title: Relevance feedback with too much data

abstract: Relevance Feedbacvk With Too Much Data A UMass Technical Report James Allan IR-57 (ref.TR95-6) Modern text collections often contain large documents which span several subject areas. Such documents are problematic for relevance feedback since inappropriate terms can easily be chosen. This study explores the highly effective approach of feeding back passages of large documents. A less- expensive method which discards long documents is also reviewed and found to be effective if there are enough relevant documents. A hybrid approach which feeds back short documents and passages of long documents may be the best compromise.


title: Battery Energy Storage for Enabling Integration of Distributed Solar Power Generation

abstract: As solar photovoltaic power generation becomes more commonplace, the inherent intermittency of the solar resource poses one of the great challenges to those who would design and implement the next generation smart grid. Specifically, grid-tied solar power generation is a distributed resource whose output can change extremely rapidly, resulting in many issues for the distribution system operator with a large quantity of installed photovoltaic devices. Battery energy storage systems are increasingly being used to help integrate solar power into the grid. These systems are capable of absorbing and delivering both real and reactive power with sub-second response times. With these capabilities, battery energy storage systems can mitigate such issues with solar power generation as ramp rate, frequency, and voltage issues. Beyond these applications focusing on system stability, energy storage control systems can also be integrated with energy markets to make the solar resource more economical. Providing a high-level introduction to this application area, this paper presents an overview of the challenges of integrating solar power to the electricity distribution system, a technical overview of battery energy storage systems, and illustrates a variety of modes of operation for battery energy storage systems in grid-tied solar applications. The real-time control modes discussed include ramp rate control, frequency droop response, power factor correction, solar time-shifting, and output leveling.


title: MMSE hybrid precoder design for millimeter-wave massive MIMO systems

abstract: This paper studies hybrid RF/baseband linear pre-coding design to minimize the mean square error (MSE) for millimeter-wave massive multiple-input multiple-output (MIMO) systems using optimal linear equalizer. Instead of dealing with the objective function of sum MSE, which involves matrix inverses, we approach this problem by minimizing the Euclidean distance between the hybrid precoder and the optimal minimum MSE precoder. In an effort to impose the optimal structure of channel diagonalization, we separate the design of modulus-constrained RF precoder from the design of unconstrained baseband pre-coder. Magnitude-least-squares approximation is introduced to formulate the RF precoder design problem, and is subsequently transformed into a simultaneous matrix diagonalization problem. Such transformation enables application of a simple and numerically stable Jacobi-like algorithm. The effective channel representing a cascade of the derived RF precoder and the MIMO channel, is diagonalized by the baseband precoder. The error performance of the proposed solution is examined by numerical results where the effectiveness is verified by its closeness to the optimal design and its noticeable gain over sparse approximation based schemes.


title: Automatic approximation of the marginal likelihood in non-Gaussian hierarchical models

abstract: Fitting of non-Gaussian hierarchical random effects models by approximate maximum likelihood can be made automatic to the same extent that Bayesian model fitting can be automated by the program BUGS. The word ''automatic'' means that the technical details of computation are made transparent to the user. This is achieved by combining a technique from computer science known as ''automatic differentiation'' with the Laplace approximation for calculating the marginal likelihood. Automatic differentiation, which should not be confused with symbolic differentiation, is mostly unknown to statisticians, and hence basic ideas and results are reviewed. The computational performance of the approach is compared to that of existing mixed-model software on a suite of datasets selected from the mixed-model literature.


Predicting Business Ratings on Yelp


title: Experiences from NFC Supported School Attendance Supervision for Children

abstract: Our paper presents a field study arranged at a Finnish primary school where two classes and a total of 23 pupils between the ages of 6 and 8 trialed an attendance supervision system supported by Near Field Communication technology in 2008. In the trial the pupils marked their arrival at and departure from the school by touching a reader device or NFC-enabled mobile phone with a smart contactless card. The system simplified attendance monitoring by replacing manual roll calls leaving thus more time for teaching. Parents were able to receive real-time information on children’s attendance, which reduced their concerns about whether their child had arrived at school safely. Information about user experience was obtained by using a variety of data collection methods. Children, as well as their teachers, became fast familiar with the touch-based interaction, and the attendance supervision was soon integrated into their everyday school routines. Our analysis shows that a technology-supported attendance supervision system can bring value for all end-user groups but it seems that the system will serve primarily the teachers and the parents.


title: Enhancing Immediate Retention with Clickers Through Individual Response Identification

abstract: Summary ::: ::: Classroom audience response systems, in which students respond to class questions via a remote ‘clicker’ unit, are widely used as a method for increasing student participation and providing immediate feedback in the form of a group frequency distribution. The phenomenon of social facilitation shows that task performance can be enhanced with co-action of others or with the presence of an audience. To enhance the audience effect, we employed a unique feedback system that displays each individual's response. After reading a text passage, participants responded via a remote clicker to a series of comprehension questions. Participants were provided with no feedback regarding other respondents' answers, group feedback, or individual feedback. The results demonstrated significantly higher test performance with individual response identification. Implications are discussed in terms of applied classroom settings and social facilitation with enhanced options for displaying different types of feedback with clickers during instruction. Copyright © 2014 John Wiley & Sons, Ltd.


Pathogenesis of gout


title: On the robust control design for a class of nonlinearly affine control systems: The attractive ellipsoid approach

abstract: This paper is devoted to a problem of robust control design for a class of continuous-time dynamic systems with bounded uncertainties. ::: We study a family of nonlinearly affine control systems and develop a computational extension of the conventional invariant ::: ellipsoid techniques. The obtained method can be considered as a powerful numerical approach that makes it possible to design ::: a concrete stabilizing control strategies for the resulting closed-loop systems. The design procedure for this feedback-type control is based ::: on the classic Lyapunov-type stability analysis of invariant sets for the given dynamic system. We study the necessary theoretic basis and ::: propose a computational algorithm that guarantee some minimality properties of the stability/attractivity regions for dynamic systems under ::: consideration. The complete solution procedure contains an auxiliary LMI-constrained optimization problem. The effectiveness of the proposed ::: robust control design is illustrated by a numerical example.


title: Imaging the developing brain: what have we learned about cognitive development?

abstract: The human brain undergoes significant changes in both its structural architecture and functional organization across the life span. Advances in neuroimaging techniques over the past decade have allowed us to track these changes safely in the human in vivo. We review the imaging literature on the neurobiology of cognitive development, focusing specifically on cognitive task-dependent changes observed in brain physiology and anatomy across childhood and adolescence. The findings suggest that cortical function becomes fine-tuned with development. Brain regions associated with more basic functions such as sensory and motor processes mature first, followed by association areas involved in top-down control of behavior.


title: Cuckoo Search: A Brief Literature Review

abstract: Cuckoo search (CS) was introduced in 2009, and it has attracted great attention due to its promising efficiency in solving many optimization problems and real-world applications. In the last few years, many papers have been published regarding cuckoo search, and the relevant literature has expanded significantly. This chapter summarizes briefly the majority of the literature about cuckoo search in peer-reviewed journals and conferences found so far. These references can be systematically classified into appropriate categories, which can be used as a basis for further research.


Fuzzy Logic Based Maximum Power Point Tracking Designed for 10kW Solar Photovoltaic System with Different Membership Functions


title: Traveling-wave antenna array (TWAA) with multioctave scan-gain-bandwidth

abstract: This paper presents the latest progress in demonstrating and characterizing the Traveling-Wave Antenna Array (TWAA) as a Multioctave Planar Phased Arrays (MPPA) capable of efficient wide-angle scan over multioctave bandwidth. (Beam Steering Network (BSN) and T/R module are not included here.) Four brass board TWAA panels having 16∗16 array elements were fabricated and tested for wide-angle scan up to 60° off broadside over 2–12 GHz using a True-Time Delay (TTD) BSN in a corporate feed configuration. Fairly good performance was exhibited using conventional far-field antenna range tests in anechoic chambers. This study appears to be the first time that an MPPA is demonstrated to be capable of multioctave bandwidth for efficient 60° scan in a full-fledged manner with direct calibration. The test data are in good agreements with computer simulation data generated by The Ohio State University. MPPA's state-of-the-art is reviewed, and relevant ambiguities and controversies are discussed.


title: Creating interactive physics education books with augmented reality

abstract: Augmented Books show three-dimensional animated educational content and provide a means for students to interact with this content in an engaging learning experience. In this paper we present a framework for creating educational Augmented Reality (AR) books that overlay virtual content over real book pages. The framework features support for certain types of user interaction, model and texture animations, and an enhanced marker design suitable for educational books. Three books teaching electromagnetism concepts were created with this framework. To evaluate the effectiveness in helping students learn, we conducted a small pilot study with ten secondary school students, studying electromagnetism concepts using the three books. Half of the group used the books with the diagrams augmented, while the other half used the books without augmentation. Participants completed a pre-test, a test after the learning session and a retention test administered 1 month later. Results suggest that AR has potential to be effective in teaching complex 3D concepts.


title: Developing a car gesture interface for use as a secondary task

abstract: Existing gesture-interface research has centered on controlling the user's primary task. This paper explores the use of gestures to control secondary tasks while the user is focused on driving. Through contextual inquiry, ten iterative prototypes, and a Wizard of Oz experiment, we show that a gesture interface is a viable alternative for completing secondary tasks in the car.


