nils-herrmann/clean_scirepeval huggingface dataset:
Understanding the perceived logic of care by vaccine-hesitant and vaccine-refusing parents: A qualitative study in Australia

In terms of public health, childhood vaccination programs have benefits that far outweigh risks. However, some parents decide not to vaccinate their children. This paper explores the ways in which such parents talked about the perceived risks and benefits incurred by vaccinating (or not vaccinating) their children. Between 2013-2016 we undertook 29 in-depth interviews with non-vaccinating and/or 'vaccine hesitant' parents in Australia. Interviews were conducted in an open and non-judgmental manner, akin to empathic neutrality. Interviews focused on parents talking about the factors that shaped their decisions not to (or partially) vaccinate their children. All interviews were transcribed and analysed using both inductive and deductive processes. The main themes focus on parental perceptions of: 1. their capacity to reason; 2. their rejection of Western medical epistemology; and 3. their participation in labour intensive parenting practices (which we term salutogenic parenting). Parents engaged in an ongoing search for information about how best to parent their children (capacity to reason), which for many led to questioning/distrust of traditional scientific knowledge (rejection of Western medical epistemology). Salutogenic parenting spontaneously arose in interviews, whereby parents practised health promoting activities which they saw as boosting the natural immunity of their children and protecting them from illness (reducing or negating the perceived need for vaccinations). Salutogenic parenting practices included breastfeeding, eating organic and/or home-grown food, cooking from scratch to reduce preservative consumption and reducing exposure to toxins. We interpret our data as a 'logic of care', which is seen by parents as internally consistent, logically inter-related and inter-dependent. Whilst not necessarily sharing the parents' reasoning, we argue that an understanding of their attitudes towards health and well-being is imperative for any efforts to engage with their vaccine refusal at a policy level.
A Cost-Effective Deadline-Constrained Dynamic Scheduling Algorithm for Scientific Workflows in a Cloud Environment

Cloud computing, a distributed computing paradigm, enables delivery of IT resources over the Internet and follows the pay-as-you-go billing model. Workflow scheduling is one of the most challenging problems in cloud computing. Although, workflow scheduling on distributed systems like grids and clusters have been extensively studied, however, these solutions are not viable for a cloud environment. It is because, a cloud environment differs from other distributed environment in two major ways: on-demand resource provisioning and pay-as-you-go pricing model. Thus, to achieve the true benefits of workflow orchestration onto cloud resources novel approaches that can capitalize the advantages and address the challenges specific to a cloud environment needs to be developed. This work proposes a dynamic cost-effective deadline-constrained heuristic algorithm for scheduling a scientific workflow in a public cloud. The proposed technique aims to exploit the advantages offered by cloud computing while taking into account the virtual machine (VM) performance variability and instance acquisition delay to identify a just-in-time schedule of a deadline constrained scientific workflow at lesser costs. Performance evaluation on some well-known scientific workflows exhibit that the proposed algorithm delivers better performance in comparison to the current state-of-the-art heuristics.
Design of Electronic Differential System for an Electric Vehicle with in-wheel motor

This paper presents design of Electronic Differential System (EDS) for an Electric Vehicle (EV) with in-wheel motor. EDS is generally used in EVs due to some drawbacks of mechanical differential such as being heavy systems and mechanical losses caused by the powertrains. According to the turning angle of the wheel, task of EDS for front wheels is to adjust rpm of the wheels. On the contrary for rear wheels, only rpm control is realized due to not steering. Hence, there are less studies on EDS for front wheels of EV in the literature. In this study, an EDS for front wheels of EV is designed. According to steering angle and speed of EV, the speeds of the front wheels are estimated by equations derived from Ackermann-Jeantand model using Codesys Software Package. The estimated speeds are sent to Induction Motor (IM) Drives via Controller Area Network-Bus (CAN-Bus). EDS is also simulated by Matlab/Simulink. Then, the speeds of the front wheels are experimentally measured by a tachometer. Codesys results are verified by both Simulink and experimental results. It is observed that the designed EDS is convenient for EVs with in-wheel motor.
Assembly algorithms for next-generation sequencing data.

