title: MemeSequencer: Sparse Matching for Embedding Image Macros

abstract: The analysis of the creation, mutation, and propagation of social media content on the Internet is an essential problem in computational social science, affecting areas ranging from marketing to political mobilization. A first step towards understanding the evolution of images online is the analysis of rapidly modifying and propagating memetic imagery or `memes'. However, a pitfall in proceeding with such an investigation is the current incapability to produce a robust semantic space for such imagery, capable of understanding differences in Image Macros. In this study, we provide a first step in the systematic study of image evolution on the Internet, by proposing an algorithm based on sparse representations and deep learning to decouple various types of content in such images and produce a rich semantic embedding. We demonstrate the benefits of our approach on a variety of tasks pertaining to memes and Image Macros, such as image clustering, image retrieval, topic prediction and virality prediction, surpassing the existing methods on each. In addition to its utility on quantitative tasks, our method opens up the possibility of obtaining the first large-scale understanding of the evolution and propagation of memetic imagery.


title: Evaluation of Northwest University, Kano Post-UTME Test Items using Item Response Theory

abstract: High-stakes testing is used for the purposes of providing results that have important consequences. Validity is the cornerstone upon which all measurement systems are built. This study applied the Item Response Theory principles to analyse Northwest University Kano Post-UTME Economics test items. The developed fifty (50) economics test items was administered to a sample of 600 students. The data obtained was analysed using XCALIBRE 4 and SPSS 20v softwares to determine items parameters base on IRT models. Indicate that, the test measure single trait by satisfying the condition of unidimensionality. Similarly, the goodness of fit test revealed that, the two parameter IRT model was more suitable since no misfit item was observed and the test reliability was 0.86. The mean examinee ability was 0.07 (SD = 0.94). The mean item difficulty was -0.63(SD = 2.54) and mean item discrimination was 0.28 (SD = 0.04). 16 (33%) items were identified as “problematic” based on difficulty indices, 35(71%) also failed to meet the set standards on the basis of discrimination parameters. it can be concluded that, using the IRT approach, the NWU Post-UTME items are not stable as far as item difficulty and discrimination indices are concerned. It is recommended that, the Post-UTME items should be made to pass through all process of standardisation and validation; test development and content experts should be involve in developing and validating the test items in order to obtain valid and reliable results which will lead to valid inferences


title: Multi-slice to volume registration of ultrasound data to a statistical atlas of human pelvis

abstract: Identifying the proper orientation of the pelvis is a critical step in accurate placement of the femur prosthesis in the ::: acetabulum in Total Hip Replacement (THR) surgeries. The general approach to localize the orientation of the pelvis ::: coordinate system is to use X-ray fluoroscopy to guide the procedure. An alternative can be employing intra-operative ::: ultrasound (US) imaging with pre-operative CT scan or fluoroscopy imaging. In this paper, we propose to replace the ::: need of pre-operative imaging by using a statistical shape model of the pelvis, constructed from several CT images. We ::: then propose an automatic deformable intensity-based registration of the anatomical atlas to a sparse set of 2D ::: ultrasound images of the pelvis in order to localize its anatomical coordinate system. In this registration technique, we ::: first extract a set of 2D slices from a single instance of the pelvic atlas. Each individual 2D slice is generated based on ::: the location of a corresponding 2D ultrasound image. Next, we create simulated ultrasound images out of the 2D atlas ::: slices and calculate a similarity metric between the simulated images and the actual ultrasound images. The similarity ::: metric guides an optimizer to generate an instance of the atlas that best matches the ultrasound data. We demonstrated ::: the feasibility of our proposed approach on two male human cadaver data. The registration was able to localize a ::: patient-specific pelvic coordinate system with origin translation error of 2 mm and 3.45 mm, and average axes rotation ::: error of 3.5 degrees and 3.9 degrees for the two cadavers, respectively.


title: A Saliency-based Convolutional Neural Network for Table and Chart Detection in Digitized Documents

abstract: Within the realm of information extraction from documents, detection of tables and charts is particularly needed as they contain a visual summary of the most valuable information contained in a document. For a complete automation of the visual information extraction process from tables and charts, it is necessary to develop techniques that localize them and identify precisely their boundaries. In this paper we aim at solving the table/chart detection task through an approach that combines deep convolutional neural networks, graphical models and saliency concepts. In particular, we propose a saliency-based fully-convolutional neural network performing multi-scale reasoning on visual cues followed by a fully-connected conditional random field (CRF) for localizing tables and charts in digital/digitized documents. Performance analysis, carried out on an extended version of the ICDAR 2013 (with annotated charts as well as tables) dataset, shows that our approach yields promising results, outperforming existing models.


Fast Onboard Stereo Vision for UAVs


title: IoT-Based Big Data Storage Systems in Cloud Computing: Perspectives and Challenges

abstract: Internet of Things (IoT) related applications have emerged as an important field for both engineers and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations especially in cloud computing. This paper first provides a functional framework that identifies the acquisition, management, processing and mining areas of IoT big data, and several associated technical modules are defined and described in terms of their key characteristics and capabilities. Then current research in IoT application is analyzed, moreover, the challenges and opportunities associated with IoT big data research are identified. We also report a study of critical IoT application publications and research topics based on related academic and industry publications. Finally, some open issues and some typical examples are given under the proposed IoT-related research framework.


title: Production, Manufacturing and Logistics Dynamic pricing with real-time demand learning

abstract: In many service industries, the firm adjusts the product price dynamically by taking into account the current product inventory and the future demand distribution. Because the firm can easily monitor the product inventory, the success of dynamic pricing relies on an accurate demand forecast. In this paper, we consider a situation where the firm does not have an accurate demand forecast, but can only roughly estimate the customer arrival rate before the sale begins. As the sale moves forward, the firm uses real-time sales data to fine-tune this arrival rate estimation. We show how the firm can first use this modified arrival rate estimation to forecast the future demand distribution with better precision, and then use the new information to dynamically adjust the product price in order to maximize the expected total revenue. Numerical study shows that this strategy not only is nearly optimal, but also is robust when the true customer arrival rate is much different from the original forecast. Finally, we extend the results to four situations commonly encountered in practice: unobservable lost customers, time dependent arrival rate, batch demand, and discrete set of allowable prices.


title: Cancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine

abstract: This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized version of original QPSO for binary 0-1 optimization problems. Then, we present the principle and procedure for cancer feature gene selection and cancer classification based on BQPSO and SVM with leave-one-out cross validation (LOOCV). Finally, the BQPSO coupling SVM (BQPSO/SVM), binary PSO coupling SVM (BPSO/SVM), and genetic algorithm coupling SVM (GA/SVM) are tested for feature gene selection and cancer classification on five microarray data sets, namely, Leukemia, Prostate, Colon, Lung, and Lymphoma. The experimental results show that BQPSO/SVM has significant advantages in accuracy, robustness, and the number of feature genes selected compared with the other two algorithms.


title: Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM

abstract: Although deep learning models are highly effective for various learning tasks, their high computational costs prohibit the deployment to scenarios where either memory or computational resources are limited. In this paper, we focus on compressing and accelerating deep models with network weights represented by very small numbers of bits, referred to as extremely low bit neural network. We model this problem as a discretely constrained optimization problem. Borrowing the idea from Alternating Direction Method of Multipliers (ADMM), we decouple the continuous parameters from the discrete constraints of network, and cast the original hard problem into several subproblems. We propose to solve these subproblems using extragradient and iterative quantization algorithms that lead to considerably faster convergency compared to conventional optimization methods. Extensive experiments on image recognition and object detection verify that the proposed algorithm is more effective than state-of-the-art approaches when coming to extremely low bit neural network.


title: Selectively diversifying web search results

abstract: Search result diversification is a natural approach for tackling ambiguous queries. Nevertheless, not all queries are equally ambiguous, and hence different queries could benefit from different diversification strategies. A more lenient or more aggressive diversification strategy is typically encoded by existing approaches as a trade-off between promoting relevance or diversity in the search results. In this paper, we propose to learn such a trade-off on a per-query basis. In particular, we examine how the need for diversification can be learnt for each query - given a diversification approach and an unseen query, we predict an effective trade-off between relevance and diversity based on similar previously seen queries. Thorough experiments using the TREC ClueWeb09 collection show that our selective approach can significantly outperform a uniform diversification for both classical and state-of-the-art diversification approaches.


title: Broadband UHF flexible vivaldi antenna

abstract: A novel Vivaldi flexible antenna with a -6 dB bandwidth from 150 MHz up to 900 MHz is presented. The whole antenna is realized on a 600 × 600 mm2 silicone foam substrate. In this paper we describe the design, the realization and the performances of this high gain and directive antenna. This antenna is easy to make and does not require matching network or balun.


