title: An intelligent event-driven approach for efficient energy consumption in commercial buildings: smart office use case

text:
In this paper we present a use case related to the intelligent processing of events coming from the conventional ("cheap") sensors in order to support better energy consumption in commercial buildings. The approach has been implemented using our iCEP framework and deployed in the office space of a real working environment. This research is a kind of the proof of the concept for a new technology that the industry partner would like to exploit.
 The results are very encouraging: smart decisions for the efficient usage of energy can be made by the intelligent processing of "cheap" sensor events.


title: IT Governance and Sarbanes-Oxley: The Latest Sales Pitch or Real Challenges for the IT Function?

text: Building on an analysis of the Sarbanes-Oxley Act, literature, as well as private and public interviews, this paper hopes to shed light on the potential impact of Sarbanes-Oxley for IT governance, IT budgets, and relationships with vendors and outsourcers. Findings have implications for research as well as practical lessons-learned for American firms, and for IT vendors or other companies doing business with American companies.


title: Attention Strategies for Multi-Source Sequence-to-Sequence Learning

text: Modeling attention in neural multi-source sequence-to-sequence learning remains a relatively unexplored area, despite its usefulness in tasks that incorporate multiple source languages or modalities. We propose two novel approaches to combine the outputs of attention mechanisms over each source sequence, flat and hierarchical. We compare the proposed methods with existing techniques and present results of systematic evaluation of those methods on the WMT16 Multimodal Translation and Automatic Post-editing tasks. We show that the proposed methods achieve competitive results on both tasks.


title: Printed $\lambda/8$ -PIFA for Penta-Band WWAN Operation in the Mobile Phone

text: A small-size printed planar inverted-F antenna (PIFA) operated at its one-eighth wavelength (lambda/8) mode as the fundamental resonant mode for achieving WWAN (wireless wide area network) operation in the mobile phone is presented. The proposed PIFA has a simple structure of comprising two radiating strips of length about lambda/8 at 900 MHz and is fed using a coupling feed. Compared to the traditional PIFA using a direct feed, the coupling feed greatly decreases the very large input impedance seen at the lambda/8 mode for the traditional PIFA and results in successful excitation of the lambda/8 mode for the proposed PIFA. Two lambda/8 modes are generated by the two radiating strips and occur at close frequencies at about 900 MHz to form a wide lower band to cover GSM850/900 operation. The two radiating strips also generate two higher-order modes or lambda/4 modes at about 1900 MHz to form a wide upper band for GSM1800/1900/UMTS operation. Penta-band WWAN operation is hence achieved, yet the proposed PIFA only occupies a small printed area of 15times31 mm2 or 465 mm2 on the system circuit board of the mobile phone, which is about the smallest for the internal uniplanar printed antenna capable of penta-band operation that have been reported. Details of the proposed PIFA are presented. The specific absorption rate (SAR) and hearing aid compatibility (HAC) results for the proposed PIFA are also analyzed.


title: Learning to Doodle with Stroke Demonstrations and Deep Q-Networks

text: Doodling is a useful and common intelligent skill that people can learn and master. In this work, we propose a two-stage learning framework to teach a machine to doodle in a simulated painting environment via Stroke Demonstration and deep Q-learning (SDQ). The developed system, Doodle-SDQ, generates a sequence of pen actions to reproduce a reference drawing and mimics the behavior of human painters. In the first stage, it learns to draw simple strokes by imitating in supervised fashion from a set of strokeaction pairs collected from artist paintings. In the second stage, it is challenged to draw real and more complex doodles without ground truth actions; thus, it is trained with Qlearning. Our experiments confirm that (1) doodling can be learned without direct stepby-step action supervision and (2) pretraining with stroke demonstration via supervised learning is important to improve performance. We further show that Doodle-SDQ is effective at producing plausible drawings in different media types, including sketch and watercolor. A short video can be found at https://www.youtube.com/watch? v=-5FVUQFQTaE.


title: Automated Music Emotion Recognition: A Systematic Evaluation

text: Automated music emotion recognition (MER) is a challenging task in Music Information Retrieval (MIR) with wide-ranging applications. Some recent studies pose MER as a continuous regression problem in the ArousalValence (AV) plane. These consist of variations on a common architecture having a universal model of emotional response, a common repertoire of low-level audio features, a bag-of-frames approach to audio analysis, and relatively small data sets. These approaches achieve some success at MER and suggest that further improvements are possible with current technology. Our contribution to the state of the art is to examine just how far one can go within this framework, and to investigate what the limitations of this framework are. We present the results of a systematic study conducted in an attempt to maximize the prediction performance of an automated MER system using the architecture described. We begin with a carefully constructed data set, emphasizing quality over quantity. We address affect induction rather than affect attribution. We consider a variety of algorithms at each stage of the training process, from preprocessing to feature selection and model selection, and we report the results of extensive testing. We found that: (1) none of the variations we considered leads to a substantial improvement in performance, which we present as evidence of a limit on what is achievable under this architecture, and (2) the size of the small data sets that are commonly used in the MER literature limits the possibility of improving the set of features used in MER due to the phenomenon of Subset Selection Bias. We conclude with some proposals for advancing the state of the art.


title: Efficient Hardware Architectures for Deep Convolutional Neural Network

text: Convolutional neural network (CNN) is the state-of-the-art deep learning approach employed in various applications. Real-time CNN implementations in resource limited embedded systems are becoming highly desired recently. To ensure the programmable flexibility and shorten the development period, field programmable gate array is appropriate to implement the CNN models. However, the limited bandwidth and on-chip memory storage are the bottlenecks of the CNN acceleration. In this paper, we propose efficient hardware architectures to accelerate deep CNN models. The theoretical derivation of parallel fast finite impulse response algorithm (FFA) is introduced. Based on FFAs, the corresponding fast convolution units (FCUs) are developed for the computation of convolutions in the CNN models. Novel data storage and reuse schemes are proposed, where all intermediate pixels are stored on-chip and the bandwidth requirement is reduced. We choose one of the largest and most accurate networks, VGG16, and implement it on Xilinx Zynq ZC706 and Virtex VC707 boards, respectively. We achieve the top-5 accuracy of 86.25% using an equal distance non-uniform quantization method. It is estimated that the average performances are 316.23 GOP/s under 172-MHz working frequency on Xilinx ZC706 and 1250.21 GOP/s under 170-MHz working frequency on VC707, respectively. In brief, the proposed design outperforms the existing works significantly, in particular, surpassing related designs by more than two times in terms of resource efficiency.


title: Inverse Combinatorial Optimization: A Survey on Problems, Methods, and Results

text: Given a (combinatorial) optimization problem and a feasible solution to it, the corresponding inverse optimization problem is to find a minimal adjustment of the cost function such that the given solution becomes optimum. Several such problems have been studied in the last twelve years. After formalizing the notion of an inverse problem and its variants, we present various methods for solving them. Then we discuss the problems considered in the literature and the results that have been obtained. Finally, we formulate some open problems.


title: The Iowa Model of Evidence-Based Practice to Promote Quality Care.

text: The UIHC Department of Nursing is nationally known for its work on use of research to improve patient care. This reputation is attributable to staff members who continue to question "how can we improve practice?" or "what does the latest evidence tell us about this patient problem?" and to administrators who support, value, and reward EBP. The revisions made in the original Iowa Model are based on suggestions from staff at UIHC and other practitioners across the country who have implemented the model. We value their feedback and have set forth this revised model for evaluation and adoption by others.


title: Coupled semi-supervised learning for information extraction

text: We consider the problem of semi-supervised learning to extract categories (e.g., academic fields, athletes) and relations (e.g., PlaysSport(athlete, sport)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semi-supervised training using only a few labeled examples is typically unreliable because the learning task is underconstrained. This paper pursues the thesis that much greater accuracy can be achieved by further constraining the learning task, by coupling the semi-supervised training of many extractors for different categories and relations. We characterize several ways in which the training of category and relation extractors can be coupled, and present experimental results demonstrating significantly improved accuracy as a result.


title: Context adaptive deep neural networks for fast acoustic model adaptation

text: Deep neural networks (DNNs) are widely used for acoustic modeling in automatic speech recognition (ASR), since they greatly outperform legacy Gaussian mixture model-based systems. However, the levels of performance achieved by current DNN-based systems remain far too low in many tasks, e.g. when the training and testing acoustic contexts differ due to ambient noise, reverberation or speaker variability. Consequently, research on DNN adaptation has recently attracted much interest. In this paper, we present a novel approach for the fast adaptation of a DNN-based acoustic model to the acoustic context. We introduce a context adaptive DNN with one or several layers depending on external factors that represent the acoustic conditions. This is realized by introducing a factorized layer that uses a different set of parameters to process each class of factors. The output of the factorized layer is then obtained by weighted averaging over the contribution of the different factor classes, given posteriors over the factor classes. This paper introduces the concept of context adaptive DNN and describes preliminary experiments with the TIMIT phoneme recognition task showing consistent improvement with the proposed approach.


