Miracle-dz/UniFAR-FTUs huggingface dataset:
Immunized adaptive critics for level 2 intelligent control

Immunized computational systems combine a priori knowledge with the adapting capabilities of immune systems to provide a powerful alternative to currently available techniques for intelligent control. In this paper, we present a perspective on various levels of intelligent control and relate them to similar functioning in human immune systems. A technique for implementing immunized computational systems as adaptive critics is presented and the technique is then applied to a simple two degree-of-freedom control problem as well as a more complex flight path generator for level 2, nonlinear, full-envelope, intelligent aircraft control.
Most expressive feature extracted by half-quadratic theory and multiresolution analysis in face recognition

In this paper, a new model for extracting most expressive feature by half-quadratic theory and multiresolution analysis is proposed. The new model has two main advantages over some famous feature extraction algorithms. First, it is robust towards a number of outliers, especially when applied in face recognition. A robust M-estimator has been introduced into the new model to estimate robust scale parameter, so the generalizing ability of the new model is enhanced. Second, the new model could extract features in multiscale space with the help of 2D wavelet decomposition. The validity of the new algorithm is confirmed by applied in face recognition. Yale and FERET face database are used in experiments. The experimental results have confirmed that the new model could cope with various outliers in face recognition, such as occlusions, making up, lighting conditions and incomplete face images, etc. And it could outperform some famous algorithms (PCA, RPCA, FLD and ICA).
Minimum Spanning Tree-Based Approach for Reliability Estimation of COTS-Based Software Applications

Software reliability is defined as the probability of the failure-free operation of a software system for a specified period of time in a specified environment. Traditional approaches for software reliability analysis treat the software as a whole, use test data during software test phase and model only the interactions with outside world; these are known as black box models. Day-by-day the complexity of software application is growing and there is more emphasis on reuse. So, Component-Based Software (CBS) applications have come into existence. The black box models ignore the structure of the software made out of components and reliability of individual components, and thus are not appropriate to model the CBS. This has generated the research interest of architecture-based reliability. This paper introduces a reliability model and reliability analysis technique for architecture-based reliability evaluation.
A general framework for describing the requirements engineering process

This article concerns automated guidance of the requirements engineering process in a CASE environment. We see guidance as part of a broader framework for process tracing, method engineering and process control which is sketched in the paper. This framework supports the claim that a process knowledge base cannot be built once and for all but must be constantly improved. This leads to the idea of a process knowledge base composed of a set of process chunks which are progressively defined. This is achieved by using the concepts of a guidance meta-model which is the kernel of the paper. <<ETX>>
Color printed image defect detection based on the image feature match

Color printed image defect detection algorithm based on the sift method and YIQ model is proposed. At first, pre-treatment is conducted with median filter for the color printing image. Feature points of color printing image are extracted with Beaudet algorithm, and then, they are described with sift algorithm. For each sift feature point, YIQ color feature is extracted. Finally, the feature vector about sift and YIQ feature of the feature points in the color printing image and standard specimen page image are matched, for determining the defect region of the color printing image. According to the experimental results, this method can detect the defects of color printing image rapidly and effectively
Content Analysis: Frequency Distribution of Words

Many academic fields use content analysis. At the core of most common content analysis lies frequency distribution of individual words. Websites and documents are mined for usage and frequency of certain words. In this article, we introduce a community-contributed command, wordfreq, to process content (online and local) and to prepare a frequency distribution of individual words. Additionally, another community-contributed command, wordcloud, is introduced to draw a simple word cloud graph for visual analysis of the frequent usage of specific words.
Classification system using parallel genetic algorithm

Classification task aims at predicting the value of the class attribute of new input data on the basis of a set of pre-classified samples. Traditional machine learning algorithms for classification are usually domain specific or produce unsatisfactory results whenever applied to classification problems with larger size or imbalanced data. Thus, to accumulate genuine useful knowledge for making decision, we introduce here a new intelligent knowledge discovery model, combining C4.5 (a decision tree-based rule inductive algorithm) with a new parallel genetic algorithm (GA) based on the idea of massive parallelism (MP). The model is named as CGAMP (C4.5 and GA based on MP). More specifically, the suggested model receives a base method C4.5 to produce rules which are then refined by the proposed parallel GA to provide more accurate rules. The strength of the developed system has been compared with pure C4.5 and a hybrid system (combining C4.5 and sequential genetic algorithm) on six real world benchmark data sets (collected from University of California at Irvine machine learning repository). The experimental results validate the effectiveness of the new model.
Structural and Semantic Information Extraction

