title: TM-LDA: efficient online modeling of latent topic transitions in social media

abstract: Latent topic analysis has emerged as one of the most effective methods for classifying, clustering and retrieving textual data. However, existing models such as Latent Dirichlet Allocation (LDA) were developed for static corpora of relatively large documents. In contrast, much of the textual content on the web, and especially social media, is temporally sequenced, and comes in short fragments, including microblog posts on sites such as Twitter and Weibo, status updates on social networking sites such as Facebook and LinkedIn, or comments on content sharing sites such as YouTube. In this paper we propose a novel topic model, Temporal-LDA or TM-LDA, for efficiently mining text streams such as a sequence of posts from the same author, by modeling the topic transitions that naturally arise in these data. TM-LDA learns the transition parameters among topics by minimizing the prediction error on topic distribution in subsequent postings. After training, TM-LDA is thus able to accurately predict the expected topic distribution in future posts. To make these predictions more efficient for a realistic online setting, we develop an efficient updating algorithm to adjust the topic transition parameters, as new documents stream in. Our empirical results, over a corpus of over 30 million microblog posts, show that TM-LDA significantly outperforms state-of-the-art static LDA models for estimating the topic distribution of new documents over time. We also demonstrate that TM-LDA is able to highlight interesting variations of common topic transitions, such as the differences in the work-life rhythm of cities, and factors associated with area-specific problems and complaints.


title: Light-Field Surface Color Segmentation with an Application to Intrinsic Decomposition

abstract: To enable light fields of large environments to be captured, they would have to be sparse, i.e. with a relatively large distance between views. Such sparseness, however, causes subsequent processing to be much more difficult than would be the case with dense light fields. This includes segmentation. In this paper, we address the problem of meaningful segmentation of a sparse planar light field, leading to segments that are coherent between views. In addition, uniquely our method does not make the assumption that all surfaces in the environment are perfect Lambertian reflectors, which further broadens its applicability. Our fully automatic segmentation pipeline leverages scene structure, and does not require the user to navigate through the views to fix inconsistencies. The key idea is to combine coarse estimations given by an over-segmentation of the scene into super-rays, with detailed ray-based processing. We show the merit of our algorithm by means of a novel way to perform intrinsic light field decomposition, outperforming state-of-the-art methods.


title: Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs

abstract: A number of problems can be formulated as prediction on graph-structured data. In this work, we generalize the convolution operator from regular grids to arbitrary graphs while avoiding the spectral domain, which allows us to handle graphs of varying size and connectivity. To move beyond a simple diffusion, filter weights are conditioned on the specific edge labels in the neighborhood of a vertex. Together with the proper choice of graph coarsening, we explore constructing deep neural networks for graph classification. In particular, we demonstrate the generality of our formulation in point cloud classification, where we set the new state of the art, and on a graph classification dataset, where we outperform other deep learning approaches.


title: Incremental Generation of Self-corrections Using Underspecification

abstract: The focus of the present paper is on the interface between the first two components of a language production model, i.e. conceptualiser and formulator. We propose a method of how the incremental input for the formulator can be represented within an underspecification formalism. The proposed method can be used in dynamically changing environments, e.g. an airport supervision and information system, to produce descriptions of ongoing events. Because such a highly dynamic setting requires a real time production, an incremental conceptualiser that produces increments piece by piece is used in order to speed up the production process. Moreover, changes in the environment have to be recognised and quickly reported to the user. For this reason an underspecified representation for the increments is crucial. Underspecification allows information to be added monotonically as soon as a change in the environment has been noticed, e.g. ‘CK-314 is on time . . . uh . . . is delayed.’


title: TUMOR BRAIN DETECTION THROUGH MR IMAGES: A REVIEW OF LITERATURE

abstract: Today's modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by using different tools and techniques. However, this paper presents a comprehensive review of the methods and techniques used to detect brain tumor through MRI image segmentation. Lastly, the paper concludes with a concise discussion and provides a direction toward the upcoming trend of more advanced research studies on brain image segmentation and Tumor detection.


title: An N-way transformer based Wilkinson power divider in CMOS

abstract: This paper presents a transformer based N-way Wilkinson power divider. The λ/4 transmission lines in a conventional N-way Wilkinson divider are replaced by a fully symmetric N-coil transformer. The proposed design features low insertion loss as well as good input/output matching in an ultracompact chip area. By adding cancellation capacitors, excellent isolation is achieved between any two output ports. Through evenodd mode analysis, the closed-form design equations are derived and presented. As a proof-of-concept demonstration, a four-way transformer based Wilkinson power divider is implemented at 4.60GHz in a standard 130nm bulk CMOS process. The measurement results demonstrate 1.10dB insertion loss, 17.0dB isolation, and 20.9% bandwidth, advancing the state-of-the-art performance of fully integrated Wilkinson power dividers.


Survey of tools for Linked Data consumption


title: Gravity-Balancing Leg Orthosis and Its Performance Evaluation

abstract: In this paper, we propose a device to assist persons with hemiparesis to walk by reducing or eliminating the effects of gravity. The design of the device includes the following features: 1) it is passive, i.e., it does not include motors or actuators, but is only composed of links and springs; 2) it is safe and has a simple patient-machine interface to accommodate variability in geometry and inertia of the subjects. A number of methods have been proposed in the literature to gravity-balance a machine. Here, we use a hybrid method to achieve gravity balancing of a human leg over its range of motion. In the hybrid method, a mechanism is used to first locate the center of mass of the human limb and the orthosis. Springs are then added so that the system is gravity-balanced in every configuration. For a quantitative evaluation of the performance of the device, electromyographic (EMG) data of the key muscles, involved in the motion of the leg, were collected and analyzed. Further experiments involving leg-raising and walking tasks were performed, where data from encoders and force-torque sensors were used to compute joint torques. These experiments were performed on five healthy subjects and a stroke patient. The results showed that the EMG activity from the rectus femoris and hamstring muscles with the device was reduced by 75%, during static hip and knee flexion, respectively. For leg-raising tasks, the average torque for static positioning was reduced by 66.8% at the hip joint and 47.3% at the knee joint; however, if we include the transient portion of the leg-raising task, the average torque at the hip was reduced by 61.3%, and at the knee was increased by 2.7% at the knee joints. In the walking experiment, there was a positive impact on the range of movement at the hip and knee joints, especially for the stroke patient: the range of movement increased by 45% at the hip joint and by 85% at the knee joint. We believe that this orthosis can be potentially used to design rehabilitation protocols for patients with stroke


title: Measurement-Based Harmonic Modeling of an Electric Vehicle Charging Station Using a Three-Phase Uncontrolled Rectifier

abstract: The harmonic pollution of electric vehicle chargers is increasing rapidly as the scale of electric vehicle charging stations enlarges to meet the rising demand for electric vehicles. This paper investigates the operating characteristics of a three-phase uncontrolled rectification electric vehicle charger with passive power factor correction. A method for estimating the equivalent circuit parameters of chargers is proposed based on the measured feature data of voltage and current at the ac side and on the circuit constraint during the conduction charging process. A harmonic analytical model of the charging station is then established by dividing the same charger types into groups. The parameter estimation method and the harmonic model of the charging station are verified through simulation, experiment, and field test. The parameter sensitivities of the equivalent circuit to the charging current harmonic are also studied.


title: A survey on provenance: What for? What form? What from?

abstract: Provenance refers to any information describing the production process of an end product, which can be anything from a piece of digital data to a physical object. While this survey focuses on the former type of end product, this definition still leaves room for many different interpretations of and approaches to provenance. These are typically motivated by different application domains for provenance (e.g., accountability, reproducibility, process debugging) and varying technical requirements such as runtime, scalability, or privacy. As a result, we observe a wide variety of provenance types and provenance-generating methods. This survey provides an overview of the research field of provenance, focusing on what provenance is used for (what for?), what types of provenance have been defined and captured for the different applications (what form?), and which resources and system requirements impact the choice of deploying a particular provenance solution (what from?). For each of these three key questions, we provide a classification and review the state of the art for each class. We conclude with a summary and possible future research challenges.


title: A Silicon PUF Based Entropy Pump

abstract: The security level of many cryptographic protocols and secure systems is determined by the strength of the cryptographic keys, which can be measured by entropy. Finding an entropy source that can generate secure keys with high entropy is a very challenging problem and it is normally associated with high cost. Instead of looking for a low-cost entropy source, we study in this article how to improve the entropy generated by a low-entropy source. Unlike the existing approaches based on hash function or cryptographic protocols, our solution leverages the intrinsic randomness in physical properties such as silicon physical unclonable functions (PUFs). Silicon PUF is a piece of circuitry that can capture certain intrinsic on-chip variations that were introduced during the chip fabrication process. It is generally believed that such variations are random and unpredictable. In this article, we demonstrate that the silicon PUF can be used as an effective entropy pump to boost low-entropy keys. Our approach is based on a recently developed highly flexible ring oscillator (RO) PUF. When we use the low-entropy key to configure the RO PUF, we find that the corresponding PUF response exhibits higher entropy, which means that the key's entropy has been improved. We implement our design on Nexys 4 Artix-7 FPGA board and demonstrate that the configurable PUF structure can successfully enhance the entropy of input keys. Compared to the other entropy enhancement methods, our PUF based entropy pump has the lowest hardware cost. Moreover, we apply this in a password enhancement application to provide robust high entropy passwords that can resist attacks such as the pre-compute attack.


