query: Autoencoders trained with relevant information: Blending Shannon and Wiener's perspectives

positive: Mean Shift, Mode Seeking, and Clustering Mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some k-means like clustering algorithms its special cases. It is shown that mean shift is a mode-seeking process on a surface constructed with a “shadow” kernel. For Gaussian kernels, mean shift is a gradient mapping. Convergence is studied for mean shift iterations. Cluster analysis is treated as a deterministic problem of finding a fixed point of mean shift that characterizes the data. Applications in clustering and Hough transform are demonstrated. Mean shift is also considered as an evolutionary strategy that performs multistart global optimization.

positive: Information Theoretic-Learning auto-encoder We propose Information Theoretic-Learning (ITL) divergence measures for variational regularization of neural networks. We also explore ITL-regularized autoencoders as an alternative to variational autoencoding bayes, adversarial autoencoders and generative adversarial networks for randomly generating sample data without explicitly defining a paritition function. This paper also formalizes, generative moment matching networks under the ITL framework.

positive: Adversarial Autoencoders In this paper we propose a new method for regularizing autoencoders by imposing an arbitrary prior on the latent representation of the autoencoder. Our method, named “adversarial autoencoder”, uses the recently proposed generative adversarial networks (GAN) in order to match the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior. Matching the aggregated posterior to the prior ensures that there are no “holes” in the prior, and generating from any part of prior space results in meaningful samples. As a result, the decoder of the adversarial autoencoder learns a deep generative model that maps the imposed prior to the data distribution. We show how adversarial autoencoders can be used to disentangle style and content of images and achieve competitive generative performance on MNIST, Street View House Numbers and Toronto Face datasets.

positive: Autoencoders, Unsupervised Learning, and Deep Architectures To better understand deep architectures and unsupervised learning, uncluttered by hardware details, we develop a general autoencoder framework for the comparative study of autoencoders, including Boolean autoencoders. We derive several results regarding autoencoders and autoencoder learning, including results on learning complexity, vertical and horizontal composition, and fundamental connections between critical points and clustering. Possible implications for the theory of deep architectures are discussed.

positive: Information Theory, Inference, and Learning Algorithms

negative: EPIC: Efficient Private Image Classification (or: Learning from the Masters) Outsourcing an image classification task raises privacy concerns, both from the image provider’s perspective, who wishes to keep their images confidential, and from the classification algorithm provider’s perspective, who wishes to protect the intellectual property of their classifier. We propose EPIC, an efficient private image classification system based on support vector machine (SVM) learning, secure against malicious adversaries. EPIC builds upon transfer learning techniques known from the Machine Learning (ML) literature and minimizes the load on the privacy-preserving part. Our solution is based on Multiparty Computation (MPC), it is 34 times faster than Gazelle (USENIX’18) –the state-of-the-art in private image classification– and it improves the communication cost by 50 times, with a 7% higher accuracy on CIFAR-10 dataset. For the same accuracy as Gazelle achieves on CIFAR-10, EPIC is 700 times faster and the communication cost is reduced by 500 times.

negative: Web-Based Ontology Languages and its Based Description Logics The main object of semantic web languages is to add semantics to the existing information on the Web. These web languages have been developed to represent or express ontologies. Therefore, these description languages provide richer constructors for forming complex class expressions and axioms. An ontology is expressed in a knowledge representation language, which provides a formal frame of semantics. Since description logic is the basis of most ontology language, it is appropriate to explain briefly the base of the description to understand the ontology language clearly. The aim of this paper is to provide a brief survey of state-of-the-art ontology languages which are used to express ontology over the Web is provided. Also the gaol of this paper is to provide a basic understanding of ontologies and description logics, which are the basis of ontology languages.

negative: Feature Mining for Localised Crowd Counting This paper presents a multi-output regression model for crowd counting in public scenes. Existing counting by regression methods either learn a single model for global counting, or train a large number of separate regressors for localised density estimation. In contrast, our single regression model based approach is able to estimate people count in spatially localised regions and is more scalable without the need for training a large number of regressors proportional to the number of local regions. In particular, the proposed model automatically learns the functional mapping between interdependent low-level features and multi-dimensional structured outputs. The model is able to discover the inherent importance of different features for people counting at different spatial locations. Extensive evaluations on an existing crowd analysis benchmark dataset and a new more challenging dataset demonstrate the effectiveness of our approach.

negative: Transformation of generalized chebyshev lowpass filter prototype to Suspended Stripline Structure highpass filter for wideband communication systems This paper presents the transformation of generalized Chebyshev lowpass filter prototype to highpass filter using Suspended Stripline Structure (SSS) technology. The study involves circuit analysis to determine generalized Chebyshev responses with a transmission zero at finite frequency. The transformation of the highpass filter from the lowpass filter prototype provides a cutoff frequency of 3.1 GHz with a return loss better than -20 dB. The design is simulated on a Roger Duroid RO4350 with a dielectric constant, εr of 3.48 and a thickness of 0.168 mm. The simulation performance results show promising results that could be further examined during the experimental works. This class of generalized Chebyshev highpass filter with finite transmission zero would be useful in any RF/ microwave communication systems particularly in wideband applications where the reduction of overall physical volume and weight as well as cost very important, while maintaining its excellent performance.

negative: Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data Detecting anomalous activity in human mobility data has a number of applications including road hazard sensing, telematic based insurance, and fraud detection in taxi services and ride sharing. In this paper we address two challenges that arise in the study of anomalous human trajectories: 1) a lack of ground truth data on what defines an anomaly and 2) the dependence of existing methods on significant pre-processing and feature engineering. While generative adversarial networks seem like a natural fit for addressing these challenges, we find that existing GAN based anomaly detection algorithms perform poorly due to their inability to handle multimodal patterns. For this purpose we introduce an infinite Gaussian mixture model coupled with (bi-directional) generative adversarial networks, IGMM-GAN, that is able to generate synthetic, yet realistic, human mobility data and simultaneously facilitates multimodal anomaly detection. Through estimation of a generative probability density on the space of human trajectories, we are able to generate realistic synthetic datasets that can be used to benchmark existing anomaly detection methods. The estimated multimodal density also allows for a natural definition of outlier that we use for detecting anomalous trajectories. We illustrate our methodology and its improvement over existing GAN anomaly detection on several human mobility datasets, along with MNIST.

negative: Lexical Analysis for the Webshell Attacks In this paper, we proposed a design considering for web application firewall system. Anyway, the website managing, web technology and hacker technology are vastly different from each other, whilst hacker techs are keep on moving forward, while company and government MIS are difficult of updating their information to the latest state due to working, they are often not updated and followed to the latest stuffs. And moreover, these newest techs are in generally needed to be figured out whether it matches the compatibility of the old versions, such as the recent PHP 7, many MIS are not capable to update their software to handle the variants of WAF bypass methods.

negative: An Italian Twitter Corpus of Hate Speech against Immigrants The paper describes a recently-created Twitter corpus of about 6,000 tweets, annotated for hate speech against immigrants, and developed to be a reference dataset for an automatic system of hate speech monitoring. The annotation scheme was therefore specifically designed to account for the multiplicity of factors that can contribute to the definition of a hate speech notion, and to offer a broader tagset capable of better representing all those factors, which may increase, or rather mitigate, the impact of the message. This resulted in a scheme that includes, besides hate speech, the following categories: aggressiveness, offensiveness, irony, stereotype, and (on an experimental basis) intensity. The paper hereby presented namely focuses on how this annotation scheme was designed and applied to the corpus. In particular, also comparing the annotation produced by CrowdFlower contributors and by expert annotators, we make some remarks about the value of the novel resource as gold standard, which stems from a preliminary qualitative analysis of the annotated data and on future corpus development.

negative:
Towards reducing the attack surface of software backdoors Backdoors in software systems probably exist since the very first access control mechanisms were implemented and they are a well-known security problem. Despite a wave of public discoveries of such backdoors over the last few years, this threat has only rarely been tackled so far.
 In this paper, we present an approach to reduce the attack surface for this kind of attacks and we strive for an automated identification and elimination of backdoors in binary applications. We limit our focus on the examination of server applications within a client-server model. At the core, we apply variations of the delta debugging technique and introduce several novel heuristics for the identification of those regions in binary application that backdoors are typically installed in (i.e., authentication and command processing functions). We demonstrate the practical feasibility of our approach on several real-world backdoors found in modified versions of the popular software tools ProFTPD and OpenSSH. Furthermore, we evaluate our implementation not only on common instruction set architectures such as x86-64, but also on commercial off-the-shelf embedded devices powered by a MIPS32 processor.