Information Extraction, the procedure where only relevant segments of text are extracted from a document, has been applied to several real world problems including stock quote extraction and comparison shopping. This thesis presents a method of Information Extraction where HTML pages are analyzed first at the structural level and secondly at a semantic level. Furthermore, this thesis presents a software agent that crawls the web in search of ongoing MIT lectures, seminars, and events. The software agent individually parses events and provides users with a web interface to a database of heterogeneous events. Thesis Supervisor: Peter Szolovits Title: Professor
ON THE STRUCTURE AND INTERPRETATION OF ANTECEDENT-CONTAINED DELETION IN ENGLISH

and any remaining errors and shortcomings are my The purpose of this paper is to make a proposal for Antecedent-Contained Deletion under the copy theory of movement (cf. Chomsky (1995)). Although the puzzles this construction poses have been discussed in Fox (2002) and Chomsky (2004), problems still remain to be solved. The proposal is to base-generate the head of the relative clause within the antecedent VP, while base-generating the relative clause containing the deletion site in an adjoined position of the antecedent VP. This paper pays special attention to the tricky construction in which the relative clause containing the ACD site is contained within the antecedent VP at surface structure, and explains this construction under McCawley’s (1982) theory of discontinuous constituent structure. *
On Enhancements of Physical Layer Secret Key Generation and Its Application in Wireless Communication Systems

As an alternative and appealing approach to providing information security in wireless communication systems, secret key generation at physical layer has demonstrated its potential in terms of efficiency and reliability over traditional cryptographic methods. Without the necessity of a management centre for key distribution or reliance on computational complexity, physical layer key generation protocols enable two wireless entities to extract identical and dynamic keys from the randomness of the wireless channels associated with them. In this thesis, the reliability of secret key generation at the physical layer is examined in practical wireless channels with imperfect channel state information (CSI). Theoretical analyses are provided to relate key match rate with channel’s signal-to-noise ratio (SNR), degrees of channel reciprocity, and iterations of information reconciliation. In order to increase key match rate of physical layer secret key generation, improved schemes in the steps of channel estimation and sample quantization are proposed respectively. In the channel estimation step, multiple observations of the wireless channels are integrated with a linear processor to provide a synthesized and more accurate estimation of the wireless channel. In the sample quantization step, a magnitude based quantization method with two thresholds is proposed to quantize partial samples, where specific quantization areas are selected to reduce cross-over errors. Significant improvements in key match rate are proven for both schemes in theoretical analysis and numerical simulations. Key match rate can even achieve 100% in both schemes with the assistance of information reconciliation process. In the end, a practical application of physical layer secret key generation is presented, where dynamic keys extracted from the wireless channels are utilized for securing secret data
The Dutch National Atlas in a Service - Oriented Architecture

In this paper we describe the National Atlas of the Netherlands map viewer, that visualizes data from the Dutch National GeoData Infrastructure, and that is based on combining a Service-Oriented Architecture with the Open Web Platform. The objective was to investigate how the national atlas could be organised as an integral part of the spatial data infrastructure (SDI). Such an atlas would benefit from up-to-date data flow, and the SDI would benefit from integrated visual summaries of available spatial data and geoservices in well-designed comparable maps. As such a national atlas provides alternative interactive and dynamic ways to access the SDI. The map viewer part of the current prototype is based on the Open Web Platform, enabling a browser-based client without the need for proprietary technology. We used the D3 Javascript API, which allows one to bind arbitrary data to web visualizations. In the paper, we describe the set–up of the National Atlas prototype. We also look forward, by describing what further work is needed to fully implement the concept of a National Atlas in a SOA-based environment.
Maintainability Analysis Software of Mine’s Hoist System Based on Genetic Algorithms for Data Collection Periods of Three and Six Months

