title: Spatiotemporal analysis of rural–urban land conversion

abstract: Understanding the complexity of urban expansion requires an analysis of the factors influencing the spatial and temporal processes of rural-urban land conversion. This study aims at building a statistical land conversion model to assist in understanding land use change patterns. Specifically, GIS coupled with a logistic regression model and exponential smoothing techniques is used for exploring the effects of various factors on land use change. These factors include population density, slope, proximity to roads, and surrounding land use, and their influence on land use change is studied for generating a predictive model. Methods to reduce spatial autocorrelation in a logistic regression framework are also discussed. Primarily, an optimal sampling scheme that can eliminate spatial autocorrelation while maintaining adequate samples to allow the model to achieve the comparable accuracy as the spatial autoregressive model is developed. Since many of the previous studies on modeling the spatial complexity of urban growth ignored temporal complexity, a modified exponential smoothing technique is employed to produce a smoothed model from a series of bi-temporal models obtained from different time periods. The proposed model is validated using the multi-temporal land use data in New Castle County, DE, USA. It is demonstrated that our approach provides an effective option for multi-temporal land use change modeling and the modeling results help interpret the land use change patterns.


title: Channel Sounding for the Masses: Low Complexity GNU 802.11b Channel Impulse Response Estimation

abstract: New techniques in cross-layer wireless networks are building demand for ubiquitous channel sounding, that is, the capability to measure channel impulse response (CIR) with any standard wireless network and node. Towards that goal, we present a software-defined IEEE 802.11b receiver and CIR estimation system with little additional computational complexity compared to 802.11b reception alone. The system implementation, using the universal software radio peripheral (USRP) and GNU Radio, is described and compared to previous work. By overcoming computational limitations and performing direct-sequence spread-spectrum (DS-SS) matched filtering on the USRP, we enable high-quality yet inexpensive CIR estimation. We validate the channel sounder and present a drive test campaign which measures hundreds of channels between WiFi access points and an in-vehicle receiver in urban and suburban areas.


title: Cultural influences on knowledge sharing through online communities of practice

abstract: Purpose – The purpose of this study is to explore cultural factors influencing knowledge sharing strategies in virtual communities of practice.Design/methodology/approach – A qualitative research design was employed. Data collection was based on in‐depth interviews. The authors assumed that such factors as degree of collectivism, competitiveness, the importance of saving face, in‐group orientation, attention paid to power and hierarchy, and culture‐specific preferences for communication modes, would explain differences in knowledge seeking and sharing patterns.Findings – The results showed that these factors had different levels of importance among employees in the three participating countries. The issue of saving face was less important than expected in China. Modesty requirements as well as a high degree of competitiveness among employees were found to be serious barriers to information sharing in China, but not in Russia and Brazil. Perceived differences in power and hierarchy seemed to be less critic...


title: Opportunistic Wireless Energy Harvesting in Cognitive Radio Networks

abstract: Wireless networks can be self-sustaining by harvesting energy from ambient radio-frequency (RF) signals. Recently, researchers have made progress on designing efficient circuits and devices for RF energy harvesting suitable for low-power wireless applications. Motivated by this and building upon the classic cognitive radio (CR) network model, this paper proposes a novel method for wireless networks coexisting where low-power mobiles in a secondary network, called secondary transmitters (STs), harvest ambient RF energy from transmissions by nearby active transmitters in a primary network, called primary transmitters (PTs), while opportunistically accessing the spectrum licensed to the primary network. We consider a stochastic-geometry model in which PTs and STs are distributed as independent homogeneous Poisson point processes (HPPPs) and communicate with their intended receivers at fixed distances. Each PT is associated with a guard zone to protect its intended receiver from ST's interference, and at the same time delivers RF energy to STs located in its harvesting zone. Based on the proposed model, we analyze the transmission probability of STs and the resulting spatial throughput of the secondary network. The optimal transmission power and density of STs are derived for maximizing the secondary network throughput under the given outage-probability constraints in the two coexisting networks, which reveal key insights to the optimal network design. Finally, we show that our analytical result can be generally applied to a non-CR setup, where distributed wireless power chargers are deployed to power coexisting wireless transmitters in a sensor network.


title: Autonomous UAV with vision based on-board decision making for remote sensing and precision agriculture

abstract: In recent years, a phenomenal increase in the development of Unmanned Aerial Vehicles (UAVs) has been observed in a broad range of applications in various fields of study. Precision agriculture has emerged as a major field of interest, integrating unmanned monitoring of crop health into general agricultural practices for researchers are utilizing UAV to collect data for post-analysis. This paper describes a modular and generic system that is able to control the UAV using computer vision. A configuration approach similar to the Observation, Orientation, Decision and Action (OODA) loop has been implemented to allow the system to perform on-board decision making. The detection of an object of interest is performed by computer vision functionality. This allows the UAV to change its planned path accordingly and approach the target in order to perform a close inspection, or conduct a manoeuvres such as the application of herbicide or collection of higher resolution agricultural images. The results show the ability of the developed system to dynamically change its current goal and implement an inspection manoeuvre to perform necessary actions after detecting the target. The vision based navigation system and on-board decision making were demonstrated in three types of tests: ArUco Marker detection, colour detection and weed detection. The results are measured based on the sensitivity and the selectivity of the algorithm. The sensitivity is the ability of the algorithm to identify and detect the true positive target while the selectivity is the capability of the algorithm to filter out the false negatives for detection targets. Results indicate that the system is capable of detecting ArUco Markers with 99% sensitivity and 100% selectivity at 5 m above the ground level. The system is also capable of detecting a red target with 96% sensitivity and 99% selectivity at the same height during a test height at 5 metres. This system has potential applicability in the field of precision agriculture such as, crop health monitoring, pest plant detection which causes detrimental financial damage to crop yields if not noticed at an early stage.


title: SlimShot: In-Database Probabilistic Inference for Knowledge Bases

abstract: Increasingly large Knowledge Bases are being created, by crawling the Web or other corpora of documents, and by extracting facts and relations using machine learning techniques. To manage the uncertainty in the data, these KBs rely on probabilistic engines based on Markov Logic Networks (MLN), for which probabilistic inference remains a major challenge. Today's state of the art systems use variants of MCMC, which have no theoretical error guarantees, and, as we show, suffer from poor performance in practice. ::: ::: In this paper we describe SlimShot (Scalable Lifted Inference and Monte Carlo Sampling Hybrid Optimization Technique), a probabilistic inference engine for knowledge bases. SlimShot converts the MLN to a tuple-independent probabilistic database, then uses a simple Monte Carlo-based inference, with three key enhancements: (1) it combines sampling with safe query evaluation, (2) it estimates a conditional probability by jointly computing the numerator and denominator, and (3) it adjusts the proposal distribution based on the sample cardinality. In combination, these three techniques allow us to give formal error guarantees, and we demonstrate empirically that SlimShot outperforms to-day's state of the art probabilistic inference engines used in knowledge bases.


title: Exaggerating Facial Expressions: A Way to Intensify Emotion or a Way to the Uncanny Valley?

abstract: Exaggeration of facial expressions is used in animation and robotics to intensify emotions. However, modifying a human-like face can lead to an unsettling outcome. This phenomenon is known as uncanny valley. The goal of this study was to identify the realism level and magnitude of facial expression that produce the maximum amount of emotional intensity and the minimum amount of perceived strangeness. We studied the perceived intensity of emotion and perceived strangeness of faces with varying levels of realism (from schematic to photorealistic) and magnitude of facial expressions (from neutral to extremely exaggerated). We found that less realistic faces required more exaggeration to reach the emotional intensity of a real human face. While there is a range of emotional intensity that can be expressed by real human faces (from neutral to full intensity), we found that the same range of emotional intensity could be expressed by artificial faces when exaggeration was used. However, attempts to express emotional intensities outside this range using exaggeration led to strange-looking faces at all levels of realism.


title: Pyramidal Recurrent Unit for Language Modeling

abstract: LSTMs are powerful tools for modeling contextual information, as evidenced by their success at the task of language modeling. However, modeling contexts in very high dimensional space can lead to poor generalizability. We introduce the Pyramidal Recurrent Unit (PRU), which enables learning representations in high dimensional space with more generalization power and fewer parameters. PRUs replace the linear transformation in LSTMs with more sophisticated interactions including pyramidal and grouped linear transformations. This architecture gives strong results on word-level language modeling while reducing the number of parameters significantly. In particular, PRU improves the perplexity of a recent state-of-the-art language model Merity et al. (2018) by up to 1.3 points while learning 15-20% fewer parameters. For similar number of model parameters, PRU outperforms all previous RNN models that exploit different gating mechanisms and transformations. We provide a detailed examination of the PRU and its behavior on the language modeling tasks. Our code is open-source and available at this https URL


title: Sexual Harassment in Academia: Ethical Climates and Bounded Ethicality

abstract: This article reviews research on sexual harassment, particularly that pertaining to academia, to understand its underlying causes. Arguing that sexual harassment is an ethical issue, we draw on the...


title: Smart Parking System with Image Processing Facility

abstract: Smart Parking Systems obtain information about available parking spaces, process it and then place the car at a certain position. A prototype of the parking assistance system based on the proposed architecture was constructed here. The adopted hardware, software, and implementation solutions in this prototype construction are described in this paper. The effective circular design is introduced here having rack-pinion special mechanism which is used to lift and place the car in the certain position. The design of rack pinion mechanism is also simulated using AUTODESK INVENTOR and COMSOL software.


