nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of the Relationship Between Consumer Based Brand Equity, Experiential Marketing, Customer Satisfaction, and Customer Loyalty at Starbucks Coffee in Karawang
Year: 2020

This study aims to analyze the relationship between consumer based brand equity, experiential marketing, customer satisfaction, and customer loyalty at Starbucks Coffee in Karawang. One of the marketing approaches to support the creation of customer loyalty is to market products using experiential marketing. By integrating the elements of emotion, logic, and general thought processes, a relationship with customers can be built which can not only increase customer satisfaction but also foster customer loyalty. The number of samples studied was 110 respondents via the internet, customers who had visited Starbucks Coffee in Karawang using non probability sampling. The analytical tool used descriptive analysis and multiple regression methods. The results showed that the dimensions of consumer based brand equity, namely physical quality, ideal self congruence, and lifestyle congruence are factors that have a positive effect on customer satisfaction. However, staff behavior does not affect customer satisfaction. The experiential marketing dimension consisting of feelings, thoughts, and actions also has a positive relationship with customer satisfaction, although feelings do not affect customer satisfaction. In the evaluation of customer satisfaction, it is proven that customer satisfaction has a significant effect. This shows that the higher the customer satisfaction, the higher the loyalty of the Starbucks Coffee Karawang outlet customers
Is Human Scoring the Best Criteria for Summary Evaluation?
Venue: FINDINGS
Year: 2021

Normally, summary quality measures are compared with quality scores produced by human annotators. A higher correlation with human scores is considered to be a fair indicator of a better measure. We discuss observations that cast doubt on this view. We attempt to show a possibility of an alternative indicator. Given a family of measures, we explore a criterion of selecting the best measure not relying on correlations with human scores. Our observations for the BLANC family of measures suggest that the criterion is universal across very different styles of summaries.
Atomically thin two dimensional materials for functional electrodes of electrochemical devices
Venue: Ionics
Year: 2012

The development of atomically thin semiconductor nanocrystals for electrodes of various electrochemical devices is probably one of the fastest growing fields in the modern condensed conductive matter research. Two dimensional (2D) nanocrystals have been established as a large class of nanostructures with unusual properties. In this study, specific characteristic, features and technologies of making the 2D nanocrystals including graphene for composite nanostructured electrodes of electrochemical devices are reviewed.
Celastrol protects TGF b1 induced endothelial mesenchymal transition
Venue: Journal of Huazhong University of Science and Technology [Medical Sciences]
Year: 2017

The endothelial to mesenchymal transition (EndMT) in endothelial cells contributes to the development of cardiac fibrosis, ultimately leading to cardiac remodeling. In this study, the effects and molecular mechanisms of celastrol (CEL) on transforming growth factor b1 (TGF b1) induced EndMT in human umbilical vein endothelial (HUVEC 12) cells were investigated. The presented data demonstrated that CEL significantly blocked the morphology change of HUVEC 12 cells induced by TGF b1 without cell cytotoxicity. In accordance with these findings, CEL blocked TGF b1 induced EndMT as evidenced by the inhibition of the mesenchymal markers, including collagen I, III, a SMA, fibronectin mRNA expression, and the increase in the mRNA expression of endothelial cell marker CD31. These changes were also confirmed by double immunofluorescence staining of CD31 and vimentin. The in vitro scratch assay showed that CEL inhibited the migration capacity of the transitioned endothelial cells induced by TGF b1. Further experiments showed that the beneficial effect of CEL on blocking the EndMT in HUVEC 12 cells was associated with the suppression of the TGF b1/Smads signalling pathway, which was also confirmed by the inhibition of its downstream transcription factor snail1, twist1, twist2, ZEB1 and ZEB2. These results indicate that CEL blocks TGF b1 induced EndMT through TGF b1/Smads signalling pathway and suggest that it may be a feasible therapy for cardiac fibrosis diseases.
REAL TIME GYM ACTIVITY DETECTION USING MONOCULAR RGB CAMERA
Year: 2020

Action detection is an attractive area for researchers in computer vision, healthcare, physiotherapy, psychology, and others. Intensive work has been done in this area due to its wide range of applications such as security surveillance, video tagging, Human Computer Interaction (HCI) robotics, medical diagnosis, sports analysis, interactive gaming, and many others. After the deep learning booming results in computer vision tasks like image classification, many researchers have tried to extend the success of deep learning models to video classification and activity recognition. The research question of this thesis is to study the use of the 2D human poses extracted by a DNN based model from RGB frames only, for the online activity detection task and comparing it with the state of the art solutions that utilize the human 3D skeletal data extracted by a depth sensor as an input. At the same time, this work showed the importance of input pre processing and filtering on improving the performance of the online human activity detector. Detecting gym exercises and counting the repetitions in real time using the human skeletal data versus the 2D poses have been studied in depth in this work. The contributions of this work are as follows: 1) generating RGB D dataset for a set of gym exercises, 2) proposing a novel real time skeleton based Double Representational RNN (DR RNN) network architecture for the online action detection, 3) Demonstrating the ability of the proposed model to achieve satisfiable results using pose estimation models applied on RGB frames, 4) introducing a novel learnable exponential filter for the online low latency filtering applications.
Flood susceptibility mapping in Dingnan County (China) using adaptive neuro fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm.
Venue: Journal of environmental management
Year: 2019

