title: An ethnographic study exploring the role of ward-based Advanced Nurse Practitioners in an acute medical setting

abstract: AIM ::: This article is a report of a study that aimed to examine the role of ward-based Advanced Nurse Practitioners and their impact on patient care and nursing practice. ::: ::: ::: BACKGROUND ::: Revised doctor/nurse skill mix combined with a focus on improving quality of care while reducing costs has had an impact on healthcare delivery in the western world. Diverse advanced nursing practice roles have developed and their function has varied globally over the last decade. However, roles and expectations for ward-based Advanced Nurse Practitioners lack clarity, which may hinder effective contribution to practice. ::: ::: ::: DESIGN ::: An ethnographic approach was used to explore the advanced nurse practitioner role. ::: ::: ::: METHODS ::: Participant observation and interviews of five ward-based Advanced Nurse Practitioners working in a large teaching hospital in the North West of England during 2009 were complemented by formal and informal interviews with staff and patients. Data were descriptive and broken down into themes, patterns and processes to enable interpretation and explanation. ::: ::: ::: RESULTS ::: The overarching concept that ran through data analysis was that of Advanced Nurse Practitioners as a lynchpin, using their considerable expertise, networks and insider knowledge of health care not only to facilitate patient care but to develop a pivotal role facilitating nursing and medical practice. Sub-themes included enhancing communication and practice, acting as a role model, facilitating the patients' journey and pioneering the role. ::: ::: ::: CONCLUSION ::: Ward-based Advanced Nurse Practitioners are pivotal and necessary for providing quality holistic patient care and their role can be defined as more than junior doctor substitutes.


title: Cooperative localization and mapping of MAVs using RGB-D sensors

abstract: The fusion of IMU and RGB-D sensors presents an interesting combination of information to achieve autonomous localization and mapping using robotic platforms such as ground robots and flying vehicles. In this paper, we present a software framework for cooperative localization and mapping while simultaneously using multiple aerial platforms. We employ a monocular visual odometry algorithm to solve the localization task, where the depth data flow associated to the RGB image is used to estimate the scale factor associated with the visual information. The current framework enables autonomous onboard control of each vehicle with cooperative localization and mapping. We present a methodology that provides both a sparse map generated by the monocular SLAM and a multiple resolution dense map generated by the associated depth. The localization algorithm and both 3D mapping algorithms work in parallel improving the system real-time reliability. We present experimental results to show the effectiveness of the proposed approach using two quadrotors platforms.


title: Gain enhancement of butler matrix fed antenna array system by using planar circular EBG units

abstract: In this paper planar circular Electromagnetic Band Gap (EBG) structures have been implemented in the Butler matrix fed antenna array system to improve the radiation performance of the system. Initially a conventional 4×4 Butler matrix fed antenna array system has been designed at 14.5 GHz. Asymmetricity in the gain values of major beams of the antenna array system has been observed both in simulation and measurement. Generally due to the design simplicity, light weight and compactness, the rectangular patch microstrip antenna has been used as the radiating element in the Butler matrix fed antenna array system. At higher frequencies microstrip antenna suffers from losses due to surface wave excitation. It degrades the radiation efficiency of the antenna array system and also increases the level of mutual coupling between the radiating elements in the antenna array. EBG (Electromagnetic Band Gap) units are used to improve the radiation performance of the system by the generation of stop band. The propagation of surface wave will be suppressed in this stop band region and may improve the performance of the system. To fulfill this requirement, 3 × 1 planar circular EBG unit has been designed. A stop band of 2.15 GHz with the centre frequency of 14.5 GHz has been obtained by the proposed EBG unit. The 3×1 planar circular EBG unit has been integrated between the radiating elements of the antenna array system. As a result the gain enhancement has been achieved for the 1R, 2L, 2R and 1L beams both in simulation and measurement at 14.5 GHz.


title: What Do Developers Use the Crowd For? A Study Using Stack Overflow

abstract: Stack Overflow relies on the crowd to construct quality developer-related knowledge. To determine what developers use this knowledge for, researchers analyzed 1,414 Stack Overflow-related code commits. The developers used this knowledge to support development tasks and collect user feedback. The researchers also studied Stack Overflow posts' helpfulness and timeliness. The crowd was the most helpful on topics such as development tools and programming languages. The questions that took the longest to resolve were related to Web frameworks. The study findings can help developers better understand how to effectively use Stack Overflow, can help Stack Overflow designers improve their platform, and can help the research community understand Stack Overflow's strengths and weaknesses as a development tool. This article is part of a special issue on Crowdsourcing for Software Engineering.


title: Flying Ad-Hoc Networks: Technological and Social Implications

abstract: With the world turning into a global village due to technological advancements, automation in all aspects of life is gaining momentum. Wireless technologies address the everincreasing demands of portable and flexible communications. Wireless ad-hoc networks, which allow communication between devices without the need for any central infrastructure, are gaining significance, particularly for monitoring and surveillance applications. A relatively new research area of ad-hoc networks is flying ad-hoc networks (FANETs), governing the autonomous movement of unmanned aerial vehicles (UAVs) [1]. In such networks multiple UAVs are allowed to communicate so that an ad-hoc network is established between them. All UAVs in the network carry UAV-to-UAV communication and only groups of UAVs interact with the ground station. This feature eliminates the need for deployment of complex hardware in each UAV. Moreover, even if one of the UAV communication links breaks down; there is no link breakage with the base station due to the ad-hoc network between UAVs.


title: Adopting a Digital Business Operating System

abstract: The role of software in society and in industry in particular continues to grow exponentially. Most companies either have or are in the process of adoption continuous deployment of their software at products in the field and collect data concerning the performance of their systems. The continuous, fast feedback loops that companies now have available allow for a fundamentally different way of organizing. In fact, based on our work with dozens of companies, we have come to the conclusion that companies are moving towards a new, digital operating system. In this paper, we first present the key elements of the digital operating system and then discuss some of the challenges companies experience during the transformation.


title: Choice of Agile Methodologies in Software Development: A Vendor Perspective

abstract: ABSTRACT The purpose of this research was to develop understanding about how vendor firms make choice about agile methodologies in software projects and their fit. Two analytical frameworks were developed from extant literature and the findings were compared with real world decisions. Framework 1 showed that the choice of XP for one project was not supported by the guidelines given by the framework. The choices of SCRUM for other two projects, were partially supported. Analysis using the framework 2 showed that except one XP project, all others had sufficient project management support, limited scope for adaptability and had prominence for rules. Keywords: Agile, software development, vendor perspective INTRODUCTION Software development methodology has been understood as a model used to plan, design, test and control the processes for developing an information system, furnished with one or more techniques. In this context, model refers to a logical description of development processes, which can be sequential or iterative (Matkovic & Tumbas, 2010). A development methodology has a direct relationship with managing project complexity. Use of methodology has also implications for usability, maintainability, adaptability, reliability and portability of the software being developed. Further, adopting an incorrect methodology could result in slippages, lack of communication and administrative overheads, leading to customer dissatisfaction. A recent study has reported a significant relationship between supplier (vendor) satisfaction and the choice of methodology (Wright, 2013). In the past two decades, fast paced evolution of software development methodologies has effected significant improvements in software quality (Huo et al., 2004). During late 1990's, agile methodologies became prevalent to address some shortcomings of traditional methodologies like heavy documentation, lack of productivity, reliability and simplicity (Cho, 2009).Agile alliance, in response to more process driven traditional methodologies, stresses upon people, communication and customer priorities (Beck et al., 2001). Also, different agile methodologies has exhibited flexibility to working within constraints, without demanding major upfront investments, while being adaptable to changing market conditions(Mohammad et al., 2013). Inspite of their widespread use in the last 15 years, agile methodologies have also shown many limitations. Some notable limitations include dependence on run-time tacit knowledge rather than more documented information, lack of traceable and proved implementation guidelines for mission critical projects, lack of adequate support for repetitive and large scale projects and team requirement of highly talented, self-motivated individuals with a high degree of implementation freedom (Cho, 2009).The outcomes of projects developed using agile methodologies are dependent mainly on organisational factors like customer commitment, decision time, team location and composition, corporate culture and people factors like competency and self-motivation (Vinekar et al., 2006). In order to address various concerns, several methodologies within the agile category evolved; Abrahamsson et al. (2010) identifies ten of them as truly agile. While there is considerable interest in the IT industry to adopt newer methods fostering agility, guidelines in choosing the right methodologies, based on relevant factors, remain scanty (Wright, 2013; Coram & Bohner, 2005).What are the key determinants in the choice of agile methodologies in order to meet project goals? Are these factors identified in extant literature followed in practice? How are decisions on the choice of agile methodologies taken in practice? Prior research has provided some frameworks for analysing characteristics of agile methods. For example, Qumer and Henderson-Sellers (2008) identifies four dimensions for analysing agility of various agile methods. …


title: An Empirical Study of the Factors Influencing Use of Social Network Service

abstract: Social network service (SNS) has become a popular media for social interaction and information exchange. However, it is not clear why people use SNS. In addition, human behaviour researches related to SNS are not sufficient despite of its prominence. Hence, the goal of this paper is to explain why people use SNS. First, we empirically examined what individual’s characteristics affect the use of SNS. To examine these characteristics, we developed a structural model on the basis of three popular theories; Technology Acceptance Model (TAM), Network Externality, and Innovation Diffusion Theory (IDT). Also, to achieve our goal, we applied Structural Equation Modeling (SEM) approach to evaluate the effect of each construct. The finding shows that Perceived Usefulness (PU), Perceived Ease of Use (PEU), Members (M), and Compatibility (C) have a positive significant effect on the Actual Use (AU) of SNS. Moreover, we identified the differences between Facebook users and Twitter users. From the control variable (Facebook and Twitter users) analysis, we concluded that PU and M are important factors to Facebook users, while PEU and C are prominent to Twitter users.


