nils-herrmann/clean_scirepeval huggingface dataset:
A comparison of three insect-inspired locomotion controllers

This paper compares three insect inspired controllers which were implemented on an autonomous hexapod robot There is a growing interest in using insect locomotion schemes to control walking robots Researchers interest in insect based controllers ranges from understanding the biological basis of locomotion control in insects Cruse Full Dean to building real time walking machines which require relatively little computational power Ferrell Donner Brooks Beer Chiel Several models for insect locomotion exist and robotics re searchers tend to adopt one approach and experiment with it In contrast this paper o ers a comparison of three insect inspired controllers all of which were implemented and tested on the same autonomous hexapod robot Some of the controllers used re ex based mechanisms whereas others used pattern based mecha nisms Re exive controllers exploit sensory stimulus and response reactions to produce leg motion and gait coordination In contrast pattern based controllers depend more upon pre programmed patterns of behavior which may be in uenced by external events Typically these pre programmed patterns of behavior are implemented using central pattern generators CPGs In this work we compare gait coordination performance of three controllers on at terrain We extend the comparison to include leg loading considerations disabled leg compensation and externally applied leg perturbations We discuss the di erences between controllers with respect to inconsistent leg retraction velocities leg design issues sensing requirements and computational issues The robot performed quite di erently under varying experimental conditions depending upon which controller was used We found that controller performance was the most sensitive to robot design parameters For our case we had the most success with pattern based mechanisms given the leg design of our robot and its limitations in controlling the retraction velocity of its legs The pattern based mechanisms allowed the robot to remain stable over a variety of gaits while the robot was subjected to loading the legs disabling a leg and physically disturbing the legs The re exive mechanisms were less successful at maintaining stability when the robot s legs were increasingly disrupted
Bridging OpenCL and CUDA: a comparative analysis and translation

Heterogeneous systems are widening their user-base, and heterogeneous computing is becoming popular in supercomputing. Among others, OpenCL and CUDA are the most popular programming models for heterogeneous systems. Although OpenCL inherited many features from CUDA and they have almost the same platform model, they are not compatible with each other. In this paper, we present similarities and differences between them and propose an automatic translation framework for both OpenCL to CUDA and CUDA to OpenCL. We describe features that make it difficult to translate from one to the other and provide our solution. We show that our translator achieves comparable performance between the original and target applications in both directions. Since each programming model separately has a wide user-base and large code-base, our translation framework is useful to extend the code-base for each programming model and unifies the efforts to develop applications for heterogeneous systems.
Generalizing Generalizability in Information Systems Research

Generalizability is a major concern to those who do, and use, research. Statistical, sampling-based generalizability is well known, but methodologists have long been aware of conceptions of generalizability beyond the statistical. The purpose of this essay is to clarify the concept of generalizability by critically examining its nature, illustrating its use and misuse, and presenting a framework for classifying its different forms. The framework organizes the different forms into four types, which are defined by the distinction between empirical and theoretical kinds of statements. On the one hand, the framework affirms the bounds within which statistical, sampling-based generalizability is legitimate. On the other hand, the framework indicates ways in which researchers in information systems and other fields may properly lay claim to generalizability, and thereby broader relevance, even when their inquiry falls outside the bounds of sampling-based research.
U-AIDA: a customizable system for named entity recognition, classification, and disambiguation

Recognizing and disambiguating entities such as people, organizations, events or places in natural language text are essential steps for many linguistic tasks such as information extraction and text categorization. A variety of named entity disambiguation methods have been proposed, but most of them focus on Wikipedia as a sole knowledge resource. This focus does not fit all application scenarios, and customization to the respective application domain is crucial. This dissertation addresses the problem of building an easily customizable system for named entity disambiguation. The first contribution is the development of a universal and flexible architecture that supports plugging in different knowledge resources. The second contribution is utilizing the flexible architecture to develop two domain-specific disambiguation systems. The third contribution is the design of a complete pipeline for building disambiguation systems for languages other than English that have poor annotated resources such as Arabic. The fourth contribution is a novel approach that performs fine-grained type classification of names in natural language text.
Opioid overdose prevention and naloxone rescue kits: what we know and what we don’t know

