title: The posterior parietal cortex: Sensorimotor interface for the planning and online control of visually guided movements

abstract: We present a view of the posterior parietal cortex (PPC) as a sensorimotor interface for visually guided movements. Special attention is given to the role of the PPC in arm movement planning, where representations of target position and current hand position in an eye-centered frame of reference appear to be mapped directly to a representation of motor error in a hand-centered frame of reference. This mapping is direct in the sense that it does not require target position to be transformed into intermediate reference frames in order to derive a motor error signal in hand-centered coordinates. Despite being direct, this transformation appears to manifest in the PPC as a gradual change in the functional properties of cells along the ventro–dorsal axis of the superior parietal lobule (SPL), i.e. from deep in the sulcus to the cortical surface. Possible roles for the PPC in context dependent coordinate transformations, formation of intrinsic movement representations, and in online control of visually guided arm movements are also discussed. Overall these studies point to the emerging view that, for arm movements, the PPC plays a role not only in the inverse transformations required to convert sensory information into motor commands but also in ‘forward’ transformations as well, i.e. in integrating sensory input with previous and ongoing motor commands to maintain a continuous estimate of arm state that can be used to update present and future movement plans. Critically, this state estimate appears to be encoded in an eye-centered frame of reference. © 2005 Elsevier Ltd. All rights reserved.


title: Uptake of abdominal aortic aneurysm screening. A cohort study.

abstract: BACKGROUND ::: Abdominal aortic aneurysms (AAA) are responsible for 1.4% of UK deaths. Deprivation is a risk factor for AAA. Screening reduces AAA related mortality and is cost effective if uptake remains high. The Highland aneurysm screening programme (HASP) began in 2001 offering screening to men in a sparsely populated area. The aim was to identify whether uptake varies with deprivation or rurality, in the context of an established programme. ::: ::: ::: METHODS ::: Retrospective interrogation of HASP records was performed on all men offered screening from 2001 until 2010. Deprivation and rurality status were derived from postcode of residence (SIMD'09 and URC'08) and the relationships with screening uptake were examined. ::: ::: ::: RESULTS ::: Mean uptake over the decade was 90.1%. There was a strong association between deprivation and uptake, which ranged from 79.5% in the most deprived population to 97.5% in the least deprived (p < 0.001). The odds of men who were least deprived attending was 10.6 times higher than those who were most deprived (p < 0.001). Higher uptake was observed in more rural areas (p = 0.02). When combined in a logistic regression model, only deprivation remained significant, indicating any apparent effect of rurality was explained by deprivation. No change was observed in the mean aortic diameter of 65-year-old men or the incidence of AAA. ::: ::: ::: CONCLUSION ::: HASP has a high uptake even in the most deprived and rural populations, demonstrating that programme design has overcome any potential rural disadvantage. A gradient of uptake associated with deprivation remains, although even the most deprived have an uptake of almost 80%.


title: UAV position and attitude estimation using IMU, GNSS and camera

abstract: The aim of this paper is to present a method for integration of measurements provided by inertial sensors (gyroscopes and accelerometers), GPS and a video system in order to estimate position and attitude of an UAV (Unmanned Aerial Vehicle). Inertial sensors are widely used for aircraft navigation because they represent a low cost and compact solution, but their measurements suffer of several errors which cause a rapid divergence of position and attitude estimates. To avoid divergence inertial sensors are usually coupled with other systems as for example GNSS (Global Navigation Satellite System). In this paper it is examined the possibility to couple the inertial sensors also with a camera. A camera is generally installed on-board UAVs for surveillance purposes, it presents several advantages with respect to GNSS as for example great accuracy and higher data rate. Moreover, it can be used in urban area or, more in general, where multipath effects can forbid the application of GNSS. A camera, coupled with a video processing system, can provide attitude and position (up to a scale factor), but it has lower data rate than inertial sensors and its measurements have latencies which can prejudice the performances and the effectiveness of the flight control system. The integration of inertial sensors with a camera allows exploiting the better features of both the systems, providing better performances in position and attitude estimation.


title: Switched-Band Vivaldi Antenna

abstract: A novel Vivaldi antenna with added switched band functionality to operate in a wideband or narrowband mode is presented. The antenna reconfiguration is realized by inserting four pairs of switchable ring slots into the ground plane of the structure. A wide bandwidth mode from 1.0-3.2 GHz and three narrowband modes can be selected. A fully functional prototype with PIN diodes switches has been developed. Measured results shows good performance of the proposed designs. The antenna could be a suitable solution for a multimode application requiring wideband and frequency reconfigurable antennas, such as in military applications and cognitive radio.


title: Hoax News Detection on Social Media: A Survey

abstract: Information and Communication Technology (ICT) is a tool to spread and share news effectively. Social media is an Information and Communication Technology product which is a trend of future communication styles, and communication is all about an activity to share the news. The news shared on social media are not always incredible resources, or on the other hand, we can say that most of them are a hoax. According to this condition, research would like to explore what kind of method approach to detect hoax news. This research uses a survey approach to papers published during 2016–2018. By doing this work, we can know the kind of algorithms used for a similar research topic. The most popular approach according to this work is the Classification using Support Vector Machine (SVM), and the most used social media platform is Twitter.


title: Data mining tools

abstract: The development and application of data mining algorithms requires the use of powerful software tools. As the number of available tools continues to grow, the choice of the most suitable tool becomes increasingly difficult. This paper attempts to support the decision-making process by discussing the historical development and presenting a range of existing state-of-the-art data mining and related tools. Furthermore, we propose criteria for the tool categorization based on different user groups, data structures, data mining tasks and methods, visualization and interaction styles, import and export options for data and models, platforms, and license policies. These criteria are then used to classify data mining tools into nine different types. The typical characteristics of these types are explained and a selection of the most important tools is categorized. This paper is organized as follows: the first section Historical Development and State-of-the-Art highlights the historical development of data mining software until present; the criteria to compare data mining software are explained in the second section Criteria for Comparing Data Mining Software. The last section Categorization of Data Mining Software into Different Types proposes a categorization of data mining software and introduces typical software tools for the different types. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 431-443 DOI: 10.1002/widm.24


title: The Linux implementation of a log-structured file system

abstract: Toward enhancing the reliability of the Linux file system, we are developing a new log-structured file system (NILFS) for the Linux operating system. Instead of overwriting existing blocks, NILFS appends consistent sets of modified or newly created blocks continuously into segmented disk regions. This writing method allows NILFS to achieve faster recovery time and higher write performance. The address of the block that is written to changes for each write, which makes it difficult to apply modern file system technologies such as B-tree structures. To permit such writing on the Linux kernel basis, NILFS has its own write mechanism that handles data and meta data as one unit and allows them to be relocated. This paper presents the design and implementation of NILFS focussing on the write mechanism.


title: Self-Organising Maps in Computer Security

abstract: Some argue that biologically inspired algorithms are the future of solving difficult problems in computer science. Others strongly believe that the future lies in the exploration of mathematical foundations of problems at hand. The field of computer security tends to accept the latter view as a more appropriate approach due to its more workable validation and verification possibilities. The lack of rigorous scientific practices prevalent in biologically inspired security research does not aid in presenting bio-inspired security approaches as a viable way of dealing with complex security problems. This chapter introduces a biologically inspired algorithm, called the SelfOrganising Map (SOM), that was developed by Teuvo Kohonen in 1981. Since the algorithm’s inception it has been scrutinised by the scientific community and analysed in more than 4000 research papers, many of which dealt with various computer security issues, from anomaly detection, analysis of executables all the way to wireless network monitoring. In this chapter a review of security related SOM research undertaken in the past is presented and analysed. The algorithm’s biological analogies are detailed and the author’s view on the future possibilities of this successful bio-inspired approach are given. The SOM algorithm’s close relation to a number of vital functions of the human brain and the emergence of multi-core computer architectures are the two main reasons behind our assumption that the future of the SOM algorithm and its variations is promising, notably in the field of computer security.


title: Low-connectivity and full-coverage three dimensional wireless sensor networks

abstract: Low-connectivity and full-coverage three dimensional Wireless Sensor Networks (WSNs) have many real-world applications. By low connectivity, we mean there are at least k disjoint paths between any two sensor nodes in a WSN, where k ≤ 4. In this paper, we design a set of patterns for these networks. In particular, we design and prove the optimality of 1- and 2-connectivity patterns under any value of the ratio of communication range rc over sensing range rs, among regular lattice deployment patterns. We further propose a set of patterns to achieve 3- and 4-connectivity patterns and investigate the evolutions among all the proposed low-connectivity patterns. Finally, we study the proposed patterns under several practical settings.


title: Toward a Model of Domain-Specific Search

abstract: We examine what makes a search system domain-specific and find that previous definitions are incomplete. We propose a new definition of domain specific search, together with a corresponding model, to assist researchers, systems designers and system beneficiaries in their analysis of their own domain. This model is then instantiated for two domains: intellectual property search (i.e. patent search) and medical or healthcare search. For each of the two we follow the theoretical model and identify outstanding issues. We find that the choice of dimensions is still an open issue, as linear independence is often absent and specific use-cases, particularly those related to interactive IR, still cannot be covered by the proposed model.


