title: SR-4000 and CamCube 3 . 0 Time of Flight ( ToF ) Cameras : Tests and Comparison

abstract: In this paper experimental comparisons between two Time-of-Flight (ToF) cameras are reported in order to test their performance and to give some procedures for testing data delivered by this kind of technology. In particular, the SR-4000 camera by Mesa Imaging AG and the CamCube3.0 by PMD Technologies have been evaluated since they have good performances and are well known to researchers dealing with Time-of-Flight (ToF) cameras. After a brief overview of commercial ToF cameras available on the market and the main specifications of the tested devices, two topics are presented in this paper. First, the influence of camera warm-up on distance measurement is analyzed: a warm-up of 40 minutes is suggested to obtain the measurement stability, especially in the case of the CamCube3.0 camera, that exhibits distance measurement variations of several centimeters. Secondly, the variation of distance measurement precision variation over integration time is presented: distance measurement precisions of some millimeters are obtained in both cases. Finally, a comparison between the two cameras based on the experiments and some information about future work on evaluation of sunlight influence on distance measurements are reported.


Authorship Attribution Using Text Distortion


The 2018 SIM IT Issues and Trends Study


title: An Asynchronous P300-Based Brain-Computer Interface Web Browser for Severely Disabled People

abstract: This paper presents an electroencephalographic (EEG) P300-based brain–computer interface (BCI) Internet browser. The system uses the “odd-ball” row-col paradigm for generating the P300 evoked potentials on the scalp of the user, which are immediately processed and translated into web browser commands. There were previous approaches for controlling a BCI web browser. However, to the best of our knowledge, none of them was focused on an assistive context, failing to test their applications with a suitable number of end users. In addition, all of them were synchronous applications, where it was necessary to introduce a “read-mode” command in order to avoid a continuous command selection. Thus, the aim of this study is twofold: 1) to test our web browser with a population of multiple sclerosis (MS) patients in order to assess the usefulness of our proposal to meet their daily communication needs; and 2) to overcome the aforementioned limitation by adding a threshold that discerns between control and non-control states, allowing the user to calmly read the web page without undesirable selections. The browser was tested with sixteen MS patients and five healthy volunteers. Both quantitative and qualitative metrics were obtained. MS participants reached an average accuracy of 84.14%, whereas 95.75% was achieved by control subjects. Results show that MS patients can successfully control the BCI web browser, improving their personal autonomy.


title: A visual query language for software visualization

abstract: Visualization for software understanding requires that the user be able to define specific visualizations that are specialized to the particular understanding task at hand. In this paper we describe a system that lets the user define such visualizations quickly and effectively. The system uses a visual query language over a variety of data sources to let the user specify what information is relevant to the understanding task and to correlate that information. Then it provides a mechanism for letting the user select and customize an appropriate visualization for this data.


title: Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment

abstract: A key step in mass spectrometry (MS)-based proteomics is the identification of peptides in sequence databases by their fragmentation spectra. Here we describe Andromeda, a novel peptide search engine using a probabilistic scoring model. On proteome data, Andromeda performs as well as Mascot, a widely used commercial search engine, as judged by sensitivity and specificity analysis based on target decoy searches. Furthermore, it can handle data with arbitrarily high fragment mass accuracy, is able to assign and score complex patterns of post-translational modifications, such as highly phosphorylated peptides, and accommodates extremely large databases. The algorithms of Andromeda are provided. Andromeda can function independently or as an integrated search engine of the widely used MaxQuant computational proteomics platform and both are freely available at www.maxquant.org. The combination enables analysis of large data sets in a simple analysis workflow on a desktop computer. For searching individual spect...


title: Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies

abstract: This paper proposes GProp, a deep reinforcement learning algorithm for continuous policies with compatible function approximation. The algorithm is based on two innovations. Firstly, we present a temporal-difference based method for learning the gradient of the value-function. Secondly, we present the deviator-actor-critic (DAC) model, which comprises three neural networks that estimate the value function, its gradient, and determine the actor's policy respectively. We evaluate GProp on two challenging tasks: a contextual bandit problem constructed from nonparametric regression datasets that is designed to probe the ability of reinforcement learning algorithms to accurately estimate gradients; and the octopus arm, a challenging reinforcement learning benchmark. GProp is competitive with fully supervised methods on the bandit task and achieves the best performance to date on the octopus arm.


title: STRUDEL: a Web site management system

abstract: The growth of the World-Wide Web has created a new kind of data management problem: building and maintaining Web sites. Building a Web site involves several tasks, such as choosing what information will be available at the site, organizing that information in individual pages or in graphs of linked pages, and specifying the visual presentation of pages in HTML. Creating and managing large sites is tedious, because a user often must perform these tasks simultaneously when creating HTML pages. The task of building and managing Web sites presents a unique opportunity for applying concepts from database management systems, such as the separation between the logical view of information and its storage and maintenance and the ability to restructure information via queries. Furthermore, recent research results on information integration [Ill1971 and managing semi-structured data [ACM97, Abi97] can play a key role in managing Web sites. The goal of STRUDEL project is to extend and adapt these concepts to the problem of Web-site management. Consider several tasks required of a Web-site manager. Site managers often want to manage a single repository of site data, but present different browsable “views” of the site based on criteria such as the type of user accessing the site, e.g., external or internal, expert or novice. Morever, a manager might want to modify the data repository by editing simple text files or by updating external databases, to reorganize the structure of the pages by manipulating graphs that represent the linked pages, or to design multiple presentations of a single page by editing HTML files or by using a WYSIWYG HTML generator. Currently, such operations on Web sites involve significant effort. The key idea in the STRUDEL system is the separation of the logical view of information available at a Web site, the structure of that information in linked pages, and


title: Impact of the current profile evolution on tearing stability of ITER demonstration discharges in DIII-D

abstract: A set of >100 DIII-D ITER demonstration discharges was analysed with the goal of characterizing the tearing stability of ITER baseline scenario plasmas on the energy and resistive evolution time scales. In DIII-D these discharges are limited by the appearance of an n = 1 tearing instability, after the discharge has run at constant pressure for several confinement times (?E 200?ms). Since the resistive time is 1?s, the current profile is still evolving when the modes appear. Across the ranges of pressure explored around the ITER design value, the probability of a discharge remaining stable equals that of encountering a mode; therefore, it seems that the tearing stability boundary cannot be characterized as a pressure limit. The internal inductance, a measure of the current distribution, does not contain enough detail to describe the tearing stability limits precisely, despite clear evidence that the evolution of the current profile is the cause of the instability and not the reaching of a ? limit. The onset of the instability does not seem to be correlated with the plasma rotation or the presence of ELMs.


title: Data warehouse design to support customer relationship management analyses

abstract: CRM is a strategy that integrates the concepts of Knowledge Management, Data Mining, and Data Warehousing in order to support the organization's decision-making process to retain long-term and profitable relationships with its customers. In this paper, we first present the design implications that CRM poses to data warehousing, and then propose a robust multidimensional starter model that supports CRM analyses. We then present sample CRM queries, test our starter model using those queries and define two measures (% success ratio and CRM suitability ratio) by which CRM models can be evaluated. We finally introduce a preliminary heuristic for designing data warehouses to support CRM analyses. Our study shows that our starter model can be used to analyze various profitability analyses such as customer profitability analysis, market profitability analysis, product profitability analysis, and channel profitability analysis.


title: The iSLIP Scheduling Algorithm for Input-Queued Switches

abstract: An increasing number of high performance internetworking protocol routers, LAN and asynchronous transfer mode (ATM) switches use a switched backplane based on a crossbar switch. Most often, these systems use input queues to hold packets waiting to traverse the switching fabric. It is well known that if simple first in first out (FIFO) input queues are used to hold packets then, even under benign conditions, head-of-line (HOL) blocking limits the achievable bandwidth to approximately 58.6% of the maximum. HOL blocking can be overcome by the use of virtual output queueing, which is described in this paper. A scheduling algorithm is used to configure the crossbar switch, deciding the order in which packets will be served. Previous results have shown that with a suitable scheduling algorithm, 100% throughput can be achieved. In this paper, we present a scheduling algorithm called iSLIP. An iterative, round-robin algorithm, iSLIP can achieve 100% throughput for uniform traffic, yet is simple to implement in hardware. Iterative and noniterative versions of the algorithms are presented, along with modified versions for prioritized traffic. Simulation results are presented to indicate the performance of iSLIP under benign and bursty traffic conditions. Prototype and commercial implementations of iSLIP exist in systems with aggregate bandwidths ranging from 50 to 500 Gb/s. When the traffic is nonuniform, iSLIP quickly adapts to a fair scheduling policy that is guaranteed never to starve an input queue. Finally, we describe the implementation complexity of iSLIP. Based on a two-dimensional (2-D) array of priority encoders, single-chip schedulers have been built supporting up to 32 ports, and making approximately 100 million scheduling decisions per second.


