutative algebras to a duality between Quotient spaces and Noncommutative algebras. The basic tools of the theory, K-theory, Cyclic cohomology, Morita equivalence, Operator theoretic index theorems, Hopf algebra symmetry are reviewed. They cover the global aspects of noncommutative spaces, such as the transformation θ → 1/θ for the noncommutative torus T θ 2 which are unseen in perturbative expansions in θ such as star or Moyal products. We discuss the foundational problem of “what is a manifold in NCG” and explain the fundamental role of Poincare duality in K-homology which is the basic reason for the spectral point of view. This leads us, when specializing to 4-geometries to a universal algebra called the “Instanton algebra”. We describe our joint work with G. Landi which gives noncommutative spheres S θ 4 from representations of the Instanton algebra. We show that any compact Riemannian spin manifold whose isometry group has rank r ≥ 2 admits isospectral deformations to noncommutative geometries. We give a survey of several recent developments. First our joint work with H. Moscovici on the transverse geometry of foliations which yields a diffeomorphism invariant (rather than the usual covariant one) geometry on the bundle of metrics on a manifold and a natural extension of cyclic cohomology to Hopf algebras. Second, our joint work with D. Kreimer on renormalization and the Riemann-Hilbert problem. Finally we describe the spectral realization of zeros of zeta and L-functions from the noncommutative space of Adele classes on a global field and its relation with the Arthur-Selberg trace formula in the Langlands program. We end with a tantalizing connection between the renormalization group and the missing Galois theory at Archimedean places.


Building ASR Corpora Using Eyra


Automated Generation of Event-Oriented Exploits in Android Hybrid Apps


title: Summary Performance Analysis of Public key Cryptographic Systems RSA and NTRU

abstract: In many business sectors secure and efficient data transfer is essential. To ensure the security to the applications of business, the business sectors use Public Key Cryptographic Systems (PKCS). An RSA and NTRU system generally belongs to the category of PKCS. The efficiency of a public key cryptographic system is mainly measured in computational overheads, key size and bandwidth. In particular, the RSA algorithm is used in many applications. Although the security of RSA is beyond doubt, the evolution in computing power has caused a growth in the necessary key length. The fact that most chips on smart cards cannot process keys extending 1024 bit shows that there is a need for alternative. NTRU is such an alternative and it is a collection of mathematical algorithms based on manipulating lists of very small integers and polynomials. This allows NTRU to achieve high speeds with the use of minimal computing power. NTRU is the first secure public key cryptosystem not based on factorization or discrete logarithm problems. This means that given sufficient computational resources and time, an adversary, should not be able to break the key. The performance characteristics of NTRU and RSA are observed by implementing the algorithms for computation and comparing their experimental running times. Encryption and decryption speeds take O (n log (n)) operations. The RSA’s complexity is O (n ) operations and it compares with NTRU’s O (n log(n) operations. In this paper, we proposed and presented these two well known Public Key Cryptographic Systems, and were implemented to verify their performance for different text files of variable sizes.


title: Effect of blood transfusion on antigen presentation function and on interleukin 2 generation.

abstract: To study the effect of blood transfusion (BT) on cell-mediated immunity, we examined the antigen presentation function of peritoneal macrophages and interleukin 2 (IL-2) generation by splenocytes. C3H/HEJ mice were transfused with 0.2 mL of fresh allogeneic blood obtained from C57BL/6 mice; they were killed on days 1, 3, and 7 after BT. A second group of C3H/HEJ mice was transfused with 0.2 mL/d of the same allogeneic blood on three successive days; they were killed on day 7 following the last BT. The antigen presentation function of peritoneal macrophages was measured by utilizing a D10.G4.1 T-helper cell clone; IL-2 activity in supernatants of concanavalin A-stimulated splenocytes was tested by utilizing an IL-2-dependent HT-2 cell line. The results indicate that although antigen presentation function remains unaffected after single and multiple BTs, the ability of splenocytes to generate IL-2 decreases significantly even after a single BT. Thus, the increased susceptibility to infection and the additional immune perturbations in malignant neoplasms following BT may be due in part to decreased IL-2 generation.


