nils-herrmann/clean_scirepeval huggingface dataset:
Artificial intelligence techniques for driving safety and vehicle crash prediction

Accident prediction is one of the most critical aspects of road safety, whereby an accident can be predicted before it actually occurs and precautionary measures taken to avoid it. For this purpose, accident prediction models are popular in road safety analysis. Artificial intelligence (AI) is used in many real world applications, especially where outcomes and data are not same all the time and are influenced by occurrence of random changes. This paper presents a study on the existing approaches for the detection of unsafe driving patterns of a vehicle used to predict accidents. The literature covered in this paper is from the past 10 years, from 2004 to 2014. AI techniques are surveyed for the detection of unsafe driving style and crash prediction. A number of statistical methods which are used to predict the accidents by using different vehicle and driving features are also covered in this paper. The approaches studied in this paper are compared in terms of datasets and prediction performance. We also provide a list of datasets and simulators available for the scientific community to conduct research in the subject domain. The paper also identifies some of the critical open questions that need to be addressed for road safety using AI techniques.
Practical Lineage Tracing in Data Warehouses

We consider the view data lineage problem in a warehousing environment: For a given data item in a materialized warehouse view, we want to identify the set of source data items that produced the view item. We formalize the problem, and we present a lineage tracing algorithm for relational views with aggregation. Based on our tracing algorithm, we propose a number of schemes for storing auxiliary views that enable consistent and e cient lineage tracing in a multisource data warehouse. We report on a performance study of the various schemes, identifying which schemes perform best in which settings. Based on our results, we have implemented a lineage tracing package in the WHIPS data warehousing system prototype at Stanford. With this package, users can select view tuples of interest, then e ciently \drill through" to examine the exact source tuples that produced the view tuples of interest.
Finding transactive contributions in whole group classroom discussions

Automatic analysis of discussion logs from studies of computer supported collaborative learning has become more prevalent as technology for machine learning and text mining have become more powerful and usable. We extend this effort to analysis of transcripts of whole group classroom discussions. We argue in favor of adapting a transactivity based framework developed for analysis of collaborative learning discussions for use in the context of whole class discussions. Results from an evaluation of this technology applied to transcripts from 3 class periods of middle school math demonstrate promising results, specifically Cohen’s Kappa of .69 in comparison with human annotators for identifying transactive contributions and .68 for identifying which prior utterance a transactive contribution relates to. Potential applications for teacher professional development, automatic assessment of learning processes, and real time support for classroom discussion facilitators is discussed. Implications for theory building related to learning in classroom discussions is also addressed.
Environmental Justice : A Panoptic Overview Using Scientometrics

Since its initial introduction in the 1970s, the field of environmental justice (EJ) continues to grow, with significant contributions from the disciplines of sustainability science, geography, political science, public policy and administration, urban planning, law, and many others. Each of these disciplines approach EJ research from slightly different perspectives, but all offer unique and valuable insight to the EJ knowledge domain. Although the interdisciplinary nature of environmental justice should be viewed as a strength, it presents a challenge when attempting to both summarize and synthesize key contributions to the field, due to disciplinary bias, narrow subfield foci, or gaps in knowledge by a research team without a representative disciplinary composition. The purpose of this paper is to provide a succinct, panoptic review of key research contributions to environmental justice, while simultaneously minimizing common problems associated with traditional reviews. In particular, this paper explores the utility of co-citation network analysis, to provide insight into the most important subdomains of environmental justice research. The results suggest that while early EJ research is initially focused on environmental disamenities and a continued focus on race and inequality, the research gradually shifts to foci more concerned with environmental amenities, such as parks and greenspace. We also find that race and inequality remain an important and consist line of research over the duration of the study time period. Implications for environmental justice research and its allied subfields are discussed.
Consensus guidelines for periprocedural management of coagulation status and hemostasis risk in percutaneous image-guided interventions.

I H g a c a c t p w m c p p o b a PREAMBLE The membership of the Society of Interventional Radiology (SIR) Standards of Practice Committee represents experts in a broad spectrum of interventional procedures from both the private and academic sectors of medicine. Generally, Standards of Practice Committee members dedicate the vast majority of their professional time to performing interventional procedures; as such, they represent a valid broad expert constituency of the subject matter under consideration for standards production. Technical documents specifying the exact consensus and literature review methodologies, as well as the institutional affiliations and professional credentials of the authors of this document, are available upon request from SIR, 3975 Fair Ridge Dr., Suite 400 N., Fairfax, VA 22033.
The spring in the arch of the human foot

Large mammals, including humans, save much of the energy needed for running by means of elastic structures in their legs and feet1,2. Kinetic and potential energy removed from the body in the first half of the stance phase is stored briefly as elastic strain energy and then returned in the second half by elastic recoil. Thus the animal runs in an analogous fashion to a rubber ball bouncing along. Among the elastic structures involved, the tendons of distal leg muscles have been shown to be important2,3. Here we show that the elastic properties of the arch of the human foot are also important.
Cyberbullying among students with intellectual and developmental disability in special education settings.

