nils-herrmann/clean_scirepeval huggingface dataset:
Voxel-Based Morphometry—The Methods

At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data.
Robust Segmentation and Measurements Techniques of White Cells in Blood Microscope Images

The analysis and the count of blood cell in microscope image can provide useful information concerning the health of the patients. In particular, morphological analysis of white cell deformations can effectively detect important diseases such as the acute lymphoblastic leukemia. Blood images obtained by microscopes coupled with a digital camera are simple to obtain and can be more simply transmitted to clinical centers than liquid blood samples. Automatic measurement systems for white cells in blood microscope image can greatly help blood experts that typically inspect blood films manually. Unfortunately, the analysis made by human experts is not rapid and it presents a not standardized accuracy due to the operator's capabilities and tiredness. The presented paper shows how that it is effectively possible to accurately measure the white cells properties in order to allow, at a second stage, the leukemia identification. In particular, the paper presents how to suitably enhance the microscope image by removing the undesired microscope background and a new segmentation strategy to robustly identify white cells permitting to better extract their features for subsequent automatic diagnosis of diseases
Design for Reliability of Low-voltage, Switched-capacitor Circuits

Design for Reliability of Low-voltage, Switched-capacitor Circuits by Andrew Masami Abo Doctor of Philosophy in Engineering University of California, Berkeley Professor Paul R. Gray, Chair Analog, switched-capacitor circuits play a critical role in mixed-signal, analogto-digital interfaces. They implement a large class of functions, such as sampling, filtering, and digitization. Furthermore, their implementation makes them suitable for integration with complex, digital-signal-processing blocks in a compatible, low-cost technology–particularly CMOS. Even as an increasingly larger amount of signal processing is done in the digital domain, this critical, analogto-digital interface is fundamentally necessary. Examples of some integrated applications include camcorders, wireless LAN transceivers, digital set-top boxes, and others. Advances in CMOS technology, however, are driving the operating voltage of integrated circuits increasingly lower. As device dimensions shrink, the applied voltages will need to be proportionately scaled in order to guarantee long-term reliability and manage power density. The reliability constraints of the technology dictate that the analog circuitry operate at the same low voltage as the digital circuitry. Furthermore, in achieving low-voltage operation, the reliability constraints of the technology must not be violated. This work examines the voltage limitations of CMOS technology and how analog circuits can maximize the utility of MOS devices without degrading relia-
Branch vanguard: Decomposing branch functionality into prediction and resolution instructions

While control speculation is highly effective for generating good schedules in out-of-order processors, it is less effective for in-order processors because compilers have trouble scheduling in the presence of unbiased branches, even when those branches are highly predictable. In this paper, we demonstrate a novel architectural branch decomposition that separates the prediction and deconvergence point of a branch from its resolution, which enables the compiler to profitably schedule across predictable, but unbiased branches. We show that the hardware support for this branch architecture is a trivial extension of existing systems and describe a simple code transformation for exploiting this architectural support. As architectural changes are required, this technique is most compelling for a dynamic binary translation-based system like Project Denver.
 We evaluate the performance improvements enabled by this transformation for several in-order configurations across the SPEC 2006 benchmark suites. We show that our technique produces a Geomean speedup of 11% for SPEC 2006 Integer, with speedups as large as 35%. As floating point benchmarks contain fewer unbiased, but predictable branches, our Geomean speedup on SPEC 2006 FP is 7%, with a maximum speedup of 26%.
Towards a taxonomy of intrusion-detection systems

Intrusion-detection systems aim at detecting attacks against computer systems and networks, or against information systems in general, as it is difficult to provide provably secure information systems and maintain them in such a secure state for their entire lifetime and for every utilization. Sometimes, legacy or operational constraints do not even allow a fully secure information system to be realized at all. Therefore, the task of intrusion-detection systems is to monitor the usage of such systems and to detect the apparition of insecure states. They detect attempts and active misuse by legitimate users of the information systems or external parties to abuse their privileges or exploit security vulnerabilities. In this paper, we introduce a taxonomy of intrusion-detection systems that highlights the various aspects of this area. This taxonomy defines families of intrusion-detection systems according to their properties. It is illustrated by numerous examples from past and current projects. q 1999 Elsevier Science B.V. All rights reserved.
Systematic Reviews: The Good, the Bad, and the Ugly

