query: Learning Sensor-Specific Spatial-Spectral Features of Hyperspectral Images via Convolutional Neural Networks

positive: A global geometric framework for nonlinear dimensionality reduction. Scientists working with large volumes of high-dimensional data, such as global climate patterns, stellar spectra, or human gene distributions, regularly confront the problem of dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. The human brain confronts the same problem in everyday perception, extracting from its high-dimensional sensory inputs-30,000 auditory nerve fibers or 10(6) optic nerve fibers-a manageably small number of perceptually relevant features. Here we describe an approach to solving dimensionality reduction problems that uses easily measured local metric information to learn the underlying global geometry of a data set. Unlike classical techniques such as principal component analysis (PCA) and multidimensional scaling (MDS), our approach is capable of discovering the nonlinear degrees of freedom that underlie complex natural observations, such as human handwriting or images of a face under different viewing conditions. In contrast to previous algorithms for nonlinear dimensionality reduction, ours efficiently computes a globally optimal solution, and, for an important class of data manifolds, is guaranteed to converge asymptotically to the true structure.

positive: ImageNet Classification with Deep Convolutional Neural Networks We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.

positive: Advances in Spectral-Spatial Classification of Hyperspectral Images Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

positive: Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning Advances in hyperspectral sensing provide new capability for characterizing spectral signatures in a wide range of physical and biological systems, while inspiring new methods for extracting information from these data. HSI data often lie on sparse, nonlinear manifolds whose geometric and topological structures can be exploited via manifold-learning techniques. In this article, we focused on demonstrating the opportunities provided by manifold learning for classification of remotely sensed data. However, limitations and opportunities remain both for research and applications. Although these methods have been demonstrated to mitigate the impact of physical effects that affect electromagnetic energy traversing the atmosphere and reflecting from a target, nonlinearities are not always exhibited in the data, particularly at lower spatial resolutions, so users should always evaluate the inherent nonlinearity in the data. Manifold learning is data driven, and as such, results are strongly dependent on the characteristics of the data, and one method will not consistently provide the best results. Nonlinear manifold-learning methods require parameter tuning, although experimental results are typically stable over a range of values, and have higher computational overhead than linear methods, which is particularly relevant for large-scale remote sensing data sets. Opportunities for advancing manifold learning also exist for analysis of hyperspectral and multisource remotely sensed data. Manifolds are assumed to be inherently smooth, an assumption that some data sets may violate, and data often contain classes whose spectra are distinctly different, resulting in multiple manifolds or submanifolds that cannot be readily integrated with a single manifold representation. Developing appropriate characterizations that exploit the unique characteristics of these submanifolds for a particular data set is an open research problem for which hierarchical manifold structures appear to have merit. To date, most work in manifold learning has focused on feature extraction from single images, assuming stationarity across the scene. Research is also needed in joint exploitation of global and local embedding methods in dynamic, multitemporal environments and integration with semisupervised and active learning.

positive: Deep Learning-Based Classification of Hyperspectral Data Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a huge number of methods were proposed to deal with the hyperspectral data classification problem. However, most of them do not hierarchically extract deep features. In this paper, the concept of deep learning is introduced into hyperspectral data classification for the first time. First, we verify the eligibility of stacked autoencoders by following classical spectral information-based classification. Second, a new way of classifying with spatial-dominated information is proposed. We then propose a novel deep learning framework to merge the two features, from which we can get the highest classification accuracy. The framework is a hybrid of principle component analysis (PCA), deep learning architecture, and logistic regression. Specifically, as a deep learning architecture, stacked autoencoders are aimed to get useful high-level features. Experimental results with widely-used hyperspectral data indicate that classifiers built in this deep learning-based framework provide competitive performance. In addition, the proposed joint spectral-spatial deep neural network opens a new window for future research, showcasing the deep learning-based methods' huge potential for accurate hyperspectral data classification.

