Large language models (LLMs) have been increasingly adopted to support patients' healthcare-seeking in recent years. While prior patient-centered studies have examined the capabilities and experience of LLM-based tools in specific health-related tasks such as information-seeking, diagnosis, or decision-supporting, the inherently longitudinal nature of healthcare in real-world practice has been underexplored. This paper presents a four-week diary study with 25 patients to examine LLMs' roles across healthcare-seeking trajectories. Our analysis reveals that patients integrate LLMs not just as simple decision-support tools, but as dynamic companions that scaffold their journey across behavioral, informational, emotional, and cognitive levels. Meanwhile, patients actively assign diverse socio-technical meanings to LLMs, altering the traditional dynamics of agency, trust, and power in patient-provider relationships. Drawing from these findings, we conceptualize future LLMs as a longitudinal boundary companion that continuously mediates between patients and clinicians throughout longitudinal healthcare-seeking trajectories.

This paper examines how informal care work in community-based aging systems is assetized through the institutionalization of timebanking. Drawing on long-term ethnography following the implementation of digital timebanking systems as part of government-led aging-in-community initiatives in Shanghai, this paper teases out the labor of navigating multi-layered temporal tensions in informal caregiving, from interactional to personal to generational. We demonstrate how present informal care work has been transformed into exchangeable, bankable, and quantifiable assets, promising future social and economic benefits. We analyze how timebanking assetizes, institutionalizes, and governs informal care work by mobilizing younger generations of older adults (i.e., those recently retired) to perform unpaid caregiving for older generations in the communities under the rhetoric of mutual support and uncertain promises of future care. We discuss how CSCW can critically engage with the temporal navigations that are integral to informal care work, particularly in the context of demographic shifts, labor shortages, and the broken formal aging care infrastructure. From there, we question whether timebanking constitutes a sustainable sociotechnical alternative to formal care systems, or if it is increasingly co-opted for labor extraction that reproduces the precarity of aging futures.

Peer Reviewed

The rise of generative AI technology has sparked interest in using digital information to create AI-generated agents as digital legacy.These agents, often referred to as "AI Afterlives", present unique challenges compared to traditional digital legacy.Yet, there is limited human-centered research on "AI Afterlife" as digital legacy, especially from the perspectives of the individuals being represented by these agents.This paper presents a qualitative study examining users' perceptions, expectations, and concerns regarding AI-generated agents as digital legacy.We identify factors shaping people's attitudes, their perceived differences compared with the traditional digital legacy, and concerns they might have in real practices.We also examine the design aspects throughout the life cycle and interaction process.Based on these findings, we situate "AI Afterlife" in digital legacy, and delve into design implications for maintaining identity consistency and balancing intrusiveness and support in "AI Afterlife" as digital legacy.

Educational inequalities in disadvantaged areas have long been a global concern. While Information and Communication Technologies (ICTs) have shown great potential in addressing this issue, the unique challenges in disadvantaged areas often hinder the practical effectiveness of such technologies. This paper examines live-streaming-based dual-teacher classes (LSDC) through a qualitative study in disadvantaged regions of China. Our findings indicate that, although LSDC offers students in these regions access to high-quality educational resources, its practical implementation is fraught with challenges. Specifically, we foreground the pivotal role of local teachers in mitigating these challenges. Through a series of situated efforts, local teachers contextualize high-quality lectures to the local classroom environment, ensuring the expected educational outcomes. Based on our findings, we argue that greater recognition and support for the situational practices of local teachers is essential for fostering a more equitable, sustainable, and scalable technology-driven educational model in disadvantaged areas.

