Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalization Performance of Support Vector Machines and Other Pattern Classifiers

The aim of this chapter is to summarise results that have been obtained for high conndence generalization error bounds for the Support Vector Machine (SVM) and other pattern classiiers related to the SVM. As a by-product of the analysis we argue that the margin and number of support vectors are both estimators of the degree to which the distribution generating the inputs assists identiication of the target hyperplane. 1.1 Introduction Generalization analysis of pattern classiiers is concerned with determining the factors that aaect the accuracy of a pattern classiier. Such an analysis requires assumptions to be made about how the data used to train the classiier was gathered and how subsequent data will be generated. One of the most popular assumptions originally championed by Vapnik and Chervonenkis 12] is to assume that the training and testing data are both generated according to the same probability distribution. The distribution can be viewed as a model of the natural processes which give rise to the observed phenomenon. Since it is usually more diicult to estimate the distribution than to learn the classiication function, it is important that no assumptions are made about the distribution, resulting in a so-called distribution-free analysis. We will consider bounds on the generalization error, that is the probability of
A New Scientometric Dimension for User Profile

Personalization aims to facilitate the expression of user needs and enables him/her to obtain relevant information. The data describing the user are grouped in a profile, which varies according to the application context and the type of users and their needs. By focusing on a particular type of applications (dedicated to scientific research) and a particular type of users (researchers), we note that existing personalization approaches only partially solve problems related to personalization quality (accuracy, freshness, validity period, response time, source credibility), but lacks a model involving scientific quality (content, author, container and affiliation quality). The scientific quality is assessed by a set of quantitative and qualitative measures, which are the scientometric indicators. In this paper, we propose a profile model based on scientometrics to involve the qualitative relevance of information in a retrieval system dedicated to scientific research. The proposed model will be the basis of building an ontological user profile able to store scientometric
Optimizing Network Performance

This thesis investigates and improves hardware and software architectural aspects that directly influence the TCP network transmission in order to reduce CPU utilization. The main areas of investigation have been Linux kernel network implementation handling video data before handing over to device driver, cache hierarchy, DMA transmit rings, and specialized network offloading hardware. The final results of the thesis yielded an overall improvement of 10% in throughput while the CPU usage is reduced by approximately 60%.
Architecture and development of distributed WWW caching proxy

In order to alleviate heavy network overloads and slow network response, peer group, a new kind of distributed WWW caching proxy, is presented. Each caching proxy is a peer in the group and stores local data and maintains the cache index. Required data stored in local or remote proxy is accessible through the indexes. When peer proxies exchange cache information, only indexes are included, thus alleviating traffic overload. The peer group algorithm keeps track of any changes in the group by periodically broadcasting the cache. Therefore, the WWW distributed caching proxy is able to automatically update cache information, share cache indexes with other peers and alleviate slow convergence and inconsistency among peers and traffic overloads.
Java Sorting Programs

This is a dataset of 126 Java computer programs, all sorting an array of integers in ascending order. The programs have been gathered from the web in 2015 as part of the DFG funded project _Learning Feedback for Dynamic Tutoring Systems_ (FIT) with grant number HA 2719/6-1. It is meant as a benchmark dataset for methods working on clustering and/or classification of structured data (sequences, trees or graphs). For copyright reasons, we can not provide the source code directly. Instead, this dataset contains meta data about the data points as well as pre-calculated distance matrices.
Dynamic job shop scheduling using variable neighbourhood search

In this paper a scheduling method based on variable neighbourhood search (VNS) is introduced to address a dynamic job shop scheduling problem that considers random job arrivals and machine breakdowns. To deal with the dynamic nature of the problem, an event-driven policy is selected. To enhance the efficiency and effectiveness of the scheduling method, an artificial neural network with a back propagation error learning algorithm is used to update parameters of the VNS at any rescheduling point according to the problem condition. The proposed method is compared with some common dispatching rules that have been widely used in the literature for the dynamic job shop scheduling problem. Results illustrate the high efficiency and effectiveness of the proposed method in a variety of shop floor conditions.
Three-Axis Constrained Attitude Pathfinding and Visualization: Charting a Course on Higher-Dimensional Maps

A new algorithm for designing attitude maneuver paths in the presence of multiple threeaxis attitude constraints is presented. The algorithm projects the full three-axis attitude representation into a three-dimensional space using a new distortion minimizing transformation. The projected representation is discretized on a three-dimensional Cartesian grid and the admissible regions emerge in the projected space from the discretized representation via interpolation of the constraint satisfaction across the grid points. The points are enumerated and graph search algorithms are employed to find minimum cost paths using the appropriately selected cost definitions. The paths are then smoothed and visualized alongside with the admissible regions in three dimensions. Ultimately, the paths can serve as targets for attitude tracking control algorithms.
Models of RNA Interaction from Experimental Datasets: Framework of Resilience

