title: A Low-Complexity Pedestrian Detection Framework for Smart Video Surveillance Systems

abstract: Pedestrian detection is a key problem in computer vision and is currently addressed with increasingly complex solutions involving compute-intensive features and classification schemes. In this scope, histogram of oriented gradients (HOG) in conjunction with linear support vector machine (SVM) classifier is considered to be the single most discriminative feature that has been adopted as a stand-alone detector as well as a key instrument in advance systems involving hybrid features and cascaded detectors. In this paper, we propose a pedestrian detection framework that is computationally less expensive as well as more accurate than HOG-linear SVM. The proposed scheme exploits the discriminating power of the locally significant gradients in building orientation histograms without involving complex floating point operations while computing the feature. The integer-only feature allows the use of powerful histogram inter-section kernel SVM classifier in a fast lookup-table-based implementation. Resultantly, the proposed framework achieves at least 3% more accurate detection results than HOG on standard data sets while being 1.8 and 2.6 times faster on conventional desktop PC and embedded ARM platforms, respectively, for a single scale pedestrian detection on VGA resolution video. In addition, hardware implementation on Altera Cyclone IV field-programmable gate array results in more than 40% savings in logic resources compared with its HOG-linear SVM competitor. Hence, the proposed feature and classification setup is shown to be a better candidate as the single most discriminative pedestrian detector than the currently accepted HOG-linear SVM.


title: A Comparative Evaluation of SiC Power Devices for High-Performance Domestic Induction Heating

abstract: This paper presents a comparative evaluation of silicon carbide power devices for the domestic induction heating (IH) application, which currently has a major industrial, economic and social impact. The compared technologies include MOSFETs, normally on and normally off JFETs, as well as BJTs. These devices have been compared according to different figure-of-merit evaluating conduction and switching performance, efficiency, impact of temperature, as well as other driving and protection issues. To perform the proposed evaluation, a versatile test platform has been developed. As a result of this study, several differential features are identified and discussed, taking into account the pursued induction heating application.


title: Botnets and Internet of Things Security

abstract: Recent distributed denial-of-service attacks demonstrate the high vulnerability of Internet of Things (IoT) systems and devices. Addressing this challenge will require scalable security solutions optimized for the IoT ecosystem.


title: Coexistence of UWB and Narrowband Systems in Nakagami/Rice Channels

abstract: This work evaluates the performance of ultrawide bandwidth (UWB) communication systems in the presence of narrowband interference. A closed-form bit error probability (BEP) expression for binary coherent spread-spectrum (SS) systems based on matched filter reception is derived by approximating the narrowband interference as a tone interferer. The approach developed is specialized to derive exact expressions for the BEP of time hopping (TH) and direct sequence (DS) system with an asynchronous tone interferer. The scenario considered includes Rice fading on the interferer and Nakagami-m fading on the desired signal. The analysis developed proves that the analytical result derived is useful in assessing the possible coexistence of TH and DS systems with existing narrowband interferers. Finally, the approach is extended to frequency-hopping (FH) interferers.


title: Defining the business model in the new world of digital business

abstract: Recent rapid advances in ICTs, specifically in Internet and mobile technologies, have highlighted the rising importance of the Business Model (BM) in Information Systems (IS). Despite agreement on its importance to an organization’s success, the concept is still fuzzy and vague, and there is no consensus regarding its definition. Furthermore, understanding the BM domain by identifying its meaning, fundamental pillars, and its relevance to other business concepts is by no means complete. In this paper we aim to provide further clarification by first presenting a classification of definitions found in the IS literature; second, proposing guidelines on which to develop a more comprehensive definition in order to reach consensus; and third, identifying the four main business model concepts and values and their interaction, and thus place the business model within the world of digital business. Based on this discussion, we propose a new definition for the business model that we argue is more appropriate to this new world.


title: Expressive speech-driven facial animation

abstract: Speech-driven facial motion synthesis is a well explored research topic. However, little has been done to model expressive visual behavior during speech. We address this issue using a machine learning approach that relies on a database of speech-related high-fidelity facial motions. From this training set, we derive a generative model of expressive facial motion that incorporates emotion control, while maintaining accurate lip-synching. The emotional content of the input speech can be manually specified by the user or automatically extracted from the audio signal using a Support Vector Machine classifier.


title: Defining the Quality Business Process Reference Models

abstract: This paper presents an empirical contribution to the quality of business process reference models, a tool used within enterprises for knowledge management and business process management. Quality in this case is defined as the existence of user desired attributes. The primary goal of the case studies was to examine user opinions of the quality attributes of reference models. The paper concludes that consistency and completeness of the model and access to feedback mechanisms are amongst the most critical attributes of reference model quality. New concepts such as a terminology-matching-dictionary and the dimension of economic efficiency of use are also introduced.


title: Application of Support Vector Machine for Detecting Rice Diseases Using Shape and Color Texture Features

abstract: For detecting rice disease early and accurately, we presented an application of image processing techniques andSupport Vector Machine (SVM) for detecting rice diseases.Rice disease spots were segmented and their shape and texture features were extracted. The SVM method was employed to classify rice bacterial leaf blight, rice sheath blight and rice blast. The results showed that SVM could effectively detect and classify these disease spots to an accuracy of 97.2%.


title: An isolated multiport bidirectional DC-DC converter for PV-battery-DC microgrid applications

abstract: This paper proposes a new isolated, multiport, bidirectional DC-DC converter with the least number of switches for photovoltaic (PV)-battery-DC microgrid applications. The proposed converter is able to perform the maximum power point tracking (MPPT) control for the PV system when solar energy is available as well as control the charge and discharge of the battery with the PV system and the DC microgrid. Simulation and experimental studies are carried out and validate the proposed converter for simultaneous power management of a PV-battery system connected to a DC microgrid.


title: Segmentation and pre-recognition of Arabic handwriting

abstract: We propose a novel algorithm for the segmentation and prerecognition of offline handwritten Arabic text. Our character segmentation method over-segments each word, and then removes extra breakpoints using knowledge of letter shapes. On a test set of 200 images, 92.3% of the segmentation points were detected correctly, with 5.1% instances of over-segmentation. The prerecognition component annotates each detected letter with shape information, to be used for recognition in future work.


title: Exogenous hormones induce poor rates of oviposition in the anurans, Litoria moorei and L. aurea

abstract: Combinations of commercially available hormones (luteinising hormone-realeasing hormone agonist, human chorionic gonadotrophin and progesterone) were given intra-coelomically to gravid females of two Australian bell frogs, Litoria moorei and L. aurea. The injections were not sufficient to cause normal ovulation and egg laying in the absence of males. Further studies are still required to develop a safe method of programming ovulation and oviposition in these and other recalcitrant species to obtain eggs that have not been exposed to sperm.


title: Cognitive Techniques for Early Detection of Cybersecurity Events

abstract: The early detection of cybersecurity events such as attacks is challenging given the constantly evolving threat landscape. Even with advanced monitoring, sophisticated attackers can spend as many as 146 days in a system before being detected. This paper describes a novel, cognitive framework that assists a security analyst by exploiting the power of semantically rich knowledge representation and reasoning with machine learning techniques. Our Cognitive Cybersecurity system ingests information from textual sources, and various agents representing host and network-based sensors, and represents this information in a knowledge graph. This graph uses terms from an extended version of the Unified Cybersecurity Ontology. The system reasons over the knowledge graph to derive better actionable intelligence to security administrators, thus decreasing their cognitive load and increasing their confidence in the system. We have developed a proof of concept framework for our approach and demonstrate its capabilities using a custom-built ransomware instance that is similar to WannaCry.


title: Hierarchical Bayesian networks: an approach to classification and learning for structured data

abstract: Bayesian Networks are one of the most popular formalisms for reasoning under uncertainty. Hierarchical Bayesian Networks (HBNs) are an extension of Bayesian Networks that are able to deal with structured domains, using knowledge about the structure of the data to introduce a bias that can contribute to improving inference and learning methods. In effect, nodes in an HBN are (possibly nested) aggregations of simpler nodes. Every aggregate node is itself an HBN modelling independences inside a subset of the whole world under consideration. In this paper we discuss how HBNs can be used as Bayesian classifiers for structured domains. We also discuss how HBNs can be further extended to model more complex data structures, such as lists or sets, and we present the results of preliminary experiments on the mutagenesis dataset.


