title: Epidemic malaria and warmer temperatures in recent decades in an East African highland

abstract: Climate change impacts on malaria are typically assessed with scenarios for the long-term future. Here we focus instead on the recent past (1970-2003) to address whether warmer temperatures have already increased the incidence of malaria in a highland region of East Africa. Our analyses rely on a new coupled mosquito-human model of malaria, which we use to compare projected disease levels with and without the observed temperature trend. Predicted malaria cases exhibit a highly nonlinear response to warming, with a significant increase from the 1970s to the 1990s, although typical epidemic sizes are below those observed. These findings suggest that climate change has already played an important role in the exacerbation of malaria in this region. As the observed changes in malaria are even larger than those predicted by our model, other factors previously suggested to explain all of the increase in malaria may be enhancing the impact of climate change.


Relation Classification Slot Filling


Real Behavior of Floating Point Numbers *


title: Spectral clustering: A semi-supervised approach

abstract: Recently, graph-based spectral clustering algorithms have been developing rapidly, which are proposed as discrete combinatorial optimization problems and approximately solved by relaxing them into tractable eigenvalue decomposition problems. In this paper, we first review the current existing spectral clustering algorithms in a unified-framework way and give a straightforward explanation about spectral clustering. We also present a novel model for generalizing the unsupervised spectral clustering to semi-supervised spectral clustering. Under this model, prior information given by some instance-level constraints can be generalized to space-level constraints. We find that (undirected) graph built on the enlarged prior information is more meaningful, hence the boundaries of the clusters are more correct. Experimental results based on toy data, real-world data and image segmentation demonstrate the advantages of the proposed model.


title: Cascaded H-bridge multilevel converter multistring topology for large scale photovoltaic systems

abstract: Large scale grid connected photovoltaic (PV) energy conversion systems have reached the megawatt level. This imposes new challenges on existing grid interface converter topologies and opens new opportunities to be explored. In this paper a new medium voltage multilevel-multistring configuration is introduced based on a three-phase cascaded H-bridge (CHB) converter and multiple string dc-dc converters. The proposed configuration enables a large increase of the total capacity of the PV system, while improving power quality and efficiency. The converter structure is very flexible and modular since it decouples the grid converter from the PV string converter, which allows to accomplish independent control goals. The main challenge of the proposed configuration is to handle the inherent power imbalances that occur not only between the different cells of one phase of the converter but also between the three phases. The control strategy to deal with these imbalances is also introduced in this paper. Simulation results of a 7-level CHB for a multistring PV system are presented to validate the proposed topology and control method.


title: Ka-Band Rectangular Waveguide to Suspended Stripline Transition

abstract: A Ka-band transition between a rectangular waveguide and a suspended stripline (SSL) is proposed. It uses a configuration in which the SSL substrate is perpendicular to the waveguide main axis. A patch printed on the SSL substrate is used to match the characteristic impedance of the waveguide. A set of formulas to facilitate the design of the element is presented. A prototype of the transition was fabricated and the experimental results agree well with the simulations. The proposed element can have the input matching below -15 dB in a 13% bandwidth while maintaining the insertion loss below 0.22 dB.


A Semiautomatic Approach for Accurate and Low-Effort Spreadsheet Data Extraction


CLASSIFICATION OF DIABETIC RETINOPATHY PATIENTS USING SUPPORT VECTOR MACHINES ( SVM ) BASED ON DIGITAL RETINAL IMAGE 3


title: Topological evolution of surface transportation networks

abstract: This study explores the topological evolution of surface transportation networks, using empirical evidence and a simulation model validated on that data. Evolution is an iterative process of interaction, investment, and disinvestment. The temporal change of topological attributes for the network is also evaluated using measures of connectivity, density, heterogeneity, and connection patterns. The simulation model is validated using historical data from the Indiana interurban network. Statistical analyses suggest that the simulation model performs well in predicting the sequence of link abandonment in the interurban network as well as the temporal change of topological attributes. The simulation model is then applied on different idealized network structures. Typical connection patterns such as rings, webs, hub-and-spokes, and cul-de-sac emerge in the networks; the spontaneous organization of network hierarchies, the temporal change of spacing between parallel links, and the rise-and-fall of places in terms of their relative importance are also observed, providing evidence for the claim that network topology is an emergent property of network dynamics.


The effect of ageing on the ocular surface parameters


title: Online evolutionary collaborative filtering

abstract: Collaborative filtering algorithms attempt to predict a user's interests based on his past feedback. In real world applications, a user's feedback is often continuously collected over a long period of time. It is very common for a user's interests or an item's popularity to change over a long period of time. Therefore, the underlying recommendation algorithm should be able to adapt to such changes accordingly. However, most existing algorithms do not distinguish current and historical data when predicting the users' current interests. In this paper, we consider a new problem - online evolutionary collaborative filtering, which tracks user interests over time in order to make timely recommendations. We extended the widely used neighborhood based algorithms by incorporating temporal information and developed an incremental algorithm for updating neighborhood similarities with new data. Experiments on two real world datasets demonstrated both improved effectiveness and efficiency of the proposed approach.


title: Community detection in social networks with node attributes based on multi-objective biogeography based optimization

abstract: Detecting communities in complex networks is one of the most important issues considered when analyzing these kinds of networks. A majority of studies in the field of community detection tend to detect communities through analyzing linkages of the networks. What this paper aims to achieve is to reach to a trade-off between similarity of nodes' attributes and density of connections in finding communities of social networks with node attributes. Since the community detection problem can be modeled as a seriously non-linear discrete optimization problem, we have hereby proposed a multi-objective discrete Biogeography Based Optimization (BBO) algorithm to find communities in social networks with node attributes. This algorithm uses the Pareto-based approach for community detection. Also, we introduced a new metric, SimAtt, to measure the similarity of node attributes in a community of a network and used it along with Modularity, which considers the linkage structure of a network to detect communities, as the two objective functions of the proposed method to be maximized. In the proposed method, a two phase sorting strategy is introduced which uses the non-dominated sorting and Crowding-distance to sort the generated solution of a population in each iteration. Moreover, this paper introduces a method for mutation probability approximation and uses a chaotic mechanism to dynamically tune the mutation probability in each iteration. Additionally, two novel strategies are introduced for mutation in unweighted and weighted networks. Since the final output of the proposed method is a set of non-dominated (Pareto-optimal) solutions, a metric named alpha_SAM is proposed to determine the best compromise solution among these non-dominated ones. Quantitative evaluations based on extensive experiments on 14 real-life data sets reveals that the method presented in this study achieves favorable results which are quite superior to other relevant algorithms in the literature.


title: Managing Risk of Bidding in Display Advertising

abstract: In this paper, we deal with the uncertainty of bidding for display advertising. Similar to the financial market trading, real-time bidding (RTB) based display advertising employs an auction mechanism to automate the impression level media buying; and running a campaign is no different than an investment of acquiring new customers in return for obtaining additional converted sales. Thus, how to optimally bid on an ad impression to drive the profit and return-on-investment becomes essential. However, the large randomness of the user behaviors and the cost uncertainty caused by the auction competition may result in a significant risk from the campaign performance estimation. In this paper, we explicitly model the uncertainty of user click-through rate estimation and auction competition to capture the risk. We borrow an idea from finance and derive the value at risk for each ad display opportunity. Our formulation results in two risk-aware bidding strategies that penalize risky ad impressions and focus more on the ones with higher expected return and lower risk. The empirical study on real-world data demonstrates the effectiveness of our proposed risk-aware bidding strategies: yielding profit gains of 15.4% in offline experiments and up to 17.5% in an online A/B test on a commercial RTB platform over the widely applied bidding strategies.


title: The Psychological Effects of Meditation: A Meta-Analysis.

abstract: In this meta-analysis, we give a comprehensive overview of the effects of meditation on psychological variables that can be extracted from empirical studies, concentrating on the effects of meditation on nonclinical groups of adult meditators. Mostly because of methodological problems, almost 3⁄4 of an initially identified 595 studies had to be excluded. Most studies appear to have been conducted without sufficient theoretical background. To put the results into perspective, we briefly summarize the major theoretical approaches from both East and West. The 163 studies that allowed the calculation of effect sizes exhibited medium average effects (r .28 for all studies and r .27 for the n 125 studies from reviewed journals), which cannot be explained by mere relaxation or cognitive restructuring effects. In general, results were strongest (medium to large) for changes in emotionality and relationship issues, less strong (about medium) for measures of attention, and weakest (small to medium) for more cognitive measures. However, specific findings varied across different approaches to meditation (transcendental meditation, mindfulness meditation, and other meditation techniques). Surprisingly, meditation experience only partially covaried with long-term impact on the variables examined. In general, the dependent variables used cover only some of the content areas about which predictions can be made from already existing theories about meditation; still, such predictions lack precision at present. We conclude that to arrive at a comprehensive understanding of why and how meditation works, emphasis should be placed on the development of more precise theories and measurement devices.


