title: Zero-latency tapping: using hover information to predict touch locations and eliminate touchdown latency

abstract: A method of reducing the perceived latency of touch input by employing a model to predict touch events before the finger reaches the touch surface is proposed. A corpus of 3D finger movement data was collected, and used to develop a model capable of three granularities at different phases of movement: initial direction, final touch location, time of touchdown. The model is validated for target distances >= 25.5cm, and demonstrated to have a mean accuracy of 1.05cm 128ms before the user touches the screen. Preference study of different levels of latency reveals a strong preference for unperceived latency touchdown feedback. A form of 'soft' feedback, as well as other uses for this prediction to improve performance, is proposed.


title: Crop Pests Prediction Method Using Regression and Machine Learning Technology: Survey☆

abstract: Abstract This paper describes current trends in the prediction of crop pests using machine learning technology. With the advent of data mining, the field of agriculture is also focused on it. Currently, various studies, domestic and overseas, are under progress using machine learning technology, and cases of its utilization are increasing. This paper classifies and introduces SVM (Support Vector Machine), Multiple Linear Regression, Neural Network, and Bayesian Network based techniques, and describes some cases of their utilization.


title: Improving on Q&A Recurrent Neural Networks Using Noun-Tagging

abstract: Often, more time is spent on finding a model that works well, rather than tuning the model and working directly with the dataset. Our research began as an attempt to improve upon a simple Recurrent Neural Network for answering"simple"first-order questions (QA-RNN), developed by Ferhan Ture and Oliver Jojic, from Comcast Labs, using the SimpleQuestions dataset. Their baseline model, a bidirectional, 2-layer LSTM RNN and a GRU RNN, have accuracies of 0.94 and 0.90, for entity detection and relation prediction, respectively. We fine tuned these models by doing substantial hyper-parameter tuning, getting resulting accuracies of 0.70 and 0.80, for entity detection and relation prediction, respectively. An accuracy of 0.984 was obtained on entity detection using a 1-layer LSTM, where preprocessing was done by removing all words not part of a noun chunk from the question. 100% of the dataset was available for relation prediction, but only 20% of the dataset, was available for entity detection, which we believe to be much of the reason for our initial difficulties in replicating their result, despite the fact we were able to improve on their entity detection results.


title: Understanding Virtual RealityInterface, Application, and Design

abstract: Understanding Virtual Reality arrives at a time when the technologies behind virtual reality have advanced to the point that it is possible to develop and deploy meaningful, productive virtual reality applications. The aim of this thorough, accessible exploration is to help you take advantage of this moment, equipping you with the understanding needed to identify and prepare for ways VR can be used in your field, whatever your field may be. ::: ::: ::: By approaching VR as a communications medium, the authors have created a resource that will remain relevant even as the underlying technologies evolve. You get a history of VR, along with a good look at systems currently in use. However, the focus remains squarely on the application of VR and the many issues that arise in the application design and implementation, including hardware requirements, system integration, interaction techniques, and usability. This book also counters both exaggerated claims for VR and the view that would reduce it to entertainment, citing dozens of real-world examples from many different fields and presenting (in a series of appendices) four in-depth application case studies. ::: ::: * Substantive, illuminating coverage designed for technical and business readers and well-suited to the classroom. ::: * Examines VR's constituent technologies, drawn from visualization, representation, graphics, human-computer interaction, and other fields, and explains how they are being united in cohesive VR systems. ::: * Via a companion Web site, provides additional case studies, tutorials, instructional materials, and a link to an open-source VR programming system. ::: ::: Table of Contents ::: ::: Foreword ::: Preface ::: ::: Part I - What is Virtual Reality? ::: Chapter 1: Introduction to Virtual Reality - What it is and Where it Comes From ::: Chapter 2: VR the Medium ::: ::: Part II - Virtual Reality Systems ::: Chapter 3: Interface to the Virtual World -- Input ::: Chapter 4: Interface to the Virtual World -- Output ::: Chapter 5: Rendering a Virtual World ::: Chapter 6: Interacting with a Virtual World ::: Chapter 7: Virtual Reality Experience ::: Chapter 8: Experience Design: Applying VR to a Problem ::: Chapter 9: What Dreams May Come: The Future of VR ::: ::: Appendices ::: A: NICE educational application (EVL) ::: B: Crumbs visualization application (NCSA) ::: C: Aircraft wiring application (Boeing, Inc.) ::: D: Placeholder artistic application (Interval Research)


title: Geometry of the Fluorides, Oxofluorides, Hydrides, and Methanides of Vanadium(V), Chromium(VI), and Molybdenum(VI): Understanding the Geometry of Non-VSEPR Molecules in Terms of Core Distortion

abstract: This paper describes a study of the topology of the electron density and its Laplacian for the molecules VF5, VMe5, VH5, CrF6, CrMe6, CrOF4, MoOF4, CrO2F2, CrO2F42- and CrOF5- all of which, except VF5, CrF6, and CrOF5- have a non-VSEPR geometry. It is shown that in each case the interaction of the ligands with the metal atom core causes it to distort to a nonspherical shape. In particular, the Laplacian of the electron density reveals the formation of local concentrations of electron density in the outer shell of the core, which have a definite geometrical arrangement such as four in a tetrahedral arrangement or five in a square pyramidal or trigonal bipyramidal and six in an octahedral arrangement. Ligands that are predominately covalently bonded are found opposite regions of charge depletion between these core charge concentrations. In VH5, VMe5, CrOF4, and MoOF4, these core charge concentrations have a square pyramidal arrangement, and the regions of charge depletions have the corresponding inverse squ...


title: Peer Influence on Attrition in Massively Open Online Courses

abstract: In this work, we investigate the role of relational bonds in keeping students engaged in online courses. Specifically, we quantify the manner in which students who demonstrate similar behavior patterns influence each other’s commitment to the course through their interaction with them either explicitly or implicitly. To this end, we design five alternative operationalizations of relationship bonds, which together allow us to infer a scaled measure of relationship between pairs of students. Using this, we construct three variables, namely number of significant bonds, number of significant bonds with people who have dropped out in the previous week, and number of such bonds with people who have dropped in the current week. Using a survival analysis, we are able to measure the prediction strength of these variables with respect to dropout at each time point. Results indicate that higher numbers of significant bonds predicts lower rates of dropout; while loss of significant bonds is associated with higher rates of dropout.


title: Global joint models for coreference resolution and named entity classification

abstract: on Lineal Entera (ILP) Abstract: In this paper, we combine models for coreference, anaphoricity and named entity classication


title: CERA-TVR: A framework for interactive high-quality teravoxel volume visualization on standard PCs

abstract: We present a volume visualization framework that allows high-quality interactive rendering of teravoxel scale volume data sets on standard PCs. It is based on a progressive multi-resolution out-of-core volume rendering approach. Data loading is controlled by rays that are cast through the volume data set. The tree structure representing the multi-resolution hierarchy is traversed and updated directly by the GPU during ray casting. Consequently, occluded and empty data is never loaded or rendered. The framework is able to render dense, anisotropic, scalar volume data on regular grids with all common rendering modes.


PERT - perfect random tree ensembles


title: A compact broadband hybrid ridged SIW and GCPW coupler

abstract: In this paper, a novel hybrid ridged substrate integrated waveguide (RSIW) and ground coplanar waveguide (GCPW) structure coupler is presented. In this coupler, the RSIW and GCPW are arranged back to back to realize high-integration. Such hybrid coupler combines the broadband advantage of RSIW and the flexibility of GCPW. According to the simulated results, it can operate at 28 GHz and achieve a relative bandwidth of 17%.


Crowdsourcing Critical Success Factor Model Strategies to harness the collective intelligence of the crowd Ankit


title: Lingering Effects: Stereotype Threat Hurts More than You Think

abstract: Starting with the first realization that negative stereotypes can cause people to underperform in the stereotyped domain, an impressive body of work has documented the robust and wide-ranging nature of stereotype and social identity threat. In this article, we look beyond the stereotyped ability domain and present evidence that coping with stereotypes and prejudice can linger, affecting a broad range of behaviors even in areas unrelated to the stigmatized ability. This stereotype threat spillover occurs because coping with negative stereotypes and prejudice leaves self-control resources depleted for challenges that arise later, even in unrelated situations. We suggest a number of different ways that individuals can empower and hopefully inoculate themselves against spillover including shifting appraisals and adopting positive coping strategies. We also discuss societal changes, encouraging governments and other organizations to enact policy that will reduce the prevalence of stereotyping and cultivate feelings of intrinsic motivation to reduce prejudice.


title: Action classification using a discriminative multilevel HDP-HMM

abstract: We classify human actions occurring in depth image sequences using features based on skeletal joint positions. The action classes are represented by a multi-level Hierarchical Dirichlet Process-Hidden Markov Model (HDP-HMM). The non-parametric HDP-HMM allows the inference of hidden states automatically from training data. The model parameters of each class are formulated as transformations from a shared base distribution, thus promoting the use of unlabelled examples during training and borrowing information across action classes. Further, the parameters are learnt in a discriminative way. We use a normalized gamma process representation of HDP and margin based likelihood functions for this purpose. We sample parameters from the complex posterior distribution induced by our discriminative likelihood function using elliptical slice sampling. Experiments with two different datasets show that action class models learnt using our technique produce good classification results.


