title: Fast Eigenspace Approximation using Random Signals

abstract: We focus in this work on the estimation of the first $k$ eigenvectors of any graph Laplacian using filtering of Gaussian random signals. We prove that we only need $k$ such signals to be able to exactly recover as many of the smallest eigenvectors, regardless of the number of nodes in the graph. In addition, we address key issues in implementing the theoretical concepts in practice using accurate approximated methods. We also propose fast algorithms both for eigenspace approximation and for the determination of the $k$th smallest eigenvalue $\lambda_k$. The latter proves to be extremely efficient under the assumption of locally uniform distribution of the eigenvalue over the spectrum. Finally, we present experiments which show the validity of our method in practice and compare it to state-of-the-art methods for clustering and visualization both on synthetic small-scale datasets and larger real-world problems of millions of nodes. We show that our method allows a better scaling with the number of nodes than all previous methods while achieving an almost perfect reconstruction of the eigenspace formed by the first $k$ eigenvectors.


title: A trade-off between space and efficiency for routing tables

abstract: Two conflicting goals play a crucial role in the design of routing schemes for communication networks. A routing scheme should use paths that are as short as possible for routing messages in the network, while keeping the routing information stored in the processors' local memory as succinct as possible. The efficiency of a routing scheme is measured in terms of its stretch factor -the maximum ratio between the length of a route computed by the scheme and that of a shortest path connecting the same pair of vertices. Most previous work has concentrated on finding good routing schemes (with a small fixed stretch factor) for special classes of network topologies. In this paper the problem for general networks is studied, and the entire range of possible stretch factors is examined. The results exhibit a trade-off between the efficiency of a routing scheme and its space requirements. Almost tight upper and lower bounds for this trade-off are presented. Specifically, it is proved that any routing scheme for general n -vertex networks that achieves a stretch factor k ≥ 1 must use a total of O( n 1+1/(2 k +4) ) bits of routing information in the networks. This lower bound is complemented by a family K ( k ) of hierarchical routing schemes (for every k ≥ l) for unit-cost general networks, which guarantee a stretch factor of O ( k ), require storing a total of O ( k 3 n 1+(1/h) log n )- bits of routing information in the network, name the vertices with O (log 2 n )-bit names and use O (log n )-bit headers.


title: A Survey and Evaluation of FPGA High-Level Synthesis Tools

abstract: High-level synthesis (HLS) is increasingly popular for the design of high-performance and energy-efficient heterogeneous systems, shortening time-to-market and addressing today’s system complexity. HLS allows designers to work at a higher-level of abstraction by using a software program to specify the hardware functionality. Additionally, HLS is particularly interesting for designing field-programmable gate array circuits, where hardware implementations can be easily refined and replaced in the target device. Recent years have seen much activity in the HLS research community, with a plethora of HLS tool offerings, from both industry and academia. All these tools may have different input languages, perform different internal optimizations, and produce results of different quality, even for the very same input description. Hence, it is challenging to compare their performance and understand which is the best for the hardware to be implemented. We present a comprehensive analysis of recent HLS tools, as well as overview the areas of active interest in the HLS research community. We also present a first-published methodology to evaluate different HLS tools. We use our methodology to compare one commercial and three academic tools on a common set of C benchmarks, aiming at performing an in-depth evaluation in terms of performance and the use of resources.


title: Application of Bayesian Network to stock price prediction

abstract: Authors present the stock price prediction algorithm by using Bayesian network. The present algorithm uses the networktwice. First, the network is determined from the daily stock price and then, it is applied for predicting the daily stock pricewhich was already observed. The prediction error is evaluated from the daily stock price and its prediction. Second, thenetwork is determined again from both the daily stock price and the daily prediction error and then, it is applied for thefuture stock price prediction. The present algorithm is applied for predicting NIKKEI stock average and Toyota motorcorporation stock price. Numerical results show that the maximum prediction error of the present algorithm is 30% inNIKKEI stock average and 20% in Toyota Motor Corporation below that of the time-series prediction algorithms such asAR, MA, ARMA and ARCH models.


Improving neural knowledge base completion with cross-lingual projections


title: Development of Smart Insulin Device for Non Invasive Blood Glucose Level Monitoring

abstract: Diabetes is one of the life threatening diseases in theworld. Nowadays, diabetes patients are increasing due toimproper monitoring of blood glucose level. The diabetes infectedpatients have to check the amount of glucose level present in thebody using invasive method. By using this method, they have totake a drop of blood from the body and check the amount ofglucose level, by which they can inject the required amount ofinsulin into the body. To overcome the difficulties caused byinvasive method, in this prototype we are using non-invasivemethodology. The main objective of this work is to design aportable non-invasive blood glucose level monitoring device usingnear infrared sensors. The device includes Infrared LED,Photodiode and ATMEGA328 Microcontroller. Besides beingable to detect glucose concentration in blood, the device alsodisplays the required insulin dose based on glucose levelcorresponding to the Body Mass Index (BMI) of the user.


title: The Design of Approximation Algorithms

abstract: Discrete optimization problems are everywhere, from traditional operations research planning problems, such as scheduling, facility location, and network design; to computer science problems in databases; to advertising issues in viral marketing. Yet most such problems are NP-hard. Thus unless P = NP, there are no efficient algorithms to find optimal solutions to such problems. This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The book is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization. Each chapter in the first part of the book is devoted to a single algorithmic technique, which is then applied to several different problems. The second part revisits the techniques but offers more sophisticated treatments of them. The book also covers methods for proving that optimization problems are hard to approximate. Designed as a textbook for graduate-level algorithms courses, the book will also serve as a reference for researchers interested in the heuristic solution of discrete optimization problems.


title: Different metastatic pattern according to the KRAS mutational status and site-specific discordance of KRAS status in patients with colorectal cancer

abstract: BackgroundWe evaluated the association between a KRAS mutational status and various clinicopathologic features including the metastatic pattern in patients with metastatic or recurrent colorectal cancer (MRCRC). The concordance rates of the KRAS status between primary tumor sites and paired metastatic organs were also analyzed.MethodsThe KRAS mutational status in codons 12, 13, and 61 from formalin-fixed sections of both primary tumors and related metastases was determined by sequencing analysis. One hundred forty-three Korean patients with MRCRC with available tissues (resection or biopsy) from both primary tumors and related metastatic sites were consecutively enrolled.ResultsThe KRAS mutation rate was 52.4% (75/143) when considering both the primary and metastatic sites. When the relationship between the KRAS status and initial metastatic sites at the time of diagnosis of MRCRC was analyzed, lung metastasis was more frequent as the initial metastatic site in patients with the KRAS mutation than in patients without the KRAS mutation (45.3% vs. 22.1%; P = 0.003). However, liver (37.3% vs. 70.6%; P < 0.001) or distant lymph node metastases (6.7% vs. 19.1%; P = 0.025) were less frequent as the initial metastatic organ in patients with the KRAS mutation than in patients without the KRAS mutation. The discordance rate of KRAS mutational status between primary and paired metastatic sites other than the lung was 12.3% (13/106). Compared with primary tumor sites, the KRAS discordance rate was significantly higher in matched lung metastases [32.4% (12/37)] than in other matched metastatic organs (P = 0.005).ConclusionsOrgans initially involved by distant metastasis were different according to the KRAS mutational status in MRCRC patients. The concordance rate (87.7%) of the KRAS mutation status at metastatic sites other than the lung was generally high compared with primary tumor sites; however, lung metastasis had a high rate of KRAS discordance (32.4%).


title: Practical Design and Implementation of Metamaterial-Enhanced Magnetic Induction Communication

abstract: The wireless communications in complex environments, such as underground and underwater, can enable various applications in the environmental, industrial, homeland security, law enforcement, and military fields. However, conventional electromagnetic wave-based techniques do not work due to the lossy media and complicated structures. Magnetic induction (MI) has been proved to achieve reliable communication in such environments. However, due to the small antenna size, the communication range of MI is still very limited, especially for the portable mobile devices. To this end, Metamaterial-enhanced MI (M2I) communication has been proposed, where the theoretical results predict that it can significantly increase the data rate and range. Nevertheless, there exists a significant gap between the theoretical prediction and the practical realization of M2I; the theoretical model relies on an ideal spherical metamaterial, while it does not exist in nature. In this paper, a practical design is proposed by leveraging a spherical coil array to realize M2I communication. The full-wave simulation is conducted to validate the design objectives. By using the spherical coil array-based M2I communication, the communication range can be significantly extended, exactly as we predicted in the ideal M2I model. Finally, the proposed M2I communication is implemented and tested in various environments.


title: Unsupervised Image-to-Image Translation Networks

abstract: Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains. Since there exists an infinite set of joint distributions that can arrive the given marginal distributions, one could infer nothing about the joint distribution from the marginal distributions without additional assumptions. To address the problem, we make a shared-latent space assumption and propose an unsupervised image-to-image translation framework based on Coupled GANs. We compare the proposed framework with competing approaches and present high quality image translation results on various challenging unsupervised image translation tasks, including street scene image translation, animal image translation, and face image translation. We also apply the proposed framework to domain adaptation and achieve state-of-the-art performance on benchmark datasets. Code and additional results are available in this https URL .


