title: Compositionality of optimal control laws

abstract: We present a theory of compositionality in stochastic optimal control, showing how task-optimal controllers can be constructed from certain primitives. The primitives are themselves feedback controllers pursuing their own agendas. They are mixed in proportion to how much progress they are making towards their agendas and how compatible their agendas are with the present task. The resulting composite control law is provably optimal when the problem belongs to a certain class. This class is rather general and yet has a number of unique properties - one of which is that the Bellman equation can be made linear even for non-linear or discrete dynamics. This gives rise to the compositionality developed here. In the special case of linear dynamics and Gaussian noise our framework yields analytical solutions (i.e. non-linear mixtures of LQG controllers) without requiring the final cost to be quadratic. More generally, a natural set of control primitives can be constructed by applying SVD to Green's function of the Bellman equation. We illustrate the theory in the context of human arm movements. The ideas of optimality and compositionality are both very prominent in the field of motor control, yet they have been difficult to reconcile. Our work makes this possible.


title: Social games, virtual goods

abstract: The popularity of virtual goods and currencies in online gaming is changing how people think and act about money.


title: What is the role of IT in innovation? A bibliometric analysis of research development in IT innovation

abstract: With the wide diffusion of information technology IT in our daily life and work, it is clear that product innovation and service innovation have more and more connection with IT, and IT has become an important tool or component in innovation. The purpose of this paper is to provide insights into future studies pertaining to this area by investigating the research development of IT innovation using bibliometric analysis. The status of IT innovation study is analysed through citation analysis with the help of CiteSpace. Influential references, hot topics, top-tier journals and important institutes are all detected, and the intellectual structure of recent studies is also mapped in this study, and we find that research on IT innovation is mainly from two directions, innovation study group and information systems study group. Finally, we follow the logic of Nambisan [2013. “Information Technology and Product/Service Innovation: A Brief Assessment and Some Suggestions for Future Research.” Journal of the Association for Information Systems 14 4: 215–226] to explore the relationship between IT and innovation through reviewing papers in the top journals in this field. We find that most studies treat IT as an enabler of innovation. Although some recent studies try to pay attention to the role of IT as a trigger for innovation and give some rationale for the IT–innovation relationship, further studies are still required to uncover the trigger effect mechanism.


title: Applied Geostatistics with SGeMS: A User's Guide

abstract: 1. Introduction 2. General overview 3. Geostatistics: a recall of concepts 4. Data sets & SGeMS EDA tools 5. Variogram computation and modeling 6. Common parameter input interfaces 7. Estimation algorithms 8. Stochastic simulation algorithms 9. Utilities 10. Scripting, commands and plug-ins List of programs List of symbols Bibliography.


Introducing Speech and Language Processing, by John Coleman


title: The imperative of influencing citizen attitude toward e-government adoption and use

abstract: We investigate factors that impact e-government adoption.Government officials need to pay attention to the dominant culture.Attitude is the most significant determinant of citizen intention.Citizen attitude is jointly determined by public value and ease of use.Positive relationships between trust and public value and ease of use. With the imperative of e-government for better transparency, accountability and public services, the problem of low-level citizen adoption of e-government services has been recognized in developed and developing countries. This pressing problem needs socio-technological, political, and cultural perspectives. In this study we develop an integrative research model by extending extant Technology Acceptance Model through the incorporation of a set of social, political, and cultural constructs: trust, perceived public value, and national culture. The model is then tested using a large-scale, multi-site survey research of 413 Jordanian citizens. Our results find strong evidence that citizen attitude toward using e-government services is the most significant determinant of citizen intention to adopt and use e-government services. Citizen attitude, in turn, is jointly determined by perceived public value and perceived ease of use. These results have managerial implications that the government needs to pay closer attention to influencing citizen attitude toward using e-government services.


title: Differential effects of cognitive inhibition and intelligence on creativity

abstract: There are different conceptions about how cognitive inhibition is related to creativity. Creativity has either been associated with effective inhibition, or with disinhibition, or with an adaptive engagement of inhibition. In this study, we examined the relationship of cognitive inhibition, assessed by means of the random motor generation task, with different measures of creativity. We also analyzed whether this relation is mediated by intelligence. We generally found a positive correlation of inhibition and creativity measures. Moreover, latent variable analyses indicate that inhibition may primarily promote the fluency of ideas, whereas intelligence specifically promotes the originality of ideas. These findings support the notion that creative thought involves executive processes and may help to better understand the differential role of inhibition and intelligence in creativity.


title: A survey of the use of crowdsourcing in software engineering

abstract: Abstract The term ‘crowdsourcing’ was initially introduced in 2006 to describe an emerging distributed problem-solving model by online workers. Since then it has been widely studied and practiced to support software engineering. In this paper we provide a comprehensive survey of the use of crowdsourcing in software engineering, seeking to cover all literature on this topic. We first review the definitions of crowdsourcing and derive our definition of Crowdsourcing Software Engineering together with its taxonomy. Then we summarise industrial crowdsourcing practice in software engineering and corresponding case studies. We further analyse the software engineering domains, tasks and applications for crowdsourcing and the platforms and stakeholders involved in realising Crowdsourced Software Engineering solutions. We conclude by exposing trends, open issues and opportunities for future research on Crowdsourced Software Engineering.


title: Classification for talent management using Decision Tree Induction techniques

abstract: Classification is one of the tasks in Data mining. Nowadays, there are many classification techniques being used to solve classification problems such as Neural Network, Genetic Algorithm, Bayesian and others. In this article, we attempt to present a study on how talent management can be implemented using Decision Tree Induction techniques. By using this approach, talent performance can be predicted using past experience knowledge discovered from the existing database. In the experimental phase, we use selected classification algorithms from Decision tree techniques to propose suitable classifier for the dataset. As a result, the C4.5 classifier algorithm shows the highest accuracy of model for the dataset. Consequently, the possible talent rules are generated based on C4.5 classifier especially for the talent forecasting purposes.


title: Duckweed rising at Chengdu: summary of the 1st International Conference on Duckweed Application and Research

abstract: Duckweeds, plants of the Lemnaceae family, have the distinction of being the smallest angiosperms in the world with the fastest doubling time. Together with its naturally ability to thrive on abundant anthropogenic wastewater, these plants hold tremendous potential to helping solve critical water, climate and fuel issues facing our planet this century. With the conviction that rapid deployment and optimization of the duckweed platform for biomass production will depend on close integration between basic and applied research of these aquatic plants, the first International Conference on Duckweed Research and Applications (ICDRA) was organized and took place in Chengdu, China, from October 7th to 10th of 2011. Co-organized with Rutgers University of New Jersey (USA), this Conference attracted participants from Germany, Denmark, Japan, Australia, in addition to those from the US and China. The following are concise summaries of the various oral presentations and final discussions over the 2.5 day conference that serve to highlight current research interests and applied research that are paving the way for the imminent deployment of this novel aquatic crop. We believe the sharing of this information with the broad Plant Biology community is an important step toward the renaissance of this excellent plant model that will have important impact on our quest for sustainable development of the world.


title: Opposing Transcriptional Mechanisms Regulate Toxoplasma Development

abstract: ABSTRACT The Toxoplasma biology that underlies human chronic infection is developmental conversion of the acute tachyzoite stage into the latent bradyzoite stage. We investigated the roles of two alkaline-stress-induced ApiAP2 transcription factors, AP2IV-3 and AP2IX-9, in bradyzoite development. These factors were expressed in two overlapping waves during bradyzoite development, with AP2IX-9 increasing expression earlier than AP2IV-3, which peaked as AP2IX-9 expression was declining. Disruption of the AP2IX-9 gene enhanced, while deletion of AP2IV-3 gene decreased, tissue cyst formation, demonstrating that these factors have opposite functions in bradyzoite development. Conversely, conditional overexpression of FKBP-modified AP2IX-9 or AP2IV-3 with the small molecule Shield 1 had a reciprocal effect on tissue cyst formation, confirming the conclusions of the knockout experiments. The AP2IX-9 repressor and AP2IV-3 activator tissue cyst phenotypes were borne out in gene expression studies that determined that many of the same bradyzoite genes were regulated in an opposite manner by these transcription factors. A common gene target was the canonical bradyzoite marker BAG1, and mechanistic experiments determined that, like AP2IX-9, AP2IV-3 regulates a BAG1 promoter-luciferase reporter and specifically binds the BAG1 promoter in parasite chromatin. Altogether, these results suggest that the AP2IX-9 transcriptional repressor and the AP2IV-3 transcriptional activator likely compete to control bradyzoite gene expression, which may permit Toxoplasma to better adapt to different tissue environments and select a suitable host cell for long-term survival of the dormant tissue cyst. IMPORTANCE Toxoplasma infections are lifelong because of the development of the bradyzoite tissue cyst, which is effectively invisible to the immune system. Despite the important clinical consequences of this developmental pathway, the molecular basis of the switch mechanisms that control tissue cyst formation is still poorly understood. Significant changes in gene expression are associated with tissue cyst development, and ApiAP2 transcription factors are an important mechanism regulating this developmental transcriptome. However, the molecular composition of these ApiAP2 complexes and the operating principles of ApiAP2 mechanisms are not well defined. Here we establish that competing ApiAP2 transcriptional mechanisms operate to regulate this clinically important developmental pathway.


