title: What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator?

abstract: In neural image captioning systems, a recurrent neural network (RNN) is typically viewed as the primary `generation' component. This view suggests that the image features should be `injected' into the RNN. This is in fact the dominant view in the literature. Alternatively, the RNN can instead be viewed as only encoding the previously generated words. This view suggests that the RNN should only be used to encode linguistic features and that only the final representation should be `merged' with the image features at a later stage. This paper compares these two architectures. We find that, in general, late merging outperforms injection, suggesting that RNNs are better viewed as encoders, rather than generators.


title: Workflow Working Group in the frame of ASOV

abstract: Started in 2005 in the frame of the french Action Specifique Observatoire Virtuel, the aim of the Workflow Working Group is to provide Workflow use cases in several domains (image, spectrum, etc.) and to improve them with VO (or other) existing tools. Another important point is to develop collaborations with people from the IT community (links to EGEE, ...). The last meeting (16 June 2006) was organised in Paris at IAP involving about thirty participants.


title: Cloud computing services: taxonomy and comparison

abstract: Cloud computing is a highly discussed topic in the technical and economic world, and many of the big players of the software industry have entered the development of cloud services. Several companies what to explore the possibilities and benefits of incorporating such cloud computing services in their business, as well as the possibilities to offer own cloud services. However, with the amount of cloud computing services increasing quickly, the need for a taxonomy framework rises. This paper examines the available cloud computing services and identifies and explains their main characteristics. Next, this paper organizes these characteristics and proposes a tree-structured taxonomy. This taxonomy allows quick classifications of the different cloud computing services and makes it easier to compare them. Based on existing taxonomies, this taxonomy provides more detailed characteristics and hierarchies. Additionally, the taxonomy offers a common terminology and baseline information for easy communication. Finally, the taxonomy is explained and verified using existing cloud services as examples.


title: Based on GDPR privacy in UML: Case of e-learning program

abstract: Data privacy regulatory framework is evolving. The long awaited EU General Data Protection Regulation (GDPR) sets new standards by increasing and improving online personal data protection (i.e. privacy). In order for informatics to take and safeguard a firm compliance stand, privacy concerns should be addressed both by design and by default. In the same time special attention is needed as to avoid smothering the added value provided by the (vast) quantity and (low) cost of web-residing-information. By combining regulation, information privacy and best practices into UML use case diagrams, paper in hand examines GDPR requirements by utilizing the educational e-platform paradigm “Law Courses”1. Ultimately it aims to equip engineers with a GDPR-standards compatible software incorporation case.


title: Detection of closed sharp edges in point clouds using normal estimation and graph theory

abstract: The reconstruction of a surface model from a point cloud is an important task in the reverse engineering of industrial parts. We aim at constructing a curve network on the point cloud that will define the border of the various surface patches. In this paper, we present an algorithm to extract closed sharp feature lines, which is necessary to create such a closed curve network. We use a first order segmentation to extract candidate feature points and process them as a graph to recover the sharp feature lines. To this end, a minimum spanning tree is constructed and afterwards a reconnection procedure closes the lines. The algorithm is fast and gives good results for real-world point sets from industrial applications.


title: System identification of discrete model for DC motor positioning

abstract: This paper presents modeling of a discrete DC motor positioning using system identification. The identification of most suitable discrete model of DC motor was done by using MATLAB/Simulink. The best discrete model was determined by testing with four different sampling times. Result presented shows that the best model achieved performance of 87.79% at 55ms sampling time.


title: Variational method to the second-order impulsive PDE with non-periodic boundary value problems (I)

abstract: By using the critical point theory of variational method and Lax-Milgram theorem, we obtain the new results for the existence of the solution of the second-order impulsive elliptic differential equations with non-periodic boundary value problems.


title: Geolocalization of Crowdsourced Images for 3-D Modeling of City Points of Interest

abstract: Geolocalization of crowdsourced images is a challenging task that is getting increased attention nowadays due to the rise in popularity of geotagging and its applications. Among these applications, 3-D modeling from Internet photograph collections is a very active research topic with great promise and potential. In order to automize and optimize the crowdsourced 3-D modeling process, this letter proposes a novel framework that can be used for automatic 3-D modeling of city points of interest (POIs), such as statues, buildings, and temporary artworks. Crowdsourced images related to the POI and its location are collected using a geographical Web search process based on geotags and semantic geodata. Subsequently, panoramic Google Street View (SV) images are used to geolocalize the images. If enough feature matches are found between the image and one of the SV images, the image is annotated with the location metadata of the best matching image from the SV database. Otherwise, when too few matches are found, the image most probably will not contain the POI in its field of view (FOV), and it is filtered out. For optimal performance, the equirectangular panoramic SV images are transformed into an SV data set of perspective cutouts facing the POI with different pitches and FOVs. From this data set, a basic 3-D model of the POI and its environment is generated. Finally, the geolocalized crowdsourced images refine and optimize the 3-D model using the matching matrix that is generated from the geolocalization results. Experiments show the feasibility of our approach on different types of city POIs. Our main contribution is that we can decrease the 3-D modeling computation time by more than half and significantly improve the model completeness. Finally, it is important to remark that the applicability of the proposed framework is not limited to 3-D modeling but can also be used in other domains, such as geoaugmented reality and location-based media annotation.


title: Benchmarking File System Benchmarking: It *IS * Rocket Science

abstract: The quality of file system benchmarking has not improved in over a decade of intense research spanning hundreds of publications. Researchers repeatedly use a wide range of poorly designed benchmarks, and in most cases, develop their own ad-hoc benchmarks. Our community lacks a definition of what we want to benchmark in a file system. We propose several dimensions of file system benchmarking and review the wide range of tools and techniques in widespread use. We experimentally show that even the simplest of benchmarks can be fragile, producing performance results spanning orders of magnitude. It is our hope that this paper will spur serious debate in our community, leading to action that can improve how we evaluate our file and storage systems.


title: Childhood immunization refusal: provider and parent perceptions.

abstract: Approximately 73% of children in the United States are immunized, and most parents permit their children to receive immunizations. However, a sizable minority of parents have concerns about immunizations, and a few refuse some or all vaccines. The national childhood immunization refusal rate is not precisely known. A 1999 national telephone survey indicated that almost one fourth of parents felt uncertain about the increasing number of childhood vaccines. Parents with alternative medical orientations had more concerns and were


Chestnutʼs Obstetric Anesthesia: Principles and Practice, 5th ed.


title: Terabit/Sec VCSEL-Based 48-Channel Optical Module Based on Holey CMOS Transceiver IC

abstract: We report here on the design, fabrication and characterization of 48-channel parallel optical transceivers demonstrating terabit/sec data transfer rate. The 0.48 Tb/s transmit plus 0.48 Tb/s receive throughput was achieved using a second-generation single-chip holey CMOS transceiver IC. In addition to 24 receiver (RX) and 24 laser diode driver circuits, the 5.2 mm × 5.8 mm single CMOS chip incorporates 48 through-substrate optical vias (holes), one for each transmitter (TX) and RX channel. A complete holey Optochip is formed following direct flip-chip attachment of 24-channel 850-nm VCSEL and PD arrays. The 48 optical vias enable optical access to the 24 VCSELs and 24 PDs. The holey Optochip concept provides a dense chip-scale package which is fully compatible with industry-standard top emitting/detecting 850-nm VCSELs/PDs providing optimized high-speed performance through close integration of the optoelectronic (OE) devices with their drive electronics. Furthermore, the optical vias and OE devices are arranged in a 4 × 12 array on 250 μm × 250 μ m pitch to facilitate direct fiber-coupling to a standard 4 × 12 multi-mode fiber array. The Optochips are packaged into complete modules by flip-chip soldering to high-density, high-speed organic carriers. A pluggable connector soldered to the bottom of the carrier provides all module electrical I/O. The 18 mm × 18 mm overall module area is dictated by the 0.8 mm-pitch ball grid array (BGA) of the organic carrier and connector. Fully functional holey Optomodules with 24 TX and 24 RX channels operate up to 20 Gb/s/ch achieving efficiencies (including both TX and RX) of 7.3 pJ/bit. The terabit/sec data rate (480 Gb/s TX + 480 Gb/s RX) is highest reported for single-chip CMOS transceiver modules.


title: Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification

abstract: Knowledge distillation is a potential solution for model compression. The idea is to make a small student network imitate the target of a large teacher network, then the student network can be competitive to the teacher one. Most previous studies focus on model distillation in the classification task, where they propose different architectures and initializations for the student network. However, only the classification task is not enough, and other related tasks such as regression and retrieval are barely considered. To solve the problem, in this paper, we take face recognition as a breaking point and propose model distillation with knowledge transfer from face classification to alignment and verification. By selecting appropriate initializations and targets in the knowledge transfer, the distillation can be easier in non-classification tasks. Experiments on the CelebA and CASIA-WebFace datasets demonstrate that the student network can be competitive to the teacher one in alignment and verification, and even surpasses the teacher network under specific compression rates. In addition, to achieve stronger knowledge transfer, we also use a common initialization trick to improve the distillation performance of classification. Evaluations on the CASIA-Webface and large-scale MS-Celeb-1M datasets show the effectiveness of this simple trick.