title: A novel compact compliant actuator design for rehabilitation robots

abstract: Rehabilitation robots have direct physical interaction with human body. Ideally, actuators for rehabilitation robots should be compliant, force controllable, and back drivable due to safety and control considerations. Various designs of Series Elastic Actuators (SEA) have been developed for these applications. However, current SEA designs face a common performance limitation due to the compromise on the spring stiffness selection. This paper presents a novel compact compliant force control actuator design for portable rehabilitation robots to overcome the performance limitations in current SEAs. Our design consists of a servomotor, a ball screw, a torsional spring between the motor and the ball screw, and a set of translational springs between the ball screw nut and the external load. The soft translational springs are used to handle the low force operation and reduce output impedance, stiction, and external shock load. The torsional spring, being in the high speed range, has high effective stiffness and improves the system bandwidth in large force operation when the translational springs are fully compressed. This design is also more compact due to the smaller size of the springs. We explain the construction and the working principle of our new design, followed by the dynamic modeling and analysis of the actuator. We also show the preliminary testing results of a prototype actuator designed for a lower limb exoskeleton for gait rehabilitation.


title: Microscopy Cell Segmentation Via Convolutional LSTM Networks

abstract: Live cell microscopy sequences exhibit complex spatial structures and complicated temporal behaviour, making their analysis a challenging task. Considering cell segmentation problem, which plays a significant role in the analysis, the spatial properties of the data can be captured using Convolutional Neural Networks (CNNs). Recent approaches show promising segmentation results using convolutional encoder-decoders such as the U-Net. Nevertheless, these methods are limited by their inability to incorporate temporal information, that can facilitate segmentation of individual touching cells or of cells that are partially visible. In order to exploit cell dynamics we propose a novel segmentation architecture which integrates Convolutional Long Short Term Memory (C-LSTM) with the U-Net. The network’s unique architecture allows it to capture multi-scale, compact, spatio-temporal encoding in the C-LSTMs memory units. The method was evaluated on the Cell Tracking Challenge and achieved state-of-the-art results (1st on Fluo-N2DH-SIM + and 2nd on DIC-C2DLHeLa datasets) The code is freely available at: https://github.com/arbellea/LSTM-UNet.git


title: Goal-Oriented Requirements Engineering: A Systematic Literature Map

abstract: Over the last two decades, much attention hasbeen paid to the area of Goal-Oriented Requirements Engineering(GORE), where goals are used as a useful conceptualizationto elicit, model and analyze requirements, capturingalternatives and conflicts. Goal modeling has been adaptedand applied to many sub-topics within RE and beyond, suchas agent-orientation, aspect-orientation, business intelligence, model-driven development, security, and so on. Despite extensiveefforts in this field, the RE community lacks a recent, generalsystematic literature review of the area. As a first step towardsproviding a GORE overview, we present a Systematic LiteratureMap, focusing on GORE-related publications at a high-level, categorizing and analyzing paper information in order to answerseveral research questions, while omitting a detailed analysis ofindividual paper quality. Our Literature Map covers the 246 topcitedGORE-related conference and journal papers, accordingto Scopus, classifying them into a number of descriptive papertypes and topics, providing an analysis of the data, which ismade publicly available. We use our analysis results to makerecommendations concerning future GORE research.


title: Automatic Image Colorization Using Adversarial Training

abstract: The paper presents a fully automatic end-to-end trainable system to colorize grayscale images. Colorization is a highly under-constrained problem. In order to produce realistic outputs, the proposed approach takes advantage of the recent advances in deep learning and generative networks. To achieve plausible colorization, the paper investigates conditional Wasserstein Generative Adversarial Networks (WGAN) [3] as a solution to this problem. Additionally, a loss function consisting of two classification loss components apart from the adversarial loss learned by the WGAN is proposed. The first classification loss provides a measure of how much the predicted colored images differ from ground truth. The second classification loss component makes use of ground truth semantic classification labels in order to learn meaningful intermediate features. Finally, WGAN training procedure pushes the predictions to the manifold of natural images. The system is validated using a user study and a semantic interpretability test and achieves results comparable to [1] on Imagenet dataset [10].


title: Big Data for Infectious Disease Surveillance and Modeling

abstract: We devote a special issue of the Journal of Infectious Diseases to review the recent advances of big data in strengthening disease surveillance, monitoring medical adverse events, informing transmission models, and tracking patient sentiments and mobility. We consider a broad definition of big data for public health, one encompassing patient information gathered from high-volume electronic health records and participatory surveillance systems, as well as mining of digital traces such as social media, Internet searches, and cell-phone logs. We introduce nine independent contributions to this special issue and highlight several cross-cutting areas that require further research, including representativeness, biases, volatility, and validation, and the need for robust statistical and hypotheses-driven analyses. Overall, we are optimistic that the big-data revolution will vastly improve the granularity and timeliness of available epidemiological information, with hybrid systems augmenting rather than supplanting traditional surveillance systems, and better prospects for accurate infectious diseases models and forecasts.


title: Masked 3D conditional generative adversarial network for rock mesh generation

abstract: 3D-GAN (Wu et al. in: Advances in Neural Information Processing Systems, pp. 82–90, 2016) has been introduced as a novel way to generate 3D models. In this paper, we propose a 3D-Masked-CGAN approach to apply in the generation of irregular 3D mesh geometry such as rocks. While there are many ways to generate 3D objects, the generation of irregular 3D models has its own peculiarity. To make a model realistic is extremely time-consuming and in high cost. In order to control the shape of generated 3D models, we extend 3D-GAN by adding conditional information into both the generator and discriminator. It is shown that that this model can generate 3D rock models with effective control over the shapes of generated models.


title: Processing Hyperspectral Data in Machine Learning

abstract: The adaptive and automated analysis of hyperspectral data is mandatory in many areas of research such as physics, astronomy and geophysics, chemistry, bioinformatics, medicine, biochemistry, engineering, and others. Hyperspectra di er from other spectral data that a large frequency range is uniformly sampled. The resulting discretized spectra have a huge number of spectral bands and can be seen as good approximations of the underlying continuous spectra. The large dimensionality causes numerical di culties in e cient data analysis. Another aspect to deal with is that the amount of data may range from several billion samples in geophysics to only a few in medical applications. In consequence, dedicated machine learning algorithms and approaches are required for precise while e cient processing of hyperspectral data, which should include also expert knowledge of the application domain as well as mathematical properties of the hyperspectral data.


title: Control Methodologies in Networked Control Systems

abstract: The use of a data network in a control loop has gained increasing attentions in recent years due to its cost effective and flexible applications. One of the major challenges in this so-called networked control system (NCS) is the network-induced delay effect in the control loop. Network delays degrade the NCS control performance and destabilize the system. A significant emphasis has been on developing control methodologies to handle the network delay effect in NCS. This survey paper presents recent NCS control methodologies. The overview on NCS structures and description of network delays including characteristics and effects are also covered.


title: Interaction between Topically and Systemically Coadministered P-Glycoprotein Substrates/Inhibitors: Effect on Vitreal Kinetics

abstract: The objective of the present study was to investigate the effect of topically coadministered P-glycoprotein (P-gp) substrates/inhibitors on the vitreal kinetics of a systemically administered P-gp substrate. Anesthetized male rabbits were used in these studies. The concentration-time profile of quinidine in the vitreous humor, after intravenous administration, was determined alone and in the presence of topically coadministered verapamil, prednisolone sodium phosphate (PP), and erythromycin. The vitreal pharmacokinetic parameters of quinidine in the presence of verapamil [apparent elimination rate constant (λz), 0.0027 ± 0.0002 min−1; clearance (CL_F), 131 ± 21 ml/min; area under the curve (AUC0–∞), 39 ± 7.0 μg · min/ml; and mean residence time, 435 ± 20 min] were significantly different from those of the control (0.0058 ± 0.0006 min−1, 296 ± 46 ml/min, 17 ± 3 μg · min/ml, and 232 ± 20 min, respectively). A 1.7-fold decrease in the vitreal λz and a 1.5-fold increase in the vitreal AUC of quinidine were observed in the presence of topical PP. Statistically significant differences between the vitreal profiles of the control and erythromycin-treated group were also observed. Plasma concentration-time profiles of quinidine, alone or in the presence of the topically instilled compounds, remained unchanged, indicating uniform systemic quinidine exposure across groups. This study demonstrates an interaction between topically and systemically coadministered P-gp substrates, probably through the modulation of P-gp on the basolateral membrane of the retinal pigmented epithelium, leading to changes in the vitreal kinetics of the systemically administered agent.


title: Harvesting Multiple Sources for User Profile Learning: a Big Data Study

abstract: User profile learning, such as mobility and demographic profile learning, is of great importance to various applications. Meanwhile, the rapid growth of multiple social platforms makes it possible to perform a comprehensive user profile learning from different views. However, the research efforts on user profile learning from multiple data sources are still relatively sparse, and there is no large-scale dataset released towards user profile learning. In our study, we contribute such benchmark and perform an initial study on user mobility and demographic profile learning. First, we constructed and released a large-scale multi-source multi-modal dataset from three geographical areas. We then applied our proposed ensemble model on this dataset to learn user profile. Based on our experimental results, we observed that multiple data sources mutually complement each other and their appropriate fusion boosts the user profiling performance.


