title: Finite element analysis of rough surfaces based on ultimate-stress asperity concept

abstract: An improved mathematical elastic–plastic model for the contact of rough surfaces that is based on an accurate finite element solution of a deformable single asperity and a rigid flat surface is developed to provide dimensionless expressions for the contact area and contact load. This model differs from the existing models, in that it accounts for the level of interference beyond expected failure. The finite element solution is used to define the limits at which failure occurs. A fictitious ultimate-stress asperity is introduced to facilitate the derivation of the contact model. This model considers a realistic picture of elastic–plastic deformation where elastic, plastic and failure behaviors can occur simultaneously for an asperity. Subsequent comparison of the results for estimating contact area and load using the present model and the earlier methods shows identical results for pure elastic contacts with plasticity index values at about 0.5 but substantial difference for the net elastic–plastic contacts having plasticity index values above 0.8. When plasticity index reaches 6 and beyond, the three models predict similar total contact area and load values and that the contact is purely plastic. Copyright © 2008 John Wiley & Sons, Ltd.


title: Previous civic experience and Asian adolescents’ expected participation in legal protest: mediating role of self-efficacy and interest

abstract: ABSTRACT Not yet citizens, adolescents are encouraged through civic education to consider an active role in the future political life of their societies. This study seeks to understand the mediating role of political self-efficacy and political interest in the association between previous civic experience and Asian adolescents’ expected participation in legal protest. Using data from the International Civic and Citizenship Education Study (ICCS) 2009, which included 23,654 students from South Korea, Chinese Taipei, Hong Kong, Indonesia, and Thailand, a multi-group multi-level path analysis based on Rasch measures was employed to investigate how adolescent civic experiences both in and out of school influence their expected participation in legal protest. The results showed the effect of political discussion and that of school participation were fully mediated by political interest and self-efficacy. The effect of community participation was partially mediated by political interest and self-efficacy in the five societies except South Korea. While the total effect of school participation is relatively low across the five societies, the total effect of political discussion is stronger for adolescents in East Asia (South Korea, Chinese Taipei, and Hong Kong) and that of community participation is stronger in South East Asia (Thailand and Indonesia).


title: Novel hydrazine-bridged covalent triazine polymer for CO 2 capture and catalytic conversion

abstract: Abstract Carbon dioxide (CO2) capture and catalytic conversion has become an attractive and challenging strategy for CO2 utilization since it is an abundant, inexpensive, and renewable C1 resource and a main greenhouse gas. Herein, a novel hydrazine-bridged covalent triazine polymer (HB-CTP) was first designed and synthesized through simple polymerization of cyanuric chloride with 2,4,6-trihydrazinyl-1,3,5-triazine. The resultant HB-CTP exhibited good CO2 capture capacity (8.2 wt%, 0 °C, and 0.1 MPa) as well as satisfactory recyclability after five consecutive adsorption-desorption cycles. Such a polymer was subsequently employed as a metal-free heterogeneous catalyst for the cyclo-addition of CO2 with various epoxides under mild and solvent-free conditions, affording cyclic carbonates with good to excellent yields (67%–99%) and high functional-group tolerance. The incorporation of hydrazine linkages into HB-CTP's architecture was suggested to play the key role in activating epoxides through hydrogen bonding. Moreover, HB-CTP can be reused at least five times without significant loss of its catalytic activity.


title: Incremental synthesis of control policies for heterogeneous multi-agent systems with linear temporal logic specifications

abstract: We consider automatic synthesis of control policies for non-independent, heterogeneous multi-agent systems with the objective of maximizing the probability of satisfying a given specification. The specification is expressed as a formula in linear temporal logic. The agents are modeled by Markov decision processes with a common set of actions. These actions, however, may or may not affect the behaviors of all the agents. To alleviate the well-known state explosion problem, an incremental approach is proposed where only a small subset of agents is incorporated in the synthesis procedure initially and more agents are successively added until the limitations on computational resources are reached. The proposed algorithm runs in an anytime fashion, where the probability of satisfying the specification increases as the algorithm progresses.


title: Waveform multiplexing for 5G: A concept and 3D evaluation

abstract: Fifth generation (5G) communication systems include enhanced mobile broadband, massive machine-type communication, and ultra-reliable low-latency communication. To support various 5G applications, researchers have considered multiple scalable subcarrier spacing and transmission time interval (TTI). In this paper, we introduce a concept of waveform multiplexing for frequency multiplexing of numerologies. Waveform multiplexing consists of different waveforms on subbands with scalable subcarrier spacing and TTI operating on one frequency band. The Waveform multiplexing also possesses dynamic cyclic prefix and minimum guard band, which are the key features for the high spectral efficiency. We verify the performance and the potential of the waveform multiplexing using 3-dimensional (3D) ray-tracing based system-level evaluation in a realistic environment and a 3D channel.


title: A recurrent deletion syndrome at chromosome bands 2p11.2-2p12 flanked by segmental duplications at the breakpoints and including REEP1

abstract: We identified an identical and recurrent 9.4-Mbp deletion at chromosome bands 2p11.2-2p12, which occurred de novo in two unrelated patients. It is flanked at the distal and proximal breakpoints by two homologous segmental duplications consisting of low copy repeat (LCR) blocks in direct orientation, which have >99% sequence identity. Despite the fact that the deletion was almost 10 Mbp in size, the patients showed a relatively mild clinical phenotype, that is, mild-to-moderate intellectual disability, a happy disposition, speech delay and delayed motor development. Their phenotype matches with that of previously described patients. The 2p11.2-2p12 deletion includes the REEP1 gene that is associated with spastic paraplegia and phenotypic features related to this are apparent in most 2p11.2-2p12 deletion patients, but not in all. Other hemizygous genes that may contribute to the clinical phenotype include LRRTM1 and CTNNA2. We propose a recurrent but rare 2p11.2-2p12 deletion syndrome based on (1) the identical, non-random localisation of the de novo deletion breakpoints in two unrelated patients and a patient from literature, (2) the patients' phenotypic similarity and their phenotypic overlap with other 2p deletions and (3) the presence of highly identical LCR blocks flanking both breakpoints, consistent with a non-allelic homologous recombination (NAHR)-mediated rearrangement.


title: Fast and accurate image recognition using Deeply-Fused Branchy Networks

abstract: In order to achieve higher accuracy of image recognition, deeper and wider networks have been used. However, when the network size gets bigger, its forward inference time also takes longer. To address this problem, we propose Deeply-Fused Branchy Network (DFB-Net) by adding small but complete side branches to the target baseline main branch. DFB-Net allows easy-to-discriminate samples to be classified faster. For hard-to-discriminate samples, DFB-Net makes probability fusion by averaging softmax probabilities to make collaborative predictions. Extensive experiments on the two CIFAR datasets show that DFB-Net achieves state-of-the-art results to obtain an error rate of 3.07% on CIFAR-10 and 16.01% on CIFAR-100. Meanwhile, the forward inference time (with a batch size of 1 and averaged among all test samples) only takes 10.4 ms on CIFAR-10, 18.8 ms on CIFAR-100, using GTX 1080 GPU with cuDNN 5.1.


title: LTE/LTE-A Network Security Data Collection and Analysis for Security Measurement: A Survey

abstract: The long-term evolution (LTE)/LTE-advanced (LTE-A) network provides advanced services for billions of users with its higher bandwidths, better spectrum efficiency, and lower latency than legacy cellular networks. But it still suffers from new security threats due to its all IP-based heterogeneous architecture. Therefore, there is a critical need to perform a rapid and accurate network security measurement in the LTE/LTE-A network. To achieve LTE/LTE-A network security measurement, security-relevant data (in short security data) collection and data analysis for attack detection are required as prerequisites. However, most of the existing work only focuses on data collection and analysis for a certain type of LTE/LTE-A attacks. Little work has been done to comprehensively perform data collection and analysis for detecting various attacks on the LTE/LTE-A network. Different from previous work, in this paper, we review the security data collection and data analysis methods in terms of various attacks in order to provide the basis of security measurement in the LTE/LTE-A network. We first present a comprehensive taxonomy of attacks according to the LTE/LTE-A network structure. Then, we propose a number of criteria for evaluating the performance of data collection and analysis methods. And we lay our emphasis on the survey of data collection and analysis methods for significant active attack detection in the LTE/LTE-A network. All the reviewed methods are analyzed and discussed based on the proposed evaluation criteria. Furthermore, current open issues and future research challenges are presented with a view to stimulating future research. Finally, an adaptive data collection and data analysis model for security measurement in the LTE/LTE-A network is proposed.


title: Approximate Hybrid High Radix Encoding for Energy-Efficient Inexact Multipliers

abstract: Approximate computing forms a design alternative that exploits the intrinsic error resilience of various applications and produces energy-efficient circuits with small accuracy loss. In this paper, we propose an approximate hybrid high radix encoding for generating the partial products in signed multiplications that encodes the most significant bits with the accurate radix-4 encoding and the least significant bits with an approximate higher radix encoding. The approximations are performed by rounding the high radix values to their nearest power of two. The proposed technique can be configured to achieve the desired energy-accuracy tradeoffs. Compared with the accurate radix-4 multiplier, the proposed multipliers deliver up to 56% energy and 55% area savings, when operating at the same frequency, while the imposed error is bounded by a Gaussian distribution with near-zero average. Moreover, the proposed multipliers are compared with state-of-the-art inexact multipliers, outperforming them by up to 40% in energy consumption, for similar error values. Finally, we demonstrate the scalability of our technique.


