title: Immune Disorders in Hashimoto's Thyroiditis: What Do We Know So Far?

abstract: This review of literature attempts to identify the factors that are involved in the pathogenesis of Hashimoto thyroiditis, an immune defect in an individual with genetic susceptibility accompanied with environmental factors. The frequency of Hashimoto's disease is a growing trend and among Caucasians it is estimated at approximately 5%. The dysfunction of the gland may be clinically evident (0.1-2% of the population) or subclinical (10-15%). The pathology is diagnosed five to ten times more often in women than men and its incidence increases with the age (the peak of the number of cases is between 45 and 65); however, it can also be diagnosed in children. The pathogenesis of Hashimoto's thyroiditis is still not fully comprehended. In the etiology of Hashimoto thyroiditis excessively stimulated T CD4+ cells are known to play the most important role. Recent research has demonstrated an increasing role of newly discovered cells such as Th17 (CD4+IL-17+) or T regulatory cells (CD4+CD25+(high)FoxP3+) in the induction of autoimmune disorders. The process of programmed cell death also plays an equally important role in the pathogenesis and the development of hypothyroidism.


A Familial 7 q 36 . 3 Duplication Associated with Agenesis of the Corpus Callosum


title: Isolated bidirectional DC/AC and AC/DC three-phase power conversion using series resonant converter modules and a three-phase unfolder

abstract: This paper proposes a modular converter system to achieve high power density, high system bandwidth and scalability for isolated bidirectional DC/AC and AC/DC three-phase power conversion. The approach is based on high frequency isolated series resonant converter (SRC) modules with series output connection and a low frequency three-phase unfolder. The performance objectives are realized through elimination of traditional low frequency passive filters used in PWM inverters and instead require high control bandwidth in the SRC modules to achieve high quality AC waveforms. The system operation and performance are verified with simulation and experimental results for a 1 kW prototype.


title: Portfolio Choice Based on Third-Degree Stochastic Dominance

abstract: We develop an optimization method for constructing investment portfolios that dominate a given benchmark portfolio in terms of third-degree stochastic dominance. Our approach relies on the properties of the semivariance function, a refinement of an existing “superconvex” dominance condition, and quadratic constrained programming. We apply our method to historical stock market data using an industry momentum strategy. Our enhanced portfolio generates important performance improvements compared with alternatives based on mean-variance dominance and second-degree stochastic dominance. Relative to the Center for Research in Security Prices all-share index, our portfolio increases average out-of-sample return by almost seven percentage points per annum without incurring more downside risk, using quarterly rebalancing and without short selling. This paper was accepted by Lauren Cohen, finance.


Modular Self-reconfigurable Robot Systems: Challenges and Opportunities for the Future


title: Folding: Why Good Models Sometimes Make Spurious Recommendations

abstract: In recommender systems based on low-rank factorization of a partially observed user-item matrix, a common phenomenon that plagues many otherwise effective models is the interleaving of good and spurious recommendations in the top-K results. A single spurious recommendation can dramatically impact the perceived quality of a recommender system. Spurious recommendations do not result in serendipitous discoveries but rather cognitive dissonance. In this work, we investigate folding, a major contributing factor to spurious recommendations. Folding refers to the unintentional overlap of disparate groups of users and items in the low-rank embedding vector space, induced by improper handling of missing data. We formally define a metric that quantifies the severity of folding in a trained system, to assist in diagnosing its potential to make inappropriate recommendations. The folding metric complements existing information retrieval metrics that focus on the number of good recommendations and their ranks but ignore the impact of undesired recommendations. We motivate the folding metric definition on synthetic data and evaluate its effectiveness on both synthetic and real world datasets. In studying the relationship between the folding metric and other characteristics of recommender systems, we observe that optimizing for goodness metrics can lead to high folding and thus more spurious recommendations.


title: Reconfigurable microwave photonic in-phase and quadrature detector for frequency agile radar.

abstract: A microwave photonic in-phase and quadrature detector is conceived and practically demonstrated. The detector has the ability to become electronically reconfigured to operate at any frequency over a wide range. This makes it an excellent candidate for frequency agile radars and other electronic warfare systems based on frequency hopping. The detector exhibits a very low amplitude and phase imbalance, which removes the need for any imbalance compensation technique. The system is designed based on the transversal filtering concept and reconfigurability is achieved via wavelength control in a dispersive fiber. The system operation was demonstrated over a frequency range of 3.5-35 GHz, with a maximum of -32 dB amplitude imbalance.


title: Synthesis, antibacterial and antifungal possession of amino acids containing sulfonamide moieties.

abstract: Sulfonamides were developed by the simple reaction of amino acid with p-toluenesulfonyl chloride and structures of the new products (2a, 2b and 2c) were confirmed by elemental and spectral analysis (FT-IR, 1HNMR and13CNMR). In vitro, developed compounds were screened for their antibacterial and antifungal activities against two sensitive bacteria belonging to both gram positive and gram-negative types and two fungi. The synthesized sulfonamides (2a, 2b, 2c) exhibited excellent antifungal activities against the tested fungi. Among the tested compounds 2a and 2b have marked activity against E. coli with zone of inhibition (mm) 22.3±0.11and 20.2±0.26 (MIC: 12.5µg/mL, 12.5µg/mL) and S. aureus with zone of inhibition (mm) 20.2±0.26 and 23.2±0.55 (MIC: 12.5µg/mL, 6.25µg/mL). Compound 2c is moderately efficient towards E. coli (zone of inhibition (mm) 14.2±0.64, MIC: 100µg/mL) and no activity against S. aureus.


title: Citizen Trust Development for E-Government Adoption and Usage: Insights from Young Adults in Singapore

abstract: Trust, which has been found to be a significant facilitator for the adoption and usage of new business paradigms like e-commerce, is relatively unexplored in the context of e-government. Using trust literature as the theoretical lens, we propose an e-government trust grid for the adoption and usage of e-government, comprising two dimensions: ‘trust in government' and ‘trust in Internet technology.' Based on their levels of trust in the two identified dimensions, nations can fall into one of four quadrants: Adversarial, Competitive, Cooperative, and Collaborative. Using focus groups and interviews with young adults in Singapore, we find that in recent years, Singapore is evolving from the cooperative (low trust in Internet technology and high trust in government) to the collaborative (high trust in Internet technology and high trust in government) quadrant. The study delineates a set of lessons learned from the Singapore experience for engendering citizen trust in e-government. These lessons for governments are: solicit feedback from citizens, demonstrate top leadership commitment and support, build institutional trust, cultivate IT literacy, and enact comprehensive and effective legal systems.


title: Dietary fibre: challenges in production and use of food composition data.

abstract: Dietary fibre is a heterogeneous group of components for which several definitions and analytical methods were developed over the past decades, causing confusion among users and producers of dietary fibre data in food composition databases. An overview is given of current definitions and analytical methods. Some of the issues related to maintaining dietary fibre values in food composition databases are discussed. Newly developed AOAC methods (2009.01 or modifications) yield higher dietary fibre values, due to the inclusion of low molecular weight dietary fibre and resistant starch. For food composition databases procedures need to be developed to combine 'classic' and 'new' dietary fibre values since re-analysing all foods on short notice is impossible due to financial restrictions. Standardised value documentation procedures are important to evaluate dietary fibre values from several sources before exchanging and using the data, e.g. for dietary intake research.


title: Closed loop task space control of an interleaved continuum-rigid manipulator

abstract: A new manipulation approach, referred to as interleaved continuum-rigid manipulation, which combines inherently safe, flexible actuated segments with more precise embedded rigid-link joints has recently been introduced [1], [2]. The redundantly actuated manipulator possesses the safety characteristics inherent in flexible segment devices while gaining some of the performance attributes of rigid-link joint systems. In this paper, we describe a general controller developed for an interleaved manipulator. The controller is implemented on a clinically-relevant prototype, the results of which demonstrate the advantages of an interleaved manipulator. We also consider kinematic drivers of the interleaved manipulator workspace, showing that careful kinematic considerations can substantially improve manipulator workspace and task accuracy.


title: Virtual museums, a survey and some issues for consideration

abstract: Abstract Museums are interested in the digitizing of their collections not only for the sake of preserving the cultural heritage, but to also make the information content accessible to the wider public in a manner that is attractive. Emerging technologies, such as VR, AR and Web3D are widely used to create virtual museum exhibitions both in a museum environment through informative kiosks and on the World Wide Web. This paper surveys the field, and while it explores the various kinds of virtual museums in existence, it discusses the advantages and limitation involved with a presentation of old and new methods and of the tools used for their creation.


title: Pretreatment methods of lignocellulosic biomass for anaerobic digestion

abstract: Agricultural residues, such as lignocellulosic materials (LM), are the most attractive renewable bioenergy sources and are abundantly found in nature. Anaerobic digestion has been extensively studied for the effective utilization of LM for biogas production. Experimental investigation of physiochemical changes that occur during pretreatment is needed for developing mechanistic and effective models that can be employed for the rational design of pretreatment processes. Various-cutting edge pretreatment technologies (physical, chemical and biological) are being tested on the pilot scale. These different pretreatment methods are widely described in this paper, among them, microaerobic pretreatment (MP) has gained attention as a potential pretreatment method for the degradation of LM, which just requires a limited amount of oxygen (or air) supplied directly during the pretreatment step. MP involves microbial communities under mild conditions (temperature and pressure), uses fewer enzymes and less energy for methane production, and is probably the most promising and environmentally friendly technique in the long run. Moreover, it is technically and economically feasible to use microorganisms instead of expensive chemicals, biological enzymes or mechanical equipment. The information provided in this paper, will endow readers with the background knowledge necessary for finding a promising solution to methane production.