negative: Ovariohysterectomy versus ovariectomy for elective sterilization of female dogs and cats: is removal of the uterus necessary? Views: Commentary 1409 E sterilization of female dogs and cats is one of the most common procedures performed in veterinary practice and is considered by private veterinary practitioners as one of the most important skills required of new graduates. Potential benefits of sterilization include population control, prevention of diseases of the reproductive tract, and elimination of undesirable behaviors associated with hormonal cycling. The AVMA and the Association of Shelter Veterinarians both promote elective sterilization of female dogs and cats as integral to reducing euthanasia of unwanted dogs and cats. Sterilization of female dogs and cats can be accomplished by removing both the ovaries and uterus (ovariohysterectomy) or by removing the ovaries alone (ovariectomy). Ovariohysterectomy has historically been recommended in the United States and Canada and is currently emphasized at schools and colleges of veterinary medicine in these countries. To our knowledge, only one of the most frequently used surgical textbooks in the United States and Canada describes ovariectomy, and ovariectomy is generally not taught to veterinary students during their surgical laboratories. The Clinical Proficiency Examination, which is the final step in the Educational Commission for Foreign Veterinary Graduates’ program for establishing the educational equivalence of graduates of nonaccredited veterinary schools seeking licensure in the United States or Canada, requires “complete removal of both ovaries and removal of the majority of the uterus” to obtain a passing grade. Despite the apparent preference for ovariohysterectomy in the United States and Canada, ovariectomy appears to have become the standard of care in many European countries. In addition, with the development of minimally invasive surgical techniques, laparoscopic ovariectomy has gained popularity. Thus, it may be helpful to review the scientific evidence comparing ovariohysterectomy and ovariectomy for elective sterilization of healthy female dogs and cats.

negative: Discovering Texture Regularity as a Higher-Order Correspondence Problem Understanding texture regularity in real images is a challenging computer vision task. We propose a higher-order feature matching algorithm to discover the lattices of near-regular textures in real images. The underlying lattice of a near-regular texture identifies all of the texels as well as the global topology among the texels. A key contribution of this paper is to formulate lattice-finding as a correspondence problem. The algorithm finds a plausible lattice by iteratively proposing texels and assigning neighbors between the texels. Our matching algorithm seeks assignments that maximize both pair-wise visual similarity and higher-order geometric consistency. We approximate the optimal assignment using a recently developed spectral method. We successfully discover the lattices of a diverse set of unsegmented, real-world textures with significant geometric warping and large appearance variation among

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

negative: Mining frequent closed graphs on evolving data streams Graph mining is a challenging task by itself, and even more so when processing data streams which evolve in real-time. Data stream mining faces hard constraints regarding time and space for processing, and also needs to provide for concept drift detection. In this paper we present a framework for studying graph pattern mining on time-varying streams. Three new methods for mining frequent closed subgraphs are presented. All methods work on coresets of closed subgraphs, compressed representations of graph sets, and maintain these sets in a batch-incremental manner, but use different approaches to address potential concept drift. An evaluation study on datasets comprising up to four million graphs explores the strength and limitations of the proposed methods. To the best of our knowledge this is the first work on mining frequent closed subgraphs in non-stationary data streams.

negative: A Model-Driven Framework to Develop Personalized Health Monitoring Both distributed healthcare systems and the Internet of Things (IoT) are currently hot topics. The latter is a new computing paradigm to enable advanced capabilities in engineering various applications, including those for healthcare. For such systems, the core social requirement is the privacy/security of the patient information along with the technical requirements (e.g., energy consumption) and capabilities for adaptability and personalization. Typically, the functionality of the systems is predefined by the patient’s data collected using sensor networks along with medical instrumentation; then, the data is transferred through the Internet for treatment and decision-making. Therefore, systems creation is indeed challenging. In this paper, we propose a model-driven framework to develop the IoT-based prototype and its reference architecture for personalized health monitoring (PHM) applications. The framework contains a multi-layered structure with feature-based modeling and feature model transformations at the top and the application software generation at the bottom. We have validated the framework using available tools and developed an experimental PHM to test some aspects of the functionality of the reference architecture in real time. The main contribution of the paper is the development of the model-driven computational framework with emphasis on the synergistic effect of security and energy issues.

negative: Recognizing human activity in smart home using deep learning algorithm There is a topic of interest for many researchers about human activities recognition in recent years. In this paper, we propose the research to recognize human activities by deep learning (DL) algorithm. We collected the data from participants performing activities in order to evaluate the human activities recognition results. After pre-training the deep network, the fine-tuning process begins. Compared with Hidden Markov Model (HMM) and naïve Bayes Classifier (NBC), the experiment results show that the proposed deep learning algorithm is an effective way for recognizing human activities in smart home.

negative: Real User Evaluation of Spoken Dialogue Systems Using Amazon Mechanical Turk This paper describes a framework for evaluation of spoken dialogue systems. Typically, evaluation of dialogue systems is performed in a controlled test environment with carefully selected and instructed users. However, this approach is very demanding. An alternative is to recruit a large group of users who evaluate the dialogue systems in a remote setting under virtually no supervision. Crowdsourcing technology, for example Amazon Mechanical Turk (AMT), provides an efficient way of recruiting subjects. This paper describes an evaluation framework for spoken dialogue systems using AMT users and compares the obtained results with a recent trial in which the systems were tested by locally recruited users. The results suggest that the use of crowdsourcing technology is feasible and it can provide reliable results.

negative:
Pocket reflectometry We present a simple, fast solution for reflectance acquisition using tools that fit into a pocket. Our method captures video of a flat target surface from a fixed video camera lit by a hand-held, moving, linear light source. After processing, we obtain an SVBRDF.
 We introduce a BRDF chart, analogous to a color "checker" chart, which arranges a set of known-BRDF reference tiles over a small card. A sequence of light responses from the chart tiles as well as from points on the target is captured and matched to reconstruct the target's appearance.
 We develop a new algorithm for BRDF reconstruction which works directly on these LDR responses, without knowing the light or camera position, or acquiring HDR lighting. It compensates for spatial variation caused by the local (finite distance) camera and light position by warping responses over time to align them to a specular reference. After alignment, we find an optimal linear combination of the Lambertian and purely specular reference responses to match each target point's response. The same weights are then applied to the corresponding (known) reference BRDFs to reconstruct the target point's BRDF. We extend the basic algorithm to also recover varying surface normals by adding two spherical caps for diffuse and specular references to the BRDF chart.
 We demonstrate convincing results obtained after less than 30 seconds of data capture, using commercial mobile phone cameras in a casual environment.

negative: Un Método para definir la Arquitectura de Procesos In this paper we present a method for systematically defining the process architecture of any organization. We begin by presenting a detailed interpretation of the upper levels of Zachman’s framework, providing an answer to the cell formed by the intersection of the perspective of the Planner (row 1) and the dimension of Function (column 2). Our method consists of four steps: capturing the organizational structure; exhaustively analyzing the flow of information between the different organizational units, customers, and providers, allowing for a high-level understanding of the organization’s operation; identifying and modeling the configuration of value that best adapts itself to the creation of value for the organization (value chain, workshop, or network); and, given the specified value configuration, identifying, defining, and interrelating the different essential processes.

negative: Development of Self Balance Transporter : A Segway This paper deals with constructing the vehicle which is fully functional to transport the one person only and movement of it is achieve by naturally. Forward and backwards motion is achieved by leaning forwards and backwards. Segway uses handlebar for turning purpose. In Segway technique uses principle of inverted pendulum in which it keeps an angle of Zero degrees with vertical at all times. The rider motion enables this technique to accelerate, brake or steer the vehicle, for this it uses Gyroscopic Sensor and it is Eco-friendly and non polluting.

negative: Can Active Memory Replace Attention? Several mechanisms to focus attention of a neural network on selected parts of its input or memory have been used successfully in deep learning models in recent years. Attention has improved image classification, image captioning, speech recognition, generative models, and learning algorithmic tasks, but it had probably the largest impact on neural machine translation. Recently, similar improvements have been obtained using alternative mechanisms that do not focus on a single part of a memory but operate on all of it in parallel, in a uniform way. Such mechanism, which we call active memory, improved over attention in algorithmic tasks, image processing, and in generative modelling. So far, however, active memory has not improved over attention for most natural language processing tasks, in particular for machine translation. We analyze this shortcoming in this paper and propose an extended model of active memory that matches existing attention models on neural machine translation and generalizes better to longer sentences. We investigate this model and explain why previous active memory models did not succeed. Finally, we discuss when active memory brings most benefits and where attention can be a better choice.