negative: How to Work a Crowd: Developing Crowd Capital Through Crowdsourcing Traditionally, the term ‘crowd’ was used almost exclusively in the context of people who selforganized around a common purpose, emotion, or experience. Today, however, firms often refer to crowds in discussions of how collections of individuals can be engaged for organizational purposes. Crowdsourcing–defined here as the use of information technologies to outsource business responsibilities to crowds–can now significantly influence a firm’s ability to leverage previously unattainable resources to build competitive advantage. Nonetheless, many managers are hesitant to consider crowdsourcing because they do not understand how its various types can add value to the firm. In response, we explain what crowdsourcing is, the advantages it offers, and how firms can pursue crowdsourcing. We begin by formulating a crowdsourcing typology and show how its four categories—crowd voting, micro-task, idea, and solution crowdsourcing—can help firms develop ‘crowd capital,’ an organizational-level resource harnessed from the crowd. We then present a three-step process model for generating crowd capital. Step one includes important considerations that shape how a crowd is to be constructed. Step two outlines the capabilities firms need to develop to acquire and assimilate resources (e.g., knowledge, labor, funds) from the crowd. Step three outlines key decision areas that executives need to address to effectively engage crowds. 1. Crowds and Crowdsourcing Not too long ago, the term ‘crowd’ was used almost exclusively in the context of people who selforganized around a common purpose, emotion, or experience. Crowds were sometimes seen as a positive occurrence– for instance, when they formed for political rallies or to support sports teams— but were more often associated negatively with riots, a mob mentality, or looting. Under today’s lens, they are viewed more positively (Wexler, 2011). Crowds have become useful! It all started in 2006, when crowdsourcing was introduced as ‘‘taking a function once performed by employees and outsourcing it to an undefined (and generally large) network of people in the form of an “open call’’ (Howe, 2006, p. 1). The underlying concept of crowdsourcing, a combination of crowd and outsourcing, is that many hands make light work and that wisdom can be gleaned from crowds (Surowiecki, 2005) to overcome groupthink, leading to superior results (Majchrzak & Malhotra, 2013). Of course, such ambitions are not new, and organizations have long desired to make the most of dispersed knowledge whereby each individual has certain knowledge advantages over every other (Hayek, 1945). Though examples of using crowds to harness what is desired are abundant (for an interesting application, see Table 1), until recently, accessing and harnessing such resources at scale has been nearly impossible for organizations. Due in large part to the proliferation of the Internet, mobile technologies, and the recent explosion of social media (Kietzmann, Hermkens, McCarthy, & Silvestre, 2011), organizations today are in a much better position to engage distributed crowds (Lakhani & Panetta, 2007) of individuals for their innovation and problem-solving needs (Afuah & Tucci, 2012; Boudreau & Lakhani, 2013). As a result, more and more executives–—from small startups to Fortune 500 companies alike–— are trying to figure out what crowdsourcing really is, the benefits it can offer, and the processes they should follow to engage a crowd. In this formative stage of crowdsourcing, multiple streams of academic and practitioner-based literature–—each using their own language–—are developing independently of one another, without a unifying framework to understand the burgeoning phenomenon of crowd engagement. For executives who would like to explore crowdbased opportunities, this presents a multitude of options and possibilities, but also difficulties. One problem entails lack of a clear understanding of crowds , the various forms they can take, and the value they can offer. Another problem entails absence of a well-defined process to engage crowds. As a result, many executives are unable to develop strategies or are hesitant to allocate resources to crowdsourcing, resulting in missed opportunities for new competitive advantages resulting from engaging crowds. To help provide clarity, we submit an overview of the different types of crowdsourcing. Then we introduce the crowd capital framework, supplying a systematic template for executives to recognize the value of information from crowds, therein mapping the steps to acquire and assimilate resources from crowds. Finally, we discuss the unique benefits that can be gained from crowds before concluding with some advice on how to best ‘work a crowd.’ 2. Types of crowdsourcing Crowdsourcing as an online, distributed problemsolving model (Brabham, 2008) suggests that approaching crowds and asking for contributions can help organizations develop solutions to a variety of business challenges. In this context, the crowd is often treated as a single construct: a general collection of people that can be targeted by firms. However, just as organizations and their problems vary, so do the types of crowds and the different kinds of contributions they can offer the firm. The following typology of crowdsourcing suggests that managers can begin by identifying a business problem and then working outward from there, considering (1) what type of contributions are required from members of the crowd and (2) how these contributions will collectively help find a solution to their business problem. First, the types of contributions required from the crowd could either call for specific objective contributions or for subjective content. Specific objective contributions help to achieve an impartial and unbiased result; here, bare facts matter and crowds can help find or create them. Subjective content contributions revolve around the judgments, opinions, perceptions, and beliefs of individuals in a crowd that are sought to collectively help solve a problem that calls for a subjective result. Second, contributions need to be processed collectively to add value. Depending on the problem to be solved, the contributions must either be aggregated or filtered. Under aggregation, contributions collectively yield value when they are simply combined at face value to inform a decision, without requiring any prior validation. For instance, political elections call for people to express their choices via electoral ballots, which are then tallied to calculate the sums and averages of their collective preferences; the reasons for their choices are not important at this stage. Other problems, however, are more complex and call for crowd contributions to be qualitatively evaluated and filtered before being considered on their relative merits (e.g., when politicians invite constituents’ opinions before campaigning). Together, these two dimensions help executives distinguish among and understand the variety of crowdsourcing alternatives that exist today (see Figure 1). Two forms of crowdsourcing rely on aggregation as the primary process: crowd voting and microtask crowdsourcing. In crowd voting, organizations pose an issue to a crowd and aggregate the subjective responses derived from crowd participants to make a decision. Consider the popular television show American Idol, which allows viewers to support their preferred contestants by submitting votes online or via telephone or text. These votes are tallied at the end of the show and contestants with the fewest votes are eliminated from the competition. Similarly, so-called prediction markets (Arrow et al., 2008) activate the wisdom of the crowd through crowd voting. But rather than simply adding up votes, these markets arrive at specific predictions that can exceed the accuracy of experts by averaging the independent responses of crowd participants. For instance, Starwood Hotels and Resorts utilized an internal prediction market by asking a crowd of its own employees to select the best choice among a variety of potential marketing campaigns (Barlow, 2008). In micro-task crowdsourcing, organizations engage a crowd to undertake work that is often unachievable through standard procedures due to its sheer size or complexity. An organization may need to assemble a large data set, have numerous photos labeled and tagged, translate documents, or transcribe audio transcripts. Breaking such work into micro-tasks (Gino & Staats, 2012) allows daunting undertakings to be completed more quickly, cheaply, and efficiently. Consider how Google uses reCAPTCHA (von Ahn, Maurer, McMillen, Abraham, & Blum, 2008) and the little–—and admittedly annoying–—dialogue boxes that ask users to enter the text snippets they see of distorted images onscreen. It is commonly believed that this web utility is only for authenticating human users, thus keeping websites from spambots. However, every time the task of entering characters is completed, individuals are actually digitizing what optical character recognition (OCR) software has been unable to read. In this way, micro-task crowdsourcing is helping to digitize the archives of The New York Times and moving old manuscripts into Google Books. Similarly, crowdfunding (Stemler, 2013) endeavors are a form of micro-task crowdsourcing whereby an overall highly ambitious financial goal is broken into smaller funding tasks and contributions consist of objective resources (herein ‘funds’) that are simply aggregated for each venture. Figure 1. Crowdsourcing alternatives Whether objective or subjective, crowdsourced contributions must be processed to be valuable. In idea crowdsourcing, organizations seek creativity from a crowd, hoping to leverage its diversity to generate unique solutions to problems/issues. An organization may receive many ideas from a crowd, which it will need to filter before one or more can be implemented. For instance, online artist community and e-comm

