title: Conducting behavioral research on Amazon's Mechanical Turk

abstract: Amazon’s Mechanical Turk is an online labor market where requesters post jobs and workers choose which jobs to do for pay. The central purpose of this article is to demonstrate how to use this Web site for conducting behavioral research and to lower the barrier to entry for researchers who could benefit from this platform. We describe general techniques that apply to a variety of types of research and experiments across disciplines. We begin by discussing some of the advantages of doing experiments on Mechanical Turk, such as easy access to a large, stable, and diverse subject pool, the low cost of doing experiments, and faster iteration between developing theory and executing experiments. While other methods of conducting behavioral research may be comparable to or even better than Mechanical Turk on one or more of the axes outlined above, we will show that when taken as a whole Mechanical Turk can be a useful tool for many researchers. We will discuss how the behavior of workers compares with that of experts and laboratory subjects. Then we will illustrate the mechanics of putting a task on Mechanical Turk, including recruiting subjects, executing the task, and reviewing the work that was submitted. We also provide solutions to common problems that a researcher might face when executing their research on this platform, including techniques for conducting synchronous experiments, methods for ensuring high-quality work, how to keep data private, and how to maintain code security.


title: Path generation and tracking based on a Bézier curve for a steering rate controller of autonomous vehicles

abstract: The present paper demonstrates a feasible real-time path planning algorithm and tracking method for autonomous driving vehicles based on a 5th-order Bezier curve. It is demonstrated that a 5th-degree Bezier curve is required to generate a feasible path to achieve lane change maneuvers in a dynamic on-road environment. The steering rate control input is calculated based on the curvature extracted from the generated trajectory. Matlab simulations were conducted both for path generation and instantaneous planning of driving path for single and double lane change maneuvers. The proposed method was evaluated in an experiment using a miniature-sized unmanned ground vehicle.


title: Latent Hierarchical Part Based Models for Road Scene Understanding

abstract: Road scenes can be naturally interpreted in terms of a hierarchical structure consisting of parts and sub-parts, which captures different degrees of abstraction at different levels of the hierarchy. We introduce Latent Hierarchical Part based Models (LHPMs), which provide a promising framework for interpreting an image using a tree structure, in the case when the root filter for non-leaf nodes may not be available. While HPMs have been developed in the context of object detection and pose estimation, their application to scene understanding is restricted, due to the requirement of having root filters for non-leaf nodes. In this work, we propose a generalization of HPMs that dispenses with the need for having root filters for non-leaf nodes, by treating them as latent variables within a Dynamic Programming based optimization scheme. We experimentally demonstrate the importance of LHPMs for road scene understanding on Continental and KITTI datasets respectively. We find that the hierarchical interpretation leads to intuitive scene descriptions, that is central for autonomous driving.


title: Ferroresonance in a 13.8 kV Distribution Line

abstract: Overvoltages, for which ferroresonance was the suspected cause, were observed in the meter shop of an electric utility company, which was correlated with a broken conductor in a 13.8 kV overhead line. The shape of the distorted waveforms and the presence of many power-factor correction capacitors on the lightly loaded line led to the suspicion that this was the cause. A time-domain electromagnetic transient simulation was used to reproduce the waveforms, verifying that ferroresonance was indeed the cause of the overvoltages.


title: Kalman Filter for Robot Vision: A Survey

abstract: Kalman filters have received much attention with the increasing demands for robotic automation. This paper briefly surveys the recent developments for robot vision. Among many factors that affect the performance of a robotic system, Kalman filters have made great contributions to vision perception. Kalman filters solve uncertainties in robot localization, navigation, following, tracking, motion control, estimation and prediction, visual servoing and manipulation, and structure reconstruction from a sequence of images. In the 50th anniversary, we have noticed that more than 20 kinds of Kalman filters have been developed so far. These include extended Kalman filters and unscented Kalman filters. In the last 30 years, about 800 publications have reported the capability of these filters in solving robot vision problems. Such problems encompass a rather wide application area, such as object modeling, robot control, target tracking, surveillance, search, recognition, and assembly, as well as robotic manipulation, localization, mapping, navigation, and exploration. These reports are summarized in this review to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed in an abstract level.


title: Polarization and Frequency Spectra Characteristics of Chaff Cloud Clutter Signals

abstract: This Influence on the echoes of the chaff cloud by the translation and rotation of chaffs are studied according to the chaff spiral descent model. The characteristics of the frequency spectra and polarization of the chaff cloud are studied using the statistic method. Firstly, the spiral descent model of the chaff cloud is introduced, as well as the polarization scattering matrix of the rotation dipoles. Then, the self-correlation functions and cross-correlation functions of the elements of scattering matrix of the chaff cloud are analyzed, presenting the power spectra and cross power spectra of the elements of scattering matrix. Finally, the numerical results are given when the orientations of chaffs obey the uniform distribution and horizontal distribution.


title: CleanM: An Optimizable Query Language for Unified Scale-Out Data Cleaning

abstract: Data cleaning has become an indispensable part of data analysis due to the increasing amount of dirty data. Data scientists spend most of their time preparing dirty data before it can be used for data analysis. At the same time, the existing tools that attempt to automate the data cleaning procedure typically focus on a specific use case and operation. Still, even such specialized tools exhibit long running times or fail to process large datasets. Therefore, from a user's perspective, one is forced to use a different, potentially inefficient tool for each category of errors. ::: ::: This paper addresses the coverage and efficiency problems of data cleaning. It introduces CleanM (pronounced clean'em), a language which can express multiple types of cleaning operations. CleanM goes through a three-level translation process for optimization purposes; a different family of optimizations is applied in each abstraction level. Thus, CleanM can express complex data cleaning tasks, optimize them in a unified way, and deploy them in a scaleout fashion. We validate the applicability of CleanM by using it on top of CleanDB, a newly designed and implemented framework which can query heterogeneous data. When compared to existing data cleaning solutions, CleanDB a) covers more data corruption cases, b) scales better, and can handle cases for which its competitors are unable to terminate, and c) uses a single interface for querying and for data cleaning.


Expected goals in soccer:explaining match results using predictive analytics


title: The butterfly effect of caring – clinical nursing teachers ’ understanding of self-compassion as a source to compassionate care

abstract: This study has its roots in a clinical application project, focusing on the development of a teaching–learning model enabling participants to understand compassion. During that project four clinica ...


title: DC wander effect of DC blocking capacitors on PCIe Gen3 signal integrity

abstract: This paper discusses the impact of DC wander also called baseline wander resulting from AC-coupling on signal integrity in receive waveforms in AC-coupled serial bus links with focus on PCIe Gen3 signaling. Receive signal behavior from charging and discharging activities of AC-coupling circuit is studied for fundamental understanding of baseline wander and its effect through simulations of short and long channels from various aspects of PCIe Gen3 signaling and high speed serial links in general.


title: Classification of Wounds at Risk and Their Antimicrobial Treatment with Polihexanide: A Practice-Oriented Expert Recommendation

abstract: Currently, there are no generally accepted definitions for wounds at risk of infection. In clinical practice, too many chronic wounds are regarded as being at risk of infection, and therefore many topical antimicrobials – in terms of frequency and duration of use – are applied to wounds. Based on expert discussion and current knowledge, a clinical assessment score was developed. The objective of this wounds at risk (W.A.R.) score is to allow decision-making on the indication for the use of antiseptics on the basis of polihexanide. The proposed clinical classification of W.A.R. shall facilitate the decision for wound antisepsis and allow an appropriate general treatment regimen with the focus on the prevention of wound infection. The W.A.R. score is based on a clinically


title: Classifiers as a model-free group comparison test

abstract: The conventional statistical methods to detect group differences assume correct model specification, including the origin of difference. Researchers should be able to identify a source of group differences and choose a corresponding method. In this paper, we propose a new approach of group comparison without model specification using classification algorithms in machine learning. In this approach, the classification accuracy is evaluated against a binomial distribution using Independent Validation. As an application example, we examined false-positive errors and statistical power of support vector machines to detect group differences in comparison to conventional statistical tests such as t test, Levene’s test, K-S test, Fisher’s z-transformation, and MANOVA. The SVMs detected group differences regardless of their origins (mean, variance, distribution shape, and covariance), and showed comparably consistent power across conditions. When a group difference originated from a single source, the statistical power of SVMs was lower than the most appropriate conventional test of the study condition; however, the power of SVMs increased when differences originated from multiple sources. Moreover, SVMs showed substantially improved performance with more variables than with fewer variables. Most importantly, SVMs were applicable to any types of data without sophisticated model specification. This study demonstrates a new application of classification algorithms as an alternative or complement to the conventional group comparison test. With the proposed approach, researchers can test two-sample data even when they are not certain which statistical test to use or when data violates the statistical assumptions of conventional methods.


