title: Current Trends in Educational Technology Research

abstract: ABSTRACT Educational technology research has moved through several stages or "ages", focusing at the beginning on the content to be learned, then on the format of instructional messages and, finally on the interaction between computers and students. The present paper reviews the research in technology-based learning environments in order to give both a historical perspective on educational technology research and a view of the current state of this discipline. We conclude that: 1) trends in educational technology research were forged by the evolution of learning theories and the technological changes, 2) a clear shift from the design of instruction to the design of learning environments can be noticed; 3) there is a positive effect of educational technology on learning, but the size of the effect varies considerably; 4) learning is much more dependent on the activity of the learner than on the quantity of information and processing opportunities provided by the environment. KEYWORDS: educational technology, computer- based learning, learning environments, research methods, virtual reality. Computer technology and the tremendous development of information technologies over the last few years have transformed the way education is conducted nowadays (Lou et al., 2001). Although computer technology has the potential of a powerful and flexible tool (Scardamalia & Bereiter, 1996), the previous experiences with the integration of early technologies in schools (e.g., radio, television, early computer-assisted instruction) underline the fact that the mere installing of hardware does not lead to desired results (Clark, 1983). Thus, the main question, research tried to find an answer for, was and still remains whether computer technology is benefic for the learning process and if yes, in which conditions? The "proper research of educational technology" has represented the subject of a debate for more than a decade (Lagemann, 2000; Levin & O'Donnell, 1999; Shavelson & Towne, 2002; Slavin, 2002). Despite of the accumulated experience in conducting research and publishing results in specialized journals, there is no consensus regarding methods that are used, or results and interpretations that are given to them (Hannafin, 2006). Reeves (1993) claimed that an important part of the research in the educational technology field is "pseudoscience", because it fails in meeting the high level of theoretical, conceptual, methodological and/or analytical requests of the paradigm that it is based upon. The purpose of this article is to review the research performed in the educational technology field in order to understand the nature of questions and problems that researchers had to face in this field over the years, and also to place the current research in the context of educational technology research. THE EVOLUTION OF RESEARCH IN EDUCATIONAL TECHNOLOGY The field of educational technology found its origins in the discovery made by researchers and practitioners of the fact that the instruction can be planned, projected, evaluated and revised before being applied on students. In other words it can be treated as an object on which a set of procedures, (i.e., technologies) can be applied (Winn, 2002). Educational technology is, according to the definition of the Association for Educational Communications and Technology (AECT), "...the theory and practice of design, development, utilization, management and evaluation of processes and resources for learning" (Seels & Richey, 1994, p. 231). Another definition is the one offered by Reisser (1987), who states that educational technology is the systematic way of designing, utilization and evaluation of the teaching/learning process, in terms of specific objectives, based on research in human learning and communication fields and on combining human and technical resources. The research made in the educational technology field, according to Winn (2002) has moved through four stages or "ages", each being built on the previous one and each of them being characterized by a specific focus, specific theoretical assumptions and practical implications. …


Estimating the Economic Benefits of Bicycling and Bicycle Facilities: An Interpretive Review and Proposed Methods In "Essays on Transportation Economics"


title: Discriminative Training of Deep Fully-connected Continuous CRF with Task-specific Loss

abstract: Recent works on deep conditional random fields (CRFs) have set new records on many vision tasks involving structured predictions. Here, we propose a fully connected deep continuous CRF model with task-specific losses for both discrete and continuous labeling problems. We exemplify the usefulness of the proposed model on multi-class semantic labeling (discrete) and the robust depth estimation (continuous) problems. In our framework, we model both the unary and the pairwise potential functions as deep convolutional neural networks (CNNs), which are jointly learned in an end-to-end fashion. The proposed method possesses the main advantage of continuously valued CRFs, which is a closed-form solution for the maximum a posteriori (MAP) inference. To better take into account the quality of the predicted estimates during the cause of learning, instead of using the commonly employed maximum likelihood CRF parameter learning protocol, we propose task-specific loss functions for learning the CRF parameters. It enables direct optimization of the quality of the MAP estimates during the learning process. Specifically, we optimize the multi-class classification loss for the semantic labeling task and the Tukey’s biweight loss for the robust depth estimation problem. Experimental results on the semantic labeling and robust depth estimation tasks demonstrate that the proposed method compare favorably against both baseline and state-of-the-art methods. In particular, we show that although the proposed deep CRF model is continuously valued, with the equipment of task-specific loss, it achieves impressive results even on discrete labeling tasks.


title: Hacking Chinese Touclick CAPTCHA by Multi-Scale Corner Structure Model with Fast Pattern Matching

abstract: In this paper, we tackle the challenge of hacking CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), which is widely used to identify machine and human in webpage registration and authorization [17]. More specially, we target at touclick Chinese CAPTCHA that is recently popular in mobile application scenario. Hacking such CAPTCHA is much more challenging and left unexploited in the literature. Our main idea is a multi-scale Corner based Structure Model, termed CSM, with a very efficient pattern matching scheme. CSM can accurately capture the intrinsic statistics of touclick Chinese CAPTCHA against background clutters. We demonstrate the efficiency and effectiveness of the proposed approach by extensive experiments on a Chinese touclick CAPTCHA dataset collected from Internet forums. We report encouraging results with an overall success rate of almost 100% and an averaged detection speed of 170 millisecond. Upon our work, we also provide suggestions on improving the current CAPTCHA-based human-machine identification systems.


title: The Spillover Effect of Fraudulent Financial Reporting on Peer Firms’ Investments

abstract: We investigate how high-profile accounting frauds affect peer firms' investment. We document that peers react to the fraudulent reports by increasing investment during fraud periods. We show that this finding is not driven by frauds that have a higher ex ante likelihood of detection or by an association between fraud and investment booms. In addition, we find that peers’ investments increase in fraudulent earnings overstatements, and in industries with higher investor sentiment, lower cost of capital and higher private benefits of control. We also find evidence consistent with equity analysts potentially facilitating the spillover effect.


title: Missing and spurious interactions and the reconstruction of complex networks

abstract: Network analysis is currently used in a myriad of contexts, from identifying potential drug targets to predicting the spread of epidemics and designing vaccination strategies and from finding friends to uncovering criminal activity. Despite the promise of the network approach, the reliability of network data is a source of great concern in all fields where complex networks are studied. Here, we present a general mathematical and computational framework to deal with the problem of data reliability in complex networks. In particular, we are able to reliably identify both missing and spurious interactions in noisy network observations. Remarkably, our approach also enables us to obtain, from those noisy observations, network reconstructions that yield estimates of the true network properties that are more accurate than those provided by the observations themselves. Our approach has the potential to guide experiments, to better characterize network data sets, and to drive new discoveries.


title: Visual domain adaptation via transfer feature learning

abstract: One of the serious challenges in computer vision and image classification is learning an accurate classifier for a new unlabeled image dataset, considering that there is no available labeled training data. Transfer learning and domain adaptation are two outstanding solutions that tackle this challenge by employing available datasets, even with significant difference in distribution and properties, and transfer the knowledge from a related domain to the target domain. The main difference between these two solutions is their primary assumption about change in marginal and conditional distributions where transfer learning emphasizes on problems with same marginal distribution and different conditional distribution, and domain adaptation deals with opposite conditions. Most prior works have exploited these two learning strategies separately for domain shift problem where training and test sets are drawn from different distributions. In this paper, we exploit joint transfer learning and domain adaptation to cope with domain shift problem in which the distribution difference is significantly large, particularly vision datasets. We therefore put forward a novel transfer learning and domain adaptation approach, referred to as visual domain adaptation (VDA). Specifically, VDA reduces the joint marginal and conditional distributions across domains in an unsupervised manner where no label is available in test set. Moreover, VDA constructs condensed domain invariant clusters in the embedding representation to separate various classes alongside the domain transfer. In this work, we employ pseudo target labels refinement to iteratively converge to final solution. Employing an iterative procedure along with a novel optimization problem creates a robust and effective representation for adaptation across domains. Extensive experiments on 16 real vision datasets with different difficulties verify that VDA can significantly outperform state-of-the-art methods in image classification problem.


Aesthetics and apparent usability: empirically assessing cultural and methodological issues


title: Effort estimation in agile software development: a systematic literature review

abstract: Context: Ever since the emergence of agile methodologies in 2001, many software companies have shifted to Agile Software Development (ASD), and since then many studies have been conducted to investigate effort estimation within such context; however to date there is no single study that presents a detailed overview of the state of the art in effort estimation for ASD. Objectives: The aim of this study is to provide a detailed overview of the state of the art in the area of effort estimation in ASD. Method: To report the state of the art, we conducted a systematic literature review in accordance with the guidelines proposed in the evidence-based software engineering literature. Results: A total of 25 primary studies were selected; the main findings are: i) Subjective estimation techniques (e.g. expert judgment, planning poker, use case points estimation method) are the most frequently applied in an agile context; ii) Use case points and story points are the most frequently used size metrics respectively; iii) MMRE (Mean Magnitude of Relative Error) and MRE (Magnitude of Relative Error) are the most frequently used accuracy metrics; iv) team skills, prior experience and task size are cited as the three important cost drivers for effort estimation in ASD; and v) Extreme Programming (XP) and SCRUM are the only two agile methods that are identified in the primary studies. Conclusion: Subjective estimation techniques, e.g. expert judgment-based techniques, planning poker or the use case points method, are the one used the most in agile effort estimation studies. As for the size metrics, the ones that were used the most in the primary studies were story points and use case points. Several research gaps were identified, relating to the agile methods, size metrics and cost drivers, thus suggesting numerous possible avenues for future work.


title: Physical telepresence: shape capture and display for embodied, computer-mediated remote collaboration

abstract: We propose a new approach to Physical Telepresence, based on shared workspaces with the ability to capture and remotely render the shapes of people and objects. In this paper, we describe the concept of shape transmission, and propose interaction techniques to manipulate remote physical objects and physical renderings of shared digital content. We investigate how the representation of user's body parts can be altered to amplify their capabilities for teleoperation. We also describe the details of building and testing prototype Physical Telepresence workspaces based on shape displays. A preliminary evaluation shows how users are able to manipulate remote objects, and we report on our observations of several different manipulation techniques that highlight the expressive nature of our system.


