nils-herrmann/clean_scirepeval huggingface dataset:
A Control Theory Perspective on Agile Methodology Use and Changing User Requirements

I this paper, we draw on control theory to understand the conditions under which the use of agile practices is most effective in improving software project quality. Although agile development methodologies offer the potential of improving software development outcomes, limited research has examined how project managers can structure the software development environment to maximize the benefits of agile methodology use during a project. As a result, project managers have little guidance on how to manage teams who are using agile methodologies. Arguing that the most effective control modes are those that provide teams with autonomy in determining the methods for achieving project objectives, we propose hypotheses related to the interaction between control modes, agile methodology use, and requirements change. We test the model in a field study of 862 software developers in 110 teams. The model explains substantial variance in four objective measures of project quality—bug severity, component complexity, coordinative complexity, and dynamic complexity. Results largely support our hypotheses, highlighting the interplay between project control, agile methodology use, and requirements change. The findings contribute to extant literature by integrating control theory into the growing literature on agile methodology use and by identifying specific contingencies affecting the efficacy of different control modes. We discuss the theoretical and practical implications of our results.
An iCRISPR platform for rapid, multiplexable, and inducible genome editing in human pluripotent stem cells.

Human pluripotent stem cells (hPSCs) offer a unique platform for elucidating the genes and molecular pathways that underlie complex traits and diseases. To realize this promise, methods for rapid and controllable genetic manipulations are urgently needed. By combining two newly developed gene-editing tools, the TALEN and CRISPR/Cas systems, we have developed a genome-engineering platform in hPSCs, which we named iCRISPR. iCRISPR enabled rapid and highly efficient generation of biallelic knockout hPSCs for loss-of-function studies, as well as homozygous knockin hPSCs with specific nucleotide alterations for precise modeling of disease conditions. We further demonstrate efficient one-step generation of double- and triple-gene knockout hPSC lines, as well as stage-specific inducible gene knockout during hPSC differentiation. Thus the iCRISPR platform is uniquely suited for dissection of complex genetic interactions and pleiotropic gene functions in human disease studies and has the potential to support high-throughput genetic analysis in hPSCs.
Towards multiobjective procedural map generation

A search-based procedural content generation (SBPCG) algorithm for strategy game maps is proposed. Two representations for strategy game maps are devised, along with a number of objectives relating to predicted player experience. A multiobjective evolutionary algorithm is used for searching the space of maps for candidates that satisfy pairs of these objectives. As the objectives are inherently partially conflicting, the algorithm generates Pareto fronts showing how these objectives can be balanced. Such fronts are argued to be a valuable tool for designers looking to balance various design needs. Choosing appropriate points (manually or automatically) on the Pareto fronts, maps can be found that exhibit good map design according to specified criteria, and could either be used directly in e.g. an RTS game or form the basis for further human design.
Computational Text Analysis for Social Science: Model Assumptions and Complexity

