title: Active Control of 3-D Irregular Building by Using Energy Based Neuro-Controller

abstract: In this paper, a methodology of active control of structures based on modal energy is extended to control the response of irregular structures. The nonlinearities of material and geometry are taken into account in the training algorithm. A three-dimensional (3-D) multi-story irregular building is employed to scrutinize the performance of the improved control strategy. The torsional and lateral responses of the structure and the dynamic behavior of actuators have been incorporated in the control model simultaneously. Thus, the lateral-torsional coupling as well as the structure-actuator interaction is considered. The incident angle of ground motion and the time delay of actuators are also included in the model. El-Centro 1940 earthquake is used to train the algorithm, and then five different earthquake records have been used to evaluate the trained control system. The results of controlled structural responses in each case proved that the proposed control algorithm may be promising in vibration control of ...


A Survey of Digital Mosaic Techniques


title: Neuronlike adaptive elements that can solve difficult learning control problems

abstract: It is shown how a system consisting of two neuronlike adaptive elements can solve a difficult learning control problem. The task is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. It is argued that the learning problems faced by adaptive elements that are components of adaptive networks are at least as difficult as this version of the pole-balancing problem. The learning system consists of a single associative search element (ASE) and a single adaptive critic element (ACE). In the course of learning to balance the pole, the ASE constructs associations between input and output by searching under the influence of reinforcement feedback, and the ACE constructs a more informative evaluation function than reinforcement feedback alone can provide. The differences between this approach and other attempts to solve problems using neurolike elements are discussed, as is the relation of this work to classical and instrumental conditioning in animal learning studies and its possible implications for research in the neurosciences.


title: The impact of individual, competitive, and collaborative mathematics game play on learning, performance, and motivation.

abstract: The present research examined how mode of play in an educational mathematics video game impacts learning, performance, and motivation. The game was designed for the practice and automation of arithmetic skills to increase fluency and was adapted to allow for individual, competitive, or collaborative game play. Participants (N 58) from urban middle schools were randomly assigned to each experimental condition. Results suggested that, in comparison to individual play, competition increased in-game learning, whereas collaboration decreased performance during the experimental play session. Although out-of-game math fluency improved overall, it did not vary by condition. Furthermore, competition and collaboration elicited greater situational interest and enjoyment and invoked a stronger mastery goal orientation. Additionally, collaboration resulted in stronger intentions to play the game again and to recommend it to others. Results are discussed in terms of the potential for mathematics learning games and technology to increase student learning and motivation and to demonstrate how different modes of engagement can inform the instructional design of such games.


title: Towards Safe Deep Learning: Unsupervised Defense Against Generic Adversarial Attacks

abstract: Recent advances in adversarial Deep Learning (DL) have opened up a new and largely unexplored surface for malicious attacks jeopardizing the integrity of autonomous DL systems. We introduce a novel automated countermeasure called Parallel Checkpointing Learners (PCL) to thwart the potential adversarial attacks and significantly improve the reliability (safety) of a victim DL model. The proposed PCL methodology is unsupervised, meaning that no adversarial sample is leveraged to build/train parallel checkpointing learners. We formalize the goal of preventing adversarial attacks as an optimization problem to minimize the rarely observed regions in the latent feature space spanned by a DL network. To solve the aforementioned minimization problem, a set of complementary but disjoint checkpointing modules are trained and leveraged to validate the victim model execution in parallel. Each checkpointing learner explicitly characterizes the geometry of the input data and the corresponding high-level data abstractions within a particular DL layer. As such, the adversary is required to simultaneously deceive all the defender modules in order to succeed. We extensively evaluate the performance of the PCL methodology against the state-of-the-art attack scenarios, including Fast-Gradient-Sign (FGS), Jacobian Saliency Map Attack (JSMA), Deepfool, and Carlini&WagnerL2 algorithm. Extensive proof-of-concept evaluations for analyzing various data collections including MNIST, CIFAR10, and ImageNet corroborate the effectiveness of our proposed defense mechanism against adversarial samples.


title: Global cancer statistics

abstract: The global burden of cancer continues to increase largely because of the aging and growth of the world population alongside an increasing adoption of cancer-causing behaviors, particularly smoking, in economically developing countries. Based on the GLOBOCAN 2008 estimates, about 12.7 million cancer cases and 7.6 million cancer deaths are estimated to have occurred in 2008; of these, 56% of the cases and 64% of the deaths occurred in the economically developing world. Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer death among females, accounting for 23% of the total cancer cases and 14% of the cancer deaths. Lung cancer is the leading cancer site in males, comprising 17% of the total new cancer cases and 23% of the total cancer deaths. Breast cancer is now also the leading cause of cancer death among females in economically developing countries, a shift from the previous decade during which the most common cause of cancer death was cervical cancer. Further, the mortality burden for lung cancer among females in developing countries is as high as the burden for cervical cancer, with each accounting for 11% of the total female cancer deaths. Although overall cancer incidence rates in the developing world are half those seen in the developed world in both sexes, the overall cancer mortality rates are generally similar. Cancer survival tends to be poorer in developing countries, most likely because of a combination of a late stage at diagnosis and limited access to timely and standard treatment. A substantial proportion of the worldwide burden of cancer could be prevented through the application of existing cancer control knowledge and by implementing programs for tobacco control, vaccination (for liver and cervical cancers), and early detection and treatment, as well as public health campaigns promoting physical activity and a healthier dietary intake. Clinicians, public health professionals, and policy makers can play an active role in accelerating the application of such interventions globally. CA Cancer J Clin 2011;61:69–90. V C


title: The acquisition of gender labels in infancy: Implications for gender-typed play.

abstract: Two aspects of children’s early gender development - the spontaneous production of gender labels and sex-typed play - were examined longitudinally in a sample of 82 children. Survival analysis, a statistical technique well suited to questions involving developmental transitions, was used to investigate the timing of the onset of children’s gender labeling as based on mothers’ biweekly reports on their children’s language from 9 through 21 months. Videotapes of children’s play both alone and with mother at 17 and 21 months were independently analyzed for play with gender stereotyped and neutral toys. Finally, the relation between gender labeling and sex-typed play was examined. Children transitioned to using gender labels at approximately 19 months on average. Although girls and boys showed similar patterns in the development of gender labeling, girls began labeling significantly earlier than boys. Modest sex differences in play were present at 17 months and increased at 21 months. Gender labeling predicted increases in sex-typed play, suggesting that knowledge of gender categories might influence sex-typing before the age of 2.


title: Representing Knowledge within the Situation Calculus using Interval-valued Epistemic Fluents

abstract: The ability of interval arithmetic to provide a finite (and succinct) way to represent uncertainty about a large, possibly uncountable, set of alternatives turns out to be useful in building "intelligent" autonomous agents. In particular, consider the two important issues of reasoning and sensing in intelligent control for autonomous agents. Developing a principled way to combine the two raises complicated issues in knowledge representation. In this paper we describe a solution to the problem. The idea is to incorporate interval arithmetic into the situation calculus. The situation calculus is a well known formalism for describing changing worlds using sorted first-order logic. It can also be used to describe how an agent's knowledge of its world changes. Potentially, this provides a sound basis for incorporating sensing into logic programming. Previous work has relied on a possible worlds approach to knowledge. This leads to an elegant mathematical specification language. Unfortunately, there have been no proposals on how to implement the approach. This is because the number of possible worlds is potentially uncountable. We propose an alternative formalization of knowledge within the situation calculus. Our approach is based on intervals. The advantage is that it is straightforward to implement. Moreover, we can prove that it is sound and (sometimes) complete with respect to the previous possible worlds approach.


title: OI-RAID: A Two-Layer RAID Architecture towards Fast Recovery and High Reliability

abstract: A lot of inexpensive disks in modern storage systems induce frequent disk failures. It takes a long time to recover a failed disk due to its large capacity and limited I/O. This paper proposes a hierarchical architecture of erasure code, OI-RAID. OI-RAID consists of two layers of codes, outer layer code and inner layer code. The outer layer code is based on disk grouping and Balanced Incomplete Block Design (BIBD) with skewed data layout to provide efficient parallel I/O of all disks for failure recovery. Inner layer code is deployed within a group of disks. As an example, we deploy RAID5 in both layers and present detailed performance analysis. With RAID5 in both layers, OI-RAID tolerates at least three disk failures meeting practical data availability, and achieves much higher speed up of disk failure recovery than existing approaches, while keeping optimal data update complexity and practically low storage overhead.


title: Transferable Semi-supervised Semantic Segmentation

abstract: The performance of deep learning based semantic segmentation models heavily depends on sufficient data with careful annotations. However, even the largest public datasets only provide samples with pixel-level annotations for rather limited semantic categories. Such data scarcity critically limits scalability and applicability of semantic segmentation models in real applications. In this paper, we propose a novel transferable semi-supervised semantic segmentation model that can transfer the learned segmentation knowledge from a few strong categories with pixel-level annotations to unseen weak categories with only image-level annotations, significantly broadening the applicable territory of deep segmentation models. In particular, the proposed model consists of two complementary and learnable components: a Label transfer Network (L-Net) and a Prediction transfer Network (P-Net). The L-Net learns to transfer the segmentation knowledge from strong categories to the images in the weak categories and produces coarse pixel-level semantic maps, by effectively exploiting the similar appearance shared across categories. Meanwhile, the P-Net tailors the transferred knowledge through a carefully designed adversarial learning strategy and produces refined segmentation results with better details. Integrating the L-Net and P-Net achieves 96.5% and 89.4% performance of the fully-supervised baseline using 50% and 0% categories with pixel-level annotations respectively on PASCAL VOC 2012. With such a novel transfer mechanism, our proposed model is easily generalizable to a variety of new categories, only requiring image-level annotations, and offers appealing scalability in real applications.


