title: Coarse-to-fine low-rank structure-from-motion

abstract: We address the problem of deformable shape and motion recovery from point correspondences in multiple perspective images. We use the low-rank shape model, i.e. the 3D shape is represented as a linear combination of unknown shape bases. We propose a new way of looking at the low-rank shape model. Instead of considering it as a whole, we assume a coarse-to-fine ordering of the deformation modes, which can be seen as a model prior. This has several advantages. First, the high level of ambiguity of the original low-rank shape model is drastically reduced since the shape bases can not anymore be arbitrarily re-combined. Second, this allows us to propose a coarse-to-fine reconstruction algorithm which starts by computing the mean shape and iteratively adds deformation modes. It directly gives the sought after metric model, thereby avoiding the difficult upgrading step required by most of the other methods. Third, this makes it possible to automatically select the number of deformation modes as the reconstruction algorithm proceeds. We propose to incorporate two other priors, accounting for temporal and spatial smoothness, which are shown to improve the quality of the recovered model parameters. The proposed model and reconstruction algorithm are successfully demonstrated on several videos and are shown to outperform the previously proposed algorithms.


title: Graph Regularized Sparse Coding for Image Representation

abstract: Sparse coding has received an increasing amount of interest in recent years. It is an unsupervised learning algorithm, which finds a basis set capturing high-level semantics in the data and learns sparse coordinates in terms of the basis set. Originally applied to modeling the human visual cortex, sparse coding has been shown useful for many applications. However, most of the existing approaches to sparse coding fail to consider the geometrical structure of the data space. In many real applications, the data is more likely to reside on a low-dimensional submanifold embedded in the high-dimensional ambient space. It has been shown that the geometrical information of the data is important for discrimination. In this paper, we propose a graph based algorithm, called graph regularized sparse coding, to learn the sparse representations that explicitly take into account the local manifold structure of the data. By using graph Laplacian as a smooth operator, the obtained sparse representations vary smoothly along the geodesics of the data manifold. The extensive experimental results on image classification and clustering have demonstrated the effectiveness of our proposed algorithm.


title: Email classification for Spam Detection using Word Stemming

abstract: Unsolicited emails, known as spam, are one of the fast growing and costly problems associated with the Internet today. Among the many proposed solutions, a technique using Bayesian filtering is considered as the most effective weapon against spam. Bayesian filtering works by evaluating the probability of different words appearing in legitimate and spam mails and then classifying them based on that probabilities.Most of the current spam email detection systems use keywords to detect spam emails.These keywords can be written as misspellings eg: baank or bannk instead of bank. Misspellings are changed from time to time and hence spam email detection system needs to constantly update the blacklist to detect spam emails containing misspellings. It"s impossible to predict all possible misspellings for a given keyword and add those to the blacklist. In this paper a better and more successful approach for improving E-mail content classification for spam control is proposed. It used the Word Stemming or Word Hashing Technique for improving the efficiency of the content based spam filter.The proposed system extract the base or stem of a misspelled or modified word, to detect spam emails. It considers every misspelled keyword applies a word stemming technique and passes the base word to the content based filter. Using a proposed if-then rule, we can decide whether or not this unknown mail is spam (1).This paper also provides an Email archiving solution which classifies the E-mail relating to a person, family, corporation, association, community, or nation. KeywordsFilters, Bayesian, content based spam filter, Word Stemming, Email, Email archiving.


title: Fine-grained financial news sentiment analysis

abstract: The 24-hour news cycle and barrage of online media is a constant drum beat. The flow of positive and negative news is always in flux, influencing our current perspective and reassessing our future outlook. Nowhere is this more true than in the capital markets where assets are priced and risk assessed based on future expectations. While many factors influence a trader's decision to buy or sell an asset it can be argued that the sentiment from the 24-hour news cycle greatly impacts their outlook on the future value of an asset. In this paper we propose new methods to predict the positive or negative sentiment of financial news. Our analysis has found that contemporary document level sentiment analysis methods break down at fine-grained levels. Fine-grained analysis methods are vitally important as the velocity and impact of small texts, such as tweets and news flashes, increase their influence over the decision process. Using Natural Language Processing methods we extract syntactic sentence patterns from financial news headlines. From these patterns we conduct experiments using both lexicon and machine learning sentiment analysis approaches to predict sentiment. We find that our sentiment prediction methods are able to consistently out perform lexicon methods. Our robust techniques give the financial practitioner a method to fold a fine-grained news sentiment factor into their pricing or risk prediction models.


title: Adversarial Multi-task Learning for Text Classification

abstract: Neural network models have shown their promising opportunities for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant features. However, in most existing approaches, the extracted shared features are prone to be contaminated by task-specific features or the noise brought by other tasks. In this paper, we propose an adversarial multi-task learning framework, alleviating the shared and private latent feature spaces from interfering with each other. We conduct extensive experiments on 16 different text classification tasks, which demonstrates the benefits of our approach. Besides, we show that the shared knowledge learned by our proposed model can be regarded as off-the-shelf knowledge and easily transferred to new tasks. The datasets of all 16 tasks are publicly available at \url{this http URL}


title: Data gathering in sensor networks using the energy*delay metric

abstract: In this paper we consider the problem of data collection from a sensor web consisting of N nodes, where nodes have packets of data in each round of communication that need to be gathered and fused with other nodes’ packets into one packet and transmitted to a distant base station. Nodes have power control in their wireless communications and can transmit directly to any node in the network or to the base station. With unit delay cost for each packet transmission, if all nodes transmit data directly to the base station, then both high energy and high delay per round will occur. In our prior work [6], we developed an algorithm to minimize the energy cost per round, where a linear chain of all the nodes are formed to gather data, and nodes took turns to transmit to the base station. If the goal is to minimize the delay cost, then a binary combining scheme can be used to accomplish this task in about log N units of delay with parallel communications and incurring a slight increase in energy cost. The goal is to find data gathering schemes that balance the energy and delay cost, as measured by energy*delay. We conducted extensive simulation experiments with a number of schemes for this problem with 100 nodes in playing fields of 50m x 50m and 100m x 100m and the base station located at least 100 meters and 200 meters, respectively, from any node. With CDMA capable sensor nodes, a chain-based binary scheme performs best in terms of energy*delay. If the sensor nodes are not CDMA capable, then parallel communications are possible only among spatially separated nodes, and a chain-based 3 level hierarchy scheme performs well. These schemes perform 60 to 100 times better than direct scheme and also outperform a cluster based scheme, called LEACH [3].


title: Segmentation and semantic labelling of RGBD data with convolutional neural networks and surface fitting

abstract: We present an approach for segmentation and semantic labelling of RGBD data exploiting together geometrical cues and deep learning techniques. An initial over-segmentation is performed using spectral clustering and a set of non-uniform rational B-spline surfaces is fitted on the extracted segments. Then a convolutional neural network (CNN) receives in input colour and geometry data together with surface fitting parameters. The network is made of nine convolutional stages followed by a softmax classifier and produces a vector of descriptors for each sample. In the next step, an iterative merging algorithm recombines the output of the over-segmentation into larger regions matching the various elements of the scene. The couples of adjacent segments with higher similarity according to the CNN features are candidate to be merged and the surface fitting accuracy is used to detect which couples of segments belong to the same surface. Finally, a set of labelled segments is obtained by combining the segmentation output with the descriptors from the CNN. Experimental results show how the proposed approach outperforms state-of-the-art methods and provides an accurate segmentation and labelling.


title: Null space versus orthogonal linear discriminant analysis

abstract: Dimensionality reduction is an important pre-processing step for many applications. Linear Discriminant Analysis (LDA) is one of the well known methods for supervised dimensionality reduction. However, the classical LDA formulation requires the nonsingularity of scatter matrices involved. For undersampled problems, where the data dimension is much larger than the sample size, all scatter matrices are singular and classical LDA fails. Many extensions, including null space based LDA (NLDA), orthogonal LDA (OLDA), etc, have been proposed in the past to overcome this problem. In this paper, we present a computational and theoretical analysis of NLDA and OLDA. Our main result shows that under a mild condition which holds in many applications involving high-dimensional data, NLDA is equivalent to OLDA. We have performed extensive experiments on various types of data and results are consistent with our theoretical analysis. The presented analysis and experimental results provide further insight into several LDA based algorithms.


