title: Gamification of online surveys: conceptual foundations and a design process based on the MDA framework

abstract: Gamification has been employed to make online surveys more engaging to fill. Related work has evaluated the psychological and behavioral outcome of gamified surveys, but has been unclear about design methods and best practices. This work discusses foundations, relevant design dimensions (game elements, survey areas and the design process), and critical issues concerning validity. It then proposes a structured process for survey gamification based on the MDA (mechanics, dynamics, aesthetics) framework. An evaluation of the proposed process within a case study is briefly presented along with preliminary, but promising results. The gamification process is put forth in the CHI community for further discussion, evaluation, and application.


title: Revealing the Traces of Median Filtering Using High-Order Local Ternary Patterns

abstract: Recently, detecting the traces introduced by the content-preserving image manipulations has received a great deal of attention from forensic analyzers. It is well known that the median filter is a widely used nonlinear denoising operator. Therefore, the detection of median filtering is of important realistic significance in image forensics. In this letter, a novel local texture operator, named the second-order local ternary pattern (LTP), is proposed for median filtering detection. The proposed local texture operator encodes the local derivative direction variations by using a 3-valued coding function and is capable of effectively capturing the changes of local texture caused by median filtering. In addition, kernel principal component analysis (KPCA) is exploited to reduce the dimensionality of the proposed feature set, making the computational cost manageable. The experiment results have shown that the proposed scheme performs better than several state-of-the-art approaches investigated.


Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control.


title: Learning disentangled representation from 12-lead electrograms: application in localizing the origin of Ventricular Tachycardia

abstract: The increasing availability of electrocardiogram (ECG) data has motivated the use of data-driven models for automating various clinical tasks based on ECG data. The development of subject-specific models are limited by the cost and difficulty of obtaining sufficient training data for each individual. The alternative of population model, however, faces challenges caused by the significant inter-subject variations within the ECG data. We address this challenge by investigating for the first time the problem of learning representations for clinically-informative variables while disentangling other factors of variations within the ECG data. In this work, we present a conditional variational autoencoder (VAE) to extract the subject-specific adjustment to the ECG data, conditioned on task-specific representations learned from a deterministic encoder. To encourage the representation for inter-subject variations to be independent from the task-specific representation, maximum mean discrepancy is used to match all the moments between the distributions learned by the VAE conditioning on the code from the deterministic encoder. The learning of the task-specific representation is regularized by a weak supervision in the form of contrastive regularization. We apply the proposed method to a novel yet important clinical task of classifying the origin of ventricular tachycardia (VT) into pre-defined segments, demonstrating the efficacy of the proposed method against the standard VAE.


title: Image and Depth from a Single Defocused Image Using Coded Aperture Photography

abstract: Depth from defocus and defocus deblurring from a single image are two challenging problems that are derived from the finite depth of field in conventional cameras. Coded aperture imaging is one of the techniques that is used for improving the results of these two problems. Up to now, different methods have been proposed for improving the results of either defocus deblurring or depth estimation. In this paper, a multi-objective function is proposed for evaluating and designing aperture patterns with the aim of improving the results of both depth from defocus and defocus deblurring. Pattern evaluation is performed by considering the scene illumination condition and camera system specification. Based on the proposed criteria, a single asymmetric pattern is designed that is used for restoring a sharp image and a depth map from a single input. Since the designed pattern is asymmetric, defocus objects on the two sides of the focal plane can be distinguished. Depth estimation is performed by using a new algorithm, which is based on image quality assessment criteria and can distinguish between blurred objects lying in front or behind the focal plane. Extensive simulations as well as experiments on a variety of real scenes are conducted to compare our aperture with previously proposed ones.


title: Impact of systemic antifungal therapy on the detection of Candida species in blood cultures in clinical cases of candidemia

abstract: The diagnosis and follow-up of candidemia still rely on blood cultures (BCs). In vitro studies show that antifungals can significantly modify the result of blood culture not containing adsorbing agents. We aimed to evaluate, under clinical conditions, the impact on BC yeast detection of systemic antifungal therapy (SAT). Patients (n = 125) experiencing candidemia at Grenoble University Hospital (France) were included in a 4-year retrospective study. The Plus Aerobic/F (Aerobic) and Plus Anaerobic/F (Anaerobic) bottles, which both contain adsorbing resins and the non-resin selective Mycosis IC/F (Mycosis) bottles, were compared using multivariate hierarchical models adjusted for clinical characteristics. The positivity rate (PR) is decreased in patients with SAT (p < 0.01), abdominal surgery (p = 0.01), and hemodialysis (p = 0.02). In all bottles, SAT reduces PR by a factor of 0.16 (95 % CI: [0.08; 0.32]) and increases the time to positivity (TTP) by a factor of 1.76 ([1.30; 2.40]; p < 0.01). In the presence of SAT, TTP is higher in non-resin bottles (Mycosis) than in resin bottles (RR = 1.76, [1.30; 2.40]); however, the TTP in nonresin and resin bottles remains comparable. Although discordant results are observed with and without SAT (37 and 58 % respectively), we showed that the presence of SAT decreases significantly the agreement rate by a factor of 0.29 (CI: [0.12; 0.68]). The combination of Anaerobic and Mycosis bottles allowed a 100 % positivity rate for C. glabrata. SAT significantly affects BC results. Because they provide additional and complementary results, this study supports the concomitant use of resin and selective bottles, especially in patients receiving SAT.


title: Problems in measuring effectiveness in software process improvement: A longitudinal study of organizational change at Danske Data

abstract: Software process improvement (SPI) is a widely recognized approach that software companies implement to improve quality, productivity, and time-to-market. Assessing and analyzing performance improvements are important SPI activities. However, many SPI managers have found it difficult to develop and implement effective performance measurement programs for SPI, in part because guidelines for conducting SPI measurements are scarce. We address this gap in the SPI literature by examining major problems that SPI change agents encounter when developing and implementing SPI measurement programs. We report on a longitudinal study of an SPI change initiative and the challenges that the SPI Team faced in dealing with the issues of measuring effectiveness of the initiative. We systematically analyze an SPI performance measurement program to understand its limitations and the problems that the SPI Team encountered when implementing it. We used an organizational change theory framework to derive theoretical and practical insights that can help managers and researchers develop and implement better SPI performance measurement programs.


title: There's nothing more practical than a good theory : Integrating motivational interviewing and self-determination theory

abstract: In this article we compare and integrate two well-established approaches to motivating therapeutic change, namely self-determination theory (SDT; Deci & Ryan, 1985, 2000) and motivational interviewing (MI; Miller & Rollnick, 1991, 2002). We show that SDT's theoretical focus on the internalization of therapeutic change and on the issue of need-satisfaction is fully compatible with key principles and clinical strategies within MI. We further suggest that basic need-satisfaction might be an important mechanism accounting for the positive effects of MI. Conversely, MI principles may provide SDT researchers with new insight into the application of SDT's theoretical concept of autonomy-support, and suggest new ways of testing and developing SDT. In short, the applied approach of MI and the theoretical approach of SDT might be fruitfully married, to the benefit of both.


title: VoxelMorph: A Learning Framework for Deformable Medical Image Registration

abstract: We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich deformation models. In contrast to this approach and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images. We parameterize the function via a convolutional neural network and optimize the parameters of the neural network on a set of images. Given a new pair of scans, VoxelMorph rapidly computes a deformation field by directly evaluating the function. In this paper, we explore two different training strategies. In the first (unsupervised) setting, we train the model to maximize standard image matching objective functions that are based on the image intensities. In the second setting, we leverage auxiliary segmentations available in the training data. We demonstrate that the unsupervised model’s accuracy is comparable to the state-of-the-art methods while operating orders of magnitude faster. We also show that VoxelMorph trained with auxiliary data improves registration accuracy at test time and evaluate the effect of training set size on registration. Our method promises to speed up medical image analysis and processing pipelines while facilitating novel directions in learning-based registration and its applications. Our code is freely available at https://github.com/voxelmorph/voxelmorph .


title: Why grit requires perseverance and passion to positively predict performance

abstract: Prior studies linking grit—defined as perseverance and passion for long-term goals—to performance are beset by contradictory evidence. As a result, commentators have increasingly declared that grit has limited effects. We propose that this inconsistent evidence has occurred because prior research has emphasized perseverance and ignored, both theoretically and empirically, the critical role of passion, which we define as a strong feeling toward a personally important value/preference that motivates intentions and behaviors to express that value/preference. We suggest that combining the grit scale—which only captures perseverance—with a measure that assesses whether individuals attain desired levels of passion will predict performance. We first metaanalyzed 127 studies ( n = 45,485) that used the grit scale and assessed performance, and found that effect sizes are larger in studies where participants were more passionate for the performance domain. Second, in a survey of employees matched to supervisor-rated job performance ( n = 422), we found that the combination of perseverance, measured through the grit scale, and passion attainment, measured through a new scale, predicted higher performance. A final study measured perseverance and passion attainment in a sample of students ( n = 248) and linked these to their grade-point average (GPA), finding that the combination of perseverance and passion attainment predicted higher GPAs in part through increased immersion. The present results help resolve the mixed evidence of grit’s relationship with performance by highlighting the important role that passion plays in predicting performance. By adequately measuring both perseverance and passion, the present research uncovers grit’s true predictive power.