negative: Magnetic resonance imaging findings associated with surgically proven rotator interval lesions To identify shoulder magnetic resonance imaging (MRI) findings associated with surgically proven rotator interval abnormalities. The preoperative MRI examinations of five patients with surgically proven rotator interval (RI) lesions requiring closure were retrospectively evaluated by three musculoskeletal-trained radiologists in consensus. We assessed the structures in the RI, including the coracohumeral ligament, superior glenohumeral ligament, fat tissue, biceps tendon, and capsule for variations in size and signal alteration. In addition, we noted associated findings of rotator cuff and labral pathology. Three of three of the MR arthrogram studies demonstrated extension of gadolinium to the cortex of the undersurface of the coracoid process compared with the control images, seen best on the sagittal oblique images. Four of five of the studies demonstrated subjective thickening of the coracohumeral ligament, and three of five of the studies demonstrated subjective thickening of the superior glenohumeral ligament. Five of five of the studies demonstrated a labral tear. The MRI arthrogram finding of gadolinium extending to the cortex of the undersurface of the coracoid process was noted on the studies of those patients with rotator interval lesions at surgery in this series. Noting this finding—especially in the presence of a labral tear and/or thickening of the coracohumeral ligament or superior glenohumeral ligament—may be helpful in the preoperative diagnosis of rotator interval lesions.

negative: A Tidy Data Model for Natural Language Processing using cleanNLP Recent advances in natural language processing have produced libraries that extract lowlevel features from a collection of raw texts. These features, known as annotations, are usually stored internally in hierarchical, tree-based data structures. This paper proposes a data model to represent annotations as a collection of normalized relational data tables optimized for exploratory data analysis and predictive modeling. The R package cleanNLP, which calls one of two state of the art NLP libraries (CoreNLP or spaCy), is presented as an implementation of this data model. It takes raw text as an input and returns a list of normalized tables. Specific annotations provided include tokenization, part of speech tagging, named entity recognition, sentiment analysis, dependency parsing, coreference resolution, and word embeddings. The package currently supports input text in English, German, French, and Spanish.

negative: Learning Anisotropic RBF Kernels We present an approach for learning an anisotropic RBF kernel in a game theoretical setting where the value of the game is the degree of separation between positive and negative training examples. The method extends a previously proposed method (KOMD) to perform feature re-weighting and distance metric learning in a kernel-based classification setting. Experiments on several benchmark datasets demonstrate that our method generally outperforms stateof-the-art distance metric learning methods, including the Large Margin Nearest Neighbor Classification family of methods.

negative:
Evaluation of Consumer Understanding of Different Front-of-Package Nutrition Labels, 2010–2011 INTRODUCTION
Governments throughout the world are using or considering various front-of-package (FOP) food labeling systems to provide nutrition information to consumers. Our web-based study tested consumer understanding of different FOP labeling systems.

METHODS
Adult participants (N = 480) were randomized to 1 of 5 groups to evaluate FOP labels: 1) no label; 2) multiple traffic light (MTL); 3) MTL plus daily caloric requirement icon (MTL+caloric intake); 4) traffic light with specific nutrients to limit based on food category (TL+SNL); or 5) the Choices logo. Total percentage correct quiz scores were created reflecting participants' ability to select the healthier of 2 foods and estimate amounts of saturated fat, sugar, and sodium in foods. Participants also rated products on taste, healthfulness, and how likely they were to purchase the product. Quiz scores and product perceptions were compared with 1-way analysis of variance followed by post-hoc Tukey tests.

RESULTS
The MTL+caloric intake group (mean [standard deviation], 73.3% [6.9%]) and Choices group (72.5% [13.2%]) significantly outperformed the no label group (67.8% [10.3%]) and the TL+SNL group (65.8% [7.3%]) in selecting the more healthful product on the healthier product quiz. The MTL and MTL+caloric intake groups achieved average scores of more than 90% on the saturated fat, sugar, and sodium quizzes, which were significantly better than the no label and Choices group average scores, which were between 34% and 47%.

CONCLUSION
An MTL+caloric intake label and the Choices symbol hold promise as FOP labeling systems and require further testing in different environments and population subgroups.

negative: Does High Self-Esteem Cause Better Performance, Interpersonal Success, Happiness, or Healthier Lifestyles? Self-esteem has become a household word. Teachers, parents, therapists, and others have focused efforts on boosting self-esteem, on the assumption that high self-esteem will cause many positive outcomes and benefits-an assumption that is critically evaluated in this review. Appraisal of the effects of self-esteem is complicated by several factors. Because many people with high self-esteem exaggerate their successes and good traits, we emphasize objective measures of outcomes. High self-esteem is also a heterogeneous category, encompassing people who frankly accept their good qualities along with narcissistic, defensive, and conceited individuals. The modest correlations between self-esteem and school performance do not indicate that high self-esteem leads to good performance. Instead, high self-esteem is partly the result of good school performance. Efforts to boost the self-esteem of pupils have not been shown to improve academic performance and may sometimes be counterproductive. Job performance in adults is sometimes related to self-esteem, although the correlations vary widely, and the direction of causality has not been established. Occupational success may boost self-esteem rather than the reverse. Alternatively, self-esteem may be helpful only in some job contexts. Laboratory studies have generally failed to find that self-esteem causes good task performance, with the important exception that high self-esteem facilitates persistence after failure. People high in self-esteem claim to be more likable and attractive, to have better relationships, and to make better impressions on others than people with low self-esteem, but objective measures disconfirm most of these beliefs. Narcissists are charming at first but tend to alienate others eventually. Self-esteem has not been shown to predict the quality or duration of relationships. High self-esteem makes people more willing to speak up in groups and to criticize the group's approach. Leadership does not stem directly from self-esteem, but self-esteem may have indirect effects. Relative to people with low self-esteem, those with high self-esteem show stronger in-group favoritism, which may increase prejudice and discrimination. Neither high nor low self-esteem is a direct cause of violence. Narcissism leads to increased aggression in retaliation for wounded pride. Low self-esteem may contribute to externalizing behavior and delinquency, although some studies have found that there are no effects or that the effect of self-esteem vanishes when other variables are controlled. The highest and lowest rates of cheating and bullying are found in different subcategories of high self-esteem. Self-esteem has a strong relation to happiness. Although the research has not clearly established causation, we are persuaded that high self-esteem does lead to greater happiness. Low self-esteem is more likely than high to lead to depression under some circumstances. Some studies support the buffer hypothesis, which is that high self-esteem mitigates the effects of stress, but other studies come to the opposite conclusion, indicating that the negative effects of low self-esteem are mainly felt in good times. Still others find that high self-esteem leads to happier outcomes regardless of stress or other circumstances. High self-esteem does not prevent children from smoking, drinking, taking drugs, or engaging in early sex. If anything, high self-esteem fosters experimentation, which may increase early sexual activity or drinking, but in general effects of self-esteem are negligible. One important exception is that high self-esteem reduces the chances of bulimia in females. Overall, the benefits of high self-esteem fall into two categories: enhanced initiative and pleasant feelings. We have not found evidence that boosting self-esteem (by therapeutic interventions or school programs) causes benefits. Our findings do not support continued widespread efforts to boost self-esteem in the hope that it will by itself foster improved outcomes. In view of the heterogeneity of high self-esteem, indiscriminate praise might just as easily promote narcissism, with its less desirable consequences. Instead, we recommend using praise to boost self-esteem as a reward for socially desirable behavior and self-improvement.

negative: Phantom vibrations among undergraduates: Prevalence and associated psychological characteristics

negative: A New Approach to Linear Filtering and Prediction Problems AN IMPORTANT class of theoretical and practical problems in communication and control is of a statistical nature. Such problems are: (i) Prediction of random signals; (ii) separation of random signals from random noise; (iii) detection of signals of known form (pulses, sinusoids) in the presence of random noise. In his pioneering work, Wiener [1]3 showed that problems (i) and (ii) lead to the so-called Wiener-Hopf integral equation; he also gave a method (spectral factorization) for the solution of this integral equation in the practically important special case of stationary statistics and rational spectra. Many extensions and generalizations followed Wiener’s basic work. Zadeh and Ragazzini solved the finite-memory case [2]. Concurrently and independently of Bode and Shannon [3], they also gave a simplified method [2) of solution. Booton discussed the nonstationary Wiener-Hopf equation [4]. These results are now in standard texts [5-6]. A somewhat different approach along these main lines has been given recently by Darlington [7]. For extensions to sampled signals, see, e.g., Franklin [8], Lees [9]. Another approach based on the eigenfunctions of the WienerHopf equation (which applies also to nonstationary problems whereas the preceding methods in general don’t), has been pioneered by Davis [10] and applied by many others, e.g., Shinbrot [11], Blum [12], Pugachev [13], Solodovnikov [14]. In all these works, the objective is to obtain the specification of a linear dynamic system (Wiener filter) which accomplishes the prediction, separation, or detection of a random signal.4 ——— 1 This research was supported in part by the U. S. Air Force Office of Scientific Research under Contract AF 49 (638)-382. 2 7212 Bellona Ave. 3 Numbers in brackets designate References at end of paper. 4 Of course, in general these tasks may be done better by nonlinear filters. At present, however, little or nothing is known about how to obtain (both theoretically and practically) these nonlinear filters. Contributed by the Instruments and Regulators Division and presented at the Instruments and Regulators Conference, March 29– Apri1 2, 1959, of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS. NOTE: Statements and opinions advanced in papers are to be understood as individual expressions of their authors and not those of the Society. Manuscript received at ASME Headquarters, February 24, 1959. Paper No. 59-IRD—11. A New Approach to Linear Filtering and Prediction Problems

