nils-herrmann/clean_scirepeval huggingface dataset:
Flaming on YouTube

Flaming is defined as " displaying hostility by insulting, swearing or using otherwise offensive language. " It seems to be common in comments on the video sharing website YouTube. In this explorative study, flaming on YouTube was studied using surveys among YouTube users. Three general conclusions were drawn. First, flaming is indeed very common on YouTube, although many users say not to flame themselves. Second, views on flaming are varied, but more often negative than positive. Some people refrain from uploading videos because of flaming, but most users do not think of flaming as a problem for themselves. Third, several explanations of flaming were found to be plausible, among which were perceived flaming norms and reduced awareness of other people's feelings. Although some YouTube users flame for entertainment, flaming is more often meant to express disagreement or to respond to perceived offense by others. A.1 Invitation for " Senders " 49 A.2 Invitation for " Receivers " 49 A.3 Invitation to the General Questionnaire 49 Appendix B – Questionnaires 51 B.1 Items Measuring  The Last Page 51 B.3 Questionnaire for " Senders " 51 B.4 Questionnaire for " Receivers " 53 B.5 General Questionnaire 54
Shared and nonshared neural networks of cognitive and affective theory-of-mind: a neuroimaging study using cartoon picture stories.

Theory of mind (ToM) refers to the ability to represent one's own and others' cognitive and affective mental states. Recent imaging studies have aimed to disentangle the neural networks involved in cognitive as opposed to affective ToM, based on clinical observations that the two can functionally dissociate. Due to large differences in stimulus material and task complexity findings are, however, inconclusive. Here, we investigated the neural correlates of cognitive and affective ToM in psychologically healthy male participants (n = 39) using functional brain imaging, whereby the same set of stimuli was presented for all conditions (affective, cognitive and control), but associated with different questions prompting either a cognitive or affective ToM inference. Direct contrasts of cognitive versus affective ToM showed that cognitive ToM recruited the precuneus and cuneus, as well as regions in the temporal lobes bilaterally. Affective ToM, in contrast, involved a neural network comprising prefrontal cortical structures, as well as smaller regions in the posterior cingulate cortex and the basal ganglia. Notably, these results were complemented by a multivariate pattern analysis (leave one study subject out), yielding a classifier with an accuracy rate of more than 85% in distinguishing between the two ToM-conditions. The regions contributing most to successful classification corresponded to those found in the univariate analyses. The study contributes to the differentiation of neural patterns involved in the representation of cognitive and affective mental states of others.
A Comparative Study of Alternative FACS Coding Algorithms

Two groups were contracted to experiment with coding of FACS (Ekman & Friesen, 1978) action units on a common database. One group is ours at CMU and the University of Pittsburgh, and the other is at UCSD. The database is from Frank and Ekman (1997) who video-recorded an interrogation in which subjects lied or told the truth about a mock crime. Subjects were ethnically diverse, action units occurred during speech, and out-of-plane motion and occlusion from head motion and glasses were common. The video data were originally collected to answer substantive questions in psychology, and represent a substantial challenge to automated AU recognition. This report describes the results of automated facial expression analysis by the CMU/Pittsburgh group. An interdisciplinary team of consultants, who have combined expertise in computer vision and in facial analysis, will compare the results of this report with those in a separate report submitted by UCSD group.
Knowledge Diffusion for Neural Dialogue Generation

๏ The experimental results show that the proposed model effectively generate more diverse and meaningful responses involving more accurate relevant entities compared with the state-of-the-art baselines. We collect a multi-turn conversation corpus which includes not only facts related inquiries but also knowledge-based chit-chats. The data is publicly available at https:// github.com/liushuman/neural-knowledge-diffusion. We obtain the element information of each movie from https://movie.douban.com/ and build the knowledge base K. The question-answering dialogues and knowledge related chit-chat are crawled from https://zhidao.baidu.com/ and https://www.douban.com/group/. The conversations are grounded on the knowledge using NER, string match, and artificial scoring and filtering rules. The total 32977 conversations consisting of 104567 utterances are divided into training (32177) and testing set (800).
Enhancing student learning experience with technology-mediated gamification: An empirical study

