title: Depth enhanced visual-inertial odometry based on Multi-State Constraint Kalman Filter

abstract: There have been increasing demands for developing robotic system combining camera and inertial measurement unit in navigation task, due to their low-cost, lightweight and complementary properties. In this paper, we present a Visual Inertial Odometry (VIO) system which can utilize sparse depth to estimate 6D pose in GPS-denied and unstructured environments. The system is based on Multi-State Constraint Kalman Filter (MSCKF), which benefits from low computation load when compared to optimization-based method, especially on resource-constrained platform. Features are enhanced with depth information forming 3D landmark position measurements in space, which reduces uncertainty of position estimate. And we derivate measurement model to access compatibility with both 2D and 3D measurements. In experiments, we evaluate the performance of the system in different in-flight scenarios, both cluttered room and industry environment. The results suggest that the estimator is consistent, substantially improves the accuracy compared with original monocular-based MSKCF and achieves competitive accuracy with other research.


abstract: A synthesis technique for grounded theory data analysis ::: ::: Aims. The purposes of this paper are to examine the issues surrounding current changes in grounded theory (GT) research methods and to explicate an innovative synthesis technique to GT data analysis. ::: ::: Background. In recent years there has been a steady rise in the number of published research reports that use the GT method. However, this growing body of GT literature has been criticized for its lack of adherence to the method as explicated by its originators, Glaser and Strauss. ::: ::: Methods. Recent and past literature that explicates, describes, and discusses GT methods is reviewed. A synthesis technique for grounded theory data analysis was developed to analyse qualitative data collected for a grounded theory study on caregiving. This synthesis technique was derived from the works of four grounded theorists (Kathy Charmaz, Mark Chesler, Juliet Corbin and Anselm Strauss). ::: ::: Results. The lack of clarity and the inconsistencies surrounding GT analysis, as reported in the literature, resulted in the development of a synthesis technique based on the works of the aforementioned-grounded theorists. The product was a synthesis approach that included analytical steps from each of these authors. ::: ::: Conclusions. This synthesis approach increased understanding and enhanced clarity of GT data analysis techniques. This paper illustrates how integration of works by noted qualitative scholars is an appropriate and effective means to advance the discourse on data analysis for GT research studies.


title: Behavior of dissolved inorganic arsenic in the Yellow Sea and East China Sea

abstract: The Yellow Sea and East China Sea are strongly affected by input from freshwater discharge (e.g., from the Changjiang (Yangtze River) in the west and the Kuroshio incursion in the east. The R/V Dong Fang Hong 2 carried out two cruises in September of 2002 and 2003 to understand the behavior of arsenic in the Yellow Sea and East China Sea. Arsenic species (total dissolved inorganic arsenic and arsenite) were determined by selective hydride generation–atomic fluorescence spectrometry. High concentrations of total dissolved inorganic arsenic species (TDIAs; [As(V)]+[As(III)]) appeared near the Changjiang Estuary and at the bottom of the shelf-break in the East China Sea where the incursion of the Kuroshio reaches the shelf. TDIAs were moderately depleted in the surface water and enriched in the deep water. The distribution of arsenite (AsO33−, As(III)) was opposite that of arsenate (AsO43−, As(V)) in seawater, but was positively correlated with that of Chlorophyll-a. The As(III)/TDIAs ratio at the surface of coastal stations was lower than 0.3 in the summer of 2002 and as high as 0.5 in the summer of 2003. A limited effect from the Changjiang discharge in September 2003 according to the salinity profile and/or high phytoplankton conversion in the study area was hypothesized for the observed interannual variation. The stoichiometric ratio of As to P in the biological assimilation was estimated to be about 2×10−3 at the PN section (a southeast transect from the Changjiang Estuary to the Ryukyu Islands) of the East China Sea in the summer. A preliminary box model was established to estimate the water-mass balance and arsenic budgets for the East China Sea Shelf in the summer. Compared with other areas in the world, the concentration of dissolved inorganic arsenic in the Yellow Sea and East China Sea remained at natural levels.


title: Segmentation and recognition of multi-food meal images for carbohydrate counting

abstract: In this paper, we propose novel methodologies for the automatic segmentation and recognition of multi-food images. The proposed methods implement the first modules of a carbohydrate counting and insulin advisory system for type 1 diabetic patients. Initially the plate is segmented using pyramidal mean-shift filtering and a region growing algorithm. Then each of the resulted segments is described by both color and texture features and classified by a support vector machine into one of six different major food classes. Finally, a modified version of the Huang and Dom evaluation index was proposed, addressing the particular needs of the food segmentation problem. The experimental results prove the effectiveness of the proposed method achieving a segmentation accuracy of 88.5% and recognition rate equal to 87%.


title: Track tension optimization for stair-climbing of a wheelchair robot with Variable Geometry Single Tracked Mechanism

abstract: This paper introduces an originally designed wheelchair robot. This robot is equipped with a novel type of Variable Geometry Single Tracked Mechanism (VGSTM) that can actively control the robot shape and the track tension. So it becomes possible to improve the obstacle clearing capability of the robot by adapting the robot shape for the obstacle. The track tension is closely related to the performance of this original wheelchair robot, so with the aim of stair-climbing, which is the most fundamental obstacle clearing performance of the wheelchair robot, the optimization model of the track tension is established based on the geometric model and the static model of the robot. Then the simulation is performed, and the optimal track tension values of the robot for different phases of stair-climbing are obtained. Finally, these track tension values are verified through the experiment.


title: The effect of Twitter on college student engagement and grades

abstract: Despite the widespread use of social media by students and its increased use by instructors, very little empirical evidence is available concerning the impact of social media use on student learning and engagement. This paper describes our semester-long experimental study to determine if using Twitter – the microblogging and social networking platform most amenable to ongoing, public dialogue – for educationally relevant purposes can impact college student engagement and grades. A total of 125 students taking a first year seminar course for pre-health professional majors participated in this study (70 in the experimental group and 55 in the control group). With the experimental group, Twitter was used for various types of academic and co-curricular discussions. Engagement was quantified by using a 19-item scale based on the National Survey of Student Engagement. To assess differences in engagement and grades, we used mixed effects analysis of variance (ANOVA) models, with class sections nested within treatment groups. We also conducted content analyses of samples of Twitter exchanges. The ANOVA results showed that the experimental group had a significantly greater increase in engagement than the control group, as well as higher semester grade point averages. Analyses of Twitter communications showed that students and faculty were both highly engaged in the learning process in ways that transcended traditional classroom activities. This study provides experimental evidence that Twitter can be used as an educational tool to help engage students and to mobilize faculty into a more active and participatory role.


title: Investigation on the ZnO:N films grown on (0001) and (0001) ZnO templates by plasma-assisted molecular beam epitaxy

abstract: Abstract Nitrogen-doped ZnO films (ZnO:N) were grown on (0 0 0 1) and (0 0 0 1¯) ZnO templates by plasma-assisted molecular beam epitaxy (PA-MBE). High nitrogen concentration (>5×10 20 /cm 3 ) was achieved from ZnO:N/(0 0 0 1)ZnO films, while relatively low nitrogen concentration (∼10 19 /cm 3 ) was observed from ZnO:N/(0 0 0 1¯)ZnO layers at the same growth temperature ( T g ). High-resolution X-ray diffraction (HRXRD) revealed considerable lattice deformation. At T g =300 °C, the c -lattice expansion in ZnO:N/(0 0 0 1)ZnO film is almost 36% larger than that in ZnO:N/(0 0 0 1¯)ZnO due to the high nitrogen concentration in the film. Electron concentration of the ZnO:N/(0 0 0 1)ZnO films gradually decreases down to ∼10 16 /cm 3 as the nitrogen concentration increases. Moreover, donor–acceptor-pair emission dominates the photoluminescence (PL) spectrum (3.23 eV) of ZnO:N/(0 0 0 1)ZnO films grown at 300 °C and that emission is 3.1 times stronger than that of ZnO:N/(0 0 0 1¯)ZnO.


title: An Impossibility Theorem for Clustering

abstract: Although the study of clustering is centered around an intuitively compelling goal, it has been very difficult to develop a unified framework for reasoning about it at a technical level, and profoundly diverse approaches to clustering abound in the research community. Here we suggest a formal perspective on the difficulty in finding such a unification, in the form of an impossibility theorem: for a set of three simple properties, we show that there is no clustering function satisfying all three. Relaxations of these properties expose some of the interesting (and unavoidable) trade-offs at work in well-studied clustering techniques such as single-linkage, sum-of-pairs, k-means, and k-median.