title: A Machine Learning Approach to Predict Chemical Reactions

abstract: Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Previous approaches are not high-throughput, are not generalizable or scalable, or lack sufficient data to be effective. We describe single mechanistic reactions as concerted electron movements from an electron orbital source to an electron orbital sink. We use an existing rule-based expert system to derive a dataset consisting of 2,989 productive mechanistic steps and 6.14 million non-productive mechanistic steps. We then pose identifying productive mechanistic steps as a ranking problem: rank potential orbital interactions such that the top ranked interactions yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.0% of non-productive reactions with less than a 0.1% false negative rate. Then, we train an ensemble of ranking models on pairs of interacting orbitals to learn a relative productivity function over single mechanistic reactions in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanisms at the top 89.1% of the time, rising to 99.9% of the time when top ranked lists with at most four nonproductive reactions are considered. The final system allows multi-step reaction prediction. Furthermore, it is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert system does not handle.


title: Characteristics of the multiple-input DC-DC converter

abstract: In the zero-emission electric power generation system, a multiple-input DC-DC converter is useful to obtain the regulated output voltage from several input power sources such as a solar array, wind generator, fuel cell, and so forth. A new multiple-input DC-DC converter is proposed and analyzed. As a result, the static and dynamic characteristics are clarified theoretically, and the results are confirmed by experiment.


title: Modeling of Photovoltaic Panel by using Proteus

abstract: This paper focuses on a Proteus Spice model of the photovoltaic Panel. This model is based on a mathematical equation which is got from the equivalent circuit of the photovoltaic Panel; it includes a photocurrent source, a diode, a series resistor and a shunt resistor. Next, this model is validated by comparing its data with the experimental data. In addition, since Proteus provides in its library different microcontrollers and electronic boards , this model is connected to the Arduino UNO Board through the voltage and current sensors, that in order to acquire and supervise the photovoltaic voltage, current and power. And for experimental validation, a prototype using real components has been developed.


title: Factors Influencing Quality of Mobile Apps:Role of Mobile App Development Life Cycle

abstract: In this paper, The mobile application field has been receiving astronomical attention from the past few years due to the growing number of mobile app downloads and withal due to the revenues being engendered .With the surge in the number of apps, the number of lamentable apps/failing apps has withal been growing.Interesting mobile app statistics are included in this paper which might avail the developers understand the concerns and merits of mobile apps.The authors have made an effort to integrate all the crucial factors that cause apps to fail which include negligence by the developers, technical issues, inadequate marketing efforts, and high prospects of the users/consumers.The paper provides suggestions to eschew failure of apps. As per the various surveys, the number of lamentable/failing apps is growing enormously, primarily because mobile app developers are not adopting a standard development life cycle for the development of apps. In this paper, we have developed a mobile application with the aid of traditional software development life cycle phases (Requirements, Design, Develop, Test, and, Maintenance) and we have used UML, M-UML, and mobile application development technologies.


title: Improving the capability of an integrated CA-Markov model to simulate spatio-temporal urban growth trends using an Analytical Hierarchy Process and Frequency Ratio

abstract: The creation of an accurate simulation of future urban growth is considered one of the most important challenges in urban studies that involve spatial modeling. The purpose of this study is to improve the simulation capability of an integrated CA-Markov Chain (CA-MC) model using CA-MC based on the Analytical Hierarchy Process (AHP) and CA-MC based on Frequency Ratio (FR), both applied in Seremban, Malaysia, as well as to compare the performance and accuracy between the traditional and hybrid models. Various physical, socio-economic, utilities, and environmental criteria were used as predictors, including elevation, slope, soil texture, population density, distance to commercial area, distance to educational area, distance to residential area, distance to industrial area, distance to roads, distance to highway, distance to railway, distance to power line, distance to stream, and land cover. For calibration, three models were applied to simulate urban growth trends in 2010; the actual data of 2010 were used for model validation utilizing the Relative Operating Characteristic (ROC) and Kappa coefficient methods Consequently, future urban growth maps of 2020 and 2030 were created. The validation findings confirm that the integration of the CA-MC model with the FR model and employing the significant driving force of urban growth in the simulation process have resulted in the improved simulation capability of the CA-MC model. This study has provided a novel approach for improving the CA-MC model based on FR, which will provide powerful support to planners and decision-makers in the development of future sustainable urban planning.


title: Building comprehensible customer churn prediction models with advanced rule induction techniques

abstract: Customer churn prediction models aim to detect customers with a high propensity to attrite. Predictive accuracy, comprehensibility, and justifiability are three key aspects of a churn prediction model. An accurate model permits to correctly target future churners in a retention marketing campaign, while a comprehensible and intuitive rule-set allows to identify the main drivers for customers to churn, and to develop an effective retention strategy in accordance with domain knowledge. This paper provides an extended overview of the literature on the use of data mining in customer churn prediction modeling. It is shown that only limited attention has been paid to the comprehensibility and the intuitiveness of churn prediction models. Therefore, two novel data mining techniques are applied to churn prediction modeling, and benchmarked to traditional rule induction techniques such as C4.5 and RIPPER. Both AntMiner+ and ALBA are shown to induce accurate as well as comprehensible classification rule-sets. AntMiner+ is a high performing data mining technique based on the principles of Ant Colony Optimization that allows to include domain knowledge by imposing monotonicity constraints on the final rule-set. ALBA on the other hand combines the high predictive accuracy of a non-linear support vector machine model with the comprehensibility of the rule-set format. The results of the benchmarking experiments show that ALBA improves learning of classification techniques, resulting in comprehensible models with increased performance. AntMiner+ results in accurate, comprehensible, but most importantly justifiable models, unlike the other modeling techniques included in this study.


title: Brown Adipose Tissue Development and Metabolism

abstract: Brown adipose tissue is well known to be a thermoregulatory organ particularly important in small rodents and human infants, but it was only recently that its existence and significance to metabolic fitness in adult humans have been widely realized. The ability of active brown fat to expend high amounts of energy has raised interest in stimulating thermogenesis therapeutically to treat metabolic diseases related to obesity and type 2 diabetes. In parallel, there has been a surge of research aimed at understanding the biology of rodent and human brown fat development, its remarkable metabolic properties, and the phenomenon of white fat browning, in which white adipocytes can be converted into brown like adipocytes with similar thermogenic properties. Here, we review the current understanding of the developmental and metabolic pathways involved in forming thermogenic adipocytes, and highlight some of the many unknown functions of brown fat that make its study a rich and exciting area for future research.


title: Scalable Metadata Management Techniques for Ultra-Large Distributed Storage Systems -- A Systematic Review

abstract: The provisioning of an efficient ultra-large scalable distributed storage system for expanding cloud applications has been a challenging job for researchers in academia and industry. In such an ultra-large-scale storage system, data are distributed on multiple storage nodes for performance, scalability, and availability. The access to this distributed data is through its metadata, maintained by multiple metadata servers. The metadata carries information about the physical address of data and access privileges. The efficiency of a storage system highly depends on effective metadata management. This research presents an extensive systematic literature analysis of metadata management techniques in storage systems. This research work will help researchers to find the significance of metadata management and important parameters of metadata management techniques for storage systems. Methodical examination of metadata management techniques developed by various industry and research groups is described. The different metadata distribution techniques lead to various taxonomies. Furthermore, the article investigates techniques based on distribution structures and key parameters of metadata management. It also presents strengths and weaknesses of individual existing techniques that will help researchers to select the most appropriate technique for specific applications. Finally, it discusses existing challenges and significant research directions in metadata management for researchers.


title: Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks

abstract: In this paper, we introduce a novel method to interpret recurrent neural networks (RNNs), particularly long short-term memory networks (LSTMs) at the cellular level. We propose a systematic pipeline for interpreting individual hidden state dynamics within the network using response characterization methods. The ranked contribution of individual cells to the network’s output is computed by analyzing a set of interpretable metrics of their decoupled step and sinusoidal responses. As a result, our method is able to uniquely identify neurons with insightful dynamics, quantify relationships between dynamical properties and test accuracy through ablation analysis, and interpret the impact of network capacity on a network’s dynamical distribution. Finally, we demonstrate the generalizability and scalability of our method by evaluating a series of different benchmark sequential datasets.


