title: Efficient schema extraction from a large collection of XML documents

abstract: XML is becoming the standard format for data exchange on the Internet. In this paper, we present a system that is effective in extracting schema information from a large collection of XML documents. Based on Xtract, we propose using the cost of an NFA simulation to compute the Minimum Length Description. We also studied using frequencies of the sample inputs to improve the effectiveness of the schema extraction. Experimental studies were conducted on synthesized XML data sets, suggesting the efficiency and effectiveness of our approach as a solution for schema inference.


title: The Málaga urban dataset: High-rate stereo and LiDAR in a realistic urban scenario

abstract: This paper introduces a dataset gathered entirely in urban scenarios with a car equipped with one stereo camera and five laser scanners, among other sensors. One distinctive feature of the present dataset is the existence of high-resolution stereo images grabbed at a high rate (20 fps) during a 36.8 km trajectory, which allows the benchmarking of a variety of computer vision techniques. We describe the sensors employed and highlight some applications which could be benchmarked using the present work. Both plain text and binary files are provided, as well as open-source tools for working with the binary versions. The dataset is available for download at http://www.mrpt.org/MalagaUrbanDataset.


title: THE EFFECT OF SEVERITY OF INITIATION ON LIKING FOR A GROUP 1

abstract: "An experiment was conducted to test the hypothesis that persons who undergo an unpleasant initiation to become members of a group increase their liking for the group; that is, they find the group more attractive than do persons who become members without going through a severe initiation. This hypothesis was derived from Festinger's theory of cognitive dissonance." 3 conditions were employed: reading of "embarrassing material" before a group, mildly embarrassing material to be read, no reading. "The results clearly verified the hypothesis." (PsycINFO Database Record (c) 2012 APA, all rights reserved)


title: Feature-based extraction of plucking and expression styles of the electric bass guitar

abstract: In this paper,we present a feature-based approach for the classification of different playing techniques in bass guitar recordings. The applied audio features are chosen to capture typical instrument sounds induced by 10 different playing techniques. A novel database that consists of approx. 4300 isolated bass notes was assembled for the purpose of evaluation. The usage of domain-specific features in a combination of feature selection and feature space transformation techniques improved the classification accuracy by over 27% points in comparison to a state-of-the-art baseline system. Classification accuracy reached 93.25% and 95.61% for the recognition of plucking and expression styles respectively.


title: A performance comparison of document oriented NoSQL databases

abstract: Due to exponential growth of data over the years, there are lot of formats in which the data is available. Many schema-less databases are identified as the data these days do not pertain itself to a particular scheme. So, the effective storage and processing of such data were not possible with the existing RDBMS. The looming of NoSQL databases proved to be one of the best solutions for handling these kind of schema-less data. This work comprises about the various characteristics of NOSQL databases. The performance comparison of two widely used Document-oriented NOSQL databases viz MongoDB and CouchDB are analysed in this work. Both qualitative as well as quantitative features are taken and a comparison for streaming applications among those features are provided using the two databases under study.


title: Imaging brain development: The adolescent brain

abstract: Abstract The past 15 years has seen a rapid expansion in the number of studies using neuroimaging techniques to investigate maturational changes in the human brain. In this paper, I review MRI studies on structural changes in the developing brain, and fMRI studies on functional changes in the social brain during adolescence. Both MRI and fMRI studies point to adolescence as a period of continued neural development. In the final section, I discuss a number of areas of research that are just beginning and may be the subject of developmental neuroimaging in the next twenty years. Future studies might focus on complex questions including the development of functional connectivity; how gender and puberty influence adolescent brain development; the effects of genes, environment and culture on the adolescent brain; development of the atypical adolescent brain; and implications for policy of the study of the adolescent brain.


title: Improving Recommender Systems by Incorporating Social Contextual Information

abstract: Due to their potential commercial value and the associated great research challenges, recommender systems have been extensively studied by both academia and industry recently. However, the data sparsity problem of the involved user-item matrix seriously affects the recommendation quality. Many existing approaches to recommender systems cannot easily deal with users who have made very few ratings. In view of the exponential growth of information generated by online users, social contextual information analysis is becoming important for many Web applications. In this article, we propose a factor analysis approach based on probabilistic matrix factorization to alleviate the data sparsity and poor prediction accuracy problems by incorporating social contextual information, such as social networks and social tags. The complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations. Moreover, the experimental results show that our method performs much better than the state-of-the-art approaches, especially in the circumstance that users have made few ratings.


title: Using an Unmanned Aerial Vehicle (UAV) to capture micro-topography of Antarctic moss beds

abstract: Mosses, the dominant flora of East Antarctica, show evidence of drying in recent decades, likely due to the regional effects of climate change. Given the relatively small area that such moss beds occupy, new tools are needed to map and monitor these fragile ecosystems in sufficient detail. In this study, we collected low altitude aerial photography with a small multi-rotor Unmanned Aerial Vehicle (UAV). Structure from Motion (SfM) computer vision techniques were applied to derive ultra-high resolution 3D models from multi-view aerial photography. A 2 cm digital surface model (DSM) and 1 cm orthophoto mosaic were derived from the 3D model and aerial photographs, respectively. The geometric accuracy of the orthophoto and DSM was 4 cm. A weighted contributing upstream area was derived with the D-infinity algorithm, based on the DSM and a snow cover map derived from the orthophoto. The contributing upstream area was used as a proxy for water availability from snowmelt, one of the key environmental drivers of moss health. A Monte Carlo simulation with 300 realisations was implemented to model the impact of error in the DSM on runoff direction. Significant correlations were found between these simulated water availability values and field measurements of moss health and water content. In the future ultra-high spatial resolution DSMs acquired with a UAV could thus be used to determine the impact of changing snow cover on the health and spatial distribution of polar vegetation non-destructively.


title: Mobility Increases Localizability: A Survey on Wireless Indoor Localization using Inertial Sensors

abstract: Wireless indoor positioning has been extensively studied for the past 2 decades and continuously attracted growing research efforts in mobile computing context. As the integration of multiple inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) to nowadays smartphones in recent years, human-centric mobility sensing is emerging and coming into vogue. Mobility information, as a new dimension in addition to wireless signals, can benefit localization in a number of ways, since location and mobility are by nature related in the physical world. In this article, we survey this new trend of mobility enhancing smartphone-based indoor localization. Specifically, we first study how to measure human mobility: what types of sensors we can use and what types of mobility information we can acquire. Next, we discuss how mobility assists localization with respect to enhancing location accuracy, decreasing deployment cost, and enriching location context. Moreover, considering the quality and cost of smartphone built-in sensors, handling measurement errors is essential and accordingly investigated. Combining existing work and our own working experiences, we emphasize the principles and conduct comparative study of the mainstream technologies. Finally, we conclude this survey by addressing future research directions and opportunities in this new and largely open area.


title: Multiple-shot human re-identification by Mean Riemannian Covariance Grid

abstract: Human re-identification is defined as a requirement to determine whether a given individual has already appeared over a network of cameras. This problem is particularly hard by significant appearance changes across different camera views. In order to re-identify people a human signature should handle difference in illumination, pose and camera parameters. We propose a new appearance model combining information from multiple images to obtain highly discriminative human signature, called Mean Riemannian Covariance Grid (MRCG). The method is evaluated and compared with the state of the art using benchmark video sequences from the ETHZ and the i-LIDS datasets. We demonstrate that the proposed approach outperforms state of the art methods. Finally, the results of our approach are shown on two other more pertinent datasets.


Bayesian Logic Networks (Extended Version ∗)


title: Featherweight Java: a minimal core calculus for Java and GJ

abstract: Several recent studies have introduced lightweight versions of Java: reduced languages in which complex features like threads and reflection are dropped to enable rigorous arguments about key properties such as type safety. We carry this process a step further, omitting almost all features of the full language (including interfaces and even assignment) to obtain a small calculus, Featherweight Java, for which rigorous proofs are not only possible but easy. Featherweight Java bears a similar relation to Java as the lambda-calculus does to languages such as ML and Haskell. It offers a similar computational "feel," providing classes, methods, fields, inheritance, and dynamic typecasts with a semantics closely following Java's. A proof of type safety for Featherweight Java thus illustrates many of the interesting features of a safety proof for the full language, while remaining pleasingly compact. The minimal syntax, typing rules, and operational semantics of Featherweight Java make it a handy tool for studying the consequences of extensions and variations. As an illustration of its utility in this regard, we extend Featherweight Java with generic classes in the style of GJ (Bracha, Odersky, Stoutamire, and Wadler) and give a detailed proof of type safety. The extended system formalizes for the first time some of the key features of GJ.


title: Attribute-centric recognition for cross-category generalization

abstract: We propose an approach to find and describe objects within broad domains. We introduce a new dataset that provides annotation for sharing models of appearance and correlation across categories. We use it to learn part and category detectors. These serve as the visual basis for an integrated model of objects. We describe objects by the spatial arrangement of their attributes and the interactions between them. Using this model, our system can find animals and vehicles that it has not seen and infer attributes, such as function and pose. Our experiments demonstrate that we can more reliably locate and describe both familiar and unfamiliar objects, compared to a baseline that relies purely on basic category detectors.


