title: [Methimazole and propylthiouracil induced acute toxic hepatitis].

abstract: Hyperthyroidism is one of the most common endocrinology disorders. Treatment can be either pharmacological, surgical or using radioactive iodine. In Europe methimazole is the antithyroid drug of choice because it can be administered in a single daily dose and has a lower risk of adverse reactions. Around 5% of patients taking thionamides can present any of their side effects, which are usually mild. Liver toxicity due to thionamides is very rare, and severe due to propylthiouracil. We present a clinical case of a cholestatic jaundice and acute toxic hepatitis due to methimazole and a cross-reaction with propylthiouracil. Based on this case a review is presented.


title: The effectiveness of virtual reality distraction for pain reduction: A systematic review

abstract: Abstract Virtual reality technology enables people to become immersed in a computer-simulated, three-dimensional environment. This article provides a comprehensive review of controlled research on the effectiveness of virtual reality (VR) distraction for reducing pain. To be included in the review, studies were required to use a between-subjects or mixed model design in which VR distraction was compared with a control condition or an alternative intervention in relieving pain. An exhaustive search identified 11 studies satisfying these criteria. VR distraction was shown to be effective for reducing experimental pain, as well as the discomfort associated with burn injury care. Studies of needle-related pain provided less consistent findings. Use of more sophisticated virtual reality technology capable of fully immersing the individual in a virtual environment was associated with greater relief. Overall, controlled research suggests that VR distraction may be a useful tool for clinicians who work with a variety of pain problems.


title: Adversarial classification

abstract: Essentially all data mining algorithms assume that the data-generating process is independent of the data miner's activities. However, in many domains, including spam detection, intrusion detection, fraud detection, surveillance and counter-terrorism, this is far from the case: the data is actively manipulated by an adversary seeking to make the classifier produce false negatives. In these domains, the performance of a classifier can degrade rapidly after it is deployed, as the adversary learns to defeat it. Currently the only solution to this is repeated, manual, ad hoc reconstruction of the classifier. In this paper we develop a formal framework and algorithms for this problem. We view classification as a game between the classifier and the adversary, and produce a classifier that is optimal given the adversary's optimal strategy. Experiments in a spam detection domain show that this approach can greatly outperform a classifier learned in the standard way, and (within the parameters of the problem) automatically adapt the classifier to the adversary's evolving manipulations.


title: Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups

abstract: Anomaly detection is one of the frequent and important subroutines deployed in large-scale data processing systems. Even being a well-studied topic, existing techniques for unsupervised anomaly detection require storing significant amounts of data, which is prohibitive from memory and latency perspective. In the big-data world existing methods fail to address the new set of memory and latency constraints. In this paper, we propose ACE (Arrays of (locality-sensitive) Count Estimators) algorithm that can be 60x faster than the ELKI package~\cite{DBLP:conf/ssd/AchtertBKSZ09}, which has the fastest implementation of the unsupervised anomaly detection algorithms. ACE algorithm requires less than $4MB$ memory, to dynamically compress the full data information into a set of count arrays. These tiny $4MB$ arrays of counts are sufficient for unsupervised anomaly detection. At the core of the ACE algorithm, there is a novel statistical estimator which is derived from the sampling view of Locality Sensitive Hashing(LSH). This view is significantly different and efficient than the widely popular view of LSH for near-neighbor search. We show the superiority of ACE algorithm over 11 popular baselines on 3 benchmark datasets, including the KDD-Cup99 data which is the largest available benchmark comprising of more than half a million entries with ground truth anomaly labels.


title: A job shop scheduling system based on Parallel Control Mechanism

abstract: Job shop scheduling has been an active area of research for several decades. However, there seems to have been a significant gap between the theoretical research in academia and practical application in industry. The scheduling system, which are developed aiming at the vast library of benchmark problems, usually faces some challenges when applying in the real manufacturing environment to support the day-to-day running of a factory. This paper will present a job shop scheduling system, which is inspired by the Parallel Control Mechanism. The system consists of two interacting subsystems, the artificial sub-system, which could provide optimal scheduling scheme, and the actual one, which would re-generate the schedule according to the real would situation. A hybrid scheduling strategy is proposed, which supports the system to make scheduling decision in job shop that operates in a dynamic environment. The strategy harmonize the different approaches, ant colony optimization algorithm, rule-based scheduling, or real-time simulation, into a unified framework, and therefore can be used as an online scheduler. The presented system and strategy offers better performance than conventional single-optimal-algorithm-based scheduling system.


title: GLStyleNet: Higher Quality Style Transfer Combining Global and Local Pyramid Features

abstract: Recent studies using deep neural networks have shown remarkable success in style transfer especially for artistic and photo-realistic images. However, the approaches using global feature correlations fail to capture small, intricate textures and maintain correct texture scales of the artworks, and the approaches based on local patches are defective on global effect. In this paper, we present a novel feature pyramid fusion neural network, dubbed GLStyleNet, which sufficiently takes into consideration multi-scale and multi-level pyramid features by best aggregating layers across a VGG network, and performs style transfer hierarchically with multiple losses of different scales. Our proposed method retains high-frequency pixel information and low frequency construct information of images from two aspects: loss function constraint and feature fusion. Our approach is not only flexible to adjust the trade-off between content and style, but also controllable between global and local. Compared to state-of-the-art methods, our method can transfer not just large-scale, obvious style cues but also subtle, exquisite ones, and dramatically improves the quality of style transfer. We demonstrate the effectiveness of our approach on portrait style transfer, artistic style transfer, photo-realistic style transfer and Chinese ancient painting style transfer tasks. Experimental results indicate that our unified approach improves image style transfer quality over previous state-of-the-art methods, while also accelerating the whole process in a certain extent. Our code is available at this https URL.


title: Processing Social Media Images by Combining Human and Machine Computing during Crises

abstract: ABSTRACTThe extensive use of social media platforms, especially during disasters, creates unique opportunities for humanitarian organizations to gain situational awareness as disaster unfolds. In addition to textual content, people post overwhelming amounts of imagery content on social networks within minutes of a disaster hit. Studies point to the importance of this online imagery content for emergency response. Despite recent advances in computer vision research, making sense of the imagery content in real-time during disasters remains a challenging task. One of the important challenges is that a large proportion of images shared on social media is redundant or irrelevant, which requires robust filtering mechanisms. Another important challenge is that images acquired after major disasters do not share the same characteristics as those in large-scale image collections with clean annotations of well-defined object categories such as house, car, airplane, cat, dog, etc., used traditionally in computer visi...


title: Probing mammalian centrosome structure using BioID proximity-dependent biotinylation.

abstract: Abstract Understanding the structure and function of the centrosome will require identification of its constituent components and a detailed characterization of the interactions among these components. Here, we describe the application of proximity-dependent biotin identification (BioID) to identify spatial and temporal relationships among centrosome proteins. The BioID method relies on protein fusions to a promiscuous mutant of the Escherichia coli biotin ligase BirA, which biotinylates proteins that are in a ∼10 nm labeling radius of the enzyme. The biotinylated proteins are captured by affinity and are identified by mass spectrometry. Proteins identified in this way are referred to as “proximity interactors.” Application of BioID to a set of centrosome proteins demonstrated the utility of this approach in overcoming inherent limitations in probing centrosome structure. These studies also demonstrated the potential of BioID for building large-scale proximity interaction maps among centrosome proteins. In this chapter, we describe the work flow for identification of proximity interactions of centrosome proteins, including materials and methods for the generation and characterization of a BirA*-fusion protein expression plasmid, expression of BirA*-fusion proteins in cells, and purification and identification of proximity partners by mass spectrometry.


title: I think therefore i om: cognitive distortions and coping style as mediators for the effects of mindfulness meditation on anxiety, positive and negative affect, and hope

abstract: This study examined cognitive distortions and coping styles as potential mediators for the effects of mindfulness meditation on anxiety, negative affect, positive affect, and hope in college students. Our pre- and postintervention design had four conditions: control, brief meditation focused on attention, brief meditation focused on loving kindness, and longer meditation combining both attentional and loving kindness aspects of mindfulness. Each group met weekly over the course of a semester. Longer combined meditation significantly reduced anxiety and negative affect and increased hope. Changes in cognitive distortions mediated intervention effects for anxiety, negative affect, and hope. Further research is needed to determine differential effects of types of meditation. © 2009 Wiley Periodicals, Inc. J Clin Psychol 65: 1–13, 2009.


title: A new active clamping soft switching weinberg converter

abstract: In this paper a new zero voltage switching flyback current-fed push-pull converter with an active circuit is proposed. The active clamp circuit not only absorbs the voltage surge across the switches at turn off instant, but also provides zero voltage switching condition for all switches. Main switches are controlled by PWM scheme and the auxiliary switches turn on are complementary with respect to main switches. Thus, the implementation of control circuit is simple. Both buck and boost modes of converter operates at soft switching conditions. Therefore, this converter can operate over a wide input voltage range. In this paper the operation of proposed converter is presented and the experimental results verify the converter analysis.