title: A Benchmark Dataset to Study the Representation of Food Images

abstract: It is well-known that people love food. However, an insane diet can cause problems in the general health of the people. Since health is strictly linked to the diet, advanced computer vision tools to recognize food images (e.g. acquired with mobile/wearable cameras), as well as their properties (e.g., calories), can help the diet monitoring by providing useful information to the experts (e.g., nutritionists) to assess the food intake of patients (e.g., to combat obesity). The food recognition is a challenging task since the food is intrinsically deformable and presents high variability in appearance. Image representation plays a fundamental role. To properly study the peculiarities of the image representation in the food application context, a benchmark dataset is needed. These facts motivate the work presented in this paper. In this work we introduce the UNICT-FD889 dataset. It is the first food image dataset composed by over \(800\) distinct plates of food which can be used as benchmark to design and compare representation models of food images. We exploit the UNICT-FD889 dataset for Near Duplicate Image Retrieval (NDIR) purposes by comparing three standard state-of-the-art image descriptors: Bag of Textons, PRICoLBP and SIFT. Results confirm that both textures and colors are fundamental properties in food representation. Moreover the experiments point out that the Bag of Textons representation obtained considering the color domain is more accurate than the other two approaches for NDIR.


title: Review of charging power levels and infrastructure for plug-in electric and hybrid vehicles

abstract: This paper reviews the current status and implementation of battery chargers, charging power levels and infrastructure for plug-in electric vehicles and hybrids. Battery performance depends both on types and design of the batteries, and on charger characteristics and charging infrastructure. Charger systems are categorized into off-board and on-board types with unidirectional or bidirectional power flow. Unidirectional charging limits hardware requirements and simplifies interconnection issues. Bidirectional charging supports battery energy injection back to the grid. Typical onboard chargers restrict the power because of weight, space and cost constraints. They can be integrated with the electric drive for avoiding these problems. The availability of a charging infrastructure reduces on-board energy storage requirements and costs. On-board charger systems can be conductive or inductive. While conductive chargers use direct contact, inductive chargers transfer power magnetically. An off-board charger can be designed for high charging rates and is less constrained by size and weight. Level 1 (convenience), Level 2 (primary), and Level 3 (fast) power levels are discussed. These system configurations vary from country to country depending on the source and plug capacity standards. Various power level chargers and infrastructure configurations are presented, compared, and evaluated based on amount of power, charging time and location, cost, equipment, effect on the grid, and other factors.


title: Extraction of land use / landcover – related information from very high resolution data in urban and suburban areas

abstract: Very High Resolution (VHR) satellite images offer a great potential for the extraction of landuse and land-cover related information for urban areas. The available techniques are diverse and need to be further examined before operational use is possible. In this paper we applied two pixel-by-pixel classification techniques and the object-oriented image analysis approach (eCognition) for a land-cover classification of a Quickbird image of a study area in the northern part of the city of Ghent (Belgium). Only small differences in overall Kappa were noted between the best results of the pixel-based approach (neural network classification with Haralick texture measures) and the object-oriented classification (eCognition). A rule-based procedure using ancillary information on elevation derived from a digital surface model was applied on the pixel-based land-cover classification in order to obtain information on the spatial distribution of buildings and artificial surfaces.


title: Sequential Person Recognition in Photo Albums with a Recurrent Network

abstract: Recognizing the identities of people in everyday photos is still a very challenging problem for machine vision, due to issues such as non-frontal faces, changes in clothing, location, lighting. Recent studies have shown that rich relational information between people in the same photo can help in recognizing their identities. In this work, we propose to model the relational information between people as a sequence prediction task. At the core of our work is a novel recurrent network architecture, in which relational information between instances labels and appearance are modeled jointly. In addition to relational cues, scene context is incorporated in our sequence prediction model with no additional cost. In this sense, our approach is a unified framework for modeling both contextual cues and visual appearance of person instances. Our model is trained end-to-end with a sequence of annotated instances in a photo as inputs, and a sequence of corresponding labels as targets. We demonstrate that this simple but elegant formulation achieves state-of-the-art performance on the newly released People In Photo Albums (PIPA) dataset.


title: THE ALGORITHMS FOR FPGA IMPLEMENTATION OF SPARSE MATRICES MULTIPLICATION

abstract: In comparison to dense matrices multiplication, sparse matrices multiplication real performance for CPU is roughly 5--100 times lower when expressed in GFLOPs. For sparse matrices, microprocessors spend most of the time on comparing matrices indices rather than performing floating-point multiply and add operations. For 16-bit integer operations, like indices comparisons, computational power of the FPGA significantly surpasses that of CPU. Consequently, this paper presents a novel theoretical study how matrices sparsity factor influences the indices comparison to floating-point operation workload ratio. As a result, a novel FPGAs architecture for sparse matrix-matrix multiplication is presented for which indices comparison and floating-point operations are separated. We also verified our idea in practice, and the initial implementations results are very promising. To further decrease hardware resources required by the floating-point multiplier, a reduced width multiplication is proposed in the case when IEEE-754 standard compliance is not required.


Leukemia Detection using Digital Image Processing Techniques


title: SAPPHIRE: Stochastically acquired photoplethysmogram for heart rate inference in realistic environments

abstract: A novel method, Stochastically Acquired Photoplethysmo-gram for Heart rate Inference in Realistic Environments (SAPPHIRE), is proposed for robust remote heart rate measurement through broadband video. A set of stochastically sampled points from the cheek region is tracked and used to construct corresponding time series observations via skin erythema transforms. From these observations, a photo-plethysmogram (PPG) waveform is estimated via Bayesian minimization, with the required posterior probability inferred using a Monte Carlo approach. To mitigate the effects of noise, the contribution of each observation is weighted based on the observation's likelihood to contain relevant data. A bandpass filter is applied to the estimated PPG waveform to omit implausible heart rate frequencies, and the heart rate is estimated through frequency domain analysis. Experimental results acquired from a set of thirty videos indicate significantly improved performance in comparison to state-of-the-art methods.


title: Forensic feature extraction and cross-drive analysis

abstract: This paper introduces Forensic Feature Extraction (FFE) and Cross-Drive Analysis (CDA), two new approaches for analyzing large data sets of disk images and other forensic data. FFE uses a variety of lexigraphic techniques for extracting information from bulk data; CDA uses statistical techniques for correlating this information within a single disk image and across multiple disk images. An architecture for these techniques is presented that consists of five discrete steps: imaging, feature extraction, first-order cross-drive analysis, cross-drive correlation, and report generation. CDA was used to analyze 750 images of drives acquired on the secondary market; it automatically identified drives containing a high concentration of confidential financial records as well as clusters of drives that came from the same organization. FFE and CDA are promising techniques for prioritizing work and automatically identifying members of social networks under investigation. We believe it is likely to have other uses as well.


title: The theoretical cognitive process of visualization for science education

abstract: The use of visual models such as pictures, diagrams and animations in science education is increasing. This is because of the complex nature associated with the concepts in the field. Students, especially entrant students, often report misconceptions and learning difficulties associated with various concepts especially those that exist at a microscopic level, such as DNA, the gene and meiosis as well as those that exist in relatively large time scales such as evolution. However the role of visual literacy in the construction of knowledge in science education has not been investigated much. This article explores the theoretical process of visualization answering the question “how can visual literacy be understood based on the theoretical cognitive process of visualization in order to inform the understanding, teaching and studying of visual literacy in science education?” Based on various theories on cognitive processes during learning for science and general education the author argues that the theoretical process of visualization consists of three stages, namely, Internalization of Visual Models, Conceptualization of Visual Models and Externalization of Visual Models. The application of this theoretical cognitive process of visualization and the stages of visualization in science education are discussed.


title: Providing Real-time Feedback for Student Teachers in a Virtual Rehearsal Environment

abstract: Research in learning analytics and educational data mining has recently become prominent in the fields of computer science and education. Most scholars in the field emphasize student learning and student data analytics; however, it is also important to focus on teaching analytics and teacher preparation because of their key roles in student learning, especially in K-12 learning environments. Nonverbal communication strategies play an important role in successful interpersonal communication of teachers with their students. In order to assist novice or practicing teachers with exhibiting open and affirmative nonverbal cues in their classrooms, we have designed a multimodal teaching platform with provisions for online feedback. We used an interactive teaching rehearsal software, TeachLivE, as our basic research environment. TeachLivE employs a digital puppetry paradigm as its core technology. Individuals walk into this virtual environment and interact with virtual students displayed on a large screen. They can practice classroom management, pedagogy and content delivery skills with a teaching plan in the TeachLivE environment. We have designed an experiment to evaluate the impact of an online nonverbal feedback application. In this experiment, different types of multimodal data have been collected during two experimental settings. These data include talk-time and nonverbal behaviors of the virtual students, captured in log files; talk time and full body tracking data of the participant; and video recording of the virtual classroom with the participant. 34 student teachers participated in this 30-minute experiment. In each of the settings, the participants were provided with teaching plans from which they taught. All the participants took part in both of the experimental settings. In order to have a balanced experiment design, half of the participants received nonverbal online feedback in their first session and the other half received this feedback in the second session. A visual indication was used for feedback each time the participant exhibited a closed, defensive posture. Based on recorded full-body tracking data, we observed that only those who received feedback in their first session demonstrated a significant number of open postures in the session containing no feedback. However, the post-questionnaire information indicated that all participants were more mindful of their body postures while teaching after they had participated in the study.


