nils-herrmann/clean_scirepeval huggingface dataset:
A Primer on Data-Driven Gamification Design

Gamification gradually gains more attention. However, gamification and its successful application is still unclear. There is a lack of insights and theory on the relationships between game design elements, motivation, domain context and user behavior. We want to discover the potentials of data-driven optimization of gamification design, e.g. by the application of machine learning techniques on user interaction data. Therefore, we propose data-driven gamification design (DDGD) and conducted a questionnaire with 17 gamification experts. Our results show that respondents regard DDGD as a promising method to improve gamification design and lead to a general definition for DDGD.
An infinite chainmail of M6L6 metallacycles featuring multiple Borromean links.

Borromean rings or links are topologically complex assemblies of three entangled rings where no two rings are interlinked in a chain-like catenane, yet the three rings cannot be separated. We report here a metallacycle complex whose crystalline network forms the first example of a new class of entanglement. The complex is formed from the self-assembly of CuBr2 with the cyclotriveratrylene-scaffold ligand (±)-tris(iso-nicotinoyl)cyclotriguaiacylene. Individual metallacycles are interwoven into a two-dimensional chainmail network where each metallacycle exhibits multiple Borromean-ring-like associations with its neighbours. This only occurs in the solid state, and also represents the first example of a crystalline infinite chainmail two-dimensional network. Crystals of the complex were twinned and have an unusual hollow tubular morphology that is likely to result from a localized dissolution-recrystallization process.
Optimized Methods to Explore the Mechanistic and Biomarker Potential of Hepatocyte-Derived Exosomes in Drug-Induced Liver Injury.

Recent evidence supports that alterations in hepatocyte-derived exosomes (HDE) may play a role in the pathogenesis of drug-induced liver injury (DILI). HDE-based biomarkers also hold promise to improve the sensitivity of existing in vitro assays for predicting DILI liability. Primary human hepatocytes (PHH) provide a physiologically relevant in vitro model to explore the mechanistic and biomarker potential of HDE in DILI. However, optimal methods to study exosomes in this culture system have not been defined. Here we use HepG2 and HepaRG cells along with PHH to optimize methods for in vitro HDE research. We compared the quantity and purity of HDE enriched from HepG2 cell culture medium by 3 widely used methods: ultracentrifugation (UC), OptiPrep density gradient ultracentrifugation (ODG), and ExoQuick (EQ)-a commercially available exosome precipitation reagent. Although EQ resulted in the highest number of particles, UC resulted in more exosomes as indicated by the relative abundance of exosomal CD63 to cellular prohibitin-1 as well as the comparative absence of contaminating extravesicular material. To determine culture conditions that best supported exosome release, we also assessed the effect of Matrigel matrix overlay at concentrations ranging from 0 to 0.25 mg/ml in HepaRG cells and compared exosome release from fresh and cryopreserved PHH from same donor. Sandwich culture did not impair exosome release, and freshly prepared PHH yielded a higher number of HDE overall. Taken together, our data support the use of UC-based enrichment from fresh preparations of sandwich-cultured PHH for future studies of HDE in DILI.
Sparse-to-Continuous: Enhancing Monocular Depth Estimation using Occupancy Maps

This paper addresses the problem of single image depth estimation (SIDE), focusing on improving the accuracy of deep neural network predictions. In a supervised learning scenario, the quality of predictions is intrinsically related to the training labels, which guide the optimization process. For indoor scenes, structured-light-based depth sensors (e.g. Kinect) are able to provide dense, albeit short-range, depth maps. On the other hand, for outdoor scenes, LiDARs are still considered the standard sensor, which comparatively provide much sparser measurements, especially in areas further away. Rather than modifying the neural network architecture to deal with sparse depth maps, this article introduces a novel densification method for depth maps, using the Hilbert Maps framework. A continuous occupancy map is produced based on 3D points from LiDAR scans, and the resulting reconstructed surface is projected into a 2D depth map with arbitrary resolution. Experiments conducted with various subsets of the KITTI dataset show a significant improvement produced by the proposed Sparse-to-Continuous technique, without the introduction of extra information into the training stage.
Techniques of Acoustic Feature Extraction for Detection and Classification of Ground Vehicles

