title: A Probabilistic Earley Parser As A Psycholinguistic Model

abstract: In human sentence processing, cognitive load can be defined many ways. This report considers a definition of cognitive load in terms of the total probability of structural options that have been disconfirmed at some point in a sentence: the surprisal of word wi given its prefix wo...i-1 on a phrase-structural language model. These loads can be efficiently calculated using a probabilistic Earley parser (Stolcke, 1995) which is interpreted as generating predictions about reading time on a word-by-word basis. Under grammatical assumptions supported by corpus-frequency data, the operation of Stolcke's probabilistic Earley parser correctly predicts processing phenomena associated with garden path structural ambiguity and with the subject/object relative asymmetry.


title: Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges

abstract: Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the basis for collaborative filtering (CF) techniques, the dominant framework for recommender systems. Currently, new recommendation scenarios are emerging that offer promising new information that goes beyond the U-I matrix. This information can be divided into two categories related to its source: rich side information concerning users and items, and interaction information associated with the interplay of users and items. In this survey, we summarize and analyze recommendation scenarios involving information sources and the CF algorithms that have been recently developed to address them. We provide a comprehensive introduction to a large body of research, more than 200 key references, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. On the basis of this material, we identify and discuss what we see as the central challenges lying ahead for recommender system technology, both in terms of extensions of existing techniques as well as of the integration of techniques and technologies drawn from other research areas.


title: Characterization of Fusarium species associated with tobacco diseases in Northwestern Argentina

abstract: Tobacco (Nicotiana tabacum L.) production is centred in the northwestern region of Argentina (NWA), where the incidence of root rot and stem diseases has increased considerably in recent years. This study aimed to analyse the genetic, morphological and pathogenic diversity of the Fusarium oxysporum and F. solani complexes (hereafter FOSC-FSSC), causing Fusarium wilt and root rot. One hundred tobacco fields were surveyed at six locations during two consecutive seasons, and 130 isolates were recovered from symptomatic tobacco plants. The isolates were characterized by morphological traits, molecular characteristics (EF1-α sequence) and pathogenicity tests. All of the isolates were identified as members of the FOSC or FSSC, exhibiting considerable intra-group variation. Three morphotypes were differentiated based on morphological characters in both complexes. The phylogenetic tree generated from the EF1-α sequences confirmed the isolates’ identification. The pathogenicity of the isolates towards tobacco seedlings was assessed in a greenhouse. Considerable variability in pathogenicity was observed among the isolates. Differences in the levels of pathogenicity were recorded. In the FOSC and FSSC, 81% and 60% of the isolates were pathogenic, respectively. In this study, members of FOSC and FSSC exhibited considerable variability in morphological characteristics and virulence, and a portion of them were non-pathogenic for tobacco. To the best of our knowledge, this study is the first to provide information on the variability of the pathogens associated with tobacco wilt and root rot in NWA. This work contributes to the development of sustainable management strategies in tobacco production.


title: DFNet: Semantic Segmentation on Panoramic Images with Dynamic Loss Weights and Residual Fusion Block

abstract: For the domain of self-driving and automatic parking, perception is a basic and critical technique, moreover, the detection of lane markings and parking slots is an important part of visual perception. Compared with front sight images, panoramic images(PI) can capture more comprehensive pavement information. However, the imbalance of different classes in PI is even more serious. Additionally, the judgment of boundary information between areas is a hard problem in deep models. Therefore, we propose a new model named DFNet to solve these problems. The proposed model has two main contributions, one is dynamic loss weights, and the other is residual fusion block(RFB). DFNet use dynamic loss weights to overcome the negative effect of imbalance dataset, which are calculated according to the pixel number of each class in a batch. RFB is composed of several convolutional layers, a pooling layer, and a fusion layer to combine the feature maps by pixel multiplication, which can reduce boundary information loss. We evaluate our method on PSV dataset, and the achieved advanced results demonstrate the effectiveness of the proposed model.


title: End-to-End Neural Ranking for eCommerce Product Search: an application of task models and textual embeddings

abstract: We consider the problem of retrieving and ranking items in an eCommerce catalog, often called SKUs, in order of relevance to a user-issued query. The input data for the ranking are the texts of the queries and textual fields of the SKUs indexed in the catalog. We review the ways in which this problem both resembles and differs from the problems of IR in the context of web search. The differences between the product-search problem and the IR problem of web search necessitate a different approach in terms of both models and datasets. We first review the recent state-of-the-art models for web search IR, distinguishing between two distinct types of model which we call the distributed type and the local-interaction type. The different types of relevance models developed for IR have complementary advantages and disadvantages when applied to eCommerce product search. Further, we explain why the conventional methods for dataset construction employed in the IR literature fail to produce data which suffices for training or evaluation of models for eCommerce product search. We explain how our own approach, applying task modeling techniques to the click-through logs of an eCommerce site, enables the construction of a large-scale dataset for training and robust benchmarking of relevance models. Our experiments consist of applying several of the models from the IR literature to our own dataset. Empirically, we have established that, when applied to our dataset, certain models of local-interaction type reduce ranking errors by one-third compared to the baseline tf-idf. Applied to our dataset, the distributed models fail to outperform the baseline. As a basis for a deployed system, the distributed models have several advantages, computationally, over the local-interaction models. This motivates an ongoing program of work, which we outline at the conclusion of the paper.


title: Compensation of Transmitter I/Q Imbalance in Millimeter-Wave SC-FDE Systems

abstract: Due to imperfect analog processing, millimeter-wave communication is confronted with severe radio frequency impairments, such as in-phase/quadrature (IQ) imbalance. The presence of I/Q imbalance introduces image interference to the desired signal and can significantly degrade the performance if not compensated. In this paper, we propose a novel training-based compensation method for transmitter (TX) I/Q imbalance in millimeter-wave single carrier with frequency domain equalization (SC-FDE) systems. The novelty of the proposed method is that it can obtain the separate estimates of the TX I/Q imbalance and multipath channel. With the obtained estimates, we can easily conduct the FDE and compensate for the TX I/Q imbalance by simple operations. Simulations show that the proposed method can accurately estimate the TX I/Q imbalance and multipath channel and, thus, can achieve almost the same performance as the ideal case free of TX I/Q imbalance.


title: Scalar Timing in Animals and Humans

abstract: Abstract John Gibbon's lifetime work provided a deep understanding of the mechanisms whereby the time sense indexes the passage of time (its accumulation) and records, that is, stores, relevant time intervals in memory, enabling behavior to occur at the right time. The Scalar Expectancy Theory (SET; Gibbon, 1977) remains the most prominent of the theoretical accounts of animal and human timing. SET deals with the three principle psychophysical properties of timing data: flexible accuracy, multiplicative variance, and ratio comparisons. It differs from many other timing theories in its emphasis on scalar variability, a term that refers to the linear increase in the standard deviation of timing errors as a task's criterion time increases. Recently, research based on the conceptual framework and analytic tools of SET in John Gibbon's lab was expanded from a decades-long focus on nonhuman species to an assessment of timing performance in “normal” and brain-diseased human subjects, aimed at understanding the functional and neural mechanisms underlying interval timing in humans. This review is aimed at showing that animal and human data obtained with a variety of timing paradigms are both amenable to analyses of accuracy and scalar variability under the SET framework. In the second part of this report we discuss advances made in our understanding of neurobiological mechanisms underlying interval timing by taking advantage of the SET framework. Issues awaiting new theoretical developments in modeling time production and perception, as revealed by psychophysical findings of recent clinical research that are still not well understood (i.e., sources of nonscalar variability), are raised at the end.


title: The Half-Width Microstrip Leaky Wave Antenna With the Periodic Short Circuits

abstract: A half-width microstrip leaky wave antenna (LWA) with periodic short circuits is presented. The backward-to-forward beam-scanning capability is achieved by periodic construction. The proposed antenna consists of a long rectangular patch with the short circuits that are placed with a series of shorting pins between the antenna patch and the ground plane periodically. The measurement results show that the main lobe scans electronically from 144° to 41° in H-plane (y-z plane) toward endfire (+z direction) with an open stopband when the operating frequency is increased from 4.4 GHz to 8.8 GHz.


title: Carotene: A Job Title Classification System for the Online Recruitment Domain

abstract: In the online job recruitment domain, accurate classification of jobs and resumes to occupation categories is important for matching job seekers with relevant jobs. An example of such a job title classification system is an automatic text document classification system that utilizes machine learning. Machine learning-based document classification techniques for images, text and related entities have been well researched in academia and have also been successfully applied in many industrial settings. In this paper we present Carotene, a machine learning-based semi-supervised job title classification system that is currently in production at CareerBuilder. Carotene leverages a varied collection of classification and clustering tools and techniques to tackle the challenges of designing a scalable classification system for a large taxonomy of job categories. It encompasses these techniques in a cascade classifier architecture. We first present the architecture of Carotene, which consists of a two-stage coarse and fine level classifier cascade. We compare Carotene to an early version that was based on a flat classifier architecture and also compare and contrast Carotene with a third party occupation classification system. The paper concludes by presenting experimental results on real world industrial data using both machine learning metrics and actual user experience surveys.


title: Image Super-Resolution Using Deep Convolutional Networks

abstract: We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.


