title: Real-Time Probabilistic Data Fusion for Large-Scale IoT Applications

abstract: Internet of Things (IoT) data analytics is underpinning numerous applications, however, the task is still challenging predominantly due to heterogeneous IoT data streams, unreliable networks, and ever increasing size of the data. In this context, we propose a two-layer architecture for analyzing IoT data. The first layer provides a generic interface using a service oriented gateway to ingest data from multiple interfaces and IoT systems, store it in a scalable manner and analyze it in real-time to extract high-level events; whereas second layer is responsible for probabilistic fusion of these high-level events. In the second layer, we extend state-of-the-art event processing using Bayesian networks in order to take uncertainty into account while detecting complex events. We implement our proposed solution using open source components optimized for large-scale applications. We demonstrate our solution on real-world use-case in the domain of intelligent transportation system where we analyzed traffic, weather, and social media data streams from Madrid city in order to predict probability of congestion in real-time. The performance of the system is evaluated qualitatively using a web-interface where traffic administrators can provide the feedback about the quality of predictions and quantitatively using F-measure with an accuracy of over 80%.


title: Improved Boundary Equilibrium Generative Adversarial Networks

abstract: Boundary equilibrium generative adversarial networks (BEGANs) can generate impressively realistic face images, but there is a trade-off between the quality and the diversity of generated images. Based on BEGANs, we propose an effective approach to generate images with higher quality and better diversity. By adding a second loss function (a denoising loss) to the discriminator, the discriminator can learn more useful information about the distribution of real images. Naturally, the ability of discriminator in distinguishing between real and generated images is improved, which further guides the generator to produce more realistic images to confuse the discriminator. We also find that using technique of batch normalization in BEGANs architecture can improve the diversity of generated images. By using batch normalization and adding a denoising loss to the objective of discriminator, we achieve comparative generations on CIFAR-10 and CelebA data sets. In addition, we evaluate the effect of several techniques on BEGANs framework through ”Inception-Score”, a measure which has been found to correlate well with human assessment of generated samples.


title: Collaborative filtering with graph information: consistency and scalable methods

abstract: Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space. Often, additional information about the variables is known, and it is reasonable to assume that incorporating this information will lead to better predictions. We tackle the problem of matrix completion when pairwise relationships among variables are known, via a graph. We formulate and derive a highly efficient, conjugate gradient based alternating minimization scheme that solves optimizations with over 55 million observations up to 2 orders of magnitude faster than state-of-the-art (stochastic) gradient-descent based methods. On the theoretical front, we show that such methods generalize weighted nuclear norm formulations, and derive statistical consistency guarantees. We validate our results on both real and synthetic datasets.


title: A SVM and K-means Clustering based Fast and Efficient Intrusion Detection System ABSTRACT

abstract: The intrusion or attack in the computer network is one of the most important issues creating problems for the network managers. However many countermeasures are taken for the security of the network but continuous growth of hackers requires to maintain the defending system up to data. This paper presents a K-means and support vector machine based intrusion detection system. The support vector machine is optimal partitioning based linear classifier and at least theoretically better other classifier also because only small numbers of classes required during classification SVM with one against one technique can be the best option and the K-means clustering filters the un-useful similar data points hence reduces the training time also hence provides an overall enhanced performance by reducing the training time while maintaining the accuracy. The proposed algorithm is tested using KDD99 dataset and results show the effectiveness of the algorithm. The paper also analyzed the effect of different input parameters on classification accuracy.


title: Cyber Security of Smart Grid Infrastructure

abstract: Smart grid security is crucial to maintain stable and reliable power system operation during the contingency situation due to the failure of any critical power system component. Ensuring a secured smart grid involves with a less possibility of power grid collapse or equipment malfunction. Due to lack of the proper security measures, a major blackout may occur which can even lead to a cascading failure. Therefore, to protect this critical power system infrastructure and to ensure a reliable and an uninterrupted power supply to the end users, smart grid security issues must be addressed with high priority. In a smart grid environment, electric power infrastructure is modernized by incorporating the current and future requirements and advanced functionalities to its consumers. To make the smart grid happen, cyber system is integrated with the physical power system. Although adoption of cyber system has made the grid more energy efficient and modernized, it has introduced cyber-attack issues which are critical for national infrastructure security and customer satisfaction. Due to the cyber-attack, power grid may face operational failures and loss of synchronization. This operational failure may damage critical power system components which may interrupt the power supply and make the system unstable resulting high financial penalties. In this chapter, some recent cyber attack related incidents into a smart grid environment are discussed. The requirements and the state of the art of cyber security issues of a critical power system infrastructure are illustrated elaborately.


title: Learning nonstationary models of normal network traffic for detecting novel attacks

abstract: Traditional intrusion detection systems (IDS) detect attacks by comparing current behavior to signatures of known attacks. One main drawback is the inability of detecting new attacks which do not have known signatures. In this paper we propose a learning algorithm that constructs models of normal behavior from attack-free network traffic. Behavior that deviates from the learned normal model signals possible novel attacks. Our IDS is unique in two respects. First, it is nonstationary, modeling probabilities based on the time since the last event rather than on average rate. This prevents alarm floods. Second, the IDS learns protocol vocabularies (at the data link through application layers) in order to detect unknown attacks that attempt to exploit implementation errors in poorly tested features of the target software. On the 1999 DARPA IDS evaluation data set [9], we detect 70 of 180 attacks (with 100 false alarms), about evenly divided between user behavioral anomalies (IP addresses and ports, as modeled by most other systems) and protocol anomalies. Because our methods are unconventional there is a significant non-overlap of our IDS with the original DARPA participants, which implies that they could be combined to increase coverage.


title: Survey of maneuvering target tracking. Part V. Multiple-model methods

abstract: This is the fifth part of a series of papers that provide a comprehensive survey of techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. Part I and Part II deal with target motion models. Part III covers measurement models and associated techniques. Part IV is concerned with tracking techniques that are based on decisions regarding target maneuvers. This part surveys the multiple-model methods $the use of multiple models (and filters) simultaneously - which is the prevailing approach to maneuvering target tracking in recent years. The survey is presented in a structured way, centered around three generations of algorithms: autonomous, cooperating, and variable structure. It emphasizes the underpinning of each algorithm and covers various issues in algorithm design, application, and performance.


title: Performance analysis of a novel LTCC UWB 60 GHz semi-shielded aperture stacked patch antenna with differential feeding

abstract: Performance of a novel differentially-fed UWB antenna operating at 60 GHz frequency range and manufactured in the LTCC technology is analysed theoretically. The antenna is based on aperture stacked patch fed via an H-shaped slot and is fed by a parallel transmission line which enables the antenna to be directly integrated with differential RF MMICs. Influence of the substrate size has been investigated‥


title: Closed loop deep brain stimulation: an evolving technology

abstract: Deep brain stimulation is an effective and safe medical treatment for a variety of neurological and psychiatric disorders including Parkinson’s disease, essential tremor, dystonia, and treatment resistant obsessive compulsive disorder. A closed loop deep brain stimulation (CLDBS) system automatically adjusts stimulation parameters by the brain response in real time. The CLDBS continues to evolve due to the advancement in the brain stimulation technologies. This paper provides a study on the existing systems developed for CLDBS. It highlights the issues associated with CLDBS systems including feedback signal recording and processing, stimulation parameters setting, control algorithm, wireless telemetry, size, and power consumption. The benefits and limitations of the existing CLDBS systems are also presented. Whilst robust clinical proof of the benefits of the technology remains to be achieved, it has the potential to offer several advantages over open loop DBS. The CLDBS can improve efficiency and efficacy of therapy, eliminate lengthy start-up period for programming and adjustment, provide a personalized treatment, and make parameters setting automatic and adaptive.


title: A Personal Health Recommender System incorporating personal health records, modular ontologies, and crowd-sourced data

abstract: We present an architecture for a Personal Health Recommender System (PHRS) that begins with a person's personal electronic health record (PEHR) and augments it by combining crowd-sourced data mined for symptoms, diseases, treatments and best practices, together with authoritative sources and curated domain ontologies. This novel approach is patient-centric in that the PEHR contains the patient's health stats, specific symptoms, diagnoses, illnesses, medications and treatment plans. By accessing anonymized crowd-sourced data for similar cases, the patient and healthcare providers can ascertain the best course of treatment. In addition, the data in the PEHR can be classified according to authoritative ontologies using Semantic Web services. A modular ontology may be constructed for each of the patient's illnesses, and then they may be combined into an integrated ontology by patient or illness. As the PEHR is populated with more data, the ontology may evolve, guided by resources and services available on the Semantic Web.


title: DUAL-BAND CIRCULARLY POLARIZED MICROSTRIP ANTENNA WITH SMALL FREQUENCY RATIO

abstract: A compact single-feed dual-band circularly polarized (CP) microstrip antenna is evaluated numerically and experimentally. The dual-band performances with small frequency ratio (about 1 : 1.1) are achieved by a circular patch and a narrow annular-ring, which have small difierence in radius. The CP characteristics are achieved by an unequal cross-slot embedded in the circular patch and two orthogonal linear stubs spurred from the annular-ring. The antenna is easy to fabricate. Good agreement is obtained between measured and simulated results.


