title: Integration of Mobile AR Technology in Performance Assessment

abstract: Introduction Based on the popular educational philosophy of allowing students to develop diverse capabilities and achieve active knowledge building, performance assessment (PA) should be considered a vital link in teaching. In addition to assigning a final score to students, the purpose of assessment is to develop a highly in-depth understanding regarding the process that students undergo during learning and provide feedback to assist in student growth. Toptas (2011) indicated that an effective evaluation of the students who answered the questions in a particular period of time will be insufficient. If we want to correct this weakness, the performances of the students must be measured with the observation of the process as well. O'Neil and Osif (1993), and VanTassel- Baska (2014) indicated that assessment plays a vital role in teaching and that the process of assessment consists of goal setting, data collection, organisation, and result analysis. The results can be used to enhance teaching and report the actual progress of students. Turgut and Baykul (2012) point out that the process can be measured alongside the results of learning outputs by measuring the performances. In addition, it is asserted that the measurement of students' performance gives them the opportunity to effectively learn the concepts, complex events, and their structures. Nevertheless, a major problem encountered by the education community is determining the appropriateness of educational evaluation. PA has been recognised as one of the most effective methods for assessing this type of high-level thinking because this approach emphasises the application and demonstration of abilities in problem- solving situations and the complexity of problem-solving processes (Wiggins, 1993; VanTassel-Baska, 2014). Previous studies (Bay, Kucukoglu, Kaya, Gundogdu, Kose, Ozan, & Tasgin 2010; Jiang, Smith, & Nichols, 1997) have indicated that the primary limitations and disadvantages of the PA approach include the lack of comparison, limited reliability, unsatisfactory economic performance, and low validity. However, the majority of these factors can be attributed to the subjective consciousness of the assessors and errors in the measured situations. By contrast, augmented reality (AR) technology can be employed to display, in real situations, real- time information that is necessary for assessing or learning. From the perspective of cognitive psychology, this approach can be applied to reduce the errors resulting from the process of PA and to minimise the time and economic costs that teachers must bear when observing student behaviour. Therefore, we examined the meaning, relevant studies, and limitations of PA before investigating the effects that incorporating AR technology exert on improving PA systems. Subsequently, we applied an AR-based PA system to a cooking course to explore the effects of the application. The results of this study can serve as a reference for implementing PA in teaching. Performance Assessment (PA) Performance Assessment (PA) requires students to apply the knowledge and skills they have learned to perform hands-on practice rather than simply revalidating and recollecting the experience of learning (VanTassel-Baska, 2014). This assessment method satisfies the needs of the current trend of constructivist learning and teaching (Chang, 2002). PA motivates students to integrate the knowledge, skills, and dispositions required in the subject, and the results of the assessment can reflect students' problem-solving abilities in real life and the interest and needs of the students. Performance assessments, which can be conducted to evaluate high-level cognitive abilities and the dispositions and skills of students, are more comprehensive compared with conventional paper-based tests. When evaluating a student for periodic checks or a promotion, there has to be a list of measurable performance criteria that can be applied consistently to all members of a particular class. …


title: ORB feature based web pornographic image recognition

abstract: Taken the requirements of web pornographic image recognition both on precision and speed, a pornographic image recognition method based on Oriented FAST and Rotated BRIEF (ORB) is proposed in this paper. The whole recognition process can be divided into two parts: coarse detection and fine detection. Coarse detection can identify the non-pornographic images with no or fewer skin-color regions and facial images quickly. For the remaining images containing much more skin-color regions, fine detection is conducted, which includes three steps: (1) extract ORB descriptors from the skin-color regions and represent the descriptors based on Bag of Words (BOW) model, (2) construct the feature vector combining ORB feature with 72-dimensional Hue, Saturation, Value (HSV) color feature of the whole image, (3) train the classification model using Support Vector Machine (SVM) and apply it for image recognition. The experimental results show that the proposed method can obtain better recognition precision and drastically reduce the average time cost to 1/4 of the method based on Scale Invariant Feature Transform (SIFT).


title: Remote monitoring and smart sensing for water meter system and leakage detection

abstract: Electrical water meters are commonly used for remote monitoring through networks, however, these devices waste energy because they must be switched on constantly. This study proposes a novel metering system, combining traditional mechanical devices with electronic water meters. The proposed device is a low-cost, non-contact arrow sensor based on capacitive signal sensing, in which the circuit is only supplied power when the server sends a request for a measured value, thereby reducing power consumption. We also propose a water-flow monitoring system operable on a wireless network in which water leakage can be detected through software analysis performed by a control centre at the server. The proposed system improves the efficiency of water management and reduces energy consumption, human errors and the waste of water resources.


title: Development of a flexible tail for legged robot

abstract: Some creatures can realize dynamic locomotions with a slender body by utilizing the dynamic effect by moving their whole body besides their legs. For example, some mammals moving by hopping with two legs at high speed and high efficiency have a tail as long as their body length. Their tails are composed of multi-joint and are flexible. The dynamic effect by swinging their flexible tails is useful for fast running and high jumping. In this paper, we develop a flexible tail mechanism for two-legged robot inspired by the flexibility of the animal tail. This tail is comprised of 6 linkages in series and the linkages are connected with elastic passive joints. First, the elasticity of tail joints and the motion of the tail base joint are designed by jumping simulations. To evaluate the effectiveness of the flexible tail mechanism, jumping experiments have been conducted using the prototype of the two-legged robot with the flexible tail as well as rigid tail. The results of these experiments show that the legged robot realized higher jumps with using the effect of swinging its flexible tail.


title: Demand forecasting and inventory control: A simulation study on automotive spare parts

abstract: This paper presents results of a large-scale simulation study on spare parts demand forecasting and inventory control to select best policies within each SKU category. Simulations were conducted over 10,032 SKUs of an automaker that operates in Brazil, considering six years of demand data. Literature review drove the selection of different models simulated. The study included three alternatives to record demand data (individual orders data, weekly and monthly time buckets), three demand forecasting models (SMA – Simple Moving Average, SBA – Syntetos–Boylan Approximation and Bootstrapping) and six models for demand distribution during lead-time (Normal, Gamma, NBD-Negative Binomial Distribution, compound Poisson-Normal, compound Poisson-Gamma and Bootstrapping) resulting in 17 “combined” policies. These policies were applied under (s, nQ) inventory control (reorder point, multiples of fixed order quantity), considering two alternative frequencies for model parameters revision (monthly and semi-annually) and four Target-Fill-Rates (TFR=80%, 90%, 95% and 99%), totalizing 136 simulation runs over each SKU. Parameter values (s, Q) were calculated towards TFR using methods from literature. Performance of each combined policy was measured by total costs and RFR – Realized-Fill-Rate. Major contributions of the research are the policy recommendations within each SKU category, a new Bootstrapping procedure and the highlight of Single Demand Approach (SDA) as a promising area for future theoretical and empirical studies. Results shall be used as guideline for practitioners under similar operations.


title: Limited channel feedback for RF lens antenna based massive MIMO systems

abstract: In this paper, we propose a radio frequency (RF) lens-embedded massive multiple-input multiple-output (MIMO) system. To analyze the characteristics of RF lens antennas, we first introduce the beam propagation method (BPM) and use it to construct a new channel model. We next design a low complexity codebook reflecting the characteristics of the RF lens antennas for limited feedback. Numerical results confirm that, compared to conventional approaches such as random vector quantization (RVQ), the proposed system shows significant performance enhancements in terms of the achievable rate and computational complexity.


title: Multi-modal local terrain maps from vision and LiDAR

abstract: In this paper, we present a method to build precise local terrain maps for an autonomous vehicle from vision and LiDAR that surpass most existing maps used in the field, both in the details they represent and in efficiency of construction. The high level of detail is obtained by spatio-temporal fusion of data from multiple, complementary sensors in a grid map. The map not only consists of obstacle probabilities, but contains different features of the environment: elevation, color, infrared reflectivity, terrain slopes and surface roughness. Still, an efficient way to manage the map's memory allows us to build the maps online on-board our autonomous vehicle. As we demonstrate by describing some of its applications, the maps can serve as a unified representation to solve further perception and navigation problems without having to resort to individual sensor data again. The proposed terrain maps have proven their robustness and precision in many real-world scenarios, leading to award-winning performances at international robotics competitions.


title: RESEARCH Open Access Data feature selection based on Artificial Bee Colony algorithm

abstract: Classification of data in large repositories requires efficient techniques for analysis since a large amount of features is created for better representation of such images. Optimization methods can be used in the process of feature selection to determine the most relevant subset of features from the data set while maintaining adequate accuracy rate represented by the original set of features. Several bioinspired algorithms, that is, based on the behavior of living beings of nature, have been proposed in the literature with the objective of solving optimization problems. This paper aims at investigating, implementing, and analyzing a feature selection method using the Artificial Bee Colony approach to classification of different data sets. Various UCI data sets have been used to demonstrate the effectiveness of the proposed method against other relevant approaches available in the literature.


