title: Monocular Multiview Object Tracking with 3D Aspect Parts

abstract: In this work, we focus on the problem of tracking objects under significant viewpoint variations, which poses a big challenge to traditional object tracking methods. We propose a novel method to track an object and estimate its continuous pose and part locations under severe viewpoint change. In order to handle the change in topological appearance introduced by viewpoint transformations, we represent objects with 3D aspect parts and model the relationship between viewpoint and 3D aspect parts in a part-based particle filtering framework. Moreover, we show that instance-level online-learned part appearance can be incorporated into our model, which makes it more robust in difficult scenarios with occlusions. Experiments are conducted on a new dataset of challenging YouTube videos and a subset of the KITTI dataset [14] that include significant viewpoint variations, as well as a standard sequence for car tracking. We demonstrate that our method is able to track the 3D aspect parts and the viewpoint of objects accurately despite significant changes in viewpoint.


title: Real time monitoring of vital signs using wireless technique

abstract: Recent wireless technology gives new possibilities for monitoring vital parameters like electrocardiogram (ECG), Blood Pressure (BP), Temperature, Respiration rate with portable system and still be under continuously monitoring and thereby to better quality of patient health care. This paper describes the design of a wireless ECG portable systems used for remote monitoring of cardiac activity is one of the most important fields in telemedicine and telecare. ECG signals and Heart beats of all patients are recorded in a single computer then processed with a microcontroller. After processing, all data are sent to a communication interface that can send this information to a personal computer. This work covers the wireless telemedicine applications with the use of zigbee technology. This work is also included to implement various filters using LabVIEW software.


title: Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model

abstract: Most human behaviors consist of multiple parts, steps, or subtasks. These structures guide our action planning and execution, but when we observe others, the latent structure of their actions is typically unobservable, and must be inferred in order to learn new skills by demonstration, or to assist others in completing their tasks. For example, an assistant who has learned the subgoal structure of a colleague's task can more rapidly recognize and support their actions as they unfold. Here we model how humans infer subgoals from observations of complex action sequences using a nonparametric Bayesian model, which assumes that observed actions are generated by approximately rational planning over unknown subgoal sequences. We test this model with a behavioral experiment in which humans observed different series of goal-directed actions, and inferred both the number and composition of the subgoal sequences associated with each goal. The Bayesian model predicts human subgoal inferences with high accuracy, and significantly better than several alternative models and straightforward heuristics. Motivated by this result, we simulate how learning and inference of subgoals can improve performance in an artificial user assistance task. The Bayesian model learns the correct subgoals from fewer observations, and better assists users by more rapidly and accurately inferring the goal of their actions than alternative approaches.


title: Kernel Density Estimation of traffic accidents in a network space

abstract: Abstract A standard planar Kernel Density Estimation (KDE) aims to produce a smooth density surface of spatial point events over a 2-D geographic space. However, the planar KDE may not be suited for characterizing certain point events, such as traffic accidents, which usually occur inside a 1-D linear space, the roadway network. This paper presents a novel network KDE approach to estimating the density of such spatial point events. One key feature of the new approach is that the network space is represented with basic linear units of equal network length, termed lixel (linear pixel), and related network topology. The use of lixel not only facilitates the systematic selection of a set of regularly spaced locations along a network for density estimation, but also makes the practical application of the network KDE feasible by significantly improving the computation efficiency. The approach is implemented in the ESRI ArcGIS environment and tested with the year 2005 traffic accident data and a road network in the Bowling Green, Kentucky area. The test results indicate that the new network KDE is more appropriate than standard planar KDE for density estimation of traffic accidents, since the latter covers space beyond the event context (network space) and is likely to overestimate the density values. The study also investigates the impacts on density calculation from two kernel functions, lixel lengths, and search bandwidths. It is found that the kernel function is least important in structuring the density pattern over network space, whereas the lixel length critically impacts the local variation details of the spatial density pattern. The search bandwidth imposes the highest influence by controlling the smoothness of the spatial pattern, showing local effects at a narrow bandwidth and revealing “ hot spots ” at larger or global scales with a wider bandwidth. More significantly, the idea of representing a linear network by a network system of equal-length lixel s may potentially lead the way to developing a suite of other network related spatial analysis and modeling methods.


Indoor localization method comparison : Fingerprinting and Trilateration algorithm


title: Cross-Calibration of RGB and Thermal Cameras with a LIDAR for RGB-Depth-Thermal Mapping

abstract: We present a method for calibrating the extrinsic parameters between a RGB camera, a thermal camera, and a LIDAR. The calibration procedure we use is common to both the RGB and thermal cameras. The extrinsic calibration procedure assumes that the cameras are geometrically calibrated. To aid the geometric calibration of the thermal camera, we use a calibration target made of black-and-white melamine that looks like a checkerboard pattern in the thermal and RGB images. For the extrinsic calibration, we place a circular calibration target in the common field of view of the cameras and the LIDAR and compute the extrinsic parameters by minimizing an objective function that aligns the edges of the circular target in the LIDAR to its corresponding edges in the RGB and thermal images. We illustrate the convexity of the objective function and discuss the convergence of the algorithm. We then identify the various sources of coloring errors (after cross-calibration) as (a) noise in the LIDAR points, (b) error in the...


title: Intrinsic and Extrinsic Risk Factors for Muscle Strains in Australian Football ∗

abstract: Muscle strains are common injuries in Australian football and other sports involving sprinting. Between 1992 and 1999, 83,503 player-matches in the Australian Football League were analyzed for risk of muscle strain injuries using logistic regression analysis. There were 672 hamstring, 163 quadriceps, and 140 calf muscle strain injuries. All three types of muscle strains were associated with significant risk factors. For all injuries, the strongest risk factor was a recent history of that same injury and the next strongest risk factor was a past history of the same injury. History of one type of muscle strain increased the risk for certain types of other muscle strains. Age was a risk factor for hamstring and calf muscle strains (even when adjusted for injury history) but was not a risk factor for quadriceps muscle strains. Quadriceps muscle injuries were more common in shorter players and were more likely when there had been less rainfall at the match venue in the previous week. Quadriceps muscle injuries...


title: Coercion Theory, Self-Control, and Social Information Processing: Understanding Potential Mediators for How Parents Influence Deviant Behaviors

abstract: Research has demonstrated that (in)effective parenting influences whether a child/adolescent engages in deviant behaviors; however, research is mixed regarding whether that influence is direct. After a review of theoretical and empirical evidence, parenting factors outlined by several theories appear important in explaining the association between parenting and deviance. More importantly, however, is that the parental influence may not be direct, but rather mediated through a child/adolescent's level of self-control and social information processing skills. As such, evidence suggests that to fully understand the complex association between parenting and deviance, parenting and mediating factors from multiple theories must be explored simultaneously.


title: Cost-sensitive learning by cost-proportionate example weighting

abstract: We propose and evaluate a family of methods for converting classifier learning algorithms and classification theory into cost-sensitive algorithms and theory. The proposed conversion is based on cost-proportionate weighting of the training examples, which can be realized either by feeding the weights to the classification algorithm (as often done in boosting), or by careful subsampling. We give some theoretical performance guarantees on the proposed methods, as well as empirical evidence that they are practical alternatives to existing approaches. In particular, we propose costing, a method based on cost-proportionate rejection sampling and ensemble aggregation, which achieves excellent predictive performance on two publicly available datasets, while drastically reducing the computation required by other methods.


title: Heritage BIM integration with mixed reality for building preventive maintenance

abstract: Historical and cultural building conservation is nowadays limited to high value buildings and only receive financial support when serious issues arise. HeritageCARE project is willing to shift this mentality, towards "prevention is better than the cure". New technologies are able to support such motivation by improving building inspector work during their inspection routine. In that regard, mixed reality will be explored to offer easy and efficient interaction while remaining focused on the task at hand. Moreover, integration with historical building information model will ensure capture of localized data and easier exchange with stakeholders in case of renovation projects. This paper presents an architecture for a mixed reality application working towards this goal and a first prototype.


title: Extension of a two-step calibration methodology to include nonorthogonal sensor axes

abstract: We present an extension of the nonlinear two-step estimation algorithm originally developed for the calibration of solid-state strapdown magnetometers. We expand the algorithm to include nonorthogonality within a sensor set for both two- and three-axis sensors. Nonorthogonality can result from manufacturing issues, installation geometry, and in the case of magnetometers, from soft iron bias errors. Simulation studies for both two- and three-axis sensors show convergence of the improved algorithm to the true values, even in the presence of realistic measurement noise. Finally the algorithm is experimentally validated on a low-cost solid-state three-axis magnetometer set, which shows definite improvement postcalibration. We note that the algorithm is general and can be applied to any two- or three-axis sensor set (such as accelerometers) with an error model consisting of scale, offset, and nonorthogonality errors.