title: Implementation Cost and Efficiency for AI Experience Managers

abstract: The study of Artificial Intelligence (AI) experience managers seeks to create software agents that can support compelling, interactive user experiences without needing any online guidance from human experts. Evaluating the utility of such AI managers is important in both academia and industry, both for measuring our progress in the field and for estimating a given manager's practical viability. While several methods have been studied that evaluate a manager's effectiveness, relatively few have explored the question of how costly a manager might be to implement in practice. We explore the latter question in this paper, presenting a formal way to estimate the cost of implementing an AI experience manager at scale.


title: Towards Prediction of Security Attacks on Software Defined Networks: A Big Data Analytic Approach

abstract: Cyber-physical systems (CPS) tightly integrate physical and computing processes by monitoring and control data interacting between them via underlying networks. Software Defined Network (SDN) Technology has increasingly become essential in many advanced computer networks, including those in modern CPS, to provide flexible and agile network development. Despite many benefits that SDN offers, malicious attacks that can eventually prevent network services are unavoidable. Among the most predominant attacks on SDN controller layer, Link Discovery Attack and ARP (Address Resolution Protocol) Spoofing Attack are fundamental in that they are the gateways of many other SDN threats and attacks. To defend these attacks, most existing techniques either rely on relatively complex data validation techniques or use thresholds that can be subjective and unable to detect more than one type of attacks at a time if one deciding factor is used. While Big data technology, particularly machine learning, has been widely used for intrusion/anomaly detection, little has been done in SDN. This paper explores how well this technology can be used to predict these SDN attacks. By employing typical machine learning algorithms on simulated data of routing in SDN when attacks occur, preliminary results, obtained from four machine learning models, show the average area under ROC curve of over 96% and 92% for sample size 50,970 (12 switches) and 60,000 (20 switches), respectively. Further experiments show near-linear scaling in training time for the best performing algorithm when sample size grows up to 100,000.


title: LEAF: Latent Extended Attribute Features Discovery for Visual Classification

abstract: To improve the discrimination of attribute representation, in this paper, we propose to extend the traditional attribute representations via embedding the latent high-order structure between attributes. Specifically, our aim is to construct the Latent Extended Attribute Features (LEAF) for visual classification. Since there only exist weak label for each attribute, we firstly propose a feature selection method to explore the common feature structures across categories. After that, the attribute classifiers are trained based on the selected features. Then, the category specific graph is introduced, which is composed of single attributes and their co-occurrence attribute pairs. This attribute graph is used as the initialized representation of each image. Considering our aim, we should discover the discriminative latent structure between attributes and train the robust category classifiers. To that end, we develop a joint learning objective function which is composed of the high-order representation mining term and the classifier training term. The mining term can both preserve category-specific information and discover the common structure between categories. Based on the discovery representation, the robust visual classifiers could be trained by the classifier term. Finally, an alternating optimization method is designed to seek the optimal solution of our objective function. Experimental results on the challenging datasets demonstrate the advantages of our proposed model over existing work.


title: Severity of atrophic gastritis related to antiparietal cell antibody and gastric carcinogenesis, including p53 mutations

abstract: BACKGROUND AND AIMS ::: Infection with Helicobacter pylori (Hp) has been linked to atrophic gastritis, an inflammatory precursor of non-cardia gastric carcinoma. Mutations in the p53 gene are one of the most frequent genetic alterations in gastric carcinoma. In a subgroup of atrophic gastritis, antiparietal cell antibody (APCA) has been detected. This study was aimed to clarify the role of APCA in the progression of atrophic gastritis and gastric carcinogenesis, and to determine the relationship of the severity of atrophic gastritis to gastric carcinoma and to p53 mutations. ::: ::: ::: METHODS ::: In 494 control subjects and 284 gastric carcinoma patients, serum APCA was evaluated and all subjects and patients were classified into four groups using serologic markers (anti-Hp IgG antibody and pepsinogen (PG) test: positive; PG I < 70 microg/L and PG I/II ratio < 3.0) as follows: A, HP- PG-; B, HP+ PG-; C, HP+ PG+ and D, HP- PG+. p53 mutations were analyzed in 174 of 284 patients. ::: ::: ::: RESULTS ::: Antiparietal cell antibody seropositivity increased from group B to D, however, no difference in its positivity was found between controls and patients. The incidence of gastric carcinoma increased from A to D, especially the intestinal subtype. The frequency of p53 gene mutations was higher in PG+ than in PG- gastric carcinoma. ::: ::: ::: CONCLUSIONS ::: Antiparietal cell antibody seropositivity is involved in the progression of a subgroup of atrophic gastritis, but not associated with gastric carcinogenesis. Severe atrophic gastritis is associated with susceptibility to gastric carcinoma, especially the intestinal subtype, and p53 mutations.


title: Sexual behaviour in context: a global perspective

abstract: Summary Research aimed at investigating sexual behaviour and assessing interventions to improve sexual health has increased in recent decades. The resulting data, despite regional differences in quantity and quality, provide a historically unique opportunity to describe patterns of sexual behaviour and their implications for attempts to protect sexual health at the beginning of the 21st century. In this paper we present original analyses of sexual behaviour data from 59 countries for which they were available. The data show substantial diversity in sexual behaviour by region and sex. No universal trend towards earlier sexual intercourse has occurred, but the shift towards later marriage in most countries has led to an increase in premarital sex, the prevalence of which is generally higher in developed countries than in developing countries, and is higher in men than in women. Monogamy is the dominant pattern everywhere, but having had two or more sexual partners in the past year is more common in men than in women, and reported rates are higher in industrialised than in non-industrialised countries. Condom use has increased in prevalence almost everywhere, but rates remain low in many developing countries. The huge regional variation indicates mainly social and economic determinants of sexual behaviour, which have implications for intervention. Although individual behaviour change is central to improving sexual health, efforts are also needed to address the broader determinants of sexual behaviour, particularly those that relate to the social context. The evidence from behavioural interventions is that no general approach to sexual-health promotion will work everywhere and no single-component intervention will work anywhere. Comprehensive behavioural interventions are needed that take account of the social context in mounting individual-level programmes, attempt to modify social norms to support uptake and maintenance of behaviour change, and tackle the structural factors that contribute to risky sexual behaviour.


title: Classifying Socially Sensitive Data Without Discrimination: An Analysis of a Crime Suspect Dataset

abstract: Social discrimination against certain sensitive groups within society (e.g., females, blacks, minorities) is prohibited by law in many countries. To prevent discrimination arising from the use of discriminatory data, recent data mining research has focused on methods for making classifiers learned over discriminatory data discrimination-aware. Most of these methods have been tested on standard classification datasets that have been tweaked for discrimination analysis rather than over actual discriminatory data. In this paper, we study discrimination-aware classification when applied to a real world dataset of Statistics Netherlands, which is a census body in the Netherlands. Specifically, we consider the use of classifiers for predicting whether an individual is a crime suspect, or not, to support law enforcement and security agencies' decision making. Our results show that discrimination does exist in real world datasets and blind use of classifiers learned over such datasets can exacerbate the discrimination problem. We demonstrate that discrimination-aware classification methods can mitigate the discriminatory effects and that they lead to rational and legally acceptable decisions.


The Web of Things : A Survey


Difference in details: transfer learning case study of "cryptic" plants and moths


title: Using Facebook for Image Steganography

abstract: Because Facebook is available on hundreds of millions of desktop and mobile computing platforms around the world and because it is available on many different kinds of platforms (from desktops and laptops running Windows, Unix, or OS X to hand held devices running iOS, Android, or Windows Phone), it would seem to be the perfect place to conduct steganography. On Facebook, information hidden in image files will be further obscured within the millions of pictures and other images posted and transmitted daily. Facebook is known to alter and compress uploaded images so they use minimum space and bandwidth when displayed on Facebook pages. The compression process generally disrupts attempts to use Facebook for image steganography. This paper explores a method to minimize the disruption so JPEG images can be used as steganography carriers on Facebook.


title: Virtual Reality in Psychotherapy: Review

abstract: Virtual reality (VR) has recently emerged as a potentially effective way to provide general and specialty health care services, and appears poised to enter mainstream psychotherapy delivery. Because VR could be part of the future of clinical psychology, it is critical to all psychotherapists that it be defined broadly. To ensure appropriate development of VR applications, clinicians must have a clear understanding of the opportunities and challenges it will provide in professional practice. This review outlines the current state of clinical research relevant to the development of virtual environments for use in psychotherapy. In particular, the paper focuses its analysis on both actual applications of VR in clinical psychology and how different clinical perspectives can use this approach to improve the process of therapeutic change.


