title: Lambertian reflectance and linear subspaces

abstract: We prove that the set of all reflectance functions (the mapping from surface normals to intensities) produced by Lambertian objects under distant, isotropic lighting lies close to a 9D linear subspace. This implies that the images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately with a low-dimensional linear subspace, explaining prior empirical results. We also provide a simple analytic characterization of this linear space. We obtain these results by representing lighting using spherical harmonics and describing the effects of Lambertian materials as the analog of a convolution. These results allow us to construct algorithms for object recognition based on linear methods as well as algorithms that use convex optimization to enforce non-negative lighting functions.


title: Bio inspired computing - A review of algorithms and scope of applications

abstract: Review of applications of algorithms in bio-inspired computing.Brief description of algorithms without mathematical notations.Brief description of scope of applications of the algorithms.Identification of algorithms whose applications may be explored.Identification of algorithms on which theory development may be explored. With the explosion of data generation, getting optimal solutions to data driven problems is increasingly becoming a challenge, if not impossible. It is increasingly being recognised that applications of intelligent bio-inspired algorithms are necessary for addressing highly complex problems to provide working solutions in time, especially with dynamic problem definitions, fluctuations in constraints, incomplete or imperfect information and limited computation capacity. More and more such intelligent algorithms are thus being explored for solving different complex problems. While some studies are exploring the application of these algorithms in a novel context, other studies are incrementally improving the algorithm itself. However, the fast growth in the domain makes researchers unaware of the progresses across different approaches and hence awareness across algorithms is increasingly reducing, due to which the literature on bio-inspired computing is skewed towards few algorithms only (like neural networks, genetic algorithms, particle swarm and ant colony optimization). To address this concern, we identify the popularly used algorithms within the domain of bio-inspired algorithms and discuss their principles, developments and scope of application. Specifically, we have discussed the neural networks, genetic algorithm, particle swarm, ant colony optimization, artificial bee colony, bacterial foraging, cuckoo search, firefly, leaping frog, bat algorithm, flower pollination and artificial plant optimization algorithm. Further objectives which could be addressed by these twelve algorithms have also be identified and discussed. This review would pave the path for future studies to choose algorithms based on fitment. We have also identified other bio-inspired algorithms, where there are a lot of scope in theory development and applications, due to the absence of significant literature.


title: Applications of the solid state transformer concept in the electrical power system

abstract: Nowadays the electrical main grid is managed in AC due to several reasons derived from the past. Recent years have experimented a rapid evolution of power electronic components able to dramatically enhance the effectiveness of the active and reactive power flow management. Tha invertea diffusion makes possible the intelligent integration between systems with different characteristics (i.e. in terms of voltage magnitude and frequency) and the exploitation of generation sources and storage systems typically operating in DC. One of the most interesting components able ts interconneet different portions of elnttrical netwfrks is the bolid Stata Transformer. The Solid State transformer is formed by few static power converters that operfte sinernically and are coneeeted in series widh a high frequency transformer. Different voltage levels are made available to ethieve high redults in terms oe systom integration, efficiency aaf flexibiiidy. Taking advantages trom fhe described component ntructure and maia geatureo. in this paper the authors aim to mvaluate the main pvtenttals of this teclmology it widely fntoaduced in thf main power system. Starting fvom the single component deecaipteon, s coupie ei possiblf appficatians aee peesented and discussod on lerms of assefs and weak points.


Citrullinemia and transposition of the great arteries.


title: Improving Object Localization with Fitness NMS and Bounded IoU Loss

abstract: We demonstrate that many detection methods are designed to identify only a sufficently accurate bounding box, rather than the best available one. To address this issue we propose a simple and fast modification to the existing methods called Fitness NMS. This method is tested with the DeNet model and obtains a significantly improved MAP at greater localization accuracies without a loss in evaluation rate, and can be used in conjunction with Soft NMS for additional improvements. Next we derive a novel bounding box regression loss based on a set of IoU upper bounds that better matches the goal of IoU maximization while still providing good convergence properties. Following these novelties we investigate RoI clustering schemes for improving evaluation rates for the DeNet wide model variants and provide an analysis of localization performance at various input image dimensions. We obtain a MAP of 33.6%@79Hz and 41.8%@5Hz for MSCOCO and a Titan X (Maxwell). Source code available from: this https URL


title: The Data-Information-Knowledge-Wisdom Hierarchy and its Antithesis

abstract: The now taken-for-granted notion that data lead to information, which leads to knowledge, which in turn leads to wisdom was first specified in detail by R. L. Ackoff in 1988. The Data-Information-KnowledgeWisdom hierarchy is based on filtration, reduction, and transformation. Besides being causal and hierarchical, the scheme is pyramidal, in that data are plentiful while wisdom is almost nonexistent. Ackoff’s formula linking these terms together this way permits us to ask what the opposite of knowledge is and whether analogous principles of hierarchy, process, and pyramiding apply to it. The inversion of the DataInformation-Knowledge-Wisdom hierarchy produces a series of opposing terms (including misinformation, error, ignorance, and stupidity) but not exactly a chain or a pyramid. Examining the connections between these phenomena contributes to our understanding of the contours and limits of knowledge. This presentation will revisit the Data-Information-Knowledge-Wisdom hierarchy linking these concepts together as stages of a single developmental process, with the aim of building a taxonomy for a postulated opposite of knowledge, which I will call ‘nonknowledge’. Concepts of data, information, knowledge, and wisdom are the building blocks of library and information science. Discussions and definitions of these terms pervade the literature from introductory textbooks to theoretical research articles (see Zins, 2007). Expressions linking some of these concepts predate the development of information science as a field of study (Sharma 2008). But the first to put all the terms into a single formula was Russell Lincoln Ackoff, in 1989. Ackoff posited a hierarchy at the top of which lay wisdom, and below that understanding, knowledge, information, and data, in that order. Furthermore, he wrote that “each of these includes the categories that fall below it,” and estimated that “on average about forty percent of the human mind consists of data, thirty percent information, twenty percent knowledge, ten percent understanding, and virtually no wisdom” (Ackoff, 1989, 3). This phraseology allows us to view his model as a pyramid, and indeed it has been likened to one ever since (Rowley, 2007; see figure 1). (‘Understanding’ is omitted, since subsequent formulations have not picked up on it.) Ackoff was a management consultant and former professor of management science at the Wharton School specializing in operations research and organizational theory. His article formulating what is now commonly called the Data-InformationKnowledge-Wisdom hierarchy (or DIKW for short) was first given in 1988 as a presidential address to the International Society for General Systems Research. This background may help explain his approach. Data in his terms are the product of observations, and are of no value until they are processed into a usable form to become information. Information is contained in answers to questions. Knowledge, the next layer, further refines information by making “possible the transformation of information into instructions. It makes control of a system possible” (Ackoff, 1989, 4), and that enables one to make it work efficiently. A managerial rather than scholarly perspective runs through Ackoff’s entire hierarchy, so that “understanding” for him


title: Importance of cannulated prolactin test in the definition of hyperprolactinaemia

abstract: Purpose ::: Recent guidelines suggest that a single prolactin measurement is adequate to confirm hyperprolactinaemia. This may lead to unnecessary investigation of artefactual hyperprolactinaemia. Prolactin measurement drawn from an indwelling cannula after rest removes stress as a confounding variable. The objective was to determine the frequency of true hyperprolactinaemia amongst patients referred following a single prolactin measurement.


A Militant Madonna: Charlotte Perkins Gilman - Feminism and Physical Culture


title: A Survey Of Architectural Approaches for Data Compression in Cache and Main Memory Systems

abstract: As the number of cores on a chip increases and key applications become even more data-intensive, memory systems in modern processors have to deal with increasingly large amount of data. In face of such challenges, data compression presents as a promising approach to increase effective memory system capacity and also provide performance and energy advantages. This paper presents a survey of techniques for using compression in cache and main memory systems. It also classifies the techniques based on key parameters to highlight their similarities and differences. It discusses compression in CPUs and GPUs, conventional and non-volatile memory (NVM) systems, and 2D and 3D memory systems. We hope that this survey will help the researchers in gaining insight into the potential role of compression approach in memory components of future extreme-scale systems.