title: Dynamic behaviors on the NAO robot with closed-loop whole body operational space control

abstract: Exploiting full-body dynamics in feedback control can enhance the balancing capability of a legged system using various techniques such as Whole-Body Control (WBC) or Centroidal Momentum control. However, motion control of the NAO robot based on full-body dynamics has not been extensively studied due to its limited computation power, limited sensors, and restricted access to its low-level controllers. Whole-Body Operational Space Control (WBOSC) is a promising WBC approach for NAO, since its closed form solution provides computational efficiency. But, users need to provide the velocity map (Jacobian) between operational space and configuration space to add the balancing control task. Thus, in this paper, we formulate the Jacobians incorporating the Capture Point (CP) technique [1] and the Centroidal Angular Momentum (CAM) [2], [3], and demonstrate the enhancement of balancing capability in a physics-based simulation. While WBOSC reduces the computational load, implementing WBC in the real system with limited sensing capability and built-in joint position control is challenging. We show that the combination of a virtual model as an interface to the real robot and an Extended Kalman-filter based orientation estimator results in a stable implementation of a closed-loop WBOSC. We demonstrate the validity of our approach by performing a dynamic kicking motion on the physical NAO robot. Overall, the contributions of this paper are: (1) to extend WBOSC by adding CAM and CP control tasks, and (2) to implement WBOSC in a restricted physical system by utilizing a virtual model and an orientation estimator.


title: Web-based recruiting’s impact on organizational image and familiarity: too much of a good thing?

abstract: AbstractLittle is known about the efficacy of many of the newer forms of online recruitment. Using a quasi-experimental design, we tested the impact of individual exposure to corporate recruitment ...


title: Fitness and Novelty in Evolutionary Art

abstract: In this paper the effects of introducing novelty search in evolutionary art are explored. Our algorithm combines fitness and novelty metrics to frame image evolution as a multi-objective optimisation problem, promoting the creation of images that are both suitable and diverse. The method is illustrated by using two evolutionary art engines for the evolution of figurative objects and context free design grammars. The results demonstrate the ability of the algorithm to obtain a larger set of fit images compared to traditional fitness-based evolution, regardless of the engine used.


title: Semisupervised Hyperspectral Classification Using Task-Driven Dictionary Learning With Laplacian Regularization

abstract: We present a semisupervised method for single-pixel classification of hyperspectral images. The proposed method is designed to address the special problematic characteristics of hyperspectral images, namely, high dimensionality of hyperspectral pixels, lack of labeled samples, and spatial variability of spectral signatures. To alleviate these problems, the proposed method features the following components. First, being a semisupervised approach, it exploits the wealth of unlabeled samples in the image by evaluating the confidence probability of the predicted labels, for each unlabeled sample. Second, we propose to jointly optimize the classifier parameters and the dictionary atoms by a task-driven formulation, to ensure that the learned features (sparse codes) are optimal for the trained classifier. Finally, it incorporates spatial information through adding a Laplacian smoothness regularization to the output of the classifier, rather than the sparse codes, making the spatial constraint more flexible. The proposed method is compared with a few comparable methods for classification of several popular data sets, and it produces significantly better classification results.


title: 4×25 Gb/s Transceiver With Optical Front-end for 100 GbE System in 65 nm CMOS Technology

abstract: This paper presents a fully-integrated chipset for 4×25 Gb/s transceiver gearbox along with laser driver and photo detector front-ends. The transceiver provides 10:4 multiplexing and 4:10 demultiplexing conversion, with built-in clock generation, equalization, and amplification. The optical front-ends are realized as 4-element arrays, presenting remarkable performance with commercial vertical-cavity surface-emitting lasers and photodiodes. Feedforward equalizers and continuous-time linear equalizers are employed to compensate for loss and distortion in both electrical and optical domains. Fabricated in 65 nm CMOS technology, these chips can be lumped together as a compact module with performance exceeding typical 100 GbE standards.


title: A survey on wireless security protocols (WEP, WPA and WPA2/802.11i)

abstract: Wireless technology has been gaining rapid popularity for some years. Adaptation of a standard depends on the ease of use and level of security it provides. In this case, contrast between wireless usage and security standards show that the security is not keeping up with the growth paste of end user's usage. Current wireless technologies in use allow hackers to monitor and even change the integrity of transmitted data. Lack of rigid security standards has caused companies to invest millions on securing their wireless networks. There are three major types of security standards in wireless. In our previous papers which registered in ICFCC 2009 Malaysia and ICCDA 2009 Singapore [1] [2], we explained the structure of WEP and WPA as first and second wireless security protocols and discussed all their versions, problems and improvements. Now, we try to explain WPA2 versions, problems and enhancements that have done solve the WPA major weakness. Finally we make a comparison among WEP and WPA and WPA2 as all wireless security protocols in Wi-Fi technology. In the next phase we hope that we will publish a complete comparison among wireless security techniques by add the WiMax security technique and make a whole comparison among all security protocols in this area.


title: Chapter 3 Biometric Recognition: An Overview

abstract: Prevailing methods of human identification based on credentials (identification documents and PIN) are not able to meet the growing demands for stringent security in applications such as national ID cards, border crossings, government benefits, and access control. As a result, biometric recognition, or simply biometrics, which is based on physiological and behavioural characteristics of a person, is being increasingly adopted and mapped to rapidly growing person identification applications. Unlike credentials (documents and PIN), biometric traits (e.g., fingerprint, face, and iris) cannot be lost, stolen, or easily forged; they are also considered to be persistent and unique. Use of biometrics is not new; fingerprints have been successfully used for over 100 years in law enforcement and forensics to identify and apprehend criminals. But, as biometrics permeates our society, this recognition technology faces new challenges. The design and suitability of biometric technology for person identification depends on the application requirements. These requirements are typically specified in terms of identification accuracy, throughput, user acceptance, system security, robustness, and return on investment. The next generation biometric technology must overcome many hurdles and challenges to improve the recognition accuracy. These include ability to handle poor quality and incomplete data, achieve scalability to accommodate hundreds of millions of users, ensure interoperability, and protect user privacy while reducing system cost and enhancing system integrity. This chapter presents an overview of biometrics, some of the emerging biometric technologies and their limitations, and examines future challenges.


title: A Decision Tree and S-transform based approach for power quality disturbances classification

abstract: In this paper, it is presented an automated classification based on S-transform as feature extraction tool and Decision Tree as algorithm classifier. The signals generated according to mathematical models, including complex disturbances, have been used to design and test this approach, where noise is added to the signals from 40dB to 20dB. Finally, several disturbances, simple and complex, have been considered to test the implemented system. Evaluation results verifying the accuracy of the proposed method are presented.


title: The study and implementation of real-time face recognition and tracking system

abstract: During the past several years, face recognition in video has received significant attention. For the video monitoring and artificial vision, real time face recognition has very important meaning. The current method is still very susceptible to the illumination condition, non-real time and very common to fail to track the target face especially when partly covered or moving fast. In this paper, we propose to use AdaBoost Cascade for face detection and then in order to recognize the candidate faces, they will be analyzed by the hybrid Wavelet and LDA method. After that, Mean shift will be invoked to track the face. The implementation shows that the algorithm has quite good performance in terms of real-time and the tracking procedure is triggered accurately.


title: A Survey of Standards for Machine-to-Machine and the Internet of Things

abstract: Over the last decade, there has been a growing research interest in machine-to-machine communications and the Internet of Things (IoT) as a milestone embodiment of the continuous convergence between the physical facet of human activities and its reflection on the information world. IoT is understood as the (r)evolutionary transition into an era where physical assets and virtual assets will be treated uniformly and, for all intents and purposes, be largely indistinguishable to the processes involving them. The sheer scale of IoT suggests that harmonized global standards will be paramount in realizing a seamless treatment across the physical facet and the virtual facet of things. In this paper, we survey the—;admittedly babelian—standardization landscape of IoT and present its major efforts. Our survey concludes with a comparative discussion that highlights the key challenges of future standardization in IoT.


Embedded Emotion-based Classification of Stack Overflow Questions Towards the Question Quality Prediction


title: Combination of 595-nm pulsed dye laser, long-pulsed 755-nm alexandrite laser, and microdermabrasion treatment for keratosis pilaris: retrospective analysis of 26 Korean patients.

abstract: Keratosis pilaris (KP) has beenpresented as small keratotic follicular papules with or without surrounding erythema. Various treatments with laser or light therapy have been used for the management of KP with various clinical outcomes. In the present study, we investigated the efficacy and safety of a combination therapy for KP. A total of 29 anatomical sites with KP in 26 patients were treated using a 595-nm pulsed dye laser (PDL) with nonpurpuragenic fluences, a long-pulsed 755-nm alexandrite laser, and microdermabrasion. Clinical improvement was assessed by comparing preand posttreatment clinical photographs and patient satisfaction rates. Evaluation of the clinical results three months after the treatments showed that 12 of the 29 anatomical sites (41.4%) demonstrated Grade 3 clinical improvement, ten (34.5%) had Grade 2 clinical improvement, four (13.8%) showed Grade 1 improvement, and three (10.3%) showed Grade 4 improvement. We observed that KP lesions improved not only in erythema and skin texture, but also in brownish dyschromias. Potential adverse events were not observed, except prolonged posttherapy scaling. Our observations demonstrate that combination therapy using a 595-nm PDL, a long-pulsed 755-nm alexandrite laser, and microdermabrasion can have a positive therapeutic effect on KP.


title: Collaborative Knowledge Base Embedding for Recommender Systems

abstract: Among different recommendation techniques, collaborative filtering usually suffer from limited performance due to the sparsity of user-item interactions. To address the issues, auxiliary information is usually used to boost the performance. Due to the rapid collection of information on the web, the knowledge base provides heterogeneous information including both structured and unstructured data with different semantics, which can be consumed by various applications. In this paper, we investigate how to leverage the heterogeneous information in a knowledge base to improve the quality of recommender systems. First, by exploiting the knowledge base, we design three components to extract items' semantic representations from structural content, textual content and visual content, respectively. To be specific, we adopt a heterogeneous network embedding method, termed as TransR, to extract items' structural representations by considering the heterogeneity of both nodes and relationships. We apply stacked denoising auto-encoders and stacked convolutional auto-encoders, which are two types of deep learning based embedding techniques, to extract items' textual representations and visual representations, respectively. Finally, we propose our final integrated framework, which is termed as Collaborative Knowledge Base Embedding (CKE), to jointly learn the latent representations in collaborative filtering as well as items' semantic representations from the knowledge base. To evaluate the performance of each embedding component as well as the whole system, we conduct extensive experiments with two real-world datasets from different scenarios. The results reveal that our approaches outperform several widely adopted state-of-the-art recommendation methods.


