title: A review of speech-based bimodal recognition

abstract: Speech recognition and speaker recognition by machine are crucial ingredients for many important applications such as natural and flexible human-machine interfaces. Most developments in speech-based automatic recognition have relied on acoustic speech as the sole input signal, disregarding its visual counterpart. However, recognition based on acoustic speech alone can be afflicted with deficiencies that preclude its use in many real-world applications, particularly under adverse conditions. The combination of auditory and visual modalities promises higher recognition accuracy and robustness than can be obtained with a single modality. Multimodal recognition is therefore acknowledged as a vital component of the next generation of spoken language systems. The paper reviews the components of bimodal recognizers, discusses the accuracy of bimodal recognition, and highlights some outstanding research issues as well as possible application domains.


title: ARVIKA: augmented reality for development, production and service

abstract: The project ARVIKA, sponsored by the BMBF (ministry of education and research) and supervised by the DLR (German Aerospace Center), uses augmented reality (AR) technologies to research and create a user-oriented and system-driven support of operation procedures. It focuses on the development, production, and service of complex technical products and systems. This article aims to give an survey of the project ARVIKA. The realisation of the particular AR-applications results from the partners specified at the end of this article.


title: Wearable haptic glove using micro hydraulic system for control of construction robot system with VR environment

abstract: Over the past few decades, various haptic devices have been developed in order to control various robot systems remotely. The actuating systems for most existing haptic devices including glove type require lots of external auxiliary equipment. Because of this, the motion of the user is restricted by the length of the electric wires or pneumatic tubes attached to this equipment. A compact actuation system, including related equipment, is thus indispensable for a wearable haptic device to be truly effective. To resolve the problem of hampered motion and reach, a micro hydraulic actuating system was developed in this research. It was composed of a slim, flexible artificial muscle, a compact hydraulic module for actuating the muscle, and a micro pressure sensor for measuring without flux loss. The characteristics of the muscle were investigated for their control capacity. Once these analyses were completed, a lightweight and compact actuation system was built incorporating a glove typed wearable haptic device. By virtue of the developed micro hydraulic system, the wearable haptic glove was able to operate independently of any external equipment, and movement was completely free of any restrictions from wires or tubes. The construction robot remotely controlled needs a compact haptic device as a motion controller for it and the haptic glove we developed makes it possible to control the motion of construction robot with easy.


title: Context-Associative Hierarchical Memory Model for Human Activity Recognition and Prediction

abstract: Human activity recognition is a challenging high-level vision task, for which multiple factors, such as subject, object, and their diverse interactions, have to be considered and modeled. Current learning-based methods are limited in the capability to integrate human-level concepts into an easily extensible computational framework. Inspired by the existing human memory model, we present a context-associative approach to recognize activity with human-object interaction. The proposed system can recognize incoming visual content based on the previous experienced activities. The high-level activity is parsed into consecutive subactivities, and we build a context cluster to model the temporal relations. The semantic attributes of the subactivity are organized by a concept hierarchy. Based on the hierarchy, a series of similarity functions are defined to turn the recognition computing into retrievals over the contextual memory, similar to the auto-associative characteristics of human memory. Partially matching in retrieval and stored memory make the activity prediction possible. The dynamical evolution of the brain memory is mimicked to allow decay and reinforcement of the input information, providing a natural way to maintain data and save computational time. We evaluate our approach on three data sets: CAD-120, MHOI, and OPPORTUNITY. The proposed method demonstrates promising results compared with other state-of-the-art techniques.


title: SnapMirror: File System Based Asynchronous Mirroring for Disaster Recovery

abstract: Computerized data has become critical to the survival of an enterprise. Companies must have a strategy for recovering their data should a disaster such as a fire destroy the primary data center. Current mechanisms offer data managers a stark choice: rely on affordable tape but risk the loss of a full day of data and face many hours or even days to recover, or have the benefits of a fully synchronized on-line remote mirror, but pay steep costs in both write latency and network bandwidth to maintain the mirror. In this paper, we argue that asynchronous mirroring, in which batches of updates are periodically sent to the remote mirror, can let data managers find a balance between these extremes. First, by eliminating the write latency issue, asynchrony greatly reduces the performance cost of a remote mirror. Second, by storing up batches of writes, asynchronous mirroring can avoid sending deleted or overwritten data and thereby reduce network bandwidth requirements. Data managers can tune the update frequency to trade network bandwidth against the potential loss of more data. We present Snap-Mirror, an asynchronous mirroring technology that leverages file system snapshots to ensure the consistency of the remote mirror and optimize data transfer. We use traces of production filers to show that even updating an asynchronous mirror every 15 minutes can reduce data transferred by 30% to 80%. We find that exploiting file system knowledge of deletions is critical to achieving any reduction for no-overwrite file systems such as WAFL and LFS. Experiments on a running system show that using file system metadata can reduce the time to identify changed blocks from minutes to seconds compared to purely logical approaches. Finally, we show that using SnapMirror to update every 30 minutes increases the response time of a heavily loaded system only 22%.


title: Millimeter-Wave Antenna in Fan-Out Wafer Level Packaging for 60 GHz WLAN Application

abstract: In this paper, a 3D integration package of antenna and chip is proposed. A patch antenna array is integrated with RF chip on fan-out wafer level packaging (FOWLP) platform with two molding compound layers and four metallic layers for 60 GHz wireless local area network (WLAN) application. The patch radiator is fed by a slot in the intermediate metal layers in the package and has small crosstalk with the chip signal routing layer. The patch antenna element achieves a 10-dB input impedance bandwidth of 10.64 GHz from 56.08 to 66.72 GHz and a maximal peak realized gain of 5.48 dBi at 63 GHz. A 4×4 antenna array is formed in a small form factor of 10×10×0.25 mm2 and achieved a maximal peak realized gain of 13.61 dBi at 65 GHz.


title: uLink: Enabling User-Defined Deep Linking to App Content

abstract: Web deep links are instrumental to many fundamental user experiences such as navigating to one web page from another, bookmarking a page, or sharing it with others. Such experiences are not possible with individual pages inside mobile apps, since historically mobile apps did not have links equivalent to web deep links. Mobile deep links, introduced in recent years, still lack many important properties of web deep links. Unlike web links, mobile deep links need significant developer effort, cover a small number of predefined pages, and are defined statically to navigate to a page for a given link, but not to dynamically generate a link for a given page. We propose uLink, a novel deep linking mechanism that addresses these problems. uLink is implemented as an application library, which transparently tracks data- and UI-event-dependencies of app pages, and encodes the information in links to the pages; when a link is invoked, the information is utilized to recreate the target page quickly and accurately. uLink also employs techniques, based on static and dynamic analysis of the app, that can provide feedback to users about whether a link may break in the future due to, e.g., modifications of external resources such as a file the link depends on. We have implemented uLink on Android. Our evaluation with 34 (of 1000 most downloaded) Android apps shows that compared to existing mobile deep links, uLink requires minimal developer effort, achieves significantly higher coverage, and can provide accurate user feedback on a broken link.


title: OPAQUE: An Asymmetric PAKE Protocol Secure Against Pre-computation Attacks

abstract: Password-Authenticated Key Exchange (PAKE) protocols allow two parties that only share a password to establish a shared key in a way that is immune to offline attacks. Asymmetric PAKE (aPAKE) strengthens this notion for the more common client-server setting where the server stores a mapping of the password and security is required even upon server compromise, that is, the only allowed attack in this case is an (inevitable) offline exhaustive dictionary attack against individual user passwords. Unfortunately, most suggested aPAKE protocols (that dispense with the use of servers’ public keys) allow for pre-computation attacks that lead to the instantaneous compromise of user passwords upon server compromise, thus forgoing much of the intended aPAKE security. Indeed, these protocols use – in essential ways – deterministic password mappings or use random “salt” transmitted in the clear from servers to users, and thus are vulnerable to pre-computation attacks.


title: Gazelle: A Low Latency Framework for Secure Neural Network Inference

abstract: The growing popularity of cloud-based machine learning raises a natural question about the privacy guarantees that can be provided in such a setting. Our work tackles this problem in the context where a client wishes to classify private images using a convolutional neural network (CNN) trained by a server. Our goal is to build efficient protocols whereby the client can acquire the classification result without revealing their input to the server, while guaranteeing the privacy of the server's neural network. To this end, we design Gazelle, a scalable and low-latency system for secure neural network inference, using an intricate combination of homomorphic encryption and traditional two-party computation techniques (such as garbled circuits). Gazelle makes three contributions. First, we design the Gazelle homomorphic encryption library which provides fast algorithms for basic homomorphic operations such as SIMD (single instruction multiple data) addition, SIMD multiplication and ciphertext permutation. Second, we implement the Gazelle homomorphic linear algebra kernels which map neural network layers to optimized homomorphic matrix-vector multiplication and convolution routines. Third, we design optimized encryption switching protocols which seamlessly convert between homomorphic and garbled circuit encodings to enable implementation of complete neural network inference. We evaluate our protocols on benchmark neural networks trained on the MNIST and CIFAR-10 datasets and show that Gazelle outperforms the best existing systems such as MiniONN (ACM CCS 2017) by 20 times and Chameleon (Crypto Eprint 2017/1164) by 30 times in online runtime. Similarly when compared with fully homomorphic approaches like CryptoNets (ICML 2016) we demonstrate three orders of magnitude faster online run-time.