title: Leveraging big data technology for small and medium-sized enterprises (SMEs)

abstract: Wisdom aligns with technology is the key factor for sustainable business development. By increasing amount of public and private data, organizations need to find new solutions to manage data and information which lead to knowledge, better decision making, and value. In the big data-bang, smart organization surfing on-line technology and start planning big data strategy. However, many organizations do not yet have a big data strategy. A challenge facing SMEs is that they may not have the same capacity as large companies to analysis new data sets. Also, traditional data processing tools are not capable for SMEs decision making because of volume, velocity and variety if data. For address this problem we need new leveraging technology, tools and talent. SMEs which have risen to leveraging the value of big data are using advantage of cloud computing and open-source software to realize various goals. The main goal of this investment is about value as a new concept in a big data era. In this study, we focus on emerging trends and future requirement: technology and tools for SMEs.


title: Investigation of a new method for improving image resolution for camera tracking applications

abstract: Camera based systems have been a preferred choice in many motion tracking applications due to the ease of installation and the ability to work in unprepared environments. The concept of these systems is based on extracting image information (colour and shape properties) to detect the object location. However, the resolution of the image and the camera field-of- view (FOV) are two main factors that can restrict the tracking applications for which these systems can be used. Resolution can be addressed partially by using higher resolution cameras but this may not always be possible or cost effective. ::: This research paper investigates a new method utilising averaging of offset images to improve the effective resolution using a standard camera. The initial results show that the minimum detectable position change of a tracked object could be improved by up to 4 times.


title: A modified roach infestation optimization

abstract: Swarm intelligence algorithms are candidate solutions to complex problems. This paper proposes a modified roach infestation optimization (MRIO) algorithm that is absolutely tied to social cockroach behaviours. MRIO improves the performance of the existing roach infestation optimization (RIO) using partial differential equation, crossover and mutation methods. The existing RIO models, made up of three components is modified and two new components are added. Simulation studies were conducted on the proposed algorithm with established benchmarks, the obtained result were compared with the results of the existing roach infestation optimization and hungry roach infestation optimization. The comparison results clearly show that the proposed algorithm outperforms the existing algorithms; and finds global optima of multi-dimensional functions.


title: Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition XuechuanWang

abstract: Feature extraction is an important component of a pattern recognition system. It performs two tasks: transforming input parameter vector into a feature vector and/or reducing its dimensionality. A well-defined feature extraction algorithm makes the classification process more effective and efficient. Two popular methods for feature extraction are linear discriminant analysis (LDA) and principal component analysis (PCA). In this paper, the minimum classification error (MCE) training algorithm (which was originally proposed for optimizing classifiers) is investigated for feature extraction. A generalized MCE (GMCE) training algorithm is proposed to mend the shortcomings of the MCE training algorithm. LDA, PCA, and MCE and GMCE algorithms extract features through linear transformation. Support vector machine (SVM) is a recently developed pattern classification algorithm, which uses non-linear kernel functions to achieve non-linear decision boundaries in the parametric space. In this paper, SVM is also investigated and compared to linear feature extraction algorithms.


title: A meta-analytic assessment of the DeLone and McLean IS success model: An examination of IS success at the individual level

abstract: Fifteen years ago, DeLone and McLean published their original model of IS success, which received considerable attention in the literature. Given the widespread acceptance of the model, we conducted a meta-analysis to determine whether the model had been validated by research studies reported in the literature. By aggregating the results of 52 empirical studies that examined relationships within the IS success model at the individual level of analysis, we found support for the relationships that encompass the model. We also offer insights on IS success based on the findings of our work.


title: Online learning classifier based behavioral biometrie authentication

abstract: In this paper, we implement a behavioral biometric-based smartphone authentication technique with online training methods. The most obvious difference between online and traditional classification is that online methods allow model training and data collection to be in progress simultaneously. Therefore, our proposed authentication system is superior when dealing with time series data and its model can be updated to adapt the change of user's habit. To verify the feasibility of online training methods used in behavioral biometric authentication, three online algorithms and four traditional classification algorithms are tested with collected dataset. They all achieved an accuracy over 96%. Two additional experiments are designed to test the stability of this authentication system. The results show that the system has the ability to prevent targeted attack, such as shoulder surfing. Furthermore, it keeps high performance that accuracy greater than 95% when user holds smartphone in different scenarios, sitting and walking. Finally, we conclude that online training methods based behavioral biometric smartphone authentication system is very stable with targeted attack and different unlock scenarios.


title: Combating the Sting of Rejection With the Pleasure of Revenge: A New Look at How Emotion Shapes Aggression

abstract: How does emotion explain the relationship between social rejection and aggression? Rejection reliably damages mood, leaving individuals motivated to repair their negatively valenced affective state. Retaliatory aggression is often a pleasant experience. Rejected individuals may then harness revenge’s associated positive affect to repair their mood. Across 6 studies (total N = 1,516), we tested the prediction that the rejection–aggression link is motivated by expected and actual mood repair. Further, we predicted that this mood repair would occur through the positive affect of retaliatory aggression. Supporting these predictions, naturally occurring (Studies 1 and 2) and experimentally manipulated (Studies 3 and 4) motives to repair mood via aggression moderated the rejection–aggression link. These effects were mediated by sadistic impulses toward finding aggression pleasant (Studies 2 and 4). Suggesting the occurrence of actual mood repair, rejected participants’ affective states were equivalent to their accepted counterparts after an act of aggression (Studies 5 and 6). This mood repair occurred through a dynamic interplay between preaggression affect and aggression itself, and was driven by increases in positive affect (Studies 5 and 6). Together, these findings suggest that the rejection–aggression link is driven, in part, by the desire to return to affective homeostasis. Additionally, these findings implicate aggression’s rewarding nature as an incentive for rejected individuals’ violent tendencies. (PsycINFO Database Record (c) 2017 APA, all rights reserved)


title: Amperometric Electrochemical Microsystem for a Miniaturized Protein Biosensor Array

abstract: Protein-based bioelectrochemical interfaces offer great potential for rapid detection, continuous use, and miniaturized sensor arrays. This paper introduces a microsystem platform that enables multiple bioelectrochemical interfaces to be interrogated simultaneously by an onchip amperometric readout system. A post-complementary metal-oxide semiconductor (CMOS) fabrication procedure is described that permits the formation of planar electrode arrays and self assembly of biosensor interfaces on the electrodes. The onchip, 0.5-mum CMOS readout electronics include a compact potentiostat that supports a very broad range of input currents-6 pA to 10 muA-to accommodate diverse biosensor interfaces. The 2.3 times 2.2-mm chip operates from a 5-V supply at 0.6 mA. A prototype electrochemical sensor platform, including an onchip potentiostat and miniaturized biosensor array, was characterized by using cyclic voltammetry. The linear relationship between the oxidization peak values and the concentrations of target analytes in the solution verifies functionality of the system and demonstrates the potential for future implementations of this platform in high sensitivity, low cost, and onchip protein-based sensor arrays.


title: Word Embeddings and Their Use In Sentence Classification Tasks

abstract: This paper have two parts. In the first part we discuss word embeddings. We discuss the need for them, some of the methods to create them, and some of their interesting properties. We also compare them to image embeddings and see how word embedding and image embedding can be combined to perform different tasks. In the second part we implement a convolutional neural network trained on top of pre-trained word vectors. The network is used for several sentence-level classification tasks, and achieves state-of-art (or comparable) results, demonstrating the great power of pre-trainted word embeddings over random ones.


title: Obesity and the four facets of impulsivity

abstract: Abstract Objective Obesity is a complex condition involving biological, psychological, sociocultural and environmental components. Impulsivity seems to be a particularly important factor. Whiteside and Lynam recently proposed dividing impulsivity into four separate dimensions: Urgency, lack of Premeditation, lack of Perseverance and Sensation Seeking (associated with a tendency to exaggerate the impact of rewards). The objective of this article is to examine how obesity and eating disorder symptoms may be related to the four facets of impulsivity. Methods Whiteside and Lynam's Impulsive Behavior Scale, the Sensitivity to Punishment and Sensitivity to Reward Questionnaire, the Eating Disorder Examination Questionnaire and the Mizes Anorectic Cognitions Questionnaire were used to explore the association between the cognitive and motivational facets of impulsivity and obesity in 47 overweight or obese persons with eating disorders and 47 normal-weight controls. Results Results suggest that overweight and obese persons have higher levels of Urgency, lack of Perseverance and Sensitivity to Reward. Conclusion These results suggest that obese and overweight persons have difficulty inhibiting automatic or dominant behavior and intrusive thoughts and a higher sensitivity to reward. Practice implications Overweight and obese persons may benefit from psychological interventions targeting self-control problems associated with impulsive eating behaviors.


