title: Scene Text Recognition using Higher Order Language Priors

abstract: The problem of recognizing text in images taken in the wild has gained significant attention from the computer vision community in recent years. Contrary to recognition of printed documents, recognizing scene text is a challenging problem. We focus on the problem of recognizing text extracted from natural scene images and the web. Significant attempts have been made to address this problem in the recent past. However, many of these works benefit from the availability of strong context, which naturally limits their applicability. In this work we present a framework that uses a higher order prior computed from an English dictionary to recognize a word, which may or may not be a part of the dictionary. We show experimental results on publicly available datasets. Furthermore, we introduce a large challenging word dataset with five thousand words to evaluate various steps of our method exhaustively. The main contributions of this work are: (1) We present a framework, which incorporates higher order statistical language models to recognize words in an unconstrained manner (i.e. we overcome the need for restricted word lists, and instead use an English dictionary to compute the priors). (2) We achieve significant improvement (more than 20%) in word recognition accuracies without using a restricted word list. (3) We introduce a large word recognition dataset (atleast 5 times larger than other public datasets) with character level annotation and benchmark it.


title: What leads developers towards the choice of a JavaScript framework?

abstract: Context: The increasing popularity of JavaScript (JS) has lead to a variety of frameworks that aim to help developers to address programming tasks. However, the number of JS Frameworks (JSF) has risen rapidly to thousands and more. It is difficult for practitioners to identify the frameworks that best fit to their needs and to develop new frameworks that fit such needs. Existing research has focused in proposing software metrics for the frameworks, which do not carry a high value to practitioners. While benchmarks, technical reports, and experts' opinions are available, they suffer the same issue that they do not carry much value. In particular, there is a lack of knowledge regarding the processes and reasons that drive developers towards the choice. Objective: This paper explores the human aspects of software development behind the decision-making process that leads to a choice of a JSF. Method: We conducted a qualitative interpretive study, following the grounded theory data analysis methodology. We interviewed 18 participants who are decision makers in their companies or entrepreneurs, or are able to motivate the JSF decision-making process. Results: We offer a model of factors that are desirable to be found in a JSF and a representation of the decision makers involved in the frameworks selection. The factors are usability (attractiveness, learnability, understandability), cost, efficiency (performance, size), and functionality (automatisation, extensibility, flexibility, isolation, modularity, suitability, updated). These factors are evaluated by a combination of four possible decision makers, which are customer, developer, team, and team leader. Conclusion: Our model contributes to the body of knowledge related to the decision-making process when selecting a JSF. As a practical implication, we believe that our model is useful for (1) Web developers and (2) JSF developers.


title: Identifying authoritative actors in question-answering forums: the case of Yahoo! answers

abstract: We consider the problem of identifying authoritative users in Yahoo! Answers. A common approach is to use link analysis techniques in order to provide a ranked list of users based on their degree of authority. A major problem for such an approach is determining how many users should be chosen as authoritative from a ranked list. To address this problem, we propose a method for automatic identification of authoritative actors. In our approach, we propose to model the authority scores of users as a mixture of gamma distributions. The number of components in the mixture is estimated by the Bayesian Information Criterion (BIC) while the parameters of each component are estimated using the Expectation-Maximization (EM) algorithm. This method allows us to automatically discriminate between authoritative and non-authoritative users. The suitability of our proposal is demonstrated in an empirical study using datasets from Yahoo! Answers.


title: Fast LIDAR-based Road Detection Using Convolutional Neural Networks

abstract: In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean height and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast convolutional neural network (CNN). The CNN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieves state-of-the-art results on the KITTI road benchmark. It is currently the top-performing algorithm among the published methods in the overall category urban road and outperforms the second best LIDAR-only approach by 7.4 percentage points. Its fast inference makes it particularly suitable for real-time applications.


title: Crosswind kite control - A benchmark problem for advanced control and dynamic optimization

abstract: This article presents a kite control and optimization problem intended as a benchmark problem for advanced control and optimization. We provide an entry point to this exciting renewable energy system for researchers in control and optimization methods looking for a realistic test bench, and/or a useful application case for their theory. The benchmark problem in this paper can be studied in simulation, and a complete Simulink model is provided to facilitate this. The simulated scenario, which reproduces many of the challenges presented by a real system, is based on experimental studies from the literature, industrial data and the first author’s own experience in experimental kite control. In par- ticular, an experimentally validated wind turbulence model is included, which subjects the kite to realistic disturbances. The benchmark problem is that of controlling a kite such that the average line tension is maximized. Two different models are provided: A more comprehensive one is used to simulate the ’plant’, while a simpler ’model’ is used to design and implement control and optimization strategies. This way, uncertainty is present in the form of plant-model mismatch. The outputs of the plant are corrupted by measurement noise. The maximum achievable average line tension for the plant is calculated, which should facilitate the performance comparison of different algorithms. A simple control strategy is implemented on the plant and found to be quite suboptimal, even if the free parameters of the algorithm are well tuned. An open question is whether or not more advanced control algorithms could do better.


title: Different Access Control Mechanisms for Data Security in Cloud Computing

abstract: IT companies are largely adopting the emerging technology of cloud computing. Cloud computing technology provides cost-efficient computing resources with increased flexibility, and scalability. The primary concerns in cloud computing implementation are security and privacy. Access control models are used to address security threats and vulnerabilities. There are a variety of access control models, including DAC (Discretionary Access Control), MAC (Mandatory Access Control), RBAC (Role Base Access Control), ORBAC (Organizational Role Based Access Control) and ABAC (Attributes Based Access Control). As such it is important to analyze and compare the different existing access control models. To achieve this goal, we determine well defined criteria necessary within the context of current cloud computing requirements. Accordingly, we choose the appropriate model for the cloud environment.


Effect of Sentiment on Bitcoin Price Formation


title: Canny Text Detector: Fast and Robust Scene Text Localization Algorithm

abstract: This paper presents a novel scene text detection algorithm, Canny Text Detector, which takes advantage of the similarity between image edge and text for effective text localization with improved recall rate. As closely related edge pixels construct the structural information of an object, we observe that cohesive characters compose a meaningful word/sentence sharing similar properties such as spatial location, size, color, and stroke width regardless of language. However, prevalent scene text detection approaches have not fully utilized such similarity, but mostly rely on the characters classified with high confidence, leading to low recall rate. By exploiting the similarity, our approach can quickly and robustly localize a variety of texts. Inspired by the original Canny edge detector, our algorithm makes use of double threshold and hysteresis tracking to detect texts of low confidence. Experimental results on public datasets demonstrate that our algorithm outperforms the state-of the-art scene text detection methods in terms of detection rate.


title: Mixed reality simulation with physical mobile display devices

abstract: This paper presents the design and implementation of a system for simulating mixed reality in setups combining mobile devices and large backdrop displays. With a mixed reality simulator, one can perform usability studies and evaluate mixed reality systems while minimizing confounding variables. This paper describes how mobile device AR design factors can be flexibly and systematically explored without sacrificing the touch and direct unobstructed manipulation of a physical personal MR display. First, we describe general principles to consider when implementing a mixed reality simulator, enumerating design factors. Then, we present our implementation which utilizes personal mobile display devices in conjunction with a large surround-view display environment. Standing in the center of the display, a user may direct a mobile device, such as a tablet or head-mounted display, to a portion of the scene, which affords them a potentially annotated view of the area of interest. The user may employ gesture or touch screen interaction on a simulated augmented camera feed, as they typically would in video-see-through mixed reality applications. We present calibration and system performance results and illustrate our system's flexibility by presenting the design of three usability evaluation scenarios.


title: Self-calibration of the 6-DOF parallel robot TRIPLANAR by identification of the geometry parameters

abstract: This paper presents a model-based procedure for the self-calibration of the parallel robot, TRIPLANAR, that gets by on just a relatively simple measuring system. The measuring device continuously quantifies the distance between the tool-center point (TCP) that is adjustable in all 6 degrees of freedom and a fixed reference point in the inertial coordinate system (ICS). After just one run over a suitable measuring trajectory and recording of the measuring data (distance TCP-ICS and actuator positions) a parameter identification is performed by minimizing the deviation between the measured pose (position and orientation) and the one that was determined from a model. The identified parameters are taken into account in an inverse kinematics model that is used for the feedforward control of the TriPlanar; thus positioning accuracy can be greatly increased.


title: PassChords: secure multi-touch authentication for blind people

abstract: Blind mobile device users face security risks such as inaccessible authentication methods, and aural and visual eavesdropping. We interviewed 13 blind smartphone users and found that most participants were unaware of or not concerned about potential security threats. Not a single participant used optional authentication methods such as a password-protected screen lock. We addressed the high risk of unauthorized user access by developing PassChords, a non-visual authentication method for touch surfaces that is robust to aural and visual eavesdropping. A user enters a PassChord by tapping several times on a touch surface with one or more fingers. The set of fingers used in each tap defines the password. We give preliminary evidence that a four-tap PassChord has about the same entropy, a measure of password strength, as a four-digit personal identification number (PIN) used in the iPhone's Passcode Lock. We conducted a study with 16 blind participants that showed that PassChords were nearly three times as fast as iPhone's Passcode Lock with VoiceOver, suggesting that PassChords are a viable accessible authentication method for touch screens.


