title: Gain Enhancement of Asymmetric Horn for Secondary Radar Antenna System using Hybrid Metamaterials

abstract: This paper presents the combination technique of two different structures of metamaterials (hybrid) in order to enhance the gain of an asymmetric horn for the secondary radar system. The hybrid metamaterials consist of a structure of woodpile Electromagnetic Band Gap (EBG) for gaining the radar main beam at 0 of direction and wire medium structure for gaining the IFF (Identification Friend or Foe) main beam at 6 shifted left from main direction. While the EBG and wire medium structures are designed at 1,300 MHz and 1,060 MHz of the center frequencies, respectively. Furthermore, the EBG structure must also response to the horizontal polarization of the radar main beam, whereas the wire medium structure must response to the vertical polarization of the IFF main beam. These are necessary to use the hybrid metamaterials to overcome such conditions. The hybrid metamaterial that is placed at the front of the aperture of asymmetric horn with the proper spacing is calculated by using CST software to consider its performance. We found that this proposed metamaterial could maintain the different directions of both main beams. While the gain of both frequencies, which have different polarization, are enhanced more than the single asymmetric horn around 2 dB.


title: Continuous Influence Maximization: What Discounts Should We Offer to Social Network Users?

abstract: Imagine we are introducing a new product through a social network, where we know for each user in the network the purchase probability curve with respect to discount. Then, what discount should we offer to those social network users so that the adoption of the product is maximized in expectation under a predefined budget? Although influence maximization has been extensively explored, surprisingly, this appealing practical problem still cannot be answered by the existing influence maximization methods. In this paper, we tackle the problem systematically. We formulate the general continuous influence maximization problem, investigate the essential properties, and develop a general coordinate descent algorithm as well as the engineering techniques for practical implementation. Our investigation does not assume any specific influence model and thus is general and principled. At the same time, using the most popularly adopted independent influence model as a concrete example, we demonstrate that more efficient methods are feasible under specific influence models. Our extensive empirical study on four benchmark real world networks with synthesized purchase probability curves clearly illustrates that continuous influence maximization can improve influence spread significantly with very moderate extra running time comparing to the classical influence maximization methods.


title: On the Canny edge detector

abstract: Abstract The Canny edge detector is widely used in computer vision to locate sharp intensity changes and to find object boundaries in an image. The Canny edge detector classifies a pixel as an edge if the gradient magnitude of the pixel is larger than those of pixels at both its sides in the direction of maximum intensity change. In this paper we will show that defining edges in this manner causes some obvious edges to be missed. We will also show how to revise the Canny edge detector to improve its detection accuracy.


title: UWB ranging accuracy

abstract: The accuracy of ranging of commercially available ultra-wide-band radio module was evaluated. Such modules are capable of very precise measurements of Time of Arrival of RF signal and consequently very precise range measurements or localizations. We measured and compared the distances with the reference distance using one of these commercial modules. The results were very promising for indoor localization because the modules achieved ranging accuracy below 10 cm.


title: Integrated Design & Test: Conquering the Conflicting Requirements of Low-Power, Variation-Tolerance and Test Cost

abstract: Design objectives of robustness and low-power usually do not go hand in hand with the test objectives of maximum test coverage and minimum test cost. Low power robust design techniques such as dual-Vth, dual-VDD, or adaptive body biasing have negative impact on the associated test cost. Similarly, test techniques like enhanced scan have large overhead in terms of area and power. In this paper, we try to mitigate the conflicting design and test requirements using an integrated approach to design and test that utilizes the existing low power and error resilient design techniques and augments them to improve test coverage and cost. Simulation results on an example 8x8 Wallace tree multiplier in 90nm technology node show 20% reduction in operating power, 60% reduction in test power and 99% reduction in critical paths while at the same time improving the yield from 96% to 100%, compared to existing design and test methodologies. All this comes at the cost of a marginal increase in area (7.8%).


title: A Randomized Clinical Trial Comparison Between Pivotal Response Treatment (PRT) and Adult-Driven Applied Behavior Analysis (ABA) Intervention on Disruptive Behaviors in Public School Children with Autism

abstract: Children with autism often demonstrate disruptive behaviors during demanding teaching tasks. Language intervention can be particularly difficult as it involves social and communicative areas, which are challenging for this population. The purpose of this study was to compare two intervention conditions, a naturalistic approach, Pivotal Response Treatment (PRT) with an adult-directed ABA approach on disruptive behavior during language intervention in the public schools. A randomized clinical trial design was used with two groups of children, matched according to age, sex and mean length of utterance. The data showed that the children demonstrated significantly lower levels of disruptive behavior during the PRT condition. The results are discussed with respect to antecedent manipulations that may be helpful in reducing disruptive behavior.


title: Demonstration of MOS capacitor measurement for wafer manufacturing using a Direct Charge Measurement

abstract: Direct Charge Measurement (DCM) has a capability to improve the capacitance measurement time in parametric test. Through an actual wafer measurement, we have successfully verified that DCM can measure MOS capacitor much faster than an LCR meter while keeping good correlations for wafer manufacturing.


title: Research on soft starting control strategy for LCI-fed synchronous motor

abstract: This paper describes a soft starting control strategy for synchronous motors fed by load-commutated inverters (LCI). At low speed, high frequency (HF) current injection method is employed to obtain rotor position. When the motor speed reaches 5% of the rated motor speed, zero-crossing detection of back electromotive force (EMF) is adopted to monitor rotor position. According to the rotor position, SCRs can exactly be triggered at the right time to regulate inverter. Meanwhile, there is a transition between forced commutation mode at low speed and load commutation mode at medium and high speed. Then the soft start of synchronous motor, namely motor operates from standstill to rated speed, and grid-connection without negative impacts can be realized finally. The feasibility of the proposed control strategy is validated on a 15kW LCI-fed synchronous motor.


title: Semantic Sensor Data Search in a Large-Scale Federated Sensor Network

abstract: Sensor network deployments are a primary source of massive amounts of data about the real world that surrounds us, measuring a wide range of physical properties in real time. However, in large-scale deployments it becomes hard to effectively exploit the data captured by the sensors, since there is no precise information about what devices are available and what properties they measure. Even when metadata is available, users need to know low-level details such as database schemas or names of properties that are specific to a device or platform. Therefore the task of coherently searching, correlating and combining sensor data becomes very challenging. We propose an ontology-based approach, that consists in exposing sensor observations in terms of ontologies enriched with semantic metadata, providing information such as: which sensor recorded what, where, when, and in which conditions. For this, we allow defining virtual semantic streams, whose ontological terms are related to the underlying sensor data schemas through declarative mappings, and can be queried in terms of a high level sensor network ontology.


title: A Review of Anomaly Detection in Automated Surveillance

abstract: As surveillance becomes ubiquitous, the amount of data to be processed grows along with the demand for manpower to interpret the data. A key goal of surveillance is to detect behaviors that can be considered anomalous. As a result, an extensive body of research in automated surveillance has been developed, often with the goal of automatic detection of anomalies. Research into anomaly detection in automated surveillance covers a wide range of domains, employing a vast array of techniques. This review presents an overview of recent research approaches on the topic of anomaly detection in automated surveillance. The reviewed studies are analyzed across five aspects: surveillance target, anomaly definitions and assumptions, types of sensors used and the feature extraction processes, learning methods, and modeling algorithms.


title: Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis

abstract: It is widely thought that news organizations exhibit ideological bias, but rigorously quantifying such slant has proven methodologically challenging. Through a combination of machine learning and crowdsourcing techniques, we investigate the selection and framing of political issues in 15 major U.S. news outlets. Starting with 803,146 news stories published over 12 months, we first used supervised learning algorithms to identify the 14% of articles pertaining to political events. We then recruited 749 online human judges to classify a random subset of 10,950 of these political articles according to topic and ideological position. Our analysis yields an ideological ordering of outlets consistent with prior work. We find, however, that news outlets are considerably more similar than generally believed. Specifically, with the exception of political scandals, we find that major news organizations present topics in a largely non-partisan manner, casting neither Democrats nor Republicans in a particularly favorable or unfavorable light. Moreover, again with the exception of political scandals, there is little evidence of systematic differences in story selection, with all major news outlets covering a wide variety of topics with frequency largely unrelated to the outlet's ideological position. Finally, we find that news organizations express their ideological bias not by directly advocating for a preferred political party, but rather by disproportionately criticizing one side, a convention that further moderates overall differences.


title: Computational Theories of Curiosity-Driven Learning

abstract: What are the functions of curiosity? What are the mechanisms of curiosity-driven learning? We approach these questions using concepts and tools from machine learning and developmental robotics. We argue that curiosity-driven learning enables organisms to make discoveries to solve complex problems with rare or deceptive rewards. By fostering exploration and discovery of a diversity of behavioural skills, and ignoring these rewards, curiosity can be efficient to bootstrap learning when there is no information, or deceptive information, about local improvement towards these problems. We review both normative and heuristic computational frameworks used to understand the mechanisms of curiosity in humans, conceptualizing the child as a sense-making organism. These frameworks enable us to discuss the bi-directional causal links between curiosity and learning, and to provide new hypotheses about the fundamental role of curiosity in self-organizing developmental structures through curriculum learning. We present various developmental robotics experiments that study these mechanisms in action, both supporting these hypotheses and opening new research avenues in machine learning and artificial intelligence. Finally, we discuss challenges for the design of experimental paradigms for studying curiosity in psychology and cognitive neuroscience. Keywords: Curiosity, intrinsic motivation, lifelong learning, predictions, world model, rewards, free-energy principle, learning progress, machine learning, AI, developmental robotics, development, curriculum learning, self-organization.


