title: Deep Convolutional Networks as shallow Gaussian Processes

abstract: We show that the output of a (residual) convolutional neural network (CNN) with an appropriate prior over the weights and biases is a Gaussian process (GP) in the limit of infinitely many convolutional filters, extending similar results for dense networks. For a CNN, the equivalent kernel can be computed exactly and, unlike"deep kernels", has very few parameters: only the hyperparameters of the original CNN. Further, we show that this kernel has two properties that allow it to be computed efficiently; the cost of evaluating the kernel for a pair of images is similar to a single forward pass through the original CNN with only one filter per layer. The kernel equivalent to a 32-layer ResNet obtains 0.84% classification error on MNIST, a new record for GPs with a comparable number of parameters.


title: Initial Psychoeducational Assessment and Related Consultation

abstract: Criticism of current approaches to assessment indicates the need for effective alternatives. Given a general understanding of the criticism, the authors describe the bases for an alternative approach to initial assessment activity. Specifically, the discussion focuses on (1) a conceptualization of initial assessment and consultation, (2) the problem-solving paradigm as a framework for guiding this activity, and (3) a description of procedures and initial data from a demonstration program.


title: Public opinion on automated driving: Results of an international questionnaire among 5000 respondents

abstract: This study investigated user acceptance, worries, and willingness to buy partially, highly, and fully automated vehicles. By means of a 63-question Internet-based survey, we collected 5,000 responses from 109 countries (40 countries with at least 25 respondents). We determined cross-national differences, and assessed correlations with personal variables, such as age, gender, and personality traits as measured with a short version of the Big Five Inventory. Results showed that respondents, on average, found manual driving the most enjoyable mode of driving. Responses were diverse: 22% of the respondents did not want to pay more than $0 for a fully automated driving system, whereas 5% indicated they would be willing to pay more than $30,000 for it, and 33% indicated that fully automated driving would be highly enjoyable. 69% of respondents estimated that fully automated driving will reach a 50% market share between now and 2050. Respondents were found to be most concerned about software hacking/misuse, and were also concerned about legal issues and safety. More neurotic people were slightly less comfortable about data transmitting, whereas agreeable persons were slightly more comfortable with that. The more developed countries (in terms of accident statistics, education, and income) were less comfortable with their vehicle transmitting data, with cross-national correlations between ρ= -0.80 and ρ= -0.90. The present results indicate the major areas of promise and concern among the international public, and could be useful for vehicle developers and other stakeholders.


title: Scene Construction, Visual Foraging, and Active Inference

abstract: This paper describes an active inference scheme for visual searches and the perceptual synthesis entailed by scene construction. Active inference assumes that perception and action minimize variational free energy, where actions are selected to minimize the free energy expected in the future. This assumption generalizes risk-sensitive control and expected utility theory to include epistemic value; namely, the value (or salience) of information inherent in resolving uncertainty about the causes of ambiguous cues or outcomes. Here, we apply active inference to saccadic searches of a visual scene. We consider the (difficult) problem of categorizing a scene, based on the spatial relationship among visual objects where, crucially, visual cues are sampled myopically through a sequence of saccadic eye movements. This means that evidence for competing hypotheses about the scene has to be accumulated sequentially, calling upon both prediction (planning) and postdiction (memory). Our aim is to highlight some simple but fundamental aspects of the requisite functional anatomy; namely, the link between approximate Bayesian inference under mean field assumptions and functional segregation in the visual cortex. This link rests upon the (neurobiologically plausible) process theory that accompanies the normative formulation of active inference for Markov decision processes. In future work, we hope to use this scheme to model empirical saccadic searches and identify the prior beliefs that underwrite intersubject variability in the way people forage for information in visual scenes (e.g., in schizophrenia).


title: Multiple motion control system of robotic car based on IoT to produce cloud service

abstract: The world of control is an exciting field that has exploded with new technologies where the Internet of Things (IoT) vision becomes reality. This paper proposes a multiple motion controlling mechanism of a robotic car using Raspberry Pi which works as master and Arduino UNO which works as slave. Each device is uniquely identifiable by the controlling software which is the core concept of IoT. Client manages the activities of the car from remote or distant places over the internet by voice commands and Universal Windows Application and also able to get data and feedback. The main contribution of this paper is that it leverages the efficiency of robot's motion controlling system because robotic car can receive direct commands at a time from multiple sources which make the maneuvering system more efficient. Both device and client do not need to be online at the same time. Commands and data are stored in cloud service which delivers them when the device is ready to receive. A GPS system is incorporated thus clients can trace the car. The system has ultrasonic distance sensor for avoiding obstacles coming in between its path. We present the architecture and design of the Raspberry Pi and Arduino communication software and illustrate how to control the car by means of commands and application.


title: Decision Tree Ensemble-Based Wafer Map Failure Pattern Recognition Based on Radon Transform-Based Features

abstract: Wafer maps contain information about defects and clustered defects that form failure patterns. Failure patterns exhibit the information related to defect generation mechanisms. The accurate classification of failure patterns in wafer maps can provide crucial information for engineers to recognize the causes of the fabrication problems. In this paper, we proposed a decision tree ensemble learning-based wafer map failure pattern recognition method based on radon transform-based features. Radon transform is applied on raw wafer map data to generate the new features which are exhibiting the geometric information of failure patterns in wafer map. Decision tree algorithm is applied to build decision tree ensemble and the final decision is made by aggregating the prediction results of decision trees. The effectiveness of the proposed method has been verified by using the real world wafer map data set (WM-811K).


title: Cognitive maps beyond the hippocampus

abstract: We present a conceptual framework for the role of the hippocampus and its afferent and efferent structures in rodent navigation. Our proposal is compatible with the behavioral, neurophysiological, anatomical, and neuropharmacological literature, and suggests a number of practical experiments that could support or refute it. ::: ::: ::: ::: We begin with a review of place cells and how the place code for an environment might be aligned with sensory cues and updated by self-motion information. The existence of place fields in the dark suggests that location information is maintained by path integration, which requires an internal representation of direction of motion. This leads to a consideration of the organization of the rodent head direction system, and thence into a discussion of the computational structure and anatomical locus of the path integrator. ::: ::: ::: ::: If the place code is used in navigation, there must be a mechanism for selecting an action based on this information. We review evidence that the nucleus accumbens subserves this function. From there, we move to interactions between the hippocampal system and the environment, emphasizing mechanisms for learning novel environments and for aligning the various subsystems upon re-entry into familiar environments. We conclude with a discussion of the relationship between navigation and declarative memory. Hippocampus 7:15–35, 1997. © 1997 Wiley-Liss, Inc.


title: An Intelligent System for False Alarm Reduction in Infrared Forest-Fire Detection

abstract: Forest fires cause many environmental disasters, creating economical and ecological damage as well as endangering people's lives. Heightened interest in automatic surveillance and early forest-fire detection has taken precedence over traditional human surveillance because the latter's subjectivity affects detection reliability, which is the main issue for forest-fire detection systems. In current systems, the process is tedious, and human operators must manually validate many false alarms. Our approach, the False Alarm Reduction system, proposes an alternative real-time infrared-visual system that overcomes this problem. The FAR system consists of applying new infrared-image processing techniques and artificial neural networks (ANNs), using additional information from meteorological sensors and from a geographical information database, taking advantage of the information redundancy from visual and infrared cameras through a matching process, and designing a fuzzy expert rule base to develop a decision function. Furthermore, the system provides the human operator with new software tools to verify alarms.


title: Towards energy-aware scheduling in data centers using machine learning

abstract: As energy-related costs have become a major economical factor for IT infrastructures and data-centers, companies and the research community are being challenged to find better and more efficient power-aware resource management strategies. There is a growing interest in "Green" IT and there is still a big gap in this area to be covered. In order to obtain an energy-efficient data center, we propose a framework that provides an intelligent consolidation methodology using different techniques such as turning on/off machines, power-aware consolidation algorithms, and machine learning techniques to deal with uncertain information while maximizing performance. For the machine learning approach, we use models learned from previous system behaviors in order to predict power consumption levels, CPU loads, and SLA timings, and improve scheduling decisions. Our framework is vertical, because it considers from watt consumption to workload features, and cross-disciplinary, as it uses a wide variety of techniques. We evaluate these techniques with a framework that covers the whole control cycle of a real scenario, using a simulation with representative heterogeneous workloads, and we measure the quality of the results according to a set of metrics focused toward our goals, besides traditional policies. The results obtained indicate that our approach is close to the optimal placement and behaves better when the level of uncertainty increases.


title: Surfactant effects on aerosolized and instilled adenoviral-mediated gene transfer.

