title: The Transport Layer Security (TLS) Protocol Version 1.2

abstract: This document specifies Version 1.2 of the Transport Layer Security ::: (TLS) protocol. The TLS protocol provides communications security over ::: the Internet. The protocol allows client/server applications to ::: communicate in a way that is designed to prevent eavesdropping, ::: tampering, or message forgery. [STANDARDS-TRACK]


title: Microbial production of fatty-acid-derived fuels and chemicals from plant biomass

abstract: Increasing energy costs and environmental concerns have emphasized the need to produce sustainable renewable fuels and chemicals. Major efforts to this end are focused on the microbial production of high-energy fuels by cost-effective 'consolidated bioprocesses'. Fatty acids are composed of long alkyl chains and represent nature's 'petroleum', being a primary metabolite used by cells for both chemical and energy storage functions. These energy-rich molecules are today isolated from plant and animal oils for a diverse set of products ranging from fuels to oleochemicals. A more scalable, controllable and economic route to this important class of chemicals would be through the microbial conversion of renewable feedstocks, such as biomass-derived carbohydrates. Here we demonstrate the engineering of Escherichia coli to produce structurally tailored fatty esters (biodiesel), fatty alcohols, and waxes directly from simple sugars. Furthermore, we show engineering of the biodiesel-producing cells to express hemicellulases, a step towards producing these compounds directly from hemicellulose, a major component of plant-derived biomass.


title: Arthropod relationships revealed by phylogenomic analysis of nuclear protein-coding sequences

abstract: The evolutionary interrelationship of arthropods (jointed-legged animals) has long been a matter of dispute. A new phylogeny based on an analysis of over 41,000 base pairs of DNA from 75 species, including representatives of every major arthropod lineage, should ease the way towards a consensus on the matter. The data support the idea that insects are land-living crustaceans, that crustaceans comprise a diverse assemblage of at last three distinct arthropod types, and that myriapods (millipedes and centipedes) are the closest relatives of this great 'pancrustacean' group.


title: Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework

abstract: Hardware accelerations of deep learning systems have been extensively investigated in industry and academia. The aim of this paper is to achieve ultra-high energy efficiency and performance for hardware implementations of deep neural networks (DNNs). An algorithm-hardware co-optimization framework is developed, which is applicable to different DNN types, sizes, and application scenarios. The algorithm part adopts the general block-circulant matrices to achieve a fine-grained tradeoff between accuracy and compression ratio. It applies to both fully-connected and convolutional layers and contains a mathematically rigorous proof of the effectiveness of the method. The proposed algorithm reduces computational complexity per layer from O($n^2$) to O($n\log n$) and storage complexity from O($n^2$) to O($n$), both for training and inference. The hardware part consists of highly efficient Field Programmable Gate Array (FPGA)-based implementations using effective reconfiguration, batch processing, deep pipelining, resource re-using, and hierarchical control. Experimental results demonstrate that the proposed framework achieves at least 152X speedup and 71X energy efficiency gain compared with IBM TrueNorth processor under the same test accuracy. It achieves at least 31X energy efficiency gain compared with the reference FPGA-based work.


title: MRAS based estimation of speed in sensorless PMSM drive

abstract: To increase the mechanical robustness of drive system and to make the drive cheaper elimination of the position sensor is highly encouraged. Sensors are not reliable in explosive environment like in chemical industries and may cause the EMI problem. This has made the speed and position sensorless drive very attractive. A speed and position estimator for PMSM drive has been investigated. In the algorithm the PMSM used as reference model. The adaptation mechanism uses a PI controller to process the error between reference model and adjustable model. The estimation algorithm used is independent of stator resistance, computationally less complex, free from integrator problem because back-emf estimation is not required and provides stable operation of drive system. So the performance at low and zero speed is also good. The estimation algorithm is implemented in MATLAB. Simulations results show the validity of MRAS estimator presented here.


title: The State of the Art in HDR Deghosting: A Survey and Evaluation

abstract: Obtaining a high quality high dynamic range HDR image in the presence of camera and object movement has been a long-standing challenge. Many methods, known as HDR deghosting algorithms, have been developed over the past ten years to undertake this challenge. Each of these algorithms approaches the deghosting problem from a different perspective, providing solutions with different degrees of complexity, solutions that range from rudimentary heuristics to advanced computer vision techniques. The proposed solutions generally differ in two ways: 1 how to detect ghost regions and 2 what to do to eliminate ghosts. Some algorithms choose to completely discard moving objects giving rise to HDR images which only contain the static regions. Some other algorithms try to find the best image to use for each dynamic region. Yet others try to register moving objects from different images in the spirit of maximizing dynamic range in dynamic regions. Furthermore, each algorithm may introduce different types of artifacts as they aim to eliminate ghosts. These artifacts may come in the form of noise, broken objects, under- and over-exposed regions, and residual ghosting. Given the high volume of studies conducted in this field over the recent years, a comprehensive survey of the state of the art is required. Thus, the first goal of this paper is to provide this survey. Secondly, the large number of algorithms brings about the need to classify them. Thus the second goal of this paper is to propose a taxonomy of deghosting algorithms which can be used to group existing and future algorithms into meaningful classes. Thirdly, the existence of a large number of algorithms brings about the need to evaluate their effectiveness, as each new algorithm claims to outperform its precedents. Therefore, the last goal of this paper is to share the results of a subjective experiment which aims to evaluate various state-of-the-art deghosting algorithms.


Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization


title: A 78mW 11.8Gb/s serial link transceiver with adaptive RX equalization and baud-rate CDR in 32nm CMOS

abstract: The last few years have witnessed a rapid increase in serial IO data rates as well as number of IO ports in microprocessors. This trend, poses significant challenges to the serial IO design because of area and power budget limitations but, above all, by the need to achieve stringent bit error rates in the presence of interconnect constraints such as channel loss, impedance discontinuities induced by package and connectors, and crosstalk effects due to routing limitations in the package and on the motherboard. To effectively meet these challenges, the serial IO architecture has evolved to include more complex equalization schemes. Building on the previous work on a 4-tap DFE [1], this paper presents the design of a complete serial IO capable of operating up to 11.8Gb/s, and in particular, focuses on the integration of an adaptive equalizer and baud-rate CDR in the receiver of the serial IO.


Analysis and modeling of YouTube traffic


title: Neuropsychiatry of frontal lobe dysfunction in violent and criminal behaviour: a critical review

abstract: OBJECTIVES—To establish the link between frontal lobe dysfunction and violent and criminal behaviour, based on a review of relevant literature. ::: METHODS—Articles relating evidence of frontal lobe dysfunction with violence or crime were collected through a MEDLINE search using the keyword "frontal lobe" combined with the terms "aggression," "violence," "crime," "antisocial personality disorder," "psychopathy," "impulse control disorders", and "episodic dyscontrol." Reference lists were then searched for additional articles. ::: RESULTS—High rates of neuropsychiatric abnormalities reported in persons with violent and criminal behaviour suggest an association between aggressive dyscontrol and brain injury, especially involving the frontal lobes. The studies reviewed support an association between frontal lobe dysfunction and increased aggressive and antisocial behaviour. Focal orbitofrontal injury is specifically associated with increased aggression. Deficits in frontal executive function may increase the likelihood of future aggression, but no study has reliably demonstrated a characteristic pattern of frontal network dysfunction predictive of violent crime. ::: CONCLUSIONS—Clinically significant focal frontal lobe dysfunction is associated with aggressive dyscontrol, but the increased risk of violence seems less than is widely presumed. Evidence is strongest for an association between focal prefrontal damage and an impulsive subtype of aggressive behaviour.


title: From Mindless to Mindful Practice — Cognitive Bias and Clinical Decision Making

abstract: Much diagnostic error is caused by cognitive bias. More than 100 biases affecting clinical decision making have been described, and many medical disciplines acknowledge their pervasive influence on our thinking. Training in critical thinking may ameliorate the problem.


title: Computing and calibrating collision impulses and its application for air hockey game

abstract: In this paper, we calibrate Mirtich's approach (1996) for computing a collision impulse by two kinds of randomized algorithms, and then apply the model for an air hockey game. Mirtich proposed a smart dynamic simulation called impulse-based simulation. He considers twin goals of physical accuracy and computational efficiency. The simulation can accurately model complex dynamic system in real-time. However, whether his impulse-based model is reasonable or not has not been investigated at all by any experimental result. For example, his impulse model requires the dynamic friction coefficient /spl mu//sub D/ and the coefficient e of restitution in advance. However, they strongly depend on shape and material of colliding objects. Because of noise and error of sensing, it is difficult for us to measure the parameters exactly. For this reason, in this paper, we calibrate these by using two kinds of randomized algorithms based on many reductions of momentums before and after the collision. Each momentum (multiplying mass and velocity) can be easily obtained by measuring relative linear velocities and angular velocities before and after the collision. They are practically and easily measured by a smart imaging of two objects. The air hockey game designed consists of two PC with two joysticks including force feedback, which are connecting each other by the fast Ethernet.


