Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid model for throughput evaluation of orthogonal frequency division multiple access networks

Data throughput is an important metric used in the performance evaluation of the next generation cellular networks such as long-term evolution (LTE) and LTE-advanced. To evaluate the performance of these networks, Monte Carlo simulation schemes are usually used. Such simulations do not provide the throughput of intermediate call state; instead it gives the overall performance of this network. The authors propose a hybrid model consisting of both analysis and simulation. The benefit of this model is that the throughput of any possible call state in the system can be evaluated. Here, the probability of possible call distribution is first obtained by analysis, which is used as input to the event-driven-based simulator to calculate the throughput of a call state. Comparison is made between throughput obtained from the author's hybrid model with that obtained from event-driven-based simulation. Numerical results are presented and show good agreement between both the proposed hybrid model and the simulation. The maximum difference of relative throughput between their hybrid model and the simulation is found in the interval of (0.04 and 1.06%) over a range of call arrival rates, mean holding times and number of resource blocks in the system.
Design techniques for high-resolution current-mode sigma-delta modulators

This paper describes techniques for the design of high-resolution oversampling analog-to-digital converters based on current memories. A key point is the reduction of nonlinearities, in particular those introduced by the current switches. A current-memory cell with very high precision and linearity has been designed and used in an experimental third-order /spl Sigma/-/spl delta/ modulator in a 0.8-/spl mu/m digital CMOS process. A linearity of better than 14 b and a maximum signal-to-noise+distortion ratio (SNDR) of 80 dB has been measured for an oversampling ratio (OSR) of 64.
A Ten-year Bibliometric Analysis of Research Trends in Three Leading Ecology Journals during 2003-2012

This paper attempts to highlight quantitatively the growth and development of literature in the field of ecology in terms of publication output using the resource Web of Science®. The focus of this analysis was to study the literature on ecology published in three journals, viz., Ecology Letters, Trends in Ecology & Evolution, and Annual Review of Ecology Evolution and Systematics. 2946 records were retrieved for 10 years (2003–2012). The study revealed that multiple authorship in the field with collaborations of two (30.31%) and three authors (19.89%) was dominant. The Degree of collaboration, Collaborative coefficient, and Collaborative index were calculated and the applicability of Lotka’s law was tested. The study identified five-year patterns in research trends, using the three studied journals, to see if the subjects of focus changed within a decade. The most productive institution was University Calif. Davis, USA, followed by University Calif. Santa Barbara, USA, and University Queensland, Australia, and the most productive countries were the USA followed by UK and Canada.
Extending model driven architecture benefits to requirements engineering

This work focuses on developing a requirement engineering model (RSPL) based on a Model Driven Architecture (MDA) and Web-tier Application Framework (WAF), to support automatic and interactive requirements generation when creating families of systems. In realizing the model, two goals were targeted namely (i) to construct a RE model that support automatic transformation of domain features into actor-specific requirements; and (ii) to design and implement an interactive web based tool for requirements engineering. The result obtained is twofold: (i) adopting MDA during RE for a product line reduced costs and development time; (ii) tool implementation based on WAF ensured that support for different client types was possible. In conclusion, the study is a contribution to a recently advocated idea that requirements generation could be model-driven. The result shows that the idea is promising with respect to requirement reuse and improving communication barriers among members of a system development team.
Molecular analysis of Malaysian Chinese D- donors: a single centre experience

Abstract Background: The Rh blood group system is highly polymorphic and next to the ABO system is the most clinically significant in transfusion medicine. The frequency of D- phenotypes and the underlying molecular genetics vary widely in different populations. Objectives: We determined the prevalence of different D- phenotypes among Malaysian blood donors in a tertiary medical centre and identified the molecular basis of Chinese D- donors in this population. Materials and methods: A total of 146 D- Chinese donors with various Rh phenotypes were identified from review of blood donor records between January 2003 and September 2008. Fresh blood samples from 36 of these donors were obtained and further characterized by PCR-SSP to determine the molecular basis of these D- individuals. Results: A total of 86,620 blood donor records were reviewed. Of these 911 were D-, consisting of 483 Indians, 189 Malays and 146 Chinese. The ccee phenotype was the most common among D- individuals with a prevalence of 91.51% (442/483) in Indians, 74.60% (141/189) in Malays and 55.48% (81/146) in Chinese. D- phenotypes with C and/or E antigens were most common in Chinese {44.52% (65/146)}. In the molecular analysis of the 36 D- Chinese donor samples, 19 samples with ccee phenotype and 5/17 of samples with Ccee phenotype showed no detectable RHD gene. The remaining 12/17 Ccee samples had intact RHD genes with RHD (K409K) mutation. Conclusion: In our donor population, we found a wide variation in the incidence of D- as well as the distribution of various D-phenotypes among the three major ethnic groups. A significant number of D- Chinese donors with Ccee phenotype were found to be DEL with RHD (K409K) mutation. DEL red cells are known to cause anti-D alloimmunization. Therefore, in clinical practice, it is important to exclude DEL RBCs from D- donor pools.
A Taxonomy of Technology: Defining Augmented Reality