PESA: Phrase Pair Extraction as Sentence Splitting


title: Automatic, On-Line Tuning of YARN Container Memory and CPU Parameters

abstract: Big data analytic technologies such as Hadoop and Spark run on compute clusters that are managed by resource managers such as YARN. YARN manages resources available to individual applications, thereby affecting job performance. Manual tuning of YARN tuning parameters can result in sub-optimal and brittle performance. Parameters that are optimal for one job may not be well suited to another. In this paper we present KERMIT, the first on-line automatic tuning system for YARN. KERMIT optimizes in real-time YARN memory and CPU allocations to individual YARN containers by analysing container response-time performance. Unlike previous automatic tuning methods for specific systems such as Spark or Hadoop, this is the first study that focuses on the more general case of on-line, real-time tuning of YARN container density and how this affects performance of applications running on YARN. KERMIT employs the same tuning code to automatically tune any system that uses YARN, including both Spark and Hadoop. The effectiveness of our technique was evaluated for Hadoop and Spark jobs using the Terasort, TPCx-HS, and SMB benchmarks. KERMIT was able to achieve an efficiency of more than 92% of the best possible tuning configuration (exhaustive search of the parameter space) and up to 30% faster than basic manual tuning.


title: Semantic and Verbatim Word Spotting Using Deep Neural Networks

abstract: In the last few years, deep convolutional neural networks have become ubiquitous in computer vision, achieving state-of-the-art results on problems like object detection, semantic segmentation, and image captioning. However, they have not yet been widely investigated in the document analysis community. In this paper, we present a word spotting system based on convolutional neural networks. We train a network to extract a powerful image representation, which we then embed into a word embedding space. This allows us to perform word spotting using both query-by-string and query-by-example in a variety of word embedding spaces, both learned and handcrafted, for verbatim as well as semantic word spotting. Our novel approach is versatile and the evaluation shows that it outperforms the previous state-of-the-art for word spotting on standard datasets.


title: The rise of graphene

abstract: Graphene is a rapidly rising star on the horizon of materials science and condensed-matter physics. This strictly two-dimensional material exhibits exceptionally high crystal and electronic quality, and, despite its short history, has already revealed a cornucopia of new physics and potential applications, which are briefly discussed here. Whereas one can be certain of the realness of applications only when commercial products appear, graphene no longer requires any further proof of its importance in terms of fundamental physics. Owing to its unusual electronic spectrum, graphene has led to the emergence of a new paradigm of 'relativistic' condensed-matter physics, where quantum relativistic phenomena, some of which are unobservable in high-energy physics, can now be mimicked and tested in table-top experiments. More generally, graphene represents a conceptually new class of materials that are only one atom thick, and, on this basis, offers new inroads into low-dimensional physics that has never ceased to surprise and continues to provide a fertile ground for applications.


title: Broadband Planar Transition to Dielectric Image Line by Substrate Truncated Microstrip Line for Millimeter-Wave Circuit Integration

abstract: In this letter, a broadband planar substrate truncated tapered microstrip line-to-dielectric image line transition on a single substrate is proposed. The design uses substrate truncated microstrip line which helps to minimize the losses due to the surface wave generation on thick microstrip line. Generalized empirical equations are proposed for the transition design and validated for different dielectric constants in millimeter-wave frequency band. Full-wave simulations are carried out using high frequency structural simulator. A back-to-back transition prototype of Ku-band is fabricated and measured. The measured return loss for 80-mm-long structure is better than 10 dB and the insertion loss is better than 2.5 dB in entire Ku-band (40% impedance bandwidth).


CALYPSO : A Neural Network Model for Natural Language Inference


title: Interest inference via structure-constrained multi-source multi-task learning

abstract: User interest inference from social networks is a fundamental problem to many applications. It usually exhibits dual-heterogeneities: a user's interests are complementarily and comprehensively reflected by multiple social networks; interests are inter-correlated in a nonuniform way rather than independent to each other. Although great success has been achieved by previous approaches, few of them consider these dual-heterogeneities simultaneously. In this work, we propose a structure-constrained multi-source multitask learning scheme to co-regularize the source consistency and the tree-guided task relatedness. Meanwhile, it is able to jointly learn the task-sharing and task-specific features. Comprehensive experiments on a real-world dataset validated our scheme. In addition, we have released our dataset to facilitate the research communities.


title: Gait recognition using wearable motion recording sensors

abstract: This paper presents an alternative approach, where gait is collected by the sensors attached to the person's body. Such wearable sensors record motion (e.g. acceleration) of the body parts during walking. The recorded motion signals are then investigated for person recognition purposes. We analyzed acceleration signals from the foot, hip, pocket and arm. Applying various methods, the best EER obtained for foot-, pocket-, arm-and hip-based user authentication were 5%, 7%, 10% and 13%, respectively. Furthermore, we present the results of our analysis on security assessment of gait. Studying gait-based user authentication (in case of hip motion) under three attack scenarios, we revealed that a minimal effort mimicking does not help to improve the acceptance chances of impostors. However, impostors who know their closest person in the database or the genders of the users can be a threat to gait-based authentication. We also provide some new insights toward the uniqueness of gait in case of foot motion. In particular, we revealed the following: a sideway motion of the foot provides the most discrimination, compared to an up-down or forward-backward directions; and different segments of the gait cycle provide different level of discrimination.


title: CAKE: Compact and Accurate K-dimensional representation of Emotion

abstract: Numerous models describing the human emotional states have been built by the psychology community. Alongside, Deep Neural Networks (DNN) are reaching excellent performances and are becoming interesting features extraction tools in many computer vision tasks.Inspired by works from the psychology community, we first study the link between the compact two-dimensional representation of the emotion known as arousal-valence, and discrete emotion classes (e.g. anger, happiness, sadness, etc.) used in the computer vision community. It enables to assess the benefits -- in terms of discrete emotion inference -- of adding an extra dimension to arousal-valence (usually named dominance). Building on these observations, we propose CAKE, a 3-dimensional representation of emotion learned in a multi-domain fashion, achieving accurate emotion recognition on several public datasets. Moreover, we visualize how emotions boundaries are organized inside DNN representations and show that DNNs are implicitly learning arousal-valence-like descriptions of emotions. Finally, we use the CAKE representation to compare the quality of the annotations of different public datasets.


title: Design of an Ultrawideband Antipodal Tapered Slot Antenna Using Elliptical Strip Conductors

abstract: This letter presents the design of an ultrawideband antipodal tapered slot antenna printed with elliptically shaped strip conductors. A new design equation was empirically determined and used to implement these antennas. The fabricated prototypes were experimentally verified through measurements, and good agreement with simulations was obtained. The measured result of one of the prototypes shows 24:1 bandwidth with good radiation characteristics.


title: A 0.6mW 31MHz 4th-order low-pass filter with +29dBm IIP3 using self-coupled source follower based biquads in 0.18µm CMOS

abstract: A highly compact low-pass filter (LPF) using self-coupled source follower based biquads is presented. The biquad cell synthesizes a 2nd-order low-pass transfer function in a single branch, using only two capacitors and a source follower with embedded local feedback for excellent linearity. A 4th-order Chebyshev LPF prototype is designed with a cascade of two biquads in 0.18µm CMOS, and occupies an active area of 0.1mm2. A cut-off frequency of 31MHz is measured with a stop-band rejection of 76dB. The prototype filter draws only 0.46mA current from a 1.35V supply, and achieves an in-band IIP3 of +29dBm. The averaged in-band input-referred noise is 22.8nV/√Hz, resulting in a dynamic range of 71dB.


100 Years of Consolidation — Remembering Müller and Pilzecker


title: Ethical leadership: A review and future directions.

abstract: Abstract Our literature review focuses on the emerging construct of ethical leadership and compares this construct with related concepts that share a common concern for a moral dimension of leadership (e.g., spiritual, authentic, and transformational leadership). Drawing broadly from the intersection of the ethics and leadership literatures, we offer propositions about the antecedents and outcomes of ethical leadership. We also identify issues and questions to be addressed in the future and discuss their implications for research and practice. Our review indicates that ethical leadership remains largely unexplored, offering researchers opportunities for new discoveries and leaders opportunities to improve their effectiveness.


Knowledge Management Frameworks: A Review of Conceptual Foundations and a KMF for IT-based Organizations.


Obfuscation-Resilient Privacy Leak Detection for Mobile Apps Through Differential Analysis


Dewey's Contribution to a Standard of Problem-Based Learning Practice


title: Using an SMT solver for interactive requirements prioritization

abstract: The prioritization of requirements is a crucial activity in the early phases of the software development process. It consists of finding an order relation among requirements, considering several requirements characteristics, such as stakeholder preferences, technical constraints, implementation costs and user perceived value. We propose an interactive approach to the problem of prioritization based on Satisfiability Modulo Theory (SMT) techniques and pairwise comparisons. Our approach resorts to interactive knowledge acquisition whenever the relative priority among requirements cannot be determined based on the available information. Synthesis of the final ranking is obtained via SMT constraint solving. The approach has been evaluated on a set of requirements from a real healthcare project. Results show that it overcomes other interactive state-of-the-art prioritization approaches in terms of effectiveness, efficiency and robustness to decision maker errors.


title: A Publish/Subscribe Protocol for Event-Driven Communications in the Internet of Things

abstract: Internet of Things is a futuristic vision of networked objects able to take smart decisions and to assist humans in their daily activities. The founding factor of such a vision is the capacity of objects to exchange data by means of wireless networks, and the applied communication pattern is event-driven and multicast. Publish/subscribe services are a promising communication technology for building solutions according to the Internet of Things' vision. The available solutions consist in the adaptation of widely-known products for publish/subscribe services taken from other contexts and application domains, without considering the key peculiarities of the Internet of Things. Our driving idea is to consider beaconing as the basic communication means, and to build a publish/subscribe service based on it. Our solution can be used stand-alone for event-driven communications and/or integrated within standardized protocols to provide basic communication capabilities. We present a preliminary set of experiments showing the efficiency of our solution.


title: Fifty Years of Artificial Reverberation

abstract: The first artificial reverberation algorithms were proposed in the early 1960s, and new, improved algorithms are published regularly. These algorithms have been widely used in music production since the 1970s, and now find applications in new fields, such as game audio. This overview article provides a unified review of the various approaches to digital artificial reverberation. The three main categories have been delay networks, convolution-based algorithms, and physical room models. Delay-network and convolution techniques have been competing in popularity in the music technology field, and are often employed to produce a desired perceptual or artistic effect. In applications including virtual reality, predictive acoustic modeling, and computer-aided design of acoustic spaces, accuracy is desired, and physical models have been mainly used, although, due to their computational complexity, they are currently mainly used for simplified geometries or to generate reverberation impulse responses for use with a convolution method. With the increase of computing power, all these approaches will be available in real time. A recent trend in audio technology is the emulation of analog artificial reverberation units, such as spring reverberators, using signal processing algorithms. As a case study we present an improved parametric model for a spring reverberation unit.


