Privacy-Preserving Healthcare and Medical Data Collaboration Service System Based on Blockchain and Federated Learning

As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.

Robust and Trustworthy Data Sharing Framework Leveraging On-Chain and Off-Chain Collaboration

The proliferation of Internet of Things(IoT)systems has resulted in the generation of substantial data,presenting new challenges in reliable storage and trustworthy sharing.Conventional distributed storage systems are hindered by centralized management and lack traceability,while blockchain systems are limited by low capacity and high latency.To address these challenges,the present study investigates the reliable storage and trustworthy sharing of IoT data,and presents a novel system architecture that integrates on-chain and off-chain data manage systems.This architecture,integrating blockchain and distributed storage technologies,provides high-capacity,high-performance,traceable,and verifiable data storage and access.The on-chain system,built on Hyperledger Fabric,manages metadata,verification data,and permission information of the raw data.The off-chain system,implemented using IPFS Cluster,ensures the reliable storage and efficient access to massive files.A collaborative storage server is designed to integrate on-chain and off-chain operation interfaces,facilitating comprehensive data operations.We provide a unified access interface for user-friendly system interaction.Extensive testing validates the system’s reliability and stable performance.The proposed approach significantly enhances storage capacity compared to standalone blockchain systems.Rigorous reliability tests consistently yield positive outcomes.With average upload and download throughputs of roughly 20 and 30 MB/s,respectively,the system’s throughput surpasses the blockchain system by a factor of 4 to 18.

Optimal edge-cloud collaboration based strategies for minimizing valid latency of railway environment monitoring system

Response speed is vital for the railway environment monitoring system,especially for the sudden-onset disasters.The edge-cloud collaboration scheme is proved efficient to reduce the latency.However,the data characteristics and communication demand of the tasks in the railway environment monitoring system are all different and changeable,and the latency contribution of each task to the system is discrepant.Hence,two valid latency minimization strategies based on the edge-cloud collaboration scheme is developed in this paper.First,the processing resources are allocated to the tasks based on the priorities,and the tasks are processed parallly with the allocated resources to minimize the system valid latency.Furthermore,considering the differences in the data volume of the tasks,which will induce the waste of the resources for the tasks finished in advance.Thus,the tasks with similar priorities are graded into the same group,and the serial and parallel processing strategies are performed intra-group and inter-group simultaneously.Compared with the other four strategies in four railway monitoring scenarios,the proposed strategies proved latency efficiency to the high-priority tasks,and the system valid latency is reduced synchronously.The performance of the railway environment monitoring system in security and efficiency will be promoted greatly with the proposed scheme and strategies.

A New Solution to Intrusion Detection Systems Based on Improved Federated-Learning Chain

In the context of enterprise systems,intrusion detection(ID)emerges as a critical element driving the digital transformation of enterprises.With systems spanning various sectors of enterprises geographically dispersed,the necessity for seamless information exchange has surged significantly.The existing cross-domain solutions are challenged by such issues as insufficient security,high communication overhead,and a lack of effective update mechanisms,rendering them less feasible for prolonged application on resource-limited devices.This study proposes a new cross-domain collaboration scheme based on federated chains to streamline the server-side workload.Within this framework,individual nodes solely engage in training local data and subsequently amalgamate the final model employing a federated learning algorithm to uphold enterprise systems with efficiency and security.To curtail the resource utilization of blockchains and deter malicious nodes,a node administration module predicated on the workload paradigm is introduced,enabling the release of surplus resources in response to variations in a node’s contribution metric.Upon encountering an intrusion,the system triggers an alert and logs the characteristics of the breach,facilitating a comprehensive global update across all nodes for collective defense.Experimental results across multiple scenarios have verified the security and effectiveness of the proposed solution,with no loss of its recognition accuracy.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Global collaboration of eye research--personal experience

On December 9,2023,I was privileged to be honored and participate in the Dr.Chi Chao Chan Symposium on Global Collaboration of Eye Research as the Global Eye Genetic Consortium(GEGC)session,which was held in the 16th Congress of the Asia-Pacific Vitreo-Retina Society(APVRS)in Hong Kong.Along with my talk on“Global collaboration of eye research:personal experience”,other prominent international speakers provided their own perspectives on opportunities for networking,collaboration,and exchange of ideas with global leaders and experts in ophthalmic practice,research,and education.

Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.

Exploration of the “Four-Three-Four” Internship and Training Mode in Higher Vocational Colleges Under the Modern Vocational Education System

This article mainly introduces the innovative internship and training model of vocational colleges,including five aspects such as“four-party collaboration,three-stage progression,four-way integration,value-added evaluation,and double selection and promotion.”The model aims to improve students’practical skills and professional quality to better adapt to market demand and social development.The article also presents the prospects of the future internship and training model,including strengthening cooperation and communication with industry enterprises,focusing on students’personalized development and practical skill cultivation,and establishing a scientific and objective evaluation and feedback mechanism.

Autonomous process collaboration framework for C4ISR system interoperation

Interoperability plays an important role in the joint command, control, communication, computer, intelligence, surveillance, reconnaissance(C4 ISR) operations. Coordinating and integrating operational processes to fulfill a common mission are challenged by the ever-changing battlefield and hence requires a cross-organizational process management that produces an autonomous, flexible and adaptable architecture for collaborative process evolution. The traditional business process collaboration pattern is based on the predefined "public-view" perspective and cannot meet the requirement of the joint task operations. This paper proposes a flexible visibility control mechanism and a dynamic collaboration framework for modeling and generating collaborative processes. The mechanism allows collaborators to define a set of visibility rules to generate different views of the private processes for different collaborations, which gives a great flexibility for the collaboration initiator to decide on an appropriate collaboration pattern. The framework supports collaborators to dynamically and recursively add a new process or even a new organization to an existing collaboration. Moreover, a formal representation of the processes and a set of generation algorithms are provided to consolidate the proposed theory.

Prototyping and Evaluating a Wearable System for Mobile Distributed Collaboration

We have developed a wearable system for mobile distributed collaboration called HandsInAir using emerging wireless and mobile technologies. This system was developed to support real world scenarios in which a remote mobile helper guides a local mobile worker in the completion of a physical task. HandsInAir consists of a helper unit and a worker unit. Both units are equipped with wearable devices having the same hardware configuration, but running different pieces of software to support the distinct roles of the collaborators (helper and worker). The two sides are connected via a wireless network and the collaboration partners can communicate with each other via audio and visual links. In this paper we describe the technical implementation of the system and present a preliminary evaluation of it. The paper concludes with a brief discussion of possible future work for further improvements and new developments.

A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

Virtual fire drill system supporting co-located collaboration

Background Due to the restriction of display mode,in most of the virtual reality systems with multiple people in the same physical space,the program renders the scene based on the position and perspective of the one user,so that other users just see the same scene,resulting in vision disorder.Methods To improve experience of multi-user co-located collaboration,in this study,we propose a fire drill system supporting co-located collaboration,in which three co-located users can collaborate to complete the virtual firefighting mission.Firstly,with multi-view stereoscopic projective display technology and ultra wideband(UWB)technology,co-located users can roam independently and watch virtual scenes through the correct perspective view based on their own position by wearing dedicated shutter glasses,thus carrying out different virtual tasks,which improves the flexibility of co-located collaboration.Secondly,we design simulated firefighting water-gun using the micro-electromechanical system sensor,through which users can interact with virtual environment,and thus provide a better interactive experience.Finally,we develop a workbench including a holographic display module and multi touch operation module for virtual scene assembly and virtual environment control.Results The controller can use the workbench to adjust the virtual layout in real time,and control the virtual task process to increase the flexibility and playability of system.Conclusions Our work can be employed in a wide range of related virtual reality applications.