title: Portrait painting using active templates

abstract: Portraiture plays a substantial role in traditional painting, yet it has not been studied in depth in painterly rendering research. The difficulty in rendering human portraits is due to our acute visual perception to the structure of human face. To achieve satisfactory results, a portrait rendering algorithm should account for facial structure. In this paper, we present an example-based method to render portrait paintings from photographs, by transferring brush strokes from previously painted portrait templates by artists. These strokes carry rich information about not only the facial structure but also how artists depict the structure with large and decisive brush strokes and vibrant colors. With a dictionary of portrait painting templates for different types of faces, we show that this method can produce satisfactory results.


title: Data Extraction from Web Tables: The Devil is in the Details

abstract: We present a method based on header paths for efficient and complete extraction of labeled data from tables meant for humans. Although many table configurations yield to the proposed syntactic analysis, some require access to semantic knowledge. Clicking on one or two critical cells per table, through a simple interface, is sufficient to resolve most of these problem tables. Header paths, a purely syntactic representation of visual tables, can be transformed ("factored") into existing representations of structured data such as category trees, relational tables, and RDF triples. From a random sample of 200 web tables from ten large statistical web sites, we generated 376 relational tables and 34,110 subject-predicate-object RDF triples.


title: Strategies for Combating Dark Networks

abstract: Our goal in this paper is to explore two generic approaches to disrupting dark networks: kinetic and nonkinetic. The kinetic approach involves aggressive and offensive measures to eliminate or capture network members and their supporters, while the non-kinetic approach involves the use of subtle, non-coercive means for combating dark networks. Two strategies derive from the kinetic approach: Targeting and Capacity-building. Four strategies derive from the non-kinetic approach: Institution-Building, Psychological Operations, Information Operations and Rehabilitation. We use network data from Noordin Top’s South East Asian terror network to illustrate how both kinetic and non-kinetic strategies could be pursued depending on a commander’s intent. Using this strategic framework as a backdrop, we strongly advise the use of SNA metrics in developing alterative counter-terrorism strategies that are contextdependent rather than letting SNA metrics define and drive a particular strategy.


title: Shape modeling with front propagation: A level set approach

abstract: Shape modeling is an important constituent of computer vision as well as computer graphics research. Shape models aid the tasks of object representation and recognition. This paper presents a new approach to shape modeling which retains some of the attractive features of existing methods and overcomes some of their limitations. The authors' techniques can be applied to model arbitrarily complex shapes, which include shapes with significant protrusions, and to situations where no a priori assumption about the object's topology is made. A single instance of the authors' model, when presented with an image having more than one object of interest, has the ability to split freely to represent each object. This method is based on the ideas developed by Osher and Sethian (1988) to model propagating solid/liquid interfaces with curvature-dependent speeds. The interface (front) is a closed, nonintersecting, hypersurface flowing along its gradient field with constant speed or a speed that depends on the curvature. It is moved by solving a "Hamilton-Jacobi" type equation written for a function in which the interface is a particular level set. A speed term synthesized from the image is used to stop the interface in the vicinity of object boundaries. The resulting equation of motion is solved by employing entropy-satisfying upwind finite difference schemes. The authors present a variety of ways of computing the evolving front, including narrow bands, reinitializations, and different stopping criteria. The efficacy of the scheme is demonstrated with numerical experiments on some synthesized images and some low contrast medical images. >


title: Hybrid approach for aligning parallel sentences for languages without a written form using standard Malay and Malay dialects

abstract: Alignment of parallel text is a step for building a machine translation. Parallel text alignment is important because linguistic information can be retrieved from the result of alignment which including bilingual dictionaries and grammars correspondence of each language. In this paper, we propose a hybrid approach for standard Malay-dialectal Malay parallel text alignment. The Malay dialects in Malaysia can be grouped according to the states such as Perak dialect, Kedah dialect and Terengganu dialect. It is important to learn Malay dialects as it is still flourished and widely used in many areas especially for unofficial matters. Kelantanese Malay is used as an example for dialectal Malay in this research. The obtained precision and recall values of the proposed alignment methods are above 90%.


title: Batch normalized recurrent neural networks

abstract: Recurrent Neural Networks (RNNs) are powerful models for sequential data that have the potential to learn long-term dependencies. However, they are computationally expensive to train and difficult to parallelize. Recent work has shown that normalizing intermediate representations of neural networks can significantly improve convergence rates in feedforward neural networks . In particular, batch normalization, which uses mini-batch statistics to standardize features, was shown to significantly reduce training time. In this paper, we show that applying batch normalization to the hidden-to-hidden transitions of our RNNs doesn't help the training procedure. We also show that when applied to the input-to-hidden transitions, batch normalization can lead to a faster convergence of the training criterion but doesn't seem to improve the generalization performance on both our language modelling and speech recognition tasks. All in all, applying batch normalization to RNNs turns out to be more challenging than applying it to feedforward networks, but certain variants of it can still be beneficial.


title: Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application

abstract: Robotic vision-based crack detection in concrete bridges is an essential task to preserve these assets and their safety. The conventional human visual inspection method is time consuming and cost inefficient. In this paper, we propose a robust algorithm to detect cracks in a pixel-wise manner from real concrete surface images. In practice, crack detection remains challenging in the following aspects: (1) detection performance is disturbed by noises and clutters of environment; and (2) the requirement of high pixel-wise accuracy is difficult to obtain. To address these limitations, three steps are considered in the proposed scheme. First, a local pattern predictor (LPP) is constructed using convolutional neural networks (CNN), which can extract discriminative features of images. Second, each pixel is efficiently classified into crack categories or non-crack categories by LPP, using as context a patch centered on the pixel. Lastly, the output of CNN-i.e., confidence map-is post-processed to obtain the crack areas. We evaluate the proposed algorithm on samples captured from several concrete bridges. The experimental results demonstrate the good performance of the proposed method.


title: Giving too much social support: social overload on social networking sites

abstract: As the number of messages and social relationships embedded in social networking sites (SNS) increases, the amount of social information demanding a reaction from individuals increases as well. We observe that, as a consequence, SNS users feel they are giving too much social support to other SNS users. Drawing on social support theory (SST), we call this negative association with SNS usage ‘social overload’ and develop a latent variable to measure it. We then identify the theoretical antecedents and consequences of social overload and evaluate the social overload model empirically using interviews with 12 and a survey of 571 Facebook users. The results show that extent of usage, number of friends, subjective social support norms, and type of relationship (online-only vs offline friends) are factors that directly contribute to social overload while age has only an indirect effect. The psychological and behavioral consequences of social overload include feelings of SNS exhaustion by users, low levels of user satisfaction, and a high intention to reduce or even stop using SNS. The resulting theoretical implications for SST and SNS acceptance research are discussed and practical implications for organizations, SNS providers, and SNS users are drawn.


title: HeartSense: Ubiquitous Accurate Multi-Modal Fusion-based Heart Rate Estimation Using Smartphones

abstract: Heart rate is one of the most important vital signals for personal health tracking. A number of smartphone-based heart rate estimation systems have been proposed over the years. However, they either depend on special hardware sensors or suffer from the high noise due to the weakness of the heart signals, affecting their accuracy in many practical scenarios. Inspired by medical studies about the heart motion mechanics, we propose the HeartSense heart rate estimation system. Specifically, we show that the gyroscope sensor is the most sensitive sensor for measuring the heart rate. To further counter noise and handle different practical scenarios, we introduce a novel quality metric that allows us to fuse the different gyroscope axes in a probabilistic framework to achieve a robust and accurate estimate. We have implemented and evaluated our system on different Android phones. Results using 836 experiments on different subjects in practical scenarios with a side-by-side comparison with other systems show that HeartSense can achieve 1.03 bpm median absolute error for heart rate estimation. This is better than the state-of-the-art by more than 147% in median error, highlighting HeartSense promise as a ubiquitous system for medical and personal well-being applications.


title: Lexical and Machine Learning approaches toward Online Reputation Management

abstract: With the popularity of social media, people are more and more interested in mining opinions from it. Learning from social media not only has value for research, but also good for business use. RepLab 2012 had Proling task and Monitoring task to understand the company related tweets. Proling task aims to determine the Ambiguity and Po- larity for tweets. In order to determine this Ambiguity and Polarity for the tweets in RepLab 2012 Proling task, we built Google Adwords Fil- ter for Ambiguity and several approaches like SentiWordNet, Happiness Score and Machine Learning for Polarity. We achieved good performance in the training set, and the performance in test set is also acceptable.


title: A novel schmitt trigger with low temperature coeficient

abstract: A novel Schmitt trigger circuit, implemented by a RS trigger and two simple distinct inverters, is proposed. Its trigger levels are determined by two CMOS inverters. Contrasted with traditional six transistors Schmitt trigger, its temperature and supply voltage characteristics have been analyzed. Apply these two triggers into relaxation oscillator and the result shows that the proposed one in this paper has better performance over wide temperature range, whose temperature coefficient from 0degC to 85degC is 1/5 of traditional.