title: Two-Wire Bus Combining Full Duplex Body-Sensor Network and Multilead Biopotential Measurements

abstract: Classical approaches to make high-quality measurements of biopotential signals require the use of shielded or multiwire cables connecting the electrodes to a central unit in a star arrangement. As a consequence, increasing the number of leads increases cabling and connector complexity, which is not only limiting the patient comfort but is also anticipated as the main limiting factor to future miniaturization and cost reduction of tomorrow's wearables. We have recently introduced a novel sensing architecture that significantly reduces the cabling complexity by eliminating shielded or multiwire cables and by allowing simple connectors, thanks to a bus arrangement. In this architecture, electrodes are replaced by so-called cooperative sensors that require synchronous operation for systems larger than two sensors. This paper presents a novel full duplex body-sensor network based on a simple two-wire bus that combines biopotential measurements, synchronization, and gathering of data in a single cooperative sensor with a throughput up to 2 Mb/s. When compared to others, the suggested approach is advantageous as it keeps the cabling complexity at its minimum and does not require every sensor to be equipped with wireless communication capabilities. First experimental measurements demonstrated the reliability of the approach for a wearable 12-lead electrocardiogram monitoring system tested in real-life scenario.


title: The State-of-the-Art in Predictive Visual Analytics

abstract: Predictive analytics embraces an extensive range of techniques including statistical modeling, machine learning, and data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline. Primary uses have been in data cleaning, exploratory analysis, and diagnostics. For example, scatterplots and bar charts are used to illustrate class distributions and responses. More recently, extensive visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. In this state-of-the-art report, we catalogue recent advances in the visualization community for supporting predictive analytics. First, we define the scope of predictive analytics discussed in this article and describe how visual analytics can support predictive analytics tasks in a predictive visual analytics PVA pipeline. We then survey the literature and categorize the research with respect to the proposed PVA pipeline. Systems and techniques are evaluated in terms of their supported interactions, and interactions specific to predictive analytics are discussed. We end this report with a discussion of challenges and opportunities for future research in predictive visual analytics.


title: Semantic annotation of BPMN: current approaches and new methodologies

abstract: Business Process management has attracted the attention of companies and organizations, which have heavily invested in the research and development of tools and standards for process representation and business requirement formalization. Several tools have been produced for the representation and management of Business processes, and many companies have designed their enterprise models using business process standards. In this paper we analyse different approaches to enrich the existing standards, in particular the Business Process Model Notation (BPMN), with the semantic information provided by OWL ontologies. We also discuss the motivations behind the need of a semantic annotation for Business Processes and provide an overview of existing research efforts and results.


title: Lyapunov-function-based control method for three-phase grid-tied quasi-Z-source inverter with LCL filter

abstract: In this work, a Lyapunov-function based control method for three-phase grid-tied LCL-filtered quasi-z-source source inverters (qZSIs) is proposed. The mathematical model of grid-tied qZSI is modeled in the synchronously rotating dq-frame. Motivated from the energy distribution in qZSI, an energylike function (referred to as Lyapunov-function) is formed. Then, using Lyapunov's direct method a Lyapunov-function based control is described to guarantee the global stability of the closed-loop system. However, since the Lyapunov-function based control method involves a single-loop, its damping performance is not satisfactory. Therefore, the Lyapunov-function based control approach is modified by adding a second loop involving the capacitor voltage feedback into the control variable. The proposed control method requires the computation of ac-side reference inverter current and capacitor voltages which can be obtained by using the ac-side reference grid currents. The control of dc-side variables is achieved by using the traditional simple boost control technique. The generated shoot through state is combined with the pulse width modulation (PWM) signals generated from the proposed control method. Computer simulation results are presented to show the performance of the proposed control method in the steady-state and transients.


title: The township plays

abstract: PREFACE INTRODUCTION NO-GOOD FRIDAY NONGOGO THE COAT SIZWE BANSI IS DEAD THE ISLAND NOTES GLOSSARY.


title: A biologically inspired robot for lunar In-Situ Resource Utilization

abstract: Successful long-term settlements on the Moon will need a supply of resources such as oxygen and water, yet the process of regularly transporting these resources from Earth would be prohibitively costly and dangerous. One alternative would be an approach using heterogeneous, autonomous robotic teams, which could collect and extract these resources from the surrounding environment (In-Situ Resource Utilization). The Whegs™ robotic platform, with its demonstrated capability to negotiate obstacles and traverse irregular terrain, is a good candidate for a lunar rover concept. In this research, Lunar Whegs™ is constructed as a proof-of-concept rover that would be able to navigate the surface of the moon, collect a quantity of regolith, and transport it back to a central processing station. The robot incorporates an actuated scoop, specialized feet for locomotion on loose substrates, Light Detection and Ranging (LIDAR) obstacle sensing and avoidance, and sealing and durability features for operation in an abrasive environment.


title: Anisotropic diffusion-based detail-preserving smoothing for image restoration

abstract: It is important in image restoration to remove noise while preserving meaningful details such as edges and fine features. The existing edge-preserving smoothing methods may inevitably take fine detail as noise or vice versa. In this paper, we propose a new edge-preserving smoothing technique based on a modified anisotropic diffusion. The proposed method can simultaneously preserve edges and fine details while filtering out noise in the diffusion process. Since the fine detail in the neighborhood of a small image window generally have a gray-level variance larger than that of the noisy background, the proposed diffusion model incorporates both local gradient and gray-level variance to preserve edges and fine details while effectively removing noise. Experimental results have shown that the proposed anisotropic diffusion scheme can effectively smooth noisy background, yet well preserve edge and fine details in the restored image. The proposed method has the best restoration result compared with other edge-preserving methods.


title: Abuse Reporting and the Fight Against Cybercrime

abstract: Cybercriminal activity has exploded in the past decade, with diverse threats ranging from phishing attacks to botnets and drive-by-downloads afflicting millions of computers worldwide. In response, a volunteer defense has emerged, led by security companies, infrastructure operators, and vigilantes. This reactionary force does not concern itself with making proactive upgrades to the cyber infrastructure. Instead, it operates on the front lines by remediating infections as they appear. We construct a model of the abuse reporting infrastructure in order to explain how voluntary action against cybercrime functions today, in hopes of improving our understanding of what works and how to make remediation more effective in the future. We examine the incentives to participate among data contributors, affected resource owners, and intermediaries. Finally, we present a series of key attributes that differ among voluntary actions to investigate further through experimentation, pointing toward a research agenda that could establish causality between interventions and outcomes.


title: Opinion Fraud Detection via Neural Autoencoder Decision Forest

abstract: Online reviews play an important role in influencing buyers' daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users' genuine purchase experience and opinions, widely exist on the Web and pose great challenges for users to make right choices. Therefore,it is desirable to build a fair model that evaluates the quality of products by distinguishing spamming reviews. We present an end-to-end trainable unified model to leverage the appealing properties from Autoencoder and random forest. A stochastic decision tree model is implemented to guide the global parameter learning process. Extensive experiments were conducted on a large Amazon review dataset. The proposed model consistently outperforms a series of compared methods.


title: Dense 3D face alignment from 2D video for real-time use

abstract: To enable real-time, person-independent 3D registration from 2D video, we developed a 3D cascade regression approach in which facial landmarks remain invariant across pose over a range of approximately 60. From a single 2D image of a person's face, a dense 3D shape is registered in real time for each frame. The algorithm utilizes a fast cascade regression framework trained on high-resolution 3D face-scans of posed and spontaneous emotion expression. The algorithm first estimates the location of a dense set of landmarks and their visibility, then reconstructs face shapes by fitting a part-based 3D model. Because no assumptions are required about illumination or surface properties, the method can be applied to a wide range of imaging conditions that include 2D video and uncalibrated multi-view video. The method has been validated in a battery of experiments that evaluate its precision of 3D reconstruction, extension to multi-view reconstruction, temporal integration for videos and 3D head-pose estimation. Experimental findings strongly support the validity of real-time, 3D registration and reconstruction from 2D video. The software is available online at http://zface.org. Display Omitted 3D cascade regression approach is proposed in which facial landmarks remain invariant.From a single 2D image of a person's face, a dense 3D shape is registered in real time for each frame.Multi-view reconstruction and temporal integration for videos are presented.Method is robust for 3D head-pose estimation under various conditions.


title: Farther on down the road: transport costs, trade and urban growth in sub-Saharan Africa

abstract: This paper investigates the role of inter-city transport costs in determining the income of sub-Saharan African cities. In particular, focusing on fifteen countries whose largest city is a port, I find that an oil price increase of the magnitude experienced between 2002 and 2008 induces the income of cities near that port to increase by 7 percent relative to otherwise identical cities 500 kilometers farther away. Combined with external estimates, this implies an elasticity of city economic activity with respect to transport costs of -0.28 at 500 kilometers from the port. Moreover, the effect differs by the surface of roads between cities. Cities connected to the port by paved roads are chiefly affected by transport costs to the port, while cities connected to the port by unpaved roads are more affected by connections to secondary centers.


title: A scalable process for manufacturing integrated, washable smart garments applied to heart rate monitoring

abstract: We present a new method for building wearable electronic and sensor systems in which all components are permanently integrated directly into garments in high volume except for the battery. We discuss the design and construction of the first such fully-integrated sensor system, wearable heart rate monitoring garments (a sports bra, a compression short, and a compression shirt) that are machine washable. We demonstrate that heart rate measurements can be detected by our system's fabric-based electrodes. We also show experimental results from wash testing of the garment. The process described herein can be applied to the construction of computational and sensor systems for healthcare, sports, virtual reality, and first responder and military personnel monitoring, among others.


