Mutual Learning Research Articles

The new paradigm of future cities? Facilitating dialogues between the 15-minute city and the 15-minute life circle in China based on a bibliometric analysis

Cities worldwide are facing increasing disturbances due to the long-lasting COVID-19 pandemic, the unpredictable climate change, and the intensifying geopolitical clashes. The uncertainties associated with the future drive a backlash against contemporary planning practices that center on growth-oriented agenda and placemaking to survive neoliberal inter-city competitions. A noticeable paradigm shift towards micro-scale and human-centric planning practices has gained traction. The 15-minute city model (FMC), which was proposed by Carlos Moreno in 2016 and went viral after the pandemic, has become a prominent concept in steering future city development. Interestingly, China has also embarked on a transition from physical-oriented to human-centric planning, which is featured by the concept of the 15-minute life circle (FLC) and its planning guidelines first promulgated in Shanghai in 2016. While both concepts have thrived since then, few scholarly attempts have been made to advance the mutual learning process. This research aims to facilitate a dialogue between the two research communities, starting with a systematic literature review of the FMC. Using a bibliometric analysis, it maps the development and evolution of research on the FMC and identifies four key themes: sustainability, resilience, utopian city models, and technology. Based on the findings, the research discusses the complexity and current trends in the FMC literature. It compares the human-centric and chrono-urbanism planning principles in FMC and FLC cases, highlighting the differences in origins, priorities, and planning principles. The study concludes with three directions for mutual learning: integrating resilient thinking, synthesizing theoretical development and practical accumulation, and adopting a new urban regeneration perspective.

Pesaguan Dayak Kanjan Serayong Custom: The Relationship between Catholic Faith and Culture

Integrating Catholicism and Kanjan Serayong culture within the Pesaguan Dayak community exemplifies the dynamic interplay between religious and cultural rituals, particularly in funeral ceremonies, fostering communal solidarity and cultural resilience through a seamless blend of traditional and Catholic practices. This research aims to elucidate how these cultural and religious elements coexist and reinforce each other within the community. Employing qualitative methods, the study includes interviews with three Pesaguan Dayak Indigenous Leaders and informants, participant observations, and literature analysis guided by Victor Turner's theory of ritual structure. The findings highlight that both Catholicism and Kanjan Serayong culture places a strong emphasis on rituals, particularly funeral ceremonies, which are central to honoring the deceased and fostering communal solidarity. The study reveals a profound respect for sacred traditions and cultural heritage, showing that rituals reflect a seamless blend of Catholic and indigenous practices, indicative of cultural adaptation and resilience. This research underscores the need for further studies on cultural inculturation within Indigenous communities, comparative analyses across different cultural contexts, and interdisciplinary collaborations to enhance the understanding of Indigenous spirituality and cultural heritage preservation. Additionally, this study recommends involving local communities in the research process to promote mutual learning and effective cultural preservation strategies.

Creating Kaveri Delta beneath our feet: An experiment in grounding socio-hydrology in Tamil Nadu, India

This paper presents an experiment in grounding socio-hydrology in the Kaveri Delta, Tamil Nadu. It follows a care-ful research attitude that shows a willingness to be blindsided and confronted with surprises, cultivates concerns by immersion in the case, and actively contributes to desired realities and futures. The paper centres and reflects on ways of participatory data collection, from monitoring borewells to gathering groundwater ethnographies. An earlier developed hydrogeological model is introduced and then mirrored to collected well data and grounded in the practices, knowledges and logics of engineers, activists and farmers working and living in the delta. We show engineers maintaining a network of weirs and irrigation canals, water worship and grassroots rejuvenation of tanks, and farmers’ borewell adaptations and insights to oscillating water levels. Bringing logics into dialogue offers openings to reconsider aquifer conceptualisations, embrace recharge opportunities and appreciate local ways of dealing with groundwater. The message from of this experiment is that it is difficult, if not impossible, to integrate all logics and knowledges in one overarching model or framework but that there are merits when staying with, and respecting, the differences among logics as it offers a potential for mutual learning, reflection and discussion.