title: Low-level laser therapy/photobiomodulation in the management of side effects of chemoradiation therapy in head and neck cancer: part 2: proposed applications and treatment protocols

abstract: Purpose ::: There is a large body of evidence supporting the efficacy of low-level laser therapy (LLLT), more recently termed photobiomodulation (PBM) for the management of oral mucositis (OM) in patients undergoing radiotherapy for head and neck cancer (HNC). Recent advances in PBM technology, together with a better understanding of mechanisms involved and dosimetric parameters may lead to the management of a broader range of complications associated with HNC treatment. This could enhance patient adherence to cancer therapy, and improve quality of life and treatment outcomes. The mechanisms of action, dosimetric, and safety considerations for PBM have been reviewed in part 1. Part 2 discusses the head and neck treatment side effects for which PBM may prove to be effective. In addition, PBM parameters for each of these complications are suggested and future research directions are discussed.


title: Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

abstract: Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.


title: The naturalness of reproduced high dynamic range images

abstract: The problem of visualizing high dynamic range images on the devices with restricted dynamic range has recently gained a lot of interest in the computer graphics community. Various so-called tone mapping operators have been proposed to face this issue. The field of tone mapping assumes thorough knowledge of both the objective and subjective attributes of an image. However, there no published analysis of such attributes exists so far. In this paper, we present an overview of image attributes which are used extensively in different tone mapping methods. Furthermore, we propose a scheme of relationships between these attributes, leading to the definition of an overall quality measure which we call naturalness. We present results of the subjective psychophysical testing that we have performed to prove the proposed relationship scheme. Our effort sets the stage for well-founded quality comparisons between tone mapping operators. By providing good definitions of the different attributes, comparisons, be they user-driven or fully automatic, are made possible at all.


title: A V-band waveguide to microstrip inline transition

abstract: A wideband, low loss inline transition from microstrip line to rectangular waveguide is presented. This transition efficiently couples energy from a microstrip line to a ridge and subsequently to a TE10 waveguide. This unique structure requires no mechanical pressure for electrical contact between the microstrip probe and the ridge because the main planar circuitry and ridge sections are placed on a single housing. The measured insertion loss for back-to-back transition is 0.5 – 0.7 dB (0.25 – 0.35 dB/transition) in the band 50 – 72 GHz.


title: Designing Planar Deployable Objects via Scissor Structures

abstract: Scissor structure is used to generate deployable objects for space-saving in a variety of applications, from architecture to aerospace science. While deployment from a small, regular shape to a larger one is easy to design, we focus on a more challenging task: designing a planar scissor structure that deploys from a given source shape into a specific target shape. We propose a two-step constructive method to generate a scissor structure from a high-dimensional parameter space. Topology construction of the scissor structure is first performed to approximate the two given shapes, as well as to guarantee the deployment. Then the geometry of the scissor structure is optimized in order to minimize the connection deflections and maximize the shape approximation. With the optimized parameters, the deployment can be simulated by controlling an anchor scissor unit. Physical deployable objects are fabricated according to the designed scissor structures by using 3D printing or manual assembly. We show a number of results for different shapes to demonstrate that even with fabrication errors, our designed structures can deform fluently between the source and target shapes.


Analysis of Error Floors for Non-binary LDPC Codes over General Linear Group through q-Ary Memoryless Symmetric Channels


title: Covariance Descriptors for 3D Shape Matching and Retrieval

abstract: Several descriptors have been proposed in the past for 3D shape analysis, yet none of them achieves best performance on all shape classes. In this paper we propose a novel method for 3D shape analysis using the covariance matrices of the descriptors rather than the descriptors themselves. Covariance matrices enable efficient fusion of different types of features and modalities. They capture, using the same representation, not only the geometric and the spatial properties of a shape region but also the correlation of these properties within the region. Covariance matrices, however, lie on the manifold of Symmetric Positive Definite (SPD) tensors, a special type of Riemannian manifolds, which makes comparison and clustering of such matrices challenging. In this paper we study covariance matrices in their native space and make use of geodesic distances on the manifold as a dissimilarity measure. We demonstrate the performance of this metric on 3D face matching and recognition tasks. We then generalize the Bag of Features paradigm, originally designed in Euclidean spaces, to the Riemannian manifold of SPD matrices. We propose a new clustering procedure that takes into account the geometry of the Riemannian manifold. We evaluate the performance of the proposed Bag of Covariance Matrices framework on 3D shape matching and retrieval applications and demonstrate its superiority compared to descriptor-based techniques.


title: Applications of Bio-Inspired Special Wettable Surfaces

abstract: In this review we focus on recent developments in applications of bioinspired special wettable surfaces. We highlight surface materials that in recent years have shown to be the most promising in their respective fi elds for use in future applications. The selected topics are divided into three groups, applications of superhydrophobic surfaces, surfaces of patterned wettability and integrated multifunctional surfaces and devices. We will present how the bio-inspired wettability has been integrated into traditional materials or devices to improve their performances and to extend their practical applications by developing new functionalities.


title: Generating optimal DAG-structured query evaluation plans

abstract: In many database queries relations are access multiple times during query processing. In these cases query processing can be accelerated by sharing scan operators and possibly other operators based upon the common relations. The standard approach to achieve sharing works as follows. In a first phase, a non-shared tree-shaped plan is generated via a traditional plan generator. In a second phase, common instances of a scan are detected and shared. After that, other possible operators are shared. The result is an operator DAG (directed acyclic graph).


title: Feature Extraction Techniques for Recognition of Malayalam Handwritten Characters: Review

abstract:  Abstract: The Character recognition is one of the most important areas in the field of pattern recognition. Recently Indian Handwritten character recognition is getting much more attention and researchers are contributing a lot in this field. But Malayalam, a South Indian language has very less works in this area and needs further attention. Malayalam OCR is a complex task owing to the various character scripts available and more importantly the difference in ways in which the characters are written. The dimensions are never the same and may be never mapped on to a square grid unlike English characters. Selection of a feature extraction method is the most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representation of characters. As an important component of pattern recognition, feature extraction has been paid close attention by many scholars, and currently has become one of the research hot spots in the field of pattern recognition. This article gives a general discussion of feature extraction techniques used in handwritten character recognition of other Indian languages and some of them are implemented for Malayalam handwritten characters.


Scalable Packet Classification Using Bit Vector Aggregating and Folding


title: An application of homomorphic encryption on IoT based green house

abstract: Every system must have some security if it would be used as commercial product. In the era of Internet of Things, many systems are developed but most of them failed in term of providing security. One of the common issue was Data Privacy and Data Security. To Provide Data Privacy we can use Homomorphic Encryption which allow user to do processing on encrypted data. In the Green House, when data are stored in cloud we may find Data Privacy issue in Cloud. To Solve this problem, we have proposed a system on the base of Homomorphic Encryption. This paper discusses how we can apply homomorphic encryption on any IoT application to provide data Security. For the shake of simplicity, we had taken example of Green House System.


title: Verification of Markov Decision Processes with Risk-Sensitive Measures

abstract: We develop a method for computing policies in Markov decision processes with risk-sensitive measures subject to temporal logic constraints. Specifically, we use a particular risk-sensitive measure from cumulative prospect theory, which has been previously adopted in psychology and economics. The nonlinear transformation of the probabilities and utility functions yields a nonlinear programming problem, which makes computation of optimal policies typically challenging. We show that this nonlinear weighting function can be accurately approximated by the difference of two convex functions. This observation enables efficient policy computation using convex-concave programming. We demonstrate the effectiveness of the approach on several scenarios.


title: Phase Diagram of Restricted Boltzmann Machines and Generalised Hopfield Networks with Arbitrary Priors

abstract: Restricted Boltzmann machines are described by the Gibbs measure of a bipartite spin glass, which in turn can be seen as a generalized Hopfield network. This equivalence allows us to characterize the state of these systems in terms of their retrieval capabilities, both at low and high load, of pure states. We study the paramagnetic-spin glass and the spin glass-retrieval phase transitions, as the pattern (i.e., weight) distribution and spin (i.e., unit) priors vary smoothly from Gaussian real variables to Boolean discrete variables. Our analysis shows that the presence of a retrieval phase is robust and not peculiar to the standard Hopfield model with Boolean patterns. The retrieval region becomes larger when the pattern entries and retrieval units get more peaked and, conversely, when the hidden units acquire a broader prior and therefore have a stronger response to high fields. Moreover, at low load retrieval always exists below some critical temperature, for every pattern distribution ranging from the Boolean to the Gaussian case.


