Semantics and implementation of continuous sliding window queries over data streams. In recent years the processing of continuous queries over potentially infinite data streams has attracted a lot of research attention. We observed that the majority of work addresses individual stream operations and system-related issues rather than the development of a general-purpose basis for stream processing systems. Furthermore, example continuous queries are often formulated in some declarative query language without specifying the underlying semantics precisely enough. To overcome these deficiencies, this article presents a consistent and powerful operator algebra for data streams which ensures that continuous queries have well-defined, deterministic results. In analogy to traditional database systems, we distinguish between a logical and a physical operator algebra. While the logical algebra specifies the semantics of the individual operators in a descriptive but concrete way over temporal multisets, the physical algebra provides efficient implementations in the form of stream-to-stream operators. By adapting and enhancing research from temporal databases to meet the challenging requirements in streaming applications, we are able to carry over the conventional transformation rules from relational databases to stream processing. For this reason, our approach not only makes it possible to express continuous queries with a sound semantics, but also provides a solid foundation for query optimization, one of the major research topics in the stream community. Since this article seamlessly explains the steps from query formulation to query execution, it outlines the innovative features and operational functionality implemented in our state-of-the-art stream processing infrastructure.

Question: What is a continuous query?


A survey of unmanned aerial vehicles (UAVs) for traffic monitoring. The focus of this paper is on surveying UAV-based systems for traffic monitoring and management. Although there has been voluminous research on the subject, unmanned aerial vehicles (UAVs) are proven to be a viable and less time-consuming alternative to real-time traffic monitoring and management, providing the eye-in-the-sky solution to the problem.

Question: What is the focus of this paper?


EMD-L1: An Efficient and Robust Algorithm for Comparing Histogram-Based Descriptors. We propose a fast algorithm, EMD-L1, for computing the Earth Mover's Distance (EMD) between a pair of histograms. Compared to the original formulation, EMD-L1 has a largely simplified structure. The number of unknown variables in EMD-L1 is O(N) that is significantly less than O(N) of the original EMD for a histogram with N bins. In addition, the number of constraints is reduced by half and the objective function is also simplified. We prove that the EMD-L1 is formally equivalent to the original EMD with L1 ground distance without approximation. Exploiting the L1 metric structure, an efficient tree-based algorithm is designed to solve the EMD-L1 computation. An empirical study demonstrates that the new algorithm has the time complexity of O(N), which is much faster than previously reported algorithms with super-cubic complexities. The proposed algorithm thus allows the EMD to be applied for comparing histogram-based features, which is practically impossible with previous algorithms. We conducted experiments for shape recognition and interest point matching. EMD-L1 is applied to compare shape contexts on the widely tested MPEG7 shape dataset and SIFT image descriptors on a set of images with large deformation, illumination change and heavy noise. The results show that our EMD-L1based solutions outperform previously reported state-of-the-art features and distance measures in solving the two tasks.

Question: What is the earth mover's distance?


Consistent sparsification for graph optimization. In a standard pose-graph formulation of simultaneous localization and mapping (SLAM), due to the continuously increasing numbers of nodes (states) and edges (measurements), the graph may grow prohibitively too large for long-term navigation. This motivates us to systematically reduce the pose graph amenable to available processing and memory resources. In particular, in this paper we introduce a consistent graph sparsification scheme: (i) sparsifying nodes via marginalization of old nodes, while retaining all the information (consistent relative constraints) - which is conveyed in the discarded measurements - about the remaining nodes after marginalization; and (ii) sparsifying edges by formulating and solving a consistent ℓ1-regularized minimization problem, which automatically promotes the sparsity of the graph. The proposed approach is validated on both synthetic and real data.

Question: How to sparsify a graph?


Case study of the Miner Botnet. Malware and botnets are one of the most serious threats to today's Internet security. In this paper, we characterise the so-called &Miner Botnet". It received major media attention after massive distributed denial of service attacks against a wide range of German and Russian websites, mainly during August and September 2011. We use our insights on this botnet to outline current botnet-related money-making concepts and to show that multiple activities of this botnet are actually centred on the virtual anonymised currency Bitcoin, thus justifying the name. Furthermore, we provide a binary-level analysis of the malware's design and components to illustrate the modularity of the previously mentioned concepts. We give an overview of the structure of the command-and-control protocol as well as of the botnet's architecture. Both centralised as well as distributed infrastructure aspects realised through peer-to-peer are present to run the botnet, the latter for increasing its resiliency. Finally, we provide the results of our ongoing tracking efforts that started in September 2011, focusing on the development of the botnet's size and geographic distribution. In addition we point out the challenge that is generally connected with size measurements of botnets due to the reachability of individual nodes and the persistence of IP addresses over time.

Question: What is the Miner Botnet?


Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction. This study investigates the use of unsupervised features derived from word embedding approaches and novel sequence representation approaches for improving clinical information extraction systems. Our results corroborate previous findings that indicate that the use of word embeddings significantly improve the effectiveness of concept extraction models; however, we further determine the influence that the corpora used to generate such features have. We also demonstrate the promise of sequence-based unsupervised features for further improving concept extraction.

Question: What is the influence of corpora on the effectiveness of word embeddings?


Demand Response as a Market Resource Under the Smart Grid Paradigm. Demand response (DR), distributed generation (DG), and distributed energy storage (DES) are important ingredients of the emerging smart grid paradigm. For ease of reference we refer to these resources collectively as distributed energy resources (DER). Although much of the DER emerging under smart grid are targeted at the distribution level, DER, and more specifically DR resources, are considered important elements for reliable and economic operation of the transmission system and the wholesale markets. In fact, viewed from transmission and wholesale operations, sometimes the term ¿virtual power plant¿ is used to refer to these resources. In the context of energy and ancillary service markets facilitated by the independent system operators (ISOs)/regional transmission organizations (RTOs), the market products DER/DR can offer may include energy, ancillary services, and/or capacity, depending on the ISO/RTO market design and applicable operational standards. In this paper we first explore the main industry drivers of smart grid and the different facets of DER under the smart grid paradigm. We then concentrate on DR and summarize the existing and evolving programs at different ISOs/RTOs and the product markets they can participate in. We conclude by addressing some of the challenges and potential solutions for implementation of DR under smart grid and market paradigms.

Question: What is the difference between demand response and distributed generation?


ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation. This paper reports our submissions to semantic textual similarity task, i.e., task 2 in Semantic Evaluation 2015. We built our systems using various traditional features, such as string-based, corpus-based and syntactic similarity metrics, as well as novel similarity measures based on distributed word representations, which were trained using deep learning paradigms. Since the training and test datasets consist of instances collected from various domains, three different strategies of the usage of training datasets were explored: (1) use all available training datasets and build a unified supervised model for all test datasets; (2) select the most similar training dataset and separately construct a individual model for each test set; (3) adopt multi-task learning framework to make full use of available training sets. Results on the test datasets show that using all datasets as training set achieves the best averaged performance and our best system ranks 15 out of 73.

Question: What is the best strategy for using training datasets?


Learning the Kernel Matrix with Semi-Definite Programming. Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space—classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm— using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

Question: What is kernel-based learning?


Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching. End-to-end deep-learning networks recently demonstrated extremely good performance for stereo matching. However, existing networks are difficult to use for practical applications since (1) they are memory-hungry and unable to process even modest-size images, (2) they have to be trained for a given disparity range. The Practical Deep Stereo (PDS) network that we propose addresses both issues: First, its architecture relies on novel bottleneck modules that drastically reduce the memory footprint in inference, and additional design choices allow to handle greater image size during training. This results in a model that leverages large image context to resolve matching ambiguities. Second, a novel sub-pixel crossentropy loss combined with a MAP estimator make this network less sensitive to ambiguous matches, and applicable to any disparity range without re-training. We compare PDS to state-of-the-art methods published over the recent months, and demonstrate its superior performance on FlyingThings3D and KITTI sets.

Question: What is the practical deep stereo network?


Survey on Cloud Computing Security. Cloud is a modern age computing paradigm which has potential to bring great benefits to information and Communication Technology (ICT) and ICT enabled business. The term Cloud comes from the graphic that was often used to show the heterogeneous networks and complex infrastructure. This graphic was adopted to describe the many aspects of Cloud Computing. In this paper, we aim to identify the security issues in cloud computing. Here we present an analysis of security issues in a cloud environment. Solutions exist to a certain extent for various issues. Keywords—Cloud computing, security challenges, threats

Question: What are the security issues in cloud computing?


