nils-herrmann/clean_scirepeval huggingface dataset:
Modeling the ownership of source code topics

Exploring linguistic topics in source code is a program comprehension activity that shows promise in helping a developer to become familiar with an unfamiliar software system. Examining ownership in source code can reveal complementary information, such as who to contact with questions regarding a source code entity, but the relationship between linguistic topics and ownership is an unexplored area. In this paper we combine software repository mining and topic modeling to measure the ownership of linguistic topics in source code. We conduct an exploratory study of the relationship between linguistic topics and ownership in source code using 10 open source Java systems. We find that classes that belong to the same linguistic topic tend to have similar ownership characteristics, which suggests that conceptually related classes often share the same owner(s). We also find that similar topics tend to share the same ownership characteristics, which suggests that the same developers own related topics.
Active capacitor multiplier in Miller-compensated circuits

A technique is presented whereby the compensating capacitor of an internally compensated linear regulator, Miller-compensated two-stage amplifier, is effectively multiplied. Increasing the capacitance with a current-mode multiplier allows the circuit to occupy less silicon area and to more effectively drive capacitive loads. Reducing physical area requirements while producing the same or perhaps better performance is especially useful in complex systems where most, if not all, functions are integrated onto a single integrated circuit. Die area in such systems is a luxury. The increasing demand for mobile battery-operated devices is a driving force toward higher integration. The enhanced Miller-compensation technique developed in this paper helps enable higher integration while being readily applicable to any process technology, be it CMOS, bipolar, or BiCMOS. Furthermore, the technique applies, in general, to amplifier circuits in feedback configuration. Experimentally, the integrated linear regulator (fabricated in a 1-/spl mu/m BiCMOS process technology) proved to be stable for a wide variety of loading conditions: load currents of up to 200 mA, equivalent series resistance of up to 3 /spl Omega/, and load capacitors ranging from 1.5 nF to 20 /spl mu/E The total quiescent current flowing through the regulator was less than 30 /spl mu/A during zero load-current conditions.
Advanced Data Mining Techniques

The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. I dedicate this book to my grandchildren. Preface The intent of this book is to describe some recent data mining tools that have proven effective in dealing with data sets which often involve uncertain description or other complexities that cause difficulty for the conventional approaches of logistic regression, neural network models, and decision trees. Among these traditional algorithms, neural network models often have a relative advantage when data is complex. We will discuss methods with simple examples, review applications, and evaluate relative advantages of several contemporary methods. Our intent is to cover the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. We have organized the material into three parts. Part I introduces concepts. Part II contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining. Not all of these chapters need to be covered, and their sequence could be varied at instructor design. The book will include short vignettes of how specific concepts have been applied in real practice. A series of representative data sets will be generated to demonstrate specific methods and concepts. References to data mining software and sites such as www.kdnuggets.com will be provided. Chapter 1 gives an overview of data mining, and provides a description of the data mining process. An overview of useful business applications is provided. Chapter 2 presents the data mining process in more detail. It demonstrates this process with a typical set of data. Visualization of data through data mining software is addressed. Chapter 3 presents memory-based reasoning methods of data mining. Major real applications are described. Algorithms are demonstrated with prototypical data based on real applications. Chapter 4 discusses association rule methods. Application in the form of market basket analysis is discussed. A real data set is described, and a simplified version used to demonstrate association rule methods. Chapter 5 presents fuzzy data mining approaches. Fuzzy decision tree approaches are described, as well as fuzzy association rule applications. Real data mining applications are described and demonstrated Chapter 6 presents Rough …
Launchpad: A Rhythm-Based Level Generator for 2-D Platformers

Launchpad is an autonomous level generator that is based on a formal model of 2-D platformer level design. Levels are built out of small segments called “rhythm groups,” which are generated using a two-tiered, grammar-based approach. These segments are pieced together into complete levels that are then rated according to a set of design heuristics. Generation can be controlled using a set of parameters that influence the level pacing and geometry. The approach minimizes the amount of content that must be manually authored: instead of piecing together large segments of a level, Launchpad uses base components that are commonly found in a number of 2-D platformers. Launchpad produces an impressive variety of levels which are all guaranteed to be playable.
Microtia repair with rib cartilage grafts: a review of personal experience with 1000 cases.