To explore the types, prevalence and associated variables of cyberbullying among students with intellectual and developmental disability attending special education settings.



Students (n = 114) with intellectual and developmental disability who were between 12-19 years of age completed a questionnaire containing questions related to bullying and victimization via the internet and cellphones. Other questions concerned sociodemographic characteristics (IQ, age, gender, diagnosis), self-esteem and depressive feelings.



Between 4-9% of students reported bullying or victimization of bullying at least once a week. Significant associations were found between cyberbullying and IQ, frequency of computer usage and self-esteem and depressive feelings. No associations were found between cyberbullying and age and gender.



Cyberbullying is prevalent among students with intellectual and developmental disability in special education settings. Programmes should be developed to deal with this issue in which students, teachers and parents work together.
Improbotics: Exploring the Imitation Game Using Machine Intelligence in Improvised Theatre

Theatrical improvisation (impro or improv) is a demanding form of live, collaborative performance. Improv is a humorous and playful artform built on an open-ended narrative structure which simultaneously celebrates effort and failure. It is thus an ideal test bed for the development and deployment of interactive artificial intelligence (AI)-based conversational agents, or artificial improvisors. This case study introduces an improv show experiment featuring human actors and artificial improvisors. We have previously developed a deeplearning-based artificial improvisor, trained on movie subtitles, that can generate plausible, context-based, lines of dialogue suitable for theatre (Mathewson and Mirowski 2017b). In this work, we have employed it to control what a subset of human actors say during an improv performance. We also give human-generated lines to a different subset of performers. All lines are provided to actors with headphones and all performers are wearing headphones. This paper describes a Turing test, or imitation game, taking place in a theatre, with both the audience members and the performers left to guess who is a human and who is a machine. In order to test scientific hypotheses about the perception of humans versus machines we collect anonymous feedback from volunteer performers and audience members. Our results suggest that rehearsal increases proficiency and possibility to control events in the performance. That said, consistency with real world experience is limited by the interface and the mechanisms used to perform the show. We also show that human-generated lines are shorter, more positive, and have less difficult words with more grammar and spelling mistakes than the artificial improvisor generated lines.
Digitalization in the process industries – Evidence from the German water industry

Recent developments in digital technologies bring about considerable business opportunities but also impose significant challenges on firms in all industries. While some industries, e.g., newspapers, have already profoundly reorganized the mechanisms of value creation, delivery, and capture during the course of digitalization (Karimi & Walter, 2015, 2016), many process-oriented and asset intensive industries have not yet fully evaluated and exploited the potential applications (Rigby, 2014). Although the process industries have successfully used advancements in technologies to optimize processes in the past (Kim et al., 2011), digitalization poses an unprecedented shift in technology that exceeds conventional technological evolution (Svahn et al., 2017). Driven by augmented processing power, connectivity of devices (IoT), advanced data analytics, and sensor technology, innovation activities in the process industries now break away from established innovation paths (Svahn et al., 2017; Tripsas, 2009). In contrast to prior innovations that were primarily bound to physical devices, new products are increasingly embedded into systems of value creation that span the physical and digital world (Parmar et al., 2014; Rigby, 2014; Yoo et al., 2010a). On this new playing field, firms and researchers are jointly interested in the organizational characteristics and capabilities that are required to gain a competitive advantage (e.g. Fink, 2011). Whereas prior studies cover the effect of digital transformation on innovation in various industries like newspaper (Karimi and Walter, 2015, 2016), automotive (Henfridsson and Yoo, 2014; Svahn et al., 2017), photography (Tripsas, 2009), and manufacturing (Jonsson et al., 2008), there is a relative dearth of studies that cover the impact of digital transformation in the process industries (Westergren and Holmström, 2012). The process industries are characterized by asset and research intensity, strong integration into physical locations, and often include value chains that are complex and feature aspects of rigidity (Lager Research Paper Digitalization in the process industries – Evidence from the German water industry
Citations, contexts, and humanistic discourse: Toward automatic extraction and classification

This paper examines prospects and limitations of citation studies in the humanities. We begin by presenting an overview of bibliometric analysis, noting several barriers to applying this method in the humanities. Following that, we present an experimental tool for extracting and classifying citation contexts in humanities journal articles. This tool reports the bibliographic information about each reference, as well as three features about its context(s): frequency, locationin-document, and polarity. We found that extraction was highly successful (above 85%) for three of the four journals, and statistics for the three citation figures were broadly consistent with previous research. We conclude by noting several limitations of the sentiment classifier and suggesting future areas for refinement.
Complementary Switching in Oxide-Based Bipolar Resistive-Switching Random Memory

