title: Ultrasonic pulse velocity test of concrete properties as specified in various standards

abstract: Abstract This is a review paper comparing critically eight standards. Methods for the determination of longitudinal pulse velocity and assessment of concrete properties by ultrasonic pulse velocity, as recommended by standards of the UK, USA, Germany, Russia, Slovakia, Hungary, and RILEM, are evaluated. It is shown that, despite the common basis of the measurement of ultrasonic longitudinal wave velocity, there are differences among the procedures as recommended by different nations. For instance, the most frequent use of pulse velocity, the assessment of concrete strength, is discussed only briefly in ASTM and in DIN; the other standards, especially the Russian and the Slovak standards, provide much more detail and description. The assessments of other concrete properties are also compared: dynamic elastic constants, defects inside concrete, concrete uniformity, and changes in concrete properties with time. The inherent uncertainty in the various assessments is so high that the assessments are not suitable for many practical purposes. The common weakness of the analyzed standards is that they do not warn the user strongly enough about the uncertainties. For instance, the assessment procedures could be rated according to their reliability.


title: Distributed delayed stochastic optimization

abstract: We analyze the convergence of gradient-based optimization algorithms whose updates depend on delayed stochastic gradient information. The main application of our results is to the development of distributed minimization algorithms where a master node performs parameter updates while worker nodes compute stochastic gradients based on local information in parallel, which may give rise to delays due to asynchrony. Our main contribution is to show that for smooth stochastic problems, the delays are asymptotically negligible. In application to distributed optimization, we show n-node architectures whose optimization error in stochastic problems—in spite of asynchronous delays—scales asymptotically as O(1/√nT), which is known to be optimal even in the absence of delays.


title: CaRINA Intelligent Robotic Car: Architectural design and applications

abstract: Abstract This paper presents the development of two outdoor intelligent vehicles platforms named CaRINA I and CaRINA II, their system architecture, simulation tools, and control modules. It also describes the development of the intelligent control system modules allowing the mobile robots and vehicles to navigate autonomously in controlled urban environments. Research work has been carried out on tele-operation, driver assistance systems, and autonomous navigation using the vehicles as platforms to experiments and validation. Our robotic platforms include mechanical adaptations and the development of an embedded software architecture. This paper addresses the design, sensing, decision making, and acting infrastructure and several experimental tests that have been carried out to evaluate both platforms and proposed algorithms. The main contributions of this work is the proposed architecture, that is modular and flexible, allowing it to be instantiated into different robotic platforms and applications. The communication and security aspects are also investigated.


title: Aging in place: fall detection and localization in a distributed smart camera network

abstract: This paper presents the design, implementation and evaluation of a distributed network of smart cameras whose function is to detect and localize falls, an important application in elderly living environments. A network of overlapping smart cameras uses a decentralized procedure for computing inter-image homographies that allows the location of a fall to be reported in 2D world coordinates by calibrating only one camera. Also, we propose a joint routing and homography transformation scheme for multi-hop localization that yields localization errors of less than 2 feet using very low resolution images. Our goal is to demonstrate that such a distributed low-power system can perform adequately in this and related applications. A prototype implementation is given for low-power Agilent/UCLA Cyclops cameras running on the Crossbow MICAz platform. We demonstrate the effectiveness of the fall detection as well as the precision of the localization using a simulation of our sample implementation.


title: Learn Convolutional Neural Network for Face Anti-Spoofing

abstract: Though having achieved some progresses, the hand-crafted texture features, e.g., LBP [23], LBP-TOP [11] are still unable to capture the most discriminative cues between genuine and fake faces. In this paper, instead of designing feature by ourselves, we rely on the deep convolutional neural network (CNN) to learn features of high discriminative ability in a supervised manner. Combined with some data pre-processing, the face anti-spoofing performance improves drastically. In the experiments, over 70% relative decrease of Half Total Error Rate (HTER) is achieved on two challenging datasets, CASIA [36] and REPLAY-ATTACK [7] compared with the state-of-the-art. Meanwhile, the experimental results from inter-tests between two datasets indicates CNN can obtain features with better generalization ability. Moreover, the nets trained using combined data from two datasets have less biases between two datasets.


title: Algorithmic Statistics

abstract: While Kolmogorov (1965, 1983) complexity is the accepted absolute measure of information content of an individual finite object, a similarly absolute notion is needed for the relation between an individual data sample and an individual model summarizing the information in the data, for example, a finite set (or probability distribution) where the data sample typically came from. The statistical theory based on such relations between individual objects can be called algorithmic statistics, in contrast to classical statistical theory that deals with relations between probabilistic ensembles. We develop the algorithmic theory of statistic, sufficient statistic, and minimal sufficient statistic. This theory is based on two-part codes consisting of the code for the statistic (the model summarizing the regularity, the meaningful information, in the data) and the model-to-data code. In contrast to the situation in probabilistic statistical theory, the algorithmic relation of (minimal) sufficiency is an absolute relation between the individual model and the individual data sample. We distinguish implicit and explicit descriptions of the models. We give characterizations of algorithmic (Kolmogorov) minimal sufficient statistic for all data samples for both description modes-in the explicit mode under some constraints. We also strengthen and elaborate on earlier results for the "Kolmogorov structure function" and "absolutely nonstochastic objects"-those objects for which the simplest models that summarize their relevant information (minimal sufficient statistics) are at least as complex as the objects themselves. We demonstrate a close relation between the probabilistic notions and the algorithmic ones: (i) in both cases there is an "information non-increase" law; (ii) it is shown that a function is a probabilistic sufficient statistic iff it is with high probability (in an appropriate sense) an algorithmic sufficient statistic.


title: Electrically Small Resonators for Planar Metamaterial, Microwave Circuit and Antenna Design: A Comparative Analysis

abstract: Planar metamaterials and many microwave circuits and antennas are designed by means of resonators with dimensions much smaller than the wavelength at their resonance frequency. There are many types of such electrically small resonators, and the main purpose of this paper is to compare them as building blocks for the implementation of microwave components. Aspects such as resonator size, bandwidth, their circuit models when they are coupled to transmission lines (as is usually required), as well as key applications, will be considered.


title: Correlation Between Insulin Resistance and Obesity in Normoglycemic Obese Syrian Subjects

abstract: Insulin resistance is a pathophysiologic link to development of several metabolic disorders in obese subjects. The aim of this study is to evaluate the association between obesity and insulin resistance using HOMA-IR index in normoglycemic obese subjects and to compare the results with the result of healthy non obese subjects. The importance is to identify the subjects in risk for developing preventive strategies before the manifest disease as type 2 diabetes. A total 54 obese subjects (54 men and 52 women ) and 45 lean subjects ( 25 men and 20 women) were enrolled to evaluate obesity by body mass index BMI, waist circumference, waist to hip ratio WHR and insulin resistance measured by HOMA-IR index. The results demonstrate that HOMA-IR is significantly higher in obese groups, males and females, compared with the control group of the same sexes and was positively correlated with the elevated values of body mass index, waist circumference, and waist to hip ratio in the tow sex.


title: Phishing Websites Detection based on Phishing Characteristics in the Webpage Source Code

abstract: World Wide Web Consortium (W3C) is the international standards organization for the World Wide Web (www). It develops standards, specifications and recommendations to enhance the interoperability and maximize consensus about the content of the web and define major parts of what makes the World Wide Web work. Phishing is a type of Internet scams that seeks to get a user‟s credentials by fraud websites, such as passwords, credit card numbers, bank account details and other sensitive information. There are some characteristics in webpage source code that distinguish phishing websites from legitimate websites and violate the w3c standards, so we can detect the phishing attacks by check the webpage and search for these characteristics in the source code file if it exists or not. In this paper, we propose a phishing detection approach based on checking the webpage source code, we extract some phishing characteristics out of the W3C standards to evaluate the security of the websites, and check each character in the webpage source code, if we find a phishing character, we will decrease from the initial secure weight. Finally we calculate the security percentage based on the final weight, the high percentage indicates secure website and others indicates the website is most likely to be a phishing website. We check two webpage source codes for legitimate and phishing websites and compare the security percentages between them, we find the phishing website is less security percentage than the legitimate website; our approach can detect the phishing website based on checking phishing characteristics in the webpage source code.


title: Current concepts in lip augmentation.

abstract: Facial rejuvenation, particularly lip augmentation, has gained widespread popularity. An appreciation of perioral anatomy as well as the structural characteristics that define the aging face is critical to achieve optimal patient outcomes. Although techniques and technology evolve continuously, hyaluronic acid (HA) dermal fillers continue to dominate aesthetic practice. A combination approach including neurotoxin and volume restoration demonstrates superior results in select settings.


title: A Supervised Clustering Method for Text Classification

abstract: This paper describes a supervised three-tier clustering method for classifying students' essays of qualitative physics in the Why2-Atlas tutoring system. Our main purpose of categorizing text in our tutoring system is to map the students' essay statements into principles and misconceptions of physics. A simple ‘bag-of-words' representation using a naive-bayes algorithm to categorize text was unsatisfactory for our purposes of analyses as it exhibited many misclassifications because of the relatedness of the concepts themselves and its inability to handle misconceptions. Hence, we investigate the performance of the k-nearest neighborhood algorithm coupled with clusters of physics concepts on classifying students' essays. We use a three-tier tagging schemata (cluster, sub-cluster and class) for each document and found that this kind of supervised hierarchical clustering leads to a better understanding of the student's essay.