Flooding is one of the most significant environmental challenges and can easily cause fatal incidents and economic losses. Flood reduction is costly and time consuming task; so it is necessary to accurately detect flood susceptible areas. This work presents an effective flood susceptibility mapping framework by involving an adaptive neuro fuzzy inference system (ANFIS) with two metaheuristic methods of biogeography based optimization (BBO) and imperialistic competitive algorithm (ICA) A total of 13 flood influencing factors, including slope, altitude, aspect, curvature, topographic wetness index, stream power index, sediment transport index, distance to river, landuse, normalized difference vegetation index, lithology, rainfall and soil type, were used in the proposed framework for spatial modeling and Dingnan County in China was selected for the application of the proposed methods due to data availability. There are 115 flood occurrences in the study area which were randomly separated into training (70% of the total) and verification (30% sets. To perform the proposed framework, the step wise weight assessment ratio analysis algorithm is first used to evaluate the correlation between influencing factors and floods. Then, two ensemble methods of ANFIS BBO and ANFIS ICA are constructed for spatial prediction and producing flood susceptibility maps. Finally, these resultant maps are assessed in terms of several statistical and error measures, including receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) root mean square error (RMSE) The experimental results demonstrated that the two ensemble methods were more effective than ANFIS in the study area. For instance, the predictive AUC values of 0.8407, 0.9045 and 0.9044 were achieved by the methods of ANFIS, ANFIS BBO and ANFIS ICA, respectively. Moreover, the RMSE values for ANFIS, ANFIS BBO and ANFIS ICA using the verification set were 0.3100, 0.2730 and 0.2700, respectively. In addition, as regards ANFIS BBO and ANFIS ICA, a total areas of 39.30% and 35.39% were classified as highly susceptible to flooding. Therefore, the proposed ensemble framework can be used for flood susceptibility mapping in other sites with similar geo environmental characteristics for taking measures to manage and prevent flood damages.
Spectral Video Compression Using Convolutional Sparse Coding
Venue: 2020 Data Compression Conference (DCC)
Year: 2020

Spectral Videos (SV) are datasets containing spatial spectral and temporal information of a moving scene and this kind of information have been successfully used in medicine, remote sensing, and military application. However, expensive acquisition processes and difficulties in equipment manufacture lead to low resolution datasets. Therefore, super resolution (SR) techniques have emerged as a processing tool that recovers a high resolution dataset by expressing the measurements as compressed versions of the desired data. Furthermore, the Convolutional Sparse Coding (CSC) has been developed as a signal model that learns a dictionary directly from the target signal, improving the reconstruction quality. This work proposes to extend the CSC formulation to consider temporal correlations in SVs, exploiting the shifting invariance property of the CSC model. The simulation results show a PSNR improvement in up to 2.5dB with respect to the state of the art methods, preserving the edges and textures of the spectral video frames.
Emerging Bilinguals Future Teachers' Digital Behavior in Online Pre Calculus Class: Mixed Methods Study
Year: 2019

The educational landscape is becoming a digital learning environment. Students in today's digital world draw from multiple sources of information; from hypertext, videos, social media, to video games and internet searches (Luke, 2005) Emerging bilinguals, individuals learning two languages at once, who use software written in English have a passive relationship with the computer when software is not in their native language. They feel that this educational software belongs to another culture. This paper will present findings from a study with emergent bilinguals' engagement in a fully online pre calculus course. The authors utilized the Cultural Historical Activity Theory to describe how emergent bilinguals (perspective teachers) created authentic bilingual learning environments and improved their self efficacy for mathematics. This study utilized Activity Theory to explicate the complex digital practices of emergent bilinguals while engaged in an online mathematics course. This mixed methods study was conducted over four semesters at a university on the U.S. Mexico border. Data collected from demographic survey, class forum questions, daily logs with snapshots, self efficacy surveys, and emails as well as face to face interviews, was analysed through a constant comparison method. Two tensions emerged from the findings, the importance of learning English and encountering unfamiliar Spanish dialects or translations. The results of this study demonstrated that emergent bilinguals mediated several forms of translators and culturally relevant videos for meaning making and to make cognitive connections with the topics in an online mathematics course. They further developed agency in creating an equitable educational digital space where they developed mathematical biliteracy.
Cl( 2 P)+D 2 DCl+DFan Ying Zhong Bo En Ao Ben Hai Mo Jin Si You Xiao Xing De Jiao Cha Fen Zi Shu Yan Jiu
Year: 2020

a. Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China b. State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China c. College of Science, Southern University of Science and Technology, Shenzhen 518055, China d. School of Physical Science Technology, ShanghaiTech University, Shanghai 201210, China e. University of Chinese Academy of Sciences, Beijing 100049, China
Cooperative multi agent traffic signal control system using fast gradient descent function approximation for V2I networks
Venue: 2014 IEEE International Conference on Communications (ICC)
Year: 2014

The traffic signal control is the basic method to solve the urban congestions problem coming with the accelerating urbanization. For large city, it is challenge to improve the traffic signal control flexibility to adapt the real time traffic change while to decrease the computation complexity. This paper proposes a cooperative Q learning with function approximation(CQFA) algorithm for vehicle to infrastructure (V2I) networks. By gathering the local intersection traffic information from V2I networks and employing cooperative behaviors with neighboring intersections, the algorithm can achieve the optimal policy without any central supervising agents. To address the curse of dimensionality effectively, the Q learning function is approximated by using a fast gradient descent function approximation method to pick out the optimal Q learning action. The Q learning with Function Approximation algorithm combining the cooperative mechanism balances the urban traffic flow and uses approximating strategy to decrease the computation dimensionality. It can improve the traffic throughout, reduce the average waiting time and avoid congestions. Simulation results verify the effectiveness of the proposed algorithm.
"Hello AI" Uncovering the Onboarding Needs of Medical Practitioners for Human AI Collaborative Decision Making
Venue: Proc. ACM Hum. Comput. Interact.
Year: 2019

Although rapid advances in machine learning have made it increasingly applicable to expert decision making, the delivery of accurate algorithmic predictions alone is insufficient for effective human AI collaboration. In this work, we investigate the key types of information medical experts desire when they are first introduced to a diagnostic AI assistant. In a qualitative lab study, we interviewed 21 pathologists before, during, and after being presented deep neural network (DNN) predictions for prostate cancer diagnosis, to learn the types of information that they desired about the AI assistant. Our findings reveal that, far beyond understanding the local, case specific reasoning behind any model decision, clinicians desired upfront information about basic, global properties of the model, such as its known strengths and limitations, its subjective point of view, and its overall design objective what it's designed to be optimized for. Participants compared these information needs to the collaborative mental models they develop of their medical colleagues when seeking a second opinion: the medical perspectives and standards that those colleagues embody, and the compatibility of those perspectives with their own diagnostic patterns. These findings broaden and enrich discussions surrounding AI transparency for collaborative decision making, providing a richer understanding of what experts find important in their introduction to AI assistants before integrating them into routine practice.
Imaging the neural effects of cognitive bias modification training
Venue: NeuroImage
Year: 2017

&NA; Cognitive bias modification (CBM) was first developed as an experimental tool to examine the causal role of cognitive biases, and later developed into complementary interventions in experimental psychopathology research. CBM involves the "re training" of implicit biases by means of multiple trials of computerized tasks, and has been demonstrated to change anxious, depressive and drug seeking behavior, including clinically relevant effects. Recently, the field has progressed by combining CBM with neuroimaging techniques, which provides insight into neural mechanisms underlying how CBM affects implicit biases in anxiety, depression, and addiction, and potentially other pathologies. This narrative literature review summarizes the state of the art of studies on the neural effects of CBM and provides directions for future research in the field. A total of 13 published studies were found and discussed: n 9 in anxiety, n 2 in depressive behavior, and n 2 in addiction. HighlightsCognitive bias modification (CBM) has been shown to change clinically relevant behavior in anxiety, depression and addictionFirst neuroimaging studies suggest a role of the fronto amygdalar circuitry in the efficacy of CBM in anxiety and addiction; and effects on connectivity between frontal gyrus, anterior cingulate and insula in depressive behaviorReviewed findings are of clinical importance and may provide insight into target regions for neuromodulatory techniques
Apparatus and system for measuring force operating tool in minimally invasive environment
Year: 2011