As a flexible, scalable, and affordable learning paradigm, live-streaming-based learning (LS learning) has become increasingly popular among older adults, shaping a digitally mediated ''silver classroom''. Despite its growing prevalence and its potential to address gaps in lifelong learning access and educational equity, meet older adults' learning needs, enhance their social connection, this learning paradigm remains largely underexplored in the CSCW literature. Given older adults' unique learning characteristics in terms of motivations, expectations, and learning abilities, it is critical to deeply examine their LS learning behaviors and experiences to inform better design. This study presents an empirical study in China to unpack this LS-based silver classroom phenomenon, focusing on its infrastructure, practices and lived experiences. Our findings reveal a human-technology integrated infrastructure alongside a volunteer-based, self-organized, autonomous collaborative community, which works together in fostering a supportive LS learning environment for older adults and meeting their additional emotional and social needs. We discuss how these sociotechnical arrangements shape the unique learning experiences of older adults and highlight the opportunities and challenges in designing for later-life learning.

Aging in place refers to the enabling of individuals to age comfortably and securely within their own homes and communities. Aging in place relies on robust infrastructure, prompting the development and implementation of both human-led care services and information and communication technologies to provide support. Through a long-term ethnographic study that includes semi-structured interviews with 24 stakeholders, we consider these human- and technology-driven care infrastructures for aging in place, examining their origins, deployment, interactions with older adults, and challenges. In doing so, we reconsider the value of these different forms of older adult care, highlighting the various issues associated with using, for instance, health monitoring technology or appointment scheduling systems to care for older adults aging in place. We suggest that technology should take a supportive, not substitutive role in older adult care infrastructure. Furthermore, we note that designing for aging in place should move beyond a narrow focus on independence in one's home to instead encompass the broader community and its dynamics.

Traditional Medicine (TM) is the oldest healthcare form and has been increasingly adopted as the primary or complementary medical therapy in the world. However, TM's practical development remains highly challenging. While artificial intelligence (AI) has become powerful in advancing modern medicine, limited attention has been paid to its potential and usage in TM. This study addresses this gap through a probe-based interview study with 16 TM clinicians, examining their experiences, perceptions, and expectations of AI-empowered clinical support systems. Our findings reveal that despite numerous AI-CDS systems, their practical usage in TM settings was still limited. We identify a series of practical challenges when integrating AI-CDS into TM clinical scenarios, largely due to TM's unique features and the significant data work challenges these features present. We end by critically discussing the potential issues that may arise when integrating AI into practical TM scenarios, and proposing a series of practical recommendations for future studies.

The increasing global aging brings the substantial demand for healthcare knowledge among the elderly. Large Language Models (LLMs) based Conversation Agents (CAs) hold significant promise for addressing the elderly’s healthcare knowledge inquiries. Yet, general LLMs often fall short in providing professional and practically usable healthcare conversations due to the lack of specific knowledge, possible hallucination issues and contextual comprehension biases. To address these challenges, we first propose a cost-effective, domain-specific questioning-answering (QA) generation framework based on knowledge distillation (KD). Based on this framework, we then built CareQA, the first Chinese healthcare QA dataset specifically for the elderly, with 41,694 QA pairs spanning geriatric diseases covering multiple categories. A comprehensive benchmarking experiment, including both automated and human evaluation, is conducted to examine the usability of CareQA. The results demonstrate that the LLMs fine-tuned on CareQA perform better in answering elderly healthcare-related questions.

Migration and aging-related dilemmas have limited the opportunities for late-life migrants to rebuild social connections and access support. While research on migrants has drawn increasing attention in HCI, limited attention has been paid to the increasing number of late-life migrants. This paper reports a qualitative study examining the social connections and support of late-life migrants. In particular, drawing on the social convoy model, we pay specific attention to the dynamic changes of late-life migrants’ social convoy, the supporting roles each convoy plays, the functions ICT plays in the process, as well as the encountered challenges and expectations of late-life migrants regarding ICT-supported social convoys. Based on these findings, we deeply discuss the role of the social convoy in supporting more targeted social support for late-life migrants, as well as broader migrant communities. Finally, we offer late-life migrant-oriented design considerations.

