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Hosseini MS, Bejnordi BE, Trinh VQH, Chan L, Hasan D, Li X, Yang S, Kim T, Zhang H, Wu T, Chinniah K, Maghsoudlou S, Zhang R, Zhu J, Khaki S, Buin A, Chaji F, Salehi A, Nguyen BN, Samaras D, Plataniotis KN. Computational pathology: A survey review and the way forward. J Pathol Inform 2024; 15:100357. [PMID: 38420608 PMCID: PMC10900832 DOI: 10.1016/j.jpi.2023.100357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 03/02/2024] Open
Abstract
Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.
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Affiliation(s)
- Mahdi S Hosseini
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | | | - Vincent Quoc-Huy Trinh
- Institute for Research in Immunology and Cancer of the University of Montreal, Montreal, QC H3T 1J4, Canada
| | - Lyndon Chan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Danial Hasan
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Xingwen Li
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Stephen Yang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Taehyo Kim
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Haochen Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Theodore Wu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Kajanan Chinniah
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Sina Maghsoudlou
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ryan Zhang
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Jiadai Zhu
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Samir Khaki
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
| | - Andrei Buin
- Huron Digitial Pathology, St. Jacobs, ON N0B 2N0, Canada
| | - Fatemeh Chaji
- Department of Computer Science and Software Engineering (CSSE), Concordia Univeristy, Montreal, QC H3H 2R9, Canada
| | - Ala Salehi
- Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
| | - Bich Ngoc Nguyen
- University of Montreal Hospital Center, Montreal, QC H2X 0C2, Canada
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, United States
| | - Konstantinos N Plataniotis
- The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE), University of Toronto, Toronto, ON M5S 3G4, Canada
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Jeffs LV, Dunbar JC, Syed S, Ng C, Pollack AH. Navigating normalcy: designing personal health visualizations for pediatric kidney transplant recipients and caregivers. J Am Med Inform Assoc 2024:ocae206. [PMID: 39078283 DOI: 10.1093/jamia/ocae206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 06/20/2024] [Accepted: 07/19/2024] [Indexed: 07/31/2024] Open
Abstract
OBJECTIVES Patients with chronic illnesses, including kidney disease, consider their sense of normalcy when evaluating their health. Although this concept is a key indicator of their self-determined well-being, they struggle to understand if their experience is typical. To address this challenge, we set out to explore how to design personal health visualizations that aid participants in better understanding their experiences post-transplant, identifying barriers to normalcy, and achieving their desired medical outcomes. MATERIALS AND METHODS Pediatric kidney transplant patients and their caregivers participated in three asynchronous design sessions involving sharing experiences, presenting symbolic objects, and providing feedback on visualizations to understand their perceptions of normalcy post-transplant. Data analysis of design session 1 and 2 comprised deductive and inductive analysis. We used affinity diagramming to identify thematic areas about participants' transplant experiences. Comprehension of design session three normalcy visualizations was also evaluated. RESULTS Participants effectively engaged in the design sessions, revealing diverse perspectives on their experiences. We found there is a significant need for visualizations that depict normalcy to better inform patients and caregivers about their health. DISCUSSION Normalcy Visualizations should incorporate three key design principles: personal values, facilitating peer and self-comparison, and seamlessly communicating abstract concepts to help youth kidney transplant recipients comprehend and contextualize if their transplant experience is normal and what normalcy means to them. CONCLUSION By incorporating holistic aspects of patients' and caregivers' lives into personal health visualizations, they can be cognizant of their progress to normalcy and empowered to make decisions that help them feel normal.
