title: The experience of living with chronic heart failure: a narrative review of qualitative studies

abstract: BackgroundChronic heart failure (CHF) is the leading cause of all hospitalisations and readmissions in older people, accounting for a large proportion of developed countries' national health care expenditure. CHF can severely affect people's quality of life by reducing their independence and ability to undertake certain activities of daily living, as well as affecting their psychosocial and economic capacity. This paper reports the findings of a systematic narrative review of qualitative studies concerning people's experience of living with CHF, aiming to develop a wide-ranging understanding of what is known about the patient experience.MethodsWe searched eight relevant electronic databases using the terms based on the diagnosis of 'chronic heart failure', 'heart failure' and 'congestive heart failure' and qualitative methods, with restrictions to the years 1990-May 2008. We also used snowballing, hand searching and the expert knowledge of the research team to ensure all relevant papers were included in the review. Of 65 papers collected less than half (n = 30) were found relevant for this review. These papers were subsequently summarised and entered into QSR NVivo7 for data management and analysis.ResultsThe review has identified the most prominent impacts of CHF on a person's everyday life including social isolation, living in fear and losing a sense of control. It has also identified common strategies through which patients with CHF manage their illness such as sharing experiences and burdens with others and being flexible to changing circumstances.Finally, there are multiple factors that commonly impact on patients' self care and self-management in the disease trajectory including knowledge, understanding and health service encounters. These health service encounters encompass access, continuity and quality of care, co-morbid conditions, and personal relationships.ConclusionsThe core and sub-concepts identified within this study provide health professionals, service providers, policy makers and educators with broad insights into common elements of people's experiences of CHF and potential options for improving their health and wellbeing. Future studies should focus on building a comprehensive picture of CHF through examination of differences between genders, and differences within age groups, socioeconomic groups and cultural groups.


title: Using Syntactic Features for Phishing Detection

abstract: This paper reports on the comparison of the subject and object of verbs in their usage between phishing emails and legitimate emails. The purpose of this research is to explore whether the syntactic structures and subjects and objects of verbs can be distinguishable features for phishing detection. To achieve the objective, we have conducted two series of experiments: the syntactic similarity for sentences, and the subject and object of verb comparison. The results of the experiments indicated that both features can be used for some verbs, but more work has to be done for others.


title: From computational thinking to computational making

abstract: Computational thinking is considered best practice for teaching computing and more broadly to solve problems and design systems, however as computing extends beyond the desktop (for instance increased integration of ubicomp technologies) so too must our educational methods. Exposure to ubicomp technologies is most accessible through the maker movement. With this in mind we argue we must move from computational thinking to computational making as an educational framework. Here we present a case study of children's making to support our vision for a broader conception of computational making.


title: Image processing based forest fire detection using YCbCr colour model

abstract: In this paper image processing based forest fire detection using YCbCr colour model is proposed. The proposed method adopts rule based colour model due to its less complexity and effectiveness. YCbCr colour space effectively separates luminance from chrominance compared to other colour spaces like RGB and rgb(normalized RGB). The proposed method not only separates fire flame pixels but also separates high temperature fire centre pixels by taking in to account of statistical parameters of fire image in YCbCr colour space like mean and standard deviation. In this method four rules are formed to separate the true fire region. Two rules are used for segmenting the fire region and other two rules are used for segmenting the high temperature fire centre region. The results obtained are compared with the other methods in the literature and shows higher true fire detection rate and less false detection rate. The proposed method can be used for real time forest fire detection with moving camera.


title: Hydraulic spool valve modeling for system level analysis

abstract: This paper presents a parameterized, dynamic model of Moog D671 hydraulic servovalve. The valve is a 4/3, center closed configuration. The model is developed based on first principles and is compared to a dynamic model in the form of a Simulink S-function made available by Moog Inc. The model is developed as part of a system level modeling, simulation, and analysis activity at Clemson University's Wind Turbine Drivetrain Testing Facility. The valve model is valuable because the valve is used to control a primary piece of test equipment at the facility and the model will be used for subsequent control system development activities. Results show that the derived model adequately reproduces the behavior of the supplied S-function while offering greatly improved functionality. Additionally, a discussion section address the observed differences between the derived model and the supplied S-function and offers a critique of the S-function's performance.


title: Non-functional requirements in software engineering

abstract: Essentially a software system's utility is determined by both its functionality and its non-functional characteristics, such as usability, flexibility, performance, interoperability and security. Nonetheless, there has been a lop-sided emphasis in the functionality of the software, even though the functionality is not useful or usable without the necessary non-functional characteristics. In this chapter, we review the state of the art on the treatment of non-functional requirements (hereafter, NFRs), while providing some prospects for future directions.


title: Fiber Bragg Grating Sensors toward Structural Health Monitoring in Composite Materials: Challenges and Solutions

abstract: Nowadays, smart composite materials embed miniaturized sensors for structural health monitoring (SHM) in order to mitigate the risk of failure due to an overload or to unwanted inhomogeneity resulting from the fabrication process. Optical fiber sensors, and more particularly fiber Bragg grating (FBG) sensors, outperform traditional sensor technologies, as they are lightweight, small in size and offer convenient multiplexing capabilities with remote operation. They have thus been extensively associated to composite materials to study their behavior for further SHM purposes. This paper reviews the main challenges arising from the use of FBGs in composite materials. The focus will be made on issues related to temperature-strain discrimination, demodulation of the amplitude spectrum during and after the curing process as well as connection between the embedded optical fibers and the surroundings. The main strategies developed in each of these three topics will be summarized and compared, demonstrating the large progress that has been made in this field in the past few years.


title: A Multi-Mode Multi-Band Reconfigurable Power Amplifier for 2G/3G/4G Handset Applications

abstract: Fully-integrated multi-mode multi-band (MMMB) reconfigurable power amplifier (PA) is implemented using single PA-cores for high- and low-frequency bands, respectively. This PA has five output paths and covers quad-band Gaussian Minimum Shift Keying/Enhanced Data Rates for Global Evolution and penta-band Universal Mobile Telecommunications System/Long Term Evolution mode operation with band combination in a small form-factor. To optimize the PA, the proposed structure reconfigures the PA-core cells as well as the interstage/output matching network. When compared with the single-mode single-band dedicated PAs, the fabricated PA showed comparable RF performance for all modes and bands, except for PAE degradations of 3.1% and 1.9% for high- and low-band, respectively, thus validating the usefulness of the proposed structure for MMMB PA applications.


title: Memory Engram Cells Have Come of Age

abstract: The idea that memory is stored in the brain as physical alterations goes back at least as far as Plato, but further conceptualization of this idea had to wait until the 20 th century when two guiding theories were presented: the "engram theory" of Richard Semon and Donald Hebb's "synaptic plasticity theory." While a large number of studies have been conducted since, each supporting some aspect of each of these theories, until recently integrative evidence for the existence of engram cells and circuits as defined by the theories was lacking. In the past few years, the combination of transgenics, optogenetics, and other technologies has allowed neuroscientists to begin identifying memory engram cells by detecting specific populations of cells activated during specific learning epochs and by engineering them not only to evoke recall of the original memory, but also to alter the content of the memory.


title: Cowden Syndrome – Diagnostic Skin Signs

abstract: Cowden syndrome is a rare autosomal dominant familial cancer syndrome with a high risk of breast cancer. The most important clinical features include carcinomas of the breast and thyroid, and hamartomatous polyps of the gastrointestinal tract. There are characteristic mucocutaneous features which allow early recognition of the disease and are generally present before internal malignancies develop. We report on a woman in whom the diagnosis of Cowden syndrome was first made after she had been treated for both breast cancer and melanoma.


title: Wideband Triple- and Quad-Resonance Substrate Integrated Waveguide Cavity-Backed Slot Antennas With Shorting Vias

abstract: A triple-resonance and a quad-resonance substrate integrated waveguide (SIW) cavity-backed slot antennas using shorting vias are presented in this paper. The antennas have wide bandwidths and low profiles. The working mechanism of the triple-resonance antenna is explained with its electric field distributions and equivalent circuit models. By loading the SIW cavity with shorting vias, the lowest mode (half-TE110 mode) is shifted upward and coupled with two higher modes (even and odd TE210 modes). As a result, a wide bandwidth with triple resonances is achieved for the antenna. Based on the similar principle, a quad-resonance antenna having an even wider bandwidth is also developed. Prototypes of the two antennas are fabricated and measured. With a low profile of $0.03\lambda _{0}$ (wavelength in free space), the triple-resonance design has a bandwidth of 15.2% and a peak gain of 4.80 dBi, and the quad-resonance design has a bandwidth of 17.5% and a peak gain of 7.27 dBi.


title: Design of 60GHz planar array antennas using PCB-based microstrip-ridge gap waveguide and SIW

abstract: A wideband 2×2-slot element for a 60 GHz antenna array is designed by making use of two double-sided printed circuit boards (PCBs). The upper PCB contains the four radiating cavity-backed slots backed by a substrate-integrated waveguide (SIW) cavity. The SIW cavity is excited by a coupling slot fed by a microstrip-ridge gap waveguide formed in the air gap between the upper and lower PCBs. The designed 2×2-slot element is considered in an infinite array environment. A sample 32×32 slot array aperture is simulated using infinite array approach and the relative bandwidth of 16% over 57-66GHz frequency range with the directivity of more than 38 dBi over the operating bandwidth is obtained. The simulations are verified by measurements on a smaller 4×4 slot array surrounded by dummy elements.