title: Robust principal component analysis?

abstract: This paper is about a curious phenomenon. Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Can we recover each component individually? We prove that under some suitable assumptions, it is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted. This extends to the situation where a fraction of the entries are missing as well. We discuss an algorithm for solving this optimization problem, and present applications in the area of video surveillance, where our methodology allows for the detection of objects in a cluttered background, and in the area of face recognition, where it offers a principled way of removing shadows and specularities in images of faces.


title: Shoot regeneration from petioles of coral bells (Heuchera sanguinea Engelm.) cultured in vitro, and subsequent planting and flowering ex vitro

abstract: Successful shoot regeneration from petioles, leaves, and petioles with leaves cultured in vitro is reported in Heuchera sanguinea. Petioles or petioles with leaves regenerated more shoots than leaves alone. For culture, the optimum hormonal concentrations were 0.19 μM α-naphthaleneacetic acid combined with 0.44 or 4.4 μM benzyladenine in Murashige and Skoog-based (MS) medium: the regenerating rate and the number of shoots per explant were 60% and 8.6–9.7, respectively. Histological study on petiole culture showed dividing cell clusters including vascular tissues after 1 wk, callus including several dividing cell clusters at the periphery after 3 wk and then apical meristems with immature leaves after 5 wk. Rooting from the regenerated shoots was highest (95%) on MS medium containing 4.9 μM indole-3-butyric acid. Seventy-three percent of rooted plants were successfully acclimatized in pots. When they were cultured in the field, the plants grew and most flowered the following year over winter.


title: Location-based system : Comparative effects of personalization vs ease of use

abstract: Few studies investigated the economic impact of an LBS-enabled mobile app in a field setting.Consumer search theory and theory of IS critical success are theoretical backgrounds.Financial gains, perceived benefits, and system usage are used as success measures of LBS.A field experiment and two complementary field surveys are used for this empirical study.We conduct a field experiment in the domain of gas purchase decision-making. Context-driven, personalized information services have become extremely popular for numerous mobile businesses to influence consumers' decision-making and improve subsequent benefits. In this research, we examine the effects of two explanatory variables (i.e., personalization and ease of use), arguably the two most critical factors, on the success of location-based system (LBS) in terms of economic gains, perceived benefits, and intention to continue service usage. Personalization enabled by contextualization (personalization for short) of LBS enhances the quality of information provided, and ease of use is a representative system quality variable. To examine the association, our research focuses on consumers' decision-making regarding where to buy gasoline and the effects of LBS-assisted searching for gas price information on user perceptions and actual behavior. We utilize two research methods for this empirical study: a field experiment and two complementary field surveys. The empirical findings confirm the strong association between the explanatory and consequence variables.


"Cheap" Rapid Locomotion of a Quadruped Robot: Self-Stabilization of Bounding Gait


An artificial beehive algorithm for continuous optimization


title: A new wearable fingertip haptic interface for the rendering of virtual shapes and surface features

abstract: This work presents the Haptic Thimble, a novel wearable haptic device for surface exploration. The Haptic Thimble combines rendering of surface orientation with fast transient and wide frequency bandwidth tactile cues. Such features allow surface exploration with rich tactile feedback, including reactive contact — no contact transition, rendering of collisions, surface asperities and textures. Above capabilities were obtained through a novel serial kinematics wrapped around the finger, actuated by compact servo motor for orienting the last link, and by a custom voice coil for actuating the plate in contact with the fingerpad. Performance of the voice coil were measured at the bench in static and dynamic conditions, assessing the capability of reproducing generic, wide-bandwidth (0–300 Hz) tactile cues. Overall usability of the Haptic Thimble was explored within a virtual environment involving exploration of virtual surfaces. Finally, a perceptual experiment executed in a teleoperated environment with kinesthetic feedback, showed that the addition of tactile feedback, provided through the Haptic Thimble, significantly improved performance of an exploratory task.


title: SPECIAL ISSUE ON DESIGNING FOR AESTHETICS OF INTERACTION Computational Compositions: Aesthetics, Materials, and Interaction Design

abstract: This paper advocates re-focusing interaction design towards aesthetics and compositions. Inspired by current movements within human- computer interaction, product design, materials science, and architecture, we argue that a central problematic concerned with reconciling the digital and physical has always shaped the field. Rather than bridging atoms and bits, an approach that systematically keeps the digital and physical apart, we advocate thinking of design as composition. Doing so will require articulating an aesthetic vocabulary that formally relates diverse materials whether digital or physical. Texture, a word that applies across materials, substrates, and scales, is advanced as a candidate term within our approach. We provide a model for thinking about texture as a relation and then ground the model through a design project, Icehotel X, rendered in ice and digital displays. Texture guided the quality of composition achieved in the final design executed by a team of diverse experts. This approach opens new avenues for the analysis and practice of interaction design.


title: Inferring Visual Persuasion via Body Language, Setting, and Deep Features

abstract: The computer vision community has reached a point when it can start considering high-level reasoning tasks such as the "communicative intents" of images, or in what light an image portrays its subject. For example, an image might imply that a politician is competent, trustworthy, or energetic. We explore a variety of features for predicting these communicative intents. We study a number of facial expressions and body poses as cues for the implied nuances of the politician's personality. We also examine how the setting of an image (e.g. kitchen or hospital) influences the audience's perception of the portrayed politician. Finally, we improve the performance of an existing approach on this problem, by learning intermediate cues using convolutional neural networks. We show state of the art results on the Visual Persuasion dataset of Joo et al. [11].


title: Increased Na+-taurine symporter in rat renal brush border membranes: preformed or newly synthesized?

abstract: The renal adaptive response to a varied intake of sulfur amino acids is demonstrated by an increase in the initial rate of Na+-taurine symport (cotransport) by rat renal brush border membrane vesicles (BBMVs) after 8-14 days of a low methionine diet. A high (3%) taurine diet reduces Na+-taurine symport. Fasting for 3 days, which depletes renal tubule cell taurine content, also enhances Na+-taurine symport both initially (15 s) and throughout the overshoot. In this study we examine the possibility that a rapid-onset adaptive response is expressed in BBMV, with the increased Na+-taurine symport reflecting the incorporation of preformed symporter into membranes rather than new synthesis. Rats fed the low methionine diet for 14 days were placed on the high taurine diet for 12-18 h; Na+-taurine symport activity fell by 40%. Fasting for 4 h restored low methionine diet levels of Na+-taurine symport activity (92 pmol.mg protein-1.15 s-1), defining a rapidly induced rise in uptake. Colchicine (0.6 mg) was injected prior to fasting in a group of rats because it blocks the incorporation (import) of preformed symporter into the membrane. Animals injected with colchicine had a pattern of BBMV uptake similar to that found in animals switched to the high taurine diet for 18 h. This agent blocked the rapidly induced rise in uptake. Feeding with the high taurine diet for 4 h caused a fall in uptake of 16.5%; colchicine blocked this reduction in uptake. These results indicate that the nephron can respond rapidly to changes in the intake of amino acids, conserving taurine in periods of nutrient lack and excreting excess taurine within 4 h in periods of surfeit. This rapid response is expressed at the brush border surface. The use of cholchicine indicates that the increase or reduction in Na+-taurine symport activity is due to incorporation (import) of transporter into the BBMV rather than to de novo synthesis.


title: Learning Deep Latent Spaces for Multi-Label Classification

abstract: Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.


title: Information theoretic sensor data selection for active object recognition and state estimation

abstract: We introduce a formalism for optimal sensor parameter selection for iterative state estimation in static systems. Our optimality criterion is the reduction of uncertainty in the state estimation process, rather than an estimator-specific metric (e.g., minimum mean squared estimate error). The claim is that state estimation becomes more reliable if the uncertainty and ambiguity in the estimation process can be reduced. We use Shannon's information theory to select information-gathering actions that maximize mutual information, thus optimizing the information that the data conveys about the true state of the system. The technique explicitly takes into account the a priori probabilities governing the computation of the mutual information. Thus, a sequential decision process can be formed by treating the a priori probability at a certain time step in the decision process as the a posteriori probability of the previous time step. We demonstrate the benefits of our approach in an object recognition application using an active camera for sequential gaze control and viewpoint selection. We describe experiments with discrete and continuous density representations that suggest the effectiveness of the approach.


title: Cognitive systems based on adaptive algorithms

abstract: The type of cognitive system (CS) studied here has four basic parts: (1) a set of interacting elementary productions, called classifiers, (2) a performance algorithm that directs the action of the system in the environment, (3) a simple learning algorithm that keeps a record of each classifier's success in bringing about rewards, and (4) a more complex learning algorithm, called the genetic algorithm, that modifies the set of classifiers so that variants of good classifiers persist and new, potentially better ones are created in a provably efficient manner.