title: A software engineering approach to ontology building

abstract: Ontologies are the backbone of the Semantic Web, a semantic-aware version of the World Wide Web. The availability of large-scale high quality domain ontologies depends on effective and usable methodologies aimed at supporting the crucial process of ontology building. Ontology building exhibits a structural and logical complexity that is comparable to the production of software artefacts. This paper proposes an ontology building methodology that capitalizes the large experience drawn from a widely used standard in software engineering: the Unified Software Development Process or Unified Process (UP). In particular, we propose UP for ONtology (UPON) building, a methodology for ontology building derived from the UP. UPON is presented with the support of a practical example in the eBusiness domain. A comparative evaluation with other methodologies and the results of its adoption in the context of the Athena EU Integrated Project are also discussed.


title: Exploring the Conceptual Universe

abstract: Humans can learn to organize many kinds of domains into categories, including real-world domains such as kinsfolk and synthetic domains such as sets of geometric figures that vary along several dimensions. Psychologists have studied many individual domains in detail, but there have been few attempts to characterize or explore the full space of possibilities. This article provides a formal characterization that takes objects, features, and relations as primitives and specifies conceptual domains by combining these primitives in different ways. Explaining how humans are able to learn concepts within all of these domains is a challenge for computational models, but I argue that this challenge can be met by models that rely on a compositional representation language such as predicate logic. The article presents such a model and demonstrates that it accounts well for human concept learning across 11 different domains.


title: An electrostatic gripper for flexible objects

abstract: We demonstrate a flexible, electrostatic adhesive gripper designed to controllably grasp and manipulate soft goods in space. The 8-fingered gripper has 50 cm2 of active electrodes operating at 3 kV. It generates electrostatic adhesion forces up to 3.5 N (0.70 kPa) on Ge-coated polyimide film and 1.2 N on MLI blanket, a film composite used for satellite thermal insulation. Extremely low-force gripper engagement (0.08 N) and release (0.04 N) of films is ideal for micro-gravity. Individual fingers generate shear adhesion forces up to 4.76 N (5.04 kPa) using electrostatic adhesive and 45.0 N (47.6 kPa) with a hybrid electrostatic / gecko adhesive. To simulate a satellite servicing task, the gripper was mounted on a 7-DoF robot arm and performed a supervised grasp, manipulate, and release sequence on a hanging, Al-coated PET film.


title: Decision Making with the Analytic Network Process

abstract: One of the most advanced and complex multi- criteria decision-making methods is the analytic network process (ANP). This method supports modelling dep


title: Learning with hypergraphs: Clustering, classification, and embedding

abstract: We usually endow the investigated objects with pairwise relationships, which can be illustrated as graphs. In many real-world problems, however, relationships among the objects of our interest are more complex than pair-wise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected valuable for our learning tasks however. Therefore we consider using hypergraphs instead to completely represent complex relationships among the objects of our interest, and thus the problem of learning with hypergraphs arises. Our main contribution in this paper is to generalize the powerful methodology of spectral clustering which originally operates on undirected graphs to hypergraphs, and further develop algorithms for hypergraph embedding and transductive classification on the basis of the spectral hypergraph clustering approach. Our experiments on a number of benchmarks showed the advantages of hypergraphs over usual graphs.


title: Short Communication

abstract: We addressed the problem of discriminating between 24 diseased and 17 healthy specimens on the basis of protein mass spectra. To prepare the data, we performed mass to charge ratio (m/z) normalization, baseline elimination, and conversion of absolute peak height measures to height ratios. After preprocessing, the major difficulty encountered was the extremely large number of variables (1676 m/z values) versus the number of examples (41). Dimensionality reduction was treated as an integral part of the classification process; variable selection was coupled with model construction in a single ten-fold cross-validation loop. We explored different experimental setups involving two peak height representations, two variable selection methods, and six induction algorithms, all on both the original 1676-mass data set and on a prescreened 124-mass data set. Highest predictive accuracies (1-2 off-sample misclassifications) were achieved by a multilayer perceptron and Naive Bayes, with the latter displaying more consistent performance (hence greater reliability) over varying experimental conditions. We attempted to identify the most discriminant peaks (proteins) on the basis of scores assigned by the two variable selection methods and by neural network based sensitivity analysis. These three scoring schemes consistently ranked four peaks as the most relevant discriminators: 11683, 1403, 17350 and 66107.


title: Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting

abstract: Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all clusters will be fed into independent GFS models with the ability of rule base extraction and data base tuning. We evaluate capability of the proposed approach by applying it on stock price data gathered from IT and Airlines sectors, and compare the outcomes with previous stock price forecasting methods using mean absolute percentage error (MAPE). Results show that the proposed approach outperforms all previous methods, so it can be considered as a suitable tool for stock price forecasting problems.


title: One-Shot Segmentation in Clutter

abstract: We tackle the problem of one-shot segmentation: finding and segmenting a previously unseen object in a cluttered scene based on a single instruction example. We propose a novel dataset, which we call $\textit{cluttered Omniglot}$. Using a baseline architecture combining a Siamese embedding for detection with a U-net for segmentation we show that increasing levels of clutter make the task progressively harder. Using oracle models with access to various amounts of ground-truth information, we evaluate different aspects of the problem and show that in this kind of visual search task, detection and segmentation are two intertwined problems, the solution to each of which helps solving the other. We therefore introduce $\textit{MaskNet}$, an improved model that attends to multiple candidate locations, generates segmentation proposals to mask out background clutter and selects among the segmented objects. Our findings suggest that such image recognition models based on an iterative refinement of object detection and foreground segmentation may provide a way to deal with highly cluttered scenes.


title: Digital Certificates: Applied Internet Security

abstract: Preface. I. SECURITY, CRYPTOGRAPHY, AND DIGITAL CERTIFICATES. 1. Security and the Internet. 2. Cryptography. 3. Digital Certificates, Certification Authorities, and Public-Key Infrastructures. II. APPLIED INTERNET SECURITY. 4. Browser Security and Trust-Based Software Management. 5. Secure Messaging and S/MIME. 6. Web Server Security, Certificates, and Access Control. 7. Integrity and Open Standards. III. SECURITY MANAGEMENT PRACTICE. 8. Security Management Solutions. 9. Certification Services. 10. Local Registration Authorities (LRAs). IV. THE TRUST DILEMMA. 11. Certificate Policy & Certification Practice. 12. Secure Electronic Commerce. 13. Computer Security Management. V. WEB SECURITY AND CERTIFICATES. 14. Secure Web Communications n Server Authentication. 15. Secure Web Communications n Client Authentication. 16. Microsoft Certificate Server n The Architecture. VI. MICROSOFT CERTIFICATE SERVER. 17. Programming Microsoft Certificate Server n The Policy Module and Extension Handlers. 18. Programming Microsoft Certificate Server n The Exit Module. 19. Programming Microsoft Certificate Server n Certificate Clients and Intermediaries. 20. Programming Microsoft Certificate Server n Admin Programs. Appendix A. Abstract Syntax Notation (ASN.1). Appendix B. Structuring X.509 Certificate for Use with Microsoft Products. Index. 0201309807T04062001


title: Machine learning for stress detection from ECG signals in automobile drivers

abstract: Physiological sensor analytics is becoming an important tool to monitor health as the availability of sensor-enabled portable, wearable, and implantable devices becomes ubiquitous in the growing Internet of Things (IoT). Physiological multi-sensor studies have been conducted previously to detect stress. In this study, we focus on ECG monitoring that can now be performed with minimally invasive wearable patches and sensors, to develop an efficient and robust mechanism for accurate stress identification. A unique aspect of our research is personalized individual stress analysis including three stress levels: low, medium and high. Using machine learning algorithms from the ECG signals alone, we could achieve 88.24% accuracy in detecting the three classes of stress. We also find that high stress can be successfully detected for a person in comparison to his or her rest period with 100% accuracy.


title: Trial for RDF: adapting graph query languages for RDF data

abstract: Querying RDF data is viewed as one of the main applications of graph query languages, and yet the standard model of graph databases -- essentially labeled graphs -- is different from the triples-based model of RDF. While encodings of RDF databases into graph data exist, we show that even the most natural ones are bound to lose some functionality when used in conjunction with graph query languages. The solution is to work directly with triples, but then many properties taken for granted in the graph database context (e.g., reachability) lose their natural meaning. Our goal is to introduce languages that work directly over triples and are closed, i.e., they produce sets of triples, rather than graphs. Our basic language is called TriAL, or Triple Algebra: it guarantees closure properties by replacing the product with a family of join operations. We extend TriAL with recursion, and explain why such an extension is more intricate for triples than for graphs. We present a declarative language, namely a fragment of datalog, capturing the recursive algebra. For both languages, the combined complexity of query evaluation is given by low-degree polynomials. We compare our languages with relational languages, such as finite-variable logics, and previously studied graph query languages such as adaptations of XPath, regular path queries, and nested regular expressions; many of these languages are subsumed by the recursive triple algebra. We also provide examples of the usefulness of TriAL in querying graph and RDF data.