title: The MGB challenge: Evaluating multi-genre broadcast media recognition

abstract: This paper describes the Multi-Genre Broadcast (MGB) Challenge at ASRU 2015, an evaluation focused on speech recognition, speaker diarization, and "lightly supervised" alignment of BBC TV recordings. The challenge training data covered the whole range of seven weeks BBC TV output across four channels, resulting in about 1,600 hours of broadcast audio. In addition several hundred million words of BBC subtitle text was provided for language modelling. A novel aspect of the evaluation was the exploration of speech recognition and speaker diarization in a longitudinal setting — i.e. recognition of several episodes of the same show, and speaker diarization across these episodes, linking speakers. The longitudinal tasks also offered the opportunity for systems to make use of supplied metadata including show title, genre tag, and date/time of transmission. This paper describes the task data and evaluation process used in the MGB challenge, and summarises the results obtained.


title: Multigranulation decision-theoretic rough sets

abstract: The Bayesian decision-theoretic rough sets propose a framework for studying rough set approximations using probabilistic theory, which can interprete the parameters from existing forms of probabilistic approaches to rough sets. Exploring rough sets in the viewpoint of multigranulation is becoming one of desirable directions in rough set theory, in which lower/upper approximations are approximated by granular structures induced by multiple binary relations. Through combining these two ideas, the objective of this study is to develop a new multigranulation rough set model, called a multigranulation decision-theoretic rough set. Many existing multigranulation rough set models can be derived from the multigranulation decision-theoretic rough set framework.


title: Binary Adsorption of Vapours in Active Carbons Described by the Dubinin Equation

abstract: It is shown that the use of the Dubinin–Radushkevich isotherm within the framework of the Myers–Prausnitz theory provides a satisfactory description of the adsorption of mixtures of chlorobenzene + carbon tetrachloride vapours by active carbons. This approach can be extended to other systems and full use can be made of the advantages of Dubinin's theory. The experimental data was obtained by Headspace-GC, a rapid and reliable technique.


title: Pegasus: a framework for mapping complex scientific workflows onto distributed systems

abstract: This paper describes the Pegasus framework that can be used to map complex scientific workflows onto distributed resources. Pegasus enables users to represent the workflows at an abstract level without needing to worry about the particulars of the target execution systems. The paper describes general issues in mapping applications and the functionality of Pegasus. We present the results of improving application performance through workflow restructuring which clusters multiple tasks in a workflow into single entities. A real-life astronomy application is used as the basis for the study.


title: Mid-Career professionals in STEM transiting to school teaching: Barriers at the border

abstract: In many countries there is a shortage of quality teachers in areas of science, technology, engineering and mathematics (STEM). Additional to the low levels of recruitment is an extraordinary high attrition rate with some 50% of beginning teachers leaving the profession within five years. One solution implemented in several countries has been to encourage mid-career professionals in the area of STEM to become school teachers. These professionals are said to bring to teaching enthusiasm, knowledge and a passion for their subject which will impact engagement and learning by students. However, these career-changers have constructed professional identities and are accustomed to working within a culture of collaboration and inquiry. In contrast, school cultures are quite different and often teaching is a lonely solitary affair with little opportunity for collegial relationships aimed at knowledge building in the context of teaching. Crossing from a culture of STEM to a culture of schools and teaching can be challenging. This study was conducted with 13 teachers who were followed for three years. However, this paper reports on the experiences of one teacher with an engineering background crossing the boundaries from practising STEM to Teaching STEM.


title: Detecting telecommunication fraud by understanding the contents of a call

abstract: Telecommunication fraud has continuously been causing severe financial loss to telecommunication customers in China for several years. Traditional approaches to detect telecommunication frauds usually rely on constructing a blacklist of fraud telephone numbers. However, attackers can simply evade such detection by changing their numbers, which is very easy to achieve through VoIP (Voice over IP). To solve this problem, we detect telecommunication frauds from the contents of a call instead of simply through the caller’s telephone number. Particularly, we collect descriptions of telecommunication fraud from news reports and social media. We use machine learning algorithms to analyze data and to select the high-quality descriptions from the data collected previously to construct datasets. Then we leverage natural language processing to extract features from the textual data. After that, we build rules to identify similar contents within the same call for further telecommunication fraud detection. To achieve online detection of telecommunication frauds, we develop an Android application which can be installed on a customer’s smartphone. When an incoming fraud call is answered, the application can dynamically analyze the contents of the call in order to identify frauds. Our results show that we can protect customers effectively.


title: Image denoising by non-local averaging

abstract: In this work, we present and analyze an image denoising method, the NL-means algorithm, based on a non local averaging of all pixels in the image. We also introduce the concept of method noise, that is, the difference between the original (always slightly noisy) digital image and its denoised version. Finally, we present some experiences comparing the NL-means results with some classical denoising methods.


title: A Method for Taxonomy Development and its Application in Information Systems

abstract: A fundamental problem in many disciplines is the classification of objects in a domain of interest into a taxonomy. Developing a taxonomy, however, is a complex process that has not been adequately addressed in the information systems (IS) literature. The purpose of this paper is to present a method for taxonomy development that can be used in IS. First, this paper demonstrates through a comprehensive literature survey that taxonomy development in IS has largely been ad hoc. Then the paper defines the problem of taxonomy development. Next, the paper presents a method for taxonomy development that is based on taxonomy development literature in other disciplines and shows that the method has certain desirable qualities. Finally, the paper demonstrates the efficacy of the method by developing a taxonomy in a domain in IS.


title: Deparaffinization and processing of pathologic material.

abstract: Methods for preparation of whole nuclei from paraffin embedded tissue are given. The combination of cell sorting of isolated nuclei combined with the polymerase chain reaction can be used to investigate other cellular parameters such as nuclear proteins and proliferation factors.


title: The design structure system: A method for managing the design of complex systems

abstract: Systems design involves the determination of interdependent variables. Thus the precedence ordering for the tasks of determining these variables involves circuits. Circuits require planning decisions about how to iterate and where to use estimates. Conventional planning techniques, such as critical path, do not deal with these problems. Techniques are shown which acknowledge these circuits in the design of systems. These techniques can be used to develop an effective engineering plan, showing where estimates are to be used, how design iterations and reviews are handled, and how information flows during the design work. This information flow can be used to determine the consequences of a change in any variable on the rest of the variables in the system, and thus which engineers must be informed and which documents must be changed. From this, a critical path schedule can be developed for implementing the change. This method is ideally suited to an automated design office where data, computer input and output, and communications are all handled through the use of computer terminals and data bases. However, these same techniques can also be effectively used in classical engineering environments.


A la francaise: Correct French for English Speakers


Introducing DRAIL – a Step Towards Declarative Deep Relational Learning


title: Developing and Validating Trust Measures for e-Commerce: An Integrative Typology

abstract: Evidence suggests that consumers often hesitate to transact with Web-based vendors because of uncertainty about vendor behavior or the perceived risk of having personal information stolen by hackers. Trust plays a central role in helping consumers overcome perceptions of risk and insecurity. Trust makes consumers comfortable sharing personal information, making purchases, and acting on Web vendor advice--behaviors essential to widespread adoption of e-commerce. Therefore, trust is critical to both researchers and practitioners. Prior research on e-commerce trust has used diverse, incomplete, and inconsistent definitions of trust, making it difficult to compare results across studies. This paper contributes by proposing and validating measures for a multidisciplinary, multidimensional model of trust in e-commerce. The model includes four high-level constructs--disposition to trust, institution-based trust, trusting beliefs, and trusting intentions--which are further delineated into 16 measurable, literature-grounded subconstructs. The psychometric properties of the measures are demonstrated through use of a hypothetical, legal advice Web site. The results show that trust is indeed a multidimensional concept. Proposed relationships among the trust constructs are tested (for internal nomological validity), as are relationships between the trust constructs and three other e-commerce constructs (for external nomological validity)--Web experience, personal innovativeness, and Web site quality. Suggestions for future research as well as implications for practice are discussed.


title: Road Extraction by Deep Residual U-Net

abstract: Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network, which combines the strengths of residual learning and U-Net, is proposed for road area extraction. The network is built with residual units and has similar architecture to that of U-Net. The benefits of this model are twofold: first, residual units ease training of deep networks. Second, the rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters, however, better performance. We test our network on a public road data set and compare it with U-Net and other two state-of-the-art deep-learning-based road extraction methods. The proposed approach outperforms all the comparing methods, which demonstrates its superiority over recently developed state of the arts.


title: Exposure: A White-Box Photo Post-Processing Framework

abstract: Retouching can significantly elevate the visual appeal of photos, but many casual photographers lack the expertise to do this well. To address this problem, previous works have proposed automatic retouching systems based on supervised learning from paired training images acquired before and after manual editing. As it is difficult for users to acquire paired images that reflect their retouching preferences, we present in this paper a deep learning approach that is instead trained on unpaired data, namely a set of photographs that exhibits a retouching style the user likes, which is much easier to collect. Our system is formulated using deep convolutional neural networks that learn to apply different retouching operations on an input image. Network training with respect to various types of edits is enabled by modeling these retouching operations in a unified manner as resolution-independent differentiable filters. To apply the filters in a proper sequence and with suitable parameters, we employ a deep reinforcement learning approach that learns to make decisions on what action to take next, given the current state of the image. In contrast to many deep learning systems, ours provides users with an understandable solution in the form of conventional retouching edits, rather than just a "black-box" result. Through quantitative comparisons and user studies, we show that this technique generates retouching results consistent with the provided photo set.