title: A Computational Framework for Modelling and Analyzing Ice Storms

abstract: Ice storms are extreme weather events that can have devastating implications for the sustainability of natural ecosystems as well as man made infrastructure. Ice storms are caused by a complex mix of atmospheric conditions and are among the least understood of severe weather events. Our ability to model ice storms and characterize storm features will go a long way towards both enabling support systems that offset storm impacts and increasing our understanding of ice storms. In this paper, we present a holistic computational framework to answer key questions of interest about ice storms. We model ice storms as a function of relevant surface and atmospheric variables. We learn these models by adapting and applying supervised and unsupervised machine learning algorithms on data with missing or incorrect labels. We also include a knowledge representation module that reasons with domain knowledge to revise the output of the learned models. Our models are trained using reanalysis data and historical records of storm events. We evaluate these models on reanalyis data as well as Global Climate Model (GCM) data for historical and future climate change scenarios. Furthermore, we discuss the use of appropriate bias correction approaches to run such modeling frameworks with GCM data.


title: Semantic Cluster Unary Loss for Efficient Deep Hashing

abstract: With the rapid development of deep learning, deep hashing methods have achieved promising results in efficient information retrieval. The hashing method maps similar data to binary hashcodes with smaller hamming distance, which has received broad attention due to its low storage cost and fast retrieval speed. Most of the existing deep hashing methods adopt pairwise or triplet losses to deal with similarities underlying the data, but their training is difficult and less efficient because $O(n^{2})$ data pairs and $O(n^{3})$ triplets are involved. To address these issues, we propose a novel deep hashing algorithm with the unary loss which can be trained very efficiently. First, we introduce a Unary Upper Bound of the traditional triplet loss, thus reducing the complexity to $O(n)$ and bridging the classification-based unary loss and the triplet loss. Second, we propose a novel Semantic Cluster Deep Hashing (SCDH) algorithm by introducing a modified Unary Upper Bound loss, called Semantic Cluster Unary Loss. The resultant hashcodes form several compact clusters, which means the hashcodes in the same cluster have similar semantic information. We also demonstrate that the proposed SCDH is easy to extend to semi-supervised settings by incorporating the state-of-the-art semi-supervised learning algorithms. The experiments on large-scale datasets show that the proposed method is superior to the state-of-the-art hashing algorithms.


title: Comparisons of machine learning techniques for detecting malicious webpages

abstract: 3 supervised and 2 unsupervised techniques are modeled to detect malicious webpages.Supervised machine learning (ML) techniques accuracies are above 89%.Unsupervised ML techniques accuracies have at least a silhouette coefficient of 0.87.Information obtained from URLs, page links, semantics and visual features of webpages.Chrome extension, lightweight and heavyweight classifiers and online learning are used. This paper compares machine learning techniques for detecting malicious webpages. The conventional method of detecting malicious webpages is going through the black list and checking whether the webpages are listed. Black list is a list of webpages which are classified as malicious from a user's point of view. These black lists are created by trusted organizations and volunteers. They are then used by modern web browsers such as Chrome, Firefox, Internet Explorer, etc. However, black list is ineffective because of the frequent-changing nature of webpages, growing numbers of webpages that pose scalability issues and the crawlers' inability to visit intranet webpages that require computer operators to log in as authenticated users. In this paper therefore alternative and novel approaches are used by applying machine learning algorithms to detect malicious webpages. In this paper three supervised machine learning techniques such as K-Nearest Neighbor, Support Vector Machine and Naive Bayes Classifier, and two unsupervised machine learning techniques such as K-Means and Affinity Propagation are employed. Please note that K-Means and Affinity Propagation have not been applied to detection of malicious webpages by other researchers. All these machine learning techniques have been used to build predictive models to analyze large number of malicious and safe webpages. These webpages were downloaded by a concurrent crawler taking advantage of gevent. The webpages were parsed and various features such as content, URL and screenshot of webpages were extracted to feed into the machine learning models. Computer simulation results have produced an accuracy of up to 98% for the supervised techniques and silhouette coefficient of close to 0.96 for the unsupervised techniques. These predictive models have been applied in a practical context whereby Google Chrome can harness the predictive capabilities of the classifiers that have the advantages of both the lightweight and the heavyweight classifiers.


title: A Smoke Removal Method for Laparoscopic Images

abstract: In laparoscopic surgery, image quality can be severely degraded by surgical smoke, which not only introduces error for the image processing (used in image guided surgery), but also reduces the visibility of the surgeons. In this paper, we propose to enhance the laparoscopic images by decomposing them into unwanted smoke part and enhanced part using a variational approach. The proposed method relies on the observation that smoke has low contrast and low inter-channel differences. A cost function is defined based on this prior knowledge and is solved using an augmented Lagrangian method. The obtained unwanted smoke component is then subtracted from the original degraded image, resulting in the enhanced image. The obtained quantitative scores in terms of FADE, JNBM and RE metrics show that our proposed method performs rather well. Furthermore, the qualitative visual inspection of the results show that it removes smoke effectively from the laparoscopic images.


Humor and the good character 1 HUMOR AND STRENGTHS OF CHARACTER


A Matlab-based Simulator for Autonomous Mobile Robots


title: A Pi-Calculus Based Context-Aware Model for Web Service Composition

abstract: Formal methods are effective ways to model and verify software system. To Describe and verify Web services by formal method is considered as an important research field. Guaranteeing the correctness of Web services composition is indispensable for enhancing the value of services. Pi-calculus is a kind of process algebra which can be used to model asynchronous concurrent communication network. A Picalculus based context aware model for Web service composition is presented. Web services behavior are described and modeled based on Pi-calculus in this paper. Finally, a case study is presented and the validity of composition model is verified.


title: Time Series Analysis of Sports Activity?s Influence on Psychological Health based on ARIMA Model

abstract: Time series analysis is a mathematics method for dynamic data processing. This technology makes use of random process theory, mathematical statistics, numerical analysis and other related methods to study the statistical rules observed by random data series, so as to solve practical problems. Time series analysis requisite mathematical model constructed to predict practical matters. ARIMA model(Autore gressive Integrated Moving Average) is a mathematics model which plays a significant role in predicting sports activity?s influence on middle school students? psychological health. By making use of mathematical statistics and logical reasoning methods, the paper has investigated and analyzed 20 students (10 male students and 10 female students) from A Middle School with Chinese Middle School Student Psychological Health Scale and Grading Standard. During the investigation, data of totally 30 time points was collected for modeling. By constructing the ARIMA model, we are to analyze and predict the collected data. The result show that It is an innovation to apply time series model in predicting and analyzing sports activity?s influence on students? psychological health.


title: Hiding feelings: The acute effects of inhibiting negative and positive emotion.

abstract: Emotion regulation plays a central role in mental health and illness, but little is known about even the most basic forms of emotion regulation. To examine the acute effects of inhibiting negative and positive emotion, we asked 180 female participants to watch sad, neutral, and amusing films under 1 of 2 conditions. Suppression participants (N = 90) inhibited their expressive behavior while watching the films; no suppression participants (N = 90) simply watched the films. Suppression diminished expressive behavior in all 3 films and decreased amusement self-reports in sad and amusing films. Physiologically, suppression had no effect in the neutral film, but clear effects in both negative and positive emotional films, including increased sympathetic activation of the cardiovascular system. On the basis of these findings, we suggest several ways emotional inhibition may influence psychological functioning.


title: State Transition Analysis: A Rule-Based Intrusion Detection Approach

abstract: A new approach to representing computer penetrations is introduced called penetration state transition analysis. This approach models penetrations as a series of state transitions described in terms of signature actions and state descriptions. State transition diagrams are written to correspond to the states of an actual computer system, and these diagrams form the basis of a rule-based expert system for detecting penetrations, referred to as STAT. >


title: Skyline Travel Routes: Exploring Skyline for Trip Planning

abstract: In this paper, given a spatial range Q and a set of query points specified by users, the goal of this paper is to return the travel routes that fulfill two requirements: 1.) travel routes should contain all those query points specified, and 2.) travel routes should be within the spatial range Q. Furthermore, we claim that each query point may have its proper visiting time. As such, the travel routes should go through these query points at their corresponding proper visiting time. To avoid some redundant information in the travel routes, we utilize the skyline concept to retrieve travel routes with more diversity. Specifically, in our paper, we consider some factors, such as the visiting time information of POIs and the set of query points, in retrieving travel routes. These factors could be mapped into dimensional spaces. Then, each travel route is viewed as a data point in the dimensional space. Thus, skyline data points (referred to as skyline travel routes) are returned as the query result. Skyline travel routes could provide more diversity in the query result of trip route recommendations. To evaluate our proposed methods, we conducted extensive experiments on real datasets. The experimental results show that skyline travel routes indeed provide more diversity in the query result. In addition, we evaluate the efficiency of retrieving skyline travel routes.


title: Probabilistic data association for semantic SLAM

abstract: Traditional approaches to simultaneous localization and mapping (SLAM) rely on low-level geometric features such as points, lines, and planes. They are unable to assign semantic labels to landmarks observed in the environment. Furthermore, loop closure recognition based on low-level features is often viewpoint-dependent and subject to failure in ambiguous or repetitive environments. On the other hand, object recognition methods can infer landmark classes and scales, resulting in a small set of easily recognizable landmarks, ideal for view-independent unambiguous loop closure. In a map with several objects of the same class, however, a crucial data association problem exists. While data association and recognition are discrete problems usually solved using discrete inference, classical SLAM is a continuous optimization over metric information. In this paper, we formulate an optimization problem over sensor states and semantic landmark positions that integrates metric information, semantic information, and data associations, and decompose it into two interconnected problems: an estimation of discrete data association and landmark class probabilities, and a continuous optimization over the metric states. The estimated landmark and robot poses affect the association and class distributions, which in turn affect the robot-landmark pose optimization. The performance of our algorithm is demonstrated on indoor and outdoor datasets.