title: Analysis of Invariance and Robustness via Invertibility of ReLU-Networks

abstract: Studying the invertibility of deep neural networks (DNNs) provides a principled approach to better understand the behavior of these powerful models. Despite being a promising diagnostic tool, a consistent theory on their invertibility is still lacking. We derive a theoretically motivated approach to explore the preimages of ReLU-layers and mechanisms affecting the stability of the inverse. Using the developed theory, we numerically show how this approach uncovers characteristic properties of the network.


title: OFDM and TDM based sensing method for large projected mutual-capacitance touch screens

abstract: This paper presents a new technique for reading out projected mutual capacitance touch screen panels. It models touch screen panel as a communication system. Orthogonal frequency division multiplexing (OFDM) is used along with time division multiplexing (TDM) to read out all channels of touch screen panels. Different carrier frequencies are applied to touch screen panel concurrently using OFDM, while the sensing lines are sensed sequentially using TDM. The proposed technique ensures a high frame scan rate by using simultaneous driving signals, and it provides high signal to noise ratio (SNR) by avoiding frequency band with high noise power. Simulation results demonstrate a SNR improvement of 8.2 dB by selecting adequate carrier frequencies.


title: A 3.9 mW 25-Electrode Reconfigured Sensor for Wearable Cardiac Monitoring System

abstract: A low power highly sensitive Thoracic Impedance Variance (TIV) and Electrocardiogram (ECG) monitoring SoC is designed and implemented into a poultice-like plaster sensor for wearable cardiac monitoring. 0.1 Ω TIV detection is possible with a sensitivity of 3.17 V/Ω and SNR > 40 dB. This is achieved with the help of a high quality (Q-factor > 30) balanced sinusoidal current source and low noise reconfigurable readout electronics. A cm-range 13.56 MHz fabric inductor coupling is adopted to start/stop the SoC remotely. Moreover, a 5% duty-cycled Body Channel Communication (BCC) is exploited for 0.2 nJ/b 1 Mbps energy efficient external data communication. The proposed SoC occupies 5 mm × 5 mm including pads in a standard 0.18 μm 1P6M CMOS technology. It dissipates a peak power of 3.9 mW when operating in body channel receiver mode, and consumes 2.4 mW when operating in TIV and ECG detection mode. The SoC is integrated on a 15 cm × 15 cm fabric circuit board together with a flexible battery to form a compact wearable sensor. With 25 adhesive screen-printed fabric electrodes, detection of TIV and ECG at 16 different sites of the heart is possible, allowing optimal detection sites to be configured to accommodate different user dependencies.


title: Clustering Affective Qualities of Classical Music: Beyond the Valence-Arousal Plane

abstract: The important role of the valence and arousal dimensions in representing and recognizing affective qualities in music is well established. There is less evidence for the contribution of secondary dimensions such as potency, tension and energy. In particular, previous studies failed to find significant relations between computable musical features and affective dimensions other than valence and arousal. Here we present two experiments aiming at assessing how musical features, directly computable from complex audio excerpts, are related to secondary emotion dimensions. To this aim, we imposed some constraints on the musical features, namely modality and tempo, of the stimuli.The results show that although arousal and valence dominate for many musical features, it is possible to identify features, in particular Roughness, Loudness, and SpectralFlux, that are significantly related to the potency dimension. As far as we know, this is the first study that gained more insight into the affective potency in the music domain by using real music recordings and a computational approach.


title: megaman: Manifold Learning with Millions of points

abstract: Manifold Learning is a class of algorithms seeking a low-dimensional non-linear representation of high-dimensional data. Thus manifold learning algorithms are, at least in theory, most applicable to high-dimensional data and sample sizes to enable accurate estimation of the manifold. Despite this, most existing manifold learning implementations are not particularly scalable. Here we present a Python package that implements a variety of manifold learning algorithms in a modular and scalable fashion, using fast approximate neighbors searches and fast sparse eigendecompositions. The package incorporates theoretical advances in manifold learning, such as the unbiased Laplacian estimator and the estimation of the embedding distortion by the Riemannian metric method. In benchmarks, even on a single-core desktop computer, our code embeds millions of data points in minutes, and takes just 200 minutes to embed the main sample of galaxy spectra from the Sloan Digital Sky Survey --- consisting of 0.6 million samples in 3750-dimensions --- a task which has not previously been possible.


title: Emptying, refurnishing, and relighting indoor spaces

abstract: Visualizing changes to indoor scenes is important for many applications. When looking for a new place to live, we want to see how the interior looks not with the current inhabitant's belongings, but with our own furniture. Before purchasing a new sofa, we want to visualize how it would look in our living room. In this paper, we present a system that takes an RGBD scan of an indoor scene and produces a scene model of the empty room, including light emitters, materials, and the geometry of the non-cluttered room. Our system enables realistic rendering not only of the empty room under the original lighting conditions, but also with various scene edits, including adding furniture, changing the material properties of the walls, and relighting. These types of scene edits enable many mixed reality applications in areas such as real estate, furniture retail, and interior design. Our system contains two novel technical contributions: a 3D radiometric calibration process that recovers the appearance of the scene in high dynamic range, and a global-illumination-aware inverse rendering framework that simultaneously recovers reflectance properties of scene surfaces and lighting properties for several light source types, including generalized point and line lights.


title: Community Informatics and Information Systems: Can They Be Better Connected?

abstract: There is an ongoing debate in community informatics about the need for a stronger conceptual and theoretical base in order to give the field disciplinary cohesion and direction. By investigating the body of reflective thinking in information systems, researchers in community informatics can develop a more rigorous theoretical context for their work. Information systems can be considered as a fragmented adhocracy that allows many intellectual communities to coexist under its umbrella. A sympathetic reading of information systems offers an opportunity to community informatics, in spite of its different orientation, to address both social and technological issues in its theoretical framework. This framework would be based on a common language that expresses a shared ontology and epistemology with information systems. Such a framework then allows community informatics to fully address its information systems problem-solving agenda as well as its community problem-solving activities. Strengthening this dual agenda will allow community informatics to work effectively with both the technical and social design and implementation problems. But it also provides community informatics with an opportunity to contribute to a discourse within information systems in order to broaden the traditional information systems concept of organization and social action.


title: Using information technology: engagement modes, flow experience, and personality orientations

abstract: The engagement mode (EM) model describes how an IT user (subject) engages in an activity with an object in a certain mode. The model specifies five engagement modes (Enjoying/Acceptance, Ambition/C ...


title: A new cluster validity index for the fuzzy c-mean

abstract: Abstract In this paper a new cluster validity index is introduced, which assesses the average compactness and separation of fuzzy partitions generated by the fuzzy c -means algorithm. To compare the performance of this new index with a number of known validation indices, the fuzzy partitioning of two data sets was carried out. Our validation performed favorably in all studies, even in those where other validity indices failed to indicate the true number of clusters within each data set.


title: Comparing the Effects of Discretionary Tax Changes between the US and the UK

abstract: The remarkable similarities of the effects of discretionary tax changes between the US and the UK, shown in (Cloyne, J. 2013. “Discretionary Tax Changes and the Macroeconomy: New Narrative Evidence from the United Kingdom. American Economic Review 103: 1507–1528.), raise the obvious concern whether the effects of tax changes at disaggregated levels in the UK still resemble those in the US. This paper investigates the issue along three dimensions – corporate and personal income tax changes, anticipated and unanticipated tax changes, and positive and negative tax changes. An important contribution of this paper is to construct a data set on exogenous changes of corporate and personal income taxes in the UK, identified from narrative sources along the lines of Cloyne (2013).


title: Improving Physical-Layer Security in Wireless Communications Using Diversity Techniques

abstract: Due to the broadcast nature of radio propagation, wireless transmission can be readily overheard by unauthorized users for interception purposes and is thus highly vulnerable to eavesdropping attacks. To this end, physical-layer security is emerging as a promising paradigm to protect the wireless communications against eavesdropping attacks by exploiting the physical characteristics of wireless channels. This article is focused on the investigation of diversity techniques to improve physical-layer security differently from the conventional artificial noise generation and beamforming techniques, which typically consume additional power for generating artificial noise and exhibit high implementation complexity for beamformer design. We present several diversity approaches to improve wireless physical-layer security, including multiple-input multiple-output (MIMO), multiuser diversity, and cooperative diversity. To illustrate the security improvement through diversity, we propose a case study of exploiting cooperative relays to assist the signal transmission from source to destination while defending against eavesdropping attacks. We evaluate the security performance of cooperative relay transmission in Rayleigh fading environments in terms of secrecy capacity and intercept probability. It is shown that as the number of relays increases, both the secrecy capacity and intercept probability of cooperative relay transmission improve significantly, implying there is an advantage in exploiting cooperative diversity to improve physical-layer security against eavesdropping attacks.


title: The future of scientific workflows

abstract: Today’s computational, experimental, and observational sciences rely on computations that involve many related tasks. The success of a scientific mission often hinges on the computer automation of these workflows. In April 2015, the US Department of Energy (DOE) invited a diverse group of domain and computer scientists from national laboratories supported by the Office of Science, the National Nuclear Security Administration, from industry, and from academia to review the workflow requirements of DOE’s science and national security missions, to assess the current state of the art in science workflows, to understand the impact of emerging extreme-scale computing systems on those workflows, and to develop requirements for automated workflow management in future and existing environments. This article is a summary of the opinions of over 50 leading researchers attending this workshop. We highlight use cases, computing systems, workflow needs and conclude by summarizing the remaining challenges this community...