title: Word Sense Disambiguation with Neural Language Models

abstract: Determining the intended sense of words in text -- word sense disambiguation (WSD) -- is a long-standing problem in natural language processing. In this paper, we present WSD algorithms which use neural network language models to achieve state-of-the-art precision. Each of these methods learns to disambiguate word senses using only a set of word senses, a few example sentences for each sense taken from a licensed lexicon, and a large unlabeled text corpus. We classify based on cosine similarity of vectors derived from the contexts in unlabeled query and labeled example sentences. We demonstrate state-of-the-art results when using the WordNet sense inventory, and significantly better than baseline performance using the New Oxford American Dictionary inventory. The best performance was achieved by combining an LSTM language model with graph label propagation.


title: Understanding Image Representations by Measuring Their Equivariance and Equivalence

abstract: Despite the importance of image representations such as histograms of oriented gradients and deep Convolutional Neural Networks (CNN), our theoretical understanding of them remains limited. Aiming at filling this gap, we investigate three key mathematical properties of representations: equivariance, invariance, and equivalence. Equivariance studies how transformations of the input image are encoded by the representation, invariance being a special case where a transformation has no effect. Equivalence studies whether two representations, for example two different parametrisations of a CNN, capture the same visual information or not. A number of methods to establish these properties empirically are proposed, including introducing transformation and stitching layers in CNNs. These methods are then applied to popular representations to reveal insightful aspects of their structure, including clarifying at which layers in a CNN certain geometric invariances are achieved. While the focus of the paper is theoretical, direct applications to structured-output regression are demonstrated too.


title: Solving Sudoku with genetic operations that preserve building blocks

abstract: Genetic operations that consider effective building blocks are proposed for using genetic algorithms to solve Sudoku puzzles. A stronger local search function is also proposed. Evaluation of the proposed techniques using commercial Sudoku puzzle sets and three puzzles ranked as super difficult compared with previously reported examples show that the rate of optimum solutions can be greatly improved. It is demonstrated that even further improvement in accuracy is expected from a correction technique based on disparity hypothesis.


title: Gaps in Performance between the Levels of Inter-corporate Relationships in Port Logistics Processes

abstract: Abstract The objective of this research is to verify gaps in firm performance between the levels of relationships between international freight forwarders and port logistics service providers. To achieve the objective, this research developed the conceptual and operational definitions of the variables through prior research and the questionnaire was sent to the forwarders. The collected data were analyzed by cluster analysis, analysis of variance (ANOVA) and analysis of covariance (ANCOVA). The results are as follows. First, the high level of the inter-corporate collaboration is based on the high level of customer performance. In this regard, the forwarders have the need of operational collaboration with port logistics service providers and as a result, they achieve a high level of customer performance. Second, the high level of the inter-corporate commitment is connected to the high level of firm performance. The forwarders provide shippers with the quality of customer service through commitment with port logistics service providers and this is the cause of firm performance. Third, the high level of the inter-corporate communication is based on a gap in customer performance. The forwarders can achieve the needs of shippers through communication with port logistics service providers and this is connected with a high level of customer performance.


title: Identification of signal peptide domain SOST mutations in autosomal dominant craniodiaphyseal dysplasia

abstract: Sclerosteosis and Van Buchem disease are related recessive sclerosing bone dysplasias caused by alterations in the SOST gene. We tested the hypothesis that craniodiaphyseal dysplasia (CDD) (MIM 122860), an extremely rare sclerosing bone dysplasia resulting facial distortion referred to as "leontiasis ossea", could also be caused by SOST mutations. We discovered mutations c.61G>A (Val21Met) and c.61G>T (Val21Leu) two children with CDD. As these mutations are located in the secretion signal of the SOST gene, we tested their effect on secretion by transfecting the mutant constructs into 293E cells. Intriguingly, these mutations greatly reduced the secretion of SOST. We conclude that CDD, the most severe form of sclerotic bone disease, is part of a spectrum of disease caused by mutations in SOST. Unlike the other SOST-related conditions, sclerosteosis and Van Buchem disease that are inherited as recessive traits seem to be caused by a dominant negative mechanism.


title: Stability analysis of robot manipulators subject to feedforward neural network controllers

abstract: In this paper a new approach, based on Lyapunov's direct method, for the design of multilayer feedforward neural network (NN) controllers for uncertain robot manipulators is presented. Furthermore, two different feedforward NN controllers are proposed to stabilize uncertain robot manipulators. The first NN controller is shown to render the closed-loop system globally practically stable while the second NN controller guarantees global uniform asymptotic stability of the system. The new approach ensures stability of the control system without using any learning or adaptive algorithm. Moreover, using nonlinear control theory, the proposed approach to neural networks provides sufficient conditions for determining the number of hidden layers, the dimension of neurons, the architecture of the neural network and the weights among the layers in order to guarantee stability of the system. The theoretical results are illustrated by application to a two-link manipulator. >


title: Cryptanalysis of Block Ciphers with Overdefined Systems of Equations

abstract: Several recently proposed ciphers, for example Rijndael and Serpent, are built with layers of small S-boxes interconnected by linear key-dependent layers. Their security relies on the fact, that the classical methods of cryptanalysis (e.g. linear or differential attacks) are based on probabilistic characteristics, which makes their security grow exponentially with the number of rounds N r . In this paper we study the security of such ciphers under an additional hypothesis: the S-box can be described by an overdefined system of algebraic equations (true with probability 1). We show that this is true for both Serpent (due to a small size of S-boxes) and Rijndael (due to unexpected algebraic properties). We study general methods known for solving overdefined systems of equations, such as XL from Eurocrypt'00, and show their inefficiency. Then we introduce a new method called XSL that uses the sparsity of the equations and their specific structure. The XSL attack uses only relations true with probability 1, and thus the security does not have to grow exponentially in the number of rounds. XSL has a parameter P, and from our estimations is seems that P should be a constant or grow very slowly with the number of rounds. The XSL attack would then be polynomial (or subexponential) in N r , with a huge constant that is double-exponential in the size of the S-box. The exact complexity of such attacks is not known due to the redundant equations. Though the presented version of the XSL attack always gives always more than the exhaustive search for Rijndael, it seems to (marginally) break 256-bit Serpent. We suggest a new criterion for design of S-boxes in block ciphers: they should not be describable by a system of polynomial equations that is too small or too overdefined.


title: Structured Labels in Random Forests for Semantic Labelling and Object Detection

abstract: Ensembles of randomized decision trees, known as Random Forests, have become a valuable machine learning tool for addressing many computer vision problems. Despite their popularity, few works have tried to exploit contextual and structural information in random forests in order to improve their performance. In this paper, we propose a simple and effective way to integrate contextual information in random forests, which is typically reflected in the structured output space of complex problems like semantic image labelling. Our paper has several contributions: We show how random forests can be augmented with structured label information and be used to deliver structured low-level predictions. The learning task is carried out by employing a novel split function evaluation criterion that exploits the joint distribution observed in the structured label space. This allows the forest to learn typical label transitions between object classes and avoid locally implausible label configurations. We provide two approaches for integrating the structured output predictions obtained at a local level from the forest into a concise, global, semantic labelling. We integrate our new ideas also in the Hough-forest framework with the view of exploiting contextual information at the classification level to improve the performance on the task of object detection. Finally, we provide experimental evidence for the effectiveness of our approach on different tasks: Semantic image labelling on the challenging MSRCv2 and CamVid databases, reconstruction of occluded handwritten Chinese characters on the Kaist database and pedestrian detection on the TU Darmstadt databases.


title: Young Children's Interpretation of Multidigit Number Names: From Emerging Competence to Mastery

abstract: This study assessed whether a sample of two hundred seven 3- to 7-year-olds could interpret multidigit numerals using simple identification and comparison tasks. Contrary to the view that young children do not understand place value, even 3-year-olds demonstrated some competence on these tasks. Ceiling was reached by first grade. When training was provided, there were significant gains, suggesting that children can improve their partial understandings with input. Findings add to what is known about the processes of symbolic development and the incidental learning that occurs prior to schooling, as well as specifying more precisely what place value misconceptions remain as children enter the educational system.


