Miracle-dz/UniFAR-FTUs huggingface dataset:
Traffic flow forecasting based on multitask ensemble learning

A new method for traffic flow forecasting based on multitask ensemble learning, which combines the advantages of multitask learning and ensemble learning, is proposed. Traditional traffic flow forecasting methods are a single task learning mode, which may neglect potential rich information embedded in some related tasks. In contrast to this, multitask learning can integrate information from related tasks for effective induction. Recent developments also witness the potential of ensemble learning for traffic flow forecasting. This paper devises a new method named MTLBag, a combination of multitask learning and a famous ensemble learning method bagging, for traffic flow forecasting. Using a neural network predictor, this paper first empirically shows the superiority of multitask learning over single task learning for traffic flow forecasting. Experimental results also indicate that the performance of MTLBag is statistically significantly better than that of the multitask neural network predictor, and that MTLBag outperforms a state-of-the-art method Bayesian networks.
Neonatal outcomes among singleton births after blastocyst versus cleavage stage embryo transfer: a systematic review and meta-analysis.

BACKGROUND Several studies have evaluated outcomes of singleton pregnancies after blastocyst versus cleavage stage embryo transfer. Higher incidences of preterm birth (PTB), very preterm birth (VPTB), low birthweight (LBW) and congenital malformations were identified in a few of them. The objective of our study was to systematically review and meta-analyze pregnancy and neonatal outcomes among singleton births following blastocyst versus cleavage stage embryo transfer. METHODS EMBASE, MEDLINE, EBM Reviews and bibliographies of included studies were searched from their inception until March 2013. Observational studies or clinical trials comparing blastocyst with cleavage stage embryo transfer and reporting on outcomes of PTB (<37 weeks), VPTB (<32 weeks), LBW (<2500 g), very low birthweight (VLBW) (<1500 g) and/or congenital anomalies in singleton neonates were included. Data on the outcomes were extracted by two reviewers. Statistical heterogeneity among studies was evaluated by calculating I(2) values and χ(2) statistics. Meta-analyses were conducted to estimate the pooled unadjusted odds ratio (OR) and the adjusted OR (AOR) with a 95% confidence interval (CI) using the random effect model. RESULTS Six observational studies, of low to moderate risk of bias, were included in this review. There were significantly higher odds of PTB (four studies, 54 792 cleavage stage and 20 724 blastocyst stage births; AOR 1.32, 95% CI 1.19-1.46) and congenital anomalies (two studies, 22 068 cleavage stage and 4517 blastocyst stage births; AOR 1.29, 95% CI 1.03-1.62) among births after blastocyst transfer compared with cleavage stage transfer. There was no difference in the adjusted odds of VPTB (four studies, 54 792 cleavage stage and 20 724 blastocyst stage births; AOR 1.18, 95% CI 0.93-1.49), LBW (four studies, 54 109 cleavage stage and 20 392 blastocyst stage births; AOR 1.06, 95% CI 0.99-1.15) or VLBW (three studies, 22 088 cleavage stage and 5772 blastocyst stage births; AOR 1.01, 95% CI 0.73-1.38). CONCLUSIONS Risk of PTB in IVF singleton pregnancies is significantly higher following blastocyst transfer compared with cleavage stage transfer. Risk of congenital anomalies may also be higher but further studies are needed to confirm this finding and to identify reasons for such outcomes.
Study Paper on Test Case generation for GUI Based Testing

With the advent of WWW and outburst in technology and software development, testing the software became a major concern. Due to the importance of the testing phase in a software development life cycle, testing has been divided into graphical user interface (GUI) based testing, logical testing, integration testing, etc.GUI Testing has become very important as it provides more sophisticated way to interact with the software. The complexity of testing GUI increased over time. The testing needs to be performed in a way that it provides effectiveness, efficiency, increased fault detection rate and good path coverage. To cover all use cases and to provide testing for all possible (success/failure) scenarios the length of the test sequence is considered important. Intent of this paper is to study some techniques used for test case generation and process for various GUI based software applications.
The Common Geometry Module (CGM): A Generic, Extensible Geometry Interface