Identifying factors influencing faculty attitude towards digital technology in higher education and its impact on faculty engagement and work performance: a mediating analysis

Background This study investigated the influence of various factors on Faculty Attitude, Faculty Engagement, and Work Performance in higher education. The study specifically examined the impact of faculty attitudes towards digital technology on their level of involvement and effectiveness in the classroom. Furthermore, it investigated how the utilization of digital technology by faculty members influences the connection between their attitudes and job performance. Methods The study utilized a mixed-methods approach, combining quantitative and qualitative methodologies to collect thorough data to explore the factors influencing faculty attitude. A survey was performed among 438 faculty members from higher educational institutions in the Delhi and NCR region. Results The results indicated a strong positive relationship of perceived usefulness, mutual learning, institutional support, collaborativeness, creativity, critical thinking, and faculty attitude towards digital technology adoption in academic setting. Nevertheless, External Pressure did not exhibit a significant relationship. The study revealed that the attitude of faculty members has a significant impact on both their work performance and engagement. Additionally, faculty engagement was found to have a positive impact on work performance. The mediation Analysis revealed that Faculty Engagement served as a partial mediator in the association between Faculty Attitude and Work Performance. Conclusions The study’s implications underscore the urgent need of fostering positive attitudes and engagement among faculty members for enhancing their performance in academic settings. Some of the limitations and implication of the study and future direction of the research directions are suggested.

Deep spatial–spectral difference network with heterogeneous feature mutual learning for sea fog detection

Multispectral remote sensing image-based sea fog detection (SFD) is both important and challenging. Deep learning methods for SFD have become mainstream due to their powerful nonlinear learning capabilities and flexibility. However, existing methods have not fully utilized the physical difference priors in multispectral images (MSI) for SFD, making it difficult to skillfully capture the shape and appearance characteristics of sea fog, thus leading to uncertainties in SFD. We propose the spatial–spectral difference network (S2DNet), a deep encoding–decoding framework that merges inter-spectral and intra-spectral heterogeneous difference features. Specifically, inspired by physics-based difference threshold methods, we developed a physics-inspired inter-spectral difference module (PIDM) that combines feature-level difference with deep neural networks to capture the shape characteristics of sea fog. We designed the intra-spectral difference module (ISDM) using difference convolution to represent sea fog’s fine-grained and dynamic appearance information. Furthermore, inspired by multi-view learning, we propose heterogeneous feature mutual learning (HFML) that seeks robust representations by focusing on semantically invariant aspects within heterogeneous difference features, adapting to the dynamic nature of sea fog. HFML is achieved through global feature mutual learning using an adversarial procedure and local feature mutual learning supported by a novel information-theoretic objective function that links maximizing statistical correlation with expectation maximization. Experiments on two SFD datasets show that integrating physical difference priors into deep learning improves SFD. In both continuous temporal and high spatial resolution SFD tasks, S2DNet outperforms existing advanced deep learning methods. Moreover, S2DNet demonstrates stronger robustness under degraded remote sensing image conditions, highlighting its potential usefulness and practicality in real-world applications.

A Hybrid Intelligent Optimization Algorithm Based on a Learning Strategy

To overcome the limitations of single-type intelligent optimization algorithms prone to becoming stuck in local optima for complex problems, a hybrid intelligent optimization algorithm named SDIQ is proposed. This algorithm combines simulated annealing (SA), differential evolution (DE), quantum-behaved particle swarm optimization (QPSO), and improved particle swarm optimization (IPSO) into a unified framework. Initially, SA is used to explore the solution space and guide individuals toward preliminary optimization. The individuals are then ranked by fitness and divided into three subpopulations, each optimized by DE, QPSO, and IPSO, respectively. After each iteration, probabilistic learning based on fitness logarithms facilitates mutual learning among subpopulations, enabling global information sharing and improvement. The experimental results demonstrate that SDIQ exhibits strong global search capability and stability in solving both standard test functions and real-world engineering problems. Compared to traditional algorithms, SDIQ enhances global convergence and solution efficiency by integrating multiple optimization strategies and leveraging inter-individual learning, providing an effective solution for complex optimization problems.