abstract: The effects of surfactant on aerosolization of adenovirus and the distribution of aerosolized and instilled adenovirus in rabbit lungs were measured. Using 99Tc sulfur colloid as a marker for the aerosol, 33P-adenovirus with and without 3H-surfactant, we found that the aerosol had the same particle size distribution independent of surfactant and the 33P-adenovirus distributed proportionately to the other radiolabels in the aerosol. Rabbits received aerosolized or instilled suspensions of adenovirus and 99Tc-sulfur colloid with or without 10 mg/ml surfactant. Rabbit lungs were separated into large airways (trachea, carina, and primary bronchi) and more distal lung parenchyma either immediately after treatment for measurement of 99Tc distribution or 3 days after treatment for luciferase transgene expression. More instillate (94 +/- 1%) than aerosol (68 +/- 2%) was recovered in the parenchyma immediately after treatment (p < 0.01). Transgene expression in the parenchyma as a percent of total lung expression was lower for both instillation (72 +/- 8%) and aerosolization (30 +/- 8%) than for the initial distributions (p < 0.01). Surfactant did not change the distributions of 99Tc sulfur colloid or transgene expression. The adenovirus was aerosolized successfully but the distribution of the aerosol did not favor transgene expression in the lung parenchyma.


title: Activity-Aware Mental Stress Detection Using Physiological Sensors

abstract: Continuous stress monitoring may help users better under- stand their stress patterns and provide physicians with more reliable data for interventions. Previously, studies on mental stress detection were lim- ited to a laboratory environment where participants generally rested in a sedentary position. However, it is impractical to exclude the effects of physical activity while developing a pervasive stress monitoring appli- cation for everyday use. The physiological responses caused by mental stress can be masked by variations due to physical activity.


title: A Minimal Spiking Neural Network to Rapidly Train and Classify Handwritten Digits in Binary and 10-Digit Tasks

abstract: This paper reports the results of experiments to develop a minimal neural network for pattern classification. The network uses biologically plausible neural and learning mechanisms and is applied to a subset of the MNIST dataset of handwritten digits. The research goal is to assess the classification power of a very simple biologically motivated mechanism. The network architecture is primarily a feedforward spiking neural network (SNN) composed of Izhikevich regular spiking (RS) neurons and conductance-based synapses. The weights are trained with the spike timing-dependent plasticity (STDP) learning rule. The proposed SNN architecture contains three neuron layers which are connected by both static and adaptive synapses. Visual input signals are processed by the first layer to generate input spike trains. The second and third layers contribute to spike train segmentation and STDP learning, respectively. The network is evaluated by classification accuracy on the handwritten digit images from the MNIST dataset. The simulation results show that although the proposed SNN is trained quickly without error-feedbacks in a few number of iterations, it results in desirable performance (97.6%) in the binary classification (0 and 1). In addition, the proposed SNN gives acceptable recognition accuracy in 10-digit (0-9) classification in comparison with statistical methods such as support vector machine (SVM) and multi-perceptron neural network.


title: Watertight trimmed NURBS

abstract: This paper addresses the long-standing problem of the unavoidable gaps that arise when expressing the intersection of two NURBS surfaces using conventional trimmed-NURBS representation. The solution converts each trimmed NURBS into an untrimmed T-Spline, and then merges the untrimmed T-Splines into a single, watertight model. The solution enables watertight fillets of NURBS models, as well as arbitrary feature curves that do not have to follow iso-parameter curves. The resulting T-Spline representation can be exported without error as a collection of NURBS surfaces.


title: A Neural Algorithm of Artistic Style

abstract: In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.


title: Calibrating On-Wafer Probes to the Probe Tips

abstract: This paper investigates the accuracy of on-wafer scattering-parameter calibrations at the probe tips. Data show the extent to which certain probe-tip calibrations are consistent with one another and applicable to the characterization of devices or circuits fabricated on different wafers or embedded in different transmission-line media. Calibrations to the probe tips are especially well suited to lower-frequency microwave measurements. Further results demonstrate conditions under which probe-tip calibrations fail.


title: STEM: a suffix tree-based method for web data records extraction

abstract: To automatically extract data records from Web pages, the data record extraction algorithm is required to be robust and efficient. However, most of existing algorithms are not robust enough to cope with rich information or noisy data. In this paper, we propose a novel suffix tree-based extraction method (STEM) for this challenging task. First, we extract a sequence of identifiers from the tag paths of Web pages. Then, a suffix tree is built on top of this sequence and four refining filters are proposed to screen out data regions that might not contain data records. To evaluate model performance, we define an evaluation metric called pattern similarity and perform rigorous experiments on five real data sets. The promising experimental results have demonstrated that the proposed STEM is superior to the state-of-the-art algorithms like MDR, TPC and CTVS with respect to precision, recall and pattern similarity. Moreover, the time complexity of STEM is linear to the total number of HTML tags contained in Web pages, which indicates the potential applicability of STEM in a wide range of Web-scale data record extraction applications.


title: A Current Sensor Based on the Giant Magnetoresistance Effect: Design and Potential Smart Grid Applications

abstract: Advanced sensing and measurement techniques are key technologies to realize a smart grid. The giant magnetoresistance (GMR) effect has revolutionized the fields of data storage and magnetic measurement. In this work, a design of a GMR current sensor based on a commercial analog GMR chip for applications in a smart grid is presented and discussed. Static, dynamic and thermal properties of the sensor were characterized. The characterizations showed that in the operation range from 0 to ±5 A, the sensor had a sensitivity of 28 mV·A−1, linearity of 99.97%, maximum deviation of 2.717%, frequency response of −1.5 dB at 10 kHz current measurement, and maximum change of the amplitude response of 0.0335%·°C−1 with thermal compensation. In the distributed real-time measurement and monitoring of a smart grid system, the GMR current sensor shows excellent performance and is cost effective, making it suitable for applications such as steady-state and transient-state monitoring. With the advantages of having a high sensitivity, high linearity, small volume, low cost, and simple structure, the GMR current sensor is promising for the measurement and monitoring of smart grids.


title: An improved harmony search based energy-efficient routing algorithm for wireless sensor networks

abstract: Graphical abstractDisplay Omitted HighlightsA new encoding of harmony memory for routing in WSNs has been proposed.A new generation method of a new harmony for routing in WSNs has been proposed.The dynamic adaptation is introduced for the parameter HMCR to improve the performance of the proposed routing algorithm.An effective local search strategy is proposed to improve the convergence speed and the accuracy of the proposed routing algorithm.An energy efficient objective function model is proposed. Wireless sensor networks (WSNs) is one of the most important technologies in this century. As sensor nodes have limited energy resources, designing energy-efficient routing algorithms for WSNs has become the research focus. And because WSNs routing for maximizing the network lifetime is a NP-hard problem, many researchers try to optimize it with meta-heuristics. However, due to the uncertain variable number and strong constraints of WSNs routing problem, most meta-heuristics are inappropriate in designing routing algorithms for WSNs. This paper proposes an Improved Harmony Search Based Energy Efficient Routing Algorithm (IHSBEER) for WSNs, which is based on harmony search (HS) algorithm (a meta-heuristic). To address the WSNs routing problem with HS algorithm, several key improvements have been put forward: First of all, the encoding of harmony memory has been improved based on the characteristics of routing in WSNs. Secondly, the improvisation of a new harmony has also been improved. We have introduced dynamic adaptation for the parameter HMCR to avoid the prematurity in early generations and strengthen its local search ability in late generations. Meanwhile, the adjustment process of HS algorithm has been discarded to make the proposed routing algorithm containing less parameters. Thirdly, an effective local search strategy is proposed to enhance the local search ability, so as to improve the convergence speed and the accuracy of routing algorithm. In addition, an objective function model that considers both the energy consumption and the length of path is developed. The detailed descriptions and performance test results of the proposed approach are included. The experimental results clearly show the advantages of the proposed routing algorithm for WSNs.


title: Learning 3D scene synthesis from annotated RGB-D images

abstract: We present a data-driven method for synthesizing 3D indoor scenes by inserting objects progressively into an initial, possibly, empty scene. Instead of relying on few hundreds of hand-crafted 3D scenes, we take advantage of existing large-scale annotated RGB-D datasets, in particular, the SUN RGB-D database consisting of 10,000+ depth images of real scenes, to form the prior knowledge for our synthesis task. Our object insertion scheme follows a co-occurrence model and an arrangement model, both learned from the SUN dataset. The former elects a highly probable combination of object categories along with the number of instances per category while a plausible placement is defined by the latter model. Compared to previous works on probabilistic learning for object placement, we make two contributions. First, we learn various classes of higher-order object-object relations including symmetry, distinct orientation, and proximity from the database. These relations effectively enable considering objects in semantically formed groups rather than by individuals. Second, while our algorithm inserts objects one at a time, it attains holistic plausibility of the whole current scene while offering controllability through progressive synthesis. We conducted several user studies to compare our scene synthesis performance to results obtained by manual synthesis, state-of-the-art object placement schemes, and variations of parameter settings for the arrangement model.


title: RecLand: A Recommender System for Social Networks

abstract: Social networks have become an important information source. Due to their unprecedented success, these systems have to face an exponentially increasing amount of user generated content. As a consequence, finding relevant users or data matching specific interests is a challenging. We present RecLand, a recommender system that takes advantage of the social graph topology and of the existing contextual information to recommend users. The graphical interface of RecLand shows recommendations that match the topical interests of users and allows to tune the parameters to adapt the recommendations to their needs.