The emergence of next-generation sequencing platforms led to resurgence of research in whole-genome shotgun assembly algorithms and software. DNA sequencing data from the Roche 454, Illumina/Solexa, and ABI SOLiD platforms typically present shorter read lengths, higher coverage, and different error profiles compared with Sanger sequencing data. Since 2005, several assembly software packages have been created or revised specifically for de novo assembly of next-generation sequencing data. This review summarizes and compares the published descriptions of packages named SSAKE, SHARCGS, VCAKE, Newbler, Celera Assembler, Euler, Velvet, ABySS, AllPaths, and SOAPdenovo. More generally, it compares the two standard methods known as the de Bruijn graph approach and the overlap/layout/consensus approach to assembly.
Generating Typed Dependency Parses from Phrase Structure Parses

This paper describes a system for extracting typed dependency parses of English sentences from phrase structure parses. In order to capture inherent relations occurring in corpus texts that can be critical in real-world applications, many NP relations are included in the set of grammatical relations used. We provide a comparison of our system with Minipar and the Link parser. The typed dependency extraction facility described here is integrated in the Stanford Parser, available for download.
Genomics of cellulosic biofuels

The development of alternatives to fossil fuels as an energy source is an urgent global priority. Cellulosic biomass has the potential to contribute to meeting the demand for liquid fuel, but land-use requirements and process inefficiencies represent hurdles for large-scale deployment of biomass-to-biofuel technologies. Genomic information gathered from across the biosphere, including potential energy crops and microorganisms able to break down biomass, will be vital for improving the prospects of significant cellulosic biofuel production.
Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors

In this paper, we extend existing work on latent attribute inference by leveraging the principle of homophily: we evaluate the inference accuracy gained by augmenting the user features with features derived from the Twitter profiles and postings of her friends. We consider three attributes which have varying degrees of assortativity: gender, age, and political affiliation. Our approach yields a significant and robust increase in accuracy for both age and political affiliation, indicating that our approach boosts performance for attributes with moderate to high assortativity. Furthermore, different neighborhood subsets yielded optimal performance for different attributes, suggesting that different subsamples of the user’s neighborhood characterize different aspects of the user herself. Finally, inferences using only the features of a user’s neighbors outperformed those based on the user’s features alone. This suggests that the neighborhood context alone carries substantial information about the user.
The Effect of the Performance Appraisal System on Trust for Management : A Field Quasi-Experiment

Recent theoretical developments have enabled the empirical study of trust for specific referents in organizations. The authors conducted a 14-month field study of employee trust for top management. A 9-month quasi-experiment found that the implementation of a more acceptable performance appraisal system increased trust for top management. The 3 proposed factors of trustworthiness (ability, benevolence, and integrity) mediated the relationship between perceptions of the appraisal system and trust.
Engineering exosomes as refined biological nanoplatforms for drug delivery

Exosomes, a subgroup of extracellular vesicles (EVs), have been recognized as important mediators of long distance intercellular communication and are involved in a diverse range of biological processes. Because of their ideal native structure and characteristics, exosomes are promising nanocarriers for clinical use. Exosomes are engineered at the cellular level under natural conditions, but successful exosome modification requires further exploration. The focus of this paper is to summarize passive and active loading approaches, as well as specific exosome modifications and examples of the delivery of therapeutic and imaging molecules. Examples of exosomes derived from a variety of biological origins are also provided. The biocompatible characteristics of exosomes, with suitable modifications, can increase the stability and efficacy of imaging probes and therapeutics while enhancing cellular uptake. Challenges in clinical translation of exosome-based platforms from different cell sources and the advantages of each are also reviewed and discussed.
Analysis of user keyword similarity in online social networks

How do two people become friends? What role does homophily play in bringing two people closer to help them forge friendship? Is the similarity between two friends different from the similarity between any two people? How does the similarity between a friend of a friend compare to similarity between direct friends? In this work, our goal is to answer these questions. We study the relationship between semantic similarity of user profile entries and the social network topology. A user profile in an on-line social network is characterized by its profile entries. The entries are termed as user keywords. We develop a model to relate keywords based on their semantic relationship and define similarity functions to quantify the similarity between a pair of users. First, we present a ‘forest model’ to categorize keywords across multiple categorization trees and define the notion of distance between keywords. Second, we use the keyword distance to define similarity functions between a pair of users. Third, we analyze a set of Facebook data according to the model to determine the effect of homophily in on-line social networks. Based on our evaluations, we conclude that direct friends are more similar than any other user pair. However, the more striking observation is that except for direct friends, similarities between users are approximately equal, irrespective of the topological distance between them.
Culture and Negotiation

This article develops a model of how culture affects negotiation processes and outcomes. It begins with a description of negotiation from a Western perspective: confrontational, focused on transactions or the resolutionof disputes, evaluated in terms of integrative and distributive outcomes. It proposes that power and information processes are fundamental to negotiations and that one impact of culture on negotiations is through these processes. The cultural value of individualism versus collectivismis linked to goals in negotiation; the cultural value of egalitarianism versus hierarchy is linked to power in negotiation; and the cultural value for high versus low context communication is linked to information sharing in negotiation. The article describes why inter-cultural negotiations pose signi® cant strategic challenges, but concludes that negotiators who are motivated to search for information, and are ̄ exible about how that search is carried out, can reach high-quality negotiated outcomes.
ENDING CORRUPTION : THE INTERPLAY AMONG INSTITUTIONAL LOGICS , RESOURCES , AND INSTITUTIONAL ENTREPRENEURS