title: Adaptive Online Hyper-Parameters Tuning for Ad Event-Prediction Models

abstract: Yahoo's native advertising (also known as Gemini native) is one of its fastest growing businesses, reaching a run-rate of several hundred Millions USD in the past year. Driving the Gemini native models that are used to predict both, click probability (pCTR) and conversion probability (pCONV), is OFFSET - a feature enhanced collaborative-filtering (CF) based event prediction algorithm. OFFSET is a one-pass algorithm that updates its model for every new batch of logged data using a stochastic gradient descent (SGD) based approach. As most learning algorithms, OFFSET includes several hyper-parameters that can be tuned to provide best performance for a given system conditions. Since the marketplace environment is very dynamic and influenced by seasonality and other temporal factors, having a fixed single set of hyper-parameters (or configuration) for the learning algorithm is sub-optimal. ::: ::: In this work we present an online hyper-parameters tuning algorithm, which takes advantage of the system parallel map-reduce based architecture, and strives to adapt the hyper-parameters set to provide the best performance at a specific time interval. Online evaluation via bucket testing of the tuning algorithm showed a significant 4.3% revenue lift overall traffic, and a staggering 8.3% lift over Yahoo Home-Page section traffic. Since then, the tuning algorithm was pushed into production, tuning both click- and conversion-prediction models, and is generating a hefty estimated revenue lift of 5% yearly for Yahoo Gemini native. ::: ::: The proposed tuning mechanism can be easily generalized to fit any learning algorithm that continuously learns on incoming streaming data, in order to adapt its hyper-parameters to temporal changes.


User-generated content: tourists’ profiles on TripAdvisor


title: Fast Automatic Vehicle Annotation for Urban Traffic Surveillance

abstract: Automatic vehicle detection and annotation for streaming video data with complex scenes is an interesting but challenging task for intelligent transportation systems. In this paper, we present a fast algorithm: detection and annotation for vehicles (DAVE), which effectively combines vehicle detection and attributes annotation into a unified framework. DAVE consists of two convolutional neural networks: a shallow fully convolutional fast vehicle proposal network (FVPN) for extracting all vehicles’ positions, and a deep attributes learning network (ALN), which aims to verify each detection candidate and infer each vehicle’s pose, color, and type information simultaneously. These two nets are jointly optimized so that abundant latent knowledge learned from the deep empirical ALN can be exploited to guide training the much simpler FVPN. Once the system is trained, DAVE can achieve efficient vehicle detection and attributes annotation for real-world traffic surveillance data, while the FVPN can be independently adopted as a real-time high-performance vehicle detector as well. We evaluate the DAVE on a new self-collected urban traffic surveillance data set and the public PASCAL VOC2007 car and LISA 2010 data sets, with consistent improvements over existing algorithms.


title: A Probabilistic Quality Representation Approach to Deep Blind Image Quality Prediction

abstract: Blind image quality assessment (BIQA) remains a very challenging problem due to the unavailability of a reference image. Deep learning based BIQA methods have been attracting increasing attention in recent years, yet it remains a difficult task to train a robust deep BIQA model because of the very limited number of training samples with human subjective scores. Most existing methods learn a regression network to minimize the prediction error of a scalar image quality score. However, such a scheme ignores the fact that an image will receive divergent subjective scores from different subjects, which cannot be adequately represented by a single scalar number. This is particularly true on complex, real-world distorted images. Moreover, images may broadly differ in their distributions of assigned subjective scores. Recognizing this, we propose a new representation of perceptual image quality, called probabilistic quality representation (PQR), to describe the image subjective score distribution, whereby a more robust loss function can be employed to train a deep BIQA model. The proposed PQR method is shown to not only speed up the convergence of deep model training, but to also greatly improve the achievable level of quality prediction accuracy relative to scalar quality score regression methods. The source code is available at this https URL


Submandibular Gland Reduction in Aesthetic Surgery of the Neck: Review of 112 Consecutive Cases


title: A Novel Learning-based Global Path Planning Algorithm for Planetary Rovers

abstract: Abstract Autonomous path planning algorithm is significant to planetary exploration rovers, since relying on commands from Earth will heavily reduce their efficiency of executing exploration missions. In this paper, a novel learning-based algorithm is proposed to deal with global path planning problem for planetary exploration rovers. Specifically, a novel deep convolutional neural network with dual branches (DB-CNN) is designed and trained, which can plan path directly from orbital images of planetary surfaces without implementing environment mapping. Moreover, the planning procedure requires no prior knowledge about planetary surface terrains. Finally, experimental results demonstrate that DB-CNN achieves better performance on global path planning and faster convergence during training compared with the existing Value Iteration Network (VIN).


title: Accelerating convolutional neural networks by group-wise 2D-filter pruning

abstract: Network pruning is an effective way to accelerate Convolutional Neural Networks (CNNs). In recent years, structured pruning methods are proposed in favor of unstructured methods as they have shown greater speedup in practical use. Existing structured methods does pruning along two main dimensions: 3D-filter wise, i.e., remove a 3D-fllter as a whole, and filter-shape wise, i.e., remove a same position from all 3D-filters. In this work, we propose a new group-wise 2D-fllter pruning approach that is orthogonal and complementary to the existing methods. The proposed approach removes a portion of 2D-fllters from each 3D-filter according to the pruning patterns learned from the data, and leads to compressed models that do not require sophisticated implementation of convolution operations. A fine-tuning process is followed to recover the accuracy. The knowledge distillation (KD) framework is explored in the fine-tuning process to improve the performance. We present our method for learning the pruning pattens as well as the fine-tuning strategy based on knowledge distillation. The proposed approach is validated on two representative CNN models — ZF and VGG16, pre-trained on ILSVRC12. Experimental results demonstrate the effectiveness of our approach. In VGG16, we get even higher accuracy after speeding-up the network by 4 times.


title: The Forgotten Needle in My Collections: Task-Aware Ranking of Documents in Semantic Information Space

abstract: With the growing amount of content stored in personal and organizational information spaces, finding and re-finding documents becomes both more crucial and challenging. In this work, we propose an approach to reduce information overload in navigation by automatically focusing on important documents, adaptively to the tasks at hand. Based on the idea of managed forgetting, we present a ranking method, which unifies activity logs and semantic information about documents into a common framework to identify important documents to the user's current tasks. Our experiments on two real-world datasets, both collected from knowledge work activities in professional scenarios, show that our ranking approach outperforms the baseline methods for both subsequent access prediction and the effectiveness in ranking important documents. Furthermore, we implemented and demonstrated a system for decluttering information spaces as a proof of concept of our managed forgetting approach.


A Close Look at Tinder Bots


title: Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter

abstract: In this paper, we perform an empirical analysis of the cyber criminal ecosystem on Twitter. Essentially, through analyzing inner social relationships in the criminal account community, we find that criminal accounts tend to be socially connected, forming a small-world network. We also find that criminal hubs, sitting in the center of the social graph, are more inclined to follow criminal accounts. Through analyzing outer social relationships between criminal accounts and their social friends outside the criminal account community, we reveal three categories of accounts that have close friendships with criminal accounts. Through these analyses, we provide a novel and effective criminal account inference algorithm by exploiting criminal accounts' social relationships and semantic coordinations.


title: Real-time monocular dense mapping on aerial robots using visual-inertial fusion

abstract: In this work, we present a solution to real-time monocular dense mapping. A tightly-coupled visual-inertial localization module is designed to provide metric and high-accuracy odometry. A motion stereo algorithm is proposed to take the video input from one camera to produce local depth measurements with semi-global regularization. The local measurements are then integrated into a global map for noise filtering and map refinement. The global map obtained is able to support navigation and obstacle avoidance for aerial robots through our indoor and outdoor experimental verification. Our system runs at 10Hz on an Nvidia Jetson TX1 by properly distributing computation to CPU and GPU. Through onboard experiments, we demonstrate its ability to close the perception-action loop for autonomous aerial robots. We release our implementation as open-source software1.


title: Protecting browsers from dns rebinding attacks

abstract: DNS rebinding attacks subvert the same-origin policy of browsers and convert them into open network proxies. We survey new DNS rebinding attacks that exploit the interaction between browsers and their plug-ins, such as Flash and Java. These attacks can be used to circumvent firewalls and are highly cost-effective for sending spam e-mail and defrauding pay-per-click advertisers, requiring less than $100 to temporarily hijack 100,000 IP addresses. We show that the classic defense against these attacks, called "DNS pinning," is ineffective in modern browsers. The primary focus of this work, however, is the design of strong defenses against DNS rebinding attacks that protect modern browsers: we suggest easy-to-deploy patches for plug-ins that prevent large-scale exploitation, provide a defense tool, dnswall, that prevents firewall circumvention, and detail two defense options, policy-based pinning and host name authorization.