Geometry modeling has in the past five years emerged as a commodity capability; several geometry modeling engines are available which provide largely the same capability, and most high-end CAD systems provide access to their geometry through APIs. However, subtle differences still exist between these modelers, both at the syntax level and in the underlying topological models. A modeler-independent interface to geometry is critical to solving many of the geometry-based problems which exist in applications like mesh generation. The Common Geometry Module, or CGM, provides such an interface to geometry. CGM consists of a set of wrapper functions which translate function calls such that they access geometry in its native format. However, CGM also provides functionality not found in most modelers, like support for non-manifold topology, and alternative geometric representations, including mesh-based and facet-based geometry. CGM is designed to be extensible, so that applications can define application-specific behavior for geometry entities. CGM is also designed to be easily portable to other geometry modeling engines. Ports to SolidWorks and Pro/Engineer are underway. 1 SANDIA IS A MULTIPROGRAM LABORATORY OPERATED BY SANDIA CORPORATION, A LOCKHEED MARTIN COMPANY, FOR THE UNITED STATES DEPARTMENT OF ENERGY UNDER CONTRACT DE-AC04-94AL85000.
Counting and Classification of Highway Vehicles by Regression Analysis

In this paper, we describe a novel algorithm that counts and classifies highway vehicles based on regression analysis. This algorithm requires no explicit segmentation or tracking of individual vehicles, which is usually an important part of many existing algorithms. Therefore, this algorithm is particularly useful when there are severe occlusions or vehicle resolution is low, in which extracted features are highly unreliable. There are mainly two contributions in our proposed algorithm. First, a warping method is developed to detect the foreground segments that contain unclassified vehicles. The common used modeling and tracking (e.g., Kalman filtering) of individual vehicles are not required. In order to reduce vehicle distortion caused by the foreshortening effect, a nonuniform mesh grid and a projective transformation are estimated and applied during the warping process. Second, we extract a set of low-level features for each foreground segment and develop a cascaded regression approach to count and classify vehicles directly, which has not been used in the area of intelligent transportation systems. Three different regressors are designed and evaluated. Experiments show that our regression-based algorithm is accurate and robust for poor quality videos, from which many existing algorithms could fail to extract reliable features.
Checking models based on an iterative co-specification process of a critical system

Recurrent incident reports indicate that critical systems such as power plants exhibit unintended emergent behaviors outside of acceptable limits, despite of the increasing development of dependable automation technologies as well as of a lot of techniques related to dependability issues. Among many causes, the role of human for operating technical artefacts is of importance, but also for designing them from the early stages of specification in order to check the basic property of wholeness of any system. A mean is to ensure a continuum of compliant models to component integration through an iterative process between all the disciplines involved to engineer these systems as whole all along their life cycle. However, a human-based process to check the “right-system requirements-right” remains not fully adequate at the scale of real systems engineering projects and in any case to critical issues. This paper explores the formal checking compliance of architecting models with dependability requirements. These models are refined iteratively by specialist and specialty engineers interoperating with a system engineer through a co-specification process on a particular case study of a critical power-plant sub-system.
Statistical Learning for Texture Characterization

In this paper, we propose a novel method for image texture characterization. Characterization is governed by simple perceptual variations in relative orientations in terms of either no variations present or variations present as row specific, column specific or diagonal specific. This generalization is obtained by modeling the input as a whole or image blocks depending on the broader or narrow coverage respectively. Most of the texture characterization is done either keeping a specific domain (synthetic or natural images specific to a category) or is application specific (segmentation on specific benchmark dataset or image retrieval). Contrary to this, our method is not biased towards any domain or application; rather it acts as a pre-processing step for guiding towards locating both non-textural and orientation specific textural image blocks. The proposed method quantifies the texture-tonal characterization of an image or image blocks using statistical ANOVA grading system. Once the grading for abstraction is assigned for both image as a whole and also for image blocks, the decision as to which higher-level algorithms need to be implemented on which block will become easier. The proposed method can be considered to be a three stage process - progressive sampling, image partitioning in blocks, ANOVA analysis and grading.
Proposal of paper-based cross-media cooperation system

This paper presents a paper-based cross-media cooperation concept, in which paper materials are used for portals of digital contents to overcome the limitation of digital content accessibility. The concept and approaches of the paper-based cross-media cooperation are introduced and the system features of the experimental system are described. Through these discussions, the effectiveness and practicality of paper-based cross-media cooperation are identified.
A survey of prediction models for breast cancer survivability

Breast cancer prognosis poses a great challenge to the researchers. Recently, there have been breakthroughs in the field of bioinformatics and because of that a new realm of breast cancer prognosis has opened. The use of machine learning and data mining techniques has revolutionized the whole process of breast cancer prognosis. In this paper we present a survey of those models that are being used to enhance the breast cancer prognosis prediction. Firstly, we introduce these models and secondly we give an overview of the current research being carried out using these models. We specify different level of accuracies being claimed by different researchers. Lastly, we conclude that despite the ongoing research efforts towards achieving better capabilities for prediction system, we still need much more to build a more accurate and less invasive prognostic system that can benefit the mankind.
Variable Timestep Integrators for Long-Term Orbital Integrations

