scholarly journals Flexible learning, rather than inveterate innovation or copying, drives cumulative knowledge gain

2020 ◽  
Vol 6 (23) ◽  
pp. eaaz0286 ◽  
Author(s):  
Elena Miu ◽  
Ned Gulley ◽  
Kevin N. Laland ◽  
Luke Rendell
Keyword(s):  
Information Transfer ◽  
Technological Progress ◽  
Large Scale ◽  
Information Use ◽  
Cultural Knowledge ◽  
Knowledge Gain ◽  
Large Scale Dataset ◽  
Social Information Use ◽  
And Performance ◽  
Cumulative Knowledge

Human technology is characterized by cumulative cultural knowledge gain, yet researchers have limited knowledge of the mix of copying and innovation that maximizes progress. Here, we analyze a unique large-scale dataset originating from collaborative online programming competitions to investigate, in a setting of real-world complexity, how individual differences in innovation, social-information use, and performance generate technological progress. We find that cumulative knowledge gain is primarily driven by pragmatists, willing to copy, innovate, explore, and take risks flexibly, rather than by pure innovators or habitual copiers. Our study also reveals a key role for prestige in information transfer.

scholarly journals Operation and performance of VRF systems: Mining a large-scale dataset

2021 ◽  
Vol 230 ◽  
pp. 110519
Author(s):  
Mingyang Qian ◽  
Da Yan ◽  
Tianzhen Hong ◽  
Hua Liu
Keyword(s):  
Large Scale ◽  
Large Scale Dataset ◽  
And Performance

Sentiment Analysis of Users’ Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset (Preprint)

2021 ◽  
Author(s):  
Kashif Ahmad ◽  
Firoj Alam ◽  
Junaid Qadir ◽  
Basheer Qolomony ◽  
Imran Khan ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Mobile Applications ◽  
Large Scale ◽  
Benchmark Dataset ◽  
Contact Tracing ◽  
Future Research ◽  
Crowd Sourcing ◽  
Large Scale Dataset ◽  
And Performance ◽  
Different Sources

BACKGROUND Contact tracing has been globally adopted in the fight to control the infection rate of COVID-19. Thanks to digital technologies, such as smartphones and wearable devices, contacts of COVID-19 patients can be easily traced and informed about their potential exposure to the virus. To this aim, several interesting mobile applications have been developed. However, there are ever-growing concerns over the working mechanism and performance of these applications. The literature already provides some interesting exploratory studies on the community’s response to the applications by analyzing information from different sources, such as news and users’ reviews of the applications. However, to the best of our knowledge, there is no existing solution that automatically analyzes users’ reviews and extracts the evoked sentiments. OBJECTIVE In this paper, we analyze how AI models can help in automatically extract and classify the polarity of users’ sentiments and propose a sentiment analysis framework to automatically analyze users’ reviews on COVID-19 contact tracing mobile applications. METHODS we propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding on the development and training of AI models for automatic sentiment analysis of users’ reviews. In detail, we collected and annotated a large-scale dataset of Android and iOS mobile application users’ reviews for COVID-19 contact tracing. After manually analyzing and annotating users’ reviews, we employed both classical (i.e., Naïve Bayes, SVM, Random Forest) and deep learning (i.e., fastText, and different transformers) methods for classification experiments. This resulted in eight different classification models. RESULTS We employed eight different methods on three different tasks achieving up to an average F1-Scores 94.8% indicating the feasibility of automatic sentiment analysis of users’ reviews on the COVID-19 contact tracing applications. Moreover, the crowd-sourcing activity resulted in a large-scale benchmark dataset composed of 34,534 reviews manually annotated from the contract tracing applications of 46 distinct countries. CONCLUSIONS The existing literature mostly relies on the manual/exploratory analysis of users’ reviews on the application, which is a tedious and time-consuming process. Moreover, in the existing studies, generally, data from fewer applications are analyzed. In this work, we showed that automatic sentiment analysis can help in analyzing users’ responses to the application more quickly with significant accuracy. Moreover, we also provided a large-scale benchmark dataset composed of 34,534 reviews from 47 different applications. We believe the presented analysis and the dataset will support future research on the topic.

