nils-herrmann/clean_scirepeval huggingface dataset:
A content-driven reputation system for the wikipedia

We present a content-driven reputation system for Wikipedia authors. In our system, authors gain reputation when the edits they perform to Wikipedia articles are preserved by subsequent authors, and they lose reputation when their edits are rolled back or undone in short order. Thus, author reputation is computed solely on the basis of content evolution; user-to-user comments or ratings are not used. The author reputation we compute could be used to flag new contributions from low-reputation authors, or it could be used to allow only authors with high reputation to contribute to controversialor critical pages. A reputation system for the Wikipedia could also provide an incentive for high-quality contributions. We have implemented the proposed system, and we have used it to analyze the entire Italian and French Wikipedias, consisting of a total of 691, 551 pages and 5, 587, 523 revisions. Our results show that our notion of reputation has good predictive value: changes performed by low-reputation authors have a significantly larger than average probability of having poor quality, as judged by human observers, and of being later undone, as measured by our algorithms.
Innovation : Mapping the winds of creative destruction *

This paper develops a framework for analyzing the competitive implications of innovation. The framework is based on the concept of transilience the capacity of an innovation to influence the established systems of production and marketing. Application of the concept results in a categorization of innovation into four types. Examples from the technical history of the US auto industry are used to illustrate the concepts and their applicability. The analysis shows that the categories of innovation are closely linked to different patterns of evolution and to different managerial environments. Special emphasis is placed on the role of incremental technical change in shaping competition and on the possibilities for a technology based reversal in the process of industrial maturity.
Transmission Lines Loaded With Pairs of Stepped Impedance Resonators: Modeling and Application to Differential Permittivity Measurements

Differential techniques are widely used in communication and sensor systems, as these techniques have been shown to improve the performance. This paper shows how differential sensing of permittivity can be conducted in a simple way. For that purpose, a microstrip line loaded with a pair of stepped-impedance resonators is used in two different resonator connections: parallel and cascade. Each resonator is individually perturbed dielectrically so that: 1) when the two individual permittivities are identical, the structure exhibits a single resonance frequency and 2) when the permittivities are different, resonance frequency splitting occurs, giving rise to two resonances (all these resonances are seen in the form of transmission zeroes). The two sensing approaches are successfully validated through electromagnetic simulations and experiments. By virtue of a differential measurement, robustness against changing ambient factors that may produce sensor miscalibration is expected.
The phone rings but the user doesn't answer: unavailability in mobile communication

We know that phone calls and mobile text messages are not always promptly answered and responded to, yet we know little about the reasons for unavailability, its effects on a user's image, the ways in which users explain the reasons for it, and actions when users cannot reach someone. Usage logs (2,983 phone use events), Web-based diaries, and interviews (N = 20) were used to investigate occasional unavailability in a mobile communication context. We identified four categories of unavailability and found that 31.1% of the phone calls consisted of unsuccessful communication attempts and reciprocal calls back from people who were unavailable earlier. Interestingly, while participants paid attention to the need to give reasons for unavailability, they did not require the explanations to be truthful. These findings have implications for design of systems that better support the needs to manage and explain unavailability and manage pending communication requests.
Code-Red: a case study on the spread and victims of an internet worm

On July 19, 2001, more than 359,000 computers connected to the Internet were infected with the Code-Red (CRv2) worm in less than 14 hours. The cost of this epidemic, including subsequent strains of Code-Red, is estimated to be in excess of $2.6 billion. Despite the global damage caused by this attack, there have been few serious attempts to characterize the spread of the worm, partly due to the challenge of collecting global information about worms. Using a technique that enables global detection of worm spread, we collected and analyzed data over a period of 45 days beginning July 2nd, 2001 to determine the characteristics of the spread of Code-Red throughout the Internet.In this paper, we describe the methodology we use to trace the spread of Code-Red, and then describe the results of our trace analyses. We first detail the spread of the Code-Red and CodeRedII worms in terms of infection and deactivation rates. Even without being optimized for spread of infection, Code-Red infection rates peaked at over 2,000 hosts per minute. We then examine the properties of the infected host population, including geographic location, weekly and diurnal time effects, top-level domains, and ISPs. We demonstrate that the worm was an international event, infection activity exhibited time-of-day effects, and found that, although most attention focused on large corporations, the Code-Red worm primarily preyed upon home and small business users. We also qualified the effects of DHCP on measurements of infected hosts and determined that IP addresses are not an accurate measure of the spread of a worm on timescales longer than 24 hours. Finally, the experience of the Code-Red worm demonstrates that wide-spread vulnerabilities in Internet hosts can be exploited quickly and dramatically, and that techniques other than host patching are required to mitigate Internet worms.
TRUSTED CODE EXECUTION ON UNTRUSTED PLATFORMS USING INTEL SGX