title: Numerical study on the along-track interferometric radar imaging mechanism of oceanic surface currents

abstract: The phase information in along-track interferometric synthetic aperture radar (along-track INSAR, ATI) images is a measure of the Doppler shift of the backscattered signal and thus of the line-of-sight velocity of the scatterers. It can be exploited for oceanic surface current measurements from aircraft or spacecraft. However, as already discussed in previous publications, the mean Doppler frequency of the radar backscatter from the ocean is not exclusively determined by the mean surface current, but it includes contributions associated with surface wave motion. The authors present an efficient new model for the simulation of Doppler spectra and ATI signatures. The model is based on Bragg scattering theory in a composite surface model approach. They show that resulting Doppler spectra are consistent with predictions of an established model based on fundamental electrodynamic expressions, while computation times are reduced by more than one order of magnitude. This can be a key advantage with regard to operational applications of ATI. Based on model calculations for two simple current fields and various wind conditions and radar configurations, they study theoretical possibilities and limitations of oceanic current measurements by ATI. They find that best results can be expected from ATI systems operated at high microwave frequencies like 10 GHz (X band), high incidence angles like 60/spl deg/, low platform altitude/speed ratios, and vertical (VV) polarization. The ATI time lag should be chosen long enough to obtain measurable phase differences, but much shorter than the decorrelation time of the backscattered field.


title: Expanding cellular coverage via cell-edge deployment in heterogeneous networks: spectral efficiency and backhaul power consumption perspectives

abstract: Heterogeneous small-cell networks (HetNets) are considered to be a standard part of future mobile networks where operator/consumer deployed small-cells, such as femtocells, relays, and distributed antennas (DAs), complement the existing macrocell infrastructure. This article proposes the need-oriented deployment of smallcells and device-to-device (D2D) communication around the edge of the macrocell such that the small-cell base stations (SBSs) and D2D communication serve the cell-edge mobile users, thereby expanding the network coverage and capacity. In this context, we present competitive network configurations, namely, femto-on-edge, DA-onedge, relay-on-edge, and D2D-communication on- edge, where femto base stations, DA elements, relay base stations, and D2D communication, respectively, are deployed around the edge of the macrocell. The proposed deployments ensure performance gains in the network in terms of spectral efficiency and power consumption by facilitating the cell-edge mobile users with small-cells and D2D communication. In order to calibrate the impact of power consumption on system performance and network topology, this article discusses the detailed breakdown of the end-to-end power consumption, which includes backhaul, access, and aggregation network power consumptions. Several comparative simulation results quantify the improvements in spectral efficiency and power consumption of the D2D-communication-onedge configuration to establish a greener network over the other competitive configurations.


title: The acquisition of skilled motor performance: Fast and slow experience-driven changes in primary motor cortex

abstract: Behavioral and neurophysiological studies suggest that skill ::: learning can be mediated by discrete, experience-driven changes within ::: specific neural representations subserving the performance of the ::: trained task. We have shown that a few minutes of daily practice on a ::: sequential finger opposition task induced large, incremental ::: performance gains over a few weeks of training. These gains did not ::: generalize to the contralateral hand nor to a matched sequence of ::: identical component movements, suggesting that a lateralized ::: representation of the learned sequence of movements evolved through ::: practice. This interpretation was supported by functional MRI data ::: showing that a more extensive representation of the trained sequence ::: emerged in primary motor cortex after 3 weeks of training. The imaging ::: data, however, also indicated important changes occurring in primary ::: motor cortex during the initial scanning sessions, which we proposed ::: may reflect the setting up of a task-specific motor processing routine. ::: Here we provide behavioral and functional MRI data on ::: experience-dependent changes induced by a limited amount of repetitions ::: within the first imaging session. We show that this limited training ::: experience can be sufficient to trigger performance gains that require ::: time to become evident. We propose that skilled motor performance is ::: acquired in several stages: “fast” learning, an initial, ::: within-session improvement phase, followed by a period of consolidation ::: of several hours duration, and then “slow” learning, consisting of ::: delayed, incremental gains in performance emerging after continued ::: practice. This time course may reflect basic mechanisms of neuronal ::: plasticity in the adult brain that subserve the acquisition and ::: retention of many different skills.


title: IoT based intelligent billboard using data mining

abstract: The paper presents the design and implementation of an intelligent advertising system using Raspberry Pi B+ v1.2 development board, RFID and display panels integrated over a network. This system can be used in multi retail stores, malls and shopping complexes. The proposed system works on performing data mining and trend analysis over sales, inventory data which is collected in real time by using POS system implemented using RFID tags attached to products. The display panels(billboards) will be placed at strategic locations. The proposed outcome of the project aims to provide better offers and deals to customers without targeting them individually, rather using a segmented approach.


title: PL-SVO: Semi-direct Monocular Visual Odometry by combining points and line segments

abstract: Most approaches to visual odometry estimates the camera motion based on point features, consequently, their performance deteriorates in low-textured scenes where it is difficult to find a reliable set of them. This paper extends a popular semi-direct approach to monocular visual odometry known as SVO [1] to work with line segments, hence obtaining a more robust system capable of dealing with both textured and structured environments. The proposed odometry system allows for the fast tracking of line segments since it eliminates the necessity of continuously extracting and matching features between subsequent frames. The method, of course, has a higher computational burden than the original SVO, but it still runs with frequencies of 60Hz on a personal computer while performing robustly in a wider variety of scenarios.


A Survey of Machine Learning Approaches to Robotic Path-Planning


title: The Architect's Role in Software Ecosystems Health

abstract: Software ecosystems have adopted many different strategies to achieve success and good health. The role of software architect is one of the main contributors to that success. Their activities are crucial for realizing the business strategy of their organization. Software ecosystems define multi-sided markets that require different strategies from a traditional two-sided market. The architectural practices in the multi-sided market must support the need for flexibility and rapid reaction. These new demands broaden our understanding of the software architect's role and the impact of their action on ecosystem structure. In particular the need to understand how to operate in a collaborative, cooperative environment to exploit competition. In this position paper, we discuss how software architect's actions influence the ecosystem health through their indicators: productivity, niche creation, and robustness.


title: Challenging Complexity of Maximum Common Subgraph Detection Algorithms: A Performance Analysis of Three Algorithms on a Wide Database of Graphs

abstract: Graphs are an extremely general and powerful data structure. In pattern recognition and computer vision, graphs are used to represent patterns to be recognized or classified. Detection of maximum common subgraph (MCS) is useful for matching, comparing and evaluate the similarity of patterns. MCS is a well known NP-complete problem for which optimal and suboptimal algorithms are known from the literature. Nevertheless, until now no effort has been done for characterizing their performance. The lack of a large database of graphs makes the task of comparing the performance of different graph matching algorithms difficult, and often the selection of an algorithm is made on the basis of a few experimental results available. In this paper, three optimal and well-known algorithms for maximum common subgraph detection are described. Moreover a large database containing various categories of pairs of graphs (e.g. random graphs, meshes, bounded valence graphs), is presented, and the performance of the three algorithms is evaluated on this database.


title: An Efficient Distributed Trust Model for Wireless Sensor Networks

abstract: Trust models have been recently suggested as an effective security mechanism for Wireless Sensor Networks (WSNs). Considerable research has been done on modeling trust. However, most current research work only takes communication behavior into account to calculate sensor nodes’ trust value, which is not enough for trust evaluation due to the widespread malicious attacks. In this paper, we propose an Efficient Distributed Trust Model (EDTM) for WSNs. First, according to the number of packets received by sensor nodes, direct trust and recommendation trust are selectively calculated. Then, communication trust, energy trust and data trust are considered during the calculation of direct trust. Furthermore, trust reliability and familiarity are defined to improve the accuracy of recommendation trust. The proposed EDTM can evaluate trustworthiness of sensor nodes more precisely and prevent the security breaches more effectively. Simulation results show that EDTM outperforms other similar models, e.g., NBBTE trust model.


title: Modeling and simulation analysis of micro thermoelectric generator

abstract: This paper presents a reliable solution for power supply by using thermal energy harvesting by means of the micro thermoelectric generator (TEG). The construction of thermocouple in achieving maximum power output is investigated which include the width and length of the thermoelements for a fixed value of the thickness of thermoelements. For the structure design, several TEGs configuration is tested to foresee the temperature difference achieved between the two junctions. The materials used as substrate and thermocouple pairs are a silicon substrate and n-type/p-type polySi, respectively. The result of thermal simulations using CoventorWare shows that the etching of substrate in order to create trenches improved the efficiency of the generator significantly by providing a larger temperature difference between two junctions. Additional thermal insulator layer on top TEG surface also contributes great impact on the temperature difference achieved. A power factor of 0.0867 μW/cm−2−K2 can be obtained when the temperature difference between the bottom and the top surface of TEG is 7 K.