Vehicles may be recognized from the sound from the sound they make when moving i.e. from their acoustic signature. These sounds may come from various sources including rotational parts, vibrations in the engine, friction between the tires and the pavement, wind effect, gears, fans. Similar vehicles working in comparable conditions would have a similar acoustic signature that could be used for recognition. Characteristic patterns may be extracted from the Fourier description of the signature and used for recognition. Classification of ground vehicles based on acoustic signals can be employed effectively in battlefield surveillance, traffic control, and many other applications. The classification performance depends on the selection of signal features that determine the separation of different signal classes. This paper compares various available techniques of acoustic feature extraction for detection and classification of ground vehicles. Finally we present an overview of the methods discussed and their success rate in tabular form along with the classifier used.
Predictive data mining in clinical medicine: Current issues and guidelines

The widespread availability of new computational methods and tools for data analysis and predictive modeling requires medical informatics researchers and practitioners to systematically select the most appropriate strategy to cope with clinical prediction problems. In particular, the collection of methods known as 'data mining' offers methodological and technical solutions to deal with the analysis of medical data and construction of prediction models. A large variety of these methods requires general and simple guidelines that may help practitioners in the appropriate selection of data mining tools, construction and validation of predictive models, along with the dissemination of predictive models within clinical environments.



The goal of this review is to discuss the extent and role of the research area of predictive data mining and to propose a framework to cope with the problems of constructing, assessing and exploiting data mining models in clinical medicine.



We review the recent relevant work published in the area of predictive data mining in clinical medicine, highlighting critical issues and summarizing the approaches in a set of learned lessons.



The paper provides a comprehensive review of the state of the art of predictive data mining in clinical medicine and gives guidelines to carry out data mining studies in this field.



Predictive data mining is becoming an essential instrument for researchers and clinical practitioners in medicine. Understanding the main issues underlying these methods and the application of agreed and standardized procedures is mandatory for their deployment and the dissemination of results. Thanks to the integration of molecular and clinical data taking place within genomic medicine, the area has recently not only gained a fresh impulse but also a new set of complex problems it needs to address.
Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation

Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and their surroundings make it difficult for robust nodule segmentation. In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to segment lung nodules from heterogeneous CT images. Our approach combines two key insights: 1) the proposed model captures a diverse set of nodule-sensitive features from both 3-D and 2-D CT images simultaneously; 2) when classifying an image voxel, the effects of its neighbor voxels can vary according to their spatial locations. We describe this phenomenon by proposing a novel central pooling layer retaining much information on voxel patch center, followed by a multi-scale patch learning strategy. Moreover, we design a weighted sampling to facilitate the model training, where training samples are selected according to their degree of segmentation difficulty. The proposed method has been extensively evaluated on the public LIDC dataset including 893 nodules and an independent dataset with 74 nodules from Guangdong General Hospital (GDGH). We showed that CF-CNN achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively. Moreover, we compared our results with the inter-radiologists consistency on LIDC dataset, showing a difference in average dice score of only 1.98%.
Representational Learning for Fault Diagnosis of Wind Turbine Equipment : A Multi-Layered Extreme Learning Machines Approach †

Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extreme learning machines (ELM) in a hierarchical structure, where a forwarding list of ELM layers is concatenated and each of them is processed independently for its corresponding role. The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. An ELM based autoencoder is trained to generate a hidden layer output weight matrix, which is then used to transform the input dataset into a new feature representation. Compared with the traditional feature extraction methods which may empirically wipe off some “insignificant’ feature information that in fact conveys certain undiscovered important knowledge, the introduced representational learning method could overcome the loss of information content. The computed output weight matrix projects the high dimensional input vector into a compressed and orthogonally weighted distribution. The last single layer of ELM is applied for fault classification. Unlike the greedy layer wise learning method adopted in back propagation based deep learning (DL), the proposed framework does not need iterative fine-tuning of parameters. To evaluate its experimental performance, comparison tests are carried out on a wind turbine generator simulator. The results show that the proposed diagnostic framework achieves the best performance among the compared approaches in terms of accuracy and efficiency in multiple faults detection of wind turbines.
Towards Deep Cellular Phenotyping in Placental Histology

The placenta is a complex organ, playing multiple roles during fetal development. Very little is known about the association between placental morphological abnormalities and fetal physiology. In this work, we present an open sourced, computationally tractable deep learning pipeline to analyse placenta histology at the level of the cell. By utilising two deep convolutional neural network architectures and transfer learning, we can robustly localise and classify placental cells within five classes with an accuracy of 89%. Furthermore, we learn deep embeddings encoding phenotypic knowledge that is capable of both stratifying five distinct cell populations and learn intraclass phenotypic variance. We envisage that the automation of this pipeline to population scale studies of placenta histology has the potential to improve our understanding of basic cellular placental biology and its variations, particularly its role in predicting adverse birth outcomes.
Automated CT Perfusion Ischemic Core Volume and Noncontrast CT ASPECTS (Alberta Stroke Program Early CT Score): Correlation and Clinical Outcome Prediction in Large Vessel Stroke.

