Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global...Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.展开更多
In this paper,we study scene image recognition with knowledge transfer for drone navigation.We divide navigation scenes into three macro-classes,namely outdoor special scenes(OSSs),the space from indoors to outdoors o...In this paper,we study scene image recognition with knowledge transfer for drone navigation.We divide navigation scenes into three macro-classes,namely outdoor special scenes(OSSs),the space from indoors to outdoors or from outdoors to indoors transitional scenes(TSs),and others.However,there are difficulties in how to recognize the TSs,to this end,we employ deep convolutional neural network(CNN)based on knowledge transfer,techniques for image augmentation,and fine tuning to solve the issue.Moreover,there is still a novelty detection prob-lem in the classifier,and we use global navigation satellite sys-tems(GNSS)to solve it in the prediction stage.Experiment results show our method,with a pre-trained model and fine tun-ing,can achieve 91.3196%top-1 accuracy on Scenes21 dataset,paving the way for drones to learn to understand the scenes around them autonomously.展开更多
In the big data environment, enterprises must constantly assimilate big dataknowledge and private knowledge by multiple knowledge transfers to maintain theircompetitive advantage. The optimal time of knowledge transfe...In the big data environment, enterprises must constantly assimilate big dataknowledge and private knowledge by multiple knowledge transfers to maintain theircompetitive advantage. The optimal time of knowledge transfer is one of the mostimportant aspects to improve knowledge transfer efficiency. Based on the analysis of thecomplex characteristics of knowledge transfer in the big data environment, multipleknowledge transfers can be divided into two categories. One is the simultaneous transferof various types of knowledge, and the other one is multiple knowledge transfers atdifferent time points. Taking into consideration the influential factors, such as theknowledge type, knowledge structure, knowledge absorptive capacity, knowledge updaterate, discount rate, market share, profit contributions of each type of knowledge, transfercosts, product life cycle and so on, time optimization models of multiple knowledgetransfers in the big data environment are presented by maximizing the total discountedexpected profits (DEPs) of an enterprise. Some simulation experiments have beenperformed to verify the validity of the models, and the models can help enterprisesdetermine the optimal time of multiple knowledge transfer in the big data environment.展开更多
Purpose:This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years.Design/m...Purpose:This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years.Design/methodology/approach:We collected publications on CRISPR between 2011 and2020 from the Web of Science,and traced all the patents citing them from lens.org.15,904 articles and 18,985 patents in total are downloaded and analyzed.The LDA model was applied to identify underlying research topics in related research.In addition,some indicators were introduced to measure the knowledge transfer from research topics of scientific publications to IPC-4 classes of patents.Findings:The emerging research topics on CRISPR were identified and their evolution over time displayed.Furthermore,a big picture of knowledge transition from research topics to technological classes of patents was presented.We found that for all topics on CRISPR,the average first transition year,the ratio of articles cited by patents,the NPR transition rate are respectively 1.08,15.57%,and 1.19,extremely shorter and more intensive than those of general fields.Moreover,the transition patterns are different among research topics.Research limitations:Our research is limited to publications retrieved from the Web of Science and their citing patents indexed in lens.org.A limitation inherent with LDA analysis is in the manual interpretation and labeling of"topics".Practical implications:Our study provides good references for policy-makers on allocating scientific resources and regulating financial budgets to face challenges related to the transformative technology of CRISPR.Originality/value:The LDA model here is applied to topic identification in the area of transformative researches for the first time,as exemplified on CRISPR.Additionally,the dataset of all citing patents in this area helps to provide a full picture to detect the knowledge transition between S&T.展开更多
A study on knowledge transfer in a mutli-agent organization is performed by applying the basic principle in physics such as the kinetic theory.Based on the theoretical analysis of the knowledge accumulation process an...A study on knowledge transfer in a mutli-agent organization is performed by applying the basic principle in physics such as the kinetic theory.Based on the theoretical analysis of the knowledge accumulation process and knowledge transfer attributes,a special type of knowledge field(KF)is introduced and the knowledge diffusion equation(KDE)is developed.The evolution of knowledge potential is modeled by lattice kinetic equation and verified by numerical experiments.The new equation-based modeling developed in this paper is meaningful to simulate and predict the knowledge transfer process in firms.The development of the lattice kinetic model(LKM)for knowledge transfer can contribute to the knowledge management theory,and the managers can also simulate the knowledge accumulation process by using the LKM.展开更多