A Simple, Fast, and Accurate Method to Estimate Large Phylogenies by Maximum Likelihood


title: Learning from Multiple Teacher Networks

abstract: Training thin deep networks following the student-teacher learning paradigm has received intensive attention because of its excellent performance. However, to the best of our knowledge, most existing work mainly considers one single teacher network. In practice, a student may access multiple teachers, and multiple teacher networks together provide comprehensive guidance that is beneficial for training the student network. In this paper, we present a method to train a thin deep network by incorporating multiple teacher networks not only in output layer by averaging the softened outputs (dark knowledge) from different networks, but also in the intermediate layers by imposing a constraint about the dissimilarity among examples. We suggest that the relative dissimilarity between intermediate representations of different examples serves as a more flexible and appropriate guidance from teacher networks. Then triplets are utilized to encourage the consistence of these relative dissimilarity relationships between the student network and teacher networks. Moreover, we leverage a voting strategy to unify multiple relative dissimilarity information provided by multiple teacher networks, which realizes their incorporation in the intermediate layers. Extensive experimental results demonstrated that our method is capable of generating a well-performed student network, with the classification accuracy comparable or even superior to all teacher networks, yet having much fewer parameters and being much faster in running.


title: Multi-classification approaches for classifying mobile app traffic

abstract: Abstract The growing usage of smartphones in everyday life is deeply (and rapidly) changing the nature of traffic traversing home and enterprise networks, and the Internet. Different tools and middleboxes, such as performance enhancement proxies, network monitors and policy enforcement devices, base their functions on the knowledge of the applications generating the traffic. This requirement is tightly coupled to an accurate traffic classification, being exacerbated by the (daily) expanding set of apps and the moving-target nature of mobile traffic. On the top of that, the increasing adoption of encrypted protocols (such as TLS) makes classification even more challenging, defeating established approaches (e.g., Deep Packet Inspection). To this end, in this paper we aim to improve the performance of classification of mobile apps traffic by proposing a multi-classification (viz. fusion) approach, intelligently-combining outputs from state-of-the-art classifiers proposed for mobile and encrypted traffic classification. Under this framework, four classes of different combiners (differing in whether they accept soft or hard classifiers' outputs, the training requirements, and the learning philosophy) are taken into account and compared. The present approach enjoys modularity, as any classifier may be readily plugged-in/out to improve performance further. Finally, based on a dataset of (true) users' activity collected by a mobile solutions provider, our results demonstrate that classification performance can be improved according to all considered metrics, up to + 9.5 % (recall score) with respect to the best state-of-the-art classifier. The proposed system is also capitalized to validate a novel pre-processing of traffic traces, here developed, and assess performance sensitivity to traffic object (temporal) segmentation, before actual classification.


title: HUE: A hybrid ultrasonic and electrostatic variable friction touchscreen

abstract: The HUE tablet, presented in this work, leverages ultrasonic friction reduction and electrovibration to enhance the dynamic friction range of its surface. Prototypes using a single friction-modulation approach are numerous, but none offer the option to perform friction-switching above and below a base level on a single platform. In applications involving active touch, the ability to dynamically change between high and low friction states may allow for more discernible haptic interactions. In this paper we present the design of a hybrid touchscreen and perform a frequency analysis of friction force measurements taken along its surface to assess the efficacy of hybrid texturerendering using alternating ultrasonic and electrostatic signals.


title: SERvICE: A Software Defined Framework for Integrated Space-Terrestrial Satellite Communication

abstract: The existing satellite communication systems suffer from traditional design, such as slow configuration, inflexible traffic engineering, and coarse-grained Quality of Service (QoS) guarantee. To address these issues, in this paper, we propose SERvICE, a Software dEfined fRamework for Integrated spaCe-tErrestrial satellite Communication, based on Software Defined Network (SDN) and Network Function Virtualization (NFV). We first introduce the three planes of SERvICE, Management Plane, Control Plane, and Forwarding Plane. The framework is designed to achieve flexible satellite network traffic engineering and fine-grained QoS guarantee. We analyze the agility of the space component of SERvICE. Then, we give a description of the implementation of the prototype with the help of the Delay Tolerant Network (DTN) and OpenFlow. We conduct two experiments to validate the feasibility of SERvICE and the functionality of the prototype. In addition, we propose two heuristic algorithms, namely the QoS-oriented Satellite Routing (QSR) algorithm and the QoS-oriented Bandwidth Allocation (QBA) algorithm, to guarantee the QoS requirement of multiple users. The algorithms are also evaluated in the prototype. The experimental results show the efficiency of the proposed algorithms in terms of file transmission delay and transmission rate.


title: Revisiting IM2GPS in the Deep Learning Era

abstract: Image geolocalization, inferring the geographic location of an image, is a challenging computer vision problem with many potential applications. The recent state-of-the-art approach to this problem is a deep image classification approach in which the world is spatially divided into cells and a deep network is trained to predict the correct cell for a given image. We propose to combine this approach with the original Im2GPS approach in which a query image is matched against a database of geotagged images and the location is inferred from the retrieved set. We estimate the geographic location of a query image by applying kernel density estimation to the locations of its nearest neighbors in the reference database. Interestingly, we find that the best features for our retrieval task are derived from networks trained with classification loss even though we do not use a classification approach at test time. Training with classification loss outperforms several deep feature learning methods (e.g. Siamese networks with contrastive of triplet loss) more typical for retrieval applications. Our simple approach achieves state-of-the-art geolocalization accuracy while also requiring significantly less training data.


Chapter in Wiley Encyclopedia of Electrical and Electronics Engineering


title: A Naïve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices

abstract: This paper proposes a hybridized framework of Support Vector Machine (SVM) with K-Nearest Neighbor approach for Indian stock market indices prediction. The objective of this paper is to get in-depth knowledge in the stock market in Indian Scenario with the two indices such as, Bombay Stock Exchange (BSE Sensex) and CNX Nifty using technical analysis methods and tools such as predicting closing price, volatility and momentum of the stock market for the available data. This hybrid model uses SVM with different kernel functions to predict profit or loss, and the output of SVM helps to compute best nearest neighbor from the training set to predict future of stock value in the horizon of 1 day, 1 week and 1 month. The proposed SVM and KNN based prediction model is experienced with the above mentioned distinguished stock market indices and the performance of proposed model has been computed using Mean Squared Error and also been compared with recent developed models such as FLIT2NS and CEFLANN respectively. The limitation of both of those existing models undergoes complex weight updating procedures, whereas, proposed SVM-KNN hybridized model scales relatively well to high dimensional data and the trade-off between classifier complexity and error can be controlled explicitly and have better prediction capability.


title: When big data meets software-defined networking: SDN for big data and big data for SDN

abstract: Both big data and software-defined networking (SDN) have attracted great interests from both academia and industry. These two important areas have traditionally been addressed separately in the most of previous works. However, on the one hand, the good features of SDN can greatly facilitate big data acquisition, transmission, storage, and processing. On the other hand, big data will have profound impacts on the design and operation of SDN. In this paper, we present the good features of SDN in solving several issues prevailing with big data applications, including big data processing in cloud data centers, data delivery, joint optimization, scientific big data architectures and scheduling issues. We show that SDN can manage the network efficiently for improving the performance of big data applications. In addition, we show that big data can benefit SDN as well, including traffic engineering, cross-layer design, defeating security attacks, and SDN-based intra and inter data center networks. Moreover, we discuss a number of open issues that need to be addressed to jointly consider big data and SDN in future research.


title: Copy-Move Forgery Detection Using Color Space and Moment Invariants-Based Features

abstract: Due to the availability of different image processing tools, now-a-days Copy-move forgery is emerging immensely. Addressing this problem is being more deterrent because of different types of image post processing techniques. In this paper, we propose a robust approach to detect copy-move forgery detection which ensembles human vision information from color space of the image and moment invariants features to represent the objects of the images. Integration of color perception and object representation generates a powerful feature vector of reasonable size which ensures detection against different types of challenges. Experiments on benchmark dataset demonstrate the efficiency and robustness of the proposed approach on different image post processing methods.


title: Meta-Data Adapter: A Way to Enable Spatial Information Data Grid

abstract: Spatial information data grid is powerful for distributed data sharing and collaborating. But the difference among meta-data produced by space agencies around the world makes it difficult to re-organize data and share them when enabling spatial information data grid. In this paper, a way named meta-data adapter is proposed, which can interpreter original meta-data with different syntax and semantic rules to required meta-data so that they can be organized as a distributed, heterogeneous, synergic, uniform interfaced, and inherent features based virtual remote sensing data catalog. Based on the meta-data adapter, spatial information data grid can be enabled. The experience of ldquoremote sensing data distribution system for WenChuan earthquake rescuingrdquo amazingly quick founding shows that meta-data adapter is a powerful and economical way to solve the problem of meta-data difference and enable spatial information grid.


title: Intelligent transport management system using sensor networks

abstract: This paper proposes distributed traffic monitoring and controlling model using sensors and dedicated traffic servers. This model is described as basic role-oriented processes communicating though primitive interaction protocols. The model is aimed to provide an enabling communications framework upon which multi-agent system models can be organized and built to be used for an simulation of an road map and to estimate the traffic behavior (to provide information about the best routes). This paper presents a hierarchical routing strategy for controlling the traffic of that region. The model assists the drivers to get the desired destination taking into account the current situation of traffic characteristics. It gives the estimated arrival time and the corresponding distance between a start and an arrival point. The necessary information is obtained from current traffic position using sensors (used as dynamic information and to characterize the traffic, for example if traffic is jammed on an area, we can predict alter paths). The information given by the advisory system has the form of self generated message according to the condition of traffic using the given algorithm. The proposed algorithm uses well known searching techniques and data structures for appropriate handling of traffic in a sensor based web model for traffic management. To achieve this objective the model uses proposed algorithm on parallel communicating servers which also communicate with end users.