title: New neutrosophic approach to image segmentation

abstract: Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature and scope of neutralities, as well as their interactions with different ideational spectra. NS is a formal framework that has been recently proposed. However, NS needs to be specified from a technical point of view for a given application or field. We apply NS, after defining some concepts and operations, for image segmentation. The image is transformed into the NS domain, which is described using three membership sets: T, I and F. The entropy in NS is defined and employed to evaluate the indeterminacy. Two operations, @a-mean and @b-enhancement operations are proposed to reduce the set indeterminacy. Finally, the proposed method is employed to perform image segmentation using a @c-means clustering. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment the images automatically and effectively. Especially, it can segment the ''clean'' images and the images having noise with different noise levels.


title: Cirrin: a word-level unistroke keyboard for pen input

abstract: We present a new system, called Cirrin, for pen input of ASCII characters using word-level unistrokes. Our system addresses the tradeoff between speed and accuracy of penbased text entry by substituting precision on the part of the user for ease of recognitionon the part of the computer. Cirrin supports ease of recognition by the computer combined with natural, script-like input. This paper discusses the design space of word-level, unistroke input, focusing on the choices made in the circular model of Cirrin that is currently in daily use by the first author.


title: Crowdsourcing for Chromosome Segmentation and Deep Classification

abstract: Metaphase chromosome analysis is one of the primary techniques utilized in cytogenetics. Observations of chromosomal segments or translocations during metaphase can indicate structural changes in the cell genome, and is often used for diagnostic purposes. Karyotyping of the chromosomes micro-photographed under metaphase is done by characterizing the individual chromosomes in cell spread images. Currently, considerable effort and time is spent to manually segment out chromosomes from cell images, and classifying the segmented chromosomes into one of the 24 types, or for diseased cells to one of the known translocated types. Segmenting out the chromosomes in such images can be especially laborious and is often done manually, if there are overlapping chromosomes in the image which are not easily separable by image processing techniques. Many techniques have been proposed to automate the segmentation and classification of chromosomes from spread images with reasonable accuracy, but given the criticality of the domain, a human in the loop is often still required. In this paper, we present a method to segment out and classify chromosomes for healthy patients using a combination of crowdsourcing, preprocessing and deep learning, wherein the non-expert crowd from CrowdFlower is utilized to segment out the chromosomes from the cell image, which are then straightened and fed into a (hierarchical) deep neural network for classification. Experiments are performed on 400 real healthy patient images obtained from a hospital. Results are encouraging and promise to significantly reduce the cognitive burden of segmenting and karyotyping chromosomes.


Feature Article


title: On-Street and Off-Street Parking Availability Prediction Using Multivariate Spatiotemporal Models

abstract: Parking guidance and information (PGI) systems are becoming important parts of intelligent transportation systems due to the fact that cars and infrastructure are becoming more and more connected. One major challenge in developing efficient PGI systems is the uncertain nature of parking availability in parking facilities (both on-street and off-street). A reliable PGI system should have the capability of predicting the availability of parking at the arrival time with reliable accuracy. In this paper, we study the nature of the parking availability data in a big city and propose a multivariate autoregressive model that takes into account both temporal and spatial correlations of parking availability. The model is used to predict parking availability with high accuracy. The prediction errors are used to recommend the parking location with the highest probability of having at least one parking spot available at the estimated arrival time. The results are demonstrated using real-time parking data in the areas of San Francisco and Los Angeles.


title: Nomadic Virtual Reality: Exploring New Interaction Concepts for Mobile Virtual Reality Head-Mounted Displays

abstract: Technical progress and miniaturization enables virtual reality (VR) head-mounted displays (HMDs) now to be solely operated using a smartphone as a display, processing unit and sensor unit. These mobile VR HMDs (e.g. Samsung GearVR) allow for a whole new interaction scenario, where users can bring their HMD with them wherever they want and immerse themselves anytime at any place (nomadic VR). However, most of the early research on interaction with VR HMDs focused around stationary setups. My research revolves around enabling new forms of interaction for these nomadic VR scenarios. In my research I choose a user-centered design approach where I build research prototypes to solve potential problems of nomadic VR and evaluate those prototypes in user studies. I am going to present three prototypes revolving around current challenges of nomadic VR (input and feedback).


A Large-Scale Study of Mobile Web App Security


title: Indian Sign Language gesture classification as single or double handed gestures

abstract: The development of a sign language recognition system can have a great impact on the daily lives of humans with hearing disabilities. Recognizing gestures from the Indian Sign Language (ISL) with a camera can be difficult due to complexity of various gestures. The motivation behind the paper is to develop an approach to successfully classify gestures in the ISL under ambiguous conditions from static images. A novel approach involving the decomposition of gestures into single handed or double handed gesture has been presented in this paper. Classifying gesture into these subcategories simplifies the process of gesture recognition in the ISL due to presence of lesser number of gestures in each subcategory. Various approaches making use of Histogram of Gradients (HOG) features and geometric descriptors using KNN and SVM classifiers were tried on a dataset consisting of images of all 26 English alphabets present in the ISL under variable background. HOG features when classified with Support Vector Machine were found to be the most efficient approach resulting in an accuracy of 94.23%.


title: Effect of a Task-Oriented Rehabilitation Program on Upper Extremity Recovery Following Motor Stroke

abstract: Importance Clinical trials suggest that higher doses of task-oriented training are superior to current clinical practice for patients with stroke with upper extremity motor deficits. Objective To compare the efficacy of a structured, task-oriented motor training program vs usual and customary occupational therapy (UCC) during stroke rehabilitation. Design, Setting, and Participants Phase 3, pragmatic, single-blind randomized trial among 361 participants with moderate motor impairment recruited from 7 US hospitals over 44 months, treated in the outpatient setting from June 2009 to March 2014. Interventions Structured, task-oriented upper extremity training (Accelerated Skill Acquisition Program [ASAP]; n = 119); dose-equivalent occupational therapy (DEUCC; n = 120); or monitoring-only occupational therapy (UCC; n = 122). The DEUCC group was prescribed 30 one-hour sessions over 10 weeks; the UCC group was only monitored, without specification of dose. Main Outcomes and Measures The primary outcome was 12-month change in log-transformed Wolf Motor Function Test time score (WMFT, consisting of a mean of 15 timed arm movements and hand dexterity tasks). Secondary outcomes were change in WMFT time score (minimal clinically important difference [MCID] = 19 seconds) and proportion of patients improving ≥25 points on the Stroke Impact Scale (SIS) hand function score (MCID = 17.8 points). Results Among the 361 randomized patients (mean age, 60.7 years; 56% men; 42% African American; mean time since stroke onset, 46 days), 304 (84%) completed the 12-month primary outcome assessment; in intention-to-treat analysis, mean group change scores (log WMFT, baseline to 12 months) were, for the ASAP group, 2.2 to 1.4 (difference, 0.82); DEUCC group, 2.0 to 1.2 (difference, 0.84); and UCC group, 2.1 to 1.4 (difference, 0.75), with no significant between-group differences (ASAP vs DEUCC: 0.14; 95% CI, −0.05 to 0.33; P = .16; ASAP vs UCC: −0.01; 95% CI, −0.22 to 0.21; P = .94; and DEUCC vs UCC: −0.14; 95% CI, −0.32 to 0.05; P = .15). Secondary outcomes for the ASAP group were WMFT change score, −8.8 seconds, and improved SIS, 73%; DEUCC group, WMFT, −8.1 seconds, and SIS, 72%; and UCC group, WMFT, −7.2 seconds, and SIS, 69%, with no significant pairwise between-group differences (ASAP vs DEUCC: WMFT, 1.8 seconds; 95% CI, −0.8 to 4.5 seconds; P = .18; improved SIS, 1%; 95% CI, −12% to 13%; P = .54; ASAP vs UCC: WMFT, −0.6 seconds, 95% CI, −3.8 to 2.6 seconds; P = .72; improved SIS, 4%; 95% CI, −9% to 16%; P = .48; and DEUCC vs UCC: WMFT, −2.1 seconds; 95% CI, −4.5 to 0.3 seconds; P = .08; improved SIS, 3%; 95% CI, −9% to 15%; P = .22). A total of 168 serious adverse events occurred in 109 participants, resulting in 8 patients withdrawing from the study. Conclusions and Relevance Among patients with motor stroke and primarily moderate upper extremity impairment, use of a structured, task-oriented rehabilitation program did not significantly improve motor function or recovery beyond either an equivalent or a lower dose of UCC upper extremity rehabilitation. These findings do not support superiority of this program among patients with motor stroke and primarily moderate upper extremity impairment. Trial Registration clinicaltrials.gov Identifier:NCT00871715


title: Augmented Reality: An Overview and Five Directions for AR in Education

abstract: Augmented Reality (AR) is an emerging form of experience in which the Real World (RW) is enhanced by computer-generated content tied to specific locations and/or activities. Over the last several years, AR applications have become portable and widely available on mobile de- vices. AR is becoming visible in our audio-visual media (e.g., news, entertainment, sports) and is beginning to enter other aspects of our lives (e.g., e-commerce, travel, marketing) in tangible and exciting ways. Facilitating ubiquitous learning, AR will give learners instant access to location- specific information compiled and provided by numerous sources (2009). Both the 2010 and 2011 Horizon Reports predict that AR will soon see widespread use on US college campuses. In prepa- ration, this paper offers an overview of AR, examines recent AR developments, explores the impact of AR on society, and evaluates the implications of AR for learning and education.