title: DeepIntent: Learning Attentions for Online Advertising with Recurrent Neural Networks

abstract: In this paper, we investigate the use of recurrent neural networks (RNNs) in the context of search-based online advertising. We use RNNs to map both queries and ads to real valued vectors, with which the relevance of a given (query, ad) pair can be easily computed. On top of the RNN, we propose a novel attention network, which learns to assign attention scores to different word locations according to their intent importance (hence the name DeepIntent). The vector output of a sequence is thus computed by a weighted sum of the hidden states of the RNN at each word according their attention scores. We perform end-to-end training of both the RNN and attention network under the guidance of user click logs, which are sampled from a commercial search engine. We show that in most cases the attention network improves the quality of learned vector representations, evaluated by AUC on a manually labeled dataset. Moreover, we highlight the effectiveness of the learned attention scores from two aspects: query rewriting and a modified BM25 metric. We show that using the learned attention scores, one is able to produce sub-queries that are of better qualities than those of the state-of-the-art methods. Also, by modifying the term frequency with the attention scores in a standard BM25 formula, one is able to improve its performance evaluated by AUC.


title: Thermostat: Application-transparent Page Management for Two-tiered Main Memory

abstract: The advent of new memory technologies that are denser and cheaper than commodity DRAM has renewed interest in two-tiered main memory schemes. Infrequently accessed application data can be stored in such memories to achieve significant memory cost savings. Past research on two-tiered main memory has assumed a 4KB page size. However, 2MB huge pages are performance critical in cloud applications with large memory footprints, especially in virtualized cloud environments, where nested paging drastically increases the cost of 4KB page management. We present Thermostat, an application-transparent huge-page-aware mechanism to place pages in a dual-technology hybrid memory system while achieving both the cost advantages of two-tiered memory and performance advantages of transparent huge pages. We present an online page classification mechanism that accurately classifies both 4KB and 2MB pages as hot or cold while incurring no observable performance overhead across several representative cloud applications. We implement Thermostat in Linux kernel version 4.5 and evaluate its effectiveness on representative cloud computing workloads running under KVM virtualization. We emulate slow memory with performance characteristics approximating near-future high-density memory technology and show that Thermostat migrates up to 50% of application footprint to slow memory while limiting performance degradation to 3%, thereby reducing memory cost up to 30%.


title: Sensor scheduling algorithms requiring limited computation

abstract: In this paper, we consider the scenario where many sensors co-operate to estimate a process. Only one sensor can take a measurement at any time step. We wish to come up with optimal sensor scheduling algorithms. The problem is motivated by the use of sonar range-finders used by the vehicles on the Caltech Multi-Vehicle Wireless Testbed. We see that this problem involves searching a tree in general and propose and analyze two strategies for pruning the tree to keep the computation limited. The first is a sliding window strategy motivated by the Viterbi algorithm, and the second one uses thresholding. We also study a technique that employs choosing the sensors randomly from a probability distribution which can then be optimized. The performance of the algorithms are illustrated with the help of numerical examples.


title: An asymmetric distributed shared memory model for heterogeneous parallel systems

abstract: Heterogeneous computing combines general purpose CPUs with accelerators to efficiently execute both sequential control-intensive and data-parallel phases of applications. Existing programming models for heterogeneous computing rely on programmers to explicitly manage data transfers between the CPU system memory and accelerator memory. ::: This paper presents a new programming model for heterogeneous computing, called Asymmetric Distributed Shared Memory (ADSM), that maintains a shared logical memory space for CPUs to access objects in the accelerator physical memory but not vice versa. The asymmetry allows light-weight implementations that avoid common pitfalls of symmetrical distributed shared memory systems. ADSM allows programmers to assign data objects to performance critical methods. When a method is selected for accelerator execution, its associated data objects are allocated within the shared logical memory space, which is hosted in the accelerator physical memory and transparently accessible by the methods executed on CPUs. ::: We argue that ADSM reduces programming efforts for heterogeneous computing systems and enhances application portability. We present a software implementation of ADSM, called GMAC, on top of CUDA in a GNU/Linux environment. We show that applications written in ADSM and running on top of GMAC achieve performance comparable to their counterparts using programmer-managed data transfers. This paper presents the GMAC system and evaluates different design choices. We further suggest additional architectural support that will likely allow GMAC to achieve higher application performance than the current CUDA model.


title: TectoMT: modular NLP framework

abstract: In the present paper we describe TectoMT, a multi-purpose open-source NLP framework. It allows for fast and efficient development of NLP applications by exploiting a wide range of software modules already integrated in TectoMT, such as tools for sentence segmentation, tokenization, morphological analysis, POS tagging, shallow and deep syntax parsing, named entity recognition, anaphora resolution, tree-to-tree translation, natural language generation, word-level alignment of parallel corpora, and other tasks. One of the most complex applications of TectoMT is the English-Czech machine translation system with transfer on deep syntactic (tectogrammatical) layer. Several modules are available also for other languages (German, Russian, Arabic).Where possible, modules are implemented in a language-independent way, so they can be reused in many applications.


title: Neocognitron: A neural network model for a mechanism of visual pattern recognition

abstract: A recognition with a large-scale network is simulated on a PDP-11/34 minicomputer and is shown to have a great capability for visual pattern recognition. The model consists of nine layers of cells. The authors demonstrate that the model can be trained to recognize handwritten Arabic numerals even with considerable deformations in shape. A learning-with-a-teacher process is used for the reinforcement of the modifiable synapses in the new large-scale model, instead of the learning-without-a-teacher process applied to a previous model. The authors focus on the mechanism for pattern recognition rather than that for self-organization.


title: Measurement of eye size illusion caused by eyeliner, mascara, and eye shadow.

abstract: Do eyeliner, mascara, and eye shadow actually make the eyes appear larger than they really are? If so, by what percentage? To answer these questions, we used psychophysical experiments. Experiment 1 manipulated the degree of eyeliner (four levels) and mascara (five levels), and measured perceived eye size using a psychophysical procedure called the staircase method. The results showed that both eyeliner and mascara make the eyes appear larger than they really are by up to 6% (13% in area), but their effects are not additive. Eyeliner increased perceived eye size only in the absence of mascara. In the presence of mascara, however, eyeliner has no additional effect. Experiment 2 measured perceived eye size with or without eye shadow and demonstrated that eye shadow increases perceived eye size by about 5% (10% in area). These findings indicate that one mechanism by which makeup and cosmetics alter facial appearances involves inducing visual illusions. In addition, it is suggested that the eye size illusion caused by eyeliner, mascara, and eye shadow uses the same mechanism as that of the Delboeuf illusion, a geometric illusion of assimilation.


title: So long, and no thanks for the externalities: the rational rejection of security advice by users

abstract: It is often suggested that users are hopelessly lazy and unmotivated on security questions. They chose weak passwords, ignore security warnings, and are oblivious to certificates errors. We argue that users' rejection of the security advice they receive is entirely rational from an economic perspective. The advice offers to shield them from the direct costs of attacks, but burdens them with far greater indirect costs in the form of effort. Looking at various examples of security advice we find that the advice is complex and growing, but the benefit is largely speculative or moot. For example, much of the advice concerning passwords is outdated and does little to address actual treats, and fully 100% of certificate error warnings appear to be false positives. Further, if users spent even a minute a day reading URLs to avoid phishing, the cost (in terms of user time) would be two orders of magnitude greater than all phishing losses. Thus we find that most security advice simply offers a poor cost-benefit tradeoff to users and is rejected. Security advice is a daily burden, applied to the whole population, while an upper bound on the benefit is the harm suffered by the fraction that become victims annually. When that fraction is small, designing security advice that is beneficial is very hard. For example, it makes little sense to burden all users with a daily task to spare 0.01% of them a modest annual pain.


title: Quick shift and kernel methods for mode seeking

abstract: We show that the complexity of the recently introduced medoid-shift algorithm in clustering N points is O(N 2), with a small constant, if the underlying distance is Euclidean. This makes medoid shift considerably faster than mean shift, contrarily to what previously believed. We then exploit kernel methods to extend both mean shift and the improved medoid shift to a large family of distances, with complexity bounded by the effective rank of the resulting kernel matrix, and with explicit regularization constraints. Finally, we show that, under certain conditions, medoid shift fails to cluster data points belonging to the same mode, resulting in over-fragmentation. We propose remedies for this problem, by introducing a novel, simple and extremely efficient clustering algorithm, called quick shift, that explicitly trades off under- and over-fragmentation. Like medoid shift, quick shift operates in non-Euclidean spaces in a straightforward manner. We also show that the accelerated medoid shift can be used to initialize mean shift for increased efficiency. We illustrate our algorithms to clustering data on manifolds, image segmentation, and the automatic discovery of visual categories.


title: Transaldolase deficiency: report of 12 new cases and further delineation of the phenotype

abstract: Purpose ::: Transaldolase deficiency is a recently described inborn error of pentose phosphate pathway. We conducted this study to further delineate the associated phenotype.


title: Universal Adversarial Perturbations

abstract: Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability. We propose a systematic algorithm for computing universal perturbations, and show that state-of-the-art deep neural networks are highly vulnerable to such perturbations, albeit being quasi-imperceptible to the human eye. We further empirically analyze these universal perturbations and show, in particular, that they generalize very well across neural networks. The surprising existence of universal perturbations reveals important geometric correlations among the high-dimensional decision boundary of classifiers. It further outlines potential security breaches with the existence of single directions in the input space that adversaries can possibly exploit to break a classifier on most natural images.