title: Distributed Word Representations Improve NER for e-Commerce

abstract: This paper presents a case study of using distributed word representations, word2vec in particular, for improving performance of Named Entity Recognition for the eCommerce domain. We also demonstrate that distributed word representations trained on a smaller amount of in-domain data are more effective than word vectors trained on very large amount of out-of-domain data, and that their combination gives the best results.


title: The design and control of the multi-modal locomotion origami robot, Tribot

abstract: Origami robots (Robogamis) use architecture to strategically activate different sets and sequence of actuators to achieve large variety of reconfigurable forms. Tribot is a unique mobile origami robot that can simultaneously choose between two modes of locomotion: jumping and crawling. When assembled, Tribot measures 64 × 34 × 20 mm3, weighs 4 g, crawls at 17% of its body length per gait cycle and jumps seven times its height repeatedly without needing to be reset. To optimize the practicality of the nominally 2D design, we made two different approaches to build the prototypes. For one of them, we used the “traditional”, monolithic, layer-by-layer robogami fabrication method and the second, we printed out most parts using a multi-material 3D printer. By showing the performance of two prototypes side-by-side, we show that with the 3D printer, we can minimize the number of functional layers and reduce the fabrication time. The embedded sensors allow Tribot's crawling gait pattern and jumping height to be modulated with a closed loop control. We compare the expected gait step size and displacement to that of the presented prototype while describing the design and control parameters to achieve the experimental results. We also illustrated the preliminary graphical design tool platform developed to optimize the next design iteration of Tribot.


title: Metaviz: interactive statistical and visual analysis of metagenomic data

abstract: Large studies profiling microbial communities and their association with healthy or disease phenotypes are now commonplace. Processed data from many of these studies are publicly available but significant effort is required for users to effectively organize, explore and integrate it, limiting the utility of these rich data resources. Effective integrative and interactive visual and statistical tools to analyze many metagenomic samples can greatly increase the value of these data for researchers. We present Metaviz, a tool for interactive exploratory data analysis of annotated microbiome taxonomic community profiles derived from marker gene or whole metagenome shotgun sequencing. Metaviz is uniquely designed to address the challenge of browsing the hierarchical structure of metagenomic data features while rendering visualizations of data values that are dynamically updated in response to user navigation. We use Metaviz to provide the UMD Metagenome Browser web service, allowing users to browse and explore data for more than 7000 microbiomes from published studies. Users can also deploy Metaviz as a web service, or use it to analyze data through the metavizr package to interoperate with state-of-the-art analysis tools available through Bioconductor. Metaviz is free and open source with the code, documentation and tutorials publicly accessible.


title: Deep Learning in Mobile and Wireless Networking: A Survey

abstract: The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.


title: 5G NR Jamming, Spoofing, and Sniffing: Threat Assessment and Mitigation

abstract: In December 2017, the Third Generation Partnership Project (3GPP) released the first set of specifications for 5G New Radio (NR), which is currently the most widely accepted 5G cellular standard. 5G NR is expected to replace LTE and previous generations of cellular technology over the next several years, providing higher throughput, lower latency, and a host of new features. Similar to LTE, the 5G NR physical layer consists of several physical channels and signals, most of which are vital to the operation of the network. Unfortunately, like for any wireless technology, disruption through radio jamming is possible. This paper investigates the extent to which 5G NR is vulnerable to jamming and spoofing, by analyzing the physical downlink and uplink control channels and signals. We identify the weakest links in the 5G NR frame, and propose mitigation strategies that should be taken into account during implementation of 5G NR chipsets and base stations.


for free : Efficient and provably secure two-factor authentication scheme with user


Singing in Dutch Dialects: Language Choice in Music and the Dialect Renaissance


title: STT-MRAM with double magnetic tunnel junctions

abstract: We report switching performance of perpendicularly magnetized Spin-Transfer Torque MRAM (STT-MRAM) devices with double tunnel barriers and two reference layers. We show that stacks with double tunnel barriers improve the switching efficiency (Eb/Ic0) by 2x, when compared to similar stacks with a single tunnel barrier. Switching efficiency up to 10 kBT/uA was observed in single devices. A large operating window, Vbreakdown-Vc10ns ∼ 0.7 V was achieved for 40nm devices, compared to 0.2V in single tunnel barrier devices.


title: Design and Validation of a Reconfigurable Single Varactor-Tuned Reflectarray

abstract: An aperture-coupled reflectarray element giving a full phase tuning range with a single varactor diode is proposed in this paper for pattern reconfigurability applications. The full phase agility is achieved by a proper optimization of the phase tuning line, thus providing an alternate inductive/capacitive effect able to avoid the use of two varactor diodes, usually adopted in similar existing configurations. The proposed active element structure is adopted to design a demonstrative reflectarray prototype of 3 × 15 radiators. Furthermore, an own synthesis procedure is applied to obtain the proper biasing voltages giving the prescribed H-plane field. Test examples of beam-scanning, multibeam, and shaped-beam patterns are discussed to demonstrate the effectiveness of the approach.


Crack Detection of a 15 Meter Long Reinforced Concrete Girder with a Single Distributed Cable Sensor


title: A performance study of BitTorrent-like peer-to-peer systems

abstract: This paper presents a performance study of BitTorrent-like P2P systems by modeling, based on extensive measurements and trace analysis. Existing studies on BitTorrent systems are single-torrent based and usually assume the process of request arrivals to a torrent is Poisson-like. However, in reality, most BitTorrent peers participate in multiple torrents and file popularity changes over time. Our study of representative BitTorrent traffic provides insights into the evolution of single-torrent systems and several new findings regarding the limitations of BitTorrent systems: (1) Due to the exponentially decreasing peer arrival rate in a torrent, the service availability of the corresponding file becomes poor quickly, and eventually it is hard to locate and download this file. (2) Client performance in the BitTorrent-like system is unstable, and fluctuates significantly with the changes of the number of online peers. (3) Existing systems could provide unfair services to peers, where a peer with a higher downloading speed tends to download more and upload less. Motivated by the analysis and modeling results, we have further proposed a graph based model to study interactions among multiple torrents. Our model quantitatively demonstrates that inter-torrent collaboration is much more effective than stimulating seeds to serve longer for addressing the service unavailability in BitTorrent systems. An architecture for inter-torrent collaboration under an exchange based instant incentive mechanism is also discussed and evaluated by simulations


title: An efficient and robust method for detecting copy-move forgery

abstract: Copy-move forgery is a specific type of image tampering, where a part of the image is copied and pasted on another part of the same image. In this paper, we propose a new approach for detecting copy-move forgery in digital images, which is considerably more robust to lossy compression, scaling and rotation type of manipulations. Also, to improve the computational complexity in detecting the duplicated image regions, we propose to use the notion of counting bloom filters as an alternative to lexicographic sorting, which is a common component of most of the proposed copy-move forgery detection schemes. Our experimental results show that the proposed features can detect duplicated region in the images very accurately, even when the copied region was undergone severe image manipulations. In addition, it is observed that use of counting bloom filters offers a considerable improvement in time efficiency at the expense of a slight reduction in the robustness.


title: Evidence for two distinct cross-bridge populations in tetanized frog muscle fibers stretched with moderate velocities.

abstract: When tetanized frog skeletal muscle fibers are subjected to moderate-velocity stretches (< 1 L0/s), the tension developed above the level of isometric tension starts to decay after a sudden reduction of stretch velocity by more than 40-50%, though the fibers are still being stretched. We analysed the decay of tension response caused by the sudden reduction of stretch velocity, by applying three different types of stretch to a tetanized fiber, i.e., a 1.5% stretch with velocity V1 (stretch-1), a 1.5% stretch with velocity V2 < V1 (stretch-2), and a 3% stretch consisting of stretch-1 and stretch-2 applied in succession (stretch-3) and comparing the corresponding tension responses, TR-1, TR-2, and TR-3. It was found that TR-3 to stretch-3 resulted from algebraical summation of TR-1 to the preceding stretch-1 and TR-2 to the subsequent stretch-2. These results can be accounted for by assuming two distinct cross-bridge populations in stretched fibers.


title: Plastic surgical management of children with Down's syndrome: indications and results.

abstract: The face of a child with Down's syndrome is stigmatised by the large tongue, open mouth, saddle nose and epicanthal folds. Operative reduction of macroglossia facilitates nose-breathing, comprehensible speech and eating. Raising the nasal bridge with Cialit-preserved homograft cartilage obliterates the epicanthal folds and conceals the oblique slant of the eyelids. The child should be operated on from the third year onwards.


title: Altering prosthetic foot stiffness influences foot and muscle function during below-knee amputee walking: A modeling and simulation analysis

abstract: Most prosthetic feet are designed to improve amputee gait by storing and releasing elastic energy during stance. However, how prosthetic foot stiffness influences muscle and foot function is unclear. Identifying these relationships would provide quantitative rationale for prosthetic foot prescription that may lead to improved amputee gait. The purpose of this study was to identify the influence of altered prosthetic foot stiffness on muscle and foot function using forward dynamics simulations of amputee walking. Three 2D muscle-actuated forward dynamics simulations of unilateral below-knee amputee walking with a range of foot stiffness levels were generated, and muscle and prosthetic foot contributions to body support and propulsion and residual leg swing were quantified. As stiffness decreased, the prosthetic keel provided increased support and braking (negative propulsion) during the first half of stance while the heel contribution to support decreased. During the second half of stance, the keel provided decreased propulsion and increased support. In addition, the keel absorbed less power from the leg, contributing more to swing initiation. Thus, several muscle compensations were necessary. During the first half of stance, the residual leg hamstrings provided decreased support and increased propulsion. During the second half of stance, the intact leg vasti provided increased support and the residual leg rectus femoris transferred increased energy from the leg to the trunk for propulsion. These results highlight the influence prosthetic foot stiffness has on muscle and foot function throughout the gait cycle and may aid in prescribing feet of appropriate stiffness.