title: Survey of Nearest Neighbor Techniques

abstract: The nearest neighbor (NN) technique is very simple, highly efficient and effective in the field of pattern recognition, text categorization, object recognition etc. Its simplicity is its main advantage, but the disadvantages can't be ignored even. The memory requirement and computation complexity also matter. Many techniques are developed to overcome these limitations. NN techniques are broadly classified into structure less and structure based techniques. In this paper, we present the survey of such techniques. Weighted kNN, Model based kNN, Condensed NN, Reduced NN, Generalized NN are structure less techniques whereas k-d tree, ball tree, Principal Axis Tree, Nearest Feature Line, Tunable NN, Orthogonal Search Tree are structure based algorithms developed on the basis of kNN. The structure less method overcome memory limitation and structure based techniques reduce the computational complexity.


title: Perceptual dimensions of vibrotactile actuators

abstract: The objective of the present research was to determine how variations in the signals generated by different vibrotactile actuators are perceived and which features are judged as being distinctive. For this purpose, three different types of actuator were used to generate signals that varied in amplitude, waveform and frequency. Participants were required to judge the degree of similarity-dissimilarity between pairs of stimuli. Multi-dimensional scaling (MDS) techniques were then used as an exploratory data analysis technique to create a spatial map that depicted the relations among the various vibrotactile signals. The first dimension that emerged from the MDS represented a continuum associated with transitions in the amplitudes of the signals, with a smooth sine wave pattern contrasting with the more abrupt transitions in square waves. This may be considered a smooth-rough dimension. The second dimension extracted from the data was more difficult to characterize in that each of the two clusters along this dimension involved signals of varying waveform and frequency. Further work will aim at defining the perceptual qualities of this dimension.


title: The free-energy self: A predictive coding account of self-recognition

abstract: Abstract Recognising and representing one's self as distinct from others is a fundamental component of self-awareness. However, current theories of self-recognition are not embedded within global theories of cortical function and therefore fail to provide a compelling explanation of how the self is processed. We present a theoretical account of the neural and computational basis of self-recognition that is embedded within the free-energy account of cortical function. In this account one's body is processed in a Bayesian manner as the most likely to be “me”. Such probabilistic representation arises through the integration of information from hierarchically organised unimodal systems in higher-level multimodal areas. This information takes the form of bottom-up “surprise” signals from unimodal sensory systems that are explained away by top-down processes that minimise the level of surprise across the brain. We present evidence that this theoretical perspective may account for the findings of psychological and neuroimaging investigations into self-recognition and particularly evidence that representations of the self are malleable, rather than fixed as previous accounts of self-recognition might suggest.


title: Labelling as an unsupervised learning problem

abstract: Unravelling hidden patterns in datasets is a classical problem with many potential applications. In this paper, we present a challenge whose objective is to discover nonlinear relationships in noisy cloud of points. If a set of point satisfies a nonlinear relationship that is unlikely to be due to randomness, we will label the set with this relationship. Since points can satisfy one, many or no such nonlinear relationships, cloud of points will typically have one, multiple or no labels at all. This introduces the labelling problem that will be studied in this paper. The objective of this paper is to develop a framework for the labelling problem. We introduce a precise notion of a label, and we propose an algorithm to discover such labels in a given dataset, which is then tested in synthetic datasets. We also analyse, using tools from random matrix theory, the problem of discovering false labels in the dataset.


title: Scalable Deep $k$-Subspace Clustering

abstract: Subspace clustering algorithms are notorious for their scalability issues because building and processing large affinity matrices are demanding. In this paper, we introduce a method that simultaneously learns an embedding space along subspaces within it to minimize a notion of reconstruction error, thus addressing the problem of subspace clustering in an end-to-end learning paradigm. To achieve our goal, we propose a scheme to update subspaces within a deep neural network. This in turn frees us from the need of having an affinity matrix to perform clustering. Unlike previous attempts, our method can easily scale up to large datasets, making it unique in the context of unsupervised learning with deep architectures. Our experiments show that our method significantly improves the clustering accuracy while enjoying cheaper memory footprints.


title: The NIC is the hypervisor: bare-metal guests in IaaS clouds

abstract: Cloud computing does not inherently require the use of virtual machines, and some cloud customers prefer or even require "bare metal" systems, where no hypervisor separates the guest operating system from the CPU. Even for bare-metal nodes, the cloud provider must find a means to isolate the guest system from other cloud resources, and to manage the instantiation and removal of guests. We argue that an enhanced NIC, together with standard features of modern servers, can provide all of the functions for which a hypervisor would normally be required.


title: Towards IEEE 802.1 Ethernet AVB for Advanced Driver Assistance Systems: A preliminary assessment

abstract: Advanced Driver Assistance Systems involving cameras and the multimedia domain are both quite challenging for automotive network technologies, due to the bandwidth requirements and latency/jitter constraints they impose. Ethernet as in-car network is expected to breakthrough in both domains and the IEEE Audio Video Bridging (AVB) standard is a promising candidate. This paper reports preliminary performance assessments of AVB for ADAS, multimedia and infotainment, obtained through the simulation of realistic traffic patterns.


title: Rest is best: The role of rest and task interruptions on vigilance

abstract: We examined the impact task interruptions have on visuospatial vigilance in two experiments. In the first experiment participants were randomly assigned to one of three interruptions: participants were given a complete rest (rest), participants completed an alphanumeric vigilance task (letter), or participants performed the primary vigilance task (continuous). In the second experiment participants were randomly assigned to one of the conditions from the first experiment or to two further conditions, in which participants (spatial memory) performed a spatial match to sample task, or participants (verbal memory) performed a letter match to sample task. Vigilance performance post-interruption was best for rest, worst for continuous, and varied for the other interruption tasks. Overall, the results suggest the vigilance decrement is due to the repeated use of particular executive resources, but there may, in addition be domain specific interference when the primary task and activities during a break make use of the same resources. Language: en


title: YodaNN: An Architecture for Ultralow Power Binary-Weight CNN Acceleration

abstract: Convolutional neural networks (CNNs) have revolutionized the world of computer vision over the last few years, pushing image classification beyond human accuracy. The computational effort of today’s CNNs requires power-hungry parallel processors or GP-GPUs. Recent developments in CNN accelerators for system-on-chip integration have reduced energy consumption significantly. Unfortunately, even these highly optimized devices are above the power envelope imposed by mobile and deeply embedded applications and face hard limitations caused by CNN weight I/O and storage. This prevents the adoption of CNNs in future ultralow power Internet of Things end-nodes for near-sensor analytics. Recent algorithmic and theoretical advancements enable competitive classification accuracy even when limiting CNNs to binary (+1/−1) weights during training. These new findings bring major optimization opportunities in the arithmetic core by removing the need for expensive multiplications, as well as reducing I/O bandwidth and storage. In this paper, we present an accelerator optimized for binary-weight CNNs that achieves 1.5 TOp/s at 1.2 V on a core area of only 1.33 million gate equivalent (MGE) or 1.9 mm 2 and with a power dissipation of 895 $\mu$ W in UMC 65-nm technology at 0.6 V. Our accelerator significantly outperforms the state-of-the-art in terms of energy and area efficiency achieving 61.2 TOp/s/W@0.6 V and 1.1 TOp/s/MGE@1.2 V, respectively.


title: Establishing a standardized clinical assessment tool of pathologic and prosthetic hand function: normative data, reliability, and validity.

abstract: OBJECTIVE ::: To develop a new assessment procedure, the Southampton Hand Assessment Procedure (SHAP), that allows contextual results of hand function to be obtained readily in a clinical environment. ::: ::: ::: DESIGN ::: Reliability (test-retest, interrater) and validity (criterion, content) of new assessment procedure against standard medical outcome measure techniques. ::: ::: ::: SETTING ::: Normative data collected in a university laboratory. ::: ::: ::: PARTICIPANTS ::: Twenty-four volunteers selected on the basis of optimum hand function using these criteria: age (range, 18-25 y), and no adverse hand trauma, neurologic condition, or disabling effects of the upper limb. ::: ::: ::: INTERVENTIONS ::: Not applicable. ::: ::: ::: MAIN OUTCOME MEASURES ::: The normative control group was assessed for variability, and the procedure measured in terms of interrater and test-retest reliability. The absence of a direct comparison prevents a criterion standard from being established; however, content validity was shown by expert peer review. ::: ::: ::: RESULTS ::: The control group data were shown to be multivariate gaussian; test-retest and interrater reliability were demonstrated at the 95% confidence level. The content validity was demonstrated by peer panel approval. ::: ::: ::: CONCLUSIONS ::: Results of the control group established the statistical integrity of SHAP. Clinical trials are underway, although more extensive use of the procedure is advocated in primary care and rehabilitation centers where physiotherapy and occupational therapy are actively used in hand rehabilitation.


