title: Object-based classification of urban areas using VHR imagery and height points ancillary data. Remote Sens

abstract: Land cover classification of very high resolution (VHR) imagery over urban areas is an extremely challenging task. Impervious land covers such as buildings, roads, and parking lots are spectrally too similar to be separated using only the spectral information of VHR imagery. Additional information, therefore, is required for separating such land covers by the classifier. One source of additional information is the vector data, which are available in archives for many urban areas. Further, the object-based approach provides a more effective way to incorporate vector data into the classification process as the misregistration between different layers is less problematic in object-based compared to pixel-based image analysis. In this research, a hierarchical rule-based object-based classification framework was developed based on a small subset of QuickBird (QB) imagery coupled with a layer of height points called Spot Height (SH) to classify a complex urban environment. In the rule-set, different spectral, morphological, contextual, class-related, and thematic layer features were employed. To assess the general applicability of the rule-set, the same classification framework and a similar one using slightly different thresholds applied to larger subsets of QB and IKONOS (IK), respectively. Results show an overall accuracy of 92% and 86% and a Kappa coefficient of 0.88 and 0.80 for the QB and IK Test image, respectively. The average producers’ accuracies for impervious land cover types were also 82% and 74.5% for QB and IK.


title: Image classification based on support vector machine and the fusion of complementary features

abstract: Image Classification based on BOW (Bag-of-words) has broad application prospect in pattern recognition field but the shortcomings are existed because of single feature and low classification accuracy. To this end we combine three ingredients: (i) Three features with functions of mutual complementation are adopted to describe the images, including PHOW (Pyramid Histogram of Words), PHOC (Pyramid Histogram of Color) and PHOG (Pyramid Histogram of Orientated Gradients). (ii) The improvement of traditional BOW model is presented by using dense sample and an improved K-means clustering method for constructing the visual dictionary. (iii) An adaptive feature-weight adjusted image categorization algorithm based on the SVM and the fusion of multiple features is adopted. Experiments carried out on Caltech 101 database confirm the validity of the proposed approach. From the experimental results can be seen that the classification accuracy rate of the proposed method is improved by 7%-17% higher than that of the traditional BOW methods. This algorithm makes full use of global, local and spatial information and has significant improvements to the classification accuracy.


title: Continuous monitoring of top-k queries over sliding windows

abstract: Given a dataset P and a preference function f, a top-k query retrieves the k tuples in P with the highest scores according to f. Even though the problem is well-studied in conventional databases, the existing methods are inapplicable to highly dynamic environments involving numerous long-running queries. This paper studies continuous monitoring of top-k queries over a fixed-size window W of the most recent data. The window size can be expressed either in terms of the number of active tuples or time units. We propose a general methodology for top-k monitoring that restricts processing to the sub-domains of the workspace that influence the result of some query. To cope with high stream rates and provide fast answers in an on-line fashion, the data in W reside in main memory. The valid records are indexed by a grid structure, which also maintains book-keeping information. We present two processing techniques: the first one computes the new answer of a query whenever some of the current top-k points expire; the second one partially pre-computes the future changes in the result, achieving better running time at the expense of slightly higher space requirements. We analyze the performance of both algorithms and evaluate their efficiency through extensive experiments. Finally, we extend the proposed framework to other query types and a different data stream model.


title: SBMDS: an interpretable string based malware detection system using SVM ensemble with bagging

abstract: Malicious executables are programs designed to infiltrate or damage a computer system without the owner’s consent, which have become a serious threat to the security of computer systems. There is an urgent need for effective techniques to detect polymorphic, metamorphic and previously unseen malicious executables of which detection fails in most of the commercial anti-virus software. In this paper, we develop interpretable string based malware detection system (SBMDS), which is based on interpretable string analysis and uses support vector machine (SVM) ensemble with Bagging to classify the file samples and predict the exact types of the malware. Interpretable strings contain both application programming interface (API) execution calls and important semantic strings reflecting an attacker’s intent and goal. Our SBMDS is carried out with four major steps: (1) first constructing the interpretable strings by developing a feature parser; (2) performing feature selection to select informative strings related to different types of malware; (3) followed by using SVM ensemble with bagging to construct the classifier; (4) and finally conducting the malware detector, which not only can detect whether a program is malicious or not, but also can predict the exact type of the malware. Our case study on the large collection of file samples collected by Kingsoft Anti-virus lab illustrate that: (1) The accuracy and efficiency of our SBMDS outperform several popular anti-virus software; (2) Based on the signatures of interpretable strings, our SBMDS outperforms data mining based detection systems which employ single SVM, Naive Bayes with bagging, Decision Trees with bagging; (3) Compared with the IMDS which utilizes the objective-oriented association (OOA) based classification on API calls, our SBMDS achieves better performance. Our SBMDS system has already been incorporated into the scanning tool of a commercial anti-virus software.


title: Network Environment and Financial Risk Using Machine Learning and Sentiment Analysis

abstract: ABSTRACT Under the network environment, the trading volume and asset price of a financial commodity or instrument are affected by various complicated factors. Machine learning and sentiment analysis provide powerful tools to collect a great deal of data from the website and retrieve useful information for effectively forecasting financial risk of associated companies. This article studies trading volume and asset price risk when sentimental financial information data are available using both sentiment analysis and popular machine learning approaches: artificial neural network (ANN) and support vector machine (SVM). Nonlinear GARCH-based mining models are developed by integrating GARCH (generalized autoregressive conditional heteroskedasticity) theory and ANN and SVM. Empirical studies in the U.S. stock market show that the proposed approach achieves favorable forecast performances. GARCH-based SVM outperforms GARCH-based ANN for volatility forecast, whereas GARCH-based ANN achieves a better forecast result for the volatility trend. Results also indicate a strong correlation between information sentiment and both trading volume and asset price volatility.


title: Software engineering issues for mobile application development

abstract: This paper provides an overview of important software engineering research issues related to the development of applications that run on mobile devices. Among the topics are development processes, tools, user interface design, application portability, quality, and security.


title: The geometry of optimal transportation

abstract: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 1. Summary of main results . . . . . . . . . . . . . . . . . . . . . . . . 120 2. Background on optimal measures . . . . . . . . . . . . . . . . . . . 126 Part I. Strictly convex costs . . . . . . . . . . . . . . . . . . . . . . . . . . 133 3. Existence and uniqueness of optimal maps . . . . . . . . . . . . . 133 4. Characterization of the optimal map . . . . . . . . . . . . . . . . . 137 Part II. Costs which are strictly concave as a function of d i s t a n c e . . . 141 5. The role of optimal maps . . . . . . . . . . . . . . . . . . . . . . . . 141 6. Uniqueness of optimal solutions . . . . . . . . . . . . . . . . . . . . 144 Part III. Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 A. Legendre transforms and conjugate costs . . . . . . . . . . . . . . 148 B. Examples of c-concave potentials . . . . . . . . . . . . . . . . . . . 152 C. Regularity of c-concave potentials . . . . . . . . . . . . . . . . . . 154 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159


title: Knowledge sharing and yahoo answers: Everyone knows something

abstract: Yahoo Answers (YA) is a large and diverse question-answer forum, acting not only as a medium for sharing technical knowledge, but as a place where one can seek advice, gather opinions, and satisfy one's curiosity about a countless number of things. In this paper, we seek to understand YA's knowledge sharing and activity. We analyze the forum categories and cluster them according to content characteristics and patterns of interaction among the users. While interactions in some categories resemble expertise sharing forums, others incorporate discussion, everyday advice, and support. With such a diversity of categories in which one can participate, we find that some users focus narrowly on specific topics, while others participate across categories. This not only allows us to map related categories, but to characterize the entropy of the users' interests. We find that lower entropy correlates with receiving higher answer ratings, but only for categories where factual expertise is primarily sought after. We combine both user attributes and answer characteristics to predict, within a given category, whether a particular answer will be chosen as the best answer by the asker.


title: An object-oriented representation for efficient reinforcement learning

abstract: Rich representations in reinforcement learning have been studied for the purpose of enabling generalization and making learning feasible in large state spaces. We introduce Object-Oriented MDPs (OO-MDPs), a representation based on objects and their interactions, which is a natural way of modeling environments and offers important generalization opportunities. We introduce a learning algorithm for deterministic OO-MDPs and prove a polynomial bound on its sample complexity. We illustrate the performance gains of our representation and algorithm in the well-known Taxi domain, plus a real-life videogame.


title: Review of NoSQL databases and performance testing on HBase

abstract: NoSQL (Not Only SQL) is the generic term of a kind of non-relational database products. This paper, firstly, lists the disadvantages of traditional relational databases, introduces NoSQL databases including their advantages, disadvantages and their application status. Then, a comparison is made between NoSQL databases and SQL database, also another comparison between different NoSQL products. Finally, we introduce the architecture and data model of HBase database, which is a representative of NoSQL databases, and did some performance tests on HBase database, including the column family test, the sort test, the random read/write test and the query test. Test results show that written and query speed of HBase is slow under a single machine environment, but can be significantly improved in multimachine cluster environment.