FACTORS AFFECTING THE ADOPTION OF ENTERPRISE RESOURCE PLANNING ( ERP ) ON CLOUD AMONG SMALL AND MEDIUM ENTERPRISES ( SMES ) IN PENANG , MALAYSIA


Automatic Shortlisting of Candidates in Recruitment


title: A New Modified Playfair Algorithm Using CBC

abstract: Security is required in order to defend information from unauthorized person on the network. Therefore, securely exchanging data over the internet is vital. In this context, cryptography is used that transmute facts from its under stable form into an indecipherable form by using various encryption techniques. Play air cipher is the form of block cipher which has no limit on the number of characters in a message it can do, but it operates on block of characters encrypting and decrypting two characters at a time cipher. In this, the plain text digraphs are converted to cipher text digraphs and vice versa using a pre-shared key. This is achieved by performing several operations column wise row wise and by creating rectangular form. This paper deals with a new solution approach to overcome the shortcomings of the Play air algorithm. In this paper, the presented Play air CBC encryption mechanism makes the cryptanalysis complex. The encrypted text obtained is almost unreadable.


title: Factors Influencing The Performance of Image Captioning Model: An Evaluation

abstract: Recently, neural network-based methods have shown impressive performances in captioning task. There have been numerous attempts with many proposed architectures to solve this captioning problem. In this paper, we present the evaluation of different alternatives in architecture and optimization algorithms for a neural image captioning model. First, we present the study of a image captioning model that is comprised of two modules -- a convolutional neural network which encodes the input image into a fixed-dimensional feature vector and a recurrent neural network to decode that representation into a sequence of words describing the input image. After that, we consider different alternatives regarding architecture and optimization algorithm to train the model. We conduct a set of experiments on standard benchmark datasets to evaluate different aspects of the captioning system using standard evaluation methods that are utilized in image captioning literatures. Based on the results of those experiments, we propose several suggestions on architecture and optimization algorithm of the image captioning model that is balanced in terms of the performance and the feasibility to be deployed on real-world problems with commodity hardware.


title: Remote Field gap evaluation for nuclear fuel channel tubes

abstract: Remote field eddy current (RFEC) inspection is based on the diffusion of electromagnetic energy through a pipe wall. This through-wall transmission characteristic is of interest as a potential technique for inspecting tube-in-tube systems such as a pressure tube in a calandria tube of a CANDU nuclear reactor. We report tests of RFEC to inspect the gap between a nonmagnetic Zr-2.5 percent Nb pressure tube and a Zircaloy-2 calandria tube from a CANDU fuel channel. The gap is evaluated using a new RFEC value - the length of the RFEC signal trace from the (1, 0) normalizing point on the voltage plane polar plot (VPPP). The results show that RFEC is a promising method for gap determination in tube-in-tube systems. It is relatively inexpensive compared with the precision eddy current and ultrasonic techniques currently used for gap measurement.


title: Building a Data Collection for Deception Research

abstract: Research in high stakes deception has been held back by the sparsity of ground truth verification for data collected from real world sources. We describe a set of guidelines for acquiring and developing corpora that will enable researchers to build and test models of deceptive narrative while avoiding the problem of sanctioned lying that is typically required in a controlled experiment. Our proposals are drawn from our experience in obtaining data from court cases and other testimony, and uncovering the background information that enabled us to annotate claims made in the narratives as true or false.


title: Learning Probabilistic Programs Using Backpropagation

abstract: Probabilistic modeling enables combining domain knowledge with learning from data, thereby supporting learning from fewer training instances than purely data-driven methods. However, learning probabilistic models is difficult and has not achieved the level of performance of methods such as deep neural networks on many tasks. In this paper, we attempt to address this issue by presenting a method for learning the parameters of a probabilistic program using backpropagation. Our approach opens the possibility to building deep probabilistic programming models that are trained in a similar way to neural networks.


title: Ion beam modification of CR-39 (DOP) and polyamide nylon-6 polymers

abstract: Abstract Swift heavy ions interact predominantly through inelastic scattering while traversing any polymeric medium and produce excited/ionized atoms. This results into the modification of physical and chemical response of the polymer. CR-39 (DOP) and polyamide nylon-6 polymer samples were irradiated with 70 MeV C 5+ ion beam to the fluences of 10 11 , 10 12 and 10 13 ions cm −2 . Modifications due to irradiation were studied using Fourier transform infra red (FTIR) spectroscopic and X-ray diffraction (XRD) technique. Considerable deformation has been observed in both the polymers at the fluence of 10 12 and 10 13 ions cm −2 . XRD analyses show slight shift of peak position and significant changes in peak width and intensity. Particle size or grain size, calculated by applying Scherrer formula, indicates measurable changes in particle size in the irradiated samples. XRD peaks at d -values of 4.335 A and 3.838 A for the virgin polyamide nylon-6 gradually change with increasing fluence into one broad peak. Some difference in the irradiation effect in these two different polymers has been observed.


title: On a New Paradigm for Stock Trading Via a Model-Free Feedback Controller

abstract: This paper describes a new paradigm for stock trading involving the use of classical feedback controllers which are “model free” in that they use neither parameterization nor estimation of stock price dynamics. At time $t$ , the control signal is the investment level $I(t)$ , obtained via a mapping on the so-called gain-loss function $g(t)$ . While such strategies fall under the umbrella of technical analysis, our approach differs from the literature in a fundamental way: Whereas existing work in finance involves statistical analysis via historical back-testing, our new control-theoretic paradigm aims to provide “certification theorems” giving conditions under which certain robustness properties are guaranteed with respect to benchmark classes for the time-varying stock price $p(t)$ . We demonstrate our ideas using a linear feedback implementation of a new stock-trading scheme called Simultaneous Long-Short. The analysis is carried out in a so-called idealized frictionless market first using smooth prices for pedagogical purposes and then using a more realistic benchmark involving Geometric Brownian Motion. Finally, simulations are given which include real-world implementation issues.


title: Predicting software change-proneness with code smells and class imbalance learning

abstract: The objective of this paper is to study the relationship between different types of object-oriented software metrics, code smells and actual changes in software code that occur during maintenance period. It is hypothesized that code smells are indicators of maintenance problems. To understand the relationship between code smells and maintenance problems, we extract code smells in a Java based mobile application called MOBAC. Four versions of MOBAC are studied. Machine learning techniques are applied to predict software change-proneness with code smells as predictor variables. The results of this paper indicate that codes smells are more accurate predictors of change-proneness than static code metrics for all machine learning methods. However, class imbalance techniques did not outperform class balance machine learning techniques in change-proneness prediction. The results of this paper are based on accuracy measures such as F-measure and area under ROC curve.


title: Interest in the biosphere and students environmental awareness and optimism: A global perspective

abstract: Abstract This study focuses on students' interest in ecosystem services and sustainability to explain their awareness and optimism about the environmental issues of air pollution, water shortage and greenhouse gas emission in 50 countries around the world. We achieve this using the cross-sectional survey data of the 2015 OECD's Program for International Student Assessment (PISA), along with seemingly unrelated bi-variate weighted ordered probit modeling. We found that in addition to factors such as age, gender, immigration status, and economic, social and cultural status, interest in the biosphere is a significant determinant of students' environmental awareness and optimism. In fact, a level increase in students' interest in ecosystem services and sustainability is found to raise on average their awareness level by 15.3% for the issue of air pollution, 15.7% for the issue of water shortage, and 24.6% for the issue of greenhouse gas emission. Although students' interest in the biosphere seems to not have a significant effect on their expectations about the issue of greenhouse gas emission, it does however raise their level of optimism by 0.8% for the issue of air pollution, and 0.2% for the issue of water shortage. Furthermore, every level increase in students' environmental awareness is found to raise their optimism level by 17.3% for the issue of air pollution, 15.8% for the issue of water shortage, and 17.4% for the issue of greenhouse gas emission. Therefore, relying on the Theory of Planned Behavior (TPB), our results suggest that governments and policy makers around the world could successfully leverage the biospheric interests of their youth populations to successfully achieve their 2030 goals for sustainability.


title: Quantitative Classification of Eyes with and without Intermediate Age-related Macular Degeneration Using Optical Coherence Tomography

abstract: Objective To define quantitative indicators for the presence of intermediate age-related macular degeneration (AMD) via spectral-domain optical coherence tomography (SD-OCT) imaging of older adults. Design Evaluation of diagnostic test and technology. Participants and Controls One eye from 115 elderly subjects without AMD and 269 subjects with intermediate AMD from the Age-Related Eye Disease Study 2 (AREDS2) Ancillary SD-OCT Study. Methods We semiautomatically delineated the retinal pigment epithelium (RPE) and RPE drusen complex (RPEDC, the axial distance from the apex of the drusen and RPE layer to Bruch's membrane) and total retina (TR, the axial distance between the inner limiting and Bruch's membranes) boundaries. We registered and averaged the thickness maps from control subjects to generate a map of "normal" non-AMD thickness. We considered RPEDC thicknesses larger or smaller than 3 standard deviations from the mean as abnormal, indicating drusen or geographic atrophy (GA), respectively. We measured TR volumes, RPEDC volumes, and abnormal RPEDC thickening and thinning volumes for each subject. By using different combinations of these 4 disease indicators, we designed 5 automated classifiers for the presence of AMD on the basis of the generalized linear model regression framework. We trained and evaluated the performance of these classifiers using the leave-one-out method. Main Outcome Measures The range and topographic distribution of the RPEDC and TR thicknesses in a 5-mm diameter cylinder centered at the fovea. Results The most efficient method for separating AMD and control eyes required all 4 disease indicators. The area under the curve (AUC) of the receiver operating characteristic (ROC) for this classifier was >0.99. Overall neurosensory retinal thickening in eyes with AMD versus control eyes in our study contrasts with previous smaller studies. Conclusions We identified and validated efficient biometrics to distinguish AMD from normal eyes by analyzing the topographic distribution of normal and abnormal RPEDC thicknesses across a large atlas of eyes. We created an online atlas to share the 38 400 SD-OCT images in this study, their corresponding segmentations, and quantitative measurements.


title: Mobility management for IoT: a survey

abstract: Internet of Thing (IoT) or also referred to as IP-enabled wireless sensor network (IP-WSN) has become a rich area of research. This is due to the rapid growth in a wide spectrum of critical application domains. However, the properties within these systems such as memory size, processing capacity, and power supply have led to imposing constraints on IP-WSN applications and its deployment in the real world. Consequently, IP-WSN is constantly faced with issues as the complexity further rises due to IP mobility. IP mobility management is utilized as a mechanism to resolve these issues. The management protocols introduced to support mobility has evolved from host-based to network-based mobility management protocols. The presence of both types of solutions is dominant but depended on the nature of systems being deployed. The mobile node (MN) is involved with the mobility-related signaling in host-based protocols, while network-based protocols shield the host by transferring the mobility-related signaling to the network entities. The features of the IoT are inclined towards the network-based solutions. The wide spectrum of strategies derived to achieve enhanced performance evidently displays superiority in performance and simultaneous issues such as long handover latency, intense signaling, and packet loss which affects the QoS for the real-time applications. This paper extensively reviews and discusses the algorithms developed to address the challenges and the techniques of integrating IP over WSNs, the attributes of mobility management within the IPv4 and IPv6, respectively, and special focus is given on a comprehensive review encompassing mechanisms, advantages, and disadvantages on related work within the IPv6 mobility management. The paper is concluded with the proposition of several pertinent open issues which are of high research value.