title: A 2-DOF MEMS Ultrasonic Energy Harvester

abstract: This paper reports a novel ultrasonic-based wireless power transmission technique that has the potential to drive implantable biosensors. Compared with commonly used radio-frequency (RF) radiation methods, the ultrasonic power transmission is relatively safe for the human body and does not cause electronic interference with other electronic circuits. To extract ambient kinetic energy with arbitrary in-plane motion directions, a novel 2-D MEMS power harvester has been designed with resonance frequencies of 38520 and 38725 Hz. Frequency-response characterization results verify that the device can extract energy from the directions of X, Y, and diagonals. Working in the diagonal direction, the device has a bandwidth of 302 Hz, which is twice wider than a comparable 1-D resonator device. A 1- storage capacitor is charged up from 0.51 to 0.95 V in 15 s, when the harvester is driven by an ultrasonic transducer at a distance of 0.5 cm in the X-direction, and is biased by 60 Vdc, indicating the energy harvesting capability of 21.4 nW in the X-direction. When excited along the Y-axis, the harvester has an energy-harvesting capacity of 22.7 nW. The harvester was modeled and simulated using an equivalent electrical circuit model in Saber, and the simulation results showed good agreement with the experimental results. The ultrasonic energy harvesting was also investigated using a 1-D piezoelectric micro-cantilever.


title: An 0(n log n) sorting network

abstract: The purpose of this paper is to describe a sorting network of size 0(n log n) and depth 0(log n). A natural way of sorting is through consecutive halvings: determine the upper and lower halves of the set, proceed similarly within the halves, and so on. Unfortunately, while one can halve a set using only 0(n) comparisons, this cannot be done in less than log n (parallel) time, and it is known that a halving network needs (½)n log n comparisons. It is possible, however, to construct a network of 0(n) comparisons which halves in constant time with high accuracy. This procedure (e-halving) and a derived procedure (e-nearsort) are described below, and our sorting network will be centered around these elementary steps.


title: ModEM: A modular system for inversion of electromagnetic geophysical data

abstract: Abstract We describe implementation of a modular system of computer codes for inversion of electromagnetic geophysical data, referred to as ModEM. The system is constructed with a fine level of modular granularity, with basic components of the inversion – forward modeling, sensitivity computations, inversion search algorithms, model parametrization and regularization, data functionals – interchangeable, reusable and readily extensible. Modular sensitivity computations and generic interfaces to parallelized inversion algorithms provide a ready framework for rapid implementation of new applications or inversion algorithms. We illustrate the code׳s versatility and capabilities for code reuse through implementation of 3D magnetotelluric (MT) and controlled-source EM (CSEM) inversions, using essentially the same components.


title: Single event effects test and analysis results from the Boeing Radiation Effects Laboratory (BREL)

abstract: We describe the results of single event effects testing conducted by the Boeing Radiation Effects Laboratory on a variety of devices. The data include SEU, SEL and SEFI cross sections induced by both heavy ions and protons.


title: Gate driver design for 1.7kV SiC MOSFET module with Rogowski current sensor for shortcircuit protection

abstract: This paper shows a gate driver design for 1.7 kV SiC MOSFET module as well a Rogowski-coil based current sensor for effective short circuit protection. The design begins with the power architecture selection for better common-mode noise immunity as the driver is subjected to high dv/dt due to the very high switching speed of the SiC MOSFET modules. The selection of the most appropriate gate driver IC is made to ensure the best performance and full functionalities of the driver, followed by the circuitry designs of paralleled external current booster, Soft Turn-Off, and Miller Clamp. In addition to desaturation, a high bandwidth PCB-based Rogowski current sensor is proposed to serve as a more effective method for the short circuit protection for the high-cost SiC MOSFET modules.


title: Digital Forensic Science: Issues, Methods, and Challenges

abstract: Digital forensic science, or digital forensics, is the application of scientific tools and methods to identify, collect, and analyze digital (data) artifacts in support of legal proceedings. From a more technical perspective, it is the process of reconstructing the relevant sequence of events that have led to the currently observable state of a target IT system or (digital) artifacts. Over the last three decades, the importance of digital evidence has grown in lockstep with the fast societal adoption of information technology, which has resulted in the continuous accumulation of data at an exponential rate. Simultaneously, there has been a rapid growth in network connectivity and the complexity of IT systems, leading to more complex behavior that needs to be investigated. The goal of this book is to provide a systematic technical overview of digital forensic techniques, primarily from the point of view of computer science. This allows us to put the field in the broader perspective of a host of related areas and gain better insight into the computational challenges facing forensics, as well as draw inspiration for addressing them. This is needed as some of the challenges faced by digital forensics, such as cloud computing, require qualitatively different approaches; the sheer volume of data to be examined also requires new means of processing it.


title: Watch out for This Commit! A Study of Influential Software Changes

abstract: One single code change can significantly influence a wide range of software systems and their users. For example, 1) adding a new feature can spread defects in several modules, while 2) changing an API method can improve the performance of all client programs. Developers often may not clearly know whether their or others' changes are influential at commit time. Rather, it turns out to be influential after affecting many aspects of a system later. This paper investigates influential software changes and proposes an approach to identify them early, i.e., immediately when they are applied. We first conduct a post-mortem analysis to discover existing influential changes by using intuitions such as isolated changes and changes referred by other changes in 10 open source projects. Then we re-categorize all identified changes through an open-card sorting process. Subsequently, we conduct a survey with 89 developers to confirm our influential change categories. Finally, from our ground truth we extract features, including metrics such as the complexity of changes, terms in commit logs and file centrality in co-change graphs, to build machine learning classifiers. The experiment results show that our prediction model achieves overall with random samples 86.8% precision, 74% recall and 80.4% F-measure respectively.


title: Eliminating Channel Feedback in Next-Generation Cellular Networks

abstract: This paper focuses on a simple, yet fundamental question: ``Can a node infer the wireless channels on one frequency band by observing the channels on a different frequency band?'' This question arises in cellular networks, where the uplink and the downlink operate on different frequencies. Addressing this question is critical for the deployment of key 5G solutions such as massive MIMO, multi-user MIMO, and distributed MIMO, which require channel state information. We introduce R2-F2, a system that enables LTE base stations to infer the downlink channels to a client by observing the uplink channels from that client. By doing so, R2-F2 extends the concept of reciprocity to LTE cellular networks, where downlink and uplink transmissions occur on different frequency bands. It also removes a major hurdle for the deployment of 5G MIMO solutions. We have implemented R2-F2 in software radios and integrated it within the LTE OFDM physical layer. Our results show that the channels computed by R2-F2 deliver accurate MIMO beamforming (to within 0.7~dB of beamforming gains with ground truth channels) while eliminating channel feedback overhead.


title: Natural-Parameter Networks: A Class of Probabilistic Neural Networks

abstract: Neural networks (NN) have achieved state-of-the-art performance in various applications. Unfortunately in applications where training data is insufficient, they are often prone to overfitting. One effective way to alleviate this problem is to exploit the Bayesian approach by using Bayesian neural networks (BNN). Another shortcoming of NN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models. To address these problems, we propose a class of probabilistic neural networks, dubbed natural-parameter networks (NPN), as a novel and lightweight Bayesian treatment of NN. NPN allows the usage of arbitrary exponential-family distributions to model the weights and neurons. Different from traditional NN and BNN, NPN takes distributions as input and goes through layers of transformation before producing distributions to match the target output distributions. As a Bayesian treatment, efficient backpropagation (BP) is performed to learn the natural parameters for the distributions over both the weights and neurons. The output distributions of each layer, as byproducts, may be used as second-order representations for the associated tasks such as link prediction. Experiments on real-world datasets show that NPN can achieve state-of-the-art performance.


title: Inverse kinematic implementation of four-wheels mecanum drive mobile robot using stepper motors

abstract: An implementation of inverse kinematic model is applied for the mobile robot using four-wheels mecanum drive. The implementation is designed for omni-directional movement without changes the robot position on a facing direction. Four stepper motors are used to drive the mecanum wheels due to these types have a good precision. This speed control feedback is not necessary. The radius of the mobile robot dimension is defined by the same distance of a and b length between wheel axis and body center of 170 mm. The inverse kinematic is conducted to control the mobile robot movement and to convert the robot velocity component of v x , v y , and ω toward angular velocity each wheels of ω 1 , ω 2 , ω 3 , ω 4 and wheel turn direction. Kinematic calculation and control mechanism are proceed by a master microcontroller and multi-slave microcontrollers which connected using SPI communication protocol. The theta angle θ is described by v x and v y vector velocity direction toward center point of the mobile robot. The movement capabilities are performed by linear direction according to the certain angle of the robot movement θ of 0° to each multiples of 45° thus obtained wheel velocity and angle movement average errors.


title: Intelligent Software Engineering: Synergy between AI and Software Engineering

abstract: There has been a long history of applying AI technologies to address software engineering problems especially on tool automation. On the other hand, given the increasing importance and popularity of AI software, recent research efforts have been on exploring software engineering solutions to improve the productivity of developing AI software and the dependability of AI software. The emerging field of intelligent software engineering is to focus on two aspects: (1) instilling intelligence in solutions for software engineering problems; (2) providing software engineering solutions for intelligent software. This extended abstract shares perspectives on these two aspects of intelligent software engineering.


title: A Novel Robust Trilateration Method Applied to Ultra-Wide Bandwidth Location Systems

abstract: Due to the non-line-of-sight (NLOS) and multipath fading channel (MPF) of the wireless networks, the non-existence of the intersection point often occurs in the range-based localization methods, e.g., the centroid-based trilateration method. To alleviate the problem, a confidence-based intersection method which expands the range of the circle within a certain confidence interval is proposed. In the method, the confidence interval is estimated based on the Cramér-Rao lower bound of the time of flight (TOF) measurement. Furthermore, an intersection determination method is proposed to select the intersection point with higher confidence level. The simulation and experimental results show the superiority of the proposed method in localization accuracy and robustness to noise compared to the conventional trilateration method, e.g., the centroid-based and least squares based trilateration methods.


