Miracle-dz/UniFAR-FTUs huggingface dataset:
Xml pointer language (xpointer)

Status of this document This is a W3C Working Draft for review by W3C members and other interested parties. It is a draft document and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use W3C Working Drafts as reference material or to cite them as other than "work in progress". A list of current W3C working drafts can be found at Abstract This document specifies constructs that support addressing into the internal structures of XML documents. In particular, it provides for specific reference to elements, character strings, and other parts of XML documents, whether or not they bear an explicit ID attribute.
Towards a Rule Based System for Automatic Simplification of Texts

The CogFLUX system is based on transformation rules used to reduce complexity of texts. The rules were compiled by Anna Decker (2003) based on studies of corpora of easy to read texts and normal texts. She has identified 25 general syntactic transformation rules for simplification. The rules can be grouped into two subsets of rules; 1) rules that remove or replace sub phrases and 2) rules that add new syntactical information to the text. An example of a rule from the first category is: np(det+ap+n) → np(n). This rule will replace any nominal phrase containing a determiner, an adjective phrase and a noun with a nominal phrase containing only the noun. CogFLUX implements the first subset of Decker's rules. The system consist of several modules, eg, GRANSKA tagger for part of speech tagging and Malt parser for syntactic analysis, and a module to replace abbreviations with its extended form.
Network reliability estimation using the tree cut and merge algorithm with importance sampling

It is well known that the exact calculation of network reliability is a NP-complete problem and that for large networks estimating the reliability using simulation techniques becomes attractive. For highly reliable networks, a Monte Carlo scheme called the Merge Process is one of the best performing algorithms, but with a relatively high computational cost per sample. The authors previously proposed a hybrid Monte Carlo scheme called the Tree Cut and Merge algorithm which can improve simulation performance by over seven orders of magnitude in some heterogeneous networks. In homogeneous networks, however, the performance of the algorithm may degrade. In this paper, we first analyse the Tree Cut and Merge algorithm and explain why it does not perform well in some networks. Then a modification is proposed that subdivides the problem into smaller problems and introduces the Importance Sampling technique to the simulation process. The modified algorithm addresses the slow convergence problem in those hard cases while keeping the performance improvement in heterogeneous networks. Experiments and results are presented with some discussions.
A Packet Based Scheduling Mechanism in Real Time Traffic for LTE Downlink Networks

The increasing demand for cellular communications leads to the development of a 4G technology known as Long Term Evolution (LTE). LTE is the evolution for existing 3G mobile networks which offers higher capacity, higher spectral efficiency efficient utilization of radio resources and bandwidth, lower latency.3GPP LTE is an emerging and promising technology for improving the performance which is achieved through streamlining the system for packet services as long term evolution is an all Internet protocol based network. Traffic scheduling plays an important role in LTE technology by assigning the shared resources among users in the most efficient manner. This paper presents the a scheduling mechanism for the different traffic flows by comparing and analyzing the effect of varying the number of packets in the queue in terms of the Average Goodput, Average Delay time, Invalid Packet rate, Packet loss ratio and Spectral efficiency. Keywords—Long term evolution (LTE), Channel Quality Indication (CQI), Physical downlink common control channel (PDCCH), User Equipment (UE), Quality of Service (QoS).
Étude et analyse des RPVs basés sur la couche liaison en comparaison avec les RPVs basés sur la couche réseau

La technologie Multi Protocol Label Switching (MPLS) offre aux operateurs une infrastructure unique pour converger leurs services. MPLS ameliore les services de Internet Protocol (IP) en offrant l'ingenierie de trafic, la qualite de services (QdS) et le support des reseaux prives virtuels (RPVs). Ce memoire etudie et analyse les services Layer 2 Virtual Private Network (L2VPN). L2VPN est une nouvelle approche pour les RPVs a travers un reseau MPLS. Elle est basee uniquement sur la couche liaison. L'etude realise une evaluation comparative des performances de L2VPN et de l'approche Layer 3 VPN (L3VPN) qui est basee sur la couche reseau. Dans la deuxieme partie du memoire l'algorithme Dynamic Distributed Token Bucket (DDTB) a ete propose pour repondre a une problematique reliee a la QdS dans l'acces au reseau MPLS pour les services L2VPN.
Using SiteRank for P2P Web Retrieval

Studies of the Web graph at the granularity of documents have revealed many interesting link distributions. Similarly, studies of the Web graph at the granularity of Web sites, the so-called hostgraph, revealed relationships among hosts based on linkage and co-citation. However, to the best of our knowledge, the graph of Web sites has not been exploited for the purpose of ranking in search engines. In this paper, we first identify the necessity of a SiteGraph abstraction. We derive the SiteRank, a ranking of general importance among the Web sites in such a graph. We then show that SiteRank follows a power-law distribution. As experimental data set we were using the Web of our campus with over two million documents. We uncover interesting relationships between PageRank and SiteRank. Based on these results and observations, we conclude that the decomposition of global Web document ranking computation by making use of SiteRank is a very promising approach for computing global document rankings in a decentralized P2P search system. In particular, by sharing SiteRanks peers would not only be able to efficiently compute global document rankings in a decentralized manner, but also obtain a new means to fight link spamming. Our experiments give very promising results to back up the proposed ideas.
GENERATING NEW STYLES OF CHINESE STROKES BASED ON STATISTICAL MODEL