title: Adversarial Phenomenon in the Eyes of Bayesian Deep Learning

abstract: Deep Learning models are vulnerable to adversarial examples, i.e.\ images obtained via deliberate imperceptible perturbations, such that the model misclassifies them with high confidence. However, class confidence by itself is an incomplete picture of uncertainty. We therefore use principled Bayesian methods to capture model uncertainty in prediction for observing adversarial misclassification. We provide an extensive study with different Bayesian neural networks attacked in both white-box and black-box setups. The behaviour of the networks for noise, attacks and clean test data is compared. We observe that Bayesian neural networks are uncertain in their predictions for adversarial perturbations, a behaviour similar to the one observed for random Gaussian perturbations. Thus, we conclude that Bayesian neural networks can be considered for detecting adversarial examples.


title: Text Mining Infrastructure in R

abstract: During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facilities in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classification and string kernels.


title: High speed single phase SOGI-PLL with high resolution implementation on an FPGA

abstract: Every power converter needs a precise synchronization method to be able to connect to the utility grid. The synchronization impacts directly on the performance of the converter. Synchronization algorithms are often based on the well known Synchronous Reference Frame Phase Locked Loop (SRF-PLL). These are usually implemented in software on Digital signal Processors (DSP) or Microcontrollers and the maximum sample rate that can be achieved is limited by the hardware used. In this paper a simple and highly effective Field Programmable Gate Array (FPGA) implementation of a Phase Locked Loop (PLL) algorithm based on a Second Order Generalized Integrator PLL (SOGI-PLL) with a high sampling frequency of 500 kHz and 16 bit resolution is presented in detail. Design time is drastically reduced by the use of a fast-prototype high-level synthesis tool without the need of any Hardware Description Language (HDL) code. Both simulation and experimental results on a Xilinx FPGA show an excellent behavior even against severe distortion and frequency or phase steps in the input voltage.


title: Clonal evolution in cancer

abstract: Cancers evolve by a reiterative process of clonal expansion, genetic diversification and clonal selection within the adaptive landscapes of tissue ecosystems. The dynamics are complex, with highly variable patterns of genetic diversity and resulting clonal architecture. Therapeutic intervention may destroy cancer clones and erode their habitats, but it can also inadvertently provide a potent selective pressure for the expansion of resistant variants. The inherently Darwinian character of cancer is the primary reason for this therapeutic failure, but it may also hold the key to more effective control.


An Indoor Bluetooth-Based Positioning System: Concept, Implementation and Experimental Evaluation


title: On Detecting Adversarial Perturbations

abstract: Machine learning and deep learning in particular has advanced tremendously on perceptual tasks in recent years. However, it remains vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to a human. In this work, we propose to augment deep neural networks with a small ``detector'' subnetwork which is trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations. Our method is orthogonal to prior work on addressing adversarial perturbations, which has mostly focused on making the classification network itself more robust. We show empirically that adversarial perturbations can be detected surprisingly well even though they are quasi-imperceptible to humans. Moreover, while the detectors have been trained to detect only a specific adversary, they generalize to similar and weaker adversaries. In addition, we propose an adversarial attack that fools both the classifier and the detector and a novel training procedure for the detector that counteracts this attack.


title: Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition

abstract: In the recent past, the recognition and localization of objects based on local point features has become a widely accepted and utilized method. Among the most popular features are currently the SIFT features, the more recent SURF features, and region-based features such as the MSER. For time-critical application of object recognition and localization systems operating on such features, the SIFT features are too slow (500–600 ms for images of size 640×480 on a 3GHz CPU). The faster SURF achieve a computation time of 150–240 ms, which is still too slow for active tracking of objects or visual servoing applications. In this paper, we present a combination of the Harris corner detector and the SIFT descriptor, which computes features with a high repeatability and very good matching properties within approx. 20 ms. While just computing the SIFT descriptors for computed Harris interest points would lead to an approach that is not scale-invariant, we will show how scale-invariance can be achieved without a time-consuming scale space analysis. Furthermore, we will present results of successful application of the proposed features within our system for recognition and localization of textured objects. An extensive experimental evaluation proves the practical applicability of our approach.


title: When Machine Learning meets Security Issues: A survey

abstract: Machine learning is one of the most prevalent techniques in recent decades which has been widely applied in various fields. Among them, the applications that detect and defend potential adversarial attacks using machine learning method provide promising solutions in cybersecurity. At the same time, machine learning algorithms and systems are vulnerable to multiple security threats. In this paper, we revisit certain literatures and present a comprehensive survey from two respects, application of machine learning on cybersecurity and reliability and security of machine learning system. We then overview security issues of mobile AI devices and propose two notable focus, which are worthy in-depth studies in future. Researchers can regard this survey as a navigating reference in both machine learning and cybersecurity fields.


Visual Attention Guided Deep Imitation Learning


Multi-Touch Surfaces - A Technical Guide Technical Report TUM-I0833 Categories and Subject Descriptors


Domain Specific Automatic Question Generation from Text


title: Emergent themes in the study of emotional development and emotion regulation.

abstract: Reflexions sur les modifications survenues dans la conceptualisation des emotions et sur leurs implications dans l'approche de la regulation des emotions et dans l'etude du developpement affectif chez l'enfant


title: Adversarial Delays in Online Strongly-Convex Optimization

abstract: We consider the problem of strongly-convex online optimization in presence of adversarial delays; in a T-iteration online game, the feedback of the player's query at time t is arbitrarily delayed by an adversary for d_t rounds and delivered before the game ends, at iteration t+d_t-1. Specifically for \algo{online-gradient-descent} algorithm we show it has a simple regret bound of \Oh{\sum_{t=1}^T \log (1+ \frac{d_t}{t})}. This gives a clear and simple bound without resorting any distributional and limiting assumptions on the delays. We further show how this result encompasses and generalizes several of the existing known results in the literature. Specifically it matches the celebrated logarithmic regret \Oh{\log T} when there are no delays (i.e. d_t = 1) and regret bound of \Oh{\tau \log T} for constant delays d_t = \tau.


title: Scalable and cost-efficient algorithms for VNF chaining and placement problem

abstract: This paper focuses on placement and chaining of virtualized network functions (VNFs) in Network Function Virtualization Infrastructures (NFVI) for emerging software networks serving multiple tenants. Tenants can request network services to the NFVI in the form of service function chains (in the IETF SFC sense) or VNF Forwarding Graphs (VNF-FG in the case of ETSI) in support of their applications and business. This paper presents efficient algorithms to provide solutions to this NP-Hard chain placement problem to support NFVI providers. Cost-efficient and improved scalability multi-stage graph and 2-Factor algorithms are presented and shown to find near-optimal solutions in few seconds for large instances.


title: Image2Scene: Transforming Style of 3D Room

abstract: We propose a style transformation system to transform a 3D room into one that resembles the style of a photograph. We focus on two major components of interior scene style: layout and color. Using an interior image database, we learn the related style guidelines. Given a reference image and a 3D room of two different interior rooms, we first establish semantic correspondence between the two scenes. The styles of the reference image are then extracted in the form of layout constraints and color schemes. Finally, our framework performs layout rearrangement followed by recoloring of the scene to match the learned style of the reference image. We show style transformation results on numerous examples to demonstrate the effectiveness and efficiency of our system.


title: Implementation techniques for main memory database systems

abstract: With the availability of very large, relatively inexpensive main memories, it is becoming possible keep large databases resident in main memory In this paper we consider the changes necessary to permit a relational database system to take advantage of large amounts of main memory We evaluate AVL vs B+-tree access methods for main memory databases, hash-based query processing strategies vs sort-merge, and study recovery issues when most or all of the database fits in main memory As expected, B+-trees are the preferred storage mechanism unless more than 80--90% of the database fits in main memory A somewhat surprising result is that hash based query processing strategies are advantageous for large memory situations


title: A Comparison of Multi-label Feature Selection Methods using the Problem Transformation Approach

abstract: Feature selection is an important task in machine learning, which can effectively reduce the dataset dimensionality by removing irrelevant and/or redundant features. Although a large body of research deals with feature selection in single-label data, in which measures have been proposed to filter out irrelevant features, this is not the case for multi-label data. This work proposes multi-label feature selection methods which use the filter approach. To this end, two standard multi-label feature selection approaches, which transform the multi-label data into single-label data, are used. Besides these two problem transformation approaches, we use ReliefF and Information Gain to measure the goodness of features. This gives rise to four multi-label feature selection methods. A thorough experimental evaluation of these methods was carried out on 10 benchmark datasets. Results show that ReliefF is able to select fewer features without diminishing the quality of the classifiers constructed using the features selected.