Navigating Chinese international students’ inquiry learning in a multilingual and multicultural environment

ABSTRACT In the past few decades, inquiry learning, as a research-based learning approach, has been increasingly emphasised in developed Western countries. This study examined the inquiry learning experiences of 17 Chinese international students at their host universities in the USA, the UK, Australia, and Canada, along with their achievements and challenges. Through two rounds of one-on-one semi-structured interviews, this study found a close relationship between the emphasis on inquiry learning in host universities and neoliberal demands for students to acquire knowledge and diverse practical competences. Urged by the learning demands in the host universities, participants advanced in knowledge acquisition, acquired active, passionate, responsible, and inclusive learning attitudes, as well as English language competence, intercultural competence, and other practical competences through inquiry learning. They also faced additional workload and challenges regarding language difficulties and sociocultural factors mediated by home and host educational, social, and cultural contexts. This study advises international and domestic staff and students to engage in constant, candid dialogue, try to understand the specific learning challenges that international students face, and the support they desire. Collaborative efforts from all relevant stakeholders are necessary to create a diversified, caring, inclusive, and supportive inquiry learning environment conducive to mutual learning and thriving.

Leaving No One Behind- Teacher Peer Mentoring Before and After the Pandemics: A Mixed-Methods Systematic Review of Spanish and English Literature

<p style="text-align:justify">This paper examines how peer mentoring strengthens teaching practices in Regular Basic Education considering the changes undertaken since the COVID-19 pandemics. Peer mentoring is an in-service teacher training strategy that includes mutual collaboration, learning and monitoring. In this systematic review, we retrieved 24 articles since 2020 from Scopus (8), Web of Science (8), Dialnet (5), Google Scholar (2) and SciELO (1) to find out about the benefits of peer mentoring. Our review was guided by the PRISMA criteria. We found that educational companionship has a positive impact on reducing knowledge gaps regarding new technologies through the exchange of experiences, promotes openness to criticism, as well as the interest in learning and unlearning, supporting learning self-regulation, and guarantees consultation, reflection, and agreement between members of the teaching communities. We conclude that peer mentoring fulfilled, to a large extent, its purpose of improving teacher performance as one of the keys to educational quality.</p>

The discourses of green modernization and eco-civilizational progress in contemporary China: convergence, tension and mutual learning

“Chinese modernization”, articulated systematically by the working report to the 20th National Congress of Communist Party of China(CPC) in 2022, has been gradually become a worldwide-known popular concept, which emphasizes harmonious coexistence between humanity and nature as one of its “five unique features”. A significant change arisen from it is that, two green theoretical discourses of “eco-civilizational progress” and “green modernization” in contemporary China have historically converged. In the Chinese context, the two discourses can be to a large extent understood as characteristic forms of a more general green discourse. Both the goals or the objectives to be transformed and the dependent environment are common or shared. As self-contained discursive theories, though, there are also noticeable differences and even a certain degree of tension between them. An ideal situation would be that, on the one hand, both of them are consciously put under a broader framework of green discourse theory, from which they can mutually learn and promote from each other, and on the other hand, it is expected that that can better serve China’s practice of building a modern socialist country in all respects.

Swarm mutual learning

With the rapid growth of big data, extracting meaningful knowledge from data is crucial for machine learning. The existing Swarm Learning data collaboration models face challenges such as data security, model security, high communication overhead, and model performance optimization. To address this, we propose the Swarm Mutual Learning (SML). Firstly, we introduce an Adaptive Mutual Distillation Algorithm that dynamically controls the learning intensity based on distillation weights and strength, enhancing the efficiency of knowledge extraction and transfer during mutual distillation. Secondly, we design a Global Parameter Aggregation Algorithm based on homomorphic encryption, coupled with a Dynamic Gradient Decomposition Algorithm using singular value decomposition. This allows the model to aggregate parameters in ciphertext, significantly reducing communication overhead during uploads and downloads. Finally, we validate the proposed methods on real datasets, demonstrating their effectiveness and efficiency in model updates. On the MNIST dataset and CIFAR-10 dataset, the local model accuracies reached 95.02% and 55.26%, respectively, surpassing those of the comparative models. Furthermore, while ensuring the security of the aggregation process, we significantly reduced the communication overhead for uploading and downloading.