Speech Emotion Recognition with Emotion-Pair Based Framework Considering Emotion Distribution Information in Dimensional Emotion Space


title: Data envelopment analysis classification machine

abstract: This paper establishes the equivalent relationship between the data classification machine and the data envelopment analysis (DEA) model, and thus build up a DEA based classification machine. A data is characterized by a set of values. Without loss of the generality, it is assumed that the data with a set of smaller values is preferred. The classification is to label if a particular data belongs to a specified group according to a set of predetermined characteristic or attribute values. We treat such a data as a decision making unit (DMU) with these given attribute values as input and an artificial output of identical value 1. Then classifying a data is equivalent to testing if the DMU is in the production possibility set, called acceptance domain, constructed by a sample training data set. The proposed DEA classification machine consists of an acceptance domain and a classification function. The acceptance domain is given by an explicit system of linear inequalities. This makes the classification process computationally convenient. We then discuss the preference cone restricted classification process. The method can be applied to classifying large amount of data. Furthermore, the research finds that DEA classification machines based on different DEA models have the same format. Input-oriented and output-oriented DEA classification machines have similar properties. The method developed has great potential in practice with its computational efficiency.


title: On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets, and Beyond

abstract: We propose a generalization error bound for a general family of deep neural networks based on the depth and width of the networks, as well as the spectral norm of weight matrices. Through introducing a novel characterization of the Lipschitz properties of neural network family, we achieve a tighter generalization error bound. We further obtain a result that is free of linear dependence on norms for bounded losses. Besides the general deep neural networks, our results can be applied to derive new bounds for several popular architectures, including convolutional neural networks (CNNs), residual networks (ResNets), and hyperspherical networks (SphereNets). When achieving same generalization errors with previous arts, our bounds allow for the choice of much larger parameter spaces of weight matrices, inducing potentially stronger expressive ability for neural networks.


title: Colcemid treatment of myeloma prior to cell fusion increases the yield of hybridomas between myeloma and splenocyte.

abstract: Effect of Colcemid treatment of myeloma (X63-Ag8-6.5.3.) prior to fusion with mouse spleen cell was studied in terms of hybridoma formation. Spleen cells from BALB/c mice immunized with various soluble antigens were fused with the myeloma cells by using polyethylene glycol solution. Colcemid treatment of myeloma cells prior to fusion increased the average number of hybridoma colonies per well by 26-570%. The yield of hybridomas producing antigen-specific antibodies was also higher with the Colcemid treatment. The results suggest that most of the proliferative hybridomas are formed by fusion of cells in the M-phase of the cell cycle.


title: Design of 16T full adder circuit using 6TXNOR gates

abstract: Adders are most useful circuits for processor and microcontroller designs such as multipliers, shifters and other complex VLSI applications. This paper proposes design of 16T-one bit full adder circuit comprising of XNOR gates, pass transistors and transmission gate logic. The design includes two 6T XNOR gates and one 2:1 MUX. Functionality of the design is faithfully verified using Cadence Virtuoso Simulation tool using GPDK 180nm technology. The W/L ratio of 2μm/180nm is considered for all the transistors. The performance of 16T single bit Full Adder has been investigated and evaluated by the simulation results obtained from the improved design of XNOR gate and has resulted in improvement of power dissipation and output voltage.


title: Constructing and characterizing covert channels on GPGPUs

abstract: General Purpose Graphics Processing Units (GPGPUs) are present in most modern computing platforms. They are also increasingly integrated as a computational resource on clusters, data centers, and cloud infrastructure, making them possible targets for attacks. We present a first study of covert channel attacks on GPGPUs. GPGPU attacks offer a number of attractive properties relative to CPU covert channels. These channels also have characteristics different from their counterparts on CPUs. To enable the attack, we first reverse engineer the hardware block scheduler as well as the warp to warp scheduler to characterize how co-location is established. We exploit this information to manipulate the scheduling algorithms to create co-residency between the trojan and the spy. We study contention on different resources including caches, functional units and memory, and construct operational covert channels on all these resources. We also investigate approaches to increase the bandwidth of the channel including: (1) using synchronization to reduce the communication cycle and increase robustness of the channel; (2) exploiting the available parallelism on the GPU to increase the bandwidth; and (3) exploiting the scheduling algorithms to create exclusive co-location to prevent interference from other possible applications. We demonstrate operational versions of all channels on three different Nvidia GPGPUs, obtaining error-free bandwidth of over 4 Mbps, making it the fastest known microarchitectural covert channel under realistic conditions. CCS CONCEPTS • Security and privacy → Security in hardware; Hardware attacks and countermeasures; Hardware reverse engineering; • Computer systems organization → Single instruction, multiple data;


Predicting forest fire occurrence and incremental fire rate using SAS ® 9 . 4 and SAS ® Enterprise


title: A Consistent Regularization Approach for Structured Prediction

abstract: We propose and analyze a regularization approach for structured prediction problems. We characterize a large class of loss functions that allows to naturally embed structured outputs in a linear space. We exploit this fact to design learning algorithms using a surrogate loss approach and regularization techniques. We prove universal consistency and finite sample bounds characterizing the generalization properties of the proposed methods. Experimental results are provided to demonstrate the practical usefulness of the proposed approach.


title: On participation factors for linear systems

abstract: Participation factors are nondimensional scalars that measure the interaction between the modes and the state variables of a linear system. Since their introduction by Verghese, Perez-Arriaga and Schweppe, participation factors have been used for analysis, order reduction and controller design in a variety of fields. In this paper, participation factors are revisited, resulting in new definitions. The aim of these definitions is to achieve a conceptual framework that does not hinge on any particular choice of initial condition. The initial condition is modeled as an uncertain quantity, which can be viewed either in a set-valued or a probabilistic setting. If the initial condition uncertainty obeys a symmetry condition, the new definitions are found to reduce to the original definition of participation factors.


title: Fattiviracin A1, a novel antiherpetic agent produced by Streptomyces microflavus strain No. 2445. I. Taxonomy, fermentation, isolation, physico-chemical properties and structure elucidation

abstract: A novel antiherpetic agent, fattiviracin Al, was isolated from the culture broth of strain No. 2445 identified as Streptomyces microflavus, It was purified through 1-butanol extraction, column chromatographies on Diaion HP-10 and silica get and HPLC using a reverse phase column. The structure of fattiviracin A1 was determined by several spectroscopic experiments and chemical degradations. It is a new macrocyclic diester consisting of four D-glucose units and two (C 24 and C 33 ) hydroxy fatty acids. It is closely related to cycloviracins B 1 and B 2 , but differs from these known compounds in both the length of its side chain and the sugar moiety.


title: A higher order estimate of the optimum checkpoint interval for restart dumps

abstract: This paper examines methods of approximating the optimum checkpoint restart strategy for minimizing application run time on a system exhibiting Poisson single component failures. Two different models will be developed and compared. We will begin with a simplified cost function that yields a first-order model. Then we will derive a more complete cost function and demonstrate a perturbation solution that provides accurate high order approximations to the optimum checkpoint interval.


title: A Probabilistic Associative Model for Segmenting Weakly Supervised Images

abstract: Weakly supervised image segmentation is an important yet challenging task in image processing and pattern recognition fields. It is defined as: in the training stage, semantic labels are only at the image-level, without regard to their specific object/scene location within the image. Given a test image, the goal is to predict the semantics of every pixel/superpixel. In this paper, we propose a new weakly supervised image segmentation model, focusing on learning the semantic associations between superpixel sets (graphlets in this paper). In particular, we first extract graphlets from each image, where a graphlet is a small-sized graph measures the potential of multiple spatially neighboring superpixels (i.e., the probability of these superpixels sharing a common semantic label, such as the sky or the sea). To compare different-sized graphlets and to incorporate image-level labels, a manifold embedding algorithm is designed to transform all graphlets into equal-length feature vectors. Finally, we present a hierarchical Bayesian network to capture the semantic associations between postembedding graphlets, based on which the semantics of each superpixel is inferred accordingly. Experimental results demonstrate that: 1) our approach performs competitively compared with the state-of-the-art approaches on three public data sets and 2) considerable performance enhancement is achieved when using our approach on segmentation-based photo cropping and image categorization.


title: A structured observation of behavioral self-regulation and its contribution to kindergarten outcomes.

abstract: The authors examined a new assessment of behavioral regulation and contributions to achievement and teacher-rated classroom functioning in a sample (N 343) of kindergarteners from 2 geographical sites in the United States. Behavioral regulation was measured with the Head-Toes-Knees-Shoulders (HTKS) task, a structured observation requiring children to perform the opposite of a dominant response to 4 different oral commands. Results revealed considerable variability in HTKS scores. Evidence for construct validity was found in positive correlations with parent ratings of attentional focusing and inhibitory control and teacher ratings of classroom behavioral regulation. Hierarchical linear modeling indicated that higher levels of behavioral regulation in the fall predicted stronger levels of achievement in the spring and better teacher-rated classroom self-regulation (all ps .01) but not interpersonal skills. Evidence for domain specificity emerged, in which gains in behavioral regulation predicted gains in mathematics but not in language and literacy over the kindergarten year (p .01) after site, child gender, and other background variables were controlled. Discussion focuses on the importance of behavioral regulation for successful adjustment to the demands of kindergarten.


title: SMART: A process-oriented methodology for resilient smart cities

abstract: Cities are engines of economic prosperity and social development. Rapid urbanization and the impacts of climate change have resulted in increased vulnerabilities in cities. On the other hand, the increasing proliferation of connected devices and distributed monitoring of the environment around us has opened up an opportunity to transform the way we create and manage cities. Contextual evidence of performance, outcome and efficiency can now be readily collected at a higher resolution to aid multi-disciplinary and multi-objective decision-making, enabling optimal evolution of cities against the backdrop of constrained resources and intensified vulnerabilities. This paper first argues that distributed and ubiquitous monitoring is at the heart of smart cities. Insights can be inferred from the gathered data with potential for evidence-based decisions at the required spatial and temporal scales. The paper then discusses the development of a comprehensive but concise frameworks called DICES (data, insights, citizen, evidence and standards) for conceptualizing smart cities. The dimensions of DICES are then translated into a process oriented methodology called SMART (specify, monitor, analyze, resolve and transform) by formalizing key aspects of the smart city process. Generality and scalability of DICES and SMART are demonstrated through the development of REPRO, a risk- and evidence-based platform for resilient and optimal design of buildings and infrastructure in a smart city.


