title: Chest diseases diagnosis using artificial neural networks

abstract: Chronic obstructive pulmonary, pneumonia, asthma, tuberculosis, lung cancer diseases are the most important chest diseases. These chest diseases are important health problems in the world. In this study, a comparative chest diseases diagnosis was realized by using multilayer, probabilistic, learning vector quantization, and generalized regression neural networks. The chest diseases dataset were prepared by using patient's epicrisis reports from a chest diseases hospital's database.


title: Assessing the Reliability, Validity and Adaptability of PSSUQ

abstract: Psychometrically qualified usability evaluation instruments offer many advantages to the usability practitioner. Advantages include objectivity, replicability, quantification, economy, communication, and scientific generalization. It is important that instruments used in usability evaluation have shown acceptable estimates of reliability, validity, adaptability and practicality. This paper compares the psychometric properties of the Post-Study System Usability Questionnaire (PSSUQ) instrument in Lewis’ study to a recent study that tailored the PSSUQ instrument to measure the user satisfaction of the usability of a webbased health provider interface. Lewis reported that the PSSUQ had acceptable psychometric properties. However, Lewis’ stated PSSUQ had limited generalizability and needed further examination. This research validated the PSSUQ instrument using a larger sample size in a different domain. The factor analysis and the clustering of the sub-scale items were different than Lewis’ results. Nevertheless, this research concluded that the PSSUQ instrument is adaptable and produced solid psychometric results.


title: Ontology learning with complex data type for Web service clustering

abstract: Clustering Web services into functionally similar clusters is a very efficient approach to service discovery. A principal issue for clustering is computing the semantic similarity between services. Current approaches use similarity-distance measurement methods such as keyword, information-retrieval or ontology based methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information and a shortage of high-quality ontologies. Further, current clustering approaches are considered only have simple data types in services' input and output. However, services that published on the web have input/ output parameter of complex data type. In this research, we propose clustering approach that considers the simple type as well as complex data type in measuring the service similarity. We use hybrid term similarity method which we proposed in our previous work to measure the similarity. We capture the semantic pattern exist in complex data types and simple data types to improve the ontology learning method. Experimental results show our clustering approach which uses complex data types in measuring similarity works efficiently.


title: An XML-based Multi-Agent System for Supporting Online Recruitment Services

abstract: In this paper, we propose an Extensible Markup Language (XML)-based multiagent recommender system for supporting online recruitment services. Our system is characterized by the following features: 1) it handles user profiles for personalizing the job search over the Internet; 2) it is based on the intelligent agent technology; and 3) it uses XML for guaranteeing a light, versatile, and standard mechanism for information representation, storing, and exchange. This paper discusses the basic features of the proposed system, presents the results of an experimental study we have carried out for evaluating its performance, and makes a comparison between the proposed system and other e-recruitment systems already presented in the past.


title: Commensal gut flora and brain autoimmunity: a love or hate affair?

abstract: Multiple sclerosis (MS) and other chronic inflammatory autoimmune diseases represent major public health challenges in industrialised Western society. MS results from an autoimmune attack against myelin structures by self-reactive lymphocytes, which are normal components of the healthy immune repertoire. The nature of the triggers that convert the innocuous self-reactive lymphocytes into an autoaggressive phenotype is poorly understood. In the past, it was primarily suspected that pathogenic infections trigger MS. However, so far, none of the incriminated pathogenic microbes were firmly associated with the disease. A growing body of evidence in animal models of MS implicates the gut microbiota in the induction of central nervous system (CNS) autoimmunity. The mammalian gut harbors a diverse population of microbial organisms which are essential for our well being. There is an increasing understanding that the gut microbiota not only modulates the local immune functions but also affects the systemic immune system. We are only just beginning to understand the nature of the interactions of the gut microbiota with the host’s immune system especially in the context of autoimmune diseases. This review will address the influence of intestinal microbiota on immune homeostasis and on the development of autoimmune responses at sites distal to the intestine with a particular emphasis placed on a discussion about CNS autoimmunity.


title: A Multicenter, Double-Blind, Randomized, Placebo-Controlled Study to Assess Whether Antibiotic Administration Should Be Recommended During Office Operative Hysteroscopy

abstract: We did a double-blind, randomized, placebo-controlled study to assess the incidence of infectious complications and the protective effect of antibiotic administration during operative hysteroscopic procedures in an office setting. A total of 1046 consecutively enrolled women with intrauterine lesions were randomly allocated to the reference group (523 patients administered with 1 g of cefazolin intramuscularly) and the study group (523 patients administered with 10 mL of isotonic sodium chloride solution), and treated in office setting by operative hysteroscopy for endometrial polypectomy, uterine septa, submucosal myomas, and intrauterine adhesions. The primary outcome measure was the computation of difference between groups in postsurgical infectious complications occurring in the 5 days after the procedures. The time spent in performing the various procedures did not differ significantly (P > .05) between the groups. With respect to the overall rate of postsurgical infection, we found that 12 (1.15%) of 1046 patients referred with symptoms related to infective complications, 7 (1.3% of 523 women) in the study—untreated—group and 5 (1.0% of 523 women) in the reference group. Such incidences did not differ significantly between the groups (P > .05). Antibiotics were prescribed in all cases of postsurgical infection and the infective process resolved in few days. None of these patients developed serious infections with adnexal involvement, as confirmed by clinical and ultrasounds evaluation. The results of the current study would support the American College of Obstetricians and Gynecologists recommendation not to prescribe routine antibiotic administration in the case of hysteroscopic surgery.


title: SVM optimization: inverse dependence on training set size

abstract: We discuss how the runtime of SVM optimization should decrease as the size of the training data increases. We present theoretical and empirical results demonstrating how a simple subgradient descent approach indeed displays such behavior, at least for linear kernels.


title: TrafficIntel: Smart traffic management for smart cities

abstract: In India the unbalanced growth in the freight volume as compared to the road length has led to severe issues of traffic congestion, road safety and health hazards, which, as estimated by recent studies will surely escalate very rapidly within coming years. The use of a real time dynamic traffic management system to intelligently navigate the vehicles can optimize the traffic flow on the roads, thus utilizing the infrastructure efficiently and providing an environment conducive to solving gridlock in cities. The authors propose a real time traffic management system (RTMS) consisting of real time traffic monitoring system formed by small network of road side units, junction units and mobile units to dynamically decide the time of traffic lights to discourage formation of gridlock, coupled with a web based application for vehicle drivers that will derive the data from real time traffic analysis to indicate the local traffic flow and suggest the incoming vehicles to make use of alternative routes in order to further alleviate growth of the gridlock.


title: Associations Between Exposure to and Expression of Negative Opinions About Human Papillomavirus Vaccines on Social Media: An Observational Study

abstract: Background: Groups and individuals that seek to negatively influence public opinion about the safety and value of vaccination are active in online and social media and may influence decision making within some communities. Objective: We sought to measure whether exposure to negative opinions about human papillomavirus (HPV) vaccines in Twitter communities is associated with the subsequent expression of negative opinions by explicitly measuring potential information exposure over the social structure of Twitter communities. Methods: We hypothesized that prior exposure to opinions rejecting the safety or value of HPV vaccines would be associated with an increased risk of posting similar opinions and tested this hypothesis by analyzing temporal sequences of messages posted on Twitter (tweets). The study design was a retrospective analysis of tweets related to HPV vaccines and the social connections between users. Between October 2013 and April 2014, we collected 83,551 English-language tweets that included terms related to HPV vaccines and the 957,865 social connections among 30,621 users posting or reposting the tweets. Tweets were classified as expressing negative or neutral/positive opinions using a machine learning classifier previously trained on a manually labeled sample. Results: During the 6-month period, 25.13% (20,994/83,551) of tweets were classified as negative; among the 30,621 users that tweeted about HPV vaccines, 9046 (29.54%) were exposed to a majority of negative tweets. The likelihood of a user posting a negative tweet after exposure to a majority of negative opinions was 37.78% (2780/7361) compared to 10.92% (1234/11,296) for users who were exposed to a majority of positive and neutral tweets corresponding to a relative risk of 3.46 (95% CI 3.25-3.67, P <.001). Conclusions: The heterogeneous community structure on Twitter appears to skew the information to which users are exposed in relation to HPV vaccines. We found that among users that tweeted about HPV vaccines, those who were more often exposed to negative opinions were more likely to subsequently post negative opinions. Although this research may be useful for identifying individuals and groups currently at risk of disproportionate exposure to misinformation about HPV vaccines, there is a clear need for studies capable of determining the factors that affect the formation and adoption of beliefs about public health interventions. [J Med Internet Res 2015;17(6):e144]


title: Identity-Based Aggregate and Verifiably Encrypted Signatures from Bilinear Pairing

abstract: An aggregate signature scheme is a digital signature that supports aggregation: Given n signatures on n distinct messages from n distinct users, it is possible to aggregate all these signatures into a single short signature. This single signature (and the n original messages) will convince the verifier that the n users did indeed sign the n original messages (i.e., user i signed message M i for i = 1,...,n). In this paper we introduce the concept of an aggregate signature, present security models for such signatures, and give several applications for aggregate signatures. We construct an efficient aggregate signature from a recent short signature scheme based on bilinear maps due to Boneh, Lynn, and Shacham. Aggregate signatures are useful for reducing the size of certificate chains (by aggregating all signatures in the chain) and for reducing message size in secure routing protocols such as SBGP. We also show that aggregate signatures give rise to verifiably encrypted signatures. Such signatures enable the verifier to test that a given ciphertext C is the encryption of a signature on a given message M. Verifiably encrypted signatures are used in contract-signing protocols. Finally, we show that similar ideas can be used to extend the short signature scheme to give simple ring signatures.


