title: Programmable 2D Arrangements for Element Texture Design

abstract: This article introduces a programmable method for designing stationary 2D arrangements for element textures, namely textures made of small geometric elements. These textures are ubiquitous in numerous applications of computer-aided illustration. Previous methods, whether they be example-based or layout-based, lack control and can produce a limited range of possible arrangements. Our approach targets technical artists who will design an arrangement by writing a script. These scripts are using three types of operators: partitioning operators for defining the broad-scale organization of the arrangement, mapping operators for controlling the local organization of elements, and merging operators for mixing different arrangements. These operators are designed so as to guarantee a stationary result, meaning that the produced arrangements will always be repetitive. We show that this simple set of operators is sufficient to reach a much broader variety of arrangements than previous methods. Editing the script leads to predictable changes in the synthesized arrangement, which allows an easy iterative design of complex structures. Finally, our operator set is extensible and can be adapted to application-dependent needs.


title: Routing protocols in Vehicular Delay Tolerant Networks: A comprehensive survey

abstract: This article presents a comprehensive survey of routing protocols proposed for routing in Vehicular Delay Tolerant Networks (VDTN) in vehicular environment. DTNs are utilized in various operational environments, including those subject to disruption and disconnection and those with high-delay, such as Vehicular Ad-Hoc Networks (VANET). We focus on a special type of VANET, where the vehicular traffic is sparse and direct end-to-end paths between communicating parties do not always exist. Thus, communication in this context falls into the category of Vehicular Delay Tolerant Network (VDTN). Due to the limited transmission range of an RSU (Road Side Unit), remote vehicles, in VDTN, may not connect to the RSU directly and thus have to rely on intermediate vehicles to relay the packets. During the message relay process, complete end-to-end paths may not exist in highly partitioned VANETs. Therefore, the intermediate vehicles must buffer and forward messages opportunistically. Through buffer, carry and forward, the message can eventually be delivered to the destination even if an end-to-end connection never exists between source and destination. The main objective of routing protocols in DTN is to maximize the probability of delivery to the destination while minimizing the end-to-end delay. Also, vehicular traffic models are important for DTN routing in vehicle networks because the performance of DTN routing protocols is closely related to population and mobility models of the network.


title: Properties of Farey Sequence and their Applications to Digital Image Processing

abstract: Farey sequence has been a topic of interest to the mathematicians since the very beginning of last century. With the emergence of various algorithms involving the digital plane in recent times, several interesting works related with the Farey sequence have come up. Our work is related with the problem of searching an arbitrary fraction in a Farey sequence and its relevance to image processing. Given an arbitrary fraction p/q (0<p<q) and a Farey sequence Fn of order n, we propose a novel algorithm using the Regula Falsi method and the concept of Farey table to efficiently find the fraction of Fn closest to p/q. All computations are in the integer domain only, which is its added benefit. Some contemporary applications of image processing have also been shown where such concepts can be incorporated. Experimental results have been furnished to demonstrate its efficiency and elegance.


Ionic Species in Orthophosphate Absorption by Barley Roots.


title: Managerial perspectives on risk and risk taking

abstract: This paper explores the relation between decision theoretic conceptions of risk and the conceptions held by executives. It considers recent studies of risk attitudes and behavior among managers against the background of conceptions of risk derived from theories of choice. We conclude that managers take risks and exhibit risk preferences, but the processes that generate those observables are somewhat removed from the classical processes of choosing from among alternative actions in terms of the mean (expected value) and variance (risk) of the probability distributions over possible outcomes. We identify three major ways in which the conceptions of risk and risk taking held by these managers lead to orientations to risk that are different from what might be expected from a decision theory perspective: Managers are quite insensitive to estimates of the probabilities of possible outcomes; their decisions are particularly affected by the way their attention is focused on critical performance targets; and they make a sharp distinction between taking risks and gambling. These differences, along with closely related observations drawn from other studies of individual and organizational choice, indicate that the behavioral phenomenon of risk taking in organizational settings will be imperfectly understood within a classical conception of risk.


A Survey of Gait Recognition Approaches Using PCA & ICA By


title: Liver Fibrosis Classification Based on Transfer Learning and FCNet for Ultrasound Images

abstract: Diagnostic ultrasound offers great improvements in diagnostic accuracy and robustness. However, it is difficult to make subjective and uniform diagnoses, because the quality of ultrasound images can be easily influenced by machine settings, the characteristics of ultrasonic waves, the interactions between ultrasound and body tissues, and other uncontrollable factors. In this paper, we propose a novel liver fibrosis classification method based on transfer learning (TL) using VGGNet and a deep classifier called fully connected network (FCNet). In case of insufficient samples, deep features extracted using TL strategy can provide sufficient classification information. These deep features are then sent to FCNet for the classification of different liver fibrosis statuses. With this framework, tests show that our deep features combined with the FCNet can provide suitable information to enable the construction of the most accurate prediction model when compared with other methods.


title: pi-football: A Bayesian network model for forecasting association football match outcomes

abstract: A Bayesian network is a graphical probabilistic model that represents the conditional dependencies among uncertain variables, which can be both objective and subjective. We present a Bayesian network model for forecasting Association Football matches in which the subjective variables represent the factors that are important for prediction but which historical data fails to capture. The model (pi-football) was used to generate forecasts about the outcomes of the English Premier League (EPL) matches during season 2010/11 (but is easily extended to any football league). Forecasts were published online prior to the start of each match. We show that:(a)using an appropriate measure of forecast accuracy, the subjective information improved the model such that posterior forecasts were on par with bookmakers' performance; (b)using a standard profitability measure with discrepancy levels at >=5%, the model generates profit under maximum, mean, and common bookmakers' odds, even allowing for the bookmakers' built-in profit margin. Hence, compared with other published football forecast models, pi-football not only appears to be exceptionally accurate, but it can also be used to 'beat the bookies'.


title: Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings

abstract: We consider the task of named entity recognition for Chinese social media. The long line of work in Chinese NER has focused on formal domains, and NER for social media has been largely restricted to English. We present a new corpus of Weibo messages annotated for both name and nominal mentions. Additionally, we evaluate three types of neural embeddings for representing Chinese text. Finally, we propose a joint training objective for the embeddings that makes use of both (NER) labeled and unlabeled raw text. Our methods yield a 9% improvement over a stateof-the-art baseline.


title: Towards Question Improvement on Knowledge Sharing Platforms: A Stack Overflow Case Study

abstract: Community-driven Knowledge Sharing (KS) platforms have gained immense popularity in recent years among the Internet users to seek, learn measured as the reputation on platforms. Thus, peers on the platform dissuade other users to post low quality content by closing, disliking or not answering their questions. The aggressive attitude of the community leads to a negative impact on the users' experience (especially for newbies) and the users tend to lose interest in the platform. Since the quality management on KS platforms is a necessity, we aim to emphasize the need of a mechanism to help users in improving their questions. To study this, we perform experiments on the Stack Overflow (SO) platform, however, the study can be adapted for other KS platforms.The SO community aggressively mark questions, even with slightest deformity, as closed. The non-triviality of reopening process and the rigorous review mechanism can be handled only by proper editing of the closed questions. Thus we present first-of-a-kind study, to assist the SO platform users, in the reopening process of their closed questions. We build predictive models to suggest the users that if their edited version of closed question will lead to a successful reopen or not. This can assist users at large by retracting them from entering the review process with improper edits. To learn these models effectively, we consider the user categories on the platform, based on their reputation: established & non-established. In addition to being the major contributor to closed questions, the non-established users have lower odds of getting their closed questions reopened than established users. Thus, we leverage the better editing skills of established users to learn question edit models by employing their reopened closed questions. We present the results of predictive model and suggest insights on the useful edits in the reopening process on the SO platform.


title: Advanced correlation-based anomaly detection method for predictive maintenance

abstract: Variations in sensor data collected from equipment have been widely analyzed by using anomaly detection methods for predictive maintenance. Our experience shows that correlations between sensors effectively predict failures because the correlations usually reflect the status of equipment with higher sensitivity. In this paper, we present a method that exploits correlations between sensors for pre-processing and enables anomalies to be detected using both sensor data and correlations. The method was evaluated by applying it to compact electric generators, and the results showed it detected anomalies more accurately than when only sensor data were used. This method is expected to predict failures earlier and reduce the cost of downtime and maintenance.


title: Improving the robustness of convolutional networks to appearance variability in biomedical images

abstract: While convolutional neural networks (CNN) produce state-of-the-art results in many applications including biomedical image analysis, they are not robust to variability in the data that is not well represented by the training set. An important source of variability in biomedical images is the appearance of objects such as contrast and texture due to different imaging settings. We introduce the neighborhood similarity layer (NSL) which can be used in a CNN to improve robustness to changes in the appearance of objects that are not well represented by the training data. The proposed NSL transforms its input feature map at a given pixel by computing its similarity to the surrounding neighborhood. This transformation is spatially varying, hence not a convolution. It is differentiable; therefore, networks including the proposed layer can be trained in an end-to-end manner. We demonstrate the advantages of the NSL for the vasculature segmentation and cell detection problems.


title: Detecting Key Players in Terrorist Networks

abstract: The interest in analyzing loosely connected and decentralized terrorist networks of global reach has grown during the past decade. Social Network Analysis (SNA) is one approach towards understanding terrorist networks since it can be used to analyze the structure of a network and to detect important persons and links. In this work we study decentralized terrorist networks with different types of nodes. The nodes can be either organizations, places or persons. We use a combination of different centrality measures to detect key players in such networks.