Distributed Computing and Artificial Intelligence


title: Intersectional Invisibility: The Distinctive Advantages and Disadvantages of Multiple Subordinate-Group Identities

abstract: The hypothesis that possessing multiple subordinate-group identities renders a person “invisible” relative to those with a single subordinate-group identity is developed. We propose that androcentric, ethnocentric, and heterocentric ideologies will cause people who have multiple subordinate-group identities to be defined as non-prototypical members of their respective identity groups. Because people with multiple subordinate-group identities (e.g., ethnic minority woman) do not fit the prototypes of their respective identity groups (e.g., ethnic minorities, women), they will experience what we have termed “intersectional invisibility.” In this article, our model of intersectional invisibility is developed and evidence from historical narratives, cultural representations, interest-group politics, and anti-discrimination legal frameworks is used to illustrate its utility. Implications for social psychological theory and research are discussed.


title: Interactive control of avatars animated with human motion data

abstract: Real-time control of three-dimensional avatars is an important problem in the context of computer games and virtual environments. Avatar animation and control is difficult, however, because a large repertoire of avatar behaviors must be made available, and the user must be able to select from this set of behaviors, possibly with a low-dimensional input device. One appealing approach to obtaining a rich set of avatar behaviors is to collect an extended, unlabeled sequence of motion data appropriate to the application. In this paper, we show that such a motion database can be preprocessed for flexibility in behavior and efficient search and exploited for real-time avatar control. Flexibility is created by identifying plausible transitions between motion segments, and efficient search through the resulting graph structure is obtained through clustering. Three interface techniques are demonstrated for controlling avatar motion using this data structure: the user selects from a set of available choices, sketches a path through an environment, or acts out a desired motion in front of a video camera. We demonstrate the flexibility of the approach through four different applications and compare the avatar motion to directly recorded human motion.


title: Ranking Nodes in Signed Social Networks

abstract: Social networks are inevitable part of modern life. A class of social networks is those with both positive (friendship or trust) and negative (enmity or distrust) links. Ranking nodes in signed networks remains a hot topic in computer science. In this manuscript, we review different ranking algorithms to rank the nodes in signed networks, and apply them to the sign prediction problem. Ranking scores are used to obtain reputation and optimism, which are used as features in the sign prediction problem. Reputation of a node shows patterns of voting towards the node and its optimism demonstrates how optimistic a node thinks about others. To assess the performance of different ranking algorithms, we apply them on three signed networks including Epinions, Slashdot and Wikipedia. In this paper, we introduce three novel ranking algorithms for signed networks and compare their ability in predicting signs of edges with already existing ones. We use logistic regression as the predictor and the reputation and optimism values for the trustee and trustor as features (that are obtained based on different ranking algorithms). We find that ranking algorithms resulting in correlated ranking scores, leads to almost the same prediction accuracy. Furthermore, our analysis identifies a number of ranking algorithms that result in higher prediction accuracy compared to others.


title: Cloning Strategies from Trading Records using Agent-based Reinforcement Learning Algorithm

abstract: Investment decision making is considered as a series of complicated processes, which are difficult to be analyzed and imitated. Given large amounts of trading records with rich expert knowledge in financial domain, extracting its original decision logics and cloning the trading strategies are also quite challenging. In this paper, an agent-based reinforcement learning (RL) system is proposed to mimic professional trading strategies. The concept of continuous Markov decision process (MDP) in RL is similar to the trading decision making in financial time series data. With the specific-designed RL components, including states, actions, and rewards for financial applications, policy gradient method can successfully imitate the expert's strategies. In order to improve the convergence of RL agent in such highly dynamic environment, a pre-trained model based on supervised learning is transferred to the deep policy networks. The experimental results show that the proposed system can reproduce around eighty percent trading decisions both in training and testing stages. With the discussion of the tradeoff between explorations and model updating, this paper tried to fine-tuning the system parameters to get reasonable results. Finally, an advanced strategy is proposed to dynamically adjust the number of explorations in each episode to achieve better results.


title: Altitude and latitude distribution of atmospheric aerosol and water vapor from the narrow-band lunar eclipse photometry

abstract: This work contains the description of two narrow IR-bands observational data of the total lunar eclipse of March 3, 2007, one- and two-dimensioned procedures of radiative transfer equation solution. The results of the procedure are the extinction values for atmospheric aerosol and water vapor at different altitudes in the troposphere along the Earth's terminator crossing North America, Arctic, Siberia and South-Eastern Asia. The altitude range and possible latitude and altitude resolution of atmospheric remote sensing by the lunar eclipse observation are described. The results of water vapor retrieval are compared with data of space experiments, and the scale of vertical water vapor distribution is found.


title: Bringing order to the Web: automatically categorizing search results

abstract: We developed a user interface that organizes Web search results into hierarchical categories. Text classification algorithms were used to automatically classify arbitrary search results into an existing category structure on-the-fly. A user study compared our new category interface with the typical ranked list interface of search results. The study showed that the category interface is superior both in objective and subjective measures. Subjects liked the category interface much better than the list interface, and they were 50% faster at finding information that was organized into categories. Organizing search results allows users to focus on items in categories of interest rather than having to browse through all the results sequentially.


title: Controlling the False Discovery Rate: a Practical and Powerful Approach to Multiple Testing

abstract: SUMMARY The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.


title: Testing-for-manufacturing (TFM) for ultra-thin IPD on InFO

abstract: The Integrated Fan Out (InFO) technology can accom-plish package miniaturization and successfully achieve “More than Moore's Law.” Its substrate-free technology also brings great cost-effective attraction to mobile and wearable applications. Ultra-thin integrated passive devices (IPDs) with high capacitance density can further shrink the InFO size and boost the bandwidth. Although the cost for the IPD in the InFO package is negligible, failed IPDs can shut down the whole InFO package. This would cause an entire mobile system loss about 104 times of cost penalty. Thus the quality of each IPD chip must be ensured before surface-mounted on the InFO, and applying the final testing (FT) of the IPD is necessary. In this paper, we propose a novel optical positioning FT system combined with testing-for-manufacturing (TFM) methodology for the ultra-thin IPD chips testing. For validation of the novel comprehensive FT solution in the volume production domain, ultra-thin and ultra-small-area IPD chips with several millions sample sizes have passed through the FT system.


title: Trajectory generation for continuous leg forces during double support and heel-to-toe shift based on divergent component of motion

abstract: This paper works with the concept of Divergent ::: Component of Motion (DCM), also called ’(instantaneous) ::: Capture Point’. We present two real-time DCM trajectory generators for uneven (three-dimensional) ground surfaces, which lead to continuous leg (and corresponding ground reaction) force profiles and facilitate the use of toe-off motion during double support. Thus, the resulting DCM trajectories are well suited for real-world robots and allow for increased step length and step height. The performance of the proposed methods was tested in numerous simulations and experiments on IHMC’s Atlas robot and DLR’s humanoid robot TORO.


title: XDoG: An eXtended difference-of-Gaussians compendium including advanced image stylization

abstract: Recent extensions to the standard difference-of-Gaussians (DoG) edge detection operator have rendered it less susceptible to noise and increased its aesthetic appeal. Despite these advances, the technical subtleties and stylistic potential of the DoG operator are often overlooked. This paper offers a detailed review of the DoG operator and its extensions, highlighting useful relationships to other image processing techniques. It also presents many new results spanning a variety of styles, including pencil-shading, pastel, hatching, and woodcut. Additionally, we demonstrate a range of subtle artistic effects, such as ghosting, speed-lines, negative edges, indication, and abstraction, all of which are obtained using an extended DoG formulation, or slight modifications thereof. In all cases, the visual quality achieved by the extended DoG operator is comparable to or better than those of systems dedicated to a single style.


title: Reinforcement learning design for cancer clinical trials

abstract: There has been significant recent research activity in developing therapies that are tailored to each individual. Finding such therapies in treatment settings involving multiple decision times is a major challenge. In this dissertation, we develop reinforcement learning trials for discovering these optimal regimens for life-threatening diseases such as cancer. A temporal-difference learning method called Q-learning is utilized which involves learning an optimal policy from a single training set of finite longitudinal patient trajectories. Approximating the Q-function with time-indexed parameters can be achieved by using support vector regression or extremely randomized trees. Within this framework, we demonstrate that the procedure can extract optimal strategies directly from clinical data without relying on the identification of any accurate mathematical models, unlike approaches based on adaptive design. We show that reinforcement learning has tremendous potential in clinical research because it can select actions that improve outcomes by taking into account delayed effects even when the relationship between actions and outcomes is not fully known. ::: To support our claims, the methodology's practical utility is firstly illustrated in a virtual simulated clinical trial. We then apply this general strategy with significant refinements to studying and discovering optimal treatments for advanced metastatic stage IIIB/IV non-small cell lung cancer (NSCLC). In addition to the complexity of the NSCLC problem of selecting optimal compounds for first and second-line treatments based on prognostic factors, another primary scientific goal is to determine the optimal time to initiate second-line therapy, either immediately or delayed after induction therapy, yielding the longest overall survival time. We show that reinforcement learning not only successfully identifies optimal strategies for two lines of treatment from clinical data, but also reliably selects the best initial time for second-line therapy while taking into account heterogeneities of NSCLC across patients.


title: Integrating robot ethics and machine morality: the study and design of moral competence in robots

abstract: Robot ethics encompasses ethical questions about how humans should design, deploy, and treat robots; machine morality encompasses questions about what moral capacities a robot should have and how these capacities could be computationally implemented. Publications on both of these topics have doubled twice in the past 10 years but have often remained separate from one another. In an attempt to better integrate the two, I offer a framework for what a morally competent robot would look like (normally considered machine morality) and discuss a number of ethical questions about the design, use, and treatment of such moral robots in society (normally considered robot ethics). Instead of searching for a fixed set of criteria of a robot's moral competence I identify the multiple elements that make up human moral competence and probe the possibility of designing robots that have one or more of these human elements, which include: moral vocabulary; a system of norms; moral cognition and affect; moral decision making and action; moral communication. Juxtaposing empirical research, philosophical debates, and computational challenges, this article adopts an optimistic perspective: if robotic design truly commits to building morally competent robots, then those robots could be trustworthy and productive partners, caretakers, educators, and members of the human community. Moral competence does not resolve all ethical concerns over robots in society, but it may be a prerequisite to resolve at least some of them.