title: Conceptual Modeling for Business Analytics: A Framework and Potential Benefits

abstract: Advanced analytics solutions are becoming widespread in business organizations. While data scientists create, implement, or apply machine learning algorithms, business stakeholders need the ultimate solution to gain competitive advantage and performance improvement. How can one, systematically, elicit analytical requirements? How can one design the analytics system for addressing such requirement? How can one assure the alignment between data analytics solutions and business strategies? How can one codify and represent analytics know-how in terms of design patterns? This paper has two contributions. First, it introduces a conceptual modeling framework for addressing those challenges. Second, it assesses the potential use cases and limitations of the framework by applying it to two case studies.


title: Visual Analytics for Explainable Deep Learning

abstract: Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever. However, even with such unprecedented advancements, the lack of explanation regarding the decisions made by deep learning models and absence of control over their internal processes act as major drawbacks in critical decision-making processes, such as precision medicine and law enforcement. In response, efforts are being made to make deep learning interpretable and controllable by humans. This article reviews visual analytics, information visualization, and machine learning perspectives relevant to this aim, and discusses potential challenges and future research directions.


title: Concavity and efficient points of discrete distributions in probabilistic programming

abstract: We consider stochastic programming problems with probabilistic constraints involving integer-valued random variables. The concept of a p-efficient point of a probability distribution is used to derive various equivalent problem formulations. Next we introduce the concept of r-concave discrete probability distributions and analyse its relevance for problems under consideration. These notions are used to derive lower and upper bounds for the optimal value of probabilistically constrained stochastic programming problems with discrete random variables. The results are illustrated with numerical examples.


The Effects of PolyMem(R) on the Wound Healing.


title: Recognising personality traits using facebook status updates

abstract: Gaining insight in a web user's personality is very valuable for applications that rely on personalisation, such as recommender systems and personalised advertising. In this paper we explore the use of machine learning techniques for inferring a user's personality traits from their Facebook status updates. Even with a small set of training examples we can outperform the majority class baseline algorithm. Furthermore, the results are improved by adding training examples from another source. This is an interesting result because it indicates that personality trait recognition generalises across social media platforms.


title: An Exploration of Automated Grading of Complex Assignments

abstract: Automated grading is essential for scaling up learning. In this paper, we conduct the first systematic study of how to automate grading of a complex assignment using a medical case assessment as a test case. We propose to solve this problem using a supervised learning approach and introduce three general complementary types of feature representations of such complex assignments for use in supervised learning. We first show with empirical experiments that it is feasible to automate grading of such assignments provided that the instructor can grade a number of examples. We further study how to integrate an automated grader with human grading and propose to frame the problem as learning to rank assignments to exploit pairwise preference judgments and use NDPM as a measure for evaluation of the accuracy of ranking. We then propose a sequential pairwise online active learning strategy to minimize the effort of human grading and optimize the collaboration of human graders and an automated grader. Experiment results show that this strategy is indeed effective and can substantially reduce human effort as compared with randomly sampling assignments for manual grading.


title: Multi-view regression via canonical correlation analysis

abstract: In the multi-view regression problem, we have a regression problem where the input variable (which is a real vector) can be partitioned into two different views, where it is assumed that either view of the input is sufficient to make accurate predictions -- this is essentially (a significantly weaker version of) the co-training assumption for the regression problem. ::: ::: We provide a semi-supervised algorithm which first uses unlabeled data to learn a norm (or, equivalently, a kernel) and then uses labeled data in a ridge regression algorithm (with this induced norm) to provide the predictor. The unlabeled data is used via canonical correlation analysis (CCA, which is a closely related to PCA for two random variables) to derive an appropriate norm over functions. We are able to characterize the intrinsic dimensionality of the subsequent ridge regression problem (which uses this norm) by the correlation coefficients provided by CCA in a rather simple expression. Interestingly, the norm used by the ridge regression algorithm is derived from CCA, unlike in standard kernel methods where a special apriori norm is assumed (i.e. a Banach space is assumed). We discuss how this result shows that unlabeled data can decrease the sample complexity.


title: AReN: Assured ReLU NN Architecture for Model Predictive Control of LTI Systems

abstract: Author(s): Ferlez, James; Shoukry, Yasser | Abstract: In this paper, we consider the problem of automatically designing a Rectified Linear Unit (ReLU) Neural Network (NN) architecture that is sufficient to implement the optimal Model Predictive Control (MPC) strategy for an LTI system with quadratic cost. Specifically, we propose AReN, an algorithm to generate Assured ReLU Architectures. AReN takes as input an LTI system with quadratic cost specification, and outputs a ReLU NN architecture with the assurance that there exist network weights that exactly implement the associated MPC controller. AReN thus offers new insight into the design of ReLU NN architectures for the control of LTI systems: instead of training a heuristically chosen NN architecture on data -- or iterating over many architectures until a suitable one is found -- AReN can suggest an adequate NN architecture before training begins. While several previous works were inspired by the fact that both ReLU NN controllers and optimal MPC controller are both Continuous, Piecewise-Linear (CPWL) functions, exploiting this similarity to design NN architectures with correctness guarantees has remained elusive. AReN achieves this using two novel features. First, we reinterpret a recent result about the implementation of CPWL functions via ReLU NNs to show that a CPWL function may be implemented by a ReLU architecture that is determined by the number of distinct affine regions in the function. Second, we show that we can efficiently over-approximate the number of affine regions in the optimal MPC controller without solving the MPC problem exactly. Together, these results connect the MPC problem to a ReLU NN implementation without explicitly solving the MPC and directly translates this feature to a ReLU NN architecture that comes with the assurance that it can implement the MPC controller. We show through numerical results the effectiveness of AReN in designing an NN architecture.


title: Use of Clear Aligners in Open Bite Cases: An Unexpected Treatment Option

abstract: ABSTRACTIn open bite case treatments, a proper diagnostic differentiation is essential in determining the appropriate corrective procedures. Dental open bites are generally more responsive to treatment with orthodontics alone, whereas skeletal open bites often require a combination of orthodontics and orthognathic surgery. Patient selection and treatment principles for non-surgical open bite treatment routinely include fixed appliances both labial or lingual. However, removable clear aligners have gained a consistent popularity in the treatment of complex cases including open bite malocclusions. In this article, the authors describe three different clinical cases in which open bite cases had been satisfactorily treated by using clear aligners.


title: A comparison of machine learning algorithms for regional wheat yield prediction using NDVI time series of SPOT-VGT

abstract: This study compares two machine learning algorithms to predict regional winter wheat yields. The models, based on Boosted Regression Trees (BRT) and Support Vector Machines (SVM), are constructed of Normalized Difference Vegetation Indices (NDVI) derived from low resolution SPOT VEGETATION imagery. Three types of NDVI-related predictors were used: Single NDVI, Incremental NDVI and Targeted NDVI. BRT and SVM were first used to select features with high relevance for predicting the yield. Periods of high influence spanning from March to June were detected by both machine learning methods. After feature selection, BRT and SVM models were applied to the subset of selected features for yield forecasting. BRT seems to consistently outperform SVM.


title: Minimally invasive hybrid ablation procedure for the treatment of persistent atrial fibrillation: one year results.

abstract: BACKGROUND ::: The concept of a hybrid approach, combining the most effective techniques of surgical and endocardial catheter ablation has resulted in the creation of the convergent ablation procedure. This novel, pericardioscopic, hybrid approach can be an effective option for highly symptomatic patients with persistent atrial fibrillation (PSAF) and longstanding persistent atrial fibrillation (LSPAF) for whom standalone surgical or endocardial ablation procedures offer sometimes unsatisfactory outcomes. ::: ::: ::: AIM ::: To assess the safety, efficacy and effectiveness of a hybrid epicardial and endocardial radiofrequency ablation for the treatment of PSAF and LSPAF. ::: ::: ::: METHODS ::: Single-centre, prospective, non-randomised clinical study. Between August 2009 and December 2011, 27 patients with PSAF (n = 5) and LSPAF (n = 22) underwent hybrid ablation (HABL). Mean age was 52.52 ± 11.27 years, and the mean EHRA class was 2.5; 14 (51.8%) patients had a history of electrical cardioversion (n = 6) or catheter ablation (n = 8). Five patients had left ventricular ejection fraction (LVEF) of less than 35%. Mean AF duration for all patients was 3.46 ± 2.5 years. All patients were on antiarrhythmic drugs (AAD) and oral anticoagulation. Patients were scheduled for three, six and 12 month follow-up with seven day Holters, REVEAL® XT and ECHO measurements. ::: ::: ::: RESULTS ::: The HABL procedure was feasible in all patients. At six months post procedure, 72.2% (13/18) of patients were in SR, and 66.5% (12/18) were off class I/III AADs. Four patients were in AF and one patient developed right atrial flutter. At one year post procedure, 80% (8/10) of patients were in SR and off class I/III AADs. At two year post procedure, 100% (6/6) of patients were in SR and off class I/III AADs. Rapid change in left ventricular function was noted in patients with low LVEF (≤ 35%) prior to the procedure. Patients with LVEF +40% had less apparent improvement. ::: ::: ::: CONCLUSIONS ::: Hybrid, epicardial and endocardial, radiofrequency ablation is feasible and safe, effectively restoring sinus rhythm in the vast majority of patients with PSAF and LSPAF.


