title: Chondrocyte sheet in vivo cartilage regeneration technique using miR-193b-3p to target MMP16

abstract: Stable cartilage regeneration has always been a challenge in both tissue engineering research and clinical practice. This study explored the feasibility of using a chondrocyte sheet technique stimulated by microRNAs to regenerate cartilage. We tested the involvement of hsa-miR-193b-3p in the microtia patient remnant auricular chondrocyte extracellular matrix (ECM). We observed in vitro chondrocyte proliferation, ECM synthesis, as well as the increase in the expression of type II collagen (COL2A1) and decrease in the expression of matrix metalloproteinase 16 (MMP16) of the chondrocyte sheets. COL2A1 deposition and MMP16 degradation of regenerative cartilage tissue were examined in vivo. A dual-luciferase reporter showed that the MMP16 gene was the direct target of miR-193b-3p. These results suggested that miR-193b-3p promotes chondrocyte sheet ECM synthesis by inhibiting MMP16. Since the evidence suggests that MMP16 is a critical regulator of chondrocyte ECM, this finding points the way towards a method that both strengthens the ECM and inhibits MMPs.


title: Behavioral Oscillations in Attention: Rhythmic Pulses Mediated through Band

abstract: Neuronal oscillations are ubiquitous in the brain and contribute to perception and attention. However, most associated evidence derives from post hoc correlations between brain dynamics and behavior. Although a few recent studies demonstrate rhythms in behavior, it remains largely unknown whether behavioral performances manifest spectrotemporal dynamics in a neurophysiologically relevant manner (e.g., the temporal modulation of ongoing oscillations, the cross-frequency coupling). To investigate the issue, we examined fine spectrotemporal dynamics of behavioral time courses in a large sample of human participants ( n = 49), by taking a high time-resolved psychophysical measurement in a precuing attentional task. We observed compelling dynamic oscillatory patterns directly in behavior. First, typical attentional effects are demonstrated in low-pass (0–2 Hz) filtered time courses of behavioral responses. Second, an uninformative peripheral cue elicits recurring α-band (8–20 Hz) pulses in behavioral performances, and the elicited α pulses for cued and uncued conditions are in a temporally alternating relationship. Finally, ongoing α-band power is phase locked to ongoing θ-bands (3–5 Hz) in behavioral time courses. Our findings constitute manifestation of oscillations at physiologically relevant rhythms and power-phase locking, as widely observed in neurophysiological recordings, in behavior. The findings suggest that behavioral performance actually consists of rich dynamic information and may reflect underlying neuronal oscillatory substrates. Our data also speak to a neural mechanism for item attention based on successive cycles (θ) of a sequential attentional sampling (α) process.


title: An Overview of Quadtrees, Octrees, and Related Hierarchical Data Structures

abstract: An overview of hierarchical data structures for representing images, such as the quadtree and octree, is presented. They are based on the principle of recursive decomposition. The emphasis is on the representation of data used in applications in computer graphics, computer-aided design, robotics, computer vision, and cartography. There is a greater emphasis on region data (i.e., 2-dimensional shapes) and to a lesser extent on point, line, and 3-dimensional data.


title: Detecting Click Fraud in Pay-Per-Click Streams of Online Advertising Networks

abstract: With the rapid growth of the Internet, online advertisement plays a more and more important role in the advertising market. One of the current and widely used revenue models for online advertising involves charging for each click based on the popularity of keywords and the number of competing advertisers. This pay-per-click model leaves room for individuals or rival companies to generate false clicks (i.e., click fraud), which pose serious problems to the development of healthy online advertising market. To detect click fraud, an important issue is to detect duplicate clicks over decaying window models, such as jumping windows and sliding windows. Decaying window models can be very helpful in defining and determining click fraud. However, although there are available algorithms to detect duplicates, there is still a lack of practical and effective solutions to detect click fraud in pay-per-click streams over decaying window models. In this paper, we address the problem of detecting duplicate clicks in pay-per-click streams over jumping windows and sliding windows, and are the first that propose two innovative algorithms that make only one pass over click streams and require significantly less memory space and operations. GBF algorithm is built on group Bloom filters which can process click streams over jumping windows with small number of sub-windows, while TBF algorithm is based on a new data structure called timing Bloom filter that detects click fraud over sliding windows and jumping windows with large number of sub-windows. Both GBF algorithm and TBF algorithm have zero false negative. Furthermore, both theoretical analysis and experimental results show that our algorithms can achieve low false positive rate when detecting duplicate clicks in pay-per-click streams over jumping windows and sliding windows.


title: Coresets and Sketches

abstract: Geometric data summarization has become an essential tool in both geometric approximation algorithms and where geometry intersects with big data problems. In linear or near-linear time large data sets can be compressed into a summary, and then more intricate algorithms can be run on the summaries whose results approximate those of the full data set. Coresets and sketches are the two most important classes of these summaries. We survey five types of coresets and sketches: shape-fitting, density estimation, high-dimensional vectors, high-dimensional point sets / matrices, and clustering.


title: Multi-label CNN based pedestrian attribute learning for soft biometrics

abstract: Recently, pedestrian attributes like gender, age and clothing etc., have been used as soft biometric traits for recognizing people. Unlike existing methods that assume the independence of attributes during their prediction, we propose a multi-label convolutional neural network (MLCNN) to predict multiple attributes together in a unified framework. Firstly, a pedestrian image is roughly divided into multiple overlapping body parts, which are simultaneously integrated in the multi-label convolutional neural network. Secondly, these parts are filtered independently and aggregated in the cost layer. The cost function is a combination of multiple binary attribute classification cost functions. Moreover, we propose an attribute assisted person re-identification method, which fuses attribute distances and low-level feature distances between pairs of person images to improve person re-identification performance. Extensive experiments show: 1) the average attribute classification accuracy of the proposed method is 5.2% and 9.3% higher than the SVM-based method on three public databases, VIPeR and GRID, respectively; 2) the proposed attribute assisted person re-identification method is superior to existing approaches.


title: Smart home garden irrigation system using Raspberry Pi

abstract: Irrigation system is a method of allowing water to drip slowly to the roots of plants, either onto the soil surface or directly onto the root zone, through solenoid valve. However, it is found that the market price of the system is expensive for small area coverage. Thus, this paper proposes a design for smart home garden irrigation system that implements ready-to-use, energy-efficient, and cost effective devices. Raspberry Pi, which is implemented in this system is integrated with multi-sensors such as soil moisture sensors, ultrasonic sensors, and light sensors. This proposed system managed to reduce cost, minimize waste water, and reduce physical human interface. In this paper, the relay is utilized to control the switching of solenoid valve. The system also managed to measure moisture of the soil and control the solenoid valve according to human's requirements. It is conducted with Graphical User Interface (GUI) using Android application to activate watering activity. Email notification is also sent to the home user for alert purposes either for normal or critical operations. An experimental setup has been tested and it is proven that the system can intelligently control and monitor the soil moisture levels in the experiment field.


title: Depot: Cloud Storage with Minimal Trust

abstract: The paper describes the design, implementation, and evaluation of Depot, a cloud storage system that minimizes trust assumptions. Depot tolerates buggy or malicious behavior by any number of clients or servers, yet it provides safety and liveness guarantees to correct clients. Depot provides these guarantees using a two-layer architecture. First, Depot ensures that the updates observed by correct nodes are consistently ordered under Fork-Join-Causal consistency (FJC). FJC is a slight weakening of causal consistency that can be both safe and live despite faulty nodes. Second, Depot implements protocols that use this consistent ordering of updates to provide other desirable consistency, staleness, durability, and recovery properties. Our evaluation suggests that the costs of these guarantees are modest and that Depot can tolerate faults and maintain good availability, latency, overhead, and staleness even when significant faults occur.


title: Training induces changes in white-matter architecture

abstract: Although previous work has shown that extensive training in the complex visuo-motor skills involved in juggling results in adult gray-matter changes, it is unclear whether such practice can produce similar changes in adult white matter. This paper now uses diffusion tensor imaging to demonstrate structural white-matter changes when adults practice juggling.


title: Deadbeat Predictive Current Control of Permanent-Magnet Synchronous Motors with Stator Current and Disturbance Observer

abstract: In order to optimize the current-control performance of the permanent-magnet synchronous motor (PMSM) system with model parameter mismatch and one-step control delay, an improved deadbeat predictive current control (DPCC) algorithm for the PMSM drive systems is proposed in this paper. First, the performance of the conventional predictive current control, when parameter mismatch exist, is analyzed, and then a stator current and disturbance observer (SCDO) based on sliding-mode exponential reaching law, which is able to simultaneously predict future value of stator current and track system disturbance caused by parameter mismatch in real time, is proposed. Based on this SCDO, prediction currents are used for replacing the sampled current in DPCC to compensate one-step delay, and estimated parameter disturbances are considered as the feedforward value to compensate the voltage reference calculated by deadbeat predictive current controller. Thus, a composite control method combining the DPCC part and current prediction and feedforward compensation part based on SCDO, called DPCC + SCDO method, is developed. Moreover, based on conventional exponential reaching law, a novel sliding-mode exponential reaching law is proposed to further improve the performance of the DPCC + SCDO method. Simulation and experimental results both show the validity of the proposed current control approach.