Resilience is a network property of systems responding under stress, which for biomed- icine correlates to chronic or acute insults. Current need exists for models and algo-rithms to study whole transcriptome differences between tissues and disease states to understand resilience. Goal of this effort is to interpret cellular transcription in a dynamic system biology framework of RNA molecules forming an information struc- ture with regulatory properties acting on individual transcripts. We develop and evalu-ate a bioinformatics framework based on information theory that utilizes RNA expression data to create a whole transcriptome model of interaction that could lead to the discovery of new biological control mechanisms. This addresses a fundamental question as to why transcription yields such a small fraction of protein products. We focus on a transformative concept that individual transcripts collectively form an “ infor- mation cloud ” of sequence words, which for some genes may have significant regulatory impact. Extending the concept of cis - and trans -regulation, we propose to search for RNAs that are modulated by interactions with the transcriptome cloud and calling such examples nebula regulation. This framework has implications as a paradigm change for RNA regulation and provides a deeper understanding of nucleotide sequence structure and -omic language meaning.
WALKING AND CRAWLING WITH ALoF - A ROBOT FOR AUTONOMOUS LOCOMOTION ON FOUR LEGS

Purpose – The purpose of this paper is to introduce the robotic quadrupedal platform ALoF that is designed to aid research on perception in legged locomotion. Design/methodology/approach – A well-balanced size and complexity of the robot results in a robust platform that is easy to handle, yet able to perform complex maneuvers as well as to carry sophisticated 3D sensors. A very large range of motion allows the robot to actively explore its surroundings through haptic interaction, and to choose between a wide range of planning options. Findings – This robot was employed and tested in the lunar robotics challenge organized by the European Space Agency, for which the authors also developed a novel crawling gait, in which the weight of the robot is alternately supported by scaled plates under the main body and the four shank segments. This allowed for stable locomotion in steep terrain with very loose soil. Originality/value – The paper describes how a very large range of motion allows the robot to actively explore its surroundings through haptic interaction, and to choose between a wide range of planning options. The paper describes how the authors developed a novel crawling gait, in which the weight of the robot is alternately supported by scaled plates under the main body and the four shank segments.
From Cloud Computing to Language Engineering, Affective Computing and Advanced Intelligence ∗

This paper discusses the definition, intension, and extension of language engineering, affective computing, and advanced intelligence, as well as the relationship among the three fields. By reporting the latest progress and future prospects, we attempt to unify language engineering and affective computing with the concept of advanced intelligence. “Cloud computing” has recently become a very popular topic. Instead of discussing the concept and intension of cloud computing, this paper focuses on how progress in language engineering, including natural language processing and natural language understanding, will enormously aid in the achievement of cloud computing. It particularly deals with how to construct clouds, how to sweep clouds, and how to predict and exploit clouds. Another concept discussed in this paper is “affective computing”. To a large extent, this is a breakthrough in advanced intelligence. Here, it refers to a high fusion of natural and artificial intelligence, and depends on the emotional capacity entrusted to the computer, including the capability of affective recognition and affective generation.
Sequential application of the hierarchy analysis method and associative training of a neural network in examination problems

We propose development of examination methodology based on a sequential application of the MAI method (i.e., the hierarchy analysis method) and associative training of neural networks. The proposed method is an alternative to the usual methods to solve a direct examination problem. We present a methodological approach to the examination problem. The approach allows to save information about all objects and consider their indicators in total. Therefore, there is the soft maximum principle (softmax), based on the model of expert evaluations mixing. This approach allows di erent interpretations of the examination results, which save quality unchanged overall picture of the examination object indicators ratio, and to get more reliable examination results, especially in cases where the objects characteristics are very di erent.
Reducing Energy Consumption in Wireless Sensor Networks Using Hash Distribution Table

Reducing Energy Consumption in Wireless Sensor Networks Using Hash Distribution Table Wireless sensor networks include many small sensor nodes. These nodes have limitations with respect to energy level, bandwidth, processing power, and memory. Because of these limitations, navigation, clustering, reduction of energy consumption, an increase of lifespan are much challenges in navigation through wireless sensor networks. This article presents a dispersed navigation protocol called “Bloom Filter”, which is based on older navigation protocols including Anita Kanavalli. Its aim is to optimize energy consumption of networks. This protocol was simulated using NS_2 and assessed. This study well with inexact and nonhomogeneous input and does not require heavy processing. This scheme uses the number of “sends” to a node instead of that node’s residual energy to determine its eligibility as a receiving node, and these results in drastic energy savings.
Compiler Transformations to Generate Reentrant C Programs to Assist Software Parallelization

