title: Affordance-based imitation learning in robots

abstract: In this paper we build an imitation learning algorithm for a humanoid robot on top of a general world model provided by learned object affordances. We consider that the robot has previously learned a task independent affordance-based model of its interaction with the world. This model is used to recognize the demonstration by another agent (a human) and infer the task to be learned. We discuss several important problems that arise in this combined framework, such as the influence of an inaccurate model in the recognition of the demonstration. We illustrate the ideas in the paper with some experimental results obtained with a real robot.


title: Convolutional neural networks application in plastic waste recognition and sorting

abstract: In this paper authors describe their results in IoT and Convolutional Neural Networks application in reverse vending machine project. In Europe and United States a reverse vending machine is a device that accepts used (empty) beverage containers and returns money to the user. In Russia we can provide the concept of automatic processing of plastic and metal waste combined with another mechanism for economic motivation of end users — system of discounts and bonuses in large trading networks. These machines have to be as cheap as possible but with rich functionality and security. Traditionally the basics of this machine is assembled with IoT controllers and tiny single-board computers which have heavy memory and computational restrictions. Nevertheless these controllers are able to recognize the waste types using cameras and provide sorting and some kind of preprocessing. We show our CNN implementation on IoT for reverse vending machine.


title: Coordinating Text And Graphics In Explanation Generation

abstract: To generate multimedia explanations, a system must be able to coordinate the use of different media in a single explanation. In this paper, we present an architecture that we have developed for COMET (COordinated Multimedia Explanation Testbed), a system that generates directions for equipment maintenance and repair, and we show how it addresses the coordination problem. In particular, we focus on the use of a single content planner that produces a common content description used by multiple media-specific generators, a media coordinator that makes a fine-grained division of information between media, and bidirectional interaction between media-specific generators to allow influence across media.


title: Active Learning with Logged Data

abstract: We consider active learning with logged data, where labeled examples are drawn conditioned on a predetermined logging policy, and the goal is to learn a classifier on the entire population, not just conditioned on the logging policy. Prior work addresses this problem either when only logged data is available, or purely in a controlled random experimentation setting where the logged data is ignored. In this work, we combine both approaches to provide an algorithm that uses logged data to bootstrap and inform experimentation, thus achieving the best of both worlds. Our work is inspired by a connection between controlled random experimentation and active learning, and modifies existing disagreement-based active learning algorithms to exploit logged data.


title: Inpainting of Long Audio Segments With Similarity Graphs

abstract: We present a novel method for the compensation of long duration data loss in audio signals, in particular music. The concealment of such signal defects is based on a graph that encodes signal structure in terms of time-persistent spectral similarity. A suitable candidate segment for the substitution of the lost content is proposed by an intuitive optimization scheme and smoothly inserted into the gap , i.e., the lost or distorted signal region. Extensive listening tests show that the proposed algorithm provides highly promising results when applied to a variety of real-world music signals.


title: Real-Time End-to-End Action Detection with Two-Stream Networks

abstract: Two-stream networks have been very successful for solving the problem of action detection. However, prior work using two-stream networks train both streams separately, which prevents the network from exploiting regularities between the two streams. Moreover, unlike the visual stream, the dominant forms of optical flow computation typically do not maximally exploit GPU parallelism. We present a real-time end-to-end trainable two-stream network for action detection. First, we integrate the optical flow computation in our framework by using Flownet2. Second, we apply early fusion for the two streams and train the whole pipeline jointly end-to-end. Finally, for better network initialization, we transfer from the task of action recognition to action detection by pre-training our framework using the recently released large-scale Kinetics dataset. Our experimental results show that training the pipeline jointly end-to-end with fine-tuning the optical flow for the objective of action detection improves detection performance significantly. Additionally, we observe an improvement when initializing with parameters pre-trained using Kinetics. Last, we show that by integrating the optical flow computation, our framework is more efficient, running at real-time speeds (up to 31 fps).


title: Fractional order total variation regularization for image super-resolution

abstract: In this paper, we present a fractional order total variation (TV) regularization functional for image super-resolution, the role of which is to better handle the texture details of image. This regularization functional is then incorporated into a variational formulation with an image fidelity term and the usual TV regularization that can efficiently preserve the discontinuities and image structures. The resulting evolution equation is the gradient descent flow that minimizes the overall functional. The proposed model has been applied to eight real images with promising results; unlike the existing TV-based image super-resolution models, the proposed model does not suffer from block artifacts, staircase edges and false edge near the edges.


title: Microstrip high-pass filter on two resonators of same shape

abstract: This paper describes the design and study of the microstrip high-pass filter structure. The suggested filter is implemented on two resonators of same shape, situated on same side of the substrate, and two microstrip lines with serially connected interdigital capacitors on the other side. The filter design and the numerical results were obtained by means of specialized Ansoft HFSS software. Microwave material was selected for the purpose of mathematical simulation and prototype production, with dielectric permeability ∊ = 4.4 and thickness of h=1.5 mm. Filter cutoff frequency equals to 2000 MHz and working band is 1500 MHz. These results were obtained experimentally using vector network analyzer. The prototype was obtained using standard printed circuit boards production method and has a surface areas of 30 × 30 = 900 mm2.


title: TG-Pro: A SAT-based ATPG System

abstract: Automatic Test Pattern Generation (ATPG) is arguably one of the practical applications that motivated the development of modern Boolean Satisfiability (SAT) solvers in the mid 90s. Despite the interest of using SAT in ATPG, the original model remained mostly unchanged for nearly two decades, even in the presence of renewed interest in applying modern SAT technology to large-scale hardware designs. This paper describes the SAT-based ATPG system TG-Pro. In contrast to all SAT-based ATPG work over the last two decades, TG-Pro is based on a new fundamentally different SAT-based ATPG model. Experimental results, obtained on well-known and publicly available benchmarks, demonstrate that TG-Pro achieves major performance improvements over other well-established SAT-based ATPG models.


title: Learning Capsules for Vehicle Logo Recognition

abstract: Vehicle logo recognition is an important part of vehicle identification in intelligent transportation systems. State-of-the-art vehicle logo recognition approaches use automatically learned features from Convolutional Neural Networks (CNNs). However, CNNs do not perform well when images are rotated and very noisy. This paper proposes an image recognition framework with a capsule network. A capsule is a group of neurons, whose length can represent the existence probability of an entity or part of an entity. The orientation of a capsule contains information about the instantiation parameters such as positions and orientations. Capsules are learned by a routing process, which is more effective than the pooling process in CNNs. This paper, for the first time, develops a capsule learning framework in the field of intelligent transportation systems. By testing with the largest publicly available vehicle logo dataset, the proposed framework gives a quick solution and achieves the highest accuracy (100%) on this dataset. The learning capsules have been tested with different image changes such as rotation and occlusion. Image degradations including blurring and noise effects are also considered, and the proposed framework has proven to be superior to CNNs.


title: 802.11s based multi-radio multi-channel mesh networking for fractionated spacecraft

abstract: 802.11s is a new IEEE standard for mesh networking. It defines the protocols needed to build mobile ad hoc networks that operate over 802.11a, b, g and n waveforms running on inexpensive, and high performance commercial WiFi stations. We have developed a new capability to add to the 802.11s that uses multiple directional radio links that can operate simultaneously within a single mesh node. This is the basis of our multi-channel multi-radio mesh network used in the DARPA F6 program called F6Net. We have developed an analysis and emulation facility that lets us model the F6Net and evaluate the performance in a real world experimentation setup. This paper presents an “Over-the-Air” experimentation testbed that uses standard Commercial Off-The-Shelf (COTS) 2.4GHz WiFi dongles in an indoor environment, and a shared-code simulation testbed that uses hardware simulated drivers within NS3's channel simulation facility to test 80211s network. To the best of our knowledge, this is the first work that provides a comprehensive evaluation platform with a full-fledged COTS hardware/software prototype to evaluate 802.11s network. Furthermore, we explain the design and development of multi-radio mesh extension for 802.11s that yields a robust and scalable mesh network suitable for clusters of LEO satellites.


title: Reconstructing the lost reality archaeological analysis and Transmedial Technologies for a perspective of Virtual Reality in the Etruscan city of Kainua

abstract: This paper presents the very first results of a three-year research program concerning the digital reconstruction of an entire Etruscan town, based on a philological analysis of archaeological data. The archaeological site and the adjacent museum will be then provided with suitable systems of perception and discovery of the archaeological reality, fully reconstructed due to innovative Transmedial Technologies based on Virtual Reality and, since this work is still in progress, studied through future digital models based on historic BIM process.


title: Using the Structure of a Conceptual Network in Computing Semantic Relatedness

abstract: We present a new method for computing semantic relatedness of concepts. The method relies solely on the structure of a conceptual network and eliminates the need for performing additional corpus analysis. The network structure is employed to generate artificial conceptual glosses. They replace textual definitions proper written by humans and are processed by a dictionary based metric of semantic relatedness [1]. We implemented the metric on the basis of GermaNet, the German counterpart of WordNet, and evaluated the results on a German dataset of 57 word pairs rated by human subjects for their semantic relatedness. Our approach can be easily applied to compute semantic relatedness based on alternative conceptual networks, e.g. in the domain of life sciences.


title: Analysis of waveguide slot-based structures using wide-band equivalent-circuit model

abstract: Analysis of geometrically complicated waveguide-based slotted arrays and filters is performed using a simple equivalent-circuit model. First, the circuit parameters (inductance and capacitance) of a simple waveguide slot-coupler problem are obtained through moment-method (MoM) analysis. The values of the lumped LC elements are virtually constant over the frequency range of interest (the X-band) for specific waveguide and slot dimensions. Based on the equivalent-circuit model of a single slot of two coupled waveguides, more complicated structures are then analyzed, such as slot coupler arrays and slot-based waveguide filters. The scattering parameters of these structures are obtained through circuit analysis, and are verified using the MoM and finite-difference time-domain method. Excellent agreement is observed over a wide band of frequencies and is confirmed by experimental results.