Biomedical Implants: Corrosion and its Prevention - A Review~!2009-12-22~!2010-01-20~!2010-05-25~!


title: Creating Brand Equity by Leveraging Value Creation and Consumer Commitment in Online Brand Communities: A Conceptual Framework

abstract: Advancement in information technologies has created change in our economics as well as in our societies because companies strive to adapt the conditions that prevail in marketplace. In this scenario, marketing professionals are exploring advanced and effective ways to build a strong brand. The research objectives explore the impact of online brand communities on brand equity dimensions through which consumer could achieve commitment and value-creation. While, marketing managers could explore attributes regarding brand perception, information and resource sharing and brand loyalty. A framework is proposed based on the drivers of online brand community and its influence on the dimensions of brand equity. From theoretical perspective, this study contributes to branding literature by exploring strategies for value-creation, brand image and integrated marketing communication. Additionally from marketing perspective, the drivers will guide managers to understand the want and desire of target audience. The building of such online brand communities will raise the return of investment, save marketing cost, enhance consumer loyalty, improving product development and providing circumstances for co-creation. Thus, it will provide ever-lasting consumer-brand relationship and substantial information about consumer behavior.


title: Multisensors realtime data fusion optimization for IOT systems

abstract: This study proposed an IoT (Internet of Things) system for the monitoring and control of the aquaculture platform. The proposed system is network surveillance combined with mobile devices and a remote platform to collect real-time farm environmental information. The real-time data is captured and displayed via ZigBee wireless transmission signal transmitter to remote computer terminals. This study permits real-time observation and control of aquaculture platform with dissolved oxygen sensors, temperature sensing elements using A/D and microcontrollers signal conversion. The proposed system will use municipal electricity coupled with a battery power source to provide power with battery intervention if municipal power is interrupted. This study is to make the best fusion value of multi-odometer measurement data for optimization via the maximum likelihood estimation (MLE).Finally, this paper have good efficient and precise computing in the experimental results.


title: Impulse Control Disorders Following Deep Brain Stimulation of the Subthalamic Nucleus in Parkinson's Disease: Clinical Aspects

abstract: Parkinson's disease (PD) has been associated with the development of impulse control disorders (ICDs), possibly due to overstimulation of the mesolimbic system by dopaminergic medication. Preliminary reports have suggested that deep brain stimulation (DBS), a neurosurgical procedure offered to patients with treatment-resistant PD, affects ICD in a twofold way. Firstly, DBS allows a decrease in dopaminergic medication and hence causes an improvement in ICDs. Secondly, some studies have proposed that specific ICDs may develop after DBS. This paper addresses the effects of DBS on ICDs in patients with PD. A literature search identified four original studies examining a total of 182 patients for ICDs and nine case reports of 39 patients that underwent DBS and developed ICDs at some point. Data analysis from the original studies did not identify a significant difference in ICDs between patients receiving dopaminergic medication and patients on DBS, whilst the case reports showed that 56% of patients undergoing DBS had poor outcome with regards to ICDs. We discuss these ambivalent findings in the light of proposed pathogenetic mechanisms. Longitudinal, prospective studies with larger number of patients are required in order to fully understand the role of DBS on ICDs in patients with PD.


title: IMPURE ALTRUISM AND DONATIONS TO PUBLIC GOODS : A THEORY OF WARM-GLOW GIVING *

abstract: When people make donations to privately provided public goods, such as charity, there may be many factors influencing their decision other than altruism. Social pressure, guilt, sympathy, or simply a desire for a "warm glow" may all be important. This paper considers such impure altruism formally and develops a wide set of implications. In particular, this paper discusses the invariance proposition of public goods, solves for the sufficient conditions for neutrality to hold, examines the optimal tax treatment of charitable giving, and calibrates the model based on econometric studies in order to consider policy experiments. Impure altruism is shown to be more consistent with observed patterns of giving than the conventional pure altruism approach, and to have policy implications that may differ widely from those of the conventional models. Copyright 1990 by Royal Economic Society.


title: Resolution enhanced SAR tomography: A nonparametric iterative adaptive approach

abstract: The ground-volume separation of radar scattering plays an important role in the analysis of forested scenes. For this purpose, the data covariance matrix of multi-polarimetric (MP) multi-baseline (MB) SAR surveys can be represented thru a sum of two Kronecker products composed of the data covariance matrices and polarimetric signatures that correspond to the ground and canopy scattering mechanisms (SMs), respectively. The sum of Kronecker products (SKP) decomposition allows the use of different tomographic SAR focusing methods on the ground and canopy structural components separately, nevertheless, the main drawback of this technique relates to the rank-deficiencies of the resultant data covariance matrices, which restrict the usage of the adaptive beamforming techniques, requiring more advanced beamforming methods, such as compressed sensing (CS). This paper proposes a modification of the nonparametric iterative adaptive approach for amplitude and phase estimation (IAA-APES), which applied to MP-MB SAR data, serves as an alternative to the SKP-based techniques for ground-volume reconstruction, which main advantage relates precisely to the non-need of the SKP decomposition technique as a pre-processing step.


title: Scene Text Detection via Holistic, Multi-Channel Prediction

abstract: Recently, scene text detection has become an active research topic in computer vision and document analysis, because of its great importance and significant challenge. However, vast majority of the existing methods detect text within local regions, typically through extracting character, word or line level candidates followed by candidate aggregation and false positive elimination, which potentially exclude the effect of wide-scope and long-range contextual cues in the scene. To take full advantage of the rich information available in the whole natural image, we propose to localize text in a holistic manner, by casting scene text detection as a semantic segmentation problem. The proposed algorithm directly runs on full images and produces global, pixel-wise prediction maps, in which detections are subsequently formed. To better make use of the properties of text, three types of information regarding text region, individual characters and their relationship are estimated, with a single Fully Convolutional Network (FCN) model. With such predictions of text properties, the proposed algorithm can simultaneously handle horizontal, multi-oriented and curved text in real-world natural images. The experiments on standard benchmarks, including ICDAR 2013, ICDAR 2015 and MSRA-TD500, demonstrate that the proposed algorithm substantially outperforms previous state-of-the-art approaches. Moreover, we report the first baseline result on the recently-released, large-scale dataset COCO-Text.


Galaxy Morphological Classification


title: Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images

abstract: Object detection in very high resolution optical remote sensing images is a fundamental problem faced for remote sensing image analysis. Due to the advances of powerful feature representations, machine-learning-based object detection is receiving increasing attention. Although numerous feature representations exist, most of them are handcrafted or shallow-learning-based features. As the object detection task becomes more challenging, their description capability becomes limited or even impoverished. More recently, deep learning algorithms, especially convolutional neural networks (CNNs), have shown their much stronger feature representation power in computer vision. Despite the progress made in nature scene images, it is problematic to directly use the CNN feature for object detection in optical remote sensing images because it is difficult to effectively deal with the problem of object rotation variations. To address this problem, this paper proposes a novel and effective approach to learn a rotation-invariant CNN (RICNN) model for advancing the performance of object detection, which is achieved by introducing and learning a new rotation-invariant layer on the basis of the existing CNN architectures. However, different from the training of traditional CNN models that only optimizes the multinomial logistic regression objective, our RICNN model is trained by optimizing a new objective function via imposing a regularization constraint, which explicitly enforces the feature representations of the training samples before and after rotating to be mapped close to each other, hence achieving rotation invariance. To facilitate training, we first train the rotation-invariant layer and then domain-specifically fine-tune the whole RICNN network to further boost the performance. Comprehensive evaluations on a publicly available ten-class object detection data set demonstrate the effectiveness of the proposed method.


title: The Perception-Distortion Tradeoff

abstract: Image restoration algorithms are typically evaluated by some distortion measure (e.g. PSNR, SSIM, IFC, VIF) or by human opinion scores that quantify perceived perceptual quality. In this paper, we prove mathematically that distortion and perceptual quality are at odds with each other. Specifically, we study the optimal probability for correctly discriminating the outputs of an image restoration algorithm from real images. We show that as the mean distortion decreases, this probability must increase (indicating worse perceptual quality). As opposed to the common belief, this result holds true for any distortion measure, and is not only a problem of the PSNR or SSIM criteria. However, as we show experimentally, for some measures it is less severe (e.g. distance between VGG features). We also show that generative-adversarial-nets (GANs) provide a principled way to approach the perception-distortion bound. This constitutes theoretical support to their observed success in low-level vision tasks. Based on our analysis, we propose a new methodology for evaluating image restoration methods, and use it to perform an extensive comparison between recent super-resolution algorithms.