title: Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback

abstract: Machine translation is a natural candidate problem for reinforcement learning from human feedback: users provide quick, dirty ratings on candidate translations to guide a system to improve. Yet, current neural machine translation training focuses on expensive human-generated reference translations. We describe a reinforcement learning algorithm that improves neural machine translation systems from simulated human feedback. Our algorithm combines the advantage actor-critic algorithm (Mnih et al., 2016) with the attention-based neural encoder-decoder architecture (Luong et al., 2015). This algorithm (a) is well-designed for problems with a large action space and delayed rewards, (b) effectively optimizes traditional corpus-level machine translation metrics, and (c) is robust to skewed, high-variance, granular feedback modeled after actual human behaviors.


title: Image and video segmentation: the normalized cut framework

abstract: We propose a segmentation system based on the normalized cut framework proposed by Shi and Malik (see Proc. IEEE Conf. Computer Vision and Pattern Recognition, San Juan, Puerto Rico, p.731-7, 1997). The goal is to partition the image from a big picture point of view. Perceptually significant groups are detected first while small variations and details are treated later. Different image features-intensity, color, texture, contour continuity, motion and stereo disparity are treated in one uniform framework.


title: MetaTutor: A MetaCognitive Tool for Enhancing Self-Regulated Learning

abstract: Learning about complex and challenging science topics with advanced learning technologies requires students to regulate their learning. The deployment of key cognitive and metacognitive regulatory processes is key to enhancing learning in open-ended learning environments such as hypermedia. In this paper, we propose a metaphor—Computers as MetaCognitive tools—to characterize the complex nature of the learning context, self- regulatory processes, task conditions, and features of advanced learning technologies. We briefly outline the theoretical and conceptual assumptions of self-regulated learning (SRL) underlying MetaTutor, a hypermedia environment designed to train and foster students’ SRL processes in biology. Lastly, we provide preliminary learning outcome and SRL process data on the deployment of SRL processes during learning with MetaTutor.


DYNAMIC MODEL OF PM SYNCHRONOUS MOTORS


Social Media Usage, Self-efficacy and Cultural Intelligence: A Longitudinal Empirical Research in China.


title: A general EM-based design procedure for single-layer substrate integrated waveguide interconnects with microstrip transitions

abstract: We propose in this work a general procedure to efficient EM-based design of single-layer SIW interconnects, including their transitions to microstrip lines. Our starting point is developed by exploiting available empirical knowledge for SIW. We propose an efficient SIW surrogate model for direct EM design optimization in two stages: first optimizing the SIW width to achieve the specified low cutoff frequency, followed by the transition optimization to reduce reflections and extend the dominant mode bandwidth. Our procedure is illustrated by designing a SIW interconnect on a standard FR4-based substrate.


title: Multi-band multi-mode SDR radar platform

abstract: This paper presents the design results of the multi-band, multi-mode software-defined radar (SDR) system. The SDR platform consists of a multi-band RF modules of S, X, K-bands, and a multi-mode digital modules with a waveform generator for CW, Pulse, FMCW, and LFM Chirp as well as reconfigurable SDR-GUI software module for user interface. This platform can be used for various applications such as security monitoring, collision avoidance, traffic monitoring, and a radar imaging.


title: Compact architecture for high-throughput regular expression matching on FPGA

abstract: In this paper we present a novel architecture for high-speed and high-capacity regular expression matching (REM) on FPGA. The proposed REM architecture, based on nondeterministic finite automaton (RE-NFA), efficiently constructs regular expression matching engines (REME) of arbitrary regular patterns and character classes in a uniform structure, utilizing both logic slices and block memory (BRAM) available on modern FPGA devices. The resulting circuits take advantage of synthesis and routing optimizations to achieve high operating speed and area efficiency. The uniform structure of our RE-NFA design can be stacked in a simple way to produce multi-character input circuits to scale up throughput further. An n-state m-character input REME takes only O (n X log2 m) time to construct and occupies no more than O (n X m) logic units. The REMEs can be staged and pipelined in large numbers to achieve high parallelism without sacrificing clock frequency. Using the proposed RE-NFA architecture, we are able to implement 3 copies of two-character input REMEs, each with 760 regular expressions, 18715 states and 371 character classes, onto a single Xilinx Virtex 4 LX-100-12 device. Each copy processes 2 characters per clock cycle at 300 MHz, resulting in a concurrent throughput of 14.4 Gbps for 760 REMEs. Compared with the automatic NFA-to-VHDL REME compilation [13], our approach achieves over 9x throughput efficiency (Gbps*state/LUT). Compared with state-of-the-art REMEs on FPGA, our approach also indicates up to 70% better throughput efficiency.


title: 5.2 Distributed system of digitally controlled microregulators enabling per-core DVFS for the POWER8TM microprocessor

abstract: Integrated voltage regulator modules (iVRMs) [1] provide a cost-effective path to realizing per-core dynamic voltage and frequency scaling (DVFS), which can be used to optimize the performance of a power-constrained multi-core processor. This paper presents an iVRM system developed for the POWER8™ microprocessor, which functions as a very fast, accurate low-dropout regulator (LDO), with 90.5% peak power efficiency (only 3.1% worse than an ideal LDO). At low output voltages, efficiency is reduced but still sufficient to realize beneficial energy savings with DVFS. Each iVRM features a bypass mode so that some of the cores can be operated at maximum performance with no regulator loss. With the iVRM area including the input decoupling capacitance (DCAP) (but not the output DCAP inherent to the cores), the iVRMs achieve a power density of 34.5W/mm2, which exceeds that of inductor-based or SC converters by at least 3.4× [2].


title: Abdominal multi-organ segmentation with organ-attention networks and statistical fusion

abstract: Abstract Accurate and robust segmentation of abdominal organs on CT is essential for many clinical applications such as computer-aided diagnosis and computer-aided surgery. But this task is challenging due to the weak boundaries of organs, the complexity of the background, and the variable sizes of different organs. To address these challenges, we introduce a novel framework for multi-organ segmentation of abdominal regions by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity. More specifically, OAN is a two-stage deep convolutional network, where deep network features from the first stage are combined with the original image, in a second stage, to reduce the complex background and enhance the discriminative information for the target organs. Intuitively, OAN reduces the effect of the complex background by focusing attention so that each organ only needs to be discriminated from its local background. RCs are added to the first stage to give the lower layers more semantic information thereby enabling them to adapt to the sizes of different organs. Our networks are trained on 2D views (slices) enabling us to use holistic information and allowing efficient computation (compared to using 3D patches). To compensate for the limited cross-sectional information of the original 3D volumetric CT, e.g., the connectivity between neighbor slices, multi-sectional images are reconstructed from the three different 2D view directions. Then we combine the segmentation results from the different views using statistical fusion, with a novel term relating the structural similarity of the 2D views to the original 3D structure. To train the network and evaluate results, 13 structures were manually annotated by four human raters and confirmed by a senior expert on 236 normal cases. We tested our algorithm by 4-fold cross-validation and computed Dice–Sorensen similarity coefficients (DSC) and surface distances for evaluating our estimates of the 13 structures. Our experiments show that the proposed approach gives strong results and outperforms 2D- and 3D-patch based state-of-the-art methods in terms of DSC and mean surface distances.


title: Millimeter wave FMCW radar for Foreign object debris (FOD) detection at airport runways

abstract: Presence of Foreign object debris (FOD) at airport runways is an immense threat to flight safety operation. Such unwanted objects may cause potential damage to the aircraft during critical phases like take off and landing. Due to the miniaturized nature of FOD parts, a very high resolution sensor is required to resolve and locate such objects with higher extent of accuracy such as less than 1 meter. The reported FOD systems operate at W-band. This paper discusses the design considerations for millimeter wave FOD radar system operating at Ka-band and presents a simulation platform comprising of FOD radar model and test setup for design evaluation. The radar model is developed using the specifications of the selected commercially off-the-shelf (COTS) components and is evaluated under different scenarios in ADS®. The design addresses inherent issues associated with FMCW architecture such as mutual coupling between transmit and receive antennae and self mixing due to substrate coupling. For improved azimuth accuracy, two channels receiver for amplitude comparison monopulse is incorporated. Transmit power is 10 dBm and receiver's minimum detectable strength is −122 dBm. The simulation results indicate radar's ability to detect very small objects − 20 dBsm radar cross section (RCS) up to 300 meter range.