title: Node Importance Ranking of Complex Networks with Entropy Variation

abstract: The heterogeneous nature of a complex network determines the roles of each node in the network that are quite different. Mechanisms of complex networks such as spreading dynamics, cascading reactions, and network synchronization are highly affected by a tiny fraction of so-called important nodes. Node importance ranking is thus of great theoretical and practical significance. Network entropy is usually utilized to characterize the amount of information encoded in the network structure and to measure the structural complexity at the graph level. We find that entropy can also serve as a local level metric to quantify node importance. We propose an entropic metric, Entropy Variation, defining the node importance as the variation of network entropy before and after its removal, according to the assumption that the removal of a more important node is likely to cause more structural variation. Like other state-of-the-art methods for ranking node importance, the proposed entropic metric is also used to utilize structural information, but at the systematical level, not the local level. Empirical investigations on real life networks, the Snake Idioms Network, and several other well-known networks, demonstrate the superiority of the proposed entropic metric, notably outperforming other centrality metrics in identifying the top-k most important nodes.


title: A Convenient Multicamera Self-Calibration for Virtual Environments

abstract: Virtual immersive environments or telepresence setups often consist of multiple cameras that have to be calibrated, We present a convenient method for doing this. The minimum is three cameras, but there is no upper limit. The method is fully automatic and a freely moving bpdght spot is the only calibration object, A set of virtual 3D points is made by waving the bright spot through the working volume. Its projections are found with subpixel precision and verified by a robust RANSAC analysis. The cameras do not have to see all points; only reasonable overlap between camera subgroups is necessary. Projective structures are computed via rank-4 factorization and the Euclidean stratification is done by imposing geometric constraints. This linear estimate initializes a postprocessing computation of nonlinear distortion, which is also fully automatic. We suggest a trick on how to use a very ordinary laser pointer as the calibration object. We show that it is possible to calibrate an immersive virtual environment with 16 cameras in less than 60 minutes reaching about 1/5 pixel reprojection error, The method has been successfully tested on numerous multicamera environments using varying numbers of cameras of varying quality.


title: UNDERSTANDING VIRTUAL EXPERIENCE AND TELEPRESENCE - A REVIEW AND SYNTHESIS OF LITERATURE

abstract: To enable buyers to be better informed before purchasing, products and services can be virtually experienced on the internet. Research into virtual experience (VE) and the related construct of telepresence (TP) as means of online marketing has made great progress in recent years. However, there is still disagreement in the literature concerning the exact understanding of these terms. In this study, the two terms are analyzed by means of a systematically executed literature review, differentiated from one another, and their understandings explained. This study is to our knowledge the first to compare the concepts of VE and TP in a systematic way. The analysis shows that TP is regarded as the feeling of presence conveyed by a communication medium. VE, on the other hand, is to be defined as an active state of a consumer through the use of computer-based presentation formats, and constituting a subtype of TP. These findings are intended to help VE and TP become more uniformly understood and make it easier to compare the results of future studies. Finally, from the literature review, it is possible to derive focal points for research in future studies.


Computing the Stereo Matching Cost with a Convolutional Neural Network Seminar Recent Trends in 3 D Computer Vision


title: Automatic Lung Cancer Prediction from Chest X-ray Images Using the Deep Learning Approach

abstract: Since, cancer is curable when diagnosed at an early stage, lung cancer screening plays an important role in preventive care. Although both low dose computed tomography (LDCT) and computed tomography (CT) scans provide more medical information than normal chest x-rays, there is very limited access to these technologies in rural areas. Recently, there is a trend in using computer-aided diagnosis (CADx) to assist in screening and diagnosing of cancer from biomedical images. In this study, the 121-layer convolutional neural network also known as DenseNet-121 by G. Huang et. al., along with the transfer learning scheme was explored as a means to classify lung cancer using chest X-ray images. The model was trained on a lung nodules dataset before training on the lung cancer dataset to alleviate the problem of a small dataset. The proposed model yields 74.43$\pm$6.01\% of mean accuracy, 74.96$\pm$9.85\% of mean specificity, and 74.68$\pm$15.33\% of mean sensitivity. The proposed model also provides a heatmap for identifying the location of the lung nodule. These findings are promising for further development of chest x-ray-based lung cancer diagnosis using the deep learning approach. Moreover, these findings solve the problem of small dataset.


title: An iPhone Application for Blood Pressure Monitoring via the Oscillometric Finger Pressing Method

abstract: We developed an iPhone X application to measure blood pressure (BP) via the “oscillometric finger pressing method”. The user presses her fingertip on both the front camera and screen to increase the external pressure of the underlying artery, while the application measures the resulting variable-amplitude blood volume oscillations via the camera and applied pressure via the strain gauge array under the screen. The application also visually guides the fingertip placement and actuation and then computes BP from the measurements just like many automatic cuff devices. We tested the application, along with a finger cuff device, against a standard cuff device. The application yielded bias and precision errors of −4.0 and 11.4 mmHg for systolic BP and −9.4 and 9.7 mmHg for diastolic BP (n = 18). These errors were near the finger cuff device errors. This proof-of-concept study surprisingly indicates that cuff-less and calibration-free BP monitoring may be feasible with many existing and forthcoming smartphones.


title: Time series forecasting of cyber attack intensity

abstract: Cyber attacks occur on a near daily basis and are becoming exponentially more common. While some research aims to detect the characteristics of an attack, little focus has been given to patterns of attacks in general. This paper aims to exploit temporal correlations between the number of attacks per day in order to predict future intensity of cyber incidents. Through analysis of attack data collected from Hackmageddon, correlation was found among reported attack volume in consecutive days. This paper presents a forecasting system that aims to predict the number of cyber attacks on a given day based only on a set of historical attack count data. Our system conducts ARIMA time series forecasting on all previously collected incidents to predict the expected number of attacks on a future date. Our tool is able to use only a subset of data relevant to a specific attack method. Prediction models are dynamically updated over time as new data is collected to improve accuracy. Our system outperforms naive forecasting methods by 14.1% when predicting attacks of any type, and up to 21.2% when forecasting attacks of a specific type. Our system also produces a model which more accurately predicts future cyber attack intensity behavior.


title: Critical success factors behind the sustainability of the Sharing Economy

abstract: Whether you are renting a text book (textbookrentals.com), sharing a ride (Uber), or looking for a safe and convenient place to spend the night (Airbnb) you are part of a growing economy referred to as the “Sharing Economy”. There are an increasing number of emerging startups that have embraced this economic model and have become giant enterprises with record-breaking revenues in an abnormally short period of time. But is the Sharing Economy sustainable enough to grow and prosper in the future? This paper investigated the critical success factors behind the promising prospects of the Sharing Economy and concludes that the Sharing Economy is a sustainable economic model. There are various factors behind this premise such as a new emerging class of consumers with a keen interest in sharing; price consciousness; a robust business model; a growing perception of the eco-efficiency of the sharing economy; and an emergent business-to-business (B2B) sector that contributes to operating at maximum capacity. All the aforementioned factors coupled with advances in information technology and software engineering, especially in social media and digital platforms, have created a unique environment for the growth and acceleration of the sharing economy.


title: Multitask learning for neural generative question answering

abstract: Neural generative model in question answering (QA) usually employs sequence-to-sequence (Seq2Seq) learning to generate answers based on the user’s questions as opposed to the retrieval-based model selecting the best matched answer from a repository of pre-defined QA pairs. One key challenge of neural generative model in QA lies in generating high-frequency and generic answers regardless of the questions, partially due to optimizing log-likelihood objective function. In this paper, we investigate multitask learning (MTL) in neural network-based method under a QA scenario. We define our main task as agenerative QA via Seq2Seq learning. And we define our auxiliary task as a discriminative QA via binary QAclassification. Both main task and auxiliary task are learned jointly with shared representations, allowing to obtain improved generalization and transferring classification labels as extra evidences to guide the word sequence generation of the answers. Experimental results on both automatic evaluations and human annotations demonstrate the superiorities of our proposed method over baselines.


title: The immunological life cycle of tuberculosis

abstract: Immune responses to Mycobacterium tuberculosis are only partially effective; they drive the bacteria into a latent state, but rarely eliminate them. Unfortunately, the latent state of M. tuberculosis is reversible, and reactivation tuberculosis is the source of most transmission. Studies in animal models and in humans have not yet yielded a comprehensive picture of the mechanisms or correlates of immunity to M. tuberculosis infection, or of why immunity fails to eradicate the pathogen. This Review proposes that there are distinct stages in the immune response to M. tuberculosis that form an 'immunological life cycle'. It is hoped that this thesis will provide a framework for investigation to understand immunity to M. tuberculosis and to guide tuberculosis vaccine discovery and development.