title: Tracheostomy Care Practices in a Simulated Setting: An Exploratory Study

abstract: Purpose/Aims:Many critically ill patients undergo a tracheostomy. Tracheostomy care is performed to maintain the airway and promote stoma skin integrity. Variation in practices may affect outcomes. The study purpose was to describe tracheostomy care practices of caregivers who perform tracheostomy c


title: Retrosplenial cortex and its role in spatial cognition

abstract: Retrosplenial cortex is a region within the posterior neocortical system, heavily interconnected with an array of brain networks, both cortical and subcortical, that is, engaged by a myriad of cognitive tasks. Although there is no consensus as to its precise function, evidence from both human and animal studies clearly points to a role in spatial cognition. However, the spatial processing impairments that follow retrosplenial cortex damage are not straightforward to characterise, leading to difficulties in defining the exact nature of its role. In this article, we review this literature and classify the types of ideas that have been put forward into three broad, somewhat overlapping classes: (1) learning of landmark location, stability and permanence; (2) integration between spatial reference frames; and (3) consolidation and retrieval of spatial knowledge (schemas). We evaluate these models and suggest ways to test them, before briefly discussing whether the spatial function may be a subset of a more gen...


title: Online nonparametric anomaly detection based on geometric entropy minimization

abstract: We consider the online and nonparametric detection of abrupt and persistent anomalies, such as a change in the regular system dynamics at a time instance due to an anomalous event (e.g., a failure, a malicious activity). Combining the simplicity of the nonparametric Geometric Entropy Minimization (GEM) method with the timely detection capability of the Cumulative Sum (CUSUM) algorithm we propose a computationally efficient online anomaly detection method that is applicable to high-dimensional datasets, and at the same time achieve a near-optimum average detection delay performance for a given false alarm constraint. We provide new insights to both GEM and CUSUM, including new asymptotic analysis for GEM, which enables soft decisions for outlier detection, and a novel interpretation of CUSUM in terms of the discrepancy theory, which helps us generalize it to the nonparametric GEM statistic. We numerically show, using both simulated and real datasets, that the proposed nonparametric algorithm attains a close performance to the clairvoyant parametric CUSUM test.


title: The role of the media in body image concerns among women: A meta-analysis of experimental and correlational studies.

abstract: Research suggests that exposure to mass media depicting the thin-ideal body may be linked to body image disturbance in women. This meta-analysis examined experimental and correlational studies testing the links between media exposure to women’s body dissatisfaction, internalization of the thin ideal, and eating behaviors and beliefs with a sample of 77 studies that yielded 141 effect sizes. The mean effect sizes were small to moderate (ds –.28, –.39, and –.30, respectively). Effects for some outcome variables were moderated by publication year and study design. The findings support the notion that exposure to media images depicting the thin-ideal body is related to body image concerns for women.


title: A New Architecture of Data Access Middleware under Grid Environment

abstract: Data sharing is one of the most important research areas in data grid. Distributed data resource and heterogeneous data schema bring difficulties to data Access and sharing. This article mainly focuses on how to deal with the heterogeneity of data schema and put forward a blueprint to solve the data access and sharing problem of heterogeneous physical data resources in the grid. To solve this problem we propose the SDB resource model which extracts three data layers from the physical data resource in order to facilitate the access and sharing of data resource.


title: Utilization and predictability in scheduling the IBM SP2 with backfilling

abstract: Scheduling jobs on the IBM SP2 system is usually done by giving each job a partition of the machine for its exclusive use. Allocating such partitions in the order that the jobs arrive (FCFS scheduling) is fair and predictable, but suffers from severe fragmentation, leading to low utilization. An alternative is to use the EASY scheduler, which uses aggressive backfilling: small jobs are moved ahead to fill in holes in the schedule, provided they do not delay the first job in the queue. The authors show that a more conservative approach, in which small jobs move ahead only if they do not delay any job in the queue, produces essentially the same benefits in terms of utilization. The conservative scheme has the added advantage that queueing times can be predicted in advance, whereas in EASY the queueing time is unbounded.


title: Analyzing Computer Vision Data — The Good, the Bad and the Ugly

abstract: In recent years, a great number of datasets were published to train and evaluate computer vision (CV) algorithms. These valuable contributions helped to push CV solutions to a level where they can be used for safety-relevant applications, such as autonomous driving. However, major questions concerning quality and usefulness of test data for CV evaluation are still unanswered. Researchers and engineers try to cover all test cases by using as much test data as possible. In this paper, we propose a different solution for this challenge. We introduce a method for dataset analysis which builds upon an improved version of the CV-HAZOP checklist, a list of potential hazards within the CV domain. Picking stereo vision as an example, we provide an extensive survey of 28 datasets covering the last two decades. We create a tailored checklist and apply it to the datasets Middlebury, KITTI, Sintel, Freiburg, and HCI to present a thorough characterization and quantitative comparison. We confirm the usability of our checklist for identification of challenging stereo situations by applying nine state-of-the-art stereo matching algorithms on the analyzed datasets, showing that hazard frames correlate with difficult frames. We show that challenging datasets still allow a meaningful algorithm evaluation even for small subsets. Finally, we provide a list of missing test cases that are still not covered by current datasets as inspiration for researchers who want to participate in future dataset creation.


title: Appearance Characterization of Linear Lambertian Objects, Generalized Photometric Stereo, and Illumination-Invariant Face Recognition

abstract: Traditional photometric stereo algorithms employ a Lambertian reflectance model with a varying albedo field and involve the appearance of only one object. In this paper, we generalize photometric stereo algorithms to handle all appearances of all objects in a class, in particular the human face class, by making use of the linear Lambertian property. A linear Lambertian object is one which is linearly spanned by a set of basis objects and has a Lambertian surface. The linear property leads to a rank constraint and, consequently, a factorization of an observation matrix that consists of exemplar images of different objects (e.g., faces of different subjects) under different, unknown illuminations. Integrability and symmetry constraints are used to fully recover the subspace bases using a novel linearized algorithm that takes the varying albedo field into account. The effectiveness of the linear Lambertian property is further investigated by using it for the problem of illumination-invariant face recognition using just one image. Attached shadows are incorporated in the model by a careful treatment of the inherent nonlinearity in Lambert's law. This enables us to extend our algorithm to perform face recognition in the presence of multiple illumination sources. Experimental results using standard data sets are presented


title: Three-Dimensional and 2.5 Dimensional Interconnection Technology: State of the Art

abstract: Three-dimensional (3D) packaging with through-silicon-vias (TSVs) is an emerging technology featuring smaller package size, higher interconnection density, and better performance; 2.5D packaging using silicon interposers with TSVs is an incremental step toward 3D packaging. Formation of TSVs and interconnection between chips and/or wafers are two key enabling technologies for 3D and 2.5D packaging, and different interconnection methods in chip-to-chip, chip-to-wafer, and wafer-to-wafer schemes have been developed. This article reviews state-of-the-art interconnection technologies reported in recent technical papers. Issues such as bump formation, assembly/bonding process, as well as underfill dispensing in each interconnection type are discussed. [DOI: 10.1115/1.4026615]


title: Object Hallucination in Image Captioning

abstract: Despite continuously improving performance, contemporary image captioning models are prone to "hallucinating" objects that are not actually in a scene. One problem is that standard metrics only measure similarity to ground truth captions and may not fully capture image relevance. In this work, we propose a new image relevance metric to evaluate current models with veridical visual labels and assess their rate of object hallucination. We analyze how captioning model architectures and learning objectives contribute to object hallucination, explore when hallucination is likely due to image misclassification or language priors, and assess how well current sentence metrics capture object hallucination. We investigate these questions on the standard image captioning benchmark, MSCOCO, using a diverse set of models. Our analysis yields several interesting findings, including that models which score best on standard sentence metrics do not always have lower hallucination and that models which hallucinate more tend to make errors driven by language priors.


title: Robot arm based flat panel CT-guided electromagnetic tracked spine interventions: phantom and animal model experiments

abstract: Purpose: ::: To evaluate accuracy and procedure times of electromagnetic tracking (EMT) in a robotic arm mounted flat panel setting using phantom and animal cadaveric models.