SELF-EFFICACY DEVELOPMENT IN ADOLESCENCES


title: Making flexible magnetic aerogels and stiff magnetic nanopaper using cellulose nanofibrils as templates.

abstract: Nanostructured biological materials inspire the creation of materials with tunable mechanical properties. Strong cellulose nanofibrils derived from bacteria or wood can form ductile or tough networks that are suitable as functional materials. Here, we show that freeze-dried bacterial cellulose nanofibril aerogels can be used as templates for making lightweight porous magnetic aerogels, which can be compacted into a stiff magnetic nanopaper. The 20-70-nm-thick cellulose nanofibrils act as templates for the non-agglomerated growth of ferromagnetic cobalt ferrite nanoparticles (diameter, 40-120 nm). Unlike solvent-swollen gels and ferrogels, our magnetic aerogel is dry, lightweight, porous (98%), flexible, and can be actuated by a small household magnet. Moreover, it can absorb water and release it upon compression. Owing to their flexibility, high porosity and surface area, these aerogels are expected to be useful in microfluidics devices and as electronic actuators.


title: Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits

abstract: Mobile payment is an emerging and important application of mobile commerce. The adoption and use of mobile payment services are critical for both service providers and investors to profit from such an innovation. The present study attempts to identify the determinants of pre-adoption of mobile payment services and explore the temporal evolution of these determinants across the pre-adoption and post-adoption stages from a holistic perspective including behavioral beliefs, social influences, and personal traits. A research model that reflects the characteristics and usage contexts of mobile payment services is developed and empirically tested by using structural equation modeling on datasets consisting of 483 potential adopters and 156 current users of a mobile payment service in China. Our findings show that behavioral beliefs in combination with social influences and personal traits are all important determinants for mobile payment services adoption and use, but their impacts on behavioral intention do vary across in different stages. Theoretical and practical implications of the findings are presented.


title: Artificial intelligence and the future of cybersecurity

abstract: A position paper toward an important and urgent discussion on how best use the potential of Artificial Intelligence in the context of cybersecurity. AI is often mentioned in papers on cybersecurity. But what is meant is using pre-existing AI techniques in cybersecurity. AI techniques are developed around applications. Cybersecurity has never been an area of concentration in AI. In this paper we argue that cybersecurity calls for new and specific AI techniques developed with that kind of application in mind. In practice, this paper is based on a broad overview of different approaches, which have the potential to be game changers in cybersecurity. This paper focuses on web application security and advocates the use of Knowledge Based Systems, probabilistic reasoning and Bayesian updating to control the probability of false positives and false negatives.


title: Can Accurate Predictions Improve Video Streaming in Cellular Networks?

abstract: Existing video streaming algorithms use various estimation approaches to infer the inherently variable bandwidth in cellular networks, which often leads to reduced quality of experience (QoE). We ask the question: "If accurate bandwidth prediction were possible in a cellular network, how much can we improve video QoE?". Assuming we know the bandwidth for the entire video session, we show that existing streaming algorithms only achieve between 69%-86% of optimal quality. Since such knowledge may be impractical, we study algorithms that know the available bandwidth for a few seconds into the future. We observe that prediction alone is not sufficient and can in fact lead to degraded QoE. However, when combined with rate stabilization functions, prediction outperforms existing algorithms and reduces the gap with optimal to 4%. Our results lead us to believe that cellular operators and content providers can tremendously improve video QoE by predicting available bandwidth and sharing it through APIs.


title: HOW TOURISM COMMUNITIES CAN CHANGE TRAVEL INFORMATION QUALITY

abstract: Largely ignored by research, online travel communities have already changed the travel behavior of the younger generation. They retrieve and exchange information prior to travelling and share their experiences afterwards. This paper presents some empirical evidence that the quality of the information retrieved justifies their behavior. The evidence is embedded in a larger framework and a set of hypotheses that establish a relationship between the choice of a travel information system and attributes of information quality. The paper argues that relevant attributes of information quality are timeliness, completeness, structure and personalization. Three studies support our proposition that a traditional discussion-based online tourism community provides more timely, more complete and more personalized information than a commercial guidebook. A major deficiency is particularly their lack of structure, but also the other attributes of information quality can be improved by more advanced online tourism communities. A second section thus proposes that a) Wiki communities improve timeliness, completeness and structure of online communities b) personal spaces improve the structure and the personalization of traditional online tourist communities and c) Mobile communities provide higher quality information than traditional online tourist communities.


title: KNIME - the Konstanz information miner: version 2.0 and beyond

abstract: The Konstanz Information Miner is a modular environment, which enables easy visual assembly and interactive execution of a data pipeline. It is designed as a teaching, research and collaboration platform, which enables simple integration of new algorithms and tools as well as data manipulation or visualization methods in the form of new modules or nodes. In this paper we describe some of the design aspects of the underlying architecture, briey sketch how new nodes can be incorporated, and highlight some of the new features of version 2.0.


title: Access Control for the Internet of Things

abstract: As we are moving from networked "Things" towards the Internet of Things (IoT), new security requirements arise. Access control in this new environment is a burgeoning and challenging problem. On the one hand, an access control system should be generic enough to cover the equirements of all the new exciting applications that become pervasive with the IoT. On the other hand, an access control system should be lightweight and easily implementable, considering at the same time the restrictions that Things impose. In this paper, we develop an access control system which enables offloading of complex access control decisions to third, trusted parties. Our system provides Thing authentication without public keys and establishes a shared symmetric encryption key that can be used to secure the communication between authorized users and Things. Our design imposes minimal overhead and it is based on a simple communication protocol. He resulting system is secure, enhances end-user privacy and the architecture facilitates the creation of new applications.


title: Natural Language Inference by Tree-Based Convolution and Heuristic Matching

abstract: In this paper, we propose the TBCNN-pair model to recognize entailment and contradiction between two sentences. In our model, a tree-based convolutional neural network (TBCNN) captures sentence-level semantics; then heuristic matching layers like concatenation, element-wise product/difference combine the information in individual sentences. Experimental results show that our model outperforms existing sentence encoding-based approaches by a large margin.


title: MINING GRAPH PATTERNS

abstract: Graph pattern mining becomes increasingly crucial to applications in a variety of domains including bioinformatics, cheminformatics, social network analysis, computer vision and multimedia. In this chapter, we first examine the existing frequent subgraph mining algorithms and discuss their computational bottleneck. Then we introduce recent studies on mining various types of graph patterns, including significant, representative and dense subgraph patterns. We also discuss the mining tasks in new problem settings such as a graph stream and an uncertain graph model. These new mining algorithms represent the state-of-the-art graph mining techniques: they not only avoid the exponential size of mining result, but also improve the applicability of graph patterns significantly.


title: The addition of a water flosser to power tooth brushing: effect on bleeding, gingivitis, and plaque.

abstract: OBJECTIVE ::: The primary objective of this study was to compare the effectiveness of a water flosser plus sonic toothbrush to a sonic toothbrush alone on the reduction of bleeding, gingivitis, and plaque. The secondary objective was to compare the effectiveness of different sonic toothbrushes on bleeding, gingivitis, and plaque. ::: ::: ::: METHODS ::: One-hundred and thirty-nine subjects completed this randomized, four-week, single-masked, parallel clinical study. Subjects were assigned to one of four groups: Waterpik Complete Care, which is a combination of a water flosser plus power toothbrush (WFS); Sensonic Professional Plus Toothbrush (SPP); Sonicare FlexCare toothbrush (SF); or an Oral-B Indicator manual toothbrush (MT). Subjects were provided written and verbal instructions for all power products at baseline, and instructions were reviewed at the two-week visit. Data were evaluated for whole mouth, facial, and lingual surfaces for bleeding on probing (BOP) and gingivitis (MGI). Plaque data were evaluated for whole mouth, lingual, facial, approximal, and marginal areas of the tooth using the Rustogi Modification of the Navy Plaque Index (RMNPI). Data were recorded at baseline (BL), two weeks (W2), and four weeks (W4). ::: ::: ::: RESULTS ::: All groups showed a significant reduction from BL in BOP, MGI, and RMNPI for all areas measured at the W2 and W4 visits (p < 0.001). The reduction of BOP was significantly higher for the WFS group than the other three groups at W2 and W4 for all areas measured (p < 0.001 for all, except p = 0.007 at W2 and p = 0.008 for W4 lingual comparison to SPP). The WFS group was 34% more effective than the SPP group, 70% more effective than the SF group, and 1.59 times more effective than the MT group for whole mouth bleeding scores (p < 0.001) at W4. The reduction of MGI was significantly higher for the WFS group; 23% more effective than SPP, 48% more effective than SF, and 1.35 times more effective than MT for whole mouth (p <0.001) at W4. The reduction of MGI was significantly higher for WFS than the SF and MT for facial and lingual surfaces, and more effective than the SPP for facial surfaces (p < 0.001) at W4. The WFS group showed significantly better reductions for plaque than the SF and MT groups for whole mouth, facial, lingual, approximal, and marginal areas at W4 (p < 0.001; SF facial p = 0.025). For plaque reduction, the WFS was significantly better than the SPP for whole mouth (p = 0.003) and comparable for all other areas and surfaces at W4. The WFS was 52% more effective for whole mouth, 31% for facial, 77% for lingual, 1.22 times for approximal, and 1.67 times for marginal areas compared to the SF for reducing plaque scores at W4 (p < 0.001; SF facial p = 0.025). The SPP had significantly higher reductions than the SF for whole mouth and lingual BOP and MGI scores, and whole mouth, approximal, marginal, and lingual areas for plaque at W4. ::: ::: ::: CONCLUSION ::: The Waterpik Complete Care is significantly more effective than the Sonicare FlexCare toothbrush for reducing gingival bleeding, gingivitis, and plaque. The Sensonic Professional Plus Toothbrush is significantly more effective than the Sonicare Flex-Care for reducing gingival bleeding, gingivitis, and plaque.