We evaluated the use of gamification to facilitate a student-centered learning environment within an undergraduate Year 2 Personal and Professional Development (PPD) course. In addition to face-to-face classroom practices, an information technology-based gamified system with a range of online learning activities was presented to students as support material. The implementation of the gamified course lasted two academic terms. The subsequent evaluation from a cohort of 136 students indicated that student performance was significantly higher among those who participated in the gamified system than in those who engaged with the nongamified, traditional delivery, while behavioral engagement in online learning activities was positively related to course performance, after controlling for gender, attendance, and Year 1 PPD performance. Two interesting phenomena appeared when we examined the influence of student background: female students participated significantly more in online learning activities than male students, and students with jobs engaged significantly more in online learning activities than students without jobs. The gamified course design advocated in this work may have significant implications for educators who wish to develop engaging technology-mediated learning environments that enhance students’ learning, or for a broader base of professionals who wish to engage a population of potential users, such as managers engaging employees or marketers engaging customers.
RAERA: A Robust Auctioning Approach for Edge Resource Allocation

In edge computing, content and service providers aim at enhancing user experience by providing services closer to the user. At the same time, infrastructure providers such as access ISPs aim at utilizing their infrastructure by selling edge resources to these content and service providers. In this context, auctions are widely used to set a price that reflects supply and demand in a fair way. In this work, we propose RAERA, the first robust auction scheme for edge resource allocation that is suitable to work with the market uncertainty typical for edge resources---here, customers typically have different valuation distribution for a wide range of heterogeneous resources. Additionally, RAERA encourages truthful bids and allows the infrastructure provider to maximize its break-even profit. Our preliminary evaluations highlight that REARA offers a time dependent fair price. Sellers can achieve higher revenue in the range of 5%-15% irrespective of varying demands and the buyers pay up to 20% lower than their top bid amount.
The diagnostic odds ratio: a single indicator of test performance.

Diagnostic testing can be used to discriminate subjects with a target disorder from subjects without it. Several indicators of diagnostic performance have been proposed, such as sensitivity and specificity. Using paired indicators can be a disadvantage in comparing the performance of competing tests, especially if one test does not outperform the other on both indicators. Here we propose the use of the odds ratio as a single indicator of diagnostic performance. The diagnostic odds ratio is closely linked to existing indicators, it facilitates formal meta-analysis of studies on diagnostic test performance, and it is derived from logistic models, which allow for the inclusion of additional variables to correct for heterogeneity. A disadvantage is the impossibility of weighing the true positive and false positive rate separately. In this article the application of the diagnostic odds ratio in test evaluation is illustrated.
AN EFFECTIVE PRE -PROCESSING ALGORITHM FOR INFORMATION RETRIEVAL SYSTEMS

The Internet is probably the most successful distributed computing system ever. However, our capabilities for data querying and manipulation on the internet are primordial at best. The user expectations are enhancing over the period of time along with increased amount of operational data past few decades. The data-user expects more deep, exact, and detailed results. Result retrieval for the user query is always relative o the pattern of data storage and index. In Information retrieval systems, tokenization is an integrals part whose prime objective is to identifying the token and their count. In this paper, we have proposed an effective tokenization approach which is based on training vector and result shows that efficiency/ effectiveness of proposed algorithm. Tokenization on documents helps to satisfy user’s information need more precisely and reduced search sharply, is believed to be a part of information retrieval. Pre-processing of input document is an integral part of Tokenization, which involves preprocessing of documents and generates its respective tokens which is the basis of these tokens probabilistic IR generate its scoring and gives reduced search space. The comparative analysis is based on the two parameters; Number of Token generated, Pre-processing time.
The Pascal Visual Object Classes Challenge: A Retrospective

The Pascal Visual Object Classes (VOC) challenge consists of two components: (i) a publicly available dataset of images together with ground truth annotation and standardised evaluation software; and (ii) an annual competition and workshop. There are five challenges: classification, detection, segmentation, action classification, and person layout. In this paper we provide a review of the challenge from 2008–2012. The paper is intended for two audiences: algorithm designers, researchers who want to see what the state of the art is, as measured by performance on the VOC datasets, along with the limitations and weak points of the current generation of algorithms; and, challenge designers, who want to see what we as organisers have learnt from the process and our recommendations for the organisation of future challenges. To analyse the performance of submitted algorithms on the VOC datasets we introduce a number of novel evaluation methods: a bootstrapping method for determining whether differences in the performance of two algorithms are significant or not; a normalised average precision so that performance can be compared across classes with different proportions of positive instances; a clustering method for visualising the performance across multiple algorithms so that the hard and easy images can be identified; and the use of a joint classifier over the submitted algorithms in order to measure their complementarity and combined performance. We also analyse the community’s progress through time using the methods of Hoiem et al. ( 2012) to identify the types of occurring errors. We conclude the paper with an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.
OCR Error Correction: State-of-the-Art vs an NMT-based Approach

