title: Differential evolution algorithm with multiple mutation strategies based on roulette wheel selection

abstract: In this paper, we propose a differential evolution (DE) algorithm variant with a combination of multiple mutation strategies based on roulette wheel selection, which we call MMRDE. We first propose a new, reflection-based mutation operation inspired by the reflection operations in the Nelder–Mead method. We design an experiment to compare its performance with seven mutation strategies, and we prove its effectiveness at balancing exploration and exploitation of DE. Although our reflection-based mutation strategy can balance exploration and exploitation of DE, it is still prone to premature convergence or evolutionary stagnation when solving complex multimodal optimization problems. Therefore, we add two basic strategies to help maintain population diversity and increase the robustness. We use roulette wheel selection to arrange mutation strategies based on their success rates for each individual. MMRDE is tested with some improved DE variants based on 28 benchmark functions for real-parameter optimization that have been recommended by the Institute of Electrical and Electronics Engineers CEC2013 special session. Experimental results indicate that the proposed algorithm shows its effectiveness at cooperative work with multiple strategies. It can obtain a good balance between exploration and exploitation. The proposed algorithm can guide the search for a global optimal solution with quick convergence compared with other improved DE variants.


title: Virtual Reality for Enhanced Ecological Validity and Experimental Control in the Clinical, Affective and Social Neurosciences

abstract: An essential tension can be found between researchers interested in ecological validity and those concerned with maintaining experimental control. Research in the human neurosciences often involves the use of simple and static stimuli lacking many of the potentially important aspects of real world activities and interactions. While this research is valuable, there is a growing interest in the human neurosciences to use cues about target states in the real world via multimodal scenarios that involve visual, semantic, and prosodic information. These scenarios should include dynamic stimuli presented concurrently or serially in a manner that allows researchers to assess the integrative processes carried out by perceivers over time. Furthermore, there is growing interest in contextually embedded stimuli that can constrain participant interpretations of cues about a target’s internal states. Virtual reality environments proffer assessment paradigms that combine the experimental control of laboratory measures with emotionally engaging background narratives to enhance affective experience and social interactions. The present review highlights the potential of virtual reality environments for enhanced ecological validity in the clinical, affective, and social neurosciences.


title: Characterization of the role of mononuclear cell subpopulations in the in vitro lymphocyte proliferation assay.

abstract: Purified human mononuclear cell subpopulations have been evaluated in the in vitro lymphocyte proliferation assay. Monocyte-depleted mononuclear cells had reduced or absent responses to mitogens and antigens which could be restored to the original mononuclear cell response by addition of purified plastic adherent cells. Purified T lymphocytes obtained by density gradient centrifugation of E-rosette-forming cells demonstrated low but significant proliferative responses to mitogens, but no significant response to antigens. The addition of monocytes potentiated the response of purified T cells to mitogens and antigens, but did not fully reconstitute the original mononuclear cell response unless non-T lymphocytes were also present. It is concluded that mononuclear cell proliferation represents a complex mechanism of cellular interaction involving multiple subpopulations of cells.


title: Learning Common Representation from RGB and Depth Images

abstract: We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGB-D images. We revise the state of art based on the RGB and depth feature fusion, where both modalities are assumed to be available at train and test time. We propose a new architecture where the feature fusion is replaced with a common deep representation. Combined with an encoder-decoder type of the network, the architecture can jointly learn models for semantic segmentation and depth estimation based on their common representation. This representation, inspired by multi-view learning, offers several important advantages, such as using one modality available at test time to reconstruct the missing modality. In the RGB-D case, this enables the cross-modality scenarios, such as using depth data for semantically segmentation and the RGB images for depth estimation. We demonstrate the effectiveness of the proposed network on two publicly available RGB-D datasets. The experimental results show that the proposed method works well in both semantic segmentation and depth estimation tasks.


title: An improved apriori algorithm

abstract: In this study, it proposes a new optimization algorithm called APRIORI-IMPROVE based on the insufficient of Apriori. APRIORI-IMPROVE algorithm presents optimizations on 2-items generation, transactions compression and so on. APRIORI-IMPROVE uses hash structure to generate L2, uses an efficient horizontal data representation and optimized strategy of storage to save time and space. The performance study shows that APRIORI-IMPROVE is much faster than Apriori.


title: Measurement and Prediction of Effective Thermal Conductivity for Woven Fabric Composites

abstract: The current paper deals with the measurement and prediction of thermal conductivities for plain weave fabric composites. An experimental apparatus was setup to measure the temperature gradients from which the thermal conductivities were obtained. The thermal conductivities were also calculated using finite element analyses for plain weave unit cell models and then compared with experimental results. In addition, the effect of a phase shift and the fiber volume fraction in the tow on the thermal conductivities was addressed.


title: SentiPers: A Sentiment Analysis Corpus for Persian

abstract: Sentiment Analysis (SA) is a major field of study in natural language processing, computational linguistics and information retrieval. Interest in SA has been constantly growing in both academia and industry over the recent years. Moreover, there is an increasing need for generating appropriate resources and datasets in particular for low resource languages including Persian. These datasets play an important role in designing and developing appropriate opinion mining platforms using supervised, semi-supervised or unsupervised methods. In this paper, we outline the entire process of developing a manually annotated sentiment corpus, SentiPers, which covers formal and informal written contemporary Persian. To the best of our knowledge, SentiPers is a unique sentiment corpus with such a rich annotation in three different levels including document-level, sentence-level, and entity/aspect-level for Persian. The corpus contains more than 26000 sentences of users opinions from digital product domain and benefits from special characteristics such as quantifying the positiveness or negativity of an opinion through assigning a number within a specific range to any given sentence. Furthermore, we present statistics on various components of our corpus as well as studying the inter-annotator agreement among the annotators. Finally, some of the challenges that we faced during the annotation process will be discussed as well.


A Fully-Dynamic Closed-Form Solution for ∆-Hedging with Market Impact


title: TrackMeNot: Enhancing the privacy of Web Search

abstract: Most search engines can potentially infer the preferences and interests of a user based on her history of search queries. While search engines can use these inferences for a variety of tasks, including targeted advertisements, such tasks do impose an serious threat to user privacy. In 2006, after AOL disclosed the search queries of 650,000 users, TrackMeNot was released as a simple browser extension that sought to hide user search preferences in a cloud of queries. The first versions of TrackMeNot, though used extensively in the past three years, was fairly simplistic in design and did not provide any strong privacy guarantees. In this paper, we present the new design and implementation of TrackMeNot, which address many of the limitations of the first release. TrackMeNot addresses two basic problems. First, using a model for characterizing search queries, TrackMeNot provides a mechanism for obfuscating the search preferences of a user from a search engine. Second, TrackMeNot prevents the leakage of information revealing the use of obfuscation to a search engine via several potential side channels in existing browsers such as clicks, cookies etc. Finally, we show that TrackMeNot cannot be detected by current search bot detection mechanisms and demonstrate the effectiveness of TrackMeNot in obfuscating user interests by testing its efficiency on a major search engine.


title: Learning to Explore with Meta-Policy Gradient

abstract: The performance of off-policy learning, including deep Q-learning and deep deterministic policy gradient (DDPG), critically depends on the choice of the exploration policy. Existing exploration methods are mostly based on adding noise to the on-going actor policy and can only explore \emph{local} regions close to what the actor policy dictates. In this work, we develop a simple meta-policy gradient algorithm that allows us to adaptively learn the exploration policy in DDPG. Our algorithm allows us to train flexible exploration behaviors that are independent of the actor policy, yielding a \emph{global exploration} that significantly speeds up the learning process. With an extensive study, we show that our method significantly improves the sample-efficiency of DDPG on a variety of reinforcement learning tasks.


Design of Self-Tuning Fuzzy PI controller in LABVIEW for Control of a Real Time Process


title: Chapter 13 A SURVEY OF DISTRIBUTED MINING OF DATA STREAMS

abstract: With advances in data collection and generation technologies, organizations and researchers are faced with the ever growing problem of how to manage and analyze large dynamic datasets. Environments that produce streaming sources of data are becoming common place. Examples include stock market, sensor, web click stream, and network data. In many instances, these environments are also equipped with multiple distributed computing nodes that are often located near the data sources. Analyzing and monitoring data in such environments requires data mining technology that is cognizant of the mining task, the distributed nature of the data, and the data influx rate. In this chapter, we survey the current state of the field and identify potential directions of future research.


title: Automatic Depression Scale Prediction using Facial Expression Dynamics and Regression

abstract: Depression is a state of low mood and aversion to activity that can affect a person's thoughts, behavior, feelings and sense of well-being. In such a low mood, both the facial expression and voice appear different from the ones in normal states. In this paper, an automatic system is proposed to predict the scales of Beck Depression Inventory from naturalistic facial expression of the patients with depression. Firstly, features are extracted from corresponding video and audio signals to represent characteristics of facial and vocal expression under depression. Secondly, dynamic features generation method is proposed in the extracted video feature space based on the idea of Motion History Histogram (MHH) for 2-D video motion extraction. Thirdly, Partial Least Squares (PLS) and Linear regression are applied to learn the relationship between the dynamic features and depression scales using training data, and then to predict the depression scale for unseen ones. Finally, decision level fusion was done for combining predictions from both video and audio modalities. The proposed approach is evaluated on the AVEC2014 dataset and the experimental results demonstrate its effectiveness.