title: The Impact on Individualizing Student Models on Necessary Practice Opportunities

abstract: When modeling student learning, tutors that use the Knowledge Tracing framework often assume that all students have the same set of model parameters. We find that when fitting parameters to individual students, there is significant variation among the individual’s parameters. We examine if this variation is important in terms of instructional decisions by computing the difference in the expected number of practice opportunities required if mastery is assessed using an individual student’s own estimated model parameters, compared to the population model. In the dataset considered, we find that a significant portion of students are expected to perform twice as many practice opportunities if the student is modeled using a population-based model, compared to the number needed if the student’s own model parameters were used. We also find an additional significant portion of students will be likely to receive less practice opportunities than needed, implying that such students will be advanced too early. Though further work on additional datasets is needed to explore this issue in more depth, our results suggest that considering individual variation in student parameters may have important implications for the instructional decisions made in intelligent tutoring systems that use a Knowledge Tracing model.


title: CRF-Filters: Discriminative Particle Filters for Sequential State Estimation

abstract: Particle filters have been applied with great success to various state estimation problems in robotics. However, particle filters often require extensive parameter tweaking in order to work well in practice. This is based on two observations. First, particle filters typically rely on independence assumptions such as "the beams in a laser scan are independent given the robot's location in a map". Second, even when the noise parameters of the dynamical system are perfectly known, the sample-based approximation can result in poor filter performance. In this paper we introduce CRF-filters, a novel variant of particle filtering for sequential state estimation. CRF-filters are based on conditional random fields, which are discriminative models that can handle arbitrary dependencies between observations. We show how to learn the parameters of CRF-filters based on labeled training data. Experiments using a robot equipped with a laser range-finder demonstrate that our technique is able to learn parameters of the robot's motion and sensor models that result in good localization performance, without the need of additional parameter tweaking.


Rephrasing Profanity in Chinese Text


title: Cloud-Based Automated Design and Additive Manufacturing: A Usage Data-Enabled Paradigm Shift

abstract: Integration of sensors into various kinds of products and machines provides access to in-depth usage information as basis for product optimization. Presently, this large potential for more user-friendly and efficient products is not being realized because (a) sensor integration and thus usage information is not available on a large scale and (b) product optimization requires considerable efforts in terms of manpower and adaptation of production equipment. However, with the advent of cloud-based services and highly flexible additive manufacturing techniques, these obstacles are currently crumbling away at rapid pace. The present study explores the state of the art in gathering and evaluating product usage and life cycle data, additive manufacturing and sensor integration, automated design and cloud-based services in manufacturing. By joining and extrapolating development trends in these areas, it delimits the foundations of a manufacturing concept that will allow continuous and economically viable product optimization on a general, user group or individual user level. This projection is checked against three different application scenarios, each of which stresses different aspects of the underlying holistic concept. The following discussion identifies critical issues and research needs by adopting the relevant stakeholder perspectives.


title: Development and comparison of homography based estimation techniques for camera to road surface orientation

abstract: This paper focuses on dynamic orientation estimation of a vehicle-mounted mono camera. In particular, the pitch and roll angles of the camera relative to the road surface. Information about the orientation angles is included in the homography (projective transformation) between two images of a planar road surface. The extraction of angles from a homography matrix is possible but not recommended due to parameter ambiguities. For this reason we do not estimate a full homography matrix but reduce the parameter space to two parameters. In this area of parameter estimation there are mainly two different approaches: The optical flow based approach and the image registration based approach. In order to decide which of these approaches is more favorable for the angle estimation problem, we develop one optical flow based and one image registration based angle estimation algorithm. We are the first directly evaluating and comparing both approaches with each other with the help of an artificial image sequence as well as real-world driving scenarios. In addition, this paper lifts the common limitation of roll angle of zero degree for dynamic camera orientation estimation. Our research finds that there is only a small difference between the parameter estimation results of the optical flow and image registration based approach.


title: A Hybrid Algorithm for Stemming of Nepali Text

abstract: In this paper, a new context free ::: stemmer is proposed which consists of the combination of traditional rule based system with string ::: similarity approach. This algorithm can be called as hybrid algorithm. It is ::: language dependent algorithm. Context free stemmer means that stemmer which ::: stems the word that is not based on the context i.e., for every context such rule is applied. After stripping the ::: words using traditional context free rule based approach, it may over stem or ::: under stem the inflected words which are overcome by applying string similarity ::: function of dynamic programming. For measuring the string similarity function, ::: edit distance is used. The stripped inflected word is compared with the words ::: stored in a text database available. That word having minimum distance is taken ::: as the substitution of the stripped inflected word which leads to the stem of ::: it. The concept of traditional rule based system and corpus based approach is heavily used in this approach. ::: This algorithm is tested for Nepali Language which is based on Devanagari ::: Script. The approach has given better result in comparison to traditional rule ::: based system particularly for Nepali Language only. The total accuracy of ::: this hybrid algorithm is 70.10% whereas the total accuracy of traditional rule ::: based system is 68.43%.


title: Three New Probabilistic Models For Dependency Parsing: An Exploration

abstract: After presenting a novel O(n3) parsing algorithm for dependency grammar, we develop three contrasting ways to stochasticize it. We propose (a) a lexical affinity model where words struggle to modify each other, (b) a sense tagging model where words fluctuate randomly in their selectional preferences, and (c) a generative model where the speaker fleshes out each word's syntactic and conceptual structure without regard to the implications for the hearer. We also give preliminary empirical results from evaluating the three models' parsing performance on annotated Wall Street Journal training text (derived from the Penn Treebank). In these results, the generative model performs significantly better than the others, and does about equally well at assigning part-of-speech tags.


title: The meaning of manufacturing know-how

abstract: This paper investigates the phenomenon of manufacturing know-how. First, the abstract notion of knowledge is discussed, and a terminological basis is introduced to treat know-how as a kind of knowledge. Next, a brief survey of the recently reported works dealt with manufacturing know-how is presented, and an explicit definition of know-how is formulated. Finally, the problem of utilizing know-how with knowledge-based systems is analyzed, and some ideas useful for its solving are given.


title: Topological overview of hybrid electric and fuel cell vehicular power system architectures and configurations

abstract: This paper discusses the operational characteristics of the topologies for hybrid electric vehicles (HEV), fuel cell vehicles (FCV), and more electric vehicles (MEV). A brief description of series hybrid, parallel hybrid, and fuel cell-based propulsion systems are presented. The paper also presents fuel cell propulsion applications, more specific to light-duty passenger cars as well as heavy-duty buses. Finally, some of the major fundamental issues that currently face these advanced vehicular technologies including the challenges for market penetration are highlighted.


title: Local region detector + CNN based landmarks for practical place recognition in changing environments

abstract: Visual place recognition is a mature field in mobile robotics research. Recognizing places in datasets covering traversals of hundreds or thousands of kilometres and accurate localization in small and medium size environments have been successfully demonstrated. However, for real world long term operation, visual place recognition has to face severe environmental appearance changes due to day-night cycles, seasonal or weather changes. Existing approaches for recognizing places in such changing environments provide solutions for matching images from the exact same viewpoint using powerful holistic descriptors, using less sophisticated holistic descriptors in combinations with images sequences, and/or pose strong requirements on training data to learn systematic appearance changes. In this paper, we present a novel, training free, single image matching procedure that builds upon local region detectors for powerful Convolutional Neural Network (CNN) based descriptors. It can be used with a broad range of local region detectors including keypoints, segmentation based approaches and object proposals. We propose a novel evaluation criterion for selection of an appropriate local region detector for changing environments and compare several available detectors. The scale space extrema detector known from the SIFT keypoint detector in combination with appropriate magnification factors performs best. We present preliminary results of the proposed image matching procedure with several region detectors on the challenging Nordland dataset on place recognition between different seasons and a dataset including severe viewpoint changes. The proposed method outperforms the best existing holistic method for place recognition in such changing environments and can additionally handle severe viewpoint changes. Additionally, the combination of the best performing detectors with superpixel based spatial image support shows promising results.


title: Dual-Mode Microstrip Open-Loop Resonators and Filters

abstract: A miniature dual-mode microstrip open-loop resonator is proposed. Distinct characteristics of this new type of dual-mode resonator are investigated using full-wave electromagnetic simulations. It is shown that the two operating modes, i.e., the even and odd modes, within a single dual-mode resonator of this type do not couple. It is also found that there is a finite-frequency transmission zero inherently associated with the even mode. Two two-pole filters using this type of dual-mode resonator are demonstrated with opposite asymmetric responses, which result from different locations of the transmission zero. Higher order filters of this type are also investigated. Both simulated and measured results are presented.


title: Exact Maximum Power Point Tracking of Grid-Connected Partially Shaded PV Source Using Current Compensation Concept

abstract: Under partially shaded conditions, series connection of photovoltaic (PV) modules results in the flow of lowest current in the string corresponding to the most shaded module, reducing the overall power output. Power output can be maximized by incorporating module-level “distributed” maximum power point tracking (DMPPT) wherein the current of each module is compensated by regulating its voltage at the respective maximum power point (MPP) value by connecting a dc-dc converter in parallel. Existing current compensation schemes are either too complex or inaccurate. This paper proposes a novel DMPPT scheme using current compensation, which is simple to implement and yet provides an accurate compensation, resulting in exact maximum power point tracking (MPPT). In the proposed scheme, each PV module is resonated through a special arrangement in the shunt-connected flyback dc-dc converter. The secondary-side diode in the flyback converter is replaced by a power mosfet with an antiparallel diode. The converter operates in two modes: resonant MPPT mode and normal flyback mode. An intelligent controller tracks the MPP of the modules while they resonate. An output switch decouples the PV source from the grid momentarily, while the MPP is tracked. All the results of this work, including experimental validation, are presented.