title: TURNOVER CONTAGION: HOW COWORKERS' JOB EMBEDDEDNESS AND JOB SEARCH BEHAVIORS INFLUENCE QUITTING

abstract: This research developed and tested a model of turnover contagion in which the job embeddedness and job search behaviors of coworkers influence employees' decisions to quit. In a sample of 45 branches of a regional bank and 1,038 departments of a national hospitality firm, multilevel analysis revealed that coworkers' job embedded-ness and job search behaviors explain variance in individual “voluntary turnover” over and above that explained by other individual and group-level predictors. Broadly speaking, these results suggest that coworkers' job embeddedness and job search behaviors play critical roles in explaining why people quit their jobs. Implications are discussed.


title: Decomposing Joint Distortion for Adaptive Steganography

abstract: Recent advances on adaptive steganography imply that the security of steganography can be improved by exploiting the mutual impact of modifications between adjacent cover elements, such as pixels of images, which is called a nonadditive distortion model. In this paper, we propose a framework for nonadditive distortion steganography by defining joint distortion on pixel blocks. To reduce the complexity for minimizing joint distortion, we design a coding method to decompose the joint distortion (abbreviated to DeJoin) into distortion on individual pixels; thus, the message can be efficiently embedded with syndrome-trellis codes. We prove that DeJoin can approach the lower bound of joint distortion. As an example, we define joint distortion according to the principle of synchronizing modification direction and then design steganographic algorithms with DeJoin. The experimental results show that the proposed method outperforms previous nonadditive distortion steganography when resisting the state-of-the-art steganalysis.


title: Statistical analysis of motion patterns in AIS Data: Anomaly detection and motion prediction

abstract: The paper is devoted to statistical analysis of vessel motion patterns in the ports and waterways using AIS ship self-reporting data. From the real historic AIS data we extract motion patterns which are then used to construct the corresponding motion anomaly detectors. This is carried out in the framework of adaptive kernel density estimation. The anomaly detector is then sequentially applied to the real incoming AIS data for the purpose of anomaly detection. Under the null hypothesis (no anomaly), using the historic motion pattern data, we predict the motion of vessels using the Gaussian sum tracking filter.


title: Quality measurement of unwrapped three-dimensional fingerprints: A neural networks approach

abstract: Traditional biometric systems based on the fingerprint characteristics acquire the biometric samples using touch-based sensors. Some recent researches are focused on the design of touch-less fingerprint recognition systems based on CCD cameras. Most of these systems compute three-dimensional fingertip models and then apply unwrapping techniques in order to obtain images compatible with biometric methods designed for images captured by touch-based sensors. Unwrapped images can present different problems with respect to the traditional fingerprint images. The most important of them is the presence of deformations of the ridge pattern caused by spikes or badly reconstructed regions in the corresponding three-dimensional models. In this paper, we present a neural-based approach for the quality estimation of images obtained from the unwrapping of three-dimensional fingertip models. The paper also presents different sets of features that can be used to evaluate the quality of fingerprint images. Experimental results show that the proposed quality estimation method has an adequate accuracy for the quality classification. The performances of the proposed method are also evaluated in a complete biometric system and compared with the ones obtained by a well-known algorithm in the literature, obtaining satisfactory results.


title: Characterizing Data Structures for Volatile Forensics

abstract: Volatile memory forensic tools can extract valuable evidence from latent data structures present in memory dumps. However, current techniques are generally limited by a lack of understanding of the underlying data without the use of expert knowledge. In this paper, we characterize the nature of such evidence by using deep analysis techniques to better understand the life-cycle and recoverability of latent program data in memory. We have developed Cafegrind, a tool that can systematically build an object map and track the use of data structures as a program is running. Statistics collected by our tool can show which data structures are the most numerous, which structures are the most frequently accessed and provide summary statistics to guide forensic analysts in the evidence gathering process. As programs grow increasingly complex and numerous, the ability to pinpoint specific evidence in memory dumps will be increasingly helpful. Cafegrind has been tested on a number of real-world applications and we have shown that it can successfully map up to 96% of heap accesses.


title: Matchmaking for online games and other latency-sensitive P2P systems

abstract: The latency between machines on the Internet can dramatically affect users' experience for many distributed applications. Particularly, in multiplayer online games, players seek to cluster themselves so that those in the same session have low latency to each other. A system that predicts latencies between machine pairs allows such matchmaking to consider many more machine pairs than can be probed in a scalable fashion while users are waiting. Using a far-reaching trace of latencies between players on over 3.5 million game consoles, we designed Htrae, a latency prediction system for game matchmaking scenarios. One novel feature of Htrae is its synthesis of geolocation with a network coordinate system. It uses geolocation to select reasonable initial network coordinates for new machines joining the system, allowing it to converge more quickly than standard network coordinate systems and produce substantially lower prediction error than state-of-the-art latency prediction systems. For instance, it produces 90th percentile errors less than half those of iPlane and Pyxida. Our design is general enough to make it a good fit for other latency-sensitive peer-to-peer applications besides game matchmaking.


title: Multi-UAV collaborative monocular SLAM

abstract: With systems performing Simultaneous Localization And Mapping (SLAM) from a single robot reaching considerable maturity, the possibility of employing a team of robots to collaboratively perform a task has been attracting increasing interest. Promising great impact in a plethora of tasks ranging from industrial inspection to digitization of archaeological structures, collaborative scene perception and mapping are key in efficient and effective estimation. In this paper, we propose a novel, centralized architecture for collaborative monocular SLAM employing multiple small Unmanned Aerial Vehicles (UAVs) to act as agents. Each agent is able to independently explore the environment running limited-memory SLAM onboard, while sending all collected information to a central server, a ground station with increased computational resources. The server manages the maps of all agents, triggering loop closure, map fusion, optimization and distribution of information back to the agents. This allows an agent to incorporate observations from others in its SLAM estimates on the fly. We put the proposed framework to the test employing a nominal keyframe-based monocular SLAM algorithm, demonstrating the applicability of this system in multi-UAV scenarios.


title: Multilevel Inverter Topologies for Stand-Alone PV Systems

abstract: This paper shows that versatile stand-alone photovoltaic (PV) systems still demand on at least one battery inverter with improved characteristics of robustness and efficiency, which can be achieved using multilevel topologies. A compilation of the most common topologies of multilevel converters is presented, and it shows which ones are best suitable to implement inverters for stand-alone applications in the range of a few kilowatts. As an example, a prototype of 3 kVA was implemented, and peak efficiency of 96.0% was achieved.


title: FInCo 2005 Preliminary Version A reflective higher-order calculus

abstract: The @p-calculus is not a closed theory, but rather a theory dependent upon some theory of names. Taking an operational view, one may think of the @p-calculus as a procedure that when handed a theory of names provides a theory of processes that communicate over those names. This openness of the theory has been exploited in @p-calculus implementations, where ancillary mechanisms provide a means of interpreting of names, e.g. as tcp/ip ports. But, foundationally, one might ask if there is a closed theory of processes, i.e. one in which the theory of names arises from and is wholly determined by the theory of processes. Here we present such a theory in the form of an asynchronous message-passing calculus built on a notion of quoting. Names are quoted processes, and as such represent the code of a process, a reification of the syntactic structure of the process as an object for process manipulation. Name- passing in this setting becomes a way of passing the code of a process as a message. In the presence of a dequote operation, turning the code of a process into a running instance, this machinery yields higher-order characteristics without the introduction of process variables. As is standard with higher-order calculi, replication and/or recursion is no longer required as a primitive operation. Somewhat more interestingly, the introduction of a process constructor to dynamically convert a process into its code is essential to obtain computational completeness, and simultaneously supplants the function of the @n operator. In fact, one may give a compositional encoding of the @n operator into a calculus featuring dynamic quote as well as dequote.


title: Association rule mining using binary particle swarm optimization

abstract: In this paper, we developed a binary particle swarm optimization (BPSO) based association rule miner. Our BPSO based association rule miner generates the association rules from the transactional database by formulating a combinatorial global optimization problem, without specifying the minimum support and minimum confidence unlike the a priori algorithm. Our algorithm generates the best M rules from the given database, where M is a given number. The quality of the rule is measured by a fitness function defined as the product of support and confidence. The effectiveness of our algorithm is tested on a real life bank dataset from commercial bank in India and three transactional datasets viz. books database, food items dataset and dataset of the general store taken from literature. Based on the results, we infer that our algorithm can be used as an alternative to the a priori algorithm and the FP-growth algorithm.


title: Feedback relations and causal orders between sea surface temperature and convection within the Western Pacific Warm Pool

abstract: Analysis of time series of standardized anomalies of sea surface temperature and outgoing longwave radiation from the western Pacific warm pool revealed two distinct modes of atmosphere-ocean interaction. A statistically binding positive feedback relationship between convection and ocean temperature exists over the equatorial region surrounding the international date line. Here, the contemporaneous feedback between these two variables is not statistically significant. A causal order from the ocean temperature to outgoing longwave radiation is detected in a region of the Southwest Pacific, where ocean patches with temperatures greater than or equal to 29.75°C form most frequently. Dynamical implications associated with the aforementioned feedback relationship and causal order are illustrated by estimating the impact of a sudden transient increase in convection on future values of sea surface temperature and vice versa.