title: Image Orientation Estimation with Convolutional Networks

abstract: Rectifying the orientation of scanned documents has been an important problem that was solved long ago. In this paper, we focus on the harder case of estimating and correcting the exact orientation of general images, for instance, of holiday snapshots. Especially when the horizon or other horizontal and vertical lines in the image are missing, it is hard to find features that yield the canonical orientation of the image. We demonstrate that a convolutional network can learn subtle features to predict the canonical orientation of images. In contrast to prior works that just distinguish between portrait and landscape orientation, the network regresses the exact orientation angle. The approach runs in real-time and, thus, can be applied also to live video streams.


title: Molecular classification of multiple tumor types

abstract: Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. Previous works show several pairs of tumor types can be successfully distinguished by their gene expression patterns (Golub et al. (1999), Ben-Dor et al. (2000), Alizadeh et al. (2000)). However, the simultaneous classification across a heterogeneous set of tumor types has not been well studied yet. We obtained 190 samples from 14 tumor classes and generated a combined expression dataset containing 16063 genes for each of those samples. We performed multi-class classification by combining the outputs of binary classifiers. Three binary classifiers (k-nearest neighbors, weighted voting, and support vector machines) were applied in conjunction with three combination scenarios (one-vs-all, all-pairs, hierarchical partitioning). We achieved the best cross validation error rate of 18.75% and the best test error rate of 21.74% by using the one-vs-all support vector machine algorithm. The results demonstrate the feasibility of performing clinically useful classification from samples of multiple tumor types.


title: Simultaneous all-optical manipulation and recording of neural circuit activity with cellular resolution in vivo

abstract: Here, the authors present an approach for the simultaneous optogenetic manipulation and recording of neural activity at cellular resolution using two-photon microscopy and apply their strategy in the mouse barrel cortex.


title: The Potential of Neuromarketing as a Marketing Tool

abstract: In recent years, the emerging field of neuromarketing, which makes use of brain research in a managerial context, has gained increasing popularity in the academic literature as well as the practical world. Therefore, the purpose of the present paper is to evaluate upon the influence of neuromarketing tools on traditional marketing inputs in order to complete the understanding of consumer behavior. Yet, no available neuromarketing study provided an detailed overview of neuromarketing‘s influence on traditional marketing inputs while considering technical as well as ethical issues. The article uses the approach of an in-depth literature review in order to do so. ::: The increased use of neuromarketing techniques for the evaluation of customer preferences and decision-making processes is considered to be an advantage for customers and marketers simultaneously. EEG, fMRI and MEG rank among the most appropriate technicalities in the area of neuroscience. However, critics emphasize the restriction of the consumer‘s free will and their reduced ability to make individual decisions in their buying behavior due to neuromarketing‘s implementation. ::: After an evaluation of Outside reflexes, input-/Output models and Inside reflexes to analyze brain activities, the current paper evaluates upon the influence of neuromarketing on different relevant marketing inputs, followed by the consideration of its ethical defensibleness. ::: The results indicate that neuromarketing is associated to have a high influence on consumer buying behavior, advertising, pricing, distribution of products, branding and decision-making as marketing inputs. ::: Therefore, neuromarketing may be considered to be a remarkable extension in the research of human behavior and the brain as the ―black box‖ which can positively contribute to its practical applicableness.


title: A Neuromorphic Event-Based Neural Recording System for Smart Brain-Machine-Interfaces

abstract: Neural recording systems are a central component of Brain-Machince Interfaces (BMIs). In most of these systems the emphasis is on faithful reproduction and transmission of the recorded signal to remote systems for further processing or data analysis. Here we follow an alternative approach: we propose a neural recording system that can be directly interfaced locally to neuromorphic spiking neural processing circuits for compressing the large amounts of data recorded, carrying out signal processing and neural computation to extract relevant information, and transmitting only the low-bandwidth outcome of the processing to remote computing or actuating modules. The fabricated system includes a low-noise amplifier, a delta-modulator analog-to-digital converter, and a low-power band-pass filter. The bio-amplifier has a programmable gain of 45–54 dB, with a Root Mean Squared (RMS) input-referred noise level of 2.1 $\mu {\rm V}$ , and consumes 90 $\mu {\rm W}$ . The band-pass filter and delta-modulator circuits include asynchronous handshaking interface logic compatible with event-based communication protocols. We describe the properties of the neural recording circuits, validating them with experimental measurements, and present system-level application examples, by interfacing these circuits to a reconfigurable neuromorphic processor comprising an array of spiking neurons with plastic and dynamic synapses. The pool of neurons within the neuromorphic processor was configured to implement a recurrent neural network, and to process the events generated by the neural recording system in order to carry out pattern recognition.


title: A First Look at Performance in Mobile Virtual Network Operators

abstract: Recent industry trends suggest a new phenomenon in the mobile market: mobile virtual network operators or MVNOs that operate on top of existing cellular infrastructures. While MVNOs have shown significant growth in the US and elsewhere in the past two years and have been successful in attracting customers, there is anecdotal evidence that users are concerned about cellular performance when choosing MVNOs over traditional cellular operators. In this paper, we present the first systematic measurement study to shed light on this emerging phenomenon. We study the performance of 3 key applications: web access, video streaming and voice, in 2 popular MVNO families (a total of 8 carriers) in the US, where each MVNO family consists of a major base carrier and 3 MVNOs running on top of it. We observe that some MVNOs do indeed exhibit significant performance degradation and that there are key differences between the two MVNO families.


title: Effects of Collaboration and Multimedia Annotations on Vocabulary Learning and Listening Comprehension

abstract: Sixty-eight college students enrolled in a French course listened to a multimedia-based French passage in one of four groups to which they were randomly assigned: the listening text (a) alone, with no annotations; (b) in pairs, with no annotations; (c) alone, with written and pictorial annotations; and (d) in pairs, with written and pictorial annotations. The students identified or recalled vocabulary best when working with both annotation types either alone or in pairs. However, when they worked collaboratively with annotations available, they showed the highest level of aural comprehension. These outcomes suggest that accessing pictorial and written annotations in multimedia listening comprehension activities, while one is collaborating with a peer, results in relatively high vocabulary recall and recognition, as well as improved aural comprehension.


title: Structure Learning for Deep Neural Networks Based on Multiobjective Optimization

abstract: This paper focuses on the connecting structure of deep neural networks and proposes a layerwise structure learning method based on multiobjective optimization. A model with better generalization can be obtained by reducing the connecting parameters in deep networks. The aim is to find the optimal structure with high representation ability and better generalization for each layer. Then, the visible data are modeled with respect to structure based on the products of experts. In order to mitigate the difficulty of estimating the denominator in PoE, the denominator is simplified and taken as another objective, i.e., the connecting sparsity. Moreover, for the consideration of the contradictory nature between the representation ability and the network connecting sparsity, the multiobjective model is established. An improved multiobjective evolutionary algorithm is used to solve this model. Two tricks are designed to decrease the computational cost according to the properties of input data. The experiments on single-layer level, hierarchical level, and application level demonstrate the effectiveness of the proposed algorithm, and the learned structures can improve the performance of deep neural networks.


title: Secure and Efficient In-Hypervisor Memory Introspection Using Nested Virtualization

abstract: In JointCloud computing, the hypervisor used by each cloud plays a key role in providing services and protection for guest virtual machines (VMs). Unfortunately, the commodity hypervisor usually has a considerable attack surface and its memory is especially prone to be tampered with by an attacker who resides in one VM and then threatens the security of other co-located VMs. To mitigate such threat, previous solutions proposed an out-of-the-box design which leverages the nested virtualization to introduce a higher privileged software layer (a nested hypervisor) below the hypervisor. It also installs a security monitor into a trusted VM which is protected by the nested hypervisor and isolated from the untrusted hypervisor. The monitor is responsible for dynamically validating the behaviors of the untrusted hypervisor. Although monitoring from outside of the hypervisor can help ensure security, the large number of context switches caused by the nested virtualization incurs unacceptable overheads and makes this approach unsuitable for the cloud environment. In this paper, we introduce In-Hypervisor Memory Introspection (IHMI), an in-the-box way to monitor the hypervisor based on the nested virtualization. Our system puts the monitor into the untrusted hypervisor for efficiency while guaranteeing the same level of memory security as monitoring the hypervisor from a separated secure VM. By leveraging hardware virtualization features of current processors, IHMI isolates the monitor from the hypervisor via the nested page table and implements an efficient switch between them. Further, IHMI configures a uni-directional mapping for the monitor which allows the monitor to access the hypervisor’s memory at native speed while forbidding the hypervisor from accessing the monitor’s memory. Our IHMI system is currently still in an early stage and we report our design as well as preliminary evaluation results in this paper.


title: Pregnancy in chronic myeloid leukemia patients treated with alpha interferon.

abstract: Chronic myeloid leukemia (CML) is uncommon during pregnancy. Bone marrow transplantation is the only curative modality for the disease and is not recommended in pregnant women. We report two cases treated with interferon alpha (IFN-a). A 30-year-old Arabic woman in her 16th week of pregnancy presented in August 2002 with splenomegaly. Investigations revealed a hemoglobin blood concentration of 8.3 gydl, a white cell count (WBC) of 299.45=10 yl, and a platelet count of 9


title: Question Generation from Concept Maps

abstract: In this paper we present a question generation approach suitable for tutorial dialogues. The approach is based on previous psychological theories that hypothesize questions are generated from a knowledge representation modeled as a concept map. Our model automatically extracts concept maps from a textbook and uses them to generate questions. The purpose of the study is to generate and evaluate pedagogically-appropriate questions at varying levels of specificity across one or more sentences. The evaluation metrics include scales from the Question Generation Shared Task and Evaluation Challenge and a new scale specific to the pedagogical nature of questions in tutoring.