title: Both CRISPR/Cas-based nucleases and nickases can be used efficiently for genome engineering in Arabidopsis thaliana

abstract: Summary ::: Engineered nucleases can be used to induce site-specific double-strand breaks (DSBs) in plant genomes. Thus, homologous recombination (HR) can be enhanced and targeted mutagenesis can be achieved by error-prone non-homologous end-joining (NHEJ). Recently, the bacterial CRISPR/Cas9 system was used for DSB induction in plants to promote HR and NHEJ. Cas9 can also be engineered to work as a nickase inducing single-strand breaks (SSBs). Here we show that only the nuclease but not the nickase is an efficient tool for NHEJ-mediated mutagenesis in plants. We demonstrate the stable inheritance of nuclease-induced targeted mutagenesis events in the ADH1 and TT4 genes of Arabidopsis thaliana at frequencies from 2.5 up to 70.0%. Deep sequencing analysis revealed NHEJ-mediated DSB repair in about a third of all reads in T1 plants. In contrast, applying the nickase resulted in the reduction of mutation frequency by at least 740-fold. Nevertheless, the nickase is able to induce HR at similar efficiencies as the nuclease or the homing endonuclease I–SceI. Two different types of somatic HR mechanisms, recombination between tandemly arranged direct repeats as well as gene conversion using the information on an inverted repeat could be enhanced by the nickase to a similar extent as by DSB-inducing enzymes. Thus, the Cas9 nickase has the potential to become an important tool for genome engineering in plants. It should not only be applicable for HR-mediated gene targeting systems but also by the combined action of two nickases as DSB-inducing agents excluding off-target effects in homologous genomic regions.


Char-Net: A Character-Aware Neural Network for Distorted Scene Text Recognition


title: High Accuracy Stereovision Approach for Obstacle Detection on Non-Planar Roads

abstract: This paper will present an obstacle detection system that that relies on the 3D information provided by stereo reconstruction. The 3D features must be separated in road features and obstacle features. Instead of relying on the flatness of the road, the vertical road profile is modeled as a clothoid, and is estimated from the lateral projection of the 3D points. The points above the road are selected for grouping into objects, based on vicinity criteria and the variation of the point density with the distance. The resulted objects are used as measurements for a model-based tracking algorithm. The resulted system is a high-accuracy, far distance obstacle detector, able to function in a large variety of real-world scenarios.


title: Modelling and Optimisation of a Spring-Supported Diaphragm Capacitive MEMS Microphone

abstract: Audio applications such as mobile communication and hearing aid devices demand a small size but high performance, stable and low cost microphone to reproduce a high quality sound. Capacitive microphone can be designed to fulfill such requirements with some trade-offs between sensitivity, operating frequency range, and noise level mainly due to the effect of device structure dimensions and viscous damping. Smaller microphone size and air gap will gradually decrease its sensitivity and increase the viscous damping. The aim of this research was to develop a mathematical model of a spring-supported diaphragm capacitive MEMS microphone as well as an approach to optimize a microphone’s performance. Because of the complex shapes in this latest type of diaphragm design trend, analytical modelling has not been previously attempted. A novel diaphragm design is proposed that offers increased mechanical sensitivity of a capacitive microphone by reducing its diaphragm stiffness. A lumped element model of the spring-supported diaphragm microphone is developed to analyze the complex relations between the microphone performance factors and to find the optimum dimensions based on the design requirements. It is shown analytically that the spring dimensions of the spring-supported diaphragm do not have large effects on the microphone performance com pared to the diaphragm and backplate size, diaphragm thickness, and air-gap distance. A 1 mm2 spring-supported diaphragm microphone is designed using several optimized performance parameters to give a –3 dB operating bandwidth of 10.2 kHz, a sensitivity of 4.67 mV/Pa (–46.5 dB ref. 1 V/Pa at 1 kHz using a bias voltage of 3 V), a pull-in voltage of 13 V, and a thermal noise of –22 dBA SPL.


title: Accuracy of Tetrode Spike Separation as Determined by Simultaneous Intracellular and Extracellular Measurements

abstract: Simultaneous recording from large numbers of neurons is a prerequisite for understanding their cooperative behavior. Various recording techniques and spike separation methods are being used toward this goal. However, the error rates involved in spike separation have not yet been quantified. We studied the separation reliability of “tetrode” (4-wire electrode)-recorded spikes by monitoring simultaneously from the same cell intracellularly with a glass pipette and extracellularly with a tetrode. With manual spike sorting, we found a trade-off between Type I and Type II errors, with errors typically ranging from 0 to 30% depending on the amplitude and firing pattern of the cell, the similarity of the waveshapes of neighboring neurons, and the experience of the operator. Performance using only a single wire was markedly lower, indicating the advantages of multiple-site monitoring techniques over single-wire recordings. For tetrode recordings, error rates were increased by burst activity and during periods of ...


title: Deep convolutional neural fields for depth estimation from a single image

abstract: We consider the problem of depth estimation from a single monocular image in this work. It is a challenging task as no reliable depth cues are available, e.g., stereo correspondences, motions etc. Previous efforts have been focusing on exploiting geometric priors or additional sources of information, with all using hand-crafted features. Recently, there is mounting evidence that features from deep convolutional neural networks (CNN) are setting new records for various vision applications. On the other hand, considering the continuous characteristic of the depth values, depth estimations can be naturally formulated into a continuous conditional random field (CRF) learning problem. Therefore, we in this paper present a deep convolutional neural field model for estimating depths from a single image, aiming to jointly explore the capacity of deep CNN and continuous CRF. Specifically, we propose a deep structured learning scheme which learns the unary and pairwise potentials of continuous CRF in a unified deep CNN framework. The proposed method can be used for depth estimations of general scenes with no geometric priors nor any extra information injected. In our case, the integral of the partition function can be analytically calculated, thus we can exactly solve the log-likelihood optimization. Moreover, solving the MAP problem for predicting depths of a new image is highly efficient as closed-form solutions exist. We experimentally demonstrate that the proposed method outperforms state-of-the-art depth estimation methods on both indoor and outdoor scene datasets.


title: Metabolism of cyclopropane fatty acids by Tetrahymena pyriformis

abstract: SUMMARY Cyclopropane fatty acids are readily metabolized by whole cells of Tetrahymena pyriformis when the fatty acids are presented either as components of intact Escherichia coli cells or as free fatty acids added to the medium. cis-ll,lZ[methylene-14C]Methyleneoctadecanoic acid was degraded with the production of 14COz by whole T. pyriformis cells or a particulate fraction derived from them. In the presence of malonate, [W4C]acetate was formed. A pathway for degradation of the cyclopropane fatty acids, requiring only minor modification of the usual /3 oxidation pathway, is proposed.


title: An Attentive Neural Architecture for Fine-grained Entity Type Classification

abstract: In this work we propose a novel attention-based neural network model for the task of fine-grained entity type classification that unlike previously proposed models recursively composes representations of entity mention contexts. Our model achieves state-of-the-art performance with 74.94% loose micro F1-score on the well-established FIGER dataset, a relative improvement of 2.59%. We also investigate the behavior of the attention mechanism of our model and observe that it can learn contextual linguistic expressions that indicate the fine-grained category memberships of an entity.


title: Letting Artificial Intelligence in Education Out of the Box: Educational Cobots and Smart Classrooms

abstract: This paper proposes that the field of AIED is now mature enough to break away from being delivered mainly through computers and pads so that it can engage with students in new ways and help teachers to teach more effectively. Mostly, the intelligent systems that AIED has delivered so far have used computers and other devices that were essentially designed for businesses or personal use, and not specifically for education. The future holds the promise of creating technologies designed specifically for learning and teaching by combining the power of AIED with advances in the field of robotics and in the increasing use of sensor devices to monitor our surroundings and actions. The paper assumes that “schools” (i.e., a place where children will gather to learn) will still exist in some shape or form in 25 years and that teachers will continue to oversee and promote learning among the students. It proposes that there will be educational cobots assisting teachers in the classrooms of tomorrow and provides examples from current work in robotics. It also envisions smart classrooms that make use of sensors to support learning and illustrates how they might be used in new ways if AIED applications are embedded into them.


title: An image-warping VR-architecture: design, implementation and applications

abstract: We describe an architecture that provides a programmable display layer in order to allow the execution of custom programs on consecutive display frames. This replaces the default display behavior of repeating application frames until an update is available. The architecture is implemented using a multi-GPU system. We will show three applications of the architecture typical to VR. First, smooth motion is provided by generating intermediate display frames by per-pixel depth-image warping using 3D motion fields. Smooth motion can be beneficial for walk-throughs of large scenes. Second, we implement fine-grained latency reduction at the display frame level using a synchronized prediction of simulation objects and the viewpoint. This improves the average quality and consistency of latency reduction. Third, a crosstalk reduction algorithm for consecutive display frames is implemented, which improves the quality of stereoscopic images.