Analysis of Hadoop’s Performance under Failures


title: A logical formalization of the OCC theory of emotions

abstract: In this paper, we provide a logical formalization of the emotion triggering process and of its relationship with mental attitudes, as described in Ortony, Clore, and Collins’s theory. We argue that modal logics are particularly adapted to represent agents’ mental attitudes and to reason about them, and use a specific modal logic that we call Logic of Emotions in order to provide logical definitions of all but two of their 22 emotions. While these definitions may be subject to debate, we show that they allow to reason about emotions and to draw interesting conclusions from the theory.


title: PC algorithm for nonparanormal graphical models

abstract: The PC algorithm uses conditional independence tests for model selection in graphical modeling with acyclic directed graphs. In Gaussian models, tests of conditional independence are typically based on Pearson correlations, and high-dimensional consistency results have been obtained for the PC algorithm in this setting. Analyzing the error propagation from marginal to partial correlations, we prove that high-dimensional consistency carries over to a broader class of Gaussian copula or nonparanormal models when using rank-based measures of correlation. For graph sequences with bounded degree, our consistency result is as strong as prior Gaussian results. In simulations, the 'Rank PC' algorithm works as well as the 'Pearson PC' algorithm for normal data and considerably better for non-normal data, all the while incurring a negligible increase of computation time. While our interest is in the PC algorithm, the presented analysis of error propagation could be applied to other algorithms that test the vanishing of low-order partial correlations.


Validation of the Somnotouch-NIBP noninvasive continuous blood pressure monitor according to the European Society of Hypertension International Protocol revision 2010


title: Fourier PCA and robust tensor decomposition

abstract: Fourier PCA is Principal Component Analysis of a matrix obtained from higher order derivatives of the logarithm of the Fourier transform of a distribution.We make this method algorithmic by developing a tensor decomposition method for a pair of tensors sharing the same vectors in rank-$1$ decompositions. Our main application is the first provably polynomial-time algorithm for underdetermined ICA, i.e., learning an $n \times m$ matrix $A$ from observations $y=Ax$ where $x$ is drawn from an unknown product distribution with arbitrary non-Gaussian components. The number of component distributions $m$ can be arbitrarily higher than the dimension $n$ and the columns of $A$ only need to satisfy a natural and efficiently verifiable nondegeneracy condition. As a second application, we give an alternative algorithm for learning mixtures of spherical Gaussians with linearly independent means. These results also hold in the presence of Gaussian noise.


title: A decentralized training algorithm for Echo State Networks in distributed big data applications

abstract: The current big data deluge requires innovative solutions for performing efficient inference on large, heterogeneous amounts of information. Apart from the known challenges deriving from high volume and velocity, real-world big data applications may impose additional technological constraints, including the need for a fully decentralized training architecture. While several alternatives exist for training feed-forward neural networks in such a distributed setting, less attention has been devoted to the case of decentralized training of recurrent neural networks (RNNs). In this paper, we propose such an algorithm for a class of RNNs known as Echo State Networks. The algorithm is based on the well-known Alternating Direction Method of Multipliers optimization procedure. It is formulated only in terms of local exchanges between neighboring agents, without reliance on a coordinating node. Additionally, it does not require the communication of training patterns, which is a crucial component in realistic big data implementations. Experimental results on large scale artificial datasets show that it compares favorably with a fully centralized implementation, in terms of speed, efficiency and generalization accuracy.


title: Field acceleration factor extraction in MOL and BEOL TDDB

abstract: Recently a BEOL (Back End Of Line) and MOL (Middle Of Line) Time-dependent dielectric breakdown (TDDB) reliability test and fail rate projection methodology based on large data analysis was proposed and studied. However the extraction of one of the key model parameters, the field acceleration factor Y e was ambiguously defined. If no enough caution is taken, a systematically lower γ E will be obtained which leads to a pessimistic projection. In this work, the fundamental differences with two different extraction methods are systematically studied by both experiments and Monte Carlo simulations. A more realistic and robust method is then proposed, for a more accurate reliability projection.


title: Active-Metric Learning for Classification of Remotely Sensed Hyperspectral Images

abstract: Classification of remotely sensed hyperspectral images via supervised approaches is typically affected by high dimensionality of the spectral data and a limited number of labeled samples. Dimensionality reduction via feature extraction and active learning (AL) are two approaches that researchers have investigated independently to deal with these two problems. In this paper, we propose a new method in which the feature extraction and AL steps are combined into a unique framework. The idea is to learn and update a reduced feature space in a supervised way at each iteration of the AL process, thus taking advantage of the increasing labeled information provided by the user. In particular, the computation of the reduced feature space is based on the large-margin nearest neighbor (LMNN) metric learning principle. This strategy is applied in conjunction with $k$ -nearest neighbor ( $k$ -NN) classification, for which a new sample selection strategy is proposed. The methodology is validated experimentally on four benchmark hyperspectral data sets. Good improvements in terms of classification accuracy and computational time are achieved with respect to the state-of-the-art strategies that do not combine feature extraction and AL.


title: Accurately Measuring Overhead, CommunicationTimeand Progressionof Blocking and Nonblocking Collective Operations at MassiveScale

abstract: Accurate, reproducible and comparable measurement of the overheads, communication times and progression behaviour of blocking and nonblocking collective operations is a complicated task. Although different measurement schemes for blocking collective operations are implemented in well-known benchmarks, many of these schemes introduce different systematic errors in their measurements. We characterise these errors and select a window-based approach as the most accurate method. However, this approach complicates measurements significantly and introduces clock synchronisation as a new source of errors. We analyse approaches to avoid or correct those errors and develop a scalable synchronisation scheme to conduct benchmarks on massively parallel systems. Our results are compared to the window-based scheme implemented in the SKaMPI benchmarks and show a reduction of the synchronisation overhead by a factor of 16 on 128 processes. We also describe two different measurement schemes for the overhead and asynchronous progress of nonblocking collective communications. An implementation and results of both measurement schemes are presented.


title: Content-based recommendation techniques for requirements engineering

abstract: Assuring quality in software development processes is often a complex task. In many cases there are numerous needs which cannot be fulfilled with the limited resources given. Consequently it is crucial to identify the set of necessary requirements for a software project which needs to be complete and conflict-free. Additionally, the evolution of single requirements (artifacts) plays an important role because the quality of these artifacts has an impact on the overall quality of the project. To support stakeholders in mastering these tasks there is an increasing interest in AI techniques. In this paper we presents two content-based recommendation approaches that support the Requirements Engineering (RE) process. First, we propose a Keyword Recommender to increase requirements reuse. Second, we define a thesaurus enhanced Dependency Recommender to help stakeholders finding complete and conflict-free requirements. Finally, we present studies conducted at the Graz University of Technology to evaluate the applicability of the proposed recommendation technologies.


title: InfoPods: Zigbee-based remote information monitoring devices for smart-homes

abstract: Recent developments in ad-hoc wireless networks, single-chip embedded systems and the wide-spread availability of Internet in homes has made it possible to remotely monitor status of external data sources and home appliances using small, independent wireless devices. This paper presents an open-architecture and an implementation of such a system called the IPnfopods System. This architecture is based on a Zigbee-based controller. The architecture allows multiple family members to simultaneously monitor their home appliances as well as external Internet resources using cheap, stand-alone hand-held mobile wireless devices. The proposed architecture can be easily integrated with existing smart-home systems. The architecture is implemented and demonstrated in the context of an existing Java-based smarthome architecture.


title: EBI metagenomics in 2016 - an expanding and evolving resource for the analysis and archiving of metagenomic data

abstract: EBI metagenomics (https://www.ebi.ac.uk/metagenomics/) is a freely available hub for the analysis and archiving of metagenomic and metatranscriptomic data. Over the last 2 years, the resource has undergone rapid growth, with an increase of over five-fold in the number of processed samples and consequently represents one of the largest resources of analysed shotgun metagenomes. Here, we report the status of the resource in 2016 and give an overview of new developments. In particular, we describe updates to data content, a complete overhaul of the analysis pipeline, streamlining of data presentation via the website and the development of a new web based tool to compare functional analyses of sequence runs within a study. We also highlight two of the higher profile projects that have been analysed using the resource in the last year: the oceanographic projects Ocean Sampling Day and Tara Oceans.


title: Extracellular vesicles – biogenesis, composition, function, uptake and therapeutic applications

abstract: Extracellular vesicles are heterogenous, nano-sized, membrane-limited structures which not only represent a specific way of intercellular communication but also endow cells with many capabilities. Apoptotic bodies created during apoptosis, microvesicles shed from the plasma membrane, and exosomes originated inside the cell and released to extracellular space upon fusion with the plasma membrane, they all belong to extracellular vesicles. Extracellular vesicles contain lipids, proteins, and nucleic acids. In this review, we describe their biogenesis, release and uptake by recipient cells, their composition, functions and potential therapeutic and diagnostic applications.


title: Antibody coating approach involving gamma globulins from non-immunized animal and second antibody antiserum.

abstract: Abstract An alternative protocol for immobilization of antibodies onto plastic solid supports is presented. According to the proposed protocol, tubes are first coated with γ-globulins from non-immunized animal of the same species as that from which the antigen-specific antibody has been developed. Then, an excess of second antibody is added to the tubes and the anti-species specific antibodies present in the antiserum are immunoadsorbed on the immobilized γ-globulins. Finally, the antigen specific antibody is immunoadsorbed on the immobilized second antibody. We found that the coating protocol developed allows the use of antigen-specific and second antibody antisera dilutions, thus avoiding the need for affinity purification of antibodies. Additionally, it provides solid-phase


title: tclust: An R Package for a Trimming Approach to Cluster Analysis

abstract: Outlying data can heavily influence standard clustering methods. At the same time, clustering principles can be useful when robustifying statistical procedures. These two reasons motivate the development of feasible robust model-based clustering approaches. With this in mind, an R package for performing non-hierarchical robust clustering, called tclust, is presented here. Instead of trying to “fit” noisy data, a proportion α of the most outlying observations is trimmed. The tclust package efficiently handles different cluster scatter constraints. Graphical exploratory tools are also provided to help the user make sensible choices for the trimming proportion as well as the number of clusters to search for.