title: Intense Pulsed Light Was Effective for Solar Lentigines and Ephelides

abstract: A noncoherent, broadband, intense pulsed light source has been used for the symptoms of photoaging skin as a nonablative method. The purpose of this study is to investigate the efficacy and tolerability of intense pulsed light in solar lentigines and ephelides on the face. An open study was performed in patients with solar lentigines and ephelides who received three to five treatments of intense pulsed light. Forty-eight percent of patients had more than 50% improvement and 20% had more than 75% improvement. In the group of solar lentigines, 40% of patients showed more than 50% improvement and 16% did more than 75% improvement. Patients with solar lentigines+ephelides and ephelides responded remarkably with 75% and 71% of patients having more than 50% improvement, respectively. Intense pulsed light was well tolerated and may be a new modality for the therapy of solar lentigines and ephelides.


title: Sensorimotor Training in a Virtual Reality Environment: Does It Improve Functional Recovery Poststroke?

abstract: Objective. To investigate the effectiveness of computerized virtual reality (VR) training of the hemiparetic hand of patients poststroke using a system that provides repetitive motor reeducation and skill reacquisition. Methods. Eight subjects in the chronic phase poststroke participated in a 3-week program using their hemiparetic hand in a series of interactive computer games for 13 days of training, weekend breaks, and pretests and posttests. Each subject trained for about 2 to 2.5 h per day. Outcome measures consisted of changes in the computerized measures of thumb and finger range of motion, thumb and finger velocity, fractionation (the ability to move fingers independently), thumb and finger strength, the Jebsen Test of Hand Function, and a Kinematic reach to grasp test. Results. Subjects as a group improved in fractionation of the fingers, thumb and finger range of motion, and thumb and finger speed, retaining those gains at the 1-week retention test. Transfer of these improvements was demonstrated through changes in the Jebsen Test of Hand Function and a decrease after the therapy in the overall time from hand peak velocity to the moment when an object was lifted from the table. Conclusions. It is difficult in current service delivery models to provide the intensity of practice that appears to be needed to effect neural reorganization and functional changes poststroke. Computerized exercise systems may be a way to maximize both the patients’ and the clinicians’ time. The data in this study add support to the proposal to explore novel technologies for incorporation into current practice.


title: Big Data Visual Analytics System for Disease Pattern Analysis

abstract: As the world trend is elapsing toward the big data world, researchers have gradually become interested in data analysis and data visualization. We propose a visual analytics system for analyzing the big data for disease pattern analysis. The system is designed for not only experts but also novice users. We have focused on designing the system to support a short learning curve and scalability. The system is implemented on the web and we utilize Spring Framework for distributed processing with consideration to scalability. Furthermore, we have collected epidemic disease data from a public data repository that the government opens to the public and we analyze the data to reveal the causality of disease outbreak through our system.


title: Preventing drive-by download via inter-module communication monitoring

abstract: Drive-by download attack is one of the most severe threats to Internet users. Typically, only visiting a malicious page will result in compromise of the client and infection of malware. By the end of 2008, drive-by download had already become the number one infection vector of malware [5]. The downloaded malware may steal the users' personal identification and password. They may also join botnet to send spams, host phishing site or launch distributed denial of service attacks. Generally, these attacks rely on successful exploits of the vulnerabilities in web browsers or their plug-ins. Therefore, we proposed an inter-module communication monitoring based technique to detect malicious exploitation of vulnerable components thus preventing the vulnerability being exploited. We have implemented a prototype system that was integrated into the most popular web browser Microsoft Internet Explorer. Experimental results demonstrate that, on our test set, by using vulnerability-based signature, our system could accurately detect all attacks targeting at vulnerabilities in our definitions and produced no false positive. The evaluation also shows the performance penalty is kept low.


title: Learning Halfspaces and Neural Networks with Random Initialization

abstract: We study non-convex empirical risk minimization for learning halfspaces and neural networks. For loss functions that are $L$-Lipschitz continuous, we present algorithms to learn halfspaces and multi-layer neural networks that achieve arbitrarily small excess risk $\epsilon>0$. The time complexity is polynomial in the input dimension $d$ and the sample size $n$, but exponential in the quantity $(L/\epsilon^2)\log(L/\epsilon)$. These algorithms run multiple rounds of random initialization followed by arbitrary optimization steps. We further show that if the data is separable by some neural network with constant margin $\gamma>0$, then there is a polynomial-time algorithm for learning a neural network that separates the training data with margin $\Omega(\gamma)$. As a consequence, the algorithm achieves arbitrary generalization error $\epsilon>0$ with ${\rm poly}(d,1/\epsilon)$ sample and time complexity. We establish the same learnability result when the labels are randomly flipped with probability $\eta<1/2$.


title: A survey of depth and inertial sensor fusion for human action recognition

abstract: A number of review or survey articles have previously appeared on human action recognition where either vision sensors or inertial sensors are used individually. Considering that each sensor modality has its own limitations, in a number of previously published papers, it has been shown that the fusion of vision and inertial sensor data improves the accuracy of recognition. This survey article provides an overview of the recent investigations where both vision and inertial sensors are used together and simultaneously to perform human action recognition more effectively. The thrust of this survey is on the utilization of depth cameras and inertial sensors as these two types of sensors are cost-effective, commercially available, and more significantly they both provide 3D human action data. An overview of the components necessary to achieve fusion of data from depth and inertial sensors is provided. In addition, a review of the publicly available datasets that include depth and inertial data which are simultaneously captured via depth and inertial sensors is presented.


title: Cloud Supported Building Data Analytics

abstract: With increasing availability of instrumented infrastructures in built environments, it is necessary to understand how such data will be stored, processed and analysed in a timely manner. Many "smart cities" applications, for instance, identify how data from building sensors can be combined together to support applications such as emergency response, energy management, etc. Enabling sensor data to be transmitted to a Cloud environment for processing provides a number of benefits, such as scalability and elastic provisioning of computational resources - as the total data size may not be known apriori. In this application-based case study, we describe the integration of an in-building sensor network (both for sensing and actuation) with a distributed Cloud environment. Energy optimisation in buildings represents a class of problems that requires significant computational resources and generally is a time consuming process. We describe the use of Cloud computing for efficiently running and deploying Energy Plus simulations with sensor data in order to fulfil a number of energy related objectives for buildings. We describe and evaluate the establishment of such a sensor based application using a Comet Cloud implementation with data collection from a real building pilot. Although our focus is on a single application, the general architecture and analysis carried out can be generalised to other similar scenarios.


title: Do Less and Achieve More: Training CNNs for Action Recognition Utilizing Action Images from the Web

abstract: We collect three large web action image datasets.We verify that web action images are complementary to training videos by extensive experiments.We show both filtered and unfiltered web action images are complementary to training videos.We show usefulness of web action images in solving an artifact of finetuning CNN model. Recently, attempts have been made to collect millions of videos to train Convolutional Neural Network (CNN) models for action recognition in videos. However, curating such large-scale video datasets requires immense human labor, and training CNNs on millions of videos demands huge computational resources. In contrast, collecting action images from the Web is much easier and training on images requires much less computation. In addition, labeled web images tend to contain discriminative action poses, which highlight discriminative portions of a videos temporal progression. Through extensive experiments, we explore the question of whether we can utilize web action images to train better CNN models for action recognition in videos. We collect 23.8K manually filtered images from the Web that depict the 101 actions in the UCF101 action video dataset. We show that by utilizing web action images along with videos in training, significant performance boosts of CNN models can be achieved. We also investigate the scalability of the process by leveraging crawled web images (unfiltered) for UCF101 and ActivityNet. Using unfiltered images we can achieve performance improvements that are on-par with using filtered images. This means we can further reduce annotation labor and easily scale-up to larger problems. We also shed light on an artifact of finetuning CNN models that reduces the effective parameters of the CNN and show that using web action images can significantly alleviate this problem.


title: Epidermoid cyst of the clitoris: a case report.

abstract: We report a rare case of spontaneous clitoral epidermal cyst without any declared previous female genital mutilation. This patient was successfully and surgically resected with good local and cosmetic results.


title: A survey and comparison of tree generation algorithms

abstract: This paper discusses and compares five major tree-generation algorithms for genetic programming, and their effects on fitness: RAMPED HALF-AND-HALF, PTC1, PTC2, RANDOM-BRANCH, and UNIFORM. The paper compares the performance of these algorithms on three genetic programming problems (11-Boolean Multiplexer, Artificial Ant, and Symbolic Regression), and discovers that the algorithms do not have a significant impact on fitness. Additional experimentation shows that tree size does have an important impact on fitness, and further that the ideal initial tree size is very different from that used in traditional GP.


title: The R*-tree: an efficient and robust access method for points and rectangles

abstract: The R-tree, one of the most popular access methods for rectangles, is based on the heuristic optimization of the area of the enclosing rectangle in each inner node. By running numerous experiments in a standardized testbed under highly varying data, queries and operations, we were able to design the R * -tree which incorporates a combined optimization of area, margin and overlap of each enclosing rectangle in the directory. Using our standardized testbed in an exhaustive performance comparison, it turned out that the R * -tree clearly outperforms the existing R-tree variants. Guttman's linear and quadratic R-tree and Greene's variant of the R-tree. This superiority of the R * -tree holds for different types of queries and operations, such as map overlay, for both rectangles and multidimensional points in all experiments. From a practical point of view the R * -tree is very attractive because of the following two reasons 1 it efficiently supports point and spatial data at the same time and 2 its implementation cost is only slightly higher than that of other R-trees.


