title: Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG+SVM Methods

abstract: In this paper, we present two supervised machine learning methods to automatically detect and recognize coral reef fishes in underwater HD videos. The first method relies on a traditional two-step approach: extraction of HOG features and use of a SVM classifier. The second method is based on Deep Learning. We compare the results of the two methods on real data and discuss their strengths and weaknesses.


title: A reconfigurable stepped frequency GPR (GPR-R)

abstract: Ground Penetrating Radars are a unique and well known tool for soil investigation. In order to improve its penetration depth and/or its resolution limits, in the past several radar designs, based on the so called Continuous Wave-Stepped Frequency technique have been proposed. However, the resulting design was often a quite complex equipment. The radar system prototype proposed in this paper, while exhibits state of the art performances with a contained hardware size, it explores a new horizon applicable only to the CW-SF radar technique: the radar reconfigurability (GPR-R).


title: Extreme Value Theory for Open Set Classification - GPD and GEV Classifiers

abstract: Classification tasks usually assume that all possible classes are present during the training phase. This is restrictive if the algorithm is used over a long time and possibly encounters samples from unknown classes. The recently introduced extreme value machine, a classifier motivated by extreme value theory, addresses this problem and achieves competitive performance in specific cases. We show that this algorithm can fail when the geometries of known and unknown classes differ. To overcome this problem, we propose two new algorithms relying on approximations from extreme value theory. We show the effectiveness of our classifiers in simulations and on the LETTER and MNIST data sets.


title: Multi-Relational Factorization Models for Predicting Student Performance

abstract: Predicting student performance (PSP) is an important task in educational data mining, where we can give the students early feedbacks to help them improving their study results. A good and reliable model which accurately predicts the student performance may replace the current standardized tests, thus, reducing the pressure on teaching and learning for examinations as well as saving a lot of time and effort for both teachers and students [Feng et al. 2009; Thai-Nghe et al. 2011]. Precisely, PSP is the task where we would like to know how the students learn (e.g. generally or narrowly), how quickly or slowly they adapt to new problems or if it is possible to infer the knowledge requirements to solve the problems directly from student performance data [Corbett and Anderson 1995; Feng et al. 2009], and eventually, we would like to know whether the students perform the tasks (exercises) correctly (or with some levels of certainty). The benefits of PSP have been vastly discussed in the literature [Cen et al. 2006; Feng et al. 2009; Thai-Nghe et al. 2011]. To address the problem of PSP, several works have been published, e.g. as summarized in Romero et al. [2010], but most of them relied on traditional classification/regression techniques. For example, Corbett and Anderson [1995] proposed the Knowledge Tracing (KT) model, which is usually used for tracing the students’ knowledge in applying their skills as well as for PSP; Cen et al. [2006] proposed a semi-automated method for improving a cognitive model called Learning Factors Analysis that combines a statistical model, human expertise and a combinatorial search; Yu et al. [2010] used linear support vector machines together with feature engineering and ensembling techniques for predicting student performance. This approach, however, requires intensive computer memory and much human effort on data pre-processing. Recently, researchers have proposed using recommender system techniques, e.g. k-NN collaborative filtering and matrix factorization, for PSP [Cetintas et al. 2010; Toscher and Jahrer 2010; Thai-Nghe et al. 2011]. The literature have shown that PSP can be considered as rating prediction task in recommender systems since the student, task, and performance would become user, item, and rating, respectively. The authors also shown that matrix factorization is a promising approach for PSP.


title: Deep neural network-based speaker embeddings for end-to-end speaker verification

abstract: In this study, we investigate an end-to-end text-independent speaker verification system. The architecture consists of a deep neural network that takes a variable length speech segment and maps it to a speaker embedding. The objective function separates same-speaker and different-speaker pairs, and is reused during verification. Similar systems have recently shown promise for text-dependent verification, but we believe that this is unexplored for the text-independent task. We show that given a large number of training speakers, the proposed system outperforms an i-vector baseline in equal error-rate (EER) and at low miss rates. Relative to the baseline, the end-to-end system reduces EER by 13% average and 29% pooled across test conditions. The fused system achieves a reduction of 32% average and 38% pooled.


title: Agent-Based Model of Sand Supply Governance Employing Blockchain Technology

abstract: Sand is a key ingredient for many industries, including concrete, glass, and electronics. Sand extraction is now exceeding fossil fuels and biomass. The absence of data on aggregates sand mining makes assessments difficult and has contributed to the lack of awareness about this issue. A sand governance business framework is developed applying the blockchain technology as the main goal of this study to regulate the sand extraction and trade. Blockchain technology provides a distributed concurrency monitoring system for the supply management. Agent-Based Modeling and Simulation (ABMS) as an effective bottom-up tool is applied to demonstrate the application of the model. The sand providers and users are modeled as a collection of autonomous decision-making entities called agents. The agents interact with each other, the regulators participate in making decisions on the basis of a set of rules that are defined within the blockchain network.


title: Negative comparisons about one's appearance mediate the relationship between Facebook usage and body image concerns

abstract: Abstract Use of social media, such as Facebook, is pervasive among young women. Body dissatisfaction is also highly prevalent in this demographic. The present study examined the relationship between Facebook usage and body image concerns among female university students ( N = 227), and tested whether appearance comparisons on Facebook in general, or comparisons to specific female target groups (family members, close friends, distant peers [women one may know but do not regularly socialize with], celebrities) mediated this relationship. Results showed a positive relationship between Facebook usage and body image concerns, which was mediated by appearance comparisons in general, frequency of comparisons to close friends and distant peers, and by upward comparisons (judging one's own appearance to be worse) to distant peers and celebrities. Thus, young women who spend more time on Facebook may feel more concerned about their body because they compare their appearance to others (especially to peers) on Facebook.


title: A 154–165 GHz LNA and receiver in CMOS 65 nm technology

abstract: This paper presents a fully differential G-band receiver design in CMOS 65 nm technology. First a low noise amplifier (LNA) was designed using neutralized, cross-coupled common source (CC CS) differential pair topology. The measured peak power gain was 36 dB and the minimum noise figure (NF) was 8.3 dB at around 158 GHz. These are the best gain and NF results in CMOS beyond 110 GHz. With exceptionally low DC power consumption of 34.8 mW, this LNA achieves a record gain efficiency of 1.03 dB/mW compared to any integrated circuit technology at this band. Due to the excellent performance of the LNA, a highly sensitive low noise G band receiver was also designed by adding a single transistor passive mixer at the LNA output. The measured receiver conversion gain was up to 20 dB at 158 GHz and the minimum NF was 8 dB. To the best knowledge of the authors, these are the best results in CMOS receivers beyond 110 GHz.


title: Improving image generative models with human interactions

abstract: GANs provide a framework for training generative models which mimic a data distribution. However, in many cases we wish to train a generative model to optimize some auxiliary objective function within the data it generates, such as making more aesthetically pleasing images. In some cases, these objective functions are difficult to evaluate, e.g. they may require human interaction. Here, we develop a system for efficiently training a GAN to increase a generic rate of positive user interactions, for example aesthetic ratings. To do this, we build a model of human behavior in the targeted domain from a relatively small set of interactions, and then use this behavioral model as an auxiliary loss function to improve the generative model. As a proof of concept, we demonstrate that this system is successful at improving positive interaction rates simulated from a variety of objectives, and characterize s


title: MitoP2: the mitochondrial proteome database—now including mouse data

abstract: The MitoP2 database (http://www.mitop.de) integrates information on mitochondrial proteins, their molecular functions and associated diseases. The central database features are manually annotated reference proteins localized or functionally associated with mitochondria supplied for yeast, human and mouse. MitoP2 enables (i) the identification of putative orthologous proteins between these species to study evolutionarily conserved functions and pathways; (ii) the integration of data from systematic genome-wide studies such as proteomics and deletion phenotype screening; (iii) the prediction of novel mitochondrial proteins using data integration and the assignment of evidence scores; and (iv) systematic searches that aim to find the genes that underlie common and rare mitochondrial diseases. The data and analysis files are referenced to data sources in PubMed and other online databases and can be easily downloaded. MitoP2 users can explore the relationship between mitochondrial dysfunctions and disease and utilize this information to conduct systems biology approaches on mitochondria.


title: ClusterViz: a cytoscape APP for cluster analysis of biological network

abstract: Cluster analysis of biological networks is one of the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization of clustering results is crucial to uncover the structure of biological networks. In this paper, ClusterViz, an APP of Cytoscape 3 for cluster analysis and visualization, has been developed. In order to reduce complexity and enable extendibility for ClusterViz, we designed the architecture of ClusterViz based on the framework of Open Services Gateway Initiative. According to the architecture, the implementation of ClusterViz is partitioned into three modules including interface of ClusterViz, clustering algorithms and visualization and export. ClusterViz fascinates the comparison of the results of different algorithms to do further related analysis. Three commonly used clustering algorithms, FAG-EC, EAGLE and MCODE, are included in the current version. Due to adopting the abstract interface of algorithms in module of the clustering algorithms, more clustering algorithms can be included for the future use. To illustrate usability of ClusterViz, we provided three examples with detailed steps from the important scientific articles, which show that our tool has helped several research teams do their research work on the mechanism of the biological networks.