title: Migration transitions: A theoretical and empirical inquiry into the developmental drivers of international migration

abstract: This paper aims to advance a conceptual framework on the developmental drivers of international migration processes and to provide an empirical test drawing on the global migrant origin database. Conventional ideas that development in origin countries will reduce international migration are ultimately based on “push-pull”, neoclassical and other equilibrium models which assume an inversely proportional relationship between absolute levels and relative differences of wealth and migration. By contrast, another group of theories postulate that development leads to generally increased levels of migration and that societies go through migration transitions characterised by an inverted U-shaped pattern of emigration. ::: ::: The paper discusses as yet unobserved conceptual parallels and differences between separately evolved ‘transition’ theories. It subsequently amends and synthesises prior theories, based on a criticism of their evolutionary character and sedentary bias, their inclination towards demographic determinism, their limited conceptualisation of structure and agency as well as the causal mechanisms underlying the correlations they describe. Sen’s capabilities-based development concept is applied to migration to create analytical room to analyse most forms of migration within a single perspective. Structure and agency are incorporated by conceptualising migration as a function of (1) capabilities, (2) aspirations and, on a macro-level, (3) opportunity rather than income differentials. ::: ::: Because of the contested nature of migration transition theory, the paper provides an empirical test. Drawing on the World Bank/University of Sussex global migrant origin database, it estimates the effect of theoretically relevant development indicators on immigrant, emigrant, net immigrant and total migrant stocks. The results largely confirm transition theory. Higher levels of economic and human development are associated to higher overall levels of migration and have the predicted U-curve effect on emigration. The results also suggest that demographic factors do not have a direct effect on migration. Although several empirical puzzles remain, particularly on the effects of political freedoms, the results suggest that take-off development in the least developed countries is likely to lead to take-off emigration. The analysis exemplifies the need to conceptualise migration as an integral part of broader development processes rather than as problem to be “solved”.


title: Defining Computational Thinking for Mathematics and Science Classrooms

abstract: Science and mathematics are becoming computational endeavors. This fact is reflected in the recently released Next Generation Science Standards and the decision to include “computational thinking” as a core scientific practice. With this addition, and the increased presence of computation in mathematics and scientific contexts, a new urgency has come to the challenge of defining computational thinking and providing a theoretical grounding for what form it should take in school science and mathematics classrooms. This paper presents a response to this challenge by proposing a definition of computational thinking for mathematics and science in the form of a taxonomy consisting of four main categories: data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices. In formulating this taxonomy, we draw on the existing computational thinking literature, interviews with mathematicians and scientists, and exemplary computational thinking instructional materials. This work was undertaken as part of a larger effort to infuse computational thinking into high school science and mathematics curricular materials. In this paper, we argue for the approach of embedding computational thinking in mathematics and science contexts, present the taxonomy, and discuss how we envision the taxonomy being used to bring current educational efforts in line with the increasingly computational nature of modern science and mathematics.


title: Decentralized Robust Adaptive Control for the Multiagent System Consensus Problem Using Neural Networks

abstract: A robust adaptive control approach is proposed to solve the consensus problem of multiagent systems. Compared with the previous work, the agent's dynamics includes the uncertainties and external disturbances, which is more practical in real-world applications. Due to the approximation capability of neural networks, the uncertain dynamics is compensated by the adaptive neural network scheme. The effects of the approximation error and external disturbances are counteracted by employing the robustness signal. The proposed algorithm is decentralized because the controller for each agent only utilizes the information of its neighbor agents. By the theoretical analysis, it is proved that the consensus error can be reduced as small as desired. The proposed method is then extended to two cases: agents form a prescribed formation, and agents have the higher order dynamics. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed method.


title: Automated EEG-Based Epileptic Seizure Detection Using Deep Neural Networks

abstract: Millions of people around the world suffer from epilepsy. It is very important to provide a method to efficiently monitor the seizures and alert the caregivers to help patients. It is proven that EEG signals are the best markers for diagnosis of the epileptic seizures. In this paper, we used the frequency domain features (normalized in-band power spectral density) to extract information from EEG signals. We applied a deep learning technique based on multilayer perceptrons to improve the accuracy of seizure detection. The results indicate that our nonlinear technique is able to efficiently and automatically detect seizure and non-seizure episodes with an F-measure accuracy of around 95%.


Sentiment Classification based on Latent Dirichlet Allocation


title: Stream data clustering based on grid density and attraction

abstract: Clustering real-time stream data is an important and challenging problem. Existing algorithms such as CluStream are based on the k-means algorithm. These clustering algorithms have difficulties finding clusters of arbitrary shapes and handling outliers. Further, they require the knowledge of k and user-specified time window. To address these issues, this article proposes D-Stream, a framework for clustering stream data using a density-based approach. Our algorithm uses an online component that maps each input data record into a grid and an offline component that computes the grid density and clusters the grids based on the density. The algorithm adopts a density decaying technique to capture the dynamic changes of a data stream and a attraction-based mechanism to accurately generate cluster boundaries. Exploiting the intricate relationships among the decay factor, attraction, data density, and cluster structure, our algorithm can efficiently and effectively generate and adjust the clusters in real time. Further, a theoretically sound technique is developed to detect and remove sporadic grids mapped by outliers in order to dramatically improve the space and time efficiency of the system. The technique makes high-speed data stream clustering feasible without degrading the clustering quality. The experimental results show that our algorithm has superior quality and efficiency, can find clusters of arbitrary shapes, and can accurately recognize the evolving behaviors of real-time data streams.


title: A DNA nanorobot functions as a cancer therapeutic in response to a molecular trigger in vivo

abstract: Nanoscale robots have potential as intelligent drug delivery systems that respond to molecular triggers. Using DNA origami we constructed an autonomous DNA robot programmed to transport payloads and present them specifically in tumors. Our nanorobot is functionalized on the outside with a DNA aptamer that binds nucleolin, a protein specifically expressed on tumor-associated endothelial cells, and the blood coagulation protease thrombin within its inner cavity. The nucleolin-targeting aptamer serves both as a targeting domain and as a molecular trigger for the mechanical opening of the DNA nanorobot. The thrombin inside is thus exposed and activates coagulation at the tumor site. Using tumor-bearing mouse models, we demonstrate that intravenously injected DNA nanorobots deliver thrombin specifically to tumor-associated blood vessels and induce intravascular thrombosis, resulting in tumor necrosis and inhibition of tumor growth. The nanorobot proved safe and immunologically inert in mice and Bama miniature pigs. Our data show that DNA nanorobots represent a promising strategy for precise drug delivery in cancer therapy.


title: Review and Role of Plyometrics and Core Rehabilitation in Competitive Sport

abstract: Core stability and plyometric training have become common elements of training programs in competitive athletes. Core stability allows stabilization of the spine and trunk of the body in order to allow maximal translation of force to the extremities. Plyometric training is more dynamic and involves explosive-strength training. Integration of these exercises theoretically begins with core stabilization using more static exercises, allowing safe and effective transition to plyometric exercises. Both core strengthening and plyometric training have demonstrated mixed but generally positive results on injury prevention rehabilitation of certain types of injuries. Improvement in performance compared to other types of exercise is unclear at this time. This article discusses the theory and strategy behind core stability and plyometric training; reviews the literature on injury prevention, rehabilitation of injury, and performance enhancement with these modalities; and discusses the evaluation and rehabilitation of core stability.


title: A Tractable Approach to Coverage and Rate in Cellular Networks

abstract: Cellular networks are usually modeled by placing the base stations on a grid, with mobile users either randomly scattered or placed deterministically. These models have been used extensively but suffer from being both highly idealized and not very tractable, so complex system-level simulations are used to evaluate coverage/outage probability and rate. More tractable models have long been desirable. We develop new general models for the multi-cell signal-to-interference-plus-noise ratio (SINR) using stochastic geometry. Under very general assumptions, the resulting expressions for the downlink SINR CCDF (equivalent to the coverage probability) involve quickly computable integrals, and in some practical special cases can be simplified to common integrals (e.g., the Q-function) or even to simple closed-form expressions. We also derive the mean rate, and then the coverage gain (and mean rate loss) from static frequency reuse. We compare our coverage predictions to the grid model and an actual base station deployment, and observe that the proposed model is pessimistic (a lower bound on coverage) whereas the grid model is optimistic, and that both are about equally accurate. In addition to being more tractable, the proposed model may better capture the increasingly opportunistic and dense placement of base stations in future networks.


title: Efficient and accurate approximations of nonlinear convolutional networks

abstract: This paper aims to accelerate the test-time computation of deep convolutional neural networks (CNNs). Unlike existing methods that are designed for approximating linear filters or linear responses, our method takes the nonlinear units into account. We minimize the reconstruction error of the nonlinear responses, subject to a low-rank constraint which helps to reduce the complexity of filters. We develop an effective solution to this constrained nonlinear optimization problem. An algorithm is also presented for reducing the accumulated error when multiple layers are approximated. A whole-model speedup ratio of 4× is demonstrated on a large network trained for ImageNet, while the top-5 error rate is only increased by 0.9%. Our accelerated model has a comparably fast speed as the “AlexNet” [11], but is 4.7% more accurate.


title: Uncovering the overlapping community structure of complex networks in nature and society

abstract: Many complex systems in nature and society can be described in terms of networks capturing the intricate web of connections among the units they are made of. A key question is how to interpret the global organization of such networks as the coexistence of their structural subunits (communities) associated with more highly interconnected parts. Identifying these a priori unknown building blocks (such as functionally related proteins, industrial sectors and groups of people) is crucial to the understanding of the structural and functional properties of networks. The existing deterministic methods used for large networks find separated communities, whereas most of the actual networks are made of highly overlapping cohesive groups of nodes. Here we introduce an approach to analysing the main statistical features of the interwoven sets of overlapping communities that makes a step towards uncovering the modular structure of complex systems. After defining a set of new characteristic quantities for the statistics of communities, we apply an efficient technique for exploring overlapping communities on a large scale. We find that overlaps are significant, and the distributions we introduce reveal universal features of networks. Our studies of collaboration, word-association and protein interaction graphs show that the web of communities has non-trivial correlations and specific scaling properties.