title: Task Scheduling Using Meta-Heuristic Optimization Techniques in Cloud Environment

abstract: Cloud computing provides various services for the resource allocation like computation, storage in a virtualization. The virtual machine in cloud, allocate the job and schedules the task efficiently. The task scheduling and resources utilization are the key issues in the cloud environment. Scheduling allocate different types of job in the resources. Scheduling is decided based on the feedback of the Quality of Services (Qos). Quality of Service is the guaranteed service which handles the different task in the job allocation. Therefore, in order to schedule the intensive data, numerous heuristic algorithms have been recommended such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Ant Colony Optimization Algorithms (ACO), Artificial Bee Colony Algorithms (ABC), to solve the task scheduling and resource matter. This project proposes a Bat Algorithm to solve task scheduling and resource allocation problem in cloud computing. The Bat algorithm is implemented to enforce a better efficient scheduling mechanism which will increase the performance and efficiency of the system by minimizing the execution time (makespan), execution cost, deadline and load balancing. This service is used to complete the task as soon as possible without any delay in task scheduling.


title: Social media in qualitative research: Challenges and recommendations

abstract: The emergence of social media on the Internet provides an opportunity for information systems researchers to examine new phenomena in new ways. However, for various reasons qualitative researchers in IS have not fully embraced this opportunity. This paper looks at the potential use of social media in qualitative research in information systems. It discusses some of the challenges of using social media and suggests how qualitative IS researchers can design their studies to capitalize on social media data. After discussing an illustrative qualitative study, the paper makes recommendations for the use of social media in qualitative research in IS. The emergence of social media provides an opportunity for IS researchers to examine new phenomena in new ways.This paper looks at the potential use of social media in qualitative research in information systems.This paper suggests how qualitative IS researchers can design their studies to capitalize on social media data.This paper makes recommendations for the use of social media in qualitative research in information systems research.


title: Diagnosis and treatment of streptococcal toxic shock syndrome in the pediatric intensive care unit: a case report

abstract: Among the infections caused by Streptococcus β hemolyticus from the Lancefield serogroup A, toxic shock syndrome is perhaps the most severe, and its mortality rate is high. Its clinical similarity to other forms of shock, especially septic shock, can often confuse the evaluator and interfere with the selection of the most appropriate therapy. This report aims to inform readers of the need to add this syndrome as a differential diagnosis in cases of shock, especially those with no well-defined clinical manifestations. For this purpose, we present the case of an infant with common flu-like symptoms who progressed rapidly with a rash, a reduced level of consciousness and clinical and laboratory signs of shock that required intensive support. In addition to cultures indicating the etiological agent, the appearance of exanthema and necrotizing fasciitis led to the diagnosis. However, less than 50% of cases present classic clinical signs of this entity. Penicillins combined with aminoglycosides are still the therapy of choice and are supported by a high level of evidence. Despite the severity of this patient's presentation, the progression was satisfactory.


title: Discipline Matters: Refactoring of Preprocessor Directives in the #ifdef Hell

abstract: The C preprocessor is used in many C projects to support variability and portability. However, researchers and practitioners criticize the C preprocessor because of its negative effect on code understanding and maintainability and its error proneness. More importantly, the use of the preprocessor hinders the development of tool support that is standard in other languages, such as automated refactoring. Developers aggravate these problems when using the preprocessor in undisciplined ways (e.g., conditional blocks that do not align with the syntactic structure of the code). In this article, we proposed a catalogue of refactorings and we evaluated the number of application possibilities of the refactorings in practice, the opinion of developers about the usefulness of the refactorings, and whether the refactorings preserve behavior. Overall, we found 5,670 application possibilities for the refactorings in 63 real-world C projects. In addition, we performed an online survey among 246 developers, and we submitted 28 patches to convert undisciplined directives into disciplined ones. According to our results, 63 percent of developers prefer to use the refactored (i.e., disciplined) version of the code instead of the original code with undisciplined preprocessor usage. To verify that the refactorings are indeed behavior preserving, we applied them to more than 36 thousand programs generated automatically using a model of a subset of the C language, running the same test cases in the original and refactored programs. Furthermore, we applied the refactorings to three real-world projects: BusyBox , OpenSSL , and SQLite . This way, we detected and fixed a few behavioral changes, 62 percent caused by unspecified behavior in the C programming language.


title: Evolutionary undersampling for imbalanced big data classification

abstract: Classification techniques in the big data scenario are in high demand in a wide variety of applications. The huge increment of available data may limit the applicability of most of the standard techniques. This problem becomes even more difficult when the class distribution is skewed, the topic known as imbalanced big data classification. Evolutionary undersampling techniques have shown to be a very promising solution to deal with the class imbalance problem. However, their practical application is limited to problems with no more than tens of thousands of instances. In this contribution we design a parallel model to enable evolutionary undersampling methods to deal with large-scale problems. To do this, we rely on a MapReduce scheme that distributes the functioning of these kinds of algorithms in a cluster of computing elements. Moreover, we develop a windowing approach for class imbalance data in order to speed up the undersampling process without losing accuracy. In our experiments we test the capabilities of the proposed scheme with several data sets with up to 4 million instances. The results show promising scalability abilities for evolutionary undersampling within the proposed framework.


title: Adaptive stabilization of a reaction wheel pendulum on moving LEGO platform

abstract: In this paper an adaptive control algorithm of a reaction wheel pendulum on a movable platform is proposed. Obtained hybrid control system allows to stabilize a pendulum fixed on the movable platform at the top unstable equilibrium. Tasks of swinging up and stabilizing of a pendulum is solved in conditions of a full parametric uncertainty of a plant and constrained control channel. In contrast to other approaches, this algorithm has adaptive adjusting of controller's parameters in “on-line” mode without any preliminary identification procedure. The second goal of this work is a development of applications used in an educational process because an investigation of control theory problems with real plants very important for students and their experience.


title: Hexagonal microstrip antenna simulation for breast cancer detection

abstract: As an emerging technology, UltraWide Band (UWB) wireless communications provides a very different approach to the antenna technology compared to narrow band systems, which has been a very attractive choice for medical antenna development. In this paper presents the design and simulation of a hexagonal microstrip antenna along with a breast phantom simulation. The antenna is simulated by introducing a hexagon slot in the center of the patch an impedance bandwidth nearly 5 GHz is achieved. The presented antenna has been designed and simulated successfully. The simulation analysis of designed antenna is carried out using HFSS software. The obtained results with this antenna make it a suitable antenna for UWB systems and portable applications.


title: A study of influencing factors for repurchase intention in Internet shopping malls

abstract: This research studied the effect of 15 variables on the consumers' overall satisfaction and repurchase intention in Internet shopping malls. As the value of loyal customers is incomparably high in electronic commerce, winning customers' loyalty is vital to success of shopping malls. In this study, a customer is defined as one who has purchased goods or services at least once from Internet shopping malls. The research variables are demographic factors, product perceptions, customer service, perceived ease of use, site image, promotion, perceived consumer risk, personal characteristics and Internet communications environments. The outcome of the research is as follows: perceived consumer risk shows a negative relationship with the repurchase intention, and all the other variables - product perceptions, customer service, perceived ease of use, site image, promotion, communications environments - are positively related with the repurchase intention. Also, the overall satisfaction level of customers for the Internet shopping malls positively influences repurchase intention.


title: Ontology-based information extraction for subject-focussed automatic essay evaluation

abstract: Automatic essay evaluation (AEE) systems are designed to assist a teacher in the task of classroom assessment in order to alleviate the demands of manual subject evaluation. However, although numerous AEE systems are available, most of these systems do not use elaborate domain knowledge for evaluation, which limits their ability to give informative feedback to students and also their ability to constructively grade a student based on a particular domain of study. This paper is aimed at improving on the achievements of previous studies by providing a subject-focussed evaluation system that considers the domain knowledge while scoring and provides informative feedback to its user. The study employs a combination of techniques such as system design and modelling using Unified Modelling Language (UML), information extraction, ontology development, data management, and semantic matching in order to develop a prototype subject-focussed AEE system. The developed system was evaluated to determine its level of performance and usability. The result of the usability evaluation showed that the system has an overall mean rating of 4.17 out of maximum of 5, which indicates ‘good usability’. In terms of performance, the assessment done by the system was also found to have sufficiently high correlation with those done by domain experts, in addition to providing appropriate feedback to the user.


Algorithms for efficient evaluation of multi-pass attribute grammars without a parse tree


title: Neural Inverse Rendering of an Indoor Scene From a Single Image

abstract: Inverse rendering aims to estimate physical attributes of a scene, e.g., reflectance, geometry, and lighting, from image(s). Inverse rendering has been studied primarily for single objects or with methods that solve for only one of the scene attributes. We propose the first learning-based approach that jointly estimates albedo, normals, and lighting of an indoor scene from a single image. Our key contribution is the Residual Appearance Renderer (RAR), which can be trained to synthesize complex appearance effects (e.g., inter-reflection, cast shadows, near-field illumination, and realistic shading), which would be neglected otherwise. This enables us to perform self-supervised learning on real data using a reconstruction loss, based on re-synthesizing the input image from the estimated components. We finetune with real data after pretraining with synthetic data. To this end, we use physically-based rendering to create a large-scale synthetic dataset, which is a significant improvement over prior datasets. Experimental results show that our approach outperforms state-of-the-art methods that estimate one or more scene attributes.


