Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring the compositionality of bigrams using statistical methodologies: shared task system description

The measurement of relative compositionality of bigrams is crucial to identify Multi-word Expressions (MWEs) in Natural Language Processing (NLP) tasks. The article presents the experiments carried out as part of the participation in the shared task 'Distributional Semantics and Compositionality (DiSCo)' organized as part of the DiSCo workshop in ACL-HLT 2011. The experiments deal with various collocation based statistical approaches to compute the relative compositionality of three types of bigram phrases (Adjective-Noun, Verb-subject and Verb-object combinations). The experimental results in terms of both fine-grained and coarse-grained compositionality scores have been evaluated with the human annotated gold standard data. Reasonable results have been obtained in terms of average point difference and coarse precision.
A new platform for studying human balance control : design, validation, and experiments

This thesis provides insight and novel tools for investigation into the neuromotor control of human standing balance. To maintain upright standing, the human body integrates sensory inputs and activates lower limb muscles in a coordinated manner. Balance control mechanisms are not well understood, largely due to the lack of experimental tools. Existing devices modify sensory information or mechanically disturb the body; however, both approaches can induce unnatural corrective responses. One approach that does not perturb normal control mechanisms is to allow humans to balance in an immersive physics simulator, but no appropriate tool has been available. Furthermore, control models that describe unperturbed (quiet) standing are typically evaluated in computer simulations and rarely experimentally tested by activating human muscles. This thesis presents two studies that seek to answer the questions: a) Can we engage humans in an immersive balancing task decoupled from their body mechanics? b) Which control models accurately characterize standing balance? The first study validates the design of a novel robotic system that enables subjects to safely balance according to a programmable physical model. When programmed with a subject’s own body mechanics, results show that the torque‐angle relationship (load stiffness) is similar to that of normal standing, and that load stiffness increases, as expected, with increasing sway frequency. By providing decoupled control over balance physics, this system enables novel investigations into the neural mechanisms of human standing. The second study evaluates proposed control models for quiet standing within a control loop that stimulates human muscle actuation. Two factors differentiate the models: activation type and delay‐ reducing prediction. All evaluated models successfully balance in the absence of natural muscle activation but increase corrective activity and mechanical effort relative to natural standing. Intermittent activation reduces stimulation energy but increases sway. Prediction reduces sway for the intermittent case only. To develop more accurate control models, future work is recommended to reduce sway during intermittent activation, reduce feedback gains, increase predictor compensation, and vary the setpoint angle. This work contributes to the understanding of balance neurophysiology and may lead to improved control models for body movement in healthy and balance‐impaired individuals.
Breast Cancer Decisive Parameters for Iraqi Women via Data Mining Techniques

Objective This research investigates Breast Cancer real data for Iraqi women, these data are acquired manually from several Iraqi Hospitals of early detection for Breast Cancer. Data mining techniques are used to discover the hidden knowledge, unexpected patterns, and new rules from the dataset, which implies a large number of attributes. Methods Data mining techniques manipulate the redundant or simply irrelevant attributes to discover interesting patterns. However, the dataset is processed via Weka (The Waikato Environment for Knowledge Analysis) platform. The OneR technique is used as a machine learning classifier to evaluate the attribute worthy according to the class value. Results The evaluation is performed using a training data rather than cross validation. The decision tree algorithm J48 is applied to detect and generate the pattern of attributes, which have the real effect on the class value. Furthermore, the experiments are performed with three machine learning algorithms J48 decision tree, simple logistic, and multilayer perceptron using 10-folds cross validation as a test option, and the percentage of correctly classified instances as a measure to determine the best one from them. As well as, this investigation used the iteration control to check the accuracy gained from the three mentioned above algorithms. Hence, it explores whether the error ratio is decreasing after several iterations of algorithm execution or not. Conclusion It is noticed that the error ratio of classified instances are decreasing after 5-10 iterations, exactly in the case of multilayer perceptron algorithm rather than simple logistic, and decision tree algorithms. This study realized that the TPS_pre is the most common effective attribute among three main classes of examined dataset. This attribute highly indicates the BC inflammation.
Propensity Score Analysis: Statistical Methods and Applications