title: The genome of the recently domesticated crop plant sugar beet (Beta vulgaris)

abstract: Sugar beet (Beta vulgaris ssp. vulgaris) is an important crop of temperate climates which provides nearly 30% of the world's annual sugar production and is a source for bioethanol and animal feed. The species belongs to the order of Caryophylalles, is diploid with 2n = 18 chromosomes, has an estimated genome size of 714-758 megabases and shares an ancient genome triplication with other eudicot plants. Leafy beets have been cultivated since Roman times, but sugar beet is one of the most recently domesticated crops. It arose in the late eighteenth century when lines accumulating sugar in the storage root were selected from crosses made with chard and fodder beet. Here we present a reference genome sequence for sugar beet as the first non-rosid, non-asterid eudicot genome, advancing comparative genomics and phylogenetic reconstructions. The genome sequence comprises 567 megabases, of which 85% could be assigned to chromosomes. The assembly covers a large proportion of the repetitive sequence content that was estimated to be 63%. We predicted 27,421 protein-coding genes supported by transcript data and annotated them on the basis of sequence homology. Phylogenetic analyses provided evidence for the separation of Caryophyllales before the split of asterids and rosids, and revealed lineage-specific gene family expansions and losses. We sequenced spinach (Spinacia oleracea), another Caryophyllales species, and validated features that separate this clade from rosids and asterids. Intraspecific genomic variation was analysed based on the genome sequences of sea beet (Beta vulgaris ssp. maritima; progenitor of all beet crops) and four additional sugar beet accessions. We identified seven million variant positions in the reference genome, and also large regions of low variability, indicating artificial selection. The sugar beet genome sequence enables the identification of genes affecting agronomically relevant traits, supports molecular breeding and maximizes the plant's potential in energy biotechnology.


title: Autonomous local path planning for a mobile robot using a genetic algorithm

abstract: This work presents results of our work in development of a genetic algorithm based path-planning algorithm for local obstacle avoidance (local feasible path) of a mobile robot in a given search space. The method tries to find not only a valid path but also an optimal one. The objectives are to minimize the length of the path and the number of turns. The proposed path-planning method allows a free movement of the robot in any direction so that the path-planner can handle complicated search spaces.


title: Loss distribution analysis of three-level active neutral-point-clamped (3L-ANPC) converter with different PWM strategies

abstract: The three-level (3L) Active Neutral-Point-Clamped (ANPC) topology has been introduced to resolve the uneven loss distribution issue in the classical 3L Neutral-Point-Clamped (NPC) topology. The Pulse-Width-Modulation (PWM) strategy is then an important means to fully explore the potential benefits of the 3L-ANPC topology. This paper presents a novel loss balancing modulation strategy — Splitting Switching Loss Distribution PWM (SSLD-PWM) strategy to achieve more even loss distribution in the 3L-ANPC converter. Basically, in the proposed PWM strategy, the switching-on losses and switching-off losses are separated in order to improve the loss distribution among the power devices. A comparison between the proposed strategy and other major PWM strategies for the loss distribution is also carried out in this paper. Benchmarking results show that the proposed strategy has effective and superior performance in contrast to the prior-art PWM strategies in terms of even power loss distribution.


title: Automated cell nuclei segmentation for breast fine needle aspiration cytology

abstract: Abstract Breast cancer detection and segmentation of cytological images is the standard clinical practice for the diagnosis and prognosis of breast cancer. This paper presents a fully automated method for cell nuclei detection and segmentation in breast cytological images. The images are enhanced with histogram stretching and contrast-limited adaptive histogram equalization (CLAHE). The locations of the cell nuclei in the image are detected with circular Hough transform (CHT) and local maximum filtering. The elimination of false positive findings (noisy circles and blood cells) is achieved using Otsu’s thresholding method and fuzzy C-means clustering technique. The segmentation of the nuclei boundaries is accomplished with the application of the marker controlled watershed transform in the gradient image, using the nuclei markers extracted in the detection step. The proposed method is evaluated using 92 breast cytological images containing 11,502 cell nuclei. Experimental evidence shows that the proposed method has very effective results even in the case of images with high degree of blood cells, noisy circles.


title: Sinomenine, a natural dextrorotatory morphinan analog, is anti-inflammatory and neuroprotective through inhibition of microglial NADPH oxidase

abstract: Background ::: The mechanisms involved in the induction and regulation of inflammation resulting in dopaminergic (DA) neurotoxicity in Parkinson's disease (PD) are complex and incompletely understood. Microglia-mediated inflammation has recently been implicated as a critical mechanism responsible for progressive neurodegeneration.


title: Challenges in Deploying CoAP Over DTLS in Resource Constrained Environments

abstract: In the world of Internet of Things IoT, huge number of resource constrained devices are directly accessible over the Internet. For allowing the constrained devices to exchange information, the IETF standard group has specified the CoAP which works on top of UDP/IP. Also, Datagram TLS DTLS binding is recommended to make the CoAP secure. When DTLS is enabled, a device can select one of three security modes that are PreSharedKey, RawPublicKey and Certificate mode. Especially, the RawPublicKey mode, which uses an asymmetric-key pair without a certificate, is mandatory to implement CoAP over DTLS. But there are several challenges in using the asymmetric-key based secure mode in resource constrained device. This paper compares the RawPublicKey mode and the PreSharedKey mode, which uses a symmetric-key, to discuss DTLS performance in resource constrained devices and networks. For the comparison, we implemented an experimental environment based on IEEE 802.15.4 wireless networks consisting of resource constrained devices in the Cooja Simulator and in the real test-bed as well. Then we analyze the comparison results with regard to code size, energy consumption and processing and receiving time.


title: Integration of LiDAR and IfSAR for mapping. The

abstract: LiDAR and IfSAR data is now widely used for a number of applications, particularly those needing a digital elevation model. The data is often complementary to other data such as aerial imagery and high resolution satellite data. This paper will review the current data sources and the products and then look at the ways in which the data can be integrated for particular applications. The main ::: platforms for LiDAR are either helicopter or fixed wing aircraft, often operating at low altitudes, a digital camera is frequently included on the platform, there is an interest in using other sensors such as 3 line cameras of hyperspectral scanners. IfSAR is used from satellite platforms, or from aircraft, the latter are more compatible with LiDAR for integration. The paper will examine the advantages and disadvantages of LiDAR and IfSAR for DEM generation and discuss the issues which still need to be dealt with. Examples of applications will be given and particularly those involving the integration of different types of data. Examples will be given from various sources and future trends examined.


title: Computability Theory of Closed Timelike Curves

abstract: We ask, and answer, the question of what's computable by Turing machines equipped with time travel into the past: that is, closed timelike curves or CTCs (with no bound on their size). We focus on a model for CTCs due to Deutsch, which imposes a probabilistic consistency condition to avoid grandfather paradoxes. Our main result is that computers with CTCs can solve exactly the problems that are Turing-reducible to the halting problem, and that this is true whether we consider classical or quantum computers. Previous work, by Aaronson and Watrous, studied CTC computers with a polynomial size restriction, and showed that they solve exactly the problems in PSPACE, again in both the classical and quantum cases. ::: Compared to the complexity setting, the main novelty of the computability setting is that not all CTCs have fixed-points, even probabilistically. Despite this, we show that the CTCs that do have fixed-points suffice to solve the halting problem, by considering fixed-point distributions involving infinite geometric series. The tricky part is to show that even quantum computers with CTCs can be simulated using a Halt oracle. For that, we need the Riesz representation theorem from functional analysis, among other tools. ::: We also study an alternative model of CTCs, due to Lloyd et al., which uses postselection to "simulate" a consistency condition, and which yields BPP_path in the classical case or PP in the quantum case when subject to a polynomial size restriction. With no size limit, we show that postselected CTCs yield only the computable languages if we impose a certain finiteness condition, or all languages nonadaptively reducible to the halting problem if we don't.


title: Automatic reconstruction of personalized avatars from 3D face scans

abstract: We present a simple algorithm for computing a high-quality personalized avatar from a single color image and the corresponding depth map which have been captured by Microsoft's Kinect sensor. Due to the low market price of our hardware setup, 3D face scanning becomes feasible for home use. The proposed algorithm combines the advantages of robust non-rigid registration and fitting of a morphable face model. We obtain a high-quality reconstruction of the facial geometry and texture along with one-to-one correspondences with our generic face model. This representation allows for a wide range of further applications such as facial animation or manipulation. Our algorithm has proven to be very robust. Since it does not require any user interaction, even non-expert users can easily create their own personalized avatars. Copyright © 2011 John Wiley & Sons, Ltd.


