nils-herrmann/clean_scirepeval huggingface dataset:
RoboCup: A Challenge Problem for AI

attempt to foster AI and intelligent robotics research by providing a standard problem where a wide range of technologies can be integrated and examined. The first RoboCup competition will be held at the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan. A robot team must actually perform a soccer game, incorporating various technologies, including design principles of autonomous agents, multiagent collaboration, strategy acquisition, real-time reasoning, robotics, and sensor fusion. RoboCup is a task for a team of multiple fast-moving robots under a dynamic environment. Although RoboCup’s final target is a world cup with real robots, RoboCup offers a software platform for research on the software aspects of RoboCup. This article describes technical challenges involved in , rules, and the simulation environment.
Formalizing cardinality-based feature models and their specialization

Feature modeling is an important approach to capture the commonalities and variabilities in system families and product lines. Cardinality-based feature modeling integrates a number of existing extensions of the original feature-modeling notation from Feature-Oriented Domain Analysis. Staged configuration is a process that allows the incremental configuration of cardinality-based feature models. It can be achieved by performing a step-wise specialization of the feature model. In this paper, we argue that cardinality-based feature models can be interpreted as a special class of context-free grammars. We make this precise by specifying a translation from a feature model into a context-free grammar. Consequently, we provide a semantic interpretation for cardinalitybased feature models by assigning an appropriate semantics to the language recognized by the corresponding grammar. Finally, we give an account on how feature model specialization can be formalized as transformations on the grammar equivalent of feature models.
Development of independent EtherCAT slave module and application to closed loop step motor drive with multi-axis

Recently, as real-time Ethernet based protocols, especially EtherCAT have become more widely used in various fields such as automation systems and motion control, many studies on their design have been conducted. In this paper, we describe a method for the design of an EtherCAT slave module we developed and its application to a closed loop motor drive. Our EtherCAT slave module consists of the ARM Cortex-M3 as the host controller and ET1100 as the EtherCAT slave controller. These were interfaced with a parallel interface instead of the SPI used by many researchers and developers. To measure the performance of this device, 32-axis closed loop step motor drives were used and the experimental results in the test environment are described.
An outpatient program in behavioral medicine for chronic pain patients based on the practice of mindfulness meditation: theoretical considerations and preliminary results.

The practice of mindfulness meditation was used in a 10-week Stress Reduction and Relaxation Program to train chronic pain patients in self-regulation. The meditation facilitates an attentional stance towards proprioception known as detached observation. This appears to cause an "uncoupling " of the sensory dimension of the pain experience from the affective/evaluative alarm reaction and reduce the experience of suffering via cognitive reappraisal. Data are presented on 51 chronic pain patients who had not improved with traditional medical care. The dominant pain categories were low back, neck and shoulder, and headache. Facial pain, angina pectoris, noncoronary chest pain, and GI pain were also represented. At 10 weeks, 65% of the patients showed a reduction of greater than or equal to 33% in the mean total Pain Rating Index (Melzack) and 50% showed a reduction of greater than or equal to 50%. Similar decreases were recorded on other pain indices and in the number of medical symptoms reported. Large and significant reductions in mood disturbance and psychiatric symptomatology accompanied these changes and were relatively stable on follow-up. These improvements were independent of the pain category. We conclude that this form of meditation can be used as the basis for an effective behavioral program in self-regulation for chronic pain patients. Key features of the program structure, and the limitations of the present uncontrolled study are discussed.
Developing legacy system migration methods and tools for technology transfer

This paper presents the research results of an ongoing technology transfer project carried out in cooperation between the University of Salerno and a small software company. The project is aimed at developing and transferring migration technology to the industrial partner. The partner should be enabled to migrate monolithic multi-user COBOL legacy systems to a multi-tier Web-based architecture. The assessment of the legacy systems of the partner company revealed that these systems had a very low level of decomposability with spaghetti-like code and embedded control flow and database accesses within the user interface descriptions. For this reason, it was decided to adopt an incremental migration strategy based on the reengineering of the user interface using Web technology, on the transformation of interactive legacy programs into batch programs, and the wrapping of the legacy programs. A middleware framework links the new Web-based user interface with the Wrapped Legacy System. An Eclipse plug-in, named MELIS (migration environment for legacy information systems), was also developed to support the migration process. Both the migration strategy and the tool have been applied to two essential subsystems of the most business critical legacy system of the partner company.
Real-time Encrypted Traffic Identification using Machine Learning

