nils-herrmann/clean_scirepeval huggingface dataset:
Tweets as impact indicators: Examining the implications of automated "bot" accounts on Twitter

This brief communication presents preliminary findings on automated Twitter accounts distributing links to scientific papers deposited on the preprint repository arXiv. It discusses the implication of the presence of such bots from the perspective of social media metrics (altmetrics), where mentions of scholarly documents on Twitter have been suggested as a means of measuring impact that is both broader and timelier than citations. We present preliminary findings that automated Twitter accounts create a considerable amount of tweets to scientific papers and that they behave differently than common social bots, which has critical implications for the use of raw tweet counts in research evaluation and assessment. We discuss some definitions of Twitter cyborgs and bots in scholarly communication and propose differentiating between different levels of engagement from tweeting only bibliographic information to discussing or commenting on the content of a paper.  The so-called altmetrics movement promotes the use of metrics generated from social media platforms as broader and timelier evidence of research impact than citations (Priem, Taraborelli, Groth, & Neylon, 2010). Among these social media platforms, the microblogging platform Twitter has been shown to be one of the most frequently used social media tools that mentions scholarly documents (Costas, Zahedi, & Wouters, 2014; Haustein et al., 2014b; 2014c). As of July 2014, Twitter reported 500 million tweets per day and 255 million monthly active users with 16% of the U.S. population purportedly on Twitter. Scholarly use is proportionally less: it is estimated that between 3% and 15% of researchers utilize this service (Priem & Costello, 2010; Pscheida, Albrecht, Herbst, Minet, & Köhler, 2013; Rowlands, Nicholas, Russell, Canty, & Watkinson, 2011). Therefore, some authors have argued that Twitter is potentially able to capture the impact of scholarly documents on a nonresearching public and might, thus, serve as a measure of broader impact of research. However, systematic evidence as to whether tweets are valid markers of actual societal and/or scientific impact
Impact of digital control in power electronics

In this paper, we discuss the impact of digital control in high-frequency switched-mode power supplies (SMPS), including point-of-load and isolated DC-DC converters, microprocessor power supplies, power-factor-correction rectifiers, electronic ballasts, etc., where switching frequencies are typically in the hundreds of kHz to MHz range, and where high efficiency, static and dynamic regulation, low size and weight, as well as low controller complexity and cost are very important. To meet these application requirements, a digital SMPS controller may include fast, small analog-to-digital converters, hardware-accelerated programmable compensators, programmable digital modulators with very fine time resolution, and a standard microcontroller core to perform programming, monitoring and other system interface tasks. Based on recent advances in circuit and control techniques, together with rapid advances in digital VLSI technology, we conclude that high-performance digital controller solutions are both feasible and practical, leading to much enhanced system integration and performance gains. Examples of experimentally demonstrated results are presented, together with pointers to areas of current and future research and development.
Self-Calibration of a 3-D-Digital Beamforming Radar System for Automotive Applications With Installation Behind Automotive Covers

Given the complexity of a radar system with digital beamforming, a number of error influences can appear due to hardware imperfections, temperature drift, and aging. These errors can result in a reduced target detection probability or even lead to false detections. For automotive driver assistance applications, there is a growing demand for hidden integration of radar systems. When installed behind a painted bumper or design radome, additional performance degradation may occur. First, the hardware imperfections according to their appearance in the presented radar system and their effects are analyzed with respect to the angular spectrum estimation. A software-based self-calibration algorithm is proposed and evaluated by means of simulation and measurement. Finally, the extended performance capability of the self-calibration procedure to compensate additional error components from automotive covers is investigated by means of a painted bumper in front of the radar sensor.
Visual interest and NPR: an evaluation and manifesto

Using eye tracking, we study the way viewers look at photos and image based NPR illustrations. Viewers examine the same number of locations in photos and in NPR images with uniformly high or low detail. In contrast, viewers are attracted to areas where detail is locally preserved in meaningfully abstracted images. This accords with the idea that artists carefully manipulate detail to control interest and understanding. It also validates the method of meaningful abstraction used in DeCarlo and Santella [2002]. Results also suggest eye tracking can be a useful tool for evaluation of NPR systems.
Automatic detection of learning styles: state of the art

