title: Measuring and predicting software productivity

abstract: ContextSoftware productivity measurement is essential in order to control and improve the performance of software development. For example, by identifying role models (e.g. projects, individuals, tasks) when comparing productivity data. The prediction is of relevance to determine whether corrective actions are needed, and to discover which alternative improvement action would yield the best results. ObjectiveIn this study we identify studies for software productivity prediction and measurement. Based on the identified studies we first create a classification scheme and map the studies into the scheme (systematic map). Thereafter, a detailed analysis and synthesis of the studies is conducted. MethodAs a research method for systematically identifying and aggregating the evidence of productivity measurement and prediction approaches systematic mapping and systematic review have been used. ResultsIn total 38 studies have been identified, resulting in a classification scheme for empirical research on software productivity. The mapping allowed to identify the rigor of the evidence with respect to the different productivity approaches. In the detailed analysis the results were tabulated and synthesized to provide recommendations to practitioners. ConclusionRisks with simple ratio-based measurement approaches were shown. In response to the problems data envelopment analysis seems to be a strong approach to capture multivariate productivity measures, and allows to identify reference projects to which inefficient projects should be compared. Regarding simulation no general prediction model can be identified. Simulation and statistical process control are promising methods for software productivity prediction. Overall, further evidence is needed to make stronger claims and recommendations. In particular, the discussion of validity threats should become standard, and models need to be compared with each other.


title: Design of double L-slot microstrip patch antenna array for WiMAX/WLAN application using step width junction feed

abstract: In this paper, a double L-slot microstrip patch antenna array using an infinite ground plane and step width junction-feed for Worldwide Interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN) applications were presented. The proposed microstrip patch antenna array consists of two rectangular patch element embedded with two L shaped slot with a compact size of 30.65mm×36.35mm×1mm. The proposed antenna was analyzed with two different feed arrangements and operates at 3.6GHz to cover the frequency bands of WLAN (2.4 to 5.8GHz) and WiMAX (2.5 to 5.5GHz) and satisfying the antenna requirements using Ansoft (HFSS). Antenna parameters such as return loss, VSWR, impedance matching and radiation pattern were analyzed. Simulated results for the designs are studied and investigated.


title: Sudden-Death Aversion: Avoiding Superior Options Because They Feel Riskier

abstract: We present evidence of sudden-death aversion (SDA)—the tendency to avoid “fast” strategies that provide a greater chance of success, but include the possibility of immediate defeat, in favor of “slow” strategies that reduce the possibility of losing quickly, but have lower odds of ultimate success. Using a combination of archival analyses and controlled experiments, we explore the psychology behind SDA. First, we provide evidence for SDA and its cost to decision makers by tabulating how often NFL teams send games into overtime by kicking an extra point rather than going for the 2-point conversion (Study 1) and how often NBA teams attempt potentially game-tying 2-point shots rather than potentially game-winning 3-pointers (Study 2). To confirm that SDA is not limited to sports, we demonstrate SDA in a military scenario (Study 3). We then explore two mechanisms that contribute to SDA: myopic loss aversion and concerns about “tempting fate.” Studies 4 and 5 show that SDA is due, in part, to myopic loss aversion, such that decision makers narrow the decision frame, paying attention to the prospect of immediate loss with the “fast” strategy, but not the downstream consequences of the “slow” strategy. Study 6 finds that people are more pessimistic about a risky strategy that needn’t be pursued (opting for sudden death) than the same strategy that must be pursued. We end by discussing how these twin mechanisms lead to differential expectations of blame from the self and others, and how SDA influences decisions in several different walks of life. (PsycINFO Database Record (c) 2018 APA, all rights reserved)


title: The politics of social policy: welfare expansion in Brazil, China, India and South Africa in comparative perspective

abstract: This introductory essay reviews the scholarship on the politics of social policy, and shows the contribution of the special issue to explaining expanded welfare commitments in Brazil, China, India and South Africa in the twenty-first century. Much literature on welfare expansion in lower- and middle-income contexts views it primarily as a policy corrective to the economic dislocations produced by global economic integration. This special issue focuses on the political factors that are critical to understanding the shape social policies have taken and their effectiveness in ameliorating poverty and inequality.


title: PL-SLAM: Real-time monocular visual SLAM with points and lines

abstract: Low textured scenes are well known to be one of the main Achilles heels of geometric computer vision algorithms relying on point correspondences, and in particular for visual SLAM. Yet, there are many environments in which, despite being low textured, one can still reliably estimate line-based geometric primitives, for instance in city and indoor scenes, or in the so-called “Manhattan worlds”, where structured edges are predominant. In this paper we propose a solution to handle these situations. Specifically, we build upon ORB-SLAM, presumably the current state-of-the-art solution both in terms of accuracy as efficiency, and extend its formulation to simultaneously handle both point and line correspondences. We propose a solution that can even work when most of the points are vanished out from the input images, and, interestingly it can be initialized from solely the detection of line correspondences in three consecutive frames. We thoroughly evaluate our approach and the new initialization strategy on the TUM RGB-D benchmark and demonstrate that the use of lines does not only improve the performance of the original ORB-SLAM solution in poorly textured frames, but also systematically improves it in sequence frames combining points and lines, without compromising the efficiency.


title: Recurrent Neural Networks for Language Understanding

abstract: Recurrent Neural Network Language Models (RNN-LMs) have recently shown exceptional performance across a variety of applications. In this paper, we modify the architecture to perform Language Understanding, and advance the state-of-the-art for the widely used ATIS dataset. The core of our approach is to take words as input as in a standard RNN-LM, and then to predict slot labels rather than words on the output side. We present several variations that differ in the amount of word context that is used on the input side, and in the use of non-lexical features. Remarkably, our simplest model produces state-of-the-art results, and we advance state-of-the-art through the use of bagof-words, word embedding, named-entity, syntactic, and wordclass features. Analysis indicates that the superior performance is attributable to the task-specific word representations learned by the RNN.


title: A robotic exoskeleton for rehabilitation and assessment of the upper limb following incomplete spinal cord injury

abstract: Robotic devices have been shown to be efficacious in the delivery of therapy to treat upper limb motor impairment following stroke. However, the application of this technology to other types of neurological injury has been limited to case studies. In this paper, we present a multi degree of freedom robotic exoskeleton, the MAHI Exo II, intended for rehabilitation of the upper limb following incomplete spinal cord injury (SCI). We present details about the MAHI Exo II and initial findings from a clinical evaluation of the device with eight subjects with incomplete SCI who completed a multi-session training protocol. Clinical assessments show significant gains when comparing pre- and post-training performance in functional tasks. This paper explores a range of robotic measures capturing movement quality and smoothness that may be useful in tracking performance, providing as feedback to the subject, or incorporating into an adaptive training protocol. Advantages and disadvantages of the various investigated measures are discussed with regard to the type of movement segmentation that can be applied to the data collected during unassisted movements where the robot is backdriven and encoder data is recorded for post-processing.


title: LSTM-based Deep Learning Models for Answer Ranking

abstract: The learning problem of ranking arises in many tasks, including the question answering, information retrieval, and movie recommendation. In these tasks, the ordering of the answers, documents or movies returned is a critical aspect of the system. Recently, deep learning approaches have gained a lot of attention from the research community and industry for their ability to automatically learn optimal feature representation for a given task. We aim to solve the answer ranking problem in practical question answering system with deep learning approaches. In this paper, we define a composite representation for questions and answers by combining convolutional neural network (CNN) with bidirectional long short-term memory (biLSTM) models, and learn a similarity function to relate them in a supervised way from the available training data. Considering the limited training data, we propose a hypernym strategy to get more general text pairs and test the effectiveness of different strategies. Experimental results on a public benchmark dataset from TREC demonstrate that our system outperforms previous work which requires syntactic features and some deep learning models.


The limits of automatic summarisation according to ROUGE.


title: Tropical-Forest-Parameter Estimation by Means of Pol-InSAR: The INDREX-II Campaign

abstract: This paper addresses the potential and limitations of polarimetric synthetic aperture radar (SAR) interferometry (Pol-InSAR) inversion techniques for quantitative forest-parameter estimation in tropical forests by making use of the unique data set acquired in the frame of the second Indonesian Airborne Radar Experiment (INDREX-II) campaign - including Pol-InSAR, light detection and ranging (LIDAR), and ground measurements - over typical Southeast Asia forest formations. The performance of Pol-InSAR inversion is not only assessed primarily at L- and P-band but also at higher frequencies, namely, X-band. critical performance parameters such as the ldquovisibility of the groundrdquo at L- and P-band as well as temporal decorrelation in short-time repeat-pass interferometry are discussed and quantitatively assessed. Inversion performance is validated against LIDAR and ground measurements over different test sites.


title: Measuring end-to-end bandwidth with Iperf using Web100

abstract: End-to-end bandwidth estimation tools like Iperf though fairly accurate are intrusive. In this paper, we describe how with an instrumented TCP stack (Web100), we can estimate the end-to-end bandwidth accurately, while consuming significantly less network bandwidth and time. We modified Iperf to use Web100 to detect the end of slow-start and estimate the end-to-end bandwidth by measuring the amount of data sent for a short period (1 second) after the slow-start, when the TCP throughput is relatively stable. We obtained bandwidth estimates differing by less than 10% when compared to running Iperf for 20 seconds, and savings in bandwidth estimation time of up to 94% and savings in network traffic of up to 92%.