Symplectic integration algorithms have become popular in recent years in long-term orbital integrations because these algorithms enforce certain conservation laws that are intrinsic to Hamiltonian systems. For problems with large variations in timescale, it is desirable to use a variable timestep. However, naively varying the timestep destroys the desirable properties of symplectic integrators. We discuss briefly the idea that choosing the timestep in a time symmetric manner can improve the performance of variable timestep integrators. Then we present a symplectic integrator which is based on decomposing the force into components and applying the component forces with different timesteps. This multiple timescale symplectic integrator has all the desirable properties of the constant timestep symplectic integrators.
Hadoop based real-time Big Data Architecture for remote sensing Earth Observatory System

Recently, Big Data analytics emerged as a hot topic because of the incredible growth of the information and communication technology. One of the exceedingly anticipated key contributors of the Big Data is real-time Earth Observatory System (EOS). Although the data generated by the individual satellite in EOS may not be significant, the overall data generated across numerous satellites may yield to the significant amount of the Big Data. Thus, extracting the useful information in an efficient manner leads a system towards major computational challenges in EOS, such as, to analyze, to aggregate, and to store, where data is remotely collected. Therefore, the paper proposes a set of requirements for achieving pervasive, integrated information system of EOS and associated services (real-time and offline data processing). The Big Data Architecture is also proposed to address all the aspect of the Big Data ecosystem and includes the following components: Data Acquisition Unit, Data Processing Unit, Data Storage Unit, and Data Analysis and Decision Unit. The proposed architecture is termed as Holistic as it considers the flow of data from satellites to services, which is designed for efficiently process and analyze the Big Data. Finally, a detailed analysis of remotely sensed earth observatory Big Data for Land and Sea area are provided using UBUNTU 14.04 LTS core™i5 machine with 3.2 GHz processor and 4 GB memory. The results show that the proposed network architecture efficiently process EOS data at a real-time as well as offline.
Performance of coded multi-hop ad-hoc networks in OFDM wireless environments

Multi-hop networks help in combating fading by employing a distributed space diversity system. The performance of the system can be further enhanced by employing a good error control code such as convolutional codes, which provide low implementation complexity and short delays. In this paper we investigate the error performance of a convolutionally coded multi-hop ad-hoc network in an OFDM wireless environment with serial and selection relaying. In particular, we investigate the effect of relaying scheme, channel, bandwidth, and path loss exponent on the performance. Results show that the relaying improves the system greatly especially in the case of selection relaying and in high path loss environments.
Entropy-Driven Parameter Control for Evolutionary Algorithms

Every evolutionary algorithm needs to address two important facets: exploration and exploitation of a search space. Evolutionary search must combine exploration of the new regions of the space with exploitation of the potential solutions already identified. The necessity of balancing exploration with exploitation needs to be intelligent. This paper introduces an entropy-driven parameter control approach for exploring and exploiting evolutionary algorithms. Entropy represents the amount of disorder of the population, where an increase in entropy represents an increase in diversity. Four kinds of entropy to express diversity and to control the entropy-driven approach are discussed. The experimental results of a unimodal, a multimodal with many local minima, and a multimodal with only a few local minima functions show that the entropy-driven approach achieves good and explicit balance between exploration and exploitation.
Researching Systemic Therapy History: In Search of a Definition

ABSTRACT This article reviews literature describing systemic therapy and it’s basic elements. Today, many different models appear under the same umbrella term, “systemic therapy,” sharing little more than a few features. However, a comprehensive definition is needed to meet political and ethical demands, especially within the field of mental health. Our proposed definition draws on elements from the Batesonian and Milan tradition, as well as the narrative, postmodern, and dialogical developments. Our aim is to help practitioners outside the systemic field to understand what systemic therapy is, and to support “insiders” in better describing the foundations and practices systemic therapy.
Online support for tracking of publication resources

The paper presents an online application that enables to follow citations of own scientific publication resources. The application cooperates with Web of Science database, Scopus, Google Scholar and ResearchGate. Its main aim is to eliminate duplicates of resources and to enable to track own scientific progress. It also enables to prepare BibTeX file of publications for later use in other papers.
Distributed Consensus With Limited Communication Data Rate