title: Judging Grammaticality with Tree Substitution Grammar Derivations

abstract: In this paper, we show that local features computed from the derivations of tree substitution grammars --- such as the identify of particular fragments, and a count of large and small fragments --- are useful in binary grammatical classification tasks. Such features outperform n-gram features and various model scores by a wide margin. Although they fall short of the performance of the hand-crafted feature set of Charniak and Johnson (2005) developed for parse tree reranking, they do so with an order of magnitude fewer features. Furthermore, since the TSGs employed are learned in a Bayesian setting, the use of their derivations can be viewed as the automatic discovery of tree patterns useful for classification. On the BLLIP dataset, we achieve an accuracy of 89.9% in discriminating between grammatical text and samples from an n-gram language model.


title: Diagnostics and prognostics for multiple induction machines using a single set of current transducers

abstract: This paper describes a field-tested technique that uses aggregate current measurements to detect faults in multiple induction machines. This new approach reduces costs and potentially increases reliability. The paper begins with a description of the field-tested system. It then describes three analytical techniques that have been applied to aggregate data in order to identify faults. The paper uses specific examples taken from applications in power plants and shipboard environments.


title: Indicator Qualities of the NAPM Report On BusinessR

abstract: SUMMARY ::: ::: Much has been written over many years concerning use of the data in the NAPM Report On Business® as economic indicators but there has not been any recent attempt to review and evaluate the research. This article reviews the representation of business activity by economic indicators, compares qualities of NAPM Report On Business® data to those desirable in indicators, summarizes previously published research on the use of NAPM data as indicators, and presents some new and previously unpublished research on this subject. The use and value of NAPM data in the development of purchasing and supply strategy is also discussed. It is concluded that NAPM data possesses many desirable indicator qualities, are useful in the analysis of business and economic activity, and have value in the development of purchasing and supply strategy.


title: Infants' reasoning about others' false perceptions.

abstract: Prior research suggests that children younger than age 3 or 4 do not understand that an agent may be deceived by an object’s misleading appearance. The authors asked whether 14.5-month-olds would give evidence in a violation-of-expectation task that they understand that agents may form false perceptions. Infants first watched events in which an agent faced a stuffed skunk and a doll with blue pigtails; the agent consistently reached for the doll, suggesting that she preferred it over the skunk. Next, while the agent was absent, the doll was hidden in a plain box, and the skunk was hidden in a box with a tuft of blue hair protruding from under its lid. Infants expected the agent to be misled by the tuft’s resemblance to the doll’s hair and to falsely perceive it as belonging to the doll. These and other results indicate that 14.5-month-old infants can already reason about agents’ false perceptions.


title: Energy-traffic tradeoff cooperative offloading for mobile cloud computing

abstract: This paper presents a quantitative study on the energy-traffic tradeoff problem from the perspective of entire Wireless Local Area Network (WLAN). We propose a novel Energy-Efficient Cooperative Offloading Model (E2COM) for energy-traffic tradeoff, which can ensure the fairness of energy consumption of mobile devices and reduce the computation repetition and eliminate the Internet data traffic redundancy through cooperative execution and sharing computation results. We design an Online Task Scheduling Algorithm (OTS) based on a pricing mechanism and Lyapunov optimization to address the problem without predicting future information on task arrivals, transmission rates and so on. OTS can achieve a desirable trade- off between the energy consumption and Internet data traffic by appropriately setting the tradeoff coefficient. Simulation results demonstrate that E2COM is more efficient than no offloading and cloud offloading for a variety of typical mobile devices, applications and link qualities in WLAN.


title: Quantitative information architecture, granular computing and rough set models in the double-quantitative approximation space of precision and grade

abstract: Because precision and grade act as fundamental quantitative information in approximation space, they are used in relative and absolute quantifications, respectively. At present, the double quantification regarding precision and grade is a novel and valuable subject, but quantitative information fusion has become a key problem. Thus, this paper constructs the double-quantitative approximation space of precision and grade (PG-Approx-Space) and tackles the fusion problem using normal logical operations. It further conducts double-quantification studies on granular computing and rough set models. (1) First, for quantitative information organization and storage, we construct space and plane forms of PG-Approx-Space using the Cartesian product, and for quantitative information extraction and fusion, we establish semantics construction and semantics granules of PG-Approx-Space. (2) Second, by granular computing, we investigate three primary granular issues: quantitative semantics, complete system and optimal calculation. Accordingly, six types of fundamental granules are proposed based on the semantic, microscopic and macroscopic descriptions; their semantics, forms, structures, calculations and relationships are studied, and the granular hierarchical structure is achieved. (3) Finally, we investigate rough set models in PG-Approx-Space. Accordingly, model regions are proposed by developing the classical regions, model expansion is systematically analyzed, some models are constructed as their structures are obtained, and a concrete model is provided. Based on the quantitative information architecture, this paper systematically conducts and investigates double quantification and establishes a fundamental and general exploration framework.


title: LCNN: Low-level Feature Embedded CNN for Salient Object Detection

abstract: In this paper, we propose a novel deep neural network framework embedded with low-level features (LCNN) for salient object detection in complex images. We utilise the advantage of convolutional neural networks to automatically learn the high-level features that capture the structured information and semantic context in the image. In order to better adapt a CNN model into the saliency task, we redesign the network architecture based on the small-scale datasets. Several low-level features are extracted, which can effectively capture contrast and spatial information in the salient regions, and incorporated to compensate with the learned high-level features at the output of the last fully connected layer. The concatenated feature vector is further fed into a hinge-loss SVM detector in a joint discriminative learning manner and the final saliency score of each region within the bounding box is obtained by the linear combination of the detector's weights. Experiments on three challenging benchmark (MSRA-5000, PASCAL-S, ECCSD) demonstrate our algorithm to be effective and superior than most low-level oriented state-of-the-arts in terms of P-R curves, F-measure and mean absolute errors.


The concept of the "World Economic Triangle" : global governance patterns and options for regions


title: A Study on Text-Score Disagreement in Online Reviews

abstract: In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understand the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that (1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa), and (2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from TripAdvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well-established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that—on a scale of five stars—those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews—on an initial very large dataset—that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement,which conveniently convey to the user a summary of positive and negative features of the review target.


title: COMPARISON OF MACHINE LEARNING ALGORITHMS RANDOM FOREST, ARTIFICIAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE TO MAXIMUM LIKELIHOOD FOR SUPERVISED CROP TYPE CLASSIFICATION

abstract: The classification and recognition of agricultural crop types is an important application of remote sensing. New machine learning algorithms have emerged in the last years, but so far, few studies only have compared their performance and usability. Therefore, we compared three different state-of-the-art machine learning classifiers, namely Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF) as well as the traditional classification method Maximum Likelihood (ML) among each other. For this purpose we classified a dataset of more than 500 crop fields located in the Canadian Prairies with a stratified randomized sampling approach. Up to four multi-spectral RapidEye images from the 2009 growing season were used. We compared the mean overall classification accuracies as well as standard deviations. Furthermore, the classification accuracy of single crops was analysed. Support Vector Machine classifiers using radial basis function or polynomial kernels exhibited superior results to ANN and RF in terms of overall accuracy and robustness, while ML exhibited inferior accuracies and higher variability. Grassland exhibited the best results for early-season mono-temporal analysis. With a multi-temporal approach, the highest accuracies were achieved for Rapeseed and Field Peas. Other crops, such as Wheat, Flax and Lentils were also successfully classified. The user’s and producer’s accuracies were higher than 85 %.


title: Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks

abstract: This article explores the use of scalar and multivariate autoregressive (AR) models to extract features from the human electroencephalogram (EEG) with which mental tasks can be discriminated. This is part of a larger project to investigate the feasibility of using EEG to allow paralyzed persons to control a device such as a wheelchair. EEG signals from four subjects were recorded while they performed two mental tasks. Quarter-second windows of six-channel EEG were transformed into four different representations: scalar AR model coefficients, multivariate AR coefficients, eigenvalues of a correlation matrix, and the Karhunen-Loeve transform of the multivariate AR coefficients. Feature vectors defined by these representations were classified with a standard, feedforward neural network trained via the error backpropagation algorithm. The four representations produced similar results, with the multivariate AR coefficients performing slightly better and more consistently with an average classification accuracy of 91.4% on novel, untrained, EEG signals.


title: Automatic Segmentation of Objects of Interest from an Image

abstract: In this paper, we propose a novel variational energy formulation for image segmentation. Traditional variational energy formulation for image segmentation like that in [41, 30] only incorporates local region potentials. We introduce a global image data likelihood potential to address the problem that each local region usually contains a portion of incorrectly classified pixels during the iterative energy minimization process. By combining it with local region potentials, we obtain more robust and accurate estimation of the foreground and background distributions. The minimization of the proposed local-global energy is achieved in two steps: the evolution of the foreground and background boundary curve by level set; and the robust estimation of the foreground and background model by fixed-point iteration, called quasi-semisupervised EM, which is particularly suited for the learning problem where some unknown portion of the data are labeled incorrectly. Extensive experimental results including business card extraction, road sign extraction and general object-of-interest segmentation, demonstrate the robustness, effectiveness, and efficiency of the proposedapproach.