Music and Emotions Research on the Theory of Musical Equilibration ( die Strebetendenz-Theorie )


title: Dual band dual polarized conformai microstrip array for base stations

abstract: A new design of a 1×8 dual-band dual-polarized microstrip conformal array antenna, for GSM900/DCS1800 MHz Base Stations with the requirement of polarization diversity, is presented. The series feeding technique is used for feeding the array which is composed of aperture coupled microstrip antenna elements. The simulation results of conformal array antenna show a VSWR< 2 and a gain of about 17dBi at both GSM (890–960 MHz) and DCS (1710–1880 MHz) bands. It also provides a good port decoupling, less than −30dB over its operating bands. One element of planar array is fabricated and measured and a good agreement between simulation and measurement results is achieved.


title: A Systematic Method for Designing a PR Controller and Active Damping of the LCL Filter for Single-Phase Grid-Connected PV Inverters

abstract: The Proportional Resonant (PR) current controller provides gains at a certain frequency (resonant frequency) and eliminates steady state errors. Therefore, the PR controller can be successfully applied to single grid-connected PV inverter current control. On the contrary, a PI controller has steady-state errors and limited disturbance rejection capability. Compared with the L- and LC filters, the LCL filter has excellent harmonic suppression capability, but the inherent resonant peak of the LCL filter may introduce instability in the whole system. Therefore, damping must be introduced to improve the control of the system. Considering the controller and the LCL filter active damping as a whole system makes the controller design method more complex. In fact, their frequency responses may affect each other. The traditional trial-and-error procedure is too time-consuming and the design process is inefficient. This paper provides a detailed analysis of the frequency response influence between the PR controller and the LCL filter regarded as a whole system. In addition, the paper presents a systematic method for designing controller parameters and the capacitor current feedback coefficient factor of LCL filter active-damping. The new method relies on meeting the stable margins of the system. Moreover, the paper also clarifies the impact of the grid on the inverter output current. Numerical simulation and a 3 kW laboratory setup assessed the feasibility and effectiveness of the proposed method.


title: Cleft lip and palate: current management

abstract: Cleft lip and palate are the most common presenting congenital conditions of the face and cranial bones. This article describes current understanding of the aetiology and presentation of the deformity and management of the child from prenatal diagnosis until maturity. Principle concerns include correction of the physical defect with the best possible functional and cosmetic outcome, optimal speech correction, satisfactory feeding and hearing, and dental and orthodontic health. The value of comprehensive management of all aspects of care within a multidisciplinary team including clinical psychology support for child and family is discussed.


title: Research on Distributed Integrated Modular Avionics system architecture design and implementation

abstract: A well-designed Distributed Integrated Modular Avionics (DIMA) should keep the complexity of system interconnections to a minimum with the constraints set by communication links, LRM size, and fault tolerance requirements. In this paper, a basic DIMA model is put forward and the design philosophy is detailed for constructing DIMA system framework which enhanced capabilities with improved reliability and lower cost. The paper analyzes the system framework model, ability and effectiveness, discusses the organization mode, management mode and process scheduling management mode of DIAM system, and created an organization and management mode for DIMA system architecture. Furthermore, the key enable technologies for DIMA system implementation are analyzed which can guide implementing DIMA system.


title: Multi-Cell High-Current Rectifier

abstract: A multicell rectifier (MC) structure with N + 2 redundancy is presented. The topology is based on power cells implemented with the integrated gate commuted thyristors (IGCTs) to challenge the SCR standard industry solution for the past 35 years. This rectifier is a reliable, compact, efficient, nonpolluting alternative and cost-effective solution for electrolytic applications. Its structure, based on power cells, enables load shedding to ensure power delivery even in the event of power cell failures. It injects quasi-sinusoidal input currents and provides unity power factor without the use of passive or active filters. A complete evaluation based on IEEE standards 493-1997 and IEEE C57.18.10 for average downtime, failures rates, and efficiency is included. For comparison purposes, results are shown against conventional systems known for their high efficiency and reliability.


title: Strategic Orientation of the Firm and New Product Performance

abstract: The authors seek to understand which of three different strategic orientations of the firm (customer, competitive, and technological) is more appropriate, when, and why it is so in the context of d...


title: 3D Menagerie: Modeling the 3D Shape and Pose of Animals

abstract: There has been significant work on learning realistic, articulated, 3D models of the human body. In contrast, there are few such models of animals, despite many applications. The main challenge is that animals are much less cooperative than humans. The best human body models are learned from thousands of 3D scans of people in specific poses, which is infeasible with live animals. Consequently, we learn our model from a small set of 3D scans of toy figurines in arbitrary poses. We employ a novel part-based shape model to compute an initial registration to the scans. We then normalize their pose, learn a statistical shape model, and refine the registrations and the model together. In this way, we accurately align animal scans from different quadruped families with very different shapes and poses. With the registration to a common template we learn a shape space representing animals including lions, cats, dogs, horses, cows and hippos. Animal shapes can be sampled from the model, posed, animated, and fit to data. We demonstrate generalization by fitting it to images of real animals including species not seen in training.


title: Deep Association Learning for Unsupervised Video Person Re-identification

abstract: Deep learning methods have started to dominate the research progress of video-based person re-identification (re-id). However, existing methods mostly consider supervised learning, which requires exhaustive manual efforts for labelling cross-view pairwise data. Therefore, they severely lack scalability and practicality in real-world video surveillance applications. In this work, to address the video person re-id task, we formulate a novel Deep Association Learning (DAL) scheme, the first end-to-end deep learning method using none of the identity labels in model initialisation and training. DAL learns a deep re-id matching model by jointly optimising two margin-based association losses in an end-to-end manner, which effectively constrains the association of each frame to the best-matched intra-camera representation and cross-camera representation. Existing standard CNNs can be readily employed within our DAL scheme. Experiment results demonstrate that our proposed DAL significantly outperforms current state-of-the-art unsupervised video person re-id methods on three benchmarks: PRID 2011, iLIDS-VID and MARS.


Mapping the Medical Device Development Process


Long-term Child-Robot Tutoring Interactions : Lessons Learned


title: IoT system for monitoring vital signs of elderly population

abstract: The leading cause of death of elderly Europeans (over 65 years) is heart diseases [1]. Adding up to that, there is also the fact that 30% of elderly people encounter unintentional falls each year, and most of them are fatal [2]. Our paper describes the concept and architecture of a system which is able to monitor the body's vital signs, pre-process the data, generate emergency alerts and use the power of cloud machine learning to predict medical states.


Churn Prediction


title: Dynamic Clustering of Streaming Short Documents

abstract: Clustering technology has found numerous applications in mining textual data. It was shown to enhance the performance of retrieval systems in various different ways, such as identifying different query aspects in search result diversification, improving smoothing in the context of language modeling, matching queries with documents in a latent topic space in ad-hoc retrieval, summarizing documents etc. The vast majority of clustering methods have been developed under the assumption of a static corpus of long (and hence textually rich) documents. Little attention has been given to streaming corpora of short text, which is the predominant type of data in Web 2.0 applications, such as social media, forums, and blogs. In this paper, we consider the problem of dynamically clustering a streaming corpus of short documents. The short length of documents makes the inference of the latent topic distribution challenging, while the temporal dynamics of streams allow topic distributions to change over time. To tackle these two challenges we propose a new dynamic clustering topic model - DCT - that enables tracking the time-varying distributions of topics over documents and words over topics. DCT models temporal dynamics by a short-term or long-term dependency model over sequential data, and overcomes the difficulty of handling short text by assigning a single topic to each short document and using the distributions inferred at a certain point in time as priors for the next inference, allowing the aggregation of information. At the same time, taking a Bayesian approach allows evidence obtained from new streaming documents to change the topic distribution. Our experimental results demonstrate that the proposed clustering algorithm outperforms state-of-the-art dynamic and non-dynamic clustering topic models in terms of perplexity and when integrated in a cluster-based query likelihood model it also outperforms state-of-the-art models in terms of retrieval quality.


title: A real time system for robust 3D voxel reconstruction of human motions

abstract: We present a multi-PC/camera system that can perform 3D reconstruction and ellipsoid fitting of moving humans in real time. The system consists of five cameras. Each camera is connected to a PC which locally extracts the silhouettes of the moving person in the image captured by the camera. The five silhouette images are then sent, via local network, to a host computer to perform 3D voxel-based reconstruction by an algorithm called SPOT. Ellipsoids are then used to fit the reconstructed data. By using a simple and user-friendly interface, the user can display and observe, in real time and from any view-point, the 3D models of the moving human body. With a rate of higher than 15 frames per second, the system is able to capture non-intrusively, a sequence of human motions.


title: Passivation and chloride-induced corrosion of a duplex alloy steel in alkali-activated slag extract solutions

abstract: Abstract In this study, the passivation and chloride-induced corrosion behavior of a novel duplex alloy steel (00Cr10MoV) and low-carbon (LC) steel in ordinary Portland cement (OPC) extract solution and alkali-activated slag (AAS) extract solution were evaluated by electrochemical measurements and corrosion morphology observation. Seawater corrosion-resistant 00Cr10MoV steel with the addition of alloying elements Cr and Mo was designed to be used in concrete structures exposed to the tidal zone. The results show that 00Cr10MoV steel exhibits significantly higher corrosion resistance than LC steel in OPC extract with chlorides due to the formation of a more protective passive film. However, the high corrosion resistance of 00Cr10MoV steel is less pronounced in AAS extract with chlorides. Accordingly, this duplex alloy steel is not recommended to be utilized in combination with AAS materials in marine environments.


title: A hierarchical clustering algorithm based on the hungarian method

abstract: We propose a novel hierarchical clustering algorithm for data-sets in which only pairwise distances between the points are provided. The classical Hungarian method is an efficient algorithm for solving the problem of minimal-weight cycle cover. We utilize the Hungarian method as the basic building block of our clustering algorithm. The disjoint cycles, produced by the Hungarian method, are viewed as a partition of the data-set. The clustering algorithm is formed by hierarchical merging. The proposed algorithm can handle data that is arranged in non-convex sets. The number of the clusters is automatically found as part of the clustering process. We report an improved performance of our algorithm in a variety of examples and compare it to the spectral clustering algorithm.