End-cloud collaboration method enables accurate state of health and remaining useful life online estimation in lithium-ion batteries

Though the lithium-ion battery is universally applied,the reliability of lithium-ion batteries remains a challenge due to various physicochemical reactions,electrode material degradation,and even thermal runaway.Accurate estimation and prediction of battery health conditions are crucial for battery safety management.In this paper,an end-cloud collaboration method is proposed to approach the track of battery degradation process,integrating end-side empirical model with cloud-side data-driven model.Based on ensemble learning methods,the data-driven model is constructed by three base models to obtain cloud-side highly accurate results.The double exponential decay model is utilized as an empirical model to output highly real-time prediction results.With Kalman filter,the prediction results of end-side empirical model can be periodically updated by highly accurate results of cloud-side data-driven model to obtain highly accurate and real-time results.Subsequently,the whole framework can give an accurate prediction and tracking of battery degradation,with the mean absolute error maintained below 2%.And the execution time on the end side can reach 261μs.The proposed end-cloud collaboration method has the potential to approach highly accurate and highly real-time estimation for battery health conditions during battery full life cycle in architecture of cyber hierarchy and interactional network.

Human-Robot Collaboration Framework Based on Impedance Control in Robotic Assembly

Human–robot(HR)collaboration(HRC)is an emerging research field because of the complementary advantages of humans and robots.An HRC framework for robotic assembly based on impedance control is proposed in this paper.In the HRC framework,the human is the decision maker,the robot acts as the executor,while the assembly environment provides constraints.The robot is the main executor to perform the assembly action,which has the position control,drag and drop,positive impedance control,and negative impedance control modes.To reveal the characteristics of the HRC framework,the switch condition map of different control modes and the stability analysis of the HR coupled system are discussed.In the end,HRC assembly experiments are conducted,where the HRC assembly task can be accomplished when the assembling tolerance is 0.08 mm or with the interference fit.Experiments show that the HRC assembly has the complementary advantages of humans and robots and is efficient in finishing complex assembly tasks.

Evolution of Manufacturing Strategy and System in Multinational Corporations:Internationalisation and Inter-firm Collaboration Redefine the Boundaries of Manufacturing and Firm

In recent decades, the world economy has been stron gl y driven by worldwide foreign direct investment (FDI) of transnational corporati ons (TNCs). And business globalisation is leading to widespread re-structuring of international manufacturing systems in TNCs. However, still very little sys tematic research in manufacturing engineering or production/operations managemen t (P/O M) has focused on these new types of globalised manufacturing systems, an d little communication is organised between different academic disciplines such as manufacturing, management/ business studies, economics, geography, internatio nal development studies and so on. Many critical issues are still poorly unders tood and are not covered by current research agendas, for example: · properties of international manufacturing network systems in terms of structu ral architecture, dynamic mechanisms, strategic capabilities, strategy processes , new technology, and alliance relationships · knowledge management and organisational learning mechanisms in international manufacturing context, especially in globally coordinated networks at both intra - and inter- organisational levels · global economic trends, regional collaboration, national culture/identity, an d their impacts on industrial sector development, and company’s manufacturin g system design/re-structuring In industry, lack of global vision and appropriate strategies during internation alisation have become major barrier to the effective management of international operations. In academia, limited multi-discipline exchanges have inhibited de velopments not only in each area but also knowledge contribution to industry whi ch could arise from discipline integration. This paper, based on recent years case observations and action research work on manufacturing internationalisation, especially globalisation, introduces an evol utionary process of manufacturing systems. It seeks to explore the external and internal driving forces of the transformation and the responses of manufacturin g systems in terms of static architectures, dynamic mechanisms, and strategic ca pabilities in the evolutionary process. The paper Reviews recent researches on manufacturing internationalisation in dif ferent disciplines to gain a comprehensive picture about international manufactu ring system from different perspectives. Based on the empirical observations of industrial issues and difficulties, a further research framework is proposed to tackle wider issues in international manufacturing covering manufacturing value creation process, internationalisation process, and strategic alliance formatio n process. The paper argues that, besides deep research on three dimensions of international manufacturing system, development of strategy process to synthesis e these three processes is more important in the future research work. It sugge sts that it is necessary to enhance the collaboration between countries and disc iplines, and stimulate explorations of new frameworks that can provide more insi ghts about new manufacturing systems and foresights their development.