title: A Comparison of Generalized Linear Discriminant Analysis Algorithms

abstract: Linear discriminant analysis (LDA) is a dimension reduction method which finds an optimal linear transformation that maximizes the class separability. However, in undersampled problems where the number of data samples is smaller than the dimension of data space, it is difficult to apply LDA due to the singularity of scatter matrices caused by high dimensionality. In order to make LDA applicable, several generalizations of LDA have been proposed recently. In this paper, we present theoretical and algorithmic relationships among several generalized LDA algorithms and compare their computational complexities and performances in text classification and face recognition. Towards a practical dimension reduction method for high dimensional data, an efficient algorithm is proposed, which reduces the computational complexity greatly while achieving competitive prediction accuracies. We also present nonlinear extensions of these LDA algorithms based on kernel methods. It is shown that a generalized eigenvalue problem can be formulated in the kernel-based feature space, and generalized LDA algorithms are applied to solve the generalized eigenvalue problem, resulting in nonlinear discriminant analysis. Performances of these linear and nonlinear discriminant analysis algorithms are compared extensively.


title: Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images

abstract: In this paper, a new computer tomography (CT) lung nodule computer-aided detection (CAD) method is proposed for detecting both solid nodules and ground-glass opacity (GGO) nodules (part solid and nonsolid). This method consists of several steps. First, the lung region is segmented from the CT data using a fuzzy thresholding method. Then, the volumetric shape index map, which is based on local Gaussian and mean curvatures, and the ldquodotrdquo map, which is based on the eigenvalues of a Hessian matrix, are calculated for each voxel within the lungs to enhance objects of a specific shape with high spherical elements (such as nodule objects). The combination of the shape index (local shape information) and ldquodotrdquo features (local intensity dispersion information) provides a good structure descriptor for the initial nodule candidates generation. Antigeometric diffusion, which diffuses across the image edges, is used as a preprocessing step. The smoothness of image edges enables the accurate calculation of voxel-based geometric features. Adaptive thresholding and modified expectation-maximization methods are employed to segment potential nodule objects. Rule-based filtering is first used to remove easily dismissible nonnodule objects. This is followed by a weighted support vector machine (SVM) classification to further reduce the number of false positive (FP) objects. The proposed method has been trained and validated on a clinical dataset of 108 thoracic CT scans using a wide range of tube dose levels that contain 220 nodules (185 solid nodules and 35 GGO nodules) determined by a ground truth reading process. The data were randomly split into training and testing datasets. The experimental results using the independent dataset indicate an average detection rate of 90.2%, with approximately 8.2 FP/scan. Some challenging nodules such as nonspherical nodules and low-contrast part-solid and nonsolid nodules were identified, while most tissues such as blood vessels were excluded. The method's high detection rate, fast computation, and applicability to different imaging conditions and nodule types shows much promise for clinical applications.


title: Direct Visual Odometry using Bit-Planes

abstract: Feature descriptors, such as SIFT and ORB, are well-known for their robustness to illumination changes, which has made them popular for feature-based VSLAM\@. However, in degraded imaging conditions such as low light, low texture, blur and specular reflections, feature extraction is often unreliable. In contrast, direct VSLAM methods which estimate the camera pose by minimizing the photometric error using raw pixel intensities are often more robust to low textured environments and blur. Nonetheless, at the core of direct VSLAM is the reliance on a consistent photometric appearance across images, otherwise known as the brightness constancy assumption. Unfortunately, brightness constancy seldom holds in real world applications. In this work, we overcome brightness constancy by incorporating feature descriptors into a direct visual odometry framework. This combination results in an efficient algorithm that combines the strength of both feature-based algorithms and direct methods. Namely, we achieve robustness to arbitrary photometric variations while operating in low-textured and poorly lit environments. Our approach utilizes an efficient binary descriptor, which we call Bit-Planes, and show how it can be used in the gradient-based optimization required by direct methods. Moreover, we show that the squared Euclidean distance between Bit-Planes is equivalent to the Hamming distance. Hence, the descriptor may be used in least squares optimization without sacrificing its photometric invariance. Finally, we present empirical results that demonstrate the robustness of the approach in poorly lit underground environments.


title: A Situation-Adaptive Lane-Keeping Support System: Overview of the SAFELANE Approach

abstract: Going beyond standard lane-departure-avoidance systems, this paper addresses the development of a system that is able to deal with a large set of different traffic situations. Its foundation lies on a thoroughly constituted environment detection through which a decision system is built. From the output of the decision module, the driver is warned or corrected through suited actuators that are coupled to control strategies. The input to the system comes from cameras, which are supplemented by active sensors (such as radar and laser scanners) and vehicle dynamic data, digital road maps, and precise vehicle-positioning data. In this paper, the presented system design is divided into three layers: the perception layer, which is responsible for the environment perception, and the decision and action layers, which are responsible for evaluating and executing actions, respectively.


title: Darwin the detective: Observable facial muscle contractions reveal emotional high-stakes lies ☆

abstract: Abstract Deception—a fundamental aspect of human communication—often is accompanied by the simulation of unfelt emotions or the concealment of genuine emotions to correspond to the false message. We investigated the consequences of extremely high-stakes emotional deception on the engagement of particular facial muscles, posited by Darwin [Darwin, C. (1872/2005). The expression of the emotions in man and animals. In J. D. Watson (Ed.), Darwin: The indelible stamp (pp. 1066–1257) Philadelphia: Running Press] to reveal the false face. The videotaped facial actions of a sample of individuals ( N =52) emotionally pleading to the public for the return of a missing relative—half of whom eventually were convicted of murdering that person—were coded frame by frame (30 frames/s for a total of 23,622 frames). Findings support the view that emotional "leakage," particularly via those facial muscles under less cortical control, is a byproduct of the overextended cognitive resources available to convey elaborate lies. Specifically, the "grief" muscles ( corrugator supercilii , depressor anguli oris ) were more often contracted in the faces of genuine than deceptive pleaders. Subtle contraction of the zygomatic major (masking smiles) and full contraction of the frontalis (failed attempts to appear sad) muscles were more commonly identified in the faces of deceptive pleaders. Thus, while interpersonal deception often is highly successful, signs of covert emotional states are communicated clearly to the informed observer.


Bearing Health Condition Prediction Using Deep Belief Network


title: Locality-Sensitive Bloom Filter for Approximate Membership Query

abstract: In many network applications, Bloom filters are used to support exact-matching membership query for their randomized space-efficient data structure with a small probability of false answers. In this paper, we extend the standard Bloom filter to Locality-Sensitive Bloom Filter (LSBF) to provide Approximate Membership Query (AMQ) service. We achieve this by replacing uniform and independent hash functions with locality-sensitive hash functions. Such replacement makes the storage in LSBF to be locality sensitive. Meanwhile, LSBF is space efficient and query responsive by employing the Bloom filter design. In the design of the LSBF structure, we propose a bit vector to reduce False Positives (FP). The bit vector can verify multiple attributes belonging to one member. We also use an active overflowed scheme to significantly decrease False Negatives (FN). Rigorous theoretical analysis (e.g., on FP, FN, and space overhead) shows that the design of LSBF is space compact and can provide accurate response to approximate membership queries. We have implemented LSBF in a real distributed system to perform extensive experiments using real-world traces. Experimental results show that LSBF, compared with a baseline approach and other state-of-the-art work in the literature (SmartStore and LSB-tree), takes less time to respond AMQ and consumes much less storage space.


title: An Anomaly-Based Method for DDoS Attacks Detection using RBF Neural Networks

abstract: Distributed denial of service (DDoS) attacks are serious threats for availability of the internet services. These types of attacks command multiple agents to send a great number of packets to a victim and thus can easily exhaust the resources of the victim. In this paper we propose an anomaly-based DDoS detection method based on the various features of attack packets, obtained from study the incoming network traffic and using of Radial Basis Function (RBF) neural networks to analyze these features. We evaluate the proposed method using our simulated network and UCLA Dataset. The results show that the proposed system can make real-time detection accuracy better than 96% for DDoS attacks.


Manifesto for the New Humanities : A Review of Digital _ Humanities


title: Efficacy and safety of dupilumab in adults with moderate-to-severe atopic dermatitis inadequately controlled by topical treatments: a randomised, placebo-controlled, dose-ranging phase 2b trial

abstract: Summary Background Data from early-stage studies suggested that interleukin (IL)-4 and IL-13 are requisite drivers of atopic dermatitis, evidenced by marked improvement after treatment with dupilumab, a fully-human monoclonal antibody that blocks both pathways. We aimed to assess the efficacy and safety of several dose regimens of dupilumab in adults with moderate-to-severe atopic dermatitis inadequately controlled by topical treatments. Methods In this randomised, placebo-controlled, double-blind study, we enrolled patients aged 18 years or older who had an Eczema Area and Severity Index (EASI) score of 12 or higher at screening (≥16 at baseline) and inadequate response to topical treatments from 91 study centres, including hospitals, clinics, and academic institutions, in Canada, Czech Republic, Germany, Hungary, Japan, Poland, and the USA. Patients were randomly assigned (1:1:1:1:1:1), stratified by severity (moderate or severe, as assessed by Investigator's Global Assessment) and region (Japan vs rest of world) to receive subcutaneous dupilumab: 300 mg once a week, 300 mg every 2 weeks, 200 mg every 2 weeks, 300 mg every 4 weeks, 100 mg every 4 weeks, or placebo once a week for 16 weeks. We used a central randomisation scheme, provided by an interactive voice response system. Drug kits were coded, providing masking to treatment assignment, and allocation was concealed. Patients on treatment every 2 weeks and every 4 weeks received volume-matched placebo every week when dupilumab was not given to ensure double blinding. The primary outcome was efficacy of dupilumab dose regimens based on EASI score least-squares mean percentage change (SE) from baseline to week 16. Analyses included all randomly assigned patients who received one or more doses of study drug. This trial is registered with ClinicalTrials.gov, number NCT01859988. Findings Between May 15, 2013, and Jan 27, 2014, 452 patients were assessed for eligibility, and 380 patients were randomly assigned. 379 patients received one or more doses of study drug (300 mg once a week [n=63], 300 mg every 2 weeks [n=64], 200 mg every 2 weeks [n=61], 300 mg every 4 weeks [n=65], 100 mg every 4 weeks [n=65]; placebo [n=61]). EASI score improvements favoured all dupilumab regimens versus placebo (p Interpretation Dupilumab improved clinical responses in adults with moderate-to-severe atopic dermatitis in a dose-dependent manner, without significant safety concerns. Our findings show that IL-4 and IL-13 are key drivers of atopic dermatitis. Funding Sanofi and Regeneron Pharmaceuticals.