title: Generalized channel inversion methods for multiuser MIMO systems

abstract: Block diagonalization (BD) is a well-known precoding method in multiuser multi-input multi-output (MIMO) broadcast channels. This scheme can be considered as a extension of the zero-forcing (ZF) channel inversion to the case where each receiver is equipped with multiple antennas. One of the limitation of the BD is that the sum rate does not grow linearly with the number of users and transmit antennas at low and medium signal-to-noise ratio regime, since the complete suppression of multi-user interference is achieved at the expense of noise enhancement. Also it performs poorly under imperfect channel state information. In this paper, we propose a generalized minimum mean-squared error (MMSE) channel inversion algorithm for users with multiple antennas to overcome the drawbacks of the BD for multiuser MIMO systems. We first introduce a generalized ZF channel inversion algorithm as a new approach of the conventional BD. Applying this idea to the MMSE channel inversion for identifying orthonormal basis vectors of the precoder, and employing the MMSE criterion for finding its combining matrix, the proposed scheme increases the signal-to-interference-plus-noise ratio at each user's receiver. Simulation results confirm that the proposed scheme exhibits a linear growth of the sum rate, as opposed to the BD scheme. For block fading channels with four transmit antennas, the proposed scheme provides a 3 dB gain over the conventional BD scheme at 1% frame error rate. Also, we present a modified precoding method for systems with channel estimation errors and show that the proposed algorithm is robust to channel estimation errors.


title: Design and Implementation of a Network Security Model using Static VLAN and AAA Server

abstract: This paper gives the details of typical network security model using static VLAN technology and TACACS+ AAA server, also discusses security issues, and their risks, caused by different types of vulnerability, threat, attacks and misconfiguration for VLAN technology, and how prevent it and protect the network by using TACACS+ AAA server, switch and router.


Newborn Genetic Screening : A Proposal of New Ethical Guidelines


title: A Tale of Two Topologies: Exploring Convertible Data Center Network Architectures with Flat-tree

abstract: This paper promotes convertible data center network architectures, which can dynamically change the network topology to combine the benefits of multiple architectures. We propose the flat-tree prototype architecture as the first step to realize this concept. Flat-tree can be implemented as a Clos network and later be converted to approximate random graphs of different sizes, thus achieving both Clos-like implementation simplicity and random-graph-like transmission performance. We present the detailed design for the network architecture and the control system. Simulations using real data center traffic traces show that flat-tree is able to optimize various workloads with different topology options. We implement an example flat-tree network on a 20-switch 24-server testbed. The traffic reaches the maximal throughput in 2.5s after a topology change, proving the feasibility of converting topology at run time. The network core bandwidth is increased by 27.6% just by converting the topology from Clos to approximate random graph. This improvement can be translated into acceleration of applications as we observe reduced communication time in Spark and Hadoop jobs.


title: Evaluation of mesh routing protocols for wireless community networks

abstract: In recent years, we have witnessed the exponential growth of wireless community networks as a response to the clear necessity of Internet access for participation in society. For wireless mesh networks that can scale up to thousands of nodes, which are owned and managed in a decentralized way, it is imperative for their survival to provide the network with self-management mechanisms that reduce the requirements of human intervention and technological knowledge in the operation of a community network. In this paper, we focus on one important self-management mechanism, routing, and we study the scalability, performance, and stability of three proactive mesh routing protocols: BMX6, OLSR, and Babel. We study different metrics on an emulation framework and on the W-ILab.T testbed at iMinds, making the most of the two worlds. Emulation allows us to have more control over the topology and more systematically repeat the experiments, whereas a testbed provides a realistic wireless medium and more reliable measurements, especially in terms of interference and CPU consumption. Results show the relative merits, costs, and limitations of the three protocols.


title: Perceptions of the School Psychological Environment and Early Adolescents' Psychological and Behavioral Functioning in School: The Mediating Role of Goals and Belonging

abstract: In a sample of 296 8th-grade middle school students, the authors examined the role of personal achievement goals and feelings of school belonging in mediating the relation between perceptions of the school psychological environment and school-related beliefs, affect, and achievement. Sequential regression analyses indicated that perceiving a task goal structure in middle school was positively related to academic self-efficacy and that this relation was mediated through personal task goals. Perceiving an ability goal structure was related to academic self-consciousness and this relation was mediated through personal relative ability goals. Perceiving positive teacher-student relationships predicted positive school-related affect and this relation was mediated through feelings of school belonging. Feelings of academic efficacy and school belonging in turn were positively related to final-semester academic grades. Results are discussed in relation to current middle school reform efforts. During the early adolescent years, middle schools play an important role in facilitating or inhibiting successful adolescent development (Carnegie Council on Adolescent Development, 1989). Schools potentially can provide early adolescents with opportunities to develop their intellectual capacities, to experience a sense of competence and belonging, and to interact with supportive, nonparental adults. Unfortunately, just when adolescents are particularly in need of these opportunities, the middle-school learning environment often fails to provide them (Carnegie Council on Adolescent Development, 1989; Eccles & Midgley, 1989). For instance, at a time when adolescents are known to be sensitive about how they appear to others, middle schools emphasize relative ability and social comparison in learning situations (Midgley, Anderman, & Hicks, 1995); and at a time when adolescents are particularly in need of supportive relationships with adults outside the home, the quality of relationships with teachers is less than optimal (Midgley, Feldlaufer, & Eccles, 1989). Understanding how particular


WME 3 . 0 : An Enhanced and Validated Lexicon of Medical Concepts


title: Combining Facility-Location Approaches for Public Schools Expansion

abstract: This study proposes determining optimal locations for expanding a higher education system by using populational and social criteria. With this aim, this work evaluates single objective location models in determining the optimal distribution of higher education facilities in Amazonas State, Brazil. Three optimization options are evaluated and made available to decision makers: 1) prioritize cities with a lower United Nation Human Development Index; 2) prioritize cities with a higher population, 3) favor both criteria. Also, the location must equalize student distribution between the regions of Amazonas State. With this aim, three discrete location models were evaluated: p-center model, p-median model, and p-dispersion model. The location models were designed using a Genetic Algorithm metaheuristic. A state-of-art implementation of the Genetic Algorithm that optimizes the solution and converge time was used. The expansions proposed here present lower mean values of United Nation Human Development Index compared with 0.619 from the existing distribution of campuses. The best results were obtained with the p-median model.


title: A Large Scale Study to Understand the Relation between Twitter and Financial Market

abstract: Twitter has transformed from an online platform for communication to a mega content generator for all kinds of topics. The topic of posts (or tweets) generated on Twitter cover diverse topics of interests. For example, politics, public personalities, events and corporate organizations. In this study, we analysed if tweets related to corporate organizations can predict the financial market. Our analysis is performed on a Twitter dataset which spans over more than two years and is related to more than 1723 stocks. To the best of our knowledge this amount of large and big dataset, specifically in terms of time length and stocks has not been studied in the past. Our quantitative analysis shows that on an average correlation between tweets and stocks' volume being traded is 0.29 on an average. In this empirical study, we also evaluated the stocks from Yahoo's sectors' categories perspective to find which sectors are more correlated than others. We also looked at the influence of important users, that is users with a large number of followers. Our results ally with the fact that important users contribute more in influencing the market [3] rather than the wisdom of the crowd [6]. The verification of our results using statistical approaches on a large dataset can be seen as a contribution in the area of financial studies using data from online platforms.


title: Towards optimization of design decisions for embedded systems by exploiting dependency relationships

abstract: Design decisions for the development of embedded systems demand for a consideration of complex goals and constraints. In order to reduce risks and optimize the design, model-based approaches are needed for an explicit representation of goals and constraints as well as for early assessments. The explicit representation of dependencies is required to make design decisions in a reasonable way. Existing works do not sufficiently support the mapping between problem space and solution space together with a consideration of technological constraints. In this paper the Goal Solution Scheme approach developed for software architectural design is extended for the development of embedded systems considering specific needs for flexible decisions late in the development process. The adaptation of the approach for the relevant goals and development steps of embedded systems is illustrated by its application in a case study of a complex embedded system project.


title: Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection

abstract: Motivation: Owing to its importance in both basic research (such as molecular evolution and protein attribute prediction) and practical application (such as timely modeling the 3D structures of proteins targeted for drug development), protein remote homology detection has attracted a great deal of interest. It is intriguing to note that the profilebased approach is promising and holds high potential in this regard. To further improve protein remote homology detection, a key step is how to find an optimal means to extract the evolutionary information into the profiles. Results: Here, we propose a novel approach, the so-called profilebased protein representation, to extract the evolutionary information via the frequency profiles. The latter can be calculated from the multiple sequence alignments generated by PSI-BLAST. Three top performing sequence-based kernels (SVM-Ngram, SVM-pairwise and SVM-LA) were combined with the profile-based protein representation. Various tests were conducted on a SCOP benchmark dataset that contains 54 families and 23 superfamilies. The results showed that the new approach is promising, and can obviously improve the performance of the three kernels. Furthermore, our approach can also provide useful insights for studying the features of proteins in various families. It has not escaped our notice that the current approach can be easily combined with the existing sequence-based methods so as to improve their performance as well. Availability and implementation: For users’ convenience, the source code of generating the profile-based proteins and the multiple kernel learning was also provided at


title: Interactive Recommender Systems: Tutorial

abstract: In this tutorial we will explore the field of interactive video and music recommendations and their application at Netflix and Spotify. Interactive recommender systems enable the user to steer the received recommendations in the desired direction through explicit interaction with the system. In this tutorial, we outline the various aspects that are crucial for a smooth and effective user experience. In particular, we present our insights from several A/B tests. The tutorial will help researchers and practitioners in the RecSys community to gain a deeper understanding of the challenges related to the application of recommender systems in the online video and music entertainment business.