List of Tables List of Figures Preface About the Authors Chapter 1: Introduction Observational Studies History and Development Randomized Experiments Why and When a Propensity Score Analysis Is Needed Computing Software Packages Plan of the Book Chapter 2: Counterfactual Framework and Assumptions Causality, Internal Validity, and Threats Counterfactuals and the Neyman-Rubin Counterfactual Framework The Ignorable Treatment Assignment Assumption The Stable Unit Treatment Value Assumption Methods for Estimating Treatment Effects The Underlying Logic of Statistical Inference Types of Treatment Effects Treatment Effect Heterogeneity Heckman's Econometric Model of Causality Conclusion Chapter 3: Conventional Methods for Data Balancing Why Is Data Balancing Necessary? A Heuristic Example Three Methods for Data Balancing Design of the Data Simulation Results of the Data Simulation Implications of the Data Simulation Key Issues Regarding the Application of OLS Regression Conclusion Chapter 4: Sample Selection and Related Models The Sample Selection Model Treatment Effect Model Overview of the Stata Programs and Main Features of treatreg Examples Conclusion Chapter 5: Propensity Score Matching and Related Models Overview The Problem of Dimensionality and the Properties of Propensity Scores Estimating Propensity Scores Matching Postmatching Analysis Propensity Score Matching With Multilevel Data Overview of the Stata and R Programs Examples Conclusion Chapter 6: Propensity Score Subclassification Overview The Overlap Assumption and Methods to Address Its Violation Structural Equation Modeling With Propensity Score Subclassification The Stratification-Multilevel Method Examples Conclusion Chapter 7: Propensity Score Weighting Overview Weighting Estimators Examples Conclusion Chapter 8: Matching Estimators Overview Methods of Matching Estimators Overview of the Stata Program nnmatch Examples Conclusion Chapter 9: Propensity Score Analysis With Nonparametric Regression Overview Methods of Propensity Score Analysis With Nonparametric Regression Overview of the Stata Programs psmatch2 and bootstrap Examples Conclusion Chapter 10: Propensity Score Analysis of Categorical or Continuous Treatments Overview Modeling Doses With a Single Scalar Balancing Score Estimated by an Ordered Logistic Regression Modeling Doses With Multiple Balancing Scores Estimated by a Multinomial Logit Model The Generalized Propensity Score Estimator Overview of the Stata gpscore Program Examples Conclusion Chapter 11: Selection Bias and Sensitivity Analysis Selection Bias: An Overview A Monte Carlo Study Comparing Corrective Models Rosenbaum's Sensitivity Analysis Overview of the Stata Program rbounds Examples Conclusion Chapter 12: Concluding Remarks Common Pitfalls in Observational Studies: A Checklist for Critical Review Approximating Experiments With Propensity Score Approaches Other Advances in Modeling Causality Directions for Future Development References Index
Improving spatial decision making in cloud computing

The increasing capabilities of Internet have caused a qualitative change in the management of spatial information while recent advances in Web 2.0 technologies have enabled the integration of data and knowledge in intuitive thematic maps. This has wide-ranging indirect effects in supporting the ways stakeholders make a decision based on information coming from various distributed resources, but the real question is, What applications and technologies are in place to deal with these decisional environments? Aiming at giving an answer to this question, this chapter explores the feasibility of a computational environment that supports the Web-based exploration and the spatial analysis in real estate decisional processes. It relies on the concept of dataspace as a new scenario for accessing, integrating, and analyzing geo-spatial information regardless of its format and location. Built on top of a cloud environment, it is made up of specialized modules, each of which provides a well-defined service. Mash-ups integrate data from different resources on the Internet and provide the user with a flexible and easy-to-use way for geo-referencing data in the maps provided by Google Maps and Google Earth. Through an interactive process, the user arrives at some interesting maps, glimpses the most important facets of the decisional problem, and combines them to fashion a solution. Applicative experiments demonstrate the effectiveness of the computational environment proposed. Michele Argiolas Università degli Studi di Cagliari, Italy Maurizio Atzori Università degli Studi di Cagliari, Italy Nicoletta Dessì Università degli Studi di Cagliari, Italy Barbara Pes Università degli Studi di Cagliari, Italy DOI: 10.4018/978-1-4666-6252-0.ch005
Frequency estimation utilizing the Hadamard transform

Fast analog to digital conversion with only one bit per sample not only makes high sampling rates possible but also reduces the required hardware complexity. For short data buffers or block lengths, it has been shown that tone frequency estimators can be implemented by a simple table look-up. We present an analysis of such tables using the Hadamard transform. As an outcome of the analysis, we propose a class of nonlinear estimators of low complexity. Their performance is evaluated using numerical simulations. Comparisons are made with the proper Cramer-Rao bound and with the table look-up approach.
Knockdown of RPL34 suppresses osteosarcoma cell proliferation likely through EIF3/FAU signaling pathway