Vision-Based State Estimation and Trajectory Control Towards High-Speed Flight with a


title: Scene-centric Joint Parsing of Cross-view Videos

abstract: Cross-view video understanding is an important yet under-explored area in computer vision. In this paper, we introduce a joint parsing framework that integrates view-centric proposals into scene-centric parse graphs that represent a coherent scene-centric understanding of cross-view scenes. Our key observations are that overlapping fields of views embed rich appearance and geometry correlations and that knowledge fragments corresponding to individual vision tasks are governed by consistency constraints available in commonsense knowledge. The proposed joint parsing framework represents such correlations and constraints explicitly and generates semantic scene-centric parse graphs. Quantitative experiments show that scene-centric predictions in the parse graph outperform view-centric predictions.


title: Essential amino acids are primarily responsible for the amino acid stimulation of muscle protein anabolism in healthy elderly adults

abstract: Background: Nutritional supplementation may be used to treat muscle loss with aging (sarcopenia). However, if physical activity does not increase, the elderly tend to compensate for the increased energy delivered by the supplements with reduced food intake, which results in a calorie substitution rather than supplementation. Thus, an effective supplement should stimulate muscle anabolism more efficiently than food or common protein supplements. We have shown that balanced amino acids stimulate muscle protein anabolism in the elderly, but it is unknown whether all amino acids are necessary to achieve this effect. Objective: We assessed whether nonessential amino acids are required in a nutritional supplement to stimulate muscle protein anabolism in the elderly. Design: We compared the response of muscle protein metabolism to either 18 g essential amino acids (EAA group: n = 6, age 69 ± 2 y; x ± SD) or 40 g balanced amino acids (18 g essential amino acids + 22 g nonessential amino acids, BAA group; n = 8, age 71 ± 2 y) given orally in small boluses every 10 min for 3 h to healthy elderly volunteers. Muscle protein metabolism was measured in the basal state and during amino acid administration via L-[ring- 2 H5]phenylalanine infusion, femoral arterial and venous catheterization, and muscle biopsies. Results: Phenylalanine net balance (in nmol · min � 1 · 100 mL leg volume � 1 ) increased from the basal state (P < 0.01), with no differences between groups (BAA: from � 16 ± 5 to 16 ± 4; EAA: from � 18 ± 5 to 14 ± 13) because of an increase (P < 0.01) in muscle protein synthesis and no change in breakdown. Conclusion: Essential amino acids are primarily responsible for the amino acid‐induced stimulation of muscle protein anabolism in the elderly. Am J Clin Nutr 2003;78:250‐8.


title: An integrated design and fabrication strategy for entirely soft, autonomous robots

abstract: Soft robots possess many attributes that are difficult, if not impossible, to achieve with conventional robots composed of rigid materials. Yet, despite recent advances, soft robots must still be tethered to hard robotic control systems and power sources. New strategies for creating completely soft robots, including soft analogues of these crucial components, are needed to realize their full potential. Here we report the untethered operation of a robot composed solely of soft materials. The robot is controlled with microfluidic logic that autonomously regulates fluid flow and, hence, catalytic decomposition of an on-board monopropellant fuel supply. Gas generated from the fuel decomposition inflates fluidic networks downstream of the reaction sites, resulting in actuation. The body and microfluidic logic of the robot are fabricated using moulding and soft lithography, respectively, and the pneumatic actuator networks, on-board fuel reservoirs and catalytic reaction chambers needed for movement are patterned within the body via a multi-material, embedded 3D printing technique. The fluidic and elastomeric architectures required for function span several orders of magnitude from the microscale to the macroscale. Our integrated design and rapid fabrication approach enables the programmable assembly of multiple materials within this architecture, laying the foundation for completely soft, autonomous robots.


title: User similarities on social networks

abstract: A key problem in online social networks is the identification of user characteristics and the analysis of how these are reflected in the graph structure evolution. The basis to tackle this issue is user similarity measures. In this paper, we propose a novel user similarity measure for online social networks, which combines both network and profile similarity. Since user profile data could be missing proposed measure is complemented by a technique to infer missing items from profile of the user’s contacts. The second main contribution of this paper is an extensive performance evaluation of the proposed measures with respect to some of the most relevant measures already proposed in the literature. The performance evaluation study has been conducted on a variety of data sets (i.e., Facebook, Youtube, Epinions and DBLP data sets) to see how different scenarios and graph characteristics affect the measures’ performance.


title: Approaches to energy efficient wireless access networks

abstract: Due to increasing data traffic rates and rollout of advanced radio transmission technologies wireless networks consume increasing amount of energy and contribute a growing fraction to the CO2 emissions of ICT industry. Thus, climate and cost issues now shift the research focus of wireless communications to energy consumption and energy efficiency. Two approaches can be followed: Incremental improvements of existing systems or a clean slate re-design with a fundamental change of paradigms. We describe two such initiatives and discuss their differences. The EC FP7 project EARTH is a 30 month project aiming for a reduction of the overall energy consumption of 4G mobile broadband networks by 50%, regarding network aspects and individual radio components from a holistic point of view. The Green Touch Initiative is a privately financed consortium addressing fundamental research that will pave the way to much higher reductions for future systems in the order of several magnitudes, with first proof of concepts available in 5 years.


title: openEyes: a low-cost head-mounted eye-tracking solution

abstract: Eye tracking has long held the promise of being a useful methodology for human computer interaction. However, a number of barriers have stood in the way of the integration of eye tracking into everyday applications, including the intrusiveness, robustness, availability, and price of eye-tracking systems. To lower these barriers, we have developed the openEyes system. The system consists of an open-hardware design for a digital eye tracker that can be built from low-cost off-the-shelf components, and a set of open-source software tools for digital image capture, manipulation, and analysis in eye-tracking applications. We expect that the availability of this system will facilitate the development of eye-tracking applications and the eventual integration of eye tracking into the next generation of everyday human computer interfaces. We discuss the methods and technical challenges of low-cost eye tracking as well as the design decisions that produced our current system.


title: Drive Now, Text Later: Nonintrusive Texting-While-Driving Detection Using Smartphones

abstract: Texting-while-driving (T&D) is one of the top dangerous behaviors for drivers. Many interesting systems and mobile phone applications have been designed to help to detect or combat T&D. However, for a T&D detection system to be practical, a key property is its capability to distinguish driver's mobile phone from passengers’. Existing solutions to this problem generally rely on the user's manual input, or utilize specific localization devices to determine whether a mobile phone is at the driver's location. In this paper, we propose a method which is able to detect T&D automatically without using any extra devices. The idea is very simple: when a user is composing messages, the smartphone embedded sensors (i.e., gyroscopes, accelerometers, and GPS) collect the associated information such as touchstrokes, holding orientation and vehicle speed. This information will then be analyzed to see whether there exists some specific T&D patterns. Extensive experiments have been conducted by different persons and in different driving scenarios. The results show that our approach can achieve a good detection accuracy with low false positive rate. Besides being infrastructure-free and with high accuracy, the method does not access the content of messages and therefore is privacy-preserving.


title: Haptics for Robot-Assisted Minimally Invasive Surgery

abstract: Robot-assisted minimally invasive surgery (RMIS) holds great promise for improving the accuracy and dexterity of a surgeon while minimizing trauma to the patient. However, widespread clinical success with RMIS has been marginal and it is hypothesized by engineers and surgeons alike that the lack of haptic feedback presented to the surgeon is a limiting factor. The objective of our research is to acquire, display, and determine the utility of haptic information during RMIS. This overview paper examines the design, analysis, practicality, and effectiveness of various force estimation and display methods. In particular, we describe our experience in adding force feedback to an experimental version of the da Vinci surgical system, a commercially available teleoperated RMIS system.


title: MyLearningMentor: A Mobile App to Support Learners Participating in MOOCs

abstract: MOOCs have brought a revolution to education. However, their impact is mainly benefiting people with Higher Education degrees. The lack of support and personalized advice in MOOCs is causing that many of the learners that have not developed work habits and self- learning skills give them up at the first obstacle, and do not see MOOCs as an alternative for their education and training. MyLearningMentor (MLM) is a mobile application that addresses the lack of support and personalized advice for learners in MOOCs. This paper presents the architecture of MLM and practical examples of use. The architecture of MLM is designed to provide MOOC participants with a personalized planning that facilitates them following up the MOOCs they enroll. This planning is adapted to learners' profiles, preferences, priorities and previous performance (measured in time devoted to each task). The architecture of MLM is also designed to provide tips and hints aimed at helping learners develop work habits and study skills, and eventually become self-learners.