title: A high voltage, constant current stimulator for electrocutaneous stimulation through small electrodes

abstract: A high-voltage stimulator has been designed to allow transcutaneous stimulation of tactile fibers of the fingertip. The stimulator's output stage was based upon an improved Howland current pump topology, modified to allow high load impedances and small currents, The compliance voltage of approximately 800 V is achieved using commercially available high-voltage operational amplifiers. The output current accuracy is better than /spl plusmn/5% over the range of 1 to 25 mA for 30 /spl mu/s or longer pulses. The rise time for square pulses is less than 1 /spl mu/s. High-voltage, common-mode, latch-up power supply problems and solutions are discussed. The stimulator's input stage is optically coupled to the controlling computer and complies with applicable safety standards for use in a hospital environment. The design presented here is for monophasic stimulation only, but could be modified for biphasic stimulation.


title: Development and Evolution of the Human Neocortex

abstract: The size and surface area of the mammalian brain are thought to be critical determinants of intellectual ability. Recent studies show that development of the gyrated human neocortex involves a lineage of neural stem and transit-amplifying cells that forms the outer subventricular zone (OSVZ), a proliferative region outside the ventricular epithelium. We discuss how proliferation of cells within the OSVZ expands the neocortex by increasing neuron number and modifying the trajectory of migrating neurons. Relating these features to other mammalian species and known molecular regulators of the mouse neocortex suggests how this developmental process could have emerged in evolution.


title: Convolutional Low-Resolution Fine-Grained Classification

abstract: Abstract Successful fine-grained image classification methods learn subtle details between visually similar (sub-)classes, but the problem becomes significantly more challenging if the details are missing due to low resolution. Encouraged by the recent success of Convolutional Neural Network (CNN) architectures in image classification, we propose a novel resolution-aware deep model which combines convolutional image super-resolution and convolutional fine-grained classification into a single model in an end-to-end manner. Extensive experiments on multiple benchmarks demonstrate that the proposed model consistently performs better than conventional convolutional networks on classifying fine-grained object classes in low-resolution images.


title: Measurement of lumbar multifidus muscle contraction with rehabilitative ultrasound imaging.

abstract: Rehabilitative Ultrasound Imaging (RUSI) has been validated as a noninvasive method to measure activation of selected muscles. The purpose of this study was to determine the relationship between muscle thickness change, as measured by ultrasonography, and electromyography (EMG) activity of the lumbar multifidus (LM) muscle in normal subjects. Bipolar fine wire electrodes were inserted into the LM at the L4 level of five subjects. Simultaneous EMG and RUSI data (muscle thickness) were collected while subjects performed increasingly demanding postural response tasks thought to activate the LM muscle. To determine the relationship between muscle thickness change and EMG activity, the normalized EMG data were correlated to normalized RUSI data. To determine if the tasks increased the demand on the LM, the mean EMG data were compared over each of the four tasks. Muscle thickness change as measured by RUSI was highly correlated with EMG activity of LM in asymptomatic subjects (r=.79,P<.001). Mean EMG data showed increasing levels of activation across tasks (19-34% of maximum voluntary isometric contraction (MVIC)). The results of the repeated measures ANOVA demonstrated theses differences were significant (F(3,12)=25.39,P<.001). Measurement of muscle thickness change utilizing RUSI is a valid and potentially useful method to measure activation of the LM muscle in a narrow range (19-34% of MVIC) in an asymptomatic population.


title: Disrupting Industries With Blockchain: The Industry, Venture Capital Funding, and Regional Distribution of Blockchain Ventures

abstract: The blockchain (i.e., a decentralized and encrypted digital ledger) has the potential to disrupt many traditional business models. This study investigates the emerging blockchain business-application landscape by analyzing its industry, venture capital funding, and regional distribution. We do so by matching four venture databases on blockchain-based startups. First, our results show that blockchain startups are most prominently represented in the Finance & Insurance and Information & Communication industries. A fine-grained analysis of financial services yields increasing novel applications in existing service offerings – especially in payment networks and processing. Second, consistent with the first result, we find that mainly Finance & Insurance and Information & Communication industries are funded by venture capital. Third, our regional distribution analysis of the emerging ventures identifies two leading geographical blockchain clusters (i.e., the US and UK). Overall, our study contributes to the literature by providing one of the first analyses of blockchain-based business applications.


title: Evaluation of the Spiritual Well-Being Scale in a Sample of Korean Adults

abstract: This study explored the psychometric qualities and construct validity of the Spiritual Well-Being Scale (SWBS; Ellison in J Psychol Theol 11:330–340, 1983) using a sample of 470 Korean adults. Two factor analyses, exploratory factor analysis and confirmatory factor analysis, were conducted in order to test the validity of the SWBS. The results of the factor analyses supported the original two-dimensional structure of the SWBS—religious well-being (RWB) and existential well-being (EWB) with method effects associated with negatively worded items. By controlling for method effects, the evaluation of the two-factor structure of SWBS is confirmed with clarity. Further, the differential pattern and magnitude of correlations between the SWB subscales and the religious and psychological variables suggested that two factors of the SWBS were valid for Protestant, Catholic, and religiously unaffiliated groups except Buddhists. The Protestant group scored higher in RWB compared to the Buddhist, Catholic, and unaffiliated groups. The Protestant group scored higher in EWB compared to the unaffiliated groups. Future studies may need to include more Buddhist samples to gain solid evidence for validity of the SWBS on a non-Western religious tradition.


title: A scalable system for real-time control of dexterous surgical robots

abstract: The requisite cabling and control processing for surgical robots can become unwieldy as dexterity is increased, due to the additional degrees of freedom. Motivated by dexterous snake-like robots for minimally invasive surgery, this paper details the development of a low-level control system that uses IEEE 1394 (FireWire), linking the computer to low-latency field-programmable gate arrays, to distribute I/O while centralizing all control computations. A standard programming interface is defined as part of the control system to enhance its scalability. These features increase the viability of complex surgical robots and ease their development by reducing cables and enabling the processing of many axes of control on a single computer.


Using Video Analysis and Machine Learning for Predicting Shot Success in Table Tennis


title: A Computing Procedure for Quantification Theory

abstract: The hope that mathematical methods employed in the investigation of formal logic would lead to purely computational methods for obtaining mathematical theorems goes back to Leibniz and has been revived by Peano around the turn of the century and by Hilbert's school in the 1920's. Hilbert, noting that all of classical mathematics could be formalized within quantification theory, declared that the problem of finding an algorithm for determining whether or not a given formula of quantification theory is valid was the central problem of mathematical logic. And indeed, at one time it seemed as if investigations of this “decision” problem were on the verge of success. However, it was shown by Church and by Turing that such an algorithm can not exist. This result led to considerable pessimism regarding the possibility of using modern digital computers in deciding significant mathematical questions. However, recently there has been a revival of interest in the whole question. Specifically, it has been realized that while no decision procedure exists for quantification theory there are many proof procedures available—that is, uniform procedures which will ultimately locate a proof for any formula of quantification theory which is valid but which will usually involve seeking “forever” in the case of a formula which is not valid—and that some of these proof procedures could well turn out to be feasible for use with modern computing machinery. ::: Hao Wang [9] and P. C. Gilmore [3] have each produced working programs which employ proof procedures in quantification theory. Gilmore's program employs a form of a basic theorem of mathematical logic due to Herbrand, and Wang's makes use of a formulation of quantification theory related to those studied by Gentzen. However, both programs encounter decisive difficulties with any but the simplest formulas of quantification theory, in connection with methods of doing propositional calculus. Wang's program, because of its use of Gentzen-like methods, involves exponentiation on the total number of truth-functional connectives, whereas Gilmore's program, using normal forms, involves exponentiation on the number of clauses present. Both methods are superior in many cases to truth table methods which involve exponentiation on the total number of variables present, and represent important initial contributions, but both run into difficulty with some fairly simple examples. ::: In the present paper, a uniform proof procedure for quantification theory is given which is feasible for use with some rather complicated formulas and which does not ordinarily lead to exponentiation. The superiority of the present procedure over those previously available is indicated in part by the fact that a formula on which Gilmore's routine for the IBM 704 causes the machine to computer for 21 minutes without obtaining a result was worked successfully by hand computation using the present method in 30 minutes. Cf. §6, below. ::: It should be mentioned that, before it can be hoped to employ proof procedures for quantification theory in obtaining proofs of theorems belonging to “genuine” mathematics, finite axiomatizations, which are “short,” must be obtained for various branches of mathematics. This last question will not be pursued further here; cf., however, Davis and Putnam [2], where one solution to this problem is given for ele


title: Network of Bandits

abstract: The distribution of machine learning tasks on the user’s devices offers several advantages for application purposes: scalability, reduction of deployment costs and privacy. We propose a basic brick, DISTRIBUTED MEDIAN ELIMINATION, which can be used to distribute the best arm identification task in various schemes. In comparison to MEDIAN ELIMINATION run on a single player, we showed a near optimal speed-up factor in O(α.N/K lnK), where K is the number of actions, N is the number of players, and α ∈ (0, 1/2]. This speed-up factor is reached with a near optimal communication cost. Experiments illustrate and complete the analysis: in comparison to MEDIAN ELIMINATION with unconstrained communication cost, the distributed version shows practical improvements.


title: Redirecting dns for ads and profit

abstract: Internet Service Providers (ISPs) increasingly try to grow their profit margins by employing “error traffic monetization,” the practice of redirecting customers whose DNS lookups fail to advertisement-oriented Web servers. A small industry of companies provides the associated machinery for ISPs to engage in this monetization, with the companies often participating in operating the service as well. We conduct a technical analysis of DNS error traffic monetization evident in 66,000 Netalyzr sessions, including fingerprinting derived from patterns seen in the resulting ad landing pages. We identify major players in this industry, their ISP affiliations over time, and available user opt-out mechanisms. One monetization vendor, Paxfire, transgresses the error-based model and also reroutes all user search queries to Bing, Yahoo, and (sometimes) Google via proxy servers controlled or provided by Paxfire.