title: Triboelectric Nanogenerators as New Energy Technology for Self-Powered Systems and as Active Mechanical and Chemical Sensors

abstract: Triboelectrification is an effect that is known to each and every one probably since ancient Greek time, but it is usually taken as a negative effect and is avoided in many technologies. We have recently invented a triboelectric nanogenerator (TENG) that is used to convert mechanical energy into electricity by a conjunction of triboelectrification and electrostatic induction. As for this power generation unit, in the inner circuit, a potential is created by the triboelectric effect due to the charge transfer between two thin organic/inorganic films that exhibit opposite tribo-polarity; in the outer circuit, electrons are driven to flow between two electrodes attached on the back sides of the films in order to balance the potential. Since the most useful materials for TENG are organic, it is also named organic nanogenerator, which is the first using organic materials for harvesting mechanical energy. In this paper, we review the fundamentals of the TENG in the three basic operation modes: vertical contact-...


title: Super-resolution from a single image

abstract: Methods for super-resolution can be broadly classified into two families of methods: (i) The classical multi-image super-resolution (combining images obtained at subpixel misalignments), and (ii) Example-Based super-resolution (learning correspondence between low and high resolution image patches from a database). In this paper we propose a unified framework for combining these two families of methods. We further show how this combined approach can be applied to obtain super resolution from as little as a single image (with no database or prior examples). Our approach is based on the observation that patches in a natural image tend to redundantly recur many times inside the image, both within the same scale, as well as across different scales. Recurrence of patches within the same image scale (at subpixel misalignments) gives rise to the classical super-resolution, whereas recurrence of patches across different scales of the same image gives rise to example-based super-resolution. Our approach attempts to recover at each pixel its best possible resolution increase based on its patch redundancy within and across scales.


title: Perception of wearable computers for everyday life by the general public: impact of culture and gender on technology

abstract: This paper examines the perception of wearable computers for everyday life by the general public, in order to foster the adoption of this technology. We present a social study that focuses on sensors, actuators, autonomy, uses, and privacy. Carried out in 2005, it considers gender and cultural disparities in two dissimilar groups: French (115 males, 59 females) and Japanese (61 males, 54 females) citizens. Acknowledging that exposition to wearables can alter perception about them, we designed a garment-shaped prototype to check our results, estimate shifts of perception, and define guidelines for equipment and services. We describe our prototype, and future experiments dealing with face-to-face contacts, community awareness, and relaxing environments.


title: Annotating Students’ Understanding of Science Concepts

abstract: This paper summarizes the annotation of fine-grained entailment relationships in the context of student answers to science assessment questions. We annotated a corpus of 15,357 answer pairs with 145,911 fine-grained entailment relationships. We provide the rationale for such fine-grained analysis and discuss its perceived benefits to an Intelligent Tutoring System. The corpus also has potential applications in other areas, such as question answering and multi-document summarization. Annotators achieved 86.2% inter-annotator agreement (Kappa=0.728, corresponding to substantial agreement) annotating the fine-grained facets of reference answers with regard to understanding expressed in student answers and labeling from one of five possible detailed relationship categories. The corpus described in this paper, which is the only one providing such detailed entailment annotations, is available as a public resource for the research community. The corpus is expected to enable application development, not only for intelligent tutoring systems, but also for general textual entailment applications, that is currently not practical.


title: Lower-face and neck antiaging treatment and prevention using onabotulinumtoxin A: the 2010 multidisciplinary French consensus - part 2

abstract: Summary ::: ::: Background Onabotulinumtoxin A has been used for many years in the aging face treatment. A survey was organized to identify current practices in France. ::: ::: ::: ::: Objective To develop consensual recommendations for treating aging lower face and neck with onabotulinumtoxin A. ::: ::: ::: ::: Methods and materials Fifty-seven participants to six regional surveys reviewed practices and techniques for each individual treatment indication. From conference summaries and data from a questionnaire, consensual recommendations were developed. ::: ::: ::: ::: Results General considerations, key treatment rules, injection specifics (dose, site, and techniques), associated procedures/treatments, and procedure follow-up were defined by indication, i.e., nasolabial angle, nasal tip repositioning, dilated nostrils, lips and perioral area, Marionette lines and depressor anguli oris, gingival smile, risorius and zygomatic perioral muscles, masseters, chin, and platysma. For the consensus participants, current onabotulinumtoxin A use is a global preventive and corrective treatment. Overall, judicious treatment of multiple sites and adjunctive modalities, such as fillers, peels, and laser, leads to satisfactory results with a youthful, harmonious, animated, and natural-looking face. ::: ::: ::: ::: Conclusion Years of experience using onabotulinumtoxin A result in sophisticated treatment approaches, more specific targeted injections, and better understanding of lower facial and neck aging, leading to satisfying therapeutic results for patients and clinicians.


title: Artificial neural networks for pattern recognition

abstract: This tutorial article deals with the basics of artificial neural networks (ANN) and their applications in pattern recognition. ANN can be viewed as computing models inspired by the structure and function of the biological neural network. These models are expected to deal with problem solving in a manner different from conventional computing. A distinction is made between pattern and data to emphasize the need for developing pattern processing systems to address pattern recognition tasks. After introducing the basic principles of ANN, some fundamental networks are examined in detail for their ability to solve simple pattern recognition tasks. These fundamental networks together with the principles of ANN will lead to the development of architectures for complex pattern recognition tasks. A few popular architectures are described to illustrate the need to develop an architecture specific to a given pattern recognition problem. Finally several issues that still need to be addressed to solve practical problems using ANN approach are discussed.


title: Apiary: Easy-to-Use Desktop Application Fault Containment on Commodity Operating Systems

abstract: Desktop computers are often compromised by the interaction of untrusted data and buggy software. To address this problem, we present Apiary, a system that transparently contains application faults while retaining the usage metaphors of a traditional desktop environment. Apiary accomplishes this with three key mechanisms. It isolates applications in containers that integrate in a controlled manner at the display and file system. It introduces ephemeral containers that are quickly instantiated for single application execution, to prevent any exploit that occurs from persisting and to protect user privacy. It introduces the Virtual Layered File System to make instantiating containers fast and space efficient, and to make managing many containers no more complex than a single traditional desktop. We have implemented Apiary on Linux without any application or operating system kernel changes. Our results with real applications, known exploits, and a 24-person user study show that Apiary has modest performance overhead, is effective in limiting the damage from real vulnerabilities, and is as easy for users to use as a traditional desktop.


title: Broadband Circularly Polarized Annular-Ring Microstrip Antenna

abstract: A wideband circularly polarized (CP) annular-ring patch antenna with two proximity-coupled L-probe feeds orientated to have phases of 0deg and 90deg, using a broadband 90deg hybrid feed, is proposed. It is found that the current distribution for CP operation can be improved by cutting a smaller concentric circular slot on the original larger circular patch to form an annular-ring patch. With such an arrangement, the proposed antenna delivers a wider axial ratio (AR) bandwidth than that of the conventional circular patch with the identical feeding technique. Considering the common overlapped bandwidth limited by the impedance, AR and gain, the proposed annular-ring patch antenna exhibits an effective bandwidth of 38% from 1.5 to 2.2 GHz, which is wider than the corresponding bandwidth of 29.7% from 1.35 to 1.82 GHz for the conventional circular patch. Moreover, the effective bandwidth of 38% for the proposed annular-ring antenna is also much wider than those for the other annular-ring patch antennas in the literature.


title: Optimum control strategies in energy conversion of PMSG wind turbine system without mechanical sensors

abstract: The amount of energy obtained from a wind energy conversion system (WECS) depends not only on the characteristics of the wind regime at the site, but it also depends on the control strategy used for the WECS. In order to determine the gain in energy derived from one concept as compared against another, models of several autonomous WECS have been developed using Matlab Simulink software. These allow easy performance evaluations and comparisons on different control strategies used, and determine the amount of energy injected to the grid in the case of the grid-connected systems. This paper also proposes a prototype version of the control strategy of a 20-kW permanent-magnet synchronous generator (PMSG) for maximum power tracking and compares with the results produced by previous strategies. Advantages of this mechanical sensorless control strategy for maximum power estimation are demonstrated by digital simulation of the system.


title: Reinforcement learning of clothing assistance with a dual-arm robot

abstract: This study aims at robotic clothing assistance as it is yet an open field for robotics despite it is one of the basic and important assistance activities in daily life of elderly as well as disabled people. The clothing assistance is a challenging problem since robots must interact with non-rigid clothes generally represented in a high-dimensional space, and with the assisted person whose posture can vary during the assistance. Thus, the robot is required to manage two difficulties to perform the task of the clothing assistance: 1) handling of non-rigid materials and 2) adaptation of the assisting movements to the assisted person's posture. To overcome these difficulties, we propose to use reinforcement learning with the cloth's state which is low-dimensionally represented in topology coordinates, and with the reward defined in the low-dimensional coordinates. With our developed experimental system, for T-shirt clothing assistance, including an anthropomorphic dual-arm robot and a soft mannequin, we demonstrate the robot quickly learns a suitable arm motion for putting the mannequin's head into a T-shirt.


title: Enumeration of immunoglobulin-bearing lymphocytes in whole peripheral blood.