Designing and using technologies to support Non-Pharmacological Interventions (NPI) for People with Dementia (PwD) has drawn increasing attention in HCI, with the potential expectations of higher user engagement and positive outcomes. Yet, technologies for NPI can only be valuable if practitioners successfully incorporate them into their ongoing intervention practices beyond a limited research period. Currently, we know little about how practitioners experience and perceive these technologies in practical NPI for PwD. In this paper, we investigate this question through observations of five in-person NPI activities and interviews with 11 therapists and 5 caregivers. Our findings elaborate the practical NPI workflow process and characteristics, and practitioners’ attitudes, experiences, and perceptions to technology-mediated NPI in practice. Generally, our participants emphasized practical NPI is a complex and professional practice, needing fine-grained, personalized evaluation and planning, and the practical executing process is situated, and multi-stakeholder collaborative. Yet, existing technologies often fail to consider these specific characteristics, which leads to limitations in practical effectiveness or sustainable use. Drawing on our findings, we discuss the possible implications for designing more useful and practical NPI intervention technologies.

Jiaju Chen, Yuxuan Lu, Shao Zhang, Bingsheng Yao, Yuanzhe Dong, Ying Xu, Yunyao Li, Qianwen Wang, Dakuo Wang, Yuling Sun. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Interactive storytelling is vital for preschooler development. While children's interactive partners have traditionally been their parents and teachers, recent advances in artificial intelligence (AI) have sparked a surge of AI-based storytelling and reading technologies. As these technologies become increasingly ubiquitous in preschoolers' lives, questions arise regarding how they function in practical storytelling and reading scenarios and, how parents, the most critical stakeholders, experience and perceive these technologies. This paper investigates these questions through a qualitative study with 17 parents of children aged 3-6. Our findings suggest that even though AI-based storytelling and reading technologies provide more immersive and engaging interaction, they still cannot meet parents' expectations due to a series of interactive and algorithmic challenges. We elaborate on these challenges and discuss the possible implications of future AI-based interactive storytelling technologies for preschoolers.

Dementia has increasingly impacted the health of older adults, necessitating precise prediction of cognitive levels to deliver targeted healthcare services and mitigate disease progression. In recent years, although there has been surging interest in research on AI-driven dementia prediction, existing methods commonly rely on background-specific datasets and utilize multi-models with equal weights for predictions. These methods limit the result to the inherent features of the datasets on the one hand and ignore the differences among various feature channels on the other hand. To address these limitations, this paper proposes a dynamic composite ensemble learning framework, including the following three novel modules. Firstly, based on the selected common feature set, we construct a top-level feature set through dynamically determined screening thresholds. Subsequently, we deploy transfer learning models for knowledge complementation to enhance model generalization. In addition, we purposefully designed a dynamic weighting module to prioritize feature channels with stronger relevance to the target task. We deploy our model on the ELSA-HCAP dataset and conduct a series of experiments to evaluate its practical effectiveness. The results demonstrate that the proposed feature engineering, dynamic weighting, and knowledge transfer modules collectively enhance the overall model performance. Moreover, the combination of these three modules in the fusion model attains optimal performance, achieving an accuracy rate of 95.76%.

This paper examines the social, technological, and emotional labor of maintaining China’s data-driven governance broadly, and dynamic zero-COVID management in particular. Drawing on ethnographic research in China, we examine the sociotechnical work of maintenance during the 2022 Shanghai lockdown. This labor included coordinating mass testing, quarantine, and lockdown procedures as well as implementing ad-hoc technological workarounds and managing public sentiments. We demonstrate that, far from being effected from the top down, China’s data-driven governance relies on the circumscribed participation of citizens. During Shanghai’s lockdown, citizens with relevant expertise helped to maintain technological stability by fixing or programming data systems, but also to ensure the ongoing production of“positive feelings” about social stability through data-driven governance. In so doing, such citizens simultaneously enacted an ambivalent and circumscribed form of agency, and maintained social and by extension political stability. This article sheds light on data-driven governance and political processes of maintenance.