title: ERP system implementation in SMEs: exploring the influences of the SME context

abstract: Small and medium-sized enterprises SMEs are increasingly implementing enterprise resource planning ERP systems. Compared to large enterprises, SMEs differ in a number of inherent characteristics, which are likely to impact the ERP system implementations. The purpose of this study is to explore these influences of the SME context on the ERP system implementation process. SME characteristics are synthesised from relevant literature and the influences of the contextual factors on various activities across the ERP life cycle are investigated. The study presents findings from a multiple case study of four SMEs. The ownership type of the companies and limited resources were identified as the most influential contextual factors. Among the ERP life-cycle phases, the implementation phase was affected most by the SME context. The case studies also illustrate the need for a more nuanced view on what should be considered general characteristics of SMEs; for example, regarding the level of IS knowledge, business processes, and market characteristics.


title: Learning to Rank from Noisy Data

abstract: Learning to rank, which learns the ranking function from training data, has become an emerging research area in information retrieval and machine learning. Most existing work on learning to rank assumes that the training data is clean, which is not always true, however. The ambiguity of query intent, the lack of domain knowledge, and the vague definition of relevance levels all make it difficult for common annotators to give reliable relevance labels to some documents. As a result, the relevance labels in the training data of learning to rank usually contain noise. If we ignore this fact, the performance of learning-to-rank algorithms will be damaged. In this article, we propose considering the labeling noise in the process of learning to rank and using a two-step approach to extend existing algorithms to handle noisy training data. In the first step, we estimate the degree of labeling noise for a training document. To this end, we assume that the majority of the relevance labels in the training data are reliable and we use a graphical model to describe the generative process of a training query, the feature vectors of its associated documents, and the relevance labels of these documents. The parameters in the graphical model are learned by means of maximum likelihood estimation. Then the conditional probability of the relevance label given the feature vector of a document is computed. If the probability is large, we regard the degree of labeling noise for this document as small; otherwise, we regard the degree as large. In the second step, we extend existing learning-to-rank algorithms by incorporating the estimated degree of labeling noise into their loss functions. Specifically, we give larger weights to those training documents with smaller degrees of labeling noise and smaller weights to those with larger degrees of labeling noise. As examples, we demonstrate the extensions for McRank, RankSVM, RankBoost, and RankNet. Empirical results on benchmark datasets show that the proposed approach can effectively distinguish noisy documents from clean ones, and the extended learning-to-rank algorithms can achieve better performances than baselines.


title: The perfect gift card: An exploration of teenagers' gift card associations

abstract: This study provides new insights into the meaning and value of gift cards in gift exchange among teenagers who gave and/or received gift cards during the Christmas/Hanukkah season. From their reflections of the reasons for giving gift cards and their feelings about receiving gift cards from others, the social roles played out in buying gift cards as a gift selection strategy are assessed and the values associated with gift cards explored. The results suggest that givers playing the roles of acknowledger and pleaser are heavy gift card givers when buying gifts for difficult recipients. Acknowledgers incorporate gift cards into existing strategies for gift selection (especially “settle,” “pawn off,” and “buy relationship affirming gifts”), but for pleasers, buying gift cards may be part of a new gift selection strategy—“play it safe.” Importantly, although gift cards can reflect economic, social, and functional value, the giving of gift cards seems to result in a new form of value for the recipients—the value of choice, which occurs when the control over gift selection is transferred from giver to receiver. © 2008 Wiley Periodicals, Inc.


title: Service Level Agreement (SLA) in Utility Computing Systems

abstract: In recent years, extensive research has been conducted in the area of Service Level Agreement (SLA) for utility computing systems. An SLA is a formal contract used to guarantee that consumers' service quality expectation can be achieved. In utility computing systems, the level of customer satisfaction is crucial, making SLAs significantly important in these environments. Fundamental issue is the management of SLAs, including SLA autonomy management or trade off among multiple Quality of Service (QoS) parameters. Many SLA languages and frameworks have been developed as solutions; however, there is no overall classification for these extensive works. Therefore, the aim of this chapter is to present a comprehensive survey of how SLAs are created, managed and used in utility computing environment. We discuss existing use cases from Grid and Cloud computing systems to identify the level of SLA realization in state-of-art systems and emerging challenges for future research.


title: DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks

abstract: In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled with bounding box annotations. It extends the approach of the well-known GrabCut method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations. Additionally, we propose variants of the DeepCut method and compare those to a naive approach to CNN training under weak supervision. We test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset and obtain encouraging results in terms of accuracy.


title: Locality Preserving Projections

abstract: Many problems in information processing involve some form of dimensionality reduction. In this paper, we introduce Locality Preserving Projections (LPP). These are linear projective maps that arise by solving a variational problem that optimally preserves the neighborhood structure of the data set. LPP should be seen as an alternative to Principal Component Analysis (PCA) – a classical linear technique that projects the data along the directions of maximal variance. When the high dimensional data lies on a low dimensional manifold embedded in the ambient space, the Locality Preserving Projections are obtained by finding the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the manifold. As a result, LPP shares many of the data representation properties of nonlinear techniques such as Laplacian Eigenmaps or Locally Linear Embedding. Yet LPP is linear and more crucially is defined everywhere in ambient space rather than just on the training data points. This is borne out by illustrative examples on some high dimensional data sets.


title: Biochemical aspects of overtraining in endurance sports: a review.

abstract: Top-level performances in endurance sports require several years of hard training loads. A major objective of this endurance training is to reach the most elevated metabolic adaptations the athlete will be able to support. As a consequence, overtraining is a recurrent problem that highly-trained athletes may experience during their career. Many studies have revealed that overtraining could be highlighted by various biochemical markers but a principal discrepancy in the diagnosis of overtraining stems from the fact that none of these markers may be considered as universal. In endurance sports, the metabolic aspects of training fatigue appear to be the most relevant parameters that may characterise overtraining when recovery is not sufficient, or when dietary habits do not allow an optimal replenishment of substrate stores. From the skeletal muscle functions to the overall energetic substrate availability during exercise, six metabolic schemes have been studied in relation to overtraining, each one related to a central parameter, i.e. carbohydrates, branched-chain amino acids, glutamine, polyunsaturated fatty acids, leptin, and proteins.


title: Visualizing the Intellectual Structure with Paper-Reference Matrices

abstract: Visualizing the intellectual structure of scientific domains using co-cited units such as references or authors has become a routine for domain analysis. In previous studies, paper-reference matrices are usually transformed into reference-reference matrices to obtain co-citation relationships, which are then visualized in different representations, typically as node-link networks, to represent the intellectual structures of scientific domains. Such network visualizations sometimes contain tightly knit components, which make visual analysis of the intellectual structure a challenging task. In this study, we propose a new approach to reveal co-citation relationships. Instead of using a reference-reference matrix, we directly use the original paper-reference matrix as the information source, and transform the paper-reference matrix into an FP-tree and visualize it in a Java-based prototype system. We demonstrate the usefulness of our approach through visual analyses of the intellectual structure of two domains: information visualization and Sloan Digital Sky Survey (SDSS). The results show that our visualization not only retains the major information of co-citation relationships, but also reveals more detailed sub-structures of tightly knit clusters than a conventional node-link network visualization.


title: 60 GHz Plated Through Hole Printed Magneto-Electric Dipole Antenna

abstract: A wideband unidirectional antenna, which is composed of a planar electric dipole and a magnetic dipole formed by a vertically-oriented shorted patch antenna, is presented for millimeter wave applications. The antenna is realized by a plated through hole printed technique with the use of a microwave substrate and is excited by a T-shaped coupled strip feed. An impedance bandwidth of 33% (S11 ≤-15 dB) from 50 to 70 GHz is achieved. Stable radiation patterns with low cross polarizations and a stable antenna gain of ~7.5 dBi are achieved across the entire operating bandwidth. This single antenna element yields advantages of wideband, good directional radiation pattern and low fabrication cost.


title: Multipath TCP path schedulers for streaming video

abstract: Modern internet traffic is heavily composed of some form of video communication. In addition, video streaming is slowly replacing regular phone connections. Given that most video transport utilizes Video over Hypertext Transfer Protocol/Transmission Control Protocol (HTTP/TCP), it is important to understand TCP performance in transporting video streams. Recently, multipath transport protocols have allowed video streaming over multiple paths to become a reality. In this paper, we investigate packet scheduling disciplines for injecting video stream packets into multiple paths at the video server. We study video streaming performance when subjected to these schedulers in conjunction with various TCP variants. We utilize network performance measures, as well as video quality metrics, to characterize the performance and interactions between network and application layers of video streams for various network scenarios.