The opioid use and overdose crisis is persistent and dynamic. Opioid overdoses were initially driven in the 1990s and 2000s by the increasing availability and misuse of prescription opioids. More recently, opioid overdoses are increasing at alarming rates due to wider use of heroin, which in some places is mixed with fentanyl or fentanyl derivatives. Naloxone access for opioid overdose rescue is one of the US Department of Health and Human Services' three priority areas for responding to the opioid crisis. This article summarizes the known benefits of naloxone access and details unanswered questions about overdose education and naloxone rescue kits. Hopefully future research will address these knowledge gaps, improve the effectiveness of opioid overdose education and naloxone distribution programs, and unlock the full promise of naloxone rescue kits.
Multiscale Categorical Object Recognition Using Contour Fragments

Psychophysical studies show that we can recognize objects using fragments of outline contour alone. This paper proposes a new automatic visual recognition system based only on local contour features, capable of localizing objects in space and scale. The system first builds a class-specific codebook of local fragments of contour using a novel formulation of chamfer matching. These local fragments allow recognition that is robust to within-class variation, pose changes, and articulation. Boosting combines these fragments into a cascaded sliding-window classifier, and mean shift is used to select strong responses as a final set of detection. We show how learning can be performed iteratively on both training and test sets to bootstrap an improved classifier. We compare with other methods based on contour and local descriptors in our detailed evaluation over 17 challenging categories and obtain highly competitive results. The results confirm that contour is indeed a powerful cue for multiscale and multiclass visual object recognition.
Chapter 3 Clustered Systems for Massive Parallelism Summary : Clustering

Clustering enables the construction of scalable parallel and distributed systems for both HPC and HTC applications. Today's cluster nodes are built with either physical servers or virtual machines. In this chapter, we study clustered computing systems and massively parallel processors. We focus on design principles and assess the hardware, software and middleware support needed. We study scalability, availability, programmability, single system image, job management, and fault tolerance. Clustering techniques are integrated to build HTC and cloud systems. In particular, we study recent advances in virtual clustering to build MPP systems.
How Positive Informational Social Influence Affects Consumers&#146; Decision of Internet Shopping?

Given the amount of perceived risk involved in Internet shopping, many potential Internet shoppers tend to wait and observe the experiences of others who have tried it before considering adopting it. This study explores how positive informational social influence affects consumers’ decision of Internet shopping using a laboratory experiment. The research results reveal that positive informational social influence reinforces the relationships between perceived ease of use and consumer attitude toward Internet shopping, as well as attitude and their intention to shop. Implications for the current investigation and future research directions are provided.
Using artificial neural network models in stock market index prediction

Forecasting stock exchange rates is an important financial problem that is receiving increasing attention. During the last few years, a number of neural network models and hybrid models have been proposed for obtaining accurate prediction results, in an attempt to outperform the traditional linear and nonlinear approaches. This paper evaluates the effectiveness of neural network models which are known to be dynamic and effective in stock-market predictions. The models analysed are multi-layer perceptron (MLP), dynamic artificial neural network (DAN2) and the hybrid neural networks which use generalized autoregressive conditional heteroscedasticity (GARCH) to extract new input variables. The comparison for each model is done in two view points: Mean Square Error (MSE) and Mean Absolute Deviate (MAD) using real exchange daily rate values of NASDAQ Stock Exchange index.
Interventions in Small Food Stores to Change the Food Environment, Improve Diet, and Reduce Risk of Chronic Disease

Many small-store intervention trials have been conducted in the United States and other countries to improve the food environment and dietary behaviors associated with chronic disease risk. However, no systematic reviews of the methods and outcomes of these trials have been published. The objective of this study was to identify small-store interventions and to determine their impact on food availability, dietary behaviors, and psychosocial factors that influence chronic disease risk.



From May 2009 through September 2010, we used PubMed, web-based searches, and listservs to identify small-store interventions that met the following criteria: 1) a focus on small food stores, 2) a completed impact evaluation, and 3) English-written documentation (peer-reviewed articles or other trial documents). We initially identified 28 trials; 16 met inclusion criteria and were used for analysis. We conducted interviews with project staff to obtain additional information. Reviewers extracted and reported data in a table format to ensure comparability between data.



Reviewed trials were implemented in rural and urban settings in 6 countries and primarily targeted low-income racial/ethnic minority populations. Common intervention strategies included increasing the availability of healthier foods (particularly produce), point-of-purchase promotions (shelf labels, posters), and community engagement. Less common strategies included business training and nutrition education. We found significant effects for increased availability of healthy foods, improved sales of healthy foods, and improved consumer knowledge and dietary behaviors.