SegBot: A Generic Neural Text Segmentation Model with Pointer Network


title: Generative Adversarial Imitation from Observation

abstract: Imitation from observation (IfO) is the problem of learning directly from state-only demonstrations without having access to the demonstrator's actions. The lack of action information both distinguishes IfO from most of the literature in imitation learning, and also sets it apart as a method that may enable agents to learn from a large set of previously inapplicable resources such as internet videos. In this paper, we propose both a general framework for IfO approaches and also a new IfO approach based on generative adversarial networks called generative adversarial imitation from observation (GAIfO). We conduct experiments in two different settings: (1) when demonstrations consist of low-dimensional, manually-defined state features, and (2) when demonstrations consist of high-dimensional, raw visual data. We demonstrate that our approach performs comparably to classical imitation learning approaches (which have access to the demonstrator's actions) and significantly outperforms existing imitation from observation methods in high-dimensional simulation environments.


title: Improved Person Re-Identification Based on Saliency and Semantic Parsing with Deep Neural Network Models

abstract: Abstract Given a video or an image of a person acquired from a camera, person re-identification is the process of retrieving all instances of the same person from videos or images taken from a different camera with non-overlapping view. This task has applications in various fields, such as surveillance, forensics, robotics, multimedia. In this paper, we present a novel framework, named Saliency-Semantic Parsing Re-Identification (SSP-ReID), for taking advantage of the capabilities of both clues: saliency and semantic parsing maps, to guide a backbone convolutional neural network (CNN) to learn complementary representations that improves the results over the original backbones. The insight of fusing multiple clues is based on specific scenarios in which one response is better than another, thus favoring the combination of them to increase performance. Due to its definition, our framework can be easily applied to a wide variety of networks and, in contrast to other competitive methods, our training process follows simple and standard protocols. We present extensive evaluation of our approach through five backbones and three benchmarks. Experimental results demonstrate the effectiveness of our person re-identification framework. In addition, we combine our framework with re-ranking techniques and compare it against state-of-the-art approaches, achieving competitive results.


title: Robust Head-Shoulder Detection by PCA-Based Multilevel HOG-LBP Detector for People Counting

abstract: Robustly counting the number of people for surveillance systems has widespread applications. In this paper, we propose a robust and rapid head-shoulder detector for people counting. By combining the multilevel HOG (Histograms of Oriented Gradients) with the multilevel LBP (Local Binary Pattern) as the feature set, we can detect the head-shoulders of people robustly, even though there are partial occlusions occurred. To further improve the detection performance, Principal Components Analysis (PCA) is used to reduce the dimension of the multilevel HOG-LBP feature set. Our experiments show that the PCA based multilevel HOG-LBP descriptors are more discriminative, more robust than the state-of-the-art algorithms. For the application of the real-time people-flow estimation, we also incorporate our detector into the particle filter tracking and achieve convincing accuracy


title: Neural Network training model for weather forecasting using Fireworks Algorithm

abstract: Weather forecasting is the application of science and technology in order to predict the weather conditions. It is important for agricultural and industrial sectors. Models of Artificial Neural Networks with supervised learning paradigm are suitable for weather forecasting in complexity atmosphere. Training algorithm is required for providing weight and bias values to the model. This research proposed a weather forecasting method using Artificial Neural Networks trained by Fireworks Algorithm. Fireworks Algorithm is a recently developed Swarm Intelligence Algorithm for optimization. The main objective of the method is to predict daily mean temperature based on various measured parameters gained from the Meteorological Station, located in Bangkok. The experimental results indicate that the proposed method is advantageous for weather forecasting.


Deep Reinforcement Learning for Simulated Autonomous Driving


title: Staff - student relationships and their impact on nursing students’ belongingness and learning

abstract: Aim: this paper is a report of selected findings from a study exploring the relationship between belongingness and placement experiences of preregistration nursing students. Background: staff-student relationships are an important influence on students' experiences of belongingness and their clinical learning. The need to belong is universal and pervasive, exerting a powerful influence on thought processes, emotions, behaviour, health and happiness. People deprived of belongingness are more likely to experience diminished self-esteem, increased stress and anxiety, depression and a decrease in general well-being. Nursing students' motivation and capacity to learn, self-concept, confidence, the extent to which they are willing to question or conform to poor practice and their future career decisions are influenced by the extent to which they experience belongingness. Method: during 2006, 18 third year students from two Australian universities and one United Kingdom university participated in in-depth semi-structured interviews. Data were analysed thematically. Findings: participants described placement experiences spanning a continuum from those promoting a high degree of belongingness to those provoking intense feelings of alienation. Staff-student relationships (including receptiveness, inclusion/exclusion, legitimization of the student role, recognition and appreciation, challenge and support) were the most important influence on students' sense of belonging and learning. Similarities between sites were remarkable, despite the differences in healthcare and higher education systems. Conclusion: staff-student relationships are key to students' experience of belongingness. Understanding the types of interactions and behaviours that facilitate or impede students' belongingness and learning are essential to the creation of positive clinical experiences.


title: Marketing Malpractice: The Cause and the Cure

abstract: Marketing executives focus too much on ever-narrower demographic segments and ever-more-trivial product extensions. They should find out, instead, what jobs consumers need to get done. Those jobs will point the way to purposeful products-and genuine innovation.


title: Young Children's Understanding of Other People's Feelings and Beliefs: Individual Differences and Their Antecedents

abstract: Individual differences in young children's understanding of others' feelings and in their ability to explain human action in terms of beliefs, and the earlier correlates of these differences, were studied with 50 children observed at home with mother and sibling at 33 months, then tested at 40 months on affective-labeling, perspective-taking, and false-belief tasks. Individual differences in social understanding were marked; a third of the children offered explanations of actions in terms of false belief, though few predicted actions on the basis of beliefs. These differences were associated with participation in family discourse about feelings and causality 7 months earlier, verbal fluency of mother and child, and cooperative interaction with the sibling. Differences in understanding feelings were also associated with the discourse measures, the quality of mother-sibling interaction, SES, and gender, with girls more successful than boys. The results support the view that discourse about the social world may in part mediate the key conceptual advances reflected in the social cognition tasks; interaction between child and sibling and the relationships between other family members are also implicated in the growth of social understanding.


title: Finding community structure in mega-scale social networks: [extended abstract]

abstract: Community analysis algorithm proposed by Clauset, Newman, and Moore (CNM algorithm) finds community structure in social networks. Unfortunately, CNM algorithm does not scale well and its use is practically limited to networks whose sizes are up to 500,000 nodes. The paper identifies that this inefficiency is caused from merging communities in unbalanced manner. The paper introduces three kinds of metrics (consolidation ratio) to control the process of community analysis trying to balance the sizes of the communities being merged. Three flavors of CNM algorithms are built incorporating those metrics. The proposed techniques are tested using data sets obtained from existing social networking service that hosts 5.5 million users. All the methods exhibit dramatic improvement of execution efficiency in comparison with the original CNM algorithm and shows high scalability. The fastest method processes a network with 1 million nodes in 5 minutes and a network with 4 million nodes in 35 minutes, respectively. Another one processes a network with 500,000 nodes in 50 minutes (7 times faster than the original algorithm), finds community structures that has improved modularity, and scales to a network with 5.5 million.


title: The mechanics of state-dependent neural correlations

abstract: Simultaneous recordings from large neural populations are becoming increasingly common. An important feature of population activity is the trial-to-trial correlated fluctuation of spike train outputs from recorded neuron pairs. Similar to the firing rate of single neurons, correlated activity can be modulated by a number of factors, from changes in arousal and attentional state to learning and task engagement. However, the physiological mechanisms that underlie these changes are not fully understood. We review recent theoretical results that identify three separate mechanisms that modulate spike train correlations: changes in input correlations, internal fluctuations and the transfer function of single neurons. We first examine these mechanisms in feedforward pathways and then show how the same approach can explain the modulation of correlations in recurrent networks. Such mechanistic constraints on the modulation of population activity will be important in statistical analyses of high-dimensional neural data.