title: On the definition and classification of cybercrime

abstract: The idea of Cybercrime is not new, yet there is significant confusion amongst academics, computer security experts and users as to the extent of real Cybercrime. In this paper, we explore the breadth of computer-based crime, providing a definition of the emerging terms “Cybercrime” and “crimeware”. We then divide Cybercrime into two distinct categories: Type I Cybercrime, which is mostly technological in nature, and Type II Cybercrime, which has a more pronounced human element. We then use two case studies to illustrate the role of crimeware in different types of Cybercrime, and offer some observations on the role of cognition in the process of Cybercrime. Finally we provide several suggestions for future work in the area of Cybercrime.


title: A Framework to Assess Legacy Software Systems

abstract: Enterprise organizations require flexible software systems that can handle emerging market needs from time to time, chiefly to ensure the organization has a competitive edge in the market. On the other hand, several organizations still possess legacy software systems, which have definite functionalities but may limit their development progress to comply with emerging e-business needs. Off-the- shelf systems might be one possible option if a decision for replacement is made. Equally, improving the architecture of a legacy system can be a valid option to consider as well. The decision on which approach to follow when dealing with a legacy software system must be made based on a thorough investigation of the nature of the system. This paper develops a conceptual framework to analyse legacy software systems in order to support CEOs in making informed decisions about the appropriate approaches to follow in order to keep their organizations functional and competitive. Index Terms—legacy system, software architecture, decision making


title: Rapid physics-based rough-terrain rover planning with sensor and control uncertainty

abstract: In future planetary exploration missions, rovers will be required to autonomously traverse challenging environments. Much of the previous work in robot motion planning cannot be successfully applied to the rough-terrain planning problem. A model-based planning method is presented in this paper that is computationally efficient and takes into account uncertainty in the robot model, terrain model, range sensor data, and rover path following errors. It is based on rapid path planning through the visible terrain map with a simple graph-search algorithm, followed by a physics-based evaluation of the path with a rover model. Simulation results are presented which demonstrate the effectiveness of the method presented.


title: Toward Integrating Catholic Social Teaching into Graduate Social Work Education.

abstract: Social work programs are one mechanism within Catholic institutions of higher education for advancing the social mission of the Church. The social work profession seeks the achievement of a just society that promotes the dignity of the human person in the context of community and social relationships. This article describes beginning efforts to integrate Catholic Social Teaching (CST) into an integrative seminar on social justice and social change in a graduate social work program. It also identifi es existing models and organizations that universities can look to for support if they endeavor to bring CST into their course content in a meaningful way.


title: Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks

abstract: Parallelization framework has become a necessity to speed up the training of deep neural networks (DNN) recently. Such framework typically employs the Model Average approach, denoted as MA-DNN, in which parallel workers conduct respective training based on their own local data while the parameters of local models are periodically communicated and averaged to obtain a global model which serves as the new start of local models. However, since DNN is a highly non-convex model, averaging parameters cannot ensure that such global model can perform better than those local models. To tackle this problem, we introduce a new parallel training framework called Ensemble-Compression, denoted as EC-DNN. In this framework, we propose to aggregate the local models by ensemble, i.e., averaging the outputs of local models instead of the parameters. As most of prevalent loss functions are convex to the output of DNN, the performance of ensemble-based global model is guaranteed to be at least as good as the average performance of local models. However, a big challenge lies in the explosion of model size since each round of ensemble can give rise to multiple times size increment. Thus, we carry out model compression after each ensemble, specialized by a distillation based method in this paper, to reduce the size of the global model to be the same as the local ones. Our experimental results demonstrate the prominent advantage of EC-DNN over MA-DNN in terms of both accuracy and speedup.


title: Top-down facilitation of visual recognition

abstract: Cortical analysis related to visual object recognition is traditionally thought to propagate serially along a bottom-up hierarchy of ventral areas. Recent proposals gradually promote the role of top-down processing in recognition, but how such facilitation is triggered remains a puzzle. We tested a specific model, proposing that low spatial frequencies facilitate visual object recognition by initiating top-down processes projected from orbitofrontal to visual cortex. The present study combined magnetoencephalography, which has superior temporal resolution, functional magnetic resonance imaging, and a behavioral task that yields successful recognition with stimulus repetitions. Object recognition elicited differential activity that developed in the left orbitofrontal cortex 50 ms earlier than it did in recognition-related areas in the temporal cortex. This early orbitofrontal activity was directly modulated by the presence of low spatial frequencies in the image. Taken together, the dynamics we revealed provide strong support for the proposal of how top-down facilitation of object recognition is initiated, and our observations are used to derive predictions for future research.


title: Performance Comparison of Various Face Detection Techniques

abstract: Face recognition has been a fast growing, challenging and interesting area in real time applications. A large number of face recognition algorithms have been developed in last decades. In this paper an attempt is made to review a wide range of methods used for face recognition comprehensively. This include PCA, LDA, ICA, SVM, Gabor wavelet soft computing tool like ANN for recognition and various hybrid combination of this techniques. This review investigates all these methods with parameters that challenges face recognition like illumination, pose variation, facial expressions.


title: The study and application of the IOT technology in agriculture

abstract: in recent years, greenhouse technology in agriculture is to automation, information technology direction with the IOT (internet of things) technology rapid development and wide application. In the paper, control networks and information networks integration of IOT technology has been studied based on the actual situation of agricultural production. Remote monitoring system with internet and wireless communications combined is proposed. At the same time, taking into account the system, information management system is designed. The collected data by the system provided for agricultural research facilities.


title: A Morphological Analyzer to Enable English to Sinhala Machine Translation

abstract: Morphological analysis plays a key role in effective functioning of a parser of any machine translation system. Further, morphological analyzers are useful as supportive software tools to coin terms for a given language. This paper reports on the first morphological analysis system for Sinhala language. This comes out as a major step in the development of a Sinhala parser for machine translation from English to Sinhala. The paper describes how the morphological analyzer can detect grammatical information of a given Sinhala word, and generation of all possible forms of the given word. It is also presented how a language specialist can use our system to device Sinhala terms that are agreeable with Sinhala grammar. The system has been developed with the use of SWI-Prolog, and to runs on Windows and Linux.


title: Implementing remote procedure calls

abstract: Remote procedure calls (RPC) appear to be a useful paradig m for providing communication across a network between programs written in a high-level language. This paper describes a package providing a remote procedure call facility, the options that face the designer of such a package, and the decisions ~we made. We describe the overall structure of our RPC mechanism, our facilities for binding RPC clients, the transport level communication protocol, and some performance measurements. We include descriptioro~ of some optimizations used to achieve high performance and to minimize the load on server machines that have many clients.


title: Memory reduction method for deep neural network training

abstract: Training deep neural networks requires a large amount of memory, making very deep neural networks difficult to fit on accelerator memories. In order to overcome this limitation, we present a method to reduce the amount of memory for training a deep neural network. The method enables to suppress memory increase during the backward pass, by reusing the memory regions allocated for the forward pass. Experimental results exhibit our method reduced the occupied memory size in training by 44.7% on VGGNet with no accuracy affection. Our method also enabled training speedup by increasing the mini batch size up to double.


title: A MATLAB-Based Virtual Tool for the Electromagnetic Wave Scattering from a Perfectly Reflecting Wedge

abstract: A MATLAB-based virtual diffraction tool, using analytical exact as well as well-known high-frequency asymptotic (HFA) techniques, is introduced. Electromagnetic wave scattering from a wedge-shaped object with perfect electrical conductor (PEC) boundaries, under both line-source and plane-wave illuminations, can be automatically analyzed. Effects of various parameters on the reflection, refraction, and diffraction can be investigated. Comparisons among Geometrical Optics (GO), Uniform Theory of Diffraction (UTD), Physical Optics (PO), Physical Theory of Diffraction (PTD), and Parabolic Equation (PE) models are possible through many scenarios and illustrations.


title: Diferulates as structural components in soluble and insoluble cereal dietary fibre

abstract: Cell wall cross-linking can have a substantial effect on the properties of the wall. To estimate cross-linking (between arabinoxylans) in cereal fibres, dehydrodiferulate levels were measured in soluble and insoluble dietary fibre (SDF and IDF) isolated from whole grains of maize (Zea mays L), wheat (Triticum aestivum L), spelt (Triticum spelta L), rice (Oryza sativa L), wild rice (Zizania aquatica L), barley (Hordeum vulgare L), rye (Secale cereale L), oat (Avena sativa L) and millet (Panicum miliaceum L). After saponification of the cereal fibres the extracts were investigated for dehydrodimers of ferulic acid using GLC–MS and GLC–FID. From most cereal IDF the whole spectrum of dehydrodiferulic acids (DFAs) (8-5′-, 8-8′-, 5-5′-, 8-O-4′- and 4-O-5′-coupled) could be identified. The absolute contents of total DFAs ranged between 2.4 and 12.6 mg g−1. With the exception of 4-O-5′-coupled DFA, the whole range of DFAs was also detected from cereal SDF but only in amounts of 40–230 µg g−1. It was estimated that arabinoxylans of cereal IDF contain 8–39 times more diferulates than arabinoxylans of cereal SDF (where measurement of DFA levels in SDF was possible). In cereal IDF, 8-5′-coupled dimers dominated, whereas in cereal SDF, 8-8′-coupled dimers were relatively enhanced and often became the major dimers. ::: ::: ::: ::: © 2001 Society of Chemical Industry


title: Fine-Grained Crowdsourcing for Fine-Grained Recognition

abstract: Fine-grained recognition concerns categorization at sub-ordinate levels, where the distinction between object classes is highly local. Compared to basic level recognition, fine-grained categorization can be more challenging as there are in general less data and fewer discriminative features. This necessitates the use of stronger prior for feature selection. In this work, we include humans in the loop to help computers select discriminative features. We introduce a novel online game called "Bubbles" that reveals discriminative features humans use. The player's goal is to identify the category of a heavily blurred image. During the game, the player can choose to reveal full details of circular regions ("bubbles"), with a certain penalty. With proper setup the game generates discriminative bubbles with assured quality. We next propose the "Bubble Bank" algorithm that uses the human selected bubbles to improve machine recognition performance. Experiments demonstrate that our approach yields large improvements over the previous state of the art on challenging benchmarks.