title: Recognition of plants by Leaf Image using Moment Invariant and Texture Analysis

abstract: This paper presents a simple and computationally good method for plant species recognition using leaf images. Recognition of plant images is one of the research topics of computer vision. The use of shape for recognizing objects has been actively studied since the beginning of object recognition in 1950s. Several authors suggest that object shape is more informative than its appearance properties such as texture and color vary between object instances more than the shape. Initially we have scanned leaf images which are two dimensional in nature and segmented the images by mathematical morphological segmentation and then extracted the high frequency feature of image. For removing the noise, the image has been converted into binary, than complemented and multiplied by filtered image. We quantitatively establish the use of texture for detection various leaf images of same tree that are difficult by other classical methods of image processing. Further we use Nearest Neighborhood classification method to classify plant leaf. In this paper we focuses mainly on image enhancement, image segmentation, high frequency feature extraction, noise remove from background, volume fraction, inverse difference moment, moment invariant and morphological feature such as area convexity.


title: Maximum Output Power Tracking Control in Variable-Speed Wind Turbine Systems Considering Rotor Inertial Power

abstract: This paper proposes a new maximum power point tracking (MPPT) algorithm for variable-speed wind turbine systems, which takes advantage of the rotor inertia power. In this method, a proportional controller is added to the power control to effectively reduce the moment of inertia of the wind turbines, which can improve the fast performance of the MPPT control. The PSIM simulation and experimental results for a doubly-fed induction generator wind turbine system have proved the validity of the proposed algorithm.


title: Multi-Task Learning as Multi-Objective Optimization

abstract: In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them. Multi-task learning is inherently a multi-objective problem because different tasks may conflict, necessitating a trade-off. A common compromise is to optimize a proxy objective that minimizes a weighted linear combination of per-task losses. However, this workaround is only valid when the tasks do not compete, which is rarely the case. In this paper, we explicitly cast multi-task learning as multi-objective optimization, with the overall objective of finding a Pareto optimal solution. To this end, we use algorithms developed in the gradient-based multi-objective optimization literature. These algorithms are not directly applicable to large-scale learning problems since they scale poorly with the dimensionality of the gradients and the number of tasks. We therefore propose an upper bound for the multi-objective loss and show that it can be optimized efficiently. We further prove that optimizing this upper bound yields a Pareto optimal solution under realistic assumptions. We apply our method to a variety of multi-task deep learning problems including digit classification, scene understanding (joint semantic segmentation, instance segmentation, and depth estimation), and multi-label classification. Our method produces higher-performing models than recent multi-task learning formulations or per-task training.


title: Dalvik Opcode Graph Based Android Malware Variants Detection Using Global Topology Features

abstract: Since Android has become the dominator of smartphone operating system market with a share of 86.8%, the number of Android malicious applications are increasing rapidly as well. Such a large volume of diversified malware variants has forced researchers to investigate new methods by using machine learning since it provides a powerful ability for variants detection. Since the static analysis of malware plays an important role in system security and the opcode has been shown as an effective representation of malware, some of them use the Dalvik opcodes as features of malware and adopt machine learning to detect Android malware. However, current opcode-based methods are also facing some problems, such as considering both of accuracy and time cost, selection of features, and the lack of understanding or description of the characteristics of malware. To overcome the existing challenges, we propose a novel method to build a graph of Dalvik opcode and analyze its global topology properties, which will first construct a weighted probability graph of operations, and then we use information entropy to prune this graph while retaining information as more as possible, the next we extract several global topology features of the graph to represent malware, finally search the similarities with these features between programs. These global topology features formulate the high-level characteristics of malware. Our approach provides a light weight framework to detect Android malware variants based on graph theory and information theory. Theoretical analysis and real-life experimental results show the effectiveness, efficiency, and robustness of our approach, which achieves high detection accuracy and cost little training and detection time.


title: A 65-fJ/Conversion-Step 0.9-V 200-kS/s Rail-to-Rail 8-bit Successive Approximation ADC

abstract: An 8-bit successive approximation (SA) analog-to- digital converter (ADC) in 0.18 mum CMOS dedicated for energy-limited applications is presented. The SA ADC achieves a wide effective resolution bandwidth (ERBW) by applying only one bootstrapped switch, thereby preserving the desired low power characteristic. Measurement results show that at a supply voltage of 0.9 V and an output rate of 200 kS/s, the SA ADC performs a peak signal-to-noise-and-distortion ratio of 47.4 dB and an ERBW up to its Nyquist bandwidth (100 kHz). It consumes 2.47 muW in the test, corresponding to a figure of merit of 65 f J/conversion-step.


title: Experimental Validation of Physics-Based Planning and Control Algorithms for Planetary Robotic Rovers

abstract: Robotic planetary exploration is a major component of the United States’ NASA space science program. The focus of our research is to develop rover planning and control algorithms for high-performance robotic planetary explorers based on the physics of these systems. Experimental evaluation is essential to ensure that unmodeled effects do not degrade algorithm performance. To perform this evaluation a low-cost rover test-bed has been developed. It consists of a rocker-bogie type rover with an on-board manipulator operating in a rough-terrain environment. In this paper the design and fabrication of an experimental rover system is described, and the experimental validation of several rover control algorithms is presented. The experimental results obtained are key to the evaluation and validation of our research.


title: Application of the Functionally Generated Path Technique to Restore Mandibular Molars in Bilateral Group Function Occlusion

abstract: The functionally generated path (FGP) is a static representation of the opposing cusps’ dynamic eccentric movements from a centric position to achieve optimal articulation and occlusal harmony. When understood and appreciated, use of the FGP technique is a straightforward and practical method to achieve harmonious occlusal anatomy of restorations with the anterior determinant/anterior guidance, the posterior determinant/condylar guidance, existing occlusal and cuspal anatomy, and the neuromuscular system. Although the FGP technique is normally used in the fabrication of maxillary posterior indirect restorations, it is described and applied here in the fabrication of mandibular posterior restorations that maintained the patient's bilateral group function occlusion while eliminating the nonworking side and protrusive interferences. This novel procedure involved the use of a stone crib to intraorally construct a stone core that captured the FGP recording while simultaneously indexing to the contralateral and ipsilateral mandibular dentition. This technique lends additional stability to the stone core to minimize error during the mounting process.


title: A dynamic core loss model for soft ferromagnetic and power ferrite materials in transient finite element analysis

abstract: A dynamic core loss model is proposed to estimate core loss in both soft ferromagnetic and power ferrite materials with arbitrary flux waveforms. The required parameters are the standard core loss coefficients that are either directly provided by manufacturers or extracted from the loss curve associated with sinusoidal excitation. The model is applied to calculating core loss in both two-dimensional and three-dimensional transient finite element analysis, and the results are compared with measured data.


title: CMOS Distributed Active Power Combiners and Splitters for Multi-Antenna UWB Beamforming Transceivers

abstract: This paper presents the design of the first CMOS distributed active power combiners and splitters with wideband variable delay and gain. These circuits are the key components for use in multi-antenna (MA) ultra-wideband (UWB) point-to-point beamforming communication systems with multiple transmit and receive antennas. Two broadband circuit topologies for each active power combiner and splitter are proposed, one of which being fabricated in a 0.13-μm CMOS process. The proposed fabricated distributed active power combiner and splitter operate across wide range of frequencies that cover the UWB frequency range from 3.1 to 10.6 GHz. The gain of each RF path of the power combiner and splitter is independently controllable from -15 to 6 dB and from -16 to 9.5 dB, respectively. The wideband variable delay of each RF path varies from 32 to 42 ps for the two-stage power combiner, and from 43 to 53 ps for the three-stage power splitter across the UWB frequency rang. Supplied from 1.8-V DC voltage, the power combiner and splitter consume 8.5 mA and 11.4 mA, respectively.


title: Effect of grammar on security of long passwords

abstract: Use of long sentence-like or phrase-like passwords such as "abiggerbetterpassword" and "thecommunistfairy" is increasing. In this paper, we study the role of grammatical structures underlying such passwords in diminishing the security of passwords. We show that the results of the study have direct bearing on the design of secure password policies, and on password crackers used for enforcing password security. Using an analytical model based on Parts-of-Speech tagging we show that the decrease in search space due to the presence of grammatical structures can be more than 50%. A significant result of our work is that the strength of long passwords does not increase uniformly with length. We show that using a better dictionary e.g. Google Web Corpus, we can crack more long passwords than previously shown (20.5% vs. 6%). We develop a proof-of-concept grammar-aware cracking algorithm to improve the cracking efficiency of long passwords. In a performance evaluation on a long password dataset, 10% of the total dataset was exclusively cracked by our algorithm and not by state-of-the-art password crackers.


title: Showing Off? Human Mobility and the Interplay of Traits, Self-Disclosure, and Facebook Check-Ins:

abstract: Mobile and location-based media refer to technologies that can openly and dynamically portray the characteristics of the users and their mundane life. Facebook check-ins highlights physical and informational mobility of the users relating individual activities into spaces. This study explored how personality traits like extraversion and narcissism function to influence self-disclosure that, in turn, impacts the intensity of check-ins on Facebook. Using survey data collected through Facebook check-in users in Taiwan (N = 523), the results demonstrated that although extraversion and narcissism might not directly impact check-in intensity on Facebook, the indirect effects of self-disclosure and exhibitionism were particularly salient. Moreover, a complete path from extraversion to Facebook check-in through self-disclosure and exhibitionism was discovered. Theoretical implications on human mobility and selective self-presentation are also discussed.


title: Cause-Effect Relation Learning

abstract: To be able to answer the question What causes tumors to shrink?, one would require a large cause-effect relation repository. Many efforts have been payed on is-a and part-of relation leaning, however few have focused on cause-effect learning. This paper describes an automated bootstrapping procedure which can learn and produce with minimal effort a cause-effect term repository. To filter out the erroneously extracted information, we incorporate graph-based methods. To evaluate the performance of the acquired cause-effect terms, we conduct three evaluations: (1) human-based, (2) comparison with existing knowledge bases and (3) application driven (SemEval-1 Task 4) in which the goal is to identify the relation between pairs of nominals. The results show that the extractions at rank 1500 are 89% accurate, they comprise 61% from the terms used in the SemEval-1 Task 4 dataset and can be used in the future to produce additional training examples for the same task.