Analysis of the wrench-closure workspace of planar parallel cable-driven mechanisms. The mobile platform of a parallel cable-driven mechanism is connected in parallel to a base by lightweight links, such as cables. Since the cables can only work in tension, the set of poses of the mobile platform for which the cables can balance any external wrench, i.e., for which the platform of the mechanism is fully constrained, is often limited or even nonexistent. Thus, the study and determination of this set of poses, called the wrench-closure workspace (WCW), is an important issue for parallel cable-driven mechanisms. In this paper, the case of planar parallel cable-driven mechanisms is addressed. Theorems that characterize the poses of the WCW are proposed. Then, these theorems are used to disclose the parts of the reachable workspace which belong to the WCW. Finally, an efficient algorithm that determines the constant-orientation cross-sections of these parts is introduced.

Question: What is the wrench-closure workspace of a planar parallel cable-driven mechanism?


Predicting the Success of Online Petitions Leveraging Multidimensional Time-Series. Applying classical time-series analysis techniques to online content is challenging, as web data tends to have data quality issues and is often incomplete, noisy, or poorly aligned. In this paper, we tackle the problem of predicting the evolution of a time series of user activity on the web in a manner that is both accurate and interpretable, using related time series to produce a more accurate prediction. We test our methods in the context of predicting signatures for online petitions using data from thousands of petitions posted on The Petition Site—one of the largest platforms of its kind. We observe that the success of these petitions is driven by a number of factors, including promotion through social media channels and on the front page of the petitions platform. We propose an interpretable model that incorporates seasonality, aging effects, self-excitation, and external effects. The interpretability of the model is important for understanding the elements that drives the activity of an online content. We show through an extensive empirical evaluation that our model is significantly better at predicting the outcome of a petition than state-of-the-art techniques.

Question: How can we predict the success of online petitions?


Mood Effects on Cognition : Affective Influences on the Content and Process of Information Processing and Behavior.

Question: What is the mood effects on cognition?


2-Point-based outlier rejection for camera-IMU systems with applications to micro aerial vehicles. This paper presents a novel method to perform the outlier rejection task between two different views of a camera rigidly attached to an Inertial Measurement Unit (IMU). Only two feature correspondences and gyroscopic data from IMU measurerments are used to compute the motion hypothesis. By exploiting this 2-point motion parametrization, we propose two algorithms to remove wrong data associations in the feature-matching process for case of a 6DoF motion. We show that in the case of a monocular camera mounted on a quadrotor vehicle, motion priors from IMU can be used to discard wrong estimations in the framework of a 2-point-RANSAC based approach. The proposed methods are evaluated on both synthetic and real data.

Question: How to reject outliers in a 2-point-based outlier rejection for camera-IMU systems with applications to micro aerial vehicles.


As Time Goes By: Comprehensive Tagging of Textual Phrases with Temporal Scopes. Temporal expressions (TempEx's for short) are increasingly important in search, question answering, information extraction, and more. Techniques for identifying and normalizing explicit temporal expressions work well, but are not designed for and cannot cope with textual phrases that denote named events, such as "Clinton's term as secretary of state". This paper addresses the problem of detecting such temponyms, inferring their temporal scopes, and mapping them to events in a knowledge base if present there. We present methods for this kind of temponym resolution, using an entityand TempEx-oriented document model and the Yago knowledge base for distant supervision. We develop a family of Integer Linear Programs for jointly inferring temponym mappings to the timeline and knowledge base. This enriches the document representation and also extends the knowledge base by obtaining new alias names for events. Experiments with three different corpora demonstrate the viability of our methods.

Question: What is the name of the first large-scale Canadian peacekeeping mission?


node2vec: Scalable Feature Learning for Networks. Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations.  We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

Question: What is the difference between node2vec and other feature learning algorithms?


Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection. Recently, microblogging sites such as Twitter have garnered a great deal of attention as an advanced form of location-aware social network services, whereby individuals can easily and instantly share their most recent updates from any place. In this study, we aim to develop a geo-social event detection system by monitoring crowd behaviors indirectly via Twitter. In particular, we attempt to find out the occurrence of local events such as local festivals; a considerable number of Twitter users probably write many posts about these events. To detect such unusual geo-social events, we depend on geographical regularities deduced from the usual behavior patterns of crowds with geo-tagged microblogs. By comparing these regularities with the estimated ones, we decide whether there are any unusual events happening in the monitored geographical area. Finally, we describe the experimental results to evaluate the proposed unusuality detection method on the basis of geographical regularities obtained from a large number of geo-tagged tweets around Japan via Twitter.

Question: How to measure the geographical regularities of crowd behaviors for Twitter-based geo-social event detection?


Key Estimation in Electronic Dance Music. In this paper we study key estimation in electronic dance music, an umbrella term referring to a variety of electronic music subgenres intended for dancing at nightclubs and raves. We start by defining notions of tonality and key before outlining the basic architecture of a template-based key estimation method. Then, we report on the tonal characteristics of electronic dance music, in order to infer possible modifications of the method described. We create new key profiles combining these observations with corpus analysis, and add two pre-processing stages to the basic algorithm. We conclude by comparing our profiles to existing ones, and testing our modifications on independent datasets of pop and electronic dance music, observing interesting improvements in the performance or our algorithms, and suggesting paths for future research.

Question: What is the key of electronic dance music?


Linear scan register allocation. We describe a new algorithm for fast global register allocation called linear scan. This algorithm is not based on graph coloring, but allocates registers to variables in a single linear-time scan of the variables' live ranges. The linear scan algorithm is considerably faster than algorithms based on graph coloring, is simple to implement, and results in code that is almost as efficient as that obtained using more complex and time-consuming register allocators based on graph coloring. The algorithm is of interest in applications where compile time is a concern, such as dynamic compilation systems, "just-in-time" compilers, and interactive development environments.

Question: What is a "just-in-time" compiler?


Business Intelligence and Business Process Management in Banking Operations. Success of banking operations is strongly correlated with the quality of customer relations and efficacy of banks processes. Banks seek means for efficient analysis of vast amount of gathered data from their IT systems. They are exploiting business intelligence (BI) technology to analyze every aspect of their data to understand behavior of their clients, striving to satisfy client's needs, in an endless race for a competitive advantage in the market. The intention of this paper is to review and discuss the most significant applications of business intelligence in banking as well as point to new technology trends that will affect bank's development.

Question: What is business intelligence?


Silhouette Smoothing for Real-Time Rendering of Mesh Surfaces. Coarse piecewise linear approximation of surfaces causes the undesirable polygonal appearance of silhouettes. We present an efficient method for smoothing the silhouettes of coarse triangle meshes using efficient 3D curve reconstruction and simple local remeshing. It does not assume the availability of a fine mesh and generates only a moderate amount of additional data at runtime. Furthermore, polygonal feature edges are also smoothed in a unified framework. Our method is based on a novel interpolation scheme over silhouette triangles, and this ensures that smooth silhouettes are faithfully reconstructed and always change continuously with respect to the continuous movement of the viewpoint or objects. We speed up computation with GPU assistance to achieve real-time rendering of coarse meshes with the smoothed silhouettes. Experiments show that this method outperforms previous methods for silhouette smoothing.

Question: How to smooth the silhouette of a mesh?


CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images.. With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.

Question: How to generate a large number of photo-realistic face images?


Agent and Cyber-Physical System Based Self-Organizing and Self-Adaptive Intelligent Shopfloor. The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities.

Question: What is the self-organizing model?


Seeded watershed cut uncertainty estimators for guided interactive segmentation. Watershed cuts are among the fastest segmentation algorithms and therefore well suited for interactive segmentation of very large 3D data sets. To minimize the number of user interactions ("seeds") required until the result is correct, we want the computer to actively query the human for input at the most critical locations, in analogy to active learning. These locations are found by means of suitable uncertainty measures. We propose various such measures for watershed cuts along with a theoretical analysis of some of their properties. Extensive evaluation on two types of 3D electron microscopic volumes of neural tissue shows that measures which estimate the non-local consequences of new user inputs achieve performance close to an oracle endowed with complete knowledge of the ground truth.

Question: What is the size of the neural tissue?