With the fast growth of aging population and the spread of various chronic diseases such as heart disease and arthritis among older adults, elderly care has become an urgent topic facing today’s society. Consequently, technologies mediated remote care has become a widely-used method, with the significant promise of reducing cost and improving the efficiency and quality of healthcare. Yet, most remote-caring technologies, especially surveillance video based remote care, face the challenge of privacy issues. For addressing this issue, this paper proposes a privacy- preserved remote care method. Specially, we use ROMP to extract the 3D human model of the elderly in the surveillance video, and use KNN pose estimation algorithm to detect the potential abnormal behaviors. Compared to existing methods, which mainly replace the privacy information with totally different contents, our method not only protects the personal privacy information of the elderly, but also provides clear and identifiable posture information which could better support remote care.

In the era of informatization, a large amount of elderly care related information is published on the Internet, which brings huge difficulties for older adults to seek useful and targeted information, also known as digital divide. In order to make it more convenient for the elderly to obtain digital information and effectively narrow the digital divide among older adults, we design a senior-friendly care information recommendation framework named "Policy Finds the Target Elderly". The framework can be divided into knowledge, algorithm, and application three levels. Based on this frame, we develop a prototype based on Shanghai’s elderly care information. We end by discussing the limitations and future work of our study.

This paper zooms in on a particularly precarious and largely invisibilized group of care workers: middle-aged and less-educated female migrant workers from rural China. Drawing from a mixed-methods study, we specifically examine how the extensive use of data-driven technologies impacts care workers' wellbeing in the workplace. Our findings suggest the extensive use of data-driven healthcare technologies are eroding care workers' workplace wellbeing, especially their sense of identity, agency, and perceived justice. Specifically, in the data-driven workplace, care workers are treated as a servant to data, instead of a human with agency and knowledge. They are no merely care workers who provide various care services for care receivers, but also data workers, whose practices and agency are greatly limited by data. This aggravates preexisting hardship of care workers, and reproduces new social injustice. We suggest CSCW researchers and practitioners take into account how pre-existing social structures shaped the designs of socio-technological systems, and reconceptualize the paradigm of "data-drivenness" for more just and ethical data-driven healthcare technologies.

Using data and data technologies to support healthcare has drawn significant attention recently. While CSCW and HCI have largely celebrated the tremendous promise of 'data-driven healthcare' in reforming the healthcare sector, this paper reveals 'labor-driven reality' of this promised data-driven future. Drawing from a qualitative study in a real-world data-driven long-term care (LTC) facility in China, we demonstrate how data-driven technologies work in practice, and especially how frontline workers, as the crux of this data-driven configuration, conduct a tremendous amount of "data work" to make data-drivenness work. This data work, we argue, goes beyond the "clerical work" and functions as a labor of maintenance, articulation, and repair, that both guarantees data technologies' functionalities and acts as an interface between stakeholders. We conclude by discussing the practices, problems and opportunities of this data work in a boarder socio-cultural context.

Unsupervised sentence embeddings learning has been recently dominated by contrastive learning methods (e.g., SimCSE), which keep positive pairs similar and push negative pairs apart. The contrast operation aims to keep as much information as possible by maximizing the mutual information between positive instances, which leads to redundant information in sentence embedding. To address this problem, we present an information minimization based contrastive learning InforMin-CL model to retain the useful information and discard the redundant information by maximizing the mutual information and minimizing the information entropy between positive instances meanwhile for unsupervised sentence representation learning. Specifically, we find that information minimization can be achieved by simple contrast and reconstruction objectives. The reconstruction operation reconstitutes the positive instance via the other positive instance to minimize the information entropy between positive instances. We evaluate our model on fourteen downstream tasks, including both supervised and unsupervised (semantic textual similarity) tasks. Extensive experimental results show that our InforMin-CL obtains a state-of-the-art performance.