Reranking-based Recommender System with Deep Learning


Procedural Landscapes with Overhangs


title: PAPER Grayscale Image Segmentation Using Color Space

abstract: A novel approach for segmentation of grayscale images, which are color scene originally, is proposed. Many algorithms have been elaborated for a grayscale image segmentation. All those approaches have been discussed in a luminance space, because it has been considered that grayscale images do not have any color information. However, a luminance value has color information as a set of corresponding colors. In this paper, an inverse mapping of luminance values is carried out to CIELAB color space, and the image segmentation for grayscale images is performed based on a distance in the color space. The proposed scheme is applied to a region growing segmentation and the performance is verified.


title: Recent advances in noninvasive glucose monitoring

abstract: The race for the next generation of painless and reliable glucose monitoring for diabetes mellitus is on. As technology advances, both diagnostic techniques and equipment improve. This review describes the main technologies currently being explored for noninvasive glucose monitoring. The principle of each technology is mentioned; its advantages and limitations are then discussed. The general description and the corresponding results for each device are illustrated, as well as the current status of the device and the manufacturer; internet references for the devices are listed where appropriate. Ten technologies and eleven potential devices are included in this review. Near infrared spectroscopy has become a promising technology, among others, for blood glucose monitoring. Although some reviews have been published already, the rapid development of technologies and information makes constant updating mandatory. While advances have been made, the reliability and the calibration of noninvasive instruments could still be improved, and more studies carried out under different physiological conditions of metabolism, bodily fluid circulation, and blood components are needed.


title: Restricted Distribution Automatizability in PAC-Semantics

abstract: We consider the proof search (--automatizability--) problem for propositional proof systems in the context of knowledge discovery (or data mining and analytics). Discovered knowledge necessarily features a weaker semantics than usually employed in mathematical logic, and in this work we find that these weaker semantics may result in a proof search problem that seems easier than the classical problem, but that is nevertheless nontrivial. Specifically, if we consider a knowledge discovery task corresponding to the unsupervised learning of parities over the uniform distribution from partial information, then we find the following: Proofs in the system polynomial calculus with resolution (PCR) can be detected in quasipolynomial time, in contrast to the nO(√n)-time best known algorithm for classical proof search for PCR.By contrast, a quasipolynomial time algorithm that distinguishes whether a formula of PCR is satisfied a 1-ε fraction of the time or merely an -ε-fraction of the time (for polynomially small -ε would give a randomized quasipolynomial time algorithm for NP, so the use of the promise of a small PCR proof is essential in the above result.Likewise, if integer factoring requires subexponential time, we find that bounded-depth Frege proofs cannot be detected in quasipolynomial time. ::: The final result essentially shows that negative results based on the hardness of interpolation [31, 13, 11] persist under this new semantics, while the first result suggests, in light of negative results for PCR [22] and resolution [2] under the classical semantics, that there are intriguing new possibilities for proof search in the context of knowledge discovery and data analysis.


title: Structured flowcharts outperform pseudocode: an experimental comparison

abstract: The author discovered, while teaching a course on data structures, that his students overwhelmingly preferred structured flowcharts over pseudocode for comprehending the algorithms presented. He describes an experiment that he designed to find out if real differences in comprehension exist between structured flowcharts and pseudocode when used to describe conditional logic. He hypothesized that structured flowcharts (1) take less time to comprehend, (2) produce fewer errors in understanding, (3) give students more confidence in their understanding of an algorithm, (4) reduce the time spent answering questions about an algorithm, and (5) reduce the number of times students need to look at an algorithm. These hypotheses were tested on three algorithms of varying complexity. The results strongly indicate that structured flowcharts do indeed aid algorithm comprehension. A large difference was found even for the simplest algorithm. >


title: Stroke prediction using artificial intelligence

abstract: A stroke occurs when the blood supply to a person's brain is interrupted or reduced. The stroke deprives person's brain of oxygen and nutrients, which can cause brain cells to die. Numerous works have been carried out for predicting various diseases by comparing the performance of predictive data mining technologies. In this work, we compare different methods with our approach for stroke prediction on the Cardiovascular Health Study (CHS) dataset. Here, decision tree algorithm is used for feature selection process, principle component analysis algorithm is used for reducing the dimension and adopted back propagation neural network classification algorithm, to construct a classification model. After analyzing and comparing classification efficiencies with different methods and variation models accuracy, our work has the optimum predictive model for the stroke disease with 97.7% accuracy.


Copper Nanoparticles for Printed Electronics: Routes Towards Achieving Oxidation Stability


title: Application of data mining in a maintenance system for failure prediction

abstract: Publicado em : "Safety, reliability and risk analysis : beyond the horizon", ISBN 978-1-138-00123-7


title: Modelling Sarcasm in Twitter, a Novel Approach

abstract: Automatic detection of figurative language is a challenging task in computational linguistics. Recognising both literal and figurative meaning is not trivial for a machine and in some cases it is hard even for humans. For this reason novel and accurate systems able to recognise figurative languages are necessary. We present in this paper a novel computational model capable to detect sarcasm in the social network Twitter (a popular microblogging service which allows users to post short messages). Our model is easy to implement and, unlike previous systems, it does not include patterns of words as features. Our seven sets of lexical features aim to detect sarcasm by its inner structure (for example unexpectedness, intensity of the terms or imbalance between registers), abstracting from the use of specific terms.


title: Gotta catch 'em all!: innoculous: enabling epidemiology of computer viruses in the developing world

abstract: Computer users in the developing world are faced with myriad challenges, from limited bandwidth to higher costs of usage and ownership. However, among the most pernicious problems is the prevalence of computer viruses, which have immediate and unexpected economic costs, often to those who are least able to bear the burden of such costs. While statistics are available for virus infection rates, these rates only reflect reports from legally purchased copies of antivirus software run on internet-connected machine, and not the preponderance of software in the developing world, which is illegally obtained, out of its license period, or operated offline and therefore not updated. Anecdotal evidence on the ground indicates infection rates an order of magnitude higher, indicating a dearth of accurate information. In this paper I present Innoculous, a system consisting of a specially crafted USB key, software, and an incentivization strategy aimed towards disinfecting infected machines, creating revenue streams for small businesses and individuals in the developing world, and obtaining rich information about computer virus infections in the environment in question.


title: A Survey on perceived speaker traits: Personality, likability, pathology, and the first challenge

abstract: The INTERSPEECH 2012 Speaker Trait Challenge aimed at a unified test-bed for perceived speaker traits – the first challenge of this kind: personality in the five OCEAN personality dimensions, likability of speakers, and intelligibility of pathologic speakers. In the present article, we give a brief overview of the state-of-the-art in these three fields of research and describe the three sub-challenges in terms of the challenge conditions, the baseline results provided by the organisers, and a new openSMILE feature set, which has been used for computing the baselines and which has been provided to the participants. Furthermore, we summarise the approaches and the results presented by the participants to show the various techniques that are currently applied to solve these classification tasks.


title: Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy

abstract: This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task.


title: Design in robotics based in the voice of the customer of household robots

abstract: The aim of this study is the analysis of the state of the art in consumer robotics dedicated to personal care and housekeeping, for further critical analysis of it. After interviewing different users we will follow a methodology to find the voice of the customer. From these interviews we get the customer's needs which will be structured and ordered in a hierarchy that will help us to set the priorities, concluding with the design requirements and the basic features that the robot must have. The state of the art in welfare-oriented and housekeeping robots are analyzed.Customer requirements for welfare-oriented and housekeeping robots are analyzed.Customer needs are transferred to a design features for reaching large masses.Definition of priorities and design inputs for housekeeping robots.List of design features for welfare-oriented robots for care of elderly with dementia.


title: Artificial Neural Network and Time Series Modeling Based Approach to Forecasting the Exchange Rate in a Multivariate Framework

abstract: Any discussion on exchange rate movements and forecasting should include explanatory variables from both the current account and the capital account of the balance of payments. In this paper, we include such factors to forecast the value of the Indian rupee vis a vis the US Dollar. Further, factors reflecting political instability and lack of mechanism for enforcement of contracts that can affect both direct foreign investment and also portfolio investment, have been incorporated. The explanatory variables chosen are the 3 month Rupee Dollar futures exchange rate (FX4), NIFTY returns (NIFTYR), Dow Jones Industrial Average returns (DJIAR), Hang Seng returns (HSR), DAX returns (DR), crude oil price (COP), CBOE VIX (CV) and India VIX (IV). To forecast the exchange rate, we have used two different classes of frameworks namely, Artificial Neural Network (ANN) based models and Time Series Econometric models. Multilayer Feed Forward Neural Network (MLFFNN) and Nonlinear Autoregressive models with Exogenous Input (NARX) Neural Network are the approaches that we have used as ANN models. Generalized Autoregressive Conditional Heteroskedastic (GARCH) and Exponential Generalized Autoregressive Conditional Heteroskedastic (EGARCH) techniques are the ones that we have used as Time Series Econometric methods. Within our framework, our results indicate that, although the two different approaches are quite efficient in forecasting the exchange rate, MLFNN and NARX are the most efficient.