title: Gated Neural Networks for Option Pricing: Rationality by Design

abstract: We propose a neural network approach to price EU call options that significantly outperforms some existing pricing models and comes with guarantees that its predictions are economically reasonable. To achieve this, we introduce a class of gated neural networks that automatically learn to divide-and-conquer the problem space for robust and accurate pricing. We then derive instantiations of these networks that are 'rational by design' in terms of naturally encoding a valid call option surface that enforces no arbitrage principles. This integration of human insight within data-driven learning provides significantly better generalisation in pricing performance due to the encoded inductive bias in the learning, guarantees sanity in the model's predictions, and provides econometrically useful byproduct such as risk neutral density.


title: Face recognition using Weber local descriptors

abstract: This paper presents a method for face recognition using multi-scale Weber local descriptors (WLDs) and multi-level information fusion. Our method introduces the WLD, a novel and robust local descriptor, to describe the facial images and modifies it by a non-linear quantization approach to enhance its discriminative power. Moreover, a multi-scale framework for WLD extraction with multi-level information fusion approaches is provided for face representation and recognition. The proposed method has four main steps: (1) image partition: under given rules, each facial image is uniformly divided into a set of non-overlapped sub-regions; in this way, for a set of facial images, we therefore have a large pool of this type of sub-regions; (2) feature extraction: in this pool of sub-regions, taking one sub-region as a center, a group of similar ones are chosen for extraction of WLD histogram features; (3) features measurement: these WLD histograms are then fused into a single vector - as the feature of the center sub-region. Nearest neighborhood on chi-square is employed for similarity measurement between two sub-regions; and (4) voting: the recognition result of the entire probe (a face in sub-regions) is obtained via a voting function on the recognition result of all its sub-regions. Experimental results demonstrate the effectiveness of the proposed method upon three popular datasets.


Kafka: The definitive guide


title: Deep Variational Information Bottleneck

abstract: We present a variational approximation to the information bottleneck of Tishby et al. (1999). This variational approach allows us to parameterize the information bottleneck model using a neural network and leverage the reparameterization trick for efficient training. We call this method"Deep Variational Information Bottleneck", or Deep VIB. We show that models trained with the VIB objective outperform those that are trained with other forms of regularization, in terms of generalization performance and robustness to adversarial attack.


Racism and the health of indigenous Australians


title: 3D building reconstruction from LiDAR data

abstract: Airborne LiDAR data can be used to create 3D terrain models useful in augmented or virtual reality in which real-world landscapes are incorporated. Automatic generation of 3D models of man-made structures such as buildings is essential for creating these detailed models. We present a method that builds a 3D mesh, segments the triangles, and forms planar facets that correspond to a roof structure of a target building. We illustrate our method with real-world data sets.


Edge landmarks in monocular slam


title: Creating music videos using automatic media analysis

abstract: We present methods for automatic and semi-automatic creation of music videos, given an arbitrary audio soundtrack and source video. Significant audio changes are automatically detected; similarly, the source video is automatically segmented and analyzed for suitability based on camera motion and exposure. Video with excessive camera motion or poor contrast is penalized with a high unsuitability score, and is more likely to be discarded in the final edit. High quality video clips are then automatically selected and aligned in time with significant audio changes. Video clips are adjusted to match the audio segments by selecting the most suitable region of the desired length. Besides a fully automated solution, our system can also start with clips manually selected and ordered using a graphical interface. The video is then created by truncating the selected clips (preserving the high quality portions) to produce a video digest that is synchronized with the soundtrack music, thus enhancing the impact of both.


title: Multi-VNF performance characterization for virtualized network functions

abstract: Network Function Virtualization promises to reduce the overall operational and capital expenses experienced by the network operators. Running multiple network functions on top of a standard x86 server instead of dedicated appliances can increase the utilization of the underlying hardware and reduce the maintenance and management costs. However, total cost of ownership calculations are typically a function of the attainable network throughput, which in a virtualized system is highly dependent on the overall system architecture - in particular the input/ output (I/O) path. In this paper, we investigate the attainable performance of an x86 host running multiple Virtualized Network Functions (VNFs) under different I/O architectures: OVS, SRIOV and FD.io VPP. We show that the system throughput in a multi-VNF environment differs significantly from deployments where only a single VNF is running on a server, while different I/O architectures can achieve different levels of performance.


title: Locally Linear Support Vector Machines

abstract: Linear support vector machines (SVMs) have become popular for solving classification tasks due to their fast and simple online application to large scale data sets. However, many problems are not linearly separable. For these problems kernel-based SVMs are often used, but unlike their linear variant they suffer from various drawbacks in terms of computational and memory efficiency. Their response can be represented only as a function of the set of support vectors, which has been experimentally shown to grow linearly with the size of the training set. In this paper we propose a novel locally linear svm classifier with smooth decision boundary and bounded curvature. We show how the functions defining the classifier can be approximated using local codings and show how this model can be optimized in an online fashion by performing stochastic gradient descent with the same convergence guarantees as standard gradient descent method for linear svm. Our method achieves comparable performance to the state-of-the-art whilst being significantly faster than competing kernel SVMs. We generalise this model to locally finite dimensional kernel SVM.


title: In vivo retinal optical coherence tomography at 1040 nm - enhanced penetration into the choroid

abstract: For the first time in vivo retinal imaging has been performed with a new compact, low noise Yb-based ASE source operating in the 1 microm range (NP Photonics, lambdac = 1040 nm, Deltalambda = 50 nm, Pout = 30 mW) at the dispersion minimum of water with ~7 microm axial resolution. OCT tomograms acquired at 800 nm are compared to those achieved at 1040 nm showing about 200 microm deeper penetration into the choroid below the retinal pigment epithelium. Retinal OCT at longer wavelengths significantly improves the visualization of the retinal pigment epithelium/choriocapillaris/choroids interface and superficial choroidal layers as well as reduces the scattering through turbid media and therefore might provide a better diagnosis tool for early stages of retinal pathologies such as age related macular degeneration which is accompanied by choroidal neovascularization, i.e., extensive growth of new blood vessels in the choroid and retina.


title: Quantitative analysis of the effects of polarization and pattern reconfiguration for mmWave 5G mobile antenna prototypes

abstract: Real-time beam-steering of highly directive antennas at mmWave spectrum under unpredictable propagation environments remains a highly elusive challenge for the upcoming 5G era. Reconfigurable and multifunctional mobile 5G antenna and RF architectures which are desired to achieve sufficient QoS introduce additional technical and economic costs which may outweigh the benefits. This paper investigates the effects and relative significance of key antenna design parameters with QoS benchmarks based on real-life mmWave antennas integrated in fully operable 5G mobile device prototypes.


title: Theoretical prediction of a novel inorganic fullerene-like family of silicon–carbon materials

abstract: Abstract In an effort to search for new inorganic fullerene-like structures, we designed a series of novel silicon–carbon cages, (SiC) n ( n = 6–36), based on the uniformly hybrid Si–C four- and six-membered-rings, and researched their geometrical and electronic structures, as well as their relative stabilities using the density function theory. Among these cages, the structures for n = 12, 16, and 36 were found to been energetically more favorable. The calculated disproportionation energy and binding energy per SiC unit show that the (SiC) 12 cage is the most stable one among these designed structures. The present calculations not only indicate that silicon–carbon fullerenes are promised to be synthesized in future, but also provide a new way for stabilizing silicon cages by uniformly doping carbon atoms into silicon structures.


title: Microelectromechanical systems (MEMS): fabrication, design and applications

abstract: Micromachining and micro-electromechanical system (MEMS) technologies can be used to produce complex structures, devices and systems on the scale of micrometers. Initially micromachining techniques were borrowed directly from the integrated circuit (IC) industry, but now many unique MEMS-specific micromachining processes are being developed. In MEMS, a wide variety of transduction mechanisms can be used to convert real-world signals from one form of energy to another, thereby enabling many different microsensors, microactuators and microsystems. Despite only partial standardization and a maturing MEMS CAD technology foundation, complex and sophisticated MEMS are being produced. The integration of ICs with MEMS can improve performance, but at the price of higher development costs, greater complexity and a longer development time. A growing appreciation for the potential impact of MEMS has prompted many efforts to commercialize a wide variety of novel MEMS products. In addition, MEMS are well suited for the needs of space exploration and thus will play an increasingly large role in future missions to the space station, Mars and beyond. (Some figures in this article are in colour only in the electronic version)


title: Identity, credential, and access management at NASA, from Zachman to attributes

abstract: To achieve the ultimate goal of attribute-based access control (ABAC), a robust architecture for Identity, Credential, and Access Management must first be established. The National Aeronautics and Space Administration (NASA) began formal development of its Identity, Credential, and Access Management Architecture using the Zachman Framework for Enterprise Architecture in June 2006. The Architecture provided the necessary structure to meet aggressive deadlines for issuance and use of the PIV smartcard. It also led to the development of NASA's Logical Access Control infrastructure to support not only PIV smartcards, but all authentication credentials in use at NASA. Use of the Zachman Framework has transformed the way that NASA looks at Logical Access Control, and has positioned NASA to provide robust attributed-based access control in the future. In this paper, we will discuss the Logical Access Control System (LACS) we are implementing at NASA, changes in the way NASA views Identity Trust and Level of Assurance, technical challenges to implementation, and our future vision for Identity, Credential, and Access Management.