VARIETY MODES AND CHAOS IN SMOKE VENTILATION BY CEILING CHAMBER SYSTEM


Path Planning and Steering Control for an Automatic Perpendicular Parking Assist System


title: Initialization techniques for 3D SLAM: A survey on rotation estimation and its use in pose graph optimization

abstract: Pose graph optimization is the non-convex optimization problem underlying pose-based Simultaneous Localization and Mapping (SLAM). If robot orientations were known, pose graph optimization would be a linear least-squares problem, whose solution can be computed efficiently and reliably. Since rotations are the actual reason why SLAM is a difficult problem, in this work we survey techniques for 3D rotation estimation. Rotation estimation has a rich history in three scientific communities: robotics, computer vision, and control theory. We review relevant contributions across these communities, assess their practical use in the SLAM domain, and benchmark their performance on representative SLAM problems (Fig. 1). We show that the use of rotation estimation to bootstrap iterative pose graph solvers entails significant boost in convergence speed and robustness.


title: Introducing IoT and Wearable Technologies into Task-Based Language Learning for Young Children

abstract: In the last few years, in an attempt to further motivate students to learn a foreign language, there has been an increasing interest in task-based teaching techniques, which emphasize communication and the practical use of language, thus moving away from the repetitive grammar-translation methods. Within this approach, the significance of situating foreign language learners in scenarios where they can meaningfully learn has become a major priority for many educators. This approach is particularly relevant in the context of teaching foreign languages to young children, who need to be introduced to a new language by means of very concrete vocabulary, which is facilitated by the use of objects that they can handle and see. In this study, we investigate the benefits of using wearable and Internet-of-Things (IoT) technologies in streamlining the creation of such realistic task-based language learning scenarios. We show that the use of these technologies will prove beneficial by freeing the instructors of having to keep records of the tasks performed by each student during the class session. Instead, instructors can focus their efforts on creating a friendly environment and encouraging students to participate. Our study sets up a basis for showing the great benefits of using wearable and IoT technologies in streamlining 1) the creation of realistic scenarios in which young foreign language learners can feel comfortable engaging in chat and becoming better prepared for social interaction in a foreign language, and 2) the acquisition and processing of performance metrics.


title: Broadband Compact Horn Antennas by Using EPS-ENZ Metamaterial Lens

abstract: We present the design of a flat lens, made by a conventional material and an epsilon near-zero metamaterial, to plug up the aperture of a short horn antenna, in order to achieve radiation performances similar to the ones of the corresponding optimum horn over a broad frequency range. Lens operation is based on the phase-compensation concept: phase-fronts of the field propagating along the short flare of the horn propagate with different phase velocities in the two lens materials, resulting in an uniform phase distribution on the aperture. Starting from the theoretical study of the transmission properties of a bulk epsilon near-zero slab, we derive the analytical formulas for the design of the flat lens and validate them through full-wave numerical simulations. Then, a realistic version of the lens, realized with a wire-medium and exhibiting a near-zero real part of the effective permittivity in the frequency range of interest, is presented. Considering two examples working in the C-band, we show that the lens can be designed for both conical and pyramidal horn antennas. In both cases, the length of the horns is half the one of the corresponding optimum versions, while the obtained radiation performances are similar to those of the optimum horns over a broad frequency band. This result may open the door to several interesting applications in satellite and radar systems.


title: SoK: The Evolution of Sybil Defense via Social Networks

abstract: Sybil attacks in which an adversary forges a potentially unbounded number of identities are a danger to distributed systems and online social networks. The goal of sybil defense is to accurately identify sybil identities. This paper surveys the evolution of sybil defense protocols that leverage the structural properties of the social graph underlying a distributed system to identify sybil identities. We make two main contributions. First, we clarify the deep connection between sybil defense and the theory of random walks. This leads us to identify a community detection algorithm that, for the first time, offers provable guarantees in the context of sybil defense. Second, we advocate a new goal for sybil defense that addresses the more limited, but practically useful, goal of securely white-listing a local region of the graph.


ine blast algorithm : A new population based algorithm for solving constrained ngineering optimization problems


title: Data security and privacy in wireless body area networks

abstract: The wireless body area network has emerged as a new technology for e-healthcare that allows the data of a patient's vital body parameters and movements to be collected by small wearable or implantable sensors and communicated using short-range wireless communication techniques. WBAN has shown great potential in improving healthcare quality, and thus has found a wide range of applications from ubiquitous health monitoring and computer assisted rehabilitation to emergency medical response systems. The security and privacy protection of the data collected from a WBAN, either while stored inside the WBAN or during their transmission outside of the WBAN, is a major unsolved concern, with challenges coming from stringent resource constraints of WBAN devices, and the high demand for both security/privacy and practicality/usability. In this article we look into two important data security issues: secure and dependable distributed data storage, and fine-grained distributed data access control for sensitive and private patient medical data. We discuss various practical issues that need to be taken into account while fulfilling the security and privacy requirements. Relevant solutions in sensor networks and WBANs are surveyed, and their applicability is analyzed.


title: Visual Human+Machine Learning

abstract: In this paper we describe a novel method to integrate interactive visual analysis and machine learning to support the insight generation of the user. The suggested approach combines the vast search and processing power of the computer with the superior reasoning and pattern recognition capabilities of the human user. An evolutionary search algorithm has been adapted to assist in the fuzzy logic formalization of hypotheses that aim at explaining features inside multivariate, volumetric data. Up to now, users solely rely on their knowledge and expertise when looking for explanatory theories. However, it often remains unclear whether the selected attribute ranges represent the real explanation for the feature of interest. Other selections hidden in the large number of data variables could potentially lead to similar features. Moreover, as simulation complexity grows, users are confronted with huge multidimensional data sets making it almost impossible to find meaningful hypotheses at all. We propose an interactive cycle of knowledge-based analysis and automatic hypothesis generation. Starting from initial hypotheses, created with linking and brushing, the user steers a heuristic search algorithm to look for alternative or related hypotheses. The results are analyzed in information visualization views that are linked to the volume rendering. Individual properties as well as global aggregates are visually presented to provide insight into the most relevant aspects of the generated hypotheses. This novel approach becomes computationally feasible due to a GPU implementation of the time-critical parts in the algorithm. A thorough evaluation of search times and noise sensitivity as well as a case study on data from the automotive domain substantiate the usefulness of the suggested approach.


title: Emerging ethical issues in neuroscience

abstract: There is growing public awareness of the ethical issues raised by progress in many areas of neuroscience. This commentary reviews the issues, which are triaged in terms of their novelty and their imminence, with an exploration of the relevant ethical principles in each case.


title: Taverna: A tool for the composition and enactment of bioinformatics workflows

abstract: Motivation:In silico experiments in bioinformatics involve the co-ordinated use of computational tools and information repositories. A growing number of these resources are being made available with programmatic access in the form of Web services. Bioinformatics scientists will need to orchestrate these Web services in workflows as part of their analyses. ::: ::: Results: The Taverna project has developed a tool for the composition and enactment of bioinformatics workflows for the life sciences community. The tool includes a workbench application which provides a graphical user interface for the composition of workflows. These workflows are written in a new language called the simple conceptual unified flow language (Scufl), where by each step within a workflow represents one atomic task. Two examples are used to illustrate the ease by which in silico experiments can be represented as Scufl workflows using the workbench application. ::: ::: Availability: The Taverna workflow system is available as open source and can be downloaded with example Scufl workflows from http://taverna.sourceforge.net


title: Value from hedonic experience and engagement

abstract: Recognizing that value involves experiencing pleasure or pain is critical to understanding the psychology of value. But hedonic experience is not enough. I propose that it is also necessary to recognize that strength of engagement can contribute to experienced value through its contribution to the experience of motivational force—an experience of the intensity of the force of attraction to or repulsion from the value target. The subjective pleasure/pain properties of a value target influence strength of engagement, but factors separate from the hedonic properties of the value target also influence engagement strength and thus contribute to the experience of attraction or repulsion. These additional sources of engagement strength include opposition to interfering forces, overcoming personal resistance, using the right or proper means of goal pursuit, and regulatory fit between the orientation and manner of goal pursuit. Implications of the contribution of engagement strength to value are discussed for judgment and decision making, persuasion, and emotional experiences.