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Affiliation(s)
- Lily V Jeffs
- Information School, University of Washington, Seattle, WA 98195, United States
| | - Julia C Dunbar
- Information School, University of Washington, Seattle, WA 98195, United States
| | - Sanaa Syed
- Information School, University of Washington, Seattle, WA 98195, United States
| | - Chelsea Ng
- Division of Nephrology, Seattle Children's Hospital, Seattle, WA 98105, United States
| | - Ari H Pollack
- Information School, University of Washington, Seattle, WA 98195, United States
- Division of Nephrology, Seattle Children's Hospital, Seattle, WA 98105, United States
- Department of Pediatrics, University of Washington, Seattle, WA 98105, United States
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3
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Sefidgar YS, Castillo CL, Chopra S, Jiang L, Jones T, Mittal A, Ryu H, Schroeder J, Cole A, Murinova N, Munson SA, Fogarty J. MigraineTracker: Examining Patient Experiences with Goal-Directed Self-Tracking for a Chronic Health Condition. PROCEEDINGS OF THE SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS. CHI CONFERENCE 2024; 2024:129. [PMID: 38741616 PMCID: PMC11090491 DOI: 10.1145/3613904.3642075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Self-tracking and personal informatics offer important potential in chronic condition management, but such potential is often undermined by difficulty in aligning self-tracking tools to an individual's goals. Informed by prior proposals of goal-directed tracking, we designed and developed MigraineTracker, a prototype app that emphasizes explicit expression of goals for migraine-related self-tracking. We then examined migraine patient experiences in a deployment study for an average of 12+ months, including a total of 50 interview sessions with 10 patients working with 3 different clinicians. Patients were able to express multiple types of goals, evolve their goals over time, align tracking to their goals, personalize their tracking, reflect in the context of their goals, and gain insights that enabled understanding, communication, and action. We discuss how these results highlight the importance of accounting for distinct and concurrent goals in personal informatics together with implications for the design of future goal-directed personal informatics tools.
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Affiliation(s)
| | | | - Shaan Chopra
- University of Washington Seattle, Washington, USA
| | - Liwei Jiang
- University of Washington Seattle, Washington, USA
| | - Tae Jones
- University of Washington Seattle, Washington, USA
| | - Anant Mittal
- University of Washington Seattle, Washington, USA
| | - Hyeyoung Ryu
- University of Washington Seattle, Washington, USA
| | | | - Allison Cole
- University of Washington Seattle, Washington, USA
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4
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Oewel B, Areán PA, Agapie E. Approaches to Tailoring Between-Session Mental Health Therapy Activities. PROCEEDINGS OF THE SIGCHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS. CHI CONFERENCE 2024; 2024:696. [PMID: 38919830 PMCID: PMC11197942 DOI: 10.1145/3613904.3642856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2024]
Abstract
Mental health activities conducted by patients between therapy sessions (or "therapy homework") are a component of addressing anxiety and depression. However, to be effective, therapy homework must be tailored to the client's needs to address the numerous barriers they encounter in everyday life. In this study, we analyze how therapists and clients tailor therapy homework to their client's needs. We interviewed 13 therapists and 14 clients about their experiences tailoring and engaging in therapy homework. We identify criteria for tailoring homework, such as client skills, discomfort, and external barriers. We present how homework gets adapted, such as through changes in difficulty or by identifying alternatives. We discuss how technologies can better use client information for personalizing mental health interventions, such as adapting to client barriers, adjusting homework to these barriers, and creating a safer environment to support discomfort.
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Affiliation(s)
- Bruna Oewel
- University of California, Irvine Irvine, USA
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5
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Qu Y, Wei C, Du P, Che W, Zhang C, Ouyang W, Bian Y, Xu F, Hu B, Du K, Wu H, Liu J, Liu Q. Integration of cognitive tasks into artificial general intelligence test for large models. iScience 2024; 27:109550. [PMID: 38595796 PMCID: PMC11001637 DOI: 10.1016/j.isci.2024.109550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024] Open
Abstract
During the evolution of large models, performance evaluation is necessary for assessing their capabilities. However, current model evaluations mainly rely on specific tasks and datasets, lacking a united framework for assessing the multidimensional intelligence of large models. In this perspective, we advocate for a comprehensive framework of cognitive science-inspired artificial general intelligence (AGI) tests, including crystallized, fluid, social, and embodied intelligence. The AGI tests consist of well-designed cognitive tests adopted from human intelligence tests, and then naturally encapsulates into an immersive virtual community. We propose increasing the complexity of AGI testing tasks commensurate with advancements in large models and emphasizing the necessity for the interpretation of test results to avoid false negatives and false positives. We believe that cognitive science-inspired AGI tests will effectively guide the targeted improvement of large models in specific dimensions of intelligence and accelerate the integration of large models into human society.