title: Improving HPC Application Performance in Cloud through Dynamic Load Balancing

abstract: Driven by the benefits of elasticity and pay-as-you-go model, cloud computing is emerging as an attractive alternative and addition to in-house clusters and supercomputers for some High Performance Computing (HPC) applications. However, poor interconnect performance, heterogeneous and dynamic environment, and interference by other virtual machines (VMs) are some bottlenecks for efficient HPC in cloud. For tightly-coupled iterative applications, one slow processor slows down the entire application, resulting in poor CPU utilization. In this paper, we present a dynamic load balancer for tightly-coupled iterative HPC applications in cloud. It infers the static hardware heterogeneity in virtualized environments, and also adapts to the dynamic heterogeneity caused by the interference arising due to multi-tenancy. Through continuous live monitoring, instrumentation, and periodic refinement of task distribution to VMs, our load balancer adapts to the dynamic variations in cloud resources. Through experimental evaluation on a private cloud with 64 VMs using benchmarks and a real science application, we demonstrate performance benefits up to 45%. Finally, we analyze the effect of load balancing frequency, problem size, and computational granularity (problem decomposition) on the performance and scalability of our techniques.


title: SCADA controlled smart home using Raspberry Pi3

abstract: The main objective of this article is to develop a smart home system using SCADA, Raspberry Pi3 Module and Wemos-Dl board. Nowadays people are expecting to control the home appliances through remote control. This expectation is fulfilled by our advanced technology. In this paper, we are using SCADA, Transmission Control Protocol/Internet Protocol (TCP/IP) and Message Queuing Telemetry Transport (MQTT) to develop a smart home system. Modern homes are expected to have centralized control system, instead of conventional switches which are fitted on walls. If the user wants to control the appliances, he has to move near the wall and control the switches. This method is inconvenient for the elderly and disabled people. So their difficulty to operate the appliances can be supported by the proposed smart home system which has a remote module to control the appliances. Remote controlled smart home system fulfills the needs of the people. Remote control operation is achieved by a PC (Personal Computer) is supported by IGSS SCADA. In the PC with TCP/IP protocol acts as a transmitter, which sends commands to the Raspberry Pi3 module. Raspberry Pi3 receives the commands from SCADA. Raspberry Pi3 send the commands with addresses to a terminal units (Wemos-Dl board) by MQTT. Each terminal unit receives the address from the Raspberry Pi3, if the terminal unit address matches the Raspberry Pi3, it can control devices or measured parameter, then sends this parameter with its address to the Raspberry Pi3. The Raspberry Pi3 sends these data to owner's PC. In this article Wemos-D1 controller board which contains an ESP8266 IC is used. The loads are interfaced to the Wemos-D1 controller using electromagnetic relays.


title: Stitch meshes for modeling knitted clothing with yarn-level detail

abstract: Recent yarn-based simulation techniques permit realistic and efficient dynamic simulation of knitted clothing, but producing the required yarn-level models remains a challenge. The lack of practical modeling techniques significantly limits the diversity and complexity of knitted garments that can be simulated. We propose a new modeling technique that builds yarn-level models of complex knitted garments for virtual characters. We start with a polygonal model that represents the large-scale surface of the knitted cloth. Using this mesh as an input, our interactive modeling tool produces a finer mesh representing the layout of stitches in the garment, which we call the stitch mesh. By manipulating this mesh and assigning stitch types to its faces, the user can replicate a variety of complicated knitting patterns. The curve model representing the yarn is generated from the stitch mesh, then the final shape is computed by a yarn-level physical simulation that locally relaxes the yarn into realistic shape while preserving global shape of the garment and avoiding "yarn pull-through," thereby producing valid yarn geometry suitable for dynamic simulation. Using our system, we can efficiently create yarn-level models of knitted clothing with a rich variety of patterns that would be completely impractical to model using traditional techniques. We show a variety of example knitting patterns and full-scale garments produced using our system.


title: SPLENDID: SPARQL Endpoint Federation Exploiting VOID Descriptions

abstract: In order to leverage the full potential of the Semantic Web it is necessary to transparently query distributed RDF data sources in the same way as it has been possible with federated databases for ages. However, there are significant differences between the Web of (linked) Data and the traditional database approaches. Hence, it is not straightforward to adapt successful database techniques for RDF federation. Reasons are the missing cooperation between SPARQL end-points and the need for detailed data statistics for estimating the costs of query execution plans. We have implemented SPLENDID, a query optimization strategy for federating SPARQL endpoints based on statistical data obtained from voiD descriptions.


title: AntHocNet: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks

abstract: In this paper, we describe AntHocNet, an algorithm for routing in mobile ad hoc networks. It is a hybrid algorithm, which combines reactive path setup with proactive path probing, maintenance and improvement. The algorithm is based on the nature-inspired ant colony optimisation framework. Paths are learned by guided Monte Carlo sampling using ant-like agents communicating in a stigmergic way. In an extensive set of simulation experiments, we compare AntHocNet with AODV, a reference algorithm in the field. We show that our algorithm can outperform AODV on different evaluation criteria. AntHocNet's performance advantage is visible over a broad range of possible network scenarios, and increases for larger, sparser and more mobile networks. Copyright © 2005 AEIT.


title: Automatic Model Generation Strategies for Model Transformation Testing

abstract: Testing model transformations requires input models which are graphs of inter-connected objects that must conform to a meta-model and meta-constraints from heterogeneous sources such as well-formedness rules, transformation pre-conditions, and test strategies. Manually specifying such models is tedious since models must simultaneously conform to several meta-constraints. We propose automatic model generation via constraint satisfaction using our tool Cartier for model transformation testing. Due to the virtually infinite number of models in the input domain we compare strategies based on input domain partitioning to guide model generation. We qualify the effectiveness of these strategies by performing mutation analysis on the transformation using generated sets of models. The test sets obtained using partitioning strategies gives mutation scores of up to 87% vs. 72% in the case of unguided/random generation. These scores are based on analysis of 360 automatically generated test models for the representative transformation of UML class diagram models to RDBMS models.


title: Understanding emotions in others: mirror neuron dysfunction in children with autism spectrum disorders

abstract: To examine mirror neuron abnormalities in autism, high-functioning children with autism and matched controls underwent fMRI while imitating and observing emotional expressions. Although both groups performed the tasks equally well, children with autism showed no mirror neuron activity in the inferior frontal gyrus (pars opercularis). Notably, activity in this area was inversely related to symptom severity in the social domain, suggesting that a dysfunctional 'mirror neuron system' may underlie the social deficits observed in autism.


Composing Video Game Levels with Music Metaphors through Functional Scaffolding


title: DVS Benchmark Datasets for Object Tracking, Action Recognition, and Object Recognition

abstract: Benchmarks have played a vital role in the advancement of visual object recognition and other fields of computer vision (LeCun et al., 1998; Deng et al., 2009;). The challenges posed by these standard datasets have helped identify and overcome the shortcomings of existing approaches, and have led to great advances of the state of the art. Even the recent massive increase of interest in deep learning methods can be attributed to their success in difficult benchmarks such as ImageNet (Krizhevsky et al., 2012; LeCun et al., 2015). Neuromorphic vision uses silicon retina sensors such as the dynamic vision sensor (DVS; Lichtsteiner et al., 2008). These sensors and their DAVIS (Dynamic and Active-pixel Vision Sensor) and ATIS (Asynchronous Time-based Image Sensor) derivatives (Brandli et al., 2014; Posch et al., 2014) are inspired by biological vision by generating streams of asynchronous events indicating local log-intensity brightness changes. They thereby greatly reduce the amount of data to be processed, and their dynamic nature makes them a good fit for domains such as optical flow, object tracking, action recognition, or dynamic scene understanding. Compared to classical computer vision, neuromorphic vision is a younger and much smaller field of research, and lacks benchmarks, which impedes the progress of the field. To address this we introduce the largest event-based vision benchmark dataset published to date, hoping to satisfy a growing demand and stimulate challenges for the community. In particular, the availability of such benchmarks should help the development of algorithms processing event-based vision input, allowing a direct fair comparison of different approaches. We have explicitly chosen mostly dynamic vision tasks such as action recognition or tracking, which could benefit from the strengths of neuromorphic vision sensors, although algorithms that exploit these features are largely missing. ::: ::: A major reason for the lack of benchmarks is that currently neuromorphic vision sensors are only available as RD see Tan et al. (2015) for an informative review. Unlabeled DVS data was made available around 2007 in the jAER project1 and was used for development of spike timing-based unsupervised feature learning e.g., in Bichler et al. (2012). The first labeled and published event-based neuromorphic vision sensor benchmarks were created from the MNIST digit recognition dataset by jiggling the image on the screen (see Serrano-Gotarredona and Linares-Barranco, 2015 for an informative history) and later to reduce frame artifacts by jiggling the camera view with a pan-tilt unit (Orchard et al., 2015). These datasets automated the scene movement necessary to generate DVS output from the static images, and will be an important step forward for evaluating neuromorphic object recognition systems such as spiking deep networks (Perez-Carrasco et al., 2013; O'Connor et al., 2013; Cao et al., 2014; Diehl et al., 2015), which so far have been tested mostly on static image datasets converted into Poisson spike trains. But static image recognition is not the ideal use case for event-based vision sensors that are designed for dynamic scenes. Recently several additional DVS datasets were made available in the Frontiers research topic “Benchmarks and Challenges for Neuromorphic Engineering”2; in particular for navigation using multiple sensor modalities (Barranco et al., 2016) and for developing and benchmarking DVS and DAVIS optical flow methods (Rueckauer and Delbruck, 2016). ::: ::: This data report summarizes a new benchmark dataset in which we converted established visual video benchmarks for object tracking, action recognition and object recognition into spiking neuromorphic datasets, recorded with the DVS output (Lichtsteiner et al., 2008) of a DAVIS camera (Berner et al., 2013; Brandli et al., 2014). This report presents our approach for sensor calibration and capture of frame-based videos into neuromorphic vision datasets with minimal human intervention. We converted four widely used dynamic datasets: the VOT Challenge 2015 Dataset (Kristan et al., 2016), TrackingDataset3, the UCF-50 Action Recognition Dataset (Reddy and Shah, 2012), and the Caltech-256 Object Category Dataset (Griffin et al., 2006). We conclude with statistics and summaries of the datasets.