title: Pre-trained multi-view word embedding using two-side neural network

abstract: Word embedding aims to learn a continuous representation for each word. It attracts increasing attention due to its effectiveness in various tasks such as named entity recognition and language modeling. Most existing word embedding results are generally trained on one individual data source such as news pages or Wikipedia articles. However, when we apply them to other tasks such as web search, the performance suffers. To obtain a robust word embedding for different applications, multiple data sources could be leveraged. In this paper, we proposed a two-side multimodal neural network to learn a robust word embedding from multiple data sources including free text, user search queries and search click-through data. This framework takes the word embeddings learned from different data sources as pre-train, and then uses a two-side neural network to unify these embeddings. The pre-trained embeddings are obtained by adapting the recently proposed CBOW algorithm. Since the proposed neural network does not need to re-train word embeddings for a new task, it is highly scalable in real world problem solving. Besides, the network allows weighting different sources differently when applied to different application tasks. Experiments on two real-world applications including web search ranking and word similarity measuring show that our neural network with multiple sources outperforms state-of-the-art word embedding algorithm with each individual source. It also outperforms other competitive baselines using multiple sources.


title: Design of frequency reconfigurable microstrip patch antenna

abstract: Recent developments in the wireless communication industry continue to drive the requirements for small, compatible, and affordable reconfigurable antennas. To overcome the challenges of multi-frequency operation a new scheme of frequency reconfiguration is proposed. In the proposed design a rectangular patch antenna with slots at the centre frequency 10 GHz that can be reconfigured in the frequency range of 10–10.5 GHz. The reconfiguration is carried out by switching the diodes ON/OFF. The antenna is designed on FR4 substrate (e r = 4.54) of thickness (H) 1.50 mm. The proposed structure was simulated by using the electromagnetic (EM) simulation software. The simulated return loss for different configuration are less than −10dB at resonance frequencies. The simulated are very exciting and show the close agreement with proposed design.


title: Circularly Polarized Patch Antenna for Future 5G Mobile Phones

abstract: A circularly polarized patch antenna for future fifth-generation mobile phones is presented in this paper. Miniaturization and beamwidth enhancement of a patch antenna are the two main areas to be discussed. By folding the edge of the radiating patch with loading slots, the size of the patch antenna is 44.8% smaller than a conventional half wavelength patch, which allows it to be accommodated inside handsets easily. Wide beamwidth is obtained by surrounding the patch with a dielectric substrate and supporting the antenna by a metallic block. A measured half power beamwidth of 124° is achieved. The impedance bandwidth of the antenna is over 10%, and the 3-dB axial ratio bandwidth is 3.05%. The proposed antenna covers a wide elevation angle and complete azimuth range. A parametric study of the effect of the metallic block and the surrounding dielectric substrate on the gain at a low elevation angle and the axial ratio of the proposed antenna are presented.


Performance Evaluation of a large 3 D Fingerprint database


Inference of Plant Diseases from Leaf Images through Deep Learning


title: Exploiting Cross-Sentence Context for Neural Machine Translation

abstract: In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation (NMT). First, this history is summarized in a hierarchical way. We then integrate the historical representation into NMT in two strategies: 1) a warm-start of encoder and decoder states, and 2) an auxiliary context source for updating decoder states. Experimental results on a large Chinese-English translation task show that our approach significantly improves upon a strong attention-based NMT system by up to +2.1 BLEU points.


title: QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

abstract: In many real-world settings, a team of agents must coordinate their behaviour while acting in a decentralised way. At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifted. Learning joint action-values conditioned on extra state information is an attractive way to exploit centralised learning, but the best strategy for then extracting decentralised policies is unclear. Our solution is QMIX, a novel value-based method that can train decentralised policies in a centralised end-to-end fashion. QMIX employs a network that estimates joint action-values as a complex non-linear combination of per-agent values that condition only on local observations. We structurally enforce that the joint-action value is monotonic in the per-agent values, which allows tractable maximisation of the joint action-value in off-policy learning, and guarantees consistency between the centralised and decentralised policies. We evaluate QMIX on a challenging set of StarCraft II micromanagement tasks, and show that QMIX significantly outperforms existing value-based multi-agent reinforcement learning methods.


title: Steel Catenary Riser for the Marlim Field FPS P-XVIII

abstract: Steel catenary risers (SCRs) have been successfully deployed on the Shell Auger TLP in the Gulf of Mexico and will also be deployed on the Shell Mars TLP. There is considerable interest in using SCRs for other applications, in particular for semi-sub based FPS systems. Petrobras is developing an SCR for installation on the PXVIII platform, located in the Marlim Field, Campos Basin, in a water depth of 910 meters. The riser will be a dead-ended prototype which will be monitored for a period of up to one year. Subsequently, it will be connected via flexible piping to the PXXVI platform, located approximately 5 kilometres from PXVIII. This paper describes the design, materials, components, specification and installation of the Petrobras P-XVIII SCR. The static/dynamic design, VIV analysis and fatigue analysis methodologies are discussed in detail and results are presented. Important aspects of the flex joint design and specification are discussed. This project represents the first application of SCRs to semi-sub based FPS systems. In addition, it is the first use of SCRs outside of the Gulf of Mexico.


title: A Critical Discourse Analysis of the Reports Issued by the International Atomic Energy Agency (IAEA) Director General on Iran"s Nuclear Program during the Last Decade

abstract: Regarding the importance of Critical Discourse Analysis (CDA) in discovering the way ideology is diffused through the prevalence of various discourses, the present paper is an attempt to illustrate how the ideological and political considerations are realized through textual features of Negation and Repetition as well as intertextual features of Argumentation and Persuasion. Reports about Iran’s nuclear program provided by IAEA’s Director General are examples of mediating role which such genres play to dictate the underlying contemplations of their producers. Using Fairclough three-dimensional framework for studying discourse at three levels of micro, meso and macro, it is shown how the language used in these 38 reports is not merely a vehicle of explicit transmission of knowledge but a means of implicit construction of ambiguity. An attempt has been made to demonstrate how an organization such as IAEA with a technical body through politicized linguistic elements directs the readers’ impression of the IAEA reports and consequently revises and constructs their ideologies.


title: The theory of joins in relational databases

abstract: Answering queries in a relational database often requires that the natural join of two or more relations be computed. However, the result of a join may not be what one expects. In this paper we give efficient algorithms to determine whether the join of several relations has the intuitively expected value (is lossless ) and to determine whether a set of relations has a subset with a lossy join. These algorithms assume that all data dependencies are functional. We then discuss the extension of our techniques to the case where data dependencies are multivalued.


title: Real-time video-based fire smoke detection system

abstract: A real-time video-based fire smoke detection method that can be incorporated with a automatic monitoring system for early alerts is proposed by this paper. The successive processing steps of our real-time algorithm are using the motion history segmentation algorithm to register the possible fire smoke position in a video and then analyze the spectral, spatial and temporal characteristics of the fire smoke regions in the image sequences. The spectral probability density is represented by comparing the fire smoke color histogram model, where HSI color spaces are used. The spatial probability density is represented by computing the fire smoke turbulent phenomena with the relation of perimeter and area. Statistical distribution of the spectral and spatial probability density is weighted with the fuzzy reasoning system to give the potential fire smoke candidate region. The temporal probability density is represented by extracting the flickering area with level crossing and separating the alias objects from the fire smoke region. Then, the continuously adaptive mean shift (CAMSHIFT) vision tracking algorithm is employed to provide feedback of the fire smoke real-time position at a high frame rate. Experimental results in a variety of conditions show the proposed method is capable of detecting fire smoke reliably.


title: Substrate Integrated Waveguide Cavity-Backed Self-Triplexing Slot Antenna

abstract: In this letter, a novel substrate integrated waveguide (SIW) cavity-backed self-triplexing slot antenna is proposed for triple-band communication. It is realized on a single-layer printed circuit board. The proposed antenna consists of a pair of bow-tie slots etched on an SIW cavity, and three different resonant modes are excited by two microstrip lines and a coaxial probe to operate at three distinct frequencies (7.89, 9.44, and 9.87 GHz). The band around 9.48 GHz is obtained due to radiation from one bow-tie slot fed by adjacent microstrip feedline and band around 9.87 GHz is due to radiation from other bow-tie slot fed by its adjacent microstrip feedline. On the other hand, lower band around 7.89 GHz is achieved because of radiation from both bow-tie slots excited by coaxial probe feedline. The measured isolation between any two feedlines is better than 22.5 dB. The measured realized gain is more than 7.2 dB at all the bands. Cross polarization below 36.5, 29.3, and 24.45 dB in broad sight direction and high front-to-back ratio of more than 17.3 dB at each resonant frequency are obtained.


title: Medical image classification with convolutional neural network

abstract: Image patch classification is an important task in many different medical imaging applications. In this work, we have designed a customized Convolutional Neural Networks (CNN) with shallow convolution layer to classify lung image patches with interstitial lung disease (ILD). While many feature descriptors have been proposed over the past years, they can be quite complicated and domain-specific. Our customized CNN framework can, on the other hand, automatically and efficiently learn the intrinsic image features from lung image patches that are most suitable for the classification purpose. The same architecture can be generalized to perform other medical image or texture classification tasks.


title: “SEIS ” – SECURITY IN EMBEDDED IP-BASED SYSTEMS

abstract: The constantly increasing variety of networking technologies used in automobiles today results in complex and cost-intensive E/E architectures. For this reason the innovation alliance for automobile electronics E|ENOVA initiated the research project “SEIS — Safety in Embedded IP-based Systems”, with the aim of creating a consistent security solution for internet protocol based communication inside the vehicle and for the vehicle's communication with the environment. The project partners now report for the first time on the success of the security initiative, which was started one year ago.


