nils-herrmann/clean_scirepeval huggingface dataset:
Predicting the Popularity of GitHub Repositories

GitHub is the largest source code repository in the world. It provides a git-based source code management platform and also many features inspired by social networks. For example, GitHub users can show appreciation to projects by adding stars to them. Therefore, the number of stars of a repository is a direct measure of its popularity. In this paper, we use multiple linear regressions to predict the number of stars of GitHub repositories. These predictions are useful both to repository owners and clients, who usually want to know how their projects are performing in a competitive open source development market. In a large-scale analysis, we show that the proposed models start to provide accurate predictions after being trained with the number of stars received in the last six months. Furthermore, specific models---generated using data from repositories that share the same growth trends---are recommended for repositories with slow growth and/or for repositories with less stars. Finally, we evaluate the ability to predict not the number of stars of a repository but its rank among the GitHub repositories. We found a very strong correlation between predicted and real rankings (Spearman's rho greater than 0.95).
Understanding LDA in source code analysis

Latent Dirichlet Allocation (LDA) has seen increasing use in the understanding of source code and its related artifacts in part because of its impressive modeling power. However, this expressive power comes at a cost: the technique includes several tuning parameters whose impact on the resulting LDA model must be carefully considered. An obvious example is the burn-in period; too short a burn-in period leaves excessive echoes of the initial uniform distribution. The aim of this work is to provide insights into the tuning parameter's impact. Doing so improves the comprehension of both, 1) researchers who look to exploit the power of LDA in their research and 2) those who interpret the output of LDA-using tools. It is important to recognize that the goal of this work is not to establish values for the tuning parameters because there is no universal best setting. Rather, appropriate settings depend on the problem being solved, the input corpus (in this case, typically words from the source code and its supporting artifacts), and the needs of the engineer performing the analysis. This work's primary goal is to aid software engineers in their understanding of the LDA tuning parameters by demonstrating numerically and graphically the relationship between the tuning parameters and the LDA output. A secondary goal is to enable more informed setting of the parameters. Results obtained using both production source code and a synthetic corpus underscore the need for a solid understanding of how to configure LDA's tuning parameters.
Towards robotic assistants in nursing homes: Challenges and results

This paper describes a mobile robotic assistant, developed to assist elderly individuals with mild cognitive and physical impairments, as well as support nurses in their daily activities. We present three software modules relevant to ensure successful humanâ€“robot interaction: an automated reminder system; a people tracking and detection system; and finally a high-level robot controller that performs planning under uncertainty by incorporating knowledge from low-level modules, and selecting appropriate courses of actions. During the course of experiments conducted in an assisted living facility, the robot successfully demonstrated that it could autonomously provide reminders and guidance for elderly residents. a a Purchase Export
A Survey on Trends and Techniques used in Hyperspectral Image Processing

Recent advancement in remote sensing facilitates collection of hyperspectral images (HSIs) in hundreds of bands which provides a potential platform to detect and identify the unique trends in land and atmospheric datasets with high accuracy. But along with the detailed information, HSIs also pose several processing problems such as1) increase in computational complexity due to high dimensionality. So dimension reduction without losing information is one of the major concerns in this area and 2) limited availability of labeled training sets causes the ill posed problem which is needed to be addressed by the classification algorithms. Initially classification techniques of HSIs were based on spectral information only. Gradually researchers started utilizing both spectral and spatial information to increase classification accuracy. Also the classification algorithms have evolved from supervised to semi supervised mode. This paper presents a survey about the techniques available in the field of HSI processing to provide a seminal view of how the field of HSI analysis has evolved over the last few decades and also provides a snapshot of the state of the art techniques used in this area. General Terms Classification algorithms, image processing, supervised, semi supervised techniques.
DSCRETE: Automatic Rendering of Forensic Information from Memory Images via Application Logic Reuse

State-of-the-art memory forensics involves signaturebased scanning of memory images to uncover data structure instances of interest to investigators. A largely unaddressed challenge is that investigators may not be able to interpret the content of data structure fields, even with a deep understanding of the data structure’s syntax and semantics. This is very common for data structures with application-specific encoding, such as those representing images, figures, passwords, and formatted file contents. For example, an investigator may know that a buffer field is holding a photo image, but still cannot display (and hence understand) the image. We call this the data structure content reverse engineering challenge. In this paper, we present DSCRETE, a system that enables automatic interpretation and rendering of inmemory data structure contents. DSCRETE is based on the observation that the application in which a data structure is defined usually contains interpretation and rendering logic to generate human-understandable output for that data structure. Hence DSCRETE aims to identify and reuse such logic in the program’s binary and create a “scanner+renderer” tool for scanning and rendering instances of the data structure in a memory image. Different from signature-based approaches, DSCRETE avoids reverse engineering data structure signatures. Our evaluation with a wide range of real-world application binaries shows that DSCRETE is able to recover a variety of application data — e.g., images, figures, screenshots, user accounts, and formatted files and messages — with high accuracy. The raw contents of such data would otherwise be unfathomable to human investigators.
The LRU-K Page Replacement Algorithm For Database Disk Buffering