title: LazyLSH: Approximate Nearest Neighbor Search for Multiple Distance Functions with a Single Index

abstract: Due to the "curse of dimensionality" problem, it is very expensive to process the nearest neighbor (NN) query in high-dimensional spaces; and hence, approximate approaches, such as Locality-Sensitive Hashing (LSH), are widely used for their theoretical guarantees and empirical performance. Current LSH-based approaches target at the L1 and L2 spaces, while as shown in previous work, the fractional distance metrics (Lp metrics with 0


title: word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method

abstract: The word2vec software of Tomas Mikolov and colleagues (this https URL ) has gained a lot of traction lately, and provides state-of-the-art word embeddings. The learning models behind the software are described in two research papers. We found the description of the models in these papers to be somewhat cryptic and hard to follow. While the motivations and presentation may be obvious to the neural-networks language-modeling crowd, we had to struggle quite a bit to figure out the rationale behind the equations. ::: This note is an attempt to explain equation (4) (negative sampling) in "Distributed Representations of Words and Phrases and their Compositionality" by Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado and Jeffrey Dean.


title: CPU DB: recording microprocessor history

abstract: With this open database, you can mine microprocessor trends over the past 40 years.


title: A High Reliability Wearable Device for Elderly Fall Detection

abstract: Falls are critical events among elderly people that requires timely rescue. In this paper, we propose a fall detection system consisting of an inertial unit that includes triaxial accelerometer, gyroscope, and magnetometer with efficient data fusion and fall detection algorithms. Starting from the raw data, the implemented orientation filter provides the correct orientation of the subject in terms of yaw, pitch, and roll angles. The system is tested according to experimental protocols, engaging volunteers who performed simulated falls, simulated falls with recovery, and activities of daily living. By placing our wearable sensor on the waist of the subject, the unit is able to achieve fall detection performance above those of similar systems proposed in literature. The results obtained through commonly adopted protocols show excellent accuracy, sensitivity and specificity, improving the results of other techniques proposed in the literature.


title: Metabolite extract of Streptomyces hygroscopicus Hygroscopicus inhibit the growth of Plasmodium berghei through inhibition of ubiquitin - proteasome system.

abstract: Streptomyces hygroscopicus Hygroscopicus, a member of family of Actinomycetes produces eponemycin a proteasome inhibitor that can inhibit Ubiquitin-Proteasome System (UPS) function in eukaryotic cell. Previous study showed that coronamycin, an active substrate isolated from Streptomyces sp. can act as anti-plasmodial, antibacterial, and antifungal, however the research did not show the mechanism of coronamycin in inhibiting the growth of Plasmodium. This research was done to reveal if eponemycin that is contained in metabolite extract of S. hygroscopicus can inhibit UPS function of Plasmodium berghei. This study was an experimental study using P. berghei infected Balb/C mice as malaria model. Samples were divided into 1 control group (group infected with P. berghei without treatment) and 3 treatment groups (mice infected with P. berghei and treated intra-peritoneal with metabolite extract of S. hygroscopicus dose 130 μg/kgBW, 580 μg/kgBW, and 2600 μg/kgBW for 5 days). The degree of parasitemia and morphology of the parasite were measured from the first day of malaria induction until the last treatment. The accumulation level of polyubiquitin was measured using Western blot and ELISA method. The degree of parasitemia on day 6 showed significant differences among treatment groups and control (p=0,000). Percentage of inhibition showed significant differences between control and group treated with metabolite extract of S. hygroscopicus 2600 μg/kgBW. An increasing dose of extract of S. hygroscopicus followed by an increasing of inhibition in parasite growth (r=0,850). Probit analysis showed that ED50 was 9.418 μg/kgBW. There was a change in morphology of the parasite after treatment. Parasite morphology became crisis form. There was an accumulation of polyubiquitinated protein in the group treated with metabolite extract of S. hygroscopicus 2600 μg/kgBW. It can be concluded that analog eponemycin in metabolite of S. hygroscopicus is a potential candidate for new malarial drug by inhibiting UPS function of the parasite and cause stress and dead of the parasite.


title: Tutorial on Answering Questions about Images with Deep Learning

abstract: Together with the development of more accurate methods in Computer Vision and Natural Language Understanding, holistic architectures that answer on questions about the content of real-world images have emerged. In this tutorial, we build a neural-based approach to answer questions about images. We base our tutorial on two datasets: (mostly on) DAQUAR, and (a bit on) VQA. With small tweaks the models that we present here can achieve a competitive performance on both datasets, in fact, they are among the best methods that use a combination of LSTM with a global, full frame CNN representation of an image. We hope that after reading this tutorial, the reader will be able to use Deep Learning frameworks, such as Keras and introduced Kraino, to build various architectures that will lead to a further performance improvement on this challenging task.


title: Manifold denoising based on spectral graph wavelets

abstract: We propose a new framework for manifold denoising using the Spectral Graph Wavelet transform, which enables non-iterative denoising directly in the graph frequency domain, an approach inspired by conventional wavelet-based signal denoising methods. We theoretically justify our approach, based on the fact that for smooth manifolds the coordinate information tends to create energy in the low spectral graph wavelet coefficients, while the noise affects all frequency bands in a similar way. Experimental results show that our suggested manifold frequency denoising (MFD) approach significantly outperforms the state of the art manifold denosing methods, and is robust to a wide range of parameter selections, e.g., the choice of k nearest neighbor connectivity of the graph.


Large Cystic Degeneration of Subserosal Fibroid and Diagnostic Dilemma


title: Embedded wafer level ball grid array (eWLB) technology for millimeter-wave applications

abstract: The embedded wafer level ball grid array (eWLB) is a novel packaging technology that shows excellent performance for millimeter-wave (mm-wave) applications. We present simulation and measurement results of single-ended and differential transmission lines realized using the thin-film redistribution layers (RDL) of an eWLB. We demonstrate the capabilities for the integration of passives on example of a configurable 17/18 GHz down-converter circuit realized in silicon-germanium (SiGe) technology with a fan-in eWLB differential inductor used for the LC tank. We compare the performance of differential chip-package-board transitions realized in an eWLB and in other common package types. We report an optimized compact chip-package-board transition in the eWLB. We obtain a simulated insertion loss as low as −0.65 dB and a return loss below −16 dB at 77 GHz without external matching networks. We introduce the concept of antenna integration in the eWLB and show examples of single-ended and differential antenna structures. Finally, we present for the first time a single-chip four-channel 77 GHz transceiver in SiGe integrated in the eWLB package together with four dipole antennas. The presented examples demonstrate that the eWLB technology is an attractive candidate for mm-wave applications including system-in-package (SiP).


title: Abductive reasoning through Filtering

abstract: Abstract Abduction is an inference mechanism where given a knowledge base and some observations, the reasoner tries to find hypotheses which together with the knowledge base explain the observations. A reasoning based on such an inference mechanism is referred to as abductive reasoning . Given a theory and some observations, by filtering the theory with the observations, we mean selecting only those models of the theory that entail the observations. Entailment with respect to these selected models is referred to as filter entailment. In this paper we give necessary and sufficient conditions when abductive reasoning with respect to a theory and some observations is equivalent to the corresponding filter entailment. We then give sufficiency conditions for particular knowledge representation formalisms that guarantee that abductive reasoning can indeed be done through filtering and present examples from the knowledge representation literature where abductive reasoning is done through filtering. We extend the notions of abductive reasoning and filter entailment to allow preferences among explanations and models respectively and give conditions when they are equivalent. Finally, we give a weaker notion of abduction and abductive reasoning and show the later to be equivalent to filter entailment under less restrictive conditions.


on the Ward BRDF


Analysis of the Protected Extensible Authentication Protocol


title: Nonparametric econometrics: The np package

abstract: We describe the R np package via a series of applications that may be of interest to applied econometricians. The np package implements a variety of nonparametric and semiparametric kernel-based estimators that are popular among econometricians. There are also procedures for nonparametric tests of significance and consistent model specification tests for parametric mean regression models and parametric quantile regression models, among others. The np package focuses on kernel methods appropriate for the mix of continuous, discrete, and categorical data often found in applied settings. Data-driven methods of bandwidth selection are emphasized throughout, though we caution the user that data-driven bandwidth selection methods can be computationally demanding.


title: Decentralized Energy Management System for Networked Microgrids in Grid-Connected and Islanded Modes

abstract: This paper proposes a decentralized energy management system for the coordinated operation of networked microgirds (MGs) in a distribution system. In the grid-connected mode, the distribution network operator and each MG are considered as distinct entities with individual objectives to minimize their own operation costs. It is assumed that both dispatchable and renewable energy source-based distributed generators (DGs) exist in the distribution network and the networked MGs. In order to coordinate the operation of all entities, we apply a decentralized bi-level algorithm to solve the problem with the first level to conduct negotiations among all entities and the second level to update the non-converging penalties. In the islanded mode, the objective of each MG is to maintain a reliable power supply to its customers. In order to take into account the uncertainties of DG outputs and load consumption, we formulate the problems as two-stage stochastic programs. The first stage is to determine base generation setpoints based on the forecasts and the second stage is to adjust the generation outputs based on the realized scenarios. Case studies of a distribution system with networked MGs demonstrate the effectiveness of the proposed methodology in both grid-connected and islanded modes.