title: The GNUnet System

abstract: GNUnet is an alternative network stack for building secure, decentralized and privacy-preserving distributed applications. Our goal is to replace the old insecure Internet protocol stack. Starting from an application for secure publication of files, it has grown to include all kinds of basic protocol components and applications towards the creation of a GNU internet. ::: ::: This habilitation provides an overview of the GNUnet architecture, including the development process, the network architecture and the software architecture. The goal of Part 1 is to provide an overview of how the various parts of the project work together today, and to then give ideas for future directions. The text is a first attempt to provide this kind of synthesis, and in return does not go into extensive technical depth on any particular topic. Part 2 then gives selected technical details based on eight publications covering many of the core components. This is a harsh selection; on the GNUnet website there are more than 50 published research papers and theses related to GNUnet, providing extensive and in-depth documentation. Finally, Part 3 gives an overview of current plans and future work.


title: Test Prioritization in Continuous Integration Environments

abstract: Abstract Two heuristics namely diversity-based (DBTP) and history-based test prioritization (HBTP) have been separately proposed in the literature. Yet, their combination has not been widely studied in continuous integration (CI) environments. The objective of this study is to catch regression faults earlier, allowing developers to integrate and verify their changes more frequently and continuously. To achieve this, we investigated six open-source projects, each of which included several builds over a large time period. Findings indicate that previous failure knowledge seems to have strong predictive power in CI environments and can be used to effectively prioritize tests. HBTP does not necessarily need to have large data, and its effectiveness improves to a certain degree with larger history interval. DBTP can be used effectively during the early stages, when no historical data is available, and also combined with HBTP to improve its effectiveness. Among the investigated techniques, we found that history-based diversity using NCD Multiset is superior in terms of effectiveness but comes with relatively higher overhead in terms of method execution time. Test prioritization in CI environments can be effectively performed with negligible investment using previous failure knowledge, and its effectiveness can be further improved by considering dissimilarities among the tests.


External Propagators in WASP: Preliminary Report


title: Synthesis and analysis of a flexible elephant trunk robot

abstract: This paper presents a novel synthesis and analysis of a flexible elephant trunk robot (biological continuum–style manipulator). The robot includes eight flexible segments, although it can be extended to more segments as necessary. In this study the gravity of the springs is neglected due to the fact that the manipulation force is much larger than these gravity forces. This mechanism exhibits a wide range of maneuverability and has a large number of degrees of freedom. Each segment is designed using a novel flexible mechanism based on the loading of a compression spring in both transverse and axial directions, and using cable–conduit systems. The rotational motion is transformed to tendon-like behavior, which enables the location of the actuators away from the trunk (e.g. at the end of the trunk). The forward kinematics of the mechanism is also presented and lends itself well to computer control. It is shown that the solution of the transverse deflection of each segment is obtained in a general form, while...


title: What Is the Best Practice for CNNs Applied to Visual Instance Retrieval?

abstract: Previous work has shown that feature maps of deep convolutional neural networks (CNNs) ::: can be interpreted as feature representation of a particular image region. Features aggregated from ::: these feature maps have been exploited for image retrieval tasks and achieved state-of-the-art performances in ::: recent years. The key to the success of such methods is the feature representation. However, the different ::: factors that impact the effectiveness of features are still not explored thoroughly. There are much less ::: discussion about the best combination of them. ::: ::: The main contribution of our paper is the thorough evaluations of the various factors that affect the ::: discriminative ability of the features extracted from CNNs. Based on the evaluation results, we also identify ::: the best choices for different factors and propose a new multi-scale image feature representation method to ::: encode the image effectively. Finally, we show that the proposed method generalises well and outperforms ::: the state-of-the-art methods on four typical datasets used for visual instance retrieval.


title: Incorporating Selectional Preferences in Multi-hop Relation Extraction

abstract: Relation extraction is one of the core challenges in automated knowledge base construction. One line of approach for relation extraction is to perform multi-hop reasoning on the paths connecting an entity pair to infer new relations. While these methods have been successfully applied for knowledge base completion, they do not utilize the entity or the entity type information to make predictions. In this work, we incorporate selectional preferences, i.e., relations enforce constraints on the allowed entity types for the candidate entities, to multi-hop relation extraction by including entity type information. We achieve a 17.67% (relative) improvement in MAP score in a relation extraction task when compared to a method that does not use entity type information.


title: Monitoring senior wellness status using multimodal biosensors

abstract: Because of the increased lifespan, there is an increasing demand on prevention of disease for senior wellness. For monitoring senior wellness status, biosensors such as Electroencephalography (EEG), Electrocardiography (ECG), blood pressure (BP), and respiration rate (RR) sensors and environmental sensors (temperature, humidity, motion, and light sensors) were used for data collection. Sensing data from bio- and environmental sensors are transferred to gateway in smart home and gateway send data into smart home server for the storage and analysis. Sensing data is analyzed using SVM for filtering, decision tree for health risk ratio, and EM algorithm for decision making. In this paper, we develop an EM-based inspection service middleware for monitoring elderly wellness status based on time and zone transition. This inspection service middleware for the prediction of abnormal health status has three steps as follows; monitoring, activity assessment, health risk assessment and decision-making. The activity assessment step used fuzzy logic, the health risk assessment step uses the decision-tree model for the classification of health status. Finally, EM-based decision-making step recommend exercise and housework for healthy status and rest or hospital checkup for tired/sick status.


title: Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time

abstract: User experience in modern content discovery applications critically depends on high-quality personalized recommendations. However, building systems that provide such recommendations presents a major challenge due to a massive pool of items, a large number of users, and requirements for recommendations to be responsive to user actions and generated on demand in real-time. Here we present Pixie, a scalable graph-based real-time recommender system that we developed and deployed at Pinterest. Given a set of user-specific pins as a query, Pixie selects in real-time from billions of possible pins those that are most related to the query. To generate recommendations, we develop Pixie Random Walk algorithm that utilizes the Pinterest object graph of 3 billion nodes and 17 billion edges. Experiments show that recommendations provided by Pixie lead up to 50% higher user engagement when compared to the previous Hadoop-based production system. Furthermore, we develop a graph pruning strategy at that leads to an additional 58% improvement in recommendations. Last, we discuss system aspects of Pixie, where a single server executes 1,200 recommendation requests per second with 60 millisecond latency. Today, systems backed by Pixie contribute to more than 80% of all user engagement on Pinterest.


title: VisualCloud Demonstration: A DBMS for Virtual Reality

abstract: We demonstrate VisualCloud, a database management system designed to efficiently ingest, store, and deliver virtual reality (VR) content at scale. VisualCloud targets both live and prerecorded spherical panoramic (a.k.a. 360°) VR videos. It persists content as a multidimensional array that utilizes both dense (e.g., space and time) and sparse (e.g., bitrate) dimensions. VisualCloud uses orientation prediction to reduce data transfer by degrading out-of-view portions of the video. Content delivered through VisualCloud requires up to 60% less bandwidth than existing methods and scales to many concurrent connections. This demonstration will allow attendees to view both live and prerecorded VR video content served through VisualCloud. Viewers will be able to dynamically adjust tuning parameters (e.g., bitrates and path prediction) and observe changes in visual fidelity.


title: Camera as Position Sensor for a Ball and Beam Control System

abstract: This paper describes a novel strategy to use a digital camera as a position sensor to control a ball and beam system. A linear control law is used to position the ball at the desired location on the beam. The experiments show how this method controls the positioning of the ball in any location on the beam using a camera with a sampling rate of 30 frames per second (fps), and these results are compared with those using an analog resistive sensor with a feedback signal sampled at rate 1000 samples per second. The mechanical characteristics of this ball and beam system are used to simplify the calculation of the ball position using our vision system, and in order to easy camera calibration with respect to the ball and beam system. Our proposal use circularity feature of blobs in a binary image, instead of the classic correlation or Hough transform techniques for ball’s tracking. The main control system is implemented in Simulink with Real Time Workshop (RTW) and the vision processing with OpenCV libraries.


title: Gradient boosting for kernelized output spaces

abstract: A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting in a principled way to complex output spaces (images, text, graphs etc.) and can be applied to a general class of base learners working in kernelized output spaces. Empirical results are provided on three problems: a regression problem, an image completion task and a graph prediction problem. In these experiments, the framework is combined with tree-based base learners, which have interesting algorithmic properties. The results show that gradient boosting significantly improves these base learners and provides competitive results with other tree-based ensemble methods based on randomization.


title: Resistive Associative Processor

abstract: Associative Processor (AP) combines data storage and data processing, and functions simultaneously as a massively parallel array SIMD processor and memory. Traditionally, AP is based on CMOS technology, similar to other classes of massively parallel SIMD processors. The main component of AP is a Content Addressable Memory (CAM) array. As CMOS feature scaling slows down, CAM experiences scalability problems. In this work, we propose and investigate an AP based on resistive CAM—the Resistive AP (ReAP). We show that resistive memory technology potentially allows scaling the AP from a few millions to a few hundred millions of processing units on a single silicon die. We compare the performance and power consumption of a ReAP to a CMOS AP and a conventional SIMD accelerator (GPU) and show that ReAP, although exhibiting higher power density, allows better scalability and higher performance.