title: An unsupervised learning approach to content-based image retrieval

abstract: "Semantic gap" is an open challenging problem in content-based image retrieval. It rejects the discrepancy between low-level imagery features used by the retrieval algorithm and high-level concepts required by system users. This paper introduces a novel image retrieval scheme, CLUster-based rEtrieval of images by unsupervised learning (CLUE), to tackle the semantic gap problem. CLUE is built on a hypothesis that images of the same semantics tend to be clustered. It attempts to narrow the semantic gap by retrieving image clusters based on not only the feature similarity of images to the query, but also how images are similar to each other. CLUE has been tested using examples from a database of about 60,000 general-purpose images. Empirical results demonstrate the effectiveness of CLUE.


title: Design, implementation, and thermal modeling of embedded reconfigurable magnetorquer system for nanosatellites

abstract: A reconfigurable magnetorquer coil has been designed and implemented for the CubeSat power management, attitude determination, and control tile (CubePMT) module of CubeSat-standard nanosatellites. Magnetorquer coil is a good choice for attitude control and stabilization of nanosatellites. The goal of this work is to provide CubeSat with a magnetorquer coil, which has small dimensions, low weight, and low heat dissipation. The designed magnetorquer coil is reconfigurable and integrated within the printed circuit board internal layers, occupying no extra space on CubeSat. Coils in each layer are treated separately and can be attached and detached through straps. Changing the arrangement of these straps, one can use 1, 2, 3, or 4 coils in series, parallel, or any hybrid combination. This reconfigurable design provides an option for generating different amounts of magnetic moment and resultant torque to stabilize and rotate the satellite. Emissivity of the CubePMT module was found through lab experiments. Power dissipation and the corresponding temperature increase of the CubePMT module are evaluated by thermal modeling.


title: A review of federated identity management of OpenStack cloud

abstract: With technology advancement adoption of service oriented architecture is gaining momentum, Identity Management) is an authentication and authorization service provided for secure storage and information access. It is used to manage data security and confidentiality which is beneficial to both client side and provider side. Some of the authentication techniques like OpenStack Keystone have the drawback of data violation, inappropriate access and use of centralized service architecture makes it vulnerable to single point of failure. For providing better data security, authentication and authorization to end users' Federated Identity Management is systematic, adoptable and well-structured approach which can be applied to the cloud for its enhancement. This paper describes the different implementation of the protocol independent federated identity management (FIM) system components on cloud. Comparison chart covering merit and demerit of the different independent approaches are also covered in this paper.


title: Tourism and Tourism Spaces

abstract: Introduction PART ONE: PRODUCTION, REGULATION AND COMPETITION Production and Regulation Tourism Firms and the Organization of Production Inter-company Cooperation and Competition PART TWO: CONSUMPTION, EXPERIENCE AND COMMODIFICATION Mapping Tourism Consumption From Fordism to McDonaldization Engineering the Tourist Experience Tourism and the Commodification of Local Communities Impacts and Relationships PART THREE: CONSTRUCTING AND RECONSTRUCTING TOURISM PLACES AND SPACES Tourism Places, Spaces and Change Established Tourism Spaces in Transition Changes in Coastal Resorts Landscapes of Pleasure The Construction of New Tourism Spaces and Places Conclusions


title: Extended Depth-of-Field Microscope Imaging: MPP Image Fusion VS. WAVEFRONT CODING

abstract: Biomedical specimens thicker than the microscope depth-of-field (DOF) produces images of limited quality where a part of the specimen is in focus and others out of focus. Even the best in-focus image always contains objects that are blurred. These blurred contents make the quantitative and accurate analysis of the specimen difficult. The problem can be remedied by extending the DOF of the imaging system. One way to achieve this is to apply digital image fusion, a software technique that combines multiple images into a carefully synthesized image. Another way, albeit more expensive, is to incorporate modification in the optical system. In this paper we describe an extended DOF method that is based on multiscale pointwise product (MPP) image fusion, and compare it with the state-of-the-art wavefront coding technique which involves optical modification. We show competitive performance of our method and contrast its pros and cons with those of the wavefront coding.


title: Deep person re-identification with improved embedding and efficient training

abstract: Person re-identification task has been greatly boosted by deep convolutional neural networks (CNNs) in recent years. The core of which is to enlarge the inter-class distinction as well as reduce the intra-class variance. However, to achieve this, existing deep models prefer to adopt image pairs or triplets to form verification loss, which is inefficient and unstable since the number of training pairs or triplets grows rapidly as the number of training data grows. Moreover, their performance is limited since they ignore the fact that different dimension of embedding may play different importance. In this paper, we propose to employ identification loss with center loss to train a deep model for person re-identification. The training process is efficient since it does not require image pairs or triplets for training while the inter-class distinction and intra-class variance are well handled. To boost the performance, a new feature reweighting (FRW) layer is designed to explicitly emphasize the importance of each embedding dimension, thus leading to an improved embedding. Experiments on several benchmark datasets have shown the superiority of our method over the state-of-the-art alternatives on both accuracy and speed.


title: A scoping review of epidemiologic risk factors for pediatric obesity: Implications for future childhood obesity and dental caries prevention research

abstract: RESEARCH QUESTIONS ::: What are the non-modifiable (socioeconomic, genetic) and modifiable factors (physical activity, dietary behaviors) related to childhood (under age 12) obesity? How can this knowledge be applied to oral health professionals' efforts to prevent or manage dental caries in children? ::: ::: ::: OBJECTIVES ::: Studies have identified risk factors for childhood obesity. The purpose of this scoping review was to develop a conceptual model to identify non-modifiable and modifiable risk factors for childhood obesity and to illustrate how these findings are relevant in developing interventions aimed at preventing obesity and dental caries in children. ::: ::: ::: METHODS ::: The authors searched PubMed and Embase and limited the study to English-language publications. A total of 2,572 studies were identified. After de-duplication, 2,479 studies remained and were downloaded into a citation-management tool. Two authors screened the titles and abstracts for relevance. Two hundred and sixty studies remained and were retrieved for a full-text review, and 80 studies were excluded, resulting in 180 studies included in the scoping review. An inductive content analytic methods was used to organize all statistically significant obesity risk factors into seven domains, which were classified as non-modifiable or modifiable; then a conceptual model of common risk factors associated with childhood obesity and dental caries was developed. ::: ::: ::: RESULTS ::: Non-modifiable obesity risk factors include biological and developmental (e.g., genes, developmental conditions, puberty), sociodemographic and household (e.g., race/ethnicity, socioeconomic status, parent education, unemployment), cultural (e.g., degree of acculturation), and community (e.g., neighborhood composition). Modifiable risk factors included behavioral (e.g., diet, physical activity, weight), psychosocial (e.g., maternal stress, family functioning, parenting practices, child temperament), and medical (e.g., parent smoking, maternal health, child health). ::: ::: ::: CONCLUSIONS ::: Identifying common risk factors has important implications for future oral health research aimed at preventing childhood obesity and dental caries. Epidemiologic knowledge gleaned from the literature can be used to develop rigorous interventions and programs aimed at preventing these highly prevalent diseases and improving health outcomes for children.


title: Design of an LCL-filter for space vector PWM in grid-connected 3-level inverters system

abstract: This paper proposes a design scheme of the LCL-filter for space vector PWM (SVM) at the three-level inverter system. Although there are several studies in progress, the conventional methods are erroneous because they do not focus on the other switching methods. This paper shows the design methodology for an LCL-filter optimized in SVM switching. The design sequence for the LCL-filter is presented gradually. The inverter-side inductor was decided by an analysis of the ripple components mathematically. Based on the reactive power absorption ratio, the filter capacitor was determined. The grid-side inductor was determined by the ripple factor. Simulation results are shown to verify the validity of the proposed filter design method


title: Experimental Study Evaluating the Effect of a Barrier Method on Postoperative Intraabdominal Adhesions

abstract: The purpose of this animal study was to determine if tissue glue-coated collagen sponge is an effective barrier method to prevent localized adhesions in a modified rabbit sidewall model. Rabbits were divided into two groups and underwent laparotomy with subsequent creation of a cecal wound according to the rabbit sidewall model. Rabbits of group I (treatment group; n = 10) were treated with a TachoComb H patch placed on the defect, whereas group II animals (control group; n = 6) did not receive further treatment. All animals were sacrificed 2 weeks postoperatively and adhesions were evaluated using special adhesion score. A further six rabbits underwent TachoComb H application at the cecum to investigate the histological changes during a course of 12 weeks. The average adhesion scores were significantly (P < 0.05) reduced in the treatment group compared to the controls. Histologically the TachoComb H patch was surrounded by granulation tissue without signs of infection. Tissue glue-coated collagen sponge (TachoComb H) is effective to prevent localized intraabdominal adhesions in the modified rabbit sidewall model.


title: Prevalence and factors associated with female genital mutilation among women of reproductive age in the Bawku municipality and Pusiga District of northern Ghana

abstract: Globally, three million girls are at risk of female genital mutilation (FGM) and an estimated 200 million girls and women in the world have undergone FGM. While the overall prevalence of FGM in Ghana is 4%, studies have shown that the overall prevalence in the Upper East Region is 38%, with Bawku municipality recording the highest at 82%. This study used a cross-sectional design with a quantitative approach: a survey with women of reproductive age (15–49). Among all respondents, 830 women who participated in the study, 61% reported having undergone FGM. Of those circumcised, 66% indicated their mothers influenced it. Three quarters of the women think FGM could be stopped through health education. Women who live in the Pusiga district (AOR: 1.66; 95% CI: 1.16–2.38), are aged 35–49 (AOR: 4.24; 95% CI: 2.62–6.85), and have no formal education (AOR: 2.78; 95% CI: 1.43–5.43) or primary education (AOR: 2.10; 95% CI: 1.03–4.31) were more likely to be circumcised relative to those who reside in Bawku Municipal, are aged 15–24, and had tertiary education. Likewise, married women (AOR: 3.82; 95% CI: 2.53–5.76) were more likely to have been circumcised compared with unmarried women. At a site-specific level, factors associated with FGM included age and marital status in Bawku, and age, marital status, and women’s education in Pusiga. Female Genital Mutilation is still being practiced in the Bawku Municipality and the Pusiga District of northern Ghana, particularly among women with low socio-economic status. Implementing interventions that would provide health education to communities and promote girl-child education beyond the primary level could help end the practice.