Real-time rendering, 3rd Edition


title: jSymbolic: A Feature Extractor for MIDI Files

abstract: A library of 160 high-level features is presented along with jSymbolic, a software package that extracts these features from MIDI files. jSymbolic is intended both as a platform for developing new features as well as a tool for providing features to data mining software that can be used to automatically classify music or evaluate musical similarity.


title: Semi-Markov Conditional Random Fields for Information Extraction

abstract: We describe semi-Markov conditional random fields (semi-CRFs), a conditionally trained version of semi-Markov chains. Intuitively, a semi-CRF on an input sequence x outputs a "segmentation" of x, in which labels are assigned to segments (i.e., subsequences) of x rather than to individual elements xi of x. Importantly, features for semi-CRFs can measure properties of segments, and transitions within a segment can be non-Markovian. In spite of this additional power, exact learning and inference algorithms for semi-CRFs are polynomial-time—often only a small constant factor slower than conventional CRFs. In experiments on five named entity recognition problems, semi-CRFs generally outperform conventional CRFs.


title: ICPR2018 Contest on Object Detection in Aerial Images (ODAI-18)

abstract: Object detection in aerial images plays a significant role in intelligent interpretation of aerial images. Hence many effective methods, especially the new-generation data-driven methods, have been developed for this task. Here, we hold the ODAI, a new contest that focused on object detection in aerial images, based on a new large-scale aerial image dataset called DOTA [1]. This contest contains over 3000 large-size images ( $4k\times 4k$ pixels), which cover 211,581 instances divided into 15 categories. Each instance is labeled by an arbitrary (8 d.o.f.) quadrilateral. Besides, we propose two tasks for this contest, named object detection with the horizontal bounding box (OD-HBB) and object detection with the oriented bounding box (OD-OBB). The contest was opened on February 7, 2018, and ended on April 30, 2018. A website is open to the public, which provides links to download data and evaluation server. We have totally received 60 registrations. There are 8 teams that have successfully submitted results on the OD-HBB task with the top mAP as 0.719, and 9 teams that have successfully submitted results on the OD-OBB task with the top mAP as 0.705. Through the contest, we hope to draw extensive attention from a wide range of communities and call for more future research and efforts for the task of object detection in aerial images.


title: Word of mouth marketing through Online Social Networks

abstract: This paper proposes a research agenda for studying factors that may affect marketing effectiveness in the context of online communities. Findings of prior studies are synthesized into a more comprehensive review of the state of the art of research on word of mouth marketing through online social networks. Based on the review, we propose a research model that incorporates both network and individual factors, present the research plan, and discuss the potential implications of the research.


title: Structure–LCST relationships for end-functionalized water-soluble polymers: an “accelerated” approach to phase behaviour studies

abstract: A novel “high throughput” technique for LCST measurement was developed which is able to identify the effect of subtle changes in end group composition on the aqueous phase behaviour of water-soluble poly(2-(dimethylamino)ethyl methacrylate).


OBSERVATIONS ON THE ELECTRON-BEAM MELTING OF TITANIUM AND ITS


title: Multifocus Image Fusion and Restoration With Sparse Representation

abstract: To obtain an image with every object in focus, we always need to fuse images taken from the same view point with different focal settings. Multiresolution transforms, such as pyramid decomposition and wavelet, are usually used to solve this problem. In this paper, a sparse representation-based multifocus image fusion method is proposed. In the method, first, the source image is represented with sparse coefficients using an overcomplete dictionary. Second, the coefficients are combined with the choose-max fusion rule. Finally, the fused image is reconstructed from the combined sparse coefficients and the dictionary. Furthermore, the proposed fusion scheme can simultaneously resolve the image restoration and fusion problem by changing the approximate criterion in the sparse representation algorithm. The proposed method is compared with spatial gradient (SG)-, morphological wavelet transform (MWT)-, discrete wavelet transform (DWT)-, stationary wavelet transform (SWT)-, curvelet transform (CVT)-, and nonsubsampling contourlet transform (NSCT)-based methods on several pairs of multifocus images. The experimental results demonstrate that the proposed approach performs better in both subjective and objective qualities.


NEPAL : A Tool for Never-Ending Paraphrase Learning


title: Improved Neighborhood-based Collaborative Filtering

abstract: Recommender systems based on collaborative filtering predi ct user preferences for products or services by learning past user- item relationships. A predominant approach to collaborative filte ring is neighborhood based (“k-nearest neighbors"), where a user-item preference rating is interpolated from ratings of similar items and/or users. In this work, we enhance the neighborhood-based approach leading to a substantial improvement of prediction accuracy, without a meaningful increase in running time. First, we remove certain so-called “global effects" from the data to make the different ratings more comparable, thereby improving interpolation accuracy. Second, we show how to simultaneously derive interpolation weights for all nearest neighbors. Unlike previous approaches where each interpolation weight is computed separately, simultaneous interpolation accounts for the many interactions between neighbors by globally solving a suitable optimization problem, also lea ding to improved accuracy. Our method is very fast in practice, generating a prediction in about 0.2 milliseconds. Importantly, it does not require training many parameters or a lengthy preprocessing, making it very practical for large scale applications. The meth od was evaluated on the Netflix dataset. We could process the 2.8 mil lion queries of the Qualifying set in 10 minutes yielding a RMSE of 0.9086. Moreover, when an extensive training is allowed, such as SVD-factorization at the preprocessing stage, our method can produce results with a RMSE of 0.8982.


title: Coplanar 122-GHz Antenna Array With Air Cavity Reflector for Integration in Plastic Packages

abstract: This letter introduces the concept of a surface-mountable millimeter-wave radar sensor, which becomes possible by integrating the complete radar front end including the antennas into a single plastic package. The packaging concept is based on a matched coplanar wire-bond interconnect between the semiconductor die and the off-chip antenna. A 122-GHz antenna design is presented that uses an air cavity within the center die pad to improve the bandwidth-efficiency product. The antenna is designed in an array configuration to reduce the effect of surface waves. Measurements of the antenna prototype in the package, as well as a study on the influence of the package lid on the antenna performance, are given.


title: Efficient incremental parsing for context-free languages

abstract: An incremental parsing algorithm based on dynamic programming techniques is described. The analyzer takes the automaton generated from a general class of context-free grammars as driver, and any finite string as input. Given an input string that has been modified, the algorithm cuts out the parts of the old analysis that had been generated by the parts of the input that has changed. What remains are the parts of the analysis that were generated by the stable part of the input. The new system has been baptized ICE, after Incremental Context-Free Environment. In an empirical comparison, ICE appears to be superior to the other context-free analyzers and comparable to standard deterministic parsers, when the input string is not ambiguous. >


title: Exploring place through user-generated content: Using Flickr to describe city cores

abstract: Terms used to describe city centers, such as Downtown, are key concepts in everyday or vernacular language. Here, we explore such language by harvesting georeferenced and tagged metadata associated with 8 million Flickr images and thus consider how large numbers of people name city core areas. The nature of errors and imprecision in tagging and georeferencing are quantified, and automatically generated precision measures appear to mirror errors in the positioning of images. Users seek to ascribe appropriate semantics to images, though bulk-uploading and bulk-tagging may introduce bias. Between 0.5–2% of tags associated with georeferenced images analyzed describe city core areas generically, while 70% of all georeferenced images analyzed include specific place name tags, with place names at the granularity of city names being by far the most common. Using Flickr metadata, it is possible not only to describe the use of the term Downtown across the USA, but also to explore the borders of city center neighborhoods at the level of individual cities, whilst accounting for bias by the use of tag profiles.


title: Neuroplasticity and memory formation in major depressive disorder: An imaging genetics perspective on serotonin and BDNF

abstract: A vast number of imaging studies have demonstrated the impact of serotonin (5-HT) and brain-derived neurotrophic factor (BDNF) on emotion and memory-related networks in the context of Major Depressive Disorder (MDD). Underlying molecular mechanisms that affect the functionality of these networks have been examined in detail in animals and corroborate imaging findings. The crucial role of 5-HT and BDNF signaling in the context of MDD is reflected in the etiologic models of MDD such as the monoamine or neuroplasticity hypothesis as well as in pharmacological models of antidepressant response. While antidepressant drug treatment has been primarily linked to the modulation of emotion-related networks, cognitive behavioral therapy has been implicated in a top-down control of limbic structures. Initially, a simple lack of monoamines or BDNF has been proposed as causal factor of MDD etiology. However, recent findings suggest a much more complex neurobiology emphasizing epistatic and epigenetic mechanisms responsible for structural and functional changes observed in emotion and memory-related brain regions of healthy subjects and MDD patients. In this review, which focuses on neuroimaging studies in the context of MDD, the authors will provide a comprehensive overview of these networks as well as on the specific role of 5-HT and BDNF in their development and function.