title: The influence of photoperiod and light intensity on the growth and photosynthesis of Dunaliella salina (chlorophyta) CCAP 19/30

abstract: The green microalga Dunaliella salina survives in a wide range of salinities via mechanisms involving glycerol synthesis and degradation and is exploited for large amounts of nutraceutical carotenoids produced under stressed conditions. In this study, D. salina CCAP 19/30 was cultured in varying photoperiods and light intensities to study the relationship of light with different growth measurement parameters, with cellular contents of glycerol, starch and carotenoids, and with photosynthesis and respiration. Results show CCAP 19/30 regulated cell volume when growing under light/dark cycles: cell volume increased in the light and decreased in the dark, and these changes corresponded to changes in cellular glycerol content. The decrease in cell volume in the dark was independent of cell division and biological clock and was regulated by the photoperiod of the light/dark cycle. When the light intensity was increased to above 1000 μmol photons m(-2) s(-1), cells displayed evidence of photodamage. However, these cells also maintained the maximum level of photosynthesis efficiency and respiration possible, and the growth rate increased as light intensity increased. Significantly, the intracellular glycerol content also increased, >2-fold compared to the content in light intensity of 500 μmol photons m(-2) s(-1), but there was no commensurate increase in the pool size of carotenoids. These data suggest that in CCAP 19/30 glycerol stabilized the photosynthetic apparatus for maximum performance in high light intensities, a role normally attributed to carotenoids.


title: The Versatile Posterior Auricular Fascia in Secondary Rhinoplasty Procedures

abstract: Background ::: Secondary rhinoplasty often involves the addition of autogenous graft material to add volume, structure, support and to camouflage cartilage and bone grafts. A variety of fascias have been used to camouflage, wrap and give “fill” in secondary rhinoplasty. The posterior auricular fascia is a source of material ideal for such purposes, and its quantity, anatomical structure and ease of harvest with minimal donor site morbidity make it a versatile and attractive autogenous graft.


On impact and evaluation in Computational Creativity: A discussion of the Turing Test and an alternative proposal


title: Supervised Hashing Using Graph Cuts and Boosted Decision Trees

abstract: To build large-scale query-by-example image retrieval systems, embedding image features into a binary Hamming space provides great benefits. Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the binary Hamming space. Most existing approaches apply a single form of hash function, and an optimization process which is typically deeply coupled to this specific form. This tight coupling restricts the flexibility of those methods, and can result in complex optimization problems that are difficult to solve. In this work we proffer a flexible yet simple framework that is able to accommodate different types of loss functions and hash functions. The proposed framework allows a number of existing approaches to hashing to be placed in context, and simplifies the development of new problem-specific hashing methods. Our framework decomposes the hashing learning problem into two steps: binary code (hash bit) learning and hash function learning. The first step can typically be formulated as binary quadratic problems, and the second step can be accomplished by training a standard binary classifier. For solving large-scale binary code inference, we show how it is possible to ensure that the binary quadratic problems are submodular such that efficient graph cut methods may be used. To achieve efficiency as well as efficacy on large-scale high-dimensional data, we propose to use boosted decision trees as the hash functions, which are nonlinear, highly descriptive, and are very fast to train and evaluate. Experiments demonstrate that the proposed method significantly outperforms most state-of-the-art methods, especially on high-dimensional data.


title: Simple Method for High-Performance Digit Recognition Based on Sparse Coding

abstract: In this brief paper, we propose a method of feature extraction for digit recognition that is inspired by vision research: a sparse-coding strategy and a local maximum operation. We show that our method, despite its simplicity, yields state-of-the-art classification results on a highly competitive digit-recognition benchmark. We first employ the unsupervised Sparsenet algorithm to learn a basis for representing patches of handwritten digit images. We then use this basis to extract local coefficients. In a second step, we apply a local maximum operation to implement local shift invariance. Finally, we train a support vector machine (SVM) on the resulting feature vectors and obtain state-of-the-art classification performance in the digit recognition task defined by the MNIST benchmark. We compare the different classification performances obtained with sparse coding, Gabor wavelets, and principal component analysis (PCA). We conclude that the learning of a sparse representation of local image patches combined with a local maximum operation for feature extraction can significantly improve recognition performance.


title: Coherence of comments and method implementations: a dataset and an empirical investigation

abstract: In this paper, we present the results of a manual assessment on the coherence between the comments and the implementation of 3636 methods in three open source software applications (for one of these applications, we considered two different subsequent versions) implemented in Java. The results of this assessment have been collected in a dataset we made publicly available on the Web. The creation of this dataset is based on a protocol that is detailed in this paper. We present that protocol to let researchers evaluate the goodness of our dataset and to ease its future possible extensions. Another contribution of this paper consists in preliminarily investigating on the effectiveness of adopting a Vector Space Model (VSM) with the tf-idf schema to discriminate coherent and non-coherent methods. We observed that the lexical similarity alone is not sufficient for this distinction, while encouraging results have been obtained by applying an Support Vector Machine (SVM) classifier on the whole vector space.


title: Ball and plate position control based on fuzzy logic with adaptive integral control action

abstract: This paper proposes a position control technique for ball and plate system using fuzzy logic with adaptive integral control action. The adaptive integral gain can automatically adjust its value linearly and only active when the ball's position is within the specified distance error. This novel scheme utilizes the ability of integral gain to eliminate steady-state error and adopts fuzzy logic technique because of its simplicity of not having to find mathematical model for this nonlinear system. Current position of a ball is determined by using a webcam positioned right above a plate. Four experimental results verify the effectiveness of this control scheme.


title: A HYBRID TECHNIQUE FOR AUTOMATIC MRI BRAIN IMAGES CLASSIFICATION

abstract: This paper presents two hybrid techniques for the classica- tion of the magnetic resonance human brain images. The proposed hybrid technique consists of three stages, namely, feature extraction, dimension- ality reduction, and classication. In the rst stage, we have obtained the features related with MRI images using discrete wavelet transformation (DWT). In the second stage, the features of magnetic resonance images (MRI) have been reduced using principles component analysis (PCA) to the more essential features. In the classication stage, two classiers based on supervised machine learning have been developed. The rst classi- er based on feed forward back-propagation articial neural network (FP-


title: Operculo-insular pain (parasylvian pain): a distinct central pain syndrome

abstract: Central pain with dissociated thermoalgesic sensory loss is common in spinal and brainstem syndromes but not in cortical lesions. Out of a series of 270 patients investigated because of somatosensory abnormalities, we identified five subjects presenting with central pain and pure thermoalgesic sensory loss contralateral to cortical stroke. All of the patients had involvement of the posterior insula and inner parietal operculum. Lemniscal sensory modalities (position sense, graphaestesia, stereognosis) and somatosensory evoked potentials to non-noxious inputs were always preserved, while thermal and pain sensations were profoundly altered, and laser-evoked potentials to thermo-nocoiceptive stimuli were always abnormal. Central pain resulting from posterior parasylvian lesions appears to be a distinct entity that can be identified unambiguously on the basis of clinical, radiological and electrophysiological data. It presents with predominant or isolated deficits for pain and temperature sensations, and is paradoxically closer to pain syndromes from brainstem lesions affecting selectively the spinothalamic pathways than to those caused by focal lesions of the posterior thalamus. The term ‘pseudo-thalamic’ is therefore inappropriate to describe it, and we propose parasylvian or operculo-insular pain as appropriate labels. Parasylvian pain may be extremely difficult to treat; the magnitude of pain-temperature sensory disturbances may be prognostic for its development, hence the importance of early sensory assessment with quantitative methods.


title: Codes in spherical caps

abstract: We consider bounds on codes in spherical caps and related problems in geometry and coding theory. An extension of the Delsarte method is presented that relates upper bounds on the size of spherical codes to upper bounds on codes in caps. Several new upper bounds on codes in caps are derived. Applications of these bounds to estimates of the kissing numbers and one-sided kissing numbers are considered. ::: It is proved that the maximum size of codes in spherical caps for large dimensions is determined by the maximum size of spherical codes, so these problems are asymptotically equivalent.


title: A causal framework for explaining the predictions of black-box sequence-to-sequence models

abstract: We interpret the predictions of any black-box structured input-structured output model around a specific input-output pair. Our method returns an "explanation" consisting of groups of input-output tokens that are causally related. These dependencies are inferred by querying the black-box model with perturbed inputs, generating a graph over tokens from the responses, and solving a partitioning problem to select the most relevant components. We focus the general approach on sequence-to-sequence problems, adopting a variational autoencoder to yield meaningful input perturbations. We test our method across several NLP sequence generation tasks.


title: Adaptive colour constancy algorithm using discrete wavelet transform

abstract: The colours of chromatically homogeneous object surfaces measured by a sensor vary with the illuminant colour used to illuminate the objects. In contrast, colour constancy enables humans to identify the true colours of the surfaces under varying illumination. This paper proposes an adaptive colour constancy algorithm which estimates the illuminant colour from wavelet coefficients at each scale of the decomposition by discrete wavelet transform of the input image. The angular error between the estimated illuminant colours in consecutive scales are used to determine the optimum scale for the best estimate of the true illuminant colour. The estimated illuminant colour is then used to modify the approximation subbands of the image so as to generate the illuminant-colour corrected image via inverse discrete wavelet transform. The experiments show that the colour constancy results generated by the proposed algorithm are comparable or better than those of the state-of-the-art colour constancy algorithms that use low-level image features.