Although the performance of the state-of-the-art OCR systems is very high, they can still introduce errors due to various reasons, and when it comes to historical documents with old manuscripts the performance of such systems gets even worse. That is why Post-OCR error correction has been an open problem for many years. Many state-of-the-art approaches have been introduced through the recent years. This paper contributes to the field of Post-OCR Error Correction by introducing two novel deep learning approaches to improve the accuracy of OCR systems, and a post processing technique that can further enhance the quality of the output results. These approaches are based on Neural Machine Translation (NMT) and were motivated by the great success that deep learning introduced to the field of Natural Language Processing. Finally, we will compare the state-of-the-art approaches in Post-OCR Error Correction with the newly introduced systems and discuss the results.
Understanding Human-Centric Images: From Geometry to Fashion

Understanding humans from photographs has always been a fundamental goal of computer vision. Early works focused on simple tasks such as detecting the location of individuals by means of bounding boxes. As the field progressed, harder and more higher level tasks have been undertaken. For example, from human detection came the 2D and 3D human pose estimation in which the task consisted of identifying the location in the image or space of all different body parts, e.g., head, torso, knees, arms, etc. Human attributes also became a great source of interest as they allow recognizing individuals and other properties such as gender or age. Later, the attention turned to the recognition of the action being performed. This, in general, relies on the previous works on pose estimation and attribute classification. Currently, even higher level tasks are being conducted such as predicting the motivations of human behaviour or identifying the fashionability of an individual from a photograph. In this thesis we have developed a hierarchy of tools that cover all these range of problems, from low level feature point descriptors to high level fashion-aware conditional random fields models, all with the objective of understanding humans from monocular RGB images. In order to build these high level models it is paramount to have a battery of robust and reliable low and mid level cues. Along these lines, we have proposed two low-level keypoint descriptors: one based on the theory of the heat diffusion on images, and the other that uses a convolutional neural network to learn discriminative image patch representations. We also introduce distinct low-level generative models for representing human pose: in particular we present a discrete model based on a directed acyclic graph and a continuous model that consists of poses clustered on a Riemannian manifold. As mid level cues we propose two 3D human pose estimation algorithms: one that estimates the 3D pose given a noisy 2D estimation, and an approach that simultaneously estimates both the 2D and 3D pose. Finally, we formulate higher level models built upon low and mid level cues for understanding humans from single images. Concretely, we focus on two different tasks in the context of fashion: semantic segmentation of clothing, and predicting the fashionability from images with metadata to ultimately provide fashion advice to the user. In summary, to robustly extract knowledge from images with the presence of humans it is necessary to build high level models that integrate low and mid level cues. In general, using and understanding strong features is critical for obtaining reliable performance. The main contribution of this thesis is in proposing a variety of low, mid and high level algorithms for human-centric images that can be integrated into higher level models for comprehending humans from photographs, as well as tackling novel fashionoriented problems.
Conceptual framework for personal recovery in mental health: systematic review and narrative synthesis.

No systematic review and narrative synthesis on personal recovery in mental illness has been undertaken.



To synthesise published descriptions and models of personal recovery into an empirically based conceptual framework.



Systematic review and modified narrative synthesis.



Out of 5208 papers that were identified and 366 that were reviewed, a total of 97 papers were included in this review. The emergent conceptual framework consists of: (a) 13 characteristics of the recovery journey; (b) five recovery processes comprising: connectedness; hope and optimism about the future; identity; meaning in life; and empowerment (giving the acronym CHIME); and (c) recovery stage descriptions which mapped onto the transtheoretical model of change. Studies that focused on recovery for individuals of Black and minority ethnic (BME) origin showed a greater emphasis on spirituality and stigma and also identified two additional themes: culturally specific facilitating factors and collectivist notions of recovery.