TA-COS 2018 : 2 nd Workshop on Text Analytics for Cybersecurity and Online Safety


title: Host–microorganism interactions in lung diseases

abstract: The recent realization that the airways harbour a steady-state microbiota necessitates a shift in our understanding of respiratory health and disease, drawing from similarities with host–microorganism relationships in the intestines.


title: Inside PageRank

abstract: Although the interest of a Web page is strictly related to its content and to the subjective readers' cultural background, a measure of the page authority can be provided that only depends on the topological structure of the Web. PageRank is a noticeable way to attach a score to Web pages on the basis of the Web connectivity. In this article, we look inside PageRank to disclose its fundamental properties concerning stability, complexity of computational scheme, and critical role of parameters involved in the computation. Moreover, we introduce a circuit analysis that allows us to understand the distribution of the page score, the way different Web communities interact each other, the role of dangling pages (pages with no outlinks), and the secrets for promotion of Web pages.


title: Coordinating advertising and pricing in a manufacturer-retailer channel

abstract: Cooperative advertising is a practice that a manufacturer pays retailers a portion of the local advertising cost in order to induce sales. Cooperative advertising plays a significant role in marketing programs of channel members. Nevertheless, most studies to date on cooperative advertising have assumed that the market demand is only influenced by advertising expenditures but not by retail price. This paper addresses channel coordination by seeking optimal cooperative advertising strategies and equilibrium pricing in a two-member distribution channel. We establish and compare two models: a non-cooperative, leader-follower game and a cooperative game. We develop propositions and insights from the comparison of these models. The cooperative model achieves better coordination by generating higher channel-wide profits than the non-cooperative model with these features: (a) the retailer price is lower to consumers; and (b) the advertising efforts are higher for all channel members. We identify the feasible solutions to a bargaining problem where the channel members can determine how to divide the extra profits.


title: The capability approach: an interdisciplinary introduction, mimeo

abstract: A blade for use in an axial fan includes a plurality of vanes which each have a winglet formed on their front face to increase the air flow of the fan at a given blade velocity.


title: A p-norm Flow Optimization Problem in Dense Wireless Sensor Networks

abstract: In a network with a high density of wireless nodes, we model flow of information by a continuous vector field known as the information flow vector field. We use a mathematical model that translates a communication network composed of a large but finite number of sensors into a continuum of nodes on which information flow is formulated by a vector field. The magnitude of this vector field is the intensity of the communication activity, and its orientation is the direction in which the traffic is forwarded. The information flow vector field satisfies a set of Neumann boundary conditions and a partial differential equation (PDE) involving the divergence of information, but the divergence constraint and Neumann boundary conditions do not specify the information flow vector field uniquely, and leave us freedom to optimize certain measures within their feasible set. Therefore, we introduce a p-norm flow optimization problem in which we minimize the p-norm of information flow vector field over the area of the network. This problem is a convex optimization problem, and we use sequential quadratic programming (SQP) to solve it. SQP is known for numerical stability and fast convergence to the optimal solution in convex optimization problems. By using standard SQP on p-norm flow optimization, we prove that the solution of each iteration of SQP is uniquely specified by an elliptic PDE with generalized Neumann boundary conditions. The p-norm flow optimization shows interesting properties for different values of p. For example, if p is close to one, the information routes resemble the geometric shortest paths of the sources and sinks, and for p = 2, the information flow shows an analogy to electrostatics. For infinitely large values of p, the problem minimizes the maximum magnitude of the information vector field over the network, and hence it achieves maximum load balancing.


title: Real-time illumination and shadow invariant lane detection on mobile platform

abstract: In this work, a novel lane detection method using a single input image is presented. The proposed method adopts a color and shadow invariant preprocessing stage including a feature region detection method called as maximally stable extremal regions. Next, candidate lane regions are examined according to their structural properties such as width–height ratio and orientation. This stage is followed by a template matching-based approach to decide final candidates for lane markings. At the final stage of the proposed method, outliers are eliminated using the random sample consensus approach. The proposed method is computationally lightweight, and thus, it is possible to execute it in real-time on consumer-grade mobile devices. Experimental results show that the proposed method is able to provide shadow, illumination and road defects invariant performance compared to the existing methods.


A question of leadership: Are there any potential downsides to emotional intelligence for executives, and if so, what are they?


title: Chest X-rays Classification: A Multi-Label and Fine-Grained Problem

abstract: The widely used ChestX-ray14 dataset addresses an important medical image classification problem and has the following caveats: 1) many lung pathologies are visually similar, 2) a variant of diseases including lung cancer, tuberculosis, and pneumonia are present in a single scan, i.e. multiple labels and 3) The incidence of healthy images is much larger than diseased samples, creating imbalanced data. These properties are common in medical domain. Existing literature uses stateof- the-art DensetNet/Resnet models being transfer learned where output neurons of the networks are trained for individual diseases to cater for multiple diseases labels in each image. However, most of them don't consider relationship between multiple classes. In this work we have proposed a novel error function, Multi-label Softmax Loss (MSML), to specifically address the properties of multiple labels and imbalanced data. Moreover, we have designed deep network architecture based on fine-grained classification concept that incorporates MSML. We have evaluated our proposed method on various network backbones and showed consistent performance improvements of AUC-ROC scores on the ChestX-ray14 dataset. The proposed error function provides a new method to gain improved performance across wider medical datasets.


title: Detection of opinion spam based on anomalous rating deviation

abstract: We propose a novel approach for detecting opinion spam based on reviewer ratings, with no need for text analysis.Our approach identifies reviewers having a significant number of reviews that disagree with majority opinion.Our approach requires significantly less running time than other, comparable approaches.Our approach is shown to successfully identify opinion spammers in synthetic and real world data sets. The publication of fake reviews by parties with vested interests has become a severe problem for consumers who use online product reviews in their decision making. To counter this problem a number of methods for detecting these fake reviews, termed opinion spam, have been proposed. However, to date, many of these methods focus on analysis of review text, making them unsuitable for many review systems where accompanying text is optional, or not possible. Moreover, these approaches are often computationally expensive, requiring extensive resources to handle text analysis over the scale of data typically involved.In this paper, we consider opinion spammers manipulation of average ratings for products, focusing on differences between spammer ratings and the majority opinion of honest reviewers. We propose a lightweight, effective method for detecting opinion spammers based on these differences. This method uses binomial regression to identify reviewers having an anomalous proportion of ratings that deviate from the majority opinion. Experiments on real-world and synthetic data show that our approach is able to successfully identify opinion spammers. Comparison with the current state-of-the-art approach, also based only on ratings, shows that our method is able to achieve similar detection accuracy while removing the need for assumptions regarding probabilities of spam and non-spam reviews and reducing the heavy computation required for learning.


title: The attention habit: how reward learning shapes attentional selection

abstract: There is growing consensus that reward plays an important role in the control of attention. Until recently, reward was thought to influence attention indirectly by modulating task-specific motivation and its effects on voluntary control over selection. Such an account was consistent with the goal-directed (endogenous) versus stimulus-driven (exogenous) framework that had long dominated the field of attention research. Now, a different perspective is emerging. Demonstrations that previously reward-associated stimuli can automatically capture attention even when physically inconspicuous and task-irrelevant challenge previously held assumptions about attentional control. The idea that attentional selection can be value driven, reflecting a distinct and previously unrecognized control mechanism, has gained traction. Since these early demonstrations, the influence of reward learning on attention has rapidly become an area of intense investigation, sparking many new insights. The result is an emerging picture of how the reward system of the brain automatically biases information processing. Here, I review the progress that has been made in this area, synthesizing a wealth of recent evidence to provide an integrated, up-to-date account of value-driven attention and some of its broader implications.


title: Understanding adolescence as a period of social–affective engagement and goal flexibility

abstract: Research has demonstrated that extensive structural and functional brain development continues throughout adolescence. A popular notion emerging from this work states that a relative immaturity in frontal cortical neural systems could explain adolescents' high rates of risk-taking, substance use and other dangerous behaviours. However, developmental neuroimaging studies do not support a simple model of frontal cortical immaturity. Rather, growing evidence points to the importance of changes in social and affective processing, which begin around the onset of puberty, as crucial to understanding these adolescent vulnerabilities. These changes in social-affective processing also may confer some adaptive advantages, such as greater flexibility in adjusting one's intrinsic motivations and goal priorities amidst changing social contexts in adolescence.


Results of m-polar Fuzzy Graphs with Application


title: Barriers to ITC Integration into Teachers ’ Classroom Practices: Lessons from a Case Study on Social Studies Teachers in Turkey

abstract: This study analyses the difficulties and obstacles faced by teachers of social studies education while using ICT-based teaching equipment and methods in their classes. Although ICT-based equipment and methods create important opportunities for the development of teaching, the literature shows that the ICT integration into teachers' classroom practices is not at the desired level. The study aims to use case study methods to analyze the reasons underlying this situation. Eighteen teachers of social studies education participated in the study. Classroom observation and semi-structured interview were used as data collection methods. According to the results of the study, the main barriers against the use of ICT-based methods and equipment in teachers' instructional practices are lack of ICT equipment in classrooms, lack of ICT-based teaching resources, the effect of traditional approaches on teachers' practices, inadequacies regarding in-service teacher training and lack of time.


title: Transferring Grasp Configurations using Active Learning and Local Replanning

abstract: We present a new approach to transfer grasp configurations from prior example objects to novel objects. We assume the novel and example objects have the same topology and similar shapes. We perform 3D segmentation on these objects using geometric and semantic shape characteristics. We compute a grasp space for each part of the example object using active learning. We build bijective contact mapping between these model parts and compute the corresponding grasps for novel objects. Finally, we assemble the individual parts and use local replanning to adjust grasp configurations while maintaining its stability and physical constraints. Our approach is general, can handle all kind of objects represented using mesh or point cloud and a variety of robotic hands.


title: Pair-Aware Neural Sentence Modeling for Implicit Discourse Relation Classification

abstract: Implicit discourse relation recognition is an extremely challenging task, for it lacks of explicit connectives between two arguments. Currently, most methods to address this problem can be regarded as to solve it in two stages, the first is to extract features from two arguments separately, and the next is to apply those features to some standard classifier. However, during the first stage, those methods neglect the links between two arguments and thus are blind to find pair-specified clues at the very beginning. This paper therefore makes an attempt to model sentence with its targeted pair in mind. Concretely, an LSTM model with attention mechanism is adapted to accomplish this idea. Experiments on the benchmark dataset show that without the help of feature engineering or any external linguistic knowledge, our proposed model outperforms previous state-of-the-art systems.