This paper outlines a functional decomposition of Augmented Reality (AR), from both a technological and human factors perspective, with a view to providing a coherent and structured framework for its development and evaluation in the future. Without a formal framework to underpin the description and development of AR technology, confusion may arise in comparing systems and applications. The proposed framework allows for the user's capabilities to be described in all their sensory detail, which, in turn, furnishes the taxonomy with a power to account for these in the development of advanced technologies. This top-level analysis can then be decomposed into discrete elements representing the enabling technologies that make up a given AR domain. This provides a much richer descriptive power within the framework and consistent criteria for comparing systems, applications and indeed conventional human-computer interfaces with AR. Furthermore, as it is a ‘user-centred’ approach, the framework does not assume that visual augmentation is the only means of providing an AR facility. Implicit in the framework is the notion that AR can be delivered through other sensory augmentation, even if the technical means is not available at the moment. In this way, the taxonomy offers a concise and precise means of categorising the technology, understanding the application, and, ultimately, supporting the user of an AR system.
Learning to Search: From Weak Methods to Domain-Specific Heuristics

Learning from experience involves three distinct components—generating behavior, assigning credit, and modifying behavior. We discuss these components in the context of learning search heuristics, along with the types of learning that can occur. We then focus on SAGE, a system that improves its search strategies with practice. The program is implemented as a production system, and learns by creating and strengthening rules for proposing moves. SAGE incorporates five different heuristics for assigning credit and blame, and employs a discrimination process to direct its search through the space of rules. The system has shown its generality by learning heuristics for directing search in six different task domains. In addition to improving its search behavior on practice problems, SAGE is able to transfer its expertise to scaled-up versions of a task, and in one case, transfers its acquired search strategy to problems with different initial and goal states.
Establishing an MPP guidepost

It is noted that no system has appeared which has been recognized by the high-performance computing community as the guidepost for massively parallel processing (MPP). The author describes what is necessary for a system to become such a guidepost. A comparison is made to the CRAY-1, a system which serves as a guidepost for vector computing and serves as a standard by which all types of high-performance computing systems are measured. It is claimed that establishing an MPP guidepost requires building a system that delivers on the promised potential of scalable parallel computing, by providing high sustained performance on a wide variety of production application, with very little programming effort. <<ETX>>
The Research of Data Stream Classification Based on Rough Set Theory-Neural Network Integration

According to the high speed of data arriving, a large amount of data and concept drifting in the stream model, combining the techniques of rough set theory, neural network and voting rule, we put forward a new data stream classification model, which is a multi-classifier integration based on rough set theory, neural network. Firstly, it reduces all attributes using rough set theory; secondly, it constructs base classifiers on the data chunks after the reduction of attributes using the improved BP neural network; finally, it fuses various base classifiers into an ensemble by voting rule. Through applying the model to classify data stream, the experiment results show that the ensemble method is feasible and effective.
Early Endovascular Treatment in Renal Transplant: A Case Report

Background: A 46-year-old female kidney transplant recipient developed acutely impaired graft function on the first postoperative day and received early endovascular treatment. Case report: The patient underwent living donor kidney transplantation from a sister in January 2011. She showed good diuresis during the early hours, followed by abruptly decreased urine output. Ultrasound examination suggested thrombosis of the intrarenal arteries or renal artery stenosis. Arteriography showed no stenosis at the site of anastomosis, but the presence of faint contrast filling the intrarenal arteries. She received intrarenal thrombolysis with 15 mg of recombinant tissue plasmin activator (rTPA), balloon angioplasty and a post anastomotic intra luminal renal artery stent with good perfusion and complete graft recovery. Conclusion: Early endovascular treatment is a possible therapeutic option in acute vascular renal transplant complications like intrarenal arterial thrombosis or renal artery stenosis. Excellent results were achieved with the procedure in the present case.
A new CUSUM‐S2 control chart for monitoring the process variance