negative:
Comparative study of a new dermal filler Uma Jeunesse® and Juvéderm®. INTRODUCTION
Innovation in technology has resulted in the emergence of better, longer-lasting hyaluronic acid implants with fewer side effects. The new dermal implant Uma Jeunesse® was compared to Juvéderm® in this split-face study.

METHODS
Uma Jeunesse® is crosslinked with butanediol diglycidyl ether (BDDE) using a new crosslinking technology. Uma Jeunesse® and Juvéderm® Ultra 3 were injected in a split-face study on 17 healthy volunteers, whose ages ranged from 33-58 years. There were 14 women and three men with medium to deep nasolabial folds. All subjects randomly received either Uma Jeunesse® or Juvéderm®) Ultra 3 on one half of their face. Patients were followed up for 9 months.

RESULTS
Juvéderm® was easier to inject with lesser injection pain because of lidocaine, but late postinjection pain was much less with Uma Jeunesse® as compared to Juvéderm®. Overall rate of early and late complications as well as adverse events was lower with Uma Jeunesse® than Juvéderm(®) . After 9 months of follow-up, Uma Jeunesse® lasted in tissues for longer as compared to Juvéderm(®) even in patients injected for the first time (P<0.0001). Patient acceptability rate of Uma Jeunesse® was also much higher. Perception of pain during injection was lesser with Juvéderm® probably because of the presence of lidocaine.

CONCLUSION
The new dermal implant Uma Jeunesse® is a safe and patient-friendly product which resides in the tissues for longer with maintenance of aesthetic effect over and beyond 6 months, reaching 9 months in over 80% of patients, and Juvéderm® injection is less painful.

negative: Direct path and multipath cancellation with discrete distribution structure in passive radar The Direct path and multipath interference (DPI and MPI) Cancellation is a crucial issue in the passive radar. In this paper, two improvements have been done in NLMS algorithm. One is to estimate the values and distribution of DPI and MPI roughly, and then remove them from the echo signal before filtering. The other one is to vary the step size of NLMS according to the cross correlation function of the reference signal and the output signal of the adaptive filter. The performance of the improved algorithm in passive radar DPI and MPI Cancellation is reported. The computer simulation results show that the algorithm has a faster convergence rate and a smaller steady state error.

negative: Interactive bookshelf surface for in situ book searching and storing support We propose an interactive bookshelf surface to augment a human ability for in situ book searching and storing. In book searching support, when a user touches the edge of the bookshelf, the cover image of a stored book located above the touched position is projected directly onto the book spine. As a result, the user can search for a desired book by sliding his (or her) finger across the shelf edge. In book storing support, when a user brings a book close to the bookshelf, the place where the book should be stored is visually highlighted by a projection light. This paper also presents sensing technologies to achieve the above mentioned interactive techniques. In addition, by considering the properties of the human visual system, we propose a simple visual effect to reduce the legibility degradation of the projected image contents by the complex textures and geometric irregularities of the spines. We confirmed the feasibility of the system and the effectiveness of the proposed interaction techniques through user studies.

negative: Experiments with crowdsourced re-annotation of a POS tagging data set Crowdsourcing lets us collect multiple annotations for an item from several annotators. Typically, these are annotations for non-sequential classification tasks. While there has been some work on crowdsourcing named entity annotations, researchers have largely assumed that syntactic tasks such as part-of-speech (POS) tagging cannot be crowdsourced. This paper shows that workers can actually annotate sequential data almost as well as experts. Further, we show that the models learned from crowdsourced annotations fare as well as the models learned from expert annotations in downstream tasks.

