Artificial neural networks in time series forecasting: a comparative analysis. Artificial neural networks (ANN) have received a great deal of attention in many fields of engineering and science. Inspired by the study of brain architecture, ANN represent a class of non-linear models capable of learning from data. ANN have been applied in many areas where statistical methods are traditionally employed. They have been used in pattern recognition, classification, prediction and process control. The purpose of this paper is to discuss ANN and compare them to non-linear time series models. We begin exploring recent developments in time series forecasting with particular emphasis on the use of non-linear models. Thereafter we include a review of recent results on the topic of ANN. The relevance of ANN models for the statistical methods is considered using time series prediction problems. Finally we construct asymptotic prediction intervals for ANN and show how to use prediction intervals to choose the number of nodes in the ANN.

Question: What is the difference between ANN and non-linear time series models?


Curvature preserving fingerprint ridge orientation smoothing using Legendre polynomials. Smoothing fingerprint ridge orientation involves a principal discrepancy. Too little smoothing can result in noisy orientation fields (OF), too much smoothing will harm high curvature areas, especially singular points (SP). In this paper we present a fingerprint ridge orientation model based on Legendre polynomials. The motivation for the proposed method can be found by analysing the almost exclusively used method in literature for orientation smoothing, proposed by Witkin and Kass (1987) more than two decades ago. The main contribution of this paper argues that the vectorial data (sine and cosine data) should be smoothed in a coupled way and the approximation error should not be evaluated employing vectorial data. For evaluating the proposed method we use a Poincare-Index based SP detection algorithm. The experiments show, that in comparison to competing methods the proposed method has improved orientation smoothing capabilities, especially in high curvature areas.

Question: What is a principal discrepancy?


Predictive Data Mining for Diagnosis of Thyroid Disease using Neural Network. This paper presents a systematic approach for earlier diagnosis of Thyroid disease using back propagation algorithm used in neural network. Back propagation algorithm is a widely used algorithm in this field. ANN has been developed based on back propagation of error used for earlier prediction of disease. ANN was subsequently trained with experimental data and testing is carried out using data that was not used during training process. Results show that outcome of ANN is in good agreement with experimental data; this indicates that developed neural network can be used as an alternative for earlier prediction of a disease. Keywords—Back propagation, decision tree, gradient descent, prediction, Supervised Learning

Question: What is the difference between supervised and unsupervised learning?


Enhanced Operating System Security Through Efficient and Fine-grained Address Space Randomization. In recent years, the deployment of many applicationlevel countermeasures against memory errors and the increasing number of vulnerabilities discovered in the kernel has fostered a renewed interest in kernel-level exploitation. Unfortunately, no comprehensive and wellestablished mechanism exists to protect the operating system from arbitrary attacks, due to the relatively new development of the area and the challenges involved. In this paper, we propose the first design for finegrained address space randomization (ASR) inside the operating system (OS), providing an efficient and comprehensive countermeasure against classic and emerging attacks, such as return-oriented programming. To motivate our design, we investigate the differences with application-level ASR and find that some of the wellestablished assumptions in existing solutions are no longer valid inside the OS; above all, perhaps, that information leakage becomes a major concern in the new context. We show that our ASR strategy outperforms stateof-the-art solutions in terms of both performance and security without affecting the software distribution model. Finally, we present the first comprehensive live rerandomization strategy, which we found to be particularly important inside the OS. Experimental results demonstrate that our techniques yield low run-time performance overhead (less than 5% on average on both SPEC and syscall-intensive benchmarks) and limited run-time memory footprint increase (around 15% during the execution of our benchmarks). We believe our techniques can greatly enhance the level of OS security without compromising the performance and reliability of the OS.

Question: What is the difference between application-level ASR and OS-level ASR?


Temporal Segmentation of Egocentric Videos. The use of wearable cameras makes it possible to record life logging egocentric videos. Browsing such long unstructured videos is time consuming and tedious. Segmentation into meaningful chapters is an important first step towards adding structure to egocentric videos, enabling efficient browsing, indexing and summarization of the long videos. Two sources of information for video segmentation are (i) the motion of the camera wearer, and (ii) the objects and activities recorded in the video. In this paper we address the motion cues for video segmentation. Motion based segmentation is especially difficult in egocentric videos when the camera is constantly moving due to natural head movement of the wearer. We propose a robust temporal segmentation of egocentric videos into a hierarchy of motion classes using a new Cumulative Displacement Curves. Unlike instantaneous motion vectors, segmentation using integrated motion vectors performs well even in dynamic and crowded scenes. No assumptions are made on the underlying scene structure and the method works in indoor as well as outdoor situations. We demonstrate the effectiveness of our approach using publicly available videos as well as choreographed videos. We also suggest an approach to detect the fixation of wearer's gaze in the walking portion of the egocentric videos.

Question: How to segment egocentric videos?


Deep Analysis of Binary Code to Recover Program Structure. Title of dissertation: DEEP ANALYSIS OF BINARY CODE TO RECOVER PROGRAM STRUCTURE Khaled ElWazeer, Doctor of Philosophy, 2014 Dissertation directed by: Professor Rajeev Barua Department of Electrical and Computer Engineering Reverse engineering binary executable code is gaining more i nterest in the research community. Agencies as diverse as anti-virus companies, se curity consultants, code forensics consultants, law-enforcement agencies and nati onal security agencies routinely try to understand binary code. Engineers also often need to d ebug, optimize or instrument binary code during the software development process. In this dissertation, we present novel techniques to extend he capabilities of existing binary analysis and rewriting tools to be more scalab le, handling a larger set of stripped binaries with better and more understandable outp uts as well as ensuring correct recovered intermediate representation (IR) from binar ies such that any modified or rewritten binaries compiled from this representation work correctly. In the first part of the dissertation, we present techniques t o recover accurate function boundaries from stripped executables. Our techniques as opposed to current techniques ensure complete live executable code coverage, high quality recovered code, and functional behavior for most application binaries. We use s tatic and dynamic based techniques to remove as much spurious code as possible in a safe ma nner that does not hurt code coverage or IR correctness. Next, we present static tec hniques to recover correct prototypes for the recovered functions. The recovered prot otypes include the complete set of all arguments and returns. Our techniques ensure corr ct behavior of rewritten binaries for both internal and external functions. Finally, we present scalable and precise techniques to reco ver l cal variables for every function obtained as well as global and heap variables . Differe

Question: What is the difference between a thesis and a dissertation?


Integrating Classification and Association Rule Mining: A Concept Lattice Framework. Concept lattice is an efficient tool for data analysis. In this paper we show how classification and association rule mining can be unified under concept lattice framework. We present a fast algorithm to extract association and classification rules from concept lattice.

Question: What is a concept lattice?


Combining Bottom-Up, Top-Down, and Smoothness Cues for Weakly Supervised Image Segmentation. This paper addresses the problem of weakly supervised semantic image segmentation. Our goal is to label every pixel in a new image, given only image-level object labels associated with training images. Our problem statement differs from common semantic segmentation, where pixel-wise annotations are typically assumed available in training. We specify a novel deep architecture which fuses three distinct computation processes toward semantic segmentation – namely, (i) the bottom-up computation of neural activations in a CNN for the image-level prediction of object classes, (ii) the top-down estimation of conditional likelihoods of the CNNs activations given the predicted objects, resulting in probabilistic attention maps per object class, and (iii) the lateral attention-message passing from neighboring neurons at the same CNN layer. The fusion of (i)-(iii) is realized via a conditional random field as recurrent network aimed at generating a smooth and boundary-preserving segmentation. Unlike existing work, we formulate a unified end-to-end learning of all components of our deep architecture. Evaluation on the benchmark PASCAL VOC 2012 dataset demonstrates that we outperform reasonable weakly supervised baselines and state-of-the-art approaches.

Question: What is the goal of semantic image segmentation?


TS-CLOCK: temporal and spatial locality aware buffer replacement algorithm for NAND flash storages. NAND flash storage is widely adopted in all classes of computing devices. However, random write performance and lifetime issues remain to be addressed. In this paper, we propose a novel buffer replacement algorithm called TS-CLOCK that effectively resolves the remaining problems. Our experimental results show that TS-CLOCK outperforms state-of-the-art algorithms in terms of performance and lifetime.

Question: What is the lifetime of a nand flash storage?


End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies. Steering a car through traffic is a complex task that is difficult to cast into algorithms. Therefore, researchers turn to training artificial neural networks from front-facing camera data stream along with the associated steering angles. Nevertheless, most existing solutions consider only the visual camera frames as input, thus ignoring the temporal relationship between frames. In this work, we propose a Convolutional Long Short-Term Memory Recurrent Neural Network (C-LSTM), that is end-to-end trainable, to learn both visual and dynamic temporal dependencies of driving. Additionally, We introduce posing the steering angle regression problem as classification while imposing a spatial relationship between the output layer neurons. Such method is based on learning a sinusoidal function that encodes steering angles. To train and validate our proposed methods, we used the publicly available Comma.ai dataset. Our solution improved steering root mean square error by 35% over recent methods, and led to a more stable steering by 87%.