Clustering of Gaze During Dynamic Scene Viewing is Predicted by Motion. Where does one attend when viewing dynamic scenes? Research into the factors influencing gaze location during static scene viewing have reported that low-level visual features contribute very little to gaze location especially when opposed by high-level factors such as viewing task. However, the inclusion of transient features such as motion in dynamic scenes may result in a greater influence of visual features on gaze allocation and coordination of gaze across viewers. In the present study, we investigated the contribution of low- to mid-level visual features to gaze location during free-viewing of a large dataset of videos ranging in content and length. Signal detection analysis on visual features and Gaussian Mixture Models for clustering gaze was used to identify the contribution of visual features to gaze location. The results show that mid-level visual features including corners and orientations can distinguish between actual gaze locations and a randomly sampled baseline. However, temporal features such as flicker, motion, and their respective contrasts were the most predictive of gaze location. Additionally, moments in which all viewers' gaze tightly clustered in the same location could be predicted by motion. Motion and mid-level visual features may influence gaze allocation in dynamic scenes, but it is currently unclear whether this influence is involuntary or due to correlations with higher order factors such as scene semantics.

Question: How does the motion of a scene influence the gaze of viewers?


Segment-aware energy-efficient management of heterogeneous memory system for ultra-low-power IoT devices. The emergence of IoT (Internet of Things) has brought various studies on low-power techniques back to embedded systems. In general, minimizing power consumed by program executions is the main consideration of system design. While running programs, executing data-dependent program code results in a large number of memory accesses which consume huge amount of power. Further, most embedded systems consist of multiple types of memory devices, i.e., heterogeneous memory system, to benefit the different characteristics of memory devices. In this paper, we conduct a research on low-power techniques to reduce the power consumption of heterogeneous memory to achieve ultra-low-power in the system level. This study proposes a segment-aware energy-efficient management to improve the power efficiency considering the characteristics and structures of the memory devices. In the proposed approach, the technique migrates program code from allocated memory device to another if the consumed power is considered to be less. We also analyze and evaluate the comprehensive effects on energy efficiency by applying the technique as well. Compared to the unmodified program code, our model reduces power consumption up to 12.98% by migrating functions.

Question: How to reduce power consumption in heterogeneous memory system?


Mapping science through bibliometric triangulation: An experimental approach applied to water research. The idea of constructing science maps based on bibliographic data has intrigued researchers for decades, and various techniques have been developed to map the structure of research disciplines. Most science mapping studies use a single method. However, as research fields have various properties, a valid map of a field should actually be composed of a set of maps derived from a series of investigations using different methods. That leads to the question what can be learned from a combination – triangulation – of these different science maps. In this paper, we propose a method for triangulation, using the example of water science. We combine three different mapping approaches: journal-journal citation relations (JJCR), shared author keywords (SAK), and title word-cited reference co-occurrence (TWRC). Our results demonstrate that triangulation of JJCR, SAK, and TWRC produces a more comprehensive picture than each method does individually. The outcomes from the three different approaches are associated with each other and can be systematically interpreted, and provide insights into the complex multidisciplinary structure of the field of water

Question: What is the multidisciplinary structure of the field of water?


Wideband Patch Antenna for HPM Applications. This paper presents the design, fabrication, and characterization of a compact wideband antenna for high-power microwave applications. The antennas proposed are array of high-power wideband patches with high compactness and less than λ/10 thick. The concept developed can be fed by high-voltage signals (up to 60 kV) in repetitive operation. Two designs are produced at central frequencies of 350 MHz and 1 GHz. Their relative bandwidth is larger than 40% at 350 MHz and 25% at 1 GHz for S11 <; - 10 dB, respectively. The arrays studied produce a gain of more than 14 dB.

Question: What is a patch antenna?


Cloud-assisted body area networks: state-of-the-art and future challenges. Body Area Networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed.

Question: What is the state-of-the-art in cloud-assisted body area networks?


TumorNet: Lung nodule characterization using multi-view Convolutional Neural Network with Gaussian Process. Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a fast and robust computer aided system. In this work, we propose an end-to-end trainable multi-view deep Convolutional Neural Network (CNN) for nodule characterization. First, we use median intensity projection to obtain a 2D patch corresponding to each dimension. The three images are then concatenated to form a tensor, where the images serve as different channels of the input image. In order to increase the number of training samples, we perform data augmentation by scaling, rotating and adding noise to the input image. The trained network is used to extract features from the input image followed by a Gaussian Process (GP) regression to obtain the malignancy score. We also empirically establish the significance of different high level nodule attributes such as calcification, sphericity and others for malignancy determination. These attributes are found to be complementary to the deep multi-view CNN features and a significant improvement over other methods is obtained.

Question: What is the significance of different high level nodule attributes for malignancy determination?


Copysets: Reducing the Frequency of Data Loss in Cloud Storage. Random replication is widely used in data center storage systems to prevent data loss. However, random replication is almost guaranteed to lose data in the common scenario of simultaneous node failures due to cluster-wide power outages. Due to the high fixed cost of each incident of data loss, many data center operators prefer to minimize the frequency of such events at the expense of losing more data in each event. We present Copyset Replication, a novel generalpurpose replication technique that significantly reduces the frequency of data loss events. We implemented and evaluated Copyset Replication on two open source data center storage systems, HDFS and RAMCloud, and show it incurs a low overhead on all operations. Such systems require that each node's data be scattered across several nodes for parallel data recovery and access. Copyset Replication presents a near optimal tradeoff between the number of nodes on which the data is scattered and the probability of data loss. For example, in a 5000-node RAMCloud cluster under a power outage, Copyset Replication reduces the probability of data loss from 99.99% to 0.15%. For Facebook's HDFS cluster, it reduces the probability from 22.8% to 0.78%.

Question: How does copyset replication work?


Pellet: A practical OWL-DL reasoner. In this report, we present Pellet: a complete and capable OWL-DL reasoner with acceptable to very good performance, extensive middleware, and a number of unique features. Pellet is written in Java and is open source under a very liberal license. It is used in a number of projects, from pure research to industrial settings. Pellet is the first sound and complete OWL-DL reasoner with extensive support for rea-soning with individuals (including nominal support and conjunctive query), user-defined datatypes, and debugging support for ontologies. It implements several extensions to OWL-DL including a combination formalism for OWL-DL ontologies, a non-monotonic operator, and preliminary support for OWL/Rule hybrid reasoning. It has proven to be a reliable tool for working with OWL-DL ontologies and experimenting with OWL extensions. In this paper we describe Pellet's features, architecture and special capabilities, along with an empirical comparison of its performance against other leading OWL-DL reasoners.

Question: What is the first large-scale Canadian peacekeeping mission?


Exercise with blood flow restriction: an updated evidence-based approach for enhanced muscular development.. A growing body of evidence supports the use of moderate blood flow restriction (BFR) combined with low-load resistance exercise to enhance hypertrophic and strength responses in skeletal muscle. Research also suggests that BFR during low-workload aerobic exercise can result in small but significant morphological and strength gains, and BFR alone may attenuate atrophy during periods of unloading. While BFR appears to be beneficial for both clinical and athletic cohorts, there is currently no common consensus amongst scientists and practitioners regarding the best practice for implementing BFR methods. If BFR is not employed appropriately, there is a risk of injury to the participant. It is also important to understand how variations in the cuff application can affect the physiological responses and subsequent adaptation to BFR training. The optimal way to manipulate acute exercise variables, such as exercise type, load, volume, inter-set rest periods and training frequency, must also be considered prior to designing a BFR training programme. The purpose of this review is to provide an evidence-based approach to implementing BFR exercise. These guidelines could be useful for practitioners using BFR training in either clinical or athletic settings, or for researchers in the design of future studies investigating BFR exercise.

Question: What is the purpose of this review?


Product Geometric Crossover for the Sudoku Puzzle. Geometric crossover is a representation-independent definition of crossover based on the distance of the search space interpreted as a metric space. It generalizes the traditional crossover for binary strings and other important recombination operators for the most used representations. Using a distance tailored to the problem at hand, the abstract definition of crossover can be used to design new problem specific crossovers that embed problem knowledge in the search. In recent work, we have introduced the important notion of product geometric crossover that enables the construction of new geometric crossovers combining preexisting geometric crossovers in a simple way. In this paper, we use it to design an evolutionary algorithm to solve the Sudoku puzzle. The different types of constraints make Sudoku an interesting study case for crossover design. We conducted extensive experimental testing and found that, on medium and hard problems, the new geometric crossovers perform significantly better than hill-climbers and mutations alone.