title: Detecting Singleton Review Spammers Using Semantic Similarity

abstract: Online reviews have increasingly become a very important resource for consumers when making purchases. Though it is becoming more and more difficult for people to make well-informed buying decisions without being deceived by fake reviews. Prior works on the opinion spam problem mostly considered classifying fake reviews using behavioral user patterns. They focused on prolific users who write more than a couple of reviews, discarding one-time reviewers. The number of singleton reviewers however is expected to be high for many review websites. While behavioral patterns are effective when dealing with elite users, for one-time reviewers, the review text needs to be exploited. In this paper we tackle the problem of detecting fake reviews written by the same person using multiple names, posting each review under a different name. We propose two methods to detect similar reviews and show the results generally outperform the vectorial similarity measures used in prior works. The first method extends the semantic similarity between words to the reviews level. The second method is based on topic modeling and exploits the similarity of the reviews topic distributions using two models: bag-of-words and bag-of-opinion-phrases. The experiments were conducted on reviews from three different datasets: Yelp (57K reviews), Trustpilot (9K reviews) and Ott dataset (800 reviews).


title: Association between obesity and high blood pressure among Lithuanian adolescents: a cross-sectional study

abstract: Most epidemiological studies have shown that the prevalence of high blood pressure (BP) has significantly increased among children and adolescents in various countries of the world. The aim of this study was to examine the associations between overweight, obesity, abdominal obesity and prehypertension and hypertension among Lithuanian adolescents aged 12–15 years. The subjects with increased BP (≥90th percentile) were screened on two separate occasions. Data on the body mass index (BMI), waist circumference (WC), and BP were analysed in 7,457 adolescents aged 12–15 years. Adjusted odds ratios (aORs) with 95% confidence intervals (CI) for the associations were estimated using multivariate logistic regression models. After two screenings, the study participants were categorised as prehypertensive (12.8%), hypertensive (22.2%), and normotensive (65%). The overall prevalence of overweight, obesity, and abdominal obesity (if WC was in the ≥75th percentile) were 12.1%, 2.4%, and 9%, respectively. After adjusting for age and sex, significant associations were found between overweight and obesity and high BP, namely, prehypertension (overweight: aOR = 2.62; 95% CI 2.13–3.23; obesity: aOR = 4.81; 95% CI 3.08–7.52) and hypertension (overweight: aOR = 3.56; 95% CI 3.02–4.19; obesity: aOR = 6.64; 95% CI 4.65–9.49). Prehypertension was found to be significantly associated with WC in the 75th– < 90th percentiles (aOR = 3.16; 95% CI 2.43–4.10) and WC in the ≥90th percentile (aOR = 4.08; 95% CI 2.35–7.10). For hypertension, significant associations were detected with WC in the 75th– < 90th percentiles (aOR = 3.92; 95% CI 3.18–4.82) and WC in the ≥90th percentile (aOR = 7.41; 95% CI 4.97–11.05). Overweight, obesity, and abdominal obesity were associated with prehypertension and hypertension.


title: Capturing the Semantics of Key Phrases Using Multiple Languages for Question Retrieval

abstract: In the age of Web 2.0, community user contributed questions and answers provide an important alternative for knowledge acquisition through web search. Question retrieval in current community-based question answering (CQA) services do not, in general, work well for long and complex queries, such as the questions. The main reasons are the verboseness in natural language queries and the word mismatch between the queries and the candidate questions in the CQA archive during retrieval. To address these two problems, existing solutions try to refine the search queries by distinguishing the key concepts in the queries and expanding the queries with relevant content. However, using the existing query refinement approaches can only identify the key and non-key concepts, while the differences between the key concepts are overlooked. Moreover, the existing query expansion approaches, not only overlook the weights of key concepts in the queries, but also fail to consider concept level expansion for them. In this paper, we explore a key concept identification approach for query refinement and a pivot language translation based approach to explore key concept paraphrasing. We further propose a new question retrieval model which can seamlessly integrate the key concepts and their paraphrases. The experimental results demonstrate that the integrated retrieval model significantly outperforms the state-of-the-art models in question retrieval.