title: ARCTIC: metadata extraction from scientific papers in pdf using two-layer CRF

abstract: Most scientific articles are available in PDF format. The PDF standard allows the generation of metadata that is included within the document. However, many authors do not define this information, making this feature unreliable or incomplete. This fact has been motivating research which aims to extract metadata automatically. Automatic metadata extraction has been identified as one of the most challenging tasks in document engineering. This work proposes Artic, a method for metadata extraction from scientific papers which employs a two-layer probabilistic framework based on Conditional Random Fields. The first layer aims at identifying the main sections with metadata information, and the second layer finds, for each section, the corresponding metadata. Given a PDF file containing a scientific paper, Artic extracts the title, author names, emails, affiliations, and venue information. We report on experiments using 100 real papers from a variety of publishers. Our results outperformed the state-of-the-art system used as the baseline, achieving a precision of over 99%.


title: A Miniature Soft Robotic Manipulator Based on Novel Fabrication Methods

abstract: Flexible robotic manipulators have been widely used in minimally invasive surgery (MIS) and many other applications requiring closer inspection and operation. Although a variety of manipulators enabled by different mechanism have been developed, few of them can preserve softness, thinness, and decent bending capability simultaneously. In this letter, we present a miniature soft robotic manipulator made of hyper-elastic silicone rubber. Along with the manipulator design, two novel fabrication methods are proposed and elaborated. Detailed characterizations are specified to show the bending capability of the manipulator given different air pressure. Specifically, our manipulator, as thin as 6 mm, is able to achieve 360° directional bending, and, when given pressure over 70 kPa, it can reach 180° bending angle and around 5 mm bending radius easily. Due to its innate compliance and small dimension, this type of robotic manipulator can deliver safe and comfortable interactions with the subjects. More significantly, the novel fabrications in this letter diversify the fabrication methods for soft pneumatic robots and actuators (SPRA) and further scale down their sizes.


title: Naive Bayes face-nonface classifier: a study of preprocessing and feature extraction techniques

abstract: This paper presents a classifier of face and nonface patterns that is based on the naive Bayes model. Using this classifier as a tool. We analyze the effects on classification performance of preprocessing, feature extraction and classifier combination techniques. Our analysis shows that image normalization techniques that reduce the effects of different lighting conditions improve face-nonface classification significantly. In addition, techniques such as background masking and combining classifiers that use different feature vectors are shown to enhance classification performance. Over a test set of 12,000 patterns, the combined classifier using four feature vectors has correct detection rates (CDRs) of 96.2% and 99.2% at false detection rates (FDRs) of 1% and 5%, respectively.


title: Personalized Food Recommendation

abstract: In this paper a food recommender system giving general and personalized advice is described. The system is based on semi-formal knowledge of personal nutrition gained in a previous project (Food4Me). This project implements a manual expert knowledge based approach using a decision tree for recommending optimal adaptions to the individual nutrition behavior. The system was evaluated in a Proof-of-Principle study whose results lead to the current two research projects. The first project aims at an automated recommender system based on formalized expert knowledge building on the insights from Food4Me. This recommendation system is then extended by including it into an application which provides continuous feedback on the participant’s nutritional behavior. Also individual preferences are integrated using approaches from critique basedand persuasive recommender systems. The second system uses an adapted collaborative filtering approach to recommend food based on healthiness and taste ratings of other users augmented by a rating based recommender system for sports. This recommender system is further extended by social support groups and game interaction in view of social motivation. In the future both projects might be used in combination to provide optimal health support.


title: On predicting social unrest using social media

abstract: We study the possibility of predicting a social protest (planned, or unplanned) based on social media messaging. We consider the process called mobilization, described in the literature as the precursor of participation. Mobilization includes four stages: being sympathetic to the cause, being aware of the movement, motivation to take part and ability to participate. We suggest that expressions of mobilization in communications of individuals may be used to predict the approaching protest. We have utilized several Natural Language Processing techniques to create a methodology to identify mobilization in social media communication. Results of experimentation with Twitter data collected before and during the 2015 Baltimore events and the information on actual protests taken from news media show a correlation over time between volume of Twitter communications related to mobilization and occurrences of protest at certain geographical locations. We conclude with discussion of possible theoretical explanations and practical applications of these results.


Presence in virtual environments Five Theses on the Book Problem: Presence in Books, Film and VR


title: A review of function modeling: Approaches and applications

abstract: This work is aimed at establishing a common frame and understanding of function modeling (FM) for our ongoing research activities. A comparative review of the literature is performed to grasp the various FM approaches with their commonalities and differences. The relations of FM with the research fields of artificial intelligence, design theory, and maintenance are discussed. In this discussion the goals are to highlight the features of various classical approaches in relation to FM, to delineate what FM introduces to these fields, and to discuss the applicability of various FM approaches in these fields. Finally, the basic ideas underlying our projects are introduced with reference to the general framework of FM.


title: Fast, Automated, Scalable Generation of Textured 3D Models of Indoor Environments

abstract: 3D modeling of building architecture from mobile scanning is a rapidly advancing field. These models are used in virtual reality, gaming, navigation, and simulation applications. State-of-the-art scanning produces accurate point-clouds of building interiors containing hundreds of millions of points. This paper presents several scalable surface reconstruction techniques to generate watertight meshes that preserve sharp features in the geometry common to buildings. Our techniques can automatically produce high-resolution meshes that preserve the fine detail of the environment by performing a ray-carving volumetric approach to surface reconstruction. We present methods to automatically generate 2D floor plans of scanned building environments by detecting walls and room separations. These floor plans can be used to generate simplified 3D meshes that remove furniture and other temporary objects. We propose a method to texture-map these models from captured camera imagery to produce photo-realistic models. We apply these techniques to several data sets of building interiors, including multi-story datasets.


title: A Simple Compact Reconfigurable Slot Antenna With a Very Wide Tuning Range

abstract: A simple and compact slot antenna with a very wide tuning range is proposed. A 25 mm (roughly equal to λH/8, where λH corresponds to the highest frequency of the tuning range) open slot is etched at the edge of the ground. To achieve the tunability, only two lumped elements, namely, a PIN diode and a varactor diode are used in the structure. By switching the PIN diode placed at the open end of the slot, the slot antenna can resonate as a standard slot (when the switch is on) or a half slot (when the switch is off). Continuous tuning over a wide frequency range in those two modes can be achieved by adjusting the reverse bias (giving different capacitances) of the varactor diode loaded in the slot. Through optimal design, the tuning bands of the two modes are stitched together to form a very wide tuning range. The fabricated prototype has a tuning frequency range from 0.42 GHz to 1.48 GHz with Sll better than -10 dB, giving a frequency ratio (fR = fu/fL) of 3.52:1. The measured full spherical radiation patterns show consistent radiation characteristics of the proposed antenna within the whole tuning range.


title: Novel concept for mains voltage proportional input current shaping of a VIENNA rectifier eliminating controller multipliers

abstract: This paper proposes a novel mains voltage proportional input current control concept eliminating the multiplication of the output voltage controller output and the mains ac phase voltages for the derivation of mains phase current reference values of a three-phase/level/switch pulsewidth-modulated (VIENNA) rectifier system. Furthermore, the concept features low input current ripple amplitude as, e.g., achieved for space-vector modulation, a low amplitude of the third harmonic of the current flowing into the output voltage center point, and a wide range of modulation. The practical realization of the analog control concept as well as experimental results for application with a 5-kW prototype of the pulsewidth-modulated rectifier are presented. Furthermore, a control scheme which relies only on the absolute values of the input phase currents and a modified control scheme which does not require information about the mains phase voltages are presented.


title: Copyright @ 1986 Pergamon Journals Lid FUTURE PATHS FOR INTEGER PROGRAMMING AND LINKS TO

abstract: Abstract Integer programming has benefited from many innovations in models and methods. Some of the promising directions for elaborating these innovations in the future may be viewed from a framework that links the perspectives of artificial intelligence and operations research. To demonstrate this, four key areas are examined: 1. (1) controlled randomization, 2. (2) learning strategies, 3. (3) induced decomposition and 4. (4) tabu search. Each of these is shown to have characteristics that appear usefully relevant to developments on the horizon.


title: DensePose: Dense Human Pose Estimation in the Wild

abstract: In this work we establish dense correspondences between an RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation. We gather dense correspondences for 50K persons appearing in the COCO dataset by introducing an efficient annotation pipeline. We then use our dataset to train CNN-based systems that deliver dense correspondence 'in the wild', namely in the presence of background, occlusions and scale variations. We improve our training set's effectiveness by training an inpainting network that can fill in missing ground truth values and report improvements with respect to the best results that would be achievable in the past. We experiment with fully-convolutional networks and region-based models and observe a superiority of the latter. We further improve accuracy through cascading, obtaining a system that delivers highly-accurate results at multiple frames per second on a single gpu. Supplementary materials, data, code, and videos are provided on the project page http://densepose.org.


title: Augmented reality and representation in vehicle for safe driving at night

abstract: To reduce traffic accidents at night driving, we introduce an augmented reality system using head-up display (HUD) in a vehicle and discuss the effectiveness and usefulness of the proposed system with representation strategy. The proposed system embraces recognition of dynamic object by radar-vision fusion, forward collision warning by threat assessment, and representation strategy using HUD. For warning information to prevent collision at night driving, an augmented reality information aligned at driver's eye and overlapped with real world is shown through the proposed system, and the usefulness of the system is validated in the experiments.