title: Deep convolutional neural networks on multichannel time series for human activity recognition

abstract: This paper focuses on human activity recognition (HAR) problem, in which inputs are multichannel time series signals acquired from a set of bodyworn inertial sensors and outputs are predefined human activities. In this problem, extracting effective features for identifying activities is a critical but challenging task. Most existing work relies on heuristic hand-crafted feature design and shallow feature learning architectures, which cannot find those distinguishing features to accurately classify different activities. In this paper, we propose a systematic feature learning method for HAR problem. This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. Through the deep architecture, the learned features are deemed as the higher level abstract representation of low level raw time series signals. By leveraging the labelled information via supervised learning, the learned features are endowed with more discriminative power. Unified in one model, feature learning and classification are mutually enhanced. All these unique advantages of the CNN make it outperform other HAR algorithms, as verified in the experiments on the Opportunity Activity Recognition Challenge and other benchmark datasets.


title: Modeling and analysis of nonuniform substrate temperature effects on global ULSI interconnects

abstract: Nonuniform thermal profiles on the substrate in high-performance ICs can significantly impact the performance of global on-chip interconnects. This paper presents a detailed modeling and analysis of the interconnect performance degradation due to the nonuniform temperature profiles that are encountered along long metal interconnects as a result of existing thermal gradients in the underlying Silicon substrate. A nonuniform temperature-dependent distributed RC interconnect delay model is proposed. The model is applied to a wide variety of interconnect layouts and substrate temperature distributions to quantify the impact of such thermal nonuniformities on signal integrity issues including speed degradation in global interconnect lines and skew fluctuations in clock signal distribution networks. Subsequently, a new thermally dependent zero-skew clock-routing methodology is presented. This study suggests that thermally aware analysis should become an integrated part of the various optimization steps in physical-synthesis flow to improve the performance and integrity of signals in global ultra large scale integration interconnects.


title: Stackable 4-BAR mechanisms and their robotic applications

abstract: This paper proposes a new planar robotic manipulator using stackable 4-BAR mechanisms for various applications such as a manipulator of surgical robot and a manipulator of field robot. The proposed manipulator has an advantage that all driving actuators can be separated from the mechanical mechanism, in other words, we are able to separate all electrical components such as electrical actuators and wirings from mechanical linkage/joint components in the robotic manipulator. Thus the robotic manipulator including working joints and linkages can be manufactured using one or a few material(s) with light-weight and slim-size. Also, the proposed mechanism does not require the actuators to attach directly to driving joints and it can be independently controlled. In addition, we suggest the kinematic analysis of the proposed manipulator composed of input mechanisms, multiple 4-BAR mechanisms and output mechanisms. Finally, a variety of simulation results and a prototype are suggested to show the effectiveness of the stackable 4-BAR mechanisms.


title: Security Improvement for Management Frames in IEEE 802.11 Wireless Networks

abstract: Summary IEEE 802.11 Wireless LAN (WLAN) has gained popularity. WLANs use different security protocols like WEP, WPA and WPA2. The newly ratified WPA2 provides the highest level of security for data frames. However WPA2 does not really mention about protection of management frames. In other words IEEE 802.11 management frames are always sent in an unsecured manner. In fact the only security mechanism for management frames is CRC-32 bit algorithm. While useful for unintentional error detection, CRC-32 bit is not safe to completely verify data integrity in the face of intentional modifications. Therefore an unsecured management frame allows an attacker to start different kinds of attack. This paper proposes a new model to address these security problems in management frames. First we summarize security threats on management frames and their influences in WLANs. Then based on these security threats, we propose a new per frames security model to provide efficient security for these frames. Finally simulation methodology is presented and results are provided. Mathematical probabilities are discussed to demonstrate that the proposed security model is robust and efficient to secure management frames.


title: Trust transfer in social media brand communities: The role of consumer engagement

abstract: Abstract Social media brand communities (SMBCs) provide firms with a potential tool to develop brand relationships. The goal of this study is to understand the value of an SMBC to that brand by examining how the community contributes to one of the central brand relationship variables—brand trust. From the perspective of trust transfer, this study considers whether and how consumer trust in a brand can be transferred from other trusted parties in the SMBC, and the mediation of consumer engagement in this process. Based on a survey of 279 SMBC participants, this study demonstrates that consumer-to-consumer trust and consumer-to-marketer trust have positive impact on consumer engagement, which subsequently influence brand trust. Also the device usage was found to moderate the impact of consumer engagement on brand trust.


title: Speaker adaptive training of deep neural network acoustic models using i-vectors

abstract: In acoustic modeling, speaker adaptive training (SAT) has been a long-standing technique for the traditional Gaussian mixture models (GMMs). Acoustic models trained with SAT become independent of training speakers and generalize better to unseen testing speakers. This paper ports the idea of SAT to deep neural networks (DNNs), and proposes a framework to perform feature-space SAT for DNNs. Using i-vectors as speaker representations, our framework learns an adaptation neural network to derive speaker-normalized features. Speaker adaptive models are obtained by fine-tuning DNNs in such a feature space. This framework can be applied to various feature types and network structures, posing a very general SAT solution. In this paper, we fully investigate how to build SAT-DNN models effectively and efficiently. First, we study the optimal configurations of SAT-DNNs for large-scale acoustic modeling tasks. Then, after presenting detailed comparisons between SAT-DNNs and the existing DNN adaptation methods, we propose to combine SAT-DNNs and model-space DNN adaptation during decoding. Finally, to accelerate learning of SAT-DNNs, a simple yet effective strategy, frame skipping, is employed to reduce the size of training data. Our experiments show that compared with a strong DNN baseline, the SAT-DNN model achieves 13.5% and 17.5% relative improvement on word error rates (WERs), without and with model-space adaptation applied respectively. Data reduction based on frame skipping results in 2 $ \times $ speed-up for SAT-DNN training, while causing negligible WER loss on the testing data.


Extraction from Biomedical Literature Using Predicate-Argument Structure Patterns


Deep Learning and Convolutional Neural Networks in the Aid of the Classification of Melanoma


title: OS 1 Reference Model-The IS 0 Model of Architecture for Open Systems Interconnection

abstract: Considering the urgency of the need for standards which would allow constitution of heterogeneous computer networks, ISO created a new subcommittee for "Open Systems Interconnection" (ISO/ TC97/SC 16) in 1977. The first priority of subcommittee 16 was to develop an architecture for open systems interconnection which could serve as a framework for the definition of standard protocols. As a result of 18 months of studies and discussions, SC16 adopted a layered architecture comprising seven layers (Physical, Data Link, Network, Transport, Session, Presentation, and Application). In July 1979 the specifications of this architecture, established by SC16, were passed under the name of "OSI Reference Model" to Technical Committee 97 "Data Processing" along with recommendations to start officially, on this basis, a set of protocols standardization projects to cover the most urgent needs. These recommendations were adopted by T.C97 at the end of 1979 as the basis for the following development of standards for Open Systems Interconnectlon within ISO. The OSI Reference Model was also recognized by CCITT Rapporteur's Group on "Layered Model for Public Data Network Services." This paper presents the model of architecture for Open Systems Interconnection developed by SC16. Some indications are also given on the initial set of protocols which will-likely be developed in this OSI Reference Model.


title: Security Analytics: Big Data Analytics for cybersecurity: A review of trends, techniques and tools

abstract: The rapid growth of the Internet has brought with it an exponential increase in the type and frequency of cyber attacks. Many well-known cybersecurity solutions are in place to counteract these attacks. However, the generation of Big Data over computer networks is rapidly rendering these traditional solutions obsolete. To cater for this problem, corporate research is now focusing on Security Analytics, i.e., the application of Big Data Analytics techniques to cybersecurity. Analytics can assist network managers particularly in the monitoring and surveillance of real-time network streams and real-time detection of both malicious and suspicious (outlying) patterns. Such a behavior is envisioned to encompass and enhance all traditional security techniques. This paper presents a comprehensive survey on the state of the art of Security Analytics, i.e., its description, technology, trends, and tools. It hence aims to convince the reader of the imminent application of analytics as an unparalleled cybersecurity solution in the near future.