title: Narcissism, Shame, Masochism, and Object Relations: An Exploratory Correlational Study

abstract: A correlational study (N == 701) examined three measures of narcissism, a measure of shame, two measures of masochism, a measure of object relations, and a measure of social desirability. Moderate correlations were found for a core of constructs which have been described in the clinical and theoretical literature. These are narcissism, shame, object relations, and masochism. Narcissism seemed to divide into two different styles, a "phallic," grandiose style and a narcissistically vulnerable style. Shame primarily accounted for the differences in these styles, correlating negatively with the grandiose style, positively with the more vulnerable style. The narcissistically vulnerable style correlated more with the core pathology measures; that is, object relations and masochism. Social desirability did not mediate the relationship between grandiose narcissism and shame. Only small univariate gender differences were found, but masochism was a better predictor of shame in women than was narcissism whereas there was little difference between masochism and narcissism for predicting shame in men.


title: A Hybrid BCI System Combining P300 and SSVEP and Its Application to Wheelchair Control

abstract: In this paper, a hybrid brain-computer interface (BCI) system combining P300 and steady-state visual evoked potential (SSVEP) is proposed to improve the performance of asynchronous control. The four groups of flickering buttons were set in the graphical user interface. Each group contained one large button in the center and eight small buttons around it, all of which flashed at a fixed frequency (e.g., 7.5 Hz) to evoke SSVEP. At the same time, the four large buttons of the four groups were intensified through shape and color changes in a random order to produce P300 potential. During the control state, the user focused on a desired group of buttons (target buttons) to evoke P300 potential and SSVEP, simultaneously. Discrimination between the control and idle states was based on the detection of both P300 and SSVEP on the same group of buttons. As an application, this method was used to produce a “go/stop” command in real-time wheelchair control. Several experiments were conducted, and data analysis results showed that combining P300 potential and SSVEP significantly improved the performance of the BCI system in terms of detection accuracy and response time.


title: A Simple Instrument to Measure IT-Business Alignment Maturity

abstract: The challenge of aligning Information Technology (IT) to business has often been cited as a key issue by IT executives. This paper presents a simple, flexible, and easy-to-use instrument that measures the alignment maturity between business and IT and identifies major gaps. The proposed instrument is based on Luftman's “Strategy Alignment Maturity Model” (SAMM); it directly encodes all attributes of SAMM alignment areas using a unidimensional framework. The instrument supports multiple levels of analysis with minimum assumptions about data using non-parametric statistical tools. In addition, the instrument provides an aggregation procedure to summarize the alignment maturity level for high-level executives. The instrument can also be customized to incorporate the contextual parameters of a company. In addition to the development of the instrument, this paper also shows how this instrument was applied to assess the alignment maturity level between IT and business in a rapidly growing company that had recently been publicly listed. The instrument was successful in identifying six major gaps for the company across the various alignment areas. These gaps were benchmarking, business metrics, strategic business planning, inter / intra organizational learning, architectural integration, and the impact of IT on business processes.


FastHybrid: A Hybrid Model for Efficient Answer Selection


title: Drag and drop the apple: the semantic weight of words and images in touch-based interaction

abstract: In this paper we report a user study to investigate the effect of semantic weight in a touch-based drag and drop task. The study was motivated by our own interest in exploring potential factors that influence touch behavior and supported by results in related neuroscience research. The question we intended to answer is: "Do people drag the representation of a smaller and lighter real world object (e.g. an apple) different than the representation of a heavier and larger real world object (e.g. a car)?". Participants were asked to perform a drag and drop task repeatedly on a tablet device. Dragged objects were the same physical size on screen, but represented real world objects that were either heavy and large or light and small. We studied two representation modalities (i.e. image and text). In both representation modalities, semantically heavier objects were dragged significantly faster than semantically lighter objects.


title: The Normalized Compression Distance Is Resistant to Noise

abstract: This correspondence studies the influence of noise on the normalized compression distance (NCD), a measure based on the use of compressors to compute the degree of similarity of two files. This influence is approximated by a first order differential equation which gives rise to a complex effect, which explains the fact that the NCD may give values greater than 1, observed by other authors. The model is tested experimentally with good adjustment. Finally, the influence of noise on the clustering of files of different types is explored, finding that the NCD performs well even in the presence of quite high noise levels


title: Fast Marine Route Planning for UAV Using Improved Sparse A* Algorithm

abstract: This paper focuses on route planning, especially for unmanned aircrafts in marine environment. Firstly, new heuristic information is adopted such as threat-zone, turn maneuver and forbid-zone based on voyage heuristic information. Then, the cost function is normalized to obtain more flexible and reasonable routes. Finally, an improved sparse A* search algorithm is employed to enhance the planning efficiency and reduce the planning time. Experiment results showed that the improved algorithm for aircraft in maritime environment could find a combinational optimum route quickly, which detoured threat-zones, with fewer turn maneuver, totally avoiding forbid-zones, and shorter voyage.


title: Dynamic Software Birthmarks to Detect the Theft of Windows Applications Haruaki Tamada Keiji Okamoto Masahide Nakamura Akito Monden

abstract: This paper proposes dynamic software birthmarks which can be extracted during execution of Windows applications. Birthmarks are unique and native characteristics of software. For a pair of software p and q, if q has the same birthmarks as p’s, q is suspected as a copy of p. Our security analysis showed that the proposed birthmark has good tolerance against various kinds of program transformation attacks.


title: A Review on Architectures and Communications Technologies for Wearable Health-Monitoring Systems

abstract: Nowadays society is demanding more and more smart healthcare services that allow monitoring patient status in a non-invasive way, anywhere and anytime. Thus, healthcare applications are currently facing important challenges guided by the u-health (ubiquitous health) and p-health (pervasive health) paradigms. New emerging technologies can be combined with other widely deployed ones to develop such next-generation healthcare systems. The main objective of this paper is to review and provide more details on the work presented in “LOBIN: E-Textile and Wireless-Sensor-Network-Based Platform for Healthcare Monitoring in Future Hospital Environments”, published in the IEEE Transactions on Information Technology in Biomedicine, as well as to extend and update the comparison with other similar systems. As a result, the paper discusses the main advantages and disadvantages of using different architectures and communications technologies to develop wearable systems for pervasive healthcare applications.


title: Mesolimbic Functional Magnetic Resonance Imaging Activations during Reward Anticipation Correlate with Reward-Related Ventral Striatal Dopamine Release

abstract: The dopaminergic mechanisms that control reward-motivated behavior are the subject of intense study, but it is yet unclear how, in humans, neural activity in mesolimbic reward-circuitry and its functional neuroimaging correlates are related to dopamine release. To address this question, we obtained functional magnetic resonance imaging (fMRI) measures of reward-related neural activity and [11C]raclopride positron emission tomography measures of dopamine release in the same human participants, while they performed a delayed monetary incentive task. Across the cohort, a positive correlation emerged between neural activity of the substantia nigra/ventral tegmental area (SN/VTA), the main origin of dopaminergic neurotransmission, during reward anticipation and reward-related [11C]raclopride displacement as an index of dopamine release in the ventral striatum, major target of SN/VTA dopamine neurons. Neural activity in the ventral striatum/nucleus accumbens itself also correlated with ventral striatal dopamine release. Additionally, high-reward-related dopamine release was associated with increased activation of limbic structures, such as the amygdala and the hippocampus. The observed correlations of reward-related mesolimbic fMRI activation and dopamine release provide evidence that dopaminergic neurotransmission plays a quantitative role in human mesolimbic reward processing. Moreover, the combined neurochemical and hemodynamic imaging approach used here opens up new perspectives for the investigation of molecular mechanisms underlying human cognition.


title: Where's that phone?: geolocating IP addresses on 3G networks

abstract: Cell phones connected to high-speed 3G networks constitute an increasingly important class of clients on the Internet. From the viewpoint of the servers they connect to, such devices are virtually indistinguishable from conventional end-hosts. In this study, we examine the IP addresses seen by Internet servers for cell phone clients and make two observations. First, individual cell phones can expose different IP addresses to servers within time spans of a few minutes, rendering IP-based user identification and blocking inadequate. Second, cell phone IP addresses do not embed geographical information at reasonable fidelity, reducing the effectiveness of commercial geolocation tools used by websites for fraud detection, server selection and content customization. In addition to these two observations, we show that application-level latencies between cell phones and Internet servers can differ greatly depending on the location of the cell phone, but do not vary much at a given location over short time spans; as a result, they provide fine-grained location information that IPs do not.


title: Declarative Process Mining: Reducing Discovered Models Complexity by Pre-Processing Event Logs

abstract: The discovery of declarative process models by mining event logs aims to represent flexible or unstructured processes, making them visible to business and improving their manageability. Although promising, the declarative perspective may still produce models that are hard to understand, both due to their size and to the high number of restrictions of the process activities. This work presents an approach to reduce declarative model complexity by aggregating activities according to inclusion and hierarchy semantic relations. The approach was evaluated through a case study with an artificial event log and its results showed complexity reduction on the resulting hierarchical model.


