nils-herrmann/clean_scirepeval huggingface dataset:
Full color fluorescent carbon quantum dots
Venue: Science Advances
Year: 2020

Efficient full color fluorescent carbon quantum dots are synthesized by acid reagent engineering for white light emitting devices. Quantum dots have innate advantages as the key component of optoelectronic devices. For white light emitting diodes (WLEDs) the modulation of the spectrum and color of the device often involves various quantum dots of different emission wavelengths. Here, we fabricate a series of carbon quantum dots (CQDs) through a scalable acid reagent engineering strategy. The growing electron withdrawing groups on the surface of CQDs that originated from acid reagents boost their photoluminescence wavelength red shift and raise their particle sizes, elucidating the quantum size effect. These CQDs emit bright and remarkably stable full color fluorescence ranging from blue to red light and even white light. Full color emissive polymer films and all types of high color rendering index WLEDs are synthesized by mixing multiple kinds of CQDs in appropriate ratios. The universal electron donating/withdrawing group engineering approach for synthesizing tunable emissive CQDs will facilitate the progress of carbon based luminescent materials for manufacturing forward looking films and devices.
An Efficient Method Based on Conditional Generative Adversarial Networks for Imbalanced Fault Diagnosis of Rolling Bearing
Venue: 2019 Prognostics and System Health Management Conference (PHM Qingdao)
Year: 2019

Fault diagnosis of rolling bearing has always been a vital component in industrial field, and effective fault diagnostic methods can guarantee normal progress of manufacturing production. However, the scarcity of fault samples in practical scenarios is still a vexed question, which will seriously affect the accuracy of data driven diagnostic methods. For the settlement of above problem, this paper introduces a supervised generation model CGAN (Conditional Generative Adversarial Network) to generate multitudinal fault data, and replaces the real fault data with the generated one to constitute a new dataset to train the classifiers adequately. In order to verify the effectiveness of the proposed method, the experiments are carried out on both artificial dataset and real one. The results show that the generated data of CGAN not only has a high degree of similarity with the real data, but also effectively improves the fault diagnosis accuracy of rolling bearing.
Artificial intelligence assisted personalized language learning: systematic review and co citation analysis
Venue: 2021 International Conference on Advanced Learning Technologies (ICALT)
Year: 2021

Artificial intelligence (AI) for personalized learning has attracted increasing attention in various educational contexts and domains, including language learning. This study systematically reviewed academic studies on AI assisted personalized language learning (PLL) from the perspectives of article trends, top journals, countries/regions and institutions, AI technology types, learning outcomes and supports, participants, scientific collaborations, and co citation relations.  Taiwanese institutions' predominance in the field and the prevalent use of intelligent tutoring systems, natural language processing, and artificial neural network in facilitating personalized diagnosis and learning path and material recommendations in language learning. Furthermore, students' improved language outcomes and positive perception, satisfaction, or motivation towards language learning and AI technologies were commonly reported. The co authorship analysis results indicated the close inter regional collaborations, while the cross regional collaborations are expected to be enhanced. The co citation network analysis results highlighted the significance of fuzzy systems and item response theory. Additionally, learner profiling mining and learning resource adaptation were important directions to realize mobile and web based PLL.
Solar PV Array Fed Water Pumping System Using Zeta Converter based Closed Loop Control of BLDC Motor Drive
Year: 2018

This paper proposes a solar photovoltaic (SPV) array fed water pumping system utilizing a zeta converter as an intermediate DC DC converter in order to extract the maximum available power from the SPV array. Controlling the zeta converter in an intelligent manner through the incremental conductance maximum power point tracking (INC MPPT) algorithm offers the soft starting of the brushless DC (BLDC) motor employed to drive a centrifugal water pump coupled to its shaft. Soft starting i.e. the reduced current starting inhibits the harmful effect of the high starting current on the windings of the BLDC motor. A fundamental frequency switching of the voltage source inverter (VSI) is accomplished by the electronic commutation of the BLDC motor, thereby avoiding the VSI losses occurred owing to the high frequency switching. A new design approach for the low valued DC link capacitor of VSI is proposed. The proposed water pumping system is designed and modeled such that the performance is not affected even under the dynamic conditions. Suitability of the proposed system under dynamic conditions is demonstrated by the simulation results using MATLAB/Simulink software.
Automatic Active Compensation Method of Cross Coupling in Multiple receiver Resonant Inductive Coupling Wireless Power Transfer Systems
Venue: 2019 IEEE Energy Conversion Congress and Exposition (ECCE)
Year: 2019