title: Piles, tabs and overlaps in navigation among documents

abstract: Navigation among documents is a frequent, but ill supported activity. Overlapping or tabbed documents are widespread, but they offer limited visibility of their content. We explore variations on navigation support: arranging documents with tabs, as overlapping windows, and in piles. In an experiment we compared 11 participants' navigation with these variations and found strong task effects. Overall, overlapping windows were preferred and their structured layout worked well with some tasks. Surprisingly, tabbed documents were efficient in tasks requiring simply finding a document. Piled documents worked well for tasks that involved visual features of the documents, but the utility of recency or stable ordering of documents was task dependent. Based on the results, we discuss the effects of spatial arrangement, visibility, and task-dependency, and suggest areas for future research on document navigation and its support by piling.


title: Lexical Analysis for the Webshell Attacks

abstract: In this paper, we proposed a design considering for web application firewall system. Anyway, the website managing, web technology and hacker technology are vastly different from each other, whilst hacker techs are keep on moving forward, while company and government MIS are difficult of updating their information to the latest state due to working, they are often not updated and followed to the latest stuffs. And moreover, these newest techs are in generally needed to be figured out whether it matches the compatibility of the old versions, such as the recent PHP 7, many MIS are not capable to update their software to handle the variants of WAF bypass methods.


title: A two-step method for detecting selection signatures using genetic markers

abstract: A two-step procedure is presented for analysis of θ (F ST ) statistics obtained for a battery of loci, which eventually leads to a clustered structure of values. The first step uses a simple Bayesian model for drawing samples from posterior distributions of θ-parameters but without constructing Markov chains. This step assigns a weakly informative prior to allelic frequencies and does not make any assumptions about evolutionary models. The second step regards samples from these posterior distributions as “data” and fits a sequence of finite mixture models, with the aim of identifying clusters of θ-statistics. Hopefully, these would reflect different types of processes and would assist in interpreting results. Procedures are illustrated with hypothetical data, and with published allelic frequency data for Type-II diabetes in three human populations, and for 12 isozyme loci in 12 populations of the argan tree in Morocco.


title: Game theory for cognitive radio networks : An overview

abstract: Cognitive radio technology, a revolutionary communication paradigm that can utilize the existing wireless spectrum resources more efficiently, has been receiving a growing attention in recent years. As network users need to adapt their operating parameters to the dynamic environment, who may pursue different goals, traditional spectrum sharing approaches based on a fully cooperative, static, and centralized network environment are no longer applicable. Instead, game theory has been recognized as an important tool in studying, modeling, and analyzing the cognitive interaction process. In this tutorial survey, we introduce the most fundamental concepts of game theory, and explain in detail how these concepts can be leveraged in designing spectrum sharing protocols, with an emphasis on state-of-the-art research contributions in cognitive radio networking. Research challenges and future directions in game theoretic modeling approaches are also outlined. This tutorial survey provides a comprehensive treatment of game theory with important applications in cognitive radio networks, and will aid the design of efficient, self-enforcing, and distributed spectrum sharing schemes in future wireless networks.


title: OptiML: An Implicitly Parallel Domain-Specific Language for Machine Learning

abstract: As the size of datasets continues to grow, machine learning applications are becoming increasingly limited by the amount of available computational power. Taking advantage of modern hardware requires using multiple parallel programming models targeted at different devices (e.g. CPUs and GPUs). However, programming these devices to run efficiently and correctly is difficult, error-prone, and results in software that is harder to read and maintain. We present OptiML, a domain-specific language (DSL) for machine learning. OptiML is an implicitly parallel, expressive and high performance alternative to MATLAB and C++. OptiML performs domain-specific analyses and optimizations and automatically generates CUDA code for GPUs. We show that OptiML outperforms explicitly parallelized MATLAB code in nearly all cases.


title: A Parallel-Strip Ring Power Divider With High Isolation and Arbitrary Power-Dividing Ratio

abstract: In this paper, a new power divider concept, which provides high flexibility of transmission line characteristic impedance and port impedance, is proposed. This power divider is implemented on a parallel-strip line, which is a balanced transmission line. By implementing the advantages and uniqueness of the parallel-strip line, the divider outperforms the conventional divider in terms of isolation bandwidths. A swap structure of the two lines of the parallel-strip line is employed in this design, which is critical for the isolation enhancements. A lumped-circuit model of the parallel-strip swap including all parasitic effects has been analyzed. An equal power divider with center frequency of 2 GHz was designed to demonstrate the idea. The experimental results show that the equal power divider has 96.5% -10-dB impedance bandwidth with more than 25-dB isolation and less than 0.7-dB insertion loss. In order to generalize the concept with an arbitrary power ratio, we also realize unequal power dividers with the same isolation characteristics. The impedance bandwidth of the proposed power divider will increase with the dividing ratio, which is opposite to the conventional Wilkinson power divider. Unequal dividers with dividing ratios of 1 : 2 and 1 : 12 are designed and measured. Additionally, a frequency independent 180 power divider has been realized with less than 2 phase errors.


title: Efficient Computation of the Tree Edit Distance

abstract: We consider the classical tree edit distance between ordered labelled trees, which is defined as the minimum-cost sequence of node edit operations that transform one tree into another. The state-of-the-art solutions for the tree edit distance are not satisfactory. The main competitors in the field either have optimal worst-case complexity but the worst case happens frequently, or they are very efficient for some tree shapes but degenerate for others. This leads to unpredictable and often infeasible runtimes. There is no obvious way to choose between the algorithms. In this article we present RTED, a robust tree edit distance algorithm. The asymptotic complexity of our algorithm is smaller than or equal to the complexity of the best competitors for any input instance, that is, our algorithm is both efficient and worst-case optimal. This is achieved by computing a dynamic decomposition strategy that depends on the input trees. RTED is shown optimal among all algorithms that use LRH (left-right-heavy) strategies, which include RTED and the fastest tree edit distance algorithms presented in literature. In our experiments on synthetic and real-world data we empirically evaluate our solution and compare it to the state-of-the-art.


title: Model-driven synthesis of SOA solutions

abstract: The current approach to the design, maintenance, and governance of service-oriented architecture (SOA) solutions has focused primarily on flow-driven assembly and orchestration of reusable service components. The practical application of this approach in creating industry solutions has been limited, because flow-driven assembly and orchestration models are too rigid and static to accommodate complex, real-world business processes. Furthermore, the approach assumes a rich, easily configured library of reusable service components when in fact the development, maintenance, and governance of these libraries is difficult. An alternative approach pioneered by the IBM Research Division, model-driven business transformation (MDBT), uses a model-driven software synthesis technology to automatically generate production-quality business service components from high-level business process models. In this paper, we present the business entity life cycle analysis (BELA) technique for MDBT-based SOA solution realization and its integration into service-oriented modeling and architecture (SOMA), the end-to-end method from IBM for SOA application and solution development. BELA shifts the process-modeling paradigm from one that is centered on activities to one that is centered on entities. BELA teams process subject-matter experts with IT and data architects to identify and specify business entities and decompose business processes. Supporting synthesis tools then automatically generate the interacting business entity service components and their associated data stores and service interface definitions. We use a large-scale project as an example demonstrating the benefits of this innovation, which include an estimated 40 percent project cost reduction and an estimated 20 percent reduction in cycle time when compared with conventional SOA approaches.


title: A method to analyse neighbourhood characteristics of land use patterns

abstract: Neighbourhood interactions between land use types are often included in the spatially explicit analysis of land use change. Especially in the context of urban growth, neighbourhood interactions are often addressed both in theories for urban development and in dynamic models of (urban) land use change. Neighbourhood interactions are one of the main driving factors in a large group of land use change models based on cellular automata (CA). ::: ::: This paper introduces a method to analyse the neighbourhood characteristics of land use. For every location in a rectangular grid the enrichment of the neighbourhood by specific land use types is studied. An application of the method for the Netherlands indicates that different land use types have clearly distinct neighbourhood characteristics. Land use conversions can be explained, for a large part, by the occurrence of land uses in the neighbourhood. ::: ::: The neighbourhood characterization introduced in this paper can help to further unravel the processes of land use change allocation and assist in the definition of transition rules for cellular automata and other land use change models.


title: A System for Mining Temporal Physiological Data Streams for Advanced Prognostic Decision Support

abstract: We present a mining system that can predict the future health status of the patient using the temporal trajectories of health status of a set of similar patients. The main novelties of this system are its use of stream processing technology for handling the incoming physiological time series data and incorporating domain knowledge in learning the similarity metric between patients represented by their temporal data. The proposed approach and system were tested using the MIMIC II database, which consists of physiological waveforms, and accompanying clinical data obtained for ICU patients. The study was carried out on 1500 patients from this database. In the experiments we report the efficiency and throughput of the stream processing unit for feature extraction, the effectiveness of the supervised similarity measure both in the context of classification and retrieval tasks compared to unsupervised approaches, and the accuracy of the temporal projections of the patient data.