Behavior and performance of emerging market investors: Evidence from China


title: Statistical debugging for real-world performance problems

abstract: Design and implementation defects that lead to inefficient computation widely exist in software. These defects are difficult to avoid and discover. They lead to severe performance degradation and energy waste during production runs, and are becoming increasingly critical with the meager increase of single-core hardware performance and the increasing concerns about energy constraints. Effective tools that diagnose performance problems and point out the inefficiency root cause are sorely needed. The state of the art of performance diagnosis is preliminary. Profiling can identify the functions that consume the most computation resources, but can neither identify the ones that waste the most resources nor explain why. Performance-bug detectors can identify specific type of inefficient computation, but are not suited for diagnosing general performance problems. Effective failure diagnosis techniques, such as statistical debugging, have been proposed for functional bugs. However, whether they work for performance problems is still an open question. In this paper, we first conduct an empirical study to understand how performance problems are observed and reported by real-world users. Our study shows that statistical debugging is a natural fit for diagnosing performance problems, which are often observed through comparison-based approaches and reported together with both good and bad inputs. We then thoroughly investigate different design points in statistical debugging, including three different predicates and two different types of statistical models, to understand which design point works the best for performance diagnosis. Finally, we study how some unique nature of performance bugs allows sampling techniques to lower the overhead of run-time performance diagnosis without extending the diagnosis latency.


title: Pre-Processing Steps for Segmentation of Retinal Blood Vessels

abstract: Segmentation of blood vessels in retinal images is an important part in retinal image analysis for diagnosis and treatment of eye diseases for large screening systems. In this paper, we addressed the problem of background and noise extraction from retinal images. Blood vessels usually have central light reflex and poor local contrast, hence the results yield by blood vessel segmentation algorithms are not satisfactory. We used different preprocessing steps which includes central light reflex removal, background homogenization and vessel enhancement to make retinal image noise-free for post-processing. We used mean and Gaussian filtering along with Top-Hat transformation for noise extraction. The preprocessing steps were applied on 40 retinal images of DRIVE database available publically. Results show the darker retinal structures like blood vessels, fovea, and possible presence of microaneurysms or hemorrhages, get enhanced as compared to original retinal image and the brighter structures like optic disc and possible presence of exudates were get removed . The presented technique will definitely improve automatic fundus images analysis also be very useful to eye specialists in their visual examination of retina.


title: Real-time reciprocal collision avoidance with elliptical agents

abstract: We present a novel algorithm for real-time collision-free navigation between elliptical agents. Each robot or agent is represented using a tight-fitting 2D ellipse in the plane. We extend the reciprocal velocity obstacle formulation by using conservative linear approximations of ellipses and derive sufficient conditions for collision-free motion based on low-dimensional linear programming. We use precomputed Minkowski Sum approximations for real-time and conservative collision avoidance in large multi-agent environments. Finally, we present efficient techniques to update the orientation to compute collision-free trajectories. Our algorithm can handle thousands of elliptical agents in real-time on a single core and provides significant speedups over prior algorithms for elliptical agents. We compare the runtime performance and behavior with circular agents on different benchmarks.


title: Embedded control of a four-rotor UAV

abstract: This paper presents the design of an embedded-control architecture for a four-rotor unmanned air vehicle (UAV) to perform autonomous hover flight. A non-linear control law based on nested saturations technique is presented that stabilizes the state of the aircraft around the origin. The control law was implemented in a microcontroller to stabilize the aircraft in real time. In order to obtain experimental results we have built a low-cost on-board system which drives the aircraft in position and orientation. The nonlinear controller has been successfully tested experimentally.


title: Hidden Markov Models and their Applications in Biological Sequence Analysis

abstract: Hidden Markov models (HMMs) have been extensively used in biological sequence analysis. In this paper, we give a tutorial review of HMMs and their applications in a variety of problems in molecular biology. We especially focus on three types of HMMs: the profile-HMMs, pair-HMMs, and context-sensitive HMMs. We show how these HMMs can be used to solve various sequence analysis problems, such as pairwise and multiple sequence alignments, gene annotation, classification, similarity search, and many others.


title: An Analytical Way to Find Influencers on Social Networks and Validate their Effects in Disseminating Social Games

abstract: As the weight of online social networks (OSNs) which provide powerful means of sharing common interests and communicating has gradually grown up, influencers are also given the great attention. This research proposes and evaluates a methodology for discovering influencers within OSNs. Finding strong influencers out is momentous perspective in success of viral marketing. The methodology consists of two-step process. In step one, structural analysis, we extract the influencer candidate subgroup by the degree centrality computation based on the social graph. Next, we analyze each user’s activity history. After whole process, we finally obtain a few powerful influencers who can effectively affect other people than mediocrities. The positive effects of influencers are confirmed by analyzing the result of spreading social games out over Facebook. The results are compared with other game promotions without utilizing influencers. This methodology can be used in various marketing practices on social platforms.


title: Analysis of sports data by using bivariate Poisson models

abstract: Summary. Models based on the bivariate Poisson distribution are used for modelling sports data. Independent Poisson distributions are usually adopted to model the number of goals of two competing teams. We replace the independence assumption by considering a bivariate Poisson model and its extensions. The models proposed allow for correlation between the two scores, which is a plausible assumption in sports with two opposing teams competing against each other. The effect of introducing even slight correlation is discussed. Using just a bivariate Poisson distribution can improve model fit and prediction of the number of draws in football games. The model is extended by considering an inflation factor for diagonal terms in the bivariate joint distribution. This inflation improves in precision the estimation of draws and, at the same time, allows for overdispersed, relative to the simple Poisson distribution, marginal distributions. The properties of the models proposed as well as interpretation and estimation procedures are provided. An illustration of the models is presented by using data sets from football and water-polo.


title: Emotion-regulation strategies across psychopathology: A meta-analytic review.

abstract: We examined the relationships between six emotion-regulation strategies (acceptance, avoidance, problem solving, reappraisal, rumination, and suppression) and symptoms of four psychopathologies (anxiety, depression, eating, and substance-related disorders). We combined 241 effect sizes from 114 studies that examined the relationships between dispositional emotion regulation and psychopathology. We focused on dispositional emotion regulation in order to assess patterns of responding to emotion over time. First, we examined the relationship between each regulatory strategy and psychopathology across the four disorders. We found a large effect size for rumination, medium to large for avoidance, problem solving, and suppression, and small to medium for reappraisal and acceptance. These results are surprising, given the prominence of reappraisal and acceptance in treatment models, such as cognitive-behavioral therapy and acceptance-based treatments, respectively. Second, we examined the relationship between each regulatory strategy and each of the four psychopathology groups. We found that internalizing disorders were more consistently associated with regulatory strategies than externalizing disorders. Lastly, many of our analyses showed that whether the sample came from a clinical or normative population significantly moderated the relationships. This finding underscores the importance of adopting a multi-sample approach to the study of psychopathology.


title: Vehicle licence plate recognition using super-resolution technique

abstract: Due to the development of economy and technology, the people's demands on cars are growing and so are the problems, such as finding stolen car, banning violation and parking lot management. It will be time-consuming and low-efficiency if we do those jobs only by human because of the limitation of human being's concentration. Therefore, it has been a popular topic to develop intelligent monitoring system under new video technology within a decade. There are still many rooms for future development and application in the car license detection and recognition fields. For finding stolen car, we can integrate car license detection system and road monitoring system to analyze the videos and trace the objects, so we can gain high-efficiency and low-cost results. For parking lot management system, we can have the result of access management and automatic charge to reduce the human resource through car license recognition system. Automated toll of highway can be done through car license recognition system as well. Because the car license recognition system is mostly applied to security monitoring field and business purposes, the demand of the accuracy is quite strict. There are causes which make the inaccuracy of the license recognition, such as the lack of video resolution, too small license due to the distance and the light and shadow. We will discuss the license recognition system and how to use apply the super-resolution to overcome those above problems.


title: eSports in EVE Online: Skullduggery, fair play and acceptability in an unbounded competition

abstract: Officially organized, spectator driven eSport tournaments occur within the notoriously transgressive and ruthless game EVE Online. In these tournaments spying, bribing and throwing matches is commonplace. Based on results from interviews with players, spectators and commentators, this paper discusses the way in which the ‘unbounded’ attitude of the developers of EVE Online towards acceptable forms of play has been transposed into its eSport iteration. Indicative of professionalization, EVE’s developer CCP Games has recently begun bounding tournament conduct and offering their first real-money tournament prizes. We argue that this is problematic, against the ‘spirit’ of EVE Online and a misreading of the effect of transgressive acts on the success and enjoyment of the emerging eSport.


title: ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages

abstract: We present ABRA, a suite of algorithms to compute and maintain probabilistically-guaranteed, high-quality, approximations of the betweenness centrality of all nodes (or edges) on both static and fully dynamic graphs. Our algorithms use progressive random sampling and their analysis rely on Rademacher averages and pseudodimension, fundamental concepts from statistical learning theory. To our knowledge, this is the first application of these concepts to the field of graph analysis. Our experimental results show that ABRA is much faster than exact methods, and vastly outperforms, in both runtime and number of samples, state-of-the-art algorithms with the same quality guarantees.


title: Structural equation modelling: guidelines for determining model fit

abstract: The following paper presents current thinking and research on fit indices for structural equation modelling. The ::: paper presents a selection of fit indices that are widely regarded as the most informative indices available to researchers. ::: As well as outlining each of these indices, guidelines are presented on their use. The paper also provides reporting ::: strategies of these indices and concludes with a discussion on the future of fit indices.


title: Ultra-wideband symmetric G-shape metamaterial-based microwave absorber

abstract: ABSTRACTThis paper investigates the absorption features of the symmetric G-shape metamaterial absorber (MA) at the microwave frequency range for the ultra-wideband. The proposed metamaterial structure attains above 95% absorptivity in the frequency range of 20–60 GHz. It also analyses the absorptivity of the ultra-wideband MA for the different incidence angles of the electromagnetic (EM) wave. The absorptivity is the function of the angle of incidence of EM waves for the proposed G-shape metamaterial structure and it attains maximum absorptivity for the normal incidence. Such a wideband absorber would be useful for the multifarious applications including wideband filtering and stealth metamaterial in the Ku, K-, Ka- Q- and U-bands.