Ensemble Correlation Coefficient


title: Dilated Residual Networks

abstract: Convolutional networks for image classification progressively reduce resolution until the image is represented by tiny feature maps in which the spatial structure of the scene is no longer discernible. Such loss of spatial acuity can limit image classification accuracy and complicate the transfer of the model to downstream applications that require detailed scene understanding. These problems can be alleviated by dilation, which increases the resolution of output feature maps without reducing the receptive field of individual neurons. We show that dilated residual networks (DRNs) outperform their non-dilated counterparts in image classification without increasing the model's depth or complexity. We then study gridding artifacts introduced by dilation, develop an approach to removing these artifacts (`degridding'), and show that this further increases the performance of DRNs. In addition, we show that the accuracy advantage of DRNs is further magnified in downstream applications such as object localization and semantic segmentation.


title: Design and Implementation of a Diplexer and a Dual-Band VHF/UHF Antenna for Nanosatellites

abstract: CONSAT1 is a nanosatellite of the form 3U CubeSat designed and built by the Space Concordia group at Concordia University during the first Canadian Satellite Design Challenge (CSDC). Its mission is to detect and study high-energy particles in low earth orbits. This CubeSat relies on a single antenna to communicate with a ground station. The antenna feeds a diplexer that separates the transmit and receive frequencies. This letter shows the design and implementation of a dual-band spring-steel monopole antenna that resonates at both the uplink frequency of 146 MHz and the downlink at 438 MHz, and the associated diplexer. Agreement between simulation and pre-launch measurement on the flight hardware is demonstrated.


title: Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification

abstract: Collecting labeled samples is quite costly and time-consuming for hyperspectral image (HSI) classification task. Semisupervised learning framework, which combines the intrinsic information of labeled and unlabeled samples, can alleviate the deficient labeled samples and increase the accuracy of HSI classification. In this letter, we propose a novel semisupervised learning framework that is based on spectral–spatial graph convolutional networks ( $\text{S}^{2}$ GCNs). It explicitly utilizes the adjacency nodes in graph to approximate the convolution. In the process of approximate convolution on graph, the proposed method makes full use of the spatial information of the current pixel. The experimental results on three real-life HSI data sets, i.e., Botswana Hyperion, Kennedy Space Center, and Indian Pines, show that the proposed $\text{S}^{2}$ GCN can significantly improve the classification accuracy. For instance, the overall accuracy on Indian data is increased from 66.8% (GCN) to 91.6%.


title: A data mining approach to employee turnover prediction (case study: Arak automotive parts manufacturing)

abstract: Pricing is one of the major aspects of decision making in supply chain. In the previous works mostly a centralized environment is considered indicating the retailers cannot independently apply their decisions on the pricing strategy. Although in a two-echelon decentralized environment it may be possible that supply chain contributors have encountered with different market power situations which provide that some of them try to impose their interests in pricing and/or volume of the products. In such situations the leader-follower Stackelberg game or more specifically bi-level programming seems to be the best approach to overcome the problem. Furthermore, in this study we consider the impacts of disruption risk caused by foreign exchange uncertainty on pricing decisions in a multi-product two-echelon supply chain. Also it is assumed that the market is partitioned to domestic and international retailers with segmented market for each retailer. The purpose of this paper is to introduce decisions policy on the pricing such that the utility of both manufacturer and retailers is met. Since the proposed bi-level model is NP-hard, a simulated annealing method combining with Tabu search is proposed to solve the model. A numerical example is presented to investigate the effect of foreign exchange variation on the decision variables through different scenarios. The results from numerical example indicate that the international retailers are indifferent to the manufacture undergoes changes where the domestic retailers react to changes, dramatically.


title: 2-20 GHz non-uniform coupler for six-port reflectometer

abstract: In this paper a six-port reflectometer to be included in a near-field microwave microscope (NFMM) is presented. The reflectometer, made of five couplers based on non-uniform and offset parallel-coupled transmission lines, is proposed to achieve a broad frequency band [2-20 GHz], corresponding to the frequency range of the interferometer based NFMM. It becomes then possible to replace the vector network analyzer classically used in NFMMS.


Evolution of Modular Neural Network in Medical Diagnosis


title: Adaptive Least Squares Correlation: A Powerful Image Matching Technique

abstract: The Adaptive Least Squares Correlation is a very potent and flexible technique for all kinds of data matching problems. Here its application to image matching is outlined. It allows for simultaneous radiometric corrections and local geometrical image shaping, whereby the system parameters are automatically assessed, corrected, and thus optimized during the least squares iterations. The various tools of least squares estimation can be favourably utilized for the assessment of the correlation quality. Furthermore, the system allows for stabilization and improvement of the correlation procedure through the simultaneous consideration of geometrical constraints, e.g. the collinearity condition. Some exciting new perspectives are emphasized, as for example multiphoto correlation, multitemporal and multisensor correlation, multipoint correlation, and simultaneous correlation/triangulation.


title: Design Analysis of a Mobile-Based Gait Analyzer

abstract: Considering a system capable of identifying abnormalities in people's walking conditions in real-time, simply by studying his/her walking profile over a short period of time is a phenomenal breakthrough in the field of biotechnology. Such abnormalities could be as a result of injury, old age, or disease termed gait which could be analyzed using the pressure mapping technology. Pressure points in the feet of an injured person as he/she walks is analyzed by sets of sensors (capacitive sensors) carefully design with a rectangular 5.1cm by 2cm parallel aluminium plate and placed on developed footwear with a uniform distance of 1cm across the dielectric material. The output of the pre-processing stage gives varying values which are calibrated and sent to the microcontroller. All placed on a portable sized Printed Circuit Board (PCB) making it moveable from one place to another (that is, mobile), is the pre-processing circuit that converts measured or evaluated result to the transmittable signal through a Mobile Communication System which can be received on a Personal Computer (PC) in form of a periodic chat and/ or report. The result of the analysis is shown both in simulation and hardware implementation of the system


title: Efficient music recommender system using context graph and particle swarm

abstract: Music recommender systems is an important field of research because of easy availability and use of online music. The most existing models only focus on explicit data like ratings and other user-item dimensions. A challenging problem in music recommendation is to model a variety of contextual information, such as feedback, time and location. In this article, we proposed a competent hybrid music recommender system (HMRS), which works on context and collaborative approaches. The timestamp is extracted from users listening log to construct a decision context behavior that extracted various temporal features like a week, sessions(as morning, evening or night). We used depth-first-search (DFS) algorithm which traverses the whole graph through the paths in different contexts. Bellman-Ford algorithm provides ranked list of recommended items with multi-layer context graph. We enhanced the process using particle swarm optimization (PSO) which produced highly optimized results. The dataset is used from Last.fm which contains 19,150,868 music listening logs of 992 users (till May, 4th 2009). We extract the properties of music from user’s listening history and evaluate the efficient system to recommend music based on user’s contextual preferences. Our system noticeably delivers the best recommendations regarding recall results when compared to existing methods.


title: Bad news travel fast: a content-based analysis of interestingness on Twitter

abstract: On the microblogging site Twitter, users can forward any message they receive to all of their followers. This is called a retweet and is usually done when users find a message particularly interesting and worth sharing with others. Thus, retweets reflect what the Twitter community considers interesting on a global scale, and can be used as a function of interestingness to generate a model to describe the content-based characteristics of retweets. In this paper, we analyze a set of high- and low-level content-based features on several large collections of Twitter messages. We train a prediction model to forecast for a given tweet its likelihood of being retweeted based on its contents. From the parameters learned by the model we deduce what are the influential content features that contribute to the likelihood of a retweet. As a result we obtain insights into what makes a message on Twitter worth retweeting and, thus, interesting.


title: Novice-based data collection methods for the study of IOIS: practice probes and learning communities

abstract: In response to the increasing influence of practice theory perspectives for studying organisational and inter-organisational information systems, we demonstrate that an important dilemma from this perspective for data collection methods is between authentic access to practices and the ability to thematize knowledge of practices. We propose a promising new approach to this dilemma that uses the learning experiences of novice practitioners to collect data as they are progressively enrolled in the practice, and describe two instantiations of this new approach, practice probes and learning communities.