Question: How to train a neural network to predict steering angles?


Educational games for improving the teaching-learning process of a CLIL subject: Physics and chemistry in secondary education. The use of educational games in an academic context seems to be a superb alternative to traditional learning activities, such as drill-type exercises, in order to engage 21st-century students. Consequently, this work tries to raise the following objectives: analyze the effectiveness of game-based learning, characterize game elements that may contribute to create playing experiences, comprehend how different player types interact with games and, finally, design interactive games which may create challenges, set goals and provide feedback on progress while motivating learners to study physics and chemistry in a foreign language in the second cycle of Secondary Education (4th E.S.O. that corresponds to the age of 15-16 years old). Specifically, we have used several Web 2.0 tools (Hot Potatoes, Scratch, What2Learn and SMART Notebook 11) and applications (Microsoft PowerPoint and Microsoft Excel) in order to create the games; and these games are based on the successive contents: laboratory safety, laboratory equipment, stoichiometry, atomic structure, electronic configuration, the periodic table, forces, motion and energy.

Question: What is a CLIL subject?


Adaptive Spatial-Scale-Aware Deep Convolutional Neural Network for High-Resolution Remote Sensing Imagery Scene Classification. High-resolution remote sensing (HRRS) scene classification plays an important role in numerous applications. During the past few decades, a lot of remarkable efforts have been made to develop various methods for HRRS scene classification. In this paper, focusing on the problems of complex context relationship and large differences of object scale in HRRS scene images, we propose a deep CNN-based scene classification method, which not only enables to enhance the ability of spatial representation, but adaptively recalibrates channel-wise feature responses to suppress useless feature channels. We evaluated the proposed method on a publicly large-scale dataset with several state-of-the-art convolutional neural network (CNN) models. The experimental results demonstrate that the proposed method is effective to extract high-level category features for HRRS scene classification.

Question: What is the name of the dataset?


An estimation of the number of cells in the human body.. BACKGROUND All living organisms are made of individual and identifiable cells, whose number, together with their size and type, ultimately defines the structure and functions of an organism. While the total cell number of lower organisms is often known, it has not yet been defined in higher organisms. In particular, the reported total cell number of a human being ranges between 10(12) and 10(16) and it is widely mentioned without a proper reference.   AIM To study and discuss the theoretical issue of the total number of cells that compose the standard human adult organism.   SUBJECTS AND METHODS A systematic calculation of the total cell number of the whole human body and of the single organs was carried out using bibliographical and/or mathematical approaches.   RESULTS A current estimation of human total cell number calculated for a variety of organs and cell types is presented. These partial data correspond to a total number of 3.72 × 10(13).   CONCLUSIONS Knowing the total cell number of the human body as well as of individual organs is important from a cultural, biological, medical and comparative modelling point of view. The presented cell count could be a starting point for a common effort to complete the total calculation.

Question: How many cells are in the human body?


Improved Redirection with Distractors: A large-scale-real-walking locomotion interface and its effect on navigation in virtual environments. Users in virtual environments often find navigation more difficult than in the real world. Our new locomotion interface, Improved Redirection with Distractors (IRD), enables users to walk in larger-than-tracked space VEs without predefined waypoints. We compared IRD to the current best interface, really walking, by conducting a user study measuring navigational ability. Our results show that IRD users can really walk through VEs that are larger than the tracked space and can point to targets and complete maps of VEs no worse than when really walking.

Question: How does the improved redirection with distractors interface work?


A low-kickback-noise latched comparator for high-speed flash analog-to-digital converters. In traditional comparators especially for flash ADCs, one serious problem is the kick back noise, which disturbs the input signal voltages and consequently might cause errors at the outputs of the ADCs. In this paper, we propose a novel CMOS latched comparator with very low kickback noise for high-speed flash ADCs. The proposed comparator separates analog preamplifier from the positive feedback digital dynamic latch so as to reduce the influence of the kickback noise. Simulation results based on a mixed signal CMOS 0.35 /spl mu/m technology show that, this comparator can work at a maximum clock frequency of 500 MHz with very reduced kickback noise compared with conventional architectures.

Question: What is a comparator?


ltm : An R Package for Latent Variable Modeling and Item Response Theory Analyses. The R package ltm has been developed for the analysis of multivariate dichotomous and polytomous data using latent variable models, under the Item Response Theory approach. For dichotomous data the Rasch, the Two-Parameter Logistic, and Birnbaum's Three-Parameter models have been implemented, whereas for polytomous data Semejima's Graded Response model is available. Parameter estimates are obtained under marginal maximum likelihood using the Gauss-Hermite quadrature rule. The capabilities and features of the package are illustrated using two real data examples.

Question: What is the package ltm?


A Fuzzy Approach for Mining Quantitative Association Rules.

Question: What is a fuzzy approach for mining quantitative association rules?


Question: What is a program for machine learning?


On Controllable Sparse Alternatives to Softmax. Converting an n-dimensional vector to a probability distribution over n objects is a commonly used component in many machine learning tasks like multiclass classification, multilabel classification, attention mechanisms etc. For this, several probability mapping functions have been proposed and employed in literature such as softmax, sum-normalization, spherical softmax, and sparsemax, but there is very little understanding in terms how they relate with each other. Further, none of the above formulations offer an explicit control over the degree of sparsity. To address this, we develop a unified framework that encompasses all these formulations as special cases. This framework ensures simple closed-form solutions and existence of sub-gradients suitable for learning via backpropagation. Within this framework, we propose two novel sparse formulations, sparsegen-lin and sparsehourglass, that seek to provide a control over the degree of desired sparsity. We further develop novel convex loss functions that help induce the behavior of aforementioned formulations in the multilabel classification setting, showing improved performance. We also demonstrate empirically that the proposed formulations, when used to compute attention weights, achieve better or comparable performance on standard seq2seq tasks like neural machine translation and abstractive summarization.

Question: What is the difference between softmax and sparsemax?


Use of Web 2 . 0 technologies in K-12 and higher education : The search for evidence-based practice. Evidence-based practice in education entails making pedagogical decisions that are informed by relevant empirical research evidence. The main purpose of this paper is to discuss evidence-based pedagogical approaches related to the use of Web 2.0 technologies in both K-12 and higher education settings. The use of such evidence-based practice would be useful to educators interested in fostering student learning through Web 2.0 tools. A comprehensive literature search across the Academic Search Premier, Education Research Complete, ERIC, and PsycINFO databases was conducted. Empirical studies were included for review if they specifically examined the impact of Web 2.0 technologies on student learning. Articles that merely described anecdotal studies such as student perception or feeling toward learning using Web 2.0, or studies that relied on student self-report data such as student questionnaire survey and interview were excluded. Overall, the results of our review suggested that actual evidence regarding the impact of Web 2.0 technologies on student learning is as yet fairly weak. Nevertheless, the use of Web 2.0 technologies appears to have a general positive impact on student learning. None of the studies reported a detrimental or inferior effect on learning. The positive effects are not necessarily attributed to the technologies per se but to how the technologies are used, and how one conceptualizes learning. It may be tentatively concluded that a dialogic, constructionist, or coconstructive pedagogy supported by activities such as Socratic questioning, peer review and self-reflection appeared to increase student achievement in blog-, wiki-, and 3-D immersive virtual world environments, while a transmissive pedagogy supported by review activities appeared to enhance student learning using podcast. 2012 Elsevier Ltd. All rights reserved.

Question: What is the impact of web 2.0 technologies on student learning?


Skin Segmentation Using YCBCR and RGB Color Models. Face detection is one of the challenging problems in image processing. This report proposes a novel technique for detecting faces in color images using skin color model algorithm combined with skin likely-hood, skin Segmentation, Morphological operation and Template matching. Color images with skin color in the chromatic and pure color space YCrCb, which separates luminance and chrominance components. A Gaussian probability density is estimated from skin samples, collected from different ethnic groups, via the maximum-likelihood criterion. Adaptive thresholding for segmentation to localize the faces within the detected skin regions. Further, mathematical morphological operators are used to remove noisy regions and fill holes in the skin-color region, so that candidate human face regions can be extracted. These systems can achieve high detection accuracy, high detection speed and reduce the false detecting rate. The two methodology used for the Skin Segmentation is YCbCr and RGB Model. In YCbCr model both the skin colour and texture of the image can be used to identify the particular object in the image,where as in RGB model only the skin color has to be used for identification of the person.Hence the YCbCr model is better than the RGB model . From our analysis we conclude that the new approach in modeling skin color can achieve good detection success rate. The algorithm gives computationally a very efficient as well as an accurate approach for skin detection. The performance of different color space may be dependent on the method used to model the color for skin pixel. Keywords—Gaussian probability, YCrCb,RGB Model

Question: What is the difference between YCbCr and RGB model?