title: Systematic Design of the Lead-Lag Network Method for Active Damping in LCL-Filter Based Three Phase Converters

abstract: Three-phase active rectifiers guarantee sinusoidal input currents and unity power factor at the price of a high switching frequency ripple. To adopt an LCL-filter, instead of an L-filter, allows using reduced values for the inductances and so preserving dynamics. However, stability problems can arise in the current control loop if the present resonance is not properly damped. Passive damping simply adds resistors in series with the LCL-filter capacitors. This simplicity is at the expense of increased losses and encumbrances. Active damping modifies the control algorithm to attain stability without using dissipative elements but, sometimes, needing additional sensors. This solution has been addressed in many publications. The lead-lag network method is one of the first reported procedures and continues being in use. However, neither there is a direct tuning procedure (without trial and error) nor its rationale has been explained. Thus, in this paper a straightforward procedure is developed to tune the lead-lag network with the help of software tools. The rationale of this procedure, based on the capacitor current feedback, is elucidated. Stability is studied by means of the root locus analysis in z-plane. Selecting the lead-lag network for the maximum damping in the closed-loop poles uses a simple optimization algorithm. The robustness against the grid inductance variation is also analyzed. Simulations and experiments confirm the validity of the proposed design flow.


title: Parallel organization of functionally segregated circuits linking basal ganglia and cortex.

abstract: Information about the basal ganglia has accumulated at a prodigious pace over the past decade, necessitating major revisions in our concepts of the structural and functional organization of these nuclei. From earlier data it had appeared that the basal ganglia served primarily to integrate diverse inputs from the entire cerebral cortex and to "funnel" these influences, via the ventrolateral thalamus, to the motor cortex (Allen & Tsukahara 1974, Evarts & Thach 1969, Kemp & Powell 1971). In particular, the basal


title: Stochastic properties of the random waypoint mobility model: epoch length, direction distribution, and cell change rate

abstract: The random waypoint model is a commonly used mobility model for simulations of wireless communication networks. In this paper, we present analytical derivations of some fundamental stochastic properties of this model with respect to: (a) the length and duration of a movement epoch, (b) the chosen direction angle at the beginning of a movement epoch, and (c) the cell change rate of the random waypoint mobility model when used within the context of cellular networks. Our results and methods can be used to compare the random waypoint model with other mobility models. The results on the movement epoch duration as well as on the cell change rate enable us to make a statement about the 'degree of mobility' of a certain simulation scenario. The direction distribution explains in an analytical manner the effect that nodes tend to move back to the middle of the system area.


title: Data-driven nonsmooth optimization

abstract: In this work, we consider methods for solving large-scale optimization problems with a possibly nonsmooth objective function. The key idea is to first parametrize a class of optimization methods us...


title: An Attention-Based Word-Level Interaction Model: Relation Detection for Knowledge Base Question Answering

abstract: Relation detection plays a crucial role in Knowledge Base Question Answering (KBQA) because of the high variance of relation expression in the question. Traditional deep learning methods follow an encoding-comparing paradigm, where the question and the candidate relation are represented as vectors to compare their semantic similarity. Max- or average- pooling operation, which compresses the sequence of words into fixed-dimensional vectors, becomes the bottleneck of information. In this paper, we propose to learn attention-based word-level interactions between questions and relations to alleviate the bottleneck issue. Similar to the traditional models, the question and relation are firstly represented as sequences of vectors. Then, instead of merging the sequence into a single vector with pooling operation, soft alignments between words from the question and the relation are learned. The aligned words are subsequently compared with the convolutional neural network (CNN) and the comparison results are merged finally. Through performing the comparison on low-level representations, the attention-based word-level interaction model (ABWIM) relieves the information loss issue caused by merging the sequence into a fixed-dimensional vector before the comparison. The experimental results of relation detection on both SimpleQuestions and WebQuestions datasets show that ABWIM achieves state-of-the-art accuracy, demonstrating its effectiveness.


title: Choosing effective colours for data visualization

abstract: We describe a technique for choosing multiple colours for use during data visualization. Our goal is a systematic method for maximizing the total number of colours available for use, while still allowing an observer to rapidly and accurately search a display for any one of the given colours. Previous research suggests that we need to consider three separate effects during colour selection: colour distance, linear separation, and colour category. We describe a simple method for measuring and controlling all of these effects. Our method was tested by performing a set of target identification studies; we analysed the ability of thirty eight observers to find a colour target in displays that contained differently coloured background elements. Results showed our method can be used to select a group of colours that will provide good differentiation between data elements during data visualization.


title: Computing Semantic Similarity of Concepts in Knowledge Graphs

abstract: This paper presents a method for measuring the semantic similarity between concepts in Knowledge Graphs (KGs) such as WordNet and DBpedia. Previous work on semantic similarity methods have focused on either the structure of the semantic network between concepts (e.g., path length and depth), or only on the Information Content (IC) of concepts. We propose a semantic similarity method, namely wpath, to combine these two approaches, using IC to weight the shortest path length between concepts. Conventional corpus-based IC is computed from the distributions of concepts over textual corpus, which is required to prepare a domain corpus containing annotated concepts and has high computational cost. As instances are already extracted from textual corpus and annotated by concepts in KGs, graph-based IC is proposed to compute IC based on the distributions of concepts over instances. Through experiments performed on well known word similarity datasets, we show that the wpath semantic similarity method has produced a statistically significant improvement over other semantic similarity methods. Moreover, in a real category classification evaluation, the wpath method has shown the best performance in terms of accuracy and F score.


title: Non-Monotonic Sequential Text Generation

abstract: Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy's own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order, while achieving competitive performance with conventional left-to-right generation.


title: Geometric Understanding of Deep Learning

abstract: Abstract This work introduces an optimal transportation (OT) view of generative adversarial networks (GANs). Natural datasets have intrinsic patterns, which can be summarized as the manifold distribution principle: the distribution of a class of data is close to a low-dimensional manifold. GANs mainly accomplish two tasks: manifold learning and probability distribution transformation. The latter can be carried out using the classical OT method. From the OT perspective, the generator computes the OT map, while the discriminator computes the Wasserstein distance between the generated data distribution and the real data distribution; both can be reduced to a convex geometric optimization process. Furthermore, OT theory discovers the intrinsic collaborative—instead of competitive—relation between the generator and the discriminator, and the fundamental reason for mode collapse. We also propose a novel generative model, which uses an autoencoder (AE) for manifold learning and OT map for probability distribution transformation. This AE–OT model improves the theoretical rigor and transparency, as well as the computational stability and efficiency; in particular, it eliminates the mode collapse. The experimental results validate our hypothesis, and demonstrate the advantages of our proposed model.


title: Denoising Distant Supervision for Relation Extraction via Instance-Level Adversarial Training

abstract: Existing neural relation extraction (NRE) models rely on distant supervision and suffer from wrong labeling problems. In this paper, we propose a novel adversarial training mechanism over instances for relation extraction to alleviate the noise issue. As compared with previous denoising methods, our proposed method can better discriminate those informative instances from noisy ones. Our method is also efficient and flexible to be applied to various NRE architectures. As shown in the experiments on a large-scale benchmark dataset in relation extraction, our denoising method can effectively filter out noisy instances and achieve significant improvements as compared with the state-of-the-art models.


title: Neural Joking Machine : Humorous image captioning

abstract: What is an effective expression that draws laughter from human beings? In the present paper, in order to consider this question from an academic standpoint, we generate an image caption that draws a "laugh" by a computer. A system that outputs funny captions based on the image caption proposed in the computer vision field is constructed. Moreover, we also propose the Funny Score, which flexibly gives weights according to an evaluation database. The Funny Score more effectively brings out "laughter" to optimize a model. In addition, we build a self-collected BoketeDB, which contains a theme (image) and funny caption (text) posted on "Bokete", which is an image Ogiri website. In an experiment, we use BoketeDB to verify the effectiveness of the proposed method by comparing the results obtained using the proposed method and those obtained using MS COCO Pre-trained CNN+LSTM, which is the baseline and idiot created by humans. We refer to the proposed method, which uses the BoketeDB pre-trained model, as the Neural Joking Machine (NJM).


psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis


title: Evaluation of chronological age based on third molar development in the Spanish population

abstract: A cross-sectional study was carried out to assess chronological age estimation based on the stages of third lower molar development, following the eight stages (A-H) method of Demirjian et al. The final sample consisted of 1,054 orthopantomograms from Spanish individuals of known chronological age (range 14-21 years) and gender (462 males and 592 females). Results showed a stronger correlation for males (r(2)=0.54) than for females (r(2)=0.45). Root formation occurred earlier in males than females, in stages 5, 6 and 7. The mean difference between chronological and estimated age was -0.10 years (+/-1.23 SD) for left third molar, and -0.07 years (+/-1.22 SD) for right third molar, with slight variations regarding sex. Comparative tables are provided regarding medicolegal questions concerning age 18 prediction in the Spanish population, showing that legal age is reached in stage 7 (G) by women and in stage 8 (H) by men. No differences have been observed between sides (p<0.0001). Differences were observed between Spaniards and other previously studied populations. Third molar maturity takes place earlier in the Spanish than French-Canadian, Scandinavian, American, German, Japanese and South African populations and is more similar to US Hispanics in root development.