title: Transfer Deep Learning for Low-Resource Chinese Word Segmentation with a Novel Neural Network

abstract: Recent studies have shown effectiveness in using neural networks for Chinese word segmentation. However, these models rely on large-scale data and are less effective for low-resource datasets because of insufficient training data. We propose a transfer learning method to improve low-resource word segmentation by leveraging high-resource corpora. First, we train a teacher model on high-resource corpora and then use the learned knowledge to initialize a student model. Second, a weighted data similarity method is proposed to train the student model on low-resource data. Experiment results show that our work significantly improves the performance on low-resource datasets: 2.3% and 1.5% F-score on PKU and CTB datasets. Furthermore, this paper achieves state-of-the-art results: 96.1%, and 96.2% F-score on PKU and CTB datasets.


title: Predicting While Comprehending Language: A Theory and Review

abstract: Researchers agree that comprehenders regularly predict upcoming language, but they do not always agree on what prediction is (and how to differentiate it from integration) or what constitutes evidence for it. After defining prediction, we show that it occurs at all linguistic levels from semantics to form, and then propose a theory of which mechanisms comprehenders use to predict. We argue that they most effectively predict using their production system (i.e., prediction-by-production): They covertly imitate the linguistic form of the speaker’s utterance and construct a representation of the underlying communicative intention. Comprehenders can then run this intention through their own production system to prepare the predicted utterance. But doing so takes time and resources, and comprehenders vary in the extent of preparation, with many groups of comprehenders (non-native speakers, illiterates, children, and older adults) using it less than typical native young adults. We thus argue that prediction-by-production is an optional mechanism, which is augmented by mechanisms based on association. Support for our proposal comes from many areas of research (electrophysiological, eye-tracking, and behavioral studies of reading, spoken language processing in the context of visual environments, speech processing, and dialogue).


title: The fractional Fourier transform and time-frequency representations

abstract: The functional Fourier transform (FRFT), which is a generalization of the classical Fourier transform, was introduced a number of years ago in the mathematics literature but appears to have remained largely unknown to the signal processing community, to which it may, however, be potentially useful. The FRFT depends on a parameter /spl alpha/ and can be interpreted as a rotation by an angle /spl alpha/ in the time-frequency plane. An FRFT with /spl alpha/=/spl pi//2 corresponds to the classical Fourier transform, and an FRFT with /spl alpha/=0 corresponds to the identity operator. On the other hand, the angles of successively performed FRFTs simply add up, as do the angles of successive rotations. The FRFT of a signal can also be interpreted as a decomposition of the signal in terms of chirps. The authors briefly introduce the FRFT and a number of its properties and then present some new results: the interpretation as a rotation in the time-frequency plane, and the FRFT's relationships with time-frequency representations such as the Wigner distribution, the ambiguity function, the short-time Fourier transform and the spectrogram. These relationships have a very simple and natural form and support the FRFT's interpretation as a rotation operator. Examples of FRFTs of some simple signals are given. An example of the application of the FRFT is also given. >


title: Touchless capacitive sensor for hand gesture detection

abstract: This paper presents a capacitive motion-tracking sensor system including the electrodes and interface electronics. The main advantages of the presented method over existing methods include low power consumption, seamless integration, and low cost. The experimental results were obtained which demonstrated the capability of the sensor system to track a human's finger within a 10cm range from the electrodes' plane. The sensor system can be easily integrated into the design of new user interfaces for mobile computing devices such as cellular phones and tablets in order to enable numerous novel applications.


title: Blind Inpainting Using $\ell _{0}$ and Total Variation Regularization

abstract: In this paper, we address the problem of image reconstruction with missing pixels or corrupted with impulse noise, when the locations of the corrupted pixels are not known. A logarithmic transformation is applied to convert the multiplication between the image and binary mask into an additive problem. The image and mask terms are then estimated iteratively with total variation regularization applied on the image, and $\ell _{0}$ regularization on the mask term which imposes sparseness on the support set of the missing pixels. The resulting alternating minimization scheme simultaneously estimates the image and mask, in the same iterative process. The logarithmic transformation also allows the method to be extended to the Rayleigh multiplicative and Poisson observation models. The method can also be extended to impulse noise removal by relaxing the regularizer from the $\ell _{0}$ norm to the $\ell _{1}$ norm. Experimental results show that the proposed method can deal with a larger fraction of missing pixels than two phase methods, which first estimate the mask and then reconstruct the image.


Cancer incidence and survival among children and adolescents: United States SEER Program 1975-1995.


title: Symmetrical dc-link capacitor voltage for cascaded H-bridge inverter supported from solar PV Array

abstract: This paper proposes an algorithm for maintaining symmetrical voltage across H-bridges of cascaded multilevel inverter fed through boost converter in solar photovoltaic system (SPV). During change in environmental conditions such as insolation and temperature, the power across each dc link capacitor changes. It might cause the change in voltage across these capacitors. Because of which the output voltage of cascaded H-bridge inverter becomes asymmetrical. The two or more modules of the H-bridges will have different switching stresses if the voltages across dc link capacitors are asymmetrical. To maintain the symmetrical voltage across capacitors of cascaded multilevel inverter, boost converters is operated according to proposed algorithm. The output of the boost converters with proposed algorithm keeps the voltage symmetrical both under uniform and non-uniform environmental conditions on each SPV array. Carrier phase shifted sinusoidal pulse width modulation is used to operate the multilevel inverter. The proposed algorithm and performance of SPV fed inverter is simulated using power system computer aided design (PSCAD/EMTDC).


title: Architectures and patterns for developing high performance real-time ORB endsystems

abstract: Abstract Many types of applications can benefit from flexible and open middleware. CORBA is an emerging middleware standard for Object Request Brokers (ORBs) that simplifies the development of distributed applications and services. Experience with CORBA demonstrates that it is suitable for traditional RPC-style applications. However, the lack of performance optimizations and quality of service (QoS) features in conventional CORBA implementations make them unsuited for high-performance and real-time applications. This paper makes four contributions to the design of CORBA ORBs for applications with high-performance and real-time requirements. First, it describes the design of TAO, which is our high-performance, real-time CORBA-compliant ORB. Second, it presents TAO’s real-time scheduling service, which provides QoS guarantees for deterministic real-time CORBA applications. Third, it empirically evaluates the effects of priority inversion and non-determinism in conventional ORBs and shows how these hazards are avoided in TAO. Fourth, it presents a case study of key patterns used to develop TAO and quantifies the impact of applying patterns to reduce the complexity of common ORB tasks.


title: Averaging and derivative estimation within stochastic approximation algorithms

abstract: Stochastic Approximation (SA) is arguably the most investigated amongst algorithms for solving local continuous simulation optimization problems. Despite its enduring popularity, the prevailing opinion is that the finite-time performance of SA-type algorithms is still not robust to SA's sequence of algorithm parameters. In the last two decades, two major advances have been proposed toward alleviating this issue: (i) Polyak-Ruppert averaging where SA is executed in multiple time scales to allow for the algorithm iterates to use large (initial) step sizes for better finite time performance, without sacrificing the asymptotic convergence rate; and (ii) efficient derivative estimation to allow for better searching within the solution space. Interestingly, however, all existing literature on SA seems to treat each of these advances separately. In this article, we present two results which characterize SA's convergence rates when both (i) and (ii) are be applied simultaneously. Our results should be seen as simply providing a theoretical basis for applying ideas that seem reasonable in practice.


title: A MultiPath Network for Object Detection

abstract: The recent COCO object detection dataset presents several new challenges for object detection. In particular, it contains objects at a broad range of scales, less prototypical images, and requires more precise localization. To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization. The result of these modifications is that information can flow along multiple paths in our network, including through features from multiple network layers and from multiple object views. We refer to our modified classifier as a "MultiPath" network. We couple our MultiPath network with DeepMask object proposals, which are well suited for localization and small objects, and adapt our pipeline to predict segmentation masks in addition to bounding boxes. The combined system improves results over the baseline Fast R-CNN detector with Selective Search by 66% overall and by 4x on small objects. It placed second in both the COCO 2015 detection and segmentation challenges.


title: Towards a Concept Inventory for Algorithm Analysis Topics

abstract: We present initial results from our work towards developing a concept inventory for algorithm analysis (AACI) at the post-CS2 level. We used a Delphi process to identify a list of algorithm analysis topics that were considered both important and hard by surveying a panel of experienced instructors. Through a similar survey process, we identified a list of student misconceptions related to the identified topics. Based on this, a set of pilot AACI items were developed. We validated the misconceptions list by analyzing student responses to four administrations of the pilot AACI in two different universities during Fall 2015 and Spring 2016. Results revealed that a sufficient number of students held most of the misconceptions identified in the list.


title: Theory Solving Made Easy with Clingo 5

abstract: Answer Set Programming (ASP) is a model, ground, and solve paradigm. The integration of application- or theory-specific reasoning into ASP systems thus impacts on many if not all elements of its workflow, viz. input language, grounding, intermediate language, solving, and output format. We address this challenge with the fifth generation of the ASP system clingo and its grounding and solving components by equipping them with well-defined generic interfaces facilitating the manifold integration efforts. On the grounder's side, we introduce a generic way of specifying language extensions and propose an intermediate format accommodating their ground representation. At the solver end, this is accompanied by high-level interfaces easing the integration of theory propagators dealing with these extensions.


