Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary algorithms and weighting strategies for feature selection in predictive data mining

The improvements in Deoxyribonucleic Acid (DNA) microarray technology mean that thousands of genes can be profiled simultaneously in a quick and efficient manner. DNA microarrays are increasingly being used for prediction and early diagnosis in cancer treatment. Feature selection and classification play a pivotal role in this process. The correct identification of an informative subset of genes may directly lead to putative drug targets. These genes can also be used as an early diagnosis or predictive tool. However, the large number of features (many thousands) present in a typical dataset present a formidable barrier to feature selection efforts. Many approaches have been presented in literature for feature selection in such datasets. Most of them use classical statistical approaches (e.g. correlation). Classical statistical approaches, although fast, are incapable of detecting non-linear interactions between features of interest. By default, Evolutionary Algorithms (EAs) are capable of taking non-linear interactions into account. Therefore, EAs are very promising for feature selection in such datasets. It has been shown that dimensionality reduction increases the efficiency of feature selection in large and noisy datasets such as DNA microarray data. The two-phase Evolutionary Algorithm/k-Nearest Neighbours (EA/k-NN) algorithm is a promising approach that carries out initial dimensionality reduction as well as feature selection and classification. This thesis further investigates the two-phase EA/k-NN algorithm and also introduces an adaptive weights scheme for the k-Nearest Neighbours (k-NN) classifier. It also introduces a novel weighted centroid classification technique and a correlation guided mutation approach. Results show that the weighted centroid approach is capable of out-performing the EA/k-NN algorithm across five large biomedical datasets. It also identifies promising new areas of research that would complement the techniques introduced and investigated.
Nonequilibrium dynamics of mixtures of active and passive colloidal particles

We develop a mesoscopic field theory for the collective nonequilibrium dynamics of multicomponent mixtures of interacting active (i.e., motile) and passive (i.e., nonmotile) colloidal particles with isometric shape in two spatial dimensions. By a stability analysis of the field theory, we obtain equations for the spinodal that describes the onset of a motility-induced instability leading to cluster formation in such mixtures. The prediction for the spinodal is found to be in good agreement with particle-resolved computer simulations. Furthermore, we show that in active-passive mixtures the spinodal instability can be of two different types. One type is associated with a stationary bifurcation and occurs also in one-component active systems, whereas the other type is associated with a Hopf bifurcation and can occur only in active-passive mixtures. Remarkably, the Hopf bifurcation leads to moving clusters. This explains recent results from simulations of active-passive particle mixtures, where moving clusters and interfaces that are not seen in the corresponding one-component systems have been observed.
Decentralized interaction and co-adaptation in the repeated prisoner's dilemma

The purpose of this paper is to propose a nonparametric interest rate term structure model and investigate its implications on term structure dynamics and prices of interest rate derivative securities. The nonparametric spot interest rate process is estimated from the observed short-term interest rates following a robust estimation procedure and the market price of interest rate risk is estimated as implied from the historical term structure data. That is, instead of imposing a priori restrictions on the model, data are allowed to speak for themselves, and at the same time the model retains a parsimonious structure and the computational tractability. The model is implemented using historical Canadian interest rate term structure data. The parametric models with closed form solutions for bond and bond option prices, namely the Vasicek (1977) and CIR (1985) models, are also estimated for comparison purpose. The empirical results not only provide strong evidence that the traditional spot interest rate models and market prices of interest rate risk are severely misspecified but also suggest that different model specifications have significant impact on term structure dynamics and prices of interest rate derivative securities.
Explorando a combinação de visualização de software com clusterização de dados em um processo de reconstrução de arquitetura

Modernizing a legacy system is a costly process that requires deep understanding of the system architecture and its components. Without an understanding of the software architecture that will be rewritten, the entire process of reengineering can fail. When there is absence of architectural documents, it is important to have a recovery process of architecture that allows the complete understanding of the software. Such process involves mapping of source code entities in high-level models. Previous work using visualization and clustering techniques has been proposed and extensively used. However, there is still important improvements that need to be addressed based on this theme. Thus, this work proposes to explore if an approach where visualization and clustering applied together can provide a higher accuracy on the software architecture recovery process. An experimental study was conducted to empirically evaluate our investigation. The results indicated a statistically significant increase in the accuracy of the models produced.
IOS Auto Layout Demystified (Mobile Programming)