title: Researching Mental Health Disorders in the Era of Social Media: Systematic Review

abstract: Background: Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose. Objective: The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research. Methods: We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals. Results: The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis. Conclusions: Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques. [J Med Internet Res 2017;19(6):e228]


title: Rethinking Procrastination: Positive Effects of "Active" Procrastination Behavior on Attitudes and Performance

abstract: Researchers and practitioners have long regarded procrastination as a self-handicapping and dysfunctional behavior. In the present study, the authors proposed that not all procrastination behaviors either are harmful or lead to negative consequences. Specifically, the authors differentiated two types of procrastinators: passive procrastinators versus active procrastinators. Passive procrastinators are procrastinators in the traditional sense. They are paralyzed by their indecision to act and fail to complete tasks on time. In contrast, active procrastinators are a "positive" type of procrastinator. They prefer to work under pressure, and they make deliberate decisions to procrastinate. The present results showed that although active procrastinators procrastinate to the same degree as passive procrastinators, they are more similar to nonprocrastinators than to passive procrastinators in terms of purposive use of time, control of time, self-efficacy belief, coping styles, and outcomes including academic per...


title: Natural Language Processing for EHR-Based Computational Phenotyping

abstract: This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping. NLP-based computational phenotyping has numerous applications including diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction (DDI), and adverse drug event (ADE) detection, as well as genome-wide and phenome-wide association studies. Significant progress has been made in algorithm development and resource construction for computational phenotyping. Among the surveyed methods, well-designed keyword search and rule-based systems often achieve good performance. However, the construction of keyword and rule lists requires significant manual effort, which is difficult to scale. Supervised machine learning models have been favored because they are capable of acquiring both classification patterns and structures from data. Recently, deep learning and unsupervised learning have received growing attention, with the former favored for its performance and the latter for its ability to find novel phenotypes. Integrating heterogeneous data sources have become increasingly important and have shown promise in improving model performance. Often, better performance is achieved by combining multiple modalities of information. Despite these many advances, challenges and opportunities remain for NLP-based computational phenotyping, including better model interpretability and generalizability, and proper characterization of feature relations in clinical narratives.


title: Multi-Band Compact Antenna for Smartphones Supporting LTE Carrier Aggregation

abstract: In this paper, we are proposing a dual feed compact antenna for a mobile phones that is capable of supporting the latest features of LTE, Carrier Aggregation (CA), which has been introduced to satisfy the ever increasing demand for data-rate. The antenna makes use of a dual resonator design to cover the low bands used in Long Term Evolution (LTE) from 699 to 960 MHz. The design of the RF Front End(FE) is simplified by the dual feed design which can have distributed components of the FE to relax its design and packaging requirements. The main antenna switch design can be relaxed and the high isolation between the two antennas removes the need for a diplexer. The antenna supporting the high bands ( from 1710 to 2700 MHz) is fed through a matching network to increase the bandwidth while having a small volume. Inductive loading is used for the minimization of both elements. Even though the antenna occupies only an area of 30x⌉10mm2, many combinations of low and high band are available for CA. Despite the small volume it has a good efficiency which is close to -3 dB. The design principle has been evaluated through simulations and measurement with good agreement between them.


title: Analyzing Android Encrypted Network Traffic to Identify User Actions

abstract: Mobile devices can be maliciously exploited to violate the privacy of people. In most attack scenarios, the adversary takes the local or remote control of the mobile device, by leveraging a vulnerability of the system, hence sending back the collected information to some remote web service. In this paper, we consider a different adversary, who does not interact actively with the mobile device, but he is able to eavesdrop the network traffic of the device from the network side (e.g., controlling a Wi-Fi access point). The fact that the network traffic is often encrypted makes the attack even more challenging. In this paper, we investigate to what extent such an external attacker can identify the specific actions that a user is performing on her mobile apps. We design a system that achieves this goal using advanced machine learning techniques. We built a complete implementation of this system, and we also run a thorough set of experiments, which show that our attack can achieve accuracy and precision higher than 95%, for most of the considered actions. We compared our solution with the three state-of-the-art algorithms, and confirming that our system outperforms all these direct competitors.


title: Temporal Segment Networks for Action Recognition in Videos

abstract: Deep convolutional networks have achieved great success for image recognition. However, for action recognition in videos, their advantage over traditional methods is not so evident. We present a general and flexible video-level framework for learning action models in videos. This method, called temporal segment network (TSN), aims to model long-range temporal structures with a new segment-based sampling and aggregation module. This unique design enables our TSN to efficiently learn action models by using the whole action videos. The learned models could be easily adapted for action recognition in both trimmed and untrimmed videos with simple average pooling and multi-scale temporal window integration, respectively. We also study a series of good practices for the instantiation of TSN framework given limited training samples. Our approach obtains the state-the-of-art performance on four challenging action recognition benchmarks: HMDB51 (71.0%), UCF101 (94.9%), THUMOS14 (80.1%), and ActivityNet v1.2 (89.6%). Using the proposed RGB difference for motion models, our method can still achieve competitive accuracy on UCF101 (91.0%) while running at 340 FPS. Furthermore, based on the temporal segment networks, we won the video classification track at the ActivityNet challenge 2016 among 24 teams, which demonstrates the effectiveness of TSN and the proposed good practices.


title: Real-time monocular visual odometry for on-road vehicles with 1-point RANSAC

abstract: The first biggest problem in visual motion estimation is data association; matched points contain many outliers that must be detected and removed for the motion to be accurately estimated. In the last few years, a very established method for removing outliers has been the "5-point RANSAC" algorithm which needs a minimum of 5 point correspondences to estimate the model hypotheses. Because of this, however, it can require up to thousand iterations to find a set of points free of outliers. In this talk, I will show that by exploiting the non-holonomic constraints of wheeled vehicles (e.g. cars, bikes, mobile robots) it is possible to use a restrictive motion model which allows us to parameterize the motion with only 1 point correspondence. Using a single feature correspondence for motion estimation is the lowest model parameterization possible and results in the most efficient algorithm for removing outliers: 1-point RANSAC. ::: The second problem in monocular visual odometry is the estimation of the absolute scale. I will show that vehicle non-holonomic constraints make it also possible to estimate the absolute scale completely automatically whenever the vehicle turns. ::: In this talk, I will give a mathematical derivation and provide experimental results on both simulated and real data over a large image dataset collected during a 25 Km path.


title: Early filtering of ephemeral malicious accounts on Twitter

abstract: Cybercriminals exploit a large number of ephemeral malicious accounts for conducting large-scale simple attacks such as spam distribution on online social networks. However, conventional detection schemes relying on account or message information take a considerable time to collect such information before running detection algorithms so criminals utilize their accounts until suspension and exploit others again. In this paper, we propose a new detection scheme to filter potentially malicious account groups around their creation time. Our scheme utilizes the differences between algorithmically generated account names and human-made account names to identify malicious accounts generated using the similar algorithms. For accounts created within a short period of time, we apply a clustering algorithm to group accounts sharing similar name-based features and a classification algorithm to classify malicious account clusters. As a case study, we analyze 4.7 million accounts collected from Twitter. Even though our scheme only relies on account names and their creation time, it achieves reasonable accuracy. Therefore, we can use it as a fast filter against malicious account groups to selectively conduct an in-depth analysis.


title: Sky: Opinion Dynamics Based Consensus for P2P Network with Trust Relationships

abstract: Traditional Byzantine consensus does not work in P2P network due to Sybil attack while the most prevalent Sybil-proof consensus at present still can't resist adversary with dominant compute power. This paper proposed opinion dynamics based consensus for P2P network with trust relationships, consisting of the sky framework and the sky model. With the sky framework, opinion dynamics can be applied in P2P network for consensus which is Sybil-proof through trust relationships and emerges from local interactions of each node with its direct contacts without topology, global information or even sample of the network involved. The sky model has better performance of convergence than existing models including MR, voter and Sznajd, and its lower bound of fault tolerance performance is also analyzed and proved. Simulations show that our approach can tolerant failures by at least 13% random nodes or 2% top influential nodes while over 96% correct nodes still make correct decision within 70 seconds on the SNAP Wikipedia who-votes-on-whom network for initial configuration of convergence>0.5 with reasonable latencies. Comparing to compute power based consensus, our approach can resist any faulty or malicious nodes by unfollowing them. To the best of our knowledge, it's the first work to bring opinion dynamics to P2P network for consensus.