title: Extended Random Walker for Shadow Detection in Very High Resolution Remote Sensing Images

abstract: The existence of shadows in very high resolution satellite images obstructs image interpretation and the following applications, such as target detection and recognition. Traditional shadow detection methods consider only the pixel-level properties, such as color and intensity of image pixels, and thus, may produce errors around object boundaries. To overcome this problem, a novel shadow detection algorithm based on extended random walker (ERW) is proposed by jointly integrating both shadow property and spatial correlations among adjacent pixels. First, a set of training samples is automatically generated via an improved Otsu-based thresholding method. Then, the support vector machine is applied to obtain an initial detection map, which categorizes all the pixels in the scene into shadow and nonshadow. Finally, the initial detection map is refined with the ERW model, which can simultaneously characterize the shadow property and spatial information in satellite images to further improve shadow detection accuracy. Experiments performed on five real remote sensing images demonstrate the superiority of the proposed method over several state-of-the-art methods in terms of detection accuracy.


title: Architectures and Control of Submodule Integrated DC–DC Converters for Photovoltaic Applications

abstract: This paper describes photovoltaic (PV) module architectures with parallel-connected submodule-integrated dc-dc converters (subMICs) that improve efficiency of energy capture in the presence of partial shading or other mismatch conditions. The subMICs are bidirectional isolated dc-dc converters capable of injecting or subtracting currents to balance the module substring voltages. When no mismatches are present, the subMICs are simply shut down, resulting in zero insertion losses. It is shown that the objective of minimum subMIC power processing can be solved as a linear programming problem. A simple close-to-optimal distributed control approach is presented that allows autonomous subMIC control without the need for a central controller or any communication among the subMICs. Furthermore, the proposed control approach is well suited for an isolated-port architecture, which yields additional practical advantages including reduced subMIC power and voltage ratings. The architectures and the control approach are validated by simulations and experimental results using three bidirectional flyback subMICs attached to a standard 180-W, 72-cell PV module, yielding greater than 98% module-level power processing efficiency for a mismatch less than 25%.


title: Public knowledge of dehydration and fluid intake practices: variation by participants’ characteristics

abstract: Dehydration results from a decrease in total body water content either due to less intake or more fluid loss. Common symptoms of dehydration are dry mouth/tongue, thirst, headache, and lethargy. The aim of this study was to assess knowledge of dehydration definition, symptoms, causes, prevention, water intake recommendations and water intake practices among people living in Riyadh, Saudi Arabia. A cross-sectional study using self-reported questionnaire was employed. Participants were invited during their visit to shopping malls. The shopping malls were selected based on geographical location covering east, west, north and southern part of the city. Self-filled questionnaires were distributed to 393 participants, using systematic sampling technique. Variables recorded included demographics, past medical history, knowledge of dehydration definition, symptoms, causes, prevention and daily water intake practices. Descriptive statistics were summarised as mean, standard deviation and proportions. Negative binomial model was used to identify the predictors of water intake. Analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA). Out of 393 participants, 273(70%) were Saudi, 209(53%) were female, average age was 32.32 ± 8.78 years. Majority 366(93%) had good knowledge of dehydration definition, 332(84%) for dehydration prevention, and 293(74%) of water intake recommendation. Top three recognized dehydration symptoms were: dry lips (87%), thirst (84%), dry tongue (76%) and recognized dehydration causes were: diarrhoea (81%), sweating (68%) and vomiting (62%). The less recognized symptoms were fatigue 176(44.78%), lack of focus 171(43.5%), headache/dizziness 160(40.71%), light headedness 117(29.7%), muscle weakness 98(24.94%), rapid breathing 90(22.9%), and muscle cramps 64(16.28%).The participants had reported an average of 5.39 ± 3.32 water glasses intake per day. The total volume of water intake was significantly different between males n = 184 (3.935 ± 2.10 l) and females n = 209 (3.461 ± 2.59 l) (p = 0.046). The participants’ weight status, intake of juice or tea were the significant predictors of more water intake. The participants displayed good knowledge of dehydration definition, prevention, and water intake recommendation. The participants also displayed good knowledge of the common symptoms, however knowledge was lacking for uncommon symptoms. Moreover, participants had reported adequate water intake, meeting the daily water intake recommendation of ≥3.7 l for men and ≥ 2.7 l for women.


title: Mobile technology habits: patterns of association among device usage, intertemporal preference, impulse control, and reward sensitivity

abstract: Mobile electronic devices are playing an increasingly pervasive role in our daily activities. Yet, there has been very little empirical research investigating how mobile technology habits might relate to individual differences in cognition and affect. The research presented in this paper provides evidence that heavier investment in mobile devices is correlated with a relatively weaker tendency to delay gratification (as measured by a delay discounting task) and a greater inclination toward impulsive behavior (i.e., weaker impulse control, assessed behaviorally and through self-report) but is not related to individual differences in sensitivity to reward. Analyses further demonstrated that individual variation in impulse control mediates the relationship between mobile technology usage and delay of gratification. Although based on correlational results, these findings lend some backing to concerns that increased use of portable electronic devices could have negative impacts on impulse control and the ability to appropriately valuate delayed rewards.


title: Representation learning of knowledge graphs with hierarchical types

abstract: Representation learning of knowledge graphs aims to encode both entities and relations into a continuous low-dimensional vector space. Most existing methods only concentrate on learning representations with structured information located in triples, regardless of the rich information located in hierarchical types of entities, which could be collected in most knowledge graphs. In this paper, we propose a novel method named Type-embodied Knowledge Representation Learning (TKRL) to take advantages of hierarchical entity types. We suggest that entities should have multiple representations in different types. More specifically, we consider hierarchical types as projection matrices for entities, with two type encoders designed to model hierarchical structures. Meanwhile, type information is also utilized as relation-specific type constraints. We evaluate our models on two tasks including knowledge graph completion and triple classification, and further explore the performances on long-tail dataset. Experimental results show that our models significantly outperform all baselines on both tasks, especially with long-tail distribution. It indicates that our models are capable of capturing hierarchical type information which is significant when constructing representations of knowledge graphs. The source code of this paper can be obtained from https://github.com/thunlp/TKRL.


title: Reorder buffer: an energy-efficient multithreading architecture for hardware MIMD ray traversal

abstract: In this paper, we present an energy- and area-efficient multithreading architecture for Multiple Instruction, Multiple Data (MIMD) ray tracing hardware targeted at low-power devices. Recent ray tracing hardware has predominantly adopted an MIMD approach for efficient parallel traversal of incoherent rays, and supports a multithreading scheme to hide latency and to resolve memory divergence. However, the conventional multithreading scheme has problems such as increased memory cost for thread storage and consumption of additional energy for bypassing threads to the pipeline. Consequently, we propose a new multithreading architecture called Reorder Buffer. Reorder Buffer solves these problems by constituting a dynamic reordering of the rays in the input buffer according to the results of cache accesses. Unlike conventional schemes, Reorder Buffer is cost-effective and energy-efficient because it does not need additional thread memory nor does it consume more energy because it makes use of existing resources. Simulation results show that our architecture is a potentially versatile solution for future ray tracing hardware in low-energy devices because it provides as much as 11.7% better cache utilization and is up to 4.7 times more energy-efficient than the conventional architecture.


title: Can virtual reality exposure therapy gains be generalized to real-life? : A meta-analysis of studies applying behavioral assessments

abstract: In virtual reality exposure therapy (VRET), patients are exposed to virtual environments that resemble feared real-life situations. The aim of the current study was to assess the extent to which VRET gains can be observed in real-life situations. We conducted a meta-analysis of clinical trials applying VRET to specific phobias and measuring treatment outcome by means of behavioral laboratory tests or recordings of behavioral activities in real-life. Data sources were searches of databases (Medline, PsycInfo, and Cochrane). We included in total 14 clinical trials on specific phobias. Results revealed that patients undergoing VRET did significantly better on behavioral assessments following treatment than before treatment, with an aggregated uncontrolled effect size of g = 1.23. Furthermore, patients undergoing VRET performed better on behavioral assessments at post-treatment than patients on wait-list (g = 1.41). Additionally, results of behavioral assessment at post-treatment and at follow-up revealed no significant differences between VRET and exposure in vivo (g = -0.09 and 0.53, respectively). Finally, behavioral measurement effect sizes were similar to those calculated from self-report measures. The findings demonstrate that VRET can produce significant behavior change in real-life situations and support its application in treating specific phobias.


title: Post-Processing of Word Representations via Variance Normalization and Dynamic Embedding

abstract: Language processing becomes more and more important in multimedia processing. Although embedded vector representations of words offer impressive performance on many natural language processing (NLP) applications, the information of ordered input sequences is lost to some extent if only context-based samples are used in the training. For further performance improvement, two new post-processing techniques, called post-processing via variance normalization (PVN) and post-processing via dynamic embedding (PDE), are proposed in this work. The PVN method normalizes the variance of principal components of word vectors, while the PDE method learns orthogonal latent variables from ordered input sequences. The PVN and the PDE methods can be integrated to achieve better performance. We apply these post-processing techniques to several popular word embedding methods to yield their post-processed representations. Extensive experiments are conducted to demonstrate the effectiveness of the proposed post-processing techniques.