abstract: A new method is described for the detection by immunofluorescence of lymphocyte populations in whole peripheral blood. Lymphocytes are identified by their lack of intracellular peroxidase, and the absolute number of different populations can be accurately enumerated by avoiding procedures for isolating mononuclear cells before fluorescent staining. Among twenty-two normal individuals the mean +/- s.d. percentage and absolute number of lymphocytes with stable surface immunoglobulin were as follows: IgM, 8-1+/-2-3%, 142+/-60/mu1; IgD,8-9+/-2-2%, 212+/-85/mu1;IgA, 2-2+/-1-0%, 41+/-24/mu1; IgG, 2-3+/-1-6%, 60+/-47/mu1. It was confirmed that the detection of true IgG-bearing lymphocytes depends on washing the blood at 37degrees before staining or the use of F(ab)2 anti-IgG antibodies. The proportion of lymphocytes stained by whole IgG polyvalent anti-immunoglobulin antibody, which detects both true immunoglobulin-bearing lymphocytes (B cells) and Fc receptor cells was constant (20-5+/-1-0%) both in and between individuals, but the absolute numbers (380+/-117/mu1) varied with the lymphocyte count.


title: A novel modular compliant knee joint actuator for use in assistive and rehabilitation orthoses

abstract: Despite significant advancements in the field of wearable robots (WRs), commercial WRs still use traditional direct-drive actuation units to power their joints. On the other hand, in research prototypes compliant actuators are increasingly being used to more adequately address the issues of safety, robustness, control and overall system efficiency. The advantages of mechanical compliance are exploited in a novel modular actuator prototype designed for the knee joint. Due to its modularity, the actuator can be implemented in a knee joint of a standalone or a multi-joint lower-limbs orthosis, for use in gait rehabilitation and/or walking assistance. Differently from any other actuator used in orthotic research prototypes, it combines a Variable Stiffness Actuator (VSA) and a Parallel Elasticity Actuation (PEA) unit in a single modular system. Although independent, the units are designed to work together in order to fully mimic dynamic behavior of the human knee joint. In this paper, design aspects and functional evaluation of the new actuator are presented and a rationale for such a design in biomechanics of the human knee joint is given. The VSA subsystem is characterized in a quasi-static benchmarking environment and the results showing main performance indicators are presented.


title: Casting Random Forests as Artificial Neural Networks (and Profiting from It)

abstract: While Artificial Neural Networks (ANNs) are highly expressive models, they are hard to train from limited data. Formalizing a connection between Random Forests (RFs) and ANNs allows exploiting the former to initialize the latter. Further parameter optimization within the ANN framework yields models that are intermediate between RF and ANN, and achieve performance better than RF and ANN on the majority of the UCI datasets used for benchmarking.


title: Autocomplete hand-drawn animations

abstract: Hand-drawn animation is a major art form and communication medium, but can be challenging to produce. We present a system to help people create frame-by-frame animations through manual sketches. We design our interface to be minimalistic: it contains only a canvas and a few controls. When users draw on the canvas, our system silently analyzes all past sketches and predicts what might be drawn in the future across spatial locations and temporal frames. The interface also offers suggestions to beautify existing drawings. Our system can reduce manual workload and improve output quality without compromising natural drawing flow and control: users can accept, ignore, or modify such predictions visualized on the canvas by simple gestures. Our key idea is to extend the local similarity method in [Xing et al. 2014], which handles only low-level spatial repetitions such as hatches within a single frame, to a global similarity that can capture high-level structures across multiple frames such as dynamic objects. We evaluate our system through a preliminary user study and confirm that it can enhance both users' objective performance and subjective satisfaction.


title: Partial fingerprint matching: Fusion of level 2 and level 3 features

abstract: Increase in identity frauds, crimes, security there is growing need of fingerprint technology in civilian and law enforcement applications. Partial fingerprints are of great interest which are either found at crime scenes or resulted from improper scanning. These fingerprints are poor in quality and the number of features present depends on size of fingerprint. Due to the lack of features such as core and delta, general fingerprint matching algorithms do not perform well for partial fingerprint matching. By using combination of level1 and level 2 features accuracy of partial matching cannot be increased. Therefore, we utilize extended features in combination with other feature set. Efficacious fusion methods for coalesce of different modality systems perform better for these types of prints. In this paper, we propose a method for partial fingerprint matching using score level fusion of minutiae based radon transform and pores based LBP extraction. To deal with broken ridges and fragmentary information, radon transform is used to get local information around minutiae. Finally, we evaluate the performance by comparing Equal Error Rate (ERR) of proposed method and existing method and proposed method reduces the error rate to 1.84%.


title: Knowledge Representation: Logical, Philosophical, and Computational Foundations Computational Foundations by John F. Sowa (Book Review)

abstract: 1. Logic. 2. Ontology. 3. Knowledge Representation. 4. Processes. 5. Purposes, Contexts, And Agents. 6. Knowledge Soup. 7. Knowledge Acquisition And Sharing. Appendixes: Appendix A: Summary Of Notations Appendix B: Sample Ontology. Appendix C: Extended Example. Answers To Selected Exercises. Bibliography. Name Index. Subject Index. Special Symbols.


title: Text Clustering for Topic Detection

abstract: Abstract : The world wide web represents vast stores of information. However, the sheer amount of such information makes it practically impossible for any human user to be aware of much of it. Therefore, it would be very helpful to have a system that automatically discovers relevant, yet previously unknown information, and reports it to users in human-readable form. As the first attempt to accomplish such a goal, we proposed a new clustering algorithm and compared it with existing clustering algorithms. The proposed method is motivated by constructive and competitive learning from neural network research. In the construction phase, it tries to find the optimal number of clusters by adding a new cluster when the intrinsic difference between the instance presented and the existing clusters is detected. Each cluster then moves toward the optimal cluster center according to the learning rate by adjusting its weight vector. From the experimental results on the three different real world data sets, the proposed method shows an even trend of performance across the different domains, while the performance of our algorithm on text domains was better than that reported in previous research.


title: Chemical named entities recognition: a review on approaches and applications

abstract: The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to “text mine” these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracted.


title: The anchoring bias reflects rational use of cognitive resources

abstract: Cognitive biases, such as the anchoring bias, pose a serious challenge to rational accounts of human cognition. We investigate whether rational theories can meet this challenge by taking into account the mind’s bounded cognitive resources. We asked what reasoning under uncertainty would look like if people made rational use of their finite time and limited cognitive resources. To answer this question, we applied a mathematical theory of bounded rationality to the problem of numerical estimation. Our analysis led to a rational process model that can be interpreted in terms of anchoring-and-adjustment. This model provided a unifying explanation for ten anchoring phenomena including the differential effect of accuracy motivation on the bias towards provided versus self-generated anchors. Our results illustrate the potential of resource-rational analysis to provide formal theories that can unify a wide range of empirical results and reconcile the impressive capacities of the human mind with its apparently irrational cognitive biases.


title: CertLedger: A New PKI Model with Certificate Transparency Based on Blockchain

abstract: Abstract In conventional PKI, CAs are assumed to be fully trusted. However, in practice, CAs’ absolute responsibility for providing trustworthiness caused major security and privacy issues. To prevent such issues, Google introduced the concept of Certificate Transparency (CT) in 2013. Later, several new PKI models are proposed to reduce the level of trust to the CAs. However, all of these proposals are still vulnerable to split-world attacks if the adversary is capable of showing different views of the log to the targeted victims. In this paper, we propose a new PKI architecture with certificate transparency based on blockchain, what we called CertLedger, to eliminate the split-world attacks and to provide certificate/revocation transparency. All TLS certificates’ validation, storage, and entire revocation process is conducted in CertLedger as well as Trusted CA certificate management. During a TLS connection, TLS clients get an efficient proof of existence of the certificate directly from its domain owners. Hence, privacy is now perfectly preserved by eliminating the traceability issue via OCSP servers. It also provides a unique, efficient, and trustworthy certificate validation process eliminating the conventional inadequate and incompatible certificate validation processes implemented by different software vendors. TLS clients in CertLedger also do not require to make certificate validation and store the trusted CA certificates anymore. We analyze the security and performance of CertLedger and provide a comparison with the previous proposals. Finally, we implement its protoype on Ethereum to demonstrate experimental results. The results show that the performance of the TLS handshake and certificate validation through CertLedger is significantly improved compared to the current TLS protocol.


title: Design of a novel six-axis force/torque sensor based on strain gauges by finite element method

abstract: This paper presents a novel six-axis force/torque sensor based on strain gauges. Firstly, the six-axis force/torque sensor's design criterion is systematically expounded by calibration matrix, condition number, coupling error, sensor accuracy, strain gauge sensitivity, sensor sensitivity and sensor stiffness. Secondly, a novel sensing element is designed and simulated by finite element method. Then, the performance of the sensor is obtained by analysis. In the end, the sensor was manufactured.


title: Single Event Upset Characterization of the Zynq-7000 ARM® Cortex™-A9 Processor Unit Using Proton Irradiation

abstract: Proton induced SEU cross-section of the Zynq-7000 ARM® Cortex™-A9 Processor Unit is presented. Upset rates in the space radiation environment are estimated.