As we move through the multi-core era into the many-core era it becomes obvious that thread-based programming is here to stay. This trend in the development of general purpose hardware is augmented by the fact that while writing sequential programs is considered a non-trivial task, writing parallel applications to take advantage of the advances in the number of cores in a processor severely complicates the process. Writing parallel applications requires programs and functions to be reentrant. Therefore, we cannot use globals and statics. However, globals and statics are useful in certain contexts. Globals allow an easy programming mechanism to share data between several functions. Statics provide the only mechanism of data hiding in C for variables that are global in scope. Writing parallel programs restricts users from using globals and statics in their programs, as doing so would make the program non-reentrant. Moreover, there is a large existing legacy code base of sequential programs that are non-reentrant, since they rely on statics and globals. Several of these sequential programs display significant amounts of data parallelism by operating on independent chunks of input data, and therefore can be easily converted into parallel versions to exploit multi-core processors. Indeed, several such programs have been manually converted into parallel versions. However, manually eliminating all globals and statics to make the program reentrant is tedious, time-consuming, and error-prone. In this paper we describe a system to provide a semi-automated mechanism for users to still be able to use statics and globals in their programs, and to let the compiler automatically convert them into their semantically-equivalent reentrant versions enabling their parallelization later.
ID 3 Algorithm for Intrusion Detection

ID3 algorithm is a standard, popular, and simple method for data classification and decision tree creation. Since privacy-preserving data mining should be taken into consideration, several secure multi protoco ls have been presented based on this technique. Many times the system get vulnerable to new attacks. This paper is the implementation of proposed model described in (1). We have implemented a model to embed primitive intelligence in the network intrusion de tection systems using C# . This model is based on Quinlain's ID3 algorithm of decision tree construction and inductive learning. This model detects unknown attacks with the help of optimized decision tree from available set of data also follow predefined r decision making for system Administrator. The communication overhead has been kept reasonably low to make the whole protocol efficient and practical.
Enhancing the Operating Efficiency ofMembrane Filter Using Image SegmentationTechniques

This Review paper describes the methodology adopted for the Qualitative analysis of Images for feature extraction. The microscopic image of membrane filter is taken which is made up of different number of polymer beads. Various segmentation techniques such as Thresholding, Edge detection, Active contouring, Watershed algorithm etc. are used for analysing the quality of Membrane filter for its efficient operation. The main goal is to use this proposed approach for estimating the uniformity of beads in the membrane filter.
A resource allocation analyzer for source programs written in atlas

This paper describes the design of a compu ter program which will read a source program written in abbreviated test language for all systems (ATLAS) and furnish a list of resources required by the ATLAS program to successfully conduct a test. These resources include specification requirements of test equipment used to generate or to sense test signals and impedance load requirements needed for the unit under test. This design uses high level data flow analysis of ATLAS (IEEE STD 416-1976) source code to determine the required resources of an ATLAS program.
A high-linearity 50- Omega CMOS differential driver for ISDN applications

A CMOS differential line-driver amplifier that uses positive feedback in the input stage to give transconductance multiplication and pole-zero doublet insertion is reported. The gain-bandwidth product at 60 kHz is 30 MHz and the unity-gain frequency is 2.7 MHz. The circuit operates from a single 5-V power supply and can achieve a total harmonic distortion (THD) of -78 dB for a 6-V/sub pp/ differential output signal at 40 kHz and for a load of 100 Omega and/or 150 pF. For the same measuring condition but with a load of 50 Omega and/or 150 pF, the THD is -73 dB. A power supply rejection of more than 76 dB up to 150 kHz was obtained. The chip occupies an area of 1200 mil/sup 2/ in a 1.5- mu m CMOS technology and dissipates 20 mW. >
Camera Based Label Reader for Blind People