title: Retinoids and proliferative verrucous leukoplakia (PVL). A preliminary study

abstract: Objective. A study is made of the efficacy and adverse effects of retinoid therapy applied to the white lesions of proliferative verrucous leukoplakia (PVL). Material and methods. The results of retinoid therapy were evaluated in 17 patients diagnosed with PVL. Topical retinoids were used in 5 patients, in the form of two daily applications of 0.1% 13-cis-retinoic acid in orabase for an average of 6.17+/-3.13 months. Systemic retinoids were used in 11 patients, with the administration of 25 mg/day of acitretin in tablet form for an average of 5.41+/-2.02 months. One patient successively received the topical and systemic retinoid formulations. The course and results were evaluated on a blind basis by two investigators. The adverse effects of the medication were also assessed. Results. Clinical improvement was recorded for 7 lesions (38.8%) (six involving systemic treatment and one as a result of topical application). Clinical worsening was recorded in the same proportion (5 lesions with systemic therapy and two with topical treatment), while four lesions (22.4%) showed no changes (one lesion with systemic therapy and three with topical treatment). Adverse effects were documented in all the patients administered the systemic formulation, versus in only one patient administered topical retinoids. The most frequent problems were desquamation and pruritus. Conclusion. Although topical or systemic retinoic acid produces some improvement in about one-third of all patients with PVL, further studies are needed to assess the efficacy and safety of these products, in view of the important percentage of individuals who worsen despite therapy, and the frequent appearance of adverse effects.


title: AppInsight: Mobile App Performance Monitoring in the Wild

abstract: The mobile-app marketplace is highly competitive. To maintain and improve the quality of their apps, developers need data about how their app is performing in the wild. The asynchronous, multithreaded nature of mobile apps makes tracing difficult. The difficulties are compounded by the resource limitations inherent in the mobile platform. To address this challenge, we develop AppInsight, a system that instruments mobile-app binaries to automatically identify the critical path in user transactions, across asynchronous-call boundaries. AppInsight is lightweight, it does not require any input from the developer, and it does not require any changes to the OS. We used AppInsight to instrument 30 marketplace apps, and carried out a field trial with 30 users for over 4 months. We report on the characteristics of the critical paths that AppInsight found in this data. We also give real-world examples of how AppInsight helped developers improve the quality of their app.


title: Microservices-based software architecture and approaches

abstract: In the last few years, a revised software architecture style has been developed to design new software applications. This architecture style is particularly suited for use cases in the aerospace industry, from an independently deployable software service. The microservices architectural style develops a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms. These services are built around business and mission capabilities and independently deployable by fully automated machinery. With microservices, some types of applications become easier to build and maintain when they are broken down into smaller, composable pieces that work together. Each component is then developed separately, and the application is then simply the sum of its constituent components. This is in contrast to a traditional, “monolithic” application which is all developed in one piece. This paper will discuss, several aspects of microservices-based architecture, including several potential use cases for the aerospace industry. The characteristics of microservice-based architecture such as componentization, organization, endpoints and messaging mechanisms. The technical implementation of microservices by reviewing containerization, services communication and related architectural components. Specific open source projects and components that can be utilized to build microservices-based architecture. A sample set of use cases.


title: Maturation of widely distributed brain function subserves cognitive development

abstract: Abstract Cognitive and brain maturational changes continue throughout late childhood and adolescence. During this time, increasing cognitive control over behavior enhances the voluntary suppression of reflexive/impulsive response tendencies. Recently, with the advent of functional MRI, it has become possible to characterize changes in brain activity during cognitive development. In order to investigate the cognitive and brain maturation subserving the ability to voluntarily suppress context-inappropriate behavior, we tested 8–30 year olds in an oculomotor response–suppression task. Behavioral results indicated that adult-like ability to inhibit prepotent responses matured gradually through childhood and adolescence. Functional MRI results indicated that brain activation in frontal, parietal, striatal, and thalamic regions increased progressively from childhood to adulthood. Prefrontal cortex was more active in adolescents than in children or adults; adults demonstrated greater activation in the lateral cerebellum than younger subjects. These results suggest that efficient top-down modulation of reflexive acts may not be fully developed until adulthood and provide evidence that maturation of function across widely distributed brain regions lays the groundwork for enhanced voluntary control of behavior during cognitive development.


title: Fairness as a constraint on profit seeking: entitlements in the market

abstract: Community standards of fairness for the setting of prices and wages were elicited by telephone surveys. In customer or labor markets it isacceptable for a firm to raise prices (or cut wages) when profits arethreatened, and to maintain prices when costs diminish. It is unfair toexploit shifts in demand by raising prices or cutting wages. Several market anomalies are explained by assuming that these standards of fairness influence the behavior of firms. Copyright 1986 by American Economic Association.


title: Challenges of using homomorphic encryption to secure cloud computing

abstract: With the emergence of cloud computing, the concept of information security has become a major issue. Indeed, the security of such a system is the greatest concern of computer scientists, providers of cloud and organizations who want to adopt and benefit from these services. Cloud computing providers must implement concepts ensuring network security, hardware, data storage and strategies of control and access to services. All these elements help to preserve data security and ensuring the availability of services associated with the Cloud, to better satisfy clients and acquire and build their trust. However, even if the data storage security in cloud servers is assured, reluctance remain when it comes to process the confidential data. Indeed, the fear that sensitive data is being used is a major obstacle in the adoption of cloud services by enterprises. To overcome this obstacle, the use of methods that can perform operations on encrypted data without knowing the secret key, seems to be an effective way to strengthen the confidentiality of information. In this paper we will examine the challenges facing Homomorphic Encryption methods to allow suppliers of cloud to perform operations on encrypted data, and provide the same results after treatment, as if they were performing calculations on raw data.


title: cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey

abstract: The paper gives futuristic challenges disscussed in the cvpaper.challenge. In 2015 and 2016, we thoroughly study 1,600+ papers in several conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV.


title: Valence-arousal prediction of Chinese Words with multi-layer corpora

abstract: This paper presents our valence-arousal (VA) prediction method in the IALP 2016 Shared Task of Dimensional Sentiment Analysis for Chinese Words. Dimensional approach represents affective states as continuous numerical values in multiple dimensions, such as the VA space, thus allowing for more fine-grained sentiment analysis. For VA prediction, existing works usually selected similar seeds for an unseen word based on semantic similarity by using ontology or word2vec. However the semantic similarity is sometimes quite different from the similarity of valence/arousal. Therefore, this paper proposes a VA prediction method with multi-layer corpora to address such difference. In semantic layer, we get the top N most similar words by word2vec. Then, we screen the selected similar words based on the training data in VA layer. Finally, we use external corpora of affective polarity and intensity lexicons to make further filtering. Experimental results show that the proposed methods in this study to predict the value of VA yields good performance for Chinese words, especially in V dimension.


title: Parsimonious Topic Models with Salient Word Discovery

abstract: We propose a parsimonious topic model for text corpora. In related models such as Latent Dirichlet Allocation (LDA), all words are modeled topic-specifically, even though many words occur with similar frequencies across different topics. Our modeling determines salient words for each topic, which have topic-specific probabilities, with the rest explained by a universal shared model. Further, in LDA all topics are in principle present in every document. By contrast, our model gives sparse topic representation, determining the (small) subset of relevant topics for each document. We derive a Bayesian Information Criterion (BIC), balancing model complexity and goodness of fit. Here, interestingly, we identify an effective sample size and corresponding penalty specific to each parameter type in our model. We minimize BIC to jointly determine our entire model—the topic-specific words, document-specific topics, all model parameter values, and the total number of topics—in a wholly unsupervised fashion. Results on three text corpora and an image dataset show that our model achieves higher test set likelihood and better agreement with ground-truth class labels, compared to LDA and to a model designed to incorporate sparsity.


title: Applying organizational routines in understanding organizational change

abstract: Organizational routines are considered basic components of organizational behavior and repositories of Organizational capabilities (Nelson and Winter, 1982). They do, therefore, hold one of the keys to understanding organizational change. We identify problems encountered in such research and present proposal for how to deal with them, in order to advance our knowledge of routines and our understanding or organizational change. Developing these themes, we also introduce the articles in the special section 'Towards an Operationalization of the Routines concept'.


title: Charge and current neutralization of an ion-beam pulse propagating in a background plasma along a solenoidal magnetic field.

abstract: The analytical studies show that the application of a small solenoidal magnetic field can drastically change the self-magnetic and self-electric fields of the beam pulse propagating in a background plasma. Theory predicts that when omega_{ce} approximately omega_{pe}beta_{b}, where omega_{ce} is the electron gyrofrequency, omega_{pe} is the electron plasma frequency, and beta_{b} is the ion-beam velocity relative to the speed of light, there is a sizable enhancement of the self-electric and self-magnetic fields due to the dynamo effect. Furthermore, the combined ion-beam-plasma system acts as a paramagnetic medium; i.e., the solenoidal magnetic field inside the beam pulse is enhanced.


title: Improving the understanding of spoken referring expressions through syntactic-semantic and contextual-phonetic error-correction

abstract: Abstract Despite recent advances in automatic speech recognition, one of the main stumbling blocks to the widespread adoption of Spoken Dialogue Systems is the lack of reliability of automatic speech recognizers. In this paper, we offer a two-tier error-correction process that harnesses syntactic, semantic and pragmatic information to improve the understanding of spoken referring expressions, specifically descriptions of objects in physical spaces. A syntactic-semantic tier offers generic corrections to perceived ASR errors on the basis of syntactic expectations of a semantic model, and passes the corrected texts to a language understanding system. The output of this system, which consists of pragmatic interpretations, is then refined by a contextual-phonetic tier, which prefers interpretations that are phonetically similar to the mis-heard words. Our results, obtained on a corpus of 341 referring expressions, show that syntactic-semantic error correction significantly improves interpretation performance, and contextual-phonetic refinements yield further improvements.