title: Anthropometry-based structural design of a hand exoskeleton for rehabilitation

abstract: We propose a novel exoskeleton for grasping hand rehabilitation based on anthropometry. The proposed design has one degree of freedom (DOF) for each finger, yielding coordinated movement across the distal interphalangeal (DIP), proximal interphalangeal (PIP), and metacarpophalangeal (MCP) joints for each finger. The dimension of each segment is determined by hand anthropometric data obtained from measurements. Each finger is controlled by one motor to allow for independent movement of each finger, which is fundamental for hand dexterity. The design was guided by a proposed mechanical model (the exo-finger model) which was verified by simulation and validated by the movement recorded by prototype fingers. It is concluded that, in the present study, anthropometry-based structural design provides a framework for the development of exoskeletal robotic devices.


title: A multi-level analysis of the impact of shared leadership in diverse virtual teams

abstract: Although organizations are using more virtual teams to accomplish work, they are finding it difficult to use traditional forms of leadership to manage these teams. Many organizations are encouraging a shared leadership approach over the traditional individual leader. Yet, there have been only a few empirical studies directly examining the effectiveness of such an approach and none have taken into account the team diversity. To address this gap, this paper reports the results of an empirical examination of the impacts of shared leadership in virtual teams. Results confirm the proposed research model. The impacts of shared leadership are multilevel and vary by race and gender. In addition, while shared leadership promotes team satisfaction despite prior assumptions, it actually reduces rather than increases team performance.


title: Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification

abstract: Robust low-level image features have been proven to be effective representations for a variety of visual recognition tasks such as object recognition and scene classification; but pixels, or even local image patches, carry little semantic meanings. For high level visual tasks, such low-level image representations are potentially not enough. In this paper, we propose a high-level image representation, called the Object Bank, where an image is represented as a scale-invariant response map of a large number of pre-trained generic object detectors, blind to the testing dataset or visual task. Leveraging on the Object Bank representation, superior performances on high level visual recognition tasks can be achieved with simple off-the-shelf classifiers such as logistic regression and linear SVM. Sparsity algorithms make our representation more efficient and scalable for large scene datasets, and reveal semantically meaningful feature patterns.


title: gSLICr: SLIC superpixels at over 250Hz

abstract: We introduce a parallel GPU implementation of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Using a single graphic card, our implementation achieves speedups of up to $83\times$ from the standard sequential implementation. Our implementation is fully compatible with the standard sequential implementation and the software is now available online and is open source.


title: Revealing information while preserving privacy

abstract: We examine the tradeoff between privacy and usability of statistical databases. We model a statistical database by an n-bit string d 1 ,..,d n , with a query being a subset q ⊆ [n] to be answered by Σ ieq d i . Our main result is a polynomial reconstruction algorithm of data from noisy (perturbed) subset sums. Applying this reconstruction algorithm to statistical databases we show that in order to achieve privacy one has to add perturbation of magnitude (Ω√n). That is, smaller perturbation always results in a strong violation of privacy. We show that this result is tight by exemplifying access algorithms for statistical databases that preserve privacy while adding perturbation of magnitude O(√n).For time-T bounded adversaries we demonstrate a privacypreserving access algorithm whose perturbation magnitude is ≈ √T.


title: Adaptive underfrequency load shedding based on the magnitude of the disturbance estimation

abstract: Summary form only given. In the paper a new approach to adaptive underfrequency load shedding, a procedure for protecting electric power systems from dynamic instability and frequency collapse is presented. It consists of two main stages. In the first stage, the frequency and the rate of frequency change are estimated by the nonrecursive Newton type algorithm. By using the simplest expression of the generator swing equation, in the second algorithm stage the magnitude of the disturbance is determined. The underfrequency load shedding plan is adapted to the magnitude estimated, obtaining in this way a more efficient system operation during emergency conditions. Results of procedure testing are demonstrated through the dynamic simulations by using: a) a simple 3-machines test system and b) a 10-machines New England system.


title: Heavy metal hyperaccumulating plants: How and why do they do it? And what makes them so interesting?

abstract: The term “hyperaccumulator” describes a number of plants that belong to distantly related families, but share the ability to grow on metalliferous soils and to accumulate extraordinarily high amounts of heavy metals in the aerial organs, far in excess of the levels found in the majority of species, without suffering phytotoxic effects. Three basic hallmarks distinguish hyperaccumulators from related non-hyperaccumulating taxa: a strongly enhanced rate of heavy metal uptake, a faster root-to-shoot translocation and a greater ability to detoxify and sequester heavy metals in leaves. An interesting breakthrough that has emerged from comparative physiological and molecular analyses of hyperaccumulators and related non-hyperaccumulators is that most key steps of hyperaccumulation rely on different regulation and expression of genes found in both kinds of plants. In particular, a determinant role in driving the uptake, translocation to leaves and, finally, sequestration in vacuoles or cell walls of great amounts of heavy metals, is played in hyperaccumulators by constitutive overexpression of genes encoding transmembrane transporters, such as members of ZIP, HMA, MATE, YSL and MTP families. Among the hypotheses proposed to explain the function of hyperaccumulation, most evidence has supported the “elemental defence” hypothesis, which states that plants hyperaccumulate heavy metals as a defence mechanism against natural enemies, such as herbivores. According to the more recent hypothesis of “joint effects”, heavy metals can operate in concert with organic defensive compounds leading to enhanced plant defence overall. Heavy metal contaminated soils pose an increasing problem to human and animal health. Using plants that hyperaccumulate specific metals in cleanup efforts appeared over the last 20 years. Metal accumulating species can be used for phytoremediation (removal of contaminant from soils) or phytomining (growing plants to harvest the metals). In addition, as many of the metals that can be hyperaccumulated are also essential nutrients, food fortification and phytoremediation might be considered two sides of the same coin. An overview of literature discussing the phytoremediation capacity of hyperaccumulators to clean up soils contaminated with heavy metals and the possibility of using these plants in phytomining is presented. © 2010 Elsevier Ireland Ltd. All rights reserved.


title: Simulation Research on Sensorless Speed Control System of Permanent Magnet Synchronous Motor Based on SMO

abstract: The vector control system of permanent magnet synchronous motor based on sliding mode observer (SMO) is studied in this paper. On the basis of analyzing the traditional sliding mode observer, an improved sliding mode observer is proposed. Firstly, by using the hyperbolic tangent function instead of the traditional symbol function, the chattering of the system is suppressed. Secondly, a low pass filter which has the variable cutoff frequency along with the rotor speed is designed to reduce the phase delay. Then, by using Kalman filter, it could make back EMF information more smoothly. Finally, in order to obtain accurate position and velocity information, the method of phase-locked loop (PLL) is proposed to estimate the position and speed of the rotor. The simulation results show that the new algorithm can not only improve the accuracy of the position and speed estimation of the rotor but reduce the chattering of the system.


title: Multi-joint gripper with Differential Gear System

abstract: This paper proposes a multi-joint-gripper that achieves envelope grasping for unknown shape objects. Proposed mechanism is based on a chain of Differential Gear Systems (DGS) controlled by only one motor. It also has a Variable Stiffness Mechanism (VSM) that controls joint stiffness to relieve interfering effects suffered from grasping environment and achieve a dexterous grasping. The experiments elucidate that the developed gripper achieves envelop grasping; the posture of the gripper automatically fits the shape of the object with no sensory feedback. And they also show that the VSM effectively works to relieve external interfering. This paper shows the mechanism and experimental results of the second test machine that was developed inheriting the idea of DGS used in the first test machine but has a completely altered VSM.


title: Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine

abstract: The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information. Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.


title: Natural history of "intrinsic" atopic dermatitis

abstract: . IT is well known that allergy plays a pathogenic role in the development of atopic dermatitis (AD) (1). However, approximately 20% of AD patients suffer from a skin disease that clinically resembles the skin lesions and the distribution pattern of AD, but is not associated with atopic status. This kind of ‘‘intrinsic’’ AD (iAD) seems to differ from the classical or ‘‘extrinsic’’ form (eAD) both in the T-cell cytokine production and in the immunohistology (2, 3), but no data are available about its natural history. In this study, we investigated the persistence of AD and the development of respiratory allergic diseases in a group of children seen at the ages of 2 and 11 years according to the skin prick test (SPT) reactivity to allergens at those ages. The parents of 111 children with AD evaluated at our allergy unit in 1989–90 were asked for re-evaluation of the children about 9 years later. Seventy percent of the children (77/111) underwent clinical history, and SPTs to food (egg white, wheat, milk, tomato, codfish, and soy [Lofarma, Milan, Italy]) and inhalant allergens (dust mite, cat, dog, Alternaria, grass pollen, Parietaria, mugwort, olive pollen, and Cupressus sempervires [Lofarma]). AD was diagnosed according to Hanifin & Rajka (4). The presence of asthma and SPT reactivity was studied as previously described (5, 6). According to the SPT results, we defined the following groups: 1) ‘‘early atopic’’, i.e., those children who were already SPT positive at age 2 and were still SPT positive at age 11 2) ‘‘late-onset atopic’’, i.e., those who were negative at age 2 but became SPT positive by the age of 11 3) ‘‘nonatopic’’, i.e., those who were SPT negative at the ages of both 2 and 11. We considered patients SPT negative at age 2 as having iAD. The data were analyzed by the statistical program SPSS 6.0 for Windows with the chi-square and chi-square for trend tests. A two-sided type 1 error lower than 0.05 was considered statistically significant. Seventy-seven AD patients, with a mean age (uSE) of 24.1u2.28 months at the first evaluation, and 132u3.24 months at the second evaluation, were identified. At diagnosis, all of them had AD (77/77), 22% (17/77) had asthma, and 64% (49/77) had at least one positive SPT. At 11 years of age, 46% of them had AD (36/77), 43% (33/77) had asthma, and 84% had (65/77) at least one positive SPT. Forty-nine out of the 77 (64%) studied children had early atopy, 16/77 (21%) had late-onset atopy, and 12/77 (15%) were nonatopic. None of the nonatopic children developed asthma by age 11 (0/12), compared with the 25% (4/16) and the 59% (29/49) of the late-onset and early atopic children, respectively (chi square for trend, P,0.0002). AD was still present by age 11 in 67% (8/12) of nonatopic AD as compared with 44% (7/16) and 43% (21/49) of the late-onset and early atopic children (Fig. 1). Altogether, the 28 AD children ALLERGY 2001: 56 :452^463 . COPYRIGHT G MUNKSGAARD 2001 . ISSN 0105-4538 . ALL RIGHTS RESERVED . CONTRIBUTIONS TO THIS SECTION WILL NOT UNDERGO PEER REVIEW, BUT WILL BE REVIEWED BY THE ASSOCIATE EDITORS .