title: A fair online payment system for digital content via subliminal channel

abstract: In recent years, purchase of digital content using the Internet has been increasing both in popularity and convenience. On the other hand, there are a multitude of pirated editions for digital products which have become more available and easier to attain. Therefore, proving legal ownership of digital content has become important. Many researchers have proposed various schemes to protect consumer's ownership using watermark mechanisms on secure payment systems. In our scheme, we want to achieve this same result with the concept of subliminal channels and propose an intact arbitration mechanism to solve the fairness of the transaction between both the customer and the shop. Our scheme not only protects customers' legal ownership of digital content, but also provides a fair and secure protocol.


title: Improving Downlink Coordinated Multipoint Performance in Heterogeneous Networks

abstract: We propose a novel method for practical Joint Processing downlink coordinated multipoint (DL CoMP) implementation in LTE/LTE-A systems using supervised machine learning. DL CoMP has not been thoroughly studied in previous work although cluster formation and interference mitigation have been studied extensively. In this paper, we attempt to improve the cell edge data rate served by a heterogeneous network cluster by means of dynamically changing the DL SINR threshold at which the DL CoMP feature is triggered. We do so by using a support vector machine (SVM) classifier. The simulation results show a cell edge user throughput improvement of 33.3\% for pico cells and more than four-fold improvement in user throughput in the cluster. This has resulted from a reduction in the downlink block error rate (DL BLER) and an improvement in the spectral efficiency due to the informed triggering of the multiple radio streams as part of DL CoMP.


title: Social learning analytics: five approaches

abstract: This paper proposes that Social Learning Analytics (SLA) can be usefully thought of as a subset of learning analytics approaches. SLA focuses on how learners build knowledge together in their cultural and social settings. In the context of online social learning, it takes into account both formal and informal educational environments, including networks and communities. The paper introduces the broad rationale for SLA by reviewing some of the key drivers that make social learning so important today. Five forms of SLA are identified, including those which are inherently social, and others which have social dimensions. The paper goes on to describe early work towards implementing these analytics on SocialLearn, an online learning space in use at the UK's Open University, and the challenges that this is raising. This work takes an iterative approach to analytics, encouraging learners to respond to and help to shape not only the analytics but also their associated recommendations.


title: A multiarmed bandit incentive mechanism for crowdsourcing demand response in smart grids

abstract: Demand response is a critical part of renewable integration and energy cost reduction goals across the world. Motivated by the need to reduce costs arising from electricity shortage and renewable energy fluctuations, we propose a novel multiarmed bandit mechanism for demand response (MAB-MDR) which makes monetary offers to strategic consumers who have unknown response characteristics, to incetivize reduction in demand. Our work is inspired by a novel connection we make to crowdsourcing mechanisms. The proposed mechanism incorporates realistic features of the demand response problem including time varying and quadratic cost function. The mechanism marries auctions, that allow users to report their preferences, with online algorithms, that allow distribution companies to learn user-specific parameters. We show that MAB-MDR is dominant strategy incentive compatible, individually rational, and achieves sublinear regret. Such mechanisms can be effectively deployed in smart grids using new information and control architecture innovations and lead to welcome savings in energy costs.


title: Learned lessons in credit card fraud detection from a practitioner perspective

abstract: Abstract Billions of dollars of loss are caused every year due to fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to non-stationary distribution of the data, highly imbalanced classes distributions and continuous streams of transactions. At the same time public data are scarcely available for confidentiality issues, leaving unanswered many questions about which is the best strategy to deal with them. In this paper we provide some answers from the practitioner’s perspective by focusing on three crucial issues: unbalancedness, non-stationarity and assessment. The analysis is made possible by a real credit card dataset provided by our industrial partner.


title: Fast Matrix Factorization for Online Recommendation with Implicit Feedback

abstract: This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our implemented, open-source (https://github.com/hexiangnan/sigir16-eals) eALS consistently outperforms state-of-the-art implicit MF methods.


title: Cloud-based design and manufacturing : A new paradigm in digital manufacturing and design innovation

abstract: Cloud-based design manufacturing (CBDM) refers to a service-oriented networked product development model in which service consumers are enabled to configure, select, and utilize customized product realization resources and services ranging from computer-aided engineering software to reconfigurable manufacturing systems. An ongoing debate on CBDM in the research community revolves around several aspects such as definitions, key characteristics, computing architectures, communication and collaboration processes, crowdsourcing processes, information and communication infrastructure, programming models, data storage, and new business models pertaining to CBDM. One question, in particular, has often been raised: is cloud-based design and manufacturing actually a new paradigm, or is it just "old wine in new bottles"? To answer this question, we discuss and compare the existing definitions for CBDM, identify the essential characteristics of CBDM, define a systematic requirements checklist that an idealized CBDM system should satisfy, and compare CBDM to other relevant but more traditional collaborative design and distributed manufacturing systems such as web- and agent-based design and manufacturing systems. To justify the conclusion that CBDM can be considered as a new paradigm that is anticipated to drive digital manufacturing and design innovation, we present the development of a smart delivery drone as an idealized CBDM example scenario and propose a corresponding CBDM system architecture that incorporates CBDM-based design processes, integrated manufacturing services, information and supply chain management in a holistic sense. We present a new paradigm in digital manufacturing and design innovation, namely cloud-based design and manufacturing (CBDM).We identify the common key characteristics of CBDM.We define a requirement checklist that any idealized CBDM system should satisfy.We compare CBDM with other relevant but more traditional collaborative design and distributed manufacturing systems.We describe an idealized CBDM application example scenario.


title: DexLego: Reassembleable Bytecode Extraction for Aiding Static Analysis

abstract: The scale of Android applications in the market is growing rapidly. To efficiently detect the malicious behavior in these applications, an array of static analysis tools are proposed. However, static analysis tools suffer from code hiding techniques like packing, dynamic loading, self modifying, and reflection. In this paper, we thus present DexLego, a novel system that performs a reassembleable bytecode extraction for aiding static analysis tools to reveal the malicious behavior of Android applications. DexLego leverages just-in-time collection to extract data and bytecode from an application at runtime, and reassembles them to a new Dalvik Executable (DEX) file offline. The experiments on DroidBench and real-world applications show that DexLego precisely reconstructs the behavior of an application in the reassembled DEX file, and significantly improves analysis result of the existing static analysis systems.


title: Bandwidth Enhancement of a Planar Printed Quasi-Yagi Antenna With Size Reduction

abstract: A compact planar printed quasi-Yagi antenna is presented. The proposed antenna consists of a microstrip line to slotline transition structure, a driver dipole and two parasitic strips. The driver dipole is connected to the slotline through coplanar stripline (CPS). The ground plane is modified by symmetrically adding two extended stubs to reduce the lateral size. Experimental and simulated results show that the proposed quasi-Yagi antenna has a wide bandwidth and good unidirectional radiation characteristics. Compared with conventional printed quasi-Yagi antennas, the width of the proposed quasi-Yagi antenna is reduced by approximately 16.7%. The proposed antenna presents an excellent end-fire radiation with a front-to-back ratio greater than 10 dB. Its measured bandwidth is from 3.6-11.6 GHz with a ratio of about 3.22: 1. A moderate gain, which is better than 4 dBi, is obtained.


title: 0-Bit Consistent Weighted Sampling

abstract: We develop 0-bit consistent weighted sampling (CWS) for efficiently estimating min-max kernel, which is a generalization of the resemblance kernel originally designed for binary data. Because the estimator of 0-bit CWS constitutes a positive definite kernel, this method can be naturally applied to large-scale data mining problems. Basically, if we feed the sampled data from 0-bit CWS to a highly efficient linear classifier (e.g., linear SVM), we effectively (and approximately) train a nonlinear classifier based on the min-max kernel. The accuracy improves as we increase the sample size. In this paper, we first demonstrate, through an extensive classification study using kernel machines, that the min-max kernel often provides an effective measure of similarity for nonnegative data. This helps justify the use of min-max kernel. However, as the min-max kernel is nonlinear and might be difficult to be used for industrial applications with massive data, we propose to linearize the min-max kernel via 0-bit CWS, a simplification of the original CWS method. The previous remarkable work on {\em consistent weighted sampling (CWS)} produces samples in the form of (i*, t*) where the i* records the location (and in fact also the weights) information analogous to the samples produced by classical minwise hashing on binary data. Because the t* is theoretically unbounded, it was not immediately clear how to effectively implement CWS for building large-scale linear classifiers. We provide a simple solution by discarding t* (which we refer to as the "0-bit" scheme). Via an extensive empirical study, we show that this 0-bit scheme does not lose essential information. We then apply 0-bit CWS for building linear classifiers to approximate min-max kernel classifiers, as extensively validated on a wide range of public datasets. We expect this work will generate interests among data mining practitioners who would like to efficiently utilize the nonlinear information of non-binary and nonnegative data.


title: Effect of examination stress on intraocular pressure in university students.