title: Seahawk: stack overflow in the IDE

abstract: Services, such as Stack Overflow, offer a web platform to programmers for discussing technical issues, in form of Question and Answers (Q&A). Since Q&A services store the discussions, the generated “crowd knowledge” can be accessed and consumed by a large audience for a long time. Nevertheless, Q&A services are detached from the development environments used by programmers: Developers have to tap into this crowd knowledge through web browsers and cannot smoothly integrate it into their workflow. This situation hinders part of the benefits of Q&A services. To better leverage the crowd knowledge of Q&A services, we created Seahawk, an Eclipse plugin that supports an integrated and largely automated approach to assist programmers using Stack Overflow. Seahawk formulates queries automatically from the active context in the IDE, presents a ranked and interactive list of results, lets users import code samples in discussions through drag & drop and link Stack Overflow discussions and source code persistently as a support for team work. Video Demo URL: http://youtu.be/DkqhiU9FYPI.


title: Planting design for urban parks: Space syntax as a landscape design assessment tool

abstract: Trees are a major factor in defining the spatial qualities of outdoor spaces. This study investigates the influence of spatial configuration produced by the proposed tree planting design on the visual fields of an urban park using space syntax theory. Space syntax theory assumes that an urban area can be represented as a matrix of connected spaces. The quantitative properties of this matrix in the form of syntactic measures can be measured using computer simulations. This research investigates how space syntax techniques can help assess the effect of tree configurations on the social structure of a small-scale garden in an urban park. Such techniques are assumed useful in predicting the social structure of the proposed space and in assessing design alternatives. An experimental study using three different planting design proposals for an urban park is conducted. Data are analyzed using space syntax techniques. Results reveal a significant effect of planting configurations on syntactic measures across the three proposals. This study suggests that space syntax techniques may have a significant value in the assessment of schematic planting design, especially at the pedestrian movement level.


title: Decoupling the Influence of Permeability and Conductivity in Pulsed Eddy-Current Measurements

abstract: Eddy-current (EC) sensors are widely used in the industry for nondestructive evaluation (NDE) and structural health monitoring (SHM). It has this wide acceptance mainly because it has high tolerance to harsh environments, low cost, and high bandwidth. Its variant pulsed eddy current (PEC) provides even more depth information of test materials, which can be applied in liftoff measurement (displacement and coating thickness measurement), defect measurement, and material characterization. However, PEC sensors are prone to measurement errors due to a phenomenon called electrical runout (ERO), which is attributed to the inhomogeneity of the test material. The main thrust of this paper is to investigate, therefore, the contributions of the electromagnetic properties (permeability and conductivity) of the sample to ERO with a view to separate the influence of these two properties. Both time-domain and frequency-domain analyses are carried out in this investigation viz-a-viz: transient response, differential normalized response, magnitude spectrum, and normalized magnitude spectrum. This paper reveals that conductivity effects are prominent in the rising edge of the transient response, hence, changing the spectral pattern in the frequency domain while permeability effects dominate in the stable phase of the transient response, thus this effect can be suppressed or reduced by normalization showing that it is only an amplitude change.


title: Semi-supervised Hierarchical Clustering

abstract: Semi-supervised clustering (i.e., clustering with knowledge-based constraints) has emerged as an important variant of the traditional clustering paradigms. However, most existing semi-supervised clustering algorithms are designed for partitional clustering methods and few research efforts have been reported on semi-supervised hierarchical clustering methods. In addition, current semi-supervised clustering methods have been focused on the use of background information in the form of instance level must-link and cannot-link constraints, which are not suitable for hierarchical clustering where data objects are linked over different hierarchy levels. In this paper, we propose a novel semi-supervised hierarchical clustering framework based on ultra-metric dendrogram distance. The proposed framework is able to incorporate triple-wise relative constraints. We establish the connection between hierarchical clustering and ultra-metric transformation of dissimilarity matrix and propose two techniques (the constrained optimization technique and the transitive dissimilarity based technique) for semi-supervised hierarchical clustering. Experimental results demonstrate the effectiveness and the efficiency of our proposed methods.


title: Deep Learning for Anomaly Detection: A Survey

abstract: Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is two-fold, firstly we present a structured and comprehensive overview of research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art research techniques into different categories based on the underlying assumptions and approach adopted. Within each category we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. For each category, we present we also present the advantages and limitations and discuss the computational complexity of the techniques in real application domains. Finally, we outline open issues in research and challenges faced while adopting these techniques.


Business intelligence: an analysis of the literature


title: Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

abstract: Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem. Bayesian NNs, which learn a distribution over weights, are currently the state-of-the-art for estimating predictive uncertainty; however these require significant modifications to the training procedure and are computationally expensive compared to standard (non-Bayesian) NNs. We propose an alternative to Bayesian NNs that is simple to implement, readily parallelizable, requires very little hyperparameter tuning, and yields high quality predictive uncertainty estimates. Through a series of experiments on classification and regression benchmarks, we demonstrate that our method produces well-calibrated uncertainty estimates which are as good or better than approximate Bayesian NNs. To assess robustness to dataset shift, we evaluate the predictive uncertainty on test examples from known and unknown distributions, and show that our method is able to express higher uncertainty on out-of-distribution examples. We demonstrate the scalability of our method by evaluating predictive uncertainty estimates on ImageNet.


Novel Scheduling Policy for Delay Analysis of Multi-Hop Wireless Networks


title: The screens culture: impact on ADHD

abstract: Children’s use of electronic media, including Internet and video gaming, has increased dramatically to an average in the general population of roughly 3 h per day. Some children cannot control their Internet use leading to increasing research on “internet addiction.” The objective of this article is to review the research on ADHD as a risk factor for Internet addiction and gaming, its complications, and what research and methodological questions remain to be addressed. The literature search was done in PubMed and Psychinfo, as well as by hand. Previous research has demonstrated rates of Internet addiction as high as 25% in the population and that it is addiction more than time of use that is best correlated with psychopathology. Various studies confirm that psychiatric disorders, and ADHD in particular, are associated with overuse, with severity of ADHD specifically correlated with the amount of use. ADHD children may be vulnerable since these games operate in brief segments that are not attention demanding. In addition, they offer immediate rewards with a strong incentive to increase the reward by trying the next level. The time spent on these games may also exacerbate ADHD symptoms, if not directly then through the loss of time spent on more developmentally challenging tasks. While this is a major issue for many parents, there is no empirical research on effective treatment. Internet and off-line gaming overuse and addiction are serious concerns for ADHD youth. Research is limited by the lack of measures for youth or parents, studies of children at risk, and studies of impact and treatment.


title: Ka-Band Wideband-Gap Solid-State Power Amplifier: General Architecture Considerations

abstract: Motivated by recent advances in wideband-gap (WBG) gallium nitride (GaN) semiconductor technology, there is considerable interest in developing efficient solidstate power amplifiers (SSPAs) as an alternative to the traveling-wave tube amplifier (TWTA) for space applications. This article documents the general architecture considerations pertinent to developing SSPA architectures that can enable a 120-W, 40 percent power-added efficiency (PAE) SSPA at 31 to 36 GHz. This article develops an estimated loss budget for the common elements of these SSPA architectures. It then considers the bandwidth effects of the monolithic microwave integrated circuits (MMICs) and various combiners/dividers used that influence the performance of the SSPA architecture. Finally consideration is given to possible MMIC power levels, efficiencies, and gain. Using these factors, this article develops MMIC and power-combiner requirements for an SSPA architecture to reach the goal of 120 W with a 40 percent PAE.


title: Virtual reality in mental health

abstract: Background ::: Several virtual reality (VR) applications for the understanding, assessment and treatment of mental health problems have been developed in the last 10 years. The purpose of this review is to outline the current state of virtual reality research in the treatment of mental health problems.