title: Regression test selection on system requirements

abstract: Regression testing, which is performed after changes are made to a software system, can be used before release of new versions of the system. However, practitioners often have little time to perform this regression testing because of the quick-release cycles of such modified systems. Thus, they may use a random-testing approach or perform little regression testing. This lack of adequate regression testing can cause bugs in untested parts of the program to be exposed only during production or field usage. To improve the efficiency of the regression testing, and thus enable its use before release, techniques that select and run only those test cases that are related to the changes or prioritize the test cases based on criticality or perceived effectiveness have been presented. These technique typically use some representation of the software such as a system model or the source code to perform the test selection and prioritization. However, in practice, access to a system modelor the source code may not be possible. To provide regression test selection in practice, we have developed, and present in this paper, a novel approach to regression test selection that, instead of using a system model or the source code, uses the system requirements and their associated test cases, which are typically available to developers/testers. The approach uses the set of system requirements, usually in natural language or some informal notation, that represent what is to be tested about the system. The technique uses these requirements, along with the set of test cases and their criticality that are associated with the requirements, to select test cases for use in regression testing. In this paper, we also present a case study that shows the potential effectiveness of our technique


title: Fully Parallel Stochastic LDPC Decoders

abstract: Stochastic decoding is a new approach to iterative decoding on graphs. This paper presents a hardware architecture for fully parallel stochastic low-density parity-check (LDPC) decoders. To obtain the characteristics of the proposed architecture, we apply this architecture to decode an irregular state-of-the-art (1056,528) LDPC code on a Xilinx Virtex-4 LX200 field-programmable gate-array (FPGA) device. The implemented decoder achieves a clock frequency of 222 MHz and a throughput of about 1.66 Gb/s at Eb/N0=4.25 dB (a bit error rate of 10-8). It provides decoding performance within 0.5 and 0.25 dB of the floating-point sum-product algorithm with 32 and 16 iterations, respectively, and similar error-floor behavior. The decoder uses less than 40% of the lookup tables, flip-flops, and IO ports available on the FPGA device. The results provided in this paper validate the potential of stochastic LDPC decoding as a practical and competitive fully parallel decoding approach.


title: Shape Classification Using the Inner-Distance

abstract: Part structure and articulation are of fundamental importance in computer and human vision. We propose using the inner-distance to build shape descriptors that are robust to articulation and capture part structure. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show that it is articulation insensitive and more effective at capturing part structures than the Euclidean distance. This suggests that the inner-distance can be used as a replacement for the Euclidean distance to build more accurate descriptors for complex shapes, especially for those with articulated parts. In addition, texture information along the shortest path can be used to further improve shape classification. With this idea, we propose three approaches to using the inner-distance. The first method combines the inner-distance and multidimensional scaling (MDS) to build articulation invariant signatures for articulated shapes. The second method uses the inner-distance to build a new shape descriptor based on shape contexts. The third one extends the second one by considering the texture information along shortest paths. The proposed approaches have been tested on a variety of shape databases, including an articulated shape data set, MPEG7 CE-Shape-1, Kimia silhouettes, the ETH-80 data set, two leaf data sets, and a human motion silhouette data set. In all the experiments, our methods demonstrate effective performance compared with other algorithms


title: Multiattribute SCADA-Specific Intrusion Detection System for Power Networks

abstract: The increased interconnectivity and complexity of supervisory control and data acquisition (SCADA) systems in power system networks has exposed the systems to a multitude of potential vulnerabilities. In this paper, we present a novel approach for a next-generation SCADA-specific intrusion detection system (IDS). The proposed system analyzes multiple attributes in order to provide a comprehensive solution that is able to mitigate varied cyberattack threats. The multiattribute IDS comprises a heterogeneous white list and behavior-based concept in order to make SCADA cybersystems more secure. This paper also proposes a multilayer cyber-security framework based on IDS for protecting SCADA cybersecurity in smart grids without compromising the availability of normal data. In addition, this paper presents a SCADA-specific cybersecurity testbed to investigate simulated attacks, which has been used in this paper to validate the proposed approach.


title: Gordon Pask's Conversation Theory: A Domain Independent . . .

abstract: Although it is conceded (as argued by many)that distinct knowledge domains do presentparticular problems of coming to know, in thispaper it is argued that it is possible (anduseful) to construct a domain independent modelof the processes of coming to know, one inwhich observers share understandings and do soin agreed ways. The model in question is partof the conversation theory (CT) of Gordon Pask. CT, as a theory of theory construction andcommunication, has particular relevance forfoundational issues in science and scienceeducation. CT explicitly propounds a ``radicalconstructivist'' (RC) epistemology. A briefaccount is given of the main tenets of RC andCT's place in that tradition and the traditionsof cybernetics. The paper presents a briefnon-technical account of the main concepts ofCT including elaborations by Laurillard andHarri-Augstein and Thomas. As part of CT, Pask also elaborated a methodology – knowledgeand task analysis – for analysing the structureof different knowledge domains; thismethodology is sketched in outline.


The Narrative Advantage : Gender and the Language of Crowdfunding


title: Fast visual odometry and mapping from RGB-D data

abstract: An RGB-D camera is a sensor which outputs color and depth and information about the scene it observes. In this paper, we present a real-time visual odometry and mapping system for RGB-D cameras. The system runs at frequencies of 30Hz and higher in a single thread on a desktop CPU with no GPU acceleration required. We recover the unconstrained 6-DoF trajectory of a moving camera by aligning sparse features observed in the current RGB-D image against a model of previous features. The model is persistent and dynamically updated from new observations using a Kalman Filter. We formulate a novel uncertainty measure for sparse RGD-B features based on a Gaussian mixture model for the filtering stage. Our registration algorithm is capable of closing small-scale loops in indoor environments online without any additional SLAM back-end techniques.


title: Detecting anomalies in dynamic rating data: a robust probabilistic model for rating evolution

abstract: Rating data is ubiquitous on websites such as Amazon, TripAdvisor, or Yelp. Since ratings are not static but given at various points in time, a temporal analysis of rating data provides deeper insights into the evolution of a product's quality. In this work, we tackle the following question: Given the time stamped rating data for a product or service, how can we detect the general rating behavior of users as well as time intervals where the ratings behave anomalous? We propose a Bayesian model that represents the rating data as sequence of categorical mixture models. In contrast to existing methods, our method does not require any aggregation of the input but it operates on the original time stamped data. To capture the dynamic effects of the ratings, the categorical mixtures are temporally constrained: Anomalies can occur in specific time intervals only and the general rating behavior should evolve smoothly over time. Our method automatically determines the intervals where anomalies occur, and it captures the temporal effects of the general behavior by using a state space model on the natural parameters of the categorical distributions. For learning our model, we propose an efficient algorithm combining principles from variational inference and dynamic programming. In our experimental study we show the effectiveness of our method and we present interesting discoveries on multiple real world datasets.


title: UNDERSTANDING THE COST ASSOCIATED WITH DATA SECURITY BREACHES

abstract: To estimate the cost of a data breach to the inflicted firm, this study examines the relationship between a breach incident and changes in the inflicted firm’s profitability, perceived risk, and the inflicted firms’ information environment transparency. Profitability is measured as reported earnings and analysts’ earnings forecasts. Perceived risk is measured as reported stock return volatility and dispersion among analysts’ forecasts. Although a number of studies have investigated the stock market reaction surrounding the disclosure of a breach incident to quantify the cost associated with breaches, we argue that there exists information uncertainty and deficiency in the disclosure of the breach incident and stock market reaction surrounding a security breach announcement date may not be the best measure for the cost of security breaches. And research using other complementary measures is warranted. Our preliminary finding suggests that data breaches negatively impact firm profitability, perceived risk and information transparency. Nevertheless, the damage of a breach most likely stems from direct costs such as compensation and litigation costs rather than indirect costs such as tarnished reputation and a decrease in market share and sales. More sophisticated analysts are also found to add value in estimating the real cost of a security breach.


title: A Virtualized Separation Kernel for Mixed-Criticality Systems

abstract: Multi- and many-core processors are becoming increasingly popular in embedded systems. Many of these processors now feature hardware virtualization capabilities, as found on the ARM Cortex A15 and x86 architectures with Intel VT-x or AMD-V support. Hardware virtualization provides a way to partition physical resources, including processor cores, memory, and I/O devices, among guest virtual machines (VMs). Each VM is then able to host tasks of a specific criticality level, as part of a mixed-criticality system with different timing and safety requirements. However, traditional virtual machine systems are inappropriate for mixed-criticality computing. They use hypervisors to schedule separate VMs on physical processor cores. The costs of trapping into hypervisors to multiplex and manage machine physical resources on behalf of separate guests are too expensive for many time-critical tasks. Additionally, traditional hypervisors have memory footprints that are often too large for many embedded computing systems. In this article, we discuss the design of the Quest-V separation kernel, which partitions services of different criticality levels across separate VMs, or sandboxes. Each sandbox encapsulates a subset of machine physical resources that it manages without requiring intervention from a hypervisor. In Quest-V, a hypervisor is only needed to bootstrap the system, recover from certain faults, and establish communication channels between sandboxes. This not only reduces the memory footprint of the most privileged protection domain but also removes it from the control path during normal system operation, thereby heightening security.


Regerator: a Registry Generator for Blockchain


title: Low-Profile Broadband Circularly Polarized Patch Antenna Using Metasurface

abstract: A low-profile single-feed circularly polarized (CP) patch antenna using metasurface is proposed for broadband operation. The antenna is comprised of a truncated corner square patch sandwiched between a lattice of $4 \times 4$ periodic metal plates and the ground plane. Surface waves propagating on the metasurface are excited in the proposed structure. This phenomenon generates additional resonances and minimum axial ratio (AR) points for the radiating structure, consequently broadening the impedance-matching and AR bandwidths of the antenna. The final prototype, with an overall size of $32\;{\text{mm}} \times 32\;{\text{mm}} \times 3\;{\text{mm}}$ ( $0.58{\uplambda}_{\text o} \times 0.58{\uplambda}_{\text o} \times 0.056{\uplambda}_{\text o}$ at 5.5 GHz), was fabricated and tested. The measurements resulted in a $\vert{S}_11 \vert bandwidth of 4.70–7.48 GHz (45.6%) and a 3-dB AR bandwidth of 4.9–6.2 GHz (23.4%). In addition, the antenna yielded a good broadside left-hand CP radiation with a small gain variation (7.0–7.6 dBic) and a high radiation efficiency ( ${>} 90$ %) within the operational bandwidth.


title: A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

abstract: Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain function. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.


