nils-herrmann/clean_scirepeval huggingface dataset:
An accurate and efficient navigation system for omnidirectional robots in industrial environments

Enhanced logistics is widely regarded as a key technology to increase flexibility and cost efficiency of today’s factories. For example, fully autonomous transport vehicles aim to gradually replace conveyor belts, guided vehicles, and manual labor. In this context, especially omnidirectional robots are appealing thanks to their advanced maneuvering capabilities. In industrial applications, however, accuracy as well as safety and efficiency are key requirements for successful navigation systems. In this paper, we present an accurate navigation system for omnidirectional robots. Our system includes dedicated modules for mapping, localization, trajectory generation and robot control. It has been designed for accurate execution by devising smooth, curvature continuous trajectories, by planning appropriate velocities and by accounting for platform and safety constraints. In this way, it completely utilizes the maneuvering capabilities of omnidirectional robots and optimizes trajectories with respect to time of travel. We present extensive experimental evaluations in simulation and in changing real-world environments to demonstrate the robustness and accuracy of our system. This work has partly been supported by the European Commission under grant agreement numbers FP7-248258-First-MM, FP7-260026-TAPAS, and FP7-248873-RADHAR. Christoph Sprunk, Wolfram Burgard Department of Computer Science, University of Freiburg, Germany, E-mail: {sprunkc,burgard}@informatik.uni-freiburg.de Boris Lau E-mail: mail@borislau.de Patrick Pfaff KUKA Laboratories GmbH, Augsburg, Germany, E-mail: patrick.pfaff@kuka.com Fig. 1 The KUKA Moiros robot using our system for accurate omnidirectional navigation during a demonstration at the Hannover Messe 2013 fair. Our system accounts for platform and safety constraints and reliably navigated for several days in this inspection task for wind turbine blades.
Learning Deep on Cyberbullying is Always Better Than Brute Force

In this paper we present our research on detection of cyberbullying (CB), which stands for humiliating other people through the Internet. CB has become recognized as a social problem, and its mostly juvenile victims usually fall into depression, selfmutilate, or even commit suicide. To deal with the problem, school personnel performs Internet Patrol (IP) by reading through the available Web contents to spot harmful entries. It is crucial to help IP members detect malicious contents more efficiently. A number of research has tackled the problem during recent years. However, due to complexity of language used in cyberbullying, the results has remained only mildly satisfying. We propose a novel method to automatic cyberbullying detection based on Convolutional Neural Networks and increased Feature Density. The experiments performed on actual cyberbullying data showed a major advantage of our approach to all previous methods, including the best performing method so far based on BruteForce Search algorithm.
Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach

We propose a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary. Our approach decouples the source-to-target language transformation into (a) languagespecific rotations on the original embeddings to align them in a common, latent space, and (b) a language-independent similarity metric in this common space to better model the similarity between the embeddings. Overall, we pose the bilingual mapping problem as a classification problem on smooth Riemannian manifolds. Empirically, our approach outperforms previous approaches on the bilingual lexicon induction and cross-lingual word similarity tasks. We next generalize our framework to represent multiple languages in a common latent space. Language-specific rotations for all the languages and a common similarity metric in the latent space are learned jointly from bilingual dictionaries for multiple language pairs. We illustrate the effectiveness of joint learning for multiple languages in an indirect word translation setting.
Virtual to Real Reinforcement Learning for Autonomous Driving

Reinforcement learning is considered as a promising direction for driving policy learning. However, training autonomous driving vehicle with reinforcement learning in real environment involves non-affordable trial-and-error. It is more desirable to first train in a virtual environment and then transfer to the real environment. In this paper, we propose a novel realistic translation network to make model trained in virtual environment be workable in real world. The proposed network can convert non-realistic virtual image input into a realistic one with similar scene structure. Given realistic frames as input, driving policy trained by reinforcement learning can nicely adapt to real world driving. Experiments show that our proposed virtual to real (VR) reinforcement learning (RL) works pretty well. To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data.
Visual SLAM and Structure from Motion in Dynamic Environments: A Survey

In the last few decades, Structure from Motion (SfM) and visual Simultaneous Localization and Mapping (visual SLAM) techniques have gained significant interest from both the computer vision and robotic communities. Many variants of these techniques have started to make an impact in a wide range of applications, including robot navigation and augmented reality. However, despite some remarkable results in these areas, most SfM and visual SLAM techniques operate based on the assumption that the observed environment is static. However, when faced with moving objects, overall system accuracy can be jeopardized. In this article, we present for the first time a survey of visual SLAM and SfM techniques that are targeted toward operation in dynamic environments. We identify three main problems: how to perform reconstruction (robust visual SLAM), how to segment and track dynamic objects, and how to achieve joint motion segmentation and reconstruction. Based on this categorization, we provide a comprehensive taxonomy of existing approaches. Finally, the advantages and disadvantages of each solution class are critically discussed from the perspective of practicality and robustness.
Instability, stabilization, and formulation of liquid protein pharmaceuticals.