title: Intelligent Notification Systems: A Survey of the State of the Art and Research Challenges

abstract: Notifications provide a unique mechanism for increasing the effectiveness of real-time information delivery systems. However, notifications that demand users' attention at inopportune moments are more likely to have adverse effects and might become a cause of potential disruption rather than proving beneficial to users. In order to address these challenges a variety of intelligent notification mechanisms based on monitoring and learning users' behavior have been proposed. The goal of such mechanisms is maximizing users' receptivity to the delivered information by automatically inferring the right time and the right context for sending a certain type of information. ::: This article provides an overview of the current state of the art in the area of intelligent notification mechanisms that relies on the awareness of users' context and preferences. More specifically, we first present a survey of studies focusing on understanding and modeling users' interruptibility and receptivity to notifications from desktops and mobile devices. Then, we discuss the existing challenges and opportunities in developing mechanisms for intelligent notification systems in a variety of application scenarios.


title: A fast algorithm for edgepreserving variational multichannel image restoration

abstract: Variational models with $\ell_1$-norm based regularization, in particular total variation (TV) and its variants, have long been known to offer superior image restoration quality, but processing speed remained a bottleneck, preventing their widespread use in the practice of color image processing. In this paper, by extending the grayscale image deblurring algorithm proposed in [Y. Wang, J. Yang, W. Yin, and Y. Zhang, SIAM J. Imaging Sci., 1 (2008), pp. 248-272], we construct a simple and efficient algorithm for multichannel image deblurring and denoising, applicable to both within-channel and cross-channel blurs in the presence of additive Gaussian noise. The algorithm restores an image by minimizing an energy function consisting of an $\ell_2$-norm fidelity term and a regularization term that can be either TV, weighted TV, or regularization functions based on higher-order derivatives. Specifically, we use a multichannel extension of the classic TV regularizer (MTV) and derive our algorithm from an extended half-quadratic transform of Geman and Yang [IEEE Trans. Image Process., 4 (1995), pp. 932-946]. For three-channel color images, the per-iteration computation of this algorithm is dominated by six fast Fourier transforms. The convergence results in [Y. Wang, J. Yang, W. Yin, and Y. Zhang, SIAM J. Imaging Sci., 1 (2008), pp. 248-272] for single-channel images, including global convergence with a strong $q$-linear rate and finite convergence for some quantities, are extended to this algorithm. We present numerical results including images recovered from various types of blurs, comparisons between our results and those obtained from the deblurring functions in MATLAB's Image Processing Toolbox, as well as images recovered by our algorithm using weighted MTV and higher-order regularization. Our numerical results indicate that the processing speed, as attained by the proposed algorithm, of variational models with TV-like regularization can be made comparable to that of less sophisticated but widely used methods for color image restoration.


title: Arc-fault unwanted tripping survey with UL 1699B-listed products

abstract: Since adoption of the 2011 National Electrical Code®, many photovoltaic (PV) direct current (DC) arc-fault circuit interrupters (AFCIs) and arc-fault detectors (AFDs) have been introduced into the PV market. To meet the Code requirements, these products must be listed to Underwriters Laboratories (UL) 1699B Outline of Investigation. The UL 1699B test sequence was designed to ensure basic arc-fault detection capabilities with resistance to unwanted tripping; however, field experiences with AFCI/AFD devices have shown mixed results. In this investigation, independent laboratory tests were performed with UL-listed, UL-recognized, and prototype AFCI/AFDs to reveal any limitations with state-of-the-art arc-fault detection products. By running AFCIs and stand-alone AFDs through realistic tests beyond the UL 1699B requirements, many products were found to be sensitive to unwanted tripping or were ineffective at detecting harmful arc-fault events. Based on these findings, additional experiments are encouraged for inclusion in the AFCI/AFD design process and the certification standard to improve products entering the market.


title: Computer literacy: essential in today's computer-centric world

abstract: Computer literacy is a fundamental part of undergraduate curriculum today. Computer literacy is as basic to undergraduate students as the course work in core curriculum in today's computer-centric information age [6]. The number of computers-in-use worldwide is growing, especially in the developing countries [3]. Computers affect every facet of our lives and every sector of the global society. Employers prefer workers who are computer literate because they are more productive and efficient at work than those who are not computer literate. This paper focuses on computer literacy and its benefits to people of all ages -- children, teenagers, adults and seniors. I also describe the implementation of computer competency requirement at Belmont Abbey College and the process students follow to satisfy this requirement in order to graduate.


title: Deep Domain Generalization With Structured Low-Rank Constraint

abstract: Domain adaptation nowadays attracts increasing interests in pattern recognition and computer vision field, since it is an appealing technique in fighting off weakly labeled or even totally unlabeled target data by leveraging knowledge from external well-learned sources. Conventional domain adaptation assumes that target data are still accessible in the training stage. However, we would always confront such cases in reality that the target data are totally blind in the training stage. This is extremely challenging since we have no prior knowledge of the target. In this paper, we develop a deep domain generalization framework with structured low-rank constraint to facilitate the unseen target domain evaluation by capturing consistent knowledge across multiple related source domains. Specifically, multiple domain-specific deep neural networks are built to capture the rich information within multiple sources. Meanwhile, a domain-invariant deep neural network is jointly designed to uncover most consistent and common knowledge across multiple sources so that we can generalize it to unseen target domains in the test stage. Moreover, structured low-rank constraint is exploited to align multiple domain-specific networks and the domain-invariant one in order to better transfer knowledge from multiple sources to boost the learning problem in unseen target domains. Extensive experiments are conducted on several cross-domain benchmarks and the experimental results show the superiority of our algorithm by comparing it with state-of-the-art domain generalization approaches.


title: Zero ripple single stage AC-DC LED driver with unity power factor

abstract: The single stage LED Driver can achieve relatively high efficiency, however the low frequency ripple current is too significant if high power factor has been achieved. With two stages AC-DC LED Driver structure, we can achieve high power factor and tight current regulation at the same time. However, the drawback of the two stage structure is relatively low efficiency and high component cost. In this paper, an innovative single stage LED Driver with ripple cancellation technology has been proposed. We can achieve almost as high efficiency and low component cost as single stage LED Driver while maintaining comparable performance to the two stages LED Driver. Our experimental prototype can achieve 1.5mA Pk-Pk 120Hz ripple current, 0.99 power factor and 85.5% efficiency for a universal AC input, 35W (50V-0.7A) output application.


An Assessment of Data Mining Based CRM Techniques for Enhancing Profitability


title: Large-Scale 6-DOF SLAM With Stereo-in-Hand

abstract: In this paper, we describe a system that can carry out simultaneous localization and mapping (SLAM) in large indoor and outdoor environments using a stereo pair moving with 6 DOF as the only sensor. Unlike current visual SLAM systems that use either bearing-only monocular information or 3-D stereo information, our system accommodates both monocular and stereo. Textured point features are extracted from the images and stored as 3-D points if seen in both images with sufficient disparity, or stored as inverse depth points otherwise. This allows the system to map both near and far features: the first provide distance and orientation, and the second provide orientation information. Unlike other vision-only SLAM systems, stereo does not suffer from ldquoscale driftrdquo because of unobservability problems, and thus, no other information such as gyroscopes or accelerometers is required in our system. Our SLAM algorithm generates sequences of conditionally independent local maps that can share information related to the camera motion and common features being tracked. The system computes the full map using the novel conditionally independent divide and conquer algorithm, which allows constant time operation most of the time, with linear time updates to compute the full map. To demonstrate the robustness and scalability of our system, we show experimental results in indoor and outdoor urban environments of 210 m and 140 m loop trajectories, with the stereo camera being carried in hand by a person walking at normal walking speeds of 4--5 km/h.


title: Do elderly people prefer a conversational humanoid as a shopping assistant partner in supermarkets?

abstract: Assistive robots can be perceived in two main ways: tools or partners. In past research, assistive robots that offer physical assistance for the elderly are often designed in the context of a tool metaphor. This paper investigates the effect of two design considerations for assistive robots in a partner metaphor: conversation and robot-type. The former factor is concerned with whether robots should converse with people even if the conversation is not germane for completing the task. The latter factor is concerned with whether people prefer a communication/function oriented design for assistive robots. To test these design considerations, we selected a shopping assistance situation where a robot carries a shopping basket for elderly people, which is one typical scenario used for assistive robots. A field experiment was conducted in a real supermarket in Japan where 24 elderly participants shopped with robots. The experimental results revealed that they prefer a conversational humanoid as a shopping assistant partner.


