2024
A comprehensive measure assessing different types of problematic use of the internet among Chinese adolescents: The Assessment of Criteria for Specific Internet-use Disorders (ACSID-11)
Saffari M, Chen C, Chen I, Ruckwongpatr K, Griffiths M, Potenza M, Wang X, Huang Y, Chen J, Tsai C, Lin C. A comprehensive measure assessing different types of problematic use of the internet among Chinese adolescents: The Assessment of Criteria for Specific Internet-use Disorders (ACSID-11). Comprehensive Psychiatry 2024, 134: 152517. PMID: 39018815, DOI: 10.1016/j.comppsych.2024.152517.Peer-Reviewed Original ResearchBergen Social Media Addiction ScaleSocial media useSocial Media Addiction ScaleInternet Gaming Disorder Scale-Short FormMedia useCross-cultural adaptationTranslation methodInternet-use disordersInternet-useOnline gamesWeb streamingOnline platformsAddiction ScaleProblematic useInternetComparative fit indexOnline surveyComprehensive measurePornographyConfirmatory factor analysisFour-factor solutionSpectrum of activityDifferent kindsCronbach's alphaMcDonaldComparison between problematic use of social media and YouTube to insomnia among Iranian adolescents: A mediating role of psychological distress
Jafari E, Huang P, Zanjanchi F, Potenza M, Lin C, Pakpour A. Comparison between problematic use of social media and YouTube to insomnia among Iranian adolescents: A mediating role of psychological distress. Digital Health 2024, 10: 20552076241261914. PMID: 39347513, PMCID: PMC11428165, DOI: 10.1177/20552076241261914.Peer-Reviewed Original ResearchPsychological distressAddiction ScaleSocial mediaBergen Social Media Addiction ScaleEffect of psychological distressSocial Media Addiction ScaleInsomnia Severity IndexStudy investigated relationshipsIranian adolescentsHayes' PROCESS macroSleep concernsProblematic useInsomniaYouTubePROCESS macroInternet useDistressOnline platformsSeverity IndexAdolescentsPUSMOnline surveyAnxietyScaleDepression
2023
Learning Product Rankings Robust to Fake Users
Golrezaei N, Manshadi V, Schneider J, Sekar S. Learning Product Rankings Robust to Fake Users. Operations Research 2023, 71: 1171-1196. DOI: 10.1287/opre.2022.2380.Peer-Reviewed Original ResearchFake usersOnline learning algorithmLearning algorithmsProduct rankingDetect fake usersEfficient learning algorithmClick farmingImplementing multiple levelsMachine learning algorithmsE-commerce platformsFraudulent behaviorFraudulent usersSuboptimal rankingsUser feedbackCorrupted dataData analyticsFraudulent actorsE-commerceOptimal rankingOnline platformsUsersTD managementDisplay orderLearning methodologyAlgorithm
2022
Online platforms' framing around vaping
Kumar N, Chen K, Shi Y, Altice F. Online platforms' framing around vaping. Drug Testing And Analysis 2022, 15: 1297-1302. PMID: 36445242, DOI: 10.1002/dta.3417.Peer-Reviewed Original Research
2021
Medical Educators’ Response to Changes in Medical Education due to COVID‐19
Lee I, Jung H, Lee Y, Kim H, Shin J, An S. Medical Educators’ Response to Changes in Medical Education due to COVID‐19. Korean Medical Education Review 2021, 23: 168-175. DOI: 10.17496/kmer.2021.23.3.168.Peer-Reviewed Original ResearchLearning Product Rankings Robust to Fake Users
Golrezaei N, Manshadi V, Schneider J, Sekar S. Learning Product Rankings Robust to Fake Users. 2021, 560-561. DOI: 10.1145/3465456.3467580.Peer-Reviewed Original ResearchFake usersLearning algorithmsSub-optimal rankingsEfficient learning algorithmNew learning algorithmsCustomer actionsImplementing multiple levelsFraudulent behaviorFraudulent usersPerformance guaranteesIncurring large costsOptimal rankingOnline platformsUsersPairwise relationshipsClick farmingAlgorithmRanking robustnessProduct rankingInformation environmentCross-learningEfficient convergencePlatformLearningLearning process
2018
Common Object Representations for Visual Production and Recognition
Fan JE, Yamins DLK, Turk‐Browne N. Common Object Representations for Visual Production and Recognition. Cognitive Science 2018, 42: 2670-2698. PMID: 30125986, PMCID: PMC6497164, DOI: 10.1111/cogs.12676.Peer-Reviewed Original ResearchConceptsVisual object recognitionStudy of visionDeep convolutional neural network modelNatural imagesConvolutional neural network modelAbstract feature representationRecognizable drawingsHuman learningObject representationsVisual productionNeural network modelObject recognitionConceptual knowledgeVisual formVisual conceptsVisual cortexRecognition dataDeep networkFeature representationEnhanced recognitionAbstract featuresComprehensionHigher layersNetwork modelOnline platforms
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