title: Powerline perching with a fixed-wing UAV

abstract: The ability to land on a powerline to recharge will dramatically enhance the endurance and therefore mission capabilities for small UAVs. The magnetic fields around these powerlines have a well known structure. In this paper we evaluate the possibility of using onboard magnetometers, in combination with an inertial measurement unit and dynamic model of the aircraft, to localize the vehicle relative to the powerline. Specifically, we investigate the near-field structure of a dipole magnetic field configuration and demonstrate that state estimation using particle filters can provide sufficiently accurate, high-bandwidth, and lowlatency estimates to be compatible with aggressive near-field maneuvering by the aircraft to acquire to perch on the line.


Herb-Drug Interactions: Systematic Review, Mechanisms, and Therapies


title: A Probabilistic Model for the Nonlinear Electromagnetic Inverse Scattering: TM Case

abstract: Electromagnetic inverse scattering (EMIS) is a noninvasive examination tool, which holds the promising potential in science, engineering, and military applications. In contrast to conventional tomography techniques, the inverse scattering is a quantitative superresolution imaging method since it is capable of accommodating more realistic interactions between the wavefield and the probed scene. In this paper, a full probabilistic formulation of the EMIS is presented for the first time, which is then solved by applying the well-known expectation maximization method. Afterward, the concept of the complex-valued alternating direction method of multipliers has been proposed as an alternative approach to solve the resulting nonlinear optimization problem. Finally, exemplary numerical and experimental results are provided to validate the proposed method.


title: Air-launched expendable micro-sized wave buoy

abstract: As part of a Phase II Office of Naval Research (ONR) Small Business Innovative Research (SBIR) contract, Neptune Sciences, Inc. (NSI) successfully developed and tested a micro-sized expendable wave measurement buoy. This is the first in a series of microsensors to be developed by NSI and is designated the Expendable Micro-sized Environmental Sensor-Nondirectional Wave Buoy (XMES-NWB). The buoy is designed to be launched from manned or unmanned aircraft using the standard tri-service ALE-47 Countermeasure Dispenser System (CMDS), or it can be deployed by hand from a surface ship. Anticipated scientific uses for production versions of the XMES-NWB include the study of tropical cyclones, monitoring nearshore and deep-water hydrodynamics, and validation of numerical wave models covering a full range of wave conditions. Tactical uses include deployment of the XMES-NWB to provide wave and surf data in support of amphibious and nearshore operations involving critical systems such as the Marine Corps' Advanced Amphibious Assault Vehicle (AAAV) and Navy LCAC air cushion landing vehicles.


title: Serotonin alterations in anorexia and bulimia nervosa: New insights from imaging studies

abstract: Anorexia nervosa (AN) and bulimia nervosa (BN) are related disorders with relatively homogenous presentations such as age of onset and gender distribution. In addition, they share symptoms, such as extremes of food consumption, body image distortion, anxiety and obsessions, and ego-syntonic neglect, raises the possibility that these symptoms reflect disturbed brain function that contributes to the pathophysiology of this illness. Recent brain imaging studies have identified altered activity in frontal, cingulate, temporal, and parietal cortical regions in AN and BN. Importantly, such disturbances are present when subjects are ill and persist after recovery, suggesting that these may be traits that are independent of the state of the illness. Emerging data point to a dysregulation of serotonin pathways in cortical and limbic structures that may be related to anxiety, behavioral inhibition, and body image distortions. In specific, recent studies using PET with serotonin specific radioligands implicate alterations of 5-HT1A and 5-HT2A receptors and the 5-HT transporter. Alterations of these circuits may affect mood and impulse control as well as the motivating and hedonic aspects of feeding behavior. Such imaging studies may offer insights into new pharmacology and psychotherapy approaches.


title: Probabilistic Marching Cubes

abstract: In this paper we revisit the computation and visualization of equivalents to isocontours in uncertain scalar fields. We model uncertainty by discrete random fields and, in contrast to previous methods, also take arbitrary spatial correlations into account. Starting with joint distributions of the random variables associated to the sample locations, we compute level crossing probabilities for cells of the sample grid. This corresponds to computing the probabilities that the well-known symmetry-reduced marching cubes cases occur in random field realizations. For Gaussian random fields, only marginal density functions that correspond to the vertices of the considered cell need to be integrated. We compute the integrals for each cell in the sample grid using a Monte Carlo method. The probabilistic ansatz does not suffer from degenerate cases that usually require case distinctions and solutions of ill-conditioned problems. Applications in 2D and 3D, both to synthetic and real data from ensemble simulations in climate research, illustrate the influence of spatial correlations on the spatial distribution of uncertain isocontours.


title: Data-Efficient Hierarchical Reinforcement Learning

abstract: Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of current HRL methods require careful task-specific design and on-policy training, making them difficult to apply in real-world scenarios. In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control. For generality, we develop a scheme where lower-level controllers are supervised with goals that are learned and proposed automatically by the higher-level controllers. To address efficiency, we propose to use off-policy experience for both higher and lower-level training. This poses a considerable challenge, since changes to the lower-level behaviors change the action space for the higher-level policy, and we introduce an off-policy correction to remedy this challenge. This allows us to take advantage of recent advances in off-policy model-free RL to learn both higher- and lower-level policies using substantially fewer environment interactions than on-policy algorithms. We term the resulting HRL agent HIRO and find that it is generally applicable and highly sample-efficient. Our experiments show that HIRO can be used to learn highly complex behaviors for simulated robots, such as pushing objects and utilizing them to reach target locations, learning from only a few million samples, equivalent to a few days of real-time interaction. In comparisons with a number of prior HRL methods, we find that our approach substantially outperforms previous state-of-the-art techniques.


Network Externalities, Competition, and Compatibility


Understanding Social Engineering Based Scams


title: Analysis of mobile internet speed, signal strength and FMDH antenna design for improved internet speed

abstract: Internet speed is important in many applications. Mobile signal strength of − 110 to − 120 dBm provides poor internet speed by mobile phones. Mobile makes less signal strength and less internet speed from mobile towers. Signal strength ranges between − 30 and − 65 dBm must be maintained to achieve high-speed internet through mobile phones. Good antenna design is necessary to maintain good signal strength and to improve high-speed internet from weak internet speed in required applications. In this work, an FM radio receiver antenna and parabolic dish antenna with a winding helical coil is proposed for the design of an antenna (FMDH antenna) in order to improve weak internet signals to enable high-speed internet signal. Cell tower radiation is normally reduced at a distance of 1000 m, with a weak signal resulting in slow internet speeds. The reduced radiation signal density from the cell tower causes slower internet speeds in mobile. The winding coil of a helical antenna receives the weaker internet signal from the cell towers to increase gain and signal strength between − 30 and − 65 dBm, improving internet to high-speed performance. The FM radio receiver antenna length is increased, so the gain in the main lobe radiation pattern improves internet speed. High-speed internet is needed for computers through mobile internet. Most computers work through mobile internet connections. Wi-Fi connection signal strength should be − 30 to − 60 dBm for maintaining high-speed internet. The FMDH antenna design is proposed in this paper improves mobile Wi-Fi internet, increasing internet signal strength and internet speed.