Chinese calligraphy is one of the most important Chinese arts: a form of entertainment as well as an embodiment of figurative thinking. In this paper, a statistical model-based approach to generating new styles of Chinese character strokes is proposed. Original calligraphy samples are aligned in a common co-ordinate frame and a training set consisting of landmarks is generated semi-automatically. The most significant features of the training set are extracted and a statistical model is built in order to generate strokes in new styles. The Bezier curve is used to fit the discrete contour data.
High performance CMOS operational-amplifier

The design of a high-performance CMOS operational-amplifier (op-amp) in 3- mu m double-level-metal technology is presented. The amplifier makes use of stacked (cascode) current source techniques and an enhanced push-pull output stage. This op-amp will be used for the realization of high-frequency switched-capacitor (SC) filters. SPICE simulations of the op-amp show a unity gain bandwidth of 40 MHz and a DC gain of 72 dB when using a bias current of 100 mu A and a load capacitance of 15 pF. A test chip implementing the op-amp and other analog test cells was fabricated. Test results from the fabricated device demonstrate a DC gain of about 67 dB, with a unity gain bandwidth of about 40 MHz and a 36 V/ mu s slew rate when using a bias current of about 100 mu A and a 15-pF load. <<ETX>>
Social Media Marketing: An Hour a Day

If the idea of starting a social media marketing campaign overwhelms you, the author of Social Media Marketing: An Hour a Day will introduce you to the basics, demonstrate how to manage details and describe how you can track results. Case studies, step-by-step guides, checklists, quizzes and hands-on tutorials will help you execute a social media marketing campaign in just one hour a day. In addition, learn how to integrate social media metrics with traditional media measurements and how to leverage blogs, RSS feeds, podcasts, and user-generated content sharing sites like YouTube.
Compression of Video Data for Transmission Over Low Bandwidth Network

Video is the most useful and most appealing approach to represent some information. The problem with such kind of media is its large size which affects the efficiency. Video compression is required to save the storage space. The combined approach of removing spatial and temporal redundancy will return the high degree of compression ratio. This paper first targets the techniques to remove the spatial redundancy from each frame of the video. Here various transform techniques (i.e. DCT, Haar, Walsh, Kekre, and Wavelet of all transforms) are implemented and analyzed with respect to different performance evaluation parameters like Mean Square Error (MSE), Compression Ratio (CR). Then it deals with the temporal redundancy removal where the differences between the consecutive frames are found and then the above transform techniques are used in difference matrix of the frames. It also focuses on other methods like half toning and sub-band decomposition for removing the spatial redundancy from the frame of a video.
FPGA implementation of an OFDM PHY

Orthogonal frequency division multiplexing (OFDM) based communication is increasingly being used in environments that exhibit severe multipath. While there are ASSP solutions for many common (e.g. 802.11a) and emerging standards, many communication systems, for example a military software radio, demand flexibility. The arithmetic requirements of an OFDM system can be very demanding. Even the ubiquitous 802.11a WLAN system has arithmetic requirements in the billions-of-operations per second region and cannot be satisfied even by high-end DSP microprocessors. This paper reports on the FPGA implementation of an OFDM transceiver. In addition to the FFT based modulator and demodulator, receiver synchronization and channel estimation is discussed. The FPGA resource requirements of the various sub-systems are reported and the design methodology employed for system design, verification and FPGA implementation is described.
Diagnostic Measurement: Theory, Methods, and Applications

Index of Notation 1. Introduction I. Theory: Principles of Diagnostic Measurement with DCMs 2. Implementation, Design, and Validation of Diagnostic Assessments 3. Diagnostic Decision Making with DCMs 4. Attribute Specification for DCMs II. Methods: Psychometric Foundations of DCMs 5. The Statistical Nature of DCMs 6. The Statistical Structure of Core DCMs 7. The LCDM Framework 8. Modeling the Attribute Space in DCMs III. Applications: Utilizing DCMs in Practice 9. Estimating DCMs Using Mplus 10. Respondent Parameter Estimation in DCMs 11. Item Parameter Estimation in DCMs 12. Evaluating the Model Fit of DCMs 13. Item Discrimination Indices for DCMs 14. Accommodating Complex Sampling Designs in DCMs Glossary
Sensor Less Speed Control of Three Phase Induction Motor by using MRAC