title: The Safety Evaluation of Monosodium Glutamate

abstract: L-Glutamic acid and its ammonium, calcium, monosodium and potassium salts were evaluated by the Joint FAO/WHO Expert Committee on Food Additives (JECFA) in 1988. The Committee noted that intestinal and hepatic metabolism results in elevation of levels in systemic circulation only after extremely high doses given by gavage (>30mg/kg body weight). Ingestion of monosodium glutamate (MSG) was not associated with elevated levels in maternal milk, and glutamate did not readily pass the placental barrier. Human infants metabolized glutamate similarly to adults. Conventional toxicity studies using dietary administration of MSG in several species did not reveal any specific toxic or carcinogenic effects nor were there any adverse outcomes in reproduction and teratology studies. Attention was paid to central nervous system lesions produced in several species after parenteral administration of MSG or as a consequence of very high doses by gavage. Comparative studies indicated that the neonatal mouse was most sensitive to neuronal injury; older animals and other species (including primates) were less so. Blood levels of glutamate associated with lesions of the hypothalamus in the neonatal mouse were not approached in humans even after bolus doses of 10 g MSG in drinking water. Because human studies failed to confirm an involvement of MSG in Chinese Restaurant Syndrome or other idiosyncratic intolerance, the JECFA allocated an acceptable daily intake (ADI) not specified to glutamic acid and its salts. No additional risk to infants was indicated. The Scientific Committee for Food (SCF) of the European Commission reached a similar evaluation in 1991. The conclusions of a subsequent review by the Federation of American Societies for Experimental Biology (FASEB) and the Federal Drug Administration (FDA) did not discount the existence of a sensitive subpopulation but otherwise concurred with the safety evaluation of JECFA and the SCF.


title: Fast content-based image retrieval using convolutional neural network and hash function

abstract: Due to the explosive increase of online images, content-based image retrieval has gained a lot of attention. The success of deep learning techniques such as convolutional neural networks have motivated us to explore its applications in our context. The main contribution of our work is a novel end-to-end supervised learning framework that learns probability-based semantic-level similarity and feature-level similarity simultaneously. The main advantage of our novel hashing scheme that it is able to reduce the computational cost of retrieval significantly at the state-of-the-art efficiency level. We report on comprehensive experiments using public available datasets such as Oxford, Holidays and ImageNet 2012 retrieval datasets.


title: MPC-On-Chip: An Embedded GPC Coprocessor for Automotive Active Suspension Systems

abstract: Safety critical automotive systems require tight real-time constraints. This letter presents a case study for embedded implementation of a model predictive controller (MPC) used to control an automotive active suspension system. It proposes a special purpose coprocessor for solving the online mathematical optimization needed by the controller, while maintaining real-time constraints imposed by system dynamics. Mathematical formulation for the optimum usage of hardware resources is provided and verified for its applicability on a proposed platform.


title: A convolutional neural networks denoising approach for salt and pepper noise

abstract: The salt and pepper noise, especially the one with extremely high percentage of impulses, brings a significant challenge to image denoising. In this paper, we propose a non-local switching filter convolutional neural network denoising algorithm, named NLSF-CNN, for salt and pepper noise. As its name suggested, our NLSF-CNN consists of two steps, i.e., a NLSF processing step and a CNN training step. First, we develop a NLSF pre-processing step for noisy images using non-local information. Then, the pre-processed images are divided into patches and used for CNN training, leading to a CNN denoising model for future noisy images. We conduct a number of experiments to evaluate the effectiveness of NLSF-CNN. Experimental results show that NLSF-CNN outperforms the state-of-the-art denoising algorithms with a few training images.


title: Detection of Diabetic Retinopathy in Retinal Images Using MLP Classifier

abstract: The rising situation in the developing world suggests diabetic retinopathy may soon be a major problem in the clinical world [1]. Hence, detection of diabetic retinopathy is important. This paper focuses on Multi Layer Perception Neural Network (MLPNN) to detect diabetic retinopathy in retinal images. In this paper the MLPNN classifier is presented to classify retinal images as normal and abnormal. A feature vector is formed with 64-point Discrete Cosine Transform (DCT) with different 09 statistical parameters namely Entropy, mean, standard deviation, average, Euler number, contrast, correlation, energy and homogeneity. The Train N Times method was used to train the MLPNN to find best feature subset. The training and cross validation rates by the MLP NN are 100% for detection of normal and abnormal retinal images.


title: Computing arbitrary functions of encrypted data

abstract: Suppose that you want to delegate the ability to process your data, without giving away access to it. We show that this separation is possible: we describe a "fully homomorphic" encryption scheme that keeps data private, but that allows a worker that does not have the secret decryption key to compute any (still encrypted) result of the data, even when the function of the data is very complex. In short, a third party can perform complicated processing of data without being able to see it. Among other things, this helps make cloud computing compatible with privacy.


title: A new method for monitoring of distribution transformers

abstract: Distribution transformers are one of the most important equipment in power network. Because of, the large number of transformers distributed over a wide area in power electric systems, the data acquisition and condition monitoring is a important issue. This paper presents design and implementation of a mobile embedded system and a novel software to monitor and diagnose condition of transformers, by record key operation indictors of a distribution transformer like load currents, transformer oil, ambient temperatures and voltage of three phases. The proposed on-line monitoring system integrates a Global Service Mobile (GSM) Modem, with stand alone single chip microcontroller and sensor packages. Data of operation condition of transformer receives in form of SMS (Short Message Service) and will be save in computer server. Using the suggested online monitoring system will help utility operators to keep transformers in service for longer of time.


title: Integrating a security awareness program into an information security course

abstract: As Armstrong Atlantic State University (AASU) enters the wireless world during the Spring 2007 semester, it is important that those connecting to the university's network use notebook computers that are free from viruses and malware. In order to help secure this network, Information Technology students were given practical experience by organizing and performing virus and malware scan sessions on student notebook computers. This paper describes the effects of the outcome of this project on using similar hands-on activities in the teaching of Information Security.


title: Health literacy: the gap between physicians and patients.

abstract: Health literacy is basic reading and numerical skills that allow a person to function in the health care environment. Even though most adults read at an eighth-grade level, and 20 percent of the population reads at or below a fifth-grade level, most health care materials are written at a 10th-grade level. Older patients are particularly affected because their reading and comprehension abilities are influenced by their cognition and their vision and hearing status. Inadequate health literacy can result in difficulty accessing health care, following instructions from a physician, and taking medication properly. Patients with inadequate health literacy are more likely to be hospitalized than patients with adequate skills. Patients understand medical information better when spoken to slowly, simple words are used, and a restricted amount of information is presented. For optimal comprehension and compliance, patient education material should be written at a sixth-grade or lower reading level, preferably including pictures and illustrations. All patients prefer reading medical information written in dear and concise language. Physicians should be alert to this problem because most patients are unwilling to admit that they have literacy problems.


title: Attributes as Semantic Units between Natural Language and Visual Recognition

abstract: Impressive progress has been made in the fields of computer vision and natural language processing. However, it remains a challenge to find the best point of interaction for these very different modalities. In this chapter, we discuss how attributes allow us to exchange information between the two modalities and in this way lead to an interaction on a semantic level. Specifically we discuss how attributes allow using knowledge mined from language resources for recognizing novel visual categories, how we can generate sentence description about images and video, how we can ground natural language in visual content, and finally, how we can answer natural language questions about images.


title: An O(n^3)-Time Algorithm for Tree Edit Distance

abstract: The edit distance between two ordered rooted trees with vertex labels is the minimum cost of transforming one tree into the other by a sequence of elementary operations consisting of deleting and relabeling existing nodes, as well as inserting new nodes. In this article, we present a worst-case O(n3)-time algorithm for the problem when the two trees have size n, improving the previous best O(n3 log n)-time algorithm. Our result requires a novel adaptive strategy for deciding how a dynamic program divides into subproblems, together with a deeper understanding of the previous algorithms for the problem. We prove the optimality of our algorithm among the family of decomposition strategy algorithms—which also includes the previous fastest algorithms—by tightening the known lower bound of Ω(n2 log2 n) to Ω(n3), matching our algorithm's running time. Furthermore, we obtain matching upper and lower bounds for decomposition strategy algorithms of Θ(nm2 (1 + log n/m)) when the two trees have sizes m and n and m


title: Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment

abstract: The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data from ten patients with idiopathic Parkinson's disease using inertial measurement units. Several motor tasks were expert-labeled and used for classification. We specifically focused on the detection of bradykinesia. For this, we compared standard machine learning pipelines with deep learning based on convolutional neural networks. Our results showed that deep learning outperformed other state-of-the-art machine learning algorithms by at least 4.6 % in terms of classification rate. We contribute a discussion of the advantages and disadvantages of deep learning for sensor-based movement assessment and conclude that deep learning is a promising method for this field.