Question: What is a geometric crossover?


An Embedded Support Vector Machine. In this paper we work on the balance between hardware and software implementation of a machine learning algorithm, which belongs to the area of statistical learning theory. We use system-on-chip technology to demonstrate the potential usefulness of moving the critical sections of an algorithm into HW: the so-called hardware/software balance. Our experiments show that the approach can achieve speedups using a complex machine learning algorithm called a support vector machine. The experiments are conducted on a real-time Java virtual machine named Java optimized processor

Question: What is a support vector machine?


Design of biped robot inspired by cats for fast running. ELECT A novel design of a biped robot inspired by domestic cats for fast running is introduced. The skeletomuscular system of a cat is analysed and applied to determine the link parameters and the linkage structure of the proposed mechanism. The linkage design of the leg mechanism is explained and a kinematic analysis based on vector loop equations is performed. The effectiveness of the proposed mechanism is verified experimentally. The biped robot runs at an average speed of 2 m/s at a step frequency of 4 Hz. This leg mechanism can facilitate the development of fast running robot systems.

Question: What is the average speed of a cat?


Centrifugal fan design for permanent magnet synchronous motor in a traction application. The requirements of high torque density and high efficiency, which are particularly pronounced in electric traction applications, often result in substantial thermal loading of electric machines for driving trams, electric multiple units (EMU) or electric cars. Permanent magnet synchronous machines are suitable candidates for traction applications due to their inherently high torque density and high efficiency. At the same time they are sensitive to temperature rise, especially in permanent magnets, highlighting the need for implementation of efficient cooling system. The performance of the cooling system and its ability to remove heat directly affect the attainable torque and efficiency of the electric machine. In this paper, the selection and sizing of the cooling system for an interior permanent magnet motor designed to drive a low-floor tram is presented. The procedure for selecting the basic dimensions of the centrifugal fan according to the analytical formulas in combination with computational fluid dynamics (CFD) analysis are explained. In addition to the geometry of the centrifugal fan itself, the geometry of the passive system components (e.g. air flow router) which have a significant impact on the performance of the cooling system, are also considered. The results of computer aided CFD analysis, which is taken as a benchmark of system performance in the design stage of the cooling system, have been confirmed with measurements on the machine prototype.

Question: What is the difference between a centrifugal fan and a centrifugal blower?


Firewalls and internet security: Repelling the wily hacker, 2nd ed. [Book Review]. General Interest How the Internet Works, 7th ed., Preston Gralla (Que, 2004, 368 pp., ISBN 0-78972973-3, $29.99; www.quepublishing.com) Preston Gralla is a well-known technology columnist and author. In this large-format book lavishly illustrated by Michael Troller, Gralla explains all of the concepts that most people are likely to encounter in connecting to the Internet. The explanations are not deep, but they systematically cover the basics. Most people, even experts, will probably find at least some of the explanations helpful. For example, I feel that I know quite a bit about the Internet, but Gralla's explanation of proxy servers clarified the subject for me. The illustrations are colorful and attractive, but in many cases are merely decorative. Visual learners may find them helpful, though I doubt that anyone could get much from the book just by looking at the pictures. The text, on the other hand, can probably stand alone. If you know people who are just starting to use the Internet and electronic mail, consider giving them this book. It may save you from having to answer some difficult questions.

Question: What is a proxy server?


Improving classification with latent variable models by sequential constraint optimization. In this paper we propose a method to use multiple generative models with latent variables for classi cation tasks. The standard approach to use generative models for classi cation is to train a separate model for each class. A novel data point is then classi ed by the model that attributes the highest probability. The algorithm we propose modi es the parameters of the models to improve the classi cation accuracy. Our approach is made computationally tractable by assuming that each of the models is deterministic, by which we mean that a data-point is associated to only a single latent state. The resulting algorithm is a variant of the support vector machine learning algorithm and in a limiting case the method is similar to the standard perceptron learning algorithm. We apply the method to two types of latent variable models. The rst has a discrete latent state space and the second, principal component analysis, has a continuous latent state space. We compare the e6ectiveness of both approaches on a handwritten digit recognition problem and on a satellite image recognition problem. c © 2003 Elsevier B.V. All rights reserved.

Question: What is the difference between a generative model and a discriminative model?


Does telecommuting really increase productivity?. As many companies have learned in the last decade, the reality of telecommuting does not reflect the hype, the expected potential, or the existing literature.

Question: Does telecommuting really increase productivity?


Peer support services for individuals with serious mental illnesses: assessing the evidence.. OBJECTIVE This review assessed the level of evidence and effectiveness of peer support services delivered by individuals in recovery to those with serious mental illnesses or co-occurring mental and substance use disorders.   METHODS Authors searched PubMed, PsycINFO, Applied Social Sciences Index and Abstracts, Sociological Abstracts, Social Services Abstracts, Published International Literature on Traumatic Stress, the Educational Resources Information Center, and the Cumulative Index to Nursing and Allied Health Literature for outcome studies of peer support services from 1995 through 2012. They found 20 studies across three service types: peers added to traditional services, peers in existing clinical roles, and peers delivering structured curricula. Authors judged the methodological quality of the studies using three levels of evidence (high, moderate, and low). They also described the evidence of service effectiveness.   RESULTS The level of evidence for each type of peer support service was moderate. Many studies had methodological shortcomings, and outcome measures varied. The effectiveness varied by service type. Across the range of methodological rigor, a majority of studies of two service types--peers added and peers delivering curricula--showed some improvement favoring peers. Compared with professional staff, peers were better able to reduce inpatient use and improve a range of recovery outcomes, although one study found a negative impact. Effectiveness of peers in existing clinical roles was mixed.   CONCLUSIONS Peer support services have demonstrated many notable outcomes. However, studies that better differentiate the contributions of the peer role and are conducted with greater specificity, consistency, and rigor would strengthen the evidence.

Question: What is the evidence for peer support services?


New generation waveform approaches for 5G and beyond. For 5G and beyond cellular communication systems, new waveform development studies have been continuing in the whole world. Solutions for providing high throughput, high data rates, low latency, large number of terminals, supporting multiple antenna systems, and the related problems originated from these issues are playing an important role in the development of 5G and new waveform technologies. OFDM which has many important advantages continues to be included in various standards. However, because of its disadvantages, OFDM will not be sufficient for dynamic spectrum access and cognitive radio applications in new generation communication systems gradually. Additionally, OFDM is not suitable for asynchronous networks and radios which we expect to see commonly in the future. In this study, some potential candidate 5G waveforms are examined and various comparisons are carried out. Besides, assessments about future hybrid solutions are presented.

Question: What is the difference between OFDM and OFDMA?


Unsupervised Event Tracking by Integrating Twitter and Instagram. This paper proposes an unsupervised framework for tracking real world events from their traces on Twitter and Instagram. Empirical data suggests that event detection from Instagram streams errs on the false-negative side due to the relative sparsity of Instagram data (compared to Twitter data), whereas event detection from Twitter can suffer from false-positives, at least if not paired with careful analysis of tweet content. To tackle both problems simultaneously, we design a unified unsupervised algorithm that fuses events detected originally on Instagram (called I-events) and events detected originally on Twitter (called T-events), that occur in adjacent periods, in an attempt to combine the benefits of both sources while eliminating their individual disadvantages. We evaluate the proposed framework with real data crawled from Twitter and Instagram. The results indicate that our algorithm significantly improves tracking accuracy compared to baselines.

Question: How to track events from twitter and instagram?


A PSO-Based Hybrid Metaheuristic for Permutation Flowshop Scheduling Problems. This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature.

Question: What is the objective of the paper?


Learning Parameterized Skills. LEARNING PARAMETERIZED SKILLS

Question: What is a parameterized skill?