title: Dual-Axis Electromagnetic Scanning Micromirror Using Radial Magnetic Field

abstract: This paper presents the design, fabrication and measurement results of a dual-axis electromagnetic scanning micromirror utilizing radial magnetic field. The scanner is comprised of a gimbaled single-crystal-silicon micromirror with a single turn electroplated metal coil, assembled passively above a concentric permanent magnet assembly. Proposed scanner utilizes the radial magnetic field rather than using the magnetic field oriented 45 ° to the horizontal and vertical scan axes to achieve a biaxial magnetic actuation[ 1,2]. Forced actuation of the gimbal and resonant actuation of the micromirror provide slow vertical scan and fast horizontal scan respectively. A dual-axis scanner with maximum scan angle of 16.1 °, mirror size of 1.5 mm in diameter, and resonant frequency of 19.7kHz is reported.


title: Yawning Detection Using Embedded Smart Cameras

abstract: Yawning detection has a variety of important applications in a driver fatigue detection, well-being assessment of humans, driving behavior monitoring, operator attentiveness detection, and understanding the intentions of a person with a tongue disability. In all of the above applications, an automatic detection of yawning is one important system component. In this paper, we design and implement such automatic system, using computer vision, which runs on a computationally limited embedded smart camera platform to detect yawning. We use a significantly modified implementation of the Viola-Jones algorithm for face and mouth detections and, then, use a backprojection theory for measuring both the rate and the amount of the changes in the mouth, in order to detect yawning. As proof-of-concept, we have also implemented and tested our system on top of an actual smart camera embedded platform, called APEX from CogniVue Corporation. In our design and implementations, we took into consideration the practical aspects that many existing works ignore, such as real-time requirements of the system, as well as the limited processing power, memory, and computing capabilities of the embedded platform. Comparisons with existing methods show significant improvements in the correct yawning detection rate obtained by our proposed method.


title: Privacy-awareness in blockchain-based PKI

abstract: Conventional public key infrastructure (PKI) designs are not optimal and contain security flaws; there is much work underway in improving PKI. The properties given by the Bitcoin blockchain and its derivatives are a natural solution to some of the problems with PKI - in particular, certificate transparency and elimination of single points of failure. Recently-proposed blockchain PKI designs are built as public ledgers linking identity with public key, giving no provision of privacy. We consider the suitability of a blockchain-based PKI for contexts in which PKI is required, but in which linking of identity with public key is undesirable; specifically, we show that blockchain can be used to construct a privacy-aware PKI while simultaneously eliminating some of the problems encountered in conventional PKI.


title: VADI: GPU Virtualization for an Automotive Platform

abstract: Modern vehicles are evolving with more electronic components than ever before (In this paper, “vehicle” means “automotive vehicle.” It is also equal to “car.”) One notable example is graphical processing unit (GPU), which is a key component to implement a digital cluster. To implement the digital cluster that displays all the meters (such as speed and fuel gauge) together with infotainment services (such as navigator and browser), the GPU needs to be virtualized; however, GPU virtualization for the digital cluster has not been addressed yet. This paper presents a Virtualized Automotive DIsplay (VADI) system to virtualize a GPU and its attached display device. VADI manages two execution domains: one for the automotive control software and the other for the in-vehicle infotainment (IVI) software. Through GPU virtualization, VADI provides GPU rendering to both execution domains, and it simultaneously displays their images on a digital cluster. In addition, VADI isolates GPU from the IVI software in order to protect it from potential failures of the IVI software. We implement VADI with Vivante GC2000 GPU and perform experiments to ensure requirements of International Standard Organization (ISO) safety standards. The results show that VADI guarantees 30 frames per second (fps), which is the minimum frame rate for digital cluster mandated by ISO safety standards even with the failure of the IVI software. It also achieves 60 fps in a synthetic workload.


title: Chaos: scale-out graph processing from secondary storage

abstract: Chaos scales graph processing from secondary storage to multiple machines in a cluster. Earlier systems that process graphs from secondary storage are restricted to a single machine, and therefore limited by the bandwidth and capacity of the storage system on a single machine. Chaos is limited only by the aggregate bandwidth and capacity of all storage devices in the entire cluster. Chaos builds on the streaming partitions introduced by X-Stream in order to achieve sequential access to storage, but parallelizes the execution of streaming partitions. Chaos is novel in three ways. First, Chaos partitions for sequential storage access, rather than for locality and load balance, resulting in much lower pre-processing times. Second, Chaos distributes graph data uniformly randomly across the cluster and does not attempt to achieve locality, based on the observation that in a small cluster network bandwidth far outstrips storage bandwidth. Third, Chaos uses work stealing to allow multiple machines to work on a single partition, thereby achieving load balance at runtime. In terms of performance scaling, on 32 machines Chaos takes on average only 1.61 times longer to process a graph 32 times larger than on a single machine. In terms of capacity scaling, Chaos is capable of handling a graph with 1 trillion edges representing 16 TB of input data, a new milestone for graph processing capacity on a small commodity cluster.


title: Tetraodon genome analysis provides further evidence for whole-genome duplication in the ray-finned fish lineage.

abstract: A whole-genome duplication in the ray-finned fish lineage has been supported by the analyses of the genome sequence of the Japanese pufferfish, Fugu rubripes. Recently, genome sequence of a second teleost fish, the freshwater pufferfish, Tetraodon nigroviridis, was completed. Comparisons of long-range synteny between the Tetraodon and human genomes provided additional evidence for the whole-genome duplication in the ray-finned fish lineage. In the present study, we conducted phylogenetic analysis of the Tetraodon and human proteins to identify ray-finned fish lineage-specific ('fish-specific') duplicate genes in the Tetraodon genome. Our analyses provide evidence for 1087 well defined fish-specific duplicate genes in Tetraodon. We also analyzed the Fugu proteome that was predicted in the recent Fugu genome assembly, and identified 346 duplicate genes in addition to the 425 duplicates previously identified. We estimated the ages of duplicate genes using the molecular clock. The ages of duplicate genes in the two pufferfishes independently support a large-scale gene duplication around 380-400 Myr ago. In addition, a burst of recent gene duplications was evident in the Tetraodon lineage. These findings provide further evidence for a whole-genome duplication early in the evolution of ray-finned fishes, and suggest that independent gene duplications have occurred recently in the Tetraodon lineage.


title: Spatio-temporal bird's-eye view images using multiple fish-eye cameras

abstract: In camera images for urban search and rescue (USAR) robots teleoperation, it is important to reduce blind spots and get surroundings as much as possible because of safety requirements. We propose a method to create synthesized bird's-eye view images from multiple fish-eye cameras as spatiotemporal data which can reduce blind spots. In practical use, it is very important to get images robustly even when some troubles such as camera broken and network disturbances occur. This method develops showing bird's-eye view images robustly even if some of images are not acquired by compensating past stored spatio-temporal data to these images. Effectiveness of the proposed method is verified through experiments.


title: Real-time categorization of driver's gaze zone using the deep learning techniques

abstract: This paper presents a study in which driver's gaze zone is categorized using new deep learning techniques. Since the sequence of gaze zones of a driver reflects precisely what and how he behaves, it allows us infer his drowsiness, focusing or distraction by analyzing the images coming from a camera. A Haar feature based face detector is combined with a correlation filter based MOSS tracker for the face detection task to handle a tough visual environment in the car. Driving database is a big-data which was constructed using a recording setup within a compact sedan by driving around the urban area. The gaze zones consist of 9 categories depending on where a driver is looking at during driving. A convolutional neural network is trained to categorize driver's gaze zone from a given face detected image using a multi-GPU platform, and then its network parameters are transferred to a GPU within a PC running on Windows to operate in the real-time basis. Result suggests that the correct rate of gaze zone categorization reaches to 95% in average, indicating that our system outperforms the state-of-art gaze zone categorization methods based on conventional computer vision techniques.


title: Axial Force and Vibroacoustic Analysis of External-Rotor Axial-Flux Motors

abstract: This paper presents a detailed analysis of the axial electromagnetic force and vibroacoustic behavior of the external-rotor axial-flux motors. First, the spatial and temporal characteristics of axial force acting on the surface of magnets are derived analytically and validated through the 2-D Fourier decomposition. Subsequently, a multiphysics model is established to calculate the vibration and noise of an external-rotor axial-flux in-wheel motor, which integrates the control model, the electromagnetic model, the structural model, and the acoustic radiation model and takes the uneven distribution of axial force into account. The accuracy of the multiphysics model is verified by the noise test. The vibroacoustic mechanism of axial-flux motors is revealed based on the multiphysics model, the influence of current harmonics and dead time effect on the vibration and noise are also analyzed. Finally, the main orders of tested noise are explained by the theoretical analysis. It is found that the zeroth spatial order axial force is dominant for the generation of the vibration and noise in axial-flux motors. The harmonic current may deteriorate the vibroacoustic behavior, but depending on its amplitude and phase. This study provides guidance for the design of low-noise axial-flux motors.


title: A Look at the Consequences of Internet Censorship Through an ISP Lens

abstract: Internet censorship artificially changes the dynamics of resource production and consumption, affecting a range of stakeholders that include end users, service providers, and content providers. We analyze two large-scale censorship events in Pakistan: blocking of pornographic content in 2011 and of YouTube in 2012. Using traffic datasets collected at home and SOHO networks before and after the censorship events, we: a) quantify the demand for blocked content, b) illuminate challenges encountered by service providers in implementing the censorship policies, c) investigate changes in user behavior (e.g., with respect to circumvention) after censorship, and d) assess benefits extracted by competing content providers of blocked content.