title: S.O.S.: Does Your Search Engine Results Page (SERP) Need Help?

abstract: Over the past 20 years, search engines have become emph{the} entry point of the WWW. Due to evolving needs for different and new kinds of information, the interfaces of search engine results pages (SERPs) change over time. Thus, their usability must be continuously evaluated to ensure user satisfaction and competitive edge. As no complete solution exists, we present emph{S.O.S.:} the emph{SERP Optimization Suite}. Our approach comprises (a) emph{WaPPU}, which is a near real-time tool for evaluating web interfaces based on usability scores, and (b) a emph{catalog of best practices} that maps bad scores to potential causes and corresponding adjustments for optimization. During a case study in which we assessed and optimized a real-world SERP, S.O.S. has proven its feasibility and effectiveness by significantly improving the SERP's usability.


title: Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection -- Short Paper

abstract: Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based autoencoders have shown great potential in detecting anomalies in medical images. However, especially Variational Autoencoders (VAEs)often fail to capture the high-level structure in the data. We address these shortcomings by proposing the context-encoding Variational Autoencoder (ceVAE), which improves both, the sample, as well as pixelwise results. In our experiments on the BraTS-2017 and ISLES-2015 segmentation benchmarks the ceVAE achieves unsupervised AUROCs of 0.95 and 0.89, respectively, thus outperforming other reported deep-learning based approaches.


An Exposition of Adversarial Examples in Neural Networks


title: Tomographic microscopy of 3D phase objects with spatially incoherent illumination

abstract: New method of a tomographic microscopy with spatially incoherent illumination is proposed. Earlier, in our works for reconstruction of a refractive index with spatial distribution of 3D phase samples, a method of tomographic microscopy with the coherent light was used. Main disadvantage of this scheme is use of a coherent light, which brings to appearance the phase noise in projection data. Spatially incoherent tomographic microscope is based on the Linnik phase-shifting interference microscope reflected type. Using interferometry with large source of the monochromatic light allows greatly improve a quality of phase projections by averaging of interference patterns. For tomographic mode of Linnik microscope working and obtaining the oblique illumination of sample method for displacing a light source is used. Observation angle range for 100 X oil-immersion objective, N.A. equals 1.25, is 90 degrees. This method allows realizing a two-dimensional scanning trajectory for sample observation. Particularities of tomography for the phase objects, placed near plane mirror, are considered. So, a viewing angle range can be reduced to 90 degree, and sample is complement by its mirror reflection. The iterative algorithms for limited-angle tomographic reconstruction were used. Experimental results on three-dimensional reconstruction of the single human blood cell (erythrocyte) are presented.


title: Compression of soft-body animation sequences

abstract: Abstract We describe a compression scheme for the geometry component of 3D animation sequences. This scheme is based on the principle component analysis (PCA) method, which represents the animation sequence using a small number of basis functions. Second-order linear prediction coding (LPC) is applied to the PCA coefficients in order to further reduce the code size by exploiting the temporal coherence present in the sequence. Our results show that applying LPC to the PCA scheme results in significant performance improvements relative to other coding methods. The use of these codes will make animated 3D data more accessible for graphics and visualization applications.


title: Influence of interhemispheric interactions on motor function in chronic stroke

abstract: In patients with chronic stroke, the primary motor cortex of the intact hemisphere (M1(intact hemisphere)) may influence functional recovery, possibly through transcallosal effects exerted over M1 in the lesioned hemisphere (M1(lesioned hemisphere)). Here, we studied interhemispheric inhibition (IHI) between M1(intact hemisphere) and M1(lesioned hemisphere) in the process of generation of a voluntary movement by the paretic hand in patients with chronic subcortical stroke and in healthy volunteers. IHI was evaluated in both hands preceding the onset of unilateral voluntary index finger movements (paretic hand in patients, right hand in controls) in a simple reaction time paradigm. IHI at rest and shortly after the Go signal were comparable in patients and controls. Closer to movement onset, IHI targeting the moving index finger turned into facilitation in controls but remained deep in patients, a finding that correlated with poor motor performance. These results document an abnormally high interhemispheric inhibitory drive from M1(intact hemisphere) to M1(lesioned hemisphere) in the process of generation of a voluntary movement by the paretic hand. It is conceivable that this abnormality could adversely influence motor recovery in some patients with subcortical stroke, an interpretation consistent with models of interhemispheric competition in motor and sensory systems.


title: Leveraging geospatially-oriented social media communications in disaster response

abstract: Geospatially-oriented social media communications have emerged as a common information resource to support crisis management. Our research compares the capabilities of two popular systems used to collect and visualize such information Project Epic’s Tweak the Tweet (TtT) and Ushahidi. Our research uses geospatially-oriented social media gathered by both projects during recent disasters to compare and contrast the frequency, content, and location components of contributed information to both systems. We compare how data was gathered and filtered, how spatial information was extracted and mapped, and the mechanisms by which the resulting synthesized information was shared with response and recovery organizations. In addition, we categorize the degree to which each platform in each disaster led to actions by first responders and emergency managers. Based on the results of our comparisons we identify key design considerations for future social media mapping tools to support crisis management.


Research and Advocacy for Policy Change: Measuring Progress


A Study of multilabel text classification and the effect of label hierarchy


Baby talk: Understanding and generating image descriptions


title: Ultra-low on-resistance LDMOS implementation in 0.13µm CD and BiCD process technologies for analog power IC's

abstract: Toshiba's 5th generation BiCD/CD-0.13 is a new process platform for analog power applications based on 0.13µm CMOS technology. The process platform has six varieties of rated voltage, 5V, 6V, 18V, 25V, 40V, and 60V. 5 to 18V CD-0.13 process use P-type silicon substrate. 25 to 60V BiCD-0.13 process use N-Epi wafer with N+/P+ buried layer on P type silicon substrate. Each LDMOS recode ultra-low on-resistance compared with that of previous papers, and we will realize the highest performance analog power IC's using this technology.


title: The Body Appreciation Scale: Development and psychometric evaluation

abstract: Abstract Body image has been conceptualized and assessed almost exclusively in terms of its negative dimensions. Therefore, a measure reflecting body appreciation, an aspect of positive body image, was developed and evaluated via four independent samples of college women. Study 1 ( N = 181) supported the Body Appreciation Scale's (BAS) unidimensionality and construct validity, as it was related as expected to body esteem, body surveillance, body shame, and psychological well-being. Study 2 ( N = 327) cross-validated its unidimensionality. Study 3 ( N = 424) further upheld the construct validity of the BAS, as it was: (a) related as expected to appearance evaluation, body preoccupation, body dissatisfaction, and eating disorder symptomatology and (b) unrelated to impression management. Studies 1 and 3 also indicated that the BAS predicted unique variance in psychological well-being above and beyond extant measures of body image. Study 4 ( N = 177) demonstrated that its scores were stable over a 3-week period. All studies supported the internal consistency reliability of its scores. The BAS should prove useful for researchers and clinicians interested in positive body image assessment.


title: Tool for 3D Gazebo Map Construction from Arbitrary Images and Laser Scans

abstract: Algorithms for mobile robots, such as navigation, mapping, and SLAM, require proper modelling of environment. Our paper presents an automatic tool that allows creating a realistic 3D-landscape in Gazebo simulation, which is based on the real sensor-based experimental results. The tool provides an occupancy grid map automatic filtering and import to Gazebo framework as a heightmap and enables to configure the settings for created simulation environment. In addition, the tool is capable to create a 3D Gazebo map from any arbitrary image.


title: Understanding Chatbot-mediated Task Management

abstract: Effective task management is essential to successful team collaboration. While the past decade has seen considerable innovation in systems that track and manage group tasks, these innovations have typically been outside of the principal communication channels: email, instant messenger, and group chat. Teams formulate, discuss, refine, assign, and track the progress of their collaborative tasks over electronic communication channels, yet they must leave these channels to update their task-tracking tools, creating a source of friction and inefficiency. To address this problem, we explore how bots might be used to mediate task management for individuals and teams. We deploy a prototype bot to eight different teams of information workers to help them create, assign, and keep track of tasks, all within their main communication channel. We derived seven insights for the design of future bots for coordinating work.


title: Text Knowledge Mining: An Alternative to Text Data Mining

abstract: In this paper we introduced an alternative view of text mining and we review several alternative views proposed by different authors. We propose a classification of text mining techniques into two main groups: techniques based on inductive inference, that we call text data mining (TDM, comprising most of the existing proposals in the literature), and techniques based on deductive or abductive inference, that we call text knowledge mining (TKM). To our knowledge, the TKM view of text mining is new though, as we shall show, several existing techniques could be considered in this group. We discuss about the possibilities and challenges of TKM techniques. We also discuss about the application of existing theories in possible future research in this field.


title: Disjunctive Logic Programs with Inheritance

abstract: The paper proposes a new knowledge representation language, called DLP<, which extends disjunctive logic programming (with strong negation) by inheritance. The addition of inheritance enhances the knowledge modeling features of the language providing a natural representation of default reasoning with exceptions. A declarative model-theoretic semantics of DLP< is provided, which is shown to generalize the Answer Set Semantics of disjunctive logic programs. The knowledge modeling features of the language are illustrated by encoding classical nonmonotonic problems in DLP<. The complexity of DLP< is analyzed, proving that inheritance does not cause any computational overhead, as reasoning in DLP< has exactly the same complexity as reasoning in disjunctive logic programming. This is confirmed by the existence of an efficient translation from DLP< to plain disjunctive logic programming. Using this translation, an advanced KR system supporting the DLP< language has been implemented on top of the DLV system and has subsequently been integrated into DLV.