In this paper, the reinforcement learning method for cooperative multi-agent systems(MAS) with incremental number of agents is studied. The existing multi-agent reinforcement learning approaches deal with the MAS with...In this paper, the reinforcement learning method for cooperative multi-agent systems(MAS) with incremental number of agents is studied. The existing multi-agent reinforcement learning approaches deal with the MAS with a specific number of agents, and can learn well-performed policies. However, if there is an increasing number of agents, the previously learned in may not perform well in the current scenario. The new agents need to learn from scratch to find optimal policies with others,which may slow down the learning speed of the whole team. To solve that problem, in this paper, we propose a new algorithm to take full advantage of the historical knowledge which was learned before, and transfer it from the previous agents to the new agents. Since the previous agents have been trained well in the source environment, they are treated as teacher agents in the target environment. Correspondingly, the new agents are called student agents. To enable the student agents to learn from the teacher agents, we first modify the input nodes of the networks for teacher agents to adapt to the current environment. Then, the teacher agents take the observations of the student agents as input, and output the advised actions and values as supervising information. Finally, the student agents combine the reward from the environment and the supervising information from the teacher agents, and learn the optimal policies with modified loss functions. By taking full advantage of the knowledge of teacher agents, the search space for the student agents will be reduced significantly, which can accelerate the learning speed of the holistic system. The proposed algorithm is verified in some multi-agent simulation environments, and its efficiency has been demonstrated by the experiment results.展开更多
In recent years,knowledge management(KM)theory has become an omnipresent and important element of organisational development.It includes processes intended to improve organisational effectiveness and it describes the ...In recent years,knowledge management(KM)theory has become an omnipresent and important element of organisational development.It includes processes intended to improve organisational effectiveness and it describes the convergence of people,processes,and systems.However,its application is limited to the development of technology for document repository and sharing.To promote new ways of approaching KM,this paper focuses on four knowledge topics:the use of human capital,social capital,structural capital,and artificial intelligence.Accepting that the four components of KM:people,processes,tools,and organisation,are interdependent,nested,and porous,then getting relevant knowledge to those who need it,when they need it,is critical for knowledge transfer.This paper considers whether the recovery of forgotten knowledge will create value for organisations.It proposes a new holistic framework to enhance the transferability of tacit and implicit knowledge in emergency relief organisations.It considers the application of artificial intelligence in the aid sector as a means of achieving this,and it proposes its use for providing ready-to-use knowledge for decision making in emergencies.Using a quantitative and qualitative research approach,this research resolves several ambiguities in the application of the KM discipline within emergency relief organisations.It found that there is no relationship between the employees’age and their attitude to communicating across organisational boundaries to exchange knowledge,yet age is a factor in the use of organisational social networks as a communication tool.Further,it found little difference in the way employees of various designations comprehend the human,structural,and social capital elements of an organisation,yet the importance,selection,and use of each of these elements is dependent on the employees’designation and/or position in the organisational hierarchy.Finally,it found that age is a key factor in the frequency of changing jobs,which contributes to the loss of tacit and implicit knowledge in aid organisations.This paper concludes by providing recommendations for action within each of the five knowledge sharing dimensions:individual,social,managerial,cultural,and structural.展开更多
Knowledge transfer(KT)from the consultant to the client is an important area that needs to be repeatedly addressed and thoroughly understood.The aim of this research was to examine the assumption that client character...Knowledge transfer(KT)from the consultant to the client is an important area that needs to be repeatedly addressed and thoroughly understood.The aim of this research was to examine the assumption that client characteristics and consultant competencies play a defining role in the effective transfer of knowledge to the client party.The authors examined the critical aspects and competencies required of the consultant,and the characteristics and attitudes required of the client,which would contribute to a successful transfer of knowledge,through unstructured in-depth interviews and concise questionnaires.Eighty consulting assignments were studied from both the client side and the consultant side.A conceptual model is presented,factor analysis was used to validate the constructs,and partial least squares were used to test the model.The findings showed that the consultants’professionalism,skills,and behavior were significant contributors to KT to the client.Surprisingly,neither the consultant knowledge nor client characteristics had any significance to the KT to the client.展开更多