title: Robust Adaptive Control of Multivariable Nonlinear Systems

abstract: Abstract : Our research is focused on defining robust adaptive control architectures that can be completely characterized with their performance bounds and robustness/stability margins. Research in this direction over the past several years has led to a powerful set of tools, known as L1 adaptive control theory. This new paradigm for design of adaptive control systems embeds the robustness specification explicitly into the control problem formulation (control objective) and allows for decoupling adaptation from robustness, limiting the speed of adaptation only by available hardware (CPU). The performance bounds can be predicted a priori based on the conservative bounds of uncertainty, and the time-delay margin of it can be tuned systematically. The results of this research have been leveraged into different programs across the country, including WP AFRL's Certification of Advanced Flight Critical Systems: Challenge Problem Integration and NASA s Integrated Resilient Aircraft Control . We also revealed some similarities with the disturbance observer (DOB) controllers and identified the main features in the difference between them. The key feature of this difference is that the estimation loop of the L1 adaptive architectures reconstructs the inverse map of the DOB architecture by fast estimation and with that avoids the number of challenges of DOB related to the plant inversion. Insights from this comparison were used towards modification of DOB with improved transient performance.


title: Bone extraction in X-ray images by analysis of line fluctuations

abstract: Segmentation of X-ray bone images is of concern in many medical applications such as detection of osteoporosis and bone fractures. Segmentation of such images is a challenging process. Varying brightness throughout the image makes it difficult to separate bones from background and soft tissue. Costume made as well as standard segmentation methods, such as active contour and region growing, have been applied to bone X-ray images. Although each method could perform well for some images, due to variety of bone structures and lighting conditions none of these methods can be considered as complete. In this paper we present a new bone segmentation method in which an image goes through preprocessing steps such as noise cancellation and edge detection. Analysis of intensity fluctuations in all rows of the image results in more accurate segmentation of bone regions. Visual evaluation show that the proposed algorithm segments bones better than conventional and some recent bone segmentation approaches.


title: Gray-level Image Enhancement By Particle Swarm Optimization

abstract: Particle Swarm Optimization (PSO) algorithms represent a new approach for optimization. In this paper image enhancement is considered as an optimization problem and PSO is used to solve it. Image enhancement is mainly done by maximizing the information content of the enhanced image with intensity transformation function. In the present work a parameterized transformation function is used, which uses local and global information of the image. Here an objective criterion for measuring image enhancement is used which considers entropy and edge information of the image. We tried to achieve the best enhanced image according to the objective criterion by optimizing the parameters used in the transformation function with the help of PSO. Results are compared with other enhancement techniques, viz. histogram equalization, contrast stretching and genetic algorithm based image enhancement.


title: A Proximity-Aware Hierarchical Clustering of Faces

abstract: In this paper, we propose an unsupervised face clustering algorithm called “Proximity-Aware Hierarchical Clustering” (PAHC) that exploits the local structure of deep representations. In the proposed method, a similarity measure between deep features is computed by evaluating linear SVM margins. SVMs are trained using nearest neighbors of sample data, and thus do not require any external training data. Clus- ters are then formed by thresholding the similarity scores. We evaluate the clustering performance using three challenging un- constrained face datasets, including Celebrity in Frontal-Profile (CFP), IARPA JANUS Benchmark A (IJB-A), and JANUS Challenge Set 3 (JANUS CS3) datasets. Experimental results demonstrate that the proposed approach can achieve significant improvements over state-of-the-art methods. Moreover, we also show that the proposed clustering algorithm can be applied to curate a set of large-scale and noisy training dataset while maintaining sufficient amount of images and their variations due to nuisance factors. The face verification performance on JANUS CS3 improves significantly by finetuning a DCNN model with the curated MS-Celeb-1M dataset which contains over three million face images.


ASAP++: Enriching the ASAP Automated Essay Grading Dataset with Essay Attribute Scores.


Fully Automatic Camera Calibration


title: Design of a Compact Dual-Band Annular-Ring Slot Antenna

abstract: This letter aims at miniaturizing an annular-ring slot antenna that is suitable for the 2.4/5-GHz dual-band operations. The miniaturization purpose was achieved by embedding in the center patch of the antenna a pair of slots to excite three resonant modes. The resonant band of the first excited resonant mode was lowered from that of the unperturbed annular-ring slot antenna, whereas those of the second and third excited resonant modes were combined to form a wide upper operating band by appropriately adjusting the dimensions of the embedded slots. The proposed antenna proves to have very similar copolarization radiation patterns in its two operating bands and have enough antenna gains for practical applications.


title: Lex—a lexical analyzer generator

abstract: Lex helps write programs whose control flow is directed by instances of regular expressions in the input stream. It is well suited for editor-script type transformations and for segmenting input in preparation for a parsing routine. Lex source is a table of regular expressions and corresponding program fragments. The table is translated to a program which reads an input stream, copying it to an output stream and partitioning the input into strings which match the given expressions. As each such string is recognized the corresponding program fragment is executed. The recognition of the expressions is performed by a deterministic finite automaton generated by Lex. The program fragments written by the user are executed in the order in which the corresponding regular expressions occur in the input stream. The lexical analysis programs written with Lex accept ambiguous specifications and choose the longest match possible at each input point. If necessary, substantial lookahead is performed on the input, but the input stream will be backed up to the end of the current partition, so that the user has general freedom to manipulate it. Lex can generate analyzers in either C or Ratfor, a language which can be translated automatically to portable Fortran. It is available on the PDP-11 UNIX, Honeywell GCOS, and IBM OS systems. This manual, however, will only discuss generating analyzers in C on the UNIX system, which is the only supported form of Lex under UNIX Version 7. Lex is designed to simplify interfacing with Yacc, for those with access to this compiler-compiler system.


title: Management challenges to implementing agile processes in traditional development organizations

abstract: Discussions with traditional developers and managers concerning agile software development practices nearly always contain two somewhat contradictory ideas. They find that on small, stand-alone projects, agile practices are less burdensome and more in tune with the software industry's increasing needs for rapid development and coping with continuous change. Managers face several barriers, real and perceived, when they try to bring agile approaches into traditional organizations. They categorized the barriers either as problems only in terms of scope or scale, or as significant general issues needing resolution. From these two categories, we've identified three areas - development process conflicts, business process conflicts, and people conflicts - that we believe are the critical challenges to software managers of large organizations in bringing agile approaches to bear in their projects.


title: Activating analytic thinking enhances the value given to individualizing moral foundations

abstract: Abstract Two central debates within Moral Foundations Theory concern (1) which moral foundations are core and (2) how conflict between ideological camps stemming from valuing different moral foundations can be resolved. Previous studies have attempted to answer the first question by imposing cognitive load on participants to direct them toward intuitive and automatic thought. However, this method has limitations and has produced mixed findings. In the present research, in two experiments, instead of directing participants toward intuitive thought, we tested the effects of activating high-effort, analytic thought on participants’ moral foundations. In both experiments, analytic thought activation caused participants to value individualizing foundations greater than the control condition. This effect was not qualified by participants’ political orientation. No effect was observed on binding foundations. The results are consistent with the idea that upholding individualizing foundations requires mental effort and may provide the basis for reconciliation between different ideological camps.


title: [Prevention of intramammary infections in dairy cows by the use of a premilking teat dip method with a foaming iodophor dip agent].

abstract: In this study we investigated the efficacy of premilking teat dipping with a foaming iodophor teat dip in a negative controlled field study. Incidence of new intramammary infections (IMI), incidence of clinical mastitis, influence on somatic cell count (SCC) and the characteristics of udder tissue and teats were used as parameters to evaluate clinical efficacy. Predipping was compared with a negative control using a split-udder experimental design. Right teats were predipped with a foaming disinfectant containing 0.27% iodine while left teats served as controls. The latter were conventionally cleaned with damp cloth towels and dried manually with disposable paper towels ("best cleaning practice"). All teats were dipped after milking with the same dip. There were no differences between treated and control quarters with respect to incidence of new IMI during the study period (treated quarters: 6.6% vs. untreated: 6.95%), incidence of clinical mastitis (30 cases in the treatment group vs. 39 cases in the control group) and geometric mean of SCC of quarter milk samples. Spectrum of detected pathogens was also comparable. Condition of udder tissue and teat ducts did not differ between treated and control quarters.


title: The New Jersey Data Reduction Report

abstract: An arrangement is shown for controlling transmission of blocks of information to and from a plurality of major components of a digital computer system interconnected by common buses. The disclosed arrangement operates so that any component of the system may normally seize, on a nonpriority basis, one of the buses at the beginning of any time slot defined by two successive clock pulses generated by a single source and applied to all components simultaneously; however, if a special instruction is encountered during execution of a program, any component may retain a bus for more than one time slot. The disclosed arrangement also permits error checking of transmitted information from a given major component without interfering with transmission from any other major component and automatically causes retransmission of any block of information found to be improperly transmitted originally. Still further, the disclosed arrangement permits buses to be dedicated during execution of a program or, if desired, any complete major component to be replaced without affecting any other major component.


title: Reusing stanford POS tagger for tagging Urdu sentences

abstract: Several Natural Language Processing applications in a particular language consider POS tagging a necessary component. To develop a new language specific POS tagger targeting such particular language is a tedious job for unstructured data due to the variation in text, type and complexity of text. For that reason, it impacts the precision of tagging as a result of the variety of a certain language. Current research focused on the thought of reusability of a popular language specific Part of speech tagger, for example, Stanford Part of speech Tagger can be employed for tagging non-Engish phrases. For generalizeability, any translator can be used to translate the sentences, however, a well-known translator, named “Google translator” is considered for sentence translation purpose across the languages. For evaluation perspective, Urdu tweets of a hot political issue “Panama leaks” from twitter.com are extracted. To measure the accuracy, the kappa statistic along with confusion matrix is deliberated. The precision of tagging the Urdu sentences by reusing Stanford Part of speech tagger is 96.05 percent. The respected approach can be globally applied to tagging the sentences of several different languages.