title: Factors affecting the intention to use e-Government services

abstract: Nowadays, more and more people are using Information and Communication Technologies (ICTs) in order to accommodate their daily needs. E-Government (e-Gov) adopts these technologies in an effort to provide prompt and secure services to citizens. However, the intention to use e-Government services has not yet been fully examined by the international literature. The present research attempts to bridge this gap, by examining the factors affecting citizens' intention to use e-Government services. In that direction, a conceptual framework (research model), based on an extensive review of the relevant literature, has been developed. The proposed conceptual framework has been empirically tested using a newly-developed structured questionnaire. Data were collected from a sample of 547 Greek citizens. The reliability and the validity of the questionnaire have been thoroughly examined, while the Structural Equation Modeling (SEM) technique has been used to analyze the data. Results indicate that perceived usefulness is the most important determinant of the intention to use e-Government services. Other important factors are perceived trust, internet experience, peer influence, computer self-efficacy and perceived risk.


title: Information Security Policy Compliance in Higher Education: A Neo-Institutional Perspective

abstract: External pressures could be a powerful force that drives the institution of higher education to attain information security policy compliance. Drawing on the Neo-Institutional Theory (NIT), this study examined how the three external expectations: regulative, normative, and cognitive expectations, impel the higher education of the United States to reach information security policy compliance. The research findings suggest that regulatory and social normative pressures, but not cognitive pressure, have significant effects on information security policy compliance in higher education. Based on these results, this study unfolds both the practical and research implications.


title: Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study

abstract: There are several invariant features of pointto-point human arm movements: trajectories tend to be straight, smooth, and have bell-shaped velocity profiles. One approach to accounting for these data is via optimization theory; a movement is specified implicitly as the optimum of a cost function, e.g., integrated jerk or torque change. Optimization models of trajectory planning, as well as models not phrased in the optimization framework, generally fall into two main groups-those specified in kinematic coordinates and those specified in dynamic coordinates. To distinguish between these two possibilities we have studied the effects of artificial visual feedback on planar two-joint arm movements. During self-paced point-to-point arm movements the visual feedback of hand position was altered so as to increase the perceived curvature of the movement. The perturbation was zero at both ends of the movement and reached a maximum at the midpoint of the movement. Cost functions specified by hand coordinate kinematics predict adaptation to increased curvature so as to reduce the visual curvature, while dynamically specified cost functions predict no adaptation in the underlying trajectory planner, provided the final goal of the movement can still be achieved. We also studied the effects of reducing the perceived curvature in transverse movements, which are normally slightly curved. Adaptation should be seen in this condition only if the desired trajectory is both specified in kinematic coordinates and actually curved. Increasing the perceived curvature of normally straight sagittal movements led to significant (P 0.05). The results of the curvature-increasing study suggest that trajectories are planned in visually based kinematic coordinates. The results of the curvature-reducing study suggest that the desired trajectory is straight in visual space. These results are incompatible with purely dynamicbased models such as the minimum torque change model. We suggest that spatial perception-as mediated by vision-plays a fundamental role in trajectory planning.


title: Lazy K-Way Linear Combination Kernels for Efficient Runtime Sparse Jacobian Matrix Evaluations in C++

abstract: The most notoriously expensive component to develop, extend, and maintain within implicit PDAE-based predictive simulation software is the Jacobian evaluation component. While the Jacobian is invariably sparse, its structure and dimensionality are functions of the point of evaluation. The application of Automatic Differentiation to develop these tools is highly desirable. The challenge presented is in providing implementations that treat dynamic sparsity efficiently without requiring the developer to have any a priori knowledge of sparsity structure. Under the context of dynamic sparse Operator Overloading implementations, we develop a direct sparse lazy evaluation approach. In this approach, an efficient runtime variant of the classic Expression Templates technique is proposed to support sparsity. The second aspect is the development of two alternate multi-way Sparse Vector Linear Combination kernels that yield efficient runtime sparsity detection and evaluation.


title: VCCFinder: Finding Potential Vulnerabilities in Open-Source Projects to Assist Code Audits

abstract: Despite the security community's best effort, the number of serious vulnerabilities discovered in software is increasing rapidly. In theory, security audits should find and remove the vulnerabilities before the code ever gets deployed. However, due to the enormous amount of code being produced, as well as a the lack of manpower and expertise, not all code is sufficiently audited. Thus, many vulnerabilities slip into production systems. A best-practice approach is to use a code metric analysis tool, such as Flawfinder, to flag potentially dangerous code so that it can receive special attention. However, because these tools have a very high false-positive rate, the manual effort needed to find vulnerabilities remains overwhelming. In this paper, we present a new method of finding potentially dangerous code in code repositories with a significantly lower false-positive rate than comparable systems. We combine code-metric analysis with metadata gathered from code repositories to help code review teams prioritize their work. The paper makes three contributions. First, we conducted the first large-scale mapping of CVEs to GitHub commits in order to create a vulnerable commit database. Second, based on this database, we trained a SVM classifier to flag suspicious commits. Compared to Flawfinder, our approach reduces the amount of false alarms by over 99 % at the same level of recall. Finally, we present a thorough quantitative and qualitative analysis of our approach and discuss lessons learned from the results. We will share the database as a benchmark for future research and will also provide our analysis tool as a web service.


title: A Ka-band broadband traveling-wave power divider/combiner based on low-loss septum unsymmetrical E-plane T-junction series

abstract: A Ka-band broadband traveling-wave power divider based on low-loss septum unsymmetrical E-plane T-junction series has been designed and fabricated. The high isolation, which guarantees the graceful degradation of a modular solid-state device system, is realized by the septum T-junction series. The attractive features of the proposed structure are easy fabrication and low loss. Moreover, a wide operation band of 30%. The simulated isolation and reflection of the outputs are better than 20 dB and 15 dB respectively from 28 GHz to 38 GHz. The measured return loss of input port is better than 19 dB and a maximum transmission coefficient amplitude imbalance of ±1dB is achieved.


City Scale Next Place Prediction from Sparse Data through Similar Strangers


Analysis of the lipid body proteome of the oleaginous alga Lobosphaera incisa


title: A Review of Non-Invasive Sensory Feedback Methods for Transradial Prosthetic Hands

abstract: Any implant or prosthesis replacing a function or functions of an organ or group of organs should be biologically and sensorily integrated with the human body in order to increase their acceptance with their user. If this replacement is for a human hand, which is an important interface between humans and their environment, the acceptance issue and developing sensory-motor embodiment will be more challenging. Despite progress in prosthesis technologies, 50–60% of hand amputees wear a prosthetic device. One primary reason for the rejection of the prosthetic hands is that there is no or negligibly small feedback or tactile sensation from the hand to the user, making the hands less functional. In fact, the loss of a hand means interrupting the closed-loop sensory feedback between the brain (motor control) and the hand (sensory feedback through the nerves). The lack of feedback requires significant cognitive efforts from the user in order to do basic gestures and daily activities. To this aim, recently, there has been significant development in the provision of sensory feedback from transradial prosthetic hands, to enable the user take part in the control loop and improve user embodiment. Sensory feedback to the hand users can be provided via invasive and non-invasive methods. The latter includes the use of temperature, vibration, mechanical pressure and skin stretching, electrotactile stimulation, phantom limb stimulation, audio feedback, and augmented reality. This paper provides a comprehensive review of the non-invasive methods, performs their critical evaluation, and presents challenges and opportunities associated with the non-invasive sensory feedback methods.


title: Supporting Data-Intensive Workflows in Software-Defined Federated Multi-Clouds

abstract: Cloud computing is emerging as a viable platform for scientific exploration. Elastic and on-demand access to resources (and other services), the abstraction of “unlimited” resources, and attractive pricing models provide incentives for scientists to move their workflows into clouds. Generalizing these concepts beyond a single virtualized datacenter, it is possible to create federated marketplaces where different types of resources (e.g., clouds, HPC grids, supercomputers) that may be geographically distributed, are collectively exposed as a single elastic infrastructure. This presents opportunities for optimizing the execution of application workflows with heterogeneous and dynamic requirements, and tackling larger scale problems. In this paper, we introduce a framework to manage the end-to-end execution of data-intensive application workflows in dynamic software-defined resource federation. This framework enables the autonomic execution of workflows by elastically provisioning an appropriate set of resources that meet application requirements, and by adapting this set of resources at runtime as the requirements change. It also allows users to customize scheduling policies that drive the way resources federated and used. To demonstrate the benefits of our approach, we study the execution of two different data-intensive scientific workflows in a multi-cloud federation using different policies and objective functions.