Computational thinking. omputational thinking builds on the power and limits of computing processes, whether they are executed by a human or by a machine. Computational methods and models give us the courage to solve problems and design systems that no one of us would be capable of tackling alone. Computational thinking confronts the riddle of machine intelligence: What can humans do better than computers? And What can computers do better than humans? Most fundamentally it addresses the question: What is computable? Today, we know only parts of the answers to such questions.

Question: What is computable?


Reinforcement Learning with Perturbed Rewards. Recent studies have shown the vulnerability of reinforcement learning (RL) models in noisy settings. The sources of noises differ across scenarios. For instance, in practice, the observed reward channel is often subject to noise (e.g., when observed rewards are collected through sensors), and thus observed rewards may not be credible as a result. Also, in applications such as robotics, a deep reinforcement learning (DRL) algorithm can be manipulated to produce arbitrary errors. In this paper, we consider noisy RL problems where observed rewards by RL agents are generated with a reward confusion matrix. We call such observed rewards as perturbed rewards. We develop an unbiased reward estimator aided robust RL framework that enables RL agents to learn in noisy environments while observing only perturbed rewards. Our framework draws upon approaches for supervised learning with noisy data. The core ideas of our solution include estimating a reward confusion matrix and defining a set of unbiased surrogate rewards. We prove the convergence and sample complexity of our approach. Extensive experiments on different DRL platforms show that policies based on our estimated surrogate reward can achieve higher expected rewards, and converge faster than existing baselines. For instance, the state-of-the-art PPO algorithm is able to obtain 67.5% and 46.7% improvements in average on five Atari games, when the error rates are 10% and 30% respectively.

Question: What is reinforcement learning?


A review of functional Near-Infrared Spectroscopy measurements in naturalistic environments. The development of novel miniaturized wireless and wearable functional Near-Infrared Spectroscopy (fNIRS) devices have paved the way to new functional brain imaging that can revolutionize the cognitive research fields. Over the past few decades, several studies have been conducted with conventional fNIRS systems that have demonstrated the suitability of this technology for a wide variety of populations and applications, to investigate both the healthy brain and the diseased brain. However, what makes wearable fNIRS even more appealing is its capability to allow more ecologically-valid measurements in everyday life scenarios that are not possible with other gold-standard neuroimaging modalities, such as functional Magnetic Resonance Imaging. This can have

Question: What is the difference between a gold-standard neuroimaging modality and a wearable fNIRS?


On the momentum term in gradient descent learning algorithms. A momentum term is usually included in the simulations of connectionist learning algorithms. Although it is well known that such a term greatly improves the speed of learning, there have been few rigorous studies of its mechanisms. In this paper, I show that in the limit of continuous time, the momentum parameter is analogous to the mass of Newtonian particles that move through a viscous medium in a conservative force field. The behavior of the system near a local minimum is equivalent to a set of coupled and damped harmonic oscillators. The momentum term improves the speed of convergence by bringing some eigen components of the system closer to critical damping. Similar results can be obtained for the discrete time case used in computer simulations. In particular, I derive the bounds for convergence on learning-rate and momentum parameters, and demonstrate that the momentum term can increase the range of learning rate over which the system converges. The optimal condition for convergence is also analyzed.

Question: What is the momentum term in gradient descent learning algorithms?


Predictive coding: a fresh view of inhibition in the retina.. Interneurons exhibiting centre--surround antagonism within their receptive fields are commonly found in peripheral visual pathways. We propose that this organization enables the visual system to encode spatial detail in a manner that minimizes the deleterious effects of intrinsic noise, by exploiting the spatial correlation that exists within natural scenes. The antagonistic surround takes a weighted mean of the signals in neighbouring receptors to generate a statistical prediction of the signal at the centre. The predicted value is subtracted from the actual centre signal, thus minimizing the range of outputs transmitted by the centre. In this way the entire dynamic range of the interneuron can be devoted to encoding a small range of intensities, thus rendering fine detail detectable against intrinsic noise injected at later stages in processing. This predictive encoding scheme also reduces spatial redundancy, thereby enabling the array of interneurons to transmit a larger number of distinguishable images, taking into account the expected structure of the visual world. The profile of the required inhibitory field is derived from statistical estimation theory. This profile depends strongly upon the signal: noise ratio and weakly upon the extent of lateral spatial correlation. The receptive fields that are quantitatively predicted by the theory resemble those of X-type retinal ganglion cells and show that the inhibitory surround should become weaker and more diffuse at low intensities. The latter property is unequivocally demonstrated in the first-order interneurons of the fly's compound eye. The theory is extended to the time domain to account for the phasic responses of fly interneurons. These comparisons suggest that, in the early stages of processing, the visual system is concerned primarily with coding the visual image to protect against subsequent intrinsic noise, rather than with reconstructing the

Question: What is the difference between a retinal ganglion cell and an interneuron?


How to Predict Mood? Delving into Features of Smartphone-Based Data. Smartphones are increasingly utilized in society and enable scientists to record a wide range of behavioral and environmental information. These information, referred to as Unobtrusive Ecological Momentary Assessment Data, might support prediction procedures regarding the mood level of users and simultaneously contribute to an enhancement of therapy strategies. In this paper, we analyze how the mood level of healthy clients is affected by unobtrusive measures and how this kind of data contributes to the prediction performance of various statistical models (Bayesian methods, Lasso procedures, etc.). We conduct analyses on a non-user and a user level. We then compare the models by utilizing introduced performance measures. Our findings indicate that the prediction performance increases when considering individual users. However, the implemented models only perform slightly better than the introduced mean model. Indicated by feature selection methods, we assume that more meaningful variables regarding the outcome can potentially increase prediction performance.

Question: How to predict mood?


ITIL-based IT service support process reengineering. The Information Technology Infrastructure Library (ITIL) supports best practices, reengineering activities and IT service support processes. ITIL framework only provides recommendations, and companies need to utilize this framework to improve their IT service support processes and establish best practices. This study provides a methodology on how to apply the ITIL framework for evaluating the IT service support processes, its reengineering and alignment to best practices, and subsequent integration into a decision support system framework. A case study approach was used to identify a set of Key Performance Indicators (KPI) which were monitored by a decision support system (DSS) for triggering on-going reengineering of IT service support processes. This paper focuses on the implementation of the ITIL guidelines at the operational level, improvement of the service desk, and incident, problem, change, release, and configuration management. It also presents the implementation of the ITIL guidelines at the tactical level for the improvement of the service level, capacity, IT service continuity, service availability, and security management. We conclude by providing recommendations for future research.

Question: What is the ITIL framework?


Anti-jamming performance of cognitive radio networks under multiple uncoordinated jammers in fading environment. In this paper, we study both the jamming capability of the cognitive-radio-based jammers and the anti-jamming capability of the cognitive radio networks (CRN), by considering multiple uncooperative jammers and independent Rayleigh flat-fading propagations. A Markov model of CRN transmission is set up for the cross-layer analysis of the anti-jamming performance. The transitional probabilities are derived analytically by considering a smart jamming attack strategy. Average throughput expression is obtained and verified by simulations. The results indicate that CRN communications can be extremely susceptible to smart jamming attacks targeting the CRN spectrum sensing and channel switching procedures.

Question: What is the average throughput of a cognitive radio network?


Abstractive Multi-document Summarization with Semantic Information Extraction. This paper proposes a novel approach to generate abstractive summary for multiple documents by extracting semantic information from texts. The concept of Basic Semantic Unit (BSU) is defined to describe the semantics of an event or action. A semantic link network on BSUs is constructed to capture the semantic information of texts. Summary structure is planned with sentences generated based on the semantic link network. Experiments demonstrate that the approach is effective in generating informative, coherent and compact summary.

Question: What is the concept of basic semantic unit?


PKLot - A robust dataset for parking lot classification. http://dx.doi.org/10.1016/j.eswa.2015.02.009 0957-4174/ 2015 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. E-mail addresses: prlalmeida@inf.ufpr.br (P.R.L. de Almeida), lesoliveira@inf. ufpr.br (L.S. Oliveira), alceu@ppgia.pucpr.br (A.S. Britto Jr.), eunelson@ppgia.pucpr. br (E.J. Silva Jr.), alessandro.koerich@etsmtl.ca (A.L. Koerich). Paulo R.L. de Almeida , Luiz S. Oliveira , Alceu S. Britto Jr. b,⇑, Eunelson J. Silva Jr. b Alessandro L. Koerich b,c

Question: PKLot - A robust dataset for parking lot classification.


A Convolutional Attention Network for Extreme Summarization of Source Code. Attention mechanisms in neural networks have proved useful for problems in which the input and output do not have fixed dimension. Often there exist features that are locally translation invariant and would be valuable for directing the model's attention, but previous attentional architectures are not constructed to learn such features specifically. We introduce an attentional neural network that employs convolution on the input tokens to detect local time-invariant and long-range topical attention features in a context-dependent way. We apply this architecture to the problem of extreme summarization of source code snippets into short, descriptive function name-like summaries. Using those features, the model sequentially generates a summary by marginalizing over two attention mechanisms: one that predicts the next summary token based on the attention weights of the input tokens and another that is able to copy a code token as-is directly into the summary. We demonstrate our convolutional attention neural network's performance on 10 popular Java projects showing that it achieves better performance compared to previous attentional mechanisms.