Completed Updated for iOS 7 and Xcode 5 Auto Layout transforms the way you create iOS user interfaces. As flexible as it is powerful, Auto Layout gives you unprecedented control over your iOS user interfaces. But Auto Layout has a reputation for difficulty. In iOS Auto Layout Demystified, Second Edition, world-renowned iOS developer and author Erica Sadun strips away the confusion, helping you gain Auto Layout mastery the easiest way possible: by example. Fully updated for iOS 7 and Xcode 5, this tutorial delivers everything Saduns guides are famous for: clear explanations, expert tips, proven best practices--and, above all, plenty of code to learn from and reuse. Step by step, Sadun explains how Auto Layout thinks, how it works, where it fits, and why its more useful (and simpler) than you ever imagined. She offers practical solutions for a wide variety of real-world iOS development challenges, plus innovative ways to use Auto Layout to build interactive elements, animations, and more. If youre an experienced iOS developer, this guide will expand your design possibilities, helping you build apps that are superbly usable and intuitive and stand out even in the most crowded marketplace. Coverage includes Mastering Auto Layouts basic concepts, techniques, and approach Defining unambiguous, satisfiable constraints that express your layout exactly how you intend Overcoming obstacles to constraint-based user interface design in Interface Builder Using visual formatting to express how items are laid out vertically and horizontally Making visual formats more flexible with metrics dictionaries and layout options Debugging constraints and deciphering those bewildering Xcode log messages Using descriptive techniques to uncover and express natural relationships in your design Applying iOS Auto Layout techniques in Mac OS X apps too
Formation of Massive Counterrotating Disks in Spiral Galaxies

We present results of numerical simulations of the formation of a massive counterrotating gas disk in a spiral galaxy. Using a hierarchical tree gravity solver combined with a sticky-particle gas dissipation scheme for our simulations, we have investigated three mechanisms: episodic and continuous gas infall, and a merger with a gas-rich dwarf galaxy. We find that both episodic and continuous gas infall work reasonably well and are able to produce a substantial gas counterrotating disk without upsetting the stability of the existing disk drastically, but it is very important for the gas to be well-dispersed in phase-space and not form concentrated clumps prior to its absorption by the disk galaxy. The initial angular momentum of the gas also plays a crucial role in determining the scale length of the counterrotating disk formed and the time it takes to form. The rate of infall, i.e. the mass of gas falling in per unit time, has to be small enough to preclude excessive heating of the preexisting disk. It is much easier in general to produce a smaller counterrotating disk than it is to produce an extensive disk whose scale length is similar to that of the original prograde disk. A gas-rich dwarf merger does not appear to be a viable mechanism to produce a massive counterrotating disk, because only a very small dwarf galaxy can produce a counterrotating disk without increasing the thickness of the existing disk by an order of magnitude, and the time-scale for this process is prohibitively long because it makes it very unlikely that several such mergers can accumulate a massive counterrotating disk over a Hubble time.
Importance Map Computation of HD Video For The Coding In H.264/AVC

The latest standard for the HDTV is H.264/AVC, and because of the great resolution of HDTV (High Defined TV), the encoding process is computationally very expensive and is not suitable in a real-time context. The ArchiPEG project plan to pre-process the HD video before the encoding, A segmentation based on motion, texture, color and so on has been already achieved for the Group of Frames (GOF). In this report, according to the Human Visual System and some results of other researchers, we generate a new method of the Importance Map (IM) calculation by analyzing the color, size, position and motion elements of the video frame in H.264 coding, and this IM is necessary for setting up the quantization step of the encoder. For the experiment result, a comparison with eye tracking data are done.
User Interface Generation from the Data Schema

Traditionally, the data model and the process model have been considered separately when modeling an application for construction purposes. The system analysis and design area has largely ignored the issue of the relationship between the user interface (UI) and the underlying data schema, leaving UI creation within the purview of the human computer interaction (HCI) literature. Traditional HCI methods however, underutilize the information in the data schema when designing user screens. Much of the work on automatic user interface (UI) generation has met with limited success because of the added load on the human designer to use specialized scripts for UI specification. In this research in progress, the authors propose a methodology applicable to database driven systems that a) automatically infers a draft interface directly from an extended entity relationship (EER) model schema and b) lists the interactions that need to take place between the designer and the tool in order to generate the final user schema.
Large-Scale Graph Processing Analysis using Supercomputer Cluster

