2025
A qualitative study exploring motives for the transition from injecting to smoking drugs in Vancouver, British Columbia
Bonn M, Kerr T, Boyd J, Rudzinski K, McNeil R, Ivsins A. A qualitative study exploring motives for the transition from injecting to smoking drugs in Vancouver, British Columbia. International Journal Of Drug Policy 2025, 145: 104982. PMID: 40946388, DOI: 10.1016/j.drugpo.2025.104982.Peer-Reviewed Original ResearchSmoked drugsParticipants' narrativesPunitive drug lawsConsumption practicesVancouver Area Network of Drug UsersHarm reduction measuresConcept of resilienceDrug supplyModes of consumptionQualitative interviewsDrug lawsDrug toxicity deathsDrug Users StudyAIDS Care CohortQualitative studyThematic analysisBritish ColumbiaSurvival ServicesPeopleDrug usersVancouverWellbeingResilienceInterviewsUser studyEffects of Robot Competency and Motion Legibility on Human Correction Feedback
Wang S, Wang A, Goncharova S, Scassellati B, Fitzgerald T. Effects of Robot Competency and Motion Legibility on Human Correction Feedback. 2025, 00: 789-799. DOI: 10.1109/hri61500.2025.10974241.Peer-Reviewed Original ResearchCompetent robotsHuman corrective feedbackRobot's interactive behavioursPick-and-place taskLegible motionSupervised robotsUser studyPick-and-placeRobot deploymentHuman feedbackTask objectivesRobotObjective divergenceInteraction behaviorLearningCorrection precisionUsersFeedbackSuboptimal behaviorDeploymentPrecisionLegibilityTaskCorrectionPhysical effort
2024
FA-Font: Feature Aggregation for Few-shot Font Generation
Lv G, Zhao H. FA-Font: Feature Aggregation for Few-shot Font Generation. 2024, 254-261. DOI: 10.1145/3677182.3677228.Peer-Reviewed Original ResearchDomain-invariant featuresFont generationFont generation methodPleasing visual effectsFeatures of charactersFeature aggregationConsistency lossStyle transferUser studyAggregation moduleSupervised learningTraining dataAggregate featuresComponent labelingVisual effectsGeneration methodLocal inconsistenciesChinese charactersInterpolation operatorLanguage systemLabor-intensiveQualitative comparisonGlyphsUsersFeaturesTime-dependant Bayesian knowledge tracing—Robots that model user skills over time
Salomons N, Scassellati B. Time-dependant Bayesian knowledge tracing—Robots that model user skills over time. Frontiers In Robotics And AI 2024, 10: 1249241. PMID: 38469397, PMCID: PMC10925631, DOI: 10.3389/frobt.2023.1249241.Peer-Reviewed Original ResearchIntelligent tutoring systemsUser skillsTutoring systemUser's knowledge stateComplex taskBayesian Knowledge TracingUser modelUser studyUser answersKnowledge TracingUser observationsEngineering tasksUsersTask completionKnowledge stateAccurate modelTaskAlgorithmRobotPersonal helpSystemIntelligenceCorrect answersAnswersMultiple-choice questions
2020
Prompting Prosocial Human Interventions in Response to Robot Mistreatment
Connolly J, Mocz V, Salomons N, Valdez J, Tsoi N, Scassellati B, Vázquez M. Prompting Prosocial Human Interventions in Response to Robot Mistreatment. 2020, 211-220. DOI: 10.1145/3319502.3374781.Peer-Reviewed Original Research
2019
Cost-aware active learning for named entity recognition in clinical text
Wei Q, Chen Y, Salimi M, Denny J, Mei Q, Lasko T, Chen Q, Wu S, Franklin A, Cohen T, Xu H. Cost-aware active learning for named entity recognition in clinical text. Journal Of The American Medical Informatics Association 2019, 26: 1314-1322. PMID: 31294792, PMCID: PMC6798575, DOI: 10.1093/jamia/ocz102.Peer-Reviewed Original ResearchConceptsAnnotation costUser studyActive learningAL methodsAL algorithmCost-CAUSEReal-world environmentsAnnotation taskAnnotation timeAnnotation accuracyEntity recognitionClinical textAnnotation dataPassive learningInformative examplesCurve scoreMost approachesSimulation areaUsersSyntactic featuresLearningCost measuresAlgorithmCostAnnotation