title: A DUAL-BAND DUAL-POLARIZED MICROSTRIP ARRAY ANTENNA FOR BASE STATIONS

abstract: This paper presents a new dual-band, dual-polarized 1£4 antenna array design for telecommunication base station. One of the bands covers global system for mobile communication (GSM) band, while the other covers both digital communication system (DCS) and universal mobile telecommunication system (UMTS) bands. The antenna is based upon an aperture stacked patch layout and incorporates a simple and novel dual-layered feeding technique to achieve dual polarized radiation. For feeding the array elements, a corporate feed network is used. In order to achieve appropriate matching in both bands, a three-section Chebyshev transformer has been designed. The proposed antenna shows good port decoupling, less than i30dB for dual linear polarization over its operating bands. Peak antenna gains about 11dBi and 11.6dBi have been obtained for lower and upper bands, respectively. The efiort was directed toward the design of a single standalone dual-polarized antenna to cover all three bands.


title: A study of the installed capacity and electricity quality of a fuel cell-independent microgrid that uses locally produced energy for local consumption

abstract: SUMMARY ::: ::: Reducing the rates of nuclear power generation for all electric power sources has been seriously discussed in Japan since the accident at the Fukushima No. 1 nuclear power plant in March 2011. Thus, the distributed power supply is expected to expand. The local production of safe and clean energy for local consumption is greatly needed in Japan. In this paper, the Saroma Lake green microgrid (SLMG), a fuel cell microgrid using tidal power generation and photovoltaics, has been planned. Energy balance equations were used to investigate this system's method of operation. Further simulated analysis of the facility, its operation cost, and the electric power quality of the network were conducted using MATLAB/Simulink, and the relationships among the capacity of a facility, the cost, and supply rate of green energy and the electric power quality (interphase voltage, frequency, and higher harmonic wave) of the power network were clarified. The total projected cost of the equipment and operation for introducing the proposed SLMG is ¥1,500,000,000/10 years. Increasing the supply rate of green energy and reducing the facility cost will require the introduction of biofuels and the reduction of the facility costs of solid oxide fuel cells in the SLMG. Copyright © 2012 John Wiley & Sons, Ltd.


title: Syndromic Surveillance Systems: Public Health and Biodefence

abstract: Ping Yan, Hsinchun Chen, and Daniel Zeng Department of Management Information Systems University of Arizona, Tucson, Arizona pyan@email.arizona.edu; {hchen, zeng}@eller.arizona.edu


title: DrugBank: a knowledgebase for drugs, drug actions and drug targets

abstract: DrugBank is a richly annotated resource that combines detailed drug data with comprehensive drug target and drug action information. Since its first release in 2006, DrugBank has been widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release. With 4900 drug entries, it now contains 60% more FDA-approved small molecule and biotech drugs including 10% more ‘experimental’ drugs. Significantly, more protein target data has also been added to the database, with the latest version of DrugBank containing three times as many non-redundant protein or drug target sequences as before (1565 versus 524). Each DrugCard entry now contains more than 100 data fields with half of the information being devoted to drug/chemical data and the other half devoted to pharmacological, pharmacogenomic and molecular biological data. A number of new data fields, including food–drug interactions, drug–drug interactions and experimental ADME data have been added in response to numerous user requests. DrugBank has also significantly improved the power and simplicity of its structure query and text query searches. DrugBank is available at http://www.drugbank.ca


title: Geometry images

abstract: Surface geometry is often modeled with irregular triangle meshes. The process of remeshing refers to approximating such geometry using a mesh with (semi)-regular connectivity, which has advantages for many graphics applications. However, current techniques for remeshing arbitrary surfaces create only semi-regular meshes. The original mesh is typically decomposed into a set of disk-like charts, onto which the geometry is parametrized and sampled. In this paper, we propose to remesh an arbitrary surface onto a completely regular structure we call a geometry image. It captures geometry as a simple 2D array of quantized points. Surface signals like normals and colors are stored in similar 2D arrays using the same implicit surface parametrization --- texture coordinates are absent. To create a geometry image, we cut an arbitrary mesh along a network of edge paths, and parametrize the resulting single chart onto a square. Geometry images can be encoded using traditional image compression algorithms, such as wavelet-based coders.


title: Growth and morphological study of zinc oxide nanoneedles grown on the annealed titanate nanotubes using hydrothermal method

abstract: Hydrothermal growth of ZnO on the annealed titanate nanotube films results in the oriented hexagonal-needlelike structures. The size, shape, density, and alignment of ZnO film are significantly affected by annealing temperature and orientation of the beneath titanate layer. It is believed that oxygen and hydrogen vacancies, generated due to dehydration of interlayered OH groups while annealing of the titanate, are responsible for the changes in the morphology of the ZnO. Microscopic observations clearly resolved nanoneedles with the base diameter of ∼150nm and length of ∼5μm with lattice spacing of 0.52nm, indicating single crystalline ZnO and grown along the (0001) direction. A growth model is presented based on the layer-by-layer growth (three-step growth) as a function of growth time (2–6h). Thicknesses of these three steps were found increasing with growth time. The second step (II) of growth exhibits the same feature as that of the first step (I), i.e., bounded with six crystallographic, lower surfac...


title: Botnet Detection Based On Machine Learning Techniques Using DNS Query Data

abstract: In recent years, botnets have become one of the major threats to information security because they have been constantly evolving in both size and sophistication. A number of botnet detection measures, such as honeynet-based and Intrusion Detection System (IDS)-based, have been proposed. However, IDS-based solutions that use signatures seem to be ineffective because recent botnets are equipped with sophisticated code update and evasion techniques. A number of studies have shown that abnormal botnet detection methods are more effective than signature-based methods because anomaly-based botnet detection methods do not require pre-built botnet signatures and hence they have the capability to detect new or unknown botnets. In this direction, this paper proposes a botnet detection model based on machine learning using Domain Name Service query data and evaluates its effectiveness using popular machine learning techniques. Experimental results show that machine learning algorithms can be used effectively in botnet detection and the random forest algorithm produces the best overall detection accuracy of over 90%.


title: SgxPectre: Stealing Intel Secrets from SGX Enclaves Via Speculative Execution

abstract: Speculative execution side-channel vulnerabilities in micro-architecture processors have raised concerns about the security of Intel SGX. To understand clearly the security impact of this vulnerability against SGX, this paper makes the following studies: First, to demonstrate the feasibility of the attacks, we present SgxPectre Attacks (the SGX-variants of Spectre attacks) that exploit speculative execution side-channel vulnerabilities to subvert the confidentiality of SGX enclaves. We show that when the branch prediction of the enclave code can be influenced by programs outside the enclave, the control flow of the enclave program can be temporarily altered to execute instructions that lead to observable cache-state changes. An adversary observing such changes can learn secrets inside the enclave memory or its internal registers, thus completely defeating the confidentiality guarantee offered by SGX. Second, to determine whether real-world enclave programs are impacted by the attacks, we develop techniques to automate the search of vulnerable code patterns in enclave binaries using symbolic execution. Our study suggests that nearly any enclave program could be vulnerable to SgxPectre Attacks since vulnerable code patterns are available in most SGX runtimes (e.g., Intel SGX SDK, Rust-SGX, and Graphene-SGX). Third, we apply SgxPectre Attacks to steal seal keys and attestation keys from Intel signed quoting enclaves. The seal key can be used to decrypt sealed storage outside the enclaves and forge valid sealed data; the attestation key can be used to forge attestation signatures. For these reasons, SgxPectre Attacks practically defeat SGX's security protection. Finally, we evaluate Intel's existing countermeasures against SgxPectre Attacks and discusses the security implications.


title: An efficient global energy optimization approach for robust 3D plane segmentation of point clouds

abstract: Abstract Automatic 3D plane segmentation is necessary for many applications including point cloud registration, building information model (BIM) reconstruction, simultaneous localization and mapping (SLAM), and point cloud compression. However, most of the existing 3D plane segmentation methods still suffer from low precision and recall, and inaccurate and incomplete boundaries, especially for low-quality point clouds collected by RGB-D sensors. To overcome these challenges, this paper formulates the plane segmentation problem as a global energy optimization because it is robust to high levels of noise and clutter. First, the proposed method divides the raw point cloud into multiscale supervoxels, and considers planar supervoxels and individual points corresponding to nonplanar supervoxels as basic units. Then, an efficient hybrid region growing algorithm is utilized to generate initial plane set by incrementally merging adjacent basic units with similar features. Next, the initial plane set is further enriched and refined in a mutually reinforcing manner under the framework of global energy optimization. Finally, the performances of the proposed method are evaluated with respect to six metrics (i.e., plane precision, plane recall, under-segmentation rate, over-segmentation rate, boundary precision, and boundary recall) on two benchmark datasets. Comprehensive experiments demonstrate that the proposed method obtained good performances both in high-quality TLS point clouds (i.e., SEMANTIC3D.NET dataset) and low-quality RGB-D point clouds (i.e., S3DIS dataset) with six metrics of (94.2%, 95.1%, 2.9%, 3.8%, 93.6%, 94.1%) and (90.4%, 91.4%, 8.2%, 7.6%, 90.8%, 91.7%) respectively.