title: A Simple Approach to Learn Polysemous Word Embeddings

abstract: Many NLP applications require disambiguating polysemous words. Existing methods that learn polysemous word vector representations involve first detecting various senses and optimizing the sense-specific embeddings separately, which are invariably more involved than single sense learning methods such as word2vec. Evaluating these methods is also problematic, as rigorous quantitative evaluations in this space is limited, especially when compared with single-sense embeddings. In this paper, we propose a simple method to learn a word representation, given any context. Our method only requires learning the usual single sense representation, and coefficients that can be learnt via a single pass over the data. We propose several new test sets for evaluating word sense induction, relevance detection, and contextual word similarity, significantly supplementing the currently available tests. Results on these and other tests show that while our method is embarrassingly simple, it achieves excellent results when compared to the state of the art models for unsupervised polysemous word representation learning.


title: Heart rate variability and autonomic activity at rest and during exercise in various physiological conditions

abstract: The rhythmic components of heart rate variability (HRV) can be separated and quantitatively assessed by means of power spectral analysis. The powers of high frequency (HF) and low frequency (LF) components of HRV have been shown to estimate cardiac vagal and sympathetic activities. The reliability of these spectral indices, as well as that of LF/HF ratio as a marker of autonomic interaction at rest and during exercise, is briefly reviewed. Modifications in autonomic activities induced by different physiological conditions, e.g. hypoxia exposure, training, and water immersion, have been found in HRV power spectra at rest. The changes in HF and LF powers and in LF/HF ratio observed during exercise have been shown not to reflect the decrease in vagal activity and the activation of sympathetic system occurring at increasing loads. HF peak was recognised in power spectra in the entire range of relative intensity, being responsible for the most part of HR variability at maximal load. LF power did not change during low intensity exercise and decreased to negligible values at medium–high intensity, where sympathetic activity was enhanced. There was no influence from factors such as fitness level, age, hypoxia, and blood distribution. In contrast, a dramatic effect of body position has been suggested by the observation that LF power increased at medium–high intensities when exercising in the supine position. The increased respiratory activity due to exercise would be responsible of HF modulation of HR via a direct mechanical effect. The changes in LF power observed at medium–high intensity might be the expression of the modifications in arterial pressure control mechanisms occurring with exercise. The finding of opposite trends for LF rhythm in supine and sitting exercises suggests that different readjustments might have occurred in relation to different muscular inputs in the two positions.


Public Skepticism of Psychology Why Many People Perceive the Study of Human Behavior as Unscientific


title: Performance considerations for automotive antenna equipment in vehicle-to-vehicle communications

abstract: Vehicle-to-vehicle (V2V) communications systems focus on the improvement of driver safety and traffic efficiency using the recently designated frequency bands in the 5.9GHz range in Europe. Due to the time-frequency selective fading behaviour of the vehicular communication channel, antenna-related effects impacted by the vehicular mounting position and mutual coupling play an integral role in the performance of V2V communications. This paper focuses on the analysis of characteristic antenna effects that are subject to vehicular mounting. Example multi-element antenna (MEA) topologies are considered to improve the reliability of the wireless link by diversity combining in order to meet the stringent requirements of real-time communications.


title: Wearable Food Intake Monitoring Technologies: A Comprehensive Review

abstract: Wearable devices monitoring food intake through passive sensing is slowly emerging to complement self-reporting of users’ caloric intake and eating behaviors. Though the ultimate goal for the passive sensing of eating is to become a reliable gold standard in dietary assessment, it is currently showing promise as a means of validating self-report measures. Continuous food-intake monitoring allows for the validation and refusal of users’ reported data in order to obtain more reliable user information, resulting in more effective health intervention services. Recognizing the importance and strength of wearable sensors in food intake monitoring, there has been a variety of approaches proposed and studied in recent years. While existing technologies show promise, many challenges and opportunities discussed in this survey, still remain. This paper presents a meticulous review of the latest sensing platforms and data analytic approaches to solve the challenges of food-intake monitoring, ranging from ear-based chewing and swallowing detection systems that capture eating gestures to wearable cameras that identify food types and caloric content through image processing techniques. This paper focuses on the comparison of different technologies and approaches that relate to user comfort, body location, and applications for medical research. We identify and summarize the forthcoming opportunities and challenges in wearable food intake monitoring technologies.


title: Grey Wolf Optimizer

abstract: This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces.


Presence and Memory: Immersive Virtual Reality Effects on Cued Recall


title: Latent Semantic Indexing (LSI): TREC-3 Report

abstract: This paper reports on recent developments of the Latent Semantic Indexing (LSI) retrieval method for TREC-3. LSI uses a reduced-dimension vector space to represent words and documents. An important aspect of this representation is that the association between terms is automatically captured, explicitly represented, and used to improve retrieval. We used LSI for both TREC-3 routing and adhoc tasks. For the routing tasks an LSI space was constructed using the training documents. We compared profiles constructed using just the topic words (no training) with profiles constructed using the average of relevant documents (no use of the topic words). Not surprisingly, the centroid of the relevant documents was 30% better than the topic words. This simple feedback method was quite good compared to the routing performance of other systems. Various combinations of information from the topic words and relevant documents provide small additional improvements in performance. For the adhoc task we compared LSI to keyword vector matching (i.e. using no dimension reduction). Small advantages were obtained for LSI even with the long TREC topic statements.


title: Computer literacy and inquiry learning: when geeks learn less

abstract: Alow level of computer literacy has often been hypothesized as constituting a disadvantage in knowledge acquisition. However, within the field of computer-supported inquiry learning systematic investigations of these purported relations have not been conducted. This classroom study investigates the role of computer literacy (procedural computer-related knowledge, selfconfidenceinusingthecomputer,andfamiliaritywithcomputers)asalearningprerequisitefor knowledgeacquisition,andanalysesthelearners’patternsofmediauseasprocessesthatmight explain this role. Thirty-seven students from two final classes of a secondary school worked in pairs on the project ‘How far does light go?’in the Web-based Inquiry Science Environment. Findings did indicate significant relations of neither procedural computer-related knowledge nor self-confidence in using the computer to knowledge acquisition. However, students with


title: Wideband hybrid power combiner using double-sided-parallel-strip lines

abstract: A new design of a hybrid power combiner using double-sided parallel-striplines is proposed for wideband and high power applications. The circuit is used to realize a 4∶1 power combiner. The use of double-sided parallel-strip lines adds flexibility to the design, since different characteristic impedances can readily be realized by simply adjusting the offset between parallel lines, without having to change the width of the lines. Moreover, a new approach using ferrite cores in order to improve low frequency performance of the power combiner is successfully applied. The proposed power combiner is studied analytically; its performance is verified via detailed HFSS simulations and measurements results.


title: DSP-Based Laboratory Implementation of Hybrid Fuzzy-PID Controller Using Genetic Optimization for High-Performance Motor Drives

abstract: This paper presents a real-time implementation of a genetic-based hybrid fuzzy-proportional-integral-derivative (PID) controller for industrial motor drives. Both the design of fuzzy-PID (FPID) controller and its integration with the conventional PID in global control system to produce a hybrid design are demonstrated. A genetic optimization technique is used to determine the optimal values of the scaling factors of the output variables of the FPID controller. The objective is to utilize the best attributes of the PID and FPID controllers to provide a controller which will produce better response than either the PID or FPID controller. The principle of the hybrid controller is to use a PID controller, which performs satisfactorily in most cases, while keeping in the background a FPID controller, which is ready to take over the PID controller when severe disturbances occur. The hybrid controller is formulated and implemented in real time, using the speed control of a brushless drive system as a testbed. The design, analysis, and implementation stages are carried out entirely using a dSPACE DS1104 digital-signal-processor-based real-time data acquisition control system and MATLAB/Simulink environment. Experimental results show that the proposed FPID controller-based genetic optimization produces better control performance than the conventional PID controllers, particularly in handling nonlinearities and external disturbances.


title: An MLP-based representation of neural tensor networks for the RDF data models

abstract: In this paper, a new representation of neural tensor networks is presented. Recently, state-of-the-art neural tensor networks have been introduced to complete RDF knowledge bases. However, mathematical model representation of these networks is still a challenging problem, due to tensor parameters. To solve this problem, it is proposed that these networks can be represented as two-layer perceptron network. To complete the network topology, the traditional gradient based learning rule is then developed. It should be mentioned that for tensor networks there have been developed some learning rules which are complex in nature due to the complexity of the objective function used. Indeed, this paper is aimed to show that the tensor network can be viewed and represented by the two-layer feedforward neural network in its traditional form. The simulation results presented in the paper easily verify this claim.


