nils-herrmann/clean_scirepeval huggingface dataset:
Adverse Incentives in Crowdfunding

This paper analyses the substantially growing markets for crowdfunding, in which retail investors lend to borrowers without financial intermediaries. Critics suggest these markets allow sophisticated investors to take advantage of unsophisticated investors. The growth and viability of these markets critically depends on the underlying incentives. We provide evidence of perverse incentives in crowdfunding that are not fully recognized by the market. In particular we look at group leader bids in the presence of origination fees and find that these bids are (wrongly) perceived as a signal of good loan quality, resulting in lower interest rates. Yet these loans actually have higher default rates. These adverse incentives are overcome only with sufficient skin in the game and when there are no origination fees. The results provide important implications for crowdfunding, its structure and regulation.
Comparative Studies of Detecting Abusive Language on Twitter

The context-dependent nature of online aggression makes annotating large collections of data extremely difficult. Previously studied datasets in abusive language detection have been insufficient in size to efficiently train deep learning models. Recently, Hate and Abusive Speech on Twitter, a dataset much greater in size and reliability, has been released. However, this dataset has not been comprehensively studied to its potential. In this paper, we conduct the first comparative study of various learning models on Hate and Abusive Speech on Twitter, and discuss the possibility of using additional features and context data for improvements. Experimental results show that bidirectional GRU networks trained on word-level features, with Latent Topic Clustering modules, is the most accurate model scoring 0.805 F1.
Smart-home technologies to assist older people to live well at home

With the rapid population ageing that is occurring world-wide, there is increasing interest in “smart home” technologies that can assist older adults to continue living at home with safety and independence. This systematic review and critical evaluation of the world wide literature assesses the effectiveness and feasibility of smart-home technologies for promoting independence, health, well-being and quality of life, in older adults.  A total of 1877 “smart home” publications were identified by the initial search of peer reviewed journals. Of these, 21 met our inclusion criteria for the review and were subject to data extraction and quality assessment.  Smart-home technologies included different types of active and passive sensors, monitoring devices, robotics and environmental control systems. One study assessed effectiveness of a smart home technology. Sixteen reported on the feasibility of smart-home technology and four were observational studies.  Older adults were reported to readily accept smart-home technologies, especially if they benefited physical activity, independence and function and if privacy concerns were addressed. Given the modest number of objective analyses, there is a need for further scientific analysis of a range of smart home technologies to promote community living. rather than being hospitalized or institutionalized [10]. Smart-home technologies can also promote independent living and safety. This has the potential to optimize quality of life and reduce the stress on agedcare facilities and other health resources [13]. The challenge with smart-home technologies is to create a home environment that is safe and secure to reduce falls, disability, stress, fear or social isolation . Contemporary smart home technology systems are versatile in function and user friendly. Smart home technologies usually aim to perform functions without disturbing the user and without causing any pain, inconvenience or movement restrictions. Martin and colleagues performed a preliminary analysis of the acceptance of smart-home technologies . The results from this review were limited as no studies met inclusion criteria . Given however, the rapid progression of new smart home technologies, a new systematic review of the literature is required. This paper addresses that need by analysing the range of studies undertaken to assess the impact of these technologies on the quality of life experienced by an ageing population accessing these supports. The broader context incorporates consideration of the social and emotional well-being needs of this population. The current review aimed to answer the following research question: “What is the effectiveness of smart-home technologies for Citation: Morris ME, Adair B, Miller K, Ozanne E, Hansen R, et al. (2013) Smart-Home Technologies to Assist Older People to Live Well at Home. Aging Sci 1: 101.
The Unintentional Procrastination Scale

Procrastination refers to the delay or postponement of a task or decision and is often conceptualised as a failure of self-regulation. Recent research has suggested that procrastination could be delineated into two domains: intentional and unintentional. In this two-study paper, we aimed to develop a measure of unintentional procrastination (named the Unintentional Procrastination Scale or the 'UPS') and test whether this would be a stronger marker of psychopathology than intentional and general procrastination. In Study 1, a community sample of 139 participants completed a questionnaire that consisted of several items pertaining to unintentional procrastination that had been derived from theory, previous research, and clinical experience. Responses were subjected to a principle components analysis and assessment of internal consistency. In Study 2, a community sample of 155 participants completed the newly developed scale, along with measures of general and intentional procrastination, metacognitions about procrastination, and negative affect. Data from the UPS were subjected to confirmatory factor analysis and revised accordingly. The UPS was then validated using correlation and regression analyses. The six-item UPS possesses construct and divergent validity and good internal consistency. The UPS appears to be a stronger marker of psychopathology than the pre-existing measures of procrastination used in this study. Results from the regression models suggest that both negative affect and metacognitions about procrastination differentiate between general, intentional, and unintentional procrastination. The UPS is brief, has good psychometric properties, and has strong associations with negative affect, suggesting it has value as a research and clinical tool.
Use of fuzzy logic and wordnet for improving performance of extractive automatic text summarization