A Corpus of Gesture-Annotated Dialogues for Monologue-to-Dialogue Generation from Personal Narratives.


title: Asynchronous Parallel Block-Coordinate Frank-Wolfe

abstract: We study parallel and distributed Frank-Wolfe algorithms; the former on shared memory machines with mini-batching, and the latter in a delayed update framework. In both cases, we perform computations asynchronously whenever possible. We assume block-separable constraints as in Block-Coordinate Frank-Wolfe (BCFW) method (Lacoste-Julien et al., 2013), but our analysis subsumes BCFW and reveals problemdependent quantities that govern the speedups of our methods over BCFW. A notable feature of our algorithms is that they do not depend on worst-case bounded delays, but only (mildly) on expected delays, making them robust to stragglers and faulty worker threads. We present experiments on structural SVM and Group Fused Lasso, and observe significant speedups over competing state-of-the-art (and synchronous) methods.


title: Structure of the human activated spliceosome in three conformational states

abstract: During each cycle of pre-mRNA splicing, the pre-catalytic spliceosome (B complex) is converted into the activated spliceosome (Bact complex), which has a well-formed active site but cannot proceed to the branching reaction. Here, we present the cryo-EM structure of the human Bact complex in three distinct conformational states. The EM map allows atomic modeling of nearly all protein components of the U2 small nuclear ribonucleoprotein (snRNP), including three of the SF3a complex and seven of the SF3b complex. The structure of the human Bact complex contains 52 proteins, U2, U5, and U6 small nuclear RNA (snRNA), and a pre-mRNA. Three distinct conformations have been captured, representing the early, mature, and late states of the human Bact complex. These complexes differ in the orientation of the Switch loop of Prp8, the splicing factors RNF113A and NY-CO-10, and most components of the NineTeen complex (NTC) and the NTC-related complex. Analysis of these three complexes and comparison with the B and C complexes reveal an ordered flux of components in the B-to-Bact and the Bact-to-B* transitions, which ultimately prime the active site for the branching reaction.


title: Frequency-Hiding Order-Preserving Encryption

abstract: Order-preserving encryption allows encrypting data, while still enabling efficient range queries on the encrypted data. This makes its performance and functionality very suitable for data outsourcing in cloud computing scenarios, but the security of order-preserving is still debatable. We present a scheme that achieves a strictly stronger notion of security than any other scheme so far. The basic idea is to randomize the ciphertexts to hide the frequency of plaintexts. Still, the client storage size remains small, in our experiments up to 1/15 of the plaintext size. As a result, one can more securely outsource large data sets, since we can also show that our security increases with larger data sets.


title: An Empirical Comparison of Lab and Remote Usability Testing of Web Sites

abstract: This paper presents the results of two studies comparing traditional lab-based usability testing and remote Webbased usability testing of Web sites. Two sites were tested: an employee benefits site and a financial information site. The remote tests used an automated technique whereby the users participated from their normal work locations using their normal browser, and there was no real-time observation. Tasks were presented to the user in a small browser window at the top of the screen that was also used to capture their input. Results showed high correlations between lab and remote tests for the task completion data and the task time data. Subjective ratings of the sites did not correlate as well. The most critical usability issues with the sites were identified by both techniques, although each technique also uniquely uncovered other issues. In general, the results indicate that both the lab and remote tests capture very similar information about the usability of a site. Each type of test appears to offer its own advantages and disadvantages in terms of the usability issues it can uncover.


title: HCCI combustion timing control with Variable Valve Timing

abstract: Homogeneous Charge Compression Ignition (HCCI) is a promising concept for combustion engines to reduce both emissions and fuel consumption. In HCCI engines, a homogeneous air-fuel mixture auto-ignites due to compression, which is unlike traditional spark ignition or diesel engines where ignition is started with either a spark or fuel injection. HCCI combustion control is a challenging issue because there is no direct initiator of combustion in these engines. Variable Valve Timing (VVT) is one effective way to control the combustion timing in HCCI engines. VVT changes the amount of trapped residual gas and the effective compression ratio both of which have a strong effect on combustion timing. In order to control HCCI combustion, a physics based control oriented model is developed that includes the effect of trapped residual gas on combustion timing. The control oriented model is obtained by model order reduction of complex chemical kinetic reaction mechanisms. This method allows different fuels to be incorporated using a standard methodology and fills the gap between complex models with highly detailed chemical kinetics and simple black box models that have been used in model based control. The control oriented model is used to develop ignition timing PI control using simulation. The PI control modulates the trapped residual gas using variable valve timing as the actuator. The results indicate that the controller can track step changes in HCCI combustion timing.


title: The Development of Organizational Social Capital: Attributes of Family Firms

abstract: We develop and extend social capital theory by exploring the creation of organizational social capital within a highly pervasive, yet often overlooked organizational form: family firms. We argue that family firms are unique in that, although they work as a single entity, at least two forms of social capital coexist: the family's and the firm's. We investigate mechanisms that link a family's social capital to the creation of the family firm's social capital and examine how factors underlying the family's social capital affect this creation. Moreover, we identify contingency dimensions that affect these relationships and the potential risks associated with family social capital. Finally, we suggest these insights are generalizable to several other types of organizations with similar characteristics. Copyright Blackwell Publishing Ltd 2007.


title: A 10-bit 2 MS/s SAR ADC using reverse VCM-based switching scheme

abstract: This paper presents a successive-approximation-register (SAR) analogue-to-digital converter (ADC) using a tri-level switching scheme named as reverse VCM-based scheme which maintains good linearity without any driving and accuracy requirements on VCM. A 10-bit SAR ADC is designed in a 0.18 CMOS technology. With a unit capacitor size of 17.2 fF, the ADC consumes 41.9 μW from a 1.8 V voltage supply. The measured signal-to-noise-plus-distortion ratio (SNDR) is 59.6 dB at 2 MS/s. The figure-of-merit (FOM) is 26.9 fJ/conv.-step.


title: Camera and lidar fusion for pedestrian detection

abstract: The aim of this work is to propose a fusion procedure based on lidar and camera to solve the pedestrian detection problem in autonomous driving. Current pedestrian detection algorithms have focused on improving the discriminability of 2D features that capture the pedestrian appearance, and on using various classifier architectures. However, less focus on exploiting the 3D structure of object has limited the pedestrian detection performance and practicality. To tackle these issues, a lidar subsystem is applied here in order to extract object structure features and train a SVM classifier, reducing the number of candidate windows that are tested by a state-of-the-art pedestrian appearance classifier. Additionally, we propose a probabilistic framework to fuse pedestrian detection given by both subsystems. With the proposed framework, we have achieved state-of-the-art performance at 20fps on our own pedestrian dataset gathered in a challenging urban scenario.


title: Perceived organizational support and police performance: The moderating influence of socioemotional needs.

abstract: Police patrol officers were surveyed to investigate how the strength of socioemotional needs affects the relationship between perceived organizational support (POS) and work performance. The association of POS with driving-under-the-influence arrests and speeding citations generally increased with strength of the needs for esteem, affiliation, emotional support, and social approval. Patrol officers with strong socioemotional needs, but not those with weak needs, showed a positive relationship between POS and performance. The findings are consistent with social exchange views that maintain (a) work effort is encouraged by the receipt of socioemotional resources, (b) POS fulfills a variety of socioemotional needs, and (c) the value of POS and the obligation to reciprocate with high performance increase with the strength of socioemotional needs.


title: Web ontology language (OWL) and semantic web

abstract: The OWL Web Ontology Language is designed for use by applications that need to process the content of information instead of just presenting information to humans. OWL facilitates greater machine interpretability of Web content than that supported by XML, RDF, and RDF Schema (RDF-S) by providing additional vocabulary along with a formal semantics. OWL has three increasingly-expressive sublanguages: OWL Lite, OWL DL, and OWL Full. This document is written for readers who want a first impression of the capabilities of OWL. It provides an introduction to OWL by informally describing the features of each of the sublanguages of OWL. Some knowledge of RDF Schema is useful for understanding this document, but not essential. After this document, interested readers may turn to the OWL Guide for more detailed descriptions and extensive examples on the features of OWL. The normative formal definition of OWL can be found in the OWL Semantics and Abstract Syntax.


title: A deep learning based stock trading model with 2-D CNN trend detection

abstract: The success of convolutional neural networks in the field of computer vision has attracted the attention of many researchers from other fields. One of the research areas in which neural networks is actively used is financial forecasting. In this paper, we propose a novel method for predicting stock price movements using CNN. To avoid the high volatility of the market and to maximize the profit, ETFs are used as primary financial assets. We extract commonly used trend indicators and momentum indicators from financial time series data and use these as our features. Adopting a sliding window approach, we generate our images by taking snapshots that are bounded by the window over a daily period. We then perform daily predictions, namely, regression for predicting the ETF prices and classification for predicting the movement of the prices on the next day, which can be modified to estimate weekly or monthly trends. To increase the number of images, we use numerous ETFs. Finally, we evaluate our method by performing paper trading and calculating the final capital. We also compare our method's performance to commonly used classical trading strategies. Our results indicate that we can predict the next day's prices with 72% accuracy and end up with 5:1 of our initial capital, taking realistic values of transaction costs into account.


title: Evaluating bag-of-visual-words representations in scene classification

abstract: Based on keypoints extracted as salient image patches, an image can be described as a "bag of visual words" and this representation has been used in scene classification. The choice of dimension, selection, and weighting of visual words in this representation is crucial to the classification performance but has not been thoroughly studied in previous work. Given the analogy between this representation and the bag-of-words representation of text documents, we apply techniques used in text categorization, including term weighting, stop word removal, feature selection, to generate image representations that differ in the dimension, selection, and weighting of visual words. The impact of these representation choices to scene classification is studied through extensive experiments on the TRECVID and PASCAL collection. This study provides an empirical basis for designing visual-word representations that are likely to produce superior classification performance.


title: HF-BAND WIRELESS POWER TRANSFER SYSTEM: CONCEPT, ISSUES, AND DESIGN

abstract: High-frequency (HF) band wireless power transfer systems ofier the promise of cutting the last cord, allowing users to seamlessly recharge mobile devices as easily as wireless communication. Yet there are still many technical issues that need to be overcome. Among them, one of the most di-cult problems is maintaining impedance match over a short range, where the distance between a transmitter and receiver could vary. In this paper, the efiect of impedance mismatch of a HF- band wireless power transfer system is carefully investigated and two compensation methods are suggested to overcome this within a short range, where frequent impedance mismatch can occur. Each method has pros and cons. In order to verify the feasibility of the proposed methods, HF-band wireless power transfer systems, with a pair of rectangular loop resonators, were designed. The e-ciency and input impedance variation were simulated and measured. From these results, proposed methods show enhanced e-ciency performance than a typical wireless power transfer system without any compensation circuits.