An apparatus for measuring a force of a minimally invasive surgery tool and a system including the same are provided to measure a force generated in the end of the minimally invasive surgery tool by mounting a detachable insulation sensor device between a trocar and a single path guide.  A trocar(100) includes a cannula(150) a trocar housing(120) and a housing hole(140) The cannula is inserted into a body via the skin of a cut part. A flange(220) is extended from the end of a body unit(210) to the outside of the body unit. A pressure sensor(230) generates pressure sensor information corresponding force measurement information. A signal line transmits the pressure sensor information to an external computer.
Evaluate Neuroprotective Chemicals in Drosophila Models of Parkinson's Disease
Year: 2010

The Drosophila model of Parkinson's disease has been invaluable for dissecting the molecular and cellular mechanism of the disease.To evaluate the possibility of utilizing Drosophila model in drug screening,two strains based on DJ 1A and PINK1 gene interference were constructed,and chemical molecules with anti oxidant and anti inflammatory property such as minocycline and coenzyme Q10 were tested.Brain dopamine levels were used as readout.We found that minocycline prevented brain dopamine level from declining in DJ 1A Drosophila model.This protective effect was not seen in PINK1 model.Coenzyme Q10 was protective in both models.The Drosophila model of Parkinson's disease revealed the specific function of chemicals,thus provided a novel platform to search for new drugs to treat the disease.
Self labelling via simultaneous clustering and representation learning
Venue: ICLR
Year: 2020

Combining clustering and representation learning is one of the most promising approaches for unsupervised learning of deep neural networks. However, doing so naively leads to ill posed learning problems with degenerate solutions. In this paper, we propose a novel and principled learning formulation that addresses these issues. The method is obtained by maximizing the information between labels and input data indices. We show that this criterion extends standard crossentropy minimization to an optimal transport problem, which we solve efficiently for millions of input images and thousands of labels using a fast variant of the Sinkhorn Knopp algorithm. The resulting method is able to self label visual data so as to train highly competitive image representations without manual labels. Our method achieves state of the art representation learning performance for AlexNet and ResNet 50 on SVHN, CIFAR 10, CIFAR 100 and ImageNet and yields the first self supervised AlexNet that outperforms the supervised Pascal VOC detection baseline. Code and models are available.
Comparison of Different Total Ionic Strength Adjustment Buffer Compositions for Determination of Low Level Fluoride in Environmental Water Samples with Fluoride Ion Selective Electrode
Year: 2021

Fluoride content of environmental water samples collected from the vicinity of Pilanesberg National Park was determined using a Fluoride Ion Selective Electrode (F ISE) Different Total Ionic Strength Adjustment Buffers (TISABs) EDTA, CDTA, citrate and acetate buffers, were compared for their effectiveness in releasing fluorine into the solution in its ionic form, by adjusting the pH and ionic strength of the solution, as well as by chelating polyvalent cations present in the samples. Nine water samples were collected from different sites around the park, where there is a decommissioned fluorspar mine, and an extinct volcano for fluoride content. Quantification was carried out by means of multipoint calibration covering the range of interest in all treatments. The fluoride concentration was calculated using the Nernst equation with values obtained from the calibration graph. It was found that CDTA and EDTA buffers were the best TISABs as they produced a better linearity, slope and recovery in that order, whereas the other acetate also produced better parameters and results than the untreated water samples. Corresponding author: Forsido TT, Department of Environmental, Water and Earth Sciences, Tshwane University of Technology, Arcadia Campus, P. O. Box 56208, Arcadia 0001, Pretoria, Republic of South Africa, Tel: 27814705520; E mail: tedlayef@yahoo.com Received date: January 19, 2021;  2021;  Forsido TT, Ndibewu P (2021) Comparison of different Total Ionic Strength Adjustment Buffer Compositions for Determination of Low Level Fluoride in Environmental Water Samples with Fluoride Ion Selective Electrode. J Ecosys Ecograph 11: 280.
RTVD: A Real Time Volumetric Detection Scheme for DDoS in the Internet of Things
Venue: IEEE Access
Year: 2020

Distributed Denial of Service (DDoS) attacks are increasingly harmful to the cyberspace nowadays. The attackers can now easily launch a bigger and more challenging DDoS attack both towards and with Internet of Things (IoT) devices, due to the fast popularization of them. Because of the characteristic of fast overwhelming, it is important to make fast as well as accurate response to DDoS attacks, and the real time performance can be even more important to prevent and legitimate the attacks. Among the methods proposed by researchers, the entropy based detection method provides a sensitive and reliable performance. However, the balance between computational complexity and recognition accuracy remains a challenge. In this paper, we propose a detection method that consists of 3 main parts in different aspects: a sliding time window to fasten the entropy calculation, a single directional filter to realize early detection during the DDoS progress but not after the crash, and a quintile deviation check algorithm to optimize the detection result. These will eventually lead to a real time and high efficient performance to recognize IoT DDoS attacks as soon as possible.