Artificial Immune Systems : A Novel Paradigm to Pattern Recognition


title: 3D Voronoi Subdivision Tree for granular materials

abstract: We describe a method to simulate granular materials using a structure called a 3D Voronoi Subdivision Tree. The 3D Voronoi Subdivision Tree is a method to create interactive subdivided objects for a volume in a physics environment. A tree of 3D Voronoi cells is created from a convex object and the cells are subdivided at each level to the bottom-most children. We utilize and adapt this technique for granular materials to create a robust simulation environment. As such, this method creates dynamic granular materials that subdivide as needed based on interaction with other objects.


title: Recognition of Mexican banknotes via their color and texture features

abstract: Highlights? Color and texture are modeled with the RGB space and LBP method, respectively. ? High recognition rates are obtained, even on mistreated banknotes. ? Low computational load and color extraction is rotation invariant. ? The proposed approach can be applied to classify banknotes of different countries. ? I will be aware of any news about the paper. In this paper it is introduced a recognition method for Mexican banknotes by using artificial vision. It is shown that the Mexican banknotes can be classified by extracting their color and texture features, with the RGB space and the Local Binary Patterns, respectively. We show the classification results performed with the current Mexican banknotes. We state that the proposed method can be applied to recognize banknotes of other countries which employ colors to distinguish the denominations.


title: Geckos: combining magnets and pressure images to enable new tangible-object design and interaction

abstract: In this paper we present Geckos, a new type of tangible objects which are tracked using a Force-Sensitive Resistance sensor. Geckos are based on low-cost permanent magnets and can also be used on non-horizontal surfaces. Unique pressure footprints are used to identify each tangible Gecko. Two types of tangible object designs are presented: Using a single magnet in combination with felt pads provides new pressure-based interaction modalities. Using multiple separate magnets it is possible to change the marker footprint dynamically and create new haptic experiences. The tangible object design and interaction are illustrated with example applications. We also give details on the feasibility and benefits of our tracking approach and show compatibility with other tracking technologies.


title: Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art

abstract: Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme, and evaluate the variety of contemporary MOEAs. Current MOEA theoretical developments are evaluated; specific topics addressed include fitness functions, Pareto ranking, niching, fitness sharing, mating restriction, and secondary populations. Since the development and application of MOEAs is a dynamic and rapidly growing activity, we focus on key analytical insights based upon critical MOEA evaluation of current research and applications. Recommended MOEA designs are presented, along with conclusions and recommendations for future work.


title: Recruiting and retaining women in undergraduate computing majors

abstract: This paper recommends methods for increasing female participation in undergraduate computer science. The recommendations are based on recent and on-going research into the gender gap in computer science and related disciplines They are intended to work in tandem with the Computing Research Association's recommendations for graduate programs (see [18] in this issue) to promote a general increase in women's participation in computing professions.Most of the suggestions offered here could improve the educational environment for both male and female students. However, general improvements are likely to be of particular benefit to women because women in our society do not generally receive the same level of support that men receive for entering and persisting in this field. Parents, friends, and classmates seldom encourage women to choose and continue in a computing discipline. Those few women who declare a computing major tend to experience continued lack of support throughout their education. They have few female peers to call on for help. Furthermore, students in their own and other majors may consider them odd. Even faculty can discourage women with unthinking remarks or with expectations based on the assumption that men's behavior and experience are the basis for success in computing majors. This lack of support can block or weaken women's commitment to computing. Moreover, when conditions in a department are generally unfavorable, those with a weakened commitment to the discipline often leave at higher rates than those who have sufficient support to overcome the conditions.


title: Reduction of Electromagnetic Noise Coupling to Antennas in Metal-Framed Smartphones Using Ferrite Sheets and Multi-Via EBG Structures

abstract: Recent smartphones increasingly employ a metal frame for a sophisticated design. However, the metal frame becomes a good coupling path for electromagnetic (EM) noise within smartphones and increases noise coupling to radio-frequency antennas located at the top and bottom sides of the smartphones. This paper proposes two novel metal frame structures with ferrite absorbing sheets or electromagnetic band gap patterns to reduce the coupling of EM noise to the antenna. Using a fabricated test board, the effects of the proposed metal frames on noise coupling suppression are experimentally validated.


title: A Decision Theoretic Approach to Targeted Advertising

abstract: A simple advertising strategy that can be used to help increase sales of a product is to mail out special offers to selected potential customers. Because there is a cost associated with sending each offer, the optimal mailing strategy depends on both the benefit obtained from a purchase and how the offer affects the buying behavior of the customers. In this paper, we describe two methods for partitioning the potential customers into groups, and show how to perform a simple cost-benefit analysis to decide which, if any, of the groups should be targeted. In particular, we consider two decision-tree learning algorithms. The first is an "off the shelf" algorithm used to model the probability that groups of customers will buy the product. The second is a new algorithm that is similar to the first, except that for each group, it explicitly models the probability of purchase under the two mailing scenarios: (1) the mail is sent to members of that group and (2) the mail is not sent to members of that group. Using data from a real-world advertising experiment, we compare the algorithms to each other and to a naive mail-to-all strategy.


title: Attacking and repairing the winZip encryption scheme

abstract: WinZip is a popular compression utility for Microsoft Windows computers, the latest version of which is advertised as having "easy-to-use AES encryption to protect your sensitive data." We exhibit several attacks against WinZip's new encryption method, dubbed "AE-2" or "Advanced Encryption, version two." We then discuss secure alternatives. Since at a high level the underlying WinZip encryption method appears secure (the core is exactly Encrypt-then-Authenticate using AES-CTR and HMAC-SHA1), and since one of our attacks was made possible because of the way that WinZip Computing, Inc. decided to fix a different security problem with its previous encryption method AE-1, our attacks further underscore the subtlety of designing cryptographically secure software.


title: Unsupervised Event Characterization and Detection in Multichannel Signals: An EEG application

abstract: In this paper, we propose a new unsupervised method to automatically characterize and detect events in multichannel signals. This method is used to identify artifacts in electroencephalogram (EEG) recordings of brain activity. The proposed algorithm has been evaluated and compared with a supervised method. To this end an example of the performance of the algorithm to detect artifacts is shown. The results show that although both methods obtain similar classification, the proposed method allows detecting events without training data and can also be applied in signals whose events are unknown a priori. Furthermore, the proposed method provides an optimal window whereby an optimal detection and characterization of events is found. The detection of events can be applied in real-time.


title: Relay Attacks on Passive Keyless Entry and Start Systems in Modern Cars

abstract: We demonstrate relay attacks on Passive Keyless Entry and Start (PKES) systems used in modern cars. We build two efficient and inexpensive attack realizations, wired and wireless physical-layer relays, that allow the attacker to enter and start a car by relaying messages between the car and the smart key. Our relays are completely independent of the modulation, protocol, or presence of strong authentication and encryption. We perform an extensive evaluation on 10 car models from 8 manufacturers. Our results show that relaying the signal in one direction only (from the car to the key) is sufficient to perform the attack while the true distance between the key and car remains large (tested up to 50 meters, non line-of-sight). We also show that, with our setup, the smart key can be excited from up to 8 meters. This removes the need for the attacker to get close to the key in order to establish the relay. We further analyze and discuss critical system characteristics. Given the generality of the relay attack and the number of evaluated systems, it is likely that all PKES systems based on similar designs are also vulnerable to the same attack. Finally, we propose immediate mitigation measures that minimize the risk of relay attacks as well as recent solutions that may prevent relay attacks while preserving the convenience of use, for which PKES systems were initially introduced.