title: Random KNN feature selection - a fast and stable alternative to Random Forests

abstract: BackgroundSuccessfully modeling high-dimensional data involving thousands of variables is challenging. This is especially true for gene expression profiling experiments, given the large number of genes involved and the small number of samples available. Random Forests (RF) is a popular and widely used approach to feature selection for such "small n, large p problems." However, Random Forests suffers from instability, especially in the presence of noisy and/or unbalanced inputs.ResultsWe present RKNN-FS, an innovative feature selection procedure for "small n, large p problems." RKNN-FS is based on Random KNN (RKNN), a novel generalization of traditional nearest-neighbor modeling. RKNN consists of an ensemble of base k-nearest neighbor models, each constructed from a random subset of the input variables. To rank the importance of the variables, we define a criterion on the RKNN framework, using the notion of support. A two-stage backward model selection method is then developed based on this criterion. Empirical results on microarray data sets with thousands of variables and relatively few samples show that RKNN-FS is an effective feature selection approach for high-dimensional data. RKNN is similar to Random Forests in terms of classification accuracy without feature selection. However, RKNN provides much better classification accuracy than RF when each method incorporates a feature-selection step. Our results show that RKNN is significantly more stable and more robust than Random Forests for feature selection when the input data are noisy and/or unbalanced. Further, RKNN-FS is much faster than the Random Forests feature selection method (RF-FS), especially for large scale problems, involving thousands of variables and multiple classes.ConclusionsGiven the superiority of Random KNN in classification performance when compared with Random Forests, RKNN-FS's simplicity and ease of implementation, and its superiority in speed and stability, we propose RKNN-FS as a faster and more stable alternative to Random Forests in classification problems involving feature selection for high-dimensional datasets.


title: 3D foot shape generation from 2D information

abstract: Two methods to generate an individual 3D foot shape from 2D information are proposed. A standard foot shape was first generated and then scaled based on known 2D information. In the first method, the foot outline and the foot height were used, and in the second, the foot outline and the foot profile were used. The models were developed using 40 participants and then validated using a different set of 40 participants. Results show that each individual foot shape can be predicted within a mean absolute error of 1.36 mm for the left foot and 1.37 mm for the right foot using the first method, and within a mean absolute error of 1.02 mm for the left foot and 1.02 mm for the right foot using the second method. The second method shows somewhat improved accuracy even though it requires two images. Both the methods are relatively cheaper than using a scanner to determine the 3D foot shape for custom footwear design.


title: Evaluating calcified carotid artery atheromas in panoramic radiographs of patients with type 2 diabetes mellitus

abstract: Objectives ::: To determine the prevalence of calcified carotid artery atheromas (CCAAs) in panoramic radiographs of patients with type 2 diabetes mellitus.


title: A Novel Way of Identifying Cyber Predators

abstract: Recurrent Neural Networks with Long Short-Term Memory cell (LSTM-RNN) have impressive ability in sequence data processing, particularly for language model building and text classification. This research proposes the combination of sentiment analysis, new approach of sentence vectors and LSTM-RNN as a novel way for Sexual Predator Identification (SPI). LSTM-RNN language model is applied to generate sentence vectors which are the last hidden states in the language model. Sentence vectors are fed into another LSTM-RNN classifier, so as to capture suspicious conversations. Hidden state enables to generate vectors for sentences never seen before. Fasttext is used to filter the contents of conversations and generate a sentiment score so as to identify potential predators. The experiment achieves a record-breaking accuracy and precision of 100% with recall of 81.10%, exceeding the top-ranked result in the SPI competition.


title: Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries

abstract: How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implications for researchers, content creators and providers. We here investigate the effect of revisits (successive visits from a single user) on content popularity. Using four datasets of social activity, with up to tens of millions media objects (e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect of revisits in the popularity evolution of such objects. Secondly, we propose the Phoenix-R model which captures the popularity dynamics of individual objects. Phoenix-R has the desired properties of being: (1) parsimonious, being based on the minimum description length principle, and achieving lower root mean squared error than state-of-the-art baselines; (2) applicable, the model is effective for predicting future popularity values of objects.


title: Mobile Learning Projects--A Critical Analysis of the State of the Art

abstract: This paper provides a critical analysis of Mobile Learning projects published before the end of 2007. The review uses a Mobile Learning framework to evaluate and categorize 102 Mobile Learning projects, and to briefly introduce exemplary projects for each category. All projects were analysed with the criteria: context, tools, control, communication, subject and objective. Although a significant number of projects have ventured to incorporate the physical context into the learning experience, few projects include a socializing context. Tool support ranges from pure content delivery to content construction by the learners. Although few projects explicitly discuss the Mobile Learning control issues, one can find all approaches from pure teacher control to learner control. Despite the fact that mobile phones initially started as a communication device, communication and collaboration play a surprisingly small role in Mobile Learning projects. Most Mobile Learning projects support novices, although one might argue that the largest potential is supporting advanced learners. All results show the design space and reveal gaps in Mobile Learning research.


title: FACTORS INFLUENCING SEXUAL HARASSMENT IN THE MALAYSIAN WORKPLACE

abstract: This study examined the potential risks factors associated with sexually harassing behaviors within the framework of the four-factor model of sexual harassment. The factors examined are unprofessional work environment, skewed gender ratio in the workplace, knowledge of grievance procedure for sexual harassment, sexist attitudes among co-workers, privacy of workspace, physical attractiveness, dress manner of victims, job status, and sex roles. The dependent variable is incidence of sexual harassment which was evaluated using the Sexual Experience Questionnaire developed by Fitzgerald, Shullman, Bailey, Richards, Swecker, Gold, Ormerod, and Weitzman (1988). Data were collected from 657 women employees working in Malaysian organizations. The findings showed that the predictions of the four-factor model are also largely true in the case of the Malaysian workplace. The study also found that sexual harassment behaviors are fairly widespread in Malaysia.


title: SC-Conv: Sparse-Complementary Convolution for Efficient Model Utilization on CNNs

abstract: We propose sparse-complementary convolution (SC-Conv) to improve model utilization of convolution neural networks (CNNs). The networks with SC-Conv achieve better accuracy than the regular convolution under similar computations and parameters. The proposed SC-Conv is paired with two deterministic sparse kernels, and one of kernels is complementary to the other one at in either spatial or channel domain or both; the deterministic sparsity increases the computational speed theoretically and practically; furthermore, by having the complementary characteristic, SC-Conv retains the same receptive field to the conventional convolution. This insightful but straightforward SC-Conv reuses of modern network architectures (ResNet and DenseNet), and at the same FLOPs and parameters, SC-Conv improves top-1 classification accuracy on ImageNet by 0.6 points for ResNet-101 and keep the same model complexity. Furthermore, SC-Conv also outperforms recent sparse networks by 1.3 points at top-1 accuracy for ImageNet, and after integrating SC-Conv with the sparse network, we further improve another 1.8 points accuracy at similar FLOPs and parameters.


title: Analyzing Skin Lesions in Dermoscopy Images Using Convolutional Neural Networks

abstract: In this paper, we discuss the problem of automatic skin lesion analysis, specifically melanoma detection and semantic segmentation. We accomplish this by using deep learning techniques to perform classification on publicly available dermoscopic images. Skin cancer, of which melanoma is a type, is the most prevalent form of cancer in the US and more than four million cases are diagnosed in the US every year. In this work, we present our efforts towards an accessible, deep learning-based system that can be used for skin lesion classification, thus leading to an improved melanoma screening system. For classification, a deep convolutional neural network architecture is first implemented over the raw images. In addition, hand-coded features such as 166-D color histogram distribution, edge histogram and Multiscale Color local binary patterns are extracted from the images and presented to a random forest classifier. The average of the outputs from the two mentioned classifiers is taken as the final classification result. The classification task achieves an accuracy of 80.3%, AUC score of 0.69 and a precision score of 0.81. For segmentation, we implement a convolutional-deconvolutional architecture and the segmentation model achieves a Dice coefficient of 73.5%.


title: A 1.55 $\mu$m 40 Gbps EML TOSA Employing a Novel FPC Connection

abstract: We have developed a 1.55-μm 40 Gbps electro-absorption modulator laser (EML)-based transmitter optical subassembly (TOSA) using a novel flexible printed circuit (FPC). The return loss at the junctions of the printed circuit board and the FPC, and of the FPC and the ceramic feedthrough connection was held better than 20 dB at up to 40 GHz by a newly developed three-layer FPC. The TOSA was fabricated and demonstrated a mask margin of >16% and a path penalty of <0.63 dB for a 43 Gbps signal after 2.4-km SMF transmission over the entire case temperature range from −5° to 80 °C, demonstrating compliance with ITU-T G.693. These results are comparable to coaxial connector type EML modules. This TOSA is expected to be a strong candidate for 40 Gbps EML modules with excellent operating characteristics, economy, and a small footprint.