Accurate network traffic identification plays important roles in many areas such as traffic engineering, QoS and intrusion detection etc. The emergence of many new encrypted applications which use dynamic port numbers and masquerading techniques causes the most challenging problem in network traffic identification field. One of the challenging issues for existing traffic identification methods is that they can’t classify online encrypted traffic. To overcome the drawback of the previous identification scheme and to meet the requirements of the encrypted network activities, our work mainly focuses on how to build an online Internet traffic identification based on flow information. We propose real-time encrypted traffic identification based on flow statistical characteristics using machine learning in this paper. We evaluate the effectiveness of our proposed method through the experiments on different real traffic traces. By experiment results and analysis, this method can classify online encrypted network traffic with high accuracy and robustness.
Early Stage Influenza Detection from Twitter

Influenza is an acute respiratory illness that occurs virtually every year and results in substantial disease, death and expense. Detection of Influenza in its earliest stage would facilitate timely action that could reduce the spread of the illness. Existing systems such as CDC and EISS which try to collect diagnosis data, are almost entirely manual, resulting in about two-week delays for clinical data acquisition. Twitter, a popular microblogging service, provides us with a perfect source for early-stage flu detection due to its realtime nature. For example, when a flu breaks out, people that get the flu may post related tweets which enables the detection of the flu breakout promptly. In this paper, we investigate the real-time flu detection problem on Twitter data by proposing Flu Markov Network (Flu-MN): a spatio-temporal unsupervised Bayesian algorithm based on a 4 phase Markov Network, trying to identify the flu breakout at the earliest stage. We test our model on real Twitter datasets from the United States along with baselines in multiple applications, such as real-time flu breakout detection, future epidemic phase prediction, or Influenza-like illness (ILI) physician visits. Experimental results show the robustness and effectiveness of our approach. We build up a real time flu reporting system based on the proposed approach, and we are hopeful that it would help government or health organizations in identifying flu outbreaks and facilitating timely actions to decrease unnecessary mortality.
Using Text and Data Mining Techniques to extract Stock Market Sentiment from Live News Streams

Analyzing stock market trends and sentiment is an interdisciplinary area of research being undertaken by many disciplines such as Finance, Computer Science, Statistics, and Economics. It has been well established that real time news plays a strong role in the movement of stock prices. With the advent of electronic and online news sources, analysts have to deal with enormous amounts of real-time, unstructured streaming data. In this paper, we present an automated text mining based approach to aggregate news stories from diverse sources and create a News Corpus. The Corpus is filtered down to relevant sentences and analyzed using Natural Language Processing (NLP) techniques. A sentiment metric, called NewsSentiment, utilizing the count of positive and negative polarity words is proposed as a measure of the sentiment of the overall news corpus. We have used various open source packages and tools to develop the news collection and aggregation engine as well as the sentiment evaluation engine. Extensive experimentation has been done using news stories about various stocks. The time variation of NewsSentiment shows a very strong correlation with the actual stock price movement. Our proposed metric has many applications in analyzing current news stories and predicting stock trends for specific companies and sectors of the economy.
Mechanisms of low back pain: a guide for diagnosis and therapy

Chronic low back pain (CLBP) is a chronic pain syndrome in the lower back region, lasting for at least 3 months. CLBP represents the second leading cause of disability worldwide being a major welfare and economic problem. The prevalence of CLBP in adults has increased more than 100% in the last decade and continues to increase dramatically in the aging population, affecting both men and women in all ethnic groups, with a significant impact on functional capacity and occupational activities. It can also be influenced by psychological factors, such as stress, depression and/or anxiety. Given this complexity, the diagnostic evaluation of patients with CLBP can be very challenging and requires complex clinical decision-making. Answering the question "what is the pain generator" among the several structures potentially involved in CLBP is a key factor in the management of these patients, since a mis-diagnosis can generate therapeutical mistakes. Traditionally, the notion that the etiology of 80% to 90% of LBP cases is unknown has been mistaken perpetuated across decades. In most cases, low back pain can be attributed to specific pain generator, with its own characteristics and with different therapeutical opportunity. Here we discuss about radicular pain, facet Joint pain, sacro-iliac pain, pain related to lumbar stenosis, discogenic pain. Our article aims to offer to the clinicians a simple guidance to identify pain generators in a safer and faster way, relying a correct diagnosis and further therapeutical approach.
Software Architecture and the Creative Process in Game Development