title: Giant Electroresistive Ferroelectric Diode on 2DEG

abstract: Manipulation of electrons in a solid through transmitting, storing, and switching is the fundamental basis for the microelectronic devices. Recently, the electroresistance effect in the ferroelectric capacitors has provided a novel way to modulate the electron transport by polarization reversal. Here, we demonstrate a giant electroresistive ferroelectric diode integrating a ferroelectric capacitor into two-dimensional electron gas (2DEG) at oxide interface. As a model system, we fabricate an epitaxial Au/Pb(Zr0.2Ti0.8)O3/LaAlO3/SrTiO3 heterostructure, where 2DEG is formed at LaAlO3/SrTiO3 interface. This device functions as a two-terminal, non-volatile memory of 1 diode-1 resistor with a large I+/I− ratio (>108 at ±6 V) and Ion/Ioff ratio (>107). This is attributed to not only Schottky barrier modulation at metal/ferroelectric interface by polarization reversal but also the field-effect metal-insulator transition of 2DEG. Moreover, using this heterostructure, we can demonstrate a memristive behavior for an artificial synapse memory, where the resistance can be continuously tuned by partial polarization switching, and the electrons are only unidirectionally transmitted. Beyond non-volatile memory and logic devices, our results will provide new opportunities to emerging electronic devices such as multifunctional nanoelectronics and neuromorphic electronics.


title: Food Insufficiency and American School-Aged Children's Cognitive, Academic, and Psychosocial Development

abstract: Objective. This study investigates associations between food insufficiency and cognitive, academic, and psychosocial outcomes for US children and teenagers ages 6 to 11 and 12 to 16 years. Methods. Data from the Third National Health and Nutrition Examination Survey (NHANES III) were analyzed. Children were classified as food-insufficient if the family respondent reported that his or her family sometimes or often did not get enough food to eat. Regression analyses were conducted to test for associations between food insufficiency and cognitive, academic, and psychosocial measures in general and then within lower-risk and higher-risk groups. Regression coefficients and odds ratios for food insufficiency are reported, adjusted for poverty status and other potential confounding factors. Results. After adjusting for confounding variables, 6- to 11-year-old food-insufficient children had significantly lower arithmetic scores and were more likely to have repeated a grade, have seen a psychologist, and have had difficulty getting along with other children. Food-insufficient teenagers were more likely to have seen a psychologist, have been suspended from school, and have had difficulty getting along with other children. Further analyses divided children into lower-risk and higher-risk groups. The associations between food insufficiency and children9s outcomes varied by level of risk. Conclusions. The results demonstrate that negative academic and psychosocial outcomes are associated with family-level food insufficiency and provide support for public health efforts to increase the food security of American families.


title: Humanoid Cognitive Robots That Learn by Imitating: Implications for Consciousness Studies

abstract: While the concept of a conscious machine is intriguing, producing such a machine remains controversial and challenging. Here we describe how our work on creating a humanoid cognitive robot that learns to perform tasks via imitation learning relates to this issue. Our discussion is divided into three parts. First, we summarize our previously-detailed framework for advancing the understanding of the nature of phenomenal consciousness. This framework is based on identifying computational correlates of consciousness. Second, we describe a cognitive robotic system that we recently developed that learns to perform tasks by imitating human-provided demonstrations. This humanoid robot uses cause-effect reasoning to infer a demonstrator’s intentions in performing a task, rather than just imitating the observed actions verbatim. In particular, its cognitive components center on top-down control of a working memory that retains the explanatory interpretations that the robot constructs during learning. Finally, we describe our ongoing work that is focused on converting our robot’s imitation learning cognitive system into purely neurocomputational form, including both its low-level cognitive neuromotor components, its use of working memory, and its causal reasoning mechanisms. Based on our initial results, we argue that the top-down cognitive control of working memory, and in particular its gating mechanisms, is an important potential computational correlate of consciousness in humanoid robots. We conclude that developing high level neurocognitive control systems for cognitive robots and using them to search for computational correlates of consciousness provides an important approach to advancing our understanding of consciousness, and that it provides a credible and achievable route to ultimately developing a phenomenally conscious machine.


title: Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs . disbelief

abstract: Machine learning (ML) has become a popular tool for mining functional neuroimaging data, and there are now hopes of performing such analyses efficiently in real-time. Towards this goal, we compared accuracy of six different ML algorithms applied to neuroimaging data of persons engaged in a bivariate task, asserting their belief or disbelief of a variety of propositional statements. We performed unsupervised dimension reduction and automated feature extraction using independent component (IC) analysis and extracted IC time courses. Optimization of classification hyperparameters across each classifier occurred prior to assessment. Maximum accuracy was achieved at 92% for Random Forest, followed by 91% for AdaBoost, 89% for Naive Bayes, 87% for a J48 decision tree, 86% for K*, and 84% for support vector machine. For real-time decoding applications, finding a parsimonious subset of diagnostic ICs might be useful. We used a forward search technique to sequentially add ranked ICs to the feature subspace. For the current data set, we determined that approximately six ICs represented a meaningful basis set for classification. We then projected these six IC spatial maps forward onto a later scanning session within subject. We then applied the optimized ML algorithms to these new data instances, and found that classification accuracy results were reproducible. Additionally, we compared our classification method to our previously published general linear model results on this same data set. The highest ranked IC spatial maps show similarity to brain regions associated with contrasts for belief > disbelief, and disbelief < belief.


title: IoT-based occupancy monitoring techniques for energy-efficient smart buildings

abstract: With the proliferation of Internet of Things (IoT) devices such as smartphones, sensors, cameras, and RFIDs, it is possible to collect massive amount of data for localization and tracking of people within commercial buildings. Enabled by such occupancy monitoring capabilities, there are extensive opportunities for improving the energy consumption of buildings via smart HVAC control. In this respect, the major challenges we envision are 1) to achieve occupancy monitoring in a minimally intrusive way, e.g., using the existing infrastructure in the buildings and not requiring installation of any apps in the users’ smart devices, and 2) to develop effective data fusion techniques for improving occupancy monitoring accuracy using a multitude of sources. This paper surveys the existing works on occupancy monitoring and multi-modal data fusion techniques for smart commercial buildings. The goal is to lay down a framework for future research to exploit the spatio-temporal data obtained from one or more of various IoT devices such as temperature sensors, surveillance cameras, and RFID tags that may be already in use in the buildings. A comparative analysis of existing approaches and future predictions for research challenges are also provided.


title: How People Write Together Now: Beginning the Investigation with Advanced Undergraduates in a Project Course

abstract: Today's commercially available word processors allow people to write collaboratively in the cloud, both in the familiar asynchronous mode and now in synchronous mode as well. This opens up new ways of working together. We examined the data traces of collaborative writing behavior in student teams’ use of Google Docs to discover how they are writing together now. We found that student teams write both synchronously and asynchronously, take fluid roles in the writing and editing of the documents, and show a variety of styles of collaborative writing, including writing from scratch, beginning with an outline, pasting in a related example as a template to organize their own writing, and three more. We also found that the document serves as a place where they share a number of things not included in the final document, including links or references to related materials, the assignment requirements from the instructor, and informal discussions to coordinate the collaboration or to structure the document. We computed a number of measures to depict a group's collaboration behavior and asked external graders to score these documents for quality. We found that the documents that included balanced participation and/or exhibited leadership were judged higher in quality, as were those that were longer. We then suggested system design implications and behavioral guidelines to support people writing together better, and concluded the paper with future research directions.


title: Statistical Learning for OCR Text Correction

abstract: The accuracy of Optical Character Recognition (OCR) is crucial to the success of subsequent applications used in text analyzing pipeline. Recent models of OCR post-processing significantly improve the quality of OCR-generated text, but are still prone to suggest correction candidates from limited observations while insufficiently accounting for the characteristics of OCR errors. In this paper, we show how to enlarge candidate suggestion space by using external corpus and integrating OCR-specific features in a regression approach to correct OCR-generated errors. The evaluation results show that our model can correct 61.5% of the OCR-errors (considering the top 1 suggestion) and 71.5% of the OCR-errors (considering the top 3 suggestions), for cases where the theoretical correction upper-bound is 78%.


title: Differential Cryptanalysis of the Data Encryption Standard

abstract: DES, the Data Encryption Standard, is one of several cryptographic standards. The authors of this text detail their cryptanalytic "attack" upon DES and several other systems, using creative and novel tactics to demonstrate how they broke DES up into 16 rounds of coding. The methodology used offers valuable insights to cryptographers and cryptanalysts alike in creating new encryption standards, strengthening current ones, and exploring new ways to test important data protection schemes. This book introduces a new cryptographic method, called differential cryptanalysis, which can be applied to analyze cryptosystems. It describes the cryptanalysis of DES, deals with the influence of its building blocks on security, and analyzes modified variants. The differential cryptanalysis of "Feal" and several other cryptosystems is also described. This method can also be used to cryptanalyze hash functions, as is exemplified by the cryptanalysis of "Snefru".


title: A fuzzy k-modes algorithm for clustering categorical data

abstract: This correspondence describes extensions to the fuzzy k-means algorithm for clustering categorical data. By using a simple matching dissimilarity measure for categorical objects and modes instead of means for clusters, a new approach is developed, which allows the use of the k-means paradigm to efficiently cluster large categorical data sets. A fuzzy k-modes algorithm is presented and the effectiveness of the algorithm is demonstrated with experimental results.