title: Content-Based Filtering Recommendation in Abstract Search Using Neo4j

abstract: In this work, we focus on development of a content search on report documents and recommendation on related document from search result. The main contribution of this work is to model document content into graph. Document-Keyword graph was created to represent the relationship between document and its features. The data were stored as a connected graph in Ne04j graph database. The graph were used to filter keyword co-occurrence documents in order to reduce search space. The performance of the proposed model was evaluated with accuracy 0.77. To improve the accuracy, the model can be extended with collecting user selection as collaborative feedback to the system, or extended with domain specific ontology to analyze the semantic relationship of the documents.


title: Feature-Based Hand Gesture Recognition Using an FMCW Radar and its Temporal Feature Analysis

abstract: In this paper, feature-based gesture recognition in a frequency modulated continuous wave (FMCW) radar system is introduced. We obtain a range-Doppler map (RDM) from raw signals of FMCW radar and generate a variety of features from the RDM. The features are broadly defined to reflect radar-specific characteristics as well as statistical values commonly used in machine learning. Among these radar features, those that are highly correlated with gesture recognition are selected by the proposed feature selection algorithm, which is a wrapper-based feature selection algorithm incorporated with a quantum-inspired evolutionary algorithm (QEA). Furthermore, the information factor based on the minimum redundancy maximum relevance criterion is applied to QEA in order to find feature subsets effectively. The proposed algorithm is able to extract from all feature sets feature subsets related to gesture recognition, and improves the gesture recognition accuracy of the FMCW radar system. In addition, we analyze which features of the radar are helpful for gesture recognition and perform effective gesture recognition using the features determined through feature analysis.


title: Social cognition in schizophrenia

abstract: Impairment of social cognition is an important feature of schizophrenia. Green and colleagues review the social processes that are affected in people with this disorder and consider empathy — a complex social cognitive function that involves several of these processes — in such individuals.


title: A survey of combinatorial testing

abstract: Combinatorial Testing (CT) can detect failures triggered by interactions of parameters in the Software Under Test (SUT) with a covering array test suite generated by some sampling mechanisms. It has been an active field of research in the last twenty years. This article aims to review previous work on CT, highlights the evolution of CT, and identifies important issues, methods, and applications of CT, with the goal of supporting and directing future practice and research in this area. First, we present the basic concepts and notations of CT. Second, we classify the research on CT into the following categories: modeling for CT, test suite generation, constraints, failure diagnosis, prioritization, metric, evaluation, testing procedure and the application of CT. For each of the categories, we survey the motivation, key issues, solutions, and the current state of research. Then, we review the contribution from different research groups, and present the growing trend of CT research. Finally, we recommend directions for future CT research, including: (1) modeling for CT, (2) improving the existing test suite generation algorithm, (3) improving analysis of testing result, (4) exploring the application of CT to different levels of testing and additional types of systems, (5) conducting more empirical studies to fully understand limitations and strengths of CT, and (6) combining CT with other testing techniques.


title: Surgical anatomy of the trachea

abstract: Anatomy as the foundation of surgery is a concept no better exemplified than by the history of tracheal surgery. Incremental advancements in our understanding of the trachea’s position, structure, blood supply and adjacent organs each allowed for stepwise improvements in the thoracic surgeon’s ability to address upper airway disease. As such, the mastery of tracheal anatomy is fundamental to those clinicians responsible for treating such ailments. In this article, tracheal anatomy is reviewed and points critical to the thoracic surgeon are highlighted. The structure and location of the trachea, the blood supply to the trachea, and the trachea’s spatial relationship to critical mediastinal organs are presented. This material provides the groundwork for understanding all aspects of tracheal surgery today.


ALTRUISM AND GENDER : A STUDY OF SENIOR SECONDARY CLASS STUDENTS


title: 3D Densely Convolutional Networks for Volumetric Segmentation

abstract: In the isointense stage, the accurate volumetric image segmentation is a challenging task due to the low contrast between tissues. In this paper, we propose a novel very deep network architecture based on a densely convolutional network for volumetric brain segmentation. The proposed network architecture provides a dense connection between layers that aims to improve the information flow in the network. By concatenating features map of fine and coarse dense blocks, it allows capturing multi-scale contextual information. Experimental results demonstrate significant advantages of the proposed method over existing methods, in terms of both segmentation accuracy and parameter efficiency in MICCAI grand challenge on 6-month infant brain MRI segmentation.


title: Logos: a system for translating queries into narratives

abstract: This paper presents Logos, a system that provides natural language translations for relational queries expressed in SQL. Our translation mechanism is based on a graph-based approach to the query translation problem. We represent various forms of structured queries as directed graphs and we annotate the graph edges with template labels using an extensible template mechanism. Logos uses different graph traversal strategies for efficiently exploring these graphs and composing textual query descriptions. The audience may interactively explore Logos using various database schemata and issuing either sample or ad hoc queries.


title: Microblog Entity Linking with Social Temporal Context

abstract: Nowadays microblogging sites, such as Twitter and Chinese Sina Weibo, have established themselves as an invaluable information source, which provides a huge collection of manually-generated tweets with broad range of topics from daily life to breaking news. Entity linking is indispensable for understanding and maintaining such information, which in turn facilitates many real-world applications such as tweet clustering and classification, personalized microblog search, and so forth. However, tweets are short, informal and error-prone, rendering traditional approaches for entity linking in documents largely inapplicable. Recent work addresses this problem by utilising information from other tweets and linking entities in a batch manner. Nevertheless, the high computational complexity makes this approach infeasible for real-time applications given the high arrival rate of tweets. In this paper, we propose an efficient solution to link entities in tweets by analyzing their social and temporal context. Our proposed framework takes into consideration three features, namely entity popularity, entity recency, and user interest information embedded in social interactions to assist the entity linking task. Effective indexing structures along with incremental algorithms have also been developed to reduce the computation and maintenance costs of our approach. Experimental results based on real tweet datasets verify the effectiveness and efficiency of our proposals.


title: Comparison of Conventional and Computer-Aided Drafting Methods from the View of Time and Drafting Quality.

abstract: Problem Statement: Drafting course is essential for students in the design disciplines for becoming more organized and for complying with standards in the educational system. Drafting knowledge is crucial, both for comprehension of the issues and for the implementation phase. In any design project, drafting performance and success are as important as the design process, especially in the educational environments aimed at professional life. However, there have been relatively any studies undertaken in the area of the success and time saving of the conventional drafting and computer-aided drafting (CAD) techniques, besides the design process, especially for furniture and interior design educations. In this direction, some questions emerge in the study. Is there really a difference on quality, success and drafting time between conventional drafting and CAD techniques in interior drafting field? If there is a difference, at which time or process does the drafting technique gain speed? Purpose of study: The purpose of this study was to determine the effects of conventional and computer-aided drafting (CAD) techniques on drafting performances of design students. Furthermore, it was explored which technique was more time, speed and quality-efficient. Methods: To verify this study, students were asked to draw a sample of a two-dimensional technical drawing with an interior plan by using the conventional and CAD techniques at different times within a 60-minute period. The task was designed to ensure that the students understood the principles of technical drawing and could edit sufficiently to draft the plan. Findings and Results: According to the results, it can be stated that there was no meaningful difference between the decision-making process for choosing the drawing method and perceiving the drawing was made by the students with both methods in the first 5 minutes. The difference in quality may be due to the advantages of the CAD technique at the 30th minute. At the end of the 60th minute, a huge difference was observed between the conventional and CAD and the CAD was more successful than the conventional drafting. Consequently, the CAD technique has a better outcome from the aspect of time and quality compared to the conventional technique. Conclusions and Recommendations: It can be stated that CAD has a significant impact on the drafting performances of design students from the aspect of time spent. The furniture and interior design departments should place emphasis on the educational process for the drafting technique in order to increase quality and save time. The drafting course hours should be increased for the design students, so that drafting success can become more efficient for those entering in the professional area. Keywords: Architectural drafting; Conventional drafting; Computer-aided drafting; Drafting tools; Drafting quality


title: Blindcoin: Blinded, Accountable Mixes for Bitcoin

abstract: Mixcoin is a Bitcoin mixing protocol proposed by Bonneau et al. which provides strong accountability guarantees [13]. However, in the Mixcoin protocol, the mapping from a user’s input to output address is visible to the mixing server. We modify the Mixcoin protocol to provide guarantees that the input/output address mapping for any user is kept hidden from the mixing server. In order to achieve this, we make use of a blind signature scheme [14, 23] as well as an append-only public log. The scheme is fully compatible with Bitcoin, forces mixes to be accountable, preserves user anonymity even against a malicious mix, is resilient to denial of service attacks, and easily scales to many users.


title: CoFFEE : Cooperative Face2Face educational environment

abstract: Co-located collaboration in classroom is the topic we tackle in this paper. We present COFFEE a tailorable collaborative environment that is designed for interactive, co-located (i.e. Face2Face) collaboration in classroom. We present the requirements for tailorability that have driven our design, CoFFEE architecture, some tools that have been deployed and discuss the latecomer management issue that is offered by the core of our architecture to all the tools that can be developed within the framework.