Systematic reviews systematically evaluate and summarize current knowledge and have many advantages over narrative reviews. Meta-analyses provide a more reliable and enhanced precision of effect estimate than do individual studies. Systematic reviews are invaluable for defining the methods used in subsequent studies, but, as retrospective research projects, they are subject to bias. Rigorous research methods are essential, and the quality depends on the extent to which scientific review methods are used. Systematic reviews can be misleading, unhelpful, or even harmful when data are inappropriately handled; meta-analyses can be misused when the difference between a patient seen in the clinic and those included in the meta-analysis is not considered. Furthermore, systematic reviews cannot answer all clinically relevant questions, and their conclusions may be difficult to incorporate into practice. They should be reviewed on an ongoing basis. As clinicians, we need proper methodological training to perform good systematic reviews and must ask the appropriate questions before we can properly interpret such a review and apply its conclusions to our patients. This paper aims to assist in the reading of a systematic review.
A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation

Speech enhancement and separation are core problems in audio signal processing, with commercial applications in devices as diverse as mobile phones, conference call systems, hands-free systems, or hearing aids. In addition, they are crucial preprocessing steps for noise-robust automatic speech and speaker recognition. Many devices now have two to eight microphones. The enhancement and separation capabilities offered by these multichannel interfaces are usually greater than those of single-channel interfaces. Research in speech enhancement and separation has followed two convergent paths, starting with microphone array processing and blind source separation, respectively. These communities are now strongly interrelated and routinely borrow ideas from each other. Yet, a comprehensive overview of the common foundations and the differences between these approaches is lacking at present. In this paper, we propose to fill this gap by analyzing a large number of established and recent techniques according to four transverse axes: 1 the acoustic impulse response model, 2 the spatial filter design criterion, 3 the parameter estimation algorithm, and 4 optional postfiltering. We conclude this overview paper by providing a list of software and data resources and by discussing perspectives and future trends in the field.
Gaussian Process Training with Input Noise

In standard Gaussian Process regression input locations are assumed to be noise free. We present a simple yet effective GP model for training on input points corrupted by i.i.d. Gaussian noise. To make computations tractable we use a local linear expansion about each input point. This allows the input noise to be recast as output noise proportional to the squared gradient of the GP posterior mean. The input noise variances are inferred from the data as extra hyperparameters. They are trained alongside other hyperparameters by the usual method of maximisation of the marginal likelihood. Training uses an iterative scheme, which alternates between optimising the hyperparameters and calculating the posterior gradient. Analytic predictive moments can then be found for Gaussian distributed test points. We compare our model to others over a range of different regression problems and show that it improves over current methods.
Applying and Interpreting Object Oriented Metrics

Object-oriented design and development is becoming very popular in today's software development environment. Object oriented development requires not only a different approach to design and implementation, it requires a different approach to software metrics. Since object oriented technology uses objects and not algorithms as its fundamental building blocks, the approach to software metrics for object oriented programs must be different from the standard metrics set. Some metrics, such as lines of code and cyclomatic complexity, have become accepted as "standard" for traditional functional/ procedural programs, but for objectoriented, there are many proposed object oriented metrics in the literature. The question is, "Which object oriented metrics should a project use, and can any of the traditional metrics be adapted to the object oriented environment?" In this paper, the Software Assurance Technology Center (SATC) at NASA Goddard Space Flight Center discusses its approach to choosing metrics for a project by first identifying the attributes associated with object oriented development. Within this framework, nine metrics for object oriented are selected. These metrics include three traditional metrics adapted for an object oriented environment, and six "new" metrics to evaluate the principle object oriented structures and concepts. The metrics are first defined, then using a very simplistic object oriented example, the metrics are applied.  guidelines are then discussed and data from NASA projects are used to demonstrate the application of the metrics. In the experience of the SATC, projects choose the data they collect by default if the tool they are using compiles it, the project collects it. The purpose of this paper is to help project managers choose a comprehensive set of metrics, not by default, but by using a set of metrics based on attributes and features of object oriented technology. Applying and Interpreting Object Oriented Metrics
Grating Lobe Suppression of Narrow-Wall Slotted Hollow Waveguide Millimeter-Wave Planar Antenna for Arbitrarily Linear Polarization