Agree or Cancel ? Research and Terms of Service Compliance


title: Traffic sign recognition with Color-based Method, shape-arc estimation and SVM

abstract: Traffic sign recognition is studied as one of the modern assistance in driving. The main purpose of it is to help driver on realizing traffic sign around the car by using computer vision enhanced to the car. In this paper, a combination method of Color-based Method and SVM is presented to do the traffic sign recognition. Color-based Method with CIELab + hue is chosen because it gives good result on localizing traffic signs. It is first used to preprocess the image to binary image. The binary image is then processed with canny to give more accurate result on detecting traffic signs. To get the traffic sign shape, the preprocessed image is checked by using Ramer-Douglas-Peucker algorithm, this algorithm will approximate each closed object shape in the preprocessed image. Detecting closed object make it unable to detect occluded and attached road signs. In order to detect occluded and attached signs, the proposed method will use two phase of detection by estimating the shape of the arc or later called the shape-arc algorithm. Approximated circle, square, or triangle images will be marked as traffic sign and processed in the recognition step with linear c-SVM. SVM classification will be based on binary images which gives 97% accuracy. The proposed method is more accurate than methods proposed by the reference papers and the detection is able to detect harder problems such as attached signs.


title: TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries

abstract: The proliferation of massive datasets combined with the development of sophisticated analytical techniques have enabled a wide variety of novel applications such as improved product recommendations, automatic image tagging, and improved speech-driven interfaces. These and many other applications can be supported by Predictive Analytic Queries (PAQs). A major obstacle to supporting PAQs is the challenging and expensive process of identifying and training an appropriate predictive model. Recent efforts aiming to automate this process have focused on single node implementations and have assumed that model training itself is a black box, thus limiting the effectiveness of such approaches on large-scale problems. In this work, we build upon these recent efforts and propose an integrated PAQ planning architecture that combines advanced model search techniques, bandit resource allocation via runtime algorithm introspection, and physical optimization via batching. The result is TuPAQ, a component of the MLbase system, which solves the PAQ planning problem with comparable quality to exhaustive strategies but an order of magnitude more efficiently than the standard baseline approach, and can scale to models trained on terabytes of data across hundreds of machines.


Developing digital skills to solve information problems through a MOOC


title: End-to-End Relation Extraction Using Distant Supervision from External Semantic Repositories

abstract: In this paper, we extend distant supervision (DS) based on Wikipedia for Relation Extraction (RE) by considering (i) relations defined in external repositories, e.g. YAGO, and (ii) any subset of Wikipedia documents. We show that training data constituted by sentences containing pairs of named entities in target relations is enough to produce reliable supervision. Our experiments with state-of-the-art relation extraction models, trained on the above data, show a meaningful F1 of 74.29% on a manually annotated test set: this highly improves the state-of-art in RE using DS. Additionally, our end-to-end experiments demonstrated that our extractors can be applied to any general text document.


title: A Survey of Augmented Reality

abstract: This paper surveys the field of augmented reality AR, in which 3D virtual objects are integrated into a 3D real environment in real time. It describes the medical, manufacturing, visualization, path planning, entertainment, and military applications that have been explored. This paper describes the characteristics of augmented reality systems, including a detailed discussion of the tradeoffs between optical and video blending approaches. Registration and sensing errors are two of the biggest problems in building effective augmented reality systems, so this paper summarizes current efforts to overcome these problems. Future directions and areas requiring further research are discussed. This survey provides a starting point for anyone interested in researching or using augmented reality.


title: Comparative analysis of axial flux SRM topologies for electric vehicle application

abstract: In-wheel motors for electric vehicles (EVs) have a high outer diameter (D) to axial length (L) ratio. In such applications, axial flux machines are preferred over radial flux machines due to high power density. Moreover, permanent magnet (PM)-less machines are gaining interest due to increase in cost of rare-earth PM materials. In view of this, axial flux switched reluctance motor (AFSRM) is considered as a possible option for EV propulsion. Two topologies namely, toothed and segmented rotor AFSRM are designed and compared for the same overall volume. These topologies have a three-phase, 12/16 pole single-stator, dual outer-rotor configuration along with non-overlapping winding arrangement. Analytical expressions for phase inductance and average torque are derived. To verify the performance of both the topologies a finite element method (FEM) based simulation study is carried out and its results are verified with the analytical values. It is observed from simulation that the average torque is 16.2% higher and torque ripple is 17.9% lower for segmented rotor AFSRM as compared to toothed rotor AFSRM.


title: How homepage aesthetic design influences users' satisfaction: Evidence from China

abstract: Abstract In the product emotional design era, the website homepage design, especially the aesthetic design, has become a key factor that influences users’ first impressions, attitudes and behaviors. Based on the Stimulus-Organism-Response (S-O-R) theory, the current study examines the mechanism through which the aesthetic design of a comprehensive job-hunting website homepage affects users’ satisfaction. Emotion and perceived ease of use were used to describe users’ internal evaluation to the homepage (i.e. organism). The Structural Equation Modeling method was used to test the proposed hypotheses. The empirical evidence shows that aesthetic formality does not have a significant impact on positive arousal, but it has positively influences on energetic arousal through its impact on aesthetic appeal. The other hypotheses proposed in this study are all been supported. The additional results, i.e. the effects of three key design elements on homepage aesthetics, are found. The aim of this study was to provide practical recommendation in the establishment of a pattern of webpage aesthetics that influences users’ satisfaction.


title: Provable Bounds for Learning Some Deep Representations

abstract: We give algorithms with provable guarantees that learn a class of deep nets in the generative model view popularized by Hinton and others. Our generative model is an n node multilayer network that has degree at most nγ for some γ < 1 and each edge has a random edge weight in [-1, 1]. Our algorithm learns almost all networks in this class with polynomial running time. The sample complexity is quadratic or cubic depending upon the details of the model. ::: ::: The algorithm uses layerwise learning. It is based upon a novel idea of observing correlations among features and using these to infer the underlying edge structure via a global graph recovery procedure. The analysis of the algorithm reveals interesting structure of neural nets with random edge weights.


title: Attention, Uncertainty, and Free-Energy

abstract: We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this paper, we try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that neuronal activity encodes a probabilistic representation of the world that optimizes free-energy in a Bayesian fashion. Because free-energy bounds surprise or the (negative) log-evidence for internal models of the world, this optimization can be regarded as evidence accumulation or (generalized) predictive coding. Crucially, both predictions about the state of the world generating sensory data and the precision of those data have to be optimized. Here, we show that if the precision depends on the states, one can explain many aspects of attention. We illustrate this in the context of the Posner paradigm, using the simulations to generate both psychophysical and electrophysiological responses. These simulated responses are consistent with attentional bias or gating, competition for attentional resources, attentional capture and associated speed-accuracy trade-offs. Furthermore, if we present both attended and non-attended stimuli simultaneously, biased competition for neuronal representation emerges as a principled and straightforward property of Bayes-optimal perception.


title: Residual vs. Inception vs. Classical Networks for Low-Resolution Face Recognition

abstract: When analyzing surveillance footage, low-resolution face recognition is still a challenging task. While high-resolution face recognition experienced impressive improvements by Convolutional Neural Network (CNN) approaches, the benefit to low-resolution face recognition remains unclear as only few work has been done in this area. This paper adapts three popular high-resolution CNN designs to the low-resolution (LR) domain to find the most suitable architecture. Namely, the classical AlexNet/VGG architecture, Google’s inception architecture and Microsoft’s residual architecture are considered. While the inception and residual concept have been proven to be useful for very deep networks, it is shown in our case that shallower networks than for high-resolution recognition are sufficient. This leads to an advantage of the classical network architecture. Final evaluation on a downscaled version of the public YouTube Faces Database indicates a comparable performance to the high-resolution domain. Results with faces extracted from the SoBiS surveillance dataset indicate a superior performance of the trained networks in the LR domain.


title: Family Adaptation to Traumatic Spinal Cord Injury: Response to Crisis.

abstract: RESULTS show that following this particular stressor event, families are in crisis and adapt by making structural changes. RESULTS raise the question of whether postinjury family dysfunction is due to reliance on previous coping strategies. Language: en


title: A Comparison of Levels of International Understanding among Students of the International Baccalaureate Diploma and Advanced Placement Programs in the USA

abstract: This study examines whether participation in the International Baccalaureate diploma program enhances one of the goals of the program, the development of international understanding. Junior and senior International Baccalaureate students and Advanced Placement students of similar background in the United States were studied using a survey instrument, and by asking them to write their personal definition of `international understanding'. Analysis of student definitions revealed a significant difference between the mean number of categories of international understanding utilized by the two groups of students.


title: Oral diagnosis and treatment planning: part 5. Preventive and treatment planning for dental caries

abstract: The practice of operative dentistry continues to evolve, to reflect the many changes occurring in society and in dental diseases and conditions. However, the belief that all questionable and early carious lesions should be restored still persists. This belief is largely based upon the concept that the removal of all carious tissue followed by meticulous restoration of the tooth is the treatment of choice for dental caries. Yet restorations are not permanent and do not cure caries, as the causes remain. On the other hand, preventive measures can remove or partially remove the causes, thereby reducing the risks for future caries recurrence at the same site or elsewhere in the mouth.