Regularizing Prediction Entropy Enhances Deep Learning with Limited Data


title: The post-orgasmic prolactin increase following intercourse is greater than following masturbation and suggests greater satiety

abstract: Abstract Research indicates that prolactin increases following orgasm are involved in a feedback loop that serves to decrease arousal through inhibitory central dopaminergic and probably peripheral processes. The magnitude of post-orgasmic prolactin increase is thus a neurohormonal index of sexual satiety. Using data from three studies of men and women engaging in masturbation or penile–vaginal intercourse to orgasm in the laboratory, we report that for both sexes (adjusted for prolactin changes in a non-sexual control condition), the magnitude of prolactin increase following intercourse is 400% greater than that following masturbation. The results are interpreted as an indication of intercourse being more physiologically satisfying than masturbation, and discussed in light of prior research reporting greater physiological and psychological benefits associated with coitus than with any other sexual activities.


title: Superpixel-Based Tracking-by-Segmentation Using Markov Chains

abstract: We propose a simple but effective tracking-by-segmentation algorithm using Absorbing Markov Chain (AMC) on superpixel segmentation, where target state is estimated by a combination of bottom-up and top-down approaches, and target segmentation is propagated to subsequent frames in a recursive manner. Our algorithm constructs a graph for AMC using the superpixels identified in two consecutive frames, where background superpixels in the previous frame correspond to absorbing vertices while all other superpixels create transient ones. The weight of each edge depends on the similarity of scores in the end superpixels, which are learned by support vector regression. Once graph construction is completed, target segmentation is estimated using the absorption time of each superpixel. The proposed tracking algorithm achieves substantially improved performance compared to the state-of-the-art segmentation-based tracking techniques in multiple challenging datasets.


title: Explanation Awareness and Ambient Intelligence as Social Technologies

abstract: This work focuses on the socio-technical aspects of artificial intelligence, namely how (specific types of) intelligent systems function in human workplace environments. The goal is first to get a ...


title: A Survey on Semantic Parsing

abstract: A significant amount of information in today's world is stored in structured and semi-structured knowledge bases. Efficient and simple methods to query them are essential and must not be restricted to only those who have expertise in formal query languages. The field of semantic parsing deals with converting natural language utterances to logical forms that can be easily executed on a knowledge base. In this survey, we examine the various components of a semantic parsing system and discuss prominent work ranging from the initial rule based methods to the current neural approaches to program synthesis. We also discuss methods that operate using varying levels of supervision and highlight the key challenges involved in the learning of such systems.


Integrating WeChat-based Mobile-Assisted Language Learning into College English Teaching


title: Consumer Magazines in Argentina: A Market to Recover

abstract: The analysis of the magazines market in Argentina can be summarized in the following statement: “the paid circulation and the distribution through kiosks determine the size, structure and possibilities of the growth of the sector”. The statement is related to the characteristics observed on the national market: the circulation instability, the low advertising revenues, and the poor development of subscription sales.


title: Development of the Sensory Processing Measure–School: Initial Studies of Reliability and Validity

abstract: OBJECTIVE. The history and development of the Sensory Processing Measure�School are detailed, and findings of initial pilot studies are reported. ::: ::: METHOD. Multiple reviews, focus groups, case studies, and two pilot studies were used to develop the early versions of the tool. Internal consistency and discriminatory ability were examined. ::: ::: RESULTS. Internal consistency measured with Cronbach�s alphas ranged from .93 to .99 in the first pilot study and .70 to .99 in the second pilot study. Children who were typically developing were correctly classified 92.3% of the time, and children with sensory issues were correctly classified 72% of the time. ::: ::: CONCLUSION. Initial results suggest that the tool is reliable and valid and discriminates children with and without sensory processing issues. Further research is under way with larger samples.


title: Real-time dynamic reflections for realistic rendering of 3D scenes

abstract: Visual effects, such as real-time dynamic reflections, are fundamental for realistic rendering of 3D scenes and walkthrough animations containing multiple moving objects, since they provide the correct identification of their relative distance and of their material properties, generating a closer perception of reality. Most rendering algorithms that generate realistic effects are quite expensive, such as ray tracing, usually used in offline rendering. This paper presents a solution for generating reflections, that we have developed with a real-time hybrid algorithm for rendering rigid objects in realistic dynamic scenes. The algorithm combines rasterization, the screen space reflection (SSR) technique, with pure GPU-ray tracing algorithm through deferred rendering pipeline, doing SSR per pixel and creating a mask with failed pixels to apply ray tracing for those pixels instead. The results demonstrate a significant improvement in performance with a very little perceptual loss in quality of our hybrid algorithm, when compared to the full ray tracing solution. In terms of FPS results, our hybrid solution remains positioned (most of the time) in between the SSR and the pure ray tracing’s methods, during the walkthrough. Besides, it scales quite well for realistic dynamic scenes with 3D rigid objects.


title: Direct torque control of sensorless induction motor drives: a sliding-mode approach

abstract: Direct torque control (DTC) is known to produce fast response and robust control in ac adjustable-speed drives. However, in the steady-state operation, notable torque, flux, and current pulsations occur. A new, direct torque and flux control strategy based on variable-structure control and space-vector pulsewidth modulation is proposed for induction motor sensorless drives. The DTC transient merits and robustness are preserved and the steady-state behavior is improved by reducing the torque and flux pulsations. A sliding-mode observer using a dual reference frame motor model is introduced and tested. Simulations and comparative experimental results with the proposed control scheme, versus classic DTC, are presented. Very-low-speed sensorless operation (3 r/min) is demonstrated.


title: Recommending Web Services via Combining Collaborative Filtering with Content-Based Features

abstract: With increasing adoption and presence of Web services, designing novel approaches for efficient Web services recommendation has become steadily more important. Existing Web services discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant Web service search engines, which possess many limitations such as insufficient recommendation performance and heavy dependence on the input from users such as preparing complicated queries. In this paper, we propose a novel approach that dynamically recommends Web services that fit users' interests. Our approach is a hybrid one in the sense that it combines collaborative filtering and content-based recommendation. In particular, our approach considers simultaneously both rating data and content data of Web services using a three-way aspect model. Unobservable user preferences are represented by introducing a set of latent variables, which is statistically estimated. To verify the proposed approach, we conduct experiments using 3, 693 real-world Web services. The experimental results show that our approach outperforms the two conventional methods on recommendation performance.


title: SECA: security-enhanced communication architecture

abstract: In this work, we propose and investigate the idea of enhancing a System-on-Chip (SoC) communication architecture (the fabric that integrates system components and carries the communication traffic between them) to facilitate higher security. We observe that a wide range of common security attacks are manifested as abnormalities in the system-level communication traffic. Therefore, the communication architecture, with its global system-level visibility, can be used to detect them. The communication architecture can also effectively react to security attacks by disallowing the offending communication transactions, or by notifying appropriate components of a security violation. We describe the general principles involved in a security-enhanced communication architecture (SECA) and show how several security objectives can be encoded in terms of policies that govern the inter-component communication traffic. We detail the implementation of SECA in the context of a popular commercial on-chip bus architecture (the AMBA architecture from ARM) through a combination of a centralized security enforcement module, and enhancements to the bus interfaces of system components. We illustrate how SECA can be used to enhance embedded system security in several application scenarios. A simple instance of SECA has been implemented in a commercial application processor SoC for mobile phones. We provide results of experiments performed to validate the proposed concepts through system-level simulation, and evaluate their overheads through hardware implementation using a commercial design flow.


title: Generic Dynamic Models for Modeling Wind Power Plants and Other Renewable Technologies in Large-Scale Power System Studies

abstract: With the tremendous growth of wind power worldwide in the past decade, there has been an equally great demand for simplified, standard, and publicly available models for simulating wind power generators in commercially available power system simulation tools for stability analysis. Several efforts have been on the way to meet this need. The Western Electricity Coordinating Council's Renewable Energy Modeling Task Force has successfully achieved this goal and more recently has been working on expanding these models to include the ability to model complex plants, energy storage, and frequency response capabilities. This paper presents an account of these latest developments.