title: Wireless Body Area Networks for Healthcare: A Survey

abstract: Wireless body area networks (WBANs) are emerging as important networks, applicable in various fields. This paper surveys the WBANs that are designed for applications in healthcare. We present a comprehensive survey consisting of stand-alone sections focusing on important aspects of WBANs. We examine the following: monitoring and sensing, power efficient protocols, system architectures, routing and security. We conclude by discussing some open research issues, their potential solutions and future trends.


title: A Petri Nets Model for Blockchain Analysis

abstract: A Blockchain is a global shared infrastructure where cryptocurrency transactions among addresses are recorded, validated and made publicly available in a peer- to-peer network. To date the best known and important cryptocurrency is the bitcoin. In this paper we focus on this cryptocurrency and in particular on the modeling of the Bitcoin Blockchain by using the Petri Nets formalism. The proposed model allows us to quickly collect information about identities owning Bitcoin addresses and to recover measures and statistics on the Bitcoin network. By exploiting algebraic formalism, we reconstructed an Entities network associated to Blockchain transactions gathering together Bitcoin addresses into the single entity holding permits to manage Bitcoins held by those addresses. The model allows also to identify a set of behaviours typical of Bitcoin owners, like that of using an address only once, and to reconstruct chains for this behaviour together with the rate of firing. Our model is highly flexible and can easily be adapted to include different features of the Bitcoin crypto-currency system.


Effect of different plant powders for their use in a botanical insecticide strategy against Busseola fusca ( Fuller ) ( Lepidoptera : Noctuidae ) under laboratory condition


title: A Revised Neural Framework for Face Processing

abstract: Face perception relies on computations carried out in face-selective cortical areas. These areas have been intensively investigated for two decades, and this work has been guided by an influential neural model suggested by Haxby and colleagues in 2000. Here, we review new findings about face-selective areas that suggest the need for modifications and additions to the Haxby model. We suggest a revised framework based on (a) evidence for multiple routes from early visual areas into the face-processing system, (b) information about the temporal characteristics of these areas, (c) indications that the fusiform face area contributes to the perception of changeable aspects of faces, (d) the greatly elevated responses to dynamic compared with static faces in dorsal face-selective brain areas, and (e) the identification of three new anterior face-selective areas. Together, these findings lead us to suggest that face perception depends on two separate pathways: a ventral stream that represents form information and...


title: Automatic device driver synthesis with termite

abstract: Faulty device drivers cause significant damage through down time and data loss. The problem can be mitigated by an improved driver development process that guarantees correctness by construction. We achieve this by synthesising drivers automatically from formal specifications of device interfaces, thus reducing the impact of human error on driver reliability and potentially cutting down on development costs. We present a concrete driver synthesis approach and tool called Termite. We discuss the methodology, the technical and practical limitations of driver synthesis, and provide an evaluation of non-trivial drivers for Linux, generated using our tool. We show that the performance of the generated drivers is on par with the equivalent manually developed drivers. Furthermore, we demonstrate that device specifications can be reused across different operating systems by generating a driver for FreeBSD from the same specification as used for Linux.


title: A Faster Algorithm for Betweenness Centrality

abstract: Motivated by the fast‐growing need to compute centrality indices on large, yet very sparse, networks, new algorithms for betweenness are introduced in this paper. They require O(n + m) space and run in O(nm) and O(nm + n2 log n) time on unweighted and weighted networks, respectively, where m is the number of links. Experimental evidence is provided that this substantially increases the range of networks for which centrality analysis is feasible. The betweenness centrality index is essential in the analysis of social networks, but costly to compute. Currently, the fastest known algorithms require ?(n 3) time and ?(n 2) space, where n is the number of actors in the network.