Understanding user situational relevance in ranking web search results. Relevance plays a key role in determining how ranking features are weighed in rank algorithms. In particular, the Search Engine Result Page (SERP) presents documents from the algorithmic relevance perspective. User relevance, however, is situational and describes the searcher's perception of the contextual document which is interpreted at rank time. Therefore, without it the monotonic decreasing click trend portrayed by rank algorithms will not hold for real time searchers. Instead, users select low ranked documents over high ones or reject the ranking, leading to frustration and several re-query tasks. To understand users situational relevance (USR), we formulate a decreasing probability function to measure each user's personalized probabilistic relevance (PPR). Next, we find an aggregated PPR representing the USR across several query categories. Then, we apply maximum probabilities swapping to modify the rank function and re-rank documents. Finally, we use Normalized Discounted Cumulative Gain (NDCG) and click weight metrics for evaluation. Our results show that a clear interpretation and incorporation of user situational relevance improves click satisfaction by 12.86%, and optimizes click through rate up to 42%.

Question: How to understand user situational relevance?


Does the use of social networking sites increase children's risk of harm?. Although research findings have been equivocal as to whether the use of social networking sites (SNS) increases experiences of online risk among children, the affordances of SNS lend support to this possibility, attracting much policy and public concern. The present article examines whether the use of such services increases the risks that children and young people encounter by analyzing data from a random stratified sample of approximately 1000 internetusing children aged 9-16 years in each of 25 European countries. Four hypotheses were formulated and tested. The first hypothesis, namely that children who use social networking sites will encounter more risks online than those who do not, is supported by the data. The second hypothesis stated that SNS users with more digital competence will encounter more online risk than those with less competence; this was also supported, despite being counter to common assumptions. Thirdly, we hypothesized that SNS users with more risky SNS practices (e.g. a public profile, displaying identifying information, with a very large number of contacts) will encounter more online risk than those with fewer risky practices: this too was supported by the data; thus what matters for risk is how SNS are used, a useful point for awareness-raising initiatives. The fourth hypothesis stated that SNS users with more digital competence in using the internet will experience less harm associated with online risk. The data did not support this hypothesis, since digital competence did not reduce the probability of children saying that they have been bothered or upset by something on the internet. Finally, the study found that, although this had not been predicted, whether or not risks are experienced as harmful depends on the specific relation between risks and platforms (website, instant messaging, gaming or social networking). We call on future research to explore how partic

Question: What is the relationship between the use of social networking sites and the experience of online risk?


Odor Recognition in Multiple E-Nose Systems With Cross-Domain Discriminative Subspace Learning. In this paper, we propose an odor recognition framework for multiple electronic noses (E-noses), machine olfaction odor perception systems. Straight to the point, the proposed transferring odor recognition model is called cross-domain discriminative subspace learning (CDSL). General odor recognition problems with E-nose are single domain oriented, that is, recognition algorithms are often modeled and tested on the same one domain data set (i.e., from only one E-nose system). Different from that, we focus on a more realistic scenario: the recognition model is trained on a prepared source domain data set from a master E-nose system <inline-formula> <tex-math notation="LaTeX">${A}$ </tex-math></inline-formula>, but tested on another target domain data set from a slave system <inline-formula> <tex-math notation="LaTeX">${B}$ </tex-math></inline-formula> or <inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> with the same type of the master system <inline-formula> <tex-math notation="LaTeX">${A}$ </tex-math></inline-formula>. The internal device parameter variance between master and slave systems often results in data distribution discrepancy between source domain and target domain, such that single-domain-based odor recognition model may not be adapted to another domain. Therefore, we propose domain-adaptation-based odor recognition for addressing the realistic recognition scenario across systems. Specifically, the proposed CDSL method consists of three merits: 1) an intraclass scatter minimization- and an interclass scatter maximization-based discriminative subspace learning is solved on source domain; 2) a data fidelity and preservation constraint of the subspace is imposed on target domain without distortion; and 3) a minipatch feature weighted domain distance is minimized for closely connecting the source and target domains. Experiments and comp

Question: What is the difference between a single domain and a cross domain?


Which User Interactions Predict Levels of Expertise in Work-Integrated Learning?. Predicting knowledge levels from user's implicit interactions with an adaptive system is a difficult task, particularly in learning systems that are used in the context of daily work tasks. We have collected interactions of six persons working with the adaptive work-integrated learning system APOSDLE over a period of two months to find out whether naturally occurring interactions with the system can be used to predict their level of expertise. One set of interactions is based on the tasks they performed, the other on a number of additional Knowledge Indicating Events (KIE). We find that the addition of KIE significantly improves the prediction as compared to using tasks only. Both approaches are superior to a model that uses only the frequencies of events.

Question: Which User Interactions Predict Levels of Expertise in Work-Integrated Learning?


Security Issues in the Internet of Things ( IoT ) : A Comprehensive Study. Wireless communication networks are highly prone to security threats. The major applications of wireless communication networks are in military, business, healthcare, retail, and transportations. These systems use wired, cellular, or adhoc networks. Wireless sensor networks, actuator networks, and vehicular networks have received a great attention in society and industry. In recent years, the Internet of Things (IoT) has received considerable research attention. The IoT is considered as future of the internet. In future, IoT will play a vital role and will change our living styles, standards, as well as business models. The usage of IoT in different applications is expected to rise rapidly in the coming years. The IoT allows billions of devices, peoples, and services to connect with others and exchange information. Due to the increased usage of IoT devices, the IoT networks are prone to various security attacks. The deployment of efficient security and privacy protocols in IoT networks is extremely needed to ensure confidentiality, authentication, access control, and integrity, among others. In this paper, an extensive comprehensive study on security and privacy issues in IoT networks is provided. Keywords—Internet of Things (IoT); security issues in IoT; security; privacy

Question: What is the size of the canadian military?


Patient Monitoring System Using GSM Technology. In this fast pace of life, it is difficult for people to be constantly available for their near ones who might need them while they are suffering from a disease or physical disorder. So also constant monitoring of the patient's body parameters such as temperature, pulse rate, sugar level etc. becomes difficult. Hence to remove human error and to lessen the burden of monitoring patient's health from doctor's head, this paper presents the methodology for monitoring patients remotely using GSM network and Very Large Scale Integration (VLSI) technology. Patient monitoring systems measure physiological characteristics either continuously or at regular intervals of time.

Question: What is the difference between a patient monitoring system and a patient monitoring system using GSM technology?


Validation of Document Clustering based on Purity and Entropy measures. Document clustering aims to automatically group related document into clusters. If two documents are close to each other in the original document space, they are grouped into the same cluster. If the two documents are far away from each other in the original document space, they tend to be grouped into different cluster. The classical clustering algorithms assign each data to exactly one cluster, but fuzzy c-means allow data belong to different clusters. Fuzzy clustering is a powerful unsupervised method for the analysis of data. Cluster validity measure is useful in estimating the optimal number of clusters. Purity and Entropy are the validity measures used in this

Question: What is the optimal number of clusters?


Matchmaker: constructing constrained texture maps. Texture mapping enhances the visual realism of 3D models by adding fine details. To achieve the best results, it is often necessary to force a correspondence between some of the details of the texture and the features of the model.The most common method for mapping texture onto 3D meshes is to use a planar parameterization of the mesh. This, however, does not reflect any special correspondence between the mesh geometry and the texture. The Matchmaker algorithm presented here forces user-defined feature correspondence for planar parameterization of meshes. This is achieved by adding positional constraints to the planar parameterization. Matchmaker allows users to introduce scores of constraints while maintaining a valid one-to-one mapping between the embedding and the 3D surface. Matchmaker's constraint mechanism can be used for other applications requiring parameterization besides texture mapping, such as morphing and remeshing.Matchmaker begins with an unconstrained planar embedding of the 3D mesh generated by conventional methods. It moves the constrained vertices to the required positions by matching a triangulation of these positions to a triangulation of the planar mesh formed by paths between constrained vertices. The matching triangulations are used to generate a new parameterization that satisfies the constraints while minimizing the deviation from the original 3D geometry.

Question: How to construct a constrained texture map?


A Fast Computational Algorithm for the Discrete Cosine Transform. After his military service he joined N. V. Philips' Gloeilampenfabrieken, Eindhoven, where he worked in the fields of electronic design of broadcast radio sets, integrated circuits and optical character recognition. Since 1970 he has been at the Philips Research Laboratories , where he is now working on digital communication systems. Mr. Roza is a member of the Netherlands Electronics and Radio Society. he worked in the field of data transmission and on the synchronizationaspects of a videophone system. Since 1972 he has been engaged in system design and electronics of high speed digital transmission. Abstruct-A Fast Discrete Cosine Transform algorithm has been developed which provides a factor of six improvement in computational complexity when compared to conventional Discrete Cosine Transform algorithms using the Fast Fourier Transform. The algorithm is derived in the form of matrices and illustrated by a signal-flow graph, which may be readily translated to hardware or software implementations.