title: Barriers to timely discharge from the general medicine service at an academic teaching hospital

abstract: Background Reducing delays for patients who are safe to be discharged is important for minimising complications, managing costs and improving quality. Barriers to discharge include placement, multispecialty coordination of care and ineffective communication. There are a few recent studies that describe barriers from the perspective of all members of the multidisciplinary team. Study objective To identify the barriers to discharge for patients from our medicine service who had a discharge delay of over 24 hours. Methodology We developed and implemented a biweekly survey that was reviewed with attending physicians on each of the five medicine services to identify patients with an unnecessary delay. Separately, we conducted interviews with staff members involved in the discharge process to identify common barriers they observed on the wards. Results Over the study period from 28 October to 22 November 2013, out of 259 total discharges, 87 patients had a delay of over 24 hours (33.6%) and experienced a total of 181 barriers. The top barriers from the survey included patient readiness, prolonged wait times for procedures or results, consult recommendations and facility placement. A total of 20 interviews were conducted, from which the top barriers included communication both between staff members and with the patient, timely notification of discharge and lack of discharge standardisation. Conclusions There are a number of frequent barriers to discharge encountered in our hospital that may be avoidable with planning, effective communication methods, more timely preparation and tools to standardise the discharge process.


title: Document Summarization and Information Extraction for Generation of Presentation Slides

abstract: In this paper, a semi automated technique to generate slide presentations from english text documents is proposed. The technique discussed in this paper is considered to be a pioneering attempt in the field of NLP (Natural Language Processing). The technique involves an information extractor and a slide generator, which combines certain NLP methods such as segmentation, chunking, summarization etc.., with certain special linguistic features of the text such as the ontology of the words, noun phrases found, semantic links, sentence centrality etc., In order to aid the language processing task, two tools can be utilized namely, MontyLingua which helps in chunking and Doddle helps in creating an ontology for the input text represented as an OWL (Ontology Web Language) file. The process of the technique comprises of extracting text, creating an ontology, identifying important phrases for bullets and generating slides.


title: Analytic Inverse Kinematics Considering the Joint Constraints and Self-Collision for Redundant 7DOF Manipulator

abstract: This study proposes an analytic inverse kinematic solution considering joint limit and self-collision avoidance for a redundant 7DOF manipulator with spherical shoulder and wrist joints. An analytic approach is used to satisfy the sub-task. The arm angle is used to restrain the manipulator redundancy. The analytic inverse kinematic solution set satisfying the sub-task is proposed by determining the range of the feasible arm angle. The effectiveness of the proposed method was verified through a simulation. We confirmed that the solution of the proposed method always satisfies the joint limit constraints and self-collision avoidance.


title: A Comparison of Emotion Annotation Approaches for Text

abstract: While the recognition of positive/negative sentiment in text is an established task with many standard data sets and well developed methodologies, the recognition of a more nuanced affect has received less attention: there are few publicly available annotated resources and there are a number of competing emotion representation schemes with as yet no clear approach to choose between them. To address this lack, we present a series of emotion annotation studies on tweets, providing methods for comparisons between annotation methods (relative vs. absolute) and between different representation schemes. We find improved annotator agreement with a relative annotation scheme (comparisons) on a dimensional emotion model over a categorical annotation scheme on Ekman’s six basic emotions; however, when we compare inter-annotator agreement for comparisons with agreement for a rating scale annotation scheme (both with the same dimensional emotion model), we find improved inter-annotator agreement with rating scales, challenging a common belief that relative judgements are more reliable. To support these studies and as a contribution in itself, we further present a publicly available collection of 2019 tweets annotated with scores on each of four emotion dimensions: valence, arousal, dominance and surprise, following the emotion representation model identified by Fontaine et al. in 2007.


title: Internet Addiction or Excessive Internet Use

abstract: Background: Problematic Internet addiction or excessive Internet use is characterized by excessive or poorly controlled preoccupations, urges, or behaviors regarding computer use and Internet access that lead to impairment or distress. Currently, there is no recognition of internet addiction within the spectrum of addictive disorders and, therefore, no corresponding diagnosis. It has, however, been proposed for inclusion in the next version of the Diagnostic and Statistical Manual of Mental Disorder (DSM). Objective: To review the literature on Internet addiction over the topics of diagnosis, phenomenology, epidemiology, and treatment. Methods: Review of published literature between 2000–2009 in Medline and PubMed using the term “internet addiction.Results: Surveys in the United States and Europe have indicated prevalence rate between 1.5% and 8.2%, although the diagnostic criteria and assessment questionnaires used for diagnosis vary between countries. Crosssectional studies on samples of patients report high comorbidity of Internet addiction with psychiatric disorders, especially affective disorders (including depression), anxiety disorders (generalized anxiety disorder, social anxiety disorder), and attention deficit hyperactivity disorder (ADHD). Several factors are predictive of problematic Internet use, including personality traits, parenting and familial factors, alcohol use, and social anxiety. Conclusions and Scientific Significance: Although Internet-addicted individuals have difficulty suppressing their excessive online behaviors in real life, little is known about the patho-physiological and cognitive mechanisms responsible for Internet addiction. Due to the lack of methodologically adequate research, it is currently impossible to recommend any evidence-based treatment of Internet addiction.


title: Partial discharge pattern recognition of XLPE cables at DC voltage based on the compressed sensing theory

abstract: Partial discharge (PD) detection has been widely applied to high voltage cable systems for several decades. In this paper, three kinds of insulation defects in XLPE cables are designed and tested at step-wise DC voltage. The PD developing progress of each defect cable is divided into two stages based on the severity degree of PDs. Based on the compressed sensing (CS) theory, a novel method used for recognizing PD patterns at DC voltage is proposed. Firstly, both the statistical features of PD repetition rate and the norm characteristics of time domain features are extracted to create a high-dimensional feature space. Then each test sample from the feature space is sparsely represented as linear combinations of training samples, and the sufficiently sparse one is obtained via 1-norm minimization. Finally, the PD pattern can be recognized by minimizing the residuals between the test sample and the recovered one. The experimental data is analyzed by the proposed method, and the results show that the patterns of both PD source and PD stage are recognized precisely, when the combination solution of features and the 1-norm minimization algorithm are determined appropriately.


title: Access to prosthetic devices in developing countries: Pathways and challenges

abstract: As the number of amputees continues to grow in low-resource settings, their demand for prosthetic devices continues to be unmet. Consequently, these amputees face exhausting physical, emotional, and economic challenges like navigating unfriendly terrain, emotional trauma, loss of income, and social rejection, all on a daily basis. While there have been attempts to meet this demand, the challenges in doing so are difficult and complex. Many organizations, including governments, nongovernmental organizations (NGOs), and private entities, are trying to deliver low-cost and durable prostheses to amputees. However, all of these organizations face a wide array of challenges related to infrastructure, technology and business in achieving this goal. With numerous distribution approaches used amongst these organizations in low-resource settings, little has been done to provide a comprehensive study of major distribution pathways and their respective challenges. This article presents the current pathways and challenges in delivering prostheses to amputees through a rigorous analysis of current organizations' approaches and the existing infrastructure in low-resource settings. The end goal of this article is to enable the reader to thoroughly understand the available options and obstacles to deliver prostheses in low-resource settings.


title: Restorative Rhinoplasty in the Aging Patient

abstract: Objective: The objective of this study is to review our favorable experience in performing rhinoplasty in aging patients. ::: ::: ::: ::: Methods: All patients aged 65 years or greater who underwent rhinoplasty, either esthetic or functional, by the senior author (Y.D.) from August 1997 to July 2005 with a minimum follow up of 1 year were retrospectively reviewed. ::: ::: ::: ::: Results: A total of 51 patients met the inclusion criteria and had complete records available for review. The average age was 69.5 years (range, 65–82 years) with 24 female and 27 male patients. All but two patients underwent open rhinoplasty. Eighteen procedures represented secondary rhinoplasties. Seven patients required auricular cartilage grafts, and 11 patients required costal cartilage grafts. One costal cartilage graft was aborted as a result of excessive calcification. All patients underwent columellar strut placement, 92% underwent internal valve grafts, and 80.4% underwent grafting of the external nasal valves. Nasal osteotomies were performed in only 23.5% of patients, all with the percutaneous technique. Revision surgery was necessary in only three (5.8%) patients, all of whom required grafting of the external valve (not performed primarily). In each of these cases, no significant external valve collapse was noted preoperatively. Premaxillary augmentation with diced or crushed cartilage grafts was performed in 81.8% (n = 18) of patients with an edentulous maxillary arch. ::: ::: ::: ::: Conclusions: Aging patients present unique technical challenges in rhinoplasty that warrant a comprehensive approach to restore internal and external valve competency and tip support. Consideration of prophylactic external valve grafts in addition to the routine use of internal valve grafts and columellar struts may help decrease the need for revision surgery in this patient population. Reasonable functional and esthetic outcomes can be expected in the aging patient.


title: A survey of Metrics for UML Class Diagrams

abstract: The demand for increased software quality has resulted in quality being more of differentiator between products than it ever has been before. For this reason, software developers need objective and valid measures for use in the evaluation and improvement of product quality from the initial stages of development. Class diagrams are a key artifact in the development of object-oriented (OO) software because they lay the foundation for all later design and implementation work. It follows that emphasizing class diagram quality may significantly contribute to higher quality OO software systems. The primary aim of this work, therefore, is to present a survey, as complete as possible, of the existing relevant works regarding class diagram metrics. Thus, from works previously published, researchers and practitioners alike may gain broad and ready access to insights for measuring these quality characteristics. Another aim of this work is to help reveal areas of research either lacking completion or yet to undertaken.