Purpose – The purpose of this paper is to introduce and investigate the performances of a new CUSUM‐S2 control chart designed to monitor the sample variance of samples from a normally distributed population.Design/methodology/approach – The proposed chart monitors a statistic computed as a logarithmic transformation of the sample variance; the introduction of the sample variance logarithmic transformation has a twofold effect: to quickly detect the occurrence of an “out‐of‐control” condition; to deal with a quasi‐standard normal statistic.Findings – A design strategy trying to minimize the “out‐of‐control” average run length (ARL) of the chart is presented and the statistical performance of the CUSUM‐S2 chart has been assessed through a comparison with an EWMA‐S2 control chart proposed in the literature to monitor the process dispersion.Research limitations/implications – The paper only deals with uncorrelated normally distributed data.Practical implications – The obtained results show how the CUSUM‐S2 ch.
Tissue characterization by MRI and MRS: an international and multidisciplinary research programme

Essential to quantification in MRI (magnetic resonance imaging) and MRS (magnetic resonance spectroscopy) is the adoption of accurate test procedures for quality control and performance assessment of the equipment. Moreover, the wide application of agreed protocols for standardization of measurements would make results obtained by different centers comparable, whatever equipment is used. A concerted research project, begun in 1984 as part of the EEC European Medical Research Programme, addresses these objectives and involves the collaborative efforts of more than forty Institutes. Performance testing of MR imaging equipment was established and performed in a number of European facilities. Present activities include defining test objects and test procedures for assessing volume selection and quantification in MRS.<<ETX>>
Stochastic identification of malware with dynamic traces

A novel approach to malware classification is introduced based on analysis of instruction traces that are collected dynamically from the program in question. The method has been implemented online in a sandbox environment (i.e., a security mechanism for separating running programs) at Los Alamos National Laboratory, and is intended for eventual host-based use, provided the issue of sampling the instructions executed by a given process without disruption to the user can be satisfactorily addressed. The procedure represents an instruction trace with a Markov chain structure in which the transition matrix, $\mathbf {P}$, has rows modeled as Dirichlet vectors. The malware class (malicious or benign) is modeled using a flexible spline logistic regression model with variable selection on the elements of $\mathbf {P}$, which are observed with error. The utility of the method is illustrated on a sample of traces from malware and nonmalware programs, and the results are compared to other leading detection schemes (both signature and classification based). This article also has supplementary materials available online.
Economic Damage Model on Industries due to Internet Attack and A Case Study

ABSTRACT Because of the internet development, most of people can acquire the information freely. But it have the disadvantages. A remarkable thing among the disadvantages is the internet attack. Internet attacks were given damages to several fields. Specially, the damage is terrible to the economic side. Specially, company is susceptive to economic damage side. But it didn’t execute a research about model that estimates damage to the economic side due to internet attacks. This paper presents a model that estimates the damage to economic side due to internet attacks.Key Words:Internet Worm, Internet Attack, Internet Security, Economic Damage 1. 서 론 1) 인터넷은 정보가 시간과 공간을 넘어 실시간으로 전달되는 것을 가능하게 해 준다. 이런 인터넷과 네트워크 환경의 발달로 인해서 모든 업무 처리 및 일상 생활이 더욱 편리해졌다. 그러나 네트워크가 발전함에 따라 이에 대한 부정적인 면도 나타나고 있는데 가장 대표적인 것으로 인터넷 침해사고를 꼽을 수 있다. 이런 침해사고는 인터넷 웜 및 분산 서비스 거부 공격(DDoS) 등 여러 가지 형태로 나타나고 있다[3]. 이 중 분산 서비스 거부 공격(DDoS)은 인터넷상에서 다수의 시스템이 협력하여 하나의 표적 시스템을 공격함으로써 시스템을 마비시키는 공격을 말한다. 그리고 인터넷 ※ 본 연구는 사이버기술연구소 학술연구사업의 연구 결과로 수행되었음. †준 회 원:아주대학교 전자공학과 석사 ††정 회 원:아주대학교 전자공학부 교수†††정 회 원:아주대학교 정보통신전문대학원 교수 논문접수:2006년 12월 13일, 심사완료：2007년 2월 1일
MIDFOOT SHAPE FOR THE DESIGN OF LADIES' SHOES

This study is an attempt to model the midfoot height of adult females. The right feet of 24 Hong Kong Chinese females were laser scanned. The midfoot heights were then determined at intervals of 1.2 mm. These heights showed a significant relationship with normalized length along the foot. The fitted model had R 2 = 0.937 and has important implications for the design of the vamp area of a shoe.
Component Signal Separation and Recombination for Linear Amplification with Nonlinear Components