Question: What is the fast discrete cosine transform algorithm?


Fog Computing for the Internet of Mobile Things: Issues and Challenges. The Internet of Things (IoT) conceives a world where everyday objects are able to join the Internet and exchange data as well as process, store, collect them from the surrounding environment, and actively intervene on it. An unprecedented number of services may be envisioned by exploiting the Internet of Things. Fog Computing, which is also known as Edge Computing, was proposed in 2012 as the ideal paradigm to support the resource-constrained IoT devices in data processing and information delivery. Indeed, the Fog, which does not replace the centralized Cloud but cooperates with it, distributes Cloud Computing technologies and principles anywhere along the Cloud-to-Things continuum and particularly at the network edge. The Fog proximity to the IoT devices allows for several advantages that must be continuously guaranteed, also when end devices move from one place to another. In this paper, we aim at examining in depth what it means to provide mobility support in a Fog environment and at investigating what are the main challenges to be faced. Besides, in order to highlight the importance of this topic in everyday life, we provide the reader with three scenarios where there is an integration between the IoT and Fog Computing, and in which mobility support is essential. We finally point out the main research directions in the field.

Question: What is fog computing?


Connectionist speech recognition of Broadcast News. This paper describes connectionist techniques for recogni ti n of Broadcast News. The fundamental difference between connectionist systems and more convent ional mixture-of-Gaussian systems is that connectionist models directly estimate posterior probabi lities as opposed to likelihoods. Access to posterior probabilities has enabled us to develop a number of no vel approaches to confidence estimation, pronunciation modelling and search. In addition we have inv stigated a new feature extraction technique based on the modulation-filtered spectrogram, and methods f or combining multiple information sources. We have incorporated all of these techniques into a system fo r the transcription of Broadcast News, and we present results on the 1998 DARPA Hub-4E Broadcast News ev aluation data.

Question: How does connectionist speech recognition work?


An empirical analysis of the distribution of unit test smells and their impact on software maintenance. Unit testing represents a key activity in software development and maintenance. Test suites with high internal quality facilitate maintenance activities, such as code comprehension and regression testing. Several guidelines have been proposed to help developers write good test suites. Unfortunately, such rules are not always followed resulting in the presence of bad test code smells (or simply test smells). Test smells have been defined as poorly designed tests and their presence may negatively affect the maintainability of test suites and production code. Despite the many studies that address code smells in general, until now there has been no empirical evidence regarding test smells (i) distribution in software systems nor (ii) their impact on the maintainability of software systems. This paper fills this gap by presenting two empirical studies. The first study is an exploratory analysis of 18 software systems (two industrial and 16 open source) aimed at analyzing the distribution of test smells in source code. The second study, a controlled experiment involving twenty master students, is aimed at analyzing whether the presence of test smells affects the comprehension of source code during software maintenance. The results show that (i) test smells are widely spread throughout the software systems studied and (ii) most of the test smells have a strong negative impact on the comprehensibility of test suites and production code.

Question: What is the impact of test smells on the comprehension of source code?


Learning physical parameters from dynamic scenes. Humans acquire their most basic physical concepts early in development, and continue to enrich and expand their intuitive physics throughout life as they are exposed to more and varied dynamical environments. We introduce a hierarchical Bayesian framework to explain how people can learn physical parameters at multiple levels. In contrast to previous Bayesian models of theory acquisition (Tenenbaum, Kemp, Griffiths, & Goodman, 2011), we work with more expressive probabilistic program representations suitable for learning the forces and properties that govern how objects interact in dynamic scenes unfolding over time. We compare our model to human learners on a challenging task of estimating multiple physical parameters in novel microworlds given short movies. This task requires people to reason simultaneously about multiple interacting physical laws and properties. People are generally able to learn in this setting and are consistent in their judgments. Yet they also make systematic errors indicative of the approximations people might make in solving this computationally demanding problem with limited computational resources. We propose two approximations that complement the top-down Bayesian approach. One approximation model relies on a more bottom-up feature-based inference scheme. The second approximation combines the strengths of the bottom-up and top-down approaches, by taking the feature-based inference as its point of departure for a search in physical-parameter space.

Question: How do people learn physical parameters from dynamic scenes?


The hidden treasure in nursing leadership: informal leaders.. AIM The goal of the present article was to generate awareness of characteristics of informal leaders in healthcare with the emphasis on nurses in acute care settings. There is limited research or literature regarding informal leaders in nursing and how they positively impact nursing management, the organization and, ultimately, patient care. Identification of nurses with leadership characteristics is important so that leadership development and mentoring can occur within the nursing profession.   BACKGROUND More than ever, nursing needs energetic, committed and dedicated leaders to meet the challenges of the healthcare climate and the nursing shortage. This requires nurse leaders to consider all avenues to ensure the ongoing profitability and viability of their healthcare facility.   KEY ISSUES This paper discusses clinical nurses as informal leaders; characteristics of the informal nurse leader, the role they play, how they impact their unit and how they shape the organization.   IMPLICATION FOR NURSING MANAGEMENT   Informal nurse leaders are an underutilized asset in health care. If identified early, these nurses can be developed and empowered to impact unit performance, efficiency and environmental culture in a positive manner.

Question: What are the characteristics of an informal leader?


Towards a General Rule for Identifying Deceptive Opinion Spam. Consumers' purchase decisions are increasingly influenced by user-generated online reviews. Accordingly, there has been growing concern about the potential for posting deceptive opinion spam— fictitious reviews that have been deliberately written to sound authentic, to deceive the reader. In this paper, we explore generalized approaches for identifying online deceptive opinion spam based on a new gold standard dataset, which is comprised of data from three different domains (i.e. Hotel, Restaurant, Doctor), each of which contains three types of reviews, i.e. customer generated truthful reviews, Turker generated deceptive reviews and employee (domain-expert) generated deceptive reviews. Our approach tries to capture the general difference of language usage between deceptive and truthful reviews, which we hope will help customers when making purchase decisions and review portal operators, such as TripAdvisor or Yelp, investigate possible fraudulent activity on their sites.1

Question: What is the difference between truthful and deceptive reviews?


On traveling path and related problems for a mobile station in a rechargeable sensor network. Wireless power transfer is a promising technology to fundamentally address energy problems in a wireless sensor network. To make such a technology work effectively, a vehicle is needed to carry a charger to travel inside the network. On the other hand, it has been well recognized that a mobile base station offers significant advantages over a fixed one. In this paper, we investigate an interesting problem of co-locating the mobile base station on the wireless charging vehicle. We study an optimization problem that jointly optimizes traveling path, stopping points, charging schedule, and flow routing. Our study is carried out in two steps. First, we study an idealized problem that assumes zero traveling time, and develop a provably near-optimal solution to this idealized problem. In the second step, we show how to develop a practical solution with non-zero traveling time and quantify the performance gap between this solution and the unknown optimal solution to the original problem.

Question: How to optimize the traveling path of a mobile base station?


Learning Deep Features for DNA Methylation Data Analysis. Many studies demonstrated that the DNA methylation, which occurs in the context of a CpG, has strong correlation with diseases, including cancer. There is a strong interest in analyzing the DNA methylation data to find how to distinguish different subtypes of the tumor. However, the conventional statistical methods are not suitable for analyzing the highly dimensional DNA methylation data with bounded support. In order to explicitly capture the properties of the data, we design a deep neural network, which composes of several stacked binary restricted Boltzmann machines, to learn the low-dimensional deep features of the DNA methylation data. Experimental results show that these features perform best in breast cancer DNA methylation data cluster analysis, compared with some state-of-the-art methods.

Question: How to analyze the DNA methylation data?


Dirichlet Processes for Joint Learning of Morphology and PoS Tags. This paper presents a joint model for learning morphology and part-of-speech (PoS) tags simultaneously. The proposed method adopts a finite mixture model that groups words having similar contextual features thereby assigning the same PoS tag to those words. While learning PoS tags, words are analysed morphologically by exploiting similar morphological features of the learned PoS tags. The results show that morphology and PoS tags can be learned jointly in a fully unsupervised setting.

