nils-herrmann/clean_scirepeval huggingface dataset:
When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away)

In past and recent years, the issues related to managing technical debt received significant attention by researchers from both industry and academia. There are several factors that contribute to technical debt. One of these is represented by code bad smells, i.e., symptoms of poor design and implementation choices. While the repercussions of smells on code quality have been empirically assessed, there is still only anecdotal evidence on when and why bad smells are introduced. To fill this gap, we conducted a large empirical study over the change history of 200 open source projects from different software ecosystems and investigated when bad smells are introduced by developers, and the circumstances and reasons behind their introduction. Our study required the development of a strategy to identify smell-introducing commits, the mining of over 0.5M commits, and the manual analysis of 9,164 of them (i.e., those identified as smell-introducing). Our findings mostly contradict common wisdom stating that smells are being introduced during evolutionary tasks. In the light of our results, we also call for the need to develop a new generation of recommendation systems aimed at properly planning smell refactoring activities.
Matrix and Tensor Factorization for Predicting Student Performance

Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in technology enhanced learning such as recommending resources (e.g. papers, books,...) to the learners (students). In this study, we propose using state-of-the-art recommender system techniques for predicting student performance. We introduce and formulate the problem of predicting student performance in the context of recommender systems. We present the matrix factorization method, known as most effective recommendation approaches, to implicitly take into account the latent factors, e.g. “slip” and “guess”, in predicting student performance. Moreover, the knowledge of the learners has been improved over the time, thus, we propose tensor factorization methods to take the temporal effect into account. Experimental results show that the proposed approaches can improve the prediction results.
What Does the Retina Know about Natural Scenes?

By examining the experimental data on the statistical properties of natural scenes together with (retinal) contrast sensitivity data, we arrive at a first principle, theoretical hypothesis for the purpose of retinal processing and its relationship to an animal's environment. We argue that the retinal goal is to transform the visual input as much as possible into a statistically independent basis as the first step in creating a redundancy reduced representation in the cortex, as suggested by Barlow. The extent of this whitening of the input is limited, however, by the need to suppress input noise. Our explicit theoretical solutions for the retinal filters also show a simple dependence on mean stimulus luminance: they predict an approximate Weber law at low spatial frequencies and a De Vries-Rose law at high frequencies. Assuming that the dominant source of noise is quantum, we generate a family of contrast sensitivity curves as a function of mean luminance. This family is compared to psychophysical data.
User Service Innovation on Mobile Phone Platforms: Investigating Impacts of Lead Userness, Toolkit Support, and Design Autonomy

Study Term Definition Boudreau and Hagiu (2009) Multisided platform, e.g. App store Platforms are products, services, or technologies that serve as foundations upon which other parties can build complementary products, services, or technologies A multisided platform is both a platform and a market intermediary. Distinct groups of consumers and “complementors” interact through multisided platforms. Boudreau (2012) Handheld computer platforms Computer platforms are a particular type of multisided platforms, which support interactions across multiple sets of actors and can facilitate technical development. Network effects result from a large number of independent software producers creating applications. Ceccagnoli et al. (2012) Platform A platform refers to the components used in common across a product family whose functionality can be extended by applications Fichman (2004) IT platform An IT platform is broadly defined as a general-purpose technology that enables a family of applications and related business opportunities. This includes computing platforms (e.g., Palm OS), infrastructure platforms (e.g., wireless networking), software development platforms (e.g., Java), and enterprise application platforms (e.g., ERP). Tiwana et al. (2010) Software based platform Software based platform is the extensible codebase of a software-based system that provides core functionality shared by the modules that interoperate with it and the interfaces through which they interoperate. Module is an add-on software subsystem that connects to the platform to add functionality to it (e.g., iOS apps, modular innovation). the collection of the platform and the modules specific to that platform as that platform’s ecosystem .
Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization

We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research. The framework allows summaries to be generated in a greedy way while preserving near-optimal performance guarantees. Our main contribution is the novel coverage reward term of the objective function optimized by the greedy algorithm. This component builds on the graph-of-words representation of text and the k-core decomposition algorithm to assign meaningful scores to words. We evaluate our approach on the AMI and ICSI meeting speech corpora, and on the DUC2001 news corpus. We reach state-of-the-art performance on all datasets. Results indicate that our method is particularly well-suited to the meeting domain.
Motivation and personality: relationships between putative motive dimensions and the five factor model of personality.