The conceptual framework is a theoretically defensible and robust synthesis of people's experiences of recovery in mental illness. This provides an empirical basis for future recovery-oriented research and practice.
Survey of Auto Seat Design Recommendations for Improved Comfort

PREFACE This report is a revised and extended edition of an earlier report. In 1994, the present author, with colleagues Lawrence Schneider and Leda Ricci from the University of Michigan Transportation Research Institute (UMTRI), undertook a thorough review of the academic and technical literature relating to automotive seat design, with a particular emphasis on accommodation and comfort. In the intervening six years, attention to seating issues has increased, to judge from the volume of related material published at SAE meetings, in ergonomics journals, and in other forums. Technological advances have increased the use of seat surface pressure distribution in seat design and assessment. Recently, a new H-point manikin was developed at UMTRI under the auspices of the Automotive Seat and Package Evaluation and Comparison Tools (ASPECT) program. As part of the program, several detailed studies of the interaction between vehicle occupants and seats were conducted that led to new insights into how seat geometry and stiffness affects occupant posture. The updated and expanded version of this report was undertaken to incorporate the findings of the ASPECT program and other studies that have been reported in the open literature since 1994. The anthropometric analyses supporting seat dimension recommendations have been revised with reference to the latest U.S. civilian anthropometry, the third National Health and Nutrition Examination Survey (NHANES III), conducted from 1988 to 1994. Although anthropometric analyses from the survey have not yet been published, the data have recently been made available and were analyzed for this report. Much of the new information in this report is based on research conducted at UMTRI during the past six years under sponsorship of auto industry companies. ii iii CONTENTS
Zero current detection technique for fast transient response in buck DC-DC converters

A load dependent zero-current detector is proposed in this paper for speeding up the transient response when load current changes from heavy to light loads. The fast transient control signal determines how long the reversed inductor current according to sudden load variations. At the beginning of load variation from heavy to light loads, the sensed voltage compared with higher voltage to discharge the overshoot output voltage for achieving fast transient response. Besides, for an adaptive reversed current period, the fast transient mechanism is turned off since the output voltage is rapidly regulated back to the acceptable level. Simulation results demonstrate that the ZCD circuit permits the reverse current flowing back into n-type power MOSFET at the beginning of load variations. The settling time is decreased to about 35 mus when load current suddenly changes from 500mA to 10 mA.
Sample size determination.

Scientists who use animals in research must justify the number of animals to be used, and committees that review proposals to use animals in research must review this justification to ensure the appropriateness of the number of animals to be used. This article discusses when the number of animals to be used can best be estimated from previous experience and when a simple power and sample size calculation should be performed. Even complicated experimental designs requiring sophisticated statistical models for analysis can usually be simplified to a single key or critical question so that simple formulae can be used to estimate the required sample size. Approaches to sample size estimation for various types of hypotheses are described, and equations are provided in the Appendix. Several web sites are cited for more information and for performing actual calculations
A graph-based framework for analyzing SQL query logs

Analytical SQL queries are a valuable source of information. Query log analysis can provide insight into the usage of datasets and uncover knowledge that cannot be inferred from source schemas or content alone. To unlock this potential, flexible mechanisms for meta-querying are required. Syntactic and semantic aspects of queries must be considered along with contextual information.
 We present an extensible framework for analyzing SQL query logs. Query logs are mapped to a multi-relational graph model and queried using domain-specific traversal expressions. To enable concise and expressive meta-querying, semantic analyses are conducted on normalized relational algebra trees with accompanying schema lineage graphs. Syntactic analyses can be conducted on corresponding query texts and abstract syntax trees. Additional metadata allows to inspect the temporal and social context of each query.
 In this demonstration, we show how query log analysis with our framework can support data source discovery and facilitate collaborative data science. The audience can explore an exemplary query log to locate queries relevant to a data analysis scenario, conduct graph analyses on the log and assemble a customized logmonitoring dashboard.
The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans

A new era of Information Warfare has arrived. Various actors, including state-sponsored ones, are weaponizing information on Online Social Networks to run false information campaigns with targeted manipulation of public opinion on specific topics. These false information campaigns can have dire consequences to the public: mutating their opinions and actions, especially with respect to critical world events like major elections. Evidently, the problem of false information on the Web is a crucial one, and needs increased public awareness, as well as immediate attention from law enforcement agencies, public institutions, and in particular, the research community. In this paper, we make a step in this direction by providing a typology of the Web’s false information ecosystem, comprising various types of false information, actors, and their motives. We report a comprehensive overview of existing research on the false information ecosystem by identifying several lines of work: 1) how the public perceives false information; 2) understanding the propagation of false information; 3) detecting and containing false information on the Web; and 4) false information on the political stage. In this work, we pay particular attention to political false information as: 1) it can have dire consequences to the community (e.g., when election results are mutated) and 2) previous work show that this type of false information propagates faster and further when compared to other types of false information. Finally, for each of these lines of work, we report several future research directions that can help us better understand and mitigate the emerging problem of false information dissemination on the Web.