Miracle-dz/UniFAR-FTUs huggingface dataset:
Allograph-Based Categorization of Handwriting Styles

The aim of this work is to aid the development of online handwriting recognition software by using a linguistic approach to the problem of categorizing handwriting styles. This is done by exploring the wide variety of styles among users of the Latin alphabet, trying to explain the variety and finding ways of categorizing the styles. The variety is due to the many variables open to the writer and the wide range of these variables. A generative model for the production of hndwriting is proposed. Different ways of categorizing styles along the lines of the variables are explored, and one of them, allographic variation, i.e. different models of the same letter, is selected or the study of a database of online samples consisting of handwritten block letters from six European countries. Not only the shapes of letters are examined but also the number and sequence of the constituting penstrokes, which is especially interesting from an online recognition point of view. It is found that there are different 'alphabets' or categories of allographs, as well as geographical differences in the adherence to the alphabets.
Multi-Agent Residual Advantage Learning with General Function Approximation.

Abstract : A new algorithm advantage learning, is presented that improves on advantage updating by requiring that a single function be learned rather than two. Furthermore, advantage learning requires only a single type of update, the learning, while advantage updating requires two different types of updates, a learning update and a normalization update. The reinforcement learning system uses the residual form of advantage learning. An application of reinforcement learning to a Markov game is presented. The test-bed has continuous states and nonlinear dynamics. The advantage function is stored in a single-hidden-layer sigmoidal network. Speed of learning is increased by a new algorithm, Incremental Delta-Delta (IDD), which extends Jacob's (1988) Delta-Delta for use in incremental training, and differs from Sutton's Incremental Delta-Bar-Delta (1992) in that it does not require the use of a trace and is amenable for use with general function approximation systems. To our knowledge, this is the first time an approximate second order method has been used with residual algorithms. Empirical results are presented comparing convergence rates with and without the use of lDD for the reinforcement learning test-bed and for a supervised learning test-bed.
Consistency Checking of Web Service Contracts

—Behavioural properties are analyzed for web service contracts formulated in Business Process Execution Language (BPEL) and Choreography Description Language (CDL). The key result reported is an automated technique to check consistency between protocol aspects of the contracts. The contracts are abstracted to (timed) automata and from there a simulation is set up, which is checked using automated tools for analyzing networks of ﬁnite state processes. Here we use the Concurrency Work Bench. The proposed techniques are illustrated with a case study that include otherwise difﬁcult to analyze fault handlers.
Nestedness for Dummies (NeD): a User Friendly Web Interface for Exploratory Nestedness Analysis

Recent theoretical advances in nestedness analysis have led to the introduction of several alternative metrics to overcome most of the problems biasing the use of matrix 'temperature' calculated by Atmar's Nestedness Temperature Calculator. However, all of the currently available programs for nestedness analysis lack the user friendly appeal that has made the Nestedness Temperature Calculator one of the most popular community ecology programs. The software package NeD is an intuitive open source application for nestedness analysis that can be used online or locally under different operating systems. NeD is able to automatically handle different matrix formats, has batch functionalities and produces an output that can be easily copied and pasted to a spreadsheet. In addition to numerical results, NeD provides a graphic representation of the matrix under examination and of the corresponding maximally packed matrix. NeD allows users to select among the most used nestedness metrics, and to combine them with different null models. Integrating easiness of use with the recent theoretical advances in the field, NeD provides researchers not directly involved in theoretical debates with a simple yet robust statistical tool for a more conscious performance of nestedness analysis. NeD can be accessed at http: //purl.oclc. org/ned.
HUMAN MACHINE COOPERATIVE TELEROBOTICS

Due to the increasing number of work sites that are hazardous or merely inaccessible, remote manipulation has become more and more important. Nuclear, underwater, and space applications, exemplify a few of the dangerous environments in which work may be desired, while micromanipulation, which has become of more interest lately, is an example of an inherently inaccessible environment. The past 50 yr have seen great advances in remote manipulation technology, from the pioneering work of Ray Goertz in the 1950s to the ongoing development at Oak Ridge National Laboratory (ORNL) of the modular light-duty utility arm (MLDUA), which is a long-reach manipulator for use in the cleanup of the waste storage tanks. Mainly, research has either focused on the improvement of manually operated remote manipulators or teleoperators, in which a human is an integral part of the control loop, or autonomous robots, which have the required decision-making capability and sensors. However, in the past few years, it has become increasingly evident that there are limitations in each of these modalities, which make them individually unsuited for certain tasks. While a human operator may be required to make high-level decisions, fatigue and tedium can result from repetitive tasks. On the other hand,more » computers can provide fast and efficient operation but are limited by their currently inadequate decision-making abilities as well as inaccuracies in the utilized sensors. An ideal teleoperator would be one in which the human is involved in the operation only to the extent that high-level decisions must be made and corrections must be made to account for inaccuracies in the sensors. Responsibilities such as gross alignment and repetitive motions would be delegated to computer control. « less
Navigation-based speed profile generation for an open road speed assistant