Today, isolated trusted computation and code execution is of paramount importance to protect sensitive information and workfl ows from other malicious privileged or unprivileged software. Intel Software Guard Extensions (SGX) is a set of security architecture extensions fi rst introduced in the Skylake microarchitecture that enables a Trusted Execution Environment (TEE). It provides an ‘inverse sandbox’, for sensitive programs, and guarantees the integrity and confi dentiality of secure computations, even from the most privileged malicious software (e.g. OS, hypervisor). SGX-capable CPUs only became available in production systems in Q3 2015, and they are not yet fully supported and adopted in systems. Besides the capability in the CPU, the BIOS also needs to provide support for the enclaves, and not many vendors have released the required updates for the system support. This has led to many wrong assumptions being made about the capabilities, features, and ultimately dangers of secure enclaves. By having access to resources and publications such as white papers, patents and the actual SGX-capable hardware and software development environment, we are in a privileged position to be able to investigate and demystify SGX. In this paper, we fi rst review the previous trusted execution technologies, such as ARM Trust Zone and Intel TXT, to better understand and appreciate the new innovations of SGX. Then, we look at the details of SGX technology, cryptographic primitives and the underlying concepts that power it, namely the sealing, attestation, and the Memory Encryption Engine (MEE). We also consider use cases such as trusted and secure code execution on an untrusted cloud platform, and digital rights management (DRM). This is followed by an overview of the software development environment and the available libraries.
High-dimensional covariance estimation by minimizing $\ell_1$-penalized log-determinant divergence

Given i.i.d. observations of a random vector X ∈ R, we study the problem of estimating both its covariance matrix Σ, and its inverse covariance or concentration matrix Θ = (Σ). When X is multivariate Gaussian, the non-zero structure of Θ is specified by the graph of an associated Gaussian Markov random field; and a popular estimator for such sparse Θ is the l1-regularized Gaussian MLE. This estimator is sensible even for for non-Gaussian X, since it corresponds to minimizing an l1-penalized log-determinant Bregman divergence. We analyze its performance under high-dimensional scaling, in which the number of nodes in the graph p, the number of edges s, and the maximum node degree d, are allowed to grow as a function of the sample size n. In addition to the parameters (p, s, d), our analysis identifies other key quantities that control rates: (a) the l∞-operator norm of the true covariance matrix Σ; and (b) the l∞ operator norm of the sub-matrix Γ∗SS , where S indexes the graph edges, and Γ∗ = (Θ) ⊗ (Θ); and (c) a mutual incoherence or irrepresentability measure on the matrix Γ∗ and (d) the rate of decay 1/f(n, δ) on the probabilities {|Σ̂ij − Σ∗ij | > δ}, where Σ̂ is the sample covariance based on n samples. Our first result establishes consistency of our estimate Θ̂ in the elementwise maximum-norm. This in turn allows us to derive convergence rates in Frobenius and spectral norms, with improvements upon existing results for graphs with maximum node degrees d = o( √ s). In our second result, we show that with probability converging to one, the estimate Θ̂ correctly specifies the zero pattern of the concentration matrix Θ∗. We illustrate our theoretical results via simulations for various graphs and problem parameters, showing good correspondences between the theoretical predictions and behavior in simulations. AMS 2000 subject classifications: Primary 62F12; secondary 62F30.
WebWitness: Investigating, Categorizing, and Mitigating Malware Download Paths

Most modern malware download attacks occur via the browser, typically due to social engineering and driveby downloads. In this paper, we study the “origin” of malware download attacks experienced by real network users, with the objective of improving malware download defenses. Specifically, we study the web paths followed by users who eventually fall victim to different types of malware downloads. To this end, we propose a novel incident investigation system, named WebWitness. Our system targets two main goals: 1) automatically trace back and label the sequence of events (e.g., visited web pages) preceding malware downloads, to highlight how users reach attack pages on the web; and 2) leverage these automatically labeled in-the-wild malware download paths to better understand current attack trends, and to develop more effective defenses. We deployed WebWitness on a large academic network for a period of ten months, where we collected and categorized thousands of live malicious download paths. An analysis of this labeled data allowed us to design a new defense against drive-by downloads that rely on injecting malicious content into (hacked) legitimate web pages. For example, we show that by leveraging the incident investigation information output by WebWitness we can decrease the infection rate for this type of drive-by downloads by almost six times, on average, compared to existing URL blacklisting approaches.
Automatic clustering of malware variants