title: A Survey on multi-robot systems

abstract: This paper reviews the state-of-the-art research on multi-robot systems, with a focus on multi-robot cooperation and coordination. By primarily classifying multi-robot systems into active and passive cooperative systems, three main research topics of multi-robot systems are focused on: task allocation, multi-sensor fusion and localization. In addition, formation control and coordination methods for multi-robots are reviewed.


title: Semantic Parsing of Ambiguous Input through Paraphrasing and Verification

abstract: We propose a new method for semantic parsing of ambiguous and ungrammatical input, such as search queries. We do so by building on an existing semantic parsing framework that uses synchronous context free grammars (SCFG) to jointly model the input sentence and output meaning representation. We generalize this SCFG framework to allow not one, but multiple outputs. Using this formalism, we construct a grammar that takes an ambiguous input string and jointly maps it into both a meaning representation and a natural language paraphrase that is less ambiguous than the original input. This paraphrase can be used to disambiguate the meaning representation via verification using a language model that calculates the probability of each paraphrase.


title: Eliciting the potentials of mathematical mobile learning through scaffolding learning processes across contexts

abstract: Little is understood in terms of scaffolding learning processes in more dynamical contexts than the classroom environment. The scope of mobile learning research has so far been limited to the scaffolding functions of the mobile technology. Thus, in this paper, a larger grip was taken, focusing on all available means, such as teachers, mobile technology, pre-and post-activities as supportive structures. In doing that a sequence of learning activities were designed within the domain of mathematics education. We asked what scaffolding role the available resources can play in supporting the students learning processes, and further, how we are to orchestrate these resources across contexts in a pedagogical manner. The findings demonstrates how students' learning processes are to be scaffolded and how learning in an outdoor context can be meaningfully supported through the sequencing of activities and the utilisation of pre-and post-activities in indoor contexts.


A COMPACT MICROSTRIP RAT-RACE COUPLER WITH MODIFIED LANGE AND T-SHAPED ARMS


title: An affective computational model for machine consciousness

abstract: In the past, several models of consciousness have become popular and have led to the development of models for machine consciousness with varying degrees of success and challenges for simulation and implementations. Moreover, affective computing attributes that involve emotions, behavior and personality have not been the focus of models of consciousness as they lacked motivation for deployment in software applications and robots. The affective attributes are important factors for the future of machine consciousness with the rise of technologies that can assist humans. Personality and affection hence can give an additional flavor for the computational model of consciousness in humanoid robotics. Recent advances in areas of machine learning with a focus on deep learning can further help in developing aspects of machine consciousness in areas that can better replicate human sensory perceptions such as speech recognition and vision. With such advancements, one encounters further challenges in developing models that can synchronize different aspects of affective computing. In this paper, we review some existing models of consciousnesses and present an affective computational model that would enable the human touch and feel for robotic systems.


title: Quality evaluation of product reviews using an information quality framework

abstract: The ubiquity of Web2.0 makes the Web an invaluable source of business information. For instance, product reviews composed collaboratively by many independent Internet reviewers can help consumers make purchase decisions and enable enterprises to improve their business strategies. As the number of reviews is increasing exponentially, opinion mining and retrieval techniques are needed to identify important reviews and opinions to answer users' queries. Most opinion mining and retrieval approaches try to extract sentimental or bipolar expressions from a large volume of reviews. However, the process often ignores the quality of each review and may retrieve useless or even noisy documents. In this paper, we propose a method for evaluating the quality of information in product reviews. We treat the evaluation of review quality as a classification problem and employ an effective information quality framework to extract representative review features. Experiments based on an expert-composed data corpus demonstrate that the proposed method outperforms state-of-the-art approaches significantly.


title: Oxidative stress and increased formation of vasoconstricting F2-isoprostanes in patients with reversible cerebral vasoconstriction syndrome.

abstract: The pathophysiology of reversible cerebral vasoconstriction syndrome (RCVS) is unknown. Oxidative stress is detrimental to endothelial function and vascular reactivity. We hypothesized that the oxidative stress marker 8-iso-prostaglandin F2α, which is also a potent vasoconstrictor, might contribute to the pathogenesis of RCVS. Recruited participants included 103RCVS patients, 53 patients with primary headache with acute severe attacks, and 54 healthy controls. Subjects recruited prior to 2009 were discovery cohort, whereas those after 2009, replication cohort. Urine samples were obtained from all patients at registration and from 79 patients with RCVS again at remission stage. Urine 8-iso-prostaglandin F2α was analyzed by liquid chromatography-tandem mass spectrometry. Patients with RCVS received magnetic resonance angiography and transcranial color-coded sonography. In RCVS patients, the urine 8-iso-prostaglandin F2α level was higher than that in the other groups in discovery, replication, and combined cohorts (RCVS, 0.29±0.18; primary headache with acute severe attacks, 0.21±0.19; control, 0.18±0.09ng/mg creatinine; P<0.001), and it was positively correlated with the flow velocities of major intracranial arteries, especially within the first week of disease onset (middle cerebral artery, Spearman's correlation coefficient [rs]=0.580, P=0.002; anterior cerebral artery, rs=0.472, P=0.042; posterior cerebral artery, rs=0.457, P=0.022; basilar artery, rs= 0.530, P=0.002). The 8-iso-prostaglandin F2α level decreased from the ictalto remission stage in RCVS patients (0.31±0.21 vs 0.16±0.10ng/mg creatinine, P<0.001). 8-Iso-prostaglandin F2α was higher in patients with RCVS and correlated with the severity of vasoconstrictions. Further studies are required to explore its potential pathogenic role.


abstract: Nowadays, the need of sharing of dynamic open data to improve the cities intelligence are increased significantly. To share these data, MQTT, the lightweight messaging publish/subscribe protocol is one of most popular protocols. The sharing of data via MQTT includes two components; topic and message where the topic element is used to define which the subscriber are received which data, meanwhile the data are presented in the message element. However, There are none of criteria or standard of topic naming to share the open data via MQTT. It makes the sharing of open data is too difficult with MQTT protocol. In this work, we propose MQTT-Topic Naming Criteria of Open Data for Smart Cities (MTNC) to be the criteria of topic naming for the open data of any city. The proposed criteria used in both processes; publishing and subscription of MQTT messages. Then, we evaluate the impact of MTNC on the data selecting and searching of the subscribers who not know the MTNC. The results show that topic naming with MTNC is more effective than the topic naming without MTNC in selecting and searching topics of the subscribers. The average numbers of the subscribers can found their needed data when they select the data from the list of topics are 7/10 and 3/10 time with and without MTNC respectively. Moreover, the experimental results show that the most keywords that the subscribers often use in their topic searching are the objective of data presented in MTNC.


title: Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter

abstract: An analytic solution to the multi-target Bayes recursion known as the $\delta $ -Generalized Labeled Multi-Bernoulli ( $\delta $ -GLMB) filter has been recently proposed by Vo and Vo in [“Labeled Random Finite Sets and Multi-Object Conjugate Priors,” IEEE Trans. Signal Process., vol. 61, no. 13, pp. 3460–3475, 2014]. As a sequel to that paper, the present paper details efficient implementations of the $\delta $ -GLMB multi-target tracking filter. Each iteration of this filter involves an update operation and a prediction operation, both of which result in weighted sums of multi-target exponentials with intractably large number of terms. To truncate these sums, the ranked assignment and K-th shortest path algorithms are used in the update and prediction, respectively, to determine the most significant terms without exhaustively computing all of the terms. In addition, using tools derived from the same framework, such as probability hypothesis density filtering, we present inexpensive (relative to the $\delta $ -GLMB filter) look-ahead strategies to reduce the number of computations. Characterization of the $L_{1}$ -error in the multi-target density arising from the truncation is presented.


title: Multi-criteria ABC analysis using artificial-intelligence-based classification techniques

abstract: ABC analysis is a popular and effective method used to classify inventory items into specific categories that can be managed and controlled separately. Conventional ABC analysis classifies inventory items three categories: A, B, or C based on annual dollar usage of an inventory item. Multi-criteria inventory classification has been proposed by a number of researchers in order to take other important criteria into consideration. These researchers have compared artificial-intelligence (AI)-based classification techniques with traditional multiple discriminant analysis (MDA). Examples of these AI-based techniques include support vector machines (SVMs), backpropagation networks (BPNs), and the k-nearest neighbor (k-NN) algorithm. To test the effectiveness of these techniques, classification results based on four benchmark techniques are compared. The results show that AI-based techniques demonstrate superior accuracy to MDA. Statistical analysis reveals that SVM enables more accurate classification than other AI-based techniques. This finding suggests the possibility of implementing AI-based techniques for multi-criteria ABC analysis in enterprise resource planning (ERP) systems.


title: This document is under the terms of the CC-BY-NC-ND Creative Commons Attribution Ontoverse: Collaborative Knowledge Management in the Life Sciences Network

abstract: This thesis is about two topics: building a database of homologous invertebrate genes named Invhogen, and the creation of an Internet platform, Ontoverse, for collaborative ontology development and maintenance. The ﬁrst part of the thesis investigates the use of sequence similarity to group sequence entries into gene families. All gene families are explored by means of diﬀerent annotation aspects such as species distribution, sequence distribution, and descriptions of the entries to characterize them with emphasis on Gene Ontology annotations. Traditional annotations written by scientists in natural language are partially suitable for machine processing. Ontological annotations of sequence entries promise to additionally represent knowledge computationally amenable to support the sequence based approach by semantic components with ontologies. These results regarding ontological annotation quality, among other motivations, lead to the question how to bridge the two ﬁelds, database annotation and ontologies, for successful resource annotation of biological sequence data. For this purpose, an Internet-based application is created in the second part, that brings scientists (domain experts) together to oﬀer them ways to communicate among each other or with ontology designers, which act as database curators in this special context. This collaborative approach should allow experts and engineers to improve database annotations by mutual understanding of the ontology’s inner structure or even by the use of completely new designed ontologies, if special ontologies are desired for annotating sequence entries. Furthermore, existing ontologies might be extended by experts’ knowledge to increase annotation qualitites. While the widest use of bio-ontologies is for conceptual annotations, they are also used in a large range of other life science application scenarios which are manageable via the Ontoverse platform. The main focus in the second part of the thesis is on the architecture to manage scientiﬁc user communities and the integration of information extraction results into ontologies (ontology population).