title: Optimizing linear counting queries under differential privacy

abstract: Differential privacy is a robust privacy standard that has been successfully applied to a range of data analysis tasks. But despite much recent work, optimal strategies for answering a collection of related queries are not known. We propose the matrix mechanism, a new algorithm for answering a workload of predicate counting queries. Given a workload, the mechanism requests answers to a different set of queries, called a query strategy, which are answered using the standard Laplace mechanism. Noisy answers to the workload queries are then derived from the noisy answers to the strategy queries. This two stage process can result in a more complex correlated noise distribution that preserves differential privacy but increases accuracy. We provide a formal analysis of the error of query answers produced by the mechanism and investigate the problem of computing the optimal query strategy in support of a given workload. We show this problem can be formulated as a rank-constrained semidefinite program. Finally, we analyze two seemingly distinct techniques, whose similar behavior is explained by viewing them as instances of the matrix mechanism.


title: Assessing the shape symbolism of the taste, flavour, and texture of foods and beverages

abstract: Consumers reliably match a variety of tastes (bitterness, sweetness, and sourness), oral-somatosensory attributes (carbonation, oral texture, and mouth-feel), and flavours to abstract shapes varying in their angularity. For example, they typically match more rounded forms such as circles with sweet tastes and more angular shapes such as triangles and stars with bitter and/or carbonated foods and beverages. Here, we suggest that such shape symbolic associations could be, and in some cases already are being, incorporated into the labelling and/or packaging of food and beverage products in order to subconsciously set up specific sensory expectations in the minds of consumers. Given that consumers normally prefer those food and beverage products that meet their sensory expectations, as compared to those that give rise to a ‘disconfirmation of expectation’, we believe that the targeted use of such shape symbolism may provide a means for companies to gain a competitive advantage in the marketplace. Here, we review the latest research documenting a variety of examples of shape symbolism in the food and beverage sector. We also highlight a number of the explanations for such effects that have been put forward over the years. Finally, we summarise the latest evidence demonstrating that the shapes a consumer sees on the label and even the shape of the packaging in which the product is served can all impact on a consumer’s sensory-discriminative and hedonic responses to food and beverage products.


title: Financial Time Series Prediction Using Elman Recurrent Random Neural Networks

abstract: In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices.


title: SQL Schema Design: Foundations, Normal Forms, and Normalization

abstract: Normalization helps us find a database schema at design time that can process the most frequent updates efficiently at run time. Unfortunately, relational normalization only works for idealized database instances in which duplicates and null markers are not present. On one hand, these features occur frequently in real-world data compliant with the industry standard SQL, and especially in modern application domains. On the other hand, the features impose challenges that have made it impossible so far to extend the existing forty year old normalization framework to SQL. We introduce a new class of functional dependencies and show that they provide the right notion for SQL schema design. Axiomatic and linear-time algorithmic characterizations of the associated implication problem are established. These foundations enable us to propose a Boyce-Codd normal form for SQL. Indeed, we justify the normal form by showing that it permits precisely those SQL instances which are free from data redundancy. Unlike the relational case, there are SQL schemata that cannot be converted into Boyce-Codd normal form. Nevertheless, for an expressive sub-class of our functional dependencies we establish a normalization algorithm that always produces a schema in Value-Redundancy free normal form. This normal form permits precisely those instances which are free from any redundant data value occurrences other than the null marker. Experiments show that our functional dependencies occur frequently in real-world data and that they are effective in eliminating redundant values from these data sets without loss of information.


title: Control of MEMS Disc Resonance Gyroscope (DRG) using a FPGA Platform

abstract: Inertial navigation systems based upon optical gyroscopes tend to be not long lived. Micro-electromechanical systems (MEMS) based gyros do not have these shortcomings; however, until recently, the performance of MEMS based gyros had been below navigation grade. Boeing and JPL have been cooperating since 1997 to develop high performance MEMS gyroscopes for miniature, low power space inertial reference unit applications. The efforts resulted in demonstration of a post resonator gyroscope (PRG). This experience led to the more compact disc resonator gyroscope (DRG) for further reduced size and power with potentially increased performance. Currently, the mass, volume and power of the DRG are dominated by the size of the electronics. This paper will detail the FPGA based digital electronics architecture and its implementation for the DRG which will allow reduction of size and power and will increase performance through a reduction in electronics noise. Using the digital control based on FPGA, we can program and modify in real-time the control loop to adapt to the specificity of each particular gyro and the change of the mechanical characteristic of the gyro during its life time.


title: Fabrication of 2D-extruded fractal structures using repeated corner lithography and etching

abstract: We present a new wafer scale fabrication procedure for the creation of complex 2D-extruded geometries in silicon by a combination of repeated anisotropic etching of silicon and a 3D lithographic technique called corner lithography. Using corner lithography it is possible, by means of a nano-masking step, to select which corners are opened, depending on specific angles relative to the substrate normal. In this way complex 3D structures can be created in a repetitive manner. In this article we apply this procedure to the etching of trench like structures in a silicon wafer. Depending on which angles open in corner lithography, it is possible to etch just downward, downward and sideward, or a sequential combination of these two options. Examples of all three routes have been created on a micron/sub-micron scale. By depositing a conformal layer, the smallest hollow features will be auto-closed, thus forming buried channels. Continuing this deposition, automatically new closed channels will be formed with increasing diameter and wall thickness. These channel structures could find application in e.g. continuous flow microreactors.


title: A hybrid system for malware detection on big data

abstract: In recent years, the increasing diffusion of malicious software has encouraged the adoption of advanced machine learning algorithms to timely detect new threats. A cloud-based approach allows to exploit the big data produced by client agents to train such algorithms, but on the other hand, poses severe challenges on their scalability and performance. We propose a hybrid cloud-based malware detection system in which static and dynamic analyses are combined in order to find a good trade-off between response time and detection accuracy. Our system performs a continuous learning process of its models, based on deep networks, by exploiting the growing amount of data provided by clients. The preliminary experimental evaluation confirms the suitability of the approach proposed here.


title: An unsupervised framework for detecting anomalous messages from syslog log files

abstract: System logs provide valuable information about the health status of IT systems and computer networks. Therefore, log file monitoring has been identified as an important system and network management technique. While many solutions have been developed for monitoring known log messages, the detection of previously unknown error conditions has remained a difficult problem. In this paper, we present a novel data mining based framework for detecting anomalous log messages from syslog- based system log files. We also describe the implementation and performance of the framework in a large organizational network.


title: Detecting Visual Relationships Using Box Attention

abstract: We propose a new model for detecting visual relationships, such as "person riding motorcycle" or "bottle on table". This task is an important step towards comprehensive structured mage understanding, going beyond detecting individual objects. Our main novelty is a Box Attention mechanism that allows to model pairwise interactions between objects using standard object detection pipelines. The resulting model is conceptually clean, expressive and relies on well-justified training and prediction procedures. Moreover, unlike previously proposed approaches, our model does not introduce any additional complex components or hyperparameters on top of those already required by the underlying detection model. We conduct an experimental evaluation on two datasets, V-COCO and Open Images, demonstrating strong quantitative and qualitative results.


title: A dual-band bagley power divider using modified Π-network

abstract: A new methodology to design a dual-band 3-way Bagley power divider is presented. The general method of using π-network as well as the proposed modified π-network has been discussed. The proposed π-network mimics a λ/2 line at two different frequencies. Design equations in closed form are obtained by using transmission line concepts and simple network analysis techniques. The proposed design is validated using Agilent ADS. This modification also leads to wider bandwidth as observed by a careful simulation.


title: A Mathematical Model of the Human Temperature Regulatory System - Transient Cold Exposure Response

abstract: A mathematical model of the human temperature regulatory system which utilizes finite difference techniques and includes new anatomical and basal physiological information, with more detail than previous models, is presented. A cold stress control system which includes both a feedback signal based on head core temperature and feedforward signals based on the skin temperature and the skin heat flux is postulated. Model and direct experimental results (seven temperatures, cardiac output, metabolism, and arm blood flow) are compared for a transient cold exposure. The substantial agreement found suggests that the skin heat flux could be an important input to the temperature regulatory system.


title: The Cybersecurity Landscape in Industrial Control Systems

abstract: Industrial control systems (ICSs) are transitioning from legacy-electromechanical-based systems to modern information and communication technology (ICT)-based systems creating a close coupling between cyber and physical components. In this paper, we explore the ICS cybersecurity landscape including: 1) the key principles and unique aspects of ICS operation; 2) a brief history of cyberattacks on ICS; 3) an overview of ICS security assessment; 4) a survey of “uniquely-ICS” testbeds that capture the interactions between the various layers of an ICS; and 5) current trends in ICS attacks and defenses.


title: A survey of Community Question Answering

abstract: With the advent of numerous community forums, tasks associated with the same have gained importance in the recent past. With the influx of new questions every day on these forums, the issues of identifying methods to find answers to said questions, or even trying to detect duplicate questions, are of practical importance and are challenging in their own right. This paper aims at surveying some of the aforementioned issues, and methods proposed for tackling the same.


