title: Towards an Ontology for Autonomous Robots

abstract: The IEEE RAS Ontologies for Robotics and Automation Working Group is dedicated to developing a methodology for knowledge representation and reasoning in robotics and automation. As part of this working group, the Autonomous Robots sub-group is tasked with developing ontology modules for autonomous robots. This paper describes the work in progress on the development of ontologies for autonomous systems. For autonomous systems, the focus is on the cooperation, coordination, and communication of multiple unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and autonomous underwater vehicles (AUVs). The ontologies serve as a framework for working out concepts of employment with multiple vehicles for a variety of operational scenarios with emphasis on collaborative and cooperative missions.


title: A survey of the applications of text mining in financial domain

abstract: Text mining has found a variety of applications in diverse domains. Of late, prolific work is reported in using text mining techniques to solve problems in financial domain. The objective of this paper is to provide a state-of-the-art survey of various applications of Text mining to finance. These applications are categorized broadly into FOREX rate prediction, stock market prediction, customer relationship management (CRM) and cyber security. Since finance is a service industry, these problems are paramount in operational and customer growth aspects. We reviewed 89 research papers that appeared during the period 2000-2016, highlighted some of the issues, gaps, key challenges in this area and proposed some future research directions. Finally, this review can be extremely useful to budding researchers in this area, as many open problems are highlighted.


title: Hybrid Analog-Digital Beamforming: How Many RF Chains and Phase Shifters Do We Need?

abstract: This paper considers hybrid beamforming (HB) for downlink multiuser massive MIMO systems with frequency selective channels. For this system, first we quantify the required number of radio frequency (RF) chains and phase shifters (PSs) such that the proposed HB achieves the same performance as that of the digital beamforming (DB) which utilizes N (number of transmitter antennas) RF chains. We show that the performance of the DB can be achieved with our HB just by utilizing rt RF chains and 2rt(N − rt + 1) PSs, where rt ≤ N is the rank of the combined digital precoder matrices of all sub-carriers. Second, we provide a simple and novel approach to reduce the number of PSs with only a negligible performance degradation. Numerical results reveal that only 20− 40 PSs per RF chain are sufficient for practically relevant parameter settings. Finally, for the scenario where the deployed number of RF chains (Na) is less than rt, we propose a simple user scheduling algorithm to select the best set of users in each sub-carrier. Simulation results validate theoretical expressions, and demonstrate the superiority of the proposed HB design over the existing HB designs in both flat fading and frequency selective channels.


Single Wideband Microstrip Patch Antenna for 5 G Wireless Communication Application


title: Analytical Modeling of the Open-Circuit Magnetic Field in Axial Flux Permanent-Magnet Machines With Semi-Closed Slots

abstract: This paper presents a multi-slice analytical model for prediction of the open-circuit magnetic field in slotted semi-closed permanent-magnet axial flux synchronous machines. The technique is based on 2-D exact solution of the Maxwell's equations using the separation of variables method. The magnetic field expressions are developed in slots regions, slot opening regions, magnetic air gap regions, and permanent magnet regions leading to an exact calculation of the slot effects on the air gap magnetic field. The model is established considering that ferromagnetic parts are infinitely permeable, and end effects at inner and outer radii are neglected. The open circuit machine's global quantities (electromotive force and cogging torque) are then deduced from the local magnetic field expressions. Finally, the accuracy of the presented analytical model is validated by comparing its results to corresponding finite-element analyses for two different axial flux machines.


title: Text-Messaging Practices and Links to General Spelling Skill: A Study of Australian Children.

abstract: This study investigated 10- to 12-year-old Australian children’s text-messaging practices and their relationship to traditional spelling ability. Of the 227 children tested, 82% reported sending textmessages; a median of 5 per day. Use of predictive and multi-press entry methods was roughly equal. Children produced a wide range of text-message abbreviations (textisms) (M = 53%) when asked to rewrite a list of 30 conventionally-spelt words as they would in a text-message to a friend. The proportion of textisms produced was significantly positively correlated with general spelling ability, which fits with previous findings of positive relationships between children’s textism use and literacy.


title: Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters

abstract: Deep learning models can take weeks to train on a single GPU-equipped machine, necessitating scaling out DL training to a GPU-cluster. However, current distributed DL implementations can scale poorly due to substantial parameter synchronization over the network, because the high throughput of GPUs allows more data batches to be processed per unit time than CPUs, leading to more frequent network synchronization. We present Poseidon, an efficient communication architecture for distributed DL on GPUs. Poseidon exploits the layered model structures in DL programs to overlap communication and computation, reducing bursty network communication. Moreover, Poseidon uses a hybrid communication scheme that optimizes the number of bytes required to synchronize each layer, according to layer properties and the number of machines. We show that Poseidon is applicable to different DL frameworks by plugging Poseidon into Caffe and TensorFlow. We show that Poseidon enables Caffe and TensorFlow to achieve 15.5x speed-up on 16 single-GPU machines, even with limited bandwidth (10GbE) and the challenging VGG19-22K network for image classification. Moreover, Poseidon-enabled TensorFlow achieves 31.5x speed-up with 32 single-GPU machines on Inception-V3, a 50% improvement over the open-source TensorFlow (20x speed-up).


title: Performance comparison between IEEE 802.11p and LTE-V2V in-coverage and out-of-coverage for cooperative awareness

abstract: Cooperative awareness, consisting in the periodic broadcasting of messages, called beacons, to inform neighboring vehicles about maneuvers, changes of direction and other relevant mobility information, represents the core requirement to enable applications that may increase road safety and transportation efficiency. Up to few months ago, when latest 3GPP Release added to long term evolution (LTE) the support of vehicle-to-vehicle (V2V) communications, the only suitable standard was IEEE 802.11p in the U.S. and the corresponding ITS-G5 in Europe. The choice regarding the worldwide adoption of one of the two technologies is still under discussion, since both show advantages and drawbacks. In this work, we analyze IEEE 802.11p and LTE-V2V and evaluate their performance for the cooperative awareness service through simulations in a realistic highway scenario. Both in-coverage and out-of-coverage conditions are considered for LTE-V2V. Results reveal that LTE-V2V incoverage is the best solution in terms of packet reception ratio for all the considered values of beacon size and communication range. As far as the beacon update delay is concerned, we observe that LTE-V2V in-coverage still provides the best performance when small packets are transmitted, while IEEE 802.11p gives the best results for values of the communication range higher than 100 m when the packet size is increased.


Shingled Magnetic Recording for Big Data Applications


title: A unified distance measurement for orientation coding in palmprint verification

abstract: Orientation coding based palmprint verification methods, such as competitive code, palmprint orientation code and robust line orientation code, are state-of-the-art verification algorithms with fast matching speeds. Orientation code makes use of two types of distance measure, SUM_XOR (angular distance) and OR_XOR (Hamming distance), yet little is known about the similarities and differences between these two distance measures. In this paper, we propose a unified distance measure, of which both SUM_XOR and OR_XOR can be regarded as special cases, and provide some principles for determining the parameters of the unified distance. Experimental results show that, using the same feature extraction and coding methods, the unified distance measure gets lower equal error rates than the original distance measures.


title: A fast digital calibration algorithm for pipeline ADC

abstract: In this paper, a pseudo-random sequence into the digital background calibration algorithm applied in the calibration of gain error in pipeline ADC is presented. The LMS algorithm is used to establish adaptive process for gain error estimation and real-time calibration of the signal. In view of the traditional PN injection amplitude algorithms cannot satisfy redundancy calibration and the problems of slow convergence speed, the calibration scheme of reduce PN injection amplitude and using alternating sequences of plus or minus 1 instead of pseudo random sequence is proposed. Under the input signal frequency of 9.8999MHz, the sampling frequency of 100MHz, the first stage of 2.3% gain error, simulation results show that the gain speed is about 200000 clock cycles. Compared with the traditional algorithm, it is improved about 100 times. SFDR and SNR respectively by 67.2dB and 53.2dB before calibration is improved to 112dB and 85.9dB after calibrated. Effective number of bits is increased from 8.495bits before calibration to 13.978bits after calibrated. System accuracy can be greatly improved after calibration.