Text Summarization produces a shorter version of large text documents by selecting most relevant information. Text summarization systems are of two types: extractive and abstractive. This paper focuses on extractive text summarization. In extractive text summarization, important sentences are selected based on certain important features. The importance of some extractive features is more than the some other features, so they should have the balance weight in computations. The purpose of this paper is to use fuzzy logic and wordnet synonyms to handle the issue of ambiguity and imprecise values with the traditional two value or multi-value logic and to consider the semantics of the text. Three different methods: fuzzy logic based method, bushy path method, and wordnet synonyms method are used to generate 3 summaries. Final summary is generated by selecting common sentences from all the 3 summaries and from rest of the sentences in union of all summaries, selection is done based on sentence location. The proposed methodology is compared with three individual methods i.e. fuzzy logic based summarizer, bushy path summarizer, and wordnet synonyms summarizer by evaluating the performance of each on 95 documents from standard DUC 2002 dataset using ROUGE evaluation metrics. The analysis shows that the proposed method gives better average precision, recall, and f-measure.
Rethinking Binary Neural Network for Accurate Image Classification and Semantic Segmentation

In this paper, we propose to train a network with both binary weights and binary activations, designed specifically for mobile devices with limited computation capacity and power consumption. Previous works on quantizing CNNs uncritically assume the same architecture with fullprecision networks, which we term value approximation. Their objective is to preserve the floating-point information using a set of discrete values. However, we take a novel view—for best performance it is very likely that a different architecture may be better suited to deal with binary weights as well as binary activations. Thus we directly design such a highly accurate binary network structure, which is termed structure approximation. In particular, we propose a “network decomposition” strategy in which we divide the networks into groups and aggregate a set of homogeneous binary branches to implicitly reconstruct the full-precision intermediate feature maps. In addition, we also learn the connections between each group. We further provide a comprehensive comparison among all quantization categories. Experiments on ImageNet classification tasks demonstrate the superior performance of the proposed model, named Group-Net, over various popular architectures. In particular, we outperform the previous best binary neural network in terms of accuracy as well as saving huge computational complexity. Furthermore, the proposed Group-Net can effectively utilize task specific properties for strong generalization. In particular, we propose to extend Group-Net for lossless semantic segmentation. This is the first work proposed on solving dense pixels prediction based on BNNs in the literature. Actually, we claim that considering both value and structure approximation should be the future development direction of BNNs.
Integrating the SOP2 Model into the Flipped Classroom to Foster Cognitive Presence and Learning Achievements

This study explored student teachers’ cognitive presence and learning achievements by integrating the SOP Model in which self-study (S), online group discussion (O) and double-stage presentations (P) were implemented in the flipped classroom. The research was conducted at a university in Taiwan with 31 student teachers. Preand post-worksheets measuring knowledge of educational issues were administered before and after group discussion. Quantitative content analysis and behavior sequential analysis were used to evaluate cognitive presence, while a paired-samples t-test analyzed learning achievement. The results showed that the participants had the highest proportion of “Exploration,” the second largest rate of “Integration,” but rarely reached “Resolution.” The participants’ achievements were greatly enhanced using the SOP Model in terms of the scores of the preand post-worksheets. Moreover, the groups with a higher proportion of “Integration” (I) and “Resolution” (R) performed best in the post-worksheets and were also the most progressive groups. Both highand low-rated groups had significant correlations between the “I” and “R” phases, with “I”  “R” in the low-rated groups but “R”  “I” in the high-rated groups. The instructional design of the SOP Model can be a reference for future pedagogical implementations in the higher educational context.
Interaction gestalt and the design of aesthetic interactions

Although there has been a drastic increase in the research of aesthetics of interaction, we still lack well-defined practical knowledge of how to design aesthetic interactions. In order to develop such knowledge, we adapt three important ways of thinking in designing interactions influenced by traditional design disciplines, namely, 1) understanding what it is that is designed---i.e. interaction, 2) knowing what is possible to be manipulated when designing interactions---i.e. attributes of interaction, and 3) mastering how to manipulate the attributes to shape the interactions. We explain our approach by arguing from the somaesthetic perspective. We propose the concept of interaction gestalt, as a way to achieve those three ways of thinking in design. We then propose a set of interaction gestalt attributes that can be used in designing aesthetic interactions. We end with a discussion of the implications and benefits of this approach in interaction design.
On the Performance of Analytical and Pattern Matching Graph Queries in Neo4j and a Relational Database

