Miracle-dz/UniFAR-FTUs huggingface dataset:
A Recursive Method Based on Case Analysis for Computing Network Terminal Reliability

In this paper a unified treatment is provided of the methods for computing terminal reliability based on recursive case analysis. The possible choices in case enumeration are discussed, and a rationale is given to support a particular policy. Simplification and decomposition techniques are also examined, and some experimental results are described, obtained with a computer program, which is a good compromise between efficiency and simplicity.
SOME SAMPLE SURVEY DESIGN PROBLEMS IN SYRIA , NEPAL AND SOMALIA

Much of the information needed for governmental planning in developing countries is either unavailable or inadequate. Often the simplest and fastest way to obtain the needed information is by means of a sample survey. Survey data can be obtained by a relatively small number of people who are specifically trained to collect the needed information. This type of procedure permits better control over coverage, quality, and speed of gathering the data than a statistical system, such as for example, a vital registration system, that must rely on the cooperation of numerous diverse governmental bodies.
Interviewer Perceptions of Interview Quality

The purpose of this analysis was to determine whether interviewer perceptions of respondents’ performance are valid indicators of data quality in a survey of persons with mental and physical disabilities. In this paper, we describe interviewer perceptions of respondent participation and examine how interviewer perceptions correspond to data quality indicators. We also examine the relationship between interviewer perceptions and respondent and interview characteristics. We use data from the National Beneficiary Survey, a survey conducted by Mathematica Policy Research and sponsored by the Social Security Administration (SSA), for this analysis The survey gathers information about health, employment, income, and demographic characteristics. Also included are questions asked of the interviewer about his/her perception of the respondent’s intellectual capacity, response accuracy, and comprehension. Our analyses included 5,567 SSA disability beneficiaries. Chi-square analyses revealed that there were significant relationships between interviewer ratings and several data quality indicators such as cognitive screener performance, item non-response, and codeable verbatim responses. The relationship between other data quality measures, nondifferentiation and acquiescence, however, were not significant. Additionally, interviewers perceptions were related to respondent characteristics such as age, race, marital status, health and the mode of interview (CATI vs. CAPI). The results of this study suggest that interviewer perceptions are consistent with some indicators of data quality and support the inclusion of interviewer ratings within the context of the interview as a valid means to gather information on the quality of data collected
The Box ’-a 4 D PTV algorithm : Accurate and ghostless reconstruction of Lagrangian tracks in densely seeded flows

The ‘Shake The Box’ (STB) PTV method for the accurate reconstruction of particle tracks in densely seeded flows was first introduced in the scope of the 10th International Symposium on Particle Image Velocimetry (PIV13) in 2013 [Schanz 2013b]. At the time it was shown that the proposed method of incorporating the temporal information into the process of particle position reconstruction is able to improve results in various aspects compared to conventionally used techniques. However, only an experimental dataset was available to explore the abilities of the technique [Schanz 2013b]. The aim of the current work is to quantify the key features of the STB-method and to identify the benefits and drawbacks. To this end, a scheme for the creation of synthetic tracks from input vector volumes was created, allowing the calculation of physically (sufficiently) correct track systems at arbitrary seeding densities and time separations. Projections of such track systems on virtual cameras are used as input data for both STB, as well as an MLOS-SMART [Atkinson 2009] tomographic reconstruction algorithm. The results are compared with respect to positional accuracy of reconstructed particles, occurrence of ghost particles and reconstruction time. It is shown that, after a sufficient number of snapshots to allow for convergence of the algorithm, the STB-method is able to correctly identify nearly all particles in existence, even for high seeding density up to 0.125 particles per pixel (ppp). On the same time, the average position error stays very low (0.023 px for 0.125 ppp) – a gain of more than an order of magnitude compared to MLOS-SMART. The occurrence of ghost particles is almost completely suppressed (ghost level below 0.2 percent for 0.125 ppp). The efficient utilization of the temporal information allows a significant reduction in processing time, with STB being 4 to 6 times faster compared to MLOS-SMART. The introduction of noise to the particle images reduces the achievable accuracy for both STB and SMART. For high noise levels, STB shows positional errors up to 0.25 pixel (which can be improved to around 0.14 pixel using temporal fitting of the reconstructed trajectory). Still, the vast majority of particles is found and ghost levels stay very low also
Verification using PPM with Parts of Speech Tagging Notebook for PAN at CLEF 2014