Question: What is extreme summarization of source code?


DyNet: The Dynamic Neural Network Toolkit. We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then examples are fed into an engine that executes this computation and computes its derivatives. In DyNet's dynamic declaration strategy, computation graph construction is mostly transparent, being implicitly constructed by executing procedural code that computes the network outputs, and the user is free to use different network structures for each input. Dynamic declaration thus facilitates the implementation of more complicated network architectures, and DyNet is specifically designed to allow users to implement their models in a way that is idiomatic in their preferred programming language (C++ or Python). One challenge with dynamic declaration is that because the symbolic computation graph is defined anew for every training example, its construction must have low overhead. To achieve this, DyNet has an optimized C++ backend and lightweight graph representation. Experiments show that DyNet's speeds are faster than or comparable with static declaration toolkits, and significantly faster than Chainer, another dynamic declaration toolkit. DyNet is released opensource under the Apache 2.0 license, and available at http://github.com/clab/dynet. Carnegie Mellon University, Pittsburgh, PA, USA Nara Institute of Science and Technology, Ikoma, Japan DeepMind, London, UK Bar Ilan University, Ramat Gan, Israel Allen Institute for Artificial Intelligence, Seattle, WA, USA University of Notre Dame, Notre Dame, IN, USA IBM T.J. Watson Research Center, Yorktown Heights, NY, USA University of Melbourne, Melbourne, Australia Johns Hopkins University, Baltimore, MD, USA Google, New York, NY, USA Google, Mountain View, CA, USA Universi

Question: What is the dynamic neural network toolkit?


Fossil chrysophycean cyst flora of Racze Lake, Wolin Island (Poland) in relation to paleoenvironmental conditions. The study presents stratigraphic distribution of fossil chrysophycean cysts in the bottom sediments of Racze Lake (Poland). Thirty morphotypes are described, most of them for the first time. The description of the cyst includes SEM microphotographs. The long-term relationship between the lake's conditions and the occurrence of characteristic morphotypes of chrysophycean cysts is discussed.

Question: What is the relationship between the lake's conditions and the occurrence of characteristic morphotypes of chrysophycean cysts?


Rank aggregation methods for the Web. We consider the problem of combining ranking results from various sources. In the context of the Web, the main applications include building meta-search engines, combining ranking functions, selecting documents based on multiple criteria, and improving search precision through word associations. We develop a set of techniques for the rank aggregation problem and compare their performance to that of well-known methods. A primary goal of our work is to design rank aggregation techniques that can e ectively combat \spam," a serious problem in Web searches. Experiments show that our methods are simple, e cient, and e ective.

Question: What is the rank aggregation problem?


Parallel computations for controlling an arm.. In order to control a reaching movement of the arm and body, several different computational problems must be solved. Some parallel methods that could be implemented in networks of neuron-like processors are described. Each method solves a different part of the overall task. First, a method is described for finding the torques necessary to follow a desired trajectory. The methods is more economical and more versatile than table look-up and requires very few sequential steps. Then a way of generating an internal representation of a desired trajectory is described. This method shows the trajectory one piece at a time by applying a large set of heuristic rules to a "motion blackboard" that represents the static and dynamic parameters of the state of the body at the current point in the trajectory. The computations are simplified by expressing the positions, orientations, and motions of parts of the body in terms of a single, non-accelerating, world-based frame of reference, rather than in terms of the joint-angles or an egocentric frame based on the body itself.

Question: How to control an arm?


Author ' s personal copy Romancing leadership : Past , present , and future ☆. Available online 2 October 2011 This paper presents a review of the romance of leadership and the social construction of leadership theory 25 years after it was originally introduced. We trace the development of this theoretical approach from the original formulation of the romance of leadership (RoL) theory as attributional bias through its emergence as a radical, unconventional approach that views leadership as a sensemaking activity that is primarily 'in the eye of the beholder.' We subsequently review research published in management and organizational psychology journals, book chapters and special issues of journals from 1985 to 2010. Three overall themes emerged from this review: 1) biases in (mis)attributions of leadership, including attributions for organizational success and failure; 2) follower-centered approaches, including the role of follower characteristics, perceptions, and motivations in interpreting leadership ratings; and 3) the social construction of leadership, including interfollower and social contagion processes, the role of crisis and uncertainty, and constructions and deconstructions of leadership and CEO celebrity in the media. Within each of these themes, we examine developments and summarize key findings. Our review concludes with recommendations for future theoretical and empirical

Question: What is the canadian military?


An ant colony algorithm for the multi-compartment vehicle routing problem. We demonstrate the use of Ant Colony System (ACS) to solve the capacitated vehicle routing problem associated with collection of recycling waste from households, treated as nodes in a spatial network. For networks where the nodes are concentrated in separate clusters, the use of k-means clustering can greatly improve the efficiency of the solution. The ACS algorithm is extended to model the use of multi-compartment vehicles with kerbside sorting of waste into separate compartments for glass, paper, etc. The algorithm produces high-quality solutions for two-compartment test problems.

Question: How to solve the capacitated vehicle routing problem?


Digital Forensic Analysis of SIM Cards. Smart cards are fundamental technology in modern life. It is embedded in numerous devices such as GPS devices, ATM cards, Mobile SIM cards and many others. Mobile devices became the evolution of technology. It becomes smaller, faster and supports large storage capabilities. Digital forensics of mobile devices that maybe found in crime scene is becoming inevitable. The purpose of this research is to address the SIM cards digital forensics analysis. It presents sound forensic methodology and process of SIM cards forensic examination. In particular, the main aim of the research is to answer the following research questions: (1) what forensic evidence could be extracted from a SIM card and (2) what are limitations that may hinder a forensic

Question: What is the purpose of this research?


Magic Quadrant for Data Quality Tools. Extensive data on functional capabilities, customer base demographics, financial status, pricing and other quantitative attributes gained via a requestfor-information process engaging vendors in this market. Interactive briefings in which vendors provided Gartner with updates on their strategy, market positioning, recent key developments and product road map. A Web-based survey of reference customers provided by each vendor, which captured data on usage patterns, levels of satisfaction with major product functionality categories, various nontechnology-related vendor attributes (such as pricing, product support and overall service delivery), and more. In total, 333 organizations across all major world regions provided input on their experiences with vendors and tools in this manner. Feedback about tools and vendors captured during conversations with users of Gartner's client inquiry service. Market share and revenue growth estimates developed by Gartner's Technology and Service Provider research unit.

Question: What is the market share of data quality tools?


CRITICAL SUCCESS FACTORS FOR CUSTOMER RELATIONSHIP MANAGEMENT IMPLEMENTATIONS. The growing forces of increasing global competition, continuing customer demands, and the significant revolution in Commercial Off The Shelf (COTS) solutions, especially Customer Relationship Management (CRM) applications, have together put pressure upon many organisations to implement CRM solutions and to switch their organisational processes from being product-centric to being customer-centric. A CRM initiative is not only technology; it is a business strategy supported by technology which automates and enhances the processes associated with managing customer relationships. By the end of 2010, it is predicted that companies will be spending almost $11 billion yearly on CRM solutions. However, studies have found that 70% of CRM projects have failed. Understanding the factors that enable success of CRM is vital. There is very few existing specific research into Critical Success Factors (CSFs) of CRM implementations, and there is no comprehensive view that captures all the aspects for successful CRM implementation and their inter-relationships. Therefore, the aim of this paper is to explore the current literature base of CSFs for CRM implementations and proposes a taxonomy for them. Future research work will continue to investigate in depth these factors by exploring the complex system links between CSFs using systems thinking techniques such as causal maps to investigate the complex, systemic networks of CSFs in organisations which result in emergent effects which themselves influence the failure or success of a CRM.

Question: What are the critical success factors for customer relationship management implementations?


Innovation in the pharmaceutical industry: New estimates of R&D costs.. The research and development costs of 106 randomly selected new drugs were obtained from a survey of 10 pharmaceutical firms. These data were used to estimate the average pre-tax cost of new drug and biologics development. The costs of compounds abandoned during testing were linked to the costs of compounds that obtained marketing approval. The estimated average out-of-pocket cost per approved new compound is $1395 million (2013 dollars). Capitalizing out-of-pocket costs to the point of marketing approval at a real discount rate of 10.5% yields a total pre-approval cost estimate of $2558 million (2013 dollars). When compared to the results of the previous study in this series, total capitalized costs were shown to have increased at an annual rate of 8.5% above general price inflation. Adding an estimate of post-approval R&D costs increases the cost estimate to $2870 million (2013 dollars).

Question: What is the average cost of a new drug?


Functional System and Areal Organization of a Highly Sampled Individual Human Brain. Resting state functional MRI (fMRI) has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected-considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of intersubject variability. Further, although the individual's systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals. VIDEO ABSTRACT.