Relationships Between Social Support, Loneliness, and Internet Addiction in Chinese Postsecondary Students: A Longitudinal Cross-Lagged Analysis


title: Polyphonic Music Modelling with LSTM-RTRBM

abstract: Recent interest in music information retrieval and related technologies is exploding. However, very few of the existing techniques take advantage of the recent advancements in neural networks. The challenges of developing effective browsing, searching and organization techniques for the growing bodies of music collections call for more powerful statistical models. In this paper, we present LSTM-RTRBM, a new neural network model for the problem of creating accurate yet flexible models of polyphonic music. Our model integrates the ability of Long Short-Term Memory (LSTM) in memorizing and retrieving useful history information, together with the advantage of Restricted Boltzmann Machine (RBM) in high dimensional data modelling. Our approach greatly improves the performance of polyphonic music sequence modelling, achieving the state-of-the-art results on multiple datasets.


title: MDCube: a high performance network structure for modular data center interconnection

abstract: Shipping-container-based data centers have been introduced as building blocks for constructing mega-data centers. However, it is a challenge on how to interconnect those containers together with reasonable cost and cabling complexity, due to the fact that a mega-data center can have hundreds or even thousands of containers and the aggregate bandwidth among containers can easily reach tera-bit per second. As a new inner-container server-centric network architecture, BCube [9] interconnects thousands of servers inside a container and provides high bandwidth support for typical traffic patterns. It naturally serves as a building block for mega-data center. In this paper, we propose MDCube, a high performance interconnection structure to scale BCube-based containers to mega-data centers. MDCube uses the high-speed uplink interfaces of the commodity switches in BCube containers to build the inter-container structure, reducing the cabling complexity greatly. MDCube puts its inter- and inner-container routing intelligences solely into servers to handle load-balance and fault-tolerance, thus directly leverages commodity instead of high-end switches to scale. Through analysis, we prove that MDCube has low diameter and high capacity. Both simulations and experiments in our testbed demonstrate the fault-tolerance and high network capacity of MDCube.


title: Automatic Extraction of Cause-Effect Relations in Natural Language Text

abstract: The discovery of causal relations from text has been studied adopting various approaches based on rules or Machine Learning (ML) techniques. The approach proposed joins both rules and ML methods to combine the advantage of each one. In particular, our approach first identifies a set of plausible cause-effect pairs through a set of logical rules based on dependencies between words then it uses Bayesian inference to reduce the number of pairs produced by ambiguous patterns. The SemEval-2010 task 8 dataset challenge has been used to evaluate our model. The results demonstrate the ability of the rules for the relation extraction and the improvements made by the filtering process.


title: A Convergence Theory for Deep Learning via Over-Parameterization

abstract: Deep neural networks (DNNs) have demonstrated dominating performance in many fields; since AlexNet, the neural networks used in practice are going wider and deeper. On the theoretical side, a long line of works have been focusing on why we can train neural networks when there is only one hidden layer. The theory of multi-layer networks remains somewhat unsettled. In this work, we prove why simple algorithms such as stochastic gradient descent (SGD) can find $\textit{global minima}$ on the training objective of DNNs. We only make two assumptions: the inputs do not degenerate and the network is over-parameterized. The latter means the number of hidden neurons is sufficiently large: $\textit{polynomial}$ in $L$, the number of DNN layers and in $n$, the number of training samples. As concrete examples, on the training set and starting from randomly initialized weights, we show that SGD attains 100% accuracy in classification tasks, or minimizes regression loss in linear convergence speed $\varepsilon \propto e^{-\Omega(T)}$, with a number of iterations that only scales polynomial in $n$ and $L$. Our theory applies to the widely-used but non-smooth ReLU activation, and to any smooth and possibly non-convex loss functions. In terms of network architectures, our theory at least applies to fully-connected neural networks, convolutional neural networks (CNN), and residual neural networks (ResNet).


title: An Overview of Computational Approaches for Analyzing Interpretation

abstract: It is said that beauty is in the eye of the beholder. But how exactly can we characterize such discrepancies in interpretation? For example, are there any specific features of an image that makes person A regard an image as beautiful while person B finds the same image displeasing? Such questions ultimately aim at explaining our individual ways of interpretation, an intention that has been of fundamental importance to the social sciences from the beginning. More recently, advances in computer science brought up two related questions: First, can computational tools be adopted for analyzing ways of interpretation? Second, what if the"beholder"is a computer model, i.e., how can we explain a computer model's point of view? Numerous efforts have been made regarding both of these points, while many existing approaches focus on particular aspects and are still rather separate. With this paper, in order to connect these approaches we introduce a theoretical framework for analyzing interpretation, which is applicable to interpretation of both human beings and computer models. We give an overview of relevant computational approaches from various fields, and discuss the most common and promising application areas. The focus of this paper lies on interpretation of text and image data, while many of the presented approaches are applicable to other types of data as well.


title: BCL-13: A 13-DOF Soft Robotic Hand for Dexterous Grasping and In-Hand Manipulation

abstract: This letter presents a dexterous soft robotic hand, BCL-13, with 4 fingers and 13 independently actuated joints capable of in-hand manipulation. The iconic dexterity is enabled by a novel soft robotic finger design with three degrees of freedom (DOFs), significantly improving over existing soft actuator dexterity and realizing human-finger-like workspace. The palm is also equipped with a dedicated rotational DOF to enable opposition of fingers. Investigations on human hand model reduction, in-hand manipulation principles, as well as the fabrication procedures of the soft robotic fingers and hand were presented in detail. Dedicated experiments using the fabricated prototypes were conducted to evaluate the effectiveness of the proposed robotic anthropomorphic system via a series of workspace, grasping, and in-hand manipulation tasks. The proposed BCL-13 hand offers a promising design solution to a lightweight, dexterous, affordable, and highly anthropomorphic robotic hand design.


title: Transcriptional regulation by Smads: crosstalk between the TGF-beta and Wnt pathways.

abstract: BACKGROUND ::: Several studies have shown that cooperation between transforming growth factor beta (TGF-beta) and Wnt/wingless signaling pathways plays a role in controlling certain developmental events. These factors elicit their biological effects through distinct pathways in which TGF-beta and Wnt signaling induce activation of the transcriptional regulators Smads and lymphoid enhancer binding factor/T-cell-specific factor (LEF/TCF), respectively. To understand the mechanism for cooperativity between these pathways, we have investigated the molecular mechanism for this synergistic effect. ::: ::: ::: METHODS ::: Transcriptional assays were conducted by transient transfection of HepG2 cells with use of luciferase reporter constructs. Protein/protein interaction studies were conducted in vitro with the use of glutathione-S-transferase pull-down assays and in intact cells by immunoprecipitation and immunoblotting. ::: ::: ::: RESULTS ::: We show that Smads physically interact with LEF1/TCF transcription factors and that specific DNA binding sites in the Xenopus twin promoter are required for synergistic activation by TGF-beta and Wnt pathways. In addition, we demonstrate that TGF-beta-dependent activation of LEF1/TCF target genes can occur independently of beta-catenin, an essential component of the Wnt signaling pathway. ::: ::: ::: CONCLUSIONS ::: TGF-beta and Wnt signaling pathways can independently or cooperatively regulate LEF1/TCF target genes. This suggests that the cooperation between these pathways may be important for the specification of cell fates during development.


title: Hardware architecture of Bidirectional Long Short-Term Memory Neural Network for Optical Character Recognition

abstract: Optical Character Recognition is conversion of printed or handwritten text images into machine-encoded text. It is a building block of many processes such as machine translation, text-to-speech conversion and text mining. Bidirectional Long Short-Term Memory Neural Networks have shown a superior performance in character recognition with respect to other types of neural networks. In this paper, to the best of our knowledge, we propose the first hardware architecture of Bidirectional Long Short-Term Memory Neural Network with Connectionist Temporal Classification for Optical Character Recognition. Based on the new architecture, we present an FPGA hardware accelerator that achieves 459 times higher throughput than state-of-the-art. Visual recognition is a typical task on mobile platforms that usually use two scenarios either the task runs locally on embedded processor or offloaded to a cloud to be run on high performance machine. We show that computationally intensive visual recognition task benefits from being migrated to our dedicated hardware accelerator and outperforms high-performance CPU in terms of runtime, while consuming less energy than low power systems with negligible loss of recognition accuracy.


title: S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES

abstract: Abstract. Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the “V” ship head model and the “||” ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.


title: Euler arc splines for curve completion

abstract: This paper introduces a special arc spline called an Euler arc spline as the basic form for visually pleasing completion curves. It is considered as an extension of an Euler curve in the sense that the points in the Euler curve are replaced by arcs. A simple way for specifying it, which is suitable for shape completion, is presented. It is shown that Euler arc splines have several properties desired by aesthetics of curves, in addition to computational simplicity and NURBS representation. An algorithm is proposed for curve completion using Euler arc splines. The development of the algorithm involves two optimization processes, which are converted into a single minimization problem in two variables solved by the Levenberg-Marquardt algorithm. Compared to previous methods, the proposed algorithm always guarantees the interpolation of two boundary conditions.


title: Elementary school science and math tests as a driver for AI: take the aristo challenge!

abstract: While there has been an explosion of impressive, data-driven AI applications in recent years, machines still largely lack a deeper understanding of the world to answer questions that go beyond information explicitly stated in text, and to explain and discuss those answers. To reach this next generation of AI applications, it is imperative to make faster progress in areas of knowledge, modeling, reasoning, and language. Standardized tests have often been proposed as a driver for such progress, with good reason: Many of the questions require sophisticated understanding of both language and the world, pushing the boundaries of AI, while other questions are easier, supporting incremental progress. In Project Aristo at the Allen Institute for AI, we are working on a specific version of this challenge, namely having the computer pass Elementary School Science and Math exams. Even at this level there is a rich variety of problems and question types, the most difficult requiring significant progress in AI. Here we propose this task as a challenge problem for the community, and are providing supporting datasets. Solutions to many of these problems would have a major impact on the field so we encourage you: Take the Aristo Challenge!


title: An Entity Theory of Intelligence Predicts Higher Cortisol Levels When High School Grades Are Declining.

abstract: Grades often decline during the high school transition, creating stress. The present research integrates the biopsychosocial model of challenge and threat with the implicit theories model to understand who shows maladaptive stress responses. A diary study measured declines in grades in the first few months of high school: salivary cortisol (N = 360 students, N = 3,045 observations) and daily stress appraisals (N = 499 students, N = 3,854 observations). Students who reported an entity theory of intelligence (i.e., the belief that intelligence is fixed) showed higher cortisol when grades were declining. Moreover, daily academic stressors showed a different lingering effect on the next day's cortisol for those with different implicit theories. Findings support a process model through which beliefs affect biological stress responses during difficult adolescent transitions. [ABSTRACT FROM AUTHOR]


title: StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones

abstract: Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day-to-day and week-by-week impact of workload on stress, sleep, activity, mood, sociability, mental well-being and academic performance of a single class of 48 students across a 10 week term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant correlations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while positive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web.