A learning style describes the attitudes and behaviors, which determine an individual’s preferred way of learning. Learning styles are particularly important in educational settings since they may help students and tutors become more self-aware of their strengths and weaknesses as learners. The traditional way to identify learning styles is using a test or questionnaire. Despite being reliable, these instruments present some problems that hinder the learning style identification. Some of these problems include students’ lack of motivation to fill out a questionnaire and lack of self-awareness of their learning preferences. Thus, over the last years, several approaches have been proposed for automatically detecting learning styles, which aim to solve these problems. In this work, we review and analyze current trends in the field of automatic detection of learning styles. We present the results of our analysis and discuss some limitations, implications and research gaps that can be helpful to researchers working in the field of learning styles.
Testing Idempotence for Infrastructure as Code

Due to the competitiveness of the computing industry, software developers are pressured to quickly deliver new code releases. At the same time, operators are expected to update and keep production systems stable at all times. To overcome the development–operations barrier, organizations have started to adopt Infrastructure as Code (IaC) tools to efficiently deploy middleware and applications using automation scripts. These automations comprise a series of steps that should be idempotent to guarantee repeatability and convergence. Rigorous testing is required to ensure that the system idempotently converges to a desired state, starting from arbitrary states. We propose and evaluate a model-based testing framework for IaC. An abstracted system model is utilized to derive state transition graphs, based on which we systematically generate test cases for the automation. The test cases are executed in light-weight virtual machine environments. Our prototype targets one popular IaC tool (Chef), but the approach is general. We apply our framework to a large base of public IaC scripts written by operators, showing that it correctly detects non-idempotent automations.
Malware Analysis and Classification: A Survey

One of the major and serious threats on the Internet today is malicious software, often referred to as a malware. The malwares being designed by attackers are polymorphic and metamorphic which have the ability to change their code as they propagate. Moreover, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses which typically use signature based techniques and are unable to detect the previously unknown malicious executables. The variants of malware families share typical behavioral patterns reflecting their origin and purpose. The behavioral patterns obtained either statically or dynamically can be exploited to detect and classify unknown malwares into their known families using machine learning techniques. This survey paper provides an overview of techniques for analyzing and classifying the malwares.
Training Multilayer Perceptrons with the Extende Kalman Algorithm

A large fraction of recent work in artificial neural nets uses multilayer perceptrons trained with the back-propagation algorithm described by Rumelhart et. a1. This algorithm converges slowly for large or complex problems such as speech recognition, where thousands of iterations may be needed for convergence even with small data sets. In this paper, we show that training multilayer perceptrons is an identification problem for a nonlinear dynamic system which can be solved using the Extended Kalman Algorithm. Although computationally complex, the Kalman algorithm usually converges in a few iterations. We describe the algorithm and compare it with back-propagation using twodimensional examples.
Applying Machine Learning Techniques to Transportation Mode Recognition Using Mobile Phone Sensor Data

This paper adopts different supervised learning methods from the field of machine learning to develop multiclass classifiers that identify the transportation mode, including driving a car, riding a bicycle, riding a bus, walking, and running. Methods that were considered include K-nearest neighbor, support vector machines (SVMs), and tree-based models that comprise a single decision tree, bagging, and random forest (RF) methods. For training and validating purposes, data were obtained from smartphone sensors, including accelerometer, gyroscope, and rotation vector sensors. K-fold cross-validation as well as out-of-bag error was used for model selection and validation purposes. Several features were created from which a subset was identified through the minimum redundancy maximum relevance method. Data obtained from the smartphone sensors were found to provide important information to distinguish between transportation modes. The performance of different methods was evaluated and compared. The RF and SVM methods were found to produce the best performance. Furthermore, an effort was made to develop a new additional feature that entails creating a combination of other features by adopting a simulated annealing algorithm and a random forest method.
Prevalence of overweight and obesity in Turkey.

The objectives of this study were to determine the prevalence of overweight and obesity in Turkey, and to investigate their association with age, gender, and blood pressure. A crosssectional population-based study was performed. A total of 20,119 inhabitants (4975 women and 15,144 men, age > 20 years) from 11 Anatolian cities in four geographic regions were screened for body weight, height, and systolic and diastolic blood pressure between the years 1999 and 2000. The overall prevalence rate of overweight was 25.0% and of obesity was 19.4%. The prevalence of overweight among women was 24.3% and obesity 24.6%; 25.9% of men were overweight, and 14.4% were obese. Mean body mass index (BMI) of the studied population was 27.59 +/- 4.61 kg/m(2). Mean systolic and diastolic blood pressure for women were 131.0 +/- 41.0 and 80.2 +/- 16.3 mm Hg, and for men 135.0 +/- 27.3 and 83.2 +/- 16.0 mm Hg. There was a positive linear correlation between BMI and blood pressure, and between age and blood pressure in men and women. Obesity and overweight are highly prevalant in Turkey, and they constitute independent risk factors for hypertension.
The Ignorant Led by the Blind: A Hybrid Human–Machine Vision System for Fine-Grained Categorization