Towards Generating Real-life Datasets for Network Intrusion Detection


title: Channel Feedback Based on AoD-Adaptive Subspace Codebook in FDD Massive MIMO Systems

abstract: Channel feedback is essential in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Unfortunately, prior work on multiuser MIMO has shown that the feedback overhead scales linearly with the number of base station (BS) antennas, which is large in massive MIMO systems. To reduce the feedback overhead, we propose an angle-of-departure (AoD) adaptive subspace codebook for channel feedback in FDD massive MIMO systems. Our key insight is to leverage the observation that path AoDs vary more slowly than the path gains. Within the angle coherence time, by utilizing the constant AoD information, the proposed AoD-adaptive subspace codebook is able to quantize the channel vector in a more accurate way. From the performance analysis, we show that the feedback overhead of the proposed codebook only scales linearly with a small number of dominant (path) AoDs instead of the large number of BS antennas. Moreover, we compare the proposed quantized feedback technique using the AoD-adaptive subspace codebook with a comparable analog feedback method. Extensive simulations show that the proposed AoD-adaptive subspace codebook achieves good channel feedback quality, while requiring low overhead.


title: SisPorto 2.0: a program for automated analysis of cardiotocograms.

abstract: OBJECTIVE ::: To describe the latest version of SisPorto, a program for automated analysis of cardiotocograms that closely follows the FIGO guidelines, analyses ante- and intrapartum tracings, performs no signal reduction, and has the possibility of simultaneously recording twins. ::: ::: ::: METHODS ::: A detailed description of the program's processing algorithms and operation is provided, as well as the main results of the studies performed to-date with this system. ::: ::: ::: RESULTS ::: Considering both current and previous versions of the program, SisPorto has been tested in over 6000 pregnancies. The system's FHR baseline was compared with an average of three experts' estimates, and the difference was under 8 bpm in all cases. A fair to good agreement was found with experts' identification of accelerations, decelerations, contractions, and normal/reduced variability (proportions of agreement 0.64-0.89). In a preliminary validity study (n = 85), a sensitivity of 100% and a specificity of 99% were obtained in prediction of poor neonatal outcome. The system is currently undergoing an international multicentre validation study. ::: ::: ::: CONCLUSIONS ::: Although still at the research level, a considerable experience has now been gathered with this system. Promising results have been achieved in studies comparing SisPorto with experts' analysis and in those evaluating the validity of the system.


title: Profile Hidden Markov Models

abstract: Summary : The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available, HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise. Contact: eddy@genetics.wustl.edu.


title: Development and field test of novel two-wheeled UAV for bridge inspections

abstract: This paper presents the development and field test of a novel unmanned aerial vehicle (UAV) for bridge inspections. The proposed UAV, which consists of a quadrotor, a cylindrical cage installed in it, and two spokeless wheels freely rotating around the cage, can climb and run on the bridge surface. Its structure improves durability and reduces air resistance compared with conventional UAVs with a cage or wheels. These advantages expand the uses of the UAV in various fields. This paper evaluates the effectiveness of the proposed UAV in real-world bridge inspection scenarios. We tested basic locomotion and measured air resistances. Experimental results on bridges demonstrated the ability to inspect various locations on bridges that are difficult for human inspectors to access.


title: Division/Square-Root Using Comparison Multiples

abstract: A new implementation for minimally redundant radix-4 floating-point SRT div/sqrt (division/square-root) with the recurrence in the signed-digit format is introduced. The implementation is developed based on the comparison multiples idea. In the proposed approach, the magnitude of the quotient (root) digit is calculated by comparing the truncated partial remainder with 2 limited precision multiples of the divisor (partial root). The digit sign is determined by investigating the polarity of the truncated partial remainder. A timing evaluation using the logical synthesis (Synopsys DC with Artisan [email protected] typical library) shows a latency of 2.5ns for the recurrence of the proposed div/sqrt. This is less than of the conventional implementation.


title: Optimizing OpenMP parallelized DGEMM calls on SGI altix 3700

abstract: Using functions of parallelized mathematical libraries is a common way to accelerate numerical applications. Computer architectures with shared memory characteristics support different approaches for the implementation of such libraries, usually OpenMP or MPI. ::: ::: This paper's content is based on the performance comparison of DGEMM calls (floating point matrix multiplication, double precision) with different OpenMP parallelized numerical libraries, namely Intel MKL and SGI SCSL, and how they can be optimized. Additionally, we have a look at the memory placement policy and give hints for initializing data. Our attention has been focused on a SGI Altix 3700 Bx2 system using BenchIT [1] as a very convenient performance measurement suite for the examinations.


Abundance, Distribution and Drivers of Microplastic Contaminant in Urban River Environments


title: An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms

abstract: Display Omitted This study proposes an intelligent hybrid trading system for discovering technicaltrading rules.This study deals with the optimization problem of data discretization and reducts.Rough set analysis is adopted to represent trading rules.A genetic algorithm is used to discover optimal and sub-optimal trading rules.To evaluate the proposed system, a sliding window method is applied. Discovering intelligent technical trading rules from nonlinear and complex stock market data, and then developing decision support trading systems, is an important challenge. The objective of this study is to develop an intelligent hybrid trading system for discovering technical trading rules using rough set analysis and a genetic algorithm (GA). In order to obtain better trading decisions, a novel rule discovery mechanism using a GA approach is proposed for solving optimization problems (i.e., data discretization and reducts) of rough set analysis when discovering technical trading rules for the futures market. Experiments are designed to test the proposed model against comparable approaches (i.e., random, correlation, and GA approaches). In addition, these comprehensive experiments cover most of the current trading system topics, including the use of a sliding window method (with or without validation dataset), the number of trading rules, and the size of training period. To evaluate an intelligent hybrid trading system, experiments were carried out on the historical data of the Korea Composite Stock Price Index 200 (KOSPI 200) futures market. In particular, trading performance is analyzed according to the number of sets of decision rules and the size of the training period for discovering trading rules for the testing period. The results show that the proposed model significantly outperforms the benchmark model in terms of the average return and as a risk-adjusted measure.


title: Evaluating the Multifactor Structure of the Long and Short Versions of the Self-Compassion Scale in a Clinical Sample

abstract: Objective: There has been a growing interest in the concept of self-compassion in Eastern psychology. The aim of the present study was to explore the dimensionality of the widely used Self-Compassion Scale (SCS; long and short versions) in both clinical and nonclinical samples Method: Several confirmatory factor analyses (CFAs) were computed in a mixed clinical (n = 316) and a nonclinical sample (n = 1128) from the Portuguese population. Also, differences were tested between the groups in the SCS 6 factors. Results: The CFA supported both a 6-factor model and a hierarchical model in both samples. The SCS also showed good psychometric properties, with good internal consistency, test-retest reliability, and convergent validity. Our study further suggests that individuals with several psychopathological disorders showed significantly lower self-compassionate abilities. Conclusions: The SCS (long and short versions) is thus a reliable instrument to assess self-compassion and is useful for research and, in particular, clinical practice. C ⃝ 2015 Wiley Periodicals, Inc. J. Clin. Psychol. 00:1–15, 2015.


title: Fasciola hepatica infection in humans: overcoming problems for the diagnosis

abstract: Fascioliasis is a zoonosis actually considered as a foodborne trematode disease priority by the World Health Organization. Our study presents three cases of F. hepatica infection diagnosed by direct, indirect and/or imaging diagnostic techniques, showing the need of the combined use of them. In order to overcome some difficulties of the presently available methods we show for the first time the application of molecular tools to improve human fascioliasis diagnosis by employing a PCR protocol based on a repetitive element as target sequence. In conclusion, diagnosis of human fascioliasis has to be carried out by the combination of diagnostic techniques that allow the detection of infection in different disease phases, different epidemiological situations and known/new transmission patterns in the actual scenario.


title: Embedding Large Watermarks In Images Using Discrete Fractional Cosine Transform.

abstract: The amount of information that can be hidden in an image without any visually noticeable difference and retrieval of this embedded information are the difficulties in watermarking techniques. In this paper we present a novel algorithm for embedding and extracting large digital watermarks in images. This method is inspired by Cox’s watermarking strategy. Unlike embedding an image or binary watermarks that are smaller than the host image being watermarked, in our algorithm we demonstrate by embedding an audio watermark which is much larger than the host image to be watermarked. The size of the watermarked image increases only by a fraction after watermarking. The positions where the watermarks are to be embedded, intermediate frequency coefficients and the sequence of the transformation orders comprise the key. The experimental results have shown that the proposed algorithm is of good imperceptibility and security. It is also robust against common attacks like rotation, resizing, filtering and even cropping.


title: Perceptually based brush strokes for nonphotorealistic visualization

abstract: An important problem in the area of computer graphics is the visualization of large, complex information spaces. Datasets of this type have grown rapidly in recent years, both in number and in size. Images of the data stored in these collections must support rapid and accurate exploration and analysis. This article presents a method for constructing visualizations that are both effective and aesthetic. Our approach uses techniques from master paintings and human perception to visualize a multidimensional dataset. Individual data elements are drawn with one or more brush strokes that vary their appearance to represent the element's attribute values. The result is a nonphotorealistic visualization of information stored in the dataset. Our research extends existing glyph-based and nonphotorealistic techniques by applying perceptual guidelines to build an effective representation of the underlying data. The nonphotorealistic properties the strokes employ are selected from studies of the history and theory of Impressionist art. We show that these properties are similar to visual features that are detected by the low-level human visual system. This correspondence allows us to manage the strokes to produce perceptually salient visualizations. Psychophysical experiments confirm a strong relationship between the expressive power of our nonphotorealistic properties and previous findings on the use of perceptual color and texture patterns for data display. Results from these studies are used to produce effective nonphotorealistic visualizations. We conclude by applying our techniques to a large, multidimensional weather dataset to demonstrate their viability in a practical, real-world setting.