Returns on Investments in Information Technology: A Research Synthesis


title: Design of Switched Reluctance Motor Competitive to 60-kW IPMSM in Third-Generation Hybrid Electric Vehicle

abstract: In this paper, the design of a switched reluctance motor (SRM) having torque, power, speed-range, and efficiency values competitive to those of the interior permanent-magnet synchronous motor (IPMSM) employed in the 2009 Toyota Prius has been investigated. The outer diameter and axial length are the same as those of the IPMSM. A maximum torque of 207 N·m is needed up to the knee speed of 2768 r/min. An output power of 60 kW is required in the speed range from 2768 to 13 900 r/min. It is shown that the aforementioned requirements can be satisfied by the designed SRM, although the current density and weight are slightly increased. At high speed, the simulation results show that the output of the designed SRM is greatly enhanced with respect to that of the IPMSM.


title: Fair Chance Round Robin Arbiter

abstract: With the advancement of Network-on-chip (NoC), fast and fair arbiter as the basic building block for high speed switches/routers gained attention in recent years. In this paper I propose the fair chance round robin arbiter (FCRRA), a high speed, low power and area efficient RRA for NoC applications. The FCRRAG tool propose in this paper can generate a design for bus arbiter, which can handle the exact number of bus masters for both on chip and off chip buses within one short cycle. General Terms i. Arbiters are electronic devices that allocate access to shared resources. ii. Virtual Output Queues (VOQs) [4]: there are VOQs in a switch to remove possible output port contention (Head of Line (HOL) blocking). iii. Bus Arbiter resolves bus conflicts when multiple bus masters request a bus in the same cycle.[4]


title: Procedural Personas as Critics for Dungeon Generation

abstract: This paper introduces a constrained optimization method which uses procedural personas to evaluate the playability and quality of evolved dungeon levels. Procedural personas represent archetypical player behaviors, and their controllers have been evolved to maximize a specific utility which drives their decisions. A “baseline” persona evaluates whether a level is playable by testing if it can survive in a worst-case scenario of the playthrough. On the other hand, a Monster Killer persona or a Treasure Collector persona evaluates playable levels based on how many monsters it can kill or how many treasures it can collect, respectively. Results show that the implemented two-population genetic algorithm discovers playable levels quickly and reliably, while the different personas affect the layout, difficulty level and tactical depth of the generated dungeons.


title: DROPOUT RATES OF MASSIVE OPEN ONLINE COURSES: BEHAVIOURAL PATTERNS

abstract: Massive open online courses (MOOCs) have received wide publicity and many institutions have invested considerable effort in developing, promoting and delivering such courses. However, there are still many unresolved questions relating to MOOCs and their effectiveness. One of the major recurring issues raised in both academic literature and the popular press is the consistently high dropout rate of MOOC learners. Although many thousands of participants enrolled on these courses, the completion rate for most courses is below 13%. This paper investigates MOOC attrition from several different perspectives. Firstly, we review existing literature relating to MOOC dropout rates, bringing together existing findings on completion rates and analyses of specific courses which identify factors that correlate to likelihood of dropout. We provide a meta-analysis of the basic figures on overall dropout rates previously collected to identify relationships between course factors and dropout rates. In addition, the literature is reviewed from a qualitative perspective drawing together perspectives on reasons for dropout and methods suggested for resolving or reducing the dropout rate. Secondly, using themes emerging from the initial investigation, we provide a preliminary analysis of data gathered from a Computing MOOC run by the University of Warwick, UK and presented using a Moodle platform. Different aspects of students’ demographic data are examined to see if relationships to persistence exist. An important feature of this course is that it has been run in two different parallel modes (“traditional” MOOC mode with peer support, and “supported” mode with real time, tutored programming labs). This allows direct comparison between the dropout figures for the two different modes. Qualitative information from student evaluations is also considered. Finally, we discuss our findings relating MOOC dropout rates, considering what factors are within the control of a MOOC provider and suggesting the most promising avenues for improvement. Our results indicate that many participants who may be classed as dropouts (for example, because they do not complete the necessary components to gain a certificate) are still participating in the course in their own preferred way (either at a slower pace or with selective engagement). This suggests that the structure of “a course” may not be helpful to all participants and supporting different patterns of engagement and presentation of material may be beneficial.


Privacy-Preserving Multi-Keyword Ranked Search over Encrypted Cloud Data


title: E-business Adoption amongst SMEs: A Structural Equation Modeling Approach

abstract: The adoption of e-business amongst small and medium sized enterprises in the state of Terengganu, Malaysia is still quite low. Hence, identifying the success factors that contribute to e-business adoption is crucial. This article examines the factors that determine the e-business adoption amongst small and medium enterprises and its causal effects using a theoretical model based on the Innovation Diffusion Theory. The research model consists of five exogenous latent constructs, namely relative advantage, compatibility, complexity, trialability and observability. Data relating to the constructs were collected from 337 small and medium sized enterprises located in the state of Terengganu, Malaysia and subjected to Structural Equation Modeling analysis. Confirmatory Factor Analysis (CFA) was performed to examine the reliability, construct validity, convergent validity and goodness of fit of individual construct and measurement models. The hypothesized structural model fits the data well. Results indicate that the significant factor that leads to the adoption of e-business is compatibility. Finally, implications and suggestions of these findings are discussed.


title: Clustering on the unit hypersphere using von mises-fisher distributions

abstract: Several large scale data mining applications, such as text categorization and gene expression analysis, involve high-dimensional data that is also inherently directional in nature. Often such data is L2 normalized so that it lies on the surface of a unit hypersphere. Popular models such as (mixtures of) multi-variate Gaussians are inadequate for characterizing such data. This paper proposes a generative mixture-model approach to clustering directional data based on the von Mises-Fisher (vMF) distribution, which arises naturally for data distributed on the unit hypersphere. In particular, we derive and analyze two variants of the Expectation Maximization (EM) framework for estimating the mean and concentration parameters of this mixture. Numerical estimation of the concentration parameters is non-trivial in high dimensions since it involves functional inversion of ratios of Bessel functions. We also formulate two clustering algorithms corresponding to the variants of EM that we derive. Our approach provides a theoretical basis for the use of cosine similarity that has been widely employed by the information retrieval community, and obtains the spherical kmeans algorithm (kmeans with cosine similarity) as a special case of both variants. Empirical results on clustering of high-dimensional text and gene-expression data based on a mixture of vMF distributions show that the ability to estimate the concentration parameter for each vMF component, which is not present in existing approaches, yields superior results, especially for difficult clustering tasks in high-dimensional spaces.


title: A Motion Graph Approach for Interactive 3D Animation using Low-cost Sensors

abstract: Interactive 3D animation of human figures is very common in video games, animation studios and virtual environments. However, it is difficult to produce full body animation that looks realistic enough to be comparable to studio quality human motion data. The commercial motion capture systems are expensive and not suitable for capture in everyday environments. Real-time requirements tend to reduce quality of animation. We present a motion graph based framework to produce high quality motion sequences in real-time using a set of inertial sensor based controllers. The user’s action generates signals from the controllers that provide constraints to select appropriate sequence of motions from a structured database of human motions, namely motion graph. Our local search algorithm utilizes noise prone and rapidly varying input sensor signals for querying a large database in real-time. The ability to waive the controllers for producing high quality animation provides a simple 3D user interface that is intuitive to use. The proposed framework is low cost and easy to setup.


IMPLEMENTATION OF COMBINATIONAL CIRCUITS USING TERNARY MULTIPLEXER


title: Prediction of cyberbullying incidents in a media-based social network

abstract: Cyberbullying is a major problem affecting more than half of all American teens. Prior work has largely focused on detecting cyberbullying after the fact. In this paper, we investigate the prediction of cyberbullying incidents in Instagram, a popular media-based social network. The novelty of this work is building a predictor that can anticipate the occurrence of cyberbullying incidents before they happen. The Instagram media-based social network is well-suited to such prediction since there is an initial posting of an image typically with an associated text caption, followed later by the text comments that form the basis of a specific cyberbullying incident. We extract several important features from the initial posting data for automated cyberbullying prediction, including profanity and linguistic content of the text caption, image content, as well as social graph parameters and temporal content behavior. Evaluations using a real-world Instagram dataset demonstrate that our method achieves high performance in predicting the occurrence of cyberbullying incidents.