title: Technology Selection for Big Data and Analytical Applications

abstract: The term Big Data has become pervasive in recent years as smart phones, televisions, washing machines, refrigerators, smart meters, diverse sensors, eyeglasses and even clothes connect to the Internet. However, their generated data is worthless without information retrieval through data analytics. As Big Data is too big for a single person to investigate, appropriate technologies are being used. Unfortunately, there is not one solution but a large variety of different tools, each of them with other functionalities, properties and characteristics. Especially small and midsized companies have a hard time to keep track as this requires time, skills, money, and specific knowledge which result in high entrance barriers for Big Data utilization. This papers aims to reduce these barriers by explaining and structuring different classes of technologies and basic criteria for proper technology selection. It proposes a framework that guides especially small and mid-sized companies through a suitable selection process that can serve as a basis for further advances.


title: FPGA-based Multithreading for In-Memory Hash Joins

abstract: Large relational databases often rely on fast join implementations for good performance. Recent paradigm shifts in processor architectures has reinvigorated research into how the join operation can be implemented. The FPGA community has also been developing new architectures with the potential to push performance even further. Hashing is a common method used to implement joins, but its poor spatial locality can hinder performance on processor architectures. Multithreaded architectures can better cope with poor spatial locality by masking memory/cache latencies with many outstanding requests. In this paper we present the first end-to-end in-memory FPGA hash join implementation. The FGPA uses massive multithreading during the build and probe phases to mask long memory delays, while it concurrently manages hundreds of thread states locally. Throughput results show a speedup between 2x and 3.4x over the best multi-core approaches with comparable memory bandwidths on uniform and skewed datasets; however, this advantage diminishes for extremely skewed datasets. Results using the FPGA’s full 76.8 GB/s of bandwidth show throughput up to 1.6 billion tuples per second for uniform and skewed datasets.


title: Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view

abstract: Abstract Spinal cord segmentation is an important step in the analysis of neurological diseases such as multiple sclerosis. Several studies have shown correlations between disease progression and metrics relating to spinal cord atrophy and shape changes. Current practices primarily involve segmenting the spinal cord manually or semi-automatically, which can be inconsistent and time-consuming for large datasets. An automatic method that segments the spinal cord and cerebrospinal fluid from magnetic resonance images is presented. The method uses a deformable atlas and topology constraints to produce results that are robust to noise and artifacts. The method is designed to be easily extended to new data with different modalities, resolutions, and fields of view. Validation was performed on two distinct datasets. The first consists of magnetization transfer-prepared T2*-weighted gradient-echo MRI centered only on the cervical vertebrae (C1–C5). The second consists of T1-weighted MRI that covers both the cervical and portions of the thoracic vertebrae (C1–T4). Results were found to be highly accurate in comparison to manual segmentations. A pilot study was carried out to demonstrate the potential utility of this new method for research and clinical studies of multiple sclerosis.


title: TraceMixer: Privacy-preserving crowd-sensing sans trusted third party

abstract: Crowd-sensing promises cheap and easy large scale data collection by tapping into the sensing and processing capabilities of smart phone users. However, the vast amount of fine-grained location data collected raises serious privacy concerns among potential contributors. In this paper, we argue that crowd-sensing has unique requirements w.r.t. privacy and data utility which renders existing protection mechanisms infeasible. We hence propose TraceMixer, a novel location privacy protection mechanism tailored to the special requirements in crowd-sensing. TraceMixer builds upon the well-studied concept of mix zones to provide trajectory privacy while achieving high spatial accuracy. First in this line of research, TraceMixer applies secure two-party computation technologies to realize a trustless architecture that does not require participants to share locations with anyone in clear. We evaluate TraceMixer on real-world datasets to show the feasibility of our approach in terms of privacy, utility, and performance. Finally, we demonstrate the applicability of TraceMixer in a real-world crowd-sensing campaign.


title: An 8-bit 1.8 V 500 MS/s CMOS DAC with a novel four-stage current steering architecture

abstract: This paper presents an 8 bit 1.8 V 500 MSPS digital- to analog converter using 0.18mum double poly five metal CMOS technology for frequency domain applications. The proposed DAC is composed of four unit cell matrix. A novel decoding logic is used to remove the inter block code transition (IBT) glitch. The proposed DAC shows less number of switching for a monotonic input and the product of number of switching and the current value associated with switching is also less than the segmented DAC. The SPICE simulated DNL and INL is 0.1373 LSB and 0.331 LSB respectively and are better than the segmented DAC. The proposed DAC also shows better SNDR and THD than the segmented DAC. The MATLAB simulated THD, SFDR and SNDR is more than 45 dB, 35 dB and 44 dB respectively at 500MS/s with a 10 MHz input sine wave with incoherent timing response between current switches.


title: Multimodal Mixed Reality Interfaces for Visualizing Digital Heritage

abstract: We have developed several digital heritage interfaces that utilize Web3D, virtual and augmented reality technologies for visualizing digital heritage in an interactive manner through the use of several different input devices. We propose in this paper an integration of these technologies to provide a novel multimodal mixed reality interface that facilitates the implementation of more interesting digital heritage exhibitions. With such exhibitions participants can switch dynamically between virtual web-based environments to indoor augmented reality environments as well as make use of various multimodal interaction techniques to better explore heritage information in the virtual museum. The museum visitor can potentially experience their digital heritage in the physical sense in the museum, then explore further through the web, visualize this heritage in the round (3D on the web), take that 3D artifact into the augmented reality domain (the real world) and explore it further using various multimodal interfaces.


title: Atypical fibroxanthoma with keloidal collagen.

abstract: Atypical fibroxanthoma (AFX) is a lesion that typically occurs on actinically damaged skin of the head and neck. Histopathology of these neoplasms reveals atypical cells, which often demonstrate positive staining for immunohistochemical markers such as vimentin and CD10. In the case reported, keloidal collagen was discovered on histopathology. To our knowledge, only rare reports in the literature describe an AFX with similar histopathologic findings, suggesting that this is a distinctive presentation of AFX.


title: Object retrieval with large vocabularies and fast spatial matching

abstract: In this paper, we present a large-scale object retrieval system. The user supplies a query object by selecting a region of a query image, and the system returns a ranked list of images that contain the same object, retrieved from a large corpus. We demonstrate the scalability and performance of our system on a dataset of over 1 million images crawled from the photo-sharing site, Flickr [3], using Oxford landmarks as queries. Building an image-feature vocabulary is a major time and performance bottleneck, due to the size of our dataset. To address this problem we compare different scalable methods for building a vocabulary and introduce a novel quantization method based on randomized trees which we show outperforms the current state-of-the-art on an extensive ground-truth. Our experiments show that the quantization has a major effect on retrieval quality. To further improve query performance, we add an efficient spatial verification stage to re-rank the results returned from our bag-of-words model and show that this consistently improves search quality, though by less of a margin when the visual vocabulary is large. We view this work as a promising step towards much larger, "web-scale " image corpora.


title: Channel Hardening-Exploiting Message Passing (CHEMP) Receiver in Large-Scale MIMO Systems

abstract: In this paper, we propose a multiple-input multiple-output (MIMO) receiver algorithm that exploits channel hardening that occurs in large MIMO channels. Channel hardening refers to the phenomenon where the off-diagonal terms of the matrix become increasingly weaker compared to the diagonal terms as the size of the channel gain matrix increases. Specifically, we propose a message passing detection (MPD) algorithm which works with the real-valued matched filtered received vector (whose signal term becomes, where is the transmitted vector), and uses a Gaussian approximation on the off-diagonal terms of the matrix. We also propose a simple estimation scheme which directly obtains an estimate of (instead of an estimate of), which is used as an effective channel estimate in the MPD algorithm. We refer to this receiver as the channel hardening-exploiting message passing (CHEMP) receiver. The proposed CHEMP receiver achieves very good performance in large-scaleMIMO systems (e.g., in systems with 16 to 128 uplink users and 128 base station antennas). For the considered large MIMO settings, the complexity of the proposed MPD algorithm is almost the same as or less than that of the minimum mean square error (MMSE) detection. This is because the MPD algorithm does not need a matrix inversion. It also achieves a significantly better performance compared to MMSE and other message passing detection algorithms using MMSE estimate of. Further, we design optimized irregular low density parity check (LDPC) codes specific to the considered large MIMO channel and the CHEMP receiver through EXIT chart matching. The LDPC codes thus obtained achieve improved coded bit error rate performance compared to off-the-shelf irregular LDPC codes.


title: Semantic Object Parsing with Local-Global Long Short-Term Memory

abstract: Semantic object parsing is a fundamental task for understanding objects in detail in computer vision community, where incorporating multi-level contextual information is critical for achieving such fine-grained pixel-level recognition. Prior methods often leverage the contextual information through post-processing predicted confidence maps. In this work, we propose a novel deep Local-Global Long Short-Term Memory (LG-LSTM) architecture to seamlessly incorporate short-distance and long-distance spatial dependencies into the feature learning over all pixel positions. In each LG-LSTM layer, local guidance from neighboring positions and global guidance from the whole image are imposed on each position to better exploit complex local and global contextual information. Individual LSTMs for distinct spatial dimensions are also utilized to intrinsically capture various spatial layouts of semantic parts in the images, yielding distinct hidden and memory cells of each position for each dimension. In our parsing approach, several LG-LSTM layers are stacked and appended to the intermediate convolutional layers to directly enhance visual features, allowing network parameters to be learned in an end-to-end way. The long chains of sequential computation by stacked LG-LSTM layers also enable each pixel to sense a much larger region for inference benefiting from the memorization of previous dependencies in all positions along all dimensions. Comprehensive evaluations on three public datasets well demonstrate the significant superiority of our LG-LSTM over other state-of-the-art methods.


title: Cognitive big data: survey and review on big data research and its implications. What is really “new” in big data?

