Collaborative Learning Mechanism Research Articles

Multiview collaboration learning classification model of stock data based on view weighting mechanism

Machine learning methods have become an effective strategy commonly used in quantitative hedge funds, which can maximize profits and reduce investment risks to a certain extent. Traditional stock forecasting systems are usually based on a single attribute of stock data. There are two main challenges in this process: 1) Use suitable processing methods to deal with highly nonlinear time series data such as stocks. 2) Using a single class of stock data for training does not capture the correlation between other related data and the training data. Based on RBF neural network, this research introduces view weighting and collaborative learning mechanism, and proposes a MV-RBF model. It mainly includes the following contributions: 1) By comparing the experimental results of MV-RBF model with the single-view model, its effectiveness and feasibility are verified. 2) The MV-RBF model was compared with other commonly used classification models to analyze its advantages and disadvantages. 3) Study the relevant parameters, stability and other indicators of MV-RBF model. The experimental results show that compared with the single view model and most common classification models, MV-RBF has certain improvement in the prediction accuracy.

Collaborative Learning with Unreliability Adaptation for Semi-Supervised Image Classification

Constructing training goals for unlabeled data is crucial for image classification in the semi-supervised setting. Consistency regularization typically encourages a model to produce consistent predictions with the given training goals, while unreliability adaptation aims to learn the transition probabilities from model predictions to training goals, instead of enforcing their consistency. In this paper, we present a model of Collaborative learning with Unreliability Adaptation (CoUA), in which multiple constituent networks collaboratively learn with each other by adapting their predictions. Toward this end, an additional adaptation module is incorporated into each network to learn a transition probability from its own prediction to that of the paired network. Therefore, the networks can exchange training experience, without being overly sensitive to the unreliability of predictions. To further enhance the collaborative learning, each network is encouraged to produce consistent predictions with the consensus results, while being resistant to the adversarial perturbations against others. Therefore, the networks are able to mutually reinforce each other. We perform extensive experiments on multiple image classification benchmarks to verify the superiority of the co-adaptation based collaborative learning mechanism.

The role of collaborative learning in resilience in healthcare—a thematic qualitative meta-synthesis of resilience narratives

BackgroundTo provide high quality services in increasingly complex, constantly changing circumstances, healthcare organizations worldwide need a high level of resilience, to adapt and respond to challenges and changes at all system levels. For healthcare organizations to strengthen their resilience, a significant level of continuous learning is required. Given the interdependence required amongst healthcare professionals and stakeholders when providing healthcare, this learning needs to be collaborative, as a prerequisite to operationalizing resilience in healthcare. As particular elements of collaborative working, and learning are likely to promote resilience, there is a need to explore the underlying collaborative learning mechanisms and how and why collaborations occur during adaptations and responses. The aim of this study is to describe collaborative learning processes in relation to resilient healthcare based on an investigation of narratives developed from studies representing diverse healthcare contexts and levels.MethodsThe method used to develop understanding of collaborative learning across diverse healthcare contexts and levels was to first conduct a narrative inquiry of a comprehensive dataset of published health services research studies. This resulted in 14 narratives (70 pages), synthesised from a total of 40 published articles and 6 PhD synopses. The narratives where then analysed using a thematic meta-synthesis approach.ResultsThe results show that, across levels and contexts, healthcare professionals collaborate to respond and adapt to change, maintain processes and functions, and improve quality and safety. This collaboration comprises activities and interactions such as exchanging information, coordinating, negotiating, and aligning needs and developing buffers. The learning activities embedded in these collaborations are both activities of daily work, such as discussions, prioritizing and delegation of tasks, and intentional educational activities such as seminars or simulation activities.ConclusionsBased on these findings, we propose that the enactment of resilience in healthcare is dependent on these collaborations and learning processes, across different levels and contexts. A systems perspective of resilience demands collaboration and learning within and across all system levels. Creating space for reflection and awareness through activities of everyday work, could support individual, team and organizational learning.

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more advanced AI models on cloud servers that surpass traditional deep learning models owing to model innovations (e.g., Transformers, Pretrained families), explosion of training data and soaring computing capabilities. However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed. In this survey, we conduct a systematic review for both cloud and edge AI. Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism. We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning. Finally, we discuss the promising directions and challenges in this field.