This paper introduces a new approach to database disk buffering, called the LRU-K method. The basic idea of LRU-K is to keep track of the times of the last K references to popular database pages, using this information to statistically estimate the interarrival times of references on a page by page basis. Although the LRU-K approach performs optimal statistical inference under relatively standard assumptions, it is fairly simple and incurs little bookkeeping overhead. As we demonstrate with simulation experiments, the LRU-K algorithm surpasses conventional buffering algorithms in discriminating between frequently and infrequently referenced pages. In fact, LRU-K can approach the behavior of buffering algorithms in which page sets with known access frequencies are manually assigned to different buffer pools of specifically tuned sizes. Unlike such customized buffering algorithms however, the LRU-K method is self-tuning, and does not rely on external hints about workload characteristics. Furthermore, the LRU-K algorithm adapts in real time to changing patterns of access.
The Impact of Business-IT Alignment on Organizational Culture

Organizational culture continues to be cited as one of the most important factors for organizations’ success in an increasingly competitive and IT-driven global environment. Given the fact that organizational culture has an influence all over the organization, the complexity of its nature is increased when considering the relationship between business and IT. As a result, different factors that have influence on changing organizational culture were highlighted in literature. These factors are found in the research literature distributed in three main group; micro-environment factors, macro-environment factors and leader’s impact. One of the factors that have not been yet well investigated in researches is concerning business-IT alignment (BITA. Therefore the purpose of this paper is to investigate the impact of BITA maturity on organizational culture. The research process that we have followed is a literature survey followed by an in-depth case study. The result of this research shows a clear interrelation in theories of both BITA and organizational culture, and clear indications of BITA impact on organizational culture and its change. The findings may support both practitioners and researchers in order to understand the insights of the relationships between BITA and organizational culture components and provide a roadmap for improvements or desired changes in organizational culture with highlighted target business area.
QR code detection using convolutional neural networks

Barcodes have been long used for data storage. Detecting and locating barcodes in images of complex background is an essential yet challenging step in the process of automatic barcode reading. This work proposed an algorithm that localizes and segments two-dimensional quick response (QR) barcodes. The localization involved a convolutional neural network that could detect partial QR barcodes. Majority voting was then applied to determine barcode locations. Then image processing algorithms were implemented to segment barcodes from the background. Experimental results shows that the proposed approach was robust to detect QR barcodes with rotation and deformation.
Object Tracking Pose Estimation Object Detection Action Recognition / / / / Autonomous Navigation 3 D Reconstruction Crowd

We present a photo-realistic training and evaluation simulator (UE4Sim) with extensive applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator integrates full featured physics based cars, unmanned aerial vehicles (UAVs), and animated human actors in diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning. The simulator fully integrates both several state-of-the-art tracking algorithms with a benchmark evaluation tool and a deep neural network (DNN) architecture for training vehicles to drive autonomously. It generates synthetic photo-realistic datasets with automatic ground truth annotations to easily extend existing real-world datasets and provides extensive synthetic data variety through its ability to reconfigure synthetic worlds on the fly using an automatic world generation tool.
Dynamic Sampling Convolutional Neural Networks

We present Dynamic Sampling Convolutional Neural Networks (DSCNN), where the position-specific kernels learn from not only the current position but also multiple sampled neighbour regions. During sampling, residual learning is introduced to ease training and an attention mechanism is applied to fuse features from different samples. And the kernels are further factorized to reduce parameters. The multiple sampling strategy enlarges the effective receptive fields significantly without requiring more parameters. While DSCNNs inherit the advantages of DFN , namely avoiding feature map blurring by position-specific kernels while keeping translation invariance, it also efficiently alleviates the overfitting issue caused by much more parameters than normal CNNs. Our model is efficient and can be trained end-to-end via standard backpropagation. We demonstrate the merits of our DSCNNs on both sparse and dense prediction tasks involving object detection and flow estimation. Our results show that DSCNNs enjoy stronger recognition abilities and achieve 81.7% in VOC2012  detection dataset. Also, DSCNNs obtain much sharper responses in flow estimation on FlyingChairs dataset[3] compared to multiple FlowNet models’ baselines.
Cellular senescence: when bad things happen to good cells

Cells continually experience stress and damage from exogenous and endogenous sources, and their responses range from complete recovery to cell death. Proliferating cells can initiate an additional response by adopting a state of permanent cell-cycle arrest that is termed cellular senescence. Understanding the causes and consequences of cellular senescence has provided novel insights into how cells react to stress, especially genotoxic stress, and how this cellular response can affect complex organismal processes such as the development of cancer and ageing.
B-cell exhaustion in HIV infection: the role of immune activation.