title: A Probabilistic Approach to Tactile Shape Reconstruction

abstract: In this paper, we present a probabilistic spatial approach to build compact 3-D representations of unknown objects probed by tactile sensors. Our approach exploits the high frame rates provided by modern tactile sensors and utilizes Kalman filters to build a probabilistic model of the contact point cloud that is efficiently stored in a kd-tree. The quality of generated shape representations is compared with a naive averaging approach, and we show that our method provides superior accuracy. We also evaluate the feasibility of object classification combining the generated object representations, together with the iterative closest point algorithm.


title: An approach to automotive ECG measurement validation using a car-integrated test framework

abstract: Development and integration of physiological sensors into automotive applications is gaining importance. Assistance systems which possess knowledge about the driver's cognitive state could increase road safety. In this paper we present a flexible framework that enables the development, evaluation and verification of sensors and algorithms for automotive applications using physiological signals under realistic driving conditions. We have integrated a custom capacitive ECG measurement system into a test car and validated its performance in real world driving tests. During first test runs, the capacitive system achieved a sensitivity of up to 95.5% and a precision rate of up to 92.6%. Our system also records synchronized vehicle dynamics. We discuss the road test measurements which suggest that the driving situation highly impacts the quality of ECG signal. Therefore, information on driving dynamics could be used to improve the precision rate of future capacitive ECG measurement.


title: Narrow passage sampling in the observation of robotic assembly tasks

abstract: The observation of robotic assembly tasks is required as feedback for decisions and adaption of the task execution on the current situation. A sequential Monte Carlo observation algorithm is proposed, which uses a fast and accurate collision detection algorithm as a reference model for the contacts between complex shaped parts. The main contribution of the paper is the extension of the classic random motion model in the propagation step with sampling methods known from the domain of probabilistic roadmap planning in order to increase the sample density in narrow passages of the configuration space. As a result, the observation performance can be improved and a risk of sample impoverishment reduced. Experimental validation is provided for a peg-in-hole task executed by a lightweight-robot arm equipped with joint torque sensors.


title: More attentional focusing through binaural beats: evidence from the global–local task

abstract: A recent study showed that binaural beats have an impact on the efficiency of allocating attention over time. We were interested to see whether this impact affects attentional focusing or, even further, the top-down control over irrelevant information. Healthy adults listened to gamma-frequency (40 Hz) binaural beats, which are assumed to increase attentional concentration, or a constant tone of 340 Hz (control condition) for 3 min before and during a global–local task. While the size of the congruency effect (indicating the failure to suppress task-irrelevant information) was unaffected by the binaural beats, the global-precedence effect (reflecting attentional focusing) was considerably smaller after gamma-frequency binaural beats than after the control condition. Our findings suggest that high-frequency binaural beats bias the individual attentional processing style towards a reduced spotlight of attention.


Additive Manufacturing Defect Detection using Neural Networks


title: Multiple memory systems as substrates for multiple decision systems

abstract: It has recently become widely appreciated that value-based decision making is supported by multiple computational strategies. In particular, animal and human behavior in learning tasks appears to include habitual responses described by prominent model-free reinforcement learning (RL) theories, but also more deliberative or goal-directed actions that can be characterized by a different class of theories, model-based RL. The latter theories evaluate actions by using a representation of the contingencies of the task (as with a learned map of a spatial maze), called an “internal model.” Given the evidence of behavioral and neural dissociations between these approaches, they are often characterized as dissociable learning systems, though they likely interact and share common mechanisms. ::: ::: In many respects, this division parallels a longstanding dissociation in cognitive neuroscience between multiple memory systems, describing, at the broadest level, separate systems for declarative and procedural learning. Procedural learning has notable parallels with model-free RL: both involve learning of habits and both are known to depend on parts of the striatum. Declarative memory, by contrast, supports memory for single events or episodes and depends on the hippocampus. The hippocampus is thought to support declarative memory by encoding temporal and spatial relations among stimuli and thus is often referred to as a relational memory system. Such relational encoding is likely to play an important role in learning an internal model, the representation that is central to model-based RL. Thus, insofar as the memory systems represent more general-purpose cognitive mechanisms that might subserve performance on many sorts of tasks including decision making, these parallels raise the question whether the multiple decision systems are served by multiple memory systems, such that one dissociation is grounded in the other. ::: ::: Here we investigated the relationship between model-based RL and relational memory by comparing individual differences across behavioral tasks designed to measure either capacity. Human subjects performed two tasks, a learning and generalization task (acquired equivalence) which involves relational encoding and depends on the hippocampus; and a sequential RL task that could be solved by either a model-based or model-free strategy. We assessed the correlation between subjects’ use of flexible, relational memory, as measured by generalization in the acquired equivalence task, and their differential reliance on either RL strategy in the decision task. We observed a significant positive relationship between generalization and model-based, but not model-free, choice strategies. These results are consistent with the hypothesis that model-based RL, like acquired equivalence, relies on a more general-purpose relational memory system.


title: Object Skeleton Extraction in Natural Images by Fusing Scale-Associated Deep Side Outputs

abstract: Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts. While object skeleton extraction in natural images is a very challenging problem, as it requires the extractor to be able to capture both local and global image context to determine the intrinsic scale of each skeleton pixel. Existing methods rely on per-pixel based multi-scale feature computation, which results in difficult modeling and high time consumption. In this paper, we present a fully convolutional network with multiple scale-associated side outputs to address this problem. By observing the relationship between the receptive field sizes of the sequential stages in the network and the skeleton scales they can capture, we introduce a scale-associated side output to each stage. We impose supervision to different stages by guiding the scale-associated side outputs toward groundtruth skeletons of different scales. The responses of the multiple scale-associated side outputs are then fused in a scale-specific way to localize skeleton pixels with multiple scales effectively. Our method achieves promising results on two skeleton extraction datasets, and significantly outperforms other competitors.


title: P-MOD: Secure Privilege-Based Multilevel Organizational Data-Sharing in Cloud Computing

abstract: Cloud computing has changed the way enterprises store, access, and share data. Big data sets are constantly being uploaded to the cloud and shared within a hierarchy of many different individuals with different access privileges. With more data storage needs turning over to the cloud, finding a secure and efficient data access structure has become a major research issue. In this paper, a Privilege-based Multilevel Organizational Data-sharing scheme (P-MOD) is proposed that incorporates a privilege-based access structure into an attribute-based encryption mechanism to handle the management and sharing of big data sets. Our proposed privilege-based access structure helps reduce the complexity of defining hierarchies as the number of users grows, which makes managing healthcare records using mobile healthcare devices feasible. It can also facilitate organizations in applying big data analytics to understand populations in a holistic way. Security analysis shows that P-MOD is secure against adaptively chosen plaintext attack assuming the DBDH assumption holds. The comprehensive performance and simulation analyses using the real U.S. Census Income dataset demonstrate that P-MOD is more efficient in computational complexity and storage space than the existing schemes.


title: Semantic Representations for Domain Adaptation: A Case Study on the Tree Kernel-based Method for Relation Extraction

abstract: We study the application of word embeddings to generate semantic representations for the domain adaptation problem of relation extraction (RE) in the tree kernelbased method. We systematically evaluate various techniques to generate the semantic representations and demonstrate that they are effective to improve the generalization performance of a tree kernel-based relation extractor across domains (up to 7% relative improvement). In addition, we compare the tree kernel-based and the feature-based method for RE in a compatible way, on the same resources and settings, to gain insights into which kind of system is more robust to domain changes. Our results and error analysis shows that the tree kernel-based method outperforms the feature-based approach.


title: Auto-completing bug reports for Android applications

abstract: The modern software development landscape has seen a shift in focus toward mobile applications as tablets and smartphones near ubiquitous adoption. Due to this trend, the complexity of these “apps” has been increasing, making development and maintenance challenging. Additionally, current bug tracking systems are not able to effectively support construction of reports with actionable information that directly lead to a bug’s resolution. To address the need for an improved reporting system, we introduce a novel solution, called FUSION, that helps users auto-complete reproduction steps in bug reports for mobile apps. FUSION links user-provided information to program artifacts extracted through static and dynamic analysis performed before testing or release. The approach that FUSION employs is generalizable to other current mobile software platforms, and constitutes a new method by which off-device bug reporting can be conducted for mobile software projects. In a study involving 28 participants we applied FUSION to support the maintenance tasks of reporting and reproducing defects from 15 real-world bugs found in 14 open source Android apps while qualitatively and qualitatively measuring the user experience of the system. Our results demonstrate that FUSION both effectively facilitates reporting and allows for more reliable reproduction of bugs from reports compared to traditional issue tracking systems by presenting more detailed contextual app information.


title: Anarchy, stability, and utopia: creating better matchings

abstract: Historically, the analysis of matching has centered on designing algorithms to produce stable matchings as well as on analyzing the incentive compatibility of matching mechanisms. Less attention has been paid to questions related to the social welfare of stable matchings in cardinal utility models. We examine the loss in social welfare that arises from requiring matchings to be stable, the natural equilibrium concept under individual rationality. While this loss can be arbitrarily bad under general preferences, when there is some structure to the underlying graph corresponding to natural conditions on preferences, we prove worst case bounds on the price of anarchy. Surprisingly, under simple distributions of utilities, the average case loss turns out to be significantly smaller than the worst-case analysis would suggest. Furthermore, we derive conditions for the existence of approximately stable matchings that are also close to socially optimal, demonstrating that adding small switching costs can make socially (near-)optimal matchings stable. Our analysis leads to several concomitant results of interest on the convergence of decentralized partner-switching algorithms, and on the impact of heterogeneity of tastes on social welfare.


