Miracle-dz/UniFAR-FTUs huggingface dataset:
Bio-mining for biomarkers with a multi-resolution block chain

In this paper, we discuss a framework for bridging the gap between security and medical Large Data Analysis (LDA) with functional- biomarkers. Unsupervised Learning for individual e-IQ & IQ relying on memory eliciting (i.e. scent, grandmother images) and IQ baseline profiles could further enhance the ability to uniquely identify and properly diagnose individuals. Sub-threshold changes in a common/probable biomedical biomarker (disorders) means that an individual remains healthy, while a martingale would require further investigation and more measurements taken to determine credibility. Empirical measurements of human actions can discover anomalies hidden in data, which point to biomarkers revealed through stimulus response. We review the approach for forming a single-user baseline having 1-d devices and a scale-invariant representation for N users each (i) having N*d(i) total devices. Such a fractal representation of human-centric data provides self-similar levels information and relationships which are useful for diagnosis and identification causality anywhere from a mental disorder to a DNA match. Biomarkers from biomedical devices offer a robust way to collect data. Biometrics could be envisioned as enhanced and personalized biomedical devices (e.g. typing fist), but used for security. As long as the devices have a shared context origin, useful information can be found by coupling the sensors. In the case of the electroencephalogram (EEG), known patterns have emerged in low frequency Delta Theta Alpha Beta-Gamma (DTAB-G) waves when an individual views a familiar picture in the visual cortex which is shown on EEGs as a sharp peak. Using brainwaves as a functional biomarker for security can lead the industry to create more secure sessions by allowing not only passwords but also visual stimuli and/or keystrokes coupled with EEG to capture and stay informed about real time user e-IQ/IQ data changes. This holistic Computer Science (CS) Knowledge Discovery in Databases, Data Mining (KDD, DM) approach seeks to merge the fields having a shared data origin - biomarkers revealed through stimulus response.
A memetic framework for describing and simulating spatial prisoner's dilemma with coalition formation

This paper presents a framework for describing the spatial distribution and the global frequency of agents who play the spatial prisoner's dilemma with coalition formation. The agent interaction is described by a non-iterated game, where each agent only locally interacts with its neighbours. Every agent may behave as a defector or a cooperator when playing isolated, but they can join or lead coalitions (group of agents) where a leader decides the coalition strategy. Isolated agents' strategies or groups' strategies are public and therefore can be memetically imitated by neighbours. The agent strategy is selected between two possibilities: probabilistic Tit-for-Tat (pTFT) or learning automata (LA). Coalition dynamics are organized around two axes. On the one hand, agents get a percentage of compromise when cooperating with other agents. On the other hand, leaders impose taxes to the other agents belonging to its coalition. These two rules and their related parameters guide the coalition formation and the game evolution. The main contribution of the paper is the framework for memetic analysis of coalition formation in spatial prisoner's dilemma. This work also includes simulation results on a lattice showing that the pTFT memetic approach becomes more effective than an isolated learning policy.
Combining Decomposition and Hybrid Algorithms for the Satellite Range Scheduling Problems

Multiple-resource satellite scheduling problem (MuRRSP) is a complex and difficult scheduling problem, which schedules a large number of task requests onto ground-station antennas in order to communicate with the satellites. We first examined several exact algorithms that were previously implemented in the machine scheduling field. They are column generation and logic-based Benders decomposition. A new hybrid approach that combines both column generation and logic-based Benders decomposition is proposed. The hybrid performed well when there is a large number of machines. Next, we presented a connection between the parallel machine scheduling problem and MuRRSP in order to solve MuRRSP with exact algorithms. Furthermore, we proposed a strengthened cut in the sub-problem of the logic-based Bendersdecomposition. The resulting algorithm proved to be very effective. Barbulescu’s benchmark problems were solved and proved optimal with an average run-time less than one-hour. To the best of our knowledge, previous efforts to solve MuRRSP were all heuristic based and no optimal schedules existed.
Detecting Spam Zombies with Semantic Matching Based NMF Hierarchical Clustering by Monitoring Outgoing Messages