adapTaTion of The Trier soCial sTress TesT To VirTual realiTy : psyCho-phsyiologiCal and neuroendoCrine modulaTion


title: The Sensory Processing Measure–Preschool (SPM-P)—Part One: Description of the Tool and Its Use in the Preschool Environment

abstract: School-based and early intervention occupational therapy evaluation is influenced by federal policy and prevailing ideas regarding best practice. Current best practice considers a child's performance in appropriate contexts and natural environments, as well as the impact of the environment on occupational functioning and participation across settings. As a recent report suggested 3.4% to 15.6% of children in a community sample of 4-year-olds may demonstrate sensory processing difficulties, occupational therapists should be familiar with multiple methods of assessment to address these concerns. One such assessment tool, the Sensory Processing Measure–Preschool (SPM-P) for children 2- to 5-years old, provides 8 scaled scores: Vision, Hearing, Touch, Body Awareness, Balance and Motion, Total Sensory System Score, Planning and Ideas, and Social Participation. Through discussion on the development, usage, and implementation of both Home and School forms, this article illustrates how the SPM-P provides a mechan...


title: Why did my car just do that? Explaining semi-autonomous driving actions to improve driver understanding, trust, and performance

abstract: This study explores, in the context of semi-autonomous driving, how the content of the verbalized message accompanying the car’s autonomous action affects the driver’s attitude and safety performance. Using a driving simulator with an auto-braking function, we tested different messages that provided advance explanation of the car’s imminent autonomous action. Messages providing only “how” information describing actions (e.g., “The car is braking”) led to poor driving performance, whereas “why” information describing reasoning for actions (e.g., “Obstacle ahead”) was preferred by drivers and led to better driving performance. Providing both “how and why” resulted in the safest driving performance but increased negative feelings in drivers. These results suggest that, to increase overall safety, car makers need to attend not only to the design of autonomous actions but also to the right way to explain these actions to the drivers.


Diagnosis and management of mucogingival problems in children.


title: Pareto Optimal Security Resource Allocation for Internet of Things

abstract: In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is needed to ensure the proper functioning of such systems even under attack. However, it is also critical that security be at a reasonable resource and energy cost. In this article, we focus on the problem of efficiently and effectively securing IoT networks by carefully allocating security resources in the network area. In particular, given a set of security resources R and a set of attacks to be faced A, our method chooses the subset of R that best addresses the attacks in A, and the set of locations where to place them, that ensure the security coverage of all IoT devices at minimum cost and energy consumption. We model our problem according to game theory and provide a Pareto-optimal solution in which the cost of the security infrastructure, its energy consumption, and the probability of a successful attack are minimized. Our experimental evaluation shows that our technique improves the system robustness in terms of packet delivery rate for different network topologies. Furthermore, we also provide a method for handling the computation of the resource allocation plan for large-scale networks scenarios, where the optimization problem may require an unreasonable amount of time to be solved. We show how our proposed method drastically reduces the computing time, while providing a reasonable approximation of the optimal solution.


title: Part-Of-Speech Tagging In Context

abstract: We present a new HMM tagger that exploits context on both sides of a word to be tagged, and evaluate it in both the unsupervised and supervised case. Along the way, we present the first comprehensive comparison of unsupervised methods for part-of-speech tagging, noting that published results to date have not been comparable across corpora or lexicons. Observing that the quality of the lexicon greatly impacts the accuracy that can be achieved by the algorithms, we present a method of HMM training that improves accuracy when training of lexical probabilities is unstable. Finally, we show how this new tagger achieves state-of-the-art results in a supervised, non-training intensive framework.


title: Barriers to evidence-based practice in primary care nursing - why viewing decision-making as context is helpful

abstract: AIM ::: This paper reports a study examining the barriers associated with research knowledge transfer amongst primary care nurses in the context of clinical decision-making. ::: ::: ::: BACKGROUND ::: The research literature on barriers to nurses' use of research knowledge is characterized by studies that rely primarily on self-report data, making them prone to reporting biases. Studies of the barriers to evidence-based practice often fail to examine information use and behaviour in the context of clinical decision-making. ::: ::: ::: METHODS ::: A multi-site, mixed method, case study was carried out in 2001. Data were collected in three primary care organizations by means of interviews with 82 primary care nurses, 270 hours of non-participant observation and 122 Q-sorts. Nurses were selected using a published theoretical sampling frame. Between-methods triangulation was employed and data analysed according to the principles of constant comparison. Multiple linear regression was used to explore relationships between a number of independent demographic variables (such as length of clinical experience) and the dependent variable of nurses' perspectives on the barriers to their use of research knowledge. ::: ::: ::: RESULTS ::: Three perspectives on barriers to research information use emerged: the need to bridge the skills and knowledge gap for successful knowledge transfer; information formats need to maximize limited opportunities for consumption; and limited access in the context of limited time for decision-making and information consumption. Demographic variables largely failed to predict allegiance to any of the perspectives identified. ::: ::: ::: CONCLUSIONS ::: Researchers should consider using decision-making as a contextual backdrop for exploring information use and behaviour, avoid relying solely on self-reported behaviour as data, and use a variety of research methods to provide a richer picture of information-related behaviour. Practice developers need to recognize that understanding the decisions to which research knowledge is to be applied should be a characteristic of any strategy to increase research uptake by nurses.


title: SemEval-2015 Task 13: Multilingual All-Words Sense Disambiguation and Entity Linking

abstract: In this paper we present the Multilingual AllWords Sense Disambiguation and Entity Linking task. Word Sense Disambiguation (WSD) and Entity Linking (EL) are well-known problems in the Natural Language Processing field and both address the lexical ambiguity of language. Their main difference lies in the kind of meaning inventories that are used: EL uses encyclopedic knowledge, while WSD uses lexicographic information. Our aim with this task is to analyze whether, and if so, how, using a resource that integrates both kinds of inventories (i.e., BabelNet 2.5.1) might enable WSD and EL to be solved by means of similar (even, the same) methods. Moreover, we investigate this task in a multilingual setting and for some specific domains.


title: Watermarking-based copyright protection of sequential functions

abstract: Watermarking is one of several techniques available today to deter copyright infringement in electronic systems. The technique consists of implanting indelible stamps in the circuit's inner structure, while not disrupting its functionality nor degrading its performance significantly. In this paper, a novel method is proposed for the creation of watermarks in regular sequential functions. This is an important class of functions, as it is the basis of most digital controllers. Algorithms are proposed for implanting robust watermarks to minimize the overhead and, ultimately, to reduce the impact on performance. Detection methods have been discussed in the presence of infringement attacks. The resilience of the method in several tampering regimes has been estimated. Examples illustrate the suitability of the approach.


title: Guided Proofreading of Automatic Segmentations for Connectomics

abstract: Automatic cell image segmentation methods in connectomics produce merge and split errors, which require correction through proofreading. Previous research has identified the visual search for these errors as the bottleneck in interactive proofreading. To aid error correction, we develop two classifiers that automatically recommend candidate merges and splits to the user. These classifiers use a convolutional neural network (CNN) that has been trained with errors in automatic segmentations against expert-labeled ground truth. Our classifiers detect potentially-erroneous regions by considering a large context region around a segmentation boundary. Corrections can then be performed by a user with yes/no decisions, which reduces variation of information 7.5A— faster than previous proofreading methods. We also present a fully-automatic mode that uses a probability threshold to make merge/split decisions. Extensive experiments using the automatic approach and comparing performance of novice and expert users demonstrate that our method performs favorably against state-of-the-art proofreading methods on different connectomics datasets.


TECHNOLOGY USE , EMOTIONAL CONNECTION AND THEIR RELATIONSHIP : A LITERATURE REVIEW


title: Automated Test Oracles: A Survey

abstract: Abstract Software testing is an essential activity of software development, and oracles are a key pillar of testing. The increasing size of test suites, the growing availability of test case generators that produce enormous amount of test cases, and the repeated execution of large amounts of test cases require automated oracles. Although studied since the late 1970s, in the last decade, test oracles and techniques to automatically generate test oracles have attracted a lot of attention and have witnessed an impressive growth. In this chapter, we survey test oracles with particular attention to their automation. We overview the main evolution of the technology, and we propose some criteria to classify the different approaches. We present the main techniques to automatically generate test oracles classified according to the required information, and we discuss different forms of checkable oracles, defined as oracles expressed in a form directly checkable during the system execution.


title: Kernel Graph Convolutional Neural Networks

abstract: Graph kernels have been successfully applied to many graph classification problems. Typically, a kernel is first designed, and then an SVM classifier is trained based on the features defined implicitly by this kernel. This two-stage approach decouples data representation from learning, which is suboptimal. On the other hand, Convolutional Neural Networks (CNNs) have the capability to learn their own features directly from the raw data during training. Unfortunately, they cannot handle irregular data such as graphs. We address this challenge by using graph kernels to embed meaningful local neighborhoods of the graphs in a continuous vector space. A set of filters is then convolved with these patches, pooled, and the output is then passed to a feedforward network. With limited parameter tuning, our approach outperforms strong baselines on 7 out of 10 benchmark datasets.