title: Likelihood ratio gradient estimation for stochastic systems

abstract: Consider a computer system having a CPU that feeds jobs to two input/output (I/O) devices having different speeds. Let &thgr; be the fraction of jobs routed to the first I/O device, so that 1 - &thgr; is the fraction routed to the second. Suppose that α = α(&thgr;) is the steady-sate amount of time that a job spends in the system. Given that &thgr; is a decision variable, a designer might wish to minimize α(&thgr;) over &thgr;. Since α(·) is typically difficult to evaluate analytically, Monte Carlo optimization is an attractive methodology. By analogy with deterministic mathematical programming, efficient Monte Carlo gradient estimation is an important ingredient of simulation-based optimization algorithms. As a consequence, gradient estimation has recently attracted considerable attention in the simulation community. It is our goal, in this article, to describe one efficient method for estimating gradients in the Monte Carlo setting, namely the likelihood ratio method (also known as the efficient score method). This technique has been previously described (in less general settings than those developed in this article) in [6, 16, 18, 21]. An alternative gradient estimation procedure is infinitesimal perturbation analysis; see [11, 12] for an introduction. While it is typically more difficult to apply to a given application than the likelihood ratio technique of interest here, it often turns out to be statistically more accurate. ::: In this article, we first describe two important problems which motivate our study of efficient gradient estimation algorithms. Next, we will present the likelihood ratio gradient estimator in a general setting in which the essential idea is most transparent. The section that follows then specializes the estimator to discrete-time stochastic processes. We derive likelihood-ratio-gradient estimators for both time-homogeneous and non-time homogeneous discrete-time Markov chains. Later, we discuss likelihood ratio gradient estimation in continuous time. As examples of our analysis, we present the gradient estimators for time-homogeneous continuous-time Markov chains; non-time homogeneous continuous-time Markov chains; semi-Markov processes; and generalized semi-Markov processes. (The analysis throughout these sections assumes the performance measure that defines α(&thgr;) corresponds to a terminating simulation.) Finally, we conclude the article with a brief discussion of the basic issues that arise in extending the likelihood ratio gradient estimator to steady-state performance measures.


title: Multi-objectives, multi-period optimization of district energy systems: I. Selection of typical operating periods

abstract: Abstract The long term optimization of a district energy system is a computationally demanding task due to the large number of data points representing the energy demand profiles. In order to reduce the number of data points and therefore the computational load of the optimization model, this paper presents a systematic procedure to reduce a complete data set of the energy demand profiles into a limited number of typical periods, which adequately preserve significant characteristics of the yearly profiles. The proposed method is based on the use of a k-means clustering algorithm assisted by an ϵ-constraints optimization technique. The proposed typical periods allow us to achieve the accurate representation of the yearly consumption profiles, while significantly reducing the number of data points. The work goes one step further by breaking up each representative period into a smaller number of segments. This has the advantage of further reducing the complexity of the problem while respecting peak demands in order to properly size the system. Two case studies are discussed to demonstrate the proposed method. The results illustrate that a limited number of typical periods is sufficient to accurately represent an entire equipments’ lifetime.


title: A GPU-accelerated software eye tracking system

abstract: Current microcomputers are powerful enough to implement a realtime eye tracking system, but the computational throughput still limits the types of algorithms that can be implemented in real time. Many of the image processing algorithms that are typically used in eye tracking applications can be significantly accelerated when the processing is delegated to a graphics processing unit (GPU). This paper describes a real-time gaze tracking system developed using the CUDA programming environment distributed by nVidia. The current implementation of the system is capable of processing a 640 by 480 image in less than 4 milliseconds, and achieves an average accuracy close to 0.5 degrees of visual angle.


title: Non-Local Recurrent Network for Image Restoration

abstract: Many classic methods have shown non-local self-similarity in natural images to be an effective prior for image restoration. However, it remains unclear and challenging to make use of this intrinsic property via deep networks. In this paper, we propose a non-local recurrent network (NLRN) as the first attempt to incorporate non-local operations into a recurrent neural network (RNN) for image restoration. The main contributions of this work are: (1) Unlike existing methods that measure self-similarity in an isolated manner, the proposed non-local module can be flexibly integrated into existing deep networks for end-to-end training to capture deep feature correlation between each location and its neighborhood. (2) We fully employ the RNN structure for its parameter efficiency and allow deep feature correlation to be propagated along adjacent recurrent states. This new design boosts robustness against inaccurate correlation estimation due to severely degraded images. (3) We show that it is essential to maintain a confined neighborhood for computing deep feature correlation given degraded images. This is in contrast to existing practice that deploys the whole image. Extensive experiments on both image denoising and super-resolution tasks are conducted. Thanks to the recurrent non-local operations and correlation propagation, the proposed NLRN achieves superior results to state-of-the-art methods with much fewer parameters.


title: EXTRACTING FACES AND FACIAL FEATURES FROM COLOR IMAGES

abstract: In this paper, we present image processing and pattern recognition techniques to extract human faces and facial features from color images. First, we segment a color image into skin and non-skin regions by a Gaussian skin-color model. Then, we apply mathematical morphology and region filling techniques for noise removal and hole filling. We determine whether a skin region is a face candidate by its size and shape. Principle component analysis (PCA) is used to verify face candidates. We create an ellipse model to locate eyes and mouths areas roughly, and apply the support vector machine (SVM) to classify them. Finally, we develop knowledge rules to verify eyes. Experimental results show that our algorithm achieves the accuracy rate of 96.7% in face detection and 90.0% in facial feature extraction.


title: ClothCap: seamless 4D clothing capture and retargeting

abstract: Designing and simulating realistic clothing is challenging. Previous methods addressing the capture of clothing from 3D scans have been limited to single garments and simple motions, lack detail, or require specialized texture patterns. Here we address the problem of capturing regular clothing on fully dressed people in motion. People typically wear multiple pieces of clothing at a time. To estimate the shape of such clothing, track it over time, and render it believably, each garment must be segmented from the others and the body. Our ClothCap approach uses a new multi-part 3D model of clothed bodies, automatically segments each piece of clothing, estimates the minimally clothed body shape and pose under the clothing, and tracks the 3D deformations of the clothing over time. We estimate the garments and their motion from 4D scans; that is, high-resolution 3D scans of the subject in motion at 60 fps. ClothCap is able to capture a clothed person in motion, extract their clothing, and retarget the clothing to new body shapes; this provides a step towards virtual try-on.


title: Real-Time Detection of Colour and Surface Defects of Maize Kernels Using Machine Vision

abstract: Abstract Real-time feature extraction algorithms were developed using a Matrox IM-1280 real-time image processing board and an Image-RTP real-time processor. On-board hardware operations and parallel statistical look-up table (LUT) mapping were used for high-speed feature extraction. Algorithms were developed to acquire and process geometric features for an artificial intelligence-based automated on-line maize quality inspection system. Maize quality-related features included average colour in red, green, and blue (RGB) and hue, saturation, and intensity (HSI) colour discriminations for specific regions of kernels. Maize quality-related features also included a Fourier profile shape descriptor, which distinguished between shapes of whole and broken kernels. Primitive features can be extracted in a processing time of less than 1 s for one object. With increasing numbers of objects, the processing time needed decreased to 2 s for 12 objects. With proper calibration, primitive feature measurements are faster and can be more accurate than measurements determined by a micrometer. For the RGB colour discrimination, the blue component provides the most separation between the white and yellow maize kernels. The green component provides some separation and the red component provides the least separation between white and yellow maize kernels. For the HSI colour discrimination, the best separation between the white and the yellow maize varieties is provided by the hue component. The processing time was about 1·3 s. Based on the Fourier profile descriptor, the breakage classifier had an accuracy of 95% in classifying whole kernels as whole and 96% in classifying broken kernels as broken. The processing time of the breakage inspection programme was about 1·5 s.


title: A variable neighborhood search for optimal scheduling and routing of take-off and landing aircraft

abstract: This paper deals with the development of meta-heuristic algorithms for the real-time traffic management problem of scheduling and routing landing and take-off operations at busy terminal control areas. The objective is to reduce the traffic controller workload and to limit airport congestion via intelligent traffic control decisions. We formulate this problem as a mixed integer linear program and solve it via a tabu search algorithm and variable neighborhood search algorithms. The development of metaheuristics is motivated by the fact that the problem is strongly NP-hard and heuristic algorithms are typically adopted in practice to compute good quality solutions in a short computation time. The algorithmic framework starts from a good initial solution for the aircraft scheduling problem with pre-defined routes, obtained via a truncated branch-and-bound algorithm. A metaheuristic is then applied to improve the solution by changing the routing of some aircraft. The neighbourhood of a solution is characterized by the number of aircraft to be re-routed. Computational experiments are performed on two Italian airports and various sources of disturbance. The variable neighborhood search algorithms outperform the tabu search algorithm within a small computation time.


title: Design of artificial skin with integrated tactile sensors for anthropomorphic robotic hands

abstract: In order to provide anthropomorphic robotic hands with adequate frictional contact and proper tactile sensing during dexterous manipulation it is necessary to develop artificial skin that is easy-to-implement and compatible with existing designs of robotic hands. In this paper, we detail the design of a set of artificial skin pads that can accommodate a complex surface topology for a variety of robotic hands. The multi-layered artificial skin is composed of deformable thermoplastic, 3D printed frame, force sensing tactile element (sensel), and a silicon rubber epidermis. The prototyping process makes the most of 3D printing technology, and takes important factors such as maintainability and modification into consideration. A unique design for the thumb web is proposed, a crucial component for stabilizing tools and trigger operated tasks. We also address the importance of biomimetically replicating the tapered fingertip of the human hand based on an analysis of stiffness at distal joints of a robotic hand while performing a precision grip. Overall, we show our skin design to be adaptable to different design constraints.