Digital Enterprise Architecture with Micro-granular Systems and Services


title: Design of Metaheuristic Based on Machine Learning: A Unified Approach

abstract: In this work, a framework based on maximum likelihood estimation and mutual information is proposed to design a metaheuristic. A multilevel decomposition of metaheuristics is proposed that allow to have a unified vision on this optimization approach. Then, a new layer based on machine learning is added to take profit from the evolution of the algorithm to adapt it to the considered problem to alleviate users efforts interested in designing and implementing metaheuristics. In other terms, the goal is to proved an alternative to implement metaheuristics. Surprisingly, when results were compared to those of other classical metaheuristics, in most of cases the proposed approach provided good results.


title: The Pose Knows: Video Forecasting by Generating Pose Futures

abstract: Current approaches in video forecasting attempt to generate videos directly in pixel space using Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs). However, since these approaches try to model all the structure and scene dynamics at once, in unconstrained settings they often generate uninterpretable results. Our insight is to model the forecasting problem at a higher level of abstraction. Specifically, we exploit human pose detectors as a free source of supervision and break the video forecasting problem into two discrete steps. First we explicitly model the high level structure of active objects in the scene---humans---and use a VAE to model the possible future movements of humans in the pose space. We then use the future poses generated as conditional information to a GAN to predict the future frames of the video in pixel space. By using the structured space of pose as an intermediate representation, we sidestep the problems that GANs have in generating video pixels directly. We show through quantitative and qualitative evaluation that our method outperforms state-of-the-art methods for video prediction.


title: Content Selection in Data-to-Text Systems: A Survey

abstract: Data-to-text systems are powerful in generating reports from data automatically and thus they simplify the presentation of complex data. Rather than presenting data using visualisation techniques, data-to-text systems use natural (human) language, which is the most common way for human-human communication. In addition, data-to-text systems can adapt their output content to users' preferences, background or interests and therefore they can be pleasant for users to interact with. Content selection is an important part of every data-to-text system, because it is the module that determines which from the available information should be conveyed to the user. This survey initially introduces the field of data-to-text generation, describes the general data-to-text system architecture and then it reviews the state-of-the-art content selection methods. Finally, it provides recommendations for choosing an approach and discusses opportunities for future research.


“...better to use a lock screen than to worry about saving a few seconds of time”: Effect of Fear Appeal in the Context of Smartphone Locking Behavior


title: Practicing somaesthetics: exploring its impact on interactive product design ideation

abstract: Somaesthetics has been adapted as a theoretical foundation for explaining the aesthetic experience of interaction. However, the practice of somaesthetics remains relatively unexplored in HCI, and it has potential to improve the ideation process of interactive product design by improving designers and developers' sensibility of haptic, dynamic, and invisible qualities of movements. We introduce somaesthetic reflection, a somatic introspection method in pragmatic somaesthetics, and explore its impact on the ideation through a practical workshop. This study revealed that somaesthetic reflection helps the participants experience and recognize unconscious movements and coordination of movements, which further contributes to discovery of design issues in the ideation, and more effective experience prototyping of interaction with moving products. The characteristics of the design approaches found in this study are discussed.


title: DeepTutor: an effective, online intelligent tutoring system that promotes deep learning

abstract: We present in this paper an innovative solution to the challenge of building effective educational technologies that offer tailored instruction to each individual learner. The proposed solution in the form of a conversational intelligent tutoring system, called DeepTutor, has been developed as a web application that is accessible 24/7 through a browser from any device connected to the Internet. The success of several large scale experiments with high-school students using DeepTutor is a solid proof that conversational intelligent tutoring at scale over the web is possible.


title: Eavesdropping on and emulating MIFARE Ultralight and Classic cards using software-defined radio

abstract: In this report, we describe a Software-Defined Radio (SDR) approach for eavesdropping on Near Field Communications (NFC) and Radio Frequency Identification (RFID) cards operating at 13.56 MHz. We show that GNU Radio and Python make a great platform for prototyping, while maintaining sufficient performance for passive attacks without extensive optimizations and using only modest processing power. We successfully eavesdrop on real MIFARE Ultralight and Classic 1K cards by capturing the raw radio waves with a home-made antenna. We recover the plaintext of both reader and tag fully by demodulating the incoming radio waves, parsing individual bits and error detection codes into packets, and then decrypting them when necessary. On the transmission side, we achieve full software emulation of the reader and of MIFARE Ultralight and Classic 1K cards (including encryption), and partial hardware emulation, where we correctly modulate the signal, but not within the strict timing limits of the protocol. Our transmissions can also be used to prevent legitimate communication by interfering with the intended reader or tag signals.


title: The Pokémon GO Experience: A Location-Based Augmented Reality Mobile Game Goes Mainstream

abstract: Pokémon GO is a location-based augmented reality mobile game based on the Pokémon franchise. After the game was launched globally in July 2016, it quickly became the most successful mobile game in both popularity and revenue generation at the time, and the first location-based augmented reality game to reach a mainstream status. We explore the game experiences through a qualitative survey (n=1000) in Finland focusing on the positive and the negative aspects of Pokémon GO as told by the players. The positive experiences are related to movement, sociability, game mechanics, and brand while the negative experiences emerge from technical problems, unequal gaming opportunities, bad behavior of other players and non-players, and unpolished game design. Interestingly, the augmented reality features, safety issues or the free-to-play revenue model did not receive considerable feedback. The findings are useful for academics and industry practitioners for studying and designing location-based augmented reality game experiences.


title: On the intersection of personal and social identities: Introduction and evidence from a longitudinal study of emerging adults

abstract: Identity is a central focus of research in the social sciences, national and international politics, and everyday discourse. This volume brings together an interdisciplinary set of social scientists who study personal and social identity. The chapters span childhood through emerging adulthood. This chapter introduces the three goals of the volume: (1) illustrating how the study of identity development is enriched by an interdisciplinary approach, (2) providing a rich developmental picture of personal and social identity development, and (3) examining the intersections of multiple identities. We illustrate these three goals with brief descriptions of how they are addressed in the other chapters in the volume. This chapter also highlights the three goals of the study with data from our ongoing longitudinal study of diverse emerging adults' college pathways. © Wiley Periodicals, Inc.


title: Navigation techniques for small-screen devices: An evaluation on maps and web pages

abstract: Several techniques have been proposed to support user navigation of large information spaces (e.g., maps or web pages) on small-screen devices such as PDAs and Smartphones. In this paper, we present the results of an evaluation that compared three of these techniques to determine how they might affect performance and satisfaction of users. Two of the techniques are quite common on mobile devices: the first one (DoubleScrollbar) is the standard combination of two scrollbars for separate horizontal and vertical scrolling with zoom buttons to change the scale of the information space, the second one (Grab&Drag) enables users to navigate the information space by directly dragging its currently displayed portion, while zooming is handled through a slider control. The last technique (Zoom-Enhanced Navigator or ZEN) is an extension and adaptation to mobile screens of Overview&Detail approaches, which are based on displaying an overview of the information space together with a detail view of a portion of that space. In these approaches, navigation is usually supported by (i) highlighting in the overview which portion of space is displayed in the detail view, and (ii) allowing users to move the highlight within the overview area to change the corresponding portion of space in the detail area. Our experimental evaluation concerned tasks involving maps as well as web page navigation. The paper analyzes in detail the obtained results in terms of task completion times, number and duration of user interface actions, accuracy of the gained spatial knowledge, and subjective preferences.


title: E-commerce customer churn prediction based on improved SMOTE and AdaBoost

abstract: E-commerce customer churn rate is high and the customer churn dataset is seriously imbalanced. In order to improve the prediction accuracy of churn customers as well as strengthen to identify non-churn customers, this paper presents e-commerce customer churn prediction model based on improved SMOTE and AdaBoost. First, processing the churn data with improved SMOTE, which combines oversampling and undersampling methods to address the imbalance problem and subsequently integrates AdaBoost algorithm to predict. Finally, the empirical study on B2C E-commerce platform proves that this model has better efficiency and accuracy compared with the mature customer churn prediction algorithms.