Trial impact appeared to be linked to the increased provision of both healthy foods (supply) and health communications designed to increase consumption (demand).
Model-based fault-detection and diagnosis - status and applications

For the improvement of reliability, safety and efficiency advanced methods of supervision, fault detection and fault diagnosis become increasingly important for many technical processes. This holds especially for safety related processes like aircraft, trains, automobiles, power plants and chemical plants. The classical approaches are limit or trend checking of some measurable output variables. Because they do not give a deeper insight and usually do not allow a fault diagnosis, model-based methods of fault detection were developed by using input and output signals and applying dynamic process models. These methods are based, e.g., on parameter estimation, parity equations or state observers. Also signal model approaches were developed. The goal is to generate several symptoms indicating the difference between nominal and faulty status. Based on different symptoms fault diagnosis procedures follow, determining the fault by applying classification or inference methods. This contribution gives a short introduction into the field and shows some applications for an actuator, a passenger car and a combustion engine.
TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning

TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow (Abadi et al., 2015). It provides an easy-to-use Scikit-learn (Pedregosa et al., 2011) style interface to simplify the process of creating, configuring, training, evaluating, and experimenting a machine learning model. TF.Learn integrates a wide range of state-ofart machine learning algorithms built on top of TensorFlow’s low level APIs for small to large-scale supervised and unsupervised problems. This module focuses on bringing machine learning to non-specialists using a general-purpose high-level language as well as researchers who want to implement, benchmark, and compare their new methods in a structured environment. Emphasis is put on ease of use, performance, documentation, and API consistency.
Thermal modeling of Lundell alternators

Thermal analysis of Lundell alternators used in automobiles is presented. An analytical thermal model for Lundell alternators is proposed, and procedures for acquiring the model parameters are elucidated. Based on the thermal model, the temperature profile of an operating Lundell alternator can be predicted analytically. The predicted alternator temperatures are found to be consistent with the experimental measurement. The presented models and measurement methods are useful for embedding switched-mode power electronics into the alternator with low manufacturing cost.
Folded Marchand Balun Design and Validation Using Sonnet

The Marchand balun has larger bandwidth than other types of combiners and has been used extensively since its introduction. The Marchand balun has also been extensively used in planar systems in both broadside-coupled and edged-coupled configurations. This paper describes the design and development of two microstrip, folded, edge-coupled Marchand baluns for use as anti-phase power combiner. The balun form-factor is thus reduced, making the circuit more compact and amenable for use in high-density printed circuit boards.
Bi-objective conflict detection and resolution in railway traffic management

Railway conflict detection and resolution is the daily task faced by railway managers that consists of adjusting train schedules whenever disturbances make the timetable infeasible. The main objective pursued by the infrastructure managers in this task is minimization of train delays, while train operating companies are also interested in other indicators of passengers dissatisfaction. The two objectives are conflicting whenever delay reduction requires cancellation of some connections between train services, which hampers passengers transfer. In fact, the infrastructure company and the train operating companies discuss on which connection to keep or drop in order to reach a compromise solution. In this paper we face the bi-objective problem of minimizing delays and missed connections to provide a set of feasible non-dominated schedules to support this decisional process. We use a detailed alternative graph model to ensure schedule feasibility and develop two heuristic algorithms to compute the Pareto front of non-dominated schedules. Our computational study, based on a complex and densely occupied Dutch railway network, shows that good coordination of connected train services is important to achieve real-time efficiency of railway services since the management of connections may heavily affect train punctuality. The two algorithms approximate accurately the Pareto front within a limited computational time.
The game, the player, the world: looking for a heart of gameness

This paper proposes a definition of games. I describe the classic game model, a list of six features that are necessary and sufficient for something to be a game. The definition shows games to be transmedial: There is no single game medium, but rather a number of game media, each with its own strengths. The computer is simply the latest game medium to emerge. While computer games 1 are therefore part of the broader area of games, they have in many cases evolved beyond the classic game model.
High-Speed and Lightweight Humanoid Robot Arm for a Skillful Badminton Robot