Report on the Academic Round Table, 30 April 2024, New York City: Freedom of Online Communication Across Transatlantic Borders

Abstract The Alexander von Humboldt Institute for Internet and Society (HIIG), the Leibniz Institute for Media Research | Hans Bredow Institute (HBI), the Columbia Global Freedom of Expression and the UNESCO Chair on Freedom of Communication and Information co-hosted an academic workshop in New York City on 30 April 2024. European and US experts examined the current state of freedom of expression on digital platforms on both sides of the Atlantic. The round table took place as part of the Year of Science 2024 in Germany. To celebrate the 75th anniversary of the German Basic Law (‘Grundgesetz’), this Year of Science has ‘Freedom’ as its central theme. This provided an excellent opportunity to examine the various understandings of freedom as it relates to communication rights and free speech between the US and Europe. The full extent of mutual learning opportunities remains untapped, and the practical implications are vast.

Participatory design in telehealth research: Practical case examples.

Participatory design (PD) is a methodology that emphasizes user participation in the design of new technologies to leverage change within organizations and services. PD originated in the computer science field in the 1970s and 1980s when new programs and technologies were developed to empower workers, by involving them in decisions that affected them. PD in health research has been proven to change clinical practice. Genuine user involvement that includes all stakeholders, and robust collaborations across sciences, sectors, and disciplines are basic elements of successful research to change clinical practice and to implement novel technical and organizational approaches. This paper summarizes seven case studies involving the use of PD in telehealth research. All cases presented promoted organizational changes supported by health information and communications technology, and have been implemented at either international, national, regional, or local levels. We describe how PD can be applied in health sciences and used to facilitate organizational changes, new perspectives, and new communications methods. The relevance and suitability of PD as a research design in health science is explained, and recommendations for conducting PD studies in telehealth research are presented. In PD, mutual learning and co-creation is facilitated. Consequently, learning from users, rather than studying them, corroborates our understanding and the emergence of new knowledge.

Bidirectional Efficient Attention Parallel Network for Segmentation of 3D Medical Imaging

Currently, although semi-supervised image segmentation has achieved significant success in many aspects, further improvement in segmentation accuracy is necessary for practical applications. Additionally, there are fewer networks specifically designed for segmenting 3D images compared to those for 2D images, and their performance is notably inferior. To enhance the efficiency of network training, various attention mechanisms have been integrated into network models. However, these networks have not effectively extracted all the useful spatial or channel information. Particularly for 3D medical images, which contain rich spatial and channel information with tightly interconnected relationships between them, there remains a wealth of spatial and channel-specific information waiting to be explored and utilized. This paper proposes a bidirectional and efficient attention parallel network (BEAP-Net). Specifically, we introduce two modules: Supreme Channel Attention (SCA) and Parallel Spatial Attention (PSA). These modules aim to extract more spatial and channel-specific feature information and effectively utilize it. We combine the principles of consistency training and entropy regularization to enable mutual learning among sub-models. We evaluate the proposed BEAP-Net on two public 3D medical datasets, LA and Pancreas. The network outperforms the current state of the art in eight algorithms and is better suited for 3D medical images. It achieves the new best semi-supervised segmentation performance on the LA database. Ablation studies further validate the effectiveness of each component of the proposed model. Moreover, the SCA and PSA modules proposed can be seamlessly integrated into other 3D medical image segmentation networks to yield significant performance gains.

