Miracle-dz/UniFAR-FTUs huggingface dataset:
An approach to development of an ensemble classification system

Generally, information system handles huge volume of dataset. Classifiers provide poor performance when such dataset are feed into it for categorization due to their high dimension. The most important attributes are extracted from the dataset prior to classification for efficient classifier design. It is also true that there may be many classifiers of a particular system, some provide better accuracy than others but selection of single classifier with all its optimized parameters is always not a good choice for various real world applications. The paper proposes a novel method for construction of an ensemble classifier by combining multiple classifiers obtained using Rough Set Theory and Genetic algorithm. The method selects the classifiers for integration based on accuracy and develops an efficient and effective ensemble classification system. In the first phase of the work, rule based classifiers termed as base classifiers are constructed from the reduced information sub systems obtained using Rough Set Theory, where a set of high quality rules are generated for each sub system. In the second phase, base classifiers are combined and an optimal ensemble classification system is developed using Genetic Algorithm. Here, ensemble classifier takes an important role to discover the class labels of the test objects with higher accuracy. The proposed algorithm has been run on standard benchmark dataset collected from the UCI repository.
A comparative throughflow analysis of axial flow turbines

Abstract A throughflow analysis is performed for a single-stage axial flow turbine to demonstrate the predicted differences between the two-dimensional streamline curvature analysis and the mean line analysis. It is found that the streamline curvature analysis results in far better agreement with the experiment, which shows the importance of considering the spanwise variation of loss and flow. It is also revealed that the present streamline curvature analysis leads to better correspondence with the experiment than other authors’ analyses.
Scaling issues in snow hydrology

The concept of scale can be used to quantify characteristic lengths of (a) a natural process (such as the correlation length of the spatial snow water equivalent (SWE) variability); (b) a measurement (such as the size of a snow density sample or the footprint of a satellite sensor), and (c) a model (such as the grid size of a distributed snow model). The different types of scales are denoted as process scale, measurement scale and model scale, respectively. Interpolations, extrapolations, aggregations, and disaggregations are viewed as a change in model scale and/or measurement scale. In a first step we examine, in a linear stochastic analysis, the effect of measurement scale and model scale on the data and the model predictions. It is shown that the ratio of the measurement scale and the process scale, and the ratio of the model scale and the process scale are the driving parameters for the scale effects. These scale effects generally cause biases in the variances and spatial correlation lengths of satellite images, field measurements, and simulation results of snow models. It is shown, by example, how these biases can be identified and corrected by regularization methods, At the core of these analyses is the variogram. For the case of snow cover patterns, it is shown that it may be difficult to infer the true snow cover variability from the variograms, particularly when they span many orders of magnitude. In a second step we examine distributed snow models which are a non-linear deterministic approach to changing the scale. Unlike in the linear case, in these models a change of scale may also bias the mean over a catchment of snow-related variables such as SWE There are a number of fundamental scaling issues with distributed models which include subgrid variability, the question of an optimum element size, and parameter identifiability. We give methods for estimating subgrid variability. We suggest that, in general, an optimum element size may not exist and that the model element scale may in practice be dictated by data availability and the required resolution of the predictions. The scale effects in distributed non-linear models can be related to the linear stochastic case which allows us to generalize the applicability of regularization methods. While most of the paper focuses on physical snow processes, similar conclusions apply and similar methods are applicable to chemical and biological processes.
CrowdTasker: Maximizing coverage quality in Piggyback Crowdsensing under budget constraint

This paper proposes a novel task allocation framework, CrowdTasker, for mobile crowdsensing. CrowdTasker operates on top of energy-efficient Piggyback Crowdsensing (PCS) task model, and aims to maximize the coverage quality of the sensing task while satisfying the incentive budget constraint. In order to achieve this goal, CrowdTasker first predicts the call and mobility of mobile users based on their historical records. With a flexible incentive model and the prediction results, CrowdTasker then selects a set of users in each sensing cycle for PCS task participation, so that the resulting solution achieves near-maximal coverage quality without exceeding incentive budget. We evaluated CrowdTasker extensively using a large-scale real-world dataset and the results show that CrowdTasker significantly outperformed three baseline approaches by achieving 3%-60% higher coverage quality.
Visual range of LLL night vision goggle for drive

In this paper, the fundamental theory about visual range of LLL imaging system is described. Based on the revised apparent distance detecting equation and combined with the research intention and design principle of night vision goggle, the relation of parameters which have an influence on performances of system are analyzed. The visual range of the goggle under the specific circumstances is estimated, which proves the revised apparent distance detecting equation is effective and the design of the system is feasible.
A New Lookup Model for Multiple Flow Tables of Open Flow with Implementation and Optimization Considerations