title: Event Logs for the Analysis of Software Failures: A Rule-Based Approach

abstract: Event logs have been widely used over the last three decades to analyze the failure behavior of a variety of systems. Nevertheless, the implementation of the logging mechanism lacks a systematic approach and collected logs are often inaccurate at reporting software failures: This is a threat to the validity of log-based failure analysis. This paper analyzes the limitations of current logging mechanisms and proposes a rule-based approach to make logs effective to analyze software failures. The approach leverages artifacts produced at system design time and puts forth a set of rules to formalize the placement of the logging instructions within the source code. The validity of the approach, with respect to traditional logging mechanisms, is shown by means of around 12,500 software fault injection experiments into real-world systems.


title: Adaptive sequential Monte Carlo by means of mixture of experts

abstract: Appropriately designing the proposal kernel of particle filters is an issue of significant importance, since a bad choice may lead to deterioration of the particle sample and, consequently, waste of computational power. In this paper we introduce a novel algorithm adaptively approximating the so-called optimal proposal kernel by a mixture of integrated curved exponential distributions with logistic weights. This family of distributions, referred to as mixtures of experts, is broad enough to be used in the presence of multi-modality or strongly skewed distributions. The mixtures are fitted, via online-EM methods, to the optimal kernel through minimisation of the Kullback-Leibler divergence between the auxiliary target and instrumental distributions of the particle filter. At each iteration of the particle filter, the algorithm is required to solve only a single optimisation problem for the whole particle sample, yielding an algorithm with only linear complexity. In addition, we illustrate in a simulation study how the method can be successfully applied to optimal filtering in nonlinear state-space models.


title: Secure software delivery and installation in embedded systems

abstract: Increasingly, software (SW) in embedded systems can be updated due to the rising share of flashable electronic control units (ECUs). However, current SW installation procedures are insecure: an adversary can install SW in a given ECU without any sender authentication or compatibility assessment. In addition, SW is installed on an all-or-nothing base: with the installation, the user acquires full access rights to any functionality. Concepts for solving individual deficiencies of current procedures have been proposed, but no unified solution has been published so far. ::: ::: In this paper we propose a method for secure SW delivery and installation in embedded systems. The automotive industry serves as a case example leading to complex trust relations and illustrates typically involved parties and their demands. Our solution combines several cryptographic techniques. For example, public key broadcast encryption enables secure SW distribution from any provider to all relevant embedded systems. Trusted computing allows to bind the distributed SW to a trustworthy configuration of the embedded system, which then fulfills a variety of security requirements. Finally, we outline the management of flexible access rights to individual functionalities of the installed SW, thus enabling new business models.


title: Biometric recognition: Sensor characteristics and image quality

abstract: Biometric recognition, or simply biometrics, refers to recognizing a person based on one or more of his anatomical or behavioral characteristics. A good biometric trait should be measurable, distinctive (different for every person) and stable over time. The sensing method should not be intrusive or socially unacceptable, and the system should be easy to use. A biometric system based on this trait should be accurate, fast, robust and inexpensive. In this article, we discuss the signal acquisition aspects of fingerprint and iris biometrics-two of the most widely used biometric traits.Personal recognition of people is necessary to conduct many social and economic activities. Besides visual recognition of acquaintances, checking a person's government issued photo ID is the most common procedure. In electronic access or transactions, passwords and security tokens are commonly used. These credentials are surrogates of a person's identity Their major shortcoming is that they can be easily compromised by being lost, stolen, or given to someone else. For better security, it is necessary to link the digital identity of a person to his body's characteristics.


title: Practical Guidelines for Measurement-Based Process Improvement

abstract: Despite significant progress in the last 15 years, implementing a successful measurement program for software development is still a challenging undertaking. Most problems are not of theoretical but of methodological or practical nature. In this article, we present lessons learned from experiences with goal-oriented measurement. We structure them into practical guidelines for efficient and useful software measurement aimed at process improvement in industry. Issues related to setting measurement goals, defining explicit measurement models, and implementing data collection procedures are addressed from a practical perspective. In addition, guidelines for using measurement in the context of process improvement are provided.


title: A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations

abstract: Progress in science has advanced the development of human society across history, with dramatic revolutions shaped by information theory, genetic cloning, and artificial intelligence, among the many scientific achievements produced in the 20th century. However, the way that science advances itself is much less well-understood. In this work, we study the evolution of scientific development over the past century by presenting an anatomy of 89 million digitalized papers published between 1900 and 2015. We find that science has benefited from the shift from individual work to collaborative effort, with over 90% of the world-leading innovations generated by collaborations in this century, nearly four times higher than they were in the 1900s. We discover that rather than the frequent myopic- and self-referencing that was common in the early 20th century, modern scientists instead tend to look for literature further back and farther around. Finally, we also observe the globalization of scientific development from 1900 to 2015, including 25-fold and 7-fold increases in international collaborations and citations, respectively, as well as a dramatic decline in the dominant accumulation of citations by the US, the UK, and Germany, from ~95% to ~50% over the same period. Our discoveries are meant to serve as a starter for exploring the visionary ways in which science has developed throughout the past century, generating insight into and an impact upon the current scientific innovations and funding policies.


title: Siglecs and their roles in the immune system

abstract: Cell surfaces in the immune system are richly equipped with a complex mixture of glycans, which can be recognized by diverse glycan-binding proteins. The Siglecs are a family of sialic-acid-binding immunoglobulin-like lectins that are thought to promote cell-cell interactions and regulate the functions of cells in the innate and adaptive immune systems through glycan recognition. In this Review, we describe recent studies on signalling mechanisms and discuss the potential role of Siglecs in triggering endocytosis and in pathogen recognition. Finally, we discuss the postulated functions of the recently discovered CD33-related Siglecs and consider the factors that seem to be driving their rapid evolution.


title: Applications of Graph Labeling in Communication Networks

abstract: The field of Graph Theory plays vital role in various fields. One of the important areas in graph theory is Graph Labeling used in many applications like coding theory, x-ray crystallography, radar, astronomy, circuit design, communication network addressing, data base management. This paper gives an overview of labeling of graphs in heterogeneous fields to some extent but mainly focuses on the communication networks. Communication network has two types ‘Wired communication’ and ‘wireless communication’. Day by day wireless networks have been developed to ease communication between any two systems, results more efficient communication. This paper also explored role of labeling in expanding the utility of this channel assignment process in communication networks. Various papers based on graph labeling have been observed, and identified its usage towards communication networks. This paper addresses how the concept of graph labeling can be applied to network security, network addressing, channel assignment process, social networks. An overview and new ideas has been proposed here.


title: ISO26262 SEooC Compliance of a ROS Based Architecture

abstract: Robot Operating System (ROS) begins to be used in automotive industry as a component to be adapted and deployed in cars. However, its use varies according to a set of parameters, and its reliability depends on these values, and usage models. This paper proposes a certification approach based on evidences for a ROS based architecture aligned with the ISO26262 and its Safety Element out of Context (SEooC) component definition. This ROS based architecture is being tested in order to identify characteristics and thresholds to be used during the whole development life cycle, safety case definition and especially during the certification phase. Finally we have outlined an ISO26262 based certification process for this kind of component.


title: Design criteria for the RF section of UHF and microwave passive RFID transponders

abstract: A set of design criteria for the radio-frequency (RF) section of long-range passive RF identification (RFID) transponders operating in the 2.45-GHz or 868-MHz industrial, scientific, and medical (ISM) frequency ranges is derived in this paper, focusing in particular on the voltage multiplier, the power-matching network, and the backscatter modulation. The paper discusses the design tradeoffs between the error probability at the reader receiver and the converted RF-dc power at the transponder, determining the regions of the design space that allow optimization of the operating range and the data rate of the RFID system.


title: Can complex network metrics predict the behavior of NBA teams?

abstract: The United States National Basketball Association (NBA) is one of the most popular sports league in the world and is well known for moving a millionary betting market that uses the countless statistical data generated after each game to feed the wagers. This leads to the existence of a rich historical database that motivates us to discover implicit knowledge in it. In this paper, we use complex network statistics to analyze the NBA database in order to create models to represent the behavior of teams in the NBA. Results of complex network-based models are compared with box score statistics, such as points, rebounds and assists per game. We show the box score statistics play a significant role for only a small fraction of the players in the league. We then propose new models for predicting a team success based on complex network metrics, such as clustering coefficient and node degree. Complex network-based models present good results when compared to box score statistics, which underscore the importance of capturing network relationships in a community such as the NBA.


title: Natural Products As Sources of New Drugs over the 30 Years from 1981 to 2010

abstract: This review is an updated and expanded version of the three prior reviews that were published in this journal in 1997, 2003, and 2007. In the case of all approved therapeutic agents, the time frame has been extended to cover the 30 years from January 1, 1981, to December 31, 2010, for all diseases worldwide, and from 1950 (earliest so far identified) to December 2010 for all approved antitumor drugs worldwide. We have continued to utilize our secondary subdivision of a “natural product mimic” or “NM” to join the original primary divisions and have added a new designation, “natural product botanical” or “NB”, to cover those botanical “defined mixtures” that have now been recognized as drug entities by the FDA and similar organizations. From the data presented, the utility of natural products as sources of novel structures, but not necessarily the final drug entity, is still alive and well. Thus, in the area of cancer, over the time frame from around the 1940s to date, of the 175 small molecules, 131, or 74...