Osteopenia in HIV-infected patients: is it the disease or is it the treatment?


title: High Resolution Acquisition, Learning and Transfer of Dynamic 3D Facial Expressions

abstract: Synthesis and re-targeting of facial expressions is central to facial animation and often involves significant manual work in order to achieve realistic expressions, due to the difficulty of capturing high quality dynamic expression data. In this paper we address fundamental issues regarding the use of high quality dense 3-D data samples undergoing motions at video speeds, e.g. human facial expressions. In order to utilize such data for motion analysis and re-targeting, correspondences must be established between data in different frames of the same faces as well as between different faces. We present a data driven approach that consists of four parts: 1) High speed, high accuracy capture of moving faces without the use of markers, 2) Very precise tracking of facial motion using a multi-resolution deformable mesh, 3) A unified low dimensional mapping of dynamic facial motion that can separate expression style, and 4) Synthesis of novel expressions as a combination of expression styles. The accuracy and resolution of our method allows us to capture and track subtle expression details. The low dimensional representation of motion data in a unified embedding for all the subjects in the database allows for learning the most discriminating characteristics of each individual's expressions as that person's “expression style”. Thus new expressions can be synthesized, either as dynamic morphing between individuals, or as expression transfer from a source face to a target face, as demonstrated in a series of experiments. ::: ::: ::: ::: Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Animation; I.3.5 [Computer Graphics]: Curve, surface, solid, and object representations; I.3.3 [Computer Graphics]: Digitizing and scanning; I.2.10 [Artificial intelligence]: Motion ; I.2.10 [Artificial intelligence]: Representations, data structures, and transforms; I.2.10 [Artificial intelligence]: Shape; I.2.6 [Artificial intelligence]: Concept learning


title: Drowsy driver detection system using eye blink patterns

abstract: This paper presents an automatic drowsy driver monitoring and accident prevention system that is based on monitoring the changes in the eye blink duration. Our proposed method detects visual changes in eye locations using the proposed horizontal symmetry feature of the eyes. Our new method detects eye blinks via a standard webcam in real-time at 110fps for a 320×240 resolution. Experimental results in the JZU [3] eye-blink database showed that the proposed system detects eye blinks with a 94% accuracy with a 1% false positive rate.


title: Towards more diverse recommendations: Item re-ranking methods for recommender systems

abstract: Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy (as exemplified by the recent Netflix Prize competition), other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce a number of item re-ranking methods that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy. Empirical results consistently show the diversity gains of the proposed re-ranking methods for several real-world rating datasets and different rating prediction techniques.


title: Performance Optimization for All Flash Scale-Out Storage

abstract: The proliferation of the big data analysis and thewide spread usage of public/private cloud services make itimportant to expand the storage capacity as the demand isincreased. The scale-out storage is gaining more attention sinceit can inherently provide scalable storage capacity. The flashSSD, on the other hand, is getting popular as the drop-inreplacement of the slow HDD, which seems to boost the systemperformance somewhat at least. However, the performance oftraditional scale-out storage system does not get much bettereven though its HDD is replaced with the flash based highperformance SSD since the whole system is designed based onHDD as its underlying storage device. In this paper, we identifyperformance problems of a representative scale-out storagesystem, Ceph, and analyze that these problems are caused by1) Coarse-grained lock, 2) Throttling logic, 3) Batching basedoperation latency and 4) Transaction Overhead. We proposesome optimization techniques for flash-based Ceph. First, weminimize coarse-grained locking. Second, we introduce throttlepolicy and system tuning. Third, we develop non-blockinglogging and light-weight transaction processing. We found thatour optimized Ceph shows up to 20 times improvement inthe case of small random writes and it also shows more thantwo times better performance in the case of small randomread through our experiments. We also show that the systemexhibits linear performance increase as we add more nodes.


Textmining at EmoInt-2017: A Deep Learning Approach to Sentiment Intensity Scoring of English Tweets.


title: Privacy-Preserving Smart Semantic Search Based on Conceptual Graphs Over Encrypted Outsourced Data

abstract: Searchable encryption is an important research area in cloud computing. However, most existing efficient and reliable ciphertext search schemes are based on keywords or shallow semantic parsing, which are not smart enough to meet with users’ search intention. Therefore, in this paper, we propose a content-aware search scheme, which can make semantic search more smart. First, we introduce conceptual graphs (CGs) as a knowledge representation tool. Then, we present our two schemes (PRSCG and PRSCG-TF) based on CGs according to different scenarios. In order to conduct numerical calculation, we transfer original CGs into their linear form with some modification and map them to numerical vectors. Second, we employ the technology of multi-keyword ranked search over encrypted cloud data as the basis against two threat models and raise PRSCG and PRSCG-TF to resolve the problem of privacy-preserving smart semantic search based on CGs. Finally, we choose a real-world data set: CNN data set to test our scheme. We also analyze the privacy and efficiency of proposed schemes in detail. The experiment results show that our proposed schemes are efficient.


title: Research on feedback linearization control of three-phase inverter based on inverse system

abstract: To guarantee symmetric output voltage with asymmetric load for three-phase inverter, a feedback linearization based on inverse system for three-phase inverter is proposed, which can improve system adaptability and stability under complex load conditions and disturbances. A prototype consisting of 1kW three-phase inverter with asymmetric load based on feedback linearization via dq0-axis transformation is built. The simulation and experimental results demonstrate the validity of proposed scheme.


title: Exploring the Social Integration of Sexual Minority Youth Across High School Contexts

abstract: Mental health disparities between sexual minority and other youth have been theorized to result in part from the effects of the stigmatization on social integration. Stochastic actor-based modeling was applied to complete network data from two high schools in the National Longitudinal Study of Adolescent Health (Mage = 15 years, N = 2,533). Same-sex attracted youth were socially marginalized in a smaller predominantly White school but not in a larger, more racially diverse school. For both schools, homophily was a critical network feature, and could represent social support for and social segregation of such youth. These findings emphasize the school context in studying the social lives of sexual minority youth and suggest that youth may be better off socially in larger and more diverse schools.


title: Pursuing prosthetic electronic skin.

abstract: This Review discusses the materials and electronic requirements for flexible sensors and electronic systems to mimic the mechanical and sensing properties of natural skin, with the goal of providing artificial prostheses with sensing capabilities.


title: A review of brand research (1990-2010): classification, application and development trajectory

abstract: Facing rapidly changeable markets, a firm not only should make best use of its brands to strengthen recognition, attitude and purchase behaviour of consumers to enhance their purchase intention, but the firm also should build up and maintain B2B partnerships to achieve win-win situations. Unfortunately, the extant literature related to brands lacks systematic exploration and analyses. A comprehensive survey of extant brand literature was undertaken using keyword classification from 1990 to 2010. Based on 1,714 articles, we find that the number of publications on brands significantly increased since 2003, with steady growth in recent years and these selected articles are mostly published in 20 academic journals. In addition, there is no industry that has not implemented the concept of brand management and the majority of brand targeted countries come from Asia-Pacific emerging markets. This study ends with some discussions and conclusions.


title: Non-rigid Reconstruction with a Single Moving RGB-D Camera

abstract: We present a novel non-rigid reconstruction method using a moving RGB-D camera. Current approaches use only non-rigid part of the scene and completely ignore the rigid background. Non-rigid parts often lack sufficient geometric and photometric information for tracking large frame-to-frame motion. Our approach uses camera pose estimated from the rigid background for foreground tracking. This enables robust foreground tracking in situations where large frame-to-frame motion occurs. Moreover, we are proposing a multi-scale deformation graph which improves non-rigid tracking without compromising the quality of the reconstruction. We are also contributing a synthetic dataset which is made publically available for evaluating non-rigid reconstruction methods. The dataset provides frame-by-frame ground truth geometry of the scene, the camera trajectory, and masks for background foreground. Experimental results show that our approach is more robust in handling larger frame-to-frame motions and provides better reconstruction compared to state-of-the-art approaches.


title: Proportionate normalized least-mean-squares adaptation in echo cancelers

abstract: On typical echo paths, the proportionate normalized least-mean-squares (PNLMS) adaptation algorithm converges significantly faster than the normalized least-mean-squares (NLMS) algorithm generally used in echo cancelers to date. In PNLMS adaptation, the adaptation gain at each tap position varies from position to position and is roughly proportional at each tap position to the absolute value of the current tap weight estimate. The total adaptation gain being distributed over the taps is carefully monitored and controlled so as to hold the adaptation quality (misadjustment noise) constant. PNLMS adaptation only entails a modest increase in computational complexity.


title: The HASYv2 dataset

abstract: This paper describes the HASYv2 dataset. HASY is a publicly available, free of charge dataset of single symbols similar to MNIST. It contains 168233 instances of 369 classes. HASY contains two challenges: A classification challenge with 10 pre-defined folds for 10-fold cross-validation and a verification challenge.


title: Pseudoguaianolides isolated from inula britannica var. chinenis as inhibitory constituents against inducible nitric oxide synthase

abstract: Three pseudoguaianolide type sesquiterpenes, bigelovin (1), 2,3-dihydroaromaticin (2), and ergolide (3) were isolated as inhibitory constituents against inducible nitric oxide synthase (iNOS) from the flowers of Inula britannica var. chinensis. Bigelovin (1) exhibited a highly potent inhibitory activity on lipopolysaccharide (LPS)-induced iNOS in murine macrophage RAW 264.7 cells with an IC50 value of 0.46 mM, which is about 8 times more potent than the known selective inhibitor of iNOS, L-N6-(1-iminoethyl)lysine (IC50 3.49 microM). 2,3-Dihydroaromaticin (2) and ergolide (3) also exhibited potent inhibitory activities on LPS-induced iNOS with IC50 values of 1.05 and 0.69 microM, respectively.