Privacy-Preserving Data Mining


title: Totem-Pole Boost Bridgeless PFC Rectifier With Simple Zero-Current Detection and Full-Range ZVS Operating at the Boundary of DCM/CCM

abstract: A totem-pole boost bridgeless power-factor-correction rectifier with simple zero-current detection and full-range zero-voltage switching (ZVS) is proposed, which operates at the boundary of discontinuous-conduction mode and continuous-conduction mode. Comparing with the boundary dual boost bridgeless rectifier, the required number of power components is reduced by one third and two current transducers can be eliminated. The zero-current detection is achieved by sampling the diode current through a single current transducer. Besides, a soft-transition method is proposed to suppress the current spike at the line-voltage zero-crossing point. Furthermore, a ZVS range extension operation is proposed to achieve ZVS in the MOSFETs within the full range of line input, which needs no additional MOSFETs. This also makes it possible to reduce MOSFET turn-OFF losses by paralleling external capacitors. Hence, a high-efficiency and low common-mode noise-interference bridgeless rectifier is achieved. This rectifier is also a candidate for interleaving operation to upgrade the power level. The experimental results show that ZVS is achieved within the full range of universal line input. The efficiency is above 96% at full load under 90 V and the maximum efficiency is above 98.4%. The ZVS range extension operation improves the efficiency by 0.5%.


title: Soft-WSN: Software-Defined WSN Management System for IoT Applications

abstract: In this paper, we propose a software-defined wireless sensor network architecture ( Soft-WSN )—an effort to support application-aware service provisioning in Internet of Things (IoT). Detailed architecture of the proposed system is presented involving the application, control, and infrastructure layers to enable software-defined networking (SDN) in IoT. We design a software-defined controller, which includes two management policies—device management and network management. Device management facilitates users to control their devices in the network. To enable device control mechanisms, we investigate three scheduling issues in a sensor node—sensing task, sensing delay, and active sleep. On the other hand, the topology of the network is controlled by the network management policies, which can be modified in run time to deal with dynamic requirements of IoT. Furthermore, the proposed scheme is implemented in a real hardware platform without changing the underlying sensor networking concepts, so that existing sensor devices can be seamlessly integrated. Therefore, in contrast to the existing SDN solutions for WSNs, the proposed system, Soft-WSN , focuses on both device management and topology management to meet run-time application-specific requirements of IoT, while enhancing flexibility and simplicity of WSN management. Experimental results on a real hardware-based test bed indicate that the proposed scheme is beneficial to meet real-time application-specific requirements of IoT, while ensuring significant improvements on network performance over the traditional approaches.


title: An Energy Efficient Full-Frame Feature Extraction Accelerator With Shift-Latch FIFO in 28 nm CMOS

abstract: This paper presents an energy-efficient feature extraction accelerator design aimed at visual navigation. The hardware-oriented algorithmic modifications such as a circular-shaped sampling region and unified description are proposed to minimize area and energy consumption while maintaining feature extraction quality. A matched-throughput accelerator employs fully-unrolled filters and single-stream descriptor enabled by algorithm-architecture co-optimization, which requires lower clock frequency for the given throughput requirement and reduces hardware cost of description processing elements. Due to the large number of FIFO blocks, a robust low-power FIFO architecture for the ultra-low voltage (ULV) regime is also proposed. This approach leverages shift-latch delay elements and balanced-leakage readout technique to achieve 62% energy savings and 37% delay reduction. We apply these techniques to a feature extraction accelerator that can process 30 fps VGA video in real time and is fabricated in 28 nm LP CMOS technology. The design consumes 2.7 mW with a clock frequency of 27 MHz at Vdd = 470 mV, providing 3.5× better energy efficiency than previous state-of-the-art while extracting features from entire image.


title: Tools and approaches for topic detection from Twitter streams: survey

abstract: Detecting topics from Twitter streams has become an important task as it is used in various fields including natural disaster warning, users opinion assessment, and traffic prediction. In this article, we outline different types of topic detection techniques and evaluate their performance. We categorize the topic detection techniques into five categories which are clustering, frequent pattern mining, Exemplar-based, matrix factorization, and probabilistic models. For clustering techniques, we discuss and evaluate nine different techniques which are sequential k-means, spherical k-means, Kernel k-means, scalable Kernel k-means, incremental batch k-means, DBSCAN, spectral clustering, document pivot clustering, and Bngram. Moreover, for matrix factorization techniques, we analyze five different techniques which are sequential Latent Semantic Indexing (LSI), stochastic LSI, Alternating Least Squares (ALS), Rank-one Downdate (R1D), and Column Subset Selection (CSS). Additionally, we evaluate several other techniques in the frequent pattern mining, Exemplar-based, and probabilistic model categories. Results on three Twitter datasets show that Soft Frequent Pattern Mining (SFM) and Bngram achieve the best term precision, while CSS achieves the best term recall and topic recall in most of the cases. Moreover, Exemplar-based topic detection obtains a good balance between the term recall and term precision, while achieving a good topic recall and running time.


title: Tolerating hardware device failures in software

abstract: Hardware devices can fail, but many drivers assume they do not. When confronted with real devices that misbehave, these assumptions can lead to driver or system failures. While major operating system and device vendors recommend that drivers detect and recover from hardware failures, we find that there are many drivers that will crash or hang when a device fails. Such bugs cannot easily be detected by regular stress testing because the failures are induced by the device and not the software load. This paper describes Carburizer, a code-manipulation tool and associated runtime that improves system reliability in the presence of faulty devices. Carburizer analyzes driver source code to find locations where the driver incorrectly trusts the hardware to behave. Carburizer identified almost 1000 such bugs in Linux drivers with a false positive rate of less than 8 percent. With the aid of shadow drivers for recovery, Carburizer can automatically repair 840 of these bugs with no programmer involvement. To facilitate proactive management of device failures, Carburizer can also locate existing driver code that detects device failures and inserts missing failure-reporting code. Finally, the Carburizer runtime can detect and tolerate interrupt-related bugs, such as stuck or missing interrupts.


title: Liquid Crystal Based Terahertz Spatial Light Modulator for Imaging Application

abstract: We demonstrate a liquid crystal based THz spatial light modulator (SLM). Indium tin oxide or gold finger type patterns were used for transparent (>70 % at 0.08 to 1.2 THz) conductor and self-cross polarizer. The phase shift and amplitude modulation achieved are greater than 90° and 30 % in broadband in THz frequency. This SLM could become a promising candidate for high speed THz imaging application via compress sensing algorithm


title: Fast beamforming of electronically steerable parasitic array radiator antennas: theory and experiment

abstract: A low-power consumption, small-size smart antenna, named electronically steerable parasitic array radiator (ESPAR), has been designed. Beamforming is achieved by tuning the load reactances at parasitic elements surrounding the active central element. A fast beamforming algorithm based on simultaneous perturbation stochastic approximation with a maximum cross correlation coefficient criterion is proposed. The simulation and experimental results validate the algorithm. In an environment where the signal-to-interference-ratio is 0 dB, the algorithm converges within 50 iterations and achieves an output signal-to-interference-plus-noise-ratio of 10 dB. With the fast beamforming ability and its low-power consumption attribute, the ESPAR antenna makes the mass deployment of smart antenna technologies practical.


title: New Model for Cyber Crime Investigation Procedure

abstract: In this paper, we presented a new model for cyber crime investigation procedure which is as follows: readiness phase, consulting with profiler, cyber crime classification and investigation priority decision, damaged cyber crime scene investigation, analysis by crime profiler, suspects tracking, injurer cyber crime scene investigation, suspect summon, cyber crime logical reconstruction, writing report. Computer forensics emerged in response to the escalation of crimes committed by the use of computer systems either as an object of crime, an instrument used to commit a crime or a repository of evidence related to a crime. Computer forensics can be traced back to as early as 1984 when the FBI laboratory and order law enforcement agencies begun developing programs to examine computer evidence. Digital forensics has been defined as the use of scientifically derived and proven methods towards the preservation, collection, validation, identification, analysis, interpretation and presentation of digital evidence derived from digital sources for the purpose of facilitating or furthering the reconstruction of events found to be crime or helping to anticipate the unauthorized actions shown to be disruptive to planned operations. Digital evidence includes computer evidence, digital audio, digital video, cell phones, digital fax machines etc. The U.S. Department of Justice published a process model in the Electronic Crime Scene Investigation: A guide to first responders that consists of four phases: collection, examination, analysis, reporting. The analysis phase of this model is improperly defined and ambiguous. Brian Carrier (5) proposed integrated digital investigation process. It (5) was consisted of 5 phase like readiness phase, deployment phase, physical crime scene investigation phase, cyber crime scene investigation phase, review phase. The Brian Carrier (5) procedure didn't include which classifying cyber crime and deciding investigation priority, psychological profiling investigation method, and so on. In this paper, we presented a new model for cyber crime investigation procedure. The proposed procedure model is as follows readiness phase, consulting with profiler, cyber crime classification and investigation priority decision, damaged cyber crime scene investigation, analysis by crime profiler, suspects tracking, injurer cyber crime scene investigation, suspect summon, cyber crime logical reconstruction, writing report. This paper presented a new methodology of a digital forensic investigation procedure. Section 2 shows previous cyber crime investigation model. We present our method for cyber crime investigation procedure model in section 3.


