Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectra acquisition software for clinical applications of the USB4000 spectrometer

The non-invasive clinic method of diffuse reflectance spectroscopy (DRS), for the diagnosis of human skin lesions can be performed by using spectrometric devices together with fiber optics probes. However, the operation of most of these devices commercially available is not specifically designed for clinical applications. As a result, the commercial software and the optical hardware of these spectrometers are impractical when trying to conciliate the requirements of a clinical procedure with their operation to perform the DRS for diagnosis purposes. Therefore, the development of home-built acquisition software will impact in a more reliable and practical spectrometric system for clinical environment. In this work is presented the development of an automation system that includes both a user graphical interface and a control system that enable a more reliable and faster acquisition of clinical spectra. The software features a voice control to perform the acquiring spectra process. The impact of this work is mostly the use of available programming platforms to implement a preliminary spectra processing tool that will lead to real-time acquisition of skin reflectance spectra of a given patient.
Spatial-domain path-diversity using an adaptive array for mobile communications

Frequency-selective fading due to multipath propagation is serious hindrance in high-speed TDMA mobile transmission. The multipath propagation causes degradation because signals with different delay times are received. If we resolve the signals and combine them adjusting the delay difference, we can both reduce the frequency-selective fading and utilize the transmitted signal power efficiently. This technique is called path-diversity. A RAKE receiver realizes the path-diversity in a time domain. We propose an adaptive array with several sets of complex weights. Each set of the complex weights forms a different array pattern. The weights are determined by an SMI (sample matrix inverse) algorithm. The adaptive array resolves multipath components adjusting the array patterns. Thus, we ran realize spatial-domain path-diversity for high-speed mobile communications.
Symbolic analysis techniques for effective automatic parallelization

To effectively translate real programs written in standard, sequential languages into parallel computer programs, parallelizing compilers need advanced techniques such as powerful dependence tests, array privatization, generalized induction variable substitution, and reduction parallelization. All of these techniques need or can benefit from symbolic analysis. To determine what kinds of symbolic analysis techniques can significantly improve the effectiveness of parallelizing Fortran compilers, we compared the automatically and manually parallelized versions of the Perfect Benchmarks. The techniques identified include: data dependence tests for nonlinear expressions, constraint propagation, interprocedural constant propagation, array summary information, and run time tests. We have developed algorithms for two of these identified symbolic analysis techniques: nonlinear data dependence analysis and constraint propagation. For data dependence analysis nonlinear expressions, we developed a data dependence test called the Range Test. The Range Test proves independence by determining whether certain symbolic inequalities hold for a logical permutation of the loop nest. We use a technique called Range Propagation to prove these symbolic inequalities. For constraint propagation, we developed a technique called Range Propagation. Range Propagation computes the range of values that each variable can take at each point of a program. A range is a symbolic lower and upper bound on the values taken by a variable. Range propagation also includes a facility to compare arbitrary expressions under the constraints imposed by a set of ranges. We have developed both a simple but slow algorithm and a fast and demand-driven but complex algorithm to compute these ranges. The Range Test and Range Propagation have been fully implemented in Polaris, a parallelizing compiler being developed at the University of Illinois. We have found that these techniques significantly improve the effectiveness of automatic parallelization. We have also found that these techniques are reasonably efficient.
Tense and agreement markers in the interlanguage of Spanish learners of English

This paper presents an analysis of the learning process of the features of tense and agreement in the interlanguage system of L1 Spanish speakers learning L2 English. This study uses a statistically significant sample of the student population in year 6 of primary education (11-12 years old), year 4 of secondary education (15-16 years old) and upper sixth form (17-18 years old), in the Autonomous Region of the Canary Islands. The research instrument was their written production. The analysis conducted includes the bound morphemes -ed and -s and the free morpheme be (with both copular and auxiliary function). The results show a predominant omission for the agreement morpheme -s in the three groups in which the corpus has been divided. On the other hand, the bound morpheme -ed and the free morpheme be present the highest percentage of correct use. These results confirm the capacity of the subjects to learn the verb features of tense and agreement, thus supporting the arguments of the Missing Surface Inflection Hypothesis (Prevost and White, 2000). As the results for the bound morphemes are not in agreement, the influence of other factors on the good results of the bound morpheme –ed, such as the verb aspect or the absence of phonological realizations, is suggested.
Performance of a memory-based approach to the control of robotic manipulators