Abstract This paper presents a navigation-based Advanced Driver Assistance System for a longitudinal application which aims to adapt, in real-time, the vehicle speed, so as to ensure the passengers' safety and comfort. A digital map database provides information about the upcoming road so that a specific Spline trajectory can be calculated. The latter allows the determination of a safe and comfortable speed profile. The Control is based on the combination of a finite state machine and a PID controller. Constraints linked to the actuators, the sensors or the vehicle dynamics are taken into account, using an improved speed profile combined with a look ahead distance and a speed tolerance. The real time validation shows that the proposed solution gives satisfactory results.
Variable optical buffer for packet storage in OPS nodes

A novel solution for all-optical packets buffering in OPS nodes is proposed. Variable delays are performed by exploiting a low-loss optically controlled fiber-based loop configuration. XGM in SOAs allows polarization and wavelength independent operation in the whole C-band. A packet delay resolution of 5 μs is obtained as well as a storage time of 50 μs with moderate signal degradation. Performances are evaluated in terms of bit error rate measurements for 10 Gb/s NRZ data payload, providing an OSNR penalty lower than 3 dB after 10 circulations. The proposed solution is particularly attractive in slotted OPS nodes architectures where packet contention would be managed entirely in the optical domain.
Malay Part of Speech Tagger: A Comparative Study on Tagging Tools

Bahasa Melayu merupakan bahasa aglutinatif yang kaya dengan morfologi untuk menerbitkan perkataan dengan makna selain daripada kata akar yang akan memberi kesan kepada perubahan golongan katanya. Korpus beranotasi Bahasa Melayu sukar didapati, maka belum ada penerbitan tentang perbandingan prestasi penandaan golongan kata (GK) menggunakan kaedah Model Markov Tersembunyi (MMT), Entropi Maksimum (EM) dan Mesin Vektor Sokongan (MVS), terutamanya bagi melihat kesan morfologi Bahasa Melayu ke atas penandaan GK bagi perkataan anu. Kertas ini bertujuan untuk membentangkan penilaian ketiga-tiga kaedah tersebut ke atas Bahasa Melayu. Tiga alatan penanda GK telah digunakan yakni TnT mewakili MMT, MaxEnt mewakili EM dan SVMTool mewakili MVS. Bagi melengkapkan latihan dan ujian bagi ketiga-tiga alatan tersebut, usaha menganotasi korpus Bahasa Melayu bagi domain kesihatan telah dilakukan. Alatan TnT diubahsuai untuk memasukkan fitur imbuhan awalan serta apitan. Keputusan bagi seluruh eksperimen menunjukkan prestasi SVMTool mengatasi TnT dan MaxEnt bagi kejituan keseluruhan (99.23% untuk SVMTool , 94% untuk TnT dan 96% untuk MaxEnt ) serta kejituan penandaan perkataan anu (96.78% untuk SVMTool , 67% untuk TnT dan 86.23% untuk MaxEnt ). Keupayaan MaxEnt pula mengatasi TnT bagi kejituan keseluruhan serta kejituan penandaan perkataan anu. Dengan penandaan perkataan anu sebanyak 96.78% ketepatan oleh SVMTool , maka isu penandaan GK Bahasa Melayu bagi domain spesifik dianggap telah selesai.
Emergent agent communication in multi-agent systems using automatically defined function genetic programming (ADF-GP)

Multi-agent systems, in which independent software agents interact with each other to achieve common goals, complete concurrent distributed tasks under autonomous control. In this paper we propose a method applying automatically defined function genetic programming (ADF-GP) to allow agents to autonomously learn effective agent communication messaging in order to achieve group behavior. A software simulation of a multi-agent system was used to observe the effectiveness of the proposed method. Using the proposed method, emergence of a compact and efficient agent communication protocol for the multi-agent system was observed.
Supervised Classification Leveraging Refined Unlabeled Data

This thesis focuses on how unlabeled data can improve supervised learning classi-fiers in all contexts, for both scarce to abundant label situations. This is meant toaddress the limitations within supervised learning with regards to label availability.Extending the training set with unlabeled data can overcome issues such as selec-tion bias, noise and insufficient data. Based on the overall data distribution andthe initial set of labels, semi-supervised methods provide labels for additional datapoints. The semi-supervised approaches considered in this thesis belong to one ofthe following categories: transductive SVMs, Cluster-then-Label and graph-basedtechniques. Further, we evaluate the behavior of: Logistic regression, Single layerperceptron, SVM and Decision trees. By learning on the extended training set,supervised classifiers are able to generalize better. Based on the results, this the-sis recommends data-processing and algorithmic solutions appropriate to real-worldsituations.
Analysis of the multisensor multitarget tracking resource allocation problem