Supporting Our Students (SOS): A multi-component program to increase perceived safety and social support in LGBTQ+ youth in Washington County


title: A Wearable Inertial Measurement System With Complementary Filter for Gait Analysis of Patients With Stroke or Parkinson’s Disease

abstract: This paper presents a wearable inertial measurement system and its associated spatiotemporal gait analysis algorithm to obtain quantitative measurements and explore clinical indicators from the spatiotemporal gait patterns for patients with stroke or Parkinson’s disease. The wearable system is composed of a microcontroller, a triaxial accelerometer, a triaxial gyroscope, and an RF wireless transmission module. The spatiotemporal gait analysis algorithm, consisting of procedures of inertial signal acquisition, signal preprocessing, gait phase detection, and ankle range of motion estimation, has been developed for extracting gait features from accelerations and angular velocities. In order to estimate accurate ankle range of motion, we have integrated accelerations and angular velocities into a complementary filter for reducing the accumulation of integration error of inertial signals. All 24 participants mounted the system on their foot to walk along a straight line of 10 m at normal speed and their walking recordings were collected to validate the effectiveness of the proposed system and algorithm. Experimental results show that the proposed inertial measurement system with the designed spatiotemporal gait analysis algorithm is a promising tool for automatically analyzing spatiotemporal gait information, serving as clinical indicators for monitoring therapeutic efficacy for diagnosis of stroke or Parkinson’s disease.


title: An ultra-wideband monocycle pulse generator with good performance

abstract: A new ultra-wideband monocycle pulse generator with good performance is designed and demonstrated. The pulse generator circuits employ SRD(step recovery diode), Schottky diode, and simple RC coupling and decoupling circuit, and are completely fabricated on the planar microstrip structure, which have the characteristic of low cost and small size. Through SRD modeling, the accuracy of the simulation is improved, which save the design period greatly. The generated monocycle pulse has the peak-to-peak amplitude 1.3V, pulse width 370ps and pulse repetition rate of 10MHz, whose waveform features are symmetric well and low ringing level. Good agreement between the measured and calculated results is achieved.


title: Mapping eye movements to cognitive processes

abstract: Eye movements provide a rich and informative window into a personOs thoughts and intentions. In recent years researchers have increasingly employed eye movements to study cognition in psychological experiments, to understand behavior in user interfaces, and even to control computers through eye-based input devices. Unfortunately, like speech and handwriting, eye movements generate vast amounts of data with significant individual variability and equipment noise. Thus, the analysis of eye-movement dataNthat is, determining what people are thinking based on where they are lookingNcan be extremely tedious and time-consuming. Typical eye-movement data sets are simply too large and complex to be analyzed by hand or by naive automated methods. This thesis formalizes a new class of algorithms that provide fast and robust analysis of eye-movement data. Specifically, the thesis describes three novel algorithms for tracing eye movementsNmapping eye-movement protocols to the sequential predictions of a cognitive process model. Two algorithms, fixation tracing and point tracing, employ hidden Markov models to determine the best probabilistic interpretation of the data given the model. The third algorithm, target tracing, extends an existing tracing algorithm based on sequence matching to eye movements. The thesis also formalizes several algorithms for identifying fixations in raw eye-movement protocols and provides a working system, EyeTracer, that embodies the proposed tracing and fixation-identification algorithms. To demonstrate the power of the proposed algorithms, the thesis applies them in three real-world domains: equation solving, reading, and eye typing. The equation-solving studies show how the algorithms can code, or interpret, eye-movement protocols as accurately as expert human coders in significantly less time. The studies also illustrate how the algorithms facilitate the prototyping and refinement of cognitive models. The reading study demonstrates how the algorithms help to evaluate and compare two existing computational models of reading and clear up temporal aspects of reading data using sequential aspects of the data. The eye-typing study shows how the algorithms can interpret eye movements in real time and help eliminate usability restrictions imposed by existing eye-based interfaces.


title: A guideline for requirements management in GitHub with lean approach

abstract: ion level, requirements can be distinguished between user requirements and system requirements. User requirements are written in plain language, possibly utilizing other tools – like diagrams or mockups – to illustrate them better. They are more abstract than system requirements which should be described precisely. User requirements should not take a stance on low-level design aspects they are for system requirements which line out functions and operational services the product is supposed to provide. Requirements can also be divided to functional (FR) and non-functional requirements (NFR). FRs state the functional operation of the system, for example how it should respond to certain input. NFRs on the other hand are constraints on the services or functions and they usually govern the whole system. However, as the name suggests, they are not directly concerned with specific functions delivered by the system. FRs and NFRs can be divided to subcategories depending on the nature of the requirement. It is also possible to consider the third requirement level beside the FR and NFR: domain requirements. These requirements come from the domain itself – not from a need of the customer – and can affect for example how certain calculations are done, thus they often cross with FRs. [Sommerville, 2007] The requirements engineering process consists of five parts: elicitation, analysis, documentation, validation and management [Paetsch et al., 2003]. Though, as pointed out by Sommerville [2007], requirements process can start with feasibility study. As mentioned in the beginning of this Chapter, the sole purpose of this process is to identify


Quantify-Me : Consumer Acceptance of Wearable Self-Tracking Devices


title: Vehicle Type Classification Using Unsupervised Convolutional Neural Network

abstract: In this paper, we propose an appearance-based vehicle type classification method from vehicle frontal view images. Unlike other methods using hand-crafted visual features, our method is able to automatically learn good features for vehicle type classification by using a convolutional neural network. In order to capture rich and discriminative information of vehicles, the network is pre-trained by the sparse filtering which is an unsupervised learning method. Besides, the network is with layer-skipping to ensure that final features contain both high-level global and low-level local features. After the final features are obtained, the soft max regression is used to classify vehicle types. We build a challenging vehicle dataset called BIT-Vehicle dataset to evaluate the performance of our method. Experimental results on a public dataset and our own dataset demonstrate that our method is quite effective in classifying vehicle types.


title: IR night vision video-based estimation of heart and respiration rates

abstract: With the aging of the population, comes increased incidence of chronic diseases affecting the cardiac and respiratory systems. Monitoring of these chronic conditions at home via family members or in institutions via healthcare providers is usually adequate during the day. Non-intrusive video-based monitoring approaches have been proposed using optical cameras whose performance significantly deteriorates in low light conditions. This paper proposed the use of infrared night vision cameras to monitor the heart and respiration rates in low light conditions and in complete darkness. An infrared camera in conjunction with video magnification method is used to capture and analyze the video of subjects in dark conditions. To validate the extracted heart rate, a finger photoplethysmograph (PPG) device that can display the real-time heart rate was used. To validate the respiration rate a BioHarness chest strap was used. The proposed framework was tested on different sizes of regions of interest (ROIs) and different distances between the subject and the camera. A post-processing procedure was applied on the video magnification signal to reduce noise. To characterize and rule out artifacts, an experiment on inanimate objects was also conducted. Results indicate that the non-intrusive approach based on infrared night vision cameras and video magnification method can accurately extract heart and respiration rates, and can be used for continuous healthcare monitoring in the night.


title: The Yagi-helix: A multi-director configuration for the backfire-helix antenna

abstract: Analyses on the diameter of the ground plane of helical antennas show that, when the diameter of this plane is slightly smaller than the one of the helix itself, backfire radiation is achieved. Considering this ground plane as a director, the addition of more ground planes - or directors - is proven to increase the directivity of the antenna. With these reduced ground planes not causing any blockage in the axial direction, its good circular polarization and its broadband characteristic, this antenna is a suitable choice for feeding parabolic dishes for satellite communications. The construction of the antenna and its subsequent measurements are described, as well as an application of it serving as the feed for a parabolic dish.


title: Neural Processes

abstract: A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision. A Gaussian process (GP), on the other hand, is a probabilistic model that defines a distribution over possible functions, and is updated in light of data via the rules of probabilistic inference. GPs are probabilistic, data-efficient and flexible, however they are also computationally intensive and thus limited in their applicability. We introduce a class of neural latent variable models which we call Neural Processes (NPs), combining the best of both worlds. Like GPs, NPs define distributions over functions, are capable of rapid adaptation to new observations, and can estimate the uncertainty in their predictions. Like NNs, NPs are computationally efficient during training and evaluation but also learn to adapt their priors to data. We demonstrate the performance of NPs on a range of learning tasks, including regression and optimisation, and compare and contrast with related models in the literature.


title: Comparing comprehension measured by multiple-choice and open-ended questions.

abstract: This study compared the nature of text comprehension as measured by multiple-choice format and open-ended format questions. Participants read a short text while explaining preselected sentences. After reading the text, participants answered open-ended and multiple-choice versions of the same questions based on their memory of the text content. The results indicated that performance on open-ended questions was correlated with the quality of self-explanations, but performance on multiple-choice questions was correlated with the level of prior knowledge related to the text. These results suggest that open-ended and multiple-choice format questions measure different aspects of comprehension processes. The results are discussed in terms of dual process theories of text comprehension.


REMAINING LIFE PREDICTION OF ELECTRONIC PRODUCTS USING LIFE CONSUMPTION MONITORING APPROACH


title: Fast Queries Over Heterogeneous Data Through Engine Customization

abstract: Industry and academia are continuously becoming more data-driven and data-intensive, relying on the analysis of a wide variety of heterogeneous datasets to gain insights. The different data models and formats pose a significant challenge on performing analysis over a combination of diverse datasets. Serving all queries using a single, general-purpose query engine is slow. On the other hand, using a specialized engine for each heterogeneous dataset increases complexity: queries touching a combination of datasets require an integration layer over the different engines. ::: ::: This paper presents a system design that natively supports heterogeneous data formats and also minimizes query execution times. For multi-format support, the design uses an expressive query algebra which enables operations over various data models. For minimal execution times, it uses a code generation mechanism to mimic the system and storage most appropriate to answer a query fast. We validate our design by building Proteus, a query engine which natively supports queries over CSV, JSON, and relational binary data, and which specializes itself to each query, dataset, and workload via code generation. Proteus outperforms state-of-the-art open-source and commercial systems on both synthetic and real-world workloads without being tied to a single data model or format, all while exposing users to a single query interface.