title: Text summarization using concept graph and BabelNet knowledge base

abstract: With rapid increasing text information, the need for a computer system to processing and analyzing this information are felt. One of the systems that exist in analyzing and processing of text is a text summarization in which large volume of text is summarized based on different algorithms. In this paper, by using BabelNet knowledge base and its concept graph, a system for summarizing text is offered. In proposed approach, concepts of words by using BabelNet knowledge base are extracted and concept graphs are produced and sentences, according to concepts and resulting graph are rated. Therefore, these rating concepts are utilized in final summarization. Also, a replication control approach is proposed in a way that selected concepts in each state are punished and this causes to produce summaries with less redundancy. To compare and evaluate the performance of the proposed method, DUC2004 is used and ROUGE used as evaluation metric. The proposed method by compared to other methods produces summaries with more quality and fewer redundancies.


title: Elimination of Dead-time in PWM Controlled Inverters

abstract: A dead-time elimination method is presented in this paper for PWM controlled inverters. In comparison to using expensive current sensors, this method precisely determines the load current direction by detecting the operating conditions of active devices and their anti-parallel diodes. A low-cost diode-conducting detection circuit is developed to measure the operating status of the anti-parallel diode. In comparison with complicated compensators, this method features with simple logic and flexible implementation. This method significantly reduces the output distortion and regains the RMS value. The principle of proposed dead-time elimination method is described. Simulation and experimental results are given to demonstrate the validity and features of the dead-time elimination method.


title: AN EXPERIMENT FOR MEASURING BUSINESS PROCESS MATURITY WITH DIFFERENT MATURITY MODELS

abstract: Since the 2000s, Business Process Management (BPM) has evolved into a comprehensively studied discipline that goes beyond the boundaries of particular business processes. By also affecting enterprise-wide capabilities (such as an organisational culture and structure that support a processoriented way of working), BPM can now correctly be called Business Process Orientation (BPO). Meanwhile, various maturity models have been developed to help organisations adopt a processoriented way of working based on step-by-step best practices. The present article reports on a case study in which the process portfolio of an organisation is assessed by different maturity models that each cover a different set of process-oriented capabilities. The purpose is to reflect on how business process maturity is currently measured, and to explore relevant considerations for practitioners, scholars and maturity model designers. Therefore, we investigate a possible difference in maturity scores that are obtained based on model-related characteristics (e.g. capabilities, scale and calculation technique) and respondent-related characteristics (e.g. organisational function). For instance, based on an experimental design, the original maturity scores are recalculated for different maturity scales and different calculation techniques. Follow-up research can broaden our experiment from multiple maturity models in a single case to multiple maturity models in multiple cases.


title: Research on a High-Efficiency LED Driving Circuit Based on Buck Topology

abstract: A high efficiency LED(Light Emitting Diode) driver based on Buck converter, which could operate under a wide AC input voltage range(85V~265V) and drive a series of high power LEDs, is presented in this paper. The operation principles, power loss factors of the LED driver in this study are analyzed and discussed in detail and some effective ways to improve efficiency are proposed through system design considerations. To verify the feasibility, a laboratory prototype is also designed and tested for an LED lamp which consists of 16 LUMILEDS LEDs in series. Experimental results show that a high efficiency of 92% at I0=350mA can be achieved and the studied driver might be practical for driving high power LEDs. In the last, the overall efficiency over 90% is gained through some experiments under variable input and output voltages and verifies the validity of the designed driver.


title: Multi-Tier Architecture

abstract: Definition A Multi-tier Architecture is a software architecture in which different software components, organized in tiers (layers), provide dedicated functionality. The most common occurrence of a multi-tier architecture is a three-tier architecture consisting of a data management tier (mostly encompassing one or several database servers), an application tier (business logic) and a client tier (interface functionality). Novel deployments come with additional tiers. Web information systems, for instance, encompass a dedicated tier (web tier) between client and application layer. Conceptually, a multi-tier architecture results from a repeated application of the client/server paradigm. A component in one of the middle tiers is client to the next lower tier and at the same time acts as server to the next higher tier.


title: Establishment of two Epstein-Barr virus negative Burkitt cell lines from a patient with AIDS and B-cell lymphoma

abstract: To analyze the pathogenesis of B-cell lymphomas in patients with acquired immunodeficiency syndrome (AIDS), we studied two cell lines, Es I and Es III, established from one such lymphoma for the presence of sequences of the Epstein-Barr virus (EBV) and the human immunodeficiency virus [HIV; lymphadenopathy-associated virus (LAV/HTLV-III)] as well as for the presence of cytogenetic abnormalities and monoclonal rearrangements of immunoglobulin and T-cell receptor genes. Both cell lines expressed the same IgM, kappa phenotype as the original lymphoma. The karyotype of Es I was 46, XY, t(8;14), 2 p+, inv (6p), 17p-, and the cells of Es III had an additional i(7q). Immunoglobulin gene studies demonstrated the identical monoclonal rearrangements in both cell lines. Neither EBV nor HIV sequences were detectable in the malignant B cells at the genomic level, leading to the conclusion that mechanisms other than transformation by EBV or HIV may have contributed to the B-cell lymphoma in this patient and possibly also to the generally increased frequency in patients with AIDS.


title: Into the deep end: Incorporating a global health governance and diplomacy experience in graduate public health training

abstract: Global health governance benefits from participants well-versed in the realities of international policy-making. Consequently, educational programmes must establish more opportunities for students to engage in global health policy development. This paper examines a unique global health governance and diplomacy practicum programme at the University of Southern California, designed for Master of Public Health candidates. Through the programme, students act as official non-governmental delegates to the World Health Assembly in Geneva, Switzerland through organisational partnerships. Students and collaborating organisations were asked to complete an online post-participation survey examining the perceived quality of the experience. Through the survey, students indicated reinforcement of classroom learning, continued or heightened interest in global health policy and enthusiasm in recommending the programme to other students. Organisations perceived students to be adequately prepared and indicated their contin...


title: A New Sub-Pixel Detector for X-Corners in Camera Calibration Targets

abstract: X-corner patterns are most widely used in camera calibration. In this paper, we propose a new sub-pixel detector for X-corners, which is much simpler than the traditional sub-pixel detection algorithm. In this new algorithm, the pixel position of X-corner is firstly detected by a new operator. Then a second order Taylor polynomial describing the local intensity profile around the corner is educed. The sub-pixel position of X-corner can be determined directly by calculating the saddle point of this intensity profile. Neither preliminary intensity interpolation nor quadratic fitting of the intensity surface is necessary, which greatly reduces the computation load of the detection process. Furthermore, computer simulation results indicate that our new algorithm is slightly more accurate than the traditional algorithm.


title: Characteristic Analysis for CPW′s on Thin Substrate

abstract: To avoid the “crowding phenomenon” in conformal mapping,approximate analytical expressions of characteristic parameter for CPW′s on a thin substrate have been derived.Combining these expressions with the partial capacitance method,we can calculate the characteristic parameters for CPW′s with a thin insulative film between the conductors and substrate.A practical example is analyzed and agrees well with the test data.Finally the effect of an insulated thin film between the conductors and Si substrate on loss factor of microwave transmission lines is analyzed.


A Markov Random Field Model for Image Segmentation of Rice Planthopper in Rice Fields


title: Objective evaluation of the visual acuity in human eyes

abstract: Traditionally, the quality of the human vision is evaluated by a subjective test in which the examiner asks the patient to read a series of characters of different sizes, located at a certain distance of the patient. Typically, we need to ensure a subtended angle of vision of 5 minutes, which implies an object of 8.8 mm high located at 6 meters (normal or 20/20 visual acuity). These characters constitute what is known as the Snellen chart, universally used to evaluate the spatial resolution of the human eyes. The mentioned process of identification of characters is carried out by means of the eye - brain system, giving an evaluation of the subjective visual performance. In this work we consider the eye as an isolated image-forming system, and show that it is possible to isolate the function of the eye from that of the brain in this process. By knowing the impulse response of the eye´s system we can obtain, in advance, the image of the Snellen chart simultaneously. From this information, we obtain the objective performance of the eye as the optical system under test. This type of results might help to detect anomalous situations of the human vision, like the so called "cerebral myopia".


title: Word Epoch Disambiguation: Finding How Words Change Over Time

abstract: In this paper we introduce the novel task of "word epoch disambiguation," defined as the problem of identifying changes in word usage over time. Through experiments run using word usage examples collected from three major periods of time (1800, 1900, 2000), we show that the task is feasible, and significant differences can be observed between occurrences of words in different periods of time.


title: The Infancy of the Human Brain

abstract: The human infant brain is the only known machine able to master a natural language and develop explicit, symbolic, and communicable systems of knowledge that deliver rich representations of the external world. With the emergence of noninvasive brain imaging, we now have access to the unique neural machinery underlying these early accomplishments. After describing early cognitive capacities in the domains of language and number, we review recent findings that underline the strong continuity between human infants' and adults' neural architecture, with notably early hemispheric asymmetries and involvement of frontal areas. Studies of the strengths and limitations of early learning, and of brain dynamics in relation to regional maturational stages, promise to yield a better understanding of the sources of human cognitive achievements.