title: Wearable Noncontact Armband for Mobile ECG Monitoring System

abstract: One of the best ways to obtain health information is from an electrocardiogram (ECG). Through an ECG, characteristics such as patients’ heartbeats, heart conditions, and heart disease can be analyzed. Unfortunately, most available healthcare devices do not provide clinical data such as information regarding patients’ heart activities. Many researchers have tried to solve this problem by inventing wearable heart monitoring systems with a chest strap or wristband, but their performances were not feasible for practical applications. Thus, the aim of this study is to build a new system to monitor heart activity through ECG signals. The proposed system consists of capacitive-coupled electrodes embedded in an armband. It is considered to be a reliable, robust, and low-power-transmission ECG monitoring system. The reliability of this system was achieved by the careful placement of sensors in the armband. Bluetooth low energy (BLE) was used as the protocol for data transmission; this protocol was proposed to develop the low-power-transmission system. For robustness, the proposed system is equipped with analysis capabilities–e.g., real-time heartbeat detection and a filter algorithm to ignore distractions from body movements or noise from the environment.


title: STDP-based spiking deep neural networks for object recognition

abstract: Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated - using rate-based neural networks trained with back-propagation - that having many layers increases the recognition robustness, an approach known as deep learning. We thus designed a deep SNN, comprising several convolutional (trainable with STDP) and pooling layers. We used a temporal coding scheme where the most strongly activated neurons fire first, and less activated neurons fire later or not at all. The network was exposed to natural images. Thanks to STDP, neurons progressively learned features corresponding to prototypical patterns that were both salient and frequent. Only a few tens of examples per category were required and no label was needed. After learning, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first layer to object prototypes in the last layer. Coding was very sparse, with only a few thousands spikes per image, and in some cases the object category could be reasonably well inferred from the activity of a single higher-order neuron. More generally, the activity of a few hundreds of such neurons contained robust category information, as demonstrated using a classifier on Caltech 101, ETH-80, and MNIST databases. We also demonstrate the superiority of STDP over other unsupervised techniques such as random crops (HMAX) or auto-encoders. Taken together, our results suggest that the combination of STDP with latency coding may be a key to understanding the way that the primate visual system learns, its remarkable processing speed and its low energy consumption.


title: Have a snack, pay with Bitcoins

abstract: Cashless payments are nowadays ubiquitous and decentralized digital currencies like Bitcoin are increasingly used as means of payment. However, due to the delay of the transaction confirmation in Bitcoin, it is not used for payments that rely on quick transaction confirmation. We present a concept that addresses this drawback of Bitcoin and allows it to be used for fast transactions. We evaluate the performance of the concept using double-spending attacks and show that, employing our concept, the success of such attacks diminishes to less than 0.09%. Moreover, we present a real world application: We modified a snack vending machine to accept Bitcoin payments and make use of fast transaction confirmations.


title: A Design Space for Self-Adaptive Systems

abstract: Self-adaptive systems research is expanding as systems professionals recognize the importance of automation for managing the growing complexity, scale, and scope of software systems. The current approach to designing such systems is ad hoc, varied, and fractured, often resulting in systems with parts of multiple, sometimes poorly compatible designs. In addition to the challenges inherent to all software, this makes evaluating, understanding, comparing, maintaining, and even using such systems more difficult. This paper discusses the importance of systematic design and identifies the dimensions of the self-adaptive system design space. It identifies key design decisions, questions, and possible answers relevant to the design space, and organizes these into five clusters: observation, representation, control, identification, and enacting adaptation. This characterization can serve as a standard lexicon, that, in turn, can aid in describing and evaluating the behavior of existing and new self-adaptive systems. The paper also outlines the future challenges for improving the design of self-adaptive systems.


Energy Efficient MAC protocols for Wireless Sensor Networks : A Survey


Gaussian Process Clustering


title: Efficient learning algorithms for changing environments

abstract: We study online learning in an oblivious changing environment. The standard measure of regret bounds the difference between the cost of the online learner and the best decision in hindsight. Hence, regret minimizing algorithms tend to converge to the static best optimum, clearly a suboptimal behavior in changing environments. On the other hand, various metrics proposed to strengthen regret and allow for more dynamic algorithms produce inefficient algorithms. We propose a different performance metric which strengthens the standard metric of regret and measures performance with respect to a changing comparator. We then describe a series of data-streaming-based reductions which transform algorithms for minimizing (standard) regret into adaptive algorithms albeit incurring only poly-logarithmic computational overhead. Using this reduction, we obtain efficient low adaptive-regret algorithms for the problem of online convex optimization. This can be applied to various learning scenarios, i.e. online portfolio selection, for which we describe experimental results showing the advantage of adaptivity.


title: Unit sizing of stand-alone hybrid wind/PV/fuel cell power generation systems

abstract: An economic evaluation of a hybrid wind/photovoltaic/fuel cell generation system for a typical home in the Pacific Northwest is performed. In this configuration the combination of a fuel cell stack, an electrolyzer, and a hydrogen storage tank is used for the energy storage system. This system is compared to a traditional hybrid energy system with battery storage. A computer program has been developed to size system components in order to match the load of the site in the most cost effective way. A cost of electricity and an overall system cost are also calculated for each configuration. The study was performed using a graphical user interface programmed in MATLAB.


title: StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

abstract: Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. Such a unified model architecture of StarGAN allows simultaneous training of multiple datasets with different domains within a single network. This leads to StarGAN's superior quality of translated images compared to existing models as well as the novel capability of flexibly translating an input image to any desired target domain. We empirically demonstrate the effectiveness of our approach on a facial attribute transfer and a facial expression synthesis tasks.


title: An Efficient Method for High Quality and Cohesive Topical Phrase Mining

abstract: A phrase is a natural, meaningful, and essential semantic unit. In topic modeling, visualizing phrases for individual topics is an effective way to explore and understand unstructured text corpora. However, from phrase quality and topical cohesion perspectives, the outcomes of existing approaches remain to be improved. Usually, the process of topical phrase mining is twofold: phrase mining and topic modeling. For phrase mining, existing approaches often suffer from order sensitive and inappropriate segmentation problems, which make them often extract inferior quality phrases. For topic modeling, traditional topic models do not fully consider the constraints induced by phrases, which may weaken the cohesion. Moreover, existing approaches often suffer from losing domain terminologies since they neglect the impact of domain-level topical distribution. In this paper, we propose an efficient method for high quality and cohesive topical phrase mining. A high quality phrase should satisfy frequency, phraseness, completeness, and appropriateness criteria. In our framework, we integrate quality guaranteed phrase mining method, a novel topic model incorporating the constraint of phrases, and a novel document clustering method into an iterative framework to improve both phrase quality and topical cohesion. We also describe efficient algorithmic designs to execute these methods efficiently. The empirical verification demonstrates that our method outperforms the state-of-the-art methods from the aspects of both interpretability and efficiency.


title: Cofabrication of Vacuum Field Emission Transistor (VFET) and MOSFET

abstract: Co-fabrication of a nanoscale vacuum field emission transistor (VFET) and a metal-oxide-semiconductor field effect transistor (MOSFET) is demonstrated on a silicon-on-insulator wafer. The insulated-gate VFET with a gap distance of 100 nm is achieved by using a conventional 0.18-μm process technology and subsequent photoresist ashing process. The VFET shows a turn-on voltage of 2 V at a cell current of 2 nA and a cell current of 3 μA at the operation voltage of 10 V with an ON/OFF current ratio of 10 $^{4}$ . The gap distance between the cathode and anode in the VFET is defined to be less than the mean free path of electrons in air, and consequently, the operation voltage is reduced to be less than the ionization potential of air molecules. This allows the relaxation of the vacuum requirement. The present integration scheme can be useful as it combines the advantages of both structures on the same chip.


title: Psoriasis Detection Based on Deep Neural Network

abstract: In this paper, a psoriasis detection method based on deep neural network (DNN) was proposed. The proposed method is composed of several learned layers: convolutional layers and fully-connected layers. These four convolutional layers are used to extract some useful multilevel features from the input image. The max-pooling operation is used to down-sample the feature maps to make the deep network faster. The fully-connected layers similar to the traditional multi-layer perceptron neural networks are used to a classifier. Experimental results show that the proposed method can determine whether the input image is psoriasis well.


title: One step towards an industry 4.0 component

abstract: One research and development direction towards flexible production systems is coded under the term Industry 4.0. It envisions a production system component with increased flexibility and networking capabilities usable to implement production systems with increased self∗ abilities. Independent of the fact that there are several activities to develop an all-embracing Industry 4.0 component architecture and its demonstration by a prototype, practitioners call for a first possibility to implement an Industry 4.0 component based on existing technologies and to provide a migration path from existing production system architectures to Industry 4.0 systems. The aim of this paper is to represent a first attempt towards an Industry 4.0 component implementation using existing standardized technologies.


title: Learning to rank query suggestions for adhoc and diversity search

abstract: Query suggestions have become pervasive in modern web search, as a mechanism to guide users towards a better representation of their information need. In this article, we propose a ranking approach for producing effective query suggestions. In particular, we devise a structured representation of candidate suggestions mined from a query log that leverages evidence from other queries with a common session or a common click. This enriched representation not only helps overcome data sparsity for long-tail queries, but also leads to multiple ranking criteria, which we integrate as features for learning to rank query suggestions. To validate our approach, we build upon existing efforts for web search evaluation and propose a novel framework for the quantitative assessment of query suggestion effectiveness. Thorough experiments using publicly available data from the TREC Web track show that our approach provides effective suggestions for adhoc and diversity search.