The paper deals with a study of the multisensor management problem. The main tool is the classical assignment formulation, using Kullback-Leibler entropy as costs. In order to use the benefit brought by the data fusion, coalitions or pseudosensors must be created at each step of time, creating an exponential calculus of all the possible sensor partitions. We compare this method and a predefinite strategy using different scenarios.
Comparison of three orientation agreement strategies in self‐propelled particle systems with turn angle restrictions in synchronous and asynchronous settings

In this study, we compare three different orientation agreement strategies of multi‐agent/particle systems under different conditions. We investigate the behavior of multi‐agent systems utilizing these strategies with different combinations of the following properties: (i) the multi‐agent systems may be synchronous or asynchronous, (ii) they may travel in bounded or unbounded regions and (iii) the mobile agents may have turning speed restrictions. The agents/particles are assumed to move with constant speed and update their orientation of motion based on three different strategies. Based on these strategies, simulations are performed and the effects on the clustering performance are investigated. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
Analysis of the dynamical performance of the two-mass drive system with the modified state controller

In this publication a modified state controller applied in the speed control loop of two-mass drive system is shown. A characteristic feature of this type of a drive system is an existence of oscillations of electromagnetic state variables, resulting from the finite stiffness of the connection between driven motor and the load machine. Effective damping of torsional vibrations in such a system is achieved by introducing additional feedback in the control structures of the two-mass drive. In the described control system the reduction of the feedbacks number is achieved by elimination of the one coming from the torsional torque. At the same time, it was assumed that the beneficial dynamic properties of the two-mass drive obtained in a case of the application of all possible additional feedbacks should be kept. Calculation of the controller gains was done using the pole placement method. Application of the pole placement method gives a possibility for setting the parameters of the control structure for given values of damping coefficient and resonance frequency of the close-loop system, so it is possible to influence the dynamic properties of the drive.
A QoS management platform for multicast environments

The paper discusses an integrated network management platform for multicast environment, IPME, which is designed to address some of the current research issues. The IPME platform mainly consists of three components; manager, agents and participants. Their functions are defined, as well as the relationship among the components. Within the framework of the IPME platform, a QoS management platform, IPME-QoS, is added to accommodate real time multicast applications. The architecture of the QoS management platform is described, together with QoS establishment procedure and QoS management policies. In order to validate the proposed QoS management platform, a multicasting based VOD system is studied and evaluated.
Reconstruction building 3D model from close-range images based on line and plane feature

Now, use close-range sequential images getting from ordinary digital camera to reconstruct building 3D model is becoming to a convenient, fast and economical method. In the traditional building 3D model reconstruction, always use feature points. Considering there are abundant of lines and planes on the building, a new method was carried out in this paper is that the automatic matching of line segments across images of scenes containing buildings, and the output is a 3D plane combination of the building. The system is largely line feature based, starting with line segments, matching, grouping, and proceed them to higher level features. The approach has been tested on a triplet of close-range sequential images of a common building in the campus.
Maximal Frequent Item Sequences Mining

This work proposes a new fast algorithm finding maximal frequent item sequences from transaction database. Itemset is defined as item sequence (IS) for mining. Two lists called ISL (Item Sequence List) and FISL (Frequent Item Sequence List) are created by scanning database once for dividing n-IS into two categories depending on whether the IS to achieve minimum support number (n is the number of attributes). Sub item sequences (SIS) whose n-superset is in ISL are generated by recursion to make sure that each k-SIS appeared before its (k+1)-superset. As current k-SIS being joined to FISL, its (k-1)-SIS are pruned (k range from 2 to n-1). At last, all SISs whose n-superset is in FISL are pruned from FISL. We compare our new algorithm and FP-Growth by experiment to prove its superiority.
Semantic fields of problem in business English: Malaysian and British journalistic business texts

Abstract This paper reports on an LSP-based research project dealing with a contrastive analysis of business and management texts taken from the Malaysian business magazine Malaysian Business (MB) and its British counterpart, Management Today (MT). The objective of the research was to study the semantic fields and linguistic signals of Problem patterns in order to determine whether they display specific differences which can be ascribed to their linguistic and cultural contexts. The study adopted a corpus-based approach based on a corpus containing fifty feature-articles from each magazine. The text corpus was analysed according to Hoey's Problem-Solution textual patterns and the corpus tool, Wmatrix, was used to identify the semantic fields in the Problem patterns. Key semantic fields were found for Problem in MB and MT compared with a normative corpus (the BNC Written Informative Sampler).
Genetic search for optimal ensemble of feature-classifier pairs in DNA gene expression profiles