title: 2.2 A scalable 28GHz coupled-PLL in 65nm CMOS with single-wire synchronization for large-scale 5G mm-wave arrays

abstract: Demonstrations of mm-Wave arrays with >50 elements in silicon has led to an interest in large-scale mm-Wave MIMO arrays for 5G networks, which promise substantial improvements in network capacity [1,2]. Practical considerations result in such arrays being developed with a tiled approach, where N unit cells with M elements each are tiled to achieve large MIMO/phased arrays with NM elements [2]. Achieving stringent phase-noise specifications and scalable LO distribution to maintain phase coherence across different unit cell ICs/PCBs are a critical challenge. In this paper, we demonstrate a scalable, single-wire-synchronization architecture and circuits for mm-Wave arrays that preserve the simplicity of daisy-chained LO distribution, compensate for phase offset due to interconnects, and provide phase-noise improvement with increasing number of PLLs [3]. Measurements on a scalable 28GHz prototype demonstrate a 21% improvement in rms jitter and a 3.4dB improvement in phase noise at 10MHz offset when coupling 28GHz PLLs across three different ICs.


title: Multilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised Learning

abstract: Highly frequent in language and communication, metaphor represents a significant challenge for Natural Language Processing NLP applications. Computational work on metaphor has traditionally evolved around the use of hand-coded knowledge, making the systems hard to scale. Recent years have witnessed a rise in statistical approaches to metaphor processing. However, these approaches often require extensive human annotation effort and are predominantly evaluated within a limited domain. In contrast, we experiment with weakly supervised and unsupervised techniques-with little or no annotation-to generalize higher-level mechanisms of metaphor from distributional properties of concepts. We investigate different levels and types of supervision learning from linguistic examples vs. learning from a given set of metaphorical mappings vs. learning without annotation in flat and hierarchical, unconstrained and constrained clustering settings. Our aim is to identify the optimal type of supervision for a learning algorithm that discovers patterns of metaphorical association from text. In order to investigate the scalability and adaptability of our models, we applied them to data in three languages from different language groups-English, Spanish, and Russian-achieving state-of-the-art results with little supervision. Finally, we demonstrate that statistical methods can facilitate and scale up cross-linguistic research on metaphor.


Studies in language technology: an object-oriented computer model of morphonological aspects of dutc


title: CODE: Coherence Based Decision Boundaries for Feature Correspondence

abstract: A key challenge in feature correspondence is the difficulty in differentiating true and false matches at a local descriptor level. This forces adoption of strict similarity thresholds that discard many true matches. However, if analyzed at a global level, false matches are usually randomly scattered while true matches tend to be coherent (clustered around a few dominant motions), thus creating a coherence based separability constraint. This paper proposes a non-linear regression technique that can discover such a coherence based separability constraint from highly noisy matches and embed it into a correspondence likelihood model. Once computed, the model can filter the entire set of nearest neighbor matches (which typically contains over 90 percent false matches) for true matches. We integrate our technique into a full feature correspondence system which reliably generates large numbers of good quality correspondences over wide baselines where previous techniques provide few or no matches.


title: Chromatin organization: form to function.

abstract: Recent developments in technology have made it possible to create high resolution genome-wide maps of histone marks, DNA binding proteins and physical interactions along genomic regions. Chromatin features are found together in different combinations, dividing the genome up into domains with distinct functional properties. Microscopy and chromatin conformation capture techniques have shown that the 3D structure of chromosomes is constrained by nuclear features and functional links between different parts of chromatin. These results provide insights about the 3D and domain organization of the genome and their connection to gene regulation and other nuclear functions.


title: Experimental validation of energy consumption model for the four-wheeled omnidirectional Mecanum robots for energy-optimal motion control

abstract: The Mecanum wheel, due to its omnidirectional mobility and heavy-duty transporting ability on the ground plane, is widely applied in the industry. However, the Mecanum wheel trades off energy efficiency for maneuverability. This paper proposes a novel energy consumption model of the four-wheel omnidirectional Mecanum mobile robots. The model is built based on a comprehensive understanding of the kinematics, dynamics, and energy flow of the Mecanum mobile robot. The energy consumption model has been mathematically implemented in MATLAB, and experimentally validated on Auckbot, the Mecanum mobile robot, developed in our lab. Simulation and experimental results show that for omnidirectional motion primitives on the ground plane, the energy consumption model has over 98% accuracy. This proposed energy consumption model is essential to the energy-optimal motion planning for the Mecanum mobile robot.


title: Weaving Multi-scale Context for Single Shot Detector

abstract: Aggregating context information from multiple scales has been proved to be effective for improving accuracy of Single Shot Detectors (SSDs) on object detection. However, existing multi-scale context fusion techniques are computationally expensive, which unfavorably diminishes the advantageous speed of SSD. In this work, we propose a novel network topology, called WeaveNet, that can efficiently fuse multi-scale information and boost the detection accuracy with negligible extra cost. The proposed WeaveNet iteratively weaves context information from adjacent scales together to enable more sophisticated context reasoning while maintaining fast speed. Built by stacking light-weight blocks, WeaveNet is easy to train without requiring batch normalization and can be further accelerated by our proposed architecture simplification. Experimental results on PASCAL VOC 2007, PASCAL VOC 2012 benchmarks show signification performance boost brought by WeaveNet. For 320x320 input of batch size = 8, WeaveNet reaches 79.5% mAP on PASCAL VOC 2007 test in 101 fps with only 4 fps extra cost, and further improves to 79.7% mAP with more iterations.


SPECIALTHEME Business engagement on Twitter : a path analysis


title: Abnormal crowd behavior detection using social force model

abstract: In this paper we introduce a novel method to detect and localize abnormal behaviors in crowd videos using Social Force model. For this purpose, a grid of particles is placed over the image and it is advected with the space-time average of optical flow. By treating the moving particles as individuals, their interaction forces are estimated using social force model. The interaction force is then mapped into the image plane to obtain Force Flow for every pixel in every frame. Randomly selected spatio-temporal volumes of Force Flow are used to model the normal behavior of the crowd. We classify frames as normal and abnormal by using a bag of words approach. The regions of anomalies in the abnormal frames are localized using interaction forces. The experiments are conducted on a publicly available dataset from University of Minnesota for escape panic scenarios and a challenging dataset of crowd videos taken from the web. The experiments show that the proposed method captures the dynamics of the crowd behavior successfully. In addition, we have shown that the social force approach outperforms similar approaches based on pure optical flow.


Corporate social responsibility : the 3 CSR model


title: Infrastructuring: Towards an integral perspective on the design and use of information technology

abstract: In this contribution we investigate how results from the ongoing discussion about ‘eInfrastructures’ can be used to improve the design of IT infrastructures in organizations. We first establish a perspective on organizational IT as ‘work infrastructure’ that focuses on the infrastructural nature of organizational Information Systems and describe challenges for designing within and for this type of infrastructure. Then we elaborate on possible use of concepts from the e-infrastructure discussion, in particular on the concept of ‘infrastructuring’ as it was developed by Star and Ruhleder (1996) and Star and Bowker (2002). Using their ‘salient characteristics of infrastructure’ we describe the methodological approach of ‘Infrastructuring’ to develop methodological and tool support for all stakeholders’ activities that contribute to the successful establishment of an information system usage (equivalent to a work infrastructure improvement). We illustrated our ideas by drawing on a case in which new work infrastructures were introduced into an organizational context and by mapping out existing and possible tool support for ‘infrastructuring’.


title: Two-dimensional phonon transport in graphene

abstract: Properties of phonons-quanta of the crystal lattice vibrations-in graphene have recently attracted significant attention from the physics and engineering communities. Acoustic phonons are the main heat carriers in graphene near room temperature, while optical phonons are used for counting the number of atomic planes in Raman experiments with few-layer graphene. It was shown both theoretically and experimentally that transport properties of phonons, i.e. energy dispersion and scattering rates, are substantially different in a quasi-two-dimensional system such as graphene compared to the basal planes in graphite or three-dimensional bulk crystals. The unique nature of two-dimensional phonon transport translates into unusual heat conduction in graphene and related materials. In this review, we outline different theoretical approaches developed for phonon transport in graphene, discuss contributions of the in-plane and cross-plane phonon modes, and provide comparison with available experimental thermal conductivity data. Particular attention is given to analysis of recent results for the phonon thermal conductivity of single-layer graphene and few-layer graphene, and the effects of the strain, defects, and isotopes on phonon transport in these systems.


title: The Role of Online Travel Agents in the Experience Economy

abstract: Our society has evolved through the product economy and the service economy to the experience economy. Due to this fact the value of a travel product, nowadays, is far beyond the goods and services. Experiences are driving the travel industry and make the travel product more and more diverse. Moreover, not only the travel experience itself, but also an experience of buying a travel package becomes extremely important for tourists. In such new economical circumstances it is necessary for travel providers to understand the desires, motivations and requirements of the travellers, as well as unique features of the tourism product. This paper provides a detailed analysis of the above-mentioned factors and suggests that online travel agents have the highest potential to exploit the benefits of the experience economy. The presented “World Trotter” travel agency prototype addresses the identified drawbacks of existing travel providers and gives motivation for future development of online travel agents.


title: A Novel Miniaturized Broadband Dual-Polarized Dipole Antenna for Base Station

abstract: A novel dual-polarized dipole antenna for base-station applications is presented in this letter. The proposed antenna is composed of a cross dipole, a square loop, a square plate, and a small-size reflector. The square loop and the square plate act as a parasitic radiator and a director, respectively. By introducing the two parts, the impedance bandwidth of the cross dipole can be significantly enhanced, and broadside radiation patterns with narrow beam can be also obtained. Experimental results show that the proposed antenna can operate from 1.71 to 2.69 GHz with low VSWRs at the two ports. High isolation ( > 22 dB) and stable antenna gain (~ 8 dB) are also achieved over the entire operating frequency band. The antenna has symmetrical radiation patterns both in vertical and horizontal planes, and the half-power beamwidth is 70°±5°. Furthermore, the size of the proposed antenna is very compact, which is only 0.513 λ0 ×0.513 λ0 ×0.388 λ0 at the center of the operating frequency band.