To discuss a component of the pathogenic mechanisms of HIV infection in the context of phenotypic and functional alterations in B cells that are due to persistent viral replication leading to aberrant immune activation and cellular exhaustion. We explore how B-cell exhaustion arises during persistent viremia and how it compares with T-cell exhaustion and similar B-cell alterations in other diseases.



HIV-associated B-cell exhaustion was first described in 2008, soon after the demonstration of persistent virus-induced T-cell exhaustion, as well as the identification of a subset B cells in tonsil tissues with immunoregulatory features similar to those observed in T-cell exhaustion. Our understanding of B-cell exhaustion has since expanded in two important areas: the role of inhibitory receptors in the unresponsiveness of exhausted B cells and the increasing evidence that similar B cells are found in other diseases that are associated with aberrant immune activation and inflammation.



The phenomenon of B-cell exhaustion is now well established in HIV infection and other diseases characterized by immune activation. Over the coming years, it will be important to understand how cellular exhaustion affects the capacity of the immune system to respond to persisting primary pathogens, as well as to other microbial antigens, whether encountered as secondary infections or following immunization.
Age, coloration and dominance in nonbreeding hummingbirds: A test of the asymmetry hypothesis

During the nonbreeding season, adult Anna and black-chinned hummingbirds (Calypte anna and Archilochus alexandri) have lower defense costs and more exclusive territories than juveniles. Adult C. anna are victorious over juveniles in aggressive encounters, and tend to monopolize the most temporally predictable resources. Juveniles are more successful than adults at stealing food from territories (the primary alternative to territoriality), presumably because juveniles are less brightly colored. Juveniles have lighter wing disc loading than adults, and consequently should have lower rates of energy expenditure during flight. Reduced flight expenditures may be more important for juveniles because their foraging strategy requires large amounts of flight time. These results support the contention of the asymmetry hypothesis that dominance can result from a contested resource being more valuable to one contestant than to the other. Among juveniles, defence costs are also negatively correlated with age and coloration; amount of conspicucus coloration is negatively correlated with the number of bill striations, an inverse measure of age.
Computational Approaches for the Understanding of Melody in Carnatic Music

The classical music traditions of the Indian subcontinent, Hindustani and Carnatic, offer an excellent ground on which to test the limitations of current music information research approaches. At the same time, studies based on these music traditions can shed light on how to solve new and complex music modeling problems. Both traditions have very distinct characteristics, specially compared with western ones: they have developed unique instruments, musical forms, performance practices, social uses and context. In this article, we focus on the Carnatic music tradition of south India, especially on its melodic characteristics. We overview the theoretical aspects that are relevant for music information research and discuss the scarce computational approaches developed so far. We put emphasis on the limitations of the current methodologies and we present open issues that have not yet been addressed and that we believe are important to be worked on.
Ossifying fibroma of the jaws: a clinicopathological case series study.

The aim of this study was to assess the clinical, radiographic and microscopic features of a case series of ossifying fibroma (OF) of the jaws. For the study, all cases with OF diagnosis from the files of the Oral Pathology Laboratory, University of Ribeirão Preto, Ribeirão Preto, SP, Brazil, were reviewed. Clinical data were obtained from the patient files and the radiographic features were evaluated in each case. All cases were reviewed microscopically to confirm the diagnosis. Eight cases were identified, 5 in females and 3 in males. The mean age of the patients was 33.7 years and most lesions (7 cases) occurred in the mandible. Radiographically, all lesions appeared as unilocular images and most of them (5 cases) were of mixed type. The mean size of the tumor was 3.1 cm and 3 cases caused displacement of the involved teeth. Microscopically, all cases showed several bone-like mineralized areas, immersed in the cellular connective tissue. From the 8 cases, 5 underwent surgical excision and 1 patient refused treatment. In the remaining 2 cases, this information was not available. In conclusion, OF occurs more commonly in women in the fourth decade of life, frequently as a mixed radiographic image in the mandible. Coherent differential diagnoses are important to guide the most adequate clinical approach. A correlation between clinical, imaginological and histopathological features is the key to establish the correct diagnosis.
Chapter 1 Artificial Neurogenesis : An Introduction and Selective Review