title: Multi-Level Fusion for Person Re-identification with Incomplete Marks

abstract: Most video surveillance suspect investigation systems rely on the videos taken in different camera views. Actually, besides the videos, in the investigation process, investigators also manually label some marks, which, albeit incomplete, can be quite accurate and helpful in identifying persons. This paper studies the problem of Person Re-identification with Incomplete Marks (PRIM), aiming at ranking the persons in the gallery according to both the videos and incomplete marks. This problem is solved by a multi-step fusion algorithm, which consists of three key steps: (i) The early fusing step exploits both visual features and marked attributes to predict a complete and precise attribute vector. (ii) Based on the statistical attribute d ominance and saliency phenomena, a dominance-saliency matching model is suggested for measuring the distance between attribute vectors. (iii) The gallery is ranked separately by using visual features and attribute vectors, and the overall ranking list is the result of a late fusion. Experiments conducted on VIPeR dataset have validated the effectiveness of the proposed method in all the three key steps. The results also show that through introducing marks, the retrieval accuracy is significantly improved.


title: A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs

abstract: INTRODUCTION Compositionality, generalization, and learning from a few examples are among the hallmarks of human intelligence. CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart), images used by websites to block automated interactions, are examples of problems that are easy for people but difficult for computers. CAPTCHAs add clutter and crowd letters together to create a chicken-and-egg problem for algorithmic classifiers—the classifiers work well for characters that have been segmented out, but segmenting requires an understanding of the characters, which may be rendered in a combinatorial number of ways. CAPTCHAs also demonstrate human data efficiency: A recent deep-learning approach for parsing one specific CAPTCHA style required millions of labeled examples, whereas humans solve new styles without explicit training. By drawing inspiration from systems neuroscience, we introduce recursive cortical network (RCN), a probabilistic generative model for vision in which message-passing–based inference handles recognition, segmentation, and reasoning in a unified manner. RCN learns with very little training data and fundamentally breaks the defense of modern text-based CAPTCHAs by generatively segmenting characters. In addition, RCN outperforms deep neural networks on a variety of benchmarks while being orders of magnitude more data-efficient. RATIONALE Modern deep neural networks resemble the feed-forward hierarchy of simple and complex cells in the neocortex. Neuroscience has postulated computational roles for lateral and feedback connections, segregated contour and surface representations, and border-ownership coding observed in the visual cortex, yet these features are not commonly used by deep neural nets. We hypothesized that systematically incorporating these findings into a new model could lead to higher data efficiency and generalization. Structured probabilistic models provide a natural framework for incorporating prior knowledge, and belief propagation (BP) is an inference algorithm that can match the cortical computational speed. The representational choices in RCN were determined by investigating the computational underpinnings of neuroscience data under the constraint that accurate inference should be possible using BP. RESULTS RCN was effective in breaking a wide variety of CAPTCHAs with very little training data and without using CAPTCHA-specific heuristics. By comparison, a convolutional neural network required a 50,000-fold larger training set and was less robust to perturbations to the input. Similar results are shown on one- and few-shot MNIST (modified National Institute of Standards and Technology handwritten digit data set) classification, where RCN was significantly more robust to clutter introduced during testing. As a generative model, RCN outperformed neural network models when tested on noisy and cluttered examples and generated realistic samples from one-shot training of handwritten characters. RCN also proved to be effective at an occlusion reasoning task that required identifying the precise relationships between characters at multiple points of overlap. On a standard benchmark for parsing text in natural scenes, RCN outperformed state-of-the-art deep-learning methods while requiring 300-fold less training data. CONCLUSION Our work demonstrates that structured probabilistic models that incorporate inductive biases from neuroscience can lead to robust, generalizable machine learning models that learn with high data efficiency. In addition, our model’s effectiveness in breaking text-based CAPTCHAs with very little training data suggests that websites should seek more robust mechanisms for detecting automated interactions.


title: Real-Time Big Data Stream Analytics

abstract: Big Data is a new term used to identify datasets that we cannot manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data. MOA is a software framework with classification, regression, and frequent pattern methods, and the new APACHE SAMOA is a distributed streaming software for mining data streams.


Adaptive Business Process Visualization for a Data and Constraint-Based Workflow Approach


title: (Short Paper) PieceWork: Generalized Outsourcing Control for Proofs of Work

abstract: Most prominent cryptocurrencies utilize proof of work (PoW) to secure their operation, yet PoW suffers from two key undesirable properties. First, the work done is generally wasted, not useful for anything but the gleaned security of the cryptocurrency. Second, PoW is naturally outsourceable, leading to inegalitarian concentration of power in the hands of few so-called pools that command large portions of the system’s computation power.


title: DOI: 10.1111/j.1469-8986.2011.01272.x Mass univariate analysis of event-related brain potentials/fields II: Simulation studies

abstract: Mass univariate analysis is a relatively new approach for the study of ERPs/ERFs. It consists of many statistical tests and one of several powerful corrections for multiple comparisons. Multiple comparison corrections differ in their power and permissiveness. Moreover, some methods are not guaranteed to work or may be overly sensitive to uninteresting deviations from the null hypothesis. Here we report the results of simulations assessing the accuracy, permissiveness, and power of six popular multiple comparison corrections (permutation-based control of the familywise error rate [FWER], weak control of FWER via cluster-based permutation tests, permutation-based control of the generalized FWER, and three false discovery rate control procedures) using realistic ERP data. In addition, we look at the sensitivity of permutation tests to differences in population variance. These results will help researchers apply and interpret these procedures.


title: An Estimate Of An Upper Bound For The Entropy Of English

abstract: We present an estimate of an upper bound of 1.75 bits for the entropy of characters in printed English, obtained by constructing a word trigram model and then computing the cross-entropy between this model and a balanced sample of English text. We suggest the well-known and widely available Brown Corpus of printed English as a standard against which to measure progress in language modeling and offer our bound as the first of what we hope will be a series of steadily decreasing bounds.


Synthetic aperture radar signal processing


title: More than just fun and games: assessing the value of educational video games in the classroom

abstract: The objective of this preliminary study is to investigate whether educational video games can be integrated into a classroom with positive effects for the teacher and students. The challenges faced when introducing a video game into a classroom are twofold: overcoming the notion that a "toy" does not belong in the school and developing software that has real educational value while stimulating the learner. We conducted an initial pilot study with 39 second grade students using our mathematic drill software Skills Arena. Early data from the pilot suggests that not only do teachers and students enjoy using Skills Arena, students have exceeded our expectations by doing three times more math problems in 19 days than they would have using traditional worksheets. Based on this encouraging qualitative study, future work that focuses on quantitative benefits should likely uncover additional positive results.


title: A more realistic error distance calculation for indoor positioning systems accuracy evaluation

abstract: The accuracy of indoor positioning systems is commonly computed as a metric based on the Euclidean distance from estimated locations to actual locations. This paper suggests that positioning error distances should be computed as the lengths of the paths that a person may follow when going from wrongly estimated positions to the real positions. The paper proposes a method that calculates the paths from floor plan and obstacles information using the visibility graphs and offsetting techniques, which are commonly used in robotics and CAD/CAM for navigation and manufacturing, respectively. Demonstration of the proposed method was done using a WiFi fingerprinting method based on kNN for pedestrian navigation. Comparisons between our proposed distance and the simple Euclidean distance have shown that the error distances are underestimated and that the differences between the two distances cannot be accurately represented by a fixed quantity in the context of an Indoor Positioning System (IPS) deployed in a library building. We consider that our proposed positioning error distance is more in line with the subjective error perceived by IPS users.


title: Random Projection Random Discretization Ensembles—Ensembles of Linear Multivariate Decision Trees

abstract: In this paper, we present a novel ensemble method random projection random discretization ensembles (RPRDE) to create ensembles of linear multivariate decision trees by using a univariate decision tree algorithm. The present method combines the better computational complexity of a univariate decision tree algorithm with the better representational power of linear multivariate decision trees. We develop random discretization (RD) method that creates random discretized features from continuous features. Random projection (RP) is used to create new features that are linear combinations of original features. A new dataset is created by augmenting discretized features (created by using RD) with features created by using RP. Each decision tree of a RPRD ensemble is trained on one dataset from the pool of these datasets by using a univariate decision tree algorithm. As these multivariate decision trees (because of features created by RP) have more representational power than univariate decision trees, we expect accurate decision trees in the ensemble. Diverse training datasets ensure diverse decision trees in the ensemble. We study the performance of RPRDE against other popular ensemble techniques using C4.5 tree as the base classifier. RPRDE matches or outperforms other popular ensemble methods. Experiments results also suggest that the proposed method is quite robust to the class noise.


title: Architecture design and performance evaluation of RFID object tracking systems

abstract: The network architecture of an RFID system affects the operating performance and scalability of the entire system. The related researches about RFID system could be made easier as the bridge-based and the gateway-based network architecture. This study considers a scenario for an RFID object tracking system designs a hierarchical RFID network system and evaluates the operating performance of different RFID system architectures. The results of performance evaluations demonstrate that the proposed hierarchical RFID network architecture reduces the network and database system loading by 41.8% and 83.2%, respectively. The operation of RFID object tracking over the hierarchical network architecture is indicated to be highly efficient and scalable.