title: Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing

abstract: The cloud is migrating to the edge of the network, where routers themselves may become the virtualisation infrastructure, in an evolution labelled as "the fog". However, many other complementary technologies are reaching a high level of maturity. Their interplay may dramatically shift the information and communication technology landscape in the following years, bringing separate technologies into a common ground. This paper offers a comprehensive definition of the fog, comprehending technologies as diverse as cloud, sensor networks, peer-to-peer networks, network virtualisation functions or configuration management techniques. We highlight the main challenges faced by this potentially breakthrough technology amalgamation.


title: Hierarchical Attention Networks

abstract: We propose a novel attention network, which accurately attends to target objects of various scales and shapes in images through multiple stages. The proposed network enables multiple layers to estimate attention in a convolutional neural network (CNN). The hierarchical attention model gradually suppresses irrelevant regions in an input image using a progressive attentive process over multiple CNN layers. The attentive process in each layer determines whether to pass or suppress feature maps for use in the next convolution. We employ local contexts to estimate attention probability at each location since it is difficult to infer accurate attention by observing a feature vector from a single location only. The experiments on synthetic and real datasets show that the proposed attention network outperforms traditional attention methods in various attribute prediction tasks.


title: Intrusion detection systems (IDS) & future challenges in cloud based environment

abstract: Cloud computing is an attention grabbing service model as a result of it reduces investment as its prices are in guide corresponding to use and request. Due to economic and technical edges it offers, Cloud computing has recently obtained major acceptance. However, as a result of the conveyed nature of distributed computing situations, its significant asset total, wide client get to, conservative and customized sharing of assets change gatecrashers to utilize cloud for his or her benefit. Crucial applications are getting additional associated with the web for cost-effectiveness and quantifiable causes; however this leaves them receptive attack. Most systems these days don't seem to be designed in contact up subtle attacks; an attacker who is ready to compromise one machine usually gains the facility to require down the complete system. To fight intruders, variety of security solution for cloud situations executes Intrusion Detection Systems (IDS). Be that as it may, most IDS arrangements are wrong for cloud conditions, because of numerous issues. During this paper, Intrusion Detection Systems (IDS) and also the varied associated difficulties of threats is given which supplies direction for future researchers in terms of IDS for Cloud computing setting.


title: Occult hepatitis B virus infection among anti-HBc only positive individuals in the Southeast of Iran in high prevalence of HBV infection region.

abstract: Background: The persistence of HBV-DNA in the serum of hepatitis B surface antigen negative individuals with or without the presence of HBV antibodies is termed occult HBV infection.


title: Internet-of-Things Communication Protocol for Low-Cost Devices in Heterogeneous Wireless Networks

abstract: In this paper, we present a new communication protocol for low-cost Internet-of-Things (IoT) devices that need to gather and transmit sensor data and receive remote commands. Taking into account that an IoT-network may employ different communication technologies, we define several important protocol features such as data encryption, integrity checking, sender authentication and data retransmission in case of packet delivery failures. We describe our approach to implementing each of the protocol features as part of our HybridNET system for industrial device control and communication. The hardware base includes cheap 32-bit microcontrollers, radio connectivity (SX1276 and others) and wired connectivity (mainly RS485 and Ethernet). We also describe the protocol packet structure in detail as well as our implementation for routing packets in larger IoT-networks. The experimental results are very promising and show that our protocol performs very well and delivers very good performance and reliability.


title: A Procedure for Checking Equality of Regular Expressions

abstract: A simple “mechanical” procedure is described for checking equality of regular expressions. The procedure, based on the work of A. Salomaa, uses derivatives of regular expressions and transition graphs. ::: Given a regular expression <italic>R</italic>, a corresponding transition graph is constructed. It is used to generate a finite set of left-linear equations which characterize <italic>R</italic>. Two regular events <italic>R</italic> and <italic>S</italic> are equal if and only if each constant term in the set of left-linear equations formed for the pair (<italic>R S</italic>) is (φ φ) or (^ ^). ::: The procedure does not involve any computations with or transformations of regular expressions and is especially appropriate for the use of a computer.


Implementing Multi-Perspective Context Matching for the SQuAD Task in TensorFlow


title: A method of acoustic landmark extraction for mobile robot navigation

abstract: Describes a method to extract multiple acoustic landmarks for the indoor navigation of a mobile robot. The environment is modeled by specular vertical walls. An ultrasonic sensor mounted on the mobile robot scans around the surrounding walls and detects multiple echo pulses. On the horizontal plane at the height of the ultrasonic sensor from the floor, the two-dimensional environment is described by the distance as a function of angle in the scan. Due to the specularity of the walls, the distance function has a manageable number of retroreflective parts that can be used as landmarks. By virtue of the constancy of the relative positions between the ultrasonic sensor and the walls in scanning, the echo pulses reflected from a certain reflection points (RP) have the same time-of-flight in the ultrasonic scan data. The authors determine the position of an RP from the collection of the echo pulses that have the same time-of-flight. The direction to an RP is estimated from the orientation of the ultrasonic sensor at which the maximum magnitude is obtained for the group of echo pulses that correspond to the RP. The mean time-of-flight of the group of echo pulses provides the distance to the RP. Some experimental results are provided to show the validity and the performance of the proposed method.


title: Evaluating the accuracy of Java profilers

abstract: Performance analysts profile their programs to find methods that are worth optimizing: the "hot" methods. This paper shows that four commonly-used Java profilers (xprof , hprof , jprofile, and yourkit) often disagree on the identity of the hot methods. If two profilers disagree, at least one must be incorrect. Thus, there is a good chance that a profiler will mislead a performance analyst into wasting time optimizing a cold method with little or no performance improvement. This paper uses causality analysis to evaluate profilers and to gain insight into the source of their incorrectness. It shows that these profilers all violate a fundamental requirement for sampling based profilers: to be correct, a sampling-based profilermust collect samples randomly. We show that a proof-of-concept profiler, which collects samples randomly, does not suffer from the above problems. Specifically, we show, using a number of case studies, that our profiler correctly identifies methods that are important to optimize; in some cases other profilers report that these methods are cold and thus not worth optimizing.


title: Reducing car dependence in the heart of Europe: lessons from Germany, Austria, and Switzerland

abstract: Munich, Berlin, Hamburg, Vienna, and Zurich – the largest cities in Germany, Austria, and Switzerland – have significantly reduced the car share of trips over the past 25 years in spite of high motorisation rates. The key to their success has been a coordinated package of mutually reinforcing transport and land-use policies that have made car use slower, less convenient, and more costly, while increasing the safety, convenience, and feasibility of walking, cycling, and public transport. The mix of policies implemented in each city has been somewhat different. The German cities have done far more to promote cycling, while Zurich and Vienna offer more public transport service per capita at lower fares. All five of the cities have implemented roughly the same policies to promote walking, foster compact mixed-use development, and discourage car use. Of the car-restrictive policies, parking management has been by far the most important. The five case study cities demonstrate that it is possible to reduce car dependence even in affluent societies with high levels of car ownership and high expectations for quality of travel.


title: SESRA: a web-based automated tool to support the systematic literature review process

abstract: Systematic Literature Review (SLR) is a key tool for evidence-based practice as it combines results from multiple studies of a specific topic of research. Due its characteristics, it is a time consuming, hard process that requires a properly documented protocol for scientific acknowledgment. In this context, this paper presents the SESRA -- a web-based automated tool to support all phases of the SLR process, contributing to its productivity and reliability. We also present a use case on the software engineering field, applying specific knowledge to set the process in the discipline. Further, we discuss how to use the tool to establish a more formal and controlled process and to reduce effort on its repetitive activities. The outcomes and feedback obtained in early use have shown that SESRA could support the SLR process, automating some of its key activities.


title: Hardware and Software Support for Virtualization

abstract: This book focuses on the core question of the necessary architectural support provided by hardware to efficiently run virtual machines, and of the corresponding design of the hypervisors that run them. Virtualization is still possible when the instruction set architecture lacks such support, but the hypervisor remains more complex and must rely on additional techniques. Despite the focus on architectural support in current architectures, some historical perspective is necessary to appropriately frame the problem. The first half of the book provides the historical perspective of the theoretical framework developed four decades ago by Popek and Goldberg. It also describes earlier systems that enabled virtualization despite the lack of architectural support in hardware. As is often the case, theory defines a necessary-but not sufficient-set of features, and modern architectures are the result of the combination of the theoretical framework with insights derived from practical systems. The second half of the book describes state-of-the-art support for virtualization in both x86-64 and ARM processors. This book includes an in-depth description of the CPU, memory, and I/O virtualization of these two processor architectures, as well as case studies on the Linux/KVM, VMware, and Xen hypervisors. It concludes with a performance comparison of virtualization on current-generation x86- and ARM-based systems across multiple hypervisors.