A Qualitative Investigation of Bank Employee Experiences of Information Security and Phishing


title: A Self-Regulated Interval Type-2 Neuro-Fuzzy Inference System for Handling Nonstationarities in EEG Signals for BCI

abstract: This paper addresses the key problems of nonstationarity and influence of artifacts in electroencephalogram (EEG)-based brain–computer-interface (BCI) systems. The nonstationary nature of EEG data arises due to the physiological/instrumental differences in intra/intersession of the data generation process. This paper proposes a robust common spatial pattern feature extraction algorithm (RoCSP) to overcome the effects of artifacts and a self-regulated interval type-2 neuro-fuzzy inference system (SRIT2NFIS) to handle this inherent nonstationarity. Combined together, this approach is referred to as RoCSP-SRIT2NFIS. The RoCSP algorithm provides better features than the CSP algorithm by excluding those trials that are affected by the artifacts. SRIT2NFIS uses the features generated by the RoCSP algorithm as input and handles the nonstationarity as an uncertainty using the interval type-2 fuzzy sets in the antecedent of fuzzy rules. A self-regulatory learning mechanism is used to evolve the structure automatically and learn the parameters of the network. A regularized projection-based learning algorithm and a modified rule addition criterion are also proposed to improve the generalization performance of SRIT2NFIS. Using benchmark datasets, performance evaluation has been carried out, and the results indicate that, compared with other existing algorithms, RoCSP-SRIT2NFIS produces higher classification accuracy of 3–5% in simple tasks like left-right classification and 6–8% in complex tasks like foot–tongue classification. In addition, a statistical analysis of the performance results indicates that RoCSP-SRIT2NFIS performs better and is more suitable for an efficient BCI.


title: Virtual clay: a real-time sculpting system with haptic toolkits

abstract: In this paper we systematically develop a novel, interactive sculpting framework founded upon subdivision solids and physics-based modeling. In contrast with popular subdivision surfaces, subdivision solids have the unique advantage offering both the boundary representation and the interior material of a solid object. We unify the geometry of subdivision solids with the principle of physicsbased models and formulate dynamic subdivision solids. Dynamic subdivision solids respond to applied forces in a natural and predictive manner and give the user the illusion of manipulating semielastic virtual clay. We have developed a real-time sculpting system that provides the user with a wide array of intuitive sculpting toolkits. The flexibility of the subdivision solid approach allows users to easily modify the topology of sculpted objects, while the inherent physical properties are exploited to provide a natural interface for direct, force-based deformation. More importantly, our sculpting system is equipped with natural, haptic-based interaction to provide the user with a realistic sculpting experience. CR Categories: I.3.5 [Computer Graphics]: Physics-based modeling; I.3.3 [Computer Graphics]: Modeling packages; I.3.6 [Computer Graphics]: Interaction techniques; H.5.2 [User Interfaces]: Haptic I/O; I.3.7 [Computer Graphics]: Virtual reality; I.3.7 [Computer Graphics]: Animation.


title: Lightweight DDoS flooding attack detection using NOX/OpenFlow

abstract: Distributed denial-of-service (DDoS) attacks became one of the main Internet security problems over the last decade, threatening public web servers in particular. Although the DDoS mechanism is widely understood, its detection is a very hard task because of the similarities between normal traffic and useless packets, sent by compromised hosts to their victims. This work presents a lightweight method for DDoS attack detection based on traffic flow features, in which the extraction of such information is made with a very low overhead compared to traditional approaches. This is possible due to the use of the NOX platform which provides a programmatic interface to facilitate the handling of switch information. Other major contributions include the high rate of detection and very low rate of false alarms obtained by flow analysis using Self Organizing Maps.


title: Ultra-Wideband Bandpass Filter With a Notch-Band Using Stub-Loaded Ring Resonator

abstract: A quadruple-mode ultra-wideband (UWB) bandpass filter with a notch-band and wide upper stopband is presented in this letter. The UWB filter is realized using a ring resonator which is loaded with two sets of stubs, i.e., one is loaded in the center and two little stubs at the symmetrical side locations. The loaded stubs are used to create transmission zeros in upper stopband thus high modes are suppressed greatly. Moreover, two identical via-loaded ring resonators are inserted within the stub-loaded ring resonator, to create a notch-band around 5-6 GHz to avoid WLAN signals. Two reflection poles appear in the notch-band, resulting in a switchable bandwidth of the notch-band. Finally, a UWB filter with a notch-band is simulated and fabricated, and measured results provide an experimental validation of predicted performance of the filter.


title: Cognitive reserve in ageing and Alzheimer's disease

abstract: Summary The concept of cognitive reserve provides an explanation for differences between individuals in susceptibility to age-related brain changes or pathology related to Alzheimer's disease, whereby some people can tolerate more of these changes than others and maintain function. Epidemiological studies suggest that lifelong experiences, including educational and occupational attainment, and leisure activities in later life, can increase this reserve. For example, the risk of developing Alzheimer's disease is reduced in individuals with higher educational or occupational attainment. Reserve can conveniently be divided into two types: brain reserve, which refers to differences in the brain structure that may increase tolerance to pathology, and cognitive reserve, which refers to differences between individuals in how tasks are performed that might enable some people to be more resilient to brain changes than others. Greater understanding of the concept of cognitive reserve could lead to interventions to slow cognitive ageing or reduce the risk of dementia.


title: A joint segmentation and classification framework for sentence level sentiment classification

abstract: In this paper, we propose a joint segmentation and classification framework for sentence-level sentiment classification. It is widely recognized that phrasal information is crucial for sentiment classification. However, existing sentiment classification algorithms typically split a sentence as a word sequence, which does not effectively handle the inconsistent sentiment polarity between a phrase and the words it contains, such as {"not bad," "bad"} and {"a great deal of," "great"}. We address this issue by developing a joint framework for sentence-level sentiment classification. It simultaneously generates useful segmentations and predicts sentence-level polarity based on the segmentation results. Specifically, we develop a candidate generation model to produce segmentation candidates of a sentence; a segmentation ranking model to score the usefulness of a segmentation candidate for sentiment classification; and a classification model for predicting the sentiment polarity of a segmentation. We train the joint framework directly from sentences annotated with only sentiment polarity, without using any syntactic or sentiment annotations in segmentation level. We conduct experiments for sentiment classification on two benchmark datasets: a tweet dataset and a review dataset. Experimental results show that: 1) our method performs comparably with state-of-the-art methods on both datasets; 2) joint modeling segmentation and classification outperforms pipelined baseline methods in various experimental settings.


title: On entropy generation and dissipation of kinetic energy in high-resolution shock-capturing schemes

abstract: This paper addresses entropy generation and the corresponding dissipation of kinetic energy associated with high-resolution, shock-capturing (Godunov) methods. Analytical formulae are derived for the rate of increase of entropy given arbitrary jumps in primitive variables at a cell interface. It is demonstrated that for general continuously varying flows the inherent numerical entropy increase of Godunov methods is not proportional to the velocity jump cubed as is commonly assumed, but it is proportional to the velocity jump squared. Furthermore, the dissipation of kinetic energy is directly linked to temperature multiplied by change in entropy at low Mach numbers. The kinetic energy dissipation rate is shown to be proportional to the velocity jump squared and the speed of sound. The leading order dissipation rate associated with jumps in pressure, density and shear waves is detailed and further shown that at low Mach number it is the dissipation due to the perpendicular velocity jumps which dominates. This explains directly the poor performance of Godunov methods at low Mach numbers. The analysis is also applied to high-order accurate methods in space and time and all analytical results are validated with simple numerical experiments.


title: Collaborative Indexing and Knowledge Exploration: A Social Learning Model

abstract: A computational social learning model shows how meaning is constructed, shared, and learned through social tags contributed by Web users as they perform exploratory search.


title: Optical add-drop filters based on photonic crystal ring resonators.

abstract: We present here an optical add-drop filter (ADF) design based on ultra-compact photonic crystal ring resonators (PCRRs). The normalized transmission spectra for single-ring and dual-ring configurations have been investigated by using the two-dimensional finite-difference time-domain (FDTD) technique in a square lattice dielectric-rod photonic-crystal structure. With the introduction of four scatterers at the corners of quasisquare- ring PCRR, high wavelength selectivity and close to 100% drop efficiency can be obtained. Both backward- and forward-dropping were achieved by controlling the coupling efficiency between two PCRR rings for resonant modes with different symmetry. The resonant-mode quality factor Q and the wavelength tunability were also analyzed, opening opportunities for PCRRs as ultra-compact filters, optical add-drop multiplexers, electrooptical N x N switches and electrooptical modulators.


title: Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks

abstract: With the revival of neural networks, many studies try to adapt powerful sequential neural models, ie Recurrent Neural Networks (RNN), to sequential recommendation. RNN-based networks encode historical interaction records into a hidden state vector. Although the state vector is able to encode sequential dependency, it still has limited representation power in capturing complicated user preference. It is difficult to capture fine-grained user preference from the interaction sequence. Furthermore, the latent vector representation is usually hard to understand and explain. To address these issues, in this paper, we propose a novel knowledge enhanced sequential recommender. Our model integrates the RNN-based networks with Key-Value Memory Network (KV-MN). We further incorporate knowledge base (KB) information to enhance the semantic representation of KV-MN. RNN-based models are good at capturing sequential user preference, while knowledge-enhanced KV-MNs are good at capturing attribute-level user preference. By using a hybrid of RNNs and KV-MNs, it is expected to be endowed with both benefits from these two components. The sequential preference representation together with the attribute-level preference representation are combined as the final representation of user preference. With the incorporation of KB information, our model is also highly interpretable. To our knowledge, it is the first time that sequential recommender is integrated with external memories by leveraging large-scale KB information.