Open Flow has become the key standard for the southbound interface of software defined networking. The single flow table of Open Flow implementation can lead to fast storage space growth, and finally cause table-overflow, the multiple flow tables can address this problem and provide greater efficiency and flexibility. Through analyzing the potential deployment challenges of Open Flow, this paper proposes a new lookup model with implementation and optimization considerations for multiple flow tables in an Open Flow switch. With the developed lookup model, the original single flow table is split into multiple sub-flow tables, and the fields in each sub-flow table are further divided into several categories according to different field types. Preliminary simulation results demonstrate that the proposed solution can effectively reduce the storage space of flow tables.
Association of GST Genetic Polymorphisms with the Susceptibility to Hepatocellular Carcinoma (HCC) in Chinese Population Evaluated by an Updated Systematic Meta-Analysis

Background Due to the possible involvement of Glutathione S-transferase Mu-1 (GSTM1) and Glutathione S-transferase theta-1 (GSTT1) in the detoxification of environmental carcinogens, environmental toxins, and oxidative stress products, genetic polymorphisms of these two genes may play important roles in the susceptibility of human being to hepatocellular carcinoma. However, the existing research results are not conclusive. Methods A systematic literature search using databases (PubMed, Scopus, Embase, Chinese Biomedical Database, Chinese National Knowledge Infrastructure, Wanfang Data, etc.) for the eligible studies meeting the inclusion criteria including case-control studies or cohort studies is evaluated using an updated systematic meta-analysis. Results Significant increase in the risk of HCC in the Chinese population is found in GSTM1 null genotype (OR = 1.47, 95% CI: 1.21 to 1.79, P<0.001) and GSTT1 null genotype (OR = 1.38, 95% CI: 1.14 to 1.65, P<0.001). Analysis using the random-effects model found an increased risk of HCC in GSTM1-GSTT1 dual null population (OR = 1.79, 95% CI: 1.26 to 2.53, P<0.001). In addition, subgroup analyses showed a significant increase in the association of GST genetic polymorphisms (GSTM1, GSTT1, and GSTM1-GSTT1) with HCC in southeast and central China mainland. However, available data collected by this study fail to show an association between GST genetic polymorphisms and HCC in people from the Taiwan region (for GSTM1: OR = 0.78, 95% CI: 0.60 to 1.01, P = 0.06; for GSTT1: OR = 0.94, 95% CI: 0.78 to 1.14, P = 0.546; for GSTM1-GSTT1: OR = 1.04, 95% CI: 0.81 to 1.32, P = 0.77). Sensitivity analysis and publication bias diagnostics confirmed the reliability and stability of this meta-analysis. Conclusions Our results indicate that both GSTM1 and GSTT1 null genotypes are associated with an increased HCC risk in Chinese population. Peoples with dual null genotypes of GSTM1-GSTT1 are more susceptible to developing HCC. In conclusion, GST genetic polymorphisms play vital roles in the development of HCC in the Chinese population.
Formulas relating KL stability estimates of discrete-time and sampled-data nonlinear systems

We provide an explicit KL stability or input-to-state stability (ISS) estimate for a sampled-data nonlinear system in terms of the KL estimate for the corresponding discrete-time system and a K function describing inter-sample growth. It is quite obvious that a uniform inter-sample growth condition, plus an ISS property for the exact discrete-time model of a closed-loop system, implies uniform ISS of the sampled-data nonlinear system. Our results serve to quantify these facts by means of comparison functions. Our results can be used as an alternative to prove and extend results in [1] or extend some results in [4] to a class of nonlinear systems. Finally, the formulas we establish can be used as a tool for some other problems which we indicate. c © 1999 Elsevier Science B.V. All rights reserved.
Modulatory effect of rat small intestinal epithelial cell-conditioned medium on lymphocyte proliferation

SummaryThe small intestinal epithelium plays an important role in the mucosal host defense. Intestinal epithelial cells have been known to release substances that suppress lymphocyte proliferation, suggesting an immunoregulatory function. We investigated how intestinal epithelial cells affect lymphocyte proliferation. Serum-free medium that was conditioned by incubating epithelial cells, particularly crypt cells, of the rat small intestine affected proliferation of allogeneic spleen lymphocytes stimulated with concanavalin A, as assessed by measuring cellular [3H]thymidine incorporation. Less than 1% and greater than 2% of the conditioned medium enhanced and suppressed, respectively, lymphocyte proliferation. The causative substances found in the conditioned medium were dialyzable and heat-stable. Suppression was not due to toxicity to splenocytes. Exposure of splenocytes to a suppressive concentration of the conditioned medium beginning at 30 min before an onset of lectin stimulation decreased the suppression of lymphocyte proliferation. Splenocyte exposure to the suppressive concentration of the conditioned medium beginning at 30 min to 4 h after the onset of the stimulation inversely strengthened the suppression. A brief exposure of splenocytes to the conditioned medium for the last 4 h during a total 72-h culture period still suppressed lymphocyte proliferation. Thus, intestinal epithelial cells produce low-molecular-weight lymphocyte proliferation-modulating substances that suppress the proliferation of lectin-activated lymphocytes, but not resting ones, by affecting earlier intracellular events and the following DNA synthesis when incubated in culture medium.
Join synopses for approximate query answering