title: DBSM: The combination of DBSCAN and SMOTE for imbalanced data classification

abstract: Many applications in the real world encounter the class imbalance problem. This problem affects the performance of the model prediction. Nowadays, resampling technique is a popular technique to handle the class imbalance problem such as oversampling, undersampling, and hybridsampling. Thus, this paper proposes a new hybrid resampling technique to deal with the class imbalance problem, called DBSM. The concept of DBSM is to use DBSCAN algorithm for undersampling and apply SMOTE technique for oversampling. The experimental results of the DBSM algorithm are compared with an original datasets and other sampling techniques, which are SMOTE, Tomek Links, SMOTE+Tomek Links and DBSCAN. The results show that the DBSM can improve the predictive performance of the classifiers. In addition, it yields the best in the average of AUC, F-measure, and accuracy.


title: Single-unit pattern generators for quadruped locomotion

abstract: Legged robots can potentially venture beyond the limits of wheeled vehicles. While creating controllers for such robots by hand is possible, evolutionary algorithms are an alternative that can reduce the burden of hand-crafting robotic controllers. Although major evolutionary approaches to legged locomotion can generate oscillations through popular techniques such as continuous time recurrent neural networks (CTRNNs) or sinusoidal input, they typically face a challenge in maintaining long-term stability. The aim of this paper is to address this challenge by introducing an effective alternative based on a new type of neuron called a single-unit pattern generator (SUPG). The SUPG, which is indirectly encoded by a compositional pattern producing network (CPPN) evolved by HyperNEAT, produces a flexible temporal activation pattern that can be reset and repeated at any time through an explicit trigger input, thereby allowing it to dynamically recalibrate over time to maintain stability. The SUPG approach, which is compared to CTRNNs and sinusoidal input, is shown to produce natural-looking gaits that exhibit superior stability over time, thereby providing a new alternative for evolving oscillatory locomotion.


title: Phased Array Radar Polarimetry for Weather Sensing: A Theoretical Formulation for Bias Corrections

abstract: It is becoming widely accepted that radar polarimetry provides accurate and informative weather measurements, while phased-array technology can shorten data updating time. In this paper, a theory of phased array radar (PAR) polarimetry is developed to establish the relation between electric fields at the antenna of the PAR and the fields in a resolution volume filled with hydrometeors. It is shown that polarimetric measurements with an electronically steered beam can cause measurement biases that are comparable to or even larger than the intrinsic polarimetric characteristics of hydrometeors. However, these biases are correctable if the transmitted electric fields are known. A correction to the measured scattering matrix that removes biases in meteorological variables is derived. The challenges and opportunities for weather sensing with a polarimetric PAR are discussed.


title: A Patch Antenna With a Varactor-Loaded Slot for Reconfigurable Dual-Band Operation

abstract: A new design approach for a microstrip patch antenna to achieve reconfigurable dual-band operation with tunable frequency ratio is introduced. The approach uses a lumped capacitor in the middle of a slotted patch antenna, which results in two resonant frequencies. The two resonant frequencies and their ratio are functions of the capacitance value. If a varactor with an appropriate biasing network is used, electronic tuning is realized by changing the applied DC voltage. To understand the dual-resonance behavior of the proposed antenna, an equivalent circuit model based on the transmission line theory of the antenna is established, considering the slot effect and the lumped capacitor. The results are verified with full wave simulation. Furthermore, measurements for fabricated antenna prototypes operating in 2–4.5 GHz are carried out for validation, and the performance shows a tunable frequency ratio from 1.45 to 1.93 with a capacitance range from 0.31 to 0.74 pF. It is worthwhile to point out that the radiation patterns are similar at both bands because both resonances are due to the fundamental TM01 mode.


title: Fuzzy multiple objective decision making approach to prioritize design requirements in quality function deployment

abstract: Quality function deployment (QFD) is a customer-oriented design tool for developing new or improved products to increase customer satisfaction by integrating marketing, design engineering, manufacturing, and other related functions of an organization. QFD focuses on delivering value by taking into account customer needs and then deploying this information throughout the development process. Although QFD aims to maximize customer satisfaction, technology and cost considerations limit the number and the extent of the possible design requirements that can be incorporated into a product. This paper presents a fuzzy multiple objective programming approach that incorporates imprecise and subjective information inherent in the QFD planning process to determine the level of fulfilment of design requirements. Linguistic variables are employed to represent the imprecise design information and the importance degree of each design objective. The fuzzy Delphi method is utilized to achieve the consensus of customers in...


title: Analytical Methods for Minimizing Cogging Torque in Permanent-Magnet Machines

abstract: Cogging torque in permanent-magnet machines causes torque and speed ripples, as well as acoustic noise and vibration, especially in low speed and direct drive applications. In this paper, a general analytical expression for cogging torque is derived by the energy method and the Fourier series analysis, based on the air gap permeance and the flux density distribution in an equivalent slotless machine. The optimal design parameters, such as slot number and pole number combination, skewing, pole-arc to pole-pitch ratio, and slot opening, are derived analytically to minimize the cogging torque. Finally, the finite-element analysis is adopted to verify the correctness of analytical methods.


title: Gated neural networks for targeted sentiment analysis

abstract: Targeted sentiment analysis classifies the sentiment polarity towards each target entity mention in given text documents. Seminal methods extract manual discrete features from automatic syntactic parse trees in order to capture semantic information of the enclosing sentence with respect to a target entity mention. Recently, it has been shown that competitive accuracies can be achieved without using syntactic parsers, which can be highly inaccurate on noisy text such as tweets. This is achieved by applying distributed word representations and rich neural pooling functions over a simple and intuitive segmentation of tweets according to target entity mentions. In this paper, we extend this idea by proposing a sentence-level neural model to address the limitation of pooling functions, which do not explicitly model tweet-level semantics. First, a bi-directional gated neural network is used to connect the words in a tweet so that pooling functions can be applied over the hidden layer instead of words for better representing the target and its contexts. Second, a three-way gated neural network structure is used to model the interaction between the target mention and its surrounding contexts. Experiments show that our proposed model gives significantly higher accuracies compared to the current best method for targeted sentiment analysis.


title: A design of large space automatic cleaning robot with omni-directional wheels

abstract: The methods for a large space cleaning task are usually using human-operated cleaning vehicle or brooms, even in the period of mid-night. In addition to hiring a large amount of people (usually senior), the over-work issue during mid-night period is another concern to the cleaning company. Therefore, an omni-directional mechanism/robot suitable for mid-night cleaning period in a large space is proposed; the hardware core is the Arduino microcontroller in association with TETRIX mechanism and sensors. Intelligent obstacle avoidance is fulfilled via sensors while the path planning is throughout the use of fuzzy control.


title: Gamification: an effective mechanism to promote civic engagement and generate trust?

abstract: This document addresses the issue of citizen participation within the framework of governance and analyzes the elements that make up the theory with which Putnam defined social capital: civic engagement and trust in institutions. The second part describes the different game mechanisms and dynamics used in gamification and finally, a reflection is made about the impact gamification could have in participation.


title: Application of Machine Learning to Algorithm Selection for TSP

abstract: The Travelling Salesman Problem (TSP) has been extensively studied in the literature and various solvers are available. However, none of the state-of-the-art solvers for TSP outperforms the others in all problem instances within a given time limit. Therefore, the prediction of the best performing algorithm can save computational resources and optimise the results. In this paper, the TSP is studied in context of automated algorithm selection. Our aim is to identify the relevant features of problem instances and tackle this scenario as a machine learning task. We extend the set of existing features in the literature and propose several novel features to better characterise the problem. The contribution of the new features is statistically analysed and experiments show that adding our new features improves the prediction accuracy. We identified that our features based on kNN graph transformation are especially helpful. To create the training datasets, two state-of-the-art (meta-) heuristic algorithms are systematically evaluated on more than 2000 problems. Overall, we show that our prediction can be substantially more accurate than simple preference of an algorithm with the best performance for a majority of problem instances.


title: Preliminary Evidence for Sensitive Periods in the Effect of Childhood Sexual Abuse on Regional Brain Development

abstract: Volumetric MRI scans from 26 women with repeated episodes of childhood sexual abuse (CSA), and 17 healthy women (18–22 years) were analyzed for sensitive periods effects on hippocampal and amydgala volume, frontal cortex gray matter volume and corpus callosum area. Hipppocampal volume was reduced in association with CSA at 3–5 years (β=−0.69, p<0.0001) and 11–13 years (β=−0.25, p<0.05). Corpus callosum was reduced with CSA at 9–10 years (β=−0.44, p<0.005), and frontal cortex was attenuated in subjects with CSA at ages 14–16 (β=−0.48, p<0.005). Brain regions have unique windows of vulnerability to the effects of traumatic stress.


title: Microcontroller based drip irrigation system using smart sensor

abstract: In the past couple of decades, there is rapid growth in terms of technology in the field of agriculture. Different monitoring and controlled systems are installed in order to increase the yield. The yield rate may deceases due to numerous factors. Disease is one of the key factors that cause the degradation of yield. So the developed monitoring system mainly focuses on predicting the start of germination of the disease. Sensor module is used to detect different environmental condition across the farm and the sensed data is displayed on Liquid crystal display using microcontroller. Microcontroller wirelessly transmits different environment conditions across the farm to central unit where data is stored, and analysed. Central unit checks the present data with disease condition and if matches then it commands microcontroller to operate relay. Sensor module is tested for different temperature range and it is found that there are little variations in recorded values. Wireless data transfer is tested with the introduction of various obstacles like wall, metal body, magnet, etc. and it is found that same data is transferred to central unit but with some amount of delay in it. The developed system nearly predicts the start of germination of disease.