There are few multidimensional measures of individual differences in motivation available. The Assessment of Individual Motives-Questionnaire assesses 15 putative dimensions of motivation. The dimensions are based on evolutionary theory and preliminary evidence suggests the motive scales have good psychometric properties. The scales are reliable and there is evidence of their consensual validity (convergence of self-other ratings) and behavioral validity (relationships with self-other reported behaviors of social importance). Additional validity research is necessary, however, especially with respect to current models of personality. The present study tested two general and 24 specific hypotheses based on proposed evolutionary advantages/disadvantages and fitness benefits/costs of the five-factor model of personality together with the new motive scales in a sample of 424 participants (M age=28.8 yr., SD=14.6). Results were largely supportive of the hypotheses. These results support the validity of new motive dimensions and increase understanding of the five-factor model of personality.
A Pipeline for the Segmentation and Classification of 3D Point Clouds

This paper presents algorithms for fast segmentation of 3D point clouds and subsequent classification of the obtained 3D segments. The method jointly determines the ground surface and segments individual objects in 3D, including overhanging structures. When compared to six other terrain modelling techniques, this approach has minimal error between the sensed data and the representation; and is fast (processing a Velodyne scan in approximately 2 seconds). Applications include improved alignment of successive scans by enabling operations in sections (Velodyne scans are aligned 7% sharper compared to an approach using raw points) and more informed decision-making (paths move around overhangs). The use of segmentation to aid classification through 3D features, such as the Spin Image or the Spherical Harmonic Descriptor, is discussed and experimentally compared. Moreover, the segmentation facilitates a novel approach to 3D classification that bypasses feature extraction and directly compares 3D shapes via the ICP algorithm. This technique is shown to achieve accuracy on par with the best feature based classifier (92.1%) while being significantly faster and allowing a clearer understanding of the classifier’s behaviour.
Optimization-based formation control of underactuated magnetic microrobots via inter-agent forces

This paper presents a new optimization-based method to control three micro-scale magnetic agents operating in close proximity to each other for applications in microrobotics. Controlling multiple magnetic microrobots close to each other is difficult due to magnetic interactions between the agents, and here we seek to control those interactions for the creation of desired multi-agent formations. Our control strategy arises from physics that apply force in the negative direction of states errors. The objective is to regulate the inter-agent spacing, heading and position of the set of agents, for motion in two dimensions, while the system is inherently underactuated. Simulation results on three agents and a proof-of-concept experiment on two agents show the feasibility of the idea to shed light on future micro/nanoscale multi-agent explorations. Average tracking error of less than 50 micrometers and 1.85 degrees is accomplished for the regulation of the inter-agent space and the pair heading angle, respectively, for identical spherical-shape agents with nominal radius less than of 250 micrometers operating within several body-lengths of each other.
Deep State Space Models for Time Series Forecasting

We present a novel approach to probabilistic time series forecasting that combines state space models with deep learning. By parametrizing a per-time-series linear state space model with a jointly-learned recurrent neural network, our method retains desired properties of state space models such as data efficiency and interpretability, while making use of the ability to learn complex patterns from raw data offered by deep learning approaches. Our method scales gracefully from regimes where little training data is available to regimes where data from large collection of time series can be leveraged to learn accurate models. We provide qualitative as well as quantitative results with the proposed method, showing that it compares favorably to the state-of-the-art.
Piecewise Linear Neural Network verification: A comparative study

The success of Deep Learning and its potential use in many important safety- critical applications has motivated research on formal verification of Neural Network (NN) models. Despite the reputation of learned NN models to behave as black boxes and the theoretical hardness of proving their properties, researchers have been successful in verifying some classes of models by exploiting their piecewise linear structure. Unfortunately, most of these approaches test their algorithms without comparison with other approaches. As a result, the pros and cons of the different algorithms are not well understood. Motivated by the need to accelerate progress in this very important area, we investigate the trade-offs of a number of different approaches based on Mixed Integer Programming, Satisfiability Modulo Theory, as well as a novel method based on the Branch-and-Bound framework. We also propose a new data set of benchmarks, in addition to a collection of pre- viously released testcases that can be used to compare existing methods. Our analysis not only allows a comparison to be made between different strategies, the comparison of results from different solvers also revealed implementation bugs in published methods. We expect that the availability of our benchmark and the analysis of the different approaches will allow researchers to develop and evaluate promising approaches for making progress on this important topic.
BIM AND AGENT-BASED MODEL INTEGRATION FOR CONSTRUCTION MANAGEMENT OPTIMIZATION