title: Ab Initio Molecular Dynamics Simulation of the Interaction between Water and Ti in Zeolitic Systems

abstract: We present the results of ab initio molecular dynamics simulations on titanium offretite. The aim of this work is to investigate at a microscopic level the room temperature behavior of titanium inserted into a fully periodic zeolitic framework in dry and hydrated conditions. Structural analysis indicates that titanium, at low water loading, is tetrahedrally coordinated by framework oxygens only. On the other hand, at higher loading, when water molecules are activated via hydrogen bonding, they interact with the Ti site, leading to an increase of the Ti coordination number up to 5. The insertion of a Ti center causes only moderate and local distortions to the silicate framework. Features due to Ti in the calculated vibrational spectra are in line with IR and Raman experiments.


title: Dual-Polarized Tightly Coupled Array With Substrate Loading

abstract: A dual-polarized, tightly coupled dipole array (TCDA), delivering 13.1:1 bandwidth, is presented. The array incorporates a resistive sheet (R-Card) in the substrate to suppress resonances within the operational band and employs a dielectric layer as a superstrate to maintain high efficiency. The simulated infinite array demonstrates broadside VSWR -2.2 is dB across all frequencies and scanning angles. An 8×8 prototype was fabricated and tested to validate the design. Measured array gain and mid-band and high-frequency patterns for the two principal scanning planes are presented, demonstrating good agreement with the respective simulations.


title: OpenIGTLink: an open network protocol for image-guided therapy environment

abstract: BACKGROUND ::: With increasing research on system integration for image-guided therapy (IGT), there has been a strong demand for standardized communication among devices and software to share data such as target positions, images and device status. ::: ::: ::: METHOD ::: We propose a new, open, simple and extensible network communication protocol for IGT, named OpenIGTLink, to transfer transform, image and status messages. We conducted performance tests and use-case evaluations in five clinical and engineering scenarios. ::: ::: ::: RESULTS ::: The protocol was able to transfer position data with submillisecond latency up to 1024 fps and images with latency of <10 ms at 32 fps. The use-case tests demonstrated that the protocol is feasible for integrating devices and software. ::: ::: ::: CONCLUSION ::: The protocol proved capable of handling data required in the IGT setting with sufficient time resolution and latency. The protocol not only improves the interoperability of devices and software but also promotes transitions of research prototypes to clinical applications.


title: Simulation of orthocyclic windings using the linear winding technique

abstract: A continuously rising number of electric vehicle licensing is mentioned since the last few years in Germany. For a cost-efficient production of electrical engines in first-class quality and in sufficient quantity, it is indispensable to understand the process of coil winding. Thereby, the prediction of wire behavior is one of the key challenges. Therefore, a detailed model is built to investigate wire behavior during the linear winding process. The finite element based simulation tool LS-DYNA serves as explicit dynamics tool. The tool works with an explicit time integration method for time discretization. To represent the high dynamic process of winding within this simulation, dynamic influences such as rotational speed or acceleration of the coil body are definable. Within process simulation, the given boundary conditions are applied to the model. The non-linear material properties of the wire are validated under scrutiny by a tensile test and by values out of datasheets in previous research work. Simulation results of orthocyclic windings using the linear winding technique are presented within this paper. The dynamic simulation model is validated by experiments using the caster angle of the wire guide as reference parameter. The caster angel rises during the winding process of the first layer until the wire jumps to the next layer. Hence, it is possible to identify the maximum caster angle and match the simulation value against the experiment value. The travel profile of the wire guide is identified as extremely important to generate an orthocyclic winding. Another substantial part is the wire fixation respectively the geometry to support the first winding offset from winding one to winding two. Results of orthocyclic windings are simulated with and without grooves on the coil body surface and demonstrate the positive influence of grooves for an accurate orthocyclic winding picture.


title: Preventing Conduct Problems, Promoting Social Competence: A Parent and Teacher Training Partnership in Head Start

abstract: Studied the effectiveness of parent and teacher training as a selective prevention program for 272 Head Start mothers and their 4-year-old children and 61 Head Start teachers. Fourteen Head Start centers (34 classrooms) were randomly assigned to (a) an experimental condition in which parents, teachers, and family service workers participated in the prevention program (Incredible Years) or (b) a control condition consisting of the regular Head Start program. Assessments included teacher and parent reports of child behavior and independent observations at home and at school. Construct scores combining observational and report data were calculated for negative and positive parenting style, parent-teacher bonding, child conduct problems at home and at school, and teacher classroom management style. Following the 12-session weekly program, experimental mothers had significantly lower negative parenting and significantly higher positive parenting scores than control mothers. Parent-teacher bonding was significa...


title: Structure, binding interface and hydrophobic transitions of Ca2+-loaded calbindin-D28K

abstract: Calbindin-D(28K) is a Ca2+-binding protein, performing roles as both a calcium buffer and calcium sensor. The NMR solution structure of Ca2+-loaded calbindin-D(28K) reveals a single, globular fold consisting of six distinct EF-hand subdomains, which coordinate Ca2+ in loops on EF1, EF3, EF4 and EF5. Target peptides from Ran-binding protein M and myo-inositol monophosphatase, along with a new target from procaspase-3, are shown to interact with the protein on a surface comprised of alpha5 (EF3), alpha8 (EF4) and the EF2-EF3 and EF4-EF5 loops. Fluorescence experiments reveal that calbindin-D(28K) adopts discrete hydrophobic states as it binds Ca2+. The structure, binding interface and hydrophobic characteristics of Ca2+-loaded calbindin-D(28K) provide the first detailed insights into how this essential protein may function. This structure is one of the largest high-resolution NMR structures and the largest monomeric EF-hand protein to be solved to date.


title: Are algorithms good judges

abstract: Every day, judges across the United States face an important decision: Should they jail a defendant awaiting trial—whose innocence or guilt has not yet been determined—or should they release the person on bail back into the community, where they might commit a crime? Increasingly, courts are turning to computer-based tools to help make those decisions, lured by the promise of complex algorithms that use an array of factors to spit out risk scores. But a new study suggests that at least one widely used algorithm produces risk assessments that are no better than those reached by online volunteers given just a few key pieces of information about a defendant.


title: Characterizing Conversation Patterns in Reddit: From the Perspectives of Content Properties and User Participation Behaviors

abstract: It becomes the norm for people to communicate with one another through various online social channels, where different conversation structures are formed depending on platforms. One of the common online communication patterns is a threaded conversation where a user brings up a conversation topic, and then other people respond to the initiator or other participants by commenting, which can be modeled as a tree structure. This paper seeks to investigate (i) the characteristics of online threaded conversations in terms of volume, responsiveness, and virality and (ii) what and how content properties and user participation behaviors are associated with such characteristics. To this end, we collect 700~K threaded conversations from 1.5~M users in Reddit, one of the most popular online communities allowing people to communicate with others in the form of threaded conversations. Using the collected dataset, we find that `social' words, difficulties of texts, and document relevancy are associated with the volume, responsiveness, and virality of conversations. We also discover that large, viral conversations are mostly formed by a small portion of users who are reciprocally communicate with others by analyzing user interactions. Our analysis on discovering user roles in conversations reveal that users who are interested in multiple topics play important roles in large and viral conversations, whereas heavy posting users play important roles in responsive conversations. We expand our analysis to topical communities (i.e., subreddits) and find that news-related, image-based, and discussion-related communities are more likely to have large, responsive, and viral conversations, respectively.


title: Snapback and safe operating area of LDMOS transistors

abstract: This paper deals with the measurement and prediction of the SOA boundary and the accompanying device physics. Results are given for n-channel LDMOS transistors having a self-aligned body diffusion. LDMOS SOA has been discussed previously in a number of papers, however, there is still a need for improving the overall understanding of this rather complicated subject. To be complete, thermal effects should also be included in the definition of SOA, however, these can be treated separately and we focus here on the "electrical SOA", which is defined by the Id-Vds boundary where snapback occurs.


title: A Multi-scale Triplet Deep Convolutional Neural Network for Person Re-identification

abstract: Person re-identification, aiming to identify images of the same person from non-overlapping camera views in different places, has attracted a lot of interests in intelligent video surveillance. As one of the newly emerging applications, deep learning has been incorporated into the feature representation of person re-identification. However, the existing deep feature learning methods are difficult to generate the robust and discriminative features since they use a fixed scale training and thus fail to adapt to diversitified scales for the same persons under realistic conditions. In this paper, a multi-scale triplet deep convolutional neural network (MST-CNN) is proposed to produce multi-scale features for person re-identification. The proposed MST-CNN consists of three sub-CNNs with respect to full scale, top scale (top part of persons) and half scale of the person images, respectively. In addition, these complementary scale-specific features are then passed to the l2-normalization layer for feature selection to obtain a more robust person descriptor. Experimental results on two public person re-identification datasets, i.e., CUHK-01 and PRID450s, demonstrate that our proposed MVT-CNN method outperforms most of the existing feature learning algorithms by 8%–10% at rank@1 in term of the cumulative matching curve (CMC) criterion.