title: The Agile Requirements Refinery: Applying SCRUM Principles to Software Product Management

abstract: Although agile software development methods such as SCRUM and DSDM are gaining popularity, the consequences of applying agile principles to software product management have received little attention until now. In this paper, this gap is filled by the introduction of a method for the application of SCRUM principles to software product management. For this purpose, the 'agile requirements refinery' is presented, an extension to the SCRUM process that enables product managers to cope with large requirements in an agile development environment. A real-life case study is presented to illustrate how agile methods can be applied to software product management. The experiences of the case study company are provided as a set of lessons learned that will help others to apply agile principles to their software product management process.


title: Fully Online Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm

abstract: We propose a new multistage procedure for a real-time brain-machine/computer interface (BCI). The developed system allows a BCI user to navigate a small car (or any other object) on the computer screen in real time, in any of the four directions, and to stop it if necessary. Extensive experiments with five young healthy subjects confirmed the high performance of the proposed online BCI system. The modular structure, high speed, and the optimal frequency band characteristics of the BCI platform are features which allow an extension to a substantially higher number of commands in the near future.


title: Immune checkpoint inhibitor-induced colitis: A comprehensive review

abstract: Immune checkpoint inhibitors (ICIs) are monoclonal antibodies that target down-regulators of the anti-cancer immune response: Cytotoxic T-lymphocyte antigen-4, programmed cell death protein-1, and its ligand programmed death-ligand 1. ICIs have revolutionized the treatment of a variety of malignancies. However, many immune-related adverse events have also been described which mainly occurs as the immune system becomes less suppressed, affecting various organs including the gastrointestinal tract and causing diarrhea and colitis. The incidence of immune-mediated colitis (IMC) ranges from 1%-25% depending on the type of ICI and if used in combination. Endoscopically and histologically there is a significant overlap between IMC and inflammatory bowel disease, however more neutrophilic inflammation without chronic inflammation is usually present in IMC. Corticosteroids are recommended for grade 2 or more severe colitis while holding the immunotherapy. About one third to two thirds of patients are steroid refractory and benefit from infliximab. Recently vedolizumab has been found to be efficacious in steroid and infliximab refractory cases. While in grade 4 colitis, the immunotherapy is permanently discontinued, the decision is controversial in grade 3 colitis.


title: Life-Style and Drug Involvement Among Youths in an Inner City Junior High School

abstract: Data gathered in a winter 1976 survey of youths attending a New York City junior high school were analyzed to learn how features of their life-styles related to their involvement with drugs. The results indicate that the youths' substance use is an environmentally related phenomenon. The factors of peer-held esteem of drug using, gang involved persons, friends' use of alcohol and marijuana, and participation in spare-time activities of a drug/street culture nature were found to provide important insights into the youths' drug taking.


title: A Framework for Self-Verification of Firmware Updates over the Air in Vehicle ECUs

abstract: An upcoming trend for automobile manufacturers is to provide firmware updates over the air (FOTA) as a service. Since the firmware controls the functionality of a vehicle, security is important. To this end, several secure FOTA protocols have been developed. However, the secure FOTA protocols only solve the security for the transmission of the firmware binary. Once the firmware is downloaded, an attacker could potentially modify its contents before it is flashed to the corresponding ECU'S ROM. Thus, there is a need to extend the flashing procedure to also verify that the correct firmware has been flashed to the ECU. We present a framework for self-verification of firmware updates over the air. We include a verification code in the transmission to the vehicle, and after the firmware has been flashed, the integrity of the memory contents can be verified using the verification code. The verification procedure entails only simple hash functions and is thus suitable for the limited resources in the vehicle. Virtualization techniques are employed to establish a trusted computing base in the ECU, which is then used to perform the verification. The proposed framework allows the ECU itself to perform self-verification and can thus ensure the successful flashing of the firmware.


title: Cognitive developmental robotics as a new paradigm for the design of humanoid robots

abstract: This paper proposes cognitive developmental robotics(CDR) as a new principle for the design of humanoid robots. This principle may provide ways of understanding human beings that go beyond the current level of explanation found in the natural and social sciences. Furthermore, a methodological emphasis on humanoid robots in the design of artificial creatures holds promise because they have many degrees of freedom and sense modalities and, thus, must face the challenges of scalability that are often side-stepped in simpler domains. We examine the potential of this new principle as well as issues that are likely to be important to CDR in the future. © 2001 Elsevier Science B.V. All rights reserved.


title: Benefits of statins in elderly subjects without established cardiovascular disease. a meta-analysis

abstract: Background: Since ageing of the population is steadily raising, prevention of Cardiovascular (CV) disease in the elderly is relevant. In elderly patients with previous CV events, use of statins is recommended by guidelines, whereas the employ of these drugs in elderly subjects without previous CV events is still debated. The aim of the present study was to verify whether statins reduce all-cause mortality and CV events in elderly people without previous established CV disease. ::: ::: Methods: MEDLINE, Cochrane, ISI Web of Science and SCOPUS databases were searched for articles about statin treatment in patients without CV disease until December 2012. Study inclusion criteria were: randomized allocation to statin or placebo; age at randomization >65 years or report of outcomes in the subgroup of patients with age >65 years; report of at least 1 clinical event among all-cause death, CV death, Myocardial Infarction (MI), coronary revascularization, stroke and new cancer onset; report of outcomes separately for patients without established CV disease. Meta-analysis was performed to assess the influence of treatments on outcomes. Meta-regression analysis was performed to test the influence of potential effect modifiers on our results. Publication bias was assessed by a modified Macaskill's test. ::: ::: Results: Seven randomized trials comparing statins versus placebo in 23,258 subjects without CV disease were included in a meta-analysis. Statins compared to placebo reduced the risk of MI by 39.5% (relative risk [RR]: 0.605 [95% confidence interval (CI): 0.459 to 0.797]; p=0.000), coronary revascularization by 50.3% (RR: 0.497 [95% CI: 0.326 to 0.758]; p=0.001) and stroke by 24.6% (RR: 0.754 [95% CI: 0.588 to 0.966]; p=0.026), whereas the risk of all-cause death (RR: 0.938 [95% CI: 0.851 to 1.034]; p=0.196, CV death (RR: 0.911 [95% CI: 0.681 to 1.219]; p=0.530) and new cancer onset (RR: 0.934 [95% CI: 0.776 to 1.126]; p=0.476) was not influenced by treatment. ::: ::: Conclusions: In elderly subjects at high CV risk without established CV disease statins substantially reduce the incidence CV morbidities, but do not significantly prolong survival.


title: Bounds For Stable Measures Of Convex Shells And Stable Approximations

abstract: The standard normal distribution Φ on R d satisfies Φ ((∂C) e ) ≤ c d e, for all e > 0 and for all convex subsets C ⊂ R d , with a constant c d which depends on the dimension d only. Here ∂C denotes the boundary of C, and (∂C) e stands for the e-neighborhood of ∂C. Such bounds for the normal measure of convex shells are extensively used to estimate the accuracy of normal approximations. We extend the inequality to all (nondegenerate) stable distributions on R d , with a constant which depends on the dimension, the characteristic exponent and the spectral measure of the distribution only. As a corollary we provide an explicit bound for the accuracy of stable approximations on the class of all convex subsets of R d .


title: Self-tuning fuzzy PID controller for electro-hydraulic cylinder

abstract: Hydraulic systems are widely used in industrial applications. This is due to its high speed of response with fast start, stop and speed reversal possible. The torque to inertia ratio is also large with resulting high acceleration capability. The nonlinear properties of hydraulic cylinder make the position tracking control design challenging. This paper presents the development and implementation of self-tuning fuzzy PID controller in controlling the position variation of electro-hydraulic actuator. The hydraulic system mathematical model is approximated using system identification technique. The simulation studies were done using Matlab Simulink environment. The output performance was compared with the design using pole-placement controller. The roots mean squared error for both techniques showed that self-tuning Fuzzy PID produced better result compared to using pole-placement controller.