negative: Sequence-discriminative training of deep neural networks Sequence-discriminative training of deep neural networks (DNNs) is investigated on a standard 300 hour American English conversational telephone speech task. Different sequencediscriminative criteria — maximum mutual information (MMI), minimum phone error (MPE), state-level minimum Bayes risk (sMBR), and boosted MMI — are compared. Two different heuristics are investigated to improve the performance of the DNNs trained using sequence-based criteria — lattices are regenerated after the first iteration of training; and, for MMI and BMMI, the frames where the numerator and denominator hypotheses are disjoint are removed from the gradient computation. Starting from a competitive DNN baseline trained using cross-entropy, different sequence-discriminative criteria are shown to lower word error rates by 7-9% relative, on average. Little difference is noticed between the different sequencebased criteria that are investigated. The experiments are done using the open-source Kaldi toolkit, which makes it possible for the wider community to reproduce these results.

negative: Diabetes Data Analysis and Prediction Model Discovery Using RapidMiner Data mining techniques have been extensively applied in bioinformatics to analyze biomedical data. In this paper, we choose the Rapid-I¿s RapidMiner as our tool to analyze a Pima Indians Diabetes Data Set, which collects the information of patients with and without developing diabetes. The discussion follows the data mining process. The focus will be on the data preprocessing, including attribute identification and selection, outlier removal, data normalization and numerical discretization, visual data analysis, hidden relationships discovery, and a diabetes prediction model construction.

negative: Production and structural elucidation of exopolysaccharide from endophytic Pestalotiopsis sp. BC55. There is a little information on exopolysaccharide production by endophytic fungi. In this investigation endophytic Pestalotiopsis sp. BC55 was used for optimization of exopolysaccharide production. One variable at a time method and response surface methodology were adopted to find out the best culture conditions and medium compositions for maximum exopolysaccharide production. The organism produced maximum exopolysaccharide (4.320 ± 0.022 g/l EPS) in 250 ml Erlenmeyer flask containing 75 ml potato dextrose broth supplemented with (g%/l) glucose, 7.66; urea, 0.29; CaCl2, 0.05 with medium pH 6.93; after 3.76 days of incubation at 24°C. Exopolysaccharide [EPS (EP-I)] produced by this organism have Mw ∼2×10(5)Da with a melting point range of 122-124°C. Structural elucidation of the EPS (PS-I) was carried out after a series of experiments. Result indicated the presence of only (1→3)-linked β-d-glucopyranosyl moiety. The structure of the repeating unit was established as - →3)-β-d-Glcp-(1→.

negative: Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions. Nanopore sequencing technology has the potential to render other sequencing technologies obsolete with its ability to generate long reads and provide portability. However, high error rates of the technology pose a challenge while generating accurate genome assemblies. The tools used for nanopore sequence analysis are of critical importance, as they should overcome the high error rates of the technology. Our goal in this work is to comprehensively analyze current publicly available tools for nanopore sequence analysis to understand their advantages, disadvantages and performance bottlenecks. It is important to understand where the current tools do not perform well to develop better tools. To this end, we (1) analyze the multiple steps and the associated tools in the genome assembly pipeline using nanopore sequence data, and (2) provide guidelines for determining the appropriate tools for each step. Based on our analyses, we make four key observations: (1) the choice of the tool for basecalling plays a critical role in overcoming the high error rates of nanopore sequencing technology. (2) Read-to-read overlap finding tools, GraphMap and Minimap, perform similarly in terms of accuracy. However, Minimap has a lower memory usage, and it is faster than GraphMap. (3) There is a trade-off between accuracy and performance when deciding on the appropriate tool for the assembly step. The fast but less accurate assembler Miniasm can be used for quick initial assembly, and further polishing can be applied on top of it to increase the accuracy, which leads to faster overall assembly. (4) The state-of-the-art polishing tool, Racon, generates high-quality consensus sequences while providing a significant speedup over another polishing tool, Nanopolish. We analyze various combinations of different tools and expose the trade-offs between accuracy, performance, memory usage and scalability. We conclude that our observations can guide researchers and practitioners in making conscious and effective choices for each step of the genome assembly pipeline using nanopore sequence data. Also, with the help of bottlenecks we have found, developers can improve the current tools or build new ones that are both accurate and fast, to overcome the high error rates of the nanopore sequencing technology.

negative: Forensic analysis of video file formats Video file format standards define only a limited number of mandatory features and leave room for interpretation. Design decisions of device manufacturers and software vendors are thus a fruitful resource for forensic video authentication. This paper explores AVI and MP4-like video streams of mobile phones and digital cameras in detail. We use customized parsers to extract all file format structures of videos from overall 19 digital camera models, 14 mobile phone models, and 6 video editing toolboxes. We report considerable differences in the choice of container formats, audio and video compression algorithms, acquisition parameters, and internal file structure. In combination, such characteristics can help to authenticate digital video files in forensic settings by distinguishing between original and post-processed videos, verifying the purported source of a file, or identifying the true acquisition device model or the processing software used for video processing. a 2014 The Authors. Published by Elsevier Ltd on behalf of DFRWS. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