title: Information privacy: measuring individuals' concerns about organizational practices

abstract: Information privacy has been called one of the most important ethical issues of the informa1 Alien Lee was the accepting senior for this paper. tion age. Public opinion polls show rising levels of concer about privacy among Americans. Against this backdrop, research into issues associated with information privacy is increasing. Based on a number of preliminary studies, it has become apparent that organizational practices, individuals' perceptions of these practices, and societal responses are inextricably linked in many ways. Theories regarding these relationships are slowly emerging. Unfortunately, researchers attempting to examine such relationships through confirmatory empirical approaches may be impeded by the lack of validated instruments for measuring individuals' concerns about organizational information privacy practices. To enable future studies in the information privacy research stream, we developed and validated an instrument that identifies and measures the primary dimensions of individuals' concers about organizational information privacy practices. The development process included examinations of privacy literature; experience surveys and focus groups; and the use of expert judges. The result was a parsimonious 15-item instrument with four subscales tapping into dimensions of individuals' concerns about organizational information privacy practices. The instrument was rigorously tested and validated across several heterogenous populations, providing a high degree of confidence in the scales' validity, reliability, and generalizability.


title: Ant colony optimization for finding the global minimum

abstract: The ant colony optimization (ACO) algorithms are multi-agent systems in which the behaviour of each ant is inspired by the foraging behaviour of real ants to solve optimization problem. This paper presents the ACO based algorithm to find global minimum. Algorithm is based on that each ant searches only around the best solution of the previous iteration. This algorithm was experimented on test problems, and successful results were obtained. The algorithm was compared with other methods which had been experimented on the same test problems, and observed to be better.


title: Trustworthiness in electronic commerce: the role of privacy, security, and site attributes

abstract: Abstract While the growth of business-to-consumer electronic commerce seems phenomenal in recent years, several studies suggest that a large number of individuals using the Internet have serious privacy concerns, and that winning public trust is the primary hurdle to continued growth in e-commerce. This research investigated the relative importance, when purchasing goods and services over the Web, of four common trust indices (i.e. (1) third party privacy seals, (2) privacy statements, (3) third party security seals, and (4) security features). The results indicate consumers valued security features significantly more than the three other trust indices. We also investigated the relationship between these trust indices and the consumer's perceptions of a marketer's trustworthiness. The findings indicate that consumers' ratings of trustworthiness of Web merchants did not parallel experts' evaluation of sites' use of the trust indices. This study also examined the extent to which consumers are willing to provide private information to electronic and land merchants. The results revealed that when making the decision to provide private information, consumers rely on their perceptions of trustworthiness irrespective of whether the merchant is electronic only or land and electronic. Finally, we investigated the relative importance of three types of Web attributes: security, privacy and pleasure features (convenience, ease of use, cosmetics). Privacy and security features were of lesser importance than pleasure features when considering consumers' intention to purchase. A discussion of the implications of these results and an agenda for future research are provided.


title: A general construction scheme for unit quaternion curves with simple high order derivatives

abstract: This paper proposes a new class of unit quaternion curves in 3 . A general method is developed that transforms a curve in 3 (defined as a weighted sum of basis functions) into its unit quaternion analogue in 3 . Applying the method to well-known spline curves (such as B´ ezier, Hermite, and B-spline curves), we are able to construct various unit quaternion curves which share many important differential properties with their original curves. Many of our naive common beliefs in geometry break down even in the simple non-Euclidean space 3 or 3 . For example, the de Casteljau type construction of cubic B-spline quaternion curves does not preserve 2 -continuity [10]. Through the use of decomposition into simple primitive quaternion curves, our quaternion curves preserve most of the algebraic and differential properties of the original spline curves.


title: Multi-Task Learning for Machine Reading Comprehension.

abstract: We propose a multi-task learning framework to jointly train a Machine Reading Comprehension (MRC) model on multiple datasets across different domains. Key to the proposed method is to learn robust and general contextual representations with the help of out-domain data in a multi-task framework. Empirical study shows that the proposed approach is orthogonal to the existing pre-trained representation models, such as word embedding and language models. Experiments on the Stanford Question Answering Dataset (SQuAD), the Microsoft MAchine Reading COmprehension Dataset (MS MARCO), NewsQA and other datasets show that our multi-task learning approach achieves significant improvement over state-of-the-art models in most MRC tasks.


title: Synthesizing open worlds with constraints using locally annealed reversible jump MCMC

abstract: We present a novel Markov chain Monte Carlo (MCMC) algorithm that generates samples from transdimensional distributions encoding complex constraints. We use factor graphs, a type of graphical model, to encode constraints as factors. Our proposed MCMC method, called locally annealed reversible jump MCMC, exploits knowledge of how dimension changes affect the structure of the factor graph. We employ a sequence of annealed distributions during the sampling process, allowing us to explore the state space across different dimensionalities more freely. This approach is motivated by the application of layout synthesis where relationships between objects are characterized as constraints. In particular, our method addresses the challenge of synthesizing open world layouts where the number of objects are not fixed and optimal configurations for different numbers of objects may be drastically different. We demonstrate the applicability of our approach on two open world layout synthesis problems: coffee shops and golf courses.


title: Extending the Internet of Things to the Future Internet through IPv6 support

abstract: Emerging Internet of Things IoT/Machine-to-Machine M2M systems require a transparent access to information and services through a seamless integration into the Future Internet. This integration exploits infrastructure and services found on the Internet by the IoT. On the one hand, the so-called Web of Things aims for direct Web connectivity by pushing its technology down to devices and smart things. On the other hand, the current and Future Internet offer stable, scalable, extensive, and tested protocols for node and service discovery, mobility, security, and auto-configuration, which are also required for the IoT. In order to integrate the IoT into the Internet, this work adapts, extends, and bridges using IPv6 the existing IoT building blocks such as solutions from IEEE 802.15.4, BT-LE, RFID while maintaining backwards compatibility with legacy networked embedded systems from building and industrial automation. Specifically, this work presents an extended Internet stack with a set of adaptation layers from non-IP towards the IPv6-based network layer in order to enable homogeneous access for applications and services.


title: A Formal Analysis of Required Cooperation in Multi-agent Planning

abstract: It is well understood that, through cooperation, multiple agents can achieve tasks that are unachievable by a single agent. However, there are no formal characterizations of situations where cooperation is required to achieve a goal, thus warranting the application of multiple agents. In this paper, we provide such a formal characterization for multi-agent planning problems with sequential action execution. We first show that determining whether there is required cooperation (RC) is in general intractable even in this limited setting. As a result, we start our analysis with a subset of more restrictive problems where agents are homogeneous. For such problems, we identify two conditions that can cause RC. We establish that when none of these conditions hold, the problem is single-agent solvable; otherwise, we provide upper bounds on the minimum number of agents required. For the remaining problems with heterogeneous agents, we further divide them into two subsets. For one of the subsets, we propose the concept of transformer agent to reduce the number of agents to be considered which is used to improve planning performance. We implemented a planner using our theoretical results and compared it with one of the best IPC CoDMAP planners in the centralized track. Results show that our planner provides significantly improved performance on IPC CoDMAP domains.


title: Factor Augmented Artificial Neural Network Model

abstract: This paper brings together two important developments in forecasting literature; the artificial neural networks and factor models. The paper introduces the factor augmented artificial neural network (FAANN) hybrid model in order to produce a more accurate forecasting. Theoretical and empirical findings have indicated that integration of various models can be an effective way of improving on their predictive performance, especially when the models in the ensemble are quite different. The proposed model is used to forecast three time series variables using large South African monthly panel, namely, deposit rate, gold mining share prices and Long term interest rate, using monthly data over the in-sample period (training set) 1992:1---2006:12. The variables are used to compute out-of-sample (testing set) results for 3, 6 and 12 month-ahead forecasts for the period of 2007:1---2011:12. The out-of-sample root mean square error findings show that the FAANN model yields substantial improvements over the autoregressive AR benchmark model and standard dynamic factor model (DFM). The Diebold---Mariano test results also further confirm the superiority of the FAANN model forecast performance over the AR benchmark model and the DFM model forecasts. The superiority of the FAANN model is due to the ANN flexibility to account for potentially complex nonlinear relationships that are not easily captured by linear models.


title: Macroesthetic elements of smile design.

abstract: BACKGROUND ::: Clinicians' expanding use of cosmetic restorative procedures has generated greater interest in the determination of esthetic guidelines and standards. The overall esthetic impact of a smile can be divided into four specific areas: gingival esthetics, facial esthetics, microesthetics and macroesthetics. In this article, the authors focus on the principles of macroesthetics, which represents the relationships and ratios of relating multiple teeth to each other, to soft tissue and to facial characteristics. ::: ::: ::: CASE DESCRIPTION ::: The authors categorize macroesthetic criteria based on two reference points: the facial midline and the amount and position of tooth reveal. The facial midline is a critical reference position for determining multiple design criteria. The amount and position of tooth reveal in various views and lip configurations also provide valuable guidelines in determining esthetic tooth positions and relationships. ::: ::: ::: CLINICAL IMPLICATIONS ::: Esthetics is an inherently subjective discipline. By understanding and applying simple esthetic rules, tools and strategies, dentists have a basis for evaluating natural dentitions and the results of cosmetic restorative procedures. Macroesthetic components of teeth and their relationship to each other can be influenced to produce more natural and esthetically pleasing restorative care.