title: Peyronie's Reconstruction for Maximum Length and Girth Gain: Geometrical Principles

abstract: Peyronie's disease has been associated with penile shortening and some degree of erectile dysfunction. Surgical reconstruction should be based on giving a functional penis, that is, rectifying the penis with rigidity enough to make the sexual intercourse. The procedure should be discussed preoperatively in terms of length and girth reconstruction in order to improve patient satisfaction. The tunical reconstruction for maximum penile length and girth restoration should be based on the maximum length of the dissected neurovascular bundle possible and the application of geometrical principles to define the precise site and size of tunical incision and grafting procedure. As penile rectification and rigidity are required to achieve complete functional restoration of the penis and 20 to 54% of patients experience associated erectile dysfunction, penile straightening alone may not be enough to provide complete functional restoration. Therefore, phosphodiesterase inhibitors, self-injection, or penile prosthesis may need to be added in some cases.


title: Robotic test bench for CubeSat ground testing: Concept and satellite dynamic parameter identification

abstract: This paper introduces a novel concept of an air bearing test bench for CubeSat ground testing together with the corresponding dynamic parameter identification method. Contrary to existing air bearing test benches, the proposed concept allows three degree-of-freedom unlimited rotations and minimizes the influence of the test bench on the tested CubeSat. These advantages are made possible by the use of a robotic wrist which rotates air bearings in order to make them follow the CubeSat motion. Another keystone of the test bench is an accurate balancing of the tested CubeSat. Indeed, disturbing factors acting on the satellite shall be minimized, the most significant one being the gravity torque. An efficient balancing requires the CubeSat center of mass position to be accurately known. Usual techniques of dynamic parameter identification cannot be directly applied because of the frictionless suspension of the CubeSat in the test bench and, accordingly, due to the lack of external actuation. In this paper, a new identification method is proposed. This method does not require any external actuation and is based on the sampling of free oscillating motions of the CubeSat mounted on the test bench.


title: Vertical Fragmentation and Allocation in Distributed Deductive Database Systems

abstract: Abstract Although approaches for vertical fragmentation and data allocation have been proposed, algorithms for vertical fragmentation and allocation of data and rules in distributed deductive database systems (DDDBSs) are lacking. In this paper, we present different approaches for vertical fragmentation of relations that are referenced by rules and an allocation strategy for rules and fragments in a DDDBS. The potential advantages of the proposed fragmentation and allocation scheme include maximal locality of query evaluation and minimization of communication cost in a distributed system, in addition to the desirable properties of (vertical) fragmentation and rule allocation as discussed in the literature. We also formulate the mathematical interpretation of the proposed vertical fragmentation and allocation algorithms.


title: Validation of Power Output for the WIND Toolkit

abstract: Renewable energy integration studies require wind data sets of high quality with realistic representations of the variability, ramping characteristics, and forecast performance for current wind power plants. The Wind Integration National Data Set (WIND) Toolkit is meant to be an update for and expansion of the original data sets created for the weather years from 2004 through 2006 during the Western Wind and Solar Integration Study and the Eastern Wind Integration Study. The WIND Toolkit expands these data sets to include the entire continental United States, increasing the total number of sites represented, and it includes the weather years from 2007 through 2012. In addition, the WIND Toolkit has a finer resolution for both the temporal and geographic dimensions. Three separate data sets will be created: a meteorological data set, a wind power data set, and a forecast data set. This report describes the validation of the wind power data set.


title: Modernizing Legacy Systems: Software Technologies, Engineering Processes, and Business Practices

abstract: From the Book: ::: Software systems become legacy systems when they begin to resist modification and evolution. However, the knowledge embodied in legacy systems constitutes significant corporate assets. Assuming these system still provide significant business value, they must then be modernized or replaced. This book describes a risk-managed approach to legacy system modernization that applies a knowledge of software technologies and an understanding of engineering processes within a business context.Audience ::: Modernizing Legacy Systems: Software Technologies, Engineering Processes and Business Practices should be useful to anyone involved in modernizing a legacy system. ::: As a software engineer, it should help you understand some of the larger business concerns that drive a modernization effort. ::: As a software designer, this book should help you understand the impact of legacy code, coupled with incremental development and deployment practices, on design activities. ::: As a system architect, this book explains the processes and techniques that have failed or succeeded in practice. It should also provide insight into how you can repeat these successes and avoid the failures. ::: As an IT manager, this book explains how technology and business objectives influence the software modernization processes. ::: ::: ::: In particular, it should help you answer the following questions: ::: When and how do I decide if a modernization or replacement effort is justified? ::: How do I develop an understanding of the legacy system? ::: How do I gain an understanding of, and evaluate the applicability of, infsystem technologies that can be used in the modernization of my system? ::: When do I involve the stakeholders and how can I reconcile their conflicting needs? ::: What role does architecture play in legacy system modernization? ::: How can I estimate the cost of a legacy system modernization? ::: How can I evaluate and select a modernization strategy? ::: How can I develop a detailed modernization plan? ::: ::: Organization and Content ::: Modernizing Legacy Systems: Software Technologies, Engineering Processes and Business Practices shows how legacy systems can be incrementally modernized. It uses and extends the methods and techniques described in Building Systems from Commercial Components Wallnau, 2001 to draw upon engineering expertise early in the conceptual phase to ensure realistic and comprehensive planning. ::: This book features an extensive case study involving a major modernization effort. The legacy system in this case study consists of nearly 2 million lines of COBOL code developed over 30 years. The system is being replaced with a modern system based on the Java 2 Enterprise Edition (J2EE) architecture. Additional challenges include a requirement to incrementally develop and deploy the system. We look at the strategy used to modernize the system; the use of Enterprise JavaBeans, message-oriented middleware, Java, and other J2EE technologies to produce the modern system; the supporting software engineering processes and techniques; and the resulting system. ::: Chapters 1 of this book provides an introduction to the challenges and practices of software evolution and Chapter 2 introduces the major case study in the bo introduces the Risk-Managed Modernization (RMM) approach which is elaborated in Chapters 4 through 17 and illustrated by the case study. Throughout Chapters 4 through 17 we provide an activity diagram of RMM as a road map to each chapter. ::: Chapter 18 provides some recommendations to help guide your modernization efforts (although these recommendations cannot be fully appreciated without reading the main body of the book).Throughout this book we use the Unified Modelling Language (UML) to represent architecture drawings and design patterns. A brief introduction to UML is provided in Chapter 6.


title: Tree inference for single-cell data

abstract: Understanding the mutational heterogeneity within tumors is a keystone for the development of efficient cancer therapies. Here, we present SCITE, a stochastic search algorithm to identify the evolutionary history of a tumor from noisy and incomplete mutation profiles of single cells. SCITE comprises a flexible Markov chain Monte Carlo sampling scheme that allows the user to compute the maximum-likelihood mutation history, to sample from the posterior probability distribution, and to estimate the error rates of the underlying sequencing experiments. Evaluation on real cancer data and on simulation studies shows the scalability of SCITE to present-day single-cell sequencing data and improved reconstruction accuracy compared to existing approaches.


title: Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams

abstract: Prediction in streaming data is an important activity in the modern society. Two major challenges posed by data streams are (1) the data may grow without limit so that it is difficult to retain a long history of raw data; and (2) the underlying concept of the data may change over time. The novelties of this paper are in four folds. First, it uses a measure of conceptual equivalence to organize the data history into a history of concepts. This contrasts to the common practice that only keeps recent raw data. The concept history is compact while still retains essential information for learning. Second, it learns concept-transition patterns from the concept history and anticipates what the concept will be in the case of a concept change. It then proactively prepares a prediction model for the future change. This contrasts to the conventional methodology that passively waits until the change happens. Third, it incorporates proactive and reactive predictions. If the anticipation turns out to be correct, a proper prediction model can be launched instantly upon the concept change. If not, it promptly resorts to a reactive mode: adapting a prediction model to the new data. Finally, an efficient and effective system RePro is proposed to implement these new ideas. It carries out prediction at two levels, a general level of predicting each oncoming concept and a specific level of predicting each instance's class. Experiments are conducted to compare RePro with representative existing prediction methods on various benchmark data sets that represent diversified scenarios of concept change. Empirical evidence offers inspiring insights and demonstrates the proposed methodology is an advisable solution to prediction in data streams.


title: Alone we can do so little, together we can do so much: a combinatorial approach for generating game content

abstract: In this paper we present a procedural content generator using Non-negative Matrix Factorisation (NMF). We use representative levels from five dissimilar content generators to train NMF models that learn patterns about the various components of the game. The constructed models are then used to automatically generate content that resembles the training data as well as to generate novel content through exploring new combinations of patterns. We describe the methodology followed and we show that the generator proposed has a more powerful capability than each of generator taken individually. The generator's output is compared to the other generators using a number of expressivity metrics. The results show that the proposed generator is able to resemble each individual generator as well as demonstrating ability to cover a wider and more novel content space.


title: Optical Flow Estimation

abstract: This chapter provides a tutorial introduction to gradient- based optical flow estimation. We discuss least-squares and robust estima- tors, iterative coarse-to-fine refinement, different forms of parametric mo- tion models, different conservation assumptions, probabilistic formulations, and robust mixture models.