We present a visual recognition system for fine-grained visual categorization. The system is composed of a human and a machine working together and combines the complementary strengths of computer vision algorithms and (non-expert) human users. The human users provide two heterogeneous forms of information object part clicks and answers to multiple choice questions. The machine intelligently selects the most informative question to pose to the user in order to identify the object class as quickly as possible. By leveraging computer vision and analyzing the user responses, the overall amount of human effort required, measured in seconds, is minimized. Our formalism shows how to incorporate many different types of computer vision algorithms into a human-in-the-loop framework, including standard multiclass methods, part-based methods, and localized multiclass and attribute methods. We explore our ideas by building a field guide for bird identification. The experimental results demonstrate the strength of combining ignorant humans with poor-sighted machines the hybrid system achieves quick and accurate bird identification on a dataset containing 200 bird species.
Online Shopping Hesitation

This study was designed to understand which factors influence consumer hesitation or delay in online product purchases. The study examined four groups of variables (i.e., consumer characteristics, contextual factors perceived uncertainty factors, and medium/channel innovation factors) that predict three types of online shopping hesitation (i.e., overall hesitation, shopping cart abandonment, and hesitation at the final payment stage). We found that different sets of delay factors are related to different aspects of online shopping hesitation. The study concludes with suggestion for various delay-reduction devices to help consumers close their online decision hesitation.
Autominder: an intelligent cognitive orthotic system for people with memory impairment

The world’s population is aging at a phenomenal rate. Certain types of cognitive decline, in particular some forms of memory impairment, occur much more frequently in the elderly. This paper describes Autominder, a cognitive orthotic system intended to help older adults adapt to cognitive decline and continue the satisfactory performance of routine activities, thereby potentially enabling them to remain in their own homes longer. Autominder achieves this goal by providing adaptive, personalized reminders of (basic, instrumental, and extended) activities of daily living. Cognitive orthotic systems on the market today mainly provide alarms for prescribed activities at fixed times that are specified in advance. In contrast, Autominder uses a range of AI techniques to model an individual’s daily plans, observe and reason about the execution of those plans, and make decisions about whether and when it is most appropriate to issue reminders. Autominder is currently deployed on a mobile robot, and is being developed as part of the Initiative on Personal Robotic Assistants for the Elderly (the Nursebot project).
The geometry of nesting problems: A tutorial

Cutting and packing problems involving irregular shapes is an important problem variant with a wide variety of industrial applications. Despite its relevance to industry, research publications are relatively low when compared to other cutting and packing problems. One explanation offered is the perceived difficulty and substantial time investment of developing a geometric tool box to assess computer generated solutions. In this paper we set out to provide a tutorial covering the core geometric methodologies currently employed by researchers in cutting and packing of irregular shapes. The paper is not designed to be an exhaustive survey of the literature but instead will draw on the literature to illustrate the theory and implementation of the approaches. We aim to provide a sufficiently instructive description to equip new and current researchers in the area to select the most appropriate methodology for their needs.
Modeling Camera Effects to Improve Deep Vision for Real and Synthetic Data

Recent work has focused on generating synthetic imagery and augmenting real imagery to increase the size and variability of training data for learning visual tasks in urban scenes. This includes increasing the occurrence of occlusions or varying environmental and weather effects. However, few have addressed modeling the variation in the sensor domain. Unfortunately, varying sensor effects can degrade performance and generalizability of results for visual tasks trained on human annotated datasets. This paper proposes an efficient, automated physicallybased augmentation pipeline to vary sensor effects – specifically, chromatic aberration, blur, exposure, noise, and color cast – across both real and synthetic imagery. In particular, this paper illustrates that augmenting training datasets with the proposed pipeline improves the robustness and generalizability of object detection on a variety of benchmark vehicle datasets.
Visual odometry and control for an omnidirectional mobile robot with a downward-facing camera