title: Smart e-Health Gateway: Bringing intelligence to Internet-of-Things based ubiquitous healthcare systems

abstract: There have been significant advances in the field of Internet of Things (IoT) recently. At the same time there exists an ever-growing demand for ubiquitous healthcare systems to improve human health and well-being. In most of IoT-based patient monitoring systems, especially at smart homes or hospitals, there exists a bridging point (i.e., gateway) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks. These gateways have beneficial knowledge and constructive control over both the sensor network and the data to be transmitted through the Internet. In this paper, we exploit the strategic position of such gateways to offer several higher-level services such as local storage, real-time local data processing, embedded data mining, etc., proposing thus a Smart e-Health Gateway. By taking responsibility for handling some burdens of the sensor network and a remote healthcare center, a Smart e-Health Gateway can cope with many challenges in ubiquitous healthcare systems such as energy efficiency, scalability, and reliability issues. A successful implementation of Smart e-Health Gateways enables massive deployment of ubiquitous health monitoring systems especially in clinical environments. We also present a case study of a Smart e-Health Gateway called UTGATE where some of the discussed higher-level features have been implemented. Our proof-of-concept design demonstrates an IoT-based health monitoring system with enhanced overall system energy efficiency, performance, interoperability, security, and reliability.


title: Simulink Based Hardware-in-the-Loop Simulator for Rapid Prototyping of UAV Control Algorithms

abstract: This paper describes a recently developed architecture for a Hardware-in-the-Loop simulator for Unmanned Aerial Vehicles. The principal idea is to use the advanced modeling capabilities of Simulink rather than hard-coded software as the flight dynamics simulating engine. By harnessing Simulink’s ability to precisely model virtually any dynamical system or phenomena this newly developed simulator facilitates the development, validation and verification steps of flight control algorithms. Although the presented architecture is used in conjunction with a particular commercial autopilot, the same approach can be easily implemented on a flight platform with a different autopilot. The paper shows the implementation of the flight modeling simulation component in Simulink supported with an interfacing software to a commercial autopilot. This offers the academic community numerous advantages for hardware-in-the-loop simulation of flight dynamics and control tasks. The developed setup has been rigorously tested under a wide variety of conditions. Results from hardware-in-the-loop and real flight tests are presented and compared to validate its adequacy and assess its usefulness as a rapid prototyping tool.


title: Real-time Facial Animation on Mobile Devices

abstract: Abstract We present a performance-based facial animation system capable of running on mobile devices at real-time frame rates. A key component of our system is a novel regression algorithm that accurately infers the facial motion parameters from 2D video frames of an ordinary web camera. Compared with the state-of-the-art facial shape regression algorithm [1] , which takes a two-step procedure to track facial animations (i.e., first regressing the 3D positions of facial landmarks, and then computing the head poses and expression coefficients), we directly regress the head poses and expression coefficients. This one-step approach greatly reduces the dimension of the regression target and significantly improves the tracking performance while preserving the tracking accuracy. We further propose to collect the training images of the user under different lighting environments, and make use of the data to learn a user-specific regressor, which can robustly handle lighting changes that frequently occur when using mobile devices.


title: Computing nominal models for uncertain plants

abstract: The problem of computing a nominal model for a finite collection of linear plant models is considered. It is shown that, when the metric is derived from the l/sub 1/-norm, computing the nominal model reduces to a standard linear programming problem. An aircraft engine example is presented to illustrate the algorithm. >


title: Herd immunity and herd effect: new insights and definitions

abstract: The term herd immunity has been used by various authors to conform to different definitions. Earlier this situation had been identified but not corrected. We propose that it should have precise meaning for which purpose a new definition is offered: “the proportion of subjects with immunity in a given population”. This definition dissociates herd immunity from the indirect protection observed in the unimmunised segment of a population in which a large proportion is immunised, for which the term ‘herd effect’ is proposed. It is defined as: “the reduction of infection or disease in the unimmunised segment as a result of immunising a proportion of the population”. Herd immunity can be measured by testing a sample of the population for the presence of the chosen immune parameter. Herd effect can be measured by quantifying the decline in incidence in the unimmunised segment of a population in which an immunisation programme is instituted. Herd immunity applies to immunisation or infection, human to human transmitted or otherwise. On the other hand, herd effect applies to immunisation or other health interventions which reduce the probability of transmission, confined to infections transmitted human to human, directly or via vector. The induced herd immunity of a given vaccine exhibits geographic variation as it depends upon coverage and efficacy of the vaccine, both of which can vary geographically. Herd effect is determined by herd immunity as well as the force of transmission of the corresponding infection. Clear understanding of these phenomena and their relationships will help improve the design of effective and efficient immunisation programmes aimed at control, elimination or eradication of vaccine preventable infectious diseases.


title: A Visual Analytics Approach for Categorical Joint Distribution Reconstruction from Marginal Projections

abstract: Oftentimes multivariate data are not available as sets of equally multivariate tuples, but only as sets of projections into subspaces spanned by subsets of these attributes. For example, one may find data with five attributes stored in six tables of two attributes each, instead of a single table of five attributes. This prohibits the visualization of these data with standard high-dimensional methods, such as parallel coordinates or MDS, and there is hence the need to reconstruct the full multivariate (joint) distribution from these marginal ones. Most of the existing methods designed for this purpose use an iterative procedure to estimate the joint distribution. With insufficient marginal distributions and domain knowledge, they lead to results whose joint errors can be large. Moreover, enforcing smoothness for regularizations in the joint space is not applicable if the attributes are not numerical but categorical. We propose a visual analytics approach that integrates both anecdotal data and human experts to iteratively narrow down a large set of plausible solutions. The solution space is populated using a Monte Carlo procedure which uniformly samples the solution space. A level-of-detail high dimensional visualization system helps the user understand the patterns and the uncertainties. Constraints that narrow the solution space can then be added by the user interactively during the iterative exploration, and eventually a subset of solutions with narrow uncertainty intervals emerges.


title: Arachneum: Blockchain meets Distributed Web

abstract: Appearance of Bitcoin raise up evolution in currency. Blockchain database also raise up possibilities to share important data between untrusted peers. In this paper, we propose Arachneum, a decentralized, distributed, and model-view-controller-based web service using blockchain and new privileges model. Therefore, we can enjoy the freedom against censorship of government or organizations, keep transparency of our web services, fork our web services, and create/read/update/delete(CRUD) all model-view-controller(MVC) components dynamically.


title: Computing Web-scale Topic Models using an Asynchronous Parameter Server

abstract: Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory search and discovery. However, classical methods for inferring topic models do not scale up to the massive size of today's publicly available Web-scale data sets. The state-of-the-art approaches rely on custom strategies, implementations and hardware to facilitate their asynchronous, communication-intensive workloads. We present APS-LDA, which integrates state-of-the-art topic modeling with cluster computing frameworks such as Spark using a novel asynchronous parameter server. Advantages of this integration include convenient usage of existing data processing pipelines and eliminating the need for disk writes as data can be kept in memory from start to finish. Our goal is not to outperform highly customized implementations, but to propose a general high-performance topic modeling framework that can easily be used in today's data processing pipelines. We compare APS-LDA to the existing Spark LDA implementations and show that our system can, on a 480-core cluster, process up to 135× more data and 10× more topics without sacricing model quality.


title: New Metrics for the Reliability of Approximate and Probabilistic Adders

abstract: Addition is a fundamental function in arithmetic operation; several adder designs have been proposed for implementations in inexact computing. These adders show different operational profiles; some of them are approximate in nature while others rely on probabilistic features of nanoscale circuits. However, there has been a lack of appropriate metrics to evaluate the efficacy of various inexact designs. In this paper, new metrics are proposed for evaluating the reliability as well as the power efficiency of approximate and probabilistic adders. Reliability is analyzed using the so-called sequential probability transition matrices (SPTMs). Error distance (ED) is initially defined as the arithmetic distance between an erroneous output and the correct output for a given input. The mean error distance (MED) and normalized error distance (NED) are then proposed as unified figures that consider the averaging effect of multiple inputs and the normalization of multiple-bit adders. It is shown that the MED is an effective metric for measuring the implementation accuracy of a multiple-bit adder and that the NED is a nearly invariant metric independent of the size of an adder. The MED is, therefore, useful in assessing the effectiveness of an approximate or probabilistic adder implementation, while the NED is useful in characterizing the reliability of a specific design. Since inexact adders are often used for saving power, the product of power and NED is further utilized for evaluating the tradeoffs between power consumption and precision. Although illustrated using adders, the proposed metrics are potentially useful in assessing other arithmetic circuit designs for applications of inexact computing.