title: Micropropagation of Bambusa balcooa Roxb. through axillary shoot proliferation

abstract: Bambusa balcooa is one of the most commercially important bamboo species. Regeneration of this species by sexual means is impossible because no seeds are set after flowering. Vegetative propagation is hindered due to bulky propagules, low rooting ability of the culm and branch cuttings, and seasonal specificity. This makes in vitro-based methods of regeneration important. This paper describes an efficient micropropagation protocol for multiplication of B. balcooa from nodal explants. Nodal segments were surface sterilized with 0.1% mercuric chloride for 10 min, and cultured on Murashige and Skoog (MS) medium supplemented with 4.4 μM 6-benzylaminopurine (BAP), 2.32 μM kinetin (Kn), and gelled with 0.2% w/v gelrite. Eighty-five percent of explants could be established in vitro with 90% of these achieving bud break. In vitro-formed shoots were successfully multiplied in MS liquid medium supplemented with 6.6 μM BAP, 2.32 μM Kn, 2.5% v/v coconut water, and 100 mg l−1 myo-inositol. Subculturing shoots every 3 wk yielded a consistent proliferation rate of 4.11-fold without decline in vigor. Shoot clusters, containing 5 to 8 shoots, were rooted with 87.5% success in 1/2 MS supplemented with 5.71 μM indole-3-acetic acid (IAA), 4.9 μM indole-3-butyric acid (IBA), and 5.37 μM naphthaleneacetic acid (NAA) within 3 wk. Plants regenerated in this manner were acclimatized in the greenhouse and under a shade net with 88% success.


Cluster Randomization


Estimating Ad Effectiveness using Geo Experiments in a Time-Based Regression Framework


title: Optimizing broadside array antenna with adaptive interference cancellation using amplitude-position perturbations in a linear array

abstract: Conventional broadside antennas can form a main lobe always towards the array normal. Their structures are simple, but they are not able to suppress interference. Implementing the function of the broadside array antenna with an adaptive antenna array is a possible approach to improve performance. The paper presents such an implementation. The performance can be improved because it is possible to null the interfering signals and maximize the main lobe towards the array normal at the same time. Thus the optimal radiation pattern can be obtained. Genetic algorithms are used for the search of the optimal radiation pattern by amplitude-position perturbations. Details of the structure of the system, radiation pattern formulation, the application of the genetic algorithm and simulation results are given.


title: Design and Implementation of a Low-Cost Real-Time Ultra-Wide Band See-Through-Wall Imaging Radar System

abstract: The technology of ultra wideband (UWB) spectrum is in a real expansion. One of the most promising applications is the See-Through-Wall (STW) imaging radar system for civilian and military use. The STW is based on the transmission and reception of UWB pulses in the time domain using synthetic aperture radar (SAR). A low-cost standalone real-time UWB STW radar system with customized and off-the-shelf components has been developed. The developed system is an affordable practical platform for, not only STW application, but also related UWB applications such as precise indoor localization. This paper is concerned with the implementation of this real-time STW system, and especially addresses the design of an efficient high-performance UWB data acquisition subsystem based on FPGSs.


title: Capture-Replay Testing for Android Applications

abstract: With the widespread popularity of Android devices, the number of Android applications has increased dramatically in recent years. In order to assure the quality of the applications, Android testing has drawn extensive attention. This paper proposes an approach to automate the testing of Android applications based on the Capture and Replay method. Particularly, the user events of Android applications are captured and converted into Robotium test scripts that can be executed to replay the recorded actions of users. The approach also allows inserting assertions when capturing user interactions for verifying the outputs of Android UI components. A supporting tool is implemented to illustrate the usefulness of the proposed approach.


title: The economics of pensions : principles, policies, and international experience

abstract: 1. Introduction and overview Salvador Valdes-Prieto Part I. The Politics of Mandatory Pensions: 2. Insulation of pensions from political risk Peter Diamond 3. Democracy and pensions in Chile: experience with two systems Oscar Godoy and Salvador Valdes-Prieto 4. Public pension governance and performance Olivia Mitchell and Ping-Lung Hsin Part II. Fiscal Deficits and Private Saving in Pension Reform: 5. Pension reform and growth Gian Carlo Corsetti and Klaus Schmidt-Hebbel 6. Pension reforms in the presence of credit constraints Rodrigo Cifuentes and Salvador Valdes-Prieto 7. Financing a pension reform towards private funded pensions Salvador Valdes-Prieto Part III. Macroeconomic Policy and Private Pensions: 8. Pension funds, capital controls, and macroeconomic stability Helmut Reisen and John Williamson 9. Are there (good) macroeconomic reasons for limiting external investments by pension funds? The Chilean experience Juan Andres Fontaine Part IV. Regulation of Privately-Managed Pension Systems: 10. Pension choice and pensions policy in the United Kingdom David Blake 11. Mandatory retirement saving: Australia and Malaysia compared Hazel Bateman and John Piggott 12. Conclusion: public pension plans in international perspective: problems, reforms and research issues Estelle James.


title: The social organization of the common vampire bat H . Mating system , genetic structure , and relatedness

abstract: Variability at seven polymorphic allozyme loci and observations of dispersal and mating provide evidence for nonrandom genetic structure among adult female groups of the highly social bat, Desmodus rotundus. The average degree of relatedness, estimated by allelic correlations at each locus, within three and six groups of females is 0.018 (SE=0.013) and 0.032 (SE=0.023), respectively. Even though these estimates do not differ significantly from zero, a multivariate analysis of variance of individual allele frequencies reveals that three of six pairwise comparisons of groups reach significance. This genetic heterogeneity within a population does not lead to increased genetic subdivision between populations. Mean classificatory ability of the discriminant functions drops from 84% for assignment to group to 56% for assignment to population. This pattern of genetic variability is due to recruitment of female offspring into their natal groups and forced male dispersal. Occasional movements of unrelated females between groups lead to the formation of multiple matrilines within groups. Although males fight viciously for access to the top of preferred female roosting sites and top males mate preferentially with females in that roost, mean maximum paternity for top males is only 46%. Consequently, male mating success is sufficiently random to maintain gametic equilibria among all pairs of loci. Given an infant mortality of 54%, mean male tenure of 17 months, and a birth interval of 10 months, females are unlikely to be related through common male ancestors. In one group, the average degree of relatedness, derived from matrilineal pedigrees, is 0.11 (SD=0.17). Computer simulations of the growth of a group of female D. routundus show that the low level of relatedness within groups is expected even if the proportion of unrelated females allowed into a group decreases. This pattern holds for any animal which recruits one sex into its social group and has relatively high juvenile mortality followed by low adult mortality.


title: Efficacy and safety of over-the-counter whitening strips as compared to home-whitening with 10 % carbamide peroxide gel—systematic review of RCTs and metanalysis

abstract: OBJECTIVES ::: The study aims to compare the efficacy and safety of over-the-counter whitestrips with the American Dental Association (ADA)-recommended home-whitening using the 10 % carbamide peroxide gel. ::: ::: ::: MATERIALS AND METHODS ::: Randomized controlled trials (RCTs) comparing the clinical efficacy and safety of the whitestrips with the 10 % carbamide peroxide (10 % CP) gel applied on tray for tooth whitening in adults were searched at PubMed and Cochrane Central Register of Controlled Trials databases and selected up to October 2014. Efficacy of the whitening techniques was assessed through ∆E, ∆L, and ∆b parameters, while side effects were analyzed as dichotomous variables. Data was extracted independently by two reviewers. Metanalysis was performed using random- and fixed-effect models (RevMan 5.3). ::: ::: ::: RESULTS ::: Eight studies were included in the metanalysis. The metanalysis revealed no significant difference between the intervention groups for tooth-whitening efficacy measured as ΔE (mean difference [MD]-0.53; 95 % CI [-1.72;0.66]; Z = 0.88; p = 0.38) and ΔL (MD-0.22; 95 % CI [-0.81;0.36]; z = 0.75; p = 0.45); reduction of yellowing was higher with the whitestrips (MD-0.47; 95 % CI [-0.89; -0.06]; Z = 2.25; p = 0.02). Tooth sensitivity (risk ratio [RR] 1.17; 95 % CI [0.81-1.69]; Z = 0.81; p = 0.42) and gingival sensitivity (RR 0.76; 95 % CI [0.53-1.10]; Z = 1.44; p = 0.15) were similar, regardless of the whitening method used. The observed gingival irritation was higher when the 10 % CP gel was applied on tray (RR 0.43; 95 % CI [0.20-0.93]; Z = 2.14; p = 0.03). The quality of evidence generated was rated very low for all outcomes. ::: ::: ::: CONCLUSIONS ::: There is no sound evidence to support the use of the whitening strips in detriment of the ADA-recommended technique based on the 10 % carbamide peroxide gel applied on tray. ::: ::: ::: CLINICAL RELEVANCE ::: To the moment, there is no sound evidence in dental literature to suggest that the ADA-recommended whitening technique based on 10 % carbamide peroxide gel could be substituted by the whitening strips. The existing studies, with their limitations, revealed similar tooth whitening and tooth and gingival sensitivity for both whitening techniques.