The University's Role in Economic Development: Lessons for Academic Leaders.


title: Using contours to detect and localize junctions in natural images

abstract: Contours and junctions are important cues for perceptual organization and shape recognition. Detecting junctions locally has proved problematic because the image intensity surface is confusing in the neighborhood of a junction. Edge detectors also do not perform well near junctions. Current leading approaches to junction detection, such as the Harris operator, are based on 2D variation in the intensity signal. However, a drawback of this strategy is that it confuses textured regions with junctions. We believe that the right approach to junction detection should take advantage of the contours that are incident at a junction; contours themselves can be detected by processes that use more global approaches. In this paper, we develop a new high-performance contour detector using a combination of local and global cues. This contour detector provides the best performance to date (F=0.70) on the Berkeley Segmentation Dataset (BSDS) benchmark. From the resulting contours, we detect and localize candidate junctions, taking into account both contour salience and geometric configuration. We show that improvements in our contour model lead to better junctions. Our contour and junction detectors both provide state of the art performance.


title: The BCI competition III: validating alternative approaches to actual BCI problems

abstract: A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results.


title: MatConvNet: Convolutional Neural Networks for MATLAB

abstract: MatConvNet is an implementation of Convolutional Neural Networks (CNNs) for MATLAB. The toolbox is designed with an emphasis on simplicity and flexibility. It exposes the building blocks of CNNs as easy-to-use MATLAB functions, providing routines for computing linear convolutions with filter banks, feature pooling, and many more. In this manner, MatConvNet allows fast prototyping of new CNN architectures; at the same time, it supports efficient computation on CPU and GPU allowing to train complex models on large datasets such as ImageNet ILSVRC. This document provides an overview of CNNs and how they are implemented in MatConvNet and gives the technical details of each computational block in the toolbox.


title: Functional atlas of emotional faces processing: a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies

abstract: ies using a variant of the emotional faces paradigm in healthy participants. The search terms were: “fMRI AND happy faces,” “fMRI AND sad faces,” “fMRI AND fearful faces,” “fMRI AND angry faces,” “fMRI AND disgusted faces” and “fMRI AND neutral faces.” We extracted spatial coordinates and inserted them in an electronic database. We performed activation likelihood estimation analysis for voxel-based meta-analyses. Results: Of the originally identified studies, 105 met our inclusion criteria. The overall database consisted of 1785 brain coordinates that yielded an overall sample of 1600 healthy participants. Quantitative voxel-based meta-analysis of brain activation provided neurofunctional maps for 1) main effect of human faces; 2) main effect of emotional valence; and 3) modulatory effect of age, sex, explicit versus implicit processing and magnetic field strength. Processing of emotional faces was associated with increased activation in a number of visual, limbic, temporoparietal and prefrontal areas; the putamen; and the cerebellum. Happy, fearful and sad faces specifically activated the amygdala, whereas angry or disgusted faces had no effect on this brain region. Furthermore, amygdala sensitivity was greater for fearful than for happy or sad faces. Insular activation was selectively reported during processing of disgusted and angry faces. However, insular sensitivity was greater for disgusted than for angry faces. Conversely, neural response in the visual cortex and cerebellum was observable across all emotional conditions. Limitations: Although the activation likelihood estimation approach is currently one of the most powerful and reliable meta-analytical methods in neuroimaging research, it is insensitive to effect sizes. Conclusion: Our study has detailed neurofunctional maps to use as normative references in future fMRI studies of emotional facial processing in psychiatric populations. We found selective differences between neural networks underlying the basic emotions in limbic and insular brain regions.


title: Crowd-Based Personalized Natural Language Explanations for Recommendations

abstract: Explanations are important for users to make decisions on whether to take recommendations. However, algorithm generated explanations can be overly simplistic and unconvincing. We believe that humans can overcome these limitations. Inspired by how people explain word-of-mouth recommendations, we designed a process, combining crowdsourcing and computation, that generates personalized natural language explanations. We modeled key topical aspects of movies, asked crowdworkers to write explanations based on quotes from online movie reviews, and personalized the explanations presented to users based on their rating history. We evaluated the explanations by surveying 220 MovieLens users, finding that compared to personalized tag-based explanations, natural language explanations: 1) contain a more appropriate amount of information, 2) earn more trust from users, and 3) make users more satisfied. This paper contributes to the research literature by describing a scalable process for generating high quality and personalized natural language explanations, improving on state-of-the-art content-based explanations, and showing the feasibility and advantages of approaches that combine human wisdom with algorithmic processes.


title: An Enhanced Dynamic Model for McKibben Pneumatic Muscle Actuators

abstract: An enhanced dynamic force model of a type of small and soft McKibben-type pneumatic muscle (PM) actuator has been developed. This model takes the factor of external loads and a more sophisticated form of friction into account, and is presented as a polynomial function of pressure, contraction length, contraction velocity and external load. The coefficients in this model are determined from a series of experiments with constant loads and step pressure inputs. Comparison study with other models is conducted assuming the Coulomb friction as a constant force. The results demonstrate a solid enhancement of the presented model.


title: A Two-Dimensional Framework of Multiple Kernel Subspace Learning for Recognizing Emotion in Speech

abstract: As a highly active topic in computational paralinguistics, speech emotion recognition (SER) aims to explore ideal representations for emotional factors in speech. In order to improve the performance of SER, multiple kernel learning (MKL) dimensionality reduction has been utilized to obtain effective information for recognizing emotions. However, the solution of MKL usually provides only one nonnegative mapping direction for multiple kernels; this may lead to loss of valuable information. To address this issue, we propose a two-dimensional framework for multiple kernel subspace learning. This framework provides more linear combinations on the basis of MKL without nonnegative constraints, which preserves more information in the learning procedures. It also leverages both of MKL and two-dimensional subspace learning, combining them into a unified structure. To apply the framework to SER, we also propose an algorithm, namely generalised multiple kernel discriminant analysis (GMKDA), by employing discriminant embedding graphs in this framework. GMKDA takes advantage of the additional mapping directions for multiple kernels in the proposed framework. In order to evaluate the performance of the proposed algorithm a wide range of experiments is carried out on several key emotional corpora. These experimental results demonstrate that the proposed methods can achieve better performance compared with some conventional and subspace learning methods in dealing with SER.


title: Channel-Dependent Scheduling of Uplink Single Carrier FDMA Systems

abstract: We examine single carrier frequency division multiple access (SC-FDMA) with frequency domain equalization for uplink data transmission. We investigate channel-dependent scheduling schemes to achieve multi-user diversity and frequency selective diversity. There are two subcarrier mapping schemes in SC-FDMA: Localized FDMA (L-FDMA) and Interleaved FDMA (I- FDMA). L-FDMA benefits from frequency selective scheduling, but it incurs higher peak-to-average power ratio than I-FDMA. Throughout our work, we provide low complexity channel- dependent scheduling (CDS) methods for L-FDMA and I-FDMA. The results show that rate-sum capacity can increase up to 130% for L-FDMA and 40% for I-FDMA relative to static round robin scheduling.


COMPUTING IN SCHOOLSComputer science, computational thinking, programming, coding: the anomalies of transitivity in K-12 computer science education


A Hybrid Text Classification Approach Using KNN And SVM


title: A critique of ANSI SQL isolation levels

abstract: ANSI SQL-92 [MS, ANSI] defines Isolation Levels in terms of phenomena: Dirty Reads, Non-Repeatable Reads, and Phantoms. This paper shows that these phenomena and the ANSI SQL definitions fail to properly characterize several popular isolation levels, including the standard locking implementations of the levels covered. Ambiguity in the statement of the phenomena is investigated and a more formal statement is arrived at; in addition new phenomena that better characterize isolation types are introduced. Finally, an important multiversion isolation type, called Snapshot Isolation, is defined.


title: Feature Importance Measure for Non-linear Learning Algorithms

abstract: Complex problems may require sophisticated, non-linear learning methods such as kernel machines or deep neural networks to achieve state of the art prediction accuracies. However, high prediction accuracies are not the only objective to consider when solving problems using machine learning. Instead, particular scientific applications require some explanation of the learned prediction function. Unfortunately, most methods do not come with out of the box straight forward interpretation. Even linear prediction functions are not straight forward to explain if features exhibit complex correlation structure. ::: In this paper, we propose the Measure of Feature Importance (MFI). MFI is general and can be applied to any arbitrary learning machine (including kernel machines and deep learning). MFI is intrinsically non-linear and can detect features that by itself are inconspicuous and only impact the prediction function through their interaction with other features. Lastly, MFI can be used for both --- model-based feature importance and instance-based feature importance (i.e, measuring the importance of a feature for a particular data point).


title: Synthetic Aperture Radar Target Recognition with Feature Fusion Based on a Stacked Autoencoder

abstract: Feature extraction is a crucial step for any automatic target recognition process, especially in the interpretation of synthetic aperture radar (SAR) imagery. In order to obtain distinctive features, this paper proposes a feature fusion algorithm for SAR target recognition based on a stacked autoencoder (SAE). The detailed procedure presented in this paper can be summarized as follows: firstly, 23 baseline features and Three-Patch Local Binary Pattern (TPLBP) features are extracted. These features can describe the global and local aspects of the image with less redundancy and more complementarity, providing richer information for feature fusion. Secondly, an effective feature fusion network is designed. Baseline and TPLBP features are cascaded and fed into a SAE. Then, with an unsupervised learning algorithm, the SAE is pre-trained by greedy layer-wise training method. Capable of feature expression, SAE makes the fused features more distinguishable. Finally, the model is fine-tuned by a softmax classifier and applied to the classification of targets. 10-class SAR targets based on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset got a classification accuracy up to 95.43%, which verifies the effectiveness of the presented algorithm.


title: Detection by detections: Non-parametric detector adaptation for a video

abstract: We propose an approach to improving the detection results of a generic offline trained detector on a specific video. Our method does not leverage visual tracking as most detection by tracking methods do. Instead, the proposed detection by detections approach can serve as a more confident initialization for detection by tracking methods. Different from other supervised detector adaptation methods, we constrain the task to videos and no supervised labels for the target video are required for the adaptation; we intend to fill the gap between detection by tracking and pure detection by frames. As a non-parametric detector adaptation method, confident detections are collected to re-rank and to group other detections. We focus on methods with high precision detection results since it is necessitated in real application. Extensive experiments with two state-of-the-art detectors demonstrate the efficacy of our approach.


title: The spatial arrangement of L and M cones in the peripheral human retina

abstract: The spatial arrangement of L and M cones in the human peripheral retina was estimated from red-green color naming of small test flashes (0.86 min of arc, 555 nm, constant intensity) presented at different locations (grid with 1.5 min of arc steps) centered at 17°temporal eccentricity. Simulated red-green color naming ratings were generated by a model based on an ideal observer for all possible patterns of placement and relative numerosities of L and M cones, constrained by the anatomical data on the statistics of cone spacing at this retinal location. The best matching simulated performance as compared to the human observer's data determined the cone array most likely to produce that observer's color naming results. The mosaics for two color normal observers showed L and M cones randomly arrayed over this retinal region. Consequences of random cone placements for spectral sampling and color opponency are discussed. Copyright (C) 2000 Elsevier Science Ltd.


title: A Co-Matching Model for Multi-choice Reading Comprehension

abstract: Multi-choice reading comprehension is a challenging task, which involves the matching between a passage and a question-answer pair. This paper proposes a new co-matching approach to this problem, which jointly models whether a passage can match both a question and a candidate answer. Experimental results on the RACE dataset demonstrate that our approach achieves state-of-the-art performance.