abstract: Intraocular pressure (IOP) has been investigated as a possible objective index of mental stressors. Here, we assessed the effect of examination stress on IOP in 33 university students. A repeated-measures design was used with two experimental conditions (examination and control) and two points of measurements (pre- and post-sessions). Also, the cardiovascular response, subjective perceived stress, as well as calculated ocular perfusion pressure and blood-pulse pressure were determined. A Bayesian statistical analysis showed higher IOP values in the examination in comparison to the control condition (BF01 < 0.001). A similar pattern was found for the cardiovascular indices (diastolic and systolic blood pressure, and heart rate), and these findings were corroborated by subjective reports (BF01 < 0.001 in all cases). Our data incorporates evidence in relation to the utility of IOP as an objective marker of examination stress, and it may help in the assessment and management of stress in applied scenarios.


title: Increasing ATM Efficiency withAssistant Based Speech Recognition

abstract: Abstract—Initiatives to integrate Automatic Speech Recognition ::: into Air Traffic Management (ATM) exists at least since the late ::: 90s. Some success to replace pseudo pilots have been reported, ::: but its integration into controller assistant tools is missing. German ::: Aerospace Center (DLR) and Saarland University developed ::: Assistant Based Speech Recognition (ABSR) enabling command ::: recognition rates better than 95%. However, good recognition ::: rates are no convincing argument for decision makers. ::: Therefore, we conducted an ABSR validation study with eight air ::: traffic controllers to quantify the benefits with respect to workload ::: and efficiency. The study validates that ABSR does not just ::: reduce controllers’ workload, which would already be a lot, but ::: this paper presents that ABSR significantly increases ATM efficiency. ::: Fuel reductions of 60 liters (16 gallons) per flight and a ::: throughput increase by two arrivals per hour are possible.


title: AR postcard: the augmented reality system with a postcard

abstract: This paper proposes the postcard system which recognizes a card using a camera within a device (PC/Smart TV/Tablet PC) and augments the video of the sender fit to the card size. The postcards or the picture cards used in our work are trained in advance. The proposed system consists of two parts, the video management module and the augmented reality module. The video management module manages playable videos and makes ID as a link between the video and the card. The augmented reality module recognizes the postcard or the picture card using the trained data which is mentioned above and estimates ID of the card and also its three-dimensional position. The ID is sent to the video management module to get the video according to the ID of the card. After that, the augmented reality module synthesizes the video to the three-dimensional position of the card. As a result, the sender shows the video to the receiver fit to the card. All process in the proposed paper is performed in real-time.


title: Tracking control with adaption of kites

abstract: A novel tracking paradigm for flying geometric trajectories using tethered kites is presented. It is shown how the differential-geometric notion of turning angle can be used as a one-dimensional representation of the kite trajectory, and how this leads to a single-input single-output (SISO) tracking problem. Based on this principle a Lyapunov-based nonlinear adaptive controller is developed that only needs control derivatives of the kite aerodynamic model. The resulting controller is validated using simulations with a point-mass kite model.


title: A case of caudal regression syndrome: walking or sitting?

abstract: Caudal regression syndrome (CRS) is a congenital disorder which is seen vertebral anomalies in varying degrees from lower thoracic spineto the level of the coccyx. We present a case of CRS which is not intended operation for orthopedic deformities considering functionality. 2, 5 year-old girl referred to our clinic with complaints about walking disability, knee and foot deformities. Patient's mother with unregulated diabetes did not have a history of drug use, radiation exposure and serious illness during pregnancy. Diagnosis had been put during antenatal follow-ups. On physical examination, her lower extremities were hypoplastic and had no muscle activity. Her hips were flexed and abducted, but did not have contractures. Her knees had 75 degrees of flexion contractures with popliteal webs and feet had equinovarus deformity. Frog belly was present due to abdominal muscles weakness. Also hypoplasic labia majora has been identified. In lumbar MRI, spinal cord was terminated at 6th thoracic (T6) vertebrae and the last solid vertebrae level was at T10. Patient who has been following by urology with clean intermittent catheterization had also severe urological problems including horseshoe kidney, neurologic bladder, vesico-ureteral reflux and grade 2 hydronephrosis. Orthopedic consultation was made for her deformities. They decided that ambulation unexpected patient's knee flexion contractures were helping sitting balance. Because of this operation was not considered. Prognosis, treatment options, strength exercises for upper extremities, skin care were told to parents and patient was taken to follow. CRS is a rare congenital abnormality which is associated with orthopedic deformities, as well as urological, anorectal and cardiac malformations. Treatment requires a multidisciplinary approach. It should not be forgotten that purpose of rehabilitation is not to correct all deformities but increase the functionality of everyday life.


title: Anonymous Alone? Measuring Bitcoin’s Second-Generation Anonymization Techniques

abstract: This paper contributes a systematic account of transaction anonymization techniques that do not require trust in a single entity and support the existing cryptographic currency Bitcoin. It surveys and compares four known techniques, proposes tailored metrics to identify the use of each technique (but not necessarily its users), and presents longitudinal measurements indicating adoption trends and teething troubles. There is a tradeoff between the choice of users' preferred protection mechanisms and the risk that pertaining transactions can be singled out, which hurts privacy due to smaller anonymity sets unless a critical mass adopts the mechanism.


title: Grouped Convolutional Neural Networks for Multivariate Time Series

abstract: Analyzing multivariate time series data is important for many applications such as automated control, fault diagnosis and anomaly detection. One of the key challenges is to learn latent features automatically from dynamically changing multivariate input. In visual recognition tasks, convolutional neural networks (CNNs) have been successful to learn generalized feature extractors with shared parameters over the spatial domain. However, when high-dimensional multivariate time series is given, designing an appropriate CNN model structure becomes challenging because the kernels may need to be extended through the full dimension of the input volume. To address this issue, we present two structure learning algorithms for deep CNN models. Our algorithms exploit the covariance structure over multiple time series to partition input volume into groups. The first algorithm learns the group CNN structures explicitly by clustering individual input sequences. The second algorithm learns the group CNN structures implicitly from the error backpropagation. In experiments with two real-world datasets, we demonstrate that our group CNNs outperform existing CNN based regression methods.


title: An insight into classification with imbalanced data : Empirical results and current trends on using data intrinsic characteristics

abstract: Training classifiers with datasets which suffer of imbalanced class distributions is an important problem in data mining. This issue occurs when the number of examples representing the class of interest is much lower than the ones of the other classes. Its presence in many real-world applications has brought along a growth of attention from researchers. We shortly review the many issues in machine learning and applications of this problem, by introducing the characteristics of the imbalanced dataset scenario in classification, presenting the specific metrics for evaluating performance in class imbalanced learning and enumerating the proposed solutions. In particular, we will describe preprocessing, costsensitive learning and ensemble techniques, carrying out an experimental study to contrast these approaches in an intra and inter-family comparison. We will carry out a thorough discussion on the main issues related to using data intrinsic characteristics in this classification problem. This will help to improve the current models with respect to: the presence of small disjuncts, the lack of density in the training data, the overlapping between classes, the identification of noisy data, the significance of the borderline instances, and the dataset shift between the training and the test distributions. Finally, we introduce several approaches and recommendations to address these problems in conjunction with imbalanced data, and we will show some experimental examples on the behavior of the learning algorithms on data with such intrinsic characteristics.


title: Classification of Alzheimer Disease on Imaging Modalities with Deep CNNs Using Cross-Modal Transfer Learning

abstract: A recent imaging modality Diffusion Tensor Imaging completes information used from Structural MRI in studies of Alzheimer disease. A large number of recent studies has explored pathologic staging of Alzheimer disease using the Mean Diffusivity maps extracted from the Diffusion Tensor Imaging modality. The Deep Neural Networks are seducing tools for classification of subjects' imaging data in computer-aided diagnosis of Alzheimer's disease. The major problem here is the lack of a publicly available large amount of training data in both modalities. The lack number of training data yields over-fitting phenomena. We propose a method of a cross-modal transfer learning: from Structural MRI to Diffusion Tensor Imaging modality. Models pre-trained on a structural MRI dataset with domain-depended data augmentation are used as initialization of network parameters to train on Mean Diffusivity data. The method shows a reduction of the over-fitting phenomena, improves learning performance, and thus increases the accuracy of prediction. Classifiers are then fused by a majority vote resulting in augmented scores of classification between Normal Control, Alzheimer Patients and Mild Cognitive Impairment subjects on a subset of ADNI dataset.


title: A planar 75% efficient GaN 1.2-GHz DC-DC converter with self-synchronous rectifier

abstract: This paper presents the design and characterization of a DC-DC converter operating at 1.2GHz with a maximum efficiency of 75% at 4.6W output power, and with a maximum output power of 13W at 58% efficiency. The microwave converter consists of a GaN class-E power amplifier coupled to a GaN class-E rectifier. The circuit is planar and exhibits a power density of approximately 0.65W/cm3 at the 75% efficiency point. The rectifier can be operated with input gate drive or selfsynchronously with a single RF input.