title: Interferometric Synthetic Aperture Radar (SAR) Missions Employing Formation Flying

abstract: This paper presents an overview of single-pass interferometric Synthetic Aperture Radar (SAR) missions employing two or more satellites flying in a close formation. The simultaneous reception of the scattered radar echoes from different viewing directions by multiple spatially distributed antennas enables the acquisition of unique Earth observation products for environmental and climate monitoring. After a short introduction to the basic principles and applications of SAR interferometry, designs for the twin satellite missions TanDEM-X and Tandem-L are presented. The primary objective of TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) is the generation of a global Digital Elevation Model (DEM) with unprecedented accuracy as the basis for a wide range of scientific research as well as for commercial DEM production. This goal is achieved by enhancing the TerraSAR-X mission with a second TerraSAR-X like satellite that will be launched in spring 2010. Both satellites act then as a large single-pass SAR interferometer with the opportunity for flexible baseline selection. Building upon the experience gathered with the TanDEM-X mission design, the fully polarimetric L-band twin satellite formation Tandem-L is proposed. Important objectives of this highly capable interferometric SAR mission are the global acquisition of three-dimensional forest structure and biomass inventories, large-scale measurements of millimetric displacements due to tectonic shifts, and systematic observations of glacier movements. The sophisticated mission concept and the high data-acquisition capacity of Tandem-L will moreover provide a unique data source to systematically observe, analyze, and quantify the dynamics of a wide range of additional processes in the bio-, litho-, hydro-, and cryosphere. By this, Tandem-L will be an essential step to advance our understanding of the Earth system and its intricate dynamics. Enabling technologies and techniques are described in detail. An outlook on future interferometric and tomographic concepts and developments, including multistatic SAR systems with multiple receivers, is provided.


title: Biomechanical Properties of Insect Wings: The Stress Stiffening Effects on the Asymmetric Bending of the Allomyrina dichotoma Beetle's Hind Wing

abstract: Although the asymmetry in the upward and downward bending of insect wings is well known, the structural origin of this asymmetry is not yet clearly understood. Some researchers have suggested that based on experimental results, the bending asymmetry of insect wings appears to be a consequence of the camber inherent in the wings. Although an experimental approach can reveal this phenomenon, another method is required to reveal the underlying theory behind the experimental results. The finite element method (FEM) is a powerful tool for evaluating experimental measurements and is useful for studying the bending asymmetry of insect wings. Therefore, in this study, the asymmetric bending of the Allomyrina dichotoma beetle's hind wing was investigated through FEM analyses rather than through an experimental approach. The results demonstrated that both the stressed stiffening of the membrane and the camber of the wing affect the bending asymmetry of insect wings. In particular, the chordwise camber increased the rigidity of the wing when a load was applied to the ventral side, while the spanwise camber increased the rigidity of the wing when a load was applied to the dorsal side. These results provide an appropriate explanation of the mechanical behavior of cambered insect wings, including the bending asymmetry behavior, and suggest an appropriate approach for analyzing the structural behavior of insect wings.


title: Long-term potentiation in the Eocene

abstract: The first ten years of long-term potentiation (LTP) research are reviewed. Surprisingly, given the intensity of current interest, the discovery paper did not trigger a wave of follow-on experiments. Despite this, the initial work laid out what ultimately became standard questions and paradigms. The application of the then still novel hippocampal slice technique oriented LTP towards basic neuroscience, perhaps somewhat at the cost of lesser attention to its functional significance. The use of slices led to the discovery of the events that trigger the formation of LTP and provided some first clues about its extraordinary persistence. Signs of the intense controversy over the nature of LTP expression (release vs receptors) emerged towards the end of the first decade of work. What appears to be lacking in the literature of that time is a widespread concern about LTP and memory. This may reflect a somewhat different attitude that neurobiologists then had towards memory research and a perceived need to integrate the new potentiation phenomenon into the web of established science before advancing extended arguments about its contributions to behaviour.


title: Porosity of 3D biomaterial scaffolds and osteogenesis

abstract: Porosity andpore size of biomaterial scaffolds play a critical role in bone formation in vitro andin vivo. This review explores the state of knowledge regarding the relationship between porosity and pore size of biomaterials used for bone regeneration. The effect of these morphological features on osteogenesis in vitro andin vivo, as well as relationships to mechanical properties of the scaffolds, are addressed. In vitro, lower porosity stimulates osteogenesis by suppressing cell proliferation and forcing cell aggregation. In contrast, in vivo, higher porosity andpore size result in greater bone ingrowth, a conclusion that is supportedby the absence of reports that show enhancedosteogenic outcomes for scaffold s with low voidvolumes. However, this trendresults in diminished mechanical properties, thereby setting an upper functional limit for pore size and porosity. Thus, a balance must be reached depending on the repair, rate of remodeling and rate of degradation of the scaffold material. Based on early studies, the minimum requirement for pore size is considered to be � 100mm due to cell size, migration requirements and transport. However, pore sizes 4300mm are recommended, due to enhanced new bone formation and the formation of capillaries. Because of vasculariziation, pore size has been shown to affect the progression of osteogenesis. Small pores favoredhypoxic cond itions and induced osteochondral formation before osteogenesis, while large pores, that are well-vascularized, lead to direct osteogenesis (without preceding cartilage formation). Gradients in pore sizes are recommended for future studies focused on the formation of multiple tissues andtissue interfaces. New fabrication techniques, such as solid -free form fabrication, can potentially be used to generate scaffolds with morphological and mechanical properties more selectively designed to meet the specificity of bonerepair needs. r 2005 Elsevier Ltd. All rights reserved.


title: Efficient Hybrid Semantic Text Similarity using Wordnet and a Corpus

abstract: Text similarity plays an important role in natural language processing tasks such as answering questions and summarizing text. At present, state-of-the-art text similarity algorithms rely on inefficient word pairings and/or knowledge derived from large corpora such as Wikipedia. This article evaluates previous word similarity measures on benchmark datasets and then uses a hybrid word similarity in a novel text similarity measure (TSM). The proposed TSM is based on information content and WordNet semantic relations. TSM includes exact word match, the length of both sentences in a pair, and the maximum similarity between one word and the compared text. Compared with other well-known measures, results of TSM are surpassing or comparable with the best algorithms in the literature.


title: Multilayer motif analysis of brain networks

abstract: In the last decade, network science has shed new light both on the structural (anatomical) and on the functional (correlations in the activity) connectivity among the different areas of the human brain. The analysis of brain networks has made possible to detect the central areas of a neural system and to identify its building blocks by looking at overabundant small subgraphs, known as motifs. However, network analysis of the brain has so far mainly focused on anatomical and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows us to perform an analysis of the human brain where the structural and functional layers are considered together. In this work, we describe how to classify the subgraphs of a multiplex network, and we extend the motif analysis to networks with an arbitrary number of layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, anatomical and functional, respect...


The Wason Selection task: A Meta-Analysis.


title: Machine Learning for Real-time Prediction of Damaging Straight-line Convective Wind

abstract: AbstractThunderstorms in the United States cause over 100 deaths and $10 billion (U.S. dollars) in damage per year, much of which is attributable to straight-line (nontornadic) wind. This paper describes a machine-learning system that forecasts the probability of damaging straight-line wind (≥50 kt or 25.7 m s−1) for each storm cell in the continental United States, at distances up to 10 km outside the storm cell and lead times up to 90 min. Predictors are based on radar scans of the storm cell, storm motion, storm shape, and soundings of the near-storm environment. Verification data come from weather stations and quality-controlled storm reports. The system performs very well on independent testing data. The area under the receiver operating characteristic (ROC) curve ranges from 0.88 to 0.95, the critical success index (CSI) ranges from 0.27 to 0.91, and the Brier skill score (BSS) ranges from 0.19 to 0.65 (>0 is better than climatology). For all three scores, the best value occurs for the smallest dist...


title: Working Memory Capacity and Fluid Intelligence Maintenance and Disengagement

abstract: Working memory capacity and fluid intelligence have been demonstrated to be strongly correlated traits. Typically, high working memory capacity is believed to facilitate reasoning through accurate maintenance of relevant information. In this article, we present a proposal reframing this issue, such that tests of working memory capacity and fluid intelligence are seen as measuring complementary processes that facilitate complex cognition. Respectively, these are the ability to maintain access to critical information and the ability to disengage from or block outdated information. In the realm of problem solving, high working memory capacity allows a person to represent and maintain a problem accurately and stably, so that hypothesis testing can be conducted. However, as hypotheses are disproven or become untenable, disengaging from outdated problem solving attempts becomes important so that new hypotheses can be generated and tested. From this perspective, the strong correlation between working memory capa...