title: Averaged Modeling of PWM Converters Operating in Discontinuous Conduction Mode

abstract: Various aspects of averaged modeling of hard-switching pulse-width modulated (PWM) converters operating in the discontinuous conduction mode (DCM) are studied. A more streamlined modeling procedure is proposed which serves as a general framework for comparing different models. A duty ratio constraint that defines the diode conduction interval is identified to be the key to accurate prediction of high-frequency behavior. A new duty-ratio constraint is proposed that leads to full-order averaged models of DCM converters. Numerical analyses and experimental measurements confirm that the new models correctly predict the small-signal responses up to one third of the switching frequency and are more accurate than all previous models. Moreover, new analytical results are included to show the origin of the high-frequency pole in DCM operation and to explain why the full-order model is capable of accurately predicting it. Averaged circuit counterparts of the new models are developed in the form of averaged switch models to facilitate circuit simulation.


title: Developing Simplified Chinese Psychological Linguistic Analysis Dictionary for Microblog

abstract: The words that people use could reveal their emotional states, intentions, thinking styles, individual differences, etc. LIWC (Linguistic Inquiry and Word Count) has been widely used for psychological text analysis, and its dictionary is the core. The Traditional Chinese version of LIWC dictionary has been released, which is a translation of LIWC English dictionary. However, Simplified Chinese which is the world's most widely used language has subtle differences with Traditional Chinese. Furthermore, both English LIWC dictionary and Traditional Chinese version dictionary were both developed for relatively formal text. Microblog has become more and more popular in China nowadays. Original LIWC dictionaries take less consideration on microblog popular words, which makes it less applicable for text analysis on microblog. In this study, a Simplified Chinese LIWC dictionary is established according to LIWC categories. After translating Traditional Chinese dictionary into Simplified Chinese, five thousand words most frequently used in microblog are added into the dictionary. Four graduate students of psychology rated whether each word belonged in a category. The reliability and validity of Simplified Chinese LIWC dictionary were tested by these four judges. This new dictionary could contribute to all the text analysis on microblog in future.


title: Data Analysis of Digital Currency Networks: Namecoin Case Study

abstract: Financial transaction networks are some of the largest networks in existence. A relatively new type of financial networks is the digital (crypto) currency network, e.g., Bitcoin. Namecoin is an alternative crypto currency, based on Bitcoin, with additional features such as DNS. Namecoin network has more than 2 million nodes and almost 17 million edges. The analysis of such a crypto currency network can help us model or predict the future growth of the transaction networks. In order to analyze the transaction network graph over time, we analyzed the Namecoin blockchain data in 7 six months intervals. Our findings suggest different user behavior and developing pattern compared to Bitcoin.


title: Ambient point clouds for view interpolation

abstract: View interpolation and image-based rendering algorithms often produce visual artifacts in regions where the 3D scene geometry is erroneous, uncertain, or incomplete. We introduce ambient point clouds constructed from colored pixels with uncertain depth, which help reduce these artifacts while providing non-photorealistic background coloring and emphasizing reconstructed 3D geometry. Ambient point clouds are created by randomly sampling colored points along the viewing rays associated with uncertain pixels. Our real-time rendering system combines these with more traditional rigid 3D point clouds and colored surface meshes obtained using multiview stereo. Our resulting system can handle larger-range view transitions with fewer visible artifacts than previous approaches.


title: Novel Word-sense Identification

abstract: Automatic lexical acquisition has been an active area of research in computational linguistics for over two decades, but the automatic identification of new word-senses has received attention only very recently. Previous work on this topic has been limited by the availability of appropriate evaluation resources. In this paper we present the largest corpus-based dataset of diachronic sense differences to date, which we believe will encourage further work in this area. We then describe several extensions to a state-of-the-art topic modelling approach for identifying new word-senses. This adapted method shows superior performance on our dataset of two different corpus pairs to that of the original method for both: (a) types having taken on a novel sense over time; and (b) the token instances of such novel senses.


title: Optical flow modeling and computation

abstract: We propose a survey of optical flow estimation focusing on recent developments.We adopt a classification approach organizing methods in a comprehensive framework.The paper is conceived as a tutorial introducing and explaining the main concepts. Optical flow estimation is one of the oldest and still most active research domains in computer vision. In 35years, many methodological concepts have been introduced and have progressively improved performances, while opening the way to new challenges. In the last decade, the growing interest in evaluation benchmarks has stimulated a great amount of work. In this paper, we propose a survey of optical flow estimation classifying the main principles elaborated during this evolution, with a particular concern given to recent developments. It is conceived as a tutorial organizing in a comprehensive framework current approaches and practices. We give insights on the motivations, interests and limitations of modeling and optimization techniques, and we highlight similarities between methods to allow for a clear understanding of their behavior.


title: SortMeRNA: Fast and accurate filtering of ribosomal RNAs in metatranscriptomic data

abstract: MOTIVATION ::: The application of next-generation sequencing (NGS) technologies to RNAs directly extracted from a community of organisms yields a mixture of fragments characterizing both coding and non-coding types of RNAs. The task to distinguish among these and to further categorize the families of messenger RNAs and ribosomal RNAs (rRNAs) is an important step for examining gene expression patterns of an interactive environment and the phylogenetic classification of the constituting species. ::: ::: ::: RESULTS ::: We present SortMeRNA, a new software designed to rapidly filter rRNA fragments from metatranscriptomic data. It is capable of handling large sets of reads and sorting out all fragments matching to the rRNA database with high sensitivity and low running time.


title: The psychometric properties of Beck Depression Inventory for adolescent depression in a primary-care paediatric setting in India

abstract: BackgroundThere is increasing interest in identifying adolescents with depression in primary care settings by paediatricians in India. This article studied the diagnostic accuracy, reliability and validity of Beck Depression Inventory (BDI) while used by paediatricians in a primary care setting in India.Methods181 adolescents attending 3 schools were administered a back translated Tamil version of BDI by a paediatrician to evaluate its psychometric properties along with Children's Depression Rating Scale (CDRS-R) for convergent validity. Clinical diagnosis of depressive disorders, for reference standard, was based on ICD-10 interview by an independent psychiatrist who also administered the Impact of Event Scale (IES) for divergent validity. Appropriate analyses for validity and diagnostic accuracy both at the item and scale levels were conducted.ResultsA cut-off score of ≥ 5 (Sn = 90.9%, Sp = 17.6 %) for screening and cut-off score of ≥ 22 (Sn = 27.3%, Sp = 90%) for diagnostic utility is suggested. The 4 week test – retest reliability was good (r = 0.82). In addition to the adequate face and content validity, BDI has very good internal consistency (α = 0.96), high convergent validity with CDRS-R (r = 0.72; P = 0.001), and high discriminant validity with IES (r = 0.26; P = 0.23). There was a moderate concordance rate with the reference standard (54.5%) in identifying depression among the adolescents. Factor analysis replicated the 2-factor structure explaining 30.5 % of variance.ConclusionThe BDI proved to be a psychometrically sound measure for use by paediatricians in a primary care setting in India. The possibility of screening for depressive disorders through the use of BDI may be helpful in identifying probable cases of the disorder among adolescents.


title: Broadband Long Rectangular Patch Antenna With High Gain and Vertical Polarization

abstract: A new microstrip rectangular patch antenna with high gain and vertically polarized radiation at endfires is proposed. The antenna has a wide bandwidth with a very low profile. Such antenna is based on a long microstrip line with periodic shorting-vias along the longitudinal direction. When both ends of the line are also shorted by vias, a resonant type of antenna is constructed. Two resonant modes with similar radiation patterns are generated. By optimizing the separation of the resonate frequencies of the two modes, a wide bandwidth is achieved. Measured results show that the antenna with a profile of 0.03 wavelengths in free space and a length of about 1.25 wavelengths can produce a bandwidth of 12.48% and a maximum gain of about 9 dBi.


title: A Network Coding Approach to Secret Key Distribution

abstract: We consider the problem of secret key distribution in a sensor network with multiple scattered sensor nodes and a mobile device that can be used to bootstrap the network. Our main contribution is a set of secure protocols that rely on simple network coding operations to provide a robust and low-complexity solution for sharing secret keys among sensor nodes, including pairwise keys, cluster keys, key revocation, and mobile node authentication. Despite its role as a key enabler for this approach, the mobile node only has access to an encrypted version of the keys, providing information-theoretic security with respect to attacks focused on the mobile node. Our results include performance evaluation in terms of security metrics and a detailed analysis of resource utilization. The basic scheme was implemented and tested in a real-life sensor network testbed. We deem this class of network coding protocols to be particularly well suited for highly constrained dynamic systems such as wireless sensor networks.


title: A new ZVS-PWM full-bridge converter

abstract: A full-bridge converter which employs a coupled inductor to achieve zero-voltage switching of the primary switches in the entire line and load range is described. Because the coupled inductor does not appear as a series inductance in the load current path, it does not cause a loss of duty cycle or severe voltage ringing across the output rectifier. The operation and performance of the proposed converter is verified on a 670 W prototype.


title: Learning When to Use a Decomposition

abstract: Applying a Dantzig-Wolfe decomposition to a mixed-integer program (MIP) aims at exploiting an embedded model structure and can lead to significantly stronger reformulations of the MIP. Recently, automating the process and embedding it in standard MIP solvers have been proposed, with the detection of a decomposable model structure as key element. If the detected structure reflects the (usually unknown) actual structure of the MIP well, the solver may be much faster on the reformulated model than on the original. Otherwise, the solver may completely fail. We propose a supervised learning approach to decide whether or not a reformulation should be applied, and which decomposition to choose when several are possible. Preliminary experiments with a MIP solver equipped with this knowledge show a significant performance improvement on structured instances, with little deterioration on others.