title: Unsupervised storyline extraction from news articles

abstract: Storyline extraction from news streams aims to extract events under a certain news topic and reveal how those events evolve over time. It requires algorithms capable of accurately extracting events from news articles published in different time periods and linking these extracted events into coherent stories. The two tasks are often solved separately, which might suffer from the problem of error propagation. Existing unified approaches often consider events as topics, ignoring their structured representations. In this paper, we propose a non-parametric generative model to extract structured representations and evolution patterns of storylines simultaneously. In the model, each storyline is modelled as a joint distribution over some locations, organizations, persons, keywords and a set of topics. We further combine this model with the Chinese restaurant process so that the number of storylines can be determined automatically without human intervention. Moreover, per-token Metropolis-Hastings sampler based on light latent Dirichlet allocation is employed to reduce sampling complexity. The proposed model has been evaluated on three news corpora and the experimental results show that it outperforms several baseline approaches.


A Practical Bayesian Framework for Backprop Networks


title: Narrative Therapy for Treating Video Game Addiction

abstract: Video game addiction is increasingly prevalent and treatment modalities have ignored the unique aspects of this particular process addiction. This article reviews the construct of video game addiction and posits utilizing narrative therapy in a manner tailored to the video game culture. By focusing upon the skills inherent in being a successful gamer, clients can translate those skills from online to offline and make substantial changes in their lives. Also, an appendix with commonly used terms in the video game culture is provided.


title: A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems

abstract: Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria.


title: Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image Processing

abstract: Recently, convex solutions to low-rank matrix factorization problems have received increasing attention in machine learning. However, in many applications the data can display other structures beyond simply being low-rank. For example, images and videos present complex spatio-temporal structures, which are largely ignored by current low-rank methods. In this paper we explore a matrix factorization technique suitable for large datasets that captures additional structure in the factors by using a projective tensor norm, which includes classical image regularizers such as total variation and the nuclear norm as particular cases. Although the resulting optimization problem is not convex, we show that under certain conditions on the factors, any local minimizer for the factors yields a global minimizer for their product. Examples in biomedical video segmentation and hyperspectral compressed recovery show the advantages of our approach on high-dimensional datasets.


title: Key ingredients in an IoT recipe: Fog Computing, Cloud computing, and more Fog Computing

abstract: This paper examines some of the most promising and challenging scenarios in IoT, and shows why current compute and storage models confined to data centers will not be able to meet the requirements of many of the applications foreseen for those scenarios. Our analysis is particularly centered on three interrelated requirements: 1) mobility; 2) reliable control and actuation; and 3) scalability, especially, in IoT scenarios that span large geographical areas and require real-time decisions based on data analytics. Based on our analysis, we expose the reasons why Fog Computing is the natural platform for IoT, and discuss the unavoidable interplay of the Fog and the Cloud in the coming years. In the process, we review some of the technologies that will require considerable advances in order to support the applications that the IoT market will demand.


title: Foresight: Rapid Data Exploration Through Guideposts

abstract: Current tools for exploratory data analysis (EDA) require users to manually select data attributes, statistical computations and visual encodings. This can be daunting for large-scale, complex data. We introduce Foresight, a visualization recommender system that helps the user rapidly explore large high-dimensional datasets through "guideposts." A guidepost is a visualization corresponding to a pronounced instance of a statistical descriptor of the underlying data, such as a strong linear correlation between two attributes, high skewness or concentration about the mean of a single attribute, or a strong clustering of values. For each descriptor, Foresight initially presents visualizations of the "strongest" instances, based on an appropriate ranking metric. Given these initial guideposts, the user can then look at "nearby" guideposts by issuing "guidepost queries" containing constraints on metric type, metric strength, data attributes, and data values. Thus, the user can directly explore the network of guideposts, rather than the overwhelming space of data attributes and visual encodings. Foresight also provides for each descriptor a global visualization of ranking-metric values to both help orient the user and ensure a thorough exploration process. Foresight facilitates interactive exploration of large datasets using fast, approximate sketching to compute ranking metrics. We also contribute insights on EDA practices of data scientists, summarizing results from an interview study we conducted to inform the design of Foresight.


title: Deep learning for content-based video retrieval in film and television production

abstract: While digitization has changed the workflow of professional media production, the content-based labeling of image sequences and video footage, necessary for all subsequent stages of film and television production, archival or marketing is typically still performed manually and thus quite time-consuming. In this paper, we present deep learning approaches to support professional media production. In particular, novel algorithms for visual concept detection, similarity search, face detection, face recognition and face clustering are combined in a multimedia tool for effective video inspection and retrieval. The analysis algorithms for concept detection and similarity search are combined in a multi-task learning approach to share network weights, saving almost half of the computation time. Furthermore, a new visual concept lexicon tailored to fast video retrieval for media production and novel visualization components are introduced. Experimental results show the quality of the proposed approaches. For example, concept detection achieves a mean average precision of approximately 90% on the top-100 video shots, and face recognition clearly outperforms the baseline on the public Movie Trailers Face Dataset.


title: Personalized Academic Paper Recommendation System

abstract: Recommendation systems can take advantage of social media in various ways. One common example is combining social relationship into neighborhood-based recommendation systems, under the assumption that social relationship affects individuals’ interest or preference. Although this assumption may not be always true, this paper presents a realistic application, personalized academic paper recommendation system, which social relationship can be closely related to taste. There is an increasing number of academic papers being published each year, but most researchers rely on keyword-based search or browsing through proceedings of top conferences and journals to nd their related work. Personalized academic paper recommendation system is designed to reduce their workload. With a collaborative-lt eringbased approach, it recommends potentially preferred articles for each researcher in personalized manner. Both computational evaluation and user study demonstrate that our system recommends a useful set of research papers.


An Analysis for Big Data and its Technologies


title: An Online Learning Approach to Generative Adversarial Networks

abstract: We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be difficult to train due to instabilities caused by a difficult minimax optimization problem. In this paper, we view the problem of training GANs as finding a mixed strategy in a zero-sum game. Building on ideas from online learning we propose a novel training method named Chekhov GAN 1 . On the theory side, we show that our method provably converges to an equilibrium for semi-shallow GAN architectures, i.e. architectures where the discriminator is a one layer network and the generator is arbitrary. On the practical side, we develop an efficient heuristic guided by our theoretical results, which we apply to commonly used deep GAN architectures. On several real world tasks our approach exhibits improved stability and performance compared to standard GAN training.


title: Reconfigurable software defined radio modem for Personal Area Network

abstract: The spectrum is the most crucial resource in today's world of modern wireless communication. The most of the spectrum under licensed category have already been allocated for licensed users. Therefore the remaining unlicensed bands are of high demand. One of such unlicensed industrial, scientific and medical (ISM) band falls under Personal Area Networking services governed by the standard IEEE802.15.4. This standard mainly features the three different frequency bands 868 MHz, 915 MHz and 2450 MHz for physical layer implementation supporting varied data rate with different digital modulation techniques. It is necessary to have a reprogrammable multi band/multi modem transceiver in order to support both regional and global frequency bands usage with a one product. The objective of this work is to develop different bands usage by one single product. Hence this paper focuses on designing of reconfigurable modem covering BPSK and O-QPSK for four different bit rates 20 kb/s, 40 kb/s, 100 kb/s and 250 kb/s, which can be ported on to hardware to a build a communication system for 3 various bands of IEEE802.15.4. The reconfigurable feature of Software Defined Radio (SDR) was exploited for design of programmable modem using Matlab/Simulink tool.


title: Semantic Segmentation Refinement by Monte Carlo Region Growing of High Confidence Detections

abstract: The semantic segmentation produced by most state-of-the-art methods does not show satisfactory adherence to object boundaries. Methods such as fully-connected conditional random fields (CRFs) can significantly refine segmentation predictions. However, they rely on supervised parameter optimization that depends upon specific datasets and predictor modules. We propose an unsupervised method for semantic segmentation refinement that takes as input the confidence scores generated by a segmentation network and re-labels pixels with low confidence levels. More specifically, a region growing mechanism aggregates these pixels to neighboring areas with high confidence scores and similar appearance. To minimize the impact of high-confidence prediction errors, our algorithm performs multiple growing steps by Monte Carlo sampling initial seeds in high-confidence regions. Our method provides both running time and segmentation improvements comparable to state-of-the-art refinement approaches for semantic segmentation, as demonstrated by evaluations on multiple publicly available benchmark datasets.