title: Transform Coding Techniques for Lossy Hyperspectral Data Compression

abstract: Transform-based lossy compression has a huge potential for hyperspectral data reduction. Hyperspectral data are 3-D, and the nature of their correlation is different in each dimension. This calls for a careful design of the 3-D transform to be used for compression. In this paper, we investigate the transform design and rate allocation stage for lossy compression of hyperspectral data. First, we select a set of 3-D transforms, obtained by combining in various ways wavelets, wavelet packets, the discrete cosine transform, and the Karhunen-Loegraveve transform (KLT), and evaluate the coding efficiency of these combinations. Second, we propose a low-complexity version of the KLT, in which complexity and performance can be balanced in a scalable way, allowing one to design the transform that better matches a specific application. Third, we integrate this, as well as other existing transforms, in the framework of Part 2 of the Joint Photographic Experts Group (JPEG) 2000 standard, taking advantage of the high coding efficiency of JPEG 2000, and exploiting the interoperability of an international standard. We introduce an evaluation framework based on both reconstruction fidelity and impact on image exploitation, and evaluate the proposed algorithm by applying this framework to AVIRIS scenes. It is shown that the scheme based on the proposed low-complexity KLT significantly outperforms previous schemes as to rate-distortion performance. As for impact on exploitation, we consider multiclass hard classification, spectral unmixing, binary classification, and anomaly detection as benchmark applications


title: Information Sharing Online: A Research Challenge

abstract: We view information sharing as a promising target of research. technology and human practice have been evolving toward more opportunities and varied manners in which information can be shared. We propose that 'information sharing' is best understood as a continuum that concomitantly reflects behavioural, social, economic, legal, and technological influences. 'Information' is a hybrid of both 'public' and 'private' goods. The mechanisms that enable, predict, and catalyse sharing behaviour in online contexts should be of major interest to both scholars and practitioners of knowledge systems. We cite a spectrum of analytical as well as empirical research on the topic of sharing, identifying the methods and theories used in the approaches to date. We also review the major online technology genres of import for sharing. We suggest an initial map of constructs to chart future research on sharing.


title: Belvedere: Engaging Students in Critical Discussion of Science and Public Policy Issues

abstract: We describe "Belvedere," a system to support students engaged in crit- ical discussion of science issues. The design is intended to address cognitive and metacognitive limitations of unpracticed beginners while supporting their practice of this complex skill. Both prior psychological research and formative evaluation studies with users shaped the interface design. We discuss our design rationale, describe our formative evaluation studies and provide examples of student sessions, and discuss ongoing work with the argumentation advisor.


title: Bilingualism does not alter cognitive decline or dementia risk among Spanish-speaking immigrants.

abstract: Objective: Clinic-based studies suggest that dementia is diagnosed at older ages in bilinguals compared with monolinguals. The current study sought to test this hypothesis in a large, prospective, communitybased study of initially nondemented Hispanic immigrants living in a Spanish-speaking enclave of northern Manhattan. Method: Participants included 1,067 participants in the Washington/Hamilton Heights Inwood Columbia Aging Project (WHICAP) who were tested in Spanish and followed at 18–24 month intervals for up to 23 years. Spanish-English bilingualism was estimated via both self-report and an objective measure of English reading level. Multilevel models for change estimated the independent effects of bilingualism on cognitive decline in 4 domains: episodic memory, language, executive function, and speed. Over the course of the study, 282 participants developed dementia. Cox regression was used to estimate the independent effect of bilingualism on dementia conversion. Covariates included country of origin, gender, education, time spent in the United States, recruitment cohort, and age at enrollment. Results: Independent of the covariates, bilingualism was associated with better memory and executive function at baseline. However, bilingualism was not independently associated with rates of cognitive decline or dementia conversion. Results were similar whether bilingualism was measured via self-report or an objective test of reading level. Conclusions: This study does not support a protective effect of bilingualism on age-related cognitive decline or the development of dementia. In this sample of Hispanic immigrants, bilingualism is related to higher initial scores on cognitive tests and higher educational attainment and may not represent a unique source of cognitive reserve.


title: Deceiving Others: Distinct Neural Responses of the Prefrontal Cortex and Amygdala in Simple Fabrication and Deception with Social Interactions

abstract: Brain mechanisms for telling lies have been investigated recently using neuroimaging techniques such as functional magnetic resonance imaging and positron emission tomography. Although the advent of these techniques has gradually enabled clarification of the functional contributions of the prefrontal cortex in deception with respect to executive function, the specific roles of subregions within the prefrontal cortex and other brain regions responsible for emotional regulation or social interactions during deception are still unclear. Assuming that the processes of falsifying truthful responses and deceiving others are differentially associated with the activities of these regions, we conducted a positron emission tomography experiment with 2 (truth, lie) × 2 (honesty, dishonesty) factorial design. The main effect of falsifying the truthful responses revealed increased brain activity of the left dorsolateral and right anterior prefrontal cortices, supporting the interpretation of previous studies that executive functions are related to making untruthful responses. The main effect of deceiving the interrogator showed activations of the ventromedial prefrontal (medial orbitofrontal) cortex and amygdala, adding new evidence that the brain regions assumed to be responsible for emotional processing or social interaction are active during deceptive behavior similar to that in real-life situations. Further analysis revealed that activity of the right anterior prefrontal cortex showed both effects of deception, indicating that this region has a pivotal role in telling lies. Our results provide clear evidence of functionally dissociable roles of the prefrontal subregions and amygdala for human deception.


title: Novelty detection: a review - part 1: statistical approaches

abstract: Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information about objects that were not known at the time of training the model. In this paper we provide state-of-the-art review in the area of novelty detection based on statistical approaches. The second part paper details novelty detection using neural networks. As discussed, there are a multitude of applications where novelty detection is extremely important including signal processing, computer vision, pattern recognition, data mining, and robotics.


title: Musical instrument identification: A pattern-recognition approach

abstract: A statistical pattern-recognition technique was applied to the classification of musical instrument tones within a taxonomic hierarchy. Perceptually salient acoustic features— related to the physical properties of source excitation and resonance structure—were measured from the output of an auditory model (the log-lag correlogram) for 1023 isolated tones over the full pitch ranges of 15 orchestral instruments. The data set included examples from the string (bowed and plucked), woodwind (single, double, and air reed), and brass families. Using 70%/30% splits between training and test data, maximum a posteriori classifiers were constructed based on Gaussian models arrived at through Fisher multiplediscriminant analysis. The classifiers distinguished transient from continuant tones with approximately 99% correct performance. Instrument families were identified with approximately 90% performance, and individual instruments were identified with an overall success rate of approximately 70%. These preliminary analyses compare favorably with human performance on the same task and demonstrate the utility of the hierarchical approach to classification.


title: Semi-flexible directed polymers in a strip with attractive walls

abstract: We study a model of a semiflexible long chain polymer confined to a two-dimensional slit of width $w$, and interacting with the walls of the slit. The interactions with the walls are controlled by Boltzmann weights $a$ and $b$, and the flexibility of the polymer is controlled by another Boltzmann weight $c$. This is a simple model of the steric stabilisation of colloidal dispersions by polymers in solution. We solve the model exactly and compute various quantities in $(a,b,c)$-space, including the free energy and the force exerted by the polymer on the walls of the slit. In some cases these quantities can be computed exactly for all $w$, while for others only asymptotic expressions can be found. Of particular interest is the zero-force surface -- the manifold in $(a,b,c)$-space where the free energy is independent of $w$, and the loss of entropy due to confinement in the slit is exactly balanced by the energy gained from interactions with the walls.


title: P2P Lending Platform Risk Observing Method Based on Short-Time Multi-Source Regression Algorithm

abstract: Peer-to-Peer (P2P) lending is a popular way of lending in contemporary Internet financial filed. Comparing with the traditional bank lending, the annual risk evaluation is no longer applicable for P2P platform because of the short life cycle and a lot of transaction records. This paper presents a method to dynamically evaluate the operation risk of P2P plat- forms based on a short-time multi-source regression algorithm. Dynamic time windows are used to split up the lending records and linear regression method is used to quantify the dynamic risk index of P2P platforms. The experimental results show that the proposed method can reflect the visible operation situation of platforms, and give investors dynamic risk assessment and effective tips of the platforms.


title: Experimental Analysis of Subscribers' Privacy Exposure by LTE Paging

abstract: Over the last years, considerable attention has been given to the privacy of individuals in wireless environments. Although significantly improved over the previous generations of mobile networks, LTE still exposes vulnerabilities that attackers can exploit. This might be the case of paging messages, wake-up notifications that target specific subscribers, and that are broadcasted in clear over the radio interface. If they are not properly implemented, paging messages can expose the identity of subscribers and furthermore provide information about their location. It is therefore important that mobile network operators comply with the recommendations and implement the appropriate mechanisms to mitigate attacks. In this paper, we verify by experiment that paging messages can be captured and decoded by using minimal technical skills and publicly available tools. Moreover, we present a general experimental method to test privacy exposure by LTE paging messages, and we conduct a case study on three different LTE mobile operators.