title: Learning 3D Keypoint Descriptors for Non-rigid Shape Matching

abstract: In this paper, we present a novel deep learning framework ::: that derives discriminative local descriptors for 3D surface shapes. In ::: contrast to previous convolutional neural networks (CNNs) that rely on ::: rendering multi-view images or extracting intrinsic shape properties, we ::: parameterize the multi-scale localized neighborhoods of a keypoint into ::: regular 2D grids, which are termed as ‘geometry images’. The benefits of ::: such geometry images include retaining sufficient geometric information, ::: as well as allowing the usage of standard CNNs. Specifically, we leverage ::: a triplet network to perform deep metric learning, which takes a set of ::: triplets as input, and a newly designed triplet loss function is minimized ::: to distinguish between similar and dissimilar pairs of keypoints. At the ::: testing stage, given a geometry image of a point of interest, our network ::: outputs a discriminative local descriptor for it. Experimental results for ::: non-rigid shape matching on several benchmarks demonstrate the superior ::: performance of our learned descriptors over traditional descriptors ::: and the state-of-the-art learning-based alternatives.


title: A New Metric for Evaluating Semantic Segmentation: Leveraging Global and Contour Accuracy

abstract: Semantic segmentation of images is an important issue for intelligent vehicles and mobile robotics because it offers basic information which can be used for complex reasoning and safe navigation. Different solutions have been proposed for this problem along the last two decades, where recent deep neural networks approaches have shown very promising results in the context of urban navigation. One of the main problems when comparing different semantic segmentation solutions is how to select an appropriate metric to evaluate their accuracy. On the one hand, classic metrics do not measure properly the accuracy on the object contours, which is important in urban driving to differentiate road from sidewalk for instance. On the other hand, contour-based metrics [1] disregard the information far from class contours. This paper explores the problem multi-modal image segmentation, and presents a new metric to leverage global and contour accuracy in a simple formulation. This metric is validated with the evaluation of several semantic segmentation solutions that exploit RGB-D images to rank these solutions taking into account the quality of the segmented contours. We also present a comparative analysis of several commonly used metrics together with a statistical analysis of their correlation.


title: Energy Efficient Clustering and Routing Algorithms for Wireless Sensor Networks: GA Based Approach

abstract: Energy efficient clustering and routing are two well known problems in wireless sensor networks. In this paper, we propose genetic algorithm based approaches for clustering and routing in wireless sensor networks. The clustering is based on residual energy of the gateways and distance from sensor nodes to their corresponding cluster head. The routing scheme is also based on the residual energy of the gateways along with a trade-off between transmission distance and number of forwards. We perform extensive simulations of the proposed algorithms and compare the simulation results with that of the existing algorithms. The results demonstrate that the proposed algorithms outperform the existing algorithms in terms of various performance metrics including energy consumption, number of active nodes, first gateway die and number of dead gateway per round.


title: MPFR: A multiple-precision binary floating-point library with correct rounding

abstract: This article presents a multiple-precision binary floating-point library, written in the ISO C language, and based on the GNU MP library. Its particularity is to extend to arbitrary-precision, ideas from the IEEE 754 standard, by providing correct rounding and exceptions. We demonstrate how these strong semantics are achieved---with no significant slowdown with respect to other arbitrary-precision tools---and discuss a few applications where such a library can be useful.


title: Broadband Slotline-to-Rectangular Waveguide Transition Using a Truncated Bow-Tie Antenna

abstract: In this paper, we propose a broadband slotline-to-rectangular waveguide transition using a truncated bow-tie antenna. Our design yields a single transition that has a 37% fractional bandwidth for which the return loss is larger than 20 dB, almost covering the whole X-band (8.2–12.4 GHz). Furthermore, in order to miniaturize the proposed transition, a stepped-impedance resonator (SIR) is used in the placement of the quarter-wavelength section in the transition. The resulting slotline-to-rectangular waveguide transition using the SIR not only greatly reduces 17% of physical size but also maintains the broadband performance, which possesses a 36.57% fractional bandwidth of 20-dB return loss. In order to verify these simulation results, back-to-back structures for both transitions are fabricated and measured where the measurement results are in good agreement with the simulation results.


title: Characterizing and Countering Communal Microblogs During Disaster Events

abstract: The huge amount of tweets posted during a disaster event includes information about the present situation as well as the emotions/opinions of the masses. While looking through these tweets, we realized that a large amount of communal tweets, i.e., abusive posts targeting specific religious/racial groups are posted even during natural disasters—this paper focuses on such category of tweets, which is in sharp contrast to most of the prior research concentrating on extracting situational information. Considering the potentially adverse effects of communal tweets during disasters, in this paper, we develop a classifier to distinguish communal tweets from noncommunal ones, which performs significantly better than existing approaches. We also characterize the communal tweets posted during five recent disaster events, and the users who posted such tweets. Interestingly, we find that a large proportion of communal tweets are posted by popular users (having tens of thousands of followers), most of whom are related to media and politics. Further, users posting communal tweets form strong connected groups in the social network. As a result, the reach of communal tweets is much higher than noncommunal tweets. We also propose an event-independent classifier to automatically identify anticommunal tweets and also indicate a way to counter communal tweets, by utilizing such anticommunal tweets posted by some users during disaster events. Finally, we develop a real-time service to automatically collect tweets related to a disaster event and identify communal and anticommunal tweets from that set. We believe that such a system is really helpful for government and local monitoring agencies to take appropriate decisions like filtering or promoting some particular contents.


title: 7 key challenges for visualization in cyber network defense

abstract: What does it take to be a successful visualization in cyber security? This question has been explored for some time, resulting in many potential solutions being developed and offered to the cyber security community. However, when one reflects upon the successful visualizations in this space they are left wondering where all those offerings have gone. Excel and Grep are still the kings of cyber security defense tools; there is a great opportunity to help in this domain, yet many visualizations fall short and are not utilized. In this paper we present seven challenges, informed by two user studies, to be considered when developing a visualization for cyber security purposes. Cyber security visualizations must go beyond isolated solutions and "pretty picture" visualizations in order to impact users. We provide an example prototype that addresses the challenges with a description of how they are met. Our aim is to assist in increasing utility and adoption rates for visualization capabilities in cyber security.


title: Three-Dimensional Face Recognition

abstract: An expression-invariant 3D face recognition approach is presented. Our basic assumption is that facial expressions can be modelled as isometries of the facial surface. This allows to construct expression-invariant representations of faces using the bending-invariant canonical forms approach. The result is an efficient and accurate face recognition algorithm, robust to facial expressions, that can distinguish between identical twins (the first two authors). We demonstrate a prototype system based on the proposed algorithm and compare its performance to classical face recognition methods.The numerical methods employed by our approach do not require the facial surface explicitly. The surface gradients field, or the surface metric, are sufficient for constructing the expression-invariant representation of any given face. It allows us to perform the 3D face recognition task while avoiding the surface reconstruction stage.


title: Three-Dimensional Object Recognition from Single Two-Dimensional Images

abstract: Abstract A computer vision system has been implemented that can recognize three-dimensional objects from unknown viewpoints in single gray-scale images. Unlike most other approaches, the recognition is accomplished without any attempt to reconstruct depth information bottom-up from the visual input. Instead, three other mechanisms are used that can bridge the gap between the two-dimensional image and knowledge of three-dimensional objects. First, a process of perceptual organization is used to form groupings and structures in the image that are likely to be invariant over a wide range of viewpoints. Second, a probabilistic ranking method is used to reduce the size of the search space during model-based matching. Finally, a process of spatial correspondence brings the projections of three-dimensional models into direct correspondence with the image by solving for unknown viewpoint and model parameters. A high level of robustness in the presence of occlusion and missing data can be achieved through full application of a viewpoint consistency constraint. It is argued that similar mechanisms and constraints form the basis for recognition in human vision.


title: High-Efficiency Current-Regulated Charge Pump for a White LED Driver

abstract: This brief presents a high-efficiency current-regulated charge pump for a white light-emitting diode driver. The charge pump incorporates no series current regulator, unlike conventional voltage charge pump circuits. Output current regulation is accomplished by the proposed pumping current control. The experimental system, with two 1-muF flying and load capacitors, delivers a regulated 20-mA current from an input supply voltage of 2.8-4.2 V. The measured variation is less than 0.6% at a pumping frequency of 200 kHz. The active area of the designed chip is 0.43 mm2 in a 0.5-mum CMOS process.


title: The Deep Neural Network Algorithm Based on Meteorological Features is in the Medium and Long Term

abstract: In order to improve the prediction accuracy of medium and long term wind speed, the deep neural network algorithm is applied to the medium and long term wind speed forecasting in this article. Automatically extract the pattern of wind speed change through powerful nonlinear mapping ability of deep neural networks. In the specific process, take historical wind speed and meteorological as input, deep convolution neural network framework is used to train the model. Through the deep structure of the network, we can learn the internal relationship between the sequences and achieve the prediction of the future wind speed series. The effect of the results is perfect after verification. The prediction result of wind speed in some wind field in Ganhekou of Gansu Province shows, the wind speed prediction deviation of the two models of CNN+MLP and CNN+LSTM are at a relatively low level in most cases. Compared with the traditional weather forecast, improved accuracy of prediction, better acceptance of the abnormal data, and stronger generalization ability of the model. In summary, this method has certain practical value.