Discovering Mis-Categorized Entities


title: Epidemiological cut-off values for disc diffusion data generated by standard test protocols from Edwardsiella tarda and Vibrio harveyi

abstract: The susceptibility of 52 isolates of Edwardsiella tarda and 54 isolates of Vibrio harveyi to 12 antibiotics was established using internationally standardised disc diffusion protocols. Normalised resistance interpretation was employed to generate statistically valid epidemiological cut-off values for these susceptibility data. For three of these 24 species/antibiotic data sets, there were insufficient fully sensitive strains to allow analysis and for an additional two there were sufficient fully susceptible isolates to allow only a provisional estimate to be made. The data sets for a further two species/antibiotic combinations were considered too diverse to allow the calculation of a valid cut-off value. Valid epidemiological cut-off values were, therefore, calculated for 17 species/antibiotic combinations. It is argued that these cut-off values will greatly facilitate the performance of studies designed to monitor the consequence of antibiotic use in aquaculture. They will also facilitate the prudent and rational choice of antibiotics to be applied therapeutically in commercial farming of aquatic animals.


title: Adversarial Model for Radio Frequency Identification

abstract: Radio Frequency Identification (RFID) systems aim to identify objects in open environments with neither physical nor visual contact. They consist of transponders inserted into objects, of readers, and usually of a database which contains information about the objects. The key point is that authorised readers must be able to identify tags without an adversary being able to trace them. Traceability is often underestimated by advocates of the technology and sometimes exaggerated by its detractors. Whatever the true picture, this problem is a reality when it blocks the deployment of this technology and some companies, faced with being boycotted, have already abandoned its use. Using cryptographic primitives to thwart the traceability issues is an approach which has been explored for several years. However, the research carried out up to now has not provided satisfactory results as no universal formalism has been defined. In this paper, we propose an adversarial model suitable for RFID environments. We define the notions of existential and universal untraceability and we model the access to the communication channels from a set of oracles. We show that our formalisation fits the problem being considered and allows a formal analysis of the protocols in terms of traceability. We use our model on several well-known RFID protocols and we show that most of them have weaknesses and are vulnerable to traceability.


title: Nitric oxide-producing myeloid-derived suppressor cells inhibit vascular E- selectin expression in human squamous cell carcinomas

abstract: Squamous cell carcinomas (SCCs) are sun-induced skin cancers that are particularly numerous and aggressive in immunosuppressed individuals. SCCs evade immune detection at least in part by downregulating E-selectin on tumor vessels, thereby restricting entry of skin-homing T cells into tumors. We find that nitric oxide (NO) potently suppresses E-selectin expression on human endothelial cells and that SCCs are infiltrated by NO-producing iNOS+ CD11b+ CD33+ CD11c− HLA-DR− myeloid-derived suppressor cells (MDSCs). MDSCs from SCCs produced NO, transforming growth factor-β (TGF-β), and arginase, and inhibited endothelial E-selectin expression in vitro. MDSCs from SCCs expressed the chemokine receptor CCR2 (chemokine (C-C motif) receptor 2) and tumors expressed the CCR2 ligand human β-defensin 3 (HBD3), suggesting that CCR2/HBD3 interactions may contribute to MDSC recruitment to SCCs. Treatment of SCCs in vitro with the inducible nitric oxide synthase (iNOS) inhibitor Nω-nitro-L-arginine(L-NNA) induced E-selectin expression at levels comparable to imiquimod-treated SCCs undergoing immunologic destruction. Our results suggest that local production of NO in SCCs may impair vascular E-selectin expression. We show that MDSCs are critical producers of NO in SCCs and that NO inhibition restores vascular E-selectin expression, potentially enhancing T-cell recruitment. The iNOS inhibitors and other therapies that reduce NO production may therefore be effective in the treatment of SCCs and their premalignant precursor lesions, actinic keratoses.


title: EVSAC: Accelerating Hypotheses Generation by Modeling Matching Scores with Extreme Value Theory

abstract: Algorithms based on RANSAC that estimate models using feature correspondences between images can slow down tremendously when the percentage of correct correspondences (inliers) is small. In this paper, we present a probabilistic parametric model that allows us to assign confidence values for each matching correspondence and therefore accelerates the generation of hypothesis models for RANSAC under these conditions. Our framework leverages Extreme Value Theory to accurately model the statistics of matching scores produced by a nearest-neighbor feature matcher. Using a new algorithm based on this model, we are able to estimate accurate hypotheses with RANSAC at low inlier ratios significantly faster than previous state-of-the-art approaches, while still performing comparably when the number of inliers is large. We present results of homography and fundamental matrix estimation experiments for both SIFT and SURF matches that demonstrate that our method leads to accurate and fast model estimations.


title: Sensitization to acrylates is a common adverse reaction to artificial fingernails.

abstract: Background Although precise figures for adverse reactions to nail-care products are not available, it is thought that one of the main risks from artificial nails (ANs) is contact allergy. ::: ::: ::: ::: Objective To evaluate the role of allergic contact dermatitis (ACD) as a cause of adverse reactions related to the use of ANs. ::: ::: ::: ::: Method A 4-year retrospective study of patients with suspected ACD from ANs was conducted. Patients tested with the methacrylate artificial nail (MAAN) series were evaluated clinically and patch test results were analysed. ::: ::: ::: ::: Results ACD to components of ANs may be a frequent cause of hand eczema, as observed in more than one-third of our patients (38.2%). About half of the patients were beauticians specializing in nail sculpturing who developed occupationally related ACD. All patients had involvement of the hands and fingers. Paronychia, nail dystrophy and onycholysis were less frequent. Dorsal hands and fingers, forearms and distant sites (face and neck) were more frequently affected in patients with occupational ACD (OACD). Typical clinical features were those of chronic dermatitis but atypical forms such as lichenoid and psoriasiform ACD were also observed. Mucosal erythema and oedema developed in two patients with ACD due to MAAN, after application of dental crowns with an acrylate-based cement. The most frequent allergens triggering ACD were 2-hydroxyethyl methacrylate (2-HEMA) and 2-hydroxypropyl methacrylate (2-HPMA) (17.5% each), followed by ethyleneglycol dimethacrylate (EGDM; 13.4%). A quarter of the patients tested with ethyl cyanoacrylate (ECA), a component of nail glue, had positive results. ::: ::: ::: ::: Conclusions Acrylic monomers used when sculpturing ANs are important contact and occupational sensitizers that can produce cross-reactions with other acrylic compounds and trigger allergic reactions when re-exposure occurs in a different setting.


title: Syntactic methods for topic-independent authorship attribution

abstract: The efficacy of syntactic features for topic-independent authorship attribution is evaluated, taking a feature set of frequencies of words and punctuation marks as baseline. The features are 'deep' ...


title: Provably Secure Dynamic ID-Based Anonymous Two-Factor Authenticated Key Exchange Protocol With Extended Security Model

abstract: Authenticated key exchange (AKE) protocol allows a user and a server to authenticate each other and generate a session key for the subsequent communications. With the rapid development of low-power and highly-efficient networks, such as pervasive and mobile computing network in recent years, many efficient AKE protocols have been proposed to achieve user privacy and authentication in the communications. Besides secure session key establishment, those AKE protocols offer some other useful functionalities, such as two-factor user authentication and mutual authentication. However, most of them have one or more weaknesses, such as vulnerability against lost-smart-card attack, offline dictionary attack, de-synchronization attack, or the lack of forward secrecy, and user anonymity or untraceability. Furthermore, an AKE scheme under the public key infrastructure may not be suitable for light-weight computational devices, and the security model of AKE does not capture user anonymity and resist lost-smart-card attack. In this paper, we propose a novel dynamic ID-based anonymous two-factor AKE protocol, which addresses all the above issues. Our protocol also supports smart card revocation and password update without centralized storage. Further, we extend the security model of AKE to support user anonymity and resist lost-smart-card attack, and the proposed scheme is provably secure in extended security model. The low-computational and bandwidth cost indicates that our protocol can be deployed for pervasive computing applications and mobile communications in practice.


title: Semantics and CBIR: a medical imaging perspective

abstract: Medical CBIR (content-based image retrieval) applications pose unique challenges but at the same time offer many new opportunities. On one hand, while one can easily understand news or sports videos, a medical image is often completely incomprehensible to untrained eyes. On the other hand, semantics in the medical domain is much better defined and there is a vast accumulation of formal knowledge representations that could be exploited to support semantic search for any specialty areas in medicine. In this paper, however, we will not dwell on any one particular specialty area, but rather address the question of how to support scalable semantic search across the whole of medical CBIR field: what are the advantages to take and gaps to fill, what are the key enabling technologies, and the critical success factor from an industrial point of view. In terms of enabling technologies, we discuss three aspects: 1. anatomical, disease, and contextual semantics, and their representations using ontologies; 2. scalable image analysis and tagging algorithms; and 3. ontological reasoning and its role in guiding and improving image analysis and retrieval. More specifically, for ontological representation of medical imaging semantics, we discuss the potential use of FMA, RadLex, ICD, and AIM. For scalable image analysis we present a learning-based anatomy detection and segmentation framework using distribution-free priors. It is easily adaptable to different anatomies and different imaging modalities.