title: Kinematics simulation and analysis of 3-RPS parallel robot on SimMechanics

abstract: Compared with serial robots, because of some advantages, e.g. higher stiffness, errors will not accumulated, parallel robot has much potential use. To 3-RPS parallel robot, there are 3 controllable degrees of freedom (axis x, axis y, axis z, or angle x, angle y, axis z). It may be called position kinematics or posture kinematics. According to structural characteristics of the 3-RPS parallel robot, the mathematical model of the posture inverse kinematics is established. The simulation model is set up using the SimMechanics and Simulink module. The validity of the posture inverse kinematics is proved by comparing the two models. And during the motion simulation, the related kinetics data and motion animate are obtained.


title: Reducing Snapshots to Points: A Visual Analytics Approach to Dynamic Network Exploration

abstract: We propose a visual analytics approach for the exploration and analysis of dynamic networks. We consider snapshots of the network as points in high-dimensional space and project these to two dimensions for visualization and interaction using two juxtaposed views: one for showing a snapshot and one for showing the evolution of the network. With this approach users are enabled to detect stable states, recurring states, outlier topologies, and gain knowledge about the transitions between states and the network evolution in general. The components of our approach are discretization, vectorization and normalization, dimensionality reduction , and visualization and interaction , which are discussed in detail. The effectiveness of the approach is shown by applying it to artificial and real-world dynamic networks.


title: The Interactional Nature of Depression

abstract: The Interactional Nature of Depression. Thomas Joiner, and James C. Coyne, (Eds.)- Washington, DC: American Psychological Association (www.apa.orrg). 1999, 440 pp., $39.95 (hardcover). In 1976, James Coyne published a paper on the interactional context of depression that set the proverbial cat among the pigeons of existing psychological theories of the disorder. Whereas most theories invoked intrapsychic processes and structures, Coyne proposed that depressed people's repetitive reassurance seeking and doubting of the genuineness of the feedback that results, brings about a destructive cycle of interpersonal rejection that deepens their misery. More than two decades later, Coyne has teamed up with Thomas Joiner to review research and theory motivated by the interpersonal approach, and by the desire to integrate it with other psychological accounts of depression. The product of their labors is a hefty, well-produced volume that offers a vivid portrait of the state-of-the-art, warts and all. The book's 14 chapters bring together a collection of prominent researchers, many of them central figures in the cognitive approach to psychopathology and its treatment. Most chapters offer exhaustively referenced reviews of particular research literatures-almost one quarter of the book's pages are consumed by reference lists!-combined with models intended to integrate interpersonal variables into other established accounts. Generally, the contributors suggest directions for future research, and several draw out implications for the practice of psychotherapy for depression. Chapters provide overviews of the interpersonal dimensions of depression (Joiner, Coyne & Blalock; Hammen; Sacco), and discuss the roles of coping strategies (Holahan, Moos, & Bonin), loneliness and shyness (Dill & Anderson), maladaptive schemas (Schmidt, Schmidt, & Young), self-esteem vulnerability (Roberts & Monroe), self-verification (Giesler & Swann), and marital discord (Anderson, Beach, & Kaslow). Two chapters focus on developmental issues-the origins of attributional style (Haines, Metalsky, Cardamone, & Joiner) and the effects of depressed parents and family functioning on children (Cummings & Davies)-and two more present emerging interpersonal theories of depression, one feminist and phenomenological (Jack), the other evolutionary or "sociophysiological" (Gardner & Price). Coyne rounds out the book with a bracing postscript. Although all of the chapters deserve a close reading, a few stand out. Haines and colleagues give long overdue attention to the sources of depressogenic attributional style, a construct that is usually taken as a given in adulthood and ascribed to unspecified early experiences. Haines and colleagues show that a great variety of developmental influences, chiefly social, may bear on this important cognitive variable. Roberts and Monroe provide a masterful review of the relations between self-esteem and depression, demonstrating that in addition to the simple level of self-esteem, researchers and therapists must attend to its reactivity, stability, and regulatory processes. These variables may be more illuminating about the nature and etiology of depression, and are both comprehensible in terms of socialcognitive mechanisms and affected by social experiences. Giesler and Swann offer a fascinating review of the extensive and often counterintuitive research findings on self-confirmation in depression, according to which depressives desire verification of negative self-views and seek interactants who act as accomplices in maintaining these views. Anderson and colleagues give a fine and wide-ranging review of the links between marital distress and depression, which also has the merit of drawing heuristic connections to attachment theory. Coyne's postscript is a particularly important chapter, sounding some sour notes about the research presented in the preceding chapters. …


title: A Survey on Security for Smartphone Device

abstract: The technological advancements in mobile connectivity services such as GPRS, GSM, 3G, 4G, Blue-tooth, WiMAX, and Wi-Fi made mobile phones a necessary component of our daily lives. Also, mobile phones have become smart which let the users perform routine tasks on the go. However, this rapid increase in technology and tremendous usage of the smartphones make them vulnerable to malware and other security breaching attacks. This diverse range of mobile connectivity services, device software platforms, and standards make it critical to look at the holistic picture of the current developments in smartphone security research. In this paper, our contribution is twofold. Firstly, we review the threats, vulnerabilities, attacks and their solutions over the period of 2010-2015 with a special focus on smartphones. Attacks are categorized into two types, i.e., old attack and new attacks. With this categorization, we aim to provide an easy and concise view of different attacks and the possible solutions to improve smartphone security. Secondly, we critically analyze our findings and estimate the market growth of different operating systems for the smartphone in coming years. Furthermore, we estimate the malware growth and forecast the world in 2020.


title: A Survey of Data Mining Techniques for Social Media Analysis

abstract: Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.


title: Classifying breast cancer types based on fine needle aspiration biopsy data using random forest classifier

abstract: Breast cancer is a complex and heterogeneous disease due to its diverse morphological features, as well as different clinical outcome. As a result, breast cancer patients may response to different therapeutic options. Currently, difficulties in recognizing the breast cancer types lead to inefficient treatments. Generally, there are two types of breast cancer, known as malignant and benign. Therefore it is necessary to devise a clinically meaningful classification of the disease that can accurately classify breast cancer tissues into relevant classes. This study aims to classify breast cancer lesions which have been obtained from fine needle aspiration (FNA) procedure using random forest. Random forest is a classifier built based on the combination of decision trees and has been identified to perform well in comparison to other machine learning techniques. This method has been tested on approximately 700 data, which consists of 458 instances from benign cases and 241 instances belong to malignant cases. The performance of proposed method is measured based on sensitivity, specificity and accuracy. The experimental results show that, random forest achieved sensitivity of 75%, specificity of 70% and accuracy about 72%. Thus, it can be concluded that random forest can accurately classify breast cancer types given a small number of features and it works as a promising tool to differentiate malignant from benign tumor at early stage.


title: New(s) Racism: A discourse analytical approach

abstract: In this chapter we critically study the way news in the press may contribute to what is sometimes called the new racism . Since news reports are a type of text, our approach will be discourse analytical . This means that we do not treat news as transparent messages whose contents may be analysed in a superficial, quantitative way. Rather, we examine the complex structures and strategies of news reports and their relations to the social context. In our case, the social context consists of the activities of journalists in newsmaking, as well as the interpretations of readers, in the increasingly multicultural societies of western Europe and North America. More specifically we want to know what role the media in general, and news in particular, play in the reproduction of racial and ethnic inequality in these societies, and aim to do so through a systematic analysis of news discourse structures.


title: EEG-based BCI and video games: a progress report

abstract: This paper presents a systematic review of electroencephalography (EEG)-based brain---computer interfaces (BCIs) used in the video games, a vibrant field of research that touches upon all relevant questions concerning the future directions of BCI. The paper examines the progress of BCI research with regard to games and shows that gaming applications offer numerous advantages by orienting BCI to concerns and expectations of a gaming application. Different BCI paradigms are investigated, and future directions are discussed.


title: BOTTOM-UP CONSTRUCTION AND 2:1 BALANCE REFINEMENT OF LINEAR OCTREES IN PARALLEL ∗

abstract: In this article, we propose new parallel algorithms for the construction and 2:1 balance refinement of large linear octrees on distributed memory machines. Such octrees are used in many problems in computational science and engineering, e.g., object representation, image analysis, unstructured meshing, finite elements, adaptive mesh refinement, and N-body simulations. Fixed-size scalability and isogranular analysis of the algorithms using an MPI-based parallel implementation was performed on a variety of input data and demonstrated good scalability for different processor counts (1 to 1024 processors) on the Pittsburgh Supercomputing Center's TCS-1 AlphaServer. The results are consistent for different data distributions. Octrees with over a billion octants were constructed and balanced in less than a minute on 1024 processors. Like other existing algorithms for constructing and balancing octrees, our algorithms have $\mathcal{O}(N\log N)$ work and $\mathcal{O}(N)$ storage complexity. Under reasonable assumptions on the distribution of octants and the work per octant, the parallel time complexity is $\mathcal{O}(\frac{N}{n_p}\log(\frac{N}{n_p})+n_p\log n_p)$, where $N$ is the size of the final linear octree and $n_p$ is the number of processors.


title: Be smart, exercise your heart: exercise effects on brain and cognition

abstract: An emerging body of multidisciplinary literature has documented the beneficial influence of physical activity engendered through aerobic exercise on selective aspects of brain function. Human and non-human animal studies have shown that aerobic exercise can improve a number of aspects of cognition and performance. Lack of physical activity, particularly among children in the developed world, is one of the major causes of obesity. Exercise might not only help to improve their physical health, but might also improve their academic performance. This article examines the positive effects of aerobic physical activity on cognition and brain function, at the molecular, cellular, systems and behavioural levels. A growing number of studies support the idea that physical exercise is a lifestyle factor that might lead to increased physical and mental health throughout life.