title: Design and process variation analysis of CNTFET-based ternary memory cells

abstract: Two novel ternary CNTFET-based SRAM cells are proposed in this paper. The first proposed CNTFET SRAM uses additional CNTFETs to sink the bit lines to ground; its operation is nearly independent of the ternary values. The second cell utilizes the traditional voltage controller (or supply) of a binary SRAM in a ternary SRAM; it consists of adding two CNTFETs to the first proposed cell. CNTFET features (such as sizing and density) and performance metrics (such as SNM and PDP) and write/read times are considered and assessed in detail. The impact of different features (such as chirality and CNT density) is also analyzed with respect to the operations of the memory cells. The effects of different process variations (such as lithography and density/number of CNTs) are extensively evaluated with respect to performance metrics. In nearly all cases, the proposed cells outperform existing CNTFET-based cells by showing a small standard deviation in the simulated memory circuits. Two novel ternary CNTFET-based SRAM cells are proposed in this paper.


title: The role of MEF2 transcription factors in dehydration and anoxia survival in Rana sylvatica skeletal muscle

abstract: The wood frog (Rana sylvatica) can endure freezing of up to 65% of total body water during winter. When frozen, wood frogs enter a dormant state characterized by a cessation of vital functions (i.e., no heartbeat, blood circulation, breathing, brain activity, or movement). Wood frogs utilize various behavioural and biochemical adaptations to survive extreme freezing and component anoxia and dehydration stresses, including a global suppression of metabolic functions and gene expression. The stress-responsive myocyte enhancer factor-2 (MEF2) transcription factor family regulates the selective expression of genes involved in glucose transport, protein quality control, and phosphagen homeostasis. This study examined the role of MEF2A and MEF2C proteins as well as select downstream targets (glucose transporter-4, calreticulin, and muscle and brain creatine kinase isozymes) in 40% dehydration and 24 h anoxia exposure at the transcriptional, translational, and post-translational levels using qRT-PCR, immunoblotting, and subcellular localization. Mef2a/c transcript levels remained constant during dehydration and anoxia. Total, cytoplasmic, and nuclear MEF2A/C and phospho-MEF2A/C protein levels remained constant during dehydration, whereas a decrease in total MEF2C levels was observed during rehydration. Total and phospho-MEF2A levels remained constant during anoxia, whereas total MEF2C levels decreased during 24 h anoxia and P-MEF2C levels increased during 4 h anoxia. In contrast, cytoplasmic MEF2A levels and nuclear phospho-MEF2A/C levels were upregulated during anoxia. MEF2 downstream targets remained constant during dehydration and anoxia, with the exception of glut4 which was upregulated during anoxia. These results suggest that the upregulated MEF2 response reported in wood frogs during freezing may in part stem from their cellular responses to surviving prolonged anoxia, rather than dehydration, leading to an increase in GLUT4 expression which may have an important role during anoxia survival.


title: Aligning security and usability

abstract: Conflicts between security and usability goals can be avoided by considering the goals together throughout an iterative design process. A successful design involves addressing users' expectations and inferring authorization based on their acts of designation.


title: Thai Tone Recognition Using Ant Colony Algorithm

abstract: This paper presents a monosyllabic Thai tone recognition system, which is based on the Ant-Miner algorithm. The system is composed of three main processes, fundamental frequency (F0) extraction from input speech signal, analysis of F0 contour for feature extraction, In the F0 feature extraction, the polynomial regression functions are employed to fit the segmented F0 curve where its coefficients are used as a feature vector. In tone recognition, we used the Ant-Miner classifier to classify a tone by assuming that the feature is features vector. The hypothetical words used in this paper are composed of numerical words and monosyllabic Thai words. The experimental results show that by using the system as a speaker-dependent system, the maximum recognition rate is 96.20%.


title: Architecture of a Scalable Platform for Monitoring Multiple Big Data Frameworks

abstract: Latest advances in information technology and the widespread growth in different areas are producing large amounts of data. Consequently, in the past decade a large number of distributed platforms for storing and processing large datasets have been proposed. Whether in development or in production, monitoring applications running on these platforms is not an easy task and dedicated tools and platforms were proposed for this scope. In this paper we present a distributed, scalable, highly available platform able to collect, store, query and process monitoring data obtained from multiple Big Data frameworks. We present its architecture and initial results obtained.


title: Verification of Knowledge Bases based on Containment Checking

abstract: Building complex knowledge based applications requires encoding large amounts of domain knowledge. After acquiring knowledge from domain experts, much of the effort in building a knowledge base goes into verifying that the knowledge is encoded correctly. A knowledge base is verified if it can be shown that certain constraints always hold between the inputs and the outputs. We consider the knowledge base verification problem for Horn rule knowledge bases and for three kinds of constraints: I/O consistency constraints, I/O dependency constraints, and input completeness constraints. For the first two cases, we establish tight complexity results on the problem, and show in what cases it is decidable. In the third case, we show that the problem is, in general, undecidable, and we identify two decidable cases. In our analysis we show how the properties of the problem vary depending on the presence of recursion in the Horn rules, the presence of the interpreted predicates =, = and the presence of negation in the antecedents of the rules. Our approach to the verification problem is based on showing a close relationship to the problem of query containment, studied in the database literature. This connection also provides novel algorithms for the knowledge base verification problem. Finally, we provide the first algorithm for verifying hybrid knowledge bases that combine the expressive power of Horn rules and the description logicALCNR.


title: The DLR MIRO - A versatile lightweight robot for surgical applications; Industrial Robot – Special Issue “Medical Robots

abstract: Purpose – Surgical robotics can be divided into two groups: specialized and versatile systems. Versatile systems can be used in different surgical applications, control architectures and operating room set‐ups, but often still based on the adaptation of industrial robots. Space consumption, safety and adequacy of industrial robots in the unstructured and crowded environment of an operating room and in close human robot interaction are at least questionable. The purpose of this paper is to describe the DLR MIRO, a new versatile lightweight robot for surgical applications.Design/methodology/approach – The design approach of the DLR MIRO robot focuses on compact, slim and lightweight design to assist the surgeon directly at the operating table without interference. Significantly reduced accelerated masses (total weight 10 kg) enhance the safety of the system during close interaction with patient and user. Additionally, MIRO integrates torque‐sensing capabilities to enable close interaction with human beings ...


title: The coupling of emotion and cognition in the eye: introducing the pupil old/new effect.

abstract: The study presented here investigated the effects of emotional valence on the memory for words by assessing both memory performance and pupillary responses during a recognition memory task. Participants had to make speeded judgments on whether a word presented in the test phase of the experiment had already been presented (‘‘old’’) or not (‘‘new’’). An emotion-induced recognition bias was observed: Words with emotional content not only produced a higher amount of hits, but also elicited more false alarms than neutral words. Further, we found a distinct pupil old/ new effect characterized as an elevated pupillary response to hits as opposed to correct rejections. Interestingly, this pupil old/new effect was clearly diminished for emotional words. We therefore argue that the pupil old/new effect is not only able to mirror memory retrieval processes, but also reflects modulation by an emotion-induced recognition bias. Descriptors: Emotion, Cognition, Memory, Old/new effect, Pupillary response


title: Conversational Metaphors in Use: Exploring the Contrast between Technical and Everyday Notions of Metaphor

abstract: Much computational work has been done on identifying and interpreting the meaning of metaphors, but little work has been done on understanding the motivation behind the use of metaphor. To computationally model discourse and social positioning in metaphor, we need a corpus annotated with metaphors relevant to speaker intentions. This paper reports a corpus study as a first step towards computational work on social and discourse functions of metaphor. We use Amazon Mechanical Turk (MTurk) to annotate data from three web discussion forums covering distinct domains. We then compare these to annotations from our own annotation scheme which distinguish levels of metaphor with the labels: nonliteral, conventionalized, and literal. Our hope is that this work raises questions about what new work needs to be done in order to address the question of how metaphors are used to achieve social goals in interaction.


title: Hashing with Graphs

abstract: Hashing is becoming increasingly popular for efficient nearest neighbor search in massive databases. However, learning short codes that yield good search performance is still a challenge. Moreover, in many cases real-world data lives on a low-dimensional manifold, which should be taken into account to capture meaningful nearest neighbors. In this paper, we propose a novel graph-based hashing method which automatically discovers the neighborhood structure inherent in the data to learn appropriate compact codes. To make such an approach computationally feasible, we utilize Anchor Graphs to obtain tractable low-rank adjacency matrices. Our formulation allows constant time hashing of a new data point by extrapolating graph Laplacian eigenvectors to eigenfunctions. Finally, we describe a hierarchical threshold learning procedure in which each eigenfunction yields multiple bits, leading to higher search accuracy. Experimental comparison with the other state-of-the-art methods on two large datasets demonstrates the efficacy of the proposed method.