title: Autonomous exploration of motor skills by skill babbling

abstract: Autonomous exploration of motor skills is a key capability of learning robotic systems. Learning motor skills can be formulated as inverse modeling problem, which targets at finding an inverse model that maps desired outcomes in some task space, e.g., via points of a motion, to appropriate actions, e.g., motion control policy parameters. In this paper, autonomous exploration of motor skills is achieved by incrementally learning inverse models starting from an initial demonstration. The algorithm is referred to as skill babbling, features sample-efficient learning, and scales to high-dimensional action spaces. Skill babbling extends ideas of goal-directed exploration, which organizes exploration in the space of goals. The proposed approach provides a modular framework for autonomous skill exploration by separating the learning of the inverse model from the exploration mechanism and a model of achievable targets, i.e. the workspace. The effectiveness of skill babbling is demonstrated for a range of motor tasks comprising the autonomous bootstrapping of inverse kinematics and parameterized motion primitives.


title: Fast target prediction of human reaching motion for cooperative human-robot manipulation tasks using time series classification

abstract: Interest in human-robot coexistence, in which humans and robots share a common work volume, is increasing in manufacturing environments. Efficient work coordination requires both awareness of the human pose and a plan of action for both human and robot agents in order to compute robot motion trajectories that synchronize naturally with human motion. In this paper, we present a data-driven approach that synthesizes anticipatory knowledge of both human motions and subsequent action steps in order to predict in real-time the intended target of a human performing a reaching motion. Motion-level anticipatory models are constructed using multiple demonstrations of human reaching motions. We produce a library of motions from human demonstrations, based on a statistical representation of the degrees of freedom of the human arm, using time series analysis, wherein each time step is encoded as a multivariate Gaussian distribution. We demonstrate the benefits of this approach through offline statistical analysis of human motion data. The results indicate a considerable improvement over prior techniques in early prediction, achieving 70% or higher correct classification on average for the first third of the trajectory (< 500msec). We also indicate proof-of-concept through the demonstration of a human-robot cooperative manipulation task performed with a PR2 robot. Finally, we analyze the quality of task-level anticipatory knowledge required to improve prediction performance early in the human motion trajectory.


title: Sport expert's motor imagery: Functional imaging of professional motor skills and simple motor skills

abstract: Numerous studies provide evidence that motor skill acquisition is associated with dynamic changes in cortical and subcortical regions. Athletes are a professional population who are engaged in extensive motor training for long periods. However, the neural substrates of extreme level motor performance have not been clarified. We used kinesthetic imagery task to induce the mental representation of sport expert's extraordinary performance in view of the shared substrates of executing movement and motor imagery. For the first time, we compared, through functional magnetic resonance imaging (fMRI), the pattern of cerebral activations in 12 professional divers and 12 normal people without extensive training, during imagery of professional skills and imagery of simple motor skills. The sport experts showed significant activation in the parahippocampus during imagery of professional skills relative to the novices, which might reflect the representation adapted to experience-related motor tasks. No significant difference was found between experts and novices when they imagined simple motor skills. These results indicated the experts might utilize their kinesthetic imagery more efficiently than novices, but only for the activity in which they had expertise. The sport experts also demonstrated more focused activation patterns in prefrontal areas in both of imagery tasks, which may be relevant to higher order of motor control during motor imagery. Moreover, this study suggested that the brains of sport experts could be regarded as the ideal subjects to explore the relationship between cerebral plasticity and learning of complex motor skills. (C) 2009 Elsevier B.V. All rights reserved.


title: Survey on Sequential Pattern Mining Algorithms

abstract: Sequential pattern mining is a significant data-mining method for determining time-related behavior in sequence databases. The information achieved from sequential pattern mining can be used in marketing, medical records, sales analysis, and so on. Existing methods only focus on the concept of frequency because of the assumption that sequences’ behaviors do not change over time. Several efficient algorithms for maintaining sequential patterns have been developed. , old datasets are deleted while some other datasets are updated. It is obvious time stamp as an important attribute of each dataset, also it is important in the process of data mining and it can gives us more accurate and useful information. Although there have been many recent studies on the sequential patterns in static database. But the complexity of sequential pattern mining is when increasing the data in dynamically, As time passes by new data sets are inserted


title: RF Circuit Design Aspects of Spiral Inductors on Silicon

abstract: The design and optimization of spiral inductors on silicon substrates, the related layout issues in integrated circuits, and the effect of the inductor-Q an the performance of radio-frequency (RF) building blocks are discussed. Integrated spiral inductors with inductances of 0.5-100 nH and Q's up to 40 are shown to be feasible in very-large-scale-integration silicon technology. Circuit design aspects, such as a minimum inductor area, the cross talk between inductors, and the effect of a substrate contact on the inductor characteristics are addressed. Important RF building blocks, such as a bandpass filter, low-noise amplifier, and voltage-controlled oscillator are shown to benefit substantially from an improved inductor-Q.


title: Constance: An Intelligent Data Lake System

abstract: As the challenge of our time, Big Data still has many research hassles, especially the variety of data. The high diversity of data sources often results in information silos, a collection of non-integrated data management systems with heterogeneous schemas, query languages, and APIs. Data Lake systems have been proposed as a solution to this problem, by providing a schema-less repository for raw data with a common access interface. However, just dumping all data into a data lake without any metadata management, would only lead to a 'data swamp'. To avoid this, we propose Constance, a Data Lake system with sophisticated metadata management over raw data extracted from heterogeneous data sources. Constance discovers, extracts, and summarizes the structural metadata from the data sources, and annotates data and metadata with semantic information to avoid ambiguities. With embedded query rewriting engines supporting structured data and semi-structured data, Constance provides users a unified interface for query processing and data exploration. During the demo, we will walk through each functional component of Constance. Constance will be applied to two real-life use cases in order to show attendees the importance and usefulness of our generic and extensible data lake system.


title: Community Detection via Maximization of Modularity and Its Variants

abstract: In this paper, we first discuss the definition of modularity (Q) used as a metric for community quality and then we review the modularity maximization approaches which were used for community detection in the last decade. Then, we discuss two opposite yet coexisting problems of modularity optimization: in some cases, it tends to favor small communities over large ones while in others, large communities over small ones (so called the resolution limit problem). Next, we overview several community quality metrics proposed to solve the resolution limit problem and discuss Modularity Density (Qds) which simultaneously avoids the two problems of modularity. Finally, we introduce two novel fine-tuned community detection algorithms that iteratively attempt to improve the community quality measurements by splitting and merging the given network community structure. The first of them, referred to as Fine-tuned Q, is based on modularity (Q) while the second one is based on Modularity Density (Qds) and denoted as Fine-tuned Qds. Then, we compare the greedy algorithm of modularity maximization (denoted as Greedy Q), Fine-tuned Q, and Fine-tuned Qds on four real networks, and also on the classical clique network and the LFR benchmark networks, each of which is instantiated by a wide range of parameters. The results indicate that Fine-tuned Qds is the most effective among the three algorithms discussed. Moreover, we show that Fine-tuned Qds can be applied to the communities detected by other algorithms to significantly improve their results.


title: Hydrogen Bonding between Metal-Ion Complexes and Noncoordinated Water: Electrostatic Potentials and Interaction Energies.

abstract: The hydrogen bonding of noncoordinated water molecules to each other and to water molecules that are coordinated to metal-ion complexes has been investigated by means of a search of the Cambridge Structural Database (CSD) and through quantum chemical calculations. Tetrahedral and octahedral complexes that were both charged and neutral were studied. A general conclusion is that hydrogen bonds between noncoordinated water and coordinated water are much stronger than those between noncoordinated waters, whereas hydrogen bonds of water molecule in tetrahedral complexes are stronger than in octahedral complexes. We examined the possibility of correlating the computed interaction energies with the most positive electrostatic potentials on the interacting hydrogen atoms prior to interaction and obtained very good correlation. This study illustrates the fact that electrostatic potentials computed for ground-state molecules, prior to interaction, can provide considerable insight into the interactions.


title: Data-driven serverless functions for object storage

abstract: Traditionally, active storage techniques have been proposed to move computation tasks to storage nodes in order to exploit data locality. However, we argue in this paper that active storage is ill-suited for cloud storage for two reasons: 1. Lack of elasticity: Computing can only scale out with the number of storage nodes; and 2. Resource Contention: Sharing compute resources can produce interferences in the storage system. Serverless computing is now emerging as a promising alternative for ensuring painless scalability, and also, for simplifying the development of disaggregated computing tasks. Here we present an innovative data-driven serverless computing middleware for object storage. It is a lightweight compute solution that allows users to create small, stateless functions that intercept and operate on data flows in a scalable manner without the need to manage a server or a runtime environment. We demonstrate through different use cases how our solution scales with minimal overhead, while getting rid of the resource contention problems incurred by active storage tasks.


title: Conformal solid T-spline construction from boundary T-spline representations

abstract: To achieve a tight integration of design and analysis, conformal solid T-spline construction with the input boundary spline representation preserved is desirable. However, to the best of our knowledge, this is still an open problem. In this paper, we provide its first solution. The input boundary T-spline surface has genus-zero topology and only contains eight extraordinary nodes, with an isoparametric line connecting each pair. One cube is adopted as the parametric domain for the solid T-spline. Starting from the cube with all the nodes on the input surface as T-junctions, we adaptively subdivide the domain based on the octree structure until each face or edge contains at most one face T-junction or one edge T-junction. Next, we insert two boundary layers between the input T-spline surface and the boundary of the subdivision result. Finally, knot intervals are calculated from the T-mesh and the solid T-spline is constructed. The obtained T-spline is conformal to the input T-spline surface with exactly the same boundary representation and continuity. For the interior region, the continuity is C 2 everywhere except for the local region surrounding irregular nodes. Several examples are presented to demonstrate the performance of the algorithm.