title: Task-based limb optimization for legged robots

abstract: The design of legged robots is often inspired by animals evolved to excel at different tasks. However, while mimicking morphological features seen in nature can be very powerful, robots may need to perform motor tasks that their living counterparts do not. In the absence of designs that can be mimicked, an alternative is to resort to mathematical models that allow the relationship between a robot's form and function to be explored. In this paper, we propose such a model to co-design the motion and leg configurations of a robot such that a measure of performance is optimized. The framework begins by planning trajectories for a simplified model consisting of the center of mass and feet. The framework then optimizes the length of each leg link while solving for associated full-body motions. Our model was successfully used to find optimized designs for legged robots performing tasks that include jumping, walking, and climbing up a step. Although our results are preliminary and our analysis makes a number of simplifying assumptions, our findings indicate that the cost function, the sum of squared joint torques over the duration of a task, varies substantially as the design parameters change.


title: Smooth spline surfaces over irregular meshes

abstract: An algorithm for creating smooth spline surfaces over irregular meshes is presented. The algorithm is a generalization of quadratic B-splines; that is, if a mesh is (locally) regular, the resulting surface is equivalent to a B-spline. Otherwise, the resulting surface has a degree 3 or 4 parametric polynomial representation. A construction is given for representing the surface as a collection of tangent plane continuous triangular Be´zier patches. The algorithm is simple, efficient, and generates aesthetically pleasing shapes.


title: Using infrared sensors for distance measurement in mobile robots. Robotics and Autonomous Systems 40

abstract: Abstract The amplitude response of infrared (IR) sensors based on reflected amplitude of the surrounding objects is non-linear and depends on the reflectance characteristics of the object surface. As a result, the main use of IR sensors in robotics is for obstacle avoidance. Nevertheless, their inherently fast response is very attractive for enhancing the real-time operation of a mobile robot in, for instance, map building tasks. Thus, it seems that the development of new low-cost IR sensors able to accurately measure distances with reduced response times is worth researching. In this paper, a new IR sensor based on the light intensity back-scattered from objects and able to measure distances of up to 1 m is described. Also, the sensor model is described and the expected errors in distance estimates are analysed and modelled. Finally, the experimental results obtained are discussed.


title: From Star Schemas to Big Data: 20$$+$$ Years of Data Warehouse Research

abstract: Data Warehouses are the core of the modern systems for decision making. They store integrated information extracted from various and heterogeneous data sources, making it available in multidimensional form for analyses aimed at improving the users’ knowledge of their business. Though the first use of the term dates back to the 80s, only during the late 90s data warehousing has emerged as a research area on its own, though in strict correlation with several other research topics as database integration, view materialization, data visualization, etc. This paper surveys more than 20 years of research on data warehouse systems, from their early relational implementations (still widely adopted in corporate environments), to the new architectures solicited by Business Intelligence 2.0 scenarios during the last decade, and up to the exciting challenges now posed by the integration with big data settings. The timeline of research is organized into three interrelated tracks: techniques, architectures, and methodologies.


title: Distributed Machine Learning via Sufficient Factor Broadcasting

abstract: Matrix-parametrized models, including multiclass logistic regression and sparse coding, are used in machine learning (ML) applications ranging from computer vision to computational biology. When these models are applied to large-scale ML problems starting at millions of samples and tens of thousands of classes, their parameter matrix can grow at an unexpected rate, resulting in high parameter synchronization costs that greatly slow down distributed learning. To address this issue, we propose a Sufficient Factor Broadcasting (SFB) computation model for efficient distributed learning of a large family of matrix-parameterized models, which share the following property: the parameter update computed on each data sample is a rank-1 matrix, i.e., the outer product of two "sufficient factors" (SFs). By broadcasting the SFs among worker machines and reconstructing the update matrices locally at each worker, SFB improves communication efficiency --- communication costs are linear in the parameter matrix's dimensions, rather than quadratic --- without affecting computational correctness. We present a theoretical convergence analysis of SFB, and empirically corroborate its efficiency on four different matrix-parametrized ML models.


title: Mining online footprints to predict user’s next location

abstract: ABSTRACTSocial media applications are widely deployed in mobile platforms equipped with built-in GPS tracking devices, and these devices have led to an unprecedented collection of geolocated data (geo-tags). Geo-tags, along with place names, offer new opportunities to explore the trajectory and mobility patterns of social media users. However, trajectory data captured by social media are sparsely and irregularly spaced and therefore have varying degrees of resolution in both space and time. Previous studies on next location prediction are mostly applicable for detecting the upcoming location of a moving object using dense GPS trajectories where locations are recorded at regular time intervals (e.g., 1 minute). Additionally, point features are commonly used to represent the locations of visits, but using point features cannot capture the variability of human mobility. This article introduces a new methodology to predict an individual’s next location based on sparse footprints accumulated over a long time p...


title: StereoScan: Dense 3d reconstruction in real-time

abstract: Accurate 3d perception from video sequences is a core subject in computer vision and robotics, since it forms the basis of subsequent scene analysis. In practice however, online requirements often severely limit the utilizable camera resolution and hence also reconstruction accuracy. Furthermore, real-time systems often rely on heavy parallelism which can prevent applications in mobile devices or driver assistance systems, especially in cases where FPGAs cannot be employed. This paper proposes a novel approach to build 3d maps from high-resolution stereo sequences in real-time. Inspired by recent progress in stereo matching, we propose a sparse feature matcher in conjunction with an efficient and robust visual odometry algorithm. Our reconstruction pipeline combines both techniques with efficient stereo matching and a multi-view linking scheme for generating consistent 3d point clouds. In our experiments we show that the proposed odometry method achieves state-of-the-art accuracy. Including feature matching, the visual odometry part of our algorithm runs at 25 frames per second, while - at the same time - we obtain new depth maps at 3-4 fps, sufficient for online 3d reconstructions.


title: Hyper-invariant tensor networks and holography

abstract: We propose a new class of tensor network state as a model for the AdS/CFT correspondence and holography. This class is demonstrated to retain key features of the multiscale entanglement renormalization ansatz (MERA), in that they describe quantum states with algebraic correlation functions, have free variational parameters, and are efficiently contractible. Yet, unlike the MERA, they are built according to a uniform tiling of hyperbolic space, without inherent directionality or preferred locations in the holographic bulk, and thus circumvent key arguments made against the MERA as a model for AdS/CFT. Novel holographic features of this tensor network class are examined, such as an equivalence between the causal cones C(R) and the entanglement wedges E(R) of connected boundary regions R.


An MDA-based approach for database re-engineering


title: Error vector magnitude to SNR conversion for nondata-aided receivers

abstract: Error vector magnitude (EVM) is one of the widely accepted figure of merits used to evaluate the quality of communication systems. In the literature, EVM has been related to signal-to-noise ratio (SNR) for data-aided receivers, where preamble sequences or pilots are used to measure the EVM, or under the assumption of high SNR values. In this paper, this relation is examined for nondata-aided receivers and is shown to perform poorly, especially for low SNR values or high modulation orders. The EVM for nondata-aided receivers is then evaluated and its value is related to the SNR for quadrature amplitude modulation (QAM) and pulse amplitude modulation (PAM) signals over additive white Gaussian noise (AWGN) channels and Rayleigh fading channels, and for systems with IQ imbalances. The results show that derived equations can be used to reliably estimate SNR values using EVM measurements that are made based on detected data symbols. Thus, presented work can be quite useful for measurement devices such as vector signal analyzers (VSA), where EVM measurements are readily available.


title: Active Antenna Array Characterization for Massive MIMO 5G Scenarios

abstract: The main objective of this work is to present a characterization and measurement system using multi-sine to study active antenna array behavior. In the multi-sine approach, a main tone and a tickle tone will be explored. The tickle tones will be used as a reference to characterize each antenna element, which will allows us to study them in terms of amplitude and phase. For this purpose, each antenna element will be excited by a main tone (same frequency at each element) and a tickle tone (different frequency at each element) with lower power. The experimental setup was fully implemented using an of the shelf software defined radio (SDR) approach. With the aid of a synchronized measurement system, a conventional 16 element array was characterized and results are presented and discussed, showing that this approach is relevant for this kind of application.


title: Privacy preservation and information security protection for patients' portable electronic health records

abstract: As patients face the possibility of copying and keeping their electronic health records (EHRs) through portable storage media, they will encounter new risks to the protection of their private information. In this study, we propose a method to preserve the privacy and security of patients' portable medical records in portable storage media to avoid any inappropriate or unintentional disclosure. Following HIPAA guidelines, the method is designed to protect, recover and verify patient's identifiers in portable EHRs. The results of this study show that our methods are effective in ensuring both information security and privacy preservation for patients through portable storage medium.


title: Agricultural technology adoption and poverty reduction: A propensity-score matching analysis for rural Bangladesh

abstract: This study aims at shedding some light on the potential impact of agricultural technology adoption on poverty alleviation strategies. It does so through an empirical investigation of the relationship between technological change, of the Green Revolution type, and wellbeing of smallholder farm households in two rural Bangladeshi regions. As technology adoption is not randomly assigned but there is ‘self-selection into treatment’, the paper tackles a methodological issue in assessing the ‘causal’ effect of technology on farm-household wellbeing through the non-parametric ‘p-score matching analysis’. It pursues a targeted evaluation of whether adopting a modern seed technology causes resource-poor farmers to improve their income and decrease the propensity to fall below the poverty line. It finds a robust and positive effect of agricultural technology adoption on farm household wellbeing suggesting that there is a large scope for enhancing the role of agricultural technology in ‘directly’ contributing to poverty alleviation. 2006 Elsevier Ltd. All rights reserved.