title: A Convolutional Autoencoder Approach To Learn Volumetric Shape Representations For Brain Structures

abstract: We propose a novel machine learning strategy for studying neuroanatomical shape variation. Our model works with volumetric binary segmentation images, and requires no pre-processing such as the extraction of surface points or a mesh. The learned shape descriptor is invariant to affine transformations, including shifts, rotations and scaling. Thanks to the adopted autoencoder framework, inter-subject differences are automatically enhanced in the learned representation, while intra-subject variances are minimized. Our experimental results on a shape retrieval task showed that the proposed representation outperforms a state-of-the-art benchmark for brain structures extracted from MRI scans.


title: Business Model Innovation

abstract: The Oxford Handbook of Innovation Management offers a comprehensive and timely analysis of the nature and importance of innovation and the strategies and practices that can be used to improve organizational benefits from innovation. Innovation is centrally important for business and national competitiveness, and for the quality and standard of living around the world, but it does not happen by itself. For innovation to succeed, it needs to be properly managed. With contributions from 49 world-leading scholars, the Handbook explores the many sources of innovation, the broader social, economic, and technological contexts that encourage and constrain it, and the cutting-edge strategies and practices of innovation management. The book addresses the traditional concerns of innovation management—such as managing RD, intellectual property, and creativity, and the contributions of science and marketing—but substantially extends traditional areas of interest. In this new volume, chapters examine emerging topics including design, social networks, open and social innovation, and innovation in business models, ecosystems, services, and platforms. The book explores the importance of innovation management for environmental sustainability, and its evolving nature and practice in Asia. Written in an accessible style, and with carefully selected bibliographies and a comprehensive index, the Handbook offers a uniquely authoritative and wide-ranging source of knowledge about innovation management. Each chapter identifies key issues and reviews the most important research findings. Future research questions are identified. The Handbook will be invaluable for students and faculty studying, researching, and teaching innovation, and for managers seeking to improve innovation outcomes in their organizations. Readership: Academics, researchers, and students of innovation and technology management; managers seeking to improve innovation outcomes in their organizations.


WIDEBAND 180 ◦ PHASE SHIFTER USING MICROSTRIP-CPW-MICROSTRIP TRANSITION


title: Low Cost Indoor Positioning System

abstract: This report describes a low cost indoor position sensing system utilising a combination of radio frequency and ultrasonics. Using a single rf transmitter and four ceiling mounted ultrasonic transmitters it provides coverage in a typical room in an area greater than 8m by 8m. As well as finding position within a room, it uses data encoded into the rf signal to determine the relevant web server for a building, and which floor and room the user is in. It is intended to be used primarily by wearable/mobile computers, though it has also been extended for use as a tracking system.


title: Camera calibration with distortion models and accuracy evaluation

abstract: A camera model that accounts for major sources of camera distortion, namely, radial, decentering, and thin prism distortions is presented. The proposed calibration procedure consists of two steps: (1) the calibration parameters are estimated using a closed-form solution based on a distribution-free camera model; and (2) the parameters estimated in the first step are improved iteratively through a nonlinear optimization, taking into account camera distortions. According to minimum variance estimation, the objective function to be minimized is the mean-square discrepancy between the observed image points and their inferred image projections computed with the estimated calibration parameters. The authors introduce a type of measure that can be used to directly evaluate the performance of calibration and compare calibrations among different systems. The validity and performance of the calibration procedure are tested with both synthetic data and real images taken by tele- and wide-angle lenses. >


title: Spirituality, Ethical Behaviour and Ethical Business: The

abstract: Unethical business practices are gaining wide attention in the business circle, academia and government. Majorly, codes of ethics, codes of practice, codes of conduct and punitive measures have been variously applied to control the menace. However, despite the fact that spiritual values influences one’s perception, choices, actions and relationship with others positively, and the fact that spiritually virtuous people act in Godly ways, spirituality as a control measure for unethical behaviour and business have not been sufficiently explored. Consequently, this study investigated the relationship among behavioural characteristics of a spiritual person, spiritual values, spiritual virtues, ethical behaviour and ethical business. Survey data from 152 respondents were analysed using Pearson Product Moment Correlation. The results revealed that a spiritual person’s behavioural characteristics, spiritual values, spiritual virtues, ethical behaviour and ethical business are significantly related. Thus, we conclude that aside the punitive measures, the internalization and practice of spiritual values by businessmen/women will ensure spiritually virtuous and ethical businessmen/women and ethical businesses.


title: GEFCom2012 Hierarchical Load Forecasting Gradient boosting machines and Gaussian processes

abstract: This report discusses methods for forecasting hourly loads of a US utility as part of the load forecasting track of the Global Energy Forecasting Competition 2012 hosted on Kaggle. The methods described (gradient boosting machines and Gaussian processes) are generic machine learning/regression algorithms, and few domain-specific adjustments were made. Despite this, the algorithms were able to produce highly competitive predictions, which can hopefully inspire more refined techniques to compete with state-of-the-art load forecasting methodologies.


title: Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks

abstract: An important goal of computer vision is to build systems that learn visual representations over time that can be applied to many tasks. In this paper, we investigate a vision-language embedding as a core representation and show that it leads to better cross-task transfer than standard multitask learning. In particular, the task of visual recognition is aligned to the task of visual question answering by forcing each to use the same word-region embeddings. We show this leads to greater inductive transfer from recognition to VQA than standard multitask learning. Visual recognition also improves, especially for categories that have relatively few recognition training labels but appear often in the VQA setting. Thus, our paper takes a small step towards creating more general vision systems by showing the benefit of interpretable, flexible, and trainable core representations.


title: Research on the control strategy of three-level active power filter with single H-bridge

abstract: Aim to the harmonic compensation of three-level active power filter with single H-bridge structure, The control scheme of multi vector proportional resonant controller to suppress specific harmonic is proposed. The advantages of three-level converter topology with single H-bridge are analyzed. The mathematical model of active power filter is established based on αβ coordinate system, and current cross decoupling link is omitted. The current tracking characteristics of vector resonant controller is analyzed, and determine controller parameters. The closed-loop control system based on multi vector resonance controller is established, and the performance is analyzed. Both the simulation and experiment results showed that the three-level active power filter with the proposed control method has the advantages of good harmonic filtering effect, fast dynamic response and high tracking accuracy, which can provide reference for the experimental device.


title: Big Data Analytics: Security and privacy challenges

abstract: The digitalization of our day-to-day activities has resulted in a huge volume of data. This data, called Big Data, is used by many organizations to extract valuable information either to take marketing decisions, track specific behaviors or detect threat attacks. The processing of such data is made possible by using multiple techniques, called Big Data Analytics, which allow getting enormous benefits by dealing with any massive volume of unstructured, structured and semi-structured content that is fast changing and impossible to process using conventional database techniques. However, while Big Data represents an immense opportunity for many industries and decisions makers, it also represents a big risk for many users. This risk arises from the fact that these analytics tools consist of storing, managing and efficiently analyzing varied data gathered from all possible and available sources. The consequence is that people become widely vulnerable to exposure because of combining and exploring specific behavioral data. That is, it is possible to collect more data than it should have which leads to many security and privacy violations. Therefore, research community has to consider these issues by proposing strong protection techniques that enable getting benefits from big data without risking privacy. In this paper, we highlight the benefits of Big Data Analytics and then we review challenges of security and privacy in big data environments. Furthermore, we present some available protection techniques and propose some possible tracks that enable security and privacy in a malicious big data context.


title: A movement activity recognition pervasive system for patient monitoring in ambient assisted living

abstract: This paper presents the main features and architecture of MHARS (Mobile Human Activity Recognition System), a pervasive system for monitoring the daily movement activities of patients in the context of Ambient Assisted Living. MHARS is based on smartphones, and allows data gathering from the different sensors usually available on this class of devices, as well as from external sensors, either the ones comprising a body sensor network as well as from ambient deployed sensors. MHARS uses accelerometer data for inferring the patient performed activity and her/his heart rate for computing its intensity. It also allows to correlate the inferred data with other context data (such as altitude, body and environment temperature, etc) for detecting user defined situations related with the patient current health status and provides a decision making engine for defining an action plan (set of actions) that must be executed whenever relevant health situations are detected. This paper emphasizes the MHARS service responsible for inferring the patient movement activity. We present a comprehensive set of experiments used for building the movement activity classifier and the evaluation of its performance.


title: A descriptive study investigating the use and nature of baby-led weaning in a UK sample of mothers

abstract: An alternative to traditional weaning methods known as baby-led weaning (BLW) appears to be emerging in the UK. This approach advocates bypassing typical weaning practices of spoon-feeding pureed foods or baby rice, encouraging instead the introduction of foods in their whole form to the infant from 6 months old. A key tenet of BLW is self-feeding. Anecdotally, the practice of BLW appears to be gaining in popularity. However, research evidence is scant, and little is known about the nature of BLW and the demography of those who utilize it. This study aimed to characterize a sample of women who have chosen to adopt the BLW method and to describe associated attitudes and behaviours. Six hundred and fifty five mothers with a child between 6 months and 12 months of age provided information about timing of weaning onset, use of spoon-feeding and purees, and experiences of weaning and mealtimes. Those participants who used a BLW method reported little use of spoon-feeding and purees and were more likely to have a higher education, higher occupation, be married and have breastfed their infant. BLW was associated with a later introduction of complementary foods, greater participation in meal times and exposure to family foods. Levels of anxiety about weaning and feeding were lower in mothers who adopted a BLW approach. These findings provide an insight into BLW practices and the characteristics of a small population of users.