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Affiliation(s)
- Youzhi Qu
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Chen Wei
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Penghui Du
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Wenxin Che
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Chi Zhang
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | | | | | - Feiyang Xu
- iFLYTEK AI Research, Hefei 230088, China
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Kai Du
- Institute for Artificial Intelligence, Peking University, Beijing 100871, China
| | - Haiyan Wu
- Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Macau 999078, China
| | - Jia Liu
- Department of Psychology, Tsinghua University, Beijing 100084, China
| | - Quanying Liu
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
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6
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Shuster SM, Campos-Castillo C, Madani N, Joseph K. Who supports Bernie? Analyzing identity and ideological variation on Twitter during the 2020 democratic primaries. PLoS One 2024; 19:e0294735. [PMID: 38603640 PMCID: PMC11008827 DOI: 10.1371/journal.pone.0294735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 10/31/2023] [Indexed: 04/13/2024] Open
Abstract
Using a novel dataset of 590M messages by 21M users, we present the first large-scale examination of the behavior of likely Bernie supporters on Twitter during the 2020 U.S. Democratic primaries and presidential election. We use these data to dispel empirically the notion of a unified, stereotypical Bernie supporter (e.g., the "Bernie Bro"). Instead, our work uncovers significant variation in the identities and ideologies of Bernie supporters who were active on Twitter. Our work makes three contributions to the literature on social media and social movements. Methodologically, we present a novel mixed methods approach to surface identity and ideological variation within a movement via use of patterns in who retweets whom (i.e. who retweets which other users) and who retweets what (i.e. who retweets which specific tweets). Substantively, documentation of these variations challenges a trend in the social movement literature to assume actors within a particular movement are unified in their ideology, identity, and values.
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Affiliation(s)
- Stef M. Shuster
- Lyman Briggs College and Department of Sociology, Michigan State University, East Lansing, MI, United States of America
| | - Celeste Campos-Castillo
- Department of Media & Information, Michigan State University, East Lansing, MI, United States of America
| | - Navid Madani
- Computer Science and Engineering Department, University at Buffalo, Buffalo, NY, United States of America
| | - Kenneth Joseph
- Computer Science and Engineering Department, University at Buffalo, Buffalo, NY, United States of America
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7
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Michalsky T. Metacognitive scaffolding for preservice teachers' self-regulated design of higher order thinking tasks. Heliyon 2024; 10:e24280. [PMID: 38293459 PMCID: PMC10827503 DOI: 10.1016/j.heliyon.2024.e24280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 01/02/2024] [Accepted: 01/05/2024] [Indexed: 02/01/2024] Open
Abstract
This study compared two professional training courses targeting self-regulated learning (SRL) amongst preservice secondary science teachers in the context of didactic content knowledge for teaching higher-order thinking (HOT-PCK), either with metacognitive scaffolding (Meta group) or without (Control group). Measures included trainees' comprehension and design of HOT-PCK learning tasks, online SRL reflections about learning-teaching events, and self-reported SRL aptitude. Results indicated skill improvement in both groups, but the metacognitive support provided by the IMPROVE self-questioning technique better enhanced the preservice teachers' (PSTs) development of HOT-PCK, both as students (comprehension skills) and as future teachers (design skills), additionally as their ability to reflect on and control their studying. Findings also revealed significant correlations between SRL assessments (self-reports, event-based reflections) and between SRL and HOT-PCK indices. Consequences for teacher education combining SRL and HOT-PCK contexts are discussed.