Understanding cultural values in counseling Asian families


title: Spectral attribute learning for visual regression

abstract: A number of computer vision problems such as facial age estimation, crowd counting and pose estimation can be solved by learning regression mapping on low-level imagery features. We show that visual regression can be substantially improved by two-stage regression where imagery features are first mapped to an attribute space which explicitly models latent correlations across continuously-changing output. We propose an approach to automatically discover spectral attributes which avoids manual work required for defining hand-crafted attribute representations. Visual attribute regression outperforms direct visual regression and our spectral attribute visual regression achieves state-of-the-art accuracy in multiple applications. HighlightsSpectral attributes avoid manually-engineered attribute construction.Spectral attributes handle multiple correlated regression outputs.Spectral attributes achieve state-of-the-art performance on various benchmarks.


title: Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis

abstract: A convolutional discriminative feature learning method is presented for induction motor fault diagnosis. The approach firstly utilizes back-propagation (BP)-based neural network to learn local filters capturing discriminative information. Then, a feed-forward convolutional pooling architecture is built to extract final features through these local filters. Due to the discriminative learning of BP-based neural network, the learned local filters can discover potential discriminative patterns. Also, the convolutional pooling architecture is able to derive invariant and robust features. Therefore, the proposed method can learn robust and discriminative representation from the raw sensory data of induction motors in an efficient and automatic way. Finally, the learned representations are fed into support vector machine classifier to identify six different fault conditions. Experiments performed on a machine fault simulator indicate that compared with the current state-of-the-art methods, the proposed method shows significant performance gains, and it is effective and efficient for induction motor fault diagnosis.


title: Understanding variable importances in forests of randomized trees

abstract: Despite growing interest and practical use in various scientific areas, variable importances derived from tree-based ensemble methods are not well understood from a theoretical point of view. In this work we characterize the Mean Decrease Impurity (MDI) variable importances as measured by an ensemble of totally randomized trees in asymptotic sample and ensemble size conditions. We derive a three-level decomposition of the information jointly provided by all input variables about the output in terms of i) the MDI importance of each input variable, ii) the degree of interaction of a given input variable with the other input variables, iii) the different interaction terms of a given degree. We then show that this MDI importance of a variable is equal to zero if and only if the variable is irrelevant and that the MDI importance of a relevant variable is invariant with respect to the removal or the addition of irrelevant variables. We illustrate these properties on a simple example and discuss how they may change in the case of non-totally randomized trees such as Random Forests and Extra-Trees.


title: Broadband spiral diversityantenna with dielectric loading

abstract: In this paper, dielectric loading and scaling is used to achieve broadband circular polarization with compact size. The spirals are arranged in opposite configuration sense which is used for detection of circularly-polarized waves of either polarization sense. A wide band balun is designed to feed the antenna structure. Antenna is enclosed in elliptical shape housing. A broad impedance and AR bandwidth is obtained.


title: Goal-based requirements analysis

abstract: Goals are a logical mechanism for identifying, organizing and justifying software requirements. Strategies are needed for the initial identification and construction of goals. We discuss goals from the perspective of two themes: goal analysis and goal evolution. We begin with an overview of the goal-based method we have developed and summarize our experiences in applying our method to a relatively large example. We illustrate some of the issues that practitioners face when using a goal-based approach to specify the requirements for a system and close the paper with a discussion of needed future research on goal-based requirements analysis and evolution.


title: Internet video traffic classification using QoS features

abstract: This paper addresses the issue of effective classification of video traffic with the view of QoS guarantee, and presents a modified K-Singular Value Decomposition (K-SVD) classification framework based on the concept of QFAg (QoS based Flow Aggregation). By statistical analysis of video flows on large-scale real networks, we define 5 Quality of Service (QoS) categories with the features of downstream/upstream rates. To investigate the sparsity property of multimedia QoS feature, this paper utilizes modified K-SVD to train dictionary extracted from training samples. By learning feature-set to obtain sparse representation for video traffic, we propose a feature-based method to classify video traffic. Experimental results reveal that the proposed method can improve the classification performance significantly compared to previous methods.


title: Evaluation of a RGB-LED-based Emotion Display for Affective Agents

abstract: Technology has become an essential part in every aspect of our lives. However the key to a successful implementation of a technology depends on the acceptance by the general public. In order to increase the acceptance various approaches can be applied. In this paper, we will examine the human-robot emotional interaction by investigating the capabilities of a developed low-resolution RGB-LED display in the context of artificial emotions. We are focusing on four of the most representative human emotions which include happiness, anger, sadness and fear. We will work with colors and dynamic light patterns which are supposed to evoke various associations. In an experiment, the use these patterns as expressions of emotions are validated. The results of the conducted study show that some of the considered basic emotions can be recognized by human observers.


SUMMARIZATION USING AGGREGATE SIMILARITY


title: A neural-network-based realization of in-network computation for the Internet of Things

abstract: Ultra-dense Internet of Things (IoT) networks and machine type communications herald an enormous opportunity for new computing paradigms and are serving as a catalyst for profound change in the evolution of the Internet. We explore leveraging the communication within IoT to serve data processing by appropriately shaping the aggregate behavior of a network to parallel more traditional computation methods. This paper presents an element of this vision, whereby we map the operations of an artificial neural network onto the communication of an IoT network for simultaneous data processing and transfer. That is, we provide a framework to treat a network holistically as an artificial neural network, rather than placing neural networks within the network. The operation of components of a neural network, neurons and connections between neurons, are performed by the various elements of the IoT network, i.e., the devices and their connections. The proposed approach reduces the latency in delivering processed information and supports the locality of information inherent to IoT by removing the need for transfer to remote data processing sites.


title: Brain-Computer Interface Operation of Robotic and Prosthetic Devices

abstract: Brain-computer interfaces (BCIs) use signals recorded from the brain to operate robotic or prosthetic devices. Both invasive and noninvasive approaches have proven effective. Achieving the speed, accuracy, and reliability necessary for real-world applications remains the major challenge for BCI-based robotic control.


Pruning Basic Elements for Better Automatic Evaluation of Summaries


title: Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

abstract: Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance. This is especially true with high-capacity parametric function approximators, such as deep networks. In this paper, we study how to bridge this gap, by employing uncertainty-aware dynamics models. We propose a new algorithm called probabilistic ensembles with trajectory sampling (PETS) that combines uncertainty-aware deep network dynamics models with sampling-based uncertainty propagation. Our comparison to state-of-the-art model-based and model-free deep RL algorithms shows that our approach matches the asymptotic performance of model-free algorithms on several challenging benchmark tasks, while requiring significantly fewer samples (e.g., 8 and 125 times fewer samples than Soft Actor Critic and Proximal Policy Optimization respectively on the half-cheetah task).


title: TRANSPORTATION ENERGY DATABOOK: EDITION 17

abstract: The Transportation Energy Data Book: Edition 17 is a statistical compendium prepared and published by Oak Ridge National Laboratory (ORNL) under contract with the Office of Transportation Technologies in the Department of Energy (DOE). Designed for use as a desk-top reference, the data book represents an assembly and display of statistics and information that characterize transportation activity, and presents data on other factors that influence transportation energy use. The purpose of this document is to present relevant statistical data in the form of tables and graphs. Each of the major transportation modes is treated in separate chapters or sections. Chapter 1 compares U.S. transportation data with data from other countries. Aggregate energy use and energy supply data for all modes are presented in Chapter 2. The highway mode, which accounts for over three-fourths of total transportation energy consumption is dealt with in Chapter 3. Topics in this chapter include automobiles, trucks, buses, fleet vehicles, federal standards, fuel economies, and high- occupancy vehicle lane data. Household travel behavior characteristics are displayed in Chapter 4. Chapter 5 contains information on alternative fuels and alternative fuel vehicles. Chapter 6 covers the major nonhighway modes: air, water, and rail. The last chapter, Chapter 7, presents data on environmental issues relating to transportation.