Feature Extraction from Point Clouds


title: Blind Authentication Using Periodic Properties of Interpolation

abstract: In this paper, we analyze and analytically describe the specific statistical changes brought into the covariance structure of signal by the interpolation process. We show that interpolated signals and their derivatives contain specific detectable periodic properties. Based on this, we propose a blind, efficient, and automatic method capable of finding traces of resampling and interpolation. The proposed method can be very useful in many areas, especially in image security and authentication. For instance, when two or more images are spliced together, to create high quality and consistent image forgeries, almost always geometric transformations, such as scaling, rotation, or skewing are needed. These procedures are typically based on a resampling and interpolation step. By having a method capable of detecting the traces of resampling, we can significantly reduce the successful usage of such forgeries. Among other points, the presented method is also very useful in estimation of the geometric transformations factors.


title: Deriving market intelligence from microblogs

abstract: Given their rapidly growing popularity, microblogs have become great sources of consumer opinions. However, in the face of unique properties and the massive volume of posts on microblogs, this paper proposes a framework that provides a compact numeric summarization of opinions on such platforms. The proposed framework is designed to cope with the following tasks: trendy topics detection, opinion classification, credibility assessment, and numeric summarization. An experiment is carried out on Twitter, the largest microblog website, to prove the effectiveness of the proposed framework. We find that the consideration of user credibility and opinion subjectivity is essential for aggregating microblog opinions. The proposed mechanism can effectively discover market intelligence (MI) for supporting decision-makers.


A security survey of middleware for the Internet of Things


title: Overview of Electric Turbocharger and Supercharger for Downsized Internal Combustion Engines

abstract: Forced induction uses otherwise wasted exhaust gases to improve the volumetric efficiency of an engine allowing for higher thermodynamic efficiency. As the fuel economy and greenhouse gas emission standards are projected to be much more stringent globally, the use of a forced induction engine in passenger cars and light duty trucks has become a new trend in automotive industry. The aerodynamic matching of an exhaust driven turbocharger is a compromise of transient response at low exhaust energy levels and power targets on high. The trend toward highly boosted downsized engines results in larger aerodynamic matches compromising responsiveness on the low side, known as turbo lag. The electrification of FIS [electric FIS (EFIS)] has emerged as a feasible solution, and it also possesses numerous benefits depending on its topologies. This paper provides a comprehensive study on the EFIS by investigating system level topologies, performance, various types of high-speed machines, power electronics, and control techniques. The advantages and disadvantages of existing EFIS are summarized, and the new challenges and opportunities are also introduced.


title: Obstacle-Avoiding Wind Turbine Placement for Power Loss and Wake Effect Optimization

abstract: As finite energy resources are being consumed at faster rate than they can be replaced, renewable energy resources have drawn extensive attention. Wind power development is one such example growing significantly throughout the world. The main difficulty in wind power development is that wind turbines interfere with each other. The produced turbulence—wake effect—directly reduces the power generation. In addition, wirelength of the collection network among wind turbines is not merely an economic factor; it also decides power loss on the wind farm. Moreover, in reality, obstacles (buildings, lakes, etc.) exist on the wind farm, which are unavoidable. Nevertheless, to the best of our knowledge, none of the existing works consider wake effect, wirelength, and avoidance of obstacles all together in the wind turbine placement problem. In this article, we propose an analytical method to obtain the obstacle-avoiding placement of wind turbines, thus minimizing both power loss and wake effect. We also propose a postprocessing method to fine-tune the solution obtained from the analytical method to find a better solution. Simulation results show that our tool is 12x faster than the state-of-the-art industrial tool AWS OpenWind and 203x faster than the state-of-the-art academic tool TDA with almost the same produced power.


A Differential Subthreshold SRAM Cell for Ultra-Low Voltage Embedded Computing Applications


title: A Comparison of Global Search Algorithms for Continuous Black Box Optimization

abstract: Four methods for global numerical black box optimization with origins in the mathematical programming community are described and experimentally compared with the state of the art evolutionary method, BIPOP-CMA-ES. The methods chosen for the comparison exhibit various features that are potentially interesting for the evolutionary computation community: systematic sampling of the search space DIRECT, MCS possibly combined with a local search method MCS, or a multi-start approach NEWUOA, GLOBAL possibly equipped with a careful selection of points to run a local optimizer from GLOBAL. The recently proposed "comparing continuous optimizers" COCO methodology was adopted as the basis for the comparison. Based on the results, we draw suggestions about which algorithm should be used depending on the available budget of function evaluations, and we propose several possibilities for hybridizing evolutionary algorithms EAs with features of the other compared algorithms.


title: A Seeded Image Segmentation Framework Unifying Graph Cuts And Random Walker Which Yields A New Algorithm

abstract: In this work, we present a common framework for seeded image segmentation algorithms that yields two of the leading methods as special cases - The graph cuts and the random walker algorithms. The formulation of this common framework naturally suggests a new, third, algorithm that we develop here. Specifically, the former algorithms may be shown to minimize a certain energy with respect to either an l1 or an l2 norm. Here, we explore the segmentation algorithm defined by an linfin norm, provide a method for the optimization and show that the resulting algorithm produces an accurate segmentation that demonstrates greater stability with respect to the number of seeds employed than either the graph cuts or random walker methods.


title: Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

abstract: Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked "What vehicle is the person riding?", computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) to answer correctly that "the person is riding a horse-drawn carriage." In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models. Specifically, our dataset contains over 108K images where each image has an average of $$35$$35 objects, $$26$$26 attributes, and $$21$$21 pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. Together, these annotations represent the densest and largest dataset of image descriptions, objects, attributes, relationships, and question answer pairs.


title: Universal Sentence Encoder

abstract: We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse transfer tasks. Two variants of the encoding models allow for trade-offs between accuracy and compute resources. For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance. Comparisons are made with baselines that use word level transfer learning via pretrained word embeddings as well as baselines do not use any transfer learning. We find that transfer learning using sentence embeddings tends to outperform word level transfer. With transfer learning via sentence embeddings, we observe surprisingly good performance with minimal amounts of supervised training data for a transfer task. We obtain encouraging results on Word Embedding Association Tests (WEAT) targeted at detecting model bias. Our pre-trained sentence encoding models are made freely available for download and on TF Hub.


title: Brand-Related Twitter Sentiment Analysis Using Feature Engineering and the Dynamic Architecture for Artificial Neural Networks

abstract: We present an approach to brand-related Twitter sentiment analysis using feature engineering and the Dynamic Architecture for Artificial Neural Networks (DAN2). The approach addresses challenges associated with the unique characteristics of the Twitter language, and the recall of mild sentiment expressions that are of interest to brand management practitioners. We demonstrate the effectiveness of the approach on a Starbucks brand-related Twitter data set. The feature engineering produced a final tweet feature representation consisting of only seven dimensions, with greater feature density. Two sets of experiments were conducted in three-class and five-class tweet sentiment classification. We compare the proposed approach to the performances of two state-of-the-art Twitter sentiment analysis systems from the academic and commercial domains. The results indicate that the approach outperforms these state-of-the-art systems in both three-class and five-class tweet sentiment classification by wide margins, with classification accuracies above 80% and excellent recall of mild sentiment tweets.


title: Enhancing Transparency of Black-box Soft-margin SVM by Integrating Data-based Prior Information

abstract: The lack of transparency often makes the black-box models difficult to be applied to many practical domains. For this reason, the current work, from the black-box model input port, proposes to incorporate data-based prior information into the black-box soft-margin SVM model to enhance its transparency. The concept and incorporation mechanism of data-based prior information are successively developed, based on which the transparent or partly transparent SVM optimization model is designed and then solved through handily rewriting the optimization problem as a nonlinear quadratic programming problem. An algorithm for mining data-based linear prior information from data set is also proposed, which generates a linear expression with respect to two appropriate inputs identified from all inputs of system. At last, the proposed transparency strategy is applied to eight benchmark examples and two real blast furnace examples for effectiveness exhibition.


title: Biomedical question answering using semantic relations

abstract: Background ::: The proliferation of the scientific literature in the field of biomedicine makes it difficult to keep abreast of current knowledge, even for domain experts. While general Web search engines and specialized information retrieval (IR) systems have made important strides in recent decades, the problem of accurate knowledge extraction from the biomedical literature is far from solved. Classical IR systems usually return a list of documents that have to be read by the user to extract relevant information. This tedious and time-consuming work can be lessened with automatic Question Answering (QA) systems, which aim to provide users with direct and precise answers to their questions. In this work we propose a novel methodology for QA based on semantic relations extracted from the biomedical literature.