We draw from theories of institutions and collective identities to present a threefold framework of institutional change—involving institutional logics, resources, and social actors—that furthers our understanding of the mitigation of corruption. Those social actors intent on reforming corruption function as institutional entrepreneurs, and their success depends both on articulating an anticorruption institutional logic that incorporates corruption-disabling identities, cognitive schemas, and practices and on having or developing the resources necessary to propagate the new anticorruption institutional logic.
Enhancing perceptual and attentional skills requires common demands between the action video games and transfer tasks

Despite increasing evidence that shows action video game play improves perceptual and cognitive skills, the mechanisms of transfer are not well-understood. In line with previous work, we suggest that transfer is dependent upon common demands between the game and transfer task. In the current study, participants played one of four action games with varying speed, visual, and attentional demands for 20 h. We examined whether training enhanced performance for attentional blink, selective attention, attending to multiple items, visual search and auditory detection. Non-gamers who played the game (Modern Combat) with the highest demands showed transfer to tasks of attentional blink and attending to multiple items. The game (MGS Touch) with fewer attentional demands also decreased attentional blink, but to a lesser degree. Other games failed to show transfer, despite having many action game characteristics but at a reduced intensity. The results support the common demands hypothesis.
Cross-Point Resistive RAM Based on Field-Assisted Superlinear Threshold Selector

We report a 3-D-stackable 1S1R passive cross-point resistive random access memory (RRAM). The sneak (leakage) current challenge in the cross-point RRAM integration has been overcome utilizing a field-assisted superlinear threshold selector. The selector offers high selectivity of >107, sharp switching slope of <;5 mV/decade, ability to tune the threshold voltage, stable operation at 125°C, and endurance of >1011. Furthermore, we demonstrate 1S1R integration in which the selector-subthreshold current is <;10 pA while offering >102 memory ON/OFF ratio and >106 selectivity during cycling. Combined with self-current-controlled RRAM, the 1S1R enables high-density and high-performance memory applications.
Design of Lightweight Authentication and Key Agreement Protocol for Vehicular Ad Hoc Networks

Due to the widespread popularity in both academia and industry, vehicular ad hoc networks (VANETs) have been used in a wide range of applications starting from intelligent transportation to e-health and itinerary planning. This paper proposes a new decentralized lightweight authentication and key agreement scheme for VANETs. In the proposed scheme, there are three types of mutual authentications: 1) between vehicles; 2) between vehicles and their respective cluster heads; and 3) between cluster heads and their respective roadside units. Apart from these authentications, the proposed scheme also maintains secret keys between roadside units for their secure communications. The rigorous formal and informal security analysis shows that the proposed scheme is capable to defend various malicious attacks. Moreover, the ns-2 simulation demonstrates the practicability of the proposed scheme in VANET environment.
Detecting social media mobile botnets using user activity correlation and artificial immune system

With the rapidly growing development of cellular networks and powerful smartphones, botnets have invaded the mobile domain. Social media, like Twitter, Facebook, and YouTube have created a new communication channel for attackers. Recently, bot masters started to exploit social media for different malicious activity, such as sending spam, recruitment of new bots, and botnet command and control. In this paper we propose a detection technique for social mediabased mobile botnets using Twitter. The proposed method combines the correlation between tweeting and user activity, such as clicks or taps, and an Artificial Immune System detector, to detect tweets caused by bots and differentiate them from tweets generated by user or by user-approved applications. This detector creates a signature of the tweet and compares it with a dynamically updated signature library of bot behavior signatures. The proposed system has been fully implemented on Android platform and tested under several sets of generated tweets. The test results show that the proposed method has a very high accuracy in detecting bot tweets with about 95% detection ratio.
Learning Human Pose Models from Synthesized Data for Robust RGB-D Action Recognition

We propose Human Pose Models that represent RGB and depth images of human poses independent of clothing textures, backgrounds, lighting conditions, body shapes and camera viewpoints. Learning such universal models requires training images where all factors are varied for every human pose. Capturing such data is prohibitively expensive. Therefore, we develop a framework for synthesizing the training data. First, we learn representative human poses from a large corpus of real motion captured human skeleton data. Next, we fit synthetic 3D humans with different body shapes to each pose and render each from 180 camera viewpoints while randomly varying the clothing textures, background and lighting. Generative Adversarial Networks are employed to minimize the gap between synthetic and real image distributions. CNN models are then learned that transfer human poses to a shared high-level invariant space. The learned CNN models are then used as invariant feature extractors from real RGB and depth frames of human action videos and the temporal variations are modelled by Fourier Temporal Pyramid. Finally, linear SVM is used for classification. Experiments on three benchmark cross-view human action datasets show that our algorithm outperforms existing methods by significant margins for RGB only and RGB-D action recognition.
Phosphorylation of LC3 by the Hippo kinases STK3/STK4 is essential for autophagy.