title: Progressive photon relaxation

abstract: We introduce a novel algorithm for progressively removing noise from view-independent photon maps while simultaneously minimizing residual bias. Our method refines a primal set of photons using data from multiple successive passes to estimate the incident flux local to each photon. We show how this information can be used to guide a relaxation step with the goal of enforcing a constant, per-photon flux. Using a reformulation of the radiance estimate, we demonstrate how the resulting blue noise photon distribution yields a radiance reconstruction in which error is significantly reduced. Our approach has an open-ended runtime of the same order as unbiased and asymptotically consistent rendering methods, converging over time to a stable result. We demonstrate its effectiveness at storing caustic illumination within a view-independent framework and at a fidelity visually comparable to reference images rendered using progressive photon mapping.


title: Gait recognition using Active Energy Image and Gabor wavelet

abstract: Recently, Gait recognition has gained significant attention. It is the identification of individuals in a video sequences, the recognition method is by the way they walk. Active Energy Image (AEI) is a more efficient represent method than Gait Energy Image (GEI), Gabor wavelet is used in face recognition successfully, so we use the Gabor wavelet to extract the amplitude spectral of AEI, research the recognition ability of amplitude feature. The algorithm is tested in the CASIA Datasets B and gain high correct recognition rates.


title: DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression

abstract: Today's deep neural networks (DNNs) are becoming deeper and wider because of increasing demand on the analysis quality and more and more complex applications to resolve. The wide and deep DNNs, however, require large amounts of resources (such as memory, storage, and I/O), significantly restricting their utilization on resource-constrained platforms. Although some DNN simplification methods (such as weight quantization) have been proposed to address this issue, they suffer from either low compression ratios or high compression errors, which may introduce an expensive fine-tuning overhead (i.e., a costly retraining process for the target inference accuracy). In this paper, we propose DeepSZ: an accuracy-loss expected neural network compression framework, which involves four key steps: network pruning, error bound assessment, optimization for error bound configuration, and compressed model generation, featuring a high compression ratio and low encoding time. The contribution is threefold. (1)We develop an adaptive approach to select the feasible error bounds for each layer. (2) We build a model to estimate the overall loss of inference accuracy based on the inference accuracy degradation caused by individual decompressed layers. (3) We develop an efficient optimization algorithm to determine the best-fit configuration of error bounds in order to maximize the compression ratio under the user-set inference accuracy constraint. Experiments show that DeepSZ can compress AlexNet and VGG-16 on the ImageNet dataset by a compression ratio of 46× and 116×, respectively, and compress LeNet-300-100 and LeNet-5 on the MNIST dataset by a compression ratio of 57× and 56×, respectively, with only up to 0.3% loss of inference accuracy. Compared with other state-of-the-art methods, DeepSZ can improve the compression ratio by up to 1.43×, the DNN encoding performance by up to 4.0× with four V100 GPUs, and the decoding performance by up to 6.2×.


title: Adult stem cells and their trans-differentiation potential—perspectives and therapeutic applications

abstract: Stem cells are self-renewing multipotent progenitors with the broadest developmental potential in a given tissue at a given time. Normal stem cells in the adult organism are responsible for renewal and repair of aged or damaged tissue. Adult stem cells are present in virtually all tissues and during most stages of development. In this review, we introduce the reader to the basic information about the field. We describe selected stem cell isolation techniques and stem cell markers for various stem cell populations. These include makers for endothelial progenitor cells (CD146/MCAM/MUC18/S-endo-1, CD34, CD133/prominin, Tie-2, Flk1/KD/VEGFR2), hematopoietic stem cells (CD34, CD117/c-Kit, Sca1), mesenchymal stem cells (CD146/MCAM/MUC18/S-endo-1, STRO-1, Thy-1), neural stem cells (CD133/prominin, nestin, NCAM), mammary stem cells (CD24, CD29, Sca1), and intestinal stem cells (NCAM, CD34, Thy-1, CD117/c-Kit, Flt-3). Separate section provides a concise summary of recent clinical trials involving stem cells directed towards improvement of a damaged myocardium. In the last part of the review, we reflect on the field and on future developments.


title: Ad Injection at Scale: Assessing Deceptive Advertisement Modifications

abstract: Today, web injection manifests in many forms, but fundamentally occurs when malicious and unwanted actors tamper directly with browser sessions for their own profit. In this work we illuminate the scope and negative impact of one of these forms, ad injection, in which users have ads imposed on them in addition to, or different from, those that websites originally sent them. We develop a multi-staged pipeline that identifies ad injection in the wild and captures its distribution and revenue chains. We find that ad injection has entrenched itself as a cross-browser monetization platform impacting more than 5% of unique daily IP addresses accessing Google -- tens of millions of users around the globe. Injected ads arrive on a client's machine through multiple vectors: our measurements identify 50,870 Chrome extensions and 34,407 Windows binaries, 38% and 17% of which are explicitly malicious. A small number of software developers support the vast majority of these injectors who in turn syndicate from the larger ad ecosystem. We have contacted the Chrome Web Store and the advertisers targeted by ad injectors to alert each of the deceptive practices involved.


title: Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees

abstract: Efficient methods for storing and querying are critical for scaling high-order n-gram language models to large corpora. We propose a language model based on compressed suffix trees, a representation that is highly compact and can be easily held in memory, while supporting queries needed in computing language model probabilities on-the-fly. We present several optimisations which improve query runtimes up to 2500x, despite only incurring a modest increase in construction time and memory usage. For large corpora and high Markov orders, our method is highly competitive with the state-of-the-art KenLM package. It imposes much lower memory requirements, often by orders of magnitude, and has runtimes that are either similar (for training) or comparable (for querying).


title: PhoneTouch: a technique for direct phone interaction on surfaces

abstract: PhoneTouch is a novel technique for integration of mobile phones and interactive surfaces. The technique enables use of phones to select targets on the surface by direct touch, facilitating for instance pick&drop-style transfer of objects between phone and surface. The technique is based on separate detection of phone touch events by the surface, which determines location of the touch, and by the phone, which contributes device identity. The device-level observations are merged based on correlation in time. We describe a proof-of-concept implementation of the technique, using vision for touch detection on the surface (including discrimination of finger versus phone touch) and acceleration features for detection by the phone.


GEOGRAPHICAL DISTRIBUTION OF MYROTHECIUM LEAF SPOT DISEASE OF MOMORDICA CHARANTIA L . CAUSED BY MYROTHECIUM RORIDUM TODE IN AGRO-ECOLOGICAL ZONES OF PUNJAB , PAKISTAN


title: CNN based suburban building detection using monocular high resolution Google Earth images

abstract: This paper proposes a deep convolutional neural networks (CNNs) based method to automatically detect suburban buildings from high resolution Google Earth imagery. Traditional methods based on low-level hand-engineered features or mid-level bag of features have great limitations in complex environment, especially in suburban areas. Inspired by the astounding achievement of CNNs in object recognition and detection, we develop a novel method to detect buildings in cluttered images which consists of three main steps. Firstly, a multi-scale saliency computation is employed to extract built-up areas and a sliding windows approach is applied to generate candidate regions. Then, a CNN is applied to classify the regions. Finally, an improved non maximum suppression is used to remove false buildings. We test our method on a collection of very challenging Google Earth images and achieve 89% precision, which shows robustness and efficiency of our method.


title: Towards fourth industrial revolution impact: smart product based on RFID technology

abstract: As presented in [1], the fourth industrial revolution is coming: one which promises to marry the worlds of production and network connectivity in an Internet of Things (IOT). "Smart production" becomes the norm in a world where intelligent information and communication technology or ICT-based machines, systems and networks are capable of independently exchanging and responding to information to manage industrial production processes. Smart industry refers to the technological evolution from embedded systems to cyber-physical systems. The deployment of cyber- physical systems in production systems creates the smart factory. In this context, it is expected that smart factories, smart products, smart materials and smart machines are characterized by cyber-physical systems. There is a need to understand smart products in this context.


title: Evaluating an ambient display for the home

abstract: We present our experiences with evaluating an ambient display for the home using two different evaluation techniques: the recently proposed 'Heuristic Evaluation of Ambient Displays' and an in situ, 3-week long, Wizard of Oz evaluation. We compare the list of usability violations found in the heuristic evaluation to the set of problems that were discovered in the in situ evaluation. Overall, the 'Heuristic Evaluation of Ambient Displays' was effective - 75% of known usability problems were found by eight evaluators (39-55% were found by 3-5 evaluators). However, the most severe usability problem found in the in situ evaluation was not identified in the heuristic evaluation. Because the problem directly violated one of the heuristics, we believe that the problem is not with the heuristics, but rather that evaluators have minimal experience with ambient displays for the home.


title: Capturing Business Strategy and Value in Enterprise Architecture to Support Portfolio Valuation

abstract: This paper investigates and enhances the suitability of the Archi Mate enterprise architecture modeling language to support the modeling of business strategy concepts and architecture-based approaches to IT portfolio valuation. It gives an overview of existing strategy and valuation concepts and methods in the literature and motivates the need for enterprise architecture and business requirements modeling to capture these aspects as well. This overview results in the identification of strategy and value related concepts, such as value, risks, resources, capabilities, competencies and constraints. The paper provides an analysis of the extent to which Archi Mate may support some of the above-mentioned concepts and extends it with the missing concepts. The proposed language extension is formalized in terms of a met model fragment, which is aligned with the Archi Mate metamodel. The approach is also illustrated by means of an application portfolio consolidation case study in which we demonstrate how a constrained optimization valuation method can be applied to architecture models enhanced with the new concepts.