Graph implementation is widely use in various sector such as automotive, traffic, image processing and many more. They produce graph in large-scale dimension, cause the processing need long computational time and high specification resources. This research addressed the analysis of implementation large-scale graph using supercomputer cluster. We impelemented graph processing by using Breadth-First Search (BFS) algorithm with single destination shortest path problem. Parallel BFS implementation with Message Passing Interface (MPI) used supercomputer cluster at High Performance Computing Laboratory Computational Science Telkom University and Stanford Large Network Dataset Collection. The result showed that the implementation give the speed up averages more than 30 times and eficiency almost 90%.
Tools for Re‐Randomization

With the rising popularity of field experiments in economics, re&#8208;randomization schemes have emerged as tools to induce balance in observable variables across treatment groups. However, re&#8208;randomization is not fully understood and the methodologies to estimate its effects on the distribution of parameters are still under&#8208;developed. This paper helps to close that gap by suggesting an asymptotically normal re&#8208;randomization scheme and bootstrapping procedure to carry inference under a wide range of estimators and re&#8208;randomization&#160;schemes.
Extended gravitational decoupling in 2  +  1 dimensional space-times

In this work we extend the so-called minimal geometric deformation method in 2 + 1 dimensional space-times with cosmological constant in order to deal with the gravitational decoupling of two circularly symmetric sources. We find that, even though the system here studied is lower dimensional and it includes the cosmological constant, the conditions for gravitational decoupling of two circularly symmetric sources coincides with those found in the 3 + 1 dimensional case. We obtain that, under certain circumstances, the extended gravitational decoupling leads to the decoupling of the sources involved in the sense that both the isotropic and the anisotropic sector satisfy Einstein’s field equations and the final solution corresponds to a non-linear superposition of two metric components. As particular examples, we implement the method to generate an exterior charged BTZ solution starting from the BTZ vacuum as the isotropic sector and new 2 + 1 black hole solutions imposing a barotropic equation of state for the anisotropic sector. We also show that the imposition of a polytropic equation of state of the decoupler matter allows to construct a regular black hole solution in three-dimensional gravity.
On the application of Bayesian statistics to protein structure calculation from nuclear magnetic resonance data

In the present work, we use concepts of Bayesian statistics to infer the three-dimensional structures of proteins from experimental data. We thus build upon the method of inferential structure determination (ISD) as introduced by Rieping et al. (2005). In line with their probabilistic approach, we factor the probability of a three-dimensional protein structure given the experimental data, into a prior distribution that captures the protein-likeness of a structure and the likelihood that describes how likely the experimental data were generated from a given three-dimensional structure. In this Bayesian framework, we attempt to develop structure calculation from NMR experiments into a highly accurate, objective and parameter-free process. We start by focusing on integrating new types of data, as ISD currently does not entail a mechanism to incorporate chemical shifts in the calculation process. To alleviate this shortcoming, we propose a hidden Markov Model that captures the relationship between protein structures and chemical shifts. Based on our probabilistic model, we are able to predict the secondary structure and dihedral angles of a protein from chemical shifts. Another means to high quality structures involves improving the potential functions that form the core of ISD’s prior distributions. Although potential functions are designed to approximate physical forces, there are still parameters, such as force constants and temperatures, that are set on an ad hoc basis and can bias the structure calculation. As an alternative, we propose an algorithm based on Bayesian model comparison to determine these parameters from the data. Further, we demonstrate that optimal data-dependent parameters lead to improved accuracy and quality of the final structure, especially with sparse and noisy data. These findings dismiss the notion of a single universal parameter and advocate the estimation of free parameters based on experimental data instead. Third, we focus on the estimation of new potential functions to include even more prior information in the structure calculation process. Currently, only a few methods allow the estimation of potential functions from a database of known structures. Our method provides a sound mathematical solution of this problem, which is also known as the inverse problem of statistical mechanics.We demonstrate the effectiveness of our approach on the examples of simple fluids and a coarse-grained protein model.
Big Data Management Challenges, Approaches, Tools and their limitations