title: An experimental study of the intrinsic stability of random forest variable importance measures

abstract: BackgroundThe stability of Variable Importance Measures (VIMs) based on random forest has recently received increased attention. Despite the extensive attention on traditional stability of data perturbations or parameter variations, few studies include influences coming from the intrinsic randomness in generating VIMs, i.e. bagging, randomization and permutation. To address these influences, in this paper we introduce a new concept of intrinsic stability of VIMs, which is defined as the self-consistence among feature rankings in repeated runs of VIMs without data perturbations and parameter variations. Two widely used VIMs, i.e., Mean Decrease Accuracy (MDA) and Mean Decrease Gini (MDG) are comprehensively investigated. The motivation of this study is two-fold. First, we empirically verify the prevalence of intrinsic stability of VIMs over many real-world datasets to highlight that the instability of VIMs does not originate exclusively from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs. Second, through Spearman and Pearson tests we comprehensively investigate how different factors influence the intrinsic stability.ResultsThe experiments are carried out on 19 benchmark datasets with diverse characteristics, including 10 high-dimensional and small-sample gene expression datasets. Experimental results demonstrate the prevalence of intrinsic stability of VIMs. Spearman and Pearson tests on the correlations between intrinsic stability and different factors show that #feature (number of features) and #sample (size of sample) have a coupling effect on the intrinsic stability. The synthetic indictor, #feature/#sample, shows both negative monotonic correlation and negative linear correlation with the intrinsic stability, while OOB accuracy has monotonic correlations with intrinsic stability. This indicates that high-dimensional, small-sample and high complexity datasets may suffer more from intrinsic instability of VIMs. Furthermore, with respect to parameter settings of random forest, a large number of trees is preferred. No significant correlations can be seen between intrinsic stability and other factors. Finally, the magnitude of intrinsic stability is always smaller than that of traditional stability.ConclusionFirst, the prevalence of intrinsic stability of VIMs demonstrates that the instability of VIMs not only comes from data perturbations or parameter variations, but also stems from the intrinsic randomness of VIMs. This finding gives a better understanding of VIM stability, and may help reduce the instability of VIMs. Second, by investigating the potential factors of intrinsic stability, users would be more aware of the risks and hence more careful when using VIMs, especially on high-dimensional, small-sample and high complexity datasets.


title: Is bottom-up attention useful for object recognition?

abstract: A key problem in learning multiple objects from unlabeled images is that it is a priori impossible to tell which part of the image corresponds to each individual object, and which part is irrelevant clutter which is not associated to the objects. We investigate empirically to what extent pure bottom-up attention can extract useful information about the location, size and shape of objects from images and demonstrate how this information can be utilized to enable unsupervised learning of objects from unlabeled images. Our experiments demonstrate that the proposed approach to using bottom-up attention is indeed useful for a variety of applications.


title: Beam-Oriented Digital Predistortion for 5G Massive MIMO Hybrid Beamforming Transmitters

abstract: In this paper, we propose a beam-oriented digital predistortion (BO-DPD) technique for power amplifiers (PAs) in hybrid beamforming massive multiple-input multiple-output (MIMO) transmitters, which can achieve linearization of the transmitted signal in the main beam direction and address the DPD implementation issue in the hybrid beamforming array. In massive MIMO hybrid beamforming transmitters, the conventional DPD to linearize each PA is impractical to implement for that the number of digital chains is less than the number of PAs; however, the BO-DPD can resolve this issue by constructing and linearizing the “virtual” main beam signal rather than single PAs. According to the beamforming weights for array elements, the feedback path combines all PA’s outputs to estimate the main beam signal for DPD processing. In addition, an average estimation method is also presented to broaden the linearized angle range around the main beam direction. A $4 \times 16$ (four subarrays and 16 antennas in each subarray) uniform linear array (ULA) simulation along with a $1 \times 2$ ULA at 2.5 GHz and a $2 \times 2$ ULA at 3.5-GHz experimental tests was performed. The experimental results show that the proposed technique and linearization angle range broadening method achieve up to 15-dB adjacent channel power ratio improvement and 10° linearization angle range in the main beam direction.


title: Detection of Cattle Using Drones and Convolutional Neural Networks

abstract: Multirotor drones have been one of the most important technological advances of the last decade. Their mechanics are simple compared to other types of drones and their possibilities in flight are greater. For example, they can take-off vertically. Their capabilities have therefore brought progress to many professional activities. Moreover, advances in computing and telecommunications have also broadened the range of activities in which drones may be used. Currently, artificial intelligence and information analysis are the main areas of research in the field of computing. The case study presented in this article employed artificial intelligence techniques in the analysis of information captured by drones. More specifically, the camera installed in the drone took images which were later analyzed using Convolutional Neural Networks (CNNs) to identify the objects captured in the images. In this research, a CNN was trained to detect cattle, however the same training process could be followed to develop a CNN for the detection of any other object. This article describes the design of the platform for real-time analysis of information and its performance in the detection of cattle.