A high-gain and high-efficiency slotted waveguide planar antenna is developed in the millimeter-wave band. Forty-five degree inclined polarization is required for automotive radar systems. In the design of slotted waveguide array for arbitrarily linear polarization, slot spacing is one guide wavelength which is larger than a wavelength in free space. Consequently, grating lobes appear in the radiation pattern. So, we developed a slotted waveguide planar antenna composed of post-loaded narrow-wall slots and a single-layer alternating-phase feeding circuit. A planar antenna with suppressed grating lobes has been fabricated and its RF performance has been measured. The measured gain is 33.2 dBi and antenna efficiency is 56% at 76 GHz. Grating lobe level is -28.6 dB lower than main lobe level. Since the proposed structure remains simple, the antenna is expected to be manufactured by metal injection molding for low cost
A Low Complexity Data Detection Algorithm for Uplink Multiuser Massive MIMO Systems

A major challenge for uplink multiuser massive multiple-input and multiple-output (MIMO) systems is the data detection problem at the receiver due to the substantial increase in the dimensions of MIMO systems. The optimal maximum likelihood detector is impractical for such large wireless systems, because it suffers from exponential complexity in terms of the number of users. Therefore, suboptimal alternatives with reduced complexity, such as the linear minimum mean square error (LMMSE) detector, are necessary. However, the LMMSE detector still introduces high computational complexity, mainly caused by the computation of the Gram matrix and matrix inversion. To reduce the computational complexity of data detection while achieving satisfactory bit error rate (BER) performance, we initially proposed an iterative data detection algorithm that exploits the coordinate descent method (CDM)-based algorithmic framework for uplink multiuser massive MIMO systems. We then developed a reduced-complexity hardware implementation algorithm by leveraging the “one-at-a-time” update property of the CDM-based algorithmic framework. Simulation results revealed that the proposed CDM-based detector provides the same or improved BER performance than the classical LMMSE algorithm at a lower complexity for different test scenarios.
A GPU implementation of inclusion-based points-to analysis

Graphics Processing Units (GPUs) have emerged as powerful accelerators for many regular algorithms that operate on dense arrays and matrices. In contrast, we know relatively little about using GPUs to accelerate highly irregular algorithms that operate on pointer-based data structures such as graphs. For the most part, research has focused on GPU implementations of graph analysis algorithms that do not modify the structure of the graph, such as algorithms for breadth-first search and strongly-connected components.
 In this paper, we describe a high-performance GPU implementation of an important graph algorithm used in compilers such as gcc and LLVM: Andersen-style inclusion-based points-to analysis. This algorithm is challenging to parallelize effectively on GPUs because it makes extensive modifications to the structure of the underlying graph and performs relatively little computation. In spite of this, our program, when executed on a 14 Streaming Multiprocessor GPU, achieves an average speedup of 7x compared to a sequential CPU implementation and outperforms a parallel implementation of the same algorithm running on 16 CPU cores.
 Our implementation provides general insights into how to produce high-performance GPU implementations of graph algorithms, and it highlights key differences between optimizing parallel programs for multicore CPUs and for GPUs.
Capturing Evolving Visit Behavior in Clickstream Data

Many online sites, both retailers and content providers, routinely monitor visitor traffic as a useful measure of their overall success. However, simple summaries such as the total number of visits per month provide little insight about individual-level site-visit patterns, especially in a changing environment such as the Internet. This article develops an individual-level model for evolving visiting behavior based on Internet clickstream data. We capture cross-sectional variation in site-visit behavior as well as changes over time as visitors gain experience with the site. In addition, we examine the relationship between visiting frequency and purchasing propensity at an e-commerce site. We find evidence supporting the notion that people who visit a retail site more frequently have a greater propensity to buy. We also show that changes (i.e., evolution) in an individual's visit frequency over time provides further information regarding which customer segments are likely to have higher purchasing conversion rates. Disciplines Business | Business Analytics | E-Commerce | Management Sciences and Quantitative Methods | Marketing | Technology and Innovation This technical report is available at ScholarlyCommons: https://repository.upenn.edu/marketing_papers/275 Capturing Evolving Visit Behavior in Clickstream Data Wendy W. Moe and Peter S. Fader The Wharton School University of Pennsylvania Marketing Department 1400 Steinberg Hall-Dietrich Hall Philadelphia, PA 19104-6371 telephone: (215) 898-7603 fax: (215) 898-2534
Background : Physics and Math of Shading