title: A short form of the Diabetes Quality of Life for Youth questionnaire: exploratory and confirmatory analysis in a sample of 2,077 young people with type 1 diabetes mellitus

abstract: Aims/hypothesis ::: The aim of this study was to test the construct validity of the Diabetes Quality of Life for Youth (DQOLY) questionnaire in a large representative sample of young people with type 1 diabetes mellitus.


title: Linear Support Vector Machines for Error Correction in Optical Data Transmission

abstract: Reduction of bit error rates in optical transmission systems is an important task that is difficult to achieve. As speeds increase, the difficulty in reducing bit error rates also increases. Channels have differing characteristics, which may change over time, and any error correction employed must be capable of operating at extremely high speeds. In this paper, a linear support vector machine is used to classify large-scale data sets of simulated optical transmission data in order to demonstrate their effectiveness at reducing bit error rates and their adaptability to the specifics of each channel. For the classification, LIBLINEAR is used, which is related to the popular LIBSVM classifier. It is found that it is possible to reduce the error rate on a very noisy channel to about 3 bits in a thousand. This is done by a linear separator that can be built in hardware and can operate at the high speed required of an operationally useful decoder.


title: From Extreme to Mainstream: How Social Norms Unravel

abstract: Social norms are typically thought to be persistent and long-lasting, sometimes surviving through growth, recessions, and regime changes. In some cases, however, they can quickly change. This paper examines the unraveling of social norms in communication when new information becomes available, e.g., aggregated through elections. We build a model of strategic communication between citizens who can hold one of two mutually exclusive opinions. In our model, agents communicate their opinions to each other, and senders care about receivers' approval. As a result, senders are more likely to express the more popular opinion, while receivers make less inference about senders who stated the popular view. We test these predictions using two experiments. In the main experiment, we identify the causal effect of Donald Trump's rise in political popularity on individuals' willingness to publicly express xenophobic views. Participants in the experiment are offered a bonus reward if they authorize researchers to make a donation to an anti-immigration organization on their behalf. Participants who expect their decision to be observed by the surveyor are significantly less likely to accept the offer than those expecting an anonymous choice. Increases in participants' perceptions of Trump's popularity (either through experimental variation or through the “natural experiment” of his victory) eliminate the wedge between private and public behavior. A second experiment uses dictator games to show that participants judge a person less negatively for publicly expressing (but not for privately holding) a political view they disagree with if that person's social environment is one where the majority of people holds that view.


title: A bias correction for the minimum error rate in cross-validation

abstract: Tuning parameters in supervised learning problems are often estimated by cross-validation. The minimum value of the cross-validation error can be biased downward as an estimate of the test error at that same value of the tuning parameter. We propose a simple method for the estimation of this bias that uses information from the cross-validation process. As a result, it requires essentially no additional computation. We apply our bias estimate to a number of popular classifiers in various settings, and examine its performance.


title: Current practices of leading e-government countries

abstract: IT is transforming the way governments function and valuable lessons can be learned from the pioneering e-government programs that have led the charge.


title: Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch

abstract: This paper presents a novel way to perform multi-modal face recognition. We use Partial Least Squares (PLS) to linearly map images in different modalities to a common linear subspace in which they are highly correlated. PLS has been previously used effectively for feature selection in face recognition. We show both theoretically and experimentally that PLS can be used effectively across modalities. We also formulate a generic intermediate subspace comparison framework for multi-modal recognition. Surprisingly, we achieve high performance using only pixel intensities as features. We experimentally demonstrate the highest published recognition rates on the pose variations in the PIE data set, and also show that PLS can be used to compare sketches to photos, and to compare images taken at different resolutions.


title: The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City. ICWSM’12

abstract: Studying the social dynamics of a city on a large scale has traditionally been a challenging endeavor, often requiring long hours of observation and interviews, usually resulting in only a partial depiction of reality. To address this difficulty, we introduce a clustering model and research methodology for studying the structure and composition of a city on a large scale based on the social media its residents generate. We apply this new methodology to data from approximately 18 million check-ins collected from users of a location-based online social network. Unlike the boundaries of traditional municipal organizational units such as neighborhoods, which do not always reflect the character of life in these areas, our clusters, which we call Livehoods, are representations of the dynamic areas that comprise the city. We take a qualitative approach to validating these clusters, interviewing 27 residents of Pittsburgh, PA, to see how their perceptions of the city project onto our findings there. Our results provide strong support for the discovered clusters, showing how Livehoods reveal the distinctly characterized areas of the city and the forces that shape them.


title: Dynamic Proximity-Aware Resource Allocation in Vehicle-to-Vehicle (V2V) Communications

abstract: In this paper, a novel proximity and load-aware resource allocation for vehicle-to-vehicle (V2V) communication is proposed. The proposed approach exploits the spatio-temporal traffic patterns, in terms of load and vehicles' physical proximity, to minimize the total network cost which captures the tradeoffs between load (i.e., service delay) and successful transmissions while satisfying vehicles' quality-of-service (QoS) requirements. To solve the optimization problem under slowly varying channel information, it is decoupled the problem into two interrelated sub-problems. First, a dynamic clustering mechanism is proposed to group vehicles in zones based on their traffic patterns and proximity information. Second, a matching game is proposed to allocate resources for each V2V pair within each zone. The problem is cast as many-to-one matching game in which V2V pairs and resource blocks (RBs) rank one another in order to minimize their service delay. The proposed game is shown to belong to the class of matching games with externalities. To solve this game, a distributed algorithm is proposed using which V2V pairs and RBs interact to reach a stable matching. Simulation results for a Manhattan model shown that the proposed scheme yields a higher percentage of V2V pairs satisfying QoS as well as significant gain in terms of the signal-to- interference-plus-noise ratio (SINR) as compared to a state-of-art resource allocation baseline.


title: Indonesian License Plate Recognition Using Convolutional Neural Network

abstract: License plate is a part of vehicle's identity. In modern countries, license plate recognition has been developed to collect traffic activity information. The performance of license plate recognition system tend to drop when the input picture contains noises like illumination, dirt, and scratches which cover one or more characters in the license plate. This research was focused on Indonesian license plate as many license plates in Indonesia had various noises like plastic cover and scratches which complicate the recognition. In this study, Indonesian license plate recognition is formed using a Convolutional Neural Network (CNN) which is known to have good performance in recognizing objects, even though the objects are obscured to some degree. Sliding window is used in this study for replace character segmentation. CNN will predict images in every area of window. The highest performance for the whole system to the normal data test is 87.36% and noised data test is 44.93%.


title: Mongoloid and Non-Mongoloid Race Classification from Face Image Using Local Binary Pattern Feature Extractions

abstract: One of the areas on the human body that has the most dominant racial trait is the face. This research build the classification system for Mongoloid and non-Mongoloid race based on the area in the periorbital area of facial image. We use Local Binary Pattern to extract texture features on periorbital facial area. To classify the LBP features, we use Support Vector Machine classifier. The accuracy obtained from the system is 99.38%.


X-ray Computed Tomography


title: Velocity and shape from tightly-coupled LiDAR and camera

abstract: In this paper, we propose a multi-object tracking and reconstruction approach through measurement-level fusion of LiDAR and camera. The proposed method, regardless of object class, estimates 3D motion and structure for all rigid obstacles. Using an intermediate surface representation, measurements from both sensors are processed within a joint framework. We combine optical flow, surface reconstruction, and point-to-surface terms in a tightly-coupled non-linear energy function, which is minimized using Iterative Reweighted Least Squares (IRLS). We demonstrate the performance of our model on different datasets (KITTI with Velodyne HDL-64E and our collected data with 4-layer ScaLa Ibeo), and show an improvement in velocity error and crispness over state-of-the-art trackers.


title: Nutritional Status and Daytime Pattern of Protein Intake on Match, Post-Match, Rest and Training Days in Senior Professional and Youth Elite Soccer Players

abstract: The nutritional status of elite soccer players across match, postmatch, training and rest days has not been defined. Recent evidence suggests the pattern of dietary protein intake impacts the daytime turnover of muscle proteins and, as such, influences muscle recovery. We assessed the nutritional status and daytime pattern of protein intake in senior professional and elite youth soccer players and compared findings against published recommendations. Fourteen senior professional (SP) and 15 youth elite (YP) soccer players from the Dutch premier division completed nutritional assessments using a 24-hr web-based recall method. Recall days consisted of a match, postmatch, rest, and training day. Daily energy intake over the 4-day period was similar between SP (2988 ± 583 kcal/day) and YP (2938 ± 465 kcal/day; p = .800). Carbohydrate intake over the combined 4-day period was lower in SP (4.7 ± 0.7 g·kg-1 BM·day-1) vs. YP (6.0 ± 1.5 g·kg-1 BM·day-1, p = .006) and SP failed to meet recommended carbohydrate intak...


title: POTs: protective optimization technologies

abstract: In spite of their many advantages, optimization systems often neglect the economic, ethical, moral, social, and political impact they have on populations and their environments. In this paper we argue that the frameworks through which the discontents of optimization systems have been approached so far cover a narrow subset of these problems by (i) assuming that the system provider has the incentives and means to mitigate the imbalances optimization causes, (ii) disregarding problems that go beyond discrimination due to disparate treatment or impact in algorithmic decision making, and (iii) developing solutions focused on removing algorithmic biases related to discrimination. ::: In response we introduce Protective Optimization Technologies: solutions that enable optimization subjects to defend from unwanted consequences. We provide a framework that formalizes the design space of POTs and show how it differs from other design paradigms in the literature. We show how the framework can capture strategies developed in the wild against real optimization systems, and how it can be used to design, implement, and evaluate a POT that enables individuals and collectives to protect themselves from unbalances in a credit scoring application related to loan allocation.