title: Logo Detection in Document Images Based on Boundary Extension of Feature Rectangles

abstract: A new method of logo detection in document images is proposed in this paper. It is based on the boundary extension of feature rectangles of which the definition is also given in this paper. This novel method takes advantage of a layout assumption that logos have background (white spaces) surrounding it in a document. Compared with other logo detection methods, this new method has the advantage that it is independent on logo shapes and very fast. After the logo candidates are detected, a simple decision tree is used to reduce the false positive from the logo candidate pool. We have tested our method on a public image database involving logos. Experiments show that our method is more precise and robust than the previous methods and is well qualified as an effective assistance in document retrieval.


title: Fast detection and recognition of QR codes in high-resolution images

abstract: This paper deals with detection and recognition of matrix codes, such as the QR codes, in high-resolution images of real-world scenes. The goal is to provide a detector capable of operation in real time even on high-resolution images (several megapixels). We present an efficient algorithm for detection of possible occurrences of the codes. This algorithm is characterized by a very low false negative rate and a reasonable false alarm rate. The results of our algorithm are to be followed by an accurate detection/recognition algorithm. We propose to use a recent matrix code detection and recognition algorithm based on Hough transform, because it can reuse some information computed by our new pre-detection algorithm and thus a further reduce of computational demands can be achieved. Since there are no publicly available annotated datasets for evaluation of this kind of algorithm, we collected a number of images and annotated them; these images will be made publicly available to allow for a proper comparison. Our algorithm was evaluated on this dataset and the results are reported in the paper.


title: Instant messaging and the future of language

abstract: The writing style commonly used in IMing, texting, and other forms of computer-mediated communication need not spell the end of normative language.


title: Artemis: sequence visualization and annotation

abstract: Summary: Artemis is a DNA sequence visualization and annotation tool that allows the results of any analysis or sets of analyses to be viewed in the context of the sequence and its six-frame translation. Artemis is especially useful in analysing the compact genomes of bacteria, archaea and lower eukaryotes, and will cope with sequences of any size from small genes to whole genomes. It is implemented in Java, and can be run on any suitable platform. Sequences and annotation can be read and written directly in EMBL, GenBank and GFF format. Availability: Artemis is available under the GNU General Public License from http:// www.sanger.ac.uk/ Software/ Artemis


title: Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering

abstract: This paper introduces an automated skill acquisition framework in reinforcement learning which involves identifying a hierarchical description of the given task in terms of abstract states and extended actions between abstract states. Identifying such structures present in the task provides ways to simplify and speed up reinforcement learning algorithms. These structures also help to generalize such algorithms over multiple tasks without relearning policies from scratch. We use ideas from dynamical systems to find metastable regions in the state space and associate them with abstract states. The spectral clustering algorithm PCCA+ is used to identify suitable abstractions aligned to the underlying structure. Skills are defined in terms of the sequence of actions that lead to transitions between such abstract states. The connectivity information from PCCA+ is used to generate these skills or options. These skills are independent of the learning task and can be efficiently reused across a variety of tasks defined over the same model. This approach works well even without the exact model of the environment by using sample trajectories to construct an approximate estimate. We also present our approach to scaling the skill acquisition framework to complex tasks with large state spaces for which we perform state aggregation using the representation learned from an action conditional video prediction network and use the skill acquisition framework on the aggregated state space.


Dynamic Optimization Of Bioreactors: A Review


title: Learning 6D Object Pose Estimation Using 3D Object Coordinates

abstract: This work addresses the problem of estimating the 6D Pose of specific objects from a single RGB-D image. We present a flexible approach that can deal with generic objects, both textured and texture-less. The key new concept is a learned, intermediate representation in form of a dense 3D object coordinate labelling paired with a dense class labelling. We are able to show that for a common dataset with texture-less objects, where template-based techniques are suitable and state of the art, our approach is slightly superior in terms of accuracy. We also demonstrate the benefits of our approach, compared to template-based techniques, in terms of robustness with respect to varying lighting conditions. Towards this end, we contribute a new ground truth dataset with 10k images of 20 objects captured each under three different lighting conditions. We demonstrate that our approach scales well with the number of objects and has capabilities to run fast.


title: Inside-out: Reflecting on your inner state

abstract: We present a novel sensor system and interface that enables an individual to capture and reflect on their daily activities. The wearable system gathers both physiological responses and visual context through the use of a wearable biosensor and a cell-phone camera, respectively. Collected information is locally stored and securely transmitted to a novel digital mirror. Through interactive visualizations, this interface allows users to reflect not only on their outer appearance but also on their inner physiological responses to daily activities. Finally, we illustrate how combining a time record of physiological data with visual contextual information can improve and enhance the experience of reflection in many real-life scenarios, and serve as a useful tool for behavior science and therapy.


Text Mining: Promises And Challenges


title: Design and fabrication of a lightweight additive-manufactured Ka-band horn antenna array

abstract: In this paper a 2×2 Ka-band lightweight horn antenna array demonstrator is designed and fabricated by means of an additive manufacturing process optimized for complex RF, microwave and mm-wave antenna components. The proposed waveguide feeding network aims to exploit the flexibility offered by this novel fabrication technique and can be easily translated to antenna arrays with more elements. The electromagnetic performance of the featured structure is validated by means of full-wave numerical simulations. The horn antenna array is fabricated and under experimental RF characterization.


title: A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling

abstract: Robust automated organ segmentation is a prerequisite for computer-aided diagnosis (CAD), quantitative imaging analysis and surgical assistance. For high-variability organs such as the pancreas, previous approaches report undesirably low accuracies. We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading superpixels. There are four stages: 1) decomposing CT slice images as a set of disjoint boundary-preserving superpixels; 2) computing pancreas class probability maps via dense patch labeling; 3) classifying superpixels by pooling both intensity and probability features to form empirical statistics in cascaded random forest frameworks; and 4) simple connectivity based post-processing. The dense image patch labeling are conducted by: efficient random forest classifier on image histogram, location and texture features; and more expensive (but with better specificity) deep convolutional neural network classification on larger image windows (with more spatial contexts). Evaluation of the approach is performed on a database of 80 manually segmented CT volumes in six-fold cross-validation (CV). Our achieved results are comparable, or better than the state-of-the-art methods (evaluated by"leave-one-patient-out"), with Dice 70.7% and Jaccard 57.9%. The computational efficiency has been drastically improved in the order of 6~8 minutes, comparing with others of ~10 hours per case. Finally, we implement a multi-atlas label fusion (MALF) approach for pancreas segmentation using the same datasets. Under six-fold CV, our bottom-up segmentation method significantly outperforms its MALF counterpart: (70.7 +/- 13.0%) versus (52.5 +/- 20.8%) in Dice. Deep CNN patch labeling confidences offer more numerical stability, reflected by smaller standard deviations.


title: Model-Driven Engineering Approaches to Design Space Exploration

abstract: During the design and deployment of increasingly complex distributed embedded systems, engineers are challenged by a plethora of design choices. This often results in infeasible or sub-optimal solu ...


title: Harnessing Semantics for Answer Sentence Retrieval

abstract: Finding answer passages from the Web is a challenging task. One major difficulty is to retrieve sentences that may not have many terms in common with the question. In this paper, we experiment with two semantic approaches for finding non-factoid answers using a learning-to-rank retrieval setting. We show that using semantic representations learned from external resources such as Wikipedia or Google News may substantially improve the quality of top-ranked retrieved answers.


title: A survey on multi-agent reinforcement learning: Coordination problems

abstract: Learning in multiagent system needs to solve the complexity of the task, so multiagent reinforcement learning has been focused on theoretical research and various applications. In multiagent reinforcement learning, agents can be compete or cooperate to accomplish the goal. For cooperative multiagent reinforcement learning(CMRL), agents have to coordinate with other agents. Therefore, coordination problems in CMRL are getting more and more important because of increasing the number of agents and actions. There are several algorithms dealt with cooperative multiagent reinforcement learning using stochastic games, coordinated graph, and so on. These algorithms have some assumptions to coordinate each other, however assumptions are not consistent with characteristics of the multiagent system. In this paper, we provide a survey on coordination problems in cooperative multiagent reinforcement learning, and propose new approach to solve coordination problems.


title: Scene Graph Generation by Iterative Message Passing

abstract: Understanding a visual scene goes beyond recognizing individual objects in isolation. Relationships between objects also constitute rich semantic information about the scene. In this work, we explicitly model the objects and their relationships using scene graphs, a visually-grounded graphical structure of an image. We propose a novel end-to-end model that generates such structured scene representation from an input image. Our key insight is that the graph generation problem can be formulated as message passing between the primal node graph and its dual edge graph. Our joint inference model can take advantage of contextual cues to make better predictions on objects and their relationships. The experiments show that our model significantly outperforms previous methods on the Visual Genome dataset as well as support relation inference in NYU Depth V2 dataset.


title: Microcontroller Based Reprogrammable Digital Door Lock Security System by Using Keypad & GSM/CDMA Technology

abstract: Now a day's Security has been a prime concern in the home or office management. Digital door lock security system provides security and safety to house or office owners, belongings, assets from being damaged by external agent or undesired strangers. We have used a new technology, incoming number verification system which gives more protection for controlling & security system. As Conventional security system does not use any password, there is a chance to hack or break the system. In this regard we used a desire mobile number without verification which doesn't allow the door to be opend.This system is composed of the microcontroller based by using matrix keypad & GSM/CDMA network. The microcontroller based digital door lock security system is an access control system that allows only authorized persons to access restricted area. The password is stored in PROM so that we can change it any time. The system has a matrix keypad .When anyone enter the code in the matrix keypad, microcontroller verify the codes. If that code is correct the device will operate and the door will be open. But if someone enter wrong code a red signal will be shown which means that the entered code is wrong.GSM/CDMA module can be used to operate the device, when anyone make a call from his mobile the receiving device which is set in main circuitry will receive the call. If the call is from desired number then operate the device and the door will be unlocked. An IPS circuit can be used for giving backup in case of emergency when there is a power failure. Keywords: DTMF encoder and decoder, Global System for Mobile Communication, Micro-Controller, Mobile phone, and Matrix Keypad.