title: Detection of Spatio-Temporal Gait Parameters by Using Wearable Motion Sensors

abstract: This paper presents a method to detect the spatio-temporal parameters of gait by using wearable motion sensors with a gyro, accelerometer, and magnetic sensor. The detected gait parameters are as follows: stance (ST), double support (DS), and gait cycle (GC) time as temporal parameters, and the stride length (SL) as spatial parameter. Four motion sensors are attached on both thighs and shanks of users, and the sensor data are collected in a portable PC. The temporal parameters are estimated by finding walking events, and then the stride length is calculated with two gait models. The estimated parameters are compared to those obtained from a motion capture system (VICON system)


Where Off-Policy Deep Reinforcement Learning Fails


title: Large-scale wire-speed packet classification on FPGAs

abstract: Multi-field packet classification is a key enabling function of a variety of network applications, such as firewall processing, Quality of Service differentiation, traffic billing, and other value added services. Although a plethora of research has been done in this area, wire-speed packet classification while supporting large rule sets remains difficult. This paper exploits the features provided by current FPGAs and proposes a decision-tree-based, two-dimensional dual-pipeline architecture for multi-field packet classification. To fit the current largest rule set in the on-chip memory of the FPGA device, we propose several optimization techniques for the state-of-the-art decision-tree-based algorithm, so that the memory requirement is almost linear with the number of rules. Specialized logic is developed to support varying number of branches at each decision tree node. A tree-to-pipeline mapping scheme is carefully designed to maximize the memory utilization. Since our architecture is linear and memory-based, on-the-fly update without disturbing the ongoing operations is feasible. The implementation results show that our architecture can store 10K real-life rules in on-chip memory of a single Xilinx Virtex-5 FPGA, and sustain 80 Gbps (i.e. 2x OC-768 rate) throughput for minimum size (40 bytes) packets. To the best of our knowledge, this work is the first FPGA-based packet classification engine that achieves wire-speed throughput while supporting 10K unique rules.


title: A comprehensive framework for secure query processing on relational data in the cloud

abstract: Data security in the cloud is a big concern that blocks the widespread use of the cloud for relational data management. First, to ensure data security, data confidentiality needs to be provided when data resides in storage as well as when data is dynamically accessed by queries. Prior works on query processing on encrypted data did not provide data confidentiality guarantees in both aspects. Tradeoff between secrecy and efficiency needs to be made when satisfying both aspects of data confidentiality while being suitable for practical use. Second, to support common relational data management functions, various types of queries such as exact queries, range queries, data updates, insertion and deletion should be supported. To address these issues, this paper proposes a comprehensive framework for secure and efficient query processing of relational data in the cloud. Our framework ensures data confidentiality using a salted IDA encoding scheme and column-access-via-proxy query processing primitives, and ensures query efficiency using matrix column accesses and a secure B+-tree index. In addition, our framework provides data availability and integrity. We establish the security of our proposal by a detailed security analysis and demonstrate the query efficiency of our proposal through an experimental evaluation.


title: Realtime Two-Way Coupling of Meshless Fluids and Nonlinear FEM

abstract: In this paper, we present a novel method to couple Smoothed Particle Hydrodynamics (SPH) and nonlinear FEM to animate the interaction of fluids and deformable solids in real time. To accurately model the coupling, we generate proxy particles over the boundary of deformable solids to facilitate the interaction with fluid particles, and develop an efficient method to distribute the coupling forces of proxy particles to FEM nodal points. Specifically, we employ the Total Lagrangian Explicit Dynamics (TLED) finite element algorithm for nonlinear FEM because of many of its attractive properties such as supporting massive parallelism, avoiding dynamic update of stiffness matrix computation, and efficient solver. Based on a predictor-corrector scheme for both velocity and position, different normal and tangential conditions can be realized even for shell-like thin solids. Our coupling method is entirely implemented on modern GPUs using CUDA. We demonstrate the advantage of our two-way coupling method in computer animation via various virtual scenarios. © 2012 Wiley Periodicals, Inc.


title: Real-Time Texture Detection Using the LU-Transform

abstract: This paper introduces a fast texture descriptor, the LU-transform. Itis inspired by previous methods, the SVD-transform and Eigen-transform, whichyield measures of image roughness by considering th ...


title: Dissecting the genetic control of natural variation in salt tolerance of Arabidopsis thaliana accessions

abstract: Many accessions (ecotypes) of Arabidopsis have been collected. Although few differences exist among their nucleotide sequences, these subtle differences induce large genetic variation in phenotypic traits such as stress tolerance and flowering time. To understand the natural variability in salt tolerance, large-scale soil pot experiments were performed to evaluate salt tolerance among 350 Arabidopsis thaliana accessions. The evaluation revealed a wide variation in the salt tolerance among accessions. Several accessions, including Bu-5, Bur-0, Ll-1, Wl-0, and Zu-0, exhibited marked stress tolerance compared with a salt-sensitive experimental accession, Col-0. The salt-tolerant accessions were also evaluated by agar plate assays. The data obtained by the large-scale assay correlated well with the results of a salt acclimation (SA) assay, in which plants were transferred to high-salinity medium following placement on moderate-salinity medium for 7 d. Genetic analyses indicated that the salt tolerance without SA is a quantitative trait under polygenic control, whereas salt tolerance with SA is regulated by a single gene located on chromosome 5 that is common among the markedly salt-tolerant accessions. These results provide important information for understanding the mechanisms underlying natural variation of salt tolerance in Arabidopsis.


title: EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matching

abstract: Recent convolutional neural networks, especially end-to-end disparity estimation models, achieve remarkable performance on stereo matching task. However, existed methods, even with the complicated cascade structure, may fail in the regions of non-textures, boundaries and tiny details. Focus on these problems, we propose a multi-task network EdgeStereo that is composed of a backbone disparity network and an edge sub-network. Given a binocular image pair, our model enables end-to-end prediction of both disparity map and edge map. Basically, we design a context pyramid to encode multi-scale context information in disparity branch, followed by a compact residual pyramid for cascaded refinement. To further preserve subtle details, our EdgeStereo model integrates edge cues by feature embedding and edge-aware smoothness loss regularization. Comparative results demonstrates that stereo matching and edge detection can help each other in the unified model. Furthermore, our method achieves state-of-art performance on both KITTI Stereo and Scene Flow benchmarks, which proves the effectiveness of our design.


title: Morph-based speech recognition and modeling of out-of-vocabulary words across languages

abstract: We explore the use of morph-based language models in large-vocabulary continuous-speech recognition systems across four so-called morphologically rich languages: Finnish, Estonian, Turkish, and Egyptian Colloquial Arabic. The morphs are subword units discovered in an unsupervised, data-driven way using the Morfessor algorithm. By estimating n-gram language models over sequences of morphs instead of words, the quality of the language model is improved through better vocabulary coverage and reduced data sparsity. Standard word models suffer from high out-of-vocabulary (OOV) rates, whereas the morph models can recognize previously unseen word forms by concatenating morphs. It is shown that the morph models do perform fairly well on OOVs without compromising the recognition accuracy on in-vocabulary words. The Arabic experiment constitutes the only exception since here the standard word model outperforms the morph model. Differences in the datasets and the amount of data are discussed as a plausible explanation.


Two theorems of Gauss.


title: The CubeSat Approach to Space Access

abstract: As advances in technology make payloads and instruments for space missions smaller, lighter, and more power efficient, a niche market is emerging from the university community to perform rapidly developed, low-cost missions on very small spacecraft - micro, nano, and picosatellites. Among this class of spacecraft, are CubeSats, with a basic form of 10 times 10 times 10 cm, weighing a maximum of 1kg. In order to serve as viable alternative to larger spacecraft, small satellite platforms must provide the end user with access to space and similar functionality to mainstream missions. However, despite recent advances, small satellites have not been able to reach their full potential. Without launch vehicles dedicated to launching small satellites as primary payloads, launch opportunities only exist in the form of co-manifest or secondary payload missions, with launches often subsidized by the government. In addition, power, size, and mass constraints create additional hurdles for small satellites. To date, the primary method of increasing a small satellite's capability has been focused on miniaturization of technology. The CubeSat Program embraces this approach, but has also focused on developing an infrastructure to offset unavoidable limitations caused by the constraints of small satellite missions. The main components of this infrastructure are: an extensive developer community, standards for spacecraft and launch vehicle interfaces, and a network of ground stations. This paper will focus on the CubeSat Program, its history, and the philosophy behind the various elements that make it a practical an enabling alternative for access to space.