title: Design of compact stacked-patch antennas in LTCC multilayer packaging modules for wireless applications

abstract: A simple procedure for the design of compact stacked-patch antennas is presented based on LTCC multilayer packaging technology. The advantage of this topology is that only one parameter, i.e., the substrate thickness (or equivalently the number of LTCC layers), needs to be adjusted in order to achieve an optimized bandwidth performance. The validity of the new design strategy is verified through applying it to practical compact antenna design for several wireless communication bands, including ISM 2.4-GHz band, IEEE 802.11a 5.8-GHz, and LMDS 28-GHz band. It is shown that a 10-dB return-loss bandwidth of 7% can be achieved for the LTCC (/spl epsiv//sub r/=5.6) multilayer structure with a thickness of less than 0.03 wavelengths, which can be realized using a different number of laminated layers for different frequencies (e.g., three layers for the 28-GHz band).


title: The affect system has parallel and integrative processing components: Form follows function

abstract: The affect system has been shaped by the hammer and chisel of adaptation and natural selection such that form follows function. The characteristics of the system thus differ across the nervous system as a function of the unique constraints existent at each level. For instance, although physical limitations constrain behavioral expressions and incline behavioral predispositions toward a bipolar (good-bad, approach-withdraw) organization, these limiting conditions lose their power at the level of underlying mechanisms. According to the authors' model of evaluative space (J. T. Cacioppo & G. G. Berntson, 1994; J. T. Cacioppo, W. L. Gardner, & G. G. Berntson, 1997), the common metric governing approach-withdrawal is generally a single dimension at response stages that itself is the consequence of multiple operations, such as the activation function for positivity (appetition) and the activation function for negativity (aversion), at earlier affective processing stages.


title: Towards Privacy Protection in Smart Grid

abstract: The smart grid is an electronically controlled electrical grid that connects power generation, transmission, distribution, and consumers using information communication technologies. One of the key characteristics of the smart grid is its support for bi-directional information flow between the consumer of electricity and the utility provider. This two-way interaction allows electricity to be generated in real-time based on consumers' demands and power requests. As a result, consumer privacy becomes an important concern when collecting energy usage data with the deployment and adoption of smart grid technologies. To protect such sensitive information it is imperative that privacy protection mechanisms be used to protect the privacy of smart grid users. We present an analysis of recently proposed smart grid privacy solutions and identify their strengths and weaknesses in terms of their implementation complexity, efficiency, robustness, and simplicity.


title: Quality evaluation of HEVC and VP9 video compression in real-time applications

abstract: Video consumption over Internet has increased significantly over the recent years and occupies the majority of the overall data traffic. To decrease the load on the Internet infrastructure and reduce bandwidth taken by video, higher efficiency video codecs, such as H.265/HEVC and VP9, have been developed. The availability of these two new competing video coding formats raises the question of which is more efficient in terms of rate-distortion and by how much they outperform the current state-of-the-art coding standard, H.264/AVC. This paper provides an answer to this difficult question for low-delay video applications, e.g., real-time video streaming/conferencing or video surveillance. The benchmarking of HEVC and VP9 video compression was conducted by means of subjective evaluations, assuming web browser playback, an uncontrolled environment, and HD video content. Considering a wide range of bit rates from very low to high bit rates, corresponding to low quality up to transparent quality (when compared to the original video), results show a clear advantage of HEVC with average bit rate savings of 59.5% when compared to AVC and 42.4% when compared to VP9.


title: Evaluating features and classifiers for road weather condition analysis

abstract: Weather-dependent road conditions are a major factor in many automobile incidents; computer vision algorithms for automatic classification of road conditions can thus be of great benefit. This paper presents a system for classification of road conditions using still-frames taken from an uncalibrated dashboard camera. The problem is challenging due to variability in camera placement, road layout, weather and illumination conditions. The system uses a prior distribution of road pixel locations learned from training data then fuses normalized luminance and texture features probabilistically to categorize the segmented road surface. We attain an accuracy of 80% for binary classification (bare vs. snow/ice-covered) and 68% for 3 classes (dry vs. wet vs. snow/ice-covered) on a challenging dataset, suggesting that a useful system may be viable.


Video Captioning and Retrieval Models with Semantic Attention


title: A graph-based MAP solution for multi-person tracking using multi-camera systems

abstract: Accurate multi-person tracking under complex conditions is an important topic in computer vision with various application scenarios such as visual surveillance. Taking into account the difficulties caused by 2D occlusions, missing detections, and false positives, we propose a two-stage graph-based object tracking-by-detection approach using multiple calibrated cameras. Firstly, data association is formulated into a maximum a posteriori (MAP) problem. After transformation, we show that this single MAP problem is equivalent of finding min-cost paths in a two-stage directed acyclic graph. The first graph aims to extract an optimal set of tracklets based on the hypotheses on the ground plane by using both 2D appearance feature and 3D spatial distances. Subsequently, the tracklets are linked into complete tracks in the second graph utilizing spatial and temporal distances. This results in a global optimization over all the 2D detections obtained from multiple cameras. Finally, the experimental results on three difficult sequences of the PETS'09 dataset with comparison to the state-of-the-art methods show the precision and consistency of our approach.


title: Malware classification using self organising feature maps and machine activity data

abstract: Abstract In this article we use machine activity metrics to automatically distinguish between malicious and trusted portable executable software samples. The motivation stems from the growth of cyber attacks using techniques that have been employed to surreptitiously deploy Advanced Persistent Threats (APTs). APTs are becoming more sophisticated and able to obfuscate much of their identifiable features through encryption, custom code bases and in-memory execution. Our hypothesis is that we can produce a high degree of accuracy in distinguishing malicious from trusted samples using Machine Learning with features derived from the inescapable footprint left behind on a computer system during execution. This includes CPU, RAM, Swap use and network traffic at a count level of bytes and packets. These features are continuous and allow us to be more flexible with the classification of samples than discrete features such as API calls (which can also be obfuscated) that form the main feature of the extant literature. We use these continuous data and develop a novel classification method using Self Organizing Feature Maps to reduce over fitting during training through the ability to create unsupervised clusters of similar “behaviour” that are subsequently used as features for classification, rather than using the raw data. We compare our method to a set of machine classification methods that have been applied in previous research and demonstrate an increase of between 7.24% and 25.68% in classification accuracy using our method and an unseen dataset over the range of other machine classification methods that have been applied in previous research.


title: Optimal design and multifield coupling analysis of propelling motor used in a novel integrated motor propeller

abstract: A novel permanent-magnet (PM) motor used in an integrated motor propeller (IMP) is designed. The motor has no bearings and employs a Halbach array in its rotor structure. Optimization is conducted in such aspects as PM structure, magnetizing angle of PM, and the best ratio of PM length to poles. The designed motor has a thinner rotor and a larger air gap, and a 3-D coupled-field finite element method (FEM) is used to analyze the temperature distribution of this water-cooled thruster. Losses in windings and stator core are calculated in 3-D eddy current field, and the loss is used as the input to calculate the thermal field. Furthermore, a multicomponent fluid method is proposed to deal with the influence from rotating rotor upon the water convection in the air gap. An experiment of this IMP is done under water and temperatures in it are obtained. The temperature results of experiment and calculation are in good agreement, verifying the effectiveness of the analyzing method proposed in this paper.


title: Jamming-resistant Key Establishment using Uncoordinated Frequency Hopping

abstract: We consider the following problem: how can two devices that do not share any secrets establish a shared secret key over a wireless radio channel in the presence of a communication jammer? An inherent challenge in solving this problem is that known anti-jamming techniques (e.g., frequency hopping or direct-sequence spread spectrum) which should support device communication during the key establishment require that the devices share a secret spreading key (or code) prior to the start of their communication. This requirement creates a circular dependency between antijamming spread-spectrum communication and key establishment, which has so far not been addressed. In this work, we propose an uncoordinated frequency hopping (UFH) scheme that breaks this dependency and enables key establishment in the presence of a communication jammer. We perform a detailed analysis of our UFH scheme and show its feasibility, both in terms of execution time and resource requirements.