negative: Optimization Criteria, Sensitivity and Robustness of Motion and Structure Estimation The prevailing efforts to study the standard formulation of motion and structure recovery have been recently focused on issues of sensitivity and and robustness of existing techniques. While many cogent observations have been made and verified experimentally, many statements do not hold in general settings and make a comparison of existing techniques difficult. With an ultimate goal of clarifying these issues we study the main aspects of the problem: the choice of objective functions, optimization techniques and the sensitivity and robustness issues in the presence of noise. We clearly reveal the relationship among different objective functions, such as “(normalized) epipolar constraints”, “reprojection error” or “triangulation”, which can all be be unified in a new “ optimal triangulation” procedure formulated as a constrained optimization problem. Regardless of various choices of the objective function, the optimization problems all inherit the same unknown parameter space, the so called “essential manifold”, making the new optimization techniques on Riemanian manifolds directly applicable. Using these analytical results we provide a clear account of sensitivity and robustness of the proposed linear and nonlinear optimization techniques and study the analytical and practical equivalence of different objective functions. The geometric characterization of critical points of a function defined on essential manifold and the simulation results clarify the difference between the effect of bas relief ambiguity and other types of local minima leading to a consistent interpretations of simulation results over large range of signal-to-noise ratio and variety of configurations. Further more we justify the choice of the linear techniques for (re)initialization and for detection of incorrect local minima.

negative: A Security approach for Data Migration in Cloud Computing Cloud computing is a new paradigm that combines several computing concepts and technologies of the Internet creating a platform for more agile and cost-effective business applications and IT infrastructure. The adoption of Cloud computing has been increasing for some time and the maturity of the market is steadily growing. Security is the question most consistently raised as consumers look to move their data and applications to the cloud. I justify the importance and motivation of security in the migration of legacy systems and I carry out an approach related to security in migration processes to cloud with the aim of finding the needs, concerns, requirements, aspects, opportunities and benefits of security in the migration process of legacy systems.

negative: SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art. Our model has the additional advantage of being very interpretable, since it allows visualization of its predictions broken up by abstract features such as information content, salience and novelty. Another novel contribution of our work is abstractive training of our extractive model that can train on human generated reference summaries alone, eliminating the need for sentence-level extractive labels.

negative: Clustering Based URL Normalization Technique for Web Mining URL (Uniform Resource Locator) normalization is an important activity in web mining. Web data can be retrieved in smoother way using effective URL normalization technique. URL normalization also reduces lot of calculations in web mining activities. A web mining technique for URL normalization is proposed in this paper. The proposed technique is based on content, structure and semantic similarity and web page redirection and forwarding similarity of the given set of URLs. Web page redirection and forward graphs can be used to measure the similarities between the URL’s and can also be used for URL clusters. The URL clusters can be used for URL normalization. A data structure is also suggested to store the forward and redirect URL information.

negative: Towards Data-Driven Autonomics in Data Centers Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on holistic predictive models that are built and updated using generated data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in a 12K-node cluster with the goal of building and evaluating a predictive model for node failures. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set characterizing machine state over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if machines will fail in a future 24-hour window. Our evaluation reveals that if we limit false positive rates to 5%, we can achieve true positive rates between 27% and 88% with precision varying between 50% and 72%. We discuss the practicality of including our predictive model as the central component of a data-driven autonomic manager and operating it on-line with live data streams (rather than off-line on data logs). All of the scripts used for BigQuery and classification analyses are publicly available from the authors' website.

negative: PyElph - a software tool for gel images analysis and phylogenetics This paper presents PyElph, a software tool which automatically extracts data from gel images, computes the molecular weights of the analyzed molecules or fragments, compares DNA patterns which result from experiments with molecular genetic markers and, also, generates phylogenetic trees computed by five clustering methods, using the information extracted from the analyzed gel image. The software can be successfully used for population genetics, phylogenetics, taxonomic studies and other applications which require gel image analysis. Researchers and students working in molecular biology and genetics would benefit greatly from the proposed software because it is free, open source, easy to use, has a friendly Graphical User Interface and does not depend on specific image acquisition devices like other commercial programs with similar functionalities do. PyElph software tool is entirely implemented in Python which is a very popular programming language among the bioinformatics community. It provides a very friendly Graphical User Interface which was designed in six steps that gradually lead to the results. The user is guided through the following steps: image loading and preparation, lane detection, band detection, molecular weights computation based on a molecular weight marker, band matching and finally, the computation and visualization of phylogenetic trees. A strong point of the software is the visualization component for the processed data. The Graphical User Interface provides operations for image manipulation and highlights lanes, bands and band matching in the analyzed gel image. All the data and images generated in each step can be saved. The software has been tested on several DNA patterns obtained from experiments with different genetic markers. Examples of genetic markers which can be analyzed using PyElph are RFLP (Restriction Fragment Length Polymorphism), AFLP (Amplified Fragment Length Polymorphism), RAPD (Random Amplification of Polymorphic DNA) and STR (Short Tandem Repeat). The similarity between the DNA sequences is computed and used to generate phylogenetic trees which are very useful for population genetics studies and taxonomic classification. PyElph decreases the effort and time spent processing data from gel images by providing an automatic step-by-step gel image analysis system with a friendly Graphical User Interface. The proposed free software tool is suitable for researchers and students which do not have access to expensive commercial software and image acquisition devices.