title: ACQR: A Novel Framework to Identify and Predict Influential Users in Micro-Blogging

text: As key roles of online social networks, influential users in micro-blogging have the ability to influence the attitudes or behaviour of others. When it comes to marketing, the users’ influence should be associated with a certain topic or field on which people have different levels of preference and expertise. In order to identify and predict influential users in a specific topic more effectively, users’ actual influential capability on a certain topic and potential influence unlimited by topics is combined into a novel comprehensive framework named “ACQR” in this research. ACQR framework depicts the attributes of the influentials from four aspects, including activeness (A), centrality (C), quality of post (Q) and reputation (R). Based on this framework, a data mining method is developed for discovering and forecasting the top influentials. Empirical results reveal that our ACQR framework and the data mining method by TOPSIS and SVMs (with polynomial and RBF kernels) can perform very well in identifying and predicting influential users in a certain topic (such as iPhone 5). Furthermore, the dynamic change processes of users’ influence from longitudinal perspective are analysed and suggestions to the sales managers are provided.


title: CYC: Using Common Sense Knowledge to Overcome Brittleness and Knowledge Acquisition Bottlenecks

text: MC& CYC project is the building, over the coming decade, of a large knowledge base (or KB) of real world facts and heuristics and-as a part of the KB itself-methods for efficiently reasoning over the KB. As the title of this article suggests, our hypothesis is that the two major limitations to building large intelligent programs might be overcome by using such a system. We briefly illustrate how common sense reasoning and analogy can widen the knowledge acquisition bottleneck The next section (“How CYC Works”) illustrates how those same two abilities can solve problems of the type that stymie current expert systems. We then report how the project is being conducted currently: its strategic philosophy, its tactical methodology, and a case study of how we are currently putting that into practice. We conclude with a discussion of the project’s feasibility and timetable.


title: Privacy-Preserving Attribute-Based Access Control Model for XML-Based Electronic Health Record System

text: Cloud-based electronic health record (EHR) systems enable medical documents to be exchanged between medical institutions; this is expected to contribute to improvements in various medical services in the future. However, as the system architecture becomes more complicated, cloud-based EHR systems may introduce additional security threats when compared with the existing singular systems. Thus, patients may experience exposure of private data that they do not wish to disclose. In order to protect the privacy of patients, many approaches have been proposed to provide access control to patient documents when providing health services. However, most current systems do not support fine-grained access control or take into account additional security factors such as encryption and digital signatures. In this paper, we propose a cloud-based EHR model that performs attribute-based access control using extensible access control markup language. Our EHR model, focused on security, performs partial encryption and uses electronic signatures when a patient’s document is sent to a document requester. We use XML encryption and XML digital signature technology. Our proposed model works efficiently by sending only the necessary information to the requesters who are authorized to treat the patient in question.


title: An empirical approach to the experience of architectural space in virtual reality — exploring relations between features and affective appraisals of rectangular indoor spaces

text: In the presented exploratory study, quantitative relations between the experience of architectural spaces and physical properties were investigated using virtual reality (VR) simulations. Geometric properties from a component-based description of rectangular rooms were tested on their suitability as predictor variables for experiential qualities. In a virtual-reality-based perceptual experiment, qualities of 16 vacant rectangular interiors were rated in eight principal categories by 16 participants using the semantic differential scaling technique. The simulated scenes were automatically generated by means of a custom made utility that also provided for the component-based room descriptions. The data analysis revealed strong correlations between several scene features and the averaged rated experience. For example, a preference for ratios near to the golden section could be observed for spatial proportions, which are not directly perceivable. Altogether, a set of five widely independent factors (openness, two room proportions, room area, and balustrade height) turned out to be most effective for describing the observed variance in the attributed experiential qualities. The combination of realistic virtual reality simulations and psychophysical data raising methods proved to be an effective means for basic architectural research. Several quantitative relations between physical properties and the emotional experience of architectural space could be demonstrated. D 2004 Elsevier B.V. All rights reserved.


title: Mutual Coupling Reduction Using F-Shaped Stubs in UWB-MIMO Antenna

text: A compact, high performance, and novel-shaped ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna with low mutual coupling is presented in this paper. The proposed antenna consists of two radiating elements with shared ground plane having an area of <inline-formula> <tex-math notation="LaTeX">$50\times 30$ </tex-math></inline-formula> mm2. F-shaped stubs are introduced in the shared ground plane of the proposed antenna to produce high isolation between the MIMO antenna elements. The designed MIMO antenna has very low mutual coupling of (S21 < −20 dB), low envelop correlation coefficient (ECC <0.04), high diversity gain (DG >7.4 dB), high multiplexing efficiency (<inline-formula> <tex-math notation="LaTeX">${\eta }_{\mathrm {Mux}}> -3.5$ </tex-math></inline-formula>), and high peak gain over the entire UWB frequencies. The antenna performance is studied in terms of S-Parameters, radiation properties, peak gain, diversity gain, envelop correlation coefficient, and multiplexing efficiency. A good agreement between the simulated and measured results is observed.


title: Building a Literal Bridge Between Robotics and Neuroscience using Functional Near Infrared Spectroscopy ( NIRS )

text: Functional near infrared spectroscopy (NIRS) is a promising new tool for research in human-robot interaction (HRI). The use of NIRS in HRI has already been demonstrated both as a means for investigating brain activity during human-robot interactions, as well as in the development of brain-robot interfaces that passively monitor a person’s cognitive state to adapt robots’ behaviors accordingly. In this paper, we survey the utility of NIRS and its range of applications in HRI. We discuss both some exemplary applications as well as several pressing challenges to the deployment of NIRS in more realistic settings.


title: Automatic Code Completion

text: Code completion software is an important tool for many developers, but it traditionally fails to model any long term program dependencies such as scoped variables, instead settling for suggestions based on static libraries. In this paper we present a deep learning approach to code completion for non-terminals (program structural components) and terminals (program text) that takes advantage of running dependencies to improve predictions. We develop an LSTM model and augment it with several approaches to Attention in order to better capture the relative value of the input, hidden state, and context. After evaluating on a large dataset of JavaScript programs, we demonstrate that our Gated LSTM model significantly improves over a Vanilla LSTM baseline, achieving an accuracy of 77% on the non-terminal prediction problem and 46% accuracy on the terminal prediction problem.


title: Could on-line voting boost desire to vote? - Technology acceptance perceptions of young Hungarian citizens

text: Article history: Received 24 May 2015 Received in revised form 2 October 2016 Accepted 18 November 2016 In our paper we develop and test the argument that intent to i-vote (to use on-line voting systems) drives intent to vote, while intent to i-vote is influenced by four key attitudes: performance expectation, perception on ease of use, trust in the internet and trust in the government.We show that these findings contradict thosewhich exclusively identified economical, legal, and cultural drivers to enhance democratic participation in the Central and Eastern European region. Rooted cardinally in the Technology Acceptance Model (TAM) six hypotheses were set, and then tested with partial least square (PLS) structural equation modelling. In the context of young, educated and internet-ready Hungarian voters the testing of the hypotheses has shown high level of on-line voting intent and that perception of on-line voting would enhance voting desire amongst young Hungarian internet users. Also, our findings show that performance expectation, perception on ease of use and trust in the internet are positively associated with i-voting intent. © 2016 Elsevier Inc. All rights reserved.


title: Feature selection with harmony search and its applications

text: Feature selection is a term given to the problem of selecting important domain attributes which are most predictive of a given outcome. Unlike other dimensionality reduction methods, feature selection approaches seek to preserve the semantics of the original data following reduction. Many strategies have been exploited for this task in an effort to identify more compact and better quality feature subsets. A number of group-based feature subset evaluation measures have been developed, which have the ability to judge the quality of a given feature subset as a whole, rather than assessing the qualities of individual features. Techniques of stochastic nature have also emerged, which are inspired by nature phenomena or social behaviour, allowing good solutions to be discovered without resorting to exhaustive search. In this thesis, a novel feature subset search algorithm termed “feature selection with harmony search” is presented. The proposed approach utilises a recently developed meta-heuristic: harmony search, that is inspired by the improvisation process of musical players. The proposed approach is general, and can be employed in conjunction with many feature subset evaluation measures. The simplicity of harmony search is exploited to reduce the overall complexity of the search process. The stochastic nature of the resultant technique also allows the search process to escape from local optima, while identifying multiple, distinctive candidate solutions. Additional parameter control schemes are introduced to reduce the effort and impact of static parameter configuration of HS, which are further combined with iterative refinement, in order to enforce the discovery of more compact feature subsets. The flexibility of the proposed approach, and its powerful performance in selecting multiple, good quality feature subsets lead to a number of further theoretical developments. These include the generation and reduction of feature subset-based classifier ensembles; feature selection and adaptive classifier ensemble for dynamic data; hybrid rule induction on the basis of fuzzy-rough set theory; and antecedent selection for fuzzy rule interpolation. The resultant techniques are experimentally evaluated using data sets drawn from real-world problem domains, and systematically compared with leading methodologies in their respective areas, demonstrating the efficacy and competitive performance of the present work.