The Role of Research and Innovation in the Transformation of Agri-Food Systems

2018 ◽  
Vol 12 (2) ◽  
pp. 60-63
Author(s):  
Mariana Sandu ◽  
Stefan Mantea
Keyword(s):  
Technological Progress ◽  
Food Systems ◽  
Food System ◽  
Agricultural Research ◽  
Research And Innovation ◽  
And Performance ◽  
The Impact ◽  
Determinant Factor ◽  
Rapid Transformation

Abstract Agri-food systems include branching ramifications, which connect in the upstream the input suppliers with farmers, and downstream farmers, processors, retailers and consumers. In the last decades, at the level of the regions, food systems have undergone rapid transformation as a result of technological progress. The paper analyzes the changes made to the structure, behavior and performance of the agri-food system and the impact on farmers and consumers. Also, the role of agricultural research as a determinant factor of transformation of agri-food system is analyzed. The research objective is to develop technologies that cover the entire food chain (from farm to fork) and meet the specific requirements of consumers (from fork to farm) through scientific solutions in line with the principles of sustainable agriculture and ensuring the safety and food safety of the population.

scholarly journals Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

2021 ◽  
Author(s):  
Jin Zhou ◽  
Qing Zhang ◽  
Jian-Hao Fan ◽  
Wei Sun ◽  
Wei-Shi Zheng
Keyword(s):  
Large Scale ◽  
Assessment Model ◽  
Generic Model ◽  
Small Subset ◽  
Deep Convolutional Neural Networks ◽  
Personal Taste ◽  
Hinge Loss ◽  
Novel Approach ◽  
Large Scale Dataset ◽  
Image Pairs

AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.

scholarly journals VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

2021 ◽  
Vol 7 (3) ◽  
pp. 50
Author(s):  
Anselmo Ferreira ◽  
Ehsan Nowroozi ◽  
Mauro Barni
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Child Pornography ◽  
Forensic Analysis ◽  
Synthetic Image ◽  
Image Detection ◽  
Face Images ◽  
Large Scale Dataset ◽  
Scanned Images ◽  
Analysis Of The Images

The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.

scholarly journals INTRACISTRONIC MAPPING OF ELECTROPHORETIC SITES IN DROSOPHILA MELANOGASTER: FIDELITY OF INFORMATION TRANSFER BY GENE CONVERSION

Genetics ◽  
1974 ◽  
Vol 76 (2) ◽  
pp. 289-299
Author(s):  
Margaret McCarron ◽  
William Gelbart ◽  
Arthur Chovnick
Keyword(s):  
Drosophila Melanogaster ◽  
Information Transfer ◽  
Large Scale ◽  
Genetic Basis ◽  
Xanthine Dehydrogenase ◽  
Specific Protein ◽  
Wild Type ◽  
Electrophoretic Variation ◽  
The Stability ◽  
Recombination Experiments

ABSTRACT A convenient method is described for the intracistronic mapping of genetic sites responsible for electrophoretic variation of a specific protein in Drosophila melanogaster. A number of wild-type isoalleles of the rosy locus have been isolated which are associated with the production of electrophoretically distinguishable xanthine dehydrogenases. Large-scale recombination experiments were carried out involving null enzyme mutants induced on electrophoretically distinct wild-type isoalleles, the genetic basis for which is followed as a nonselective marker in the cross. Additionally, a large-scale recombination experiment was carried out involving null enzyme rosy mutants induced on the same wild-type isoallele. Examination of the electrophoretic character of crossover and convertant products recovered from the latter experiment revealed that all exhibited the same parental electrophoretic character. In addition to documenting the stability of the xanthine dehydrogenase electrophoretic character, this observation argues against a special mutagenesis hypothesis to explain conversions resulting from allele recombination studies.

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