Question: How do you measure the brain of a single individual?


Spreadsheet Validation and Analysis through Content Visualization. Visualizing spreadsheet content provides analytic insight and visual validation of large amounts of spreadsheet data. Oculus Excel Visualizer is a point and click data visualization experiment which directly visualizes Excel data and re-users the layout and formatting already present in the spreadsheet.

Question: What is a spreadsheet?


A functional imaging study of translation and language switching.. The neural systems underlying translation and language switching were investigated using PET. Proficient German-English adult bilinguals were scanned whilst either translating or reading visually presented words in German (L1), English (L2) or alternating L1/L2. We refer to alternating L1/L2 as 'switching'. The results revealed contrasting patterns of activation for translation and switching, suggesting at least partially independent mechanisms. Translation, but not switching, increased activity in the anterior cingulate and subcortical structures whilst decreasing activation in several other temporal and parietal language areas associated with the meaning of words. Translation also increased activation in regions associated with articulation (the anterior insula, cerebellum and supplementary motor area) arguably because the reading response to the stimulus must be inhibited whilst a response in a different language is activated. In contrast, switching the input language resulted in activation of Broca's area and the supramarginal gyri, areas associated with phonological recoding. The results are discussed in terms of the cognitive control of language processes.

Question: What is the difference between translation and switching?


NB-IoT Technology Overview and Experience from Cloud-RAN Implementation. The 3GPP has introduced a new narrowband radio technology called narrowband Internet of Things (NB-IoT) in Release 13. NB-IoT was designed to support very low power consumption and low-cost devices in extreme coverage conditions. NB-IoT operates in very small bandwidth and will provide connectivity to a large number of low-data-rate devices. This article highlights some of the key features introduced in NB-IoT and presents performance results from real-life experiments. The experiments were carried out using an early-standard-compliant prototype based on a software defined radio partial implementation of NB-IoT that runs on a desktop computer connected to the network. It is found that a cloud radio access network is a good candidate for NB-IoT implementation.

Question: What is the difference between NB-IoT and LTE-M?


What's in an Embedding? Analyzing Word Embeddings through Multilingual Evaluation. In the last two years, there has been a surge of word embedding algorithms and research on them. However, evaluation has mostly been carried out on a narrow set of tasks, mainly word similarity/relatedness and word relation similarity and on a single language, namely English. We propose an approach to evaluate embeddings on a variety of languages that also yields insights into the structure of the embedding space by investigating how well word embeddings cluster along different syntactic features. We show that all embedding approaches behave similarly in this task, with dependency-based embeddings performing best. This effect is even more pronounced when generating low dimensional embed-

Question: What is the difference between embedding and embedding?


Simulated Annealing Algorithm for 2D Image Compression. In this paper a new sign coding approximation method for the wavelet coefficients in a 2D image codec based on a simulated annealing metaheuristic is presented. The efficiency of the proposed algorithm versus a genetic algorithm using benchmarks of Kodak is compared and showing that the proposed sign prediction algorithm is efficient and provides a significant reduction of wavelet coefficients sign information in the final bit-stream. The results show that, by including sign coding capabilities to a nonembedded encoder, the sign compression gain is up to 17.35%, being the rate-distortion (R/D) performance improvement up to 0.25 dB.

Question: What is a simulated annealing algorithm?


Evaluation of Cost Functions for Stereo Matching. Stereo correspondence methods rely on matching costs for computing the similarity of image locations. In this paper we evaluate the insensitivity of different matching costs with respect to radiometric variations of the input images. We consider both pixel-based and window-based variants and measure their performance in the presence of global intensity changes (e.g., due to gain and exposure differences), local intensity changes (e.g., due to vignetting, non-Lambertian surfaces, and varying lighting), and noise. Using existing stereo datasets with ground-truth disparities as well as six new datasets taken under controlled changes of exposure and lighting, we evaluate the different costs with a local, a semi-global, and a global stereo method.

Question: What is the cost function for stereo matching?


Deep Image Segmentation by Quality Inference. Traditionally, convolutional neural networks are trained for semantic segmentation by having an image given as input and the segmented mask as output. In this work, we propose a neural network trained by being given an image and mask pair, with the output being the quality of that pairing. The segmentation is then created afterwards through backpropagation on the mask. This allows enriching training with semi-supervised synthetic variations on the ground-truth. The proposed iterative segmentation technique allows improving an existing segmentation or creating one from scratch. We compare the performance of the proposed methodology with state-of-the-art deep architectures for image segmentation and achieve competitive results, being able to improve their segmentations.

Question: How does deep image segmentation work?


Knowledge management and organizational culture: a theoretical integrative framework. Purpose – Organizational culture is a critical factor in building and reinforcing knowledge management in organizations. However, there is no theoretical framework that comprehensively explains the effect of organizational culture on knowledge management in organizations. This paper endeavors to develop a theoretical integrative framework for organizational knowledge management and organizational culture. Design/methodology/approach – This is a conceptual paper. It modifies the ''competing value framework'' by adding a new dimension representing ethical and trusting culture, and then integrates it with the SECI model of knowledge creation and conversion by identifying the conceptual parallels between the two frameworks and then analyzing the interaction effects among the dimensions. Findings – Based on the congruity between the modified competing values framework and the knowledge creation and conversion framework, the paper formulates six propositions about the propensity of organizations of different dominant cultural styles to engage in the four processes of knowledge creation and conversion. Research limitations/implications – The dynamic nature of the framework presented in the paper points to the importance of longitudinal and comparative research in understanding the effects of organizational culture on organizational knowledge management systems in organizations. Practical implications – The proposed integrative framework would facilitate organizational learning and lead to the improvement of knowledge management practices in organizations as it helps managers to understand the linkages between culture and knowledge management. Originality/value – This paper presents a new framework linking organizational culture to knowledge management. It moves away from analyzing culture only in terms of its positive and negative influences on knowledge management. Instead, it suggests a

Question: What is the difference between knowledge management and organizational culture?


Current location-based next POI recommendation. Availability of large volume of community contributed location data enables a lot of location providing services and these services have attracted many industries and academic researchers by its importance. In this paper we propose the new recommender system that recommends the new POI for next hours. First we find the users with similar check-in sequences and depict their check-in sequences as a directed graph, then find the users current location. To recommend the new POI recommendation for next hour we refer to the directed graph we have created. Our algorithm considers both the temporal factor i.e., recommendation time, and the spatial(distance) at the same time. We conduct an experiment on random data collected from Foursquare and Gowalla. Experiment results show that our proposed model outperforms the collaborative-filtering based state-of-the-art recommender techniques.

Question: What is the location-based next POI recommendation?


Communication-Efficient Learning of Deep Networks from Decentralized Data. Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10–100× as compared to synchronized stochastic gradient descent.

Question: What is the difference between federated learning and distributed learning?


CS Unplugged and Middle-School Students' Views, Attitudes, and Intentions Regarding CS. Many students hold incorrect ideas and negative attitudes about computer science (CS). In order to address these difficulties, a series of learning activities called Computer Science Unplugged was developed by Tim Bell and his colleagues. These activities expose young people to central concepts in CS in an entertaining way without requiring a computer. The CS Unplugged activities have become more and more popular among CS educators and several activities are recommended in the ACM K-12 curriculum for elementary schools. CS Unplugged is used worldwide and has been translated into many languages.  We examined the effect of the CS Unplugged activities on middle-school students' ideas about CS and their desire to consider and study it in high school. The results indicate that following the activities the ideas of the students on what CS is about were partially improved, but their desire to study CS lessened.  In order to provide possible explanations to these results, we analyzed the CS Unplugged activities to determine to what extent the objectives of CS Unplugged were addressed in the activities. In addition, we checked whether the activities were designed according to constructivist principles and whether they were explicitly linked to central concepts in CS. We found that only some of the objectives were addressed in the activities, that the activities do not engage with the students' prior knowledge and that most of the activities are not explicitly linked to central concepts in CS. We offer suggestions for modifying the CS Unplugged activities so that they will be more likely to achieve their objectives.

Question: What is the effect of the CS Unplugged activities on middle-school students' ideas about CS and their desire to consider and study it in high school?


Will NoSQL Databases Live Up to Their Promise?. Many organizations collect vast amounts of customer, scientific, sales, and other data for future analysis. Traditionally, most of these organizations have stored structured data in relational databases for subsequent access and analysis. However, a growing number of developers and users have begun turning to various types of nonrelational, now frequently called NoSQL-databases. Nonrelational databases, including hierarchical, graph, and object-oriented databases-have been around since the late 1960s. However, new types of NoSQL databases are being developed. And only now are they beginning to gain market traction. Different NoSQL databases take different approaches. What they have in common is that they're not relational. Their primary advantage is that, unlike relational databases, they handle unstructured data such as word-processing files, e-mail, multimedia, and social media efficiently. This paper discuss issues such as limitations, advantages, concerns and doubts regarding NoSQL databases.

Question: What are the advantages and disadvantages of NoSQL databases?