title: Fast and robust keypoint detection in unstructured 3-D point clouds

abstract: In robot perception, as well as in other areas of 3-D computer vision, keypoint detection is the first major ::: ::: step for an efficient and accurate 3-D perception of the environment. Thus, a fast and robust algorithm for an ::: ::: automatic identification of keypoints in unstructured 3-D point clouds is essential. The presented algorithm is ::: ::: designed to be highly parallelizable and can be implemented on modern GPUs for fast execution. The computation ::: ::: is based on a convolution of a voxel based representation of the point cloud and a voxelized integral ::: ::: volume. The generation of the voxel-based representation neither requires additional surface information or ::: ::: normals nor needs to approximate them. The proposed approach is robust against noise up to the mean distance ::: ::: between the 3-D points. In addition, the algorithm provides moderate scale invariance, i. e., it can approximate ::: ::: keypoints for lower resolution versions of the input point cloud. This is particularly useful, if keypoints are ::: ::: supposed to be used with any local 3-D point cloud descriptor to recognize or classify point clouds at different ::: ::: scales. We evaluate our approach in a direct comparison with state-of-the-art keypoint detection algorithms in ::: ::: terms of repeatability and computation time.


This information is current as Animal Models of Multiple Sclerosis Disease of the Central Nervous System in Metformin Attenuated the Autoimmune


title: Novel use of electronic whiteboard in the operating room increases surgical team compliance with pre-incision safety practices.

abstract: BACKGROUND ::: Despite evidence that use of a checklist during the pre-incision time out improves patient morbidity and mortality, compliance with performing the required elements of the checklist has been low. In an effort to improve compliance, a standardized time out interactive Electronic Checklist System [iECS] was implemented in all hospital operating room (OR) suites at 1 institution. The purpose of this 12-month prospective observational study was to assess whether an iECS in the OR improves and sustains improved surgical team compliance with the pre-incision time out. ::: ::: ::: METHODS ::: Direct observational analyses of preprocedural time outs were performed on 80 cases 1 month before, and 1 and 9 months after implementation of the iECS, for a total of 240 observed cases. Three observers, who achieved high interrater reliability (kappa = 0.83), recorded a compliance score (yes, 1; no, 0) on each element of the time out. An element was scored as compliant if it was clearly verbalized by the surgical team. ::: ::: ::: RESULTS ::: Pre-intervention observations indicated that surgical staff verbally communicated the core elements of the time out procedure 49.7 ± 12.9% of the time. After implementation of the iECS, direct observation of 80 surgical cases at 1 and 9 months indicated that surgical staff verbally communicated the core elements of the time out procedure 81.6 ± 11.4% and 85.8 ± 6.8% of the time, respectively, resulting in a statistically significant (P < .0001) increase in time out procedural compliance. ::: ::: ::: CONCLUSION ::: Implementation of a standardized, iECS can dramatically increase compliance with preprocedural time outs in the OR, an important and necessary step in improving patient outcomes and reducing preventable complications and deaths.


MACHINING WITH ROBOTS: A CRITICAL REVIEW


title: Fingerprint matching by genetic algorithms

abstract: Fingerprint matching is still a challenging problem for reliable person authentication because of the complex distortions involved in two impressions of the same finger. In this paper, we propose a fingerprint-matching approach based on genetic algorithms (GA), which tries to find the optimal transformation between two different fingerprints. In order to deal with low-quality fingerprint images, which introduce significant occlusion and clutter of minutiae features, we design a fitness function based on the local properties of each triplet of minutiae. The experimental results on National Institute of Standards and Technology fingerprint database, NIST-4, not only show that the proposed approach can achieve good performance even when a large portion of fingerprints in the database are of poor quality, but also show that the proposed approach is better than another approach, which is based on mean-squared error estimation.


title: Importance Sampling for Minibatches

abstract: Minibatching is a very well studied and highly popular technique in supervised learning, used by practitioners due to its ability to accelerate training through better utilization of parallel processing power and reduction of stochastic variance. Another popular technique is importance sampling -- a strategy for preferential sampling of more important examples also capable of accelerating the training process. However, despite considerable effort by the community in these areas, and due to the inherent technical difficulty of the problem, there is no existing work combining the power of importance sampling with the strength of minibatching. In this paper we propose the first {\em importance sampling for minibatches} and give simple and rigorous complexity analysis of its performance. We illustrate on synthetic problems that for training data of certain properties, our sampling can lead to several orders of magnitude improvement in training time. We then test the new sampling on several popular datasets, and show that the improvement can reach an order of magnitude.


title: Information revelation and privacy in online social networks

abstract: Participation in social networking sites has dramatically increased in recent years. Services such as Friendster, Tribe, or the Facebook allow millions of individuals to create online profiles and share personal information with vast networks of friends - and, often, unknown numbers of strangers. In this paper we study patterns of information revelation in online social networks and their privacy implications. We analyze the online behavior of more than 4,000 Carnegie Mellon University students who have joined a popular social networking site catered to colleges. We evaluate the amount of information they disclose and study their usage of the site's privacy settings. We highlight potential attacks on various aspects of their privacy, and we show that only a minimal percentage of users changes the highly permeable privacy preferences.


title: Exploiting Timelines to Enhance Multi-document Summarization

abstract: We study the use of temporal information in the form of timelines to enhance multidocument summarization. We employ a fully automated temporal processing system to generate a timeline for each input document. We derive three features from these timelines, and show that their use in supervised summarization lead to a significant 4.1% improvement in ROUGE performance over a state-of-the-art baseline. In addition, we propose TIMEMMR, a modification to Maximal Marginal Relevance that promotes temporal diversity by way of computing time span similarity, and show its utility in summarizing certain document sets. We also propose a filtering metric to discard noisy timelines generated by our automatic processes, to purify the timeline input for summarization. By selectively using timelines guided by filtering, overall summarization performance is increased by a significant 5.9%.


A Survey on Smart Automobiles using Internet of Things for Digital India


title: Cardiac tumors--diagnosis and surgical treatment.

abstract: Although the existence of cardiac tumors has been known since the Middle Ages (1), they remain a diagnostic puzzle and a therapeutic challenge. The first successful resection of a cardiac tumor was carried out by Crafoord in 1954, using a heart–lung machine (2), and ensuing advances in cardiosurgical technique enabled resection of malignant tumors. There are no large studies of the best treatment for cardiac tumors, particularly the malignant forms; often, only single case reports are available. With the exception of lymphoma, the treatment of choice is complete resection in combination with chemo-/radiotherapy. Large autopsy series show that the incidence of cardiac tumors is 0.02% (3). Some 75% of the tumors are benign, 25% malignant (3). Metastases are discovered much more frequently than primary neoplasms of the heart, being diagnosed in over 10% of tumor patients at autopsy. Atrial myxoma is the most frequent primary cardiac tumor in adults, while rhabdomyosarcoma predominates in children (3, 4). Our experience over a period of 23 years is summarized in Figure 1. ::: ::: ::: ::: Figure 1 ::: ::: Distribution of cardiac tumors diagnosed and treated at the Center for Cardiac Surgery of Munster University Hospital between 1989 and 2012


Adaptive Multi-Scale Information Flow for Object Detection


title: A Hierarchical Oil Tank Detector With Deep Surrounding Features for High-Resolution Optical Satellite Imagery

abstract: Automatic oil tank detection plays a very important role for remote sensing image processing. To accomplish the task, a hierarchical oil tank detector with deep surrounding features is proposed in this paper. The surrounding features extracted by the deep learning model aim at making the oil tanks more easily to recognize, since the appearance of oil tanks is a circle and this information is not enough to separate targets from the complex background. The proposed method is divided into three modules: 1) candidate selection; 2) feature extraction; and 3) classification. First, a modified ellipse and line segment detector (ELSD) based on gradient orientation is used to select candidates in the image. Afterward, the feature combing local and surrounding information together is extracted to represent the target. Histogram of oriented gradients (HOG) which can reliably capture the shape information is extracted to characterize the local patch. For the surrounding area, the convolutional neural network (CNN) trained in ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012) contest is applied as a blackbox feature extractor to extract rich surrounding feature. Then, the linear support vector machine (SVM) is utilized as the classifier to give the final output. Experimental results indicate that the proposed method is robust under different complex backgrounds and has high detection rate with low false alarm.


title: A Cloud-Based Internet of Things Platform for Ambient Assisted Living

abstract: A common feature of ambient intelligence is that many objects are inter-connected and act in unison, which is also a challenge in the Internet of Things. There has been a shift in research towards integrating both concepts, considering the Internet of Things as representing the future of computing and communications. However, the efficient combination and management of heterogeneous things or devices in the ambient intelligence domain is still a tedious task, and it presents crucial challenges. Therefore, to appropriately manage the inter-connection of diverse devices in these systems requires: (1) specifying and efficiently implementing the devices (e.g., as services); (2) handling and verifying their heterogeneity and composition; and (3) standardizing and managing their data, so as to tackle large numbers of systems together, avoiding standalone applications on local servers. To overcome these challenges, this paper proposes a platform to manage the integration and behavior-aware orchestration of heterogeneous devices as services, stored and accessed via the cloud, with the following contributions: (i) we describe a lightweight model to specify the behavior of devices, to determine the order of the sequence of exchanged messages during the composition of devices; (ii) we define a common architecture using a service-oriented standard environment, to integrate heterogeneous devices by means of their interfaces, via a gateway, and to orchestrate them according to their behavior; (iii) we design a framework based on cloud computing technology, connecting the gateway in charge of acquiring the data from the devices with a cloud platform, to remotely access and monitor the data at run-time and react to emergency situations; and (iv) we implement and generate a novel cloud-based IoT platform of behavior-aware devices as services for ambient intelligence systems, validating the whole approach in real scenarios related to a specific ambient assisted living application.