A Case Study: Beliefs and Metaphors of a Japanese Teacher of English


title: Intrusion detection using reduced-size RNN based on feature grouping

abstract: Intrusion detection is well-known as an essential component to secure the systems in Information and Communication Technology (ICT). Based on the type of analyzing events, two kinds of Intrusion Detection Systems (IDS) have been proposed: anomaly-based and misuse-based. In this paper, three-layer Recurrent Neural Network (RNN) architecture with categorized features as inputs and attack types as outputs of RNN is proposed as misuse-based IDS. The input features are categorized to basic features, content features, time-based traffic features, and host-based traffic features. The attack types are classified to Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R). For this purpose, in this study, we use the 41 features per connection defined by International Knowledge Discovery and Data mining group (KDD). The RNN has an extra output which corresponds to normal class (no attack). The connections between the nodes of two hidden layers of RNN are considered partial. Experimental results show that the proposed model is able to improve classification rate, particularly in R2L attacks. This method also offers better Detection Rate (DR) and Cost Per Example (CPE) when compared to similar related works and also the simulated Multi-Layer Perceptron (MLP) and Elman-based intrusion detectors. On the other hand, False Alarm Rate (FAR) of the proposed model is not degraded significantly when compared to some recent machine learning methods.


title: Musiplectics: computational assessment of the complexity of music scores

abstract: In the Western classical tradition, musicians play music from notated sheet music, called a score. When playing music from a score, a musician translates its visual symbols into sequences of instrument-specific physical motions. Hence, a music score's overall complexity represents a sum of the cognitive and mechanical acuity required for its performance. For a given instrument, different notes, intervals, articulations, dynamics, key signatures, and tempo represent dissimilar levels of difficulty, which vary depending on the performer's proficiency. Individual musicians embrace this tenet, but may disagree about the degrees of difficulty. This paper introduces musiplectics (musiplectics = music + plectics, Greek for the study of complexity), a systematic and objective approach to computational assessment of the complexity of a music score for any instrument. Musiplectics defines computing paradigms for automatically and accurately calculating the complexity of playing a music score on a given instrument. The core concept codifies a two-phase process. First, music experts rank the relative difficulty of individual musical components (e.g., notes, intervals, dynamics, etc.) for different playing proficiencies and instruments. Second, a computing engine automatically applies this ranking to music scores and calculates their respective complexity. As a proof of concept of musiplectics, we present an automated, Web-based application called Musical Complexity Scoring (MCS) for music educators and performers. Musiplectics can engender the creation of practical computing tools for objective and expeditious assessment of a music score's suitability for the abilities of intended performers.


title: Detection of Plagiarism in Arabic Documents

abstract: Many language-sensitive tools for detecting plagiarism in natural language documents have been developed, particularly for English. Language- independent tools exist as well, but are considered restrictive as they usually do not take into account specific language features. Detecting plagiarism in Arabic documents is particularly a challenging task because of the complex linguistic structure of Arabic. In this paper, we present a plagiarism detection tool for comparison of Arabic documents to identify potential similarities. The tool is based on a new comparison algorithm that uses heuristics to compare suspect documents at different hierarchical levels to avoid unnecessary comparisons. We evaluate its performance in terms of precision and recall on a large data set of Arabic documents, and show its capability in identifying direct and sophisticated copying, such as sentence reordering and synonym substitution. We also demonstrate its advantages over other plagiarism detection tools, including Turnitin, the well-known language-independent tool.


title: Low-cost, high-throughput, automated counting of bacterial colonies

abstract: Research involving bacterial pathogens often requires enumeration of bacteria colonies. Here we present a low-cost, high-throughput colony counting system consisting of colony counting software and a consumer-grade digital camera or document scanner. We demonstrate that this software, called “NICE” (NIST's Integrated Colony Enumerator), can count bacterial colonies as part of a high-throughput multiplexed opsonophagocytic killing assay (MOPA) used to characterize pneumococcal vaccine efficacy. The results obtained with NICE correlate well with the results obtained from manual counting, with a mean difference of less than 3 %. NICE is also rapid; it can count colonies from multiple reaction wells within minutes and export the results to a spreadsheet for data processing. As this program is freely available from NIST, NICE should be helpful in bacteria colony enumeration required in many microbiological studies, and in standardizing colony counting methods.


title: Disinfection of Multidrug Resistant Escherichia coli by Solar-Photocatalysis using Fe-doped ZnO Nanoparticles

abstract: Spread of antibiotic resistant bacteria through water, is a threat to global public health. Here, we report Fe-doped ZnO nanoparticles (Fe/ZnO NPs) based solar-photocatalytic disinfection (PCD) of multidrug resistant Escherichia coli (MDR E. coli). Fe/ZnO NPs were synthesized by chemical precipitation technique, and when used as photocatalyst for disinfection, proved to be more effective (time for complete disinfection = 90 min) than ZnO (150 min) and TiO2 (180 min). Lipid peroxidation and potassium (K+) ion leakage studies indicated compromisation of bacterial cell membrane and electron microscopy and live-dead staining confirmed the detrimental effects on membrane integrity. Investigations indicated that H2O2 was the key species involved in solar-PCD of MDR E. coli by Fe/ZnO NPs. X-ray diffraction and atomic absorption spectroscopy studies showed that the Fe/ZnO NPs system remained stable during the photocatalytic process. The Fe/ZnO NPs based solar-PCD process proved successful in the disinfection of MDR E. coli in real water samples collected from river, pond and municipal tap. The Fe/ZnO NPs catalyst made from low cost materials and with high efficacy under solar light may have potential for real world applications, to help reduce the spread of resistant bacteria.


title: Democracy online: civility, politeness, and the democratic potential of online political discussion groups

abstract: The proponents of cyberspace promise that online discourse will increase political participation and pave the road for a democratic utopia. This article explores the potential for civil discourse in cyberspace by examining the level of civility in 287 discussion threads in political newsgroups. While scholars often use civility and politeness interchangeably, this study argues that this conflation ignores the democratic merit of robust and heated discussion. Therefore, civility was defined in a broader sense, by identifying as civil behaviors that enhance democratic conversation. In support of this distinction, the study results revealed that most messages posted on political newsgroups were civil, and further suggested that because the absence of face-to-face communication fostered more heated discussion, cyberspace might actually promote Lyotard’s vision of democratic emancipation through disagreement and anarchy (Lyotard, 1984). Thus, this study supported the internet’s potential to revive the public s...


title: A fast large neighbourhood based heuristic for the Two-Echelon Vehicle Routing Problem

abstract: In this paper, we address two optimisation problems arising in the context of city logistics and two-level transportation systems. The two-echelon vehicle routing problem and the two-echelon location routing problem seek to produce vehicle itineraries to deliver goods to customers, with transits through intermediate facilities. To efficiently solve these problems, we propose a hybrid metaheuristic which combines enumerative local searches with destroy-and-repair principles, as well as some tailored operators to optimise the selections of intermediate facilities. We conduct extensive computational experiments to investigate the contribution of these operators to the search performance, and measure the performance of the method on both problem classes. The proposed algorithm finds the current best known solutions, or better ones, for 95% of the two-echelon vehicle routing problem benchmark instances. Overall, for both problems, it achieves high-quality solutions within short computing times. Finally, for future reference, we resolve inconsistencies between different versions of benchmark instances, document their differences, and provide them all online in a unified format. HighlightsWe introduce a simple metaheuristic based on LNS for the 2E-VRP and the 2E-LRP.The algorithm is tested with benchmark instances form literature, and outperforms previous methods.We clarified several mistakes and inconsistencies in the benchmark instances.


title: Movie Description

abstract: Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total). The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016.


title: Design of a Marx Generator for HEMP filter evaluation taking account of parasitic effect of components

abstract: This paper introduces the design of a Marx Generator that can generate high attitude pulses up to 25 kV and 2.5 kA. The pulses have a wave shape representative of that used to test High-altitude ElectroMagnetic (HEMP) filters against the pulse current injection (PCI) requirements. The rise time is 20 ns and the pulse width is 230 ns on a 10 Ω resistor. In the design, it was found that parasitic effect of circuit components could significantly affect the performance of the generator. This is mainly because the pulse width is very narrow and its frequency spectrum is very wideband. The high-frequency circuit models of the components used in the generator were obtained by calculation and optimization, and then further verified by experiment. The verified circuit models were then used to design the three-stage generator. The measured performance agrees very well with the simulated one using the proposed circuit models.


title: Self-Autonomous Wireless Sensor Nodes With Wind Energy Harvesting for Remote Sensing of Wind-Driven Wildfire Spread

abstract: The satellite-based remote sensing technique has been widely used in monitoring wildfire spread. There are two prominent drawbacks with this approach of using satellites located in space: (1) very low sampling rate (temporal resolution problem) and (2) lack of accuracy (spatial and spectral resolution problem). To address these challenges, a wireless sensor network deployed at ground level with high-fidelity and low-altitude atmospheric sensing for wind speed of local wildfire spread has been used. An indirect approach in sensing wind speed has been proposed in this paper as an alternative to the bulky conventional wind anemometer to save cost and space. The wind speed is sensed by measuring the equivalent electrical output voltage of the wind turbine generator (WTG). The percentage error in the wind speed measurement using the proposed indirect method is measured to be well within the ±4% limit with respect to wind anemometer accuracy. The same WTG also functions as a wind energy harvesting (WEH) system to convert the available wind energy into electrical energy to sustain the operation of the wireless sensor node. The experimental results show that the designed WEH system is able to harvest an average electrical power of 7.7 mW at an average wind speed of 3.62 m/s for powering the operation of the wireless sensor node that consumes 3.5 mW for predicting the wildfire spread. Based on the sensed wind speed information, the fire control management system determines the spreading condition of the wildfire, and an adequate fire suppression action can be performed by the fire-fighting experts.


title: A Learning Automata Based Algorithm For Solving Capacitated Vehicle Routing Problem

abstract: This paper presents an approximate algorithm based on distributed learning automata for solving capacitated vehicle routing problem. The vehicle routing problem (VRP) is an NPhard problem and capacitated vehicle routing problem variant (CVRP) is considered here. This problem is one of the NP-hard problems and for this reason many approximate algorithms have been designed for solving it. Distributed learning automata that is a general searching tool and is a solving tool for variety of NPcomplete problems, is used to solve this problem and tested on fourteen benchmark problems. Our results were compared to the best known results. The results of comparison have shown the efficiency of the proposed algorithm.