title: Aspectizing Java Access Control

abstract: It is inevitable that some concerns crosscut a sizeable application, resulting in code scattering and tangling. This issue is particularly severe for security-related concerns: It is difficult to be confident about the security of an application when the implementation of its security-related concerns is scattered all over the code and tangled with other concerns, making global reasoning about security precarious. In this study, we consider the case of access control in Java, which turns out to be a crosscutting concern with a nonmodular implementation based on runtime stack inspection. We describe the process of modularizing access control in Java by means of Aspect-Oriented Programming (AOP). We first show a solution based on AspectJ, the most popular aspect-oriented extension to Java, that must rely on a separate automata infrastructure. We then put forward a novel solution via dynamic deployment of aspects and scoping strategies. Both solutions, apart from providing a modular specification of access control, make it possible to easily express other useful policies such as the Chinese wall policy. However, relying on expressive scope control results in a compact implementation, which, at the same time, permits the straightforward expression of even more interesting policies. These new modular implementations allowed by AOP alleviate maintenance and evolution issues produced by the crosscutting nature of access control.


A test of automatic road extraction approaches


title: Properties and Therapeutic Application of Bromelain: A Review

abstract: Bromelain belongs to a group of protein digesting enzymes obtained commercially from the fruit or stem of pineapple. Fruit bromelain and stem bromelainare prepared differently and they contain different enzymatic composition. "Bromelain" refers usually to the "stem bromelain." Bromelain is a mixture of different thiol endopeptidases and other components like phosphatase, glucosidase, peroxidase, cellulase, escharase, and several protease inhibitors. In vitro and in vivo studies demonstrate that bromelain exhibits various fibrinolytic, antiedematous, antithrombotic, and anti-inflammatory activities. Bromelain is considerably absorbable in the body without losing its proteolytic activity and without producing any major side effects. Bromelain accounts for many therapeutic benefits like the treatment of angina pectoris, bronchitis, sinusitis, surgical trauma, and thrombophlebitis, debridement of wounds, and enhanced absorption of drugs, particularly antibiotics. It also relieves osteoarthritis, diarrhea, and various cardiovascular disorders. Bromelain also possesses some anticancerous activities and promotes apoptotic cell death. This paper reviews the important properties and therapeutic applications of bromelain, along with the possible mode of action.


title: The transport geography of logistics and freight distribution

abstract: Abstract Goods movement and freight distribution are widely underrepresented in regional science and geographical research. This is surprising since a large body of traditional spatial theory has been developed with respect to transportation costs or to trade areas: those aspects that were originally closely connected with the exchange of goods. Growing attention is being paid in geography to related subjects, such as the emergence of global production networks, to structural changes in retail or to the commodification of modern consumption. To a certain extent, these processes depend upon the efficient transfer of information, finance and physical goods. Yet, with a few exceptions, the freight sector appears to be neglected in contemporary research. This paper provides an overview of the emerging transport geography of logistics and freight distribution. It challenges the traditional perspective where transportation is considered as a derived demand with the idea that logistical requirements underline transportation as a component of an integrated demand. The paper provides an analysis of the evolution of logistics as it pertains to the core dimensions of transport geography (flows, nodes/locations and networks). The concept of logistical friction is also introduced to illustrate the inclusion of the multidimensional notion of impedance in integrated freight transport demand.


title: Toward multi-modal music emotion classification

abstract: The performance of categorical music emotion classification that divides emotion into classes and uses audio features alone for emotion classification has reached a limit due to the presence of a semantic gap between the object feature level and the human cognitive level of emotion perception. Motivated by the fact that lyrics carry rich semantic information of a song, we propose a multi-modal approach to help improve categorical music emotion classification. By exploiting both the audio features and the lyrics of a song, the proposed approach improves the 4-class emotion classification accuracy from 46.6% to 57.1%. The results also show that the incorporation of lyrics significantly enhances the classification accuracy of valence.


title: PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

abstract: Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As Python is gaining more ground in scientific computing, an open source Python module for extracting EEG features has the potential to save much time for computational neuroscientists. In this paper, we introduce PyEEG, an open source Python module for EEG feature extraction.


title: A roadmap for traffic engineering in SDN-OpenFlow networks

abstract: Software Defined Networking (SDN) is an emerging networking paradigm that separates the network control plane from the data forwarding plane with the promise to dramatically improve network resource utilization, simplify network management, reduce operating cost, and promote innovation and evolution. Although traffic engineering techniques have been widely exploited in the past and current data networks, such as ATM networks and IP/MPLS networks, to optimize the performance of communication networks by dynamically analyzing, predicting, and regulating the behavior of the transmitted data, the unique features of SDN require new traffic engineering techniques that exploit the global network view, status, and flow patterns/characteristics available for better traffic control and management. This paper surveys the state-of-the-art in traffic engineering for SDNs, and mainly focuses on four thrusts including flow management, fault tolerance, topology update, and traffic analysis/characterization. In addition, some existing and representative traffic engineering tools from both industry and academia are explained. Moreover, open research issues for the realization of SDN traffic engineering solutions are discussed in detail.


title: Robots, Love, and Sex: The Ethics of Building a Love Machine

abstract: This paper will explore the ethical impacts of the use of affective computing by engineers and roboticists who program their machines to mimic and manipulate human emotions in order to evoke loving or amorous reactions from their human users. We will see that it does seem plausible that some people might buy a love machine if it were created, but it is argued here that principles from machine ethics have a role to play in the design of these machines. This is best achieved by applying what is known about the philosophy of love, the ethics of loving relationships, and the philosophical value of the erotic in the early design stage of building robust artificial companions. The paper concludes by proposing certain ethical limits on the manipulation of human psychology when it comes to building sex robots and in the simulation of love in such machines. In addition, the paper argues that the attainment of erotic wisdom is an ethically sound goal and that it provides more to loving relationships than only satisfying physical desire. This fact may limit the possibility of creating a machine that can fulfill all that one should want out of erotic love unless a machine can be built that would help its user attain this kind of love.


A Model for Impact of E-Government on Corruption: Exploring Theoretical Foundations


Magnitude and frequency of floods in suburban areas


title: Non-repudiable disk I/O in untrusted kernels

abstract: It is currently impossible for an application to verify that the data it passes to the kernel for storage is actually submitted to an underlying device or that the data returned to an application by the kernel has actually originated from an underlying device. A compromised or malicious OS can silently discard data written by the application or return fabricated data during a read operation. This is a serious data integrity issue for use-cases where verifiable storage and retrieval of data is a necessary precondition for ensuring correct operation, for example with secure logging, APT monitoring and compliance. We outline a solution for verifiable data storage and retrieval by providing a trustworthy mechanism, based on Intel SGX, to authenticate and verify request data at both the application and storage device endpoints. Even in the presence of a malicious OS our design ensures the authenticity and integrity of data while performing disk I/O and detects any data loss attributable to the untrusted OS fabricating or discarding read and write requests respectively. We provide a nascent prototype implementation for the core system together with an evaluation highlighting the temporal overheads imposed by this mechanism.


title: Where do people draw lines?

abstract: This paper presents the results of a study in which artists made line drawings intended to convey specific 3D shapes. The study was designed so that drawings could be registered with rendered images of 3D models, supporting an analysis of how well the locations of the artists' lines correlate with other artists', with current computer graphics (CG) line definitions, and with the underlying differential properties of the 3D surface. Lines drawn by artists in this study largely overlapped one another, particularly along the occluding contours of the object. Most lines that do not overlap contours overlap large gradients of the image intensity and correlate strongly with predictions made by recent line-drawing algorithms in CG. A few were not well described by any of the local properties considered in this study. The result of our work is a publicly available data set of aligned drawings, an analysis of where lines appear in that data set based on local properties of 3D models, and algorithms to predict where artists will draw lines for new scenes.


title: EyeLinks: A Gaze-Only Click Alternative for Heterogeneous Clickables

abstract: In this paper, we introduce a novel gaze-only interaction technique called EyeLinks, which was designed i) to support various types of discrete clickables (e.g. textual links, buttons, images, tabs, etc.); ii) to be easy to learn and use; iii) to mitigate the inaccuracy of affordable eye trackers. Our technique uses a two-step fixation approach: first, we assign numeric identifiers to clickables in the region where users gaze at and second, users select the desired clickable by performing a fixation on the corresponding confirm button, displayed in a sidebar. This two-step selection enables users to freely explore Web pages, avoids the Midas touch problem and improves accuracy. We evaluated our approach by comparing it against the mouse and another gaze-only technique (Actigaze). The results showed no statistically significant difference between EyeLinks and Actigaze, but users considered EyeLinks easier to learn and use than Actigaze and it was also the most preferred. Of the three, the mouse was the most accurate and efficient technique.