In large data warehousing environments, it is often advantageous to provide fast, approximate answers to complex aggregate queries based on statistical summaries of the full data. In this paper, we demonstrate the difficulty of providing good approximate answers for join-queries using only statistics (in particular, samples) from the base relations. We propose join synopses as an effective solution for this problem and show how precomputing just one join synopsis for each relation suffices to significantly improve the quality of approximate answers for arbitrary queries with foreign key joins. We present optimal strategies for allocating the available space among the various join synopses when the query work load is known and identify heuristics for the common case when the work load is not known. We also present efficient algorithms for incrementally maintaining join synopses in the presence of updates to the base relations. Our extensive set of experiments on the TPC-D benchmark database show the effectiveness of join synopses and various other techniques proposed in this paper.
RF propagation through vegetation with time-varying moisture

Six broadband measurements were made of the transmission at 3-4 GHz between two horn antennas 30 cm above the ground through 80 cm tall grass. Water was sprayed over the tall grass, and the variation in transmission was captured as the water evaporated. The results show an increase in transmission through the wet grass of 6.5 dB at an 8 m distance. This partially supports our hypothesis that moistened grass may act as a dielectric waveguide for ground-based wireless devices and sensors. The results not only provide a working model for predicting path loss for ground-based radio sensors, but suggest a potential method for wireless sensing of bulk moisture content in vegetation.
EULAR recommendations for terminology and research in individuals at risk of rheumatoid arthritis: report from the Study Group for Risk Factors for Rheumatoid Arthritis

The Study Group for Risk Factors for Rheumatoid Arthritis was established by the EULAR Standing Committee on Investigative Rheumatology to facilitate research into the preclinical and earliest clinically apparent phases of rheumatoid arthritis (RA). This report describes the recommendation for terminology to be used to define specific subgroups during different phases of disease, and defines the priorities for research in this area. Terminology was discussed by way of a three-stage structured process: A provisional list of descriptors for each of the possible phases preceding the diagnosis of RA were circulated to members of the study group for review and feedback. Anonymised comments from the members on this list were fed back to participants before a 2-day meeting. 18 participants met to discuss these data, agree terminologies and prioritise important research questions. The study group recommended that, in prospective studies, individuals without RA are described as having: genetic risk factors for RA; environmental risk factors for RA; systemic autoimmunity associated with RA; symptoms without clinical arthritis; unclassified arthritis; which may be used in a combinatorial manner. It was recommended that the prefix ‘pre-RA with:’ could be used before any/any combination of the five points above but only to describe retrospectively a phase that an individual had progressed through once it was known that they have developed RA. An approach to dating disease onset was recommended. In addition, important areas for research were proposed, including research of other tissues in which an adaptive immune response may be initiated, and the identification of additional risk factors and biomarkers for the development of RA, its progression and the development of extra-articular features. These recommendations provide guidance on approaches to describe phases before the development of RA that will facilitate communication between researchers and comparisons between studies. A number of research questions have been defined, requiring new cohorts to be established and new techniques to be developed to image and collect material from different sites.
An exploration methodology for a customizable OpenCL stereo-matching application targeted to an industrial multi-cluster architecture

Open Computing Language (OpenCL) is emerging as a standard for parallel programming of heterogeneous hardware accelerators. With respect to device specific languages, OpenCL enables application portability but does not guarantee performance portability, eventually requiring additional tuning of the implementation to a specific platform or to unpredictable dynamic workloads. In this paper, we present a methodology to analyze the customization space of an OpenCL application in order to improve performance portability and to support dynamic adaptation. We formulate our case study by implementing an OpenCL image stereo-matching application (which computes the relative depth of objects from a pair of stereo images) customized to the STMicroelectronics Platform 2012 many-core computing fabric. In particular, we use design space exploration techniques to generate a set of operating points that represent specific configurations of the parameters allowing different trade-offs between performance and accuracy of the algorithm itself. These points give detailed knowledge about the interaction between the application parameters, the underlying architecture and the performance of the system; they could also be used by a run-time manager software layer to meet dynamic Quality-of-Service (QoS) constraints. To analyze the customization space, we use cycle-accurate simulations for the target architecture. Since the profiling phase of each configuration takes a long simulation time, we designed our methodology to reduce the overall number of simulations by exploiting some important features of the application parameters; our analysis also enables the identification of the parameters that could be explored on a high-level simulation model to reduce the simulation time. The resulting methodology is one order of magnitude more efficient than an exhaustive exploration and, given its randomized nature, it increases the probability to avoid sub-optimal trade-offs.
An empiric weight computation for record linkage using linearly combined fields' similarity scores