title: Inference of k-testable languages in the strict sense and application to syntactic pattern recognition

abstract: The inductive inference of the class of k-testable languages in the strict sense (k-TSSL) is considered. A k-TSSL is essentially defined by a finite set of substrings of length k that are permitted to appear in the strings of the language. Given a positive sample R of strings of an unknown language, a deterministic finite-state automation that recognizes the smallest k-TSSL containing R is obtained. The inferred automation is shown to have a number of transitions bounded by O(m) where m is the number of substrings defining this k-TSSL, and the inference algorithm works in O(kn log m) where n is the sum of the lengths of all the strings in R. The proposed methods are illustrated through syntactic pattern recognition experiments in which a number of strings generated by ten given (source) non-k-TSSL grammars are used to infer ten k-TSSL stochastic automata, which are further used to classify new strings generated by the same source grammars. The results of these experiments are consistent with the theory and show the ability of (stochastic) k-TSSLs to approach other classes of regular languages. >


title: Universal Transformers

abstract: Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been shown to achieve superior results on some sequence modeling tasks such as machine translation, with the added advantage that they concurrently process all inputs in the sequence, leading to easy parallelization and faster training times. Despite these successes, however, popular feed-forward sequence models like the Transformer fail to generalize in many simple tasks that recurrent models handle with ease, e.g. copying strings or even simple logical inference when the string or formula lengths exceed those observed at training time. We propose the Universal Transformer (UT), a parallel-in-time self-attentive recurrent sequence model which can be cast as a generalization of the Transformer model and which addresses these issues. UTs combine the parallelizability and global receptive field of feed-forward sequence models like the Transformer with the recurrent inductive bias of RNNs. We also add a dynamic per-position halting mechanism and find that it improves accuracy on several tasks. In contrast to the standard Transformer, under certain assumptions, UTs can be shown to be Turing-complete. Our experiments show that UTs outperform standard Transformers on a wide range of algorithmic and language understanding tasks, including the challenging LAMBADA language modeling task where UTs achieve a new state of the art, and machine translation where UTs achieve a 0.9 BLEU improvement over Transformers on the WMT14 En-De dataset.


title: Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals

abstract: Interpretability of deep neural networks is a recently emerging area of machine learning research targeting a better understanding of how models perform feature selection and derive their classification decisions. In this paper, two neural network architectures are trained on spectrogram and raw waveform data for audio classification tasks on a newly created audio dataset and layer-wise relevance propagation (LRP), a previously proposed interpretability method, is applied to investigate the models' feature selection and decision making. It is demonstrated that the networks are highly reliant on feature marked as relevant by LRP through systematic manipulation of the input data. Our results show that by making deep audio classifiers interpretable, one can analyze and compare the properties and strategies of different models beyond classification accuracy, which potentially opens up new ways for model improvements.


title: Performance Study of Classification Algorithms for Consumer Online Shopping Attitudes and Behavior Using Data Mining

abstract: The online retail industry is one of the world's largest and fastest growing industries having huge amount of online sales data. This sales data includes information about customer buying history, goods or services offered for the customers. Hidden relationships in sales data can be discovered from the application of data mining techniques. Data mining is an inter disciplinary promising field that focuses on access of information useful for high level decisions and also include machine learning to help online shopping stores to indentify online customer behavior to recommend for him the appropriate products he/she is interesting to them, because the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the large number of online offers. Thus, techniques for personalization and shopping guides are needed by users. For a pleasant and successful shopping experience, users need to know easily which products to buy with high confidence. In this paper eleven data mining classification techniques will be comparatively tested to find the best classifier fit for consumer online shopping attitudes and behavior according to obtained dataset for big agency of online shopping, the results shows that decision table classifier and filtered classifier gives the highest accuracy and the lowest accuracy is achieved by classification via clustering and simple cart, also this paper will provide a recommender system based on decision table classifier helping the customer to find the products he/she is searching for in some ecommerce web sites. Recommender system learns from the information about customers and products and provides appropriate personalized recommendations to customers to find the desired products.


title: Carpooling Service for Large-Scale Taxicab Networks

abstract: Carpooling has long held the promise of reducing gas consumption by decreasing mileage to deliver coriders. Although ad hoc carpools already exist in the real world through private arrangements, little research on the topic has been done. In this article, we present the first systematic work to design, implement, and evaluate a carpool service, called coRide, in a large-scale taxicab network intended to reduce total mileage for less gas consumption. Our coRide system consists of three components, a dispatching cloud server, passenger clients, and an onboard customized device, called TaxiBox. In the coRide design, in response to the delivery requests of passengers, dispatching cloud servers calculate cost-efficient carpool routes for taxicab drivers and thus lower fares for the individual passengers. To improve coRide’s efficiency in mileage reduction, we formulate an NP-hard route calculation problem under different practical constraints. We then provide (1) an optimal algorithm using Linear Programming, (2) a 2-approximation algorithm with a polynomial complexity, and (3) its corresponding online version with a linear complexity. To encourage coRide’s adoption, we present a win-win fare model as the incentive mechanism for passengers and drivers to participate. We test the performance of coRide by a comprehensive evaluation with a real-world trial implementation and a data-driven simulation with 14,000 taxi data from the Chinese city Shenzhen. The results show that compared with the ground truth, our service can reduce 33p of total mileage; with our win-win fare model, we can lower passenger fares by 49p and simultaneously increase driver profit by 76p.


title: Floor Time Play with a Child with Autism: A Single-Subject Study

abstract: Background. Children with autism exhibit di!culties with social interaction and communication skills, and they present with restricted interests and stereotyped patterns of behaviour that a"ect their daily lives. Floor time play (FTP) is an intervention approach that addresses these issues; however, there are few published studies on its e"ectiveness. Purpose. This study determines the e"ectiveness of FTP intervention with a child diagnosed with autism. Methods. A single subject AB design was used with circles of communication as the behaviour indicator for improvement. Visual and statistical analyses were completed. The child’s mother kept a daily journal describing FTP intervention sessions at home. Findings. Despite variability in the data, statistical analyses indicate a significant di"erence between the numbers of circles of communication during the intervention phase as compared with the observation phase. Implications. This study provides preliminary evidence for the use of the FTP approach with a child with autism.


title: Broadband Millimeter-Wave Power Combiner Using Compact SIW to Waveguide Transition

abstract: A novel broadband millimeter-wave passive power combiner based on substrate integrated waveguide (SIW) to waveguide transition is presented in this letter. Two transition circuits are symmetrically inserted into the E-plane of the rectangular waveguide and function as power-combining network. To satisfy the wideband request of the power combiner, a broadband and compact SIW-to-waveguide transition circuit has been developed using antisymmetric tapered probe. A Ka-band four-way power combiner was fabricated and the measured results agree well with the simulated ones. The measured results show that the proposed combiner achieved a bandwidth of 52% from 23.5 to 40 GHz with better than 15 dB return loss and an insertion loss of 0.75 to 1.4 dB. This millimeter-wave power combiner can be employed in high power-combining system for its simple structure and broadband performance.


title: A generalized capacitor voltage balancing scheme for flying capacitor multilevel converters

abstract: Multilevel power electronic converters are the converter of choice in medium-voltage applications due to their reduced switch voltage stress, better harmonic performance, and lower switching losses. Although it has received little attention, the flying-capacitor multilevel converter has a distinct advantage in terms of its ease of capacitor voltage balancing. A number of techniques have been presented in the literature for capacitor voltage balancing, some relying on “self-balancing” properties. However, self balancing cannot guarantee balancing of capacitor voltages in practical applications. Other researchers present closed-loop control schemes which force voltage balancing of capacitors. In this paper, a new closed loop control scheme is proposed which regulates the capacitor voltages for a multilevel flying capacitor converter. The proposed scheme is based on the converter equations and involves implementing simple rules. In particular, multiple duty cycles are defined and modulated in direct response to the capacitor voltages. Through simulation, the method is shown to work on four, eight and nine-level flying capacitor inverters.


title: Apposcopy: semantics-based detection of Android malware through static analysis

abstract: We present Apposcopy, a new semantics-based approach for identifying a prevalent class of Android malware that steals private user information. Apposcopy incorporates (i) a high-level language for specifying signatures that describe semantic characteristics of malware families and (ii) a static analysis for deciding if a given application matches a malware signature. The signature matching algorithm of Apposcopy uses a combination of static taint analysis and a new form of program representation called Inter-Component Call Graph to efficiently detect Android applications that have certain control- and data-flow properties. We have evaluated Apposcopy on a corpus of real-world Android applications and show that it can effectively and reliably pinpoint malicious applications that belong to certain malware families.