title: Mining spatio-temporal information on microblogging streams using a density-based online clustering method

abstract: Highlights? We applied a density-based stream clustering method for mining Twitter data. ? The developed method can detect real-time and geospatial event features. ? Using the detection results can estimate the temporal and spatial impacts of events. ? Our method is well suited for awareness of large-scale events and risk management. Social networks have been regarded as a timely and cost-effective source of spatio-temporal information for many fields of application. However, while some research groups have successfully developed topic detection methods from the text streams for a while, and even some popular microblogging services such as Twitter did provide information of top trending topics for selection, it is still unable to fully support users for picking up all of the real-time event topics with a comprehensive spatio-temporal viewpoint to satisfy their information needs. This paper aims to investigate how microblogging social networks (i.e. Twitter) can be used as a reliable information source of emerging events by extracting their spatio-temporal features from the messages to enhance event awareness. In this work, we applied a density-based online clustering method for mining microblogging text streams, in order to obtain temporal and geospatial features of real-world events. By analyzing the events detected by our system, the temporal and spatial impacts of the emerging events can be estimated, for achieving the goals of situational awareness and risk management.


title: MATLAB toolboxes: robotics and vision for students and teachers

abstract: In this article, the author gives us a description of Matlab toolboxes. The author has been passionately developing tools to enable students and teachers to better understand the theoretical concepts behind classical robotics and computer vision through easy and intuitive simulation and visualization. The results of this labor of love have been packaged as Matlab toolboxes: the robotics toolbox and the vision toolbox.


title: Conditional bernoulli mixtures for multi-label classification

abstract: Multi-label classification is an important machine learning task wherein one assigns a subset of candidate labels to an object. In this paper, we propose a new multi-label classification method based on Conditional Bernoulli Mixtures. Our proposed method has several attractive properties: it captures label dependencies; it reduces the multi-label problem to several standard binary and multi-class problems; it subsumes the classic independent binary prediction and power-set subset prediction methods as special cases; and it exhibits accuracy and/or computational complexity advantages over existing approaches. We demonstrate two implementations of our method using logistic regressions and gradient boosted trees, together with a simple training procedure based on Expectation Maximization. We further derive an efficient prediction procedure based on dynamic programming, thus avoiding the cost of examining an exponential number of potential label subsets. Experimental results show the effectiveness of the proposed method against competitive alternatives on benchmark datasets.


title: A semiotic analysis of the genetic information system

abstract: Terms loaded with informational connotations are often employed to refer to genes and their dynamics. Indeed, genes are usually perceived by biologists as basically ‘the carriers of hereditary information.’ Nevertheless, a number of researchers consider such talk as inadequate and ‘just metaphorical,’ thus expressing a skepticism about the use of the term ‘information’ and its derivatives in biology as a natural science. First, because the meaning of that term in biology is not as precise as it is, for instance, in the mathematical theory of communication. Second, because it seems to refer to a purported semantic property of genes without theoretically clarifying if any genuinely intrinsic semantics is involved. Biosemiotics, a field that attempts to analyze biological systems as semiotic systems, makes it possible to advance in the understanding of the concept of information in biology. From the perspective of Peircean biosemiotics, we develop here an account of genes as signs, including a detailed analysis of two fundamental processes in the genetic information system (transcription and protein synthesis) that have not been made so far in this field of research. Furthermore, we propose here an account of information based on Peircean semiotics and apply it to our analysis of transcription and protein synthesis.


title: Low-Loss Integrated Passive CMOS Electrical Balance Duplexers With Single-Ended LNA

abstract: A tunable integrated electrical balanced duplexer (EBD) is presented as a compact alternative to multiple bulky surface acoustic wave (SAW) and bulk acoustic wave (BAW) duplexers in third-generation (3G)/fourth-generation (4G) cellular transceivers. A balancing network creates a replica of the transmitter (TX) signal for cancellation at the input of a single-ended low-noise amplifier (LNA) to isolate the receive path from the TX. The proposed passive EBD is based on a cross-connected transformer topology without the need of any extra baluns at the antenna side. The balancing network enables a single-ended LNA with reliable high TX power operation up to 22 dBm by alleviating the common-mode coupling. The duplexer achieves around 50-dB transmitter–receiver (TX-RX) isolation within a 1.6–2.2-GHz range. The cascaded noise figure (NF) of the duplexer and LNA is 6.5 dB, and TX insertion loss (TXIL) of the duplexer is about 3.2 dB. The duplexer and LNA are implemented in a ${\hbox{0.18-}}\mu{\hbox{m}}$ CMOS process and occupy an active area of ${\hbox{0.35 mm}}^{2}$ .


title: REFRESH: REDEFINE for Face Recognition Using SURE Homogeneous Cores

abstract: In this paper we present design and analysis of a scalable real-time Face Recognition (FR) module to perform 450 recognitions per second. We introduce an algorithm for FR, which is a combination of Weighted Modular Principle Component Analysis and Radial Basis Function Neural Networks. This algorithm offers better recognition accuracy in various practical conditions than algorithms used in existing architectures for real-time FR. To meet real-time requirements, a Scalable Parallel Pipelined Architecture (SPPA) is developed by realizing the above FR algorithm as independent parallel streams and sub-streams of computations. SPPA is capable of supporting large databases maintained in external (DDR) memory. By casting the computations in a stream into hardware, we present the design of a Scalable Unit for Region Evaluation (SURE) core. Using SURE cores as computer elements in a massively parallel CGRA, like REDFINE, we provide a FR system on REDEFINE called REFRESH. We report FPGA and ASIC synthesis results for SPPA and REFRESH. Through analysis using these results, we show that excellent scalability and added programmability in REFRESH makes it a flexible and favorable solution for real-time FR.


title: Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning

abstract: Text-based adventure games provide a platform on which to explore reinforcement learning in the context of a combinatorial action space, such as natural language. We present a deep reinforcement learning architecture that represents the game state as a knowledge graph which is learned during exploration. This graph is used to prune the action space, enabling more efficient exploration. The question of which action to take can be reduced to a question-answering task, a form of transfer learning that pre-trains certain parts of our architecture. In experiments using the TextWorld framework, we show that our proposed technique can learn a control policy faster than baseline alternatives. We have also open-sourced our code at this https URL.


title: A 3D-BPP approach for optimising stowage plans and terminal productivity

abstract: Abstract This paper addresses the problem of determining stowage plans for containers in a ship, that is the so-called master bay plan problem (MBPP). MBPP is NP-complete [Botter, R.C., Brinati, M.A., 1992. Stowage container planning: A model for getting an optimal solution. IFIP Transactions B (Applications in Technology) B-5, 217–229; Avriel, M., Penn, M., Shpirer, N., 2000. Container ship stowage problem: Complexity and connection to the colouring of circle graphs. Discrete Applied Mathematics 103, 271–279]. We present a heuristic method for solving MBPP based on its relation with the three-dimensional bin packing problem (3D-BPP), where items are containers and the only bin is the ship . We look for stowage plans that take into a proper account structural and operational constraints, related to both the containers and the ship, and maximise some important terminal performance indexes, such as the effective and mean net crane productivity. Our aim is to evaluate how stowage plans can influence the performance of the quay. A validation of the proposed approach with some test cases related to containership docks at the port of Genoa (Italy) is given. The results of real instances of the problem and the comparison with a validated heuristic for MBPP, show the effectiveness of the proposed approach in producing stowage plans that minimise the total loading time and allow an efficient use of the quay equipment.


title: tamable looper: creature-like expressions and interactions by movement and deformation of clusters of sphere magnets

abstract: Researches using unique materials to move or deform objects are now popular. There are generally two approaches to accomplish this achievement. By implanting actuators, such as bio-metals, directly into the object is a common solution. In this way, actuators can disturb the materials behaviors or characteristics and ruin some performances. On the other hand, researches that enable to move or deform objects without implanting any actuators directly into the material also have been accomplished. In this approach, magnetic force is appropriate and useful for moving or deforming objects.


title: An experimental study of Bitcoin fluctuation using machine learning methods

abstract: In this paper, we study the ability to make the short-term prediction of the exchange price fluctuations towards the United States dollar for the Bitcoin market. We use the data of realized volatility collected from one of the largest Bitcoin digital trading offices in 2016 and 2017 as well as order information. Experiments are performed to evaluate a variety of statistical and machine learning approaches.


title: Multi-vehicle Convoy Analysis Based on ANPR data

abstract: This paper focuses on the development and novel application of data mining techniques for convoy analysis of vehicles based on the automatic number plate recognition (ANPR) system. The amount of ANPR data captured daily by traffic cameras in the road networks is very substantial. Data mining techniques are commonly used to extract relevant information and to reduce the amount of data processing and storage. In this paper, we apply data clustering techniques to extract relevant traffic patterns from the ANPR data to detect and identify unusual patterns and irregular behaviour of multi-vehicle convoy activities. (5 pages)


title: Microglial cell origin and phenotypes in health and disease

abstract: Microglia are brain-resident macrophages with a distinct origin. This Review discusses the development and function of these cells, and describes the association between the different microglial cell phenotypes and disease.