Accurate Linear-Time Chinese Word Segmentation via Embedding Matching. This paper proposes an embedding matching approach to Chinese word segmentation, which generalizes the traditional sequence labeling framework and takes advantage of distributed representations. The training and prediction algorithms have linear-time complexity. Based on the proposed model, a greedy segmenter is developed and evaluated on benchmark corpora. Experiments show that our greedy segmenter achieves improved results over previous neural network-based word segmenters, and its performance is competitive with state-of-the-art methods, despite its simple feature set and the absence of external resources for training.

Question: What is the linear-time complexity of the proposed model?


Comparison of the Hardware Performance of the AES Candidates Using Reconfigurable Hardware. The results of implementations of all five AES finalists using Xilinx Field Programmable Gate Arrays are presented and analyzed. Performance of four alternative hardware architectures is discussed and compared. The AES candidates are divided into three classes depending on their hardware performance characteristics. Recommendation regarding the optimum choice of the algorithms for AES is provided.

Question: What is the hardware performance of the AES candidates?


Advanced image fusion to overlay coronary sinus anatomy with real-time fluoroscopy to facilitate left ventricular lead implantation in CRT.. BACKGROUND Failure rate for left ventricular (LV) lead implantation in cardiac resynchronization therapy (CRT) is up to 12%. The use of segmentation tools, advanced image registration software, and high-fidelity images from computerized tomography (CT) and cardiac magnetic resonance (CMR) of the coronary sinus (CS) can guide LV lead implantation. We evaluated the feasibility of advanced image registration onto live fluoroscopic images to allow successful LV lead placement.   METHODS Twelve patients (11 male, 59 ± 16.8 years) undergoing CRT had three-dimensional (3D) whole-heart imaging (six CT, six CMR). Eight patients had at least one previously failed LV lead implant. Using segmentation software, anatomical models of the cardiac chambers, CS, and its branches were overlaid onto the live fluoroscopy using a prototype version of the Philips EP Navigator software to guide lead implantation.   RESULTS We achieved high-fidelity segmentations of cardiac chambers, coronary vein anatomy, and accurate registration between the 3D anatomical models and the live fluoroscopy in all 12 patients confirmed by balloon occlusion angiography. The CS was cannulated successfully in every patient and in 11, an LV lead was implanted successfully. (One patient had no acceptable lead values due to extensive myocardial scar).   CONCLUSION Using overlaid 3D segmentations of the CS and cardiac chambers, it is feasible to guide CRT implantation in real time by fusing advanced imaging and fluoroscopy. This enabled successful CRT in a group of patients with previously failed implants. This technology has the potential to facilitate CRT and improve implant success.

Question: How to guide CRT implantation?


An in-depth study of the promises and perils of mining GitHub. With over 10 million git repositories, GitHub is becoming one of the most important sources of software artifacts on the Internet. Researchers mine the information stored in GitHub's event logs to understand how its users employ the site to collaborate on software, but so far there have been no studies describing the quality and properties of the available GitHub data. We document the results of an empirical study aimed at understanding the characteristics of the repositories and users in GitHub; we see how users take advantage of GitHub's main features and how their activity is tracked on GitHub and related datasets to point out misalignment between the real and mined data. Our results indicate that while GitHub is a rich source of data on software development, mining GitHub for research purposes should take various potential perils into consideration. For example, we show that the majority of the projects are personal and inactive, and that almost 40 % of all pull requests do not appear as merged even though they were. Also, approximately half of GitHub's registered users do not have public activity, while the activity of GitHub users in repositories is not always easy to pinpoint. We use our identified perils to see if they can pose validity threats; we review selected papers from the MSR 2014 Mining Challenge and see if there are potential impacts to consider. We provide a set of recommendations for software engineering researchers on how to approach the data in GitHub.

Question: What is the size of the canadian military?


ProM: The Process Mining Toolkit. Nowadays, all kinds of information systems store detailed information in logs. Process mining has emerged as a way to analyze these systems based on these detailed logs. Unlike classical data mining, the focus of process mining is on processes. First, process mining allows us to extract a process model from an event log. Second, it allows us to detect discrepancies between a modeled process (as it was envisioned to be) and an event log (as it actually is). Third, it can enrich an existing model with knowledge derived from an event log. This paper presents our tool ProM, which is the world-leading tool in the area of process mining.

Question: What is process mining?


OpenHAR: A Matlab Toolbox for Easy Access to Publicly Open Human Activity Data Sets. This study introduces OpenHAR, a free Matlab toolbox to combine and unify publicly open data sets. It provides an easy access to accelerometer signals of ten publicly open human activity data sets. Data sets are easy to access as OpenHAR provides all the data sets in the same format. In addition, units, measurement range and labels are unified, as well as, body position IDs. Moreover, data sets with different sampling rates are unified using downsampling. What is more, data sets have been visually inspected to find visible errors, such as sensor in wrong orientation. OpenHAR improves re-usability of data sets by fixing these errors. Altogether OpenHAR contains over 65 million labeled data samples. This is equivalent to over 280 hours of data from 3D accelerometers. This includes data from 211 study subjects performing 17 daily human activities and wearing sensors in 14 different body positions.

Question: What is the name of the toolbox?


Megaspeed Drive Systems: Pushing Beyond 1 Million r/min. The latest research in mesoscale drive systems is targeting rotational speeds toward 1 million r/min for a power range of 1-1 kW. Emerging applications for megaspeed drives (MegaNdrives) are to be found in future turbo compressor systems for fuel cells and heat pumps, generators/starters for portable nanoscale gas turbines, printed circuit board drilling and machining spindles, and electric power generation from pressurized gas flow. The selection of the machine type and the challenges involved in designing a machine for megaspeed operation such as the winding concepts, a mechanical rotor design capable of 1 000 000 r/min, the selection of magnetic materials for the stator, and the optimization concerning high-frequency losses and torque density are presented. Furthermore, a review of the advantageous inverter topologies, taking into account the extremely low stator inductance and possible high-speed bearing types such as ball bearings, air bearings, foil bearings, and magnetic bearings, are given. Finally, prototypes and experimental results originating from MegaNdrive research at Swiss Federal Institute of Technology Zurich are discussed and extreme temperature operation and power microelectricalmechanical system are identified as targets for future research.

Question: What is the highest rotational speed of a motor?


Solving optimization problems using black hole algorithm. Various meta-heuristic optimization approaches have been recently created and applied in different areas. Many of these approaches are inspired by swarm behaviors in the nature. This paper studies the solving optimization problems using Black Hole Algorithm (BHA) which is a population-based algorithm. Since the performance of this algorithm was not tested in mathematical functions, we have studied this issue using some standard functions. The results of the BHA are compared with the results of GA and PSO algorithms which indicate that the performance of BHA is better than the other two mentioned algorithms.

Question: What is a meta-heuristic optimization approach?


LiTell: robust indoor localization using unmodified light fixtures. Owing to dense deployment of light fixtures and multipath-free propagation, visible light localization technology holds potential to overcome the reliability issue of radio localization. However, existing visible light localization systems require customized light hardware, which increases deployment cost and hinders near term adoption. In this paper, we propose LiTell, a simple and robust localization scheme that employs unmodified fluorescent lights (FLs) as location landmarks and commodity smartphones as light sensors. LiTell builds on the key observation that each FL has an inherent characteristic frequency which can serve as a discriminative feature. It incorporates a set of sampling, signal amplification and camera optimization mechanisms, that enable a smartphone to capture the extremely weak and high frequency ( > 80 kHz) features. We have implemented LiTell as a real-time localization and navigation system on Android. Our experiments demonstrate LiTell's high reliability in discriminating different FLs, and its potential to achieve sub-meter location granularity. Our user study in a multi-storey office building, parking lot and grocery store further validates LiTell as an accurate, robust and ready-to-use indoor localization system.

Question: How does LiTell work?


The efficacy and safety of postoperative adjuvant transarterial embolization and radiotherapy in hepatocellular carcinoma patients with portal vein tumor thrombus. OBJECTIVE This study aims to find out the safety and efficiency of postoperative adjuvant transarterial chemoembolization (TACE) and radiotherapy (RT) in hepatocellular carcinoma (HCC) patients with portal vein tumor thrombus (PVTT).   METHODS From 2009 to 2010, a total of 92 HCC patients with PVTT were enrolled in this retrospective study. Patients were divided into three groups according to their adjuvant therapies (conservative group, n=51; TACE group, n=31; RT group, n=10).   RESULTS In our analysis, median survival in patients with postoperative adjuvant TACE (21.91±3.60 months) or RT (14.53±1.61 months) was significantly longer than patients with hepatectomy alone (8.99±1.03 months). But the difference between adjuvant TACE and RT was of no significance (P=0.716). Also a similar result could be observed in median disease-free survival: conservative group (6.51±1.44 months), TACE group (13.98±3.38 months), and RT group (14.03±2.40 months). Treatment strategies (hazard ratio [HR] =0.411, P<0.001) and PVTT type (HR =4.636, P<0.001) were the independent prognostic factors for overall survival. Similarly, the risk factors were the same when multivariate analysis was conducted in disease-free survival (treatment strategies, HR =0.423, P<0.001; PVTT type, HR =4.351, P<0.001) and recurrence (treatment strategies, HR =0.459, P=0.030; PVTT type, HR =2.908, P=0.047). Patients with PVTT type I had longer overall survival than patients with PVTT type II (median survival: 18.43±2.88 months vs 11.59±1.45 months, P=0.035).   CONCLUSION Postoperative adjuvant TACE and RT may be a choice for HCC patients with PVTT.