title: A Hybrid Model of Hierarchical Clustering and Decision Tree for Rule-based Classification of Diabetic Patients

abstract: Hybrid models in data mining have recently gained attention including in the study of medical research. Various studies in this domain using hybrid models have shown different results. This paper presents the new hybrid model by exploring Agglomerative Hierarchical Clustering and Decision Tree Classifier on Pima Indians Diabetes dataset. The experiments compared performance accuracy of the Decision Tree Classifier against the same classifier augmented with Hierarchical Clustering. Results showed that the hybrid model achieved higher accuracy with 80.8% as compared to 76.9% of the standard model. This is a promising result for adoption of hierarchical clustering in a rule-based classifier. Keyword- Medical Data Mining, Hierarchical Clustering, Classification, Decision Tree, Hybrid Model


title: Few-Shot Object Detection via Feature Reweighting

abstract: Conventional training of a deep CNN based object detector demands a large number of bounding box annotations, which may be unavailable for rare categories. In this work we develop a few-shot object detector that can learn to detect novel objects from only a few annotated examples. Our proposed model leverages fully labeled base classes and quickly adapts to novel classes, using a meta feature learner and a reweighting module within a one-stage detection architecture. The feature learner extracts meta features that are generalizable to detect novel object classes, using training data from base classes with sufficient samples. The reweighting module transforms a few support examples from the novel classes to a global vector that indicates the importance or relevance of meta features for detecting the corresponding objects. These two modules, together with a detection prediction module, are trained end-to-end based on an episodic few-shot learning scheme and a carefully designed loss function. Through extensive experiments we demonstrate that our model outperforms well-established baselines by a large margin for few-shot object detection, on multiple datasets and settings. We also present analysis on various aspects of our proposed model, aiming to provide some inspiration for future few-shot detection works.


title: Mitochondrial stress signaling in longevity: A new role for mitochondrial function in aging

abstract: Mitochondria are principal regulators of cellular function and metabolism through production of ATP for energy homeostasis, maintenance of calcium homeostasis, regulation of apoptosis and fatty acid oxidation to provide acetyl CoA for fueling the electron transport chain. In addition, mitochondria play a key role in cell signaling through production of reactive oxygen species that modulate redox signaling. Recent findings support an additional mechanism for control of cellular and tissue function by mitochondria through complex mitochondrial–nuclear communication mechanisms and potentially through extracellular release of mitochondrial components that can act as signaling molecules. The activation of stress responses including mitophagy, mitochondrial number, fission and fusion events, and the mitochondrial unfolded protein response (UPRMT) requires mitochondrial–nuclear communication for the transcriptional activation of nuclear genes involved in mitochondrial quality control and metabolism. The induction of these signaling pathways is a shared feature in long-lived organisms spanning from yeast to mice. As a result, the role of mitochondrial stress signaling in longevity has been expansively studied. Current and exciting studies provide evidence that mitochondria can also signal among tissues to up-regulate cytoprotective activities to promote healthy aging. Alternatively, mitochondria release signals to modulate innate immunity and systemic inflammatory responses and could consequently promote inflammation during aging. In this review, established and emerging models of mitochondrial stress response pathways and their potential role in modulating longevity are discussed.


title: Automatic makeup based on generative adversarial nets

abstract: Automatic makeup aims to compose and synthesize makeup on human face through computer algorithm, which belongs to the field of face image analysis. It plays an important role in interactive entertainment applications, image and video editing and face recognition assistance. However, as a face editing task, automatic makeup has several challenges as following: 1) It is usually difficult to meet the editing requirements while ensuring that makeup results are naturally looking without obvious artifacts. 2) Designed network should preserve the content (human identity) of input images but only edits the makeup-related parts of input images such as lip gloss and eye shadow. 3) Number of face makeup dataset is limited. To address these difficulties, we propose an automatic makeup generative adversarial network, a novel approach that can perform makeup attribute editing for multiple makeup styles. The network is an end-to-end model with several specially designed skip layers in generator network and allows simultaneous training of multiple datasets in addition to makeup datasets. Finally, the performance of proposed method was compared with state-of-art method qualitatively and quantitatively.


title: Inventory control with Markovian capacity and the option of order rejection

abstract: We consider a stochastic inventory control problem with Markovian capacity and the option of order rejection. We show its optimal policy to be the combination of a capacity-dependent modified base-stock production policy and a capacity-dependent critical-point order acceptance policy. When capacity is stochastically monotone, we show that the policy parameters change over the current capacity level in an intuitive way. All these results can be extended to the infinite-horizon case, with or without cost discounting. Our computational study substantiates the benefits from both exploiting the Markovian behavior of the capacity and rejecting orders when necessary.


title: A Mini Review of Neuromorphic Architectures and Implementations

abstract: Neuromorphic architectures are hardware systems that aim to use the principles of neural function for their basis of operation. Their goal is to harness biologically inspired concepts such as weighted connections, activation thresholds, short-and long-term potentiation, and inhibition to solve problems in distributed computation. Compared with today’s methods of emulating neural function in software on conventional von Neumann hardware, neuromorphic systems provide the promise of inherently low power and fault-tolerant operation directly implemented into hardware, for application in distributed and embedded computing tasks, where the vast scaling of today’s architectures does not provide a long-term solution. This mini review is intended for a general engineering audience not currently familiar with this exciting research area. It provides descriptions of some of the recent advances, including supercomputer and single-device implementations, approaches based on spiking and nonspiking neurons, machine learning hardware accelerators, and those utilizing memristive devices. Hardware implementations utilizing both conventional electronic materials and organic electronic materials are reviewed.


title: Production systems as a programming language for artificial intelligence applications.

abstract: Abstract : EPAM is a simple model of verbal learning that was developed to simulate certain features of human learning, but it has also turned out to be useful for certain kinds of discriminations in Al programs. This chapter describes a production system for EPAM, featuring the automatic addition of productions by the basic system to represent incremental learning of three-letter nonsense syllables. The design of the network represented by the added productions is discussed and its growth described. Details of the EPAM production system raise several issues with respect to general EPAM variations and with respect to production system issues such as the right set of production-building primitives. A comparison of the present program to a similar one by Waterman, using a radically different production system architecture, is carried out, highlighting the advantages of the present one. (Author)


title: Friends or Foes: Is Empathy Necessary for Moral Behavior?

abstract: In the past decade, a flurry of empirical and theoretical research on morality and empathy has taken place, and interest and usage in the media and the public arena have increased. At times, in both popular culture and academia, morality and empathy are used interchangeably, and quite often the latter is considered to play a foundational role for the former. In this article, we argue that although there is a relationship between morality and empathy, it is not as straightforward as apparent at first glance. Moreover, it is critical to distinguish among the different facets of empathy (emotional sharing, empathic concern, and perspective taking), as each uniquely influences moral cognition and predicts differential outcomes in moral behavior. Empirical evidence and theories from evolutionary biology as well as developmental, behavioral, and affective and social neuroscience are comprehensively integrated in support of this argument. The wealth of findings illustrates a complex and equivocal relationship between morality and empathy. The key to understanding such relations is to be more precise on the concepts being used and, perhaps, abandoning the muddy concept of empathy.


Computational Drug Discovery with Dyadic Positive-Unlabeled Learning


title: EXAMINING THE PSYCHOMETRIC PROPERTIES OF THE WONG AND LAW EMOTIONAL INTELLIGENCES SCALE (WLEIS)

abstract: Emotional intelligence is an individual’s ability to perceive accurately, evaluate and express emotions. One of the instruments to measure emotional intelligence is the Wong and Law Emotional Intelligence Scale (WLEIS) which consist of four dimensions namely self-emotional appraisal, others’ emotional appraisal, regulation of emotion and use of emotion. The main aim of this research was to evaluate the psychometric properties of the Wong and Law Emotional Intelligence Scale (WLEIS). This was a survey research using a set of questionnaires. A total of 150 newly appointed administrative officers who were undergoing a compulsory course participated in this study. The instruments used were the Wong and Law Emotional Intelligence Scale (WLEIS), Organisational Commitment Questionnaire and the Satisfaction with Life Scale (SWLS). In evaluating the reliability of WLEIS, alpha Cronbach and split half methods were used. In addition, criterion and construct validity methods were used to test its validity. Results obtained showed that the Bahasa Malaysia version of the WLEIS was valid and using principal component analysis with varimax rotation method, four components were extracted with 75.1% variance. The WLEIS also showed good criterion validity from the significant correlations with the criteria of organizational commitment and satisfaction with life. Furthermore, the results of reliability were satisfactory with alpha Cronbach ranging from 0.83 to 0.92 for all the dimensions. Results of split half reliability also showed the instrument was reliable with the coefficient ranging from 0.81 to 0.95.


title: Smart city digital twins

abstract: Driven by the challenges of rapid urbanization, cities are determined to implement advanced socio-technological changes and transform into smarter cities. The success of such transformation, however, greatly relies on a thorough understanding of the city's states of spatiotemporal flux. The ability to understand such fluctuations in context and in terms of interdependencies that exist among various entities across time and space is crucial, if cities are to maintain their smart growth. Here, we introduce a Smart City Digital Twin paradigm that can enable increased visibility into cities' human-infrastructure-technology interactions, in which spatiotemporal fluctuations of the city are integrated into an analytics platform at the real-time intersection of reality-virtuality. Through learning and exchange of spatiotemporal information with the city, enabled through virtualization and the connectivity offered by Internet of Things (IoT), this Digital Twin of the city becomes smarter over time, able to provide predictive insights into the city's smarter performance and growth.


title: Transformer winding deformation diagnostic system using online high frequency signal injection by capacitive coupling

abstract: Transformer winding deformation is common among all sorts of transformer failures. Cumulative deformation can eventually burgeon into catastrophic faults and result in entire network outage. It is possible to detect the early signs of faults with continuous online monitoring transformer. However, frequency response analysis (FRA) is considered to be a useful and accurate tool for sufficient detection. This paper aims at proposing a novel diagnostic system for online monitoring power transformer winding deformation based on FRA. In order to realize online monitoring transformer winding, the system uses the capacitive coupling method to inject controllable nanosecond pulses, which function as the excitation signal of winding, and to obtain the response signal. This proposed method may extend frequency range for analysis and perhaps could early detect minor winding movement and looseness. Transformer experiments show great prospect in the application of the system.