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to introduce and define Cognitive Big Data as a concept. Furthermore, it investigates what is really “new” in Big Data, as it seems to be a hyped-up concept that has emerged during recent years. The purpose is also to broaden the discussion around Big Data far beyond the common 4V (velocity, volume, veracity and variety) model. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The authors established an expert think tank to discuss the notion of Big Data, identify new characteristics and re-think what really is new in the idea of Big Data, by analyzing over 60 literature resources. They identified typical baseline scenarios (traffic, business processes, retail, health and social media) as a starting point from which they explored the notion of Big Data from different perspectives. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: They concluded that the idea of Big Data is simply not new and recognized the need to re-think a new approach toward Big Data. The authors also introduced a five-Trait Framework for “Cognitive Big Data”, socio-technical system, data space, data richness, knowledge management (KM)/decision-making and visualization/sensory presentation. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: The viewpoint is centered on cognitive processes as KM process. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: Data need to be made available in an understandable form for the right application context and in the right portion size that it can be turned into knowledge and eventually wisdom. The authors need to know about data that can be ignored, data that they are not aware of (dark data) and data that can be fully utilized for analysis (light data). In the foreground is the extension of human mental capabilities and data understandability. ::: ::: ::: ::: ::: Social implications ::: ::: ::: ::: ::: Cognitive Big Data implies a socio-technological knowledge system. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: Introduction of cognitive Big Data as concept and framework.


title: Unrolled Optimization with Deep Priors

abstract: A broad class of problems at the core of computational imaging, sensing, and low-level computer vision reduces to the inverse problem of extracting latent images that follow a prior distribution, from measurements taken under a known physical image formation model. Traditionally, hand-crafted priors along with iterative optimization methods have been used to solve such problems. In this paper we present unrolled optimization with deep priors, a principled framework for infusing knowledge of the image formation into deep networks that solve inverse problems in imaging, inspired by classical iterative methods. We show that instances of the framework outperform the state-of-the-art by a substantial margin for a wide variety of imaging problems, such as denoising, deblurring, and compressed sensing magnetic resonance imaging (MRI). Moreover, we conduct experiments that explain how the framework is best used and why it outperforms previous methods.


title: Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis

abstract: The widespread use of multisensor technology and the emergence of big data sets have highlighted the limitations of standard flat-view matrix models and the necessity to move toward more versatile data analysis tools. We show that higher-order tensors (i.e., multiway arrays) enable such a fundamental paradigm shift toward models that are essentially polynomial, the uniqueness of which, unlike the matrix methods, is guaranteed under very mild and natural conditions. Benefiting from the power of multilinear algebra as their mathematical backbone, data analysis techniques using tensor decompositions are shown to have great flexibility in the choice of constraints which match data properties and extract more general latent components in the data than matrix-based methods.


title: Emotion-Preserving Representation Learning via Generative Adversarial Network for Multi-View Facial Expression Recognition

abstract: Face frontalization is one way to overcome the pose variation problem, which simplifies multi-view recognition into one canonical-view recognition. This paper presents a multi-task learning approach based on the generative adversarial network (GAN) that learns the emotion-preserving representations in the face frontalization framework. Taking advantage of adversarial relationship between the generator and the discriminator in GAN, the generator can frontalize input non-frontal face images into frontal face images while preserving the identity and expression characteristics; in the meantime, it can employ the learnt emotion-preserving representations to predict the expression class label from the input face. The proposed network is optimized by combining both synthesis and classification objective functions to make the learnt representations generative and discriminative simultaneously. Experimental results demonstrate that the proposed face frontalization system is very effective for expression recognition with large head pose variations.


title: Low cost transceiver for DSRC applications

abstract: In this paper a low cost transceiver for DSRC applications is presented. First a brief overview of the new European Standards is given followed by a possible implementation of a on board unit (OBU). The implementation proposed for the RF part can be implemented with very low cost components, making this solution viable for the huge DSRC market.


title: Compact Circularly Polarized Asymmetrical Fractal Boundary Microstrip Antenna for Wireless Applications

abstract: Compact fractal boundary microstrip antenna is proposed for circular polarization (CP). By replacing the sides of a square patch with asymmetrical prefractal curves, two orthogonal modes are excited for CP operation. The structure is asymmetric along the principal axes (x, y). The indentation parameter of the fractal boundary curve is optimized to design compact CP antennas. Experimental results show that 10-dB return loss and 3-dB axial-ratio bandwidths of the proposed fractal boundary Ant 2 are 162 and 50 MHz, respectively, at operating frequency of around 2540 MHz. Results show that an excellent CP is achieved with a single probe feed, besides reduction in the antenna size by applying fractal boundary concept.


title: Millimeter wave FMCW radar system simulations including a 3D ray tracing channel simulator

abstract: In this paper a millimeter-wave radar system simulation environment for frequency-modulated continuous-wave (FMCW) radar using a 3D ray tracing channel simulator is presented. Therefore we use Agilents' RF design environment ADS® for system simulation and calculate the channel impulse response based on the data received from a 3D ray tracer in a Matlab® co-simulation. To reduce computing time the whole signal flow is processed in a complex baseband representation and scattering center models are used as substitute for complex polygon targets as further improvement. Nevertheless all effects occurring at harmonic bands and at DC due to nonlinearities in the transmitter and receiver circuits are well reflected in the simulation. A typical road scenario is simulated and the results show the capabilities of the simulation technique in terms of speed and accuracy.


title: Towards Information-Seeking Agents

abstract: We develop a general problem setting for training and testing the ability of agents to gather information efficiently. Specifically, we present a collection of tasks in which success requires searching through a partially-observed environment, for fragments of information which can be pieced together to accomplish various goals. We combine deep architectures with techniques from reinforcement learning to develop agents that solve our tasks. We shape the behavior of these agents by combining extrinsic and intrinsic rewards. We empirically demonstrate that these agents learn to search actively and intelligently for new information to reduce their uncertainty, and to exploit information they have already acquired.


title: Computer-assisted authoring of interactive narratives

abstract: This paper explores new authoring paradigms and computer-assisted authoring tools for free-form interactive narratives. We present a new design formalism, Interactive Behavior Trees (IBT's), which decouples the monitoring of user input, the narrative, and how the user may influence the story outcome. We introduce automation tools for IBT's, to help the author detect and automatically resolve inconsistencies in the authored narrative, or conflicting user interactions that may hinder story progression. We compare IBT's to traditional story graph representations and show that our formalism better scales with the number of story arcs, and the degree and granularity of user input. The authoring time is further reduced with the help of automation, and errors are completely avoided. Our approach enables content creators to easily author complex, branching narratives with multiple story arcs in a modular, extensible fashion while empowering players with the agency to freely interact with the characters in the story and the world they inhabit.


title: Leafsnap: A Computer Vision System for Automatic Plant Species Identification

abstract: We describe the first mobile app for identifying plant species using automatic visual recognition. The system --- called Leafsnap --- identifies tree species from photographs of their leaves. Key to this system are computer vision components for discarding non-leaf images, segmenting the leaf from an untextured background, extracting features representing the curvature of the leaf's contour over multiple scales, and identifying the species from a dataset of the 184 trees in the Northeastern United States. Our system obtains state-of-the-art performance on the real-world images from the new Leafsnap Dataset --- the largest of its kind. Throughout the paper, we document many of the practical steps needed to produce a computer vision system such as ours, which currently has nearly a million users.


title: CUSTOMER SATISFACTION AS THE MODERATING VARIABLE OF CUSTOMER LOYALTY OF INDIHOME CUSTOMERS

abstract: The internet is an aspect that become one in the daily activities of Indonesian society, even in Purwokerto, almost all activities involve the help of the internet. PT Telkom as the largest internet service provider in Indonesia has facilitate the society to get internet access with its products, Indihome. Lately, a competitor is emerging which threatens the existence of PT Telkom as an internet service provider in Purwokerto, which is Biznet. Therefore, PT Telkom needs to examine what factors that make Indihome's customers loyal. Based on the problems above, this study was conducted to analyze the components of perceived product quality, perceived service quality, perceived value, and perceived price to the customer loyalty, mediated by customer satisfaction. The research methodology used for this study is a case study with survey research methods. The sample for this study were 155 people selected through convenience sampling technique derived from non-probability sampling technique. The sample comes from Indihome internet users in Purwokerto. The software used to analyze data is SPSS and AMOS statistical software. The research conclude that Perceived product quality, Perceived value, Perceived price has a positive effect on customer satisfaction. Perceived service quality has no effect on customer satisfaction. Customer satisfaction, Perceived service quality has a positive effect on customer loyalty. Perceived product quality, Perceived value, Perceived price has no effect on customer loyalty.