title: Camera calibration from video of a walking human

abstract: A self-calibration method to estimate a camera's intrinsic and extrinsic parameters from vertical line segments of the same height is presented. An algorithm to obtain the needed line segments by detecting the head and feet positions of a walking human in his leg-crossing phases is described. Experimental results show that the method is accurate and robust with respect to various viewing angles and subjects


title: Cache-Oblivious R-Trees

abstract: We develop a cache-oblivious data structure for storing a set S of N axis-aligned rectangles in the plane, such that all rectangles in S intersecting a query rectangle or point can be found efficiently. Our structure is an axis-aligned bounding-box hierarchy and as such it is the first cache-oblivious R-tree with provable performance guarantees. If no point in the plane is contained in more than a constant number of rectangles in S, we can construct, for any constant e, a structure that answers a rectangle query using $O(\sqrt{N/B}+T/B)$memory transfers and a point query using O((N/B)e ) memory transfers, where T is the number of reported rectangles and B is the block size of memory transfers between any two levels of a multilevel memory hierarchy. We also develop a variant of our structure that achieves the same performance on input sets with arbitrary overlap among the rectangles. The rectangle query bound matches the bound of the best known linear-space cache-aware structure.


title: Decentralized and centralized model predictive control to reduce the bullwhip effect in supply chain management

abstract: Abstract Mitigating the bullwhip effect is one of crucial problems in supply chain management. In this research, centralized and decentralized model predictive control strategies are applied to control inventory positions and to reduce the bullwhip effect in a benchmark four-echelon supply chain. The supply chain under consideration is described by discrete dynamic models characterized by balance equations on product and information flows with an ordering policy serving as the control schemes. In the decentralized control strategy, a MPC-EPSAC (Extended Prediction Self-Adaptive Control) approach is used to predict the changes in the inventory position levels. A closed-form solution of an optimal ordering decision for each echelon is obtained by locally minimizing a cost function, which consists of the errors between predicted inventory position levels and their setpoints, and a weighting function that penalizes orders. The single model predictive controller used in centralized control strategy optimizes globally and finds an optimal ordering policy for each echelon. The controller relies on a linear discrete-time state-space model to predict system outputs. But the predictions are approached by either of two multi-step predictors depending on whether the states of the controller model are directly observed or not. The objective function takes a quadratic form and thus the resulting optimization problem can be solved via standard quadratic programming method. The comparisons on performances of the two MPC strategies are illustrated with a numerical example.


Attitudes toward Facebook advertising


title: PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment

abstract: {PG,1 A dwarf bush rose of the hybrid miniature moss rose class. The plant has many moss-scented stipitate glands on its penduncles, buds, sepals and hips, and blooms almost continuously throughout the growing season. The overall color effect of the flowers is a deep velvety red. There are from about 40 to about 50 relatively broad petals in the flower, with from 7 to 20 petaloids. The plant manifests a better-than-average resistance to mildew, as compared to roses of its class under parallel conditions.


title: Medical text simplification using synonym replacement: Adapting assessment of word difficulty to a compounding language

abstract: Medical texts can be difficult to understand for laymen, due to a frequent occurrence of specialised medical terms. Replacing these difficult terms with easier synonyms can, however, lead to improved readability. In this study, we have adapted a method for assessing difficulty of words to make it more suitable to medical Swedish. The difficulty of a word was assessed not only by measuring the frequency of the word in a general corpus, but also by measuring the frequency of substrings of words, thereby adapting the method to the compounding nature of Swedish. All words having a MeSH synonym that was assessed as easier, were replaced in a corpus of medical text. According to the readability measure LIX, the replacement resulted in a slightly more difficult text, while the readability increased according to the OVIX measure and to a preliminary reader study.


title: Unsupervised learning for human activity recognition using smartphone sensors

abstract: Abstract To provide more sophisticated healthcare services, it is necessary to collect the precise information on a patient. One impressive area of study to obtain meaningful information is human activity recognition, which has proceeded through the use of supervised learning techniques in recent decades. Previous studies, however, have suffered from generating a training dataset and extending the number of activities to be recognized. In this paper, to find out a new approach that avoids these problems, we propose unsupervised learning methods for human activity recognition, with sensor data collected from smartphone sensors even when the number of activities is unknown. Experiment results show that the mixture of Gaussian exactly distinguishes those activities when the number of activities k is known, while hierarchical clustering or DBSCAN achieve above 90% accuracy by obtaining k based on Calinski–Harabasz index, or by choosing appropriate values for ɛ and MinPts when k is unknown. We believe that the results of our approach provide a way of automatically selecting an appropriate value of k at which the accuracy is maximized for activity recognition, without the generation of training datasets by hand.


title: The variable radius covering problem

abstract: In this paper we propose a covering problem where the covering radius of a facility is controlled by the decision-maker; the cost of achieving a certain covering distance is assumed to be a monotonically increasing function of the distance (i.e., it costs more to establish a facility with a greater covering radius). The problem is to cover all demand points at a minimum cost by finding optimal number, locations and coverage radii for the facilities. Both, the planar and discrete versions of the model are considered. Heuristic approaches are suggested for solving large problems in the plane. These methods were tested on a set of planar problems. Mathematical programming formulations are proposed for the discrete problem, and a solution approach is suggested and tested.


Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring


title: ReGAN: RE[LAX|BAR|INFORCE] based Sequence Generation using GANs

abstract: Generative Adversarial Networks (GANs) have seen steep ascension to the peak of ML research zeitgeist in recent years. Mostly catalyzed by its success in the domain of image generation, the technique has seen wide range of adoption in a variety of other problem domains. Although GANs have had a lot of success in producing more realistic images than other approaches, they have only seen limited use for text sequences. Generation of longer sequences compounds this problem. Most recently, SeqGAN (Yu et al., 2017) has shown improvements in adversarial evaluation and results with human evaluation compared to a MLE based trained baseline. The main contributions of this paper are three-fold: 1. We show results for sequence generation using a GAN architecture with efficient policy gradient estimators, 2. We attain improved training stability, and 3. We perform a comparative study of recent unbiased low variance gradient estimation techniques such as REBAR (Tucker et al., 2017), RELAX (Grathwohl et al., 2018) and REINFORCE (Williams, 1992). Using a simple grammar on synthetic datasets with varying length, we indicate the quality of sequences generated by the model.


The Security Impact of HTTPS Interception.


title: Automatic fire detection based on soft computing techniques: review from 2000 to 2010

abstract: Automatic fire detection system is a system that is capable of assessing environmental factors and their effects on the environment as well as predicting the occurrence of fire in the early stages and even before the outbreak. There are two perspectives in fire detection: fire detection in forests or jungles and fire detection in occupied or residential areas. Automatic fire detection has attracted increased attention due to its importance in decreasing fire damage. There are many studies that have considered appropriate techniques for early fire detection. In recent years researches have been studying technical developments in this field aimed at exploiting wireless communications networks, detection systems and fire prediction systems design. In this paper the automatic fire detection researches using intelligent techniques from 2000 to 2010 is reviewed. We could classify researches to four categories: fire detectors, reduce false alarms systems, fire data analysis and fire predictors. We also classify the intelligent techniques outlined in the researches for each category.


title: A Soft-Switched Three-Port Bidirectional Converter for Fuel Cell and Supercapacitor Applications

abstract: This paper presents a zero-voltage-switching (ZVS) three-port bidirectional converter for fuel cell and supercapacitor applications. A simple and effective method to extend the soft-switching range is proposed, especially for the three-port active-bridge topology. By continuously adjusting the duty cycle on the supercapacitor side H-bridge according to the supercapacitor voltage level, soft-switching conditions are achieved over the full operating range due to the equivalent volt-second products applied to the transformer over half the switching period. Detailed analysis is provided for both the two-port and the three-port converters. Furthermore, a dual PI-loop based control strategy is proposed to achieve constant output voltage and precise power flow management, as well as soft-switching. The converter is implemented and tested


title: Tornado Detection using a Dual Polarimetric X-band Doppler Weather Radar in The Netherlands: A Case Study

abstract: Hazardous whirlwind and tornado events are uncommon in the Netherlands. Though, the phenomenon is more frequently spotted over the last years, where significant damage is reported. In the United States, literature studies suggest that due to the increase of the Earth's surface temperature tornadoes will become more frequent and intense. This consequence might affect Europe as well. Therefore, a case study is conducted in order to investigate the current technological abilities within the Netherlands in order to protect itself against possible tornado hazards. In this study the development of an EF1 tornado, reaching wind speed between 138-177 km/h, was detected on 03 November 2013 by the high resolution polarimetric X-band radar IDRA. Results have shown that IDRA was able to detect tornado signatures such as hook echo, V-notch and wind shear, 20 minutes prior touchdown. These observations were verified by multiple systems, located near IDRA site, including radar profiles, C-band radars. Moreover, in-situ measurements were available during the evolution of the tornado storm. From this synergy-based analysis, made possible by the Cabauw Experimental Site for Atmospheric Research (CESAR) and the Royal Netherlands Meteorological Institute (KNMI), it was found that their combined efforts can potentially lead to an effective system for early warning detection of tornadoes.