title: Quantitative Network Monitoring with NetQRE

abstract: In network management today, dynamic updates are required for traffic engineering and for timely response to security threats. Decisions for such updates are based on monitoring network traffic to compute numerical quantities based on a variety of network and application-level performance metrics. Today's state-of-the-art tools lack programming abstractions that capture application or session-layer semantics, and thus require network operators to specify and reason about complex state machines and interactions across layers. To address this limitation, we present the design and implementation of NetQRE, a high-level declarative toolkit that aims to simplify the specification and implementation of such quantitative network policies. NetQRE integrates regular-expression-like pattern matching at flow-level as well as application-level payloads with aggregation operations such as sum and average counts. We describe a compiler for NetQRE that automatically generates an efficient implementation with low memory footprint. Our evaluation results demonstrate that NetQRE allows natural specification of a wide range of quantitative network tasks ranging from detecting security attacks to enforcing application-layer network management policies. NetQRE results in high performance that is comparable with optimized manually-written low-level code and is significantly more efficient than alternative solutions, and can provide timely enforcement of network policies that require quantitative network monitoring.


title: The Youth Anxiety Measure for DSM-5 (YAM-5): Development and First Psychometric Evidence of a New Scale for Assessing Anxiety Disorders Symptoms of Children and Adolescents

abstract: The Youth Anxiety Measure for DSM-5 (YAM-5) is a new self- and parent-report questionnaire to assess anxiety disorder symptoms in children and adolescents in terms of the contemporary classification system. International panels of childhood anxiety researchers and clinicians were used to construct a scale consisting of two parts: part one consists of 28 items and measures the major anxiety disorders including separation anxiety disorder, selective mutism, social anxiety disorder, panic disorder, and generalized anxiety disorder, whereas part two contains 22 items that focus on specific phobias and (given its overlap with situational phobias) agoraphobia. In general, the face validity of the new scale was good; most of its items were successfully linked to the intended anxiety disorders. Notable exceptions were the selective mutism items, which were frequently considered as symptoms of social anxiety disorder, and some specific phobia items especially of the natural environment, situational and other type, that were regularly assigned to an incorrect category. A preliminary investigation of the YAM-5 in non-clinical (N = 132) and clinically referred (N = 64) children and adolescents indicated that the measure was easy to complete by youngsters. In addition, support was found for the psychometric qualities of the measure: that is, the internal consistency was good for both parts, as well as for most of the subscales, the parent–child agreement appeared satisfactory, and there was also evidence for the validity of the scale. The YAM-5 holds promise as a tool for assessing anxiety disorder symptoms in children and adolescents.


title: Mitigation of Solar Irradiance Intermittency in Photovoltaic Power Systems With Integrated Electric-Vehicle Charging Functionality

abstract: Mitigation of the variability in output power of renewable generators such as solar photovoltaic (PV) systems is a growing concern as these generators reach higher penetrations on electric grids. Furthermore, increased penetration of electric vehicle (EV) loads presents a challenge for distribution feeders. This paper presents a system where a bidirectional, highly efficient, dc-dc EV charger is placed between the high-voltage dc bus of a PV inverter and the EV battery. The system partially alleviates feeder overloading by providing fast charging for the EV battery from the PV system. In addition, the charger is capable of diverting fast changes in PV power output to the battery, thereby reducing the rate of change of inverter output power to a level below the ramp rate of existing grid resources. The paper addresses sizing of the charger and energy storage based on the PV system rating, the desired maximum ramp rate, and site solar irradiation characteristics, including geographic dispersion of PV arrays. Analysis suggests that small amounts of energy storage can accomplish large reductions in output power ramp rate. Experimental results are shown for a 10 kW, 98% efficient dc-dc charger based on bidirectional four-phase zero-voltage-switching converter.


title: Traffic Accident Analysis Using Machine Learning Paradigms

abstract: Engineers and researchers in the automobile industry have tried to design and build safer automobiles, but traffic accidents are unavoidable. Patterns involved in dangerous crashes could be detected if we develop accurate prediction models capable of automatic classification of type of injury severity of various traffic accidents. These behavioral and roadway accident patterns can be useful to develop traffic safety control policies. We believe that to obtain the greatest possible accident reduction effects with limited budgetary resources, it is important that measures be based on scientific and objective surveys of the causes of accidents and severity of injuries. This paper summarizes the performance of four machine learning paradigms applied to modeling the severity of injury that occurred during traffic accidents. We considered neural networks trained using hybrid learning approaches, support vector machines, decision trees and a concurrent hybrid model involving decision trees and neural networks. Experiment results reveal that among the machine learning paradigms considered the hybrid decision tree-neural network approach outperformed the individual approaches. Povzetek: Stirje pristopi strojnega ucenja so uporabljeni za preiskovanje zakonitosti poskodb v prometnih nesrecah.


title: Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries

abstract: Interactive analytics increasingly involves querying for quantiles over sub-populations of high cardinality datasets. Data processing engines such as Druid and Spark use mergeable summaries to estimate quantiles, but summary merge times can be a bottleneck during aggregation. We show how a compact and efficiently mergeable quantile sketch can support aggregation workloads. This data structure, which we refer to as the moments sketch, operates with a small memory footprint (200 bytes) and computationally efficient (50ns) merges by tracking only a set of summary statistics, notably the sample moments. We demonstrate how we can efficiently and practically estimate quantiles using the method of moments and the maximum entropy principle, and show how the use of a cascade further improves query time for threshold predicates. Empirical evaluation on real-world datasets shows that the moments sketch can achieve less than 1 percent error with 15 times less merge overhead than comparable summaries, improving end query time in the MacroBase engine by up to 7 times and the Druid engine by up to 60 times.


title: Real-time human gesture grading based on OpenPose

abstract: In this paper, we presented a real-time 2D human gesture grading system from monocular images based on OpenPose, a library for real-time multi-person keypoint detection. After capturing 2D positions of a person's joints and skeleton wireframe of the body, the system computed the equation of motion trajectory for every joint. Similarity metric was defined as distance between motion trajectories of standard and real-time videos. A modifiable scoring formula was used for simulating the gesture grading scenario. Experimental results showed that the system worked efficiently with high real-time performance, low cost of equipment and strong robustness to the interference of noise.


title: No Training Required: Exploring Random Encoders for Sentence Classification

abstract: We explore various methods for computing sentence representations from pre-trained word embeddings without any training, i.e., using nothing but random parameterizations. Our aim is to put sentence embeddings on more solid footing by 1) looking at how much modern sentence embeddings gain over random methods---as it turns out, surprisingly little; and by 2) providing the field with more appropriate baselines going forward---which are, as it turns out, quite strong. We also make important observations about proper experimental protocol for sentence classification evaluation, together with recommendations for future research.


title: PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs

abstract: Natural graphs with skewed distributions raise unique challenges to distributed graph computation and partitioning. Existing graph-parallel systems usually use a “one-size-fits-all” design that uniformly processes all vertices, which either suffer from notable load imbalance and high contention for high-degree vertices (e.g., Pregel and GraphLab) or incur high communication cost and memory consumption even for low-degree vertices (e.g., PowerGraph and GraphX). In this article, we argue that skewed distributions in natural graphs also necessitate differentiated processing on high-degree and low-degree vertices. We then introduce PowerLyra, a new distributed graph processing system that embraces the best of both worlds of existing graph-parallel systems. Specifically, PowerLyra uses centralized computation for low-degree vertices to avoid frequent communications and distributes the computation for high-degree vertices to balance workloads. PowerLyra further provides an efficient hybrid graph partitioning algorithm (i.e., hybrid-cut) that combines edge-cut (for low-degree vertices) and vertex-cut (for high-degree vertices) with heuristics. To improve cache locality of inter-node graph accesses, PowerLyra further provides a locality-conscious data layout optimization. PowerLyra is implemented based on the latest GraphLab and can seamlessly support various graph algorithms running in both synchronous and asynchronous execution modes. A detailed evaluation on three clusters using various graph-analytics and MLDM (Machine Learning and Data Mining) applications shows that PowerLyra outperforms PowerGraph by up to 5.53X (from 1.24X) and 3.26X (from 1.49X) for real-world and synthetic graphs, respectively, and is much faster than other systems like GraphX and Giraph, yet with much less memory consumption. A porting of hybrid-cut to GraphX further confirms the efficiency and generality of PowerLyra.


title: Deep Learning–Based Multimedia Analytics: A Review

abstract: The multimedia community has witnessed the rise of deep learning–based techniques in analyzing multimedia content more effectively. In the past decade, the convergence of deep-learning and multimedia analytics has boosted the performance of several traditional tasks, such as classification, detection, and regression, and has also fundamentally changed the landscape of several relatively new areas, such as semantic segmentation, captioning, and content generation. This article aims to review the development path of major tasks in multimedia analytics and take a look into future directions. We start by summarizing the fundamental deep techniques related to multimedia analytics, especially in the visual domain, and then review representative high-level tasks powered by recent advances. Moreover, the performance review of popular benchmarks gives a pathway to technology advancement and helps identify both milestone works and future directions.