title: INTERSECTION OF CHILD ABUSE AND CHILDREN’S EXPOSURE TO DOMESTIC VIOLENCE

abstract: This review addresses research on the overlap in physical child abuse and domestic violence, the prediction of child outcomes, and resilience in children exposed to family violence. The authors explore current findings on the intersection of physical child abuse and domestic violence within the context of other risk factors, including community violence and related family and environmental stressors. Evidence from the studies reviewed suggests considerable overlap, compounding effects, and possible gender differences in outcomes of violence exposure. The data indicate a need to apply a broad conceptualization of risk to the study of family violence and its effects on children. Further testing of competing theoretical models will advance understanding of the pathways through which exposure leads to later problems in youth, as well as protective factors and processes through which resilience unfolds.


title: Who Will Win Practical Artificial Intelligence? AI Engineerings in China

abstract: Currently, Artificial Intelligence (AI) has won unprecedented attention and is becoming the increasingly popular focus in China. This change can be judged by the impressive record of academic publications, the amount of state-level investment and the presence of nation-wide participation and devotion. In this paper, we place emphasis on discussing the progress of artificial intelligence engineerings in China. We first introduce the focus on AI in Chinese academia, including the supercomputing brain system, Cambrian Period supercomputer of neural networks, and biometric systems. Then, the development of AI in industrial circles and the latest layout of AI products in companies, such as Baidu, Tencent, and Alibaba, are introduced. Last, we bring in the opinions and arguments of the main intelligentsia of China about the future development of AI, including how to examine the relationship between humanity on one side and science and technology on the other.


title: Building a sentiment summarizer for local service reviews

abstract: Online user reviews are increasingly becoming the de-facto standard for measuring the quality of electronics, restau- rants, merchants, etc. The sheer volume of online reviews makes it difficult for a human to process and extract all meaningful information in order to make an educated pur- chase. As a result, there has been a trend toward systems that can automatically summarize opinions from a set of re- views and display them in an easy to process manner (1, 9). In this paper, we present a system that summarizes the sen- timent of reviews for a local service such as a restaurant or hotel. In particular we focus on aspect-based summarization models (8), where a summary is built by extracting relevant aspects of a service, such as service or value, aggregating the sentiment per aspect, and selecting aspect-relevant text. We describe the details of both the aspect extraction and sentiment detection modules of our system. A novel aspect of these models is that they exploit user provided labels and domain specific characteristics of service reviews to increase quality.


Adaptable and Adaptive Automation for Supervisory Control of Multiple Autonomous Vehicles: (572172013-089)


title: Orbitofrontal cortex, decision-making and drug addiction

abstract: The orbitofrontal cortex, as a part of prefrontal cortex, is implicated in executive function. However, within this broad region, the orbitofrontal cortex is distinguished by its unique pattern of connections with crucial subcortical associative learning nodes, such as basolateral amygdala and nucleus accumbens. By virtue of these connections, the orbitofrontal cortex is uniquely positioned to use associative information to project into the future, and to use the value of perceived or expected outcomes to guide decisions. This review will discuss recent evidence that supports this proposal and will examine evidence that loss of this signal, as the result of drug-induced changes in these brain circuits, might account for the maladaptive decision-making that characterizes drug addiction.


title: Differential privacy via t-closeness in data publishing

abstract: k-Anonymity and e-differential privacy are two main privacy models proposed within the computer science community. Whereas the former was proposed for privacy-preserving data publishing, i.e. data set anonymization, the latter initially arose in the context of interactive databases and was later extended to data publishing. We show here that t-closeness, one of the extensions of k-anonymity, can actually yielde-differential privacy in data publishing when t =exp(e). We detail a construction based on bucketization that realizes the previous implication; hence, as an ancillary result, we provide a new computational procedure to achieve t-closeness and e-differential privacy in data publishing.


title: Ubiquitous Document Capturing with Deep Learning

abstract: Digital and paper based documents co-exist in our daily lives. Seamless integration of information from both sources is crucial for efficient knowledge management. This paper address the algorithm that can handle the detection of document so that it can be captured easily to convert it into a digital form for automatic integration of relevant information in electronic work flows. It uses the deep learning technique to provide a solution which is more generalized and flexible than other available solutions.


title: Economic Agendas in Civil Wars: What We Know, What We Need to Know

abstract: Since the mid-1990s, the political economy of civil wars has acquired unprecedented relevance for scholars and policymakers dealing with pre-venting and mitigating armed conflict. The issue is now under scrutiny by non-governmental organizations (NGOs), research institutes, humanitarian and aid organizations, governments, international financial organizations (IFIs), and, importantly, the United Nations (UN). This chapter provides a tour d’horizon of this field, based largely on the research findings and policy debates that have emerged from the three-year project by the International Peace Academy (now the International Peace Institute) on Economic Agendas in Civil Wars (EACW), which concluded in 2004.


title: Conditional Random Field (CRF)-Boosting: Constructing a Robust Online Hybrid Boosting Multiple Object Tracker Facilitated by CRF Learning

abstract: Due to the reasonably acceptable performance of state-of-the-art object detectors, tracking-by-detection is a standard strategy for visual multi-object tracking (MOT). In particular, online MOT is more demanding due to its diverse applications in time-critical situations. A main issue of realizing online MOT is how to associate noisy object detection results on a new frame with previously being tracked objects. In this work, we propose a multi-object tracker method called CRF-boosting which utilizes a hybrid data association method based on online hybrid boosting facilitated by a conditional random field (CRF) for establishing online MOT. For data association, learned CRF is used to generate reliable low-level tracklets and then these are used as the input of the hybrid boosting. To do so, while existing data association methods based on boosting algorithms have the necessity of training data having ground truth information to improve robustness, CRF-boosting ensures sufficient robustness without such information due to the synergetic cascaded learning procedure. Further, a hierarchical feature association framework is adopted to further improve MOT accuracy. From experimental results on public datasets, we could conclude that the benefit of proposed hybrid approach compared to the other competitive MOT systems is noticeable.


title: Heterogeneous Face Recognition with CNNs

abstract: Heterogeneous face recognition aims to recognize faces across different sensor modalities. Typically, gallery images are normal visible spectrum images, and probe images are infrared images or sketches. Recently significant improvements in visible spectrum face recognition have been obtained by CNNs learned from very large training datasets. In this paper, we are interested in the question to what extent the features from a CNN pre-trained on visible spectrum face images can be used to perform heterogeneous face recognition. We explore different metric learning strategies to reduce the discrepancies between the different modalities. Experimental results show that we can use CNNs trained on visible spectrum images to obtain results that are on par or improve over the state-of-the-art for heterogeneous recognition with near-infrared images and sketches.


title: Surveillance Drone for Landmine Detection

abstract: Mine detection using a surveillance drone is a modern conceptual prototype, which has been designed to detect landmines. Landmines were primarily used to create defensive and tactical barriers during the Second World War. They are still very much employed in large quantities in countries such as Afghanistan, Korea. A lot of these land mines still go undetected, increasing the death rate and creating havoc on the surroundings. The prototype developed helps us to detect a landmine using a flying drone. The prototype has a quad copter which has a mine detector mounted on it. This utilizes two different modes of detection, which are an IR camera and a metal detector. These are extensively used in aiding this whole operation. The location of the mine can be traced back by the GPS and the detected location can be transmitted using the GSM module. There is a lot of untapped potential and scope of improvement for this prototype in the future. In this paper, we have successfully worked on a mine detector that performs functions of vested interest towards the military and commercial organizations.


title: Textual Analysis of Stock Market Prediction Using Financial News Articles

abstract: This paper examines the role of financial news articles on three different textual representations; Bag of Words, Noun Phrases, and Named Entities and their ability to predict discrete number stock prices twenty minutes after an article release. Using a Support Vector Machine (SVM) derivative, we show that our model had a statistically significant impact on predicting future stock prices compared to linear regression. We further demonstrate that using a Noun Phrase representation scheme performs better than the de facto standard of Bag of Words.


title: Large-scale community detection on youtube for topic discovery and exploration

abstract: Detecting coherent, well-connected communities in large graphs provides insight into the graph structure and can serve as the basis for content discovery. Clustering is a popular technique for community detection but global algorithms that examine the entire graph do not scale. Local algorithms are highly parallelizable but perform sub-optimally, especially in applications where we need to optimize multiple metrics. We present a multi-stage algorithm based on local-clustering that is highly scalable, combining a pre-processing stage, a lo- cal clustering stage, and a post-processing stage. We apply it to the YouTube video graph to generate named clusters of videos with coherent content. We formalize coverage, co- herence, and connectivity metrics and evaluate the quality of the algorithm for large YouTube graphs. Our use of local algorithms for global clustering, and its implementation and practical evaluation on such a large scale is a ﬁrst of its kind.


title: Party Polarization in Congress: A Network Science Approach

abstract: We measure polarization in the United States Congress using the network science concept of modularity. Modularity provides a conceptually-clear measure of polarization that reveals both the number of relevant groups and the strength of inter-group divisions without making restrictive assumptions about the structure of the party system or the shape of legislator utilities. We show that party influence on Congressional blocs varies widely throughout history, and that existing measures underestimate polarization in periods with weak party structures. We demonstrate that modularity is a significant predictor of changes in majority party and that turnover is more prevalent at medium levels of modularity. We show that two variables related to modularity, called `divisiveness' and `solidarity,' are significant predictors of reelection success for individual House members. Our results suggest that modularity can serve as an early warning of changing group dynamics, which are reflected only later by changes in party labels.