Big Data is the buzzword everyone talks about. Independently of the application domain, today there is a consensus about the V's characterizing Big Data: Volume, Variety, and Velocity. By focusing on Data Management issues and past experiences in the area of databases systems, this chapter examines the main challenges involved in the three V's of Big Data. Then it reviews the main characteristics of existing solutions for addressing each of the V's (e.g., NoSQL, parallel RDBMS, stream data management systems and complex event processing systems). Finally, it provides a classification of different functions offered by NewSQL systems and discusses their benefits and limitations for processing Big Data.
Tool-Support for Software Development Processes

The quality demands on to-day’s software together with the demands on the development processes are increasing due to the growing complexity of today’s software products. In answering these demands the guidance of software development by a software development process model and the support of all activities by tools has nowadays become state-ofthe-art, leading to so-called software engineering environments (SEE). Due to increased awareness of project risks and cheaper software and hardware, the utilization of SEEs becomes interesting not only for large but also for small and medium enterprises (SME). Traditionally tools always supported software development for certain development tasks like code-generation, design support etc. The understanding that the development process needs also to be incorporated and supported has brought AbstrAct
Relationship between female secondary education and economic growth of Pakistan

The main focusing of this study is to examine empirically the connection between female secondary school enrollment and economic growth of Pakistan taking the period of 1975-2014. The variables of the series passed the test of stationary by the first difference as evaluated by the ADF and PP test. Therefore, by employing the Johansen test of cointegration, the result shows that female secondary school enrollment and labor employment have insignificantly long run positive influence on economic growth, however, capital formation has significantly positive impact on economic growth of Pakistan. The Granger causality test based on VECM shows that female secondary school enrollment and GDP have long run two-way causality, however, the short run bidirectional causality does not exist but unidirectional causality, which is running from GDP to female secondary school enrollment.
Single machine scheduling with slack due dates assignment

ABSTRACT This paper considers a single machine scheduling problem in which each job is assigned an individual due date based on a common flow allowance (i.e. all jobs have slack due date). The goal is to find a sequence for jobs, together with a due date assignment, that minimizes a non-regular criterion comprising the total weighted absolute lateness value and common flow allowance cost, where the weight is a position-dependent weight. In order to solve this problem, an time algorithm is proposed. Some extensions of the problem are also shown.
Socioeconomic status permanent income and fertility: a latent variable approach.

Regardless of the specific underlying theoretical model it is safe to say that most studies of the determinants of childbearing focus on or control for the role of socioeconomic factors. In this paper we examine how permanent income one of several components of SES relates to fertility in developing countries and also pay attention to the role of other socioeconomic factors. Given that we cannot directly measure permanent income we employ a latent variable approach to studying its impact on fertility. We compare our results to the more common practice of using a proxy variable to measure permanent income and thereby investigate the consequences of not accounting for the measurement error that is inherent in proxies. Using micro survey data from Ghana and Peru we find that permanent income has a large negative influence on fertility and that research must take the latent nature of permanent income into account to uncover its influence. Our results also show that controlling for measurement error in the proxies of permanent income can lead to substantial changes in the estimated effects of control variables. Finally we examine which of the common proxies for permanent income most closely capture the concept. Though our focus is on childbearing our results have implications beyond this specific dependent variable providing evidence on the sensitivity of microanalyses to the treatment of long-term economic status. (authors)
How Chinese Commercial Banks Innovate: Process and Practice

This paper describes the financial innovation approach and process adopted by large commercial banks operating in China. Data are collected from three case firms using a qualitative case study method. The research shows that regulation can be a catalyst or a hindrance to financial innovation, depending upon the degree of coherence between a bank’s innovation strategy and local government policy. Performance pressure helps drive innovation, whereas organisational bureaucracy and trajectory are major barriers to innovation. Banks adopt different approaches to the innovation process due to differences in institutional arrangement and institutional environment. Practitioners should consider the institutional effects when engaging in financial innovation. This research contributes to current practice by pointing out an integrated approach to financial innovation and regulatory dialectic.
Efficient algorithm for XML query based sequence matching——SCALER+

The problems of SequenCe bAsed XML QuEry AlgoRithm(SCALER)were solved.The first was definitely supporting wildcard and descendant axis //,and the second was supporting queries of pattern trees with out-of-order brother nodes.The extended algorithm was named SCALER+. Experimental results prove that it is efficient as SCALER and stronger for twig query evaluation.