title: A Field Relevance Model for Structured Document Retrieval

abstract: Many search applications involve documents with structure or fields. Since query terms often are related to specific structural components, mapping queries to fields and assigning weights to those fields is critical for retrieval effectiveness. Although several field-based retrieval models have been developed, there has not been a formal justification of field weighting. ::: ::: In this work, we aim to improve the field weighting for structured document retrieval. We first introduce the notion of field relevance as the generalization of field weights, and discuss how it can be estimated using relevant documents, which effectively implements relevance feedback for field weighting. We then propose a framework for estimating field relevance based on the combination of several sources. Evaluation on several structured document collections show that field weighting based on the suggested framework improves retrieval effectiveness significantly.


title: CS50 sandbox: secure execution of untrusted code

abstract: We introduce CS50 Sandbox, an environment for secure execution of untrusted code. Implemented as an asynchronous HTTP server, CS50 Sandbox offers clients the ability to execute programs (both interactive and non-interactive) written in any compiled or interpreted language in a tightly controlled, resource-constrained environment. CS50 Sandbox's HTTP-based API takes files, command lines, and standard input as inputs and returns standard output and error plus exit codes as outputs. Atop CS50 Sandbox, we have built CS50 Run, a web-based code editor that enables students to write code in a browser in any language, whether compiled or interpreted, that's executed server-side within a sandboxed environment. And we have built CS50 Check, an autograding framework that supports black- and white-box testing of students' code, leveraging CS50 Sandbox to run series of checks against students' programs, no matter the language of implementation. We present in this work the pedagogical motivations for each of these tools, along with the underlying designs thereof. Each is available as open source.


title: Improving the Estimation of Word Importance for News Multi-Document Summarization

abstract: We introduce a supervised model for predicting word importance that incorporates a rich set of features. Our model is superior to prior approaches for identifying words used in human summaries. Moreover we show that an extractive summarizer using these estimates of word importance is comparable in automatic evaluation with the state-of-the-art.


Centroid-based Text Summarization through Compositionality of Word Embeddings


title: Experimental analysis of a vibrating tribocharging device of plastic particles for free-fall triboelectric separator

abstract: The selective sorting of mixed granular plastics can be achieved by tribocharging them in a vibrated parallelepiped chamber of variable height prior to introducing them in a free fall electrostatic separator. Although such vibrating tribochargers are currently used in industry, no experimental investigation of these devices has been carried out, yet. The objective of this work is to characterize this class of tribochargers by estimating both the electric charge imparted to the granules and the electrostatic separation efficiency. The granular product (a mixture of high-density poly-ethylene - HDPE - and poly-vinyl-chloride - PVC) is introduced through a funnel into the vibrating chamber, where the particles get triboelectrically charged by undergoing collisions between themselves and with the walls of the device. The obtained results showed that the efficiencies of both the charging and the separation depend on the geometry and the operating conditions of the tribocharger (height, length. inclination angle, motor speed). Charge per mass ratios beyond 3 nC/g have been easily attained and the efficiency of the separation was excellent: more than 90% of the HDPE and PVC in the feed are recovered at the outlet of the installation.


title: Efficient Nonsmooth Nonconvex Optimization for Image Restoration and Segmentation

abstract: In this article, we introduce variational image restoration and segmentation models that incorporate the $$L^1$$ L 1 data-fidelity measure and a nonsmooth, nonconvex regularizer. The $$L^1$$ L 1 fidelity term allows us to restore or segment an image with low contrast or outliers, and the nonconvex regularizer enables homogeneous regions of the objective function (a restored image or an indicator function of a segmented region) to be efficiently smoothed while edges are well preserved. To handle the nonconvexity of the regularizer, a multistage convex relaxation method is adopted. This provides a better solution than the classical convex total variation regularizer, or than the standard $$L^1$$ L 1 convex relaxation method. Furthermore, we design fast and efficient optimization algorithms that can handle the non-differentiability of both the fidelity and regularization terms. The proposed iterative algorithms asymptotically solve the original nonconvex problems. Our algorithms output a restored image or segmented regions in the image, as well as an edge indicator that characterizes the edges of the output, similar to Mumford---Shah-like regularizing functionals. Numerical examples demonstrate the promising results of the proposed restoration and segmentation models.