title: SMC: A Practical Schema for Privacy-Preserved Data Sharing over Distributed Data Streams

abstract: Data collection is required to be safe and efficient considering both data privacy and system performance. In this paper, we study a new problem: distributed data sharing with privacy-preserving requirements. Given a data demander requesting data from multiple distributed data providers, the objective is to enable the data demander to access the distributed data without knowing the privacy of any individual provider. The problem is challenged by two questions: how to transmit the data safely and accurately; and how to efficiently handle data streams? As the first study, we propose a practical method, Shadow Coding, to preserve the privacy in data transmission and ensure the recovery in data collection, which achieves privacy preserving computation in a data-recoverable, efficient, and scalable way. We also provide practical techniques to make Shadow Coding efficient and safe in data streams. Extensive experimental study on a large-scale real-life dataset offers insight into the performance of our schema. The proposed schema is also implemented as a pilot system in a city to collect distributed mobile phone data.


title: DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News

abstract: While many lexica annotated with words polarity are available for sentiment analysis, very few tackle the harder task of emotion analysis and are usually quite limited in coverage. In this paper, we present a novel approach for extracting - in a totally automated way - a high-coverage and high-precision lexicon of roughly 37 thousand terms annotated with emotion scores, called DepecheMood. Our approach exploits in an original way 'crowd-sourced' affective annotation implicitly provided by readers of news articles from rappler.com. By providing new state-of-the-art performances in unsupervised settings for regression and classification tasks, even using a na\"{\i}ve approach, our experiments show the beneficial impact of harvesting social media data for affective lexicon building.


title: ZhuSuan: A Library for Bayesian Deep Learning

abstract: In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike existing deep learning libraries, which are mainly designed for deterministic neural networks and supervised tasks, ZhuSuan is featured for its deep root into Bayesian inference, thus supporting various kinds of probabilistic models, including both the traditional hierarchical Bayesian models and recent deep generative models. We use running examples to illustrate the probabilistic programming on ZhuSuan, including Bayesian logistic regression, variational auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural networks.


Towards an Intelligent Interior design system


Visual Control Of Robot Manipulators -- A Review


title: Performance evaluation of cryptographic algorithms: DES and AES

abstract: With the fast progression of digital data exchange in electronic way, information security is becoming much more important in data storage and transmission. Cryptography has come up as a solution which plays a vital role in information security system against malicious attacks. This security mechanism uses some algorithms to scramble data into unreadable text which can be only being decoded or decrypted by party those possesses the associated key. These algorithms consume a significant amount of computing resources such as CPU time, memory and computation time. In this paper two most widely used symmetric encryption techniques i.e. data encryption standard (DES) and advanced encryption standard (AES) have been implemented using MATLAB software. After the implementation, these techniques are compared on some points. These points are avalanche effect due to one bit variation in plaintext keeping the key constant, avalanche effect due to one bit variation in key keeping the plaintext constant, memory required for implementation and simulation time required for encryption.


title: Planar omnidirectional microstrip antenna array for 5 GHz ISM and UNII band

abstract: In this paper we propose for development and testing a low profile omnidirectional microstrip antenna. The antenna operating bandwidth covers 5-5.2 and 5.8-5.9 GHz bands being suitable for 5GHz ISM and UNII bands operation. The antenna is fabricated in FR4 due to low cost with overall dimensions of 77 × 10 × 1.6 mm3. The antenna is designed such that the currents on the top and bottom radiating elements are in phase. This antenna has the advantage of having a simple configuration, low profile, compactness, and low fabrication cost. Microwave experiments and numerical simulations are also performed to demonstrate its functionality.


title: An Improved Image Steganography Method Based on LSB Technique with Random Pixel Selection

abstract: with the rapid advance in digital network, information technology, digital libraries, and particularly World Wide Web services, many kinds of information could be retrieved any time. Thus, the security issue has become one of the most significant problems for distributing new information. It is necessary to protect this information while passing over insecure channels. Steganography introduces a strongly approach to hide the secret data in an appropriate media carriers such as images, audio files, text files, and video files. In this paper, a new image steganography method based on spatial domain is proposed. According to the proposed method, the secret message is embedded randomly in the pixel location of the cover image using Pseudo Random Number Generator (PRNG) of each pixel value of the cover image instead of embedding sequentially in the pixels of the cover image. This randomization is expected to increase the security of the system. The proposed method works with two layers (Blue and Green), as (2-1-2) layer, and the byte of the message will be embedded in three pixels only in this form (3-2-3). From the experimental results, it has found that the proposed method achieves a very high Maximum Hiding Capacity (MHC), and higher visual quality as indicated by the Peak Signal-to- Noise Ratio (PSNR).


Anticipating and Responding to Fraud in the 2014 Afghan Elections


title: Superresolving SAR Tomography for Multidimensional Imaging of Urban Areas: Compressive sensing-based TomoSAR inversion

abstract: Synthetic aperture radar (SAR) is capable of assessing the deformation of the ground and buildings in the order of centimeters and millimeters due to its coherent nature and short wavelengths. Spaceborne SAR systems are particularly suited for long-term monitoring of such dynamic processes. A single SAR image, however, only provides a two-dimensional (2-D) projection of the objects, which is in many cases noninjective (i.e., suffers from layover). To retrieve the real three-?dimensional (3-D) localization and motion of ?scattering objects, advanced interferometric methods, like persistent scatterer interferometry (PSI) or SAR tomography (TomoSAR), are required, which exploit stacks of complex-valued SAR images with diversity in space and time [1]?[6]. Modern spaceborne SAR sensors like TerraSAR-X, TanDEM-X, and COSMO-Skymed, provide data with very high spatial resolution (VHR) in the order of 1 m, which matches well with the scale of building features (typical floor height and window size and distance). This motivated the further development of existing TomoSAR techniques for exploring the potentials of VHR SAR data for urban infrastructure mapping [6]?[8]. In the last decade, conventional spectral estimation methods have been implemented for tomographic SAR imaging [3]?[6], [8]. However, for VHR urban monitoring, the ?following requirements should be met:


title: EFFICIENT HOUGH TRANSFORM FOR AUTOMATIC DETECTION OF CYLINDERS IN POINT CLOUDS

abstract: We present an efficient Hough transform for automatic detection of cylinders in point clouds. As cylinders are one of the most frequently used primitives for industrial design, automatic and robust methods for their detection and fitting are essential for reverse engineering from point clouds. The current methods employ automatic segmentation followed by geometric fitting, which requires a lot of manual interaction during modelling. Although Hough transform can be used for automatic detection of cylinders, the required 5D Hough space has a prohibitively high time and space complexity for most practical applications. We address this problem in this paper and present a sequential Hough transform for automatic detection of cylinders in point clouds. Our algorithm consists of two sequential steps of low dimensional Hough transforms. The first step, called Orientation Estimation, uses the Gaussian sphere of the input data and performs a 2D Hough Transform for finding strong hypotheses for the direction of cylinder axis. The second step of Position and Radius Estimation, consists of a 3D Hough transform for estimating cylinder position and radius. This sequential breakdown reduces the space and time complexity while retaining the advantages of robustness against outliers and multiple instances. The results of applying this algorithm to real data sets from two industrial sites are presented that demonstrate the effectiveness of this procedure for automatic cylinder detection.


title: Multiple design patterns for voice over IP (VoIP) security

abstract: Design patterns capture software solutions to specific problems that have evolved over time and reflect many iterations of work. Documenting such patterns promotes proven design and software reuse. There has been a growing amount of work documenting design patterns for security, however, little work specific to VoIP security. In 2005 NIST released a report on recommendations and best practices for securing VoIP, however it lacks the structure, terminology, and ease-of-understanding needed for both technical and non-technical audiences that is an inherent feature of design patterns. In this paper, we document three design patterns for VoIP implementations related to specific security problems: (1) secure traversal of firewalls and NATs; (2) detecting and mitigating DDoS attacks; and (3) securing against eavesdropping. With many VoIP vendors rushing products to market with overlapping functionality and requirements for interoperability, documenting design patterns is poised to become an important part of secure programming processes for VoIP.


FTB-2058 ) Microbial Cellulose : Fermentative Production and Applications


title: Hyaluronic acid (hyaluronan): a review

abstract: Hyaluronic acid (HA) is a high molecular weight biopolysacharide, discovered in 1934, by Karl Meyer and his assistant, John Palmer in the vitreous of bovine eyes. Hyaluronic acid is a naturally occurring biopolymer, which has important biological functions in bacteria and higher animals including humans. It is found in most connective tissues and is particularly concentrated in synovial fluid, the vitreous fluid of the eye, umbilical cords and chicken combs. It is naturally synthesized by a class of integral membrane protein s called hyaluronan synthases, and degraded by a family of enzymes called hyaluronidases. This review describes metabolisms, different physi - ological and pathological functions, basic pharmacological properties, and the clinical use of hyaluronic acid.


title: Composing high-performance memory allocators

abstract: Current general-purpose memory allocators do not provide sufficient speed or flexibility for modern high-performance applications. Highly-tuned general purpose allocators have per-operation costs around one hundred cycles, while the cost of an operation in a custom memory allocator can be just a handful of cycles. To achieve high performance, programmers often write custom memory allocators from scratch - a difficult and error-prone process. In this paper, we present a flexible and efficient infrastructure for building memory allocators that is based on C++ templates and inheritance. This novel approach allows programmers to build custom and general-purpose allocators as heap layers that can be composed without incurring any additional runtime overhead or additional programming cost. We show that this infrastructure simplifies allocator construction and results in allocators that either match or improve the performance of heavily-tuned allocators written in C, including the Kingsley allocator and the GNU obstack library. We further show this infrastructure can be used to rapidly build a general-purpose allocator that has performance comparable to the Lea allocator, one of the best uniprocessor allocators available. We thus demonstrate a clean, easy-to-use allocator interface that seamlessly combines the power and efficiency of any number of general and custom allocators within a single application.