title: Starting from scratch: Developing a pre-service teacher training program in computational thinking

abstract: This paper details a series of pre-professional development interventions to assist teachers in utilizing computational thinking and programming as an instructional tool within other subject areas (i.e. music, language arts, mathematics, and science). It describes the lessons utilized in the interventions along with the instruments used to evaluate them, and offers some preliminary findings.


title: The intractability of resolution

abstract: Abstract We prove that, for infinitely many disjunctive normal form propositional calculus tautologies ξ, the length of the shortest resolution proof of ξ cannot be bounded by any polynomial of the length of ξ. The tautologies we use were introduced by Cook and Reckhow (1979) and encode the pigeonhole principle. Extended resolution can furnish polynomial length proofs of these formulas.


title: Smart collaboration between humans and machines based on mutual understanding

abstract: To improve the safety and comfort of a human-machine system, the machine needs to 'know,' in a real time manner, the human operator in the system. The machine's assistance to the human can be fine tuned if the machine is able to sense the human's state and intent. Related to this point, this paper discusses issues of human trust in automation, automation surprises, responsibility and authority. Examples are given of a driver assistance system for advanced automobile.


title: Stock market value prediction using neural networks

abstract: Neural networks, as an intelligent data mining method, have been used in many different challenging pattern recognition problems such as stock market prediction. However, there is no formal method to determine the optimal neural network for prediction purpose in the literature. In this paper, two kinds of neural networks, a feed forward multi layer Perceptron (MLP) and an Elman recurrent network, are used to predict a company's stock value based on its stock share value history. The experimental results show that the application of MLP neural network is more promising in predicting stock value changes rather than Elman recurrent network and linear regression method. However, based on the standard measures that will be presented in the paper we find that the Elman recurrent network and linear regression can predict the direction of the changes of the stock value better than the MLP.


title: Incident Ticket Analytics for IT Application Management Services

abstract: An important IT service outsourcing business is to resolve incidents related to IT infrastructures our clients contract our company to support. Incidents are recorded as structured and unstructured data in tickets, which contain various characteristics


title: An Evaluation of the Hibernate Object-Relational Mapping for Processing Interactive Social Networking Actions

abstract: With object-oriented programming languages, Object Relational Mapping (ORM) frameworks such as Hibernate have gained popularity due to their ease of use and portability to different relational database management systems. Hibernate implements the Java Persistent API, JPA, and frees a developer from authoring software to address the impedance mismatch between objects and relations. In this paper, we evaluate the performance of Hibernate by comparing it with a native JDBC implementation using a benchmark named BG. BG rates the performance of a system for processing interactive social networking actions such as view profile, extend an invitation from one member to another, and other actions. Our key findings are as follows. First, an object-oriented Hibernate implementation of each action issues more SQL queries than its JDBC counterpart. This enables the JDBC implementation to provide response times that are significantly faster. Second, one may use the Hibernate Query Language (HQL) to refine the object-oriented Hibernate implementation to provide performance that approximates the JDBC implementation.


title: Structural Sentence Similarity Estimation for Short Texts.

abstract: Sentence similarity is the basis of most text-related tasks. In this paper, we define a new task of sentence similarity estimation specifically for short while informal, social-network styled sentences. The new type of sentence similarity, which we call Structural Similarity, eliminates syntactic or grammatical features such as dependency paths and Part-of-Speech (POS) tagging which do not have enough representativeness on short sentences. Structural Similarity does not consider actual meanings of the sentences either but puts more emphasis on the similarities of sentence structures, so as to discover purpose- or emotion-level similarities. The idea is based on the observation that people tend to use sentences with similar structures to express similar feelings. Besides the definition, we present a new feature set and a mechanism to calculate the scores, and, for the needs of disambiguating word senses we propose a variant of the Word2Vec model to represent words. We prove the correctness and advancement of our sentence similarity measurement by experiments.


From big data analysis to personalized medicine for all: challenges and opportunities


title: Effect of Gen2 protocol parameters on RFID tag performance

abstract: In this paper, we analyze the effect of Gen2 protocol parameters on RFID tag performance (tag sensitivity and backscatter efficiency). We describe our measurement methodology and perform characterization of several tags with different latest Gen2 ICs available on the market (Monza, UCODE, and Higgs families). To confirm our findings, we repeat measurements using conducted tag setup. We analyze data and draw conclusions on how the protocol parameters affect the tag performance in forward and reverse links.


title: Cascaded Subpatch Networks for Effective CNNs

abstract: Conventional convolutional neural networks use either a linear or a nonlinear filter to extract features from an image patch (region) of spatial size $ H\times W $ (typically, $ H $ is small and is equal to $ W$ , e.g., $ H $ is 5 or 7). Generally, the size of the filter is equal to the size $ H\times W $ of the input patch. We argue that the representational ability of equal-size strategy is not strong enough. To overcome the drawback, we propose to use subpatch filter whose spatial size $ h\times w $ is smaller than $ H\times W $ . The proposed subpatch filter consists of two subsequent filters. The first one is a linear filter of spatial size $ h\times w $ and is aimed at extracting features from spatial domain. The second one is of spatial size $ 1\times 1 $ and is used for strengthening the connection between different input feature channels and for reducing the number of parameters. The subpatch filter convolves with the input patch and the resulting network is called a subpatch network. Taking the output of one subpatch network as input, we further repeat constructing subpatch networks until the output contains only one neuron in spatial domain. These subpatch networks form a new network called the cascaded subpatch network (CSNet). The feature layer generated by CSNet is called the csconv layer. For the whole input image, we construct a deep neural network by stacking a sequence of csconv layers. Experimental results on five benchmark data sets demonstrate the effectiveness and compactness of the proposed CSNet. For example, our CSNet reaches a test error of 5.68% on the CIFAR10 data set without model averaging. To the best of our knowledge, this is the best result ever obtained on the CIFAR10 data set.


title: Coherent stylized silhouettes

abstract: We describe a way to render stylized silhouettes of animated 3D models with temporal coherence. Coherence is one of the central challenges for non-photorealistic rendering. It is especially difficult for silhouettes, because they may not have obvious correspondences between frames. We demonstrate various coherence effects for stylized silhouettes with a robust working system. Our method runs in real-time for models of moderate complexity, making it suitable for both interactive applications and offline animation.


Comparative Evaluation of Process Mining Tools


title: Can emerging market bank regulators establish credible discipline? The case of Argentina

abstract: In the early 1990s, after decades of high inflation and financial repression, Argentina embarked on a course of macroeconomic and bank regulatory reform. Bank regulatory policy promoted privatization, financial liberalization, and free entry, limited safety net support, and established a novel mix of regulatory and market discipline to ensure stable growth of the banking system during the liberalization process. Argentina suffered some fallout from the Mexican tequila crisis of 1995, but its response to that crisis (allowing weak banks to close) and the redoubling of regulatory efforts to promote market discipline after the crisis made Argentina's banking system quite resilient during the Asian, Russian, and Brazilian crises. Argentina's bank regulatory system now is widely regarded as one of the two or three most successful among emerging market economies. This paper traces the evolution of the regulatory policy changes of the 1990s and shows that the reliance on market discipline has played an important role in prudential regulation by encouraging proper risk management by banks. There is substantial heterogeneity among banks in the interest rates they pay for debt and the rate of growth of their deposits, and that heterogeneity is traceable to fundamental attributes of banks that affect the riskiness of deposits (i.e. asset risk and leverage). Moreover, market perceptions of default risk are mean-reverting, indicating that market discipline encourages banks to respond to increases in default risk by limiting asset risk or lowering leverage.


Lezical Structures in Syntaz and Semantics


abstract: A flat car-roof antenna module for phone and GPS applications is proposed. The module consists of a planar inverted-F antenna (PIFA) for the phone operation and a planar loop antenna for the GPS one. The two antennas are located in a same plane parallel to car roof and put close to each other so that the dimension of the module is only 50 × 80 mm2 in plane. Gap between the antenna plane and the car roof is only 19 mm. This low profile makes the module almost invisible when it is installed on a car roof. Even with this narrow gap, the two antennas keep high performance in their operation frequencies.


title: The firefighter problem: Empirical results on random graphs

abstract: The firefighter problem is a deterministic discrete-time model for the spread and containment of fire on a graph. Once the fire breaks out at a set of vertices, the goal addressed in this work is to save as many vertices as possible from burning. Although the problem finds applications in various real-world problems, such as the spread of diseases or hoaxes contention in communication networks, this problem has not been addressed from a practical point of view so far, in the sense of finding a good strategy for the general case. In this work, we develop and compare several integer linear programming techniques and heuristic methods. Random graphs are used for the purpose of comparison. The obtained results shed some light on the challenges for computational tools as caused by graph topology, graph size, and the number of firefighters per iteration, when looking for the best strategy for an a priori unknown graph.