A Systematic Gap Analysis of Social Engineering Defence Mechanisms Considering Social Psychology


title: Functional brain networks: great expectations, hard times and the big leap forward

abstract: Many physical and biological systems can be studied using complex network theory, a new statistical physics understanding of graph theory. The recent application of complex network theory to the study of functional brain networks generated great enthusiasm as it allows addressing hitherto non-standard issues in the field, such as efficiency of brain functioning or vulnerability to damage. However, in spite of its high degree of generality, the theory was originally designed to describe systems profoundly different from the brain. We discuss some important caveats in the wholesale application of existing tools and concepts to a field they were not originally designed to describe. At the same time, we argue that complex network theory has not yet been taken full advantage of, as many of its important aspects are yet to make their appearance in the neuroscience literature. Finally, we propose that, rather than simply borrowing from an existing theory, functional neural networks can inspire a fundamental reformulation of complex network theory, to account for its exquisitely complex functioning mode.


title: Novel considerations on the negative pressure adhesion of electric ducted fans: An experimental study

abstract: In this article, the potential of utilizing an Electric Ducted Fan (EDF) as an adhesion actuator is investigated in detail, where an experimental setup is implemented for evaluating the EDF's ability to adhere to a test surface through negative pressure generation. Different design variables and modifications to the original EDF structure are tested, while their impact on the adhesion efficiency is experimentally evaluated. The presented investigation acts as a preliminary study to the goal of incorporating the resulting optimized negative pressure-based actuation method in a wall-climbing robot for inspection of aircraft fuselages.


title: Microstrip antenna array with ratrace comparator at X-band for monopulse tracking radar

abstract: In this paper, a microstrip antenna array with ratrace comparator at X-band for monopulse tracking radar has been designed and simulated using IE3D software. To achieve better side lobe level and low cross polar components, the radiating antenna consists of mixed feeding technique. The bandwidth of 13.9% has been achieved for VSWR < 2. The peak gain achieved is equal to 24.7 dBi with less than 1 dBi variations over the entire range of bandwidth. Side lobe level of better than 15 dB and a null depth of better than 30 dB has been obtained. The radiation pattern is symmetric in both the planes with HPBW nearly equal to 9.0°.


A framework for evaluating websites of public authorities


A framework for navigation-driven lazy mediators


title: Uncertainty and Expectation in Sentence Processing: Evidence From Subcategorization Distributions

abstract: There is now considerable evidence that human sentence processing is expectation based: As people read a sentence, they use their statistical experience with their language to generate predictions about upcoming syntactic structure. This study examines how sentence processing is affected by readers' uncertainty about those expectations. In a self-paced reading study, we use lexical subcategorization distributions to factorially manipulate both the strength of expectations and the uncertainty about them. We compare two types of uncertainty: uncertainty about the verb's complement, reflecting the next prediction step; and uncertainty about the full sentence, reflecting an unbounded number of prediction steps. We find that uncertainty about the full structure, but not about the next step, was a significant predictor of processing difficulty: Greater reduction in uncertainty was correlated with increased reading times (RTs). We additionally replicated previously observed effects of expectation violation (surprisal), orthogonal to the effect of uncertainty. This suggests that both surprisal and uncertainty affect human RTs. We discuss the consequences for theories of sentence comprehension.


title: Semantic Composition and Decomposition: From Recognition to Generation

abstract: Semantic composition is the task of understanding the meaning of text by composing the meanings of the individual words in the text. Semantic decomposition is the task of understanding the meaning of an individual word by decomposing it into various aspects (factors, constituents, components) that are latent in the meaning of the word. We take a distributional approach to semantics, in which a word is represented by a context vector. Much recent work has considered the problem of recognizing compositions and decompositions, but we tackle the more dicult generation problem. For simplicity, we focus on noun-modier bigrams and noun unigrams. A test for semantic composition is, given context vectors for the noun and modier in a noun-modier bigram ( red salmon), generate a noun unigram that is synonymous with the given bigram (sockeye). A test for semantic decomposition is, given a context vector for a noun unigram (snifter), generate a noun-modier bigram that is synonymous with the given unigram ( brandy glass). With a vocabulary of about 73,000 unigrams from WordNet, there are 73,000 candidate unigram compositions for a bigram and 5,300,000,000 (73,000 squared) candidate bigram decompositions for a unigram. We generate ranked lists of potential solutions in two passes. A fast unsupervised learning algorithm generates an initial list of candidates and then a slower supervised learning algorithm renes the list. We evaluate the candidate solutions by comparing them to WordNet synonym sets. For decomposition (unigram to bigram), the top 100 most highly ranked bigrams include a WordNet synonym of the given unigram 50.7% of the time. For composition (bigram to unigram), the top 100 most highly ranked unigrams include a WordNet synonym of the given bigram 77.8% of the time.


title: Opportunities and Challenges of Using UAVs for Dubai Smart City

abstract: This paper discusses the applications of unmanned aerial vehicles (UAVs) in smart cities, their opportunities and their challenges. It focuses on the city of Dubai as an example. UAVs have a wide range of applications in many fields like environmental hazards monitoring, traffic management and pollution monitoring, all of which contributes greatly to the development of any smart city. These opportunities among several others are discussed in this paper. Several challenges and issues such as safety, privacy and ethical use are of great concern and are also outlined.


Understanding the Twitter User Networks of Viruses and Ransomware Attacks


title: Measuring flow in gamification: Dispositional Flow Scale-2

abstract: Psychometric properties of the DFS-2 are examined in gamification of exercise.A good model fit for the original DFS-2 structure is found.A respecification of the model is created to reach higher level of goodness-of-fit.The data suggest a division of the components of flow into conditions and outcomes.The study also shows which components of flow are salient in gamification. This paper measures flow in the context of gamification and investigates the psychometric properties of the Dispositional Flow Scale-2 (DFS-2). We employ data gathered from users of an exercise gamification service (N=200). The results show that the original DFS-2 factorial structure does result in a similar model fit as the original work. However, we also present a factorial respecification that satisfies more recent model fit thresholds. Beyond validating the original DFS-2 instrument in the context of gamification, the psychometric analysis and the respecifications suggest that the components of flow divide into highly correlated conditions of flow (which were also found to be more salient in the context of gamification: autotelic experience, balance of skill and challenge, control, clear goals, and feedback) and into possible outcomes (merging action-awareness, concentration, loss of sense of time, and loss of self-consciousness) from achieving flow.


title: Provably secure ciphertext policy ABE

abstract: In ciphertext policy attribute-based encryption (CP-ABE), every secret key is associated with a set of attributes, and every ciphertext is associated with an access structure on attributes. Decryption is enabled if and only if the user's attribute set satisfies the ciphertext access structure. This provides fine-grained access control on shared data in many practical settings, e.g., secure database and IP multicast. In this paper, we study CP-ABE schemes in which access structures are AND gates on positive and negative attributes. Our basic scheme is proven to be chosen plaintext (CPA) secure under the decisional bilinear Diffie-Hellman (DBDH) assumption. We then apply the Canetti-Halevi-Katz technique to obtain a chosen ciphertext (CCA) secure extension using one-time signatures. The security proof is a reduction to the DBDH assumption and the strong existential unforgeability of the signature primitive. In addition, we introduce hierarchical attributes to optimize our basic scheme - reducing both ciphertext size and encryption/decryption time while maintaining CPA security. We conclude with a discussion of practical applications of CP-ABE.


title: Collective frequency variation in network synchronization and reverse PageRank

abstract: A wide range of natural and engineered phenomena rely on large networks of interacting units to reach a dynamical consensus state where the system collectively operates. Here we study the dynamics of self-organizing systems and show that for generic directed networks the collective frequency of the ensemble is {\it not} the same as the mean of the individuals' natural frequencies. Specifically, we show that the collective frequency equals a weighted average of the natural frequencies, where the weights are given by an out-flow centrality measure that is equivalent to a reverse PageRank centrality. Our findings uncover an intricate dependence of the collective frequency on both the structural directedness and dynamical heterogeneity of the network, and also reveal an unexplored connection between synchronization and PageRank, which opens the possibility of applying PageRank optimization to synchronization. Finally, we demonstrate the presence of collective frequency variation in real-world networks by considering the UK and Scandinavian power grids.


title: Unsupervised learning algorithms for MRI brain tumor segmentation

abstract: The structure of the brain can be seen by the Magnetic Resonance (MR) image output. MR scanned image of the brain is utilized for the entire study in this paper. The MR image filter is more agreeable than some other outputs for analysis. It will not influence the human body since it does not hone any radiation. In digitization of MR scanned image, segmentation of brain tumor is one kind of challenging problems and it is critical to clinical diagnosis. So segmentation needs to be accurate, robust, and efficient to avoid impacts caused by various large and complex biases added to images. Clustering algorithms have been widely used for the segmentation. In this paper, the K-means (KM) clustering and Fuzzy C-means (FCM) clustering algorithms are used to locate the tumor and extract it. Comparative analysis in terms of Segmented area, Relative area, Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) is performed between K-means clustering and FCM clustering algorithms. The obtained performance measures from the experiments indicate the superiority of the chosen FCM algorithm over the K-means algorithm. That is 0.93% of relative segmented tumor area for FCM shows that the area which was effected by the tumor in the original MR image is segmented as a tumor. The FCM Algorithm has less processing time of 8.639 seconds compared to 22.831 seconds for KM algorithm.


title: Electricity Demand Forecasting by Multi-Task Learning

abstract: We explore the application of kernel-based multi-task learning techniques to forecast the demand of electricity in multiple nodes of a distribution network. We show that recently developed output kernel learning techniques are particularly well suited to solve this problem, as they allow to flexibly model the complex seasonal effects that characterize electricity demand data, while learning and exploiting correlations between multiple demand profiles. We also demonstrate that kernels with a multiplicative structure yield superior predictive performance with respect to the widely adopted (generalized) additive models. Our study is based on residential and industrial smart meter data provided by the Irish Commission for Energy Regulation (CER).