title: RETracer: triaging crashes by reverse execution from partial memory dumps

abstract: Many software providers operate crash reporting services to automatically collect crashes from millions of customers and file bug reports. Precisely triaging crashes is necessary and important for software providers because the millions of crashes that may be reported every day are critical in identifying high impact bugs. However, the triaging accuracy of existing systems is limited, as they rely only on the syntactic information of the stack trace at the moment of a crash without analyzing program semantics. In this paper, we present RETracer, the first system to triage software crashes based on program semantics reconstructed from memory dumps. RETracer was designed to meet the requirements of large-scale crash reporting services. RETracer performs binary-level backward taint analysis without a recorded execution trace to understand how functions on the stack contribute to the crash. The main challenge is that the machine state at an earlier time cannot be recovered completely from a memory dump, since most instructions are information destroying. We have implemented RETracer for x86 and x86-64 native code, and compared it with the existing crash triaging tool used by Microsoft. We found that RETracer eliminates two thirds of triage errors based on a manual analysis of 140 bugs fixed in Microsoft Windows and Office. RETracer has been deployed as the main crash triaging system on Microsoft's crash reporting service.


title: Active Suspension Control With Direct-Drive Tubular Linear Brushless Permanent-Magnet Motor

abstract: Recently, active suspension is gaining popularity in commercial automobiles. To develop the control methodologies for active suspension control, a quarter-car test bed was built employing a direct-drive tubular linear brushless permanent-magnet motor (LBPMM) as a force-generating component. Two accelerometers and a linear variable differential transformer (LVDT) are used in this quarter-car test bed. Three pulse-width-modulation (PWM) amplifiers supply the currents in three phases. Simulated road disturbance is generated by a rotating cam. Modified lead-lag control, linear-quadratic (LQ) servo control with a Kalman filter, fuzzy control methodologies were implemented for active-suspension control. In the case of fuzzy control, an asymmetric membership function was introduced to eliminate the DC offset in sensor data and to reduce the discrepancy in the models. This controller could attenuate road disturbance by up to 77% in the sprung mass velocity and 69% in acceleration. The velocity and the acceleration data of the sprung mass are presented to compare the controllers' performance in the ride comfort of a vehicle. Both simulation and experimental results are presented to demonstrate the effectiveness of these control methodologies.


title: Network forensics: Notions and challenges

abstract: Network forensics is an extension of the network security model which traditionally emphasizes prevention and detection of network attacks. It addresses the need for dedicated investigative capabilities in the current model to allow investigating malicious behavior in networks. It helps organizations in investigating outside and inside network attacks. It is also important for law enforcement investigations. In this paper, various aspects of network forensics are reviewed as well as related technologies and their limitations. Also, challenges in deploying a network forensics infrastructure are highlighted.


title: Structural basis of PROTAC cooperative recognition for selective protein degradation

abstract: Inducing macromolecular interactions with small molecules to activate cellular signaling is a challenging goal. PROTACs (proteolysis-targeting chimeras) are bifunctional molecules that recruit a target protein in proximity to an E3 ubiquitin ligase to trigger protein degradation. Structural elucidation of the key ternary ligase-PROTAC-target species and its impact on target degradation selectivity remain elusive. We solved the crystal structure of Brd4 degrader MZ1 in complex with human VHL and the Brd4 bromodomain (Brd4BD2). The ligand folds into itself to allow formation of specific intermolecular interactions in the ternary complex. Isothermal titration calorimetry studies, supported by surface mutagenesis and proximity assays, are consistent with pronounced cooperative formation of ternary complexes with Brd4BD2. Structure-based-designed compound AT1 exhibits highly selective depletion of Brd4 in cells. Our results elucidate how PROTAC-induced de novo contacts dictate preferential recruitment of a target protein into a stable and cooperative complex with an E3 ligase for selective degradation.


title: viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia

abstract: New high-dimensional, single-cell technologies offer unprecedented resolution in the analysis of heterogeneous tissues. However, because these technologies can measure dozens of parameters simultaneously in individual cells, data interpretation can be challenging. Here we present viSNE, a tool that allows one to map high-dimensional cytometry data onto two dimensions, yet conserve the high-dimensional structure of the data. viSNE plots individual cells in a visual similar to a scatter plot, while using all pairwise distances in high dimension to determine each cell's location in the plot. We integrated mass cytometry with viSNE to map healthy and cancerous bone marrow samples. Healthy bone marrow automatically maps into a consistent shape, whereas leukemia samples map into malformed shapes that are distinct from healthy bone marrow and from each other. We also use viSNE and mass cytometry to compare leukemia diagnosis and relapse samples, and to identify a rare leukemia population reminiscent of minimal residual disease. viSNE can be applied to any multi-dimensional single-cell technology.


title: Pencil-Beam Full-Space Scanning 2D CRLH Leaky-Wave Antenna Array

abstract: A novel pencil-beam, full-space scanning 2D composite right/left-handed (CRLH) transmission line (TL) leaky-wave antenna (LWA) array is proposed. The proposed array achieves the same performance of conventional phase array antennas in a simpler manner. Moreover, array elements are fed by a CRLH infinite-wavelength series power divider and therefore the separation between array elements and the number of array element can be extended arbitrary. The CRLH series power divider eliminates the requirement of complex and high loss feeding networks commonly found in conventional arrays. Experimental results of each system components are presented along with analytical and simulation results of the complete array system.


title: StreamWiki: Enabling Viewers of Knowledge Sharing Live Streams to Collaboratively Generate Archival Documentation for Effective In-Stream and Post Hoc Learning

abstract: Knowledge-sharing live streams are distinct from traditional educational videos, at least because of the large concurrently-viewing audience and the real-time discussions between viewers and the streamer. Though this creates unique opportunities for interactive learning, it also brings a challenge for creating a useful archive for post hoc learning. This paper presents the results of interviews with knowledge sharing streamers, their moderators, and viewers to understand current experiences and needs for sharing and learning knowledge through live streaming. Based on those findings, we built StreamWiki, a tool which leverages the viewers during live streams to produce useful archives of the interactive learning experience. On StreamWiki, moderators initiate tasks that viewers complete by conducting microtasks, such as writing a summary, commenting, and voting for informative comments. As a result, a summary document is built in real time. Through the tests of our prototype with streamers and viewers, we found that StreamWiki could help understanding the content and the context of the stream, during the stream and for post hoc learning.


title: Dual-Polarized, Differential Fed Microstrip Patch Antennas With Very High Interport Isolation for Full-Duplex Communication

abstract: This communication presents two dual-polarized microstrip patch antenna systems based on a single radiating element with 180° ring hybrid coupler for differential feeding to achieve high interport RF isolation. One of the implemented single-layer patch antennas provides more than 67 dB isolation between transmit (Tx) and receive (Rx) ports at 2.4 GHz while the second fabricated antenna with slot coupled Tx port along with differential feeding for Rx operation provides more than 90 dB RF isolation at 2.41 GHz between dc isolated Tx–Rx ports. Moreover, the single-layer antenna provides better than 62 dB interport isolation for 10 dB-return loss impedance bandwidth (BW) of 50 MHz. Measured interport isolation for implemented antenna with slot coupled Tx port achieves around 70 and 79 dB port to port RF isolation for 50 and 20 MHz BWs, respectively, and these are the highest amounts of isolation for a single antenna to best of our knowledge. The antenna with slot coupled Tx port provides more interport RF isolation as compared to single-layer antenna due to low coupling between slot coupled and co-planar quarter-wave microstrip fed ports. Both antenna structures have the capability to remove in-band nonlinear self-interference (SI) components without complex SI cancellation circuits.


title: Adaptive weighted imbalance learning with application to abnormal activity recognition

abstract: Abnormal activity recognition has been paid much attention in the field of healthcare and related applications, especially for the elderly people's physical and mental health, the high risk of the fall accident and its caused injures have gradually attracted more and more concerns. At present, wearable devices based fall detection technology can effectively and timely monitor the occurrence of fall accidents and help the injured person receive the first aid. However, the built classifiers of traditional approaches for fall detecting and monitoring suffer from a high false-alarm rate though they can reach a relatively high detection accuracy, further they have to face with the imbalance problem because sensor data of abnormal activities are usually rare in the realistic application. To address this challenge, we propose two-stage adaptive weighted extreme learning machine (AWELM) method for eyeglass and watch wearables based fall detecting and monitoring. Experimental results validate and illustrate significant efficiency and effectiveness of the proposed method and show that, our approach firstly achieves a good balance between high detection accuracy and low false-alarm rate based on our two-stage recognition scheme; secondly enables our imbalance learning approach for scarce abnormal activity data by two-stage adaptive weighted method; thirdly provides a light-weight classifier solution to resource constrained wearable devices using extreme learning machine with the fast training speed and good generalization capability, which enables large-scale mHealth applications and especially helps the elderly people to greatly reduce the risk of fall accidents finally.