title: A randomized controlled trial into the effects of neurofeedback, methylphenidate, and physical activity on EEG power spectra in children with ADHD

abstract: Background ::: The clinical and neurophysiological effects of neurofeedback (NF) as treatment for children with ADHD are still unclear. This randomized controlled trial (RCT) examined electroencephalogram (EEG) power spectra before and after NF compared to methylphenidate (MPH) treatment and physical activity (PA) – as semi-active control group – during resting and active (effortful) task conditions to determine whether NF can induce sustained alterations in brain function. ::: ::: Methods ::: Using a multicentre three-way parallel group RCT design, 112 children with a DSM-IV diagnosis of ADHD, aged between 7 and 13 years, were initially included. NF training consisted of 30 sessions of theta/beta training at Cz over a 10-week period. PA training was a semi-active control group, matched in frequency and duration. Methylphenidate was titrated using a double-blind placebo controlled procedure in 6 weeks, followed by a stable dose for 4 weeks. EEG power spectra measures during eyes open (EO), eyes closed (EC) and task (effortful) conditions were available for 81 children at pre- and postintervention (n = 29 NF, n = 25 MPH, n = 27 PA). Clinical trials registration: Train Your Brain? Exercise and Neurofeedback Intervention for ADHD, https://clinicaltrials.gov/show/;NCT01363544, Ref. No. NCT01363544. ::: ::: Results ::: Both NF and MPH resulted in comparable reductions in theta power from pre- to postintervention during the EO condition compared to PA (ηp2 = .08 and .12). For NF, greater reductions in theta were related to greater reductions in ADHD symptoms. During the task condition, only MPH showed reductions in theta and alpha power compared to PA (ηp2 = .10 and .12). ::: ::: Conclusions ::: This study provides evidence for specific neurophysiological effects after theta/beta NF and MPH treatment in children with ADHD. However, for NF these effects did not generalize to an active task condition, potentially explaining reduced behavioural effects of NF in the classroom.


title: Design of MPPT algorithm under partial shadows

abstract: Owing to the complexity of the photovoltaic system structure and their environment, especially under the partial shadows environment, the output characteristics of photovoltaic arrays are greatly affected. Under the partial shadows environment, power-voltage (P-V) characteristics curve is of multi-peak. This makes that it is a difficult task to track the actual maximum power point. In addition, most programs are not able to get the maximum power point under these conditions. In this paper, we study the P-V curves under both uniform illumination and partial shadows environments, and then design an algorithm to track the maximum power point and select the strategy to deal with the MPPT algorithm by DSP chips and DC-DC converters. It is simple and easy to allow solar panels to maintain the best solar energy utilization resulting in increasing output at all times. Meanwhile, in order to track local peak point and improve the tracking speed, the algorithm proposed DC-DC converters operating feed-forward control scheme. Compared with the conventional controller, this controller costs much less time. This paper focuses mainly on specific processes of the algorithm, and being the follow-up basis for implementation of control strategies.


title: POLYGLOT-NER: Massive Multilingual Named Entity Recognition

abstract: The increasing diversity of languages used on the web introduces a new level of complexity to Information Retrieval (IR) systems. We can no longer assume that textual content is written in one language or even the same language family. In this paper, we demonstrate how to build massive multilingual annotators with minimal human expertise and intervention. We describe a system that builds Named Entity Recognition (NER) annotators for 40 major languages using Wikipedia and Freebase. Our approach does not require NER human annotated datasets or language specific resources like treebanks, parallel corpora, and orthographic rules. The novelty of approach lies therein - using only language agnostic techniques, while achieving competitive performance. ::: Our method learns distributed word representations (word embeddings) which encode semantic and syntactic features of words in each language. Then, we automatically generate datasets from Wikipedia link structure and Freebase attributes. Finally, we apply two preprocessing stages (oversampling and exact surface form matching) which do not require any linguistic expertise. ::: Our evaluation is two fold: First, we demonstrate the system performance on human annotated datasets. Second, for languages where no gold-standard benchmarks are available, we propose a new method, distant evaluation, based on statistical machine translation.


title: On SCADA control system command and response injection and intrusion detection

abstract: SCADA systems are widely used in critical infrastructure sectors, including electricity generation and distribution, oil and gas production and distribution, and water treatment and distribution. SCADA process control systems are typically isolated from the internet via firewalls. However, they may still be subject to illicit cyber penetrations and may be subject to cyber threats from disgruntled insiders. We have developed a set of command injection, data injection, and denial of service attacks which leverage the lack of authentication in many common control system communication protocols including MODBUS, DNP3, and EtherNET/IP. We used these exploits to aid in development of a neural network based intrusion detection system which monitors control system physical behavior to detect artifacts of command and response injection attacks. Finally, we present intrusion detection accuracy results for our neural network based IDS which includes input features derived from physical properties of the control system.


title: Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server

abstract: This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to expectation propagation (EP), a popular variational inference algorithm. SNEP is a black box variational algorithm, in that it does not require any simplifying assumptions on the distribution of interest, beyond the existence of some Monte Carlo sampler for estimating the moments of the EP tilted distributions. Further, as opposed to EP which has no guarantee of convergence, SNEP can be shown to be convergent, even when using Monte Carlo moment estimates. Secondly, we propose a novel architecture for distributed Bayesian learning which we call the posterior server. The posterior server allows scalable and robust Bayesian learning in cases where a data set is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data. An independent Monte Carlo sampler is run on each compute node, with direct access only to the local data subset, but which targets an approximation to the global posterior distribution given all data across the whole cluster. This is achieved by using a distributed asynchronous implementation of SNEP to pass messages across the cluster. We demonstrate SNEP and the posterior server on distributed Bayesian learning of logistic regression and neural networks. ::: Keywords: Distributed Learning, Large Scale Learning, Deep Learning, Bayesian Learn- ing, Variational Inference, Expectation Propagation, Stochastic Approximation, Natural Gradient, Markov chain Monte Carlo, Parameter Server, Posterior Server.


title: Cluster I/O with River: making the fast case common

abstract: We introduce River, a data-flow programming environment and I/O substrate for clusters of computers. River is designed to provide maximum performance in the common case — even in the face of nonuniformities in hardware, software, and workload. River is based on two simple design features: a high-performance distributed queue, and a storage redundancy mechanism called graduated declustering. We have implemented a number of data-intensive applications on River, which validate our design with near-ideal performance in a variety of non-uniform performance scenarios.


title: A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

abstract: Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (kim 2014, kalchbrenner 2014, johnson 2014). However, these models require practitioners to specify an exact model architecture and set accompanying hyperparameters, including the filter region size, regularization parameters, and so on. It is currently unknown how sensitive model performance is to changes in these configurations for the task of sentence classification. We thus conduct a sensitivity analysis of one-layer CNNs to explore the effect of architecture components on model performance; our aim is to distinguish between important and comparatively inconsequential design decisions for sentence classification. We focus on one-layer CNNs (to the exclusion of more complex models) due to their comparative simplicity and strong empirical performance, which makes it a modern standard baseline method akin to Support Vector Machine (SVMs) and logistic regression. We derive practical advice from our extensive empirical results for those interested in getting the most out of CNNs for sentence classification in real world settings.


title: Artificial magnetic conductor surfaces and their application to low-profile high-gain planar antennas

abstract: Planar periodic metallic arrays behave as artificial magnetic conductor (AMC) surfaces when placed on a grounded dielectric substrate and they introduce a zero degrees reflection phase shift to incident waves. In this paper the AMC operation of single-layer arrays without vias is studied using a resonant cavity model and a new application to high-gain printed antennas is presented. A ray analysis is employed in order to give physical insight into the performance of AMCs and derive design guidelines. The bandwidth and center frequency of AMC surfaces are investigated using full-wave analysis and the qualitative predictions of the ray model are validated. Planar AMC surfaces are used for the first time as the ground plane in a high-gain microstrip patch antenna with a partially reflective surface as superstrate. A significant reduction of the antenna profile is achieved. A ray theory approach is employed in order to describe the functioning of the antenna and to predict the existence of quarter wavelength resonant cavities.


title: Thermal Analysis of a Tubular Permanent Magnet Linear Generator using Multiphysics Solver

abstract: This paper presents the thermal design aspect of linear generator. A study is carried out to investigate the temperature effects on the components in linear generator. One of the most important components in the linear generator, neodymium Iron boron (Nd-Fe-B) permanent magnet is emphasized in this study since it provides a magnetic field source for the linear generator and the influence of its electromagnetic properties towards temperature will affect the performance of linear generator. In this paper, the generator is coupled with the internal combustion engine which tends to produce heat. Due to the high temperature from the combustion engine and the heat produced by the current carrying conductors, thermal analysis on different physical aspects has been done. In order to achieve an accurate result, the multiphysics solver is used to analyze the thermal analysis for both electrical and thermal problem. Finite element method (FEM) is used to observe this coupled field effect.