The primary objective of this study is to examine the effectiveness of a memory-based control strategy for robotic manipulators. The main idea behind the memory-based control method is to use certain gathered information, such as most recent tracking error, current tracking error and past control experience, to directly modify the current control command. Simulations on a 3 DOF robotic arm show that this method is very effective in dealing with system nonlinearities and parameter uncertainties. Comparisons between the memory-based control and the existing model-based control are also provided.
Use of artificial neural networks for clinical decision-making (Maldescensus testis)

The application of artificial neural networks (ANNs) to medical diagnosis and in particular for the Maldescensus testis domain is presented. Various architectures and schemes using the traditional backpropagation are considered. The input data to the neural networks were encoded in two different ways, that is, using real values and gray code representation. These different architectures and schemes were evaluated and compared in terms of classification accuracy and speed. <<ETX>>
Simple Guidelines for Testing VR Applications

In recent years the number of virtual reality (VR) applications and devices employed in companies as well as in research facilities has increased remarkably. The applications developed call for human-computer interaction, which in turn calls for system and usability evaluations. These evaluations are usually conducted by means of measurement of human behaviour including aspects of perception, action, and task-performance. Traditionally, evaluations are held in specially designed usability laboratories but the new tendency requires researchers and even students that are non-experts in the field of usability, to evaluate applications. Hence, these studies take place at the premises of laboratories at universities or research institutes. This raises the question of whether non-experts are able to conduct evaluations in a professional manner. Furthermore, the evaluation issue calls for multiand interdisciplinary collaboration, where technical expertise is combined with humanistic knowledge and methodology. Several experts in the field of VR, as well as in the field of usability studies, call for producing helpful guidelines in order to be able to evaluate VR applications. This chapter gives an overview of the problem and introduces a guideline for evaluations of VR applications which aims to assist researchers in evaluating VR devices and installations,and in particular usability novices. This chapter also aims to facilitate multidisciplinary activities through the use of an evaluation guideline which would be simple and focused on VR. The applicability and usefulness of the proposed guideline are based on the authors’ experience in supervising and coordinating several university student projects related to the development of VR visualization technologies and applications. Furthermore, the guideline has been tested through several case studies where students were asked to evaluate their final year projects. The results showed how the proposed guideline could successfully be employed by non-experts to carry out pilot and formal evaluations. Therefore, this chapter is expected to represent a valuable reference for students and non-usability experts who design VR systems or applications and wish to run a user study to assess usefulness and general usability characteristics of their products.
A Novel Efficient SDN Based Broadcast Scheme

Data centers with millions of physical and virtual end hosts are now the basis for many Internet and cloud computing services. Hence, it is no longer practical to employ the traditional flooding method for the broadcast tasks in such large networks. In order to improve the efficiency of broadcasting among millions of hosts, we present a novel Software Defined Networking (SDN) based broadcast scheme in this paper. In most exsiting SDN technologies, the centralized Controller usually outputs messages to every port of all switches for broadcasting. However, in our scheme, a broadcast tree is previously created and corresponding instructions are installed into the switches as flow entries by the Controller. Hence, the Controller only needs to send a message to one switch, and then the informed switch will automatically spread the message to entire network along the broadcast tree. Therefore, the heavy broadcast task is transferred from the Controller to the distributed switches, which greatly alleviates the burden on the Controller and reduces the interactions between the Controller and switches. Moreover, a prototype is developed and the experimental results prove the good efficiency of the presented broadcast scheme. Keywords—Broadcast; SDN; Controller
Speech dynamics and recurrent neural networks

Recently, connectionist models have been recognized as an interesting alternative tool to hidden Markov models for speech recognition. Their main property lies in their combination of good discriminating power and the ability to capture input-output relations. They have also been proved useful in dealing with statistical data. However, the serial aspect remains difficult to handle in that kind of model, and several authors have proposed original architectures to deal with this problem. This study establishes links among them and compares their respective advantages. Relations with hidden Markov models are explained. <<ETX>>
KONSEP METADATA UNTUK APLIKASI E-LEARNING

E-Learning is one of the strategies to improve education, to the implementation needs to be developed and expanded. E-Learning systems must be able to provide information learning materials, including for users who are outside the environment in which the system of E- learning is used. Dissemination of information is often hampered because users usually not allowed to enter into the system. This study is intended to provide a solution in providing wider access to the E-Learning system is non-intrusive through the concept of metadata. A form of metadata structures of organizing learning material contained in the databases on the E- Learning system built by providing a query to databases to read the structure and content of learning materials, and then represented in an XML document format. Generated metadata catalogs used as learning materials are placed in a data warehouse and can be accessed through a web browser, and can be used by anyone, anytime and anywhere, in a variety of ways without having to log into the system, thus providing greater access to information in effective and efficient.
A classifier with the fuzzy Hopfield network for human chromosomes