title: Text summarization using enhanced MMR technique

text: Now a day when huge amount of documents and web contents are available, so reading of full content is somewhat difficult. Summarization is a way to give abstract form of large document so that the moral of the document can be communicated easily. Current research in automatic summarization is dominated by some effective, yet naive approaches: summarization through extraction, summarization through Abstraction and multi-document summarization. These techniques are used to building a summary of a document. Although there are a number of techniques implemented for the summarization of text for the single document or for the online web data or for any language. Here in this paper we are implemented an efficient technique for text summarization to reduce the computational cost and time and also the storage capacity.


title: Modeling temporal effects of human mobile behavior on location-based social networks

text: The rapid growth of location-based social networks (LBSNs) invigorates an increasing number of LBSN users, providing an unprecedented opportunity to study human mobile behavior from spatial, temporal, and social aspects. Among these aspects, temporal effects offer an essential contextual cue for inferring a user's movement. Strong temporal cyclic patterns have been observed in user movement in LBSNs with their correlated spatial and social effects (i.e., temporal correlations). It is a propitious time to model these temporal effects (patterns and correlations) on a user's mobile behavior. In this paper, we present the first comprehensive study of temporal effects on LBSNs. We propose a general framework to exploit and model temporal cyclic patterns and their relationships with spatial and social data. The experimental results on two real-world LBSN datasets validate the power of temporal effects in capturing user mobile behavior, and demonstrate the ability of our framework to select the most effective location prediction algorithm under various combinations of prediction models.


title: Harnessing the Crowdsourcing Power of Social Media for Disaster Relief

text: This article briefly describes the advantages and disadvantages of crowdsourcing applications applied to disaster relief coordination. It also discusses several challenges that must be addressed to make crowdsourcing a useful tool that can effectively facilitate the relief progress in coordination, accuracy, and security.


title: BeAMS: A Beacon-Based Angle Measurement Sensor for Mobile Robot Positioning

text: Positioning is a fundamental issue in mobile robot applications, and it can be achieved in multiple ways. Among these methods, triangulation based on angle measurements is widely used, robust, accurate, and flexible. This paper presents BeAMS, which is a new active beacon-based angle measurement system used for mobile robot positioning. BeAMS introduces several major innovations. One innovation is the use of a unique unsynchronized channel with on-off keying modulated infrared signals to measure angles and to identify the beacons. We also introduce a new mechanism to measure angles: Our system detects a beacon when it enters and leaves an angular window. We show that the estimator resulting from the center of this angular window provides an unbiased estimate of the beacon angle. A theoretical framework for a thorough performance analysis of BeAMS is provided. We establish the upper bound of the variance and validate this bound through experiments and simulations; the overall error measure of BeAMS is lower than 0.24° for an acquisition rate of 10 Hz. In conclusion, BeAMS is a low-power, flexible, and robust solution for angle measurement and a reliable component for robot positioning.


title: Brain impalement by an angle metal bar

text: The author reports the case of a 37-year-old right-handed man who was impaled in the head by an angle metal bar at a construction work site. Impalement injuries of the brain are rare, and their management is complex. The surgical treatment of the injury and the medical management of complications are described in detail. The patient made a good recovery although he has functional deficits related to the injury to his frontal lobes.


title: CGBoost : Conjugate Gradient in Function Space

text: The superior out-of-sample performance of AdaBoost has been attributed to the fact that it minimizes a cost function based on margin, in that it can be viewed as a special case of AnyBoost, an abstract gradient descent algorithm. In this paper, we provide a more sophisticated abstract boosting algorithm, CGBoost, based on conjugate gradient in function space. When the AdaBoost exponential cost function is optimized, CGBoost generally yields much lower cost and training error but higher test error, which implies that the exponential cost is vulnerable to overfitting. With the optimization power of CGBoost, we can adopt more “regularized” cost functions that have better out-of-sample performance but are difficult to optimize. Our experiments demonstrate that CGBoost generally outperforms AnyBoost in cost reduction. With suitable cost functions, CGBoost can have better out-of-sample performance.


title: An Efficient Approach to Model-Based Hierarchical Reinforcement Learning

text: We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowledge and selective execution at different levels of abstraction, to efficiently solve large, complex problems. Our framework adopts a new transition dynamics learning algorithm that identifies the common action-feature combinations of the subtasks, and evaluates the subtask execution choices through simulation. The framework is sample efficient, and tolerates uncertain and incomplete problem characterization of the subtasks. We test the framework on common benchmark problems and complex simulated robotic environments. It compares favorably against the stateof-the-art algorithms, and scales well in very large problems.


title: Introducing 'Bones': a parallelizing source-to-source compiler based on algorithmic skeletons

text:
Recent advances in multi-core and many-core processors requires programmers to exploit an increasing amount of parallelism from their applications. Data parallel languages such as CUDA and OpenCL make it possible to take advantage of such processors, but still require a large amount of effort from programmers.
 A number of parallelizing source-to-source compilers have recently been developed to ease programming of multi-core and many-core processors. This work presents and evaluates a number of such tools, focused in particular on C-to-CUDA transformations targeting GPUs. We compare these tools both qualitatively and quantitatively to each other and identify their strengths and weaknesses.
 In this paper, we address the weaknesses by presenting a new classification of algorithms. This classification is used in a new source-to-source compiler, which is based on the algorithmic skeletons technique. The compiler generates target code based on skeletons of parallel structures, which can be seen as parameterisable library implementations for a set of algorithm classes. We furthermore demonstrate that the presented compiler requires little modifications to the original sequential source code, generates readable code for further fine-tuning, and delivers superior performance compared to other tools for a set of 8 image processing kernels.


title: Data-Driven De-Anonymization in Bitcoin

text: We analyse the performance of several clustering algorithms in the digital peerto-peer currency Bitcoin. Clustering in Bitcoin refers to the task of finding addresses that belongs to the same wallet as a given address. In order to assess the effectiveness of clustering strategies we exploit a vulnerability in the implementation of Connection Bloom Filtering to capture ground truth data about 37,585 Bitcoin wallets and the addresses they own. In addition to well-known clustering techniques, we introduce two new strategies, apply them on addresses of the collected wallets and evaluate precision and recall using the ground truth. Due to the nature of the Connection Bloom Filtering vulnerability the data we collect is not without errors. We present a method to correct the performance metrics in the presence of such inaccuracies. Our results demonstrate that even modern wallet software can not protect its users properly. Even with the most basic clustering technique known as multiinput heuristic, an adversary can guess on average 68.59% addresses of a victim. We show that this metric can be further improved by combining several more sophisticated heuristics.


title: A probabilistic model for component-based shape synthesis

text: We present an approach to synthesizing shapes from complex domains, by identifying new plausible combinations of components from existing shapes. Our primary contribution is a new generative model of component-based shape structure. The model represents probabilistic relationships between properties of shape components, and relates them to learned underlying causes of structural variability within the domain. These causes are treated as latent variables, leading to a compact representation that can be effectively learned without supervision from a set of compatibly segmented shapes. We evaluate the model on a number of shape datasets with complex structural variability and demonstrate its application to amplification of shape databases and to interactive shape synthesis.


title: Analysis and Design of an Ultrabroadband Stacked Power Amplifier in CMOS Technology

text: This brief presents the analysis and design of a two-stage stacked power amplifier (PA) with very broadband gain frequency response and power performance in a small chip size. The broadband load impedance match is realized using modified stacked field-effect transistors (FETs) with a resistive feedback by analyzing the matching condition of the source input impedance of the stacked FET. In order to further improve the broadband gain frequency response, the effectiveness of a gain expansion from a stacked driver amplifier is demonstrated to compensate the gain compression of the last-stage amplifier. To verify the design concept, a two-stage three-stacked PA has been implemented in a 0.18-μm CMOS technology. The PA achieves a saturated output power of 22-24.3 dBm and a power added efficiency of 13%-20% within a 194% fractional bandwidth from 0.1 to 6.5 GHz. It also demonstrates better than 11-dB input return loss (RL) and better than 5.1-dB output RL. This PA occupies a chip size of 0.64 mm2 including pads.


title: Designing an Indonesian part of speech tagset and manually tagged Indonesian corpus

text: We describe our work on designing a linguistically principled part of speech (POS) tagset for the Indonesian language. The process involves a detailed study and analysis of existing tagsets and the manual tagging of an Indonesian corpus. The results of this work are an Indonesian POS tagset consisting of 23 tags and an Indonesian corpus of over 250.000 lexical tokens that have been manually tagged using this tagset.


title: Radar Communication for Combating Mutual Interference of FMCW Radars

text: Commercial automotive radars used today are based on frequency modulated continuous wave signals due to the simple and robust detection method and good accuracy. However, the increase in both the number of radars deployed per vehicle and the number of such vehicles leads to mutual interference, cutting short future plans for autonomous driving and active safety functionality. We propose and analyze a radar communication (RadCom) approach to reduce this mutual interference while simultaneously offering communication functionality. We achieve this by frequency division multiplexing radar and communication, where communication is built on a decentralized carrier sense multiple access protocol and is used to adjust the timing of radar transmissions. Our simulation results indicate that radar interference can be significantly reduced, at no cost in radar accuracy.