Background This study aims to explore the effect of knockdown of the ribosomal protein L34 (RPL34) on osteosarcoma cell proliferation through EIF3/FAU. Methods The mRNA expression levels of RPL34, EIF3A, EIF3F, EIF3G, UBA52, UBC, and FAU were quantified using quantitative real-time PCR (qRT-PCR) in two human cell lines: the hFOB1.19 osteoblast cell line and the Saos-2 osteosarcoma cell line. The qRT-PCR analysis was performed to confirm the shRNA-mediated depletion of RPL34, and a western blot analysis was done to confirm the knockdown of protein levels. qRT-PCR was also used to examine the effects of RPL34 knockdown on the above genes. Results The mRNA expression levels of RPL34, EIF3A, EIF3G, UBA52, UBC, and FAU were increased in the Saos-2 cells relative to the hFOB1.19 osteoblast cells. EIF3F expression was decreased in Saos-2 cells relative to hFOB1.19 cells. Knockdown of RPL34 mRNA significantly suppressed EIF3A and EIF3F expression and significantly up-regulated FAU expression in the Saos-2 cell line, but there were no remarkable differences in the relative expression in the EIF3G, UBA52, and UBC genes. Conclusions Knockdown of RPL34 suppresses osteosarcoma cells proliferation probably through EIF3A, EIF3F, and FAU.
Probability Estimation of Change Maps Using Spectral Similarity

Change Detection (CD), which is a process of identifying changes that have occurred in a geographical area over the time, plays a key role in many applications, including assessing natural disasters, monitoring crops, and managing water resources. In the past decades, many CD techniques have been proposed. Hence, evaluating and analyzing of the probability of changes and interpreting them is an essential task which leads to better management of natural resources and the prevention of disasters. For this purpose, we adopted an approach to estimate the probability of changes that have occurred in the image scene. Based on this approach, changed pixels are categorized and labeled as probabilities (in percentage format). In this paper, the proposed framework consists of the following four steps. Firstly, this research produces a final binary change map (BCM) through combining the results of some of popular binary CD methods that have been proposed in the literature. Then an unmixing process is adopted and in the next step the spectral similarity of pixels is calculated in the abundance maps of endmembers. A measurement of spectral similarity identifies the finer spectral differences between the two hyperspectral images (HSIs). Finally, spectral similarity values are masked by the final BCM resulting in a probability map of changes. The experimental results verified that the method is able to obtain good results and may be well suited for hyperspectral CD applications.
What comparisons of natural and chimeric contacts reveal about inhibition of human cathepsins K, L and S by their prosegments

Human cathepsins K, L, and S, which are involved in the development of several serious diseases, are strongly inhibited by their related prosegments, to which they are covalently bound or simply forming complexes. In this work, three-dimensional structures of the three natural complexes of these enzymes with their related proregions were constructed, as well as six chimeric complexes of the same three prosegments with their non-cognate enzymes. We made a comparative study of the contacts in all nine structures throughout their active sites. The analysis was performed looking for a structural parameter that could agree with the values of the inhibition constants reported experimentally for each of the nine complexes. We found that this correlating parameter was the difference of the electrostatic energy (involving hydrogen bonds and ion pairs) at the binding interface of a 13-amino acid fragment of the prosegments. We used the results of this work, on the one hand, to identify the key residues involved in the electrostatic intermolecular recognition in each studied complex and, on the other, to explain some results achieved by different research groups on the inhibition of the same enzymes analyzed here. It was found that the natural cathepsin L complex showed a higher number of electrostatic interactions, some of them interconnected, when compared to the other two natural complexes. In addition, the chimeric contacts revealed binding sites that could be used to achieve a more potent inhibition of these cathepsins, avoiding cross-interactions.
Una primera aproximación para la extracción de patrones emergentes en flujos continuos de datos

Resumen—A dı́a de hoy, la cantidad de información proveniente de fuentes que emiten constantemente datos es inmensa, por lo que se hace necesario la extracción automática de conocimiento para la mejora de servicios en la vida cotidiana de las personas. La búsqueda de patrones emergentes permite la descripción de las caracterı́sticas discriminativas entre clases o la descripción de tendencias emergentes en los datos. En este trabajo se presenta un nuevo enfoque basado en un sistema evolutivo difuso para la extracción de patrones emergentes en flujos continuos de datos. Los resultados del estudio experimental muestran unos resultados prometedores para la extracción de este tipo de conocimiento en el ámbito de la minerı́a de flujo de datos.
Resistance to change? The impact of group medication on AMR gene dynamics during commercial pig production