The ability to charge to multiple receivers simultaneously with a single transmitter is one of the advantages of resonant inductive coupling wireless power transfer (RIC WPT) technologies. However, in multiple receiver RIC WPT systems, each receiver often suffers from the magnetic coupling among receivers, i.e. cross coupling. The cross coupling not only complicates the control of the receivers but also significantly decreases the output power of each receiver. Hence, the purpose of this paper is to propose a compensation method of the influence due to the cross coupling. To achieve this purpose, we first analyze the requirement to compensate for the influence of the cross coupling. As a result, we reveal that it is necessary to control the phase of the current in all the receiver to be orthogonal to the phase of the transmitter current. Then, we propose the method to adjust the current phase of each receiver automatically to the desired phase by using only the phase information of the transmitter current, which results in the full compensation of the influence due to the cross coupling. Furthermore, the proposed method also compensates for the influence of a detuning of the resonant frequency of each receiver due to the natural tolerance. Experiments of a two receiver RIC WPT system successfully verify the effectiveness of the proposed method.
HistoTransfer: Understanding Transfer Learning for Histopathology
Venue: 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)
Year: 2021

Advancement in digital pathology and artificial intelligence has enabled deep learning based computer vision techniques for automated disease diagnosis and prognosis. However, WSIs present unique computational and algorithmic challenges. WSIs are gigapixel sized, making them infeasible to be used directly for training deep neural networks. Hence, for modeling, a two stage approach is adopted: Patch representations are extracted first, followed by the aggregation for WSI prediction. These approaches require detailed pixel level annotations for training the patch encoder. However, obtaining these annotations is time consuming and tedious for medical experts. Transfer learning is used to address this gap and deep learning architectures pre trained on ImageNet are used for generating patch level representation. Even though ImageNet differs significantly from histopathology data, pre trained networks have been shown to perform impressively on histopathology data. Also, progress in self supervised and multi task learning coupled with the release of multiple histopathology data has led to the release of histopathology specific networks. In this work, we compare the performance of features extracted from networks trained on ImageNet and histopathology data. We use an attention pooling network over these extracted features for slide level aggregation. We investigate if features learned using more complex networks lead to gain in performance. We use a simple top k sampling approach for fine tuning framework and study the representation similarity between frozen and fine tuned networks using Centered Kernel Alignment. Further, to examine if intermediate block representation is better suited for feature extraction and ImageNet architectures are unnecessarily large for histopathology, we truncate the blocks of ResNet18 and DenseNet121 and examine the performance.
Preoperative predictors for outcomes after total hip replacement in patients with osteoarthritis: a systematic review
Venue: BMC Musculoskeletal Disorders
Year: 2016

BackgroundThis systematic review examines which patient related factors influence functional and clinical outcomes after total hip arthroplasty (THA) in patients with hip osteoarthritis (OA).MethodsWe performed a systematic review according to the PRISMA guidelines. We searched databases and trial registries for prospective studies including OA patients who underwent primary THA. Studies with preoperative measurements on predictors, with at least 1 year follow up were included. Risk of bias and confounding was assessed for two domains: follow up rate and looking at independent effects.ResultsThirty five studies were included (138,039 patients) Only nine studies (29 had low risk of bias for all domains thus suggesting an overall low quality of evidence. Studies were heterogeneous in the predictors tested and in the observed directions of the associations. Overall, preoperative function (13 studies (37 2 with low risk of bias) and radiological OA (6 studies (17 1 with low risk of bias) were predictors with the most consistent findings. Worse preoperative function and more severe radiological OA were associated with larger postoperative improvement. However, these patients never reached the level of postoperative functioning as patients with better preoperative function or less severe radiological OA. For age, gender, comorbidity, pain and quality of life the results of studies were conflicting. For BMI, some studies (n 5, 2 with low risk of bias) found worse outcomes for patients with higher BMI. However, substantial improvement was still achieved regardless of their BMI.ConclusionThere is not enough evidence to draw succinct conclusions on preoperative predictors for postoperative outcome in THA, as results of studies are conflicting and the methodological quality is low. Results suggest to focus on preoperative function and radiological osteoarthritis to decide when THA will be most effective. The present mapping of current evidence on the relationship between patient related factors and outcomes provides better information compared to individual studies and may help to set patient expectations before surgery. In addition, these findings may contribute to discussions on how to achieve the best possible postoperative outcome for specific patient groups.
Piecewise 3D printing of crystallographic data for post printing construction
Year: 2019

A method of 3D printing complex or challenging structures by breaking them into parts with connectors, printing each part separately, and then assembling the structure post printing has been developed. This has advantages such as multicoloured printing, framework optimization and reduction, print time reduction, and can be used to bypass print tray size limits. This method is particularly applicable to extended structures such as coordination polymers, metal organic frameworks, and hydrogen bonding networks, but examples where it can be used to simplify the printing of small molecules are also shown.
Improved Mechanical and Moisture Resistant Properties of Woven Hybrid Epoxy Composites by Graphene Nanoplatelets (GNP)
Venue: Materials
Year: 2019