Light is an electromagnetic transverse wave, which means that it oscillates in directions perpendicular to its propagation . Since it is a wave, light is characterized by its wavelength—the distance from peak to peak. Electromagnetic wavelengths cover a very wide range but only a tiny part of this range (about 400 to 700 nanometers) is visible to humans and thus of interest for shading . The effect matter has on light is defined by a property called the refractive index. The refractive
Neural Network based Performance Tuning in Distributed Database Environment

The self-tuning of database management systems has important advantages like efficient use of resources, improved performance, reduced total cost of ownership, improved customer satisfaction etc. To support large database driven web-applications with high scalability and availability, the data tier invariably will have distributed database systems. The self-tuning of database systems in a distributed database environment is most desirable as it not only improves the quality of service to the end user applications, but it also provides central control over the entire tuning process. In this paper, a novel technique that leverages the learning ability of the Artificial Neural Network(ANN) has been employed to estimate the extent of tuning required at each database location. Further, the estimated values are used to fine-tune the Database systems at every location from remote control panel. The proposed method has been tested under OLTP workload-type with database distributed on three nodes and the experimental results are validated with workload characteristics based selftuning method. The results show a significant performance improvement og 18.64% as compared to built in selftuning feature of the Database Management System. The self-tuning method not only makes the end-user applications more responsive, but it will also help improve the business prospects of the enterprise.
Psychometric Properties of the Portuguese Version of the Treatment Self-Regulation Questionnaire for Physical Activity ( TSRQ-PA )

The aim of the present study was to analyze the reliability and factorial validity of the Portuguese Version of the Treatment Self-Regulation Questionnaire for physical activity (TSRQ-PA).  In this study, 148 healthy adults (Mean age = 44.70, 96.6% women) and 89 chronic fatigue patients (Mean age = 47.39, 97.8% women) filled out questionnaires related to behaviour regulation style (TSRQ – PA) and physical activity.  The confirmatory factor analysis adjustment indices of a two-factor structure (Autonomous Regulation scale and Controlled Regulation scale) of the TSRQ-PA were satisfactory and internal consistency estimates were acceptable for both factors. A higher degree of autonomous behaviour regulation was significantly associated with higher levels of physical activity.  These findings provide support for the validity and reliability of the TSRQ – PA for measuring behaviour regulation style for engaging in physical activity, in different settings.
UMLtoCSP: a tool for the formal verification of UML/OCL models using constraint programming

We present UMLtoCSP, a tool for the formal verification of UML/OCL models. Given a UML class diagram annotated with OCL constraints, UMLtoCSP is able to automatically check several correctness properties, such as the strong and weak satisfiability of the model or the lack of redundant constraints. The tool uses Constraint Logic Programming as the underlying formalism and the constraint solver ECLiPSe as the verification engine.
The use of big data analytics in the retail industries in South Africa

The majority of publications around big data analytics are centred on technical algorithms or systems development. While research has been conducted on the use of big data analytics there is an apparent lack of studies which focus on industry-specific usage in South Africa. The purpose of the study was to assess the usage of big data analytics in the retail industries in South Africa. The benefits of using big data analytics are not specific to a particular industry. Retailers, for example, can use big data analytics to gain new insights about their customers in order to inform decision making around pricing and marketing. The usage of big data analytics was assessed by collecting data from interviews with retailers, big data vendors and professional services. The main finding of the study was that South African retailers are not using big data analytics. Some retailers are, however, using big data analytic platforms to improve the speed of processing large amounts of structured data and to deliver information cost effectively. The findings show that South African retailers find it difficult to identify a use case to justify the investment required for using big data analytics.