title: WHY 'OPEN INNOVATION' IS OLD WINE IN NEW BOTTLES

abstract: The concept of 'open innovation' has received a considerable amount of coverage within the academic literature and beyond. Much of this seems to have been without much critical analysis of the evidence. In this paper, we show how Chesbrough creates a false dichotomy by arguing that open innovation is the only alternative to a closed innovation model. We systematically examine the six principles of the open innovation concept and show how the Open Innovation paradigm has created a partial perception by describing something which is undoubtedly true in itself (the limitations of closed innovation principles), but false in conveying the wrong impression that firms today follow these principles. We hope that our examination and scrutiny of the 'open innovation' concept contributes to the debate on innovation management and helps enrich our understanding.


title: A framework of changing image emotion using emotion prediction

abstract: Most works about affective image classification in computer vision treat each emotion category independently and predict hard labels, ignoring the correlation between emotion categories. In this work, inspired by psychological theories, we adopt a dimensional emotion model to model the correlation among certain emotion categories. We also propose a framework of changing image emotion by using our emotion predictor. Easily extendable to other feature transformations, our framework changes image emotion by color histogram specification, relaxing the limitation of the previous method that each emotion is associated with a monotonic palette. Effective and comparable to the previous work of changing image emotion shown by user study, our proposed framework provides users with more flexible control in changing image emotion compared with the previous work.


title: Fear and Loathing across Party Lines: New Evidence on Group Polarization

abstract: When defined in terms of social identity and affect toward copartisans and opposing partisans, the polarization of the American electorate has dramatically increased. We document the scope and consequences of affective polarization of partisansusingimplicit,explicit,andbehavioralindicators.Ourevidencedemonstratesthathostilefeelingsfortheopposing party are ingrained or automatic in voters’ minds, and that affective polarization based on party is just as strong as polarization based on race. We further show that party cues exert powerful effects on nonpolitical judgments and behaviors. Partisans discriminate against opposing partisans, doing so to a degree that exceeds discrimination based on race. We note that the willingness of partisans to display open animus for opposing partisans can be attributed to the absence of norms governing the expression of negative sentiment and that increased partisan affect provides an incentive for elites to engage in confrontation rather than cooperation.


Approximated and Domain-Adapted LSTM Language Models for First-Pass Decoding in Speech Recognition


title: An Improved Phase-Shifted Full-Bridge Converter With Wide-Range ZVS and Reduced Filter Requirement

abstract: The phase-shifted full-bridge (PSFB) converter is widely employed in high-power applications. However, circulating current, narrow range of zero-voltage switching (ZVS), duty-cycle loss, and secondary parasitic oscillation are the main drawbacks of the traditional PSFB converter. This paper proposes an improved full-bridge converter to solve the drawbacks of the traditional PSFB converter. In the proposed converter, a coupled inductor with three windings is introduced to adjust the available ZVS energy and to transfer power to the output side during freewheeling period. Due to this structure, output filter requirement, circulating current, the required ZVS energy, and secondary voltage oscillation can be reduced. Thus, the proposed converter can obtain higher conversion efficiency than the traditional PSFB converter. In this paper, the detailed operation principles and experimental results are presented.


title: Design considerations of a CMOS envelope detector for low power wireless receiver applications

abstract: Previous studies have shown that a transistor's transconductance-to-drain-current ratio is useful for optimizing analog circuits. In this paper, we derive an expression that captures the second order distortion generated at the output of an envelope detector. We use the derived expression, along with the nonlinear Taylor series coefficients to design a 3 µA envelope detector in 0.13 µm CMOS process.


Super-Resolution Methods for Wideband Radar


title: Software project management tools: a brief comparative view

abstract: The task of managing a software project can be an extremely complex one, drawing on many personal, team, and organizational resources. The quality of a software product is dependent on the process in which the project is completed. Time delays in software development project and low productivity tend to fall right to the bottom line. Recently, the evolution of project management tools for both software and non-software applications has been accelerating at a rapid pace, and the number of available products have grown significantly. There are many project management tools and software being developed everyday to help managers to automate the administration of individual projects or groups of projects during their life-cycle. This article presents a short comparison of popular software project management tools.


title: Customer-developer links in software development

abstract: Many of the best ideas for new products and product improvements come from the customer or end user of the product [15]. In the software arena, tapping into this source of information requires the establishment of one or more customer-developer links . These links are defined as the techniques and/or channels that allow customers and developers to exchange information.


title: Where do developers log? an empirical study on logging practices in industry

abstract: System logs are widely used in various tasks of software system management. It is crucial to avoid logging too little or too much. To achieve so, developers need to make informed decisions on where to log and what to log in their logging practices during development. However, there exists no work on studying such logging practices in industry or helping developers make informed decisions. To fill this significant gap, in this paper, we systematically study the logging practices of developers in industry, with focus on where developers log. We obtain six valuable findings by conducting source code analysis on two large industrial systems (2.5M and 10.4M LOC, respectively) at Microsoft. We further validate these findings via a questionnaire survey with 54 experienced developers in Microsoft. In addition, our study demonstrates the high accuracy of up to 90% F-Score in predicting where to log.


Hand Gesture Recognition based on Digital Image Processing using MATLAB


title: Effects of plyometric training on soccer players

abstract: Plyometric training (PT) is a technique used to increase strength and explosiveness. It consists of physical exercises in which muscles exert maximum force at short intervals to increase dynamic performances. In such a training, muscles undergo a rapid elongation followed by an immediate shortening (stretch-shortening contraction), utilizing the elastic energy stored during the stretching phase. There is consensus on the fact that when used, PT contributes to improvement in vertical jump performance, acceleration, leg strength, muscular power, increase of joint awareness and overall sport-specific skills. Consequently, PT which was primarily used by martial artists, sprinters and high jumpers to improve performances has gained in popularity and has been used by athletes in all types of sports. However, although PT has been shown to increase performance variables in many sports, little scientific information is currently available to determine whether PT actually enhances skill performance in soccer players, considering that soccer is an extremely demanding sport. Soccer players require dynamic muscular performance for fighting at all levels of training status, including rapid movements such as acceleration and deceleration of the body, change of direction, vertical and horizontal jumps, endurance, speed as well as power for kicking and tackling. In this review we discussed the effects of PT on soccer players by considering gender and age categories.


title: Generating Realistic Training Images Based on Tonality-Alignment Generative Adversarial Networks for Hand Pose Estimation

abstract: Hand pose estimation from a monocular RGB image is an important but challenging task. The main factor affecting its performance is the lack of a sufficiently large training dataset with accurate hand-keypoint annotations. In this work, we circumvent this problem by proposing an effective method for generating realistic hand poses and show that state-of-the-art algorithms for hand pose estimation can be greatly improved by utilizing the generated hand poses as training data. Specifically, we first adopt an augmented reality (AR) simulator to synthesize hand poses with accurate hand-keypoint labels. Although the synthetic hand poses come with precise joint labels, eliminating the need of manual annotations, they look unnatural and are not the ideal training data. To produce more realistic hand poses, we propose to blend a synthetic hand pose with a real background, such as arms and sleeves. To this end, we develop tonality-alignment generative adversarial networks (TAGANs), which align the tonality and color distributions between synthetic hand poses and real backgrounds, and can generate high quality hand poses. We evaluate TAGAN on three benchmarks, including the RHP, STB, and CMU-PS hand pose datasets. With the aid of the synthesized poses, our method performs favorably against the state-of-the-arts in both $2$D and $3$D hand pose estimations.


title: High efficiency aperture-coupled stacked-patch antennas with foam substrate

abstract: High efficiency microstrip patch antenna elements developed in the framework of a green radio technology European Union research project are presented. Wide band aperture-coupled stacked-patch configurations with foam substrates are used. The cheap and low losses low dielectric constant foam substrate used leads to a high efficiency and wide bandwidth printed antenna solution. The good agreement between numerical simulation and experimental input impedance, radiation pattern and efficiency results provides validation of the design and optimization procedure and confirm the proof of concept. A 16% (impedance) bandwidth and 93% radiation efficiency are obtained.


title: Recent updates on different methods of pretreatment of lignocellulosic feedstocks: a review

abstract: Lignocellulosic feedstock materials are the most abundant renewable bioresource material available on earth. It is primarily composed of cellulose, hemicellulose, and lignin, which are strongly associated with each other. Pretreatment processes are mainly involved in effective separation of these complex interlinked fractions and increase the accessibility of each individual component, thereby becoming an essential step in a broad range of applications particularly for biomass valorization. However, a major hurdle is the removal of sturdy and rugged lignin component which is highly resistant to solubilization and is also a major inhibitor for hydrolysis of cellulose and hemicellulose. Moreover, other factors such as lignin content, crystalline, and rigid nature of cellulose, production of post-pretreatment inhibitory products and size of feed stock particle limit the digestibility of lignocellulosic biomass. This has led to extensive research in the development of various pretreatment processes. The major pretreatment methods include physical, chemical, and biological approaches. The selection of pretreatment process depends exclusively on the application. As compared to the conventional single pretreatment process, integrated processes combining two or more pretreatment techniques is beneficial in reducing the number of process operational steps besides minimizing the production of undesirable inhibitors. However, an extensive research is still required for the development of new and more efficient pretreatment processes for lignocellulosic feedstocks yielding promising results.