This paper focuses on automatic classification of human chromosomes and develops a classifier based on the fuzzy Hopfield network, a dynamic neuro-fuzzy system. The classifier is a neurocomputing model as well as a fuzzy computing model. It holds fuzzy clustering capability, and is able to acquire knowledge about human chromosome data from some samples. Its task is to identify human chromosomes and assign each of them to one of the 24 human chromosome classes (22 autosomes and two sex chromosomes). The research results show that the classifier has a lower unidentified rate and the wrong identification rate of zero. The classifier offers us a promising computerized intelligent machine for automatic analysis of human chromosomes.
Using unified process to develop an online survey application.

This paper presents an online survey application that helps researchers to create online questionnaires, collect data, and analyse results easily. The development of the system follows Unified Process, where the workflow of requirements, analysis, design, implementation and testing are carried out respectively. The system has been implemented with high quality using various techniques, programming languages and tools. It has also been tested by users and the feedback is positive. The software can be found as an Open Source at http://surveyexpert.sf. net.
Data-driven decision making with applications to healthcare systems

Reconstructing a high-dimensional sparse vector from a small number of observations is a well-studied problem in many scientific, economic and engineering disciplines, and a number of tools have been designed to address this problem. It is currently experiencing a resurgence due to new applications, such as data-driven medicine and online advertising, and due to the need for accurate predictions under time and complexity constraints. This talk describes contributions in both directions. In the first portion of this talk, I will describe the application of such tools for minimizing rehospitalizations—the admission of a patient to a hospital soon after discharge. Nearly one in every five patients is readmitted within 30 days of their discharge, and the estimated cost of such rehospitalizations to Medicare in 2004 was $17.4 billion. Hospitals aim to avoid rehospitalizations in a number of ways; for example, through patient education programs, follow- up home visits by pharmacists, and by supplying extensive discharge packages. It is important to properly allocate these costly and limited resources. Using electronic health records from a major hospital in the U.S., we have designed a predictive model which identifies patients with the highest risk of being rehospitalized, making it possible to significantly reduce rehospitalization costs. In the second portion, I will focus on the rigorous analysis of a recent family of iterative algorithms for solving the above learning problems that are inspired by graphical models and ideas from statistical physics. These algorithms are exceptionally fast in yielding accurate predictions. Our analysis of these algorithms yields sharp formulas for their asymptotic performance. In particular, we derive rigorous formulas for the mean square error of the LASSO estimator.
A divide-and-conquer methodology for modular supervised neural network design

A novel learning strategy based on the divide-and-conquer concept is proposed to effectively overcome the slow learning speed and hard-determined network size problems in supervised learning neural networks. The proposed method first partitions the whole complex training set into several manageable subsets and then generates small size networks to 'conquer' (or learn) all these training subsets. In order to achieve efficient partition on a train set, we have proposed an error correlation partitioning (ECP) scheme such that sub-training-sets are formed with small (acceptable) training error. Based on this learning strategy, a self-growing modular neural network system can be developed. By applying the proposed learning strategy, a neural network is not only useful for pattern classification problems but also for continuous valued function approximation problems. <<ETX>>
Differentiating ethernet devices using normal link pulse with efficient computation and the impacts on performance

viii CHAPTER 1. Overview and Introduction . 1 1.1 Goals of Research . 1 1.1.1 Normal Link Pulse . 1 1.1.2 Expand Data Collected . 2 1.1. 3 Examine Design Space . 3 1.1. 4 Explore Efficient Design Possibilities . 3 1.2 General Algorithm . 4 CHAPTER 2. Review of Literature . 6 2.1 Previous DILON Work . 6 2.2 WINLAB at Rutgers University Research . 7 2.3 High Resolution Alignment . 8 2.4 More Identification of Transmitters . 8 CHAPTER 3. Design Reasoning . 10 3.1 Daubechie’s D4 Wavelet Transform . 10 3.2 Low-Pass Filtering . 12 3.3 Scaling by Noise Spectral Amplitude . 13 3.4 Waveform Length Adjustment . 15 3.4. 1 Standard Length Adjustment . 15
Capacity analysis of voting networks with application to human face recognition