Surgical construction of the auricle with autogenous tissues is a unique marrying of science and art. Although the surgeon's facility with both sculpture and design is imperative, the surgical result is equally influenced by adherence to sound principles of plastic surgery and tissue transfer. The material reviewed in this article is derived from clinical experience with congenital microtia: 1094 completed ears in 1000 patients (94 cases were bilateral). This article focuses on total repair of major congenital ear defects, but includes relevant supplementary input from experience gained by managing more than 125 traumatic auricular deformities.
Towards A Framework for the Evaluation of Machine Compositions

We outlinea framework within which machinecompositionsmaybeevaluatedobjectively. In particular, theframework allows statementsaboutthosecompositionsto berefutedon thebasisof empiricalexperimentation.We considerthis to befundamental if we wish to evaluatethedegreeto whichourprogramsachieve their compositionalaims.Furthermore, a review of the literaturerevealsthat this is a largely ignoredaspectof researchinto algorithmic composition. Our framework involvesfour components: specifyingthecompositional aims;inducingacritic from asetof examplemusical phrases;composingmusicthatsatisfiesthecritic; andevaluatingspecificclaimsaboutthecompositionsin experiments usinghumansubjects.Wedescribeasystemwhichexemplifiesthesefour stagesandwhichdemonstrates thepracticality of the framework. Finally, the applicationof the framework to the evaluationof musicalcreati vity is discussedand directionsfor futureresearcharesuggested.
Modeling User Concerns in the App Store: A Case Study on the Rise and Fall of Yik Yak

Mobile application (app) stores have lowered the barriers to app market entry, leading to an accelerated and unprecedented pace of mobile software production. To survive in such a highly competitive and vibrant market, release engineering decisions should be driven by a systematic analysis of the complex interplay between the user, system, and market components of the mobile app ecosystem. To demonstrate the feasibility and value of such analysis, in this paper, we present a case study on the rise and fall of Yik Yak, one of the most popular social networking apps at its peak. In particular, we identify and analyze the design decisions that led to the downfall of Yik Yak and track rival apps' attempts to take advantage of this failure. We further perform a systematic in-depth analysis to identify the main user concerns in the domain of anonymous social networking apps and model their relations to the core features of the domain. Such a model can be utilized by app developers to devise sustainable release engineering strategies that can address urgent user concerns and maintain market viability.
Speech recognition using factorial hidden Markov models for separation in the feature space

This paper proposes an algorithm for the recognition and separation of speech signals in non-stationary noise, such as another speaker. We present a method to combine hidden Markov models (HMMs) trained for the speech and noise into a factorial HMM to model the mixture signal. Robustness is obtained by separating the speech and noise signals in a feature domain, which discards unnecessary information. We use mel-cepstral coefficients (MFCCs) as features, and estimate the distribution of mixture MFCCs from the distributions of the target speech and noise. A decoding algorithm is proposed for finding the state transition paths and estimating gains for the speech and noise from a mixture signal. Simulations were carried out using speech material where two speakers were mixed at various levels, and even for high noise level (9 dB above the speech level), the method produced relatively good (60% word recognition accuracy) results. Audio demonstrations are available at www.cs.tut.fi/ ̃tuomasv.
Access Control of Door and Home Security by Raspberry Pi Through Internet

In the present age Internet of things (IOT) has enterned a golden era of rapid growth. The Internet of things is a concept that aims to extend the benefits of the regular Internet—constant connectivity, remote control ability, data sharing, and so on—to goods in the physical world. Everyday things are getting connected with the internet. This concept can be used tomanage the security concerned issues in a cost effective way. In ths paper work a system is being developed to connect any door with the internet, so that the access control system can be controlled from any where in the world. In a case that one is not at home and a vistor is at his door steps then the authorized person will be notified about the visitor via twitter and the person can see the visitor from the web through the camera from any where and the system will take a picture of the visitor and keep a record by sending an attachment through E-mail or tweet in twitter. If the authorized person wants to give a message the visitor it can be sent easily through the internet and it will appear in a screen on the front face of the door. The door lock can be controlled through the internet. With the help of this system an evidence of the visitor can be kept as a record if any emergency case or situtaion occurs.
Global geometric similarity scheme for feature selection in fault diagnosis