Best Practices for Implementing Electronic Health Records and Information Systems


Optimizing Storage System Design for Timeseries Processing


Education Policies and Reform in South Korea


title: A random graph model for massive graphs

abstract: We propose a random graph model which is a special case of sparse random graphs with given degree sequences. This model involves only a small number of parameters, called logsize and log-log growth rate. These parameters capture some universal characteristics of massive graphs. Furthermore, from these parameters, various properties of the graph can be derived. For example, for certain ranges of the parameters, we will compute the expected distribution of the sizes of the connected components which almost surely occur with high probability. We will illustrate the consistency of our model with the behavior of some massive graphs derived from data in telecommunications. We will also discuss the threshold function, the giant component, and the evolution of random graphs in this model.


Development of Worm-Rack Driven Cylindrical Crawler Unit


title: [Cervical spine osteoblastoma in children: Selective diagnostics as the basis for effective surgical management].

abstract: Expansively growing cervical spine osteoblastomas are rare but can cause severe neurological damage as a result of their anatomical relationship to nerve structures. Also, cerebral vessels, especially the vertebral artery in its transvertebral position, are often covered by tumor tissue. In complete resection of the tumor, it is sometimes possible to retain the affected vessel. In addition to conventional radiographic diagnostics, computed tomography, and magnetic resonance imaging, angiography and Doppler sonography of the intracranial arteries executed in parallel can provide evidence of the dimension of the neurological deficit to be expected during resection. This case report describes the staged diagnostic procedure and successful complete resection, retaining the affected vertebral artery, of an osteoblastoma of the 5th cervical body with massive intraspinal expansion in an 11-year-old child.


Visualizing a Sequence of a Thousand Graphs (or Even More)


Implementation of Energy Efficient Clustering Using Firefly Algorithm in Wireless Sensor Networks


title: Biosynthesis and Toxicological Effects of Patulin

abstract: Patulin is a toxic chemical contaminant produced by several species of mold, especially within Aspergillus, Penicillium and Byssochlamys. It is the most common mycotoxin found in apples and apple-derived products such as juice, cider, compotes and other food intended for young children. Exposure to this mycotoxin is associated with immunological, neurological and gastrointestinal outcomes. Assessment of the health risks due to patulin consumption by humans has led many countries to regulate the quantity in food. A full understanding of the molecular genetics of patulin biosynthesis is incomplete, unlike other regulated mycotoxins (aflatoxins, trichothecenes and fumonisins), although the chemical structures of patulin precursors are now known. The biosynthetic pathway consists of approximately 10 steps, as suggested by biochemical studies. Recently, a cluster of 15 genes involved in patulin biosynthesis was reported, containing characterized enzymes, a regulation factor and transporter genes. This review includes information on the current understanding of the mechanisms of patulin toxinogenesis and summarizes its toxicological effects.


title: MPE-seq, a new method for the genome-wide analysis of chromatin structure

abstract: The analysis of chromatin structure is essential for the understanding of transcriptional regulation in eukaryotes. Here we describe methidiumpropyl-EDTA sequencing (MPE-seq), a method for the genome-wide characterization of chromatin that involves the digestion of nuclei withMPE-Fe(II) followed by massively parallel sequencing. Like micrococcal nuclease (MNase), MPE-Fe(II) preferentially cleaves the linker DNA between nucleosomes. However, there are differences in the cleavage of nuclear chromatin by MPE-Fe(II) relative to MNase. Most notably, immediately upstream of the transcription start site of active promoters, we frequently observed nucleosome-sized (141-190 bp) and subnucleosome-sized (such as 101-140 bp) peaks of digested chromatin fragments with MPE-seq but not with MNase-seq. These peaks also correlate with the presence of core histones and could thus be due, at least in part, to noncanonical chromatin structures such as labile nucleosome-like particles that have been observed in other contexts. The subnucleosome-sized MPE-seq peaks exhibit a particularly distinct association with active promoters. In addition, unlike MNase, MPE-Fe(II) cleaves nuclear DNA with little sequence bias. In this regard, we found that DNA sequences at RNA splice sites are hypersensitive to digestion by MNase but not by MPE-Fe(II). This phenomenon may have affected the analysis of nucleosome occupancy over exons. These findings collectively indicate that MPE-seq provides a unique and straightforward means for the genome-wide analysis of chromatin structure with minimal DNA sequence bias. In particular, the combined use of MPE-seq and MNase-seq enables the identification of noncanonical chromatin structures that are likely to be important for the regulation of gene expression.


title: Automatic Goal Generation for Reinforcement Learning Agents

abstract: Reinforcement learning is a powerful technique to train an agent to perform a task. However, an agent that is trained using reinforcement learning is only capable of achieving the single task that is specified via its reward function. Such an approach does not scale well to settings in which an agent needs to perform a diverse set of tasks, such as navigating to varying positions in a room or moving objects to varying locations. Instead, we propose a method that allows an agent to automatically discover the range of tasks that it is capable of performing. We use a generator network to propose tasks for the agent to try to achieve, specified as goal states. The generator network is optimized using adversarial training to produce tasks that are always at the appropriate level of difficulty for the agent. Our method thus automatically produces a curriculum of tasks for the agent to learn. We show that, by using this framework, an agent can efficiently and automatically learn to perform a wide set of tasks without requiring any prior knowledge of its environment. Our method can also learn to achieve tasks with sparse rewards, which traditionally pose significant challenges.


title: Labeled pupils in the wild: A dataset for studying pupil detection in unconstrained environments

abstract: We present labelled pupils in the wild (LPW), a novel dataset of 66 high-quality, high-speed eye region videos for the development and evaluation of pupil detection algorithms. The videos in our dataset were recorded from 22 participants in everyday locations at about 95 FPS using a state-of-the-art dark-pupil head-mounted eye tracker. They cover people of different ethnicities and a diverse set of everyday indoor and outdoor illumination environments, as well as natural gaze direction distributions. The dataset also includes participants wearing glasses, contact lenses, and make-up. We benchmark five state-of-the-art pupil detection algorithms on our dataset with respect to robustness and accuracy. We further study the influence of image resolution and vision aids as well as recording location (indoor, outdoor) on pupil detection performance. Our evaluations provide valuable insights into the general pupil detection problem and allow us to identify key challenges for robust pupil detection on head-mounted eye trackers.


Iterative Back-Translation for Neural Machine Translation


title: Second Language Perception of Mandarin Vowels and Tones

abstract: This study examines the discrimination of Mandarin vowels and tones by native English speakers with varying amounts of Mandarin experience, aiming to investigate the relative difficulty of these tw...


title: Genetic Programming and Autoconstructive Evolution with the Push Programming Language

abstract: Push is a programming language designed for the expression of evolving programs within an evolutionary computation system. This article describes Push and illustrates some of the opportunities that it presents for evolutionary computation. Two evolutionary computation systems, PushGP and Pushpop, are described in detail. PushGP is a genetic programming system that evolves Push programs to solve computational problems. Pushpop, an “autoconstructive evolution” system, also evolves Push programs but does so while simultaneously evolving its own evolutionary mechanisms.


title: A rectangular defected ground structure (DGS) for reduction of mutual coupling between closely-spaced microstrip antennas

abstract: A sufficient and simple method to minimize the mutual coupling between closely-spaced microstrip antennas in a H-plane array format, operating at the same frequency is presented. More isolation can be provided through multiple rectangular defected ground structures (DGSs) between the antennas. Interelement mutual coupling before and after using the proposed DGSs is investigated. The length, width and number of the rectangular DGSs have been optimized for more mitigating the mutual coupling (S 12 ). Position of the DGSs is also the other important factor to have a best performance. By using this technique, more than 31 dB isolation between two patch antennas with a side by side distance of only 1/4 free-space wavelength at resonance frequency of 9.2 GHz can be obtained.


title: Investigation of friction characteristics of a tendon driven wearable robotic hand

abstract: Wearable robots can assist people with disabilities to perform their daily tasks. However, the size, weight and wearability are important factors in the design because it is worn by the person controlling it. Various disabilities can be assisted with wearable robot technology, from the lower to upper body. The hand is an important part of the body for the disabled to perform their daily tasks. However, compared to the arms or legs, the degree of freedom is much higher, which makes it difficult to fabricate a compact wearable robot. We propose a frameless structure and modified differential mechanism to make the wearable robot compact. For the evaluation and control, it is necessary to analyze the friction force because the mechanism we proposed delivers power through more tube than the previous tendon tube transmission. Different from the previous friction model, we consider the friction at the edge of tube ends. This paper contains the design concept of the developed wearable robotic hand and its friction characteristics.