title: Physical activity in the United States measured by accelerometer.

abstract: ABSTRACTPurpose:To describe physical activity levels of children (6-11 yr), adolescents (12-19 yr), and adults (20+ yr), using objective data obtained with accelerometers from a representative sample of the U.S. population.Methods:These results were obtained from the 2003-2004 National Health and Nu


title: Do Parents Influence the Sexual Orientation of Their Children? Findings from a Longitudinal Study of Lesbian Families," Developmental Psychology

abstract: Findings are presented of a longitudinal study of the sexual orientation of adults who had been raised as children in lesbian families. Twenty-five children of lesbian mothers and a control group of 21 children of heterosexual single mothers were first seen at age 9.5 years on average, and again at age 23.5 years on average. Standardized interviews were used to obtain data on sexual orientation from the young adults in the follow-up study, and on family characteristics and children's gender role behavior from the mothers and their children in the initial study. Although those from lesbian families were more likely to explore same-sex relationships, particularly if their childhood family environment was characterized by an openness and acceptance of lesbian and gay relationships, the large majority of children who grew up in lesbian families identified as heterosexual. Opinion varies among biological and psychological theorists regarding the extent to which it is possible for parents to influence the sexual orientation of their children. From a purely biological perspective, parents should make little difference. In contrast, psychoanalytic theorists believe that relationships with parents in childhood are central to the development of sexual orientation in adult life. Research on adults raised in lesbian families provides an opportunity to test theoretical assumptions about the role of parents in their children's sexual orientation; if parents are influential in whether their children grow up to be heterosexual, lesbian, or gay, then it might be expected that lesbian parents would be more likely than heterosexual parents to have lesbian daughters and gay sons. With the exception of Gottman's (1990) investigation of adult daughters of lesbian mothers in which actual sexual behavior was not reported, research on lesbian families has focused on children rather than adults, and sexual orientation has not been assessed (Golombok, Spencer, & Rutter, 1983; Green, Mandel, Hotvedt, Gray,SHoeffer, 1981; Kirkpatrick, Smith, & Roy,


title: Defining a Digital Earth System

abstract: In a 1998 speech before the California Science Center in Los Angeles, then US VicePresident Al Gore called for a global undertaking to build a multi-faceted computing system for education and research, which he termed “Digital Earth.” The vision was that of a system providing access to what is known about the planet and its inhabitants’ activities – currently and for any time in history – via responses to queries and exploratory tools. Furthermore, it would accommodate modeling extensions for predicting future conditions. Organized efforts towards realizing that vision have diminished significantly since 2001, but progress on key requisites has been made. As the 10 year anniversary of that influential speech approaches, we re-examine it from the perspective of a systematic software design process and find the envisioned system to be in many respects inclusive of concepts of distributed geolibraries and digital atlases. A preliminary definition for a particular digital earth system as: “a comprehensive, distributed geographic information and knowledge organization system,” is offered and discussed. We suggest that resumption of earlier design and focused research efforts can and should be undertaken, and may prove a worthwhile “Grand Challenge” for the GIScience community.


title: Quality through flow and immersion: gamifying crowdsourced relevance assessments

abstract: Crowdsourcing is a market of steadily-growing importance upon which both academia and industry increasingly rely. However, this market appears to be inherently infested with a significant share of malicious workers who try to maximise their profits through cheating or sloppiness. This serves to undermine the very merits crowdsourcing has come to represent. Based on previous experience as well as psychological insights, we propose the use of a game in order to attract and retain a larger share of reliable workers to frequently-requested crowdsourcing tasks such as relevance assessments and clustering. In a large-scale comparative study conducted using recent TREC data, we investigate the performance of traditional HIT designs and a game-based alternative that is able to achieve high quality at significantly lower pay rates, facing fewer malicious submissions.


title: Depression Duration But Not Age Predicts Hippocampal Volume Loss in Medically Healthy Women with Recurrent Major Depression

abstract: This study takes advantage of continuing advances in the precision of magnetic resonance imaging (MRI) to quantify hippocampal volumes in a series of human subjects with a history of depression compared with controls. We sought to test the hypothesis that both age and duration of past depression would be inversely and independently correlated with hippocampal volume. A sample of 24 women ranging in age from 23 to 86 years with a history of recurrent major depression, but no medical comorbidity, and 24 case-matched controls underwent MRI scanning. Subjects with a history of depression (post-depressed) had smaller hippocampal volumes bilaterally than controls. Post-depressives also had smaller amygdala core nuclei volumes, and these volumes correlated with hippocampal volumes. In addition, post-depressives scored lower in verbal memory, a neuropsychological measure of hippocampal function, suggesting that the volume loss was related to an aspect of cognitive functioning. In contrast, there was no difference in overall brain size or general intellectual performance. Contrary to our initial hypothesis, there was no significant correlation between hippocampal volume and age in either post-depressive or control subjects, whereas there was a significant correlation with total lifetime duration of depression. This suggests that repeated stress during recurrent depressive episodes may result in cumulative hippocampal injury as reflected in volume loss.


title: An evaluation of the robustness of existing supervised machine learning approaches to the classification of emotions in speech

abstract: In this study, the robustness of approaches to the automatic classification of emotions in speech is addressed. Among the many types of emotions that exist, two groups of emotions are considered, adult-to-adult acted vocal expressions of common types of emotions like happiness, sadness, and anger and adult-to-infant vocal expressions of affective intents also known as ''motherese''. Specifically, we estimate the generalization capability of two feature extraction approaches, the approach developed for Sony's robotic dog AIBO (AIBO) and the segment-based approach (SBA) of [Shami, M., Kamel, M., 2005. Segment-based approach to the recognition of emotions in speech. In: IEEE Conf. on Multimedia and Expo (ICME05), Amsterdam, The Netherlands]. Three machine learning approaches are considered, K-nearest neighbors (KNN), Support vector machines (SVM) and Ada-boosted decision trees and four emotional speech databases are employed, Kismet, BabyEars, Danish, and Berlin databases. Single corpus experiments show that the considered feature extraction approaches AIBO and SBA are competitive on the four databases considered and that their performance is comparable with previously published results on the same databases. The best choice of machine learning algorithm seems to depend on the feature extraction approach considered. Multi-corpus experiments are performed with the Kismet-BabyEars and the Danish-Berlin database pairs that contain parallel emotional classes. Automatic clustering of the emotional classes in the database pairs shows that the patterns behind the emotions in the Kismet-BabyEars pair are less database dependent than the patterns in the Danish-Berlin pair. In off-corpus testing the classifier is trained on one database of a pair and tested on the other. This provides little improvement over baseline classification. In integrated corpus testing, however, the classifier is machine learned on the merged databases and this gives promisingly robust classification results, which suggest that emotional corpora with parallel emotion classes recorded under different conditions can be used to construct a single classifier capable of distinguishing the emotions in the merged corpora. Such a classifier is more robust than a classifier learned on a single corpus as it can recognize more varied expressions of the same emotional classes. These findings suggest that the existing approaches for the classification of emotions in speech are efficient enough to handle larger amounts of training data without any reduction in classification accuracy.


title: When Firmware Modifications Attack: A Case Study of Embedded Exploitation

abstract: The ability to update firmware is a feature that is found in nearly all modern embedded systems. We demonstrate how this feature can be exploited to allow attackers to inject malicious firmware modifications into vulnerable embedded devices. We discuss techniques for exploiting such vulnerable functionality and the implementation of a proof of concept printer malware capable of network reconnaissance, data exfiltration and propagation to general purpose computers and other embedded device types. We present a case study of the HP-RFU (Remote Firmware Update) LaserJet printer firmware modification vulnerability, which allows arbitrary injection of malware into the printer’s firmware via standard printed documents. We show vulnerable population data gathered by continuously tracking all publicly accessible printers discovered through an exhaustive scan of IPv4 space. To show that firmware update signing is not the panacea of embedded defense, we present an analysis of known vulnerabilities found in third-party libraries in 373 LaserJet firmware images. Prior research has shown that the design flaws and vulnerabilities presented in this paper are found in other modern embedded systems. Thus, the exploitation techniques presented in this paper can be generalized to compromise other embedded systems. Keywords-Embedded system exploitation; Firmware modification attack; Embedded system rootkit; HP-RFU vulnerability.


title: A scalable two-stage approach for a class of dimensionality reduction techniques

abstract: Dimensionality reduction plays an important role in many data mining applications involving high-dimensional data. Many existing dimensionality reduction techniques can be formulated as a generalized eigenvalue problem, which does not scale to large-size problems. Prior work transforms the generalized eigenvalue problem into an equivalent least squares formulation, which can then be solved efficiently. However, the equivalence relationship only holds under certain assumptions without regularization, which severely limits their applicability in practice. In this paper, an efficient two-stage approach is proposed to solve a class of dimensionality reduction techniques, including Canonical Correlation Analysis, Orthonormal Partial Least Squares, linear Discriminant Analysis, and Hypergraph Spectral Learning. The proposed two-stage approach scales linearly in terms of both the sample size and data dimensionality. The main contributions of this paper include (1) we rigorously establish the equivalence relationship between the proposed two-stage approach and the original formulation without any assumption; and (2) we show that the equivalence relationship still holds in the regularization setting. We have conducted extensive experiments using both synthetic and real-world data sets. Our experimental results confirm the equivalence relationship established in this paper. Results also demonstrate the scalability of the proposed two-stage approach.