title: MMed cohort supervision: A path out of the swamp?

abstract: The authors present the case for collaborative cohort supervision (CCM), including both master's students and novice supervisors, as a possible way to rapidly increase the number of supervisors needed to address the recent implementation of a compulsory research component to specialist registration with the Health Professions Council of South Africa. Different models of CCM are discussed and possible pitfalls highlighted.


title: Indoor Robot Localization System Using WiFi Signal Measure and Minimizing Calibration Effort

abstract: This paper presents a robot localization system for indoor environments using WiFi signal strength measure. We analyse the main causes of the WiFi signal strength variation and we experimentally demonstrate that a localization technique based on a propagation model doesn't work properly in our test-bed. We have carried out a localization system based on a priori radio-map obtained automatically from a robot navigation in the environment in a semi-autonomous way. We analyse the effect of reducing calibration effort in order to diminish practical barriers to wider adoption of this type of location measurement technique. Experimental results using a real robot moving are shown. Finally, the conclusions and future works are presented.


title: Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors

abstract: We present a novel algorithm for detection of certain types of unusual events. The algorithm is based on multiple local monitors which collect low-level statistics. Each local monitor produces an alert if its current measurement is unusual and these alerts are integrated to a final decision regarding the existence of an unusual event. Our algorithm satisfies a set of requirements that are critical for successful deployment of any large-scale surveillance system. In particular, it requires a minimal setup (taking only a few minutes) and is fully automatic afterwards. Since it is not based on objects' tracks, it is robust and works well in crowded scenes where tracking-based algorithms are likely to fail. The algorithm is effective as soon as sufficient low-level observations representing the routine activity have been collected, which usually happens after a few minutes. Our algorithm runs in real-time. It was tested on a variety of real-life crowded scenes. A ground-truth was extracted for these scenes, with respect to which detection and false-alarm rates are reported.


title: Looking toward the future of IT-bUSINESS strategic alignment through the past: a meta-analysis

abstract: Research examining the relationship between IT-business strategic alignment (hereafter referred to as alignment) and firm performance (hereafter referred to as performance) has produced apparently conflicting findings (i.e., an alignment paradox). To examine the alignment paradox, we conducted a meta-analysis that probed the interrelationships between alignment, performance, and context constructs. We found the alignment dimensions (intellectual, operational, and cross-domain) demonstrate unique relationships with the different performance types (financial performance, productivity, and customer benefit) and with many of the other constructs in alignment's nomological network. All mean corrected correlations between dimensions of alignment and dependent variables were positive and most of the credibility interval values in these analyses were also positive. Overall, the evidence gathered from the extant literature suggests there is not much of an alignment paradox. This study contributes to the literature by clarifying the relationships between alignment and performance outcomes and offering insight into sources of inconsistencies in alignment research. By doing so, this paper lays a foundation for more consistent treatment of alignment in future IT research.


title: The effects of harmonics on overcurrent relays

abstract: This paper analyses the influence of current harmonics on protections devices connected to the power system which can cause serious problems. This paper outlines work that has been conducted to test of overcurrent relay and to identify the impacts of harmonics on distribution utility protection and control systems. The overcurrent relay REJ 525 is based on a microprocessor environment. A self-supervision system continuously monitors the operation of the relay.


Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network


title: Procedural wood textures

abstract: Existing bidirectional reflectance distribution function (BRDF) models are capable of capturing the distinctive highlights produced by the fibrous nature of wood. However, capturing parameter textures for even a single specimen remains a laborious process requiring specialized equipment. In this paper we take a procedural approach to generating parameters for the wood BSDF. We characterize the elements of trees that are important for the appearance of wood, discuss techniques appropriate for representing those features, and present a complete procedural wood shader capable of reproducing the growth patterns responsible for the distinctive appearance of highly prized ``figured'' wood. Our procedural wood shader is random-access, 3D, modular, and is fast enough to generate a preview for design.


title: A New Local Adaptive Thresholding Technique in Binarization

abstract: Image binarization is the process of separation of pixel values into two groups, white as background and black as foreground. Thresholding plays a major in binarization of images. Thresholding can be categorized into global thresholding and local thresholding. In images with uniform contrast distribution of background and foreground like document images, global thresholding is more appropriate. In degraded document images, where considerable background noise or variation in contrast and illumination exists, there exists many pixels that cannot be easily classified as foreground or background. In such cases, binarization with local thresholding is more appropriate. This paper describes a locally adaptive thresholding technique that removes background by using local mean and mean deviation. Normally the local mean computational time depends on the window size. Our technique uses integral sum image as a prior processing to calculate local mean. It does not involve calculations of standard deviations as in other local adaptive techniques. This along with the fact that calculations of mean is independent of window size speed up the process as compared to other local thresholding techniques.


title: Comparison of Induction and PM Synchronous Motor Drives for EV Application Including Design Examples

abstract: Three different motor drives for electric traction are compared, in terms of output power and efficiency at the same stack dimensions and inverter size. Induction motor (IM), surface-mounted permanent-magnet (PM) (SPM), and interior PM (IPM) synchronous motor drives are investigated, with reference to a common vehicle specification. The IM is penalized by the cage loss, but it is less expensive and inherently safe in case of inverter unwilled turnoff due to natural de-excitation. The SPM motor has a simple construction and shorter end connections, but it is penalized by eddy-current loss at high speed, has a very limited transient overload power, and has a high uncontrolled generator voltage. The IPM motor shows the better performance compromise, but it might be more complicated to be manufactured. Analytical relationships are first introduced and then validated on three example designs and finite element calculated, accounting for core saturation, harmonic losses, the effects of skewing, and operating temperature. The merits and limitations of the three solutions are quantified comprehensively and summarized by the calculation of the energy consumption over the standard New European Driving Cycle.


title: Gender recognition from mobile biometric data

abstract: This paper investigates gender recognition from keystroke dynamics data and from touchscreen swipes. Classification measurements were performed using 10-fold cross-validation and leave-one-user-out cross-validation (LOUOCV). We show that when the target is unseen user data classification, only the second approach is viable. Based on our limited datasets, we show that gender cannot be reliably predicted. The best results were 64.76% for the keystroke dataset and 57.16% for the swipes dataset. However, the classification accuracy is over 80% for more than half of the users in the case of keystroke dynamics dataset.


title: MDAC design for 1.5-bit pipeline stage of high-speed high-resolution ADC

abstract: This paper presents a design of residue amplification circuit (MDAC) used in the first 1.5-bit pipeline stage of an ADC, and the MDAC should meet requirements of a 100MS/s 14-bit pipeline ADC with 1.8V supply voltage. In order to obtain the corresponding performance, the circuits such as operational amplifier and bootstrap circuit are designed which could realize the objective of high-speed and high-resolution. The gain-boost structure is used in the amplifier to obtain the specified resolution, and an optimization between speed and power dissipation should be carefully conducted as a high speed requires a large slew rate or trans-conductance which is proportional to power dissipation. The design is implemented in the 0.18µm CMOS process with 9.6mW power consumption and the simulation results in Spectre illustrate that, the designed operational amplifier could reach the fixed objective and the residue signal of MDAC could set up completely in the specified time 3ns.


title: Gait, wrist, and sensors: Detecting freezing of gait in Parkinson's disease from wrist movement

abstract: We investigate the correlation between wrist movement and freezing of the gait in Parkinsons disease. Detecting such freezes allows real-time monitoring to reduce the risk of falls in subjects with Parkinson's. While most of research focuses on placing inertial sensors on lower limb, i.e., foot, ankle, thigh, lower back, we focus on the wrist as an alternative placement. Commonly worn accessories at the wrist such as watches or wristbands are easier to be accepted and worn by elderly users, in special subjects with motor problems. Experiments on data from 11 subjects show that freezing of gait episodes can be detected using the wrist movements, with a freeze hit-rate of 90% and 83% specificity in a subject-dependent evaluation scheme. This suggests that wrist sensors can be a feasible alternative to the cumbersome placement on the legs.


title: Robotic Online Path Planning on Point Cloud

abstract: This paper deals with the path-planning problem for mobile wheeled- or tracked-robot which drive in 2.5-D environments, where the traversable surface is usually considered as a 2-D-manifold embedded in a 3-D ambient space. Specially, we aim at solving the 2.5-D navigation problem using raw point cloud as input. The proposed method is independent of traditional surface parametrization or reconstruction methods, such as a meshing process, which generally has high-computational complexity. Instead, we utilize the output of 3-D tensor voting framework on the raw point clouds. The computation of tensor voting is accelerated by optimized implementation on graphics computation unit. Based on the tensor voting results, a novel local Riemannian metric is defined using the saliency components, which helps the modeling of the latent traversable surface. Using the proposed metric, we prove that the geodesic in the 3-D tensor space leads to rational path-planning results by experiments. Compared to traditional methods, the results reveal the advantages of the proposed method in terms of smoothing the robot maneuver while considering the minimum travel distance.