title: Wealth and happiness across the world: material prosperity predicts life evaluation, whereas psychosocial prosperity predicts positive feeling.

text: The Gallup World Poll, the first representative sample of planet Earth, was used to explore the reasons why happiness is associated with higher income, including the meeting of basic needs, fulfillment of psychological needs, increasing satisfaction with one's standard of living, and public goods. Across the globe, the association of log income with subjective well-being was linear but convex with raw income, indicating the declining marginal effects of income on subjective well-being. Income was a moderately strong predictor of life evaluation but a much weaker predictor of positive and negative feelings. Possessing luxury conveniences and satisfaction with standard of living were also strong predictors of life evaluation. Although the meeting of basic and psychological needs mediated the effects of income on life evaluation to some degree, the strongest mediation was provided by standard of living and ownership of conveniences. In contrast, feelings were most associated with the fulfillment of psychological needs: learning, autonomy, using one's skills, respect, and the ability to count on others in an emergency. Thus, two separate types of prosperity-economic and social psychological-best predict different types of well-being.


title: A Survey of Recent Results in Networked Control Systems

text: Networked control systems (NCSs) are spatially distributed systems for which the communication between sensors, actuators, and controllers is supported by a shared communication network. We review several recent results on estimation, analysis, and controller synthesis for NCSs. The results surveyed address channel limitations in terms of packet-rates, sampling, network delay, and packet dropouts. The results are presented in a tutorial fashion, comparing alternative methodologies


title: Advanced image fusion to overlay coronary sinus anatomy with real-time fluoroscopy to facilitate left ventricular lead implantation in CRT.

text:
BACKGROUND
Failure rate for left ventricular (LV) lead implantation in cardiac resynchronization therapy (CRT) is up to 12%. The use of segmentation tools, advanced image registration software, and high-fidelity images from computerized tomography (CT) and cardiac magnetic resonance (CMR) of the coronary sinus (CS) can guide LV lead implantation. We evaluated the feasibility of advanced image registration onto live fluoroscopic images to allow successful LV lead placement.

METHODS
Twelve patients (11 male, 59 ± 16.8 years) undergoing CRT had three-dimensional (3D) whole-heart imaging (six CT, six CMR). Eight patients had at least one previously failed LV lead implant. Using segmentation software, anatomical models of the cardiac chambers, CS, and its branches were overlaid onto the live fluoroscopy using a prototype version of the Philips EP Navigator software to guide lead implantation.

RESULTS
We achieved high-fidelity segmentations of cardiac chambers, coronary vein anatomy, and accurate registration between the 3D anatomical models and the live fluoroscopy in all 12 patients confirmed by balloon occlusion angiography. The CS was cannulated successfully in every patient and in 11, an LV lead was implanted successfully. (One patient had no acceptable lead values due to extensive myocardial scar).

CONCLUSION
Using overlaid 3D segmentations of the CS and cardiac chambers, it is feasible to guide CRT implantation in real time by fusing advanced imaging and fluoroscopy. This enabled successful CRT in a group of patients with previously failed implants. This technology has the potential to facilitate CRT and improve implant success.


title: Extracting Aspect-Evaluation and Aspect-Of Relations in Opinion Mining

text: The technology of opinion extraction allows users to retrieve and analyze people’s opinions scattered over Web documents. We define an opinion unit as a quadruple consisting of the opinion holder, the subject being evaluated, the part or the attribute in which the subject is evaluated, and the value of the evaluation that expresses a positive or negative assessment. We use this definition as the basis for our opinion extraction task. We focus on two important subtasks of opinion extraction: (a) extracting aspect-evaluation relations, and (b) extracting aspect-of relations, and we approach each task using methods which combine contextual and statistical clues. Our experiments on Japanese weblog posts show that the use of contextual clues improve the performance for both tasks.


title: Being overweight has limited effect on SCARF osteotomy outcome for hallux valgus correction

text: The purpose of this study was to investigate the association between body mass index (BMI) and the results of SCARF osteotomy of the first metatarsal for hallux valgus (HV) correction, as the literature on this is scant. This prospective study was carried out between 2011 and 2015. One hundred and thirty-three patients diagnosed with moderate to severe HV underwent a SCARF corrective osteotomy. We divided the patients into two groups according to their BMI: normal and overweight. Postoperative follow-up was two years. All patients were examined twice by two medical doctors simultaneously: pre-operatively and post-operatively at two years’ follow-up. Data collected included biometrical records, X-rays [HV angle (HVA), intermetatarsal angle (IMA), American Orthopaedic Foot and Ankle Society Hallux Metatarsophalangeal Index (AOFAS-HMI) and visual analogue scale (VAS) for pain and satisfaction]. There was a significant difference between patient age (p = 0.001), age at onset (p < 0.001) and AOFAS-HMI (p = 0.035) at follow-up. Other parameters were similar in both groups. Regardless of BMI, the radiological outcome was comparable. Despite a significant difference in AOFAS-HMI results, pain and satisfaction level were similar. The authors agreed that high BMI has protective role in the prevalence of HV.


Tweeties Squabbling: Positive and Negative Results in Applying Argument Mining on Social Media


title: A honeycomb-shaped planar monopole antenna for broadband millimeter-wave applications

text: This paper investigates a planar monopole antenna for fifth generation (5G) wireless communication networks. The proposed antenna has an ultra-wide band impedance response in millimeter wave (mmW) spectrum, 25–39 GHz covering Ka band. The antenna has unique structural layout resembling hexagonal honeycomb and has low profile (8×7 mm2) on 0.254 mm thick Rogers substrate, enabling the design for incorporation into future mobile phones. This antenna provides peak gain of 4.15 dBi along with 90% efficiency in the working band. The design is also extended to an 8×1 element array presenting maximum gain of 12.7 dBi at central frequency of the antenna.


title: Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning

text: Ranking is a fundamental and widely studied problem in scenarios such as search, advertising, and recommendation. However, joint optimization for multi-scenario ranking, which aims to improve the overall performance of several ranking strategies in different scenarios, is rather untouched. Separately optimizing each individual strategy has two limitations. The first one is lack of collaboration between scenariosmeaning that each strategy maximizes its own objective but ignores the goals of other strategies, leading to a suboptimal overall performance. The second limitation is the inability of modeling the correlation between scenarios meaning that independent optimization in one scenario only uses its own user data but ignores the context in other scenarios. In this paper, we formulate multi-scenario ranking as a fully cooperative, partially observable, multi-agent sequential decision problem. We propose a novel model named Multi-Agent Recurrent Deterministic Policy Gradient (MA-RDPG) which has a communication component for passing messages, several private actors (agents) for making actions for ranking, and a centralized critic for evaluating the overall performance of the co-working actors. Each scenario is treated as an agent (actor). Agents collaborate with each other by sharing a global action-value function (the critic) and passing messages that encodes historical information across scenarios. The model is evaluated with online settings on a large E-commerce platform. Results show that the proposed model exhibits significant improvements against baselines in terms of the overall performance.


title: Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks

text: Designing optimal treatment plans for patients with comorbidities requires accurate cause-specific mortality prognosis. Motivated by the recent availability of linked electronic health records, we develop a nonparametric Bayesian model for survival analysis with competing risks, which can be used for jointly assessing a patient’s risk of multiple (competing) adverse outcomes. The model views a patient’s survival times with respect to the competing risks as the outputs of a deep multi-task Gaussian process (DMGP), the inputs to which are the patients’ covariates. Unlike parametric survival analysis methods based on Cox and Weibull models, our model uses DMGPs to capture complex non-linear interactions between the patients’ covariates and cause-specific survival times, thereby learning flexible patient-specific and cause-specific survival curves, all in a data-driven fashion without explicit parametric assumptions on the hazard rates. We propose a variational inference algorithm that is capable of learning the model parameters from time-to-event data while handling right censoring. Experiments on synthetic and real data show that our model outperforms the state-of-the-art survival models.


title: Fairness in Package-to-Group Recommendations

text: Recommending packages of items to groups of users has several applications, including recommending vacation packages to groups of tourists, entertainment packages to groups of friends, or sets of courses to groups of students. In this paper, we focus on a novel aspect of package-to-group recommendations, that of fairness. Specifically, when we recommend a package to a group of people, we ask that this recommendation is fair in the sense that every group member is satisfied by a sufficient number of items in the package. We explore two definitions of fairness and show that for either definition the problem of finding the most fair package is NP-hard. We exploit the fact that our problem can be modeled as a coverage problem, and we propose greedy algorithms that find approximate solutions within reasonable time. In addition, we study two extensions of the problem, where we impose category or spatial constraints on the items to be included in the recommended packages. We evaluate the appropriateness of the fairness models and the performance of the proposed algorithms using real data from Yelp, and a user study.