title: Nutritional Aspects of Depression

abstract: Several nutrition, food and dietary compounds have been suggested to be involved in the onset and maintenance of depressive disorders and in the severity of depressive symptoms. Nutritional compounds might modulate depression associated biomarkers and parallel the development of depression, obesity and diabetes. In this context, recent studies revealed new mediators of both energy homeostasis and mood changes (i.e. IGF-1, NPY, BDNF, ghrelin, leptin, CCK, GLP-1, AGE, glucose metabolism and microbiota) acting in gut brain circuits. In this context several healthy foods such as olive oil, fish, fruits, vegetables, nuts, legumes, poultry, dairy and unprocessed meat have been inversely associated with depression risk and even have been postulated to improve depressive symptoms. In contrast, unhealthy western dietary patterns including the consumption of sweetened beverage, refined food, fried food, processed meat, refined grain, and high fat diary, biscuits, snacking and pastries have been shown to be associated with an increased risk of depression in longitudinal studies. However, it is always difficult to conclude a real prospective causal relationship from these mostly retrospective studies as depressed individuals might also change their eating habits secondarily to their depression. Additionally specific selected nutritional compounds, e.g. calcium, chromium, folate, PUFAs, vitamin D, B12, zinc, magnesium and D-serine have been postulated to be used as ad-on strategies in antidepressant treatment. In this context, dietary and lifestyle interventions may be a desirable, effective, pragmatical and non-stigmatizing prevention and treatment strategy for depression. At last, several medications (pioglitazone, metformin, exenatide, atorvastatin, gram-negative antibiotics), which have traditionally been used to treat metabolic disorders showed a certain potential to treat depression in first randomized controlled clinical trials.


title: Improved Local Search in Artificial Bee Colony using Golden Section Search

abstract: Artificial bee colony (ABC), an optimization algorithm is a recent addition to the family of population based search algorithm. ABC has taken its inspiration from the collective intelligent foraging behavior of honey bees. In this study we have incorporated golden section search mechanism in the structure of basic ABC to improve the global convergence and prevent to stick on a local solution. The proposed variant is termed as ILS-ABC. Comparative numerical results with the state-of-art algorithms show the performance of the proposal when applied to the set of unconstrained engineering design problems. The simulated results show that the proposed variant can be successfully applied to solve real life problems.


title: Impact of tinnitus as measured by the Tinnitus Handicap Inventory among tinnitus sufferers in Singapore.

abstract: INTRODUCTION ::: The effects of tinnitus on quality of life (QOL) have never been extensively studied in Singapore. We describe the characteristics of tinnitus and its impact on QOL as measured by the Tinnitus Handicap Inventory (THI) in a series of ear, nose and throat clinic patients. ::: ::: ::: METHODS ::: A total of 327 patients who attended a tinnitus counselling clinic completed the THI questionnaire, a self-report measure with 25 items grouped into functional, emotional and catastrophic subscales. ::: ::: ::: RESULTS ::: The mean age of the 134 female and 193 male patients was 48.9 years. 36.7 percent of these patients had bilateral tinnitus and 64.6 percent had symptoms for less than one year. 270 patients had hearing loss, 74 percent of whom presented with bilateral high frequency hearing loss. Most patients (84.1 percent) perceived only one type of sound. The total THI score distribution was: 107 (33 percent) patients had THI less than 16, 100 (31 percent) had THI 18 to 36, 59 (18 percent) had THI 38 to 56, and 61 (19 percent) had THI more than 58. There were no differences in the overall THI and subscale scores between the patients' gender, those with or without hearing loss, and those with unilateral or bilateral tinnitus. However, significantly higher total THI and all subscale scores were found among patients who were hearing more than one type of tinnitus sound. The areas of concern that were commonly reported by the patients in this series were a lack of control over tinnitus, frustration and stress. ::: ::: ::: CONCLUSION ::: Tinnitus patients who hear multiple sounds tend to have a higher THI and subscale scores. The management of tinnitus should address common areas of concern, and may include counselling. The THI is a potential screening tool to determine if patients require counselling. A series of THI assessments can be used to chart the progress of treatment.


title: Identification and Evaluation of Discriminative Lexical Features of Malware URL for Real-Time Classification

abstract: This study identifies and evaluates discriminative lexical features of malware URLs for building a real-time malware URL classification. The lexical features of malware URL are first identified from existing blacklisted malware URLs through manual examination. Feature identification is followed by studying the prevalence of these features on newly collected malware URLs through empirical analysis. Our empirical analysis revealed that attackers follow the same pattern in crafting malware URL. To evaluate the performance and effectiveness of these features, we applied a Support Vector Machine (SVM) classification algorithm on a dataset comprising of benign and malware URLs. By applying the WEKA data mining tool on our trained dataset, a 96.95 % accuracy was achieved with a low False Negative Rate (FNR) of 0.018 and a moderate False Positive Rate (FPR) of 0.046.


title: Temperature-aware microarchitecture

abstract: With power density and hence cooling costs rising exponentially, processor packaging can no longer be designed for the worst case, and there is an urgent need for runtime processor-level techniques that can regulate operating temperature when the package's capacity is exceeded. Evaluating such techniques, however, requires a thermal model that is practical for architectural studies.This paper describes HotSpot, an accurate yet fast model based on an equivalent circuit of thermal resistances and capacitances that correspond to microarchitecture blocks and essential aspects of the thermal package. Validation was performed using finite-element simulation. The paper also introduces several effective methods for dynamic thermal management (DTM): "temperature-tracking" frequency scaling, localized toggling, and migrating computation to spare hardware units. Modeling temperature at the microarchitecture level also shows that power metrics are poor predictors of temperature, and that sensor imprecision has a substantial impact on the performance of DTM.


title: Sonography of the iliopsoas tendon and injection of the iliopsoas Bursa for diagnosis and management of the painful snapping hip

abstract: Objective ::: The purpose of this study was to compare sonographic evaluations of patients referred with suspected snapping of their iliopsoas tendon with the pain relief achieved from anesthetic injection of the iliopsoas bursa, and with the subsequent surgical outcome. This study also assessed the effectiveness of Kenalog injection into the iliopsoas bursa for long-term pain relief.


title: Design and Analysis of Real-time Network Intrusion Detection and Prevention System using Open Source Tools

abstract: detection and prevention is one of the most important and fundamental task in an organization's computer network. Commercially available intrusion detection and prevention systems are costly and overkill for small and medium sized organizations. This paper describes the design and analysis of a network intrusion detection system (NIDS) and network intrusion prevention system (NIPS) using open source tools. The study also describes an open source Database to store the alerts and an open source front end management console application to view the alerts and logs from the proposed Database in any of the modern day web browser. In this particular research Snort was used as an NIDS to detect intrusions and attacks. Snort is a popular open source NIDS with signature based rules for detecting thousands of known attacks. The rules are regularly updated by Snort team to include new attacks and intrusions. SnortSam was used as an NIPS to act upon the alerts detected by Snort. SnortSam blocks the intrusions by sending intruders and attacker's source IP addresses to firewall in real time. MySQL was used as the Database to store alerts and BASE (Basic Analysis and Security Engine) was chosen as the open source management console application. Juniper Networks switch EX-3200 and Firewall SSG-20 were used as the network devices for connectivity and working of the system. Any other vendor network devices can also effectively be used in design and configuration of the system. The design successfully detected and prevented network intrusions and same can be implemented in any small and medium sized organization for protection of their Computer Networks.


title: Minimizing the Communication Cost of Aggregation in Publish/Subscribe Systems

abstract: Modern applications for distributed publish/subscribe systems often require stream aggregation capabilities along with rich data filtering. When compared to other distributed systems, aggregation in pub/sub differentiates itself as a complex problem which involves dynamic dissemination paths that are difficult to predict and optimize for a priori, temporal fluctuations in publication rates, and the mixed presence of aggregated and non-aggregated workloads. In this paper, we propose a formalization for the problem of minimizing communication traffic in the context of aggregation in pub/sub. We present a solution to this minimization problem by using a reduction to the well-known problem of minimum vertex cover in a bipartite graph. This solution is optimal under the strong assumption of complete knowledge of future publications. We call the resulting algorithm "Aggregation Decision, Optimal with Complete Knowledge" (ADOCK). We also show that under a dynamic setting without full knowledge, ADOCK can still be applied to produce a low, yet not necessarily optimal, communication cost. We also devise a computationally cheaper dynamic approach called "Aggregation Decision with Weighted Publication" (WAD). We compare our solutions experimentally using two real datasets and explore the trade-offs with respect to communication and computation costs.


title: Argumentation in artificial intelligence

abstract: Over the last ten years, argumentation has come to be increasingly central as a core study within Artificial Intelligence (AI). The articles forming this volume reflect a variety of important trends, developments, and applications covering a range of current topics relating to the theory and applications of argumentation. Our aims in this introduction are, firstly, to place these contributions in the context of the historical foundations of argumentation in AI and, subsequently, to discuss a number of themes that have emerged in recent years resulting in a significant broadening of the areas in which argumentation based methods are used. We begin by presenting a brief overview of the issues of interest within the classical study of argumentation: in particular, its relationship-in terms of both similarities and important differences-to traditional concepts of logical reasoning and mathematical proof. We continue by outlining how a number of foundational contributions provided the basis for the formulation of argumentation models and their promotion in AI related settings and then consider a number of new themes that have emerged in recent years, many of which provide the principal topics of the research presented in this volume.