title: Comparison between three-capacitance, analytical-based and finite element analysis partial discharge models in condition monitoring

abstract: An important and essential diagnostic tool for insulation systems in condition monitoring is partial discharge (PD) measurement. Through PD measurement, the results may be used for insulation condition assessment. However, the modelling of PD is important because it is helpful in attaining a better understanding of PD phenomenon. The physical mechanisms and critical parameters influencing PD events under various conditions of defects and stresses can be identified. The relationship between the insulation design parameters, test conditions and defect characteristics with PD activities can also be developed. There have been many researches that reported on simulation of PD events in a void in insulation material. PD models can be categorized in three models, they are the induced charge concept model, three-capacitance model and the finite element method models. Each model has its own strengths and limitations in certain aspects. Therefore, in this paper, comparative studies and simulations for various PD models that have been developed to date are reported and discussed. The disadvantages and advantages of every model are presented in details. The results from the simulation using those models were compared with the measurement data to observe their performance in terms of physical parameters values. From comparative studies between each model, the most suitable PD model can be chosen to simulate PD events depending on the requirement selected by the users.


title: Maximized Inter-Class Weighted Mean for Fast and Accurate Mitosis Cells Detection in Breast Cancer Histopathology Images

abstract: Based on the Nottingham criteria, the number of mitosis cells in histopathological slides is an important factor in diagnosis and grading of breast cancer. For manual grading of mitosis cells, histopathology slides of the tissue are examined by pathologists at 40× magnification for each patient. This task is very difficult and time-consuming even for experts. In this paper, a fully automated method is presented for accurate detection of mitosis cells in histopathology slide images. First a method based on maximum-likelihood is employed for segmentation and extraction of mitosis cell. Then a novel Maximized Inter-class Weighted Mean (MIWM) method is proposed that aims at reducing the number of extracted non-mitosis candidates that results in reducing the false positive mitosis detection rate. Finally, segmented candidates are classified into mitosis and non-mitosis classes by using a support vector machine (SVM) classifier. Experimental results demonstrate a significant improvement in accuracy of mitosis cells detection in different grades of breast cancer histopathological images.


title: Annotating omission in statement pairs

abstract: In this piece of industrial application, we focus on the identification of omission in statement pairs for an online news platform. We compare three annotation schemes, namely two crowdsourcing schemes and an expert annotation. The simplest of the two crowdsourcing approaches yields a better annotation quality than the more complex one. We use a dedicated classifier to assess whether the annotators' behaviour can be explained by straightforward linguistic features. However , for our task, we argue that expert and not crowdsourcing-based annotation is the best compromise between cost and quality.


title: A SLAM Overview from a User’s Perspective

abstract: This paper gives a brief overview on the Simultaneous Localization and Mapping (SLAM) problem from the perspective of using SLAM for an application as opposed to the common view in SLAM research papers that focus on investigating SLAM itself.


Histopathological classification of precursor lesions of esophageal adenocarcinoma: A Deep Multiple Instance Learning Approach


Virtual environments for vocational training : user experience in culinary education


title: Zoning Features and 2DLSTM for Urdu Text-line Recognition

abstract: Recognition of Urdu cursive script is a challenging task due to the implicit complexities associated with it. The performance of a recognition system is immensely dependent on extracted features. There are various features extraction approaches proposed in recent years. Among many, an approach based on zoning features proved to be efficient and popular. Such zoning features represent significant information with low complexity and high speed. In this paper, we used zoning features for the classification of Urdu Nasta'liq text lines, with a combination of 2-Dimensional Long Short Term Memory networks (2DLSTM) as learning classifier. The proposed model is evaluated on publicly available UPTI dataset and character recognition rate of 93.39% is obtained.


Material Participation: Technology, the Environment and Everyday Publics


title: The Ecology of Human Performance: A Framework for Considering the Effect of Context

abstract: In theory and in practice, context (as an area of concern to occupational therapists) has not received the same attention as performance components and performance areas. The Ecology of Human Performance serves as a framework for considering the effect of context. Context is described as a lens from which persons view their world. The interrelationship of person and context determines which tasks fall within the person's performance range. The Ecology of Human Performance framework provides guidelines for encompassing context in occupational therapy theory, practice, and research. Language: en


title: Spectrogram segmentation by means of statistical features for non-stationary signal interpretation

abstract: Time-frequency representations (TFRs) are suitable tools for nonstationary signal analysis, but their reading is not straightforward for a signal interpretation task. This paper investigates the use of TFR statistical properties for classification or recognition purposes, focusing on a particular TFR: the spectrogram. From the properties of a stationary process periodogram, we derive the properties of a nonstationary process spectrogram. It leads to transform the TFR to a local statistical features space from which we propose a method of segmentation. We illustrate our matter with first- and second-order statistics and identify the information they, respectively, provide. The segmentation is operated by a region growing algorithm, which does not require any prior knowledge on the nonstationary signal. The result is an automatic extraction of informative subsets from the TFR, which is relevant for the signal understanding. Examples are presented concerning synthetic and real signals.


title: Explicative factors of face-to-face harassment and cyberbullying in a sample of primary students.

abstract: BACKGROUND ::: Research has shown that there is a co-occurrence between bullying and cyberbullying in relation to certain variables that describe and explain them. The present study aims to examine the differential influence of individual and contextual variables on perception of the role played in the involvement in both phenomena. ::: ::: ::: METHOD ::: Participants were 1278 schoolchildren (47.7 % girls) of primary education, aged 10 to 14 years ( M =11.11, SD = 0.75). ::: ::: ::: RESULTS ::: Logistic regression analysis indicated that social adjustment, normative adjustment, disruptiveness, gender, and self-esteem explain a substantial part of the involvement in both violent phenomena as victims, aggressors, and bully/victims. ::: ::: ::: CONCLUSIONS ::: The results are discussed regarding the weight that must attributed to individual versus contextual factors, concluding that the explicative weight of the immediate social elements and educational context may make the difference.


title: DNACLUST: accurate and efficient clustering of phylogenetic marker genes

abstract: BackgroundClustering is a fundamental operation in the analysis of biological sequence data. New DNA sequencing technologies have dramatically increased the rate at which we can generate data, resulting in datasets that cannot be efficiently analyzed by traditional clustering methods.This is particularly true in the context of taxonomic profiling of microbial communities through direct sequencing of phylogenetic markers (e.g. 16S rRNA) - the domain that motivated the work described in this paper. Many analysis approaches rely on an initial clustering step aimed at identifying sequences that belong to the same operational taxonomic unit (OTU). When defining OTUs (which have no universally accepted definition), scientists must balance a trade-off between computational efficiency and biological accuracy, as accurately estimating an environment's phylogenetic composition requires computationally-intensive analyses. We propose that efficient and mathematically well defined clustering methods can benefit existing taxonomic profiling approaches in two ways: (i) the resulting clusters can be substituted for OTUs in certain applications; and (ii) the clustering effectively reduces the size of the data-sets that need to be analyzed by complex phylogenetic pipelines (e.g., only one sequence per cluster needs to be provided to downstream analyses).ResultsTo address the challenges outlined above, we developed DNACLUST, a fast clustering tool specifically designed for clustering highly-similar DNA sequences.Given a set of sequences and a sequence similarity threshold, DNACLUST creates clusters whose radius is guaranteed not to exceed the specified threshold. Underlying DNACLUST is a greedy clustering strategy that owes its performance to novel sequence alignment and k- mer based filtering algorithms.DNACLUST can also produce multiple sequence alignments for every cluster, allowing users to manually inspect clustering results, and enabling more detailed analyses of the clustered data.ConclusionsWe compare DNACLUST to two popular clustering tools: CD-HIT and UCLUST. We show that DNACLUST is about an order of magnitude faster than CD-HIT and UCLUST (exact mode) and comparable in speed to UCLUST (approximate mode). The performance of DNACLUST improves as the similarity threshold is increased (tight clusters) making it well suited for rapidly removing duplicates and near-duplicates from a dataset, thereby reducing the size of the data being analyzed through more elaborate approaches.


title: Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions

abstract: In this paper, we propose a novel approach to extract primary object segments in videos in the `object proposal' domain. The extracted primary object regions are then used to build object models for optimized video segmentation. The proposed approach has several contributions: First, a novel layered Directed Acyclic Graph (DAG) based framework is presented for detection and segmentation of the primary object in video. We exploit the fact that, in general, objects are spatially cohesive and characterized by locally smooth motion trajectories, to extract the primary object from the set of all available proposals based on motion, appearance and predicted-shape similarity across frames. Second, the DAG is initialized with an enhanced object proposal set where motion based proposal predictions (from adjacent frames) are used to expand the set of object proposals for a particular frame. Last, the paper presents a motion scoring function for selection of object proposals that emphasizes high optical flow gradients at proposal boundaries to discriminate between moving objects and the background. The proposed approach is evaluated using several challenging benchmark videos and it outperforms both unsupervised and supervised state-of-the-art methods.


title: Materializing Energy

abstract: Motivated and informed by perspectives on sustainability and design, this paper draws on a diverse body of scholarly works related to energy and materiality to articulate a perspective on energy-as-materiality and propose a design approach of materializing energy. Three critical themes are presented: the intangibility of energy, the undifferentiatedness of energy, and the availability of energy. Each theme is developed through combination of critical investigation and design exploration, including the development and deployment of several novel design artifacts: Energy Mementos and The Local Energy Lamp. A framework for interacting with energy-as-materiality is proposed involving collecting, keeping, sharing, and activating energy. A number of additional concepts are also introduced, such as energy attachment, energy engagement, energy attunement, local energy and energy meta-data. Our work contributes both a broader, more integrative design perspective on energy and materiality as well as a diversity of more specific concepts and artifacts that may be of service to designers and researchers of interactive systems concerned with sustainability and energy.