This work presents a global geometric similarity scheme (GGSS) for feature selection in fault diagnosis, which is composed of global geometric model and similarity metric. The global geometric model is formed to construct connections between disjoint clusters in fault diagnosis. The similarity metric of the global geometric model is applied to filter feature subsets. To evaluate the performance of GGSS, fault data from wind turbine test rig is collected, and condition classification is carried out with classifiers established by Support Vector Machine (SVM) and General Regression Neural Network (GRNN). The classification results are compared with feature ranking methods and feature wrapper approaches. GGSS achieves higher classification accuracy than the feature ranking methods, and better time efficiency than the feature wrapper approaches. The hybrid scheme, GGSS with wrapper, obtains optimal classification accuracy and time efficiency. The proposed scheme can be applied in feature selection to get better accuracy and efficiency in condition classification of fault diagnosis.
Color in the Cortex: single- and double-opponent cells

This is a review of the research during the past 25years on cortical processing of color signals. At the beginning of the period the modular view of cortical processing predominated. However, at present an alternative view, that color and form are linked inextricably in visual cortical processing, is more persuasive than it seemed in 1985. Also, the role of the primary visual cortex, V1, in color processing now seems much larger than it did in 1985. The re-evaluation of the important role of V1 in color vision was caused in part by investigations of human V1 responses to color, measured with functional magnetic resonance imaging, fMRI, and in part by the results of numerous studies of single-unit neurophysiology in non-human primates. The neurophysiological results have highlighted the importance of double-opponent cells in V1. Another new concept is population coding of hue, saturation, and brightness in cortical neuronal population activity.
A Comprehensive Comparison between Neural Style Transfer and Universal Style Transfer

Style transfer aims to transfer arbitrary visual styles to content images. We explore algorithms adapted from two papers that try to solve the problem of style transfer while generalizing on unseen styles or compromised visual quality. Majority of the improvements made focus on optimizing the algorithm for real-time style transfer while adapting to new styles with considerably less resources and constraints. We compare these strategies and compare how they measure up to produce visually appealing images. We explore two approaches to style transfer: neural style transfer with improvements and universal style transfer. We also make a comparison between the different images produced and how they can be qualitatively measured.
An energy framework for the network simulator 3 (NS-3)

The Network Simulator-3 (ns-3) is rapidly developing into a flexible and easy-to-use tool suitable for wireless network simulation. Since energy consumption is a key issue for wireless devices, wireless network researchers often need to investigate the energy consumption at a battery powered node or in the overall network, while running network simulations. This requires the underlying simulator to support energy consumption and energy source modeling. Currently however, ns-3 does not provide any support for modeling energy consumption or energy sources. In this paper, we introduce an integrated energy framework for ns-3, with models for energy source as well as energy consumption. We present the design and implementation of the overall framework and the specific models therein. Further, we show how the proposed framework can be used in ns-3 to simulate energy-aware protocols in a wireless network.
Comparative Analysis Between Gazebo and V-REP Robotic Simulators

Robotic simulators are normally used in the design and testing of control algorithms for different platforms. But they can also be seen in a more broad context of Artificial Creatures. This a natural development of the cognitive paradigm proposed by Brooks. Related to this, in recent years the Robot Operating System (ROS) has emerged as a de facto standard for robot software architecture. I selected two of the best-known simulators and performed a comparison between them, evaluating the strengths and weaknesses for each one, and how well to these simulators interact with ROS, based on an experiment with a Pioneer 2DX robot. robotic simulators, ROS, Gazebo, V-REP, cognitive science
PLINK: a tool set for whole-genome association and population-based linkage analyses.

Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
The Impact of Social Media on Academic Performance of Selected College Students

The purpose of this article is to assess the impact of social media on academic performance of selected college students. In this article, the authors raise the actual impact of daily communication of youth in social media. Descriptive research design was utilized to gain accurate profile of situation.Sixty (60) Business Administration and Management Information System students who are actively using social media are the respondents of the study. It was conducted during the summer semester of academic year 20172018.Summing-up,social networks becomes an integral part of the students’ full life, took up most of their free time.Undoubtedly, in social networks, there are also things useful for the development of the students. In addition, communication with peers through social networks can help a student socialize, find new friends, discuss with them issues related to studies. Thus, it can be concluded that social media have a dual impact on student achievement, and it is necessary to approach adolescents' use of social networks with ultimate responsibility.
Transition-based Dependency Parsing with Rich Non-local Features