title: Privacy Concerns Versus Desire for Interpersonal Awareness in Driving the Use of Self-Disclosure Technologies: The Case of Instant Messaging in Two Cultures

abstract: Social computing technologies typically have multiple features that allow users to reveal their personal information to other users. Such self-disclosure (SD) behavior is generally considered positive and beneficial in interpersonal communication and relationships. Using a newly proposed model based on social exchange theory, this paper investigates and empirically validates the relationships between SD technology use and culture. In particular, we explore the effects of culture on information privacy concerns and the desire for online interpersonal awareness, which influence attitudes toward, intention to use, and actual use of SD technologies. Our model was tested using arguably the strongest social computing technology for online SD-instant messaging (IM)-with users from China and the United States. Our findings reveal that cross-cultural dimensions are significant predictors of information privacy concerns and desire for online awareness, which are, in turn, found to be predictors of attitude toward, intention to use, and actual use of IM. Overall, our proposed model is applicable to both cultures. Our findings enhance the theoretical understanding of the effects of culture and privacy concerns on SD technologies and provide practical suggestions for developers of SD technologies, such as adding additional control features to applications.


title: The Ties That Bind: Measuring the Strength of Consumers’ Emotional Attachments to Brands

abstract: Extant research suggests that consumers can become emotionally attached to consumption objects, including brands. However, a scale to measure the strength of consumers’ emotional attachments to brands has yet to be devised. We develop such a scale in Studies 1 and 2. Study 3 validates the scale's internal consistency and dimensional structure. Study 4 examines its convergent validity with respect to four behavioral indicators of attachments. Study 5 demonstrates discriminant validity, showing that the scale is differentiated from measures of satisfaction, involvement, and brand attitudes. That study also examines the scale's predictive validity, showing that it is positively associated with indicators of both commitment and investment. The limitations of the scale and the boundary conditions of its applicability are also discussed.


title: Bayesian Model Averaging of Bayesian Network Classifiers for Intrusion Detection

abstract: Bayesian network (BN) classifiers with powerful reasoning capabilities have been increasingly utilized to detect intrusion with reasonable accuracy and efficiency. However, existing BN classifiers for intrusion detection suffer two problems. First, such BN classifiers are often trained from data using heuristic methods that usually select suboptimal models. Second, the classifiers are trained using very large datasets which may be time consuming to obtain in practice. When the size of training dataset is small, the performance of a single BN classifier is significantly reduced due to its inability to represent the whole probability distribution. To alleviate these problems, we build a Bayesian classifier by Bayesian Model Averaging(BMA) over the k-best BN classifiers, called Bayesian Network Model Averaging (BNMA) classifier. We train and evaluate BNMA classifier on the NSL-KDD dataset, which is less redundant, thus more judicial than the commonly used KDD Cup 99 dataset. We show that the BNMA classifier performs significantly better in terms of detection accuracy than the Naive Bayes classifier and the BN classifier built with heuristic method. We also show that the BNMA classifier trained using a smaller dataset outperforms two other classifiers trained using a larger dataset. This also implies that the BNMA is beneficial in accelerating the detection process due to its less dependance on the potentially prolonged process of collecting large training datasets.


Reflections on Manner/Result Complementarity


title: Applications of data hiding in digital images

abstract: Summary form only given, as follows. The author introduces data hiding in digital imagery as a new and powerful technology with applications including robust digital watermarking of images for copyright protection, image fingerprinting and authentication, tamper detection in digital imagery, covert (invisible) communication using images, access control, copy control in DVDs, embedding subtitles and audio tracks in video signals, etc. Data hiding is a highly multidisciplinary field that combines image and signal processing with cryptography, communication theory, coding theory, signal compression, and the theory of visual perception.


title: Band-pass shielding enclosure for 2.4/5 GHz WLAN applications

abstract: This paper presents a design of band-pass shielding enclosure for two frequency bands of WLAN. The proposed design employs a compact frequency selective surface to highlight the advantages of these miniaturized structures for the selective shielding of such enclosures that are electrically small. The results indicate that the proposed shielding enclosure has high transmittance in both the pass bands for horizontal and vertical polarizations, while offers a good amount of electromagnetic shielding outside these frequency bands.


title: Quantitative fuzzy semantics

abstract: The point of departure in this paper is the definition of a language, L, as a fuzzy relation from a set of terms, T = x, to a universe of discourse, U = y. As a fuzzy relation, L is characterized by its membership function @m"L:T x U -> [0,1], which associates with each ordered pair (x,y) its grade of membership, @m"L(x,y), in L. Given a particular x in T, the membership function @m"L(x,y) defines a fuzzy set, M(x), in U whose membership function is given by @m"M"("x")(y) = @m"L(x,y). The fuzzy set M(x) is defined to be the meaning of the term x, with x playing the role of a name for M(x). If a term x in T is a concatenation of other terms in T, that is, x = x"1 ... x"n, x"i @e T, i = 1,...,n, then the meaning of x can be expressed in terms of the meanings of x"1,...,x"n through the use of a lambda-expression or by solving a system of equations in the membership functions of the x"i which are deduced from the syntax tree of x. The use of this approach is illustrated by examples.


title: Do not cite or quote Early Childhood Poverty and Adult Attainment, Behavior and Health 1

abstract: This article assesses the consequences of poverty between a child’s prenatal year and 5th birthday for several adult achievement, health, and behavior outcomes, measured as late as age 37. Using data from the Panel Study of Income Dynamics (1,589) and controlling for economic conditions in middle childhood and adolescence, as well as demographic conditions at the time of the birth, findings indicate statistically significant and, in some cases, quantitatively large detrimental effects of early poverty on a number of attainment-related outcomes (adult earnings and work hours). Early-childhood poverty was not associated with such behavioral measures as out-of-wedlock childbearing and arrests. Most of the adult earnings effects appear to operate through early poverty’s association with adult work hours.


title: The epidemiology of bacterial vaginosis in relation to sexual behaviour

abstract: BackgroundBacterial vaginosis (BV) has been most consistently linked to sexual behaviour, and the epidemiological profile of BV mirrors that of established sexually transmitted infections (STIs). It remains a matter of debate however whether BV pathogenesis does actually involve sexual transmission of pathogenic micro-organisms from men to women. We therefore made a critical appraisal of the literature on BV in relation to sexual behaviour.DiscussionG. vaginalis carriage and BV occurs rarely with children, but has been observed among adolescent, even sexually non-experienced girls, contradicting that sexual transmission is a necessary prerequisite to disease acquisition. G. vaginalis carriage is enhanced by penetrative sexual contact but also by non-penetrative digito-genital contact and oral sex, again indicating that sex per se, but not necessarily coital transmission is involved. Several observations also point at female-to-male rather than at male-to-female transmission of G. vaginalis, presumably explaining the high concordance rates of G. vaginalis carriage among couples. Male antibiotic treatment has not been found to protect against BV, condom use is slightly protective, whereas male circumcision might protect against BV. BV is also common among women-who-have-sex-with-women and this relates at least in part to non-coital sexual behaviours. Though male-to-female transmission cannot be ruled out, overall there is little evidence that BV acts as an STD. Rather, we suggest BV may be considered a sexually enhanced disease (SED), with frequency of intercourse being a critical factor. This may relate to two distinct pathogenetic mechanisms: (1) in case of unprotected intercourse alkalinisation of the vaginal niche enhances a shift from lactobacilli-dominated microflora to a BV-like type of microflora and (2) in case of unprotected and protected intercourse mechanical transfer of perineal enteric bacteria is enhanced by coitus. A similar mechanism of mechanical transfer may explain the consistent link between non-coital sexual acts and BV. Similar observations supporting the SED pathogenetic model have been made for vaginal candidiasis and for urinary tract infection.SummaryThough male-to-female transmission cannot be ruled out, overall there is incomplete evidence that BV acts as an STI. We believe however that BV may be considered a sexually enhanced disease, with frequency of intercourse being a critical factor.


title: Deceiving Cyber Adversaries: A Game Theoretic Approach

abstract: An important way cyber adversaries find vulnerabilities in modern networks is through reconnaissance, in which they attempt to identify configuration specifics of network hosts. To increase uncertainty of adversarial reconnaissance, the network administrator (henceforth, defender) can introduce deception into responses to network scans, such as obscuring certain system characteristics. We introduce a novel game theoretic model of deceptive interactions of this kind between a defender and a cyber attacker, which we call the Cyber Deception Game. We consider both a powerful (rational) attacker, who is aware of the defender's exact deception strategy, and a naive attacker who is not. We show that computing the optimal deception strategy is NP-hard for both types of attackers. For the case with a powerful attacker, we provide a mixed-integer linear program solution as well as a fast and effective greedy algorithm. Similarly, we provide complexity results and propose exact and heuristic approaches when the attacker is naive. Our extensive experimental analysis demonstrates the effectiveness of our approaches.


title: Learning full pairwise affinities for spectral segmentation

abstract: Segmenting a single image into multiple coherent groups remains a challenging task in the field of computer vision. Particularly, spectral segmentation which uses the global information embedded in the spectrum of a given image's affinity matrix is a major trend in image segmentation. This paper focuses on the problem of efficiently learning a full range of pairwise affinities gained by integrating local grouping cues for spectral segmentation. We first construct a sparse multilayer graph whose nodes are both the pixels and the oversegmented regions obtained by an unsupervised segmentation algorithm. By applying the semi-supervised learning strategy to this graph, the intra and interlayer affinities between all pairs of nodes can be estimated without iteration. These pairwise affinities are then applied into the spectral segmentation algorithms. In this paper, two types of spectral segmentation algorithms are introduced: K-way segmentation and hierarchical segmentation. Our algorithms provide high-quality segmentations which preserve object details by directly incorporating the full-range connections. Moreover, since our full affinity matrix is defined by the inverse of a sparse matrix, its eigendecomposition can be efficiently computed. The experimental results on the BSDS and MSRC image databases demonstrate the superiority of our segmentation algorithms in terms of relevance and accuracy compared with existing popular methods.