title: Estimation of frequency and its rate of change for applications in power systems

abstract: A new method for estimation of power frequency and its rate of change is presented. Unlike conventional methods which are based on the concept of linearization, the proposed scheme accommodates the inherent nonlinearity of the frequency estimation problem. This makes the method capable of providing a fast and accurate estimate of the frequency when its deviation from the nominal value is incremental or large. The estimator is based on a newly developed quadrature phase-locked loop concept. The method is highly immune to noise and distortions. The estimator performance is robust with respect to the parameters of its structure. Structural simplicity and performance robustness are other salient features of the method.


title: Contactless haptic feedback: state of the art

abstract: This paper discusses some of the recent advances in contactless haptic feedback. We mainly discuss two research methods to produce haptic feedback in 3D space: Air-jet and ultrasound. We discuss and compare technical basics of each technology, and then give a literature review of some of the research done that is closely related to this field. This paper also surveys the stages of the design and implementation of airborne ultrasonic tactile displays (AUTD) by researchers in the University of Tokyo, as well as an application of this research done in the University of Bristol. A comparison is presented showing the main advances in the Tokyo research and the technical tests and implementation differences. A discussion follows covering possible improvements and safety issues on the contactless haptic feedback research in general. We show comments and drawbacks of the current technology. For future work in the field of mid-air haptic feedback, we propose a design method to build a "Touchable Avatar", which is a holographic display with contactless haptic feedback properties. Finally, a conclusion is provided including an outlook of the future applications in the field of contactless haptic feedback.


title: Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

abstract: BACKGROUND ::: Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis. ::: ::: ::: OBJECTIVE ::: The objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges. ::: ::: ::: METHODS ::: A review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review. ::: ::: ::: RESULTS ::: We propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results. ::: ::: ::: CONCLUSIONS ::: We obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients' quality of life.


title: BTS guidelines for the insertion of a chest drain

abstract: These guidelines have been replaced by BTS Pleural Disease Guideline 2010 Superseded By BTS Pleural Disease Guideline 2010: BTS Guidelines for the Management of Pleural Disease. Thorax 2003 May; 58(Suppl 2): 1–59.


title: Rekindling network protocol innovation with user-level stacks

abstract: Recent studies show that more than 86% of Internet paths allow well-designed TCP extensions, meaning that it is still possible to deploy transport layer improvements despite the existence of middleboxes in the network. Hence, the blame for the slow evolution of protocols (with extensions taking many years to nbecome widely used) should be placed on end systems. In this paper, we revisit the case for moving protocols stacks up into user space in order to ease the deployment of new protocols, extensions, or performance optimizations. We present MultiStack, operating system support for user-level protocol stacks. MultiStack runs within commodity operating systems, can concurrently host a large number of isolated stacks, has a fall-back path to the legacy host stack, and is able to process packets at rates of 10Gb/s. We validate our design by showing that our mux/demux layer can validate and switch packets at line rate (up to 14.88 Mpps) on a 10 Gbit port using 1-2 cores, and that a proof-of-concept HTTP server running over a basic userspace TCP outperforms by 18-90% both the same server and nginx running over the kernel's stack.


title: Input-series output-parallel full-bridge DC-DC converter for high power applications

abstract: Input-series and output-parallel (ISOP) connected DC-DC converters are suitable for high input voltage and high output current applications by using a low voltage rating power switches. This paper proposes an input-series-output-parallel Full-Bridge DC-DC converter with low-voltage switches under high frequency operation with low losses switching, low harmonic spikes with high efficiency and high power density. In this proposed work, Arduino mega microcontroller has been used to generate Pulse Width Modulation (PWM) for ISOP configuration. Under different duty cycle PWM and switching frequency, the simulation and experimental results with appropriate theoretical analysis of an ISOP full-bridge converter topology are presented.


title: How translation alters sentiment

abstract: Sentiment analysis research has predominantly been on English texts. Thus there exist many sentiment resources for English, but less so for other languages. Approaches to improve sentiment analysis in a resource-poor focus language include: (a) translate the focus language text into a resource-rich language such as English, and apply a powerful English sentiment analysis system on the text, and (b) translate resources such as sentiment labeled corpora and sentiment lexicons from English into the focus language, and use them as additional resources in the focus-language sentiment analysis system. In this paper we systematically examine both options. We use Arabic social media posts as stand-in for the focus language text. We show that sentiment analysis of English translations of Arabic texts produces competitive results, w.r.t. Arabic sentiment analysis. We show that Arabic sentiment analysis systems benefit from the use of automatically translated English sentiment lexicons. We also conduct manual annotation studies to examine why the sentiment of a translation is different from the sentiment of the source word or text. This is especially relevant for building better automatic translation systems. In the process, we create a state-of-the-art Arabic sentiment analysis system, a new dialectal Arabic sentiment lexicon, and the first Arabic-English parallel corpus that is independently annotated for sentiment by Arabic and English speakers.


title: Computational design of reconfigurables

abstract: A reconfigurable is an object or collection of objects whose transformation between various states defines its functionality or aesthetic appeal. For example, consider a mechanical assembly composed of interlocking pieces, a transforming folding bicycle, or a space-saving arrangement of apartment furniture. Unlike traditional computer-aided design of static objects, specialized tools are required to address problems unique to the computational design and revision of objects undergoing rigid transformations. Collisions and interpenetrations as objects transition from one configuration to another prevent the physical realization of a design. We present a software environment intended to support fluid interactive design of reconfigurables, featuring tools that identify, visualize, monitor and resolve infeasible configurations. We demonstrate the versatility of the environment on a number of examples spanning mechanical systems, urban dwelling, and interlocking puzzles, some of which we then realize via additive manufacturing. ::: Spatial-temporal information about collisions between objects is presented to the designer according to a cascading order of precedence. A designer may quickly determine when, and then where, and then how objects are colliding. This precedence guides the design and implementation of our four-dimensional spacetime bounding volume hierarchy for interactive-rate collision detection. On screen, the designer experiences a suite of interactive visualization and monitoring tools during editing: timeline notifications of new collisions, picture-in-picture windows for tracking collisions and suggestive hints for contact resolution. Contacts too tedious to remove manually can be eliminated automatically via our proposed constrained numerical optimization and swept-volume carving.


title: Real-time EEG-based emotion recognition for music therapy

abstract: Music could change our emotions, could have an influence on our mood, and finally could affect our health. Music therapy is one of the oldest methods used for treating some diseases. Since music therapy is proved to be the helpful approach, we proposed to combine music therapy process with the real-time EEG-based human emotion recognition algorithm. By this, we could identify the user’s current emotional state, and based on such neurofeedback we could adjust the music therapy to the patient’s needs. The proposed emotion recognition algorithm could recognize in real-time six emotions such as fear, frustrated, sad, happy, pleasant, and satisfied. As the algorithm is based on an Arousal-Valence emotion model, it has a potential to recognize all emotions that could be defined by the 2-dimensional model. The experiments on emotion induction with sound stimuli from International Affective Digitized Sounds (IADS) database and with music stimuli and implemented questionnaire were proposed and realized. In this paper, we proposed a general EEG-enabled music therapy algorithm. It allows us to adapt the therapy to the predefined time of the treatment and adjust the music therapy session to the current emotional state of the user in a way as an experienced music therapist works.


title: ThrottleBot - Performance without Insight

abstract: Large scale applications are increasingly built by composing sets of microservices. In this model the functionality for a single application might be split across 100s or 1000s of microservices. Resource provisioning for these applications is complex, requiring administrators to understand both the functioning of each microservice, and dependencies between microservices in an application. In this paper we present ThrottleBot, a system that automates the process of determining what resource when allocated to which microservice is likely to have the greatest impact on application performance. We demonstrate the efficacy of our approach by applying ThrottleBot to both synthetic and real world applications. We believe that ThrottleBot when combined with existing microservice orchestrators, e.g., Kubernetes, enables push-button deployment of web scale applications.


title: Gesture Modeling by Hanklet-Based Hidden Markov Model

abstract: In this paper we propose a novel approach for gesture modeling. We aim at decomposing a gesture into sub-trajectories that are the output of a sequence of atomic linear time invariant (LTI) systems, and we use a Hidden Markov Model to model the transitions from the LTI system to another. For this purpose, we represent the human body motion in a temporal window as a set of body joint trajectories that we assume are the output of an LTI system. We describe the set of trajectories in a temporal window by the corresponding Hankel matrix (Hanklet), which embeds the observability matrix of the LTI system that produced it. We train a set of HMMs (one for each gesture class) with a discriminative approach. To account for the sharing of body motion templates we allow the HMMs to share the same state space. We demonstrate by means of experiments on two publicly available datasets that, even with just considering the trajectories of the 3D joints, our method achieves state-of-the-art accuracy while competing well with methods that employ more complex models and feature representations.