title: Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons

abstract: This article introduces a new general-purpose sentiment lexicon called WKWSCI Sentiment Lexicon and compares it with five existing lexicons: Hu & Liu Opinion Lexicon, Multi-perspective Question Answering (MPQA) Subjectivity Lexicon, General Inquirer, National Research Council Canada (NRC) Word-Sentiment Association Lexicon and Semantic Orientation Calculator (SO-CAL) lexicon. The effectiveness of the sentiment lexicons for sentiment categorisation at the document level and sentence level was evaluated using an Amazon product review data set and a news headlines data set. WKWSCI, MPQA, Hu & Liu and SO-CAL lexicons are equally good for product review sentiment categorisation, obtaining accuracy rates of 75%–77% when appropriate weights are used for different categories of sentiment words. However, when a training corpus is not available, Hu & Liu obtained the best accuracy with a simple-minded approach of counting positive and negative words for both document-level and sentence-level sentiment categorisatio...


title: Compact rectangular U-shaped slot microstrip patch antenna for UWB applications

abstract: In this paper, the performance of a rectangular microstrip patch antenna fed by microstrip line is designed to operate for ultra-wide band applications. It consists of a rectangular patch with U-shaped slot on one side of the substrate and a finite ground plane on the other side. The U-shaped slot and the finite ground plane are used to achieve an excellent impedance matching to increase the bandwidth. The proposed antenna is designed and optimized based on extensive 3D EM simulation studies. The proposed antenna is designed to operate over a frequency range from 3.6 to 15 GHz.


title: FACIAL EXPRESSION DETECTION AND RECOGNITION SYSTEM

abstract: In this paper, the integration of face feature detection and extraction, and facial expression recognition are discussed. In this paper, we propose an algorithm that utilizes multi-stage integral projection to extract facial features. Furthermore, in this project, we propose a statistical approach to process the optical flow data to obtain the overall value for the respective feature region in the face. This approach has eliminated the requirement of accurate identification of the feature boundary. Optical flow computations are utilized to identify the directions and the amount of motions in image sequences that are caused by human facial expressions. The optical flow computation results are processed using Kalman filtering. The filtered results are given to a neural network to realize a mapping into the facial expression space. This technique is used on a set of training and testing face images. Preliminary experiments indicate an accuracy between 60% - 80% on the Kalman filtered data when recognizing four types of expressions: anger, sad, happy and surprise. In an attempt to further improve the recognition results, we proposed a technique to process the optical flow results using a statistical approach instead of using Kalman filtering. The preliminary experiments on this proposal approach produced accuracy between 70% - 100% on the original optical flow results that is better than the Kalman filter technique.


title: An empirically-developed framework for Agile transition and adoption

abstract: We show how a large-scale empirical study using Grounded Theory has been used to study Agile transition and adoption process.An empirically-developed transition framework to facilitate Agile transformation is proposed.We describe the key structural characteristics and activities of the transition framework.We report how the inductively developed framework can be a good replacement for the previous one.We report empirical evaluation of the findings and theoretical and practical implications of them. To date, few Agile transition and adoption frameworks have been proposed in the software industry. However, using them is not easy in practice and primarily requires a huge organizational overhead because of their complex and non-flexible structure. These drawbacks make such frameworks difficult to apply in small and medium-sized companies. We have conducted a large-scale empirical research study using Grounded Theory approach with the participation of 49 Agile experts from 13 different countries. This study inductively developed a substantive Agile transition and adoption framework which appears to be simple and flexible. The main aim of this paper is to present the developed framework. The primary characteristics of this framework, including iterative, gradual, continuous, and value-based are in line with the Agile approach and show promise of being useful in software companies and organizations, regardless of size. This paper also describes how various steps of this framework could help software companies to achieve Agile transformation.


title: Nitrogen cycling in a hypothetical scenario of generalised organic agriculture in the Seine, Somme and Scheldt watersheds

abstract: Nitrogen contamination of ground and surface water in the Seine, Somme and Scheldt watersheds, as well as in the receiving coastal marine zones, results in severe ecological problems. Previous modelling results showed that the implementation of classical management measures involving improvement of wastewater purification and “good agricultural practices” are not sufficient to obviate these problems. A more radical scenario was therefore established, consisting of a generalised shift to organic agriculture of all agricultural areas in the three basins, with the additional constraints that livestock is fed only on local fodder production. This scenario involves an increased livestock density in the Seine and Somme and a decrease in livestock in the Scheldt basin. It leads to a significant reduction of agricultural production that finally brings the three basins closer to autotrophy/heterotrophy equilibrium. Nitrate concentrations in most of the drainage network would drop below the threshold of 2.25 mgN/l in the most optimistic hypothesis. The excess of nitrogen over silica (with respect to the requirements of marine diatoms) delivered into the coastal zones would be decreased by a factor from 2 to 5, thus strongly reducing, but not entirely eliminating the potential for marine eutrophication.


title: The dynamics of molecular bearings

abstract: Various types of molecular bearings have recently been proposed in the growing nanotechnology literature. Using novel molecular dynamics methods, we have simulated several model graphite bearings. The bearings varied in size from inner shafts of between 4 and 16 A in diameter, up to 120 A in length, and outer cylinders of between 10 and 23 A in diameter, up to 40 A in length. The turning shaft was either instantaneously started or torqued up to the desired rotational speeds. Frictional properties were size-, temperature- and velocity-dependent. The presence of more than one bearing vibrational mode in some simulations created beats that could possibly adversely affect bearing performance; placing a stretching tension on the bearing suppressed one of the modes and therefore the beats. These and future studies will help evaluate the performance, wear and load-bearing properties of fundamental components of nanomachines such as bearings.


title: Nasal tip projection and facial attractiveness.

abstract: OBJECTIVES/HYPOTHESIS ::: Six nasal tip projection (NTP) ratios from Goode, Simons, Baum, Powell, and Crumley guide clinical and academic practice on quantifying NTP, but none have been empirically correlated with facial attractiveness. This study's objectives were to determine: 1) if there is a correlation between these ratios and facial attractiveness; and 2) which of the six ratios has the greatest linkage to overall facial attractiveness. ::: ::: ::: STUDY DESIGN ::: Basic research study. ::: ::: ::: METHODS ::: There were 300 digital portraits of women (ages 18-25 years) randomly paired and morphed to create 300 synthetic lateral facial images rated by 78 raters in the community. NTP ratios were measured in each portrait. ::: ::: ::: RESULTS ::: None of the ratios correlated with facial attractiveness. For the Baum, Powell, and Simons ratio, facial attractiveness increased as NTP deviated 1 and 2 standard deviations from the ideal, whereas facial attractiveness decreased as NTP deviated from the Goode and Crumley ideal ratios. The most attractive faces had NTP ratios consistent with previous expert opinion findings. ::: ::: ::: CONCLUSIONS ::: To our knowledge, this is the first study to empirically correlate these six landmark NTP ratios with facial attractiveness. Although there was no correlation with any of the six ratios, the ideal ratios proposed by Goode and Crumley impacted facial aesthetics the most. Although the ideal ratios are useful in establishing rhinoplasty guidelines, they should only be used as a part of the management in achieving an aesthetic face on the whole, as they may not be robust enough to correlate with overall facial attractiveness.


title: BabelNet: The automatic construction, evaluation and application of a . . .

abstract: We present an automatic approach to the construction of BabelNet, a very large, wide-coverage multilingual semantic network. Key to our approach is the integration of lexicographic and encyclopedic knowledge from WordNet and Wikipedia. In addition, Machine Translation is applied to enrich the resource with lexical information for all languages. We first conduct in vitro experiments on new and existing gold-standard datasets to show the high quality and coverage of BabelNet. We then show that our lexical resource can be used successfully to perform both monolingual and cross-lingual Word Sense Disambiguation: thanks to its wide lexical coverage and novel semantic relations, we are able to achieve state-of the-art results on three different SemEval evaluation tasks.


Fingerprinting information in javascript implementations


title: Problem characterization and abstraction for visual analytics in behavior-based malware pattern analysis

abstract: Behavior-based analysis of emerging malware families involves finding suspicious patterns in large collections of execution traces. This activity cannot be automated for previously unknown malware families and thus malware analysts would benefit greatly from integrating visual analytics methods in their process. However existing approaches are limited to fairly static representations of data and there is no systematic characterization and abstraction of this problem domain. Therefore we performed a systematic literature study, conducted a focus group as well as semi-structured interviews with 10 malware analysts to elicit a problem abstraction along the lines of data, users, and tasks. The requirements emerging from this work can serve as basis for future design proposals to visual analytics-supported malware pattern analysis.


title: Attacking an SRAM-Based PUF through Wearout

abstract: Physical unclonable functions (PUFs) provide a fast and cheap solution to secret key generation. Natural variations in silicon create unique “fingerprints” that are useful for identification. In a 6T SRAM array, these variations cause individual cells to skew their power-on tendency toward storing a 0 or a 1. Wearout effects interfere with those variations, changing the power-on behavior of an SRAM cell in the opposite direction of a stored bit and affecting its reliability as a PUF. In this work, we take advantage of this effect by exposing an SRAM array to high voltage and temperature to activate and accelerate wearout and show that it can cause significant changes to the SRAM's fingerprint. Then we propose an attack on an SRAM PUF that makes use of these conditions with several stored data patterns to modify its fingerprint and then compare the effectiveness of each pattern at producing false negatives for identification challenges. In doing so, we show that false negatives can be increased to 100% in less than 24 hours, effectively erasing the fingerprint and rendering the PUF unusable.