negative: Explicit Solution for Constrained Stochastic Linear-Quadratic Control with Multiplicative Noise We study in this paper a class of constrained linear-quadratic (LQ) optimal control problem formulations for the scalar-state stochastic system with multiplicative noise, which has various applications, especially in the financial risk management. The linear constraint on both the control and state variables considered in our model destroys the elegant structure of the conventional LQ formulation and has blocked the derivation of an explicit control policy so far in the literature. We successfully derive in this paper the analytical control policy for such a class of problems by utilizing the state separation property induced from its structure. We reveal that the optimal control policy is a piece-wise affine function of the state and can be computed off-line efficiently by solving two coupled Riccati equations. Under some mild conditions, we also obtain the stationary control policy for infinite time horizon. We demonstrate the implementation of our method via some illustrative examples and show how to calibrate our model to solve dynamic constrained portfolio optimization problems.

negative: Efficient Multipattern Event Processing Over High-Speed Train Data Streams Big data is becoming a key basis for productivity growth, innovation, and consumer surplus, but also bring us great challenges in its volume, velocity, variety, value, and veracity. The notion of event is an important cornerstone to manage big data. High-speed railway is one of the most typical application domains for event-based system, especially for the train onboard system. There are usually numerous complex event patterns subscribed in system sharing the same prefix, suffix, or subpattern; consequently, multipattern complex event detection often results in plenty of redundant detection operations and computations. In this paper, we propose a multipattern complex event detection model, multipattern event processing (MPEP), constructed by three parts: 1) multipattern state transition; 2) failure transition; and 3) state output. Based on MPEP, an intelligent onboard system for high-speed train is preliminarily implemented. The system logic is described using our proposed complex event description model and compiled into a multipattern event detection model. Experimental results show that MPEP can effectively optimize the complex event detection process and improve its throughput by eliminating duplicate automata states and redundant computations. This intelligent onboard system also provides better detection ability than other models when processing real-time events stored in high-speed train Juridical Recording Unit (JRU).

negative: Genetic Algorithm-Based Motion Estimation Method using Orientations and EMGs for Robot Controls Demand for interactive wearable devices is rapidly increasing with the development of smart devices. To accurately utilize wearable devices for remote robot controls, limited data should be analyzed and utilized efficiently. For example, the motions by a wearable device, called Myo device, can be estimated by measuring its orientation, and calculating a Bayesian probability based on these orientation data. Given that Myo device can measure various types of data, the accuracy of its motion estimation can be increased by utilizing these additional types of data. This paper proposes a motion estimation method based on weighted Bayesian probability and concurrently measured data, orientations and electromyograms (EMG). The most probable motion among estimated is treated as a final estimated motion. Thus, recognition accuracy can be improved when compared to the traditional methods that employ only a single type of data. In our experiments, seven subjects perform five predefined motions. When orientation is measured by the traditional methods, the sum of the motion estimation errors is 37.3%; likewise, when only EMG data are used, the error in motion estimation by the proposed method was also 37.3%. The proposed combined method has an error of 25%. Therefore, the proposed method reduces motion estimation errors by 12%.

negative: A Nonlinear Unified State-Space Model for Ship Maneuvering and Control in a Seaway This article presents a unified state-space model for ship maneuvering, station-keeping, and control in a seaway. The frequency-dependent potential and viscous damping terms, which in classic theory results in a convolution integral not suited for real-time simulation, is compactly represented by using a state-space formulation. The separation of the vessel model into a low-frequency model (represented by zero-frequency added mass and damping) and a wave-frequency model (represented by motion transfer functions or RAOs), which is commonly used for simulation, is hence made superfluous.

negative: Tasks and scenario-based evaluation of information visualization techniques Usability evaluation of an information visualization technique can only be done by the joint evaluation of both the visual representation and the interaction techniques. This work proposes task models as a key element for carrying out such evaluations in a structured way. We base our work on a taxonomy abstracting from rendering functions supported by information visualization techniques. CTTE is used to model these abstract visual tasks as well as to generate scenarios from this model for evaluation purposes. We conclude that the use of task models allows generating test scenarios which are more effective than informal and unstructured evaluations.

negative: VMHunt: A Verifiable Approach to Partially-Virtualized Binary Code Simplification Code virtualization is a highly sophisticated obfuscation technique adopted by malware authors to stay under the radar. However, the increasing complexity of code virtualization also becomes a "double-edged sword" for practical application. Due to its performance limitations and compatibility problems, code virtualization is seldom used on an entire program. Rather, it is mainly used only to safeguard the key parts of code such as security checks and encryption keys. Many techniques have been proposed to reverse engineer the virtualized code, but they share some common limitations. They assume the scope of virtualized code is known in advance and mainly focus on the classic structure of code emulator. Also, few work verifies the correctness of their deobfuscation results. In this paper, with fewer assumptions on the type and scope of code virtualization, we present a verifiable method to address the challenge of partially-virtualized binary code simplification. Our key insight is that code virtualization is a kind of process-level virtual machine (VM), and the context switch patterns when entering and exiting the VM can be used to detect the VM boundaries. Based on the scope of VM boundary, we simplify the virtualized code. We first ignore all the instructions in a given virtualized snippet that do not affect the final result of that snippet. To better revert the data obfuscation effect that encodes a variable through bitwise operations, we then run a new symbolic execution called multiple granularity symbolic execution to further simplify the trace snippet. The generated concise symbolic formulas facilitate the correctness testing of our simplification results. We have implemented our idea as an open source tool, VMHunt, and evaluated it with real-world applications and malware. The encouraging experimental results demonstrate that VMHunt is a significant improvement over the state of the art.