title: ARSA: a sentiment-aware model for predicting sales performance using blogs

abstract: Due to its high popularity, Weblogs (or blogs in short) present a wealth of information that can be very helpful in assessing the general public's sentiments and opinions. In this paper, we study the problem of mining sentiment information from blogs and investigate ways to use such information for predicting product sales performance. Based on an analysis of the complex nature of sentiments, we propose Sentiment PLSA (S-PLSA), in which a blog entry is viewed as a document generated by a number of hidden sentiment factors. Training an S-PLSA model on the blog data enables us to obtain a succinct summary of the sentiment information embedded in the blogs. We then present ARSA, an autoregressive sentiment-aware model, to utilize the sentiment information captured by S-PLSA for predicting product sales performance. Extensive experiments were conducted on a movie data set. We compare ARSA with alternative models that do not take into account the sentiment information, as well as a model with a different feature selection method. Experiments confirm the effectiveness and superiority of the proposed approach.


title: Concept Decompositions for Large Sparse Text Data Using Clustering

abstract: Unlabeled document collections are becoming increasingly common and availables mining such data sets represents a major contemporary challenge. Using words as features, text documents are often represented as high-dimensional and sparse vectors–a few thousand dimensions and a sparsity of 95 to 99% is typical. In this paper, we study a certain spherical k-means algorithm for clustering such document vectors. The algorithm outputs k disjoint clusters each with a concept vector that is the centroid of the cluster normalized to have unit Euclidean norm. As our first contribution, we empirically demonstrate that, owing to the high-dimensionality and sparsity of the text data, the clusters produced by the algorithm have a certain “fractal-like” and “self-similar” behavior. As our second contribution, we introduce concept decompositions to approximate the matrix of document vectorss these decompositions are obtained by taking the least-squares approximation onto the linear subspace spanned by all the concept vectors. We empirically establish that the approximation errors of the concept decompositions are close to the best possible, namely, to truncated singular value decompositions. As our third contribution, we show that the concept vectors are localized in the word space, are sparse, and tend towards orthonormality. In contrast, the singular vectors are global in the word space and are dense. Nonetheless, we observe the surprising fact that the linear subspaces spanned by the concept vectors and the leading singular vectors are quite close in the sense of small principal angles between them. In conclusion, the concept vectors produced by the spherical k-means algorithm constitute a powerful sparse and localized “basis” for text data sets.


title: Mobile Security Catching Up? Revealing the Nuts and Bolts of the Security of Mobile Devices

abstract: We are currently moving from the Internet society to a mobile society where more and more access to information is done by previously dumb phones. For example, the number of mobile phones using a full blown OS has risen to nearly200% from Q3/2009 to Q3/2010. As a result, mobile security is no longer immanent, but imperative. This survey paper provides a concise overview of mobile network security, attack vectors using the back end system and the web browser, but also the hardware layer and the user as attack enabler. We show differences and similarities between "normal" security and mobile security, and draw conclusions for further research opportunities in this area.


title: Optimal design of corona ring on HV composite insulator using PSO approach with dynamic population size

abstract: This paper deals with the use of corona ring at the HV end fitting for improving the electric field and potential distributions and then for minimizing the corona discharges on 230 kV AC transmission line composite insulator. A single corona ring is installed at the energized end side. Three-dimensional finite element method (FEM) software is employed to compute the electric field. As the performance of high voltage insulator strings closely depends on designs and locations of corona ring, the effects of the corona ring radius, the ring tube radius and the ring vertical position are examined. The minimization of the electric field necessitates the optimization of corona ring. For this purpose, new nonlinear mathematical objective function linking the electric field strength to the corona ring structure parameters is established. The optimization problem is achieved by minimizing the objective function using a modified particles swarm optimization (PSO) algorithm with a dynamic population size. The algorithm adjusts the size of population for each iteration. Based on the average value and the best solution of the objective function, we propose a new mathematical model to update the population size. This algorithm enables the population size reduction leading to computing time decrease. According to the results, FEM-PSO hybridization technique could be very helpful in optimization of corona ring design.


title: Prevalence of burnout among nurses in Iran: a systematic review and meta‐analysis

abstract: AIM ::: This study aimed to summarize the available information in the literature to make an accurate estimate of the prevalence of burnout among Iranian nurses. ::: ::: ::: BACKGROUND AND INTRODUCTION ::: Burnout is a work-related stress syndrome that has negative impact on healthcare providers, patients and healthcare delivery systems. ::: ::: ::: METHOD ::: A comprehensive search of literature using international [PubMed, Scopus and the Institute for Scientific Information (ISI)] and Iranian scientific data bases [Scientific Information Database (SID), IranMedex and Magiran] was conducted to identify English and Persian language studies, published between 2000 and 2016, that examined the prevalence of burnout among nurses in Iran. The I-squared test and Chi-squared-based Q-test suggested heterogeneity of reported prevalence among the qualified studies; thus, a random-effects model was applied to estimate the overall prevalence of burnout among nurses in Iran. ::: ::: ::: RESULTS ::: Based on 21 selected articles with 4180 participants, the overall prevalence of burnout among Iranian nurses was estimated to be 36% [95% confidence interval (CI), 20-53%] in Iran. Meta-regression indicated that sample size and year of data collection, mean age of samples, female to male ratio and geographic regions were not statistically significantly associated with the prevalence of burnout. Also, based on Egger's test and funnel plot, there is no publication bias among studies included in the analysis. ::: ::: ::: CONCLUSION ::: Professional burnout affects more than one-third of nursing staff in Iran; thus, effective interventions and strategies are required to reduce and prevent burnout among nurses. ::: ::: ::: IMPLICATION FOR NURSING AND HEALTH POLICY ::: Due to the negative consequences of burnout on patients, nurses and organizations, nursing and healthcare managers should intervene to prevent and reduce burnout among nurses in Iran. Policy attention should focus on developing effective interventions to prevent and minimize the burden of burnout among nurses in Iran. Nurses' involvement in the policy-making process is crucial in the implementation of effective programs and initiatives tailored to address the higher prevalence of burnout among Iranian nurses.


title: A d–q Voltage Droop Control Method With Dynamically Phase-Shifted Phase-Locked Loop for Inverter Paralleling Without Any Communication Between Individual Inverters

abstract: This paper presents a modified droop control method for equal load sharing of parallel-connected inverters, without any communication between individual inverters. Droop in d - and q -axis voltages are given depending upon d - and q -axis currents, respectively. Each inverter works in the voltage control mode, where it controls the filter capacitor voltage. Voltage references of each inverter come from d - and q -axis voltage droops. This d - and q -axis voltage droops force the parallel-connected inverters to share equal current and hence equal active and reactive power. A dynamically phase-shifted phase-locked loop (PLL) technique is locally designed for generating phase reference of each inverter. The phase angle between filter capacitor voltage vector and d -axis is dynamically adjusted with the change in q -axis inverter current to generate the phase reference of each inverter. The strategy has been verified with simulations and experiments and results are presented in this paper.


title: Pulmonary involvement in ankylosing spondylitis

abstract: This is a prospective study analyzing 52 asymptomatic, consecutive patients with ankylosing spondylitis (AS), who submitted to a pulmonary investigation that included plain chest radiography, pulmonary function test (PFT), and thoracic high-resolution computed tomography (HRCT). The results were compared according to sex, race, dorsal spine involvement, thoracic diameter, smoking status, and HLA-B27. There were four patients (8%) with an altered plain chest radiograph. PFT presented a restrictive pattern in 52% of the patients. Thoracic HRCT showed abnormalities in 21 patients (40%), predominantly nonspecific linear parenchymal opacities (19%), lymphadenopathy (12%), emphysema (10%), bronchiectasis (8%), and pleural involvement (8%). Linear parenchymal opacities were associated with a smoking history (p=0.026) and dorsal spine involvement (p=0.032). HLA-B27 was not associated with any abnormality. A lower thoracic diameter was observed in patients with dorsal spine involvement (p=0.0001), restrictive pattern at PFT (p=0.023), and linear parenchymal opacities (p=0.015). The study concluded that nonspecific subclinical pulmonary involvement is frequent in AS.