This research investigated the effect of adding different wt. (0, 0.25, 0.50, and 0.75) of GNP (graphene nanoplatelets) to improve the mechanical and moisture resistant properties of Kevlar (K)/cocos nucifera sheath (CS)/epoxy hybrid composites. The laminates were fabricated with different K/CS weight ratios such as 100/0 (S1) 75/25 (S2) 50/50 (S3) 25/75 (S4) and 0/100 (S5) The results revealed that the addition of GNP improved the tensile, flexural, and impact properties of laminated composites. However, the optimal wt. of GNP varies with different laminates. A moisture diffusion analysis showed that the laminates with a 0.25 wt. of GNP content efficiently hindered water uptake by closing all the unoccupied pores inside the laminate. Morphological investigations (SEM and FE SEM (Field Emission Scanning Electron Microscope) proved that the addition of GNP improved the interfacial adhesion and dispersion. Structural (XRD and FTIR) analyses reveals that at 0.25 wt. of GNP, all the hybrid composites showed a better crystallinity index and the functional groups presents in the GNP can form strong interactions with the fibers and matrix. A statistical analysis was performed using One way ANOVA, and it corroborates that the mechanical properties of different laminates showed a statistically significant difference. Hence, these GNP modified epoxy hybrid composites can be efficiently utilized in load bearing structures.
[Development of a three dimensional occlusal force sensor]
Venue: Kokubyo Gakkai zasshi. The Journal of the Stomatological Society, Japan
Year: 2007

The aim of this study was to develop a three dimensional small occlusal force sensor which could be set up inside the crown of the tooth and measure both the three dimensional occlusal force and the tooth displacement path during function at the same time in vivo. The measurement error of this occlusal force sensor was 11.9% 8.7% and 5.9% at maximum in the mesio distal, bucco lingual and colono apical directions, respectively. The subject was a Japanese male who had good periodontal conditions and an acceptable centric occlusion. After this occlusal force sensor and tooth displacement transducer were set up at the upper left first molar, the three dimensional occlusal force and the tooth displacement were measured simultaneously during clenching at the intercuspal position and biting a ready made monoaxial occlusal force meter. During clenching at the intercuspal position, the tooth displaced toward the palato disto apical direction and this was almost the same direction in which the occlusal force acted. The difference between the values measured by this sensor and a ready made monoaxial occlusal force meter was about 9.5% at maximum. This three dimensional occlusal force sensor is able to reveal how occlusal force is diffused.
A Cumulant Tensor Based Probabilistic Load Flow Method
Venue: IEEE Transactions on Power Systems
Year: 2018

Probabilistic load flow analysis is an important part of grid design, optimization, and operation due to the uncertainties in the power network for both generation and demand, increasingly so for newly integrated technologies including wind power and plug in vehicles. A reliable, fast, and robust mathematical method for such analyses is a key requirement to help support widespread integration of these new generation and load sources. Conventional deterministic Monte Carlo analyses, though simple in implementation, becomes too slow as networks become more complex. In this paper, a new cumulant tensor based method is used to assess power flows. Probability distribution functions and reliability indices are generated as final outputs. Furthermore, general correlation between input random variables is included in the analysis. An illustrative 2 bus network is presented along 24 bus IEEE system as case studies, showing the capabilities and increased reliability of the method.
GPU accelerated foreground segmentation using CodeBook model and shadow removal using CUDA
Venue: 2017 International Conference on Computing, Communication and Automation (ICCCA)
Year: 2017

Subtraction is the major important step in many image processing applications which can be applied in much of video surveillances. The major result of this method is accuracy as well as processing time. So we mainly focused on these two challenges. We parallelized the Two Layered CodeBook Model on Graphical Processing Unit (GPU) for increasing the processing speed and the accuracy of the object is increased by using Shadow Removal technique in this proposed method. This algorithm uses CUDA programming language on GPU for making the computation faster when compared with the existing Two Layered CodeBook Model on CPU.
Heritage resources as a driver for cultural tourism in Nigeria
Year: 2020

Nigeria has outstanding and diverse heritage resources, products of its numerous ethnic groups. These heritage resources were created from human interaction with the environment. Under this guise, they have become a source of cultural identity and national pride. They are grouped as tangible and intangible heritages which are today a driver of cultural tourism travel to experience unique traditional beliefs and practices. This form of travel does not only uncover unique heritage landscape but also allows the tourist to be immersed in intangible heritage processes traditional rites, festivities, ritual enactment, etc. Such experience could not have been possible without tourism the process through which culture can be consumed by tourists and positively exploited by host community. Therefore, this work is set to establish heritage resources as of the major reasons for cultural tourism in the country. To achieve the set goal, the paper will explore UNESCO enlisted heritage resources in Nigeria stating their significance and contributions to sustainable cultural tourism.
Direct Current Stimulation Does Little to Improve the Outcome of Working Memory Training in Older Adults
Venue: Psychological science
Year: 2017

