nils-herrmann/clean_scirepeval huggingface dataset:
PCG-based game design: creating Endless Web

This paper describes the creation of the game Endless Web, a 2D platforming game in which the player's actions determine the ongoing creation of the world she is exploring. Endless Web is an example of a PCG-based game: it uses procedural content generation (PCG) as a mechanic, and its PCG system, Launchpad, greatly influenced the aesthetics of the game. All of the player's strategies for the game revolve around the use of procedural content generation. Many design challenges were encountered in the design and creation of Endless Web, for both the game and modifications that had to be made to Launchpad. These challenges arise largely from a loss of fine-grained control over the player's experience; instead of being able to carefully craft each element the player can interact with, the designer must instead craft algorithms to produce a range of content the player might experience. In this paper we provide a definition of PCG-based game design and describe the challenges faced in creating a PCG-based game. We offer our solutions, which impacted both the game and the underlying level generator, and identify issues which may be particularly important as this area matures.
A Software-Based Sonar Ranging Sensor for Smart Phones

We live in a 3-D world. However, the smart phones that we use every day are incapable of sensing depth, without the use of custom hardware. By creating new depth sensors, we can provide developers with the tools that they need to create immersive mobile applications that take advantage of the 3-D nature of our world. In this paper, we propose a new sonar sensor for smart phones. This sonar sensor does not require any additional hardware, and utilizes the phone's microphone and rear speaker. The sonar sensor calculates distances by measuring the elapsed time between the initial pulse and its reflection. We evaluate the accuracy of the sonar sensor by using it to measure the distance from the phone to an object. We found that we were able to measure the distances of objects accurately with an error bound of 12 cm.
SemEval 2016 Task 11: Complex Word Identification

We report the findings of the Complex Word Identification task of SemEval 2016. To create a dataset, we conduct a user study with 400 non-native English speakers, and find that complex words tend to be rarer, less ambiguous and shorter. A total of 42 systems were submitted from 21 distinct teams, and nine baselines were provided. The results highlight the effectiveness of Decision Trees and Ensemble methods for the task, but ultimately reveal that word frequencies remain the most reliable predictor of word complexity.
USARSim: a robot simulator for research and education

This paper presents USARSim, an open source high fidelity robot simulator that can be used both for research and education. USARSim offers many characteristics that differentiate it from most existing simulators. Most notably, it constitutes the simulation engine used to run the virtual robots competition within the Robocup initiative. We describe its general architecture, describe examples of utilization, and provide a comprehensive overview for those interested in robot simulations for education, research and competitions.
Disclosure , Liquidity , and the Cost of Capital

This paper shows that revealing public information to reduce information asymmetry can reduce a firm's cost of capital by attracting increased demand from large investors due to increased liquidity of its securities. Large firms will disclose more information since they benefit most. Disclosure also reduces the risk bearing capacity available through market makers. If initial information asymmetry is large, reducing it will increase the current price of the security. However, the maximum current price occurs with some asymmetry of information: further reduction of information asymmetry accentuates the undesirable effects of exit from market making. THIS PAPER STUDIES THE causes and consequences of a security's liquidity, especially the effect of future liquidity on the security's current price-equivalently the effect on its required expected rate of return, its cost of capital. Under conditions that we identify, reducing information asymmetry reduces the cost of capital. Under other (less typical) conditions, this reduced information asymmetry can have the opposite effect. We use public disclosure of information as the means of changing information asymmetry, but the points are more general. Our model is related to those of Kyle (1985), Glosten and Milgrom (1985), and Admati and Pfleiderer (1988). They assume that there is unlimited risk bearing capacity devoted to market making, which implies that changes in future liquidity never influence a security's cost of capital.1 In contrast, we develop a model of trade in an illiquid market with limited risk bearing capacity of risk-averse market makers and examine the effects of private information on the incentives of market makers to provide risk bearing *The University of Chicago and the University of Pennsylvania, respectively. Diamond gratefully acknowledges support from NSF grant SES-8896223 and from a gift to the University of Chicago from Dimensional Fund Advisors. Verrecchia gratefully acknowledges financial assistance from Ernst & Young and the Wharton School of the University of Pennsylvania. We are grateful to Franklin Allen, Ren6 Stulz, Robert Vishny, an anonymous referee, and workshop participants at the University of Chicago, the Federal Reserve Bank of Richmond, Georgetown University, and UCLA for helpful comments on a previous draft. 'Given the assumption, one could even close the secondary market for an arbitrarily long time in the future without influencing the current price. As a security becomes less liquid in these models, more of the security is permanently held by the market makers who do not care about liquidity.
Mixed state discrimination using optimal control.