title: Separating the Real from the Synthetic: Minutiae Histograms as Fingerprints of Fingerprints

abstract: In this study we show that by the current state-of-the-art synthetically generated ngerprints can easily be discriminated from real ngerprints. We propose a method based on second order extended minutiae histograms (MHs) which can distinguish between real and synthetic prints with very high accuracy. MHs provide a xed-length feature vector for a ngerprint which are invariant under rotation and translation. This ’test of realness’ can be applied to synthetic ngerprints produced by any method. In this work, tests are conducted on the 12 publicly available databases of FVC2000, FVC2002 and FVC2004 which are well established benchmarks for evaluating the performance of ngerprint recognition algorithms; 3 of these 12 databases consist of articial ngerprints generated by the SFinGe software. Additionally, we evaluate the discriminative performance on a database of synthetic ngerprints generated by the software of Bicz versus real ngerprint images. We conclude with suggestions for the improvement of synthetic ngerprint generation.


title: Bayesian Optimization with Unknown Constraints

abstract: Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions. Many real-world optimization problems of interest also have constraints which are unknown a priori. In this paper, we study Bayesian optimization for constrained problems in the general case that noise may be present in the constraint functions, and the objective and constraints may be evaluated independently. We provide motivating practical examples, and present a general framework to solve such problems. We demonstrate the effectiveness of our approach on optimizing the performance of online latent Dirichlet allocation subject to topic sparsity constraints, tuning a neural network given test-time memory constraints, and optimizing Hamiltonian Monte Carlo to achieve maximal effectiveness in a fixed time, subject to passing standard convergence diagnostics.


title: A comparative study of walkthrough paradigms for virtual environments using kinect based natural interaction

abstract: This study analyzes and compares several gestural schemes using Kinect based interaction on virtual walkthroughs. The text describes the tests performed to measure the suitability of six different combinations of body gestures for moving and turning, and compares the results in terms of usability. The tests were carried out on a real existing virtual museum installation.


title: Bayesian Nonparametric Clustering for Positive Definite Matrices

abstract: Symmetric Positive Definite (SPD) matrices emerge as data descriptors in several applications of computer vision such as object tracking, texture recognition, and diffusion tensor imaging. Clustering these data matrices forms an integral part of these applications, for which soft-clustering algorithms (K-Means, expectation maximization, etc.) are generally used. As is well-known, these algorithms need the number of clusters to be specified, which is difficult when the dataset scales. To address this issue, we resort to the classical nonparametric Bayesian framework by modeling the data as a mixture model using the Dirichlet process (DP) prior. Since these matrices do not conform to the Euclidean geometry, rather belongs to a curved Riemannian manifold,existing DP models cannot be directly applied. Thus, in this paper, we propose a novel DP mixture model framework for SPD matrices. Using the log-determinant divergence as the underlying dissimilarity measure to compare these matrices, and further using the connection between this measure and the Wishart distribution, we derive a novel DPM model based on the Wishart-Inverse-Wishart conjugate pair. We apply this model to several applications in computer vision. Our experiments demonstrate that our model is scalable to the dataset size and at the same time achieves superior accuracy compared to several state-of-the-art parametric and nonparametric clustering algorithms.


title: Comparing Twitter Summarization Algorithms for Multiple Post Summaries

abstract: Due to the sheer volume of text generated by a micro log site like Twitter, it is often difficult to fully understand what is being said about various topics. In an attempt to understand micro logs better, this paper compares algorithms for extractive summarization of micro log posts. We present two algorithms that produce summaries by selecting several posts from a given set. We evaluate the generated summaries by comparing them to both manually produced summaries and summaries produced by several leading traditional summarization systems. In order to shed light on the special nature of Twitter posts, we include extensive analysis of our results, some of which are unexpected.


title: Not all bytes are equal: Neural byte sieve for fuzzing

abstract: Fuzzing is a popular dynamic program analysis technique used to find vulnerabilities in complex software. Fuzzing involves presenting a target program with crafted malicious input designed to cause crashes, buffer overflows, memory errors, and exceptions. Crafting malicious inputs in an efficient manner is a difficult open problem and often the best approach to generating such inputs is through applying uniform random mutations to pre-existing valid inputs (seed files). We present a learning technique that uses neural networks to learn patterns in the input files from past fuzzing explorations to guide future fuzzing explorations. In particular, the neural models learn a function to predict good (and bad) locations in input files to perform fuzzing mutations based on the past mutations and corresponding code coverage information. We implement several neural models including LSTMs and sequence-to-sequence models that can encode variable length input files. We incorporate our models in the state-of-the-art AFL (American Fuzzy Lop) fuzzer and show significant improvements in terms of code coverage, unique code paths, and crashes for various input formats including ELF, PNG, PDF, and XML.


title: Collaborative Filtering with Implicit Feedbacks by Discounting Positive Feedbacks

abstract: Recommender Systems are indispensable to provide personalized services on the Web. Recommending items which match a user's preference has been researched for a long time, and there exist a lot of useful approaches. Especially, Collaborative Filtering, which gives recommendation based on users' feedbacks to items, is considered useful. Feedbacks are categorized into explicit feedbacks and implicit feedbacks. In this paper, Collaborative Filtering with implicit feedbacks is addressed. Explicit feedbacks are feedbacks provided by users intentionally and represent users' preferences for items explicitly. For example, in Netflix, users can rate movies on a scale of 1-5, and, based on these ratings, users can receive movie recommendation. On the other hand, implicit feedbacks are collected by the system automatically. In Amazon.com, products that users buy and click are used for recommendation. While Collaborative Filtering with explicit feedbacks has been a central topic for a long time, implicit feedbacks have become a more and more important research topic recently because these are easier to obtain and more abundant than explicit feedbacks. However, implicit feedbacks are often noisy. They often contain feedbacks which do not represent users' real preferences for items. Our approach addresses to this noise problem. We propose three discounting methods for observed values in implicit feedbacks. The key idea is that there is hidden uncertainty for each observed feedback, and effects by observed feedbacks of much uncertainty are discounted. The three discounting methods do not need additional information besides ordinary user-item feedbacks pairs and timestamps. Experiments with huge real-world datasets confirm that all of the three methods contribute to improving the performance. Moreover, our discounting methods can easily be combined with existing methods and improve the recommendation accuracy of existing models.


title: Visual Attention driven by Convolutional Features

abstract: The understanding of where humans look in a scene is a problem of great interest in visual perception and computer vision. When eye-tracking devices are not a viable option, models of human attention can be used to predict fixations. In this paper we give two contribution. First, we show a model of visual attention that is simply based on deep convolutional neural networks trained for object classification tasks. A method for visualizing saliency maps is defined which is evaluated in a saliency prediction task. Second, we integrate the information of these maps with a bottom-up differential model of eye-movements to simulate visual attention scanpaths. Results on saliency prediction and scores of similarity with human scanpaths demonstrate the effectiveness of this model.


title: Update on the Progress of Hydrogen-Fueled Internal Combustion Engines

abstract: This chapter provides an overview on the use of hydrogen as a fuel for internal combustion engines (ICEs). First, pros and cons are discussed for using hydrogen to fuel ICEs versus fuel cells. Then, the properties of hydrogen pertinent to engine operation are briefly reviewed, after which the present state of the art of hydrogen engines is discussed. Ongoing research efforts are highlighted next, which primarily aim at maximizing engine efficiency throughout the load range, while keeping emissions at ultralow levels. Finally, the challenges for reaching these goals and translating laboratory results to production are discussed.


title: A multi-objective approach to supply chain visibility and risk

abstract: This paper investigates the twin effects of supply chain visibility (SCV) and supply chain risk (SCR) on supply chain performance. Operationally, SCV has been linked to the capability of sharing timely and accurate information on exogenous demand, quantity and location of inventory, transport related cost, and other logistics activities throughout an entire supply chain. Similarly, SCR can be viewed as the likelihood that an adverse event has occurred during a certain epoch within a supply chain and the associated consequences of that event which affects supply chain performance. Given the multi-faceted attributes of the decision making process which involves many stages, objectives, and stakeholders, it beckons research into this aspect of the supply chain to utilize a fuzzy multi-objective decision making approach to model SCV and SCR from an operational perspective. Hence, our model incorporates the objectives of SCV maximization, SCR minimization, and cost minimization under the constraints of budget, customer demand, production capacity, and supply availability. A numerical example is used to demonstrate the applicability of the model. Our results suggest that decision makers tend to mitigate SCR first then enhance SCV.


title: "Bridging the Gap between Wound Care and Infection Prevention" A CASE REPORT

abstract: “Disconnect” in the Health Care environment between different disciplines have become more and more evident. Besides many efforts to encourage interdisciplinary association, many gaps are still apparent.


title: Study of Safety Analysis and Assessment Methodology for AADL Model

abstract: This paper focuses on safety model of embedded system architecture using AADL (Architecture Analysis and Design Language). For further integration of safety analysis and system modeling, we propose a new approach to evaluate and assess the safety property of embedded systems quantitatively. We establish the safety model of embedded systems by extending AADL with fault model, identify causal relationships between elementary failure modes, put forward the formal method to transform this safety model to DSPN (Deterministic Stochastic Petri Net) model for quantitative analysis and made transforming rules to support safety assessment automatically. A fire alarm system is discussed for further explanation.