title: A Broadband ±45° Dual-Polarized Multidipole Antenna Fed by Capacitive Coupling

abstract: A broadband ±45° dual-polarized multidipole antenna is proposed in this communication. A pair of big dipoles and a pair of small ones are printed on a square substrate. The large dipoles mainly dominate the low operating frequency of the antenna, while the small dipoles work at the high one. A pair of H-shaped coupler is introduced to feed the antenna through capacitive coupling with loop radiator dipoles, and a rectangular box-shaped reflector is used for keeping the radiation patterns over the operating frequency. The antenna achieved a wide impedance bandwidth of about 56.6% for 8.1 dBi is achieved for both ports. A stable radiation pattern with 3 dB beamwidth 62°±6° at E-plane and 75°±3° at H-plane for port 1 and 60° ±4° at E-plane and 71° ±5° at H-plane for port 2 is obtained. The antenna is manufactured and tested. The measured results of the antenna are in well agreement with the simulation ones. Design details of the proposed antenna are presented and discussed.


title: Performance comparison of extendible hashing and linear hashing techniques

abstract: Based on seven assumptions, the following comparison factors are used to compare the performance of linear hashing with extendible hashing: 1. storage utilization; 2. average unsuccessful search cost; 3. average successful search cost; 4. split cost; 5. insertion cost; 6. number of overflow buckets. The simulation is conducted with the bucket sizes of 10, 20, and 50 for both hashing techniques. In order to observe their average behavior, the simulation uses 50,000 keys which have been generated randomly. According to our simulation results, extendible hashing has an advantage of 5% over linear hashing in terms of storage utilization. Successful search, unsuccessful search, and insertions are less costly in linear hashing. However, linear hashing requires a large overflow space to handle the overflow records. Simulation shows that approximately 10% of the space should be marked as overflow space in linear hashing. Directory size is a serious bottleneck in extendible hashing. Based on the simulation results, the authors recommend linear hashing when main memory is at a premium.


A Fast Approach for Semantic Similar Short Texts Retrieval


title: Microcontroller Based Heart Rate Monitor

abstract: This paper describes the development of a heart rate monitor system based on a microcontroller. It offers the advantage of portability over tape-based recording systems. The paper explains how a single-chip microcontroller can be used to analyse heart beat rate signals in real-time. In addition, it allows doctors to get the heart beat rate file of the patient by e- mail every twenty four hours. It can also be used to control patients or athletic person over a long period. The system reads, stores and analyses the heart beat rate signals repetitively in real-time. The hardware and software design are oriented towards a single-chip microcontroller-based system, hence minimizing the size. The important feature of this paper is the use of zero crossing algorithm to compute heart rate. It then processes on real-time the information to determine some heart diseases.


A Graphical Filter/Flow Representation of Boolean Queries: A Prototype Implementation and Evaluation


title: Obesity, subliminal perception and inhibition: Neuromodulation of the prefrontal cortex.

abstract: Abstract The prefrontal cortex (PFC) plays a central role in food choice, and may be partly dysfunctional in obesity – a condition linked to altered attention and inhibition processes, particularly in response to food-related stimuli. We investigated the role of the PFC in subliminal visual processing and response inhibition to food pictures using anodal, cathodal, or sham transcranial direct current stimulation (tDCS) in a sample of 53 normal weight, overweight, or obese participants. Subliminal processing was measured with a breaking continuous flash suppression task (bCFS), and inhibition with a Go/No-Go task. BMI was included in the analyses as a continuous predictor. Higher BMI was associated with prolonged subliminal processing for both food and nonfood pictures in the bCFS task, and with longer RTs in food Go trials in the Go/No-Go task. Therefore, higher BMI was associated with an attentional bias for food images during supraliminal, but not subliminal visual processing. Moreover, anodal tDCS resulted in shorter detection times in the bCFS task, especially in participants with higher BMI. We conclude that anodal tDCS affects subliminal perception and attentional processes, and speculate that these effects may explain previous reports of reduced craving and food intake after anodal tDCS.


title: Design and stability analysis of CNTFET based SRAM Cell

abstract: Static Random Access Memory (SRAM) is one of the most crucial and critical memory devices used in today's technological environment. The continuous scaling of CMOS technology significantly limits the performance of 6T SRAM cell in terms of leakage power and stability. With remote chances to further improve the MOSFET technology in future, Carbon Nanotube Field Effect Transistors (CNTFETs) are being widely studied as the possible alternatives. In this paper, the conventional 6T SRAM cell is compared with CNTFET based SRAM cell. The conventional 6T SRAM cell is designed using Cadence Virtuoso Tool in 180nm and 45nm Technology. The Verilog-A code of CNTFET for replacing nMOS and pMOS are separately simulated in Cadence Virtuoso Tool. The CNTFET based SRAM cell is technology independent. The performances are evaluated in terms of leakage power, delay and stability to show that the CNTFET based SRAM cell can successfully replace the CMOS based SRAM cell.


title: Combining and benchmarking methods of foetal ECG extraction without maternal or scalp electrode data.

abstract: Despite significant advances in adult clinical electrocardiography (ECG) signal processing techniques and the power of digital processors, the analysis of non-invasive foetal ECG (NI-FECG) is still in its infancy. The Physionet/Computing in Cardiology Challenge 2013 addresses some of these limitations by making a set of FECG data publicly available to the scientific community for evaluation of signal processing techniques.The abdominal ECG signals were first preprocessed with a band-pass filter in order to remove higher frequencies and baseline wander. A notch filter to remove power interferences at 50 Hz or 60 Hz was applied if required. The signals were then normalized before applying various source separation techniques to cancel the maternal ECG. These techniques included: template subtraction, principal/independent component analysis, extended Kalman filter and a combination of a subset of these methods (FUSE method). Foetal QRS detection was performed on all residuals using a Pan and Tompkins QRS detector and the residual channel with the smoothest foetal heart rate time series was selected.The FUSE algorithm performed better than all the individual methods on the training data set. On the validation and test sets, the best Challenge scores obtained were E1 = 179.44, E2 = 20.79, E3 = 153.07, E4 = 29.62 and E5 = 4.67 for events 1-5 respectively using the FUSE method. These were the best Challenge scores for E1 and E2 and third and second best Challenge scores for E3, E4 and E5 out of the 53 international teams that entered the Challenge. The results demonstrated that existing standard approaches for foetal heart rate estimation can be improved by fusing estimators together. We provide open source code to enable benchmarking for each of the standard approaches described.


title: Synthesis of α-Al2O3 thin films using reactive high power impulse magnetron sputtering

abstract: α-alumina coatings have been deposited directly onto cemented-carbide and Mo substrates at a temperature as low as 650 °C using reactive high-power impulse magnetron sputtering (HiPIMS) of Al in an ...


title: Bistatic backscatter radio for tag read-range extension

abstract: This work examines the idea of dislocating the carrier transmission from the tag-modulated carrier reception, i.e. bi-static rather than mono-static backscatter radio. In that way, more than one carrier transmitters can be distributed in a given geographical area and illuminate a set of RF tags/sensors that modulate and scatter the received carrier towards a single software-defined receiver. The increased number of carrier transmitters and their distributed nature assists tags to be potentially located closer to one carrier transmitter and thus, improves the power of the scattered signals towards the receiver. Specifically, this work a) carefully derives near-optimal detectors for bi-static backscatter radio and on/off keying (OOK) tag modulation (which is widely used in commercial tags), b) analytically calculates their bit error rate (BER) performance, and c) experimentally tests them in practice with a custom bistatic backscatter radio link. As a collateral dividend, it is shown that the non-linear processing of the proposed receivers requires certain attention on the utilized tag design principles, commonly overlooked in the literature, validating recently reported theoretical results on the microwave domain.


title: Renewable energy in cellular networks: A survey

abstract: In recent years a lot of research work was dedicated to energy efficiency in cellular networks. An emerging field of interest in that direction is the use of renewable energy sources such as wind and solar - a type of energy that is bound to increase and develop. In this paper we survey the existing works and present the fundamental principles and motivations necessary for a thorough understanding of the usage of renewable energy in cellular networks. Furthermore, we introduce a reference model for renewable energy base station (REBS) and provide analysis of its components. The synthesis of dedicated REBS algorithms, system architectures, deployment strategies and evaluation metrics provides comprehensive overview and helps identify the perspectives in this promising research field.