title: A compact former of high-power bipolar subnanosecond pulses

abstract: The system comprises either a nanosecond solid-state operating switch (SOS)-generator (see, e.g., Rukin, 1995) or a RADAN pulsed power source (see, e.g., Mesyats et al., 1993) that charges resonantly a short pulse-forming line (PFL) through a decoupling inductor, an active converter, a matched load, and several built-in voltage and current probes. The active converter comprises an additional 1-ns PFL charged through a peaking spark gap (SG) and a secondary decoupling inductor by the first PFL, and two SGs (chopping and peaking gap). The peaking SG and active converter are set in one body and operate in N/sub 2/ at pressure up to 6 MPa. Driven by RADAN, the converter generates bipolar subnanosecond pulses having peak-to-peak amplitude up to 270 kV across 46.6-/spl Omega/ load. The pulsewidth of each half wave of bipolar pulses on full-width at half-maximum is 280 ps, with the risetime of the first half wave of about 160 ps. The SGs' gaps can be regulated without system depressurizing (a technique adopted from Shpak et al., 1996). Circuit analysis accounting for the distributed character of the components and numerous parasitic parameters is presented. Mapping of main SG parameters (gap distance and pressure) was performed. Waveforms probed at different locations of the pulser systems, from the SOS-generator and from the RADAN to the load, are shown. The bipolar subnanosecond pulses had very stable rise, while the fall jitter was around 50 ps. The experimental results are in fair agreement with the simulation. Pulsers were tested with a transverse electromagnetic horn antenna. An effective potential of 910 kV was obtained.


title: Exploring the Limits of Complexity: A Survey of Empirical Studies on Graph Visualisation

abstract: For decades, researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks. Experiments involving human participants have also explored the readability of different styles of layout and representations for such networks. In both bodies of literature, networks are frequently referred to as being 'large' or 'complex', yet these terms are relative. From a human-centred, experiment point-of-view, what constitutes 'large' (for example) depends on several factors, such as data complexity, visual complexity, and the technology used. In this paper, we survey the literature on human-centred experiments to understand how, in practice, different features and characteristics of node-link diagrams affect visual complexity.


title: Context-Aware Recommendation-Based Learning Analytics Using Tensor and Coupled Matrix Factorization

abstract: Student retention and timely graduation are enduring challenges in higher education. With the rapidly expanding collection and availability of learning data and related analytics, student performance can be accurately monitored, and possibly predicted ahead of time, thus, enabling early warning and degree planning “expert systems” to provide disciplined decision support to counselors, advisors, and educators. Previous work in educational data mining has explored matrix factorization techniques for grade prediction, albeit without taking contextual information into account. Temporal information should be informative as it distinguishes between the different class offerings and indirectly captures student experience as well. To exploit temporal and/or other kinds of context, we develop three approaches under the framework of collaborative filtering (CF). Two of the proposed approaches build upon coupled matrix factorization with a shared latent matrix factor. The third utilizes tensor factorization to model grades and their context, without introducing a new mode per context dimension as is common in the CF literature. The latent factors obtained can be used to predict grades and context, if desired. We evaluate these approaches on grade data obtained from the University of Minnesota. Experimental results show that fairly good prediction is possible even with simple approaches, but very accurate prediction is hard. The more advanced approaches can increase prediction accuracy, but only up to a point for the particular dataset considered.


title: Behaviour Analysis and Anomaly Detection Algorithms for the Maritime Integrated Surveillance Awareness

abstract: The increasing complexity of social-economical activities in the maritime domain is raising new challenges to maritime administrations and agencies from coastal states. These challenges require innovative approaches to support maritime safety and security and, in particular, to promote the sustainable use of the oceans. One solution is to enhance Maritime Situational Awareness (MSA) using systems with high integration and efficient analysis capability. With this problem in mind, the European Commission awarded a grant to a project which aims to develop a framework that will enable a better decision process, by the operators and decision makers, at the maritime operations centers and allowing seamless cooperation between operating authorities and on-site / at sea / in air intervention forces. The project focuses on taking benefits from the data exchange to the next level, while remaining completely integrated in the current European policy. Securing shipping and human lives is of fundamental importance for our continued well-being. The most important way to cost-effectively enhance security and safety at sea is to cooperate at national and international levels. This paper focuses on the specific contribution from some partners of the European Project Consortium - MARISA.


Morphological Tagging Approach in Document Analysis of Invoices


title: Malware Detection and Classification Based on N-Grams Attribute Similarity

abstract: Unknown malware has increased dramatically, but the existing security software cannot identify them effectively. In this paper, we propose a new malware detection and classification method based on n-grams attribute similarity. We extract all n-grams of byte codes from training samples and select the most relevant as attributes. After calculating the average value of attributes in malware and benign separately, we determine a test sample is malware or benign by attribute similarity between attributes of the test sample and the two average attributes of malware and benign. We compare our method with a variety of machine learning methods, including Naive Bayes, Bayesian Networks, Support Vector Machine and C4.5 Decision Tree. Experimental results on public (Open Malware Benchmark) and private (self-collected) datasets both reveal that our method outperforms the other four methods.


title: Applying an optimized switching strategy to a high gain boost converter for input current ripple cancellation

abstract: This paper discusses applying various switching strategies such as conventional complementary and proportional methods on a specific converter. This converter was recently presented and it can provide high voltage gain and has current ripple cancelation ability at a preselected duty-cycle. The input current ripple is zero when the converter works at a special duty-cycle. But load disturbance leads to output voltage changes that maybe cause the duty-cycle to deviate from its preselected value. In this situation, the input current ripple cannot be completely canceled. The proposed proportional strategy is an optimized method for minimizing input current ripple at other operating duty-cycles and also it provides a voltage gain lower than conventional complementary strategy. Here, the converter's performance is analyzed under two switching strategies and the effect of various strategies has been investigated on converter parameters including output voltage and input current ripple. These considerations are verified by implementing a 100-W prototype of the proposed converter in laboratory.


title: Conversational Query Understanding Using Sequence to Sequence Modeling

abstract: Understanding conversations is crucial to enabling conversational search in technologies such as chatbots, digital assistants, and smart home devices that are becoming increasingly popular. Conventional search engines are powerful at answering open domain queries but are mostly capable of stateless search. In this paper, we define a conversational query as a query that depends on the context of the current conversation, and we formulate the conversational query understanding problem as context aware query reformulation, where the goal is to reformulate the conversational query into a search engine friendly query in order to satisfy users’ information needs in conversational settings. Such context aware query reformulation problem lends itself to sequence to sequence modeling. We present a large scale open domain dataset of conversational queries and various sequence to sequence models that are learned from this dataset. The best model correctly reformulates over half of all conversational queries, showing the potential of sequence to sequence modeling for this task.


title: Heterosocial involvement, peer pressure for thinness, and body dissatisfaction among young adolescent girls

abstract: The purpose of this study was to examine longitudinal connections among young adolescent heterosocial involvement (i.e., mixed-sex interactions), peer pressure for thinness, and body dissatisfaction. Three years of self-report questionnaire data were collected from 88 adolescent girls as they completed 6th through 8th grades. Results indicated that the relation between heterosocial involvement and body dissatisfaction was mediated by perceived peer pressure for thinness. Within this model, heterosocial involvement was associated with greater peer pressure for thinness. In turn, peer pressure for thinness was associated with greater body dissatisfaction. Results are discussed in terms of their implications for prevention and intervention efforts aimed at girls during their middle-school years.


ABSTRACT A Multilevel Model Primer Using SAS ® PROC MIXED


title: Cyberbullying perpetration and victimization among adolescents in Hong Kong

abstract: Cyberbullying is a growing concern worldwide. Using a sample of 1917 secondary adolescents from seven schools, five psychometric measures (self-efficacy, empathy level, feelings regarding a harmonious school, sense of belonging to the school, and psychosocial wellbeing) and five scales regarding bullying experiences (cyber- and traditional bullying perpetration and victimization; reactions to cyberbullying victimization) were administered to explore the prevalence of cyberbullying in Hong Kong. Findings indicated that male adolescents were more likely than female adolescents to cyberbully others and to be cyber-victimized. Cyberbullying perpetration and victimization were found to be negatively associated with the adolescents' psychosocial health and sense of belonging to school. Cyber- and traditional bullying were positively correlated. Multivariate analyses indicated that being male, having a low sense of belonging to school, involvement in traditional bullying perpetration, and experiencing cyber-victimization were associated with an increased propensity to cyberbully others.


title: A Ka-Band Monopulse Microstrip Antenna Array

abstract: A 1 times 8 monopulse microstrip patch antenna array in Ka-band has been proposed. The radiating element is a modified circular patch antenna, designed for getting circular polarization. The feed network is composed of six Wilkinson power dividers, a 3 dB hybrid, and a microstrip to waveguide transition. Both the array and the feed network are placed on the same layer.


title: Rock texture retrieval using gray level co-occurrence matrix” Norsig2002

abstract: Nowadays, as the computational power increases, the role of automatic visual inspection becomes more important. Therefore, also visual quality control has gained in popularity. This paper presents an application of gray level co-occurrence matrix (GLCM) to texturebased similarity evaluation of rock images. Retrieval results were evaluated for two databases, one consisting of the whole images and the other with blocks obtained by splitting the original images. Retrieval results for both databases were obtained by calculating distance between the feature vector of the query image and other feature vectors in the database. Performance of the cooccurrence matrices was also compared to that of Gabor wavelet features. Co-occurrence matrices performed better for the given rock image dataset. This similarity evaluation application could reduce the cost of geological investigations by allowing improved accuracy in automatic rock sample selection.


title: Self-Organizing Map Based Data Detection of Hematopoietic Tumors

abstract: Data detection based on self organizing maps is presented for hematopoietic tumor patients. Learning data for the maps are generated from the screening data of examinees. The incomplete screening data without some item values is then supplemented by substituting averaged non-missing item values. In addition, redundant items, which are common to all the data and tend to have an unfavorable influence on data detection, are eliminated by a genetic algorithm and/or an immune algorithm. It is basically judged, by observing the label of a winner neuron in the map, whether the data presented to the map belongs to the class of hematopoietic tumors. Some experimental results are provided to show that the proposed methods achieve the high probability of correctly identifying examinees as hematopoietic tumor patients.


