Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural network models for forecast: a review

Neural networks are advocated as a replacement for statistical forecasting methods. The authors review the literature comparing neural networks and classical forecasting methods, particularly in causal forecasting, time series forecasting, and judgmental forecasting. They provide not only an overview and evaluation of the literature but also summarize several studies performed which address the typical criticisms of work in this area. Overall, the empirical studies find neural networks at least as good as their classical counterparts. <<ETX>>
To Combine Silhouette Detection and Stencil Buffer for Generating Real-Time Shadow

This paper describes one of the best methods to generate real-time shadow that called volume shadow. Volume shadow algorithm is possible to implement for virtual environment with moveable illuminated light source. We improve to find silhouette and implementation of the last techniques used in shadow volume algorithms, and limitation of length of volume of each semi-polygon that makes between silhouette's edge and shadow receiver. We rewrite the last volume shadow algorithm using stencil buffer and propose a pseudo code in OpenGl. The best tools that we have used in this algorithm are stencil buffer and Z-buffer.It makes realistic commercial games or it can be use in virtual reality systems.
Two approaches to generate non-equivalent ground terms as test cases for object-oriented software

Software testing is an important technique for improving the reliability and quality of software systems. Object-oriented programming is considered to be the most promising programming methodology. We propose two approaches to generate non-equivalent ground terms as test cases. These approaches are based on the technique of fundamental equivalent pair. The basic idea of these approaches is as follows. Suppose u/sub 1//spl sim/u/sub 2/ is a fundamental equivalent pair, u/sub 2/* is the normal form of u/sub 2/. Suppose further that by changing the values of parameters of constructor contained in u/sub 2/*, or by inserting a basic constructor into u/sub 2/*, we obtain another ground term u/sub 2/', then u/sub 1/ and u/sub 2/' are nonequivalent, and can be used as a test case.
An automated defect management for document images

This paper describes a new approach for automated quality improvement of grey-scale document images, called STORM. The grey-scale images are first adaptively partitioned into representative regions, whose content is analyzed using a set of document image features developed and adapted for the purpose. The document condition and quality information is evaluated for defect pattern classification in a given entity. This data is then processed using a neural network classifier to expose and prioritize the image defects, if any. The evaluation information is further partitioned using the soft control technique by mapping and parametrising the evaluation classes into available image operation techniques. The document type and domain characteristics are used to bias these operations. The experiments cover over 1000 document images in different categories having degradation types in various degree. The outcome shows good results in most of these domains with an automated process.
Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining

The security of the large database that contains certain crucial information, it will become a serious issue when sharing data to the network against unauthorized access. Privacy preserving data mining is a new research trend in privacy data for data mining and statistical database. Association analysis is a powerful tool for discovering relationships which are hidden in large database. Association rules hiding algorithms get strong an efficient performance for protecting confidential and crucial data. Data modification and rule hiding is one of the most important approaches for secure data. The objective of the proposed Weight Based Sorting Distortion (WBSD) algorithm is to distort certain data which satisfies a particular sensitive rule. Then hide those transactions which support a sensitive rule and assigns them a priority and sorts them in ascending order according to the priority value of each rule. Then it uses these weights to compute the priority value for each transaction according to how weak the rule is that a transaction supports. Data distortion is one of the important methods to avoid this kind of scalability issues.
Magnetic and structural studies of LaMnO3 thin films prepared by atomic layer deposition

Here we report the results of structural, microstructural and magnetic property characterizations of both thin films and bulk samples of LaMnO3 (LMO). Thin films were deposited by the atomic layer deposition technique on silicon (1 0 0) substrates, whereas bulk samples were prepared by a citrate combustion route. Effects of varying thickness, annealing atmosphere and temperature were studied on both LMO sample classes. Single-phase perovskite crystal structure was confirmed by x-ray diffraction and Raman spectroscopy, in thin films annealed at 700 and 800 °C as well as in bulk samples. Thin films annealed in N2 or O2 atmosphere do not vary in the crystal structure, but differ by the oxygen stoichiometry, microstructure and magnetic properties. The Curie temperature in all LMO thin films annealed in N2 was found to be around 200 K, while it was around 250 K for the films annealed in O2 as well as for the bulk samples.
Stochastic Modelling for Systems Biology