The emergence of malware creation tools in recent years has facilitated the creation of new variations of existing malware instances. Typically, Anti-Virus companies process new malware instances manually to determine their maliciousness and generate their signatures. However, with the overwhelming number of new malware variants that are created automatically to evade pattern based detection, manual analysis is becoming a bottleneck that hinders the process of responding to new threats. This paper proposes a novel method to automatically cluster malware variants into malware families based on the structured control flow graphs of the malware instances. Our final results demonstrate high effectiveness in terms of accuracy, an average of %94 accuracy, and speed in clustering malware variants.
A History of Digital Forensics

The field of digital forensics is relatively new. While its history may be chronologically short, it is complex. This paper outlines the early history of digital forensics from the perspective of an early participant. The history is divided into four epochs: pre-history, infancy, childhood and adolescence. Each of these epochs is examined from the perspective of the people involved, the criminal targets, the forensic tools utilized, the organizational structures that supported digital forensic practitioners and how the community formed. This history is, by necessity, incomplete and biased. There is a need for rigorous historical research in this area before all traces of the past are forgotten or obliterated.
Variance Reduction for Stochastic Gradient Optimization

Stochastic gradient optimization is a class of widely used algorithms for training machine learning models. To optimize an objective, it uses the noisy gradient computed from the random data samples instead of the true gradient computed from the entire dataset. However, when the variance of the noisy gradient is large, the algorithm might spend much time bouncing around, leading to slower convergence and worse performance. In this paper, we develop a general approach of using control variate for variance reduction in stochastic gradient. Data statistics such as low-order moments (pre-computed or estimated online) is used to form the control variate. We demonstrate how to construct the control variate for two practical problems using stochastic gradient optimization. One is convex—the MAP estimation for logistic regression, and the other is non-convex—stochastic variational inference for latent Dirichlet allocation. On both problems, our approach shows faster convergence and better performance than the classical approach.
TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and Context

This paper details a new approach for learning a discriminative model of object classes, incorporating texture, layout, and context information efficiently. The learned model is used for automatic visual understanding and semantic segmentation of photographs. Our discriminative model exploits texture-layout filters, novel features based on textons, which jointly model patterns of texture and their spatial layout. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating the unary classifier in a conditional random field, which (i) captures the spatial interactions between class labels of neighboring pixels, and (ii) improves the segmentation of specific object instances. Efficient training of the model on large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy is demonstrated on four varied databases: (i) the MSRC 21-class database containing photographs of real objects viewed under general lighting conditions, poses and viewpoints, (ii) the 7-class Corel subset and (iii) the 7-class Sowerby database used in He et al. (Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 695–702, June 2004), and (iv) a set of video sequences of television shows. The proposed algorithm gives competitive and visually pleasing results for objects that are highly textured (grass, trees, etc.), highly structured (cars, faces, bicycles, airplanes, etc.), and even articulated (body, cow, etc.).
A state-of-the-art 3D sensor for robot navigation

This paper relates first experiences using a state-of-the-art, time-of-flight sensor that is able to deliver 3D images. The properties and capabilities of the sensor make it a potential powerful tool for applications within mobile robotics especially for real-time tasks, as the sensor features a frame rate of up to 30 frames per second. Its capabilities in terms of basic obstacle avoidance and local path-planning are evaluated and compared to the performance of a standard laser scanner.
Compositional dynamic test generation

Dynamic test generation is a form of dynamic program analysis that attempts to compute test inputs to drive a program along a specific program path. Directed Automated Random Testing, or DART for short, blends dynamic test generation with model checking techniques with the goal of systematically executing all feasible program paths of a program while detecting various types of errors using run-time checking tools (like Purify, for instance). Unfortunately, systematically executing all feasible program paths does not scale to large, realistic programs.This paper addresses this major limitation and proposes to perform dynamic test generation compositionally, by adapting known techniques for interprocedural static analysis. Specifically, we introduce a new algorithm, dubbed SMART for Systematic Modular Automated Random Testing, that extends DART by testing functions in isolation, encoding test results as function summaries expressed using input preconditions and output postconditions, and then re-using those summaries when testing higher-level functions. We show that, for a fixed reasoning capability, our compositional approach to dynamic test generation (SMART) is both sound and complete compared to monolithic dynamic test generation (DART). In other words, SMART can perform dynamic test generation compositionally without any reduction in program path coverage. We also show that, given a bound on the maximum number of feasible paths in individual program functions, the number of program executions explored by SMART is linear in that bound, while the number of program executions explored by DART can be exponential in that bound. We present examples of C programs and preliminary experimental results that illustrate and validate empirically these properties.
Requirements Engineering in Agile Software Development