title: Virtual synchronous machine method in renewable energy integration

abstract: The virtual synchronous machine (VSM) method is a power electronics based approach which was first introduced by Beck and Hesse in 2007 for renewable energy integration modeling. This method controls grid-connected renewable energy sources as electromechanical synchronous machines, which makes renewable energy sources behave as conventional power generation stations if viewed from the power grid. During the last decade, some developments have been made to operate power electronic converters as VSMs, which offers a promising way to design advanced control systems for renewable energy integration. Most publications on this topic actually occurred within the past two years. As an emerging new method with huge potential, the VSM method has been used to reduce voltage and frequency fluctuation of renewable energy sources, and improve power system stability. In this paper, a comprehensive literature review is conducted for the VSM method. The principle, and current state of the art control techniques to implement this method are reviewed and summarized in the paper.


title: Packaging Process for Grating-Coupled Silicon Photonic Waveguides Using Angle-Polished Fibers

abstract: A novel process for fiber-packaging submicrometer silicon waveguides is presented. The process uses fibers polished at an angle to reflect light between a horizontal core and the slightly off-vertical input and output path of a grating coupler. The necessity for a reflective coating on the fiber facet is overcome through the use of total internal reflection and a novel technique of epoxy distribution based on capillary action. Simulations of alignment tolerance are presented, along with measurements confirming the applicability of passive alignment. A peak coupling efficiency within 0.2 dB of the theoretical maximum for the grating coupler is achieved.


title: Side-lobe level reduction in 1D photonic array antennas

abstract: Photonic array antennas have been proposed to improve radiation features and to enable dynamic beam steering and beam forming. In CMOS-compatible photonic array antennas, fabrication constraints, however, limit the minimum separation distance between radiation elements to several wavelengths, which results in the presence of strong side-lobes known as grating lobes. These grating lobes represent a critical impairment that prevents the adoption of these sparse array antennas in applications where crosstalk and security faults must be avoided. In this work, we numerically analyze the side-lobe level (SLL) reduction of 1D sparse antenna arrays under beam steering operation when the radiating elements are arranged non-uniformly (the separation between radiation elements is increased geometrically). The proposed method has a single parameter and, therefore, can be easily optimized. Furthermore, the approach may be extended to higher dimensions. Preliminary results reveal a SLL in the worst-case scenario reduction of approximately 5 dB for a 5-element array with a total length of 6 wavelengths.


title: CONIKS: bringing key transparency to end users

abstract: We present CONIKS, an end-user key verification service capable of integration in end-to-end encrypted communication systems. CONIKS builds on transparency log proposals for web server certificates but solves several new challenges specific to key verification for end users. CONIKS obviates the need for global third-party monitors and enables users to efficiently monitor their own key bindings for consistency, downloading less than 20 kB per day to do so even for a provider with billions of users. CONIKS users and providers can collectively audit providers for non-equivocation, and this requires downloading a constant 2.5 kB per provider per day. Additionally, CONIKS preserves the level of privacy offered by today's major communication services, hiding the list of usernames present and even allowing providers to conceal the total number of users in the system.


title: An Energy-Efficient Smart Comfort Sensing System Based on the IEEE 1451 Standard for Green Buildings

abstract: In building automation, comfort is an important aspect, and the real-time measurement of comfort is notoriously complicated. In this paper, we have developed a wireless, smart comfort sensing system. The important parameters in designing the prevalent measurement of comfort systems, such as portability, power consumption, reliability, and system cost, were considered. To achieve the target design goals, the communication module, sensor node, and sink node were developed based on the IEEE1451 standard. Electrochemical and semiconductor sensors were considered for the development of the sensor array, and the results of both technologies were compared. The sensor and sink nodes were implemented using the ATMega88 microcontroller. Microsoft Visual Studio 2013 preview was used to create the graphical user interface in C#. The sensors were calibrated after the signal processing circuit to ensure that the standard accuracy of the sensor was achieved. This paper presents detailed design solutions to problems that existed in the literature.