Transition-based dependency parsers generally use heuristic decoding algorithms but can accommodate arbitrarily rich feature representations. In this paper, we show that we can improve the accuracy of such parsers by considering even richer feature sets than those employed in previous systems. In the standard Penn Treebank setup, our novel features improve attachment score form 91.4% to 92.9%, giving the best results so far for transitionbased parsing and rivaling the best results overall. For the Chinese Treebank, they give a signficant improvement of the state of the art. An open source release of our parser is freely available.
Threat Assessment in the Cloud Environment: A Quantitative Approach for Security Pattern Selection

Cloud computing has emerged as a fast-growing technology in the past few years. It provides a great flexibility for storing, sharing and delivering data over the Internet without investing on new technology or resources. In spite of the development and wide array of cloud usage, security perspective of cloud computing still remains its infancy. Security challenges faced by cloud environment becomes more complicated when we include various stakeholders' perspectives. In a cloud environment, security perspectives and requirements are usually designed by software engineers or security experts. Sometimes clients' requirements are either ignored or given a very high importance. In order to implement cloud security by providing equal importance to client organizations, software engineers and security experts, we propose a new methodology in this paper. We use Microsoft's STRIDE-DREAD model to assess threats existing in the cloud environment and also to measure its consequences. Our aim is to rank the threats based on the nature of its severity, and also giving a significant importance for clients' requirements on security perspective. Our methodology would act as a guiding tool for security experts and software engineers to proceed with securing process especially for a private or a hybrid cloud. Once threats are ranked, we provide a link to a well-known security pattern classification. Although we have some security pattern classification schemes in the literature, we need a methodology to select a particular category of patterns. In this paper, we provide a novel methodology to select a set of security patterns for securing a cloud software. This methodology could aid a security expert or a software professional to assess the current vulnerability condition and prioritize by also including client's security requirements in a cloud environment.
Autoimmune disorders: nail signs and therapeutic approaches.

Systemic sclerosis (scleroderma, SSc) is an autoimmune disease that targets small and medium-sized arteries and arterioles in the involved tissues, resulting in a fibrotic vasculopathy and tissue fibrosis. Several prominent nail and periungual changes are apparent in scleroderma. Examination of the nail fold capillaries can reveal the nature and extent of microvascular pathology in patients with collagen vascular disease and Raynaud's phenomenon. Among the complications stemming from Raynaud's phenomenon can be painful ischemic digital ulcers. This can be managed, and potentially prevented, through pharmacologic and nonpharmacologic means. Whereas oral calcium channel blockers remain the most convenient therapy, oral endothelin receptor antagonists and intravenous prostaglandins may be important therapeutic advances for ischemic digital vascular lesions.
Noise Adaptive Fuzzy Switching Median Filter for Salt-and-Pepper Noise Reduction

This letter presents a novel two-stage noise adaptive fuzzy switching median (NAFSM) filter for salt-and-pepper noise detection and removal. Initially, the detection stage will utilize the histogram of the corrupted image to identify noise pixels. These detected ¿noise pixels¿ will then be subjected to the second stage of the filtering action, while ¿noise-free pixels¿ are retained and left unprocessed. Then, the NAFSM filtering mechanism employs fuzzy reasoning to handle uncertainty present in the extracted local information as introduced by noise. Simulation results indicate that the NAFSM is able to outperform some of the salt-and-pepper noise filters existing in literature.
Use of Fourier Series Analysis for Motion Artifact Reduction and Data Compression of Photoplethysmographic Signals

Pulse oximeters require artifact-free clean photoplethysmograph (PPG) signals obtained at red and infrared (IR) wavelengths for the estimation of the level of oxygen saturation ( SpO2) in the arterial blood of a patient. Movement of a patient corrupts a PPG signal with motion artifacts and introduces large errors in the computation of SpO2. A novel method for removing motion artifacts from corrupted PPG signals by applying Fourier series analysis on a cycle-by-cycle basis is presented in this paper. Aside from artifact reduction, the proposed method also provides data compression. Experimental results indicate that the proposed method is insensitive to heart rate variation, introduces negligible error in the processed PPG signals due to the additional processing, preserves all the morphological features of the PPG, provides 35 dB reduction in motion artifacts, and achieves a data compression factor of 12.