title: Natural Image Colorization

text: In this paper, we present an interactive system for users to easily colorize the natural images of complex scenes. In our system, colorization procedure is explicitly separated into two stages: Color labeling and Color mapping. Pixels that should roughly share similar colors are grouped into coherent regions in the color labeling stage, and the color mapping stage is then introduced to further fine-tune the colors in each coherent region. To handle textures commonly seen in natural images, we propose a new color labeling scheme that groups not only neighboring pixels with similar intensity but also remote pixels with similar texture. Motivated by the insight into the complementary nature possessed by the highly contrastive locations and the smooth locations, we employ a smoothness map to guide the incorporation of intensity-continuity and texture-similarity constraints in the design of our labeling algorithm. Within each coherent region obtained from the color labeling stage, the color mapping is applied to generate vivid colorization effect by assigning colors to a few pixels in the region. A set of intuitive interface tools is designed for labeling, coloring and modifying the result. We demonstrate compelling results of colorizing natural images using our system, with only a modest amount of user input.


title: An Optimized Transformerless Photovoltaic Grid-Connected Inverter

text: Unipolar sinusoidal pulsewidth modulation (SPWM) full-bridge inverter brings high-frequency common-mode voltage, which restricts its application in transformerless photovoltaic grid-connected inverters. In order to solve this problem, an optimized full-bridge structure with two additional switches and a capacitor divider is proposed in this paper, which guarantees that a freewheeling path is clamped to half input voltage in the freewheeling period. Sequentially, the high-frequency common-mode voltage has been avoided in the unipolar SPWM full-bridge inverter, and the output current flows through only three switches in the power processing period. In addition, a clamping branch makes the voltage stress of the added switches be equal to half input voltage. The operation and clamping modes are analyzed, and the total losses of power device of several existing topologies and proposed topology are fairly calculated. Finally, the common-mode performance of these topologies is compared by a universal prototype inverter rated at 1 kW.


title: A New High-Efficiency Single-Phase Transformerless PV Inverter Topology

text: There is a strong trend in the photovoltaic inverter technology to use transformerless topologies in order to acquire higher efficiencies combining with very low ground leakage current. In this paper, a new topology, based on the H-bridge with a new ac bypass circuit consisting of a diode rectifier and a switch with clamping to the dc midpoint, is proposed. The topology is simulated and experimentally validated, and a comparison with other existing topologies is performed. High conversion efficiency and low leakage current are demonstrated.


title: Leakage Current Analytical Model and Application in Single-Phase Transformerless Photovoltaic Grid-Connected Inverter

text: Due to the characteristics of low cost and high efficiency, the transformerless photovoltaic (PV) grid-connected inverters have been popularized in the application of solar electric generation system in residential market. Unfortunately, the leakage current through the stray capacitors between the PV array and the ground is harmful. This paper focuses on the leakage current suppressing method, in which all common-mode paths are considered. First, the common-mode analytical model at switching frequency is developed, and the rules of eliminating switching frequency common-mode source are summarized based on this model. The existing full-bridge- and half-bridge-type converters have been analyzed by using the developed model and rules, and then, a new full-bridge-type converter structure and a compensation strategy for half-bridge-type inverter have been presented finally.


title: Cyber-Physical System Security of a Power Grid : State-ofthe-Art

text: As part of the smart grid development, more and more technologies are developed and deployed on the power grid to enhance the system reliability. A primary purpose of the smart grid is to significantly increase the capability of computer-based remote control and automation. As a result, the level of connectivity has become much higher, and cyber security also becomes a potential threat to the cyber-physical systems (CPSs). In this paper, a survey of the state-of-the-art is conducted on the cyber security of the power grid concerning issues of: (1) the structure of CPSs in a smart grid; (2) cyber vulnerability assessment; (3) cyber protection systems; and (4) testbeds of a CPS. At Washington State University (WSU), the Smart City Testbed (SCT) has been developed to provide a platform to test, analyze and validate defense mechanisms against potential cyber intrusions. A test case is provided in this paper to demonstrate how a testbed helps the study of cyber security and the anomaly detection system (ADS) for substations.


title: The paradox of received social support: the importance of responsiveness.

text: Although the perception of available support is associated with positive outcomes, the receipt of actual support from close others is often associated with negative outcomes. In fact, support that is "invisible" (not perceived by the support recipient) is associated with better outcomes than "visible" support. To investigate this paradox, we proposed that received support (both visible and invisible) would be beneficial when it was responsive to the recipient's needs. Sixty-seven cohabiting couples participated in a daily-experience study in which they reported on the support they provided and received each day. Results indicated that both visible and invisible support were beneficial (i.e., associated with less sadness and anxiety and with greater relationship quality) only when the support was responsive. These findings suggest that the nature of support is an important determinant of when received support will be beneficial.


title: The study of evasion of packed PE from static detection

text: Static detection of packed portable executables (PEs) relies primarily on structural properties of the packed PE, and also on anomalies in the packed PE caused by packing tools. This paper outlines weaknesses in this method of detection. We show that these structural properties and anomalies are contingent features of the executable, and can be more or less easily modified to evade static detection.


title: BAliBASE: a benchmark alignment database for the evaluation of multiple alignment programs

text:
SUMMARY
BAliBASE is a database of manually refined multiple sequence alignments categorized by core blocks of conservation sequence length, similarity, and the presence of insertions and N/C-terminal extensions.

AVAILABILITY
From http://www-igbmc. u-strasbg.fr/BioInfo/BAliBASE/index.html


title: Pneumatic muscle actuators within robotic and mechatronic systems

text: Pneumatic artificial muscles (PAMs) as soft, lightweight and compliant actuators have great potential in applications for the actuations of new types of robots and manipulators. The favourable characteristics of fluidic muscles, such as high power-to-weight ratio and safe interaction with humans are also very suitable during the process of musculoskeletally rehabilitating patients and are often used in making artificial orthoses. This technology, despite the problems of control relatng to nonlinear phenomena, may also have wide future applications within industrial and mechatronic systems. This paper presents several experimental systems actuated by PAMs, which have been designed as test models within the fields of mobile robots, mechatronics, fluid power systems and the feedback control education of mechanical engineering students. This paper first presents the design and construction of a four legged walking robot actuated by pneumatic muscles. The robot has a fully autonomous system with a wireless application platform and can be controlled using a cell phone. Then the paper describes the design and construction of the prototype of an actively-powered ankle foot orthosis. This orthosis device actuated by a single PAM is able to provide the appropriate functions required during the rehabilitations of patients and the loss of mobility. Then the paper focuses on the design and control of a ball and beam system with an antagonistic muscle pair for generating the necessary torque for beam rotation. This mechatronic balancing mechanism falls into the category of unstable, under-actuated, multivariable systems with highly nonlinear dynamics. The final section of the article presents the design and control of a single-joint manipulator arm with pneumatic muscle actuators that enable some new features for the controlled systems. Fluid Power 2015 176


title: Diabetes management in the primary care setting: a comparison of physicians' performance by gender.

text:
BACKGROUND
A major shift in the gender of the medical-doctor workforce is now underway, and all over the world it is expected that an average 65% of the medical workforce will be women by 2030. In addition, an aging population means that chronic diseases, such as diabetes, are becoming more prevalent and the demand for care is rising. There is growing evidence of female physicians performing better than male physicians.AimOur study aimed to investigate whether any differences in diabetes process indicators are associated with gender, and/or the interaction between gender and different organizational models.Design and settingA population-based cross-sectional analysis was conducted on a large data set obtained by processing the public health administration databases of seven Italian local health units (LHUs). The seven LHUs, distributed all over the Italian peninsula in seven different regions, took part in a national project called MEDINA, with the focus on chronic disease management in primary care (PC).

METHODS
A total score was calculated for the average performance in the previously listed five indicators, representing global adherence to a quality management of patients with diabetes. A multilevel analysis was applied to see how LHUs affected the outcome. A quantile regression model was also fitted.

RESULTS
Our study included 2287 Italian general practitioners (586 of them female) caring for a total of 2 646 059 patients. Analyzing the performance scores confirmed that female general practitioners obtained better results than males. The differences between males and females were stronger on the 25th and 75th percentiles of the score than on the median values. The interaction between gender and LHU was not significant.