Question: What is a finite mixture model?


Digital Calibration Techniques for Pipelined ADC ' s. A skip and fill algorithm is developed to digitally self-calibrate pipelined analog-to-digital converters (ADC's) in real time. The proposed digital calibration technique is applicable to capacitor-ratioed multiplying digital-to-analog converters (MDAC's) commonly used in multi-step or pipelined ADC's. This background calibration process can replace, in effect, a trimming procedure usually done in the factory with a hidden electronic calibration. Unlike other self-calibration techniques working in the foreground, the proposed technique is based on the concept of skipping conversion cycles randomly but filling in data later by nonlinear interpolation. This opens up the feasibility of digitally implementing calibration hardware and simplifying the task of self-calibrating multi-step or pipelined ADC's. The proposed method improves the performance of the inherently fast ADC's by maintaining simple system architectures. To measure errors resulting from capacitor mismatch, op amp dc gain, offset, and switch feedthrough in real time, the calibration test signal is injected in place of the input signal using a split-reference injection technique. Ultimately, the missing signal within 2/3 of the Nyquist bandwidth is recovered with 16-bit accuracy using a 44-th order polynomial interpolation, behaving essentially as an FIR filter. This work was supported by the National Science Foundation under Grant MIP-9312671.

Question: What is the difference between a pipelined ADC and a multi-step ADC?


How to get mature global virtual teams: a framework to improve team process management in distributed software teams. Managing global software development teams is not an easy task because of the additional problems and complexities that have to be taken into account. This paper defines VTManager, a methodology that provides a set of efficient practices for global virtual team management in software development projects. These practices integrate software development techniques in global environments with others such as explicit practices for global virtual team management, definition of skills and abilities needed to work in these teams, availability of collaborative work environments and shared knowledge management practices. The results obtained and the lessons learned from implementing VTManager in a pilot project to develop software tools for collaborative work in rural environments are also presented. This project was carried out by geographically distributed teams involving people from seven countries with a high level of virtualness.

Question: How to get mature global virtual teams: a framework to improve team process management in distributed software teams.


Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network. A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.

Question: How to control the dissolved oxygen concentration in a wastewater treatment process?


The infrastructure problem in HCI. HCI endeavors to create human-centered computer systems, but underlying technological infrastructures often stymie these efforts. We outline three specific classes of user experience difficulties caused by underlying technical infrastructures, which we term constrained possibilities, unmediated interaction, and interjected abstractions. We explore how prior approaches in HCI have addressed these issues, and discuss new approaches that will be required for future progress. We argue that the HCI community must become more deeply involved with the creation of technical infrastructures. Doing so, however, requires a substantial expansion to the methodological toolbox of HCI.

Question: What is the infrastructure problem in HCI?


Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce. Nowadays, it is a heated topic for many industries to build automatic question-answering (QA) systems. A key solution to these QA systems is to retrieve from a QA knowledge base the most similar question of a given question, which can be reformulated as a paraphrase identification (PI) or a natural language inference (NLI) problem. However, most existing models for PI and NLI have at least two problems: They rely on a large amount of labeled data, which is not always available in real scenarios, and they may not be efficient for industrial applications. In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource-poor target domain. Specifically, since most existing transfer learning methods only focus on learning a shared feature space across domains while ignoring the relationship between the source and target domains, we propose to simultaneously learn shared representations and domain relationships in a unified framework. Furthermore, we propose an efficient and effective hybrid model by combining a sentence encoding-based method and a sentence interaction-based method as our base model. Extensive experiments on both paraphrase identification and natural language inference demonstrate that our base model is efficient and has promising performance compared to the competing models, and our transfer learning method can help to significantly boost the performance. Further analysis shows that the inter-domain and intra-domain relationship captured by our model are insightful. Last but not least, we deploy our transfer learning model for PI into our online chatbot system, which can bring in significant improvements over our existing system. Finally, we launch our new system on the chatbot

Question: What is the difference between paraphrase identification and natural language inference?


Foundations and Trends Vision for Robotics. Robot vision refers to the capability of a robot to visually perceive the environment and use this information for execution of different tasks. Visual feedback has been used extensively for robot navigation and obstacle avoidance. In the recent years, there are also examples that include interaction with people and manipulation of objects. In this paper, we review some of the work that goes beyond of using artificial landmarks and fiducial markers for the purpose of implementing vision based control in robots. We discuss different application areas, both from the systems perspective and individual problems such as object tracking and recognition.

Question: What is the vision for robotics?


Teaching Programming in Secondary Education Through Embodied Computing Platforms: Robotics and Wearables. Pedagogy has emphasized that physical representations and tangible interactive objects benefit learning especially for young students. There are many tangible hardware platforms for introducing computer programming to children, but there is limited comparative evaluation of them in the context of a formal classroom. In this work, we explore the benefits of learning to code for tangible computers, such as robots and wearable computers, in comparison to programming for the desktop computer. For this purpose, 36 students participated in a within-groups study that involved three types of target computer platform tangibility: (1) desktop, (2) wearable, and (3) robotic. We employed similar blocks-based visual programming environments, and we measured emotional engagement, attitudes, and computer programming performance. We found that students were more engaged by and had a higher intention of learning programming with the robotic rather than the desktop computer. Furthermore, tangible computing platforms, either robot or wearable, did not affect the students' performance in learning basic computational concepts (e.g., sequence, repeat, and decision). Our findings suggest that computer programming should be introduced through multiple target platforms (e.g., robots, smartphones, wearables) to engage children.

Question: What is the difference between a wearable computer and a robot?


A pattern recognition approach to voiced-unvoiced-silence classification with applications to speech recognition. Absb-act—In speech analysis, the voiced-unvoiced decision is usually performed in conjunction with pitch analysis. The linking of voiced-unvoiced (V-UV) decision to pitch analysis not only results in unnecessary complexity, but makes it difficult to classify short speech segments which are less than a few pitch periods in duration. In this paper, we describe a pattern recognition approach for deciding whether a given segment of a speech signal should be classified as voiced speech, un-voiced speech, or silence, based on measurements made on the signal. In this method, five different measurements axe made on the speech segment to be classified. The measured parameters are the zero-crossing rate, the speech energy, the correlation between adjacent speech samples, the first predictor coefficient from a 12-pole linear predictive coding (LPC) analysis, and the energy in the prediction error. The speech segment is assigned to a particular class based on a minimum-distance rule obtained under the assumption that the measured parameters are distributed according to the multidimensional Gaussian probability density function. The means and covariances for the Gaussian distribution are determined from manually classified speech data included in a training set. The method has been found to provide reliable classification with speech segments as short as 10 ms and has been used for both speech analysis-synthesis and recognition applications. A simple nonlinear smoothing algorithm is described to provide a smooth 3-level contour of an utterance for use in speech recognition applications. Quantitative results and several examples illustrating the performance of the methOd are included in the paper.

Question: What is the difference between a pattern recognition approach and a rule-based approach?


A Dictionary Approach to EBSD Indexing. We propose a framework for indexing of grain and sub-grain structures in electron backscatter diffraction (EBSD) images of polycrystalline materials. The framework is based on a previously introduced physics-based forward model by Callahan and De Graef (2013) relating measured patterns to grain orientations (Euler angle). The forward model is tuned to the microscope and the sample symmetry group. We discretize the domain of the forward model onto a dense grid of Euler angles and for each measured pattern we identify the most similar patterns in the dictionary. These patterns are used to identify boundaries, detect anomalies, and index crystal orientations. The statistical distribution of these closest matches is used in an unsupervised binary decision tree (DT) classifier to identify grain boundaries and anomalous regions. The DT classifies a pattern as an anomaly if it has an abnormally low similarity to any pattern in the dictionary. It classifies a pixel as being near a grain boundary if the highly ranked patterns in the dictionary differ significantly over the pixels 3× 3 neighborhood. Indexing is accomplished by computing the mean orientation of the closest dictionary matches to each pattern. The mean orientation is estimated using a maximum likelihood approach that models the orientation distribution as a mixture of Von MisesFisher distributions over the quaternionic 3-sphere. The proposed dictionary matching approach permits segmentation, anomaly detection, and indexing to be performed in a unified manner with the additional benefit of uncertainty quantification. We demonstrate the proposed dictionary-based approach on a Ni-base IN100 alloy. c1 c1 Part of this work was reported in the Proceedings of the IEEE International Conference on Image Processing (ICIP), Melbourne Australia, Sept 2013.