Modelling and Networks Introduction to Biological Modelling What is modelling? Aims of modelling Why is stochastic modelling necessary? Chemical reactions Modelling genetic and biochemical networks Modelling higher-level systems Representation of Biochemical Networks Coupled chemical reactions Graphical representations Petri nets Stochastic process algebras Systems Biology Markup Language (SBML) SBML-shorthand Stochastic Processes and Simulation Probability Models Probability Discrete probability models The discrete uniform distribution The binomial distribution The geometric distribution The Poisson distribution Continuous probability models The uniform distribution The exponential distribution The normal/Gaussian distribution The gamma distribution Quantifying "noise" Stochastic Simulation Introduction Monte Carlo integration Uniform random number generation Transformation methods Lookup methods Rejection samplers Importance resampling The Poisson process Using the statistical programming language, R Analysis of simulation output Markov Processes Introduction Finite discrete time Markov chains Markov chains with continuous state-space Markov chains in continuous time Diffusion processes Stochastic Chemical Kinetics Chemical and Biochemical Kinetics Classical continuous deterministic chemical kinetics Molecular approach to kinetics Mass-action stochastic kinetics The Gillespie algorithm Stochastic Petri nets (SPNs) Structuring stochastic simulation codes Rate constant conversion Kolmogorov's equations and other analytic representations Software for simulating stochastic kinetic networks Case Studies Introduction Dimerisation kinetics Michaelis-Menten enzyme kinetics An auto-regulatory genetic network The lac operon Beyond the Gillespie Algorithm Introduction Exact simulation methods Approximate simulation strategies Hybrid simulation strategies Bayesian Inference Bayesian Inference and MCMC Likelihood and Bayesian inference The Gibbs sampler The Metropolis-Hastings algorithm Hybrid MCMC schemes Metropolis-Hastings algorithms for Bayesian inference Bayesian inference for latent variable models Alternatives to MCMC Inference for Stochastic Kinetic Models Introduction Inference given complete data Discrete-time observations of the system state Diffusion approximations for inference Likelihood-free methods Network inference and model comparison Conclusions SBML Models Auto-regulatory network Lotka-Volterra reaction system Dimerisation-kinetics model References Index All chapters include exercises and further reading.
A FACE RECOGNITION SYSTEM BY HIDDEN MARKOV MODEL AND DISCRIMINING SET APPROACH

Different approaches have been proposed over the last few years for improving holistic methods for facerecognition. Some of them include color pro- cessing, different face representations and image processingtechniques to increase robustness against illumination changes. T here has been also some research about thecombination of different recognition methods, both at the feature and score levels. Embedded hidden Markov model(E-HHM) has been widely used in pattern recog- nition.The performance of Face recognition by E-HMM heavily depends on the choice of model parameters.In this paper, we proposea discriminating set ofmulti E- HMMs based face recognition algorithm. Experimental results illustrate that compared with the conventional HMMbased face recognition algorithm the proposed method obtainbetter recognition accuracies and higher generalization ability.
Processing and Analysis of Search Query Logs in Chinese

More non-English contents are now available on the World Wide Web and the number of non-English users on the Web is increasing. While it is important to understand the Web searching behavior of these non-English users, many previous studies on Web query logs have focused on analyzing English search logs and their results may not be directly applied to other languages. In this Chapter we discuss some methods and techniques that can be used to analyze search queries in Chinese. We also show an example of applying our methods on a Chinese Web search engine. Some interesting findings are reported.
The Title of an Article

This article illustrates many features of a mathematics article, but we do not explain the spurious appearance of the formula (rF) � k = z + 1 in this abstract. 1. Sample Mathematics and Text This short sample document illustrates the typeset appearance of in-line and dis- played mathematics in documents. It also illustratesve levels of section headings and three kinds of lists. Finally, the document includes entries for a manual bibli- ography and an appendix.
Mach reflection of spherical detonation waves

When two detonation waves collide, the shape of the wave front at their intersection can be used to categorize the flow as regular or irregular reflection. In the case of regular reflection, the intersection of the waves forms a cusp. In the case of irregular reflection, the cusp is replaced by a leading shock locus that bridges the incident waves. Many workers have studied irregular or Mach reflection of detonation waves, but most of the their experimental work has focused on the interaction of plane detonation waves. Reflection of spherical detonation waves has received less attention. This study also differs from previous work in that the focus is to measure the relationship between the detonation velocity and the local wave curvatue for irregular reflection of spherical detonation waves. Two explosives with different detonation properties, PBX 9501 and PBX 9502, are compared.
Supporting Heterogeneous Device Development and Communication