Forecasting Trend of Coronavirus Disease 2019 using Multi-Task Weighted TSK Fuzzy System

Artificial intelligence– (AI) based fog/edge computing has become a promising paradigm for infectious disease. Various AI algorithms are embedded in cooperative fog/edge devices to construct medical Internet of Things environments, infectious disease forecast systems, smart health, and so on. However, these systems are usually done in isolation, which is called single-task learning. They do not consider the correlation and relationship between multiple/different tasks, so some common information in the model parameters or data characteristics is lost. In this study, each data center in fog/edge computing is considered as a task in the multi-task learning framework. In such a learning framework, a multi-task weighted Takagi-Sugeno-Kang (TSK) fuzzy system, called MW-TSKFS, is developed to forecast the trend of Coronavirus disease 2019 (COVID-19). MW-TSKFS provides a multi-task learning strategy for both antecedent and consequent parameters of fuzzy rules. First, a multi-task weighted fuzzy c-means clustering algorithm is developed for antecedent parameter learning, which extracts the public information among all tasks and the private information of each task. By sharing the public cluster centroid and public membership matrix, the differences of commonality and individuality can be further exploited. For consequent parameter learning of MW-TSKFS, a multi-task collaborative learning mechanism is developed based on ε-insensitive criterion and L2 norm penalty term, which can enhance the generalization and forecasting ability of the proposed fuzzy system. The experimental results on the real COVID-19 time series show that the forecasting tend model based on multi-task the weighted TSK fuzzy system has a high application value.

Audience participation digital drama‐based learning activities for situational learning in the classroom

AbstractIn this study, the transformational play approach was further used to deploy drama‐based situational learning in the classroom through the Digital Learning Theatre (DLT) by engaging drama performers and the audience collaboratively. This study analysed the learners' learning effectiveness based on translation and sentence‐construction abilities, and their perception by focussing on concentration, and comprehension of dialogues before and after the drama‐based activity. The experimental results were based on both the DLT including tablet PCs to enable audience participation in drama performances, and the DLT for drama performances in which the audience can participate through traditional open discussion sessions with the narrator of the drama. Sixty‐five students were selected randomly as participants from the English as a Foreign Language academic program at the junior high school level. The application of the DLT including the tablet PCs approach showed considerable improvement in students' learning effectiveness as well as a significant positive effect on classroom learning, outperform the effectiveness of the DLT with traditional open discussion sessions for the audience. Accordingly, to improve students' learning effectiveness, their engagement in the learning process can be advanced through situational learning activities using educational technologies that combine with collaborative learning mechanisms for classroom learning. Practitioner notesWhat is already known about this topic The transformational play proposed by Sasha Barab is designed based on situated cognition theory to position persons, content, and context together through digital games. Dorothy Heathcote proposed a drama‐based learning approach for learning in the classroom, and learners can play various roles in the drama to learn in a situational learning setting. The teacher, too, plays a role to guide the students and engage them in the situational learning setting. Integration of immersive educational technologies supports learning by reducing obstacles to adoption in the learning process and improve learning effectiveness. Within a rich contextual learning environment, learners can be placed in different situational learning tasks that allow them to apply existing knowledge and experiences. What does this paper add This study introduces a manner of applying the transformational play approach in drama‐based situational learning activities suitable for collaborative learning in a classroom setting. It introduces a practical and simple method to transform a regular classroom into a digital version of the learning theatre, including a virtual stage to drama performances. The drama‐based situational learning approach facilitates the audience as well as the performers to transform themselves into protagonists in the learning context by collaboratively following the learning content and learning with their peers. The technology‐enhanced drama‐based situational learning approach is introduced based on the transformation play approach that allows the students to learn situationally through different script instances, and improves their learning effectiveness by helping them understand the learning content through performing, participating, watching, and interacting in the learning context. Implications for practice and/or policy Learners can apply their prior knowledge and experiences creatively through drama‐based learning activities, and interactive engagement allows the audience to participate in the learning process equally as drama performers. Educators and learning system developers can implement this approach to facilitate situational practice and discuss learning content. The learning process supports the reinterpretation of the learners' experiences and reflects on how to procure/adopt new knowledge and apply it to real‐life situations. Drama performance is a collaborative/group learning task and it is performed for the audience. Students with higher achievements will help students with lower achievements so that everyone can contribute in the learning process to achieve their learning objectives.