title: A Stock Selection Model Based on Fundamental and Technical Analysis Variables by Using Artificial Neural Networks and Support Vector Machines

abstract: The basic aim of this article is to provide a model to explain stock performance utmost level. To reach this purpose, at the initial step, the model results composed of fundamental and technical analysis variables considered separately; in the second step, building the model composed of fundamental and technical analysis parameters which has best explaining ability was the focal point of this study. Artificial Neural Network (ANN) is an approach that has been widely used for financial classification problems for a long time. In addition, promising results of a novel machine learning method known as the Support Vector Machines (SVM) have been presented in several studies compared to the ANN. The stock performance results relying on fundamental analysis have shown more successful classification rates than the models based on technical analysis. Moreover, it was also experienced that the models constructed by using SVM method in the both type of analyses have shown more prominent results.


title: A New Concave Hull Algorithm and Concaveness Measure for n-dimensional Datasets

abstract: Convex and concave hulls are useful concepts for a wide variety of application areas, such as pattern recognition, image processing, statistics, and classification tasks. Concave hull performs better than convex hull, but it is difficult to formulate and few algorithms are suggested. Especially, an n-dimensional concave hull is more difficult than a 2- or 3-dimensional one. In this paper, we propose a new concave hull algorithm for n-dimensional datasets. It is simple but creative. We show its application to dataset analysis. We also suggest a concaveness measure and a graph that captures geometric shape of an n-dimensional dataset. Proposed concave hull algorithm and concaveness measure/graph are implemented using java, and are posted to http://user.dankook.ac.kr/~bitl/dkuCH.


The Psychological Determinants of Political Judgment


title: Nutritional Assessment of Children with Autism

abstract: Abstract Differences were investigated between dietary intake of children with autism and the recommendations of the Food Guide Pyramid (Pyramid) and kcals and protein levels of the RDA, and differences in subject anthropometric measurements from the 50 th percentile for the National Center for Health Statistics (NCHS) growth charts and data adapted from the National Health and Nutrition Examination Survey (NHANES) for boys of the same age. The subjects consisted of nine Caucasian boys with autism, aged 7-10 years. Measurements included height (HT), body weight (WT), weight for height (WT/HT), estimated percent body fat (BF) from calf and triceps skinfolds and mid-arm muscle area (MAMA). A four-day food record was completed by a parent of each child, assisted by the childs teacher, for each of three consecutive months. Single Sample t-tests were utilized in data analyses. Results revealed mean HT, 34 th percentile, mean WT, 54 th percentile, WT/HT, 71 st percentile (p=.002), and mean for MAMA, 48 th percentile. Mean BF was 18%±1.7, with a norm of 15%. Findings for dietary intake indicated subjects consumed a mean of 400±220 (p=.001) fewer kcals than the RDA (2000kcals). The RDA for protein is 28g/day and mean intake was 56g/day (p=.001). Food Guide Pyramid recommended servings were not met in the following groups: milk (mean 0.8±0.8 serving/day) (p=.0001) and vegetables (mean 0.1±0.3serving/day) (p=.0001). Major kcals were contributed by fat (mean 8.6±1.5servings/day) (p=.001). Boys with autism appear to have dietary intakes different than those recommended by the Pyramid and RDA with low kcals, high protein intake, low consumption of milk, vegetables and high consumption of fat. However, anthropometric measurements were close to the 50 th percentile except for greater WT/HT. The investigators suggest monitoring dietary intake and nutrition counseling for children with autism to prevent low or excess intake.


title: Mapping the reasons for resistance to Internet banking: A means-end approach

abstract: Although Internet banking has been widely adopted in developed countries, there is still a group of customers resisting the services. In other words, Internet banking, although proven to be a successful innovation, has still not become adopted by the laggards, and hence, has not met all the expectations of banks. Therefore, the purpose of this paper is to identify the reasons for consumer resistance to Internet banking. The special interest is to explore resistance among those bank customers who already have valid contracts for Internet banking but prefer to pay their bills via ATM. The objective is to identify those characteristics generating resistance to Internet banking and their connections to values of individuals. In order to achieve the objective, 30 Finnish bank customers were interviewed in-depth using the means-end approach and the laddering interviewing technique. The findings indicate both functional and psychological barriers arising from service-, channel-, consumer- and communication-related means-end chains inhibiting Internet banking adoption. The contribution of the paper lies in achieving a more profound understanding of consumer resistance to Internet banking, and further, in offering suggestions and practical advice for service providers' decision-making.


title: Diode Opening Switch Based Nanosecond High Voltage Pulse Generators for Biological and Medical Applications

abstract: Studies of short (few ns) pulse cell electroperturbation require high-voltage nanosecond pulses delivered to low-impedance (10 Omega) electroporation cuvette loads. Here we present two pulse generators for these applications. The generators comprise of three stages of pulse modulation: an LC resonant stage switched by IGBT/MOSFET; a magnetic pulse compression stage; and a diode opening switch output stage. The all-solid-state design results in reproducible pulses and reliable, long-life operation. Pulses produced by the LC resonant stage are compressed using nonlinear properties of the magnetic material. The compressed pulses are fed to the diode opening switch, which is constructed by series and parallel connecting ordinary automotive rectifying diodes. Two versions of the pulse generator are described: The first version is capable of generating 20 ns wide, 4.5 kV amplitude pulses of 20 Hz repetition rate. A second version generates 5 ns, 7.5 kV amplitude pulses into the same 10 Omega cuvette load.


title: Near-field measurement of stochastic electromagnetic fields

abstract: Stochastic electromagnetic fields with Gaussian amplitude probability distribution can be fully described by auto- and cross correlation spectra of the field components. The cross correlation spectra have to be known for the pairs of field components taken at different spatial points. Integrated electric dipole and magnetic loop probes together with active electronics in AlGaAs/GaAs-HEMT and SiGe:C-HBT technologies are presented. While the probes allow for a spatial resolution in the 100 μm range, integrated amplifiers provide about 30 dB gain over a bandwidth of about 10 GHz.


title: Collaborative hyperparameter tuning

abstract: Hyperparameter learning has traditionally been a manual task because of the limited number of trials. Today's computing infrastructures allow bigger evaluation budgets, thus opening the way for algorithmic approaches. Recently, surrogate-based optimization was successfully applied to hyperparameter learning for deep belief networks and to WEKA classifiers. The methods combined brute force computational power with model building about the behavior of the error function in the hyperparameter space, and they could significantly improve on manual hyperparameter tuning. What may make experienced practitioners even better at hyperparameter optimization is their ability to generalize across similar learning problems. In this paper, we propose a generic method to incorporate knowledge from previous experiments when simultaneously tuning a learning algorithm on new problems at hand. To this end, we combine surrogate-based ranking and optimization techniques for surrogate-based collaborative tuning (SCoT). We demonstrate SCoT in two experiments where it outperforms standard tuning techniques and single-problem surrogate-based optimization.


title: Bankruptcy Prediction for Credit Risk Using Neural Networks: A Survey and New Results

abstract: The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work presents two contributions. First we review the topic of bankruptcy prediction, with emphasis on neural-network (NN) models. Second, we develop an NN bankruptcy prediction model. Inspired by one of the traditional credit risk models developed by Merton (1974), we propose novel indicators for the NN system. We show that the use of these indicators in addition to traditional financial ratio indicators provides a significant improvement in the (out-of-sample) prediction accuracy (from 81.46% to 85.5% for a three-year-ahead forecast).


title: Gestures for industry Intuitive human-robot communication from human observation

abstract: Human-robot collaborative work has the potential to advance quality, efficiency and safety in manufacturing. In this paper we present a gestural communication lexicon for human-robot collaboration in industrial assembly tasks and establish methodology for producing such a lexicon. Our user experiments are grounded in a study of industry needs, providing potential real-world applicability to our results. Actions required for industrial assembly tasks are abstracted into three classes: part acquisition, part manipulation, and part operations. We analyzed the communication between human pairs performing these subtasks and derived a set of communication terms and gestures. We found that participant-provided gestures are intuitive and well suited to robotic implementation, but that interpretation is highly dependent on task context. We then implemented these gestures on a robot arm in a human-robot interaction context, and found the gestures to be easily interpreted by observers. We found that observation of human-human interaction can be effective in determining what should be communicated in a given human-robot task, how communication gestures should be executed, and priorities for robotic system implementation based on frequency of use.


title: Saliency-Guided Unsupervised Feature Learning for Scene Classification

abstract: Due to the rapid technological development of various different satellite sensors, a huge volume of high-resolution image data sets can now be acquired. How to efficiently represent and recognize the scenes from such high-resolution image data has become a critical task. In this paper, we propose an unsupervised feature learning framework for scene classification. By using the saliency detection algorithm, we extract a representative set of patches from the salient regions in the image data set. These unlabeled data patches are exploited by an unsupervised feature learning method to learn a set of feature extractors which are robust and efficient and do not need elaborately designed descriptors such as the scale-invariant-feature-transform-based algorithm. We show that the statistics generated from the learned feature extractors can characterize a complex scene very well and can produce excellent classification accuracy. In order to reduce overfitting in the feature learning step, we further employ a recently developed regularization method called “dropout,” which has proved to be very effective in image classification. In the experiments, the proposed method was applied to two challenging high-resolution data sets: the UC Merced data set containing 21 different aerial scene categories with a submeter resolution and the Sydney data set containing seven land-use categories with a 60-cm spatial resolution. The proposed method obtained results that were equal to or even better than the previous best results with the UC Merced data set, and it also obtained the highest accuracy with the Sydney data set, demonstrating that the proposed unsupervised-feature-learning-based scene classification method provides more accurate classification results than the other latent-Dirichlet-allocation-based methods and the sparse coding method.