2018
Preference-Based Assistance Prediction for Human-Robot Collaboration Tasks
Grigore E, Roncone A, Mangin O, Scassellati B. Preference-Based Assistance Prediction for Human-Robot Collaboration Tasks. 2018, 00: 4441-4448. DOI: 10.1109/iros.2018.8593716.Peer-Reviewed Original ResearchHuman-robot collaborationHuman workersHuman-RobotHuman peersHuman-robot collaborative tasksHidden state representationsUser studyTraining dataCollaborative tasksAction partsTraining setRobotReal world observationsAssisted predictionState representationTaskWorld observationsBehavioral modelPhysical tasksUsersHMMTrainingBehavioral preferencesRepresentationAssistance
2017
User needs analysis and usability assessment of DataMed – a biomedical data discovery index
Dixit R, Rogith D, Narayana V, Salimi M, Gururaj A, Ohno-Machado L, Xu H, Johnson T. User needs analysis and usability assessment of DataMed – a biomedical data discovery index. Journal Of The American Medical Informatics Association 2017, 25: 337-344. PMID: 29202203, PMCID: PMC7378884, DOI: 10.1093/jamia/ocx134.Peer-Reviewed Original ResearchData discoveryUsability evaluationInformation needsUser interfaceBiomedical dataIterative usability evaluationsInformation retrieval toolsUser interface needsHigh-quality metadataResearchers informationCommon search enginesDiscovery systemRetrieval toolsDataMedUser studyRelevance judgmentsSearch enginesUser needsDataset explorationUsability assessmentRetrieval techniquesNew retrieval techniqueIncomplete metadataMetadataUsersAn active learning-enabled annotation system for clinical named entity recognition
Chen Y, Lask T, Mei Q, Chen Q, Moon S, Wang J, Nguyen K, Dawodu T, Cohen T, Denny J, Xu H. An active learning-enabled annotation system for clinical named entity recognition. BMC Medical Informatics And Decision Making 2017, 17: 82. PMID: 28699546, PMCID: PMC5506567, DOI: 10.1186/s12911-017-0466-9.Peer-Reviewed Original ResearchConceptsNovel AL algorithmAL algorithmAnnotation timeUser studyEntity recognitionAnnotation systemNatural language processing modelsLanguage processing modelsAnnotation costMedical domainAnnotation processDifferent usersNER modelProcessing modelAlgorithmAL methodsResultsThe simulation resultsUsersSimulation resultsInformation contentFuture workRecognitionLarge numberSystemReal-life setting
2015
A Preliminary Study of Clinical Abbreviation Disambiguation in Real Time
Wu Y, Denny J, Rosenbloom S, Miller R, Giuse D, Song M, Xu H. A Preliminary Study of Clinical Abbreviation Disambiguation in Real Time. Applied Clinical Informatics 2015, 06: 364-374. PMID: 26171081, PMCID: PMC4493336, DOI: 10.4338/aci-2014-10-ra-0088.Peer-Reviewed Original ResearchConceptsElectronic health record systemsUser studyClinical documentation systemNatural language processing systemsClinical NLP systemsPreliminary user studyAbbreviation recognitionExtra time costLanguage processing systemWSD methodHealth record systemsDocumentation systemPrototype applicationWord sense disambiguation methodNLP systemsCorrect sensesNote generationPrototype systemClinical sentencesCost of timeClinical documentsDocument entryDisambiguation moduleSense disambiguation methodHealthcare records
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