Distinctive Image Features from Scale-Invariant Keypoints Abstract by Matthijs Dorst Based on the paper by


Risk Assessment in Internal Auditing: A Neural Network Approach


title: Shunt Active-Power-Filter Topology Based on Parallel Interleaved Inverters

abstract: In this paper, an interleaved active-power-filter concept with reduced size of passive components is discussed. The topology is composed of two pulsewidth-modulation interleaved voltage-source inverters connected together on the ac line and sharing the same dc-link capacitor. The advantages of the proposed approach are as follows: 1. significant reduction in the linkage inductors' size by decreasing the line-current ripple due to the interleaving; 2. reduction of the switching stress in the dc-link capacitor, due to the shared connection; and 3. more accurate compensation for high-power applications, because the power sharing allows one to use a higher switching frequency in each inverter. This paper analyzes the design of the passive components and gives a practical and low-cost solution for the minimization of the circulation currents between the inverters, by using common-mode coils. Several simulation results are discussed, and experimental results with a three-phase 10-kVA 400-V unit are obtained to validate the theoretical analysis.


title: On Number of Turns in Reduced Random Lattice Paths

abstract: We consider the tree-reduced path of symmetric random walk on $\ZZ^{d}$. It is interesting to ask about the number of turns $T_n$ in the reduced path after $n$ steps. This question arises from inverting signature for lattice paths. We show that, when $n$ is large, the mean and variance of $T_n$ have the same order as $n$, while the second order terms are O(1). We then use these estimates to obtain limit theorems for $T_n$. Similar results hold for any other finite patterns as well.


title: Transaction Support over Redis: An Overview

abstract: This document outlines the approach to supporting cross-node transactions over a Redis cluster.


title: Reconfigurable Annular Ring Slot Antenna with Circular Polarization Diversity

abstract: In this paper, the reconfigurable annular ring slot antenna with circular polarization diversity is proposed for SDMB(satellite digital multimedia broadcasting) system. The proposed antenna consists of a ring slot with tuning stubs. Four PIN diodes are attached to achieve circular polarization diversity. By switching the diodes on or off, the proposed antenna can be operated either RHCP(right hand circular polarization) mode or LHCP(left hand circular polarization) mode. The experimental result shows that the proposed antenna has an impedance bandwidth( VSWR les 2 ) of 2.47~3.04 GHz(570 MHz) at LHCP mode, an impedance bandwidth( VSWR les 2 ) of 2.45~3.01GHz(560 MHz) at RHCP mode, a maximum gain of 3.1dBi at RHCP mode, 4.76dBi at LHCP mode. The 3dB CP bandwidth of about 100 MHz at both RHCP and LHCP mode is achieved at the center frequency 2.63 GHz. The proposed antenna is suitable for application such as mobile satellite communications, WLAN(wireless local area networks), and broadband wireless communication systems.


title: CONE BASED 3D RECONSTRUCTION: A FDK - SART COMPARISON FOR LIMITED NUMBER OF PROJECTIONS

abstract: 3D Cone Beam Computed Tomography is a highly investigated field, nowadays. Both analytical and iterative methods for tomographic reconstruction have been proposed. This paper compares two such CBCT image reconstruction algorithms, the FDK and the SART, in terms of their performances when a limited number of projections is used. Increased angular step between projections, allows dose reduction and shorter acquisition time. Although more computationally demanding, given the constant development of computer power, SART could be a method of interest for cone beam reconstruction using a limited number of projections.


title: Paraplay: exploring playfulness around physical console gaming

abstract: We present the concept of paraplay: playful activities that take place within the context of an interactive game or other play activity, but outside the activity itself. By critically examining work related to gaming and play goals and motivations we argue that the concept of playfulness should have a stronger role in our understanding of gaming sessions, and particularly social gaming sessions. In order to further understand the role of playfulness in social gaming we conducted an empirical field study of physical console gaming. Six families with a total of 32 participants were provided with an Xbox 360 console, Kinect sensor, and three casual physical video games to play together for a period of approximately two weeks. Participants were instructed to record their social gaming sessions. We conducted video analysis on these recordings as well as interviews with many of the participants. We found numerous types and examples of playfulness within the gaming session even from those who were not actively participating in the game. Drawing on the results of this study we present a taxonomy of paraplay and discuss the ways that playfulness can be exhibited in a social play session. We show that participants in a game situation act within a wider context of playfulness, according to a variety of significant roles ranging from active player through to audience member. We explore these roles and their attributes to provide a rich account of paraplay and its importance in understanding playful activities broadly.


title: Osteosarcoma of the mobile spine.

abstract: BACKGROUND ::: The aims of this analysis were to investigate features and outcome of high-grade osteosarcomas of the mobile spine. ::: ::: ::: PATIENTS AND METHODS ::: Since 1977, 20 Cooperative Osteosarcoma Study Group patients had a diagnosis of high-grade osteosarcomas of the mobile spine and were included in this retrospective analysis of patient-, tumor- and treatment-related variables and outcome. ::: ::: ::: RESULTS ::: The median age was 29 years (range 5-58). Most frequent tumor sites were thoracic and lumbar spine. All but three patients had nonmetastatic disease at diagnosis. Treatment included surgery and chemotherapy for all patients, 13 were also irradiated. Eight patients failed to achieve a macroscopically complete surgical remission (five local, one primary metastases, two both), six died, two are alive, both with radiotherapy. Of 12 patients with complete remission at all sites, three had a recurrence (two local, one metastases) and died. The median follow-up of the 11 survivors was 8.7 years (range 3.1-22.3), 5-year overall and event-free survival rates were 60% and 43%. Age <40 years, nonmetastatic disease at diagnosis and complete remission predicted for better overall survival (OS, P < 0.05). ::: ::: ::: CONCLUSIONS ::: Osteosarcomas of the mobile spine are rare. With complete resection (and potentially radiotherapy) and chemotherapy, prognosis may be comparable with that of appendicular osteosarcomas.


title: Modified Block Playfair Cipher using Random Shift Key Generation

abstract: In this paper conventional Playfair Cipher is being modified by encrypting the plaintext in blocks. For each block the keyword would be the same but the matrix will shift by some random value. As a result of which the diagram analysis would be very difficult which is done in the traditional Playfair Cipher to obtain the plaintext from the ciphertext. The shift value will be generated using SHA-1 which is very secure. Playfair Cipher method, based on polyalphabetic cipher is relatively easy to break because it still leaves much of the structure and a few hundred of letters of ciphertext are sufficient. To add to its security and to make it more usable we are using 6x6 matrix instead of 5x5 which will be able to cover 26 alphabets in English and ten numerals i.e. from 0 to 9. This 6x6 matrix eliminate the case of putting of 2 alphabets (I and J) together in the matrix as it was in the 5x5 matrix. Plaintext as well as key can be numeral, alphabetic or combination of both.


title: Fault diagnosis of hydraulic pump based on stacked autoencoders

abstract: The fault diagnosis of hydraulic pump is always a challenging issue in the field of machinery fault diagnosis. Not only is manual feature extraction time-consuming and laborious, but also the diagnostic result is easily affected by subjective experience. The stacked autoencoders (SAE) which has powerful learning and representation ability is applied in hydraulic pump fault diagnosis, and it is directly used for training and identification based on vibration signals, so that we don't need to extract features manually. To avoid gradient vanishing and to improve the performance for small training set, ReLU activation function and Dropout strategy are both introduced into SAE. Validated by experiments, proposed SAE is superior to the BP, SVM and traditional SAE. And it can recognize hydraulic pump condition accurately even if the training set is small, which satisfies the engineering application.


title: Digital forensics study of internet messenger: Line artifact analysis in Android OS

abstract: This paper provides a description of how to get artifacts generated by Line messenger applications such as user activities, contact information, chatting, and deleting the messages. It also provides correlation and interpretation of messages that are formed during the conversation scenario. This paper can be used as a guidance for investigators and forensics analysts in conducting investigation process. Furthermore, this paper also shows how the acquisition processes were done logically and physically. Android forensics processes were done in forensically sounds manner using three different models of smartphones and Android OS versions as the experiment objects.


title: Importance of rare gene copy number alterations for personalized tumor characterization and survival analysis

abstract: It has proven exceedingly difficult to ascertain rare copy number alterations (CNAs) that may have strong effects in individual tumors. We show that a regulatory network inferred from gene expression and gene copy number data of 768 human cancer cell lines can be used to quantify the impact of patient-specific CNAs on survival signature genes. A focused analysis of tumors from six tissues reveals that rare patient-specific gene CNAs often have stronger effects on signature genes than frequent gene CNAs. Further comparison to a related network-based approach shows that the integration of indirectly acting gene CNAs significantly improves the survival analysis.


title: Arterial blood supply variation in the anterior midline mandible: Significance to dental implantology

abstract: Background ::: Inter-foraminal implant placement poses a risk to the sublingual artery as it enters the mandibular midline lingual foramen. Lack of consensus on the source of this artery poses a dilemma to surgeons during management of haemorrhagic episodes. Determination of the exact source of this artery is therefore pivotal.


title: Agency Costs, Risk Management, and Capital Structure

abstract: The joint determination of capital structure and investment risk is examined. Optimal capital structure reflects both the tax advantages of debt less default costs (Modigliani and Miller (1958, 1963)), and the agency costs resulting from asset substitution (Jensen and Meckling (1976)). Agency costs restrict leverage and debt maturity and increase yield spreads, but their importance is small for the range of environments considered. Risk management is also examined. Hedging permits greater leverage. Even when a firm cannot precommit to hedging, it will still do so. Surprisingly, hedging benefits often are greater when agency costs are low. Copyright The American Finance Association 1998.


title: Choking under pressure: multiple routes to skill failure.