LINC is a technique that uses signal processing to produce linear amplification of bandpass signals with grossly nonlinear circuit components. Two important signal-processing functions of LINC are 1) forming two constant envelope phase-modulated signal components from the bandpass input signal and 2) recombining the amplified components to produce an amplified replica of the input signal. Two-tone laboratory tests of a LINC component separator and combiner, not including a limiter and envelope detector, show that, at full output, spurious levels 40 dB below tone level are achievable over a 1-MHz band. Because the laboratory model operated at relatively low frequencies (hundreds of megahertz), scaling up in frequency should result in a LINC with <40-dB spurious over a 10-MHz band. Spurious 30 dB below tone level should be achievable over a bandwidth of 50 to 100 MHz using the same technique of component signal separation. Lower spurious levels or greater bandwidths will require a sin-1phase modulator that is less sensitive to delay in a feedback loop.
A corpus-based error typology: towards a more objective approach to measuring quality in localization

This paper examines current localization quality assessment through a product-based analysis of error typologies and the role they play in producing reliable and valid measurements. In order to provide a more objective foundation for localization quality, the methodology used is based on a monolingual comparable corpus of original and localized Spanish corporate websites. The contrastive analysis of this corpus highlighted recurrent inadequacies that are difficult to control with current evaluation metrics and, as a result, a comprehensive error categorization for localization is presented. It incorporates certain aspects, such as pragmatic errors, that are absent in current testing instruments used in the industry. In order to observe whether this typology can provide a more holistic and valid approach, it is applied to a case study that measures the errors in a localized Spanish website from one of the largest US companies. The results shed some light onto the shortcomings of current Quality Analysis practices and illustrate the benefits of adopting a bottom-up corpus approach in quality assessment. The wider implications for testing practices in localization are also described.
Sparse Representation of the Human Vision Information and the Saliency Detection Algorithm

Representation and measurement are two important issues for saliency models. Different with previous works that learnt sparse features from large scale natural statistics, we propose to learn features from short-term statistics of single images. For saliency measurement, we defined basic firing rate (BFR) for each sparse feature, and then we propose to use feature activity rate (FAR) to measure the bottom-up visual saliency. The proposed FAR measure is biological plausible and easy to compute and with satisfied performance. Experiments on human trajectory positioning and psychological patterns demonstrate the effectiveness and robustness of our proposed method.
A Comparative Study of Clustering Algorithms

Clustering is a major problem when dealing with organizing and dividing data. There are multiple algorithms proposed to handle this issue in many scientific areas such as classifications, community detection and collaborative filtering. The need for clustering arises in Social Networks where huge data generated daily and different relations are established between users. The ability to find groups of interest in a network can help in many aspects to provide different services such as targeted advertisements. The authors surveyed different clustering algorithms from three different clustering groups: Hierarchical, Partitional, and Density-based algorithms. They then discuss and compare these algorithms from social web point view and show their strength and weaknesses in handling social web data. They also use a case study to support our finding by applying two clustering algorithms on articles collected from Delicious.com and discussing the different groups generated by each algorithm.
Next steps in understanding the multilevel determinants of health

This commentary briefly summarises past work that has used multilevel analysis to investigate the multilevel determinants of health and outlines possible new directions in this area. Topics discussed include the need to (1) examine contexts other than neighbourhoods; (2) improve measurement of group-level constructs; (3) apply techniques more appropriate for causal inference from observational data; (4) analyse data from “natural experiments” involving exogenous variations in contextual characteristics; (5) examine dependencies between groups (such as spatial dependencies) more broadly and allow for reciprocal relations between individuals and contexts; and (6) contrast multilevel statistical models (or regression models generally) and complex systems models in the study of multilevel effects.
Good visibility of TITAN-2 coronary stents demonstrable on cardiac computer tomographic angiography: A report of 2 cases

Numerous studies have sought to assess stent patency by cardiac computer tomographic angiography (CCTA) in comparison with invasive coronary angiography in patients who had undergone percutaneous coronary stenting. Even with newer generation scanners, CCTA has been of limited value in the assessment of the revascularized patient. The main reason being blooming artifact from metallic stents often obscures stent luminal dimension, making the stented segment unassessable. We report on a novel finding of good visibility of TITAN-2 coronary stents demonstrable on CCTA for 2 patients and discuss the possible mechanism and potential implications of this observation.