negative: Convolutional Sketch Inversion In this paper, we use deep neural networks for inverting face sketches to synthesize photorealistic face images. We first construct a semi-simulated dataset containing a very large number of computergenerated face sketches with different styles and corresponding face images by expanding existing unconstrained face data sets. We then train models achieving state-of-the-art results on both computer-generated sketches and hand-drawn sketches by leveraging recent advances in deep learning such as batch normalization, deep residual learning, perceptual losses and stochastic optimization in combination with our new dataset. We finally demonstrate potential applications of our models in fine arts and forensic arts. In contrast to existing patch-based approaches, our deep-neuralnetwork-based approach can be used for synthesizing photorealistic face images by inverting face sketches in the wild.

negative: A convolutional neural networks denoising approach for salt and pepper noise The salt and pepper noise, especially the one with extremely high percentage of impulses, brings a significant challenge to image denoising. In this paper, we propose a non-local switching filter convolutional neural network denoising algorithm, named NLSF-CNN, for salt and pepper noise. As its name suggested, our NLSF-CNN consists of two steps, i.e., a NLSF processing step and a CNN training step. First, we develop a NLSF pre-processing step for noisy images using non-local information. Then, the pre-processed images are divided into patches and used for CNN training, leading to a CNN denoising model for future noisy images. We conduct a number of experiments to evaluate the effectiveness of NLSF-CNN. Experimental results show that NLSF-CNN outperforms the state-of-the-art denoising algorithms with a few training images.

negative: A Systematic Literature Review: Code Bad Smells in Java Source Code Code smell is an indication of a software designing problem. The presence of code smells can have a severe impact on the software quality. Smells basically refers to the structure of the code which violates few of the design principals and so has negative effect on the quality of the software. Larger the source code, more is its presence. Software needs to be reliable, robust and easily maintainable so that it can minimize the cost of its development as well as maintenance. Smells may increase the chances of failure of the system during maintenance. A SLR has been performed based on the search of digital libraries that includes the publications since 1999 to 2016. 60 research papers are deeply analyzed that are most relevant. The objective of this paper is to provide an extensive overview of existing research in the field of bad smells, identify the detection techniques and correlation between the detection techniques, in addition to find the name of the code smells that need more attention in detection approaches. This SLR identified that code clone (code smell) receives most research attention. Our findings also show that very few papers report on the impact of code bad smells. Most of the papers focused on the detection techniques and tools. A significant correlation between detection techniques has been calculated. There are four code smells that are not yet detected are Primitive Obsession, Inappropriate Intimacy, Incomplete library class and Comments.

negative: Multimodal Classification of Remote Sensing Images: A Review and Future Directions Earth observation through remote sensing images allows the accurate characterization and identification of materials on the surface from space and airborne platforms. Multiple and heterogeneous image sources can be available for the same geographical region: multispectral, hyperspectral, radar, multitemporal, and multiangular images can today be acquired over a given scene. These sources can be combined/fused to improve classification of the materials on the surface. Even if this type of systems is generally accurate, the field is about to face new challenges: the upcoming constellations of satellite sensors will acquire large amounts of images of different spatial, spectral, angular, and temporal resolutions. In this scenario, multimodal image fusion stands out as the appropriate framework to address these problems. In this paper, we provide a taxonomical view of the field and review the current methodologies for multimodal classification of remote sensing images. We also highlight the most recent advances, which exploit synergies with machine learning and signal processing: sparse methods, kernel-based fusion, Markov modeling, and manifold alignment. Then, we illustrate the different approaches in seven challenging remote sensing applications: 1) multiresolution fusion for multispectral image classification; 2) image downscaling as a form of multitemporal image fusion and multidimensional interpolation among sensors of different spatial, spectral, and temporal resolutions; 3) multiangular image classification; 4) multisensor image fusion exploiting physically-based feature extractions; 5) multitemporal image classification of land covers in incomplete, inconsistent, and vague image sources; 6) spatiospectral multisensor fusion of optical and radar images for change detection; and 7) cross-sensor adaptation of classifiers. The adoption of these techniques in operational settings will help to monitor our planet from space in the very near future.

negative: Semantic Segmentation for High Spatial Resolution Remote Sensing Images Based on Convolution Neural Network and Pyramid Pooling Module Semantic segmentation provides a practical way to segment remotely sensed images into multiple ground objects simultaneously, which can be potentially applied to multiple remote sensed related aspects. Current classification algorithms in remotely sensed images are mostly limited by different imaging conditions, the multiple ground objects are difficult to be separated from each other due to high intraclass spectral variances and interclass spectral similarities. In this study, we propose an end-to-end framework to semantically segment high-resolution aerial images without postprocessing to refine the segmentation results. The framework provides a pixel-wise segmentation result, comprising convolutional neural network structure and pyramid pooling module, which aims to extract feature maps at multiple scales. The proposed model is applied to the ISPRS Vaihingen benchmark dataset from the ISPRS 2D Semantic Labeling Challenge. Its segmentation results are compared with previous state-of-the-art method UZ_1, UPB and three other methods that segment images into objects of all the classes (including clutter/background) based on true orthophoto tiles, and achieve the highest overall accuracy of 87.8% over the published performances, to the best of our knowledge. The results validate the efficiency of the proposed model in segmenting multiple ground objects from remotely sensed images simultaneously.