Fingerprint Recognition and Matching using Matlab


Connecting the Dots: Towards Human-Level Grammatical Error Correction


title: Color Texture Classification Approach Based on Combination of Primitive Pattern Units and Statistical Features

abstract: Texture classification became one of the problems which has been paid much attention on by image processing scientists since late 80s. Consequently, since now many different methods have been proposed to solve this problem. In most of these methods the researchers attempted to describe and discriminate textures based on linear and non-linear patterns. The linear and non-linear patterns on any window are based on formation of Grain Components in a particular order. Grain component is a primitive unit of morphology that most meaningful information often appears in the form of occurrence of that. The approach which is proposed in this paper could analyze the texture based on its grain components and then by making grain components histogram and extracting statistical features from that would classify the textures. Finally, to increase the accuracy of classification, proposed approach is expanded to color images to utilize the ability of approach in analyzing each RGB channels, individually. Although, this approach is a general one and it could be used in different applications, the method has been tested on the stone texture and the results can prove the quality of approach.


title: F1/10: An Open-Source Autonomous Cyber-Physical Platform

abstract: In 2005 DARPA labeled the realization of viable autonomous vehicles (AVs) a grand challenge; a short time later the idea became a moonshot that could change the automotive industry. Today, the question of safety stands between reality and solved. Given the right platform the CPS community is poised to offer unique insights. However, testing the limits of safety and performance on real vehicles is costly and hazardous. The use of such vehicles is also outside the reach of most researchers and students. In this paper, we present F1/10: an open-source, affordable, and high-performance 1/10 scale autonomous vehicle testbed. The F1/10 testbed carries a full suite of sensors, perception, planning, control, and networking software stacks that are similar to full scale solutions. We demonstrate key examples of the research enabled by the F1/10 testbed, and how the platform can be used to augment research and education in autonomous systems, making autonomy more accessible.


title: The role of collaborative tagging and ontologies in emerging semantic of web resources

abstract: The social web interactions have extended the sharing and the growth of web resources on the web. The collaborative web services (folksonomies) enable users to assign their freely chosen keywords (tags) to describe web resources. The advent of folksonomy has evolved the role of web users from consumers to contributors of information. Thus, users attribute their descriptive tags to annotate, organize and classify web resources of interests. Folksonomy became popular with the emergence of collaborative tagging. It offers a practical classification of web resources via the attributed tags. Nonetheless, the freely chosen tags weaken the semantic description of web resources. Folksonomy can give rise to a poor classification system based on ambiguous and inconsistent tags. Therefore, it is essential to pertinently describe the semantic of web resources to enhance their classification, findability and discoverability. The proposed approach represents a combined semantic enrichment strategy that explores collaborative tagging towards describing each web resource using different types of descriptive metadata, namely relevant folksonomy tags, content-based main keywords and matching ontology terms. The experimental evaluation has shown relevant results attesting the efficiency of our proposal. The alignment of social tagging with the ontology will not only enhances the classification of web resources but also constructs their semantic clustering. This emergent semantic will establish new challenges to improve the context-aware recommender systems of web resources in different real-world applications (healthcare, social education and cultural heritage).


title: Overcoming cultural barriers to sharing knowledge

abstract: Culture is often seen as the key inhibitor of effective knowledge sharing. A study of companies where sharing knowledge is built into the culture found that they did not change their culture to match their knowledge management initiatives. They adapted their approach to knowledge management to fit their culture. They did this by: linking sharing knowledge to solving practical business problems; tying sharing knowledge to a pre‐existing core value; introducing knowledge management in a way that matches the organization’s style; building on existing networks people use in their daily work; and encouraging peers and supervisors to exert pressure to share.


title: Accelerometer Calibration and Dynamic Bias and Gravity Estimation: Analysis, Design, and Experimental Evaluation

abstract: Tri-axial linear accelerometers are key components in a great variety of applications and, in particular, in navigation systems. Nonidealities such as scale factors, cross coupling, bias, and other higher-order nonlinearities affect the output of this sensor, leading, in general, to prohibitive errors. On the other hand, these coefficients are often slowly time-varying, which renders offline calibration less effective. One such coefficient that usually varies greatly over time and between power-ons is the bias. This paper details the calibration of an accelerometer unit and presents also a dynamic filtering solution for the bias, which also includes the estimation of the gravity in body-fixed coordinates. Simulation and experimental results obtained with a motion rate table are presented and discussed to illustrate the performance of the proposed algorithms.


title: Automatic Wrinkle Detection Using Hybrid Hessian Filter

abstract: Aging as a natural phenomenon affects different parts of the human body under the influence of various biological and environmental factors. The most pronounced changes that occur on the face is the appearance of wrinkles, which are the focus of this research. Accurate wrinkle detection is an important task in face analysis. Some have been proposed in the literature, but the poor localization limits the performance of wrinkle detection. It will lead to false wrinkle detection and consequently affect the processes such as age estimation and clinician score assessment. Therefore, we propose a hybrid Hessian filter (HHF) to cope with the identified problem. HHF is composed of the directional gradient and Hessian matrix. The proposed filter is conceptually simple, however, it significantly increases the true wrinkle localization when compared with the conventional methods. In the experimental setup, three coders have been instructed to annotate the wrinkle of 2D forehead image manually. The inter-reliability among three coders is 93 % of Jaccard similarity index (JSI). In comparison to the state-of-the-art Cula method (CLM) and Frangi filter, HHF yielded the best result with a mean JSI of 75.67 %. We noticed that the proposed method is capable of detecting the medium to coarse wrinkle but not the fine wrinkle. Although there is a gap between human annotation and automated detection, this work demonstrates that HHF is a remarkably strong filter for wrinkle detection. From the experimental results, we believe that our findings are notable in terms of the JSI.


title: Performance evaluation of component-based software systems: A survey

abstract: Performance prediction and measurement approaches for component-based software systems help software architects to evaluate their systems based on component performance specifications created by component developers. Integrating classical performance models such as queueing networks, stochastic Petri nets, or stochastic process algebras, these approaches additionally exploit the benefits of component-based software engineering, such as reuse and division of work. Although researchers have proposed many approaches in this direction during the last decade, none of them has attained widespread industrial use. On this basis, we have conducted a comprehensive state-of-the-art survey of more than 20 of these approaches assessing their applicability. We classified the approaches according to the expressiveness of their component performance modelling languages. Our survey helps practitioners to select an appropriate approach and scientists to identify interesting topics for future research.


title: Information Security in Banking and Financial Industry

abstract: It summarizes the influence elements introducing the concept of information technologies in financial and banking industries and analysis the relationship of information technology risk factors. By using the case study, research design and methods, and investigative study is conducted. IT security management practice is discussed. The potential vulnerabilities that exist in the system with reference to data leak, suggestions to safeguard from such threats are presented. It explores why information security should be a priority for businesses and deals with how a security expert can model potential losses for their organization. This paper identifies four security issues (access to information system, secure communication, security management, development of secure information systems).


title: A LabVIEW-Based GPS Receiver Development and Testing Platform with DSP Peripherals: Case study with C6713 DSK

abstract: The modernization of Global Positioning Systems (GPS) and the availability of more complex signals and modulation schemes boost the development of civil and military applications while the accuracy and coverage of receivers continually improve. Recently, software defined receiver solutions gained attention for flexible multimode operations. For them, developers address algorithmic and hardware accelerators or their hybrids for fast prototyping and testing high performance receivers for various conditions. This paper presents a new fast prototyping concept exploiting digital signal processor (DSP) peripherals and the benefits of the host environment using the National Instruments (NI) LabVIEW platform. With a reasonable distribution of tasks between the host hardware and reconfigurable peripherals, a higher performance is achieved. As a case study, in this paper the Texas Instruments (TI) TMS320C6713 DSP is used along with a Real Time Data Exchange (RTDX) communication link to compare with similar Simulink-based solutions. The proposed testbed GPS signal is created using the NI PXI signal generator and the NI GPS Simulation Toolkit.


title: The Impact of Mobile Shopping Quality on Customer Satisfaction and Purchase Intentions: The IS Success Based Model

abstract: While the popularity of mobile phones has grown enormously in recent years, mobile shopping (m-shopping) is becoming a new marketing channel where marketers can use to display or deliver their products or service. Despite mobile shopping increasing significance, there is still a lack of research work in this filed, the objectives of this study are sought to be achieved through examining factors connected with m-shopping quality, perceived usefulness, customer satisfaction, and purchase intention. A self-administered questionnaire is used as a quantitative evaluation method for data collection. This study uses the partial least squares (PLS) analytical tool to examine the model. The results were fully supported proposed model. The implications and suggestions for the further research are also discussed in this study.