title: Software Process Improvement in Very Small Organizations

abstract: Software process improvement (SPI) offers obstacles and opportunities for very small entities (VSEs), which are organizations with 25 or fewer employees. To help with this, the International Organization for Standardization and the International Electrotechnical Commission jointly developed ISO/IEC 29110 as SPI guidelines for VSEs.


title: Data routing strategies in opportunistic mobile social networks

abstract: Opportunistic Mobile Social Network (MSN) is a kind of Delay Tolerant Network (DTN) in which nodes are mobile with social characteristics. Users in such network carry data, move and forward it to others for information dissemination. To enable efficient data routing in opportunistic MSNs, the social metrics of users, such as mobility pattern, social centrality, community and etc. are leveraged in context of MSNs. In this paper, we investigate the data routing strategies in opportunistic MSNs in the following aspects: (1) the architecture of MSNs and its routing challenges and (2) routing strategies investigation on the basis of different social metrics. We study opportunistic MSN architecture and investigate the social metrics from encounter, social features and social properties, respectively. We show that encounter information is important exemplification of social metrics in opportunistic MSNs. We present other social metrics such as social features and social properties, including social graph properties and community structure. We then elaborate the routing strategies from different perspectives accordingly: encounter-based routing strategies, routing schemes according to social features and routing strategies based on social properties. We discuss the open issues for data routing in opportunistic MSNs, including limitations of routing metrics, collection of social information, social privacy and security, future applications of opportunistic MSNs, and etc.


title: Zika virus infection disrupts neurovascular development and results in postnatal microcephaly with brain damage

abstract: Zika virus (ZIKV) infection of pregnant women can result in fetal brain abnormalities. It has been established that ZIKV disrupts neural progenitor cells (NPCs) and leads to embryonic microcephaly. However, the fate of other cell types in the developing brain and their contributions to ZIKV-associated brain abnormalities remain largely unknown. Using intracerebral inoculation of embryonic mouse brains, we found that ZIKV infection leads to postnatal growth restriction including microcephaly. In addition to cell cycle arrest and apoptosis of NPCs, ZIKV infection causes massive neuronal death and axonal rarefaction, which phenocopy fetal brain abnormalities in humans. Importantly, ZIKV infection leads to abnormal vascular density and diameter in the developing brain, resulting in a leaky blood-brain barrier (BBB). Massive neuronal death and BBB leakage indicate brain damage, which is further supported by extensive microglial activation and astrogliosis in virally infected brains. Global gene analyses reveal dysregulation of genes associated with immune responses in virus-infected brains. Thus, our data suggest that ZIKV triggers a strong immune response and disrupts neurovascular development, resulting in postnatal microcephaly with extensive brain damage.


Assessments Of Different Speeded Up Robust Features (SURF) Algorithm Resolution For Pose Estimation Of UAV


Statistique et applications


title: Learning to Follow Navigational Directions

abstract: We present a system that learns to follow navigational natural language directions. Where traditional models learn from linguistic annotation or word distributions, our approach is grounded in the world, learning by apprenticeship from routes through a map paired with English descriptions. Lacking an explicit alignment between the text and the reference path makes it difficult to determine what portions of the language describe which aspects of the route. We learn this correspondence with a reinforcement learning algorithm, using the deviation of the route we follow from the intended path as a reward signal. We demonstrate that our system successfully grounds the meaning of spatial terms like above and south into geometric properties of paths.


title: An automated framework for QSAR model building

abstract: In-silico quantitative structure–activity relationship (QSAR) models based tools are widely used to screen huge databases of compounds in order to determine the biological properties of chemical molecules based on their chemical structure. With the passage of time, the exponentially growing amount of synthesized and known chemicals data demands computationally efficient automated QSAR modeling tools, available to researchers that may lack extensive knowledge of machine learning modeling. Thus, a fully automated and advanced modeling platform can be an important addition to the QSAR community. In the presented workflow the process from data preparation to model building and validation has been completely automated. The most critical modeling tasks (data curation, data set characteristics evaluation, variable selection and validation) that largely influence the performance of QSAR models were focused. It is also included the ability to quickly evaluate the feasibility of a given data set to be modeled. The developed framework is tested on data sets of thirty different problems. The best-optimized feature selection methodology in the developed workflow is able to remove 62–99% of all redundant data. On average, about 19% of the prediction error was reduced by using feature selection producing an increase of 49% in the percentage of variance explained (PVE) compared to models without feature selection. Selecting only the models with a modelability score above 0.6, average PVE scores were 0.71. A strong correlation was verified between the modelability scores and the PVE of the models produced with variable selection. We developed an extendable and highly customizable fully automated QSAR modeling framework. This designed workflow does not require any advanced parameterization nor depends on users decisions or expertise in machine learning/programming. With just a given target or problem, the workflow follows an unbiased standard protocol to develop reliable QSAR models by directly accessing online manually curated databases or by using private data sets. The other distinctive features of the workflow include prior estimation of data modelability to avoid time-consuming modeling trials for non modelable data sets, an efficient variable selection procedure and the facility of output availability at each modeling task for the diverse application and reproduction of historical predictions. The results reached on a selection of thirty QSAR problems suggest that the approach is capable of building reliable models even for challenging problems.


title: Compact arrays fed by substrate integrated waveguides

abstract: In this paper, we compare three different concepts of compact antenna arrays fed by substrate integrated waveguides (SIW). Antenna concepts differ in the type of radiators. Slots represent magnetic linear radiators, patches are electric surface radiators, and Vivaldi slots belong to travelling-wave antennas. Hence, the SIW feeders have to exploit different mechanisms of exciting antenna elements. Impedance and radiation properties of studied antenna arrays have been related to the normalized frequency. Antenna arrays have been mutually compared to show fundamental dependencies of final parameters of the designed antennas on state variables of antennas, on SIW feeder architectures and on related implementation details.


title: Design and Development of a Compact All-Solid-State High-Frequency Picosecond-Pulse Generator

abstract: The Marx circuit structure based on fast high-power semiconductor devices is the classical way of generating picosecond pulses. However, most reported studies suggest that as the number of stages in the Marx circuit increases, the output pulse amplitude tends to saturate. This has of late made high-voltage picosecond-pulse generation a challenge. In this paper, as the number of stages increases, the internal resistance of the avalanche transistor tends to decrease with increasing current flowing through the avalanche transistor. A step-by-step wiring and debugging of the avalanche transistor-based Marx circuit demonstrates this experimentally. That is, for a certain range in the number of stages, saturation of the output pulse amplitude for the Marx circuit of avalanche transistors is not apparent. Introducing a parallel structure for the transistors not only increases the current level of the entire circuit but also further reduces the equivalent resistance of the avalanche transistors, thereby improving the output efficiency. Drawing on microstrip transmission theory, and combining the Marx circuit and avalanche transistor parallel structure, component parameters and circuit topology of the picosecond-pulse generator were redesigned, and a 30-level Marx circuit with a parallel structure of two avalanche transistors was developed. The picosecond-pulse generator outputs a pulse with adjustable voltage amplitude of 0.9–3.1 kV, a 350-ps full-width at half-maximum, a 150-ps rise time, and an adjustable high-stability repetition rate of up to 10 kHz. The number of pulses is precisely controlled. The generator is an all-solid-state compact device with high frequency suitable for research needs.


title: DISCO: A multilingual database of distributionally similar words

abstract: This paper 1 presents DISCO, a tool for retrieving the distributional similarity be- tween two given words, and for retrieving the distributionally most similar words for a given word. Pre-computed word spaces are freely available for a number of languages including English, German, French and Italian, so DISCO can be used off the shelf. The tool is imple- mented in Java, provides a Java API, and can also be called from the command line. The per- formance of DISCO is evaluated by measuring the correlation with WordNet-based semantic similarities and with human relatedness judgements. The evaluations show that DISCO has a higher correlation with semantic similarities derived from WordNet than latent semantic analysis (LSA) and the web-based PMI-IR.


title: A time and space complexity analysis of model integration

abstract: The computational study of complex systems increasingly requires model integration. The drivers include a growing interest in leveraging accepted legacy models, an intensifying pressure to reduce development costs by reusing models, and expanding user requirements that are best met by combining different modeling methods. There have been many published successes including supporting theory, conceptual frameworks, software tools, and case studies. Nonetheless, on an empirical basis, the published work suggests that correctly specifying model integration strategies remains challenging. This naturally raises a question that has not yet been answered in the literature, namely ‘what is the computational difficulty of model integration?’ This paper's contribution is to address this question with a time and space complexity analysis that concludes that deep model integration with proven correctness is both NP-complete and PSPACE-complete and that reducing this complexity requires sacrificing correctness proofs in favor of guidance from both subject matter experts and modeling specialists.