title: Rotation-Invariant HOG Descriptors Using Fourier Analysis in Polar and Spherical Coordinates

abstract: The histogram of oriented gradients (HOG) is widely used for image description and proves to be very effective. In many vision problems, rotation-invariant analysis is necessary or preferred. Popular solutions are mainly based on pose normalization or learning, neglecting some intrinsic properties of rotations. This paper presents a method to build rotation-invariant HOG descriptors using Fourier analysis in polar/spherical coordinates, which are closely related to the irreducible representation of the 2D/3D rotation groups. This is achieved by considering a gradient histogram as a continuous angular signal which can be well represented by the Fourier basis (2D) or spherical harmonics (3D). As rotation-invariance is established in an analytical way, we can avoid discretization artifacts and create a continuous mapping from the image to the feature space. In the experiments, we first show that our method outperforms the state-of-the-art in a public dataset for a car detection task in aerial images. We further use the Princeton Shape Benchmark and the SHREC 2009 Generic Shape Benchmark to demonstrate the high performance of our method for similarity measures of 3D shapes. Finally, we show an application on microscopic volumetric data.


title: An algorithm for WSN clock synchronization: Uncertainty and convergence rate trade off

abstract: Abstract-Achieving tight time synchronization between wireless sensor network (WSN) nodes is essential to coordinate the activities of different devices. In this paper, a synchronization algorithm based on a linear controller is used to dynamically compensate both mutual offsets and drifts of the clock associated with the nodes of a WSN. This approach, compared with other existing solutions based on control theory, explicitly takes into account the inter-node communication latencies. Furthermore, it presents on optimal (fastest convergence) controller in the case of full visibility among nodes. In the case of noise in both the clock measurements and the clock drifts, a controller that reduces the noise effect on the synchronization accuracy is also proposed and compared to the optimal one. In all cases, the correct operation of the algorithm has been proved through simulations.


title: Diabetes in Asia Epidemiology , Risk Factors , and Pathophysiology

abstract: CONTEXT ::: With increasing globalization and East-West exchanges, the increasing epidemic of type 2 diabetes in Asia has far-reaching public health and socioeconomic implications. ::: ::: ::: OBJECTIVE ::: To review recent data in epidemiologic trends, risk factors, and complications of type 2 diabetes in Asia. ::: ::: ::: EVIDENCE ACQUISITION ::: Search of MEDLINE using the term diabetes and other relevant keywords to identify meta-analyses, systematic reviews, large surveys, and cohort studies. Separate searches were performed for specific Asian countries. The review was limited to English-language articles published between January 1980 and March 2009; publications on type 1 diabetes were excluded. ::: ::: ::: EVIDENCE SYNTHESIS ::: The prevalence of diabetes in Asian populations has increased rapidly in recent decades. In 2007, more than 110 million individuals in Asia were living with diabetes, with a disproportionate burden among the young and middle aged. Similarly, rates of overweight and obesity are increasing sharply, driven by economic development, nutrition transition, and increasingly sedentary lifestyles. The "metabolically obese" phenotype (ie, normal body weight with increased abdominal adiposity) is common in Asian populations. The increased risk of gestational diabetes, combined with exposure to poor nutrition in utero and overnutrition in later life in some populations, may contribute to the increasing diabetes epidemic through "diabetes begetting diabetes" in Asia. While young age of onset and long disease duration place Asian patients with diabetes at high risk for cardiorenal complications, cancer is emerging as an important cause of morbidity and mortality. ::: ::: ::: CONCLUSIONS ::: Type 2 diabetes is an increasing epidemic in Asia, characterized by rapid rates of increase over short periods and onset at a relatively young age and low body mass index. Prevention and control of diabetes should be a top public health priority in Asian populations.


Credit Card Fraud Detection Using Self Organised Map


title: Annotation in outdoor . . .

abstract: Annotation, the process of adding extra virtual information to an object, is one of the most common uses for augmented reality. Although annotation is widely used in augmented reality, there is no general agreed-upon definition of what precisely constitutes an annotation in this context. In this paper, we propose a taxonomy of annotation, describing what constitutes an annotation and outlining different dimensions along which annotation can vary. Using this taxonomy we also highlight what styles of annotation are used in different types of applications and areas where further work needs to be done to improve annotation. Through our taxonomy we found two primary categories into which annotations in current applications fall. Some annotations present information that is directly related to the object they are annotating, while others are only indirectly related to the object that is being annotated. We also found that there are very few applications that enable the user to either edit or create new annotations online. Instead, most applications rely on content that is created in various offline processes. There are, however, many advantages to online annotation. We summarize and formalize our recent work in this field by presenting the steps needed to build an online annotation system, looking most closely at techniques for placing annotations from a distance.


title: A Koch-Like Sided Fractal Bow-Tie Dipole Antenna

abstract: A novel Koch-like fractal curve is proposed to transform ultra-wideband (UWB) bow-tie into so called Koch-like sided fractal bow-tie dipole. A small isosceles triangle is cut off from center of each side of the initial isosceles triangle, then the procedure iterates along the sides like Koch curve does, forming the Koch-like fractal bow-tie geometry. The fractal bow-tie of each iterative is investigated without feedline in free space for fractal trait unveiling first, followed by detailed expansion upon the four-iterated pragmatic fractal bow-tie dipole fed by 50-Ω coaxial SMA connector through coplanar stripline (CPS) and comparison with Sierpinski gasket. The fractal bow-tie dipole can operate in multiband with moderate gain (3.5-7 dBi) and high efficiency (60%-80%), which is corresponding to certain shape parameters, such as notch ratio α, notch angle φ, and base angles θ of the isosceles triangle. Compared with conventional bow-tie dipole and Sierpinski gasket with the same size, this fractal-like antenna has almost the same operating properties in low frequency and better radiation pattern in high frequency in multi-band operation, which makes it a better candidate for applications of PCS, WLAN, WiFi, WiMAX, and other communication systems.


title: Parking availability prediction for sensor-enabled car parks in smart cities

abstract: The growth in low-cost, low-power sensing and communication technologies is creating a pervasive network infrastructure called the Internet of Things (IoT), which enables a wide range of physical objects and environments to be monitored in fine spatial and temporal detail. The detailed, dynamic data that can be collected from these devices provide the basis for new business and government applications in areas such as public safety, transport logistics and environmental management. There has been growing interest in the IoT for realising smart cities, in order to maximise the productivity and reliability of urban infrastructure, such as minimising road congestion and making better use of the limited car parking facilities. In this work, we consider two smart car parking scenarios based on real-time car parking information that has been collected and disseminated by the City of San Francisco, USA and the City of Melbourne, Australia. We present a prediction mechanism for the parking occupancy rate using three feature sets with selected parameters to illustrate the utility of these features. Furthermore, we analyse the relative strengths of different machine learning methods in using these features for prediction.


title: Rhythmic Similarity in Traditional Turkish Music

abstract: In this paper, the problem of automatically assigning a piece of traditional Turkish music into a class of rhythm referred to as usul is addressed. For this, an approach for rhythmic similarity measurement based on scale transforms has been evaluated on a set of MIDI data. Because this task is related to time signature estimation, the accuracy of the proposed method is evaluated and compared with a state of the art time signature estimation approach. The results indicate that the proposed method can be successfully applied to audio signals of Turkish music and that it captures relevant properties of the individual usul.


title: Uplink resource allocation algorithms for Single-Carrier FDMA systems

abstract: We have focused on SC-FDMA based resource allocation in uplink cellular systems. Subchannel and power allocation constraints specific to SC-FDMA are considered. We considered a binary integer programming-based solution recently proposed for weighted sum rate maximization and extended it to different problems. We considered problems such as rate constraint satisfaction with minimum number subchannels and sum-power minimization subject to rate constraints. Besides stating the binary integer programming formulations for these problems, we propose simpler greedy algorithms for the three problems. Numerical evaluations show that the greedy algorithms perform very close to the optimal solution, with much less computation time.


title: Semi-Global Matching: A Principled Derivation in Terms of Message Passing

abstract: Semi-global matching, originally introduced in the context of dense stereo, is a very successful heuristic to minimize the energy of a pairwise multi-label Markov Random Field defined on a grid. We offer the first principled explanation of this empirically successful algorithm, and clarify its exact relation to belief propagation and tree-reweighted message passing. One outcome of this new connection is an uncertainty measure for the MAP label of a variable in a Markov Random Field.


title: QoE QoS Mapping for YouTube Services over LTE Network

abstract: The fourth generation of mobile networks, known as Long Term Evolution (LTE), is the first 3GPP cellular fully-IP standard. Quality of Service (QoS) policies are based on bearers and QoS Class Identifier (QCI). Here we focus on Quality of Experience (QoE) for a popular service, YouTube, which also constitutes a common experience for a billion users worldwide. We choose to measure QoE via the Apdex, in order to combine the response times for both HTTP web browsing and HTTP video streaming. Two different classes have been analyzed: MNO-managed and OTT-managed. Different scenarios are simulated using the OPNET simulation software. We derived a quite good linear model which links Apdex and the three KPIs for QoS. The model exhibits fairly good statistical metrics and holds for both the classes. This is a substantial progress with respect to previous work, according to which the MLR technique gave unsatisfactory results for the OTT-managed class.