title: Analysis of wheat microspore embryogenesis induction by transcriptome and small RNA sequencing using the highly responsive cultivar “Svilena”

abstract: BackgroundMicrospore embryogenesis describes a stress-induced reprogramming of immature male plant gametophytes to develop into embryo-like structures, which can be regenerated into doubled haploid plants after whole genome reduplication. This mechanism is of high interest for both research as well as plant breeding. The objective of this study was to characterize transcriptional changes and regulatory relationships in early stages of cold stress-induced wheat microspore embryogenesis by transcriptome and small RNA sequencing using a highly responsive cultivar.ResultsTranscriptome and small RNA sequencing was performed in a staged time-course to analyze wheat microspore embryogenesis induction. The analyzed stages were freshly harvested, untreated uninucleate microspores and the two following stages from in vitro anther culture: directly after induction by cold-stress treatment and microspores undergoing the first nuclear divisions. A de novo transcriptome assembly resulted in 29,388 contigs distributing to 20,224 putative transcripts of which 9,305 are not covered by public wheat cDNAs. Differentially expressed transcripts and small RNAs were identified for the stage transitions highlighting various processes as well as specific genes to be involved in microspore embryogenesis induction.ConclusionThis study establishes a comprehensive functional genomics resource for wheat microspore embryogenesis induction and initial understanding of molecular mechanisms involved. A large set of putative transcripts presumably specific for microspore embryogenesis induction as well as contributing processes and specific genes were identified. The results allow for a first insight in regulatory roles of small RNAs in the reprogramming of microspores towards an embryogenic cell fate.


title: Trajectory Design for Completion Time Minimization in UAV-Enabled Multicasting

abstract: This paper studies an unmanned aerial vehicle (UAV)-enabled multicasting system, where a UAV is dispatched to disseminate a common file to a set of ground terminals (GTs). We aim to design the UAV trajectory to minimize its mission completion time, while ensuring that each GT successfully recovers the file with a desired high probability. The formulated problem is nonconvex and difficult to be solved in its original form. Therefore, we first derive an effective lower bound for the success file recovery probability of each GT. The problem is then reformulated in a more tractable form, where the UAV trajectory only needs to be designed to ensure the minimum connection time constraint with each GT, during which their distance is below a certain threshold. We show that without loss of optimality, the UAV trajectory consists of connected line segments only, which can be obtained by determining the optimal set of waypoints as well as the UAV speed along the path connecting the waypoints. We propose efficient schemes for the waypoint design based on a novel concept of virtual base station placement and by applying convex optimization. Furthermore, for fixed waypoints, the optimal UAV speed is efficiently obtained by solving a linear programming problem. Numerical results show that the proposed UAV-enabled multicasting with optimized trajectory design achieves significant performance gains over other benchmark schemes.


Evaluating Story Generation Systems Using Automated Linguistic Analyses


title: The role of three-dimensional printed models of skull in anatomy education: a randomized controlled trail

abstract: Three-dimensional (3D) printed models represent educational tools of high quality compared with traditional teaching aids. Colored skull models were produced by 3D printing technology. A randomized controlled trial (RCT) was conducted to compare the learning efficiency of 3D printed skulls with that of cadaveric skulls and atlas. Seventy-nine medical students, who never studied anatomy, were randomized into three groups by drawing lots, using 3D printed skulls, cadaveric skulls, and atlas, respectively, to study the anatomical structures in skull through an introductory lecture and small group discussions. All students completed identical tests, which composed of a theory test and a lab test, before and after a lecture. Pre-test scores showed no differences between the three groups. In post-test, the 3D group was better than the other two groups in total score (cadaver: 29.5 [IQR: 25–33], 3D: 31.5 [IQR: 29–36], atlas: 27.75 [IQR: 24.125–32]; p = 0.044) and scores of lab test (cadaver: 14 [IQR: 10.5–18], 3D: 16.5 [IQR: 14.375–21.625], atlas: 14.5 [IQR: 10–18.125]; p = 0.049). Scores involving theory test, however, showed no difference between the three groups. In this RCT, an inexpensive, precise and rapidly-produced skull model had advantages in assisting anatomy study, especially in structure recognition, compared with traditional education materials.


title: How to put it into words - using random forests to extract symbol level descriptions from audio content for concept detection

abstract: This paper presents a system that uses symbolic representations of audio concepts as words for the descriptions of audio tracks, that enable it to go beyond the state of the art, which is audio event classification of a small number of audio classes in constrained settings, to large-scale classification in the wild. These audio words might be less meaningful for an annotator but they are descriptive for computer algorithms. We devise a random-forest vocabulary learning method with an audio word weighting scheme based on TF-IDF and TD-IDD, so as to combine the computational simplicity and accurate multi-class classification of the random forest with the data-driven discriminative power of the TF-IDF/TD-IDD methods. The proposed random forest clustering with text-retrieval methods significantly outperforms two state-of-the-art methods on the dry-run set and the full set of the TRECVID MED 2010 dataset.


title: Nucleosome dynamics regulate Neurospora circadian clock

abstract: How are circadian rhythms epigenetically regulated? Liu and colleagues have discovered a critical circadian clock component, CATP, that regulates rhythmic nucleosome occupancy at the clock gene frequency in Neurospora.


title: A 1.5-V 2–9.6-GHz Inductorless Low-Noise Amplifier in 0.13-$\mu{\hbox {m}} $ CMOS

abstract: This paper presents an inductorless low-noise amplifier (LNA) design for an ultra-wideband (UWB) receiver front-end. A current-reuse gain-enhanced noise canceling architecture is proposed, and the properties and limitations of the gain-enhancement stage are discussed. Capacitive peaking is employed to improve the gain flatness and -3-dB bandwidth, at the cost of absolute gain value. The LNA circuit is fabricated in a 0.13-mum triple-well CMOS technology. Measurement result shows that a small-signal gain of 11 dB and a -3-dB bandwidth of 2-9.6 GHz are obtained. Over the -3-dB bandwidth, the input return loss is less than -8.3 dB, and the noise figure is 3.6-4.8 dB. The LNA consumes 19 mW from a low supply voltage of 1.5 V. It is shown that the LNA designed without on-chip inductors achieves comparable performances with inductor-based designs. The silicon area is reduced significantly in the inductorless design, the LNA core occupies only 0.05 mm2, which is among the smallest reported designs.


title: In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images

abstract: Summary Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call " in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico , the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.


title: Comparative investigation of PWM current-source inverters for future machine drives using high-frequency wide-bandgap power switches

abstract: Pulse-width-modulated (PWM) current-source inverters (CSIs) in machine drives have received limited research attention since the 1980s because they are generally considered to be inferior to PWM voltage-source inverters (VSIs) in terms of several performance metrics. However, in view of the special features of wide-bandgap (WBG) power semiconductors that distinguish them from today's silicon devices, this paper uses simulation to reexamine the PWM-CSI's key performance metrics including efficiency, electromagnetic interference (EMI), and machine over-voltage at high switching frequencies (e.g., 200kHz) compared with two-level WBG-enabled VSIs under similar operating conditions. Advantages of the CSI compared to the baseline VSI topology are revealed for each of these three performance features. Reverse-voltage-blocking switches based on gallium nitride (GaN) High Electron Mobility Transistors (HEMTs) that are key to the operation of PWM-CSIs are discussed and experimentally evaluated.


title: A novel approach for internet traffic classification based on multi-objective evolutionary fuzzy classifiers

abstract: Internet traffic classification has moved in the last years from traditional port and payload-based approaches towards methods employing statistical measurements and machine learning techniques. Despite the success achieved by these techniques, they are not able to explain the relation between the features, which describe the traffic flow, and the corresponding traffic classes. This relation can be extremely useful to network managers for quickly handling possible network drawback. In this paper, we propose to tackle the traffic classification problem by using multi-objective evolutionary fuzzy classifiers (MOEFCs). MOEFCs are characterised by good trade-offs between accuracy and interpretability. We adopt two Internet traffic datasets extracted from two real-world networks. We discuss the results obtained both by applying a cross validation on each single dataset, and by using a dataset as training set and the other as test set. We show that, in both cases, MOEFCs can achieve satisfactory accuracy in the face of low complexity and, therefore, high interpretability.


title: The Impact of Jitter on Traffic Flow Optimization in Communication Networks

abstract: Current network planning and design methods use the average delay, packet loss and throughput as metrics to optimize the network cost and performance. New multimedia applications, on the other hand, also have critical jitter requirements that are not taken into account by these methods. Here, we explore the impact on the network performance of adding these jitter constraints. We use a fast jitter calculation model to solve the optimal routing problem for flows subject to jitter or delay constraints. We find that the optimal routing is very different for the two kinds of flows: They should be routed on different paths, the jitter-constrained flows should not be split on multiple paths while the opposite conclusion is true for delay-constrained flows.


title: Proximate Time-Optimal Digital Control for Synchronous Buck DC–DC Converters

abstract: This paper introduces an approach to near time-optimal control for synchronous buck dc-dc converters. The proposed proximate time-optimal digital (PTOD) controller is a combination of a constant-frequency pulsewidth modulation (PWM) controller employing a linear PID compensator close to a reference point, and a linear or nonlinear switching surface controller (SSC) away from the reference, together with smooth transitions between the two. A hybrid capacitor current estimator enables switching surface evaluation and eliminates the need for current sensing. The SSC, which is implemented as a small Verilog HDL module, can be easily added to an existing digital PWM controller to construct the PTOD controller. In steady state, the controller operates exactly the same as a standard constant-frequency PWM controller with a linear PID compensator. Simulation and experimental results are shown for a 6.5 V-to-1.3 V, 10A synchronous buck converter.