The goal of this thesis has been to perform research on the relationship between the creative team, software architecture, and game development processes. Researching this relationship was done in three stages. The first stage was a literature review into software architecture and game development. In the second stage a questionnaire was designed based on the literature review, and this questionnaire was then distributed to several game developers. In addition to querying the game developers on their knowledge on the field, they were also asked whether or not they would be willing to answer a set of follow-up questions later. The responses to the questionnaire provided a lot of answers, but also gave rise to new questions. In the third stage these new questions were incorporated into a follow-up survey which was distributed to those respondents whom had previously answered that they were willing to answer follow-up questions. The problem definition has been divided into five research questions according to the Goal Question Metric approach. Supported by the literature review and the responses to both the questionnaire and the survey, these five research questions have been answered in detail in Chapter 11: “Research Conclusions”. The results from this thesis is not generalizable to all game developers, but provides a very interesting glimpse into how the creative team is affected by, and is allowed to affect, the software architecture and tools used, as well as the game development process.
Tracking network dynamics: a survey of distances and similarity metrics

5 From longitudinal biomedical studies to social networks, graphs 6 have emerged as a powerful framework for describing evolving inter7 actions between agents in complex systems. In such studies, after 8 pre-processing, the data can be represented by a set of graphs, each 9 graph represents a system’s state at a different point in time or space.  The analysis of the system’s dynamics depends on the selection of 11 the appropriate analytical tools. In particular, after specifying prop12 erties characterizing similarities between states, a critical step lies 13 in the choice of a distance between graphs capable of reflecting such 14 similarities. 15 While the literature offers a number of distances that one could 16 a priori choose from, their properties have been little investigated 17 and no guidelines regarding the choice of such a distance have yet 18 been provided. In particular, most graph distances consider that the 19 nodes are exchangeable and do not take into account node identities. 20 Accounting for the alignment of the graphs enables us to enhance 21 these distances’ sensitivity to perturbations in the network and de22 tect important changes in graph dynamics. Thus the selection of an 23 adequate metric is a decisive – yet delicate – practical matter. 24 In the spirit of Goldenberg, Zheng and Fienberg’s seminal 2009 25 review [21], the purpose of this article is to provide an overview of 26 commonly-used graph distances and an explicit characterization of 27 the structural changes that they are best able to capture. To see how 28 this translates in real-life situations, we use as a guiding thread to our 29 discussion the application of these distances to the analysis of both a 30 longitudinal microbiome dataset and a brain fMRI study. We show 31 examples of using permutation tests to detect the effect of covariates 32 on the graphs’ variability. Finally synthetic examples provide intuition 33 as to the qualities and drawbacks of the different distances. Above 34 all, we provide some guidance for choosing one distance over another 35 in certain types of applications. 36 Finally, extending the scope of our analysis from temporal to spatial 37 dynamics, we show an application of these different distances to a 38 network created from worldwide recipes.
JOB SATISFACTION AND EMPLOYEE TURNOVER : A FIRM-LEVEL PERSPECTIVE

In this paper, I study how companies can use their personnel data and information from job satisfaction surveys to predict employee quits. An important issue discussed at length in the paper is how employers can ensure the anonymity of employees in surveys used for management and HR analytics. I argue that a simple mechanism where the company delegates the implementation of job satisfaction surveys to an external consulting company can be optimal. In the subsequent empirical analysis, I use a unique combination of firm-level data (personnel records) and information from job satisfaction surveys to assess the benefits for companies using data in their decision-making. Moreover, I show how companies can move from a descriptive to a predictive approach.
Design of an Underactuated Compliant Gripper for Surgery using Nitinol

This paper presents the development of an underactuated compliant gripper using a biocompatible super elastic alloy, namely Nitinol. This gripper has two ngers with ve phalanges each and can be used as the end-e ector of an endoscopic instrument. Optimization procedures are required to obtain the geometry of the transmission mechanism because of its underactuated nature and its underlying complexity. A driving mechanism further incorporated in the gripper to distribute actuation to both ngers and accomplish the grasping of asymmetrical objects without requiring supplementary inputs is also discussed. Finally, the results of numerical simulations with di erent materials and di erent grasped objects are presented and discussed.  Deparment of Mechanical Engineering, École Polytechnique de Montréal, Montréal, QC, H3T 1J4, Canada, phone: 514-340-4711
An introduction to computational networks and the computational network toolkit (invited talk)

We introduce computational network (CN), a unified framework for describing arbitrary learning machines, such as deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short term memory (LSTM), logistic regression, and maximum entropy model, that can be illustrated as a series of computational steps. A CN is a directed graph in which each leaf node represents an input value or a parameter and each non-leaf node represents a matrix operation upon its children. We describe algorithms to carry out forward computation and gradient calculation in CN and introduce most popular computation node types used in a typical CN. We further introduce the computational network toolkit (CNTK), an implementation of CN that supports both GPU and CPU. We describe the architecture and the key components of the CNTK, the command line options to use CNTK, and the network definition and model editing language, and provide sample setups for acoustic model, language model, and spoken language understanding. We also describe the Argon speech recognition decoder as an example to integrate with CNTK.
Toward a more pragmatic approach to morality: a critical evaluation of Kohlberg's model.