title: Three-dimensional human shape inference from silhouettes: reconstruction and validation

abstract: Silhouettes are robust image features that provide considerable evidence about the three-dimensional (3D) shape of a human body. The information they provide is, however, incomplete and prior knowledge has to be integrated to reconstruction algorithms in order to obtain realistic body models. This paper presents a method that integrates both geometric and statistical priors to reconstruct the shape of a subject assuming a standardized posture from a frontal and a lateral silhouette. The method is comprised of three successive steps. First, a non-linear function that connects the silhouette appearances and the body shapes is learnt and used to create a first approximation. Then, the body shape is deformed globally along the principal directions of the population (obtained by performing principal component analysis over 359 subjects) to follow the contours of the silhouettes. Finally, the body shape is deformed locally to ensure it fits the input silhouettes as well as possible. Experimental results showed a mean absolute 3D error of 8 mm with ideal silhouettes extraction. Furthermore, experiments on body measurements (circumferences or distances between two points on the body) resulted in a mean error of 11 mm.


title: The frontal space measurement in euploid and aneuploid pregnancies at 11-13 weeks' gestation

abstract: Objective ::: ::: The aim of this study is to evaluate whether the measurement of the frontal space (FS) improves first trimester combined aneuploidy screening. ::: ::: ::: ::: Methods ::: ::: We have presented a retrospective study including 2D images of the nuchal translucency measurement of 300 euploid and 133 trisomic fetuses that were seen at the prenatal department at the University of Tuebingen, Germany. For the FS measurement, a mandibulo-maxillary line (MML) was created by connecting the anterior edge of the mental protuberance and anterior edge of the maxilla. The MML was then extended upwards. A measurement was taken perpendicular from the MML to the skin line over the point of largest excursion of the fetal forehead. After transformation into z-scores based on the crown rump length, the FS distribution in fetuses with trisomy 21, 18, and 13 was compared with the euploid group by using a student's t-test. ::: ::: ::: ::: Results ::: ::: The mean FS in euploid fetuses was 1.25 mm. In fetuses with trisomy 21, 18, and 13, the mean FS measurements were 2.96, 3.22, and 2.03 mm. In euploid and trisomy 21, 18, and 13 pregnancies, FS measurements were above the 95th centile in 5.3%, 41.1%, 34.3%, and 12.5% of the cases. The mean z-scores were significantly higher in trisomy 21 and 18. ::: ::: ::: ::: Conclusion ::: ::: First trimester FS measurement may improve first trimester combined screening. © 2013 John Wiley & Sons, Ltd.


title: Thermal analysis of electromagnetic levitation coil

abstract: This paper presents analytical and numerical thermal analysis of three different toroidal coils levitated on an aluminum plate. The analytical method consists of a network of thermal resistances that make it possible to estimate the average temperature of the coils. In the numerical method, by using the FEM thermal and magnetic analysis, the average temperature of the coils and the amount of the in-rush levitation current are calculated. All the numerical and analytical results are compared with experimental results and they show very good agreement. The effects of optimization parameters on the levitation system are considered.


title: Fruit recognition from images using deep learning

abstract: In this paper we introduce a new, high-quality, dataset of images containing fruits. We also present the results of some numerical experiment for training a neural network to detect fruits. We discuss the reason why we chose to use fruits in this project by proposing a few applications that could use this kind of neural network.


title: BloomCast: Efficient and Effective Full-Text Retrieval in Unstructured P2P Networks

abstract: Efficient and effective full-text retrieval in unstructured peer-to-peer networks remains a challenge in the research community. First, it is difficult, if not impossible, for unstructured P2P systems to effectively locate items with guaranteed recall. Second, existing schemes to improve search success rate often rely on replicating a large number of item replicas across the wide area network, incurring a large amount of communication and storage costs. In this paper, we propose BloomCast, an efficient and effective full-text retrieval scheme, in unstructured P2P networks. By leveraging a hybrid P2P protocol, BloomCast replicates the items uniformly at random across the P2P networks, achieving a guaranteed recall at a communication cost of O(√N), where N is the size of the network. Furthermore, by casting Bloom Filters instead of the raw documents across the network, BloomCast significantly reduces the communication and storage costs for replication. We demonstrate the power of BloomCast design through both mathematical proof and comprehensive simulations based on the query logs from a major commercial search engine and NIST TREC WT10G data collection. Results show that BloomCast achieves an average query recall of 91 percent, which outperforms the existing WP algorithm by 18 percent, while BloomCast greatly reduces the search latency for query processing by 57 percent.


title: Test-retest analysis with functional MR of the activated area in the human visual cortex

abstract: PURPOSE ::: To investigate the intersubject and intrasubject variability of the activated area in the visual cortex with functional MR imaging. ::: ::: ::: METHODS ::: Double-section gradient-echo MR images were acquired at 1.5 T in 28 healthy volunteers using the fast low-angle shot (FLASH) technique. Visual stimulation was obtained with light-emitting diode (LED) goggles. Eighteen volunteers were studied twice. The size of the activated areas in the visual cortex and the increase in signal were measured. A reproducibility ratio for size (R[size]) and for location (R[overlap]) was calculated on a scale of 0.0 to 1.0. ::: ::: ::: RESULTS ::: Activation was seen in 89% of the subjects. The size of the activated area was widely variable among subjects: mean, 460 +/- 284 mm2; range, 0 to 1029 mm2 in the first study in all volunteers. Signal increases ranged from 3.2% to 10.9%, with a mean of 6.6 +/- 1.7%. The mean values of intrasubject variability testing were R(size) = 0.83 +/- 0.16 and R(overlap) = 0.31 +/- 0.11. ::: ::: ::: CONCLUSION ::: Functional MR imaging with the FLASH technique is useful in identifying certain cortical areas that have quite variable locations among subjects. This study provides reference data for the intrasubject and intersubject variability of the activation pattern of the visual cortex.


title: Low-light image restoration using bright channel prior-based variational Retinex model

abstract: This paper presents a low-light image restoration method based on the variational Retinex model using the bright channel prior (BCP) and total-variation minimization. The proposed method first estimates the bright channel to control the amount of brightness enhancement. Next, the variational Retinex-based energy function is iteratively minimized to estimate the improved illumination and reflectance using the BCP. Contrast of the estimated illumination is enhanced using the gamma correction and histogram equalization to reduce a color distortion and noise amplification. Experimental results show that the proposed method can provide the better restored result than the existing methods without unnatural artifacts such as noise amplification and halo effects near edges.


title: Verification and validation and artificial intelligence

abstract: Artifical Intelligence (AI) is useful. AI can deliver more functionality for reduced cost. AI should be used more widely but won’t be unless developers can trust adapative, nondeterministic, or complex AI systems. Verification and validation is one method used by software analysts to gain that trust. AI systems have features that make them hard to check using conventional V&V methods. Nevertheless, as we show in this article, there are enough alternative readily-available methods that enable the V&V of AI software.


Web as a textbook: Curating Targeted Learning Paths through the Heterogeneous Learning Resources on the Web


title: Catastrophic Forgetting in Connectionist Networks: Causes, Consequences and Solutions

abstract: All natural cognitive systems, and, in particular, our own, gradually forget previously learned information. Consequently, plausible models of human cognition should exhibit similar patterns of gradual forgetting old information as new information is acquired. Only rarely (see Box 3) does new learning in natural cognitive systems completely disrupt or erase previously learned information. In other words, natural cognitive systems do not, in general, forget catastrophically. Unfortunately, however, this is precisely what occurs under certain circumstances in distributed connectionist networks. It turns out that the very features that give these networks their much-touted abilities to generalize, to function in the presence of degraded input, etc., are the root cause of catastrophic forgetting. The challenge is how to keep the advantages of distributed connectionist networks while avoiding the problem of catastrophic forgetting. In this article, we examine the causes, consequences and numerous solutions to the problem of catastrophic forgetting in neural networks. We consider how the brain might have overcome this problem and explore the consequences of this solution.


title: A Novel Similarity Measure for Clustering Categorical Data Sets

abstract: similarity between two data objects is a more challenging problem for data mining and knowledge disc overy tasks. The traditional clustering algorithms have been mainly stressed on numerical data, the implicit property of which can be exploited to define distance function between the data points to define similarity measure. The problem of similarity beco mes more complex when the data is categorical which do not have a natural ordering of values or can be called as non geometrical attributes. Clustering on relational data sets when majority of its attributes are of categorical types makes interesting facts . No earlier work has been done on clustering categorical attributes of relational data set types making use of the property of functional dependency as parameter to measure similarity. This paper is an extension of earlier work on clustering relational da ta sets where domains are unique and similarity is context based and introduces a new notion of similarity based on dependency of an attribute on other attributes prevalent in the relational data set. This paper also gives a brief overview of popular simil arity measures of categorical attributes. This novel similarity measure can be used to apply on tuples and their respective values. The important property of categorical domain is that they have smaller number of attribute values. The similarity measure of relational data sets then can be applied to the smaller data sets for efficient results.


title: The effect of high-intensity versus low-level laser therapy in the management of plantar fasciitis: a randomized clinical trial

abstract: We aimed to compare the efficacy of low-level laser therapy (LLLT) and high-intensity laser therapy (HILT) in the treatment of plantar fasciitis (PF). Seventy patients were randomized into either the LLLT (8 men, 27 women; mean age 48.65 ± 10.81 years) or HILT (7 men, 28 women; mean age 48.73 ± 11.41 years) groups. LLLT (904 nm) and HILT (1064 nm) were performed three times per week, over a period of 3 weeks. Each treatment combined with silicone insole and stretching exercises. Patients’ pain and functional status were evaluated with Visual Analog Scale, Heel Tenderness Index, and Foot and Ankle Outcome Score before and after treatment. A chi-square test was performed to compare demographic and clinical characteristics. Within-group and between-group differences were also investigated. Paired samples t test was used to analyze the differences between baseline and after treatment values, while independent samples t test was used to compare the two groups. Both groups contained similar demographic characteristics including age, sex, and body mass index (all p > 0.05). Three and two patients in the HILT and LLLT group, respectively, were lost to follow-up. At the study onset, there were no statistically significant differences between the two groups in the Visual Analog Scale, Heel Tenderness Index, and Foot And Ankle Outcome Scores. Three weeks later, both groups showed significant improvement in all parameters (p < 0.05). The HILT group demonstrated better improvement in all parameters than the LLLT group. Although both treatments improved the pain levels, function, and quality of life in patients with PF, HILT had a more significant effect than LLLT.