title: Cell Genetics and Hereditary Symbiosis

abstract: w and scope of cytoplasmic inheritance, often centering on the diagnosis of virus 2’s. plasmagene (5, 42, 43, 45, 48, 55, 197). This review is an attempt to reconcile these views and, where possible, to look for further implications of the synthesis. This account is (quite properly) limited by space allowances. Therefore, objective reviews which already survey the experiments will be cited, when available, in lieu of original references. The treatment of material which has not been condensed previously is therefore exaggerated. An eclectic construction from the freely borrowed views of many authors has been attempted, and few of the interpretations are original. The problems of cytoplasmic heredity have often merged with those of embryonic development and somatic differentiation (56, IIS, 125, 182, 183, 202, 203, 212, 220). The genetics of somatic cells has, however, been poorly studied. Their constitution has been deduced from that of the zygote or the whole organism, except for a few, but important, studies on somatic mutation (66, 119, 123) and somatic segregation (83, 88, 189). Cytological studies have taken some exceptions to the genetic uniformity of somatic cells of the differentiated organism, but have not thereby contributed affirmatively to a comprehensive theory of development (83, 121, 220). Although the pertinence of extranuclear heredity for ontogeny has been generally accepted, in the absence of extensive evidence for it, plausible theories of development have been constructed which rely on the ultimate primacy of the nucleus (73, 131, 193, 202, 220). For technical reasons, cell genetics is best studied in organisms whose germ and soma are not irreversibly differentiated, especially in the microorganisms where vegetative proliferation is preeminent. Whether methodological obstacles with extranuclear heredity factors exaggerate the seeming dominant role of the nucleus in the genetics of higher animals and plants has been debated elsewhere (37, 55, 90, 131, 181, 220). These discussions have left a plethora of terms adrift: pangenes, bioblasts, plasmagenes, plastogenes, chondriogenes, cytogenes and proviruses, which have lost their original utility owing to the accretion of vague or contradictory connotations. At the risk of adding to this list, I propose plasmid as a generic term for any extrachromosomal hereditary determinant. The plasmid itself may be genetically simple or complex. On occasion, the nuclear reference of the general term gene will be emphasized as cltromogetze (2 rgj. This review is dedicated to the reconciliation of the attitudes that plasmids are symbiotic organisms, and that they comprise part of the genetic determination of the organic whole. The conflict may arise in part from fixed conceptions of the scope of the organism. Heuristically, the taxonomic classification of plasmids as viruses, symbionts, or plasmagenes should not obscure careful descriptions of their function, hereditary or pathological, or both. This viewpoint need not prejudge


Clothing identification via deep learning: forensic applications


title: Möbius Concepts in Architecture

abstract: The familiar one-sided Mobius Band has inherent properties that can be expressed in terms of architectural form, surface, and space. This paper first investigates the basic mathematical form of the Mobius Band and its extension to a Prism and then translates these properties into a series of architectural entities differentiated on the basis of human scale.


title: A Novel Control Strategy for a Variable Speed Wind Turbine with a Permanent Magnet Synchronous Generator

abstract: This paper presents a novel control strategy for the operation of a direct drive permanent magnet synchronous generator (PMSG) based stand alone variable speed wind turbine. The control strategy for the generator side converter with maximum power extraction is discussed. The stand alone control is featured with output voltage and frequency controller capable of handling variable load. The potential excess of power is dissipated in the damp resistor with the chopper control and the dc link voltage is maintained. Dynamic representation of dc bus and small signal analysis are presented. Simulation results show that the controllers can extract maximum power and regulate the voltage and frequency under varying wind and load conditions. The controller shows very good dynamic and steady state performance.


title: Evolving Personalized Content for Super Mario Bros Using Grammatical Evolution

abstract: Adapting game content to a particular player's needs and expertise constitutes an important aspect in game design. Most research in this direction has focused on adapting game difficulty to keep the player engaged in the game. Dynamic difficulty adjustment, however, focuses on one aspect of the gameplay experience by adjusting the content to increase or decrease perceived challenge. In this paper, we introduce a method for automatic level generation for the platform game Super Mario Bros using grammatical evolution. The grammatical evolution-based level generator is used to generate player-adapted content by employing an adaptation mechanism as a fitness function in grammatical evolution to optimize the player experience of three emotional states: engagement, frustration and challenge. The fitness functions used are models of player experience constructed in our previous work from crowd-sourced gameplay data collected from over 1500 game sessions.


title: ETV6-RUNX1 + Acute Lymphoblastic Leukaemia in Identical Twins.

abstract: Acute leukaemia is the major subtype of paediatric cancer with a cumulative risk of 1 in 2000 for children up to the age of 15 years. Childhood acute lymphoblastic leukaemia (ALL) is a biologically and clinically diverse disease with distinctive subtypes; multiple chromosomal translocations exist within the subtypes and each carries its own prognostic relevance. The most common chromosome translocation observed is the t(12;21) that results in an in-frame fusion between the first five exons of ETV6 (TEL) and almost the entire coding region of RUNX1 (AML1).The natural history of childhood ALL is almost entirely clinically silent and is well advanced at the point of diagnosis. It has, however, been possible to backtrack this process through molecular analysis of appropriate clinical samples: (i) leukaemic clones in monozygotic twins that are either concordant or discordant for ALL; (ii) archived neonatal blood spots or Guthrie cards from individuals who later developed leukaemia; and (iii) stored, viable cord blood cells.Here, we outline our studies on the aetiology and pathology of childhood ALL that provide molecular evidence for a monoclonal, prenatal origin of ETV6-RUNX1+ leukaemia in monozygotic identical twins. We provide mechanistic support for the concept that altered patterns of infection during early childhood can deliver the necessary promotional drive for the progression of ETV6-RUNX1+ pre-leukaemic cells into a postnatal overt leukaemia.


title: The benefits of data warehousing: why some organizations realize exceptional payoffs

abstract: Data warehousing is one of the key developments in the information systems (IS) field. While its benefits are plentiful, some organizations are receiving more significant returns than others. The types of returns can vary in the impact they have on the organization and the ease in which they can be quantified and measured. This article presents a framework that shows how data warehouses can transform an organization; it also offers a compelling explanation for why differences in impact exist. Case studies of data warehousing initiatives at a large manufacturing company (LMC), the Internal Revenue Service, and a financial services company (FSC) are presented and discussed within the context of the framework. The analysis shows that the benefits that each company received can be tied to the way in which it conforms to the framework.


Stance Detection for Fake News Identification


title: Over-exposed?: privacy patterns and considerations in online and mobile photo sharing

abstract: As sharing personal media online becomes easier and widely spread, new privacy concerns emerge - especially when the persistent nature of the media and associated context reveals details about the physical and social context in which the media items were created. In a first-of-its-kind study, we use context-aware camerephone devices to examine privacy decisions in mobile and online photo sharing. Through data analysis on a corpus of privacy decisions and associated context data from a real-world system, we identify relationships between location of photo capture and photo privacy settings. Our data analysis leads to further questions which we investigate through a set of interviews with 15 users. The interviews reveal common themes in privacy considerations: security, social disclosure, identity and convenience. Finally, we highlight several implications and opportunities for design of media sharing applications, including using past privacy patterns to prevent oversights and errors.


title: Oculomotor learning revisited: a model of reinforcement learning in the basal ganglia incorporating an efference copy of motor actions

abstract: In its simplest formulation, reinforcement learning is based on the idea that if an action taken in a particular context is followed by a favorable outcome, then, in the same context, the tendency to produce that action should be strengthened, or reinforced. While reinforcement learning forms the basis of many current theories of basal ganglia (BG) function, these models do not incorporate distinct computational roles for signals that convey context, and those that convey what action an animal takes. Recent experiments in the songbird suggest that vocal-related BG circuitry receives two functionally distinct excitatory inputs. One input is from a cortical region that carries context information about the current ‘time’ in the motor sequence. The other is an efference copy of motor commands from a separate cortical brain region that generates vocal variability during learning. Based on these findings, I propose here a general model of vertebrate BG function that combines context information with a distinct motor efference copy signal. The signals are integrated by a learning rule in which efference copy inputs gate the potentiation of context inputs (but not efference copy inputs) onto medium spiny neurons in response to a rewarded action. The hypothesis is described in terms of a circuit that implements the learning of visually-guided saccades. The model makes testable predictions about the anatomical and functional properties of hypothesized context and efference copy inputs to the striatum from both thalamic and cortical sources.


title: Leave a Comment! An In-Depth Analysis of User Comments on YouTube

abstract: User comments are the most popular but also extremely controversial form of communication on YouTube. Their public image is very poor; users generally expect that most comments will be of little value or even in thorough- ly bad taste. Nevertheless, heaps of comments continue to be posted every day. We propose an explanation for this contradiction in user attitudes and behaviour based on a new comment classification approach which captures salient aspects of YouTube comments. We show that, based on our new classification, we are able to perform very fast lightweight semantic video analysis. In addition, our results indicate that users' video perceptions (Likes and Dislikes) are indeed in- fluenced by the dispersion of valuable and inferior comments.


title: Graph-basedmethods for analysing networks in cell biology

abstract: Availability of large-scale experimental data for cell biology is enabling computational methods to systematically model the behaviour of cellular networks. This review surveys the recent advances in the field of graph-driven methods for analysing complex cellular networks. The methods are outlined on three levels of increasing complexity, ranging from methods that can characterize global or local structural properties of networks to methods that can detect groups of interconnected nodes, called motifs or clusters, potentially involved in common elementary biological functions. We also briefly summarize recent approaches to data integration and network inference through graph-based formalisms. Finally, we highlight some challenges in the field and offer our personal view of the key future trends and developments in graph-based analysis of large-scale datasets.


title: Bitcoin Mining Pools: A Cooperative Game Theoretic Analysis

abstract: Bitcoin is an innovative decentralized cryptocurrency whose core security relies on a "proof of work" procedure, which requires network participants to repeatedly compute hashes on inputs from a large search space. Finding one of the rare inputs that generates an extremely low hash value is considered a successful attempt, allowing miners to approve new transactions and, in return, to collect rewards in bitcoins. ::: ::: This reward allocation, which provides the incentive for miners to participate, is a random process with a large variance. Miners who desire a steady income thus often participate in mining pools that divide among their members the earned rewards, and reduce this variance. Mining pools are slightly better at coordinating participants due to lower-latency communication, a fact which implies that they manage to collect slightly higher rewards. ::: ::: We examine dynamics of pooled mining and the rewards that pools manage to collect, and use cooperative game theoretic tools to analyze how pool members may share these rewards. We show that for some network parameters, especially under high transaction loads, it is difficult or even impossible to distribute rewards in a stable way: some participants are always incentivized to switch between pools.