title: What did Simon say? Revisiting the bilingual advantage

abstract: Bilingual children often outperform monolingual children in tasks of cognitive control. This advantage may be a consequence of the fact that bilinguals have more practice controlling attention due to an ongoing need to manage two languages. However, existing evidence is limited because possible differences in ethnicity and socioeconomic status have not been properly controlled. To address this issue, we administered the Simon task to bilingual and monolingual children of identical ethnic and socioeconomic backgrounds. Bilingual and monolingual children performed identically, whereas children from higher SES families were advantaged relative to children from lower SES families. Controlling differences in SES and ethnicity may attenuate the bilingual advantage in cognitive control.


title: Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres

abstract: We use complex-weighted, deep convolutional networks to invert the effects of multimode optical fibre distortion of a coherent input image. We generated experimental data based on collections of optical fibre responses to greyscale, input images generated with coherent light, and measuring only image amplitude (not amplitude and phase as is typical) at the output of the 10 metre long 105 micrometre diameter multimode fibre. This data is made available as the {\it Optical fibre inverse problem} Benchmark collection. The experimental data is used to train complex-weighted models with a range of regularisation approaches and subsequent denoising autoencoders. A new {\it unitary regularisation} approach for complex-weighted networks is proposed which performs best in robustly inverting the fibre transmission matrix, and which fits well with the physical theory. The use of unitary layers allows analytic inversion of the network via its complex conjugate transpose, and we demonstrate simultaneous optimisation of both the forward and inverse models.


Automatic Tuberculosis Screening Using Active contour segmentation using Chest Radiographs


title: Recurrent Poisson Factorization for Temporal Recommendation

abstract: Poisson factorization is a probabilistic model of users and items for recommendation systems, where the so-called implicit consumer data is modeled by a factorized Poisson distribution. There are many variants of Poisson factorization methods who show state-of-the-art performance on real-world recommendation tasks. However, most of them do not explicitly take into account the temporal behavior and the recurrent activities of users which is essential to recommend the right item to the right user at the right time. In this paper, we introduce Recurrent Poisson Factorization (RPF) framework that generalizes the classical PF methods by utilizing a Poisson process for modeling the implicit feedback. RPF treats time as a natural constituent of the model and brings to the table a rich family of time-sensitive factorization models. To elaborate, we instantiate several variants of RPF who are capable of handling dynamic user preferences and item specification (DRPF), modeling the social-aspect of product adoption (SRPF), and capturing the consumption heterogeneity among users and items (HRPF). We also develop a variational algorithm for approximate posterior inference that scales up to massive data sets. Furthermore, we demonstrate RPF's superior performance over many state-of-the-art methods on synthetic dataset, and large scale real-world datasets on music streaming logs, and user-item interactions in M-Commerce platforms.


title: Supplier selection using AHP methodology extended by D numbers

abstract: Supplier selection is an important issue in supply chain management (SCM), and essentially is a multi-criteria decision-making problem. Supplier selection highly depends on experts' assessments. In the process of that, it inevitably involves various types of uncertainty such as imprecision, fuzziness and incompleteness due to the inability of human being's subjective judgment. However, the existing methods cannot adequately handle these types of uncertainties. In this paper, based on a new effective and feasible representation of uncertain information, called D numbers, a D-AHP method is proposed for the supplier selection problem, which extends the classical analytic hierarchy process (AHP) method. Within the proposed method, D numbers extended fuzzy preference relation has been involved to represent the decision matrix of pairwise comparisons given by experts. An illustrative example is presented to demonstrate the effectiveness of the proposed method.


title: Knowledge-sharing and influence in online social networks via viral marketing

abstract: Online social networks are increasingly being recognized as an important source of information influencing the adoption and use of products and services. Viral marketing—the tactic of creating a process where interested people can market to each other—is therefore emerging as an important means to spread-the-word and stimulate the trial, adoption, and use of products and services. Consider the case of Hotmail, one of the earliest firms to tap the potential of viral marketing. Based predominantly on publicity from word-of-mouse [4], the Web-based email service provider garnered one million registered subscribers in its first six months, hit two million subscribers two months later, and passed the eleven million mark in eighteen months [7]. Wired magazine put this growth in perspective in its December 1998 issue: “The Hotmail user base grew faster than [that of ] any media company in history—faster than CNN, faster than AOL, even faster than Seinfeld’s audience. By mid-2000, Hotmail had over 66 million users with 270,000 new accounts being established each day.” While the potential of viral marketing to efficiently reach out to a broad set of potential users is attracting considerable attention, the value of this approach is also being questioned [5]. There needs to be a greater understanding of the contexts in which this strategy works and the characteristics of products and services for which it is most effective. This is particularly important because the inappropriate use of viral marketing can be counterproductive by creating unfavorable attitudes towards products. Work examining this phenomenon currently provides either descriptive accounts of particular initiatives [8] or advice based on anecdotal evidence [2]. What is missing is an analysis of viral marketing that highlights systematic patterns in the nature of knowledge-sharing and persuasion by influencers and responses by recipients in online social networks. To this end, we propose an organizing framework for viral marketing that draws on prior theory and highlights different behavioral mechanisms underlying knowledge-sharing, influence, and compliance in online social networks. Though the framework is descrip-


title: Abnormal image detection in endoscopy videos using a filter bank and local binary patterns

abstract: Finding mucosal abnormalities (e.g., erythema, blood, ulcer, erosion, and polyp) is one of the most essential tasks during endoscopy video review. Since these abnormalities typically appear in a small number of frames (around 5% of the total frame number), automated detection of frames with an abnormality can save [email protected]?s time significantly. In this paper, we propose a new multi-texture analysis method that effectively discerns images showing mucosal abnormalities from the ones without any abnormality since most abnormalities in endoscopy images have textures that are clearly distinguishable from normal textures using an advanced image texture analysis method. The method uses a ''texton histogram'' of an image block as features. The histogram captures the distribution of different ''textons'' representing various textures in an endoscopy image. The textons are representative response vectors of an application of a combination of Leung and Malik (LM) filter bank (i.e., a set of image filters) and a set of Local Binary Patterns on the image. Our experimental results indicate that the proposed method achieves 92% recall and 91.8% specificity on wireless capsule endoscopy (WCE) images and 91% recall and 90.8% specificity on colonoscopy images.


title: The effects of manual therapy on connective tissue.

abstract: The purpose of this manuscript is to examine the known and theoretical mechanical effects of therapeutic manual techniques on the connective tissue (CT) of joints and fasciae. Typical CT structures that could be influenced by manual techniques will be discussed. The behavior of CT under loading and the influence of immobilization on CT will be examined. The forces developed during manual techniques will be described, and their potential effects on the physical properties of CT will be discussed. Research priorities regarding the effects of manual therapy on CT will be outlined.


title: Clash of the Contagions: Cooperation and Competition in Information Diffusion

abstract: In networks, contagions such as information, purchasing behaviors, and diseases, spread and diffuse from node to node over the edges of the network. Moreover, in real-world scenarios multiple contagions spread through the network simultaneously. These contagions not only propagate at the same time but they also interact and compete with each other as they spread over the network. While traditional empirical studies and models of diffusion consider individual contagions as independent and thus spreading in isolation, we study how different contagions interact with each other as they spread through the network. We develop a statistical model that allows for competition as well as cooperation of different contagions in information diffusion. Competing contagions decrease each other's probability of spreading, while cooperating contagions help each other in being adopted throughout the network. We evaluate our model on 18,000 contagions simultaneously spreading through the Twitter network. Our model learns how different contagions interact with each other and then uses these interactions to more accurately predict the diffusion of a contagion through the network. Moreover, the model also provides a compelling hypothesis for the principles that govern content interaction in information diffusion. Most importantly, we find very strong effects of interactions between contagions. Interactions cause a relative change in the spreading probability of a contagion by em 71% on the average.


title: SecureDroid: Enhancing Security of Machine Learning-based Detection against Adversarial Android Malware Attacks

abstract: With smart phones being indispensable in people's everyday life, Android malware has posed serious threats to their security, making its detection of utmost concern. To protect legitimate users from the evolving Android malware attacks, machine learning-based systems have been successfully deployed and offer unparalleled flexibility in automatic Android malware detection. In these systems, based on different feature representations, various kinds of classifiers are constructed to detect Android malware. Unfortunately, as classifiers become more widely deployed, the incentive for defeating them increases. In this paper, we explore the security of machine learning in Android malware detection on the basis of a learning-based classifier with the input of a set of features extracted from the Android applications (apps). We consider different importances of the features associated with their contributions to the classification problem as well as their manipulation costs, and present a novel feature selection method (named SecCLS) to make the classifier harder to be evaded. To improve the system security while not compromising the detection accuracy, we further propose an ensemble learning approach (named SecENS) by aggregating the individual classifiers that are constructed using our proposed feature selection method SecCLS. Accordingly, we develop a system called SecureDroid which integrates our proposed methods (i.e., SecCLS and SecENS) to enhance security of machine learning-based Android malware detection. Comprehensive experiments on the real sample collections from Comodo Cloud Security Center are conducted to validate the effectiveness of SecureDroid against adversarial Android malware attacks by comparisons with other alternative defense methods. Our proposed secure-learning paradigm can also be readily applied to other malware detection tasks.