title: THE EMPATHIC COMPANION: A CHARACTER-BASED INTERFACE THAT ADDRESSES USERS ’ AFFECTIVE STATES

abstract: ABSTRACT In this paper, we report on our efforts in developing affective character-based interfaces, i.e., interfaces that recognize and measure affective information of the user and address user affect by employing embodied characters. In particular, we describe the Empathic Companion, an animated interface agent that accompanies the user in the setting of a virtual job interview. This interface application takes physiological data (skin conductance and electromyography) of a user in realtime, interprets them as emotions, and addresses the user's affective states in the form of empathic feedback. The Empathic Companion is conceived as an educational agent that supports job seekers preparing for a job interview. We also present results from an exploratory study that aims to evaluate the impact of the Empathic Companion by measuring users' skin conductance and heart rate. While an overall positive effect of the Empathic Companion could not be shown, the outcome of the experiment suggests that empathic feed...


title: Relationship between Variants of One-Class Nearest Neighbors and Creating Their Accurate Ensembles

abstract: In one-class classification problems, only the data for the target class is available, whereas the data for the non-target class may be completely absent. In this paper, we study one-class nearest neighbour (OCNN) classifiers and their different variants. We present a theoretical analysis to show the relationships among different variants of OCNN that may use different neighbours or thresholds to identify unseen examples of the non-target class. We also present a method based on inter-quartile range for optimising parameters used in OCNN in the absence of non-target data during training. Then, we propose two ensemble approaches based on random subspace and random projection methods to create accurate OCNN ensembles. We tested the proposed methods on 15 benchmark and real world domain-specific datasets and show that random-projection ensembles of OCNN perform best.


title: Treatment of selective mutism: a 5-year follow-up study

abstract: Selective mutism (SM) has been defined as an anxiety disorder in the diagnostic and statistical manual of mental disorders (DSM-5). Cognitive behavioral therapy (CBT) is the recommended approach for SM, but prospective long-term outcome studies are lacking. Reports from the children themselves, and the use of more global quality of life measures, are also missing in the literature. We have developed a school-based CBT intervention previously found to increase speech in a pilot efficacy study and a randomized controlled treatment study. Continued progress was found in our 1-year follow-up studies, where older age and more severe SM had a significant negative effect upon outcome. In the present study, we provide 5-year outcome data for 30 of these 32 children with SM who completed the same CBT for mean 21 weeks (sd 5, range 8–24) at mean age 6 years (10 boys). Mean age at the 5-year follow-up was 11 years (range 8–14). Outcome measures were diagnostic status, the teacher- and parent-rated selective mutism questionnaires, and child rated quality of life and speaking behavior. At the 5-year follow-up, 21 children were in full remission, five were in partial remission and four fulfilled diagnostic criteria for SM. Seven children (23%) fulfilled criteria for social phobia, and separation anxiety disorder, specific phobia and/or enuresis nocturna were found in a total of five children (17%). Older age and severity at baseline and familial SM were significant negative predictors of outcome. Treatment gains were maintained on the teacher- and parent questionnaires. The children rated their overall quality of life as good. Although most of them talked outside of home, 50% still experienced it as somewhat challenging. These results point to the long-term effectiveness of CBT for SM, but also highlight the need to develop more effective interventions for the subset of children with persistent symptoms.


title: Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification

abstract: PURPOSE ::: In both structural and functional MRI, there is a need for accurate and reliable automatic segmentation of brain regions. Inconsistent segmentation reduces sensitivity and may bias results in clinical studies. The current study compares the performance of publicly available segmentation tools and their impact on diffusion quantification, emphasizing the importance of using recently developed segmentation algorithms and imaging techniques. ::: ::: ::: METHODS ::: Four publicly available, automatic segmentation methods (volBrain, FSL, FreeSurfer and SPM) are compared to manual segmentation of the thalamus and hippocampus imaged with a recently proposed T1-weighted MRI sequence (MP2RAGE). We evaluate morphometric accuracy on 22 healthy subjects and impact on diffusivity measurements obtained from aligned diffusion-weighted images on a subset of 10 subjects. ::: ::: ::: RESULTS ::: Compared to manual segmentation, the highest Dice similarity index of the thalamus is obtained with volBrain using a local library ([Formula: see text], [Formula: see text]) followed by volBrain using an external library ([Formula: see text], [Formula: see text]), FSL ([Formula: see text], [Formula: see text]), FreeSurfer ([Formula: see text], [Formula: see text]) and SPM ([Formula: see text], [Formula: see text]). The same order is found for hippocampus with volBrain local ([Formula: see text], [Formula: see text]), volBrain external ([Formula: see text], [Formula: see text]), FSL ([Formula: see text], [Formula: see text]), FreeSurfer ([Formula: see text], [Formula: see text]) and SPM ([Formula: see text], [Formula: see text]). For diffusivity measurements, volBrain provides values closest to those obtained from manual segmentations. volBrain is the only method where FA values do not differ significantly from manual segmentation of the thalamus. ::: ::: ::: CONCLUSIONS ::: Overall we find that volBrain is superior in thalamus and hippocampus segmentation compared to FSL, FreeSurfer and SPM. Furthermore, the choice of segmentation technique and training library affects quantitative results from diffusivity measures in thalamus and hippocampus.


title: Challenges and opportunities for Fan-out Panel Level Packing (FOPLP)

abstract: Fan-out Wafer Level Packaging (FOWLP) is one of the latest packaging trends in microelectronics. Mold embedding for this technology is currently done on wafer level up to 12”/300 mm size. For higher productivity and therewith lower costs larger mold embedding form factors are forecasted for the near future. Following the wafer level approach then the next step will be a reconfigured wafer size of 450 mm. An alternative option would be leaving the wafer shape and moving to panel sizes leading to Fan-out Panel Level Packaging (FOPLP). Sizes for the panel could range up to 18”×24” or even larger. For reconfigured mold embedding, compression mold processes are used in combination with liquid, granular or sheet compound. As an alternative process, lamination can be also considered. Already today PCB technologies offer the potential for large area panel packaging up to 24”×18”/610 × 457 mm2 and can be applied to form a redistribution layer [RDL] for large area reconfigured wafers or panels, replacing thin film redistribution. For PCB based RDLs a resin coated copper sheet (RCC) is laminated on the reconfigured wafer or panel, respectively. Micro vias are drilled through the RCC layer to the die pads and electrically connected by Cu plating. Final process step is the etching of Cu lines using LDI techniques for maskless patterning. State of the art equipment and materials the manufacturing of structures down to 20 μm lines and spaces with a clear development trend to 10 (mi lines and spaces and hence getting close to photolithography thin film structure sizes. Using the above mentioned maskless laser direct imaging technologies (LDI) instead of photolithography have a high potential for further cost reduction with intrinsic process advantages. The LDI cost advantage is backed by LDI availability for large panel sizes, also including 450 mm wafer form factors. Based on the technology described the Fan-out Panel Level Packaging approach will be demonstrated on full 24”×18”/610 × 457 mm2 format including large area assembly, embedding and redistribution. Related technology challenges as die shift, warpage, panel handling or yield will be discussed in detail. Using maskless LDI technology real die positions could be automatically adapted to the redistribution and hence less accurate die placement can be compensated and higher die shift could be tolerated which is a big advantage when moving towards large area with acceptable yield. In summary this paper describes the technological path from wafer level embedding to 24”×18” fan-out panel level packaging technology in combination with low cost PCB based RDL processes and discusses challenges and opportunities in detail. The technology described offers a cost effective packaging solution for various application as packages for handheld consumer application or bio-medical application as sensor integration into microfluidics.


title: Subreflectarrays for ring-focus reflector antenna

abstract: In this paper, a design that utilizes subreflectarrays for ring-focus reflector antenna is proposed. A 0.3 m parabolic reflector which operates at 23 GHz is chosen as a main reflector in ring-focus reflector antenna, and a 50 mm diameter circular array consists of 68 elements is designed to emulate a metal subreflector, using a method presented in this paper. Simulation rerults of the reflector demonstrate a good availability of the presented design about utilizing reflectarrays as subreflector for ring-focus reflector antenna.


title: How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0

abstract: We are living in the era of the fourth industrial revolution, namely Industry 4.0. This paper presents the main aspects related to Industry 4.0, the technologies that will enable this revolution, and the main application domains that will be affected by it. The effects that the introduction of Internet of Things (IoT), Cyber-Physical Systems (CPS), crowdsensing, crowdsourcing, cloud computing and big data will have on industrial processes will be discussed. The main objectives will be represented by improvements in: production efficiency, quality and cost-effectiveness; workplace health and safety, as well as quality of working conditions; products’ quality and availability, according to mass customisation requirements. The paper will further discuss the common denominator of these enhancements, i.e., data collection and analysis. As data and information will be crucial for Industry 4.0, crowdsensing and crowdsourcing will introduce new advantages and challenges, which will make most of the industrial processes easier with respect to traditional technologies.


title: An Efficient VLSI Architecture for Convolution Based DWT Using MAC

abstract: The modern real time applications related to image processing and etc., demand high performance discrete wavelet transform (DWT). This paper proposes the floating point multiply accumulate circuit (MAC) based 1D/2D-DWT, where the MAC is used to find the outputs of high/low pass FIR filters. The proposed technique is implemented with 45 nm CMOS technology and the results are compared with various existing techniques. The proposed 8 x 8-point floating point 2-levels 2D-DWT achieves 27.6% and 83.7% of reduction in total area and net power respectively as compared with existing DWT [9].