title: "Bricks-and-mortar" vs. "clicks-and-mortar": An equilibrium analysis

abstract: Abstract The Internet has provided traditional retailers a new means with which to serve customers. Consequently, many “bricks-and-mortar” retailers have transformed to “clicks-and-mortar” by incorporating Internet sales. Examples of companies making such a transition include Best Buy, Wal-Mart, Barnes & Noble, etc. Despite the increasing prevalence of this practice, several fundamental questions remain: (1) Does it pay off to go online? (2) Which is the equilibrium industry structure? (3) What is the implication of this business model for consumers? We study these issues in an oligopoly setting and show that clicks-and-mortar arises as the equilibrium channel structure. However, we find that this equilibrium does not necessarily imply higher profits for the firms: in some cases, rather, it emerges as a strategic necessity. Consumers are generally better off with clicks-and-mortar retailers. If firms align with pure e-tailers to reach the online market, we show that a prisoner’s dilemma-type equilibrium may arise.


title: Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning

abstract: To improve the efficiency of big data feature learning, the paper proposes a privacy preserving deep computation model by offloading the expensive operations to the cloud. Privacy concerns become evident because there are a large number of private data by various applications in the smart city, such as sensitive data of governments or proprietary information of enterprises. To protect the private data, the proposed model uses the BGV encryption scheme to encrypt the private data and employs cloud servers to perform the high-order back-propagation algorithm on the encrypted data efficiently for deep computation model training. Furthermore, the proposed scheme approximates the Sigmoid function as a polynomial function to support the secure computation of the activation function with the BGV encryption. In our scheme, only the encryption operations and the decryption operations are performed by the client while all the computation tasks are performed on the cloud. Experimental results show that our scheme is improved by approximately 2.5 times in the training efficiency compared to the conventional deep computation model without disclosing the private data using the cloud computing including ten nodes. More importantly, our scheme is highly scalable by employing more cloud servers, which is particularly suitable for big data.


title: Experimental verification of HVDC test sources requirements for pollution tests

abstract: This paper presents the results of tests conducted to verify the effect of the voltage source on the critical flashover voltage of DC insulators under pollution. Tests were performed under 3 pollution levels using voltage sources with different voltage drop x leakage current characteristics. A data acquisition system was used to record all current pulses above a certain pre selected current level and the corresponding voltage evolution during current pulse duration, which made possible a complete analysis of current pulses and voltage drops characteristics. The experimental results show that the present restrictions imposed on the DC voltage sources can probably be relaxed. Also from the experimental data collected it was found that the insulator tested presents a constant impedance x current characteristic. This characteristic can be useful in checking theoretical models for polluted insulator flashover. >


IEEE RAM 1 Correct, Reactive Robot Control from Abstraction and Temporal Logic Specifications


title: Elasticity in cloud computing: a survey

abstract: Cloud computing is now a well-consolidated paradigm for on-demand services provisioning on a pay-as-you-go model. Elasticity, one of the major benefits required for this computing model, is the ability to add and remove resources “on the fly” to handle the load variation. Although many works in literature have surveyed cloud computing and its features, there is a lack of a detailed analysis about elasticity for the cloud. As an attempt to fill this gap, we propose this survey on cloud computing elasticity based on an adaptation of a classic systematic review. We address different aspects of elasticity, such as definitions, metrics and tools for measuring, evaluation of the elasticity, and existing solutions. Finally, we present some open issues and future directions. To the best of our knowledge, this is the first study on cloud computing elasticity using a systematic review approach.


title: Kernel Based Detection of Mislabeled Training Examples

abstract: The problem of identifying mislabeled training examples has been examined in several studies, with a variety of approaches developed for editing the training data to obtain better classifiers. Many of these approaches involve applying an individual or an ensemble of classifiers to the training set and filtering the mislabeled examples based on their consistency with respect to the classifier’s outputs. In this study, we formulate mislabeled detection as an optimization problem and introduce a kernel-based approach for filtering the mislabeled examples. Experimental results using a variety of data sets from the UCI data repository demonstrate the effectiveness of our proposed method, compared to existing nearest-neighbor and ensemble-based filtering schemes.


A Mathematical Model and Calibration Procedure for Galvanometric Laser Scanning Systems


DYNAMIC BEHAVIOURAL MODELING , SIMULATION AND ANALYSIS OF HOUSEHOLD WATER CONSUMPTION IN AN URBAN AREA : A HYBRID APPROACH


title: Experiments in Parallel Clustering with DBSCAN

abstract: We present a new result concerning the parallelisation of DBSCAN, a Data Mining algorithm for density-based spatial clustering. The overall structure of DBSCAN has been mapped to a skeletonstructured program that performs parallel exploration of each cluster. The approach is useful to improve performance on high-dimensional data, and is general w.r.t. the spatial index structure used. We report preliminary results of the application running on a Beowulf with good efficiency.


title: Efficient Global Search for Inputs Triggering High Floating-Point Inaccuracies

abstract: Floating-point rounding errors are pervasive when using numerical code to implement the real arithmetic algorithm. In particular, high floating-point inaccuracies may cause serious problems once being triggered. Hence, a testing method that can find concrete test cases to trigger high floating-point inaccuracies, is quite helpful to aid debugging and reduce high inaccuracies. Recently, two testing approaches have been proposed to find inputs triggering high floating-point inaccuracies in numerical programs: Locality-Sensitive Genetic Algorithm (LSGA) and Binary Guided Random Testing (BGRT). However, experiments show that LSGA may result in a high rate of false alarm while BART may easily fall into a local maximum when the search space is large. In this paper, we propose a novel testing approach to trigger high floating-point inaccuracies in numerical code. The main idea is utilizing heuristic rules drawn from error analysis to guide the process of global search of test cases. Comparative experiments with the random and BGRT methods are conducted on benchmarks including real-world scientific programs. Experimental results show that our approach can efficiently find inputs that trigger higher floating-point inaccuracies in 11 of 12 real-world programs (especially for programs whose input space are large) and have better stability.


title: Monochromatic and electrochemically switchable electrochemiluminescence of perovskite CsPbBr3 nanocrystals

abstract: Cubic-shaped perovskite CsPbBr3 nanocrystals (NCs) could be electrochemically injected with holes (or electrons) to produce several charged states under different oxidizing and reducing potentials, and then bring out electrochemiluminescence (ECL) of higher color purity than traditional ECL chemicals and metal chalcogenide NCs, in both annihilation and co-reactant routes. The difference of electrochemical gaps between varied hole and electron injecting potentials displayed little effect on the ECL spectrum and colour purity of CsPbBr3 NCs. All the excited states generated under different oxidizing and reducing potential couples in ECL of CsPbBr3 NCs were the same as those in photoluminescence, as all the ECL spectra were almost identical to the CsPbBr3 NCs’ photoluminescence spectrum. Importantly, the ECL of CsPbBr3 NCs was electrochemically switchable and displayed an obvious “on/off” type feature by changing the sequence of hole injecting and electron injecting processes, as strong ECL could be obtained by injecting holes onto the electron injected NCs, while no or very weak ECL was obtained in the reversed way.


title: Historic Preservation and Residential Property Values: An Analysis of Texas Cities.” Urban Studies

abstract: Designation of historic districts is increasingly used as a tool to revive or halt the deterioration of central-city neighbourhoods. While historic designation is generally thought to have a positive impact on property values, evidence on this issue is mixed. One limitation of previous research is that it typically focuses on historic neighbourhoods in one city and thus bases its conclusions on a very limited sample. This study expands upon previous work by examining the effects of designation on property values across a larger set of cities. The study employs hedonic regression models to estimate housing prices in historic districts and comparable neighbourhoods in nine Texas cities. Results suggest that, in most cases, historic designation is associated with higher property values.