title: Speaker change point detection using deep neural nets

abstract: We investigate the use of deep neural nets (DNN) to provide initial speaker change points in a speaker diarization system. The DNN trains states that correspond to the location of the speaker change point (SCP) in the speech segment input to the DNN. We model these different speaker change point locations in the DNN input by 10 to 20 states. The confidence in the SCP is measured by the number of frame synchronous states that correspond to the hypothesized speaker change point. We only keep the speaker change points with the highest confidence. We show that this DNN-based change point detector reduces the number of missed change points for both an English test set and a French dev set. We also show that the DNN-based change points reduce the diarization error rate for both an English and a French diarization system. These results show the feasibility of DNNs to provide initial speaker change points.


title: Convolutional SVM Networks for Object Detection in UAV Imagery

abstract: Nowadays, unmanned aerial vehicles (UAVs) are viewed as effective acquisition platforms for several civilian applications. They can acquire images with an extremely high level of spatial detail compared to standard remote sensing platforms. However, these images are highly affected by illumination, rotation, and scale changes, which further increases the complexity of analysis compared to those obtained using standard remote sensing platforms. In this paper, we introduce a novel convolutional support vector machine (CSVM) network for the analysis of this type of imagery. Basically, the CSVM network is based on several alternating convolutional and reduction layers ended by a linear SVM classification layer. The convolutional layers in CSVM rely on a set of linear SVMs as filter banks for feature map generation. During the learning phase, the weights of the SVM filters are computed through a forward supervised learning strategy unlike the backpropagation algorithm widely used in standard convolutional neural networks (CNNs). This makes the proposed CSVM particularly suitable for detecting problems characterized by very limited training sample availability. The experiments carried out on two UAV data sets related to vehicles and solar-panel detection issues, with a 2-cm resolution, confirm the promising capability of the proposed CSVM network compared to recent state-of-the-art solutions based on pretrained CNNs.


title: Approxilyzer: Towards a systematic framework for instruction-level approximate computing and its application to hardware resiliency

abstract: Approximate computing environments trade off computational accuracy for improvements in performance, energy, and resiliency cost. For widespread adoption of approximate computing, a fundamental requirement is to understand how perturbations to a computation affect the outcome of the execution in terms of its output quality. This paper presents a framework for approximate computing, called Approxilyzer, that quantifies the quality impact of a single-bit error in all dynamic instructions of an execution with high accuracy (95% on average). We demonstrate two uses of Approxilyzer. First, we show how Approxilyzer can be used to quantitatively tune output quality vs. resiliency vs. overhead to enable ultra-low cost resiliency solutions (with a single bit error model). For example, we show that Approxilyzer determines that a very small loss in output quality (1%) can yield large resiliency overhead reduction (up to 55%) for 99% resiliency coverage. Second, we show how Approxilyzer can be used to provide a first-order estimate of the approximation potential of general-purpose programs. It does so in an automated way while requiring minimal user input and no program modifications. This enables programmers or other tools to focus on the promising subset of approximable instructions for further analysis.


title: Metrics of the Aesthetically Perfect Breast

abstract: Breast surgery has long been viewed as an art more than a science. However, defining and objectively measuring the ideal breast morphology and aesthetic proportions are fundamental for surgical planning and to setting the goals of surgery as well as to evaluate surgical outcomes. Despite the fact that much has been written about aesthetic and reconstructive breast surgery, there is still no real consensus about what the attributes are of an ideal breast. Moreover, there are in fact no objective standard measurement systems and guidelines to describe ideal or even normal breast shape. Though there is great variability in the perception of beauty among patients and surgeons alike due to many factors among which are age, sex, and sociocultural background, there is common agreement that beauty is a universal phenomenon that has a universal standard present across all civilizations and centuries, and that perceived beauty is enhanced and optimal aesthetics are achieved when proper measurements are made and anthropometric proportions as well as attractive harmonious ratios are respected. The current review is an attempt to summarize the most relevant information available trying to introduce some harmony in our perception of aesthetic ideals of breast surgery. Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.


title: AU-aware Deep Networks for facial expression recognition

abstract: In this paper, we propose to construct a deep architecture, AU-aware Deep Networks (AUDN), for facial expression recognition by elaborately utilizing the prior knowledge that the appearance variations caused by expression can be decomposed into a batch of local facial Action Units (AUs). The proposed AUDN is composed of three sequential modules: the first module consists of two layers, i.e., a convolution layer and a max-pooling layer, which aim to generate an over-complete representation encoding all expression-specific appearance variations over all possible locations; In the second module, an AU-aware receptive field layer is designed to search subsets of the over-complete representation, each of which aims at best simulating the combination of AUs; In the last module, multilayer Restricted Boltzmann Machines (RBM) are exploited to learn hierarchical features, which are then concatenated for final expression recognition. Experiments on three expression databases CK+, MMI and SFEW demonstrate the effectiveness of AUDN in both lab-controlled and wild environments. All our results are better than or at least competitive to the best known results.


title: Understanding information technology acceptance by individual professionals: Toward an integrative view

abstract: Although information technology is becoming a vital part of the workplace of skilled professionals, it is unclear what factors contribute to its acceptance by them. Building upon and integrating the key findings of three closely related theoretical paradigms (the technology acceptance model, the theory of planned behavior, and innovation diffusion theory), we developed a more complete, coherent, and unified model and tested the resulting model in the context of PDA acceptance by healthcare professionals. Using LISREL, data collected from 222 physicians in the U.S. were tested against the model; it explained 57% of the physician's intention to accept an innovation, with good model fit. Our study produced useful insights into the factors that influence technology acceptance decisions by professionals and provided new ideas in the understanding of user acceptance of technology.


title: CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise

abstract: In this paper, we study the problem of learning image classification models with label noise. Existing approaches depending on human supervision are generally not scalable as manually identifying correct or incorrect labels is time-consuming, whereas approaches not relying on human supervision are scalable but less effective. To reduce the amount of human supervision for label noise cleaning, we introduce CleanNet, a joint neural embedding network, which only requires a fraction of the classes being manually verified to provide the knowledge of label noise that can be transferred to other classes. We further integrate CleanNet and conventional convolutional neural network classifier into one framework for image classification learning. We demonstrate the effectiveness of the proposed algorithm on both of the label noise detection task and the image classification on noisy data task on several large-scale datasets. Experimental results show that CleanNet can reduce label noise detection error rate on held-out classes where no human supervision available by 41.5% compared to current weakly supervised methods. It also achieves 47% of the performance gain of verifying all images with only 3.2% images verified on an image classification task. Source code and dataset will be available at this http URL.


title: A half-bridge series resonant inverter for induction cookers using a novel FPGA-based control strategy

abstract: Asymmetrical pulse width modulation (APWM) with frequency tracking control strategy is proposed for half bridge series resonant inverter for induction cookers, by which inverter is operated at zero-voltage-switching (ZVS) over wide load range. The FPGA-based full digital control system construction and operation are described. Steady state characteristics, such as normalized output power, turn-off current and average current through switches, are calculated. Some design considerations including selection of snubber capacitor, determination of dead time, ZVS boundary and presetting of phase-locked loop (PLL) are discussed. Finally, experiment results are given


title: Show and tell: A neural image caption generator

abstract: Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66, and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we achieve a BLEU-4 of 27.7, which is the current state-of-the-art.


The new new product development game Stop running the relay race and take up rugby


title: The cyber decade: Cyber defence at a X-ing point

abstract: As a consequence of the numerous cyber attacks over the last decade, both the consideration and use of cyberspace has fundamentally changed, and will continue to evolve. Military forces all over the world have come to value the new role of cyberspace in warfare, building up cyber commands, and establishing new capabilities. Integral to such capabilities is that military forces fundamentally depend on the rapid exchange of information in order for their decision-making processes to gain superiority on the battlefield; this compounds the need to develop network- enabled capabilities to realize network-centric warfare. This triangle of cyber offense, cyber defence, and cyber dependence creates a challenging and complex system of interdependencies. Alongside, while numerous technologies have not improved cyber security significantly, this may change with upcoming new concepts and systems, like decentralized ledger technologies (Blockchains) or quantum-secured communication. Following these thoughts, the paper analyses the development of both cyber threats and defence capabilities during the past 10 years, evaluates the current situation and gives recommendations for improvements. To this end, the paper is structured as follows: first, general conditions for military forces with respect to "cyber" are described, including an analysis of the most likely courses of action of the West and their seemingly traditional adversary in the East, Russia. The overview includes a discussion of the usefulness of the measures and an overview of upcoming technologies critical for cyber security. Finally, requirements and recommendations for the further development of cyber defence are briefly covered.


title: Natural Language Processing for Information Extraction

abstract: With rise of digital age, there is an explosion of information in the form of news, articles, social media, and so on. Much of this data lies in unstructured form and manually managing and effectively making use of it is tedious, boring and labor intensive. This explosion of information and need for more sophisticated and efficient information handling tools gives rise to Information Extraction(IE) and Information Retrieval(IR) technology. Information Extraction systems takes natural language text as input and produces structured information specified by certain criteria, that is relevant to a particular application. Various sub-tasks of IE such as Named Entity Recognition, Coreference Resolution, Named Entity Linking, Relation Extraction, Knowledge Base reasoning forms the building blocks of various high end Natural Language Processing (NLP) tasks such as Machine Translation, Question-Answering System, Natural Language Understanding, Text Summarization and Digital Assistants like Siri, Cortana and Google Now. This paper introduces Information Extraction technology, its various sub-tasks, highlights state-of-the-art research in various IE subtasks, current challenges and future research directions.