title: Software Architectures for Robotics Systems: A Systematic Mapping Study

abstract: ContextSeveral research efforts have been targeted to support architecture centric development and evolution of software for robotic systems for the last two decades. ObjectiveWe aimed to systematically identify and classify the existing solutions, research progress and directions that influence architecture-driven modeling, development and evolution of robotic software. Research MethodWe have used Systematic Mapping Study (SMS) method for identifying and analyzing 56 peer-reviewed papers. Our review has (i) taxonomically classified the existing research and (ii) systematically mapped the solutions, frameworks, notations and evaluation methods to highlight the role of software architecture in robotic systems. Results and ConclusionsWe have identified eight themes that support architectural solutions to enable (i) operations, (ii) evolution and (iii) development specific activities of robotic software. The research in this area has progressed from object-oriented to component-based and now to service-driven robotics representing different architectural models that emerged overtime. An emerging solution is cloud robotics that exploits the foundations of service-driven architectures to support an interconnected web of robots. The results of this SMS facilitate knowledge transfer - benefiting researchers and practitioners - focused on exploiting software architecture to model, develop and evolve robotic systems.


title: Combining virtual reality enabled simulation with 3D scanning technologies towards smart manufacturing

abstract: Recent introduction of low-cost 3D sensing and affordable immersive virtual reality have lowered the barriers for creating and maintaining 3D virtual worlds. In this paper, we propose a way to combine these technologies with discrete-event simulation to improve the use of simulation in decision making in manufacturing. This work will describe how feedback is possible from real world systems directly into a simulation model to guide smart behaviors. Technologies included in the research include feedback from RGBD images of shop floor motion and human interaction within full immersive virtual reality that includes the latest headset technologies.


Financial Market Time Series Prediction with Recurrent Neural Networks


title: Duplication of lower lip and mandible--a rare diprosopus.

abstract: Diprosopus or duplication of the lower lip and mandible is a very rare congenital anomaly. We report this unusual case occurring in a girl who presented to our hospital at the age of 4 months. Surgery and problems related to this anomaly are discussed.


Comparison of Naive Bayes, Random Forest, Decision Tree, Support Vector Machines, and Logistic Regression Classifiers for Text Reviews Classification


title: Low-rank sparse learning for robust visual tracking

abstract: In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1].


title: MiFID, Reg NMS and Competition Across Trading Venues in Europe and United States

abstract: This paper examines the differences between MiFID and Reg NMS and provides, based on market microstructure principles, insights as to their likely impact on European and US securities markets. Although MiFID and Reg NMS share the common objective of enhancing competition in securities markets, they adopt different provisions with respect to three issues that strongly influence the competition for order flow among trading venues. Specifically, some of the provisions set forth by the US regulation with respect to the best execution duty, the consolidation of market data and the disclosure of execution quality information appear to be more effective, compared to the EU ones, in strengthening competition for order flow among trading venues. The paper also provides an investigation of the degree of market fragmentation among incumbent exchanges and new trading venues in European and US securities markets, and suggests possible explanations for understanding the current macrostructure of such markets.


title: Using targeted symbolic execution for reducing false-positives in dataflow analysis

abstract: Static data flow analysis is an indispensable tool for finding potentially malicious data leaks in software programs. Programs, nowadays often consisting of millions of lines of code, have grown much too large to allow for a complete manual inspection. Nevertheless, security experts need to judge whether an application is trustworthy or not, developers need to find bugs, and quality experts need to assess the maturity of software products. Thus, analysts take advantage of automated data flow analysis tools to find candidates for suspicious leaks which are then further investigated. While much progress has been made in the area with a broad variety of static data flow analysis tools proposed in academia and being offered commercially, the number of false alarms raised by these tools is still a concern. Many of the false alarms are reported because the analysis tool detects data flows along paths which are not realizable at runtime, e.g., due to contradictory conditions on the path. Still, every single report is a potential issue and must be reviewed by an expert which is labor-intensive and costly. In this work, we therefore propose TASMAN, a post-analysis based on symbolic execution that removes such false data leaks along unrealizable paths from the result set. Thus, it greatly improves the usefulness of the result presented to the human analyst. In our experiments on DroidBench examples, TASMAN reduces the number of false positives by about 80% without pruning any true positives. Additionally, TASMAN also identified false positives in real-world examples which we confirmed by hand. With an average execution time of 5.4 seconds per alleged leak to be checked on large real-world applications, TASMAN is fast enough for practical use.


An instantaneous-frequency-based pitch extraction method for high-quality speech transformation: revised TEMPO in the STRAIGHT-suite.


title: Soliton solutions for quasilinear Schrödinger equations

abstract: It is established the existence of nontrivial solutions for quasilinear Schrodinger equations with subcritical or critical exponents, which appear from plasma physics as well as high-power ultrashort laser in matter.


title: Context-aware Gestures for Mixed-Initiative Text Editing UIs

abstract: This is a pre-copyedited, author-produced PDF of an article accepted for publication in Interacting with computers following peer review. The version of record is available online at: http://dx.doi.org/10.1093/iwc/iwu019


Deep Learning for Acoustic Echo Cancellation in Noisy and Double-Talk Scenarios


title: Coherent and incoherent crosstalk in WDM optical transport network

abstract: Homodyne crosstalk can be divided into two kinds: coherent and incoherent crosstalk in WDM optical network. A general analyzing model of crosstalks in WDM optical network is developed in this paper. According to the model, two different kinds of crosstalks, viz. coherent crosstalk and inconherent crosstalk, are distinguished from each other. The experimental results, 1dB power penalty for about 20dB coherent and incoherent crosstalk signal well agree with theoretical one, could be important to the WDM optical network designing.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Active lower limb prosthetics: a systematic review of design issues and solutions

abstract: This paper presents a review on design issues and solutions found in active lower limb prostheses. This review is based on a systematic literature search with a methodical search strategy. The search was carried out across four major technical databases and the retrieved records were screened for their relevance. A total of 21 different active prostheses, including 8 above-knee, 9 below-knee and 4 combined knee-ankle prostheses were identified. While an active prosthesis may help to restore the functional performance of an amputee, the requirements regarding the actuation unit as well as for the control system are high and the development becomes a challenging task. Regarding mechanical design and the actuation unit high force/torque delivery, high efficiency, low size and low weight are conflicting goals. The actuation principle and variable impedance actuators are discussed. The control system is paramount for a “natural functioning” of the prosthesis. The control system has to enable locomotion and should react to the amputee’s intent. For this, multi-level control approaches are reviewed.


title: Magneto-Gyro Wearable Sensor Algorithm for Trunk Sway Estimation During Walking and Running Gait

abstract: Trunk sway is a critical gait parameter associated with walking stability and joint loading. This paper introduces a novel algorithm to estimate trunk sway based on magnetometer and gyroscope sensor fusion. An initial experiment was performed to determine the optimal placement of the wearable sensor device on the back and a second experiment was performed to characterize the accuracy of the algorithm. Ten human subjects walked, fast walked, and ran on a treadmill with normal, slightly increased or significantly increased trunk sway. Subjects wore a single magneto-inertial measurement unit (IMU) and a standard set of reflective motion-capture markers on their back. Magneto-gyro algorithm trunk sway estimations were compared with estimations from other common wearable sensor algorithms and errors were determined via comparison with trunk sway measured from motion capture. Overall, the magneto-gyro algorithm was the most accurate (RMSE = 1.7 ± 0.7°) followed by algorithms based on a single three-axis magnetometer (RMSE = 2.5 ± 1.8°), IMU gradient descent (RMSE = 2.9 ± 3.4°), a single three-axis gyroscope (RMSE = 3.2 ± 2.4°), magneto-IMU Kalman filter (RMSE = 8.5 ± 5.5°), and a single accelerometer (RMSE = 16.7 ± 11.2°). Optimal placement of a wearable sensor for estimating trunk sway was along the spine between the $T7$ – $T12$ vertebrae. The presented algorithm based on magnetometer and gyroscope sensor fusion could enable more precise trunk sensing for clinical gait applications outside the laboratory.


title: Enterprise Architecture as Enabler of Organizational Agility - A Municipality Case Study

abstract: Organizational agility is one of the top management concerns as organizations face today increasingly changing environments. Among enterprise architecture (EA) benefits, organizational agility has been claimed as one of them, perceived as a direct or indirect benefit, for example, through business-IT alignment, another top management concern. However, even with reasonable explanations in the EA literature, there is still a lack of empirical evidence to support such claim. Our research looks for that evidence seeking to understand how the development and use of EA may contribute for organizational agility. Having one of the biggest municipalities in the country as the research setting, using a mixmethods approach, a case study was carried out to identify EA artefacts, understand EA at use and examine agility in a specific change situation. In this case, enterprise architecture was not just used but was developed and improved during the change situation to enable organizational agility.