CONCLUSION
Our study evidenced that female physicians perform better than males in providing PC for diabetes independently by the different organizational models. Further research to understand the reasons for these gender differences is needed.


title: Direct type-specific conic fitting and eigenvalue bias correction

text: A new method to fit specific types of conics to scattered data points is introduced. Direct, specific fitting of ellipses and hyperbolae is achieved by imposing a quadratic constraint on the conic coefficients, whereby an improved partitioning of the design matrix is devised so as to improve computational efficiency and numerical stability by eliminating redundant aspects of the fitting procedure. Fitting of parabolas is achieved by determining an orthogonal basis vector set in the Grassmannian space of the quadratic terms’ coefficients. The linear combination of the basis vectors that fulfills the parabolic condition and has a minimum residual norm is determined using Lagrange multipliers. This is the first known direct solution for parabola specific fitting. Furthermore, the inherent bias of a linear conic fit is addressed. We propose a linear method of correcting this bias, producing better geometric fits which are still constrained to specific conic type. 2007 Published by Elsevier B.V.


title: Articulatory feature-based pronunciation modeling

text: Spoken language, especially conversational speech, is characterized by great variability in word pronunciation, including many variants that differ grossly from dictionary prototypes. This is one factor in the poor performance of automatic speech recognizers on conversational speech, and it has been very difficult to mitigate in traditional phonebased approaches to speech recognition. An alternative approach, which has been studied by ourselves and others, is one based on sub-phonetic features rather than phones. In such an approach, a word’s pronunciation is represented as multiple streams of phonological features rather than a single stream of phones. Features may correspond to the positions of the speech articulators, such as the lips and tongue, or may be more abstract categories such as manner and place. This article reviews our work on a particular type of articulatory feature-based pronunciation model. The model allows for asynchrony between features, as well as per-feature substitutions, making it more natural to account for many pronunciation changes that are difficult to handle with phone-based models. Such models can be efficiently represented as dynamic Bayesian networks. The feature-based models improve significantly over phone-based counterparts in terms of frame perplexity and lexical access accuracy. The remainder of the article discusses related work and future directions.


title: Attacking an AES-Enabled NFC Tag: Implications from Design to a Real-World Scenario

text: Radio-frequency identification (RFID) technology is the enabler for applications like the future internet of things (IoT), where security plays an important role. When integrating security to RFID tags, not only the cryptographic algorithms need to be secure but also their implementation. In this work we present differential power analysis (DPA) and differential electromagnetic analysis (DEMA) attacks on a securityenabled RFID tag. The attacks are conducted on both an ASIC-chip version and on an FPGA-prototype version of the tag. The design of the ASIC version equals that of commercial RFID tags and has analog and digital part integrated on a single chip. Target of the attacks is an implementation of the Advanced Encryption Standard (AES) with 128-bit key length and DPA countermeasures. The countermeasures are shuffling of operations and insertion of dummy rounds. Our results illustrate that the effort for successfully attacking the ASIC chip in a real-world scenario is only 4.5 times higher than for the FPGA prototype in a laboratory environment. This let us come to the conclusion that the effort for attacking contactless devices like RFID tags is only slightly higher than that for contact-based devices. The results further underline that the design of countermeasures like the insertion of dummy rounds has to be done with great care, since the detection of patterns in power or electromagnetic traces can be used to significantly lower the attacking effort.


title: Building Program Vector Representations for Deep Learning

text: Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the “coding criterion” to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than “shallow” methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.


title: A case against convexity in conceptual spaces

text: The notion of conceptual space, proposed by Gärdenfors as a framework for the representation of concepts and knowledge, has been highly influential over the last decade or so. One of the main theses involved in this approach is that the conceptual regions associated with properties, concepts, verbs, etc. are convex. The aim of this paper is to show that such a constraint—that of the convexity of the geometry of conceptual regions—is problematic; both from a theoretical perspective and with regard to the inner workings of the theory itself. On the one hand, all the arguments provided in favor of the convexity of conceptual regions rest on controversial assumptions. Additionally, his argument in support of a Euclidean metric, based on the integral character of conceptual dimensions, is weak, and under non-Euclidean metrics the structure of regions may be non-convex. Furthermore, even if the metric were Euclidean, the convexity constraint could be not satisfied if concepts were differentially weighted. On the other hand, Gärdenfors’ convexity constraint is brought into question by the own inner workings of conceptual spaces because: (i) some of the allegedly convex properties of concepts are not convex; (ii) the conceptual regions resulting from the combination of convex properties can be non-convex; (iii) convex regions may co-vary in non-convex ways; and (iv) his definition of verbs is incompatible with a definition of properties in terms of convex regions. Therefore, the mandatory character of the convexity requirement for regions in a conceptual space theory should be reconsidered.


title: Inductive Design of Maturity Models: Applying the Rasch Algorithm for Design Science Research

text: Maturity models are an established means to systematically document and guide the development of organizations using archetypal capability levels. Often, these models lack a sound foundation and/or are derived on the basis of an arbitrary design method. In order to foster the design of relevant and rigorous artifacts, this paper presents a method for maturity model construction that applies the Rasch algorithm and cluster analysis as a sound methodical foundation. The Rasch algorithm is widely used to improve scholarly intelligence and attainment tests. In order to demonstrate the application of the proposed method and to evaluate its usability and applicability, we present a design exemplar in the business intelligence domain.


title: Increased prefrontal and parietal activity after training of working memory

text: Working memory capacity has traditionally been thought to be constant. Recent studies, however, suggest that working memory can be improved by training. In this study, we have investigated the changes in brain activity that are induced by working memory training. Two experiments were carried out in which healthy, adult human subjects practiced working memory tasks for 5 weeks. Brain activity was measured with functional magnetic resonance imaging (fMRI) before, during and after training. After training, brain activity that was related to working memory increased in the middle frontal gyrus and superior and inferior parietal cortices. The changes in cortical activity could be evidence of training-induced plasticity in the neural systems that underlie working memory.


title: Aerial-Image Denoising Based on Convolutional Neural Network with Multi-Scale Residual Learning Approach

text: Aerial images are subject to various types of noise, which restricts the recognition and analysis of images, target monitoring, and search services. At present, deep learning is successful in image recognition. However, traditional convolutional neural networks (CNNs) extract the main features of an image to predict directly and are limited by the requirements of the training sample size (i.e., a small data size is not successful enough). In this paper, using a small sample size, we propose an aerial-image denoising recognition model based on CNNs with a multi-scale residual learning approach. The proposed model has the following three advantages: (1) Instead of directly learning latent clean images, the proposed model learns the noise from noisy images and then subtracts the learned residual from the noisy images to obtain reconstructed (denoised) images; (2) The developed image denoising recognition model is beneficial to small training datasets; (3) We use multi-scale residual learning as a learning approach, and dropout is introduced into the model architecture to force the network to learn to generalize well enough. Our experimental results on aerial-image denoising recognition reveal that the proposed approach is highly superior to the other state-of-the-art methods.


title: RECONCEPTUALISING GAMIFICATION : PLAY AND PEDAGOGY

text: Gamification is a complex and controversial concept. It has been both embraced as a marketing and education revolution, and dismissed as practice of exploitation. Contested within the debate around gamification has been the very concept of what a game is, what the core mechanics of games are, and whether gamification truly mobilises these core mechanics. This paper will challenge the foundation of this debate through reconceptualising gamification not as a simple set of techniques and mechanics, but as a pedagogic heritage, an alternative framework for training and shaping participant behaviour that has at its core the concepts of entertainment and engagement. In doing so it will recontextualise current practices of gamification into a longer and deeper history, and suggest potential pathways for more sophisticated gamification in the future.


title: Minimum Near-Convex Shape Decomposition.

text: Shape decomposition is a fundamental problem for part-based shape representation. We propose the Minimum Near-Convex Decomposition (MNCD) to decompose arbitrary shapes into minimum number of "near-convex" parts. The near-convex shape decomposition is formulated as a discrete optimization problem by minimizing the number of non-intersecting cuts. Two perception rules are imposed as constraints into our objective function to improve the visual naturalness of the decomposition. With the degree of near-convexity a user specified parameter, our decomposition is robust to local distortions and shape deformation. The optimization can be efficiently solved via Binary Integer Linear Programming. Both theoretical analysis and experiment results show that our approach outperforms the state-of-the-art results without introducing redundant parts, and thus leads to robust shape representation.


title: A 3-kW Wireless Power Transfer System for Sightseeing Car Supercapacitor Charge

text: Wireless power transfer (WPT) is the preferred charging method for electric vehicles (EVs) powered by battery and supercapacitor. In this paper, a novel WPT system with constant current charging capability for sightseeing car with supercapacitor storage is designed. First, an optimized magnetic coupler using ferrite cores and magnetic shielding structure is proposed to ensure stable power transfer and high efficiency. Compared with the traditional planar shape ferrite core coupler, the proposed magnetic coupler requires lesser ferrite material without degrading the performance of the WPT system. Second, the model of supercapacitor is applied to the WPT system and the relationship between equivalent load resistances of supercapacitor and charging time is analyzed in detail. Then, a Buck converter with proportional integral (PI) controller is implemented on the secondary side to maintain constant charging current for the variable load. Finally, the proposed design is verified by experiments. Constant charging current of 31.5 A across transfer distance of 15 cm is achieved. The peak transfer power and system efficiency are 2.86 kW and 88.05%, respectively.


title: Step Climbing Omnidirectional Mobile Robot with Passive Linkages

text: In this paper, we develop a holonomic omni-directional mobile vehicle with step-climbing ability. This system realizes omni-directional motion on flat floor using special wheels and passes over the step in forward or backward direction using the passive linkage mechanism. Our key ideas are the following topics. First topic is new omnidirectional mobile mechanism which consists of special wheels and passive linkage mechanism. Second topic is new passive linkage mechanism which can change its body configuration easily when the vehicle passes over the step. Third topic is wheel control reference derivation based on the body configuration, which changes passively during step climbing for reducing wheel slippage. Last topic is wheel control method which keeps the rotation velocity coordination among the wheels for reducing wheel slippage and increasing the step-climbing performance. We verified the effectiveness of our proposed method by the computer simulations and experiments. Utilizing our proposed mechanism and control systems, the vehicle has both omnidirectional mobility and step overcoming function. Furthermore, our developing vehicle can pass over the 128[mm] height step using the wheel which radius is 66[mm]. Its performance is three times larger than one of general wheeled vehicle.