title: Bidirectional VSI as a regenerative-braking converter for BLDC motor — An analysis on a plug-in electric vehicle application

abstract: The limited distance covered by a plug-in electric vehicle is commonly caused by the limited capacity of its battery. In fact, the energy consumption of the battery can be reduced by recovering the energy wasted during braking using the regenerative braking method. In this way, it brings benefit to increase the battery efficiency while providing vehicle deceleration. Other possible advantages include reducing the maintenance cost because of the wearing out of friction pads and/or brake shoes and providing smoother braking torque. This paper analyzes two methods of regenerative braking, which become the object of many types of research nowadays, using a bidirectional voltage-source inverter. Characteristics of each method and its implementation are presented. Motor torque can be controlled and energy can be recovered to charge the battery just by interchanging the switching order of the inverter.


title: MCMLSD: A Dynamic Programming Approach to Line Segment Detection

abstract: Prior approaches to line segment detection typically involve perceptual grouping in the image domain or global accumulation in the Hough domain. Here we propose a probabilistic algorithm that merges the advantages of both approaches. In a first stage lines are detected using a global probabilistic Hough approach. In the second stage each detected line is analyzed in the image domain to localize the line segments that generated the peak in the Hough map. By limiting search to a line, the distribution of segments over the sequence of points on the line can be modeled as a Markov chain, and a probabilistically optimal labelling can be computed exactly using a standard dynamic programming algorithm, in linear time. The Markov assumption also leads to an intuitive ranking method that uses the local marginal posterior probabilities to estimate the expected number of correctly labelled points on a segment. To assess the resulting Markov Chain Marginal Line Segment Detector (MCMLSD) we develop and apply a novel quantitative evaluation methodology that controls for under-and over-segmentation. Evaluation on the YorkUrbanDB dataset shows that the proposed MCMLSD method outperforms the state-of-the-art by a substantial margin.


title: "Trust me, I'm an online vendor": towards a model of trust for e-commerce system design

abstract: Consumers' lack of trust has often been cited as a major barrier to the adoption of electronic commerce (e-commerce). To address this problem, a model of trust was developed that describes what design factors affect consumers' assessment of online vendors' trustworthiness. Six components were identified and regrouped into three categories: Prepurchase Knowledge, Interface Properties and Informational Content. This model also informs the Human-Computer Interaction (HCI) design of e-commerce systems in that its components can be taken as trust-specific high-level user requirements.


Linking Theory to Practice: Authentic Leadership


title: Discriminative Deep Face Shape Model for Facial Point Detection

abstract: Facial point detection is an active area in computer vision due to its relevance to many applications. It is a nontrivial task, since facial shapes vary significantly with facial expressions, poses or occlusion. In this paper, we address this problem by proposing a discriminative deep face shape model that is constructed based on an augmented factorized three-way Restricted Boltzmann Machines model. Specifically, the discriminative deep model combines the top-down information from the embedded face shape patterns and the bottom up measurements from local point detectors in a unified framework. In addition, along with the model, effective algorithms are proposed to perform model learning and to infer the true facial point locations from their measurements. Based on the discriminative deep face shape model, 68 facial points are detected on facial images in both controlled and "in-the-wild" conditions. Experiments on benchmark data sets show the effectiveness of the proposed facial point detection algorithm against state-of-the-art methods.


title: Naive Bayes models for probability estimation

abstract: Naive Bayes models have been widely used for clustering and classification. However, they are seldom used for general probabilistic learning and inference (i.e., for estimating and computing arbitrary joint, conditional and marginal distributions). In this paper we show that, for a wide range of benchmark datasets, naive Bayes models learned using EM have accuracy and learning time comparable to Bayesian networks with context-specific independence. Most significantly, naive Bayes inference is orders of magnitude faster than Bayesian network inference using Gibbs sampling and belief propagation. This makes naive Bayes models a very attractive alternative to Bayesian networks for general probability estimation, particularly in large or real-time domains.


title: Batched Large-scale Bayesian Optimization in High-dimensional Spaces

abstract: Bayesian optimization (BO) has become an effective approach for black-box function optimization problems when function evaluations are expensive and the optimum can be achieved within a relatively small number of queries. However, many cases, such as the ones with high-dimensional inputs, may require a much larger number of observations for optimization. Despite an abundance of observations thanks to parallel experiments, current BO techniques have been limited to merely a few thousand observations. In this paper, we propose ensemble Bayesian optimization (EBO) to address three current challenges in BO simultaneously: (1) large-scale observations; (2) high dimensional input spaces; and (3) selections of batch queries that balance quality and diversity. The key idea of EBO is to operate on an ensemble of additive Gaussian process models, each of which possesses a randomized strategy to divide and conquer. We show unprecedented, previously impossible results of scaling up BO to tens of thousands of observations within minutes of computation.


title: YARNsim: Simulating Hadoop YARN

abstract: Despite the popularity of the Apache Hadoop system, its success has been limited by issues such as single points of failure, centralized job/task management, and lack of support for programming models other than MapReduce. The next generation of Hadoop, Apache Hadoop YARN, is designed to address these issues. In this paper, we propose YARNsim, a simulation system for Hadoop YARN. YARNsim is based on parallel discrete event simulation and provides protocol-level accuracy in simulating key components of YARN. YARNsim provides a virtual platform on which system architects can evaluate the design and implementation of Hadoop YARN systems. Also, application developers can tune job performance and understand the tradeoffs between different configurations, and Hadoop YARN system vendors can evaluate system efficiency under limited budgets. To demonstrate the validity of YARNsim, we use it to model two real systems and compare the experimental results from YARNsim and the real systems. The experiments include standard Hadoop benchmarks, synthetic workloads, and a bioinformatics application. The results show that the error rate is within 10% for the majority of test cases. The experiments prove that YARNsim can provide what-if analysis for system designers in a timely manner and at minimal cost compared with testing and evaluating on a real system.


title: Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks

abstract: Biological neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifelong learning. The interplay of these elements leads to the emergence of biological intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) employ simulated evolution in-silico to breed plastic neural networks with the aim to autonomously design and create learning systems. EPANN experiments evolve networks that include both innate properties and the ability to change and learn in response to experiences in different environments and problem domains. EPANNs' aims include autonomously creating learning systems, bootstrapping learning from scratch, recovering performance in unseen conditions, testing the computational advantages of particular neural components, and deriving hypotheses on the emergence of biological learning. Thus, EPANNs may include a large variety of different neuron types and dynamics, network architectures, plasticity rules, and other factors. While EPANNs have seen considerable progress over the last two decades, current scientific and technological advances in artificial neural networks are setting the conditions for radically new approaches and results. Exploiting the increased availability of computational resources and of simulation environments, the often challenging task of hand-designing learning neural networks could be replaced by more autonomous and creative processes. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and possible developments are presented.


title: Modified global k-means algorithm for minimum sum-of-squares clustering problems

abstract: k-Means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and inefficient for solving clustering problems in large data sets. Recently, a new version of the k-means algorithm, the global k-means algorithm has been developed. It is an incremental algorithm that dynamically adds one cluster center at a time and uses each data point as a candidate for the k-th cluster center. Results of numerical experiments show that the global k-means algorithm considerably outperforms the k-means algorithms. In this paper, a new version of the global k-means algorithm is proposed. A starting point for the k-th cluster center in this algorithm is computed by minimizing an auxiliary cluster function. Results of numerical experiments on 14 data sets demonstrate the superiority of the new algorithm, however, it requires more computational time than the global k-means algorithm.


title: Removing rain and snow in a single image using saturation and visibility features

abstract: Rain and snow are two of the major obstacles in processing the photograph captured in the outdoor bad weather conditions. Rain and snow will cause the performance of vision algorithm become worse. There are a lot of methods have been proposed to reduce the raindrops and snowflakes in video. However, how to remove rain and snow and keep the detail of the background in a single image is still quite challenging since it is hard to detect the pixels of rain and snow. In this paper, we proposed a new method based on features on saturation and visibility. The results show that it can achieve better performance than previous methods.


title: Choice Under Conflict: The Dynamics of Deferred Decision

abstract: Choice often produces conflict. This notion, however, plays no role in classical decision theory, in which each alternative is assigned a value, and the decision maker selects from every choice set the option with the highest value. We contrast this principle of value maximization with the hypothesis that the option to delay choice or seek new alternatives is more likely to be selected when conflict is high than when it is low. This hypothesis is supported by several studies showing that the tendency to defer decision, search for new alternatives, or choose the default option can be increased when the offered set is enlarged or improved, contrary to the principle of value maximization.