WLAN interference signal recognition using an improved quadruple generative adversarial network

Cross-technology interference sources poses a great challenge for improving throughputs of wireless local area networks (WLAN) and wireless interference signal recognition (WISR) can provide a precondition for mitigating this problem. The quadruple generative adversarial network (QGAN) has shown its prevailing performance for specific emitter identification (SEI). In this paper, an enhanced collaborative learning mechanism is proposed to improve QGAN's performance for WISR. ACGAN is involved in the Improved-QGAN architecture to substitute original GAN, and loss functions are further optimized for generative, representation and classification sub-networks. Besides, a lightweight model based on knowledge distillation (KD) is presented to reduce memory consumption and computational complexity at inference phase. Numerical results indicate that the proposed Improved-QGAN outperforms the other baseline algorithms both on the experimental dataset and benchmark dataset.

Epilepsy Diagnosis Using Multi-view & Multi-medoid Entropy-based Clustering with Privacy Protection

Using unsupervised learning methods for clinical diagnosis is very meaningful. In this study, we propose an unsupervised multi-view & multi-medoid variant-entropy-based fuzzy clustering (M 2 VEFC) method for epilepsy EEG signals detecting. Comparing with existing related studies, M 2 VEFC has four main merits and contributions: (1) Features in original EEG data are represented from different perspectives that can provide more pattern information for epilepsy signals detecting. (2) During multi-view modeling, multi-medoids are used to capture the structure of clusters in each view. Furthermore, we assume that the medoids in a cluster observed from different views should keep invariant, which is taken as one of the collaborative learning mechanisms in this study. (3) A variant entropy is designed as another collaborative learning mechanism in which view weight learning is controlled by a user-free parameter. The parameter is derived from the distribution of samples in each view such that the learned weights have more discrimination. (4) M 2 VEFC does not need original data as its input—it only needs a similarity matrix and feature statistical information. Therefore, the original data are not exposed to users and hence the privacy is protected. We use several different kinds of feature extraction techniques to extract several groups of features as multi-view data from original EEG data to test the proposed method M 2 VEFC. Experimental results indicate M 2 VEFC achieves a promising performance that is better than benchmarking models.

Implementation of the e-SUS Primary Care system: Impact on the routine of Primary Health Care professionals.

Objective:to understand how the implementation of the e-SUS Primary Care system has been processed and its impact on the daily life of the health teams.Method:a qualitative research study, conducted in a municipality in the inland of the state of São Paulo with professionals who work in Primary Health Care and use the e-SUS Primary Care system as a work tool. Semi-structured interviews and thematic data analysis were used with Kotter’s three-phase approach.Results:a total of 17 professionals, nurses, physicians, dentists and community agents were interviewed. The implementation of e-SUS Primary Care and its impact on the daily life of health teams were understood in terms of mandatory implementation; weaknesses for implementation, such as absence of material resources and implicit imposition for the use of the system; fragile training for deployment and learning from experience.Conclusion:a harmful incentive process was observed, conducted from the perspective of institutional pressure, use of the system to justify the work performed and, on the other hand, there was the creation of collaborative learning mechanisms between the teams.

Tablet for two: How do children collaborate around single player tablet games?

• Single player games/apps on tablets are used in collaborative configurations in the classroom. • Tablets change the nature of coordination leading to more turn taking during collaborative episodes. • When children approach the task as a co-constructed space, high quality collaborative discussion can occur. • The design of the game learning task can promote, or inhibit effective collaborative discussion. Tablet computers are increasingly used in school classrooms. However, despite the fact that these devices are conceived as single-user devices, and most games or apps developed for them are designed for single-users, pairs or groups of students usually use these devices. Surprisingly little research has been done to explore the ways in which these devices support or not children's collaboration – instead research has focused on larger tabletop computers, or on collaboration around configurations of multiple tablet computers. In this paper we present a case-study analysis of pairs of children playing single player tablet games together. We use a combination of temporal video analysis and the Collaborative Learning Mechanisms (CLM) framework previously developed to understand collaboration around surfaces. This analysis aims to unpack collaborative interactions around these devices and identify ways in which successful and less successful collaborations occur. A comparison of our findings to previous studies of interactions around larger tabletop surfaces reveals some of the ways interactions around tablets differ to these. We use these understandings to begin to outline some of the issues to take into consideration when facilitating and designing for children's collaboration around single tablet computers.