title: A Nonintrusive Power Supply Design for Self-Powered Sensor Networks in the Smart Grid by Scavenging Energy From AC Power Line

abstract: An advanced metering and monitoring system based on autonomous, ubiquitous and maintenance-free wireless sensor networks is of great significance to the smart grid. However, the power supply for sensor nodes (especially those installed on high-voltage side) remains one of the most challenging issues. To date, miniaturized, reliable, low-cost and flexible designs catering the massive application of self-powered sensor nodes in the smart grid are still limited. This paper presents a nonintrusive design of power supply to support the sensor network applied in the smart grid. Using a cantilever-structured magnetoelectric (ME) composite, the energy harvester is able to scavenge energy from the power-frequency (50 Hz) magnetic field distributed around the transmission line. Design considerations for this specific type of scavenger have been discussed, and optimized energy harvester prototypes have been fabricated, which are further tested on a power line platform. Experimental results show that the single-cell and double-cell energy harvesters are capable of producing 0.62 mW and 1.12 mW at 10 A, respectively, while corresponding power outputs are enhanced to 4.11 mW and 9.40 mW at 40 A. The good energy harvesting ability of this particular ME composite indicates its great potential to make a nonintrusive, miniaturized, flexible and cost-effective power supply, which possesses great application prospects in the smart grid.


title: Broadband CPW-Fed Circularly Polarized Square Slot Antenna With Lightening-Shaped Feedline and Inverted-L Grounded Strips

abstract: A novel design is described for a circularly polarized square slot antenna (CPSSA). Circular polarization (CP) operations can be attained using a lightening-shaped feedline protruded from the signal line of the feeding coplanar waveguide (CPW). The CP bandwidth can be significantly enhanced by implanting a pair of inverted-L grounded strips into the slot and adjusting the dimensions of the lightening-shaped feedline, whereas the impedance bandwidth can be greatly enlarged through tuning embedded vertical and horizontal stubs. The designed antenna was measured to exhibit a CP bandwidth of as high as 48.8%.


title: Global Optimization for Shape Fitting

abstract: We propose a global optimization framework for 3D shape reconstruction from sparse noisy 3D measurements frequently encountered in range scanning, sparse feature-based stereo, and shape-from-X. In contrast to earlier local or banded optimization methods for shape fitting, we compute global optimum in the whole volume removing dependence on initial guess and sensitivity to numerous local minima. Our global method is based on two main ideas. First, we suggest a new regularization functional with a data alignment term that maximizes the number of (weakly-oriented) data points contained by a surface while allowing for some measurement errors. Second, we propose a touch-expand algorithm for finding a minimum cut on a huge 3D grid using an automatically adjusted band. This overcomes prohibitively high memory cost of graph cuts when computing globally optimal surfaces at high-resolution. Our results for sparse or incomplete 3D data from laser scanning and passive multi-view stereo are robust to noise, outliers, missing parts, and varying sampling density.


title: Identification and quantification of astaxanthin esters in shrimp (Pandalus borealis) and in a microalga (Haematococcus pluvialis) by liquid chromatography-mass spectrometry using negative ion atmospheric pressure chemical ionization.

abstract: Negative ion liquid chromatography-atmospheric pressure chemical ionization mass spectrometry [negative ion LC-(APCI)MS] was used for the identification of astaxanthin esters in extracts of commercial shrimp (Pandalus borealis) and dried microalga (Haematococcus pluvialis) samples. A cleanup step using a normal phase solid phase extraction (SPE) cartridge was applied prior to analysis. Recovery experiments with astaxanthin oleate as model compound proved the applicability of this step (98.5 +/- 7.6%; n = 4). The assignment of astaxanthin esters in negative ion LC-(APCI)MS was based on the detection of the molecular ion (M*-) and the formation of characteristic fragment ions, resulting from the loss of one or two fatty acids. Quantification of individual astaxanthin esters was performed using an astaxanthin calibration curve, which was found to be linear over the required range (1-51 micromol/L; r2 = 0.9996). Detection limits, based on the intensity of M*-, a signal-to-noise ratio of 3:1, and an injection volume of 20 microL, were estimated to be 0.05 microg/mL (free astaxanthin), 0.28 microg/mL (astaxanthin-C16:0), and 0.78 microg/mL (astaxanthin-C16:0/C16:0), respectively. This LC-(APCI)MS method allows for the first time the characterization of native astaxanthin esters in P. borealis and H. pluvialis without using time-consuming isolation steps with subsequent gas chromatographic analyses of fatty acid methyl esters. The results suggest that the pattern of astaxanthin-bound polyunsaturated fatty acids of P. borealis does not reflect the respective fatty acid pattern found in triacylglycerides. Application of the presented LC-(APCI)MS technique in common astaxanthin ester analysis will forestall erroneous xanthophyll ester assignment in natural sources.


title: Network of Information (NetInf) - An information-centric networking architecture

abstract: Information-centric networking (ICN) is a promising approach to networking that has the potential to provide better - more natural and more efficient - solutions for many of today's important communication applications including but not limited to large-scale content distribution. This article describes the Network of Information (NetInf) architecture - a specific ICN approach that targets global-scale communication and supports many different types of networks and deployments, including traditional Internet access/core network configurations, data centers, as well as challenged and infrastructure-less networks. NetInf's approach to connecting different technology and administrative domains into a single information-centric network is based on a hybrid name-based routing and name resolution scheme. In this article, we describe the most important requirements that motivated the NetInf design. We present an architecture overview and discuss the different architecture elements such as naming, message forwarding, caching, and a name resolution service (NRS) in detail. As efficient caching and a scalable NRS are two main success factors, we present an evaluation of both elements based on a theoretical analysis, complemental simulation results, and prototyping results. The results suggest that a scalable NRS for 10^1^5 and more objects with resolution latencies (well) below 100ms is possible, implying that a global Network of Information that removes the need for today's application-specific overlay solutions is feasible.


title: Voice-Based Gender Identification in Multimedia Applications

abstract: In the context of content-based multimedia indexing gender identification based on speech signal is an important task. In this paper a set of acoustic and pitch features along with different classifiers are compared for the problem of gender identification. We show that the fusion of features and classifiers performs better than any individual classifier. Based on such conclusions we built a system for gender identification in multimedia applications. The system uses a set of Neural Networks with acoustic and Pitch related features.90% of classification accuracy is obtained for 1 second segments and with independence to the language and the channel of the speech. Practical considerations, such as the continuity of speech and the use of mixture of experts instead of one single expert are shown to improve the classification accuracy to 93%. When used on a subset of the Switchboard database, the classification accuracy attains 98.5% for 5 seconds segments.


title: A Food Recognition System for Diabetic Patients Based on an Optimized Bag-of-Features Model

abstract: Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the bag-of-features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.


title: Review of Literature on Low-level Laser Therapy Benefits for Nonpharmacological Pain Control in Chronic Pain and Osteoarthritis.

abstract: Introduction ::: Low-level laser therapy (LLLT) is a form of light therapy that triggers biochemical changes within cells. Photons are absorbed by cellular photoreceptors, triggering chemical alterations and potential biochemical benefits to the human body. LLLT has been used in pain management for years and is also known as cold laser therapy, which uses low-frequency continuous laser of typically 600 to 1000 nm wavelength for pain reduction and healing stimulation. Many studies have demonstrated analgesic and anti-inflammatory effects provided by photobiomodulation in both experimental and clinical trials. ::: ::: ::: Objective ::: The purpose of this research article was to present a summary of the possible pain management benefits of LLLT. ::: ::: ::: Results ::: In cold laser therapy, coherent light of wavelength 600 to 1000 nm is applied to an area of concern with hope for photo-stimulating the tissues in a way that promotes and accelerates healing. This is evidenced by the similarity in absorption spectra between oxidized cytochrome c oxidase and action spectra from biological responses to light. LLLT, using the properties of coherent light, has been seen to produce pain relief and fibroblastic regeneration in clinical trials and laboratory experiments. LLLT has also been seen to significantly reduce pain in the acute setting; it is proposed that LLLT is able to reduce pain by lowering the level of biochemical markers and oxidative stress, and the formation of edema and hemorrhage. Many studies have demonstrated analgesic and anti-inflammatory effects provided by photobiomodulation in both experimental and clinical trials. ::: ::: ::: Conclusion ::: Based on current research, the utilization of LLLT for pain management and osteoarthritic conditions may be a complementary strategy used in clinical practice to provide symptom management for patients suffering from osteoarthritis and chronic pain.


title: A vision-based obstacle detection system for Unmanned Surface Vehicle

abstract: This paper describes a vision-based obstacle detection system for Unmanned Surface Vehicle (USV) towards the aim of real-time and high performance obstacle detection on the sea surface. By using both the monocular and stereo vision methods, the system offers the capacity of detecting and locating multiple obstacles in the range from 30 to 100 meters for high speed USV which runs at speeds up to 12 knots. Field tests in the real scenes have been taken and the obstacle detection system for USV is proven to provide stable and satisfactory performance.


title: Fast moving-object detection in H.264/AVC compressed domain for video surveillance

abstract: This paper discusses a novel fast approach for moving object detection in H.264/AVC compressed domain for video surveillance applications. The proposed algorithm initially segments out edges from regions with motion at macroblock level by utilizing the gradient of quantization parameter over 2D-image space. A spatial median filtering of the segmented edges followed by weighted temporal accumulation accounts for whole object segmentation. To attain sub-macroblock (4×4) level precision, the size of macroblocks (in bits) is interpolated using a two tap filter. Partial decoding rules out the complexity involved in full decoding and gives fast foreground segmentation results. Compared to other compressed domain techniques, the proposed approach allows the video streams to be encoded with different quantization parameters across macroblocks thereby increasing flexibility in bit rate adjustment.