title: BioMot exoskeleton — Towards a smart wearable robot for symbiotic human-robot interaction

abstract: This paper presents design of a novel modular lower-limb gait exoskeleton built within the FP7 BioMot project. Exoskeleton employs a variable stiffness actuator in all 6 joints, a directional-flexibility structure and a novel physical humanrobot interfacing, which allows it to deliver the required output while minimally constraining user's gait by providing passive degrees of freedom. Due to modularity, the exoskeleton can be used as a full lower-limb orthosis, a single-joint orthosis in any of the three joints, and a two-joint orthosis in a combination of any of the two joints. By employing a simple torque control strategy, the exoskeleton can be used to deliver user-specific assistance, both in gait rehabilitation and in assisting people suffering musculoskeletal impairments. The result of the presented BioMot efforts is a low-footprint exoskeleton with powerful compliant actuators, simple, yet effective torque controller and easily adjustable flexible structure.


title: A RBF neural network model for anti-money laundering

abstract: Money laundering (ML) is a serious crime which makes it necessary to develop detection methods in transactions. Some researches have been carried on, but the problem is not thoroughly solved. Aiming at the low detection rate of suspicious transaction at home and abroad in financial field, and with an analysis of radial basis function (RBF) neural network, we propose a radial basis function neural network model based on APC-III clustering algorithm and recursive least square algorithm for anti-money laundering (AML). APC-III clustering algorithm is used for determining the parameters of radial basis function in hidden layer, and recursive least square (RLS) algorithm is adopted to update weights of connections between hidden layer and output layer. The proposed method is compared against support vector machine (SVM) and outlier detection methods, which show that the proposed method has the highest detection rate and the lowest false positive rate. Thus our method is proved to have both theoretical and practical value for anti-money laundering.


title: Situation Recognition: Visual Semantic Role Labeling for Image Understanding

abstract: This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including: (1) the main activity (e.g., clipping), (2) the participating actors, objects, substances, and locations (e.g., man, shears, sheep, wool, and field) and most importantly (3) the roles these participants play in the activity (e.g., the man is clipping, the shears are his tool, the wool is being clipped from the sheep, and the clipping is in a field). We use FrameNet, a verb and role lexicon developed by linguists, to define a large space of possible situations and collect a large-scale dataset containing over 500 activities, 1,700 roles, 11,000 objects, 125,000 images, and 200,000 unique situations. We also introduce structured prediction baselines and show that, in activity-centric images, situation-driven prediction of objects and activities outperforms independent object and activity recognition.


title: Mutual Coupling Reduction in Patch Antenna Arrays Using a UC-EBG Superstrate

abstract: Reducing mutual coupling between elements of an antenna array is one of the main topics in array designs. The use of electromagnetic band-gap (EBG) structures built by microstrip technology is an attractive way to mitigate the mutual coupling problem. This letter describes a novel configuration of uniplanar compact electromagnetic band-gap (UC-EBG) structures to reduce mutual coupling between the radiating elements. The idea is to use the UC-EBG structures placed on top of the antenna layer. The main objective is to reduce both the element separation and the mutual coupling between the patch antennas, which in turn increases antenna directivity. The proposed configuration eliminates drawbacks of similar structures presented in previous works.


title: Deep Kernelized Autoencoders

abstract: In this paper we introduce the deep kernelized autoencoder, a neural network model that allows an explicit approximation of (i) the mapping from an input space to an arbitrary, user-specified kernel space and (ii) the back-projection from such a kernel space to input space. The proposed method is based on traditional autoencoders and is trained through a new unsupervised loss function. During training, we optimize both the reconstruction accuracy of input samples and the alignment between a kernel matrix given as prior and the inner products of the hidden representations computed by the autoencoder. Kernel alignment provides control over the hidden representation learned by the autoencoder. Experiments have been performed to evaluate both reconstruction and kernel alignment performance. Additionally, we applied our method to emulate kPCA on a denoising task obtaining promising results.


title: Globally Embedded Community: Satisfaction and Attachment in Vance, Alabama

abstract: Community, as an imagined representation of a social collectivity, is necessarily dynamic and subjective. In the contemporary globalized world, rapid structural changes provide an opportunity to evaluate the extent to which structure and subjectivity interact to create particular patterns of community life. Using multiple regression analysis of data from Vance, Alabama, which suddenly entered the global arena with the announcement and construction of a large German automobile factory there, the argument made here is that structural and contextual models are insufficient on their own to account for community life. Especially as our communities become increasingly incorporated into the global economy, our understanding of community must incorporate the subjectivities of community members.


title: Congenital diaphragmatic eventration and bilateral uretero-hydronephrosis in a patient with neonatal Marfan syndrome caused by a mutation in exon 25 of the FBN1 gene and review of the literature

abstract: UNLABELLED ::: Neonatal Marfan syndrome, the most severe presentation of Marfan syndrome phenotypes (MIM 154700), is characterised mainly by joint contractures, arachnodactyly, loose skin, crumpled ears, severe atrioventricular valve dysfunction and pulmonary emphysema. Death usually occurs within the first 2 years of life from congestive heart failure. We describe here a newborn male with many typical characteristics of neonatal Marfan syndrome associated with a diaphragmatic eventration and a bilateral uretero-hydronephrosis with bladder dilatation. He died from cardiac failure due to severe tricuspid and mitral regurgitation at 62 h of age. ::: ::: ::: CONCLUSION ::: Molecular analysis showed a heterozygous missense mutation at nucleotide 3165 (3165T>G) in exon 25 of the FBN1 gene, resulting in the substitution of cysteine for tryptophan (C1055W).


title: Fair enough: guaranteeing approximate maximin shares

abstract: We consider the problem of fairly allocating indivisible goods, focusing on a recently-introduced notion of fairness called maximin share guarantee: Each player's value for his allocation should be at least as high as what he can guarantee by dividing the items into as many bundles as there are players and receiving his least desirable bundle. Assuming additive valuation functions, we show that such allocations may not exist, but allocations guaranteeing each player 2/3 of the above value always exist, and can be computed in polynomial time when the number of players is constant. These theoretical results have direct practical implications.


title: Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions

abstract: Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-revealing QR decomposition, play a central role in data analysis and scientific computing. This work surveys and extends recent research which demonstrates that randomization offers a powerful tool for performing low-rank matrix approximation. These techniques exploit modern computational architectures more fully than classical methods and open the possibility of dealing with truly massive data sets. This paper presents a modular framework for constructing randomized algorithms that compute partial matrix decompositions. These methods use random sampling to identify a subspace that captures most of the action of a matrix. The input matrix is then compressed---either explicitly or implicitly---to this subspace, and the reduced matrix is manipulated deterministically to obtain the desired low-rank factorization. In many cases, this approach beats its classical competitors in terms of accuracy, speed, and robustness. These claims are supported by extensive numerical experiments and a detailed error analysis.


title: A generic camera calibration method for fish-eye lenses

abstract: Fish-eye lenses are convenient in such computer vision applications where a very wide angle of view is needed. However, their use for measurement purposes is limited by the lack of an accurate, generic, and easy-to-use calibration procedure. We hence propose a generic camera model for cameras equipped with fish-eye lenses and a method for calibration of such cameras. The calibration is possible by using only one view of a planar calibration object but more views should be used for better results. The proposed calibration method was evaluated with real images and the obtained results are promising. The calibration software becomes commonly available at the author's Web page.


title: Serious processing for frivolous purpose: a chatbot using web-mining supported affect analysis and pun generation

abstract: By our demonstration we want to introduce our achievements in combining different purpose algorithms to build a chatbot which is able to keep a conversation on any topic. It uses snippets of Internet search results to stay within a context, Nakamura's Emotion Dictionary to detect an emotional load existence and categorization of a textual utterance and a causal consequences retrieval algorithm when emotive features are not found. It is also able to detect a possibility to make a pun by analyzing the input sentence and create one if timing is adequate.


title: Orthopedics management of acromicric dysplasia: follow up of nine patients.

abstract: UNLABELLED ::: Acromicric dysplasia (AD) is an autosomal dominant disorder characterized by short stature, short extremities, stiff joint and skeleton features including brachymetacarpia, cone-shaped epiphyses, internal notch of the femoral head, and delayed bone age. Recently, we identified fibrillin 1 (FBN1) as the disease gene of AD. The aim of our study was to further describe the long-term follow up of AD patients with an emphasis on orthopedic management. Nine patients with FBN1 mutations were included in the study ranging in age from 5.5 to 64 years. For all, detailed clinical and radiological data were available. ::: ::: ::: RESULTS ::: Birth parameters were always normal and patients progressively developed short stature <-3 SD. Carpal tunnel syndrome was observed in four patients. We found discrepancy between the carpal bone age and the radius and ulna epiphysis bone ages, a variable severity of hip dysplasia with acetabular dysplasia, epiphyseal and metaphyseal femoral dysplasia resembling Legg-Perthes-Calvé disease and variable pelvic anteversion and hyperlordosis. Orthopedic surgery was required in two patients for hip dysplasia, in one for limb lengthening and in three for carpal tunnel syndrome. Our observations expand the AD phenotype and emphasize the importance of regular orthopedic survey.


title: Clustering algorithms and validity measures

abstract: Clustering aims at discovering groups and identifying interesting distributions and patterns in data sets. Researchers have extensively studied clustering since it arises in many application domains in engineering and social sciences. In the last years the availability of huge transactional and experimental data sets and the arising requirements for data mining created needs for clustering algorithms that scale and can be applied in diverse domains. The paper surveys clustering methods and approaches available in the literature in a comparative way. It also presents the basic concepts, principles and assumptions upon which the clustering algorithms are based. Another important issue is the validity of the clustering schemes resulting from applying algorithms. This is also related to the inherent features of the data set under concern. We review and compare clustering validity measures available in the literature. Furthermore, we illustrate the issues that are under-addressed by the recent algorithms and we address new research directions.


title: Personalized news recommendation based on click behavior

abstract: Online news reading has become very popular as the web provides access to news articles from millions of sources around the world. A key challenge of news websites is to help users find the articles that are interesting to read. In this paper, we present our research on developing personalized news recommendation system in Google News. For users who are logged in and have explicitly enabled web history, the recommendation system builds profiles of users' news interests based on their past click behavior. To understand how users' news interests change over time, we first conducted a large-scale analysis of anonymized Google News users click logs. Based on the log analysis, we developed a Bayesian framework for predicting users' current news interests from the activities of that particular user and the news trends demonstrated in the activity of all users. We combine the content-based recommendation mechanism which uses learned user profiles with an existing collaborative filtering mechanism to generate personalized news recommendations. The hybrid recommender system was deployed in Google News. Experiments on the live traffic of Google News website demonstrated that the hybrid method improves the quality of news recommendation and increases traffic to the site.