Ballbot : A Low-Cost Robot for Tennis Ball Retrieval


title: What if you can't trust your network card?

abstract: In the last few years, many different attacks against computing platform targeting hardware or low level firmware have been published. Such attacks are generally quite hard to detect and to defend against as they target components that are out of the scope of the operating system and may not have been taken into account in the security policy enforced on the platform. In this paper, we study the case of remote attacks against network adapters. In our case study, we assume that the target adapter is running a flawed firmware that an attacker may subvert remotely by sending packets on the network to the adapter. We study possible detection techniques and their efficiency. We show that, depending on the architecture of the adapter and the interface provided by the NIC to the host operating system, building an efficient detection framework is possible. We explain the choices we made when designing such a framework that we called NAVIS and give details on our proof of concept implementation.


title: Three-layer printed reflectarrays for contoured beam space applications

abstract: A procedure for designing contoured beam reflectarrays in a defined frequency band is proposed. The reflectarray consists of three layers of rectangular patch arrays backed by a ground plane. The phase of the reflection coefficient for both linear polarizations is controlled at each reflective element by adjusting the patch dimensions. To overcome the frequency band limitation of reflectarrays, the patch dimensions are adjusted to match the required phase distribution and its variation with frequency. A phase-only synthesis technique based on the intersection approach has been applied to obtain the phase distribution on the reflectarray surface. An experimental demonstration is presented by means of a medium-size two-layer reflectarray that radiates two pencil beams separated by 55/spl deg/. An 80-cm three-layer reflectarray has been designed for typical South America DBS coverage at Ku-band for dual polarization. After the optimization of the patch dimensions to match the phase distribution in the 12.8-14.2 GHz band significant improvements have been achieved in the contoured patterns. As a result, the theoretical radiation patterns practically fulfill the requirements in the coverage region within a 10% bandwidth.


Recent Progress in Shape Memory Alloys


title: Execution strategies for SQL subqueries

abstract: Optimizing SQL subqueries has been an active area in database research and the database industry throughout the last decades. Previous work has already identified some approaches to efficiently execute relational subqueries. For satisfactory performance, proper choice of subquery execution strategies becomes even more essential today with the increase in decision support systems and automatically generated SQL, e.g., with ad-hoc reporting tools. This goes hand in hand with increasing query complexity and growing data volumes, which all pose challenges for an industrial-strength query optimizer. This current paper explores the basic building blocks that Microsoft SQL Server utilizes to optimize and execute relational subqueries. We start with indispensable prerequisites such as detection and removal of correlations for subqueries. We identify a full spectrum of fundamental subquery execution strategies such as forward and reverse lookup as well as set-based approaches, explain the different execution strategies for subqueries implemented in SQL Server, and relate them to the current state of the art. To the best of our knowledge, several strategies discussed in this paper have not been published before. An experimental evaluation complements the paper. It quantifies the performance characteristics of the different approaches and shows that indeed alternative execution strategies are needed in different circumstances, which make a cost-based query optimizer indispensable for adequate query performance.


title: Smart BodyNet for Hypercube Body Sensor Network

abstract: Body sensor Networks (BSNs) over the span of time are increasingly becoming significant and widely adopted. They are being utilized and implemented for monitoring and measurement the physiological parameters in both human beings and animals. BSNs are comprised of number of sensor nodes located and deployed over the human body or could be deployed in wearable form over the clothes. These sensors continuously gather the set parameters data of body area, to be used in the intelligent system in smart environments. BSNs need new transmission approaches that are both effective and capable of handling the requirements that involve data transference, functioning on extremely low power levels, and motility of the body area in the smart environments. BSNs are subjected to intensive research in major methods, challenges like sensors, data gathering, and network communication and many aspects of network architecture. This paper presents a Body Sensors Network Product based on virtual Hypercube structure backbone termed Smart BodyNet. Complemented by a data gathering approach and internal routing methodology between nodes to address several challenging BSNs network design issues in a environmented contributing to the smarter world. The Hypercube is an effective interconnection network whose main hallmarks are high-quality connectivity and maximum battery life as a backbone. In this work we leverage the concept of interoperability, smart phone to perform 2 (two) tasks. First, function as a base station to process, receive the gathered data from intelligent sensor nodes and secondly, to work as a gateway for attaching the movable and portable (Smart BodyNet) product to any other networks in the potential smart world involving integrated internet of things.


title: Learning Community Embedding with Community Detection and Node Embedding on Graphs

abstract: In this paper, we study an important yet largely under-explored setting of graph embedding, i.e., embedding communities instead of each individual nodes. We find that community embedding is not only useful for community-level applications such as graph visualization, but also beneficial to both community detection and node classification. To learn such embedding, our insight hinges upon a closed loop among community embedding, community detection and node embedding. On the one hand, node embedding can help improve community detection, which outputs good communities for fitting better community embedding. On the other hand, community embedding can be used to optimize the node embedding by introducing a community-aware high-order proximity. Guided by this insight, we propose a novel community embedding framework that jointly solves the three tasks together. We evaluate such a framework on multiple real-world datasets, and show that it improves graph visualization and outperforms state-of-the-art baselines in various application tasks, e.g., community detection and node classification.


title: Taking the sting out of carrier sense: interference cancellation for wireless LANs

abstract: A fundamental problem with unmanaged wireless networks is high packet loss rates and poor spatial reuse, especially with bursty traffic typical of normal use. To address these limitations, we explore the notion of interference cancellation for unmanaged networks - the ability for a single receiver to disambiguate and successfully receive simultaneous overlapping transmissions from multiple unsynchronized sources. We describe a practical algorithm for interference cancellation, and implement it for ZigBee using software radios. In this setting, we find that our techniques can reduce packet loss rate and substantially increase spatial reuse. With carrier sense set to prevent concurrent sends, our approach reduces the packet loss rate during collisions from 14% to 8% due to improved handling of hidden terminals. Conversely, disabling carrier sense reduces performance for only 7% of all pairs of links and increases the delivery rate for the median pair of links in our testbed by a factor of 1.8 due to improved spatial reuse.


title: Color Constancy with Spatio-Spectral Statistics

abstract: We introduce an efficient maximum likelihood approach for one part of the color constancy problem: removing from an image the color cast caused by the spectral distribution of the dominating scene illuminant. We do this by developing a statistical model for the spatial distribution of colors in white balanced images (i.e., those that have no color cast), and then using this model to infer illumination parameters as those being most likely under our model. The key observation is that by applying spatial band-pass filters to color images one unveils color distributions that are unimodal, symmetric, and well represented by a simple parametric form. Once these distributions are fit to training data, they enable efficient maximum likelihood estimation of the dominant illuminant in a new image, and they can be combined with statistical prior information about the illuminant in a very natural manner. Experimental evaluation on standard data sets suggests that the approach performs well.


title: Relations between Body Motion and Emotion: Analysis based on Laban Movement Analysis

abstract: This study examined the relationship between human body movements and emotion based on Laban Movement Analysis (LMA). Ten participants participated in the experiment in which they stayed at a small resting room while hearing pleasant or unpleasant sounds. After the stay at the room, the participants rated their subjective emotional states. Participants’ body movement were also recorded with four video cameras. The movement analysis based on LMA reveled significant differences in movement features between experimental conditions. In addition, significant correlations between movement features and subjective mood ratings were observed. These results suggest a strong relationship between human body movements and emotion.


title: ORIGINAL ARTICLE Comparison of the Full Outline of UnResponsiveness and Glasgow Liege Scale/Glasgow Coma Scale in an Intensive Care Unit Population

abstract: Background ::: The Full Outline of UnResponsiveness (FOUR) has been proposed as an alternative for the Glasgow Coma Scale (GCS)/Glasgow Liege Scale (GLS) in the evaluation of consciousness in severely brain-damaged patients. We compared the FOUR and GLS/GCS in intensive care unit patients who were admitted in a comatose state.


Design of a Hybrid Controller using Differential Evolution and MIT Rule for Magnetic Levitation System


title: A Braille-based mobile communication and translation glove for deaf-blind people

abstract: Deafblind people are excluded from most forms of communication and information. This paper suggests a novel approach to support the communication and interaction of deaf- blind individuals, thus fostering their independence. It includes a smart glove that translates the Braille alphabet, which is used almost universally by the literate deafblind population, into text and vice versa, and communicates the message via SMS to a remote contact. It enables user to convey simple messages by capacitive touch sensors as input sensors placed on the palmer side of the glove and converted to text by the PC/mobile phone. The wearer can perceive and interpret incoming messages by tactile feedback patterns of mini vibrational motors on the dorsal side of the glove. The successful implementation of real-time two- way translation between English and Braille, and communication of the wearable device with a mobile phone/PC opens up new opportunities of information exchange which were hitherto un-available to deafblind individuals, such as remote communication, as well as parallel one-to many broadcast. The glove also makes communicating with laypersons without knowledge of Braille possible, without the need for trained interpreters.