title: A Current-Driving Synchronous Rectifier for an LLC Resonant Converter With Voltage-Doubler Rectifier Structure

abstract: This paper proposes a current-driving scheme for a synchronous rectifier (SR) in an LLC resonant converter with a voltage-doubler rectifier. In the proposed scheme, only one current transformer (CT) with one secondary winding is used to drive two SRs in the voltage-doubler rectifier. Also, the sensed current from the CT can be fed to the output with an energy-recovery circuit. Compared to the conventional center-tapped rectifier, the CT count for the SR drive circuit is reduced, which saves both cost and space. Although the current rating of the SR in the voltage-doubler rectifier is doubled, the voltage rating is well reduced and a lower voltage rating device with lower ON-state resistance can be used to compensate the increased current rating. Furthermore, the SR voltage stress is well clamped to the output voltage under any condition which improves the reliability of the circuit. A prototype with 16 V/5.6 A output was built to verify the theoretical analysis.


title: Solving the Resource Constrained Project Scheduling Problem with Generalized Precedences by Lazy Clause Generation

abstract: The technical report presents a generic exact solution approach for minimizing the project duration of the resource-constrained project scheduling problem with generalized precedences (Rcpsp/max). The approach uses lazy clause generation, i.e., a hybrid of finite domain and Boolean satisfiability solving, in order to apply nogood learning and conflict-driven search on the solution generation. Our experiments show the benefit of lazy clause generation for finding an optimal solutions and proving its optimality in comparison to other state-of-the-art exact and non-exact methods. The method is highly robust: it matched or bettered the best known results on all of the 2340 instances we examined except 3, according to the currently available data on the PSPLib. Of the 631 open instances in this set it closed 573 and improved the bounds of 51 of the remaining 58 instances.


title: Role of lens to sample distance during laser-induced damage in zinc targets

abstract: We report on the experimental investigation of nanosecond laser-induced Zn target damages versus the lens to sample distance (LTSD). The varieties of typical surface profiles associated with the evolutions of laser ignited plasma expansion were described in detail. According to the observed results, an established transient state of modified structure within an early phase of single-pulse laser ablation should play a critical role in the steps of final damage and plasma expanding. Due to the transient state, re-enhance absorption of laser energy will occur immediately in this case. On the other hand, it is deduced that the rise curves of transient absorption coefficient should obey an exponential growth, as the LTSD increasing from 103 mm to focal length ~ 111 mm.


title: Wide-Angle Scanning Phased Array With Pattern Reconfigurable Elements

abstract: A novel phased array is presented to extend array scanning range by using pattern reconfigurable antenna elements and weighted thinned synthesis technology in this paper. The pattern reconfigurable microstrip Yagi antenna element is used as a basic element in array and it is capable of reconfiguring its patterns from broadside to quasi-endfire radiation by shifting states of the PIN diode switches integrated on parasitic strips. A weighted thinned linear array synthesis technique is analyzed and some interesting conclusions have been made. A linear array composed of eight pattern reconfigurable antenna elements is manufactured to demonstrate the excellent performance of the array. The active element pattern of each element is measured and pre-stored. Based on active element patterns and weighted thinned linear array synthesis technique, the pattern scanning performance of the novel array is synthesized. The results indicate that the array can scan its main beam from φ = -60° to φ = 60° in H-plane with gain fluctuation less than 3 dB while maintaining low side lobes, and the -3 dB beam width coverage is about from φ = -68° to φ = 68°. The performance is superior to the traditional phased array made of wide-beam elements.


Coattention Model for Question Answering


Empirical Bayes for Learning to Learn


title: Hard drive failure prediction using Decision Trees

abstract: This paper proposes two hard drive failure prediction models based on Decision Trees (DTs) and Gradient Boosted Regression Trees (GBRTs) which perform well in prediction performance as well as stability and interpretability. The models are evaluated on a real-world dataset containing 121,698 drives in total. Experimental results show the DT model predicts over 93% of failures at a false alarm rate under 0.01%, and the GBRT model can achieve about 90% failure detection rate without any false alarms. Moreover, the GBRT model evaluates drive health (or fault probability) which provides a quantitative indicator of failure urgency. This enables operators to allocate system resources accordingly for pre-warning migrations while maintaining the quality of user services.


title: Video Affective Content Analysis: A Survey of State-of-the-Art Methods

abstract: Video affective content analysis has been an active research area in recent decades, since emotion is an important component in the classification and retrieval of videos. Video affective content analysis can be divided into two approaches: direct and implicit. Direct approaches infer the affective content of videos directly from related audiovisual features. Implicit approaches, on the other hand, detect affective content from videos based on an automatic analysis of a user’s spontaneous response while consuming the videos. This paper first proposes a general framework for video affective content analysis, which includes video content, emotional descriptors, and users’ spontaneous nonverbal responses, as well as the relationships between the three. Then, we survey current research in both direct and implicit video affective content analysis, with a focus on direct video affective content analysis . Lastly, we identify several challenges in this field and put forward recommendations for future research.


title: Antenna Design for Low-Cost Laptop-based Ground Penetrating Radar

abstract: Ground penetrating radar (GPR) is non-destructive device used for monitoring underground structures. COST Action TU1208 promoted its use outside the civil engineering applications and provided a lot of free resources to the GPR community. In this paper, we built a low-cost GPR prototype for educational purposes according to the given resources and continued the work with focus on GPR antenna design. According to the required radiation characteristics, some antenna types are thoroughly discussed, fabricated and measured.


title: Microsoft's Submission to the WMT2018 News Translation Task: How I Learned to Stop Worrying and Love the Data

abstract: This paper describes the Microsoft submission to the WMT2018 news translation shared task. We participated in one language direction -- English-German. Our system follows current best-practice and combines state-of-the-art models with new data filtering (dual conditional cross-entropy filtering) and sentence weighting methods. We trained fairly standard Transformer-big models with an updated version of Edinburgh's training scheme for WMT2017 and experimented with different filtering schemes for Paracrawl. According to automatic metrics (BLEU) we reached the highest score for this subtask with a nearly 2 BLEU point margin over the next strongest system. Based on human evaluation we ranked first among constrained systems. We believe this is mostly caused by our data filtering/weighting regime.


title: Efficient resource management for Cloud computing environments

abstract: The notion of Cloud computing has not only reshaped the field of distributed systems but also fundamentally changed how businesses utilize computing today. While Cloud computing provides many advanced features, it still has some shortcomings such as the relatively high operating cost for both public and private Clouds. The area of Green computing is also becoming increasingly important in a world with limited energy resources and an ever-rising demand for more computational power. In this paper a new framework is presented that provides efficient green enhancements within a scalable Cloud computing architecture. Using power-aware scheduling techniques, variable resource management, live migration, and a minimal virtual machine design, overall system efficiency will be vastly improved in a data center based Cloud with minimal performance overhead.


title: The Social Self in Early Adolescence: Two Longitudinal Investigations in the United States and China.

abstract: This research examined how children's inclusion of social personal attributes (e.g., talkative and argumentative) in their views of themselves changes over early adolescence in the United States and China. In 2 studies (N = 825 in Study 1 and 394 in Study 2) using open-ended methods (e.g., completion of "I . . ." stems), American and Chinese children described themselves multiple times during the 7th and 8th grades. Conceptions of the self in terms of personal attributes were the norm in both the United States and China, but personal attributes that are social became more common over early adolescence in only the United States. Study 1 indicated that increases in social personal attributes were accompanied by heightened psychological and positive descriptions of the self in both countries. Study 2 ruled out potential confounds (e.g., pubertal status) in the differences in American and Chinese children's use of social personal attributes to define themselves.


title: HopsFS: Scaling Hierarchical File System Metadata Using NewSQL Databases

abstract: Recent improvements in both the performance and scalability of shared-nothing, transactional, in-memory NewSQL databases have reopened the research question of whether distributed metadata for hierarchical file systems can be managed using commodity databases. In this paper, we introduce HopsFS, a next generation distribution of the Hadoop Distributed File System (HDFS) that replaces HDFS' single node in-memory metadata service, with a distributed metadata service built on a NewSQL database. By removing the metadata bottleneck, HopsFS enables an order of magnitude larger and higher throughput clusters compared to HDFS. Metadata capacity has been increased to at least 37 times HDFS' capacity, and in experiments based on a workload trace from Spotify, we show that HopsFS supports 16 to 37 times the throughput of Apache HDFS. HopsFS also has lower latency for many concurrent clients, and no downtime during failover. Finally, as metadata is now stored in a commodity database, it can be safely extended and easily exported to external systems for online analysis and free-text search.


title: Emotion Regulation Predicts Marital Satisfaction: More Than a Wives' Tale

abstract: Emotion regulation is generally thought to be a critical ingredient for successful interpersonal relationships. Ironically, few studies have investigated the link between how well spouses regulate emotion and how satisfied they are with their marriages. We utilized data from a 13-year, 3-wave longitudinal study of middle-aged (40–50 years old) and older (60–70 years old) long-term married couples, focusing on the associations between downregulation of negative emotion (measured during discussions of an area of marital conflict at Wave 1) and marital satisfaction (measured at all three waves). Downregulation of negative emotion was assessed by determining how quickly spouses reduced signs of negative emotion (in emotional experience, emotional behavior, and physiological arousal) after negative emotion events. Data were analyzed using actor-partner interdependence modeling. Findings showed that (a) greater downregulation of wives’ negative experience and behavior predicted greater marital satisfaction for wives and husbands concurrently and (b) greater downregulation of wives’ negative behavior predicted increases in wives’ marital satisfaction longitudinally. Wives’ use of constructive communication (measured between Waves 1 and 2) mediated the longitudinal associations. These results show the benefits of wives’ downregulation of negative emotion during conflict for marital satisfaction and point to wives’ constructive communication as a mediating pathway.