The promise of transcranial direct current stimulation (tDCS) as a modulator of cognition has appealed to researchers, media, and the general public. Researchers have suggested that tDCS may increase effects of cognitive training. In this study of 123 older adults, we examined the interactive effects of 20 sessions of anodal tDCS over the left prefrontal cortex (vs. sham tDCS) and simultaneous working memory training (vs. control training) on change in cognitive abilities. Stimulation did not modulate gains from pre to posttest on latent factors of either trained or untrained tasks in a statistically significant manner. A supporting meta analysis (n 266) including younger as well as older individuals, showed that, when combined with training, tDCS was not much more effective than sham tDCS at changing working memory performance (g 0.07, 95% confidence interval, or CI 0.21, 0.34] and global cognition performance (g 0.01, 95% CI 0.29, 0.26] assessed in the absence of stimulation. These results question the general usefulness of current tDCS protocols for enhancing the effects of cognitive training on cognitive ability.
Facebook immune system
Venue: SNS '11
Year: 2011

Popular Internet sites are under attack all the time from phishers, fraudsters, and spammers. They aim to steal user information and expose users to unwanted spam. The attackers have vast resources at their disposal. They are well funded, with full time skilled labor, control over compromised and infected accounts, and access to global botnets. Protecting our users is a challenging adversarial learning problem with extreme scale and load requirements. Over the past several years we have built and deployed a coherent, scalable, and extensible realtime system to protect our users and the social graph. This Immune System performs realtime checks and classifications on every read and write action. As of March 2011, this is 25B checks per day, reaching 650K per second at peak. The system also generates signals for use as feedback in classifiers and other components. We believe this system has contributed to making Facebook the safest place on the Internet for people and their information. This paper outlines the design of the Facebook Immune System, the challenges we have faced and overcome, and the challenges we continue to face.
An introduction to latent class growth analysis and growth mixture modeling.
Year: 2008

In recent years, there has been a growing interest among researchers in the use of latent class and growth mixture modeling techniques for applications in the social and psychological sciences, in part due to advances in and availability of computer software designed for this purpose (e.g. Mplus and SAS Proc Traj) Latent growth modeling approaches, such as latent class growth analysis (LCGA) and growth mixture modeling (GMM) have been increasingly recognized for their usefulness for identifying homogeneous subpopulations within the larger heterogeneous population and for the identification of meaningful groups or classes of individuals. The purpose of this paper is to provide an overview of LCGA and GMM, compare the different techniques of latent growth modeling, discuss current debates and issues, and provide readers with a practical guide for conducting LCGA and GMM using the Mplus software. Researchers in the fields of social and psychological sciences are often interested in modeling the longitudinal developmental trajectories of individuals, whether for the study of personality development or for better understanding how social behaviors unfold over time (whether it be days, months, or years) This usually requires an extensive dataset consisting of longitudinal, repeated measures of variables, sometimes including multiple cohorts, and analyzing this data using various longitudinal latent variable modeling techniques such as latent growth curve models (cf. MacCallum Austin, 2000) The objective of these approaches is to capture information about interindividual differences in intraindividual change over time (Nesselroade, 1991) However, conventional growth modeling approaches assume that individuals come from a single population and that a single growth trajectory can adequately approximate an entire population. Also, it is assumed that covariates that affect the growth factors influence each individual in the same way. Yet, theoretical frameworks and existing studies often categorize individuals into distinct subpopulations (e.g. socioeconomic classes, age groups, at risk populations) For example, in the field of alcohol research, theoretical literature suggests different classes
Higher Order Partial Least Squares (HOPLS) A Generalized Multilinear Regression Method
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2013

A new generalized multilinear regression model, termed the higher order partial least squares (HOPLS) is introduced with the aim to predict a tensor (multiway array) Y from a tensor X through projecting the data onto the latent space and performing regression on the corresponding latent variables. HOPLS differs substantially from other regression models in that it explains the data by a sum of orthogonal Tucker tensors, while the number of orthogonal loadings serves as a parameter to control model complexity and prevent overfitting. The low dimensional latent space is optimized sequentially via a deflation operation, yielding the best joint subspace approximation for both X and Y. Instead of decomposing X and Y individually, higher order singular value decomposition on a newly defined generalized cross covariance tensor is employed to optimize the orthogonal loadings. A systematic comparison on both synthetic data and real world decoding of 3D movement trajectories from electrocorticogram signals demonstrate the advantages of HOPLS over the existing methods in terms of better predictive ability, suitability to handle small sample sizes, and robustness to noise.