One of the most challenging tasks in the development of protein pharmaceuticals is to deal with physical and chemical instabilities of proteins. Protein instability is one of the major reasons why protein pharmaceuticals are administered traditionally through injection rather than taken orally like most small chemical drugs. Protein pharmaceuticals usually have to be stored under cold conditions or freeze-dried to achieve an acceptable shelf life. To understand and maximize the stability of protein pharmaceuticals or any other usable proteins such as catalytic enzymes, many studies have been conducted, especially in the past two decades. These studies have covered many areas such as protein folding and unfolding/denaturation, mechanisms of chemical and physical instabilities of proteins, and various means of stabilizing proteins in aqueous or solid state and under various processing conditions such as freeze-thawing and drying. This article reviews these investigations and achievements in recent years and discusses the basic behavior of proteins, their instabilities, and stabilization in aqueous state in relation to the development of liquid protein pharmaceuticals.
Active Plasmonics: Principles, Structures, and Applications.

Active plasmonics is a burgeoning and challenging subfield of plasmonics. It exploits the active control of surface plasmon resonance. In this review, a first-ever in-depth description of the theoretical relationship between surface plasmon resonance and its affecting factors, which forms the basis for active plasmon control, will be presented. Three categories of active plasmonic structures, consisting of plasmonic structures in tunable dielectric surroundings, plasmonic structures with tunable gap distances, and self-tunable plasmonic structures, will be proposed in terms of the modulation mechanism. The recent advances and current challenges for these three categories of active plasmonic structures will be discussed in detail. The flourishing development of active plasmonic structures opens access to new application fields. A significant part of this review will be devoted to the applications of active plasmonic structures in plasmonic sensing, tunable surface-enhanced Raman scattering, active plasmonic components, and electrochromic smart windows. This review will be concluded with a section on the future challenges and prospects for active plasmonics.
Deep Learning for Punctuation Restoration in Medical Reports

In clinical dictation, speakers try to be as concise as possible to save time, often resulting in utterances without explicit punctuation commands. Since the end product of a dictated report, e.g. an out-patient letter, does require correct orthography, including exact punctuation, the latter need to be restored, preferably by automated means. This paper describes a method for punctuation restoration based on a stateof-the-art stack of NLP and machine learning techniques including B-RNNs with an attention mechanism and late fusion, as well as a feature extraction technique tailored to the processing of medical terminology using a novel vocabulary reduction model. To the best of our knowledge, the resulting performance is superior to that reported in prior art on similar tasks.
A Review of the Literature on Configuration Management Tools

Configuration management tools help administrators in defining and automating system configurations. With cloud computing, host numbers are likely to grow. IaaS (infrastructure as a service) offerings with pay-per-use pricing models make fast and effective deployment of applications necessary. Configuration management tools address both challenges. In this paper, the existing research on this topic is reviewed comprehensively. Readers are provided with a descriptive analysis of the published literature as well as with an analysis of the content of the respective research works. The paper serves as an overview for researchers who are new to the topic. Furthermore, it serves to identify work related to an intended research field and identifies research gaps. Practitioners are provided with a means to identify solutions to their organizational problems.
From Fertilized Eggs to Complex Organisms: Models of Biological Pattern Formation

Error Thresholds and Their Relation to Optimal Mutation Rates p. 54 Are Artificial Mutation Biases Unnatural? p. 64 Evolving Mutation Rates for the Self-Optimisation of Genetic Algorithms p. 74 Statistical Reasoning Strategies in the Pursuit and Evasion Domain p. 79 An Evolutionary Method Using Crossover in a Food Chain Simulation p. 89 On Self-Reproduction and Evolvability p. 94 Some Techniques for the Measurement of Complexity in Tierra p. 104 A Genetic Neutral Model for Quantitative Comparison of Genotypic Evolutionary Activity p. 109
Wearable sensors: modalities, challenges, and prospects.