negative: On Divergences and Informations in Statistics and Information Theory The paper deals with the f-divergences of Csiszar generalizing the discrimination information of Kullback, the total variation distance, the Hellinger divergence, and the Pearson divergence. All basic properties of f-divergences including relations to the decision errors are proved in a new manner replacing the classical Jensen inequality by a new generalized Taylor expansion of convex functions. Some new properties are proved too, e.g., relations to the statistical sufficiency and deficiency. The generalized Taylor expansion also shows very easily that all f-divergences are average statistical informations (differences between prior and posterior Bayes errors) mutually differing only in the weights imposed on various prior distributions. The statistical information introduced by De Groot and the classical information of Shannon are shown to be extremal cases corresponding to alpha=0 and alpha=1 in the class of the so-called Arimoto alpha-informations introduced in this paper for 0<alpha<1 by means of the Arimoto alpha-entropies. Some new examples of f-divergences are introduced as well, namely, the Shannon divergences and the Arimoto alpha-divergences leading for alphauarr1 to the Shannon divergences. Square roots of all these divergences are shown to be metrics satisfying the triangle inequality. The last section introduces statistical tests and estimators based on the minimal f-divergence with the empirical distribution achieved in the families of hypothetic distributions. For the Kullback divergence this leads to the classical likelihood ratio test and estimator

negative: Autonomous robotic exploration using occupancy grid maps and graph SLAM based on Shannon and Rényi Entropy In this paper we examine the problem of autonomously exploring and mapping an environment using a mobile robot. The robot uses a graph-based SLAM system to perform mapping and represents the map as an occupancy grid. In this setting, the robot must trade-off between exploring new area to complete the task and exploiting the existing information to maintain good localization. Selecting actions that decrease the map uncertainty while not significantly increasing the robot's localization uncertainty is challenging. We present a novel information-theoretic utility function that uses both Shannon's and Rényi's definitions of entropy to jointly consider the uncertainty of the robot and the map. This allows us to fuse both uncertainties without the use of manual tuning. We present simulations and experiments comparing the proposed utility function to state-of-the-art utility functions, which only use Shannon's entropy. We show that by using the proposed utility function, the robot and map uncertainties are smaller than using other existing methods.

negative: Security and compliance challenges in complex IT outsourcing arrangements: A multi-stakeholder perspective Complex IT outsourcing arrangements promise numerous benefits such as increased cost predictability and reduced costs, higher flexibility and scalability upon demand. Organizations trying to realize these benefits, however, face several security and compliance challenges. In this article, we investigate the pressure to take action with respect to such challenges and discuss avenues toward promising responses. We collected perceptions on security and compliance challenges from multiple stakeholders by means of a series of interviews and an online survey, first, to analyze the current and future relevance of the challenges as well as potential adverse effects on organizational performance and, second, to discuss the nature and scope of potential responses. The survey participants confirmed the current and future relevance of the six challenges auditing clouds, managing heterogeneity of services, coordinating involved parties, managing relationships between clients and vendors, localizing and migrating data and coping with lack of security awareness. Additionally, they perceived these challenges as affecting organizational performance adversely in case they are not properly addressed. Responses in form of organizational measures were considered more promising than technical ones concerning all challenges except localizing and migrating data, for which the opposite was true. Balancing relational and contractual governance as well as employing specific client and vendor capabilities is essential for the success of IT outsourcing arrangements, yet do not seem sufficient to overcome the investigated challenges. Innovations connecting the technical perspective of utility software with the business perspective of application software relevant for security and compliance management, however, nourish the hope that the benefits associated with complex IT outsourcing arrangements can be realized in the foreseeable future whilst addressing the security and compliance challenges. a 2013 Elsevier Ltd. All rights reserved. 61. .fraunhofer.de (D. Bachlechner), stefan.thalmann@uibk.ac.at (S. Thalmann), ronald.maier@ ier Ltd. All rights reserved. c om p u t e r s & s e c u r i t y 4 0 ( 2 0 1 4 ) 3 8e5 9 39

negative: GP-GAN: Towards Realistic High-Resolution Image Blending Recent advances in generative adversarial networks (GANs) have shown promising potentials in conditional image generation. However, how to generate high-resolution images remains an open problem. In this paper, we aim at generating high-resolution well-blended images given composited copy-and-paste ones, i.e. realistic highresolution image blending. To achieve this goal, we propose Gaussian-Poisson GAN (GP-GAN), a framework that combines the strengths of classical gradient-based approaches and GANs, which is the first work that explores the capability of GANs in high-resolution image blending task to the best of our knowledge. Particularly, we propose GaussianPoisson Equation to formulate the high-resolution image blending problem, which is a joint optimisation constrained by the gradient and colour information. Gradient filters can obtain gradient information. For generating the colour information, we propose Blending GAN to learn the mapping between the composited image and the well-blended one. Compared to the alternative methods, our approach can deliver high-resolution, realistic images with fewer bleedings and unpleasant artefacts. Experiments confirm that our approach achieves the state-of-the-art performance on Transient Attributes dataset. A user study on Amazon Mechanical Turk finds that majority of workers are in favour of the proposed approach.

negative: Semi-supervised Sequence Learning We present two approaches that use unlabeled data to improve sequ nce learning with recurrent networks. The first approach is to predict wha t comes next in a sequence, which is a conventional language model in natural language processing. The second approach is to use a sequence autoencoder, which r eads the input sequence into a vector and predicts the input sequence again. T hese two algorithms can be used as a “pretraining” step for a later supervised seq uence learning algorithm. In other words, the parameters obtained from the unsu pervised step can be used as a starting point for other supervised training model s. In our experiments, we find that long short term memory recurrent networks after b eing pretrained with the two approaches are more stable and generalize bette r. With pretraining, we are able to train long short term memory recurrent network s up to a few hundred timesteps, thereby achieving strong performance in ma ny text classification tasks, such as IMDB, DBpedia and 20 Newsgroups.

negative: Automatic Image Alignment and Stitching of Medical Images with Seam Blending This paper proposes an algorithm which automatically aligns and stitches the component medical images (fluoroscopic) with varying degrees of overlap into a single composite image. The alignment method is based on similarity measure between the component images. As applied here the technique is intensity based rather than feature based. It works well in domains where feature based methods have difficulty, yet more robust than traditional correlation. Component images are stitched together using the new triangular averaging based blending algorithm. The quality of the resultant image is tested for photometric inconsistencies and geometric misalignments. This method cannot correct rotational, scale and perspective artifacts. Keywords—Histogram Matching, Image Alignment, Image Stitching, Medical Imaging.


query: Hierarchical Text Generation and Planning for Strategic Dialogue

positive: Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and backoff n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger questionanswer pair corpus and from pretrained word embeddings.