title: EPOBF: Energy Efficient Allocation of Virtual Machines in High Performance Computing Cloud

abstract: Cloud computing has become more popular in provision of computing resources under virtual machine (VM) abstraction for high performance computing (HPC) users to run their applications. A HPC cloud is such cloud computing environment. One of challenges of energy efficient resource allocation for VMs in HPC cloud is tradeoff between minimizing total energy consumption of physical machines (PMs) and satisfying Quality of Service (e.g. performance). On one hand, cloud providers want to maximize their profit by reducing the power cost (e.g. using the smallest number of running PMs). On the other hand, cloud customers (users) want highest performance for their applications. In this paper, we focus on the scenario that scheduler does not know global information about user jobs and user applications in the future. Users will request shortterm resources at fixed start times and non interrupted durations. We then propose a new allocation heuristic (named Energy-aware and Performance per watt oriented Bestfit (EPOBF)) that uses metric of performance per watt to choose which most energy-efficient PM for mapping each VM (e.g. maximum of MIPS per Watt). Using information from Feitelson's Parallel Workload Archive to model HPC jobs, we compare the proposed EPOBF to state of the art heuristics on heterogeneous PMs (each PM has multicore CPU). Simulations show that the EPOBF can reduce significant total energy consumption in comparison with state of the art allocation heuristics.


title: Design of broadband planar array composed of 2×2 slotted cavities fed by E-plane waveguide parallel-feeding circuit in millimeter-wave band

abstract: An 8×8 element array antenna is composed of the E-plane waveguide parallel-feeding circuit in the lower layer and the 2×2 slotted cavities in the upper layer. The E-plane waveguide parallel-feeding circuit consists of two plates and assembled at the center of the broad wall of the waveguide to achieve low-loss property and wide bandwidth simultaneously. The 2×2 slotted cavities are designed to obtain lower Q factor and an ideal radiation patter in combination with the array factor of the cavity arrangement. The simulated bandwidth with reflection lower than −15 dB were wider than 5 GHz. The sidelobe level was lower than −12 dB and the gain was 26.0 dBi at 73.5 GHz.


title: Big Data Analytics for System Stability Evaluation Strategy in the Energy Internet

abstract: With the significant improvements in the Energy Internet, we have witnessed the explosion of multisource energy big data, whose characteristics of vast volume, fast velocity, and diverse variety not only formulate an essential infrastructure of the Energy Internet, but also bring threats to the system's stability. In this paper, we concern with the system-level stability issues in the Energy Internet and study how to maintain a stable and healthy energy network environment. To this end, we propose a system-level stability evaluation model in the Energy Internet based on a critical energy function to explore small disturbance stability region (SDSR), where SDSR can be acquired via estimating the operational data threshold of distributed generations. The threshold is estimated based on energy consumption rather than equilibrium nodes, which applies the energy function theory and reduces the computation complexity. Moreover, in our proposed model, we add the big data approximate analytics algorithm into hyperplane fitting to optimize and analyze the SDSR. Simulation results on SDSR in a single dominant oscillation mode and multiple dominant oscillation mode have demonstrated the advantages and superiority of our proposed method over the prior schemes.


title: Glutamate systems in cocaine addiction.

abstract: All addictive drugs facilitate dopamine transmission, and determining the role of dopamine has been the predominant focus of biomedical research in addiction for 20 years. Newer data and hypotheses have begun to shift our focus to involvement of cortex and corticofugal glutamate projections. The rationale for shifting focus to glutamate ranges from evidence showing that cortical activity is altered in addicts to data from animal models demonstrating drug-induced changes in the function of proteins that regulate pre- and postsynaptic glutamate neurotransmission. Recent studies have particularly focused on involvement of a circuit that includes glutamate projections from the prefrontal cortex to the nucleus accumbens.


title: Exploring the Semantic Gap for Movie Recommendations

abstract: In the last years, there has been much attention given to the semantic gap problem in multimedia retrieval systems. Much effort has been devoted to bridge this gap by building tools for the extraction of high-level, semantics-based features from multimedia content, as low-level features are not considered useful because they deal primarily with representing the perceived content rather than the semantics of it. In this paper, we explore a different point of view by leveraging the gap between low-level and high-level features. We experiment with a recent approach for movie recommendation that extract low-level Mise-en-Scene features from multimedia content and combine it with high-level features provided by the wisdom of the crowd. To this end, we first performed an offline performance assessment by implementing a pure content-based recommender system with three different versions of the same algorithm, respectively based on (i) conventional movie attributes, (ii) mise-en-scene features, and (iii) a hybrid method that interleaves recommendations based on movie attributes and mise-en-scene features. In a second study, we designed an empirical study involving 100 subjects and collected data regarding the quality perceived by the users. Results from both studies show that the introduction of mise-en-scene features in conjunction with traditional movie attributes improves both offline and online quality of recommendations.


title: 1 SPITFIRE: Towards a Semantic Web of Things

abstract: The developed world is awash with sensors. However, they are typically locked into unimodal closed systems. To unleash their full potential, access to sensors should be opened such that their data and services can be integrated with data and services available in other information systems, facilitating novel applications and services that are based on the state of the real world. We describe our vision and architecture of a Semantic Web of Things: a service infrastructure that makes the deployment and use of semantic applications involving Internet-connected sensors almost as easy as building, searching, and reading a web page today.


title: Deep Bilateral Learning for Real-Time Image Enhancement

abstract: Performance is a critical challenge in mobile image processing. Given a reference imaging pipeline, or even human-adjusted pairs of images, we seek to reproduce the enhancements and enable real-time evaluation. For this, we introduce a new neural network architecture inspired by bilateral grid processing and local affine color transforms. Using pairs of input/output images, we train a convolutional neural network to predict the coefficients of a locally-affine model in bilateral space. Our architecture learns to make local, global, and content-dependent decisions to approximate the desired image transformation. At runtime, the neural network consumes a low-resolution version of the input image, produces a set of affine transformations in bilateral space, upsamples those transformations in an edge-preserving fashion using a new slicing node, and then applies those upsampled transformations to the full-resolution image. Our algorithm processes high-resolution images on a smartphone in milliseconds, provides a real-time viewfinder at 1080p resolution, and matches the quality of state-of-the-art approximation techniques on a large class of image operators. Unlike previous work, our model is trained off-line from data and therefore does not require access to the original operator at runtime. This allows our model to learn complex, scene-dependent transformations for which no reference implementation is available, such as the photographic edits of a human retoucher.


Frequency analysis of attribute identification


title: From dynamic movement primitives to associative skill memories

abstract: In recent years, research on movement primitives has gained increasing popularity. The original goals of movement primitives are based on the desire to have a sufficiently rich and abstract representation for movement generation, which allows for efficient teaching, trial-and-error learning, and generalization of motor skills (Schaal 1999). Thus, motor skills in robots should be acquired in a natural dialog with humans, e.g., by imitation learning and shaping, while skill refinement and generalization should be accomplished autonomously by the robot. Such a scenario resembles the way we teach children and connects to the bigger question of how the human brain accomplishes skill learning. In this paper, we review how a particular computational approach to movement primitives, called dynamic movement primitives, can contribute to learning motor skills. We will address imitation learning, generalization, trial-and-error learning by reinforcement learning, movement recognition, and control based on movement primitives. But we also want to go beyond the standard goals of movement primitives. The stereotypical movement generation with movement primitives entails predicting of sensory events in the environment. Indeed, all the sensory events associated with a movement primitive form an associative skill memory that has the potential of forming a most powerful representation of a complete motor skill.


title: Decomposition instead of self-composition for proving the absence of timing channels

abstract: We present a novel approach to proving the absence of timing channels. The idea is to partition the programâ s execution traces in such a way that each partition component is checked for timing attack resilience by a time complexity analysis and that per-component resilience implies the resilience of the whole program. We construct a partition by splitting the program traces at secret-independent branches. This ensures that any pair of traces with the same public input has a component containing both traces. Crucially, the per-component checks can be normal safety properties expressed in terms of a single execution. Our approach is thus in contrast to prior approaches, such as self-composition, that aim to reason about multiple (kâ � 2) executions at once. We formalize the above as an approach called quotient partitioning, generalized to any k-safety property, and prove it to be sound. A key feature of our approach is a demand-driven partitioning strategy that uses a regex-like notion called trails to identify sets of execution traces, particularly those influenced by tainted (or secret) data. We have applied our technique in a prototype implementation tool called Blazer, based on WALA, PPL, and the brics automaton library. We have proved timing-channel freedom of (or synthesized an attack specification for) 24 programs written in Java bytecode, including 6 classic examples from the literature and 6 examples extracted from the DARPA STAC challenge problems.


title: Conceptual Performance of a Satellite Borne, Wide Swath Synthetic Aperture Radar

abstract: A satellite borne synthetic aperture radar can image a wide swath in the order of 700 km with one-look 100-m resolution. If the design meets the ambiguity constraints at the far edge of the swath, the maximum swath width is independent of both radar wavelength and shape of the physical antenna aperture. The antenna pattern can be a pencil beam scanned in the elevation plane, or a fan beam formed by a long antenna. The scanning pencil beam antenna may be a phased array or multiple-feed reflector which may be more practical than a long antenna to image a wide swath. Design performance trade computations are presented involving resolution, swath width, antenna area, average transmitter power and digital data rate.


title: Active Complex Event Processing: Applications in Real-Time Health Care

abstract: Our analysis of many real-world event based applications has revealed that existing Complex Event Processing technology (CEP), while effective for efficient pattern matching on event stream, is limited in its capability of reacting in realtime to opportunities and risks detected or environmental changes. We are the first to tackle this problem by providing active rule support embedded directly within the CEP engine, henceforth called Active Complex Event Processing technology, or short, Active CEP. We design the Active CEP model and associated rule language that allows rules to be triggered by CEP system state changes and correctly executed during the continuous query process. Moreover we design an Active CEP infrastructure, that integrates the active rule component into the CEP kernel, allowing fine-grained and optimized rule processing. We demonstrate the power of Active CEP by applying it to the development of a collaborative project with UMass Medical School, which detects potential threads of infection and reminds healthcare workers to perform hygiene precautions in real-time.