title: Swallowable smart pills for local drug delivery: present status and future perspectives

abstract: Smart pills were originally developed for diagnosis; however, they are increasingly being applied to therapy – more specifically drug delivery. In addition to smart drug delivery systems, current research is also looking into localization systems for reaching the target areas, novel locomotion mechanisms and positioning systems. Focusing on the major application fields of such devices, this article reviews smart pills developed for local drug delivery. The review begins with the analysis of the medical needs and socio-economic benefits associated with the use of such devices and moves onto the discussion of the main implemented technological solutions with special attention given to locomotion systems, drug delivery systems and power supply. Finally, desired technical features of a fully autonomous robotic capsule for local drug delivery are defined and future research trends are highlighted.


title: Load profiling and data mining techniques in electricity deregulated market

abstract: This paper presents load profiles of electricity customers, using the knowledge discovery in databases (KDD) procedure, a data mining technique, to determine the load profiles for different types of customers. In this paper, the current load profiling methods are compared using data mining techniques, by analysing and evaluating these classification techniques. The objective of this study is to determine the best load profiling methods and data mining techniques to classify, detect and predict non-technical losses in the distribution sector, due to faulty metering and billing errors, as well as to gather knowledge on customer behaviour and preferences so as to gain a competitive advantage in the deregulated market. This paper focuses mainly on the comparative analysis of the classification techniques selected; a forthcoming paper focuses on the detection and prediction methods.


title: A comparison study between horn and vivaldi antennas for 1.5–6 GHz breast microwave radar imaging

abstract: Breast Microwave Radar is a novel imaging modality for breast cancer detection. This technology uses low power microwaves to illuminate the breast region and it generates a reflectivity map of the breast structures in which tumors can be detected. Although this imaging modality has been successful in limited clinical trials, additional work is needed to optimize several parameters of this technology to increase its use in routine clinical imaging procedures. One of the aspects that is crucial for the performance of a BMR system are the antennas used to record the datasets. In this study, the use of a novel horn antenna for BMR data collection is proposed. This antenna was used to record BMR datasets from synthetic phantoms that mimic the dielectric properties of breast tissues. Datasets from these phantoms were also collected using a Vivaldi antenna, which is a common design for current BMR systems. The results of this study show that images formed from datasets recorded using the horn antenna had a lower noise level and higher contrast compared to their counterparts corresponding to the Vivaldi antenna datasets.


title: Differences in sonographic features of ruptured and unruptured epidermal cysts.

abstract: OBJECTIVES ::: This study aimed to report the differences in sonographic features of ruptured and unruptured epidermal cysts. ::: ::: ::: METHODS ::: We reviewed and analyzed the sonographic features of superficial epidermal cysts of in 2 subgroups of 46 patients: 20 with ruptured cysts and 26 with unruptured cysts. All lesions were diagnosed either by excisional biopsy or by debridement biopsy. Sonographic features used in the analysis included tumor size, location, echo texture, pericystic changes, boundary, and shape on gray scale sonography and lesion vascularity on color Doppler sonography. Pathologic examinations were reviewed with emphasis on differences between ruptured and unruptured epidermal cysts. ::: ::: ::: RESULTS ::: Unruptured epidermal cysts showed significantly higher frequencies of an oval shape, presence of a halo, well-circumscribed boundaries, and lack of blood flow signals (P < .05). In contrast, ruptured cysts usually had a lobulated shape, a slightly poorly defined or poorly defined boundary, the absence of a halo, and intermediate grades of lesion vascularity. The lesion sizes and sonographic features, including dermal attachment, a hypoechoic echo texture, posterior acoustic enhancement, and the presence of intralesional hyperechoic strips and hypoechoic debris, showed no significant differences between the groups (P > .05). In pathologic studies, acute and chronic inflammatory reactions, more prominent microvessels, and abscess formation in the adjacent stromal tissue, the lesions, or both could be detected in the 20 ruptured epidermal cysts but not in the unruptured cysts. ::: ::: ::: CONCLUSIONS ::: This research shows that recognition of different sonographic features is helpful for differentiating ruptured and unruptured epidermal cysts.


Trust in decision aids: A model and its training implications


title: Functions of the skin microbiota in health and disease.

abstract: The skin, the human body's largest organ, is home to a diverse and complex variety of innate and adaptive immune functions. Despite this potent immune system present at the cutaneous barrier, the skin encourages colonization by microorganisms. Characterization these microbial communities has enhanced our knowledge of the ecology of organisms present in normal skin; furthermore, studies have begun to bring to light the intimate relationships shared between host and resident microbes. In particular, it is apparent that just as host immunological factors and behaviors shape the composition of these communities, microbes present on the skin greatly impact the functions of human immunity. Thus, today the skin immune system should be considered a collective mixture of elements from the host and microbes acting in a mutualistic relationship. In this article we will review recent findings of the interactions of skin microbial communities with host immunity, and discuss the role that dysbiosis of these communities plays in diseases of the skin.


title: A microplate assay for d-xylose/ d-glucose isomerase

abstract: Abstract A modification of the resorcinol method of Kulka 1 for the determination of ketoses is described. Though being a stopped enzyme test, it is much more sensitive than the carbazol method and by applying microtiter plates and measuring with an ELISA reader, a large number of tests can be performed within a short time, thereby facilitating initial velocity studies. The test is linear up to a concentration of 2.5 m m d -xylulose even in the presence of 10 m m d -xylose and 2 m m d -fructose in the presence of 10 m m d -glucose. The sensitivity is 25 μ m for xylulose and 38 μ m for fructose. The test method is insensitive to perturbations of substances frequently used in isolation procedures such as ammonium sulfate, Triton X-100, PEG 6000, sodium dodecyl sulfate, and ethanol in moderate concentrations.


title: Demo: Implementing advanced network functions with stateful programmable data planes

abstract: Stateful programmable dataplanes are emerging as a disruptive technology and an enabler factor for network function virtualization in SDN environments. In this demo paper we show three real world use cases with stateful forwarding requirements to validate both the hardware and software implementations of the Open Packet Processor data plane.


title: Cognitive Functions and Neurodevelopmental Disorders Involving the Prefrontal Cortex and Mediodorsal Thalamus

abstract: The mediodorsal nucleus of the thalamus (MD) has been implicated in executive functions (such as planning, cognitive control, working memory and decision-making) because of its significant interconnectivity with the prefrontal cortex (PFC). Yet, whilst the roles of the PFC have been extensively studied, how the MD contributes to these cognitive functions remains relatively unclear. Recently, causal evidence in monkeys has demonstrated that in everyday tasks involving rapid updating (e.g. while learning something new, making decisions or planning the next move), the MD and frontal cortex are working in close partnership. Furthermore, researchers studying the MD in rodents have been able to probe the underlying mechanisms of this relationship to give greater insights into how the frontal cortex and MD might interact during the performance of these essential tasks. This review summarizes the circuitry and known neuromodulators of the MD, and considers the most recent behavioral, cognitive and neurophysiological studies conducted in monkeys and rodents; in total, this evidence demonstrates that MD makes a critical contribution to cognitive functions. We propose that communication occurs between the MD and the frontal cortex in an ongoing, fluid manner during rapid cognitive operations, via the means of efference copies of messages passed through transthalamic routes; the conductance of these messages may be modulated by other brain structures interconnected to the MD. This is similar to the way in which other thalamic structures have been suggested to carry out forward modelling associated with rapid motor responding and visual processing. Given this, and the marked thalamic pathophysiology now identified in many neuropsychiatric disorders, we suggest that changes in the different subdivisions of the MD and their interconnections with the cortex could plausibly give rise to a number of the otherwise disparate symptoms (including changes to olfaction and cognitive functioning) that are associated with many different neuropsychiatric disorders. In particular, we will focus here on the cognitive symptoms of schizophrenia and suggest testable hypotheses about how changes to MD-frontal cortex interactions may affect cognitive processes in this disorder.


title: The psychology of elite cycling: a systematic review

abstract: ABSTRACTThis systematic review sought to synthesise what is currently known about the psychology of elite cycling. Nine electronic databases were searched in March 2017 for studies reporting an empirical test of any psychological construct in an elite cycling sample. Fourteen studies (total n = 427) met inclusion criteria. Eight studies were coded as having high risk of bias. Themes extracted included mood, anxiety, self-confidence, pain, and cognitive function. Few studies had similar objectives meaning that in many instances findings could not be synthesised in a meaningful way. Nevertheless, there was some cross-study evidence that elite cyclists have more positive mood states (relative to normative scores), pre-race anxiety impairs performance (among male cyclists), and associative strategies are perceived as helpful for pain management. Among single studies coded as having low risk of bias, evidence suggests that implicit beliefs affect decision making performance, elite cyclists are less susceptible...