Across many disciplines, interest is increasing in the use of computational text analysis in the service of social science questions. We survey the spectrum of current methods, which lie on two dimensions: (1) computational and statistical model complexity; and (2) domain assumptions. This comparative perspective suggests directions of research to better align new methods with the goals of social scientists. 1 Use cases for computational text analysis in the social sciences The use of computational methods to explore research questions in the social sciences and humanities has boomed over the past several years, as the volume of data capturing human communication (including text, audio, video, etc.) has risen to match the ambitious goal of understanding the behaviors of people and society . Automated content analysis of text, which draws on techniques developed in natural language processing, information retrieval, text mining, and machine learning, should be properly understood as a class of quantitative social science methodologies. Employed techniques range from simple analysis of comparative word frequencies to more complex hierarchical admixture models. As this nascent field grows, it is important to clearly present and characterize the assumptions of techniques currently in use, so that new practitioners can be better informed as to the range of available models. To illustrate the breadth of current applications, we list a sampling of substantive questions and studies that have developed or applied computational text analysis to address them. •  How do U.S. Senate speeches reflect agendas and attention? How are Senate institutions changing ? What are the agendas expressed in Senators’ press releases [28]? Do U.S. Supreme Court oral arguments predict justices’ voting behavior [29]? Does social media reflect public political opinion, or forecast elections [12, 30]? What determines international conflict and cooperation ? How much did racial attitudes affect voting in the 2008 U.S. presidential election [34]?  How does sentiment in the media affect the stock market [2, 3]? Does sentiment in social media associate with stocks [4, 5, 6]? Do a company’s SEC filings predict aspects of stock performance [7, 8]? What determines a customer’s trust in an online merchant ? How can we measure macroeconomic variables with search queries and social media text [10, 11, 12]? How can we forecast consumer demand for movies [13, 14]? • Psychology: How does a person’s mental and affective state manifest in their language ? Are diurnal and seasonal mood cycles cross-cultural [16]?
CryptoPage: An Efficient Secure Architecture with Memory Encryption, Integrity and Information Leakage Protection

Several secure computing hardware architectures using memory encryption and memory integrity checkers have been proposed during the past few years to provide applications with a tamper resistant environment. Some solutions, such as Hide, have also been proposed to solve the problem of information leakage on the address bus. We propose the CryptoPage architecture which implements memory encryption, memory integrity protection checking and information leakage protection together with a low performance penalty (3% slowdown on average) by combining the counter mode of operation, local authentication values and Merkle trees
A systematic literature review of software requirements prioritization research

During requirements engineering, prioritization is performed to grade or rank requirements in their order of importance and subsequent implementation releases. It is a major step taken in making crucial decisions so as to increase the economic value of a system.  The purpose of this study is to identify and analyze existing prioritization techniques in the context of the formulated research questions.  Search terms with relevant keywords were used to identify primary studies that relate requirements prioritization classified under journal articles, conference papers, workshops, symposiums, book chapters and IEEE bulletins.  73 Primary studies were selected from the search processes. Out of these studies; 13 were journal articles, 35 were conference papers and 8 were workshop papers. Furthermore, contributions from symposiums as well as IEEE bulletins were 2 each while the total number of book chapters amounted to 13.  Prioritization has been significantly discussed in the requirements engineering domain. However , it was generally discovered that, existing prioritization techniques suffer from a number of limitations which includes: lack of scalability, methods of dealing with rank updates during requirements evolution, coordination among stakeholders and requirements dependency issues. Also, the applicability of existing techniques in complex and real setting has not been reported yet.
Fully Automated Laser Range Calibration

We present a novel method for fully automated exterior calib r t on of a 2D scanning laser range sensor that attains accurate pose wi th respect to a fixed 3D reference frame. This task is crucial for applicatio ns that attempt to recover self-consistent 3D environment maps and produce ac cur tely registered or fused sensor data. A key contribution of our approach lies in the design of a clas s of calibration target objects whose pose can be reliably recogniz ed from a single observation (i.e. from one 2D range data stripe). Unlike oth er echniques, we do not require simultaneous camera views or motion of the sen sor, making our approach simple, flexible and environment-independent . In this paper we illustrate the target geometry and derive th e relationship between a single 2D range scan and the 3D sensor pose. We descr ibe an algorithm for closed-form solution of the 6 DOF pose that minimiz es an algebraic error metric, and an iterative refinement scheme that subseq ently minimizes geometric error. Finally, we report performance and stabil ity of our technique on synthetic and real data sets, and demonstrate accuracy wi thin 1 degree of orientation and 3 cm of position in a realistic configuration .
Approximate fair bandwidth allocation: A method for simple and flexible traffic management