title: Parametric estimation of distances with a 122 GHz FMCW radar system

abstract: Industrial applications for frequency modulated continuous wave (FMCW) radar systems often require the separation of close targets, which is limited by the available distance resolution of the radar. In general, for the evaluation of FMCW radar signals a fast Fourier transformation (FFT) based method is used. However, the FFT inherently limits the available distance resolution in dependence of the bandwidth. To overcome this limitation, super resolution algorithms or parametric methods can be used. A 122 GHz FMCW radar with a bandwidth of 1 GHz is used to take measurements of two targets, below the FFT limited distance resolution (150 mm) of the FMCW radar. It is shown by using an expectation maximization based algorithm that it is still possible to distinguish between both targets over the whole measurement distance. The maximum distance error remains in the millimeter range as long as the targets are separated by at least 50 mm, which equals an improved distance resolution of a factor 3 compared to the FFT. Furthermore, the influence of model order errors on the expectation maximization algorithm are analyzed.


title: A comparison of SVM and HMM classifiers in the off-line signature verification

abstract: The SVM is a new classification technique in the field of statistical learning theory which has been applied with success in pattern recognition applications like face and speaker recognition, while the HMM has been found to be a powerful statistical technique which is applied to handwriting recognition and signature verification. This paper reports on a comparison of the two classifiers in off-line signature verification. For this purpose, an appropriate learning and testing protocol was created to observe the capability of the classifiers to absorb intrapersonal variability and highlight interpersonal similarity using random, simple and simulated forgeries.


title: Computationally Efficient Bayesian Learning of Gaussian Process State Space Models

abstract: Gaussian processes allow for flexible specification of prior assumptions of unknown dynamics in state space models. We present a procedure for efficient Bayesian learning in Gaussian process state space models, where the representation is formed by projecting the problem onto a set of approximate eigenfunctions derived from the prior covariance structure. Learning under this family of models can be conducted using a carefully crafted particle MCMC algorithm. This scheme is computationally efficient and yet allows for a fully Bayesian treatment of the problem. Compared to conventional system identification tools or existing learning methods, we show competitive performance and reliable quantification of uncertainties in the model.


title: ICDAR 2013 Robust Reading Competition

abstract: This report presents the final results of the ICDAR 2013 Robust Reading Competition. The competition is structured in three Challenges addressing text extraction in different application domains, namely born-digital images, real scene images and real-scene videos. The Challenges are organised around specific tasks covering text localisation, text segmentation and word recognition. The competition took place in the first quarter of 2013, and received a total of 42 submissions over the different tasks offered. This report describes the datasets and ground truth specification, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods.


title: Wideband and High-Gain Composite Cavity-Backed Crossed Triangular Bowtie Dipoles for Circularly Polarized Radiation

abstract: A composite cavity-backed antenna excited by crossed triangular bowtie dipoles is proposed and investigated for circular polarization (CP) applications. It is fed by a novel balun, i.e., a transition from a microstrip line to double slot lines, providing symmetrical electric field distributions at the feeding port. Measurements of an optimized antenna prototype show that it can achieve an impedance bandwidth of over 57.6% for SWR ≤ 2, a 3-dB axial-ratio bandwidth of 39%, a broadside gain of 8-10.7 dBi, and symmetrical radiation patterns over the whole operating band. The operating principles of the proposed antenna are analyzed carefully and found quite different from crossed thin-wire dipoles with very weak coupling. Problems in the feeding balun, greatly deteriorating the CP performance at the resonance, are clearly addressed and solved. Detailed parametric studies are given in the final part of this paper.


title: Firing Circuit for Three-Phase Thyristor-Bridge Rectifier

abstract: Existing firing schemes for the firing of three-phase SCR bridge rectifiers used for industrial applications employ equidistant firing pulses. Mostly they consist of six identical phase control circuits. In this paper a compact scheme using minimum integrated circuit components is described. It has a fast response for triggering angle correction and gives a full range control of voltage.


title: A magnifier tool for video data

abstract: We describe an interface prototype, the Hierarchical Video Magnifier, which allows users to work with a video source at fine-levels of detail while maintaining an awareness of temporal context. The technique allows the user to recursively magnify the temporal resolution of a video source while preserving the levels of magnification in a spatial hierarchy. We discuss how the ability to inspect and manipulate hierarchical views of temporal magnification affords a powerful tool for navigating, analyzing and editing video streams.


title: Performance characterization and acceleration of in-memory file systems for Hadoop and Spark applications on HPC clusters

abstract: For data-intensive computing, the low throughput of the existing disk-bound storage systems is a major bottleneck. Recent emergence of the in-memory file systems with heterogeneous storage support mitigates this problem to a great extent. Parallel programming frameworks, e.g. Hadoop MapReduce and Spark are increasingly being run on such high-performance file systems. However, no comprehensive study has been done to analyze the impacts of the in-memory file systems on various Big Data applications. This paper characterizes two file systems in literature, Tachyon [17] and Triple-H [13] that support in-memory and heterogeneous storage, and discusses the impacts of these two architectures on the performance and fault tolerance of Hadoop MapReduce and Spark applications. We present a complete methodology for evaluating MapReduce and Spark workloads on top of in-memory file systems and provide insights about the interactions of different system components while running these workloads. We also propose advanced acceleration techniques to adapt Triple-H for iterative applications and study the impact of different parameters on the performance of MapReduce and Spark jobs on HPC systems. Our evaluations show that, although Tachyon is 5x faster than HDFS for primitive operations, Triple-H performs 47% and 2.4x better than Tachyon for MapReduce and Spark workloads, respectively. Triple-H also accelerates K-Means by 15% over HDFS and 9% over Tachyon.


title: Audit Log Management in MongoDB

abstract: In the past few years, web-based applications and their data management needs have changed dramatically. Relational databases are often being replaced by other viable alternatives, such as NoSQL databases, for reasons of scalability and heterogeneity. MongoDB, a NoSQL database, is an agile database built for scalability, performance and high availability. It can be deployed in single server environment and also on complex multi-site architectures. MongoDB provides high performance for read and write operations by leveraging in-memory computing. Although researchers have motivated the need for MongoDB, not much appears in the area of log management. Efficient log management techniques are needed for various reasons including security, accountability, and improving the performance of the system. Towards this end, we analyze the different logging methods offered by MongoDB and compare them to the NIST standard. Our analysis indicates that profiling and mongosniff are useful for log management and we present a simple model that combines the two techniques.


title: Post-quantum cryptography

abstract: Cryptography is essential for the security of online communication, cars and implanted medical devices. However, many commonly used cryptosystems will be completely broken once large quantum computers exist. Post-quantum cryptography is cryptography under the assumption that the attacker has a large quantum computer; post-quantum cryptosystems strive to remain secure even in this scenario. This relatively young research area has seen some successes in identifying mathematical operations for which quantum algorithms offer little advantage in speed, and then building cryptographic systems around those. The central challenge in post-quantum cryptography is to meet demands for cryptographic usability and flexibility without sacrificing confidence.


title: Damage detection from aerial images via convolutional neural networks

abstract: This paper explores the effective use of Convolutional Neural Networks (CNNs) in the context of washed-away building detection from pre- and post-tsunami aerial images. To this end, we compile a dedicated, labeled aerial image dataset to construct models that classify whether a building is washed-away. Each datum in the set is a pair of pre- and post-tsunami image patches and encompasses a target building at the center of the patch. Using this dataset, we comprehensively evaluate CNNs from a practical-application viewpoint, e.g., input scenarios (pre-tsunami images are not always available), input scales (building size varies) and different configurations for CNNs. The experimental results show that our CNN-based washed-away detection system achieves 94–96% classification accuracy across all conditions, indicating the promising applicability of CNNs for washed-away building detection.


title: Adoption of agile methodology in software development

abstract: As adopting Agile software development becomes a trend, there is a need for a more structured definition of what is Agile and what is a high-level of Agile maturity. Traditional development methodologies rely on documents to record and pass on knowledge from one specialist to the next. Feedback cycles are, in many cases, too long or even nonexistent. Agile principles emphasize building working software that people can get hands on quickly, versus spending a lot of time writing specifications up front. Agile development focuses on cross-functional teams empowered to make decisions, versus big hierarchies and splitting by function. It also focuses on rapid iteration, with continuous customer input along the way. This paper deals with Agile methodology and scaling. The special highlight is put on people investigating their contribution in Agile approach success. Some reflections after using Agile in our own organization are also presented.


title: Reactive Power Control for Single-Phase Grid-Tie Inverters Using Quasi-Sinusoidal Waveform

abstract: This paper presents a reactive power control technique for single-phase Photovoltaic (PV) inverters, especially unfolding inverter. The proposed system retains the benefit of the unfolding inverters having low BOM cost and semiconductor losses, and tackles the drawback of the standard unfolding inverter not having capability of reactive power injection. It is important to note that reactive power delivery is mandatory for PV inverters according to the recent announced regulations. The concept is based on changing the shape of the grid current waveform but keeping the same zero crossing points as in the unity power factor condition. The current waveform is governed by real power and reactive power, at the price of an acceptable deformation. The operating principles of the proposed technique and mathematical derivations of the grid current function are provided in the paper. Experimental results in a grid-tie inverter prototype have shown a good agreement with the derived theory, and they confirm the feasibility of using the proposed technique in grid-tie inverters.