With the aging of the population, caregiving for the elderly has become an urgent social topic. While the rapid development of data-driven technologies provides tremendous promises to deal with this issue, the gap between data-driven and practical healthcare challenges their effectiveness. With the aim of providing strong data basis for the large-scale data-driven healthcare, this paper proposes a human-space-service based method, named CareMap, to model and quantity the caregiving process for the elderly aging at home. We build the intelligent individual profile to model and compute seniors’ health conditions on the one hand and care network profile to model and quantify the care resources around the elderly on the other hand. We design a CareMap based prototype to illustrate the possible application and discuss its potentials and limitations.

Using data-driven technologies to support the healthcare of the elderly has been largely celebrated as an effective means. This paper focuses on the issue of using video-based sensing technologies to remotely monitor the activities and conditions of the elderly. Although it is a widely explored field, the high cost and high infrastructural requirements of most existing technologies usually challenge their effectiveness and efficiency in practical caregiving context. To address these challenges, we propose a lightweight network based real-time anomaly detection system, which consists of video-based ADL sensing and pre-processing, AI streaming aggregating and cluster computing. We examine our method by implementing and deploying it into a real-world care facility for the elderly in Shanghai China. The results show that our method has good performance in expansibility, reliability, bandwidth availability, accuracy and privacy protection.

Using data-driven technologies to support healthcare has been largely celebrated as an effective means. Yet, most existing data-driven technologies are designed with the purposes of improving productivity, efficiency, and effectiveness etc., i.e. care administer-centric design. The needs and perceptions of caregiver, the actual users of most technologies, are largely ignored. In this paper, we examine the impacts of data-driven technologies from the perspective of caregivers. Through a questionnaire study with 191 caregivers in Shanghai China, we quantify the impacts of data-driven technologies to caregivers' work and experiences. Our results show that while the embedded data-driven technologies provide significant benefits to caregivers' work, these benefits are often limited by the complex, situated and fragmented nature of healthcare. We analyze the results and propose our suggestions to the further design.

Crowdsourcing is rapidly gaining popularity among academic and business communities. Yet, our understanding of this work way is still in its incipient stage, in particular regarding the increasingly large and diverse crowdworkers. As such, we aim to understand crowdworkers' perception and experience to themselves and their work from their own perspective. We explore this by a mix-methods study of crowdworkers in Ali, one of prominent micro-task crowdsourcing platforms in China. Our findings highlight crowdworker in Ali is not only a coded name, but also an identity with some positive attitudes and beliefs towards work and life. In particular, this identity provides many socio-psychological benefits for crowdworkers, which further contributes to their consistent engagement in Ali and proactive practices to improve crowdworker communities and Ali platform collaboratively. We according suggest that taking crowdworker identity as a lens for crowdsourcing research, and turning attention towards construction and expressions of crowdworkers' identity and values in their own context.

Older adults' technology learning is a long-term process, during which family members often play significant roles. Although much research has emphasized how family support is important, little research has dove into the evolution of family dynamics when older adults are learning to use new technology. Drawing on the results from a qualitative study that performed semi-structured interviews with 20 older adults and 18 younger adults in China, we unpack how family members were involved in technology learning over time. Our findings suggest that younger family members play transformative roles throughout older adults' learning stages, i.e., as influencers, supporters, protectors, and monitors. Younger family members' roles co-evolve with not only older adults' changing needs but also their perceptions of older adults' learning abilities and online behaviors. They may struggle to adjust their teaching strategies to accommodate older adults' needs and abilities during the process. They may also worry about older adults' online benefits and safety as many older adults become far more active online than anticipated. Challenges while teaching and tensions regarding protection may thus emerge during the support process. With these findings, we suggest that older adults' technology learning should be treated as a collaborative activity with family members rather than an activity they pursue alone. We also highlight older adults' technology learning as a recurrent, dynamic, and evolving process, and call attention to the unique culture of "xiaoshun" in China that acts as a buffer to the burdens and tensions found with family support.