title: UAVs mission planning with flight level constraint using Fast Marching Square Method

abstract: Abstract In the last decade, Unmanned Aerial Vehicles (UAVs) have been a research focus for many purposes. Many of these studies require a path planning to perform autonomous flights, as well as the maintenance of a fixed flight level with respect to the ground to capture videos or overlying images. This article presents an approach to plan a mission for UAVs keeping a fixed flight level constraint. The 3D environment where the planning is carried out is an open field with non-uniform terrain. The approach proposed is based on the Fast Marching Square (FM 2 ) method, which generates a path free from obstacles. Our approach includes two adjustment parameters. Depending on the values of these parameters, the restriction of flight level can be modified, as well as the smoothness and safety of the generated paths. Simulated experiments carried out in this work demonstrate that the proposed approach generates trajectories respecting a fixed flight level over the ground with successful results.


title: Bisabolene epoxides in sex pheromone innezara viridula (L.) (Heteroptera: Pentatomidae): Role ofcis isomer and relation to specificity of pheromone

abstract: Thetrans- andcis-(Z)-α-bisabolene epoxides (trans- andcis-(Z)-αBE) are the main components of the male sex pheromone inNezara viridula. The role of thecis isomer and the importance of thecis/trans proportion for the activity and the specificity of the pheromone are not clearly elucidated and were studied here. Interindividual variation of thecis/trans proportion produced by males was studied by individual hexanic extracts in two strains originating from the south of France (SF) and French West Indies (FWI). Thetrans isomer composed 42–82% of bisabolene epoxides in SF males and 74–94% of bisabolene epoxides in FWI males. Means (± SD) significantly differ between SF (62.8%±8.4) and FWI (82.4%±5.9) males in spite of this interindividual variation. Different isomers of bisabolene epoxide were synthesized and their EAG activity on female antennae was compared. Racemictrans- andcis-(Z)-αBE elicited low EAGs, not different from the nonnaturaltrans andcis (E)-αBE that were inactive on behavior. Behavioral tests revealed that racemictrans- andcis-(Z)-αBE attracted 45% (P 0.05) of the females, respectively. Binary blends containing 75/25, 50/50, and 25/75 proportions ofcis/trans isomers were more attractive thantrans-(Z)-αBE alone and response of females to the 25%cis/75%trans blend was significantly more important than the response totrans-isomer alone (P<0.05). The importance of thecis/trans proportion in relation with the specificity of the male pheromone is discussed.


title: A Passive Approach to Rogue Access Point Detection

abstract: Unauthorized or rogue access points (APs) produce security vulnerabilities in enterprise/campus networks by circumventing inherent security mechanisms. We propose to use the round trip time (RTT) of network traffic to distinguish between wired and wireless nodes. This information coupled with a standard wireless AP authorization policy allows the differentiation (at a central location) between wired nodes, authorized APs, and rogue APs. We show that the lower capacity and the higher variability in a wireless network can be used to effectively distinguish between wired and wireless nodes. Further, this detection is not dependant upon the wireless technology (802.11a, 802.11b, or 802.11g), is scalable, does not contain the inefficiencies of current solutions, remains valid as the capacity of wired and wireless links increase, and is independent of the signal range of the rogue APs.


title: Cluster Boosted Tree Classifier for Multi-View, Multi-Pose Object Detection

abstract: Detection of object of a known class is a fundamental problem of computer vision. The appearance of objects can change greatly due to illumination, view point, and articulation. For object classes with large intra-class variation, some divide-and-conquer strategy is necessary. Tree structured classifier models have been used for multi-view multi- pose object detection in previous work. This paper proposes a boosting based learning method, called Cluster Boosted Tree (CBT), to automatically construct tree structured object detectors. Instead of using predefined intra-class sub- categorization based on domain knowledge, we divide the sample space by unsupervised clustering based on discriminative image features selected by boosting algorithm. The sub-categorization information of the leaf nodes is sent back to refine their ancestors' classification functions. We compare our approach with previous related methods on several public data sets. The results show that our approach outperforms the state-of-the-art methods.


title: Associations between human milk oligosaccharides and infant body composition in the first 6 mo of life

abstract: Evidence linking breastfeeding to reduced risk of developing childhood obesity is inconclusive, yet previous studies have not considered variation in specific components of breast milk that may affect early development.We examined whether differences in the composition of human milk oligosaccharides (HMOs) correlate with infant growth and body composition at 1 and 6 mo of age.Twenty-five mother-infant dyads were recruited from the University Hospital at the University of Oklahoma Health Sciences Center. Infants were breastfed for 6 mo. Breast-milk and infant measures were obtained at 1 and 6 mo of infant age. HMO composition was analyzed by high-pressure liquid chromatography, and infant growth (length and weight) and body composition (percentage fat, total fat, lean mass) were measured by dual-energy X-ray absorptiometry. Relations between HMOs and infant growth and body composition were examined by using multiple linear regression. A priori covariates included maternal prepregnancy body mass index, pregnancy weight gain, and infant age and sex.Higher HMO diversity and evenness at 1 mo were associated with lower total and percentage fat mass at 1 mo. At 1 mo, each 1-μg/mL increase in lacto-N-fucopentaose (LNFP) I was associated with a 0.40-kg lower infant weight (P = 0.03). At 6 mo, each 1-μg/mL increase in LNFPI was associated with a 1.11-kg lower weight (P = 0.03) and a 0.85-g lower lean mass (P = 0.01). At 6 mo, each 1-μg/mL increase in LNFPI was associated with a 0.79-g lower fat mass (P = 0.02), whereas disialyl-lacto-N-tetraose and LNFPII were associated with a 1.92-g (P = 0.02) and 0.42-g (P = 0.02) greater fat mass, respectively. At 6 mo, each 1-μg/mL increase in fucosyl-disialyl-lacto-N-hexaose and lacto-N-neotetraose was associated with 0.04% higher (P = 0.03) and 0.03% lower (P < 0.01) body fat, respectively.These findings support the hypothesis that differences in HMO composition in mother's milk are associated with infant growth and body composition. This trial was registered at clinicaltrials.gov as NCT02535637.


title: Discriminative illumination: Per-pixel classification of raw materials based on optimal projections of spectral BRDF

abstract: Classifying raw, unpainted materials--metal, plastic, ceramic, fabric, and so on--is an important yet challenging task for computer vision. Previous works measure subsets of surface spectral reflectance as features for classification. However, acquiring the full spectral reflectance is time consuming and error-prone. In this paper, we propose to use coded illumination to directly measure discriminative features for material classification. Optimal illumination patterns--which we call "discriminative illumination"--are learned from training samples, after projecting to which the spectral reflectance of different materials are maximally separated. This projection is automatically realized by the integration of incident light for surface reflection. While a single discriminative illumination is capable of linear, two-class classification, we show that multiple discriminative illuminations can be used for nonlinear and multiclass classification. We also show theoretically that the proposed method has higher signal-to-noise ratio than previous methods due to light multiplexing. Finally, we construct an LED-based multispectral dome and use the discriminative illumination method for classifying a variety of raw materials, including metal (aluminum, alloy, steel, stainless steel, brass, and copper), plastic, ceramic, fabric, and wood. Experimental results demonstrate its effectiveness.


title: Consensus Paper: The Cerebellum's Role in Movement and Cognition

abstract: While the cerebellum's role in motor function is well recognized, the nature of its concurrent role in cognitive function remains considerably less clear. The current consensus paper gathers diverse views on a variety of important roles played by the cerebellum across a range of cognitive and emotional functions. This paper considers the cerebellum in relation to neurocognitive development, language function, working memory, executive function, and the development of cerebellar internal control models and reflects upon some of the ways in which better understanding the cerebellum's status as a “supervised learning machine” can enrich our ability to understand human function and adaptation. As all contributors agree that the cerebellum plays a role in cognition, there is also an agreement that this conclusion remains highly inferential. Many conclusions about the role of the cerebellum in cognition originate from applying known information about cerebellar contributions to the coordination and quality of movement. These inferences are based on the uniformity of the cerebellum's compositional infrastructure and its apparent modular organization. There is considerable support for this view, based upon observations of patients with pathology within the cerebellum.


title: Social media, social capital, and seniors: The impact of Facebook on bonding and bridging social capital of individuals over 65.

abstract: Online social networks may be important avenues for building and maintaining social capital as adult’s age. However, few studies have explicitly examined the role online communities play in the lives of seniors. In this exploratory study, U.S. seniors were interviewed to assess the impact of Facebook on social capital. Interpretive thematic analysis reveals Facebook facilitates connections to loved ones and may indirectly facilitate bonding social capital. Awareness generated via Facebook often lead to the sharing and receipt of emotional support via other channels. As such, Facebook acted as a catalyst for increasing social capital. The implication of “awareness” as a new dimension of social capital theory is discussed. Additionally, Facebook was found to have potential negative impacts on seniors’ current relationships due to open access to personal information. Finally, common concerns related to privacy, comfort with technology, and inappropriate content were revealed.


title: MNL1 Regulates Weak Acid-induced Stress Responses of the Fungal Pathogen Candida albicans

abstract: MNL1, the Candida albicans homologue of an orphan Msn2-like gene (YER130c in Saccharomyces cerevisiae) has no known function. Here we report that MNL1 regulates weak acid stress responses. Deletion of MNL1 prevents the long-term adaptation of C. albicans cells to weak acid stresses and compromises their global transcriptional response under these conditions. The promoters of Mnl1-dependent genes contain a novel STRE-like element (SLE) that imposes Mnl1-dependent, weak acid stress–induced transcription upon a lacZ reporter in C. albicans. The SLE (HHYYCCCCTTYTY) is related to the Nrg1 response element (NRE) element recognized by the transcriptional repressor Nrg1. Deletion of NRG1 partially restores the ability of C. albicans mnl1 cells to adapt to weak acid stress, indicating that Mnl1 and Nrg1 act antagonistically to regulate this response. Molecular, microarray, and proteomic analyses revealed that Mnl1-dependent adaptation does not occur in cells exposed to proapoptotic or pronecrotic doses of weak acid, suggesting that Ras-pathway activation might suppress the Mnl1-dependent weak acid response in dying cells. Our work defines a role for this YER130c orthologue in stress adaptation and cell death.