Wearable sensors have recently seen a large increase in both research and commercialization. However, success in wearable sensors has been a mix of both progress and setbacks. Most of commercial progress has been in smart adaptation of existing mechanical, electrical and optical methods of measuring the body. This adaptation has involved innovations in how to miniaturize sensing technologies, how to make them conformal and flexible, and in the development of companion software that increases the value of the measured data. However, chemical sensing modalities have experienced greater challenges in commercial adoption, especially for non-invasive chemical sensors. There have also been significant challenges in making significant fundamental improvements to existing mechanical, electrical, and optical sensing modalities, especially in improving their specificity of detection. Many of these challenges can be understood by appreciating the body's surface (skin) as more of an information barrier than as an information source. With a deeper understanding of the fundamental challenges faced for wearable sensors and of the state-of-the-art for wearable sensor technology, the roadmap becomes clearer for creating the next generation of innovations and breakthroughs.
Automated Irrigation System Using a Wireless Sensor Network and GPRS Module

An automated irrigation system was developed to optimize water use for agricultural crops. The system has a distributed wireless network of soil-moisture and temperature sensors placed in the root zone of the plants. In addition, a gateway unit handles sensor information, triggers actuators, and transmits data to a web application. An algorithm was developed with threshold values of temperature and soil moisture that was programmed into a microcontroller-based gateway to control water quantity. The system was powered by photovoltaic panels and had a duplex communication link based on a cellular-Internet interface that allowed for data inspection and irrigation scheduling to be programmed through a web page. The automated system was tested in a sage crop field for 136 days and water savings of up to 90% compared with traditional irrigation practices of the agricultural zone were achieved. Three replicas of the automated system have been used successfully in other places for 18 months. Because of its energy autonomy and low cost, the system has the potential to be useful in water limited geographically isolated areas.
Validity and reliability of the Experience-Sampling Method.

To understand the dynamics of mental health, it is essential to develop measures for the frequency and the patterning of mental processes in every-day-life situations. The Experience-Sampling Method (ESM) is an attempt to provide a valid instrument to describe variations in self-reports of mental processes. It can be used to obtain empirical data on the following types of variables: a) frequency and patterning of daily activity, social interaction, and changes in location; b) frequency, intensity, and patterning of psychological states, i.e., emotional, cognitive, and conative dimensions of experience; c) frequency and patterning of thoughts, including quality and intensity of thought disturbance. The article reviews practical and methodological issues of the ESM and presents evidence for its short- and long-term reliability when used as an instrument for assessing the variables outlined above. It also presents evidence for validity by showing correlation between ESM measures on the one hand and physiological measures, one-time psychological tests, and behavioral indices on the other. A number of studies with normal and clinical populations that have used the ESM are reviewed to demonstrate the range of issues to which the technique can be usefully applied.
Seismic Performance Evaluation of Existing RC Buildings Without Seismic Details . Comparison of Nonlinear Static Methods and IDA

The inelastic response of existing reinforced concrete (RC) buildings without seismic details is investigated, presenting the results from more than 1000 nonlinear analyses. The seismic performance is investigated for two buildings, a typical building form of the 60s and a typical form of the 80s. Both structures are designed according to the old Greek codes. These building forms are typical for that period for many Southern European countries. Buildings of the 60s do not have seismic details, while buildings of the 80s have elementary seismic details. The influence of masonry infill walls is also investigated for the building of the 60s. Static pushover and incremental dynamic analyses (IDA) for a set of 15 strong motion records are carried out for the three buildings, two bare and one infilled. The IDA predictions are compared with the results of pushover analysis and the seismic demand according to Capacity Spectrum Method (CSM) and N2 Method. The results from IDA show large dispersion on the response, available ductility capacity, behaviour factor and failure displacement, depending on the strong motion record. CSM and N2 predictions are enveloped by the nonlinear dynamic predictions, but have significant differences from the mean values. The better behaviour of the building of the 80s compared to buildings of the 60s is validated with both pushover and nonlinear dynamic analyses. Finally, both types of analysis show that fully infilled frames exhibit an improved behaviour compared to bare frames.
Bias and high-dimensional adjustment in observational studies of peer effects

Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are posited by multiple theories in the social sciences. Other processes can also produce behaviors that are correlated in networks and groups, thereby generating debate about the credibility of observational (i.e. nonexperimental) studies of peer effects. Randomized field experiments that identify peer effects, however, are often expensive or infeasible. Thus, many studies of peer effects use observational data, and prior evaluations of causal inference methods for adjusting observational data to estimate peer effects have lacked an experimental “gold standard” for comparison. Here we show, in the context of information and media diffusion on Facebook, that high-dimensional adjustment of a nonexperimental control group (677 million observations) using propensity score models produces estimates of peer effects statistically indistinguishable from those from using a large randomized experiment (220 million observations). Naive observational estimators overstate peer effects by 320% and commonly used variables (e.g., demographics) offer little bias reduction, but adjusting for a measure of prior behaviors closely related to the focal behavior reduces bias by 91%. High-dimensional models adjusting for over 3,700 past behaviors provide additional bias reduction, such that the full model reduces bias by over 97%. This experimental evaluation demonstrates that detailed records of individuals’ past behavior can improve studies of social influence, information diffusion, and imitation; these results are encouraging for the credibility of some studies but also cautionary for studies of rare or new behaviors. More generally, these results show how large, high-dimensional data sets and statistical learning techniques can be used to improve causal inference in the behavioral sciences.  We are grateful to L. Adamic, S. Aral,  Messing, C. Nass, M. Nowak, A. B. Owen, A. Peysakhovich, B. Reeves, D. Rogosa, J. Sekhon, A. C. Thomas, J. Ugander, and participants in seminars at New York University Stern School of Business, Stanford University Graduate School of Business, UC Berkeley Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, University of Chicago Booth School of Business, Columbia University Department of Statistics, and UC Davis Department of Statistics for comments on this work. D.E. was previously an employee of Facebook while contributing to this research and is a contractor with Facebook. D.E. and E.B. have significant financial interests in Facebook. 1 ar X iv :1 70 6. 04 69 2v 1 [ st at .M E ] 1 4 Ju n 20 17
Finding and assessing social media information sources in the context of journalism

Social media is already a fixture for reporting for many journalists, especially around breaking news events where non-professionals may already be on the scene to share an eyewitness report, photo, or video of the event. At the same time, the huge amount of content posted in conjunction with such events serves as a challenge to finding interesting and trustworthy sources in the din of the stream. In this paper we develop and investigate new methods for filtering and assessing the verity of sources found through social media by journalists. We take a human centered design approach to developing a system, SRSR ("Seriously Rapid Source Review"), informed by journalistic practices and knowledge of information production in events. We then used the system, together with a realistic reporting scenario, to evaluate the filtering and visual cue features that we developed. Our evaluation offers insights into social media information sourcing practices and challenges, and highlights the role technology can play in the solution.
Assessing and conceptualizing frontal EEG asymmetry: An updated primer on recording, processing, analyzing, and interpreting frontal alpha asymmetry.

Frontal electroencephalographic (EEG) alpha asymmetry is widely researched in studies of emotion, motivation, and psychopathology, yet it is a metric that has been quantified and analyzed using diverse procedures, and diversity in procedures muddles cross-study interpretation. The aim of this article is to provide an updated tutorial for EEG alpha asymmetry recording, processing, analysis, and interpretation, with an eye towards improving consistency of results across studies. First, a brief background in alpha asymmetry findings is provided. Then, some guidelines for recording, processing, and analyzing alpha asymmetry are presented with an emphasis on the creation of asymmetry scores, referencing choices, and artifact removal. Processing steps are explained in detail, and references to MATLAB-based toolboxes that are helpful for creating and investigating alpha asymmetry are noted. Then, conceptual challenges and interpretative issues are reviewed, including a discussion of alpha asymmetry as a mediator/moderator of emotion and psychopathology. Finally, the effects of two automated component-based artifact correction algorithms-MARA and ADJUST-on frontal alpha asymmetry are evaluated.
Kinematic Analysis of the Wrist and Forearm During Baseball Pitching

Previous researchers studying baseball pitching have compared kinematic and kinetic parameters among different types of pitches, focusing on the trunk, shoulder, and elbow. The lack of data on the wrist and forearm limits the understanding of clinicians, coaches, and researchers regarding the mechanics of baseball pitching and the differences among types of pitches. The purpose of this study was to expand existing knowledge of baseball pitching by quantifying and comparing kinematic data of the wrist and forearm for the fastball (FA), curveball (CU) and change-up (CH) pitches. Kinematic and temporal parameters were determined from 8 collegiate pitchers recorded with a four-camera system (200 Hz). Although significant differences were observed for all pitch comparisons, the least number of differences occurred between the FA and CH. During arm cocking, peak wrist extension for the FA and CH pitches was greater than for the CU, while forearm supination was greater for the CU. In contrast to the current study, previous comparisons of kinematic data for trunk, shoulder, and elbow revealed similarities between the FA and CU pitches and differences between the FA and CH pitches. Kinematic differences among pitches depend on the segment of the body studied.