title: Aerial Manipulation: A Literature Review

abstract: Aerial manipulation aims at combining the versatility and the agility of some aerial platforms with the manipulation capabilities of robotic arms. This letter tries to collect the results reached by the research community so far within the field of aerial manipulation, especially from the technological and control point of view. A brief literature review of general aerial robotics and space manipulation is carried out as well.


title: Study Design, Precision, and Validity in Observational Studies

abstract: The use of observational research methods in the field of palliative care is vital to building the evidence base, identifying best practices, and understanding disparities in access to and delivery of palliative care services. As discussed in the introduction to this series, research in palliative care encompasses numerous areas in which the gold standard research design, the randomized controlled trial (RCT), is not appropriate, adequate, or even possible.1,2 The difficulties in conducting RCTs in palliative care include patient and family recruitment, gate-keeping by physicians, crossover contamination, high attrition rates, small sample sizes, and limited survival times. Furthermore, a number of important issues including variation in access to palliative care and disparities in the use and provision of palliative care simply cannot be answered without observational research methods. As research in palliative care broadens to encompass study designs other than the RCT, the collective understanding of the use, strengths, and limitations of observational research methods is critical. The goals of this first paper are to introduce the major types of observational study designs, discuss the issues of precision and validity, and provide practical insights into how to critically evaluate this literature in our field.


title: Spatial Resolution of Bistatic Synthetic Aperture Radar: Impact of Acquisition Geometry on Imaging Performance

abstract: This paper analyzes the spatial resolution of bistatic synthetic aperture radar (SAR) in general hybrid configurations, such as air- and spaceborne systems moving along independent trajectories. The gradient method is utilized to point out the effects of the acquisition geometry, namely, position and velocity of both the transmitter and the receiver, on image resolution. This general approach is applied to different realizations of bistatic SAR, such as low-Earth-orbit monostatic-bistatic SAR, spaceborne-airborne bistatic SAR, and a bistatic system consisting of a high-altitude long-endurance illuminator and lower altitude airborne receivers. The main features of the method are then put in evidence, including the derivation of analytical tools to individuate adequate relative geometries for achieving satisfactory resolutions. A comparison to the other proposed techniques for computing the spatial resolution of bistatic SAR is also reported in order to highlight some peculiarities of all presented methodologies. Finally, the good agreement between the image resolution results achieved by recently carried out bistatic SAR experiments and the ones derived by the gradient method strengthens the potentialities of the proposed approach.


title: A Review of the Mental Workload Literature 1.0

abstract: Abstract : The intent of this paper is to provide the reader with an overview of the mental workload literature. It will focus on other state-of-the-art surveys with reference to some specific reports of the practical application of mental workload measurement. The surveys will be limited to those in English. Manzey reportedly provides a review of psycho-physiological methods in German; a comparable, recent review in English was not found although a NATO RTO report (Wilson 2004, pp. 64-65 and Chapter 8) provides some guidance in this respect. Appendix 1 lists a search for references to workload measurement techniques using the GOOGLE search engine. The intent is to give the reader an appreciation of where work has been focused, or at least as reported on the Internet.


title: Predicting the next location: a recurrent model with spatial and temporal contexts

abstract: Spatial and temporal contextual information plays a key role for analyzing user behaviors, and is helpful for predicting where he or she will go next. With the growing ability of collecting information, more and more temporal and spatial contextual information is collected in systems, and the location prediction problem becomes crucial and feasible. Some works have been proposed to address this problem, but they all have their limitations. Factorizing Personalized Markov Chain (FPMC) is constructed based on a strong independence assumption among different factors, which limits its performance. Tensor Factorization (TF) faces the cold start problem in predicting future actions. Recurrent Neural Networks (RNN) model shows promising performance comparing with PFMC and TF, but all these methods have problem in modeling continuous time interval and geographical distance. In this paper, we extend RNN and propose a novel method called Spatial Temporal Recurrent Neural Networks (ST-RNN). ST-RNN can model local temporal and spatial contexts in each layer with time-specific transition matrices for different time intervals and distance-specific transition matrices for different geographical distances. Experimental results show that the proposed ST-RNN model yields significant improvements over the competitive compared methods on two typical datasets, i.e., Global Terrorism Database (GTD) and Gowalla dataset.


title: Microprocessor Optimizations for the Internet of Things

abstract: The Internet of Things (IoT) refers to a pervasive presence of interconnected and uniquely identifiable physical devices. These devices’ goal is to gather data and drive actions in order to improve productivity, and ultimately reduce or eliminate reliance on human intervention for data acquisition, interpretation, and use. The proliferation of these connected low-power devices will result in a data explosion that will significantly increase data transmission costs with respect to energy consumption and latency. Edge computing reduces these costs by performing computations at the edge nodes, prior to data transmission, to interpret and/or utilize the data. While much research has focused on the IoT’s connected nature and communication challenges, the challenges of IoT embedded computing with respect to device microprocessors has received much less attention. This paper explores IoT applications’ execution characteristics from a microarchitectural perspective and the microarchitectural characteristics that will enable efficient and effective edge computing. To tractably represent a wide variety of next-generation IoT applications, we present a broad IoT application classification methodology based on application functions, to enable quicker workload characterizations for IoT microprocessors. We then survey and discuss potential microarchitectural optimizations and computing paradigms that will enable the design of right-provisioned microprocessors that are efficient, configurable, extensible, and scalable. This paper provides a foundation for the analysis and design of a diverse set of microprocessor architectures for next-generation IoT devices.


title: Data-Driven Models for the Spatio-Temporal Interpolation of Satellite-Derived SST Fields

abstract: Satellite-derived products are of key importance for the high-resolution monitoring of the ocean surface on a global scale. Due to the sensitivity of spaceborne sensors to the atmospheric conditions as well as the associated spatio-temporal sampling, ocean remote sensing data may be subject to high-missing data rates. The spatio-temporal interpolation of these data remains a key challenge to deliver L4 gridded products to end-users. Whereas operational products mostly rely on model-driven approaches, especially optimal interpolation based on Gaussian process priors, the availability of large-scale observation and simulation datasets calls for the development of novel data-driven models. This study investigates such models. We extend the recently introduced analog data assimilation to high-dimensional spatio-temporal fields using a multiscale patch-based decomposition. Using an observing system simulation experiment for sea surface temperature, we demonstrate the relevance of the proposed data-driven scheme for the real missing data patterns of the high-resolution infrared METOP sensor. It has resulted in a significant improvement w.r.t. state-of-the-art techniques in terms of interpolation error (about 50% of relative gain) and spectral characteristics for horizontal scales smaller than 100 km. We further discuss the key features and parameterizations of the proposed data-driven approach as well as its relevance with respect to classical interpolation techniques.


title: Reflections on how designers design with data

abstract: In recent years many popular data visualizations have emerged that are created largely by designers whose main area of expertise is not computer science. Designers generate these visualizations using a handful of design tools and environments. To better inform the development of tools intended for designers working with data, we set out to understand designers' challenges and perspectives. We interviewed professional designers, conducted observations of designers working with data in the lab, and observed designers working with data in team settings in the wild. A set of patterns emerged from these observations from which we extract a number of themes that provide a new perspective on design considerations for visualization tool creators, as well as on known engineering problems.


title: Graphene: fine-grained IO management for graph computing

abstract: As graphs continue to grow, external memory graph processing systems serve as a promising alternative to inmemory solutions for low cost and high scalability. Unfortunately, not only does this approach require considerable efforts in programming and IO management, but its performance also lags behind, in some cases by an order of magnitude. In this work, we strive to achieve an ambitious goal of achieving ease of programming and high IO performance (as in-memory processing) while maintaining graph data on disks (as external memory processing). To this end, we have designed and developed Graphene that consists of four new techniques: an IO request centric programming model, bitmap based asynchronous IO, direct hugepage support, and data and workload balancing. The evaluation shows that Graphene can not only run several times faster than several external-memory processing systems, but also performs comparably with in-memory processing on large graphs.