negative: 3D Reconstruction from Hyperspectral Images 3D reconstruction from hyper spectral images has seldom been addressed in the literature. This is a challenging problem because 3D models reconstructed from different spectral bands demonstrate different properties. If we use a single band or covert the hyper spectral image to gray scale image for the reconstruction, fine structural information may be lost. In this paper, we present a novel method to reconstruct a 3D model from hyper spectral images. Our proposed method first generates 3D point sets from images at each wavelength using the typical structure from motion approach. A structural descriptor is developed to characterize the spatial relationship between the points, which allows robust point matching between two 3D models at different wavelength. Then a 3D registration method is introduced to combine all band-level models into a single and complete hyper spectral 3D model. As far as we know, this is the first attempt in reconstructing a complete 3D model from hyper spectral images. This work allows fine structural-spectral information of an object be captured and integrated into the 3D model, which can be used to support further research and applications.

negative: Capsule Networks for Hyperspectral Image Classification Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNNbased network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral–spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral–spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton’s capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral– spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral–spatial capsules, which are able to learn HSI spectral–spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted Manuscript received June 9, 2018; revised August 22, 2018; accepted September 19, 2018. This work was supported in part by the Ministerio de Educación (Resolución de 26 de diciembre de 2014 y de 19 de noviembre de 2015, de la Secretaría de Estado de Educación, Formación Profesional y Universidades, por la que se convocan ayudas para la formación de profesorado universitario, de los subprogramas de Formación y de Movilidad incluidos en el Programa Estatal de Promoción del Talento y su Empleabilidad, and en el marco del Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016), in part by the Junta de Extremadura (decreto 297/2014, ayudas para la realización de actividades de investigación y desarrollo tecnológico, and de divulgación y de transferencia de conocimiento por los Grupos de Investigación de Extremadura) under Grant GR15005, in part by the Generalitat Valenciana under Contract APOSTD/2017/007, in part by the Spanish Ministry of Economy under Project ESP2016-79503-C2-2P and Project TIN2015-63646-C5-5-R, in part by the National Natural Science Foundation of China under Grant 61771496, in part by the National Key Research and Development Program of China under Grant 2017YFB0502900, and in part by the Guangdong Provincial Natural Science Foundation under Grant 2016A030313254. (Corresponding author: Jun Li.) M. E. Paoletti, J. M. Haut, J. Plaza, and A. Plaza are with the Hyperspectral Computing Laboratory, Department of Technology of Computers and Communications, Escuela Politécnica, University of Extremadura, 10003 Cáceres, Spain (e-mail: mpaoletti@unex.es; juanmariohaut@unex.es; jplaza@unex.es; aplaza@unex.es). R. Fernandez-Beltran and F. Pla are with the Institute of New Imaging Technologies, Universitat Jaume I, 12071 Castellón de la Plana, Spain (e-mail: rufernan@uji.es; pla@uji.es). J. Li is with the Guangdong Provincial Key Laboratory of Urbanization and Geosimulation, Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China (e-mail: lijun48@mail.sysu.edu.cn). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2018.2871782 using five well-known HSI data sets and several state-of-theart classification methods, reveal that our HSI classification approach based on spectral–spatial capsules is able to provide competitive advantages in terms of both classification accuracy and computational time.

negative: Snapshot Image Mapping Spectrometer (IMS) with high sampling density for hyperspectral microscopy A snapshot Image Mapping Spectrometer (IMS) with high sampling density is developed for hyperspectral microscopy, measuring a datacube of dimensions 285 x 285 x 60 (x, y, lambda). The spatial resolution is approximately 0.45 microm with a FOV of 100 x 100 microm(2). The measured spectrum is from 450 nm to 650 nm and is sampled by 60 spectral channels with average sampling interval approximately 3.3 nm. The channel's spectral resolution is approximately 8nm. The spectral imaging results demonstrate the potential of the IMS for real-time cellular fluorescence imaging.

negative: Semi-Supervised Classification with Graph Convolutional Networks We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.

negative: Hierarchical Convolutional Features for Visual Tracking Visual object tracking is challenging as target objects often undergo significant appearance changes caused by deformation, abrupt motion, background clutter and occlusion. In this paper, we exploit features extracted from deep convolutional neural networks trained on object recognition datasets to improve tracking accuracy and robustness. The outputs of the last convolutional layers encode the semantic information of targets and such representations are robust to significant appearance variations. However, their spatial resolution is too coarse to precisely localize targets. In contrast, earlier convolutional layers provide more precise localization but are less invariant to appearance changes. We interpret the hierarchies of convolutional layers as a nonlinear counterpart of an image pyramid representation and exploit these multiple levels of abstraction for visual tracking. Specifically, we adaptively learn correlation filters on each convolutional layer to encode the target appearance. We hierarchically infer the maximum response of each layer to locate targets. Extensive experimental results on a largescale benchmark dataset show that the proposed algorithm performs favorably against state-of-the-art methods.