positive: The Dialog State Tracking Challenge In a spoken dialog system, dialog state tracking deduces information about the user’s goal as the dialog progresses, synthesizing evidence such as dialog acts over multiple turns with external data sources. Recent approaches have been shown to overcome ASR and SLU errors in some applications. However, there are currently no common testbeds or evaluation measures for this task, hampering progress. The dialog state tracking challenge seeks to address this by providing a heterogeneous corpus of 15K human-computer dialogs in a standard format, along with a suite of 11 evaluation metrics. The challenge received a total of 27 entries from 9 research groups. The results show that the suite of performance metrics cluster into 4 natural groups. Moreover, the dialog systems that benefit most from dialog state tracking are those with less discriminative speech recognition confidence scores. Finally, generalization is a key problem: in 2 of the 4 test sets, fewer than half of the entries out-performed simple baselines. 1 Overview and motivation Spoken dialog systems interact with users via natural language to help them achieve a goal. As the interaction progresses, the dialog manager maintains a representation of the state of the dialog in a process called dialog state tracking (DST). For example, in a bus schedule information system, the dialog state might indicate the user’s desired bus route, origin, and destination. Dialog state tracking is difficult because automatic speech ∗Most of the work for the challenge was performed when the second and third authors were with Honda Research Institute, Mountain View, CA, USA recognition (ASR) and spoken language understanding (SLU) errors are common, and can cause the system to misunderstand the user’s needs. At the same time, state tracking is crucial because the system relies on the estimated dialog state to choose actions – for example, which bus schedule information to present to the user. Most commercial systems use hand-crafted heuristics for state tracking, selecting the SLU result with the highest confidence score, and discarding alternatives. In contrast, statistical approaches compute scores for many hypotheses for the dialog state (Figure 1). By exploiting correlations between turns and information from external data sources – such as maps, bus timetables, or models of past dialogs – statistical approaches can overcome some SLU errors. Numerous techniques for dialog state tracking have been proposed, including heuristic scores (Higashinaka et al., 2003), Bayesian networks (Paek and Horvitz, 2000; Williams and Young, 2007), kernel density estimators (Ma et al., 2012), and discriminative models (Bohus and Rudnicky, 2006). Techniques have been fielded which scale to realistically sized dialog problems and operate in real time (Young et al., 2010; Thomson and Young, 2010; Williams, 2010; Mehta et al., 2010). In end-to-end dialog systems, dialog state tracking has been shown to improve overall system performance (Young et al., 2010; Thomson and Young, 2010). Despite this progress, direct comparisons between methods have not been possible because past studies use different domains and system components, for speech recognition, spoken language understanding, dialog control, etc. Moreover, there is little agreement on how to evaluate dialog state tracking. Together these issues limit progress in this research area. The Dialog State Tracking Challenge (DSTC) provides a first common testbed and evaluation

positive: Latent Variable Dialogue Models and their Diversity We present a dialogue generation model that directly captures the variability in possible responses to a given input, which reduces the ‘boring output’ issue of deterministic dialogue models. Experiments show that our model generates more diverse outputs than baseline models, and also generates more consistently acceptable output than sampling from a deterministic encoder-decoder model.

positive: Partially observable Markov decision processes for spoken dialog systems In a spoken dialog system, determining which action a machine should take in a given situation is a difficult problem because automatic speech recognition is unreliable and hence the state of the conversation can never be known with certainty. Much of the research in spoken dialog systems centres on mitigating this uncertainty and recent work has focussed on three largely disparate techniques: parallel dialog state hypotheses, local use of confidence scores, and automated planning. While in isolation each of these approaches can improve action selection, taken together they currently lack a unified statistical framework that admits global optimization. In this paper we cast a spoken dialog system as a partially observable Markov decision process (POMDP). We show how this formulation unifies and extends existing techniques to form a single principled framework. A number of illustrations are used to show qualitatively the potential benefits of POMDPs compared to existing techniques, and empirical results from dialog simulations are presented which demonstrate significant quantitative gains. Finally, some of the key challenges to advancing this method – in particular scalability – are briefly outlined. 2006 Elsevier Ltd. All rights reserved.

positive: Ask Me Anything: Dynamic Memory Networks for Natural Language Processing Most tasks in natural language processing can be cast into question answering (QA) problems over language input. We introduce the dynamic memory network (DMN), a unified neural network framework which processes input sequences and questions, forms semantic and episodic memories, and generates relevant answers. Questions trigger an iterative attention process which allows the model to condition its attention on the result of previous iterations. These results are then reasoned over in a hierarchical recurrent sequence model to generate answers. The DMN can be trained end-to-end and obtains state of the art results on several types of tasks and datasets: question answering (Facebook’s bAbI dataset), sequence modeling for part of speech tagging (WSJ-PTB), coreference resolution (Quizbowl dataset) and text classification for sentiment analysis (Stanford Sentiment Treebank). The model relies exclusively on trained word vector representations and requires no string matching or manually engineered features.

negative: Human balance and posture control during standing and walking The fact that we as humans are bipeds and locomote over the ground with one foot in contact (walking), no feet in contact (running), or both feet in contact (standing) creates a major challenge to our balance control system. Because two-thirds of our body mass is located two-thirds of body height above the ground we are an inherently unstable system unless a control system is continuously acting. The purpose of this review is to introduce the reader to the basic anatomy, mechanics, and neuromuscular control that is seen in the current literature related to balance control. The degeneration of the balance control system in the elderly and in many pathologies has forced researchers and clinicians to understand more about how the system works and how to quantify its status at any point in time. With the increase in our ageing population and with increased life expectancy of our elderly the importance of maintaining mobility is becoming ever more critical. Injuries and loss of life due to falls in the elderly is a very major factor facing them. Table 1 compares the deaths due to falls with the deaths due to motor vehicle accidents (MVA) as a function of age as reported by Statistics Canada in 1991. The startling fact is that the deaths due to falls in the SO+ population is almost as high as the MVA deaths in the accident-prone l5-29-year group. However, when we look at the deaths per 100 000 for these two causes the figures are even more startling. The young 15-29-year-old group had 21.5 MVA deaths per 100 000 while the elderly deaths

negative: ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection As the intermediate level task connecting image captioning and object detection, visual relationship detection started to catch researchers’ attention because of its descriptive power and clear structure. It localizes the objects and captures their interactions with a subject-predicateobject triplet, e.g. 〈person-ride-horse〉. In this paper, the visual relationship is considered as a phrase with three components. So we formulate the visual relationship detection as three inter-connected recognition problems and propose a Visual Phrase reasoning Convolutional Neural Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a Visual Phrase Reasoning Structure (VPRS) to set up the connection among the relationship components and help the model consider the three problems jointly. Corresponding non-maximum suppression method and model training strategy are also proposed. Experimental results show that our ViP-CNN outperforms the stateof-art method both in speed and accuracy. We further pretrain our model on our cleansed Visual Genome Relationship dataset, which is found to perform better than the pretraining on the ImageNet for this task.

negative: Computational Models of Reading: A Primer This article brief ly reviews four computational models of reading and the types of empirical findings that they are designed to explain: (1) theDual-Route model of word identification (Coltheart et al., 2001); (2) the SimpleRecurrent Network model of sentence processing (Elman, 1990); (3) the Construction–Integration model of discourse representation (Kintsch, 1988); and (4) the E-Z Reader model of eye-movement control in reading (Reichle et al., 2012). These particular models are reviewed because they provide comprehensive accounts of a large number of empirical findings in each of their respective domains, and because they have advanced our understanding of the cognitive processes involved in reading by motivating new empirical studies. Future models of reading will build upon the success of their predecessors by integrating models from two or more of the aforementioned domains, thereby providing a means to examine the compatibility of their theoretical assumptions and more comprehensive accounts of the cognitive processes involved in reading. The perceptual, cognitive, and motor processes that support skilled reading are numerous, extremely complex, and work together in a highly coordinated manner. For those reasons, any deep understanding of what happens in the mind during reading requires the use of computational models that describe those processes and how they jointly determine both the on-line behavior that is observed during reading (e.g., the patterns of eye movements that readers make) and the products of that behavior (e.g., the mental representations that readers generate to understand the text). As will be discussed in this article, such models provide theoretical frameworks for understanding the mental processes involved in reading, but also advance our understanding by generating novel predictions that can be tested with experiments. In the remainder of this article, I will describe four existing models that have already played important roles in furthering our understanding of the psychology of reading. These models were selected for inclusion in this article because they account for a wide range of empirical phenomena within a broad area of reading research, and because they have motivated a large number of new empirical studies. The models were also selected because they collectively span the range of topics that have traditionally been studied by reading researchers: (1) word identification; (2) sentence processing; (3) the representation of discourse; and (4) how these different processes interact with other cognitive processes (e.g., attention) to determine the patterns of eye movements that are observed during reading. Because one goal of this article is to introduce the models in a way that is accessible to a broad audience, I will describe the models using schematic diagrams rather than their equations; for a more formal description of the models, see either the original articles or Reichle’s review of the models. Word-Identification Models Word-identification models describe how the visual features corresponding to a word on a printed page are used to access that word’s pronunciation and meaning, as well as behavioral deficits that result from impairment of these processes due to developmental delay or brain © 2015 The Author Language and Linguistics Compass © 2015 John Wiley & Sons Ltd