In the past decade, sentiment analysis on social media has attracted great attention and has been used in many studies in CSCW and related fields. Recently, with the rapid development of machine learning, using machine learning methods to analyze sentiment has become an efficient experiment framework. Now, the existing sentiment analysis methods in machine learning are mainly based on supervised learning, and they need enough training data to ensure high accuracy. They encounter a common problem that they cannot recognize and calculate the emotion of samples with unseen labels, which don't belong to the training set. However, most data collected from social media is unstructured and unlabeled, which challenges the effectiveness and usability of existing methods. In our study, we first refer to existing sentiment analysis methods and zero-shot learning for addressing the problem. After that, we propose two zero-shot sentiment analysis methods and design an experiment to compare our methods and strong baselines. In conclusion, our methods obtain better results and we try to apply these methods to future social media research.

In CSCW and related fields, ICT supported collaborative design has been used in various fields. Yet, there has been little research focusing on the information collaboration between medical care and non-medical care in long-term senior caring settings. For filling this gap, this paper explores the information collaboration and communication process between formal medical carers and informal nonmedical carers in the process of long-term senior care in more depth. We report our findings from three dimensions: self-awareness of different stakeholders, their informative collaborative practices, and knowledge gap among them. Based on our findings, we proposed design considerations that combines the concept of peer to peer and machine learning algorithms, which provides alternative ideas for future design.

Participatory sensing refers to the sensing paradigm where human participants use personal mobile devices to generate and share data from their surroundings. It holds the promise of providing information that is otherwise challenging to access, which sets the stage for understanding and resolving various social issues. However, difficulties in engaging participants often hinder the fulfillment of this promise. The current paper presents a qualitative study in the context of dockless bikesharing, where participatory sensing constitutes a backbone of the bike status monitoring system. We conducted in-depth interviews with 30 participants. These participants came from different emergent groups who took part in filing status reports for shared bikes. Our analysis indicated close associations among participants' models of engagement, their perceived (dis)connections with the sensing data, and their situated interpretation of the incentives. Based on these findings, we propose ways to engage the commons in participatory sensing for dockless bikesharing and beyond.

Crowdsourcing is an online, distributed problem-solving and production model in which individuals post short micro-task that online workers can complete in exchange for a small payment. Yet, with the rapid development of crowdsourcing in different fields, many complex tasks which request workers with domain-specific skills or knowledge challenge the current crowdsourcing platforms. In this paper, we focus on the issue of producing expert-level crowdsourcing with non-expert contribution. We firstly did a survey to get a comprehensive view of the mainly research related to this issue. We summarize and categorize the existing approaches, and identify the main challenges that raise from the current approaches. After that, we propose a social network engaged crowdsourcing framework and conduct the preliminary study to explore its ability in performing the tasks with the need of domain-specific skills and knowledge.

How to provide long-term care for the elderly is one significant challenge for the current aging society. A multiform service platforms and a large number of non-professional caregivers might influence the accuracy and appropriateness of the care programs. In CSCW and related fields, the intelligent methods brought by the development of information technology and pervasive end equipment have attracted wide attention. However, most researches are conducted in a laboratory environment, which makes it difficult to meet the highly complex realities of the elderly. Based on a series of field studies, we have an in-depth understanding of practical effectiveness of directly using intelligence algorithms into the generating process of caring programs. We identify the existing challenges, and proposes a data-driven and human-engaged health care services recommendation algorithm for seniors. We implement our proposed algorithm in a prototype system, and conduct a primary study to validate the effectiveness of the framework.

In CSCW and related fields, computer supported collaborative design has been used in various fields. Yet, there has been little research focusing on the collaborative context design for elderly caring. For filling this gap, this paper explores a notion of rule-driven layout co-designing system for elderly caring. What makes it distinct from other layout designing system is that it employs hierarchically modularization to store all standardized components that are needed for the aging caring space, and embeds intelligence algorithm into the typical evidence-based design for improving design efficiency and reducing design costs. In this paper, we designed and implemented a prototype called iCare Designer, and conducted a preliminary study to understand how this notion works in reality. While findings of the preliminary study suggest promises of the notion of rule-driven space layout co-designing system, it reveals some challenges this kind of systems to truly work in practices, mainly reflecting in its flexibility and perception to ambient environment. An in-depth discussion is provided in the end.