title: Controlling UAVs with sensor input spoofing attacks

abstract: There has been a recent surge in interest in autonomous robots and vehicles. From the Google self-driving car, to autonomous delivery robots, to hobbyist UAVs, there is a staggering variety of proposed deployments for autonomous vehicles. Ensuring that such vehicles can plan and execute routes safely is crucial. ::: ::: The key insight of our paper is that the sensors that autonomous vehicles use to navigate represent a vector for adversarial control. With direct knowledge of how sensor algorithms operate, the adversary can manipulate the victim's environment to form an implicit control channel on the victim. We craft an attack based on this idea, which we call a sensor input spoofing attack. ::: ::: We demonstrate a sensor input spoofing attack against the popular Lucas-Kanade method for optical flow sensing and characterize the ability of an attacker to trick optical flow via simulation. We also demonstrate the effectiveness of our optical flow sensor input spoofing attack against two consumer-grade UAVs, the AR.Drone 2.0 and the APM 2.5 ArduCopter. Finally, we introduce a method for defending against such an attack on optical-flow sensors, both using the RANSAC algorithm and a more robust weighted RANSAC algorithm to synthesize sensor outputs.


title: Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction

abstract: Stock trend prediction plays a critical role in seeking maximized profit from the stock investment. However, precise trend prediction is very difficult since the highly volatile and non-stationary nature of the stock market. Exploding information on the Internet together with the advancing development of natural language processing and text mining techniques have enabled investors to unveil market trends and volatility from online content. Unfortunately, the quality, trustworthiness, and comprehensiveness of online content related to stock market vary drastically, and a large portion consists of the low-quality news, comments, or even rumors. To address this challenge, we imitate the learning process of human beings facing such chaotic online news, driven by three principles: sequential content dependency, diverse influence, and effective and efficient learning. In this paper, to capture the first two principles, we designed a Hybrid Attention Networks(HAN) to predict the stock trend based on the sequence of recent related news. Moreover, we apply the self-paced learning mechanism to imitate the third principle. Extensive experiments on real-world stock market data demonstrate the effectiveness of our framework. A further simulation illustrates that a straightforward trading strategy based on our proposed framework can significantly increase the annualized return.


title: SOAL: Second-Order Online Active Learning

abstract: This paper investigates the problem of online active learning for training classification models from sequentially arriving data. This is more challenging than conventional online learning tasks since the learner not only needs to figure out how to effectively update the classifier but also needs to decide when is the best time to query the label of an incoming instance given limited label budget. The existing online active learning approaches are often based on first-order online learning methods which generally fall short in slow convergence rate and sub-optimal exploitation of available information when querying the labeled data. To overcome the limitations, in this paper, we present a new framework of Second-order Online Active Learning (SOAL), which fully exploits both first-order and second-order information to achieve high learning accuracy with low labeling cost. We conduct both theoretical analysis and empirical studies for evaluating the proposed SOAL algorithm extensively. The encouraging results show clear advantages of the proposed algorithm over a family of state-of-the-art online active learning algorithms.


title: Hybrid Risk Assessment Model Based on Bayesian Networks

abstract: Because of the threat posed by advanced multi-step attacks, it is difficult for security operators to fully cover all vulnerabilities when deploying countermeasures. Deploying sensors to monitor attacks exploiting residual vulnerabilities is not sufficient and new tools are needed to assess the risk associated with the security events produced by these sensors. Although attack graphs were proposed to represent known multi-step attacks occurring in an information system, they are not directly suited for dynamic risk assessment. In this paper, we present the Hybrid Risk Assessment Model (HRAM), a Bayesian network-based extension to topological attack graphs, capable of handling topological cycles, making it fit for any information system. This hybrid model is subdivided in two complementary models: (1) Dynamic Risk Correlation Models, correlating a chain of alerts with the knowledge on the system to analyse ongoing attacks and provide the hosts’ compromise probabilities, and (2) Future Risk Assessment Models, taking into account existing vulnerabilities and current attack status to assess the most likely future attacks. We validate the performance and accuracy of this model on simulated network topologies and against diverse attack scenarios of realistic size.


title: QoS-Aware Web Service Recommendation by Collaborative Filtering

abstract: With increasing presence and adoption of Web services on the World Wide Web, Quality-of-Service (QoS) is becoming important for describing nonfunctional characteristics of Web services. In this paper, we present a collaborative filtering approach for predicting QoS values of Web services and making Web service recommendation by taking advantages of past usage experiences of service users. We first propose a user-collaborative mechanism for past Web service QoS information collection from different service users. Then, based on the collected QoS data, a collaborative filtering approach is designed to predict Web service QoS values. Finally, a prototype called WSRec is implemented by Java language and deployed to the Internet for conducting real-world experiments. To study the QoS value prediction accuracy of our approach, 1.5 millions Web service invocation results are collected from 150 service users in 24 countries on 100 real-world Web services in 22 countries. The experimental results show that our algorithm achieves better prediction accuracy than other approaches. Our Web service QoS data set is publicly released for future research.


title: Ontology Development 101: A Guide to Creating Your First Ontology

abstract: 1 Why develop an ontology? In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of ArtificialIntelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web. The ontologies on the Web range from large taxonomies categorizing Web sites (such as on Yahoo!) to categorizations of products for sale and their features (such as on Amazon.com). The WWW Consortium (W3C) is developing the Resource Description Framework (Brickley and Guha 1999), a language for encoding knowledge on Web pages to make it understandable to electronic agents searching for information. The Defense Advanced Research Projects Agency (DARPA), in conjunction with the W3C, is developing DARPA Agent Markup Language (DAML) by extending RDF with more expressive constructs aimed at facilitating agent interaction on the Web (Hendler and McGuinness 2000). Many disciplines now develop standardized ontologies that domain experts can use to share and annotate information in their fields. Medicine, for example, has produced large, standardized, structured vocabularies such as SNOMED (Price and Spackman 2000) and the semantic network of the Unified Medical Language System (Humphreys and Lindberg 1993). Broad general-purpose ontologies are emerging as well. For example, the United Nations Development Program and Dun & Bradstreet combined their efforts to develop the UNSPSC ontology which provides terminology for products and services (www.unspsc.org). An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them. Why would someone want to develop an ontology? Some of the reasons are:


title: Large-Scale Location Recognition and the Geometric Burstiness Problem

abstract: Visual location recognition is the task of determining the place depicted in a query image from a given database of geo-tagged images. Location recognition is often cast as an image retrieval problem and recent research has almost exclusively focused on improving the chance that a relevant database image is ranked high enough after retrieval. The implicit assumption is that the number of inliers found by spatial verification can be used to distinguish between a related and an unrelated database photo with high precision. In this paper, we show that this assumption does not hold for large datasets due to the appearance of geometric bursts, i.e., sets of visual elements appearing in similar geometric configurations in unrelated database photos. We propose algorithms for detecting and handling geometric bursts. Although conceptually simple, using the proposed weighting schemes dramatically improves the recall that can be achieved when high precision is required compared to the standard re-ranking based on the inlier count. Our approach is easy to implement and can easily be integrated into existing location recognition systems.


title: Investigations of SIW Leaky-Wave Antenna for Endfire-Radiation With Narrow Beam and Sidelobe Suppression

abstract: A new substrate integrated waveguide (SIW) leaky-wave antenna is investigated for endfire-radiation with a narrow beam and sidelobe suppression. Maximum directivity conditions for endfire-radiation from line sources with different amplitude distributions are theoretically discussed as a design aid. Interestingly, for endfire beams it is seen that designs that have a lower sidelobe level can also have a higher directivity, contrary to what is normally encountered for broadside beams. An SIW leaky-wave antenna with tapered transverse slots on only the top and bottom planes is presented. Compared with a previous leaky-wave antenna having uniform transverse slots on the top plane, the presented leaky-wave antenna has a main beam that can radiate exactly at endfire and also has a lower sidelobe level. The design of the low sidelobe antenna is based on the leaky mode, which loses physical significance as the beam is scanned to the endfire direction. Nevertheless, the antenna retains a good beam shape and a low sidelobe level when it radiates at endfire. A prototype is made, and measured results are consistent with theoretical and simulated results.


title: Forecasting construction industry demand, price and productivity in Singapore: the Box-Jenkins approach

abstract: In academic research, the traditional Box-Jenkins approach is widely acknowledged as a benchmark technique for univariate methods because of its structured modelling basis and acceptable forecasting performance. This study examines the versatility of this approach by applying it to analyse and forecast three distinct variables of the construction industry, namely, tender price, construction demand and productivity, based on case studies of Singapore. In order to assess the adequacy of the Box-Jenkins approach to construction industry forecasting, the models derived are evaluated on their predictive accuracy based on out-of-sample forecasts. Two measures of accuracy are adopted, the root mean-square-error (RMSE) and the mean absolute percentage error (MAPE). The conclusive findings of the study include: (1) the prediction RMSE of all three models is consistently smaller than the model's standard error, implying the models' good predictive performance; (2) the prediction MAPE of all three models consistently falls within the general acceptable limit of 10%; and (3) among the three models, the most accurate is the demand model which has the lowest MAPE, followed by the price model and the productivity model.