In this paper we describe a compression-based authorship verification model used in conjunction with a parts of speech tagger. We use standard language-specific parts of speech taggers on the texts in question to generate a stream of symbols representing each word, run the PPM (Prediction by Partial Matching) algorithm on the resulting stream, and use Bobicev’s method of calculating and comparing cross-entropies to determine text authorship.
ECONOMIC DATA COLLECTION VIA THE WEB: A CENSUS BUREAU CASE STUDY

BACKGROUND Past tests by the Census Bureau of diskette Computerized Self-Administered Questionnaires (CSAQs) showed several limitations such as paper laden mail packages, labor intensive mail out operations, costly postage and mail materials, operating system limitations, with no timing savings enroute (Sweet and Ramos, 1995). In theory, using a CSAQ over the Internet (i.e., a Web CSAQ) has the potential to increase data accuracy and reduce respondent burden by controlling the flow of survey questions and performing consistency and completeness edit checks as respondents enter data, which are advantages of diskette CSAQs, while minimizing the mail, preparation, and maintenance costs of diskette CSAQs. This, coupled with the fact that many large U.S. companies have some kind of Interact connection (Groenfeldt, 1995), and the fact that we are developing CSAQs for establishment surveys, prompted our research into using Web CSAQs. Several questions exist since this is such a new mode of collecting data for federal statistics: What is the appropriate software to use for a Web CSAQ? How should one design the questionnaire? And, what is the respondent's perception of and willingness to use the Internet for this purpose? With these questions in mind, during 1996-97 the Census Bureau investigated whether we could collect data confidentially over the Web with a CSAQ that met respondents' needs. We tested a Web CSAQ on 50 production companies selected for the 1996 Industrial Research and Development (R&D or RD-1 S) survey. The 1994 R&D survey had served as a test bed for previous diskette CSAQ studies, and the sponsor, the National Science Foundation (NSF), was agreeable to testing a Web CSAQ within the 1996 survey.
THE QUALITY OF ESTIMATES FROM THE AMERICAN COMMUNITY SURVEY FOR SMALL POPULATION GROUPS

This paper discusses the quality and usefulness of estimates from the planned American Community Survey (ACS), for very small populations groups. The ACS is intended to replace the long form survey in the 2010 census. Since the long form is unique as a source of information about smaller population groups, a priority objective of the ACS design has been to provide good information about smaller groups.
A new video object motion estimation strategy using a multipopulation modified coevolutionary genetic algorithm

This paper describes a new, very efficient video object motion estimation strategy, that considers video object translation, translation of rotation center, and planar multilayering. It is based on a Multipopulation Modified Coevolutionary Genetic Algorithm (MMCGA), that receives the video objects of a segmented sequence of video images, and outputs the corresponding motion and layer information using appropriately represented object and layer genotypes. The algorithm performs a random search for locating the global optimal solution in the searching space. This operation involves an evolutionary process wherein populations of predicted solutions evolve over a period of generations. From the possible solutions, a video test frame is created, and the fitness of the test frames is evaluated by comparing with the reference frame. Next, solutions are selected for reproduction based on their fitness values. Good solutions are selected for reproduction while bad ones are eliminated. The selected solutions undergo recombination under the genetic operations of modified crossover and dynamic mutation. This last operation increases the mutation rate and reduces the mutation range from one population generation to the next, maximizing the performance of the MMCGA. For the increase in predicted solution accuracy, and convergence speed, lifetime strategies are used. Preliminary simulations with synthetic video images have shown very encouraging results with the proposed video motion estimation technique, which competes favorably with respect to the conventional motion estimation algorithms in accuracy, robustness, simplicity and speed.
Managing updates and transformations in data sharing systems