Due to the growth of the computer technologies the usage of the internet also increased in now days. The increases of the web usage the security of the each and every user data also play major significant roles to each user, to avoid the loss of the data. Several types of the attacks have been occurred during this process those attacks are spamming, Dos, DDoS, Worms, Viruses, and individuality theft. These types of spamming motivates the attackers to hacks the secret information of the people ,in order to conquer these problem in this paper focus on the spam detection problem for outgoing the messages in the larger organization applications such as educational data, university data ,hospital sharing information data and individual mail communication . The proposed method automatically monitor the activities of the user through the post messages, these types of activities performed by spammer is known as the spam zombies. In earlier work the detection of the spam zombie’s methods is known as the SPOT, through the automatic examination of the outgoing messages. It is designed based on the probabilistic test is known as SPRT. SPRT based probabilistic measurement analysis the negative and positive results that corresponds to false positive (FP) ,False negative (FN),True Positive (TP) and true negative (TN) error rates through user defined thresholds. The methods doesn’t measure semantic measurement based matching to detect worm attacks ,in order to solve this issue additionally propose an semantic matching based Nonnegative matrix factorization clustering method in hierarchical structure . The spam and non-spam clustered are formed based on the degree value of the fuzzy membership function; it tries to divide the email message into two clusters through the non negative matrix and a nonnegative coefficient matrix. In addition, the proposed NMF-Hierarchical Clustering with existing SPOT Methods respectively, and show that proposed NMFHierarchical Clustering SPOT outperforms than existing methods. Keywords— Compromised Machines, Spam Zombies, Nonnegative Matrix Factorization (NMF) ,Compromised Machine Detection Algorithms, Sequential Probability Ratio Test(SPRT).
A novel block equalization design for wireless communication with ISI and Rayleigh fading channels

In this paper, a novel block channel equalization design for wireless communication over mobile radio channels with both inter-symbol interference (ISI) and Rayleigh fading is presented. The proposed design consists of a matched filter, a channel estimator and a block decision feedback equalizer (BDFE). The channel estimator, which is based on a revised RLS algorithm, adopts a semi-blind approach. The estimated channel impulse response h(n) is used later in both matched filtering and the BDFE update. The BDFE actually consists of a noise whitener and a maximum-likelihood block detector followed by a symbol detector. The filter coefficients of the BDFE are calculated subject to the Cholesky factorization and are updated once for each data block. Various wireless channel models, covering ISI and Rayleigh fading, have been simulated to demonstrate the effectiveness of the proposed equalization scheme. Based on this scheme, a novel systolic array design is next developed. The derived equalizer design features a highly parallel, hardware efficient and scalable design. Implementation results on a Xilinx XCV600 FPGA indicate an up-to-3-million symbols per second processing rate at 35 MHz working frequency.
XRAYL: a program for producing idealized powder diffraction line profiles from overlapped powder patterns

The X-ray diffraction patterns of samples of polycrystalline materials are used to identify and characterize phases. Very often the total (or composite) profile consists of a series of overlapping profiles. In many applications it is necessary to separate the component profiles from the total profile. (In this document the terms {ital profile, line}, and {ital peak} are used interchangeably to represent these features of X-ray or neutron diffraction patterns.) A computer program, XRAYL, first developed in the 1980s and subsequently enlarged and improved, allows the fitting of analytical functions to powder diffraction lines. The fitting process produces parameters of chosen profile functions, diffraction line by diffraction line. The resulting function parameters may then be used to generate ``idealized`` powder diffraction lines as counts at steps in 2{Omega}. The generated lines are effectively free of statistical noise and contributions from overlapping lines. Each separated line extends to background on both sides of the generated profile. XRAYL may, therefore, be used in X-ray powder diffraction profile analysis as a preprocessor program that is, separating peaks and feeding the ``resolved`` data to subsequent analysis programs. This self- contained document includes: (1) a description of the fitting functions coded into XRAYL, (2) an outline of the least-squares algorithm used in fitting the profile function, (3) the file formats and contents utilized by the computer code, (4) the user options and their presentation requirements for execution of the program, (5) an example of input and output for a test case, and (6) source code listings on a diskette.
OWL Web ontology language overview