The current necessity of manage complexity in the field of building process management push to provide process' figures of construction methodologies and tools capable to support them in a proficient way. With the scope to define in advance the places occupied by workers to accomplish a task, is defined a methodology and related tools to integrate Building Information Modeling (BIM) with an Agent-Based simulation of workers activities. The goal is to know at early project phases where it is possible to work in a more effective and safer way, how it is possible to be more efficient placing in the same working space different working phases and when it is possible to allow the continuity of building operations. The outcome of the system is predicting how much resources are involved in a project, identifying and minimizing
SOCIAL CAPITAL : Its Origins and Applications in Modern Sociology

This paper reviews the origins and definitions of social capital in the writings of Bourdieu, Loury, and Coleman, among other authors. It distinguishes four sources of social capital and examines their dynamics. Applications of the concept in the sociological literature emphasize its role in social control, in family support, and in benefits mediated by extrafamilial networks. I provide examples of each of these positive functions. Negative consequences of the same processes also deserve attention for a balanced picture of the forces at play. I review four such consequences and illustrate them with relevant examples. Recent writings on social capital have extended the concept from an individual asset to a feature of communities and even nations. The final sections describe this conceptual stretch and examine its limitations. I argue that, as shorthand for the positive consequences of sociability, social capital has a definite place in sociological theory. However, excessive extensions of the concept may jeopardize its heuristic value.  Biographical Sketch Alejandro Portes is professor of sociology at Princeton University and faculty associate of the Woodrow Wilson School of Public Affairs. He formerly taught at Johns Hopkins where he held the John Dewey Chair in Arts and Sciences, Duke University, and the University of Texas-Austin. In 1997 he held the Emilio Bacardi distinguished professorship at the University of Miami. In the same year he was elected president of the American Sociological Association. Born in Havana, Cuba, he came to the United States in 1960. He was educated at the University of Havana, Catholic University of Argentina, and Creighton University. He received his MA and PhD from the University of Wisconsin-Madison. 0360-0572/98/0815-0001$08.00 1 A nn u. R ev . S oc io l. 19 98 .2 4: 124 . D ow nl oa de d fr om w w w .a nn ua lr ev ie w s. or g A cc es s pr ov id ed b y St an fo rd U ni ve rs ity M ai n C am pu s R ob er t C ro w n L aw L ib ra ry o n 03 /1 0/ 17 . F or p er so na l u se o nl y. Portes is the author of some 200 articles and chapters on national development, international migration, Latin American and Caribbean urbanization, and economic sociology. His most recent books include City on the Edge, the Transformation of Miami (winner of the Robert Park award for best book in urban sociology and of the Anthony Leeds award for best book in urban anthropology in 1995); The New Second Generation (Russell Sage Foundation 1996); Caribbean Cities (Johns Hopkins University Press); and Immigrant America, a Portrait. The latter book was designated as a centennial publication by the University of California Press. It was originally published in 1990; the second edition, updated and containing new chapters on American immigration policy and the new second generation, was published in 1996.
Model-based Software Testing

Software testing requires the use of a model to guide such efforts as test selection and test verification. Often, such models are implicit, existing only in the head of a human tester, applying test inputs in an ad hoc fashion. The mental model testers build encapsulates application behavior, allowing testers to understand the application’s capabilities and more effectively test its range of possible behaviors. When these mental models are written down, they become sharable, reusable testing artifacts. In this case, testers are performing what has become to be known as model-based testing. Model-based testing has recently gained attention with the popularization of models (including UML) in software design and development. There are a number of models of software in use today, a few of which make good models for testing. This paper introduces model-based testing and discusses its tasks in general terms with finite state models (arguably the most popular software models) as examples. In addition, advantages, difficulties, and shortcoming of various model-based approaches are concisely presented. Finally, we close with a discussion of where model-based testing fits in the present and future of software engineering.
Accurate rough terrain estimation with space-carving kernels

Accurate terrain estimation is critical for autonomous offroad navigation. Reconstruction of a 3D surface allows rough and hilly ground to be represented, yielding faster driving and better planning and control. However, data from a 3D sensor samples the terrain unevenly, quickly becoming sparse at longer ranges and containing large voids because of occlusions and inclines. The proposed approach uses online kernel-based learning to estimate a continuous surface over the area of interest while providing upper and lower bounds on that surface. Unlike other approaches, visibility information is exploited to constrain the terrain surface and increase precision, and an efficient gradient-based optimization allows for realtime implementation.
On the Emergence of Invariance and Disentangling in Deep Representations