Documenta Math. 155 On the History of the Shortest Path Problem


title: A data mining framework for building intrusion detection models

abstract: There is often the need to update an installed intrusion detection system (IDS) due to new attack methods or upgraded computing environments. Since many current IDSs are constructed by manual encoding of expert knowledge, changes to IDSs are expensive and slow. We describe a data mining framework for adaptively building Intrusion Detection (ID) models. The central idea is to utilize auditing programs to extract an extensive set of features that describe each network connection or host session, and apply data mining programs to learn rules that accurately capture the behavior of intrusions and normal activities. These rules can then be used for misuse detection and anomaly detection. New detection models are incorporated into an existing IDS through a meta-learning (or co-operative learning) process, which produces a meta detection model that combines evidence from multiple models. We discuss the strengths of our data mining programs, namely, classification, meta-learning, association rules, and frequent episodes. We report on the results of applying these programs to the extensively gathered network audit data for the 1998 DARPA Intrusion Detection Evaluation Program.


Data Leakage Detection and Data Prevention Using Algorithm


title: Solid waste monitoring system integration based on RFID, GPS and camera

abstract: The integration of communication technologies such as radio frequency identification (RFID), global positioning system (GPS), general packet radio system (GPRS), geographic information system (GIS) with a camera are constructed for solid waste monitoring system. The aim is to improve the way of responding to customer's inquiry and emergency cases and estimate the solid waste amount without any involvement of the truck driver. The proposed system consists of RFID tag mounted on the bin, RFID reader as truck module, GPSR/GSM as web-server, GIS as map server, database server and control station server. The tracking devices mounted in the trucks collect location information in real-time via the GPS. This information is transferred continuously through GPRS to a central database. The users are able to view the current location of each truck in the collection stage via a web-based application, and thereby manage the fleet. The trucks positions and trash bin information are displayed on a digital map, which is made available by a map server. Thus, the solid waste of the bin and the truck are being monitored using the developed system.


1. Introduction Informativeness, Relevance and Scalar Implicature *


title: Millimeter-wave antennas for radio access and backhaul in 5G heterogeneous mobile networks

abstract: Millimeter-wave communications are expected to play a key role in future 5G mobile networks to overcome the dramatic traffic growth expected over the next decade. Such systems will severely challenge antenna technologies used at mobile terminal, access point or backhaul/fronthaul levels. This paper provides an overview of the authors' recent achievements in the design of integrated antennas, antenna arrays and high-directivity quasi-optical antennas for high data-rate 60-GHz communications.


Financial Administration in Local Government.


title: A new method of correcting uneven illumination problem in fundus images

abstract: Recent advancements in signal and image processing have reduced the time of diagnoses, effort and pressure on the screeners by providing auto diagnostic tools for different diseases. The success rate of these tools greatly depend on the quality of acquired images. Bad image quality can significantly reduce the specificity and the sensitivity which in turn forces screeners back to their tedious job of manual diagnoses. In acquired fundus images, some areas appear to be brighter than the other, that is areas close to the center of the image are always well illuminated, hence appear very bright while areas far from the center are poorly illuminated hence appears to be very dark. Several techniques including the simple thresholding, Naka Rushton (NR) filtering technique and histogram equalization (HE) method have been suggested by various researchers to overcome this problem. However, each of these methods has limitations at their own and hence the need to develop a more robust technique that will provide better performance with greater flexibility. A new method of compensating uneven (irregular) illumination in fundus images termed global-local adaptive histogram equalization using partially-overlapped windows (GLAPOW) is proposed in this paper. The developed algorithm has been tested and the results obtained show superior performance when compared to other known techniques for uneven illumination correction.


title: Priority Handling Aggregation Technique (PHAT) for Wireless Sensor Networks

abstract: Wireless Sensor Networks (WSNs) have limited power capabilities, whereas they serve applications which usually require specific packets, i.e. High Priority Packets (HPP), to be delivered before a deadline. Hence, it is essential to reduce the energy consumption and to have real-time behavior. To achieve this goal we propose a hybrid technique which explores the benefits of data aggregation without data size reduction in combination with prioritized queues. The energy consumption is reduced by appending data from incoming packets with already buffered Low Priority Packets (LPP). The real-time behavior is achieved by directly forwarding the HPP to the next node. Our study explores the impact of the proposed hybrid technique in several all-to-one data flow scenarios with various traffic loads, wait time intervals and percentage of HPP. Our results show gain up to 23,3% in packet loss and 36,6% in energy consumption compared with the direct forwarding of packets.


title: Supervised Dictionary Learning by a Variational Bayesian Group Sparse Nonnegative Matrix Factorization

abstract: Nonnegative matrix factorization (NMF) with group sparsity constraints is formulated as a probabilistic graphical model and, assuming some observed data have been generated by the model, a feasible variational Bayesian algorithm is derived for learning model parameters. When used in a supervised learning scenario, NMF is most often utilized as an unsupervised feature extractor followed by classification in the obtained feature subspace. Having mapped the class labels to a more general concept of groups which underlie sparsity of the coefficients, what the proposed group sparse NMF model allows is incorporating class label information to find low dimensional label-driven dictionaries which not only aim to represent the data faithfully, but are also suitable for class discrimination. Experiments performed in face recognition and facial expression recognition domains point to advantages of classification in such label-driven feature subspaces over classification in feature subspaces obtained in an unsupervised manner.


title: How to generate cryptographically strong sequences of pseudo random bits

abstract: We give a set of conditions that allow one to generate 50–50 unpredictable bits.Based on those conditions, we present a general algorithmic scheme for constructing polynomial-time deterministic algorithms that stretch a short secret random input into a long sequence of unpredictable pseudo-random bits.We give an implementation of our scheme and exhibit a pseudo-random bit generator for which any efficient strategy for predicting the next output bit with better than 50–50 chance is easily transformable to an “equally efficient” algorithm for solving the discrete logarithm problem. In particular: if the discrete logarithm problem cannot be solved in probabilistic polynomial time, no probabilistic polynomial-time algorithm can guess the next output bit better than by flipping a coin: if “head” guess “0”, if “tail” guess “1”


title: Machine printed handwritten text discrimination using Radon transform and SVM classifier

abstract: Discrimination of machine printed and handwritten text is deemed as major problem in the recognition of the mixed texts. In this paper, we address the problem of identifying each type by using the Radon transform and Support Vector Machines, which is conducted at three steps: preprocessing, feature generation and classification. New set of features is generated from each word using the Radon transform. Classification is used to distinguish printed text from handwritten. The proposed system is tested on IAM databases. The recognition rate of the proposed method is calculated to be over 98%.


title: CredFinder: A real-time tweets credibility assessing system

abstract: Lately, Twitter has grown to be one of the most favored ways of disseminating information to people around the globe. However, the main challenge faced by the users is how to assess the credibility of information posted through this social network in real time. In this paper, we present a real-time content credibility assessment system named CredFinder, which is capable of measuring the trustworthiness of information through user analysis and content analysis. The proposed system is capable of providing a credibility score for each user's tweets. Hence, it provides users with the opportunity to judge the credibility of information faster. CredFinder consists of two parts: a frontend in the form of an extension to the Chrome browser that collects tweets in real time from a Twitter search or a user-timeline page and a backend that analyzes the collected tweets and assesses their credibility.


title: From ratings to trust: an empirical study of implicit trust in recommender systems

abstract: Trust has been extensively studied and its effectiveness demonstrated in recommender systems. Due to the lack of explicit trust information in most systems, many trust metric approaches have been proposed to infer implicit trust from user ratings. However, previous works have not compared these different approaches, and oftentimes focus only on the performance of predictive item ratings. In this paper, we first analyse five kinds of trust metrics in light of the properties of trust. We conduct an empirical study to explore the ability of trust metrics to distinguish explicit trust from implicit trust and to generate accurate predictions. Experimental results on two real-world data sets show that existing trust metrics cannot provide satisfying performance, and indicate that future metrics should be designed more carefully.