title: A framework for context-aware privacy of sensor data on mobile systems

abstract: We study the competing goals of utility and privacy as they arise when a user shares personal sensor data with apps on a smartphone. On the one hand, there can be value to the user for sharing data in the form of various personalized services and recommendations; on the other hand, there is the risk of revealing behaviors to the app producers that the user would like to keep private. The current approaches to privacy, usually defined in multi-user settings, rely on anonymization to prevent such sensitive behaviors from being traced back to the user---a strategy which does not apply if user identity is already known, as is the case here. Instead of protecting identity, we focus on the more general problem of choosing what data to share, in such a way that certain kinds of inferences---i.e., those indicating the user's sensitive behavior---cannot be drawn. The use of inference functions allows us to establish a terminology to unify prior notions of privacy as special cases of this more general problem. We identify several information disclosure regimes, each corresponding to a specific privacy-utility tradeoff, as well as privacy mechanisms designed to realize these tradeoff points. Finally, we propose ipShield as a privacy-aware framework which uses current user context together with a model of user behavior to quantify an adversary's knowledge regarding a sensitive inference, and obfuscate data accordingly before sharing. We conclude by describing initial work towards realizing this framework.


title: Strategies for Context-Adaptive Message Dissemination in Vehicular Ad Hoc Networks

abstract: In future deployments of Vehicular Ad hoc Networks (VANETs) safety-related applications such as local danger warning (LDW) will use broadcast-based communication schemes to transmit information to other vehicles within the network. The unlimited flooding of a message throughout the whole network however, is neither feasible nor intended. The high resulting traffic load would congest the shared wireless medium and prevent other, potentially highly relevant and time-critical messages from getting access to the medium. Existing strategies to limit redundant packet retransmissions manage to make broadcast-based data packet dissemination more efficient and reliable. However, they do not take into account the individual network's nodes interest in information. In fact, the static mechanisms proposed are not adequate to leverage the limited network resources as efficiently as possible in varying network conditions and to transport information to where it is needed as fast as possible. Therefore, we propose an altruistic communication scheme which differentiates data traffic according to the benefit it is likely to provide to potential recipients. A system for calculating and leveraging message benefit and two different node architectures are presented. With the help of a comprehensive simulation environment, the performance of our concept is analyzed. Comparative simulative studies show that an improvement of the benefit provided to all the participants in a VANET is also possible with the help of the readily available IEEE 802.11e standard, but to a lower extent.


title: A Hybrid Bio-Inspired Algorithm for Scheduling and Resource Management in Cloud Environment

abstract: In this paper, we propose a novel HYBRID Bio-Inspired algorithm for task scheduling and resource management, since it plays an important role in the cloud computing environment. Conventional scheduling algorithms such as Round Robin, First Come First Serve, Ant Colony Optimization etc. have been widely used in many cloud computing systems. Cloud receives clients tasks in a rapid rate and allocation of resources to these tasks should be handled in an intelligent manner. In this proposed work, we allocate the tasks to the virtual machines in an efficient manner using Modified Particle Swarm Optimization algorithm and then allocation / management of resources (CPU and Memory), as demanded by the tasks, is handled by proposed HYBRID Bio-Inspired algorithm (Modified PSO + Modified CSO). Experimental results demonstrate that our proposed HYBRID algorithm outperforms peer research and benchmark algorithms (ACO, MPSO, CSO, RR and Exact algorithm based on branch-and-bound technique) in terms of efficient utilization of the cloud resources, improved reliability and reduced average response time.


title: A Semantic Web ontology for context-based classification and retrieval of music resources

abstract: In this article, we describe the MX-Onto ontology for providing a Semantic Web compatible representation of music resources based on their context. The context representation is realized by means of an OWL ontology that describes music information and that defines rules and classes for a flexible genre classification. By flexible classification we mean that the proposed approach enables capturing the subjective interpretation of music genres by defining multiple membership relations between a music resource and the corresponding music genres, thus supporting context-based and proximity-based search of music resources.


title: Centrality for graphs with numerical attributes

abstract: Identification of important actors in social networks is a hard task but with various interesting applications such as in information recommendation or for viral marketing. Existing centrality measures evaluate the importance of an actor in considering only the structural positions regardless of prior information on these actors such as their popularity, accessibility or behavior. A few measures have been proposed for weighted networks, notably the three common measures of centrality: degree, closeness, and betweenness. However, these extended versions have solely focused on the weights of ties and not on the attributes of nodes. This article proposes generalizations that combine these both aspects. We present a set of measures, based on conventional centrality indicators, suited to weighted attributed graphs where the nodes are characterized by attributes. We illustrate the benefits of this approach on real attributed graphs. Experiments have validated the contribution of the links weights and attributes, especially for the detection of information broadcasters in social networks.


Alpha 1-Adrenergic Receptor Activation Stimulates Calcium Entry and Proliferation via TRPC 6 Channels in Cultured Human Mesangial Cells


title: Toward the simulation of emotion in synthetic speech: a review of the literature on human vocal emotion.

abstract: There has been considerable research into perceptible correlates of emotional state, but a very limited amount of the literature examines the acoustic correlates and other relevant aspects of emotion effects in human speech; in addition, the vocal emotion literature is almost totally separate from the main body of speech analysis literature. A discussion of the literature describing human vocal emotion, and its principal findings, are presented. The voice parameters affected by emotion are found to be of three main types: voice quality, utterance timing, and utterance pitch contour. These parameters are described both in general and in detail for a range of specific emotions. Current speech synthesizer technology is such that many of the parameters of human speech affected by emotion could be manipulated systematically in synthetic speech to produce a simulation of vocal emotion; application of the literature to construction of a system capable of producing synthetic speech with emotion is discussed.


title: Sentence Simplification with Memory-Augmented Neural Networks

abstract: Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstream NLP applications. Recent advances in neural machine translation have paved the way for novel approaches to the task. In this paper, we adapt an architecture with augmented memory capacities called Neural Semantic Encoders (Munkhdalai and Yu, 2017) for sentence simplification. Our experiments demonstrate the effectiveness of our approach on different simplification datasets, both in terms of automatic evaluation measures and human judgments.


title: A Parallel Bandit-Based Approach for Autotuning FPGA Compilation

abstract: Mainstream FPGA CAD tools provide an extensive collection of optimization options that have a significant impact on the quality of the final design. These options together create an enormous and complex design space that cannot effectively be explored by human effort alone. Instead, we propose to search this parameter space using autotuning, which is a popular approach in the compiler optimization domain. Specifically, we study the effectiveness of applying the multi-armed bandit (MAB) technique to automatically tune the options for a complete FPGA compilation flow from RTL to bitstream, including RTL/logic synthesis, technology mapping, placement, and routing. To mitigate the high runtime cost incurred by the complex FPGA implementation process, we devise an efficient parallelization scheme that enables multiple MAB-based autotuners to explore the design space simultaneously. In particular, we propose a dynamic solution space partitioning and resource allocation technique that intelligently allocates computing resources to promising search regions based on the runtime information of search quality from previous iterations. Experiments on academic and commercial FPGA CAD tools demonstrate promising improvements in quality and convergence rate across a variety of real-life designs.


title: Content or community? a digital business strategy for content providers in the social age

abstract: The content industry has been undergoing a tremendous transformation in the last two decades. We focus in this paper on recent changes in the form of social computing. Although the content industry has implemented social computing to a large extent, it has done so from a techno-centric approach in which social features are viewed as complementary rather than integral to content. This approach does not capitalize on users' social behavior in the website and does not answer the content industry's need to elicit payment from consumers. We suggest that both of these objectives can be achieved by acknowledging the fusion between content and community, making the social experience central to the content website's digital business strategy. ::: ::: We use data from Last.fm, a site offering both music consumption and online community features. The basic use of Last.fm is free, and premium services are provided for a fixed monthly subscription fee. Although the premium services on Last.fm are aimed primarily at improving the content consumption experience, we find that willingness to pay for premium services is strongly associated with the level of community participation of the user. ::: ::: Drawing from the literature on levels of participation in online communities, we show that consumers' willingness to pay increases as they climb the so-called "ladder of participation" on the website. Moreover, we find that willingness to pay is more strongly linked to community participation than to the volume of content consumption. We control for self-selection bias by using propensity score matching. We extend our results by estimating a hazard model to study the effect of community activity on the time between joining the website and the subscription decision. Our results suggest that firms whose digital business models remain viable in a world of "freemium" will be those that take a strategic rather than techno-centric view of social media, that integrate social media into the consumption and purchase experience rather than use it merely as a substitute for offline soft marketing. We provide new evidence of the importance of fusing social computing with content delivery and, in the process, lay a foundation for a broader strategic path for the digital content industry in an age of growing user participation.


title: The design-by-adaptation approach to universal access: learning from videogame technology

abstract: This paper proposes an alternative approach to the design of universally accessible interfaces to that provided by formal design frameworks applied ab initio to the development of new software. This approach, design-by-adaptation, involves the transfer of interface technology and/or design principles from one application domain to another, in situations where the recipient domain is similar to the host domain in terms of modelled systems, tasks and users. Using the example of interaction in 3D virtual environments, the paper explores how principles underlying the design of videogame interfaces may be applied to a broad family of visualization and analysis software which handles geographical data (virtual geographic environments, or VGEs). One of the motivations behind the current study is that VGE technology lags some way behind videogame technology in the modelling of 3D environments, and has a less-developed track record in providing the variety of interaction methods needed to undertake varied tasks in 3D virtual worlds by users with varied levels of experience. The current analysis extracted a set of interaction principles from videogames which were used to devise a set of 3D task interfaces that have been implemented in a prototype VGE for formal evaluation.