title: Feature Selection: Evaluation, Application, and Small Sample Performance

abstract: A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward floating selection algorithm, proposed by Pudil et al. (1994), dominates the other algorithms tested. We study the problem of choosing an optimal feature set for land use classification based on SAR satellite images using four different texture models. Pooling features derived from different texture models, followed by a feature selection results in a substantial improvement in the classification accuracy. We also illustrate the dangers of using feature selection in small sample size situations.


title: Using Open Data to Analyze Urban Mobility from Social Networks

abstract: The need to use online technologies that favor the understanting of city dynamics has grown, mainly due to the ease in obtaining the necessary data, which, in most cases, are gathered with no cost from social networks services. With such facility, the acquisition of georeferenced data has become easier, favoring the interest and feasibility in studying human mobility patterns, bringing new challenges for knowledge discovery in GIScience. This favorable scenario also encourages governments to make their data available for public access, increasing the possibilities for data scientist to analyze such data. This article presents an approach to extracting mobility metrics from Twitter messages and to analyzing their correlation with social, economic and demographic open data. The proposed model was evaluated using a dataset of georeferenced Twitter messages and a set of social indicators, both related to Greater London. The results revealed that social indicators related to employment conditions present higher correlation with the mobility metrics than any other social indicators investigated, suggesting that these social variables may be more relevant for studying mobility behaviors.


title: Marine sponge microbial association: Towards disclosing unique symbiotic interactions

abstract: Abstract Sponges are sessile benthic filter-feeding animals, which harbor numerous microorganisms. The enormous diversity and abundance of sponge associated bacteria envisages sponges as hot spots of microbial diversity and dynamics. Many theories were proposed on the ecological implications and mechanism of sponge-microbial association, among these, the biosynthesis of sponge derived bioactive molecules by the symbiotic bacteria is now well-indicated. This phenomenon however, is not exhibited by all marine sponges. Based on the available reports, it has been well established that the sponge associated microbial assemblages keep on changing continuously in response to environmental pressure and/or acquisition of microbes from surrounding seawater or associated macroorganisms. In this review, we have discussed nutritional association of sponges with its symbionts, interaction of sponges with other eukaryotic organisms, dynamics of sponge microbiome and sponge-specific microbial symbionts, sponge-coral association etc.


title: Feature Selection for Document Flow Segmentation

abstract: In this paper, we describe a method to restore a flow of continuous documents. The flow is a collection of consecutive scanned pages without explicit separation marks between documents. Our method is based on contextual and layout descriptors meant to specify the relationship between each pair of consecutive pages. The relationships are represented using vectors of features with boolean values indicating the presence or the absence of descriptors on concerned pages. The segmentation task therefore consists in classifying such vectors into continuities or breaks. The continuity class indicates that pages belong to the same document while the break class ends the ongoing document and starts a new one. The experimental part is based on a large collection of real administrative documents.


Variational Attribute Grammars for Computer Aided Design


title: DTC-SVM control for three phase induction motors

abstract: By principle, Direct Torque Control (DTC) is used to adjust stator flux and electromagnetic torque. Simple structure and very good dynamic behavior are main features of DTC. However classical DTC has some disadvantages, the most important of them is the high ripple torque. In this paper we propose some variety of DTC combined with FOC structures. Hysteresis controllers and switching table are replaced by PI controllers and space vector modulator. Three algorithms work in fixed switching frequency are developed, can overcome the aforementioned drawback. The proposed schemes are described clearly. Simulation results prove the effectiveness, reliability and correctness of the proposed methods.


title: Large Scale Distributed Data Science from scratch using Apache Spark 2.0

abstract: Apache Spark is an open-source cluster computing framework. It has emerged as the next generation big data processing engine, overtaking Hadoop MapReduce which helped ignite the big data revolution. Spark maintains MapReduce's linear scalability and fault tolerance, but extends it in a few important ways: it is much faster (100 times faster for certain applications), much easier to program in due to its rich APIs in Python, Java, Scala, SQL and R (MapReduce has 2 core calls) , and its core data abstraction, the distributed data frame. In addition, it goes far beyond batch applications to support a variety of compute-intensive tasks, including interactive queries, streaming, machine learning, and graph processing. With massive amounts of computational power, deep learning has been shown to produce state-of-the-art results on various tasks in different fields like computer vision, automatic speech recognition, natural language processing and online advertising targeting. Thanks to the open-source frameworks, e.g. Torch, Theano, Caffe, MxNet, Keras and TensorFlow, we can build deep learning model in a much easier way. Among all these framework, TensorFlow is probably the most popular open source deep learning library. TensorFlow 1.0 was released recently, which provide a more stable, flexible and powerful computation tool for numerical computation using data flow graphs. Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. This tutorial will provide an accessible introduction to large-scale distributed machine learning and data mining, and to Spark and its potential to revolutionize academic and commercial data science practices. It is divided into three parts: the first part will cover fundamental Spark concepts, including Spark Core, functional programming ala map-reduce, data frames, the Spark Shell, Spark Streaming, Spark SQL, MLlib, and more; the second part will focus on hands-on algorithmic design and development with Spark (developing algorithms from scratch such as decision tree learning, association rule mining (aPriori), graph processing algorithms such as pagerank/shortest path, gradient descent algorithms such as support vectors machines and matrix factorization. Industrial applications and deployments of Spark will also be presented.; the third part will introduce deep learning concepts, how to implement a deep learning model through TensorFlow, Keras and run the model on Spark. Example code will be made available in python (pySpark) notebooks.


title: Enhancing Electricity Audits in Residential Buildings with Nonintrusive Load Monitoring

abstract: Nonintrusive load monitoring (NILM) is a technique for deducing the power consumption and operational schedule of individual loads in a building from measurements of the overall voltage and current feeding it, using information and communication technologies. In this article, we review the potential of this technology to enhance residential electricity audits. First, we review the currently commercially available whole-house and plug-level technology for residential electricity monitoring in the context of supporting audits. We then contrast this with NILM and show the advantages and disadvantages of the approach by discussing results from a prototype system installed in an apartment unit. Recommendations for improving the technology to allow detailed, continuous appliance-level auditing of residential buildings are provided, along with ideas for possible future work in the field.


title: Finding the largest area rectangle of arbitrary orientation in a closed contour

abstract: Abstract For many software applications, it is sometimes necessary to find the rectangle of largest area inscribed in a polygon, in any possible direction. Thus, given a closed contour C, we consider approximation algorithms for the problem of finding the largest area rectangle of arbitrary orientation that is fully contained in C. Furthermore, we compute the largest area rectangle of arbitrary orientation in a quasi-lattice polygon, which models the C contour. In this paper, we propose an approximation algorithm that solves this problem with an O ( n 3 ) computational cost, where n is the number of vertices of the polygon. There is no other algorithm having lower computational complexity regardless of any constraints. In addition, we have developed a web application that uses the proposed algorithm.


title: BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors

abstract: When incorporating deep neural networks into robotic systems, a major challenge is the lack of uncertainty measures associated with their output predictions. Methods for uncertainty estimation in the output of deep object detectors (DNNs) have been proposed in recent works, but have had limited success due to 1) information loss at the detectors non-maximum suppression (NMS) stage, and 2) failure to take into account the multitask, many-to-one nature of anchor-based object detection. To that end, we introduce BayesOD, an uncertainty estimation approach that reformulates the standard object detector inference and Non-Maximum suppression components from a Bayesian perspective. Experiments performed on four common object detection datasets show that BayesOD provides uncertainty estimates that are better correlated with the accuracy of detections, manifesting as a significant reduction of 9.77\%-13.13\% on the minimum Gaussian uncertainty error metric and a reduction of 1.63\%-5.23\% on the minimum Categorical uncertainty error metric. Code will be released at {\url{this https URL}}.


title: ICN based distributed IoT resource discovery and routing

abstract: IoT resource registration and lookup interfaces have been specified for Resource Directory (RD) with defined resource link format in IETF standard. However, it has many drawbacks due to the nature of the centralized architecture. This paper proposes an ICN-based distributed RD architecture. The proposed distributed IoT resource discovery and routing mechanisms allow and reuse the existing RD resource registration and lookup interfaces, which aligns with the IETF standard. Routers do not maintain routing table for every IoT resource, but keep routing tables on the aggregated resource attributes. A client does not need to know the resource names in advance for the routing. Resource advertisement and routing table advertisement are triggered by the updates, which prevents the network congestion due to the control messages. It also allows the client to specify how many resources would be returned. Our simulation results indicate substantial performance improvement of the proposed ICN-based distributed RD architecture in terms of network overhead and routing table size compared to the existing distributed RD deployments.


title: CRISP-DM as a framework for discovering knowledge in small and medium sized enterprises' data

abstract: Discovering knowledge from a waste amount of data has become a promising area nowadays, but at the same time it is a very intricate, uncertain and time consuming process. The complexity of a data collection, the oscillations in data quality and their impact on the discovery process, as well as the applicability of results, urge for an extensive research and gain of experience to overcome the difficulties that can jeopardize the knowledge in data discovery (KDD) process as a whole. In this article we described the limitations and challenges of discovering knowledge, that we have experienced analyzing small and medium sized enterprises' (SMEs) data.