title: Protecting Data In-Use from Firmware and Physical Attacks

abstract: Defending computers from unauthorized physical access, malicious hardware devices, or other low-level attacks has proven extremely challenging. The risks from these attacks are exacerbated in cloud-computing environments, where users lack physical control over servers executing their workloads. This paper reviews several rmware and physical attacks against x86 platforms, including bootkits, "cold booting", and malicious devices. We discuss several existing tools and technologies that can mitigate these risk such as Trusted Execution Technology (TXT) and main memory encryption. We will also discuss upcoming technologies that may help protect against rmware and physical threats.


title: Exemplars, ethics, and illness narratives

abstract: Many people report that reading first-person narratives of the experience of illness can be morally instructive or educative. But although they are ubiquitous and typically sincere, the precise nature of such educative experiences is puzzling, for those narratives typically lack the features that modern philosophers regard as constitutive of moral reason. I argue that such puzzlement should disappear, and the morally educative power of illness narratives explained, if one distinguishes two different styles of moral reasoning: an inferentialist style that generates the puzzlement and an alternative exemplarist style that offers a compelling explanation of the morally educative power of pathographic literature.


title: CMOS Voltage Level-Up Shifter – A Review

abstract: This paper presents a review on various CMOS voltage level shifters. A voltage level-shifter shifts the level of output voltage from a digital circuit. Level Shifter circuits are compared in terms of output voltage level, power consumption and delay. The input voltage is set to 1.6V. Simulations have been carried out in SPICE based on TSMC 0.18μm CMOS technology.


title: Cache Simulation for Instruction Set Simulator QEMU

abstract: In embedded system design, there is an increasing demand for modeling techniques that can provide both accurate measurements of delay and fast simulation speed. Modeling latency effects of a cache can greatly increase accuracy of the simulation and assist developers to optimize their software. Current solutions have not succeeded in balancing three important factors: speed, accuracy and usability. In this research, we created a cache simulation module inside a well-known instruction set simulator QEMU. Our implementation can simulate various cases of cache configuration and obtain every memory access. In full system simulation, speed is kept at around 73 MIPS on a personal host computer which is close to native execution of ARM Cortex-M3(125 MIPS at 100 MHz). Compared to the widely used cache simulation tool, Valgrind, our simulator is three time faster.


title: An Improved Image Segmentation Algorithm Based on Two-Dimensional Otsu Method

abstract: This paper presented an improved image segmentation algorithm based on 2D Otsu, in which two- dimensional histogram was mainly build by gray and neighborhood gray gradient of two tuples, and then calculated the biggest Otsu value of the object area and the background area in image, at last, got the threshold of the image. The experimental results indicate that we can get more smooth area of the image and more accurate shape of the image's edge, compared with Otsu method and the traditional 2D Otsu method.


title: A Survey of Distributed File Systems

abstract: Abstract This paper is a survey of the current state of the art in the design and implementation of distributed file systems. It consists of four major parts: an overview of background material, case studies of a number of contemporary file systems, identification of key design techniques, and an examination of current research issues. The systems surveyed are Sun NFS, Apollo Domain, Andrew, IBM AIX DS, AT&T RFS, and Sprite. The coverage of background material includes a taxonomy of file system issues, a brief history of distributed file systems, and a summary of empirical research on file properties. A comprehensive bibliography forms an important of the paper.


title: Expression of affect in spontaneous speech: Acoustic correlates . . .

abstract: The majority of previous studies on vocal expression have been conducted on posed expressions. In contrast, we utilized a large corpus of authentic affective speech recorded from real-life voice controlled telephone services. Listeners rated a selection of 200 utterances from this corpus with regard to level of perceived irritation, resignation, neutrality, and emotion intensity. The selected utterances came from 64 different speakers who each provided both neutral and affective stimuli. All utterances were further automatically analyzed regarding a comprehensive set of acoustic measures related to F0, intensity, formants, voice source, and temporal characteristics of speech. Results first showed that several significant acoustic differences were found between utterances classified as neutral and utterances classified as irritated or resigned using a within-persons design. Second, listeners' ratings on each scale were associated with several acoustic measures. In general the acoustic correlates of irritation, resignation, and emotion intensity were similar to previous findings obtained with posed expressions, though the effect sizes were smaller for the authentic expressions. Third, automatic classification (using LDA classifiers both with and without speaker adaptation) of irritation, resignation, and neutral performed at a level comparable to human performance, though human listeners and machines did not necessarily classify individual utterances similarly. Fourth, clearly perceived exemplars of irritation and resignation were rare in our corpus. These findings were discussed in relation to future research.


title: Image retrieval using color and shape

abstract: This paper deals with efficient retrieval of images from large databases based on the color and shape content in images. With the increasing popularity of the use of large-volume image databases in various applications, it becomes imperative to build an automatic and efficient retrieval system to browse through the entire database. Techniques using textual attributes for annotations are limited in their applications. Our approach relies on image features that exploit visual cues such as color and shape. Unlike previous approaches which concentrate on extracting a single concise feature, our technique combines features that represent both the color and shape in images. Experimental results on a database of 400 trademark images show that an integrated color- and shape-based feature representation results in 99% of the images being retrieved within the top two positions. Additional results demonstrate that a combination of clustering and a branch and bound-based matching scheme aids in improving the speed of the retrievals.


title: General Road Detection From a Single Image

abstract: Given a single image of an arbitrary road, that may not be well-paved, or have clearly delineated edges, or some a priori known color or texture distribution, is it possible for a computer to find this road? This paper addresses this question by decomposing the road detection process into two steps: the estimation of the vanishing point associated with the main (straight) part of the road, followed by the segmentation of the corresponding road area based upon the detected vanishing point. The main technical contributions of the proposed approach are a novel adaptive soft voting scheme based upon a local voting region using high-confidence voters, whose texture orientations are computed using Gabor filters, and a new vanishing-point-constrained edge detection technique for detecting road boundaries. The proposed method has been implemented, and experiments with 1003 general road images demonstrate that it is effective at detecting road regions in challenging conditions.


title: Recent Advances and Future Challenges for Artificial Neural Systems in Geotechnical Engineering Applications

abstract: Artificial neural networks (ANNs) are a form of artificial intelligence that has proved to provide a high level of competency in solving many complex engineering problems that are beyond the computational capability of classicalmathematics and traditional procedures. In particular, ANNs have been applied successfully to almost all aspects of geotechnical engineering problems. Despite the increasing number and diversity of ANN applications in geotechnical engineering, the contents of reported applications indicate that the progress in ANN development and procedures is marginal and not moving forward since the mid-1990s. This paper presents a brief overview of ANN applications in geotechnical engineering, briefly provides an overview of the operation of ANN modeling, investigates the current research directions of ANNs in geotechnical engineering, and discusses some ANN modeling issues that need further attention in the future, including model robustness; transparency and knowledge extraction; extrapolation; uncertainty.


title: Color photometric stereo for multicolored surfaces

abstract: We present a multispectral photometric stereo method for capturing geometry of deforming surfaces. A novel photometric calibration technique allows calibration of scenes containing multiple piecewise constant chromaticities. This method estimates per-pixel photometric properties, then uses a RANSAC-based approach to estimate the dominant chromaticities in the scene. A likelihood term is developed linking surface normal, image intensity and photometric properties, which allows estimating the number of chromaticities present in a scene to be framed as a model estimation problem. The Bayesian Information Criterion is applied to automatically estimate the number of chromaticities present during calibration. A two-camera stereo system provides low resolution geometry, allowing the likelihood term to be used in segmenting new images into regions of constant chromaticity. This segmentation is carried out in a Markov Random Field framework and allows the correct photometric properties to be used at each pixel to estimate a dense normal map. Results are shown on several challenging real-world sequences, demonstrating state-of-the-art results using only two cameras and three light sources. Quantitative evaluation is provided against synthetic ground truth data.


title: Microfluidics for cell separation

abstract: The need for efficient cell separation, an essential preparatory step in many biological and medical assays, has led to the recent development of numerous microscale separation techniques. This review describes the current state-of-the-art in microfluidics-based cell separation techniques. Microfluidics-based sorting offers numerous advantages, including reducing sample volumes, faster sample processing, high sensitivity and spatial resolution, low device cost, and increased portability. The techniques presented are broadly classified as being active or passive depending on the operating principles. The various separation principles are explained in detail along with popular examples demonstrating their application toward cell separation. Common separation metrics, including separation markers, resolution, efficiency, and throughput, of these techniques are discussed. Developing efficient microscale separation methods that offering greater control over cell population distribution will be important in realizing true point-of-care (POC) lab-on-a-chip (LOC) systems.