In this introduction and review—like in the book which follows—we explore the hypothesis that adaptive growth is a means of producing brain-like machines. The emulation of neural development can incorporate desirable characteristics of natural neural systems into engineered designs. The introduction begins with a review of neural development and neural models. Next, artificial development— the use of a developmentally-inspired stage in engineering design—is introduced. Several strategies for performing this “meta-design” for artificial neural systems are reviewed. This work is divided into three main categories: bio-inspired representations; developmental systems; and epigenetic simulations. Several specific network biases and their benefits to neural network design are identified in these contexts. In particular, several recent studies show a strong synergy, sometimes interchangeability, between developmental and epigenetic processes—a topic that has remained largely under-explored in the literature. T. Kowaliw (B) Institut des Systèmes Complexes Paris Île-de-France, CNRS, Paris, France e-mail: taras@kowaliw.ca N. Bredeche Sorbonne Universités, UPMC University Paris 06, UMR 7222 ISIR,F-75005 Paris, France e-mail: nicolas.bredeche@upmc.fr N. Bredeche CNRS, UMR 7222 ISIR,F-75005 Paris, France S. Chevallier Versailles Systems Engineering Laboratory (LISV), University of Versailles, Velizy, France e-mail: sylvain.chevallier@uvsq.fr R. Doursat School of Biomedical Engineering, Drexel University, Philadelphia, USA e-mail: rene.doursat@drexel.edu T. Kowaliw et al. (eds.), Growing Adaptive Machines, 1 Studies in Computational Intelligence 557, DOI: 10.1007/978-3-642-55337-0_1,
Robust non-local denoising of colored depth data

We give a brief discussion of denoising algorithms for depth data and introduce a novel technique based on the NL-means filter. A unified approach is presented that removes outliers from depth data and accordingly achieves an unbiased smoothing result. This robust denoising algorithm takes intra-patch similarity and optional color information into account in order to handle strong discontinuities and to preserve fine detail structure in the data. We achieve fast computation times with a GPU-based implementation. Results using data from a time-of-flight camera system show a significant gain in visual quality.
A Refactoring Constraint Language and Its Application to Eiffel

We generalize previous work on constraint-based refactoring and develop it into the definition of a constraint language allowing the specification of refactorings in a completely declarative way. We present a compiler that transforms specifications in our language to plug-ins for an IDE that, together with an accompanying framework providing the necessary infrastructure, implement the specified refactoring tools. We evaluate our approach by presenting specifications of three different refactorings for the Eiffel programming language, and by applying the resulting refactoring tools to several sample programs. Outcome suggests that our approach is indeed viable.
Direct manipulation of free-form deformations

Free-form deformation (FFD) is a powerful modeling tool, but controlling the shape of an object under complex deformations is often difficult. The interface to FFD in most conventional systems simply represents the underlying mathematics directly; users describe deformations by manipulating control points. The difficulty in controlling shape precisely is largely due to the control points being extraneous to the object; the deformed object does not follow the control points exactly. In addition, the number of degrees of freedom presented to the user can be overwhelming. We present a method that allows a user to control a free-form deformation of an object by manipulating the object directly, leading to better control of the deformation and a more intuitive interface. CR Categories: I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling Curve, Surface, Solid, and Object Representations; I.3.6 [Computer Graphics]: Methodology and Techniques Interaction Techniques.
A 1.5 V, 1.5 GHz CMOS low noise amplifier

A 1.5 GHz low noise amplifier for a Global Positioning System (GPS) receiver has been implemented in a 0.6 /spl mu/m CMOS process. This amplifier provides a forward gain of 22 dB with a noise figure of only 3.5 dB while drawing 30 mW from a 1.5 V supply. To the authors' knowledge, this represents the lowest noise figure reported to date for a CMOS amplifier operating above 1 GHz.
A Music Recommendation System Based on Music and User Grouping

In this paper, we present a music recommendation system, which provides a personalized service of music recommendation. The polyphonic music objects of MIDI format are first analyzed for deriving information for music grouping. For this purpose, the representative track of each polyphonic music object is first determined, and then six features are extracted from this track for proper music grouping. Moreover, the user access histories are analyzed to derive the profiles of user interests and behaviors for user grouping. The content-based, collaborative, and statistics-based recommendation methods are proposed based on the favorite degrees of the users to the music groups, and the user groups they belong to. A series of experiments are carried out to show that our approach performs well.