Asynchronous Distributed Neural Network Training using Alternating Direction Method of Multipliers


title: Context-Aware Analysis of Data Sharing Agreements

abstract: Compared with traditional images, high-resolution images obtained by unmanned aerial vehicles (UAV) are superior in many aspects such as low cost, convenience and so on. However, the attitude of unmanned aerial vehicles cannot be determined accurately, which will lead to the decrease of accuracy and efficiency of automatic matching, so image preprocessing should be done to guarantee enough degree of overlap for image mosaicking and mapping. There are some ascendant characteristics of SIFT operator such as invariant to scale, rotation, and lightness, and these characteristics are very useful in increasing the matching accuracy of UAV images. In this paper, the overlap analysis of UAV images has been processed based on the SIFT algorithm, and the steps of overlap analysis are described, and the way of treating wrong matching points is explained. Finally, satisfactory results are obtained.


Request for Comments 1945


title: Personality in Gneezy's cheap talk game: The interaction between Honesty-Humility and Extraversion in predicting deceptive behavior

abstract: Abstract Recent papers have suggested that personality traits may predict behavior in cheap talk games. This paper investigated whether Honesty-Humility predicts dishonesty and whether Extraversion moderates the relationship between Honesty-Humility and dishonesty — all as measured by senders' behaviors in a cheap talk game. We hypothesized that the correlation between Honesty-Humility and dishonesty would be stronger among those who measure high for Extraversion than among those who measure low for Extraversion. One hundred and forty-three undergraduate students participated as senders in the study. Although Honesty-Humility did not correlate significantly with dishonest behavior, the results supported the hypothesis that Honesty-Humility interacted with Extraversion to predict dishonest behavior. The results suggest that the effects of a personality trait may depend upon other traits.


title: WashInDepth: Lightweight Hand Wash Monitor Using Depth Sensor

abstract: Personnel working in health services and food preparation industries maintain hand hygiene by washing their hands. Often this follows the WHO hand hygiene guidelines. Systems exist to monitor and detect compliance with all stages of the guidelines. However, the critical step of verifying whether the subject has correctly lathered soap on the hands can only be monitored using wrist worn sensors. This comes with several disadvantages: the wearable sensors may become contaminated, require battery, the user forgets to wear or misplaces the device. We address these problems by proposing WashInDepth, a system which uses a fixed contactless depth sensor mounted above the wash basin and is activated using a wireless trigger. As the system is fixed in position and contactless, there is reduced chance of contamination, the option to not require a battery and no possibility of forgetting or misplacement of the device. This system provides the potential ability to warn a person, after they have washed their hand, if improper application of soap was detected based on their hand gestures. We evaluate the gesture detection accuracy with 15 subjects and achieve 94% gesture detection accuracy. We deploy the system on a low power Compute Stick and demonstrate that it can keep track of hand gestures in real-time when video is recorded at 20 FPS.


Analytic Error Modeling for Imprecise Arithmetic Circuits


title: Pathognomonic Cephalometric Characteristics of Angle Class II Division 2 Malocclusion

abstract: Abstract The Class II division 2 (Class II/2) malocclusion as originally defined by E.H. Angle is relatively rare. The orthodontic literature does not agree on the skeletal characteristics of this malocclusion. Several researchers claim that it is characterized by an orthognathic facial pattern and that the malocclusion is dentoalveolar per se. Others claim that the Class II/2 malocclusion has unique skeletal and dentoalveolar characteristics. The present study describes the skeletal and dentoalveolar cephalometric characteristics of 50 patients clinically diagnosed as having Class II/2 malocclusion according to Angle's original criteria. The study compares the findings with those of both a control group of 54 subjects with Class II division 1 (Class II/1) malocclusion and a second control group of 34 subjects with Class I (Class I) malocclusion. The findings demonstrate definite skeletal and dentoalveolar patterns with the following characteristics: (1) the maxilla is orthognathic, (2) the mandible has r...


title: Constrained Multi-Task Learning for Automated Essay Scoring

abstract: This is the author accepted manuscript. The final version is available from Association for Computational Linguistics at http://acl2016.org/index.php?article_id=71.


Relationship Between Psychological Empowerment and Productivity of Medical Librarians


title: Forecasting Next-Day Electricity Prices by

abstract: In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper provides two highly accurate yet efficient price forecasting tools based on time series analysis: dynamic regression and transfer function models. These techniques are explained and checked against each other. Results and discussions from real-world case studies based on the electricity markets of mainland Spain and California are presented.


title: High-dynamic single-phase Hilbert-based PLL for improved phase-jump ride-through in grid-connected inverters

abstract: The diffusion of renewable energy sources has now gained a very high importance in contributing to the electrical power needs. Grid-connected photovoltaic or wind energy conversion systems are nowadays highly integrated in the electrical grid, and they provide a consistent share of power to the nearby loads. Although this is true for high-power three-phase systems, problems related to the increasing penetration of low power, distributed generation systems into the grid structure have not been completely investigated yet. This paper proposes a control system based on a Hilbert-filter single-phase PLL with very fast response that allows the detection of fast frequency variations or phase jump in the grid voltage. The novelty resides in the use of a very short FIR transfer function to approximate the Hilbert filter in conjunction with a system that compensates the magnitude error. Simulations and experiments show that this system can be employed to increase the phase-jump ride-through capability of single phase grid-connected inverters.


title: Searching for Parking Spaces via Range-based Sensors

abstract: Real-time parking occupancy information is valuable for guiding drivers' searching for parking spaces. However, in previous practice, data acquisition requires costly infrastructure upgrades especially including installing cameras or sensors on site. In this paper, we proposed a Sensor-based Intelligent Parking System (SIPS) which is able to detect free parking spaces using range-based sensors embedded in vehicles, and share information through a connected vehicle network. A probe vehicle equipped with six Short-Range Radars (SRR) and GPS is used to collected data from field tests with known test environment configuration. Based on different scenarios of data processing, two probabilistic detection algorithms are proposed and evaluated. In the case of handling static data, a two-step clustering method reduced Type I and Type II errors to less than 15 \%. For dynamic data, a modified Simultaneous Localization and Mapping (SLAM) algorithm was applied to estimate the status of parking occupancy by using Bayesian inference and geometric approximations.


title: Cross-domain learning methods for high-level visual concept classification

abstract: Exploding amounts of multimedia data increasingly require automatic indexing and classification, e.g. training classifiers to produce high-level features, or semantic concepts, chosen to represent image content, like car, person, etc. When changing the applied domain (i.e. from news domain to consumer home videos), the classifiers trained in one domain often perform poorly in the other domain due to changes in feature distributions. Additionally, classifiers trained on the new domain alone may suffer from too few positive training samples. Appropriately adapting data/models from an old domain to help classify data in a new domain is an important issue. In this work, we develop a new cross-domain SVM (CDSVM) algorithm for adapting previously learned support vectors from one domain to help classification in another domain. Better precision is obtained with almost no additional computational cost. Also, we give a comprehensive summary and comparative study of the state- of-the-art SVM-based cross-domain learning methods. Evaluation over the latest large-scale TRECVID benchmark data set shows that our CDSVM method can improve mean average precision over 36 concepts by 7.5%. For further performance gain, we also propose an intuitive selection criterion to determine which cross-domain learning method to use for each concept.


title: Feature Selection Using Information Gain for Improved Structural-Based Alert Correlation

abstract: Grouping and clustering alerts for intrusion detection based on the similarity of features is referred to as structurally base alert correlation and can discover a list of attack steps. Previous researchers selected different features and data sources manually based on their knowledge and experience, which lead to the less accurate identification of attack steps and inconsistent performance of clustering accuracy. Furthermore, the existing alert correlation systems deal with a huge amount of data that contains null values, incomplete information, and irrelevant features causing the analysis of the alerts to be tedious, time-consuming and error-prone. Therefore, this paper focuses on selecting accurate and significant features of alerts that are appropriate to represent the attack steps, thus, enhancing the structural-based alert correlation model. A two-tier feature selection method is proposed to obtain the significant features. The first tier aims at ranking the subset of features based on high information gain entropy in decreasing order. The second tier extends additional features with a better discriminative ability than the initially ranked features. Performance analysis results show the significance of the selected features in terms of the clustering accuracy using 2000 DARPA intrusion detection scenario-specific dataset.