Physically Based Deformable Models in Computer Graphics


title: Counter-forensics in machine learning based forgery detection

abstract: With the powerful image editing tools available today, it is very easy to create forgeries without leaving visible traces. Boundaries between host image and forgery can be concealed, illumination changed, and so on, in a naive form of counter-forensics. For this reason, most modern techniques for forgery detection rely on the statistical distribution of micro-patterns, enhanced through high-level filtering, and summarized in some image descriptor used for the final classification. In this work we propose a strategy to modify the forged image at the level of micro-patterns to fool a state-of-the-art forgery detector. Then, we investigate on the effectiveness of the proposed strategy as a function of the level of knowledge on the forgery detection algorithm. Experiments show this approach to be quite effective especially if a good prior knowledge on the detector is available.


title: Investment in Enterprise Resource Planning: Business Impact and Productivity Measures

abstract: Enterprise Resource Planning (ERP)software systems integrate key business and management processes within and beyond a firm's boundary.Although the business value of ERP implementations has been extensively debated in trade periodicals in the form of qualitative discussion or detailed case studies, there is little large-sample statistical evidence on whether the benefits of ERP implementation exceed the costs and risks. With multiyear multi-firm ERP implementation and financial data, we find that firms that invest in ERP tend to show higher performance across a wide variety of financial metrics. Even though there is a slowdown in business performance and productivity shortly after the implementation, financial markets consistently reward the adopters with higher market valuation (as measured by Tobin's q). Due to the lack of mid- and long-term post-implementation data, future research on the long-run impact of ERP is proposed.


title: DimpVis: Exploring Time-varying Information Visualizations by Direct Manipulation

abstract: We introduce a new direct manipulation technique, DimpVis, for interacting with visual items in information visualizations to enable exploration of the time dimension. DimpVis is guided by visual hint paths which indicate how a selected data item changes through the time dimension in a visualization. Temporal navigation is controlled by manipulating any data item along its hint path. All other items are updated to reflect the new time. We demonstrate how the DimpVis technique can be designed to directly manipulate position, colour, and size in familiar visualizations such as bar charts and scatter plots, as a means for temporal navigation. We present results from a comparative evaluation, showing that the DimpVis technique was subjectively preferred and quantitatively competitive with the traditional time slider, and significantly faster than small multiples for a variety of tasks.


Topics in semantic representation


title: The Enactive Approach to Architectural Experience: A Neurophysiological Perspective on Embodiment, Motivation, and Affordances

abstract: Over the last few years, the efforts to reveal through neuroscientific lens the relations between the mind, body, and built environment have set a promising direction of using neuroscience for architecture. However, little has been achieved thus far in developing a systematic account that could be employed for interpreting current results and providing a consistent framework for subsequent scientific experimentation. In this context, the enactive perspective is proposed as a guide to studying architectural experience for two key reasons. Firstly, the enactive approach is specifically selected for its capacity to account for the profound connectedness of the organism and the world in an active and dynamic relationship, which is primarily shaped by the features of the body. Thus, particular emphasis is placed on the issues of embodiment and motivational factors as underlying constituents of the body-architecture interactions. Moreover, enactive understanding of the relational coupling between body schema and affordances of architectural spaces singles out the two-way bodily communication between architecture and its inhabitants, which can be also explored in immersive virtual reality settings. Secondly, enactivism has a strong foothold in phenomenological thinking that corresponds to the existing phenomenological discourse in architectural theory and qualitative design approaches. In this way, the enactive approach acknowledges the available common ground between neuroscience and architecture and thus allows a more accurate definition of investigative goals. Accordingly, the outlined model of architectural subject in enactive terms – that is, a model of a human being as embodied, enactive, and situated agent, is proposed as a basis of neuroscientific and phenomenological interpretation of architectural experience.