Food Recognition and Detection with Minimum Supervision


title: Contour-based next-best view planning from point cloud segmentation of unknown objects

abstract: A novel strategy is presented to determine the next-best view for a robot arm, equipped with a depth camera in eye-in-hand configuration, which is oriented to autonomous exploration of unknown objects. Instead of maximizing the total size of the expected unknown volume that becomes visible, the next-best view is chosen to observe the border of incomplete objects. Salient regions of space that belong to the objects are detected, without any prior knowledge, by applying a point cloud segmentation algorithm. The system uses a Kinect V2 sensor, which has not been considered in previous works on next-best view planning, and it exploits KinectFusion to maintain a volumetric representation of the environment. A low-level procedure to reduce Kinect V2 invalid points is also presented. The viability of the approach has been demonstrated in a real setup where the robot is fully autonomous. Experiments indicate that the proposed method enables the robot to actively explore the objects faster than a standard next-best view algorithm.


title: Staff scheduling and rostering: A review of applications, methods and models

abstract: Abstract This paper presents a review of staff scheduling and rostering, an area that has become increasingly important as business becomes more service oriented and cost conscious in a global environment. Optimised staff schedules can provide enormous benefits, but require carefully implemented decision support systems if an organisation is to meet customer demands in a cost effective manner while satisfying requirements such as flexible workplace agreements, shift equity, staff preferences, and part-time work. In addition, each industry sector has its own set of issues and must be viewed in its own right. There are many computer software packages for staff scheduling, ranging from spreadsheet implementations of manual processes through to mathematical models using efficient optimal or heuristic algorithms. We do not review software packages in this paper. Rather, we review rostering problems in specific application areas, and the models and algorithms that have been reported in the literature for their solution. We also survey commonly used methods for solving rostering problems.


title: TopicRank: Graph-Based Topic Ranking for Keyphrase Extraction

abstract: Keyphrase extraction is the task of identifying single or multi-word expressions that represent the main topics of a document. In this paper we present TopicRank, a graph-based keyphrase extraction method that relies on a topical representation of the document. Candidate keyphrases are clustered into topics and used as vertices in a complete graph. A graph-based ranking model is applied to assign a significance score to each topic. Keyphrases are then generated by selecting a candidate from each of the topranked topics. We conducted experiments on four evaluation datasets of different languages and domains. Results show that TopicRank significantly outperforms state-of-the-art methods on three datasets.


title: 14nm FinFET technology for analog and RF applications

abstract: This paper highlights a 14nm Analog and RF technology based on a logic FinFET platform for the first time. An optimized RF device layout shows excellent F t /F max of (314GHz/180GHz) and (285GHz/140GHz) for NFET and PFET respectively. A higher PFET RF performance compared to 28nm technology is due to a source/drain stressor mobility improvement. A benefit of better FinFET channel electrostatics can be seen in the self-gain (G m /G ds ), which shows a significant increase to 40 and 34 for NFET and PFET respectively. Superior 1/f noise of 17/35 f(V∗μm)2/Hz @ 1KHz for N/PFET respectively is also achieved. To extend further low voltage operation and power saving, ultra-low Vt devices are also developed. Furthermore, a deep N-well (triple well) process is introduced to improve the ultra-low signal immunity from substrate noise, while offering useful devices like VNPN and high breakdown voltage deep N-well diodes. A superior F t /F max , high self-gain, low 1/f noise and substrate isolation characteristics truly extend the capability of the 14nm FinFETs for analog and RF applications.


title: Measuring academic influence: Not all citations are equal

abstract: The importance of a research article is routinely measured by counting how many times it has been cited. However, treating all citations with equal weight ignores the wide variety of functions that citations perform. We want to automatically identify the subset of references in a bibliography that have a central academic influence on the citing paper. For this purpose, we examine the effectiveness of a variety of features for determining the academic influence of a citation. By asking authors to identify the key references in their own work, we created a data set in which citations were labeled according to their academic influence. Using automatic feature selection with supervised machine learning, we found a model for predicting academic influence that achieves good performance on this data set using only four features. The best features, among those we evaluated, were those based on the number of times a reference is mentioned in the body of a citing paper. The performance of these features inspired us to design an influence-primed h-index (the hip-index). Unlike the conventional h-index, it weights citations by how many times a reference is mentioned. According to our experiments, the hip-index is a better indicator of researcher performance than the conventional h-index.


title: An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture

abstract: Microseismic monitoring is one of the most critical technologies for hydraulic fracturing in oil and gas production. To detect events in an accurate and efficient way, there are two major challenges. One challenge is how to achieve high accuracy due to a poor signal-to-noise ratio (SNR). The other one is concerned with real-time data transmission. Taking these challenges into consideration, an edge-computing-based platform, namely Edge-to-Center LearnReduce, is presented in this work. The platform consists of a data center with many edge components. At the data center, a neural network model combined with convolutional neural network (CNN) and long short-term memory (LSTM) is designed and this model is trained by using previously obtained data. Once the model is fully trained, it is sent to edge components for events detection and data reduction. At each edge component, a probabilistic inference is added to the neural network model to improve its accuracy. Finally, the reduced data is delivered to the data center. Based on experiment results, a high detection accuracy (over 96%) with less transmitted data (about 90%) was achieved by using the proposed approach on a microseismic monitoring system. These results show that the platform can simultaneously improve the accuracy and efficiency of microseismic monitoring.


title: Wideband Inline Coaxial to Ridge Waveguide Transition With Tuning Capability for Ridge Gap Waveguide

abstract: An ultra-wideband inline coaxial-to-single ridge waveguide transition is designed to feed the ridge gap waveguide (RGW). The transition covers the frequency band of 11.5 to 33 GHz. This bandwidth cannot be covered by a single RGW. Therefore, two RGWs are manufactured covering two different bands to be used as devices under test. Simulations and measurements of back-to-back transitions have achieved better than 15 dB return loss. In addition, a tuning capability in the transition is simulated using two different commercial simulators showing that the transition may cover up to 40 GHz with over 90% bandwidth to cover the entire Ku , ${K}$ , and Ka -bands.


title: A Simple NLP-Based Approach to Support Onboarding and Retention in Open Source Communities

abstract: Successful open source communities are constantly looking for new members and helping them become active developers. A common approach for developer onboarding in open source projects is to let newcomers focus on relevant yet easy-to-solve issues to familiarize themselves with the code and the community. The goal of this research is twofold. First, we aim at automatically identifying issues that newcomers can resolve by analyzing the history of resolved issues by simply using the title and description of issues. Second, we aim at automatically identifying issues, that can be resolved by newcomers who later become active developers. We mined the issue trackers of three large open source projects and extracted natural language features from the title and description of resolved issues. In a series of experiments, we optimized and compared the accuracy of four supervised classifiers to address our research goals. Random Forest, achieved up to 91% precision (F1-score 72%) towards the first goal while for the second goal, Decision Tree achieved a precision of 92% (F1-score 91%). A qualitative evaluation gave insights on what information in the issue description is helpful for newcomers. Our approach can be used to automatically identify, label, and recommend issues for newcomers in open source software projects based only on the text of the issues.


title: Collaborative vehicle routing problem for urban last-mile logistics

abstract: Collaboration between logistics service providers (LSPs) becomes increasingly crucial in urban last-mile logistics as it not only helps LSPs to reduce the transportation costs and increase the truck load factor but also improve customer service levels. However, LSPs are usually against sharing of customer data and delivery orders with their competitors. This makes most existing collaboration strategies in literature impractical in reality. To deal with this issue, we proposed a new collaboration strategy that requires less coordination efforts between the LSPs involved. In addition, we carried out a numerical experiment with the data collected from the local LSPs in Singapore to illustrate our proposed strategy. The results and the analysis highlighted shows that the proposed collaboration strategy is not only more practical than other conventional strategies, it also helps to reduce the total transportation costs and increase truckload in overall.


title: The journey to autism: Insights from neuroimaging studies of infants and toddlers.