title: SWAM: Stuxnet Worm Analysis in Metasploit

abstract: Nowadays cyber security is becoming a great challenge. Attacker's community is progressing towards making smart and intelligent malwares (viruses, worms and Root kits). They stealth their existence and also use administrator rights without knowing legal user. Stuxnet worm is an example of a recent malware first detected in July 2010. Its variants were also detected earlier. It is the first type of worm that affects the normal functionality of industrial control systems (ICS) having programmable logic controllers (PLC) through PLC Root kit. Its main goal is to modify ICS behavior by changing the code of PLC and make it to behave in a way that attacker wants. It is a complex piece of malware having different operations and functionalities which are achieved by exploiting zero day vulnerabilities. Stuxnet exploits various vulnerable services in Microsoft Windows. In this paper we will show real time simulation of first three vulnerabilities of these through Metasploit Framework 3.2 and analyze results. A real time scenario is established based on some assumptions. We assumed Proteus design (pressure sensor) as PLC and showed after exploitation that the pressure value drops to an unacceptable level by changing Keil code of this design.


title: Spring parameters design to increase the loading capability of a hydraulic quadruped robot

abstract: A quadruped robot “Baby Elephant” with parallel legs has been developed. It is about 1 m tall, 1.2 m long and 0.5m wide. It weighs about 130 kg. Driven by a new type of hydraulic actuation system, the Baby Elephant is designed to work as a mechanical carrier. It can carry a payload more than 50 kg. In this study, the structure of the legs is introduced first. Then the effect of the springs for increasing the loading capability is explained and discovered. The design problem of the spring parameters is also discussed. Finally, simulations and experiments are carried out to confirm the effect.


title: Meta-learning in reinforcement learning

abstract: Meta-parameters in reinforcement learning should be tuned to the environmental dynamics and the animal performance. Here, we propose a biologically plausible meta-reinforcement learning algorithm for tuning these meta-parameters in a dynamic, adaptive manner. We tested our algorithm in both a simulation of a Markov decision task and in a non-linear control task. Our results show that the algorithm robustly finds appropriate meta-parameter values, and controls the meta-parameter time course, in both static and dynamic environments. We suggest that the phasic and tonic components of dopamine neuron firing can encode the signal required for meta-learning of reinforcement learning.


title: The influence of access to eReaders, computers and mobile phones on children's book reading frequency

abstract: Regular recreational book reading is a practice that confers substantial educative benefit. However, not all book types may be equally beneficial, with paper book reading more strongly associated with literacy benefit than screen-based reading at this stage, and a paucity of research in this area. While children in developed countries are gaining ever-increasing levels of access to devices at home, relatively little is known about the influence of access to devices with eReading capability, such as Kindles, iPads, computers and mobile phones, on young children's reading behaviours, and the extent to which these devices are used for reading purposes when access is available. Young people are gaining increasing access to devices through school-promoted programs; parents face aggressive marketing to stay abreast of educational technologies at home; and schools and libraries are increasingly their eBook collections, often at the expense of paper book collections. Data from the 997 children who participated in the 2016 Western Australian Study in Children's Book Reading were analysed to determine children's level of access to devices with eReading capability, and their frequency of use of these devices in relation to their recreational book reading frequency. Respondents were found to generally underutilise devices for reading purposes, even when they were daily book readers. In addition, access to mobile phones was associated with reading infrequency. It was also found that reading frequency was less when children had access to a greater range of these devices. Children underutilised devices for recreational book reading, even when daily book readers.Reading frequency was less when children had access to mobile phones.Reading in general was less when children were given access to more digital devices.


title: A Case Study in Access Control Requirements for a Health Information System

abstract: We present a detailed examination of the access constraints for a small real-world Health Information System with the aim of achieving minimal access rights for each of the involved principals. We show that, even for such a relatively simple system, the resulting constraints are very complex and cannot be expressed easily or clearly using the static per-method access control lists generally supported by component-based software. We derive general requirements for the expressiveness of access constraints and propose criteria for a more suitable access control mechanism in the context of component-based systems. We describe a two-level mechanism which can fulfil these criteria.


title: Statistical Methods in Psychology Journals: Guidelines and Explanations

abstract: In the light of continuing debate over the applications of significance testing in psychology journals and following the publication of Cohen's (1994) article, the Board of Scientific Affairs (BSA) of the American Psychological Association (APA) convened a committee called the Task Force on Statistical Inference (TFSI) whose charge was "to elucidate some of the controversial issues surrounding applications of statistics including significance testing and its alternatives; alternative underlying models and data transformation; and newer methods made possible by powerful computers" (BSA, personal communication, February 28, 1996). Robert Rosenthal, Robert Abelson, and Jacob Cohen (cochairs) met initially and agreed on the desirability of having several types of specialists on the task force: statisticians, teachers of statistics, journal editors, authors of statistics books, computer experts, and wise elders. Nineindividuals were subsequently invited to join and all agreed. These were Leona Aiken, Mark Appelbaum, Gwyneth Boodoo, David A. Kenny, Helena Kraemer, Donald Rubin, Bruce Thompson, Howard Wainer, and Leland Wilkinson. In addition, Lee Cronbach, Paul Meehl, Frederick Mosteller and John Tukey served as Senior Advisors to the Task Force and commented on written materials.


title: RankTopic: Ranking Based Topic Modeling

abstract: Topic modeling has become a widely used tool for document management due to its superior performance. However, there are few topic models distinguishing the importance of documents on different topics. In this paper, we investigate how to utilize the importance of documents to improve topic modeling and propose to incorporate link based ranking into topic modeling. Specifically, topical pagerank is used to compute the topic level ranking of documents, which indicates the importance of documents on different topics. By retreating the topical ranking of a document as the probability of the document involved in corresponding topic, a generalized relation is built between ranking and topic modeling. Based on the relation, a ranking based topic model Rank Topic is proposed. With Rank Topic, a mutual enhancement framework is established between ranking and topic modeling. Extensive experiments on paper citation data and Twitter data are conducted to compare the performance of Rank Topic with that of some state-of-the-art topic models. Experimental results show that Rank Topic performs much better than some baseline models and is comparable with the state-of-the-art link combined relational topic model (RTM) in generalization performance, document clustering and classification by setting a proper balancing parameter. It is also demonstrated in both quantitative and qualitative ways that topics detected by Rank Topic are more interpretable than those detected by some baseline models and still competitive with RTM.


True-~self and the uses and gratifications of Instagram among college-~aged females


title: CWRP: An efficient and classical weight ranking policy for enhancing cache performance

abstract: There is a huge difference between the performance of main memory and processor hence, caching is an essential technique to enhance the performance of computers. The access speed of cache memory is higher than the main memory which is used to bridge the performance gap between processor and main memory. Our Proposed work provides a solution for cache replacement which is easy to implement, has better performance as compared to the traditional cache replacement policies like First-In-First-Out (FIFO), Clock with Adaptive Replacement (CAR) and Least-Recently-Used (LRU).


title: Water quality monitoring with internet of things (IoT)

abstract: The paper suggests an Internet of Things (IoT) based system implementation by embedding the Radio Frequency Identification (RFID) system, Wireless Sensor Network (WSN) platform and Internet Protocol (IP) based communication into a single platform for water quality monitoring (WQM) purpose. The suggested radio frequency for the proposed WSN communication to be deployed in vegetation area is 920MHz. The measured water parameter in this proposed system is pH level by using an analog pH sensor. The ambient temperature is captured during pH measurement by using an analog temperature sensor. All the WSN nodes are deployed in a real environment at the lake in the campus area of Universiti Sains Malaysia (USM) for performance evaluation. Instead of using 2.4GHz ZigBee protocol, the 920MHz Digi Mesh protocol is proposed to be implemented for water quality monitoring in vegetation area due to its ability to surpass the signal attenuation. This novel proposed system prototype was evaluated in a real environment to ensure that the main functionality on pH measuring process is following the design requirements. Several experimental analysis were conducted including the energy analysis and communication read range analysis to study the overall performance of the proposed system.


title: Attacking convolutional neural network using differential evolution

abstract: The output of convolutional neural networks (CNNs) has been shown to be discontinuous which can make the CNN image classifier vulnerable to small well-tuned artificial perturbation. That is, images modified by conducting such alteration (i.e., adversarial perturbation) that make little difference to the human eyes can completely change the CNN classification results. In this paper, we propose a practical attack using differential evolution (DE) for generating effective adversarial perturbations. We comprehensively evaluate the effectiveness of different types of DEs for conducting the attack on different network structures. The proposed method only modifies five pixels (i.e., few-pixel attack), and it is a black-box attack which only requires the miracle feedback of the target CNN systems. The results show that under strict constraints which simultaneously control the number of pixels changed and overall perturbation strength, attacking can achieve 72.29%, 72.30%, and 61.28% non-targeted attack success rates, with 88.68%, 83.63%, and 73.07% confidence on average, on three common types of CNNs. The attack only requires modifying five pixels with 20.44, 14.28, and 22.98 pixel value distortion. Thus, we show that current deep neural networks are also vulnerable to such simpler black-box attacks even under very limited attack conditions.