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Affiliation(s)
- Tova Michalsky
- Faculty of Education, Bar-Ilan University, Ramat-Gan, 5290002, Israel
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8
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Cho YM, Rai S, Ungar L, Sedoc J, Guntuku SC. An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives. PROCEEDINGS OF THE CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING. CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING 2023; 2023:11346-11369. [PMID: 38618627 PMCID: PMC11010238 DOI: 10.18653/v1/2023.emnlp-main.698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Mental health conversational agents (a.k.a. chatbots) are widely studied for their potential to offer accessible support to those experiencing mental health challenges. Previous surveys on the topic primarily consider papers published in either computer science or medicine, leading to a divide in understanding and hindering the sharing of beneficial knowledge between both domains. To bridge this gap, we conduct a comprehensive literature review using the PRISMA framework, reviewing 534 papers published in both computer science and medicine. Our systematic review reveals 136 key papers on building mental health-related conversational agents with diverse characteristics of modeling and experimental design techniques. We find that computer science papers focus on LLM techniques and evaluating response quality using automated metrics with little attention to the application while medical papers use rule-based conversational agents and outcome metrics to measure the health outcomes of participants. Based on our findings on transparency, ethics, and cultural heterogeneity in this review, we provide a few recommendations to help bridge the disciplinary divide and enable the cross-disciplinary development of mental health conversational agents.
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9
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Kapoor S, Narayanan A. Leakage and the reproducibility crisis in machine-learning-based science. PATTERNS (NEW YORK, N.Y.) 2023; 4:100804. [PMID: 37720327 PMCID: PMC10499856 DOI: 10.1016/j.patter.2023.100804] [Citation(s) in RCA: 61] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/18/2023] [Accepted: 07/05/2023] [Indexed: 09/19/2023]
Abstract
Machine-learning (ML) methods have gained prominence in the quantitative sciences. However, there are many known methodological pitfalls, including data leakage, in ML-based science. We systematically investigate reproducibility issues in ML-based science. Through a survey of literature in fields that have adopted ML methods, we find 17 fields where leakage has been found, collectively affecting 294 papers and, in some cases, leading to wildly overoptimistic conclusions. Based on our survey, we introduce a detailed taxonomy of eight types of leakage, ranging from textbook errors to open research problems. We propose that researchers test for each type of leakage by filling out model info sheets, which we introduce. Finally, we conduct a reproducibility study of civil war prediction, where complex ML models are believed to vastly outperform traditional statistical models such as logistic regression (LR). When the errors are corrected, complex ML models do not perform substantively better than decades-old LR models.
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Affiliation(s)
- Sayash Kapoor
- Department of Computer Science and Center for Information Technology Policy, Princeton University, Princeton, NJ 08540, USA
| | - Arvind Narayanan
- Department of Computer Science and Center for Information Technology Policy, Princeton University, Princeton, NJ 08540, USA
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10
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Chen V, Bhatt U, Heidari H, Weller A, Talwalkar A. Perspectives on incorporating expert feedback into model updates. PATTERNS (NEW YORK, N.Y.) 2023; 4:100780. [PMID: 37521050 PMCID: PMC10382980 DOI: 10.1016/j.patter.2023.100780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
Machine learning (ML) practitioners are increasingly tasked with developing models that are aligned with non-technical experts' values and goals. However, there has been insufficient consideration of how practitioners should translate domain expertise into ML updates. In this review, we consider how to capture interactions between practitioners and experts systematically. We devise a taxonomy to match expert feedback types with practitioner updates. A practitioner may receive feedback from an expert at the observation or domain level and then convert this feedback into updates to the dataset, loss function, or parameter space. We review existing work from ML and human-computer interaction to describe this feedback-update taxonomy and highlight the insufficient consideration given to incorporating feedback from non-technical experts. We end with a set of open questions that naturally arise from our proposed taxonomy and subsequent survey.
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Affiliation(s)
| | - Umang Bhatt
- University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | | | - Adrian Weller
- University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
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11
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Anwar S, Rana H. Spiritual intelligence and psychological wellbeing of Pakistani University students. CURRENT PSYCHOLOGY 2023:1-8. [PMID: 37359633 PMCID: PMC10176278 DOI: 10.1007/s12144-023-04717-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/27/2023] [Indexed: 06/28/2023]
Abstract
The objective of this study was to find out the predictive role of spiritual intelligence for psychological wellbeing in university students and also to assess gender differences. For that reason, data of N= 250 (M age = 21.8; SD= 1.9) students of undergraduate programs was taken from different universities of Pakistan. Due to COVID-19 pandemic, data was collected online (google form) by using purposive sampling technique and sample was comprised of 77 men and 173 women. Spiritual Intelligence (King, 2008) and Ryff's 42-item Psychological Wellbeing Scale (Ryff, 1989, Muzzafar & Rana, 2019) were utilized for measuring variables of the study. Results were analyzed via SPSS (version 21), Hierarchical Regression and t-Test were carried out. The study results revealed that spiritual intelligence is a significant positive predictor of psychological wellbeing. It was also found that male students have high level of spiritual intelligence and psychological wellbeing in comparison to female students. Results of this study provide an implication for instructors as well as educationists to design activities which provide facilitation in increasing spiritual intelligence of students.