title: Fast facial expression recognition using local binary features and shallow neural networks

abstract: Facial expression recognition applications demand accurate and fast algorithms that can run in real time on platforms with limited computational resources. We propose an algorithm that bridges the gap between precise but slow methods and fast but less precise methods. The algorithm combines gentle boost decision trees and neural networks. The gentle boost decision trees are trained to extract highly discriminative feature vectors (local binary features) for each basic facial expression around distinct facial landmark points. These sparse binary features are concatenated and used to jointly optimize facial expression recognition through a shallow neural network architecture. The joint optimization improves the recognition rates of difficult expressions such as fear and sadness. Furthermore, extensive experiments in both within- and cross-database scenarios have been conducted on relevant benchmark data sets for facial expression recognition: CK+, MMI, JAFFE, and SFEW 2.0. The proposed method (LBF-NN) compares favorably with state-of-the-art algorithms while achieving an order of magnitude improvement in execution time.


title: Content-based recommender systems for spoken documents

abstract: Content-based recommender systems use preference ratings and features that characterize media to model users' interests or information needs for making future recommendations. While previously developed in the music and text domains, we present an initial exploration of content-based recommendation for spoken documents using a corpus of public domain internet audio. Unlike familiar speech technologies of topic identification and spoken document retrieval, our recommendation task requires a more comprehensive notion of document relevance than bags-of-words would supply. Inspired by music recommender systems, we automatically extract a wide variety of content-based features to characterize non-linguistic aspects of the audio such as speaker, language, gender, and environment. To combine these heterogeneous information sources into a single relevance judgement, we evaluate feature, score, and hybrid fusion techniques. Our study provides an essential first exploration of the task and clearly demonstrates the value of a multisource approach over a bag-of-words baseline.


title: Qualitative comparison of graph-based and logic-based multi-relational data mining: a case study

abstract: The goal of this paper is to generate insights about the differences between graph-based and logic-based approaches to multi-relational data mining by performing a case study of graph-based system, Subdue and the inductive logic programming system, CProgol. We identify three key factors for comparing graph-based and logic-based multi-relational data mining; namely, the ability to discover structurally large concepts, the ability to discover semantically complicated concepts and the ability to effectively utilize background knowledge. We perform an experimental comparison of Subdue and CProgol on the Mutagenesis domain and various artificially generated Bongard problems. Experimental results indicate that Subdue can significantly outperform CProgol while discovering structurally large multi-relational concepts. It is also observed that CProgol is better at learning semantically complicated concepts and it tends to use background knowledge more effectively than Subdue.


title: The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach

abstract: Online shopping provides convenience to Web shoppers, yet its electronic format changes information-gathering methods traditionally used by customers. This change raises questions concerning customer satisfaction with the online purchasing process. Web shopping involves a number of phases, including the information phase, in which customers search for information regarding their intended purchases. The purpose of this paper is to develop theoretically justifiable constructs for measuring Web-customer satisfaction during the information phase.By synthesizing the expectation-disconfirmation paradigm with empirical theories in user satisfaction, we separate Web site quality into information quality (IQ) and system quality (SQ), and propose nine key constructs for Web-customer satisfaction. The measurements for these constructs are developed and tested in a two-phase study. In the first phase, the IQ and SQ dimensions are identified, and instruments for measuring them are developed and tested. In the second phase, using the salient dimensions of Web-IQ and Web-SQ as the basis for formulating first-order factors, we develop and empirically test instruments for measuring IQ and SQ-satisfaction. Moreover, this phase involves the design and test of second-order factors for measuring Web-customer expectations, disconfirmation, and perceived performance regarding IQ and SQ. The analysis of the measurement model indicates that the proposed metrics have a relatively high degree of validity and reliability. The results of the study provide reliable instruments for operationalizing the key constructs in the analysis of Web-customer satisfaction within the expectation-disconfirmation paradigm.


title: Team participation and online gamer loyalty

abstract: Online games have emerged as popular computer applications and gamer loyalty is vital to game providers, since online gamers frequently switch between games. Online gamers often participate in teams also. This study investigates whether and how team participation improves loyalty. We utilized a cross-sectional design and an online survey, with 546 valid responses from online game subjects. Confirmatory factor analysis was applied to assess measurement reliability and validity directly, and structural equation modeling was utilized to test our hypotheses. The results indicate that participation in teams motivates online gamers to adhere to team norms and satisfies their social needs, also enhancing their loyalty. The contribution of this research is the introduction of social norms to explain online gamer loyalty.


title: Human body as antenna and its effect on human body communications

abstract: Human body communication (HBC) is a promising wireless technology that uses the human body as part of the communication channel. HBC operates in the near-fleld of the high frequency (HF) band and in the lower frequencies of the very high frequency (VHF) band, where the electromagnetic fleld has the tendency to be conflned inside the human body. Electromagnetic interference poses a serious reliability issue in HBC; consequently, it has been given increasing attention in regard to adapting techniques to curtail its degrading efiect. Nevertheless, there is a gap in knowledge on the mechanism of HBC interference that is prompted when the human body is exposed to electromagnetic flelds as well as the efiect of the human body as an antenna on HBC. This paper narrows the gap by introducing the mechanisms of HBC interference caused by electromagnetic fleld exposure of human body. We derived analytic expressions for induced total axial current in the body and associated flelds in the vicinity of the body when an imperfectly conducting cylindrical antenna model of the human body is illuminated by a vertically polarized plane wave within the 1{200MHz frequency range. Also, flelds in the vicinity of the human body model from an on-body HBC transmitter are calculated. Furthermore, conducted electromagnetic interference on externally embedded HBC receivers is also addressed. The results show that the maximum HBC gain near 50MHz is due to whole-body resonance, and the maximum at 80MHz is due to the resonance of the arm. Similarly, the results also suggest that the magnitude of induced axial current in the body due to electromagnetic fleld exposure of human body is higher near 50MHz.


title: Equalization in high-speed communication systems

abstract: The article first discusses the major non-ideal issues of low-cost transmission media for over Gbps data transmissions - the frequency dispersion loss and channel noise. The former causes ISI in received signal, which presents difficulty for clock and data recovery at high frequencies and results higher BER. The latter further degrades the received signal quality and further limits the data transmission rate and transmission distance. Then, two equalization approaches - transmitter pre-emphasis and receiver equalization, are reviewed, in addition to various adaptation criteria and algorithms.


title: Local and global holomorphic extensions of time-varying real analytic vector fields

abstract: In this paper, we consider time-varying real analytic vector fields as curves on the space of real analytic vector fields. Using a suitable topology on the space of real analytic vector fields, we study and characterize different properties of time-varying real analytic vector fields. We study holomorphic extensions of time-varying real analytic vector fields and show that under suitable integrability conditions, a time-varying real analytic vector field on a manifold can be extended to a time-varying holomorphic vector field on a neighbourhood of that manifold. Moreover, we develop an operator setting, where the nonlinear differential equation governing the flow of a time-varying real analytic vector field can be considered as a linear differential equation on an infinite dimensional locally convex vector space. Using the holomorphic extension results, we show that the integrability of the time-varying vector field ensures the convergence of the sequence of Picard iterations for this linear differential equation. This gives us a series representation for the flow of an integrable time-varying real analytic vector field. We also define the exponential map between integrable time-varying real analytic vector fields and their flows. Using the holomorphic extensions of time-varying real analytic vector fields, we show that the exponential map is sequentially continuous.


title: Detecting Direction of Movement Using Pyroelectric Infrared Sensors

abstract: Pyroelectric infrared (PIR) sensors are widely used as a simple but powerful people presence triggers, e.g., automatic lighting systems. In particular, by alternating the effective polarization of the sensing elements in a PIR sensor, it is possible to determine the relative direction of the movement of an object moving on the motion plane of the PIR sensor. In this paper, we present a novel method of detecting a relative direction of human movement (in eight directions uniformly distributed) with two pairs of PIR sensors whose sensing elements are orthogonally aligned. We have developed a data collection unit with four dual sensing element PIR sensors with modified lenses, and collected data set from six subjects walking in eight directions each. Based on the collected PIR signals, we have performed classification analysis with well known machine learning algorithms, including instance-based learning and support vector machine. Our findings show that with the raw data set captured from two orthogonally aligned PIR sensors with modified lenses, we were able to achieve more than 98% correct detection of direction of movement. We also found that with the reduced feature set composed of three peak values for each PIR sensor, we could achieve 89%-95% recognition accuracy according to machine learning algorithms.


title: Virtual assistant: an artificial agent for enhancing content acquisition: how ambient media elicit information from humans

abstract: In this paper, we propose a novel framework "Virtual Assistant" for enhancing content potentially procured by ambient media. The Virtual Assistant is an artificial agent simulating a human assistant shown in TV programs and prompts users to provide feedback by asking questions or back-channeling. This framework ensures that sufficient information is provided in the captured content while users interact in a natural and enjoyable way with the agent. We developed a prototype agent based on a chatbot-like approach and applied it to a daily cooking scene. The experimental results demonstrate a good potential of the Virtual Assistant framework, allowing a person to provide feedback easily with few interruptions, lifting the face towards the camera, and eliciting a variety of useful information.