title: DreamCam: A modular FPGA-based smart camera architecture

abstract: DreamCam is a modular smart camera constructed with the use of an FPGA like main processing board. The core of the camera is an Altera Cyclone-III associated with a CMOS imager and six private Ram blocks. The main novel feature of our work consists in proposing a new smart camera architecture and several modules (IP) to efficiently extract and sort the visual features in real time. In this paper, extraction is performed by a Harris and Stephen filtering associated with customized modules. These modules extract, select and sort visual features in real-time. As a result, DreamCam (with such a configuration) provides a description of each visual feature in the form of its position and the grey-level template around it.


title: Wideband Balun-LNA With Simultaneous Output Balancing, Noise-Canceling and Distortion-Canceling

abstract: An inductorless low-noise amplifier (LNA) with active balun is proposed for multi-standard radio applications between 100 MHz and 6 GHz. It exploits a combination of a common-gate (CGH) stage and an admittance-scaled common-source (CS) stage with replica biasing to maximize balanced operation, while simultaneously canceling the noise and distortion of the CG-stage. In this way, a noise figure (NF) close to or below 3 dB can be achieved, while good linearity is possible when the CS-stage is carefully optimized. We show that a CS-stage with deep submicron transistors can have high IIP2, because the nugsldr nuds cross-term in a two-dimensional Taylor approximation of the IDS(VGS, VDS) characteristic can cancel the traditionally dominant square-law term in the IDS(VGS) relation at practical gain values. Using standard 65 nm transistors at 1.2 V supply voltage, we realize a balun-LNA with 15 dB gain, NF +20 dBm, while simultaneously achieving an IIP3 > 0 dBm. The best performance of the balun is achieved between 300 MHz to 3.5 GHz with gain and phase errors below 0.3 dB and plusmn2 degrees. The total power consumption is 21 mW, while the active area is only 0.01 mm2.


title: Marloes L. van Engen & Tineke M. WillemsenGENDER AND LEADERSHIP STYLES 2 Gender and leadership styles: A review of the past decade

abstract: Research on sex differences in leadership styles over the past decade (1987-1999) is reviewed and compared with the results of a meta-analysis of studies of the previous period by Eagly and Johnson (1990). Research on transformational or charismatic leadership is included in this review. As was the case in previous overviews, the evidence for sex differences in leadership behavior is still mixed, yet it is clear that these sex differences have not vanished. It is argued that sex differences in leadership styles are largely a consequence of the context in which male and female leaders work. Organizational factors like sex-composition of the immediate working context and hierarchical level are important moderators of leadership styles. We conclude that future research should unravel the impact of structural and organizational features, that are still so confounded with gender, on leadership behavior.


title: Online crowdsourcing: Rating annotators and obtaining cost-effective labels

abstract: Labeling large datasets has become faster, cheaper, and easier with the advent of crowdsourcing services like Amazon Mechanical Turk. How can one trust the labels obtained from such services? We propose a model of the labeling process which includes label uncertainty, as well a multi-dimensional measure of the annotators' ability. From the model we derive an online algorithm that estimates the most likely value of the labels and the annotator abilities. It finds and prioritizes experts when requesting labels, and actively excludes unreliable annotators. Based on labels already obtained, it dynamically chooses which images will be labeled next, and how many labels to request in order to achieve a desired level of confidence. Our algorithm is general and can handle binary, multi-valued, and continuous annotations (e.g. bounding boxes). Experiments on a dataset containing more than 50,000 labels show that our algorithm reduces the number of labels required, and thus the total cost of labeling, by a large factor while keeping error rates low on a variety of datasets.


title: Scientific foundations and effective chemical methods of combustion, explosion, and gas detonation control

abstract: The competition between the branching and termination of reaction chains is shown to determine all general laws of gas combustion and explosion not only at pressures much lower that atmospheric, but also at atmospheric and increased pressures upon self-heating. It is established that the role of any elementary reaction in combustion is determined primarily by its effect on the relation between the rates of chain branching and termination. Scientific foundations and effective chemical methods of combustion, explosion, and combustible gas detonation control are developed.


title: High-Frequency Behavior of Graphene-Based Interconnects—Part II: Impedance Analysis and Implications for Inductor Design

abstract: This paper provides the first detailed insights into the ultrahigh-frequency behavior of graphene ribbons (GRs) and analyzes their consequences in designing interconnects and low-loss on-chip inductors. In the companion paper (part I), an accurate impedance modeling methodology has been developed based on the Boltzmann equation with the magnetic vector potential Green's function approach incorporating the dependency of current on the nonlocal electric field. Based on the developed methodology, this paper for the first time embarks on the rigorous investigation of the intricate processes occurring at high frequencies in GRs, such as anomalous skin effect (ASE), high-frequency resistance and inductance saturation, intercoupled relation between edge specularity and ASE, and the influence of the linear dimensions on impedance. A comparative study of the high-frequency response of GRs with that of carbon nanotubes (CNTs) and Cu is made to highlight the potential of GR interconnects for high-frequency applications. Subsequently, the high-frequency performance of GR inductors is analyzed, and it is shown that they can achieve 32% and 50% improvements in maximum Q-factor compared to Cu and single-walled CNT inductors with 1/3 metallic fraction, respectively.


THE MORAL EMOTIONS


Opportunities for energy saving in the Dutch household sector


title: Wave Physics as an Analog Recurrent Neural Network

abstract: Analog machine learning hardware platforms promise to be faster and more energy efficient than their digital counterparts. Wave physics, as found in acoustics and optics, is a natural candidate for building analog processors for time-varying signals. Here, we identify a mapping between the dynamics of wave physics and the computation in recurrent neural networks. This mapping indicates that physical wave systems can be trained to learn complex features in temporal data, using standard training techniques for neural networks. As a demonstration, we show that an inverse-designed inhomogeneous medium can perform vowel classification on raw audio signals as their waveforms scatter and propagate through it, achieving performance comparable to a standard digital implementation of a recurrent neural network. These findings pave the way for a new class of analog machine learning platforms, capable of fast and efficient processing of information in its native domain.


title: Shamir's key based confidentiality on cloud data storage

abstract: Confidentiality is used on accessing the set of cloud database information with high security level. The conventional encryption and decryption mechanism for the privacy maintenance in cloud zone acquire additional processing time. Hence, the cloud service did not explore effective confidentiality on information retrieval process. A new key distribution scheme for efficient privacy preserving query plans on the cloud data achieves a higher percentage of confidentiality by residing the cloud data with polynomial interpolation. To ensure high confidentiality and providing privacy to the cloud users the Shamir's Key Distribution based Confidentiality (SKDC) scheme is employed. SKDC scheme generates a polynomial of degree with the secret as the first coefficient and the remaining coefficients picked up at random to improve the privacy preserving level on the cloud infrastructure. Shamir's Key Distribution supports batch auditing where multiple user requests for data auditing is held concurrently at a higher confidentiality rate. SKDC scheme handles query processing using the matrix-structure form. An Experimental evaluation is performed with Amazon Simple Storage Service dataset to evaluate the confidentiality and privacy performance with Shamir's Key Distribution based Confidentiality (SKDC) scheme is compared with two well-known privacy schemes such as Trusted Third Party Model (TTPM) and Trusted Hardware Based Database. As a result, the SKDC scheme increases confidentiality level of the user by 22 % as compared to existing TTPM method.


title: Phase estimation for fast action recognition and trajectory generation in human–robot collaboration

abstract: This paper proposes a method to achieve fast and fluid human–robot interaction by estimating the progress of the movement of the human. The method allows the progress, also referred to as the phase of the movement, to be estimated even when observations of the human are partial and occluded; a problem typically found when using motion capture systems in cluttered environments. By leveraging on the framework of Interaction Probabilistic Movement Primitives, phase estimation makes it possible to classify the human action, and to generate a corresponding robot trajectory before the human finishes his/her movement. The method is therefore suited for semi-autonomous robots acting as assistants and coworkers. Since observations may be sparse, our method is based on computing the probability of different phase candidates to find the phase that best aligns the Interaction Probabilistic Movement Primitives with the current observations. The method is fundamentally different from approaches based on Dynamic Time Wa...


title: A scalable hierarchical distributed language model

abstract: Neural probabilistic language models (NPLMs) have been shown to be competitive with and occasionally superior to the widely-used n-gram language models. The main drawback of NPLMs is their extremely long training and testing times. Morin and Bengio have proposed a hierarchical language model built around a binary tree of words, which was two orders of magnitude faster than the non-hierarchical model it was based on. However, it performed considerably worse than its non-hierarchical counterpart in spite of using a word tree created using expert knowledge. We introduce a fast hierarchical language model along with a simple feature-based algorithm for automatic construction of word trees from the data. We then show that the resulting models can outperform non-hierarchical neural models as well as the best n-gram models.