title: ROC Graphs with Instance-Varying Costs

abstract: Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs have been used in cost-sensitive learning because of the ease with which class skew and error cost information can be applied to them to yield cost-sensitive decisions. However, they have been criticized because of their inability to handle instance-varying costs; that is, domains in which error costs vary from one instance to another. This paper presents and investigates a technique for adapting ROC graphs for use with domains in which misclassification costs vary within the instance population. pulation.


title: Technical Development on Partial Discharge Measurement and Electrical Insulation Techniques for Low Voltage Motors Driven by Voltage Inverters

abstract: This paper reviews the up-to-date research activities on electrical insulation techniques under repetitive surge voltages with steep wavefront for low voltage inverter-fed motors. Partial discharge (PD) inception, propagation, ageing and breakdown characteristics of magnet wires, including recently developed PD-resistant wires such as nanocomposite enameled wires, are described. PD measurement techniques under the steep-front voltages and IEC activity toward their standardization are also introduced.


title: The Neural Architecture of the Language Comprehension Network: Converging Evidence from Lesion and Connectivity Analyses

abstract: While traditional models of language comprehension have focused on the left posterior temporal cortex as the neurological basis for language comprehension, lesion and functional imaging studies indicate the involvement of an extensive network of cortical regions. However, the full extent of this network and the white matter pathways that contribute to it remain to be characterized. In an earlier voxel-based lesion-symptom mapping analysis of data from aphasic patients (Dronkers et al., 2004), several brain regions in the left hemisphere were found to be critical for language comprehension: the left posterior middle temporal gyrus (MTG), the anterior part of Brodmann’s area 22 in the superior temporal gyrus (anterior STG/BA22), the posterior superior temporal sulcus (STS) extending into Brodmann’s area 39 (STS/BA39), the orbital part of the inferior frontal gyrus (BA47) and the middle frontal gyrus (BA46). Here, we investigated the white matter pathways associated with these regions using diffusion tensor imaging from healthy subjects. We also used resting-state functional magnetic resonance imaging data to assess the functional connectivity profiles of these regions. Fiber tractography and functional connectivity analyses indicated that the left MTG, anterior STG/BA22, STS/BA39 and BA47 are part of a richly interconnected network that extends to additional frontal, parietal and temporal regions in the two hemispheres. The inferior occipito-frontal fasciculus, the arcuate fasciculus and the middle and inferior longitudinal fasciculi, as well as transcallosal projections via the tapetum were found to be the most prominent white matter pathways bridging the regions important for language comprehension. The left posterior MTG showed a particularly extensive structural and functional connectivity pattern which is consistent with the severity of the impairments associated with MTG lesions and which suggests a central role for this region in language comprehension.


title: The Andes physics tutoring system: five years of evaluations

abstract: Andes is a mature intelligent tutoring system that has helped hundreds of students improve their learning of university physics. It replaces pencil and paper problem solving homework. Students continue to attend the same lectures, labs and recitations. Five years of experimentation at the United States Naval Academy indicates that it significantly improves student learning. This report describes the evaluations and what was learned from them.


title: Quantifying Program Bias

abstract: With the range and sensitivity of algorithmic decisions expanding at a break-neck speed, it is imperative that we aggressively investigate whether programs are biased. We propose a novel probabilistic program analysis technique and apply it to quantifying bias in decision-making programs. Specifically, we (i) present a sound and complete automated verification technique for proving quantitative properties of probabilistic programs; (ii) show that certain notions of bias, recently proposed in the fairness literature, can be phrased as quantitative correctness properties; and (iii) present FairSquare, the first verification tool for quantifying program bias, and evaluate it on a range of decision-making programs.


title: A Novel Bayes Model: Hidden Naive Bayes

abstract: Because learning an optimal Bayesian network classifier is an NP-hard problem, learning-improved naive Bayes has attracted much attention from researchers. In this paper, we summarize the existing improved algorithms and propose a novel Bayes model: hidden naive Bayes (HNB). In HNB, a hidden parent is created for each attribute which combines the influences from all other attributes. We experimentally test HNB in terms of classification accuracy, using the 36 UCI data sets selected by Weka, and compare it to naive Bayes (NB), selective Bayesian classifiers (SBC), naive Bayes tree (NBTree), tree-augmented naive Bayes (TAN), and averaged one-dependence estimators (AODE). The experimental results show that HNB significantly outperforms NB, SBC, NBTree, TAN, and AODE. In many data mining applications, an accurate class probability estimation and ranking are also desirable. We study the class probability estimation and ranking performance, measured by conditional log likelihood (CLL) and the area under the ROC curve (AUC), respectively, of naive Bayes and its improved models, such as SBC, NBTree, TAN, and AODE, and then compare HNB to them in terms of CLL and AUC. Our experiments show that HNB also significantly outperforms all of them.


title: Architectural impact of secure socket layer on Internet servers

abstract: Secure socket layer (SSL) is the most popular protocol used in the Internet for facilitating secure communications. In this retrospective, we summarize our original paper which analyzed the performance and architectural impact of SSL on the servers and provided insights into the functioning and acceleration of SSL. In addition, we describe advancements in the area that have occurred on this topic and also discuss future research opportunities


title: Trajectory Analysis for Soccer Players

abstract: In order to make good strategies, soccer coaches analyze the archives of matches, which can be effectively considered as a set of trajectories. We can extract several useful information by analyzing the trajectories of moving objects, which consist of 22 players and a ball. Since each moving object interacts with others and produces a trajectory, its trajectory has a certain number of relationships with others, which are a basic type of information to make soccer strategies. In this paper, we propose a model to quantitatively express the performance of soccer players. This model is based on the relationships between trajectories of 22 players and a ball and allows to evaluate the performance of several players in quantitative way.


title: DYNAMIC PROGRAMMING APPROACH TO TESTING RESOURCE ALLOCATION PROBLEM FOR MODULAR SOFTWARE

abstract: Testing phase of a software begins with module testing. During this period modules are tested independently to remove maximum possible number of faults within a specified time limit or testing resource budget. This gives rise to some interesting optimization problems, which are discussed in this paper. Two Optimization models are proposed for optimal allocation of testing resources among the modules of a Software. In the first model, we maximize the total fault removal, subject to budgetary Constraint. In the second model, additional constraint representing aspiration level for fault removals for each module of the software is added. These models are solved using dynamic programming technique. The methods have been illustrated through numerical examples.


title: Traffic Engineering in SDN/OSPF Hybrid Network

abstract: Traffic engineering under OSPF routes along the shortest paths, which may cause network congestion. Software Defined Networking (SDN) is an emerging network architecture which exerts a separation between the control plane and the data plane. The SDN controller can centrally control the network state through modifying the flow tables maintained by routers. Network operators can flexibly split arbitrary flows to outgoing links through the deployment of the SDN. However, SDN has its own challenges of full deployment, which makes the full deployment of SDN difficult in the short term. In this paper, we explore the traffic engineering in a SDN/OSPF hybrid network. In our scenario, the OSPF weights and flow splitting ratio of the SDN nodes can both be changed. The controller can arbitrarily split the flows coming into the SDN nodes. The regular nodes still run OSPF. Our contribution is that we propose a novel algorithm called SOTE that can obtain a lower maximum link utilization. We reap a greater benefit compared with the results of the OSPF network and the SDN/OSPF hybrid network with fixed weight setting. We also find that when only 30% of the SDN nodes are deployed, we can obtain a near optimal performance.


title: Accessing the deep web

abstract: Attempting to locate and quantify material on the Web that is hidden from typical search techniques.


title: A Review of Substrate Integrated Waveguide End-Fire Antennas

abstract: Substrate integrated waveguide (SIW) is a planar waveguide structure, which offers the advantages of low profile, ease of fabrication, low insertion loss, and compatibility with other planar circuits. SIW end-fire antennas have drawn broad interests due to the potential applications in aircraft, missile, and radar systems. However, this planar structure suffers from narrow bandwidth due to severe impedance mismatch at the radiating aperture. Meanwhile, the narrow radiating aperture of SIW end-fire antennas also deteriorates the radiation performance. This paper presents a detailed review upon the most recent research efforts concerning the improvement of antenna performances. They are discussed and classified into three different categories from the aspect of polarization properties: horizontally polarized, vertically polarized, and circularly polarized SIW end-fire antennas. Some practical difficulties for the development of SIW end-fire antennas are pointed out and effective approaches are also provided. A wide variety of antenna examples are presented with respect to theoretical and experimental results.


title: Light - fields of Circular Camera Arrays

abstract: The ray structure and sampling properties of different light-field representations inherently determine their use-cases. Currently prevalent linear data structures do not allow for joint processing of light-fields captured from multiple sides of a scene. In this paper, we review and highlight the differences in capturing and reconstruction between light-fields captured with linear and circular camera arrays. We also examine and improve the processing of light-fields captured with circular camera arrays with a focus on their use in reconstructing dense light-fields, by proposing a new resampling technique for circular light-fields. The proposed circular epipolar light-field structure creates a simple sinusoidal relation between the objects of the scene and their curves in the epipolar image, opening the way of efficient reconstruction of circular light-fields.


title: Comparison of Encryption Algorithms for Multimedia

abstract: Cryptographic techniques play crucial role when users exchange information. Multimedia plays an important role in learning and sharing experiences. When multimedia contents are shared among the users, it faces security threats. Usually multimedia contents takes much space. Encryption technique should be time efficient. In this work we consider four encryption techniques: Blowfish, AES, XOR and RSA and four types of media content: text, image, audio and video. Simulation shows that AES is time efficient than others. Comparing between symmetric and asymmetric cryptography, symmetric cryptographic techniques take less time than asymmetric technique.