title: Image Editing and Creation with Perception-Motivated Local Features

abstract: This document describes four projects that, taken together, serve as a proof by example of the efficacy of a perception-motivated strategy for making graphics tools. More specifically, this strategy involves first selecting local features motivated by human perception and domain understanding, and then building algorithms that allow users to interactively edit these features. While the tools presented herein range over the domains of painting, compositing, and animation, all share a common philosophy—they spring from the isolation of a basic task-relevant feature and the creation of support algorithms that allow local edits of this feature at interactive rates. ::: Gradient Paint is a paint program inspired by theories of color perception which allows artists to edit edges between colors rather than colors themselves. The system includes a new real-time integrator for image display and a set of brushes suited for working with edges. ::: Local Layering introduces a notion of local overlap which allows artists to stack images as if they were paper cut-outs. In this work, I present a new way of representing stacked images and provide a provably correct and sufficient set of operators for navigating this representation. ::: Soft Stacking is a continuous-domain extension of Local Layering which allows artists to stack images as if they were volumes of fog. I present both brush-based and optimization-based creation techniques for such continuous-domain stackings. ::: 3D-like Texturing for 2D Animation is an attempt to provide 2D artists with a way to reap one of the primary benefits—coherent texturing—of creating 3D models. The system I describe, though imperfect, is able to construct a correspondence between various 2D drawings using only local shape information, and in such a way that the computer never creates a 3D model. ::: These four projects showcase a new, and somewhat effective, strategy for creating graphics tools that transcend traditional art materials and computer constructs. ::: Keywords: ambiguity, animation, artist tools, gradient domain, layering, locality, painting, perception, stacking, texturing


title: DIFUZE: Interface Aware Fuzzing for Kernel Drivers

abstract: Device drivers are an essential part in modern Unix-like systems to handle operations on physical devices, from hard disks and printers to digital cameras and Bluetooth speakers. The surge of new hardware, particularly on mobile devices, introduces an explosive growth of device drivers in system kernels. Many such drivers are provided by third-party developers, which are susceptible to security vulnerabilities and lack proper vetting. Unfortunately, the complex input data structures for device drivers render traditional analysis tools, such as fuzz testing, less effective, and so far, research on kernel driver security is comparatively sparse. In this paper, we present DIFUZE, an interface-aware fuzzing tool to automatically generate valid inputs and trigger the execution of the kernel drivers. We leverage static analysis to compose correctly-structured input in the userspace to explore kernel drivers. DIFUZE is fully automatic, ranging from identifying driver handlers, to mapping to device file names, to constructing complex argument instances. We evaluate our approach on seven modern Android smartphones. The results show that DIFUZE can effectively identify kernel driver bugs, and reports 32 previously unknown vulnerabilities, including flaws that lead to arbitrary code execution.


title: MADMM: a generic algorithm for non-smooth optimization on manifolds

abstract: Numerous problems in computer vision, pattern recognition, and machine learning are formulated as optimization with manifold constraints. In this paper, we propose the Manifold Alternating Directions Method of Multipliers (MADMM), an extension of the classical ADMM scheme for manifold-constrained non-smooth optimization problems. To our knowledge, MADMM is the first generic non-smooth manifold optimization method. We showcase our method on several challenging problems in dimensionality reduction, non-rigid correspondence, multi-modal clustering, and multidimensional scaling.


title: An efficient IP address lookup algorithm based on a small balanced tree using entry reduction

abstract: Due to a tremendous increase in internet traffic, backbone routers must have the capability to forward massive incoming packets at several gigabits per second. IP address lookup is one of the most challenging tasks for high-speed packet forwarding. Some high-end routers have been implemented with hardware parallelism using ternary content addressable memory (TCAM). However, TCAM is much more expensive in terms of circuit complexity as well as power consumption. Therefore, efficient algorithmic solutions are essentially required to be implemented using network processors as low cost solutions. Among the state-of-the-art algorithms for IP address lookup, a binary search based on a balanced tree is effective in providing a low-cost solution. In order to construct a balanced search tree, the prefixes with the nesting relationship should be converted into completely disjointed prefixes. A leaf-pushing technique is very useful to eliminate the nesting relationship among prefixes [V. Srinivasan, G. Varghese, Fast address lookups using controlled prefix expansion, ACM Transactions on Computer Systems 17 (1) (1999) 1-40]. However, it creates duplicate prefixes, thus expanding the search tree. This paper proposes an efficient IP address lookup algorithm based on a small balanced tree using entry reduction. The leaf-pushing technique is used for creating the completely disjointed entries. In the leaf-pushed prefixes, there are numerous pairs of adjacent prefixes with similarities in prefix strings and output ports. The number of entries can be significantly reduced by the use of a new entry reduction method which merges pairs with these similar prefixes. After sorting the reduced disjointed entries, a small balanced tree is constructed with a very small node size. Based on this small balanced tree, a native binary search can be effectively used in address lookup issue. In addition, we propose a new multi-way search algorithm to improve a binary search for IPv4 address lookup. As a result, the proposed algorithms offer excellent lookup performance along with reduced memory requirements. Besides, these provide good scalability for large amounts of routing data and for the address migration toward IPv6. Using both various IPv4 and IPv6 routing data, the performance evaluation results demonstrate that the proposed algorithms have better performance in terms of lookup speed, memory requirement and scalability for the growth of entries and IPv6, as compared with other algorithms based on a binary search.


title: Adapting robot task planning to user preferences: an assistive shoe dressing example

abstract: Healthcare robots will be the next big advance in humans’ domestic welfare, with robots able to assist elderly people and users with disabilities. However, each user has his/her own preferences, needs and abilities. Therefore, robotic assistants will need to adapt to them, behaving accordingly. Towards this goal, we propose a method to perform behavior adaptation to the user preferences, using symbolic task planning. A user model is built from the user’s answers to simple questions with a fuzzy inference system, and it is then integrated into the planning domain. We describe an adaptation method based on both the user satisfaction and the execution outcome, depending on which penalizations are applied to the planner’s rules. We demonstrate the application of the adaptation method in a simple shoe-fitting scenario, with experiments performed in a simulated user environment. The results show quick behavior adaptation, even when the user behavior changes, as well as robustness to wrong inference of the initial user model. Finally, some insights in a non-simulated world shoe-fitting setup are also provided.


title: A Survey on Network Security-Related Data Collection Technologies

abstract: Security threats and economic loss caused by network attacks, intrusions, and vulnerabilities have motivated intensive studies on network security. Normally, data collected in a network system can reflect or can be used to detect security threats. We define these data as network security-related data. Studying and analyzing security-related data can help detect network attacks and intrusions, thus making it possible to further measure the security level of the whole network system. Obviously, the first step in detecting network attacks and intrusions is to collect security-related data. However, in the context of big data and 5G, there exist a number of challenges in collecting these security-related data. In this paper, we first briefly introduce network security-related data, including its definition and characteristics, and the applications of network data collection. We then provide the requirements and objectives for security-related data collection and present a taxonomy of data collection technologies. Moreover, we review existing collection nodes, collection tools, and collection mechanisms in terms of network data collection and analyze them based on the proposed requirements and objectives toward high quality security-related data collection. Finally, we discuss open research issues and conclude with suggestions for future research directions.


title: DFARPA: Differential fault attack resistant physical design automation

abstract: Differential Fault Analysis (DFA), aided by sophisticated mathematical analysis techniques for ciphers and precise fault injection methodologies, has become a potent threat to cryptographic implementations. In this paper, we propose, to the best of the our knowledge, the first “DFA-aware” physical design automation methodology, that effectively mitigates the threat posed by DFA. We first develop a novel floorplan heuristic, which resists the simultaneous corruption of cipher states necessary for successful fault attack, by exploiting the fact that most fault injections are localized in practice. Our technique results in the computational complexity of the fault attack to shoot up to exhaustive search levels, making them practically infeasible. In the second part of the work, we develop a routing mechanism, which tackles more precise and costly fault injection techniques, like laser and electromagnetic guns. We propose a routing technique by integrating a specially designed ring oscillator based sensor circuit around the potential fault attack targets without incurring any performance overhead. We demonstrate the effectiveness of our technique by applying it on state of the art ciphers.


title: Massively Parallel Cross-Lingual Learning in Low-Resource Target Language Translation

abstract: We work on translation from rich-resource languages to low-resource languages. The main challenges we identify are the lack of low-resource language data, effective methods for cross-lingual transfer, and the variable-binding problem that is common in neural systems. We build a translation system that addresses these challenges using eight European language families as our test ground. Firstly, we add source and target family labels and study intra- family and inter-family influences for effective cross-lingual transfer. We achieve improvement of +8.4 BLEU score compared to single-family multi-source multi- target NMT baseline. We find that training two neighboring families closest to the low-resource language is often enough. Secondly, we construct an ablation study and find that reasonably good results can be achieved even with considerably less target data. Thirdly, we address the variable-binding problem by building an order-preserving named entity translation model. We obtain 60.6% accuracy in qualitative evaluation where our translations are akin to human translations in a preliminary study.


title: A 50V high-speed level shifter with high dv/dt immunity for multi-MHz DCDC converters

abstract: Size and cost of a switched mode power supply can be reduced by increasing the switching frequency. The maximum switching frequency and the maximum input voltage range, respectively, is limited by the minimum propagated on-time pulse, which is mainly determined by the level shifter speed. At switching frequencies above 10 MHz, a voltage conversion with an input voltage range up to 50 V and output voltages below 5 V requires an on-time of a pulse width modulated signal of less than 5 ns. This cannot be achieved with conventional level shifters. This paper presents a level shifter circuit, which controls an NMOS power FET on a high-voltage domain up to 50 V. The level shifter was implemented as part of a DCDC converter in a 180 nm BiCMOS technology. Experimental results confirm a propagation delay of 5 ns and on-time pulses of less than 3 ns. An overlapping clamping structure with low parasitic capacitances in combination with a high-speed comparator makes the level shifter also very robust against large coupling currents during high-side transitions as fast as 20 V/ns, verified by measurements. Due to the high dv/dt, capacitive coupling currents can be two orders of magnitude larger than the actual signal current. Depending on the conversion ratio, the presented level shifter enables an increase of the switching frequency for multi-MHz converters towards 100 MHz. It supports high input voltages up to 50 V and it can be applied also to other high-speed applications.