An omnidirectional Mecanum base allows for more flexible mobile manipulation. However, slipping of the Mecanum wheels results in poor dead-reckoning estimates from wheel encoders, limiting the accuracy and overall utility of this type of base. We present a system with a downward-facing camera and light ring to provide robust visual odometry estimates. We mounted the system under the robot which allows it to operate in conditions such as large crowds or low ambient lighting. We demonstrate that the visual odometry estimates are sufficient to generate closed-loop PID (Proportional Integral Derivative) and LQR (Linear Quadratic Regulator) controllers for motion control in three different scenarios: waypoint tracking, small disturbance rejection, and sideways motion. We report quantitative measurements that demonstrate superior control performance when using visual odometry compared to wheel encoders. Finally, we show that this system provides high-fidelity odometry estimates and is able to compensate for wheel slip on a four-wheeled omnidirectional mobile robot base.
Haptic Glove With MR Brakes for Virtual Reality

Haptic gloves open up the world of force feedback by allowing the user to pick up and feel virtual objects in a natural way. In most of the existing gloves, a remote box houses a large number of actuators and sensors. Power to the glove is transmitted via cables. If the haptic gloves were smaller, lighter, and easier to use and control, they could become more common as human-machine interfaces. Recent developments show that actuators based on active fluids, such as the magnetorheological (MR) fluids, can be viable alternatives in haptics. But these devices are desk- or floor-mounted and use relatively large MR brakes. In this research, we developed a compact MR brake that is about 25 mm in diameter, weighs 84 g, and can apply up to 899 Nmiddotmm torque. The compact size was achieved by stacking steel and aluminum rings to create a serpentine flux path through the fluid. Six brakes were used to build a force feedback glove called MR glove. The glove weighs 640 g and does not require any remote actuators. Results of usability experiments showed that the MR glove improved task completion times in grasping virtual objects and could convey stiffness information to the user.
Citations and annotations in classics: old problems and new perspectives

Annotations played a major role in Classics since the very beginning of the discipline. Some of the first attested examples of philological work, the so-called scholia, were in fact marginalia, namely comments written at the margins of a text. Over the centuries this kind of scholarship evolved until it became a genre on its own, the classical commentary, thus moving away from the text with the result that philologists had to devise a solution to linking together the commented and the commenting text. The solution to this problem is the system of canonical citations, a special kind of bibliographic references that are at the same time very precise and highly interoperable.
 In this paper we present HuCit, an ontology that models in depth the semantics of canonical citations. We discuss how it can be used to a) support the automatic extraction of canonical citations from texts and b) to publish them in machine-readable format on the Semantic Web. Finally, we describe how HuCit's machine-generated citation data can also be expressed as annotations by using the Open Annotation Collaboration (OAC) ontology, to the aim of increasing reuse and semantic interoperability.
Column Sampling Based Discrete Supervised Hashing

By leveraging semantic (label) information, supervised hashing has demonstrated better accuracy than unsupervised hashing in many real applications. Because the hashing-code learning problem is essentially a discrete optimization problem which is hard to solve, most existing supervised hashing methods try to solve a relaxed continuous optimization problem by dropping the discrete constraints. However, these methods typically suffer from poor performance due to the errors caused by the relaxation. Some other methods try to directly solve the discrete optimization problem. However, they are typically time-consuming and unscalable. In this paper, we propose a novel method, called column sampling based discrete supervised hashing (COSDISH), to directly learn the discrete hashing code from semantic information. COSDISH is an iterative method, in each iteration of which several columns are sampled from the semantic similarity matrix and then the hashing code is decomposed into two parts which can be alternately optimized in a discrete way. Theoretical analysis shows that the learning (optimization) algorithm of COSDISH has a constant-approximation bound in each step of the alternating optimization procedure. Empirical results on datasets with semantic labels illustrate that COSDISH can outperform the state-of-the-art methods in real applications like image retrieval.
Soft Gripper Dynamics Using a Line-Segment Model With an Optimization-Based Parameter Identification Method

Soft robotics is an emerging field that focuses on the development and application of soft robots. Due to their highly deformable features, it is difficult to model and control such robots. In this paper, we proposed a simplified model to simulate a fluidic elastomer actuator (FEA). The model consists of a series of line segments connected by viscoelastic joints. Pneumatic inputs were modeled as active torques acting at each joint. The Lagrangian dynamic equations were derived. An optimization-based method was proposed to identify the unknown model parameters. Experiments were conducted using three-dimensional (3D) printed FEAs. Calibration results of a single FEA showed the repeatability of the pressure actuated bending angles, and the proposed dynamic model can precisely reproduce the deformation behavior of the FEA. Grasping experiments showed that the proposed dynamic model can predict the grasping forces, which was validated by a separate experiment of grasping force measurement. The presented methods can be extended to model other soft robots.