Entropy-Based Subword Mining with an Application to Word Embeddings


title: Biometric smart attendance kit with fingerprint scanner by using microcontroller

abstract: The register based conventional attendance system for the students in our educational institution is laborious, time consuming and kind of boring task for the educators. An intelligent system based on fingerprint scanner have been designed and implemented that supplanted the traditional mundane attendance system. The system can acquire, store, crosscheck the fingerprint of individuals and export the data in the form of Microsoft excel spreadsheet in a memory module. The entire procedures are supervised by a microcontroller. This smart attendance kit is portable, handy, cheap and reliable.


title: Inkjet-Configurable Gate Arrays (IGA)

abstract: Implementation of organic digital circuits (or printed electronic circuits) has been under an extensive investigation, facing some critical challenges such as process variability, device performance, cell design styles and circuit yield. Failure in any single Organic Thin Film Transistor (OTFT) often causes the whole circuit to fail since integration density is still low. For the same reason, the application of fault tolerant techniques is not that popular in these circuits. In this paper, we propose an approach for the direct mapping of digital functions on top of new prefabricated structures: Inkjet-Configurable Gate Arrays (IGA). This alternative has two main advantages. First, it helps to obtain high yield circuits out of mid-yield foils, and second, it allows implementing individual circuit personalization at a very low cost by using additive mask-less printing techniques thus avoiding the need for OTPROM-like (or E2PROM) devices. All along the design process of IGA cells and structures we used the scalability and technology-independent strategies provided by parameterizable cells (PCell) what helps dealing with current fast technology evolution.


title: A Fractional-N Sub-Sampling PLL using a Pipelined Phase-Interpolator With an FoM of -250 dB

abstract: A fractional- $N$ sub-sampling PLL architecture based on pipelined phase-interpolator and Digital-to-Time-Converter (DTC) is presented in this paper. The combination of pipelined phase-interpolator and DTC enables efficient design of the multi-phase generation mechanism required for the fractional operation. This technique can be used for designing a fractional- $N$ PLL with low in-band phase noise and low spurious tones with low power consumption. The short-current-free pipelined phase-interpolator used in this work is capable of achieving high-linearity with low-power while minimizing the intrinsic jitter. A number of other circuit techniques and layout techniques are also employed in this design for ensuring high-performance operation with minimal chip area and power consumption. The proposed fractional- $N$ PLL is implemented in standard 65 nm CMOS technology. The PLL has an operating range of 600 MHz from 4.34 GHz to 4.94 GHz. In fractional- $N$ mode, the proposed PLL achieves -249.5 dB FoM and less than -59 dBc fractional spurs.


CHAPTER 14 Security and Fraud Detection in Mobile and Wireless Networks


Small Signal Analysis for LLC Resonant Converter


Emotion Detection on Twitter Data using Knowledge Base Approach


title: Self-tracking cultures: towards a sociology of personal informatics

abstract: A body of literature on self-tracking has been established in human-computer interaction studies. Contributors to this literature tend to take a cognitive or behavioural psychology approach to theorising and explaining self-tracking. Such an approach is limited to understanding individual behaviour. Yet self-tracking is a profoundly social practice, both in terms of the enculturated meanings with which it is invested and the social encounters and social institutions that are part of the self-tracking phenomenon. In this paper I contend that sociological perspectives can contribute some intriguing possibilities for human-computer interaction research, particularly in developing an understanding of the wider social, cultural and political dimensions of what I refer to as 'self-tracking cultures'. The discussion focuses on the following topics: self-optimisation and governing the self; entanglements of bodies and technologies; the valorisation of data; data doubles; and social inequalities and self-tracking. The paper ends with outlining some directions for future research on self-tracking cultures that goes beyond the individual to the social.


title: The network data repository with interactive graph analytics and visualization

abstract: (NR) is the first interactive data repository with a web-based platform for visual interactive analytics. Unlike other data repositories (e.g., UCI ML Data Repository, and SNAP), the network data repository (networkrepository.com) allows users to not only download, but to interactively analyze and visualize such data using our web-based interactive graph analytics platform. Users can in real-time analyze, visualize, compare, and explore data along many different dimensions. The aim of NR is to make it easy to discover key insights into the data extremely fast with little effort while also providing a medium for users to share data, visualizations, and insights. Other key factors that differentiate NR from the current data repositories is the number of graph datasets, their size, and variety. While other data repositories are static, they also lack a means for users to collaboratively discuss a particular dataset, corrections, or challenges with using the data for certain applications. In contrast, NR incorporates many social and collaborative aspects that facilitate scientific research, e.g., users can discuss each graph, post observations, and visualizations.


title: 56 Gb/s PAM-4 optical receiver frontend in an advanced FinFET process

abstract: This paper presents a 56Gb/s PAM-4 optical receiver analog frontend circuits which consists of three inverter stages TIA with resistive feedback in the first and third stages. An adaptively-tuned continuous-time linear equalizer (CTLE) is cascaded after the TIA for improved sensitivity and bandwidth. The overall gain is controlled by an automatic gain control (AGC) circuits to avoid the large input optical power saturates the TIA, thus distorting the PAM-4 signals. The frontend receiver circuits is designed in an advanced FinFET technology and overall gain achieves 68 dBO with a 22 GHz bandwidth. The simulated input referred current rms noise is 2.86 µA. Total chip power is 6.3 mW from a 0.83 V supply. The chip active area is 150µm × 100 µm.


title: Evaluation of deep convolutional nets for document image classification and retrieval

abstract: This paper presents a new state-of-the-art for document image classification and retrieval, using features learned by deep convolutional neural networks (CNNs). In object and scene analysis, deep neural nets are capable of learning a hierarchical chain of abstraction from pixel inputs to concise and descriptive representations. The current work explores this capacity in the realm of document analysis, and confirms that this representation strategy is superior to a variety of popular hand-crafted alternatives. Experiments also show that (i) features extracted from CNNs are robust to compression, (ii) CNNs trained on non-document images transfer well to document analysis tasks, and (iii) enforcing region-specific feature-learning is unnecessary given sufficient training data. This work also makes available a new labelled subset of the IIT-CDIP collection, containing 400,000 document images across 16 categories, useful for training new CNNs for document analysis.


title: A Recursive Bayesian Approach To Describe Retinal Vasculature Geometry

abstract: Abstract Deep networks have recently seen significant application to the analysis of medical image data, particularly for segmentation and disease classification. However, there are many situations in which the purpose of analysing a medical image is to perform parameter estimation, assess connectivity or determine geometric relationships. Some of these tasks are well served by probabilistic trackers, including Kalman and particle filters. In this work, we explore how the probabilistic outputs of a single-architecture deep network may be coupled to a probabilistic tracker, taking the form of a particle filter. The tracker provides information not easily available with current deep networks, such as a unique ordering of points along vessel centrelines and edges, whilst the construction of observation models for the tracker is simplified by the use of a deep network. We use the analysis of retinal images in several datasets as the problem domain, and compare estimates of vessel width in a standard dataset (REVIEW) with manually determined measurements.


title: Automatic Whole Brain MRI Segmentation of the Developing Neonatal Brain

abstract: Magnetic resonance (MR) imaging is increasingly being used to assess brain growth and development in infants. Such studies are often based on quantitative analysis of anatomical segmentations of brain MR images. However, the large changes in brain shape and appearance associated with development, the lower signal to noise ratio and partial volume effects in the neonatal brain present challenges for automatic segmentation of neonatal MR imaging data. In this study, we propose a framework for accurate intensity-based segmentation of the developing neonatal brain, from the early preterm period to term-equivalent age, into 50 brain regions. We present a novel segmentation algorithm that models the intensities across the whole brain by introducing a structural hierarchy and anatomical constraints. The proposed method is compared to standard atlas-based techniques and improves label overlaps with respect to manual reference segmentations. We demonstrate that the proposed technique achieves highly accurate results and is very robust across a wide range of gestational ages, from 24 weeks gestational age to term-equivalent age.


title: Sensor-Based Assistive Devices for Visually-Impaired People: Current Status, Challenges, and Future Directions

abstract: The World Health Organization (WHO) reported that there are 285 million visuallyimpaired people worldwide. Among these individuals, there are 39 million who are totally blind. There have been several systems designed to support visually-impaired people and to improve the quality of their lives. Unfortunately, most of these systems are limited in their capabilities. In this paper, we present a comparative survey of the wearable and portable assistive devices for visuallyimpaired people in order to show the progress in assistive technology for this group of people. Thus, the contribution of this literature survey is to discuss in detail the most significant devices that are presented in the literature to assist this population and highlight the improvements, advantages, disadvantages, and accuracy. Our aim is to address and present most of the issues of these systems to pave the way for other researchers to design devices that ensure safety and independent mobility to visually-impaired people.