title: Hyper Expression of Mucin 5ac Indicates Poor Cancer Prognoses: A Meta-Analysis

abstract: AbstractThe aim of the study was to explore the association between mucin 5ac expression and cancer prognosis.A systematically comprehensive search was performed through PubMed, the Web of Science, and the China National Knowledge Infrastructure (CNKI). The prognostic value of mucin 5ac expression in cancer patients was evaluated.The overexpression of mucin 5ac was found to be significantly associated with a poor prognosis in cancer patients (pooled HR: 1.53, 95%CI: 1.158–2.028, P = 0.003). This association was also detected in a biliary subgroup (pooled HR: 1.83, 95%CI: 1.269–2.639, P = 0.001) and a gastrointestinal subgroup (pooled HR: 1.44, 95%CI: 1.069–1.949 P = 0.017). In the geography subgroup analysis, a statistical association was found in the Asian subgroup (pooled HR: 1.69, 95%CI: 1.200–2.384, P = 0.003). In the clinical characteristics analysis, a statistical association was found between the hyper expression of mucin 5ac and lymphatic metastasis.We indicated that mucin 5ac is a promising prognostic predictor for cancer, especially for biliary and gastrointestinal cancer, and is more suitable for predicting cancer prognoses in Asians.


title: Automating Intention Mining

abstract: Developers frequently discuss aspects of the systems they are developing online. The comments they post to discussions form a rich information source about the system. Intention mining, a process introduced by Di Sorbo et al., classifies sentences in developer discussions to enable further analysis. As one example of use, intention mining has been used to help build various recommenders for software developers. The technique introduced by Di Sorbo et al. to categorize sentences is based on linguistic patterns derived from two projects. The limited number of data sources used in this earlier work introduces questions about the comprehensiveness of intention categories and whether the linguistic patterns used to identify the categories are generalizable to developer discussion recorded in other kinds of software artifacts (e.g., issue reports). To assess the comprehensiveness of the previously identified intention categories and the generalizability of the linguistic patterns for category identification, we manually created a new dataset, categorizing 5,408 sentences from issue reports of four projects in GitHub. Based on this manual effort, we refined the previous categories. We assess Di Sorbo et al.'s patterns on this dataset, finding that the accuracy rate achieved is low (0.31). To address the deficiencies of Di Sorbo et al.'s patterns, we propose and investigate a convolution neural network (CNN)-based approach to automatically classify sentences into different categories of intentions. Our approach optimizes CNN by integrating batch normalization to accelerate the training speed, and an automatic hyperparameter tuning approach to tune appropriate hyperparameters of CNN. Our approach achieves an accuracy of 0.84 on the new dataset, improving Di Sorbo et al.'s approach by 171%. We also apply our approach to improve an automated software engineering task, in which we use our proposed approach to rectify misclassified issue reports, thus reducing the bias introduced by such data to other studies. A case study on four open source projects with 2,076 issue reports shows that our approach achieves an average AUC score of 0.687, which improves other baselines by at least 16%.


Collective Deep Quantization for Efficient Cross-Modal Retrieval.


P53 functional abnormality in mesenchymal stem cells promotes osteosarcoma development


Flaviviral Replication Complex: Coordination between RNA Synthesis and 5′-RNA Capping


title: Dual Approach to the Modelling Single Product Demand Curves in the Next Best Offer CRM Problem

abstract: Customer Relationship Management (CRM) is a comprehensive approach for creating, maintaining and expanding customer relationships (Anderson & Kerr, 2002). The definition tells that CRM has to be a comprehensive way of doing business that touches all areas and does not belong to one department of an organization, such IT, Marketing or Sales. CRM is also a way of thinking and dealing with different aspects of relationship with customers and involves having a clear strategy and plan. In modern economy that relationship between a company and customer has various forms, from personal to completely virtual contacts with frequent customers who have never spoken to anyone from the company. Therefore, CRM also takes care about creating the idea and strategy of high touch in a high tech environment. Technology must not stay on the way of applying CRM strategy and must not be confused with it.


title: Speech Dereverberation Based on Variance-Normalized Delayed Linear Prediction

abstract: This paper proposes a statistical model-based speech dereverberation approach that can cancel the late reverberation of a reverberant speech signal captured by distant microphones without prior knowledge of the room impulse responses. With this approach, the generative model of the captured signal is composed of a source process, which is assumed to be a Gaussian process with a time-varying variance, and an observation process modeled by a delayed linear prediction (DLP). The optimization objective for the dereverberation problem is derived to be the sum of the squared prediction errors normalized by the source variances; hence, this approach is referred to as variance-normalized delayed linear prediction (NDLP). Inheriting the characteristic of DLP, NDLP can robustly estimate an inverse system for late reverberation in the presence of noise without greatly distorting a direct speech signal. In addition, owing to the use of variance normalization, NDLP allows us to improve the dereverberation result especially with relatively short (of the order of a few seconds) observations. Furthermore, NDLP can be implemented in a computationally efficient manner in the time-frequency domain. Experimental results demonstrate the effectiveness and efficiency of the proposed approach in comparison with two existing approaches.


title: Operant Procedures for Assessing Behavioral Flexibility in Rats

abstract: Executive functions consist of multiple high-level cognitive processes that drive rule generation and behavioral selection. An emergent property of these processes is the ability to adjust behavior in response to changes in one's environment (i.e., behavioral flexibility). These processes are essential to normal human behavior, and may be disrupted in diverse neuropsychiatric conditions, including schizophrenia, alcoholism, depression, stroke, and Alzheimer's disease. Understanding of the neurobiology of executive functions has been greatly advanced by the availability of animal tasks for assessing discrete components of behavioral flexibility, particularly strategy shifting and reversal learning. While several types of tasks have been developed, most are non-automated, labor intensive, and allow testing of only one animal at a time. The recent development of automated, operant-based tasks for assessing behavioral flexibility streamlines testing, standardizes stimulus presentation and data recording, and dramatically improves throughput. Here, we describe automated strategy shifting and reversal tasks, using operant chambers controlled by custom written software programs. Using these tasks, we have shown that the medial prefrontal cortex governs strategy shifting but not reversal learning in the rat, similar to the dissociation observed in humans. Moreover, animals with a neonatal hippocampal lesion, a neurodevelopmental model of schizophrenia, are selectively impaired on the strategy shifting task but not the reversal task. The strategy shifting task also allows the identification of separate types of performance errors, each of which is attributable to distinct neural substrates. The availability of these automated tasks, and the evidence supporting the dissociable contributions of separate prefrontal areas, makes them particularly well-suited assays for the investigation of basic neurobiological processes as well as drug discovery and screening in disease models.


title: A Bottom Fed Deployable Conical Log Spiral Antenna Design for CubeSat

abstract: A conical log spiral antenna is presented with a new feeding scheme. This antenna is proposed for deployment on a CubeSat platform where the new feeding technique allows for easier antenna deployment. The antenna is composed of two arms that are wrapped around each other in a log-periodic manner. It is designed on top of a ground plane allowing its bottom feeding property. The feeding network is composed of quarter-wavelengths transmission lines connected to a planar balun. The feeding network along with the balun provides the antenna with the appropriate impedance matching and phase shift between the two arms. The antenna is fabricated and tested. The measurement and simulation results show good agreement.


title: High-Order Terminal Sliding-Mode Observer for Parameter Estimation of a Permanent-Magnet Synchronous Motor

abstract: This paper proposes a terminal sliding-mode (TSM) observer for estimating the immeasurable mechanical parameters of permanent-magnet synchronous motors (PMSMs) used for complex mechanical systems. The observer can track the system states in finite time with high steady-state precision. A TSM control strategy is designed to guarantee the global finite-time stability of the observer and, meanwhile, to estimate the mechanical parameters of the PMSM. A novel second-order sliding-mode algorithm is designed to soften the switching control signal of the observer. The effect of the equivalent low-pass filter can be properly controlled in the algorithm based on requirements. The smooth signal of the TSM observer is directly used for the parameter estimation. The experimental results in a practical CNC machine tool are provided to demonstrate the effectiveness of the proposed method.


title: A variable frequency link for a power-aware . . .

abstract: Although the technology scaling has enabled designers to integrate a large number of processors onto a single chip realizing chip multi-processor (CMP), problems arising from technology scaling have made power reduction an important design issue. Since interconnection networks dissipate a significant portion of the total system power budget, it is desirable to consider interconnection network's power efficiency when designing CMP. In this paper, we present a variable frequency link for a power-aware interconnection network using the clock boosting mechanism, and apply a dynamic frequency scaling (DFS) policy, that judiciously adjusts link frequency based on link utilization parameter. Experimental result shows that history-based DFS successfully adjusts link frequency to track actual link utilization over time, demonstrating the feasibility of the proposed link as a power-aware interconnection network for system-on-chip (SoC).


title: A novel approach to characterize host cell proteins associated with therapeutic monoclonal antibodies

abstract: Recombinant monoclonal antibody (mAb) products are widely produced in the pharmaceutical industry using Chinese hamster ovary (CHO) cells. Host cell proteins (HCPs) are one of many process-related impurities generated during the production of mAb products. The multi-analyte HCP enzyme linked immunosorbent assay (ELISA) is the industry standard accepted assay to measure clearance of HCPs from recombinant protein therapeutics. While similar platform processes are used for expression and purification, varying amounts of HCPs are found in final drug substances for different mAb products. Through the use of novel ELISA formats, an HCP-mAb product interaction was identified using a variety of ELISAs to investigate the underlying protein-protein interaction. This result provides evidence to explain why some mAb products have HCPs that are not cleared during purification. This ELISA technique can be used as a high-throughput screening tool to identify conditions to improve clearance of difficult HCPs during downstream processing purification. In addition, an interaction was observed to occur between anti-CHO HCP antibodies and mAb products. However, this interaction only occurs under denaturing conditions, and does not interfere with the HCP quantitation obtained by ELISA, and it was demonstrated that the cross-reactive anti-CHO HCP antibodies can be removed by affinity purification. Biotechnol. Bioeng. 2017;114: 1208-1214. © 2017 Wiley Periodicals, Inc.