title: Social Cognition , Artefacts , and Stigmergy : A Comparative Analysis of Theoretical Frameworks for the Understanding of Artefact-mediated Collaborative Activity

abstract: Collective behaviour is often characterised by the so-called 'coordination paradox': looking at individual ants, for example, they do not seem to cooperate or communicate explicitly, but nevertheless at the social level cooperative behaviour, such as nest building, emerges, apparently without any central coordination. In the case of social insects such emergent coordination has been explained by the theory of stigmergy, which describes how individuals can effect the behaviour of others (and their own) through artefacts, i.e., the product of their own activity (e.g., building material in the ants' case). Artefacts clearly also play a strong role in human collective behaviour, which has been emphasised, for example, by proponents of activity theory and distributed cognition. However, the relation between theories of situated/social cognition and theories of social insect behaviour has so far received relatively little attention in the cognitive science literature. This paper aims to take a step in this direction by comparing three theoretical frameworks for the study of cognition in the context of agent-environment interaction (activity theory, situated action, and distributed cognition) to each other and to the theory of stigmergy as a possible minimal common ground. The comparison focuses on what each of the four theories has to say about the role/nature of (a) the agents involved in collective behaviour, (b) their environment, (c) the collective activities addressed, and (d) the role that artefacts play in the interaction between agents and their environments, and in particular in the coordination of cooperation.


title: Semantic Channels for Fast Pedestrian Detection

abstract: Pedestrian detection and semantic segmentation are high potential tasks for many real-time applications. However most of the top performing approaches provide state of art results at high computational costs. In this work we propose a fast solution for achieving state of art results for both pedestrian detection and semantic segmentation. As baseline for pedestrian detection we use sliding windows over cost efficient multiresolution filtered LUV+HOG channels. We use the same channels for classifying pixels into eight semantic classes. Using short range and long range multiresolution channel features we achieve more robust segmentation results compared to traditional codebook based approaches at much lower computational costs. The resulting segmentations are used as additional semantic channels in order to achieve a more powerful pedestrian detector. To also achieve fast pedestrian detection we employ a multiscale detection scheme based on a single flexible pedestrian model and a single image scale. The proposed solution provides competitive results on both pedestrian detection and semantic segmentation benchmarks at 8 FPS on CPU and at 15 FPS on GPU, being the fastest top performing approach.


title: Implementation of potential field method for mobile robot navigation in greenhouse environment with WSN support

abstract: This paper deals with the implementation of mobile measuring station in greenhouse environment navigated using potential field method. The function of a greenhouse is to create the optimal growing conditions for the full life of the plants. Using autonomous measuring systems helps to monitor all the necessary parameters for creating the optimal environment in the greenhouse. The robot equipped with sensors is capable of driving to the end and back along crop rows inside the greenhouse. It introduces a wireless sensor network that was used for the purpose of measuring and controlling the greenhouse application. Continuous advancements in wireless technology and miniaturization have made the deployment of sensor networks to monitor various aspects of the environment increasingly flexible.


title: World Knowledge as Indirect Supervision for Document Clustering

abstract: One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this paper, we provide an example of using world knowledge for domain dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, WordNet. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features.


title: TouRS'15: Workshop on Tourism Recommender Systems

abstract: Tourism has been one of the most prominents fields of application of recommender systems in the last ten years.This summary gives an overview of the latest advances in the area, which have been presented in the RecSys 2015 workshop on Tourism Recommender Systems.


title: Two-year effects of a school-based prevention programme on adolescent cigarette smoking in Guangzhou, China: a cluster randomized trial

abstract: BACKGROUND ::: The prevalence of adolescent smoking has been increasing rapidly in China. Theory-based smoking prevention programmes in schools may be an effective approach in preventing smoking among Chinese adolescents. ::: ::: ::: METHODS ::: A school-level cluster randomized controlled trial was conducted among 7th and 8th grade students (N = 2343) in four junior high schools in southern China during 2004-06. The theory-based, multi-level intervention was compared with the standard health curriculum. Outcome measures comprised changes in students' smoking-related knowledge, attitudes and behaviour. ::: ::: ::: RESULTS ::: The mean knowledge scores from baseline to the 1- and 2-year follow-ups increased more in the intervention group than in the control group, whereas there was little change in attitude scores. At the 1-year follow-up (the total sample), the interventions reduced the probability of baseline experimental smokers' escalating to regular smoker [7.9 vs 18.3%; adjusted odds ratio (OR) 0.34, 95% confidence interval (CI) 0.12-0.97, P = 0.043], but did not reduce the probability of baseline non-smokers' initiating smoking (7.9 vs 10.6%; adjusted OR 0.86, 95% CI 0.54-1.38, P = 0.538). At the 2-year follow-up (only 7th grade students), similar proportions of baseline non-smokers initiated smoking in the intervention group and the control group (13.5 vs 13.1%), while a possibly lower proportion of baseline experimental smokers escalated to regular smoking in the intervention group than the control group (22.6 vs 40.0%; adjusted OR 0.43, 95% CI 0.12-1.57, P = 0.199). ::: ::: ::: CONCLUSIONS ::: This multi-level intervention programme had a moderate effect on inhibiting the escalation from experimental to regular smoking among Chinese adolescents, but had little effect on the initiation of smoking. The programme improved adolescents' smoking-related knowledge, but did not change their attitudes towards smoking.


title: Addition of Lappert's Stannylenes to Carbodiimides, Providing a New Class of Tin(II) Guanidinates

abstract: Reactions of bis[bis(trimethylsilyl)amino]tin and the dimer of [bis(trimethylsilyl)amino]tin chloride with various carbodiimides give pure corresponding tin(II) guanidinates in essentially quantitative yields. Heteroleptic bis(trimethylsilyl)amido ({R-NC[N(SiMe3)2]N-R}SnN(SiMe3)2)- and chloro-substituted ({R-NC[N(SiMe3)2]N-R}SnCl) tin(II) guanidinates were obtained from reactions of N,N′-diisopropyl-, N,N′-dicyclohexyl-, N,N′-bis(4-methylphenyl)-, and N-[3-(dimethylamino)propyl]-N′-ethylcarbodiimides, respectively. Homoleptic tin(II) guanidinates {R-NC[N(SiMe3)2]N-R}2Sn were obtained from the N,N′-bis(4-methylphenyl)- and N-[3-(dimethylamino)propyl]-N′-ethyl-substituted carbodiimides. Similar reactions of N,N′-bis(2,6-diisopropylphenyl)- and N,N′-bis(trimethylsilyl)carbodiimide, respectively, having the bulkiest substituents of the series, failed to take place under various conditions. The complexes prepared were characterized as monomers in solution by 1H, 13C, and 119Sn NMR spectroscopy in C6D6 and THF-...


title: Fast high-quality non-blind deconvolution using sparse adaptive priors

abstract: We present an efficient approach for high-quality non-blind deconvolution based on the use of sparse adaptive priors. Its regularization term enforces preservation of strong edges while removing noise. We model the image-prior deconvolution problem as a linear system, which is solved in the frequency domain. This clean formulation lends to a simple and efficient implementation. We demonstrate its effectiveness by performing an extensive comparison with existing non-blind deconvolution methods, and by using it to deblur photographs degraded by camera shake. Our experiments show that our solution is faster and its results tend to have higher peak signal-to-noise ratio than the state-of-the-art techniques. Thus, it provides an attractive alternative to perform high-quality non-blind deconvolution of large images, as well as to be used as the final step of blind-deconvolution algorithms.


title: A framework for modeling the appearance of 3D articulated figures

abstract: This paper describes a framework for constructing a linear subspace model of image appearance for complex articulated 3D figures such as humans and other animals. A commercial motion capture system provides 3D data that is aligned with images of subjects performing various activities. Portions of a limb's image appearance are seen from multiple views and for multiple subjects. From these partial views, weighted principal component analysis is used to construct a linear subspace representation of the "unwrapped" image appearance of each limb. The linear subspaces provide a generative model of the object appearance that is exploited in a Bayesian particle filtering tracking system. Results of tracking single limbs and walking humans are presented.


title: M-SIFT: A new method for Vehicle Logo Recognition

abstract: In this paper, a new algorithm for Vehicle Logo Recognition is proposed, on the basis of an enhanced Scale Invariant Feature Transform (Merge-SIFT or M-SIFT). The algorithm is assessed on a set of 1500 logo images that belong to 10 distinctive vehicle manufacturers. A series of experiments are conducted, splitting the 1500 images to a training set (database) and to a testing set (query). It is shown that the MSIFT approach, which is proposed in this paper, boosts the recognition accuracy compared to the standard SIFT method. The reported results indicate an average of 94.6% true recognition rate in vehicle logos, while the processing time remains low (∼0.8sec).


title: Integrating a dual-silicon photoelectrochemical cell into a redox flow battery for unassisted photocharging

abstract: Technologies for in situ capture and storage of intermittent solar energy are an important research goal. Here the authors report a solar rechargeable flow cell based on a dual-silicon photoelectrochemical cell and a quinone/bromine redox flow battery for in situ solar energy conversion and storage.