Machine Learning Approaches for Catchphrase Extraction in Legal Documents


title: The impact of board composition on corporate financial and social responsibility performance: Evidence from public-listed companies in Turkey

abstract: In an attempt to explain the relationships between principles of good governance, marketing practices and financial performances of companies in Turkey, the authors examined the inter-relationships between board composition characteristics, corporate social responsibility practices and financial performance in this study. The population of this study is Turkish firms that are listed in the Istanbul Stock Exchange (ISE) in 2007 and have a published corporate governance compliance report. Using content and logistic regression analyses, the authors found that smaller board size leads to better financial performance, whereas inside directors and CEO duality lead to worse financial performance. On the other hand, independent directors lead to better corporate social responsibility. By providing evidence from an emerging country, Turkey, this study provided some meaningful insights into the board of directors’ composition, corporate social responsibility practices and the effects of both on the various financial output of a company. Emphasizing the importance of corporate social responsibility practices and board composition characteristics, the current study offered in-depth information to companies that aim to gain a competitive financial advantage in Turkey.


title: E-SAP: Efficient-Strong Authentication Protocol for Healthcare Applications Using Wireless Medical Sensor Networks

abstract: A wireless medical sensor network (WMSN) can sense humans’ physiological signs without sacrificing patient comfort and transmit patient vital signs to health professionals’ hand-held devices. The patient physiological data are highly sensitive and WMSNs are extremely vulnerable to many attacks. Therefore, it must be ensured that patients’ medical signs are not exposed to unauthorized users. Consequently, strong user authentication is the main concern for the success and large scale deployment of WMSNs. In this regard, this paper presents an efficient, strong authentication protocol, named E-SAP, for healthcare application using WMSNs. The proposed E-SAP includes: (1) a two-factor (i.e., password and smartcard) professional authentication; (2) mutual authentication between the professional and the medical sensor; (3) symmetric encryption/decryption for providing message confidentiality; (4) establishment of a secure session key at the end of authentication; and (5) professionals can change their password. Further, the proposed protocol requires three message exchanges between the professional, medical sensor node and gateway node, and achieves efficiency (i.e., low computation and communication cost). Through the formal analysis, security analysis and performance analysis, we demonstrate that E-SAP is more secure against many practical attacks, and allows a tradeoff between the security and the performance cost for healthcare application using WMSNs.


title: Fully depleted devices for designers: FDSOI and FinFETs

abstract: Technologies featuring fully depleted transistors are entering the mainstream for designs at the 28nm, 20nm, and 14nm nodes. Although these devices have been the playground of device engineers for more than a decade it is for the most part only recently that they have been introduced to circuit designers and logic chip integrators. The physical structure and many of the features — or lack thereof — of the transistors vis-a-vis conventional planar devices are different, opening some new doors and perhaps closing some old ones. In this paper we discuss both planar (variously called ETSOI/UTBB/FDSOI1) and three-dimensional (variously called FinFET or trigate or doublegate) fully depleted devices, comparing and contrasting them with one another and with classical devices, and in both bulk and SOI manifestations.


title: Pitfalls in reporting sample size calculation in randomized controlled trials published in leading anaesthesia journals: a systematic review

abstract: Abstract We have evaluated the pitfalls in reporting sample size calculation in randomized controlled trials (RCTs) published in the 10 highest impact factor anaesthesia journals. Superiority RCTs published in 2013 were identified and checked for the basic components required for sample size calculation and replication. The difference between the reported and replicated sample size was estimated. The sources used for estimating the expected effect size (Δ) were identified, and the difference between the expected and observed effect sizes (Δ gap) was estimated. We enrolled 194 RCTs. Sample size calculation was reported in 91.7% of studies. Replication of sample size calculation was possible in 80.3% of studies. The original and replicated sample sizes were identical in 67.8% of studies. The difference between the replicated and reported sample sizes exceeded 10% in 28.7% of studies. The expected and observed effect sizes were comparable in RCTs with positive outcomes ( P =0.1). Studies with negative outcome tended to overestimate the effect size (Δ gap 42%, 95% confidence interval 32–51%), P Post hoc power of negative studies was 20.2% (95% confidence interval 13.4–27.1%). Studies using data derived from pilot studies for sample size calculation were associated with the smallest Δ gaps ( P =0.008). Sample size calculation is frequently reported in anaesthesia journals, but the details of basic elements for calculation are not consistently provided. In almost one-third of RCTs, the reported and replicated sample sizes were not identical and the assumptions for the expected effect size and variance were not supported by relevant literature or pilot studies.


title: Duckietown: An open, inexpensive and flexible platform for autonomy education and research

abstract: Duckietown is an open, inexpensive and flexible platform for autonomy education and research. The platform comprises small autonomous vehicles (“Duckiebots”) built from off-the-shelf components, and cities (“Duckietowns”) complete with roads, signage, traffic lights, obstacles, and citizens (duckies) in need of transportation. The Duckietown platform offers a wide range of functionalities at a low cost. Duckiebots sense the world with only one monocular camera and perform all processing onboard with a Raspberry Pi 2, yet are able to: follow lanes while avoiding obstacles, pedestrians (duckies) and other Duckiebots, localize within a global map, navigate a city, and coordinate with other Duckiebots to avoid collisions. Duckietown is a useful tool since educators and researchers can save money and time by not having to develop all of the necessary supporting infrastructure and capabilities. All materials are available as open source, and the hope is that others in the community will adopt the platform for education and research.


title: Characterization of Europium and Americium Dipicolinate Complexes

abstract: Dipicolinic acid (pyridine-2,6-dicarboxylic acid, DPA) is a well-known complexant that forms moderately strong complexes with a variety of metal ions, including trivalent lanthanides. A search of open literature reports and thermodynamic databases produced no information on equilibrium constants for dipicolinate complexes of trivalent actinides. Such information represents an essential starting point for assessing the potential for application of dipicolinate to the task of trivalent lanthanide/actinide separations, specifically as a potential substitute for diethylenetriamine-N,N,N′,N″,N″-pentaacetic acid (DTPA) in the TALSPEAK process. The Am(III)/Eu(III)-dipicolinate system is the subject of the following report. Protonation constants of dipicolinic acid are pK1 = 2.15 (±0.02) and pK2 = 4.62 (±0.02) in µ = 1.0 M NaClO4 media, T = 25.0 (±0.1)°C. Stability constants of Eu3+ and Am3+ with DPA have been determined using solvent extraction distribution methods coupled with radiometric detection. The resolve...


title: Scientific Article Recommendation by using Distributed Representations of Text and Graph

abstract: Scientific article recommendation problem deals with recommending similar scientific articles given a query article. It can be categorized as a content based similarity system. Recent advancements in representation learning methods have proven to be effective in modeling distributed representations in different modalities like images, languages, speech, networks etc. The distributed representations obtained using such techniques in turn can be used to calculate similarities. In this paper, we address the problem of scientific paper recommendation through a novel method which aims to combine multimodal distributed representations, which in this case are: 1. distributed representations of paper's content, and 2. distributed representation of the graph constructed from the bibliographic network. Through experiments we demonstrate that our method outperforms the state-of-the-art distributed representation methods in text and graph, by 29.6% and 20.4%, both in terms of precision and mean-average-precision respectively.


title: Experimental reconstruction of mandibular defects with vascularized iliac bone grafts

abstract: Abstract Purpose: The study evaluated the processes of incorporation and bone remodeling after microsurgical transplantation of iliac crest grafts for mandibular defect reconstruction. Materials and Methods: A defect in the mandibular angle measuring 2 × 4 × 1 cm was reconstructed in six Gottingen minipigs with a microvascularized iliac bone graft based on the circumflex artery. The animals were killed at 14, 28, and 56 days after transplantation and the healing process was histologically examined. Results: Although a constant vascular supply to the graft was restored by the microvascular reanastomosis, necrosis occurred in the major part of the bone marrow. New bone formation from the graft was markedly delayed compred with that from the mandible. Some osteocytes and marrow survived after grafting, but the volume of iliac bone tended to decrease gradually over time. In the 56-day specimens, the border between the graft and the mandible was not clear. Conclusion: These findings indicate that survival of the vascularized iliac bone graft in the minipig is not complete.


title: Achieving low latency and energy consumption by 5G TDD mode optimization

abstract: The target for a new 5G radio access technology is to support multi-Gbps and ms latency connectivity simultaneously at noticeably lower energy consumption and cost compared to the existing 4G technologies, such as LTE-Advanced. Extremely short air interface latency is required to achieve these requirements in a TDD-based local area network. In this paper, we discuss how the required short TDD latency can be achieved and further utilized in 5G physical air interface. First, we investigate the enablers and limits of TDD latency by analyzing the performance of OFDM in different channel environments and discussing on the consequent frame length limits. We then provide a description on how the achieved short TDD latency can further be utilized to enable remarkably low energy consumption. A numerical analysis comparing the battery life time of the suggested 5G TDD air interface and LTE is provided, showing remarkable gains for the 5G air interface concept.


title: DKN: Deep Knowledge-Aware Network for News Recommendation

abstract: Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among news. The recommended results for a user are consequently limited to simple patterns and cannot be extended reasonably. Moreover, news recommendation also faces the challenges of high time-sensitivity of news and dynamic diversity of users' interests. To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. KCNN treats words and entities as multiple channels, and explicitly keeps their alignment relationship during convolution. In addition, to address users' diverse interests, we also design an attention module in DKN to dynamically aggregate a user's history with respect to current candidate news. Through extensive experiments on a real online news platform, we demonstrate that DKN achieves substantial gains over state-of-the-art deep recommendation models. We also validate the efficacy of the usage of knowledge in DKN.