title: Capabilities of three-layered perceptrons

abstract: A theorem is proved to the effect that three-layered perceptrons with an infinite number of computing units can represent arbitrary mapping if the desired mapping and the input-output characteristics of the computing units satisfy some constraints. The proof is constructive, and each coefficient is explicitly presented. The theorem theoretically guarantees a kind of universality for three-layered perceptrons. Although two-layered perceptrons (simple perceptrons) cannot represent arbitrary functions, three-layers prove necessary and sufficient. The relationship between the model used in the proof and the distributed storage and processing of information is also discussed. >


title: FEXT Crosstalk Cancellation for High-Speed Serial Link Design

abstract: We have proposed and verified an efficient architecture for a high-speed I/O transceiver design that implements far-end crosstalk (FEXT) cancellation. In this design, TX pre-emphasis, used traditionally to reduce ISI, is combined with FEXT cancellation at the transmitter to remove crosstalk-induced jitter and interference. The architecture has been verified via simulation models based on channel measurement. A prototype implementation of a 12.8Gbps source-synchronous serial link transmitter has been developed in TSMC's 0.18mum CMOS technology. The proposed design consists of three 12.8Gbps data lines that uses a half-rate PLL clock of 6.4GHz. The chip includes a PRBS generator to simplify multi-lane testing. Simulation results show that, even with a 2times reduction in line separation, FEXT cancellation can successfully reduce jitter by 51.2 %UI and widen the eye by 14.5%. The 2.5 times 1.5 mm2 core consumes 630mW per lane at 12.8Gbps with a 1.8V supply


title: Hidden images

abstract: A hidden image is a form of artistic expression in which one or more secondary objects (or scenes) are hidden within a primary image. Features of the primary image, especially its edges and texture, are used to portray a secondary object. People can recognize both the primary and secondary intent in such pictures, although the time taken to do so depends on the prior experience of the viewer and the strength of the clues. Here, we present a system for creating such images. It relies on the ability of human perception to recognize an object, e.g. a human face, from incomplete edge information within its interior, rather than its outline. Our system detects edges of the object to be hidden, and then finds a place where it can be embedded within the scene, together with a suitable transformation for doing so, by optimizing an energy based on edge differences. Embedding is performed using a modified Poisson blending approach, which strengthens matched edges of the host image using edges of the object being embedded. We show various hidden images generated by our system.


title: Classification and comparison of routing protocols in VANETs

abstract: Vehicular Ad Hoc Networks (VANETs) is a subclass of the Mobile Ad Hoc Networks, which do not rely on a preexisting infrastructure, in which each node participates in routing by forwarding data for other nodes. The main purpose of VANET is to provide road traffic safety for the drivers in addition to make a comfortable driving environment. Although the big challenge in VANET in contract to MANET is to design a routing protocols more suitable to route packets efficiently to their final destination, in spite of the high speed of vehicles, frequent disconnection and the highly changeable topology. In this paper we discussed, the details of conventional routing protocols designed for VANETs, their advantages and weaknesses and also highlights the open issues that remain to be addressed.


title: The tyranny of the Balanced Scorecard in the innovation economy

abstract: Purpose – To trace the rationale, features, development and application of the Balanced Scorecard (BSC) over the past ten years, to provide a critical review of its key problematic effects, and to suggest a future direction. Design/methodology/approach – The shift from the industrial to the innovation economy provides a background to identifying five major problem areas of the BSC which are then discussed with reference to selected case examples. An alternative systemic scorecard is then proposed. Findings – The tyranny of the BSC as a measurement “straightjacket” is beginning to jeopardize the survival of firms, hinders much‐needed business ecosystem innovation, thereby negatively affecting customer value rejuvenation, shareholders' benefits, other stakeholders as well as societal benefits in general. A more systemic alternative is proposed. Research limitations/implications – Future research might focus on further development of the systemic scorecard in different industries and organisational settings with detailed systemic measurement techniques. Practical implications – Rather than relying on the static BSC, it would be more effective to adopt a systemic perspective in measuring/managing intangible assets. Originality/value – An alternative to the BSC is proposed that involves radical change in its underlying assumptions by moving to a more systemic, dynamic framework – a systemic management system, including a systemic scorecard.


Action and degradation of dicyandiamide in soils


title: The Three Domains of Disgust Scale: Factor Structure, Psychometric Properties, and Conceptual Limitations.

abstract: This investigation examined the measurement properties of the Three Domains of Disgust Scale (TDDS). Principal components analysis in Study 1 (n = 206) revealed three factors of Pathogen, Sexual, a...


SEMANTIC WEB SERVICES -- A SURVEY


Distributed compressed sensing


title: A Game Theoretic Approach to Strategy Generation for Moving Target Defense in Web Applications

abstract: The present complexity in designing web applications makes software security a difficult goal to achieve. An attacker can explore a deployed service on the web and attack at his/her own leisure. Moving Target Defense (MTD) in web applications is an effective mechanism to nullify this advantage of their reconnaissance but the framework demands a good switching strategy when switching between multiple configurations for its web-stack. To address this issue, we propose the modeling of a real world MTD web application as a repeated Bayesian game. We formulate an optimization problem that generates an effective switching strategy while considering the cost of switching between different web-stack configurations. To use this model for a developed MTD system, we develop an automated system for generating attack sets of Common Vulnerabilities and Exposures (CVEs) for input attacker types with predefined capabilities. Our framework obtains realistic reward values for the players (defenders and attackers) in this game by using security domain expertise on CVEs obtained from the National Vulnerability Database (NVD). We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system. Lastly, we demonstrate the robustness of our proposed model by evaluating its performance when there is uncertainty about input attacker information.


title: Agent-Based Systems for Manufacturing

abstract: The emerging paradigm of agent-based computation has revolutionized the building of intelligent and decentralized systems. The new technologies met well the requirements in all domains of manufacturing where problems of uncertainty and temporal dynamics, information sharing and distributed operation, or coordination and cooperation of autonomous entities had to be tackled. In the paper software agents and multi-agent systems are introduced and through a comprehensive survey, their potential manufacturing applications are outlined. Special emphasis is laid on methodological issues and deployed industrial systems. After discussing open issues and strategic research directions, we conclude that the evolution of agent technologies and manufacturing will probably proceed hand in hand. The former can receive real challenges from the latter, which, in turn, will have more and more benefits in applying agent technologies, presumably together with well-established or emerging approaches of other disciplines.


title: Robust voltage control of dual active bridge DC-DC converters using sliding mode control

abstract: In this paper, a nonlinear voltage control scheme based on sliding mode control (SMC) is proposed for the bidirectional isolated dual active bridge (DAB) DC-DC converters. The state space model is driven for the nonlinear controller design by using the harmonic functions of the converter output voltage based on the single phase shift modulation (SPSM). The proposed controller can provide the robust control performance for the DC reference tracking. Furthermore, the SMC can achieve the fast reference tracking performance. The validity of the proposed control scheme has been verified by simulation results for 10kW DAB converter.


title: A 128.7nW neural amplifier and Gm-C filter for EEG, using gm/ID methodology and a current reference without resistance

abstract: This paper presents a 128.7nW analog front-end amplifier and Gm-C filter for biomedical sensing applications, specifically for Electroencephalogram (EEG) use. The proposed neural amplifier has a supply voltage of 1.8V, consumes a total current of 71.59nA, for a total dissipated power of 128nW and has a gain of 40dB. Also, a 3th order Butterworth Low Pass Gm-C Filter with a 14.7nS transconductor is designed and presented. The filter has a pass band suitable for use in EEG (1-100Hz). The amplifier and filter utilize current sources without resistance which provide 56nA and (1.154nA ×5) respectively. The proposed amplifier occupies and area of 0.26mm2 in 0.3μm TSMC process.


title: Adding lighting and viewing effects to digital images

abstract: Real paintings are not truly flat but change subtly with variations in viewing direction. The pigments and painting layers also interact with the lighting environment, producing changes that range from subtle to quite dramatic. These effects are lacking in digital images. This paper describes a system that allows an artist to introduce, and control, a variety of lighting and viewing effects, such as specular reflection and refraction, through the use of additional images.


title: Activation of the prefrontal cortex in the human visual aesthetic perception

abstract: Visual aesthetic perception (“aesthetics”) or the capacity to visually perceive a particular attribute added to other features of objects, such as form, color, and movement, was fixed during human evolutionary lineage as a trait not shared with any great ape. Although prefrontal brain expansion is mentioned as responsible for the appearance of such human trait, no current knowledge exists on the role of prefrontal areas in the aesthetic perception. The visual brain consists of “several parallel multistage processing systems, each specialized in a given task such as, color or motion” [Bartels, A. & Zeki, S. (1999) Proc. R. Soc. London Ser. B 265, 2327–2332]. Here we report the results of an experiment carried out with magnetoencephalography which shows that the prefrontal area is selectively activated in humans during the perception of objects qualified as “beautiful” by the participants. Therefore, aesthetics can be hypothetically considered as an attribute perceived by means of a particular brain processing system, in which the prefrontal cortex seems to play a key role.


title: Trajectory generation and control for four wheeled omnidirectional vehicles

abstract: Abstract This paper describes an algorithm to calculate near-optimal minimum time trajectories for four wheeled omnidirectional vehicles, which can be used as part of a high-level path planner. The algorithm is based on a relaxed optimal control problem. It takes limited friction and vehicle dynamics into account, as encountered in high-performance omnidirectional vehicles. The low computational complexity makes the application in real-time feasible. An implementation of the algorithm on a real vehicle is presented and discussed.


title: Treating cancer with selective CDK4/6 inhibitors

abstract: Uncontrolled cellular proliferation, mediated by dysregulation of the cell-cycle machinery and activation of cyclin-dependent kinases (CDKs) to promote cell-cycle progression, lies at the heart of cancer as a pathological process. Clinical implementation of first-generation, nonselective CDK inhibitors, designed to inhibit this proliferation, was originally hampered by the high risk of toxicity and lack of efficacy noted with these agents. The emergence of a new generation of selective CDK4/6 inhibitors, including ribociclib, abemaciclib and palbociclib, has enabled tumour types in which CDK4/6 has a pivotal role in the G1-to-S-phase cell-cycle transition to be targeted with improved effectiveness, and fewer adverse effects. Results of pivotal phase III trials investigating palbociclib in patients with advanced-stage oestrogen receptor (ER)-positive breast cancer have demonstrated a substantial improvement in progression-free survival, with a well-tolerated toxicity profile. Mechanisms of acquired resistance to CDK4/6 inhibitors are beginning to emerge that, although unwelcome, might enable rational post-CDK4/6 inhibitor therapeutic strategies to be identified. Extending the use of CDK4/6 inhibitors beyond ER-positive breast cancer is challenging, and will likely require biomarkers that are predictive of a response, and the use of combination therapies in order to optimize CDK4/6 targeting.