Studies of Thermal Injury: II. The Relative Importance of Time and Surface Temperature in the Causation of Cutaneous Burns*


title: Developments in hybrid modeling and control of Unmanned Aerial Vehicles

abstract: Hybrid modeling and control issues provide a flexible powerful tool for Unmanned Aerial Vehicles (UAVs) as complex control platforms. This paper aims to briefly review recent results in this area. Firstly, existing works on hybrid modeling and control of a single UAV are reviewed. Then multiple UAVs control in a synchronously cooperative task planning, which gives much more maneuverability, has been surveyed in the hybrid framework. Formation control of UAVs is a typical cooperative strategy with considerable applications in multi-agent aerial robotics area and can leverage limited abilities of single UAVs into complicated tasks of group missions. There are a few works in this field which we briefly reviewed their results and explored the possible future directions of these research activities.


title: Cyberbullying Identification Using Participant-Vocabulary Consistency

abstract: With the rise of social media, people can now form relationships and communities easily regardless of location, race, ethnicity, or gender. However, the power of social media simultaneously enables harmful online behavior such as harassment and bullying. Cyberbullying is a serious social problem, making it an important topic in social network analysis. Machine learning methods can potentially help provide better understanding of this phenomenon, but they must address several key challenges: the rapidly changing vocabulary involved in cyber- bullying, the role of social network structure, and the scale of the data. In this study, we propose a model that simultaneously discovers instigators and victims of bullying as well as new bullying vocabulary by starting with a corpus of social interactions and a seed dictionary of bullying indicators. We formulate an objective function based on participant-vocabulary consistency. We evaluate this approach on Twitter and Ask.fm data sets and show that the proposed method can detect new bullying vocabulary as well as victims and bullies.


Architectural Patterns for Microservices: A Systematic Mapping Study


title: A stylized software model to explore the free market equality/efficiency tradeoff

abstract: This paper provides an agent-based software exploration of the wellknown free market efficiency/equality trade-off. Our study simulates the interaction of agents producing, trading and consuming goods in the presence of different market structures, and looks at how efficient the producers/consumers mapping turn out to be as well as the resulting distribution of welfare among agents at the end of an arbitrarily large number of iterations. Two market mechanisms are compared: the competitive market (a double auction market in which agents outbid each other in order to buy and sell products) and the random one (in which products are allocated randomly). Our results confirm that the superior efficiency of the competitive market (an effective and never stopping producers/consumers mapping and a superior aggregative welfare) comes at a very high price in terms of inequality (above all when severe budget constraints are in play).


A Deep Learning-based Approach for Banana Leaf Diseases Classification


Magnetic hyperthermia effect in simple and complex biological systems: preliminary results


title: Inkjet Printing of Silver Nanowire Networks

abstract: The development of printed electronics will require the ability to deposit a wide range of nanomaterials using printing techniques. Here we demonstrate the controlled deposition of networks of silver nanowires in well-defined patterns by inkjet printing from an optimized isopropyl alcohol–diethylene glycol dispersion. We find that great care must be taken while producing the ink and during solvent evaporation. The resultant networks have good electrical properties, displaying sheet resistances as low as 8 Ω/□ and conductivities as high as 105 S/m. Such optimized performances were achieved for line widths of 1–10 mm and network thicknesses of 0.5–2 μm deposited from ∼10–20 passes while using processing temperatures of no more than 110 °C. Thin networks are semitransparent with dc to optical conductivity ratios of ∼40.


Purify: Fast detection of memory leaks and access errors


title: Solving a Practical Examination Timetabling Problem: A Case Study

abstract: This paper presents a Greedy-Least Saturation Degree (G-LSD) heuristic (which is an adaptation of the least saturation degree heuristic) to solve a real-world examination timetabling problem at the University Kebangsaan, Malaysia. We utilise a new objective function that was proposed in our previous work to evaluate the quality of the timetable. The objective function considers both timeslots and days in assigning exams to timeslots, where higher priority is given to minimise students having consecutive exams on the same day. The objective also tries to spread exams throughout the examination period. This heuristic has the potential to be used for the benchmark examination datasets (e.g. the Carter datasets) as well as other real world problems. Computational results are presented.


Title Optimal control of nonlinear system for generator bidding inderegulated power markets


title: Sim-to-Real Reinforcement Learning for Deformable Object Manipulation

abstract: We have seen much recent progress in rigid object manipulation, but interaction with deformable objects has notably lagged behind. Due to the large configuration space of deformable objects, solutions using traditional modelling approaches require significant engineering work. Perhaps then, bypassing the need for explicit modelling and instead learning the control in an end-to-end manner serves as a better approach? Despite the growing interest in the use of end-to-end robot learning approaches, only a small amount of work has focused on their applicability to deformable object manipulation. Moreover, due to the large amount of data needed to learn these end-to-end solutions, an emerging trend is to learn control policies in simulation and then transfer them over to the real world. To-date, no work has explored whether it is possible to learn and transfer deformable object policies. We believe that if sim-to-real methods are to be employed further, then it should be possible to learn to interact with a wide variety of objects, and not only rigid objects. In this work, we use a combination of state-of-the-art deep reinforcement learning algorithms to solve the problem of manipulating deformable objects (specifically cloth). We evaluate our approach on three tasks --- folding a towel up to a mark, folding a face towel diagonally, and draping a piece of cloth over a hanger. Our agents are fully trained in simulation with domain randomisation, and then successfully deployed in the real world without having seen any real deformable objects.


title: Adaptive Fog Configuration for the Industrial Internet of Things

abstract: Industrial Fog computing deploys various industrial services, such as automatic monitoring/control and imminent failure detection, at the Fog Nodes (FNs) to improve the performance of industrial systems. Much effort has been made in the literature on the design of fog network architecture and computation offloading. This paper studies an equally important but much less investigated problem of service hosting where FNs are adaptively configured to host services for Sensor Nodes (SNs), thereby enabling corresponding tasks to be executed by the FNs. The problem of service hosting emerges because of the limited computational and storage resources at FNs, which limit the number of different types of services that can be hosted by an FN at the same time. Considering the variability of service demand in both temporal and spatial dimensions, when, where, and which services to host have to be judiciously decided to maximize the utility of the Fog computing network. Our proposed Fog configuration strategies are tailored to battery-powered FNs. The limited battery capacity of FNs creates a long-term energy budget constraint that significantly complicates the Fog configuration problem as it introduces temporal coupling of decision making across the timeline. To address all these challenges, we propose an online distributed algorithm, called Adaptive Fog Configuration (AFC), based on Lyapunov optimization and parallel Gibbs sampling. AFC jointly optimizes service hosting and task admission decisions, requiring only currently available system information while guaranteeing close-to-optimal performance compared to an oracle algorithm with full future information.


An overview of the OMNeT++ simulation environment


title: Vision-based Detection of Kerbs and Steps

abstract: A key component of a technological aid for the partially sighted (TAPS) is a system to detect kerbs and steps. In this paper, a vision-based kerb detection system is described which uses the Hough Transform to find clusters of parallel lines in the image as evidence for a kerb. This is combined with a stereo vision-based obstacle detection algorithm. Experiments show that kerb regions are identified correctly from the images. An error analysis of the obstacle detection algorithm enables the kerb height, and its uncertainty, to be determined.


title: NISAN: network information service for anonymization networks

abstract: Network information distribution is a fundamental service for any anonymization network. Even though anonymization and information distribution about the network are two orthogonal issues, the design of the distribution service has a direct impact on the anonymization. Requiring each node to know about all other nodes in the network (as in Tor and AN.ON -- the most popular anonymization networks) limits scalability and offers a playground for intersection attacks. The distributed designs existing so far fail to meet security requirements and have therefore not been accepted in real networks. In this paper, we combine probabilistic analysis and simulation to explore DHT-based approaches for distributing network information in anonymization networks. Based on our findings we introduce NISAN, a novel approach that tries to scalably overcome known security problems. It allows for selecting nodes uniformly at random from the full set of all available peers, while each of the nodes has only limited knowledge about the network. We show that our scheme has properties similar to a centralized directory in terms of preventing malicious nodes from biasing the path selection. This is done, however, without requiring to trust any third party. At the same time our approach provides high scalability and adequate performance. Additionally, we analyze different design choices and come up with diverse proposals depending on the attacker model. The proposed combination of security, scalability, and simplicity, to the best of our knowledge, is not available in any other existing network information distribution system.


title: MPB: A modified Poisson blending technique

abstract: Image cloning has many useful applications, such as removing unwanted objects, fixing damaged parts of images, and panorama stitching. Instead of using pixel intensities, the gradient domain is used in Poisson image editing; however, it suffers from two main problems: color bleeding and bleeding artifacts. In this paper, a modified Poisson blending (MPB) technique is presented which ensures dependency on the boundary pixels of both target and source images rather than just those of the target. The problem of bleeding artifacts is reduced. This makes the proposed technique suitable for use in video compositing as it avoids the flickering caused by bleeding artifacts. To reduce the problem of color bleeding, we use an additional alpha compositing step. Our experimental results using the proposed technique show that MPB reduces the bleeding problems and generates more natural composited images than other techniques.