The semiquantitative noncontrast CT Alberta Stroke Program Early CT Score (ASPECTS) and RAPID automated computed tomography (CT) perfusion (CTP) ischemic core volumetric measurements have been used to quantify infarct extent. We aim to determine the correlation between ASPECTS and CTP ischemic core, evaluate the variability of core volumes within ASPECTS strata, and assess the strength of their association with clinical outcomes.



Review of a prospective, single-center database of consecutive thrombectomies of middle cerebral or intracranial internal carotid artery occlusions with pretreatment CTP between September 2010 and September 2015. CTP was processed with RAPID software to identify ischemic core (relative cerebral blood flow<30% of normal tissue).



Three hundred and thirty-two patients fulfilled inclusion criteria. Median age was 66 years (55-75), median ASPECTS was 8 (7-9), whereas median CTP ischemic core was 11 cc (2-27). Median time from last normal to groin puncture was 5.8 hours (3.9-8.8), and 90-day modified Rankin scale score 0 to 2 was observed in 54%. The correlation between CTP ischemic core and ASPECTS was fair (R=-0.36; P<0.01). Twenty-six patients (8%) had ASPECTS <6 and CTP core ≤50 cc (37% had modified Rankin scale score 0-2, whereas 29% were deceased at 90 days). Conversely, 27 patients (8%) had CTP core >50 cc and ASPECTS ≥6 (29% had modified Rankin scale 0-2, whereas 21% were deceased at 90 days). Moderate correlations between ASPECTS and final infarct volume (R=-0.42; P<0.01) and between CTP ischemic core and final infarct volume (R=0.50; P<0.01) were observed; coefficients were not significantly influenced by the time from stroke onset to presentation. Multivariable regression indicated ASPECTS ≥6 (odds ratio 4.10; 95% confidence interval, 1.47-11.46; P=0.01) and CTP core ≤50 cc (odds ratio 3.86; 95% confidence interval, 1.22-12.15; P=0.02) independently and comparably predictive of good outcome.



There is wide variability of CTP-derived core volumes within ASPECTS strata. Patient selection may be affected by the imaging selection method.
A Hybrid Approach for Music Recommendation

The task of WSDM 2018 Music Recommendation Challenge is predict the probability of a user re-listening to a song within a specified time window after the first observable listening event. This paper presents our approach to this challenge. We built our recommendation models using multiple additive decision trees and factorization machines. By capturing the time-series characteristics of the music listening data, we can achieve significant improvement over baseline models. Meanwhile, ensemble of a collection of models that take into consideration the cold-start nature of the music recommendation task can further significantly improve upon the best single model. We show how our approach achieved an AUROC score of 0.73666 on the withheld test set, and thereby attaining the overall 5th place in the competition.
Antihyperglycemic activity of bacosine, a triterpene from Bacopa monnieri, in alloxan-induced diabetic rats.

This article describes the antihyperglycemic activity, in vivo antioxidant potential, effect on hemoglobin glycosylation, estimation of liver glycogen content, and in vitro peripheral glucose utilization of bacosine, a triterpene isolated from the ethyl acetate fraction (EAF) of the ethanolic extract of Bacopa monnieri. Bacosine produced a significant decrease in the blood glucose level when compared with the diabetic control rats both in the single administration as well as in the multiple administration study. It was observed that the compound reversed the weight loss of the diabetic rats, returning the values to near normal. Bacosine also prevented elevation of glycosylated hemoglobin in vitro with an IC₅₀ value of 7.44 µg/mL, comparable with the one for the reference drug α-tocopherol. Administration of bacosine and glibenclamide significantly decreased the levels of malondialdehyde (MDA), and increased the levels of reduced glutathione (GSH) and the activities of superoxide dismutase (SOD) and catalase (CAT) in the liver of diabetic rats. Bacosine increased glycogen content in the liver of diabetic rats and peripheral glucose utilization in the diaphragm of diabetic rats in vitro, which is comparable with the action of insulin. Thus, bacosine might have insulin-like activity and its antihyperglycemic effect might be due to an increase in peripheral glucose consumption as well as protection against oxidative damage in alloxanized diabetes.
Big Data in Natural Disaster Management : A Review