title: Delay-Tolerant Stochastic Algorithms for Parking Space Assignment

abstract: This paper introduces and illustrates some novel stochastic policies that assign parking spaces to cars looking for an available parking space. We analyze in detail both the main features of a single park, i.e., how a car could conveniently decide whether to try its luck at that parking lot or try elsewhere, and the case when more parking lots are available, and how to choose the best one. We discuss the practical requirements of the proposed strategies in terms of infrastructure technology and vehicles' equipment and the mathematical properties of the proposed algorithms in terms of robustness against delays, stability, and reliability. Preliminary results obtained from simulations are also provided to illustrate the feasibility and the potential of our stochastic assignment policies.


title: Analysis of Hashrate-Based Double Spending

abstract: Bitcoin is the world's first decentralized digital currency. Its main technical innovation is the use of a blockchain and hash-based proof of work to synchronize transactions and prevent double-spending the currency. While the qualitative nature of this system is well understood, there is widespread confusion about its quantitative aspects and how they relate to attack vectors and their countermeasures. In this paper we take a look at the stochastic processes underlying typical attacks and their resulting probabilities of success.


title: Spoofing 2D face recognition systems with 3D masks

abstract: Vulnerability to spoofing attacks is a serious drawback for many biometric systems. Among all biometric traits, face is the one that is exposed to the most serious threat, since it is exceptionally easy to access. The limited work on fraud detection capabilities for face mainly shapes around 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices. A significant portion of this work is based on the flatness of the facial surface in front of the sensor. In this study, we complicate the spoofing problem further by introducing the 3rd dimension and examine possible 3D attack instruments. A small database is constructed with six different types of 3D facial masks and experimented on to determine the right direction to study 3D attacks. Spoofing performance for each type of mask is assessed and analysed thoroughly using two Gabor-wavelet-based algorithms.


title: On the Expression of Agent Emotions in Customer Support Dialogs in Social Media

abstract: Providing customer support through social media channels is gaining popularity. In this context, it is important for the agent, either human or automated, to express emotions when needed. In this paper we study the patterns of human agents emotional expression and question the need of Affective NLG. By analyzing real dialogs that were tagged by the crowd, we show that emotional responses contribute to the quality of the dialog and relate to customer satisfaction, and that human agents express emotions by simply adding emotional acknowledgment at the beginning of the response.


title: Reciprocal peer recommendation for learning purposes

abstract: Larger student intakes by universities and the rise of education through Massive Open Online Courses has led to less direct contact time with teaching staff for each student. One potential way of addressing this contact deficit is to invite learners to engage in peer learning and peer support; however, without technological support they may be unable to discover suitable peer connections that can enhance their learning experience. Two different research subfields with ties to recommender systems provide partial solutions to this problem. Reciprocal recommender systems provide sophisticated filtering techniques that enable users to connect with one another. To date, however, the main focus of reciprocal recommender systems has been on providing recommendation in online dating sites. Recommender systems for technology enhanced learning have employed and tailored exemplary recommenders towards use in education, with a focus on recommending learning content rather than other users. In this paper, we first discuss the importance of supporting peer learning and the role recommending reciprocal peers can play in educational settings. We then introduce our open-source course-level recommendation platform called RiPPLE that has the capacity to provide reciprocal peer recommendation. The proposed reciprocal peer recommender algorithm is evaluated against key criteria such as scalability, reciprocality, coverage, and quality and shows improvement over a baseline recommender. Primary results indicate that the system can help learners connect with peers based on their knowledge gaps and reciprocal preferences, with designed flexibility to address key limitations of existing algorithms identified in the literature.


title: Modelling and prediction of rainfall using artificial neural network and ARIMA techniques

abstract: Climate and rainfall are highly non-linear and complicated phenomena, which require sophisticated computer modelling and simulation for accurate prediction. An artificial intelligence technology allows knowledge processing and can be used .as forecasting tool. For example, the application of Artificial Neural Networks (ANN), to predict the behaviors of nonlinear systems has become an attractive alternative to traditional statistical methods. In this paper, we present tools for modeling and predicting the behavioral pattern in rainfall phenomena based on past observations. The paper introduces two fundamentally different approaches for designing a model, the statistical method based on autoregressive integrated moving average (ARIMA) and the emerging computationally powerful techniques based on ANN. In order to evaluate the prediction efficiency, we made use of 104 years of mean annual rainfall data from year 1901 to 2003 of Hyderabad region (India). The models were trained with 93 years of mean annual rainfall data. The ANN and the ARIMA approaches are applied to the data to derive the weights and the regression coefficients respectively. The performance of the model was evaluated by using remaining 10 years of data. The study reveals that ANN model can be used as an appropriate forecasting tool to predict the rainfall, which out performs the ARIMA model.


title: Success of government e-service delivery: does satisfaction matter?

abstract: For measuring e-government success a well-founded theory is important which can help governments to improve their services and identify how effectively public money is spent. We propose using citizen satisfaction as a measure of e-government success, as well as explore its relationships with e-government service quality. Three hypotheses have been formulated to test the model. For empirical estimation, the data used in this study was collected form Sweden. An online survey was conducted using systematic sampling among the municipalities in Sweden, 425 valid responses were received. The measures of each variables selected in this article were mainly adapted from related previous studies. Efficiency, privacy, responsiveness and web assistance were selected as e-service quality dimensions. Actual usages were measured by three items-Frequency of usage, Diversity of usages and Dependency. Confirmatory factor analyses were conducted to confirm the factor structures. The analysis shows that 43% of the variance among the factors of e-service quality, and usage is explained by citizen satisfaction. We found e-service quality has a relation with citizen satisfaction considering four dimensions of service quality. Efficiency, responsiveness and web assistance were found to be of more importance compared to privacy in determining e-service quality. Use was found to be positively and significantly related to citizen satisfaction. The results should contribute towards understanding of the key issues that influence citizens' needs and level of satisfaction with the tax services and help improve the service delivery process. Further research is suggested to explore other quality dimensions such as system and information quality.


abstract: It turns out riding across America is more than a handy metaphor for building system software.


title: MADS: Malicious Android Applications Detection through String Analysis

abstract: The use of mobile phones has increased in our lives because they offer nearly the same functionality as a personal computer. Besides, the number of applications available for Android-based mobile devices has also experienced a importat grow. Google offers to programmers the opportunity to upload and sell applications in the Android Market, but malware writers upload their malicious code there. In light of this background, we present here Malicious Android applications Detection through String analysis (MADS), a new method that extracts the contained strings from the Android applications to build machine-learning classifiers and detect malware.


title: Examining the role of information technology in cultivating firms' dynamic marketing capabilities

abstract: Most prior research has investigated an organization's dynamic capabilities in general and overlooked their effect on critical business functions. Our study considered the role of IT in improving firm's dynamic marketing capabilities. We developed a model consisting of market orientation, IT infrastructure capabilities, and the use of IT in customer relationship management (CRM). With data collected from 135 manufacturing and service firms in Taiwan, our results supported most of our hypotheses. Our results showed important direct effects of a firm's market orientation, use of IT to support CRM, and the functionality of IT infrastructure capabilities on its dynamic marketing capabilities.


title: Building a Dynamical Network Model from Neural Spiking Data: Application of Poisson Likelihood

abstract: Research showed that, the information transmitted in biological neurons is encoded in the instants of successive action potentials or their firing rate. In addition to that, in-vivo operation of the neuron makes measurement difficult and thus continuous data collection is restricted. Due to those reasons, classical mean square estimation techniques that are frequently used in neural network training is very difficult to apply. In such situations, point processes and related likelihood methods may be beneficial. In this study, we will present how one can apply certain methods to use the stimulus-response data obtained from a neural process in the mathematical modeling of a neuron. The study is theoretical in nature and it will be supported by simulations. In addition it will be compared to a similar study performed on the same network model.


title: Self-sustainable Chlorella pyrenoidosa strain NCIM 2738 based photobioreactor for removal of Direct Red-31 dye along with other industrial pollutants to improve the water-quality.

abstract: Abstract The genotoxic and carcinogenic effects of diazo dyes from industrial effluents pose a serious environmental threat by contaminating aquatic ecosystem and consequently impact human health. The potential of a diazo dye resistant, self-sustainable photosynthetic green alga Chlorella pyrenoidosa NCIM 2738 provides a viable green technology for an efficient biodegradation of diazo dye Direct Red-31 (DR-31) and overall improvement of water quality. Herein, we for the first time report the degradation of DR-31 using C. pyrenoidosa . Batch experiments were performed to optimize the effect of initial pH, contact time and toxicity-range of DR-31 in order to achieve the optimal conditions for maximum decolourization in continuous cyclic photobioreactor. In batch culture, C. pyrenoidosa exhibited 96% decolourization with 40 mg L −1 dye at pH3. The equilibrium was attained within 30 min and the maximum uptake of 30.53 mg g −1 algal biomass was observed during this period. This was found to be fitted well with Langmuir and Freundlich adsorption isotherm. The FT-IR spectra showed a change from N N to N H suggesting the possible involvement of the azoreductase enzyme. The application of C. pyrenoidosa not only degraded the DR-31 but also improved the quality of water by reducing COD (82.73%), BOD (56.44%), sulphate (54.54%), phosphate (19.88%), and TDS (84.18%) which was further enhanced in continuous cyclic bioreactor treatment. The results clearly showed that C. pyrenoidosa provides an efficient, self-sustainable green technology for decolourization of DR-31 and improved the water quality.