Effect of severe visual impairment on development


title: Vector graphics animation with time-varying topology

abstract: We introduce the Vector Animation Complex (VAC), a novel data structure for vector graphics animation, designed to support the modeling of time-continuous topological events. This allows features of a connected drawing to merge, split, appear, or disappear at desired times via keyframes that introduce the desired topological change. Because the resulting space-time complex directly captures the time-varying topological structure, features are readily edited in both space and time in a way that reflects the intent of the drawing. A formal description of the data structure is provided, along with topological and geometric invariants. We illustrate our modeling paradigm with experimental results on various examples.


title: Practical probabilistic programming with monads

abstract: The machine learning community has recently shown a lot of interest in practical probabilistic programming systems that target the problem of Bayesian inference. Such systems come in different forms, but they all express probabilistic models as computational processes using syntax resembling programming languages. In the functional programming community monads are known to offer a convenient and elegant abstraction for programming with probability distributions, but their use is often limited to very simple inference problems. We show that it is possible to use the monad abstraction to construct probabilistic models for machine learning, while still offering good performance of inference in challenging models. We use a GADT as an underlying representation of a probability distribution and apply Sequential Monte Carlo-based methods to achieve efficient inference. We define a formal semantics via measure theory. We demonstrate a clean and elegant implementation that achieves performance comparable with Anglican, a state-of-the-art probabilistic programming system.


title: Incorporate Group Information to Enhance Network Embedding

abstract: The problem of representing large-scale networks with low-dimensional vectors has received considerable attention in recent years. Except the networks that include only vertices and edges, a variety of networks contain information about groups or communities. For example, on Facebook, in addition to users and the follower-followee relations between them, users can also create and join groups. However, previous studies have rarely utilized this valuable information to generate embeddings of vertices. In this paper, we investigate a novel method for learning the network embeddings with valuable group information for large-scale networks. The proposed methods take both the inner structures of the groups and the information across groups into consideration. Experimental results demonstrate that the embeddings generated by the proposed methods significantly outperform state-of-the-art network embedding methods on two different scale real-world network


title: A literature review and novel theoretical approach on the optical properties of whole blood

abstract: Optical property measurements on blood are influenced by a large variety of factors of both physical and methodological origin. The aim of this review is to list these factors of influence and to provide the reader with optical property spectra (250–2,500 nm) for whole blood that can be used in the practice of biomedical optics (tabulated in the appendix). Hereto, we perform a critical examination and selection of the available optical property spectra of blood in literature, from which we compile average spectra for the absorption coefficient (μa), scattering coefficient (μs) and scattering anisotropy (g). From this, we calculate the reduced scattering coefficient (μs′) and the effective attenuation coefficient (μeff). In the compilation of μa and μs, we incorporate the influences of absorption flattening and dependent scattering (i.e. spatial correlations between positions of red blood cells), respectively. For the influence of dependent scattering on μs, we present a novel, theoretically derived formula that can be used for practical rescaling of μs to other haematocrits. Since the measurement of the scattering properties of blood has been proven to be challenging, we apply an alternative, theoretical approach to calculate spectra for μs and g. Hereto, we combine Kramers–Kronig analysis with analytical scattering theory, extended with Percus–Yevick structure factors that take into account the effect of dependent scattering in whole blood. We argue that our calculated spectra may provide a better estimation for μs and g (and hence μs′ and μeff) than the compiled spectra from literature for wavelengths between 300 and 600 nm.


title: A Wideband Dual-Polarized Antenna With Two Independently Controllable Resonant Modes and Its Array for Base-Station Applications

abstract: A wideband dual-polarized antenna with two independently controllable resonant modes for 2G/3G/4G bands is proposed. Its radiator mainly consists of two crossed stepped-width loop dipoles loaded with two crossed straight dipoles. Two approaches are adopted to design the proposed antenna with large freedom. First, by replacing the traditional uniform-width loop dipole with a stepped-width one, the first resonant mode can be adjusted independently by controlling the width ratio of the loop. Second, a pair of crossed straight dipoles are embedded into the gap of the crossed loop dipoles, and the second resonant mode can be controlled independently by altering the length or position of the straight dipoles. Measurements show that the antenna achieves an impedance bandwidth of 54.5% with VSWR< 1.5 (1.68–2.94 GHz) and isolation of greater than 28.5 dB within the bandwidth. Also, an antenna array with ten elements is developed with electrical downtilt from 0° to 8° for practical base-station applications.


title: A solution for SGi-LAN services virtualization using NFV and SDN

abstract: Today operators deploy functions like Deep Packet Inspection, Caches, traffic optimization, NAT and Firewall on the SGi/Gi-LAN for subscribers accessing Internet based content/services. Currently these functions are deployed on dedicated hardware components from different vendors that most often need to be managed separately. This is not cost efficient and gives long lead times for new services. The main contribution of this paper is the implementation of a solution for virtualizing the services on the SGi-LAN using NFV and SDN. The virtualized services include TCP optimization, video optimization and network analytics. In addition, a performance evaluation is presented with focus on key indicators for virtualization. It is shown that virtualizing network functions on servers using NFV adds an overhead of 20–40 µ seconds in latency per hop, but that the impact on the network function performance is negligible. It is also shown that a COTS server can support a single instance of the SDN controller programming 100K OpenFlow transactions/second and that a software switch can implement 100K rules/sec while forwarding traffic at 160 Gbps with 200M rules. To evaluate the scaling feature of NFV it is shown that the time to bring up a new virtual network function was between 90–120 seconds.


abstract: The Voronoi diagram of a set of sites partitions space into regions one per site the region for a site s consists of all points closer to s than to any other site The dual of the Voronoi diagram the Delaunay triangulation is the unique triangulation so that the circumsphere of every triangle contains no sites in its interior Voronoi diagrams and Delaunay triangulations have been rediscovered or applied in many areas of math ematics and the natural sciences they are central topics in computational geometry with hundreds of papers discussing algorithms and extensions Section discusses the de nition and basic properties in the usual case of point sites in R with the Euclidean metric while section gives basic algorithms Some of the many extensions obtained by varying metric sites environment and constraints are discussed in section Section nishes with some interesting and nonobvious structural properties of Voronoi diagrams and Delaunay triangulations


title: A Survey on Semi-Supervised Learning Techniques

abstract: Semisupervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semisupervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semisupervised approaches in the presence of large volumes of data. Labels are very hard to attain while unlabeled data are surplus, therefore semisupervised learning is a noble indication to shrink human labor and improve accuracy. There has been a large spectrum of ideas on semisupervised learning. In this paper we bring out some of the key approaches for semisupervised learning.


title: A printed antenna array fed by balanced Schiffman shifter used for UWB location system

abstract: A double-sided printed bow-tie antenna array fed by balanced Schiffman phase shifter, which used as a reader antenna in ultra-wideband (UWB) location system, is proposed in this paper. The presented antenna has a impedance bandwidth of 3.4–4.4 GHz and a high gain of more than 5dB within the whole bandwidth. Good omnidirectional radiation pattern is obtained in the plane of θ = 15°. Good simulation results are achieved by HFSS.


title: Volunteered Geographic Information and Crowdsourcing Disaster Relief: A Case Study of the Haitian Earthquake

abstract: This paper outlines the ways in which information technologies (ITs) were used in the Haiti relief effort, especially with respect to web-based mapping services. Although there were numerous ways in which this took place, this paper focuses on four in particular: CrisisCamp Haiti, OpenStreetMap, Ushahidi, and GeoCommons. This analysis demonstrates that ITs were a key means through which individuals could make a tangible difference in the work of relief and aid agencies without actually being physically present in Haiti. While not without problems, this effort nevertheless represents a remarkable example of the power and crowdsourced online mapping and the potential for new avenues of interaction between physically distant places that vary tremendously.


title: A fingerprint segmentation technique based on Morphological processing

abstract: Fingerprint segmentation is one of the most important preprocessing steps in an automatic fingerprint identification system (AFIS). It is used to separate a fingerprint area (foreground) from the image background. Accurate segmentation of a fingerprint will greatly reduce the computation time of the following processing steps, and discard many spurious minutiae. In this paper, a new segmentation algorithm is presented. Apart from its simplicity, it is characterized by being neither depend on empirical thresholds chosen by experts or a learned model trained by elements generated from manually segmented fingerprints. The algorithm uses the block range as a feature to achieve fingerprint segmentation. Then, some Morphological closing and opening operations are performed, to extract the foreground from the image. The performance of the proposed technique is checked by evaluating the classification error (Err). Experimental results have shown that when analyzing FVC2004, FVC2002, and FVC2000 databases using the proposed algorithm, the average classification error rates are much less than those obtained by other approaches. Several illustrative examples are given to verify this conclusion.