Equipment failures and associated maintenance have an impact on the profitability of mines. Implementing maintenance at suitable time intervals can save money and improve the reliability and maintainability of mining equipment. This paper discusses aspects of maintainability prediction for mining machinery. For this purpose, a software tool, called GenRel, is developed. In GenRel, it is assumed that failures of mining equipment caused by an array of factors follow the biological evolution process. GenRel then simulates the failure occurrences during a time period of interest using Genetic Algorithms (GAs) coupled with a statistical methodology. Two case studies on maintainability analysis and prediction of a mine’s hoist system in two different time intervals, three months and six months are discussed. The data are collected from a typical underground mine in the Sudbury area in Ontario, Canada. In each case study, a statistical test is carried out to examine the similarity between the predicted data set and the real-life data set in the same time period. The objectives include an assessment of the applicability of GenRel using real-life data and an investigation of the relationship between data size and prediction results. Discrete and continuous probability distribution functions are applied to the input data.
Conversion of thin surface solids to BSP solid sets with visualization and simulation applications

The advantages of modern game engines in the simulation field are numerous, but simulations need to accurately represent physical data. Real world objects can be scanned and recorded as sets of point data. Surface reconstruction from point data is only one step needed to visualize it in a virtual environment static objects can take advantage of significant optimizations by converting the surface faces into convex solids and storing them in a binary space partition tree. During this conversion, other physical characteristics of the data can be determined for use in the simulation. This thesis presents methods for conversion and data extraction, algorithm backgrounds and step-by-step procedures from spatial data through active simulation.
Interactive Re-lighting with Dynamic View using Deferred Shading

Recent techniques in lighting design for a complex 3D scene achieved high quality preview of final rendering image at interactive frame rate. However, previous works based on the traditioal deep-framebuffer approach are limited to a fixed viewpoint, which can be a critical drawback in real production environment. We present a lighting design system that can handle arbitrary view changing during the interactive re-lighting. Our system generates deep-framebuffers on-the-fly by looking up the view-independent shader cache(VISC) which are pre-computed before the rendering process. These caches are adaptively compressed by the wavelettransform and reconstructed on GPU. From the interactively generated deep-framebuffers, re-lighting can be performed using the standard deferred shading technique under an arbitrary viewpoint. We outline the algorithm with a design example.
GS-RSAR : A Technique for Feature Selection of Microarray Data

- Gene expression profiles have great potential as a medical diagnostic tool since they represent the state of a cell at the molecular level. Available training data sets for classification of cancer types generally have a fairly small sample size compared to the number of genes involved. This fact poses an insurmountable problem to some classification methodologies due to training data limitations. Feature selection is considered a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable classification accuracy. Hence, selecting relevant genes from the microarray data poses a formidable challenge to researchers due to the high-dimensionality of features, multi-class categories being involved, and the usually small sample size. To overcome this difficulty, a good selection method for genes relevant for sample classification is needed in order to improve prediction accuracy, and to avoid incomprehensibility due to the large number of genes investigated. In this paper, irrelevant genes are eliminated in two stages, employing correlation-based feature selection (CFS)as an evaluator and genetic search (GS)as a search technique at the first phase and in the second phase of elimination it an implementation of the Quick reduct algorithm of rough set attribute reduction(RSAR) and in third phase of elimination is (CFS+GS) combines Quick-Reduct algorithm and forms an integrated filter method. Since the data consist of a large number of redundant features, an initial redundancy reduction of the gene is done to enable faster convergence. Then Rough set theory is employed to generate reducts, which represent the minimal sets of non-redundant gene capable of discerning between all objects, in a multi-objective framework. The effectiveness of the proposed approach was verified on six different binary and multi-class microarray datasets using four different ANN classifier as LVQ1, LVQ2, OLVQ1 and SOM with 10 fold cross-validation method.
Sentiment Analysis for Chinese Version based on Machine Learning

Word embedding based on neural language model can automatically learn effective word representation from massive unlabeled text dataset, and has made essential progress in many natural language processing tasks. This paper present a sentimental word vector learning frameworks based on a Convolutional Neural Network respectively.in order to Sentiment Analysis model have good generalization and are effective. Our second work is to adopt a Convolutional Neural Network model to perform text modeling.
Implementing a Student-regulated Learning Tool in Reading: A Heuristic Case Study