abstract: Poor performance in pressure-filled situations, or "choking under pressure," has largely been explained by two different classes of theories. Distraction theories propose that choking occurs because attention needed to perform the task at hand is coopted by task-irrelevant thoughts and worries. Explicit monitoring theories claim essentially the opposite-that pressure prompts individuals to attend closely to skill processes in a manner that disrupts execution. Although both mechanisms have been shown to occur in certain contexts, it is unclear when distraction and/or explicit monitoring will ultimately impact performance. The authors propose that aspects of the pressure situation itself can lead to distraction and/or explicit monitoring, differentially harming skills that rely more or less on working memory and attentional control. In Experiments 1-2, it is shown that pressure that induces distraction (involving performance-contingent outcomes) hurts rule-based category learning heavily dependent on attentional control. In contrast, pressure that induces explicit monitoring of performance (monitoring by others) hurts information-integration category learning thought to run best without heavy demands on working memory and attentional control. In Experiment 3, the authors leverage knowledge about how specific types of pressure impact performance to design interventions to eliminate choking. Finally, in Experiment 4, the selective effects of monitoring-pressure are replicated in a different procedural-based task: the serial reaction time task. Skill failure (and success) depends in part on how the performance environment influences attention and the extent to which skill execution depends on explicit attentional control. (PsycINFO Database Record (c) 2011 APA, all rights reserved). Language: en


title: Performance evaluation of 6LoWPAN-based precision agriculture

abstract: The use of Wireless Sensor Network (WSN) with IP network in Precission Agriculture (PA) would be an added advantage to existing solutions of PA system. However, existing WSN Technologies, such as Zigbee and other legacy sensor networks are originally design for non IP-based WSN. Hence, they have difficulties in constructing global infrastructure for the precision agriculture. On the other hand, IETF task force has come up with an open standard of wireless sensor network called 6LoWPAN which define connectivity of WSN to IP network using IPv6 protocols. Since the 6LoWPAN is a new technology, the use of this standard for Precision Agriculture is scarce. Most of the works are based on Zigbee. Therefore, this paper is contributing on discussing the performance evaluation and fundamental infrastructure setup for 6LoWPAN based Precision Agriculture.


title: Single image haze removal with WLS-based edge-preserving smoothing filter

abstract: Images captured under hazy conditions have low contrast and poor color. This is primarily due to air-light which degrades image quality according to the transmission map. The approach to enhance these hazy images we introduce here is based on the `Dark-Channel Prior' method with image refinement by the `Weighted Least Square' based edge-preserving smoothing. Local contrast is further enhanced by multi-scale tone manipulation. The proposed method improves the contrast, color and detail for the entire image domain effectively. In the experiment, we compare the proposed method with conventional methods to validate performance.


title: Detecting functionally similar code within the same project

abstract: Multiple developers often take part in a software development project. Although these developers are collaborating towards the development within the same project, each developer creates code on their own. This may lead to duplicate or similar code appearing in different parts of the software. Such code should be removed to improve maintainability. This paper proposes an approach to automatically detect such code, which we shall call functionally similar code. The unit of detection is at the method level, and we focus on input/output and the method structure using program dependence graph. We show the results of applying our approach on open source software.


title: Perceptually Optimized Image Rendering

abstract: We develop a framework for rendering photographic images by directly optimizing their perceptual similarity to the original visual scene. Specifically, over the set of all images that can be rendered on a given display, we minimize the normalized Laplacian pyramid distance (NLPD), a measure of perceptual dissimilarity that is derived from a simple model of the early stages of the human visual system. When rendering images acquired with a higher dynamic range than that of the display, we find that the optimization boosts the contrast of low-contrast features without introducing significant artifacts, yielding results of comparable visual quality to current state-of-the-art methods, but without manual intervention or parameter adjustment. We also demonstrate the effectiveness of the framework for a variety of other display constraints, including limitations on minimum luminance (black point), mean luminance (as a proxy for energy consumption), and quantized luminance levels (halftoning). We show that the method may generally be used to enhance details and contrast, and, in particular, can be used on images degraded by optical scattering (e.g., fog). Finally, we demonstrate the necessity of each of the NLPD components-an initial power function, a multiscale transform, and local contrast gain control-in achieving these results and we show that NLPD is competitive with the current state-of-the-art image quality metrics.


title: FPGA Implementations of the Round Two SHA-3 Candidates

abstract: The second round of the NIST-run public competition is underway to find a new hash algorithm(s) for inclusion in the NIST Secure Hash Standard (SHA-3). This paper presents the full implementations of all of the second round candidates in hardware with all of their variants. In order to determine their computational efficiency, an important aspect in NIST's round two evaluation criteria, this paper gives an area/speed comparison of each design both with and without a hardware interface, thereby giving an overall impression of their performance in resource constrained and resource abundant environments. The implementation results are provided for a Virtex-5 FPGA device. The efficiency of the architectures for the hash functions are compared in terms of throughput per unit area.


title: Low-Complexity ICI Mitigation Methods for High-Mobility SISO/MIMO-OFDM Systems

abstract: In orthogonal frequency-division multiplexing (OFDM) systems, it is generally assumed that the channel response is static in an OFDM symbol period. However, the assumption does not hold in high-mobility environments. As a result, intercarrier interference (ICI) is induced, and system performance is degraded. A simple remedy for this problem is the application of the zero-forcing (ZF) equalizer. Unfortunately, the direct ZF method requires the inversion of an N times N ICI matrix, where N is the number of subcarriers. When N is large, the computational complexity can become prohibitively high. In this paper, we first propose a low-complexity ZF method to solve the problem in single-input-single-output (SISO) OFDM systems. The main idea is to explore the special structure inherent in the ICI matrix and apply Newton's iteration for matrix inversion. With our formulation, fast Fourier transforms (FFTs) can be used in the iterative process, reducing the complexity from O (N3) to O (N log2 N). Another feature of the proposed algorithm is that it can converge very fast, typically in one or two iterations. We also analyze the convergence behavior of the proposed method and derive the theoretical output signal-to-interference-plus-noise ratio (SINR). For a multiple-input-multiple-output (MIMO) OFDM system, the complexity of the ZF method becomes more intractable. We then extend the method proposed for SISO-OFDM systems to MIMO-OFDM systems. It can be shown that the computational complexity can be reduced even more significantly. Simulations show that the proposed methods perform almost as well as the direct ZF method, while the required computational complexity is reduced dramatically.


title: Maritime surveillance with GSM passive radar: Detection and tracking of small agile targets

abstract: In a previous paper we have demonstrated that GSM passive radar is well suited for maritime surveillance. In a follow-on experiment we have now extended these results to the case of tracking small, fast and maneuvering boats. We present our system design and discuss the challenges. Then the experiments with scenario and hardware setup are explained. Finally we present results for detecting and tracking a number of agile boats with small radar cross section.


title: IoTFLiP： IoT-based flipped learning platform for medical education

abstract: Case-Based Learning (CBL) has become an effective pedagogy for student-centered learning in medical education, which is founded on persistent patient cases. Flippped learning and Internet of Things (IoTs) concepts have gained significant attention in recent years. Using these concepts in conjunction with CBL can improve learning ability by providing real evolutionary medical cases. It also enables students to build confidence in their decision making, and efficiently enhances teamwork in the learing environment. We propose an IoT-based Flip Learning Platform, called IoTFLiP, where an IoT infrastructure is exploited to support flipped case-based learning in a cloud environment with state of the art security and privacy measures for personalized medical data. It also provides support for application delivery in private, public, and hybrid approaches. The proposed platform is an extension of our Interactive Case-Based Flipped Learning Tool (ICBFLT), which has been developed based on current CBL practices. ICBFLT formulates summaries of CBL cases through synergy between students' and medical expert knowledge. The low cost and reduced size of sensor device, support of IoTs, and recent flipped learning advancements can enhance medical students' academic and practical experiences. In order to demonstrate a working scenario for the proposed IoTFLiP platform, real-time data from IoTs gadgets is collected to generate a real-world case for a medical student using ICBFLT.


title: Robot Companions as Case-Scenario for Assessing the “Subjectivity ” of Autonomous Agents. Some Philosophical and Legal Remarks

abstract: In this paper the European flagship project proposal Robot Companion for Citizens (RCC), grounded on the idea of developing robot companions for citizens, is taken as a case scenario for investigating the feasibility of ascribing rights and duties to autonomous robots from a legal and philosophical standpoint. In talking about rights and duties with respect to robots endowed with autonomous decision capabilities, one should face the implications that inevitably these terms rise, especially in the field of law. The paper points out the technological problems related to the application of the notion of duty to robots and the problems deriving from attributing a legal subjectivity to nonhuman entities such as robot.