title: Unique and atypical deletions in Prader–Willi syndrome reveal distinct phenotypes

abstract: Prader–Willi syndrome (PWS) is a multisystem, contiguous gene disorder caused by an absence of paternally expressed genes within the 15q11.2-q13 region via one of the three main genetic mechanisms: deletion of the paternally inherited 15q11.2-q13 region, maternal uniparental disomy and imprinting defect. The deletion class is typically subdivided into Type 1 and Type 2 based on their proximal breakpoints (BP1–BP3 and BP2–BP3, respectively). Despite PWS being a well-characterized genetic disorder the role of the specific genes contributing to various aspects of the phenotype are not well understood. Methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) is a recently developed technique that detects copy number changes and aberrant DNA methylation. In this study, we initially applied MS-MLPA to elucidate the deletion subtypes of 88 subjects. In our cohort, 32 had a Type 1 and 49 had a Type 2 deletion. The remaining seven subjects had unique or atypical deletions that were either smaller (n=5) or larger (n=2) than typically described and were further characterized by array-based comparative genome hybridization. In two subjects both the PWS region (15q11.2) and the newly described 15q13.3 microdeletion syndrome region were deleted. The subjects with a unique or an atypical deletion revealed distinct phenotypic features. In conclusion, unique or atypical deletions were found in ∼8% of the deletion subjects with PWS in our cohort. These novel deletions provide further insight into the potential role of several of the genes within the 15q11.2 and the 15q13.3 regions.


title: Application of big data analytics in process safety and risk management

abstract: In recent years, there has been an increasing interest in the field of big data analytics. It has been established that there exist large amounts of data in the energy industry1. However, there is a need to develop methods combining domain knowledge to transform this data into meaningful information to return business intelligence. The existing literature on big data analytics focuses on applications in various fields such as healthcare, aviation industry, finance, energy industry, and supply chain. However, within the energy industry, the application of big data analytics in process safety and risk management is in the nascent stages. The objective of this study is to discuss the potential of big data analytics in the area of process safety and risk management in the energy industry. The paper outlines the systemic framework with different stakeholders, data sources, challenges, and discusses the benefits of big data analytics in process safety. Four case studies with different applications ranging from incident database analysis, predictive modeling for pump failures, dynamic risk mapping of operating plant, and image analysis to gain insights are demonstrated. It is concluded that the application of big data analytics would provide valuable insights for more informed policy, strategic, and operational risk decision-making leading to a safer and more reliable industry.


title: An Efficient Sampling Scheme For Comparison of Large Graphs

abstract: As new graph structured data is being generated, graph comparison has become an important and challenging problem in application areas such as molecular biology, telecommunications, chemoinformatics, and social networks. Graph kernels have recently been proposed as a theoretically sound approach to this problem, and have been shown to achieve high accuracies on benchmark datasets. Different graph kernels compare different types of subgraphs in the input graphs. So far, the choice of subgraphs to compare is rather ad-hoc and is often motivated by runtime considerations. There is no clear indication that certain types of subgraphs are better than the others. On the other hand, comparing all possible subgraphs has been shown to be NP-hard, thus making it practically infeasible. These difficulties seriously limit the practical applicability of graph kernels. In this article, we attempt to rectify the situation, and make graph kernels applicable for data mining on large graphs and large datasets. Our starting point is the matrix reconstruction theorem, which states that any matrix of size 5 or above can be reconstructed given all its principal minors. By applying this to the adjacency matrix of a graph, we recursively define a graph kernel and show that it can be efficiently computed by using the distribution of all size 4 subgraphs of a graph. This distribution, we argue, is similar to a sufficient statistic of the graph, especially when the graph is large. Exhaustive enumeration of these subgraphs is prohibitively expensive, scaling as O(n4). But, by bounding the deviation of the empirical estimates of the distribution from the true distribution, it suffices to sample a fixed number of subgraphs. Incidentally, our bounds are stronger than those found in the bio-informatics literature for similar techniques. In our experimental evaluation, our graph kernel outperforms state-of-the-art graph kernels both in times of time and classification accuracy.


title: Blockchain beyond bitcoin

abstract: Blockchain technology has the potential to revolutionize applications and redefine the digital economy.


title: Gradient boosting machines, a tutorial

abstract: Gradient boosting machines are a family of powerful machine-learning techniques that have shown considerable success in a wide range of practical applications. They are highly customizable to the particular needs of the application, like being learned with respect to different loss functions. This article gives a tutorial introduction into the methodology of gradient boosting methods. A theoretical information is complemented with many descriptive examples and illustrations which cover all the stages of the gradient boosting model design. Considerations on handling the model complexity are discussed. A set of practical examples of gradient boosting applications are presented and comprehensively analyzed.


title: The mediating effects of team and self-efficacy on the relationship between transformational leadership, and job satisfaction and psychological well-being in healthcare professionals: A cross-sectional questionnaire survey

abstract: Background: The importance of transformational leadership for the health and well-being of staff in the healthcare sector is increasingly acknowledged, however, there is less knowledge about the mechanisms that may explain the links between transformational


Rendering of Stereoscopic 360 o Views From Spherical Image Pairs


title: Efficient implementation of Multilevel Feedback Queue Scheduling

abstract: In CPU scheduling various algorithms exist like FCFS (First come first serve), SJF (Shortest job first), SRTF (Shortest remaining time first), Priority Scheduling, Round Robin (RR), MLQ (Multilevel queue), MLFQ (Multilevel feedback queue) scheduling. Multilevel Feedback Queue (MLFQ) algorithm allows the switching of processes between queues depending on their burst time. The processes switch to the next queue when burst time is greater than time quantum. Each queue can define its own scheduling policy. In this paper we have implemented MLFQ technique using small burst time for the first queue thus making it analogous to RR scheduling and using SJF prior to RR from second queue onwards gives better CPU utilization. Dynamic time quantum is also used which further improves the efficiency of the scheduling. Here the dynamic time quantum of the queues is calculated based on the burst time of the processes. Time quantum of the second queue is the burst time of the (2n/3)th process (where n is the number of processes remaining after the execution in the first queue) and time quantum of the third queue is burst time of the largest remaining process. Thus 66% of the processes get executed in the second queue and remaining processes in the last queue thus preventing the problem of starvation of huge burst time processes.


WORLD-WIDE CRUDE TRANSPORTATION LOGISTICS : A DECISION SUPPORT SYSTEM BASED ON SIMULATION AND OPTIMIZATION


title: Quantifying RhoA Facilitated Trafficking of the Epithelial Na+ Channel toward the Plasma Membrane with Total Internal Reflection Fluorescence-Fluorescence Recovery after Photobleaching

abstract: Abstract The epithelial Na+ channel (ENaC) plays a central role in control of epithelial surface hydration and vascular volume. Similar to other ion channels, ENaC activity is set, in part, by its membrane levels. The small G protein RhoA increases ENaC activity by increasing the membrane levels of this channel. We hypothesize that RhoA increases ENaC activity by promoting channel trafficking to the plasma membrane. Few experimental methods are available to directly visualize trafficking of ion channels to the plasma membrane. Here we combine electrophysiology with two complementary imaging methods, total internal reflection fluorescence microscopy and fluorescence recovery after photobleaching, to study the mechanistic basis of RhoA actions on ENaC. Patch clamp results demonstrate that RhoA increases ENaC activity in an additive manner with dominant-negative dynamin. This is consistent with a mechanism of increased ENaC trafficking to the membrane. Direct visualization of ENaC movement near the plasma membrane with total internal reflection fluorescence-fluorescence recovery after photobleaching revealed that RhoA accelerates ENaC trafficking toward the membrane. RhoA-facilitated movement of the channel was sensitive to disrupting the endomembrane system. Moreover, facilitating retrieval decreased ENaC activity but not trafficking toward the membrane. ENaC at the plasma membrane clustered and was laterally immobile suggesting that the cytoskeleton tethers or corrals membrane resident channels or membrane-directed vesicles containing ENaC. Disrupting microtubules but not microfilaments led to reorganization of ENaC clusters and slowed trafficking toward the membrane. The cytoskeleton is an established target for RhoA signaling. We conclude that RhoA, likely through effects on the cytoskeleton, promotes ENaC trafficking to the plasma membrane to increase channel membrane levels and activity.


title: Prevalence of physical activity and inactivity among Saudis aged 30-70 years: a population-based cross-sectional study.

abstract: OBJECTIVES ::: To assess physical activity levels among Saudi adults, and to examine the relationships of physical activity with body mass index (BMI), waist circumference (WC) and obesity prevalence. ::: METHODS ::: Data taken from the Coronary Artery Disease in Saudis Study which is a National Epidemiological Health Survey carried out between 1995 and 2000. Participants included 17395 Saudi males and females aged 30-70 years, selected randomly using a multistage stratified cluster sampling technique. Leisure-type and sport-related physical activities including walking were assessed using an interviewed-administered questionnaire. The activities were classified into five intensity categories and assigned metabolic equivalents (MET) according to the compendium of physical activity. Based on the intensity, duration and frequency of physical activity, subjects were classified into active or inactive category. ::: RESULTS ::: Inactivity prevalence (96.1%) was very high. There were significantly (p<0.001)) more inactive females (98.1%) than males (93.9%). Inactivity prevalence increases with increasing age category, especially in males, and decreases with increasing education levels. Inactivity was the highest in the central region (97.3%; 95% CI = 96.8-97.8%) and the lowest in the southern region of Saudi Arabia (94.0%; 95% CI = 93.2-94.8%). Further, active individuals exhibited lower values of BMI and WC. ::: CONCLUSIONS ::: These findings reveal the sedentary nature of Saudi population. The overwhelming majority of men and women did not reach the recommended physical activity levels necessary for promoting health and preventing diseases. The high prevalence of inactivity among Saudis represents a major public health concern.