We present theory and experiment for the task of discriminating two nonorthogonal states, given multiple copies. We implement several local measurement schemes, on both pure states and states mixed by depolarizing noise. We find that schemes which are optimal (or have optimal scaling) without noise perform worse with noise than simply repeating the optimal single-copy measurement. Applying optimal control theory, we derive the globally optimal local measurement strategy, which outperforms all other local schemes, and experimentally implement it for various levels of noise.
A Computational Approach to Edge Detection

This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge.
Domestic violence and psychological well-being of survivor women in Punjab , Pakistan

Violence against women is becoming a very critical issue, especially domestic violence. Public health practitioners of developing countries over the years had been deeply concern with trying to study and mitigate the impact of this on female survivors. It is defined as a gender based violence that results in, or is likely to result in, physical, sexual or mental harm /suffering to women, including threat of such acts, coercion or arbitrary deprivation of liberty, whether occurring in public or in private life.1,2 Domestic violence is a global issue and commonly used to describe the “women abuse” those who suffer at the hands of their male partner, because wife abused by husband is the most common form of violence against women (e.g. wife beating, burning of women, acid throwing)3−6 and according to the American Medical Association domestic violence is perceived as a pattern of physical, sexual and/or psychological abuse by a person with whom the survivor had an intimate relationship.7−10 Domestic violence is also considered as an important cause of mortality and morbidity of women in every country where these associations have been  It is considered as an important cause of intentional injuries in women, which consequently reduce women’s confidence by decreasing their desire to participate fully in  It occurs in almost all social and economic classes, races and religions in multiple patterns/trends however, not a single society could be declared free of this notion.15
Multi-label learning: a review of the state of the art and ongoing research

Multi-label learning is quite a recent supervised learning paradigm. Owing to its capabilities to improve performance in problems where a pattern may have more than one associated class, it has attracted the attention of researchers, producing an increasing number of publications. The paper presents an up-todate overview about multi-label learning with the aim of sorting and describing the main approaches developed till now. The formal definition of the paradigm, the analysis of its impact on the literature, its main applications, works developed and ongoing research are presented.
Applying Organizational Routines in understanding organizational change

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Applying Organizational Routines in understanding organizational change Markus Becker, Nathalie Lazaric, Richard Nelson, Sidney G. Winter
Q # : A Quantum Programming Language by Microsoft

The multi-paradigm quantum programming language Q# was analysed and used to study and create novel programs that were able to go beyond the capabilities of any classical program. This language was built by Microsoft to succeed LIQUi|> and is a Domain-Specific Language (DSL) that can be used within Microsoft’s Quantum Development Kit (QDK). The quantum programs are run on a quantum simulator which possesses properties that a real quantum computer would from a behavioural aspect. It uses the .NET Code SDK, allowing for easy creation, building and running of quantum projects via the command line. Initially, the main features and libraries available were studied and experimented with by analysing implementations of the Quantum Teleportation and Deutsch-Jozsa algorithms. Thereafter, an algorithm to solve an arbitrary 2 × 2 matrix system of linear equations was implemented, demonstrating a theoretical quantum speed-up with time complexities of order O(logN) compared to the classical O(N) for sparse matrices. Running the algorithm for a particular matrix achieved results that were within the range of theoretical predictions. Subsequently, as an extension to the project, concepts within Quantum Game Theory were explored. This led to the Mermin-Peres Magic Square game successfully being simulated in Q#; a game where no classical winning strategy exists, yet a quantum strategy is able to win in all possible cases of the game.
A Memory Soft Error Measurement on Production Systems