Recognition and Classification of Fast Food Images


title: FPGA based nonlinear Support Vector Machine training using an ensemble learning

abstract: Support Vector Machines (SVMs) are powerful supervised learning methods in machine learning. However, their applicability to large problems has been limited due to the time consuming training stage whose computational cost scales quadratically with the number of examples. In this work, a complete FPGA-based system for nonlinear SVM training using ensemble learning is presented. The proposed framework builds on the FPGA architecture and utilizes a cascaded multi-precision training flow, exploits the heterogeneity within the training problem by tuning the number representation used, and supports ensemble training tuned to each internal memory structure so to address very large datasets. Its performance evaluation shows that the proposed system achieves more than an order of magnitude better results compared to state-of-the-art CPU and GPU-based implementations.


title: Google Scholar as a new source for citation analysis

abstract: Traditionally, the most commonly used source of bibliometric data is Thomson ISI Web of Knowledge, in particular the Web of Science and the Journal Citation Reports (JCR), which provide the yearly Journal Impact Factors (JIF). This paper presents an alternative source of data (Google Scholar, GS) as well as 3 alternatives to the JIF to assess journal impact (h-index, g-index and the number of citations per paper). Because of its broader range of data sources, the use of GS gen- erally results in more comprehensive citation coverage in the area of management and international business. The use of GS particularly benefits academics publishing in sources that are not (well) cov- ered in ISI. Among these are books, conference papers, non-US journals, and in general journals in the field of strategy and international business. The 3 alternative GS-based metrics showed strong correlations with the traditional JIF. As such, they provide academics and universities committed to JIFs with a good alternative for journals that are not ISI-indexed. However, we argue that these metrics provide additional advantages over the JIF and that the free availability of GS allows for a democratization of citation analysis as it provides every academic access to citation data regardless of their institution's financial means.


COREGISTRATION BASED ON SIFT ALGORITHM FOR SYNTHETIC APERTURE RADAR INTERFEROMETRY


title: Experiments with Spectral Learning of Latent-Variable PCFGs

abstract: Latent-variable PCFGs (L-PCFGs) are a highly successful model for natural language parsing. Recent work (Cohen et al., 2012) has introduced a spectral algorithm for parameter estimation of L-PCFGs, which—unlike the EM algorithm—is guaranteed to give consistent parameter estimates (it has PAC-style guarantees of sample complexity). This paper describes experiments using the spectral algorithm. We show that the algorithm provides models with the same accuracy as EM, but is an order of magnitude more efficient. We describe a number of key steps used to obtain this level of performance; these should be relevant to other work on the application of spectral learning algorithms. We view our results as strong empirical evidence for the viability of spectral methods as an alternative to EM.


Evaluating ERP as a Composition of Different Functionalities from Key Stakeholder Perspectives


title: Short-Term Heart Rate Variability Strongly Predicts Sudden Cardiac Death in Chronic Heart Failure Patients

abstract: Background—The predictive value of heart rate variability (HRV) in chronic heart failure (CHF) has never been tested in a comprehensive multivariate model using short-term laboratory recordings designed to avoid the confounding effects of respiration and behavioral factors. Methods and Results—A multivariate survival model for the identification of sudden (presumably arrhythmic) death was developed with data from 202 consecutive patients referred between 1991 and 1995 with moderate to severe CHF (age 529 years, left ventricular ejection fraction 247%, New York Heart Association class 2.30.7; the derivation sample). Time- and frequency-domain HRV parameters obtained from an 8 recording of ECG at baseline and during controlled breathing (12 to 15 breaths/min) were challenged against clinical and functional parameters. This model was then validated in 242 consecutive patients referred between 1996 and 2001 (validation sample). In the derivation sample, sudden death was independently predicted by a model that included low-frequency power (LFP) of HRV during controlled breathing 13 ms 2 and left ventricular end-diastolic diameter 77 mm (relative risk [RR] 3.7, 95% CI 1.5 to 9.3, and RR 2.6, 95% CI 1.0 to 6.3, respectively). The derivation model was also a significant predictor in the validation sample (P0.04). In the validation sample, LFP 11 ms 2 during controlled breathing and 83 ventricular premature contractions per hour on Holter monitoring were both independent predictors of sudden death (RR 3.0, 95% CI 1.2 to 7.6, and RR 3.7, 95% CI 1.5 to 9.0, respectively). Conclusions—Reduced short-term LFP during controlled breathing is a powerful predictor of sudden death in patients with CHF that is independent of many other variables. These results refine the identification of patients who may benefit from prophylactic implantation of a cardiac defibrillator. (Circulation. 2003;107:565-570.)


A Survey of Cybercrime


title: Axial Flux PM Machines With Concentrated Armature Windings: Design Analysis and Test Validation of Wind Energy Generators

abstract: Some design and operation aspects of axial flux permanent magnet synchronous machines, wound with concentrated coils, are presented. Due to their high number of poles, compactness, and excellent waveform quality and efficiency, these machines show satisfactory operation at low speeds, both as direct drive generators and as motors. In this paper, after a general analysis of the model and design features of this kind of machine, the attention is focused on wind power generation: The main sizing equations are defined, and the most relevant figures of merit are examined by means of a suitable parametric analysis. Some experimental results obtained by testing a three-phase, 50-kW, and 70-rpm prototype are presented and discussed, validating the modeling theory and the design procedure.


Discovering Unique Game Variants


title: Physics-based face database for color research

abstract: Most face databases are meant for pattern recognition use. We report the creation of a unique face database which may be used for color-related studies of faces. We describe it as physics- based because it not only contains color images but also data on color image formation such as spectral reflectance measurements from facial skin, illuminant spectral power distribution, and camera spectral response. We demonstrate the usefulness of the database by an approach we developed to color correct face images taken under different illumination conditions. © 2000 SPIE and IS&T.


title: Time-dependent ROC curve analysis in medical research: current methods and applications

abstract: Abstract Background ROC (receiver operating characteristic) curve analysis is well established for assessing how well a marker is capable of discriminating between individuals who experience disease onset and individuals who do not. The classical (standard) approach of ROC curve analysis considers event (disease) status and marker value for an individual as fixed over time, however in practice, both the disease status and marker value change over time. Individuals who are disease-free earlier may develop the disease later due to longer study follow-up, and also their marker value may change from baseline during follow-up. Thus, an ROC curve as a function of time is more appropriate. However, many researchers still use the standard ROC curve approach to determine the marker capability ignoring the time dependency of the disease status or the marker. Methods We comprehensively review currently proposed methodologies of time-dependent ROC curves which use single or longitudinal marker measurements, aiming to provide clarity in each methodology, identify software tools to carry out such analysis in practice and illustrate several applications of the methodology. We have also extended some methods to incorporate a longitudinal marker and illustrated the methodologies using a sequential dataset from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver. Results From our methodological review, we have identified 18 estimation methods of time-dependent ROC curve analyses for censored event times and three other methods can only deal with non-censored event times. Despite the considerable numbers of estimation methods, applications of the methodology in clinical studies are still lacking. Conclusions The value of time-dependent ROC curve methods has been re-established. We have illustrated the methods in practice using currently available software and made some recommendations for future research.


title: Sensing as a Service: secure wireless sensor network infrastructure sharing for the Internet of Things

abstract: Internet of Things (IoT) andWireless Sensor Networks (WSN) are composed of devices capable of sensing/actuation, communication and processing. They are valuable technology for the development of applications in several areas, such as environmental, industrial and urban monitoring and processes controlling. Given the challenges of different protocols and technologies used for communication, resource constrained devices nature, high connectivity and security requirements for the applications, the main challenges that need to be addressed include: secure communication between IoT devices, network resource management and the protected implementation of the security mechanisms. In this paper, we present a secure Software-Defined Networking (SDN) based framework that includes: communication protocols, node task programming middleware, communication and computation resource management features and security services. The communication layer for the constrained devices considers IT-SDN as its basis. Concerning security, we address the main services, the type of algorithms to achieve them, and why their secure implementation is needed. Lastly, we showcase how the Sensing as a Service paradigm could enable WSN usage in more environments.


title: Energy-Efficient I/O Thread Schedulers for NVMe SSDs on NUMA

abstract: Non-volatile memory express (NVMe) based SSDs and the NUMA platform are widely adopted in servers to achieve faster storage speed and more powerful processing capability. As of now, very little research has been conducted to investigate the performance and energy efficiency of the state-of-the-art NUMA architecture integrated with NVMe SSDs, an emerging technology used to host parallel I/O threads. As this technology continues to be widely developed and adopted, we need to understand the runtime behaviors of such systems in order to design software runtime systems that deliver optimal performance while consuming only the necessary amount of energy. This paper characterizes the runtime behaviors of a Linux-based NUMA system employing multiple NVMe SSDs. Our comprehensive performance and energy-efficiency study using massive numbers of parallel I/O threads shows that the penalty due to CPU contention is much smaller than that due to remote access of NVMe SSDs. Based on this insight, we develop a dynamic "lesser evil" algorithm called ESN, to minimize the impact of these two types of penalties. ESN is an energy-efficient profiling-based I/O thread scheduler for managing I/O threads accessing NVMe SSDs on NUMA systems. Our empirical evaluation shows that ESN can achieve optimal I/O throughput and latency while consuming up to 50% less energy and using fewer CPUs.