(ABSTRACT) To increase market penetration, mobile software makers support their popular applications on all major software platforms, which currently include Android, iOS, and Windows Phone. Although these platforms often offer a drastically different look and feel, cross-platform applications deliver the same core functionality to the end user. Maintaining and evolving such applications currently requires replicating all the changes across all supported variants, a laborious and intellectually taxing enterprise. The state-of-the-practice automated source translation tools fall short, as they are incapable of handling the structural and idiomatic differences of the software frameworks driving major mobile platforms. In addition, popular mobile applications increasingly make use of distributed resources. Certain domains, including social networking, productivity enhancement, and gaming, require different application instances to continuously exchange information with each other. The current state of the art in supporting communication across heterogeneous mobile devices requires the programmer to write platform-specific, low-level API calls that are hard not only to develop but also to evolve and maintain. This thesis reports on the findings of two complementary research activities, conducted with the goal of facilitating the development and communication across heterogeneous mobile devices: (1) a programming model and runtime support for heterogeneous device-to-device communication across mobile applications; (2) a source code recommendation system that synthesizes code snippets from web-based programming resources, based on the functionality written for Android or iOS and vice versa. The conceptual and practical advancements of this research have potential to benefit fellow researchers as well as mobile software developers and users. This thesis is based on research accepted for publication to SPLASH'15 [25] and GPCE'15 [22]. Dedication I dedicate my thesis to my parents, close friends and most importantly my late grandfather, He has always showered me with wisdom and this journey would not have been possible without his blessings. " Om Sai Ram " iii Acknowledgments " Success is not a destination, it is a journey " Although only my name is listed as the main author for this thesis, there is no way I would have been able to finish the production of this manuscript without the support, wisdom, blessings, help, and love from so many people that I have encountered on this journey. Without their motivation and advice, I would not be the man that I am today. I wish to take a moment to acknowledge and thank the people who were responsible for making my journey through higher education …
Time Changes Things: Time as a Linguistic Resource for Professionals in Information-Intensive Fields / Le temps change toutes choses : Le temps comme ressource linguistique pour les professionnels dans les champs à forte concentration informationnelle

In accounts of identity and making sense of professional roles, time is a linguistic resource that librarians and pharmacists—members of information-rich professions—draw upon. Accounts of time organize narratives of professionalism, make sense of new and evolving roles, frame a profession’s future, and create meaning from the profession’s past. Quand il s’agit d’identité et de donner un sens à des rôles professionnels, le temps est une ressource linguistique très utile aux bibliothécaires et aux pharmaciens, deux professions à forte concentration informationnelle. Le temps raconté met en scène des récits sur le professionnalisme, sur les rôles nouveaux et ceux qui évoluent, cadrent l’avenir d’une profession et créent du sens à partir du passé de cette profession.
Sub-band classification of decomposed single event-related potential co-variants for multi-class brain–computer interface: a qualitative and quantitative approach

Subject dependent and co-variant nature of electroencephalography (EEG)/event-related potential (ERP) are still hurdles in the development of generalised EEG system such as clinical diagnoses and brain–computer interface (BCI) systems. Presently, classification of BCI classes is limited due to consideration of several trials of ERP as average case for generalisation or some time single-trial ERP detection is preferred for subject specific schemes. Present study tries to develop a methodology for utilisation of co-variant nature of ERP, based on the sub-band decomposed ERPs. Proposed idea also explores a new dimension in BCI system design, as multi-class classification for execution of multiple commands through single ERP. Sub-band ERPs and their power spectrum-based features are extracted and classified successfully with 70.64% accuracy using artificial neural network.
Java GPU Enhanced Multi-histogram Volume Rendering Framework for Efficient Bio-medical Visualization

In this work, we present a novel implementation of a Java GPU based multihistogram volume rendering framework that integrates into the multi-dimensional transfer functions [1]. We implement the framework within MIPAV [2], a Java based bio-medical image processing and visualization tool. The integration approach has been performed in two separate steps: Java OpenGL texture viewaligned volume rendering ported from the original C++ implementation [1], and the WildMagic [3] GPU ray-casting based volume rendering. We also demonstrate several applications integrated into the GPU based multi-histogram framework, including GPU based surface rendering, Nvidia 3D Vision stereo rendering. These flexible features make the interactive multi-histogram volume rendering an efficient framework within the MIPAV visualization.
Understanding Sampling-based Adversarial Search Methods