title: Long range transport aircraft using hydrogen fuel

abstract: Abstract Hydrogen is since long seen as an outstanding candidate for an environmentally acceptable, future aviation fuel. Given that most comprehensive studies on its use in aviation were performed over two decades ago, the current article evaluates its potential as a fuel for long range transport aircraft at current and future technology levels. The investigations show that hydrogen has the potential to reduce the energy utilisation of long range transport aircraft by approximately 11%. The use of hydrogen namely allows a much smaller wing area and span since the wing size is not restricted by its fuel storage capacity. At a given price per unit energy content, the smaller wings lead to a reduction of around 30% in take-off gross weight and 3% in direct operating costs for a given fuel price per energy content. The hydrogen-fuelled aircraft are furthermore slightly more sensitive to a possible reduction in operating empty weight in the future and 20% less sensitive to further improvements in engine thrust specific fuel consumption.


title: Estimating Three-Dimensional Orientation of Human Body Parts by Inertial/Magnetic Sensing

abstract: User-worn sensing units composed of inertial and magnetic sensors are becoming increasingly popular in various domains, including biomedical engineering, robotics, virtual reality, where they can also be applied for real-time tracking of the orientation of human body parts in the three-dimensional (3D) space. Although they are a promising choice as wearable sensors under many respects, the inertial and magnetic sensors currently in use offer measuring performance that are critical in order to achieve and maintain accurate 3D-orientation estimates, anytime and anywhere. This paper reviews the main sensor fusion and filtering techniques proposed for accurate inertial/magnetic orientation tracking of human body parts; it also gives useful recipes for their actual implementation.


title: Electrical balance isolation for flexible duplexing in 5G mobile devices

abstract: Exploiting new technologies will be vital in meeting the demanding requirements of 5G radio access. Flexible duplexing architectures will be a key enabling technology in 5G handsets, virtualising the radio spectrum, reducing cost, and increasing link capacity through full duplex radio communication. The tunability, low cost and small form factor of Electrical Balance (EB) duplexing technology makes it an attractive choice for flexible duplexing in small form factor devices. Circuit simulations incorporating measured antenna data have been used to investigate the performance of a dual notch EB duplexer in a full duplex mode at 1900MHz. Results show that, in general, the isolation bandwidth of a dual notch EB duplexer increases with the notch separation frequency, and that at bandwidths below 75MHz, dual notch balancing achieves transmit to receive isolation within 3dB of optimal balancing. The simulated duplexer provides a 30dB isolation bandwidth of up to 200MHz. Therefore, in addition to providing tunable frequency division duplexing functionality, EB duplexing technology can also serve as a component of a broadband full duplex transceiver, thus providing a fully integrated flexible duplexing solution.


title: Triglycerides and Total Cholesterol Concentrations in Association with Hyperuricemia in Chinese Adults in Qingdao, China

abstract: Objective ::: To assess the association between triglycerides (TG), total cholesterol (TC) and hyperuricemia (HUA) in the general Chinese population. ::: ::: ::: Methods ::: A population-based cross-sectional survey included 9680 participants aged 35-74 years in 2006 and 2009 in Qingdao, China. TG, TC and uric acid (UA) were measured. The logistic regression model was performed to estimate the association between TG, TC, and HUA with an odds ratio (OR) and 95% confidence intervals (CI). Meanwhile, age stratification analysis (<55 years group and ≥55 years group) was performed to evaluate whether age potentially affects the association between TG, TC and HUA using multivariable logistic regression. ::: ::: ::: Results ::: Higher TG and TC showed significantly increased HUA prevalence in both men and women (Ptrend all <0.05). Multivariate logistic regression indicated that borderline high TG (OR: 1.68, 95% CI: 1.31, 2.15 and HTG (OR: 2.98, 95% CI: 2.39, 3.72) indicated increased risk for HUA in men, and borderline high TG (OR: 2.09; 95% CI: 1.68,2.62); HTG (OR: 3.62; 95% CI: 2.90,4.51), borderline high TC (OR: 2.09, 95% CI: 1.68, 2.62) and HTC (OR: 3.62, 95% CI: 2.90, 4.51) showed significant association with HUA in women after adjusted age, school years, marital status, geographic division, personal monthly income, BMI and HDL-C. Age stratification analyses demonstrated that the association between TG and HUA was stronger in males aged ≥55 years and female aged <55 years, and the association between TC and HUA was stronger in both gender aged <55 years. ::: ::: ::: Conclusion ::: This large cross-sectional study focusing on the association between single indictor of blood lipid as exposure and HUA as outcome on the east coast of China for the first time. From a sample of Chinese adults, this study demonstrated that elevated TG in men and women and TC in women were associated with increased HUA prevalence.


Large Scale Image Annotations on Amazon Mechanical Turk


title: Mesh Merging with Mean Value Coordinates

abstract: Merging a source mesh to a target mesh is one useful and important mesh operating technology. This paper introduces a method merging with mean value coordinates. The method firstly computes the mean value coordinates of the vertices in the source mesh with respect to its boundary. After specifying the difference between the two mesh's boundaries, it interpolates the difference of the source's inside vertices through the coordinates, and then constructs a new mesh to merge. With the same framework, smoothing the seaming line can be achieved by interpolating tangent vector variations. This method avoids the mesh parameterizations or the solving a large linear system, but does merge through computing close form expressions. Experiments show that mesh merging with this method is less artifacts and faster, comparing to the method through solving Poisson equation.


title: Semi-blind source separation with multichannel variational autoencoder

abstract: This paper proposes a multichannel source separation technique called the multichannel variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model and estimate the power spectrograms of the sources in a mixture. By training the CVAE using the spectrograms of training examples with source-class labels, we can use the trained decoder distribution as a universal generative model capable of generating spectrograms conditioned on a specified class label. By treating the latent space variables and the class label as the unknown parameters of this generative model, we can develop a convergence-guaranteed semi-blind source separation algorithm that consists of iteratively estimating the power spectrograms of the underlying sources as well as the separation matrices. In experimental evaluations, our MVAE produced better separation performance than a baseline method.


title: Minimization of inter-module leakage current in cascaded H-bridge multilevel inverters for grid connected solar PV applications

abstract: Cascaded H-bridge multilevel inverters (CHB-MLIs) are well accepted for medium and high power grid connected photovoltaic (PV) applications. Independent MPPT and modularity are the advantages of these converters over the diode clamped and capacitor clamped MLIs. However, CHB-MLIs are associated with inter-module leakage current through the parasitic capacitor of PV panels even though an isolation transformer is used on the AC side. This necessitates the use of an additional isolated DC-DC converter in each power cell, which increases the cost and reduces efficiency. In order to address these limitations, a simple passive solution is proposed to minimize the intermodule leakage current. It comprises of several L-C filters placed among the power cells to mitigate the switching frequency components in voltage across the parasitic capacitors. Moreover, the proposed solution can be easily upgraded to higher number of power cells. In this paper, the expressions for inter-module leakage currents are derived for five level CHB-MLI. The required filter values are designed for various pulse width modulation (PWM) techniques and it is found that the modified carried disposition PWM and hybrid PWM techniques are superior in minimizing the leakage currents. Simulation studies validate the claims of proposed solution.


title: Method for Assessing the Reliability of Molecular Diagnostics Based on Multiplexed SERS-Coded Nanoparticles

abstract: Surface-enhanced Raman scattering (SERS) nanoparticles have been engineered to generate unique fingerprint spectra and are potentially useful as bright contrast agents for molecular diagnostics. One promising strategy for biomedical diagnostics and imaging is to functionalize various particle types (“flavors”), each emitting a unique spectral signature, to target a large multiplexed panel of molecular biomarkers. While SERS particles emit narrow spectral features that allow them to be easily separable under ideal conditions, the presence of competing noise sources and background signals such as detector noise, laser background, and autofluorescence confounds the reliability of demultiplexing algorithms. Results obtained during time-constrained in vivo imaging experiments may not be reproducible or accurate. Therefore, our goal is to provide experimentalists with a metric that may be monitored to enforce a desired bound on accuracy within a user-defined confidence level. We have defined a spectral reliability index (SRI), based on the output of a direct classical least-squares (DCLS) demultiplexing routine, which provides a measure of the reliability of the computed nanoparticle concentrations and ratios. We present simulations and experiments to demonstrate the feasibility of this strategy, which can potentially be utilized for a range of instruments and biomedical applications involving multiplexed SERS nanoparticles.