title: Neural network applications in stylometry: The Federalist Papers

abstract: Neural Networks have recently been a matter of extensive research and popularity. Their application has increased considerably in areas in which we are presented with a large amount of data and we have to identify an underlying pattern. This paper will look at their application to stylometry. We believe that statistical methods of attributing authorship can be coupled effectively with neural networks to produce a very powerful classification tool. We illustrate this with an example of a famous case of disputed authorship, The Federalist Papers. Our method assigns the disputed papers to Madison, a result which is consistent with previous work on the subject.


title: Data Acquisition Model for Analyzing Schedule Delays Using KDD: Knowledge Discovery and Datamining

abstract: Project schedule delay is a major problem in many projects. It has major consequences and effects on project cost, customer satisfaction level, market share and company reputation. Schedule delay factors can be expressed as direct or indirect causes of schedule delay. Developing an acquisition frame to study schedule delay is a need for any analytic process and there was no such generic model developed to analyze many projects at once regardless of their uniqueness. (Data Acquisition Model) DAM is the way which includes schedule delay factors selected from academic and practical field experience. DAM grouped schedule delay factors according to project life cycle level. EPC lifecycle is known as (Engineering, Procurement and Construction) phases of project life cycle. Some factors were not related to EPC, so adding a criteria using (general) was informative to group non-EPC factors. Shortlisting schedule delay factors in each group was achieved through another criteria (selection criteria). As a conclusion based on the literature review and our practical experience the model might be novel to address schedule delay problem in projects. Data mining techniques have been widely utilized in many industries to extract hidden descriptive or implicit knowledge. A framework was developed based on two main models (hybrid KDD Model) and (Fayyad Model) to discover knowledge in data. DAM might be starting point for (DM) techniques; to generate knowledge about schedule delay factors that will help many decision makers to take corrective action items and control schedule delays in projects.


title: Resonant bits: controlling digital musical instruments with resonance and the ideomotor effect

abstract: Resonant Bits proposes giving digital information resonant dynamic properties, requiring skill and concerted effort for interaction. This paper applies resonant interaction to musical control, exploring musical instruments that are controlled through both purposeful and subconscious resonance. We detail three exploratory prototypes, the first two illustrating the use of resonant gestures and the third focusing on the detection and use of the ideomotor (subconscious micro-movement) effect.


title: Intelligence without reason

abstract: Computers and Thought are the two categories that together define Artificial Intelligence as a discipline. It is generally accepted that work in Artificial Intelligence over the last thirty years has had a strong influence on aspects of computer architectures. In this paper we also make the converse claim; that the state of computer architecture has been a strong influence on our models of thought. The Von Neumann model of computation has lead Artificial Intelligence in particular directions. Intelligence in biological systems is completely different. Recent work in behavior-based Artificial Intelligence has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share many characteristics with biological computation.


title: Modeling locational factors for tourism employment

abstract: The purpose of this paper is to focus attention on developing a workforce development strategy that comprehensively includes the critical determinants of how employees fit a particular job in a particular organization at a particular location. Appropriating the Attraction-Selection-Attrition (ASA) framework, and incorporating person-location with person-job and person-organizational environment fit we present a three-dimensional eight cell model that includes the often neglected influence of job locations on tourism employment decisions. This model, supported by eight propositions, facilitates a multi-level and holistic appraisal of employee fit. The article then discusses the theoretical implications and applications of this model to raise research agendas and practical implications for meeting workforce challenges of tourism organizations, associations and destinations, especially those in remote locations.


title: How to Make Better Decisions? Lessons Learned from Behavioral Corporate Finance

abstract: This article reviews the literature in the field of Behavioral Corporate Finance. For reasons of simplicity, we distinguish between two approaches. The first approach focuses on the analysis of irrational behavior of managers in the context of efficient financial markets. Many empirical studies discover systematic irrational managerial behavior. The second approach regards rational manager decisions in the context of inefficient markets. The analysis focuses on situations where investors are systematically irrational taking rational and well-informed managers as given. Interestingly, Behavioral Corporate Finance is able to explain many empirical observations that cannot be explained by traditional Corporate Finance. In reality, both managers and investors act to some extent irrationally. Therefore, we make recommendations to both groups in order to improve their decision making. In contrast to other papers, we give specific recommendations for both managers and investors. With the help of these recommendations, managers and investors are able to improve decision-making to their mutual advantage.


title: The promise of blockchain technology for interaction design

abstract: After its first prominent application as part of the digital currency Bitcoin, blockchain technology is gaining in popularity. It comprises a chain of blocks (the ledger) that provides a secure, immutable and auditable list of ordered records. Using both cryptography and a distributed database, once a block is recorded, it can no longer be changed. In Bitcoin's case, the ledger contains the history of all currency transactions. This paper offers a critical overview of the blockchain technology for an interaction design audience, in three parts: (1) The principles making block-chain technology possible are briefly explained using examples. (2) Specific cases such as food provenance and supply chain tracing, are used to illustrate both the wide array of possible applications and the profound implications of blockchain technology. (3) Opportunities and challenges for HCI and interaction design are discussed in the form of a nascent research agenda.


title: Caenorhabditis elegans as a Model for Microbiome Research

abstract: The nematode Caenorhabditis elegans is used as a central model system across biological disciplines. Surprisingly, almost all research with this worm is performed in the absence of its native microbiome, possibly affecting generality of the obtained results. In fact, the C. elegans microbiome had been unknown until recently. This review brings together results from the first three studies on C. elegans microbiomes, all published in 2016. Meta-analysis of the data demonstrates a considerable conservation in the composition of the microbial communities, despite the distinct geographical sample origins, study approaches, labs involved and perturbations during worm processing. The C. elegans microbiome is enriched and in some cases selective for distinct phylotypes compared to corresponding substrate samples (e.g., rotting fruits, decomposing plant matter, and compost soil). The dominant bacterial groups include several Gammaproteobacteria (Enterobacteriaceae, Pseudomonaceae, and Xanthomonodaceae) and Bacteroidetes (Sphingobacteriaceae, Weeksellaceae, Flavobacteriaceae). They are consistently joined by several rare putative keystone taxa like Acetobacteriaceae. The bacteria are able to enhance growth of nematode populations, as well as resistance to biotic and abiotic stressors, including high/low temperatures, osmotic stress, and pathogenic bacteria and fungi. The associated microbes thus appear to display a variety of effects beneficial for the worm. The characteristics of these effects, their relevance for C. elegans fitness, the presence of specific co-adaptations between microbiome members and the worm, and the molecular underpinnings of microbiome-host interactions represent promising areas of future research, for which the advantages of C. elegans as an experimental system should prove of particular value.


Deep Learning for Image Denoising


title: Swivel: Improving Embeddings by Noticing What's Missing

abstract: We present Submatrix-wise Vector Embedding Learner (Swivel), a method for generating low-dimensional feature embeddings from a feature co-occurrence matrix. Swivel performs approximate factorization of the point-wise mutual information matrix via stochastic gradient descent. It uses a piecewise loss with special handling for unobserved co-occurrences, and thus makes use of all the information in the matrix. While this requires computation proportional to the size of the entire matrix, we make use of vectorized multiplication to process thousands of rows and columns at once to compute millions of predicted values. Furthermore, we partition the matrix into shards in order to parallelize the computation across many nodes. This approach results in more accurate embeddings than can be achieved with methods that consider only observed co-occurrences, and can scale to much larger corpora than can be handled with sampling methods.


DeepLife: An Entity-aware Search, Analytics and Exploration Platform for Health and Life Sciences


title: Voluntary Facial Action Generates Emotion-Specific Autonomic Nervous System Activity

abstract: Four experiments were conducted to determine whether voluntarily produced emotional facial configurations are associated with differentiated patterns of autonomic activity, and if so, how this might be mediated. Subjects received muscle-by-muscle instructions and coaching to produce facial configurations for anger, disgust, fear, happiness, sadness, and surprise while heart rate, skin conductance, finger temperature, and somatic activity were monitored. Results indicated that voluntary facial activity produced significant levels of subjective experience of the associated emotion, and that autonomic distinctions among emotions: (a) were found both between negative and positive emotions and among negative emotions, (b) were consistent between group and individual subjects' data, (c) were found in both male and female subjects, (d) were found in both specialized (actors, scientists) and nonspecialized populations, (e) were stronger when the voluntary facial configurations most closely resembled actual emotional expressions, and (f) were stronger when experience of the associated emotion was reported. The capacity of voluntary facial activity to generate emotion-specific autonomic activity: (a) did not require subjects to see facial expressions (either in a mirror or on an experimenter's face), and (b) could not be explained by differences in the difficulty of making the expressions or by differences in concomitant somatic activity. DESCRIPTORS: Facial action, Emotion, Autonomic activity during emotion, Emotion-specific autonomic activity. The experiments in this report are relevant to two major theoretical issues. The first, whether there are different patterns of autonomic nervous system (ANS) activity for different emotions, is one of psychophysiology's oldest. The second, under standing how voluntary facial activity can generate emotion-specific autonomic activity, has a much shorter history, but has potentially important im plications for emotion theory.