title: Discriminative Recurrent Sparse Auto-Encoders

abstract: We present the discriminative recurrent sparse auto-encoder model, comprising a recurrent encoder of rectified linear units, unrolled for a fixed number of iterations, and connected to two linear decoders that reconstruct the input and predict its supervised classification. Training via backpropagation-through-time initially minimizes an unsupervised sparse reconstruction error; the loss function is then augmented with a discriminative term on the supervised classification. The depth implicit in the temporally-unrolled form allows the system to exhibit all the power of deep networks, while substantially reducing the number of trainable parameters. ::: From an initially unstructured network the hidden units differentiate into categorical-units, each of which represents an input prototype with a well-defined class; and part-units representing deformations of these prototypes. The learned organization of the recurrent encoder is hierarchical: part-units are driven directly by the input, whereas the activity of categorical-units builds up over time through interactions with the part-units. Even using a small number of hidden units per layer, discriminative recurrent sparse auto-encoders achieve excellent performance on MNIST.


title: A high-efficiency Class-E power amplifier with wide-range load in WPT systems

abstract: A high-efficiency power amplifier (PA) is important in a Megahertz wireless power transfer (WPT) system. It is attractive to apply the Class-E PA for its simple structure and high efficiency. However, the conventional design for Class-E PA can only ensure a high efficiency for a fixed load. It is necessary to develop a high-efficiency Class-E PA for a wide-range load in WPT systems. A novel design method for Class-E PA is proposed to achieve this objective in this paper. The PA achieves high efficiency, above 80%, for a load ranging from 10 to 100 Ω at 6.78 MHz in the experiment.


title: An OpenCCG-Based Approach to Question Generation from Concepts

abstract: Dialogue systems are often regarded as being tedious and inflexible. We believe that one reason is rigid and inadaptable system utterances. A good dialogue system should automatically choose a formulation that reflects the user’s expectations. However, current dialogue system development environments only allow the definition of questions with unchangeable formulations. In this paper we present a new approach to the generation of system questions by only defining basic concepts. This is the basis for realising adaptive, user-tailored, and human-like system questions in dialogue systems.


title: Object-Based Image Classification of Summer Crops with Machine Learning Methods

abstract: The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR), support vector machine (SVM) and multilayer perceptron (MLP) neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous) from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86%) and notably higher than C4.5 (79%). The SVM+SVM classifier (best method) improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity). The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.


title: Contextual Weisfeiler-Lehman graph kernel for malware detection

abstract: In this paper, we propose a novel graph kernel specifically to address a challenging problem in the field of cyber-security, namely, malware detection. Previous research has revealed the following: (1) Graph representations of programs are ideally suited for malware detection as they are robust against several attacks, (2) Besides capturing topological neighbourhoods (i.e., structural information) from these graphs it is important to capture the context under which the neighbourhoods are reachable to accurately detect malicious neighbourhoods. ::: We observe that state-of-the-art graph kernels, such as Weisfeiler-Lehman kernel (WLK) capture the structural information well but fail to capture contextual information. To address this, we develop the Contextual Weisfeiler-Lehman kernel (CWLK) which is capable of capturing both these types of information. We show that for the malware detection problem, CWLK is more expressive and hence more accurate than WLK while maintaining comparable efficiency. Through our large-scale experiments with more than 50,000 real-world Android apps, we demonstrate that CWLK outperforms two state-of-the-art graph kernels (including WLK) and three malware detection techniques by more than 5.27% and 4.87% F-measure, respectively, while maintaining high efficiency. This high accuracy and efficiency make CWLK suitable for large-scale real-world malware detection.


title: The Intelligent surfer: Probabilistic Combination of Link and Content Information in PageRank

abstract: The PageRank algorithm, used in the Google search engine, greatly improves the results of Web search by taking into account the link structure of the Web. PageRank assigns to a page a score proportional to the number of times a random surfer would visit that page, if it surfed indefinitely from page to page, following all outlinks from a page with equal probability. We propose to improve PageRank by using a more intelligent surfer, one that is guided by a probabilistic model of the relevance of a page to a query. Efficient execution of our algorithm at query time is made possible by precomputing at crawl time (and thus once for all queries) the necessary terms. Experiments on two large subsets of the Web indicate that our algorithm significantly outperforms PageRank in the (human-rated) quality of the pages returned, while remaining efficient enough to be used in today's large search engines.


title: Face recognition: A literature survey

abstract: As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.


title: Analysis of bidirectional flyback converter

abstract: Power converters have great importance in this technically advanced world because many electronic devices use controlled source. Thus new converting topologies are developing to get better operation in terms of efficiency. This paper deals with a bridgeless flyback converter topology which have bidirectional power flow capability. This converter have great importance in the inverters for residential use because, a single converter can be used as AC-DC converter for battery charging and DC-AC conversion while battery discharging. Conventional flyback converter is modified to the proposed bidirectional flyback converter. It is an integration of two flyback converters. The steady state operation and its switching strategy were presented here. Bidirectional flyback converter has been designed with the requirement of 40W, 24 V output in rectification operation and inverting operation aims for the single phase AC voltage of 230V. A battery storage unit is charged by the flyback converter and is discharging through the same converter for the residential AC load, when there is no conventional supply. Matlab/Simulink model has also been developed and relevant results are also discussed in this paper. Simulation results show good correlation with theory.


title: A novel wavelet-based model for EEG epileptic seizure detection using multi-context learning

abstract: Epileptic seizure detection has gained increasing attention in clinical therapy. Scalp electroencephalogram (EEG) analysis is a common way to capture brain abnormality for seizure onset detection. This paper presents a novel context-learning based approach using multi-feature fusion to compensate for incomplete description of single feature in epileptic EEG signals. First, EEG scalogram sequence is generated using wavelet transform to represent the time-frequency information. Second, three sets of EEG context features are unsupervisedly learned in parallel by using global principal component analysis (GPCA), stacked denoising autoencoders (SDAEs) and EEG embeddings, respectively. Finally, the multi-features are concatenated into a fixed-length feature vector for seizure classification. The experimental results conducted on two real EEG datasets demonstrate that the proposed cross-patient learning model is able to extract meaningful context features from different perspectives, and hence can detect the onset of epileptic seizure effectively.


title: Learning graph structure for multi-label image classification via clique generation

abstract: Exploiting label dependency for multi-label image classification can significantly improve classification performance. Probabilistic Graphical Models are one of the primary methods for representing such dependencies. The structure of graphical models, however, is either determined heuristically or learned from very limited information. Moreover, neither of these approaches scales well to large or complex graphs. We propose a principled way to learn the structure of a graphical model by considering input features and labels, together with loss functions. We formulate this problem into a max-margin framework initially, and then transform it into a convex programming problem. Finally, we propose a highly scalable procedure that activates a set of cliques iteratively. Our approach exhibits both strong theoretical properties and a significant performance improvement over state-of-the-art methods on both synthetic and real-world data sets.


title: ECG arrhythmia classification based on optimum-path forest

abstract: An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e., cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e., support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e., there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis.


title: Targeting the DNA Damage Response in Cancer.

abstract: An underlying hallmark of cancers is their genomic instability, which is associated with a greater propensity to accumulate DNA damage. Historical treatment of cancer by radiotherapy and DNA-damaging chemotherapy is based on this principle, yet it is accompanied by significant collateral damage to normal tissue and unwanted side effects. Targeted therapy based on inhibiting the DNA damage response (DDR) in cancers offers the potential for a greater therapeutic window by tailoring treatment to patients with tumors lacking specific DDR functions. The recent approval of olaparib (Lynparza), the poly (ADP-ribose) polymerase (PARP) inhibitor for treating tumors harboring BRCA1 or BRCA2 mutations, represents the first medicine based on this principle, exploiting an underlying cause of tumor formation that also represents an Achilles' heel. This review highlights the different concepts behind targeting DDR in cancer and how this can provide significant opportunities for DDR-based therapies in the future.