The Rise and Fall of Cryptocurrencies


title: GeoSpark: a cluster computing framework for processing large-scale spatial data

abstract: This paper introduces G eo S park an in-memory cluster computing framework for processing large-scale spatial data. G eo S park consists of three layers: Apache Spark Layer, Spatial RDD Layer and Spatial Query Processing Layer. Apache Spark Layer provides basic Spark functionalities that include loading / storing data to disk as well as regular RDD operations. Spatial RDD Layer consists of three novel Spatial Resilient Distributed Datasets (SRDDs) which extend regular Apache Spark RDDs to support geometrical and spatial objects. G eo S park provides a geometrical operations library that accesses Spatial RDDs to perform basic geometrical operations (e.g., Overlap, Intersect). System users can leverage the newly defined SRDDs to effectively develop spatial data processing programs in Spark. The Spatial Query Processing Layer efficiently executes spatial query processing algorithms (e.g., Spatial Range, Join, KNN query) on SRDDs. G eo S park also allows users to create a spatial index (e.g., R-tree, Quad-tree) that boosts spatial data processing performance in each SRDD partition. Preliminary experiments show that G eo S park achieves better run time performance than its Hadoop-based counterparts (e.g., SpatialHadoop).


title: Friend or Foe?: Your Wearable Devices Reveal Your Personal PIN

abstract: The proliferation of wearable devices, e.g., smartwatches and activity trackers, with embedded sensors has already shown its great potential on monitoring and inferring human daily activities. This paper reveals a serious security breach of wearable devices in the context of divulging secret information (i.e., key entries) while people accessing key-based security systems. Existing methods of obtaining such secret information relies on installations of dedicated hardware (e.g., video camera or fake keypad), or training with labeled data from body sensors, which restrict use cases in practical adversary scenarios. In this work, we show that a wearable device can be exploited to discriminate mm-level distances and directions of the user's fine-grained hand movements, which enable attackers to reproduce the trajectories of the user's hand and further to recover the secret key entries. In particular, our system confirms the possibility of using embedded sensors in wearable devices, i.e., accelerometers, gyroscopes, and magnetometers, to derive the moving distance of the user's hand between consecutive key entries regardless of the pose of the hand. Our Backward PIN-Sequence Inference algorithm exploits the inherent physical constraints between key entries to infer the complete user key entry sequence. Extensive experiments are conducted with over 5000 key entry traces collected from 20 adults for key-based security systems (i.e. ATM keypads and regular keyboards) through testing on different kinds of wearables. Results demonstrate that such a technique can achieve 80% accuracy with only one try and more than 90% accuracy with three tries, which to our knowledge, is the first technique that reveals personal PINs leveraging wearable devices without the need for labeled training data and contextual information.


GPR ( Ground Penetrating Radar ) into Real World


title: An Experimental Evaluation of the Reliability of LoRa Long-Range Low-Power Wireless Communication

abstract: Recent technological innovations allow compact radios to transmit over long distances with minimal energy consumption and could drastically affect the way Internet of Things (IoT) technologies communicate in the near future. By extending the communication range of links, it is indeed possible to reduce the network diameter to a point that each node can communicate with almost every other node in the network directly. This drastically simplifies communication, removing the need of routing, and significantly reduces the overhead of data collection. Long-range low-power wireless technology, however, is still at its infancy, and it is yet unclear (i) whether it is sufficiently reliable to complement existing short-range and cellular technologies and (ii) which radio settings can sustain a high delivery rate while maximizing energy-efficiency. To shed light on this matter, this paper presents an extensive experimental study of the reliability of LoRa , one of the most promising long-range low-power wireless technologies to date. We focus our evaluation on the impact of physical layer settings on the effective data rate and energy efficiency of communications. Our results show that it is often not worth tuning parameters, thereby reducing the data rate in order to maximize the probability of successful reception, especially on links at the edge of their communication range. Furthermore, we study the impact of environmental factors on the performance of LoRa, and show that higher temperatures significantly decrease the received signal strength and may drastically affect packet reception.


title: Cartesian Impedance Control of a UAV with a Robotic Arm

abstract: Abstract The dynamic model of a UAV with an attached robotic arm is derived in a symbolic matrix form through the Euler-Lagrangian formalism. A Cartesian impedance control, which provides a dynamic relationship between external generalized forces acting on the structure and the system motion, is then designed. The hovering control of a quadrotor, equipped with a 3-DOF robotic arm and subject to contact forces and external disturbances, is tested in a simulated case study.


title: FM Backscatter: Enabling Connected Cities and Smart Fabrics

abstract: This paper enables connectivity on everyday objects by transforming them into FM radio stations. To do this, we show for the first time that ambient FM radio signals can be used as a signal source for backscatter communication. Our design creates backscatter transmissions that can be decoded on any FM receiver including those in cars and smartphones. This enables us to achieve a previously infeasible capability: backscattering information to cars and smartphones in outdoor environments. ::: Our key innovation is a modulation technique that transforms backscatter, which is a multiplication operation on RF signals, into an addition operation on the audio signals output by FM receivers. This enables us to embed both digital data as well as arbitrary audio into ambient analog FM radio signals. We build prototype hardware of our design and successfully embed audio transmissions over ambient FM signals. Further, we achieve data rates of up to 3.2 kbps and ranges of 5-60 feet, while consuming as little as 11.07{\mu}W of power. To demonstrate the potential of our design, we also fabricate our prototype on a cotton t-shirt by machine sewing patterns of a conductive thread to create a smart fabric that can transmit data to a smartphone. We also embed FM antennas into posters and billboards and show that they can communicate with FM receivers in cars and smartphones.


title: The Pascal Visual Object Classes Challenge: A Retrospective

abstract: The Pascal Visual Object Classes (VOC) challenge consists of two components: (i) a publicly available dataset of images together with ground truth annotation and standardised evaluation software; and (ii) an annual competition and workshop. There are five challenges: classification, detection, segmentation, action classification, and person layout. In this paper we provide a review of the challenge from 2008–2012. The paper is intended for two audiences: algorithm designers, researchers who want to see what the state of the art is, as measured by performance on the VOC datasets, along with the limitations and weak points of the current generation of algorithms; and, challenge designers, who want to see what we as organisers have learnt from the process and our recommendations for the organisation of future challenges. To analyse the performance of submitted algorithms on the VOC datasets we introduce a number of novel evaluation methods: a bootstrapping method for determining whether differences in the performance of two algorithms are significant or not; a normalised average precision so that performance can be compared across classes with different proportions of positive instances; a clustering method for visualising the performance across multiple algorithms so that the hard and easy images can be identified; and the use of a joint classifier over the submitted algorithms in order to measure their complementarity and combined performance. We also analyse the community’s progress through time using the methods of Hoiem et al. (Proceedings of European Conference on Computer Vision, 2012) to identify the types of occurring errors. We conclude the paper with an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.


title: Molecular Characterization of Sec2 Loci in Wheat—Secale africanum Derivatives Demonstrates Genomic Divergence of Secale Species

abstract: The unique 75 K γ-secalins encoded by Sec2 loci in Secale species is composed of almost half rye storage proteins. The chromosomal location of Sec2 loci in wild Secale species, Secale africanum, was carried out by the wheat--S. africanum derivatives, which were identified by genomic in situ hybridization and multi-color fluorescence in situ hybridization. The Sec2 gene-specific PCR analysis indicated that the S. cereale Sec2 was located on chromosome 2R, while the S. africanum Sec2 was localized on chromosome 6Rafr of S. africanum. A total of 38 Sec2 gene sequences were isolated from S. africanum, S. cereale and S. sylvestre by PCR-based cloning. Phylogenetic analysis showed that S. africanum Sec2 diverged from S. cereale Sec2 approximately 2-3 million years ago. The illegitimate recombination of chromosome 2R-6R involving the Sec2 loci region may accelerate sequence variation during evolutionary process from wild to cultivated Secale species.


title: EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies

abstract: This work presents EddyNet, a deep learning based architecture for automated eddy detection and classification from Sea Surface Height (SSH) maps provided by the Copernicus Marine and Environment Monitoring Service (CMEMS). EddyNet is a U-Net like network that consists of a convolutional encoder-decoder followed by a pixel-wise classification layer. The output is a map with the same size of the input where pixels have the following labels \{'0': Non eddy, '1': anticyclonic eddy, '2': cyclonic eddy\}. We investigate the use of SELU activation function instead of the classical ReLU+BN and we use an overlap based loss function instead of the cross entropy loss. Keras Python code, the training datasets and EddyNet weights files are open-source and freely available on this https URL


title: Review on mechanisms for detecting sinkhole attacks on RPLs

abstract: Internet Protocol version 6 (IPv6) over Low power Wireless Personal Area Networks (6LoWPAN) is extensively used in wireless sensor networks (WSNs) due to its ability to transmit IPv6 packet with low bandwidth and limited resources. 6LoWPAN has several operations in each layer. Most existing security challenges are focused on the network layer, which is represented by its routing protocol for low-power and lossy network (RPL). RPL components include WSN nodes that have constrained resources. Therefore, the exposure of RPL to various attacks may lead to network damage. A sinkhole attack is a routing attack that could affect the network topology. This paper aims to investigate the existing detection mechanisms used in detecting sinkhole attack on RPL-based networks. This work categorizes and presents each mechanism according to certain aspects. Then, their advantages and drawbacks with regard to resource consumption and false positive rate are discussed and compared.