Augmented reality-based BIM multi-user collaboration system

The rapid development of building information modelling(BIM) and its enabling technologies has attracted extensive attention in the field of architecture, engineering and construction(AEC). By combining BIM models with the real world, the potential of BIM can be further exploited with the help of augmented reality(AR) technology. However, a BIM model usually involves a huge amount of data. Considering the limited computing capability of current mobile devices, these applications therefore suffer from significant performance problems, especially model loading and rendering problems. To this end, an AR-based multi-user BIM collaboration system, which can realise the on-demand dynamical loading of the BIM model by using the block-wise loading strategy of model transformation, thus solving the problem of model loading delay, was proposed. In addition, dynamic rendering technology is adopted to solve the problem of rendering lag. Experimental results show that the realisation of virtual-reality fusion and interaction for the BIM model and remote multi-user collaboration can effectively improve work efficiency and intelligence in the engineering field.

A Conceptual and Computational Framework for Aspect-Based Collaborative Filtering Recommender Systems

Many datasets in E-commerce have rich information about items and users who purchase or rate them. This information can enable advanced machine learning algorithms to extract and assign user sentiments to various aspects of the items thus leading to more sophisticated and justifiable recommendations. However, most Collaborative Filtering (CF) techniques rely mainly on the overall preferences of users toward items only. And there is lack of conceptual and computational framework that enables an understandable aspect-based AI approach to recommending items to users. In this paper, we propose concepts and computational tools that can sharpen the logic of recommendations and that rely on users’ sentiments along various aspects of items. These concepts include: The sentiment of a user towards a specific aspect of a specific item, the emphasis that a given user places on a specific aspect in general, the popularity and controversy of an aspect among groups of users, clusters of users emphasizing a given aspect, clusters of items that are popular among a group of users and so forth. The framework introduced in this study is developed in terms of user emphasis, aspect popularity, aspect controversy, and users and items similarity. Towards this end, we introduce the Aspect-Based Collaborative Filtering Toolbox (ABCFT), where the tools are all developed based on the three-index sentiment tensor with the indices being the user, item, and aspect. The toolbox computes solutions to the questions alluded to above. We illustrate the methodology using a hotel review dataset having around 6000 users, 400 hotels and 6 aspects.

Training System of Innovative and Entrepreneurial Talents based on University-Enterprise Collaboration

With the continuous development of China's education,the diversification of personnel training has gradually become an important part of China's social development and construction.As a new teaching mode in higher education,the university-enterprise collaborative training mode is an important guarantee for training high-quality,compound and practical professionals.Based on this,first of all,the current situation and problems of the training mode of innovative and entrepreneurial talents based on university-enterprise collaboration and its causes were expounded;then a new innovative and entrepreneurial talents training system based on university-enterprise collaboration was constructed from four aspects of education,practice,training and guidance;finally,the guarantee mechanism of the system was proposed from the aspects of culture,system and law,so as to promote the development of education in China.

Global trends in international research collaboration, 1980-2021

Purpose:The aim of this study is to analyze the evolution of international research collaboration from 1980 to 2021.The study examines the main global patterns as well as those specific to individual countries,country groups,and different areas of research.Design/methodology/approach:The study is based on the Web of Science Core collection database.More than 50 million publications are analyzed using co-authorship data.International collaboration is defined as publications having authors affiliated with institutions located in more than one country.Findings:At the global level,the share of publications representing international collaboration has gradually increased from 4.7%in 1980 to 25.7%in 2021.The proportion of such publications within each country is higher and,in 2021,varied from less than 30%to more than 90%.There are notable disparities in the temporal trends,indicating that the process of internationalization has impacted countries in different ways.Several factors such as country size,income level,and geopolitics may explain the variance.Research limitations:Not all international research collaboration results in joint co-authored scientific publications.International co-authorship is a partial indicator of such collaboration.Another limitation is that the applied full counting method does not take into account the number of authors representing in each country in the publication.Practical implications:The study provides global averages,indicators,and concepts that can provide a useful framework of reference for further comparative studies of international research collaboration.Originality/value:Long-term macro-level studies of international collaboration are rare,and as a novelty,this study includes an analysis by the World Bank’s division of countries into four income groups.