title: A survey of cyber security management in industrial control systems

abstract: Contemporary industrial control systems no longer operate in isolation, but use other networks (e.g., corporate networks and the Internet) to facilitate and improve business processes. The consequence of this development is the increased exposure to cyber threats. This paper surveys the latest methodologies and research for measuring and managing this risk. A dearth of industrial-control-system-specific security metrics has been identified as a barrier to implementing these methodologies. Consequently, an agenda for future research on industrial control system security metrics is outlined. The "functional assurance" concept is also introduced to deal with fail-safe and fail-secure industrial control system operations.


title: Human movement analysis using stereophotogrammetry: Part 1: theoretical background

abstract: This paper sets the stage for a series of reviews dealing with the problems associated with the reconstruction and analysis of in vivo skeletal system kinematics using optoelectronic stereophotogrammetric data. Instantaneous bone position and orientation and joint kinematic variable estimations are addressed in the framework of rigid body mechanics. The conceptual background to these exercises is discussed. Focus is placed on the experimental and analytical problem of merging the information relative to movement and that relative to the morphology of the anatomical body parts of interest. The various global and local frames that may be used in this context are defined. Common anatomical and mathematical conventions that can be used to describe joint kinematics are illustrated in a comparative fashion. The authors believe that an effort to systematize the different theoretical and experimental approaches to the problems involved and related nomenclatures, as currently reported in the literature, is needed to facilitate data and knowledge sharing, and to provide renewed momentum for the advancement of human movement analysis.


title: Adaptive Intrusion Detection Using Machine Learning

abstract: This paper applies PCA for feature selection with Naive Bayes for classification in order to build a network intrusion detection system. For experimental analysis, KDDCup 1999 intrusion detection benchmark dataset have been used. The 2 class classification is performed. The experimental results show that the proposed approach is very accurate with low false positive rate and takes less time in comparison to other existing approaches while building an efficient network intrusion detection system.


title: Scan Primitives for GPU Computing

abstract: The scan primitives are powerful, general-purpose data-parallel primitives that are building blocks for a broad range of applications. We describe GPU implementations of these primitives, specifically an efficient formulation and implementation of segmented scan, on NVIDIA GPUs using the CUDA API. Using the scan primitives, we show novel GPU implementations of quicksort and sparse matrix-vector multiply, and analyze the performance of the scan primitives, several sort algorithms that use the scan primitives, and a graphical shallow-water fluid simulation using the scan framework for a tridiagonal matrix solver.


title: Using Multiple Databases for Training in Emotion Recognition: To Unite or to Vote

abstract: We present an extensive study on the performance of data agglomeration and decision-level fusion for robust cross-corpus emotion recognition. We compare joint training with multiple databases and late fusion of classifiers trained on single databases, employing six frequently used corpora of natural or elicited emotion, namely ABC, AVIC, DES, eNTERFACE, SAL, VAM, and three classifiers i. e. SVM, Random Forests, Na¨ Bayes to best cover for singular effects. On average over classifier and database, data agglomeration and majority voting deliver relative improvements of unweighted accuracy by 9.0 % and 4.8 %, respectively, over single-database cross-corpus classification of arousal, while majority voting performs best for valence recognition.


title: Modified Convolutional Neural Network Based on Dropout and the Stochastic Gradient Descent Optimizer

abstract: This study proposes a modified convolutional neural network (CNN) algorithm that is based on dropout and the stochastic gradient descent (SGD) optimizer (MCNN-DS), after analyzing the problems of CNNs in extracting the convolution features, to improve the feature recognition rate and reduce the time-cost of CNNs. The MCNN-DS has a quadratic CNN structure and adopts the rectified linear unit as the activation function to avoid the gradient problem and accelerate convergence. To address the overfitting problem, the algorithm uses an SGD optimizer, which is implemented by inserting a dropout layer into the all-connected and output layers, to minimize cross entropy. This study used the datasets MNIST, HCL2000, and EnglishHand as the benchmark data, analyzed the performance of the SGD optimizer under different learning parameters, and found that the proposed algorithm exhibited good recognition performance when the learning rate was set to [0.05, 0.07]. The performances of WCNN, MLP-CNN, SVM-ELM, and MCNN-DS were compared. Statistical results showed the following: (1) For the benchmark MNIST, the MCNN-DS exhibited a high recognition rate of 99.97%, and the time-cost of the proposed algorithm was merely 21.95% of MLP-CNN, and 10.02% of SVM-ELM; (2) Compared with SVM-ELM, the average improvement in the recognition rate of MCNN-DS was 2.35% for the benchmark HCL2000, and the time-cost of MCNN-DS was only 15.41%; (3) For the EnglishHand test set, the lowest recognition rate of the algorithm was 84.93%, the highest recognition rate was 95.29%, and the average recognition rate was 89.77%.


title: Cyber Security Situational Awareness

abstract: Situational awareness in the context of cyber security has been well recognized. In a time cyber-attacks getting increasingly sophisticated and making potentially disruptive impacts, it becomes apparent that a holistic approach is fundamentally needed to handling security data effectively. Cyber Security Situational Awareness (CSSA) emerges timely. In this paper, after revisiting the concept of CSSA, we have aligned the process of CSSA with security data lifecycle and analyzed the requirements of CSSA. Then, we have put forward a multi-level analysis framework for CSSA.


title: Planning and scheduling by logic-based benders decomposition

abstract: We combine mixed-integer linear programming (MILP) and constraint programming (CP) to solve an important class of planning and scheduling problems. Tasks are allocated to facilities using MILP and scheduled using CP, and the two are linked via logic-based Benders decomposition. Tasks assigned to a facility may run in parallel subject to resource constraints (cumulative scheduling). We solve problems in which the objective is to minimize cost, makespan, or total tardiness. We obtain significant computational speedups, of several orders of magnitude for the first two objectives, relative to the state of the art in both MILP and CP. We also obtain better solutions and bounds for problems than cannot be solved to optimality.


title: An Effective Handover Scheme Based on Antenna Selection in Ground–Train Distributed Antenna Systems

abstract: Due to frequent handovers in broadband wireless communications for the high-speed railway system, communication interruption caused by handover failures could seriously degrade the users' experience. Aiming at reducing the handover failure probability, this paper proposes an effective handover scheme based on the distributed antenna system (DAS) and mobile-relay-aided two-link architecture, i.e., ground-relay link and relay-onboard link, where the mobile relay is mounted on top of the moving train. In addition, an antenna selection scheme with optimal power allocation is proposed to improve handover performance. Our simulation and analysis results show that the DAS structure with macrodiversity can reduce the unnecessary handovers, as well as reduce the handover failure and link outage probabilities compared with the traditional cell without DAS. In addition, the proposed DAS with an antenna selection scheme performs better in terms of handover failure and link outage probabilities than that of DAS with a equal power allocation (EPA) scheme.


title: Fluidically Tunable Frequency Selective/Phase Shifting Surfaces for High-Power Microwave Applications

abstract: We examine fluidically tunable periodic structures acting as highly-selective frequency selective surfaces (FSSs) or spatial phase shifters (SPSs) capable of providing phase shifts in the range of 0°-360°. These devices are multi-layer periodic structures composed of non-rePsonant unit cells. The tuning mechanism is based on integrating small, movable liquid metal droplets with the unit cells of the periodic structure. By moving these liquid metal droplets by small distances within the unit cell, the structure's frequency response can be tuned continuously. Using this technique, a fluidically tunable FSS with a fifth-order bandpass response is designed and its tuning performance is examined for various incidence angles and polarizations of the incident EM wave. Additionally, electronically tunable counterparts of the same structure are also designed and their tuning performances are examined under short-duration high-power excitation conditions. It is demonstrated that such electronically tunable FSSs/PSSs demonstrate extremely nonlinear responses. Since the fluidically tunable structure examined in this work does not use any nonlinear devices, its response is expected to remain linear for such short-duration high-power excitation conditions. The tuning performances of these fluidically tunable periodic structures are also experimentally demonstrated by fabricating three prototypes and characterizing their responses in a waveguide environment.


title: A Novel Experimental Platform for In-Vessel Multi-Chemical Molecular Communications

abstract: This work presents a new multi-chemical experimental platform for molecular communication where the transmitter can release different chemicals. This platform is designed to be inexpensive and accessible, and it can be expanded to simulate different environments including the cardiovascular system and complex network of pipes in industrial complexes and city infrastructures. To demonstrate the capabilities of the platform, we implement a time-slotted binary communication system where a bit-0 is represented by an acid pulse, a bit-1 by a base pulse, and information is carried via pH signals. The channel model for this system, which is nonlinear and has long memories, is unknown. Therefore, we devise novel detection algorithms that use techniques from machine learning and deep learning to train a maximum-likelihood detector. Using these algorithms the bit error rate improves by an order of magnitude relative to the approach used in previous works. Moreover, our system achieves a data rate that is an order of magnitude higher than any of the previous molecular communication platforms.


title: Optimal Combination and Sizing of a New and Renewable Hybrid Generation System

abstract: Since solar, wind and tidal energies mainly depend upon statistical parameters with respect to changing climate and environment, focus on hybrid generation system design increases the availability of the power generation system. The hybrid system also reduces the dependence on one environment parameter thus providing the consumer with reliable and cheap electricity. Therefore optimal combination and sizing design of hybrid generation system considering the battery has a very important role in the use of renewable energy effectively and economically. Several approaches were developed to achieve the optimal configurations of the hybrid systems such as the least square method, the trade-off method and the probabilistic approach method. This study presents a method to design the optimal combination and unit sizing for new and renewable hybrid systems. The method is to find the optimal configuration and sizing among sets of system components, that meets the desired system requirements, with the lowest value of the energy cost. The availability of the methodology is demonstrated with the field data acquired from sets of experiments.