title: My life has become a major distraction from my cell phone: Partner phubbing and relationship satisfaction among romantic partners

abstract: Partner phubbing (Pphubbing) can be best understood as the extent to which an individual uses or is distracted by his/her cell phone while in the company of his/her relationship partner. The present study is the first to investigate the oft-occurring behavior of Pphubbing and its impact on relationship satisfaction and personal well-being. In Study 1, a nine-item scale was developed to measure Pphubbing. The scale was found to be highly reliable and valid. Study 2 assessed the study's proposed relationships among a sample of 145 adults. Results suggest that Pphubbing's impact on relationship satisfaction is mediated by conflict over cell phone use. One's attachment style was found to moderate the Pphubbing - cell phone conflict relationship. Those with anxious attachment styles reported higher levels of cell phone conflict than those with less anxious attachment styles. Importantly, Pphubbing was found to indirectly impact depression through relationship satisfaction and ultimately life satisfaction. Given the ever-increasing use of cell phones to communicate between romantic partners, the present research offers insight into the process by which such use may impact relationship satisfaction and personal well-being. Directions for future research are discussed. A valid scale of partner phubbing (Pphubbing) was developed.Pphubbing was found to have a negative impact on relationship satisfaction.Cell phone conflict mediated the impact of Pphubbing on relationship satisfaction.Attachment style moderated the Pphubbing - cell phone conflict relationship.Pphubbing was found to indirectly impact life satisfaction and depression.


title: Adaptive bootstrapping of recommender systems using decision trees

abstract: Recommender systems perform much better on users for which they have more information. This gives rise to a problem of satisfying users new to a system. The problem is even more acute considering that some of these hard to profile new users judge the unfamiliar system by its ability to immediately provide them with satisfying recommendations, and may quickly abandon the system when disappointed. Rapid profiling of new users by a recommender system is often achieved through a bootstrapping process - a kind of an initial interview - that elicits users to provide their opinions on certain carefully chosen items or categories. The elicitation process becomes particularly effective when adapted to users' responses, making best use of users' time by dynamically modifying the questions to improve the evolving profile. In particular, we advocate a specialized version of decision trees as the most appropriate tool for this task. We detail an efficient tree learning algorithm, specifically tailored to the unique properties of the problem. Several extensions to the tree construction are also introduced, which enhance the efficiency and utility of the method. We implemented our methods within a movie recommendation service. The experimental study delivered encouraging results, with the tree-based bootstrapping process significantly outperforming previous approaches.


title: Compile-time dynamic voltage scaling settings: opportunities and limits

abstract: With power-related concerns becoming dominant aspects of hardware and software design, significant research effort has been devoted towards system power minimization. Among run-time power-management techniques, dynamic voltage scaling (DVS) has emerged as an important approach, with the ability to provide significant power savings. DVS exploits the ability to control the power consumption by varying a processor's supply voltage (V) and clock frequency (f). DVS controls energy by scheduling different parts of the computation to different (V, f) pairs; the goal is to minimize energy while meeting performance needs. Although processors like the Intel XScale and Transmeta Crusoe allow software DVS control, such control has thus far largely been used at the process/task level under operating system control. This is mainly because the energy and time overhead for switching DVS modes is considered too large and difficult to manage within a single program.In this paper we explore the opportunities and limits of compile-time DVS scheduling. We derive an analytical model for the maximum energy savings that can be obtained using DVS given a few known program and processor parameters. We use this model to determine scenarios where energy consumption benefits from compile-time DVS and those where there is no benefit. The model helps us extrapolate the benefits of compile-time DVS into the future as processor parameters change. We then examine how much of these predicted benefits can actually be achieved through optimal settings of DVS modes. This is done by extending the existing Mixed-integer Linear Program (MILP) formulation for this problem by accurately accounting for DVS energy switching overhead, by providing finer-grained control on settings and by considering multiple data categories in the optimization. Overall, this research provides a comprehensive view of compile-time DVS management, providing both practical techniques for its immediate deployment as well theoretical bounds for use into the future.


title: Episodic autobiographical memories over the course of time: Cognitive, neuropsychological and neuroimaging findings

abstract: The critical attributes of episodic memory are self, autonoetic consciousness and subjectively sensed time. The aim of this paper is to present a theoretical overview of our already published researches into the nature of episodic memory over the course of time. We have developed a new method of assessing autobiographical memory (TEMPau task), which is specially designed to measure these specific aspects, based on the sense of re-experiencing events from across the entire lifespan. Based on our findings of cognitive, neuropsychological and neuroimaging studies, new insights into episodic autobiographical memories are presented, focusing on the effects of age of the subjects interacting with time interval in healthy subjects and lesioned patients. The multifaceted and complex nature of episodic memory is emphasized and it is suggested that mental time travel through subjective time, which allows individuals to re-experience specific past events through a feeling of self-awareness, is the last feature of autobiographical memory to become fully operational in development and the first feature to go in aging and most amnesias. Our findings highlight the critical role of frontotemporal areas in constructive autobiographical memory processes, and especially hippocampus, in re-experiencing episodic details from the recent or more distant past.


title: Estimation and mitigation of current harmonics using improved control schemes

abstract: Algorithms are well presented for estimation of harmonics and their elimination in a three phase system for power quality improvement. This paper presents the application of KF-Adaline Algorithm for estimation of power system harmonics and shunt active power filter with hysteresis current controller based on space vector modulation for perfect elimination of harmonics. The space vector modulation (SVM) based hysteresis current controller (HCC) combines both the advantages of SVM and HCC. This controller determines a set of space vector from a region detector and then applies a space vector according to the Hysteresis current controller. Reference current extraction is done by the synchronous detection technique which is compared to the actual current and the error current is fed to the Shunt Active Power Filter (SAPF). Shunt Active Power Filter manages to provide better compensation signals to eliminate the harmonics for power quality improvement which is fed to the distribution system. Simulation results are provided to verify the better performance of SAPF due to the proposed controller.


title: Motor Schema-Based Formation Control for Multiagent Robot Teams

abstract: New reactive behaviors that implement formations in multirobot teams are presented and evaluated. The formation behaviors are integrated with other navigational behaviors to enable a robotic team to reach navigational goals, avoid hazards and simultaneously remain in formation. The behaviors are implemented in simulation, on robots in the laboratory and aboard DARPA's HMMWV-based unmanned ground vehicles. The technique has been integrated with the autonomous robot architecture (AuRA) and the UGV Demo II architecture. The results demonstrate the value of various types of formations in autonomous, human-led and communications-restricted applications, and their appropriateness in different types of task environments.


title: ShelfNet for Real-time Semantic Segmentation

abstract: In this project, we present ShelfNet, a lightweight convolutional neural network for accurate real-time semantic segmentation. Different from the standard encoder-decoder structure, ShelfNet has multiple encoder-decoder branch pairs with skip connections at each spatial level, which looks like a shelf with multiple columns. The shelf-shaped structure provides multiple paths for information flow and improves segmentation accuracy. Inspired by the success of recurrent convolutional neural networks, we use modified residual blocks where two convolutional layers share weights. The shared-weight block enables efficient feature extraction and model size reduction. We tested ShelfNet with ResNet50 and ResNet101 as the backbone respectively: they achieved 59 FPS and 42 FPS respectively on a GTX 1080Ti GPU with a 512x512 input image. ShelfNet achieved high accuracy: on PASCAL VOC 2012 test set, it achieved 84.2% mIoU with ResNet101 backbone and 82.8% mIoU with ResNet50 backbone; it achieved 75.8% mIoU with ResNet50 backbone on Cityscapes dataset. ShelfNet achieved both higher mIoU and faster inference speed compared with state-of-the-art real-time semantic segmentation models. We provide the implementation this https URL.


Suggestibility , compliance and psychological traits in high-functioning adults with Autism Spectrum Disorder


title: Large-scale optimization for evaluation functions with minimax search

abstract: This paper presents a new method, Minimax Tree Optimization (MMTO), to learn a heuristic evaluation function of a practical alpha-beta search program. The evaluation function may be a linear or non-linear combination of weighted features, and the weights are the parameters to be optimized. To control the search results so that the move decisions agree with the game records of human experts, a well-modeled objective function to be minimized is designed. Moreover, a numerical iterative method is used to find local minima of the objective function, and more than forty million parameters are adjusted by using a small number of hyper parameters. This method was applied to shogi, a major variant of chess in which the evaluation function must handle a larger state space than in chess. Experimental results show that the large-scale optimization of the evaluation function improves the playing strength of shogi programs, and the new method performs significantly better than other methods. Implementation of the new method in our shogi program Bonanza made substantial contributions to the program's first-place finish in the 2013 World Computer Shogi Championship. Additionally, we present preliminary evidence of broader applicability of our method to other two-player games such as chess.


title: Embrace the Challenges: Software Engineering in a Big Data World

abstract: The design and development of data-intensive software systems -- systems that generate, collect, store, process, analyze, query, and visualize large sets of data -- is fraught with significant challenges both technical and social. Project EPIC has been designing and developing data-intensive systems in support of crisis informatics research since Fall 2009. Our experience working on Project EPIC has provided insight into these challenges. In this paper, we share our experience working in this design space and describe the choices we made in tackling these challenges and their attendant trade-offs. We highlight the lack of developer support tools for data-intensive systems, the importance of multidisciplinary teams, the use of highly-iterative life cycles, the need for deep understanding of the frameworks and technologies used in data intensive systems, how simple operations transform into significant challenges at scale, and the paramount significance of data modeling in producing systems that are scalable, robust, and efficient.


title: Using of Co-simulation ADAMS-SIMULINK for development of mechatronic systems

abstract: This paper deals with an efficient technique for the development of mechatronic systems. Individual parts of such system as mechanics, actuators, sensors, control system, etc. are designed in several passes through V-model with respects to mutual feedbacks. Based on this methodology the developed system is made as a virtual prototype and can be tested and simulated using co-simulation technique. The ADAMS and SIMULINK co-simulation is used and it is based on direct embedding of dynamic model of the mechanical system with sensors and actuators implemented in ADAMS into MATLAB environment to a control system design and a virtual prototype model tuning. So the complex model of mechatronic system applies the same implementation for design, simulation and testing.