Push-Net: Deep Planar Pushing for Objects with Unknown Physical Properties


The EM Side–Channel(s):Attacks and Assessment Methodologies


title: Role of Offline Handwritten Character Recognition System in Various Applications

abstract: Offline handwritten character recognition system recognizes characters from images. Since the past few decades, many researchers have been developing various handwritten recognition systems for various languages. This paper demonstrates the role and significance of an offline handwritten character recognition system in various applications. General Terms Offline Handwritten Recognition System, Pattern Recognition, Image Processing


title: Iterative Transformer Network for 3D Point Cloud

abstract: 3D point cloud is an efficient and flexible representation of 3D structures. Recently, neural networks operating on point clouds have shown superior performance on tasks such as shape classification and part segmentation. However, performance on these tasks are evaluated using complete, aligned shapes, while real world 3D data are partial and unaligned. A key challenge in learning from unaligned point cloud data is how to attain invariance or equivariance with respect to geometric transformations. To address this challenge, we propose a novel transformer network that operates on 3D point clouds, named Iterative Transformer Network (IT-Net). Different from existing transformer networks, IT-Net predicts a 3D rigid transformation using an iterative refinement scheme inspired by classical image and point cloud alignment algorithms. We demonstrate that models using IT-Net achieves superior performance over baselines on the classification and segmentation of partial, unaligned 3D shapes. Further, we provide an analysis on the efficacy of the iterative refinement scheme on estimating accurate object poses from partial observations.


title: Surveying instructor and learner attitudes toward e-learning

abstract: The trend of using e-learning as a learning and/or teaching tool is now rapidly expanding into education. Although e-learning environments are popular, there is minimal research on instructors' and learners' attitudes toward these kinds of learning environments. The purpose of this study is to explore instructors' and learners' attitudes toward e-learning usage. Accordingly, 30 instructors and 168 college students are asked to answer two different questionnaires for investigating their perceptions. After statistical analysis, the results demonstrate that instructors have very positive perceptions toward using e-learning as a teaching assisted tool. Furthermore, behavioral intention to use e-learning is influenced by perceived usefulness and self-efficacy. Regarding to learners' attitudes, self-paced, teacher-led, and multimedia instruction are major factors to affect learners' attitudes toward e-learning as an effective learning tool. Based on the findings, this research proposes guidelines for developing e-learning environments.


title: Computer games development and appreciative learning approach in enhancing students' creative perception

abstract: Creativity is an important entity in developing human capital while computer games are the current generation's contemporary tool. This study focused on the teaching of computer games development in order to enhance the creative perception of secondary school children. The study applied randomised subjects, with control group experimental design, which involved 69 Malaysian form one students, aged 13-14year-old. Different pedagogical strategies were being investigated on the abilities to enhance students' creative perception. Treatment group adopted appreciative learning approach, which was based on Appreciative Inquiry (AI) theory. Meanwhile, control group adopted self-paced learning, followed by do-it-yourself session. Previous studies indicated that although appreciative learning approach is still in its infancy development, the approach is gaining its momentum in educational settings as it focuses on strengthening a person's capacities and potential. Students' creative perception was assessed using Khatena-Torrance Creative Perception Inventory (KTCPI). It was found students in treatment group gained a mean score of 71.82, which was significantly higher at .05 level of significance compared to the mean score of 50.49 exhibited by the control group. Yet, both treatment and control groups showed significant increases in pre-to-post-test scores. Dimensions within KTCPI were further analysed in order to present a better picture of students' creative perception. As a conclusion, different pedagogical strategy generated different level of creative perception enhancement.


title: Strong Injection Locking in Low- $Q$ LC Oscillators: Modeling and Application in a Forwarded-Clock I/O Receiver

abstract: A general model for injection-locked LC oscillators (LC-ILOs) is presented that is valid for any tank quality factor and injection strength. Important properties of an ILO such as lock-range, phase shift, bandwidth and response to input jitter are described. An LC-ILO together with a half-rate data sampler is implemented as a forwarded-clock I/O receiver in 45-nm CMOS. A strongly-injected low-Q LC oscillator enables clock deskew across 1UI and rejects high-frequency clock jitter. The complete 27 Gb/s ILO-based data receiver has an overall power efficiency of 1.6 mW/Gb/s.


title: Bracken: estimating species abundance in metagenomics data

abstract: We describe a new, highly accurate statistical method that computes the abundance of species in DNA sequences from a metagenomics sample. Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN) uses the taxonomy labels assigned by Kraken, a highly accurate metagenomics classification algorithm, to estimate the number of reads originating from each species present in a sample. Kraken classifies reads to the best matching location in the taxonomic tree, but does not estimate abundances of species. We use the Kraken database itself to derive probabilities that describe how much sequence from each genome is shared with other genomes in the database, and combine this information with the assignments for a particular sample to estimate abundance at the species level, the genus level, or above. Combined with the Kraken classifier, Bracken produces accurate speciesand genus-level abundance estimates even when a sample contains multiple near-identical species.


title: Development and application of a metric on semantic nets

abstract: Motivated by the properties of spreading activation and conceptual distance, the authors propose a metric, called distance, on the power set of nodes in a semantic net. Distance is the average minimum path length over all pairwise combinations of nodes between two subsets of nodes. Distance can be successfully used to assess the conceptual distance between sets of concepts when used on a semantic net of hierarchical relations. When other kinds of relationships, like 'cause', are used, distance must be amended but then can again be effective. The judgements of distance significantly correlate with the distance judgements that people make and help to determine whether one semantic net is better or worse than another. The authors focus on the mathematical characteristics of distance that presents novel cases and interpretations. Experiments in which distance is applied to pairs of concepts and to sets of concepts in a hierarchical knowledge base show the power of hierarchical relations in representing information about the conceptual distance between concepts. >


title: Portable 3D visual sensor based indoor localization on mobile device

abstract: Smart mobile device and consumer level 3D visual sensor are becoming very cheap and widely accessible in people's daily lives. Visual information based indoor localization is getting more and more attention in the computer vision research community to achieve real time and precise localization result. In this paper, we present a 6-degree-of-freedom (6-DoF) pose estimation using a portable 3D visual sensor mounted on a mobile device. A detailed 3D model and a WiFi received signal strength model of the indoor environment are constructed in the offline training phase. During the online localization, we first use WiFi signals to locate the device in a 3D sub model. The initial pose is calculated through feature matching between the online captured 2D image and the key frame images used to build the 3D model. Then we employ iterative closest point (ICP) algorithm to estimate the rigid transform between online captured 3D point cloud and local 3D point cloud. The 6-DOF pose estimation is further refined by random sample consensus (RANSAC) algorithm. We implement the system on iOS platform and the experiments carried out in an indoor environment shows promising result of our approach. To the best of our knowledge, this is the first portable 3D visual sensor based indoor localization system on mobile device.


title: Neural Net Models for Open-Domain Discourse Coherence

abstract: Discourse coherence is strongly associated with text quality, making it important to natural language generation and understanding. Yet existing models of coherence focus on measuring individual aspects of coherence (lexical overlap, rhetorical structure, entity centering) in narrow domains. ::: In this paper, we describe domain-independent neural models of discourse coherence that are capable of measuring multiple aspects of coherence in existing sentences and can maintain coherence while generating new sentences. We study both discriminative models that learn to distinguish coherent from incoherent discourse, and generative models that produce coherent text, including a novel neural latent-variable Markovian generative model that captures the latent discourse dependencies between sentences in a text. ::: Our work achieves state-of-the-art performance on multiple coherence evaluations, and marks an initial step in generating coherent texts given discourse contexts.


title: Therapeutic advances in the treatment of Peyronie's disease

abstract: Summary ::: Peyronie's disease (PD) is an under-diagnosed condition with prevalence in the male population as high as 9%. It is a localized connective tissue disorder of the penis characterized by scarring of the tunica albuginea. Its pathophysiology, however, remains incompletely elucidated. For the management of the acute phase of PD, there are currently numerous available oral drugs, but the scientific evidence for their use is weak. In terms of intralesional injections, collagenase clostridium histolyticum is currently the only Food and Drug Administration-approved drug for the management of patients with PD and a palpable plaque with dorsal or dorsolateral curvature >30°. Other available intralesional injectable drugs include verapamil and interferon-alpha-2B, however, their use is considered off-label. Iontophoresis, shockwave therapy, and radiation therapy have also been described with unconvincing results, and as such, their use is currently not recommended. Traction therapy, as part of a multimodal approach, is an underused additional tool for the prevention of PD-associated loss of penile length, but its efficacy is dependent on patient compliance. Surgical therapy remains the gold standard for patients in the chronic phase of the disease. In patients with adequate erectile function, tunical plication and/or incision/partial excision and grafting can be offered, depending on degree of curvature and/or presence of destabilizing deformity. In patients with erectile dysfunction non-responsive to oral therapy, insertion of an inflatable penile prosthesis with or without straightening procedures should be offered.