title: Machine learning–XGBoost analysis of language networks to classify patients with epilepsy

abstract: Our goal was to apply a statistical approach to allow the identification of atypical language patterns and to differentiate patients with epilepsy from healthy subjects, based on their cerebral activity, as assessed by functional MRI (fMRI). Patients with focal epilepsy show reorganization or plasticity of brain networks involved in cognitive functions, inducing ‘atypical’ (compared to ‘typical’ in healthy people) brain profiles. Moreover, some of these patients suffer from drug-resistant epilepsy, and they undergo surgery to stop seizures. The neurosurgeon should only remove the zone generating seizures and must preserve cognitive functions to avoid deficits. To preserve functions, one should know how they are represented in the patient’s brain, which is in general different from that of healthy subjects. For this purpose, in the pre-surgical stage, robust and efficient methods are required to identify atypical from typical representations. Given the frequent location of regions generating seizures in the vicinity of language networks, one important function to be considered is language. The risk of language impairment after surgery is determined pre-surgically by mapping language networks. In clinical settings, cognitive mapping is classically performed with fMRI. The fMRI analyses allowing the identification of atypical patterns of language networks in patients are not sufficiently robust and require additional statistic approaches. In this study, we report the use of a statistical nonlinear machine learning classification, the Extreme Gradient Boosting (XGBoost) algorithm, to identify atypical patterns and classify 55 participants as healthy subjects or patients with epilepsy. XGBoost analyses were based on neurophysiological features in five language regions (three frontal and two temporal) in both hemispheres and activated with fMRI for a phonological (PHONO) and a semantic (SEM) language task. These features were combined into 135 cognitively plausible subsets and further submitted to selection and binary classification. Classification performance was scored with the Area Under the receiver operating characteristic curve (AUC). Our results showed that the subset SEM_LH BA_47-21 (left fronto-temporal activation induced by the SEM task) provided the best discrimination between the two groups (AUC of 91 ± 5%). The results are discussed in the framework of the current debates of language reorganization in focal epilepsy.


Computational Viability of Fog Methodologies in IoT Enabled Smart City Architectures-A Smart Grid Case Study


title: Expectation of f-measures: tractable exact computation and some empirical observations of its properties

abstract: We derive a tractable and exact computation for the expectation of F-measures. We also demonstrate the non-convexity of this expectation, and investigate errors of approximating the expectation under different settings.


title: Metal Additive Manufacturing: A Review

abstract: This paper reviews the state-of-the-art of an important, rapidly emerging, manufacturing technology that is alternatively called additive manufacturing (AM), direct digital manufacturing, free form fabrication, or 3D printing, etc. A broad contextual overview of metallic AM is provided. AM has the potential to revolutionize the global parts manufacturing and logistics landscape. It enables distributed manufacturing and the productions of parts-on-demand while offering the potential to reduce cost, energy consumption, and carbon footprint. This paper explores the material science, processes, and business consideration associated with achieving these performance gains. It is concluded that a paradigm shift is required in order to fully exploit AM potential.


title: Key Updating for Leakage Resiliency With Application to AES Modes of Operation

abstract: Side-channel analysis (SCA) exploits the information leaked through unintentional outputs (e.g., power consumption) to reveal the secret key of cryptographic modules. The real threat of SCA lies in the ability to mount attacks over small parts of the key and to aggregate information over different encryptions. The threat of SCA can be thwarted by changing the secret key at every run. Indeed, many contributions in the domain of leakage resilient cryptography tried to achieve this goal. However, the proposed solutions were computationally intensive and were not designed to solve the problem of the current cryptographic schemes. In this paper, we propose a generic framework of lightweight key updating that can protect the current cryptographic standards and evaluate the minimum requirements for heuristic SCA-security. Then, we propose a complete solution to protect the implementation of any standard mode of Advanced Encryption Standard. Our solution maintains the same level of SCA-security (and sometimes better) as the state of the art, at a negligible area overhead while doubling the throughput of the best previous work.


title: RStore: A Direct-Access DRAM-based Data Store

abstract: Distributed DRAM stores have become an attractive option for providing fast data accesses to analytics applications. To accelerate the performance of these stores, researchers have proposed using RDMA technology. RDMA offers high bandwidth and low latency data access by carefully separating resource setup from IO operations, and making IO operations fast by using rich network semantics and offloading. Despite recent interest, leveraging the full potential of RDMA in a distributed environment remains a challenging task. In this paper, we present RDMA Store or RStore, a DRAM-based data store that delivers high performance by extending RDMA's separation philosophy to a distributed setting. RStore achieves high aggregate bandwidth (705 Gb/s) and close-to-hardware latency on our 12-machine testbed. We developed a distributed graph processing framework and a Key-Value sorter using RStore's unique memory-like API. The graph processing framework, which relies on RStore for low-latency graph access, outperforms state-of-the-art systems by margins of 2.6 -- 4.2× when calculating Page Rank. The Key-Value sorter can sort 256 GB of data in 31.7 sec, which is 8× better than Hadoop TeraSort in a similar setting.


title: Real-Time Nonparametric Anomaly Detection in High-Dimensional Settings

abstract: Timely detection of abrupt anomalies is crucial for real-time monitoring and security of modern systems producing high-dimensional data. With this goal, we propose effective and scalable algorithms. Proposed algorithms are nonparametric as both the nominal and anomalous multivariate data distributions are assumed unknown. We extract useful univariate summary statistics and perform anomaly detection in a single-dimensional space. We model anomalies as persistent outliers and propose to detect them via a cumulative sum-like algorithm. In case the observed data have a low intrinsic dimensionality, we learn a submanifold in which the nominal data are embedded and evaluate whether the sequentially acquired data persistently deviate from the nominal submanifold. Further, in the general case, we learn an acceptance region for nominal data via Geometric Entropy Minimization and evaluate whether the sequentially observed data persistently fall outside the acceptance region. We provide an asymptotic lower bound and an asymptotic approximation for the average false alarm period of the proposed algorithm. Moreover, we provide a sufficient condition to asymptotically guarantee that the decision statistic of the proposed algorithm does not diverge in the absence of anomalies. Experiments illustrate the effectiveness of the proposed schemes in quick and accurate anomaly detection in high-dimensional settings.


title: Geo-Text Data and Data-Driven Geospatial Semantics

abstract: Many datasets nowadays contain links between geographic locations and natural language texts. These links can be geotags, such as geotagged tweets or geotagged Wikipedia pages, in which location coordinates are explicitly attached to texts. These links can also be place mentions, such as those in news articles, travel blogs, or historical archives, in which texts are implicitly connected to the mentioned places. This kind of data is referred to as geo-text data. The availability of large amounts of geo-text data brings both challenges and opportunities. On the one hand, it is challenging to automatically process this kind of data due to the unstructured texts and the complex spatial footprints of some places. On the other hand, geo-text data offers unique research opportunities through the rich information contained in texts and the special links between texts and geography. As a result, geo-text data facilitates various studies especially those in data-driven geospatial semantics. This paper discusses geo-text data and related concepts. With a focus on data-driven research, this paper systematically reviews a large number of studies that have discovered multiple types of knowledge from geo-text data. Based on the literature review, a generalized workflow is extracted and key challenges for future work are discussed.


title: Cognitive Analysis of Working Memory Load from Eeg, by a Deep Recurrent Neural Network

abstract: One of the common modalities for observing mental activity is electroencephalogram (EEG) signals. However, EEG recording is highly susceptible to various sources of noise and to inter subject differences. In order to solve these problems we present a deep recurrent neural network (RNN) architecture to learn robust features and predict the levels of cognitive load from EEG recordings. Using a deep learning approach, we first transform the EEG time series into a sequence of multispectral images which carries spatial information. Next, we train our recurrent hybrid network to learn robust representations from the sequence of frames. The proposed approach preserves spectral, spatial and temporal structures and extracts features which are less sensitive to variations along each dimension. Our results demonstrate cognitive memory load prediction across four different levels with an overall accuracy of 92.5% during the memory task execution and reduce classification error to 7.61% in comparison to other state-of-art techniques.


title: Deeptongue: Tongue Segmentation Via Resnet

abstract: Accurate tongue image segmentation is helpful to acquire correct automatic tongue diagnosis result. However, traditional methods cannot bring satisfying results in most cases. This paper proposes an end-to-end trainable tongue image segmentation method using deep convolutional neural network based on ResNet. The proposed method, named DeepTongue, segments tongue by using a forward network without preprocessing. The proposed method has no restrictions of the illumination and size of tongue images. Experimental results show that the proposed DeepTongue improves the segmentation accuracy by a noticeable margin. In addition, DeepTongue is much faster than the existing tongue image segmentation methods.


title: Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment

abstract: In the context of the Electronic Health Record, automated diagnosis coding of patient notes is a useful task, but a challenging one due to the large number of codes and the length of patient notes. We investigate four models for assigning multiple ICD codes to discharge summaries taken from both MIMIC II and III. We present Hierarchical Attention-GRU (HA-GRU), a hierarchical approach to tag a document by identifying the sentences relevant for each label. HA-GRU achieves state-of-the art results. Furthermore, the learned sentence-level attention layer highlights the model decision process, allows easier error analysis, and suggests future directions for improvement.


title: Formal Models for Computer Security

abstract: Efforts to build "secure" computer systems have now been underway for more than a decade. Many designs have been proposed, some prototypes have been constructed, and a few systems are approaching the production stage. A small number of systems are even operating in what the Department of Defense calls the "multilevel" mode some information contained m these computer systems may have a clasmfication higher than the clearance of some of the users of those systems. This paper revmws the need for formal security models, describes the structure and operation of military security controls, considers how automation has affected security problems, surveys models that have been proposed and applied to date, and suggests possible d~rectlons for future models