The use of a self-regulated learning tool can provide students with an organized and helpful avenue toward setting goals, creating action plans to meet those goals, and most importantly, reflect on the goals to monitor their progress. Then adapt or change the action plan where needed. Current research has suggested that the self-regulated learning model is an effective way to achieve desired academic outcomes. The purpose of this study was to examine the teacher’s perceptions of and experience with the use of a self-regulated learning tool known as a data notebook in reading in fourth and fifth grade classrooms in a Midwestern suburban school. Six teachers of a fourth or fifth grade classroom were interviewed and observed, and documents were examined to gain an understanding of the experiences of these teachers and the use of this tool. The overarching question that was explored is: How do teachers’ implement data notebooks to address the reading needs of their students. The sub-questions were: a) What are the fourth and fifth grade teachers’ perceptions of the data notebooks that are used by students to monitor their own reading progress, (b) What are some of the ways that data notebooks are used by fourth and fifth grade students in monitoring their own reading progress, (c) How was the data notebook iv strategy implemented and supported by the leadership in one Midwestern school district? This is a heuristic case study to gather an understanding of and articulate the experiences and perceptions of these teachers and the use of this self-regulated tool for reading known as a data notebook. The results determined that current pedagogical approaches should be changed to improve the educational experiences of learners and their use of a self-regulated learning tool for reading.
Eco-Strategy : New ICT Management Model Built on the Basis of the CSR Strategy an Green IT

The awareness of the Sustainable Development (SD) issues and stakeholder interactions in the field of information and communication technologies (ICT) management, forces companies to adopt the concepts of Corporate Social Responsibility (CSR) and Green IT to meet the challenges of innovation agility, and afterwards to create differentiation in the processes of governance and strategic alignment of ICT. In this article, we present the enhanced model of eco-strategy as a new generation model for ICT management, which will serve as a theoretical basis and aims to improve research in the field of responsible management of ICT. The updated eco-strategy model is composed of two dimensions: “ICT Green Alignment” and “ICT Green Governance”. These dimensions were designed according to a Green IT and CSR strategy to provide companies with tools for the development of coherent and sustainable managerial strategies capable of boosting overall performance and to explore new levers of transition towards renewed management modes in service the SD.
Anti-spoofing using challenge-response user interaction

2D facial identification has attracted a great amount of attention over the past years, due to its several advantages including practicality and simple requirements. However, without its capability to recognize a real user from an impersonator, face identification system becomes ineffective and vulnerable to spoof attacks. With the great evolution of smart portable devices, more advanced sorts of attacks have been developed, especially the replayed videos spoofing attempts that are becoming more difficult to recognize. Consequently, several studies have investigated the types of vulnerabilities a face biometric system might encounter and proposed various successful anti-spoofing algorithms. Unlike spoofing detection for passive or motionless authentication methods that were profoundly studied, anti-spoofing systems applied on interactive user verification methods were broadly examined as a potential robust spoofing prevention approach. This study aims first at comparing the performance of the existing spoofing detection techniques on passive and interactive authentication methods using a more balanced collected dataset and second proposes a fusion scheme that combines both texture analysis with interaction in order to enhance the accuracy of spoofing detection.
Sketches for aggregate estimations over data streams

In this work, we present methods to speed-up the sketch computation. Sketches are randomized algorithms that use small amount of memory and that can be computed in one pass over the data. Frequency moments represent important distributional characteristics of the data that are required in any statistical modeling method. This work focuses on AGMS-sketches used for the estimation of the second frequency moment. Fast-AGMS sketches use hash functions to speed-up the computation by reducing the number of basic estimators that need to be updated. We show that hashing also changes the distribution of the estimator, thus improving the accuracy by orders of magnitude. The second method to speed-up the sketch computation is related to the degree of randomization used to build the estimator. We show that by using 3-wise independent random variables instead of the proposed 4-wise, significant improvements are obtained both in computation time and memory usage while the accuracy of the estimator stays the same. The last speed-up method we discuss is combining sketches and sampling. Instead of sketching the entire data, the sketch is built only over a sample of the data. We show that the accuracy of the estimator is not drastically affected even when the sample contains a small amount of the original data. When the three speed-up methods are put together, it is possible to sketch streams having input rates of millions of tuples per second in small memory while providing similar accuracy as the original AGMS sketches.
Link adaptive multi-rate coding verification system for CDMA mobile communications

Mobile communications link adaptation maximises the efficient use of radio resources. Multi-rate source and channel coding (MRC) is one of the proposed techniques for link adaptation in future mobile communication systems. A MRC system trades-off the source codec bit rate against channel codec bit rate according to the prevailing channel conditions. This paper presents a CDMA verification environment that allows a comprehensive test of the MRC performance. It shows that by maintaining a satisfactory level of speech quality over a range of channel conditions, MRC results in an increase in the system capacity.