negative: Math Anxiety : Personal , Educational , and Cognitive Consequences Highly math-anxious individuals are characterized by a strong tendency to avoid math, which ultimately undercuts their math competence and forecloses important career paths. But timed, on-line tests reveal math-anxiety effects on w h o l e-n u m b e r a r i t h m e t i c p r o b l e m s (e. g. , 4 6 ϩ 2 7) , whereas achievement tests show no competence differences. Math anxiety disrupts cognitive processing by compromising ongoing activity in working memory. Although the causes of math anxiety are undetermined, some teaching styles are implicated as risk factors. We need research on the origins of math anxiety and on its " signature " in brain activity , to examine both its emo-t i o n a l a n d i t s c o g n i t i v e components. My graduate assistant recently told me about a participant he had tested in the lab. She exhibited increasing discomfort and nervousness as the testing session progressed , eventually becoming so distraught that she burst into tears. My assistant remarked that many of our participants show some un-ease or apprehension during test-ing—trembling hands, nervous laughter, and so forth. Many ask, defensively, if their performance says anything about their overall intelligence. These occasionally extreme emotional reactions are not triggered by deliberately provocative procedures—there are no personally sensitive questions or intentional manipulations of stress. Instead, we merely ask college adults to solve elementary-school arithmetic problems, such as 46 ϩ 18 ϭ ? and 34 Ϫ 19 ϭ ? The reactions are obvious symptoms of anxiety, in this case math anxiety induced by ordinary arithmetic problems presented in timed tasks. On the one hand, it is almost unbelievable that tests on such fundamental topics can be so upsetting ; knowing that 15 Ϫ 8 ϭ 7 ought to be as basic as knowing how to spell " cat. " On the other hand, U.S. culture abounds with attitudes that foster math anxiety: Math is thought to be inherently difficult (as Barbie dolls used to say, " Math class is hard "), aptitude is considered far more important than effort (Geary, 1994, chap. 7), and being good at math is considered relatively unimportant, or even optional. In this article, I discuss what has been learned about math anxiety across the past 30 years or so, and suggest …

negative: Enabling Public Verifiability and Data Dynamics for Storage Security Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings about many new security challenges, which have not been well understood. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The introduction of TPA eliminates the involvement of client through the auditing of whether his data stored in the cloud is indeed intact, which can be important in achieving economies of scale for Cloud Computing. The support for data dynamics via the most general forms of data operation, such as block modification, insertion and deletion, is also a significant step toward practicality, since services in Cloud Computing are not limited to archive or backup data only. While prior works on ensuring remote data integrity often lacks the support of either public verifiability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from prior works and then show how to construct an elegant verification scheme for seamless integration of these two salient features in our protocol design. In particular, to achieve efficient data dynamics, we improve the Proof of Retrievability model [1] by manipulating the classic Merkle Hash Tree (MHT) construction for block tag authentication. Extensive security and performance analysis show that the proposed scheme is highly efficient and provably secure.

negative: The Effect of Noise on Sofware Engineers' Performance Background: Noise, dened as an unwanted sound, is one of the commonest factors that could aect people’s performance in their daily work activities. e soware engineering research community has marginally investigated the eects of noise on soware engineers’ performance. Aims: We studied if noise aects soware engineers’ performance in: (i) comprehending functional requirements and (ii) xing faults in source code. Method: We conducted two experiments with nal-year undergraduate students in Computer Science. In the rst experiment, we asked 55 students to comprehend functional requirements exposing them or not to noise, while in the second experiment 42 students were asked to x faults in Java code. Results: e participants in the second experiment, when exposed to noise, had signicantly worse performance in xing faults in source code. On the other hand, we did not observe any statistically signicant dierence in the rst experiment. Conclusions: Fixing faults in source code seems to be more vulnerable to noise than comprehending functional requirements.

negative: Transcranial Direct Current Stimulation Modulates Neuronal Activity and Learning in Pilot Training Skill acquisition requires distributed learning both within (online) and across (offline) days to consolidate experiences into newly learned abilities. In particular, piloting an aircraft requires skills developed from extensive training and practice. Here, we tested the hypothesis that transcranial direct current stimulation (tDCS) can modulate neuronal function to improve skill learning and performance during flight simulator training of aircraft landing procedures. Thirty-two right-handed participants consented to participate in four consecutive daily sessions of flight simulation training and received sham or anodal high-definition-tDCS to the right dorsolateral prefrontal cortex (DLPFC) or left motor cortex (M1) in a randomized, double-blind experiment. Continuous electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) were collected during flight simulation, n-back working memory, and resting-state assessments. tDCS of the right DLPFC increased midline-frontal theta-band activity in flight and n-back working memory training, confirming tDCS-related modulation of brain processes involved in executive function. This modulation corresponded to a significantly different online and offline learning rates for working memory accuracy and decreased inter-subject behavioral variability in flight and n-back tasks in the DLPFC stimulation group. Additionally, tDCS of left M1 increased parietal alpha power during flight tasks and tDCS to the right DLPFC increased midline frontal theta-band power during n-back and flight tasks. These results demonstrate a modulation of group variance in skill acquisition through an increasing in learned skill consistency in cognitive and real-world tasks with tDCS. Further, tDCS performance improvements corresponded to changes in electrophysiological and blood-oxygenation activity of the DLPFC and motor cortices, providing a stronger link between modulated neuronal function and behavior.

negative: How to gamify software engineering Software development, like any prolonged and intellectually demanding activity, can negatively affect the motivation of developers. This is especially true in specific areas of software engineering, such as requirements engineering, test-driven development, bug reporting and fixing, where the creative aspects of programming fall short. The developers' engagement might progressively degrade, potentially impacting their work's quality.

negative: A multi-method simulation of a high-frequency bus line A methodology to model high-frequency bus lines is proposed and a realistic event-simulation model for such a line in the Netherlands is presented. This simulation model helps policy makers to predict changes that have to be made to bus routes and planned travel times before problems occur. With this model, different passenger growth scenarios can be easily evaluated. The model is validated using different key performance indicators, showing that under some model assumptions, it can realistically simulate real-life scenarios. The simulations workings are illustrated by a case study of passenger growth.

negative: The role of mindfulness in a contextual cognitive-behavioral analysis of chronic pain-related suffering and disability An increasing number of studies consider the specific processes by which distressing sensations, thoughts, and emotional experiences exert their influence on the daily functioning of those who suffer with chronic pain. Clinical methods of mindfulness and the processes that underlie them appear to have clear implications in this area, but have not been systematically investigated to this point in time. The purpose of the present study was to examine mindfulness in relation to the pain, emotional, physical, and social functioning of individuals with chronic pain. The present study included 105 consecutive patients attending a clinical assessment for treatment of chronic pain. Each completed a standardized battery of questionnaires, including a measure of mindfulness, the Mindful Attention Awareness Scale [Brown KW, Ryan RM. The benefits of being present: mindfulness and its role in psychological well-being. J Pers Soc Psychol 2003;84:822-48]. Correlation analyses indicated that mindfulness was unrelated to age, gender, education, or chronicity of pain, but was significantly related to multiple measures of patient functioning. In multiple regression analyses, after controlling for patient background variables, pain intensity, and pain-related acceptance, mindfulness accounted for significant variance in measures of depression, pain-related anxiety; physical, psychosocial, and "other" disability. In each instance greater mindfulness was associated with better functioning. The combined increments of variance explained from acceptance of pain and mindfulness were at least moderate and, in some cases, appeared potentially meaningful. The behavioral processes of mindfulness and their accessibility to scientific study are considered.

negative: Integrative Neuromuscular Training in Youth Athletes. Part II THE SECOND PART OF THIS REVIEW PROVIDES A FLEXIBLE APPROACH TO INTEGRATIVE NEUROMUSCULAR TRAINING (INT) WITH THE GOALS TO IMPROVE INJURY RESILIENCE AND TO ENHANCE SPORT AND MOTOR PERFORMANCE ABILITIES IN YOUTH POPULATIONS. THE PROPOSED MODEL OF INT IN THIS MANUSCRIPT PRESENTS 6 ESSENTIAL COMPONENTS: DYNAMIC STABILITY (LOWER LIMB AND CORE), STRENGTH, PLYOMETRICS, COORDINATION, SPEED AND AGILITY, AND FATIGUE RESISTANCE. THE DEVELOPMENT OF THESE 6 CAPACITIES ARE INTEGRAL IN ESTABLISHING AN IMPORTANT FOUNDATION BY INITIALLY DEVELOPING FUNDAMENTAL MOVEMENT SKILL COMPETENCY BEFORE BUILDING UPON THESE SKILLS TO ENRICH SPORTS-SPECIFIC AND ACTIVITY-SPECIFIC SKILL SETS. FOR A VIDEO ABSTRACT OF THIS ARTICLE, SEE SUPPLEMENTAL DIGITAL CONTENT 1 (SEE VIDEO, http://links.lww.com/SCJ/A190).