title: Power Consumption Modeling of Skid-Steer Tracked Mobile Robots on Rigid Terrain

abstract: Power consumption is a key element in outdoor mobile robot autonomy. This issue is very relevant in skid-steer tracked vehicles on account of their large ground contact area. In this paper, the power losses due to dynamic friction have been modeled from two different perspectives: 1) the power drawn by the rigid terrain and 2) the power supplied by the motors. Comparison of both approaches has provided new insight on skid steering on hard flat terrains at walking speeds. Experimental power models, which also include traction resistance and other power losses, have been obtained for two different track widths over marble flooring and asphalt with Auriga- beta, which is a full-size mobile robot. To this end, various internal probes have been set at different points of the power stream. Furthermore, new energy implications for navigation of these kinds of vehicles have been deduced and tested.


title: Atrophic Glossitis From Vitamin B12Deficiency: A Case Misdiagnosed as Burning Mouth Disorder

abstract: BACKGROUND ::: Glossodynia, or painful sensation of the tongue, can have a spectrum of etiologies, such as local infection, trauma, nerve damage, glossitis, or the enigmatic neuropathic pain syndrome, burning mouth disorder (BMD; also known as burning mouth syndrome). Careful history-taking, physical examination, and appropriate laboratory screening can differentiate these causes of glossodynia and direct further therapy. ::: ::: ::: METHODS ::: A 73-year-old woman presented with several months of glossodynia having previously been diagnosed by her primary care physician with primary BMD. Subsequently, she consulted an otolaryngologist, who pursued further diagnostic evaluation. ::: ::: ::: RESULTS ::: Examination revealed the presence of a beefy, red, smooth tongue, and further laboratory evaluation yielded a low serum vitamin B(12) level and macrocytosis. Three months of oral vitamin B(12) supplementation led to partial restoration of serum vitamin B(12) levels and a modest improvement in symptoms. Her final diagnoses were atrophic glossitis and glossodynia secondary to vitamin B(12) deficiency, most likely due to pernicious anemia. ::: ::: ::: CONCLUSIONS ::: The results of this case have important clinical implications for the diagnostic evaluation and management of patients with glossodynia and apparent BMD. Pathogenic mechanisms of nutrient deficiency in atrophic glossitis are discussed.


title: An Evaluation of the Impact of Visual Embellishments in Bar Charts

abstract: As data visualization becomes further intertwined with the field of graphic design and information graphics, small graphical alterations are made to many common chart formats. Despite the growing prevalence of these embellishments, their effects on communication of the charts' data is unknown. From an overview of the design space, we have outlined some of the common embellishments that are made to bar charts. We have studied the effects of these chart embellishments on the communication of the charts' data through a series of user studies on Amazon's Mechanical Turk platform. The results of these studies lead to a better understanding of how each chart type is perceived, and help provide guiding principles for the graphic design of charts.


title: Functional activation of the human frontal cortex during the performance of verbal working memory tasks.

abstract: Regional cerebral blood flow was measured with positron emission tomography during the performance of verbal working memory tasks. The same type of verbal response (i.e., reciting numbers) was required in the control and the two experimental tasks. In the control task, the subjects were required to count aloud. In the two experimental tasks, the subjects were required to maintain within working memory the numbers they generated (self-ordered task) or the numbers generated by the experimenter (externally ordered task). Examination of the difference in activation between these conditions revealed strong bilateral activation within the mid-dorsolateral frontal cortex during both experimental tasks. There was, however, no evidence of additional activation within the mid-dorsolateral frontal cortex when monitoring self-generated responses as compared with the monitoring of externally generated responses. These results provide evidence regarding the role of the mid-dorsolateral frontal cortex in mnemonic processing that are in agreement with recent findings from work with non-human primates.


title: Intelligent control for a drone by self-tunable Fuzzy Inference System

abstract: The work describes an automatically on-line Self-Tunable Fuzzy Inference System (STFIS) of a new configuration of mini-flying called XSF (X4 Stationnary Flyer) drone. A Fuzzy controller based on on-line optimization of a zero order Takagi-Sugeno fuzzy inference system (FIS) by a back propagation-like algorithm is successfully applied. It is used to minimize a cost function that is made up of a quadratic error term and a weight decay term that prevents an excessive growth of parameters. Thus, we carried out control for the continuation of simple trajectories such as the follow-up of straight lines, and complex (half circle, corner) by using the STFIS technique. This permits to prove the effectiveness of the proposed control law. We studied the robustness of the two controllers used in the presence of disturbances. We presented two types of disturbances, the case of a gust of wind and taking into account white noise disturbances. A comparison between the Self-Tunable Fuzzy Inference System (STFIS) and Adaptive Network based Fuzzy Inference System (ANFIS) is given.


title: Graph Convolutional Networks for Named Entity Recognition

abstract: In this paper we investigate the role of the dependency tree in a named entity recognizer upon using a set of GCN. We perform a comparison among different NER architectures and show that the grammar of a sentence positively influences the results. Experiments on the ontonotes dataset demonstrate consistent performance improvements, without requiring heavy feature engineering nor additional language-specific knowledge.


title: Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications

abstract: Transfer learning allows practitioners to recognize and apply knowledge learned in previous tasks (source task) to new tasks or new domains (target task), which share some commonality. The two important factors impacting the performance of transfer learning models are: (a) the size of the target dataset, and (b) the similarity in distribution between source and target domains. Thus far, there has been little investigation into just how important these factors are. In this paper, we investigate the impact of target dataset size and source/target domain similarity on model performance through a series of experiments. We find that more data is always beneficial, and model performance improves linearly with the log of data size, until we are out of data. As source/target domains differ, more data is required and fine tuning will render better performance than feature extraction. When source/target domains are similar and data size is small, fine tuning and feature extraction renders equivalent performance. We hope that by beginning this quantitative investigation on the effect of data volume and domain similarity in transfer learning we might inspire others to explore the significance of data in developing more accurate statistical models.


title: Exploring patterns of daily physical and sedentary behaviour in community-dwelling older adults

abstract: BACKGROUND: recent evidence suggests that the interaction between periods of sedentary and activity behaviour is important for health; providing distinctive information to assessment of activity alone. This study quantified activity and sedentary behaviour in older, community-dwelling adults. METHODS: fifty-six community-dwelling older adults with an average age 79 (SD) years wore an ActivPAL accelerometer for 7 days and were assessed for a range of motor, cognitive and affective characteristics. Seven variables derived from accelerometry considered to represent four characteristics of habitual behaviour (volume, frequency, intensity and variability) were submitted to principal components factor analysis (PCA). Factor scores were retained and used as dependent variables in regression analysis. RESULTS: three significant orthogonal factors emerged from the PCA, accounting for 80% of the variance in test scores: 'walking behaviour' which accounted for 39% of variance in the model; 'sedentary behaviour' explaining 24.3% of total variance; and 'postural transitions' which accounted for 16.7% of total variance. For the regression analysis, younger age and lower body mass index (BMI) emerged as significant predictors of physical behaviour, explaining 36% of the total variance. For postural transitions, lower BMI was the unique contributor, explaining 15% of total variance. Significant predictors of sedentary behaviours were not identified. CONCLUSIONS: walking, sedentary and transitory behaviours are distinct from each other, and together explain daily function. Further research on a larger sample is indicated to explore the characteristics that explain these behaviours, in particular the interplay between sedentary behaviour and periods of physical activity. Language: en


title: An intrusion detection system for smart grid neighborhood area network

abstract: Smart grid is expected to improve the efficiency, reliability and economics of current energy systems. Using two-way flow of electricity and information, smart grid builds an automated, highly distributed energy delivery network. In this paper, we present the requirements for intrusion detection systems (IDSs) in neighborhood area network (NAN) as a component of smart grid. We propose an IDS that is implemented in a distributed fashion with respect to NAN's communication and computation needs. An analytical approach is employed for detecting Wormhole attacks. We validate our NAN IDS scheme using OPNET Modeler [1]. The analytical part of the solution is developed in Maple [2] and integrated with OPNET.


title: A Fine-Grained Approach to Scene Text Script Identification

abstract: This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online.


title: Understanding Changes in Consumer Payment Habits - Do Mobile Payments and Electronic Invoices Attract Consumers?

abstract: Mobile payment services have, thus far, failed to entice consumers. An apparent conclusion is that these services have failed to meet consumers’ payment needs. Deeper understanding of consumer adoption motivations is thus needed to be able to develop and launch mobile payment services successfully. For more than a decade researchers have tried to uncover the generic determinants of technology adoption common to most if not all technologies. Even when armed with this knowledge mobile payment services have fallen short of gaining customer bases large enough to sustain them. This suggests that the current academic knowledge is either ignored by practitioners or that the knowledge of consumer adoption is far from clear. Our paper seeks to find out if the generic technology adoption models are sufficient to explain factors consumers consider when they decide whether or not to adopt mobile payment services. In particular, we develop two models in the payment context. One of them models the determinants of the mobile payments services adoption while the other models the determinants of electronic invoicing adoption. The comparison of the model structures suggests that perceived ease of use seems to be the least common denominator for consumer adoption of these information technology based services while the context of technology adoption determines both the non-differentiating and the differentiating determinants of technology adoption.