title: Adaptive neural reward processing during anticipation and receipt of monetary rewards in mindfulness meditators

abstract: Reward seeking is ubiquitous and adaptive in humans. But excessive reward seeking behavior, such as chasing monetary rewards, may lead to diminished subjective well-being. This study examined whether individuals trained in mindfulness meditation show neural evidence of lower susceptibility to monetary rewards. Seventy-eight participants (34 meditators, 44 matched controls) completed the monetary incentive delay task while undergoing functional magnetic resonance imaging. The groups performed equally on the task, but meditators showed lower neural activations in the caudate nucleus during reward anticipation, and elevated bilateral posterior insula activation during reward anticipation. Meditators also evidenced reduced activations in the ventromedial prefrontal cortex during reward receipt compared with controls. Connectivity parameters between the right caudate and bilateral anterior insula were attenuated in meditators during incentive anticipation. In summary, brain regions involved in reward


title: Scenario trees and policy selection for multistage stochastic programming using machine learning

abstract: In the context of multistage stochastic optimization problems, we propose a hybrid strategy for generalizing to nonlinear decision rules, using machine learning, a finite data set of constrained vector-valued recourse decisions optimized using scenario-tree techniques from multistage stochastic programming. The decision rules are based on a statistical model inferred from a given scenario-tree solution and are selected by out-of-sample simulation given the true problem. Because the learned rules depend on the given scenario tree, we repeat the procedure for a large number of randomly generated scenario trees and then select the best solution policy found for the true problem. The scheme leads to an ex post selection of the scenario tree itself. Numerical tests evaluate the dependence of the approach on the machine learning aspects and show cases where one can obtain near-optimal solutions, starting with a “weak” scenario-tree generator that randomizes the branching structure of the trees.


title: An Unsupervised Aspect Detection Model for Sentiment Analysis of Reviews

abstract: With the rapid growth of user-generated content on the internet, sentiment analysis of online reviews has become a hot research topic recently, but due to variety and wide range of products and services, the supervised and domain-specific models are often not practical. As the number of reviews expands, it is essential to develop an efficient sentiment analysis model that is capable of extracting product aspects and determining the sentiments for aspects. In this paper, we propose an unsupervised model for detecting aspects in reviews. In this model, first a generalized method is proposed to learn multi-word aspects. Second, a set of heuristic rules is employed to take into account the influence of an opinion word on detecting the aspect. Third a new metric based on mutual information and aspect frequency is proposed to score aspects with a new bootstrapping iterative algorithm. The presented bootstrapping algorithm works with an unsupervised seed set. Finally two pruning methods based on the relations between aspects in reviews are presented to remove incorrect aspects. The proposed model does not require labeled training data and can be applicable to other languages or domains. We demonstrate the effectiveness of our model on a collection of product reviews dataset, where it outperforms other techniques.


title: Evolution of reddit: from the front page of the internet to a self-referential community?

abstract: In the past few years, Reddit -- a community-driven platform for submitting, commenting and rating links and text posts -- has grown exponentially, from a small community of users into one of the largest online communities on the Web. To the best of our knowledge, this work represents the most comprehensive longitudinal study of Reddit's evolution to date, studying both (i) how user submissions have evolved over time and (ii) how the community's allocation of attention and its perception of submissions have changed over 5 years based on an analysis of almost 60 million submissions. Our work reveals an ever-increasing diversification of topics accompanied by a simultaneous concentration towards a few selected domains both in terms of posted submissions as well as perception and attention. By and large, our investigations suggest that Reddit has transformed itself from a dedicated gateway to the Web to an increasingly self-referential community that focuses on and reinforces its own user-generated image- and textual content over external sources.


title: Coresets for Scalable Bayesian Logistic Regression

abstract: The use of Bayesian methods in large-scale data settings is attractive because of the rich hierarchical models, uncertainty quantification, and prior specification they provide. Standard Bayesian inference algorithms are computationally expensive, however, making their direct application to large datasets difficult or infeasible. Recent work on scaling Bayesian inference has focused on modifying the underlying algorithms to, for example, use only a random data subsample at each iteration. We leverage the insight that data is often redundant to instead obtain a weighted subset of the data (called a coreset) that is much smaller than the original dataset. We can then use this small coreset in any number of existing posterior inference algorithms without modification. In this paper, we develop an efficient coreset construction algorithm for Bayesian logistic regression models. We provide theoretical guarantees on the size and approximation quality of the coreset -- both for fixed, known datasets, and in expectation for a wide class of data generative models. Crucially, the proposed approach also permits efficient construction of the coreset in both streaming and parallel settings, with minimal additional effort. We demonstrate the efficacy of our approach on a number of synthetic and real-world datasets, and find that, in practice, the size of the coreset is independent of the original dataset size. Furthermore, constructing the coreset takes a negligible amount of time compared to that required to run MCMC on it.


title: L-boronophenylalanine-mediated boron neutron capture therapy for malignant glioma progressing after external beam radiation therapy: a Phase I study.

abstract: PURPOSE ::: To investigate the safety of boronophenylalanine-mediated boron neutron capture therapy (BNCT) in the treatment of malignant gliomas that progress after surgery and conventional external beam radiation therapy. ::: ::: ::: METHODS AND MATERIALS ::: Adult patients who had histologically confirmed malignant glioma that had progressed after surgery and external beam radiotherapy were eligible for this Phase I study, provided that >6 months had elapsed from the last date of radiation therapy. The first 10 patients received a fixed dose, 290 mg/kg, of L-boronophenylalanine-fructose (L-BPA-F) as a 2-hour infusion before neutron irradiation, and the remaining patients were treated with escalating doses of L-BPA-F, either 350 mg/kg, 400 mg/kg, or 450 mg/kg, using 3 patients on each dose level. Adverse effects were assessed using National Cancer Institute Common Toxicity Criteria version 2.0. ::: ::: ::: RESULTS ::: Twenty-two patients entered the study. Twenty subjects had glioblastoma, and 2 patients had anaplastic astrocytoma, and the median cumulative dose of prior external beam radiotherapy was 59.4 Gy. The maximally tolerated L-BPA-F dose was reached at the 450 mg/kg level, where 4 of 6 patients treated had a grade 3 adverse event. Patients who were given >290 mg/kg of L-BPA-F received a higher estimated average planning target volume dose than those who received 290 mg/kg (median, 36 vs. 31 Gy [W, i.e., a weighted dose]; p = 0.018). The median survival time following BNCT was 7 months. ::: ::: ::: CONCLUSIONS ::: BNCT administered with an l-BPA-F dose of up to 400 mg/kg as a 2-hour infusion is feasible in the treatment of malignant gliomas that recur after conventional radiation therapy.


title: Butterfly Communication Strategies: A prospect for soft-computing techniques

abstract: This paper aims to show a new source of inspiration through Butterfly Communication Strategies to the field of Soft Computing. Inspirations and the algorithms that have been proposed already in this field have been surveyed and efforts have been put to provide the understanding of principal communication strategies of butterfly mating along with the different traits playing major role in it. The principal mating mechanisms were virtually shown with various experimental results using compatible software. The proposals were initiated from the observations to bring a collective movement or localization which could be one of the main aims in the field of soft computing.


title: Path-tracking of a tractor-trailer vehicle along rectilinear and circular paths: a Lyapunov-based approach

abstract: The problem of asymptotic stabilization for straight and circular forward/backward motions of a tractor-trailer system is addressed using Lyapunov techniques. Smooth, bounded, nonlinear control laws achieving asymptotic stability along the desired path are designed, and explicit bounds on the region of attraction are provided. The problem of asymptotic controllability with bounded control is also addressed.


title: Mining financial statement fraud: An analysis of some experimental issues

abstract: Financial statement fraud detection is an important problem with a number of design aspects to consider. Issues such as (i) problem representation, (ii) feature selection, and (iii) choice of performance metrics all influence the perceived performance of detection algorithms. Efficient implementation of financial fraud detection methods relies on a clear understanding of these issues. In this paper we present an analysis of the three key experimental issues associated with financial statement fraud detection, critiquing the prevailing ideas and providing new understandings.


title: Large Scale Terrain Real-Time Rendering on GPU Using Double LayersTile Quad Tree and Cuboids Bounding Error Metric

abstract: Improving terrain tile data selection efficiency, real-time loading of visible tile data and building GPU-based continuous Level of Details (LOD) are the key technologies for large scale terrain rendering on GPU. In this article, in or- der to reduce terrain tile data selection time, we build double layers tile quad tree for massive terrain data and organize tile data by designing Z-order space filling curve. According to the visible region coordinates obtained by GPU offscreen ren- der to texture, we realize real-time loading of visible tile data from CPU to GPU. Map visible tile quad tree into two- dimensional texture on GPU making full use of the characteristics of GPU multi-channel parallel processing. In order to execute tile error metric computation and LOD selection on GPU, we design GPU-based cuboid bounding unsaturated er- ror metric, reduce CPU computational burden and enhance the terrain rendering performance. Experiments show that our algorithm can improve the utilization rate of GPU in the terrain rendering and achieve a good visual effect and a high frame rate.