Graph databases with a custom non-relational backend promote themselves to outperform relational databases in answering queries on large graphs. Recent empirical studies show that this claim is not always true. However, these studies focus only on pattern matching queries and neglect analytical queries used in practice such as shortest path, diameter, degree centrality or closeness centrality. In addition, there is no distinction between different types of pattern matching queries. In this paper, we introduce a set of analytical and pattern matching queries, and evaluate them in Neo4j and a market-leading commercial relational database system. We show that the relational database system outperforms Neo4j for our analytical queries and that Neo4j is faster for queries that do not filter on specific edge types.
Building Streetview Datasets for Place Recognition and City Reconstruction

Google Maps API combined with Street View images can serve as a powerful tool for place recognition or city reconstruction tasks. In this paper, we present a way how to build geotagged datasets of perspective views from Google Maps. Given the initial GPS coordinates, the algorithm can build a list of panoramas in a certain area, download corresponding panoramas, and generate perspective views. In more detail, each panorama on Google Maps Street View contains meta data from which the GPS location and the direction of the view can be extracted. Moreover, the information about the neighbouring panoramas can be obtained as well, hence, a list of panoramas covering a certain area can be built. Downloading panoramas from the list and combining it with the meta data, each downloaded panorama is cut into a set of overlaping perspective views and stored while the camera GPS location, yaw, and pitch are coded in the filename of the perspective view. The geotagged database is subsequently used for place recognition and structure from motion 3D reconstruction.
WHUIRGroup at TREC 2016 Clinical Decision Support Task

The goal of Clinical Decision Support(CDS) is to help physicians find academic papers in PubMed, and link medical records to information relevant for patient care. Participants attending the track have access to a data collection which is a snapshot of 1.25 million articles from PubMed Central(PMC)and a set of 30 topics which are EHR admission notes curated by physicians in the real condition from the MIMIC-III data. Our CDS systems are based on Indri toolkit, using Continuous Space Word Vectors expanding the queries. The 2015 topics and results are used to train the re-rank model, using the LambdaMART rank algorithms. The evaluation of run submissions are used partially marked results which is a promising methodologies.
Platelet-rich plasma: a review of biology and applications in plastic surgery.

Healing of hard and soft tissue is mediated by a complex array of intracellular and extracellular events that are regulated by signaling proteins, a process that is, at present, incompletely understood. What is certain, however, is that platelets play a prominent if not deciding role. Controlled animal studies of soft and hard tissues have suggested that the application of autogenous platelet-rich plasma can enhance wound healing. The clinical use of platelet-rich plasma for a wide variety of applications has been reported; however, many reports are anecdotal and few include controls to definitively determine the role of platelet-rich plasma. The authors describe platelet biology and its role in wound healing; the preparation, characterization, and use of platelet-rich plasma; and those applications in plastic surgery for which it may be useful.
Media Bias Monitor: Quantifying Biases of Social Media News Outlets at Large-Scale

As Internet users increasingly rely on social media sites like Facebook and Twitter to receive news, they are faced with a bewildering number of news media choices. For example, thousands of Facebook pages today are registered and categorized as some form of news media outlets. Inferring the bias (or slant) of these media pages poses a difficult challenge for media watchdog organizations that traditionally rely on con-
Risk of Intracranial Hemorrhage in Ground-level Fall With Antiplatelet or Anticoagulant Agents.

Anticoagulant and antiplatelet medications are known to increase the risk and severity of traumatic intracranial hemorrhage (tICH), even with minor head trauma. Most studies on bleeding propensity with head trauma are retrospective, are based on trauma registries, or include heterogeneous mechanisms of injury. The goal of this study was to determine the rate of tICH from only a common low-acuity mechanism of injury, that of a ground-level fall, in patients taking one or more of the following antiplatelet or anticoagulant medications: aspirin, warfarin, prasugrel, ticagrelor, dabigatran, rivaroxaban, apixaban, or enoxaparin.



This was a prospective cohort study conducted at a Level I tertiary care trauma center of consecutive patients meeting the inclusion criteria of a ground-level fall with head trauma as affirmed by the treating clinician, a computed tomography (CT) head obtained, and taking and one of the above antiplatelet or anticoagulants. Patients were identified prospectively through electronic screening with confirmatory chart review. Emergency department charts were abstracted without subsequent knowledge of the hospital course. Patients transferred with a known abnormal CT head were excluded. Primary outcome was rate of tICH on initial CT head. Rates with 95% confidence intervals (CIs) were compared.