title: Three-phase power flow controller based on hybrid transformer with matrix converter

abstract: This paper deals with a three-phase hybrid transformer combined with matrix converter, which is used as a power flow controller in the power system. The main difference in the proposed solution in comparison to a conventional UPFC is the lack of DC energy storage in the power electronic converter. The main aim of this paper is to analyze the basic properties of the presented solution in a two-source system and to consider the possibility of bidirectional power flow control. The paper presents an operational description, theoretical simulation and experimental analysis of the proposed solution with the Space Vector Modulation of the matrix converter in an open feedback loop.


title: Local Correlation-based Fingerprint Matching

abstract: Most fingerprint matching systems are based on matching minutia points between two fingerprint images. Each minutia is represented by a fixed number of attributes such as the location, orientation, type and other local information. A hard decision is made on the match between a pair of minutiae based on the similarity of these attributes. In this paper, we present a minutiae matching algorithm that uses spatial correlation of regions around the minutiae to ascertain the quality of each minutia match. The proposed algorithm has two main advantages. Since the gray level values of the pixels around a minutia point retain most of the local information, spatial correlation provides an accurate measure of the similarity between minutia regions. Secondly, no hard decision is made on the correspondence between a minutia pair. Instead the quality of all the minutiae matches are accumulated to arrive at the final matching score between the template and query fingerprint impressions. Experiments on a database of 160 users (4 impressions per finger) indicate that the proposed algorithm serves well to complement the 2D dynamic programming based minutiae matching technique; a combination of these two methods can reduce the false non-match rate by approximately 3.5% at a false match rate of 0.1%.


title: Inverse Reinforcement Learning from Failure

abstract: Inverse reinforcement learning (IRL) allows autonomous agents to learn to solve complex tasks from successful demonstrations. However, in many settings, e.g., when a human learns the task by trial and error, failed demonstrations are also readily available. In addition, in some tasks, purposely generating failed demonstrations may be easier than generating successful ones. Since existing IRL methods cannot make use of failed demonstrations, in this paper we propose inverse reinforcement learning from failure (IRLF) which exploits both successful and failed demonstrations. Starting from the state-of-the-art maximum causal entropy IRL method, we propose a new constrained optimisation formulation that accommodates both types of demonstrations while remaining convex. We then derive update rules for learning reward functions and policies. Experiments on both simulated and real-robot data demonstrate that IRLF converges faster and generalises better than maximum causal entropy IRL, especially when few successful demonstrations are available.


title: Live Data Analytics With Collaborative Edge and Cloud Processing in Wireless IoT Networks

abstract: Recently, big data analytics has received important attention in a variety of application domains including business, finance, space science, healthcare, telecommunication and Internet of Things (IoT). Among these areas, IoT is considered as an important platform in bringing people, processes, data and things/objects together in order to enhance the quality of our everyday lives. However, the key challenges are how to effectively extract useful features from the massive amount of heterogeneous data generated by resource-constrained IoT devices in order to provide real-time information and feedback to the end-users, and how to utilize this data-aware intelligence in enhancing the performance of wireless IoT networks. Although there are parallel advances in cloud computing and edge computing for addressing some issues in data analytics, they have their own benefits and limitations. The convergence of these two computing paradigms, i.e., massive virtually shared pool of computing and storage resources from the cloud and real-time data processing by edge computing, could effectively enable live data analytics in wireless IoT networks. In this regard, we propose a novel framework for coordinated processing between edge and cloud computing/processing by integrating advantages from both the platforms. The proposed framework can exploit the network-wide knowledge and historical information available at the cloud center to guide edge computing units towards satisfying various performance requirements of heterogeneous wireless IoT networks. Starting with the main features, key enablers and the challenges of big data analytics, we provide various synergies and distinctions between cloud and edge processing. More importantly, we identify and describe the potential key enablers for the proposed edge-cloud collaborative framework, the associated key challenges and some interesting future research directions.


title: Measuring e-Commerce Success: Applying the DeLone & McLean Information Systems Success Model

abstract: Information technology and the Internet have had a dramatic effect on business operations. Companies are making large investments in e-commerce applications but are hard pressed to evaluate the success of their e-commerce systems. The DeLone & McLean Information Systems Success Model can be adapted to the measurement challenges of the new e-commerce world. The six dimensions of the updated model are a parsimonious framework for organizing the e-commerce success metrics identified in the literature. Two case examples demonstrate how the model can be used to guide the identification and specification of e-commerce success metrics.


title: Control and dynamic analysis of a parallel-connected single active bridge DC–DC converter for DC-grid wind farm application

abstract: This study presents a control strategy and its dynamic analysis of a high-power dc-dc converter, which is constructed with the parallel-connected single active bridge (SAB) dc-dc converters for dc-grid wind farm applications. The structural and operational characteristics of the SAB dc-dc converter have several advantages for high-power applications, and the modular concept of the parallel-connected converter is highly beneficial especially for offshore wind farm applications in terms of maintenance cost and fault tolerance. To justify the feasibility of the parallel-connected SAB dc-dc converter for dc-grid wind farm applications, an input voltage control method based on the PI control will be introduced and the dynamics of the overall system will be analysed. The analysis results are to be verified by means of simulations and experiments.


title: Circular Polarization Wide-Angle Beam Steering at Ka-Band by In-Plane Translation of a Plate Lens Antenna

abstract: A simple mechanical beam steering antenna concept is proposed for ground mobile terminals of Ka-band satellite and high altitude platform (HAP) providing broadband access services. The wide-angle elevation beam steering is achieved by in-plane translation of a thin offset flat lens in front of a stationary primary feed while full azimuth coverage is obtained by simple 360° rotation of the lens. A new strategy is also proposed to reduce the effective F/D of the focusing system and consequently the total antenna height without increasing beam distortion: a second small flat lens is added on top of the primary feed to create a virtual focus located well below the feed phase center. The challenge is to conciliate high gain both with wide beam scanning and reduced antenna height. Design rules are presented for this antenna concept along with a 27.3-dBi gain fabricated example for the up-link Ka-band (29.5–30 GHz), with circular polarization, 0° to 50° elevation scan, better than 2.8-dB scan loss and an effective F/D of only 0.55. Both lenses are 3.35-mm thick, formed by a suitable assembly of phase shifting unit cells with less than 0.4 dB of transmission loss in simulation. The main lens dimensions are $195\;\text{mm} \times 145\;\text{mm}$ and its weight is 215 g. Total antenna height, including the feed is 84 mm.


title: Moving Object Detection in Complex Scene Using Spatiotemporal Structured-Sparse RPCA

abstract: Moving object detection is a fundamental step in various computer vision applications. Robust principal component analysis (RPCA)-based methods have often been employed for this task. However, the performance of these methods deteriorates in the presence of dynamic background scenes, camera jitter, camouflaged moving objects, and/or variations in illumination. It is because of an underlying assumption that the elements in the sparse component are mutually independent, and thus the spatiotemporal structure of the moving objects is lost. To address this issue, we propose a spatiotemporal structured sparse RPCA algorithm for moving objects detection, where we impose spatial and temporal regularization on the sparse component in the form of graph Laplacians. Each Laplacian corresponds to a multi-feature graph constructed over superpixels in the input matrix. We enforce the sparse component to act as eigenvectors of the spatial and temporal graph Laplacians while minimizing the RPCA objective function. These constraints incorporate a spatiotemporal subspace structure within the sparse component. Thus, we obtain a novel objective function for separating moving objects in the presence of complex backgrounds. The proposed objective function is solved using a linearized alternating direction method of multipliers based batch optimization. Moreover, we also propose an online optimization algorithm for real-time applications. We evaluated both the batch and online solutions using six publicly available data sets that included most of the aforementioned challenges. Our experiments demonstrated the superior performance of the proposed algorithms compared with the current state-of-the-art methods.


title: Towards a Taxonomy of Problem Solving Types

abstract: Our group's work in medical decision making has led us to formulate a framework for expert system design, in particular about how the domain knowledge may be decomposed into substructures. We propose that there exist different problem-solving types, i.e., uses of knowledge, and corresponding to each is a separate substructure specializing in that type of problem-solving. Each substructure is in turn further decomposed into a hierarchy of specialist which differ from each other not in the type of problem-solving, but in the conceptual content of their knowledge; e.g.; one of them may specialize in "heart disease," while another may do so in "liver," though both of them are doing the same type of problem solving. Thus ultimately all the knowledge in the system is distributed among problem-solvers which know how to use that knowledge. This is in contrast to the currently dominant expert system paradigm which proposes a common knowledge base accessed by knowledge-free problem-solvers of various kinds. In our framework there is no distinction between knowledge bases and problem-solvers: each knowledge source is a problem-solver. We have so far had occasion to deal with three generic problem-solving types in expert clinical reasoning: diagnosis (classification), data retrieval and organization, and reasoning about consequences of actions. In novice, these expert structures are often incomplete, and other knowledge structures and learning processes are needed to construct and complete them.