Developing a scale to measure the perceived quality of an Internet shopping site (SITEQUAL


title: Expokit: a software package for computing matrix exponentials

abstract: Expokit provides a set of routines aimed at computing matrix exponentials. More precisely, it computes either a small matrix exponential in full, the action of a large sparse matrix exponential on an operand vector, or the solution of a system of linear OBEs with constant inhomogeneity. The backbone of the sparse routines consists of matrix-free Krylov subspace projection methods (Arnoldi and Lanczos processes), and that is why the toolkit is capable of coping with sparse matrices of large dimension. The software handles real and complex matrices and provides specific routines for symmetric and Hermitian matrices. The computation of matrix exponentials is a numerical issue of critical importance in the area of Markov chains and furthermore, the computed solution is subject to probabilistic constraints. In addition to addressing general matrix exponentials, a distinct attention is assigned to the computation of transient states of Markov chains.


title: Very Short-Term Load Forecasting: Wavelet Neural Networks With Data Pre-Filtering

abstract: Very short-term load forecasting predicts the loads 1 h into the future in 5-min steps in a moving window manner based on real-time data collected. Effective forecasting is important in area generation control and resource dispatch. It is however difficult in view of the noisy data collection process and complicated load features. This paper presents a method of wavelet neural networks with data pre-filtering. The key idea is to use a spike filtering technique to detect spikes in load data and correct them. Wavelet decomposition is then used to decompose the filtered loads into multiple components at different frequencies, separate neural networks are applied to capture the features of individual components, and results of neural networks are then combined to form the final forecasts. To perform moving forecasts, 12 dedicated wavelet neural networks are used based on test results. Numerical testing demonstrates the effects of data pre-filtering and the accuracy of wavelet neural networks based on a data set from ISO New England.


title: Magnetically Tunable Ferrite Loaded Substrate Integrated Waveguide Cavity Resonator

abstract: A magnetically tunable ferrite-loaded substrate integrated waveguide (SIW) cavity resonator is presented and demonstrated. X-band cavity resonator is operated in the dominant mode and the ferrite slabs are loaded onto the side walls of the cavity where the value of magnetic field is highest. Measured results for single and double ferrite-loaded SIW cavity resonators are presented. Frequency tuning range of more than 6% and 10% for single and double ferrite slabs are obtained. Unloaded Q -factor of more than 200 is achieved.


title: Security in Mobile Edge Caching with Reinforcement Learning

abstract: Mobile edge computing usually uses caching to support multimedia contents in 5G mobile Internet to reduce the computing overhead and latency. Mobile edge caching (MEC) systems are vulnerable to various attacks such as denial of service attacks and rogue edge attacks. This article investigates the attack models in MEC systems, focusing on both the mobile offloading and the caching procedures. In this article, we propose security solutions that apply reinforcement learning (RL) techniques to provide secure offloading to the edge nodes against jamming attacks. We also present lightweight authentication and secure collaborative caching schemes to protect data privacy. We evaluate the performance of the RL-based security solution for mobile edge caching and discuss the challenges that need to be addressed in the future.


title: Topical ethyl chloride to reduce pain associated with venous catheterization: a randomized crossover trial

abstract: Abstract Objective To compare pain associated with venous catheterization after administration of topical ethyl chloride vs placebo among emergency department health care providers. Methods We conducted a randomized, double-blind, placebo-controlled, crossover trial among a convenience sample of health care provider volunteers in a tertiary care urban emergency department. We randomly allocated subjects to initial treatment (ethyl chloride vs sterile water aerosol spray) and catheterization site (left or right antecubital fossa). After venous catheterization placement and discontinuation, subjects underwent a 5-minute washout period. All subjects then underwent venous catheterization in the contralateral antecubital fossa after administration of the alternative agent. We measured all outcomes after discontinuation of the second catheter. The primary outcome was difference in pain verbal numeric rating scale score (0-10) between the 2 agents. Secondary outcomes included preferred agent (binary) and future willingness to use agent on patients (5-point Likert scale). Results Thirty-eight health care providers were recruited; all completed the study. Median pain verbal numeric rating scale scores were 4 (interquartile range, 2-5) for placebo vs 2 (1-4) for ethyl chloride. The effect size for pain reduction with ethyl chloride compared with placebo was 2 (95% confidence interval, 0.5-2; P = .001). Most subjects (68.4%) preferred ethyl chloride to placebo. Five-point Likert scale scores measuring willingness to use preferred product on future patients were higher by 2 (95% confidence interval, 1-3) among subjects preferring ethyl chloride vs placebo. Conclusions We found that topical ethyl chloride yields a greater reduction in pain associated with venous catheterization compared with topical placebo.


title: A survey of video datasets for human action and activity recognition

abstract: Highlights? Description of datasets for video-based human activity and action recognition. ? 68 datasets reported: 28 for heterogeneous and 40 for specific human actions. ? Useful data, such as web for dowloading, published works or ground truth, are provided. ? Datasets are compared and classified. Vision-based human action and activity recognition has an increasing importance among the computer vision community with applications to visual surveillance, video retrieval and human-computer interaction. In recent years, more and more datasets dedicated to human action and activity recognition have been created. The use of these datasets allows us to compare different recognition systems with the same input data. The survey introduced in this paper tries to cover the lack of a complete description of the most important public datasets for video-based human activity and action recognition and to guide researchers in the election of the most suitable dataset for benchmarking their algorithms.


title: Research Report : Empirical Test of an EDI Adoption Model

abstract: This paper is the first test of a parsimonious model that posits three factors as determinants of the adoption of electronic data interchange (EDI):readiness, perceived benefits, andexternal pressure. To construct the model, we identified and organized the factors that were found to be influential in prior EDI research. By testing all these factors together in one model, we are able to investigate their relative contributions to EDI adoption decisions. Senior purchasing managers, chosen for their experience with EDI and proximity to the EDI adoption decision, were surveyed and their responses analyzed using structural equation modeling. All three determinants were found to be significant predictors of intent to adopt EDI, with external pressure and readiness being considerably more important than perceived benefits. We show that the constructs in this model can be categorized into three levels:technological, organizational, andinterorganizational. We hypothesize that these categories of influence will also be determinants of the adoption of other emerging forms of interorganizational systems (IOS). 1


title: Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction

abstract: A new practical application of neural network (NN) techniques to environmental numerical modeling has been developed. Namely, a new type of numerical model, a complex hybrid environmental model based on a synergetic combination of deterministic and machine learning model components, has been introduced. Conceptual and practical possibilities of developing hybrid models are discussed in this paper for applications to climate modeling and weather prediction. The approach presented here uses NN as a statistical or machine learning technique to develop highly accurate and fast emulations for time consuming model physics components (model physics parameterizations). The NN emulations of the most time consuming model physics components, short and long wave radiation parameterizations or full model radiation, presented in this paper are combined with the remaining deterministic components (like model dynamics) of the original complex environmental model-a general circulation model or global climate model (GCM)-to constitute a hybrid GCM (HGCM). The parallel GCM and HGCM simulations produce very similar results but HGCM is significantly faster. The speed-up of model calculations opens the opportunity for model improvement. Examples of developed HGCMs illustrate the feasibility and efficiency of the new approach for modeling complex multidimensional interdisciplinary systems.


title: Enhanced Just Noticeable Difference (JND) estimation with image decomposition

abstract: Contrast masking (CM) on edge and textured regions have to be distinguished since distortions on edge regions are easier to be noticed than that on textured regions. Therefore, how to efficiently estimate the CM on edge and textured regions of an image is a key issue for accurate JND (Just Noticeable Difference) estimation. An enhanced image domain JND estimator is devised in this paper with new model for CM. We use the total variation method to obtain a structural image (which contains edge information) and a textural image (which contains texture information) from the input image, and then evaluate the CM for the two images separately rather than the whole image, and hence edge and texture are better distinguished and the under-estimation of JND on textured regions can be effectively avoided. Experimental results of subjective viewing confirm that the proposed model is capable of determining more accurate visibility thresholds.


title: The streaming capacity of sparsely connected P2P systems with distributed control

abstract: Peer-to-Peer (P2P) streaming technologies can take advantage of the upload capacity of clients, and hence can scale to large content distribution networks with lower cost. A fundamental question for P2P streaming systems is the maximum streaming rate that all users can sustain. Prior works have studied the optimal streaming rate for a complete network, where every peer is assumed to communicate with all other peers. This is however an impractical assumption in real systems. In this paper, we are interested in the achievable streaming rate when each peer can only connect to a small number of neighbors. We show that even with a random peer selection algorithm and uniform rate allocation, as long as each peer maintains Ω(logN) downstream neighbors, where N is the total number of peers in the system, the system can asymptotically achieve a streaming rate that is close to the optimal streaming rate of a complete network.We then extend our analysis to multi-channel P2P networks, and we study the scenario where “helpers” from channels with excessive upload capacity can help peers in channels with insufficient upload capacity. We show that by letting each peer select Ω(logN) neighbors randomly from either the peers in the same channel or from the helpers, we can achieve a close-to-optimal streaming capacity region. Simulation results are provided to verify our analysis.