Improved Semantic Segmentation for Histopathology using Rotation Equivariant Convolutional Networks


title: Embedding-based Query Language Models

abstract: Word embeddings, which are low-dimensional vector representations of vocabulary terms that capture the semantic similarity between them, have recently been shown to achieve impressive performance in many natural language processing tasks. The use of word embeddings in information retrieval, however, has only begun to be studied. In this paper, we explore the use of word embeddings to enhance the accuracy of query language models in the ad-hoc retrieval task. To this end, we propose to use word embeddings to incorporate and weight terms that do not occur in the query, but are semantically related to the query terms. We describe two embedding-based query expansion models with different assumptions. Since pseudo-relevance feedback methods that use the top retrieved documents to update the original query model are well-known to be effective, we also develop an embedding-based relevance model, an extension of the effective and robust relevance model approach. In these models, we transform the similarity values obtained by the widely-used cosine similarity with a sigmoid function to have more discriminative semantic similarity values. We evaluate our proposed methods using three TREC newswire and web collections. The experimental results demonstrate that the embedding-based methods significantly outperform competitive baselines in most cases. The embedding-based methods are also shown to be more robust than the baselines.


title: Learning Memory Access Patterns

abstract: The explosion in workload complexity and the recent slow-down in Moore's law scaling call for new approaches towards efficient computing. Researchers are now beginning to use recent advances in machine learning in software optimizations, augmenting or replacing traditional heuristics and data structures. However, the space of machine learning for computer hardware architecture is only lightly explored. In this paper, we demonstrate the potential of deep learning to address the von Neumann bottleneck of memory performance. We focus on the critical problem of learning memory access patterns, with the goal of constructing accurate and efficient memory prefetchers. We relate contemporary prefetching strategies to n-gram models in natural language processing, and show how recurrent neural networks can serve as a drop-in replacement. On a suite of challenging benchmark datasets, we find that neural networks consistently demonstrate superior performance in terms of precision and recall. This work represents the first step towards practical neural-network based prefetching, and opens a wide range of exciting directions for machine learning in computer architecture research.


title: Blue-Fi: enhancing Wi-Fi performance using bluetooth signals

abstract: Mobile devices are increasingly equipped with multiple network interfaces with complementary characteristics. In particular, the Wi-Fi interface has high throughput and transfer power efficiency, but its idle power consumption is prohibitive. In this paper we present, Blue-Fi, a sytem that predicts the availability of the Wi-Fi connectivity by using a combination of bluetooth contact-patterns and cell-tower information. This allows the device to intelligently switch the Wi-Fi interface on only when there is Wi-Fi connectivity available, thus avoiding the long periods in idle state and significantly reducing the the number of scans for discovery. Our prediction results on traces collected from real users show an average coverage of 94% and an average accuracy of 84%, a 47% accuracy improvement over pure cell-tower based prediction, and a 57% coverage improvement over the pure bluetooth based prediction. For our workload, Blue-Fi is up to 62% more energy efficient, which results in increasing our mobile device's lifetime by more than a day.


Chapter 1 RULE INDUCTION


title: The ASTOOT approach to testing object-oriented programs

abstract: This article describes a new approach to the unit testing of object-oriented programs, a set of tools based on this approach, and two case studies. In this approach, each test case consists of a tuple of sequences of messages, along with tags indicating whether these sequences should put objects of the class under test into equivalent states and/or return objects that are in equivalent states. Tests are executed by sending the sequences to objects of the class under test, then invoking a user-supplied equivalence-checking mechanism. This approach allows for substantial automation of many aspects of testing, including test case generation, test driver generation, test execution, and test checking. Experimental prototypes of tools for test generation and test execution are described. The test generation tool requires the availability of an algebraic specification of the abstract data type being tested, but the test execution tool can be used when no formal specification is available. Using the test execution tools, case studies involving execution of tens of thousands of test cases, with various sequence lengths, parameters, and combinations of operations were performed. The relationships among likelihood of detecting an error and sequence length, range of parameters, and relative frequency of various operations were investigated for priority queue and sorted-list implementations having subtle errors. In each case, long sequences tended to be more likely to detect the error, provided that the range of parameters was sufficiently large and likelihood of detecting an error tended to increase up to a threshold value as the parameter range increased.


title: When deep learning meets edge computing

abstract: The state-of-the-art cloud computing platforms are facing challenges, such as the high volume of crowdsourced data traffic and highly computational demands, involved in typical deep learning applications. More recently, Edge Computing has been recently proposed as an effective way to reduce the resource consumption. In this paper, we propose an edge learning framework by introducing the concept of edge computing and demonstrate the superiority of our framework on reducing the network traffic and running time.


title: Altered Trunk Muscle Recruitment in People With Low Back Pain With Upper Limb Movement at Different Speeds

abstract: Hodges PW, Richardson CA. Altered trunk muscle recruitment in people with low back pain with upper limb movement at different speeds. Arch Phys Med Rehabil 1999;80:1005-12. Objective: To compare trunk muscle coordination in people with and without low back pain with varying speeds of limb movement. Study Design: Abdominal and back extensor muscle activity in association with upper limb movement was compared among three speeds of movement and between people with and without low back pain. Participants: Fourteen subjects with a history of recurrent low back pain and a group of age- and sex-matched control subjects. Measures: The onsets of electromyographic activity of the trunk and limb muscles, frequency of trunk muscle responses, and angular velocity of arm movements. Results: Early activation of transversus abdominis (TrA) and obliquus internus abdominis (OI) occurred in the majority of trials, with movement at both the fast and intermediate speeds for the control group. In contrast, subjects with low back pain failed to recruit TrA or OI in advance of limb movement with fast movement, and no activity of the abdominal muscles was recorded in the majority of intermediate speed trials. There was no difference between groups for slow movement. Conclusion: The results indicate that the mechanism of preparatory spinal control is altered in people with lower back pain for movement at a variety of speeds. © 1999 by the American Congress of Rehabilitation Medi- cine and the American Academy of Physical Medicine and Rehabilitation


title: To Go With the Flows or to Flow With the Nodes? An Exploration of ‘post-disciplinary’ Theories of Movement in Space Syntax and Mobilities Research

abstract: All too often the dialogue between space syntax and the social sciences pits space against society. For this reason the recent ‘mobilities’ turn in urban studies – stimulated by John Urry’s key (2007) text Mobilities – presents space syntax research with an opportunity but also a theoretical challenge. Urry’s ‘mobilities paradigm’ advocates a broad ‘post-disciplinary’ convergence on infrastructural systems as agents actively creating and restraining social competences through the effects they have on movement and communication. Space syntax theory and research is well placed to make a substantial contribution to this emerging field. For example, the theories of ‘natural movement’ and ‘movement economy’ offer a radical conceptualization of quotidian pedestrian and vehicular movement-space that, in comparison with other forms of transport and communications infrastructure, are seriously under-conceptualized by Urry. For space syntax theory, on the other hand, the mobilities emphasis on the infrastructure of movement-space as enabling social practices to emerge in particular historical, geographical and cultural contexts invites consideration on the extent to which the implicit normativity of the space syntax theory of urban movement has been repressed in the name of prioritizing the objectivity of its contribution to the design process, over the benefits of wider interdisciplinary – or indeed ‘post-disciplinary’ engagements.


title: High Resolution ESM/ELINT DOA estimation with super-heterodyne multi-octave antenna system

abstract: This paper describes a compact antenna system architecture for DOA estimation in electronic warfare environments. The single antenna modules are intended to cover a certain frequency range each. This super-heterodyne approach allows covering a bandwidth larger than four octaves. The novelty of the presented structure, with respect to typical ESM/ELINT approaches, lies in a spatially shared solution for the intermediate and high-band arrays by means of an interdigitated architecture. Throughout the document, it is also shown how mixed amplitude/phase DOA algorithms allow better angular accuracy than amplitude-based methods.


title: Multi-class active learning for image classification

abstract: One of the principal bottlenecks in applying learning techniques to classification problems is the large amount of labeled training data required. Especially for images and video, providing training data is very expensive in terms of human time and effort. In this paper we propose an active learning approach to tackle the problem. Instead of passively accepting random training examples, the active learning algorithm iteratively selects unlabeled examples for the user to label, so that human effort is focused on labeling the most “useful” examples. Our method relies on the idea of uncertainty sampling, in which the algorithm selects unlabeled examples that it finds hardest to classify. Specifically, we propose an uncertainty measure that generalizes margin-based uncertainty to the multi-class case and is easy to compute, so that active learning can handle a large number of classes and large data sizes efficiently. We demonstrate results for letter and digit recognition on datasets from the UCI repository, object recognition results on the Caltech-101 dataset, and scene categorization results on a dataset of 13 natural scene categories. The proposed method gives large reductions in the number of training examples required over random selection to achieve similar classification accuracy, with little computational overhead.


title: Development of a novel direct-drive tubular linear brushless permanent-magnet motor

abstract: This paper presents a novel design for a tubular linear brushless permanent-magnet motor. In this design, the magnets in the moving part are oriented in an NS-NS-SN-SN fashion which leads to higher magnetic force near the like-pole region. An analytical methodology to calculate the motor force and to size the actuator was developed. The linear motor is operated in conjunction with a position sensor, three power amplifiers, and a controller to form a complete solution for controlled precision actuation. Real-time digital controllers enhanced the dynamic performance of the motor, and; gain scheduling reduced the effects of a nonlinear dead band. In its current state, the motor has a rise time of 30 ms, a settling time of 60 ms, and 25% overshoot to a 5-mm step command.. The motor has a maximum speed of 1.5 m/s and acceleration up to 10 g. It has a 10-cm travel range and 26-N maximum pull-out force. The compact size of the motor suggests it could be used in robotic applications requiring moderate force and precision, such as robotic-gripper positioning or actuation. The moving part of the motor can extend significantly beyond its fixed support base. This reaching ability makes it useful in applications requiring a small, direct-drive actuator, which is required to extend into a spatially constrained environment.