title: Distribution expansion planning considering reliability and security of energy using modified PSO (Particle Swarm Optimization) algorithm

abstract: Distribution feeders and substations need to provide additional capacity to serve the growing electrical demand of customers without compromising the reliability of the electrical networks. Also, more control devices, such as DG (Distributed Generation) units are being integrated into distribution feeders. Distribution networks were not planned to host these intermittent generation units before construction of the systems. Therefore, additional distribution facilities are needed to be planned and prepared for the future growth of the electrical demand as well as the increase of network hosting capacity by DG units. This paper presents a multiobjective optimization algorithm for the MDEP (Multi-Stage Distribution Expansion Planning) in the presence of DGs using nonlinear formulations. The objective functions of the MDEP consist of minimization of costs, END (Energy-Not-Distributed), active power losses and voltage stability index based on SCC (Short Circuit Capacity). A MPSO (modified Particle Swarm Optimization) algorithm is developed and used for this multiobjective MDEP optimization. In the proposed MPSO algorithm, a new mutation method is implemented to improve the global searching ability and restrain the premature convergence to local minima. The effectiveness of the proposed method is tested on a typical 33-bus test system and results are presented.


title: A clustering-based deep autoencoder for one-class image classification

abstract: Anomaly detection in images is a topic of great interest in Computer Vision. It can be defined as an One-Class problem, where the goal is to detect deviations from known patterns, which are defined as normal. Recently, Deep Learning methods became popular due to their performance on classification tasks. This works presents an image anomaly detection classifier based on a previously known method, the Deep Embedded Clustering, which is based on a Deep Autoencoder. We show the effectiveness of the method through three different experiments. Results suggest that the method improves classification performance when compared to a Stacked Denoising Autoencoder in the image anomaly detection context.


title: Quantitative workload analysis and prediction using Google cluster traces

abstract: Resource allocation efficiency and energy consumption are among the top concerns to today's Cloud data center. Finding the optimal point where users' multiple job requests can be accomplished timely with minimum electricity and hardware cost is one of the key factors for system designers and managers to optimize the system configurations. Understanding the characteristics of the distribution of user task is an essential step for this purpose. At large-scale Cloud Computing data centers, a precise workload prediction will significantly help designers and operators to schedule hardware/software resources and power supplies in a more efficient manner, and make appropriate decisions to upgrade the Cloud system when the workload grows. While a lot of study has been conducted for hypervisor-based Cloud, container-based virtualization is becoming popular because of the low overhead and high efficiency in utilizing computing resources. In this paper, we have studied a set of real-world container data center traces from part of Google's cluster. We investigated the distribution of job duration, waiting time and machine utilization and the number of jobs submitted in a fix time period. Based on the quantitative study, an Ensemble Workload Prediction (EnWoP) method and a novel prediction evaluation parameter called Cloud Workload Correction Rate (C-Rate) have been proposed. The experimental results have verified that the EnWoP method achieved high prediction accuracy and the C-Rate evaluates the prediction methods more objective.


title: CrowdPose: Efficient Crowded Scenes Pose Estimation and a New Benchmark

abstract: Multi-person pose estimation is fundamental to many computer vision tasks and has made significant progress in recent years. However, few previous methods explored the problem of pose estimation in crowded scenes while it remains challenging and inevitable in many scenarios. Moreover, current benchmarks cannot provide an appropriate evaluation for such cases. In this paper, we propose a novel and efficient method to tackle the problem of pose estimation in the crowd and a new dataset to better evaluate algorithms. Our model consists of two key components: joint-candidate single person pose estimation (SPPE) and global maximum joints association. With multi-peak prediction for each joint and global association using the graph model, our method is robust to inevitable interference in crowded scenes and very efficient in inference. The proposed method surpasses the state-of-the-art methods on CrowdPose dataset by 5.2 mAP and results on MSCOCO dataset demonstrate the generalization ability of our method.


title: Semantic Image Inpainting Through Improved Wasserstein Generative Adversarial Networks

abstract: Image inpainting is the task of filling-in missing regions of a damaged or incomplete image. In this work we tackle this problem not only by using the available visual data but also by incorporating image semantics through the use of generative models. Our contribution is twofold: First, we learn a data latent space by training an improved version of the Wasserstein generative adversarial network, for which we incorporate a new generator and discriminator architecture. Second, the learned semantic information is combined with a new optimization loss for inpainting whose minimization infers the missing content conditioned by the available data. It takes into account powerful contextual and perceptual content inherent in the image itself. The benefits include the ability to recover large regions by accumulating semantic information even it is not fully present in the damaged image. Experiments show that the presented method obtains qualitative and quantitative top-tier results in different experimental situations and also achieves accurate photo-realism comparable to state-of-the-art works.


title: Virtual reality exposure therapy for the treatment of anxiety disorders: An evaluation of research quality

abstract: Abstract Randomized controlled trials (RCTs) support the effectiveness of virtual reality exposure therapy (VRET) for anxiety disorders; however, the overall quality of the VRET RCT literature base has yet to be evaluated. This study reviewed 27 VRET RCTs and the degree of adherence to 8 RCT research design criteria derived from existing standards. Adherence to the study quality criteria was generally low as the articles met an average 2.85 criteria (SD = 1.56). None of the studies met more than six quality criteria. Study quality did not predict effect size; however, a reduction in effect size magnitude was observed for studies with larger sample sizes when comparing VRET to non-active control groups. VRET may be an effective method of treatment but caution should be exercised in interpreting the existing body of literature supporting VRET relative to existing standards of care. The need for well-designed VRET research is discussed.


title: iHR: an online recruiting system for Xiamen Talent Service Center

abstract: Online recruiting systems have gained immense attention in the wake of more and more job seekers searching jobs and enterprises finding candidates on the Internet. A critical problem in a recruiting system is how to maximally satisfy the desires of both job seekers and enterprises with reasonable recommendations or search results. In this paper, we investigate and compare various online recruiting systems from a product perspective. We then point out several key functions that help achieve a win-win situation between job seekers and enterprises for a successful recruiting system. Based on the observations and key functions, we design, implement and deploy a web-based application of recruiting system, named iHR, for Xiamen Talent Service Center. The system utilizes the latest advances in data mining and recommendation technologies to create a user-oriented service for a myriad of audience in job marketing community. Empirical evaluation and online user studies demonstrate the efficacy and effectiveness of our proposed system. Currently, iHR has been deployed at http://i.xmrc.com.cn/XMRCIntel.


title: Time Series Clustering Based on ICA for Stock Data Analysis

abstract: Time series clustering is an important task in time series data mining. Compared to traditional clustering problems, time series clustering poses additional difficulties. The unique structure of time series makes many traditional clustering methods unable to apply directly. This paper presents a novel feature-based approach to time series clustering, which first converts the raw time series data into feature vectors of lower dimension by using ICA algorithm, and then applies a modified k-means algorithm to the extracted feature vectors. Finally, to validate effectiveness and feasibility of the presented method, we use it to analyze the real world stock time series data and achieve reasonable results.


title: Mapping right ventricular myocardial mechanics using 3D cine DENSE cardiovascular magnetic resonance

abstract: The mechanics of the right ventricle (RV) are not well understood as studies of the RV have been limited. This is, in part, due to the RV's thin wall, asymmetric geometry and irregular motion. However, the RV plays an important role in cardiovascular function. This study aims to describe the complex mechanics of the healthy RV using three dimensional (3D) cine displacement encoding with stimulated echoes (DENSE) cardiovascular magnetic resonance (CMR). Whole heart 3D cine DENSE data were acquired from five healthy volunteers. Tailored post-processing algorithms for RV mid-wall tissue tracking and strain estimation are presented. A method for sub-dividing the RV into four regions according to anatomical land marks is proposed, and the temporal evolution of strain was assessed in these regions. The 3D cine DENSE tissue tracking methods successfully capture the motion and deformation of the RV at a high spatial resolution in all volunteers. The regional Lagrangian peak surface strain and time to peak values correspond with previous studies using myocardial tagging, DENSE and strain encoded CMR. The inflow region consistently displays lower peak strains than the apical and outflow regions, and the time to peak strains suggest RV mechanical activation in the following order: inflow, outflow, mid, then apex. Model-free techniques have been developed to study the myocardial mechanics of the RV at a high spatial resolution using 3D cine DENSE CMR. The consistency of the regional RV strain patterns across healthy subjects is encouraging and the techniques may have clinical utility in assessing disrupted RV mechanics in the diseased heart.


title: Improved visual sensitivity during smooth pursuit eye movements

abstract: When we view the world around us, we constantly move our eyes. This brings objects of interest into the fovea and keeps them there, but visual sensitivity has been shown to deteriorate while the eyes are moving. Here we show that human sensitivity for some visual stimuli is improved during smooth pursuit eye movements. Detection thresholds for briefly flashed, colored stimuli were 16% lower during pursuit than during fixation. Similarly, detection thresholds for luminance-defined stimuli of high spatial frequency were lowered. These findings suggest that the pursuit-induced sensitivity increase may have its neuronal origin in the parvocellular retino-thalamic system. This implies that the visual system not only uses feedback connections to improve processing for locations and objects being attended to, but that a whole processing subsystem can be boosted. During pursuit, facilitation of the parvocellular system may reduce motion blur for stationary objects and increase sensitivity to speed changes of the tracked object.


title: Secure Database in Cloud Computing - CryptDB Revisited

abstract: Databases contain most valuable personal, economic, and government information. They are most desirable to the malicious adversaries and therefore, it is very critical to protect against all possible adversarial behavior. With the recent rapid growth in the availability and popularity of cloud services, many personal and business and government information are now moving to the Cloud. Therefore, databases are more difficult to protect because of new security and privacy issues. Various techniques have been proposed to solve the outsourcing database scenarios which preserve a certain degree of confidentiality while still allowing to execute some SQL queries efficiently. CryptDB is a new database management system for protecting data confidentiality while preserving confidentiality and performing a standard set of SQL queries in an efficient way. CryptDB seems to be practical compared to other attempts at solving the problem of computing with encrypted data and the database can be fully moved to the Cloud with no security concern because all the data are already encrypted and never revealed to the database administrator. In this paper, we revisit CryptDB from cryptographic point of view. We first describe it in more details for ease of understanding and then highlight the drawbacks of CryptDB from security and efficiency points of view.