Communication data rate and energy constraints are important factors which have to be considered when investigating distributed coordination of multi-agent networks. Although many proposed average-consensus protocols are available, a fundamental theoretic problem remains open, namely, how many bits of information are necessary for each pair of adjacent agents to exchange at each time step to ensure average consensus? In this paper, we consider average-consensus control of undirected networks of discrete-time first-order agents under communication constraints. Each agent has a real-valued state but can only exchange symbolic data with its neighbors. A distributed protocol is proposed based on dynamic encoding and decoding. It is proved that under the protocol designed, for a connected network, average consensus can be achieved with an exponential convergence rate based on merely one bit information exchange between each pair of adjacent agents at each time step. An explicit form of the asymptotic convergence rate is given. It is shown that as the number of agents increases, the asymptotic convergence rate is related to the scale of the network, the number of quantization levels and the ratio of the second smallest eigenvalue to the largest eigenvalue of the Laplacian of the communication graph. We also give a performance index to characterize the total communication energy to achieve average consensus and show that the minimization of the communication energy leads to a tradeoff between the convergence rate and the number of quantization levels.
DR-BB Rat Thymus Contains Thymocyte Populations Predisposed to Autoreactivity

We have induced autoimmune insulin-dependent diabetes mellitus (IDDM) in athymic WAG rats by transfusing thymocytes from histocompatible phenotypically normal rats of the DR-BB strain. DR-BB rats rarely develop spontaneous IDDM, but readily become hyperglycemic if depleted in vivo of regulatory T-cells that express the RT6.1 maturational alloantigen. Successful adoptive transfer of IDDM by DR-BB thymocytes required that the athymic recipients be depleted of emerging populations of donor-origin RT6.1+ T-cells. Thymocytes from both normal and RT6-depleted diabetic DR donors were equally capable of transferring autoimmunity. In contrast, thymocytes from normal histocompatible YOS rats failed to transfer IDDM. The autoreactive potential of DR-BB rat thymocytes was minimal from birth to 4 weeks of age and then increased substantially at 8–9 weeks of age. These results demonstrate that the DR-BB rat thymus harbors abnormal cell populations predisposed to autoreactivity. The data localize the developmental defect leading to diabetes in the BB rat to an abnormal intrathymic selection process.
Performance and perceptions of human computation games for image tagging

Applications that use games to harness human intelligence to perform various computational tasks are known as human computation games (HCGs). Most HCGs are collaborative in nature where players to cooperate to score points. Competitive versions, where players work against each other have been argued to address shortcomings of collaborative HCGs. However, there is yet little work done in understanding how different HCG genres affect players' perceptions and performance. In this paper, we focus on image tagging HCGs, where games are used to generate keywords for images. Three versions were created: collaborative HCG, competitive HCG and a control application for manual tagging. The applications were evaluated to examine the quality of the tags generated and users' perceptions of these genres. Results suggest while participants reported liking the collaborative and competitive HCGs over the control application, those using the latter seemed to generate better quality of tags.
Altruism in groups: An evolutionary games approach

We revisit in this paper the relation between evolution of species and the mathematical tool of evolutionary games, which has been used to model and predict it. We indicate known shortcoming of this model that restricts the capacity of evolutionary games to model groups of individuals that share a common gene or a common fitness function. In this paper we provide a new concept to remedy this shortcoming in the standard evolutionary games in order to cover this kind of behavior. Further, we explore the relationship between this new concept and Nash equilibrium or ESS. We indicate through the study of some example in the biology as Hawk-Dove game, Stag-Hunt Game and Prisoner's Dilemma, that when taking into account a utility that is common to a group of individuals, the equilibrium structure may change dramatically. We also study the multiple access control in slotted Aloha based wireless networks. We analyze the impact of the altruism behavior on the performance at the equilibrium.
Fuzzy Inference with Parameter Identification for Indoor WLAN Positioning

This paper considers the fuzzy inference as position estimator for WLAN indoor environments, based on received signal strength measurements RSS. The proposal algorithm includes a fuzzy inference which uses the k-nearest neighbor classification in signal space, where the position of target node is calculated as a weighted combination of nearest fingerprints, where the weights are estimated using enhanced Takagi–Sugeno fuzzy controller with multivariable inputs and parameter identification with constrained optimization. The new developed technique is proposed to enhance the accuracy of position estimation in WLAN indoor environments;
Virtual function placement and traffic steering in flexible and dynamic software defined networks

The integration of network function virtualization (NFV) and software defined networks (SDN) seeks to create a more flexible and dynamic software-based network environment. The line between entities involved in forwarding and those involved in more complex middle box functionality in the network is blurred by the use of high-performance virtualized platforms capable of performing these functions. A key problem is how and where network functions should be placed in the network and how traffic is routed through them. An efficient placement and appropriate routing increases system capacity while also minimizing the delay seen by flows. In this paper, we formulate the problem of network function placement and routing as a mixed integer linear programming problem. This formulation not only determines the placement of services and routing of the flows, but also seeks to minimize the resource utilization. We develop heuristics to solve the problem incrementally, allowing us to support a large number of flows and to solve the problem for incoming flows without impacting existing flows.