The protein LC3 is indispensible for the cellular recycling process of autophagy and plays critical roles during cargo recruitment, autophagosome biogenesis, and completion. Here, we report that LC3 is phosphorylated at threonine 50 (Thr(50)) by the mammalian Sterile-20 kinases STK3 and STK4. Loss of phosphorylation at this site blocks autophagy by impairing fusion of autophagosomes with lysosomes, and compromises the ability of cells to clear intracellular bacteria, an established cargo for autophagy. Strikingly, mutation of LC3 mimicking constitutive phosphorylation at Thr(50) reverses the autophagy block in STK3/STK4-deficient cells and restores their capacity to clear bacteria. Loss of STK3/STK4 impairs autophagy in diverse species, indicating that these kinases are conserved autophagy regulators. We conclude that phosphorylation of LC3 by STK3/STK4 is an essential step in the autophagy process. Since several pathological conditions, including bacterial infections, display aberrant autophagy, we propose that pharmacological agents targeting this regulatory circuit hold therapeutic potential.
Flexible Camera Calibration by Viewing a Plane from Unknown Orientations

We propose a flexible new technique to easily calibrate a camera. It only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Either the camera or the planar pattern can be freely moved. The motion need not be known. Radial lens distortion is modeled. The proposed procedure consists of a closed-form solution, followed by a nonlinear refinement based on the maximum likelihood criterion. Both computer simulation and real data have been used to test the proposed technique, and very good results have been obtained. Compared with classical techniques which use expensive equipment such as two or three orthogonal planes, the proposed technique is easy to use and flexible. It advances 3D computer vision one step from laboratory environments to real world use. The corresponding software is available from the author’s Web page.
Human auditory evoked potentials. II. Effects of attention.

Auditory attention involves the selective perception of a particular auditory message and the relative suppression of competing sensory information. Neurophysiological theories of auditory attention have ranged l¥om selective peripheral gating prior to sensory analysis (e.,q,, HernandezPeon 1966), to a more central selective response to tho,~ stimuli evaluat~xt as signilicant after sensoD' analysis (e,,q., Sokolov 1963). One approach to determining the neural mechanism of this stimulus-selection process is to study the influence of different attentive strategies upon the components of the human auditory evoked potential. The "'early" (0-8 msec) and "'middle" (8 50 msec) latency components have been found to be stable during fluctuations in subjective arousal and environmental conditions {Mendel and Goldstein 1969, 197 ! : Jewett and Williston 197 ! ; Amadeo and Shagass 1973). but have not been examined during changes in the direction of selective attention. Directing attention toward an auditory stimulus has consistently been reported to enhance the N~ (90 msec) P_, (170 msec) components ofthe evoked response (Davis 1964: Gross et al. 1965; Satterfield 1965; Spong et ai. 1965; Wilkinson and Morlock 1967; Hirsch 1971; Keating and Ruhm 1971: Picton et al. 1971). In certain conditions attended task relevant auditory stimuli may also elicit a late positive component or P3 wave (Sutton et al. 1965. 1967: Wilkinson and Morlock 1967; Ritter and Vaughan 1969: Sheatz and Chapman 1969; Smith et al. 1970; Hillyard et al. 1971:
KP-Miner: A keyphrase extraction system for English and Arabic documents

Automatic key phrase extraction has many important applications including but not limited to summarization, cataloging/indexing, feature extraction for clustering and classification, and data mining. This paper presents the KP-Miner system, and demonstrates through experimentation and comparison with existing systems that the it is effective in extracting keyphrases from both English and Arabic documents of varied length. Unlike other existing keyphrase extraction systems, the KP-Miner system has the advantage of being configurable as the rules and heuristics adopted by the system are related to the general nature of documents and keyphrase. This implies that the users of this system can use their understanding of the document(s) being input into the system, to fine tune it to their particular needs.
Geographical linked data: a Spanish use case

We present the process that has been followed for the development of an application that makes use of several heterogeneous Spanish public datasets that are related to administrative, hydrographic, and statistical domains. Our application aims at analysing existing relations between the Spanish coastal area and different statistical variables such as unemployment, population, dwelling, industry, and building trade. Moreover, we provide an important innovation with respect to other similar processes followed in other initiatives by dealing with the geometrical information of features.