Dealing with dirty data is an expensive and time consuming task. Estimates suggest that up to 80% of the total cost of large data projects is spent on data cleaning alone. This work is often done manually by domain experts in data applications, working with data copies and limited database access. We propose a new system of update propagation to manage data cleaning transformations in such data sharing scenarios. By spreading the changes made by one user to all users working with the same data, we hope to reduce repeated manual labour and improve overall data quality. We describe a modular system design, drawing from different research areas of data management, and highlight system requirements and challenges for implementation. Our goal is not to achieve full synchronization, but to propagate updates that individual users consider valuable to their operation.
NEURAL NETWORK AND WAVELETS MODEL IN SEISMIC LOCATION FOR THE CENTRAL ANDES OF BOLIVIA

The location of seismic events in the Central Andes of Bolivia frequently displays differences between the reports by international agencies such as the International Data Center (IDC) or the Preliminary Determination of Epicenters (PDE) and those obtained from the National network. Also observed is an instability for different algorithms of location. The main cause of this instability is the dispersed azimuth distribution and the small number of seismic stations that are used to locate the events. Other factors that can cause this instability are marked attenuation of the S phase in earthquakes located in the south zone of Bolivia, or phases recorded as a consequence of the complex crustal structure that includes several refracting layers causing abrupt changes of period and amplitude. In our first stage of investigation, we proposed to develop an artificial and automatic method that accomplishes localization using a Neural Network on Radial Bases Functions (NNRBF) by applying a Gaussian function to the coda of the seismic signal and its location parameters. Our results show that the NNRBF requires a better approximation of the training parameters with the activating functions. In our second stage of investigation, we used a wavelet method instead of a Gaussian function to try to retain the maximum information recorded in the seismic signal when we applied the filters. This procedure applies the discrete wavelet transform to decompose the seismic noise, based on the Haar wavelet (or first Debauchies decomposition) with an alternating introduction in the NNRBF algorithm. Initially this methodology was tested with earthquakes in the central zone of Bolivia where the occurrence of seismic events is frequent and waveforms are less complex than in the rest of the region. The 23 earthquakes tested that occurred in 2000 and consisting of magnitudes (Ml) greater than 3 were analyzed with a success rate of 83%. This first result leads us to consider using this technique as a solution for the problem involving a small number of stations and the dispersed azimuth distribution, thus increasing the database of seismic events and identifying mislocated events as 17% of the data analyzed. We will continue our investigations to determine the limitations of this method by testing it in the remaining zones of Bolivia. 25th Seismic Research Review Nuclear Explosion Monitoring: Building the Knowledge Base
Estimating HIV Incidence Using Complex Survey Data

Accurate estimation of HIV incidence in at-risk countries is an important public health challenge. The Population-based HIV Impact Assessment (PHIA) Project has been conducting large-scale national population surveys using complex survey designs. HIV incidence estimation for these surveys is performed using a survey-weighted version of the incidence estimator originally proposed in Kassanjee, McWalter, Bärnighausen & Welte (2012). We describe a comprehensive variance estimation approach for this estimator that fully accounts for the variance components due to the survey and those due to the estimation of the biomarker assay parameters. The approach is readily integrated into a large-scale survey context, including that of the PHIA Project surveys. We illustrate the approach on data from three African countries and evaluate the sensitivity of the estimates to the values provided for the biomarker assay parameters and their measures of uncertainty.
An implementation of real-time hardware for moving object detection and discrimination

In this paper, we introduce real-time hardware implemented for the purpose of detecting the boundary points of a moving object and discriminating it from non-targets, which are basic functions of an unmanned surveillance system. The implemented hardware is composed of the two parts; one is a hard-wired logic part which computes spatio-temporal derivatives, and the other is a discrimination part including a micro-controller and memory devices. The object boundary is extracted by using a moving region and an edge map which can be detected by temporal derivative followed by morphological erosion and spatial derivative, respectively. We propose newly devised logic circuits for subtraction and thresholding, and spatio-temporal derivatives. Scarcely before individual hardware modules complete their functions, the second image capturing is done by performing the above functions in real-time.
Estimation of measurement bias using a model prediction approach