Crystal structure of cis-inositol-monobenzoate, C13H16O7


title: Attention-in-Attention Networks for Surveillance Video Understanding in Internet of Things

abstract: In this paper, we propose an approach to generate the comprehensive video interpretation for the surveillance video understanding in Internet of Things. The key problem of many visual learning tasks is to adaptively select and fuse diverse and complimentary features for video representation. We design the attention-in-attention (AIA) network to hierarchically explore the attention fusion in an end-to-end manner, and demonstrate the value of this model on the multievent recognition and video captioning challenges. Particularly, it consists of multiple encoder attention modules (EAMs) and a fusion attention module (FAM). Each EAM aims to highlight the space-specific features by selecting the most salient visual features or semantic attributes and averages them into one attentive feature. The FAM can suppress or enhance the activation of multispace attentive features and adaptively co-embed them for comprehensive video representation. Then, one long short-term memory unit decodes the video representations to generate multiple event labels or video captions. This architecture is capable of: 1) adaptively learning the salient space-specific feature representation and 2) co-embedding multispace attentive features into one space for feature fusion. Experiments conducted on the surveillance video dataset (concurrent event dataset) and the popular video captioning datasets (Microsoft Research Video Description Corpus and MSR-Video to Text). It shows that the proposed AIA can achieve competitive performances against the state of the arts.


title: Eliminating Fuzzy Duplicates in Data Warehouses

abstract: The duplicate elimination problem of detecting multiple tuples, which describe the same real world entity, is an important data cleaning problem. Previous domain independent solutions to this problem relied on standard textual similarity functions (e.g., edit distance, cosine metric) between multi-attribute tuples. However, such approaches result in large numbers of false positives if we want to identify domain-specific abbreviations and conventions. In this paper, we develop an algorithm for eliminating duplicates in dimensional tables in a data warehouse, which are usually associated with hierarchies. We exploit hierarchies to develop a high quality, scalable duplicate elimination algorithm, and evaluate it on real datasets from an operational data warehouse.


title: Simulation of Object and Human Skin Deformations in a Grasping Task

abstract: This paper addresses the problem of simulating deformations between objects and the hand of a synthetic character during a grasping process. A numerical method based on finite element theory allows us to take into account the active forces of the fingers on the object and the reactive forces of the object on the fingers. The method improves control of synthetic human behavior in a task level animation system because it provides information about the environment of a synthetic human and so can be compared to the sense of touch. Finite element theory currently used in engineering seems one of the best approaches for modeling both elastic and plastic deformation of objects, as well as shocks with or without penetration between deformable objects. We show that intrinsic properties of the method based on composition/decomposition of elements have an impact in computer animation. We also state that the use of the same method for modeling both objects and human bodies improves the modeling both objects and human bodies improves the modeling of the contacts between them. Moreover, it allows a realistic envelope deformation of the human fingers comparable to existing methods. To show what we can expect from the method, we apply it to the grasping and pressing of a ball. Our solution to the grasping problem is based on displacement commands instead of force commands used in robotics and human behavior.


title: Morton Filters: Faster, Space-Efficient Cuckoo Filters via Biasing, Compression, and Decoupled Logical Sparsity

abstract: Approximate set membership data structures (ASMDSs) are ubiquitous in computing. They trade a tunable, often small, error rate (e) for large space savings. The canonical ASMDS is the Bloom filter, which supports lookups and insertions but not deletions in its simplest form. Cuckoo filters (CFs), a recently proposed class of ASMDSs, add deletion support and often use fewer bits per item for equal e. ::: ::: This work introduces the Morton filter (MF), a novel AS-MDS that introduces several key improvements to CFs. Like CFs, MFs support lookups, insertions, and deletions, but improve their respective throughputs by 1.3x to 2.5x, 0.9x to 15.5x, and 1.3x to 1.6x. MFs achieve these improvements by (1) introducing a compressed format that permits a logically sparse filter to be stored compactly in memory, (2) leveraging succinct embedded metadata to prune unnecessary memory accesses, and (3) heavily biasing insertions to use a single hash function. With these optimizations, lookups, insertions, and deletions often only require accessing a single hardware cache line from the filter. These improvements are not at a loss in space efficiency, as MFs typically use comparable to slightly less space than CFs for the same epsis;.


title: A covert channel construction in a virtualized environment

abstract: Memory deduplication has been widely used in various commodity hypervisors. However, while this technique improves memory efficiency, it has an impact on system security. In particular, memory deduplication is usually implemented using a variant of copy-on-write techniques, for which, writing to a shared page would incur a longer access time than those non-shared. By exploiting this artifact, we demonstrate a new covert channel can be built in a virtualized environment.


title: Steelix: program-state based binary fuzzing

abstract: Coverage-based fuzzing is one of the most effective techniques to find vulnerabilities, bugs or crashes. However, existing techniques suffer from the difficulty in exercising the paths that are protected by magic bytes comparisons (e.g., string equality comparisons). Several approaches have been proposed to use heavy-weight program analysis to break through magic bytes comparisons, and hence are less scalable. In this paper, we propose a program-state based binary fuzzing approach, named Steelix, which improves the penetration power of a fuzzer at the cost of an acceptable slow down of the execution speed. In particular, we use light-weight static analysis and binary instrumentation to provide not only coverage information but also comparison progress information to a fuzzer. Such program state information informs a fuzzer about where the magic bytes are located in the test input and how to perform mutations to match the magic bytes efficiently. We have implemented Steelix and evaluated it on three datasets: LAVA-M dataset, DARPA CGC sample binaries and five real-life programs. The results show that Steelix has better code coverage and bug detection capability than the state-of-the-art fuzzers. Moreover, we found one CVE and nine new bugs.


title: Pilonidal sinus destruction with a radial laser probe: technique and first Belgian experience

abstract: BACKGROUND ::: The treatment of pilonidal sinus disease still remains challenging. Despite many non-surgical and surgical methods, no consensus emerged for the best treatment. We describe a new innovative technique consisting in the destruction of the pilonidal cyst with a radial laser probe (FILACTM, Biolitec, Germany). The energy delivered causes the destruction of the sinus epithelium and the simultaneous obliteration of the tract. ::: ::: ::: METHOD ::: In December 2015, we retrospectively studied the data of our 40 first patients operated with this technique between September 2014 and September 2015. The mean follow-up period was 234 days (92-316). There were 33 men and 7 women. The mean age of the patients was 25.2 years (15-46). ::: ::: ::: RESULTS ::: The success rate was 87.5% (35 patients/40). Recurrence rate was 2.9% (1 patient/35). Hospital stay was 1 day for all the patients with no re-hospitalisation during the follow-up. The mean duration of soiling before healing was 18.6 days (2-35). The mean duration of pain-killers intake was 4.9 days (0-14). Four patients presented complications: 2 hematomas (5%) and 2 abscesses (5%), all medically treated. ::: ::: ::: CONCLUSION ::: The destruction of a pilonidal cyst with a laser probe is a safe, simple and minimally invasive technique. The success rate is good. Hospital stay is short, pain is light and complications are few as well as the number of patients needing post-operative care, allowing a rapid return to work or school. This technique could be proposed as a first-line treatment to the majority of patients with a pilonidal sinus disease.


title: Memento: A Health Monitoring System for Wireless Sensor Networks

abstract: Wireless sensor networks are deployed today to monitor the environment, but their own health status is relatively opaque to network administrators, in most cases. Our system, Memento, provides failure detection and symptom alerts, while being frugal in the use of energy and bandwidth. Memento has two parts: an energy-efficient protocol to deliver state summaries, and a distributed failure detector module. The failure detector is robust to packet losses, and attempts to ensure that reports of failure will not exceed a specified false positive rate. We show that distributed monitoring of a subset of well-connected neighbors using a variance-bound based failure detector achieves the lowest rate of false positives, suitable for use in practice. We evaluate our findings using an implementation for the TinyOS platform on the Mica2 motes on a 55-node network, and find that Memento achieves a 80-90% reduction in bandwidth use compared to standard data collection methods


title: Reduced Conductive EMI in Switched-Mode DC–DC Power Converters Without EMI Filters: PWM Versus Randomized PWM

abstract: This paper addresses improved electromagnetic compatibility (EMC) in switched-mode power converters (SMPCs), without using electromagnetic interference (EMI) filters. A practical solution is proposed for minimizing conductive EMI in a pulsewidth modulated (PWM), and random PWM dc-dc power converters. A comparative investigation is performed into the use of different random modulation schemes (driven by the DSP-2 board with the TMS320C32) as against the normal PWM. The effectiveness of randomization on spreading those dominating frequencies that normally exist in constant frequency PWM schemes is evaluated by power spectral density (PSD) estimations in the low-frequency range. Some parasitical- and topology-based EMI sources in devices are presented and frequency analysis is shown by using the discrete Fourier transform (DFT). SPICE simulations are also used to verify practical solutions for eliminating negative EMI sources and achieving EMC improvements. Finally, levels of conductive EMI are estimated with DFT, and experimentally verified to comply with the CISPR 25 (or EN 55025) regulations. Moreover, it is clearly shown, that limited speed PWM driving of the power switches with appropriate snubber circuits guarantees reduced conductive EMI. When considering the price and high EMC, in the final solution the power converter is driven by the mu-controller PIC16F876 with limited numerical and peripheral capabilities, where only normal PWM and randomized PWM can be implemented