Human face recognition is growing field of biometrics in which biological features are used to identify, individuals. In face recognition, the human face is the biological feature used to perform identification. This field has recently received an increased amount of attention because of potential applications such as security systems, law enforcement agencies, ATM/credit card applications, and human computer interfaces. The thesis is divided into two sections. In the first part, we study the capacity analysis of binary images using 2-level decoupled Hamming network which is a high performance discrete time/discrete state associative memory model. This model is a generalization of Hamming memory in the sense that it provides the local distance computations in the first level and a voting mechanism in the second level. We will study the effect of system dimension, window size, and noise on the capacity and error correction capability of the two-level decoupled Hamming memory. In the second part, we develop a fully automated face recognition system and study the classification, false positive, and temporal performance of the system using a database consisting of face images of 1200 individuals. Two techniques are developed for this purpose. The first one is a recognition algorithm based on the wavelet transform. The second one is a generalization of the 2-level decoupled Hamming network in which the local windows are optimally repositioned on a face in order to compensate for facial expressions, and small degrees of translation, tilt and rotation which is called voting with dynamic local templates. This 2-level network with its dynamic templates nature is able to simultaneously achieve a high correct classification and low false positive rate. The well known nearest neighbor algorithm is used to benchmark our results. The proposed voting with dynamic local templates gave 2.5 percent rejection with 0 percent misclassification on database of 1000 individuals.
Formal Support for Adaptive Workflow Systems in a Distributed Environment

To achieve more widespread application, Workflow Management Systems (WfMS) need to be developed to operate in dynamic environments where they are expected to ensure that users are supported in performing flexible and creative tasks while maintaining organisational norms [Alonso et al, 1997; Sheth & Kochut, 1997]. In order to cope with these demands, the systems must provide knowledge about the business process itself and the organisational context in that these processes operate [Jarvis et al, 1999]. It, however, is not an easy task to provide the appropriate and sufficient knowledge at the right level of abstraction that supports a workflow system at all stages of operation in a dynamic environment and for different types of users. At the same time, Enterprise Modelling (EM) methods are well recognised for their value in describing complex domains in an organised but usually informal structure. In particular, business process modelling techniques provide rich conceptualisations that tend to describe the type of information required by the adaptive workflow systems. However, because of their lack of formal structure the use of Enterprise Models that have been developed is limited [Junginger, 2000][Chen-Burger, 2001a]. We propose the use of a formal language within a three-layered framework. This language helps to turn the information contained in an informal Enterprise Model into the kind of formal model required by an adaptive Workflow System. In its current state of development, FBPML (Fundamental Business Process Modelling Language) covers business processes, organisational structure, agents and their capabilities as well as execution logic that gives direct instructions to a workflow engine. We assist modelling efforts of Enterprise Modellers by giving them a visual modelling language, underpinned by a formal representation, that is expressive and easy to use and that lets them specify the information required by a workflow engine. In this paper, we present our formal enterprise modelling language, FBPML. We show how adaptive workflow systems, like those developed at AIAI (e.g. the Task Based Process Manager [Stader et al, 2000], AKT Workflow [Chen-Burger, 2002a] and I-X system [Tate, 2002]), can take advantage of Enterprise Models represented in FBPML to provide effective support to users in real business environments.
A Method of Extracting Sentences Related to Protein Interaction from Literature using a Structure Database

Because a protein expresses its function through interaction with other substrates, it is vital to create a database of protein interaction. Since the total volume of information on protein interaction is described in terms of thousands of literatures, it is nearly impossible to extract all this information manually. Although extraction systems for interaction information based on the template matching method have already been developed, it is not possible to match all the sentences with interaction information due to the extent of sentence complexity.We propose a method of extracting sentences with interaction information independent of sentence structure. In a protein-compound complex structure, the interacting residue is near to its partner. The distance between them can be calculated by using the structure data in the PDB database, with a short distance indicating that the sentences associated with them might describe the interaction information. In a free-protein structure, the distance cannot be calculated because the coordinates of the protein's partner are not registered in the structure data. Hence, we use the homology protein structure data, which is complexed with the protein's parter.The proposed method was applied to seven literatures written about protein-compound complexes and four literatures written about free proteins, obtaining F-measures of 71% and 72%, respectively.
Towards Configurable Motion Capture with Prioritized Inverse Kinematics

This paper explores the potential of prioritized Inverse Kinematics for full body motion capture. We are especially interested in compensating for the use of a minimal set of sensors by proposing pertinent constraints associated with a priority level. We evaluate the believability issues and the performance requirements arising through a case study exploiting recorded motions of professional clarinet performers. We conclude by presenting a framework in which end users can easily configure the IK engine for 3D real-time interaction.