It is well-known in the survey literature that when responses are obtained from respondents in sample surveys, the actual observed values of measured characteristics may differ markedly from the true values of the characteristics. Evidence of these socalled measurement errors in surveys has been collected in a number of ways. For example, the recorded response may be checked for accuracy against administrative records or legal documents within which the true (or at least a more accurate) value of the characteristic is contained. An alternative means relies on revised reports from respondents via reinterviews. In a reinterview, a respondent is recontacted for the purpose of conducting a second interview regarding the same characteristics measured in the first interview. Rather than simply repeating the original questions in the interview, there may be extensive probes designed to elicit a more accurate response, or the respondent may be instructed to consult written records for the "book values" of the characteristics. For some reinterview surveys, descrepancies between the first and second interviews are reconciled with the respondem umil the interviewer is satisfied that a correct answer has been obtained. Forsman and Schreiner (1991) provide an overview of the literature for these types of reinterviews. Other means of checking the accuracy of survey responses include" a) comparing the survey statistics (i.e., means, totals, proportions, etc.) to statistics from external sources that are more accurate; b) using experimental designs to estimate the effects on survey estimates of interviewers and other survey personnel; and c) checking the results within the same survey for intemal consistency. The focus of the current work is on estimators of measurement bias from data collected in true value remeasurement studies, i.e., record check and reinterview studies, where the objective is to obtain the true value of the characteristic at, perhaps, a much greater cost per measurement than the original survey. Because of the high costs typically involved in conduct ing re in terv iew studies, repeated measurements are usually obtained for only a small fraction of the original survey sample. While the sample size may be quite adequate for estimating biases at the national and regional levels, they may not be adequate for estimating the error associated with small subpopulations or rare survey characteristics. In this paper, our objective is to consider estimators of response bias having better mean squared error properties than the traditional estimators. The basic idea behind our approach can be described as follows. In a typical remeasurement study, a random subsample of the survey respondents are selected and, through some means, the true values of the
Meta-cognitive sequential learning in RBF network for diagnosis of neurodegenerative diseases

This research work focuses on the development of meta-cognitive sequential learning algorithms in Radial Basis Function (RBF) network classi ers, and their application to the early diagnosis of neurodegenerative diseases. The important issues in existing sequential learning algorithms are proper selection of training samples, nding minimal network structure and selection of an appropriate learning strategy. In addition, the random sequence of sample arrival in uences the performance signi cantly. It has been reported in human learning that best learning strategies employ meta-cognition (metacognition means cognition about cognition) to address fundamental problems of what-tolearn, when-to-learn and how-to-learn. This thesis develops such meta-cognitive sequential learning algorithms in RBF network for classi cation problems. We call a RBF network employing meta-cognitive algorithm as `meta-cognitive RBF network' (McRBFN). McRBFN is developed based on Nelson and Narens model of meta-cognition for human learning. Accordingly, McRBFN has two components, namely cognitive and meta-cognitive components. A RBF network with evolving structure is the cognitive component and a self-regulatory learning mechanism is its meta-cognitive component. The meta-cognitive component controls the learning of cognitive component by choosing suitable learning strategies for each sample. When a new sample is presented, the metacognitive component either deletes the sample or learns the sample or reserves the sample for future use. Learning includes adding a new neuron or updating the parameters of the existing neurons using an extended Kalman lter (EKF). The McRBFN using EKF for parameter updates are referred as `EKF-McRBFN'. EKF-McRBFN uses computationally intensive EKF based parameter update and does not utilize the past knowledge stored in the network. Therefore, an e cient Projection Based Learning (PBL) algorithm for McRBFN referred as PBL-McRBFN has been developed. When a neuron is added to the cognitive component, the Gaussian parameters
Instrumentation and Data Acquisition Techniques in Structural Health Monitoring / Instrumentieren und Datenerfassung bei der Überwachung von Tragwerken

Life extension and replacement strategies are crucial issues in an efficient highway infrastructure management system. Due to normative and budgetary constraints, knowledge of the integrity of in-service structures on a continuous time basis has to be an ultimate objective of owners as well as maintenance authorities. A key component of any infrastructure management system is a set of surveillance techniques to assess the conditions of existing structures. Recent progress in the development of sensing technologies and material/structure damage characterization combined with current data processing techniques have resulted in a significant interest in diagnostic tools to monitor structural integrity and to detect structural diseases. The comparison of measured and calculated behavior in order to tune and calibrate mechanical and numerical model assumptions is an essential part of any system analysis. Once a model has achieved a certain level of completeness and validity, analytical prediction provides a quantitative knowledge of structural performance and safety assessment.