title: Intrinsic and extrinsic motivations: Classic definitions and new directions

abstract: Intrinsic and extrinsic types of motivation have been widely studied, and the distinction between them has shed important light on both developmental and educational practices. In this review we revisit the classic definitions of intrinsic and extrinsic motivation in light of contemporary research and theory. Intrinsic motivation remains an important construct, reflecting the natural human propensity to learn and assimilate. However, extrinsic motivation is argued to vary considerably in its relative autonomy and thus can either reflect external control or true self-regulation. The relations of both classes of motives to basic human needs for autonomy, competence and relatedness are discussed. ≈ 2000 Academic Press To be motivated means to be moved to do something. A person who feels no impetus or inspiration to act is thus characterized as unmotivated, whereas someone who is energized or activated toward an end is considered motivated. Most everyone who works or plays with others is, accordingly, concerned with motivation, facing the question of how much motivation those others, or oneself, has for a task, and practitioners of all types face the perennial task of fostering more versus less motivation in those around them. Most theories of motivation reflect these concerns by viewing motivation as a unitary phenomenon, one that varies from very little motivation to act to a great deal of it. Yet, even brief reflection suggests that motivation is hardly a unitary phenomenon. People have not only different amounts, but also different kinds of motivation. That is, they vary not only in level of motivation (i.e., how much motivation), but also in the orientation of that motivation (i.e., what type of motivation). Orientation of motivation concerns the underlying attitudes and goals that give rise to action—that is, it concerns the why of actions. As an example, a student can be highly motivated to do homework out of curiosity and interest or, alternatively, because he or she wants to procure the approval of a teacher or parent. A student could be motivated


title: Tri-band circularly polarized stacked microstrip antenna for GPS and CNSS applications

abstract: A compact single-feed stacked microstrip patch antenna for tri-band circularly polarized (CP) application is presented. The proposed antenna with two properly square patches. By inserting two pairs of narrow slots parallel to the edges of the top square patch, with two protruding slots perpendicular to the edge, the top patch can perform CNSS dual-frequency (1.61 GHz and 2.49 GHz) CP radiations using a single probe feed. The GPS frequency (1.57 GHz) is achieved by cutting a slit in the bottom square patch and by adjusting the length of the slit, the antenna can perform CP radiation. Details of the proposed antenna design and results for the obtained tri-band CP performances are presented and discussed.


title: Forecasting of Financial Markets via Neural Network

abstract: Artificial neural network is one of the intelligent methods in Artificial Intelligence. There are many decisions of different tasks using neural network approach. The forecasting problems are high challenge and researchers use different methods to solve them. The financial tasks related to forecasting, classification and management using artificial neural network are considered. The technology and methods for prediction of financial data as well as the developed system for forecasting of financial markets via neural network are described in the paper. The designed architecture of a neural network using four different technical indicators is presented. The developed neural network is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is a training algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise. The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.


title: Perception of the Image of Scientist by Israeli Student Teachers from Two Distinct Communities in Israel: Arabs and Jews

abstract: This is a comparative study of the image of the scientist held by Israeli Jewish and Arabic student teachers from various backgrounds. The image of female scientists among these groups was also investigated. Five groups of female students (N=500) from four colleges were studied. Traditional tools (DAST) were combined with more informative methods (captions, statements, and free writing) and supplemented by interviews. The stereotypic image of the scientist was found to be a bespectacled male using conventional research equipment, who prefers intellectual occupations for leisure pursuits was perceived by most participants. The image held by the secular Jewish student teachers largely conformed to the Western image of the scientist – a disheveled man working in a laboratory, with few social connections. An image of a scientist as a revered, authoritative teacher or scholar emerged amongst the traditional groups (Bedouin, Orthodox, and Ultra-Orthodox). The image found amongst Arabic student teachers of the North (a moderately traditional group) was unique: a young scientist, using computers, whose work is partially done outside of the laboratory. Among the traditional groups, the female scientist is perceived to be “torn” between her career and tradition. Conclusions from this research and implications for science study and teaching curricula are discussed.


title: Mapping and use

abstract: Analogical reasoning is a kind of reasoning that applies between specific exemplars or cases, in which what is known about one exemplar is used to infer new information about another exemplar. The basic intuition behind analogical reasoning is that when there are substantial parallels across different situations, there are likely to be further parallels. ::: ::: ::: Keywords: ::: ::: mapping; ::: alignment; ::: inference; ::: relational structure; ::: structural consistency; ::: systematicity; ::: pragmatic mapping; ::: structure-mapping; ::: structural mapping


title: Context-aware Distributed Complex Event Processing Method for Event Cloud in Internet of Things

abstract: With the rapid development of Internet of Things (IoT), enormous events are produced by various kinds of devices at high speed. Complex Event Processing (CEP) is the key part of IoT middleware which can help the user to get semantic meanings of primitive events. Context-awareness is an important feature of CEP engine. In this paper a high performance distributed context-aware CEP architecture and method is proposed for internet of things. Context is modeled as fuzzy ontology to support uncertainty and linguistic variables in event queries. Based on fuzzy ontology query and similarity based distributed reasoning, complex event query plans are generated and context-aware queries are rewritten into context independent sub-queries. Data window is partitioned according to different event patterns and context. The sub-queries are optimized and executed parallel based on data partition. The experiments show that this method can support fuzzy context in CEP and have acceptable performance and scalability.


title: Efficient algorithms for generalized subgraph query processing

abstract: We study a new type of graph queries, which injectively maps its edges to paths of the graphs in a given database, where the length of each path is constrained by a given threshold specified by the weight of the corresponding matching edge. We give important applications of the new graph query and identify new challenges of processing such a query. Then, we devise the cost model of the branch-and-bound algorithm framework for processing the graph query, and propose an efficient algorithm to minimize the cost overhead. We also develop three indexing techniques to efficiently answer the queries online. Finally, we verify the efficiency of our proposed indexes with extensive experiments on large real and synthetic datasets.


title: Historical declines and disparities in cigarette coupon saving among adolescents in the United States, 1997-2013.

abstract: Exposure to cigarette coupons is associated with smoking initiation and likelihood of cigarette purchase among adolescents. Some adolescents who are exposed to cigarette coupons take a step further by choosing to save or collect these coupons, a further risk factor for cigarette smoking. This study examines historical trends and disparities in cigarette coupon saving among adolescents in the United States from 1997 to 2013. National samples of 10th and 12th grade students (n=129,111) were obtained from Monitoring the Future surveys in 1997-2013. Prevalence of lifetime and current cigarette coupon saving was estimated in each year in the overall adolescent population, and in race/ethnicity, parent education level, sex, and urban/rural subgroups. Prevalence of lifetime and current cigarette coupon saving was then estimated in each year based on smoking status. Prevalence of cigarette coupon saving has decreased dramatically among adolescents; only 1.2% reported currently saving coupons in 2013. However, disparities in cigarette coupon saving remain with prevalence higher among rural, White, and low parental education level students. Adolescent smokers continue to save coupons at high rates; 21.2% had ever saved coupons and 6.9% currently saved coupons as of 2013. Despite overall declines in adolescent cigarette coupon saving, existing sociodemographic disparities and the considerably high prevalence of coupon saving among adolescent smokers suggest that cigarette coupons remain a threat to smoking prevention among youth. Additional research is needed to further elucidate longitudinal associations between cigarette coupon saving and smoking initiation and maintenance among adolescents.


title: Presentation mode and question format artifacts in visual assessment research

abstract: Abstract Research in assessment relies on both open-end and closed-end questions and on real and depicted presentations of scenes. Studies to date have shown that photographic depictions and “real” scenes (site views) yield similar answers to closed-end questions, but it is not known whether mode of presentation makes a difference when open-end questions are used. This paper reports on a quasi-experiment in which 24 students of landscape architecture were shown either site views or color slides of architectural views and asked to assess them with either open-end questions or a variety of closed-end questions. The scenes were five architectural views of an automated people-mover system, including both stations and elevated guideways. In line with the assumptions that (A) site views are richer and more dynamic than color slides, and (B) open-end questions allow more variation in response than closed-end questions, the authors hypothesized that open-end questions would produce quantitatively and qualitatively different data with site presentations than with color slides. Consistent with previous research, mode of presentation was not predicted to influence answers to closed-end questions. As expected, mode of presentation did not significantly affect answers to closed-end questions, but the subjects who viewed sites gave a larger number of responses to open-end questions. These responses contained significantly more descriptions of ephemeral aspects of the scenes (statistically significant) and slightly more evocative (subjective) responses (not statistically significant). In contrast, slides produced more descriptions of obvious physical features. Photographic depictions are apparently less involving than sites views. If closed-end questions are used, there is no particular advantage in using site visits, which are time-consuming, costky and difficult. Researchers using open-end questions for visual assessment may find that site visits are worth the trouble if they wish to measure responses to scenic ephemera or the subjective responses evoked by a scene. Overall, however, site and slide presentations do not produce radically different architectural assessments, and site visits do not have any strong advantage when architectural views are being assessed.


title: Recognition memory: What are the roles of the perirhinal cortex and hippocampus?

abstract: The hallmark of medial temporal lobe amnesia is a loss of episodic memory such that patients fail to remember new events that are set in an autobiographical context (an episode). A further symptom is a loss of recognition memory. The relationship between these two features has recently become contentious. Here, we focus on the central issue in this dispute — the relative contributions of the hippocampus and the perirhinal cortex to recognition memory. A resolution is vital not only for uncovering the neural substrates of these key aspects of memory, but also for understanding the processes disrupted in medial temporal lobe amnesia and the validity of animal models of this syndrome.