title: A 60-GHz Dense Dielectric Patch Antenna Array

abstract: The dense dielectric (DD) patch antenna is investigated at the 60-GHz band. By employing the printed circuit board (PCB) technology, a 4 4 aperture-coupled DD patch antenna array is designed, fabricated and studied. An impedance bandwidth of 23.7% from 51.3 to 65.1 GHz for |S11| <; -10 dB, a gain up to 16.5 dBi with a 3-dB gain bandwidth of 32.5%, and symmetrical broadside radiation patterns with -15 dB cross-polarization are achieved.


title: Mondor’s Disease of the Penis

abstract: A 36-year-old male presented to the emergency department (ED) complaining of “lumps in my penis.” The patient described 2 firm, raised areas on the dorsum of his penis that had been present 1 week. He had no pain at rest or with palpation but mild discomfort with erection. He denied trauma, dysuria, hematuria, or discharge. He had no prior medical history and took no medications. ::: ::: On physical exam, the patient had 2 palpable, firm cords wrapping around the dorsum of his penis just proximal to the glans (Figure). The affected area was non-tender and without swelling, erythema, or warmth. The skin was intact without lesions or signs of trauma. The rest of his exam was normal. ::: ::: ::: ::: Figure. ::: ::: Visible cords on the dorsolateral penis. ::: ::: ::: ::: An ultrasound showed 2 short areas of noncompressibility consistent with superficial venous thrombosis. The urology consult recommended conservative treatment with non-steroidal anti-inflammatory drugs and warm compresses. When the patient was called 2 days after ED presentation, he reported that his symptoms had almost completely resolved. ::: ::: Penile superficial venous thrombosis is an uncommon and little known benign genital condition; the largest case series in the literature describes 25 patients.1 The incidence is unknown and the condition may occur more frequently than reported given its benign symptomatology, frequent spontaneous resolution, and the possibly embarrassing nature of the complaint. ::: ::: Penile Mondor’s disease affects sexually active men of any age. No specific cause has been determined, but detailed history frequently reveals prolonged or vigorous sexual intercourse causing stretching and torsion of penile veins. The disease has also been associated with other risk factors for localized and generalized clot formation.1, 2 ::: ::: Conservative treatment is effective in the vast majority of patients.1 For clots persisting longer than 6 weeks or persistent symptoms despite medical management, thrombectomy or superficial penile vein resection may be recommended.1, 2


Stock Market Analysis Based on Artificial Neural Network with Big data Miss


title: Using web security scanners to detect vulnerabilities in web services

abstract: Although web services are becoming business-critical components, they are often deployed with critical software bugs that can be maliciously explored. Web vulnerability scanners allow detecting security vulnerabilities in web services by stressing the service from the point of view of an attacker. However, research and practice show that different scanners have different performance on vulnerabilities detection. In this paper we present an experimental evaluation of security vulnerabilities in 300 publicly available web services. Four well known vulnerability scanners have been used to identify security flaws in web services implementations. A large number of vulnerabilities has been observed, which confirms that many services are deployed without proper security testing. Additionally, the differences in the vulnerabilities detected and the high number of false-positives (35% and 40% in two cases) and low coverage (less than 20% for two of the scanners) observed highlight the limitations of web vulnerability scanners on detecting security vulnerabilities in web services.


Biased Blogging : Linguistic Evidence of Gender Bias in Political Blogs


title: ENGINEERED CEMENTITIOUS COMPOSITES (ECC) – TAILORED COMPOSITES THROUGH MICROMECHANICAL MODELING

abstract: This article provides a brief overview of several classes of fiber reinforced cement based composites and suggests future directions in FRC development. Special focus is placed on micromechanics based design methodology of strain-hardening cement based composites. As example, a particular engineered cementitious composite newly developed at the ACE-MRL at the University of Michigan is described in detail with regard to its design, material composition, processing, and mechanical properties. Three potential applications which utilize the unique properties of such composites are cited in this paper, and future research needs are identified.


title: Design and Sensitivity Analysis of Highly Compact Comparator for Ku-Band Monopulse Radar

abstract: This paper gives the design of a highly compact comparator at a Ku-band frequency and presents analysis results of the comparator for the fabrication inaccuracies. First an unconventional magic-t using a nonstandard waveguide is designed at 15.50 GHz. To reduce the volume occupied by the magic-t, its E-arm (or difference port) is kept parallel to the plane of two inputs of the magic-t instead of perpendicular to them as is done in a convention magic-t. The sum and the difference ports of the above folded magic-t are then matched using inductive windows at 15.50 GHz. Keeping the required location of the outputs of the comparator in mind, four of these matched folded magic-ts are suitably interconnected to design a highly compact comparator. The effects of the fabrication errors in the waveguide and matching elements dimensions on the centre frequency, magnitude and phase response of the comparator are also analyzed and presented.


title: Knowledge-Defined Networking

abstract: The research community has considered in the past the application of Artificial Intelligence (AI) techniques to control and operate networks. A notable example is the Knowledge Plane proposed by D.Clark et al. However, such techniques have not been extensively prototyped or deployed in the field yet. In this paper, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control. We describe a new paradigm that accommodates and exploits SDN, NA and AI, and provide use-cases that illustrate its applicability and benefits. We also present simple experimental results that support, for some relevant use-cases, its feasibility. We refer to this new paradigm as Knowledge-Defined Networking (KDN).


title: Data and Information Visualization Methods and Interactive Mechanisms: A Survey

abstract: availability of enough visualization techniques it can be very confusing to know what and when should be appropriate technique to use in order to convey maximum possible understanding. The basic purpose of visual representation is to efficiently interpret what is insight, as easy as possible. Different available visualization techniques are use for different situation which convey different level of understanding. This document is guide for the young researchers who wants to start work in visualization. The purpose of this piece of document is to collect all visualization techniques with their brief introduction. This paper deals with many definitions and aspects of visualization, how visualization take place i.e. different steps of visualization process, problems that are confront in visualization, categorization of visualization techniques on the bases of distinct perspective, typically known common data and information visualization techniques, basic interactive methods for visualization their advantages and disadvantages, interactivity process, and the scope of visualization up to some extent in different field of research. General Terms Visualization, Visualization techniques, Challenges, Interactive techniques.


title: T-Fuzz: Fuzzing by Program Transformation

abstract: Fuzzing is a simple yet effective approach to discover software bugs utilizing randomly generated inputs. However, it is limited by coverage and cannot find bugs hidden in deep execution paths of the program because the randomly generated inputs fail complex sanity checks, e.g., checks on magic values, checksums, or hashes. To improve coverage, existing approaches rely on imprecise heuristics or complex input mutation techniques (e.g., symbolic execution or taint analysis) to bypass sanity checks. Our novel method tackles coverage from a different angle: by removing sanity checks in the target program. T-Fuzz leverages a coverage-guided fuzzer to generate inputs. Whenever the fuzzer can no longer trigger new code paths, a light-weight, dynamic tracing based technique detects the input checks that the fuzzer-generated inputs fail. These checks are then removed from the target program. Fuzzing then continues on the transformed program, allowing the code protected by the removed checks to be triggered and potential bugs discovered. Fuzzing transformed programs to find bugs poses two challenges: (1) removal of checks leads to over-approximation and false positives, and (2) even for true bugs, the crashing input on the transformed program may not trigger the bug in the original program. As an auxiliary post-processing step, T-Fuzz leverages a symbolic execution-based approach to filter out false positives and reproduce true bugs in the original program. By transforming the program as well as mutating the input, T-Fuzz covers more code and finds more true bugs than any existing technique. We have evaluated T-Fuzz on the DARPA Cyber Grand Challenge dataset, LAVA-M dataset and 4 real-world programs (pngfix, tiffinfo, magick and pdftohtml). For the CGC dataset, T-Fuzz finds bugs in 166 binaries, Driller in 121, and AFL in 105. In addition, found 3 new bugs in previously-fuzzed programs and libraries.


title: A novel Arabic OCR post-processing using rule-based and word context techniques

abstract: Optical character recognition (OCR) is the process of recognizing characters automatically from scanned documents for editing, indexing, searching, and reducing the storage space. The resulted text from the OCR usually does not match the text in the original document. In order to minimize the number of incorrect words in the obtained text, OCR post-processing approaches can be used. Correcting OCR errors is more complicated when we are dealing with the Arabic language because of its complexity such as connected letters, different letters may have the same shape, and the same letter may have different forms. This paper provides a statistical Arabic language model and post-processing techniques based on hybridizing the error model approach with the context approach. The proposed model is language independent and non-constrained with the string length. To the best of our knowledge, this is the first end-to-end OCR post-processing model that is applied to the Arabic language. In order to train the proposed model, we build Arabic OCR context database which contains 9000 images of Arabic text. Also, the evaluation of the OCR post-processing system results is automated using our novel alignment technique which is called fast automatic hashing text alignment. Our experimental results show that the rule-based system improves the word error rate from 24.02% to become 20.26% by using a training data set of 1000 images. On the other hand, after this training, we apply the rule-based system on 500 images as a testing dataset and the word error rate is improved from 14.95% to become 14.53%. The proposed hybrid OCR post-processing system improves the results based on using 1000 training images from a word error rate of 24.02% to become 18.96%. After training the hybrid system, we used 500 images for testing and the results show that the word error rate enhanced from 14.95 to become 14.42. The obtained results show that the proposed hybrid system outperforms the rule-based system.