Learning to Rank Semantic Coherence for Topic Segmentation


title: Beat-to-Beat Blood Pressure and Two-dimensional (axial and radial) Motion of the Carotid Artery Wall: Physiological Evaluation of Arterial Stiffness

abstract: The physiological relationship between local arterial displacement and blood pressure (BP) plays an integral role in assess- ment of the mechanical properties of arteries. In this study, we used more advanced methods to obtain reliable continuous BP and the displacement of the common carotid artery (CCA) simultaneously. We propose a novel evaluation method for arterial stiffness that relies on determining the physiological relationship between the axial and radial displacements of the CCA wall and beat-to-beat BP. Patients (total of 138) were divided into groups according to the following three criteria: essential hyper- tension (EH) and normotension, male and female, elderly and younger. The Pearson correlation test and canonical correlation analysis showed that the CCA indices were significantly correlated with BP indices (r = 0:787; p < 0:05). The slope of the CCA displacement/pressure curve showed a progressive reduction with increasing age and EH disease occurrence (EH: 0.496 vs. normotension: 0.822; age <= 60:0.585 vs. age > 60:0.783). Our method provides an explicit reference value and relationship for the manner in which the CCA wall responds to changes in BP. Short-term and continuous BP were significantly correlated with CCA displacement and exhibited a close inverse relationship with each subject's BP and EH, age, and systolic blood pressure.


title: Purification and characterization of extracellular Staphylococcus warneri lipase

abstract: The extracellular lipase of Staphylococcus warneri was secreted as a protein with an apparent molecular mass of 90 kDa. It was then sequentially processed in the supernatant to a protein of 45 kDa. Tryptic digestion of the crude extract resulted in a homogeneous sample containing only the 45-kDa form. Purification was achieved by hydrophobic chromatography. Purified lipase had an optimum pH of 9.0 and an optimum temperature of 25°C. The enzyme was stable within the range pH 5.0–9.0; it had a broad substrate specificity. The results of inhibition studies were consistent with the view that lipases possess a serine residue at the catalytic site.


title: Schema Inference for Massive JSON Datasets

abstract: Recent years have seen the widespread use of JSON as a data format to represent massive data collections. JSON data collections are usually schemaless. While this ensures several advantages, the absence of schema information has important negative consequences: the correctness of complex queries and programs cannot be statically checked, users cannot rely on schema information to quickly figure out structural properties that could speed up the formulation of correct queries, and many schema-based optimizations are not possible. In this paper we deal with the problem of inferring a schema from massive JSON data sets. We first identify a JSON type language which is simple and, at the same time, expressive enough to capture irregularities and to give complete structural information about input data. We then present our main contribution, which is the design of a schema inference algorithm, its theoretical study and its implementation based on Spark, enabling reasonable schema inference time for massive collections. Finally, we report about an experimental analysis showing the effectiveness of our approach in terms of execution time, precision and conciseness of inferred schemas, and scalability.


title: USB Device Forensics: Insertion and removal timestamps of USB devices in Windows 8

abstract: The timeline of the events surrounding an incident being investigated is one of the most important facets to forensic investigation. As a result, sometimes it is crucial to know when a suspect USB device might have been connected or disconnected from a system .A variety of forensic artifacts associated with the insertion and removal of USB devices are located in the windows registry, event logs etc. The existing forensically important locations, methodology and techniques for determining the traces left behind by the usage of USB devices in Windows XP, Vista and Windows 7 may not be the same for upcoming versions of Windows. This work aims to mainly study the forensic artifacts related to insertion and removal time stamps of various Mass Storage Class (MSC), Media Transport Protocol (MTP) and Picture Transport Protocol (PTP) enabled USB devices in Windows 8 operating system. In this paper the Windows 8 registry and Windows event logs were mainly considered as artifacts for analysis. This research explores forensic artifacts aimed to help a forensic investigator in enumerating the first insertion and last removal time stamps of various MSC, MTP and PTP enabled USB devices in Windows 8 computing environment by demonstrating the crucial Windows 8 registry keys , event logs and related metadata.


title: Progressive Gaussian filtering using explicit likelihoods

abstract: In this paper, we introduce a new sample-based Gaussian filter. In contrast to the popular Nonlinear Kalman Filters, e.g., the UKF, we do not rely on linearizing the measurement model. Instead, we take up the Gaussian progressive filtering approach introduced by the PGF 42 but explicitly rely on likelihood functions. Progression means, we incorporate the information of a new measurement gradually into the state estimate. The advantages of this filtering method are on the one hand the avoidance of sample degeneration and on the other hand an adaptive determination of the number of likelihood evaluations required for each measurement update. By this means, less informative measurements can be processed quickly, whereas measurements containing much information automatically receive more emphasis by the filter. These properties allow the new filter to cope with the demanding problem of very narrow likelihood functions in an efficient way.


title: A Review on Recent Traffic Congestion Relief Approaches

abstract: Traffic congestion is a global and crucial problem due to increase in demand and limited network transportation structure. Complex traffic structure, imbalanced traffic flow, and uncertain event like road accident are among of the identified factors that may cause traffic congestion. Traffic congestion problem causes delay in travelling, increase in travelling cost and accident rate and may affect air pollution. Numerous approaches have been developed and proposed in solving these problems. Among them are traffic signal controls, congestion pricing, turning restrictions and traffic routing. Various methods have been employed include mathematical model, heuristic and meta-heuristics. This paper presents a review on recent approaches that have been proposed for traffic congestion problem and methods used for implementation. This review provides a bigger picture on recent approaches; provide a comparison and suggestion for future research in traffic congestion relief.


title: Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach

abstract: Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed. In this study, unscented Kalman filter (UKF) is integrated with support vector regression (SVR) based state-space model in order to precisely update the short-term estimation of wind speed sequence. In the proposed SVR–UKF approach, support vector regression is first employed to formulate a nonlinear state-space model and then unscented Kalman filter is adopted to perform dynamic state estimation recursively on wind sequence with stochastic uncertainty. The novel SVR–UKF method is compared with artificial neural networks (ANNs), SVR, autoregressive (AR) and autoregressive integrated with Kalman filter (AR-Kalman) approaches for predicting short-term wind speed sequences collected from three sites in Massachusetts, USA. The forecasting results indicate that the proposed method has much better performance in both one-step-ahead and multi-step-ahead wind speed predictions than the other approaches across all the locations.


title: A fast approach for omnidirectional surveillance with multiple virtual perspective views

abstract: In recent years, video surveillance combined with computer vision algorithms like object detection, tracking or automated behaviour analysis has become an important research topic. However, most of these systems are depending on either fixed or remotely controlled narrow angle cameras. When using the former, the area of coverage is extremely limited, while utilizing the latter leads to high failure rates and troubles in camera calibration. In this paper, a method of extracting multiple perspective views from a single omnidirectional image for realtime environments is proposed. An example application of a ceiling-mounted camera setup is used to show the functional principle. Furthermore a performance improvement strategy is both presented and evaluated.


title: A general framework for road marking detection and analysis

abstract: Road markings are paintings on road surface to provide traffic guidance information for vehicles and pedestrians. In this paper, we propose a general framework for road marking detection and analysis, which is able to support various types of markings. Marking contours of different types are extracted indiscriminately from a image processing procedure, and sent to respective modules for independent classification and analysis. Four common types of markings are studied as examples in this paper, including lanes, arrows, zebra-crossings, and words. Our proposed method is tested through experiments, and shows good performance.


title: Learning Arithmetic Circuits

abstract: Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, because it requires approximate inference. We propose an alternative: learning models with a score function that directly penalizes the cost of inference. Specifically, we learn arithmetic circuits with a penalty on the number of edges in the circuit (in which the cost of inference is linear). Our algorithm is equivalent to learning a Bayesian network with context-specific independence by greedily splitting conditional distributions, at each step scoring the candidates by compiling the resulting network into an arithmetic circuit, and using its size as the penalty. We show how this can be done efficiently, without compiling a circuit from scratch for each candidate. Experiments on several real-world domains show that our algorithm is able to learn tractable models with very large treewidth, and yields more accurate predictions than a standard context-specific Bayesian network learner, in far less time.


title: Trust Me: Social Games are Better than Social Icebreakers at Building Trust

abstract: Interpersonal trust is one of the key components of efficient teamwork. Research suggests two main approaches for trust formation: personal information exchange (e.g., social icebreakers), and creating a context of risk and interdependence (e.g., trust falls). However, because these strategies are difficult to implement in an online setting, trust is more difficult to achieve and preserve in distributed teams. In this paper, we argue that games are an optimal environment for trust formation because they can simulate both risk and interdependence. Results of our online experiment show that a social game can be more effective than a social task at fostering interpersonal trust. Furthermore, trust formation through the game is reliable, but trust depends on several contingencies in the social task. Our work suggests that gameplay interactions do not merely promote impoverished versions of the rich ties formed through conversation; but rather engender genuine social bonds. \


title: Modified sepic converter with serial magnetic coupling and voltage multiplier cell

abstract: This paper presents a new modified structure of the SEPIC converter topology using series magnetic coupling and voltage multiplier in order to obtain an elevate voltage gain. The application fields that this converter will be used includes: combined with embbeded systems and photovoltaic modules. The proposed converter is analyzed focusing in its operation stages e observing the effort on the semiconductors e voltages on the other components. The theoretical and experimental results of the proposed converter are described on this paper and shows the converter's efficiency through parameters such as: low voltage on switch, low input current ripple and high output voltage.


title: Identification of Plagiarism Using Syntactic and Semantic Filters

abstract: We present a work on detection of manual paraphrasing in documents in comparison with a set of source documents. Manual paraphrasing is a realistic type of plagiarism, where the obfuscation is introduced manually in documents. We have used PAN-PC-10 data set to develop and evaluate our algorithm. The proposed approach consists of two steps, namely, identification of probable plagiarized passages using dice similarity measure and filtering the obtained passages using syntactic rules and lexical semantic features extracted from obfuscation patterns. The algorithm works at sentence level. The results are encouraging in difficult cases of plagiarism that most of the existing approaches fail to detect.