title: Application of bagging, boosting and stacking to intrusion detection

abstract: This paper investigates the possibility of using ensemble algorithms to improve the performance of network intrusion detection systems. We use an ensemble of three different methods, bagging, boosting and stacking, in order to improve the accuracy and reduce the false positive rate. We use four different data mining algorithms, naive bayes, J48 (decision tree), JRip (rule induction) and iBK( nearest neighbour), as base classifiers for those ensemble methods. Our experiment shows that the prototype which implements four base classifiers and three ensemble algorithms achieves an accuracy of more than 99% in detecting known intrusions, but failed to detect novel intrusions with the accuracy rates of around just 60%. The use of bagging, boosting and stacking is unable to significantly improve the accuracy. Stacking is the only method that was able to reduce the false positive rate by a significantly high amount (46.84%); unfortunately, this method has the longest execution time and so is inefficient to implement in the intrusion detection field.


title: Telling Stories about Dynamic Networks with Graph Comics

abstract: In this paper, we explore graph comics as a medium to communicate changes in dynamic networks. While previous re- search has focused on visualizing dynamic networks for data exploration, we want to see if we can take advantage of the visual expressiveness and familiarity of comics to present and explain temporal changes in networks to an audience. To understand the potential of comics as a storytelling medium, we first created a variety of comics during a 3 month structured design process, involving domain experts from public education and neuroscience. This process led to the definition of 8 design factors for creating graph comics and propose design solutions for each. Results from a qualitative study suggest that a general audience is quickly able understand complex temporal changes through graph comics, provided with minimal textual annotations and no training.


title: High-Performance Parallel Implementation of Genetic Algorithm on FPGA

abstract: Genetic algorithms (GAs) are used to solve search and optimization problems in which an optimal solution can be found using an iterative process with probabilistic and non-deterministic transitions. However, depending on the problem’s nature, the time required to find a solution can be high in sequential machines due to the computational complexity of genetic algorithms. This work proposes a full-parallel implementation of a genetic algorithm on field-programmable gate array (FPGA). Optimization of the system’s processing time is the main goal of this project. Results associated with the processing time and area occupancy (on FPGA) for various population sizes are analyzed. Studies concerning the accuracy of the GA response for the optimization of two variables functions were also evaluated for the hardware implementation. However, the high-performance implementation proposed in this paper is able to work with more variable from some adjustments on hardware architecture. The results showed that the GA full-parallel implementation achieved throughput about 16 millions of generations per second and speedups between 17 and 170,000 associated with several works proposed in the literature.


title: Towards Mixed Optimization for Reinforcement Learning with Program Synthesis

abstract: Deep reinforcement learning has led to several recent breakthroughs, though the learned policies are often based on black-box neural networks. This makes them difficult to interpret and to impose desired specification constraints during learning. We present an iterative framework, MORL, for improving the learned policies using program synthesis. Concretely, we propose to use synthesis techniques to obtain a symbolic representation of the learned policy, which can then be debugged manually or automatically using program repair. After the repair step, we use behavior cloning to obtain the policy corresponding to the repaired program, which is then further improved using gradient descent. This process continues until the learned policy satisfies desired constraints. We instantiate MORL for the simple CartPole problem and show that the programmatic representation allows for high-level modifications that in turn lead to improved learning of the policies.


title: Technology assessment for mission-critical services on automotive virtual edge communicator (AVEC)

abstract: Mission critical services are beginning to migrate to broadband from the trusted-but-limited existing systems. They need assured availability and confidentiality for communication in areas affected by a disaster, flash crowding or any catastrophic network failure. This paper describes a vision for an automotive virtual edge Communication scheme that helps healing stricken networks, utilizing any available resources for hosting network functions on vehicles. Emergency and essential service organizations who rush to the area would ‘bring their own network’, i.e. computing capacity and connectivity tools, aboard their service vehicles. These vehicles form a dynamic community that shares resources, information and services, using the combined processing capacity via virtualized core functions. To realize such a scheme, innovative features are required, such as cooperative hosting, opportunistic vehicular resource virtualization, context-based edge SDN traffic prioritization, and ad-hoc vehicular community management, including multi-entity authentication.


title: Unequal Wilkinson Power Dividers With Favorable Selectivity and High-Isolation Using Coupled-Line Filter Transformers

abstract: In the conventional unequal-split Wilkinson power divider, poor selectivity for each transmission path is usually a problem. To surmount this obstacle, the parallel coupled-line bandpass filter structure is utilized as an impedance transformer as well as a band selector for each transmission path in the proposed unequal-split Wilkinson power dividers. However, the bandpass filters in the proposed dividers require careful design because they may not be functional under certain conditions. For example, the odd-order coupled-line filters are not appropriate for impedance transformers in the proposed unequal-split dividers and high-isolation requirement. Using the even-order coupled-line filter transformers, this study proposes two types of unequal-split Wilkinson power dividers. The first type of the proposed dividers arranges two filter transformers near two output ports, respectively, and is capable of achieving a highly remarkable isolation between the two output ports and a good band selection in each transmission path. Specifically, not only the operating band but also the lower and higher stopbands can achieve highly favorable isolation for this type of divider. By arranging the load impedance of each port properly, the second type of the proposed dividers, which has only one filter transformer to be shared by each transmission path near the input port, is also proposed to provide effective isolation between two output ports and favorable selectivity in each transmission path.


title: Multimodal feature fusion for robust event detection in web videos

abstract: Combining multiple low-level visual features is a proven and effective strategy for a range of computer vision tasks. However, limited attention has been paid to combining such features with information from other modalities, such as audio and videotext, for large scale analysis of web videos. In our work, we rigorously analyze and combine a large set of low-level features that capture appearance, color, motion, audio and audio-visual co-occurrence patterns in videos. We also evaluate the utility of high-level (i.e., semantic) visual information obtained from detecting scene, object, and action concepts. Further, we exploit multimodal information by analyzing available spoken and videotext content using state-of-the-art automatic speech recognition (ASR) and videotext recognition systems. We combine these diverse features using a two-step strategy employing multiple kernel learning (MKL) and late score level fusion methods. Based on the TRECVID MED 2011 evaluations for detecting 10 events in a large benchmark set of ∼45000 videos, our system showed the best performance among the 19 international teams.


title: Real-time sleep quality assessment using single-lead ECG and multi-stage SVM classifier

abstract: Sleep efficiency measures provide an objective assessment to gauge the quality of individual's sleep. In this study we present a home-based, automated and non-intrusive system that is based on Electrocardiogram (ECG) measurements and uses a multi-stage Support Vector Machines (SVM) classifier to measure three indices for sleep quality assessment per 30 s epoch segment: Sleep Efficiency Index, Delta-Sleep Efficiency Index and Sleep Onset Latency. This method provides an alternative to the intrusive and expensive Polysomnography (PSG) and scoring by Rechtschaffen and Kales visual method.


title: Open domain short text conceptualization: a generative + descriptive modeling approach

abstract: Concepts embody the knowledge to facilitate our cognitive processes of learning. Mapping short texts to a large set of open domain concepts has gained many successful applications. In this paper, we unify the existing conceptualization methods from a Bayesian perspective, and discuss the three modeling approaches: descriptive, generative, and discriminative models. Motivated by the discussion of their advantages and shortcomings, we develop a generative + descriptive modeling approach. Our model considers term relatedness in the context, and will result in disambiguated conceptualization. We show the results of short text clustering using a news title data set and a Twitter message data set, and demonstrate the effectiveness of the developed approach compared with the state-of-the-art conceptualization and topic modeling approaches.


title: Optimising area under the ROC curve using gradient descent

abstract: This paper introduces RankOpt, a linear binary classifier which optimises the area under the ROC curve (the AUC). Unlike standard binary classifiers, RankOpt adopts the AUC statistic as its objective function, and optimises it directly using gradient descent. The problems with using the AUC statistic as an objective function are that it is non-differentiable, and of complexity O(n2) in the number of data observations. RankOpt uses a differentiable approximation to the AUC which is accurate, and computationally efficient, being of complexity O(n.) This enables the gradient descent to be performed in reasonable time. The performance of RankOpt is compared with a number of other linear binary classifiers, over a number of different classification problems. In almost all cases it is found that the performance of RankOpt is significantly better than the other classifiers tested.


title: Multi-attention Network for One Shot Learning

abstract: One-shot learning is a challenging problem where the aim is to recognize a class identified by a single training image. Given the practical importance of one-shot learning, it seems surprising that the rich information present in the class tag itself has largely been ignored. Most existing approaches restrict the use of the class tag to finding similar classes and transferring classifiers or metrics learned thereon. We demonstrate here, in contrast, that the class tag can inform one-shot learning as a guide to visual attention on the training image for creating the image representation. This is motivated by the fact that human beings can better interpret a training image if the class tag of the image is understood. Specifically, we design a neural network architecture which takes the semantic embedding of the class tag to generate attention maps and uses those attention maps to create the image features for one-shot learning. Note that unlike other applications, our task requires that the learned attention generator can be generalized to novel classes. We show that this can be realized by representing class tags with distributed word embeddings and learning the attention map generator from an auxiliary training set. Also, we design a multiple-attention scheme to extract richer information from the exemplar image and this leads to substantial performance improvement. Through comprehensive experiments, we show that the proposed approach leads to superior performance over the baseline methods.