title: Birth Order Effects on Personality and Achievement Within Families

abstract: We investigated birth order effects on personality and achievement in four studies (N = 1,022 families) including both stu- dent and adult samples. Control over a wide range of variables was effected by collecting within-family data: Participants compared their siblings (and themselves) on a variety of personality and achievement dimensions. Across four diverse data sets, first-borns were nominated as most achieving and most conscientious. Later-borns were nominat- ed as most rebellious, liberal, and agreeable. The same results obtained whether or not birth order was made salient (to activate stereotypes) during the personality ratings. Overall, the results sup- port predictions from Sulloway's niche model of personality develop- ment, as well as Zajonc's confluence model of intellectual achievement.


title: Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances †

abstract: In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.


title: LASER: a scalable response prediction platform for online advertising

abstract: We describe LASER, a scalable response prediction platform currently used as part of a social network advertising system. LASER enables the familiar logistic regression model to be applied to very large scale response prediction problems, including ones beyond advertising. Though the underlying model is well understood, we apply a whole-system approach to address model accuracy, scalability, explore-exploit, and real-time inference. To facilitate training with both large numbers of training examples and high dimensional features on commodity clustered hardware, we employ the Alternating Direction Method of Multipliers (ADMM). Because online advertising applications are much less static than classical presentations of response prediction, LASER employs a number of techniques that allows it to adapt in real time. LASER models can be divided into components with different re-training frequencies, allowing us to learn from changes in ad campaign performance frequently without incurring the cost of retraining larger, more stable sections of the model. Thompson sampling during online inference further helps by efficiently balancing exploration of new ads with exploitation of long running ones. To enable predictions made with the most recent feature data, we employ a range of techniques, including extensive caching and lazy evaluation, to permit real time, low latency scoring. LASER models are defined using a configuration language that ties together the training, validation, and inference pieces and permits even non-programming analysts to experiment with different model structures without modifications to code or interruptions to running servers. Finally, we show via extensive offline experiments and online A/B tests that this system provides significant benefits to prediction accuracy, gains in revenue and CTR, and reductions in system latency.


title: Lyotropic liquid crystal systems in drug delivery: a review

abstract: Lamellar, cubic and hexagonal mesophases are some of the most common lyotropic liquid crystal systems, and have attracted much research attention because of their distinctive structures and physicochemical properties. Polar lipids and surfactants exhibit a range of phase behavior in an aqueous environment, depending on the composition of the lipids and surfactants. These characteristics have been investigated for a variety of applications in drug delivery, and lyotropic liquid crystal systems have potential as drug carriers for small molecules, peptides, and proteins. In this article we provide an overview of recent advances in the state of the art, including methods of preparation and applications in drug delivery. The scope and limitations of lyotropic liquid crystals for drug delivery are discussed, and future research perspectives are identified.


title: Cosmetics as a Feature of the Extended Human Phenotype: Modulation of the Perception of Biologically Important Facial Signals

abstract: Research on the perception of faces has focused on the size, shape, and configuration of inherited features or the biological phenotype, and largely ignored the effects of adornment, or the extended phenotype. Research on the evolution of signaling has shown that animals frequently alter visual features, including color cues, to attract, intimidate or protect themselves from conspecifics. Humans engage in conscious manipulation of visual signals using cultural tools in real time rather than genetic changes over evolutionary time. Here, we investigate one tool, the use of color cosmetics. In two studies, we asked viewers to rate the same female faces with or without color cosmetics, and we varied the style of makeup from minimal (natural), to moderate (professional), to dramatic (glamorous). Each look provided increasing luminance contrast between the facial features and surrounding skin. Faces were shown for 250 ms or for unlimited inspection time, and subjects rated them for attractiveness, competence, likeability and trustworthiness. At 250 ms, cosmetics had significant positive effects on all outcomes. Length of inspection time did not change the effect for competence or attractiveness. However, with longer inspection time, the effect of cosmetics on likability and trust varied by specific makeup looks, indicating that cosmetics could impact automatic and deliberative judgments differently. The results suggest that cosmetics can create supernormal facial stimuli, and that one way they may do so is by exaggerating cues to sexual dimorphism. Our results provide evidence that judgments of facial trustworthiness and attractiveness are at least partially separable, that beauty has a significant positive effect on judgment of competence, a universal dimension of social cognition, but has a more nuanced effect on the other universal dimension of social warmth, and that the extended phenotype significantly influences perception of biologically important signals at first glance and at longer inspection.


title: Wheel & Track hybrid robot platform for optimal navigation in an urban environment

abstract: Various robot platforms are used for tasks in a variety of disaster fields. For public safety in some urban environments, a novel wheel and track hybrid robot platform which is able to perform fast navigation in flatland and stairs is developed. For its applicability on various environments, we devised a transformable track structure which can climb stairs and navigate the flat floor rapidly. The transformable track can transfer the power with three actuators. Track arms installed in front, rear, and side are used for mode change between floor-navigation and stair-climbing. When the robot navigates on a flatland, tracks on its left and right side are down-folded and kept up folded posture in which four wheels are exposed to the outside of tracks. In this floor-navigation mode, the robot fast navigates flat floors without contact between tracks and the floor. When the robot ascends stairs, the robot spreads tracks until they enclose four wheels. In the stair-climbing mode, two tracks are working. The wheel and track hybrid robot platform was verified by real-navigation experiments in some indoor test-bed environments. The experimental result shows that the developed robot platform has good performance in climbing stair with two tracks and navigating flatland. The platform's maximum speed on flatland is 7.2 km/h and its maximum slope to climbing is an angle of 45 degrees.


title: Exemplar-based framework for 3D point cloud hole filling

abstract: Holes can arise in 3D point clouds due to a number of reasons such as incomplete scans, occlusions, and packet loss. We present an exemplar-based framework for hole filling in 3D point clouds, which exploits non-local self similarity to provide plausible reconstruction even for large holes and complex surfaces. Points along the hole boundary are given priority that determines the order in which they are processed. Hole filling is performed iteratively and uses templates near the hole boundary to find the best matching regions elsewhere in the cloud, from where existing points are transferred to the hole. The proposed method has been compared with several existing methods and has shown superior results, both visually and in terms of the Hausdorff distance.


title: SUSTAIN: A network model of category learning

abstract: SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.


title: A simulated model for a once-through boiler by parameter adjustment based on genetic algorithms

abstract: In this paper based on the physical rules, thermodynamics principles and energy mass balance, the simulation models are developed and applied to electrical power generating plants in order to characterize the essential dynamic behavior of the boiler subsystems and to use the corresponding models for the power plant processes. These models are developed for a sub-critical once through Benson type boiler based on the experimental data obtained from a complete set of field experiments. An optimization approach based on genetic algorithm (GA) is executed to estimate the model parameters and fit the models response on the real system dynamics. Comparison between the responses of the corresponding models with the response of the real plants validates the accuracy and performance of modeling approach. A similar comparison between the responses of these models with linear parametric models shows the effectiveness and feasibility of the developed model in term of more accurate and less deviation between the responses of the models and the corresponding subsystems.


title: Training Load and Fatigue Marker Associations with Injury and Illness: A Systematic Review of Longitudinal Studies

abstract: Background ::: Coaches, sport scientists, clinicians and medical personnel face a constant challenge to prescribe sufficient training load to produce training adaption while minimising fatigue, performance inhibition and risk of injury/illness.


Prevention of DDOS Attacks using New Cracking Algorithm


title: Spin observation and trajectory prediction of a ping-pong ball

abstract: For ping-pong playing robots, observing a ball and predicting a ball’s trajectory accurately in real-time is essential. However, most existing vision systems can only provide ball’s position observation, and do not take into consideration the spin of the ball, which is very important in competitions. This paper proposes a way to observe and estimate ball’s spin in real-time, and achieve an accurate prediction. Based on the fact that a spinning ball’s motion can be separated into global movement and spinning respect to its center, we construct an integrated vision system to observe the two motions separately. With a pan-tilt vision system, the spinning motion is observed through recognizing the position of the brand on the ball and restoring the 3D pose of the ball. Then the spin state is estimated with the method of plane fitting on current and historical observations. With both position and spin information, accurate state estimation and trajectory prediction are realized via Extended Kalman Filter(EKF). Experimental results show the effectiveness and accuracy of the proposed method.


title: Analysis of Drain-Induced Barrier Rising in Short-Channel Negative-Capacitance FETs and Its Applications

abstract: We investigate the performance of hysteresis-free short-channel negative-capacitance FETs (NCFETs) by combining quantum-mechanical calculations with the Landau–Khalatnikov equation. When the subthreshold swing (SS) becomes smaller than 60 mV/dec, a negative value of drain-induced barrier lowering is obtained. This behavior, drain-induced barrier rising (DIBR), causes negative differential resistance in the output characteristics of the NCFETs. We also examine the performance of an inverter composed of hysteresis-free NCFETs to assess the effects of DIBR at the circuit level. Contrary to our expectation, although hysteresis-free NCFETs are used, hysteresis behavior is observed in the transfer properties of the inverter. Furthermore, it is expected that the NCFET inverter with hysteresis behavior can be used as a Schmitt trigger inverter.