title: Generation capacity expansion in imperfectly competitive restructured electricity markets

abstract: We consider three models of investments in generation capacity in restructured electricity systems that differ with respect to their underlying economic assumptions. The first model assumes a perfect, competitive equilibrium. It is very similar to the traditional capacity expansion models even if its economic interpretation is different. The second model (open-loop Cournot game) extends the Cournot model to include investments in new generation capacities. This model can be interpreted as describing investments in an oligopolistic market where capacity is simultaneously built and sold in long-term contracts when there is no spot market. The third model (closed-loop Cournot game) separates the investment and sales decision with investment in the first stage and sales in the second stage-that is, a spot market. This two-stage game corresponds to investments in merchant plants where the first-stage equilibrium problem is solved subject to equilibrium constraints. We show that despite some important differences, the open- and closed-loop games share many properties. One of the important results is that the prices and quantities produced in the closed-loop game, when the solution exists, fall between the prices and quantities in the open-loop game and the competitive equilibrium.


title: Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization

abstract: We develop a family of accelerated stochastic algorithms that minimize sums of convex functions. Our algorithms improve upon the fastest running time for empirical risk minimization (ERM), and in particular linear least-squares regression, across a wide range of problem settings. To achieve this, we establish a framework based on the classical proximal point algorithm. Namely, we provide several algorithms that reduce the minimization of a strongly convex function to approximate minimizations of regularizations of the function. Using these results, we accelerate recent fast stochastic algorithms in a black-box fashion. Empirically, we demonstrate that the resulting algorithms exhibit notions of stability that are advantageous in practice. Both in theory and in practice, the provided algorithms reap the computational benefits of adding a large strongly convex regularization term, without incurring a corresponding bias to the original problem.


title: Construction and optimal search of interpolated motion graphs

abstract: Many compelling applications would become feasible if novice users had the ability to synthesize high quality human motion based only on a simple sketch and a few easily specified constraints. We approach this problem by representing the desired motion as an interpolation of two time-scaled paths through a motion graph. The graph is constructed to support interpolation and pruned for efficient search. We use an anytime version of A* search to find a globally optimal solution in this graph that satisfies the user's specification. Our approach retains the natural transitions of motion graphs and the ability to synthesize physically realistic variations provided by interpolation. We demonstrate the power of this approach by synthesizing optimal or near optimal motions that include a variety of behaviors in a single motion.


title: Wideband antenna array for Simultaneous Transmit and Receive (STAR) applications

abstract: A wideband antenna array for Simultaneous Transmit and Receive (STAR) applications is presented. The design is comprised of a ring array of TEM horns, and a monocone at the array's center. When the array is phased with the first order circular mode, it is isolated from the monocone. Thus, the array may be used in reception while the monocone is used in transmission, or vice versa. The array and monocone both produce quasi-omnidirectional patterns in the azimuthal planes. Simulations suggest that the design operates across an 8.4 : 1 bandwidth. This wide bandwidth is possible through the use of a novel capacitive feed employed in the TEM horn array.


title: Double-spend Attack Models with Time Advantange for Bitcoin

abstract: Bitcoin is a digital currency in which the need for a trusted third party is avoided. Instead, this digital currency is based on the concept of 'proof of work' allowing users to execute payments by digitally signing their transactions. Since electronic files can be duplicated, fraudulent transactions in the form of double-spend attacks - where users spend the same money at least twice - can happen. This paper is about attack models that can assign possible time advantage to attacker agents in the Bitcoin network. In particular, this paper presents: (i) two attack models in which partial advancement towards block production can be influenced by time and not only by the hashpower used to produce blocks of hashes, and (ii) algorithmic experimentation comparing these models against existing well-known hashrate-based attack models that do not consider time advantage. As a conclusion, this paper presents evidence on the fact that advantages are not negligible for cases in which an attacker has had enough time for secretly mining fraudulent blocks or significant control over the network. Also, the models presented in this paper help in supporting previous claims in the literature about how to correctly model and detect double-spend attacks in the Bitcoin network.


title: Adaptation in cloud resource configuration: a survey

abstract: With increased demand for computing resources at a lower cost by end-users, cloud infrastructure providers need to find ways to protect their revenue. To achieve this, infrastructure providers aim to increase revenue and lower operational costs. A promising approach to addressing these challenges is to modify the assignment of resources to workloads. This can be used, for example, to consolidate existing workloads; the new capability can be used to serve new requests or alternatively unused resources may be turned off to reduce power consumption. The goal of this paper is to highlight features, approaches and findings in the literature, in order to identify open challenges and facilitate future developments. We present a definition of cloud systems adaptation, a classification of the key features and a survey of adapting compute and storage configuration. Based on our analysis, we identify three open research challenges: characterising the workload type, accurate online profiling of workloads, and building highly scalable adaptation mechanisms.


title: A Review on Corpus Annotation for Arabic Sentiment Analysis

abstract: Mining publicly available data for meaning and value is an important research direction within social media analysis. Even for automatically analyzing collected textual data, a manual effort is needed for a successful machine learning algorithm to effectively classify text. Corpus annotation is labeling datasets with appropriate classes. There is a lack in the Arabic annotated corpus although Arabic is one of the languages that shows a fast uptake of sentiment analysis research, despite limited resources and scarce annotated corpora. In this paper, we review the most recent work on annotation carried out for papers focusing on Arabic sentiment analysis, between the years of 2010 and 2016.


title: Horizontal Dipole Located Close to Ground Plane With Bidirectional Endfire Radiation

abstract: In this letter, a single-element antenna backed by ground plane with unique endfire “8”-shaped patterns in both the azimuth ( E-plane) and elevation ( H-plane) planes is designed and experimentally demonstrated. The endfire radiation patterns benefit from the novel radiation mechanism of surface-wave diffraction. The bidirectional endfire antenna figures a low-profile configuration with overall dimensions of 0.8λ 0 ×0.8λ 0 ×0.04λ 0 , consisting of a horizontal dipole located close to a high impedance surface (HIS). Experimental results show that the antenna performs narrow-beamwidth patterns in both the E- and H-planes, resulting in a relatively high gain (maximum gain of 4.92 dBi) compared to a traditional dipole antenna.


title: A state-feedback approach to event-based control

abstract: This paper proposes a new method for event-based state-feedback control in which a control input generator mimics a continuous feedback between two consecutive event times. The performance of the event-based control system is evaluated by comparing this loop with the continuous state-feedback loop. An upper bound of the difference between both loops is derived, which shows that the approximation of the continuous state-feedback loop by the event-based control loop can be made arbitrarily tight by appropriately choosing the threshold parameter of the event generator.


title: Practical covertly secure mpc for dishonest majority or: Breaking the spdz limits. Cryptology ePrint Archive, Report 2012/642, 2012. http:// eprint.iacr.org

abstract: SPDZ (pronounced “Speedz”) is the nickname of the MPC protocol of Damgard et al. from Crypto 2012. In this paper we both resolve a number of open problems with SPDZ; and present several theoretical and practical improvements to the protocol. In detail, we start by designing and implementing a covertly secure key generation protocol for obtaining a BGV public key and a shared associated secret key. We then construct both a covertly and actively secure preprocessing phase, both of which compare favourably with previous work in terms of efficiency and provable security.


title: Ancestral Causal Inference

abstract: Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions. Several approaches to improve the reliability of the predictions by exploiting redundancy in the independence information have been proposed recently. Though promising, existing approaches can still be greatly improved in terms of accuracy and scalability. We present a novel method that reduces the combinatorial explosion of the search space by using a more coarse-grained representation of causal information, drastically reducing computation time. Additionally, we propose a method to score causal predictions based on their confidence. Crucially, our implementation also allows one to easily combine observational and interventional data and to incorporate various types of available background knowledge. We prove soundness and asymptotic consistency of our method and demonstrate that it can outperform the state-of-the-art on synthetic data, achieving a speedup of several orders of magnitude. We illustrate its practical feasibility by applying it on a challenging protein data set.


title: Opportunistic Piggyback Marking for IP Traceback

abstract: IP traceback is a solution for attributing cyber attacks, and it is also useful for accounting user traffic and network diagnosis. Marking-based traceback (MBT) has been considered a promising traceback approach, and has received considerable attention. However, we find that the traceback message delivery problem in MBT, which is important to the successful completion of a traceback, has not been adequately studied in the literature. To address this issue, we present the design, analysis, and evaluation of opportunistic piggyback marking (OPM) for IP traceback in this paper. The OPM distinguishes itself from the existing works by decoupling the traceback message content encoding and delivery functions in MBT, and efficiently achieves expedited and robust traceback message delivery by exploiting piggyback marking opportunities. Based on the proposed OPM scheme, we then present the flexible marking-based traceback framework, which is a novel design paradigm for IP traceback and has several favorable features for practical deployment of IP traceback. Through the numerical analysis and the comprehensive simulation evaluations, we demonstrate that our design effectively reduces the traceback completion delay and router processing overhead, and increases the message delivery ratio compared with other baseline approaches.