ONTAgri : Scalable Service Oriented Agriculture Ontology for Precision Farming


title: Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques

abstract: CRM gains increasing importance due to intensive competition and saturated markets. With the purpose of retaining customers, academics as well as practitioners find it crucial to build a churn prediction model that is as accurate as possible. This study applies support vector machines in a newspaper subscription context in order to construct a churn model with a higher predictive performance. Moreover, a comparison is made between two parameter-selection techniques, needed to implement support vector machines. Both techniques are based on grid search and cross-validation. Afterwards, the predictive performance of both kinds of support vector machine models is benchmarked to logistic regression and random forests. Our study shows that support vector machines show good generalization performance when applied to noisy marketing data. Nevertheless, the parameter optimization procedure plays an important role in the predictive performance. We show that only when the optimal parameter-selection procedure is applied, support vector machines outperform traditional logistic regression, whereas random forests outperform both kinds of support vector machines. As a substantive contribution, an overview of the most important churn drivers is given. Unlike ample research, monetary value and frequency do not play an important role in explaining churn in this subscription-services application. Even though most important churn predictors belong to the category of variables describing the subscription, the influence of several client/company-interaction variables cannot be neglected.


title: Sampling the disparity space image

abstract: A central issue in stereo algorithm design is the choice of matching cost. Many algorithms simply use squared or absolute intensity differences based on integer disparity steps. In this paper, we address potential problems with such approaches. We begin with a careful analysis of the properties of the continuous disparity space image (DSI) and propose several new matching cost variants based on symmetrically matching interpolated image signals. Using stereo images with ground truth, we empirically evaluate the performance of the different cost variants and show that proper sampling can yield improved matching performance.


title: Interpretive case studies in IS research: nature and method

abstract: AbstractThere has been an increase in recent years in the number of in-depth case studies which focus on human actions and interpretations surrounding the development and use of computer-based information systems (IS). This paper addresses philosophical and theoretical issues concerning the nature of such interpretive case studies, and methodological issues on the conduct and reporting of this type of research. The paper aims to provide a useful reference point for researchers who wish to work in the interpretive tradition, and more generally to encourage careful work on the conceptualisation and execution of case studies in the information systems field.


title: Experience with the automatic implantable defibrillator.

abstract: SummaryFive patients received the automatic implantable cardioverter-defibrillator (AICD-B) for °ut-of-hospital cardiac arrest.The primary clinical arrhythmia was ventricular fibrillation in two patients, recurrent mnornorphic ventricular tachycardia in another and polymorphic ventricular tachycardia in another last one. The defibrillation threshold for ventriular fibrillation was 15 Joules in all patients. The rate cut-off criterion was choscn between and 174 beats/min. During a mean follow-up period of sixteen months (range 6 to 35), the five Patients received 60 discharges. Nine shocks were given for arrhythmias induced in the laboratory and 51 were given in clinical conditions. We documented twenty-nine shocks.Sinus rhythm triggered the device once, atrial fibrillation twice and short lasting ventricular tachycardia two times. These unwanted discharges never resulated in ventricular arrhythmias. Assocciated treatment consisted of antiarrthymic drugs in four patients and a maker for bradycardia and tac...


title: Pedestrain Detection from Motion A spatial-temporal approach based on walking actions

abstract: Pedestrian detection is a challenging problem studied over decades. Most algorithms are based on human appearance. Only few works consider motion as a feature component. In this paper, however, we tackle this problem only considering short periods of pedestrian walking. This motion does not depend on the variations of pedestrian pose, body shape, illumination, and background. We model pedestrian motion that has unique properties compare to background and rigid objects motion in spatial-temporal motion profiles. This observation helps us to identify pedestrian leg motion along with body motion over a short time period. Our method also works for a vehicle borne camera where background also moves. We achieved more robust results by dealing with crowds, and other degenerating cases of human motion against background and dynamic scenes. The method has a low computational cost on a motion profile and it can be combined with a shape-based method as pre-screening for reducing the false positives. It also provides a feasible way to find human behaviors.


Prototyping a Wireless Sensor Node using FPGA for Mines Safety Application


title: First Experiments with Neural Translation of Informal to Formal Mathematics

abstract: We report on our experiments to train deep neural networks that automatically translate informalized LaTeX-written Mizar texts into the formal Mizar language. To the best of our knowledge, this is the first time when neural networks have been adopted in the formalization of mathematics. Using Luong et al.'s neural machine translation model (NMT), we tested our aligned informal-formal corpora against various hyperparameters and evaluated their results. Our experiments show that our best performing model configurations are able to generate correct Mizar statements on 65.73\% of the inference data, with the union of all models covering 79.17\%. These results indicate that formalization through artificial neural network is a promising approach for automated formalization of mathematics. We present several case studies to illustrate our results.


title: BYOD: Current state and security challenges

abstract: Recently, BYOD or Bring Your own Device has become one of the most popular models for enterprises to provide mobility and flexibility in workplaces. The emergence of new technologies and features of mobile devices makes them integral parts of every aspect of daily business activities. Also, mobile networks are now well integrated with the Internet (e.g. 3G, 4G and LTE technologies), therefore, in BYOD, the personal devices (i.e. mobile devices) can be used to increase employees' satisfaction and reduce an organization's device costs. Mobile devices are not well protected compared to computer and computer networks and users pay less attention to security updates and solutions. As a result, mobile security has become a crucial issue in BYOD as employees use their own mobile devices to access an organization's data and systems. Therefore, in this paper we present an overview of the current state of BYOD security and we discuss some future challenges in this area.


title: Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering

abstract: As a prolific research area in data mining, subspace clustering and related problems induced a vast quantity of proposed solutions. However, many publications compare a new proposition—if at all—with one or two competitors, or even with a so-called “naive” ad hoc solution, but fail to clarify the exact problem definition. As a consequence, even if two solutions are thoroughly compared experimentally, it will often remain unclear whether both solutions tackle the same problem or, if they do, whether they agree in certain tacit assumptions and how such assumptions may influence the outcome of an algorithm. In this survey, we try to clarify: (i) the different problem definitions related to subspace clustering in general; (ii) the specific difficulties encountered in this field of research; (iii) the varying assumptions, heuristics, and intuitions forming the basis of different approaches; and (iv) how several prominent solutions tackle different problems.


title: Evaluative and generative modes of thought during the creative process

abstract: article i nfo Psychological theories have suggested that creativity involves a twofold process characterized by a generative component facilitating the production of novel ideas and an evaluative component enabling the assessment of their usefulness. The present study employed a novel fMRI paradigm designed to distinguish between these two components at the neural level. Participants designed book cover illustrations while alternating between the generation and evaluation of ideas. The use of an fMRI-compatible drawing tablet allowed for a more natural drawing and creative environment. Creative generation was associated with preferential recruitment of medial temporal lobe regions, while creative evaluation was associated with joint recruitment of executive and default network regions and activation of the rostrolateral prefrontal cortex, insula, and temporopolar cortex. Executive and default regions showed positive functional connectivity throughout task performance. These findings suggest that the medial temporal lobe may be central to the generation of novel ideas and creative evaluation may extend beyond deliberate analytical processes supported by executive brain regions to include more spontaneous affective and visceroceptive evaluative processes supported by default and limbic regions. Thus, creative thinking appears to recruit a unique configuration of neural processes not typically used together during traditional problem solving tasks.


title: From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur

abstract: Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but extensive literature on the subject indicates the difficulty in identifying a prior which is suitably informative, and general. Rather than imposing a prior based on theory, we propose instead to learn one from the data. Learning a prior over the latent image would require modeling all possible image content. The critical observation underpinning our approach, however, is that learning the motion flow instead allows the model to focus on the cause of the blur, irrespective of the image content. This is a much easier learning task, but it also avoids the iterative process through which latent image priors are typically applied. Our approach directly estimates the motion flow from the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from the estimated motion flow. Our FCN is the first universal end-to-end mapping from the blurred image to the dense motion flow. To train the FCN, we simulate motion flows to generate synthetic blurred-image-motion-flow pairs thus avoiding the need for human labeling. Extensive experiments on challenging realistic blurred images demonstrate that the proposed method outperforms the state-of-the-art.


title: Neuropsychological and cerebral metabolic function in early vs late onset dementia of the Alzheimer type

abstract: Abstract Differences in age at onset of dementia of the Alzheimer type (DAT) have been associated with differences in cognitive impairments and course of the disease. To investigate this, we examined cerebral metabolism and cognitive performance in early and late onset DAT patients, who had equivalent duration and severity of illness. Regional cerebral metabolic rates for glucose were measured in the resting state by positron emission tomography using [18F]2-fluoro-2-deoxy- d -glucose. A cross-sectional analysis showed no significant differences between the two groups in performance on neuropsychological tests, but the early onset patients showed significantly more parietal metabolic dysfunction than did the late onset patients. Longitudinal analysis showed no significant differences between early and late onset patients in rate of cognitive decline over a mean interval of 19 months. Thus, our results do not support the hypothesis of different subgroups in DAT based on age at onset, nor suggest a faster rate of cognitive decline in younger patients.