title: Cooperative Game Theory for Distributed Spectrum Sharing

abstract: There is a need for new spectrum access protocols that are opportunistic, flexible and efficient, yet fair. Game theory provides a framework for analyzing spectrum access, a problem that involves complex distributed decisions by independent spectrum users. We develop a cooperative game theory model to analyze a scenario where nodes in a multi-hop wireless network need to agree on a fair allocation of spectrum. We show that in high interference environments, the utility space of the game is non-convex, which may make some optimal allocations unachievable with pure strategies. However, we show that as the number of channels available increases, the utility space becomes close to convex and thus optimal allocations become achievable with pure strategies. We propose the use of the Nash Bargaining Solution and show that it achieves a good compromise between fairness and efficiency, using a small number of channels. Finally, we propose a distributed algorithm for spectrum sharing and show that it achieves allocations reasonably close to the Nash Bargaining Solution.


title: Modeling spread of disease from social interactions

abstract: Research in computational epidemiology to date has concentrated on coarse-grained statistical analysis of populations, often synthetic ones. By contrast, this paper focuses on fine-grained modeling of the spread of infectious diseases throughout a large real-world social network. Specifically, we study the roles that social ties and interactions between specific individuals play in the progress of a contagion. We focus on public Twitter data, where we find that for every health-related message there are more than 1,000 unrelated ones. This class imbalance makes classification particularly challenging. Nonetheless, we present a framework that accurately identifies sick individuals from the content of online communication. Evaluation on a sample of 2.5 million geo-tagged Twitter messages shows that social ties to infected, symptomatic people, as well as the intensity of recent co-location, sharply increase one's likelihood of contracting the illness in the near future. To our knowledge, this work is the first to model the interplay of social activity, human mobility, and the spread of infectious disease in a large real-world population. Furthermore, we provide the first quantifiable estimates of the characteristics of disease transmission on a large scale without active user participation---a step towards our ability to model and predict the emergence of global epidemics from day-to-day interpersonal interactions.


Control Strategy on Doubly Fed Induction Generator Using PSIM


title: A kirigami-based Parylene C stretch sensor

abstract: We present the first slotted Parylene C kirigami device for transducing stretch-induced displacement under multiple modes of operation. Devices consist of thin-film gold traces wound among multiple rows of offset slits etched through a free-film Parylene substrate. Slits were etched via a switched-chemistry DRIE in oxygen plasma, resulting in densely packed slit designs. Multiple slit designs were evaluated and devices with more tightly packed slits were found endure greater stretch distance before failure, with devices containing more tightly packed slits able to stretch up to 9 mm (180%) without plastic deformation or compromise of electrical integrity. Multiple modes of stretch transduction were evaluated, including DC resistance changes due to strain at trace inflection points and high-frequency changes in self-impedance and inter-trace capacitance. DC resistance was found to increase linearly with stretch distance with sensitivities between 0.04 and 0.1 Ω/mm which are comparable to previously reported polyimide sensors but with a wider dynamic measurement range. Capacitance was also found to increase linearly with stretch distance after 2 mm, and high-frequency impedance increased nonlinearly, with different trace routing designs resulting in different patterns of impedance change with stretch.


title: Node Localization in Wireless Sensor Networks Using the M5P Tree and SMOreg Algorithms

abstract: In Wireless Sensor Networks (WSN), Node Localization is of great importance for location aware services. In this paper we propose the use of Time of Arrival (TOA) information with two popular machine learning algorithms M5 tree Model (M5P) and Sequential Minimal Optimization for Regression (SMOreg) for more accurate node localization in WSN. In this paper we also applied the same node localization problem to two previously used artificial neural network models- Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) Network. After that a comparative analysis between all selected algorithms has been made. Simulation results show the superiority of M5P and SMOreg over MLP and RBFN in high noise conditions in terms of root mean square error. At last a comparative analysis between the two new proposed algorithms was made by changing the number of training nodes. Results show that initially the performance of SMOreg is better but there is no improvement in its performance with increasing training samples. On the other hand M5P's performance can be made better by train it with more number of samples.


title: A faster parallel algorithm and efficient multithreaded implementations for evaluating betweenness centrality on massive datasets

abstract: We present a new lock-free parallel algorithm for computing betweenness centrality of massive complex networks that achieves better spatial locality compared with previous approaches. Betweenness centrality is a key kernel in analyzing the importance of vertices (or edges) in applications ranging from social networks, to power grids, to the influence of jazz musicians, and is also incorporated into the DARPA HPCS SSCA#2, a benchmark extensively used to evaluate the performance of emerging high-performance computing architectures for graph analytics. We design an optimized implementation of betweenness centrality for the massively multithreaded Cray XMT system with the Thread-storm processor. For a small-world network of 268 million vertices and 2.147 billion edges, the 16-processor XMT system achieves a TEPS rate (an algorithmic performance count for the number of edges traversed per second) of 160 million per second, which corresponds to more than a 2× performance improvement over the previous parallel implementation. We demonstrate the applicability of our implementation to analyze massive real-world datasets by computing approximate betweenness centrality for the large IMDb movie-actor network.


title: Software-Defined Networking for Smart Grid Resilience: Opportunities and Challenges

abstract: Software-defined networking (SDN) is an emerging networking paradigm that provides unprecedented flexibility in dynamically reconfiguring an IP network. It enables various applications such as network management, quality of service (QoS) optimization, and system resilience enhancement. Pilot studies have investigated the possibilities of applying SDN on smart grid communications, while the specific benefits and risks that SDN may bring to the resilience of smart grids against accidental failures and malicious attacks remain largely unexplored. Without a systematic understanding of these issues and convincing validations of proposed solutions, the power industry will be unlikely to embrace SDN, since resilience is always a key consideration for critical infrastructures like power grids. In this position paper, we aim to provide an initial understanding of these issues, by investigating (1) how SDN can enhance the resilience of typical smart grids to malicious attacks, (2) additional risks introduced by SDN and how to manage them, and (3) how to validate and evaluate SDN-based resilience solutions. Our goal is also to trigger more profound discussions on applying SDN to smart grids and inspire innovative SDN-based solutions for enhancing smart grid resilience.


title: Modeling, control, and validation of an electro-hydraulic steer-by-wire system for articulated vehicle applications

abstract: X-by-wire control systems in automotive applications refer to systems where the input device used by the operator is connected to the actuation power subsystem by electrical wires, as opposed to being connected by mechanical or hydraulic means. The "X" in the X-by-wire is replaced by "steer", "throttle", and "brake" to represent the steer-by-wire, throttle-by-wire, and brake-by-wire systems. Common to all of these subsystems is that the operator control input device (i.e., steering column, acceleration pedal, and brake pedal) is not connected to the actuation devices mechanically. Rather, it is connected to an embedded computer which, in turn, sends the control signals to the actuation devices. Current state of art steering systems used in articulated vehicles are hydro-mechanical type systems, i.e., the steering column motion is transmitted and amplified by the main hydraulic circuit by hydro-mechanical means. This paper presents a new steer-by-wire (SBW) system which we designed, modeled, analyzed, and tested on wheel type loader construction equipment. The simulation results and tests conducted on a prototype development vehicle (a medium size wheel type loader) show very good agreement. The control algorithm is modeled using graphical modeling tools similar to Simulink and StateFlow. A real-time control algorithm is implemented on a Motorola 68332 microprocessor-based embedded controller. The operational performance of the steer-by-wire system has been convincingly demonstrated.


title: Slackliner - An Interactive Slackline Training Assistant

abstract: In this paper we present Slackliner, an interactive slackline training assistant which features a life-size projection, skeleton tracking and real-time feedback. As in other sports, proper training leads to a faster buildup of skill and lessens the risk for injuries. We chose a set of exercises from slackline literature and implemented an interactive trainer which guides the user through the exercises and gives feedback if the exercise was executed correctly. A post analysis gives the user feedback about her performance. We conducted a user study to compare the interactive slackline training system with a classic approach using a personal trainer. No significant difference was found between groups regarding balancing time, number of steps and the walking distance on the line for the left and right foot. Significant main effects for the balancing time on line, without considering the group, have been found. User feedback acquired by questionnaires and semi-structured interviews was very positive. Overall, the results indicate that the interactive slackline training system can be used as an enjoyable and effective alternative to classic training methods.


title: Low-cost hardware & control design of an inverted pendulum using conventional, fuzzy and hybrid techniques

abstract: Inverted pendulum is a highly non-linear system, which makes it a complex control problem that may be articulated for various real-life applications. Usually, its linearized model is implemented and controlled using a typical Proportional-Integral-Derivate (PID) controller but a different controller is required to control the actual, non-linear model. In the current work, we aim to develop an indigenous and experimental hardware platform to implement conventional PID, Fuzzy and Hybrid techniques. Hardware setup of this model is implemented on a 4-wheeler mobile cart with an inverted rod mechanism (pendulum). Control circuit is implemented through Arduino and associated control circuitry. The controller is simulated on MATLAB/Simulink and implemented on this hardware platform to validate minimum possible deflection of pendulum from a reference vertical position using PID, Fuzzy and hybrid control schemes. Results are promising from an FLC scheme applied on the close to real non-linear model of the system.