A systematic review of calcium channel antagonists in bipolar disorder and some considerations for their future development


title: Load balancing through dynamic partitioning for hierarchical cellular networks

abstract: A novel autonomic area reconfiguration mechanism is proposed that complements hierarchical QoSR protocols in wireless access networks. The proposed mechanism revises the border between adjacent areas based on handover rates and on resource consumption in terms of bandwidth and user population. Its objectives are concentrated on reducing the overhead related with frequent inter-area handovers as well as providing bandwidth and user balancing on per area basis. This paper focuses on the load balancing aspects, exploring the contribution and effects of mobility through a simulation study.


title: The well-being of playful adults: Adult playfulness, subjective well-being, physical well-being, and the pursuit of enjoyable activities

abstract: It was hypothesized that playfulness in adults relates positively to different indicators of subjective but also physical well-being. A sample of 255 adults completed subjective measures of playfulness along with self-ratings for different facets of well-being and the endorsement to enjoyable activities. Adult playfulness demonstrated robust positive relations with life satisfaction and an inclination to enjoyable activities and an active way of life. There were also minor positive relations with physical fitness. Leading an active way of life partially mediated the relation between playfulness and life satisfaction. The study provides further evidence on the contribution of adult playfulness to different aspects of well-being.


title: Beam-Steering SIW Leaky-Wave Subarray With Flat-Topped Footprint for 5G Applications

abstract: A novel double-layered vertically stacked substrate integrated waveguide leaky-wave antenna (SIW LWA) is presented. An array of vias on the narrow wall produces leakage through excitation of TE10 fast-wave mode of the waveguide. Attenuation and phase constants of the leaky mode are controlled independently to obtain desired pattern in the elevation. In the azimuth, top and bottom layers radiate independently, producing symmetrically located beams on both sides of broadside. A new near-field analysis of single LWA is performed to determine wavenumbers and as a way to anticipate radiation characteristics of the dual layer antenna. In addition to frequency beam steering in the elevation plane, this novel topology also offers flexibility for multispot illumination of the azimuth plane with flat-topped beams at every ${\varphi }$ -cut through excitation of each layer separately or both antennas simultaneously. It is shown that the proposed antenna solution is a qualified candidate for 5G base station antenna (BSA) applications due to its capability of interference mitigation and latency reduction. Moreover, from the point of view of highly reliable connectivity, users can enjoy seamless mobility through the provided spatial diversity. A 15-GHz prototype has been fabricated and tested. Measured results are in good agreement with those of simulations.


title: Kinematics analysis and simulation of a robotic arm using MATLAB

abstract: In order to enhance the study of the kinematics of any robot arm, parameter design is directed according to certain necessities for the robot, and its forward and inverse kinematics are discussed. The DH convention Method is used to form the kinematical equation of the resultant structure. In addition, the Robotics equations are modeled in MATLAB to create a 3D visual simulation of the robot arm to show the result of the trajectory planning algorithms. The simulation has detected the movement of each joint of the robot arm, and tested the parameters, thus accomplishing the predetermined goal which is drawing a sine wave on a writing board.


title: Simulation environment for mobile robots testing using ROS and Gazebo

abstract: In the process of development a control strategy for mobile robots, simulation is important for testing the software components, robot behavior and control algorithms in different surrounding environments. In this paper we introduce a simulation environment for mobile robots based on ROS and Gazebo. We show that after properly creating the robot models under Gazebo, the code developed for the simulation process can be directly implemented in the real robot without modifications. In this paper autonomous navigation tasks and 3D-mapping simulation using control programs under ROS are presented. Both the simulation and experimental results agree very well and show the usability of the developed environment.


title: Mining Wiki Resources for Multilingual Named Entity Recognition

abstract: In this paper, we describe a system by which the multilingual characteristics of Wikipedia can be utilized to annotate a large corpus of text with Named Entity Recognition (NER) tags requiring minimal human intervention and no linguistic expertise. This process, though of value in languages for which resources exist, is particularly useful for less commonly taught languages. We show how the Wikipedia format can be used to identify possible named entities and discuss in detail the process by which we use the Category structure inherent to Wikipedia to determine the named entity type of a proposed entity. We further describe the methods by which English language data can be used to bootstrap the NER process in other languages. We demonstrate the system by using the generated corpus as training sets for a variant of BBN's Identifinder in French, Ukrainian, Spanish, Polish, Russian, and Portuguese, achieving overall F-scores as high as 84.7% on independent, human-annotated corpora, comparable to a system trained on up to 40,000 words of human-annotated newswire.


title: Webly-Supervised Video Recognition by Mutually Voting for Relevant Web Images and Web Video Frames

abstract: Video recognition usually requires a large amount of training samples, which are expensive to be collected. An alternative and cheap solution is to draw from the large-scale images and videos from the Web. With modern search engines, the top ranked images or videos are usually highly correlated to the query, implying the potential to harvest the labeling-free Web images and videos for video recognition. However, there are two key difficulties that prevent us from using the Web data directly. First, they are typically noisy and may be from a completely different domain from that of users’ interest (e.g. cartoons). Second, Web videos are usually untrimmed and very lengthy, where some query-relevant frames are often hidden in between the irrelevant ones. A question thus naturally arises: to what extent can such noisy Web images and videos be utilized for labeling-free video recognition? In this paper, we propose a novel approach to mutually voting for relevant Web images and video frames, where two forces are balanced, i.e. aggressive matching and passive video frame selection. We validate our approach on three large-scale video recognition datasets.


title: Consumers' Satisfaction With Online Information Quality: The Moderating Roles Of Consumer Decision-Making Style, Gender And Product Involvement

abstract: In the absence of an opportunity to physically interact with products in the online environment, online information plays a critical role in enabling e-Commerce consumers’ purchase decisions. Thus, it is critical to understand what leads to consumer satisfaction with online information quality. However, despite the rapidly increasing number of consumers who use websites to gather pre-purchase product information, very little is known about how to increase consumers’ satisfaction with online product information quality in different contexts. This research-in-progress study proposes a comprehensive model to investigate the impacts of perceived verbal information, nonverbal information and decision support tools qualities on consumers’ satisfaction with information quality within e-Commerce websites. Further, we also plan to investigate how the relations between the constructs in the proposed model might vary by factors such as gender, decision making style, and product involvement. A survey-based methodology is outlined to empirically validate the proposed research model using structural equation modelling techniques.


title: Automatic generation of basis test path using clonal selection algorithm

abstract: Path testing is one of the powerful coverage criteria in white box testing. In path testing every logical path has to be executed at least once but it is extremely difficult to execute or test the entire path for given program. So instead of executing all the paths a set of independent path are executed which are called as basis set path. In this paper we proposed novel approach for the utilization of Clonal selection Algorithm to automatically generate set of basis path. Experiments have been carried out to evaluate the effectiveness of the proposed path generation technique. And the results show that the proposed technique causes reduction in path generation effort and time.


title: iSpreadRank: Ranking sentences for extraction-based summarization using feature weight propagation in the sentence similarity network

abstract: Sentence extraction is a widely adopted text summarization technique where the most important sentences are extracted from document(s) and presented as a summary. The first step towards sentence extraction is to rank sentences in order of importance as in the summary. This paper proposes a novel graph-based ranking method, iSpreadRank, to perform this task. iSpreadRank models a set of topic-related documents into a sentence similarity network. Based on such a network model, iSpreadRank exploits the spreading activation theory to formulate a general concept from social network analysis: the importance of a node in a network (i.e., a sentence in this paper) is determined not only by the number of nodes to which it connects, but also by the importance of its connected nodes. The algorithm recursively re-weights the importance of sentences by spreading their sentence-specific feature scores throughout the network to adjust the importance of other sentences. Consequently, a ranking of sentences indicating the relative importance of sentences is reasoned. This paper also develops an approach to produce a generic extractive summary according to the inferred sentence ranking. The proposed summarization method is evaluated using the DUC 2004 data set, and found to perform well. Experimental results show that the proposed method obtains a ROUGE-1 score of 0.38068, which represents a slight difference of 0.00156, when compared with the best participant in the DUC 2004 evaluation.


title: No Dutch Book can be built against the TBM even though update is not obtained by Bayes rule of conditioning

abstract: Synchronic and Diachronic Dutch Books are used to justify the use of probability measures to quantify the beliefs held by a rational agent. The argument has been used to reject any non-Bayesian representation of degrees of beliefs. We show that the transferable belief model resists the criticism even though it is not a Bayesian model. We analyze the ‘Peter, Paul and Mary’ example and show how it resists to Dutch Books.


title: A tool for calculating energy consumption in wireless sensor networks

abstract: Energy and total useful lifetime are primary design concerns of fundamental importance, in a variety of real life applications, where the deployment of a Wireless Sensor Network is desired. In this paper the authors introduce AVAKIS, a tool for calculating the energy consumption of the various components of a sensor node. The proposed tool is an architectural level simulator, in which the system building blocks are described by a high level behavioral model. The methodology used in order to estimate power consumption is based on both the characteristics of the components, and on a number of user-defined initialization parameters.


title: Geometric approach for coupling enhancement of magnetically coupled coils

abstract: This paper presents a geometric approach for the enhancement of the coupling coefficient between two magnetically coupled coils. It is demonstrated that the coupling coefficient can be considerably enhanced, if the turns of the coils are not concentrated at the circumferences, but distributed across the diameters. For analysis, each of the two coils is assumed to be composed of concentric circular loops. The experimental results are in very good agreement with the theoretical results.


title: A hybrid dynamic time warping-deep neural network architecture for unsupervised acoustic modeling.

abstract: We report on an architecture for the unsupervised discovery of talker-invariant subword embeddings. It is made out of two components: a dynamic-time warping based spoken term discovery (STD) system and a Siamese deep neural network (DNN). The STD system clusters word-sized repeated fragments in the acoustic streams while the DNN is trained to minimize the distance between time aligned frames of tokens of the same cluster, and maximize the distance between tokens of different clusters. We use additional side information regarding the average duration of phonemic units, as well as talker identity tags. For evaluation we use the datasets and metrics of the Zero Resource Speech Challenge. The model shows improvement over the baseline in subword unit modeling.