title: An overview of object detection and tracking

abstract: Over the last couple of years, object detection and tracking reserachers have been developing many new techniques, which has been used widely by others. In this article, we present an extensive overview of object detection and tracking methods. At the same time, we also introduces some related theoretical knowledge (e.g., feature and classification). The reason why the object detection and tracking in summarized together, is because the object detection can be said to be the foundation of the object tracking, and they all need to choose the right features and training effective classification. Due to the application fields and emphasis may be different, the number of features which we can select is large. This paper mainly introduces some common features, such as color, histogram of gradients edges and optical flow. Then classifications are introduced, which are all classical classifications. There are many methods of detection and tracking, but now researchers will mainly consider some of the key factors, which include context, silhouette and background. Finally, we respectively introduced some common methods for object detection and object tracking. And discuss the advantages and disadvantages of principles.


title: Using Analogies across Narratives to Drive Dialogue.

abstract: This paper describes a program called Hob that uses analogical mappings across narratives to drive an experimental conversational system. Analogical mappings are used to drive internal reasoning processes and supply response templates. The knowledge base is written in simple English, and consists of three parts: a dictionary, a collection of facts and heuristics, and a collection of stories that notionally correspond to an experience base. Internally, knowledge is stored and used in the form of near-canonical English parse trees. Thus the schema is not hardwired, but is derived from the knowledge base as interpreted under the rules and implications of English grammar. An experimental “sellbot” application is described, and example runs are presented.


title: Specialised Tools for Automating Data Mining for Hospital Management

abstract: This paper presents a research project which is directed to the partial automation of Data Mining (DM) in hospital information systems (HIS). We concentrate on hospital management applications and information systems such as emergencies and ward management, human resources (services, night duties, etc.), physical resources (beds, intervention theatres, etc.), etc. We have realised how the business objectives are usually the same across several hospitals and so is the information which is gathered in several HIS (even using different DBMS). This means that although the models extracted highly differ between hospitals, data mining processes are highly similar across different hospitals. We argue how a tool can be constructed in such a way that it automates many DM processes and that can be ported to other hospitals which could benefit more quickly of a first DM experience. Our work plan covers all the stages in the process of Knowledge Discovery from Databases (KDD): data cleansing, extraction and integration from the HIS and external data, construction of tasks and minable views, model generation, and finally a module to carry out and interpret their predictions. We also consider a module to perform simulations and to integrate the models extracted by the previous modules with other decision support systems as well as model monitoring.


title: Financial early warning system model and data mining application for risk detection

abstract: One of the biggest problems of SMEs is their tendencies to financial distress because of insufficient finance background. In this study, an early warning system (EWS) model based on data mining for financial risk detection is presented. CHAID algorithm has been used for development of the EWS. Developed EWS can be served like a tailor made financial advisor in decision making process of the firms with its automated nature to the ones who have inadequate financial background. Besides, an application of the model implemented which covered 7853 SMEs based on Turkish Central Bank (TCB) 2007 data. By using EWS model, 31 risk profiles, 15 risk indicators, 2 early warning signals, and 4 financial road maps has been determined for financial risk mitigation.


title: Event-Driven Business Process Management and its Practical Application Taking the Example of DHL

abstract: The recently coined term «Event-Driven Business Process Management» (EDBPM) is a combination of actually two different disciplines: Business Process Management (BPM) and Complex Event Processing (CEP). The common understanding behind BPM is that each company’s unique way of doing business is captured in its business processes. For this reason, business processes are today seen as the most valuable corporate asset. In the context of this article, BPM means a software platform which provides companies the ability to model, manage, and optimize these processes for significant gain. As an independent system, Complex Event Processing (CEP) is a parallel running platform that analyses and processes events. The BPM- and the CEP-platform correspond via events which are produced by the BPM-workflow engine and by the IT services which are associated with the business process steps. In this paper we present a general framework for EDBPM as well as first use cases in the context of logistics and financial services.


title: Augmented Reality in Astrophysics

abstract: Augmented Reality consists of merging live images with virtual layers of information. The rapid growth in the popularity of smartphones and tablets over recent years has provided a large base of potential users of Augmented Reality technology, and virtual layers of information can now be attached to a wide variety of physical objects. In this article, we explore the potential of Augmented Reality for astrophysical research with two distinct experiments: (1) Augmented Posters and (2) Augmented Articles. We demonstrate that the emerging technology of Augmented Reality can already be used and implemented without expert knowledge using currently available apps. Our experiments highlight the potential of Augmented Reality to improve the communication of scientific results in the field of astrophysics. We also present feedback gathered from the Australian astrophysics community that reveals evidence of some interest in this technology by astronomers who experimented with Augmented Posters. In addition, we discuss possible future trends for Augmented Reality applications in astrophysics, and explore the current limitations associated with the technology. This Augmented Article, the first of its kind, is designed to allow the reader to directly experiment with this technology.


title: Multi-purpose mobile monitoring system based on automatic extraction of rule-sets

abstract: Real-time tele-monitoring technology is very useful for remotely monitoring patients' vital parameters and for ensuring mobility of both patient and doctor. In this paper a multi-purpose and multi-parametric tele-monitoring system is described. This system is based on the offline automatic extraction of knowledge from databases expressed as a set of IF…THEN rules that are used by a rule-based mobile Decision Support System (DSS) embedded in the presented system. Vital parameters are collected by wearable sensors, sent to a mobile device, and processed in real-time. If a rule is activated, the system produces an alarm for a well-timed medical intervention. Finally all monitored parameters are saved in European Data Format (EDF) files that could be used for further analysis. Two practical applications of the system are presented with reference to two important healthcare issues, namely apnea monitoring and fall detection. I. INTRODUCTION E-Health applications aiming at caring for people are very


title: CYC: Using common sense knowledge to overcome brittleness and knowledge acquistion bottlenecks

abstract: The major limitations in building large software have always been (a) its brittleness when confronted by problems that were not foreseen by its builders, and (by the amount of manpower required. The recent history of expert systems, for example highlights how constricting the brittleness and knowledge acquisition bottlenecks are. Moreover, standard software methodology (e.g., working from a detailed "spec") has proven of little use in AI, a field which by definition tackles ill- structured problems. How can these bottlenecks be widened? Attractive, elegant answers have included machine learning, automatic programming, and natural language understanding. But decades of work on such systems have convinced us that each of these approaches has difficulty "scaling up" for want a substantial base of real world knowledge.


title: Denoising without access to clean data using a partitioned autoencoder

abstract: Training a denoising autoencoder neural network requires access to truly clean data, a requirement which is often impractical. To remedy this, we introduce a method to train an autoencoder using only noisy data, having examples with and without the signal class of interest. The autoencoder learns a partitioned representation of signal and noise, learning to reconstruct each separately. We illustrate the method by denoising birdsong audio (available abundantly in uncontrolled noisy datasets) using a convolutional autoencoder.


title: Work in progress: Using one-way communication delay for in-order arrival MPTCP scheduling

abstract: We use one-way communication delay of a TCP connection to design an MPTCP scheduler that transmits data out-of-order over multiple paths such that their arrival is in-order. Our Linux implementation shows our proposed scheduler can reduce receive buffer utilization, and increase overall throughput when a small receive buffer size results in receive buffer blocking.


title: Building Change Detection Based on Satellite Stereo Imagery and Digital Surface Models

abstract: Building change detection is a major issue for urban area monitoring. Due to different imaging conditions and sensor parameters, 2-D information delivered by satellite images from different dates is often not sufficient when dealing with building changes. Moreover, due to the similar spectral characteristics, it is often difficult to distinguish buildings from other man-made constructions, like roads and bridges, during the change detection procedure. Therefore, stereo imagery is of importance to provide the height component which is very helpful in analyzing 3-D building changes. In this paper, we propose a change detection method based on stereo imagery and digital surface models (DSMs) generated with stereo matching methodology and provide a solution by the joint use of height changes and Kullback-Leibler divergence similarity measure between the original images. The Dempster-Shafer fusion theory is adopted to combine these two change indicators to improve the accuracy. In addition, vegetation and shadow classifications are used as no-building change indicators for refining the change detection results. In the end, an object-based building extraction method based on shape features is performed. For evaluation purpose, the proposed method is applied in two test areas, one is in an industrial area in Korea with stereo imagery from the same sensor and the other represents a dense urban area in Germany using stereo imagery from different sensors with different resolutions. Our experimental results confirm the efficiency and high accuracy of the proposed methodology even for different kinds and combinations of stereo images and consequently different DSM qualities.


title: An Algorithm to Determine Peer-Reviewers

abstract: The peer-review process is the most widely accepted certification mechanism for officially accepting the written results of researchers within the scientific community. An essential component of peer-review is the identification of competent referees to review a submitted manuscript. This article presents an algorithm to automatically determine the most appropriate reviewers for a manuscript by way of a co-authorship network data structure and a relative-rank particle-swarm algorithm. This approach is novel in that it is not limited to a pre-selected set of referees, is computationally efficient, requires no human-intervention, and, in some instances, can automatically identify conflict of interest situations. A useful application of this algorithm would be to open commentary peer-review systems because it provides a weighting for each referee with respects to their expertise in the domain of a manuscript. The algorithm is validated using referee bid data from the 2005 Joint Conference on Digital Libraries.