title: Maximum Power Point Tracking Converter Based on the Open-Circuit Voltage Method for Thermoelectric Generators

abstract: Thermoelectric generators (TEGs) convert heat energy into electricity in a quantity dependent on the temperature difference across them and the electrical load applied. It is critical to track the optimum electrical operating point through the use of power electronic converters controlled by a maximum power point tracking (MPPT) algorithm. The MPPT method based on the open-circuit voltage is arguably the most suitable for the linear electrical characteristic of TEGs. This paper presents an innovative way to perform the open-circuit voltage measure during the pseudonormal operation of the interfacing power electronic converter. The proposed MPPT technique is supported by theoretical analysis and used to control a synchronous Buck-Boost converter. The prototype MPPT converter is controlled by an inexpensive microcontroller, and a lead-acid battery is used to accumulate the harvested energy. Experimental results using commercial TEG devices prove that the converter accurately tracks the maximum power point during thermal transients. Precise measurements in the steady state show that the converter finds the maximum power point with a tracking efficiency of 99.85%.


title: Maximum Achievable Efficiency in Near-Field Coupled Power-Transfer Systems

abstract: Wireless power transfer is commonly realized by means of near-field inductive coupling and is critical to many existing and emerging applications in biomedical engineering. This paper presents a closed form analytical solution for the optimum load that achieves the maximum possible power efficiency under arbitrary input impedance conditions based on the general two-port parameters of the network. The two-port approach allows one to predict the power transfer efficiency at any frequency, any type of coil geometry and through any type of media surrounding the coils. Moreover, the results are applicable to any form of passive power transfer such as provided by inductive or capacitive coupling. Our results generalize several well-known special cases. The formulation allows the design of an optimized wireless power transfer link through biological media using readily available EM simulation software. The proposed method effectively decouples the design of the inductive coupling two-port from the problem of loading and power amplifier design. Several case studies are provided for typical applications.


title: Prevalence of problematic cell phone use in an adult population in Spain as assessed by the Mobile Phone Problem Use Scale (MPPUS)

abstract: Problematic cell phone use has alarmingly increased in industrialized countries in the past 10 years. For many perpetrators, it can turn into a behavioural addiction, although this is not a recognized medical condition. Although there are many tools for evaluating this use, one of the most widely used tools is the Mobile Phone Problematic Use Scale (MPPUS), which we test on a representative sample of the population in Spain to obtain an estimate of the prevalence of problematic cell phone use in our midst. The age range consists of 16–65 years, with 1,126 surveys conducted. In this population, we verify that the reliability and internal consistency of the MPPUS (α = 0.939) are maintained. Additionally, the construct validity, considering the derived factors (Abuse and Dependence, Craving and Loss of Control, and Dependence on the Social Environment) are aligned with other research and with diverse external criteria of addiction. We establish four categories of users (Casual, Regular, At Risk, and Problematic) and obtain a prevalence of 15.4% among At Risk Users and 5.1% among Problematic Users. This finding implies a total of 20.5% of Users with Problems. A binary logistic regression analysis shows that age, gender, level of education, and daily cell phone use predict problematic cell phone use. The results, based on multiple criteria, show that such problematic use shares features of recognized addictions, affecting large segments of the population and not only adolescents.


title: 30 Gbit/s 1.7 pJ/bit common-cathode tunable 850-nm-VCSEL driver in 28 nm digital CMOS

abstract: A common-cathode VCSEL driver implemented in 28-nm digital CMOS, assembled with a commercial VCSEL is presented. Electro-optical measurements show 30-Gbit/s operation with 1.7-pJ/bit from 2.9-V supply. When operated at 15-Gbit/s the power can be reduced while maintaining the modulation amplitude. Power/datarate tunability is demonstrated.


title: Analyzing Classifiers: Fisher Vectors and Deep Neural Networks

abstract: Fisher Vector classifiers and Deep Neural Networks (DNNs) are popular and successful algorithms for solving image classification problems. However, both are generally considered `black box' predictors as the non-linear transformations involved have so far prevented transparent and interpretable reasoning. Recently, a principled technique, Layer-wise Relevance Propagation (LRP), has been developed in order to better comprehend the inherent structured reasoning of complex nonlinear classification models such as Bag of Feature models or DNNs. In this paper we (1) extend the LRP framework also for Fisher Vector classifiers and then use it as analysis tool to (2) quantify the importance of context for classification, (3) qualitatively compare DNNs against FV classifiers in terms of important image regions and (4) detect potential flaws and biases in data. All experiments are performed on the PASCAL VOC 2007 data set.


title: Smartphone Applications Usability Evaluation: A Hybrid Model and Its Implementation

abstract: Evaluating the usability of smartphone applications is crucial for their success, so developers can learn how to adapt them considering the dynamicity of mobile scenarios. The HCI community recommends considering different requirements when evaluating those applications, such as quantitative data (metrics), subjective evaluation (users' impressions) and context data (e.g. environment and devices conditions). We observed a lack in the literature of approaches that support those three requirements combined into a single experiment; generally one or a pair of them is used. Besides, performing usability evaluation on real mobile scenarios is hard to achieve and most proposals are based on laboratory-controlled experiments. In this paper, we present our proposal for a hybrid usability evaluation of smartphone applications, which is composed by a model and an infrastructure that implements it. The model describes how to automatically monitor and collect context data and usability metrics, how those data can be processed for analysis support and how users' impressions can be collected. An infrastructure is provided to implement the model allowing it to be plugged into any smartphone Android-based application. To evaluate our proposal, we performed a field experiment, with 21 users using three mobile applications during a 6-month period, in their day-to-day scenarios.


title: 24.7% Record Efficiency HIT Solar Cell on Thin Silicon Wafer

abstract: A new record conversion efficiency of 24.7% was attained at the research level by using a heterojunction with intrinsic thin-layer structure of practical size (101.8 cm2, total area) at a 98-μm thickness. This is a world height record for any crystalline silicon-based solar cell of practical size (100 cm2 and above). Since we announced our former record of 23.7%, we have continued to reduce recombination losses at the hetero interface between a-Si and c-Si along with cutting down resistive losses by improving the silver paste with lower resistivity and optimization of the thicknesses in a-Si layers. Using a new technology that enables the formation of a-Si layer of even higher quality on the c-Si substrate, while limiting damage to the surface of the substrate, the Voc has been improved from 0.745 to 0.750 V. We also succeeded in improving the fill factor from 0.809 to 0.832.


title: Emotional design in multimedia learning: Differentiation on relevant design features and their effects on emotions and learning

abstract: We deduce design features for an emotional design in multimedia learning.Objectively given aesthetics and usability did not affect learner's emotional states.Perceived aesthetics and usability affected the emotional states of the learners.Emotional states had a minor impact on learning.Emotional states substantially affected learner's intrinsic motivation. Previous research into multimedia learning has mainly focused on cognitive factors to investigate different instructional conditions and design principles. Emotional factors have so far been widely neglected. However, recent studies showed that the emotional design of multimedia learning material can evoke positive emotions in learners that in turn facilitate the learning process. Following this lead, our study aims to further explore the potential of an emotional design. We seek to differentiate the current findings by systematically deducing emotionally relevant design features and also taking into account negative emotional states. In order to deduce relevant design features, we adopt concepts from web design. German college students (N=334) were assigned to one of nine conditions, created by two design factors (classical vs. expressive aesthetics), each with two levels (high vs. low) and a usability factor (high vs. low usability) as well as a control group (no color/gray scale). Unexpectedly, objective differences in aesthetics or usability did not affect learners' emotional states. However, the perceived aesthetics and usability positively affected the emotional states of the learners. Learners' emotional states had a minor impact on learning outcomes but a larger impact on learners' intrinsic motivation, including the motivation to continue working with the material.


title: Phylogenetic Inference of Colony Isolates Comprising Seasonal Microcystis Blooms in Lake Taihu, China

abstract: Blooms of the toxin-producing cyanobacterium, Microcystis spp., are an increasingly prevalent water quality problem and health hazard worldwide. China’s third largest lake, Lake Taihu, has been experiencing progressively more severe Microcystis blooms over the past three decades. In 2009 and 2010, individual Microcystis colonies, consisting of four different morphospecies, were isolated and genotyped using a whole-cell multiplex PCR assay. The 16S–23S rDNA-ITS sequences were aligned based on Bayesian inference and indicated that one morphospecies was genetically unique (Microcystis wesenbergii) and three were indistinguishable (Microcystis aeruginosa, Microcystis flos-aquae, and Microcystis ichthyoblabe). Microcystin (mcyB) genes were detected intermittently in two of the morphospecies while the other two morphospecies lacked the mcyB gene in all samples. Water temperature was found to influence bloom formation and morphotype prevalence, and chlorophyll a and temperature were positively and significantly correlated with microcystin concentration. Cooler water temperatures promoted toxigenic strains of Microcystis. Wind appeared to influence the distribution of morphotypes across the lake, with M. aeruginosa and M. ichthyoblabe being more susceptible to wind stress than M. wesenbergii and M. flos-aquae. The results of this study indicated that the blooms were composed of a variety of Microcystis morphospecies, with more genotypes observed than can be attributed to individual morphotypes. We conclude that morphology is not a reliable indicator of toxigenicity in Lake Taihu, and caution should be exercised when the M. aeruginosa morphotype is present because it is capable of producing MC-LR, the most toxic microcystin isoform.