Analyzing Dynamic Binary Instrumentation Overhead Gang -


title: Leveraging deep preference learning for indexing and retrieval of biomedical images

abstract: This paper presents an original framework based on deep learning and preference learning to retrieve and characterize biomedical images for assisting physicians in diagnosing complex diseases with potentially only small differences between them. In particular, we use deep learning to extract the high-level and compact features for biomedical images. In contrast to the traditional biomedical algorithms or general image retrieval systems that only consider the use of pixel and/or hand-crafted features to represent images, we utilize deep neural networks for feature discovery of biomedical images. Moreover, in order to be able to index the similarly referenced images, we introduce preference learning in a novel way to learn what kinds of images we need so that we can obtain the similarity ranking list of biomedical images. We evaluate the performance of our system in detailed experiments over the well-known available OASIS-MRI database for whole brain neuroimaging as a benchmark and compare it with those of the traditional biomedical and general image retrieval approaches. Our proposed system exhibits an outstanding retrieval ability and efficiency for biomedical image applications.


title: How Open is this Platform? The Meaning and Measurement of Platform Openness from the Complementors’ Perspective

abstract: Software platforms’ success largely depends on complementors’ willingness to repeatedly invest their time and effort to the development of platform applications that attract users and increase the platform’s installed base. But how can platform providers encourage desirable behaviours by complementors (i.e., application developers) in the absence of formal roles and hierarchical control structures? Although previous studies of software-based platforms have identified openness as critical instrument at the macro (i.e., platform) level and have provided initial attempts to measure the construct, no research has been dedicated to comprehensively conceptualize and operationalize platform openness at the micro level from the perspective of application developers. To go beyond these preliminary findings and to theorize about the nature and effects of platform openness as perceived by application developers, we develop a construct called perceived platform openness (PPO). Drawing on recently advanced scale development methodologies, we conceptualize PPO as a multidimensional construct and empirically validate it with important consequent variables linked to developers’ continuous platform contributions. Empirical evidence from several rounds of qualitative and quantitative steps supports the conceptual validity of the construct and empirical relevance of the scale across different smartphone platform contexts (i.e., Apple iOS and Google Android). Researchers will benefit from the study’s systematic and comprehensive conceptualization of PPO, how it is measured, and how it relates to critical application developer beliefs and attitudes. Platform managers may use our results to target the underlying facets of PPO most likely to contribute to the platform’s long-term goals.


GPU Based Generation and Real-Time Rendering of Semi-Procedural Terrain Using Features


Multi-Level μTESLA: Broadcast Authentication for Distributed Sensor Networks


title: Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization

abstract: We propose a simple and easy to implement neural network compression algorithm that achieves results competitive with more complicated state-of-the-art methods. The key idea is to modify the original optimization problem by adding K independent Gaussian priors (corresponding to the k-means objective) over the network parameters to achieve parameter quantization, as well as an L1 penalty to achieve pruning. Unlike many existing quantization-based methods, our method uses hard clustering assignments of network parameters, which adds minimal change or overhead to standard network training. We also demonstrate experimentally that tying neural network parameters provides less gain in generalization performance than changing network architecture and connectivity patterns entirely.


title: Data Gathering with Compressive Sensing in Wireless Sensor Networks: A Random Walk Based Approach

abstract: In this paper, we study the problem of data gathering with compressive sensing (CS) in wireless sensor networks (WSNs). Unlike the conventional approaches, which require uniform sampling in the traditional CS theory, we propose a random walk algorithm for data gathering in WSNs. However, such an approach will conform to path constraints in networks and result in the non-uniform selection of measurements. It is still unknown whether such a non-uniform method can be used for CS to recover sparse signals in WSNs. In this paper, from the perspectives of CS theory and graph theory, we provide mathematical foundations to allow random measurements to be collected in a random walk based manner. We find that the random matrix constructed from our random walk algorithm can satisfy the expansion property of expander graphs. The theoretical analysis shows that a k-sparse signal can be recovered using `1 minimization decoding algorithm when it takes m = O(k log(n=k)) independent random walks with the length of each walk t = O(n=k) in a random geometric network with n nodes. We also carry out simulations to demonstrate the effectiveness of the proposed scheme. Simulation results show that our proposed scheme can significantly reduce communication cost compared to the conventional schemes using dense random projections and sparse random projections, indicating that our scheme can be a more practical alternative for data gathering applications in WSNs.


title: Gas Stations Oriented Internet Service System Based on Intel Minnowboard

abstract: This work is an intelligent gas station information collection and exchange system based on the Intel processor, Minnowboard. Aim to obtain real-time data include tanks' status, guns' status and billing information from gas stations through the sensor device placed on each station. The Sensor Controllers connect to oil tanks and oil guns, to collect real time data, and then transmit these data to the server running on the Minnow IoT(Internet of Things) gateway by a network protocol WebSocket. After the server processes the data, it stores and updates the information in the cloud server database. At the same time, publishing these data to WeChat terminal for a real-time display, through another protocol, MQTT(Message Queuing Telemetry Transport). This system effectively improve the automatic management level of oil stations. In the near future, the system accomplish online payments and so on, That would be a direction for the petrochemical field in the future.


title: New face recognition method based on local binary pattern histogram

abstract: Face recognition is one of the most important tasks in computer vision and biometrics where many algorithms have been developed. The Local Binary Pattern (LBP) has been proved to be effective for facial image representation and analysis, but it is too local to be robust. In this paper, we present an improved method for face recognition named Elongated Multi-Block Local Ternary Pattern (EMBLTP), which is based on Local Binary Pattern (LBP).The proposed method is tested on Yale face database and compared with different variants of LBP. Experimental results show that, the classification rate of the proposed method is appreciable.


title: Paper vs. Computer-based Exams: A Study of Errors in Recursive Binary Tree Algorithms

abstract: This paper reports on a study of goal-plans and errors produced by students who wrote recursive solutions for a binary tree operation. This work extends a previous study of difficulties CS2 students experienced while writing solutions on paper-based exams. In this study, participants solved the same recursive binary tree problem as part of a hands-on computer-based exam where students had access to an IDE and Java API documentation. Not surprisingly, students who took the computer-based exams were more successful than those who took the paper-based exams (58% vs. 17% correct solutions). However, even with the advantage of access to an IDE, documentation, and test cases, 42% of students taking the computer-based exam still made errors, indicating that students exhibit persistent errors even with support. The most common errors observed included incorrect calculations, missing method calls and missing and incorrect base cases.


title: Statistical properties of community structure in large social and information networks

abstract: A large body of work has been devoted to identifying community structure in networks. A community is often though of as a set of nodes that has more connections between its members than to the remainder of the network. In this paper, we characterize as a function of size the statistical and structural properties of such sets of nodes. We define the network community profile plot, which characterizes the "best" possible community - according to the conductance measure - over a wide range of size scales, and we study over 70 large sparse real-world networks taken from a wide range of application domains. Our results suggest a significantly more refined picture of community structure in large real-world networks than has been appreciated previously. Our most striking finding is that in nearly every network dataset we examined, we observe tight but almost trivial communities at very small scales, and at larger size scales, the best possible communities gradually "blend in" with the rest of the network and thus become less "community-like." This behavior is not explained, even at a qualitative level, by any of the commonly-used network generation models. Moreover, this behavior is exactly the opposite of what one would expect based on experience with and intuition from expander graphs, from graphs that are well-embeddable in a low-dimensional structure, and from small social networks that have served as testbeds of community detection algorithms. We have found, however, that a generative model, in which new edges are added via an iterative "forest fire" burning process, is able to produce graphs exhibiting a network community structure similar to our observations.


On Your Social Network De-anonymizablity: Quantification and Large Scale Evaluation with Seed Knowledge


title: Motion and squeeze force control for omnidirectional wheeled mobile robots

abstract: For a redundantly-actuated wheeled mobile robot, the squeeze force between the robot body and its wheels does not contribute to the motion of the robot, but to squeeze the related rigid bodies each other. It is good to control the squeeze force, regulate it to zero if possible, from the view of energy efficiency or life span of joint components. Thus a scheme controlling robot motion while controlling squeeze force is proposed as the following procedure: After a stable controller is presented for robot motion control, a squeeze force controller is designed to regulate the squeeze force to zero. The proposed scheme is demonstrated in robot regulation and tracking control problem. A "PD+" controller is adopted for robot regulation, a modified inverse dynamics control is applied for robot tracking control. In both robot control problems, an integral feedback squeeze force controller is used to minimize the squeeze force. The presented simulation results verify the effect of the proposed scheme.