Over 30 months, we enrolled 939 subjects. The mean ± SD age was 78.3 ± 11.9 years and 44.6% were male. There were a total of 33 patients with tICH (3.5%, 95% CI = 2.5%-4.9%). Antiplatelets had a rate of tICH of 4.3% (95% CI = 3.0%-6.2%) compared to anticoagulants with a rate of 1.7% (95% CI = 0.4%-4.5%). Aspirin without other agents had an tICH rate of 4.6% (95% CI = 3.2%-6.6%); of these, 81.5% were taking low-dose 81 mg aspirin. Two patients received a craniotomy (one taking aspirin, one taking warfarin). There were four deaths (three taking aspirin, one taking warfarin). Most (72.7%) subjects with tICH were discharged home or to a rehabilitation facility. There were no tICH in 31 subjects taking a direct oral anticoagulant. CIs were overlapping for the groups.



There is a low incidence of clinically significant tICH with a ground-level fall in head trauma in patients taking an anticoagulant or antiplatelet medication. There was no statistical difference in rate of tICH between antiplatelet and anticoagulants, which is unanticipated and counterintuitive as most literature and teaching suggests a higher rate with anticoagulants. A larger data set is needed to determine if small differences between the groups exist.
The functions of the orbitofrontal cortex

The orbitofrontal cortex contains the secondary taste cortex, in which the reward value of taste is represented. It also contains the secondary and tertiary olfactory cortical areas, in which information about the identity and also about the reward value of odours is represented. The orbitofrontal cortex also receives information about the sight of objects from the temporal lobe cortical visual areas, and neurons in it learn and reverse the visual stimulus to which they respond when the association of the visual stimulus with a primary reinforcing stimulus (such as taste) is reversed. This is an example of stimulus-reinforcement association learning, and is a type of stimulus-stimulus association learning. More generally, the stimulus might be a visual or olfactory stimulus, and the primary (unlearned) positive or negative reinforcer a taste or touch. A somatosensory input is revealed by neurons that respond to the texture of food in the mouth, including a population that responds to the mouth feel of fat. In complementary neuroimaging studies in humans, it is being found that areas of the orbitofrontal cortex are activated by pleasant touch, by painful touch, by taste, by smell, and by more abstract reinforcers such as winning or losing money. Damage to the orbitofrontal cortex can impair the learning and reversal of stimulus-reinforcement associations, and thus the correction of behavioural responses when there are no longer appropriate because previous reinforcement contingencies change. The information which reaches the orbitofrontal cortex for these functions includes information about faces, and damage to the orbitofrontal cortex can impair face (and voice) expression identification. This evidence thus shows that the orbitofrontal cortex is involved in decoding and representing some primary reinforcers such as taste and touch; in learning and reversing associations of visual and other stimuli to these primary reinforcers; and in controlling and correcting reward-related and punishment-related behavior, and thus in emotion. The approach described here is aimed at providing a fundamental understanding of how the orbitofrontal cortex actually functions, and thus in how it is involved in motivational behavior such as feeding and drinking, in emotional behavior, and in social behavior.
Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling

This paper presents a Kernel Entity Salience Model (KESM) that improves text understanding and retrieval by better estimating entity salience (importance) in documents. KESM represents entities by knowledge enriched distributed representations, models the interactions between entities and words by kernels, and combines the kernel scores to estimate entity salience. The whole model is learned end-to-end using entity salience labels. The salience model also improves ad hoc search accuracy, providing effective ranking features by modeling the salience of query entities in candidate documents. Our experiments on two entity salience corpora and two TREC ad hoc search datasets demonstrate the effectiveness of KESM over frequency-based and feature-based methods. We also provide examples showing how KESM conveys its text understanding ability learned from entity salience to search.
Automotive Technology and Human Factors Research : Past , Present , and Future

This paper reviews the history of automotive technology development and human factors research, largely by decade, since the inception of the automobile. The human factors aspects were classified into primary driving task aspects (controls, displays, and visibility), driver workspace (seating and packaging, vibration, comfort, and climate), driver’s condition (fatigue and impairment), crash injury, advanced driver-assistance systems, external communication access, and driving behavior. For each era, the paper describes the SAE and ISO standards developed, the major organizations and conferences established, the major news stories affecting vehicle safety, and the general social context. The paper ends with a discussion of what can be learned from this historical review and the major issues to be addressed. A major contribution of this paper is more than 180 references that represent the foundation of automotive human factors, which should be considered core knowledge and should be familiar to those in the profession.