title: The Amsterdam Resting-State Questionnaire reveals multiple phenotypes of resting-state cognition

abstract: Resting-state neuroimaging is a dominant paradigm for studying brain function in health and disease. It is attractive for clinical research because of its simplicity for patients, straightforward standardization, and sensitivity to brain disorders. Importantly, non-sensory experiences like mind wandering may arise from ongoing brain activity. However, little is known about the link between ongoing brain activity and cognition, as phenotypes of resting-state cognition—and tools to quantify them—have been lacking. To facilitate rapid and structured measurements of resting-state cognition we developed a 50-item self-report survey, the Amsterdam Resting-State Questionnaire (ARSQ). Based on ARSQ data from 813 participants assessed after 5 min eyes-closed rest in their home, we identified seven dimensions of resting-state cognition using factor analysis: Discontinuity of Mind, Theory of Mind, Self, Planning, Sleepiness, Comfort, and Somatic Awareness. Further, we showed that the structure of cognition was similar during resting-state fMRI and EEG, and that the test-retest correlations were remarkably high for all dimensions. To explore whether inter-individual variation of resting-state cognition is related to health status, we correlated ARSQ-derived factor scores with psychometric scales measuring depression, anxiety, and sleep quality. Mental health correlated positively with Comfort and negatively with Discontinuity of Mind. Finally, we show that sleepiness may partially explain a resting-state EEG profile previously associated with Alzheimer's disease. These findings indicate that the ARSQ readily provides information about cognitive phenotypes and that it is a promising tool for research on the neural correlates of resting-state cognition in health and disease.


title: Convolutional Neural Network Architectures for Signals Supported on Graphs

abstract: Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs are introduced. We start with the selection graph neural network (GNN), which replaces linear time invariant filters with linear shift invariant graph filters to generate convolutional features and reinterprets pooling as a possibly nonlinear subsampling stage where nearby nodes pool their information in a set of preselected sample nodes. A key component of the architecture is to remember the position of sampled nodes to permit computation of convolutional features at deeper layers. The second architecture, dubbed aggregation GNN, diffuses the signal through the graph and stores the sequence of diffused components observed by a designated node. This procedure effectively aggregates all components into a stream of information having temporal structure to which the convolution and pooling stages of regular CNNs can be applied. A multinode version of aggregation GNNs is further introduced for operation in large-scale graphs. An important property of selection and aggregation GNNs is that they reduce to conventional CNNs when particularized to time signals reinterpreted as graph signals in a circulant graph. Comparative numerical analyses are performed in a source localization application over synthetic and real-world networks. Performance is also evaluated for an authorship attribution problem and text category classification. Multinode aggregation GNNs are consistently the best-performing GNN architecture.


Stochastic processes for modelling bridge deterioration


title: Shedding Light on Thirteen Years of Darkness: Content Analysis of Articles Pertaining to Transgender Issues In Marriage/Couple and Family Therapy Journals

abstract: What is the extent to which marriage/couple and family therapy (M/CFT) journals address transgender issues and how many of them say they are inclusive of transgender persons when they are not? To answer these queries, a content analysis was conducted on articles published in M/CFT literature from 1997 through 2009. Of the 10,739 articles examined in 17 journals, only nine (0.0008%) focused on transgender issues or used gender variance as a variable. Findings support the assertion that transgender issues are ignored and marginalized by M/CFT scholars and researchers alike.


title: ContikiSec: A Secure Network Layer for Wireless Sensor Networks under the Contiki Operating System

abstract: In this paper we introduce ContikiSec, a secure network layer for wireless sensor networks, designed for the Contiki Operating System. ContikiSec has a configurable design, providing three security modes starting from confidentiality and integrity, and expanding to confidentiality, authentication, and integrity. ContikiSec has been designed to balance low energy consumption and security while conforming to a small memory footprint. Our design was based on performance evaluation of existing security primitives and is part of the contribution of this paper. Our evaluation was performed in the Modular Sensor Board hardware platform for wireless sensor networks, running Contiki. Contiki is an open source, highly portable operating system for wireless sensor networks (WSN) that is widely used in WSNs.


Multi-Scale Change Point Detection in Multivariate Time Series


title: PhysioDroid: Combining Wearable Health Sensors and Mobile Devices for a Ubiquitous, Continuous, and Personal Monitoring

abstract: Technological advances on the development of mobile devices, medical sensors, and wireless communication systems support a new generation of unobtrusive, portable, and ubiquitous health monitoring systems for continuous patient assessment and more personalized health care. There exist a growing number of mobile apps in the health domain; however, little contribution has been specifically provided, so far, to operate this kind of apps with wearable physiological sensors. The PhysioDroid, presented in this paper, provides a personalized means to remotely monitor and evaluate users' conditions. The PhysioDroid system provides ubiquitous and continuous vital signs analysis, such as electrocardiogram, heart rate, respiration rate, skin temperature, and body motion, intended to help empower patients and improve clinical understanding. The PhysioDroid is composed of a wearable monitoring device and an Android app providing gathering, storage, and processing features for the physiological sensor data. The versatility of the developed app allows its use for both average users and specialists, and the reduced cost of the PhysioDroid puts it at the reach of most people. Two exemplary use cases for health assessment and sports training are presented to illustrate the capabilities of the PhysioDroid. Next technical steps include generalization to other mobile platforms and health monitoring devices.


title: Interactive Policy Learning through Confidence-Based Autonomy

abstract: We present Confidence-Based Autonomy (CBA), an interactive algorithm for policy learning from demonstration. The CBA algorithm consists of two components which take advantage of the complimentary abilities of humans and computer agents. The first component, Confident Execution, enables the agent to identify states in which demonstration is required, to request a demonstration from the human teacher and to learn a policy based on the acquired data. The algorithm selects demonstrations based on a measure of action selection confidence, and our results show that using Confident Execution the agent requires fewer demonstrations to learn the policy than when demonstrations are selected by a human teacher. The second algorithmic component, Corrective Demonstration, enables the teacher to correct any mistakes made by the agent through additional demonstrations in order to improve the policy and future task performance. CBA and its individual components are compared and evaluated in a complex simulated driving domain. The complete CBA algorithm results in the best overall learning performance, successfully reproducing the behavior of the teacher while balancing the tradeoff between number of demonstrations and number of incorrect actions during learning.


title: An inexpensive solid medium for obtaining colony-forming units of oral spirochetes.

abstract: A method for the enumeration of colony-forming units of oral anaerobic spirochetes in new oral spirochete agarose (NOS-A) medium was described recently. However, the high cost of agarose limits the extent to which large assays can be carried out. Accordingly, a search for an inexpensive gelling agent that remains molten at 37 degrees C and gels at 25 degrees C was undertaken. Varying amounts of Noble agar or Bacto agar (0.5 to 1.5%, w/v) were mixed with varying amounts of gelatin (0.5 to 1.0%, w/v) in NOS medium. NOS medium containing 0.5% gelatin-0.5% Noble agar (NOS-GN) or 0.5% gelatin-0.5% Bacto agar (NOS-GB) met the above criteria. NOS-GN and NOS-GB media yielded higher colony-forming units with Treponema denticola than NOS-A medium in that order. However, all three media, NOS-GN, NOS-GB and NOS-A, performed equally well in the recovery of viable counts of T. vincentii. The NOS-GN medium was not liquefied by subgingival bacteria or two gelatinase-producing species of bacteria, Bacillus subtilis and Staphylococcus aureus. Thus NOS-GN medium is the recommended medium both in cost and performance for obtaining colony counts of spirochetes.


title: of copyright to IEEE.) A Longitudinal Study of the Use of a Test-Driven Development Practice in Industry

abstract: Test-Driven Development (TDD) is an agile practice that is widely accepted and advocated by most agile methods and methodologists. In this paper, we report on a longitudinal case study of an IBM team who has sustained use of TDD for five years and over ten releases of a Java-implemented product. The team worked from a design and wrote tests incrementally before or while they wrote code and, in the process, developed a significant asset of automated tests. The IBM team realized sustained quality improvement relative to a pre-TDD project and consistently had defect density below industry standards. As a result, our data indicate that the TDD practice can aid in the production of high quality products. This quality improvement would compensate for the moderate perceived productivity losses. Additionally, the use of TDD may decrease the degree to which code complexity increases as software ages.


title: Learning low dimensional predictive representations

abstract: Predictive state representations (PSRs) have recently been proposed as an alternative to partially observable Markov decision processes (POMDPs) for representing the state of a dynamical system (Littman et al., 2001). We present a learning algorithm that learns a PSR from observational data. Our algorithm produces a variant of PSRs called transformed predictive state representations (TPSRs). We provide an efficient principal-components-based algorithm for learning a TPSR, and show that TPSRs can perform well in comparison to Hidden Markov Models learned with Baum-Welch in a real world robot tracking task for low dimensional representations and long prediction horizons.


title: A novel dual-polarized double-ridged horn antenna for wideband applications

abstract: Dual-polarized antenna is widely used in communication systems such as ECM and DF systems. In this paper a novel doubleridged horn antenna with dual polarizations is introduced for frequency range of 8–18 GHz. Common double ridged horn antennas have single polarization over the operating frequency. We have used five layers polarizer to provide dual polarizations performance of the doubleridged horn antenna. In order to achieve dual polarizations the strips width, strips spacing and layers distances are optimized. It is worth mentioning that the corresponding VSWR of the antenna during the optimization process should be maintain below a certain value (VSWR< 2). Simulation results show that the proposed antenna yields dual polarizations performance and low VSWR over the operating frequency. We have used CST software for antenna simulation which is based on the finite integral technique.