title: Finding beans in burgers: Deep semantic-visual embedding with localization

abstract: Several works have proposed to learn a two-path neural network that maps images and texts, respectively, to a same shared Euclidean space where geometry captures useful semantic relationships. Such a multi-modal embedding can be trained and used for various tasks, notably image captioning. In the present work, we introduce a new architecture of this type, with a visual path that leverages recent spaceaware pooling mechanisms. Combined with a textual path which is jointly trained from scratch, our semantic-visual embedding offers a versatile model. Once trained under the supervision of captioned images, it yields new state-of-the-art performance on cross-modal retrieval. It also allows the localization of new concepts from the embedding space into any input image, delivering state-of-the-art result on the visual grounding of phrases.


title: EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images

abstract: Light field cameras capture both the spatial and the angular properties of light rays in space. Due to its property, one can compute the depth from light fields in uncontrolled lighting environments, which is a big advantage over active sensing devices. Depth computed from light fields can be used for many applications including 3D modelling and refocusing. However, light field images from hand-held cameras have very narrow baselines with noise, making the depth estimation difficult. any approaches have been proposed to overcome these limitations for the light field depth estimation, but there is a clear trade-off between the accuracy and the speed in these methods. In this paper, we introduce a fast and accurate light field depth estimation method based on a fully-convolutional neural network. Our network is designed by considering the light field geometry and we also overcome the lack of training data by proposing light field specific data augmentation methods. We achieved the top rank in the HCI 4D Light Field Benchmark on most metrics, and we also demonstrate the effectiveness of the proposed method on real-world light-field images.


title: Cross-Domain Image Matching with Deep Feature Maps

abstract: We investigate the problem of automatically determining what type of shoe left an impression found at a crime scene. This recognition problem is made difficult by the variability in types of crime scene evidence (ranging from traces of dust or oil on hard surfaces to impressions made in soil) and the lack of comprehensive databases of shoe outsole tread patterns. We find that mid-level features extracted by pre-trained convolutional neural nets are surprisingly effective descriptors for this specialized domains. However, the choice of similarity measure for matching exemplars to a query image is essential to good performance. For matching multi-channel deep features, we propose the use of multi-channel normalized cross-correlation and analyze its effectiveness. Our proposed metric significantly improves performance in matching crime scene shoeprints to laboratory test impressions. We also show its effectiveness in other cross-domain image retrieval problems: matching facade images to segmentation labels and aerial photos to map images. Finally, we introduce a discriminatively trained variant and fine-tune our system through our proposed metric, obtaining state-of-the-art performance.


title: Disentangled Link Prediction for Signed Social Networks via Disentangled Representation Learning

abstract: Link prediction is an important and interesting application for social networks because it can infer potential links among network participants. Existing approaches basically work with the homophily principle, i.e., people of similar characteristics tend to befriend each other. In this way, however, they are not suitable for inferring negative links or hostile links, which usually take place among people with different characteristics. Moreover, negative links tend to couple with positive links to form signed networks. In this paper, we thus study the problem of disentangled link prediction (DLP) for signed networks, which includes two separate tasks, i.e., inferring positive links and inferring negative links. Recently, representation learning methods have been proposed to solve the link prediction problem because the entire network structure can be encoded in representations. For the DLP problem, we thus propose to disentangle a node representation into two representations, and use one for positive link prediction and another for negative link prediction. Experiments on three real-world signed networks demonstrate the proposed disentangled representation learning (DRL) method significantly outperforms alternatives in the DLP problem.


title: Development of a Single-Sided Flux Magnetic Coupler for Electric Vehicle IPT Charging Systems

abstract: Inductive power transfer is a practical method for recharging electric vehicles because it is safe, convenient, and reliable. The performance of the magnetic couplers that transfer power determines the overall feasibility of a complete system. Circular couplers are the most common topology in the literature; however, they have fundamentally limited coupling. Their flux patterns necessarily limit the operational air gap as well as tolerance to horizontal misalignment. A new polarized coupler topology [referred to as a double D (DD)] is presented, which overcomes these difficulties. DDs provide a charge zone five times larger than that possible with circular pads for a similar material cost and are smaller. A 0.31-m2 DD enables 2 kW of power transfer over an oval area measuring 540 mm × 800 mm with a 200-mm air gap. Leakage magnetic fields have been investigated and show that circular and DD couplers operating under similar power transfer conditions produce similar levels. Both topologies can be designed and operated to ensure compliance with international guidelines.


title: A Survey on Smartphones Security: Software Vulnerabilities, Malware, and Attacks

abstract: Nowadays, the usage of smartphones and their applications have become rapidly increasing popular in people's daily life. Over the last decade, availability of mobile money services such as mobile-payment systems and app markets have significantly increased due to the different forms of apps and connectivity provided by mobile devices such as 3G, 4G, GPRS, and Wi-Fi, etc. In the same trend, the number of vulnerabilities targeting these services and communication networks has raised as well. Therefore, smartphones have become ideal target devices for malicious programmers. With increasing the number of vulnerabilities and attacks, there has been a corresponding ascent of the security countermeasures presented by the researchers. Due to these reasons, security of the payment systems is one of the most important issues in mobile payment systems. In this survey, we aim to provide a comprehensive and structured overview of the research on security solutions for smartphone devices. This survey reviews the state of the art on security solutions, threats, and vulnerabilities during the period of 2011-2017, by focusing on software attacks, such those to smartphone applications. We outline some countermeasures aimed at protecting smartphones against these groups of attacks, based on the detection rules, data collections and operating systems, especially focusing on open source applications. With this categorization, we want to provide an easy understanding for users and researchers to improve their knowledge about the security and privacy of smartphones.


title: The Neuropathology of Adolescent Anabolic/Androgenic Steroid Abuse: Altered Development of the Reciprocal Hypothalamic Neural Circuit Controlling Aggressive Behavior

abstract: Considerable attention has been focused on the issue of youth violence, particularly that associated with drug use. It is well documented that anabolic steroid use by teenagers is associated with a higher incidence of violence; yet little is known about how these drugs produce the aggressive phenotype. Here we discuss work from our laboratory that has uncovered a direct relationship between the development and activity of select neurotransmitter systems in the anterior hypothalamus, i.e., the center of a neural network of reciprocal connections that regulate aggression, and anabolic steroid-induced aggressive behavior using pubertal Syrian hamsters as an adolescent animal model. Specifically, the influence of anabolic steroids on the development and activity of the vasopressin, serotonin, dopamine, γ-aminobutyric acid, and glutamate neural systems in the anterior hypothalamus and its reciprocal neural circuitry controlling aggression will be examined individually and together to provide a context in which the understanding of aggression modulation by anabolic steroids can be expanded from seemingly parallel neural systems to a single interactive neural circuitry of aggression control.


title: UAV Swarm Mission Planning Development Using Evolutionary Algorithms - Part I

abstract: Abstract : Embedding desired behaviors in autonomous vehicles is a difficult problem at best and in general probably impossible to completely resolve in complex dynamic environments. Future technology demands the deployment of small autonomous vehicles or agents with large-scale decentralized swarming capabilities and associated behaviors. Various techniques inspired by biological self-organized systems as found in forging insects and flocking birds, revolve around control approaches that have simple localized rule sets that generate some of the desired emergent behaviors. To computationally develop such a system,an underlying organizational structure or framework is required to control agent rule execution. Thus, autonomous self-organization features are identified and coalesced into an entangled-hierarchical framework. The use of this self-organizing multi-objective evolutionary algorithmic approach dynamically determines the proper weighting and control parameters providing highly dynamic swarming behavior. The system is extensively evaluated with swarms of heterogeneous vehicles in a distributed simulation system with animated graphics. Statistical measurements and observations indicate that bio-inspired techniques integrated with an entangled self-organizing framework can provide desired dynamic swarming behaviors in complex dynamic environments.


title: SEE: Syntax-aware Entity Embedding for Neural Relation Extraction

abstract: Distant supervised relation extraction is an efficient approach to scale relation extraction to very large corpora, and has been widely used to find novel relational facts from plain text. Recent studies on neural relation extraction have shown great progress on this task via modeling the sentences in low-dimensional spaces, but seldom considered syntax information to model the entities. In this paper, we propose to learn syntax-aware entity embedding for neural relation extraction. First, we encode the context of entities on a dependency tree as sentence-level entity embedding based on tree-GRU. Then, we utilize both intra-sentence and inter-sentence attentions to obtain sentence set-level entity embedding over all sentences containing the focus entity pair. Finally, we combine both sentence embedding and entity embedding for relation classification. We conduct experiments on a widely used real-world dataset and the experimental results show that our model can make full use of all informative instances and achieve state-of-the-art performance of relation extraction.