title: Pay Attention to the Ending: Strong Neural Baselines for the ROC Story Cloze Task

text: We consider the ROC story cloze task (Mostafazadeh et al., 2016) and present several findings. We develop a model that uses hierarchical recurrent networks with attention to encode the sentences in the story and score candidate endings. By discarding the large training set and only training on the validation set, we achieve an accuracy of 74.7%. Even when we discard the story plots (sentences before the ending) and only train to choose the better of two endings, we can still reach 72.5%. We then analyze this “ending-only” task setting. We estimate human accuracy to be 78% and find several types of clues that lead to this high accuracy, including those related to sentiment, negation, and general ending likelihood regardless of the story context.


title: Convenient or Useful ? Consumer Adoption of Smartphones for Mobile Commerce

text: Merchants have developed apps for the smartphone that assist consumers through the buying process, from when they gather information to when they decide to purchase. Sharing information, such as location, shopping preferences and financial data, can enhance consumers’ experience. However, they may be reluctant to disclose these details unless they perceive that the benefits gained are more than the risk of privacy loss. This privacy calculus is added to the unified theory of acceptance and use of technology (UTAUT2) in order to explain consumers’ willingness to exchange the disclosure of personal information for additional value. Sharing information makes mobile commerce more convenient by saving time and effort. Companies are able to send offers that are tailored to a specific customer. Payments are processed faster because the merchant already has the financial data on hand. UTAUT2 is further extended with the Theory of Convenience. Results from a survey of over 300 consumers show that perceived value and perceived convenience are influencing variables and that perceived value mediates the influence of perceived convenience on intention to use.


title: DeepLogo: Hitting Logo Recognition with the Deep Neural Network Hammer

text: Recently, there has been a flurry of industrial activity around logo recognition, such as Ditto’s service for marketers to track their brands in user-generated images, and LogoGrab’s mobile app platform for logo recognition. However, relatively little academic or open-source logo recognition progress has been made in the last four years. Meanwhile, deep convolutional neural networks (DCNNs) have revolutionized a broad range of object recognition applications. In this work, we apply DCNNs to logo recognition. We propose several DCNN architectures, with which we surpass published state-of-art accuracy on a popular logo recognition dataset.


title: Hidden Markov Models for Speech Recognition

text: Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.


title: Procedural Generation of Roads

text: In this paper, we propose an automatic method for generating roads based on a weighted anisotropic shortest path algorithm. Given an input scene, we automatically create a path connecting an initial and a final point. The trajectory of the road minimizes a cost function that takes into account the different parameters of the scene including the slope of the terrain, natural obstacles such as rivers, lakes, mountains and forests. The road is generated by excavating the terrain along the path and instantiating generic parameterized models.


title: Arc-Length Parameterized Spline Curves for Real-Time Simulation

text: Parametric curves are frequently used in computer animation and virtual environments to control the movements of synthetic objects. In many of these applications it is essential to efficiently relate parameter values to the arc length of the curve. Current approaches to compute arc length or to construct an arc-length parameterized curve are impractical to use in real-time applications. This paper presents a simple and efficient technique to generate approximately arc-length parameterized spline curves that closely match spline curves typically used to model roads in high-fidelity driving simulators.


title: Congested traffic states in empirical observations and microscopic simulations

text: We present data from several German freeways showing different kinds of congested traffic forming near road inhomogeneities, specifically lane closings, intersections, or uphill gradients. The states are localized or extended, homogeneous or oscillating. Combined states are observed as well, like the coexistence of moving localized clusters and clusters pinned at road inhomogeneities, or regions of oscillating congested traffic upstream of nearly homogeneous congested traffic. The experimental findings are consistent with a recently proposed theoretical phase diagram for traffic near on-ramps [D. Helbing, A. Hennecke, and M. Treiber, Phys. Rev. Lett. 82, 4360 (1999)]. We simulate these situations with a continuous microscopic single-lane model, the "intelligent driver model," using empirical boundary conditions. All observations, including the coexistence of states, are qualitatively reproduced by describing inhomogeneities with local variations of one model parameter. We show that the results of the microscopic model can be understood by formulating the theoretical phase diagram for bottlenecks in a more general way. In particular, a local drop of the road capacity induced by parameter variations has essentially the same effect as an on-ramp.


title: Computing the arc length of parametric curves

text: Specifying constraints on motion is simpler if the curve is parameterized by arc length, but many parametric curves of practical interest cannot be parameterized by arc length. An approximate numerical reparameterization technique that improves on a previous algorithm by using a different numerical integration procedure that recursively subdivides the curve and creates a table of the subdivision points is presented. The use of the table greatly reduces the computation required for subsequent arc length calculations. After table construction, the algorithm takes nearly constant time for each arc length calculation. A linear increase in the number of control points can result in a more than linear increase in computation. Examples of this type of behavior are shown.<<ETX>>


title: Approximate Arc Length Parametrization

text: Current approaches to compute the arc length of a parametric curve rely on table lookup schemes. We present an approximate closed-form solution to the problem of computing an arc length parametrization for any given parametric curve. Our solution outputs a one or two-span Bézier curve which relates the length of the curve to the parametric variable. The main advantage of our approach is that we obtain a simple continuous function relating the length of the curve and the parametric variable. This allows the length to be easily computed given the parametric values. Tests with our algorithm on several thousand curves show that the maximum error in our approximation is 8.7% and that the average of maximum errors is 1.9%. Our algorithm is fast enough to compute the closed-form solution in a fraction of a second. After that a user can interactively get an approximation of the arc length for an arbitrary parameter value.


title: NIH Image to ImageJ: 25 years of image analysis

text: For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.


title: Graph Database Applications and Concepts with Neo4j

text: Graph databases (GDB) are now a viable alternative to Relational Database Systems (RDBMS). Chemistry, biology, semantic web, social networking and recommendation engines are all examples of applications that can be represented in a much more natural form. Comparisons will be drawn between relational database systems (Oracle, MySQL) and graph databases (Neo4J) focusing on aspects such as data structures, data model features and query facilities. Additionally, several of the inherent and contemporary limitations of current offerings comparing and contrasting graph vs. relational database implementations will be explored.


title: Fully Convolutional Networks for Semantic Segmentation

text: Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional networks achieve improved segmentation of PASCAL VOC (30% relative improvement to 67.2% mean IU on 2012), NYUDv2, SIFT Flow, and PASCAL-Context, while inference takes one tenth of a second for a typical image.


title: Computational Decomposition of Style for Controllable and Enhanced Style Transfer

text: Neural style transfer has been demonstrated to be powerful in creating artistic image with help of Convolutional Neural Networks (CNN). However, there is still lack of computational analysis of perceptual components of the artistic style. Different from some early attempts which studied the style by some pre-processing or post-processing techniques, we investigate the characteristics of the style systematically based on feature map produced by CNN. First, we computationally decompose the style into basic elements using not only spectrum based methods including Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) but also latent variable models such Principal Component Analysis (PCA), Independent Component Analysis (ICA). Then, the decomposition of style induces various ways of controlling the style elements which could be embedded as modules in state-of-the-art style transfer algorithms. Such decomposition of style brings several advantages. It enables the computational coding of different artistic styles by our style basis with similar styles clustering together, and thus it facilitates the mixing or intervention of styles based on the style basis from more than one styles so that compound style or new style could be generated to produce styled images. Experiments demonstrate the effectiveness of our method on not only painting style transfer but also sketch style transfer which indicates possible applications on picture-to-sketch problems.


title: A CMOS low-power low-offset and high-speed fully dynamic latched comparator

text: This paper presents a novel dynamic latched comparator that demonstrates lower offset voltage and higher load drivability than the conventional dynamic latched comparators. With two additional inverters inserted between the input- and output-stage of the conventional double-tail dynamic comparator, the gain preceding the regenerative latch stage is improved. The complementary version of the regenerative latch stage, which provides larger output drive current than the conventional one at a limited area, is implemented. The proposed circuit is designed using 90nm CMOS technology and 1V power supply voltage, and it demonstrates up to 19% less offset voltage and 62% less sensitivity of the delay to the input voltage difference (17ps/decade) than the conventional double-tail latched comparator at approximately the same area and power consumption.


title: A DYNAMIC FUZZY-COGNITIVE-MAP APPROACH BASED ON RANDOM NEURAL NETWORKS

text: A fuzzy cognitive map is a graphical means of representing arbitrarily complex models of interrelations between concepts. The purpose of this paper is to describe a dynamic fuzzy cognitive map based on the random neural network model. Previously, we have developed a random fuzzy cognitive map and illustrated its application in the modeling of processes. The adaptive fuzzy cognitive map changes its fuzzy causal web as causal patterns change and as experts update their causal knowledge. Our model carries out inferences via numerical calculation instead of symbolic deduction. We show how the dynamic random fuzzy cognitive map can reveal implications of models composed of dynamic processes. Copyright c ©2003 Yang’s Scientific Research Institute, LLC. All rights reserved.