Resistive-switching random access memory (RRAM) devices utilizing a crossbar architecture represent a promising alternative for Flash replacement in high-density data storage applications. However, RRAM crossbar arrays require the adoption of diodelike select devices with high on-off -current ratio and with sufficient endurance. To avoid the use of select devices, one should develop passive arrays where the nonlinear characteristic of the RRAM device itself provides self-selection during read and write. This paper discusses the complementary switching (CS) in hafnium oxide RRAM, where the logic bit can be encoded in two high-resistance levels, thus being immune from leakage currents and related sneak-through effects in the crossbar array. The CS physical mechanism is described through simulation results by an ion-migration model for bipolar switching. Results from pulsed-regime characterization are shown, demonstrating that CS can be operated at least in the 10-ns time scale. The minimization of the reset current is finally discussed.
A Machine Learning Approach for Identifying Disease-Treatment Relations in Short Texts

The Machine Learning (ML) field has gained its momentum in almost any domain of research and just recently has become a reliable tool in the medical domain. The empirical domain of automatic learning is used in tasks such as medical decision support, medical imaging, protein-protein interaction, extraction of medical knowledge, and for overall patient management care. ML is envisioned as a tool by which computer-based systems can be integrated in the healthcare field in order to get a better, more efficient medical care. This paper describes a ML-based methodology for building an application that is capable of identifying and disseminating healthcare information. It extracts sentences from published medical papers that mention diseases and treatments, and identifies semantic relations that exist between diseases and treatments. Our evaluation results for these tasks show that the proposed methodology obtains reliable outcomes that could be integrated in an application to be used in the medical care domain. The potential value of this paper stands in the ML settings that we propose and in the fact that we outperform previous results on the same data set.
Neural Chinese Word Segmentation with Dictionary Knowledge

Chinese word segmentation (CWS) is an important task for Chinese NLP. Recently, many neural network based methods have been proposed for CWS. However, these methods require a large number of labeled sentences for model training, and usually cannot utilize the useful information in Chinese dictionary. In this paper, we propose two methods to exploit the dictionary information for CWS. The first one is based on pseudo labeled data generation, and the second one is based on multi-task learning. The experimental results on two benchmark datasets validate that our approach can effectively improve the performance of Chinese word segmentation, especially when training data is insufficient.
Simultaneous Transmit and Receive Operation in Next Generation IEEE 802.11 WLANs: A MAC Protocol Design Approach

Full-duplex technology is likely to be adopted in various legacy communications standards. The IEEE 802.11ax Working Group has been considering a simultaneous transmit and receive (STR) mode for the next generation WLANs. Enabling STR mode (FD communication mode) in 802.11 networks creates bidirectional FD (BFD) and unidirectional FD (UFD) links. The key challenge is to integrate STR mode with minimal protocol modifications, while considering the coexistence of FD and legacy half-duplex STAs and backward compatibility. This article proposes a simple and practical approach to enable STR mode in 802.11 networks with coexisting FD and HD STAs. The protocol explicitly accounts for the peculiarities of FD environments and backward compatibility. Key aspects of the proposed solution include FD capability discovery, a handshake mechanism for channel access, node selection for UFD transmission, adaptive ACK timeout for STAs engaged in BFD or UFD transmission, and mitigation of contention unfairness. Performance evaluation demonstrates the effectiveness of the proposed solution in realizing the gains of FD technology for next generation WLANs.
Safe Learning of Quadrotor Dynamics Using Barrier Certificates

To effectively control complex dynamical systems, accurate nonlinear models are typically needed. However, these models are not always known. In this paper, we present a data-driven approach based on Gaussian processes that learns models of quadrotors operating in partially unknown environments. What makes this challenging is that if the learning process is not carefully controlled, the system will go unstable, i.e., the quadcopter will crash. To this end, barrier certificates are employed for safe learning. The barrier certificates establish a non-conservative forward invariant safe region, in which high probability safety guarantees are provided based on the statistics of the Gaussian Process. A learning controller is designed to efficiently explore those uncertain states and expand the barrier certified safe region based on an adaptive sampling scheme. Simulation results are provided to demonstrate the effectiveness of the proposed approach.
Cloning of 16S rRNA genes amplified from normal and disturbed vaginal microflora suggests a strong association between Atopobium vaginae, Gardnerella vaginalis and bacterial vaginosis