title: Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients

abstract: The ‘default network’ is defined as a set of areas, encompassing posterior-cingulate/precuneus, anterior cingulate/mesiofrontal cortex and temporo-parietal junctions, that show more activity at rest than during attention-demanding tasks. Recent studies have shown that it is possible to reliably identify this network in the absence of any task, by resting state functional magnetic resonance imaging connectivity analyses in healthy volunteers. However, the functional significance of these spontaneous brain activity fluctuations remains unclear. The aim of this study was to test if the integrity of this resting-state connectivity pattern in the default network would differ in different pathological alterations of consciousness. Fourteen non-communicative brain-damaged patients and 14 healthy controls participated in the study. Connectivity was investigated using probabilistic independent component analysis, and an automated template-matching component selection approach. Connectivity in all default network areas was found to be negatively correlated with the degree of clinical consciousness impairment, ranging from healthy controls and locked-in syndrome to minimally conscious, vegetative then coma patients. Furthermore, precuneus connectivity was found to be significantly stronger in minimally conscious patients as compared with unconscious patients. Locked-in syndrome patient’s default network connectivity was not significantly different from controls. Our results show that default network connectivity is decreased in severely brain-damaged patients, in proportion to their degree of consciousness impairment. Future prospective studies in a larger patient population are needed in order to evaluate the prognostic value of the presented methodology.


title: Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges

abstract: Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future.


title: Analytical study of RGB vertical stripe and RGBX square-shaped subpixel arrangements

abstract: The frequency characteristics of subpixel-based decimation with RGB vertical stripe and RGBX square-shaped subpixel arrangements are studied. To achieve higher apparent resolution than pixel-based decimation, the sampling locations are specially chosen for each of two subpixel arrangements, resulting in relatively small magnitudes of horizontal and vertical aliasing spectra in frequency domain. Thanks to 2-D RGBX square-shaped subpixel arrangement, all the horizontal, vertical, diagonal and anti-diagonal aliasing spectra merely contain low-frequency information, indicating that subpixel-based decimation with RGBX square-shaped panel is more effective in retaining original high frequency details than RGB vertical stripe subpixel arrangement.


title: Kansei Engineering with Online Review Mining for Hotel Service Development

abstract: In the Internet era, consumers can comment on their hotel living experience directly through the Internet. Thus, consumers online comments might become a key factor for consumers choosing hotels when planning their tourism itinerary. The large amount of User-Generated Content (UGC) provided by the consumers has also provided chances for the hospitality industry to consumer behavior statistic inferences. This study applies Kansei engineering combined with text mining to develop guidelines for hotel services to help managers meet consumers Kansei needs. Through understanding consumers online comments by text mining, this study identifies the most important customer Kansei and hotel service characteristics, and then analyzes the relationships between them. The finds can provide suggestions for hotel service improvement and development.


Introduction to partitioning-based clustering methods with a robust example ∗


title: Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies

abstract: This paper proposes a method to predict fluctuations in the prices of cryptocurrencies, which are increasingly used for online transactions worldwide. Little research has been conducted on predicting fluctuations in the price and number of transactions of a variety of cryptocurrencies. Moreover, the few methods proposed to predict fluctuation in currency prices are inefficient because they fail to take into account the differences in attributes between real currencies and cryptocurrencies. This paper analyzes user comments in online cryptocurrency communities to predict fluctuations in the prices of cryptocurrencies and the number of transactions. By focusing on three cryptocurrencies, each with a large market size and user base, this paper attempts to predict such fluctuations by using a simple and efficient method.


Spelling Correction for Morphologically Rich Language: a Case Study of Russian.


title: Managing Disruption Risks in Supply Chains

abstract: There are two broad categories of risk affecting supply chain design and management: (1) risks arising from the problems of coordinating supply and demand, and (2) risks arising from disruptions to normal activities. This paper is concerned with the second category of risks, which may arise from natural disasters, from strikes and economic disruptions, and from acts of purposeful agents, including terrorists. The paper provides a conceptual framework that reflects the joint activities of risk assessment and risk mitigation that are fundamental to disruption risk management in supply chains. We then consider empirical results from a rich data set covering the period 1995–2000 on accidents in the U. S. Chemical Industry. Based on these results and other literature, we discuss the implications for the design of management systems intended to cope with supply chain disruption risks.


title: Methodological implications of critical realism for mixed-methods research

abstract: Building on recent developments in mixed methods, we discuss the methodological implications of critical realism and explore how these can guide dynamic mixed-methods research design in information systems. Specifically, we examine the core ontological assumptions of CR in order to gain some perspective on key epistemological issues such as causation and validity, and illustrate how these shape our logic of inference in the research process through what is known as retroduction. We demonstrate the value of a CR-led mixed-methods research approach by drawing on a study that examines the impact of ICT adoption in the financial services sector. In doing so, we provide insight into the interplay between qualitative and quantitative methods and the particular value of applying mixed methods guided by CR methodological principles. Our positioning of demi-regularities within the process of retroduction contributes a distinctive development in this regard. We argue that such a research design enables us to better address issues of validity and the development of more robust meta-inferences.


title: Resource intensity, institutions, and development

abstract: Summary. — We examine the relationship between resource abundance and several indicators of human welfare. Consistent with the existing literature on the relationship between resource abundance and economic growth we find that, given an initial income level, resource-intensive countries tend to suffer lower levels of human development. While we find only weak support for a direct link between resources and welfare, there is an indirect link that operates through institutional quality. There are also significant differences in the effects that resources have on different measures of institutional quality. These results imply that the ‘‘resource curse’’ is a more encompassing phenomenon than previously considered, and that key differences exist between the effects of different resource types on various aspects of governance and human welfare. 2005 Elsevier Ltd. All rights reserved.


title: The neurobiology of semantic memory

abstract: Semantic memory includes all acquired knowledge about the world and is the basis for nearly all human activity, yet its neurobiological foundation is only now becoming clear. Recent neuroimaging studies demonstrate two striking results: the participation of modality-specific sensory, motor, and emotion systems in language comprehension, and the existence of large brain regions that participate in comprehension tasks but are not modality-specific. These latter regions, which include the inferior parietal lobe and much of the temporal lobe, lie at convergences of multiple perceptual processing streams. These convergences enable increasingly abstract, supramodal representations of perceptual experience that support a variety of conceptual functions including object recognition, social cognition, language, and the remarkable human capacity to remember the past and imagine the future.


title: Is the childbirth experience improved by a birth plan?

abstract: From antenatal clinics in Sweden, 271 women were recruited after week 33 of pregnancy and given a questionnaire designed to assess their attitudes and feelings about the coming childbirth. Thereafter, they formulated a birth plan. The midwife in attendance at the birth was able to refer to this plan. Women who followed this program were compared with women from the same clinics who were asked to complete a questionnaire during the first postpartum week to assess their birth experience. A questionnaire at the end of pregnancy, followed by a birth plan, was not effective in improving women's experiences of childbirth. In the birth plan group, women gave significantly lower scores for the relationship to the first midwife they met during delivery, with respect to listening and paying attention to needs and desires, support, guiding, and respect. Although a birth plan did not improve the experience of childbirth in the overall group, there may be beneficial effects with regard to fear, pain, and concerns about the newborn for certain subgroups of women.


title: On the Waveforms for 5G Mobile Broadband Communications

abstract: To realize the vision of ubiquitous mobile broadband where radio access performance should not be a limiting factor for user experience, we need to access very large bandwidths, and thus consider higher frequency bands up to the millimeter wave region. Air interface design, including waveforms, is a very important component for the success of 5G mobile broadband (MBB) in terms of flexibility, energy efficiency and cost efficiency. In this paper, we compare two waveforms, orthogonal frequency division multiplexing (OFDM) and filter bank multicarrier (FBMC), in terms of these requirements. We show that OFDM is a suitable waveform for MBB due to reasonably low overhead, low cost and latency; whereas FBMC loses its spectral properties when non-linear power amplifier is used.


title: A 20μW 10MHz relaxation oscillator with adaptive bias and fast self-calibration in 40nm CMOS for micro-aerial robotics application

abstract: Efficient actuation control of flapping-wing microrobots requires a low-power frequency reference with good absolute accuracy. To meet this requirement, we designed a fully-integrated 10MHz relaxation oscillator in a 40nm CMOS process. By adaptively biasing the continuous-time comparator, we are able to achieve a power consumption of 20μW, a 68% reduction to the conventional fixed bias design. A built-in self-calibration controller enables fast post-fabrication calibration of the clock frequency. Measurements show a frequency drift of 1.2% as the battery voltage changes from 3V to 4.1V.


title: Vision based fire detection

abstract: Vision based fire detection is potentially a useful technique. With the increase in the number of surveillance cameras being installed, a vision based fire detection capability can be incorporated in existing surveillance systems at relatively low additional cost. Vision based fire detection offers advantages over the traditional methods. It will thus complement the existing devices. In this paper, we present spectral, spatial and temporal models of fire regions in visual image sequences. The spectral model is represented in terms of the color probability density of fire pixels. The spatial model captures the spatial structure within a fire region. The shape of a fire region is represented in terms of the spatial frequency content of the region contour using its Fourier coefficients. The temporal changes in these coefficients are used as the temporal signatures of the fire region. Specifically, an auto regressive model of the Fourier coefficient series is used. Experiments with a large number of scenes show that our method is capable of detecting fire reliably.


title: Rotman lens for the millimeter wave frequency range Dirk Nüβler

abstract: Applications in radar system and communications systems require very often antennas with beam steering or multi beam capabilities. For the millimetre frequency range Rotman lenses can be useful as multiple beam forming networks for linear antennas providing the advantage of broadband performance. For microwave bands lens design is predominantly performed in stripline or microstrip technology to meet the requirements for compact low weight and low costs systems. The design and development of Rotman lens at 94 GHz feeding an antenna array for beam steering applications is presented. The construction is completely realized in waveguide technology. Experimental results are compared with theoretical considerations and electromagnetic simulations. The first approach for a two dimensional lens stack for 94 GHz is demonstrated.