title: Assessment of Vasoactive Agents and Vascular Aging by the Second Derivative of Photoplethysmogram Waveform

abstract: Abstract —To evaluate the clinical application of the second derivative of the fingertip photoplethysmogram waveform, we performed drug administration studies (study 1) and epidemiological studies (study 2). In study 1, ascending aortic pressure was recorded simultaneously with the fingertip photoplethysmogram and its second derivative in 39 patients with a mean±SD age of 54±11 years. The augmentation index was defined as the ratio of the height of the late systolic peak to that of the early systolic peak in the pulse. The second derivative consists of an a, b, c, and d wave in systole and an e wave in diastole. Ascending aortic pressure increased after injection of 2.5 μg angiotensin from 126/74 to 160/91 mm Hg and decreased after 0.3 mg sublingual nitroglycerin to 111/73 mm Hg. The d/a, the ratio of the height of the d wave to that of the a wave, decreased after angiotensin from −0.40±0.13 to −0.62±0.19 and increased after nitroglycerin to −0.25±0.12 ( P P r =0.79, P r =0.80, P y ) increased with age ( x ) ( r =0.80, P y =0.023 x −1.515). The second derivative aging index was higher in 126 subjects with any history of diabetes mellitus, hypertension, hypercholesterolemia, and ischemic heart disease than in age-matched subjects without such a history (−0.06±0.36 versus −0.22±0.41, P P


title: Composite Right/Left-Handed Substrate Integrated Waveguide and Half Mode Substrate Integrated Waveguide Leaky-Wave Structures

abstract: Composite right/left-handed (CRLH) substrate integrated waveguide (SIW) and half mode substrate integrated waveguide (HMSIW) leaky-wave structures for antenna applications are proposed and investigated. Their propagation properties and radiation characteristics are studied extensively. Their backfire-to-endfire beam-steering capabilities through frequency scanning are demonstrated and discussed. These metamaterial radiating structures are realized by etching interdigital slots on the waveguide surface and the ground. The slot behaves as a series capacitor as well as a radiator leading to a CRLH leaky-wave application. Four antennas are fabricated, measured, and analyzed, including two balanced CRLH SIW designs characterized by single-side or double-side radiation, and two unbalanced HMSIW designs characterized by different boundary conditions. Antenna parameters such as return loss, radiation patterns, gain, and efficiency are all provided. Measured results are consistent with the simulation. All these proposed antennas possess the advantages of low profile, low cost, and low weight, while they are also showing their own unique features, like high directivity, quasi-omnidirectional radiation, miniaturized size, continuous beam-steering capabilities covering both the backward and forward quadrants, etc., providing much design flexibility for the real applications.


title: Machine learning based paraphrase identification system using lexical syntactic features

abstract: During the natural language communication, meaning understanding is the complex task that humans learn from their childhood but to automate this process of meaning understanding for computers has great real world applications. Simple text processing tasks are not enough to uncover the meaning from given unstructured natural language text. Our current research focuses on the issues pertaining to the same. Paraphrase identification is such important task of identifying the meaning similarity between two text segments in natural language understanding system. Proposed a machine learning system uses lexical features and dependency based features for sentence level paraphrase identification. The performance of proposed system is evaluated by conducting experiment on standard Microsoft paraphrase corpus. Moreover, a comparative study of current system with other machine learning based systems on Microsoft paraphrase corpus for paraphrase identification is carried out. The proposed system achieves competitive results compare to other state-of-the art machine learning systems by using simple linguistic features. The system using SVM classifier achieves 81.41% f-score by using simple lexical features only. Voting based classifier scores 80.97% with lexical features. Results with dependency features are highly sensitive to minor syntactic change.


title: FP-Block : Usable Web Privacy by Controlling Browser Fingerprinting

abstract: Online tracking of users is used for benign goals, such as detecting fraudulent logins, but also to invade user privacy. We posit that for non-oppressed users, tracking within one website does not have a substantial negative impact on privacy, while it enables legitimate benefits. In contrast, cross-domain tracking negatively impacts user privacy, while being of little benefit to the user.


title: Why are bilinguals better than monolinguals at false-belief tasks?

abstract: In standard Theory of Mind tasks, such as the Sally-Anne, children have to predict the behaviour of a mistaken character, which requires attributing the character a false belief. Hundreds of developmental studies in the last 30 years have shown that children under 4 fail standard false-belief tasks. However, recent studies have revealed that bilingual children and adults outperform their monolingual peers in this type of tasks. Bilinguals’ better performance in false-belief tasks has generally been interpreted as a result of their better inhibitory control; that is, bilinguals are allegedly better than monolinguals at inhibiting the erroneous response to the false-belief question. In this review, I challenge the received view and argue instead that bilinguals’ better false-belief performance results from more effective attention management. This challenge ties in with two independent lines of research: on the one hand, recent studies on the role of attentional processes in false-belief tasks with monolingual children and adults; and on the other, current research on bilinguals’ performance in different Executive Function tasks. The review closes with an exploratory discussion of further benefits of bilingual cognition to Theory of Mind development and pragmatics, which may be independent from Executive Function.


title: Price Trend Prediction of Stock Market Using Outlier Data Mining Algorithm

abstract: In this paper we present a novel data miming approach to predict long term behavior of stock trend. Traditional techniques on stock trend prediction have shown their limitations when using time series algorithms or volatility modelling on price sequence. In our research, a novel outlier mining algorithm is proposed to detect anomalies on the basis of volume sequence of high frequency tick-by tick data of stock market. Such anomaly trades always inference with the stock price in the stock market. By using the cluster information of such anomalies, our approach predict the stock trend effectively in the really world market. Experiment results show that our proposed approach makes profits on the Chinese stock market, especially in a long-term usage.


title: To compress or not to compress - compute vs. IO tradeoffs for mapreduce energy efficiency

abstract: Compression enables us to shift resource bottlenecks between IO and CPU. In modern datacenters, where energy efficiency is a growing concern, the benefits of using compression have not been completely exploited. As MapReduce represents a common computation framework for Internet datacenters, we develop a decision algorithm that helps MapReduce users identify when and where to use compression. For some jobs, using compression gives energy savings of up to 60%. We believe our findings will provide signficant impact on improving datacenter energy efficiency.


title: LG-Trader : Stock trading decision support based on feature selection by weighted localized generalization error model

abstract: Stock trading is an important financial activity of human society. Machine learning techniques are adopted to provide trading decision support by predicting the stock price or trading signals of the next day. Decisions are made by analyzing technical indices and fundamental analysis of companies. There are two major machine learning research problems for stock trading decision support: classifier architecture selection and feature selection. In this work, we propose the LG-Trader which will deal with these two problems simultaneously using a genetic algorithm minimizing a new Weighted Localized Generalization Error (wL-GEM). An issue being ignored in current machine learning based stock trading researches is the imbalance among buy, hold and sell decisions. Usually hold decision is the majority in comparison to both buy and sell decisions. So, the wL-GEM is proposed to balance classes by penalizing heavier for generalization error being made in minority classes. The feature selection based on wL-GEM helps to select most useful technical indices among choices for each stock. Experimental results demonstrate that the LG-Trader yields higher profits and rates of return in both stock and index trading.


Finite Impulse Response Neural Networks for Autoregressive Time Series Prediction


title: Dynamic Self-Attention : Computing Attention over Words Dynamically for Sentence Embedding

abstract: In this paper, we propose Dynamic Self-Attention (DSA), a new self-attention mechanism for sentence embedding. We design DSA by modifying dynamic routing in capsule network (Sabouretal.,2017) for natural language processing. DSA attends to informative words with a dynamic weight vector. We achieve new state-of-the-art results among sentence encoding methods in Stanford Natural Language Inference (SNLI) dataset with the least number of parameters, while showing comparative results in Stanford Sentiment Treebank (SST) dataset.


title: Navigating the Local Modes of Big Data: The Case of Topic Models.

abstract: We consider the problem of multi-modality as it arises in the use of latent variable models to analyze big data. We focus in particular on unsupervised topic models which provide a powerful method for discovering latent structure in large corpora of unstructured texts. Although these approaches have proven useful throughout the social sciences, these models often have extremely multi-modal likelihood surfaces and different starting values will produce different parameter estimates. In this chapter, we develop tools for assessing sensitivity to initialization, selecting a model to analyze and improving the quality of starting values. We offer the perspective that even if the user is unable to identify the global maximum, local modes can provide useful and sometimes even better solutions for the data under study. ∗Prepared for “Data Science for Politics, Policy and Government”, R. Michael Alvarez, editor. The research assistance of Antonio Coppola, under the support of the Harvard BLISS program, was extremely valuable for this paper. Our thanks to participants of the Harris School Political Economy workshop and Princeton University Political Methodology workshop for reinforcing the need to write this chapter, and Jetson Leder-Luis for comments. An R package for estimating the Structural Topic Model is freely available at www.structuraltopicmodel.com.