INFORMATION WITHOUT TRUTH ANDREA SCARANTINO AND GUALTIERO PICCININI


title: Lung Ultrasound in the Management of Acute Decompensated Heart Failure

abstract: Once thought impracticable, lung ultrasound is now used in patients with a variety of pulmonary processes. This review seeks to describe the utility of lung ultrasound in the management of patients with acute decompensated heart failure (ADHF). A literature search was carried out on PubMed/Medline using search terms related to the topic. Over three thousand results were narrowed down via title and/or abstract review. Related articles were downloaded for full review. Case reports, letters, reviews and editorials were excluded. Lung ultrasonographic multiple B-lines are a good indicator of alveolar interstitial syndrome but are not specific for ADHF. The absence of multiple B-lines can be used to rule out ADHF as a causative etiology. In clinical scenarios where the assessment of acute dyspnea boils down to single or dichotomous pathologies, lung ultrasound can help rule in ADHF. For patients being treated for ADHF, lung ultrasound can also be used to monitor response to therapy. Lung ultrasound is an important adjunct in the management of patients with acute dyspnea or ADHF.


title: Digital Watermarking and its Application in Image Copyright Protection

abstract: Digital watermarking technology is a frontier research field, and it serves an important role in information security. According to the analysis of the definition and basic characteristics of digital watermarking technology, the system model of digital watermarking is given. The system consists of two modules which are watermark embedding module and watermark detection and extraction module. In view of the importance of digital images copyright protection, based on the analysis of the main digital watermarking algorithms, the digital watermarking technology can be applied to the image copyright protection. The two dimension discrete cosine transform is encoded on the Windows platform by using Visual C++ program language. The experiment result shows that the digital watermark is non-perceptible, the watermark information can be extracted even if it has been attacked, and the expected effect can be achieved.


title: Five reasons for using social media among young terror survivors: Results from the Utøya study

abstract: Abstract This study explores how young survivors experienced social media usage after the 2011 terror attack at Utoya, Norway. In total, 112 interviews with survivors were conducted 14–15 months post-terror, and analyzed using thematic analysis. Five reasons for social media usage were identified: 1) exchange of information, 2) giving and receiving social support, 3) mourning, 4) performing different symbolic actions, and 5) partaking in debates and discussions pertaining to the terror. Our informants described how their participation in online debates existed in a space between their personal stories and the public narrative surrounding the July 22 attacks. As unedited and easily accessible platforms, social media had characteristics that differentiated them from other arenas in which the survivors were communicating about the terror attack. The potential for receiving support was described as massive, because social media were perceived as platforms where the threshold for reaching out was low. However, participants felt that although social media usage enabled survivors to take control when presenting their own narratives, it also took some of their control away as their posts could be misinterpreted. They also felt that they were unable to protect themselves from information and opinions that they found distressing.


title: Fast Labeling and Transcription with the Speechalyzer Toolkit

abstract: We describe a software tool named “Speechalyzer” which is optimized to process large speech data sets with respect to transcription, labeling and annotation. It is implemented as a client server based framework in Java and interfaces software for speech recognition, synthesis, speech classification and quality evaluation. The application is mainly the processing of training data for speech recognition and classification models and performing benchmarking tests on speech to text, text to speech and speech categorization software systems.


title: A Low-delay Protocol for Multihop Wireless Body Area Networks

abstract: Wireless body area networks (WBANs) form a new and interesting area in the world of remote health monitoring. An important concern in such networks is the communication between the sensors. This communication needs to be energy efficient and highly reliable while keeping delays low. Mobility also has to be supported as the nodes are positioned on different parts of the body that move with regard to each other. In this paper, we present a new cross-layer communication protocol for WBANs: CICADA or Cascading Information retrieval by Controlling Access with Distributed slot Assignment. The protocol sets up a network tree in a distributed manner. This tree structure is subsequently used to guarantee collision free access to the medium and to route data towards the sink. The paper analyzes CICADA and shows simulation results. The protocol offers low delay and good resilience to mobility. The energy usage is low as the nodes can sleep in slots where they are not transmitting or receiving.


title: Narrative theory and the dynamics of popular movies

abstract: Popular movies grab and hold our attention. One reason for this is that storytelling is culturally important to us, but another is that general narrative formulae have been honed over millennia and that a derived but specific filmic form has developed and has been perfected over the last century. The result is a highly effective format that allows rapid processing of complex narratives. Using a corpus analysis I explore a physical narratology of popular movies—narrational structure and how it impacts us—to promote a theory of popular movie form. I show that movies can be divided into 4 acts—setup, complication, development, and climax—with two optional subunits of prolog and epilog, and a few turning points and plot points. In 12 studies I show that normative aspects in patterns of shot durations, shot transitions, shot scale, shot motion, shot luminance, character introduction, and distributions of conversations, music, action shots, and scene transitions reduce to 5 correlated stylistic dimensions of movies and can litigate among theories of movie structure. In general, movie narratives have roughly the same structure as narratives in any other domain—plays, novels, manga, folktales, even oral histories—but with particular runtime constraints, cadences, and constructions that are unique to the medium.


title: Totem-Pole Power-Factor-Correction Converter under Critical-Conduction-Mode Interleaved Operation

abstract: This paper proposes a new power-factor-correction (PFC) topology, and explains its operation principle, its control mechanism, related application problems followed by experimental results. In this proposed topology, critical-conduction-mode (CRM) interleaved technique is applied to a bridgeless PFC in order to achieve high efficiency by combining benefits of each topology. This application is targeted toward low to middle power applications that normally employs continuous-conduction-mode boost converter.


title: A Deep Neural Network Model for Target-based Sentiment Analysis

abstract: In recent years, with the development of social networks, sentiment analysis has become one of the most important research topics in the field of natural language processing. The deep neural network model combining attention mechanism has achieved remarkable success in the task of target-based sentiment analysis. In current research, however, the attention mechanism is more combined with LSTM networks, such neural network- based architectures generally rely on complex computation and only focus on the single target, thus it is difficult to effectively distinguish the different polarities of variant targets in the same sentence. To address this problem, we propose a deep neural network model combining convolutional neural network and regional long short-term memory (CNN-RLSTM) for the task of target-based sentiment analysis. The approach can reduce the training time of neural network model through a regional LSTM. At the same time, the CNN-RLSTM uses a sentence-level CNN to extract sentiment features of the whole sentence, and controls the transmission of information through different weight matrices, which can effectively infer the sentiment polarities of different targets in the same sentence. Finally, experimental results on multi-domain datasets of two languages from SemEval2016 and auto data show that, our approach yields better performance than SVM and several other neural network models.


title: Cipher Type Detection

abstract: Manual analysis and decryption of enciphered documents is a tedious and error prone work. Often—even after spending large amounts of time on a particular cipher—no decipherment can be found. Automating the decryption of various types of ciphers makes it possible to sift through the large number of encrypted messages found in libraries and archives, and to focus human effort only on a small but potentially interesting subset of them. In this work, we train a classifier that is able to predict which encipherment method has been used to generate a given ciphertext. We are able to distinguish 50 different cipher types (specified by the American Cryptogram Association) with an accuracy of 58.5%. This is a 11.2% absolute improvement over the best previously published classifier.


title: A 25Gb/s serial-link repeater with receiver equalization and transmitter de-emphasis in 0.13μm SiGe BiCMOS

abstract: A serial-link repeater chip with a single stage continuous-time linear equalizer (CTLE) and a 3-tap feedforward equalizer (FFE) is realized in a 0.13μm SiGe BiCMOS technology. The CTLE with the negative capacitance circuits is implemented to achieve a larger high-frequency boosting at the receiver side. By utilizing the LC-based delay elements, the FFE accomplishes the transmitter de-emphasis without retiming clocks. The measurement results show that the repeater can at least compensate for a Nyquist channel loss of 23.5dB (14.4dB by the receiver equalization and 9.1dB by the transmitter de-emphasis) when operating at the data rate of 25Gb/s. The total power consumption is 230mW from a 3.3V power supply.


title: Examination of Narratives from Emergency Department Presentations to Identify Road Trauma, Crash and Injury Risk Factors for Different Age Groups

abstract: BACKGROUND: Road trauma represents a high proportion of injury-related emergency department presentations. Narrative text recorded in the emergency department could provide useful information to monitor road trauma and to identify crash and injury risk factors by age group. OBJECTIVE: To examine the Public Health Real-time Emergency Department Surveillance System (PHREDSS) to identify road users (i.e. motor vehicle drivers, motor vehicle passengers, motorcyclists, pedal cyclists and pedestrians), and crash (e.g. vehicle speed) and injury risk factors (e.g. non-restraint use) by age group. METHOD: Narrative text from the PHREDSS in New South Wales, Australia, during 1 January 2006 to 31 December 2012 was reviewed. RESULTS: A keyword search of all emergency department presentations potentially identified 388,991 road trauma-related presentations and between 6,420 motorbike crashes to 138,889 motor vehicle accident emergency department presentations. Potential crash and injury risk factors were also identified. CONCLUSION: This exploratory study demonstrated the capability of information from PHREDSS to be used to support injury prevention efforts in road safety. Language: en


title: Is Google enough? Comparison of an internet search engine with academic library resources

abstract: Purpose – The purpose of the study was to compare an internet search engine, Google, with appropriate library databases and systems, in order to assess the relative value, strengths and weaknesses of the two sorts of system.Design/methodology/approach – A case study approach was used, with detailed analysis and failure checking of results. The performance of the two systems was assessed in terms of coverage, unique records, precision, and quality and accessibility of results. A novel form of relevance assessment, based on the work of Saracevic and others was devised.Findings – Google is superior for coverage and accessibility. Library systems are superior for quality of results. Precision is similar for both systems. Good coverage requires use of both, as both have many unique items. Improving the skills of the searcher is likely to give better results from the library systems, but not from Google.Research limitations/implications – Only four case studies were included. These were limited to the kind of q...


title: Multi-view 6D Object Pose Estimation and Camera Motion Planning Using RGBD Images

abstract: Recovering object pose in a crowd is a challenging task due to severe occlusions and clutters. In active scenario, whenever an observer fails to recover the poses of objects from the current view point, the observer is able to determine the next view position and captures a new scene from another view point to improve the knowledge of the environment, which may reduce the 6D pose estimation uncertainty. We propose a complete active multi-view framework to recognize 6DOF pose of multiple object instances in a crowded scene. We include several components in active vision setting to increase the accuracy: Hypothesis accumulation and verification combines single-shot based hypotheses estimated from previous views and extract the most likely set of hypotheses; an entropy-based Next-Best-View prediction generates next camera position to capture new data to increase the performance; camera motion planning plans the trajectory of the camera based on the view entropy and the cost of movement. Different approaches for each component are implemented and evaluated to show the increase in performance.