title: HLA-DRB1 alleles associated with rheumatoid arthritis in southern France. Absence of extraarticular disease despite expression of the shared epitope.

abstract: Objective. Extraarticular rheumatoid arthritis (RA) is almost unknown in Marseille in southern France. We investigated whether this was due to rare expression of the HLA-DRB1 sharedepitope. Methods. HLA-DRB1 alleles were characterized in 73 patients with RA and 108 controls by polymerase chain reaction amplification and oligonucleotide hybridization. Results. In patients with RA, 76% expressed the shared epitope (46% DR1, 45% DR4). Four HLA-DRB1 alleles were positively associated with disease: DRB1 * 0101, DRB1 * 0401, DRB1 * 0404, DRB1 * 0405. Patients with double dose shared epitope had the most severe articular damage, but no extraarticular disease. Conclusion. In Marseille, 76% of patients with RA are shared epitope positive. Still, most do not develop extraarticular RA. This may be caused by the low frequency of HLA-DRB1 * 0401 in this population


title: A comparison of volumetric information gain metrics for active 3D object reconstruction

abstract: In this paper, we investigate the following question: when performing next best view selection for volumetric 3D reconstruction of an object by a mobile robot equipped with a dense (camera-based) depth sensor, what formulation of information gain is best? To address this question, we propose several new ways to quantify the volumetric information (VI) contained in the voxels of a probabilistic volumetric map, and compare them to the state of the art with extensive simulated experiments. Our proposed formulations incorporate factors such as visibility likelihood and the likelihood of seeing new parts of the object. The results of our experiments allow us to draw some clear conclusions about the VI formulations that are most effective in different mobile-robot reconstruction scenarios. To the best of our knowledge, this is the first comparative survey of VI formulation performance for active 3D object reconstruction. Additionally, our modular software framework is adaptable to other robotic platforms and general reconstruction problems, and we release it open source for autonomous reconstruction tasks.


title: Direct-Drive Electromechanical Linear Actuator for Shift-by-Wire Control of an Automated Transmission

abstract: Since they are potentially more efficient and simpler in construction, as well as being easier to integrate, electromechanical actuation systems are being considered as an alternative to hydraulic systems for controlling clutches and gearshifts in vehicle transmissions. A high-force, direct-drive linear electromechanical actuator has been developed which acts directly on the shift rails of either an Automated Manual Transmission (AMT) or a Dual Clutch Transmission (DCT) to facilitate gear selection and provide shift-by-wire functionality. It offers a number of advantages over electromechanical systems based on electric motors and gearboxes in that it reduces mechanical hysteresis, backlash and compliance, has fewer components, is more robust, and exhibits a better dynamic response.


title: PointGrid: A Deep Network for 3D Shape Understanding

abstract: Volumetric grid is widely used for 3D deep learning due to its regularity. However the use of relatively lower order local approximation functions such as piece-wise constant function (occupancy grid) or piece-wise linear function (distance field) to approximate 3D shape means that it needs a very high-resolution grid to represent finer geometry details, which could be memory and computationally inefficient. In this work, we propose the PointGrid, a 3D convolutional network that incorporates a constant number of points within each grid cell thus allowing the network to learn higher order local approximation functions that could better represent the local geometry shape details. With experiments on popular shape recognition benchmarks, PointGrid demonstrates state-of-the-art performance over existing deep learning methods on both classification and segmentation.


title: Globally regular instability of $AdS_3$

abstract: We consider three-dimensional AdS gravity minimally coupled to a massless scalar field and study numerically the evolution of small smooth circularly symmetric perturbations of the $AdS_3$ spacetime. As in higher dimensions, for a large class of perturbations, we observe a turbulent cascade of energy to high frequencies which entails instability of $AdS_3$. However, in contrast to higher dimensions, the cascade cannot be terminated by black hole formation because small perturbations have energy below the black hole threshold. This situation appears to be challenging for the cosmic censor. Analysing the energy spectrum of the cascade we determine the width $\rho(t)$ of the analyticity strip of solutions in the complex spatial plane and argue by extrapolation that $\rho(t)$ does not vanish in finite time. This provides evidence that the turbulence is too weak to produce a naked singularity and the solutions remain globally regular in time, in accordance with the cosmic censorship hypothesis.


title: Autonomous Rover Navigation on Unknown Terrains Functions and Integration

abstract: Autonomous long range navigation in partially known planetary-like terrain is an open challenge for robotics. Navigating several hundreds of meters without any human intervention requires the robot to be able to build various representations of its environment, to plan and execute trajectories according to the kind of terrain traversed, to localize itself as it moves, and to schedule, start, control and interrupt these various activities. In this paper, we briefly describe some functionalities that are currently running on board the Marsokhod model robot Lama at LAAS/CNRS. We then focus on the necessity to integrate various instances of the perception and decision functionalities, and on the difficulties raised by this integration.


title: Emotion regulation in preschoolers: the roles of behavioral inhibition, maternal affective behavior, and maternal depression.

abstract: Background: This study examined preschoolers’ emotion regulation (ER) strategies and the association with temperament, maternal interactive style, and maternal history of childhood-onset depression (COD). Methods: Participants were 62 children and their mothers, 37 of whom had mothers with COD. Children’s ER was assessed using a disappointment paradigm; temperament assessment also was laboratory-based. Results: Maternal COD was inversely related to offspring’s active ER and positive mood. Among children of COD mothers, behavioral inhibition was associated with passive regulation and sadness, and maternal positivity toward these children was associated with child active ER and positive mood. Conclusion: Behavioral inhibition may place children of COD mothers at risk for developing maladaptive ways of regulating negative emotion, whereas mothers’ positivity may serve as a protective factor for them. Keywords: Emotion regulation, behavioral inhibition, parent–child interaction, maternal depression. Abbreviations: COD: childhood-onset depression; ER: emotion regulation.


title: Inclusive yet Selective: Supervised Distributional Hypernymy Detection

abstract: We test the Distributional Inclusion Hypothesis, which states that hypernyms tend to occur in a superset of contexts in which their hyponyms are found. We find that this hypothesis only holds when it is applied to relevant dimensions. We propose a robust supervised approach that achieves accuracies of .84 and .85 on two existing datasets and that can be interpreted as selecting the dimensions that are relevant for distributional inclusion.


title: SLM Microscopy: Scanless Two-Photon Imaging and Photostimulation with Spatial Light Modulators

abstract: Laser microscopy has generally poor temporal resolution, because of the serial scanning of each pixel. This is a significant problem for imaging or optically manipulating neural circuits since neuronal activity is fast. To help surmount this limitation, we have developed a “scanless” microscope that does not contain mechanically moving parts. This microscope uses a diffractive Spatial Light Modulator (SLM) to shape an incoming two-photon laser source into any arbitrary light pattern. This allows the simultaneous imaging or photostimulation of different regions of a sample with three-dimensional precision. To demonstrate the usefulness of this microscope, we perform two-photon uncaging of glutamate to activate dendritic spines and cortical neurons in brain slices. We also use it to carry out two-photon calcium imaging of action potentials in neuronal populations at 60 Hz. Thus, SLM microscopy appears to be a powerful tool for imaging and optically manipulating neurons and neuronal circuits. Moreover, the use of SLMs generally expands the flexibility of laser microscopy, as it can substitute traditional fixed lenses with any calculated lens function.


title: Scaling concurrent log-structured data stores

abstract: Log-structured data stores (LSM-DSs) are widely accepted as the state-of-the-art implementation of key-value stores. They replace random disk writes with sequential I/O, by accumulating large batches of updates in an in-memory data structure and merging it with the on-disk store in the background. While LSM-DS implementations proved to be highly successful at masking the I/O bottleneck, scaling them up on multicore CPUs remains a challenge. This is nontrivial due to their often rich APIs, as well as the need to coordinate the RAM access with the background I/O. We present cLSM, an algorithm for scalable concurrency in LSM-DS, which exploits multiprocessor-friendly data structures and non-blocking synchronization. cLSM supports a rich API, including consistent snapshot scans and general non-blocking read-modify-write operations. We implement cLSM based on the popular LevelDB key-value store, and evaluate it using intensive synthetic workloads as well as ones from production web-serving applications. Our algorithm outperforms state of the art LSM-DS implementations, improving throughput by 1.5x to 2.5x. Moreover, cLSM demonstrates superior scalability with the number of cores (successfully exploiting twice as many cores as the competition).


title: Learn to Steer through Deep Reinforcement Learning

abstract: It is crucial for robots to autonomously steer in complex environments safely without colliding with any obstacles. Compared to conventional methods, deep reinforcement learning-based methods are able to learn from past experiences automatically and enhance the generalization capability to cope with unseen circumstances. Therefore, we propose an end-to-end deep reinforcement learning algorithm in this paper to improve the performance of autonomous steering in complex environments. By embedding a branching noisy dueling architecture, the proposed model is capable of deriving steering commands directly from raw depth images with high efficiency. Specifically, our learning-based approach extracts the feature representation from depth inputs through convolutional neural networks and maps it to both linear and angular velocity commands simultaneously through different streams of the network. Moreover, the training framework is also meticulously designed to improve the learning efficiency and effectiveness. It is worth noting that the developed system is readily transferable from virtual training scenarios to real-world deployment without any fine-tuning by utilizing depth images. The proposed method is evaluated and compared with a series of baseline methods in various virtual environments. Experimental results demonstrate the superiority of the proposed model in terms of average reward, learning efficiency, success rate as well as computational time. Moreover, a variety of real-world experiments are also conducted which reveal the high adaptability of our model to both static and dynamic obstacle-cluttered environments. A video of our experiments is available at https://youtu.be/yixnmFXIKf4 and http://v.youku.com/vshow/idXMzg1ODYwMzM5Ng.