Until 2007, the best computer programs for playing the board game Go performed at the level of a weak amateur, while employing the same Minimax algorithm that had proven so successful in other games such as Chess and Checkers. Thanks to a revolutionary new sampling-based planning approach named Upper Confidence bounds applied to Trees (UCT), today's best Go programs play at a master level on full-sized 19 × 19 boards. Intriguingly, UCT's spectacular success in Go has not been replicated in domains that have been the traditional stronghold of Minimax-style approaches. The focus of this thesis is on understanding this phenomenon. We begin with a thorough examination of the various facets of UCT in the games of Chess and Mancala, where we can contrast the behavior of UCT to that of the better understood Minimax approach. We then introduce the notion of shallow search traps — positions in games where short winning strategies for the opposing player exist — and demonstrate that these are distributed very differently in different games, and that this has a significant impact on the performance of UCT. Finally, we study UCT and Minimax in two novel synthetic game settings that permit mathematical analysis. We show that UCT is relatively robust to misleading heuristic feedback if the noise samples are independently drawn, whereas systematic biases in a heuristic can cause UCT to prematurely “freeze” onto sub-optimal lines of play and thus perform poorly. We conclude with a discussion of the potential avenues for future work.
Plain Language - a Panacea of Linguistic Quality Assurance?

This article questions the use of a set of principles or guidelines, often referred to as language policies or language guides, which proliferate in companies and organisations as a means of harmonizing and modernizing corporate discourse. The language guide recommendations promote certain syntactic and lexical features which seem to have been inspired by the plain language campaigns conducted in various countries with the purpose of setting up a framework for a simplifi cation of the public-sector discourse from administration to citizens. The problem of applying theses principles as an overall instrument of assuring the linguistic quality of private companies has been pointed out on various occasions. However, the argumentation has until now been made on an impressionistic or introspective basis. The article demonstrates that the inventory of linguistics in fact offers a range of possibilities to give a theoretical and empirical support to the arguments. Based on a set of language guide recommendations from Danish companies, it is illustrated how variations in discourse elements, i.e. purpose, domain and audience, conventionally call for variations in the linguistic devices used. In other words, from a discoursal point of view, the set of plain language-like recommendations for paradigmatic stylistic choices cannot be expected to cover every instance of business communication. Furthermore, it is argued that certain intralingual and interlingual features require empirical research in order to establish a stylistically adequate framework of this instrument of language management in companies.
Proceedings of the First Workshop on Unsupervised Learning in NLP

The rapid growth in the amount of computer-readable text in different languages, along with ever developing computational resources, raise much interest in fully automated algorithms for analyzing massive amounts of plain text without using any manually provided input. In addition to obviating the need for costly manual annotation, this line of research gives rise to exciting theoretical questions, exploring what information can be extracted purely by distributional analysis, and characterizing the theoretical significance of the output of such an automatic analysis. Unsupervised learning is the main approach in NLP for addressing this challenge. Although this approach has grown in popularity over the past years and increasingly sophisticated methodology has been introduced, several fundamental challenges remain which need to be resolved and which cannot be effectively discussed in major conferences. This workshop aims to bridge this gap, by summarizing what has been achieved so far in unsupervised learning in NLP, by fostering discussions on these fundamental issues, and by discussing future trends. The workshop encourages discussion on topics such as evaluation of unsupervised algorithms, comparison of different algorithmic approaches, and unsupervised learning across multiple languages. Our invited talk by Sharon Goldwater discusses the role unsupervised learning can play on shedding light on human cognition. The workshop program also includes papers that address unsupervised approaches for a broad variety of NLP tasks, ranging from syntactic parsing to lexical semantics. Finally, the workshop holds a panel discussion for exchanging ideas between leading researchers in the area, in order to gain some insight into how to best tackle the current big challenges in unsupervised NLP. It is our hope that this workshop will provide a better understanding of this research area, and will initiate a series of workshops devoted to this important topic.
Non-heads of Compounds as Valency Bearers: Extraction from Corpora, Classification and Implication for Dictionaries

This paper describes an approach to the classification of nominal compounds based on their subcategorisation. German compound noun predicates, such as Grundproblem, Beweislast and Schlussfolgerung subcategorizing for a subordinate clause are semiautomatically extracted from text corpora and classified according to which of their components, the head or the non-head, is the valency bearer. In over 40% of cases the subcategorisation of compounds is not determined by their heads. This kind of information should be included in subcategorisation lexicons as well as dictionaries for human users. We show that our semi-automatic approach can be applied in natural language processing, especially in lexicon and dictionary creation.
Intrusion detection system based on fuzzy default logic

Current IDSs usually have several shortcomings. First, the speed and sensitivity of detection are not so ideal. Secondly, the response system lacks the ability to correct errors. Thirdly, the cost of intrusion detection is not considered, that is, the response policy is static. This paper applies fuzzy default theory to transform reasoning and response engine of IDS, based on the proving of IDS as non-monotonic, and set up an intelligent IDS-FDL-IDS. The experiment result showed that FDL-IDS increased the detection speed and sensitivity and decreased the cumulative cost as compared with traditional intrusion detection expert system.