title: Task driven perceptual organization for extraction of rooftop polygons

abstract: A new method for extracting planar polygonal rooftops in monocular aerial imagery is proposed. Structural features are extracted and hierarchically related using perceptual grouping techniques. Top-down feature verification is used so that features, and links between the features, are verified with local information in the image and weighed in a graph. Cycles in the graph correspond to possible building rooftop hypotheses. Virtual features are hypothesized for the perceptual completion of partially occluded rooftops. Extraction of the "best" grouping features into a building rooftop hypothesis is posed as a graph search problem. The maximally weighted, independent set of cycles in the graph is extracted as the final set of roof boundaries. >


title: Acoustic-emission inspection of flaws during laser bonding of articles made of VT20 titanium alloy

abstract: The acoustic-emission method for inspecting flaws that form during laser bonding with the use of a CO2 continuous-wave laser with a power of up to 8 kW and characteristics that can be preadjusted prior to welding is considered. The structure of acoustic-emission signals registered in a specimen’s flawed domain has been analyzed using the wavelet transform. It has been shown that the structural coefficients of the acoustic-emission signals localized in specimen’s flawed sections become identical over time.


title: How to reach top accuracy for a visual pedestrian warning system from a car?

abstract: Due to the wide applicability of pedestrian detection in surveillance and safety, this research topic has received much attention in computer vision literature. However, the focus of this research mainly lies in detecting and locating pedestrians individually as accurate as possible. In recent years, a number of datasets are captured using a forward looking camera from a car, which imposes the application of warning the driver when pedestrians are in front of the car. For such applications, it is not required to detect each pedestrian independently, but to generate an alarm when necessary. In this paper we explore techniques to boost the accuracy of recent channel-based algorithms in this application: algorithmic refinements as well as the inclusion of an LWIR image channel. We use the KAIST dataset which is constructed from image-pairs of both the visual and the LWIR spectrum, in day and night conditions. We study the influence of techniques that have shown success in literature.


title: Robust Resource Allocation for MIMO Wireless Powered Communication Networks Based on a Non-Linear EH Model

abstract: In this paper, we consider a multiple-input multiple-output wireless powered communication network, where multiple users harvest energy from a dedicated power station in order to be able to transmit their information signals to an information receiving station. Employing a practical non-linear energy harvesting (EH) model, we propose a joint time allocation and power control scheme, which takes into account the uncertainty regarding the channel state information (CSI) and provides robustness against imperfect CSI knowledge. In particular, we formulate two non-convex optimization problems for different objectives, namely system sum throughput maximization and the maximization of the minimum individual throughput across all wireless powered users. To overcome the non-convexity, we apply several transformations along with a one-dimensional search to obtain an efficient resource allocation algorithm. Numerical results reveal that a significant performance gain can be achieved when the resource allocation is designed based on the adopted non-linear EH model instead of the conventional linear EH model. Besides, unlike a non-robust baseline scheme designed for perfect CSI, the proposed resource allocation schemes are shown to be robust against imperfect CSI knowledge.


title: Correlated Cascades: Compete or Cooperate

abstract: In real world social networks, there are multiple cascades which are rarely independent. They usually compete or cooperate with each other. Motivated by the reinforcement theory in sociology we leverage the fact that adoption of a user to any behavior is modeled by the aggregation of behaviors of its neighbors. We use a multidimensional marked Hawkes process to model users product adoption and consequently spread of cascades in social networks. The resulting inference problem is proved to be convex and is solved in parallel by using the barrier method. The advantage of the proposed model is twofold; it models correlated cascades and also learns the latent diffusion network. Experimental results on synthetic and two real datasets gathered from Twitter, URL shortening and music streaming services, illustrate the superior performance of the proposed model over the alternatives.


Clustering Based Routing for Wireless Sensor Networks in Smart Grid Environment


title: Scalable Power-Efficient Kilo-Core Photonic-Wireless NoC Architectures

abstract: As technology scales, hundreds and thousands of cores are being integrated on a single-chip. Since metallic interconnects may not scale effectively to support thousands of cores, architects have proposed emerging technologies such as photonics and wireless for intra-chip communication. While photonics technology is limited by the complexity and thermal effects, wireless technology for on-chip communication is limited by the available bandwidth. In this paper, we combine the benefits of both technologies into novel architecture that takes advantage of the communication benefits of both technologies while circumventing their limits. We discuss the scalability of the proposed architecture to kilo-core system using wireless technology. We evaluate the power consumption, throughput and latency for 256 and 1024 core architectures when compared to photonics-only, wireless-wired, wireless-photonics and wired-only architectures on synthetic traffic traces. Our simulation results indicate that the proposed architecture and design methodology can have significant impact on the overall network power and performance.


title: Neural Networks and Graph Algorithms with Next-Generation Processors

abstract: The use of graphical processors for distributed computation revolutionized the field of high performance scientific computing. As the Moore's Law era of computing draws to a close, the development of non-Von Neumann systems: neuromorphic processing units, and quantum annealers; again are redefining new territory for computational methods. While these technologies are still in their nascent stages, we discuss their potential to advance computing in two domains: machine learning, and solving constraint satisfaction problems. Each of these processors utilize fundamentally different theoretical models of computation. This raises questions about how to best use them in the design and implementation of applications. While many processors are being developed with a specific domain target, the ubiquity of spin-glass models and neural networks provides an avenue for multi-functional applications. This provides hints at the future infrastructure needed to integrate many next-generation processing units into conventional high-performance computing systems.


title: Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture

abstract: We propose a CNN-based approach for multi-camera markerless motion capture of the human body. Unlike existing methods that first perform pose estimation on individual cameras and generate 3D models as post-processing, our approach makes use of 3D reasoning throughout a multi-stage approach. This novelty allows us to use provisional 3D models of human pose to rethink where the joints should be located in the image and to recover from past mistakes. Our principled refinement of 3D human poses lets us make use of image cues, even from images where we previously misdetected joints, to refine our estimates as part of an end-to-end approach. Finally, we demonstrate how the high-quality output of our multi-camera setup can be used as an additional training source to improve the accuracy of existing single camera models.


title: Detailed Human Avatars from Monocular Video

abstract: We present a novel method for high detail-preserving human avatar creation from monocular video. A parameterized body model is refined and optimized to maximally resemble subjects from a video showing them from all sides. Our avatars feature a natural face, hairstyle, clothes with garment wrinkles, and high-resolution texture. Our paper contributes facial landmark and shading-based human body shape refinement, a semantic texture prior, and a novel texture stitching strategy, resulting in the most sophisticated-looking human avatars obtained from a single video to date. Numerous results show the robustness and versatility of our method. A user study illustrates its superiority over the state-of-the-art in terms of identity preservation, level of detail, realism, and overall user preference.


title: Performance and trends in recent opinion retrieval techniques

abstract: This paper presents trends and performance of opinion retrieval techniques proposed within the last 8 years. We identify major techniques in opinion retrieval and group them into four popular categories. We describe the state-of-the-art techniques for each category and emphasize on their performance and limitations. We then summarize with a performance comparison table for the techniques on different datasets. Finally, we highlight possible future research directions that can help solve existing challenges in opinion retrieval.


title: Speeding-up homography estimation in mobile devices

abstract: A critical problem faced by computer vision on mobile devices is reducing the computational cost of algorithms and avoiding visual stalls. In this paper, we introduce a procedure for reducing the number of samples required for fitting a homography to a set of noisy correspondences using a random sampling method. This is achieved by means of a geometric constraint that detects invalid minimal sets. In the experiments conducted, we show that this constraint not only reduces the number of random samples at a negligible computational cost but also balances the processor workload over time preventing visual stalls. In extreme situations of very large outlier proportion and noise level, it reduces in about one order of magnitude the number of required random samples.


title: Player Communities in Multiplayer Online Games: A Systematic Review of Empirical Research

abstract: Numerous researchers have written about the social dynamics of player communities in multiplayer online games. Following a systematic review of refereed empirical research publications from 2000-2010, this article synthesizes the key methods and concepts researchers have used to study and characterize player communities, as well as the aspects and operationalizations they have concentrated on. The analysis shows that qualitative approaches have been more popular than quantitative. The concepts used to characterize player communities were often not clearly defined or overlapped in meaning. Yet they revealed a prevalence of micro (groups or teams), meso (guilds or organizations) and macro (communities and networks) perspectives. Eighteen different aspects and operationalizations of player communities were identified. Six of these were clearly most popular, i.e. social structuring, rationale, culture & social norms, sed ICTs, number of members and time of existence. The article concludes with several perspectives and suggestions for future research.


title: AMEE Guide no. 34: Teaching in the clinical environment.