Gene expression profile is numerical data of gene expression levels from organism, measured on the microarray. In general, each specific tissue indicates different expression level in related genes, so that it is possible to classify disease by gene expression profile. For classification, it is needed to select related genes called feature selection, because all the genes are not useful for classification. We propose GA-based method for searching optimal ensemble of feature-classifier pairs of gene expression profile in seven feature selection methods based on correlation, distance, and information theory, and representative six classifiers. Experimental results on two gene expression profiles related to cancers show that GA finds good solution quickly. Especially, in Lymphoma dataset, GA finds the ensemble of 100% accuracy.
Enhancements of the Voxmap-PointShell Algorithm

Providing the user with touch or haptic feedback is crucial in activities performed in virtual worlds, such as assembly or maintenance simulations, and skill training. McNeely et al. introduced in 1999 the Voxmap-PointShellTM (VPS) Algorithm, an appealing haptic rendering algorithm that generates collision and force response with 1 kHz refresh rate even with geometrically complex environments. The VPS algorithm works with two types of data-structures to achieve this high frequency: voxmaps (discretized objects using voxels, equivalent to pixels in 3D), and pointshells (point-clouds representing the surface of the moving objects). This diploma thesis introduces enhancements on a modified VPS algorithm that was reimplemented at DLR. First, the development (using Matlab and C++) of new algorithms that generate these data-structures is explained. The voxmap generator is based on the Separating Axis Theorem, whereas a nonlinear analytical optimization method is applied to compute pointshells. Experiments show both quality and generation speed improvements compared to the previous algorithms. The quality is essential for the VPS algorithm, as the collision and force computation depend on the accuracy of its data-structures. Second, a sphere-point hierarchization implemented to the pointshell is introduced, in order to work at high sampling rates with high resolution. Results of generation tests performed to artificial objects and real models provided by Volkswagen are included to verify the suitability of the new algorithms.
A LOGICAL DESCRIPTION OF THE EVOLUTION OF FEEDBACK LOOP SYSTEMS AND ITS APPLICATION TO RHABDOVIRUS INFECTION

The molecular biology of viruses can be effectively described by kinetic logic, even in its so-called “naive” version, since feedback loops are implicated in all viral cycles and viral proteins generally display several functions. A general method for the evolution of systems involving feedback loops using this modelling is presented. It can be summarized as follows: (i) description of the system through boolean equations, (ii) identification of the steady states (is there a good agreement between the observations and the logical description? ), (iii) experimental determination of several parameters, (iv) computer simulation of viral cycles, (v) predictions and their relation to the biological facts. This survey of oscillation between experimentation and logical description was applied to the study of rhabdovirus cycles and allowed us to describe some aspects of their gene expression.
The Problem with Assessing Statistical Methods

In this paper, we investigate the problem of assessing statistical methods and effectively summarizing results from simulations. Specifically, we consider problems of the type where multiple methods are compared on a reasonably large test set of problems. These simulation studies are typically used to provide advice on an effective method for analyzing future untested problems. Most of these simulation studies never apply statistical methods to find which method(s) are expected to perform best. Instead, conclusions are based on a qualitative assessment of poorly chosen graphical and numerical summaries of the results. We illustrate that the Empirical Cumulative Distribution Function when used appropriately is an extremely effective tool for assessing what matters in large scale statistical simulations.
Analysis and synthesis of neural networks using linear separation

General analysis and synthesis methods for neural networks are presented. The techniques proposed are simple, efficient and not restricted to a certain network architecture, i.e., they can be any of the multilayer, fully interconnected feedforward or feedback structures. Based on the signs of connections between neurons (called weight signatures) being excitatory or inhibitory, the methods proposed provide some fundamental rules of learnability in such networks. Various design techniques are presented using these learning rules for the synthesis of neural architectures. <<ETX>>
Resizing of images by arbitrary factors in the spatial domain and implementation on Blackfin BF533 processor

A novel approach for resizing of images in the spatial domain is presented. Image transformations are seen from a Euclidean space perspective and expressions for image resizing are derived. The proposed method can be used for resizing by any arbitrary rational ratio L/M. The algorithm has been tested for resizing between standard formats like NTSC, QVGA, QCIF and CIF and the performance is compared with other algorithms in terms of PSNR and computational efficiency. Real time implementation on analog devices Blackfin-BF533 DSP processor shows promise.
Formation maneuvers in three dimensions

The flying of aircraft in formation necessitates the extension of the theory of formation flight control to allow for three dimensional formation maneuvers. A leader and wingman formation is considered. A rotating reference frame attached to the wingman is used and special attention is given to the motion of the leader relative to the wingman. A seven state, three input, and three disturbance signal control system which models the dynamics of a two-aircraft formation in three dimensional space is developed. Two formation flight control concepts are investigated. A proportional, integral, and derivative automatic control system to maintain the wing aircraft in the specified formation geometry despite the leader's maneuvers is designed, and its performance is examined in simulation experiments.