title: Ventral striatum activity when watching preferred pornographic pictures is correlated with symptoms of Internet pornography addiction

abstract: One type of Internet addiction is excessive pornography consumption, also referred to as cybersex or Internet pornography addiction. Neuroimaging studies found ventral striatum activity when participants watched explicit sexual stimuli compared to non-explicit sexual/erotic material. We now hypothesized that the ventral striatum should respond to preferred pornographic compared to non-preferred pornographic pictures and that the ventral striatum activity in this contrast should be correlated with subjective symptoms of Internet pornography addiction. We studied 19 heterosexual male participants with a picture paradigm including preferred and non-preferred pornographic materials. Subjects had to evaluate each picture with respect to arousal, unpleasantness, and closeness to ideal. Pictures from the preferred category were rated as more arousing, less unpleasant, and closer to ideal. Ventral striatum response was stronger for the preferred condition compared to non-preferred pictures. Ventral striatum activity in this contrast was correlated with the self-reported symptoms of Internet pornography addiction. The subjective symptom severity was also the only significant predictor in a regression analysis with ventral striatum response as dependent variable and subjective symptoms of Internet pornography addiction, general sexual excitability, hypersexual behavior, depression, interpersonal sensitivity, and sexual behavior in the last days as predictors. The results support the role for the ventral striatum in processing reward anticipation and gratification linked to subjectively preferred pornographic material. Mechanisms for reward anticipation in ventral striatum may contribute to a neural explanation of why individuals with certain preferences and sexual fantasies are at-risk for losing their control over Internet pornography consumption.


title: The analysis and utilization of cycling training data

abstract: Most mathematical models of athletic training require the quantification of training intensity and quantity or ‘dose’. We aim to summarize both the methods available for such quantification, particularly in relation to cycle sport, and the mathematical techniques that may be used to model the relationship between training and performance.


title: Rule induction for forecasting method selection: meta-learning the

abstract: For univariate forecasting, there are various statistical models and computational algorithms available. In real-world exercises, too many choices can create difficulties in selecting the most appropriate technique, especially for users lacking sufficient knowledge of forecasting. This study focuses on rule induction for forecasting method selection by understanding the nature of historical forecasting data. A novel approach for selecting a forecasting method for univariate time series based on measurable data characteristics is presented that combines elements of data mining, meta-learning, clustering, classification and statistical measurement. We conducted a large-scale empirical study of over 300 time series using four of the most popular forecasting methods. To provide a rich portrait of the global characteristics of univariate time series, we extracted measures from a comprehensive set of features such as trend, seasonality, periodicity, serial correlation, skewness, kurtosis, nonlinearity, self-similarity, and chaos. Both supervised and unsupervised learning methods are used to learn the relationship between the characteristics of the time series and the forecasting method suitability, providing both recommendation rules, as well as visualizations in the feature space. A derived weighting schema based on the rule induction is also used to improve forecasting accuracy based on combined forecasting models.


title: Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor

abstract: Abstract In this paper, a novel controller for brushless DC (BLDC) motor has been presented. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and the rigorous analysis through simulation is performed using simulink tool box in MATLAB environment. The performance of the motor with proposed ANFIS controller is analyzed and compared with classical Proportional Integral (PI) controller, Fuzzy Tuned PID controller and Fuzzy Variable Structure controller. The dynamic characteristics of the brushless DC motor is observed and analyzed using the developed MATLAB/simulink model. Control system response parameters such as overshoot, undershoot, rise time, recovery time and steady state error are measured and compared for the above controllers. In order to validate the performance of the proposed controller under realistic working environment, simulation result has been obtained and analyzed for varying load and varying set speed conditions.


Large-Scale Categorization of Japanese Product Titles Using Neural Attention Models.


title: Introduction: Extending Ethnographic Research with Children in the Asia-Pacific Region

abstract: While some have critiqued anthropologists' apparent neglect of children as potential research subjects, several recent works have pointed out that far more research has been done on the anthropology of childhoods than many of us may realise. The idea of doing ethnographic research with children has not yet been sufficiently mainstreamed in anthropology, though the need for such research has never been more urgent. This is nowhere more true than in the Asia-Pacific region, given its enormous diversity of cultures and the dynamism of its rapidly changing societies. The UN Convention on the Rights of the Child enshrines children's right to participation; all such research, by facilitating children's expression of their own opinions and experiences, helps to make this goal more real. Our introduction sets out some of the issues, and the reasons why as anthropologists we need to take children seriously both as research subjects and as subjects of rights.


title: Exploring the phylogeography of a hexaploid freshwater fish by RAD sequencing

abstract: Genomics Research Institute, University ::: of Pretoria; National Research Foundation, ::: Grant/Award Number: 77240


title: An integrated battery charger/discharger with power-factor correction

abstract: Distributed power supply systems are an attractive solution to meeting the requirements (redundancy, modularity, battery backup, etc.) for the next generation of power supply systems. In addition, the normalization regarding power factor and total harmonic distortion (THD) makes it necessary to include power factor correction in the input stage in such architectures. This paper presents a novel approach of an integrated battery charger/discharger which offers power factor correction and battery galvanic isolation in a simple structure. Placing the battery in the primary side overcomes the need of galvanic isolation integration in each of the DC/DC on-board power converters when this topology is used as part of a distributed power supply system.


title: Adversarial Structure Matching Loss for Image Segmentation

abstract: The per-pixel cross-entropy loss (CEL) has been widely used in structured output prediction tasks as a spatial extension of generic image classification. However, its i.i.d. assumption neglects the structural regularity present in natural images. Various attempts have been made to incorporate structural reasoning mostly through structure priors in a cooperative way where co-occuring patterns are encouraged. We, on the other hand, approach this problem from an opposing angle and propose a new framework for training such structured prediction networks via an adversarial process, in which we train a structure analyzer that provides the supervisory signals, the adversarial structure matching loss (ASML). The structure analyzer is trained to maximize ASML, or to exaggerate recurring structural mistakes usually among co-occurring patterns. On the contrary, the structured output prediction network is trained to reduce those mistakes and is thus enabled to distinguish fine-grained structures. As a result, training structured output prediction networks using ASML reduces contextual confusion among objects and improves boundary localization. We demonstrate that ASML outperforms its counterpart CEL especially in context and boundary aspects on figure-ground segmentation and semantic segmentation tasks with various base architectures, such as FCN, U-Net, DeepLab, and PSPNet.


title: Modeling and control design of Magnetic levitation system

abstract: The aim of this paper is to design of control algorithm for the Magnetic levitation system using an exact input — output feedback linearization method. In this paper is given nonlinear simulation model of the Magnetic levitation based on mathematical model of the Magnetic levitation system. Designed control algorithm together with simulation model of the Magnetic levitation is implemented into control structure with purpose of control on steady state defined by a reference trajectory, which is verified in Matlab/Simulink language.


title: Student success system: risk analytics and data visualization using ensembles of predictive models

abstract: We propose a novel design of a Student Success System (S3), a holistic analytical system for identifying and treating at-risk students. S3 synthesizes several strands of risk analytics: the use of predictive models to identify academically at-risk students, the creation of data visualizations for reaching diagnostic insights, and the application of a case-based approach for managing interventions. Such a system poses numerous design, implementation, and research challenges. In this paper we discuss a core research challenge for designing early warning systems such as S3. We then propose our approach for meeting that challenge. A practical implementation of an student risk early warning system, utilizing predictive models, must meet two design criteria: a) the methodology for generating predictive models must be flexible to allow generalization from one context to another; b) the underlying mechanism of prediction should be easily interpretable by practitioners whose end goal is to design meaningful interventions on behalf of students. Our proposed solution applies an ensemble method for predictive modeling using a strategy of decomposition. Decomposition provides a flexible technique for generating and generalizing predictive models across different contexts. Decomposition into interpretable semantic units, when coupled with data visualizations and case management tools, allows practitioners, such as instructors and advisors, to build a bridge between prediction and intervention.


title: Active Current Source IGBT Gate Drive With Closed-Loop di/dt and dv/dt Control

abstract: This paper proposes an active current source gate drive (ACSD) method based on voltage controlled current source(VCCS) feedback control strategy for high-power IGBTs. Unlike the common voltage source gate drive, the proposed ACSD method provides constant drive current to charge and discharge an IGBT. With a large gate drive current, high switching speed and low switching losses can be achieved in a power converter. However, a high-current/voltage overshoot occurs. To solve this problem, a feedback current proportional to the di/dt or dv/dt signal is generated to the IGBT gate. Thus, direct control of the net gate drive current is produced. Then, the current/voltage overshoot is controlled with little sacrifice in switching time, and the switching losses are lower than that with the conventional gate drive method operating simply by gate resistance switchover. The operation principle and circuit implementation of the proposed ACSD method are presented. The experimental results from a 1200 V/800 A IGBT module verify the performance of the proposed method.