The topic of this paper is requirements engineering (RE) in agile software development. The first part concentrates on the comparison of traditional requirements engineering approaches and agile software development. While traditional requirements engineering methods are often document-centric, agile methods often try to minimize documentation. We analyze commonalities and differences of both approaches and determine how agile software development can benefit from traditional engineering methods. The second part shows how one of these RE techniques can be integrated in a tool that supports distributed agile software development over the internet.
Which Predictability Measures Affect Content Word Durations

The pronunciation of a word can vary widely, and many factors are known to affect this variation. This paper focuses on the role of predictability on word duration. Previous research has suggested that more frequent words are shorter, as are words which are more predictable from neighboring words. This research has tended to focus only on extremely high frequency function words; previous research on content words has not been able to examine natural speech and control for key confounding factors like accent and rate of speech. We examined 1401 content words from the Switchboard corpus and studied the role of word frequency, conditional and joint probabilities with neighboring words and a measure of word semantic association, Latent Semantic Analysis. Using multiple regression to control for confounding factors, we show that two predictability variables principally affect a word’s duration—word frequency, and the conditional probability of a word given the following word. Other predictability variables do not make an additional significant contribution. We discuss the implications for pronunciation modeling.
Finding application errors and security flaws using PQL: a program query language

A number of effective error detection tools have been built in recent years to check if a program conforms to certain design rules. An important class of design rules deals with sequences of events asso-ciated with a set of related objects. This paper presents a language called PQL (Program Query Language) that allows programmers to express such questions easily in an application-specific context. A query looks like a code excerpt corresponding to the shortest amount of code that would violate a design rule. Details of the tar-get application's precise implementation are abstracted away. The programmer may also specify actions to perform when a match is found, such as recording relevant information or even correcting an erroneous execution on the fly.We have developed both static and dynamic techniques to find solutions to PQL queries. Our static analyzer finds all potential matches conservatively using a context-sensitive, flow-insensitive, inclusion-based pointer alias analysis. Static results are also use-ful in reducing the number of instrumentation points for dynamic analysis. Our dynamic analyzer instruments the source program to catch all violations precisely as the program runs and to optionally perform user-specified actions.We have implemented the techniques described in this paper and found 206 errors in 6 large real-world open-source Java applica-tions containing a total of nearly 60,000 classes. These errors are important security flaws, resource leaks, and violations of consis-tency invariants. The combination of static and dynamic analysis proves effective at addressing a wide range of debugging and pro-gram comprehension queries. We have found that dynamic analysis is especially suitable for preventing errors such as security vulner-abilities at runtime.
Design Considerations and Performance Evaluation of Single-Stage TAIPEI Rectifier for HVDC Distribution Applications

Design considerations and performance evaluations of a three-phase, four-switch, single-stage, isolated zero-voltage-switching (ZVS) rectifier are presented. The circuit is obtained by integrating the three-phase, two-switch, ZVS, discontinuous-current-mode (DCM), boost power-factorcorrection (PFC) rectifier, named for short the TAIPEI rectifier, with the ZVS full-bridge (FB) phase-shift dc/dc converter. The performance was evaluated on a three-phase 2.7-kW prototype designed for HVDC distribution applications with the line-toline voltage range from 180 VRMS to 264 VRMS and with a tightly regulated variable dc output voltage from 200 V to 300 V. The prototype operates with ZVS over the entire input-voltage and load-current range and achieves less than 5% input-current THD with the efficiency in the 95% range.
Male and Female Spoken Language Differences : Stereotypes and Evidence

Male speech and female speech have been observed to differ in their form, topic, content, and use. Early writers were largely introspective in their analyses; more recent work has begun to provide empirical evidence. Men may be more loquacious and directive; they use more nonstandard forms, talk more about sports, money, and business, and more frequently refer to time, space, quantity, destructive action, perceptual attributes, physical movements, and objects. Women are often more supportive, polite, and expressive, talk more about home and family, and use more words implying feeling, evaluation, interpretation, and psychological state. A comprehensive theory of "genderlect" must include information about linguistic features under a multiplicity of conditions.