title: Microfabricated Microneedles: A Novel Approach to Transdermal Drug Delivery

abstract: Although modern biotechnology has produced extremely sophisticated and potent drugs, many of these compounds cannot be effectively delivered using current drug delivery techniques (e.g., pills and injections). Transdermal delivery is an attractive alternative, but it is limited by the extremely low permeability of skin. Because the primary barrier to transport is located in the upper 10-15 micron of skin and nerves are found only in deeper tissue, we used a reactive ion etching microfabrication technique to make arrays of microneedles long enough to cross the permeability barrier but not so long that they stimulate nerves, thereby potentially causing no pain. These microneedle arrays could be easily inserted into skin without breaking and were shown to increase permeability of human skin in vitro to a model drug, calcein, by up to 4 orders of magnitude. Limited tests on human subjects indicated that microneedles were reported as painless. This paper describes the first published study on the use of microfabricated microneedles to enhance drug delivery across skin.


title: Answering graph pattern query using incremental views

abstract: In recent years, modeling data in graph structure became evident and effective for processing in some of the prominent application areas like social analytics, health care analytics, scientific analytics etc. The key sources of massively scaled data are petascale simulations, experimental devices, the internet and scientific applications. Hence, there is a demand for adapt graph querying techniques on such large graph data. Graphs are pervasive in large scale analytics, facing the new challenge such as data size, heterogeneity, uncertainty and data quality. Traditional graph pattern matching approaches are based on inherent isomorphism and simulation. In real life applications, many of them either fail to capture structural or semantic or both similarities. Moreover, in real life applications data graphs constantly bear modifications with small updates. In response to these challenges, we propose a notion that revises traditional notions to characterize graph pattern matching using graph views. Based on this characterization, we outline an approach that efficiently solve graph pattern queries problem over both static and dynamic real life data graphs.


title: Leave-One-Out Kernel Optimization for Shadow Detection and Removal

abstract: The objective of this work is to detect shadows in images. We pose this as the problem of labeling image regions, where each region corresponds to a group of superpixels. To predict the label of each region, we train a kernel Least-Squares SVM for separating shadow and non-shadow regions. The parameters of the kernel and the classifier are jointly learned to minimize the leave-one-out cross validation error. Optimizing the leave-one-out cross validation error is typically difficult, but it can be done efficiently in our framework. Experiments on two challenging shadow datasets, UCF and UIUC, show that our region classifier outperforms more complex methods. We further enhance the performance of the region classifier by embedding it in an MRF framework and adding pairwise contextual cues. This leads to a method that significantly outperforms the state-of-the-art.


title: A Finite-State Turn-Taking Model for Spoken Dialog Systems

abstract: This paper introduces the Finite-State Turn-Taking Machine (FSTTM), a new model to control the turn-taking behavior of conversational agents. Based on a non-deterministic finite-state machine, the FSTTM uses a cost matrix and decision theoretic principles to select a turn-taking action at any time. We show how the model can be applied to the problem of end-of-turn detection. Evaluation results on a deployed spoken dialog system show that the FSTTM provides significantly higher responsiveness than previous approaches.


title: Dual-polarized log.-periodic antenna on a conical MID substrate

abstract: This paper presents the design of a dual-polarized log.-periodic four arm antenna bent on a conical MID substrate. The bending of a planar structure in free space is highlighted and the resulting effects on the input impedance and radiation characteristic are analyzed. The subsequent design of the UWB compliant prototype is introduced. An adequate agreement between simulated and measured performance can be observed. The antenna provides an input matching of better than −8 dB over a frequency range from 3GHz to 9GHz. The antenna pattern is characterized by a radiation with two linear, orthogonal polarizations and a front-to-back ratio of 6 dB. A maximum gain of 5.6 dBi is achieved at 5.5GHz. The pattern correlation coefficients confirm the suitability of this structure for diversity and MIMO applications. The overall antenna diameter and height are 50mm and 24mm respectively. It could therefore be used as a surface mounted or ceiling antenna in buildings, vehicles or aircrafts for communication systems.


title: A Pattern Reconfigurable U-Slot Antenna and Its Applications in MIMO Systems

abstract: A new compact pattern reconfigurable U-slot antenna is presented. The antenna consists of a U-slot patch and eight shorting posts. Each edge of the square patch is connected to two shorting posts via PIN diodes. By switching between the different states of the PIN diodes, the proposed antenna can operate in either monopolar patch or normal patch mode in similar frequency ranges. Therefore, its radiation pattern can be switched between conical and boresight patterns electrically. In addition, the plane with the maximum power level of the conical pattern can be changed between two orthogonal planes. Owing to a novel design of the switch geometry, the antenna does not need dc bias lines. The measured overlapping impedance bandwidth (|S11| <; -10 dB) of the two modes is 6.6% with a center frequency of 5.32 GHz. The measured radiation patterns agree well with simulated results. The antennas are incorporated in a 2 × 2 multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system to demonstrate the improvement in system capacity. In the real-time MIMO-OFDM channel measurement, it is shown that compared to omnidirectional antennas, the pattern reconfigurable antennas can enhance the system capacity, with 17% improvement in a line-of-sight (LOS) scenario and 12% in a non-LOS (NLOS) scenario at a signal-to-noise ratio (SNR) of 10 dB.


title: Condition directed Multi-domain Adversarial Learning for Loop Closure Detection

abstract: Loop closure detection (LCD) is the key module in appearance based simultaneously localization and mapping (SLAM). However, in the real life, the appearance of visual inputs are usually affected by the illumination changes and texture changes under different weather conditions. Traditional methods in LCD usually rely on handcraft features, however, such methods are unable to capture the common descriptions under different weather conditions, such as rainy, foggy and sunny. Furthermore, traditional handcraft features could not capture the highly level understanding for the local scenes. In this paper, we proposed a novel condition directed multi-domain adversarial learning method, where we use the weather condition as the direction for feature inference. Based on the generative adversarial networks (GANs) and a classification networks, the proposed method could extract the high-level weather-invariant features directly from the raw data. The only labels required here are the weather condition of each visual input. Experiments are conducted in the GTAV game simulator, which could generated lifelike outdoor scenes under different weather conditions. The performance of LCD results shows that our method outperforms the state-of-arts significantly.


The Insights into Car Hacking EEL 6931 – Advanced Ethical Hacking Group Project – Spring 2014


title: Advanced quaternion forward kinematics algorithm including overview of different methods for robot kinematics

abstract: Formulation of proper and efficient algorithms for robot kinematics is essential for the analysis and design of serial manipulators. Kinematic modeling of manipulators is most often performed in Cartesian space. However, due to disadvantages of most widely used mathematical constructs for description of orientation such as Euler angles and rotational matrices, a need for unambiguous, compact, singularity free, computationally efficient method for representing rotational information is imposed. As a solution, unit quaternions are proposed and kinematic modeling in dual quaternion space arose. In this paper, an overview of spatial descriptions and transformations that can be applied together within these spaces in order to solve kinematic problems is presented. Special emphasis is on a different mathematical formalisms used to represent attitude of a rigid body such as rotation matrix, Euler angles, axis-angle representation, unit quaternions, and their mutual relation. Benefits of kinematic modeling in quaternion space are presented. New direct kinematics algorithm in dual quaternion space pertaining to a particular manipulator is given. These constructs and algorithms are demonstrated on the human centrifuge as 3 DoF robot manipulator.


title: WiBeD2: A communication aid for deaf and dumb

abstract: WiBeD2: As the name suggests it is a Wireless BEll for Deaf and Dumb. Generally bells are meant for calling the people in a house, but how can a deaf or a dumb respond to bells. A Deaf cannot hear to the bell and a dumb cannot respond to the calling bell. Therefore, we have brought forward a simple low cost feasible system to help the deaf and the dumb to respond effectively to the person knocking at the door. As the deaf cannot hear, we have recommended a device with vibrators attached to their body in the form of some wrist band or a watch. Similarly, for the dumb, we have developed a kind of bell with speakers which will playback some voice to the person standing at the door as an acknowledgement message. When the person presses the calling bell switch the vibrators on the hand of a deaf/dumb vibrates and s/he can also send an acknowledgment message to the person. The entire system is wireless and can work effectively within a radius of 50m i.e. the range of RF Transmitter.