title: Stochastic computing

abstract: The Stochastic Computer was developed as part of a program of research on the structure, realization and application of advanced automatic controllers in the form of Learning Machines. Although algorithms for search, identification, policy-formation and the integration of these activities, could be established and tested by simulation on conventional digital computers, there was no hardware available which would make construction of the complex computing structure required in a Learning Machine feasible. The main problem was to design an active storage element in which the stored value was stable over long periods, could be varied by small increments, and whose output could act as a 'weight' multiplying other variables. Since large numbers of these elements would be required in any practical system it was also necessary that they be small and of low cost. Conventional analog integrators and multipliers do not fulfill requirements of stability and low cost, and unconventional elements such as electro-chemical stores and transfluxors are unreliable or require sophisticated external circuitry to make them usable. Semiconductor integrated circuits have advantages in speed, stability, size and cost, and it was decided to design a computing element based on standard gates and flip-flops which would be amenable to large-scale integration.


title: Nonparametric Bayesian sparse factor models with application to gene expression modeling

abstract: A nonparametric Bayesian extension of Factor Analysis (FA) isproposed where observed data Y is modeled as a linear superposi-tion, G, of a potentially in nite number of hidden factors, X. TheIndian Bu et Process (IBP) is used as a prior on G to incorporatesparsity and to allow the number of latent features to be inferred.The model’s utility for modeling gene expression data is investigatedusing randomly generated datasets based on a known sparse connec-tivity matrix for E. Coli, and on three biological datasets of increasingcomplexity.


title: Smoke Detection in Video

abstract: In this paper, we propose a method for smoke detection in outdoor video sequences. We assume that the camera is mounted on a pan/tilt device.The proposed method is composed of three steps. The first step is to decide whether the camera is moving or not. While the camera is moving, we skip the ensuing steps. Otherwise, the second step is to detect the areas of change in the current input frame against the background image and to locate regions of interest (ROIs) by connected component analysis. The block-based approach is applied in both the first and second steps. In the final step, we decide whether the detected ROI is smoke by using the k- temporal information of its color and shape extracted from the ROI. We show the experimental results using in the forest surveillance videos.


title: Ant colony optimization with greedy migration mechanism for node deployment in wireless sensor networks

abstract: Node deployment is one of the most crucial issues in wireless sensor networks because it determines the deployment cost, the detection capability of the networks, and even the network lifetimes. To solve such a problem is an intricate task with realistic deployment factors such as deployment cost, connectivity guarantee, load balancing and channel collisions. In this paper, we consider the problem of grid-based coverage with low-cost and connectivity-guarantee (GCLC), and propose a novel deployment approach, ACO-Greedy, to settle this question. This approach is based on the ant colony optimization with greedy migration mechanism, which can quickly complete the full coverage, and markedly decrease the deployment cost. In addition, ACO-Greedy can dynamically adjust the sensing/communication radius to alleviate the energy hole problem and prolong the network lifetime. The simulation results reveal that our developed approach can not only decrease the deployment cost remarkably, but also effectively balance power consumption among sensor nodes and prolong the network lifetime in grid-based WSNs.


title: Valued Constraint Satisfaction Problems: Hard and Easy Problems

abstract: In order to deal with over-constrained Constraint Satisfaction Problems, various extensions of the CSP framework have been considered by taking into account costs, uncertainties, preferences, priorities...Each extension uses a specific mathematical operator (+, max...) to aggregate constraint violations. ::: ::: In this paper, we consider a simple algebraic framework, related to Partial Constraint Satisfaction, which subsumes most of these proposals and use it to characterize existing proposals in terms of rationality and computational complexity. We exhibit simple relationships between these proposals, try to extend some traditional CSP algorithms and prove that some of these extensions may be computationally expensive.


title: Designing the W3C open annotation data model

abstract: The Open Annotation Core Data Model specifies an interoperable framework for creating associations between related resources, called annotations, using a methodology that conforms to the Architecture of the World Wide Web. Open Annotations can easily be shared between platforms, with sufficient richness of expression to satisfy complex requirements while remaining simple enough to also allow for the most common use cases, such as attaching a piece of text to a single web resource. This paper presents the W3C Open Annotation Community Group specification and the rationale behind the scoping and technical decisions that were made. It also motivates interoperable Annotations via use cases, and provides a brief analysis of the advantages over previous specifications.


The United Nations Parallel Corpus v1.0


title: Thoughts on massively-parallel heterogeneous computing for solving large problems

abstract: In this paper, we present our view of massively-parallel heterogeneous computing for solving large scientific problems. We start by observing that computing has been the primary driver of major innovations since the beginning of the 21st century. We argue that this is the fruit of decades of progress in computing methods, technology, and systems. A high-level analysis on out-scaling and up-scaling on large supercomputers is given through a time-domain wave-scattering simulation example. The importance of heterogeneous node architectures for good up-scaling is highlighted. A case for low-complexity algorithms is made for continued scale-out towards exascale systems.


title: The Future of Digital Forensics: Challenges and the Road Ahead

abstract: Today’s huge volumes of data, heterogeneous information and communication technologies, and borderless cyberinfrastructures create new challenges for security experts and law enforcement agencies investigating cybercrimes. The future of digital forensics is explored, with an emphasis on these challenges and the advancements needed to effectively protect modern societies and pursue cybercriminals.


title: Developer Mistakes in Writing Android Manifests: An Empirical Study of Configuration Errors

abstract: Each Android app must have an Android manifest file. It is one of the most important configuration files manually written by developers. In addition to various configuration parameters required to run an app, it also contains configuration parameters which are used to implement security, compatibility, and accessibility of an app. Any mistakes in writing the manifest file can cause serious implications in terms of security, reliability, and availability of an app. In this paper, we study and report different types of mistakes committed by developers in writing Android manifest files. The study was performed on 13,483 real-world Android apps. We also present an open source rule-based static analysis tool which detects developer mistakes in the manifest file. The tool generates a warning message if it detects any misconfigurations in the manifest file. We used the tool to perform the empirical study and it generated total 59,547 configuration errors in 11,110 apps. Only 2,373 apps, among studied apps, do not have any configuration errors.


title: A Proposal for an Open Local Movie Recommender

abstract: This position paper describes the initial research assumptions and work carried on in the context of the ReAL CODE, a project aimed at realizing an intelligent and tailored distribution of digital creative contents towards end users.


title: Segmentation of telecom customers based on customer value by decision tree model

abstract: The more the telecom services marketing paradigm evolves, the more important it becomes to retain high value customers. Traditional customer segmentation methods based on experience or ARPU (Average Revenue per User) consider neither customers' future revenue nor the cost of servicing customers of different types. Therefore, it is very difficult to effectively identify high-value customers. In this paper, we propose a novel customer segmentation method based on customer lifecycle, which includes five decision models, i.e. current value, historic value, prediction of long-term value, credit and loyalty. Due to the difficulty of quantitative computation of long-term value, credit and loyalty, a decision tree method is used to extract important parameters related to long-term value, credit and loyalty. Then a judgments matrix formulated on the basis of characteristics of data and the experience of business experts is presented. Finally a simple and practical customer value evaluation system is built. This model is applied to telecom operators in a province in China and good accuracy is achieved.


title: Bandwidth Enhancement of SIW Horn Antenna Loaded With Air-Via Perforated Dielectric Slab

abstract: A substrate integrated waveguide (SIW) horn antenna loaded with air-via perforated dielectric slab for bandwidth enhancement is proposed in this letter. The narrow impedance bandwidth of the planar horn antenna is mainly resulting from the discontinuity between the substrate and air. By simply drilling air-vias with different diameters in the substrate extended from the horn aperture, a smooth transition from substrate to air can be achieved, which can enhance the impedance bandwidth of the antenna in much degree. Measured results show that the enhanced impedance bandwidth of 40% from 16 to 24 GHz is obtained with the return loss |S11| below -10 dB. In addition, stable radiation patterns are observed over the entire operating band.


Example-based Authoring of Procedural Modeling Programs with Structural and Continuous Variability


title: Mitigation of Inrush Currents in Network Transformers by Reducing the Residual Flux With an Ultra-Low-Frequency Power Source

abstract: A methodology for the reduction of the residual flux in network transformers is proposed in this paper. The purpose is the mitigation of large inrush currents taken by numerous transformers when a long feeder is energized. Time-domain simulations are used to prove that a small-power device can substantially reduce the residual flux of all transformers simultaneously. The device consists of a low-voltage dc source, a suitable power-electronic switching unit, and a simple controller. Before a feeder is re-energized, the residual flux is reduced to a minimum and, as a consequence, the large inrush currents are reduced to an acceptable level. This greatly enhances the probability for the feeder to be successfully energized when otherwise a false trip would have occurred. Inrush current reductions of more than 60% are obtained at the head of the feeder.


title: Getting a Job via Career-Oriented Social Networking Sites: The Weakness of Ties

abstract: By asking users of career-oriented social networking sites I investigated their job search behavior. For further IS-theorizing I integrated the number of a user's contacts as an own construct into Venkatesh's et al. UTAUT2 model, which substantially rose its predictive quality from 19.0 percent to 80.5 percent concerning the variance of job search success. Besides other interesting results I found a substantial negative relationship between the number of contacts and job search success, which supports the experience of practitioners but contradicts scholarly findings. The results are useful for scholars and practitioners.