title: Eclectic extraction of propositional rules from Neural Networks

text: Artificial Neural Network is among the most popular algorithm for supervised learning. However, Neural Networks have a well-known drawback of being a “Black Box” learner that is not comprehensible to the Users. This lack of transparency makes it unsuitable for many high risk tasks such as medical diagnosis that requires a rational justification for making a decision. Rule Extraction methods attempt to curb this limitation by extracting comprehensible rules from a trained Network. Many such extraction algorithms have been developed over the years with their respective strengths and weaknesses. They have been broadly categorized into three types based on their approach to use internal model of the Network. Eclectic Methods are hybrid algorithms that combine the other approaches to attain more performance. In this paper, we present an Eclectic method called HERETIC. Our algorithm uses Inductive Decision Tree learning combined with information of the neural network structure for extracting logical rules. Experiments and theoretical analysis show HERETIC to be better in terms of speed and performance.


title: High Efficiency AC – AC Power Electronic Converter Applied To Domestic Induction Heating

text: Induction heating is a well-known technique to produce very high temperature for applications. A large number of topologies have been developed in this area such as voltage and current source inverter. This project presents the analysis and design of a new ac–ac resonant converter applied to domestic induction heating. The proposed topology, based on the halfbridge series resonant inverter, uses only two diodes to rectify the mains voltage.The proposed converter can operate with zerovoltage switching during both switches-on and switch-off transitions. This topology doubles the output voltage, and therefore, the current in the load is reduced for the same output power. As a consequence, the converter efficiency is significantly improved.The output power is controlled using the PWM technique. This results in an increase of the net efficiency of the induction heating system. The circuit is simulated using MATLAB. The circuit is implemented using PIC controller. Both simulation and hardware results are compared.


title: Censoring Representations with an Adversary

text: In practice, there are often explicit constraints on what representations or decisions are acceptable in an application of machine learning. For example it may be a legal requirement that a decision must not favour a particular group. Alternatively it can be that that representation of data must not have identifying information. We address these two related issues by learning flexible representations that minimize the capability of an adversarial critic. This adversary is trying to predict the relevant sensitive variable from the representation, and so minimizing the performance of the adversary ensures there is little or no information in the representation about the sensitive variable. We demonstrate this adversarial approach on two problems: making decisions free from discrimination and removing private information from images. We formulate the adversarial model as a minimax problem, and optimize that minimax objective using a stochastic gradient alternate min-max optimizer. We demonstrate the ability to provide discriminant free representations for standard test problems, and compare with previous state of the art methods for fairness, showing statistically significant improvement across most cases. The flexibility of this method is shown via a novel problem: removing annotations from images, from separate training examples of annotated and unannotated images, and with no a priori knowledge of the form of annotation provided to the model.


title: EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect

text: Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing healthrelated data—the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients’ health conditions and responses to treatment over time. Methods: We describe a case study of 423 patients treated by Centerstone within Tennessee and Indiana in which we utilized electronic health record data to generate predictive algorithms of individual patient treatment response. Multiple models were constructed using predictor variables derived from clinical, financial and geographic data. Results: For the 423 patients, 101 deteriorated, 223 improved and in 99 there was no change in clinical condition. Based on modeling of various clinical indicators at baseline, the highest accuracy in predicting individual patient response ranged from 70% to 72% within the models tested. In terms of individual predictors, the Centerstone Assessment of Recovery Level—Adult (CARLA) baseline score was most significant in predicting outcome over time (odds ratio 4.1+2.27). Other variables with consistently significant impact on outcome included payer, diagnostic category, location and provision of case management services. Conclusions: This approach represents a promising avenue toward reducing the current gap between research and practice across healthcare, developing data-driven clinical decision support based on real-world populations, and serving as a component of embedded clinical artificial intelligences that ‘‘learn’’ over time. & 2012 Fellowship of Postgraduate Medicine. Published by Elsevier Ltd. All rights reserved. 2211-8837/$ see front matter & 2012 Fellowship of Postgraduate Medicine. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.hlpt.2012.03.001 Corresponding author at: Department of Informatics, Centerstone Research Institute, 365 South Park Ridge Road, Suite 103, Bloomington, IN 47401, United States. Tel.: +1 812 337 2302. E-mail addresses: Casey.Bennett@CenterstoneResearch.org (C.C. Bennett), Tom.Doub@CenterstoneResearch.org (T.W. Doub), RebeccaSelove@CenterstoneResearch.org (R. Selove). Health Policy and Technology (2012) 1, 105–114 Author's personal copy


title: Full-wave design of H-plane contiguous manifold output multiplexers using the fictitious reactive load concept

text: In this paper, a new procedure for the design of contiguous band manifold output multiplexers in H-plane configuration is presented. Such a configuration eliminates the slots between the T-junctions and the filters allowing high power handling, as well as dramatically reducing the risk of multipactor. This paper pursues the division of the design into tasks with low computational effort. The approach comprises: 1) the singly terminated synthesis of the channel filters and their corresponding full-wave responses; 2) the manifold design using fictitious reactive loads simulating the phase response of every channel out of its corresponding band; and 3) the final full-wave optimization of the whole structure using the simulated-annealing method. In order to validate the above procedure, a five-channel Ku-band multiplexer has been designed, manufactured, and measured.


title: Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets

text: Cells, the basic units of biological structure and function, vary broadly in type and state. Single-cell genomics can characterize cell identity and function, but limitations of ease and scale have prevented its broad application. Here we describe Drop-seq, a strategy for quickly profiling thousands of individual cells by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together. Drop-seq analyzes mRNA transcripts from thousands of individual cells simultaneously while remembering transcripts' cell of origin. We analyzed transcriptomes from 44,808 mouse retinal cells and identified 39 transcriptionally distinct cell populations, creating a molecular atlas of gene expression for known retinal cell classes and novel candidate cell subtypes. Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution. VIDEO ABSTRACT.


title: STRIP: A Defence Against Trojan Attacks on Deep Neural Networks

text: Recent trojan attacks on deep neural network (DNN) models are one insidious variant of data poisoning attacks. Trojan attacks exploit an effective backdoor created in a DNN model by leveraging the difficulty in interpretability of the learned model to misclassify any inputs signed with the attacker’s chosen trojan trigger input. Trojan attacks are easy to craft; survive even in adverse conditions such as different viewpoints, and lighting conditions on images; and threaten real world applications such as autonomous vehicles and robotics. The trojan trigger is a secret guarded and exploited by the attacker. Therefore, detecting such trojaned inputs is a challenge, especially at run-time when models are in active operation. We focus on vision systems and build the STRong Intentional Perturbation (STRIP) based run-time trojan attack detection system. We intentionally perturb the incoming input, for instance by superimposing various image patterns, and observe the randomness of the predicted classes for the perturbed inputs for a given model—malicious or benign—under deployment. A low entropy in the predicted classes violates the input-dependence property of a benign model and implies the presence of a malicious input—a characteristic of a trojaned input. The high efficacy of our method is validated through case studies on two popular and contrasting datasets: MNIST and CIFAR10. We achieve an overall false acceptance rate (FAR) of less than 1%, given a preset false rejection rate (FRR) of 1%, for four different tested trojan trigger types—three triggers are identified in previous attack works and one dedicated trigger is crafted by us to demonstrate the trigger-size insensitivity of the STRIP detection approach. In particular, on the dataset of natural images in CIFAR10, we have empirically achieved the desired result of 0% for both FRR and FAR.


Introduction to This is Watson


title: A formal ontological perspective on the behaviors and functions of technical artifacts

text: In this paper we present a formal characterization of the engineering concepts of behavior and function of technical artifacts. We capture the meanings that engineers attach to these concepts by formalizing, within the formal ontology DOLCE, the five meanings of artifact behavior and the two meanings of function that Chandrasekaran and Josephson identified in 2000 within the Functional Representation approach. We begin our formalization by reserving the term ‘behavior’ of a technical artifact as ‘the specific way in which the artifact occurs in an event.’ This general notion is characterized formally, and used to provide definitions of actual behaviors of artifacts, and the physically possible and physically impossible behaviors that rational agents believe that artifacts have. We also define several other notions, e.g., input and output behaviors of artifacts, and then show that these ontologically characterizedconcepts give a general framework in which Chandrasekaran and Josephson’s meanings of behavior can be explicitly formalized. Finally we show how Chandrasekaran and Josephson’s two meanings of artifact functions, namely, device-centric and environmental-centric functions, can be captured in DOLCE via the concepts of behavioral constraint and mode of deployment of an artifact. A more general goal of this work is to show that foundational ontologies are suited to the engineering domain: they can facilitate information sharing and exchange in the various engineering domains by providing concept structures and clarifications that make explicit and precise important engineering notions. The meanings of the terms ‘behavior’ and ‘function’ in domains like designing, redesigning, reverse engineering, product architecture, and engineering knowledge bases are often ambiguous or overloaded. Our results show that foundational ontologies can accommodate the variety of denotations these terms have and can explain their relationships.