title: AA-Sort: A New Parallel Sorting Algorithm for Multi-Core SIMD Processors

abstract: Many sorting algorithms have been studied in the past, but there are only a few algorithms that can effectively exploit both SIMD instructions and thread-level parallelism. In this paper, we propose a new parallel sorting algorithm, called aligned-access sort (AA-sort), for shared-memory multi processors. The AA-sort algorithm takes advantage of SIMD instructions. The key to high performance is eliminating unaligned memory accesses that would reduce the effectiveness of SIMD instructions. We implemented and evaluated the AA-sort on PowerPCreg 970MP and Cell Broadband Enginetrade. In summary, a sequential version of the AA-sort using SIMD instructions outperformed IBM's optimized sequential sorting library by 1.8 times and GPUTeraSort using SIMD instructions by 3.3 times on PowerPC 970MP when sorting 32 M of random 32-bit integers. Furthermore, a parallel version of AA-sort demonstrated better scalability with increasing numbers of cores than a parallel version of GPUTeraSort on both platforms.


title: Accurate estimation of influenza epidemics using Google search data via ARGO

abstract: Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search–based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people’s online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions.


title: Extracting Drug-Drug Interactions with Word and Character-Level Recurrent Neural Networks

abstract: Drug-drug interactions (DDIs) are known to be responsible for nearly a third of all adverse drug reactions. Hence several current efforts focus on extracting signal from EMRs to prioritize DDIs that need further exploration. To this end, being able to extract explicit mentions of DDIs in free text narratives is an important task. In this paper, we explore recurrent neural network (RNN) architectures to detect and classify DDIs from unstructured text using the DDIExtraction dataset from the SemEval 2013 (task 9) shared task. Our methods are in line with those used in other recent deep learning efforts for relation extraction including DDI extraction. However, to our knowledge, we are the first to investigate the potential of character-level RNNs (Char-RNNs) for DDI extraction (and relation extraction in general). Furthermore, we explore a simple but effective model bootstrapping method to (a). build model averaging ensembles, (b). derive confidence intervals around mean micro-F scores (MMF), and (c). assess the average behavior of our methods. Without any rule based filtering of negative examples, a popular heuristic used by most earlier efforts, we achieve an MMF of 69.13. By adding simple replicable heuristics to filter negative instances we are able to achieve an MMF of 70.38. Furthermore, our best ensembles produce micro F-scores of 70.81 (without filtering) and 72.13 (with filtering), which are superior to metrics reported in published results. Although Char-RNNs turnout to be inferior to regular word based RNN models in overall comparisons, we find that ensembling models from both architectures results in nontrivial gains over simply using either alone, indicating that they complement each other.


title: "23andMe confirms: I'm super white"-- Analyzing Twitter Discourse On Genetic Testing

abstract: Recent progress in genomics is bringing genetic testing to the masses, as participatory public initiatives are well underway to sequence the genome of millions of volunteers, and a new market is booming with a number of companies like 23andMe and AncestryDNA offering affordable tests directly to consumers. Consequently, news, experiences, and views on genetic testing are increasingly shared and discussed online and on social networks like Twitter. ::: In this paper, we present a large-scale analysis of Twitter discourse on genetic testing. We collect 302K tweets from 113K users, posted over 2.5 years, by using thirteen keywords related to genetic testing companies and public initiatives as search keywords. We study both the tweets and the users posting them along several axes, aiming to understand who tweets about genetic testing, what they talk about, and how they use Twitter for that. Among other things, we find that tweets about genetic testing originate from accounts that overall appear to be interested in digital health and technology. Also, marketing efforts as well as announcements, such as the FDA's suspension of 23andMe's health reports, influence the type and the nature of user engagement. Finally, we report on users who share screenshots of their results, and raise a few ethical and societal questions as we find evidence of groups associating genetic testing to racist ideologies.


title: Law, Finance, and Economic Growth in China *

abstract: China is an important counterexample to the findings in the law, institutions, finance, and growth literature: Neither its legal nor financial system is well developed, yet it has one of the fastest growing economies. While the law–finance–growth nexus applies to the State Sector and the Listed Sector, with arguably poorer applicable legal and financial mechanisms, the Private Sector grows much faster than the others and provides most of the economy’s growth. The imbalance among the three sectors suggests that alternative financing channels and ARTICLE IN PRESS


title: Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics

abstract: We introduce a new scheme for molecular simulations, based on a many-body potential and interatomic forces generated by a deep neural network trained with ab initio data. We show that the proposed scheme, which we call Deep Potential Molecular Dynamics (DeePMD), provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DeePMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size. Moreover, in a few test cases, DeePMD shows good structural transferability to thermodynamic conditions not included in the original training data.


title: A case study of the in-class use of a video game for teaching high school history

abstract: This study examines the case of a sophomore high school history class where Making History, a video game designed with educational purposes in mind, is used in the classroom to teach about World War II. Data was gathered using observation, focus group and individual interviews, and document analysis. The high school was a rural school located in a small town in the Midwestern United States. The teacher had been teaching with the game for several years and spent one school week teaching World War II, with students playing the game in class for three days of that week. The purpose of this study was to understand teacher and student experiences with and perspectives on the in-class use of an educational video game. Results showed that the use of the video game resulted in a shift from a traditional teacher-centered learning environment to a student-centered environment where the students were much more active and engaged. Also, the teacher had evolved implementation strategies based on his past experiences using the game to maximize the focus on learning.


title: A fold-back current-limit circuit with load-insensitive quiescent current for CMOS low dropout regulator

abstract: A fold-back current-limit circuit, with load-insensitive quiescent current characteristic for CMOS low dropout regulator (LDO), is proposed in this paper. This method has been designed in 0.35 µm CMOS technology and verified by Hspice simulation. The quiescent current of the LDO is 5.7 µA at 100-mA load condition. It is only 2.2% more than it in no-load condition, 5.58 µA. The maximum current limit is set to be 197 mA, and the short-current limit is 77 mA. Thus, the power consumption can be saved up to 61% at the short-circuit condition, which also decreases the risk of damaging the power transistor. Moreover, the thermal protection can be simplified and the LDO will be more reliable.


title: Storage management and caching in PAST, a large-scale, persistent peer-to-peer storage utility

abstract: This paper presents and evaluates the storage management and caching in PAST, a large-scale peer-to-peer persistent storage utility. PAST is based on a self-organizing, Internet-based overlay network of storage nodes that cooperatively route file queries, store multiple replicas of files, and cache additional copies of popular files.In the PAST system, storage nodes and files are each assigned uniformly distributed identifiers, and replicas of a file are stored at nodes whose identifier matches most closely the file's identifier. This statistical assignment of files to storage nodes approximately balances the number of files stored on each node. However, non-uniform storage node capacities and file sizes require more explicit storage load balancing to permit graceful behavior under high global storage utilization; likewise, non-uniform popularity of files requires caching to minimize fetch distance and to balance the query load.We present and evaluate PAST, with an emphasis on its storage management and caching system. Extensive trace-driven experiments show that the system minimizes fetch distance, that it balances the query load for popular files, and that it displays graceful degradation of performance as the global storage utilization increases beyond 95%.


title: Essay-Anchor Attentive Multi-Modal Bilinear Pooling for Textbook Question Answering

abstract: Textbook Question Answering (TQA) [1] is a newly proposed task to answer arbitrary questions in middle school curricula, which has particular challenges to understand the long essays in additional to the images. Bilinear models [2], [3] are effective at learning high-level associations between questions and images, but are inefficient to handle the long essays. In this paper, we propose an Essay-anchor Attentive Multi-modal Bilinear pooling (EAMB), a novel method to encode the long essays into the joint space of the questions and images. The essay-anchors, embedded from the keywords, represent the essay information in a latent space. We propose a novel network architecture to pay special attention on the keywords in the questions, consequently encoding the essay information into the question features, and thus the joint space with the images. We then use the bilinear models to extract the multi-modal interactions to obtain the answers. EAMB successfully utilizes the redundancy of the pre-trained word embedding space to represent the essay-anchors. This avoids the extra learning difficulties from exploiting large network structures. Quantitative and qualitative experiments show the outperforming effects of EAMB on the TQA dataset.


title: KASLR is Dead: Long Live KASLR

abstract: Modern operating system kernels employ address space layout randomization (ASLR) to prevent control-flow hijacking attacks and code-injection attacks. While kernel security relies fundamentally on preventing access to address information, recent attacks have shown that the hardware directly leaks this information. Strictly splitting kernel space and user space has recently been proposed as a theoretical concept to close these side channels. However, this is not trivially possible due to architectural restrictions of the x86 platform.


title: Agent based Architecture for Modeling and Analysis of Self Adaptive Systems using Formal Methods

abstract: Self-adaptive systems (SAS) can modify their behavior during execution; this modification is done because of change in internal or external environment. The need for self-adaptive software systems has increased tremendously in last decade due to ever changing user requirements, improvement in technology and need for building software that reacts to user preferences. To build this type of software we need well establish models that have the flexibility to adjust to the new requirements and make sure that the adaptation is efficient and reliable. Feedback loop has proven to be very effective in modeling and developing SAS, these loops help the system to sense, analyze, plan, test and execute the adaptive behavior at runtime. Formal methods are well defined, rigorous and reliable mathematical techniques that can be effectively used to reason and specify behavior of SAS at design and run-time. Agents can play an important role in modeling SAS because they can work independently, with other agents and with environment as well. Using agents to perform individual steps in feedback loop and formalizing these agents using Petri nets will not only increase the reliability, but also, the adaptation can be performed efficiently for taking decisions at run time with increased confidence. In this paper, we propose a multi-agent framework to model self-adaptive systems using agent based modeling. This framework will help the researchers in implementation of SAS, which is more dependable, reliable, autonomic and flexible because of use of multi-agent based formal approach.