title: Adoption of Bundled Services with Network Externalities and Correlated Affinities

abstract: The goal of this article is to develop a principled understanding of when it is beneficial to bundle technologies or services whose value is heavily dependent on the size of their user base, that is, exhibits positive exernalities. Of interest is how the joint distribution, and in particular the correlation, of the values users assign to components of a bundle affect its odds of success. The results offer insight and guidelines for deciding when bundling new Internet technologies or services can help improve their overall adoption. In particular, successful outcomes appear to require a minimum level of value correlation. Categories and Subject Descriptors (2012): Networks -- Network Algorithms -- Network economics; Networks -- Network properties -- Network dynamics; Information systems -- Information systems applications -- Collaborative and social computing systems and tools; Security and privacy -- Human and societal aspects of security and privacy; Human-centered computing -- Collaborative and social computing theory, concepts and paradigms


title: Anonymous Certificate-based Broadcast Encryption with Constant Decryption Cost

abstract: Abstract Most of the existing broadcast encryption schemes are constructed in either public key cryptosystem or identity-based cryptosystem. However, the overload of certificate management is heavy for public key cryptosystem while the key escrow problem is inherent in identity-based cryptosystem. Certificate-based cryptosystem provides a promising solution of designing more efficient and secure broadcast encryption schemes. Meanwhile, it is an important security property to protect the privacy of receivers, anonymity in many broadcast encryption application scenarios. Furthermore, in the broadcast encryption applications with a large number of computationally constrained users, it is critical to lower the computation cost, especially the decryption cost of the users. In this paper, we first introduce the notion of anonymous certificate-based broadcast encryption. Specifically, we formalize the definition and security model for anonymous certificate-based broadcast encryption. Then we construct an anonymous certificate-based broadcast encryption scheme with constant decryption cost. Compared with the existing anonymous multi-receiver certificate-based encryption scheme, the proposed scheme has advantage in the computation efficiency. Therefore, our scheme is more feasible for those broadcast encryption application scenarios where there are many receivers with limited computation ability. In the respect of security, the proposed scheme achieves anonymity and confidentiality against adaptive chosen-ciphertext attacks simultaneously under standard assumption.


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title: Behavioral/Systems/Cognitive Evidence of Mirror Neurons in Human Inferior Frontal

abstract: There is much current debate about the existence of mirror neurons in humans. To identify mirror neurons in the inferior frontal gyrus (IFG) of humans, we used a repetition suppression paradigm while measuring neural activity with functional magnetic resonance imaging. Subjects either executed or observed a series of actions. Here we show that in the IFG, responses were suppressed both when an executed action was followed by the same rather than a different observed action and when an observed action was followed by the same rather than a different executed action. This pattern of responses is consistent with that predicted by mirror neurons and is evidence of mirror neurons in the human IFG.


title: Long Range Dependence Prognostics for Bearing Vibration Intensity Chaotic Time Series

abstract: According to the chaotic features and typical fractional order characteristics of the bearing vibration intensity time series, a forecasting approach based on long range dependence (LRD) is proposed. In order to reveal the internal chaotic properties, vibration intensity time series are reconstructed based on chaos theory in phase-space, the delay time is computed with C-C method and the optimal embedding dimension and saturated correlation dimension are calculated via the Grassberger–Procaccia (G-P) method, respectively, so that the chaotic characteristics of vibration intensity time series can be jointly determined by the largest Lyapunov exponent and phase plane trajectory of vibration intensity time series, meanwhile, the largest Lyapunov exponent is calculated by the Wolf method and phase plane trajectory is illustrated using Duffing-Holmes Oscillator (DHO). The Hurst exponent and long range dependence prediction method are proposed to verify the typical fractional order features and improve the prediction accuracy of bearing vibration intensity time series, respectively. Experience shows that the vibration intensity time series have chaotic properties and the LRD prediction method is better than the other prediction methods (largest Lyapunov, auto regressive moving average (ARMA) and BP neural network (BPNN) model) in prediction accuracy and prediction performance, which provides a new approach for running tendency predictions for rotating machinery and provide some guidance value to the engineering practice.


title: Message Impartiality in Social Media Discussions

abstract: Discourse on social media platforms is often plagued by acute polarization, with different camps promoting different perspectives on the issue at hand—compare, for example, the differences in the liberal and conservative discourse on the U.S. immigration debate. A large body of research has studied this phenomenon by focusing on the affiliation of groups and individuals. We propose a new finer-grained perspective: studying the impartiality of individual messages. While the notion of message impartiality is quite intuitive, the lack of an objective definition and of a way to measure it directly has largely obstructed scientific examination. In this work we operationalize message impartiality in terms of how discernible the affiliation of its author is, and introduce a methodology for quantifying it automatically. Unlike a supervised machine learning approach, our method can be used in the context of emerging events where impartiality labels are not immediately available. Our framework enables us to study the effects of (im)partiality on social media discussions at scale. We show that this phenomenon is highly consequential, with partial messages being twice more likely to spread than impartial ones, even after controlling for author and topic. By taking this fine-grained approach to polarization, we also provide new insights into the temporal evolution of online discussions centered around major political and sporting events.


title: Hormone Treatment without Surgery for Patients Aged 75 Years or Older with Operable Breast Cancer

abstract: To evaluate the trend in the use of primary endocrine treatment (PET) for elderly patients with operable breast cancer and to study mean time to response (TTR), local control, time to progression (TTP), and overall survival. Data of 184 patients aged ≥75 years, diagnosed with breast cancer in the south of the Netherlands between 2001 and 2008 and receiving PET, were analyzed. The percentage of women ≥75 years with breast cancer receiving PET in the south of the Netherlands decreased from 23% in the period 1988–1992 to 12% in 1997–2000, and increased to 29% in 2005–2008. Mean age at diagnosis of 184 patients treated with PET in the period 2001–2008 was 84 years (range 75–89 years). Mean length of follow-up was 2.6 years. In 107 patients (58%), an initial response was achieved (mean TTR 7 months), 21 patients (12%) showed stable disease. A total of 64 patients (35%), with or without prior response, eventually displayed progression (mean TTP 20 months). No differences in TTR and TTP were observed between the patients starting with tamoxifen or an aromatase inhibitor. One hundred nineteen (65%) of 184 patients had died by January 1, 2010. In 17 patients (14%), breast cancer was the cause of death. Tumor progression was observed in a substantial proportion of the cohort, but only a small number of patients died of breast cancer. Further research is needed on the safety and effectiveness of PET for elderly women with breast cancer to justify the current widespread use.


title: A Low-Cost Localization System Based on Artificial Landmarks

abstract: The present work proposes and evaluates a technique for improving the Turtlebot mobile robot localization system for navigation in unknown structured environments. In our proposal, the localization is refined by using a dense artificial landmark setup fixed in the environment ceiling. Although the use of artificial landmarks restricts the applicability of this methodology, the proposed methodology has the advantage of not depending on a previous mapping of the landmarks distribution in the environment, which makes its adoption a simple process.


title: Supporting Transparent Snapshot for Bare-metal Malware Analysis on Mobile Devices

abstract: The increasing growth of cybercrimes targeting mobile devices urges an efficient malware analysis platform. With the emergence of evasive malware, which is capable of detecting that it is being analyzed in virtualized environments, bare-metal analysis has become the definitive resort. Existing works mainly focus on extracting the malicious behaviors exposed during bare-metal analysis. However, after malware analysis, it is equally important to quickly restore the system to a clean state to examine the next sample. Unfortunately, state-of-the-art solutions on mobile platforms can only restore the disk, and require a time-consuming system reboot. In addition, all of the existing works require some in-guest components to assist the restoration. Therefore, a kernel-level malware is still able to detect the presence of the in-guest components. We propose Bolt, a transparent restoration mechanism for bare-metal analysis on mobile platform without rebooting. Bolt achieves a reboot-less restoration by simultaneously making a snapshot for both the physical memory and the disk. Memory snapshot is enabled by an isolated operating system (BoltOS) in the ARM TrustZone secure world, and disk snapshot is accomplished by a piece of customized firmware (BoltFTL) for flash-based block devices. Because both the BoltOS and the BoltFTL are isolated from the guest system, even kernel-level malware cannot interfere with the restoration. More importantly, Bolt does not require any modifications into the guest system. As such, Bolt is the first that simultaneously achieves efficiency, isolation, and stealthiness to recover from infection due to malware execution. We have implemented a Bolt prototype working with the Android OS. Experimental results show that Bolt can restore the guest system to a clean state in only 2.80 seconds.


title: GrammarViz 2.0: a tool for grammar-based pattern discovery in time series

abstract: The problem of frequent and anomalous patterns discovery in time series has received a lot of attention in the past decade. Addressing the common limitation of existing techniques, which require a pattern length to be known in advance, we recently proposed grammar-based algorithms for efficient discovery of variable length frequent and rare patterns. In this paper we present GrammarViz 2.0, an interactive tool that, based on our previous work, implements algorithms for grammar-driven mining and visualization of variable length time series patterns1.


title: Indoor Localization Using Camera Phones

abstract: Indoor localization has long been a goal of pervasive computing research. In this paper, we explore the possibility of determining user's location based on the camera images received from a smart phone. In our system, the smart phone is worn by the user as a pendant and images are periodically captured and transmitted over GPRS to a web server. The web server returns the location of the user by comparing the received images with images stored in a database. We tested our system inside the Computer Science department building. Preliminary results show that user's location can be determined correctly with more than 80% probability of success. As opposed to earlier solutions for indoor localization, this approach does not have any infrastructure requirements. The only cost is that of building an image database.


title: Don’t Interrupt Me While I Type: Inferring Text Entered Through Gesture Typing on Android Keyboards

abstract: This work was partially supported by the Samsung Electronics Research Institute (SERI), Thales, and the Carnegie Trust for the Universities of Scotland.