NEURO-FUZZY TECHNIQUES IN FDI SYSTEM FOR SUGAR FACTORY ACTUATORS


title: Evidence that transcranial direct current stimulation (tDCS) generates little-to-no reliable neurophysiologic effect beyond MEP amplitude modulation in healthy human subjects: A systematic review

abstract: Abstract Background Transcranial direct current stimulation (tDCS) is a form of neuromodulation that is increasingly being utilized to examine and modify a number of cognitive and behavioral measures. The theoretical mechanisms by which tDCS generates these changes are predicated upon a rather large neurophysiological literature. However, a robust systematic review of this neurophysiological data has not yet been undertaken. Methods tDCS data in healthy adults (18–50) from every neurophysiological outcome measure reported by at least two different research groups in the literature was collected. When possible, data was pooled and quantitatively analyzed to assess significance. When pooling was not possible, data was qualitatively compared to assess reliability. Results Of the 30 neurophysiological outcome measures reported by at least two different research groups, tDCS was found to have a reliable effect on only one: MEP amplitude. Interestingly, the magnitude of this effect has been significantly decreasing over the last 14 years. Conclusion Our systematic review does not support the idea that tDCS has a reliable neurophysiological effect beyond MEP amplitude modulation – though important limitations of this review (and conclusion) are discussed. This work raises questions concerning the mechanistic foundations and general efficacy of this device – the implications of which extend to the steadily increasing tDCS psychological literature.


title: Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems

abstract: While variational methods have been among the most powerful tools for solving linear inverse problems in imaging, deep (convolutional) neural networks have recently taken the lead in many challenging benchmarks. A remaining drawback of deep learning approaches is their requirement for an expensive retraining whenever the specific problem, the noise level, noise type, or desired measure of fidelity changes. On the contrary, variational methods have a plug-and-play nature as they usually consist of separate data fidelity and regularization terms. In this paper we study the possibility of replacing the proximal operator of the regularization used in many convex energy minimization algorithms by a denoising neural network. The latter therefore serves as an implicit natural image prior, while the data term can still be chosen independently. Using a fixed denoising neural network in exemplary problems of image deconvolution with different blur kernels and image demosaicking, we obtain state-of-the-art reconstruction results. These indicate the high generalizability of our approach and a reduction of the need for problem-specific training. Additionally, we discuss novel results on the analysis of possible optimization algorithms to incorporate the network into, as well as the choices of algorithm parameters and their relation to the noise level the neural network is trained on.


title: Inter-dependent CNNs for joint scene and object recognition

abstract: In this paper, we consider two inter-dependent deep networks, where one network taps into the other, to perform two challenging cognitive vision tasks - scene classification and object recognition jointly. Recently, convolutional neural networks have shown promising results in each of these tasks. However, as scene and objects are interrelated, the performance of both of these recognition tasks can be further improved by exploiting dependencies between scene and object deep networks. The advantages of considering the inter-dependency between these networks are the following: 1. improvement of accuracy in both scene and object classification, and 2. significant reduction of computational cost in object detection. In order to formulate our framework, we employ two convolutional neural networks (CNNs), scene-CNN and object-CNN. We utilize scene-CNN to generate object proposals which indicate the probable object locations in an image. Object proposals found in the process are semantically relevant to the object. More importantly, the number of object proposals is fewer in amount when compared to other existing methods which reduces the computational cost significantly. Thereafter, in scene classification, we train three hidden layers in order to combine the global (image as a whole) and local features (object information in an image). Features extracted from CNN architecture along with the features processed from object-CNN are combined to perform efficient classification. We perform rigorous experiments on five datasets to demonstrate that our proposed framework outperforms other state-of-the-art methods in classifying scenes as well as recognizing objects.


title: Cooperative game theoretic approach using fuzzy Q-learning for detecting and preventing intrusions in wireless sensor networks

abstract: Abstract Owing to the distributed nature of denial-of-service attacks, it is tremendously challenging to detect such malicious behavior using traditional intrusion detection systems in Wireless Sensor Networks (WSNs). In the current paper, a game theoretic method is introduced, namely cooperative Game-based Fuzzy Q-learning (G-FQL). G-FQL adopts a combination of both the game theoretic approach and the fuzzy Q-learning algorithm in WSNs. It is a three-player strategy game consisting of sink nodes, a base station, and an attacker. The game performs at any time a victim node in the network receives a flooding packet as a DDoS attack beyond a specific alarm event threshold in WSN. The proposed model implements cooperative defense counter-attack scenarios for the sink node and the base station to operate as rational decision-maker players through a game theory strategy. In order to evaluate the performance of the proposed model, the Low Energy Adaptive Clustering Hierarchy (LEACH) was simulated using NS-2 simulator. The model is subsequently compared against other existing soft computing methods, such as fuzzy logic controller, Q-learning, and fuzzy Q-learning, in terms of detection accuracy, counter-defense, network lifetime and energy consumption, to demonstrate its efficiency and viability. The proposed model׳s attack detection and defense accuracy yield a greater improvement than existing above-mentioned machine learning methods. In contrast to the Markovian game theoretic, the proposed model operates better in terms of successful defense rate.


title: Composite Connectors for Composing Software Components

abstract: In a component-based system, connectors are used to compose components. Connectors should have a semantics that makes them simple to construct and use. At the same time, their semantics should be rich enough to endow them with desirable properties such as genericity, compositionality and reusability. For connector construction, compositionality would be particularly useful, since it would facilitate systematic construction. In this paper we describe a hierarchical approach to connector definition and construction that allows connectors to be defined and constructed from sub-connectors. These composite connectors are indeed generic, compositional and reusable. They behave like design patterns, and provide powerful composition connectors.


title: Incorporating Non-Local Information Into Information Extraction Systems By Gibbs Sampling

abstract: Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, a simple Monte Carlo method used to perform approximate inference in factored probabilistic models. By using simulated annealing in place of Viterbi decoding in sequence models such as HMMs, CMMs, and CRFs, it is possible to incorporate non-local structure while preserving tractable inference. We use this technique to augment an existing CRF-based information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints. This technique results in an error reduction of up to 9% over state-of-the-art systems on two established information extraction tasks.


title: The NSA and Edward Snowden: surveillance in the 21st century

abstract: This paper examines the case and background of Edward Snowden, the history and purpose of the National Security Agency (NSA), legality and American public opinion and its aftermath.


title: Architectural Support for Software-Defined Metadata Processing

abstract: Optimized hardware for propagating and checking software-programmable metadata tags can achieve low runtime overhead. We generalize prior work on hardware tagging by considering a generic architecture that supports software-defined policies over metadata of arbitrary size and complexity; we introduce several novel microarchitectural optimizations that keep the overhead of this rich processing low. Our model thus achieves the efficiency of previous hardware-based approaches with the flexibility of the software-based ones. We demonstrate this by using it to enforce four diverse safety and security policies---spatial and temporal memory safety, taint tracking, control-flow integrity, and code and data separation---plus a composite policy that enforces all of them simultaneously. Experiments on SPEC CPU2006 benchmarks with a PUMP-enhanced RISC processor show modest impact on runtime (typically under 10%) and power ceiling (less than 10%), in return for some increase in energy usage (typically under 60%) and area for on-chip memory structures (110%).


title: Adaptation and Evaluation of Recommendations for Short-term Shopping Goals

abstract: An essential characteristic in many e-commerce settings is that website visitors can have very specific short-term shopping goals when they browse the site. Relying solely on long-term user models that are pre-trained on historical data can therefore be insufficient for a suitable next-basket recommendation. Simple "real-time" recommendation approaches based, e.g., on unpersonalized co-occurrence patterns, on the other hand do not fully exploit the available information about the user's long-term preference profile. In this work, we aim to explore and quantify the effectiveness of using and combining long-term models and short-term adaptation strategies. We conducted an empirical evaluation based on a novel evaluation design and two real-world datasets. The results indicate that maintaining short-term content-based and recency-based profiles of the visitors can lead to significant accuracy increases. At the same time, the experiments show that the choice of the algorithm for learning the long-term preferences is particularly important at the beginning of new shopping sessions.


title: Accelerating Machine Learning Kernel in Hadoop Using FPGAs

abstract: Big data applications share inherent characteristics that are fundamentally different from traditional desktop CPU, parallel and web service applications. They rely on deep machine learning and data mining applications. A recent trend for big data analytics is to provide heterogeneous architectures to allow support for hardware specialization to construct the right processing engine for analytics applications. However, these specialized heterogeneous architectures require extensive exploration of design aspects to find the optimal architecture in terms of performance and cost. % Considering the time dedicated to create such specialized architectures, a model that estimates the potential speedup achievable through offloading various parts of the algorithm to specialized hardware would be necessary. This paper analyzes how offloading computational intensive kernels of machine learning algorithms to a heterogeneous CPU+FPGA platform enhances the performance. We use the latest Xilinx Signboards for implementation and result analysis. Furthermore, we perform a comprehensive analysis of communication and computation overheads such as data I/O movements, and calling several standard libraries that can not be offloaded to the accelerator to understand how the speedup of each application will contribute to its overall execution in an end-to-end Hadoop MapReduce environment.


title: TTS evaluation: Double-ended objective quality measures

abstract: We address the problem of TTS speech quality evaluation and propose a double-ended objective measure in the form of average spectral distortion between time-aligned reference and synthesized speech, where the reference signal is made available as the speech of the text input to the TTS spoken by the same speaker as the unit-database. We detail the time-aligned spectral distortion measure calculated via dynamic time-warping and apply this measure for comparison of the effectiveness of 5 different automatic segmentation techniques for annotating the unit-database for two Indian languages, Tamil and Kannada. We also show that the proposed measure yields more meaningful scores than the PESQ measure which is popular for codec evaluation, but likely to be less suited for TTS evaluation due to the lack of a rigorous time-alignment as is done in the proposed spectral distortion measure here.