Record linkage is the task of identifying which records from one or more data sources refer to the same entity. Many record linkage methods were introduced and applied over the last decades. In general, the principle is to compare a range of available identifier fields in record pairs among different data sources, in order to make a linkage decision. The Fellegi-Sunter probabilistic record linkage (PRL-FS) is one of the most commonly used methods. To obtain a better performance, Winkler proposed an enhanced PRL-FS method (PRL-W) that takes into account field similarity, but its implementation requires the estimation of much more parameters which complicates the task. Consequently, we propose to develop a method that contains the best features in the PRL-FS and the PRL-W methods: simplicity of parameters estimation and consideration of fields' similarities. We hypothesize that our record linkage method outperforms the PRL-FS, and can achieve a similar performance of the PRL-W. This paper presents briefly the PRL-FS and PRL-W methods, and describes in details how to combine fields' similarity scores to create a novel record pair weight. Simulated data sets were used to assess and to compare these three methods regarding their ability to reduce the rates of false matches and false non-matches.
Doping in sport: misuse, analytical tests, and legal aspects.

As part of the International Congress of Clinical Chemistry, a symposium, Doping in Sport: Misuse, Analytical Tests, and Legal Aspects, was held, a week before the start of the Olympic Games at Atlanta, bringing together a number of international experts concerned with various aspects of controlling drug abuse in sport. The presentations focused particularly on anabolic-androgenic steroids (AAS), these analytes still comprising the greatest number of positive findings in samples tested by International Olympic Committee (IOC)-accredited sports drug-testing laboratories. Although various aspects of AAS use and detection have been reported on in the last decade, the proceedings of this symposium, published in this issue of Clinical Chemistry , bring together health, social, endocrine, and analytical perspectives in a widely read peer-reviewed journal. Those who think that the harmful effects of AAS administration are relatively benign and perhaps should not be banned by the IOC may find particularly sobering the article by Franke and Berendonk (1), which describes the health and social consequences of the clandestine state-sponsored program of administration of AAS in the former East Germany. This article should be extremely useful for educational purposes and, we hope, will encourage some present users to abandon the practice. The mechanism of action of AAS is still not well understood, and the article by Wu (2) is a useful update. Catlin et al. (3) discuss the pertinent issues of detecting AAS administration, in particular of testosterone (T), which is commonly abused. Stenman et al. (4) give a clinical chemistry perspective on the problems of detecting human chorionic gonadotropin (hCG) by immunoassay, the protein hormone that may be used by competitors to stimulate testicular production of T. Bowers (5) discusses analytical advances to detect performance-enhancing compounds, particularly with reference to xenobiotic AAS and peptide hormones, such as hCG. But why bother about anabolic …
Semantic Annotation, Analysis and Comparison: A Multilingual and Cross-lingual Text Analytics Toolkit

Within the context of globalization, multilinguality and cross-linguality for information access have emerged as issues of major interest. In order to achieve the goal that users from all countries have access to the same information, there is an impending need for systems that can help in overcoming language barriers by facilitating multilingual and cross-lingual access to data. In this paper, we demonstrate such a toolkit, which supports both service-oriented and user-oriented interfaces for semantically annotating, analyzing and comparing multilingual texts across the boundaries of languages. We conducted an extensive user study that shows that our toolkit allows users to solve cross-lingual entity tracking and article matching tasks more efficiently and with higher accuracy compared to the baseline approach.
Research Ethics in HCI: A Town Hall Meeting

As interactive technologies evolve and reach into every aspect of modern life, research practices in human-computer interaction (HCI) have changed. The methodological and epistemological foundations of the field are shifting to reflect the diversity of contexts in which rapidly changing digital technology is being used. Alongside these changes, new ethical challenges emerge for the HCI community, both in terms of research ethics and responsible research and innovation. Open dilemmas include issues such as the shifting meaning of informed consent, anonymisation or privacy in an always-online world. The SIGCHI Ethics Committee has been established to look into the processes, practices and structures at SIGCHI venues to deal with such ethical dilemmas and how they can be addressed in a transparent, consistent and open way. This town hall style panel will be an opportunity to prompt community discussion and collect input into how we can further address these challenges.