Implemented Domain Model Generation


title: Flow map layout

abstract: Cartographers have long used flow maps to show the movement of objects from one location to another, such as the number of people in a migration, the amount of goods being traded, or the number of packets in a network. The advantage of flow maps is that they reduce visual clutter by merging edges. Most flow maps are drawn by hand and there are few computer algorithms available. We present a method for generating flow maps using hierarchical clustering given a set of nodes, positions, and flow data between the nodes. Our techniques are inspired by graph layout algorithms that minimize edge crossings and distort node positions while maintaining their relative position to one another. We demonstrate our technique by producing flow maps for network traffic, census data, and trade data.


title: Simple Technical Trading Rules and the Stochastic Properties of Stock Returns

abstract: This paper tests two of the simplest and most popular trading rules--moving average and trading range break--by utilizing the Dow Jones Index from 1897 to 1986. Standard statistical analysis is extended through the use of bootstrap techniques. Overall, their results provide strong support for the technical strategies. The returns obtained from these strategies are not consistent with four popular null models: the random walk, the AR(1), the GARCH-M, and the Exponential GARCH. Buy signals consistently generate higher returns than sell signals, and further, the returns following buy signals are less volatile than returns following sell signals. Moreover, returns following sell signals are negative, which is not easily explained by any of the currently existing equilibrium models. Copyright 1992 by American Finance Association.


title: Real-time tracking of 3D elastic objects with an RGB-D sensor

abstract: This paper presents a method to track in real-time a 3D textureless object which undergoes large deformations such as elastic ones, and rigid motions, using the point cloud data provided by an RGB-D sensor. This solution is expected to be useful for enhanced manipulation of humanoid robotic systems. Our framework relies on a prior visual segmentation of the object in the image. The segmented point cloud is registered first in a rigid manner and then by non-rigidly fitting the mesh, based on the Finite Element Method to model elasticity, and on geometrical point-to-point correspondences to compute external forces exerted on the mesh. The real-time performance of the system is demonstrated on synthetic and real data involving challenging deformations and motions.


title: Cluster head election techniques for coverage preservation in wireless sensor networks

abstract: Coverage preservation is one of the basic QoS requirements of wireless sensor networks, yet this problem has not been sufficiently explored in the context of cluster-based sensor networks. Specifically, it is not known how to select the best candidates for the cluster head roles in applications that require complete coverage of the monitored area over long periods of time. In this paper, we take a unique look at the cluster head election problem, specifically concentrating on applications where the maintenance of full network coverage is the main requirement. Our approach for cluster-based network organization is based on a set of coverage-aware cost metrics that favor nodes deployed in densely populated network areas as better candidates for cluster head nodes, active sensor nodes and routers. Compared with using traditional energy-based selection methods, using coverage-aware selection of cluster head nodes, active sensor nodes and routers in a clustered sensor network increases the time during which full coverage of the monitored area can be maintained anywhere from 25% to 4.5x, depending on the application scenario.


title: Premotor Cortex Activation during Observation and Naming of Familiar Tools

abstract: Abstract Positron emission tomography was used to investigate whether observation of real objects (tools of common use) activates premotor areas in the absence of any overt motor demand. Silent naming of the presented tools and silent naming of their use were also studied. Right-handed normal subjects were employed. Tool observation strongly activated the left dorsal premotor cortex. In contrast, silent tool naming activated Broca's area without additional activity in the dorsal premotor cortex. Silent tool-use naming, in addition to activating Broca's area, increased the activity in the left dorsal premotor cortex and recruited the left ventral premotor cortex and the left supplementary motor area. These data indicate that, even in the absence of any subsequent movement, the left premotor cortex processes objects that, like tools, have a motor valence. This dorsal premotor activation, which further augments when the subject names the tool use, should reflect the neural activity related to motor schemata for object use. The presence of an activation of both dorsal premotor cortex and ventral premotor cortex during tool-use naming suggests a role for these two areas in understanding object semantics.


CRYSTAL ISLAND : A Narrative-Centered Learning Environment for Eighth Grade Microbiology


title: Fusion of LiDAR and Camera Sensor Data for Environment Sensing in Driverless Vehicles

abstract: Driverless vehicles operate by sensing and perceiving its surrounding environment to make the accurate driving decisions. A combination of several different sensors such as LiDAR, radar, ultrasound sensors and cameras are utilized to sense the surrounding environment of driverless vehicles. The heterogeneous sensors simultaneously capture various physical attributes of the environment. Such multimodality and redundancy of sensing need to be positively utilized for reliable and consistent perception of the environment through sensor data fusion. However, these multimodal sensor data streams are different from each other in many ways, such as temporal and spatial resolution, data format, and geometric alignment. For the subsequent perception algorithms to utilize the diversity offered by multimodal sensing, the data streams need to be spatially, geometrically and temporally aligned with each other. In this paper, we address the problem of fusing the outputs of a Light Detection and Ranging (LiDAR) scanner and a wide-angle monocular image sensor. The outputs of LiDAR scanner and the image sensor are of different spatial resolutions and need to be aligned with each other. A geometrical model is used to spatially align the two sensor outputs, followed by a Gaussian Process (GP) regression based resolution matching algorithm to interpolate the missing data with quantifiable uncertainty. The results indicate that the proposed sensor data fusion framework significantly aids the subsequent perception steps, as illustrated by the performance improvement of a typical free space detection algorithm.


title: Connection Scan Algorithm

abstract: We introduce the Connection Scan Algorithm (CSA) to efficiently answer queries to timetable information systems. The input consists, in the simplest setting, of a source position and a desired target position. The output consist is a sequence of vehicles such as trains or buses that a traveler should take to get from the source to the target. We study several problem variations such as the earliest arrival and profile problems. We present algorithm variants that only optimize the arrival time or additionally optimize the number of transfers in the Pareto sense. An advantage of CSA is that is can easily adjust to changes in the timetable, allowing the easy incorporation of known vehicle delays. We additionally introduce the Minimum Expected Arrival Time (MEAT) problem to handle possible, uncertain, future vehicle delays. We present a solution to the MEAT problem that is based upon CSA. Finally, we extend CSA using the multilevel overlay paradigm to answer complex queries on nation-wide integrated timetables with trains and buses.


title: Switchboard: a matchmaking system for multiplayer mobile games

abstract: Supporting interactive, multiplayer games on mobile phones over cellular networks is a difficult problem. It is particularly relevant now with the explosion of mostly single-player or turn-based games on mobile phones. The challenges stem from the highly variable performance of cellular networks and the need for scalability (not burdening the cellular infrastructure, nor any server resources that a game developer deploys). We have built a service for matchmaking in mobile games -- assigning players to games such that game settings are satisfied as well as latency requirements for an enjoyable game. This requires solving two problems. First, the service needs to know the cellular network latency between game players. Second, the service needs to quickly group players into viable game sessions. In this paper, we present the design of our service, results from our experiments on predicting cellular latency, and results from efficiently grouping players into games.


title: A Survey of Feature Base Methods for Human Face Detection

abstract: The human face is among the most significant objects in an image or video, it contains many important information and specifications, also is required to be the cause of almost all achievable look variants caused by changes in scale, location, orientation, pose, facial expression, lighting conditions and partial occlusions. It plays a key role in face recognition systems and many other face analysis applications. We focus on the feature based approach because it gave great results on detect the human face. Face feature detection techniques can be mainly divided into two kinds of approaches are Feature base and image base approach. Feature base approach tries to extract features and match it against the knowledge of the facial features. This paper gives the idea about challenging problems in the field of human face analysis and as such, as it has achieved a great attention over the last few years because of its many applications in various domains. Furthermore, several existing face detection approaches are analyzed and discussed and attempt to give the issues regarding key technologies of feature base methods, we had gone direct comparisons of the method's performance are made where possible and the advantages/ disadvantages of different approaches are discussed.


title: Array SSA form and its use in parallelization

abstract: Static single assignment (SSA) form for scalars has been a significant advance. It has simplified the way we think about scalar variables. It has simplified the design of some optimizations and has made other optimizations more effective. Unfortunately none of this can be be said for SSA form for arrays. The current SSA processing of arrays views an array as a single object. But the kinds of analyses that sophisticated compilers need to perform on arrays, for example those that drive loop parallelization, are at the element level. Current SSA form for arrays is incapable of providing the element-level data flow information required for such analyses.In this paper, we introduce an Array SSA form that captures precise element-level data flow information for array variables in all cases. It is general and simple, and coincides with standard SSA form when applied to scalar variables. It can also be used for structures and other variable types that can be modeled as arrays. An important application of Array SSA form is in automatic parallelization. We show how Array SSA form can enable parallelization of any loop that is free of loop-carried true data dependences. This includes loops with loop-carried anti and output dependences, unanalyzable subscript expressions, and arbitrary control flow within an iteration. Array SSA form achieves this level of generality by making manifest its - functions as runtime computations in cases that are not amenable to compile-time analysis.


title: Skyport - Container-Based Execution Environment Management for Multi-cloud Scientific Workflows

abstract: Recently, Linux container technology has been gaining attention as it promises to transform the way software is developed and deployed. The portability and ease of deployment makes Linux containers an ideal technology to be used in scientific workflow platforms. Skyport utilizes Docker Linux containers to solve software deployment problems and resource utilization inefficiencies inherent to all existing scientific workflow platforms. As an extension to AWE/Shock, our data analysis platform that provides scalable workflow execution environments for scientific data in the cloud, Skyport greatly reduces the complexity associated with providing the environment necessary to execute complex workflows.


title: An architecture for specification-based detection of semantic integrity violations in kernel dynamic data

abstract: The ability of intruders to hide their presence in compromised systems has surpassed the ability of the current generation of integrity monitors to detect them. Once in control of a system, intruders modify the state of constantly-changing dynamic kernel data structures to hide their processes and elevate their privileges. Current monitoring tools are limited to detecting changes in nominally static kernel data and text and cannot distinguish a valid state change from tampering in these dynamic data structures. We introduce a novel general architecture for defining and monitoring semantic integrity constraints using a specification language-based approach. This approach will enable a new generation of integrity monitors to distinguish valid states from tampering.