Memory state can be corrupted by the impact of particles causing single-event upsets (SEUs). Understanding and dealing with these soft (or transient) errors is important for system reliability. Several earlier studies have p rovided field test measurement results on memory soft error rate, but no results were available for recent production computer systems. We believe the measurement results on real production systems are uniquely valuable due to various environmental effects. This paper presents methodologies for memory soft error measurement on production systems where performance impact on existing running applications must be negligible and the system administrativ e control might or might not be available. We conducted measurements in three distinct system environments: a rack-mounted server farm for a popular Internet service (Ask.com search engine), a set of office desktop computers (Univ. of Rochester), and a geographically distributed network testbed (PlanetLab). Our preliminary measurement on over 300 machines for varying multi-month periods finds 2 suspected soft errors. In particular, our result on the Internet servers indicates that, with high probability, the soft error rate is at least two orders of magnitude lower than those reported previously. We provide discussions that attribute the low error rate to several factors in today’s production system environments . As a contrast, our measurement unintentionally discovers permanent (or hard) memory faults on 9 out of 212 Ask.com machines, suggesting the relative commonness of
Towards a benchmark for RGB-D SLAM evaluation

We provide a large dataset containing RGB-D image sequences and the ground-truth camera trajectories with the goal to establish a benchmark for the evaluation of visual SLAM systems. Our dataset contains the color and depth images of a Microsoft Kinect sensor and the groundtruth trajectory of camera poses. The data was recorded at full frame rate (30 Hz) and sensor resolution (640x480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz). Further, we provide the accelerometer data from the Kinect. Finally, we propose an evaluation criterion for measuring the quality of the estimated camera trajectory of visual SLAM systems.
Fake News Detection on Social Media using Geometric Deep Learning

Social media are nowadays one of the main news sources for millions of people around the globe due to their low cost, easy access, and rapid dissemination. This however comes at the cost of dubious trustworthiness and significant risk of exposure to ‘fake news’, intentionally written to mislead the readers. Automatically detecting fake news poses challenges that defy existing content-based analysis approaches. One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or ‘common sense’, which current natural language processing algorithms are still missing. Recent studies have empirically shown that fake and real news spread differently on social media, forming propagation patterns that could be harnessed for the automatic fake news detection. Propagation-based approaches have multiple advantages compared to their content-based counterparts, among which is language independence and better resilience to adversarial attacks. In this paper, we show a novel automatic fake news detection model based on geometric deep learning. The underlying core algorithms are a generalization of classical convolutional neural networks to graphs, allowing the fusion of heterogeneous data such as content, user profile and activity, social graph, and news propagation. Our model was trained and tested on news stories, verified by professional fact-checking organizations, that were spread on Twitter. Our experiments indicate that social network structure and propagation are important features allowing highly accurate (92.7% ROC AUC) fake news detection. Second, we observe that fake news can be reliably detected at an early stage, after just a few hours of propagation. Third, we test the aging of our model on training and testing data separated in time. Our results point to the promise of propagation-based approaches for fake news detection as an alternative or complementary strategy to content-based approaches. ar X iv :1 90 2. 06 67 3v 1 [ cs .S I] 1 0 Fe b 20 19
Incremental visual text analytics of news story development