title: Automatic Extraction of Opinion Propositions and their Holders

abstract: We identify a new task in the ongoing analysis of opinions: finding propositional opinions, sentential complements which for many verbs contain the actual opinion, rather than full opinion sentences. We propose an extension of semantic parsing techniques, coupled with additional lexical and syntactic features, that can produce labels for propositional opinions as opposed to other syntactic constituents. We describe the annotation of a small corpus of 5,139 sentences with propositional opinion information, and use this corpus to evaluate our methods. We also present results that indicate that the proposed methods can be extended to the related task of identifying opinion holders and associating them with propositional


title: Acoustic event detection in real life recordings

abstract: This paper presents a system for acoustic event detection in recordings from real life environments. The events are modeled using a network of hidden Markov models; their size and topology is chosen based on a study of isolated events recognition. We also studied the effect of ambient background noise on event classification performance. On real life recordings, we tested recognition of isolated sound events and event detection. For event detection, the system performs recognition and temporal positioning of a sequence of events. An accuracy of 24% was obtained in classifying isolated sound events into 61 classes. This corresponds to the accuracy of classifying between 61 events when mixed with ambient background noise at 0dB signal-to-noise ratio. In event detection, the system is capable of recognizing almost one third of the events, and the temporal positioning of the events is not correct for 84% of the time.


title: Apriori- A Big Data Analysis in Education

abstract: Apriori is designed to operate on databases containing transactions (for example, collections of items bought by customers, or details of a website frequentation). Other algorithms are designed for finding association rules in data having no transactions , or having no timestamps (DNA sequencing). Apriori uses a "bottom up" approach, where frequent subsets are extended one item at a time (a step known as candidate generation), and groups of candidates are tested against the data. The algorithm terminates when no further successful extensions are found.


title: Findings of the 2013 Workshop on Statistical Machine Translation

abstract: We present the results of the WMT13 shared tasks, which included a translation task, a task for run-time estimation of machine translation quality, and an unofficial metrics task. This year, 143 machine translation systems were submitted to the ten translation tasks from 23 institutions. An additional 6 anonymized systems were included, and were then evaluated both automatically and manually, in our largest manual evaluation to date. The quality estimation task had four subtasks, with a total of 14 teams, submitting 55 entries.


title: Hadoop-GIS: A High Performance Spatial Data Warehousing System over MapReduce

abstract: Support of high performance queries on large volumes of spatial data becomes increasingly important in many application domains, including geospatial problems in numerous fields, location based services, and emerging scientific applications that are increasingly data- and compute-intensive. The emergence of massive scale spatial data is due to the proliferation of cost effective and ubiquitous positioning technologies, development of high resolution imaging technologies, and contribution from a large number of community users. There are two major challenges for managing and querying massive spatial data to support spatial queries: the explosion of spatial data, and the high computational complexity of spatial queries. In this paper, we present Hadoop-GIS - a scalable and high performance spatial data warehousing system for running large scale spatial queries on Hadoop. Hadoop-GIS supports multiple types of spatial queries on MapReduce through spatial partitioning, customizable spatial query engine RESQUE, implicit parallel spatial query execution on MapReduce, and effective methods for amending query results through handling boundary objects. Hadoop-GIS utilizes global partition indexing and customizable on demand local spatial indexing to achieve efficient query processing. Hadoop-GIS is integrated into Hive to support declarative spatial queries with an integrated architecture. Our experiments have demonstrated the high efficiency of Hadoop-GIS on query response and high scalability to run on commodity clusters. Our comparative experiments have showed that performance of Hadoop-GIS is on par with parallel SDBMS and outperforms SDBMS for compute-intensive queries. Hadoop-GIS is available as a set of library for processing spatial queries, and as an integrated software package in Hive.


title: Mathematical programming for piecewise linear regression analysis

abstract: A novel piece-wise linear regression method has been proposed in this work.The method partitions samples into multiple regions from a single attribute.Each region is fitted with a linear regression function.An optimisation model is proposed to decide break-points and regression functions.Benchmark examples have been used to demonstrate its efficiency. In data mining, regression analysis is a computational tool that predicts continuous output variables from a number of independent input variables, by approximating their complex inner relationship. A large number of methods have been successfully proposed, based on various methodologies, including linear regression, support vector regression, neural network, piece-wise regression, etc. In terms of piece-wise regression, the existing methods in literature are usually restricted to problems of very small scale, due to their inherent non-linear nature. In this work, a more efficient piece-wise linear regression method is introduced based on a novel integer linear programming formulation. The proposed method partitions one input variable into multiple mutually exclusive segments, and fits one multivariate linear regression function per segment to minimise the total absolute error. Assuming both the single partition feature and the number of regions are known, the mixed integer linear model is proposed to simultaneously determine the locations of multiple break-points and regression coefficients for each segment. Furthermore, an efficient heuristic procedure is presented to identify the key partition feature and final number of break-points. 7 real world problems covering several application domains have been used to demonstrate the efficiency of our proposed method. It is shown that our proposed piece-wise regression method can be solved to global optimality for datasets of thousands samples, which also consistently achieves higher prediction accuracy than a number of state-of-the-art regression methods. Another advantage of the proposed method is that the learned model can be conveniently expressed as a small number of if-then rules that are easily interpretable. Overall, this work proposes an efficient rule-based multivariate regression method based on piece-wise functions and achieves better prediction performance than state-of-the-arts approaches. This novel method can benefit expert systems in various applications by automatically acquiring knowledge from databases to improve the quality of knowledge base.


title: Fine-Grained Control-Flow Integrity Through Binary Hardening

abstract: Applications written in low-level languages without type or memory safety are prone to memory corruption. Attackers gain code execution capabilities through memory corruption despite all currently deployed defenses. Control-Flow Integrity CFI is a promising security property that restricts indirect control-flow transfers to a static set of well-known locations. ::: ::: We present Lockdown, a modular, fine-grained CFI policy that protects binary-only applications and libraries without requiring source-code. Lockdown adaptively discovers the control-flow graph of a running process based on the executed code. The sandbox component of Lockdown restricts interactions between different shared objects to imported and exported functions by enforcing fine-grained CFI checks using information from a trusted dynamic loader. A shadow stack enforces precise integrity for function returns. Our prototype implementation shows that Lockdown results in low performance overhead and a security analysis discusses any remaining gadgets.


title: Collapse Behavior of Steel Columns as Part of Steel Frame Buildings: Experiments and Numerical Models

abstract: Earthquake-induced collapse risk assessment of steel frame buildings requires the use of deterioration models that simulate instabilities that cause strength and stiffness deterioration of structural steel components. In the case of steel columns in addition to cyclic deterioration in flexural strength, such models should capture the axial load – bending interaction, the axial contraction as well as the axial strength deterioration after the formation of local buckling within the column cross-section. Based on the available literature, experimental data that characterizes the hysteretic behavior of steel columns at large deformations is scarce. Therefore, the validation and further refinement of new or available steel column deterioration models becomes challenging. This paper discusses the main findings of an extensive experimental program that characterized the hysteretic behavior of wide-flange and hollow structural steel (HSS) columns. The effect of various types of lateral loading protocols on the hysteretic behavior of steel columns was examined with nominally identical specimens. It is shown that a primary failure mode observed in steel columns is the axial shortening. The amount of axial shortening can be considerably different in end columns compared to that observed in interior columns within the same story of a steel moment-resisting frame (MRF). Symmetric loading protocols that are typically used for experimental testing of structural components provide insufficient information for modeling the cyclic deterioration in flexural strength and stiffness of steel columns near collapse. A fiberbased deterioration model is developed for HSS columns that is able to simulate the cyclic hardening prior to the formation of local buckling and the steel column post-buckling behavior including axial shortening. The proposed model is validated with steel column test data that became available from the experimental program.


title: Attribute2Image: Conditional Image Generation from Visual Attributes

abstract: This paper investigates a problem of generating images from visual attributes. Given the prevalent research for image recognition, the conditional image generation problem is relatively under-explored due to the challenges of learning a good generative model and handling rendering uncertainties in images. To address this, we propose a variety of attribute-conditioned deep variational auto-encoders that enjoy both effective representation learning and Bayesian modeling, from which images can be generated from specified attributes and sampled latent factors. We experiment with natural face images and demonstrate that the proposed models are capable of generating realistic faces with diverse appearance. We further evaluate the proposed models by performing attribute-conditioned image progression, transfer and retrieval. In particular, our generation method achieves superior performance in the retrieval experiment against traditional nearest-neighbor-based methods both qualitatively and quantitatively.