title: A Lightweight XMPP Publish/Subscribe Scheme for Resource-Constrained IoT Devices

abstract: The development of wireless technology like Internet Protocol version 6 over Low power Wireless Personal Area Networks, which defines IP communication for resource-constrained networks enables communication on the lower layers, while the diverging and incompatible application layer protocols lead to barriers in the way of information transferring between heterogeneous networks. Fortunately, extensible messaging and presence protocol (XMPP) is a preferential protocol to solve the problem of interoperability between heterogeneous networks. Hence, it is attractive to extend XMPP to the Internet of Things (IoT). However, XMPP protocol is initially designed for the Internet where the equipment is rich in resources, considering the characteristics of the IoT, the XMPP protocol needs to be optimized to meet the requirements of IoT, e.g. , downsized protocol, publish/subscribe service and sleeping mechanism. In this paper, by staying true to the IoT visions, we propose a lightweight XMPP publish/subscribe scheme for resource-constrained IoT devices to perform data exchange either periodically or upon any value change. According to the subscriber’s needs, the publisher can adjust the data information published to the server. Inherit the merits of XEP-0060, the server maintains and manages the publish/subscribe relationships with multiple subscribers, as well as distributes the received data sent by publisher to all subscribers, yielding less complexity of publication and energy efficiency. In addition, the proposed scheme strongly supports publish/subscribe architecture of the sleeping clients that actually prolongs the lifetime of battery-powered devices. Thus, based on the standardized XMPP protocol, we present a down-sized and trimmed XMPP to implement this scheme. Through the experimental results, we demonstrate our work on optimizing and improving XMPP publish/subscribe scheme for resource-constrained IoT devices and show the simplicity and efficiency of this scheme.


title: Image Segmentation - A Survey of Soft Computing Approaches

abstract: Soft Computing is an emerging field that consists of complementary elements of fuzzy logic, neural computing and evolutionary computation. Soft computing techniques have found wide applications. One of the most important applications is edge detection for image segmentation. The process of partitioning a digital image into multiple regions or sets of pixels is called image segmentation. Edge is a boundary between two homogeneous regions. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. In this paper, the main aim is to survey the theory of edge detection for image segmentation using soft computing approach based on the Fuzzy logic, Genetic Algorithm and Neural Network.


title: Next-generation genetics in plants

abstract: Natural variation presents one of the fundamental challenges of modern biology. Soon, the genome sequences of thousands of individuals will be known for each of several species. But how does the genotypic variation that will be observed among these individuals translate into phenotypic variation? Plants are in many ways ideal for addressing this question, and resources that are unmatched, except in humans, have now been developed.


Classification of Agricultural Pests Using DWT and Back Propagation Neural Networks


title: Optimised De Bruijn patterns for one-shot shape acquisition

abstract: Coded structured light is an optical technique based on active stereovision which allows shape acquisition. By projecting a suitable set of light patterns onto the surface of an object and capturing images with a camera, a large number of correspondences can be found and 3D points can be reconstructed by means of triangulation. One-shot techniques are based on projecting an unique pattern so that moving objects can be measured. A major group of techniques in this field define coloured multi-slit or stripe patterns in order to obtain dense reconstructions. The former type of patterns is suitable for locating intensity peaks in the image while the latter is aimed to locate edges. In this paper, we present a new way to design coloured stripe patterns so that both intensity peaks and edges can be located without loss of accuracy and reducing the number of hue levels included in the pattern. The results obtained by the new pattern are quantitatively and qualitatively compared to similar techniques. These results also contribute to a comparison between the peak-based and edge-based reconstruction strategies.


title: Mental Model Consensus and Shifts During Navigation System-Assisted Route Planning:

abstract: A major barrier to effective spatial decision-making in human-agent teams is that humans and algorithms use different mechanisms to solve spatial problems, frequently leading them to produce different solutions. Incongruity between algorithm-generated solutions and human spatial mental models results in higher workload in mixed-initiative systems, and potential breakdowns in trust and team situation awareness. Although these performance effects are well-understood, few methods exist for quantifying and comparing human spatial mental models and algorithm-generated solutions. To address these problems, 27 participants completed solutions to 5 spatial planning problems, before and after receiving assistance from 2 navigation algorithms. A novel path mapping and clustering approach provided a means of quantifying consensus in human mental models, and shifts in those mental models after viewing the algorithm-suggested routes. Human solutions clustered into a small number of shared mental models. Individual dif...


title: The Taming of the Shrew: Mitigating Low-Rate TCP-Targeted Attack

abstract: A Shrew attack, which uses a low-rate burst carefully designed to exploit TCP's retransmission timeout mechanism, can throttle the bandwidth of a TCP flow in a stealthy manner. While such an attack can significantly degrade the performance of all TCP-based protocols and services including Internet routing (e.g., BGP), no existing scheme clearly solves the problem in real network scenarios. In this paper, we propose a simple protection mechanism, called SAP (Shrew Attack Protection), for defending against a Shrew attack. Rather than attempting to track and isolate Shrew attackers, SAP identifies TCP victims by monitoring their drop rates and preferentially admits those packets from victims with high drop rates to the output queue. This is to ensure that well-behaved TCP sessions can retain their bandwidth shares. Our simulations indicate that under a Shrew attack, SAP can prevent TCP sessions from closing, and effectively enable TCP flows to maintain high throughput. SAP is a destination-port-based mechanism and requires only a small number of counters to find potential victims, which makes SAP readily implementable on top of existing router mechanisms.


How Consumers Perceive Trustworthiness of Providers in Sharing Economy: Effects of Photos and Comments on Demand at Airbnb.


title: Time-Decaying Bandits for Non-stationary Systems

abstract: Contents displayed on web portals (e.g., news articles at Yahoo.com) are usually adaptively selected from a dynamic set of candidate items, and the attractiveness of each item decays over time. The goal of those websites is to maximize the engagement of users (usually measured by their clicks) on the selected items. We formulate this kind of applications as a new variant of bandit problems where new arms are dynamically added into the candidate set and the expected reward of each arm decays as the round proceeds. For this new problem, a direct application of the algorithms designed for stochastic MAB (e.g., UCB) will lead to over-estimation of the rewards of old arms, and thus cause a misidentification of the optimal arm. To tackle this challenge, we propose a new algorithm that can adaptively estimate the temporal dynamics in the rewards of the arms, and effectively identify the best arm at a given time point on this basis. When the temporal dynamics are represented by a set of features, the proposed algorithm is able to enjoy a sub-linear regret. Our experiments verify the effectiveness of the proposed algorithm.


title: Antibacterial activity of South African plants used for medicinal purposes.

abstract: Crude extracts from 21 South African medicinal plants, traditionally used for ailments of an infectious or septic nature, were screened for in vitro antibacterial activity using the agar diffusion and dilution methods. Almost all the activity exhibited was against Gram-positive bacteria, with 12 of the 21 plant species tested showing some activity against Bacillus subtilis. Only the Warburgia salutaris methanol extract inhibited the growth of Escherichia coli. None of the extracts had any activity against Klebsiella pneumoniae. The highest activity was found in the methanol extracts from Bidens pilosa, Psidium guajava, Artemisia afra and Warburgia salutaris. The majority of the antibacterial activity was present in the methanolic, rather than the aqueous extracts.


title: Who to follow and why: link prediction with explanations

abstract: User recommender systems are a key component in any on-line social networking platform: they help the users growing their network faster, thus driving engagement and loyalty. In this paper we study link prediction with explanations for user recommendation in social networks. For this problem we propose WTFW ("Who to Follow and Why"), a stochastic topic model for link prediction over directed and nodes-attributed graphs. Our model not only predicts links, but for each predicted link it decides whether it is a "topical" or a "social" link, and depending on this decision it produces a different type of explanation. A topical link is recommended between a user interested in a topic and a user authoritative in that topic: the explanation in this case is a set of binary features describing the topic responsible of the link creation. A social link is recommended between users which share a large social neighborhood: in this case the explanation is the set of neighbors which are more likely to be responsible for the link creation. Our experimental assessment on real-world data confirms the accuracy of WTFW in the link prediction and the quality of the associated explanations.


title: Determinants of Customer Satisfaction in Fast Food Industry A Study of Fast Food Restaurants Peshawar Pakistan

abstract: This research is focused to find what are the key success factors for fast food industry in region of Peshawar Pakistan. Fast food concepts developed very rapidly in last few years in Peshawar region. The failure or success of a fast food industry based on some factors like Promotion, Service quality, Customer expectations, Brand, Physical Environment, Price, and Taste of the product. To find which of these factors has greater influence on consumer satisfaction, four fast food restaurants customers were targeted randomly. These four restaurants were KFC, CHIEF, ARBAIN CHICK, and PIZZA HUT. The data collected from the customers of these restaurants. The data collected from customers of these restaurants when the customers were in restaurants for refreshment. Total number of customers who were targeted was 120. From each restaurant 30 customers were targeted on availability basis. On the basis of their responses multiple regression and correlation test was applied. Findings of the study shows that service quality and brand are the key factors for satisfaction in fast food industry in Peshawar Pakistan.


title: Comparative Analysis of Improved Cuckoo Search(ICS) Algorithm and Artificial Bee Colony (ABC) Algorithm on Continuous Optimization Problems

abstract: This work is related on two well-known algorithm, Improved Cuckoo Search and Artificial Bee Colony Algorithm which are inspired from nature. Improved Cuckoo Search (ICS) algorithm is based on Levy flight and behavior of some birds and fruit flies and they have some assumptions and each assumption is highly observed to maintain their characteristics. Besides Artificial Bee Colony (ABC) algorithm is based on swarm intelligence, which is based on bee colony with the way the bees maintain their life in that colony. Bees’ characteristics are the main part of this algorithm. This is a theoretical result of this topic and a quantitative research paper.