title: The "cricket bat" flap: a one-stage free forearm flap phalloplasty.

abstract: Total and subtotal penile reconstruction represents a major surgical challenge. We present a new method and two illustrative cases using a modified design of the radial forearm free-tissue transfer: the "cricket bat" flap.


title: Code obfuscation against symbolic execution attacks

abstract: Code obfuscation is widely used by software developers to protect intellectual property, and malware writers to hamper program analysis. However, there seems to be little work on systematic evaluations of effectiveness of obfuscation techniques against automated program analysis. The result is that we have no methodical way of knowing what kinds of automated analyses an obfuscation method can withstand. This paper addresses the problem of characterizing the resilience of code obfuscation transformations against automated symbolic execution attacks, complementing existing works that measure the potency of obfuscation transformations against human-assisted attacks through user studies. We evaluated our approach over 5000 different C programs, which have each been obfuscated using existing implementations of obfuscation transformations. The results show that many existing obfuscation transformations, such as virtualization, stand little chance of withstanding symbolic-execution based deobfuscation. A crucial and perhaps surprising observation we make is that symbolic-execution based deobfuscators can easily deobfuscate transformations that preserve program semantics. On the other hand, we present new obfuscation transformations that change program behavior in subtle yet acceptable ways, and show that they can render symbolic-execution based deobfuscation analysis ineffective in practice.


title: Adaptive human silhouette extraction with chromatic distortion and contour tracking

abstract: This paper proposes a method to increase the accuracy of human silhouette extraction using background subtraction. Most silhouette extraction algorithms identify shadows when they should not, fail when illumination changes and absorb stationary objects into the background model. An algorithm is proposed that separates the chromatic and brightness changes in a video to clearly differentiate foreground features from background. Large chromatic differences between foreground and background data are used to overcome the effects of illumination changes and differences in brightness are used to overcome shadowing. Chromatic and brightness data collected over 10 frames in a video showed that chromatic distortions in the range (40, 65) indicate foreground objects and brightness distortions below 0.6 indicate shadows. Objects were prevented from disappearing into the background model by tracking the silhouette contours and adaptively updating the background. Experiments showed that silhouette regions should have a background model size of 30 frames while non-silhouette regions should have 5 frames. The proposed method successfully removed shadows and accurately output contours of human silhouettes under varying lighting conditions.


title: Towards Measuring and Inferring User Interest from Gaze

abstract: How can we reliably infer web users' interest and evaluate the content relevance when lacking active user interaction such as click behavior? In this paper, we investigate the relationship between mobile users' implicit interest inferred from attention metrics, such as eye gaze or viewport time, and explicit interest expressed by users. We present the first quantitative gaze tracking study using front-facing camera of mobile devices instead of specialized, expensive eye-tracking devices. We focus on multi-column digital media pages in Google Play Store that display 30+ items per page belonging to diverse categories. In such pages, we find significantly different distribution of gaze metrics on items that users rate as interesting vs. not. We leverage this insight by building a prediction model that is able to infer a user's interest ratings from the the non-click actions of the user. Our model is able to attain AUC of 90.32% in predicting user interest at an individual item level. In addition, our experiments on collection item re-ranking show how user gaze and viewport signals can be used to personalize item ranking on the collection page. Beyond understanding users' attention behavior in novel contexts such as multi-column digital media pages in Google Play Store, the findings in this study have implications for the design of a novel personalization and recommendation mechanism by (1) prioritizing items that are most likely of interest to users based on historical attention signals, and (2) prioritizing positions receiving significant portion of gaze attention.


title: A Comparative Study of Palmprint Recognition Algorithms

abstract: Palmprint images contain rich unique features for reliable human identification, which makes it a very competitive topic in biometric research. A great many different low resolution palmprint recognition algorithms have been developed, which can be roughly grouped into three categories: holistic-based, feature-based, and hybrid methods. The purpose of this article is to provide an updated survey of palmprint recognition methods, and present a comparative study to evaluate the performance of the state-of-the-art palmprint recognition methods. Using the Hong Kong Polytechnic University (HKPU) palmprint database (version 2), we compare the recognition performance of a number of holistic-based (Fisherpalms and DCT+LDA) and local feature-based (competitive code, ordinal code, robust line orientation code, derivative of Gaussian code, and wide line detector) methods, and then investigate the error correlation and score-level fusion performance of different algorithms. After discussing the achievements and limitations of current palmprint recognition algorithms, we conclude with providing several potential research directions for the future.


title: Automatic stylistic manga layout

abstract: Manga layout is a core component in manga production, characterized by its unique styles. However, stylistic manga layouts are difficult for novices to produce as it requires hands-on experience and domain knowledge. In this paper, we propose an approach to automatically generate a stylistic manga layout from a set of input artworks with user-specified semantics, thus allowing less-experienced users to create high-quality manga layouts with minimal efforts. We first introduce three parametric style models that encode the unique stylistic aspects of manga layouts, including layout structure, panel importance, and panel shape. Next, we propose a two-stage approach to generate a manga layout: 1) an initial layout is created that best fits the input artworks and layout structure model, according to a generative probabilistic framework; 2) the layout and artwork geometries are jointly refined using an efficient optimization procedure, resulting in a professional-looking manga layout. Through a user study, we demonstrate that our approach enables novice users to easily and quickly produce higher-quality layouts that exhibit realistic manga styles, when compared to a commercially-available manual layout tool.


title: Example-Based facial animation for blend shape interpolation

abstract: This paper presents a system for example-based character animation using shape interpolation (blend shapes) generated from tracked offline facial feature. First, the subject was recorded expressing various facial expressions (happy, sad, fear, anger, surprise). The recordings were then processed and marked on specific places (feature points) on the face where certain expressions would have the most significant change (the eyes and mouth area). The facial movements, or control parameters, of the human subject are used as movement parameter for the freeform deformation blend shape interpolation.


title: Transabdominal Ultrasound of Rectum as a Diagnostic Tool in Childhood Constipation

abstract: Purpose: We tested whether transverse rectal diameter measured by ultrasound could identify rectal impaction, investigated whether transverse diameter is enlarged in constipated children compared to healthy children and evaluated transverse diameter during treatment of constipation.Materials and Methods: A total of 51 children 4 to 12 years old were included in the study. Of the children 27 (mean age 7.0 ± 1.8 years) had been diagnosed with chronic constipation by Rome III criteria and 24 (9.1 ± 2.7 years) were healthy controls. All patients underwent a thorough medical history and physical examination, including digital rectal examination and measurement of rectal diameter by transabdominal ultrasound. Constipated children underwent repeat investigations after 4 weeks of laxative treatment.Results: Average rectal diameter of children with negative digital rectal examination was 21 ± 4.2 mm (mean ± SD), leading to the approximation that a value greater than 29.4 mm (mean ± 2 SD) indicates rectal impaction...


title: Design pattern detection by template matching

abstract: In this paper, we adopt a template matching method to detect design patterns from a software system by calculating their normalized cross correlation. Because design patterns document flexible design ideas, there can be various ways of implementing them. In our approach, not only the exact matches of pattern instances are detected from system source code, but also the variations of pattern candidates can be identified. Based on our method, we provide tool support and perform experiments on different large open-source systems.


title: Socioeconomic Factors and Processes Associated With Domestic Violence in Rural Bangladesh

abstract: CONTEXT ::: Although the pervasiveness of domestic violence against women in Bangladesh is well documented, specific risk factors, particularly those that can be affected by policies and programs, are not well understood. ::: ::: ::: METHODS ::: In 2001-2002, surveys, in-depth interviews and small group discussions were conducted with married women from six Bangladeshi villages to examine the types and severity of domestic violence, and to explore the pathways through which women's social and economic circumstances may influence their vulnerability to violence in marriage. Women's odds of experiencing domestic violence in the past year were assessed by logistic regression analysis. ::: ::: ::: RESULTS ::: Of about 1,200 women surveyed, 67% had ever experienced domestic violence, and 35% had done so in the past year. According to the qualitative findings, participants expected women with more education and income to be less vulnerable to domestic violence; they also believed (or hoped) that having a dowry or a registered marriage could strengthen a women's position in her marriage. Yet, of these potential factors, only education was associated with significantly reduced odds of violence; meanwhile, the odds were increased for women who had a dowry agreement or had personal earnings that contributed more than nominally to the marital household. Women strongly supported educating their daughters, but pressures remain to marry them early, in part to avoid high dowry costs. ::: ::: ::: CONCLUSIONS ::: In rural Bangladesh, women's social and economic circumstances may influence their risk of domestic violence in complex and contradictory ways. Findings also suggest a disconnect between women's emerging expectations and their current realities.


title: Relationships between Design Patterns

abstract: The catalogue of design patterns from [Gamm94] contains about twenty design patterns and their mutual relationships. In this paper, we organize these relationships into different categories and revise the design patterns and their relationships. We are then able to arrange the design patterns in different layers. The results simplify the understanding of the overall structure of the catalogue, thereby making it easier to classify other design patterns, and to apply these design patterns to software development.