a new model reference adaptive controller (MRAC) for the speed estimation of the vector controlled induction motor drive is presented in this paper. the sensor less speed control of induction motor is formulated by developing a new model reference adaptive system using steady–state values of X=(? ̇? x i where v= supply voltage and i= supply current vector in synchronously rotating reference frame)which is a fictitious quantity. This is no need of physical significance. This formulation not only simply and more reliable but also made the sensor less drive stable in all four quadrants of operation. Speed estimation processing techniques are does not involve the stator and rotor flux. Modification of the vector controller drive can estimate the stator resistance in all the four quadrants of operation, if speed signal is available. The proposed MRACbased speed sensor less vector control drive as well as the stator resistance estimation technique has been simulated in MATLAB/SIMULINK.
Projeto de sistema crítico para transmissão de vídeo em um link de comunicação para vants

This project aims to design a digital data communication link for unmaned aerial vehicles. We will focus the main challenges and their impacts in the communication system. The project was divided in an analog and digital block. The analog block address the power requirements that make the system works by using a link budget procedure. The digital block will use the OFDM transmission technique. In this work we also suggest a channel estimation procedure via pilot tones. The performance of this approach will be measured by Monte Carlo Simulation. Key-Words: UAVs, OFDM, Digital Communication
The use of bubble lattices for information storage

A new approach to bubble memories, called a bubble lattice file (BLF), is described. This approach employs a periodic bubble lattice to define bit positions while the information is contained in the wall structure of the bubbles. The principal advantage of the BLF is the increased storage density, sixteenfold when the resolution capability of the fabrication process is the limiting factor. The device functions required for the BLF, including accessing and write/read operation, are described. Significant differences between BLF and the T‐I bar bubble memories are discussed.
Language classification by numbers

1. How do Linguists Classify Languages? 2. Lexicostatistics 3. Tree-Based Quantitative Approaches - Computational Cladistics 4. Tree-Based Quantitative Approaches: Sublists 5. Correlations Between Genetic and Linguistic Data 6. Climbing Down from the Trees: Network Models 7. Dating 8. Quantitative Methods Beyond the Lexicon
Evaluation of a fuzzy system-based automotive copilot dedicated to lateral guidance

A fuzzy copilot system called RPV has been developed to lighten the driver's task in the lateral control of his vehicle. Fuzzy systems were designed and embedded with various sensors on a passenger car to perform the lateral control of the vehicle. A fuzzy controller outputs a torque command, which is applied to the steering column to steer the vehicle along a desired immaterial trajectory, while a fuzzy diagnosis monitors the driver's steering actions and intentions. This enables a supervisor to trigger the adequate driving strategy in accordance with driver expectations. The paper presents encouraging results from field-test experiments based on drivers' point of view, taking benefit from a positive cooperation of fuzzy logic and ergonomics.
Enhancing Data Security in Cloud Computing Using 3D Framework & Digital Signature with Encryption

Cloud Computing is emerging technology which consist of existing techniques combined with new technology paradigms. In this new technology shared resources like software’s, hardware’s and information is provided to its users and other peoples on internet whenever demanded. Today’s world relies on cloud computing to store their public as well as some personal information which is needed by the user itself or some other persons. Cloud service is any service offered to its users by cloud. As cloud computing comes in service there are some drawbacks such as privacy of user’s data, security of user data is very important aspects. In this paper we are focusing to enhance the data security in cloud computing using 3 dimensional framework and digital signature with RSA Encryption algorithm. In 3 Dimensional frameworks, at client side user select the parameters reactively between CIA (Confidentiality, Integrity & Availability) and before actual storing the data in cloud a digital signature is created using MD 5 Algorithm and then RSA Encryption algorithm is applied then it stored on cloud.
An integrated microcomputer network for experiments in distributed programming

A network computer is a computer network designed to function not as a collection of autonomous hosts but as one machine. Systems intended to support experiments with asynchronous distributed programs, including both distributed systems programs and distributed applications, form a major sub-class of network computers. The goal of the Stony Brook Network (SBN) project is to construct such a general-purpose network computer. Unlike most other systems in its class, SBN is a language-centered design. SBN's starting point is the distributed programming language Linda. To run on SBN, a distributed program must either be written in or be pre-processed into Linda; the language is intended to be a maximally powerful, flexible and expressive vehicle for distributed programming. The role of SBN's hardware and communication software is to support Linda with greatest possible efficiency, or in other words to implement a maximally-efficient Linda machine. Part I discusses the Linda design, which encompasses unusual inter-process communication primitives and program-structuring devices. Inter-process communication takes place via a shared data structured called "structured memory". Though logically shared, structured memory may be implemented over many memory-disjoint nodes. Processes in Linda communicate by means of the three operations that structured memory defines. Linda's four basic structuring tools allow the construction of a large variety of program components--sequential, concurrent and mutual-exculsion statements, tasks, remote-procedure-like blocks and monitor-like blocks, conditional and timed receives among others. Part II discusses algorithms to be incorporated in a communication system that supports Linda and structured memory. Runtime rendez-vous between outputting and inputting processes is a requirement of Linda and an implementation technique that is optimal given well-defined assumptions is described. Store-and-forward deadlock is a potential problem in any packet-swtiching communication system, and SBN is a packet-switching design; a new algorithm for packet deadlock prevention is described and its correctness is proven. Finally, a new communication protocol and associated architecture that, we argue, are well-suited to network computers of SBN's class is discussed.
A Survey of Internet Data Mining Technologies Based on Machine Learning