negative: Coping theory and research: past, present, and future. In this essay in honor of Donald Oken, I emphasize coping as a key concept for theory and research on adaptation and health. My focus will be the contrasts between two approaches to coping, one that emphasizes style—that is, it treats coping as a personality characteristic—and another that emphasizes process—that is, efforts to manage stress that change over time and are shaped by the adaptational context out of which it is generated. I begin with an account of the style and process approaches, discuss their history briefly, set forth the principles of a process approach, describe my own efforts at measurement, and define coping and its functions from a process standpoint. This is followed by a digest of major generalizations that resulted from coping process research. The essay concludes with a discussion of special issues of coping measurement, in particular, the limitations of both coping style and process approaches and how these limitations might be dealt with. There has been a prodigious volume of coping research in the last decade or two, which I can only touch on very selectively. In this essay, I also ignore a host of important developmental issues that have to do with the emergence of coping and its cognitive and motivational bases in infants, as well as a growing literature on whether, how, and why the coping process changes with aging.

negative: Sentiment-specific word embedding for Indonesian sentiment analysis Word embedding has been proven to improve performance of sentiment classification systems. A new proposed word embedding called sentiment-specific word embedding exploits sentiment information, in order to reach better performance of sentiment analysis systems using word embedding. In this paper, we build Indonesian sentiment-specific word embedding and apply it for sentiment analysis. We compare performance of sentiment classification based on sentiment-specific word embedding with other popular feature representations such as bag of words, Term Frequency-Inverse Document Frequency (TF-IDF), and also generic word embedding. Although our sentiment-specific word embedding models achieve a higher score than the Word2Vec embedding, the lexical representations, bag of words and TF-IDF still gained better performance.

negative: The Airport Ground Movement Problem : Past and Current Research and Future Directions Determining efficient airport operations is an important and critical problem for airports, airlines, passengers and other stakeholders. Moreover, it is likely to become even more so given the traffic increases which are expected over the next few years. The ground movement problem forms the link between other airside problems, such as arrival sequencing, departure sequencing and gate/stand allocation. This paper provides an overview, categorisation and critical examination of the previous research for ground movement and highlights various important open areas of research. Of particular importance is the question of the integration of various airport operations and their relationships which are considered in this paper.

negative: An Organization Ontology for Enterprise Modelling The paper presents our exploration into an organization ontology for the TOVE enterprise model. Its primary focus has been in linking structure and behavior through the concept of empowerment. Empowerment is the right of an organization agent to perform status changing actions. This linkage is critical to the unification of enterprise models and their executability.

negative: VL2: a scalable and flexible data center network To be agile and cost effective, data centers should allow dynamic resource allocation across large server pools. In particular, the data center network should enable any server to be assigned to any service. To meet these goals, we present VL2, a practical network architecture that scales to support huge data centers with uniform high capacity between servers, performance isolation between services, and Ethernet layer-2 semantics. VL2 uses (1) flat addressing to allow service instances to be placed anywhere in the network, (2) Valiant Load Balancing to spread traffic uniformly across network paths, and (3) end-system based address resolution to scale to large server pools, without introducing complexity to the network control plane. VL2's design is driven by detailed measurements of traffic and fault data from a large operational cloud service provider. VL2's implementation leverages proven network technologies, already available at low cost in high-speed hardware implementations, to build a scalable and reliable network architecture. As a result, VL2 networks can be deployed today, and we have built a working prototype. We evaluate the merits of the VL2 design using measurement, analysis, and experiments. Our VL2 prototype shuffles 2.7 TB of data among 75 servers in 395 seconds - sustaining a rate that is 94% of the maximum possible.

negative: Friendlink: Link prediction in social networks via bounded local path traversal Online social networks (OSNs) like Facebook, Myspace, and Hi5 have become popular, because they allow users to easily share content or expand their social circle. OSNs recommend new friends to registered users based on local graph features (i.e. based on the number of common friends that two users share). However, OSNs do not exploit all different length paths of the network. Instead, they consider only pathways of maximum length 2 between a user and his candidate friends. On the other hand, there are global approaches, which detect the overall path structure in a network, being computationally prohibitive for huge-size social networks. In this paper, we provide friend recommendations, also known as the link prediction problem, by traversing all paths of a bounded length, based on the “algorithmic small world hypothesis”. As a result, we are able to provide more accurate and faster friend recommendations. We perform an extensive experimental comparison of the proposed method against existing link prediction algorithms, using two real data sets (Hi5 and Epinions). Our experimental results show that our FriendLink algorithm outperforms other approaches in terms of effectiveness and efficiency in both real data sets.

negative: Planning-based prediction for pedestrians We present a novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrance-sensitive robot trajectory planning provided by our approach.

negative: Fast local search and guided local search and their application to British Telecom's workforce scheduling problem This paper reports a Fast Local Search (FLS) algorithm which helps to improve the efficiency of hill climbing and a Guided Local Search (GLS) Algorithm which is developed to help local search to escape local optima and distribute search effort. To illustrate how these algorithms work, this paper describes their application to British Telecom’s workforce scheduling problem, which is a hard real life problem. The effectiveness of FLS and GLS are demonstrated by the fact that they both out-perform all the methods applied to this problem so far, which include simulated annealing, genetic algorithms and constraint logic programming.

negative: A Partial Derandomization of PhaseLift using Spherical Designs The problem of retrieving phase information from amplitude measurements alone has appeared in many scientific disciplines over the last century. PhaseLift is a recently introduced algorithm for phase recovery that is computationally efficient, numerically stable, and comes with rigorous performance guarantees. PhaseLift is optimal in the sense that the number of amplitude measurements required for phase reconstruction scales linearly with the dimension of the signal. However, it specifically demands Gaussian random measurement vectors — a limitation that restricts practical utility and obscures the specific properties of measurement ensembles that enable phase retrieval. Here we present a partial derandomization of PhaseLift that only requires sampling from certain polynomial size vector configurations, called t-designs. Such configurations have been studied in algebraic combinatorics, coding theory, and quantum information. We prove reconstruction guarantees for a number of measurements that depends on the degree t of the design. If the degree is allowed to to grow logarithmically with the dimension, the bounds become tight up to polylog-factors. Beyond the specific case of PhaseLift, this work highlights the utility of spherical designs for the derandomization of data recovery schemes.

negative: Implementation and Optimization of the Accelerator Based on FPGA Hardware for LSTM Network Today, artificial neural networks (ANNs) are important machine learning methods which are widely used in a variety of applications. As the emerging field of ANNs, recurrent neural networks (RNNs) are often used for sequencerelated applications. And Long Short-Term Memory (LSTM) is an improved RNN which contains complex computational logic. To achieve high accuracy, researchers always build largescale LSTM networks which are time-consuming and powerconsuming. Thus the acceleration of LSTM networks, low power & energy consumption become the hot issues in today's research. In this paper, we present a hardware accelerator for the LSTM neural network layer based on FPGA Zedboard and use pipeline methods to parallelize the forward computing process. To optimize our implementation, we also use multiple methods including tiled matrix-vector multiplication, binary adder tree, and overlap of computation and data access. Through the acceleration and optimization methods, our accelerator is power-efficient and has a better performance than ARM Cortex A9 processor and Intel Core i5 processor.

negative: Demonstration of MOS capacitor measurement for wafer manufacturing using a Direct Charge Measurement Direct Charge Measurement (DCM) has a capability to improve the capacitance measurement time in parametric test. Through an actual wafer measurement, we have successfully verified that DCM can measure MOS capacitor much faster than an LCR meter while keeping good correlations for wafer manufacturing.

negative: NB-IoT and LoRA connectivity analysis for M2M/IoT smart grids applications The evolution of the new M2M/IoT paradigm suggests new applications in several fields. However, the unique characteristics of M2M communications pose different challenges in terms of connectivity and capacity with respect to the conventional telecommunications networks. In particular, the harmful locations where IoT devices could be deployed and the massive number of the devices to be connected require a different approach with respect the traditional wireless communication. In this paper, we analyzed two different technologies expressly devoted the M2M communication: the Long Term Evolution (LTE) based solution, NB-IoT (Narrow Band — Internet of Things), and the LPWAN (Low Power Wide Area Network) based solution, LoRa (Low Range). The analysis has been provided taking into account different propagation scenarios and geographical information of the real MNOs (Mobile Network Operators) sites operating in the territory under test. The aim of this work is to check wireless access method of both cellular and proprietary technologies for the novel IoT Smart Grid applications requirements.

negative: A Survey of Context-Aware Mobile Computing Research Context-aware computing is a mobile computing paradigm in which applications can discover and take advantage of contextual information (such as user location, time of day, nearby people and devices, and user activity). Since it was proposed about a decade ago, many researchers have studied this topic and built several context-aware applications to demonstrate the usefulness of this new technology. Context-aware applications (or the system infrastructure to support them), however, have never been widely available to everyday users. In this survey of research on context-aware systems and applications, we looked in depth at the types of context used and models of context information, at systems that support collecting and disseminating context, and at applications that adapt to the changing context. Through this survey, it is clear that context-aware research is an old but rich area for research. The difficulties and possible solutions we outline serve as guidance for researchers hoping to make context-aware computing a reality.