title: Polarization diversity measurements at 5.8 GHz for penetration loss and reflectivity of common building materials in an indoor environment

abstract: In this paper, we present measurement results of material penetration loss and reflection coefficient as a function of antenna polarization at 5.8 GHz. The measurements were made in a typical building hallway with a variety of test materials. This is a unique measurement campaign because the same antenna apertures were used to create three different types of polarizations: linear, circular polarized, and slant 45°. With the given antenna pair, each having a gain of 6 dBi, and a vector network analyzer; we developed measurements of penetration and reflection for dry wall, wood, door window, and steel door, along with five different combinations of antenna polarizations. For each of the above mentioned materials, we have measured the penetration loss and reflection coefficients for all five polarizations and then compared them. The results show that combinations of vertical-vertical and slant 45° — slant 45° polarizations have relative low penetration losses and high reflectivity, meaning that the signal would be contained within a room or within the hallway in such environment with an in-building base station. Also, it was concluded that co-circular polarized waves would become cross circular polarized when received after a single reflection. Lastly, a generalized comparison is made between the measurement using a slant 45° — vertical polarization pair to the other combinations of antenna polarization.


title: Iterative Neural Autoregressive Distribution Estimator (NADE-k)

abstract: Training of the neural autoregressive density estimator (NADE) can be viewed as doing one step of probabilistic inference on missing values in data. We propose a new model that extends this inference scheme to multiple steps, arguing that it is easier to learn to improve a reconstruction in k steps rather than to learn to reconstruct in a single inference step. The proposed model is an unsupervised building block for deep learning that combines the desirable properties of NADE and multi-prediction training: (1) Its test likelihood can be computed analytically, (2) it is easy to generate independent samples from it, and (3) it uses an inference engine that is a superset of variational inference for Boltzmann machines. The proposed NADE-k is competitive with the state-of-the-art in density estimation on the two datasets tested.


title: KNOWROB — knowledge processing for autonomous personal robots

abstract: Knowledge processing is an essential technique for enabling autonomous robots to do the right thing to the right object in the right way. Using knowledge processing the robots can achieve more flexible and general behavior and better performance. While knowledge representation and reasoning has been a well-established research field in Artificial Intelligence for several decades, little work has been done to design and realize knowledge processing mechanisms for the use in the context of robotic control. In this paper, we report on KNOWROB, a knowledge processing system particularly designed for autonomous personal robots. KNOWROB is a first-order knowledge representation based on description logics that provides specific mechanisms and tools for action-centered representation, for the automated acquisition of grounded concepts through observation and experience, for reasoning about and managing uncertainty, and for fast inference — knowledge processing features that are particularly necessary for autonomous robot control.


title: Sensing and classifying roadway obstacles: The street bump anomaly detection and decision support system

abstract: We develop an anomaly detection and decision support system based on data collected through the Street Bump smartphone application. The system is capable of effectively classifying roadway obstacles into predefined categories using machine learning algorithms, as well as identifying actionable ones in need of immediate attention based on a proposed “anomaly index.” We introduce appropriate regularization to the classification algorithms we employ, which has the effect of utilizing a sparse set of relevant features to perform the classification. Further, our novel “anomaly index” allows us to prioritize among actionable obstacles. Results on an actual data set provided by the City of Boston illustrate the feasibility and effectiveness of our system in practice.


title: Towards"AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies

abstract: Retrosynthesis is a technique to plan the chemical synthesis of organic molecules, for example drugs, agro- and fine chemicals. In retrosynthesis, a search tree is built by analysing molecules recursively and dissecting them into simpler molecular building blocks until one obtains a set of known building blocks. The search space is intractably large, and it is difficult to determine the value of retrosynthetic positions. Here, we propose to model retrosynthesis as a Markov Decision Process. In combination with a Deep Neural Network policy learned from essentially the complete published knowledge of chemistry, Monte Carlo Tree Search (MCTS) can be used to evaluate positions. In exploratory studies, we demonstrate that MCTS with neural network policies outperforms the traditionally used best-first search with hand-coded heuristics.


title: Position Estimation and Smooth Tracking With a Fuzzy-Logic-Based Adaptive Strong Tracking Kalman Filter for Capacitive Touch Panels

abstract: This paper presents a novel 7-in capacitive touch panel (CTP) system with a smooth tracking algorithm that accurately estimates the position where the panel is touched and tracks the trajectory of touch. The proposed CTP system consists of a microcontroller unit, a sensor IC, and an interface board. When a user draws at different speeds, the measurement noise caused by the sensor IC induces an error in the touched position and zigzag trajectory, especially when the motion is slow. The fuzzy-logic-based adaptive strong tracking Kalman filter method is implemented in a CTP system to mitigate the effect of measurement noise and provide a smooth tracking trajectory at different speeds. Moreover, the approach effectively measures and quantifies the “smoothness” of the touched trajectory. Experimental results indicate that the proposed method reduces the measurement noise and decreases the mean tracking error by 85.4% over that achieved using the moving average filter.


title: Sigma-Point Kalman Filters for Nonlinear Estimation and Sensor-Fusion: Applications to Integrated Navigation

abstract: A probabilistic framework, called Sigma-point Kalman Filters (SPKF) was applied to the problem domain addressed by the extended Kalman Filter (EKF). SPKF methods are superior to the standard EKF based estimation approaches, as an SPKF achieves second-order or higher accuracy. The SPKF has also been applied to the integrated navigation problem as it relates to unmanned aerial vehicle (UAV) autonomy. The SPKF-based sensor latency compensation technique is used to demonstrate the lagged GPS measurements.


title: A 1.6-GHz delta-sigma modulated fractional-N frequency synthesizer

abstract: The goal of this work is to design a high speed, low power consumption, and low noise RF frequency synthesizer. It can be applied to the RF front-end architecture of the IEEE 802.11 b/g channels. In this work, the VCO is an LC-tank oscillator to get the low noise performance, and an all digital delta-sigma modulator, which controls the divider of the synthesizer, is designed to present the channel selection and the fractional division ratio. Basing on the theory of phase-locked loop, the concepts, several different architectures, and the implementation of the fractional-N frequency synthesizer are given. The fractional-N frequency synthesizer is fabricated in a 1.8V, 0.18/spl mu/m CMOS technology, and consumes 29.05 mW in the operating frequency of 1.6 GHz.


title: Narcissism and attractiveness

abstract: Is narcissism related to observer-rated attractiveness? Two views imply that narcissism is unrelated to attractiveness: positive illusions theory and Feingold’s (1992) attractiveness theory (i.e., attractiveness is unrelated to personality in general). In contrast, two other views imply that narcissism is positively related to attractiveness: an evolutionary perspective on narcissism (i.e., selection pressures in shortterm mating contexts shaped the evolution of narcissism, including greater selection for attractiveness in short-term versus long-term mating contexts) and, secondly, the self-regulatory processing model of narcissism (narcissists groom themselves to bolster grandiose self-images). A meta-analysis (N > 1000) reveals a small but reliable positive narcissism–attractiveness correlation that approaches the largest known personality–attractiveness correlations. The finding supports the evolutionary and self-regulatory views of narcissism.


title: Using the foot windlass mechanism for jumping higher: A study on bipedal robot jumping

abstract: Abstract Both scientists and roboticists widely agree that the musculoskeletal system of the human foot plays an important role in locomotion. Nevertheless, the contribution of the foot musculoskeletal system has not been fully uncovered because currently it is impossible to modify and evaluate musculoskeletons in living animals. Here, to understand the effects of foot windlass mechanism, we construct a bipedal robot, which has similar musculoskeleton and dynamics to those of human. By implementing experiments on this robot, we investigate the effects (e.g. jumping height) of foot windlass mechanism on drop jumping, a simple and representative bouncing gait comprising landing and push-off. Through a significant number of drop jumping trials, the results demonstrated that (1) the windlass mechanism is passively activated in the push-off phase and that (2) it contributes to the height of jumping. Our results suggest that the foot windlass mechanism contributes to the energy efficiency and performance in locomotion.


title: What works in offender profiling? A comparison of typological, thematic, and multivariate models

abstract: Utilizing a sample of 85 stranger rapists, three models (Hazelwood's (1987) Power and Anger FBI model, the Behavioral Thematic evaluation of Canter, Bennell, Alison, and Reddy (2003), and the Massachusetts Treatment Center: Rape classification system revision 3 (MTC:R3, Knight & Prentky, 1990)) were contrasted with a multivariate regression approach to assess their ability to predict an offender's previous convictions from crime scene information. In respect of the three aforementioned models, logistic regression and AUC analysis indicated that the Power and Anger FBI model was the most effective, followed by the MTC:R3, and then the Behavioral Thematic evaluation. However, predictive analyses based on a multivariate approach using a mixture of crime scene behaviors, as opposed to the grouping of behaviors into themes or types as in the three models, far exceeded the predictive ability of the three models under AUC analysis. The results suggest that emphasis should be placed on further exploration of the predictive validity of each of the individual behaviors that comprise existing thematic, typological, and multivariate classification systems, especially those that are subject to inter-situational variation. Copyright © 2009 John Wiley & Sons, Ltd.