People without medical background usually seek health information from Web search engines using queries with the circumlocutory style, such as a description of observations, instead of professional symptoms or medical terms. However, the existing commercial search engines performs better on professional terms than the nonprofessional ones. Therefore, we propose an interaction-embedded framework for healthcare search to fit this gap. First, we absorb the MeSH and Wikipedia data as the knowledge resources to translate the circumlocutory queries into medical related terminologies. Then, we embed the user interaction into the search process to better capture users' intents. Finally, we perform a series of experiments on the TREC and CLEF eHealth collections for evaluation purpose. The experimental results show that our proposed framework are promising and outperforms the traditional retrieval algorithms.

This paper presents a notion of location-sensitive online-offline hybrid social system. What makes it distinct from other mobile social media is that it supports not simply online interaction but also online-offline hybrid interaction, and location sensitivity is a feature designed to lower cost for users to participant in offline activities. We designed and implemented a prototype mobile Application called USee, and danmaku - an emerging socio-digital paradigm, was employed as the main user interface. We introduce the design and implementation of USee, and conduct a preliminary study to understand how it works in reality. Findings of the preliminary study suggest promises of the notion of location-sensitive online-offline hybrid social system. We highlight that USee provides users a free and safe social place, in which users express high and unique enthusiasm and engagement. This paper contributes to provide meaningful insights and design implication to mobile social media.

Crowdsourcing is quite cheap and effective to get a data set labeled by multiple annotators in a short amount of time. Although there are many traditional methods focusing on quality control of crowdsourcing, little pays close attention to the intrinsic ambiguity in dataset, which is hard for workers to make the right decision. In this paper, we introduce DCRB, a collaborative crowdsourcing framework that can obtain high quality output even for ambiguous tasks. We enable users' disagreement and encourage them to provide in-depth explanations when the disagreement occurs. We use these explanations to analyze the ambiguous tasks and design collaborative crowdsourcing to improve the output. In addition, we also design “reward brave” incentive mechanism to encourage users' valuable explanations. The experimental results show that our method significantly improves the accuracy of crowdsourcing, especially for those ambiguous crowdsourcing tasks.

In CSCW and related fields, the increasing attentions of “aging in place” and health caring has been paid to copy with the rapidly aging population. Yet, how this theoretical aging caring model works in practical, and how to drive a ubiquitous computing model to support “aging in place”, are still challenging. Based on a series of field work, this study proposes a data driven collaborative caring framework to support aging in place in China. We employ a community-embedded model to structure the whole aged caring process and improve the reliability and real timing of caring. We also design the computing algorithm to support collaborative caring. We evaluate our framework in izhaohu, one well-known elderly-care company in Shanghai, China, and design a prototype to support the whole process. The experimental results demonstrate the effectiveness of our framework as well as the limitation. An in-depth discussion is provided in the end.

In micro-task crowdsourcing markets such as Amazon's Mechanical Turk, how to obtain high quality result without exceeding the limited budgets is one main challenge. The existing theory and practice of crowdsourcing suggests that uneven task difficulty plays a crucial role to task quality. Yet, it lacks a clear identifying method to task difficulty, which hinders effective and efficient execution of micro-task crowdsourcing. This paper explores the notion of task difficulty and its influence to crowdsourcing, and presents a difficulty-based crowdsourcing method to optimize the crowdsourcing process. We firstly identify task difficulty feature based on a local estimation method in the real crowdsourcing context, followed by proposing an optimization method to improve the accuracy of results, while reducing the overall cost. We conduct a series of experimental studies to evaluate our method, which show that our difficulty-based crowdsourcing method can accurately identify the task difficulty feature, improve the quality of task performance and reduce the cost significantly, and thus demonstrate the effectiveness of task difficulty as task modeling property.