title: Predisposing factors in childhood masturbation in Turkey

abstract: UNLABELLED ::: The aim of this prospective, referral-based study was to assess demographical and developmental features associated with childhood masturbation in Turkey. A total of 61 children with childhood masturbation who were referred for the first time to the Department of Child Psychiatry were examined from demographical, psychosocial and medical aspects and compared with two control groups consisting of 61 age and gender matched children who were brought to the paediatric outpatient clinics and 43 children and adolescents who were the biological siblings of the study group. In children with masturbation, sleep difficulties were more frequent (P < 0.001) and breast-feeding was shorter than in controls (P < 0.05). The onset of masturbation was often associated with a genito-urinary disorder or a stressful life event like weaning, the birth of a sibling, or separation from the parents. ::: ::: ::: CONCLUSION ::: This is the first controlled study investigating the clinical and the developmental features of childhood masturbation. These findings may help identify children who could be at risk for this condition.


title: Flights in my hands: coherence concerns in designing Strip'TIC, a tangible space for air traffic controllers

abstract: We reflect upon the design of a paper-based tangible interactive space to support air traffic control. We have observed, studied, prototyped and discussed with controllers a new mixed interaction system based on Anoto, video projection, and tracking. Starting from the understanding of the benefits of tangible paper strips, our goal is to study how mixed physical and virtual augmented data can support the controllers' mental work. The context of the activity led us to depart from models that are proposed in tangible interfaces research where coherence is based on how physical objects are representative of virtual objects. We propose a new account of coherence in a mixed interaction system that integrates externalization mechanisms. We found that physical objects play two roles: they act both as representation of mental objects and as tangible artifacts for interacting with augmented features. We observed that virtual objects represent physical ones, and not the reverse, and, being virtual representations of physical objects, should seamlessly converge with the cognitive role of the physical object. Finally, we show how coherence is achieved by providing a seamless interactive space.


title: IM Balanced: Influence Maximization Under Balance Constraints

abstract: Influence Maximization (IM) is the problem of finding a set of influential users in a social network, so that their aggregated influence is maximized. IM has natural applications in viral marketing and has been the focus of extensive recent research. One critical problem, however, is that while existing IM algorithms serve the goal of reaching a large audience, they may obliviously focus on certain well-connected populations, at the expense of key demographics, creating an undesirable imbalance, an illustration of a broad phenomenon referred to as algorithmic discrimination. Indeed, we demonstrate an inherent trade-off between two objectives: (1) maximizing the overall influence and (2) maximizing influence over a predefined "protected" demographic, with the optimal balance between the two being open to different interpretations. To this end, we present IM-Balanced, a system enabling end users to declaratively specify the desired trade-off between these objectives w.r.t. an emphasized population. IM-Balanced provides theoretical guarantees for the proximity to the optimal solution in terms of both objectives and ensures an efficient, scalable computation via careful adaptation of existing state-of-the-art IM algorithms. Our demonstration illustrates the effectiveness of our approach through real-life viral marketing scenarios in an academic social network.


title: Mining Semantics-Preserving Attention for Group Activity Recognition

abstract: In this paper, we propose a Semantics-Preserving Teacher-Student (SPTS) model for group activity recognition in videos, which aims to mine the semantics-preserving attention to automatically seek the key people and discard the misleading people. Conventional methods usually aggregate the features extracted from individual persons by pooling operations, which cannot fully explore the contextual information for group activity recognition. To address this, our SPTS networks first learn a Teacher Network in semantic domain, which classifies the word of group activity based on the words of individual actions. Then we carefully design a Student Network in vision domain, which recognizes the group activity according to the input videos, and enforce the Student Network to mimic the Teacher Network during the learning process. In this way, we allocate semantics-preserving attention to different people, which adequately explores the contextual information of different people and requires no extra labelled data. Experimental results on two widely used benchmarks for group activity recognition clearly show the superior performance of our method in comparisons with the state-of-the-arts.


title: Safety of the Peripheral Administration of Vasopressor Agents

abstract: Vasopressors are an integral component of the management of septic shock and are traditionally given via a central venous catheter (CVC) due to the risk of tissue injury and necrosis if extravasated. However, the need for a CVC for the management of septic shock has been questioned, and the risk of extravasation and incidence of severe injury when vasopressors are given via a peripheral venous line (PVL) remains poorly defined. We performed a retrospective chart review of 202 patients who received vasopressors through a PVL. The objective was to describe the vasopressors administered peripherally, PVL size and location, the incidence of extravasation events, and the management of extravasation events. The primary vasopressors used were norepinephrine and phenylephrine. The most common PVL sites used were the forearm and antecubital fossa. The incidence of extravasation was 4%. All of the events were managed conservatively; none required an antidote or surgical management. Vasopressors were restarted at an...


title: Robust gesture recognition using feature pre-processing and weighted dynamic time warping

abstract: Gesture recognition is a technology often used in human-computer interaction applications. Dynamic time warping (DTW) is one of the techniques used in gesture recognition to find an optimal alignment between two sequences. Oftentimes a pre-processing of sequences is required to remove variations due to different camera or body orientations or due to different skeleton sizes between the reference gesture sequences and the test gesture sequences. We discuss a set of pre-processing methods to make the gesture recognition mechanism robust to these variations. DTW computes a dissimilarity measure by time-warping the sequences on a per sample basis by using the distance between the current reference and test sequences. However, all body joints involved in a gesture are not equally important in computing the distance between two sequence samples. We propose a weighted DTW method that weights joints by optimizing a discriminant ratio. Finally, we demonstrate the performance of our pre-processing and the weighted DTW method and compare our results with the conventional DTW and state-of-the-art.


title: Linked Document Embedding for Classification

abstract: Word and document embedding algorithms such as Skip-gram and Paragraph Vector have been proven to help various text analysis tasks such as document classification, document clustering and information retrieval. The vast majority of these algorithms are designed to work with independent and identically distributed documents. However, in many real-world applications, documents are inherently linked. For example, web documents such as blogs and online news often have hyperlinks to other web documents, and scientific articles usually cite other articles. Linked documents present new challenges to traditional document embedding algorithms. In addition, most existing document embedding algorithms are unsupervised and their learned representations may not be optimal for classification when labeling information is available. In this paper, we study the problem of linked document embedding for classification and propose a linked document embedding framework LDE, which combines link and label information with content information to learn document representations for classification. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of link and label information in the proposed framework LDE.


title: Deep View Morphing

abstract: Recently, convolutional neural networks (CNN) have been successfully applied to view synthesis problems. However, such CNN-based methods can suffer from lack of texture details, shape distortions, or high computational complexity. In this paper, we propose a novel CNN architecture for view synthesis called "Deep View Morphing" that does not suffer from these issues. To synthesize a middle view of two input images, a rectification network first rectifies the two input images. An encoder-decoder network then generates dense correspondences between the rectified images and blending masks to predict the visibility of pixels of the rectified images in the middle view. A view morphing network finally synthesizes the middle view using the dense correspondences and blending masks. We experimentally show the proposed method significantly outperforms the state-of-the-art CNN-based view synthesis method.


title: Defect Inspection in Low-Contrast LCD Images Using Hough Transform-Based Nonstationary Line Detection

abstract: In this paper, we propose a Hough transform-based method to identify low-contrast defects in unevenly illuminated images, and especially focus on the inspection of mura defects in liquid crystal display (LCD) panels. The proposed method works on 1-D gray-level profiles in the horizontal and vertical directions of the surface image. A point distinctly deviated from the ideal line of a profile can be identified as a defect one. A 1-D gray-level profile in the unevenly illuminated image results in a nonstationary line signal. The most commonly used technique for straight line detection in a noisy image is Hough transform (HT). The standard HT requires a sufficient number of points lie exactly on the same straight line at a given parameter resolution so that the accumulator will show a distinct peak in the parameter space. It fails to detect a line in a nonstationary signal. In the proposed HT scheme, the points that contribute to the vote do not have to lie on a line. Instead, a distance tolerance to the line sought is first given. Any point with the distance to the line falls within the tolerance will be accumulated by taking the distance as the voting weight. A fast search procedure to tighten the possible ranges of line parameters is also proposed for mura detection in LCD images.


title: Shrinkable, stiffness-controllable soft manipulator based on a bio-inspired antagonistic actuation principle

abstract: This paper explores a new hybrid actuation principle combining pneumatic and tendon-driven actuators for a soft robotic manipulator. The fusion of these two actuation principles leads to an overall antagonistic actuation mechanism whereby pneumatic actuation opposes tendon actuation - a mechanism commonly found in animals where muscles can oppose each other to vary joint stiffness. We are taking especially inspiration from the octopus who belongs to the class of Cephalopoda; the octopus uses its longitudinal and transversal muscles in its arms to achieve varied motion patterns; activating both sets of muscles, the octopus can control the arm stiffness over a wide range. Our approach mimics this behavior and achieves comparable motion patterns, including bending, elongation and stiffening. The proposed method combines the advantages of tendon-driven and pneumatic actuated systems and goes beyond what current soft, flexible robots can achieve: because the new robot structure is effectively an inflatable, sleeve, it can be pumped up to its fully inflated volume and, also, completely deflated and shrunk. Since, in the deflated state, it comprises just its outer “skin” and tendons, the robot can be compressed to a very small size, many times smaller when compared to its fully-inflated state. In this paper, we describe the mechanical structure of the soft manipulator. Proof-of-concept experiments focus on the robot's ability to bend, to morph from completely shrunk to entirely inflated as well as to vary its stiffness.