abstract: Teaching in the clinical environment is a demanding, complex and often frustrating task, a task many clinicians assume without adequate preparation or orientation. Twelve roles have previously been described for medical teachers, grouped into six major tasks: (1) the information provider; (2) the role model; (3) the facilitator; (4) the assessor; (5) the curriculum and course planner; and (6) the resource material creator (Harden & Crosby 2000). It is clear that many of these roles require a teacher to be more than a medical expert. In a pure educational setting, teachers may have limited roles, but the clinical teacher often plays many roles simultaneously, switching from one role to another during the same encounter. The large majority of clinical teachers around the world have received rigorous training in medical knowledge and skills but little to none in teaching. As physicians become ever busier in their own clinical practice, being effective teachers becomes more challenging in the context of expanding clinical responsibilities and shrinking time for teaching (Prideaux et al. 2000). Clinicians on the frontline are often unaware of educational mandates from licensing and accreditation bodies as well as medical schools and postgraduate training programmes and this has major implications for staff training. Institutions need to provide necessary orientation and training for their clinical teachers. This Guide looks at the many challenges for teachers in the clinical environment, application of relevant educational theories to the clinical context and practical teaching tips for clinical teachers. This guide will concentrate on the hospital setting as teaching within the community is the subject of another AMEE guide.


title: Theoretical Bitcoin Attacks with less than Half of the Computational Power (draft)

abstract: A widespread security claim of the Bitcoin system, presented in the original Bitcoin white-paper, states that the security of the system is guaranteed as long as there is no attacker in possession of half or more of the total computational power used to maintain the system. This claim, however, is proved based on theoretically flawed assumptions. ::: In the paper we analyze two kinds of attacks based on two theoretical flaws: the Block Discarding Attack and the Difficulty Raising Attack. We argue that the current theoretical limit of attacker's fraction of total computational power essential for the security of the system is in a sense not $\frac{1}{2}$ but a bit less than $\frac{1}{4}$, and outline proposals for protocol change that can raise this limit to be as close to $\frac{1}{2}$ as we want. ::: The basic idea of the Block Discarding Attack has been noted as early as 2010, and lately was independently though-of and analyzed by both author of this paper and authors of a most recently pre-print published paper. We thus focus on the major differences of our analysis, and try to explain the unfortunate surprising coincidence. To the best of our knowledge, the second attack is presented here for the first time.


title: Alignment of 3D point clouds to overhead images

abstract: We address the problem of automatically aligning structure-from-motion reconstructions to overhead images, such as satellite images, maps and floor plans, generated from an orthographic camera. We compute the optimal alignment using an objective function that matches 3D points to image edges and imposes free space constraints based on the visibility of points in each camera. We demonstrate the accuracy of our alignment algorithm on several outdoor and indoor scenes using both satellite and floor plan images. We also present an application of our technique, which uses a labeled overhead image to automatically tag the input photo collection with textual information.


title: Multilingual Named-Entity Recognition from Parallel Corpora

abstract: We present a named-entity recognition (NER) system for parallel multilingual text. Our system handles three languages (i.e., English, French, and Spanish) and is tailored to the biomedical domain. For each language, we design a supervised knowledge-based CRF model with rich biomedical and general domain information. We use the sentence alignment of the parallel corpora, the word alignment generated by the GIZA++[8] tool, and Wikipedia-based word alignment in order to transfer system predictions made by individual language models to the remaining parallel languages. We re-train each individual language system using the transferred predictions and generate a final enriched NER model for each language. The enriched system performs better than the initial system based on the predictions transferred from the other language systems. Each language model benefits from the external knowledge extracted from biomedical and general domain resources.


title: Design and analysis of an origami continuum manipulation module with torsional strength

abstract: This paper presents an origami-inspired cable-driven continuum manipulator module that offers low-cost, low-volume deployment, light weight, and inherently safe human interaction and collaboration. Each module has a mass of around 110 g and integrates the actuation, sensing, and control sub-systems necessary for operation. The origami structure has 7.311 Nm/rad (0.128 Nm/degree) torsional stiffness while being capable of bending in two directions and changing arc-length down to a fully collapsed state. A maximum contraction of 35 mm and bending angle of 35.5 degrees were achieved with 45 mm arc length. The module is capable of passively supporting a 1-kg mass at its tip, or 4 additional serially connected modules, bending approximately 6 degrees in the worst case. We also show that we can actively compensate for external moments by pre-compressing or pre-bending the module. We utilize an inverse kinematic control scheme and use it for both open and closed loop control following a circular trajectory. Our results indicate that the module motion follows the desired trajectory with an RMS error of 0.681 mm in the horizontal (x-y) plane and 0.373 mm in the z-axis with closed-loop control. We also assembled two origami modules in series and drove them independently, demonstrating the proof-of-concept of a modular origami continuum manipulator.


title: Design of reconfigurable fractal antenna using pin diode switch for wireless applications

abstract: In this article, a frequency reconfigurable fractal patch antenna using pin diodes is proposed and studied. The antenna structure has been designed on FR-4 low-cost substrate material of relative permittivity er = 4.4, with a compact volume of 30×30×0.8 mm3. The bandwidth and resonance frequency of the antenna design will be increased when we exploit the fractal iteration on the patch antenna. This antenna covers some service bands such as: WiMAX, m-WiMAX, WLAN, C-band and X band applications. The simulation of the proposed antenna is carried out using CST microwave studio. The radiation pattern and S parameter are further presented and discussed.


title: Efficacy and safety of primaquine and methylene blue for prevention of Plasmodium falciparum transmission in Mali: a phase 2, single-blind, randomised controlled trial

abstract: Summary Background Primaquine and methylene blue are gametocytocidal compounds that could prevent Plasmodium falciparum transmission to mosquitoes. We aimed to assess the efficacy and safety of primaquine and methylene blue in preventing human to mosquito transmission of P falciparum among glucose-6-phosphate dehydrogenase (G6PD)-normal, gametocytaemic male participants. Methods This was a phase 2, single-blind, randomised controlled trial done at the Clinical Research Centre of the Malaria Research and Training Centre (MRTC) of the University of Bamako (Bamako, Mali). We enrolled male participants aged 5–50 years with asymptomatic P falciparum malaria. G6PD-normal participants with gametocytes detected by blood smear were randomised 1:1:1:1 in block sizes of eight, using a sealed-envelope design, to receive either sulfadoxine-pyrimethamine and amodiaquine, sulfadoxine-pyrimethamine and amodiaquine plus a single dose of 0·25 mg/kg primaquine, dihydroartemisinin-piperaquine, or dihydroartemisinin-piperaquine plus 15 mg/kg per day methylene blue for 3 days. Laboratory staff, investigators, and insectary technicians were masked to the treatment group and gametocyte density of study participants. The study pharmacist and treating physician were not masked. Participants could request unmasking. The primary efficacy endpoint, analysed in all infected patients with at least one infectivity measure before and after treatment, was median within-person percentage change in mosquito infectivity 2 and 7 days after treatment, assessed by membrane feeding. This study is registered with ClinicalTrials.gov, number NCT02831023. Findings Between June 27, 2016, and Nov 1, 2016, 80 participants were enrolled and assigned to the sulfadoxine-pyrimethamine and amodiaquine (n=20), sulfadoxine-pyrimethamine and amodiaquine plus primaquine (n=20), dihydroartemisinin-piperaquine (n=20), or dihydroartemisinin-piperaquine plus methylene blue (n=20) groups. Among participants infectious at baseline (54 [68%] of 80), those in the sulfadoxine-pyrimethamine and amodiaquine plus primaquine group (n=19) had a median 100% (IQR 100 to 100) within-person reduction in mosquito infectivity on day 2, a larger reduction than was noted with sulfadoxine-pyrimethamine and amodiaquine alone (n=12; −10·2%, IQR −143·9 to 56·6; p Interpretation Adding a single dose of 0·25 mg/kg primaquine to sulfadoxine-pyrimethamine and amodiaquine or 3 days of 15 mg/kg per day methylene blue to dihydroartemisinin-piperaquine was highly efficacious for preventing P falciparum transmission. Both primaquine and methylene blue were well tolerated. Funding Bill & Melinda Gates Foundation, European Research Council.


title: Predicting wins and spread in the Premier League using a sentiment analysis of twitter

abstract: Can the sentiment contained in tweets serve as a meaningful proxy to predict match outcomes and if so, can the magnitude of outcomes be predicted based on a degree of sentiment?To answer these questions we constructed the CentralSport system to gather tweets related to the twenty clubs of the English Premier League and analyze their sentiment content, not only to predict match outcomes, but also to use as a wagering decision system. From our analysis, tweet sentiment outperformed wagering on odds-favorites, with higher payout returns (best $2704.63 versus odds-only $1887.88) but lower accuracy, a trade-off from non-favorite wagering. This result may suggest a performance degradation that arises from conservatism in the odds-setting process, especially when three match results are possible outcomes. We found that leveraging a positive tweet sentiment surge over club average could net a payout of $3011.20. Lastly, we found that as the magnitude of positive sentiment between two clubs increased, so too did the point spread; 0.42 goal difference for clubs with a slight positive edge versus 0.90 goal difference for an overwhelming difference in positive sentiment. In both these cases, the cultural expectancy of positive tweet dominance within the twitter-base may be realistic. These outcomes may suggest that professional odds-making excessively predicts non-positive match outcomes and tighter goal spreads. These results demonstrate the power of hidden information contained within tweet sentiment and has predictive implications on the design of automated wagering systems. Gathered soccer tweet sentiment to predict outcomes and wagering decisionsHad higher payout returns ($2704.63) but lower accuracyLeveraging a surge over average netted $3011.20 by identifying longshot opportunityAs magnitude of sentiment increased so did the point spread, 0.42 goal difference