Understanding the canine intestinal microbiota and its modification by pro‐, pre‐ and synbiotics – what is the evidence?


title: Event-based, 6-DOF Camera Tracking for High-Speed Applications

abstract: In contrast to standard cameras, which produce frames at a fixed rate, event cameras respond asynchronously to pixel-level brightness changes, thus enabling the design of new algorithms for high-speed applications with latencies of microseconds. However, this advantage comes at a cost: because the output is composed by a sequence of events, traditional computer-vision algorithms are not applicable, so that a new paradigm shift is needed. We present an event-based approach for ego-motion estimation, which provides pose updates upon the arrival of each event, thus virtually eliminating latency. Our method is the first work addressing and demonstrating event-based pose tracking in six degrees-of-freedom (DOF) motions in realistic and natural scenes, and it is able to track high-speed motions. The method is successfully evaluated in both indoor and outdoor scenes.


title: Deep Adaptive Resonance Theory for learning biologically inspired episodic memory

abstract: Biologically inspired episodic memory is able to store time sequential events, and to recall all of them from partial information. Because of the advantages of episodic memory, the biological concepts of episodic memory have been utilized to many applications. In this research, we propose a new memory model, called Deep ART (Adaptive Resonance Theory), to make a robust memory system for learning episodic memory. Deep ART has an attribute field in the bottom layer, which is newly designed to get semantic information of inputs. After encoding all inputs with their features, events are categorized in the event field using specified inputs. Since an episode is made of a temporal sequence of events, Deep ART makes event sequences with proposed sequence encoding and decoding processes. They can encode any temporal sequence of events, even if there are duplicated events in the episode. Moreover, based on the result of the analysis of retrieval error, Deep ART does not use the complement coding for partial inputs to enhance the accuracy of episode retrieval from partial cues. The simulation results demonstrate the effectiveness of Deep ART as the long term memory.


title: Virtual Dermatologist: An application of 3D modeling to tele-healthcare

abstract: In this paper, we present preliminary results towards the development of the Virtual Dermatologist: A 3D image and tactile database for virtual examination of dermatology patients. This system, which can be installed and operated by non-dermatologists in remotes areas where access to a dermatologist is difficult, will enhance and broaden the application of tele-healthcare, and it will greatly facilitate the surveillance and consequent diagnosis of various skin diseases. Unlike other systems that monitor the progress of skin diseases using qualitative data on simple baseline (2D) photography, the proposed system will also allow for the quantitative assessment of the progress of the disease over time (e.g. thickness, size, roughness, etc). In fact, the 3D model created by the proposed system will let the dermatologist perform dermatoscopic-like examinations over specially annotated areas of the 3D model of the patient's body (i.e. higher definition areas of the 3D model). As part of its future development, the system will also allow the dermatologist to virtually touch and feel the lesion through a haptic interface. In its current form, the system can detect skin lesions smaller than 1mm, as we demonstrate in the result section.


title: ModelDB: a system for machine learning model management

abstract: Building a machine learning model is an iterative process. A data scientist will build many tens to hundreds of models before arriving at one that meets some acceptance criteria (e.g. AUC cutoff, accuracy threshold). However, the current style of model building is ad-hoc and there is no practical way for a data scientist to manage models that are built over time. As a result, the data scientist must attempt to "remember" previously constructed models and insights obtained from them. This task is challenging for more than a handful of models and can hamper the process of sensemaking. Without a means to manage models, there is no easy way for a data scientist to answer questions such as "Which models were built using an incorrect feature?", "Which model performed best on American customers?" or "How did the two top models compare?" In this paper, we describe our ongoing work on ModelDB, a novel end-to-end system for the management of machine learning models. ModelDB clients automatically track machine learning models in their native environments (e.g. scikit-learn, spark.ml), the ModelDB backend introduces a common layer of abstractions to represent models and pipelines, and the ModelDB frontend allows visual exploration and analyses of models via a web-based interface.


A Novel Iterative CT Reconstruction Approach Based on FBP Algorithm


title: Adversarially Regularized Graph Autoencoder.

abstract: Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which often results in inferior embedding in real-world graph data. In this paper, we propose a novel adversarial graph embedding framework for graph data. The framework encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure. Furthermore, the latent representation is enforced to match a prior distribution via an adversarial training scheme. To learn a robust embedding, two variants of adversarial approaches, adversarially regularized graph autoencoder (ARGA) and adversarially regularized variational graph autoencoder (ARVGA), are developed. Experimental studies on real-world graphs validate our design and demonstrate that our algorithms outperform baselines by a wide margin in link prediction, graph clustering, and graph visualization tasks.


title: Does visual attention select objects or locations

abstract: Much research supports location-based attentional selection, but J. Duncan (1984) presented data favoring object-based selection in a shape discrimination task. Does attention select objects or locations? We confirmed that Duncan's task elicits selection from spatially invariant object representations rather than from a grouped location-based representation. We next asked whether this finding was due to location-based filtering; the results again supported object-based selection. Finally, we demonstrated that when Duncan's objects were used in a cued detection task the results were consistent with location-based selection. These results suggest that there may not be a single attention mechanism, consistent with Duncan's original claim that object-based and location-based attentional selection are not mutually exclusive. Rather, attentional limitations may depend on the type of stimulus representation used in performing a given task


title: Dcell: a scalable and fault-tolerant network structure for data centers

abstract: A fundamental challenge in data center networking is how to efficiently interconnect an exponentially increasing number of servers. This paper presents DCell, a novel network structure that has many desirable features for data center networking. DCell is a recursively defined structure, in which a high-level DCell is constructed from many low-level DCells and DCells at the same level are fully connected with one another. DCell scales doubly exponentially as the node degree increases. DCell is fault tolerant since it does not have single point of failure and its distributed fault-tolerant routing protocol performs near shortest-path routing even in the presence of severe link or node failures. DCell also provides higher network capacity than the traditional tree-based structure for various types of services. Furthermore, DCell can be incrementally expanded and a partial DCell provides the same appealing features. Results from theoretical analysis, simulations, and experiments show that DCell is a viable interconnection structure for data centers.


title: Continuous Authentication Using Eye Movement Response of Implicit Visual Stimuli

abstract: Smart head-worn or head-mounted devices, including smart glasses and Virtual Reality (VR) headsets, are gaining popularity. Online shopping and in-app purchase from such headsets are presenting new e-commerce opportunities to the app developers. For convenience, users of these headsets may store account login, bank account and credit card details in order to perform quick in-app purchases. If the device is unattended, then an attacker, which can include insiders, can make use of the stored account and banking details to perform their own in-app purchases at the expense of the legitimate owner. To better protect the legitimate users of VR headsets (or head mounted displays in general) from such threats, in this paper, we propose to use eye movement to continuously authenticate the current wearer of the VR headset. We built a prototype device which allows us to apply visual stimuli to the wearer and to video the eye movements of the wearer at the same time. We use implicit visual stimuli (the contents of existing apps) which evoke eye movements from the headset wearer but without distracting them from their normal activities. This is so that we can continuously authenticate the wearer without them being aware of the authentication running in the background. We evaluated our proposed system experimentally with 30 subjects. Our results showed that the achievable authentication accuracy for implicit visual stimuli is comparable to that of using explicit visual stimuli. We also tested the time stability of our proposed method by collecting eye movement data on two different days that are two weeks apart. Our authentication method achieved an Equal Error Rate of 6.9% (resp. 9.7%) if data collected from the same day (resp. two weeks apart) were used for testing. In addition, we considered active impersonation attacks where attackers trying to imitate legitimate users' eye movements. We found that for a simple (resp. complex) eye tracking scene, a successful attack could be realised after on average 5.67 (13.50) attempts and our proposed authentication algorithm gave a false acceptance rate of 14.17% (3.61%). These results show that active impersonating attacks can be prevented using complex scenes and an appropriate limit on the number of authentication attempts. Lastly, we carried out a survey to study the user acceptability to our proposed implicit stimuli. We found that on a 5-point Likert scale, at least 60% of the respondents either agreed or strongly agreed that our proposed implicit stimuli were non-intrusive.


title: A compact electro-optical VCSEL model for high-speed IC design

abstract: This paper presents an accurate yet compact model for vertical cavity surface emitting lasers (VCSEL), which can be extracted from simple DC and small-signal electro-optical measurements. Since VCSELs are the bottleneck of high-speed electro-optical transceivers, the model is essential in the design of high-speed laser drivers. VCSEL rate equations are used to describe the electrical to optical conversion in the laser, while non-linear parasitcs are modelling the VCSEL interface. The model is applied to a commercially available VCSEL and there is an excellent agreement between the simulated and measured eye diagrams at different data rates and bias currents.


title: Locality sensitive hashing for scalable structural classification and clustering of web documents

abstract: Web content management systems as well as web front ends to databases usually use mechanisms based on homogeneous templates for generating and populating HTML documents containing structured, semi-structured or plain text data. Wrapper based information extraction techniques leverage such templates as an essential cornerstone of their functionality but rely heavily on the availability of proper training documents based on the specific template. Thus, structural classification and structural clustering of web documents is an important contributing factor to the success of those methods. We introduce a novel technique to support these two tasks: template fingerprints. Template fingerprints are locality sensitive hash values in the form of short sequences of characters which effectively represent the underlying template of a web document. Small changes in the document structure, as they may occur in template based documents, lead to no or only minor variations in the corresponding fingerprint. Based on the fingerprints we introduce a scalable index structure and algorithm for large collections of web documents, which can retrieve structurally similar documents efficiently. The effectiveness of our approach is empirically validated in a classification task on a data set of 13,237 documents based on 50 templates from different domains. The general efficiency and scalability is evaluated in a clustering task on a data set retrieved from the Open Directory Project comprising more than 3.6 million web documents. For both tasks, our template fingerprint approach provides results of high quality and demonstrates a linear runtime of O(n) w.r.t. the number of documents.