title: IBOTS: agent control through the user interface

abstract: This paper describes an ibot, a specialized software agent that exists in the environment of the user interface. Such an agent interacts with applications through the same medium as a human user. Its sensors process screen contents and mouse/keyboard events to monitor the user’s actions and the responses of the environment, while its effecters can generate such events for its own contributions to the interaction. We describe the architecture of our agent and * its algorithms for image processing, event management, and state representation. We illustrate the use of the agent with a small feasibility study in the area of software logging; results are promising for future progress.


title: Stop words in review summarization using TextRank

abstract: This paper presents a comparison of automatic review summarization with and without stop words. An extractive, unsupervised graph-based ranking model TextRank is employed to highlight the differences between both approaches. Experimental results on 50 sample reviews have shown that the usage of stop words removal can be impactful in determining the result of review summarization, which suggests that depending on the user requirements, it should be considered whether stop words removal needs to be performed or not.


title: BlurMe: inferring and obfuscating user gender based on ratings

abstract: User demographics, such as age, gender and ethnicity, are routinely used for targeting content and advertising products to users. Similarly, recommender systems utilize user demographics for personalizing recommendations and overcoming the cold-start problem. Often, privacy-concerned users do not provide these details in their online profiles. In this work, we show that a recommender system can infer the gender of a user with high accuracy, based solely on the ratings provided by users (without additional metadata), and a relatively small number of users who share their demographics. Focusing on gender, we design techniques for effectively adding ratings to a user's profile for obfuscating the user's gender, while having an insignificant effect on the recommendations provided to that user.


title: Algebraic geometry of Bayesian networks

abstract: We study the algebraic varieties defined by the conditional independence statements of Bayesian Networks. A complete algebraic classification is given for Bayesian Networks on at most five random variables. Hidden variables are related to the geometry of higher secant varieties.


title: Synthesizing data for text recognition with style transfer

abstract: Most of the existing datasets for scene text recognition merely consist of a few thousand training samples with a very limited vocabulary, which cannot meet the requirement of the state-of-the-art deep learning based text recognition methods. Meanwhile, although the synthetic datasets (e.g., SynthText90k) usually contain millions of samples, they cannot fit the data distribution of the small target datasets in natural scenes completely. To address these problems, we propose a word data generating method called SynthText-Transfer, which is capable of emulating the distribution of the target dataset. SynthText-Transfer uses a style transfer method to generate samples with arbitray text content, which preserve the texture of the reference sample in the target dataset. The generated images are not only visibly similar with real images, but also capable of improving the accuracy of the state-of-the-art text recognition methods, especially for the English and Chinese dataset with a large alphabet (in which many characters only appear in few samples, making it hard to learn for sequence models). Moreover, the proposed method is fast and flexible, with a competitive speed among common style transfer methods.


title: A Dual-Polarized Series-Fed Microstrip Antenna Array With Very High Polarization Purity for Weather Measurements

abstract: The design and analysis of a dual-polarization frequency scanning array antenna, operating in a frequency band of 2.7-3.0 GHz, for weather measurement applications are presented. Stacked patch elements are coupled to feed lines through slots etched on the ground plane. A wall of plated holes in the middle of the antenna column is designed to improve the isolation between the two input ports. Both simulation and measurements results confirm that very high polarization purity is achieved.


title: Fidelity vs. simplicity: a global approach to line drawing vectorization

abstract: Vector drawing is a popular representation in graphic design because of the precision, compactness and editability offered by parametric curves. However, prior work on line drawing vectorization focused solely on faithfully capturing input bitmaps, and largely overlooked the problem of producing a compact and editable curve network. As a result, existing algorithms tend to produce overly-complex drawings composed of many short curves and control points, especially in the presence of thick or sketchy lines that yield spurious curves at junctions. We propose the first vectorization algorithm that explicitly balances fidelity to the input bitmap with simplicity of the output, as measured by the number of curves and their degree. By casting this trade-off as a global optimization, our algorithm generates few yet accurate curves, and also disambiguates curve topology at junctions by favoring the simplest interpretations overall. We demonstrate the robustness of our algorithm on a variety of drawings, sketchy cartoons and rough design sketches.