SLEP: Sparse Learning with Efficient Projections


title: From focal stacks to tensor display: A method for light field visualization without multi-view images

abstract: A new type of light field display called a tensor display was investigated. Although this display consists of only a few light attenuating layers located in front of a backlight, many views can be emitted in different directions simultaneously without sacrificing the resolution of each view. The transmittance pattern of each layer is calculated from a light field, namely, a set of dense multi-view images (typically dozens) that are to be observed from different directions. However, preparing such images is often cumbersome for real objects. We propose a method that does not require multi-view images as the input; instead, a focal stack composed of only a few differently focused images is directly transformed into the layer patterns. Our method greatly reduces the data acquisition cost while also maintaining the quality of the output light field. We validated the method with experiments using synthetic light field datasets and a focal stack acquired by an ordinary camera.


title: Self-Similarity based Zebra-crossing Detection for Intelligent Vehicle

abstract: A zebra-crossing detection method for intelligent vehicle is proposed in this paper. The method is performed on a bird-eye view image called inverse perspective mapping image. The complete method includes two phrases. First, a morphological filer followed by horizontal projection is applied to fast extract candidate zebra-crossing regions, where the size and structure information of zebra-crossing are well utilized. Second, a recognition method which based on self- similarity is presented to identify the candidate regions. Given a seed region of a zebra-crossing, the recognition method can infer overall zebra-crossing region by matching and growing. Experiments on a great number of real images which consist of several challenge scenes demonstrate the effectiveness and efficiency of the proposed approach.


Workspace Analysis of a Linear Delta Robot: Calculating the Inscribed Radius


title: Performance Analysis of RIP, EIGRP, and OSPF using OPNET

abstract: Routing protocols are key elements of modern communication networks. Currently deployed dynamic routing protocols that are used to propagate network topology information to the neighboring routers are Routing Information Protocol (RIP), Enhanced Interior Gateway Routing Protocol (EIGRP), and the Open Shortest Path First (OSPF) protocol. The choice of the right routing protocol depends on a number of parameters. In this paper, we use OPNET Modeler to analyze the performance of RIP, EIGRP, and the OSPF protocols, which are commonly deployed in Internet Protocol (IP) networks. We designed various simulation scenarios to compare their performance.


The Epipolar Geometry Toolbox : multiple view geometry and visual servoing for MATLAB


Proactive Learning for Named Entity Recognition


title: A Master Attack Methodology for an AI-Based Automated Attack Planner for Smart Cities

abstract: America’s critical infrastructure is becoming “smarter” and increasingly dependent on highly specialized computers called industrial control systems (ICS). Networked ICS components now called the industrial Internet of Things (IIoT) are at the heart of the “smart city”, controlling critical infrastructure, such as CCTV security networks, electric grids, water networks, and transportation systems. Without the continuous, reliable functioning of these assets, economic and social disruption will ensue. Unfortunately, IIoT are hackable and difficult to secure from cyberattacks. This leaves our future smart cities in a state of perpetual uncertainty and the risk that the stability of our lives will be upended. The Local government has largely been absent from conversations about cybersecurity of critical infrastructure, despite its importance. One reason for this is public administrators do not have a good way of knowing which assets and which components of those assets are at the greatest risk. This is further complicated by the highly technical nature of the tools and techniques required to assess these risks. Using artificial intelligence planning techniques, an automated tool can be developed to evaluate the cyber risks to critical infrastructure. It can be used to automatically identify the adversarial strategies (attack trees) that can compromise these systems. This tool can enable both security novices and specialists to identify attack pathways. We propose and provide an example of an automated attack generation method that can produce detailed, scalable, and consistent attack trees–the first step in securing critical infrastructure from cyberattack.


title: A unified hardware/software runtime environment for FPGA-based reconfigurable computers using BORPH

abstract: This paper explores the design and implementation of BORPH, an operating system designed for FPGA-based reconfigurable computers. Hardware designs execute as normal UNIX processes under BORPH, having access to standard OS services, such as file system support. Hardware and software components of user designs may, therefore, run as communicating processes within BORPH's runtime environment. The familiar language independent UNIX kernel interface facilitates easy design reuse and rapid application development. To develop hardware designs, a Simulink-based design flow that integrates with BORPH is employed. Performances of BORPH on two on-chip systems implemented on a BEE2 platform are compared.


title: Vehicle Anomaly Detection Based on Trajectory Data of ANPR System

abstract: This paper proposes a machine-learning technique to detect vehicle anomalies from data captured by automatic number plate recognition (ANPR) system. The proposed anomaly detection technique is specially engineered to exploit both spatial and temporal features of vehicles captured by ANPR system, so as to accurately detect anomaly vehicles. We extensively evaluated the proposed technique using a two- month long dataset collected by a real world ANRP system, which has more than three hundred cameras deployed in a big city of China. The evaluation results show that our technique can effectively detect vehicle anomalies from the huge amount of data collected by the ANPR system. More importantly, our technique significantly outperforms existing schemes especially when the data collected by the ANRP system are noisy due to poor weather condition.


title: Clickage: towards bridging semantic and intent gaps via mining click logs of search engines

abstract: The semantic gap between low-level visual features and high-level semantics has been investigated for decades but still remains a big challenge in multimedia. When "search" became one of the most frequently used applications, "intent gap", the gap between query expressions and users' search intents, emerged. Researchers have been focusing on three approaches to bridge the semantic and intent gaps: 1) developing more representative features, 2) exploiting better learning approaches or statistical models to represent the semantics, and 3) collecting more training data with better quality. However, it remains a challenge to close the gaps. In this paper, we argue that the massive amount of click data from commercial search engines provides a data set that is unique in the bridging of the semantic and intent gap. Search engines generate millions of click data (a.k.a. image-query pairs), which provide almost "unlimited" yet strong connections between semantics and images, as well as connections between users' intents and queries. To study the intrinsic properties of click data and to investigate how to effectively leverage this huge amount of data to bridge semantic and intent gap is a promising direction to advance multimedia research. In the past, the primary obstacle is that there is no such dataset available to the public research community. This changes as Microsoft has released a new large-scale real-world image click data to public. This paper presents preliminary studies on the power of large-scale click data with a variety of experiments, such as building large-scale concept detectors, tag processing, search, definitive tag detection, intent analysis, etc., with the goal to inspire deeper researches based on this dataset.


title: Approximate Communication: Techniques for Reducing Communication Bottlenecks in Large-Scale Parallel Systems

abstract: Approximate computing has gained research attention recently as a way to increase energy efficiency and/or performance by exploiting some applications’ intrinsic error resiliency. However, little attention has been given to its potential for tackling the communication bottleneck that remains one of the looming challenges to be tackled for efficient parallelism. This article explores the potential benefits of approximate computing for communication reduction by surveying three promising techniques for approximate communication: compression, relaxed synchronization, and value prediction. The techniques are compared based on an evaluation framework composed of communication cost reduction, performance, energy reduction, applicability, overheads, and output degradation. Comparison results demonstrate that lossy link compression and approximate value prediction show great promise for reducing the communication bottleneck in bandwidth-constrained applications. Meanwhile, relaxed synchronization is found to provide large speedups for select error-tolerant applications, but suffers from limited general applicability and unreliable output degradation guarantees. Finally, this article concludes with several suggestions for future research on approximate communication techniques.


title: The CRISPR-associated DNA-cleaving enzyme Cpf1 also processes precursor CRISPR RNA

abstract: CRISPR-Cas systems that provide defence against mobile genetic elements in bacteria and archaea have evolved a variety of mechanisms to target and cleave RNA or DNA. The well-studied types I, II and III utilize a set of distinct CRISPR-associated (Cas) proteins for production of mature CRISPR RNAs (crRNAs) and interference with invading nucleic acids. In types I and III, Cas6 or Cas5d cleaves precursor crRNA (pre-crRNA) and the mature crRNAs then guide a complex of Cas proteins (Cascade-Cas3, type I; Csm or Cmr, type III) to target and cleave invading DNA or RNA. In type II systems, RNase III cleaves pre-crRNA base-paired with trans-activating crRNA (tracrRNA) in the presence of Cas9 (refs 13, 14). The mature tracrRNA-crRNA duplex then guides Cas9 to cleave target DNA. Here, we demonstrate a novel mechanism in CRISPR-Cas immunity. We show that type V-A Cpf1 from Francisella novicida is a dual-nuclease that is specific to crRNA biogenesis and target DNA interference. Cpf1 cleaves pre-crRNA upstream of a hairpin structure formed within the CRISPR repeats and thereby generates intermediate crRNAs that are processed further, leading to mature crRNAs. After recognition of a 5'-YTN-3' protospacer adjacent motif on the non-target DNA strand and subsequent probing for an eight-nucleotide seed sequence, Cpf1, guided by the single mature repeat-spacer crRNA, introduces double-stranded breaks in the target DNA to generate a 5' overhang. The RNase and DNase activities of Cpf1 require sequence- and structure-specific binding to the hairpin of crRNA repeats. Cpf1 uses distinct active domains for both nuclease reactions and cleaves nucleic acids in the presence of magnesium or calcium. This study uncovers a new family of enzymes with specific dual endoribonuclease and endonuclease activities, and demonstrates that type V-A constitutes the most minimalistic of the CRISPR-Cas systems so far described.