Nowadays, with the rapid development of technology in pervasive healthcare management field, health data shows an explosive growth, with the characters of collaborative, heterogeneous and multi-source, which increase the complexity and difficulty of data assessment. It is critical to ensure health data quality so that effective decisions can be made. In this paper, we propose a method of electronic health data quality assessment based on data provenance. We first conduct a qualitative analysis to explore the current health data condition. Then, we introduce an instancing provenance model for health data analysis. Based on this model, we propose a data assessment framework for health data and use a prototype system to verify the effectiveness of our method. It shows that by enabling data provenance, we significantly reduce the input error and improve the quality of data processing.

In this paper, we designed and implemented a lightweight mobile crowdsourcing platform called WeCrowd. What makes it distinct from other mobile crowdsourcing applications is that, it is based on WeChat and enables users to post and work on crowdsourcing tasks without setup process, which can greatly save storage space, speed up crowd work and also be convenient for users to take part in tasks. Based on this platform, we conducted a preliminary study to understand how this lightweight crowdsourcing platform worked in reality. The experiment results affirm that WeCrowd provides meaningful insights for mobile crowdsourcing, and enables mobile users to take part in tasks with more freedom, enthusiastic and high quality users experience. We end by some design implications for mobile crowdsourcing.

This paper tells a story of DIY (do it yourself) making that does not neatly fit more familiar narratives of making: as individual empowerment, as a democratizing force, and as technoscientific innovation. Drawing on ethnographic research with a collective of elderly electronic hackers in China, we provide insights into the socio-technical and politico-economic processes of hacking and making. This paper examines how the activity of making functioned for elderly DIY enthusiasts as way of remaking and reliving the past and as a means for expressing class belonging and citizenship. We show that making and hacking is not practiced in a void independent of social, political or economic forces. Rather, making unfolds in relation to, and is contingent on, societal norms and specific techno-cultural histories. As much as hacking empowers certain people, it excludes others and functions as a site for the exercise of power and social distinction making.

System design for seniors often focuses on the decline of their biological capabilities and social connectedness. This approach has been challenged as too simplistic to capture what it really means to be senior. This paper presents a qualitative study of 17 seniors in urban China (age ranging from 50s to 70s), who have adopted and incorporated ICT into their daily lives. Findings from this study show that the ways in which seniors attend to ICT are not simply shaped by changes in health or other wellbeing, but also by their life attitudes, value systems, relationships to younger generations as well as historical specifics during their coming of age. This paper contributes by showing that 1) what it means to be senior is shaped from within a whole social ecology of past and current experiences, values and interactions; 2) senior identities are not fixed, but continuously negotiated, articulated and enacted through ICT; 3) social interaction and access of technologies are highly intertwined.

Task assignment problem is one of the important research topics in a multi-agent system. It is desired to assign each task to a suited agent with a minimum total cost. For the advantages of memory, multi-character, local search and the solution improvement mechanism in artificial bee colony (ABC) algorithm, a task assignment approach based on ABC in a multi-agent cooperative design system is proposed in this paper. Experimental results demonstrate that the optimal solutions obtained by the ABC algorithm are better than genetic algorithm and particle swarm optimization on solving some task assignment problems.

This paper presents a multi-agent cooperative system for crowd animation. It analyses related work about crowd animation first. Then, a multi-agent crowd animation system architecture is introduced, which offers a promising framework for dynamically creating and managing agent communities in widely distributed environments. Next, a role modeling approach based on dynamic self-adaptive genetic algorithm and NURBS (Non Uniform Relational B Splines) technology is presented. Following, a group of fishes modelling example is illustrated for showing the modeling process in the system. Finally, the current work is summarised and an outlook for the future work is given.