Combining Progressive Rethinking and Collaborative Learning: A Deep Framework for In-Loop Filtering.

In this paper, we aim to address issues of (1) joint spatial-temporal modeling and (2) side information injection for deep-learning based in-loop filter. For (1), we design a deep network with both progressive rethinking and collaborative learning mechanisms to improve quality of the reconstructed intra-frames and inter-frames, respectively. For intra coding, a Progressive Rethinking Network (PRN) is designed to simulate the human decision mechanism for effective spatial modeling. Our designed block introduces an additional inter-block connection to bypass a high-dimensional informative feature before the bottleneck module across blocks to review the complete past memorized experiences and rethinks progressively. For inter coding, the current reconstructed frame interacts with reference frames (peak quality frame and the nearest adjacent frame) collaboratively at the feature level. For (2), we extract both intra-frame and inter-frame side information for better context modeling. A coarse-to-fine partition map based on HEVC partition trees is built as the intra-frame side information. Furthermore, the warped features of the reference frames are offered as the inter-frame side information. Our PRN with intra-frame side information provides 9.0% BD-rate reduction on average compared to HEVC baseline under All-intra (AI) configuration. While under Low-Delay B (LDB), Low-Delay P (LDP) and Random Access (RA) configuration, our PRN with inter-frame side information provides 9.0%, 10.6% and 8.0% BD-rate reduction on average respectively. Our project webpage is https://dezhao-wang.github.io/PRN-v2/.

I see what you did there! Divergent collaboration and learner transitions from unproductive to productive states in open-ended inquiry

While open-ended and tinkering-based learning environments offer considerable support for developing STEM-based reasoning and collaboration skills, understanding how and when learners are engaged in productive or unproductive exploration is a pressing challenge. This is particularly true in museums where dwell times are short and visitors can enter and exit exhibits at varying times. In response, this work aimed to answer two questions: 1) How can we combine learning analytics and qualitative approaches to understand how learners move from unproductive to productive states during open-ended inquiry? 2) What role do interactions with others play in supporting participants' transitions to productive states? To answer these questions, this study examined how combining a Hidden Markov Model (HMM) and interaction analysis together can reveal important elements of participants’ collaboration and exploration that would likely be lost if each method were applied in isolation. The methods were applied to visitors participating at an interactive multi-touch exhibit (named Oztoc). The application of HMM successfully captured when participants transitioned from persistent unproductive to productive states, while interaction analysis using the Divergent Collaborative Learning Mechanisms framework (DCLM) showed how specific divergent collaborative interactions supported these transitions. In particular, we reveal the role that visitors engaging in Boundary Spanning Perception and Boundary Spanning Action played in these transitions. More broadly, this work shows how designs that provide opportunities for these kinds of interactions may help learners effectively transition out of sustained states of unproductive persistence.

Realizing Two-View TSK Fuzzy Classification System by Using Collaborative Learning

In this paper, a novel Takagi-Sugeno–Kang (TSK) fuzzy classification system (FCS) is firstly presented for pattern classification tasks. It is distinguished by having the large margin criterion properly integrated into its objective function. In order to exploit the applicability of fuzzy systems in multiview scenarios, the proposed TSK-FCS is extended to a two-view version, called two-view TSK-FCS (TwoV-TSK-FCS), by using a collaborative learning mechanism. The adopted collaborative learning mechanism not only fully considers the independent information of each view, but also effectively discovers the correlation information hidden in the two views. Thus, the performance of TwoV-TSK-FCS can be enhanced accordingly. Comprehensive experiments on two-view synthetic and UCI datasets demonstrate the effectiveness of the proposed two-view FCS.