title: Survey of Flocking Algorithms in Multi-agent Systems

abstract: Flocking behaviour in Multi-agent Systems (MAS) has attracted tremendous attention amongst researchers in the recent past due to its potential applications in various fields where distributed work environment is desired. The flocking algorithms have the potential to introduce self-organizing, self-healing and selfconfiguring capabilities in the functioning of a distributed system. The flocking algorithms exploit various artificial intelligence techniques, mathematical potential functions and geometric approaches to realize the global objectives by controlling local parameters. The main parameters of characterization of any flocking algorithm consist of mathematical models of agents, their hierarchical or flat control structures and the control approach by which these agents are controlled to exhibit flocking behaviour along with any type of formational constraints. A rigorous survey study of flocking algorithms for agents in MAS in the perspective of various instances of agents shows that there lies a huge scope for the researchers to apply, experiment and analyse various techniques locally to achieve global objectives. This paper surveys the flocking algorithms in perspective of these parameters.


title: Bayesian structure learning in graphical models

abstract: We consider the problem of estimating a sparse precision matrix of a multivariate Gaussian distribution, including the case where the dimension $p$ exceeds the sample size $n$. Gaussian graphical models provide an important tool in describing conditional independence through presence or absence of the edges in the underlying graph. A popular non-Bayesian method of estimating a graphical structure is given by the graphical lasso. In this paper, we consider a Bayesian approach to the problem. We use priors which put a mixture of a point mass at zero and certain absolutely continuous distribution on off-diagonal elements of the precision matrix. Hence the resulting posterior distribution can be used for graphical structure learning. The posterior convergence rate of the precision matrix is obtained. The posterior distribution of different graphical models is extremely cumbersome to compute. We propose a fast computational method for approximating the posterior probabilities of various graphs using the Laplace approximation method by expanding the posterior density around the posterior mode, which is the graphical lasso by our choice of the prior distribution. We also provide estimates of the accuracy in the approximation.


title: A New Improved Vertical Partitioning Scheme for Non Relational Databases Using Greedy Method

abstract: 2 Abstract: Non Relational database is a database used to store large amounts of data. Improving the performance of a database system is one of the key research issues. As publications of context are rising, a new vertical partitioning scheme is proposed to handle better data load and to improve performance for non relational databases. In the proposed work, an algorithm is developed by concatenating the vertical partitioning scheme and greedy algorithm to enhance the performance of data load by altering the vertical partitioning method and to check out the time performance by using the greedy algorithm. In this paper, different performance parameters named data load, time in terms of execution time, communication time are analyzed against data size and nodes using non relational databases so that we get better results from existing algorithm.


CHALLENGES AND OPPORTUNITIES FOR IOT-ENABLED CYBERMANUFACTURING : SOME INITIAL FINDINGS FROM AN IOT-ENABLED MANUFACTURING TECHNOLOGY TESTBED


title: User involvement: A review of the benefits and challenges

abstract: User involvement is a widely accepted principle in development of usable systems. However, it is a vague concept covering many approaches. This study first clarifies the nature of user involvement and its expected benefits, and secondly reviews three streams of research, to evaluate the benefits and problems of varied user involvement approaches in practice. The particular focus of this study is on the early activities in the development process. An analysis of the literature suggests that user involvement has generally positive effects, especially on user satisfaction, and some evidence exists to suggest that taking users as a primary information source is an effective means of requirements capture. However, the role of users must be carefully considered and more cost-efficient practices are needed for gathering users' implicit needs and requirements in real product development contexts.


title: Mapping, Learning, Visualization, Classification, and Understanding of fMRI Data in the NeuCube Evolving Spatiotemporal Data Machine of Spiking Neural Networks

abstract: This paper introduces a new methodology for dynamic learning, visualization, and classification of functional magnetic resonance imaging (fMRI) as spatiotemporal brain data. The method is based on an evolving spatiotemporal data machine of evolving spiking neural networks (SNNs) exemplified by the NeuCube architecture [1] . The method consists of several steps: mapping spatial coordinates of fMRI data into a 3-D SNN cube (SNNc) that represents a brain template; input data transformation into trains of spikes; deep, unsupervised learning in the 3-D SNNc of spatiotemporal patterns from data; supervised learning in an evolving SNN classifier; parameter optimization; and 3-D visualization and model interpretation. Two benchmark case study problems and data are used to illustrate the proposed methodology—fMRI data collected from subjects when reading affirmative or negative sentences and another one—on reading a sentence or seeing a picture. The learned connections in the SNNc represent dynamic spatiotemporal relationships derived from the fMRI data. They can reveal new information about the brain functions under different conditions. The proposed methodology allows for the first time to analyze dynamic functional and structural connectivity of a learned SNN model from fMRI data. This can be used for a better understanding of brain activities and also for online generation of appropriate neurofeedback to subjects for improved brain functions. For example, in this paper, tracing the 3-D SNN model connectivity enabled us for the first time to capture prominent brain functional pathways evoked in language comprehension. We found stronger spatiotemporal interaction between left dorsolateral prefrontal cortex and left temporal while reading a negated sentence. This observation is obviously distinguishable from the patterns generated by either reading affirmative sentences or seeing pictures. The proposed NeuCube-based methodology offers also a superior classification accuracy when compared with traditional AI and statistical methods. The created NeuCube-based models of fMRI data are directly and efficiently implementable on high performance and low energy consumption neuromorphic platforms for real-time applications.


title: Experience and Brain Development

abstract: GREENOUGH, WILLIAM T.; BLACK, JAMES E.; and WALLACE, CHRISTOPHER S. Experience and Brain Development. CHILD DEVELOPMENT, 1987, 58, 539-559. This article considers how experience can influence the developing and mature brain and proposes a new categorization scheme based upon the type of information stored and the brain mechanisms that appear to be involved in storing it. In this scheme, experience-expectant information storage refers to incorporation of environmental information that is ubiquitous in the environment and common to all species members, such as the basic elements of pattern perception. Experience-expectant processes appear to have evolved as a neural preparation for incorporating specific information: in many sensory systems, synaptic connections between nerve cells are overproduced, and a subsequent selection process occurs in which aspects of sensory experience determine the pattern of connections that remains. Experiencedependent information storage refers to incorporation of environmental information that is idiosyncratic, or unique to the individual, such as learning about one's specific physical environment or vocabulary. The neural basis of experience-dependent processes appears to involve active formation of new synaptic connections in response to the events providing the information to be stored. Although these processes probably do not occur entirely independently of one another in development, the categories offer a new view more in accord with neural mechanisms than were terms like "critical" or "sensitive period."


title: Distributed queues in shared memory: multicore performance and scalability through quantitative relaxation

abstract: A prominent remedy to multicore scalability issues in concurrent data structure implementations is to relax the sequential specification of the data structure. We present distributed queues (DQ), a new family of relaxed concurrent queue implementations. DQs implement relaxed queues with linearizable emptiness check and either configurable or bounded out-of-order behavior or pool behavior. Our experiments show that DQs outperform and outscale in micro- and macrobenchmarks all strict and relaxed queue as well as pool implementations that we considered.


title: Measuring People-Flow through Doorways Using Easy-to-Install IR Array Sensors

abstract: People counting has many applications in smart buildings. For example, adjusting HVAC systems based on the number of occupants in each room can save a significant amount of energy. In addition, security and safety of the building can be managed by determining the number and location of occupants. Different technologies and sensing platforms have proposed for accurate and efficient people counting. However, these solutions are expensive, hard to deploy, or privacy invasive. We investigate the possibility of placing an 8×8 IR array sensor at the doorways and counting the number of people inside rooms. Our solution is real-time, inexpensive, privacy preserving with much less deployment constraints compared to its competitors. The proposed solution deals with realistic and dynamic changes in the sensing environment by leveraging a combination of Otsus thresholding and modeling thermal noise distribution. We evaluated our solution via several controlled and uncontrolled real-world environments. The results show an average of 93% accuracy in estimating the number of occupants in rooms.


title: Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step

abstract: Generative adversarial networks (GANs) are a family of generative models that do not minimize a single training criterion. Unlike other generative models, the data distribution is learned via a game between a generator (the generative model) and a discriminator (a teacher providing training signal) that each minimize their own cost. GANs are designed to reach a Nash equilibrium at which each player cannot reduce their cost without changing the other players' parameters. One useful approach for the theory of GANs is to show that a divergence between the training distribution and the model distribution obtains its minimum value at equilibrium. Several recent research directions have been motivated by the idea that this divergence is the primary guide for the learning process and that every step of learning should decrease the divergence. We show that this view is overly restrictive. During GAN training, the discriminator provides learning signal in situations where the gradients of the divergences between distributions would not be useful. We provide empirical counterexamples to the view of GAN training as divergence minimization. Specifically, we demonstrate that GANs are able to learn distributions in situations where the divergence minimization point of view predicts they would fail. We also show that gradient penalties motivated from the divergence minimization perspective are equally helpful when applied in other contexts in which the divergence minimization perspective does not predict they would be helpful. This contributes to a growing body of evidence that GAN training may be more usefully viewed as approaching Nash equilibria via trajectories that do not necessarily minimize a specific divergence at each step.