HoneyLedgerBFT : Enabling Byzantine Fault Tolerance for the Hyperledger platform


title: Source of trace element variability in Great Barrier Reef corals affected by the Burdekin flood plumes

abstract: Massive corals in the Great Barrier Reef, analyzed at high-resolution for Sr/Ca (thermal ionization mass spectrometry) and trace elements such as Ba and Mn (laser ablation inductively coupled plasma mass spectrometry), can provide continuous proxy records of dissolved seawater concentrations, as well as sea surface temperature (SST). A 10-yr record (1989 to 1998) from Pandora Reef, an inshore reef regularly impacted by the freshwater plumes of the Burdekin River, is compared with an overlapping record from a midshelf reef, away from runoff influences. Surface seawater samples, taken away from river plumes, show little variability for Sr/Ca (8484 ± 10 μmol/mol) and Ba (33.7 ± 0.7 nmol/kg). Discrete Ba/Ca peaks in the inshore coral coincide with flood events. The magnitude of this Ba/Ca enrichment is most likely controlled by the amount of suspended sediments delivered to the estuary, which remains difficult to monitor. The maximum flow rate at peak river discharge is used here as a proxy for the sediment load and is shown to be strongly correlated with coral Ba/Ca (r = 0.97). After the wet summer of 1991, the coral Ba/Ca flood peak is followed by a plateau that lingers for several months after dissipation of plume waters, signifying an additional flux of Ba that may originate from submarine groundwater seeps and/or mangrove reservoirs. Both Mn and Y are enriched by a factor of ∼5 in inshore relative to midshelf corals. Mn/Ca ratios show a seasonal cycle that follows SST (r = 0.7), not river discharge, with an additional high variability in summer suggesting a link with biological activity. P and Cd show no significant seasonal variation and are at a low level at both inshore and midreef locations. However, leaching experiments suggest that part of the coral P is not lattice bound.


title: Grounding the meaning of words through vision and interactive gameplay

abstract: Currently, there exists a need for simple, easily-accessible methods with which individuals lacking advanced technical training can expand and customize their robot's knowledge. This work presents a means to satisfy that need, by abstracting the task of training robots to learn about the world around them as a vision- and dialogue-based game, I Spy. In our implementation of I Spy, robots gradually learn about objects and the concepts that describe those objects through repeated gameplay. We show that I Spy is an effective approach for teaching robots how to model new concepts using representations comprised of visual attributes. The results from 255 test games show that the system was able to correctly determine which object the human had in mind 67% of the time. Furthermore, a model evaluation showed that the system correctly understood the visual representations of its learned concepts with an average of 65% accuracy. Human accuracy against the same evaluation standard was just 88% on average.


title: Deep Reinforcement Learning in High Frequency Trading.

abstract: The ability to give a precise and fast prediction for the price movement of stocks is the key to profitability in High Frequency Trading. The main objective of this paper is to propose a novel way of modeling the high frequency trading problem using Deep Reinforcement Learning and to argue why Deep RL can have a lot of potential in the field of High Frequency Trading. We have analyzed the model's performance based on it's prediction accuracy as well as prediction speed across full-day trading simulations.


title: Set-membership affine projection channel estimation for wireless sensor networks

abstract: A set-membership affine projection algorithm is applied to estimate the communication channel between the wireless sensor nodes in a general form, where the channel is modeled as a complex matrix in the presence of additive white gaussian noise. An efficient hybrid model for the affine projection algorithm is briefly introduced and the problem of matrix invertibility in some cases of the affine projection algorithm is resolved by a new method which does not use any matrix inversion. Simulations show good performance of our proposed algorithm in terms of convergence speed and demonstrate reduced complexity.


title: Structure analysis of soccer video with domain knowledge and hidden Markov models

abstract: In this paper, we present statistical techniques for parsing the structure of produced soccer programs. The problem is important for applications such as personalized video streaming and browsing systems, in which videos are segmented into different states and important states are selected based on user preferences. While prior work focuses on the detection of special events such as goals or corner kicks, this paper is concerned with generic structural elements of the game. We define two mutually exclusive states of the game, play and break based on the rules of soccer. Automatic detection of such generic states represents an original challenging issue due to high appearance diversities and temporal dynamics of such states in different videos. We select a salient feature set from the compressed domain, dominant color ratio and motion intensity, based on the special syntax and content characteristics of soccer videos. We then model the stochastic structures of each state of the game with a set of hidden Markov models. Finally, higher-level transitions are taken into account and dynamic programming techniques are used to obtain the maximum likelihood segmentation of the video sequence. The system achieves a promising classification accuracy of 83.5%, with light-weight computation on feature extraction and model inference, as well as a satisfactory accuracy in boundary timing.


title: Activity recall in a visual cortical ensemble

abstract: Cue-triggered recall of learned temporal sequences is an important cognitive function that has been attributed to higher brain areas. Here recordings in both anesthetized and awake rats demonstrate that after repeated stimulation with a moving spot that evoked sequential firing of an ensemble of primary visual cortex (V1) neurons, just a brief flash at the starting point of the motion path was sufficient to evoke a sequential firing pattern that reproduced the activation order evoked by the moving spot. The speed of recalled spike sequences may reflect the internal dynamics of the network rather than the motion speed. In awake rats, such recall was observed during a synchronized ('quiet wakeful') brain state having large-amplitude, low-frequency local field potential (LFP) but not in a desynchronized ('active') state having low-amplitude, high-frequency LFP. Such conditioning-enhanced, cue-evoked sequential spiking of a V1 ensemble may contribute to experience-based perceptual inference in a brain state-dependent manner.


title: A high efficiency synchronous buck converter with adaptive dead-time control

abstract: An adaptive inverter-based dead-time controller for synchronous DC-DC converter is proposed. With this controller, the switching time is precisely controlled and thus simultaneously eliminates the power losses caused by body-diode conduction, power-stage shoot-through current and inductor reverse current. A pair of inverters are used to replace the high speed comparators in this controller for even faster comparison. In addition, the proposed two-step (coarse and fine tuning) dynamic delay generator in this controller helps to achieve more accurate switching time with wider dead-time correction range. The controller can be incorporated into different converter topologies with a wide load range. In this paper, a voltage-mode buck converter with the proposed adaptive dead-time controller has been designed using 0.18 µm CMOS process. Simulation results show precisely controlled switching time under different loading conditions, and a 96% peak conversion efficiency can be achieved with the proposed technique.


title: Temporal relation classification with deep neural network

abstract: We proposed neural network architecture based on Convolution Neural Network(CNN) for temporal relation classification in sentence. First, we transformed word into vector by using word embedding. In Feature Extraction, we extracted two type of features. Lexical level feature considered meaning of marked entity and Sentence level feature considered context of the sentence. Window processing was used to reflect local context and Convolution and Max-pooling operation were used for global context. We concatenated both feature vectors and used softmax operation to compute confidence score. Because experiment results didn't outperform the state-of-the-art methods, we suggested some future works to do.


title: A Comparison Between Decision Trees and Decision Tree Forest Models for Software Development Effort Estimation

abstract: Accurate software effort estimation has been a challenge for many software practitioners and project managers. Underestimation leads to disruption in the project's estimated cost and delivery. On the other hand, overestimation causes outbidding and financial losses in business. Many software estimation models exist; however, none have been proven to be the best in all situations. In this paper, a decision tree forest (DTF) model is compared to a traditional decision tree (DT) model, as well as a multiple linear regression model (MLR). The evaluation was conducted using ISBSG and Desharnais industrial datasets. Results show that the DTF model is competitive and can be used as an alternative in software effort prediction.


title: A New Generalization of Fibonacci Sequence & Extended Binet's Formula

abstract: Abstract Consider the Fibonacci sequence having initial conditions F 0 = 0, F 1 = 1 and recurrence relation Fn = F n–1 + F n–2 (n ≥ 2). The Fibonacci sequence has been generalized in many ways, some by preserving the initial conditions, and others by preserving the recurrence relation. In this article, we study a new generalization {qn }, with initial conditions q 0 = 0 and q 1 = 1 which is generated by the recurrence relation qn = aq n–1 + q n–2 (when n is even) or qn = bq n–1 + q n–2 (when n is odd), where a and b are nonzero real numbers. Some well-known sequences are special cases of this generalization. The Fibonacci sequence is a special case of {qn } with a = b = 1. Pell's sequence is {qn } with a = b = 2 and the k-Fibonacci sequence is {qn } with a = b = k. We produce an extended Binet's formula for the sequence {qn } and, thereby, identities such as Cassini's, Catalan's, d'Ocagne's, etc.