Resistance to flow in the coronary microcirculation - we can measure it but what does it mean?


title: Design of Dual-Band Millimeter-Wave Antenna Array for 5G Communication System

abstract: In this paper, a dual-band millimeter-wave antenna array is proposed. The dual-band antenna consists of a monopole branch and a patch that are printed on a cavity-backed ceramic block. The first resonance is generated by the coupling between the monopole and the patch, and the second resonance is generated by the monopole. The antenna can be mounted on the PCB. Simulated antenna return loss indicates that the proposed antenna can meet the requirements of the two potential Chinese fifth generation(5G) millimeter-wave bands (26GHz and 40GHz). And the beam scanning performance of an eight-element antenna linear array is presented for the above two bands.


title: Learning Deep Embeddings with Histogram Loss

abstract: We suggest a new loss for learning deep embeddings. The key characteristics of the new loss is the absence of tunable parameters and very good results obtained across a range of datasets and problems. The loss is computed by estimating two distribution of similarities for positive (matching) and negative (non-matching) point pairs, and then computing the probability of a positive pair to have a lower similarity score than a negative pair based on these probability estimates. We show that these operations can be performed in a simple and piecewise-differentiable manner using 1D histograms with soft assignment operations. This makes the proposed loss suitable for learning deep embeddings using stochastic optimization. The experiments reveal favourable results compared to recently proposed loss functions.


Guidelines for handheld mobile device interface design


title: Learning and Game AI

abstract: The incorporation of learning into commercial games can enrich the player experience, but may concern developers in terms of issues such as losing control of their game world. We explore a number of applied research and some fielded applications that point to the tremendous possibilities of machine learning research including game genres such as real-time strategy games, flight simulation games, car and motorcycle racing games, board games such as Go, an even traditional ::: game-theoretic problems such as the prisoners dilemma. A common trait of these works is the potential of machine learning to reduce the burden of game developers. However a number of challenges exists that hinder the use of machine learning more broadly. We discuss some of these challenges while at the same time exploring opportunities for a wide use of machine learning in games.


title: High-dimensional sequence transduction

abstract: We investigate the problem of transforming an input sequence into a high-dimensional output sequence in order to transcribe polyphonic audio music into symbolic notation. We introduce a probabilistic model based on a recurrent neural network that is able to learn realistic output distributions given the input and we devise an efficient algorithm to search for the global mode of that distribution. The resulting method produces musically plausible transcriptions even under high levels of noise and drastically outperforms previous state-of-the-art approaches on five datasets of synthesized sounds and real recordings, approximately halving the test error rate.


title: Facial Expression Synthesis by U-Net Conditional Generative Adversarial Networks

abstract: High-level manipulation of facial expressions in images such as expression synthesis is challenging because facial expression changes are highly non-linear, and vary depending on the facial appearance. Identity of the person should also be well preserved in the synthesized face. In this paper, we propose a novel U-Net Conditioned Generative Adversarial Network (UC-GAN) for facial expression generation. U-Net helps retain the property of the input face, including the identity information and facial details. We also propose an identity preserving loss, which further improves the performance of our model. Both qualitative and quantitative experiments are conducted on the Oulu-CASIA and KDEF datasets, and the results show that our method can generate faces with natural and realistic expressions while preserve the identity information. Comparison with the state-of-the-art approaches also demonstrates the competency of our method.


title: Mobile computing: issues and challenges

abstract: Mobile Cloud Computing (MCC) is an emerging field. Due to the wide usage of mobile devices and variety of applications, mobile cloud computing becomes a necessary part for mobile devices, due to reliability and portability as data processing and storage take place outside of the mobile. It is useful in a sense to save battery and computation power of mobile devices which is a serious issue in high power mobile devices. Mobile cloud computing provide mobile users a service where they can use cloud services on their mobiles and perform computations. As mobile Cloud computing is still in early stage of development, it is useful to build a thorough understanding about existing models and future trends. The purpose of this survey is to analyze and point out the major challenges and risk involved in the mobile cloud computing as well as present new trends in this field.


title: Intelligent fault diagnosis and prediction technologies for condition based maintenance of track circuit

abstract: Condition based maintenance (CBM) is considered to be the most effective maintenance strategy in a performance contract. It significantly increases the system availability and efficiency, meanwhile reduces service affecting faults and maintenance cost, especially when combined with online monitoring of real-time condition data of assets. For the above reasons, CBM has been paid more attention in railway signal system. Under the basic principle of CBM, this paper studies the key maintenance technologies of ZPW-2000 track circuit, specifically relating to information collection, fault diagnosis and state prediction. Among them, the centralized signaling monitoring system is responsible for track circuit information collection as well as the preliminary state alarms. For advanced fault diagnosis on monitoring data, a fuzzy neural network (FNN) model is constructed based on the concept of fault tree. After training, the FNN model can realize fault diagnose in real-time. Moreover, a gray GM(1,1) predictor is further studied to estimate the evolution trend of system condition. Simulation experiments show that the proposed diagnosis and prediction methods are efficient, which makes it possible to realize the predictive maintenance of track circuit in real-time.


title: Context-aware Convolutional Neural Networks for Twitter Sentiment Analysis in Italian

abstract: This paper describes the Unitor system that participated to the SENTIment POLarity Classification task proposed in Evalita 2016. The system implements a classification workflow made of several Convolutional Neural Network classifiers, that generalize the linguistic information observed in the training tweets by considering also their context. Moreover, sentiment specific information is injected in the training process by using Polarity Lexicons automatically acquired through the automatic analysis of unlabeled collection of tweets. Unitor achieved the best results in the Subjectivity Classification sub-task, and it scored 2nd in the Polarity Classification sub-task, among about 25 different submissions.


title: No Need for Black Chambers: Testing TLS in the E-mail Ecosystem at Large

abstract: TLS is the most widely used cryptographic protocol on the Internet. While many recent studies focused on its use in HTTPS, none so far analyzed TLS usage in e-mail related protocols, which often carry highly sensitive information. Since end-to-end encryption mechanisms like PGP are seldomly used, today confidentiality in the e-mail ecosystem is mainly based on the encryption of the transport layer. A well-positioned attacker may be able to intercept plaintext passively and at global scale. In this paper we are the first to present a scalable methodology to assess the state of security mechanisms in the e-mail ecosystem using commodity hardware and open-source software. We draw a comprehensive picture of the current state of every e-mail related TLS configuration for the entire IPv4 range. We collected and scanned a massive data-set of 20 million IP/port combinations of all related protocols (SMTP, POP3, IMAP) and legacy ports. Over a time span of approx. three months we conducted more than 10 billion TLS handshakes. Additionally, we show that securing server-to-server communication using e.g. SMTP is inherently more difficult than securing client-to-server communication. Lastly, we analyze the volatility of TLS certificates and trust anchors in the e-mail ecosystem and argue that while the overall trend points in the right direction, there are still many steps needed towards secure e-mail.


title: General Video Game AI: A Multitrack Framework for Evaluating Agents, Games, and Content Generation Algorithms

abstract: General Video Game Playing (GVGP) aims at designing an agent that is capable of playing multiple video games with no human intervention. In 2014, The General Video Game AI (GVGAI) competition framework was created and released with the purpose of providing researchers a common open-source and easy to use platform for testing their AI methods with potentially infinity of games created using Video Game Description Language (VGDL). The framework has been expanded into several tracks during the last few years to meet the demand of different research directions. The agents are required either to play multiple unknown games with or without access to game simulations, or to design new game levels or rules. This survey paper presents the VGDL, the GVGAI framework, existing tracks, and reviews the wide use of GVGAI framework in research, education and competitions five years after its birth. A future plan of framework improvements is also described.


title: An Approach to Software Testing of Machine Learning Applications

abstract: Some machine learning applications are intended to learn properties of data sets where the correct answers are not already known to human users. It is challenging to test such ML software, because there is no reliable test oracle. We describe a software testing approach aimed at addressing this problem. We present our findings from testing implementations of two different ML ranking algorithms: Support Vector Machines and MartiRank.


title: High-voltage wordline generator for low-power program operation in NAND flash memories

abstract: High-voltage wordline generation circuit with stage-controlled charge pump is developed for low-power NAND flash memories. The proposed stage-controlled charge pump controls an optimized number of stages according to the required program voltage. The proposed high-voltage generator provides sleep-mode operation by turning off the driving clock and regulators during the program verify operation. Using these techniques, the proposed high-voltage generator reduces about 50 percent of power consumption in comparison with a conventional scheme.


title: Text summarization via hidden Markov models

abstract: A sentence extract summary of a document is a subset of the document's sentences that contains the main ideas in the document. We present an approach to generating such summaries, a hidden Markov model that judges the likelihood that each sentence should be contained in the summary. We compare the results of this method with summaries generated by humans, showing that we obtain significantly higher agreement than do earlier methods.


title: Embedding Entities and Relations for Learning and Inference in Knowledge Bases

abstract: Abstract: We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned from a neural network and relations are bilinear and/or linear mapping functions. Under this framework, we compare a variety of embedding models on the link prediction task. We show that a simple bilinear formulation achieves new state-of-the-art results for the task (achieving a top-10 accuracy of 73.2% vs. 54.7% by TransE on Freebase). Furthermore, we introduce a novel approach that utilizes the learned relation embeddings to mine logical rules such as "BornInCity(a,b) and CityInCountry(b,c) => Nationality(a,c)". We find that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication. More interestingly, we demonstrate that our embedding-based rule extraction approach successfully outperforms a state-of-the-art confidence-based rule mining approach in mining Horn rules that involve compositional reasoning.