title: Artificial General Intelligence

abstract: This edited volume gives the first-ever book-length presentation of contemporary research in the domain of Artificial General Intelligence (AGI). What distinguishes AGI research from run-of-the-mill AI research is that it is explicitly focused on engineering general intelligence ? autonomous, self-reflective, self-improving, commonsensical intelligence ? in the short term. Bringing the diverse body of AGI research together in a single volume reveals the common themes among various researchers? work, and makes clear what the big open questions are in this vital and critical area of research, as well as highlighting relationships between AI and related fields such as philosophy, neuroscience, linguistics, psychology, biology, sociology, anthropology and engineering.


title: Smart nodes of internet of things (IoT): a hardware perspective view & implementation

abstract: A lot of efforts have been done on software layer to propose the idea of Internet of Things (IoT). However, the functionality of IoT highly depends on the implementation of its end nodes. And there is little attention about realization of such IoT Smart Nodes. As mixed signal IC researchers, we analyze the urgent needs of IoT Smart Nodes and their implementation challenges. We focus this talk on the two of the most important practical issues: energy harvesting & regulating, and wireless transceiver. A high efficient self-sustained energy harvester inside the Smart Nodes can power numerous possibilities to IoT network. For the various inputs, it should accommodate nonlinear energy sources and achieve Maximum Power Point Tracking (MPPT). As it is considered the most power hungry part, the wireless transceiver in the IoT Smart Nodes needs special focus to minimize its power consumption. The transceiver should adapt its power consumption based on the available amount of the energy and the amount of the transferred data in time


title: Linearized Recursive Least Squares Methods for Real-Time Identification of Tire–Road Friction Coefficient

abstract: The tire-road friction coefficient is critical information for conventional vehicle safety control systems. Most previous studies on tire-road friction estimation have only considered either longitudinal or lateral vehicle dynamics, which tends to cause significant underestimation of the actual tire-road friction coefficient. In this paper, the parameters, including the tire-road friction coefficient, of the combined longitudinal and lateral brushed tire model are identified by linearized recursive least squares (LRLS) methods, which efficiently utilize measurements related to both vehicle lateral and longitudinal dynamics in real time. The simulation study indicates that by using the estimated vehicle states and the tire forces of the four wheels, the suggested algorithm not only quickly identifies the tire-road friction coefficient with great accuracy and robustness before tires reach their frictional limits but successfully estimates the two different tire-road friction coefficients of the two sides of a vehicle on a split- μ surface as well. The developed algorithm was verified through vehicle dynamics software Carsim and MATLAB/Simulink.


title: Eating habits, body weight, and insulin misuse. A longitudinal study of teenagers and young adults with type 1 diabetes.

abstract: OBJECTIVE: To examine disordered eating, insulin misuse, weight change, and their relationships with glycemic control and diabetic complications in adolescents with type 1 diabetes followed up over eight years. RESEARCH DESIGN AND METHODS: Of 76 adolescents (43 male, 33 female) with type 1 diabetes aged 11-18 years at the first assessment, 65 were interviewed as young adults (aged 20-28 years). Eating habits were assessed using a standardized Eating Disorder Examination. Height and weight were determined and BMI calculated. Three consecutive urine specimens were collected for measurement of albumin/creatinine ratio and other significant diabetic complications were recorded. Glycemic control was assessed by glycated hemoglobin. RESULTS: Weight and BMI increased from adolescence to young adulthood. Females were overweight as adolescents and both sexes were overweight as young adults. Concern over weight and shape increased significantly for both sexes from adolescence to young adulthood. This increase in concern was reflected in increased levels of dietary restraint. Features of disordered eating were apparent in females at both assessments, but no patients met the criteria for anorexia nervosa or bulimia nervosa at either assessment. A total of 10 (30%) females, but none of the males admitted underusing insulin to control weight. Five (45%) females with microvascular complications had intentionally misused insulin to prevent weight gain. CONCLUSIONS: An increase in BMI from adolescence to adulthood was associated with higher levels of concern over shape and weight and more intense dietary restraint, especially among females. Overt eating disorders were no more prevalent in these patients than in the general population, but milder forms of disordered eating were common and had implications for diabetes management. Insulin omission for weight control was frequent among females and may contribute to poor glycemic control and to risk of complications.


title: Spatial working memory in heavy cannabis users: a functional magnetic resonance imaging study

abstract: Rationale ::: Many neuropsychological studies have documented deficits in working memory among recent heavy cannabis users. However, little is known about the effects of cannabis on brain activity.


title: A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems

abstract: Nowadays, many current real financial applications have nonlinear and uncertain behaviors which change across the time. Therefore, the need to solve highly nonlinear, time variant problems has been growing rapidly. These problems along with other problems of traditional models caused growing interest in artificial intelligent techniques. In this paper, comparative research review of three famous artificial intelligence techniques, i.e., artificial neural networks, expert systems and hybrid intelligence systems, in financial market has been done. A financial market also has been categorized on three domains: credit evaluation, portfolio management and financial prediction and planning. For each technique, most famous and especially recent researches have been discussed in comparative aspect. Results show that accuracy of these artificial intelligent methods is superior to that of traditional statistical methods in dealing with financial problems, especially regarding nonlinear patterns. However, this outperformance is not absolute.


title: 6.7 A 28Gb/s digital CDR with adaptive loop gain for optimum jitter tolerance

abstract: As we move to higher data rates, the performance of clock and data recovery (CDR) circuits becomes increasingly important in maintaining low bit error rates (BER) in wireline links. Digital CDRs are popular in part for their robustness, but their use of bang-bang phase detectors (BB-PD) makes their performance sensitive to changes in jitter caused by PVT variations, crosstalk or power supply noise. This is because the gain of a BB-PD depends on the CDR input jitter, causing the loop gain of the CDR to change if the jitter magnitude or spectrum varies. This problem is illustrated in Fig. 6.7.1 where small jitter leads to excessive loop gain and hence to an underdamped behaviour in the CDR jitter tolerance (JTOL), while large jitter leads to insufficient loop gain and hence to low overall JTOL. To prevent this, we propose a CDR with an adaptive loop gain, K G , as shown in Fig. 6.7.1.


title: Imaging system having White-RGB color filter array

abstract: We have developed an imaging system comprised of a sensor having White-RGB color filter array and back end image processing chain (IPC) that enables seamless integration with existing imaging solutions.


title: Enhanced SDN security using firewall in a distributed scenario

abstract: Software Defined Network (SDN) attract much attendance from research and industrial area; reason behind is the open interface, user controlled management and lower operating cost for data/flow handling rules that implements on software module rather than embedding in hardware with greatly improve performance. Network with the high security demanding higher in this generation. New service and products provide advanced technology and flexible architecture but changing in network landscape can introduce new security issues. SDN simplifies hardware, software and management of network device but faces many forms of attack. SDN or TNA (Traditional Network Architecture) has same uses firewall to manage legacy network traffic. This paper introduce firewall for SDN infrastructure to secure network attached device from suspicious and malicious traffic regarding to attack and access control to enhancing SDN security by distributing system.


title: Classification of Schizophrenia versus normal subjects using deep learning

abstract: Motivated by deep learning approaches to classify normal and neuro-diseased subjects in functional Magnetic Resonance Imaging (fMRI), we propose stacked autoencoder (SAE) based 2-stage architecture for disease diagnosis. In the proposed architecture, a separate 4-hidden layer autoencoder is trained in unsupervised manner for feature extraction corresponding to every brain region. Thereafter, these trained autoencoders are used to provide features on class-labeled input data for training a binary support vector machine (SVM) based classifier. In order to design a robust classifier, noisy or inactive gray matter voxels are filtered out using a proposed covariance based approach. We applied the proposed methodology on a public dataset, namely, 1000 Functional Connectomes Project Cobre dataset consisting of fMRI data of normal and Schizophrenia subjects. The proposed architecture is able to classify normal and Schizophrenia subjects with 10-fold cross-validation accuracy of 92% that is better compared to the existing methods used on the same dataset.


title: Rotational data augmentation for electroencephalographic data

abstract: Motivation: For deep learning on image data, a common approach is to augment the training data by artificial new images, using techniques like moving windows, scaling, affine distortions, and elastic deformations. In contrast to image data, electroencephalographic (EEG) data suffers even more from the lack of sufficient training data. Methods: We suggest and evaluate rotational distortions similar to affine/rotational distortions of images to generate augmented data. Results: Our approach increases the performance of signal processing chains for EEG-based brain-computer interfaces when rotating only around y- and z-axis with an angle around ±18 degrees to generate new data. Conclusion: This shows that our processing efficient approach generates meaningful data and encourages to look for further new methods for EEG data augmentation.


title: End-to-End Subtitle Detection and Recognition for Videos in East Asian Languages via CNN Ensemble with Near-Human-Level Performance

abstract: Abstract In this paper, we propose an innovative end-to-end subtitle detection and recognition system for videos in East Asian languages. Our end-to-end system consists of multiple stages. Subtitles are firstly detected by a novel image operator based on the sequence information of consecutive video frames. Then, an ensemble of Convolutional Neural Networks (CNNs) trained on synthetic data is adopted for detecting and recognizing East Asian characters. Finally, a dynamic programming approach leveraging language models is applied to constitute results of the entire body of text lines. The proposed system achieves average end-to-end accuracies of 98.2% and 98.3% on 40 videos in Simplified Chinese and 40 videos in Traditional Chinese respectively, which is a significant outperformance of other existing methods. The near-perfect accuracy of our system dramatically narrows the gap between human cognitive ability and state-of-the-art algorithms used for such a task.


title: Improved lane detection based on past vehicle trajectories

abstract: Knowing where the host lane lies is paramount to the effectiveness of many advanced driver assistance systems (ADAS), such as lane keep assist (LKA) and adaptive cruise control (ACC). This paper presents an approach for improving lane detection based on the past trajectories of vehicles. Instead of expensive high-precision map, the authors use the vehicle trajectory information to provide additional lane-level spatial support of the traffic scene, and combine it with the visual evidence to improve each step of the lane detection procedure, thereby overcoming typical challenges of normal urban streets. Such an approach could serve as an Add-On to enhance the performance of existing lane detection systems in terms of both accuracy and robustness. Experimental results in various typical but challenging scenarios show the effectiveness of the proposed system.


title: A binarization method for degraded document image using artificial neural network and interpolation inpainting

abstract: The binarization of degraded document image is an important phase in the document analysis to extract text from the original image. For this purpose, we present a new multi-stage approach started by, a preprocessing phase to enhance the contrast of the original image and to eliminate noises. Then an interpolation inpainting is performing to create the background estimation and the foreground (text) estimation, that will be used as feature to train and test a supervised artificial neural network. the final stage is the use of the ANN to classify the pixels of the original image into 0 for black and 1 for white to get the final binary image. The suggested method has been tested and evaluated over DIBCO'2009 database, the results show the successful of the proposed approach.