Detecting Depression with Audio/Text Sequence Modeling of Interviews


title: BenchBox: A User-Driven Benchmarking Framework for Fat-Client Storage Systems

abstract: In many online storage services, end-users mainly interact with the system via “fat” storage clients that integrate complex functionality. This means that to obtain a complete performance evaluation of one of such systems we may need to generate workloads on the client side that reproduce the behavior of real users. Unfortunately, this remains as an open research challenge today. We present BenchBox : A distributed performance evaluation framework for fat-client storage systems. On the one hand, BenchBox can generate workloads directly in storage clients that mimic users exhibiting a certain behavior, namely, user stereotypes . To this end, the framework enables to plug-in workload models and feed them with compact recipes that capture the behavior of user stereotypes (e.g., storage activity, type of file contents, data sharing links). On the other hand, BenchBox provides researchers with management and monitoring facilities to deploy experiments and analyze the performance of groups of storage clients. To demonstrate our framework, we equipped BenchBox with a 2-layer workload model that reproduces both the activity —e.g., types of operations, frequency— and data —e.g., file sizes, data types—of users in a Personal Cloud. We used this model to generate workloads based on user stereotypes that we identified in real traces (UbuntuOne). Our experiments with public providers show how distinct types of users impact on the performance and efficiency of Personal Clouds, which may guide their optimization.


title: RPM: Representative Pattern Mining for Efficient Time Series Classification

abstract: Time series classication is an important problem that has received a great amount of attention by researchers and practitioners in the past two decades. In this work, we propose a novel algorithm for time series classication based on the discovery of class-specic representative patterns. We dene representative patterns of a class as a set of subsequences that has the greatest discriminative power to distinguish one class of time series from another. Our approach rests upon two techniques with linear complexity: symbolic discretization of time series, which generalizes the structural patterns, and grammatical inference, which automatically nds recurrent correlated patterns of variable length, producing an initial pool of common patterns shared by many instances in a class. From this pool of candidate patterns, our algorithm selects the most representative patterns that capture the class specicities, and that can be used to eectively discriminate between time series classes. Through an exhaustive experimental evaluation we show that our algorithm is competitive in accuracy and speed with the stateof-the-art classication techniques on the UCR time series repository, robust on shifted data, and demonstrates excellent performance on real-world noisy medical time series.


title: Dual-Polarized Quasi Yagi–Uda Antennas With Endfire Radiation for Millimeter-Wave MIMO Terminals

abstract: This paper presents a novel design of compact dual-polarized multi-input and multi-output (MIMO) antennas with endfire radiation for millimeter-wave wireless applications. The low-cost printed circuit board process serves as the basis for the design, fabrication, and measurement of the proposed dual-polarized quasi Yagi–Uda antennas. Addressing the potential antenna locations in a mobile terminal, this paper investigates both the corner and the lateral design of antenna modules. Each design incorporates dual-port dual-polarized antennas co-located in a compact area. The lateral design is further extended to a linear $1 \times 4$ array for high-gain and phased-scanning operation. Experimental results show that the proposed compact dual-polarized quasi Yagi–Uda antennas are very suitable for MIM terminals of next-generation (5G) mobile communications.


title: Model Based Clustering for Mixed Data: clustMD

abstract: A model based clustering procedure for data of mixed type, clustMD, is developed using a latent variable model. It is proposed that a latent variable, following a mixture of Gaussian distributions, generates the observed data of mixed type. The observed data may be any combination of continuous, binary, ordinal or nominal variables. clustMD employs a parsimonious covariance structure for the latent variables, leading to a suite of six clustering models that vary in complexity and provide an elegant and unified approach to clustering mixed data. An expectation maximisation (EM) algorithm is used to estimate clustMD; in the presence of nominal data a Monte Carlo EM algorithm is required. The clustMD model is illustrated by clustering simulated mixed type data and prostate cancer patients, on whom mixed data have been recorded.


title: Bipolar pulse generator for very high frequency (> 100 MHz) ultrasound applications

abstract: Design of a cost-effective and compact monocycle bipolar pulse generator for very high frequency (> 100 MHz) transducers and cell applications is presented. The designed pulse generator could provide a monocycle bipolar pulse with center frequency of 220 MHz, -6 dB bandwidth of 100 % and the peak-to-peak amplitude of 2 V. Moreover, it generates less ringing effect to improve the axial resolution of the ultrasound systems. For the transducers that triggered by the pulse generator, the -6 dB bandwidth of received echo signals was considerably improved by 122 % compared to the Panametrics 5900 transmitter. In addition, wire phantom measurements are presented.


title: Injection Attack Detection Using the Removal of SQL Query Attribute Values

abstract: The expansion of the Internet has made web applications become a part of everyday life. As a result the number of incidents which exploit web application vulnerabilities are increasing. A large percentage of these incidents are SQL Injection attacks which are a serious security threat to databases with potentially sensitive information. Therefore, much research has been done to detect and prevent these attacks and it resulted in a decline of SQL Injection attacks. However, there are still methods to bypass them and these methods are too complex to implement in real web applications. This paper proposes a simple and effective SQL Query removal method which uses Combined Static and Dynamic Analysis and evaluates the efficiency through various experiments.


title: Mal-ID: Automatic Malware Detection Using Common Segment Analysis and Meta-Features

abstract: This paper proposes several novel methods, based on machine learning, to detect malware in executable files without any need for preprocessing, such as unpacking or disassembling. The basic method (Mal-ID) is a new static (form-based) analysis methodology that uses common segment analysis in order to detect malware files. By using common segment analysis, Mal-ID is able to discard malware parts that originate from benign code. In addition, Mal-ID uses a new kind of feature, termed meta-feature, to better capture the properties of the analyzed segments. Rather than using the entire file, as is usually the case with machine learning based techniques, the new approach detects malware on the segment level. This study also introduces two Mal-ID extensions that improve the Mal-ID basic method in various aspects. We rigorously evaluated Mal-ID and its two extensions with more than ten performance measures, and compared them to the highly rated boosted decision tree method under identical settings. The evaluation demonstrated that Mal-ID and the two Mal-ID extensions outperformed the boosted decision tree method in almost all respects. In addition, the results indicated that by extracting meaningful features, it is sufficient to employ one simple detection rule for classifying executable files.


title: Differential Response of Central Blood Pressure to Isometric and Isotonic Exercises

abstract: Central blood pressure may be more closely associated with cardiovascular events than peripheral blood pressure. The aim of the present study was to investigate central blood pressure responses to exercise. Apparently healthy 18 subjects were enrolled in the study (38 ± 6 years) and changes in central and brachial blood pressure were recorded in response to ergometer and hand-grip exercises. Central blood pressure was estimated using an automated device (Omron HEM-9000AI). Systolic brachial blood pressure was increased after both ergometer (from 119 ± 10 to 172 ± 16 mmHg; P < 0.001) and hand-grip (from 118 ± 8 to 122 ± 9 mmHg; P = 0.001) exercises, but central systolic blood pressure was increased only after hand-grip exercise (from 117 ± 11 to 121 ± 12 mmHg; P = 0.002). The radial augmentation index was increased after hand-grip exercise, whereas ergometer exercise reduced this index. Heart rate was increased only after ergometer exercise. Thus, isometric, but not isotonic, exercise may increase central blood pressure in overall healthy subjects. The response of central blood pressure, which is a better index of cardiac load than peripheral blood pressure, to hand-grip exercise may be useful in evaluating cardiovascular risk.


Behavior in the Brain Using Functional Neuroimaging to Assess Residual Cognition and Awareness After Severe Brain Injury


title: A study of modern Linux API usage and compatibility: what to support when you're supporting

abstract: This paper presents a study of Linux API usage across all applications and libraries in the Ubuntu Linux 15.04 distribution. We propose metrics for reasoning about the importance of various system APIs, including system calls, pseudo-files, and libc functions. Our metrics are designed for evaluating the relative maturity of a prototype system or compatibility layer, and this paper focuses on compatibility with Linux applications. This study uses a combination of static analysis to understand API usage and survey data to weight the relative importance of applications to end users. This paper yields several insights for developers and researchers, which are useful for assessing the complexity and security of Linux APIs. For example, every Ubuntu installation requires 224 system calls, 208 ioctl, fcntl, and prctl codes and hundreds of pseudo files. For each API type, a significant number of APIs are rarely used, if ever. Moreover, several security-relevant API changes, such as replacing access with faccessat, have met with slow adoption. Finally, hundreds of libc interfaces are effectively unused, yielding opportunities to improve security and efficiency by restructuring libc.


title: Classification of sidewalks in street view images

abstract: Mapping sidewalks in urban environments is key in the creation of pedestrian-friendly, sustainable cities. Currently, urban planners are hindered by a lack of information available in a format suitable for the large-scale analysis of sidewalk design. To demonstrate the impact that information technology could have in this area, we leverage techniques from machine learning and computer vision to gather information about the presence and quality of sidewalks in map images. In particular, we identify sidewalk segments in street view images using a random forest classifier, utilizing a set of local and global features that include geometric context, presence of lanes, pixel color, and location. Our results illustrate that this approach is effective in classifying sidewalk segments in a large set of street view images. This algorithm can be easily extended to other datasets, and can be automated to gather complete, fine-grained details about sidewalks for arbitrarily large urban environments.