title: Adaptive Kalman filtering and smoothing for gravitation tracking in mobile systems

abstract: This paper is concerned with inertial-sensor-based tracking of the gravitation direction in mobile devices such as smartphones. Although this tracking problem is a classical one, choosing a good state-space for this problem is not entirely trivial. Even though for many other orientation related tasks a quaternion-based representation tends to work well, for gravitation tracking their use is not always advisable. In this paper we present a convenient linear quaternion-free state-space model for gravitation tracking. We also discuss the efficient implementation of the Kalman filter and smoother for the model. Furthermore, we propose an adaption mechanism for the Kalman filter which is able to filter out shot-noises similarly as has been proposed in context of adaptive and robust Kalman filtering. We compare the proposed approach to other approaches using measurement data collected with a smartphone.


title: 'Showrooming' and the Competition between Store and Online Retailers

abstract: Customers often evaluate products at brick-and-mortar stores to identify their "best fit" product, but end up buying this product not at the store but at a competing online retailer to take advantage of lower prices. This free-riding behavior by customers is referred to as "showrooming." We analyze three strategies to counter the effect of showrooming that may improve profits for the brick-and-mortar stores: (a) price matching, (b) making product matching harder between the brick-and-mortar store and the online retailer, and (c) charging customers for showrooming. We show that only the last two strategies may improve profits of the brick-and-mortar stores. We also present an analysis to illustrate when a particular strategy, (b) or (c), does better than the other.


title: Advanced image processing methods for automatic liver segmentation

abstract: Nowadays digital image processing and analysis is e xtensively used in different areas of human activities. One of these fields is medicine, where the enormous amount of data has to be processed. In diagnostic medicine, radiologists use for example c omputed tomography (CT) or magnetic resonance imaging (MRI) to help diagnose diseases or to perfo rm surgical procedures. Although those high-end technologies are very sophisticated and developed, there is still room for improvements especially in the area of post-processing. For example to plan th e liver resection, surgeon will need an accurate 3D model of a liver with its vessel system. To visuali ze 3D data, volume rendering method is available and can be directly used. Disadvantage of this tech nique is that it does not provide any other information than 3D models for visualization itself . To obtain models for hemodynamics simulations or even models where volume of certain part of the model could be measured, methods of advanced image processing have to be used. The keystone of the digital image processing method s is an image segmentation which works with pixel intensity levels. Those techniques are suffic ient in cases where different parts of the image ha ve significant differences in intensities of pixels li ke in cases of bones segmentation. The soft tissues like liver and its vessel system are much more chal lenging due to low contrast and direct influence of other organs which have the same intensity levels o f the pixels. This paper presents advanced techniques of image se gmentation suitable for automatic recognition of the human liver and its vessel system. The comparis on of studied techniques is being made in terms of segmentation quality and algorithm speed. The main criterion for quality evaluation of each selected technique is the level of conformity betwe en the automatically recognized boundary and reference boundary specified by experienced user. F or all the tests sequences of CT and MRI images were used.


A catch effort data recording system for the developing fishery on the small pelagic Rastrineobola argentea in the southern part of Lake Victoria.


Building a nation: Caribbean federation in the black diaspora


title: Deep Learning Based Photometric Stereo from Many Images and Under Unknown Illumination

abstract: Shape from X is an interesting area of research in computer vision community. This topic is divided into passive and active methods. Example of passive methods is shape from texture, shape from defocus and shape from the silhouette. For active methods, the important categories are shape from shading and photometric stereo. In shape from shading, the cue for shape reconstruction is shading which is the relation between intensity and shape. In this case, only one image is considered. In photometric stereo, where multiple vantage points exist, 3D reconstruction considers multiple images (at least three). Photometric stereo on its own can be categorised depending on pre-existing information of illumination directions, illumination intensities, Lambertian surfaces or non-Lambertian surfaces. This paper presents a method employing deep learning for photometric stereo where lighting and surface conditions are unknown. The proposed method is applied to a public dataset. Based on the experimental results, this method outperforms currently existing techniques.


title: Taxiway Navigation and Situation Awareness ( T-NASA ) System : Problem , Design Philosophy , and Description of an Integrated Display Suite for Low-Visibility Airport Surface Operations

abstract: An integrated cockpit display suite, the T-NASA (Taxiway Navigation and Situation Awareness) system, is under development for NASA's Terminal Area Productivity (TAP) Low-Visibility Landing and Surface Operations (LVLASO) program. This system has three integrated components: Moving Map -- track-up airport surface display with own-ship, traffic and graphical route guidance; SceneLinked Symbology -- route/taxi information virtually projected via a Head-up Display (HUD) onto the forward scene; and, 3-D Audio Ground Collision Avoidance Warning (GCAW) system -- spatially-localized auditory traffic alerts. In this paper, surface operations in low-visibility conditions, the design philosophy of the T-NASA system, and the TNASA system display components are described.


title: Effect of Generating Heat on Ozone Generation in Dielectric Cylinder-Cylinder DBD Ozone Generator

abstract: A cylinder-cylinder dielectric-barrier discharge (DBD) ozone generator has been developed. The ozone concentration was measured at an oxygen pressure of 1.02 atm and an oxygen flow rate of 12 L/min. The heat generated in the ozone tube during the operation of the ozone generator was investigated using a thermal camera. The infrared (IR) images and corresponding temperatures of the ozone tube surfaces at different operating times were observed. The IR images showed the generating of the inhomogeneous heat-up along the ozone tube surface which results in the decrease of the ozone production efficiency.


IntraNav – Low-Cost Indoor Localization & Navigation System For industry and consumer markets


title: A 96-GHz Ortho-Mode Transducer for the Polatron

abstract: We describe the design, simulation, fabrication, and performance of a 96-GHz ortho-mode transducer (OMT) to be used for the Polatron-a bolometric receiver with polarization capability. The OMT has low loss, good isolation, and moderately broad bandwidth, and its performance closely resembles simulation results.


title: Clinical Information Extraction via Convolutional Neural Network

abstract: We report an implementation of a clinical information extraction tool that leverages deep neural network to annotate event spans and their attributes from raw clinical notes and pathology reports. Our approach uses context words and their part-of-speech tags and shape information as features. Then we hire temporal (1D) convolutional neural network to learn hidden feature representations. Finally, we use Multilayer Perceptron (MLP) to predict event spans. The empirical evaluation demonstrates that our approach significantly outperforms baselines.


title: Probe retrieval in ptychographic coherent diffractive imaging.

abstract: Ptychography is a coherent diffractive imaging method that uses multiple diffraction patterns obtained through the scan of a localized illumination on the specimen. Until recently, reconstruction algorithms for ptychographic datasets needed the a priori knowledge of the incident illumination. A new reconstruction procedure that retrieves both the specimen's image and the illumination profile was recently demonstrated with hard X-ray data. We present here the algorithm in greater details and illustrate its practical applicability with a visible light dataset. Improvements in the quality of the reconstruction are shown and compared to previous reconstruction techniques. Implications for future applications with other types of radiation are discussed.


title: Bluetooth telemedicine Processor for multichannel biomedical signal transmission via mobile cellular networks

abstract: One of the emerging issues in m-Health is how best to exploit the mobile communications technologies that are now almost globally available. The challenge is to produce a system to transmit a patient's biomedical signals directly to a hospital for monitoring or diagnosis, using an unmodified mobile telephone. The paper focuses on the design of a processor, which samples signals from sensors on the patient. It then transmits digital data over a Bluetooth link to a mobile telephone that uses the General Packet Radio Service. The modular design adopted is intended to provide a "future-proofed" system, whose functionality may be upgraded by modifying the software.


title: Gated-Attention Architectures for Task-Oriented Language Grounding

abstract: To perform tasks specified by natural language instructions, autonomous agents need to extract semantically meaningful representations of language and map it to visual elements and actions in the environment. This problem is called task-oriented language grounding. We propose an end-to-end trainable neural architecture for task-oriented language grounding in 3D environments which assumes no prior linguistic or perceptual knowledge and requires only raw pixels from the environment and the natural language instruction as input. The proposed model combines the image and text representations using a Gated-Attention mechanism and learns a policy to execute the natural language instruction using standard reinforcement and imitation learning methods. We show the effectiveness of the proposed model on unseen instructions as well as unseen maps, both quantitatively and qualitatively. We also introduce a novel environment based on a 3D game engine to simulate the challenges of task-oriented language grounding over a rich set of instructions and environment states.


title: Widely linear general Kalman filter for stereophonic acoustic echo cancellation

abstract: The stereophonic acoustic echo cancellation (SAEC) problem is usually modelled as a two-input/two-output system with real random variables. Recently, the SAEC scheme was recast as a single-input/single-output system with complex random variables, thanks to the widely linear (WL) model. In this paper, we motivate the use of a more general form of the Kalman filter with the WL model for SAEC. Simulation results indicate that this algorithm outperforms the recursive least-squares (RLS) algorithm, which is usually considered as the benchmark for SAEC.


title: Deep Hashing Network for Unsupervised Domain Adaptation

abstract: In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a large amount of labeled data, which is an expensive process in terms of time, labor and human expertise. Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain. Further, the explosive growth of digital data has posed a fundamental challenge concerning its storage and retrieval. Due to its storage and retrieval efficiency, recent years have witnessed a wide application of hashing in a variety of computer vision applications. In this paper, we first introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms. The dataset contains images of a variety of everyday objects from multiple domains. We then propose a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data. To the best of our knowledge, this is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem. Our extensive empirical studies on multiple transfer tasks corroborate the usefulness of the framework in learning efficient hash codes which outperform existing competitive baselines for unsupervised domain adaptation.