Architectural Modeling of Ozone Widget Framework End-User Compositions (CMU-ISR-14-108)


title: Mitigating DoS Attacks Against Pseudonymous Authentication Through Puzzle-Based Co-Authentication in 5G-VANET

abstract: The upcoming Fifth Generation (5G) networks can provide ultra-reliable ultra-low latency vehicle-to-everything for vehicular ad hoc networks (VANET) to promote road safety, traffic management, information dissemination, and automatic driving for drivers and passengers. However, 5G-VANET also attracts tremendous security and privacy concerns. Although several pseudonymous authentication schemes have been proposed for VANET, the expensive cost for their initial authentication may cause serious denial of service (DoS) attacks, which furthermore enables to do great harm to real space via VANET. Motivated by this, a puzzle-based co-authentication (PCA) scheme is proposed here. In the PCA scheme, the Hash puzzle is carefully designed to mitigate DoS attacks against the pseudonymous authentication process, which is facilitated through collaborative verification. The effectiveness and efficiency of the proposed scheme is approved by performance analysis based on theory and experimental results.


title: Sexual dimorphism of brain developmental trajectories during childhood and adolescence ☆

abstract: Human total brain size is consistently reported to be ∼8–10% larger in males, although consensus on regionally specific differences is weak. Here, in the largest longitudinal pediatric neuroimaging study reported to date (829 scans from 387 subjects, ages 3 to 27 years), we demonstrate the importance of examining size-by-age trajectories of brain development rather than group averages across broad age ranges when assessing sexual dimorphism. Using magnetic resonance imaging (MRI) we found robust male/female differences in the shapes of trajectories with total cerebral volume peaking at age 10.5 in females and 14.5 in males. White matter increases throughout this 24-year period with males having a steeper rate of increase during adolescence. Both cortical and subcortical gray matter trajectories follow an inverted U shaped path with peak sizes 1 to 2 years earlier in females. These sexually dimorphic trajectories confirm the importance of longitudinal data in studies of brain development and underline the need to consider sex matching in studies of brain development.


title: A Compositional Object-Based Approach to Learning Physical Dynamics

abstract: We present the Neural Physics Engine (NPE), a framework for learning simulators of intuitive physics that naturally generalize across variable object count and different scene configurations. We propose a factorization of a physical scene into composable object-based representations and a neural network architecture whose compositional structure factorizes object dynamics into pairwise interactions. Like a symbolic physics engine, the NPE is endowed with generic notions of objects and their interactions; realized as a neural network, it can be trained via stochastic gradient descent to adapt to specific object properties and dynamics of different worlds. We evaluate the efficacy of our approach on simple rigid body dynamics in two-dimensional worlds. By comparing to less structured architectures, we show that the NPE's compositional representation of the structure in physical interactions improves its ability to predict movement, generalize across variable object count and different scene configurations, and infer latent properties of objects such as mass.


title: Least-Squares Policy Iteration

abstract: We propose a new approach to reinforcement learning for control problems which combines value-function approximation with linear architectures and approximate policy iteration. This new approach is motivated by the least-squares temporal-difference learning algorithm (LSTD) for prediction problems, which is known for its efficient use of sample experiences compared to pure temporal-difference algorithms. Heretofore, LSTD has not had a straightforward application to control problems mainly because LSTD learns the state value function of a fixed policy which cannot be used for action selection and control without a model of the underlying process. Our new algorithm, least-squares policy iteration (LSPI), learns the state-action value function which allows for action selection without a model and for incremental policy improvement within a policy-iteration framework. LSPI is a model-free, off-policy method which can use efficiently (and reuse in each iteration) sample experiences collected in any manner. By separating the sample collection method, the choice of the linear approximation architecture, and the solution method, LSPI allows for focused attention on the distinct elements that contribute to practical reinforcement learning. LSPI is tested on the simple task of balancing an inverted pendulum and the harder task of balancing and riding a bicycle to a target location. In both cases, LSPI learns to control the pendulum or the bicycle by merely observing a relatively small number of trials where actions are selected randomly. LSPI is also compared against Q-learning (both with and without experience replay) using the same value function architecture. While LSPI achieves good performance fairly consistently on the difficult bicycle task, Q-learning variants were rarely able to balance for more than a small fraction of the time needed to reach the target location.


title: The development and validation of a computational model to predict rat liver microsomal clearance.

abstract: As the cost of discovering and developing new pharmaceutically relevant compounds continues to rise, it is increasingly important to select the right molecules to prosecute very early in drug discovery. The development of high throughput in vitro assays of hepatic metabolic clearance has allowed for vast quantities of data generation; however, these large screens are still costly and remain dependant on animal usage. To further expand the value of these screens and ultimately aid in animal usage reduction, we have developed an in silico model of rat liver microsomal (RLM) clearance. This model combines a large amount of rat clearance data (n = 27,697) generated at multiple Pfizer laboratories to represent the broadest possible chemistry space. The model predicts RLM stability (with 82% accuracy and a kappa value of 0.65 for test data set) based solely on chemical structural inputs, and provides a clear assessment of confidence in the prediction. The current in silico model should help accelerate the drug discovery process by using confidence-based stability-driven prioritization, and reduce cost by filtering out the most unstable/undesirable molecules. The model can also increase efficiency in the evaluation of chemical series by optimizing iterative testing and promoting rational drug design.


title: Simulation and optimization of milk pasteurization processes using a general process simulator (ProSimPlus)

abstract: In order to increase product quality and to minimize energy consumption and impact on the environment, it is necessary to optimize the food process design and operation. Therefore, the aim of this work was to study the optimization feasibility of the milk pasteurization process using a general process simulator (ProSimPlus). To simulate the pasteurization process, a simulation flowsheet was developed using the simulator, and a database was built in the simulator with the values and/or functions to estimate the necessary physical properties of the whole milk. Using the ProSimPlus utilities (control units) an objective function was set up in order to optimize the process design and the process operation of the milk pasteurization process. For that purpose a ProSimPlus module (optimization: OPTI) was used. By introducing physical properties related to other products and redefining the objective function the application of this general simulator could be extended in the Food Industry.


title: Towards Information Profiling: Data Lake Content Metadata Management

abstract: There is currently a burst of Big Data (BD) processed and stored in huge raw data repositories, commonly called Data Lakes (DL). These BD require new techniques of data integration and schema alignment in order to make the data usable by its consumers and to discover the relationships linking their content. This can be provided by metadata services which discover and describe their content. However, there is currently a lack of a systematic approach for such kind of metadata discovery and management. Thus, we propose a framework for the profiling of informational content stored in the DL, which we call information profiling. The profiles are stored as metadata to support data analysis. We formally define a metadata management process which identifies the key activities required to effectively handle this. We demonstrate the alternative techniques and performance of our process using a prototype implementation handling a real-life case-study from the OpenML DL, which showcases the value and feasibility of our approach.


title: Fully Automated Laser Range Calibration

abstract: We present a novel method for fully automated exterior calibration of a 2D scanning laser range sensor that attains accurate pose with respect to a fixed 3D reference frame. This task is crucial for applicatio ns that attempt to recover self-consistent 3D environment maps and produce accurately registered or fused sensor data. A key contribution of our approach lies in the design of a class of calibration target objects whose pose can be reliably recognized from a single observation (i.e. from one 2D range data stripe). Unlike other techniques, we do not require simultaneous camera views or motion of the sensor, making our approach simple, flexible and environment-independent . In this paper we illustrate the target geometry and derive th e relationship between a single 2D range scan and the 3D sensor pose. We describe an algorithm for closed-form solution of the 6 DOF pose that minimizes an algebraic error metric, and an iterative refinement scheme that subseq uently minimizes geometric error. Finally, we report performance and stability of our technique on synthetic and real data sets, and demonstrate accuracy within 1 degree of orientation and 3 cm of position in a realistic configuration .


title: 0/1 Knapsack on Hardware: A Complete Solution

abstract: We present a memory efficient, practical, systolic, parallel architecture for the complete 0/1 knapsack dynamic programming problem, including backtracking. This problem was intentionally selected because its dynamic dependencies introduce difficulties in hardware implementation. The architecture uses a divide-and-conquer technique that results in a pseudo-linear memory requirement. This memory reduction comes in exchange for a factor of two slowdown due to redundant computation. The architecture uses Theta(n + p(C + Wmax)) memory and the run time is Theta(nC/p + nlog(n/p)). The heart of the architecture is a systolic module to compute the optimal profit for any problem that fits in available hardware resources. We implemented the module using 64 processors on an Alpha Data coprocessor board using a Xilinx VirtexII FPGA(2001 technology). Our implementation showed a factor of 32 improvement on the total execution time over a sequential algorithm running on a 1.5 GHz Xeon processor(2000 technology) and a factor of 16 improvement over a 3.2 GHz Pentium 4(2004 technology) and a 64 bit 3.4 GHz Pentium 4 (2006 technology). We measured complete wall-clock time, including the time to download the problem to the board, but not the time to download the bit stream to the FPGA.


title: The Ignorant Led by the Blind: A Hybrid Human–Machine Vision System for Fine-Grained Categorization

abstract: We present a visual recognition system for fine-grained visual categorization. The system is composed of a human and a machine working together and combines the complementary strengths of computer vision algorithms and (non-expert) human users. The human users provide two heterogeneous forms of information object part clicks and answers to multiple choice questions. The machine intelligently selects the most informative question to pose to the user in order to identify the object class as quickly as possible. By leveraging computer vision and analyzing the user responses, the overall amount of human effort required, measured in seconds, is minimized. Our formalism shows how to incorporate many different types of computer vision algorithms into a human-in-the-loop framework, including standard multiclass methods, part-based methods, and localized multiclass and attribute methods. We explore our ideas by building a field guide for bird identification. The experimental results demonstrate the strength of combining ignorant humans with poor-sighted machines the hybrid system achieves quick and accurate bird identification on a dataset containing 200 bird species.