Undoubtedly, the age of big data has opened new options for natural disaster management, primarily because of the varied possibilities it provides in visualizing, analyzing, and predicting natural disasters. From this perspective, big data has radically changed the ways through which human societies adopt natural disaster management strategies to reduce human suffering and economic losses. In a world that is now heavily dependent on information technology, the prime objective of computer experts and policy makers is to make the best of big data by sourcing information from varied formats and storing it in ways that it can be effectively used during different stages of natural disaster management. This paper aimed at making a systematic review of the literature in analyzing the role of big data in natural disaster management and highlighting the present status of the technology in providing meaningful and effective solutions in natural disaster management. The paper has presented the findings of several researchers on varied scientific and technological perspectives that have a bearing on the efficacy of big data in facilitating natural disaster management. In this context, this paper reviews the major big data sources, the associated achievements in different disaster management phases, and emerging technological topics associated with leveraging this new ecosystem of Big Data to monitor and detect natural hazards, mitigate their effects, assist in relief efforts, and contribute to the recovery and reconstruction processes.
Internet use and verbal aggression: The moderating role of parents and peers

This research investigated the influence of parent–adolescent communication quality, as perceived by the adolescents, on the relationship between adolescents’ Internet use and verbal aggression. Adolescents (N = 363, age range 10–16, MT1 = 12.84, SD = 1.93) were examined twice with a six-month delay. Controlling for social support in general terms, moderated regression analyses showed that Internet-related communication quality with parents determined whether Internet use is associated with an increase or a decrease in adolescents’ verbal aggression scores over time. A three way interaction indicated that high Internet-related communication quality with peers can have disadvantageous effects if the communication quality with parents is low. Implications on resources and risk factors related to the effects of Internet use are discussed.
Combating the Bandits in the Cloud: A Moving Target Defense Approach

Security and privacy in cloud computing are critical components for various organizations that depend on the cloud in their daily operations. Customers' data and the organizations' proprietary information have been subject to various attacks in the past. In this paper, we develop a set of Moving Target Defense (MTD) strategies that randomize the location of the Virtual Machines (VMs) to harden the cloud against a class of Multi-Armed Bandit (MAB) policy-based attacks. These attack policies capture the behavior of adversaries that seek to explore the allocation of VMs in the cloud and exploit the ones that provide the highest rewards (e.g., access to critical datasets, ability to observe credit card transactions, etc). We assess through simulation experiments the performance of our MTD strategies, showing that they can make MAB policy-based attacks no more effective than random attack policies. Additionally, we show the effects of critical parameters – such as discount factors, the time between randomizing the locations of the VMs and variance in the rewards obtained – on the performance of our defenses. We validate our results through simulations and a real OpenStack system implementation in our lab to assess migration times and down times under different system loads.
A Generalized Cascaded Multilevel Inverter Using Series Connection of Submultilevel Inverters

Application of multilevel inverters for higher power purposes in industries has become more popular. This is partly because of high-quality output waveform of multilevel inverters in comparison with two-level inverters. In this paper, initially a new topology for submultilevel inverter is proposed and then series connection of the submultilevel inverters is proposed as a generalized multilevel inverter. The proposed multilevel inverter uses reduced number of switching devices. Special attention has been paid to obtain optimal structures regarding different criteria such as number of switches, standing voltage on the switches, number of dc voltage sources, etc. The proposed multilevel inverter has been analyzed in both symmetric and asymmetric conditions. The validity of the proposed multilevel inverter is verified with both computer simulations using PSCAD/EMTDC software and laboratory prototype implementation.
Child physical abuse and adult mental health: a national study.

This study characterizes adults who report being physically abused during childhood, and examines associations of reported type and frequency of abuse with adult mental health. Data were derived from the 2000-2001 and 2004-2005 National Epidemiologic Survey on Alcohol and Related Conditions, a large cross-sectional survey of a representative sample (N = 43,093) of the U.S. population. Weighted means, frequencies, and odds ratios of sociodemographic correlates and prevalence of psychiatric disorders were computed. Logistic regression models were used to examine the strength of associations between child physical abuse and adult psychiatric disorders adjusted for sociodemographic characteristics, other childhood adversities, and comorbid psychiatric disorders. Child physical abuse was reported by 8% of the sample and was frequently accompanied by other childhood adversities. Child physical abuse was associated with significantly increased adjusted odds ratios (AORs) of a broad range of DSM-IV psychiatric disorders (AOR = 1.16-2.28), especially attention-deficit hyperactivity disorder, posttraumatic stress disorder, and bipolar disorder. A dose-response relationship was observed between frequency of abuse and several adult psychiatric disorder groups; higher frequencies of assault were significantly associated with increasing adjusted odds. The long-lasting deleterious effects of child physical abuse underscore the urgency of developing public health policies aimed at early recognition and prevention.