Using News Articles for Real-time Cross-Lingual Event Detection and Filtering


title: Detection of Anomalies in Large Scale Accounting Data using Deep Autoencoder Networks

abstract: Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or forensic investigations. Nowadays, the majority of applied techniques refer to handcrafted rules derived from known fraud scenarios. While fairly successful, these rules exhibit the drawback that fraudsters gradually adapt and find ways to circumvent them. In addition, these rigid rules often fail to generalize beyond known fraud scenarios. To overcome this challenge we propose a novel method of detecting anomalous journal entries using deep autoencoder networks. We demonstrate that the trained networks' reconstruction error regularized by the individual attribute probabilities of a journal entry can be interpreted as a highly adaptive anomaly assessment. Our empirical study, based on two datasets of real-world journal entries, demonstrates the effectiveness of the approach and outperforms several baseline anomaly detection methods. Resulting in a fraction of less than 0.15% (0.7%) of detected anomalous entries while achieving a high detection precision of 19.71% (9.26%). Initial feedback received by accountants underpinned the quality of our approach capturing highly relevant anomalies in the data. We envision this method as an important supplement to the forensic examiners' toolbox.


title: WORKING PAPER Joint Optimization of Customer Segmentation and Marketing Policy to Maximize Long-Term Profitability

abstract: With the advent of one-to-one marketing media, e.g. targeted direct mail or internet marketing, the opportunities to develop targeted marketing (customer relationship management) campaigns are enhanced in such a way that it is now both organizationally and economically feasible to profitably support a substantially larger number of marketing segments. However, the problem of what segments to distinguish, and what actions to take towards the different segments increases substantially in such an environment. A systematic analytic procedure optimizing both steps would be very welcome. In this study, we present a joint optimization approach addressing two issues: (1) the segmentation of customers into homogeneous groups of customers, (2) determining the optimal policy (i.e., what action to take from a set of available actions) towards each segment. We implement this joint optimization framework in a direct-mail setting for a charitable organization. Many previous studies in this area highlighted the importance of the following variables: R(ecency), F(requency), and M(onetary value). We use these variables to segment customers. In a second step, we determine which marketing policy is optimal using markov decision processes, following similar previous applications. The attractiveness of this stochastic dynamic programming procedure is based on the long-run maximization of expected average profit. Our contribution lies in the combination of both steps into one optimization framework to obtain an optimal allocation of marketing expenditures. Moreover, we control segment stability and policy performance by a bootstrap procedure. Our framework is illustrated by a real-life application. The results show that the proposed model outperforms a CHAID segmentation.


title: The Boundaries of Walking Stability: Viability and Controllability of Simple Models

abstract: From which states and with what controls can a biped avoid falling or reach a given target state? What is the most robust way to do these? So as to help with the design of walking robot controllers, and perhaps give insights into human walking, we address these questions using two simple 2-D models: the inverted pendulum (IP) and linear inverted pendulum (LIP). Each has one state variable at mid-stance, i.e., hip velocity, and two state-dependent controls at each step, i.e., push-off magnitude and step length (IP) and step time and length (LIP). Using practical targets and constraints, we compute all combinations of initial states and control actions for the next step, such that the robot can, with the best possible future controls, avoid falling for $n$ steps or reach a target within $n$ steps. All such combinations constitute regions in the combined space of states and controls. Farther from the boundaries of these regions, the robot tolerates larger errors and disturbances. Furthermore, for these models, and thus possibly real bipeds, usually if it is possible to avoid falling, it is possible to reach the target, and if it is possible to reach the target, it is possible to do so in two steps.


Thirty-Five Years of Research on Neuro-Linguistic Programming. NLP Research Data Base. State of the Art or Pseudoscientific Decoration?


title: A comparative study between LBP and Haar-like features for Face Detection using OpenCV

abstract: Face Detection is an important step in any face recognition systems, for the purpose of localizing and extracting face region from the rest of the images. There are many techniques, which have been proposed from simple edge detection techniques to advance techniques such as utilizing pattern recognition approaches. This paper evaluates two methods of face detection, her features and Local Binary Pattern features based on detection hit rate and detection speed. The algorithms were tested on Microsoft Visual C++ 2010 Express with OpenCV library. The experimental results show that Local Binary Pattern features are most efficient and reliable for the implementation of a real-time face detection system.


title: MAQS: a personalized mobile sensing system for indoor air quality monitoring

abstract: Most people spend more than 90% of their time indoors; indoor air quality (IAQ) influences human health, safety, productivity, and comfort. This paper describes MAQS, a personalized mobile sensing system for IAQ monitoring. In contrast with existing stationary or outdoor air quality sensing systems, MAQS users carry portable, indoor location tracking sensors that provide personalized IAQ information. To improve accuracy and energy efficiency, MAQS incorporates three novel techniques: (1) an accurate temporal n-gram augmented Bayesian room localization method that requires few Wi-Fi fingerprints; (2) an air exchange rate based IAQ sensing method, which measures general IAQ using only CO2 sensors; and (3) a zone-based proximity detection method for collaborative sensing, which saves energy and enables data sharing among users. MAQS has been deployed and evaluated via user study. Detailed evaluation results demonstrate that MAQS supports accurate personalized IAQ monitoring and quantitative analysis with high energy efficiency.


title: A Systematic Study of Semantic Vector Space Model Parameters

abstract: We present a systematic study of parameters used in the construction of semantic vector space models. Evaluation is carried out on a variety of similarity tasks, including a compositionality dataset, using several source corpora. In addition to recommendations for optimal parameters, we present some novel findings, including a similarity metric that outperforms the alternatives on all tasks considered.


title: Fast Algorithms for Computing the Smallest k-Enclosing Circle

abstract: For a set $P$ of $n$ points in the plane and an integer $k \leq n$, consider the problem of finding the smallest circle enclosing at least $k$ points of $P$. We present a randomized algorithm that computes in $O( n k )$ expected time such a circle, improving over previously known algorithms. Further, we present a linear time $\delta$-approximation algorithm that outputs a circle that contains at least $k$ points of $P$ and has radius less than $(1+\delta)r_{opt}(P,k)$, where $r_{opt}(P,k)$ is the radius of the minimum circle containing at least $k$ points of $P$. The expected running time of this approximation algorithm is $O(n + n \cdot\min((1/k\delta^3) \log^2 (1/\delta),k))$.


title: Design and implementation of intelligent energy distribution management with photovoltaic system

abstract: As increasing power consumption is becoming a huge problem, renewable energy has been highlighted recently. Many companies and research centers study this new sustainable energy, and various products have appeared to the public. However, this kind of researches concentrates on the elemental technologies, and now a management system is needed to manage these technologies to maximize energy efficiency. In this paper, we propose the system of Intelligent Energy Distribution Management (iEDM) to monitor fast-changing environmental variables and manage solar power flexibly. Compared with normal utility interactive systems, the iEDM improves the energy efficiency up to 5.6 percent.


title: A Motion Retargeting Method for Effective Mimicry-based Teleoperation of Robot Arms

abstract: In this paper, we introduce a novel interface that allows novice users to effectively and intuitively tele-operate robot manipulators. The premise of our method is that an interface that allows its user to direct a robot arm using the natural 6-DOF space of his/her hand would afford effective direct control of the robot; however, a direct mapping between the user's hand and the robot's end effector is impractical because the robot has different kinematic and speed capabilities than the human arm. Our key technical idea that by relaxing the constraint of the direct mapping between hand position and orientation and end effector configuration, a system can provide the user with the feel of direct control, while still achieving the practical requirements for telemanipulation, such as motion smoothness and singularity avoidance. We present methods for implementing a motion retargeting solution that achieves this relaxed control using constrained optimization and describe a system that utilizes it to provide real-time control of a robot arm. We demonstrate the effectiveness of our approach in a user study that shows novice users can complete a range of tasks more efficiently and enjoyably using our relaxed-mimicry based interface compared to standard interfaces.


title: Actions in context

abstract: This paper exploits the context of natural dynamic scenes for human action recognition in video. Human actions are frequently constrained by the purpose and the physical properties of scenes and demonstrate high correlation with particular scene classes. For example, eating often happens in a kitchen while running is more common outdoors. The contribution of this paper is three-fold: (a) we automatically discover relevant scene classes and their correlation with human actions, (b) we show how to learn selected scene classes from video without manual supervision and (c) we develop a joint framework for action and scene recognition and demonstrate improved recognition of both in natural video. We use movie scripts as a means of automatic supervision for training. For selected action classes we identify correlated scene classes in text and then retrieve video samples of actions and scenes for training using script-to-video alignment. Our visual models for scenes and actions are formulated within the bag-of-features framework and are combined in a joint scene-action SVM-based classifier. We report experimental results and validate the method on a new large dataset with twelve action classes and ten scene classes acquired from 69 movies.