abstract: By definition, autism spectrum disorder (ASD) is a neurodevelopmental disorder that emerges during early childhood. It is during this time that infants and toddlers transition from appearing typical across multiple domains to exhibiting the behavioral phenotype of ASD. Neuroimaging studies focused on this period of development have provided crucial knowledge pertaining to this process, including possible mechanisms underlying pathogenesis of the disorder and offering the possibility of prodromal or presymptomatic prediction of risk. In this paper, we review findings from structural and functional brain imaging studies of ASD focused on the first years of life and discuss implications for next steps in research and clinical applications.


title: A new method for constructing membership functions and fuzzy rules from training examples

abstract: To extract knowledge from a set of numerical data and build up a rule-based system is an important research topic in knowledge acquisition and expert systems. In recent years, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a new fuzzy learning algorithm based on the /spl alpha/-cuts of equivalence relations and the /spl alpha/-cuts of fuzzy sets to construct the membership functions of the input variables and the output variables of fuzzy rules and to induce the fuzzy rules from the numerical training data set. Based on the proposed fuzzy learning algorithm, we also implemented a program on a Pentium PC using the MATLAB development tool to deal with the Iris data classification problem. The experimental results show that the proposed fuzzy learning algorithm has a higher average classification ratio and can generate fewer rules than the existing algorithm.


title: Three-way substrate integrated waveguide (SIW) power divider design

abstract: In this paper, an X-band 1×3 substrate integrated waveguide (SIW) power divider design is presented. The designed SIW power divider provides equal amplitude with uniform phase distribution at the each output port. It has also a satisfactory operating bandwidth and low insertion loss. Moreover, the return loss is approximately 25 dB at the design frequency as shown in the EM simulation results.


title: A capacitor-less 1T-DRAM cell

abstract: A simple true 1 transistor dynamic random access memory (DRAM) cell concept is proposed for the first time, using the body charging of partially-depleted SOI devices to store the logic "1" or "0" binary states. This cell is two times smaller in area than the conventional 8F/sup 2/ 1T/1C DRAM cell and the process of its manufacturing does not require the storage capacitor fabrication steps. This concept will allow the manufacture of simple low cost DRAM and embedded DRAM chips for 100 and sub-100 nm generations.


title: Analysis of Semipermeable Containment Sleeve Technology for High-Speed Permanent Magnet Machines

abstract: This paper presents an alternative permanent magnet rotor containment technology. The use of a semipermeable material in a laminated structure is shown to result in a significant improvement in magnetic loading for a given thickness of containment compared to the use of magnetically inert materials such as carbon fiber or Inconel, while minimizing eddy current losses in the containment layer. An analytical model is presented for determining the air-gap field delivered from a permanent magnet array with a semipermeable containment. The analysis is validated through finite element analysis. The fabrication of a prototype machine is detailed and the results presented show good agreement with the analysis. The validated modeling process is used to assess the potential of the new technology.


title: Future Market Trends and Opportunities for Wearable Sensor Technology

abstract: Wearable sensors enable long term continuous psychological monitoring, which is important for the treatment and management of many chronic illnesses, such as neurological disorders and mental health issues. Examples include diabetes, problems with socials skills to some extent, empathy, communication, depression, drug addiction, and anxiety disorders. In the current paper we present a few mobile health technologies developed by our group and shall also discuss future market trends and opportunities for wearable sensor technology. Technologies presented include wearable sensors for electro-dermal activity (EDA), mobile plethysmography and the supporting wireless network architecture. The companies that are recently in the market and are putting great emphasis on wearable sensors are gradually changing the level of technology to a significant high level.


title: Deep low-rank coding for transfer learning

abstract: Recent researches on transfer learning exploit deep structures for discriminative feature representation to tackle cross-domain disparity. However, few of them are able to joint feature learning and knowledge transfer in a unified deep framework. In this paper, we develop a novel approach, called Deep Low-Rank Coding (DLRC), for transfer learning. Specifically, discriminative low-rank coding is achieved in the guidance of an iterative supervised structure term for each single layer. In this way, both marginal and conditional distributions between two domains intend to be mitigated. In addition, a marginalized denoising feature transformation is employed to guarantee the learned single-layer low-rank coding to be robust despite of corruptions or noises. Finally, by stacking multiple layers of low-rank codings, we manage to learn robust cross-domain features from coarse to fine. Experimental results on several benchmarks have demonstrated the effectiveness of our proposed algorithm on facilitating the recognition performance for the target domain.


title: 5.2/5.7-GHz 48-dB Image Rejection GaInP/GaAs HBT Weaver Down-Converter Using LO Frequency Quadrupler

abstract: A GaInP/GaAs heterojunction bipolar transistor (HBT) down-converter using the Weaver architecture is demonstrated in this paper. The Weaver system is a double-conversion image rejection heterodyne system which requires no bandpass filters in the signal path and no quadrature networks. The Weaver down-converter has the image rejection ratios of 48 dB and 44 dB when the RF frequency is 5.2 GHz and 5.7 GHz, respectively. A new frequency quadrupler is employed in the down-converter to generate the local oscillator (LO) signals. The frequency quadrupler is designed to minimize the phase error when generating LO signals and thus the image rejection performance is improved. A diagrammatic explanation using the complex mixing technique to analyze the image rejection mechanism of the Weaver architecture is developed in this paper. From our analysis, the image rejection can be further improved by making the LO1 and LO2 signals coherent


title: Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension

abstract: Within the framework of pac-learning, we explore the learnability of concepts from samples using the paradigm of sample compression schemes. A sample compression scheme of size k for a concept class C ⊆ 2X consists of a compression function and a reconstruction function. The compression function receives a finite sample set consistent with some concept in C and chooses a subset of k examples as the compression set. The reconstruction function forms a hypothesis on X from a compression set of k examples. For any sample set of a concept in C the compression set produced by the compression function must lead to a hypothesis consistent with the whole original sample set when it is fed to the reconstruction function. We demonstrate that the existence of a sample compression scheme of fixed-size for a class C is sufficient to ensure that the class C is pac-learnable. ::: ::: Previous work has shown that a class is pac-learnable if and only if the Vapnik-Chervonenkis (VC) dimension of the class is finite. In the second half of this paper we explore the relationship between sample compression schemes and the VC dimension. We define maximum and maximal classes of VC dimension d. For every maximum class of VC dimension d, there is a sample compression scheme of size d, and for sufficiently-large maximum classes there is no sample compression scheme of size less than d. We discuss briefly classes of VC dimension d that are maximal but not maximum. It is an open question whether every class of VC dimension d has a sample compression scheme of size O(d).


title: Scoring decisions in the context of economic uncertainty

abstract: We consider methods for incorporating forecasts of future economic conditions into acquisition decisions for scored retail credit and loan portfolios. We suppose that a portfolio manager is faced with two possible future economic scenarios, each characterised by a known probability of occurrence and by known performance functions that give expected profit and volume. We suppose further that he must choose in advance the scoring strategy and score cutoffs to optimise performance. We show that, despite the uncertainty of performance induced by economic conditions, every efficient policy consists of a single cutoff, provided the expected profit and volume performance curves in each scenario are concave. If these curves are not concave, efficient operating points can be characterised as cutoffs on a redefined score. In cases in which two scorecards are available, we show that it may be advantageous to randomly choose the scorecard to be employed, and we provide methods for selecting efficient operating points. Discussion is limited to cases with two scorecards and two economic scenarios, but our approach and results generalise to more scorecards and more economic scenarios.


title: Event-triggered control for multi-agent systems

abstract: Event-driven strategies for multi-agent systems are motivated by the future use of embedded microprocessors with limited resources that will gather information and actuate the individual agent controller updates. The control actuation updates considered in this paper are event-driven, depending on the ratio of a certain measurement error with respect to the norm of a function of the state, and are applied to a first order agreement problem. A centralized formulation of the problem is considered first and then the results are extended to the decentralized counterpart, in which agents require knowledge only of the states of their neighbors for the controller implementation.


title: One-Class Collective Anomaly Detection Based on LSTM-RNNs

abstract: Intrusion detection for computer network systems has been becoming one of the most critical tasks for network administrators today. It has an important role for organizations, governments and our society due to the valuable resources hosted on computer networks. Traditional misuse detection strategies are unable to detect new and unknown intrusion types. In contrast, anomaly detection in network security aims to distinguish between illegal or malicious events and normal behavior of network systems. Anomaly detection can be considered as a classification problem where it builds models of normal network behavior, of which it uses to detect new patterns that significantly deviate from the model. Most of the current approaches on anomaly detection is based on the learning of normal behavior and anomalous actions. They do not include memory that is they do not take into account previous events classify new ones. In this paper, we propose a one-class collective anomaly detection model based on neural network learning. Normally a Long Short-Term Memory Recurrent Neural Network (LSTM RNN) is trained only on normal data, and it is capable of predicting several time-steps ahead of an input. In our approach, a LSTM RNN is trained on normal time series data before performing a prediction for each time-step. Instead of considering each time-step separately, the observation of prediction errors from a certain number of time-steps is now proposed as a new idea for detecting collective anomalies. The prediction errors of a certain number of the latest time-steps above a threshold will indicate a collective anomaly. The model is evaluated on a time series version of the KDD 1999 dataset. The experiments demonstrate that the proposed model is capable to detect collective anomaly efficiently.