The Internet architecture uses congestion avoidance mechanisms implemented in the transport layer protocol like TCP to provide good service under heavy load. If network nodes distribute bandwidth fairly, the Internet would be more robust and accommodate a wide variety of applications. Various congestion and bandwidth management schemes have been proposed for this purpose and can be classified into two broad categories: packet scheduling algorithms such as fair queueing (FQ) which explicitly provide bandwidth shares by scheduling packets. They are more difficult to implement compared to FIFO queueing. The second category has active queue management schemes such as RED which use FIFO queues at the routers. They are easy to implement but don't aim to provide (and, in the presence of non-congestion-responsive sources, don't provide) fairness. An algorithm called AFD (approximate fair dropping), has been proposed to provide approximate, weighted max-min fair bandwidth allocations with relatively low complexity. AFD has since been widely adopted by the industry. This paper describes the evolution of AFD from a research project into an industry setting, focusing on the changes it has undergone in the process. AFD now serves as a traffic management module, which can be implemented either using a single FIFO or overlaid on top of extant per-flow queueing structures and which provides approximate bandwidth allocation in a simple fashion. The AFD algorithm has been implemented in several switch and router platforms at Cisco sytems, successfully transitioning from the academic world into the industry.
A smartphone-based sensing platform to model aggressive driving behaviors

Driving aggressively increases the risk of accidents. Assessing a person's driving style is a useful way to guide aggressive drivers toward having safer driving behaviors. A number of studies have investigated driving style, but they often rely on the use of self-reports or simulators, which are not suitable for the real-time, continuous, automated assessment and feedback on the road. In order to understand and model aggressive driving style, we construct an in-vehicle sensing platform that uses a smartphone instead of using heavyweight, expensive systems. Utilizing additional cheap sensors, our sensing platform can collect useful information about vehicle movement, maneuvering and steering wheel movement. We use this data and apply machine learning to build a driver model that evaluates drivers' driving styles based on a number of driving-related features. From a naturalistic data collection from 22 drivers for 3 weeks, we analyzed the characteristics of drivers who have an aggressive driving style. Our model classified those drivers with an accuracy of 90.5% (violation-class) and 81% (questionnaire-class). We describe how, in future work, our model can be used to provide real-time feedback to drivers using only their current smartphone.
THE PRIMES CONTAIN ARBITRARILY LONG ARITHMETIC PROGRESSIONS

We prove that there are arbitrarily long arithmetic progressions of primes. There are three major ingredients.   for all x ∈ ZN (here (m0, t0, L0) = (3, 2, 1)) and E ( ν((x− y)/2)ν((x− y + h2)/2)ν(−y)ν(−y − h1)× × ν((x− y′)/2)ν((x− y′ + h2)/2)ν(−y)ν(−y − h1)× × ν(x)ν(x + h1)ν(x + h2)ν(x + h1 + h2) ∣∣∣∣ x, h1, h2, y, y′ ∈ ZN) = 1 + o(1) (0.1) (here (m0, t0, L0) = (12, 5, 2)).  Proposition 0.1 (Generalised von Neumann). Suppose that ν is k-pseudorandom. Let f0, . . . , fk−1 ∈ L(ZN) be functions which are pointwise bounded by ν+νconst, or in other words |fj(x)| 6 ν(x) + 1 for all x ∈ ZN , 0 6 j 6 k − 1. (0.2) Let c0, . . . , ck−1 be a permutation of {0, 1, . . . , k − 1} (in practice we will take cj := j). Then E ( k−1 ∏ j=0 fj(x + cjr) ∣∣∣∣ x, r ∈ ZN) = O( inf 06j6k−1 ‖fj‖Uk−1) + o(1).
Robust Photometric Stereo via Low-Rank Matrix Completion and Recovery