title: Relative discrimination criterion - A novel feature ranking method for text data

abstract: Discussed characteristics of text data.Indicated that term counts are being ignored to calculated term rank.Proposed new feature ranking algorithm (RDC) which considers term counts.Compared performance of RDC with four feature ranking metrics on four datasets.RDC show highest performance in 65% of the classification cases. High dimensionality of text data hinders the performance of classifiers making it necessary to apply feature selection for dimensionality reduction. Most of the feature ranking metrics for text classification are based on document frequencies (df) of a term in positive and negative classes. Considering only document frequencies to rank features favors terms frequently occurring in larger classes in unbalanced datasets. In this paper we introduce a new feature ranking metric termed as relative discrimination criterion (RDC), which takes document frequencies for each term count of a term into account while estimating the usefulness of a term. The performance of RDC is compared with four well known feature ranking metrics, information gain (IG), CHI squared (CHI), odds ratio (OR) and distinguishing feature selector (DFS) using support vector machines (SVM) and multinomial naive Bayes (MNB) classifiers on four benchmark datasets, namely Reuters, 20 Newsgroups and two subsets of Ohsumed dataset. Our results based on macro and micro F1 measures show that the performance of RDC is superior than the other four metrics in 65% of our experimental trials. Also, RDC attains highest macro and micro F1 values in 69% of the cases.


title: M.: A taxonomy for generating explanations in recommender systems

abstract: In recommender systems, explanations serve as an additional type of information that can help users to better understand the system's output and promote objectives such as trust, confidence in decision making or utility. This article proposes a taxonomy to categorize and review the research in the area of explanations. It provides a unified view on the different recommendation paradigms, allowing similarities and differences to be clearly identified. Finally, the authors present their view on open research issues and opportunities for future work on this topic.


title: Familiar Music as an Enhancer of Self-Consciousness in Patients with Alzheimer's Disease

abstract: The main objective of this paper is to examine the impact of familiar music on self-consciousness (SC) in patients with Alzheimer's disease (AD). For this purpose, two AD groups of 20 patients matched by age, educational level, gender, illness duration, and cognitive state were assessed using an SC questionnaire before and after music intervention. The SC questionnaire measured several aspects: personal identity, anosognosia, affective state, body representation, prospective memory, introspection and moral judgments. One AD group received familiar music stimulation and another AD group unfamiliar music stimulation over three months. The AD patients who received a familiar music intervention showed a stabilization or improvement in aspects of SC. By contrast, control AD group showed a deterioration of most of the SC aspects after unfamiliar music stimulation, except the SC aspects of body representation and affective state. Familiar music stimulation could be considered as an enhancer of SC in patients with Alzheimer's disease.


title: Pathways: augmenting interoperability across scholarly repositories

abstract: In the emerging eScience environment, repositories of papers, datasets, software, etc., should be the foundation of a global and natively-digital scholarly communications system. The current infrastructure falls far short of this goal. Cross-repository interoperability must be augmented to support the many workflows and value-chains involved in scholarly communication. This will not be achieved through the promotion of single repository architecture or content representation, but instead requires an interoperability framework to connect the many heterogeneous systems that will exist. ::: ::: We present a simple data model and service architecture that augments repository interoperability to enable scholarly value-chains to be implemented. We describe an experiment that demonstrates how the proposed infrastructure can be deployed to implement the workflow involved in the creation of an overlay journal over several different repository systems (Fedora, aDORe, DSpace and arXiv).


title: Hypertableau Reasoning for Description Logics

abstract: We present a novel reasoning calculus for the description logic SHOIQ+--a knowledge representation formalism with applications in areas such as the SemanticWeb. Unnecessary nondeterminism and the construction of large models are two primary sources of inefficiency in the tableau-based reasoning calculi used in state-of-the-art reasoners. In order to reduce nondeterminism, we base our calculus on hypertableau and hyperresolution calculi, which we extend with a blocking condition to ensure termination. In order to reduce the size of the constructed models, we introduce anywhere pairwise blocking. We also present an improved nominal introduction rule that ensures termination in the presence of nominals, inverse roles, and number restrictions--a combination of DL constructs that has proven notoriously difficult to handle. Our implementation shows significant performance improvements over state-of-the-art reasoners on several well-known ontologies.


title: Posture stabilization of Skid Steer Wheeled Mobile Robots

abstract: This paper represents the posture stabilization of a Skid Steer Wheeled Mobile Robot (SSWMR). Although, in mobile robots lateral skidding of the wheels occurs when turning at high speed, wheels of a SSWMR laterally skid in every rotational maneuver even for low speeds yielded the non-skidding nonholonomic constraint to be violated. In order to compensate the effects of parameter uncertainties, an adaptive torque controller is developed based on tunable dynamic oscillator. The Globally Uniformly Ultimately Bounded (GUUB) stability of the system to an arbitrarily small neighborhood of the origin is proved. To demonstrate the performance of the proposed controller, modeling of a SSWMR was implemented through ADAMS.


title: Efficient Classification for Additive Kernel SVMs

abstract: We show that a class of nonlinear kernel SVMs admits approximate classifiers with runtime and memory complexity that is independent of the number of support vectors. This class of kernels, which we refer to as additive kernels, includes widely used kernels for histogram-based image comparison like intersection and chi-squared kernels. Additive kernel SVMs can offer significant improvements in accuracy over linear SVMs on a wide variety of tasks while having the same runtime, making them practical for large-scale recognition or real-time detection tasks. We present experiments on a variety of datasets, including the INRIA person, Daimler-Chrysler pedestrians, UIUC Cars, Caltech-101, MNIST, and USPS digits, to demonstrate the effectiveness of our method for efficient evaluation of SVMs with additive kernels. Since its introduction, our method has become integral to various state-of-the-art systems for PASCAL VOC object detection/image classification, ImageNet Challenge, TRECVID, etc. The techniques we propose can also be applied to settings where evaluation of weighted additive kernels is required, which include kernelized versions of PCA, LDA, regression, k-means, as well as speeding up the inner loop of SVM classifier training algorithms.


title: Residual Super-Resolution Single Shot Network for Low-Resolution Object Detection

abstract: For object detection in computer vision, detection models trained by high-resolution images often fail to recognize or localize objects on low-resolution images. To tackle this problem, we propose a fully convolutional network named residual super-resolution single shot network (RSRSSN). RSRSSN consists of two sub-networks, super-resolution sub-network and detection sub-network. The super-resolution sub-network in RSRSSN is achieved by stacking of identity residual blocks while the detection sub-network adopts the single shot multibox detector (SSD). Based on multi-task learning, we design a novel objective function called feature maps multibox loss to enforce low-resolution images to produce similar feature maps with their corresponding high-resolution ones. This information sharing mechanism is proved to be critical for solving the resolution mismatch problem in the experiments. A two-step training scheme is also proposed to train the RSRSSN in an end-to-end manner. Without any data augmentation, RSRSSN outperforms the SSD on both down-sampled PASCAL VOC and MS COCO in real-time object detection.


title: Time-Optimal Control of a Hovering Quad-Rotor Helicopter

abstract: The time-optimal control problem of a hovering quad-rotor helicopter is addressed in this paper. Instead of utilizing the Pontryagin's Minimum Principle (PMP), in which one needs to solve a set of highly nonlinear differential equations, a nonlinear programming (NLP) method is proposed. In this novel method, the count of control steps is fixed initially and the sampling period is treated as a variable in the optimization process. The optimization object is to minimize the sampling period such that it will be below a specific minimum value, which is set in advance considering the accuracy of discretization. To generate initial feasible solutions of the formulated NLP problem, genetic algorithms (GAs) are adopted. With the proposed method, one can find a time-optimal movement of the helicopter between two configurations. To show the feasibility of the proposed method, simulation results are included for illustration.


title: Estimation of line-of-sight rate in a homing Missile Guidance loop using optimal filters

abstract: The concept of noise is very important in a Missile Guidance system. In radar homing tactical missile the seeker measurement of line-of-sight (LOS) rate signal required for the implementation of proportional navigation guidance is not perfect, but it is corrupted by noise. To extract the signal from the measurement an understanding of the concept of noise and its various properties will be analyzed in this paper. Therefore in order to derive the line-of-sight rate estimate required by proportional navigation guidance, it is necessary to use a digital noise filter in an onboard missile guidance system. These filters are constant gain filters known as fading memory filters to derive the line-of-sight rate estimate. By considering various cases and simulation trails some of the properties of digital fading memory filters and their influence on system performance will be investigated in this paper as these fading memory filters will serve as the foundation for more advanced digital filters known as Kalman filters. This paper brings forth an approach to estimate and smooth the LOS rate and also to estimate target maneuver by using second and third order fixed gain filters.


title: MMSS: Multi-modal Sharable and Specific Feature Learning for RGB-D Object Recognition

abstract: Most of the feature-learning methods for RGB-D object recognition either learn features from color and depth modalities separately, or simply treat RGB-D as undifferentiated four-channel data, which cannot adequately exploit the relationship between different modalities. Motivated by the intuition that different modalities should contain not only some modal-specific patterns but also some shared common patterns, we propose a multi-modal feature learning framework for RGB-D object recognition. We first construct deep CNN layers for color and depth separately, and then connect them with our carefully designed multi-modal layers, which fuse color and depth information by enforcing a common part to be shared by features of different modalities. In this way, we obtain features reflecting shared properties as well as modal-specific properties in different modalities. The information of the multi-modal learning frameworks is back-propagated to the early CNN layers. Experimental results show that our proposed multi-modal feature learning method outperforms state-of-the-art approaches on two widely used RGB-D object benchmark datasets.