title: Influence of proximal box elevation on bond strength of composite inlays

abstract: OBJECTIVES ::: The objective of the present study was to evaluate the influence of proximal box elevation on microtensile bond strength (mTBS) of composite inlays to the proximal box floor, using either a total-etch or a self-adhesive resin cement. ::: ::: ::: MATERIALS AND METHODS ::: Twenty-five human molars were selected, and a class II OM (inlay) cavity preparation was performed in each tooth. Cavities were randomly assigned into four experimental groups, according to the location of the proximal cervical margin (located 1 mm below cementoenamel junction (CEJ), or with proximal box elevation with composite resin) and the resin cement used for luting (a total-etch resin cement RelyX ARC or a self-adhesive resin cement G-Cem). After 1-week water storage, samples were subjected to mTBS test. Results were analyzed by Kruskal-Wallis and Mann-Whitney U tests (p < 0.05). ::: ::: ::: RESULTS ::: Kruskal-Wallis revealed statistically significant differences among experimental groups (p = 0.007). Both resin cements showed similar bond strength values when cervical margin was located below CEJ. The proximal box elevation improved the bond strength of composite inlays for both resin cements. However, only for G-Cem was this improvement statistically significant. ::: ::: ::: CONCLUSIONS ::: The proximal box elevation improved the bond strength attained by G-Cem resin cement. For RelyX ARC, the position of the cervical margin did not affect composite inlays bond strength. ::: ::: ::: CLINICAL RELEVANCE ::: Proximal box elevation does not decline bond strength of composite inlays to the proximal floor when a total-etch or a self-adhesive resin cement is used.


title: Online Learning with Random Representations

abstract: We consider the requirements of online learning|learning which must be done incrementally and in realtime, with the results of learning available soon after each new example is acquired. Despite the abundance of methods for learning from examples, there are few that can be used eectively for online learning, e.g., as components of reinforcement learning systems. Most of these few, including radial basis functions, CMACs, Kohonen’s self-organizing maps, and those developed in this paper, share the same structure. All expand the original input representation into a higher dimensional representation in an unsupervised way, and then map that representation to the nal answer using a relatively simple supervised learner, such as a perceptron or LMS rule. Such structures learn very rapidly and reliably, but have been thought either to scale poorly or to require extensive domain knowledge. To the contrary, some researchers (Rosenblatt, 1962; Gallant & Smith, 1987; Kanerva, 1988; Prager & Fallside, 1988) have argued that the expanded representation can be chosen largely at random with good results. The main contribution of this paper is to develop and test this hypothesis. We show that simple random-representation methods can perform as well as nearest-neighbor methods (while being more suited to online learning), and signicantly better than backpropagation. We nd that the size of the random representation does increase with the dimensionality of the problem, but not unreasonably so, and that the required size can be reduced substantially using unsupervisedlearning techniques. Our results suggest that randomness has a useful role to play in online supervised learning and constructive induction. 1. Online Learning Applications of supervised learning can be divided into two types: online and oine.


title: Technology Acceptance Model and E-learning

abstract: The technology acceptance model (TAM) proposes that perceived ease of use and perceived usefulness predict applications usage. The study-in-progress investigated TAM for work-related tasks with the e-learning, and used TAM as a basis for hypothesizing the effects of such variables on the use of e-learning as the application. Elearning has become an increasingly popular learning approach in higher education institutions due to the rapid growth of Internet technologies in Malaysia. It has been integrated in many university programs, and is one of the new learning trends that challenge the banking concept of education (i.e. assumes that the instructor or teacher owns the knowledge and deposits it into the students who attend the class). E-learning is not intended to replace the traditional classroom setting, but to provide new opportunities for interaction and communication between students and instructor or teacher. This study focuses on the individual users’ acceptance investigation for the elearning in universities as an effective learning tool. It develops a technology usage model for the e-learning .The contributions of this study are three fold. First, this study may help identify whether learners or users would like to accept e-learning or vice versa. Second, this study will help to determine what are the factors that significant in explaining the intention towards e-learning. An attempt is made whether attitudinal belief such as perceived ease of use and perceived usefulness has relationship towards the e-learning adoption. Third, this study is among the first to use the technology acceptance model in the context of e-learning.


title: Some Early Developments in Synthetic Aperture Radar Systems

abstract: This paper describes some of the early developments in the synthetic aperture technique for radar application. The basic principle and later extensions to the theory are described. The results of the first experimental verification at the University of Illinois are given as well as the results of subsequent experiments. The paper also includes a section comparing some of the important features of real and synthetic aperture systems.


title: Automatic Classification of Literature Pieces by Emotion Detection: A Study on Quevedo's Poetry

abstract: In this work, we describe an experiment on the categorization of poems based on their emotional content, which is automatically measured. For that purpose, we center on the poetry of Francisco de Quevedo and a well-known sentiment categorization of it. Thereby, we explore how emotions can help in the classification process. The goal was to verify whether the information about emotional content can be used to build classifiers reproducing that categorization.


title: Spotlight Content Delivery Networks: Status and Trends

abstract: CDNs improve network performance and offer fast and reliable applications and services by distributing content to cache servers located close to users. The Web's growth has transformed communications and business services such that speed, accuracy, and availability of network-delivered content has become absolutely critical - both on their own terms and in terms of measuring Web performance. Proxy servers partially address the need for rapid content delivery by providing multiple clients with a shared cache location. In this context, if a requested object exists in a cache (and the cached version has not expired), clients get a cached copy, which typically reduces delivery time. CDNs act as trusted overlay networks that offer high-performance delivery of common Web objects, static data, and rich multimedia content by distributing content load among servers that are close to the clients. CDN benefits include reduced origin server load, reduced latency for end users, and increased throughput. CDNs can also improve Web scalability and disperse flash-crowd events. Here we offer an overview of the CDN architecture and popular CDN service providers.


Botnet Detection Through Fine Flow Classification


title: Deadlock Control of Automated Manufacturing Systems Based on Petri Nets—A Literature Review

abstract: Deadlocks are a rather undesirable situation in a highly automated flexible manufacturing system. Their occurrences often deteriorate the utilization of resources and may lead to catastrophic results in safety-critical systems. Graph theory, automata, and Petri nets are three important mathematical tools to handle deadlock problems in resource allocation systems. Particularly, Petri nets are considered as a popular formalism because of their inherent characteristics. They received much attention over the past decades to deal with deadlock problems, leading to a variety of deadlock-control policies. This study surveys the state-of-the-art deadlock-control strategies for automated manufacturing systems by reviewing the principles and techniques that are involved in preventing, avoiding, and detecting deadlocks. The focus is deadlock prevention due to its large and continuing stream of efforts. A control strategy is evaluated in terms of computational complexity, behavioral permissiveness, and structural complexity of its deadlock-free supervisor. This study provides readers with a conglomeration of the updated results in this area and facilitates engineers in finding a suitable approach for their industrial scenarios. Future research directions are finally discussed.


title: A Depth Video Sensor-Based Life-Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments

abstract: Recent advancements in depth video sensors technologies have made human activity recognition (HAR) realizable for elderly monitoring applications. Although conventional HAR utilizes RGB video sensors, HAR could be greatly improved with depth video sensors which produce depth or distance information. In this paper, a depth-based life logging HAR system is designed to recognize the daily activities of elderly people and turn these environments into an intelligent living space. Initially, a depth imaging sensor is used to capture depth silhouettes. Based on these silhouettes, human skeletons with joint information are produced which are further used for activity recognition and generating their life logs. The life-logging system is divided into two processes. Firstly, the training system includes data collection using a depth camera, feature extraction and training for each activity via Hidden Markov Models. Secondly, after training, the recognition engine starts to recognize the learned activities and produces life logs. The system was evaluated using life logging features against principal component and independent component features and achieved satisfactory recognition rates against the conventional approaches. Experiments conducted on the smart indoor activity datasets and the MSRDailyActivity3D dataset show promising results. The proposed system is directly applicable to any elderly monitoring system, such as monitoring healthcare problems for elderly people, or examining the indoor activities of people at home, office or hospital.


title: AUTOMATIC SHIP DETECTION IN SINGLE-POL SAR IMAGES USING TEXTURE FEATURES IN ARTIFICIAL NEURAL NETWORKS

abstract: This paper presents a novel method for detecting ships from high-resolution synthetic aperture radar (SAR) images. This method categorizes ship targets from single-pol SAR images using texture features in artificial neural networks. As such, the method tries to overcome the lack of an operational solution that is able to reliably detect ships with one SAR channel. The method has the following three main stages: 1) feature extraction; 2) feature selection; and 3) ship detection. The first part extracts different texture features from SAR image. These textures include occurrence and co occurrence measures with different window sizes. Then, best features are selected. Finally, the artificial neural network is used to extract ship pixels from sea ones. In post processing stage some morphological filters are used to improve the result. The effectiveness of the proposed method is verified using Sentinel-1 data in VV polarization. Experimental results indicate that the proposed algorithm can be implemented with time-saving, high precision ship extraction, feature analysis, and detection. The results also show that using texture features the algorithm properly discriminates speckle noise from ships.