title: Improvements to BM25 and Language Models Examined

abstract: Recent work on search engine ranking functions report improvements on BM25 and Language Models with Dirichlet Smoothing. In this investigation 9 recent ranking functions (BM25, BM25+, BM25T, BM25-adpt, BM25L, TF1°Δ°p×ID, LM-DS, LM-PYP, and LM-PYP-TFIDF) are compared by training on the INEX 2009 Wikipedia collection and testing on INEX 2010 and 9 TREC collections. We find that once trained (using particle swarm optimization) there is very little difference in performance between these functions, that relevance feedback is effective, that stemming is effective, and that it remains unclear which function is best over-all.


title: Overview of Data Exploration Techniques

abstract: Data exploration is about efficiently extracting knowledge from data even if we do not know exactly what we are looking for. In this tutorial, we survey recent developments in the emerging area of database systems tailored for data exploration. We discuss new ideas on how to store and access data as well as new ideas on how to interact with a data system to enable users and applications to quickly figure out which data parts are of interest. In addition, we discuss how to exploit lessons-learned from past research, the new challenges data exploration crafts, emerging applications and future research directions.


title: I-SI : Scalable Architecture for Analyzing Latent Topical-Level Information From Social Media Data

abstract: We present a general visual analytics architecture that is designed and implemented to effectively analyze unstructured social media data on a large scale. Pipelined on a high-performance cluster configuration, MPI processing, and interactive visual analytics interfaces, our architecture, I-SI, closely integrates data-driven analytical methods and user-centered visual analytics. It creates a coherent analysis environment for identifying event structures, geographical distributions, and key indicators of emerging events. This environment supports monitoring, analyzing, and responding to latent information extracted from social media. We have applied the I-SI architecture to collect social media data, analyze the data on a large scale and uncover the latent social phenomena. To demonstrate the efficacy and applicability of I-SI, we describe several social media use cases in multiple domains that were evaluated by experts. The use cases demonstrate that I-SI can benefit a range of users by constructing meaningful event structures and identifying precursors to critical events within a rich, evolving set of topics. © 2012 Wiley Periodicals, Inc.


title: Marginal Maximum Likelihood Estimation of Item Response Models in R

abstract: Item response theory (IRT) models are a class of statistical models used by researchers to describe the response behaviors of individuals to a set of categorically scored items. The most common IRT models can be classified as generalized linear fixed- and/or mixed-effect models. Although IRT models appear most often in the psychological testing literature, researchers in other fields have successfully utilized IRT-like models in a wide variety of applications. This paper discusses the three major methods of estimation in IRT and develops R functions utilizing the built-in capabilities of the R environment to find the marginal maximum likelihood estimates of the generalized partial credit model. The currently available R packages ltm is also discussed.


title: The Neurocircuitry of Fear, Stress, and Anxiety Disorders

abstract: Anxiety disorders are a significant problem in the community, and recent neuroimaging research has focused on determining the brain circuits that underlie them. Research on the neurocircuitry of anxiety disorders has its roots in the study of fear circuits in animal models and the study of brain responses to emotional stimuli in healthy humans. We review this research, as well as neuroimaging studies of anxiety disorders. In general, these studies have reported relatively heightened amygdala activation in response to disorder-relevant stimuli in post-traumatic stress disorder, social phobia, and specific phobia. Activation in the insular cortex appears to be heightened in many of the anxiety disorders. Unlike other anxiety disorders, post-traumatic stress disorder is associated with diminished responsivity in the rostral anterior cingulate cortex and adjacent ventral medial prefrontal cortex. Additional research will be needed to (1) clarify the exact role of each component of the fear circuitry in the anxiety disorders, (2) determine whether functional abnormalities identified in the anxiety disorders represent acquired signs of the disorders or vulnerability factors that increase the risk of developing them, (3) link the findings of functional neuroimaging studies with those of neurochemistry studies, and (4) use functional neuroimaging to predict treatment response and assess treatment-related changes in brain function.


title: Cr-N alloy thin-film based torque sensors and joint torque servo systems for compliant robot control

abstract: This paper proposes a new torque sensing method, Cr-N alloy strain sensitive thin-film based torque sensors and distributed joint torque servo systems that enable human support robots to have capabilities to make physical interaction in adaptive and safe operation tasks. Stiffer torque sensing with stable and high-resolution sensing to meet practical level of the developed torque sensors have been achieved. We have developed the joint torque control based 4-DOF arm models in order to verify practical effectiveness of the proposed joint torque servo systems. We also demonstrated the joint torque control based bilateral master slave system exploring future applications.


title: Low back pain in older adults: risk factors, management options and future directions

abstract: Low back pain (LBP) is one of the major disabling health conditions among older adults aged 60 years or older. While most causes of LBP among older adults are non-specific and self-limiting, seniors are prone to develop certain LBP pathologies and/or chronic LBP given their age-related physical and psychosocial changes. Unfortunately, no review has previously summarized/discussed various factors that may affect the effective LBP management among older adults. Accordingly, the objectives of the current narrative review were to comprehensively summarize common causes and risk factors (modifiable and non-modifiable) of developing severe/chronic LBP in older adults, to highlight specific issues in assessing and treating seniors with LBP, and to discuss future research directions. Existing evidence suggests that prevalence rates of severe and chronic LBP increase with older age. As compared to working-age adults, older adults are more likely to develop certain LBP pathologies (e.g., osteoporotic vertebral fractures, tumors, spinal infection, and lumbar spinal stenosis). Importantly, various age-related physical, psychological, and mental changes (e.g., spinal degeneration, comorbidities, physical inactivity, age-related changes in central pain processing, and dementia), as well as multiple risk factors (e.g., genetic, gender, and ethnicity), may affect the prognosis and management of LBP in older adults. Collectively, by understanding the impacts of various factors on the assessment and treatment of older adults with LBP, both clinicians and researchers can work toward the direction of more cost-effective and personalized LBP management for older people.


title: Optical Character Recognition for Hindi Language Using a Neural-network Approach

abstract: Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the documents textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.


Reducing Blood Testing in Pediatric Patients after Heart Surgery: Proving Sustainability


title: A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship

abstract: Abstract Online business transactions and the success of e-commerce depend greatly on the effective design of a product recommender mechanism. This study proposes a social recommender system that can generate personalized product recommendations based on preference similarity, recommendation trust, and social relations. Compared with traditional collaborative filtering approaches, the advantage of the proposed mechanism is its comprehensive consideration of recommendation sources. Accordingly, our experimental results show that the proposed model outperforms other benchmark methodologies in terms of recommendation accuracy. The proposed framework can also be effectively applied to e-commerce retailers to promote their products and services.


title: PixelVAE: A Latent Variable Model for Natural Images

abstract: Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and model global structure well but have difficulty capturing small details. PixelCNN models details very well, but lacks a latent code and is difficult to scale for capturing large structures. We present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. Our model requires very few expensive autoregressive layers compared to PixelCNN and learns latent codes that are more compressed than a standard VAE while still capturing most non-trivial structure. Finally, we extend our model to a hierarchy of latent variables at different scales. Our model achieves state-of-the-art performance on binarized MNIST, competitive performance on 64 × 64 ImageNet, and high-quality samples on the LSUN bedrooms dataset.


title: KSU Speech Database: Text Selection, Recording and Verification

abstract: King Saud University speech database (KSU-DB) is a very rich speech database of Arabic language. Its richness is in many dimensions. It has more than three hundred speakers of both genders. The speakers are Arabs and non-Arabs belonging to twenty-nine different nationalities. The database has different types of text such as isolated words, digits, phonetically rich words and sentences, phonetically balanced sentences, paragraphs, and answers to questions. The KSU-DB was recorded in three different locations, the first is an office that represents a normal environment with low noise. The second and third locations are cafeteria and soundproof room representing noisy and quiet environments, respectively. The database has different channels of recordings, mobile, medium and high quality microphones connected to recording devices of different qualities. To track the inter-session variations of the speakers, the database was recorded in three sessions with a gap of about six weeks. Though the database main goal is for speaker recognition research, nonetheless, we made it very rich so that it can be used in many speech-processing researches. A team of native Arabs verified the database manually as well as automatically.


title: Let the Cat Out of the Bag: A Holistic Approach Towards Security Analysis of the Internet of Things

abstract: The exponential increase of Internet of Things (IoT) devices have resulted in a range of new and unanticipated vulnerabilities associated with their use. IoT devices from smart homes to smart enterprises can easily be compromised. One of the major problems associated with the IoT is maintaining security; the vulnerable nature of IoT devices poses a challenge to many aspects of security, including security testing and analysis. It is trivial to perform the security analysis for IoT devices to understand the loop holes and very nature of the devices itself. Given these issues, there has been less emphasis on security testing and analysis of the IoT. In this paper, we show our preliminary efforts in the area of security analysis for IoT devices and introduce a security IoT testbed for performing security analysis. We also discuss the necessary design, requirements and the architecture to support our security analysis conducted via the proposed testbed.