title: The Business Model: Recent Developments and Future Research

abstract: This article provides a broad and multifaceted review of the received literature on business models in which the authors examine the business model concept through multiple subject-matter lenses. The review reveals that scholars do not agree on what a business model is and that the literature is developing largely in silos, according to the phenomena of interest of the respective researchers. However, the authors also found emerging common themes among scholars of business models. Specifically, (1) the business model is emerging as a new unit of analysis; (2) business models emphasize a system-level, holistic approach to explaining how firms “do business”; (3) firm activities play an important role in the various conceptualizations of business models that have been proposed; and (4) business models seek to explain how value is created, not just how it is captured. These emerging themes could serve as catalysts for a more unified study of business models.


title: Influence of heat stress on antioxidant status in Beetal goats.

abstract: Oxidative stress may be a crucial factor affecting heat tolerance, thus affecting productive / reproductive performance in farm animals. The effect of summer heat stress was evaluated on the status of various parameters related to oxidative stress. Haematological parameters (packed cell volume and haemoglobin), antioxidant vitamins (vitamins C and E) and enzymes (glutathione peroxidase and superoxide dismutase) were assessed in Beetal goats during pre-summer and summer months. Stressed goats exhibited lower levels of haemoglobin, packed cell volume, vitamin C and E. The activity of glutathione peroxidase was lowered and superoxide dismutase activity increased. It was concluded that oxidative stress was increased in goats during summer.


title: Beyond Bitcoin: A Critical Look at Blockchain-Based Systems

abstract: After more than eight years since the launch of Bitcoin, the decentralized transaction ledger functionality implemented through the blockchain technology is being used not only for cryptocurrencies, but to register, confirm and transfer any kind of contract and property. In this work, we analyze the most relevant functionalities and known issues of this technology, with the intent of pointing out the possible behaviours that are not as efficient and reliable as they should be when thinking with a broader outlook.


title: Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images

abstract: Vision impairment due to pathological damage of the retina can largely be prevented through periodic screening using fundus color imaging. However the challenge with large scale screening is the inability to exhaustively detect fine blood vessels crucial to disease diagnosis. In this work we present a computational imaging framework using deep and ensemble learning for reliable detection of blood vessels in fundus color images. An ensemble of deep convolutional neural networks is trained to segment vessel and non-vessel areas of a color fundus image. During inference, the responses of the individual ConvNets of the ensemble are averaged to form the final segmentation. In experimental evaluation with the DRIVE database, we achieve the objective of vessel detection with maximum average accuracy of 94.7\% and area under ROC curve of 0.9283.


title: Semi-supervised learning of compact document representations with deep networks

abstract: Finding good representations of text documents is crucial in information retrieval and classification systems. Today the most popular document representation is based on a vector of word counts in the document. This representation neither captures dependencies between related words, nor handles synonyms or polysemous words. In this paper, we propose an algorithm to learn text document representations based on semi-supervised autoencoders that are stacked to form a deep network. The model can be trained efficiently on partially labeled corpora, producing very compact representations of documents, while retaining as much class information and joint word statistics as possible. We show that it is advantageous to exploit even a few labeled samples during training.


title: Narrow-Band Microwave Filter Using High-Q Groove Gap Waveguide Resonators With Manufacturing Flexibility and No Sidewalls

abstract: This paper presents a new type of narrow band filter with good electrical performance and manufacturing flexibility, based on the newly introduced groove gap waveguide technology. The designed third and fifth-order filters work at Ku band with 1% fractional bandwidth. These filter structures are manufactured with an allowable gap between two metal blocks, in such a way that there is no requirement for electrical contact and alignment between the blocks. This is a major manufacturing advantage compared to normal rectangular waveguide filters. The measured results of the manufactured filters show reasonably good agreement with the full-wave simulated results, without any tuning or adjustments.


title: LTE-Fractional Frequency Reuse (FFR) optimization with femtocell network

abstract: The project to define Long Term Evolution (LTE) of UMTS cellular technology started in year 2004 is well established to the world. The subsequent evolve of LTE-Advanced however targets for enhancement in average spectrum efficiency and cell-edge user throughput. The limiting factor of the system performance at cell-edge is inter-cell interference (ICI). The challenge that is discussed in this paper focuses on ensuring the overall satisfaction mainly cell-edge system performance through the analysis on Fractional Frequency Reuse (FFR) and hybrid Macro system which involves small cell technology — Femtocell technology. This paper discussed the three frequency allocation schemes. The schemes include without the application of Femtocell, with randomly application of Femtocell and with preplanned Femtocell scheme. The FFR mechanism divides the cell into two regions (inner and outer). The inner radius and inner region resource is determined base on overall average throughput and overall user satisfaction.


title: Multi-cue pedestrian classification with partial occlusion handling

abstract: This paper presents a novel mixture-of-experts framework for pedestrian classification with partial occlusion handling. The framework involves a set of component-based expert classifiers trained on features derived from intensity, depth and motion. To handle partial occlusion, we compute expert weights that are related to the degree of visibility of the associated component. This degree of visibility is determined by examining occlusion boundaries, i.e. discontinuities in depth and motion. Occlusion-dependent component weights allow to focus the combined decision of the mixture-of-experts classifier on the unoccluded body parts. In experiments on extensive real-world data sets, with both partially occluded and non-occluded pedestrians, we obtain significant performance boosts over state-of-the-art approaches by up to a factor of four in reduction of false positives at constant detection rates. The dataset is made public for benchmarking purposes.


Morfessor 2.0: Python Implementation and Extensions for Morfessor Baseline


The economic approach to artificial intelligence


title: Cross-layer attack on anonymizing networks

abstract: Network layer anonymization protects only some of the userpsilas personal identification information, namely network addresses of the communicating parties. However, even if the lower layers of communication provide perfect protection for the userpsilas profile, information leakage on the application layer destroys the whole effort. Currently, all widespread implementations of anonymizing networks do not use a holistic approach and therefore, neither filter nor actively warn users about information leakage from the upper layers, which may look innocent to the end user. We extend existing work on security of anonymizing networks to take into account additional information leakage from the application layer. Further we show, under which conditions and how this kind of information can be used not only to build an extensive user profile at ldquolow costsrdquo, but also to speed up traditional attacks that are targeted at the network layer identification of userspsila peer partners.


title: A Planarity Test via Construction Sequences

abstract: Linear-time algorithms for testing the planarity of a graph are well known for over 35 years. However, these algorithms are quite involved and recent publications still try to give simpler linear-time tests. We give a conceptually simple reduction from planarity testing to the problem of computing a certain construction of a 3-connected graph. This implies a linear-time planarity test. Our approach is radically different from all previous linear-time planarity tests; as key concept, we maintain a planar embedding that is 3-connected at each point in time. The algorithm computes a planar embedding if the input graph is planar and a Kuratowski-subdivision otherwise.


title: Scale Invariant Feature Transform on the Sphere: Theory and Applications

abstract: A SIFT algorithm in spherical coordinates for omnidirectional images is proposed. This algorithm can generate two types of local descriptors, Local Spherical Descriptors and Local Planar Descriptors. With the first ones, point matching between two omnidirectional images can be performed, and with the second ones, the same matching process can be done but between omnidirectional and planar images. Furthermore, a planar to spherical mapping is introduced and an algorithm for its estimation is given. This mapping allows to extract objects from an omnidirectional image given their SIFT descriptors in a planar image. Several experiments, confirming the promising and accurate performance of the system, are conducted.


title: A New Low Power Dynamic Full Adder Cell Based on Majority Function 1

abstract: A new low power dynamic CMOS one bit full adder cell is presented. In this design the time consuming XOR gates are eliminated. It is based on Majority Function. This new cell is compared with two widely used dynamic adders as well as other conventional and recently proposed architectures. It is implemented in two level dynamic CMOS with zipper technique and the number of transistors, chip area and switching activity is significantly reduced.


SIMULATION STUDY OF ROUTING PROTOCOLS IN WIRELESS SENSOR NETWORKS


title: A Comprehensive Survey of Multiagent Reinforcement Learning

abstract: Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must, instead, discover a solution on their own, using learning. A significant part of the research on multiagent learning concerns reinforcement learning techniques. This paper provides a comprehensive survey of multiagent reinforcement learning (MARL). A central issue in the field is the formal statement of the multiagent learning goal. Different viewpoints on this issue have led to the proposal of many different goals, among which two focal points can be distinguished: stability of the agents' learning dynamics, and adaptation to the changing behavior of the other agents. The MARL algorithms described in the literature aim---either explicitly or implicitly---at one of these two goals or at a combination of both, in a fully cooperative, fully competitive, or more general setting. A representative selection of these algorithms is discussed in detail in this paper, together with the specific issues that arise in each category. Additionally, the benefits and challenges of MARL are described along with some of the problem domains where the MARL techniques have been applied. Finally, an outlook for the field is provided.


title: A New Chaotic Jerk Circuit

abstract: Much recent interest has been given to simple chaotic oscillators based on jerk equations that involve a third-time derivative of a single scalar variable. The simplest such equation has yet to be electronically implemented. This paper describes a particularly elegant circuit whose operation is accurately described by a simple variant of that equation in which the requisite nonlinearity is provided by a single diode and for which the analysis is particularly straightforward.