System Implementation Success Factors ; It ’ s not just the Technology


title: Fast Routing in Road Networks with Transit Nodes

abstract: When you drive to somewhere far away, you will leave your current location via one of only a few important traffic junctions. Starting from this informal observation, we developed an algorithmic approach, transit node routing, that allows us to reduce quickest path queries in road networks to a small number of table lookups. For road maps of Western Europe and the United States, our best query times improved over the best previously published figures by two orders of magnitude. This is also more than one million times faster than the best known algorithm for general networks.


title: SPOKE: Scalable Knowledge Collection and Attack Surface Analysis of Access Control Policy for Security Enhanced Android

abstract: SEAndroid is a mandatory access control (MAC) framework that can confine faulty applications on Android. Nevertheless, the effectiveness of SEAndroid enforcement depends on the employed policy. The growing complexity of Android makes it difficult for policy engineers to have complete domain knowledge on every system functionality. As a result, policy engineers sometimes craft over-permissive and ineffective policy rules, which unfortunately increased the attack surface of the Android system and have allowed multiple real-world privilege escalation attacks. We propose SPOKE, an SEAndroid Policy Knowledge Engine, that systematically extracts domain knowledge from rich-semantic functional tests and further uses the knowledge for characterizing the attack surface of SEAndroid policy rules. Our attack surface analysis is achieved by two steps: 1) It reveals policy rules that cannot be justified by the collected domain knowledge. 2) It identifies potentially over-permissive access patterns allowed by those unjustified rules as the attack surface. We evaluate SPOKE using 665 functional tests targeting 28 different categories of functionalities developed by Samsung Android Team. SPOKE successfully collected 12,491 access patterns for the 28 categories as domain knowledge, and used the knowledge to reveal 320 unjustified policy rules and 210 over-permissive access patterns defined by those rules, including one related to the notorious libstagefright vulnerability. These findings have been confirmed by policy engineers.


title: Safety of human papillomavirus vaccines: a review

abstract: Introduction: Between 2006 and 2009, two different human papillomavirus virus (HPV) vaccines were licensed for use: a quadrivalent (qHPVv) and a bivalent (bHPVv) vaccine. Since 2008, HPV vaccination programmes have been implemented in the majority of the industrialized countries. Since 2013, HPV vaccination has been part of the national programs of 66 countries including almost all countries in North America and Western Europe. Despite all the efforts made by individual countries, coverage rates are lower than expected. Vaccine safety represents one of the main concerns associated with the lack of acceptance of HPV vaccination both in the European Union/European Economic Area and elsewhere. ::: ::: Areas covered: Safety data published on bivalent and quadrivalent HPV vaccines, both in pre-licensure and post-licensure phase, are reviewed. ::: ::: Expert opinion: Based on the latest scientific evidence, both HPV vaccines seem to be safe. Nevertheless, public concern and rumors about adverse events (AE) represent an important barrier to overcome in order to increase vaccine coverage. Passive surveillance of AEs is an important tool for detecting safety signals, but it should be complemented by activities aimed at assessing the real cause of all suspect AEs. Improved vaccine safety surveillance is the first step for effective communication based on scientific evidence.


title: A survey of methods for distributed machine learning

abstract: Traditionally, a bottleneck preventing the development of more intelligent systems was the limited amount of data available. Nowadays, the total amount of information is almost incalculable and automatic data analyzers are even more needed. However, the limiting factor is the inability of learning algorithms to use all the data to learn within a reasonable time. In order to handle this problem, a new field in machine learning has emerged: large-scale learning. In this context, distributed learning seems to be a promising line of research since allocating the learning process among several workstations is a natural way of scaling up learning algorithms. Moreover, it allows to deal with data sets that are naturally distributed, a frequent situation in many real applications. This study provides some background regarding the advantages of distributed environments as well as an overview of distributed learning for dealing with “very large” data sets.


title: Simulation and Hardware Implementation of Incremental Conductance MPPT With Direct Control Method Using Cuk Converter

abstract: This paper presents simulation and hardware implementation of incremental conductance (IncCond) maximum power point tracking (MPPT) used in solar array power systems with direct control method. The main difference of the proposed system to existing MPPT systems includes elimination of the proportional-integral control loop and investigation of the effect of simplifying the control circuit. Contributions are made in several aspects of the whole system, including converter design, system simulation, controller programming, and experimental setup. The resultant system is capable of tracking MPPs accurately and rapidly without steady-state oscillation, and also, its dynamic performance is satisfactory. The IncCond algorithm is used to track MPPs because it performs precise control under rapidly changing atmospheric conditions. MATLAB and Simulink were employed for simulation studies, and Code Composer Studio v3.1 was used to program a TMS320F2812 digital signal processor. The proposed system was developed and tested successfully on a photovoltaic solar panel in the laboratory. Experimental results indicate the feasibility and improved functionality of the system.


title: Convolutional Hashing for Automated Scene Matching

abstract: We present a powerful new loss function and training scheme for learning binary hash functions. In particular, we demonstrate our method by creating for the first time a neural network that outperforms state-of-the-art Haar wavelets and color layout descriptors at the task of automated scene matching. By accurately relating distance on the manifold of network outputs to distance in Hamming space, we achieve a 100-fold reduction in nontrivial false positive rate and significantly higher true positive rate. We expect our insights to provide large wins for hashing models applied to other information retrieval hashing tasks as well.


title: Precise 2.5D facial landmarking via an analysis by synthesis approach

abstract: 3D face landmarking aims at automatic localization of 3D facial features and has a wide range of applications, including face recognition, face tracking, facial expression analysis. Methods so far developed for pure 2D texture images were shown sensitive to lighting condition changes. In this paper, we present a statistical model-based technique for accurate 3D face landmarking, thus using an “analysis by synthesis” approach. Our model learns from a training set both variations of global face shapes as well as the local ones in terms of scale-free texture and range patches around each landmark. Given an shape instance, local regions of a new face can be approximated by synthesizing texture and range instances using respectively the texture and range models. By optimizing an objective function describing the similarity of the new face and instances, we can optimize the best shape in order to locate the landmarks. Experimented on more than 1860 face models from FRGC datasets, our method achieves an average of locating errors less than 7mm for 15 feature points. Compared with a curvature analysis-based method also developed within our team, this learning-based method enables localization of more facial landmarks with a general better accuracy at the cost of a learning step.


title: Using Deep Convolutional Neural Networks in Monte Carlo Tree Search

abstract: Deep Convolutional Neural Networks have revolutionized Computer Go. Large networks have emerged as state-of-the-art models for move prediction and are used not only as stand-alone players but also inside Monte Carlo Tree Search to select and bias moves. Using neural networks inside the tree search is a challenge due to their slow execution time even if accelerated on a GPU. In this paper we evaluate several strategies to limit the number of nodes in the search tree in which neural networks are used. All strategies are assessed using the freely available cuDNN library. We compare our strategies against an optimal upper bound which can be estimated by removing timing constraints. We show that the best strategies are only 50 ELO points worse than this upper bound.


title: Model-Based Testing with a General Purpose Keyword-Driven Test Automation Framework

abstract: Model-based testing (MBT) is a relatively new approach to software testing that extends test automation from test execution to test design using automatic test generation from models. The effective use of the new approach requires new skills and knowledge, such as test modeling skills, but also good tool support. This paper focuses upon the integration of the TEMA model-based graphical user interface test generator with a keyword-driven test automation framework, Robot Framework. Both of the tools are available as open source. The purpose of the integration was to enable the wide testing library support of Robot Framework to be used in online model-based testing. The main contribution of this paper is to present the integration providing a base for future MBT utilization, but we will also describe a short case study where we experimented with the integration in testing a Java Swing GUI application and discuss early experiences in using the framework in testing Web GUIs.


title: PORTFOLIO CREDIT RISK : TOP DOWN VS . BOTTOM UP APPROACHES

abstract: Dynamic reduced form models of portfolio credit risk can be distinguished by the way in which the intensity of the default process is specied. In a bottom up model, the portfolio intensity is an aggregate of the constituent intensities. In a top down model, the portfolio intensity is specied without reference to the constituents. This expository article contrasts these modeling approaches. It emphasizes the role of the information ltration as a modeling tool.