We show that invariance in a deep neural network is equivalent to information minimality of the representation it computes, and that stacking layers and injecting noise during training naturally bias the network towards learning invariant representations. Then, we show that overfitting is related to the quantity of information stored in the weights, and derive a sharp bound between this information and the minimality and Total Correlation of the layers. This allows us to conclude that implicit and explicit regularization of the loss function not only help limit overfitting, but also foster invariance and disentangling of the learned representation. We also shed light on the properties of deep networks in relation to the geometry of the loss function.
Digital image hashing using local histogram of Oriented Gradients

The ease with which digital images can be manipulated without severe degradation of quality makes it necessary to be able to verify the authenticity of digital images. One way to establish the image authenticity is by computing a hash sequence from an image. This hash sequence must be robust against non content-altering manipulations, but must be able to show if the content of the image has been tampered with. Furthermore, the hash has to have enough differentiating power such that the hash sequences from two different images are not similar. This paper presents an image hashing system based on local Histogram of Oriented Gradients. The system is shown to have good differentiating power, robust against non content-altering manipulations such as filtering and JPEG compression and is sensitive to content-altering attacks.
Structural Deep Embedding for Hyper-Networks

Network embedding has recently attracted lots of attentions in data mining. Existing network embedding methods mainly focus on networks with pairwise relationships. In real world, however, the relationships among data points could go beyond pairwise, i.e., three or more objects are involved in each relationship represented by a hyperedge, thus forming hyper-networks. These hyper-networks pose great challenges to existing network embedding methods when the hyperedges are indecomposable, that is to say, any subset of nodes in a hyperedge cannot form another hyperedge. These indecomposable hyperedges are especially common in heterogeneous networks. In this paper, we propose a novel Deep Hyper-Network Embedding (DHNE) model to embed hypernetworks with indecomposable hyperedges. More specifically, we theoretically prove that any linear similarity metric in embedding space commonly used in existing methods cannot maintain the indecomposibility property in hypernetworks, and thus propose a new deep model to realize a non-linear tuplewise similarity function while preserving both local and global proximities in the formed embedding space. We conduct extensive experiments on four different types of hyper-networks, including a GPS network, an online social network, a drug network and a semantic network. The empirical results demonstrate that our method can significantly and consistently outperform the state-of-the-art algorithms.
Fabric Defect Detection Based on Wavelet Decomposition with One Resolution Level

According to the property of wavelet transform and fabric texture's Fourier spectrum, a new method for defect detection was presented. The proposed method is based on wavelet lifting transform with one resolution level. By using restoration scheme of the Fourier transform, the normal fabric textures of smooth sub-image in the spatial domain are removed by detecting the high-energy frequency components of sub-image in the Fourier domain, setting them to zero using frequency-domain filter, and back-transforming to a spatial domain sub-image. Then, the smooth and detail sub-images are segmented into many sub-windows, in which standard deviation are calculated as extracted features. The extracted features are compared with normal sub-window's features to determine whether there exists defect. Experimental results show that this method is validity and feasibility.
A Nation at Rest : The American Way of Homework

schooling were as universally endorsed as homework. Educators, parents, and policymakers of all political and pedagogical stripes insisted that homework is good and more is better—a view that was promoted most visibly in A Nation at Risk (National Commission on Excellence in Education, 1983) and What Works (U.S. Department of Education, 1986).1 Indeed, never in the history of American education was there a stronger professional and public consensus in favor of homework (see Gill & Schlossman, 1996; Gill & Schlossman, 2000). Homework has been touted for academic and character-building purposes, and for promoting America’s international competitiveness (see, e.g., Cooper, 2001; Keith, 1986; Maeroff, 1992; Maeroff, 1989; The Economist, 1995). It has been viewed as a key symbol, method, and yardstick of serious commitment to educational re-
Planarity Testing and Embedding

Universita’ di Roma Tre 3.1 Properties and Characterizations of Planar Graphs . . . . . 2 Basic Definitions • Properties • Characterizations 3.2 Planarity Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3.3 History of Planarity Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3.4 Common Algorithmic Techniques and Tools . . . . . . . . . . . . . 4 3.5 The Path-Addition Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Initialization Phase • Path Decomposition Phase • Path Embedding Phase 3.6 The Vertex-Addition Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Lempel, Even, and Cederbaum • PQ-Trees 3.7 The Block Embedding Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Shih and Hsu algorithm • Boyer and Myrvold algorithm