Rock Reinforcement Design For a Shallow Large-span Cavern


title: Fake News: Fundamental Theories, Detection Strategies and Challenges

abstract: The explosive growth of fake news and its erosion to democracy, justice, and public trust increased the demand for fake news detection. As an interdisciplinary topic, the study of fake news encourages a concerted effort of experts in computer and information science, political science, journalism, social science, psychology, and economics. A comprehensive framework to systematically understand and detect fake news is necessary to attract and unite researchers in related areas to conduct research on fake news. This tutorial aims to clearly present (1) fake news research, its challenges, and research directions; (2) a comparison between fake news and other related concepts (e.g., rumors); (3) the fundamental theories developed across various disciplines that facilitate interdisciplinary research; (4) various detection strategies unified under a comprehensive framework for fake news detection; and (5) the state-of-the-art datasets, patterns, and models. We present fake news detection from various perspectives, which involve news content and information in social networks, and broadly adopt techniques in data mining, machine learning, natural language processing, information retrieval and social search. Facing the upcoming 2020 U.S. presidential election, challenges for automatic, effective and efficient fake news detection are also clarified in this tutorial.


title: Isomorphism of graphs of bounded valence can be tested in polynomial time

abstract: Abstract Suppose we are given a set of generators for a group G of permutations of a colored set A . The color automorphism problem for G involves finding generators for the subgroup of G which stabilizes the color classes. It is shown that testing isomorphism of graphs of bounded valance is polynomial-time reducible to the color automorphism problem for groups with composition factors of bounded order. The algorithm for the latter problem involves three divide-and-conquer maneuvers. The problem is solved sequentially on the G-orbits. An orbit is broken into a minimal system of imprimitivity blocks. At that point, the hypothesis on G guarantees the presence of a subgroup P of “small” index which acts a p -group on the blocks. Divide-and-conquer is then used on the group, trading the problem on G for a small number of similar problems on P . In the trivalent case p = 2, P = G and the analysis requires only elementary notions. For higher valence, the justification requires some new observations about primitive permutation groups.


title: fastFM: A Library for Factorization Machines

abstract: Factorization Machines (FM) are currently only used in a narrow range of applications and are not yet part of the standard machine learning toolbox, despite their great success in collaborative filtering and click-through rate prediction. However, Factorization Machines are a general model to deal with sparse and high dimensional features. Our Factorization Machine implementation (fastFM) provides easy access to many solvers and supports regression, classification and ranking tasks. Such an implementation simplifies the use of FM for a wide range of applications. Therefore, our implementation has the potential to improve understanding of the FM model and drive new development.


title: The neuroscience of mindfulness meditation

abstract: Positive effects of mindfulness-based practices on health and cognitive performance have been reported; however, we know little about the underlying mechanisms. Tang and colleagues outline the challenges of meditation research and consider emerging information about the effects of mindfulness meditation on brain structure and function.


title: Recovery of motoneuron and locomotor function after spinal cord injury depends on constitutive activity in 5-HT2C receptors

abstract: Muscle paralysis after spinal cord injury is partly caused by a loss of brainstem-derived serotonin (5-HT), which normally maintains motoneuron excitability by regulating crucial persistent calcium currents. Here we examine how over time motoneurons compensate for lost 5-HT to regain excitability. We find that, months after a spinal transection in rats, changes in post-transcriptional editing of 5-HT2C receptor mRNA lead to increased expression of 5-HT2C receptor isoforms that are spontaneously active (constitutively active) without 5-HT. Such constitutive receptor activity restores large persistent calcium currents in motoneurons in the absence of 5-HT. We show that this helps motoneurons recover their ability to produce sustained muscle contractions and ultimately enables recovery of motor functions such as locomotion. However, without regulation from the brain, these sustained contractions can also cause debilitating muscle spasms. Accordingly, blocking constitutively active 5-HT2C receptors with SB206553 or cyproheptadine, in both rats and humans, largely eliminates these calcium currents and muscle spasms, providing a new rationale for antispastic drug therapy.


title: Foundations for smarter cities

abstract: This paper describes the information technology (IT) foundation and principles for Smarter Cities™. Smarter Cities are urban areas that exploit operational data, such as that arising from traffic congestion, power consumption statistics, and public safety events, to optimize the operation of city services. The foundational concepts are instrumented, interconnected, and intelligent. Instrumented refers to sources of near-real-time real-world data from both physical and virtual sensors. Interconnected means the integration of those data into an enterprise computing platform and the communication of such information among the various city services. Intelligent refers to the inclusion of complex analytics, modeling, optimization, and visualization in the operational business processes to make better operational decisions. This approach enables the adaptation of city services to the behavior of the inhabitants, which permits the optimal use of the available physical infrastructure and resources, for example, in sensing and controlling consumption of energy and water, managing waste processing and transportation systems, and applying optimization to achieve new efficiencies among these resources. Additional roles exist in intelligent interaction between the city and its inhabitants and further contribute to operational efficiency while maintaining or enhancing quality of life.


title: Planar Ultra-Wideband Antennas in Ku- and K-Band for Pattern or Polarization Diversity Applications

abstract: Two planar diversity antennas operating in Ku- (12.4-18 GHz) and K-band (18-26.5 GHz) are proposed for ultra-wideband (UWB) applications. Both of them consist of a monopole radiating element and two orthogonal feeding ports. Their ground planes are modified and optimized to improve the isolation as well as to control the radiation. The first one is a pattern diversity antenna with a disc monopole patch. The second one is a polarization diversity antenna based on a square patch which is able to provide both linear and circular polarizations. Different feeding approaches are adopted to obtain a broadband impedance matching. Two wideband couplers are designed to provide the required feeding circuit. Both the simulated and measured results are presented to verify the proposed idea. Good radiation efficiency and isolation between the two ports are achieved. Consistent diversity performance is observed across the whole UWB bandwidth which is demonstrated by their radiation patterns.


title: Classifying web documents in a hierarchy of categories: a comprehensive study

abstract: Most of the research on text categorization has focused on classifying text documents into a set of categories with no structural relationships among them (flat classification). However, in many information repositories documents are organized in a hierarchy of categories to support a thematic search by browsing topics of interests. The consideration of the hierarchical relationship among categories opens several additional issues in the development of methods for automated document classification. Questions concern the representation of documents, the learning process, the classification process and the evaluation criteria of experimental results. They are systematically investigated in this paper, whose main contribution is a general hierarchical text categorization framework where the hierarchy of categories is involved in all phases of automated document classification, namely feature selection, learning and classification of a new document. An automated threshold determination method for classification scores is embedded in the proposed framework. It can be applied to any classifier that returns a degree of membership of a document to a category. In this work three learning methods are considered for the construction of document classifiers, namely centroid-based, naive Bayes and SVM. The proposed framework has been implemented in the system WebClassIII and has been tested on three datasets (Yahoo, DMOZ, RCV1) which present a variety of situations in terms of hierarchical structure. Experimental results are reported and several conclusions are drawn on the comparison of the flat vs. the hierarchical approach as well as on the comparison of different hierarchical classifiers. The paper concludes with a review of related work and a discussion of previous findings vs. our findings.


title: Sexy media matter: exposure to sexual content in music, movies, television, and magazines predicts black and white adolescents' sexual behavior.

abstract: OBJECTIVE ::: To assess over time whether exposure to sexual content in 4 mass media (television, movies, music, and magazines) used by early adolescents predicts sexual behavior in middle adolescence. ::: ::: ::: METHODS ::: An in-home longitudinal survey of 1017 black and white adolescents from 14 middle schools in central North Carolina was conducted. Each teen was interviewed at baseline when he or she was 12 to 14 years old and again 2 years later using a computer-assisted self interview (audio computer-assisted self-interview) to ensure confidentiality. A new measure of each teen's sexual media diet (SMD) was constructed by weighting the frequency of use of 4 media by the frequency of sexual content in each television show, movie, music album, and magazine the teen used regularly. ::: ::: ::: RESULTS ::: White adolescents in the top quintile of sexual media diet when 12 to 14 years old were 2.2 times more likely to have had sexual intercourse when 14 to 16 years old than those who were in the lowest SMD quintile, even after a number of other relevant factors, including baseline sexual behavior, were introduced. The relationship was not statistically significant for black adolescents after controlling for other factors that were more predictive, including parental disapproval of teen sex and perceived permissive peer sexual norms. ::: ::: ::: CONCLUSIONS ::: Exposure to sexual content in music, movies, television, and magazines accelerates white adolescents' sexual activity and increases their risk of engaging in early sexual intercourse. Black teens appear more influenced by perceptions of their parents' expectations and their friends' sexual behavior than by what they see and hear in the media.