Sports, especially badminton, require participants to perform dynamic and skillful motions. Previous robots have had difficulty in performing like a human because of their severe limitations of low operating speed, heavy bodies, and simplistic mechanisms. In this letter, we propose a new robot design that consists of a structure integrated with pneumatic actuators and noninterfering many-degree-of-freedom joints, for the realization of a high-speed and lightweight humanoid robot. We made a four-degree-of-freedom robot arm for badminton, which is an especially dynamic sport, aiming for maximum speed while meeting geometric requirements. The robot swung with a racket-head speed of 21 m/s, which is a value higher than speeds achieved by previous robotic arms. The robot also realized a skillful shot, namely the spin net shot, which cannot be performed by previous badminton robots having simple mechanisms. A pneumatic robot is considered difficult to control, especially in terms of feedback control. We found that the reproducibility of the robot was as fine as 10–40 mm at the racket head for four kinds of strong swings. Using feedforward control, we also conducted an experiment in which the robot hits a flying shuttle, and achieved a high hitting rate of 69.7% for powerful swings. We believe that this research expands the possibilities of the pneumatic robot and is the first step toward developing a skillful humanoid badminton robot.
Language and life history: a new perspective on the development and evolution of human language.

It has long been claimed that Homo sapiens is the only species that has language, but only recently has it been recognized that humans also have an unusual pattern of growth and development. Social mammals have two stages of pre-adult development: infancy and juvenility. Humans have two additional prolonged and pronounced life history stages: childhood, an interval of four years extending between infancy and the juvenile period that follows, and adolescence, a stage of about eight years that stretches from juvenility to adulthood. We begin by reviewing the primary biological and linguistic changes occurring in each of the four pre-adult ontogenetic stages in human life history. Then we attempt to trace the evolution of childhood and juvenility in our hominin ancestors. We propose that several different forms of selection applied in infancy and childhood; and that, in adolescence, elaborated vocal behaviors played a role in courtship and intrasexual competition, enhancing fitness and ultimately integrating performative and pragmatic skills with linguistic knowledge in a broad faculty of language. A theoretical consequence of our proposal is that fossil evidence of the uniquely human stages may be used, with other findings, to date the emergence of language. If important aspects of language cannot appear until sexual maturity, as we propose, then a second consequence is that the development of language requires the whole of modern human ontogeny. Our life history model thus offers new ways of investigating, and thinking about, the evolution, development, and ultimately the nature of human language.
A Calibration Technique for Bang-Bang ADPLLs Using Jitter Distribution Monitoring

This brief presents a built-in self-calibration (BISC) technique for minimization of the total jitter in bang-bang all-digital phase-locked loops (ADPLLs). It is based on the addition of a monitoring phase-frequency detector (PFD) with tunable delay cells for the reference clock and the divider clock and a counter for this PFD output signal. This allows for on-chip binary comparison of the jitter distribution widths at the ADPLL PFD input, when ADPLL filter parameters are altered. Since only a relative comparison is performed, no accurate delay calibration is required. The statistical properties of this comparison of two random distributions are analyzed theoretically, and guidelines for circuit dimensioning are derived. The proposed method is used for BISC by adaption of the ADPLL filter coefficients. This allows for jitter minimization under process, voltage and temperature variations as well as gain and period jitter of the digitally controlled oscillator. The proposed calibration technique is verified by system simulations and measurements of a silicon prototype implementation in 28-nm CMOS technology.
Web 3.0 Emerging

While Web 3.0 technologies are difficult to define precisely, the outline of emerging applications has become clear over the past year. We can thus essentially view Web 3.0 as semantic Web technologies integrated into, or powering, large-scale Web applications. The base of Web 3.0 applications resides in the resource description framework (RDF) for providing a means to link data from multiple Web sites or databases. With the SPARQL query language, a SQL-like standard for querying RDF data, applications can use native graph-based RDF stores and extract RDF data from traditional databases.
Sentiment Analysis using Support Vector Machines with Diverse Information Sources

This paper introduces an approach to sentiment analysis which uses support vector machines (SVMs) to bring together diverse sources of potentially pertinent information, including several favorability measures for phrases and adjectives and, where available, knowledge of the topic of the text. Models using the features introduced are further combined with unigram models which have been shown to be effective in the past (Pang et al., 2002) and lemmatized versions of the unigram models. Experiments on movie review data from Epinions.com demonstrate that hybrid SVMs which combine unigram-style feature-based SVMs with those based on real-valued favorability measures obtain superior performance, producing the best results yet published using this data. Further experiments using a feature set enriched with topic information on a smaller dataset of music reviews handannotated for topic are also reported, the results of which suggest that incorporating topic information into such models may also yield improvement.