title: Efficient Patch-Wise Non-Uniform Deblurring for a Single Image

abstract: In this paper, we address the problem of estimating a latent sharp image from a single spatially variant blurred image. Non-uniform deblurring methods based on projective motion path models formulate the blur as a linear combination of homographic projections of a clear image. But they are computationally expensive and require large memory due to the calculation and storage of a large number of the projections. Patch-wise non-uniform deblurring algorithms have been proposed to estimate each kernel locally by a uniform deblurring algorithm, which does not require to calculate and store the projections. The key issues of these methods are the accuracy of kernel estimation and the identification of erroneous kernels. To perform accurate kernel estimation, we employ the total variation (TV) regularization to recover a latent image, in which the edges are better enhanced and the ringing artifacts are reduced, rather than Tikhonov regularization that previous algorithms adopt. Thus blur kernels can be estimated more accurately from the latent image and estimated in a closed form while previous methods cannot estimate kernels in closed forms. To identify the erroneous kernels, we develop a novel metric, which is able to measure the similarity between the neighboring kernels. After replacing the erroneous kernels with the well-estimated ones, a clear image is obtained. The experiments show that our approach can achieve better results on the real-world blurry images while using less computation and memory.


abstract: Bias in Spatial Memory: Prototypes or Relational Categories? L. Elizabeth Crawford (lcrawfor@richmond.edu) Department of Psychology, University of Richmond, 28 Westhampton Way Richmond, VA 23173 USA David Landy (dlandy@indiana.edu) Department of Psychological & Brain Sciences, Indiana University, 1101 E. 10 th St., Bloomington IN 47405 USA Amanda N. Presson (Nicole.Presson@richmond.edu) Department of Psychology, University of Richmond, 28 Westhampton Way Richmond, VA 23173 USA Abstract Two experiments examined participants’ use of independent and relational category structures in a spatial memory task. When estimating the locations of dots presented on a rectangular display, many participants appeared to divide the space into left and right relational categories, biasing estimates away from the center of the screen and toward the outer edges, in contrast to prior work showing biases toward centrally located prototypes. More participants showed this relational pattern at short interstimulus intervals than at long ones. The results suggest that participants flexibly make use of different types of spatial structure under different task demands. Keywords: spatial memory; categorization. Introduction Categories profoundly affect our interpretation of situations. But how are categories constructed, and how do particular situations impact this construction? Recent studies distinguish between categories defined in isolation and those that are defined in relation to other categories (see Chin- Parker & Ross, 2004; Genter & Kurtz, 2005; Goldstone, 1996; Yamauchi & Markman, 1998). This distinction between relational and non-relational category structures comes out of the literature on categories of objects. To our knowledge, there has been little discussion of this issue in the spatial domain, despite the large literature on spatial category effects. Thus it is not known what factors might influence the kinds of category structure people use to organize space. To the extent that categories of objects and of space serve similar functions, the distinction between relational and independent categories may emerge in the spatial domain as well. Research on categories of objects has shown that when a category is construed as independent, the category center is generally treated as the prototype, leading to fast, accurate classification and high typicality ratings for central values (e.g., Goldstone, 1996; Posner & Keele, 1968; Rosch, 1975). However, when categories are established in a contrasting relation to one another, the values that show advantages in classification and higher typicality ratings tend to be more extreme than the central values (e.g., Atran, 1999; Barsalou, 1985; Goldstone, Steyvers, & Rogosky, 2003; Davis & Love, 2010). That is, the best exemplars of contrasting categories are idealized caricatures that exaggerate the differences between the categories, rather than the most typical values. These different category structures have also been shown to influence how individual category members are remembered. When items within a single category vary along a continuous dimension such as size or hue, estimates of individual items are biased toward the central value of the presented set (e.g., Huttenlocher, Hedges, & Vevea, 2000), but when that set is divided into two distinct categories, estimates may be biased away from the value corresponding to the category boundary (Goldstone, 1995, but see also Sailor & Antoine, 2005). Patterns of bias in estimates have also been used to investigate categorization in the spatial domain. A common finding is that when participants reproduce the location of a stimulus within a bounded spatial frame, their location estimates are systematically biased toward some locations and away from others. A prominent model explains these biases as category effects that arise during reconstructive memory (Huttenlocher, Hedges, & Duncan, 1991). In a process likened to Bayesian estimation, the model posits that people use the category information as a prior distribution in order to adjust for the inexactness of fine- grained memory, leading to systematic biases toward the center of the prior distribution. As predicted by this account, when fine-grained memory is worsened by adding a delay, bias becomes more extreme, suggesting that the category is given more weight during memory reconstruction. This model offers an account of the large literature on how categories influence memories of stimulus features and suggests that bias in estimates can reveal the category structure that people impose on space. The categories that are revealed by bias in spatial memory tend to be consistent across many variations in experiment procedures. In general, estimates are biased away from outer edges and internal axes of symmetry and toward centrally located prototypes. For example, within a circle, estimates are biased toward the centers of mass of the four quadrants bounded by the horizontal and vertical axes of symmetry (Huttenlocher, et al., 1991; Huttenlocher, Hedges, Corrigan, & Crawford, 2004). Stretching the circle along one dimension so that it becomes an ellipse moves the prototypes accordingly (Wedell, Fitting, & Allen, 2007). Stimuli within a rectangular frame, such as sandbox or a rectangle drawn on paper (Huttenlocher, Newcombe, &


title: Recommender System Based on Product Taxonomy in E-Commerce Sites

abstract: This article describes a novel and fast recommender system for websites based on product taxonomy and user click patterns. The proposed system consists of the following four steps. First, a product-preference matrix for each customer is estimated through a linear combination of the click, basket placement, and purchase statuses. Second, the preference matrix of the genre and that of the specific type are constructed by computing the ratios of the number of clicks, basket placements, and purchases of a product to the totals. Third, cluster analysis is performed using the genre preference matrix, and a neighborhood formation process is conducted using the specific-type preference matrix. Finally, data are generated for prediction, in which a customer's preference for specific types is greater than a given threshold value. Using these data sets, computational burden and processing time are greatly reduced. The effectiveness of the proposed approach was assessed by applying the F1 metric to an experimental e-commerce website. The proposed method outperformed conventional methods.


title: Treatment of anxiety disorders

abstract: Anxiety disorders (generalized anxiety disorder, panic disorder/agoraphobia, social anxiety disorder, and others) are the most prevalent psychiatric disorders, and are associated with a high burden of illness. Anxiety disorders are often underrecognized and undertreated in primary care. Treatment is indicated when a patient shows marked distress or suffers from complications resulting from the disorder. The treatment recommendations given in this article are based on guidelines, meta-analyses, and systematic reviews of randomized controlled studies. Anxiety disorders should be treated with psychological therapy, pharmacotherapy, or a combination of both. Cognitive behavioral therapy can be regarded as the psychotherapy with the highest level of evidence. First-line drugs are the selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors. Benzodiazepines are not recommended for routine use. Other treatment options include pregabalin, tricyclic antidepressants, buspirone, moclobemide, and others. After remission, medications should be continued for 6 to 12 months. When developing a treatment plan, efficacy, adverse effects, interactions, costs, and the preference of the patient should be considered.


title: Sparse Representation for Color Image Restoration

abstract: Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task and shown to perform very well for various grayscale image processing tasks. In this paper, we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in. This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper.


title: A Particle-Based Solution for Modeling and Tracking Dynamic Digital Elevation Maps

abstract: Digital elevation maps are simple yet powerful representations of complex 3-D environments. These maps can be built and updated using various sensors and sensorial data processing algorithms. This paper describes a novel approach for modeling the dynamic 3-D driving environment, the particle-based dynamic elevation map, each cell in this map having, in addition to height, a probability distribution of speed in order to correctly describe moving obstacles. The dynamic elevation map is represented by a population of particles, each particle having a position, a height, and a speed. Particles move from one cell to another based on their speed vectors, and they are created, multiplied, or destroyed using an importance resampling mechanism. The importance resampling mechanism is driven by the measurement data provided by a stereovision sensor. The proposed model is highly descriptive for the driving environment, as it can easily provide an estimation of the height, speed, and occupancy of each cell in the grid. The system was proven robust and accurate in real driving scenarios, by comparison with ground truth data.


title: Multimodal speech synthesis architecture for unsupervised speaker adaptation

abstract: This paper proposes a new architecture for speaker adaptation of multi-speaker neural-network speech synthesis systems, in which an unseen speaker's voice can be built using a relatively small amount of speech data without transcriptions. This is sometimes called "unsupervised speaker adaptation". More specifically, we concatenate the layers to the audio inputs when performing unsupervised speaker adaptation while we concatenate them to the text inputs when synthesizing speech from text. Two new training schemes for the new architecture are also proposed in this paper. These training schemes are not limited to speech synthesis, other applications are suggested. Experimental results show that the proposed model not only enables adaptation to unseen speakers using untranscribed speech but it also improves the performance of multi-speaker modeling and speaker adaptation using transcribed audio files.