Online news sources produce thousands of news articles every day, reporting on local and global real-world events. New information quickly replaces the old, making it difficult for readers to put current events in the context of the past. The stories about these events have complex relationships and characteristics that are difficult to model: they can be weakly or strongly related or they can merge or split over time. In this article, we present a visual analytics system for temporal analysis of news stories in dynamic information streams, which combines interactive visualization and text mining techniques to facilitate the analysis of similar topics that split and merge over time. Text clustering algorithms extract stories from online news streams in consecutive time windows and identify similar stories from the past. The stories are displayed in a visualization, which (1) sorts the stories by minimizing clutter and overlap from edge crossings, (2) shows their temporal characteristics in different time frames with different levels of detail, and (3) allows incremental updates of the display without recalculating the past data. Stories can be interactively filtered by their duration and connectivity in order to be explored in full detail. To demonstrate the system’s capabilities for detailed dynamic text stream exploration, we present a use case with real news data about the Arabic Uprising in 2011.
Major life changes and behavioral markers in social media: case of childbirth

We explore the harnessing of social media as a window on changes around major life events in individuals and larger populations. We specifically examine patterns of activity, emotional, and linguistic correlates for childbirth and postnatal course. After identifying childbirth events on Twitter, we analyze daily posting patterns and language usage before and after birth by new mothers, and make inferences about the status and dynamics of changes in emotions expressed following childbirth. We find that childbirth is associated with some changes for most new mothers, but approximately 15% of new mothers show significant changes in their online activity and emotional expression postpartum. We observe that these mothers can be distinguished by linguistic changes captured by shifts in a relatively small number of words in their social media posts. We introduce a greedy differencing procedure to identify the type of language that characterizes significant changes in these mothers during postpartum. We conclude with a discussion about how such characterizations might be applied to recognizing and understanding health and well-being in women following childbirth.
Sentiment Analysis in MOOC Discussion Forums: What does it tell us?

Sentiment analysis is one of the great accomplishments of the last decade in the field of Language Technologies. In this paper, we explore mining collective sentiment from forum posts in a Massive Open Online Course (MOOC) in order to monitor students’ trending opinions towards the course and major course tools, such as lecture and peer-assessment. We observe a correlation between sentiment ratio measured based on daily forum posts and number of students who drop out each day. On a user-level, we evaluate the impact of sentiment on attrition over time. A qualitative analysis clarifies the subtle differences in how these language behaviors are used in practice across three MOOCs. Implications for research and practice are discussed.
Development Process for AUTOSAR-based Embedded System

Recently automotive embedded system has developed highly since the advent of smart car, electric card and so on. They have various value-added system for example IPA (Intelligent Parking Assistance), BSW (Blind Spot Warning), LDWS (Lane Departure warning System), LKS(Lane Keeping System)-these are ADAS (Advanced Driver Assistance Systems).  is the most notable industrial standard for developing automotive embedded software. AUTOSAR is a partnership of automotive manufacturers and suppliers working together to develop and establish an open industry standard for automotive E/E architectures. In this paper, we will introduce AUTOSAR briefly and demonstrate the result of automotive software LDWS (Lane Detection & Warning System) development.
A clean slate 4D approach to network control and management

Today's data networks are surprisingly fragile and difficult to manage. We argue that the root of these problems lies in the complexity of the control and management planes--the software and protocols coordinating network elements--and particularly the way the decision logic and the distributed-systems issues are inexorably intertwined. We advocate a complete refactoring of the functionality and propose three key principles--network-level objectives, network-wide views, and direct control--that we believe should underlie a new architecture. Following these principles, we identify an extreme design point that we call "4D," after the architecture's four planes: decision, dissemination, discovery, and data. The 4D architecture completely separates an AS's decision logic from pro-tocols that govern the interaction among network elements. The AS-level objectives are specified in the decision plane, and en-forced through direct configuration of the state that drives how the data plane forwards packets. In the 4D architecture, the routers and switches simply forward packets at the behest of the decision plane, and collect measurement data to aid the decision plane in controlling the network. Although 4D would involve substantial changes to today's control and management planes, the format of data packets does not need to change; this eases the deployment path for the 4D architecture, while still enabling substantial innovation in network control and management. We hope that exploring an extreme design point will help focus the attention of the research and industrial communities on this crucially important and intellectually challenging area.