title: A 5.7 GHz GaN transformer-based Doherty Power Amplifier for 40-MHz LTE-A applications

abstract: In this work, a hybrid 5.7 GHz gallium-nitride high electron mobility transistor (GaN HEMT) transformer-based Doherty power amplifier (TBD PA) to support 40-MHz bandwidth (BW) long term evolution-advanced (LTE-A) applications is presented. A series-combining transformer is used to realize Doherty action instead of using conventional quarter-wave length impedance inverter to cover wide bandwidth modulation signals. Also the AM-PM distortion generated by the Doherty action is compensated with a varactor-based linearizer to gain better linearity. The proposed hybrid TBD PA is implemented using Cree's 6 W GaN HEMT bare die (CGHV1J006) and Semco's 7 layer PCB. The TBD PA is tested at 5.7 GHz carrier frequency and showing 43% peak PAE and 39.3 dBm maximum output power with continuous wave (CW) signal, −33 dBc CA E-UTRA ACLR at 31.8 dBm output power with 40-MHz BW LTE-A Signal by using the fabricated linearizer.


title: Design of multifunctional autonomous car using ultrasonic and infrared sensors

abstract: The daily routine problems that common man faces on roads while commuting are becoming a serious problem with each passing day. People get late and meet accidents. The model of autonomous car presented in this research paper aims to solve these issues by taking humans off the wheels, so that they do not have to drive anymore and the risk of accidents, getting late and traffic congestions can be reduced to a minimum. This car is able to follow the track, overtake other cars, detect obstacles, take sharp bends and turns, follow traffic signals and turn on its lights under low light conditions. Circuit diagrams for performing all these functions have been presented and the mechanical model of the car has also been shown in the paper, which is practically implemented and successfully run by the authors.


title: DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

abstract: This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features, which require prior knowledge of sleep analysis. Only a few of them encode the temporal information, such as transition rules, which is important for identifying the next sleep stages, into the extracted features. In the proposed model, we utilize convolutional neural networks to extract time-invariant features, and bidirectional-long short-term memory to learn transition rules among sleep stages automatically from EEG epochs. We implement a two-step training algorithm to train our model efficiently. We evaluated our model using different single-channel EEGs (F4-EOG (left), Fpz-Cz, and Pz-Oz) from two public sleep data sets, that have different properties (e.g., sampling rate) and scoring standards (AASM and R&K). The results showed that our model achieved similar overall accuracy and macro F1-score (MASS: 86.2%−81.7, Sleep-EDF: 82.0%−76.9) compared with the state-of-the-art methods (MASS: 85.9%−80.5, Sleep-EDF: 78.9%−73.7) on both data sets. This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different data sets without utilizing any hand-engineered features.


Speculative Visualization : A New Rhetoric for Communicating Public Concerns


title: Manipulating the soil microbiome to increase soil health and plant fertility

abstract: A variety of soil factors are known to increase nutrient availability and plant productivity. The most influential might be the organisms comprising the soil microbial community of the rhizosphere, which is the soil surrounding the roots of plants where complex interactions occur between the roots, soil, and microorganisms. Root exudates act as substrates and signaling molecules for microbes creating a complex and interwoven relationship between plants and the microbiome. While individual microorganisms such as endophytes, symbionts, pathogens, and plant growth promoting rhizobacteria are increasingly featured in the literature, the larger community of soil microorganisms, or soil microbiome, may have more far-reaching effects. Each microorganism functions in coordination with the overall soil microbiome to influence plant health and crop productivity. Increasing evidence indicates that plants can shape the soil microbiome through the secretion of root exudates. The molecular communication fluctuates according to the plant development stage, proximity to neighboring species, management techniques, and many other factors. This review seeks to summarize the current knowledge on this topic.


title: Deep joint demosaicking and denoising

abstract: Demosaicking and denoising are the key first stages of the digital imaging pipeline but they are also a severely ill-posed problem that infers three color values per pixel from a single noisy measurement. Earlier methods rely on hand-crafted filters or priors and still exhibit disturbing visual artifacts in hard cases such as moire or thin edges. We introduce a new data-driven approach for these challenges: we train a deep neural network on a large corpus of images instead of using hand-tuned filters. While deep learning has shown great success, its naive application using existing training datasets does not give satisfactory results for our problem because these datasets lack hard cases. To create a better training set, we present metrics to identify difficult patches and techniques for mining community photographs for such patches. Our experiments show that this network and training procedure outperform state-of-the-art both on noisy and noise-free data. Furthermore, our algorithm is an order of magnitude faster than the previous best performing techniques.


title: 12 Hybrid Web Recommender Systems

abstract: Adaptive web sites may offer automated recommendations generated through any number of well-studied techniques including collaborative, content-based and knowledge-based recommendation. Each of these techniques has its own strengths and weaknesses. In search of better performance, researchers have combined recommendation techniques to build hybrid recommender systems. This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies. Implementations of 41 hybrids including some novel combinations are examined and compared. The study finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths.


title: Real-time face pose estimation from single range images

abstract: We present a real-time algorithm to estimate the 3D pose of a previously unseen face from a single range image. Based on a novel shape signature to identify noses in range images, we generate candidates for their positions, and then generate and evaluate many pose hypotheses in parallel using modern graphics processing units (GPUs). We developed a novel error function that compares the input range image to precomputed pose images of an average face model. The algorithm is robust to large pose variations of plusmn90deg yaw, plusmn45deg pitch and plusmn30deg roll rotation, facial expression, partial occlusion, and works for multiple faces in the field of view. It correctly estimates 97.8% of the poses within yaw and pitch error of 15deg at 55.8 fps. To evaluate the algorithm, we built a database of range images with large pose variations and developed a method for automatic ground truth annotation.


title: Dual Learning for Visual Question Generation

abstract: Recently, automatic answering of visually related questions (VQA) has gained a lot of attention in computer vision community. However, there is little work on automatically generating questions for images (VQG). Actually, VQG itself closes the loop to question-answering and diverse questions, which is useful to the research on VQA. Motivated by the assumption that learning to answer questions may boost the question generation, in this paper, we introduce the VQA task as the complementary of our primary VQG task, and propose a novel model that uses dual learning framework to jointly learn the dual tasks. In the framework, we devise an agent for VQG and VQA with pre-trained models respectively, and the learning tasks of the two agents form a closed loop, whose objectives are optimized together to guide each other via a reinforcement learning process. Specific rewards for each task are designed to update the models of the agents with policy gradient method. The relation of these two tasks can be exploited to further improve the performance of the primary VQG task. Extensive experiments conducted on two large-scale datasets show that the proposed method is capable to generate grounded visual questions of sufficient coverage and outperforms previous VQG methods on standard measures.


title: Online Data Organizer: Micro-Video Categorization by Structure-Guided Multimodal Dictionary Learning

abstract: Micro-videos have rapidly become one of the most dominant trends in the era of social media. Accordingly, how to organize them draws our attention. Distinct from the traditional long videos that would have multi-site scenes and tolerate the hysteresis, a micro-video: 1) usually records contents at one specific venue within a few seconds. The venues are structured hierarchically regarding their category granularity. This motivates us to organize the micro-videos via their venue structure. 2) timely circulates over social networks. Thus, the timeliness of micro-videos desires effective online processing. However, only 1.22% of micro-videos are labeled with venue information when uploaded at the mobile end. To address this problem, we present a framework to organize the micro-videos online. In particular, we first build a structure-guided multi-modal dictionary learning model to learn the concept-level micro-video representation by jointly considering their venue structure and modality relatedness. We then develop an online learning algorithm to incrementally and efficiently strengthen our model, as well as categorize the micro-videos into a tree structure. Extensive experiments on a real-world data set validate our model well. In addition, we have released the codes to facilitate the research in the community.


title: PSXO: population-wide semantic crossover

abstract: Since its introduction, Geometric Semantic Genetic Programming (GSGP) has been the inspiration to ideas on how to reach optimal solutions efficiently. Among these, in 2016 Pawlak has shown how to analytically construct optimal programs by means of a linear combination of a set of random programs. Given the simplicity and excellent results of this method (LC) when compared to GSGP, the author concluded that GSGP is "overkill". However, LC has limitations, and it was tested only on simple benchmarks. In this paper, we introduce a new method, Population-Wide Semantic Crossover (PSXO), also based on linear combinations of random programs, that overcomes these limitations. We test the first variant (Inv) on a diverse set of complex real-life problems, comparing it to LC, GSGP and standard GP. We realize that, on the studied problems, both LC and Inv are outperformed by GSGP, and sometimes also by standard GP. This leads us to the conclusion that GSGP is not overkill. We also introduce a second variant (GPinv) that integrates evolution with the approximation of optimal programs by means of linear combinations. GPinv outperforms both LC and Inv on unseen test data for the studied problems.


title: Hashcash - Amortizable Publicly Auditable Cost-Functions

abstract: We present a distributed efficiently amortizable CPU cost-function with no trap–door. The absence of a trap–door allows us to avoid needing to trust any party. Applications for such cost-functions are in distributed document popularity estimation, and metering of web advertising. None of the servers involved have any advantage over users in computing the cost function. The amortized token has a small fixed sized representation independent of the amortized value. The valuation function is efficient. Limits can be placed on the resources which can be expended in computing the cost-function to prevent the user inflating the value of his contribution by using more CPU time than the expected token value. The amortized part of the cost result can be blinded so that clients can not obtain service but avoid contributing to the amortization by expending more resources than the expected token value. Interactive and non-interactive variants of the cost-function can be constructed which can be used in situations where the server can issue a challenge (connection oriented interactive protocol), and where it can’t (where the communication is store–and–forward, or packet oriented) respectively.