An OCR (Optical Character Recognition) system, which is a branch of computer vision and in turn a sub-class of Artificial Intelligence. OCR is the translation of optically scanned bitmaps of printed or hand written text into audio output by using of Raspberry Pi. OCRs developed for many world languages are already under efficient use. The Moving object region is extracted by a mixture-of-Gaussians-based background subtraction method. A text localization and recognition are conducted to acquire text information. To localize the text regions from the object, a text localization algorithm by learning gradient features of stroke orientations and distributions of edge pixels . Text characters in the localized text regions are then binarized and recognized by off-the-shelf OCR software. The text codes recognized are output to blind users in speech. As the recognition process is completed, the character codes in the text file are processed using Raspberry pi device which recognize character using Adaboost algorithm and python programming, the audio output is listened.
A 1 mA, -120.5 dbc/Hz at 600 kHz from 1.9 GHz fully tuneable LC CMOS VCO

A 2 V, 1 mA, 1.8 GHz to 2.45 GHz tuneable LC-tank CMOS VCO is presented. The tank is made of a MOS varactor (worked between accumulation and deep depletion) and a bondwire inductor, realized connecting two pads to a package frame lead, to be compatible with the production environment. This solution enables one to tune all components variations, while achieving the lowest phase noise times current consumption product, reported to date.
A stochastic RTL circuit generator for FPGA architecture and CAD evaluation

The performance and capacity of Field-Programmable Gate Arrays (FPGAs) have dramatically improved in recent years. Today these devices are emerging as massively reconfigurable and paralleled hardware computation engines in data centers and cloud computing infrastructures. These emerging application domains require better and faster FPGAs. Designing such FPGAs requires realistic benchmark circuits to evaluate new architectural proposals. However, the number of available benchmark circuits is small, outdated, and few of these are representative of realistic circuits. A potential method to obtain more benchmark circuits is to design a generator that is capable of generating as many circuits as desired that are realistic and have specific characteristics. Previous work has focused on generating benchmark circuits at the netlist level. This limits the usefulness of these circuits in evaluating FPGA Computer Aided Design (CAD) algorithms since it does not allow for the evaluation of synthesis or related mapping algorithms. In addition, these netlist level circuit generators were calibrated using specific synthesis tools, which may no longer be state of the art. In this thesis, we introduce an Register Transfer Level (RTL) level circuit generator that can automatically create benchmark circuits that can be used for FPGA architecture studies and for evaluating CAD tools. Our generator can operate in two modes: as a random circuit generator or as a clone circuit generator. The clone circuit generator works by first analyzing an input RTL circuit then it gen-
An autofocus technique for imaging microscopy

The authors present an automatic focus (autofocus) technique for imaging microscopy. The technique successfully finds the optimum focus position for large working distance/small depth of focus optics. High throughput is realized by a combination linear-to-nonlinear digital signal processing algorithm. The optical design allows for focusing on image planes where no inherent image information exists. The optical design, the signal capture technique, the functional relationship between the definition of goodness-of-focus and objective to image distance, and how the algorithm exploits this functional relationship to achieve fast autofocus are described. <<ETX>>
Architecture for a low-level functional specification language supporting multimodeling and simulation

Multimodels are models composed of heterogeneous dynamic system components. Since most normal models like FSM and FBM represent only a part of the overall system behavior, they make only a subset of solution for analyzing the prediction and diagnosis of our real world. Multimodels can, on the other hand, give abstraction perspectives for a complex real-world system. There is a need for a unified, low-level functional specification language (and associated diagrammatic presentation) for simulations that may be expressed in a wide variety of modeling visualizations and formalisms. Our objective was to design a low-level functional specification language, with a circuit-like appearance and functionality, to more efficiently support eXtensible Markup Language (XML)-based multimodeling and simulation. For the multimodeling and simulation, there are three stages in our approach. For the first stage, we have defined traditional heterogeneous models using the Multimodeling eXchange Language (MXL). These models are transformed into a low-level functional specification language through eXtensible Stylesheet Language Transformation (XSLT) translator. For the second stage, we have designed a low-level functional specification language and its diagrammatic presentation for multimodeling and simulation, the Dynamic eXchange Language (DXL). DXL is an XML-based language that positions itself between higher-level modeling languages and a programming code. For the last stage, by using the XML Document Object Model (DOM), we demonstrate a translation approach that yields a target code for simulation such as JavaScript and Python. Through these three stages, we have developed the multimodeling methodology through the low-level functional language and its translation from MXL to simulation codes.
Research challenges and perspectives of the Semantic Web

Accessing documents and services on today's Web requires human intelligence. The interface to these documents and services is the Web page, written in natural language, which humans must understand and act upon. The paper discusses the Semantic Web which will augment the current Web with formalized knowledge and data that computers can process. In the future, some services will mix human-readable and structured data so that both humans and computers can use them. Others will support formalized knowledge that only machines will use.