title: Investigation of the effect of manufacturing errors on dynamic characteristics of herringbone planetary gear trains

abstract: A new and generalized dynamic model for a herringbone planetary gear train (HPGT) is proposed to investigate the forced vibration performances by utilizing the lumped-parameter approach. The developed model takes manufacturing eccentric errors and tooth profile errors into consideration, and can be used for the vibration performance analysis of the HPGT with different types of manufacturing errors and arbitrary number of planets. The forced vibration performances of an HPGT with three planets are numerically computed with the model presented, and are analyzed respectively in time domain and frequency domain. The effects of manufacturing errors such as the sun gear eccentric error on dynamic characteristics are discussed. Results show that manufacturing error excitations have a significant effect on dynamic characteristics of HPGT.


title: Non-Line-of-Sight Reconstruction using Efficient Transient Rendering

abstract: Being able to see beyond the direct line of sight is an intriguing prospective and could benefit a wide variety of important applications. Recent work has demonstrated that time-resolved measurements of indirect diffuse light contain valuable information for reconstructing shape and reflectance properties of objects located around a corner. In this paper, we introduce a novel reconstruction scheme that, by design, produces solutions that are consistent with state-of-the-art physically-based rendering. Our method combines an efficient forward model (a custom renderer for time-resolved three-bounce indirect light transport) with an optimization framework to reconstruct object geometry in an analysis-by-synthesis sense. We evaluate our algorithm on a variety of synthetic and experimental input data, and show that it gracefully handles uncooperative scenes with high levels of noise or non-diffuse material reflectance.


title: Pattern of Global Cyber War and Crime: A Conceptual Framework

abstract: The flourishing synergy arising between organized crimes and the Internet has increased the insecurity of the digital world. How hackers frame their actions? What factors encourage and energize their behavior? These are very important but highly underresearched questions. We draw upon literatures on psychology, economics, international relation and warfare to propose a framework that addresses these questions. We found that countries across the world differ in terms of regulative, normative and cognitive legitimacy to different types of web attacks. Cyber wars and crimes are also functions of the stocks of hacking skills relative to the availability of economic opportunities. An attacking unit's selection criteria for the target network include symbolic significance and criticalness, degree of digitization of values and weakness in defense mechanisms. Managerial and policy implications are discussed and directions for future research are suggested.


Aahaan : Using kNN and SVM based One-Class Classifier for Detecting Online Radicalization on


title: Automatic video description generation via LSTM with joint two-stream encoding

abstract: In this paper, we propose a novel two-stream framework based on combinational deep neural networks. The framework is mainly composed of two components: one is a parallel two-stream encoding component which learns video encoding from multiple sources using 3D convolutional neural networks and the other is a long-short-term-memory (LSTM)-based decoding language model which transfers the input encoded video representations to text descriptions. The merits of our proposed model are: 1) It extracts both temporal and spatial features by exploring the usage of 3D convolutional networks on both raw RGB frames and motion history images. 2) Our model can dynamically tune the weights of different feature channels since the network is trained end-to-end from learning combinational encoding of multiple features to LSTM-based language model. Our model is evaluated on three public video description datasets: one YouTube clips dataset (Microsoft Video Description Corpus) and two large movie description datasets (MPII Corpus and Montreal Video Annotation Dataset) and achieves comparable or better performance than the state-of-the-art approaches in video caption generation.


title: Native Language Identification Using Large, Longitudinal Data

abstract: Native Language Identification (NLI) is a task aimed at determining the native language (L1) of learners of second language (L2) on the basis of their written texts. To date, research on NLI has focused on relatively small corpora. We apply NLI to the recently released EFCamDat corpus which is not only multiple times larger than previous L2 corpora but also provides longitudinal data at several proficiency levels. Our investigation using accurate machine learning with a wide range of linguistic features reveals interesting patterns in the longitudinal data which are useful for both further development of NLI and its application to research on L2 acquisition.


title: Social media driven image retrieval

abstract: People often try to find an image using a short query and images are usually indexed using short annotations. Matching the query vocabulary with the indexing vocabulary is a difficult problem when little text is available. Textual user generated content in Web 2.0 platforms contains a wealth of data that can help solve this problem. Here we describe how to use Wikipedia and Flickr content to improve this match. The initial query is launched in Flickr and we create a query model based on co-occurring terms. We also calculate nearby concepts using Wikipedia and use these to expand the query. The final results are obtained by ranking the results for the expanded query using the similarity between their annotation and the Flickr model. Evaluation of these expansion and ranking techniques, over the Image CLEF 2010 Wikipedia Collection containing 237,434 images and their multilingual textual annotations, shows that a consistent improvement compared to state of the art methods.


title: An UWB planar out-of-phase power divider employing microstrip-slot and parallel stripline-microstrip transitions

abstract: The design of a planar out-of-phase power divider with an ultra wideband (UWB) performance is presented. The device employs two substrates with a common ground plane. A coupling between two microstrip lines on the top and bottom substrate layers through a slot in the common ground plane, and a transition from two microstrip lines to a parallel stripline are used to achieve an UWB operation. The simulated and measured results of the proposed divider reveal low insertion losses, good return loss at the three ports and fine isolation between the two output ports across the band 3.1 to more than 11 GHz with high phase stability.


title: In Vitro and In Vivo Antitumor Effect of Anti-CD33 Chimeric Receptor-Expressing EBV-CTL against CD33+ Acute Myeloid Leukemia

abstract: Genetic engineering of T cells with chimeric T-cell receptors (CARs) is an attractive strategy to treat malignancies. It extends the range of antigens for adoptive T-cell immunotherapy, and major mechanisms of tumor escape are bypassed. With this strategy we redirected immune responses towards the CD33 antigen to target acute myeloid leukemia. To improve in vivo T-cell persistence, we modified human Epstein Barr Virus-(EBV-) specific cytotoxic T cells with an anti-CD33.CAR. Genetically modified T cells displayed EBV and HLA-unrestricted CD33 bispecificity in vitro. In addition, though showing a myeloablative activity, they did not irreversibly impair the clonogenic potential of normal CD34(+) hematopoietic progenitors. Moreover, after intravenous administration into CD33(+) human acute myeloid leukemia-bearing NOD-SCID mice, anti-CD33-EBV-specific T cells reached the tumor sites exerting antitumor activity in vivo. In conclusion, targeting CD33 by CAR-modified EBV-specific T cells may provide additional therapeutic benefit to AML patients as compared to conventional chemotherapy or transplantation regimens alone.


title: Converting a tetrahedral mesh to a prism-tetrahedral hybrid mesh for FEM accuracy and efficiency

abstract: This paper presents a computational method for converting a tetrahedral mesh to a prism-tetrahedral hybrid mesh for improved solution accuracy and computational efficiency of finite element analysis. The proposed method inserts layers of prism elements and deletes tetrahedral elements in sweepable sub-domains, in which cross-sections remain topologically identical and geometrically similar along a certain sweeping path. The total number of finite elements is reduced because roughly three tetrahedral elements are converted to one prism element. The solution accuracy of the finite element analysis improves since a prism element yields a more accurate solution than a tetrahedral element. Only previously known method for creating such a prism-tetrahedral mesh was to manually decompose a target volume into sweepable and non-sweepable sub-volumes and mesh each sub-volume separately. The proposed method starts from a cross-section of a tetrahedral mesh and replaces the tetrahedral elements with layers of prism elements until prescribed quality criteria can no longer be satisfied. The method applies a sequence of edge-collapse, local-transformation, and smoothing operations to remove or displace nodes located within the volume to be replaced with a layer of prism elements. Series of computational fluid dynamics simulations and structural analyses have been conducted, and the results verified a better performance of prismtetrahedral hybrid mesh in finite element simulations.


title: Application of microencapsulation for toxicology studies. I. Principles and stabilization of trichloroethylene in gelatin-sorbitol microcapsules.

abstract: Microencapsulation is an innovative, alternative means of incorporating volatile, reactive, and/or unpalatable chemicals into animal feed for toxicologic studies. For such usage, the materials in the microcapsule shell must not adversely affect the laboratory animals, and the encapsulation process must not alter the chemical under study. Trichloroethylene (TCE), a volatile chemical identified in drinking water, was encapsulated in gelatin-sorbitol microcapsules. The concentration of TCE ranged from 40 to 43% (w/w) and most particles (approximately 85%) were between 300 and 420 micron in diameter. Under optimum storage conditions, loss of TCE from the microcapsules was less than 1% per month. Less than 2% of the TCE was lost from microcapsules held in uncovered petri dishes at ambient temperature and humidity for 14 days. Microencapsulated TCE was mixed with NIH-07 rodent feed at a level of 50 mg microcapsules/g feed (equivalent to 20.6 mg TCE/g feed) and stored at room temperature for 7 days in an open container or for 21 days in a sealed container. There was no detectable loss of TCE from the feed blends stored under these conditions. Thus, the stability of TCE in gelatin-sorbitol microcapsules is adequate for dosed-feed toxicity studies of this chemical.


title: Land Use and Land Cover change detection of Indra river watershed through Remote Sensing using Multi-Temporal satellite data

abstract: Land use and land cover is an important component in understanding the interactions of the human activities with the environment and thus it is necessary to be able to simulate changes. Empirical observation revealed a change in land use land cover classification in Indra river watershed located in Hoshangabad district (M.P). In this paper an attempt is made to study the changes in land use and land cover in Indra river watershed over 14 years period. The study has been done through remote sensing approach using Land Sat imageries of October 1992 and November 2006. The land use land cover classification was performed based on Satellite imageries. GIS software is used to prepare the thematic maps. The present study has brought to light that forest area that occupied about 15.01 per cent of the study area in 1992 has increased to 25.57 per cent in 2006. Built up area, water body and Waste land also have experienced change. Farm land (Agricultural land/open land) has decreased from 84.16 per cent to 68.40 per cent of the total area. Proper land use planning is essential for a sustainable development of Indra nadi watershed.