We present a new approach to robustly solve photometric stereo problems. We cast the problem of recovering surface normals from multiple lighting conditions as a problem of recovering a low-rank matrix with both missing entries and corrupted entries, which model all types of non-Lambertian effects such as shadows and specularities. Unlike previous approaches that use Least-Squares or heuristic robust techniques, our method uses advanced convex optimization techniques that are guaranteed to find the correct low-rank matrix by simultaneously fixing its missing and erroneous entries. Extensive experimental results demonstrate that our method achieves unprecedentedly accurate estimates of surface normals in the presence of significant amount of shadows and specularities. The new technique can be used to improve virtually any photometric stereo method including uncalibrated photometric stereo.
Robust Image Hashing With Ring Partition and Invariant Vector Distance

Robustness and discrimination are two of the most important objectives in image hashing. We incorporate ring partition and invariant vector distance to image hashing algorithm for enhancing rotation robustness and discriminative capability. As ring partition is unrelated to image rotation, the statistical features that are extracted from image rings in perceptually uniform color space, i.e., CIE L*a*b* color space, are rotation invariant and stable. In particular, the Euclidean distance between vectors of these perceptual features is invariant to commonly used digital operations to images (e.g., JPEG compression, gamma correction, and brightness/contrast adjustment), which helps in making image hash compact and discriminative. We conduct experiments to evaluate the efficiency with 250 color images, and demonstrate that the proposed hashing algorithm is robust at commonly used digital operations to images. In addition, with the receiver operating characteristics curve, we illustrate that our hashing is much better than the existing popular hashing algorithms at robustness and discrimination.
High-level scene perception.

Three areas of high-level scene perception research are reviewed. The first concerns the role of eye movements in scene perception, focusing on the influence of ongoing cognitive processing on the position and duration of fixations in a scene. The second concerns the nature of the scene representation that is retained across a saccade and other brief time intervals during ongoing scene perception. Finally, we review research on the relationship between scene and object identification, focusing particularly on whether the meaning of a scene influences the identification of constituent objects.
Text detection in stores using a repetition prior

Text detection in stores has valuable applications that could transform the shopping experience, yet cluttered store environments present distinct challenges for existing techniques. We propose a strategy for text detection in stores that exploits a repetition prior. Leveraging the fact that shops typically display multiple instances of the same product on the shelf, our approach localizes text regions with a global view of the image, preferring instances that have repeated support in the scene. On two challenging real-world datasets taken with a mobile phone and wearable camera, we demonstrate our method's substantial advantages compared to several state-of-the-art techniques in grocery store environments.
Decimal Floating-Point: Algorism for Computers

Origins of decimal arithmetic • Decimal (base 10) arithmetic has been used for thousands of years • Algorism (Indo-Arabic place value system) in use since 800 AD • Many early computers were decimal Decimal computation today • Pervasive in financial, commercial, and human-centric applications – often a legal requirement • Benchmarks do not reflect actual use • 55% of numeric data in databases are decimal (and a further 43% integers)
Goals as Excuses or Guides : The Liberating Effect of Perceived Goal Progress on Choice

Consumer choices are often driven by multiple goals (e.g., career and family), each of which if viewed in isolation may appear to suggest conflicting choices. This article examines the effect of initial goal pursuit on consumers’ interest in pursuing unrelated or even conflicting goals. Four studies were conducted to test whether perceived goal progress hinders the pursuit of the focal goal. These studies demonstrate that in the course of self-regulation progress along one goal liberates people to pursue inconsistent goals. Furthermore, merely planning to make goal progress in the future may facilitate incongruent choice of immediate action.
Analysis of Deep Neural Networks with Extended Data Jacobian Matrix

Deep neural networks have achieved great success on a variety of machine learning tasks. There are many fundamental and open questions yet to be answered, however. We introduce the Extended Data Jacobian Matrix (EDJM) as an architecture-independent tool to analyze neural networks at the manifold of interest. The spectrum of the EDJM is found to be highly correlated with the complexity of the learned functions. After studying the effect of dropout, ensembles, and model distillation using EDJM, we propose a novel spectral regularization method, which improves network performance.
The Multilingual Paraphrase Database