Interval Power Flow Analysis Based on Type-II Fuzzy Logic Forecasting


title: Design and Realization of an S-Band Microwave Low-Noise Amplifier for Wireless RF Subsystems

abstract: This study undertakes the theoretical design, CAD modeling, realization, and performance analysis of a microwave low-noise amplifier (LNA) which has been accurately developed for operation at 3.0 GHz (S-band). The objective of this research is to thoroughly analyze and develop a reliable microstrip LNA intended for a potential employment in wireless communication systems, and satellite applications. The S-band microwave LNA demonstrates the appropriateness to develop a high-performance and well-established device realization for wireless RF systems. The microwave amplifier simulations have been conducted using the latest version of the AWR Design Environment software.


title: Depth from Defocus vs. Stereo: How Different Really Are They?

abstract: Depth from focus (DFF) and depth from defocus (DFD) methods are shown to be realizations of the geometric triangulation principle. Fundamentally, the depth sensitivities of DFF and DFD are not different than those of stereo (or motion) based systems having the same physical dimensions. Contrary to common belief DFD does not inherently avoid the matching (correspondence) problem. Basically DFD and DFF do not avoid the occlusion problem any more than triangulation techniques, but they are more stable in the presence of such disruptions. The fundamental advantage of DFF and DFD methods is the two-dimensionality of the aperture, allowing more robust estimation. These results elucidate the limitations of methods based on depth of field and provide a foundation for fair performance comparison between DFF/DFD and shape from stereo (or motion) algorithms.


title: IoTs (Internet of Things) and DfPL (Device-free Passive Localisation) in a disaster management scenario

abstract: Abstract This paper presents an overview of the potential obstacles and challenges related to research topics such as IoT, DfPL WSNs (Wireless Sensor Networks) in IoT and disaster management using WSNs. This review will analyse key aspects of deploying a DfPL WSN in IoT scenario for disaster management. In an IoT scenario the DfPL WSN is only collecting raw data that is forwarded to the Internet using a Compressed Sensing (CS) IoT framework or other solutions including data compression. Compressed Sensing (CS) refers to a method used to reduce the number of samples collected in an IoT WSN. Thus it is possible to create stand-alone applications that require fewer resources. There is no need to process the data in the WSN as this can be done in the Data Analysis Network, after the data is reconstructed. This will enable a reduced volume of data transmitted and lower power consumption for battery-operated nodes. The detection of people in a disaster scenario who are simply moving and not in the possession of a ‘tracking device’ is revolutionary. The aim here is to build upon our patent-pended technology in order to deliver a robust field-trial ready human detection system for disaster situations.


title: Feature Subset Selection for Software Cost Modelling and Estimation

abstract: Feature selection has been recently used in the area of software engineering for improving the accuracy and robustness of software cost models. The idea behind selecting the most informative subset of features from a pool of available cost drivers stems from the hypothesis that reducing the dimensionality of datasets will significantly minimise the complexity and time required to reach to an estimation using a particular modelling technique. This work investigates the appropriateness of attributes, obtained from empirical project databases and aims to reduce the cost drivers used while preserving performance. Finding suitable subset selections that may cater improved predictions may be considered as a pre-processing step of a particular technique employed for cost estimation (filter or wrapper) or an internal (embedded) step to minimise the fitting error. This paper compares nine relatively popular feature selection methods and uses the empirical values of selected attributes recorded in the ISBSG and Desharnais datasets to estimate software development effort.


title: Convolutional neural network-based multimodal image fusion via similarity learning in the shearlet domain

abstract: Recently, deep learning has been shown effectiveness in multimodal image fusion. In this paper, we propose a fusion method for CT and MR medical images based on convolutional neural network (CNN) in the shearlet domain. We initialize the Siamese fully convolutional neural network with a pre-trained architecture learned from natural data; then, we train it with medical images in a transfer learning fashion. Training dataset is made of positive and negative patch pair of shearlet coefficients. Examples are fed in two-stream deep CNN to extract features maps; then, a similarity metric learning based on cross-correlation is performed aiming to learn mapping between features. The minimization of the logistic loss objective function is applied with stochastic gradient descent. Consequently, the fusion process flow starts by decomposing source CT and MR images by the non-subsampled shearlet transform into several subimages. High-frequency subbands are fused based on weighted normalized cross-correlation between feature maps given by the extraction part of the CNN, while low-frequency coefficients are combined using local energy. Training and test datasets include pairs of pre-registered CT and MRI taken from the Harvard Medical School database. Visual analysis and objective assessment proved that the proposed deep architecture provides state-of-the-art performance in terms of subjective and objective assessment. The potential of the proposed CNN for multi-focus image fusion is exhibited in the experiments.


title: Earth‐Observation Data Access: A Knowledge Discovery Concept for Payload Ground Segments

abstract: In recent years the ability to store large quantities of Earth Observation (EO) satellite images has greatly surpassed the ability to access and meaningfully extract information from it. The state-of-the-art of operational systems for Remote Sensing data access (in particular for images) allows queries by geographical location, time of acquisition or type of sensor. Nevertheless, this information is often less relevant than the content of the scene (e.g. specific scattering properties, structures, objects, etc.). Moreover, the continuous increase in the size of the archives and in the variety and complexity of EO sensors require new methodologies and tools - based on a shared knowledge - for information mining and management, in support of emerging applications (e.g.: change detection, global monitoring, disaster and risk management, image time series, etc.). In addition, the current Payload Ground Segments (PGS) are mainly designed for Long Term Data Preservation (LTDP), in this article we propose an alternative solution for enhancing the access to the data content. Our solution presents a knowledge discovery concept, whose intention is to implement a communication channel between the PGS (EO data sources) and the end-user who receives the content of the data sources coded in an understandable format associated with semantics and ready for the exploitation. The first implemented concepts were presented in Knowledge driven content based Image Information Mining (KIM) and Geospatial Information Retrieval and Indexing (GeoIRIS) system as examples of data mining systems. Our new concept is developed in a modular system composed of the following components 1) the data model generation implementing methods for extracting relevant descriptors (low-level features) of the sources (EO images), analyzing their metadata in order to complement the information, and combining with vector data sources coming from Geographical Information Systems. 2) A database management system, where the database structure supports the knowledge management, feature computation, and visualization tools because of the modules for analysis, indexing, training and retrieval are resolved into the database. 3) Data mining and knowledge discovery tools allowing the end-user to perform advanced queries and to assign semantic annotations to the image content. The low-level features are complemented with semantic annotations giving meaning to the image information. The semantic description is based on semi-supervised learning methods for spatio-temporal and contextual pattern discovery. 4) Scene understanding counting on annotation tools for helping the user to create scenarios using EO images as for example change detection analysis, etc. 5) Visual data mining providing Human-Machine Interfaces for navigating and browsing the archive using 2D or 3D representation. The visualization techniques perform an interactive loop in order to optimize the visual interaction with huge volumes of data of heterogeneous nature and the end-user.


title: Predictors of online buying behavior

abstract: Consumers worldwide can shop online 24 hours a day, seven days a week, 365 days a year. Some market sectors, including insurance, financial services, computer hardware and software, travel, books, music, video, flowers, and automobiles, are experiencing rapid growth in online sales. For example, in Jan. 1999, Dell Computer Corp. was selling an average of $14 million of equipment online per day, and Amazon.com has become the third largest bookseller in the U.S., despite being in business only since 1995. With projections that the Internet will generate consumer and business-to-business sales in excess of $294 billion by 2002, online retailing raises many questions about how to market on the Net.


title: Implementation of Backpropagation Algorithm: A Neural Net- work Approach for Pattern Recognition

abstract: A pattern recognition system refers to a system deployed for the classification of data patterns and categoriz- ing them into predefined set of classes. Various methods used for recognizing the patterns are studied under this paper. The objective of this paper is to study the various techniques for recognizing the complex patterns, identify and imple- ment the best suitable technique with its merits over the other techniques and analyze the result in order to achieve the accuracy in the designed system.


title: Instantaneous ego-motion estimation using Doppler radar

abstract: The growing use of Doppler radars in the automotive field and the constantly increasing measurement accuracy open new possibilities for estimating the motion of the ego-vehicle. The following paper presents a robust and self-contained algorithm to instantly determine the velocity and yaw rate of the ego-vehicle. The algorithm is based on the received reflections (targets) of a single measurement cycle. It analyzes the distribution of their radial velocities over the azimuth angle. The algorithm does not require any preprocessing steps such as clustering or clutter suppression. Storage of history and data association is avoided. As an additional benefit, all targets are instantly labeled as stationary or non-stationary.


title: The Nonsubsampled Contourlet Transform: Theory, Design, and Applications

abstract: In this paper, we develop the nonsubsampled contourlet transform (NSCT) and study its applications. The construction proposed in this paper is based on a nonsubsampled pyramid structure and nonsubsampled directional filter banks. The result is a flexible multiscale, multidirection, and shift-invariant image decomposition that can be efficiently implemented via the à trous algorithm. At the core of the proposed scheme is the nonseparable two-channel nonsubsampled filter bank (NSFB). We exploit the less stringent design condition of the NSFB to design filters that lead to a NSCT with better frequency selectivity and regularity when compared to the contourlet transform. We propose a design framework based on the mapping approach, that allows for a fast implementation based on a lifting or ladder structure, and only uses one-dimensional filtering in some cases. In addition, our design ensures that the corresponding frame elements are regular, symmetric, and the frame is close to a tight one. We assess the performance of the NSCT in image denoising and enhancement applications. In both applications the NSCT compares favorably to other existing methods in the literature.


title: Efficient, Oblivious Data Structures for MPC

abstract: We present oblivious implementations of several data structures for secure multiparty computation (MPC) such as arrays, dictionaries, and priority queues. The resulting oblivious data structures have only polylogarithmic overhead compared with their classical counterparts. To achieve this, we give secure multiparty protocols for the ORAM of Shi et al. (Asiacrypt ‘11) and the Path ORAM scheme of Stefanov et al. (CCS ‘13), and we compare the resulting implementations. We subsequently use our oblivious priority queue for secure computation of Dijkstra’s shortest path algorithm on general graphs, where the graph structure is secret. To the best of our knowledge, this is the first implementation of a non-trivial graph algorithm in multiparty computation with polylogarithmic overhead.