title: R-FFT: Function Split at IFFT/FFT in Unified LTE CRAN and Cable Access Network

abstract: The remote-PHY (R-PHY) modular cable network for data over cable service interface specification (DOCSIS) service conducts the physical layer processing for the transmissions over the broadcast cable in a remote node. In contrast, the cloud radio access network (CRAN) for long-term evolution (LTE) cellular wireless services conducts all baseband physical layer processing in a central baseband unit and the remaining physical layer processing steps toward radio frequency (RF) transmission in remote nodes. Both DOCSIS and LTE are based on orthogonal frequency division multiplexing physical layer processing. We propose to unify cable and wireless cellular access networks by utilizing the hybrid fiber-coax (HFC) cable network infrastructure as fiber fronthaul network for cellular wireless services. For efficient operation of such a unified access network, we propose a novel remote-fast-Fourier transform (R-FFT) node that conducts the physical layer processing from the FFT module toward the RF transmission, whereby DOCSIS and LTE share a common FFT module. The frequency domain in-phase and quadrature (I/Q) symbols for both DOCSIS and LTE are transmitted over the fiber between remote node and cable headend, where the remaining physical layer processing is conducted. We further propose to cache repetitive quadrature amplitude modulation (QAM) symbols in the R-FFT node to reduce the fronthaul bitrate requirements and enable statistical multiplexing. We evaluate the fronthaul bitrate reductions achieved by R-FFT node caching, the fronthaul transmission bitrates arising from the unified DOCSIS and LTE service, and illustrate the delay implications of moving part of the cable R-PHY remote node physical layer processing to the headend. Overall, our evaluations indicate that the proposed R-FFT node can effectively support unified DOCSIS and LTE services over the HFC cable plant while substantially reducing the fronthaul bitrate requirements of the existing CRAN structures.


Calibration of Panorama Based on Linear CCD and Fisheye Lens


title: Changes in employees' job characteristics during an enterprise system implementation: a latent growth modeling perspective

abstract: Enterprise system implementations often create tension in organizations. On the one hand, these systems can provide significant operational and strategic benefits. On the other hand, implementation of these systems is risky and a source of major disruptions. In particular, employees experience significant changes in their work environment during an implementation. Although the relationship between ES implementations and employees' jobs has been noted in prior research, there is limited research on the nature, extent, determinants, and outcomes of changes in employees' job characteristics following an ES implementation. This paper develops and tests a model, termed the job characteristics change model (JCCM), that posits that employees will experience substantial changes in two job characteristics (i.e., job demands and job control) during the shakedown phase (i.e., immediately after the rollout) of an ES implementation. These changes are theorized to be predicted by work process characteristics, namely perceived process complexity, perceived process rigidity, and perceived process radicalness, that in turn will be influenced by technology characteristics (i.e., perceived technology complexity, perceived technology reconfigurability, and perceived technology customization). JCCM further posits that changes in job characteristics will influence employees' job satisfaction. Longitudinal field studies conducted in two organizations (N = 281 and 141 respectively) provided support for the model. The scientific and practical implications of the findings are discussed.


title: pH-sensitive polymeric nanoparticles to improve oral bioavailability of peptide/protein drugs and poorly water-soluble drugs.

abstract: pH-sensitive polymeric nanoparticles are promising for oral drug delivery, especially for peptide/protein drugs and poorly water-soluble medicines. This review describes current status of pH-sensitive polymeric nanoparticles for oral drug delivery and introduces the mechanisms of drug release from them as well as possible reasons for absorption improvement, with emphasis on our contribution to this field. pH-sensitive polymeric nanoparticles are prepared mainly with polyanions, polycations, their mixtures or cross-linked polymers. The mechanisms of drug release are the result of carriers' dissolution, swelling or both of them at specific pH. The possible reasons for improvement of oral bioavailability include the following: improve drug stability, enhance mucoadhesion, prolong resident time in GI tract, ameliorate intestinal permeability and increase saturation solubility and dissolution rate for poorly water-soluble drugs. As for the advantages of pH-sensitive nanoparticles over conventional nanoparticles, we conclude that (1) most carriers used are enteric-coating materials and their safety has been approved. (2) The rapid dissolution or swelling of carriers at specific pH results in quick drug release and high drug concentration gradient, which is helpful for absorption. (3) At the specific pH carriers dissolve or swell, and the bioadhesion of carriers to mucosa becomes high because nanoparticles turn from solid to gel, which can facilitate drug absorption.


Predictors for Early Neurological Deterioration in Lacunar Infarction


title: How to identify new high-payoff information systems for the organization

abstract: Viewing users as partners and their personal knowledge of the organization as a strategic asset helps CIOs justify project proposals with the greatest promise for achieving organizational goals.


title: A Basic Language Resource Kit for Persian

abstract: Persian with its about 100,000,000 speakers in the world belongs to the group of languages with less developed linguistically annotated resources and tools. The few existing resources and tools are neither open source nor freely available. Thus, our goal is to develop open source resources such as corpora and treebanks, and tools for data-driven linguistic analysis of Persian. We do this by exploring the reusability of existing resources and adapting state-of-the-art methods for the linguistic annotation. We present fully functional tools for text normalization, sentence segmentation, tokenization, part-of-speech tagging, and parsing. As for resources, we describe the Uppsala PErsian Corpus (UPEC) which is a modified version of the Bijankhan corpus with additional sentence segmentation and consistent tokenization modified for more appropriate syntactic annotation. The corpus consists of 2,782,109 tokens and is annotated with parts of speech and morphological features. A treebank is derived from UPEC with an annotation scheme based on Stanford Typed Dependencies and is planned to consist of 10,000 sentences of which 215 have already been annotated.


title: Designing for video engagement on social networks: a video marketing case study

abstract: In this case study, we describe the design and execution of a video marketing campaign on Facebook. We present concrete recommendations for effective video persuasion design, focusing on a new kind of video player presentation, as well as one specific environment in which video content can be placed, seeded, and distributed. We present a number of findings that support our design recommendations. Finally, we acknowledge and discuss the limitations in our case study approach.


title: An arduino based system provided with GPS/GPRS shield for real time monitoring of traffic flows

abstract: In the past, few Public Administrations provided traffic monitoring services in real time due to the high cost of the car traffic monitoring system. The paper aims at proposing a low cost system based on the GPS signals coming from Arduino based systems to collect the traffic measurements needed to compute colored traffic map and the minimum path to the destination depending on the current position. The comparison carried between the performance of the proposed system and the one based on GPS signals coming from the user mobiles points out a higher accuracy of Arduino based tracking system. Also the system may send the user data to the main information center as anonymous messages thus satisfying the privacy requirements needed for a wide activation of such a monitoring methodology.


title: A mouse model of cytogenetic analysis to evaluate caesium137 radiation dose exposure and contamination level in lymphocytes.

abstract: In case of external overexposure to ionizing radiation, an estimation of its genotoxic effects on exposed individuals can be made retrospectively by the measurement of radiation-induced chromosome aberrations on circulating lymphocytes. Compared with external irradiation, intakes of radionuclides may, however, lead to specific features influencing dose distribution at the scale of body, of tissue or even of cell. Therefore, in case of internal contamination by radionuclides, experimental studies, particularly using animal models, are required to better understand mechanisms of their genotoxic effects and to better estimate the absorbed dose. The present study was designed to evaluate a cytogenetic method in mouse peripheral blood lymphocytes that would allow determination of yields and complexities of chromosome aberrations after low-dose rate exposure to (137)Cs delivered in vitro either by irradiation or by contamination. By using M-FISH analysis, we compared the low-dose rate responses observed in mouse to the high-dose rate responses observed both in mouse and in human. Promising similarities between the two species in the relative biological effect evaluation show that our cytogenetic model established in mouse might be useful to evaluate various radiation exposures, particularly relevant in case of intakes of radionuclides.


title: An attack on science? Media use, trust in scientists, and perceptions of global warming:

abstract: There is a growing divide in how conservatives and liberals in the USA understand the issue of global warming. Prior research suggests that the American public’s reliance on partisan media contributes to this gap. However, researchers have yet to identify intervening variables to explain the relationship between media use and public opinion about global warming. Several studies have shown that trust in scientists is an important heuristic many people use when reporting their opinions on science-related topics. Using within-subject panel data from a nationally representative sample of Americans, this study finds that trust in scientists mediates the effect of news media use on perceptions of global warming. Results demonstrate that conservative media use decreases trust in scientists which, in turn, decreases certainty that global warming is happening. By contrast, use of non-conservative media increases trust in scientists, which, in turn, increases certainty that global warming is happening.


title: The hallmark of the Methodist Diaconal Order - its life as a Religious Order - and some implications for the future of Methodism

abstract: This article argues that the distinctiveness and importance of the Methodist Diaconal Order lies primarily in its standing as a religious order. It is this standing that should shape its life and work as an order of ministry. The hallmarks of what it means to be a Christian community are described and their presence within the life, organization and work of the Order identified. It is argued that Methodism should be aware that moves to integrate presbyteral and diaconal selection, training and oversight could weaken the Order’s distinctiveness as a religious order. The article sets out what a deeper appreciation of the character of the Order as a religious community could offer Methodism, not least how this might enable the latter’s ministry to reclaim its heritage as a religious order. Issues that the Order needs to address in order to re-invigorate its life and work as a religious order, and thereby strengthen its contribution to Methodism and the wider world, are noted.