title: Consumer perception of interface quality, security, and loyalty in electronic commerce

abstract: Customer interface quality, perceived security, and customer loyalty are critical factors for success of an e-commerce website; however, the relationships among them are not fully understood. We proposed a model for testing the relationships among them and the important outcomes of the site: switching costs and customer loyalty. Data was collected to test the model using a web-based survey, and empirical analyses were performed using SEM. The analytical results demonstrated that customer interface quality and perceived security positively affected customer satisfaction and switching costs, and thus customer loyalty to an e-commerce website. Specifically, our study showed that switching costs positively moderated the effect of customer satisfaction on customer loyalty; this moderating effect is discussed.


title: An Early Performance Analysis of Cloud Computing . . .

abstract: Cloud Computing is emerging today as a commercial infrastructure that eliminates the need for maintaining expensive computing hardware. Through the use of virtualization, clouds promise to address with the same shared set of physical resources a large user base with different needs. Thus, clouds promise to be for scientists an alternative to clusters, grids, and supercomputers. However, virtualization may induce significant performance penalties for the demanding scientific computing workloads. In this work we present an evaluation of the usefulness of the current cloud computing services for scientific computing. We analyze the performance of the Amazon EC2 platform using micro-benchmarks, kernels, and e-Science workloads. We also compare using long-term traces the performance characteristics and cost models of clouds with those of other platforms accessible to scientists. While clouds are still changing, our results indicate that the current cloud services need an order of magnitude in performance improvement to be useful to the scientific community. Wp 1 http://www.pds.ewi.tudelft.nl/∼iosup/ S. Ostermann et al. Wp Early Cloud Computing EvaluationWp PDS


title: Machine learning plug-ins for GNU Radio Companion

abstract: This paper gives an insight about how to create classifier plug-ins (signal processing blocks) using hard-code input for GNU Radio Companion (GRC). GNU Radio Companion is an open source Visual programming language for any real time signal processing applications. At present there is no classifier block available inside this GRC tool. Here we are introducing a low cost classifier which utilizes the basic machine learning algorithms:linear regression and logistic regression. The creation of classifier plug-ins in an open source software enables easy manipulation of real time classification problems during the transmission and reception of signals in Software Defined Radios. So this workdescribes the development of signal processing block that can be done by changing the Python code and C++ codes of the ‘gr-modtool’ package. It is highly cost effective and with great potential since GNU Radio software is open source and free.


title: HINE: Heterogeneous Information Network Embedding

abstract: Network embedding has shown its effectiveness in embedding homogeneous networks. Compared with homogeneous networks, heterogeneous information networks (HINs) contain semantic information from multi-typed entities and relations, and are shown to be a more effective model for real world data. The existing network embedding methods fail to explicitly capture the semantics in HINs. In this paper, we propose an HIN embedding model (HINE), which consists of local and global semantic embedding. Local semantic embedding aims to incorporate entity type information via embedding the local structures and types of the entities in a supervised way. Global semantic embedding leverages multi-hop relation types among entities to propagate the global semantics via a Markov Random Field (MRF) to impact the embedding vectors. By doing so, HINE is capable to capture both local and global semantic information in the embedding vectors. Experimental results show that HINE significantly outperforms state-of-the-art methods.


title: Review of vision-based steel surface inspection systems

abstract: Steel is the material of choice for a large number and very diverse industrial applications. Surface qualities along with other properties are the most important quality parameters, particularly for flat-rolled steel products. Traditional manual surface inspection procedures are awfully inadequate to ensure guaranteed quality-free surface. To ensure stringent requirements of customers, automated vision-based steel surface inspection techniques have been found to be very effective and popular during the last two decades. Considering its importance, this paper attempts to make the first formal review of state-of-art of vision-based defect detection and classification of steel surfaces as they are produced from steel mills. It is observed that majority of research work has been undertaken for cold steel strip surfaces which is most sensitive to customers' requirements. Work on surface defect detection of hot strips and bars/rods has also shown signs of increase during the last 10 years. The review covers overall aspects of automatic steel surface defect detection and classification systems using vision-based techniques. Attentions have also been drawn to reported success rates along with issues related to real-time operational aspects.


title: Post-buckling analysis for the precisely controlled buckling of thin film encapsulated by elastomeric substrates

abstract: The precisely controlled buckling of stiff thin films (e.g., Si or GaAs nano ribbons) on the patterned surface of elastomeric substrate (e.g., poly(dimethylsiloxane) (PDMS)) with periodic inactivated and activated regions was designed by Sun et al. [Sun, Y., Choi, W.M., Jiang, H., Huang, Y.Y., Rogers, J.A., 2006. Controlled buckling of semiconductor nanoribbons for stretchable electronics. Nature Nanotechnology 1, 201-207] for important applications of stretchable electronics. We have developed a post-buckling model based on the energy method for the precisely controlled buckling to study the system stretchability. The results agree with Sun et al.'s (2006) experiments without any parameter fitting, and the system can reach 120% stretchability.


title: Review of random forest classification techniques to resolve data imbalance

abstract: In this current age, numerous ranges of real word applications with imbalanced dataset is one of the foremost focal point of researcher's inattention. There is the enormous increment of data generation and imbalance within dataset. Processing and knowledge extraction of huge amount of imbalanced data becomes a challenge related with space and time necessities. Generally there is a list of an assortment of factual humanity applications which deals with unequal data sample division in to number of classes. Due to this division of data either of class goes into majority or minority with comparably less data count. This outnumbering of data sample in either of one class directs towards the handling of minority class and target on remarkable reduction in error rate. The standard learning methods do not directly focus on this type of classes. Random Forest Classification (RFC) is an ensemble approach that utilizes a number of classifiers to work together in order to identify the class label for unlabeled instances. This approach has proved its high accuracy and superiority with imbalanced datasets. This classifier provides various techniques to resolve class imbalance problem. This paper summarizes, the literature survey from 2000 to 2016 of various techniques related to RFC to resolve class imbalance. Specifically Weighted Random Forest (WRF), Balanced Random Forest (BRF), Sampling (Under Sampling (US)) and Down Sampling (DS), Cost Sensitive Methods have been adapted more to till date. The limitation of this numerous literature is researchers can focus on dynamic integration techniques to resolve class imbalance and increase robustness and versatility of classification.


title: Identification of Discriminating Metabolic Pathways and Metabolites in Human PBMCs Stimulated by Various Pathogenic Agents

abstract: Immunity and cellular metabolism are tightly interconnected but it is not clear whether different pathogens elicit specific metabolic responses. To address this issue, we studied differential metabolic regulation in peripheral blood mononuclear cells (PBMCs) of healthy volunteers challenged by Candida albicans, Borrelia burgdorferi, lipopolysaccharide, and Mycobacterium tuberculosis in vitro. By integrating gene expression data of stimulated PBMCs of healthy individuals with the KEGG pathways, we identified both common and pathogen-specific regulated pathways depending on the time of incubation. At 4 h of incubation, pathogenic agents inhibited expression of genes involved in both the glycolysis and oxidative phosphorylation pathways. In contrast, at 24 h of incubation, particularly glycolysis was enhanced while genes involved in oxidative phosphorylation remained unaltered in the PBMCs. In general, differential gene expression was less pronounced at 4 h compared to 24 h of incubation. KEGG pathway analysis allowed differentiation between effects induced by Candida and bacterial stimuli. Application of genome-scale metabolic model further generated a Candida-specific set of 103 reporter metabolites (e.g., desmosterol) that might serve as biomarkers discriminating Candida-stimulated PBMCs from bacteria-stimuated PBMCs. Our analysis also identified a set of 49 metabolites that allowed discrimination between the effects of Borrelia burgdorferi, lipopolysaccharide and Mycobacterium tuberculosis. We conclude that analysis of pathogen-induced effects on PBMCs by a combination of KEGG pathways and genome-scale metabolic model provides deep insight in the metabolic changes coupled to host defense.


title: Domain Adaptation from Synthesis to Reality in Single-model Detector for Video Smoke Detection

abstract: This paper proposes a method for video smoke detection using synthetic smoke samples. The virtual data can automatically offer precise and rich annotated samples. However, the learning of smoke representations will be hurt by the appearance gap between real and synthetic smoke samples. The existed researches mainly work on the adaptation to samples extracted from original annotated samples. These methods take the object detection and domain adaptation as two independent parts. To train a strong detector with rich synthetic samples, we construct the adaptation to the detection layer of state-of-the-art single-model detectors (SSD and MS-CNN). The training procedure is an end-to-end stage. The classification, location and adaptation are combined in the learning. The performance of the proposed model surpasses the original baseline in our experiments. Meanwhile, our results show that the detectors based on the adversarial adaptation are superior to the detectors based on the discrepancy adaptation. Code will be made publicly available on this http URL Moreover, the domain adaptation for two-stage detector is described in Appendix A.


title: Resilient packet delivery through software defined redundancy: An experimental study

abstract: Mission-critical applications that use computer networks require guaranteed communication with bounds on the time needed to deliver packets, even if hardware along a path fails or congestion occurs. Current networking technologies follow a reactive approach in which retransmission mechanisms and routing update protocols respond once packet loss occurs. We present a proactive approach that pre-builds and monitors redundant paths to ensure timely delivery with no recovery latency, even in the case of a single-link failure. The paper outlines the approach, describes a new mechanism we call a redundancy controller, and explains the use of SDN to establish and change paths. The paper also reports on a prototype system and gives experimental measurements.