title: UltraLight: An ultrafast imaging platform based on a digital 64-channel ultrasound probe

abstract: Digital ultrasound probes include the entire analog frontend in their enclosing and are equipped with a standard digital link. This enables to build very cost-effective ultrasound systems as they can be simply connected to a commodity device, such as a desktop PC, tablet or smartphone, running an ultrasound imaging application. Up to now, digital probes have been mainly demonstrated for low-end ultrasound applications and are currently limited to a small number of frontend channels (typically 16). In addition, the available bandwidth at the digital interface (less than 10 Gb/s) limits these devices only to basic imaging modalities. In this work, we present an imaging platform built with a digital 64-channel ultrasound probe that supports ultrafast imaging. Our digital probe, called LightProbe, utilizes a 64-element phased array without multiplexing and incorporates a 64-channel 100 Vpp TX/RX stage providing a sample rate up to 32.5 MS/s @ 12bit. The probe features an optical link interface achieving 25Gb/s on a standard fiber cable. A Xilinx Artix 7 FPGA is integrated in the probe to manage the optical interface and to provide a high-degree of configurabilty. To the best of our knowledge, this is the first digital probe capable of compounded plane wave imaging. We capture plane waves with peak and average rate of 4.9 kHz and 2kHz respectively, with a peak link load of 15.36 Gb/s, while consuming just 9.25 W.


title: Winning with DNS Failures: Strategies for Faster Botnet Detection

abstract: Botnets such as Conficker and Torpig utilize high entropy domains for fluxing and evasion. Bots may query a large number of domains, some of which may fail. In this paper, we present techniques where the failed domain queries (NXDOMAIN) may be utilized for: (i) Speeding up the present detection strategies which rely only on successful DNS domains. (ii) Detecting Command and Control (C&C) server addresses through features such as temporal correlation and information entropy of both successful and failed domains. We apply our technique to a Tier-1 ISP dataset obtained from South Asia, and a campus DNS trace, and thus validate our methods by detecting Conficker botnet IPs and other anomalies with a false positive rate as low as 0.02%. Our technique can be applied at the edge of an autonomous system for real-time detection.


title: Describing the emotional states that are expressed in speech

abstract: To study relations between speech and emotion, it is necessary to have methods of describing emotion. Finding appropriate methods is not straightforward, and there are difficulties associated with the most familiar. The word emotion itself is problematic: a narrow sense is often seen as "correct", but it excludes what may be key areas in relation to speech-including states where emotion is present but not full-blown, and related states (e.g., arousal, attitude). Everyday emotion words form a rich descriptive system, but it is intractable because it involves so many categories, and the relationships among them are undefined. Several alternative types of description are available. Emotion-related biological changes are well documented, although reductionist conceptions of them are problematic. Psychology offers descriptive systems based on dimensions such as evaluation (positive or negative) and level of activation, or on logical elements that can be used to define an appraisal of the situation. Adequate descriptive systems need to recognise the importance of both time course and interactions involving multiple emotions and/or deliberate control. From these conceptions of emotion come various tools and techniques for describing particular episodes. Different tools and techniques are appropriate for different purposes.


title: Design of flexible triple-layer folded dipole antenna on curved surface for WBAN

abstract: This article presents the results of using flexible folded dipole by stacking techniques to automatically adjust the matching frequency. The designed triple-layer (3L) antenna provides the folded dipole antenna with flexibility to efficiently work with the human body part that mostly like curved surface. The flexible triple-layers folded dipole (3L-FFD) antenna can increase the length of all layer of the antenna to be automatically adjusted. The effective length of the 3L-FFD antenna can be expanded as the actual antenna's length by two, four, six and eight sliding points when compared to the former multilayer dipole antenna design. The proposed design will decrease the amount of layers of the folded dipole antenna; only three-layers still can extend the whole antenna's length as well as the multilayer dipole antenna (four-layers and five-layers). The results displayed that the resonance frequency improvement of the antenna was more stable. The 3L-FFD antenna is created for functioning at 2.45GHz with WBAN.


title: A Fast and Robust Ellipse-Detection Method Based on Sorted Merging

abstract: A fast and robust ellipse-detection method based on sorted merging is proposed in this paper. This method first represents the edge bitmap approximately with a set of line segments and then gradually merges the line segments into elliptical arcs and ellipses. To achieve high accuracy, a sorted merging strategy is proposed: the merging degrees of line segments/elliptical arcs are estimated, and line segments/elliptical arcs are merged in descending order of the merging degrees, which significantly improves the merging accuracy. During the merging process, multiple properties of ellipses are utilized to filter line segment/elliptical arc pairs, making the method very efficient. In addition, an ellipse-fitting method is proposed that restricts the maximum ratio of the semimajor axis and the semiminor axis, further improving the merging accuracy. Experimental results indicate that the proposed method is robust to outliers, noise, and partial occlusion and is fast enough for real-time applications.


title: Lexicon-based sentiment analysis of Indian Union Budget 2016–17

abstract: In this work sentiment analysis of annual budget for Financial year 2016–17 is done. Text mining is used to extract text data from the budget document and to compute the word association of significant words and their correlation in computed with the associated words. Word frequency and the corresponding word cloud is plotted. The analysis is done in R software. The corresponding sentiment score is computed and analyzed. This analysis is of significant importance keeping in mind the sentiment reflected about the budget in the official budget document.


title: Design, Optimization, and Prototyping of Segmental-Type Linear Switched-Reluctance Motor With a Toroidally Wound Mover for Vertical Propulsion Application

abstract: Linear switched-reluctance motor (LSRM) is investigated and proved to be an alternative for direct linear driving systems, especially for long-distance transportation applications, such as vertical elevator driving motor. This paper presents the design, sensitivity analysis, and optimization of a toroidally wound mover LSRM with segmental stator. The presented LSRM has much higher copper utilization ratio and flux carrying capability than the conventional LSRM for vertical linear propulsion application. The features of motor performance influenced by various geometrical parameters are described using magnetic circuit and finite element analysis to give the guidelines for selecting the reasonable range of those parameters of this kind of machine. Furthermore, an optimization procedure is performed to obtain the optimal machine dimensions considering active payload ratio and force ripples. Finally, the experimental results from the prototype validate the design procedures and analysis above.


Risk versus Uncertainty in Deep Learning : Bayes , Bootstrap and the Dangers of Dropout


Facial Expression Recognition in Older Adults using Deep Machine Learning


title: Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari

abstract: Evolution Strategies (ES) have recently been demonstrated to be a viable alternative to reinforcement learning (RL) algorithms on a set of challenging deep RL problems, including Atari games and MuJoCo humanoid locomotion benchmarks. While the ES algorithms in that work belonged to the specialized class of natural evolution strategies (which resemble approximate gradient RL algorithms, such as REINFORCE), we demonstrate that even a very basic canonical ES algorithm can achieve the same or even better performance. This success of a basic ES algorithm suggests that the state-of-the-art can be advanced further by integrating the many advances made in the field of ES in the last decades. We also demonstrate qualitatively that ES algorithms have very different performance characteristics than traditional RL algorithms: on some games, they learn to exploit the environment and perform much better while on others they can get stuck in suboptimal local minima. Combining their strengths with those of traditional RL algorithms is therefore likely to lead to new advances in the state of the art.


User embodiment in collaborative virtual environments


Selecting Exemplar Recordings of American Sign Language Non-Manual Expressions for Animation Synthesis Based on Manual Sign Timing


title: Automated Collaborative Filtering Applications for Online Recruitment Services

abstract: Online recruitment services suffer from shortcomings due to traditional search techniques. Most users fail to construct queries that provide an adequate and accurate description of their (job) requirements, leading to imprecise search results. We investigate one potential solution that combines implicit profiling methods and automated collaborative filtering (ACF) techniques to build personalised query-less job recommendations. Two ACF strategies are implemented and evaluated in the JobFinder domain.


title: Optimal Differential Energy Watermarking (DEW) of DCT Encoded Images and Video

abstract: This paper proposes the differential energy watermarking (DEW) algorithm for JPEG/MPEG streams. The DEW algorithm embeds label bits by selectively discarding high frequency discrete cosine transform (DCT) coefficients in certain image regions. The performance of the proposed watermarking algorithm is evaluated by the robustness of the watermark, the size of the watermark, and the visual degradation the watermark introduces. These performance factors are controlled by three parameters, namely the maximal coarseness of the quantizer used in pre-encoding, the number of DCT blocks used to embed a single watermark bit, and the lowest DCT coefficient that we permit to be discarded. We follow a rigorous approach to optimizing the performance and choosing the correct parameter settings by developing a statistical model for the watermarking algorithm. Using this model, we can derive the probability that a label bit cannot be embedded. The resulting model can be used, for instance, for maximizing the robustness against re-encoding and for selecting adequate error correcting codes for the label bit string.


title: Drift analysis and average time complexity of evolutionary algorithms

abstract: The computational time complexity is an important topic in the theory of evolutionary algorithms (EAs). This paper reports some new results on the average time complexity of EAs. Based on drift analysis, some useful drift conditions for deriving the time complexity of EAs are studied, including conditions under which an EA will take no more than polynomial time (in problem size) to solve a problem and conditions under which an EA will take at least exponential time (in problem size) to solve a problem. The paper first presents the general results, and then uses several problems as examples to illustrate how these general results can be applied to concrete problems in analyzing the average time complexity of EAs. While previous work only considered (1 + 1) EAs without any crossover, the EAs considered in this paper are fairly general, which use a finite population, crossover, mutation, and selection.  2001 Elsevier Science B.V. All rights reserved.