{ACES}: Automatic Compartments for Embedded Systems


title: Functional neuroimaging studies of sexual arousal and orgasm in healthy men and women: A review and meta-analysis

abstract: In the last fifteen years, functional neuroimaging techniques have been used to investigate the neuroanatomical correlates of sexual arousal in healthy human subjects. In most studies, subjects have been requested to watch visual sexual stimuli and control stimuli. Our review and meta-analysis found that in heterosexual men, sites of cortical activation consistently reported across studies are the lateral occipitotemporal, inferotemporal, parietal, orbitofrontal, medial prefrontal, insular, anterior cingulate, and frontal premotor cortices as well as, for subcortical regions, the amygdalas, claustrum, hypothalamus, caudate nucleus, thalami, cerebellum, and substantia nigra. Heterosexual and gay men show a similar pattern of activation. Visual sexual stimuli activate the amygdalas and thalami more in men than in women. Ejaculation is associated with decreased activation throughout the prefrontal cortex. We present a neurophenomenological model to understand how these multiple regional brain responses could account for the varied facets of the subjective experience of sexual arousal. Further research should shift from passive to active paradigms, focus on functional connectivity and use subliminal presentation of stimuli.


title: Music as a memory enhancer in patients with Alzheimer's disease

abstract: Musical mnemonics have a long and diverse history of popular use. In addition, music processing in gen- eral is often considered spared by the neurodegenerative effects of Alzheimer's disease (AD). Research examining these two phenomena is limited, and no work to our knowledge has explored the effectiveness of musical mnemonics in AD. The present study sought to investigate the effect of music at encoding on the subsequent recognition of associated verbal information. Lyrics of unfamiliar children's songs were presented bimodally at encoding, and visual stimuli were accompanied by either a sung or a spoken recording. Patients with AD demonstrated better recognition accuracy for the sung lyrics than the spo- ken lyrics, while healthy older adults showed no significant difference between the two conditions. We propose two possible explanations for these findings: first, that the brain areas subserving music pro- cessing may be preferentially spared by AD, allowing a more holistic encoding that facilitates recognition, and second, that music heightens arousal in patients with AD, allowing better attention and improved memory.


title: Design of a dual circular polarization microstrip patch array antenna

abstract: Design of a microstrip array antenna to achieve dual circular polarization is proposed in this paper. The proposed antenna is a 2×2 array antenna where each patch element is circularly polarized. The feed network has microstrip lines, cross slot lines and air-bridges. The array antenna can excite both right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP) without using any 90° hybrid circuit or PIN diode. “Both-sided MIC Technology” is used to design the feed network as it provides flexibility to place several types of transmission lines on both sides of the dielectric substrate. The design frequency of the proposed array antenna is 10 GHz. The simulated return loss exhibits an impedance bandwidth of greater than 5% and the 3-dB axial ratio bandwidths for both RHCP and LHCP are approximately 1.39%. The structure and the basic operation along with the simulation results of the proposed dual circularly polarized array antenna are demonstrated in this paper.


title: Industry 4.0 with cyber-physical integration: A design and manufacture perspective

abstract: Foreseeing changes in the way companies manufacture products and provide services, future trends are emerging in design and manufacture. Together with growing internet applications and technologies connected through the cloud, a new Industrial Revolution, named “Industry 4.0”, aims to integrate cyber-virtual and cyber-physical systems to aid smart manufacturing, as presented in this paper. Connecting information and physical machinery, this new paradigm relies on how effective and fast connectivity are achieved for Industry 4.0. A new generation of wireless connection, 5G, will help and accelerate this trend. Following analysis of the present cyber-physical integration for the 4th Industrial Revolution, this paper also investigates future methodologies and trends in smart manufacturing, design and innovation.


A Neural Switch for Active and Passive Fear


title: Wired Equivalent Privacy (WEP) versus Wi-Fi Protected Access (WPA)

abstract: Wireless technology has been gaining rapid popularity for some years. Adaptation of a standard depends on the ease of use and level of security it provides. In this case, contrast between wireless usage and security standards show that the security is not keeping up with the growth paste of end user’s usage. Current wireless technologies in use allow hackers to monitor and even change the integrity of transmitted data. Lack of rigid security standards has caused companies to invest millions on securing their wireless networks. There are three major types of security standards in wireless. In our previous paper which was presented in ICFCC2009 Conference in Kuala Lumpur and published by IEEE Computer Society [1], we explained the structure of WEP as a first wireless security standard and discussed all its versions, problems and improvements. Now, we try to explain all of WPA versions and problems with the best solutions and finally make a comparison between WEP and WPA. Then we are in the next phase which is to explain the structure of last standard (WPA2) and we hope that we will publish a complete comparison among wireless security techniques in the near future and recommend a new proposal as a new protocol.


title: The Daikon system for dynamic detection of likely invariants

abstract: Daikon is an implementation of dynamic detection of likely invariants; that is, the Daikon invariant detector reports likely program invariants. An invariant is a property that holds at a certain point or points in a program; these are often used in assert statements, documentation, and formal specifications. Examples include being constant (x=a), non-zero (x 0), being in a range (a@?x@?b), linear relationships (y=ax+b), ordering (x@?y), functions from a library (x=fn(y)), containment (x@?y), sortedness (xissorted), and many more. Users can extend Daikon to check for additional invariants. Dynamic invariant detection runs a program, observes the values that the program computes, and then reports properties that were true over the observed executions. Dynamic invariant detection is a machine learning technique that can be applied to arbitrary data. Daikon can detect invariants in C, C++, Java, and Perl programs, and in record-structured data sources; it is easy to extend Daikon to other applications. Invariants can be useful in program understanding and a host of other applications. Daikon's output has been used for generating test cases, predicting incompatibilities in component integration, automating theorem proving, repairing inconsistent data structures, and checking the validity of data streams, among other tasks. Daikon is freely available in source and binary form, along with extensive documentation, at http://pag.csail.mit.edu/daikon/.


title: A Java implementation of paillier homomorphic encryption scheme

abstract: This paper discusses how to implement Paillier homomorphic encryption (HE) scheme in Java as an API. We first analyze existing Pailler HE libraries and discuss their limitations. We then design a comparatively accomplished and efficient Pailler HE Java library. As a proof of concept, we applied our Pailler HE library in an electronic voting system that allows the voting server to sum up the candidates' votes in the encrypted form with voters remain anonymous. Our library records an average of only 2766ms for each vote placement through HTTP POST request.


title: Hierarchical structure and the prediction of missing links in networks

abstract: Networks are now a ubiquitous tool for representing the structure of complex systems, including the Internet, social networks, food webs, and protein and genetic networks. Unfortunately, the data describing these networks are in many cases incomplete or biased. A new study provides a general technique to divide network vertices into groups and sub-groups. Revealing such underlying hierarchies makes it possible to predict missing links from partial data with higher accuracy than previous methods. Networks have recently emerged as a powerful tool to describe and quantify many complex systems, with applications in engineering, communications, ecology, biochemistry and genetics. A general technique to divide network vertices in groups and sub-groups is reported. Revealing such underlying hierarchies in turn allows the predicting of missing links from partial data with higher accuracy than previous methods. Networks have in recent years emerged as an invaluable tool for describing and quantifying complex systems in many branches of science1,2,3. Recent studies suggest that networks often exhibit hierarchical organization, in which vertices divide into groups that further subdivide into groups of groups, and so forth over multiple scales. In many cases the groups are found to correspond to known functional units, such as ecological niches in food webs, modules in biochemical networks (protein interaction networks, metabolic networks or genetic regulatory networks) or communities in social networks4,5,6,7. Here we present a general technique for inferring hierarchical structure from network data and show that the existence of hierarchy can simultaneously explain and quantitatively reproduce many commonly observed topological properties of networks, such as right-skewed degree distributions, high clustering coefficients and short path lengths. We further show that knowledge of hierarchical structure can be used to predict missing connections in partly known networks with high accuracy, and for more general network structures than competing techniques8. Taken together, our results suggest that hierarchy is a central organizing principle of complex networks, capable of offering insight into many network phenomena.


title: TABOO: Detecting Unstructured Sensitive Information Using Recursive Neural Networks

abstract: Abstract-Leak of sensitive information from unstructured text documents is a costly problem both for government and for industrial institutions. Traditional approaches for data leak prevention are commonly based on the hypothesis that sensitive information is reflected in the presence of distinct sensitive words. However, for complex sensitive information, this hypothesis may not hold.