Question: What is the difference between a dictionary and a database?


Exploring Students' Computational Thinking Skills in Modeling and Simulation Projects: a Pilot Study. Computational Thinking (CT) is gaining a lot of attention in education. We explored how to discern the occurrences of CT in the projects of 12th grade high school students in the computer science (CS) course. Within the projects, they constructed models and ran simulations of phenomena from other (STEM) disciplines. We examined which CT aspects occurred in students' activities and how to assess students' CT accomplishments. For this purpose we employed a framework based on CT characterizations by Wing [14, 15], CSTA [4] and Comer et al. [3]. We analyzed students' project documentation, survey results and interviews with individual students. The findings indicate that this framework is suitable for detection of occurrences of CT aspects in students' data. Moreover, our preliminary results suggest that the framework is useful in assessment of the quality of the students' CT performance.

Question: What is the framework based on CT characterizations by Wing [14, 15], CSTA [4] and Comer et al. [3]?


Detection and identification algorithm of the S1 and S2 heart sounds. This article attempts to contribute to heart abnormalities detection by suggesting a segmentation algorithm of S1 and S2 heart sounds. It describes the steps followed and the results achieved during the realization of the first PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning) Classifying Heart Sounds Challenge. The performed work leads to improved results, compared to the results found by the three finalists of this challenge, using the envelope of Shannon energy and other methods, to detect and identify first and second heart sound of phonocardiogram signals.

Question: How to detect and identify heart sounds?


Markov Random Field model for single image defogging. Fog reduces contrast and thus the visibility of vehicles and obstacles for drivers. Each year, this causes traffic accidents. Fog is caused by a high concentration of very fine water droplets in the air. When light hits these droplets, it is scattered and this results in a dense white background, called the atmospheric veil. As pointed in [1], Advanced Driver Assistance Systems (ADAS) based on the display of defogged images from a camera may help the driver by improving objects visibility in the image and thus may lead to a decrease of fatality and injury rates. In the last few years, the problem of single image defogging has attracted attention in the image processing community. Being an ill-posed problem, several methods have been proposed. However, a few among of these methods are dedicated to the processing of road images. One of the first exception is the method in [2], [1] where a planar constraint is introduced to improve the restoration of the road area, assuming an approximately flat road. The single image defogging problem being ill-posed, the choice of the Bayesian approach seems adequate to set this problem as an inference problem. A first Markov Random Field (MRF) approach of the problem has been proposed recently in [3]. However, this method is not dedicated to road images. In this paper, we propose a novel MRF model of the single image defogging problem which applies to all kinds of images but can also easily be refined to obtain better results on road images using the planar constraint. A comparative study and quantitative evaluation with several state-of-the-art algorithms is presented. This evaluation demonstrates that the proposed MRF model allows to derive a new algorithm which produces better quality results, in particular in case of a noisy input image.

Question: What is the difference between a Markov Random Field and a Bayesian approach?


The human phenotype ontology.. A standardized, controlled vocabulary allows phenotypic information to be described in an unambiguous fashion in medical publications and databases. The Human Phenotype Ontology (HPO) is being developed in an effort to provide such a vocabulary. The use of an ontology to capture phenotypic information allows the use of computational algorithms that exploit semantic similarity between related phenotypic abnormalities to define phenotypic similarity metrics, which can be used to perform database searches for clinical diagnostics or as a basis for incorporating the human phenome into large-scale computational analysis of gene expression patterns and other cellular phenomena associated with human disease. The HPO is freely available at http://www.human-phenotype-ontology.org.

Question: What is the human phenotype ontology?


Image Enhancement in Encrypted Domain over Cloud. Cloud-based multimedia systems are becoming increasingly common. These systems offer not only storage facility, but also high-end computing infrastructure which can be used to process data for various analysis tasks ranging from low-level data quality enhancement to high-level activity and behavior identification operations. However, cloud data centers, being third party servers, are often prone to information leakage, raising security and privacy concerns. In this article, we present a Shamir's secret sharing based method to enhance the quality of encrypted image data over cloud. Using the proposed method we show that several image enhancement operations such as noise removal, antialiasing, edge and contrast enhancement, and dehazing can be performed in encrypted domain with near-zero loss in accuracy and minimal computation and data overhead. Moreover, the proposed method is proven to be information theoretically secure.

Question: How to enhance the quality of encrypted image data over cloud?


Question: What is the name of the algorithm?


Mechanisms of facial emotion recognition in autism spectrum disorders: Insights from eye tracking and electroencephalography. While behavioural difficulties in facial emotion recognition (FER) have been observed in individuals with Autism Spectrum Disorder (ASD), behavioural studies alone are not suited to elucidate the specific nature of FER challenges in ASD. Eye tracking (ET) and electroencephalography (EEG) provide insights in to the attentional and neurological correlates of performance, and may therefore provide insight in to the mechanisms underpinning FER in ASD. Given that these processes develop over the course of the developmental trajectory, there is a need to synthesise findings in regard to the developmental stages to determine how the maturation of these systems may impact FER in ASD. We conducted a systematic review of fifty-four studies investigating ET or EEG meeting inclusion criteria. Findings indicate divergence of visual processing pathways in individuals with ASD. Altered function of the social brain in ASD impacts the processing of facial emotion across the developmental trajectory, resulting in observable differences in ET and EEG outcomes.

Question: What is the difference between the social brain and the visual processing pathways?


A probabilistic rating inference framework for mining user preferences from reviews. We propose a novel Probabilistic Rating infErence Framework, known as Pref, for mining user preferences from reviews and then mapping such preferences onto numerical rating scales. Pref applies existing linguistic processing techniques to extract opinion words and product features from reviews. It then estimates the sentimental orientations (SO) and strength of the opinion words using our proposed relative-frequency-based method. This method allows semantically similar words to have different SO, thereby addresses a major limitation of existing methods. Pref takes the intuitive relationships between class labels, which are scalar ratings, into consideration when assigning ratings to reviews. Empirical results validated the effectiveness of Pref against several related algorithms, and suggest that Pref can produce reasonably good results using a small training corpus. We also describe a useful application of Pref as a rating inference framework. Rating inference transforms user preferences described as natural language texts into numerical rating scales. This allows Collaborative Filtering (CF) algorithms, which operate mostly on databases of scalar ratings, to utilize textual reviews as an additional source of user preferences. We integrated Pref with a classical CF algorithm, and empirically demonstrated the advantages of using rating inference to augment ratings for CF.

Question: What is a probabilistic rating inference framework?


Evaluation of information technology investment: a data envelopment analysis approach. The increasing use of information technology (IT) has resulted in a need for evaluating the productivity impacts of IT. The contemporary IT evaluation approach has focused on return on investment and return on management. IT investment has impacts on different stages of business operations. For example, in the banking industry, IT plays a key role in effectively generating (i) funds from the customer in the forms of deposits and then (ii) profits by using deposits as investment funds. Existing approaches based upon data envelopment analysis (DEA) only measure the IT efficiency or impact on one specific stage when a multi-stage business process is present. A detailed model is needed to characterize the impact of IT on each stage of the business operation. The current paper develops a DEA non-linear programming model to evaluate the impact of IT on multiple stages along with information on how to distribute the IT-related resources so that the efficiency is maximized. It is shown that this non-linear program can be treated as a parametric linear program. It is also shown that if there is only one intermediate measure, then the non-linear DEA model becomes a linear program. Our approach is illustrated with an example taken from previous studies. 2004 Elsevier Ltd. All rights reserved.

Question: How to evaluate the impact of IT on multiple stages along with information on how to distribute the IT-related resources so that the efficiency is maximized.


The Business Value of Business inTelligence. This paper focuses on the influence of BI on manufacturing and distribution organizations, although much of what's covered in it can apply to businesses in non-related industries, as well. To kick it off, the chart below illustrates the primary value propositions of business intelligence and the types of business analytics (e.g., Sales, Inventory, Supplier Performance analysis) that support each value area. The effect of BI on each of these business value areas are then discussed in more detail in the following sections.

Question: What is the business value of business intelligence?