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Affiliation(s)
- Shahzad Anwar
- Riphah Institute of Clinical & Professional Psychology, Riphah International University, Lahore, Pakistan
| | - Hina Rana
- Department of Clinical Psychology, University of Management & Technology, Lahore, Pakistan
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12
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Liang H, Liu S, Wang Y, Pan J, Zhang Y, Dong X. Multi-user upper limb rehabilitation training system integrating social interaction. COMPUTERS & GRAPHICS 2023; 111:103-110. [PMID: 36694846 PMCID: PMC9854142 DOI: 10.1016/j.cag.2023.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/07/2023] [Accepted: 01/16/2023] [Indexed: 06/17/2023]
Abstract
COVID-19 causes persistent symptoms such as weakness and myasthenia in most patients. Due to the cross-infection of COVID-19, the traditional face-to-face rehabilitation services are risky for the elderly. To ensure that the elderly in urgent need of rehabilitation services receive training while minimizing the disturbance of the COVID-19 pandemic on their social activities. We have improved the existing virtual upper limb training system, and added a social factor to the system. Seniors with upper limb rehabilitation needs can use the system to compete or collaborate with others for training. In addition, a set of natural and scientific exclusive gestures have been designed under the direction of following the doctor's advice. The experiment is conducted jointly with the chief physicians of the geriatrics department in the authoritative class-A hospitals of Class III. Our experiment, which lasted for two months, showed that the virtual training system with social factors added had the best rehabilitation effect and enhanced the initiative of patients. The system has value for popularization during the COVID-19 epidemic.
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Affiliation(s)
- Hui Liang
- Zhengzhou University of Light Industry, Zhengzhou, 450000, China
| | - Shiqing Liu
- Zhengzhou University of Light Industry, Zhengzhou, 450000, China
| | - Yi Wang
- General Hospital of Pingmei Shenma Group, Pingdingshan, 467000, China
| | - Junjun Pan
- Beihang University, Beijing, 100000, China
| | - Yazhou Zhang
- Zhengzhou University of Light Industry, Zhengzhou, 450000, China
| | - Xiaohang Dong
- Zhengzhou University of Light Industry, Zhengzhou, 450000, China
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Xiao Q, Ihnaini B. Stock trend prediction using sentiment analysis. PeerJ Comput Sci 2023; 9:e1293. [PMID: 37547393 PMCID: PMC10403218 DOI: 10.7717/peerj-cs.1293] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 02/23/2023] [Indexed: 08/08/2023]
Abstract
These days, the vast amount of data generated on the Internet is a new treasure trove for investors. They can utilize text mining and sentiment analysis techniques to reflect investors' confidence in specific stocks in order to make the most accurate decision. Most previous research just sums up the text sentiment score on each natural day and uses such aggregated score to predict various stock trends. However, the natural day aggregated score may not be useful in predicting different stock trends. Therefore, in this research, we designed two different time divisions: 0:00t∼0:00t+1 and 9:30t∼9:30t+1 to study how tweets and news from the different periods can predict the next-day stock trend. 260,000 tweets and 6,000 news from Service stocks (Amazon, Netflix) and Technology stocks (Apple, Microsoft) were selected to conduct the research. The experimental result shows that opening hours division (9:30t∼9:30t+1) outperformed natural hours division (0:00t∼0:00t+1).