title: RoleMiner: mining roles using subset enumeration

abstract: Role engineering, the task of defining roles and associating permissions to them, is essential to realize the full benefits of the role-based access control paradigm. Essentially, there are two basic approaches to accomplish this: the top-down and the bottom-up. The top-down approach relies on a careful analysis of the business processes to define job functions and then specify appropriate roles from them. While this approach can aid in defining roles more accurately, it is tedious and time consuming since it requires that the semantics of the business processes be well understood. Moreover, it ignores existing permissions within an organization and does not utilize them. On the other hand, the bottom-up approach starts with existing permissions and attempts to derive roles from them, thus helping to automate role definition. In this paper, we present an unsupervised approach called RoleMiner that mines roles from existing user-permission assignments. Since a role is nothing but a set of permissions, when no semantics are available, the task of role mining is essentially that of clustering users that have same (or similar) permissions. However, unlike the traditional applications of data mining that ideally require identification of non-overlapping clusters, roles will have overlapping permission needs and thus permission sets that define roles should be allowed to overlap. It is this distinction from traditional clustering that makes the problem of role mining non-trivial. Our experiments with real and simulated data sets indicate that our role mining process is quite accurate and efficient.


title: Comparison of aligned Friedman rank and parametric methods for testing interactions in split-plot designs

abstract: Parametric methods are commonly used despite evidence that model assumptions are often violated. Various statistical procedures have been suggested for analyzing data from multiple-group repeated measures (i.e., split-plot) designs when parametric model assumptions are violated (e.g., Akritas and Arnold (J. Amer. Statist. Assoc. 89 (1994) 336); Brunner and Langer (Biometrical J. 42 (2000) 663)), including the use of Friedman ranks. The effects of Friedman ranking on data and the resultant test statistics for single sample repeated measures designs have been examined (e.g., Harwell and Serlin (Comput. Statist. Data Anal. 17 (1994) 35; Comm. Statist. Simulation Comput. 26 (1997) 605); Zimmerman and Zumbo (J. Experiment. Educ. 62 (1993) 75)). However, there have been fewer investigations concerning Friedman ranks applied to multiple groups of repeated measures data (e.g., Beasley (J. Educ. Behav. Statist. 25 (2000) 20); Rasmussen (British J. Math. Statist. Psych. 42 (1989) 91)). We investigate the use of Friedman ranks for testing the interaction in a split-plot design as a robust alternative to parametric procedures. We demonstrated that the presence of a repeated measures main effect may reduce the power of interaction tests performed on Friedman ranks. Aligning the data before applying Friedman ranks was shown to produce more statistical power than simply analyzing Friedman ranks. Results from a simulation study showed that aligning the data (i.e., removing main effects) before applying Friedman ranks and then performing either a univariate or multivariate test can provide more statistical power than parametric tests if the error distributions are skewed.


Studies with a lymphosarcoma, sarcoma 180 and leukemia P1534 in mice.


title: A line feature based SLAM with low grade range sensors using geometric constraints and active exploration for mobile robot

abstract: This paper describes a geometrically constrained Extended Kalman Filter (EKF) framework for a line feature based SLAM, which is applicable to a rectangular indoor environment. Its focus is on how to handle sparse and noisy sensor data, such as PSD infrared sensors with limited range and limited number, in order to develop a low-cost navigation system. It has been applied to a vacuum cleaning robot in our research. In order to meet the real-time objective with low computing power, we develop an efficient line feature extraction algorithm based upon an iterative end point fit (IEPF) technique assisted by our constrained version of the Hough transform. It uses a geometric constraint that every line is orthogonal or parallel to each other because in a general indoor setting, most furniture and walls satisfy this constraint. By adding this constraint to the measurement model of EKF, we build a geometrically constrained EKF framework which can estimate line feature positions more accurately as well as allow their covariance matrices to converge more rapidly when compared to the case of an unconstrained EKF. The experimental results demonstrate the accuracy and robustness to the presence of sensor noise and errors in an actual indoor environment.


title: Redundant transformation on the binary data and its applications to image coding and transmission

abstract: Mojette transformation (MT) is the discrete domain counterpart of the Radon transformation, and the Mojette transformation on binary data has some special characteristics compared to the ones for integer data. This paper discusses the ability of reconstruction of Mojette transformation on binary data (MTBI) and finds out a group of projections by which Mojette transformation on a bilevel image of size 2/spl times/2 is reversible. Furthermore, this transformation has the ability of correcting 1 bit of error within a vector of 8 bits and some of 2-continuous-bits of errors. Finally, an application to state image coding and transmission is proposed.


title: Interpretation of Linear Classifiers by Means of Feature Relevance Bounds

abstract: Abstract Research on feature relevance and feature selection problems goes back several decades, but the importance of these areas continues to grow as more and more data becomes available, and machine learning methods are used to gain insight and interpret, rather than solely to solve classification or regression problems. Despite the fact that feature relevance is often discussed, it is frequently poorly defined, and the feature selection problems studied are subtly different. Furthermore, the problem of finding all features relevant for a classification problem has only recently started to gain traction, despite its importance for interpretability and integrating expert knowledge. In this paper, we attempt to unify commonly used concepts and to give an overview of the main questions and results. We formalize two interpretations of the all-relevant problem and propose a polynomial method to approximate one of them for the important hypothesis class of linear classifiers, which also enables a distinction between strongly and weakly relevant features.


title: A cloud computing architecture for supply chain network simulation

abstract: This paper presents a Cloud Computing Architecture For Supply Chain Network Simulation (CCAFSCNS). The structure and elements of the CCAFSCNS as well as the relations among elements are introduced. The purpose of the architecture is to facilitate the distributed simulation of large-scale multi-echelon supply chains with an arborescent structure. The simulator in the CCAFSCNS permits the user to specify the network structure, the inventory stocking policies and demand characteristics so that supply chain performance can be estimated (e.g. average inventory on hand, average fill rates, average backorders, etc.) for each stock-keeping-unit. A prototype system that implements the CCAFSCNS is presented and illustrated with three example use cases. In addition, this paper discusses the time issues associated with the cloud computing solution and shows that the cloud computing solution can significantly shorten the simulation time.


title: Cyber-physical systems clouds

abstract: Cyber-Physical Systems (CPSs) represent systems where computations are tightly coupled with the physical world, meaning that physical data is the core component that drives computation. Industrial automation systems, wireless sensor networks, mobile robots and vehicular networks are just a sample of cyber-physical systems. Typically, CPSs have limited computation and storage capabilities due to their tiny size and being embedded into larger systems. With the emergence of cloud computing and the Internet-of-Things (IoT), there are several new opportunities for these CPSs to extend their capabilities by taking advantage of the cloud resources in different ways. In this survey paper, we present an overview of research efforts on the integration of cyber-physical systems with cloud computing and categorize them into three areas: (1) remote brain, (2) big data manipulation, (3) and virtualization. In particular, we focus on three major CPSs namely mobile robots, wireless sensor networks and vehicular networks.


title: RGB-D & SVM action recognition for security improvement

abstract: The current state of art 1 allows achieving high action recognition accuracy but only after processing the entire video sequence, however for security issues, it is primordial to detect dangerous behaviors occurrence as soon as possible allowing early warnings. In this paper, we present a human activity recognition method using skeleton information provided by an RGB-D sensor by proposing a new descriptor modelling dynamic relation between 3D locations of skeleton joints, expressed in distance and spherical coordinates between the 20 available joints. First, we perform preprocessing step to recover missing skeleton information over frames, so, we normalize skeleton joints and apply a PCA dimension reduction to remove noisy information and enhance recognition accuracy while improving calculation and decision time. We also study the accuracy of the proposed descriptor calculated over limited few first frames to perform early action detection by specifying multiclass SVM classifier. We test this approach on two datasets, MSR Daily Activity 3D and our own dataset called INDACT; experimental evaluation shows that proposed approach can robustly classify actions outperforming state-of-art and maintain good accuracy score even using limited frame number.


title: Texture defect detection in gradient space

abstract: In this paper, we propose a machine vision algorithm for automatically detecting defects in patterned textures with the help of gradient space and its energy. Gradient space image is obtained from the input defective image and is split into several blocks of size same as that of the periodic unit of the input defective image. Energy of the gradient space image is used as feature space for identifying defective and nondefective periodic blocks using Ward’s hierarchical clustering. Experiments on real fabric images with defects show that the proposed method can be used for automatic detection of fabric defects in textile industries.


title: Night-time dehazing by fusion

abstract: We introduce an effective technique to enhance night-time hazy scenes. Our technique builds on multi-scale fusion approach that use several inputs derived from the original image. Inspired by the dark-channel [1] we estimate night-time haze computing the airlight component on image patch and not on the entire image. We do this since under night-time conditions, the lighting generally arises from multiple artificial sources, and is thus intrinsically non-uniform. Selecting the size of the patches is non-trivial, since small patches are desirable to achieve fine spatial adaptation to the atmospheric light, this might also induce poor light estimates and reduced chance of capturing hazy pixels. For this reason, we deploy multiple patch sizes, each generating one input to a multiscale fusion process. Moreover, to reduce the glowing effect and emphasize the finest details, we derive a third input. For each input, a set of weight maps are derived so as to assign higher weights to regions of high contrast, high saliency and small saturation. Finally the derived inputs and the normalized weight maps are blended in a multi-scale fashion using a Laplacian pyramid decomposition. The experimental results demonstrate the effectiveness of our approach compared with recent techniques both in terms of computational efficiency and quality of the outputs.