The OWL Web Ontology Language is designed for use by applications that need to process the content of information instead of just presenting information to humans. OWL facilitates greater machine interpretability of Web content than that supported by XML, RDF, and RDF Schema (RDF-S) by providing additional vocabulary along with a formal semantics. OWL has three increasingly-expressive sublanguages: OWL Lite, OWL DL, and OWL Full. This document is written for readers who want a first impression of the capabilities of OWL. It provides an introduction to OWL by informally describing the features of each of the sublanguages of OWL. Some knowledge of RDF Schema is useful for understanding this document, but not essential. After this document, interested readers may turn to the OWL Guide for more detailed descriptions and extensive examples on the features of OWL. The normative formal definition of OWL can be found in the OWL Semantics and Abstract Syntax. Status of this document OWL Web Ontology Language Overview https://www.w3.org/TR/owl-features/ 1 de 14 09/05/2017 08:32 a.m. This document has been reviewed by W3C Members and other interested parties, and it has been endorsed by the Director as a W3C Recommendation. W3C's role in making the Recommendation is to draw attention to the specification and to promote its widespread deployment. This enhances the functionality and interoperability of the Web. This is one of six parts of the W3C Recommendation for OWL, the Web Ontology Language. It has been developed by the Web Ontology Working Group as part of the W3C Semantic Web Activity (Activity Statement, Group Charter) for publication on 10 February 2004. The design of OWL expressed in earlier versions of these documents has been widely reviewed and satisfies the Working Group's technical requirements. The Working Group has addressed all comments received, making changes as necessary. Changes to this document since the Proposed Recommendation version are detailed in the change log. Comments are welcome at public-webont-comments@w3.org (archive) and general discussion of related technology is welcome at www-rdf-logic@w3.org (archive). A list of implementations is available. The W3C maintains a list of any patent disclosures related to this work. This section describes the status of this document at the time of its publication. Other documents may supersede this document. A list of current W3C publications and the latest revision of this technical report can be found in the W3C technical reports index at http://www.w3.org/TR/.
Practical sorting algorithms for hypercube computers

We consider the problem of designing a practical and efficient sorting algorithm for hypercube computers. The advent of commercial hypercube machines in the past several years has sparked much interest in this problem. We came up with one of the first such algorithms, hyperquicksort. Its description and implementation are very straightforward. Still other algorithms were devised, either concurrently with or later than hyperquicksort. Substantial amounts of empirical analysis have been carried out on their behavior. We summarize these results and show how hyperquicksort has outperformed the other, often more sophisticated, algorithms. Extensive empirical results--obtained from a custom software simulation package--as well as some rigorous theoretical analysis are provided to back up our claims. In addition, we present possible enhancements to hyperquicksort to improve its performance in special situations. Finally, we explain modifications to the basic algorithm that allow it to run on other parallel machines, not just hypercubes.
Pattern- based text mining method for the classification of research proposals

Research proposal classification is an important task for private research funding agencies. When a large number of research proposals are received, it is common to group them according to their similarities in research disciplines. Current methods for grouping proposals are based on manual matching of similar research discipline areas and/or keywords. This paper presents a pattern-based text-mining approach to classify research proposals based on their similarities in research areas. This paper focus on developing efficient mining algorithm for discovering patterns from large data collection and search for useful and interesting patterns. In the field of text mining, pattern mining techniques can be used to find various text patterns, such as frequent itemsets, closed frequent itemsets, co-occurring terms. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. In proposed system we can take sufficient . txt file as inputs and we apply various algorithms and generate expected results.
Automatic analysis of fundus eye images using mathematical morphology and neural networks for supporting glaucoma diagnosis

In this paper a new method for automatic segmentation and classification of fundus eye images (fei) into normal and glaucomatous ones is proposed. The segmentation of the cup region from the fei makes use of a morphological watershed transformation with markers imposed. New features for quantitative cup evaluation are found based on genetic algorithms with the proposed new fitness function. The computed features are then used in a classification procedure which is based on a multilayer perceptron. The mean sensitivity is 90%, while the mean specificity reaches 85%. The results obtained are encouraging.
Bactome III: OLIgonucleotide Variable Expression Ranker (OLIVER) 1.0, Tool for Identifying Suitable Reference (Invariant) Genes from Large Microarray Datasets

Reference genes are crucial in gene expression analysis where varying gene expressions are reported as changes with respect to the expression of reference genes. Hence, reference genes are assumed to be stably expressed under most circumstances. Previous studies have shown that common algorithms for identifying of potential reference genes are not suitable for large microarray-sized datasets. Our previous work had derived methods for identification of reference genes from large microarray-sized datasets and the performances of these methods are linear to sample size. These methods had been implemented as a tool, OLIgonucleotide Variable Expression Ranker (OLIVER), which can be downloaded from http://sourceforge. net/projects/bactome/files/OLIVER/OLIVER_1. zip . This manuscript documents the implementation of OLIVER, which had been incorporated as part of Bactome project ( http://www.sf.net/projects/bactome ). These codes are licensed under GNU General Public License version 3 for academic and non-for-profit use.
Cumulant-based blind optimum beamforming

The authors propose blind estimation of the source steering vector in the presence of multiple, directional, correlated or coherent Gaussian interferers via higher-order statistics. In any application, only sample statistics are available, so a robust beamforming approach that employs the steering vector estimate obtained by cumulant-based signal processing is suggested. A method that employs both covariance and cumulant information to combat finite sample effects is also proposed, as is an adaptive version of the authors' algorithm. Simulations demonstrate the excellent performance of the approach in a wide variety of situations. <<ETX>>
Working Memory Capacity: An Individual Differences Approach.