title: Combating imbalance in network intrusion datasets

abstract: An approach to combating network intrusion is the development of systems applying machine learning and data min- ing techniques. Many IDS (Intrusion Detection Systems) suffer from a high rate of false alarms and missed intrusions. We want to be able to improve the intrusion detection rate at a reduced false positive rate. The focus of this paper is rule-learning, using RIPPER, on highly imbalanced intrusion datasets with an objective to improve the true positive rate (intrusions) without significantly increasing the false positives. We use RIPPER as the underlying rule classifier. To counter imbalance in data, we implement a combination of oversampling (both by replication and synthetic generation) and undersampling techniques. We also propose a clustering based methodology for oversampling by generating synthetic instances. We evaluate our approaches on two intrusion datasets — destination and actual packets based — constructed from actual Notre Dame traffic, giving a flavor of real-world data with its idiosyncrasies. Using ROC analysis, we show that oversampling by synthetic generation of minority (intrusion) class outperforms oversampling by replication and RIPPER's loss ratio method. Additionally, we establish that our clustering based approach is more suitable for the detecting intrusions and is able to provide additional improvement over just synthetic generation of instances.


title: Combined Top-Down/Bottom-Up Segmentation

abstract: We construct a segmentation scheme that combines top-down with bottom-up processing. In the proposed scheme, segmentation and recognition are intertwined rather than proceeding in a serial manner. The top-down part applies stored knowledge about object shapes acquired through learning, whereas the bottom-up part creates a hierarchy of segmented regions based on uniformity criteria. Beginning with unsegmented training examples of class and non-class images, the algorithm constructs a bank of class-specific fragments and determines their figure-ground segmentation. This bank is then used to segment novel images in a top-down manner: the fragments are first used to recognize images containing class objects, and then to create a complete cover that best approximates these objects. The resulting segmentation is then integrated with bottom-up multi-scale grouping to better delineate the object boundaries. Our experiments, applied to a large set of four classes (horses, pedestrians, cars, faces), demonstrate segmentation results that surpass those achieved by previous top-down or bottom-up schemes. The main novel aspects of this work are the fragment learning phase, which efficiently learns the figure-ground labeling of segmentation fragments, even in training sets with high object and background variability; combining the top-down segmentation with bottom-up criteria to draw on their relative merits; and the use of segmentation to improve recognition.


Discriminative Deep Hashing for Scalable Face Image Retrieval


title: A case study of CPOE adoption and use: work-arounds and their social-technical implications.

abstract: This study applies ethnographically-based methods to investigate the socio-technical issues during and after a CPOE system implementation. The research is conducted in an inpatient unit at an academic medical center, where the CPOE is being implemented and clinicians have found to develop ways to workaround the system. The findings of the study will help improve our understanding of the interaction between people, information, and IT systems in a highly efficient and highly collaborative clinical environment.


title: Large-scale impacts of bottom trawling on shelf primary productivity

abstract: Disturbance of the seabed resulting from bottom trawling affects ecosystem processes, such as the rate and magnitude of nutrient regeneration. The potential responses of the plankton community arising from such effects can be modelled, provided that reliable data on the effects on nutrient fluxes are available. In a north Cretan outer continental shelf and upper slope fishing ground (Heraklion Bay, Crete, Eastern Mediterranean) we applied a new field instrument which can simulate the passage of trawl groundropes across the sea floor and made direct seasonal measurements of the rate of dissolved and particulate nutrient releases resulting from seabed disturbance. These observational data were then integrated in a 3D ecosystem model. Results revealed that bottom trawling may trigger off considerable productivity pulses, in addition to pulses from the natural seasonal cycle.


title: Exploring synonyms as context in zero-shot action recognition

abstract: Zero shot learning (ZSL) provides a solution to recognising unseen classes without class labelled data for model learning. Most ZSL methods aim to learn a mapping from a visual feature space to a semantic embedding space, e.g. attribute or word vector spaces. The use of word vector space is particularly attractive as compared to attribute, it offers vast auxiliary classes with free parts embedding without human annotation. However, using the word vector embedding often provides weaker discriminative power than manually labelled attributes of the auxiliary classes. This is compounded further in zero-shot action recognition due to richer content variations among action classes. In this work we propose to explore a broader semantic contextual information in the text domain to enrich the word vector representation of action classes. We show through extensive experiments that this method improves significantly the performance of a number of existing word vector embedding ZSL methods. Moreover, it also outperforms attribute embedding ZSL with human annotation.


title: A Corporate Culture Channel: How Increased Shareholder Governance Reduces Firm Value

abstract: I show corporate culture is an important channel through which shareholder governance affects firm value. I develop a novel data set to measure aspects of corporate culture and use a regression discontinuity strategy to demonstrate stronger shareholder governance significantly changes aspects of culture. I find greater results-orientation but less customer-focus, integrity, and collaboration. Consistent with a positive link between governance and value, shareholders initially realize financial gains: increases in sales, profitability, and payout occur. However, over time, I find intangible assets associated with customer satisfaction and employee integrity deteriorate, which partly reverses the gains from greater results-orientation. These findings are consistent with a model of multitasking where stronger governance incentivizes managers to concentrate on easy-to-observe benchmarks at the expense of the harder-to-measure intangibles, even though such actions are not in the firm's best long-term interest. On average, I find firm value declines 1.4% through this corporate culture channel. I use an instrumental variable design and interventions by activist hedge funds to test the external validity of the inferences. Across these complementary research designs, I consistently find strong support for the economic importance of a corporate culture channel.


Cyber Aggression / Cyber Bullying and the Dark Triad: Effect on Workplace Behavior / Performance


title: Biological and migrational characteristics of transponders implanted into beagle dogs.

abstract: Ninety transponders (microchips), 45 made of bioglass, 30 made of acid-etched bioglass, and 15 made of bioglass and provided with a polypropylene cap, were inserted into 15 dogs in six different locations: on the left and right side of the head, and on the left and right shoulders, both cranial and caudal to the dorsum. The transponders were left in place for 16 weeks, during which their position was determined by means of an electronic reader and radiographs, and they were then retrieved and examined histologically. A clinical evaluation revealed that about half of the transponders inserted in all the shoulder locations had migrated to some extent, whereas the transponders in the head location had hardly moved. There were no differences in the extent of migration between the different types of transponders. Histological analysis showed that almost all the transponders were surrounded by a thin fibrous capsule with no sign of any gross inflammatory reaction.


title: A nine-test screening battery for athletes: a reliability study

abstract: Studies have shown that reduced neuromuscular control or strength increases the risk of acute injuries. It is hypothesized that a non-functional movement pattern can predispose for injuries. In the present paper a detailed description of a test battery consisting of nine different tests to screen athletic movement pattern is provided. The aim was to evaluate the inter- and intra-rater reliability of the test battery on a group of male elite soccer players. Twenty-six healthy elite soccer players (17-28 years) were screened. Eighteen participated at a second occasion 7 days later. No significant difference (P=0.31) was found between test occasion 1 (LS means 18.3, 95% confidence interval 14.9-21.7) and test occasion 2 (18.0, 14.4-21.7) in the mean total score of the test battery. No significant difference in the inter-rater reliability was found between the eight physiotherapists at the two test occasions. The intra-class correlation coefficient was 0.80 and 0.81, respectively. The test battery showed good inter- and intra-rater reliability. The screening battery is easy to use for familiarized professionals and requires minimal equipment. However, further studies are needed to confirm the validity of the test battery in injury prevention, rehabilitation and performance enhancement.


title: A comparison of methods for sketch-based 3D shape retrieval

abstract: Sketch-based 3D shape retrieval has become an important research topic in content-based 3D object retrieval. To foster this research area, two Shape Retrieval Contest (SHREC) tracks on this topic have been organized by us in 2012 and 2013 based on a small-scale and large-scale benchmarks, respectively. Six and five (nine in total) distinct sketch-based 3D shape retrieval methods have competed each other in these two contests, respectively. To measure and compare the performance of the top participating and other existing promising sketch-based 3D shape retrieval methods and solicit the state-of-the-art approaches, we perform a more comprehensive comparison of fifteen best (four top participating algorithms and eleven additional state-of-the-art methods) retrieval methods by completing the evaluation of each method on both benchmarks. The benchmarks, results, and evaluation tools for the two tracks are publicly available on our websites [1,2].


title: Meta-DiSc: a software for meta-analysis of test accuracy data

abstract: BackgroundSystematic reviews and meta-analyses of test accuracy studies are increasingly being recognised as central in guiding clinical practice. However, there is currently no dedicated and comprehensive software for meta-analysis of diagnostic data. In this article, we present Meta-DiSc, a Windows-based, user-friendly, freely available (for academic use) software that we have developed, piloted, and validated to perform diagnostic meta-analysis.ResultsMeta-DiSc a) allows exploration of heterogeneity, with a variety of statistics including chi-square, I-squared and Spearman correlation tests, b) implements meta-regression techniques to explore the relationships between study characteristics and accuracy estimates, c) performs statistical pooling of sensitivities, specificities, likelihood ratios and diagnostic odds ratios using fixed and random effects models, both overall and in subgroups and d) produces high quality figures, including forest plots and summary receiver operating characteristic curves that can be exported for use in manuscripts for publication. All computational algorithms have been validated through comparison with different statistical tools and published meta-analyses. Meta-DiSc has a Graphical User Interface with roll-down menus, dialog boxes, and online help facilities.ConclusionMeta-DiSc is a comprehensive and dedicated test accuracy meta-analysis software. It has already been used and cited in several meta-analyses published in high-ranking journals. The software is publicly available at http://www.hrc.es/investigacion/metadisc_en.htm.


An Ant Colony Optimization Algorithm for Solving Travelling Salesman Problem