title: An emerging trend in functional foods for the prevention of cardiovascular disease and diabetes: Marine algal polyphenols

abstract: ABSTRACT Marine macroalgae are gaining recognition among the scientific community as a significant source of functional food ingredients. Due to the harsh environments in which macroalgae survive, they produce unique bioactive compounds that are not found in terrestrial plants. Polyphenols are the predominant bioactive compound in brown algae and are accountable for the majority of its biological activity. Phlorotannins are a type of polyphenol that are unique to marine sources and have exhibited protective effects against hyperglycemia, hyperlipidemia, inflammation and oxidative stress, known risk factors for cardiovascular disease and diabetic complications, in cell culture, animal studies and some human studies. This review updates the information on marine polyphenols, with a particular focus on phlorotannins and their potential health benefits in relation to the prevention and treatment of risk factors for type 2 diabetes and cardiovascular diseases.


title: Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

abstract: Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelled by radiologists but this procedure is laborious, time-consuming, and error prone. In this paper, we propose a novel automatic method to detect CMBs from magnetic resonance (MR) images by exploiting the 3D convolutional neural network (CNN). Compared with previous methods that employed either low-level hand-crafted descriptors or 2D CNNs, our method can take full advantage of spatial contextual information in MR volumes to extract more representative high-level features for CMBs, and hence achieve a much better detection accuracy. To further improve the detection performance while reducing the computational cost, we propose a cascaded framework under 3D CNNs for the task of CMB detection. We first exploit a 3D fully convolutional network (FCN) strategy to retrieve the candidates with high probabilities of being CMBs, and then apply a well-trained 3D CNN discrimination model to distinguish CMBs from hard mimics. Compared with traditional sliding window strategy, the proposed 3D FCN strategy can remove massive redundant computations and dramatically speed up the detection process. We constructed a large dataset with 320 volumetric MR scans and performed extensive experiments to validate the proposed method, which achieved a high sensitivity of 93.16% with an average number of 2.74 false positives per subject, outperforming previous methods using low-level descriptors or 2D CNNs by a significant margin. The proposed method, in principle, can be adapted to other biomarker detection tasks from volumetric medical data.


title: Mining Japanese Compound Words and Their Pronunciations from Web Pages and Tweets

abstract: Mining compound words and their pronunciations is essential for Japanese input method editors (IMEs). We propose to use a chunk-based dependency parser to mine new words, collocations and predicate-argument phrases from largescale Japanese Web pages and tweets. The pronunciations of the compound words are automatically rewritten by a statistical machine translation (SMT) model. Experiments on applying the mined lexicon to a state-of-the-art Japanese IME system 1 showthat theprecisionofKana-Kanjiconversion is significantly improved.


title: Observing industrial control system attacks launched via metasploit framework

abstract: Industrial Control Systems (ICS) are present across many industries ranging from automotive to utilities. These systems have been found to be connected to corporate enterprise servers and can communicate over unencrypted communication channels. Interconnections of this type provide an attack vector for people with malicious intent and therefore are a critical cyber security risk. To better understand these risks and possible security measures, this research presents as proof of concept several attacks against a programmable logic controller along with observations made during the attacks. Our results indicate a time sequence difference between legitimate and spoofed command and control packets. Attacks are launched using the Metasploit Framework against a simulated control scenario. Using the observations made in this paper it is then suggested that several features can be extracted and then utilized in next generation mitigation and detection techniques for the industrial control environment.


title: Hypertrophic lichen planus versus prurigo nodularis: a dermoscopic perspective

abstract: Hypertrophic lichen planus (HLP) classically involves shin and ankles and is characterized by hyperkeratotic plaques and nodules. Prurigo nodularis (PN) is a chronic neurodermatitis that presents with intensely pruritic nodules. Histopathology of HLP and PN demonstrate epidermal hyperplasia, hypergranulosis, and compact hyperkeratosis. The dermis shows vertically arranged collagen fibers and an increased number of fibroblasts and capillaries in both conditions. Moreover, basal cell degeneration is confined to the tips of rete ridges, and band-like infiltration is conspicuously absent in HLP. Therefore, both conditions mimic each other clinically, which makes diagnosis difficult. Hence, there is a need for a diagnostic technique to differentiate both conditions.To evaluate dermoscopic patterns in HLP and PN and to study these patterns histopathologically.The study was conducted at S. Nijalingappa Medical College in Bagalkot. It was an observational case series study. Ethical clearance and informed consent was obtained. A Dermlite 3 dermoscope (3Gen, San Juan Capistrano, CA, USA) attached to a Sony Cyber Shot camera DSC-W800 (Sony Electronics Inc., San Diego, California, USA) was employed. Histopathology was done to confirm the diagnosis.There were 10 patients each with HLP and PN. HLP was seen in 8 males and 2 females. PN was observed in 7 females and 3 males. Dermoscopy of HLP demonstrated pearly white areas and peripheral striations (100%), gray-blue globules (60%), comedo-like openings (30%), red dots (40%), red globules (10%), brownish-black globules (30%), and yellowish structures (90%). In PN, red dots (70%), red globules (60%), and pearly white areas with peripheral striations (100%) were observed under dermoscopy.Both HLP and PN demonstrated specific dermoscopic patterns which can be demonstrated on histopathologic findings. The authors propose that these patterns are hallmarks of each condition. Thus, dermoscopy is a good diagnostic tool in the differentiation of HLP and PN.


title: A Single-Switch AC–DC LED Driver Based on a Boost-Flyback PFC Converter With Lossless Snubber

abstract: A single-switch ac–dc light-emitting-diode (LED) driver based on boost-flyback power factor correction (PFC) converter with a lossless snubber is proposed. In the proposed LED driver, the boost PFC module is designed to be operated in the discontinuous-conduction mode to achieve a high power factor. The dc–dc flyback module is designed to provide input–output electrical isolation to improve safety. The lossless snubber circuit clamps the peak voltage spike of switch to a low voltage and the leakage inductor energy is recycled via the dc–dc flyback module. Additionally, a low-voltage-rating capacitor can be used as the dc-bus capacitor because some of the input power is directly conducted to the output; the remaining power is stored in the dc-bus capacitor. Therefore, the proposed LED driver can provide a high power factor and a high power conversion efficiency. These results are verified for an output of 48 V and 2 A for the experimental prototype.


title: On the vehicle stability control for electric vehicle based on control allocation

abstract: This paper proposes a vehicle stability control (VSC) scheme for a four-wheel-drive electric vehicle based on the control allocation techniques. The wheels of the electric vehicle are driven by the electric motor individually, which makes it more convenient to control tires forces and moments for stabilizing vehicle motion. But the over-actuated system is complex to control. The control allocation technique is well-known used in marine craft and aircraft to resolve over-actuated problems. By introduction of control allocation to electric vehicle control, a large degree of modularization of the different levels of control is obtained. A control analysis layer is designed using a sliding mode control, which specifies a desired yaw moment to satisfy the vehicle stability. And then the control allocation distributes this yaw moment among the individual wheels by commanding appropriate wheel longitudinal slip. Simulation results in the Carmakerreg environment have shown that the proposed control scheme takes advantages of electric vehicle and enhances the vehicle stability.


title: Understanding the Smart City Domain: A Literature Review

abstract: Smart cities appeared in literature in the late 1990s and various approaches have been developed so far. Until today, smart city does not describe a city with particular attributes but it is used to describe different cases in urban spaces: web portals that virtualize cities or city guides, knowledge bases that address local needs, agglomerations with information and communication technology (ICT) infrastructure that attract business relocation, metropolitan-wide ICT infrastructures that deliver e-services to the citizens, ubiquitous environments, and recently ICT infrastructure for ecological use. Researchers, practicians, businessmen, and policy makers consider smart city from different perspectives and most of them agree on a model that measures urban economy, mobility, environment, living, people, and governance. On the other hand, ICT and construction industries stress to capitalize smart city and a new market seems to be generated in this domain. This chapter aims to perform a literature review, discover and classify the particular schools of thought, universities and research centres as well as companies that deal with smart city domain and discover alternative approaches, models, architectures, and frameworks with this regard.


title: An industrial-scale software defined Internet exchange point

abstract: Software-Defined Internet Exchange Points (SDXes) promise to significantly increase the flexibility and function of interdomain traffic delivery on the Internet. Unfortunately, current SDX designs cannot yet achieve the scale required for large Internet exchange points (IXPs), which can host hundreds of participants exchanging traffic for hundreds of thousands of prefixes. Existing platforms are indeed too slow and inefficient to operate at this scale, typically requiring minutes to compile policies and millions of forwarding rules in the data plane. ::: ::: We motivate, design, and implement iSDX, the first SDX architecture that can operate at the scale of the largest IXPs. We show that iSDX reduces both policy compilation time and forwarding table size by two orders of magnitude compared to current state-of-the-art SDX controllers. Our evaluation against a trace from one of the largest IXPs in the world found that iSDX can compile a realistic set of policies for 500 IXP participants in less than three seconds. Our public release of iSDX, complete with tutorials and documentation, is already spurring early adoption in operational networks.