We release a massive expansion of the paraphrase database (PPDB) that now includes a collection of paraphrases in 23 different languages. The resource is derived from large volumes of bilingual parallel data. Our collection is extracted and ranked using state of the art methods. The multilingual PPDB has over a billion paraphrase pairs in total, covering the following languages: Arabic, Bulgarian, Chinese, Czech, Dutch, Estonian, Finnish, French, German, Greek, Hungarian, Italian, Latvian, Lithuanian, Polish, Portugese, Romanian, Russian, Slovak, Slovenian, and Swedish.
Pendulum-balanced autonomous unicycle: Conceptual design and dynamics model

A new mechanism to balance an autonomous unicycle is explored which makes use of a simple pendulum. Mounted laterally on the unicycle chassis, the pendulum provides a means of controlling the unicycle balance in the lateral (left-right) direction. Longitudinal (forward-backward) balance is achieved by controlling the unicycle wheel, a mechanism exactly the same as that of wheeled inverted pendulum. In this paper, the pendulum-balancing concept is explained and the dynamics model of an autonomous unicycle balanced by such mechanism is derived by Lagrange-Euler formulation. The behavior is analyzed by dynamic simulation in MATLAB. Dynamics comparison with wheeled inverted pendulum and Acrobot is also performed.
Emotions and Personality in Adaptive e-Learning Systems: An Affective Computing Perspective

This chapter reports how affective computing (in terms of detection methods and intervention approaches) is considered in e-learning systems. The goal behind is to enrich the personalized support provided in online educational settings by taking into account the influence that emotions and personality have in the learning process. The main contents of the chapter consist in the review of 26 works that present current research trends regarding the detection of the learners’ affective states and the delivery of the appropriate affective support in diverse educational settings. In addition, the chapter discusses open issues regarding affective computing in the educational domain.
Bayesian Poker

Poker is ideal for testing automated reason­ ing under uncertainty. It introduces un­ certainty both by physical randomization and by incomplete information about op­ ponents' hands. Another source of uncer­ tainty is the limited information available to construct psychological models of opponents, their tendencies to bluff, play conservatively, reveal weakness, etc. and the relation be­ tween their hand strengths and betting be­ haviour. All of these uncertainties must be assessed accurately and combined effectively for any reasonable level of skill in the game to be achieved, since good decision making is highly sensitive to those tasks. We de­ scribe our Bayesian Poker Program (BPP) , which uses a Bayesian network to model the program's poker hand, the opponent's hand and the opponent's playing behaviour con­ ditioned upon the hand, and betting curves which govern play given a probability of win­ ning. The history of play with opponents is used to improve BPP's understanding of their behaviour. We compare BPP experimentally with: a simple rule-based system; a program which depends exclusively on hand probabil­ ities (i.e., without opponent modeling); and with human players. BPP has shown itself to be an effective player against all these opponents, barring the better humans. We also sketch out some likely ways of improv­ ing play.
Knowing What a User Likes: A Design Science Approach to Interfaces that Automatically Adapt to Culture

The adaptation ontology (shown in Figure A1) defines an element’s possible placement areas within the UI as well as its minimum and maximum size. Its main element is the class UserInterface, which defines general layout characteristics such as the colorfulness, color saturation, and alignment of the interface. It also specifies which UI element is currently used with the datatype property isUsed. The UI is further divided into the disjoint subclasses Header, Content, and Footer. The class Header generally describes the top part of a web page, which usually features a logo, a menu, and sometimes breadcrumbs showing the exact position within the hierarchy of web pages. The class Content can be divided into the disjoint subclasses Navigation, which contains several individuals such as a tree navigation, or a flat, nonhierarchical navigation, and WorkArea. The latter describes the part of the web page where the content is being presented, and this presentation can be adapted with different levels of information density, guidance, and accessibilities of functions. Additionally, the look and feel of the Navigation and WorkArea changes according to various characteristics inherited from the classes Content and UserInterface.