In this article, the authors evaluate L. Kohlberg's (1984) cognitive- developmental approach to morality, find it wanting, and introduce a more pragmatic approach. They review research designed to evaluate Kohlberg's model, describe how they revised the model to accommodate discrepant findings, and explain why they concluded that it is poorly equipped to account for the ways in which people make moral decisions in their everyday lives. The authors outline in 11 propositions a framework for a new approach that is more attentive to the purposes that people use morality to achieve. People make moral judgments and engage in moral behaviors to induce themselves and others to uphold systems of cooperative exchange that help them achieve their goals and advance their interests.
Augmented reality visualization: A review of civil infrastructure system applications

In Civil Infrastructure System (CIS) applications, the requirement of blending synthetic and physical objects distinguishes Augmented Reality (AR) from other visualization technologies in three aspects: (1) it reinforces the connections between people and objects, and promotes engineers’ appreciation about their working context; (2) it allows engineers to perform field tasks with the awareness of both the physical and synthetic environment; and (3) it offsets the significant cost of 3D Model Engineering by including the real world background. This paper reviews critical problems in AR and investigates technical approaches to address the fundamental challenges that prevent the technology from being usefully deployed in CIS applications, such as the alignment of virtual objects with the real environment continuously across time and space; blending of virtual entities with their real background faithfully to create a sustained illusion of co-existence; and the integration of these methods to a scalable and extensible computing AR framework that is openly accessible to the teaching and research community. The research findings have been evaluated in several challenging CIS applications where the potential of having a significant economic and social impact is high. Examples of validation test beds implemented include an AR visual excavator-utility collision avoidance system that enables workers to ‘‘see’’ buried utilities hidden under the ground surface, thus helping prevent accidental utility strikes; an AR post-disaster reconnaissance framework that enables building inspectors to rapidly evaluate and quantify structural damage sustained by buildings in seismic events such as earthquakes or blasts; and a tabletop collaborative AR visualization framework that allows multiple users to observe and interact with visual simulations of engineering processes.
Continuous Space Translation Models for Phrase-Based Statistical Machine Translation

This paper presents a new approach to perform the estimation of the translation model probabilities of a phrase-based statistical machine translation system. We use neural networks to directly learn the translation probability of phrase pairs using continuous representations. The system can be easily trained on the same data used to build standard phrase-based systems. We provide experimental evidence that the approach seems to be able to infer meaningful translation probabilities for phrase pairs not seen in the training data, or even predict a list of the most likely translations given a source phrase. The approach can be used to rescore n-best lists, but we also discuss an integration into the Moses decoder. A preliminary evaluation on the English/French IWSLT task achieved improvements in the BLEU score and a human analysis showed that the new model often chooses semantically better translations. Several extensions of this work are discussed.
Data Governance on EA Information Assets: Logical Reasoning for Derived Data

Today’s companies face increased pressure regarding compliance to legal obligations. Regulations for the financial sector such as Basel II and III, Solvency II, or the Sarbanes-Oxley-Act explicitly demand various requirements. Many of those requirements address the governance and management of information assets, such as data. Companies need to report and track their information architecture, and furthermore have to provide accountability and responsibility information on their data to, e.g., supervisory authorities. Additionally, the tracking of processed data becomes increasingly difficult since the software systems and their interactions throughout the enterprise are highly complex. This paper argues for a consistent and comprehensive assignment mechanism on data governance roles. Based on logical inferences, we are able to show how accountability and responsibility can be assigned throughout processed data. Thereby, we analyze the limitations of traditional logic, such as propositional logic, and exemplarily show how non-monotonic defeasible logic can be used to keep the assignment of roles on information assets consistent.
Cut to the Chase: A Context Zoom-in Network for Reading Comprehension

In recent years many deep neural networks have been proposed to solve Reading Comprehension (RC) tasks. Most of these models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span in a given document. We present a novel neural-based architecture that is capable of extracting relevant regions based on a given question-document pair and generating a well-formed answer. To show the effectiveness of our architecture, we conducted several experiments on the recently proposed and challenging RC dataset ‘NarrativeQA’. The proposed architecture outperforms state-of-the-art results (Tay et al., 2018) by 12.62% (ROUGE-L) relative improvement.