Evolution of the roles of family physicians through collaboration with rehabilitation therapists in rural community hospitals: a grounded theory approach

BackgroundThe role of rural family physicians continues to evolve to accommodate the comprehensive care needs of aging societies. For older individuals in rural areas, rehabilitation is vital to ensure that they can continue to perform activities of daily living. In this population, a smooth discharge following periods of hospitalization is essential and requires management of multimorbidity, and rehabilitation therapists may require support from family physicians to achieve optimal outcomes. Therefore, this study aimed to investigate changes in the roles of rural family physicians in patient rehabilitation.MethodsAn ethnographic analysis was conducted with rural family physicians and rehabilitation therapists at a rural Japanese hospital. A constructivist grounded theory approach was applied as a qualitative research method. Data were collected from the participants via field notes and semi-structured interviews.ResultsUsing a grounded theory approach, the following three themes were developed regarding the establishment of effective interprofessional collaboration between family physicians and therapists in the rehabilitation of older patients in rural communities: 1) establishment of mutual understanding and the perception of psychological safety; 2) improvement of relationships between healthcare professionals and their patients; and 3) creation of new roles in rural family medicine to meet evolving needs.ConclusionEnsuring continual dialogue between family medicine and rehabilitation departments helped to establish understanding, enhance knowledge, and heighten mutual respect among healthcare workers, making the work more enjoyable. Continuous collaboration between departments also improved relationships between professionals and their patients, establishing trust in collaborative treatment paradigms and supporting patient-centered approaches to family medicine. Within this framework, understanding the capabilities of family physicians can lead to the establishment of new roles for them in rural hospitals. Family medicine plays a vital role in geriatric care in community hospitals, especially in rural primary care settings. The role of family medicine in hospitals should be investigated in other settings to improve geriatric care and promote mutual learning and improvement among healthcare professionals.

DMANet: Dual-modality alignment network for visible–infrared person re-identification

Visible–infrared person re-identification (VI-ReID) is a challenging retrieval task, which aims to match the same pedestrian between visible and infrared modalities. Most existing works achieve performance gains by solving the problem of the inherent cross-modality discrepancies. However, they cannot fully mine the modality information and lead to a poor generalization. In addition, the pedestrian images are unable to align well due to the large inter- and intra- class variations. To tackle the above limitations, we propose a novel dual-modality alignment network (DMANet) for VI-ReID. The core idea of our work is to develop multi-granularity features mutual learning (MGFML) for inadequate perception of modalities information, and to solve modality difference by proposing inter- and intra- modality alignment module (IIMA). Specifically, firstly, an effective multi-granularity features mutual learning module is proposed to mine the multi-granularity features, which combines the domain alignment and self-distillation to relieve modality discrepancy. Further, the maximum mean discrepancy loss and mutual learning loss are presented to enhance the identity-aware ability of the DMANet. Secondly, an effective inter- and intra- modality alignment module is presented to explore the potential alignment relation of inter- and intra- modalities. Finally, joint learning mechanism of multi-granularity features and modality alignment is utilized to improve the VI-ReID accuracy. Extensive experiments on mainstream benchmarks demonstrate that our method is superior to the state-of-the-art methods.

TST_MFL: Two-stage training based metric fusion learning for few-shot image classification

Addressing the limitations of most few-shot learning (FSL) methods, particularly their insufficient single-feature discriminability and generalization during pre-training encoding, this paper introduces a novel approach: Two-Stage Training based Metric Fusion Learning (TST_MFL) for few-shot image classification. TST_MFL innovatively integrates a two-stage fusion metric learning strategy, enhancing both pre-training and meta-training phases to overcome the prevalent focus on single-level feature extraction in current few-shot image classification methods. In pre-training stage, we employ prediction distillation loss and local difference loss to facilitate mutual learning between global and local features, thus obtaining more discriminative features. Moreover, meta-training further enhances feature generalization and integrates global–local metrics to improve few-shot classification accuracy. Our approach demonstrates competitive performance across three fine-grained datasets and one coarse-grained dataset. Notably, experimental results in 1-shot and 5-shot settings of fine-grained datasets underscore the superiority of TST_MFL. The code for this work is available at: https://github.com/ZitZhengWang/TST_MFL.