title: A Self-Training Approach for Visual Tracking and Recognition of Complex Human Activity Patterns

abstract: Automatically observing and understanding human activities is one of the big challenges in computer vision research. Among the potential fields of application are areas such as robotics, human computer interaction or medical research. In this article we present our work on unintrusive observation and interpretation of human activities for the precise recognition of human fullbody motions. The presented system requires no more than three cameras and is capable of tracking a large spectrum of motions in a wide variety of scenarios. This includes scenarios where the subject is partially occluded, where it manipulates objects as part of its activities, or where it interacts with the environment or other humans. Our system is self-training, i.e. it is capable of learning models of human motion over time. These are used both to improve the prediction of human dynamics and to provide the basis for the recognition and interpretation of observed activities. The accuracy and robustness obtained by our system is the combined result of several contributions. By taking an anthropometric human model and optimizing it towards use in a probabilistic tracking framework we obtain a detailed biomechanical representation of human shape, posture and motion. Furthermore, we introduce a sophisticated hierarchical sampling strategy for tracking that is embedded in a probabilistic framework and outperforms state-of-the-art Bayesian methods. We then show how to track complex manipulation activities in everyday environments using a combination of learned human appearance models and implicit environment models. Finally, we discuss a locally consistent representation of human motion that we use as a basis for learning environment- and task-specific motion models. All methods presented in this article have been subject to extensive experimental evaluation on today's benchmarks and several challenging sequences ranging from athletic exercises to ergonomic case studies to everyday manipulation tasks in a kitchen environment.


title: Network Packet Analysis for Detecting Malicious Insider

abstract: With the wide spread use of the Internet, the cyber crimes have also increased. The industries are facing more and more cyber threats. The threats to an organization come not only from the outside but also from within the organization. The malicious insider in an organization uses the network facility to do the industrial espionage. The information in the form of packets that is being transferred over the network is essential to be analyzed to identify the suspicious activities. The paper proposes a framework to identify the various activities being performed by the user based on the network packet analysis. A methodology is proposed to identify the malicious insider in an organization.


title: Forecasting Price of Cryptocurrencies Using Tweets Sentiment Analysis

abstract: The problem is to find a method to predict the two-hour price of cryptocurrencies on the basis of the Social Factors, which are increasingly used for online transactions worldwide. The few previous methods proposed to predict price of cryptocurrency are inefficient because they fail to take into consideration the differences in the attributes between real currencies and cryptocurrencies. In this paper, we focus on two cryptocurrencies, namely Bitcoin and Litecoin, each with a large market size and user base, and attempt to predict their future prices using multi-linear regression model.


title: IEEE 802.11ax: On Performance of Multi-Antenna Technologies with LDPC Codes

abstract: The implementation of low-density parity-check (LDPC) codes will be a mandatory feature in the 2019 IEEE 802.11ax amendment, the sixth generation of wireless local area networks (WLANs) for frequencies below 6 GHz. Orthogonal frequency division multiplexing multi-user multiple-input multiple-output (OFDM MU-MIMO) technologies for both downlink and uplink have been specified in Task Group (TG) 802.11ax meetings to improve the spectrum and area efficiency in ultra-dense WLANs. In this paper, we compare the performance of binary convolutional codes (BCC) and LDPC codes using different configurations of multi-antenna technologies taking into account both hardware and system impairments. We have concluded that on the most study cases, the implementation of LDPC codes in the IEEE 802.11ax physical layer allows power gains between 2.5 and 4.0 dB in relation to BCC with soft-decision Viterbi decoding.


title: Big Data Analytics to Predict Breast Cancer Recurrence on SEER Dataset using MapReduce Approach

abstract: traditional data analytic might not have the capacity to handle enormous amount of data. Due to the rapid growth of information, solutions need to be contemplated and provided in order to handle and extract value and knowledge from these data sets. Moreover, decision makers should have the capacity to increase significant bits of knowledge from such fluctuated and quickly evolving information. Such esteem can be given utilizing big data analytic, which is the utilization of advanced analytic techniques on big data using MapReduce approach. This paper examines to develop a high performance platform to efficiently analyse big SEER (Surveillance, Epidemiology, and End Results) breast cancer data set using MapReduce to find the recurrence of breast cancer. Keywordscancer; Big data, Classification; Data analytics, MapReduce, SEER.


title: Childhood Asymmetry Labium Majus Enlargement (CALME): Description of Two Cases

abstract: Background: Childhood asymmetry labium majus enlargement (CALME) is an uncommon, benign condition that occurs in pre- and early pubertal girls and is characterized by a painless, fluctuating, non-tender labial swelling with normal overlying skin. Recognition of this benign condition is essential. Differentiation with several other diseases that mimic CALME and require different diagnostic and therapeutic approaches is mandatory. Two cases of CALME are described in this report. Differential diagnoses and therapeutic approaches are highlighted. Case presentation: The first case was an 11-year-old Caucasian girl referred to our hospital for the evaluation of right labium majus, which showed a palpable, painless, soft, non-tender, non-erythematous enlargement measuring approximately 2 cm with indistinct borders. Ultrasound showed a mass 23 × 18 × 12 mm in diameter. Surgical excision of the mass was performed and in the histopathological evaluation, the tissue specimens were composed of haphazardly arranged vascular channels, adipose tissue and nervous elements that were components of the vulvar soft tissue and were compatible with the diagnosis of CALME. Case 2 was a 6-year-old Caucasian girl who presented a post-traumatic painless mass of left labium majus swelling that progressively increased in volume. Ultrasound study evidenced an ill-defined heterogeneous echotexture mass 26 × 15 × 10 mm in diameter and magnetic resonance imaging confirmed these findings. Histopathological examination was performed after bioptic sampling evidencing normal constituents of vulvar soft tissue, including fibroblast, collagen, adipose tissue, blood vessels and nerves compatible with CALME. Conclusions: CALME is a particular clinical condition that occurs mainly in pre-pubertal girls and has a benign course but poses numerous problems in differential diagnosis that can be solved only with careful clinical observation and with a careful use of radiological imaging techniques. Our cases, in agreement with recent literature, suggest that radical excision is not recommended and that surgical biopsy should be taken into consideration only in cases of doubt.


Smart Societies, Infrastructure, Technologies and Applications


title: Accurate Doppler Frequency Shift Estimation for any Satellite Orbit

abstract: One of the most important parameters in satellite communication links, particularly in low earth orbits (LEO) and elliptical orbits is Doppler frequency shift (DFS) and Doppler frequency rate (DFR) at communication channels. This paper analyzes Doppler frequency effects in various satellite orbits. For this purpose, a new orbit generator is presented for accurate simulation of satellite orbits. The non-spherical mass distribution turbulence of the earth and its effects on Doppler shift calculations is considered and simulation results of Doppler frequency shift for an elliptical LEO satellite in L-band is presented.


title: Automatic Online Calibration of Cameras and Lasers

abstract: The combined use of 3D scanning lasers with 2D cameras has become increasingly popular in mobile robotics, as the sparse depth measurements of the former augment the dense color information of the latter. Sensor fusion requires precise 6DOF transforms between the sensors, but hand-measuring these values is tedious and inaccurate. In addition, autonomous robots can be rendered inoperable if their sensors’ calibrations change over time. Yet previously published camera-laser calibration algorithms are offline only, requiring significant amounts of data and/or specific calibration targets; they are thus unable to correct calibration errors that occur during live operation. In this paper, we introduce two new real-time techniques that enable camera-laser calibration online, automatically, and in arbitrary environments. The first is a probabilistic monitoring algorithm that can detect a sudden miscalibration in a fraction of a second. The second is a continuous calibration optimizer that adjusts transform offsets in real time, tracking gradual sensor drift as it occurs. Although the calibration objective function is not globally convex and cannot be optimized in real time, in practice it is always locally convex around the global optimum, and almost everywhere else. Thus, the local shape of the objective function at the current parameters can be used to determine whether the sensors are calibrated, and allows the parameters to be adjusted gradually so as to maintain the global optimum. In several online experiments on thousands of frames in real markerless scenes, our method automatically detects miscalibrations within one second of the error exceeding .25 deg or 10cm, with an accuracy of 100%. In addition, rotational sensor drift can be tracked in real-time with a mean error of just .10 deg. Together, these techniques allow significantly greater flexibility and adaptability of robots in unknown and potentially harsh environments.


title: Autonomic disorders predicting Parkinson's disease

abstract: Summary It is now well recognized that there is a premotor phase of Parkinson's disease (PD) with hyposmia and REM sleep behavior disorder caused by degeneration of specific CNS neurons. Most patients with PD describe autonomic symptoms at the time of diagnosis suggesting that these features may have potential sensitivity as clinical biomarkers of the premotor phase. The recognition that damage to peripheral autonomic neurons is present in the early stages of PD has led to a search for specific abnormalities in autonomic function that could serve as predictive biomarkers. There is evidence that constipation, urinary and sexual dysfunction and more recently decreased cardiac chronotropic response during exercise, are part of the premotor parkinsonian phenotype. The sensitivity and specificity of these features has yet to be accurately assessed. We briefly review the evidence for autonomic dysfunction as biomarker of premotor PD.


title: A Probabilistic Topic Model for Mashup Tag Recommendation

abstract: Mashups are prevalent Service-Oriented Architecture (SOA) based applications consisting of multiple Web Application Programming Interfaces (APIs) and content. Tags have been extensively used to organize and index mashup services. However, people favor manual tags creation in the past. This approach demands user intervention, which is extremely time-consuming and probes to errors. In this paper we propose a novel Mashup-API-Tag model for automatic mashup tag recommendation. The model simultaneously incorporates the composition relationships between mashups and APIs as well as the annotation relationships between APIs and tags to discover the latent topics. Then the semantic similarity between Web APIs and mashups can be acquired. Subsequently, tags of chosen APIs are recommended to a mashup where the mashup and the APIs are most similar. In addition, we develop a tag filtering algorithm to select the most relevant tags for recommendation. The experimental results on a real world dataset prove that our approach outperforms other methods, including frequency-based methods and the methods that only consider the composition relationships and the annotation relationships separately.