Abstract : Five experiments are described that study the relationship between measures of working memory and reading comprehension. Two experiments investigated whether the complex span measure must be similar to the reading comprehension task to be predictive of comprehension. The correlation found between reading comprehension and two reading-related complex spans was similar to those found between two arithmetic-related complex spans and comprehension. The relationship remained significant when quantitative skills were factored out. The simple digit and word spans (measured without a background task) did NOT correlate with reading comprehension. The complex span/comprehension correlations were a function of the difficulty of the background task. When the difficulty level of the reading-related or arithmetic-related background tasks was moderate, the span/comprehension correlations were higher in magnitude than when the background tasks were simple or very difficult. The third experiment showed that if serial recall was required in the span tasks, simple word span did significantly predict reading comprehension but not as well as the sentence span. The fourth experiment showed that the ordering of list lengths in the span tasks had little influence on the correlation between span scores and comprehension. The fifth experiment is the first in a series investigating variables whether variables that influence simple word span also influence the sentence word span. This study demonstrated that the word length has the same effect on the sentence span task as on the simple word span.
Current practice in mobile learning : A survey of research method and purpose

In this paper we present a survey of published research in mobile learning. We investigated 76 papers from mLearn 2007 and 2008, and classified them according to two dimensions: research method and research purpose. Research methods and purposes are important parts of how research is conducted. Alas, opinions and approaches towards research differ greatly. The classified papers are evenly distributed among the research methods investigated, with one exception, being few in Basic research. In terms of research purpose, papers aimed to describe were well represented while there was a lack of papers targeting evaluation. Papers recounting both Basic research and research done to evaluate are imperative since they help a research field to mature and also help researchers to avoid repeating known pitfalls. This maturity, in turn, will lead to better scalability and sustainability for future research efforts in the community of mobile learning.
Some problems on temporally consistent video editing and object recognition

Video editing and object recognition are two significant fields in computer vi-sion: the first has remarkably assisted digital production and post-productiontasks of a digital video footage; the second is considered fundamental to imageclassification or image based search in large databases (e.g. the web). In thisthesis, we address two problems, namely we present a novel formulation thattackles video editing tasks and we develop a mechanism that allows to generatemore robust descriptors for objects in an image.Concerning the first problem, this thesis proposes two variational models toperform temporally coherent video editing. These models are applied to changean object’s (rigid or non-rigid) texture throughout a given video sequence. Onemodel is based on propagating color information from a given frame (or be-tween two given frames) along the motion trajectories of the video; while theother is based on propagating gradient domain information. The models wepresent in this thesis require minimal user intervention and they automaticallyaccommodate for illumination changes in the scene.Concerning the second problem, this thesis addresses the problem of affineinvariance in object recognition. We introduce a way to generate geometricaffine invariant quantities that are used in the construction of feature descrip-tors. We show that when these quantities are used they do indeed achieve amore robust recognition than the state of the art descriptors.i
Rethinking Literacy Instruction to Non-LEP/ESL-Labeled Language Minority Students

This qualitative study examines three non-limited English proficiency (LEP) / English as a second language (ESL)-labeled Chinese American students’ English language and literacy performance in mainstream classrooms. Multiple methods were adopted for data collection, including interviewing the focal students’ teachers and their parents, observing and audiotaping classroom interactions, and collecting student work. Findings indicate that although the focal students passed the ESL placement test and standardized tests when entering public school, they still showed needs in developing specific areas in English as a second language and sociolinguistic competence. However, findings demonstrated that such needs have been overlooked in class and also ignored by the mainstream curriculum. Even if their teachers noticed that the focal students’ use of English language was different from other English monolingual students, the teachers did not consider the difference as a serious problem and did not feel the need to make any accommodations for them. Therefore, this study attempts to expand mainstream teachers’ understanding of language minority students; it advocates that literacy developmental needs of language minority students should not be understood simply through labels used by schools to classify students and scores that students obtained on high-stakes tests which are the judgmental criterion of mainstream valuing.
Theories of the Theatre: A Historical and Critical Survey, from the Greeks to the Present

Beginning with Aristotle and the Greeks and ending with semiotics and post-structuralism, Theories of the Theatre is the first comprehensive survey of Western dramatic theory. In this expanded edition the author has updated the book and added a new concluding chapter that focuses on theoretical developments since 1980, emphasizing the impact of feminist theory.