title: Efficient Gradient Boosted Decision Tree Training on GPUs

abstract: In this paper, we present a novel parallel implementation for training Gradient Boosting Decision Trees (GBDTs) on Graphics Processing Units (GPUs). Thanks to the wide use of the open sourced XGBoost library, GBDTs have become very popular in recent years and won many awards in machine learning and data mining competitions. Although GPUs have demonstrated their success in accelerating many machine learning applications, there are a series of key challenges of developing a GPU-based GBDT algorithm, including irregular memory accesses, many small sorting operations and varying data parallel granularities in tree construction. To tackle these challenges on GPUs, we propose various novel techniques (including Run-length Encoding compression and thread/block workload dynamic allocation, and reusing intermediate training results for efficient gradient computation). Our experimental results show that our algorithm named GPU-GBDT is often 10 to 20 times faster than the sequential version of XGBoost, and achieves 1.5 to 2 times speedup over a 40 threaded XGBoost running on a relatively high-end workstation of 20 CPU cores. Moreover, GPU-GBDT outperforms its CPU counterpart by 2 to 3 times in terms of performance-price ratio.


title: Coupling Adaptive Batch Sizes with Learning Rates

abstract: Mini-batch stochastic gradient descent and variants thereof have become standard for large-scale empirical risk minimization like the training of neural networks. These methods are usually used with a constant batch size chosen by simple empirical inspection. The batch size significantly influences the behavior of the stochastic optimization algorithm, though, since it determines the variance of the gradient estimates. This variance also changes over the optimization process; when using a constant batch size, stability and convergence is thus often enforced by means of a (manually tuned) decreasing learning rate schedule. ::: We propose a practical method for dynamic batch size adaptation. It estimates the variance of the stochastic gradients and adapts the batch size to decrease the variance proportionally to the value of the objective function, removing the need for the aforementioned learning rate decrease. In contrast to recent related work, our algorithm couples the batch size to the learning rate, directly reflecting the known relationship between the two. On popular image classification benchmarks, our batch size adaptation yields faster optimization convergence, while simultaneously simplifying learning rate tuning. A TensorFlow implementation is available.


title: Output and welfare implications of monopolistic third-degree price discrimination

abstract: Focuses on output and welfare implications of monopolistic third-degree price discrimination. Maximization of profits by charging different prices to different markets or classes for customers; Maldistribution of resources for different uses. (From Ebsco)


Super-Resolution Composition in Multi-Projector Displays


title: MOBILE MARKETING USING A LOCATION BASED

abstract: Considering the recent convergence of computing and telecommunications technologies with the tremendous success of the Internet, the World Wide Web, and Mobile Communications, the next step is expected to be the Mobile Web. The main promise of the Mobile Web is to satisfy user needs for anywhere, anytime access to information and services, including Location Based Services (LBS). This paper presents an innovative LBS service applicable to the Mobile Marketing industry sector. We describe the architecture of the information system supporting the proposed service and a software prototype we implemented using a simulation environment for providing location information. The technologies adopted for this design are based on open standards and have successfully met the requirements of scalability and autonomy.


Radical treatment of extensive nevoid hyperkeratosis of the areola and breast with surgical excision after mild response to topical agents: A case report


title: Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators

abstract: This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model the observed graph as a sample from a manifold endowed with a vector field, and we design an algorithm that separates and recovers the features of this process: the geometry of the manifold, the data density and the vector field. The algorithm is motivated by our analysis of Laplacian-type operators and their continuous limit as generators of diffusions on a manifold. We illustrate the recovery algorithm on both artificially constructed and real data.


title: Model-Based Clustering of High-Dimensional Data: A review

abstract: Model-based clustering is a popular tool which is renowned for its probabilistic foundations and its flexibility. However, high-dimensional data are nowadays more and more frequent and, unfortunately, classical model-based clustering techniques show a disappointing behavior in high-dimensional spaces. This is mainly due to the fact that model-based clustering methods are dramatically over-parametrized in this case. However, high-dimensional spaces have specific characteristics which are useful for clustering and recent techniques exploit those characteristics. After having recalled the bases of model-based clustering, dimension reduction approaches, regularization-based techniques, parsimonious modeling, subspace clustering methods and clustering methods based on variable selection are reviewed. Existing softwares for model-based clustering of high-dimensional data will be also reviewed and their practical use will be illustrated on real-world data sets.


title: The natural history of prenatally diagnosed conjoined twins

abstract: Abstract Background/Purpose: Accurate prenatal diagnosis of complex anatomic connections and associated anomalies has only been possible recently with the use of ultrasonography, echocardiography, and fetal magnetic resonance imaging (MRI). To assess the impact of improved antenatal diagnosis in the management and outcome of conjoined twins, the authors reviewed their experience with 14 cases. Methods: A retrospective review of prenatally diagnosed conjoined twins referred to our institution from 1996 to present was conducted. Results: In 14 sets of conjoined twins, there were 10 thoracoomphalopagus, 2 dicephalus tribrachius dipus, 1 ischiopagus, and 1 ischioomphalopagus. The earliest age at diagnosis was 9 weeks' gestation (range, 9 to 29; mean, 20). Prenatal imaging with ultrasonography, echocardiography, and ultrafast fetal MRI accurately defined the shared anatomy in all cases. Associated anomalies included cardiac malformations (11 of 14), congenital diaphragmatic hernia (4 of 14), abdominal wall defects (2 of 14), and imperforate anus (2 of 14). Three sets of twins underwent therapeutic abortion, 1 set of twins died in utero, and 10 were delivered via cesarean section at a mean gestational age of 34 weeks. There were 5 individual survivors in the series after separation (18%). In one case, in which a twin with a normal heart perfused the cotwin with a rudimentary heart, the ex utero intrapartum treatment procedure (EXIT) was utilized because of concern that the normal twin would suffer immediate cardiac decompensation at birth. This EXIT-to-separation strategy allowed prompt control of the airway and circulation before clamping the umbilical cord and optimized control over a potentially emergent situation, leading to survival of the normal cotwin. In 2 sets of twins in which each twin had a normal heart, tissue expanders were inserted before separation. Conclusions: Advances in prenatal diagnosis allow detailed, accurate evaluations of conjoined twins. Careful prenatal studies may uncover cases in which emergent separation at birth is lifesaving.


Using TF-IDF to Determine Word Relevance in Document Queries


title: Entropy Guided Spectrum Based Bug Localization Using Statistical Language Model

abstract: Locating bugs is challenging but one of the most important activities in software development and maintenance phase because there are no certain rules to identify all types of bugs. Existing automatic bug localization tools use various heuristics based on test coverage, pre-determined buggy patterns, or textual similarity with bug report, to rank suspicious program elements. However, since these techniques rely on information from single source, they often suffer when the respective source information is inadequate. For instance, the popular spectrum based bug localization may not work well under poorly written test suite. In this paper, we propose a new approach, EnSpec, that guides spectrum based bug localization using code entropy, a metric that basically represents naturalness of code derived from a statistical language model. Our intuition is that since buggy code are high entropic, spectrum based bug localization with code entropy would be more robust in discriminating buggy lines vs. non-buggy lines. We realize our idea in a prototype, and performed an extensive evaluation on two popular publicly available benchmarks. Our results demonstrate that EnSpec outperforms a state-of-the-art spectrum based bug localization technique.


title: Economic Evaluation of the 80% Baccalaureate Nurse Workforce Recommendation: A Patient-level Analysis

abstract: BACKGROUND ::: Higher proportions of BSN-educated nurses were associated with improved outcomes in hospital-level studies. A recent Institute of Medicine report calls for increasing the proportion of BSN-educated nurses to 80% by 2020. Patient-level evidence of cost and quality implications of the 80% BSN threshold is needed for a business case to support these efforts. ::: ::: ::: OBJECTIVES ::: To conduct the economic analysis of meeting the 80% BSN threshold on patient outcomes and costs, using linked patient-nurse data. ::: ::: ::: RESEARCH DESIGN ::: Retrospective observational patient-level analysis of electronic data. Linear and logistic regression modeling with patient controls and diagnosis and unit fixed effects. ::: ::: ::: SUBJECTS ::: A total of 8526 adult medical-surgical patients matched with 1477 direct care nurses from an Eastern US academic medical center, during June 1, 2011-December 31, 2011. ::: ::: ::: MEASURES ::: Outcomes include hospital mortality, all-cause same-facility 30-day readmission, length-of-stay, and total hospitalization cost. BSN proportion is a continuous measure for the proportion of nurse assessment inputs into the patient's electronic medical record made by BSN-educated nurses; a dichotomous indicator for BSN proportion is 0.8-1.0. ::: ::: ::: RESULTS ::: Continuous BSN proportion was associated with lower mortality (OR=0.891, P<0.01). Compared with patients with <80% BSN care, patients receiving ≥ 80% of care from BSN nurses had lower odds of readmission (OR=0.813, P=0.04) and 1.9% shorter length-of-stay (P=0.03). Economic simulations support a strong business case for increasing the proportion of BSN-educated nurses to 80%. ::: ::: ::: CONCLUSIONS ::: A combined approach of increasing the hospital-level BSN proportion to 80% and assuring a high BSN dose through individual patient-level staffing assignments is needed to achieve projected quality and costs benefits.