title: Common and Unique Neural Correlates of Autobiographical Memory and Theory of Mind

abstract: There is an inconsistency regarding the relationship between thinking about personal past experiences during autobiographical memory (AM) and thinking about other people's mental states during theory of mind (ToM). Neuroimaging studies of AM and ToM consistently report overlap in the brain regions recruited. Lesion data, however, show that amnesic people with AM impairment can have intact ToM, suggesting that distinct neural mechanisms support these abilities [Rosenbaum, R. S., Stuss, D. T., Levine, B., & Tulving, E. Theory of mind is independent of episodic memory. Science,318, 1257, 2007]. The current fMRI study examined the functional and neural correlates of remembering one's own experiences in response to personal photos (AM condition) and imagining others' experiences in response to strangers' photos (ToM condition). AM and ToM conditions were matched in terms of content and vividness, and were compared directly and to a common baseline. Analyses revealed common activity within frontal and temporal-parietal regions, yet midline structures exhibited greater activity during AM. More specific analyses of event construction and detail elaboration revealed unique activation of the right hippocampus during AM construction, and of lateral regions, such as the right temporo-parietal junction (TPJ) during ToM elaboration. Moreover, a region of left hippocampus/perirhinal cortex appeared to be driven by event vividness. Thus, differences in AM and ToM emerge when a common baseline is used and temporal dynamics are taken into account. Furthermore, the right TPJ and related lateral regions, and not the hippocampus, may be needed for ToM, given that this ability is intact in amnesic people.


title: Constant-g/sub m/ techniques for rail-to-rail CMOS amplifier input stages: a comparative study

abstract: This paper presents a comparative study of a number of constant-transconductance (g/sub m/) techniques for CMOS amplifier input stages. The pros and cons of each technique are discussed. Theoretical analysis along with simulation results are discussed to demonstrate the performance of each constant-g/sub m/ technique. Finally, we propose a novel technique which achieves within /spl plusmn/3% g/sub m/ variation over the full input common-mode voltage range. The new technique exhibits improvement over other existing techniques.


title: Political Connections and Corporate Bailouts

abstract: We analyze the likelihood of government bailouts of 450 politically connected firms from 35 countries during 1997‐2002. Politically connected firms are significantly more likely to be bailed out than similar nonconnected firms. Additionally, politically connected firms are disproportionately more likely to be bailed out when the International Monetary Fund or the World Bank provides financial assistance to the firm’s home government. Further, among bailed-out firms, those that are politically connected exhibit significantly worse financial performance than their nonconnected peers at the time of and following the bailout. This evidence suggests that, at least in some countries, political connections influence the allocation of capital through the mechanism of financial assistance when connected companies confront economic distress.


title: DeepCloak: Masking DNN Models for robustness against adversarial samples

abstract: Recent studies have shown that deep neural networks (DNN) are vulnerable to adversarial samples: maliciously-perturbed samples crafted to yield incorrect model outputs. Such attacks can severely undermine DNN systems, particularly in security-sensitive settings. It was observed that an adversary could easily generate adversarial samples by making a small perturbation on irrelevant feature dimensions that are unnecessary for the current classification task. To overcome this problem, we introduce a defensive mechanism called DeepMask. By identifying and removing unnecessary features in a DNN model, DeepMask limits the capacity an attacker can use generating adversarial samples and therefore increase the robustness against such inputs. Comparing with other defensive approaches, DeepMask is easy to implement and computationally efficient. Experimental results show that DeepMask can increase the performance of state-of-the-art DNN models against adversarial samples.


title: An introduction to modern missing data analyses

abstract: Abstract A great deal of recent methodological research has focused on two modern missing data analysis methods: maximum likelihood and multiple imputation. These approaches are advantageous to traditional techniques (e.g. deletion and mean imputation techniques) because they require less stringent assumptions and mitigate the pitfalls of traditional techniques. This article explains the theoretical underpinnings of missing data analyses, gives an overview of traditional missing data techniques, and provides accessible descriptions of maximum likelihood and multiple imputation. In particular, this article focuses on maximum likelihood estimation and presents two analysis examples from the Longitudinal Study of American Youth data. One of these examples includes a description of the use of auxiliary variables. Finally, the paper illustrates ways that researchers can use intentional, or planned, missing data to enhance their research designs.


title: Contribution to a diagnosis system of the Whewellite- and Weddellite crystals in human urinary using data minig algorithms

abstract: The goal of this study is to develop a simple intelligent urolithiasis diagnosis system. The accuracy of the system was determined by comparing the recognition rates of the Artificial Neural Networks (ANNs)-, k-nearest Neighbor (kNN)-, and Support Vector Machines (SVM) algorithms. The results showed that the ANN model was superior to SVM and KNN models in prediction. We aimed through this work to classify the subjects in three classes according to the chemical concentrations of variables (Ca, Ox, pCaOx, Ca/ Ox) using and according to their clinical status. The ANN model, used to determine the first class that contains the subjects presenting their urine a calcium oxalate monohydrate (CaC 2O4,H 2O : whewellite (Wh)) crystal type. This ANN model reached a correct prediction rate of 85.3%. Using SVM- and KNN model the correct prediction rate reached 82.6% and 65.55% respectively. The second class contains the subjects presenting a calcium oxalate dihydrate (CaC 2O4,2H 2O wedellite (Wd)) crystal type. The ANN-, SVM- and KNN model reached a 93.4%-, 94.2%- and 77.25% correct prediction rate, respectively. In third class that corresponds to the subjects who have negative crystalluria (NC), ANN-, SVM- and KNN model reached a 91.7%-, 87.8%- and 69.77% correct prediction rate, respectively. Compared to SVM- and KNN models, the developed system using ANN model has allowed us to discriminate the subjects. This system is important in clinical laboratories since it could be a helpful tool for provide information about the development, formation of urinary stones crystals and the determination of their crystal type.


title: A Focused Planar Microstrip Array for 2.4 GHz RFID Readers

abstract: The specific problems encountered in the design of near-field focused planar microstrip arrays for RFID (Radio Frequency IDentification) readers are described. In particular, the paper analyzes the case of a prototype operating at 2.4 GHz, which has been designed and characterized. Improvements with respect to conventional far-field focused arrays (equal phase arrays) are discussed and quantified.


title: Distributed generation intelligent islanding detection using governor signal clustering

abstract: One of the major protection concerns with distribution networks comprising distributed generation is unintentional islanding phenomenon. Expert diagnosis system is needed to distinguish network cut off from normal occurrences. An important part of synchronous generator is automatic load-frequency controller (ALFC). In this paper, a new approach based on clustering of input signal to governor is introduced. Self-organizing map (SOM) neural network is used to identify and classify islanding and non-islanding phenomena. Simulation results show that input signal to governor has different characteristics concern with islanding conditions and other disturbances. In addition, the SOM is able to identify and classify phenomena satisfactorily. Using proposed method, islanding can be detected after 200 ms.


title: Host pigments: potential facilitators of photosynthesis in coral symbioses

abstract: Reef-building corals occur as a range of colour morphs because of varying types and concentrations of pigments within the host tissues, but little is known about their physiological or ecological significance. Here, we examined whether specific host pigments act as an alternative mechanism for photoacclimation in the coral holobiont. We used the coral Montipora monasteriata (Forskal 1775) as a case study because it occurs in multiple colour morphs (tan, blue, brown, green and red) within varying light-habitat distributions. We demonstrated that two of the non-fluorescent host pigments are responsive to changes in external irradiance, with some host pigments up-regulating in response to elevated irradiance. This appeared to facilitate the retention of antennal chlorophyll by endosymbionts and hence, photosynthetic capacity. Specifically, net P(max) Chl a(-1) correlated strongly with the concentration of an orange-absorbing non-fluorescent pigment (CP-580). This had major implications for the energetics of bleached blue-pigmented (CP-580) colonies that maintained net P(max) cm(-2) by increasing P(max) Chl a(-1). The data suggested that blue morphs can bleach, decreasing their symbiont populations by an order of magnitude without compromising symbiont or coral health.


title: Search Engine Optimization Research for Website Promotion

abstract: In order to make the search engine transfer information efficiently and accurately and do this optimization to improve the web search ranking, beginning with understanding the principle of search engine, this paper exports the specific explanation of search engine optimization. And then it introduces the new website building concepts and design concepts for the purpose of the construction of search engine optimization. Through an empirical research from the fields of the internal coding method, the website content realizable form and website overall architecture, the paper expounds search engine optimization tools, strategies and methods, and analysis the new thought that the enterprise and e-commerce sites with the search engine do the effective website promotion. And when the user through the search engine to search, the website can get a good rankings position in the search results, so as to improve the site traffic and finally enhance the website sales ability or advocacy capacity.


title: Efficient Multiview Stereo by Random-Search and Propagation

abstract: We present an efficient multi-view 3D reconstruction method based on randomization and propagation scheme. Our method progressively refines 3D point estimates by randomly perturbing the initial guess of 3D points and propagates photo-consistent ones to their neighbors. In contrast to previous refinement methods that perform local optimization for a better photo-consistency, our randomization approach takes lucky matchings for reducing the computational complexity. Experiments show favorable efficiency of the proposed method with the accuracy that is close to the state-of-the-art methods.