title: Thin slices of expressive behavior as predictors of interpersonal consequences: A meta-analysis.

abstract: A meta-analysis was conducted on the accuracy of predictions of various objective outcomes in the areas of social and clinical psychology from short observations of expressive behavior (under 5 min). The overall effect size (/) for the accuracy of predictions for 38 different results was .39. Studies using longer periods of behavioral observation did not yield greater predictive accuracy; predictions based on observations under Vi min in length did not differ significantly from predictions based on 4- and 5-min observations. The type of behavioral channel (such as the face, speech, the body, tone of voice) on which the ratings were based was not related to the accuracy of predictions. Accuracy did not vary significantly between behaviors manipulated in a laboratory and more naturally occurring behavior. Last, effect sizes did not differ significantly for predictions in the areas of clinical psychology, social psychology, and the accuracy of detecting deception. The way in which people move, talk, and gesture—their facial expressions, posture, and speech—all contribute to the formation of impressions about them. Many of the judgments we make about others in our everyday lives are based on cues from these expressive behaviors. Gordon Allport (1937) believed that expressive behaviors were important indicators of personality and that impressions from brief interactions were often verified upon further acquaintance. Allport and Vernon (1933) demonstrated that people's expressive styles were quite consistent across a variety of situations. They then began to investigate the accuracy of perceivers' impressions that were based on observations of these expressive styles. For reasons that have been discussed elsewhere, this issue—like other issues concerning the accuracy of interpersonal and social perception— was neglected for a long time (Funder, 1987; Kenny & Albright, 1987). Recently, however, there has been a resurgence of interest in the accuracy of social and interpersonal perception and judgment (Funder, 1987; Kenny & Albright, 1987; Kruglanski, 1989; Swann, 1984) and in the study of expressive behavior (Lippa, 1983; Riggio & Friedman, 1986). Recent work confirms earlier findings (Passini & Norman, 1966) that the ratings of strangers converge surprisingly well with self-ratings of personality by targets (Albright, Kenny, & Malloy, 1988; Funder & Cohan, 1988; Watson, 1989). This correspondence seems to confirm Gordon Allport's observation that there is something in the nature of individuals that


Reasoning ability predicts irrational worldview but not conspiracy belief.


title: A systematic review and meta-analysis of nut consumption and incident risk of CVD and all-cause mortality

abstract: Dietary patterns containing nuts are associated with a lower risk of CVD mortality, and increased nut consumption has been shown to have beneficial effects on CVD risk factors including serum lipid levels. Recent studies have reported on the relationship between nut intake and CVD outcomes and mortality. Our objective was to systematically review the literature and quantify associations between nut consumption and CVD outcomes and all-cause mortality. Five electronic databases (through July 2015), previous reviews and bibliographies of qualifying articles were searched. In the twenty included prospective cohort studies ( n 467 389), nut consumption was significantly associated with a lower risk of all-cause mortality (ten studies; risk ratio (RR) 0·81; 95 % CI 0·77, 0·85 for highest v . lowest quantile of intake, P het =0·04, I 2 =43 %), CVD mortality (five studies; RR 0·73; 95 % CI 0·68, 0·78; P het =0·31, I 2 =16 %), all CHD (three studies; RR 0·66; 95 % CI 0·48, 0·91; P het =0·0002, I 2 =88 %) and CHD mortality (seven studies; RR 0·70; 95 % CI 0·64, 0·76; P het =0·65, I 2 =0 %), as well as a statistically non-significant reduction in the risk of non-fatal CHD (three studies; RR 0·71; 95 % CI 0·49, 1·03; P het =0·03, I 2 =72 %) and stroke mortality (three studies; RR 0·83; 95 % CI 0·69, 1·00; P het =0·54, I 2 =0 %). No evidence of association was found for total stroke (two studies; RR 1·05; 95 % CI 0·69, 1·61; P het =0·04, I 2 =77 %). Data on total CVD and sudden cardiac death were available from one cohort study, and they were significantly inversely associated with nut consumption. In conclusion, we found that higher nut consumption is associated with a lower risk of all-cause mortality, total CVD, CVD mortality, total CHD, CHD mortality and sudden cardiac death.


Compressive sensing


title: A high efficiency low cost direct battery balancing circuit using a multi-winding transformer with reduced switch count

abstract: This paper presents a simple circuit for balancing battery cells. The circuit is composed of one low voltage MOSFET for each battery cell and a multi-winding transformer for a group of battery cells. Only one identical gate drive signal for all the MOSFETs is needed to control the balance current. Energy can be directly transferred from higher voltage cells to lower voltage cells. The balancing circuit can achieve ideal balance results as long as the multi-winding transformer has ideal symmetry. Other circuit components' asymmetry would only affect the balance speed. Simulation results are given to show the effectiveness. A circuit for balancing a four-cell battery group is tested. The experiment shows the energy transfer efficiency is up to 93% between cell energy transfers.


A Formal Definition of Data Quality Problems.


Combining Q-Learning with Artificial Neural Networks in an Adaptive Light Seeking Robot


title: Exploiting Prolog and NLP Techniques for Matching Ontologies and for Repairing Correspondences

abstract: Providing efficient ontology matching algorithms is one of the means for pursuing semantic interoperability. In this paper we discuss an algorithm that exploits natural language processing techniques for matching ontologies and that post-processes the obtained alignment in order to find semantic inconsistencies. The algorithm has been entirely implemented in Prolog, whose usefulness was mainly evident in the post-processing phase. A careful analysis of the recent state- of-the art witnesses the originality of our matching algorithm which is based on the "Adapted Lesk Algorithm" for word sense disambiguation. The experiments we carried out, although in their early stages, are encouraging.


title: Weighted k-nearest-neighbor techniques and ordinal classification

abstract: In the field of statistical discrimination k-nearest neighbor classification is a well-known, easy and successful method. In this paper we present an extended version of this technique, where the distances of the nearest neighbors can be taken into account. In this sense there is a close connection to LOESS, a local regression technique. In addition we show possibilities to use nearest neighbor for classification in the case of an ordinal class structure. Empirical studies show the advantages of the new techniques.


Continuous Optimization of Adaptive Quadtree Structures


title: Video games and education: (Education in the Face of a “Parallel School”)

abstract: Fully integrated into the everyday lives of millions of young people throughout the world, video games are a vital part of contemporary culture and society. But the reaction of many authorities and the majority of educators has been to discredit video games by assuming their negative effects. After more than two decades of research, however, many studies have been published that have gradually led to a more complex, nuanced, and useful understanding of video games. This article focuses on one of the most interesting alternative perspectives--that of their educational potential as teaching and learning tools. After justifying this point of view, we offer a critical review of previous research on the subject; and finally reach some conclusions on the advantages of using video games for educational purposes.


title: Challenges for the future modifications of the TNM staging system for head and neck cancer: Case for a new computational model?

abstract: The TNM system of staging cancers is a simple and effective method to map the extent of tumours. It had traditionally strived to maintain a balance between being simple and user-friendly on one hand and comprehensive on the other. A number of revisions have taken place over the years with the goal of improving utility. However, numerous controversies surround the TNM system. There is a school of thought that contends that patient co-morbidity and specific tumour-related factors should be incorporated to add further prognostic capabilities in the TNM system, but this raises concerns that such an approach may unnecessarily complicate the system. This review highlights some controversies that surround the TNM system and suggests prognostic indicators that may be added to make it more useful in guiding treatment decisions and predicting outcomes.


title: Automatic identification of human helminth eggs on microscopic fecal specimens using digital image processing and an artificial neural network

abstract: In order to automate routine fecal examination for parasitic diseases, the authors propose in this study a computer processing algorithm using digital image processing techniques and an artificial neural network (ANN) classifier The morphometric characteristics of eggs of human parasites in fecal specimens were extracted from microscopic images through digital image processing. An ANN then identified the parasite species based on those characteristics. The authors selected four morphometric features based on three morphological characteristics representing shape, shell smoothness, and size. A total of 82 microscopic images containing seven common human helminth eggs were used. The first stage (ANN-1) of the proposed ANN classification system isolated eggs from confusing artifacts. The second stage (ANN-2) classified eggs by species. The performance of ANN was evaluated by the tenfold cross-validation method to obviate the dependency on the selection of training samples. Cross-validation results showed 86.1% average correct classification ratio for ANN-1 and 90.3% for ANN-2 with small variances of 46.0 and 39.0, respectively. The algorithm developed will be an essential part of a completely automated fecal examination system.


title: An advanced detrending method with application to HRV analysis

abstract: An advanced, simple to use, detrending method to be used before heart rate variability analysis (HRV) is presented. The method is based on smoothness priors approach and operates like a time-varying finite-impulse response high-pass filter. The effect of the detrending on time- and frequency-domain analysis of HRV is studied.