title: Recovering pulse rate during motion artifact with a multi-imager array for non-contact imaging photoplethysmography

abstract: Photoplethysmography relies on characteristic changes in the optical absorption of tissue due to pulsatile (arterial) blood flow in peripheral vasculature. Sensors for observing the photoplethysmographic effect have traditionally required contact with the skin surface. Recent advances in non-contact imaging photoplethysmography have demonstrated that measures of cardiopulmonary system state, such as pulse rate, pulse rate variability, and respiration rate, can be obtained from a participant by imaging their face under relatively motionless conditions. A critical limitation in this method that must be resolved is the inability to recover these measures under conditions of head motion artifact. To investigate the adequacy of channel space dimensionality for the use of blind source separation in this context, nine synchronized, visible spectrum imagers positioned in a semicircular array centered on the imaged participant were used for data acquisition in a controlled lighting environment. Three-lead electrocardiogram and finger-tip reflectance photoplethysmogram were also recorded as ground truth signals. Controlled head motion artifact trial conditions were compared to trials in which the participant remained stationary, with and without the aid of a chinrest. Bootstrapped means of one-minute, non-overlapping trial segments show that, for situations involving little to no head motion, a single imager is sufficient for recovering pulse rate with an average absolute error of less than two beats per minute. However, error in the recovered pulse rate measurement for the single imager can be as high as twenty-two beats per minute when head motion artifact is severe. This increase in measurement error during motion artifact was mitigated by increasing the dimensionality of the imager channel space with multiple imagers in the array prior to applying blind source separation. In contrast to single-imager results, the multi-imager channel space resulted in an absolute error in the recovered pulse rate measurement that is comparable with pulse rate measured via fingertip reflectance photoplethysmography. These results demonstrate that non-contact, imaging photoplethysmography can be accurate in the presence of head motion artifact when a multi-imager array is implemented to increase the dimensionality of the decomposed channel space.


title: Reading comprehension and its underlying components in second-language learners: A meta-analysis of studies comparing first- and second-language learners.

abstract: We report a systematic meta-analytic review of studies comparing reading comprehension and its underlying components (language comprehension, decoding, and phonological awareness) in first- and second-language learners. The review included 82 studies, and 576 effect sizes were calculated for reading comprehension and underlying components. Key findings were that, compared to first-language learners, second-language learners display a medium-sized deficit in reading comprehension (pooled effect size d 0.62), a large deficit in language comprehension (pooled effect size d 1.12), but only small differences in phonological awareness (pooled effect size d ‐ 0.08) and decoding (pooled effect size d ‐ 0.12). A moderator analysis showed that characteristics related to the type of reading comprehension test reliably explained the variation in the differences in reading comprehension between first- and second-language learners. For language comprehension, studies of samples from low socioeconomic backgrounds and samples where only the first language was used at home generated the largest group differences in favor of first-language learners. Test characteristics and study origin reliably contributed to the variations between the studies of language comprehension. For decoding, Canadian studies showed group differences in favor of second-language learners, whereas the opposite was the case for U.S. studies. Regarding implications, unless specific decoding problems are detected, interventions that aim to ameliorate reading comprehension problems among second-language learners should focus on language comprehension skills.


The physician burnout questionnaire: A new definition and measure


title: A Four-Switch Single-Stage Single-Phase Buck–Boost Inverter

abstract: This paper proposes a single-phase, single-stage buck–boost inverter for photovoltaic (PV) systems. The presented topology has one common terminal in input and output ports which eliminates common mode leakage current problem in the grid-connected PV applications. Although it uses four switches, its operation is bimodal and only two switches receive high-frequency pulse width modulation signals in each mode. Its principle of operation is described in detail with the help of equivalent circuits. Its dynamic model is presented, based on which a bimodal controller is designed. Experimental results, in stand-alone and grid-connected mode, obtained with a 300-W laboratory prototype are presented to validate its performance.


title: Correlation Analysis of Electromyogram Signals for Multiuser Myoelectric Interfaces

abstract: An inability to adapt myoelectric interfaces to a novel user’s unique style of hand motion, or even to adapt to the motion style of an opposite limb upon which the interface is trained, are important factors inhibiting the practical application of myoelectric interfaces. This is mainly attributed to the individual differences in the exhibited Electromyogram (EMG) signals generated by the muscles of different limbs. We propose in this paper a multi-user myoelectric interface which easily adapts to novel users and maintains good movement recognition performance. The main contribution is a framework for implementing style-independent feature transformation by using Canonical Correlation Analysis (CCA) in which different users’ data is projected onto a unified-style space. The proposed idea is summarized into three steps, to: 1) train a myoelectric pattern classifier on the set of style-independent features extracted from multiple users using the proposed CCA-based mapping, 2) create a new set of features describing the movements of a novel user during a quick calibration session, and 3) project the novel user’s features onto a lower-dimensional unified-style space with features maximally correlated with training data, and classify accordingly. The proposed method has been validated on a set of eight intact-limbed subjects, left-and-right handed, performing ten classes of bilateral synchronous fingers movements with four electrodes on each forearm. The method was able to overcome individual differences through the style-independent framework with accuracies of >83% across multiple users. Testing was also performed on a set of 10 intact-limbed and 6 belowelbow amputee subjects as they performed finger and thumb movements. The proposed framework allowed us to train the classifier on a normal subject’s data while subsequently testing it on an amputee’s data after calibration with a performance of >82% on average across all amputees.


Exploration of Large Networks via Fast and Universal Latent Space Model Fitting.


title: Active Clamping Circuit With Status Feedback for Series-Connected HV-IGBTs

abstract: Active clamping circuits can effectively suppress the voltage imbalance between series-connected insulated-gate bipolar transistors (IGBTs). However, while the active clamping circuit is conducting, extra current is injected into the gate of the IGBT, the gate voltage increases, and the IGBT operates in the active region. Therefore, long time conduction and frequent actions of the active clamping circuit will lead to extremely high switching loss of the IGBT with high collector-emitter voltage and serious switching loss imbalance between the series-connected IGBTs, which imperils the efficiency and safety of the system, particularly in the applications of high-voltage IGBT (HV-IGBT) series connection. In this paper, a novel active clamping circuit with status feedback is proposed for series-connected HV-IGBTs. A high-speed status feedback subcircuit is designed for transforming the operation status of an active clamping circuit into optical signal and feeding it back to the control system. According to the feedback signals, the microcontroller regulates the switching-on time and switching-off time of the series-connected HV-IGBTs' drive signals timely in order to implement voltage balancing and, thus, to restrain the actions of active clamping circuits. By applying this voltage balancing method to the HV-IGBT series connection circuit, the voltage balancing performance is expected to improve, and high switching loss and serious switching loss imbalance are expected to be suppressed. Experimental results verify the validity and superiorities of the proposed voltage balancing circuit and control strategy.


title: A Survey on Visual Content-Based Video Indexing and Retrieval

abstract: Video indexing and retrieval have a wide spectrum of promising applications, motivating the interest of researchers worldwide. This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, video retrieval including query interfaces, similarity measure and relevance feedback, and video browsing. Finally, we analyze future research directions.


title: Deriving Topics in Twitter by Exploiting Tweet Interactions

abstract: Twitter as a big data social network becomes one of the most important sources for capturing the up-to-date events happening in the world. Topic derivation from Twitter is important for various applications such as situation awareness, market analysis, content filtering, and recommendations. However, tweets are short messages, which makes topic derivation challenging. Current methods employ various semantic features of tweet content but mostly overlook the interactions among tweets. In this paper, we propose a novel topic derivation method that takes into account the interactions among tweets, defined as the reciprocal activities related to people who send the tweets, as well as actions and tweet contents. In particular, topics are derived by performing a two-step matrix factorization jointly over the interactions and semantic features of the tweets. We have conducted a number of experiments on tweets collected over a period of time, showing that the proposed method consistently outperforms other advanced topic derivation methods in the literature. Our experiments also reveal that the interactions among tweets do significantly relieve the sparsity problem caused by the short-text nature of Twitter.


title: The effect of Jatropha oil biodiesel on the corrosion rates of aluminium and mild carbon steel

abstract: The effect of biodiesel fuel made from Jatropha curcus L. seed oil and methanol on the corrosion rates of mild carbon steel (MCS) and aluminium (Al) was investigated and compared with those of the conventional petroleum diesel and its blend. Static immersion tests in diesel (B0), biodiesel-diesel blend (B50) and biodiesel (B100) fuels were carried out at room temperature for 18 weeks. Corrosion rates of MCS and Al in these fuels were determined at an interval of 3 weeks by the weight loss method. Also, during the period of investigation, the fuels were analysed by measuring the total acid number (TAN). It was observed from the results that the corrosion rates for these metals were higher in B100 compared to the corrosion rates in B50 and B0. The corrosion rates for MCS were found to be higher than corrosion rates for Al in these fuels. The corrosion rate for MCS at the end of our investigation were 0.026, 0.022 and 0.011 mpy in B100, B50 and B0 respectively, while that for aluminium were 0.016, 0.0099 and...


Design and Implementation of a Wireless Sensor Network Based Scalable Monitoring System (WiSe-SMS)