title: Performance Evaluation of Knowledge Extraction Methods

abstract: This paper shows the precision, the recall and the F-measure for the knowledge extraction methods (under Open Information Extraction paradigm): ReVerb, OLLIE and ClausIE. For obtaining these three measures a subset of 55 newswires corpus was used. This subset was taken from the Reuters-21578 text categorization and test collection database. A handmade relation extraction was applied for each one of these newswires.


title: How social cognition can inform social decision making

abstract: Social decision-making is often complex, requiring the decision-maker to make inferences of others’ mental states in addition to engaging traditional decision-making processes like valuation and reward processing. A growing body of research in neuroeconomics has examined decision- making involving social and nonsocial stimuli to explore activity in brain regions such as the striatum and prefrontal cortex, largely ignoring the power of the social context. Perhaps more complex processes may influence decision-making in social versus nonsocial contexts. Years of social psychology and social neuroscience research have documented a multitude of processes (e.g. mental state inferences, impression formation, spontaneous trait inferences) that occur upon viewing another person. These processes rely on a network of brain regions including medial prefrontal cortex, superior temporal sulcus, temporal parietal junction, and precuneus among others. Undoubtedly, these social cognition processes affect social decision-making since mental state inferences occur spontaneously and automatically. Few studies have looked at how these social inference processes affect decision-making in a social context despite the capability of these inferences to serve as predictions that can guide future decision-making. Here we review and integrate the person perception and decision-making literatures to understand how social cognition can inform the study of social decision-making in a way that is consistent with both literatures. We identify gaps in both literatures—while behavioral economics largely ignores social processes that spontaneously occur upon viewing another person, social psychology has largely failed to talk about the implications of social cognition processes in an economic decision-making context—and examine the benefits of integrating social psychological theory with behavioral economic theory.


title: 600 V Reverse Conducting (RC-)IGBT for Drives Applications in Ultra-Thin Wafer Technology

abstract: Reverse conducting IGBTs are fabricated in a large productive volume for soft switching applications, such as inductive heaters, microwave ovens or lamp ballast, since several years. To satisfy the requirements of hard switching applications, such as inverters in refrigerators, air conditioners or general purpose drives, the reverse recovery behavior of the integrated diode has to be optimized. Two promising concepts for such an optimization are based on a reduction of the charge- carrier lifetime or the anti-latch p+ implantation dose. It is shown that a combination of both concepts will lead to a device with a good reverse recovery behavior, low forward and reverse voltage drop and excellent over current turn- off capability of a trench field-stop IGBT.


Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment


title: Multi-shot ASP solving with clingo

abstract: We introduce a new flexible paradigm of grounding and solving in Answer Set Programming (ASP), which we refer to as multi-shot ASP solving, and present its implementation in the ASP system clingo. ::: Multi-shot ASP solving features grounding and solving processes that deal with continuously changing logic programs. In doing so, they remain operative and accommodate changes in a seamless way. For instance, such processes allow for advanced forms of search, as in optimization or theory solving, or interaction with an environment, as in robotics or query-answering. Common to them is that the problem specification evolves during the reasoning process, either because data or constraints are added, deleted, or replaced. This evolutionary aspect adds another dimension to ASP since it brings about state changing operations. We address this issue by providing an operational semantics that characterizes grounding and solving processes in multi-shot ASP solving. This characterization provides a semantic account of grounder and solver states along with the operations manipulating them. ::: The operative nature of multi-shot solving avoids redundancies in relaunching grounder and solver programs and benefits from the solver's learning capacities. clingo accomplishes this by complementing ASP's declarative input language with control capacities. On the declarative side, a new directive allows for structuring logic programs into named and parameterizable subprograms. The grounding and integration of these subprograms into the solving process is completely modular and fully controllable from the procedural side. To this end, clingo offers a new application programming interface that is conveniently accessible via scripting languages.


title: Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations

abstract: We address the problem of comparing sets of images for object recognition, where the sets may represent variations in an object's appearance due to changing camera pose and lighting conditions. canonical correlations (also known as principal or canonical angles), which can be thought of as the angles between two d-dimensional subspaces, have recently attracted attention for image set matching. Canonical correlations offer many benefits in accuracy, efficiency, and robustness compared to the two main classical methods: parametric distribution-based and nonparametric sample-based matching of sets. Here, this is first demonstrated experimentally for reasonably sized data sets using existing methods exploiting canonical correlations. Motivated by their proven effectiveness, a novel discriminative learning method over sets is proposed for set classification. Specifically, inspired by classical linear discriminant analysis (LDA), we develop a linear discriminant function that maximizes the canonical correlations of within-class sets and minimizes the canonical correlations of between-class sets. Image sets transformed by the discriminant function are then compared by the canonical correlations. Classical orthogonal subspace method (OSM) is also investigated for the similar purpose and compared with the proposed method. The proposed method is evaluated on various object recognition problems using face image sets with arbitrary motion captured under different illuminations and image sets of 500 general objects taken at different views. The method is also applied to object category recognition using ETH-80 database. The proposed method is shown to outperform the state-of-the-art methods in terms of accuracy and efficiency


title: Binary optimization using hybrid particle swarm optimization and gravitational search algorithm

abstract: The PSOGSA is a novel hybrid optimization algorithm, combining strengths of both particle swarm optimization (PSO) and gravitational search algorithm (GSA). It has been proven that this algorithm outperforms both PSO and GSA in terms of improved exploration and exploitation. The original version of this algorithm is well suited for problems with continuous search space. Some problems, however, have binary parameters. This paper proposes a binary version of hybrid PSOGSA called BPSOGSA to solve these kinds of optimization problems. The paper also considers integration of adaptive values to further balance exploration and exploitation of BPSOGSA. In order to evaluate the efficiencies of the proposed binary algorithm, 22 benchmark functions are employed and divided into three groups: unimodal, multimodal, and composite. The experimental results confirm better performance of BPSOGSA compared with binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), and genetic algorithm in terms of avoiding local minima and convergence rate.


title: Ontology-Based Information Extraction: Identifying Eligible Patients for Clinical Trials in Neurology

abstract: In this paper, we present a case study in using ontologies within a system for identifying patients who are eligible for clinical trials. The main purpose of this clinical research data warehouse (CRDW) is to support patient recruitment based on routine data from the clinical information system. In contrast to most other systems for similar purposes, the CRDW also makes use of information extracted from clinical documents like admission reports, radiological findings and discharge letters. The so-called linguistic pipeline of the CDRW recognizes negated and coordinated phrases. It is supported by clinical application ontologies, which enable the identification of main terms and their properties, as well as semantic search with synonyms, hypernyms, and syntactic variants. In the paper, we discuss questions related to designing the ontologies and filling them with content. The CRDW is currently being tested at several departments of the Charite—Universitatsmedizin Berlin and the Vivantes—Netzwerk fur Gesundheit GmbH. In the article, we provide a thorough evaluation of the deployed systems based on real data related to clinical trials conducted by our neurology departments.


title: Topic Segmentation and Labeling in Asynchronous Conversations

abstract: Topic segmentation and labeling is often considered a prerequisite for higher-level conversation analysis and has been shown to be useful in many Natural Language Processing (NLP) applications. We present two new corpora of email and blog conversations annotated with topics, and evaluate annotator reliability for the segmentation and labeling tasks in these asynchronous conversations. We propose a complete computational framework for topic segmentation and labeling in asynchronous conversations. Our approach extends state-of-the-art methods by considering a fine-grained structure of an asynchronous conversation, along with other conversational features by applying recent graph-based methods for NLP. For topic segmentation, we propose two novel unsupervised models that exploit the fine-grained conversational structure, and a novel graph-theoretic supervised model that combines lexical, conversational and topic features. For topic labeling, we propose two novel (unsupervised) random walk models that respectively capture conversation specific clues from two different sources: the leading sentences and the fine-grained conversational structure. Empirical evaluation shows that the segmentation and the labeling performed by our best models beat the state-of-the-art, and are highly correlated with human annotations.


title: A Hybrid Convolutional Variational Autoencoder for Text Generation

abstract: In this paper we explore the effect of architectural choices on learning a Variational Autoencoder (VAE) for text generation. In contrast to the previously introduced VAE model for text where both the encoder and decoder are RNNs, we propose a novel hybrid architecture that blends fully feed-forward convolutional and deconvolutional components with a recurrent language model. Our architecture exhibits several attractive properties such as faster run time and convergence, ability to better handle long sequences and, more importantly, it helps to avoid some of the major difficulties posed by training VAE models on textual data.


title: Intrusion detection system for high-speed network

abstract: The increasing network throughput challenges the current Network Intrusion Detection Systems (NIDS) to have compatible high-performance data processing. In this paper, we describe an in-depth research on the related techniques of high-performance network intrusion detection and an implementation of a Rule-based High-performance Network Intrusion Detection System (RHPNIDS) for high-speed networks. By integrating several performance optimizing methods, the performance of RHPNIDS is very impressive compared with the popular open source NIDS Snort.