title: ATRIUM: A Radar Target Simulator for Complex Traffic Scenarios

abstract: Future autonomous automobiles will require extremely reliable radar sensors to guarantee the safety of road users. This requires test environments to verify that automotive radars work properly even in complex traffic situations. Radar target simulators can generate virtual scattering centers in front of the radar sensor and are therefore a promising verification technique. However, currently available systems only offer a very basic approach to manipulating scattering centers. With the workflow presented in this publication it becomes possible to decompose high-level descriptions of traffic scenarios into parameters required for radar target simulation. The workflow can therefore be used to generate realistic traffic scenarios and thus increases the reliability of automotive radar sensor tests.


title: A Novel Segmentation Technique for Urdu Type-Written Text

abstract: Text segmentation is a process of subdividing the text image into its constituent parts, such as text lines, words and isolated characters. It is the first module in design of Optical character recognition systems. The problem of automatic text segmentation algorithms is increasingly becoming an important issue. Major problems arise due to the lack of standard dataset, a wide diversity of objectives and a lack of meaningful quantitative evaluation. In this paper a new technique is proposed that segments Urdu type written text into text lines on the basis of edges information of connected components. The performance of this technique is tested over the benchmark data set using precision and recall metric with accuracy of 87.36% and 84.75% respectively. Also data set collection, compilation and organization is a part of this research.


Model Predictive Path Integral Control for Car Driving with Dynamic Cost Map


title: Automatic Quantification of Tumour Hypoxia From Multi-Modal Microscopy Images Using Weakly-Supervised Learning Methods

abstract: In recently published clinical trial results, hypoxia-modified therapies have shown to provide more positive outcomes to cancer patients, compared with standard cancer treatments. The development and validation of these hypoxia-modified therapies depend on an effective way of measuring tumor hypoxia, but a standardized measurement is currently unavailable in clinical practice. Different types of manual measurements have been proposed in clinical research, but in this paper we focus on a recently published approach that quantifies the number and proportion of hypoxic regions using high resolution (immuno-)fluorescence (IF) and hematoxylin and eosin (HE) stained images of a histological specimen of a tumor. We introduce new machine learning-based methodologies to automate this measurement, where the main challenge is the fact that the clinical annotations available for training the proposed methodologies consist of the total number of normoxic, chronically hypoxic, and acutely hypoxic regions without any indication of their location in the image. Therefore, this represents a weakly-supervised structured output classification problem, where training is based on a high-order loss function formed by the norm of the difference between the manual and estimated annotations mentioned above. We propose four methodologies to solve this problem: 1) a naive method that uses a majority classifier applied on the nodes of a fixed grid placed over the input images; 2) a baseline method based on a structured output learning formulation that relies on a fixed grid placed over the input images; 3) an extension to this baseline based on a latent structured output learning formulation that uses a graph that is flexible in terms of the amount and positions of nodes; and 4) a pixel-wise labeling based on a fully-convolutional neural network. Using a data set of 89 weakly annotated pairs of IF and HE images from eight tumors, we show that the quantitative results of methods (3) and (4) above are equally competitive and superior to the naive (1) and baseline (2) methods. All proposed methodologies show high correlation values with respect to the clinical annotations.


title: Algorithms and object-oriented programming: bridging the gap

abstract: Currently there is a serious conceptual and technical gap between ideas emphasized in object-oriented programming and ideas as taught in algorithms courses. We suggest both a paradigm for "re-expressing" algorithms in terms of classes and objects, and a set of criteria relative to which the quality of such re-expressions can be evaluated. A detailed example is provided for re-expressing the algorithmic idea of sorting.


title: Calculating the VC-dimension of decision trees

abstract: We propose an exhaustive search algorithm that calculates the VC-dimension of univariate decision trees with binary features. The VC-dimension of the univariate decision tree with binary features depends on (i) the VC-dimension values of the left and right subtrees, (ii) the number of inputs, and (iii) the number of nodes in the tree. From a training set of example trees whose VC-dimensions are calculated by exhaustive search, we fit a general regressor to estimate the VC-dimension of any binary tree. These VC-dimension estimates are then used to get VC-generalization bounds for complexity control using SRM in decision trees, i.e., pruning. Our simulation results shows that SRM-pruning using the estimated VC-dimensions finds trees that are as accurate as those pruned using cross-validation.


title: Genetic counselling and customary consanguineous marriage

abstract: Consanguineous marriage is customary in many societies, but leads to an increased birth prevalence of infants with severe recessive disorders. It is therefore often proposed that consanguineous marriage should be discouraged on medical grounds. However, several expert groups have pointed out that this proposal is inconsistent with the ethical principles of genetic counselling, overlooks the social importance of consanguineous marriage and is ineffective. Instead, they suggest that the custom increases the possibilities for effective genetic counselling, and recommend a concerted effort to identify families at increased risk, and to provide them with risk information and carrier testing when feasible.


title: Pipeline FFT architectures optimized for FPGAs

abstract: This paper presents optimized implementations of two different pipeline FFT processors on Xilinx Spartan-3 and Virtex-4 FPGAs. Different optimization techniques and rounding schemes were explored. The implementation results achieved better performance with lower resource usage than prior art. The 16-bit 1024-point FFT with the R22SDF architecture had a maximum clock frequency of 95.2 MHz and used 2802 slices on the Spartan-3, a throughput per area ratio of 0.034 Msamples/s/slice. The R4SDC architecture ran at 123.8 MHz and used 4409 slices on the Spartan-3, a throughput per area ratio of 0.028 Msamples/s/slice. On Virtex-4, the 16-bit 1024-point R22SDF architecture ran at 235.6 MHz and used 2256 slice, giving a 0.104 Msamples/s/slice ratio; the 16-bit 1024-point R4SDC architecture ran at 219.2 MHz and used 3064 slices, giving a 0.072 Msamples/s/slice ratio. The R22SDF was more efficient than the R4SDC in terms of throughput per area due to a simpler controller and an easier balanced rounding scheme. This paper also shows that balanced stage rounding is an appropriate rounding scheme for pipeline FFT processors.


title: A PATH MODEL LINKING BUSINESS ANALYTICS, DATA-DRIVEN CULTURE, AND COMPETITIVE ADVANTAGE

abstract: Business analytics (BA) has become increasingly important for companies to gain valuable insights from big data and ultimately competitive advantage. However, little empirical evidence exists regarding the mechanisms through which BA impacts on competitive advantage. In light of this paucity, this paper aims to advance our understanding of the impact of BA on competitive advantage. First, this paper provides a BA classification. Second, drawing on contingency theory and the resource-based view, it develops a research model that specifies the paths from BA to competitive advantage. Third, it empirically tests the proposed model using structural equation modelling, offering valuable insights into how different types of BA impact on competitive advantage. Fourth, it systematically tests how resource value, rarity and inimitability impact on competitive advantage. The findings demonstrate that BA, through the mediation of a data-driven culture, positively impacts on information processing capabilities, which in turn have a positive effect on competitive advantage. The findings also demonstrate that resource value, rarity and inimitability partially but strongly mediate the impact of information processing capabilities on competitive advantage. Finally, the paper contributes to managers’ knowledge by demonstrating how different types of BA should be implemented to develop information processing capabilities and gain competitive advantage.


title: Nudging in the clinic: the ethical implications of differences in doctors’ and patients’ point of view

abstract: There is an extensive ethical debate regarding the justifiability of doctors nudging towards healthy behaviour and better health-related choices. One line of argument in favour of nudging is based on empirical findings, according to which a healthy majority among the public support nudges. In this paper, we show, based on an experiment we conducted, that, in health-related choices, people’s ethical attitudes to nudging are strongly affected by the point of view from which the nudge is considered. Significant differences have been found between doctors’ ethical attitude to clinical nudging and that of patients. We show how these differences weaken the argument for nudging from public support. Moreover, our findings raise concerns regarding doctors’ ability to nudge ethically according to their own standards, as they may underestimate the degree of harm medical nudges can cause to informed consent, doctor–patient trust and other important ethically relevant features of health-related choices.


title: Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling

abstract: In this paper, we propose a novel approach based on a single convolutional neural network (CNN) for age estimation. In our proposed network architecture, we first model the randomness of aging with the Gaussian distribution which is used to calculate the Gaussian integral of an age interval. Then, we present a soft softmax regression function used in the network. The new function applies the aging modeling to compute the function loss. Compared with the traditional softmax function, the new function considers not only the chronological age but also the interval nearby true age. Moreover, owing to the complex of Gaussian integral in soft softmax function, a look up table is built to accelerate this process. All the integrals of age values are calculated offline in advance. We evaluate our method on two public datasets: MORPH II and Cross-Age Celebrity Dataset (CACD), and experimental results have shown that the proposed method has gained superior performances compared to the state of the art.


title: Persian EFL Students' Developmental versus Fossilized Prepositional Errors

abstract: This study investigated the developmental and fossilized prepositional errors in Persian EFL learners’ compositions at three levels of proficiency; participants were divided into lowerintermediate, upper-intermediate, and advanced levels. For each participant, four compositions were collected, and after identifying the prepositional errors for each level, their frequency and mean differences were compared across these levels to identify developmental and fossilized errors. Fifteen prepositional error types were identified, of which the most frequently made was the wrong selection of prepositions before nouns. Out of 15 error types, two were identified as developmental and 12 as fossilized. Findings indicate that most of the students committed fossilized errors, which shows the permanent retention of prepositional errors in their compositions. Accordingly, serious pedagogical consideration needs to be given to the teaching of prepositions to improve the writing competence of Iranian EFL learners.