title: DNorm: disease name normalization with pairwise learning to rank

abstract: Motivation: Despite the central role of diseases in biomedical research, there have been much fewer attempts to automatically determine which diseases are mentioned in a text—the task of disease name normalization (DNorm)—compared with other normalization tasks in biomedical text mining research. ::: ::: Methods: In this article we introduce the first machine learning approach for DNorm, using the NCBI disease corpus and the MEDIC vocabulary, which combines MeSH® and OMIM. Our method is a high-performing and mathematically principled framework for learning similarities between mentions and concept names directly from training data. The technique is based on pairwise learning to rank, which has not previously been applied to the normalization task but has proven successful in large optimization problems for information retrieval. ::: ::: Results: We compare our method with several techniques based on lexical normalization and matching, MetaMap and Lucene. Our algorithm achieves 0.782 micro-averaged F-measure and 0.809 macro-averaged F-measure, an increase over the highest performing baseline method of 0.121 and 0.098, respectively. ::: ::: Availability: The source code for DNorm is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/DNorm, along with a web-based demonstration and links to the NCBI disease corpus. Results on PubMed abstracts are available in PubTator: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator ::: ::: Contact: vog.hin@ul.gnoyihz


title: Semantic segmentation of urban scenes with enhanced spatial contexts

abstract: This paper proposes a method to solve the image segmentation problem effectively by applying local spatial relations. The proposed method is based on the conjecture that spatially neighboring objects have a higher probability of relating each other than nonadjacent objects. Using depth information from 3D lidar, spatial contextual information between image segments is obtained. This information is modeled in the framework of Conditional Random Field to penalize distant objects that are likely to have no particular relation between them. The proposed method is evaluated on the publicly available image dataset, and it shows better global and class classification rates compared to the previous state-of-the-art work.


title: Prescribed performance control of quadruped robot

abstract: Due to the complex and high nonlinear structure of the quadruped robots, the dynamic control of quadruped robots has long been a big challenge for the researchers. In this paper, a guaranteed performance adaptive control algorithm is proposed for tracking control of a quadruped robot with parameter uncertainties. The controller is designed based on the sliding mode theory, and incorporates a transformed error, which includes the performance indices. Parameter uncertainties are estimated by a linear network. By choosing a proper Lyapunov function, the prescribed performance with the proposed controller is proven in the scenes of the parameter uncertainties and the environment constraint. Numerical simulations demonstrate the effectiveness and superiority of the proposed controller.


title: Metro: measuring error on simplified surfaces

abstract: This paper presents a new tool, Metro, designed to compensate for a deficiency in many simplification methods proposed in literature. Metro allows one to compare the difference between a pair of surfaces (e.g. a triangulated mesh and its simplified representation) by adopting a surface sampling approach. It has been designed as a highly general tool, and it does no assuption on the particular approach used to build the simplified representation. It returns both numerical results (meshes areas and volumes, maximum and mean error, etc.) and visual results, by coloring the input surface according to the approximation error. EMAIL:: r.scopigno@cnuce.cnr.it


title: Fast Scalable State Measurement with Superconducting Qubits

abstract: Author(s): Sank, Daniel Thomas | Advisor(s): Martinis, John M | Abstract: Superconducting qubits have emerged as leading candidates as the foundation of quantum information processing systems. Progress in superconducting qubit experiments with greater numbers of qubits and advanced techniques such as feedback will require faster and more accurate quantum state measurement. In particular, cyclic fault tolerance protocols such as the surface code require high accuracy measurement on time scales significantly shorter than the coherence times of the qubits. We have designed a multiplexed measurement system with a bandpass filter that allows fast measurement without increasing environmental damping of the qubits. We use this to demonstrate simultaneous measurement of four qubits on a single superconducting integrated circuit, finding that we can measured a single qubit state to 99.8% accuracy in 140 ns. This accuracy and speed is suitable for advanced multiqubit experiments including surface-code error correction.


title: Building a 3D Virtual Museum of Native American Baskets

abstract: In this paper we report our progress in building a system for the acquisition, analysis, and visualization of a collection of Native Californian baskets from the Phoebe A. Hearst Museum of Anthropology. Our project differs from existing cultural heritage applications in terms of its focus: to build tools and techniques for visualizing and studying a large number of related objects -- in this case, baskets. We present our progress in the following system components: (i) laser-scanning of baskets, (ii) construction and processing of 3D models, and (iii) building virtual exhibits. We conclude the paper with our experiences and a summary of challenges we anticipate in building a completely automated system for processing and analyzing a large set of models -- such as might be encountered when digitizing a large museum collection. Efficient retrieval and visualization of artifact collections are important to a number of communities, including anthropology researchers, Native American tribes, and the general public.


title: SWI-Prolog and the Web

abstract: Where Prolog is commonly seen as a component in a Web application that is either embedded or communicates using a proprietary protocol, we propose an architecture where Prolog communicates to other components in a Web application using the standard HTTP protocol. By avoiding embedding in external Web servers development and deployment become much easier. To support this architecture, in addition to the transfer protocol, we must also support parsing, representing and generating the key Web document types such as HTML, XML and RDF. ::: This paper motivates the design decisions in the libraries and extensions to Prolog for handling Web documents and protocols. The design has been guided by the requirement to handle large documents efficiently. The described libraries support a wide range of Web applications ranging from HTML and XML documents to Semantic Web RDF processing. ::: To appear in Theory and Practice of Logic Programming (TPLP)


FACE RECOGNITION BASED ATTENDANCE MANAGEMENT SYSTEM WITH RASPBERRY PI 2 USING EIGEN FACES ALGORITHM


title: hTetro: A tetris inspired shape shifting floor cleaning robot

abstract: This research work presents the development of a novel tetris inspired reconfigurable floor cleaning robot — hTetro, utilizing the hinged dissection theory of polyominoes. The developed robot platform is capable of transforming between any of the seven set of one-sided tetromino morphologies according to the perceived environment with an objective of maximizing the coverage area. Experiments were performed across two different settings to systematically compare the coverage area performance of the developed hTetro robot and a commercially available fixed morphology robot platform. Results indicate significantly higher coverage area performance in the case of hTetro due to its ability to assume optimal morphology in relation to navigating environment.


title: A Stock Market Trend Prediction System Using a Hybrid Decision Tree-Neuro-Fuzzy System

abstract: Stock market prediction is of great interest to stock traders and applied researchers. Main issues in developing a fully automated stock market prediction system are: feature extraction from the stock market data, feature selection for highest prediction accuracy, the dimensionality reduction of the selected feature set and the accuracy and robustness of the prediction system. In this paper, an automated decision tree-adaptive neuro-fuzzy hybrid automated stock market trend prediction system is proposed. The proposed system uses technical analysis (traditionally used by stock traders) for feature extraction and decision tree for feature selection. Selected features are then subjected to dimensionality reduction and the reduced dataset is then applied to the adaptive neuro-fuzzy system for the next-day stock market trend prediction. The proposed system is tested on four major international stock markets. The results show that the proposed hybrid system produces much higher accuracy when compared to stand-alone decision tree based system and ANFIS based system without feature selection and dimensionality reduction.


title: Anarchy is what States Make of it: The Social Construction of Power Politics

abstract: Todas las teorias de relaciones internacionales se basan en teorias sociales de relaciones entre agentes, procesos y estructuras sociales. Las teorias sociales no determinan el contenido de nuestra teoria internacional, pero estructuran las preguntas que nos hacemos sobre la politica mundial y nuestros enfoques en las respuestas a esas cuestiones. El principal asunto que se cuestiona en los debates sobre teoria social es el tipo de fundamento que puede ofrecer el conjunto de preguntas y las estrategias de investigacion mas provechosas para poder explicar los cambios revolucionarios que parecen estar ocurriendo en el sistema internacional desde finales del siglo XX. All international relations theories are based on social theories about agents, processes and social structures. Social theories do not determine the content of our international theory but they structure our questions about global politics and our answers approaches to those questions. The main topic in the social theory debates is what kind of fundament can offer the set of questions and which are the most profitable research strategies in order to be able to explain the revolutionary changes that seems to be going on since the end of the XXth century.


title: Social Privacy Protector - Protecting Users' Privacy in Social Networks

abstract: In recent years, online social networks have grown exponentially, as these networks are fantastic places to meet and network with people who share similar personal interests. Facebook, currently the largest social network, has more than 901 million active users. The amount of personal information each user exposes on social networks like Facebook is stagger- ing. Recent research in the area of social networking evaluated that many Facebook users exposed personal information. Due to the many security concerns regarding online personal exposure, we developed the Social Privacy Protector, a software which aims to improve the security and privacy of Facebook users. The software contains three protection layers which improve user privacy by implementing different methods. The software first identifies a user's friends who might pose a threat, and then restricts this "friend"'s exposure to the user's personal information. The second layer is an expansion of Facebook's basic privacy settings based on different types of social network usage profiles. The third layer alerts the user about the number of installed applications on their Facebook profile which have access to their private information. An initial version of the Social Privacy Protection software was evaluated on 74 Facebook users and successfully assisted them in restricting the access of 392 friends.