title: Analog man-in-the-middle attack against link-based packet source identification

abstract: A novel attack model is proposed against the existing wireless link-based source identification, which classifies packet sources according to the physical-layer link signatures. A link signature is believed to be a more reliable indicator than an IP or MAC address for identifying packet source, as it is generally harder to modify/forge. It is therefore expected to be a future authentication against impersonation and DoS attacks. However, if an attacker is equipped with the same capability/hardware as the authenticator to process physical-layer signals, a link signature can be easily manipulated by any nearby wireless device during the training phase. Based on this finding, we propose an attack model, called the analog man-in-the-middle (AMITM) attack, which utilizes the latest full-duplex relay technology to inject semi-controlled link signatures into authorized packets and reproduce the injected signature in the fabricated packets. Our experimental evaluation shows that with a proper parameter setting, 90% of fabricated packets are classified as those sent from an authorized transmitter. A countermeasure against this new attack is also proposed for the authenticator to inject link-signature noise by the same attack methodology.


title: Effectiveness of a computational thinking (CS0) course on student analytical skills

abstract: : In this paper, we describe the content and evaluation of a Computational Thinking (CS0) course developed to improve the analytical problem solving of students participating in the course. The course is targeted to students who are mathematically under-prepared to enter our introductory programming sequence; however, it has recently been included in the University's general education curriculum so that students majoring in any discipline may take the course. Using the Whimbey Analytical Skills Inventory (WASI) students in the CS0 class, along with students in an analogous level engineering class (FESP), were tested at the beginning of the course and again at the end, using different versions of the test. The improvement in scores was statistically significant when measured by both the student t-test and the Cohen d (effect size) for CS0 students but not for the FESP students, providing support that the course does, in fact, increase student analytical problem solving skills. Courses in Computational Thinking have demonstrated success in many schools; however, this research demonstrates its effectiveness in improving analytical skills in majors as well as non-majors.


title: High-efficiency and flat-gain Doherty type transmitter using a 180-degree hybrid-combiner

abstract: It is well-known that the isolation provided by a hybrid combiner defeats the active load-modulation mechanism between the carrier and peaking power amplifiers in a Doherty transmitter. In this paper, a hybrid combiner is used in combination with a lattice network to combine the carrier and peaking amplifiers in order to manage load modulation between them. Both amplifiers have a driver stage. The driver of the peaking amplifier is tuned and biased so that the efficiency at 6-dB backoff power is peaked. The prototype exhibits an efficiency at peak power of 80.5% and an efficiency at 6-dB back-off of 70.5%. Alternatively, the transmitter can be optimized for flat AM-AM characteristics scarifying efficiency enhancement at back-off power.


title: A 20.7% locking range W-band fully integrated injection-locked oscillator using 90 nm CMOS technology

abstract: A 20.7% locking range W-band fully integrated injection-locked oscillator (ILO) using 90 nm CMOS technology is presented in this paper. The proposed ILO is designed using a ring-based triple-push topology. The free-running oscillation frequency of the ILO is 97.6 GHz. When the input subharmonic number is 3, the ILO demonstrates a locking range from 88.1 to 108.5 GHz without bias tuning, a minimum conversion loss of 14.6 dB, and an output power flatness of within 2 dB. When the input subharmonic number is 6, the locking range is from 96.1 to 98.4 GHz and the minimum conversion loss is 17.2 dB. The dc power consumption is 55.4 mW from 1.2-V dc supply voltage. The chip size is 0.733 × 0.492 mm2. As compared to the previously reported ILOs in the MMW band, our proposed ILO has the widest locking range and good power flatness.


title: Why change programs don't produce change.

abstract: Non-inflammable, high molecular weight polytriazines are obtained by condensing less than 3 mols of polyfunctional (amino)phenols with 1 mol phosphorus (oxy) trihalide in the presence of a base, subsequently reacting the still free primary amino or phenolic hydroxyl groups with cyanogen halide in the presence of a base to form cyanamide or organic ester groups and polymerizing thereafter the reaction product at a temperature of up to 350 DEG C, optionally in the presence of polymerization promoters. The polytriazine may be used as plastics and lacquers.


title: Enhancing wifi-based localization with visual clues

abstract: Indoor localization is of great importance to a wide range of applications in the era of mobile computing. Current mainstream solutions rely on Received Signal Strength (RSS) of wireless signals as fingerprints to distinguish and infer locations. However, those methods suffer from fingerprint ambiguity that roots in multipath fading and temporal dynamics of wireless signals. Though pioneer efforts have resorted to motion-assisted or peer-assisted localization, they neither work in real time nor work without the help of peer users, which introduces extra costs and constraints, and thus degrades their practicality. To get over these limitations, we propose Argus, an image-assisted localization system for mobile devices. The basic idea of Argus is to extract geometric constraints from crowdsourced photos, and to reduce fingerprint ambiguity by mapping the constraints jointly against the fingerprint space. We devise techniques for photo selection, geometric constraint extraction, joint location estimation, and build a prototype that runs on commodity phones. Extensive experiments show that Argus triples the localization accuracy of classic RSS-based method, in time no longer than normal WiFi scanning, with negligible energy consumption.


title: Functional architecture of basal ganglia circuits: neural substrates of parallel processing

abstract: Abstract Concepts of basal ganglia organization have changed markedly over the past decade, due to significant advances in our understanding of the anatomy, physiology and pharmacology of these structures. Independent evidence from each of these fields has reinforced a growing perception that the functional architecture of the basal ganglia is essentially parallel in nature, regardless of the perspective from which these structures are viewed. This represents a significant departure from earlier concepts of basal ganglia organization, which generally emphasized the serial aspects of their connectivity. Current evidence suggests that the basal ganglia are organized into several structurally and functionally distinct ‘circuits' that link cortex, basal ganglia and thalamus, with each circuit focused on a different portion of the frontal lobe. In this review, Garrett Alexander and Michael Crutcher, using the basal ganglia ‘motor' circuit as the principal example, discuss recent evidence indicating that a parallel functional architecture may also be characteristic of the organization within each individual circuit.


title: VQA: vertica query analyzer

abstract: Database query monitoring tools collect performance metrics, such as memory and cpu usage, while a query is executing and make them available through log files or system tables. The metrics can be used to understand and diagnose query performance issues. However, analytic queries over big data presents new challenges for query monitoring tools. A long-running query may generate tens of thousands of values so simply reporting the metrics may overwhelm the user. Second, analytic queries may be written by database novices who have trouble interpreting the metrics. Third, analytic queries may access data or processing outside the database through user-defined functions and connectors. The impact of these on query performance must be understood. Vertica Query Analyzer (VQA) is a query monitoring tool to address these challenges. VQA is both a useful tool and a research platform for query analytics. It presents query performance metrics through a variety of views and granularities. In addition, it analyzes the metrics for typical performance problems and suggests corrective actions. We demonstrate VQA using TPC-DS queries which have a wide range of query duration and complexity.


title: Answering Questions with Complex Semantic Constraints on Open Knowledge Bases

abstract: A knowledge-based question-answering system (KB-QA) is one that answers natural language questions with information stored in a large-scale knowledge base (KB). Existing KB-QA systems are either powered by curated KBs in which factual knowledge is encoded in entities and relations with well-structured schemas, or by open KBs, which contain assertions represented in the form of triples (e.g., subject; relation phrase; argument). We show that both approaches fall short in answering questions with complex prepositional or adverbial constraints. We propose using n-tuple assertions, which are assertions with an arbitrary number of arguments, and n-tuple open KB (nOKB), which is an open knowledge base of n-tuple assertions. We present TAQA, a novel KB-QA system that is based on an nOKB and illustrate via experiments how TAQA can effectively answer complex questions with rich semantic constraints. Our work also results in a new open KB containing 120M n-tuple assertions and a collection of 300 labeled complex questions, which is made publicly available for further research.


title: Evaluating boosting algorithms to classify rare classes: comparison and improvements

abstract: Classification of rare events has many important data mining applications. Boosting is a promising meta-technique that improves the classification performance of any weak classifier. So far, no systematic study has been conducted to evaluate how boosting performs for the task of mining rare classes. The authors evaluate three existing categories of boosting algorithms from the single viewpoint of how they update the example weights in each iteration, and discuss their possible effect on recall and precision of the rare class. We propose enhanced algorithms in two of the categories, and justify their choice of weight updating parameters theoretically. Using some specially designed synthetic datasets, we compare the capability of all the algorithms from the rare class perspective. The results support our qualitative analysis, and also indicate that our enhancements bring an extra capability for achieving better balance between recall and precision in mining rare classes.


title: Characterization of a Voltage Glitch Attack Detector for Secure Devices

abstract: Glitch attacks are often used to inject temporal faults into secure devices and devices manipulating or holding sensitive data. The main countermeasure against these kinds of attacks involves the correct design of a built-in voltage regulator. Another important countermeasure is a glitch detector circuit. Common glitch detection approaches, such as directly monitoring the power rails, can potentially find it hard to detect fast glitches, as these become harder to differentiate from noise. This paper presents the silicon test results of a voltage glitch detector circuit which, instead of monitoring the power rails, monitors the effect of a glitch on a sensitive circuit, hence reducing the risk of detecting noise as glitches.