title: Emergency response to the nuclear accident at the Fukushima Daiichi Nuclear Power Plants using mobile rescue robots

abstract: On March 11, 2011, a massive earthquake (magnitude 9.0) and accompanying tsunami hit the Tohoku region of eastern Japan. Since then, the Fukushima Daiichi Nuclear Power Plants have been facing a crisis due to the loss of all power that resulted from the meltdown accidents. Three buildings housing nuclear reactors were seriously damaged from hydrogen explosions, and, in one building, the nuclear reactions became out of control. It was too dangerous for humans to enter the buildings to inspect the damage because radioactive materials were also being released. In response to this crisis, it was decided that mobile rescue robots would be used to carry out surveillance missions. The mobile rescue robots needed could not be delivered to the Tokyo Electric Power Company (TEPCO) until various technical issues were resolved. Those issues involved hardware reliability, communication functions, and the ability of the robots' electronic components to withstand radiation. Additional sensors and functionality that would enable the robots to respond effectively to the crisis were also needed. Available robots were therefore retrofitted for the disaster reponse missions. First, the radiation tolerance of the electronic componenets was checked by means of gamma ray irradiation tests, which were conducted using the facilities of the Japan Atomic Energy Agency (JAEA). The commercial electronic devices used in the original robot systems operated long enough (more than 100 h at a 10% safety margin) in the assumed environment (100 mGy/h). Next, the usability of wireless communication in the target environment was assessed. Such tests were not possible in the target environment itself, so they were performed at the Hamaoka Daiichi Nuclear Power Plants, which are similar to the target environment. As previously predicted, the test results indicated that robust wireless communication would not be possible in the reactor buildings. It was therefore determined that a wired communication device would need to be installed. After TEPCO's official urgent mission proposal was received, the team mounted additional devices to facilitate the installation of a water gauge in the basement of the reactor buildings to determine flooding levels. While these preparations were taking place, prospective robot operators from TEPCO trained in a laboratory environment. Finally, one of the robots was delivered to the Fukushima Daiichi Nuclear Power Plants on June 20, 2011, where it performed a number of important missions inside the buildings. In this paper, the requirements for the exploration mission in the Fukushima Daiichi Nuclear Power Plants are presented, the implementation is discussed, and the results of the mission are reported. © 2013 Wiley Periodicals, Inc. ::: ::: (Webpage: http://www.astro.mech.tohoku.ac.jp/)


title: The designversus the analysis of observational studies for causal effects: parallels with the design of randomized trials

abstract: For estimating causal effects of treatments, randomized experiments are generally considered the gold standard. Nevertheless, they are often infeasible to conduct for a variety of reasons, such as ethical concerns, excessive expense, or timeliness. Consequently, much of our knowledge of causal effects must come from non-randomized observational studies. This article will advocate the position that observational studies can and should be designed to approximate randomized experiments as closely as possible. In particular, observational studies should be designed using only background information to create subgroups of similar treated and control units, where 'similar' here refers to their distributions of background variables. Of great importance, this activity should be conducted without any access to any outcome data, thereby assuring the objectivity of the design. In many situations, this objective creation of subgroups of similar treated and control units, which are balanced with respect to covariates, can be accomplished using propensity score methods. The theoretical perspective underlying this position will be presented followed by a particular application in the context of the US tobacco litigation. This application uses propensity score methods to create subgroups of treated units (male current smokers) and control units (male never smokers) who are at least as similar with respect to their distributions of observed background characteristics as if they had been randomized. The collection of these subgroups then 'approximate' a randomized block experiment with respect to the observed covariates.


title: Chameleon: Survey-Free Updating of a Fingerprint Database for Indoor Localization

abstract: In fingerprint-based indoor localization, keeping fingerprints current is important for localization accuracy. Because access point (AP) signals can change over time or suddenly, the traditional approach is to survey the site regularly and frequently, which is laborious and costly. The authors propose Chameleon, a novel survey-free approach to localize users and maintain an updated fingerprint database despite fingerprint signal changes. In Chameleon, clients are both location "queriers" and unconscious "surveyors" (implicit crowdsourcing). Independent of and amenable to any fingerprint-based localization algorithm, Chameleon employs a clustering algorithm to filter out the altered AP signals to achieve high localization accuracy. Using the calculated location and the collected signals of the client, the fingerprint database can be continuously updated without manual intervention. Extensive experimental trials at The Hong Kong University of Science and Technology and at the Hong Kong International Airport confirm that Chameleon can adapt the radio map to the current signal environment and achieve low localization error despite signal changes.


title: Deep Nearest Class Mean Model for Incremental Odor Classification

abstract: In recent years, more machine learning algorithms have been applied to odor classification. These odor classification algorithms usually assume that the training data sets are static. However, for some odor recognition tasks, new odor classes continually emerge. That is, the odor data sets are dynamically growing while both training samples and number of classes are increasing over time. Motivated by this concern, this paper proposes a deep nearest class mean (DNCM) model based on the deep learning framework and the nearest class mean method. The proposed model not only leverages deep neural network to extract deep features but also able to dynamically integrate new classes over time. In our experiments, the DNCM model was initially trained with 10 classes, then 25 new classes are integrated. Experiment results demonstrate that the proposed model is very efficient for incremental odor classification, especially for new classes with only a small number of training examples.


title: The VisLab Intercontinental Autonomous Challenge: An Extensive Test for a Platoon of Intelligent Vehicles

abstract: This paper presents the VisLab Intercontinental Autonomous Challenge (VIAC), an autonomous vehicles test carried out from Parma to Shanghai between July and October 2010 by the VisLab team. The vehicle equipment is explained introducing the sensing systems which were tested during the journey. Trip details and the first statistics are presented as well.


Combined effects of sleep quality and depression on quality of life in patients with type 2 diabetes


title: Human Placental Adenosine Kinase: Purification and Characterization

abstract: The regulation of adenosine metabolism is an important factor in the determination of the biological properties of this compoundl. Although the deamination of adenosine has been carefully characterized, only a limited amount of information is available concerning its phosphorylation in mammalian tissue. We have undertaken the purification and characterization of human placental adenosine kinase (E. C.2.7.1.20), which catalyzes the reaction: ::: ::: $$Adenosine + ATP \to AMP + ADP$$


title: Privacy preserving data publishing: Review

abstract: Privacy preserving data publishing (PPDP) methods a new class of privacy preserving data mining (PPDM) technology, has been developed by the research community working on security and knowledge discovery. It is common to share data between two organizations in many application areas. When data are to be shared between parties, there could be some sensitive patterns which should not be disclosed to the other parties. These methods aims to keep the underlying data useful based on privacy preservation “utility based method based on privacy preservation, and created tremendous opportunities for knowledge- and information-based decision making. Recently, PPDP has received considerable attention in research communities, and many approaches have been proposed for different data publishing scenarios. In this survey, we will systematically summarize and evaluate different approaches to PPDP, study the challenges in practical data publishing, clarify the differences and requirements that distinguish PPDP from other related problems, and propose future research directions. ::: ::: ::: ::: Key words: Privacy preserving, privacy preserving data publishing, privacy preserving data mining, republishing, security, privacy, decision making, knowledge.


title: Differential Drive Wheeled Mobile Robot (WMR) Control Using Fuzzy Logic Techniques

abstract: Wheeled Mobile Robot (WMR) is extremely important for active target tracking control and the reactive obstacle avoidance in an unstructured environment. Fuzzy control provides a mechanism for incorporating human-like reasoning capabilities and computationally in control systems. In this paper, a new type of controller based on Fuzzy Logic for differential drive WMR is proposed. The paper begins with design of the WMR which involves the kinematics model and fuzzy controller. Then, simulation and research findings are provided in detail. It was found that the Fuzzy Logic Controller with five membership functions provides better performance compared to that of a seven membership function controller.


title: Review: A review of novelty detection

abstract: Novelty detection is the task of classifying test data that differ in some respect from the data that are available during training. This may be seen as ''one-class classification'', in which a model is constructed to describe ''normal'' training data. The novelty detection approach is typically used when the quantity of available ''abnormal'' data is insufficient to construct explicit models for non-normal classes. Application includes inference in datasets from critical systems, where the quantity of available normal data is very large, such that ''normality'' may be accurately modelled. In this review we aim to provide an updated and structured investigation of novelty detection research papers that have appeared in the machine learning literature during the last decade.


title: Player Type Models: Towards Empirical Validation

abstract: Player type models -- such as the BrainHex model -- are popular approaches for personalizing digital games towards individual preferences of players. Although several player type models have been developed and are currently used in game design projects, there is still a lack of data on their validity. To close this research gap we currently investigate the psychometric properties (factor structure, reliability, stability) and predictive validity (if player type scores can predict player experience) of the player type model BrainHex in an ongoing project. Results of two online studies (n1=592, n2=243) show that the psychometric properties of the BrainHex model could be improved. We suggest to improve the according questionnaire and sketch how the predictive validity could be investigated in future studies.