The pathogenesis of bacterial vaginosis remains largely elusive, although some microorganisms, including Gardnerella vaginalis, are suspected of playing a role in the etiology of this disorder. Recently culture-independent analysis of microbial ecosystems has proven its efficacy in characterizing the diversity of bacterial populations. Here, we report on the results obtained by combining culture and PCR-based methods to characterize the normal and disturbed vaginal microflora. A total of 150 vaginal swab samples from healthy women (115 pregnant and 35 non-pregnant) were categorized on the basis of Gram stain of direct smear as grade I (n = 112), grade II (n = 26), grade III (n = 9) or grade IV (n = 3). The composition of the vaginal microbial community of eight of these vaginal swabs (three grade I, two grade II and three grade III), all from non-pregnant women, were studied by culture and by cloning of the 16S rRNA genes obtained after direct amplification. Forty-six cultured isolates were identified by tDNA-PCR, 854 cloned 16S rRNA gene fragments were analysed of which 156 by sequencing, yielding a total of 38 species, including 9 presumptively novel species with at least five species that have not been isolated previously from vaginal samples. Interestingly, cloning revealed that Atopobium vaginae was abundant in four out of the five non-grade I specimens. Finally, species specific PCR for A. vaginae and Gardnerella vaginalis pointed to a statistically significant co-occurrence of both species in the bacterial vaginosis samples. Although historically the literature regarding bacterial vaginosis has largely focused on G. vaginalis in particular, several findings of this study – like the abundance of A. vaginae in disturbed vaginal microflora and the presence of several novel species – indicate that much is to be learned about the composition of the vaginal microflora and its relation to the etiology of BV.
Secure Digital Voting System Based on Blockchain Technology

Electronic﻿voting﻿or﻿e-voting﻿has﻿been﻿used﻿in﻿varying﻿forms﻿since﻿1970s﻿with﻿fundamental﻿benefits﻿ over﻿ paper-based﻿ systems﻿ such﻿ as﻿ increased﻿ efficiency﻿ and﻿ reduced﻿ errors.﻿ However,﻿ challenges﻿ remain﻿to﻿the﻿achieving﻿of﻿wide﻿spread﻿adoption﻿of﻿such﻿systems,﻿especially﻿with﻿respect﻿to﻿improving﻿ their﻿resilience﻿against﻿potential﻿faults.﻿Blockchain﻿is﻿a﻿disruptive﻿technology﻿of﻿the﻿current﻿era﻿and﻿ promises﻿ to﻿ improve﻿ the﻿overall﻿ resilience﻿of﻿ e-voting﻿ systems.﻿This﻿ article﻿presents﻿ an﻿effort﻿ to﻿ leverage﻿benefits﻿of﻿blockchain﻿such﻿as﻿cryptographic﻿foundations﻿and﻿transparency﻿to﻿achieve﻿an﻿ effective﻿scheme﻿for﻿e-voting.﻿The﻿proposed﻿scheme﻿conforms﻿to﻿the﻿fundamental﻿requirements﻿for﻿ e-voting﻿schemes﻿and﻿achieves﻿end-to-end﻿verifiability.﻿The﻿article﻿presents﻿details﻿of﻿the﻿proposed﻿ e-voting﻿scheme﻿along﻿with﻿its﻿implementation﻿using﻿Multichain﻿platform.﻿The﻿article﻿also﻿presents﻿ an﻿in-depth﻿evaluation﻿of﻿the﻿scheme﻿which﻿successfully﻿demonstrates﻿its﻿effectiveness﻿to﻿achieve﻿an﻿ end-to-end﻿verifiable﻿e-voting﻿scheme.  Blockchain, E-Government, Electronic Voting, E-Voting, Verifiable Voting
Bile Acid Signaling Pathways from the Enterohepatic Circulation to the Central Nervous System

Bile acids are best known as detergents involved in the digestion of lipids. In addition, new data in the last decade have shown that bile acids also function as gut hormones capable of influencing metabolic processes via receptors such as FXR (farnesoid X receptor) and TGR5 (Takeda G protein-coupled receptor 5). These effects of bile acids are not restricted to the gastrointestinal tract, but can affect different tissues throughout the organism. It is still unclear whether these effects also involve signaling of bile acids to the central nervous system (CNS). Bile acid signaling to the CNS encompasses both direct and indirect pathways. Bile acids can act directly in the brain via central FXR and TGR5 signaling. In addition, there are two indirect pathways that involve intermediate agents released upon interaction with bile acids receptors in the gut. Activation of intestinal FXR and TGR5 receptors can result in the release of fibroblast growth factor 19 (FGF19) and glucagon-like peptide 1 (GLP-1), both capable of signaling to the CNS. We conclude that when plasma bile acids levels are high all three pathways may contribute in signal transmission to the CNS. However, under normal physiological circumstances, the indirect pathway involving GLP-1 may evoke the most substantial effect in the brain.