Question: What is the difference between TACE and RT?


Designing of Robust Image Steganography Technique Based on LSB Insertion and Encryption. This paper discusses the design of a Robust image steganography technique based on LSB (Least Significant Bit) insertion and RSA encryption technique. Steganography is the term used to describe the hiding of data in images to avoid detection by attackers. Steganalysis is the method used by attackers to determine if images have hidden data and to recover that data. The application discussed in this paper ranks images in a users library based on their suitability as cover objects for some data. By matching data to an image, there is less chance of an attacker being able to use steganalysis to recover the data. Before hiding the data in an image the application first encrypts it. The steganography method proposed in this paper and illustrated by the application is superior to that used by current steganography tools.

Question: What is the difference between steganography and steganalysis?


Metadating: Exploring the Romance and Future of Personal Data. We introduce Metadating -- a future-focused research and speed-dating event where single participants were invited to "explore the romance of personal data". Participants created "data profiles" about themselves, and used these to "date" other participants. In the rich context of dating, we study how personal data is used conversationally to communicate and illustrate identity. We note the manner in which participants carefully curated their profiles, expressing ambiguity before detail, illustration before accuracy. Our findings proposition a set of data services and features, each concerned with representing and curating data in new ways, beyond a focus on purely rational or analytic relationships with a quantified self. Through this, we build on emerging interest in "lived informatics" and raise questions about the experience and social reality of a "data-driven life".

Question: What is the future of personal data?


A Machine Learning Approach to Test Data Generation: A Case Study in Evaluation of Gene Finders. Programs for gene prediction in computational biology are examples of systems for which the acquisition of authentic test data is difficult as these require years of extensive research. This has lead to test methods based on semiartificially produced test data, often produced by ad hoc techniques complemented by statistical models such as Hidden Markov Models (HMM). The quality of such a test method depends on how well the test data reflect the regularities in known data and how well they generalize these regularities. So far only very simplified and generalized, artificial datasets have been tested, and a more thorough statistical foundation is required. We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM system developed by Sato and Kameya. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language. We illustrate these principles here, showing parts of a model under development for genetic sequences and indicate firstinitial experiments producing test data for evaluation of existing gene finders, exemplified by GENSCAN. The advantage of the approach is the relative ease and flexibility with which these probabilistic models can be developed.

Question: What is a gene finder?


Standing Out from the Crowd: Emotional Labor, Body Labor, and Temporal Labor in Ridesharing. CSCW researchers have become interested in crowd work as a new form of collaborative engagement, that is, as a new way in which people's actions are coordinated in order to achieve collective effects. We address this area but from a different perspective – that of the labor practices involved in taking crowd work as a form of work. Using empirical materials from a study of ride-sharing, we draw inspiration from studies of the immaterial forms of labor and alternate analyses of political economy that can cast a new light on the context of crowd labor that might matter for CSCW researchers.

Question: What is the difference between crowd work and crowd labor?


An unusual vesical calculus.. We report a 52 year old patient presenting with a bladder stone formed over a migrated intrauterine device (Copper-T). Her history was pertinent for intrauterine contraceptive (IUCD) device placement 10 years back. Investigations included plain ultrasound of abdomen, X-ray of abdomen, urinalysis, and urine culture. Ultrasound and plain X-ray of the pelvis confirmed a bladder stone formed over a migrated copper-T intrauterine device. The device was removed through suprapubic cystolithotomy. Of all the reported cases of vesical stone formation over a migrated IUCD, this case is unique as the patient was an elderly - 52 year old - female. All previously reported cases are of younger age.

Question: What is a vesical calculus?


Interpreting image databases by region classification. This paper addresses automatic interpretation of images of outdoor scenes. The method allows instances of objects from a number of generic classes to be identi ed: vegetation, buildings, vehicles, roads, etc., thereby enabling image databases to be queried on scene content. The feature set is based, in part, on psychophysical principles and includes measures of colour, texture and shape. Using a large database of ground-truth labelled images, a neural network is trained as a pattern classi er. The method is demonstrated on a large test set to provide highly accurate image interpretations, with over 90% of the image area labelled correctly. Scene Understanding Object Recognition Classi cation Image Segmentation Neural Networks

Question: What is the neural network?


A Semantic Web Methodology for Situation-Aware Curative Food Service Recommendation System. Recently curative food is becoming more popular, as more people realize its benefits. Based on the theory of Chinese medicine, food itself is medicine. The Curative food which is an ideal nutritious food can help to loss weight, increase immunity and is also good for curative effects in patients. In this paper, we propose a new curative food service (CFS) recommendation system, the designed Situation-aware Curative Food Service Recommendation System (SCFSRS), which provides the cooperation web-based platform for all related mobile users (MUs) and curative food service providers (CFSPs), could strengthen the ability of CFS suggestion. SCFSRS is a five-tier system composed of the MUs, UDDI registries (UDDIRs), CFSPs, curative food services server (CFSS), and database server (DS).

Question: What is a curative food service?


Crowdsourcing for book search evaluation: impact of hit design on comparative system ranking. The evaluation of information retrieval (IR) systems over special collections, such as large book repositories, is out of reach of traditional methods that rely upon editorial relevance judgments. Increasingly, the use of crowdsourcing to collect relevance labels has been regarded as a viable alternative that scales with modest costs. However, crowdsourcing suffers from undesirable worker practices and low quality contributions. In this paper we investigate the design and implementation of effective crowdsourcing tasks in the context of book search evaluation. We observe the impact of aspects of the Human Intelligence Task (HIT) design on the quality of relevance labels provided by the crowd. We assess the output in terms of label agreement with a gold standard data set and observe the effect of the crowdsourced relevance judgments on the resulting system rankings. This enables us to observe the effect of crowdsourcing on the entire IR evaluation process. Using the test set and experimental runs from the INEX 2010 Book Track, we find that varying the HIT design, and the pooling and document ordering strategies leads to considerable differences in agreement with the gold set labels. We then observe the impact of the crowdsourced relevance label sets on the relative system rankings using four IR performance metrics. System rankings based on MAP and Bpref remain less affected by different label sets while the Precision@10 and nDCG@10 lead to dramatically different system rankings, especially for labels acquired from HITs with weaker quality controls. Overall, we find that crowdsourcing can be an effective tool for the evaluation of IR systems, provided that care is taken when designing the HITs.

Question: What is the impact of crowdsourcing on the evaluation of information retrieval systems?


Antibiotic resistance is ancient. The discovery of antibiotics more than 70 years ago initiated a period of drug innovation and implementation in human and animal health and agriculture. These discoveries were tempered in all cases by the emergence of resistant microbes. This history has been interpreted to mean that antibiotic resistance in pathogenic bacteria is a modern phenomenon; this view is reinforced by the fact that collections of microbes that predate the antibiotic era are highly susceptible to antibiotics. Here we report targeted metagenomic analyses of rigorously authenticated ancient DNA from 30,000-year-old Beringian permafrost sediments and the identification of a highly diverse collection of genes encoding resistance to β-lactam, tetracycline and glycopeptide antibiotics. Structure and function studies on the complete vancomycin resistance element VanA confirmed its similarity to modern variants. These results show conclusively that antibiotic resistance is a natural phenomenon that predates the modern selective pressure of clinical antibiotic use.

Question: What is the oldest antibiotic?


A Survey of Location Prediction on Twitter. Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we make a conclusion of the survey and list future research directions.

Question: What is the size of the canadian military?


Robot path planning : an object-oriented approach. Path planning has important applications in many areas, for example industrial robotics, autonomous systems, virtual prototyping, and computer-aided drug design. This thesis presents a new framework for developing and evaluating path planning algorithms. The framework is named CoPP (Components for Path Planning). It consists of loosely coupled and reusable components that are useful for building path planning applications. The framework is especially designed to make it easy to do fair comparisons between different path planning algorithms. CoPP is also designed to allow almost any user-defined moving system. The default type of moving system is a robot class, which is capable of describing tree-like kinematic chains. Additional features of this robot class are: joint couplings, numerical or closed-form inverse kinematics, and hierarchical robot representations. The last feature is useful when planning for complex systems like a mobile platform equipped with an arm and a hand. During the last six years, Rapidly-exploring Random Trees (RRTs) have become a popular framework for developing randomized path planning algorithms. This thesis presents a method for augmenting bidirectional RRT-planners with local trees. For problems where the solution trajectory has to pass through several narrow passages, local trees help to reduce the required planning time. To reduce the work needed for programming of industrial robots, it is desirable to allow task specifications at a very high level, leaving it up to the robot system to figure out what to do. Here we present a fast and flexible pick-and-place planner. Given an object that has to be moved to another position, the planner chooses a suitable grasp of the object and finds motions that bring the object to the desired position. The planner can also handle constraints on, e.g., the orientation of the manipulated object. For planning of pick-and-place tasks it is necessary to c

Question: What is a pick-and-place planner?