The anthropogenic selection of antimicrobial resistance (AMR) genes is under intense scrutiny, particularly in livestock production, where group antimicrobial administration is used to control disease. Whilst large epidemiological studies provide important data on the diversity and distribution of AMR genes, we have little insight into how group antimicrobial administration impacts AMR gene abundance and diversity within a system. Here, faecal microbiome and AMR gene dynamics were tracked for six months through a standard production cycle on a commercial pig unit. Our results demonstrate that specific AMR genes have reached an equilibrium across this farming system to the extent that the levels measured were maintained from suckling through to slaughter, despite increases in microbiome diversity in early development. These levels were not influenced by antibiotic use, either during the production cycle or following whole herd medication. Some AMR genes were found at levels higher than that of the bacterial 16S rRNA gene, indicating widespread distribution across the most common bacterial genera. The targeted AMR genes were also detected in nearby soil samples, several with decreasing abundance with increasing distance from the unit, demonstrating that the farm acts as a point source of AMR gene ‘pollution’. Metagenomic sequencing of a subset of samples identified 144 AMR genes, with higher gene diversity in the piglet samples compared to the sow samples. Critically, despite overwhelming and stable levels of resistance alleles against the main antimicrobials used on this farm, these compounds continue to control the bacterial pathogens responsible for production losses and compromised welfare. Importance Group antibiotic treatment has been used for decades to control bacterial diseases that reduce the productivity and compromise the welfare of livestock. Recent increases in antibiotic resistant infections in humans has resulted in concerns that antibiotic use in livestock may contribute to the development of untreatable bacterial infections in humans. There is however little understanding as to how the genes that bacteria require to become resistant to antibiotics respond during and after group antibiotic treatment of livestock, particularly in systems where high levels of antibiotics have been used for a prolonged period of time. We show that in such a system, levels of specific antibiotic resistance genes are high irrespective of group antibiotic treatments, whilst dramatic reductions in antibiotic use also fail to reduce the levels of these genes. These findings have important implications for public policy relating to the use of antibiotic in livestock farming.
Sketch filter for feature extraction and rendering applications

First a novel sketch filter based on the maximum filter is proposed. The proposed sketch filter implicitly calculates the complexity of local area, and results in an image which expresses edges and textures as an artist draws a sketch with a pencil. It is then proposed to apply the sketch filter for edge feature extraction, colour pencil drawing, and the animation filtered image. The proposed algorithms work automatically without any interaction and demonstrate impressive results for rendering applications, while requiring a much smaller amount of computations compared to existing schemes.
Available Bandwidth Estimation and Measurement-Based Admission Control in IP Networks

The capacity of core networks has increased tremendously due to recent technology development in optical transmission and high-speed router/ethernet switches. However, IP networks originally designed to provide best-effort services can not guarantee strict or statistical quality-of-service (QoS) requirements for real-time traffic flows because resources are not reserved and all packets are treated equally in most nodes. Thus, it is very important to monitor network status and manage network resources in order to guarantee QoS for flows with real-time performance requirements. Since the delay performance strongly depends on the available bandwidth of the path among many network resources, this dissertation is concerned with monitoring the available bandwidth and proposing an admission control scheme of internet flows based on the estimated available bandwidth. First, a new mechanism is proposed to estimate the available bandwidth of a queueing system, whose service rate and the load of input traffic are not known in advance. In order to estimate the available bandwidth, we propose a probing method called a minimally backlogging method and propose two statistics. The first statistic is based on the delay of each probing packet and the second statistic is based on the amount of probing packets served in a specific time interval. We first show that an M/G/1 queueing system is stable when probing packets are sent to the system according to the minimally backlogging method. We also show that the available bandwidth can be estimated by using either of the two statistics if the probing packets are sent to the queueing system by the minimally backlogging method. Especially, the second statistic can be used to estimate the available bandwidth of a G/G/1 queueing system. We apply the theory developed for a single server in order to estimate the available bandwidth for a local server as an application. The accuracy of the two proposed statistics is evaluated numerically under Poisson and
Soft reservation multiple access with priority assignment (SRMA/PA): a novel MAC protocol for QoS-guaranteed integrated services in mobile ad-hoc networks

A new medium access control (MAC) protocol-soft reservation multiple access with priority assignment (SRMA/PA) protocol-is introduced for supporting the integrated services of real-time and non-real-time applications in mobile ad-hoc networks. The SRMA/PA protocol allows the distributed nodes to contend for and reserve time slots with a RTS/CTS-like "collision-avoidance" handshake and "soft reservation" mechanism augmented with distributed and dynamic access priority control, which virtually achieves capability of the distributed scheduling to guarantee the QoS requirements of the integrated services. We have demonstrated via simulation studies that the multiplexing gain can be significantly improved without much compromising the system complexity. We also have shown that the proposed back-off mechanism designed for the delay-constrained services is useful for further improving the channel utilization.
Logistics Systems Analysis

This monograph, viewing logistics systems from an integrated standpoint, aims to describe, and also to demonstrate how to find, rational structures for logistics systems, including their operation and organization. The work departs from traditional operations research procedures in that it avoids detailed descriptions of problems or theiH solutions. For a typical problem, for example, the book demonstrates how to achieve reasonable solutions (described in terms of their properties) based on minimum information and avoiding detailed data handling and complex numerical analyses. The book also makes the point that detailed solutions can often be improved if preceded by the kind of exploratory analysis described here. This monograph on operations research and transportation is intended for researchers.