As a knowledge discovery in internet, data mining technology is the cross of machine learning and databases technology. Data mining can provide massive amounts of data analysis using machine learning techniques and manage massive amounts of data by the use of database technology. The goal of machine learning is to simulate and realize human learning behavior in order to optimize a performance criterion using past experience and example data. Two kinds of internet data mining technologies which include rank learning and topic model learning are introduced in this paper. As a supervised learning sorting method, the task of rank learning is sorting the retrieved pages for a given query. As a Bayesian model, topic model learning is designed to automatically extract all possible topics and each topic page from the index page, so that the web page and query statement can match on the topic. The latest researches about above areas are reviewed.
Mechanism Design : Analysis and Synthesis

1. Introduction to Kinematics and Mechanisms. 2. Computer-Aided Mechanism Design Philosophy. 3. Displacement and Velocity Analysis. 4. Acceleration Analysis. 5. Introduction to Dynamics of Mechanisms. 6. Cam Design. 7. Gears and Gear Trains. 8. Introduction to Kinematic Synthesis: Graphical and Linear Analytical Methods. Appendix: Case Study-Type Synthesis of Casement Window Mechanisms. Answers to Selected Problems. References. Index.
A FRAMEWORK FOR STUDENTS’ ACADEMIC PERFORMANCE ANALYSIS USING NAÏVE BAYES CLASSIFIER

Educational database of Higher Learning Institutions holds an enormous amount of data that increases every semester. Data mining technique is usually applied to this database to discover underlying information about the students. This paper proposed a framework to predict the performance of first year bachelor students in Computer Science course. Naive Bayes Classifier was used to extract patterns using WEKA as a Data mining tool in order to build a prediction model. The data were collected from 6 year period intakes from July 2006/2007 until July 2011/2012. From the students’ data, six parameters were selected that are race, gender, family income, university entry mode, and Grade Point Average. By using Naive Bayes Classifier, it would predict the class label “Grade Point Average” as a categorical value; Poor, Average, and Good. Result from the study shows that the students’ family income, gender, and hometown parameter contribute towards students’ academic performance. The prediction model is useful to the lecturers and management of the faculty in identifying students with weak performance so that they will be able to take necessary actions to improve the students’ academic performance.
Data Cleansing for Models Trained with SGD

Data cleansing is a typical approach used to improve the accuracy of machine learning models, which, however, requires extensive domain knowledge to identify the influential instances that affect the models. In this paper, we propose an algorithm that can suggest influential instances without using any domain knowledge. With the proposed method, users only need to inspect the instances suggested by the algorithm, implying that users do not need extensive knowledge for this procedure, which enables even non-experts to conduct data cleansing and improve the model. The existing methods require the loss function to be convex and an optimal model to be obtained, which is not always the case in modern machine learning. To overcome these limitations, we propose a novel approach specifically designed for the models trained with stochastic gradient descent (SGD). The proposed method infers the influential instances by retracing the steps of the SGD while incorporating intermediate models computed in each step. Through experiments, we demonstrate that the proposed method can accurately infer the influential instances. Moreover, we used MNIST and CIFAR10 to show that the models can be effectively improved by removing the influential instances suggested by the proposed method.
Regional based query in graph active learning

Graph convolution networks (GCN) have emerged as the leading method to classify node classes in networks, and have reached the highest accuracy in multiple node classification tasks. In the absence of available tagged samples, active learning methods have been developed to obtain the highest accuracy using the minimal number of queries to an oracle. The current best active learning methods use the sample class uncertainty as selection criteria. However, in graph based classification, the class of each node is often related to the class of its neighbors. As such, the uncertainty in the class of a node's neighbor may be a more appropriate selection criterion. We here propose two such criteria, one extending the classical uncertainty measure, and the other extending the page-rank algorithm. We show that the latter is optimal when the fraction of tagged nodes is low, and when this fraction grows to one over the average degree, the regional uncertainty performs better than all existing methods. While we have tested this methods on graphs, such methods can be extended to any classification problem, where a distance metrics can be defined between the input samples. All the code used can be accessed at : this https URL All the datasets used can be accessed at : this https URL