title: The Transradial Approach to Percutaneous Coronary Intervention

abstract: Periprocedural bleeding complications after percutaneous coronary intervention (PCI) are associated with increased short- and long-term morbidity and mortality. Although clinical trials have primarily assessed pharmacological strategies for reducing bleeding risk, there is a mounting body of evidence suggesting that adoption of a transradial rather than a transfemoral approach to PCI may permit greater reductions in bleeding risk than have been achieved with pharmacological strategies alone. However, despite a long history of use, a lack of widespread uptake by physicians coupled with the technological limitations of available devices has in the past confined transradial PCI to the status of a niche procedure, and many operators lack experience in this technique. In this review, we examine the history of the transradial approach to PCI and discuss some of the circumstances that have hitherto limited its appeal. We then review the current state of the peer-reviewed literature supporting its use and summarize the unresolved issues affecting broader application of this technique, including lack of operator familiarity and an insufficient evidence base for guiding practice. Finally, we describe potential directions for future investigation in the transradial realm.


title: Detecting Retries of Voice Search Queries

abstract: When a system fails to correctly recognize a voice search query, the user will frequently retry the query, either by repeating it exactly or rephrasing it in an attempt to adapt to the system’s failure. It is desirable to be able to identify queries as retries both offline, as a valuable quality signal, and online, as contextual information that can aid recognition. We present a method than can identify retries offline with 81% accuracy using similarity measures between two subsequent queries as well as system and user signals of recognition accuracy. The retry rate predicted by this method correlates significantly with a gold standard measure of accuracy, suggesting that it may be useful as an offline predictor of accuracy.


Design and Development of Gaussian Minimum Shift Keying (GMSK) Demodulator for Satellite Communication


Cloud Computing Security Issues and Challenges


title: High-Performance 1.5–2.5-GHz RF-MEMS Tunable Filters for Wireless Applications

abstract: This paper presents high-performance RF microelectromechanical systems (RF MEMS) tunable filters with constant absolute bandwidth for the 1.5-2.5-GHz wireless band. The filter design is based on corrugated coupled lines and ceramic substrates (er=9.9) for miniaturization, and the 3-bit tuning network is fabricated using a digital/analog RF-MEMS device so as to provide a large capacitance ratio and continuous frequency coverage. Narrowband (72 ± 3 MHz) and wideband (115 ± 10 MHz) 1-dB bandwidth two-pole filters result in a measured insertion loss of 1.9-2.2 dB at 1.5-2.5 GHz with a power handling of 25 dBm and an IIP3 >> 33 dBm. The filters also showed no distortion when tested under wideband CDMA waveforms up to 24.8 dBm. The designs can be scaled to higher dielectric-constant substrates to result in smaller filters. To our knowledge, these filters represent the state-of-the-art at this frequency range using any planar tuning technology.


title: Recombinant outer-surface protein A (des-Cys1-OspA) from the Lyme disease spirochete Borrelia burgdorferi: high production levels in Saccharomyces cerevisiae yeast cultures

abstract: The recombinant outer-surface protein A with an N-terminally truncated form (des-Cys1-OspA) from the Lyme disease spirochete Borrelia burgdorferi was expressed in Saccharomyces cerevisiae at high production levels. Since the recombinant vaccine candidate expressed in Escherichia coli exhibits low production yields and the purification of lipoproteins appears to be difficult, we have investigated the secretion of a soluble recombinant OspA in the yeast S. cerevisiae. In this way, a Leu+ derivative of S. cerevisiae cI3ABYS86 was used as the host strain transformed with an expression plasmid containing the gene encoding des-Cys1-OspA and driven by the MF alpha 1 promoter. The fed-batch culture results revealed that an efficient secretion of des-Cys1-OspA is obtained with a high production level of about 2.1 g l-1 at a cell density of 101 g l-1 cell dry weight. The accumulation of recombinant protein in the supernatant exceeds 6% of the total yeast proteins when estimated by sodium dodecyl sulphate/polyacrylamide gel electrophoresis. Moreover, des-Cys1-OspA showed lower solubilities at high cell densities and, as a consequence, a fraction of the recombinant protein precipitated. An internal cleavage of the MF alpha 1 pro::des-Cys1-OspA precursor was also detected. However, in this case the cleavage occurred at a frequency such that the large amounts of the secreted des-Cys1-OspA could be employed for the evaluation of an immunogenic effect on animal immunization. These studies will extend the knowledge of the usefulness of OspA as a vaccine for Lyme borreliosis.


title: Multilevel Energy Management System for Hybridization of Energy Storages in DC Microgrids

abstract: Hybridization of energy storages (ESs) with different ramp rates helps minimization of system bus voltage variation and extension of ESs lifetime in dc microgrids. Hybrid ES system (HESS) control is normally realized with centralized coordination. In this paper, HESS distributed control, which is independent of the communication link, is proposed to enhance system reliability. All ESs are configured as slack terminals to regulate system bus voltage with droop control. System net power decomposition and ESs power sharing are realized with localized low-pass filter (LPF) applied to ESs with low-ramp rates. The relationship between LPF cut-off frequency and ES ramp rate is elaborated in detail. However, bus voltage deviation and power tracking errors are the main drawbacks of HESS distributed control. Multilevel energy management system (EMS) is thus proposed to enhance system control accuracy. HESS distributed control is scheduled as the primary control. Bus voltage restoration and power sharing compensation are applied in secondary control to eliminate the voltage deviation and power tracking errors, respectively. In tertiary control, autonomous state of charge (SoC) recovery is implemented to limit SoC variation of ES with high-ramp rate. A lab-scale dc microgrid is developed to verify the proposed multilevel EMS for HESS control.


Improved models for automatic punctuation prediction for spoken and written text.


title: Biomechanical Evaluation of Ascending Aortic Aneurysms

abstract: The biomechanical properties of ascending aortic aneurysms were investigated only in the last decade in a limited number of studies. Indeed, in recent years, there has been a growing interest in this field in order to identify new predictive parameters of risk of dissection, which may have clinical relevance. The researches performed so far have been conducted according to the methods used in the study of abdominal aortic aneurysms. In most cases, uniaxial or biaxial tensile tests were used, while in a smaller number of studies other methods, such as opening angle, bulge inflation, and inflation-extension tests, were used. However, parameters and protocols of these tests are at present very heterogeneous in the studies reported in the literature, and, therefore, the results are not comparable and are sometimes conflicting. The purpose of this review then thence to provide a comprehensive analysis of the experimental methodology for determination of biomechanical properties in the specific field of aneurysms of the ascending aorta to allow for better comparison and understanding of the results.


title: Diagnosis of aerospace structure defects by a HPC implemented soft computing algorithm

abstract: This study concerns with the diagnosis of aerospace structure defects by applying a HPC parallel implementation of a novel learning algorithm, named U-BRAIN. The Soft Computing approach allows advanced multi-parameter data processing in composite materials testing. The HPC parallel implementation overcomes the limits due to the great amount of data and the complexity of data processing. Our experimental results illustrate the effectiveness of the U-BRAIN parallel implementation as defect classifier in aerospace structures. The resulting system is implemented on a Linux-based cluster with multi-core architecture.


title: Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff

abstract: Lossy compression algorithms are typically designed and analyzed through the lens of Shannon's rate-distortion theory, where the goal is to achieve the lowest possible distortion (e.g., low MSE or high SSIM) at any given bit rate. However, in recent years, it has become increasingly accepted that "low distortion" is not a synonym for "high perceptual quality", and in fact optimization of one often comes at the expense of the other. In light of this understanding, it is natural to seek for a generalization of rate-distortion theory which takes perceptual quality into account. In this paper, we adopt the mathematical definition of perceptual quality recently proposed by Blau & Michaeli (2018), and use it to study the three-way tradeoff between rate, distortion, and perception. We show that restricting the perceptual quality to be high, generally leads to an elevation of the rate-distortion curve, thus necessitating a sacrifice in either rate or distortion. We prove several fundamental properties of this triple-tradeoff, calculate it in closed form for a Bernoulli source, and illustrate it visually on a toy MNIST example.


title: TD(λ) Converges with Probability 1

abstract: The methods of temporal differences (Samuel, 1959s Sutton, 1984, 1988) allow an agent to learn accurate predictions of stationary stochastic future outcomes. The learning is effectively stochastic approximation based on samples extracted from the process generating the agent's future. ::: ::: Sutton (1988) proved that for a special case of temporal differences, the expected values of the predictions converge to their correct values, as larger samples are taken, and Dayan (1992) extended his proof to the general case. This article proves the stronger result than the predictions of a slightly modified form of temporal difference learning converge with probability one, and shows how to quantify the rate of convergence.


title: Histogram Matching and Global Initialization for Laser-only SLAM in Large Unstructured Environments

abstract: This paper presents an enhanced algorithm for matching laser scan maps using histogram correlations. The histogram representation effectively summarizes a map's salient features such that pairs of maps can be matched efficiently without any prior guess as to their alignment. The histogram matching algorithm has been enhanced in order to work well in outdoor unstructured environments by using entropy metrics, weighted histograms and proper thresholding of quality metrics. Thus our large-scale scan-matching SLAM implementation has a vastly improved ability to close large loops in real-time even when odometry is not available. Our experimental results have demonstrated a successful mapping of the largest area ever mapped to date using only a single laser scanner. We also demonstrate our ability to solve the lost robot problem by localizing a robot to a previously built map without any prior initialization.