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Affiliation(s)
- Qianyi Xiao
- Department of Computer Science, Wenzhou Kean University, Wenzhou, Zhejiang, China
| | - Baha Ihnaini
- Department of Computer Science, Wenzhou Kean University, Wenzhou, Zhejiang, China
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14
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Seaborn K, Barbareschi G, Chandra S. Not Only WEIRD but "Uncanny"? A Systematic Review of Diversity in Human-Robot Interaction Research. Int J Soc Robot 2023; 15:1-30. [PMID: 37359427 PMCID: PMC9993363 DOI: 10.1007/s12369-023-00968-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/19/2023] [Indexed: 03/29/2023]
Abstract
Critical voices within and beyond the scientific community have pointed to a grave matter of concern regarding who is included in research and who is not. Subsequent investigations have revealed an extensive form of sampling bias across a broad range of disciplines that conduct human subjects research called "WEIRD": Western, Educated, Industrial, Rich, and Democratic. Recent work has indicated that this pattern exists within human-computer interaction (HCI) research, as well. How then does human-robot interaction (HRI) fare? And could there be other patterns of sampling bias at play, perhaps those especially relevant to this field of study? We conducted a systematic review of the premier ACM/IEEE International Conference on Human-Robot Interaction (2006-2022) to discover whether and how WEIRD HRI research is. Importantly, we expanded our purview to other factors of representation highlighted by critical work on inclusion and intersectionality as potentially underreported, overlooked, and even marginalized factors of human diversity. Findings from 827 studies across 749 papers confirm that participants in HRI research also tend to be drawn from WEIRD populations. Moreover, we find evidence of limited, obscured, and possible misrepresentation in participant sampling and reporting along key axes of diversity: sex and gender, race and ethnicity, age, sexuality and family configuration, disability, body type, ideology, and domain expertise. We discuss methodological and ethical implications for recruitment, analysis, and reporting, as well as the significance for HRI as a base of knowledge.
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Tyler J, Boldi MO, Cherubini M. Contemporary self-reflective practices: A large-scale survey. Acta Psychol (Amst) 2022; 230:103768. [DOI: 10.1016/j.actpsy.2022.103768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 09/29/2022] [Accepted: 10/09/2022] [Indexed: 11/01/2022] Open
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Mohtar S, Jomhari N, Mustafa MB, Yusoff ZM. Mobile learning: research context, methodologies and future works towards middle-aged adults - a systematic literature review. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:11117-11143. [PMID: 36035325 PMCID: PMC9391209 DOI: 10.1007/s11042-022-13698-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 06/17/2022] [Accepted: 08/15/2022] [Indexed: 06/15/2023]
Abstract
Over the past several years, mobile learning concepts have changed the way people perceived on mobile devices and technology in the learning environment. In earlier days, mobile devices were used mainly for communication purposes. Later, with many new advanced features of mobile devices, they have opened the opportunity for individuals to use them as mediated technology in learning. The traditional way of teaching and learning has shifted into a new learning dimension, where an individual can execute learning and teaching everywhere and anytime. Mobile learning has encouraged lifelong learning, in which everyone can have the opportunity to use mobile learning applications to gain knowledge. However, many of the previous studies on mobile learning have focused on the young and older adults, and less intention on middle-aged adults. In this research, it is targeted for the middle-aged adults which are described as those who are between the ages of 40 to 60. Middle-aged adults typically lead very active lives while at the same time are also very engaged in self-development programs aimed at enhancing their spiritual, emotional, and physical well-being. In this paper, we investigate the methodology used by researchers based on the research context namely, acceptance, adoption, effectiveness, impact, intention of use, readiness, and usability of mobile learning. The research context was coded to the identified methodologies found in the literature. This will help one to understand how mobile learning can be effectively implemented for middle-aged adults in future work. A systematic review was performed using EBSCO Discovery Service, Science Direct, Google Scholar, Scopus, IEEE and ACM databases to identify articles related to mobile learning adoption. A total of 65 journal articles were selected from the years 2016 to 2021 based on Kitchenham systematic review methodology. The result shows there is a need to strengthen research in the field of mobile learning with middle-aged adults.
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Affiliation(s)
- Syahida Mohtar
- Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Nazean Jomhari
- Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Mumtaz Begum Mustafa
- Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
| | - Zulkifli Mohd Yusoff
- Department of Al-Quran and Al-Hadith, Academy of Islamic Studies, Universiti Malaya, 50603 Kuala Lumpur, Malaysia
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