negative: Convolutional Neural Networks for Real-Time Beat Tracking: A Dancing Robot Application In this paper a novel approach that adopts Convolutional Neural Networks (CNN) for the Beat Tracking task is proposed. The proposed architecture involves 2 convolutional layers with the CNN filter dimensions corresponding to time and band frequencies, in order to learn a Beat Activation Function (BAF) from a time-frequency representation. The output of each convolutional layer is computed only over the past values of the previous layer, to enable the computation of the BAF in an online fashion. The output of the CNN is post-processed by a dynamic programming algorithm in combination with a bank of resonators for calculating the salient rhythmic periodicities. The proposed method has been designed to be computational efficient in order to be embedded on a dancing NAO robot application, where the dance moves of the choreography are synchronized with the beat tracking output. The proposed system was submitted to the Signal Processing Cup Challenge 2017 and ranked among the top third algorithms.

negative: Anaphoric Annotation in the ARRAU Corpus Arrau is a new corpus annotated for anaphoric relations, with information about agreement and explicit representation of multiple antecedents for ambiguous anaphoric expressions and discourse antecedents for expressions which refer to abstract entities such as events, actions and plans. The corpus contains texts from different genres: task-oriented dialogues from the Trains-91 and Trains-93 corpus, narratives from the English Pear Stories corpus, newspaper articles from the Wall Street Journal portion of the Penn Treebank, and mixed text from the Gnome corpus.

negative: Deep Learning Approaches to Text Production Text production is a key component of many NLP applications. In data-driven approaches, it is used for instance, to generate dialogue turns from dialogue moves [Wen et al., 2015, Wen et al., 2016, Novikova et al., 2017], to verbalise the content of Knowledge bases [Gardent et al., 2017a, Gardent et al., 2017b] or to generate natural English sentences from rich linguistic representations, such as dependency trees [Belz et al., 2011, Mille et al., 2018] or Abstract Meaning Representations [May and Priyadarshi, 2017, Konstas et al., 2017, Song et al., ]. In text-driven methods on the other hand, text production is at work in sentence compression [Knight and Marcu, 2000, Cohn and Lapata, 2008, Filippova and Strube, 2008, Pitler, 2010, Filippova et al., 2015, Toutanova et al., 2016]; sentence fusion [McKeown et al., 2010, Filippova, 2010, Thadani and McKeown, 2013]; paraphrasing [Dras, 1999, Barzilay and McKeown, 2001, Bannard and Callison-Burch, 2005, Wubben et al., 2010, Narayan et al., 2016, Dong et al., 2017, Mallinson et al., 2017]; sentence (or text) simplification [Siddharthan et al., 2004, Zhu et al., 2010, Woodsend and Lapata, 2011, Wubben et al., 2012, Narayan and Gardent, 2014, Xu et al., 2015, Narayan and Gardent, 2016, Zhang and Lapata, 2017, Narayan et al., 2017], text summarisation [Wan, 2010, Nenkova and McKeown, 2011, Woodsend and Lapata, 2010, Rush et al., 2015, Cheng and Lapata, 2016, Nallapati et al., 2016, Chen et al., 2016, Tan and Wan, 2017, See et al., 2017, Nallapati et al., 2017, Paulus et al., 2017, Yasunaga et al., 2017, Narayan et al., 2018a, Narayan et al., 2018b, Pasunuru and Bansal, 2018, Celikyilmaz et al., 2018] and end-to-end dialogue systems [Li et al., 2017]. Following the success of encoder-decoder models in modeling sequence-rewriting tasks such as machine translation [Sutskever et al., 2011, Bahdanau et al., 2014], deep learning models have successfully been applied to the various text production tasks. For instance, [Rush et al., 2015] utilize a local attention-based model for abstractive summarisation, [Shang et al., 2015] propose an encoder-decoder model for response generation, [See et al., 2017] uses a hybrid of encoder-decoder model and pointer network [Vinyals et al., 2015] for story highlight generation, [Dong et al., 2017] exploits an encoder-decoder model for question rephrasing and [Konstas et al., 2017] for AMR generation. In this tutorial, we will cover the fundamentals and the state-of-the-art research on neural models for text production. Each text production task raises a slightly different communication goal (e.g, how to take the dialogue context into account when producing a dialogue turn; how to detect and merge relevant information when summarising a text; or how to produce a well-formed text that correctly capture the information contained in some input data in the case of data-to-text generation). We will outline the constraints specific to each subtasks and examine how the existing neural models account for them.

negative: Cognitive Context Models and Discourse 1. Mental models Since the early 1980s, the notion of mental model has been quite successful in cognitive psychology in general, and in the theory of text processing in particu-Such models have been conceptualized as representations in episodic memory of situations, acts or events spoken or thought about, observed or participated in by human actors, that is of experiences (Ehrlich, Tardieu & Cavazza 1993). In the theory of text processing, such (situation or event) models played a crucial role in establishing the necessary referential basis for the processing of anaphora and other phenomena of coherence (Albrecht & O Brien 1993). They further explained, among many other things, why text recall does not seem to be based on semantic representations of texts, but rather on the mental model construed or updated of the event the text is about (Bower & Morrow 1990). Conversely, mental models also play a role in the much neglected theory of discourse production, viz., as the mental point of departure of all text and talk, from which relevant information may be selected for the strategic construction of their global and local semantic structures. Many experiments have confirmed these hypotheses, and have shown at text comprehension and recall essentially involve a strategic manipulation of models, for instance by matching text information with structures of the The notion of mental space is sometimes also used in formal linguistics as a construct that has similar functions as our notion of a mental model (Faucormier 1985).

negative: Customer-Centric Strategic Planning: Integrating CRM in Online Business Systems Customer Relationship Management (CRM) is increasingly found at the top of corporate agendas. Online companies in particular are embracing CRM as a major element of corporate strategy, because online technological applications permit a precise segmentation, profiling and targeting of customers, and the competitive pressures of the digital markets require a customer-centric corporate culture. The implementation of CRM systems in online organisation determines a complex restructuring of all organisational elements and processes. The strategic planning process will have to adapt to new customer-centric procedures. The present paper analyses the implementation process of a CRM system in online retail businesses and develops a model of the strategic planning function in a customer-centric context.

negative: Learning long-range vision for autonomous off-road driving Most vision-based approaches to mobile robotics suffer from the limitations imposed by stereo obstacle detection, which is short-range and prone to failure. We present a self-supervised learning process for long-range vision that is able to accurately classify complex terrain at distances up to the horizon, thus allowing superior strategic planning. The success of the learning process is due to the self-supervised training data that is generated on every frame: robust, visually consistent labels from a stereo module, normalized wide-context input windows, and a discriminative and concise feature representation. A deep hierarchical network is trained to extract informative and meaningful features from an input image, and the features are used to train a realtime classifier to predict traversability. The trained classifier sees obstacles and paths from 5 to over 100 meters, far beyond the maximum stereo range of 12 meters, and adapts very quickly to new environments. The process was developed and tested on the LAGR mobile robot. Results from a ground truth dataset are given as well as field test results.

negative: Domain Modelling in Computational Persuasion for Behaviour Change in Healthcare The aim of behaviour change is to help people to change aspects of their behaviour for the better (e.g., to decrease calorie intake, to drink in moderation, to take more exercise, to complete a course of antibiotics once started, etc.). In current persuasion technology for behaviour change, the emphasis is on helping people to explore their issues (e.g., through questionnaires or game playing) or to remember to follow a behaviour change plan (e.g., diaries and email reminders). However, recent developments in computational persuasion are leading to an argument-centric approach to persuasion that can potentially be harnessed in behaviour change applications. In this paper, we review developments in computational persuasion, and then focus on domain modelling as a key component. We present a multi-dimensional approach to domain modelling. At the core of this proposal is an ontology which provides a representation of key factors, in particular kinds of belief, which we have identified in the behaviour change literature as being important in diverse behaviour change initiatives. Our proposal for domain modelling is intended to facilitate the acquisition and representation of the arguments that can be used in persuasion dialogues, together with meta-level information about them which can be used by the persuader to make strategic choices of argument to present.

negative: Adversarial Learning for Neural Dialogue Generation In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning (RL) problem where we jointly train two systems, a generative model to produce response sequences, and a discriminator—analagous to the human evaluator in the Turing test— to distinguish between the human-generated dialogues and the machine-generated ones. The outputs from the discriminator are then used as rewards for the generative model, pushing the system to generate dialogues that mostly resemble human dialogues. In addition to adversarial training we describe a model for adversarial evaluation that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls. Experimental results on several metrics, including adversarial evaluation, demonstrate that the adversarially-trained system generates higher-quality responses than previous baselines.