Topology Derivation of Nonisolated Three-Port DC–DC Converters From DIC and DOC. A systematic approach is proposed for the derivation of nonisolated three-port converter (TPC) topologies based on dual-input converters (DIC) and dual-output converters (DOC), which serves as an interface for a renewable source, a storage battery, and a load simultaneously. The power flow in a TPC is analyzed and compared with that in a DIC or a DOC, respectively. Beginning with building the power flow paths of a TPC from a DIC or a DOC, the general principles and detailed procedures for the generation and optimization of TPC topologies are presented. Based on these works, a family of nonisolated TPC topologies is developed. The derived TPCs feature single-stage power conversion between any two of the three ports, and result in high integration and high efficiency. One of the proposed TPCs, named Boost-TPC, is taken as an example for verifying the performance of its circuit topology with related operational methods. Pulsewidth modulation and power management methods, used in this circuit, are analyzed in detail. Experiments have been carried out on a 1-kW prototype of the Boost-TPC, which demonstrate the feasibility and effectiveness of the proposed topology derivation method.

Question: How to derive a topology of a DC–DC converter?


Firefly: illuminating future network-on-chip with nanophotonics. Future many-core processors will require high-performance yet energy-efficient on-chip networks to provide a communication substrate for the increasing number of cores. Recent advances in silicon nanophotonics create new opportunities for on-chip networks. To efficiently exploit the benefits of nanophotonics, we propose Firefly - a hybrid, hierarchical network architecture. Firefly consists of clusters of nodes that are connected using conventional, electrical signaling while the inter-cluster communication is done using nanophotonics - exploiting the benefits of electrical signaling for short, local communication while nanophotonics is used only for global communication to realize an efficient on-chip network. Crossbar architecture is used for inter-cluster communication. However, to avoid global arbitration, the crossbar is partitioned into multiple, logical crossbars and their arbitration is localized. Our evaluations show that Firefly improves the performance by up to 57% compared to an all-electrical concentrated mesh (CMESH) topology on adversarial traffic patterns and up to 54% compared to an all-optical crossbar (OP XBAR) on traffic patterns with locality. If the energy-delay-product is compared, Firefly improves the efficiency of the on-chip network by up to 51% and 38% compared to CMESH and OP XBAR, respectively.

Question: What is a hybrid network?


OpenCV based disease identification of mango leaves. This paper aims in classifying and identifying the diseases of mango leaves for Indian agriculture. K-means algorithm is chosen for the disease segmentation, and the disease classification and identification is carried out using the SVM classifier. Disease identification based on analysis of patches or discoloring of leaf will hold good for some of the plant diseases, but some other diseases which will deform the leaf shape cannot be identified based on the same method. In this case leaf shape based disease identification has to be performed. Based on this analysis two topics are addressed in this research paper. (1) Disease identification using the OpenCV libraries (2) Leaf shape based disease identification. Keywordk-means,Principal Component Analysis (PCA), feature extraction, shape detection, disease identification, Elliptic fourier analysis, Support Vector Machine(SVM), Artificial Neural Network (ANN)

Question: What is the difference between k-means and PCA?


Wind Speed and Rotor Position Sensorless Control for Direct-Drive PMG Wind Turbines. This paper proposes a wind speed and rotor position sensorless control for wind turbines directly driving permanent magnetic generators (PMGs). A sliding-mode observer is designed to estimate the rotor position of the PMG by using the measured stator currents and the commanded stator voltages obtained from the control scheme of the machine-side converter of the PMG wind turbine. The rotor speed of the PMG (i.e., the turbine shaft speed) is estimated from its back electromotive force using a model adaptive reference system observer. Based on the measured output electrical power and estimated rotor speed of the PMG, the mechanical power of the turbine is estimated by taking into account the power losses of the wind turbine generator system. A back-propagation artificial neural network is then designed to estimate the wind speed in real time by using the estimated turbine shaft speed and mechanical power. The estimated wind speed is used to determine the optimal shaft speed reference for the PMG control system. Finally, a sensorless control is developed for the PMG wind turbines to continuously generate the maximum electrical power without using any wind speed or rotor position sensors. The validity of the proposed estimation and control algorithms are shown by simulation studies on a 3-kW PMG wind turbine and are further demonstrated by experimental results on a 300-W practical PMG wind turbine.

Question: How to estimate the wind speed in real time?


Review on trajectory similarity measures. The availability of devices that can be used to track moving objects has increased dramatically leading to a great growth in movement data from almost every application domain. Therefore, there has been an increasing interest in proposing new methodologies for indexing, classifying, clustering, querying and measuring similarity between moving objects' data. One of the main functions for a wide range of application domains is to measure the similarity between two moving objects' trajectories. In this paper, we present a comparative study between widely used trajectory similarity measures observing the advantages and disadvantages of these measures.

Question: What is the advantage of trajectory similarity measures?


Realized Volatility and Variance : Options via Swaps. Let St denote the value of a stock or stock index at time t. Given a variance/volatility option to be priced and hedged, let us designate as time 0 the start of its averaging period, and time T the end. For t ∈ [0, T ], and τ ≤ t, let R2 τ,t denote the realized variance of returns over the time interval [τ, t]. The mathematical results about the synthesis of volatility and variance swaps will hold exactly if R2 refers to the continuously-monitored variance. This means the quadratic variation of logS, ∗Bloomberg L.P. and NYU Courant Institute pcarr@nyc.rr.com. †University of Chicago. RL@math.uchicago.edu. We thank the International Securities Exchange, Kris Monaco,

Question: What is the mathematical definition of realized variance?


Comparative study of trust and reputation systems for wireless sensor networks. Wireless sensor networks (WSNs) are emerging as useful technology for information extraction from the surrounding environment by using numerous small-sized sensor nodes that are mostly deployed in sensitive, unattended, and (sometimes) hostile territories. Traditional cryptographic approaches are widely used to provide security in WSN. However, because of unattended and insecure deployment, a sensor node may be physically captured by an adversary who may acquire the underlying secret keys, or a subset thereof, to access the critical data and/or other nodes present in the network. Moreover, a node may not properly operate because of insufficient resources or problems in the network link. In recent years, the basic ideas of trust and reputation have been applied to WSNs to monitor the changing behaviors of nodes in a network. Several trust and reputation monitoring (TRM) systems have been proposed, to integrate the concepts of trust in networks as an additional security measure, and various surveys are conducted on the aforementioned system. However, the existing surveys lack a comprehensive discussion on trust application specific to the WSNs. This survey attempts to provide a thorough understanding of trust and reputation as well as their applications in the context of WSNs. The survey discusses the components required to build a TRM and the trust computation phases explained with a study of various security attacks. The study investigates the recent advances in TRMs and includes a concise comparison of various TRMs. Finally, a discussion on open issues and challenges in the implementation of trust-based systems is also presented. Copyright © 2012 John Wiley & Sons, Ltd.

Question: What is a wireless sensor network?


The architecture of the Festival speech synthesis system. We describe a new formalism for storing linguistic data in a text to speech system. Linguistic entities such as words and phones are stored as feature structures in a general object called an linguistic item. Items are configurable at run time and via the feature structure can contain arbitrary information. Linguistic relations are used to store the relationship between items of the same linguistic type. Relations can take any graph structure but are commonly trees or lists. Utterance structures contain all the items and relations contained in a single utterance. We first describe the design goals when building a synthesis architecture, and then describe some problems with previous architectures. We then discuss our new formalism in general along with the implementation details and consequences of our approach.

Question: What is a speech synthesis system?


Geo-parsing Messages from Microtext. Widespread use of social media during crises has become commonplace, as shown by the volume of messages during the Haiti earthquake of 2010 and Japan tsunami of 2011. Location mentions are particularly important in disaster messages as they can show emergency responders where problems have occurred. This article explores the sorts of locations that occur in disaster-related social messages, how well off-theshelf software identifies those locations, and what is needed to improve automated location identification, called geo-parsing. To do this, we have sampled Twitter messages from the February 2011 earthquake in Christchurch, Canterbury, New Zealand. We annotated locations in messages manually to make a gold standard by which to measure locations identified by a Named Entity Recognition software. The Stanford NER software found some locations that were proper nouns, but did not identify locations that were not capitalized, local streets and buildings, or nonstandard place abbreviations and mis-spellings that are plentiful in microtext. We review how these problems might be solved in software research, and model a readable crisis map that shows crisis location clusters via enlarged place labels.

Question: What is the difference between a crisis map and a disaster map?


Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science. As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning--pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.

Question: What is a tree-based pipeline optimization tool?