Participatory Design in Suicide Prevention: A Qualitative Study of International Students' Experiences of Adapting the LivingWorks safeTALK Programme.

Current suicide prevention approaches are not adapted to international student needs, and participatory design is a method that may facilitate the development or adaptation of appropriate programmes for this group. This qualitative study investigated the experiences of international university students studying in Australia who participated in a co-consultation process to adapt the LivingWorks safeTALK suicide prevention programme. Eight international students from the co-consultation workshop completed semi-structured interviews about their workshop experience. The data were analysed using reflexive thematic analysis. The findings showed that participants found the co-consultation process empowering and engaging. They also reported that the experience promoted mutual learning and challenged simplistic views of suicide. No students reported experiencing distress. Suggestions for improving participatory design for international students focussed on enhancing participant interaction, supporting quiet voices to be heardand ensuring understanding of mental health and suicide through shared language. This study underscores the value of participatory design in suicide prevention, emphasising its potential to empower international students and facilitate culturally sensitive programme adaptations. International students were involved in the co-consultation process to redevelop the training content and provided a series of recommendations for improving such processes for international students in the future. The two researchers who conducted the interviews and data analysis were former international students.

From Simple to Complex Scenes: Learning Robust Feature Representations for Accurate Human Parsing.

Human parsing has attracted considerable research interest due to its broad potential applications in the computer vision community. In this paper, we explore several useful properties, including high-resolution representation, auxiliary guidance, and model robustness, which collectively contribute to a novel method for accurate human parsing in both simple and complex scenes. Starting from simple scenes: we propose the boundary-aware hybrid resolution network (BHRN), an advanced human parsing network. BHRN utilizes deconvolutional layers and multi-scale supervision to generate rich high-resolution representations. Additionally, it includes an edge perceiving branch designed to enhance the fineness of part boundaries. Building on BHRN, we construct a dual-task mutual learning (DTML) framework. It not only provides implicit guidance to assist the parser by incorporating boundary features, but also explicitly maintains the high-order consistency between the parsing prediction and the ground truth. Toward complex scenes: we develop a domain transform method to enhance the model robustness. By transforming the input space from the spatial domain to the polar harmonic Fourier moment domain, the mapping relationship to the output semantic space is highly stable. This transformation yields robust representations for both clean and corrupted data. When evaluated on standard benchmark datasets, our method achieves superior performance compared to state-of-the-art human parsing methods. Furthermore, our domain transform strategy significantly improves the robustness of DTML dramatically in most complex scenes.

Neural-IMLS: Self-Supervised Implicit Moving Least-Squares Network for Surface Reconstruction.

Surface reconstruction is a challenging task when input point clouds, especially real scans, are noisy and lack normals. Observing that the Multilayer Perceptron (MLP) and the implicit moving least-square function (IMLS) provide a dual representation of the underlying surface, we introduce Neural-IMLS, a novel approach that directly learns a noise-resistant signed distance function (SDF) from unoriented raw point clouds in a self-supervised manner. In particular, IMLS regularizes MLP by providing estimated SDFs near the surface and helps enhance its ability to represent geometric details and sharp features, while MLP regularizes IMLS by providing estimated normals. We prove that at convergence, our neural network produces a faithful SDF whose zero-level set approximates the underlying surface due to the mutual learning mechanism between the MLP and the IMLS. Extensive experiments on various benchmarks, including synthetic and real scans, show that Neural-IMLS can reconstruct faithful shapes even with noise and missing parts. The source code can be found at https://github.com/bearprin/Neural-IMLS.

Promoting Mutual Learning and Technology Exploration of Age-Diverse Coworkers - A Field Experiment

Promoting Mutual Learning and Technology Exploration of Age-Diverse Coworkers - A Field Experiment