The Journey of Primary Care Practices to Meaningful Use: A Colorado Beacon Consortium Study

The Health Information Technology for Economic and Clinical Health Act of 2009 provides for incentive payments through Medicare and Medicaid for clinicians who implement electronic health records (EHRs) and use this technology meaningfully to improve patient care. There are few comprehensive descriptions of how primary care practices achieve the meaningful use of clinical data, including the formal stage 1 meaningful use requirements. Evaluation of the Colorado Beacon Consortium project included iterative qualitative analysis of practice narratives, provider and staff interviews, and separate focus groups with quality improvement (QI) advisors and staff from the regional health information exchange (HIE). Most practices described significant realignment of practice priorities and aims, which often required substantial education and training of physicians and staff. Re-engineering office processes, data collection protocols, EHRs, staff roles, and practice culture comprised the primary effort and commitment to attest to stage 1 meaningful use and subsequent meaningful use of clinical data. While realizing important benefits, practices bore a significant burden in learning the true capabilities of their EHRs with little effective support from vendors. Attestation was an important initial milestone in the process, but practices faced substantial ongoing work to use their data meaningfully for patient care and QI. Key resources were instrumental to these practices: local technical EHR expertise; collaborative learning mechanisms; and regular contact and support from QI advisors. Meeting the stage 1 requirements for incentives under Medicare and Medicaid meaningful use criteria is the first waypoint in a longer journey by primary care practices to the meaningful use of electronic data to continuously improve the care and health of their patients. The intensive re-engineering effort for stage 1 yielded practice changes consistent with larger practice aims and goals. While many of these practices are now poised to use data meaningfully, faster progress will likely come with continued local QI and technical support and planned community-wide learning.

A comparative study of collaborative learning in Paper Scribbles and Group Scribbles

<em>Paper Scribbles</em><span> (PS) consisting of markers, vanguard sheets and 3M </span><em>Post-It</em><span> notes, is a pedagogical tool to harness collective intelligence of groups for collaborative learning in the classroom. Borrowing the key features of PS and yet avoiding some of their physical limitations, a computer-based tool called </span><em>Group Scribbles</em><span> (GS) was designed to enable high performance synchronous, face to face collaborative experiences in the classroom. In our exploratory study, we discuss and compare the different properties of PS and GS technologies in two elementary grade 5 classes. The findings reveal that there are not only physical and technical differences but more importantly, pedagogical differences between the two technologies that accounts for different collaborative learning mechanisms.</span>

Inter-organisational routines and transformation of customer relationships in collaborative innovation

Innovation, learning and knowledge leverage are crucial for the competitive edge of knowledge-intensive firms. In recent research on business relationships, the significance of studying routines embedded in inter-organisational learning has been stressed as a key element in understanding the development of relationships. The purpose of this paper is to introduce an approach for increasing understanding of the driving forces and influence of inter-organisational routines in the transformation process towards deeper customer-orientated collaboration in innovation. In practice, the results of the paper aim to promote organisations' balance between exploitation- and exploration-related activities towards more customer-orientated innovation management. The case study on the knowledge-intensive engineering consultancy industry illustrates features, driving forces and observed routines influencing the transformation process and collaborative learning mechanisms. Final conclusions and managerial implications are based on an analysis of the theoretical and empirical parts of the study.

Knowledge management, response ability, and the agile enterprise

This paper defines the agile enterprise as one which is able to both manage and apply knowledge effectively, and suggests that value from either capability is impeded if they are not in balance. It looks at the application of knowledge as requiring a change, and overviews a body of analytical work on change proficiency in business systems and processes. It looks at knowledge management as a strategic portfolio management responsibility based on learning functionality, and shares knowledge and experience in organizational collaborative learning mechanisms. It introduces the concept of plug‐compatible knowledge packaging as a means for increasing the velocity of knowledge diffusion and the likelihood of knowledge understood at the depth of insight. Finally, it reviews a knowledge portfolio management and collaborative knowledge development architecture used successfully in a sizable cross‐industry informal‐consortia activity, and suggests that it is a good model for a corporate university architecture.