- 1-PERFLUOROCARBON FORMATION DURING RARE EARTH ELECTROLYSIS


title: HF skywave FMCW OTH-B systems expected emitter footprint

abstract: This paper begins with the introduction of the best known over-the-horizon radar (OTHR) systems around the world. A model of the maximum detection range for a high frequency (HF) frequency modulated continuous wave (FMCW) over-the-horizon backscatter (OTH-B) radar is reported. The maximum interception range for a narrowband intercept receiver is also quantified as a function of the minimum required input signal-to-noise ratio. A mathematical model of the ionosphere, PROPLAB, is also used to present a more detailed analysis of the expected performance including the electron density profile, the maximum usable frequency, the skip zone prediction, and the footprint coverage for angles from 0 to 20 degrees. As an example, the simulations focused on the Chinese OTH-B radar system to investigate the expected surveillance capabilities.


title: A pattern language for error management in voice user interfaces

abstract: Speech is not recognized with an accuracy of 100%. Even humans are not able to do that. There will always be some uncertainty in the recognized input, requiring strategies to cope. This is different from the experience with graphical user interfaces, where keyboard and mouse input are recognized without any doubts. Speech recognition and other errors occur frequently and reduce both the usefulness of applications and user satisfaction. This turns error handling into a crucial aspect of speech applications. Successful error handling methods can make even applications with poor recognition accuracy more successful. In [Sagawa et al. 2004] the authors show that the task completion rate increased from 86.4% to 93.4% and the average number of turns reduced by three after a better error handling method had been installed. On the other hand, poorly constructed error handling may bring unwanted complexity to the system and cause new errors and annoyances.


title: LSTM recurrent neural networks for high resolution range profile based radar target classification

abstract: Positive and timely identification of targets is critical in any military scenario. Target identification from backscattered electromagnetic energy is an evolving area. The objective of this paper is to study the applicability of Long Short-Term Memory Recurrent Neural Network (LSTM RNN) for High Resolution Range Profile (HRRP) based Radar target classification. Simulated Radar Range Profile data is used here. Three Different Target models are considered in this study. The classification is performed using a LSTM RNN.


title: A Lexicalized Tree Kernel for Open Information Extraction

abstract: In contrast with traditional relation extraction, which only considers a fixed set of relations, Open Information Extraction (Open IE) aims at extracting all types of relations from text. Because of data sparseness, Open IE systems typically ignore lexical information, and instead employ parse trees and Part-of-Speech (POS) tags. However, the same syntactic structure may correspond to different relations. In this paper, we propose to use a lexicalized tree kernel based on the word embeddings created by a neural network model. We show that the lexicalized tree kernel model surpasses the unlexicalized model. Experiments on three datasets indicate that our Open IE system performs better on the task of relation extraction than the stateof-the-art Open IE systems of Xu et al. (2013) and Mesquita et al. (2013).


title: A spotlight on security and privacy risks with future household robots: attacks and lessons

abstract: Future homes will be populated with large numbers of robots with diverse functionalities, ranging from chore robots to elder care robots to entertainment robots. While household robots will offer numerous benefits, they also have the potential to introduce new security and privacy vulnerabilities into the home. Our research consists of three parts. First, to serve as a foundation for our study, we experimentally analyze three of today's household robots for security and privacy vulnerabilities: the WowWee Rovio, the Erector Spykee, and the WowWee RoboSapien V2. Second, we synthesize the results of our experimental analyses and identify key lessons and challenges for securing future household robots. Finally, we use our experiments and lessons learned to construct a set of design questions aimed at facilitating the future development of household robots that are secure and preserve their users' privacy.


title: better theory about technology and organizing

abstract: Researchers have had difficulty accommodating materiality in voluntaristic theories of organizing. Although materiality surely shapes how people use technologies, materiality's role in organizational change remains under-theorized. We suggest that scholars have had difficulty grappling with materiality because they often conflate the distinction between the material and social with the distinction between determinism and voluntarism. We explain why such conflation is unnecessary and outline four challenges that researchers must address before they can reconcile the reality of materiality with the notion that outcomes of technological change are socially constructed.


title: Investigation on Passive Booster for Improving Magnetic Coupling of Metal Mounted Proximity Range HF RFIDs

abstract: In this paper, the design methodology, fabrication, and measurement of a passive circuit for increasing the read range of the near-field-communications (NFCs) gadget in handheld devices to read miniature metal mounted radio-frequency identification (RFID) are discussed. Since, the impedances at source and load are unknown, the conventional methods are not helpful for designing a proper communication. The proposed circuit is fabricated on thin flexible polyimide film hence; it can be simply attached to the back of handheld devices with arbitrary shape. Having only one discrete component in the circuit is another step toward low-cost devices. The magnetic coupling between the small metal mounted high-frequency (HF) RFID and the NFC coil are improved by adding two coils connected in parallel to each other resonating at 13.56 MHz. One coil is designed to be coupled to the NFC coil and the other one to the HF RFID. The operability of the system is investigated not only by standard measurement equipment like vector network analyzer but also utilizing different smartphones. The agreement between the simulation and practical results shows that the booster improves the read range significantly.


title: Dynamic Access Control Policy based on Blockchain and Machine Learning for the Internet of Things

abstract: The Internet of Things (IoT) is now destroying the barriers between the real and digital worlds. However, one of the huge problems that can slow down the development of this global wave, or even stop it, concerns security and privacy requirements. The criticality of these latter comes especially from the fact that the smart objects may contain very intimate information or even may be responsible for protecting people’s lives. In this paper, the focus is on access control in the IoT context by proposing a dynamic and fully distributed security policy. Our proposal will be based, on one hand, on the concept of the blockchain to ensure the distributed aspect strongly recommended in the IoT; and on the other hand on machine learning algorithms, particularly on reinforcement learning category, in order to provide a dynamic, optimized and self-adjusted security policy.


title: Data security and privacy preservation in cloud storage environments based on cryptographic mechanisms

abstract: Abstract Recent technological advances have sparked the popularity and success of cloud. This new paradigm is gaining an expanding interest, since it provides cost efficient architectures that support the transmission, storage, and intensive computing of data. However, these promising storage services bring many challenging design issues, considerably due to both loss of data control and abstract nature of clouds. The objective of this survey is to provide a consistent view about both data security concerns and privacy issues that are faced by clients in cloud storage environments. This survey brings a critical comparative analysis of cryptographic defense mechanisms, and beyond this, it explores research directions and technology trends to address the protection of outsourced data in cloud infrastructures.


title: A Broadband Dual-Mode Monopole Antenna Using NRI-TL Metamaterial Loading

abstract: A printed monopole antenna is proposed which uses negative-refractive-index transmission-line (NRI-TL) metamaterial loading in order to achieve a broadband dual-mode operation. The metamaterial-loaded monopole supports a predominately even-mode current at 5.5 GHz, which allows the antenna to be modeled as a short folded monopole. Around 3.55 GHz, the metamaterial-loaded monopole acts as a balun for the ground plane currents, therefore rendering the entire top edge of the ground plane as the main radiating element. This in turn radiates a dipolar mode that is orthogonal to the folded-monopole mode at 5.5 GHz. By virtue of the orthogonality between the two radiating modes, the metamaterial antenna exhibits a return-loss characteristic with a dual resonance, and therefore a very wide measured impedance bandwidth of 4.06 GHz. The total size of the antenna is only 22 times 30 mm, and the measured efficiency is on the order of 90% at both 3.55 and 5.5 GHz.


title: Children's text messaging: abbreviations, input methods and links with literacy

abstract: This study investigated the effects of mobile phone text-messaging method (predictive and multi-press) and experience (in texters and non-texters) on children's textism use and understanding. It also examined popular claims that the use of text-message abbreviations, or textese spelling, is associated with poor literacy skills. A sample of 86 children aged 10 to 12 years read and wrote text messages in conventional English and in textese, and completed tests of spelling, reading and non-word reading. Children took significantly longer and made more errors when reading messages written in textese than in conventional English. Further, they were no faster at writing messages in textese than in conventional English, regardless of texting method or experience. Predictive texters were faster at reading and writing messages than multi-press texters, and texting experience increased writing, but not reading, speed. General spelling and reading scores did not differ significantly with usual texting method. However, better literacy skills were associated with greater textese reading speed and accuracy. These findings add to the growing evidence for a positive relationship between texting proficiency and traditional literacy skills.