title: Visualizing the Semantic Structure in Classical Music Works

abstract: A major obstacle in the appreciation of classical music is that extensive training is required to understand musical structure and compositional techniques toward comprehending the thoughts behind the musical work. In this paper, we propose an innovative visualization solution to reveal the semantic structure in classical orchestral works such that users can gain insights into musical structure and appreciate the beauty of music. We formulate the semantic structure into macrolevel layer interactions, microlevel theme variations, and macro-micro relationships between themes and layers to abstract the complicated construction of a musical composition. The visualization has been applied with success in understanding some classical music works as supported by highly promising user study results with the general audience and very positive feedback from music students and experts, demonstrating its effectiveness in conveying the sophistication and beauty of classical music to novice users with informative and intuitive displays.


title: AUTOREB: Automatically Understanding the Review-to-Behavior Fidelity in Android Applications

abstract: Along with the increasing popularity of mobile devices, there exist severe security and privacy concerns for mobile apps. On Google Play, user reviews provide a unique understanding of security/privacy issues of mobile apps from users' perspective, and in fact they are valuable feedbacks from users by considering users' expectations. To best assist the end users, in this paper, we automatically learn the security/privacy related behaviors inferred from analysis on user reviews, which we call review-to-behavior fidelity. We design the system AUTOREB that automatically assesses the review-to-behavior fidelity of mobile apps. AUTOREB employs the state-of-the-art machine learning techniques to infer the relations between users' reviews and four categories of security-related behaviors. Moreover, it uses a crowdsourcing approach to automatically aggregate the security issues from review-level to app-level. To our knowledge, AUTOREB is the first work that explores the user review information and utilizes the review semantics to predict the risky behaviors at both review-level and app-level. We crawled a real-world dataset of 2,614,186 users, 12,783 apps and 13,129,783 reviews from Google play, and use it to comprehensively evaluate AUTOREB. The experiment result shows that our method can predict the mobile app behaviors at user-review level with accuracy as high as 94.05%, and also it can predict the security issues at app-level by aggregating the predictions at review-level. Our research offers an insight into understanding the mobile app security concerns from users' perspective, and helps bridge the gap between the security issues and users' perception.


title: Adaptive tracking for pneumatic muscle actuators in bicep and tricep configurations

abstract: Adaptive tracking techniques are applied to pneumatic muscle actuators arranged in bicep and tricep configurations. The control objective is to force the joint angle to track a specified reference path. Mathematical models are derived for the bicep and tricep configurations. The models are nonlinear and in general time-varying, making adaptive control desirable. Stability results are derived, and the results of simulation studies are presented, contrasting the nonlinear adaptive control to a nonadaptive PID control approach.


title: Real time estimation of parameters for controlling and monitoring permanent magnet synchronous motors

abstract: In permanent magnet synchronous motor (PMSM) drives, accurate knowledge of machine parameters has advantage in its control and/or monitoring its condition. This paper provides an adaptation method to determine the machine parameters. The machine's phase resistance, phase inductance and back-emf constant are updated continuously. The online adaptation helps tracking any dynamic change in the motor parameters due to environmental, aging and loading condition. The motor parameters used in the controller can be updated to enhance the performance. This method can also be used for end of line motor parameter calibration in mass production and condition monitoring of drive systems. The method eliminates the need for a priori knowledge of the machine parameters making the controller robust for high performance operation. Simulation and experiment demonstrate the validity and usefulness of the proposed algorithm for PMSM drive.


title: A Hierarchical Multi-Label Classification Algorithm for Gene Function Prediction

abstract: Gene function prediction is a complicated and challenging hierarchical multi-label classification (HMC) task, in which genes may have many functions at the same time and these functions are organized in a hierarchy. This paper proposed a novel HMC algorithm for solving this problem based on the Gene Ontology (GO), the hierarchy of which is a directed acyclic graph (DAG) and is more difficult to tackle. In the proposed algorithm, the HMC task is firstly changed into a set of binary classification tasks. Then, two measures are implemented in the algorithm to enhance the HMC performance by considering the hierarchy structure during the learning procedures. Firstly, negative instances selecting policy associated with the SMOTE approach are proposed to alleviate the imbalanced data set problem. Secondly, a nodes interaction method is introduced to combine the results of binary classifiers. It can guarantee that the predictions are consistent with the hierarchy constraint. The experiments on eight benchmark yeast data sets annotated by the Gene Ontology show the promising performance of the proposed algorithm compared with other state-of-the-art algorithms.


title: Digital Transformation in the automotive industry: towards a generic value network

abstract: The emergence of digital innovations is accelerating and intervening existing business models by delivering ::: opportunities for new services. Drawing on the automotive industry, leading trends like self-driving ::: cars, connectivity and car sharing are creating new business models. These are simultaneously giving ::: rise for innovative market entrants, which begin to transform the automotive industry. However, ::: literature does not provide a generic value network of the automotive industry, including new market ::: players. The paper aims to visualize the current automotive ecosystem, by evolving a generic value ::: network using the e³-value method. We define different roles, which are operating in the automotive ::: industry by analyzing 650 companies reported in the Crunchbase database and present the value ::: streams within the ecosystem. To validate the proposed generic value network we conducted five preliminary ::: interviews with experts from the automotive industry. Our results show the central role of mobility ::: service platforms, emerging disruptive technology providers and the dissemination of industries, ::: e.g., as OEMs collaborate with mobile payment providers. Scholars in this field can apply the developed ::: generic value network for further research, while car manufacturers may apply the model to position ::: themselves in their market and to identify possible disruptive actors or potential business opportunities


title: Fast Stereo Matching Using Rectangular Subregioning and 3D Maximum-Surface Techniques

abstract: This paper presents a fast and reliable stereo matching algorithm which produces a dense disparity map by using fast cross correlation, rectangular subregioning (RSR) and 3D maximum-surface techniques in a coarse-to-fine scheme. Fast correlation is achieved by using the box-filtering technique whose speed is invariant to the size of the correlation window and by segmenting the stereo images into rectangular subimages at different levels of the pyramid. By working with rectangular subimages, not only can the speed of the correlation be further increased, the intermediate memory storage requirement can also be reduced. The disparity map for the stereo images is found in the 3D correlation coefficient volume by obtaining the global 3D maximum-surface rather than simply choosing the position that gives the local maximum correlation coefficient value for each pixel. The 3D maximum-surface is obtained using our new two-stage dynamic programming (TSDP) technique. There are two original contributions in this paper: (1) development of the RSR technique for fast similarity measure; and (2) development of the TSDP technique for efficiently obtaining 3D maximum-surface in a 3D volume. Typical running time of our algorithm implemented in the C language on a 512 × 512 image is in the order of a few seconds on a 500 MHz PC. A variety of synthetic and real images have been tested, and good results have been obtained.


An ontology of suspicious software behavior


title: An Integrated Neural Network Model for Domain Action Determination in Goal-Oriented Dialogues

abstract: Abstract —A speaker’s intentions can be represented by domain actions (domain-independent speech act and domain-dependent concept sequence pairs). Therefore, it is essential that domain actions be determined when implementing dialogue systems because a dialogue system should determine users’ intentions from their utterances and should create counterpart intentions to the users’ intentions. In this paper, a neural network model is proposed for classifying a user’s domain actions and planning a system’s domain actions. An integrated neural network model is proposed for simultaneously determining user and system domain actions using the same framework. The proposed model performed better than previous non-integrated models in an experiment using a goal-oriented dialogue corpus. This result shows that the proposed integration method contributes to improving domain action determination performance. Keywords —Domain Action, Speech Act, Concept Sequence, Neural Network


title: Using Scrum in Distributed Agile Development: A Multiple Case Study

abstract: Distributed agile development (DAD) has received increasing interest both in industry and academia as global software development (GSD) is becoming main-stream. However, agile methods and in particular agile practices have been designed for collocated software development, and are thus not directly applicable to DAD. In this paper, we present findings from a multiple case study on agile practices in two small and one mid-sized distributed Scrum project. Based on an interview study of 19 project team members, we describe how Scrum practices, such as daily scrums, backlogs, and sprints were successfully adopted to distributed development. We also describe supporting GSD practices employed, such as frequent visits and multiple communication modes that the projects used. Finally, we depict the challenges and benefits the case projects reported, as well as lessons learned from applying Scrum in distributed settings.


title: EvolView, an online tool for visualizing, annotating and managing phylogenetic trees

abstract: EvolView is a web application for visualizing, annotating and managing phylogenetic trees. First, EvolView is a phylogenetic tree viewer and customization tool; it visualizes trees in various formats, customizes them through built-in functions that can link information from external datasets, and exports the customized results to publication-ready figures. Second, EvolView is a tree and dataset management tool: users can easily organize related trees into distinct projects, add new datasets to trees and edit and manage existing trees and datasets. To make EvolView easy to use, it is equipped with an intuitive user interface. With a free account, users can save data and manipulations on the EvolView server. EvolView is freely available at: http://www.evolgenius.info/evolview.html.