Action recognition and localization in untrimmed videos is important for many applications and have attracted a lot of attention. Since full supervision with frame-level annotation places an overwhelming burden on man...Action recognition and localization in untrimmed videos is important for many applications and have attracted a lot of attention. Since full supervision with frame-level annotation places an overwhelming burden on manual labeling effort, learning with weak video-level supervision becomes a potential solution. In this paper, we propose a novel weakly supervised framework to recognize actions and locate the corresponding frames in untrimmed videos simultaneously. Considering that there are abundant trimmed videos publicly available and well-segmented with semantic descriptions, the instructive knowledge learned on trimmed videos can be fully leveraged to analyze untrimmed videos. We present an effective knowledge transfer strategy based on inter-class semantic relevance. We also take advantage of the self-attention mechanism to obtain a compact video representation, such that the influence of background frames can be effectively eliminated. A learning architecture is designed with twin networks for trimmed and untrimmed videos, to facilitate transferable self-attentive representation learning. Extensive experiments are conducted on three untrimmed benchmark datasets (i.e., THUMOS14, ActivityNet1.3, and MEXaction2), and the experimental results clearly corroborate the efficacy of our method. It is especially encouraging to see that the proposed weakly supervised method even achieves comparable results to some fully supervised methods.展开更多
Evolutionary game theory expands into a number of areas that go beyond the biological concept of evolution to include sociology,economics,and business management.Social networks determine definite interactions between...Evolutionary game theory expands into a number of areas that go beyond the biological concept of evolution to include sociology,economics,and business management.Social networks determine definite interactions between individuals in social settings.The common nature of these two broad areas of research generates interest in applying the approaches of evolutionary game theory to social network-based problems.Knowledge transfer that occurs in the process of social interaction improves a company's innovation capability.This paper attempts to explore ways in which networks relate to knowledge transfer on the basis of evolutionary game theory.We offer a simple mathematical model to examine the interaction of knowledge transfer and actor behavior in games of coordination.展开更多
In this paper,we consider to learn the inherent probability distribution of types via knowledge transfer in a two-player repeated Bayesian game,which is a basic model in network security.In the Bayesian game,the attac...In this paper,we consider to learn the inherent probability distribution of types via knowledge transfer in a two-player repeated Bayesian game,which is a basic model in network security.In the Bayesian game,the attacker's distribution of types is unknown by the defender and the defender aims to reconstruct the distribution with historical actions.lt is dificult to calculate the distribution of types directly since the distribution is coupled with a prediction function of the attacker in the game model.Thus,we seek help from an interrelated complete-information game,based on the idea of transfer learning.We provide two different methods to estimate the prediction function in difftrent concrete conditions with knowledge transfer.After obtaining the estimated prediction function,the deiender can decouple the inherent distribution and the prediction function in the Bayesian game,and moreover,reconstruct the distribution of the attacker's types.Finally,we give numerical examples to illustrate the effectiveness of our methods.展开更多
The importance of knowledge as a strategic asset for organizations has been recognized by both researchers and practitioners.To gain a competitive advantage,firms are required to effectively manage their knowledge res...The importance of knowledge as a strategic asset for organizations has been recognized by both researchers and practitioners.To gain a competitive advantage,firms are required to effectively manage their knowledge resources.The most central activity in managing knowledge is to ensure its transfer within and between organizations.Knowledge transfer(KT)has thus been recognized as a key component of the knowledge management processes.The purpose of this research is to provide a holistic view of the KT barriers and enablers within an organization,from a multilevel and process-based perspectives.We first review the extant literature to identify the key enablers and barriers to KT.Second,we develop a multilevel conceptualization of enablers and barriers that can influence KT at different levels–individual,team/exchange and organization.The proposed model improves current understanding of KT by offering a holistic and integrated view of enablers and barriers.展开更多
A project is a specific effort to create a unique product,so it is a favorable place for knowledge creation and development.Knowledge can be transferred inside and outside projects and their parent project-based organ...A project is a specific effort to create a unique product,so it is a favorable place for knowledge creation and development.Knowledge can be transferred inside and outside projects and their parent project-based organizations,thus affecting project performance and organizational competitiveness.However,the current research on the elements and outcomes of knowledge transfer(KT)in the project environment lacks completeness and clarity,and that on the different levels of KT is fragmented.This study aims to conduct comprehensive research to determine and link the elements and outcomes of KT in the project environment.The authors systematically analyzed the relevant literature from 2000 to 2021,which showed an increasing publication trend.They divided KT in the project environment into three levels according to the transfer scenario:Intra-project,cross-project,and cross-organizational KT.Five-dimensional transfer elements and two-dimensional transfer outcomes were then identified and analyzed from previous literature.Lastly,the relationships between the transfer elements and outcomes were gathered to create a comprehensive model.Importantly,the knowledge gap in the current literature was highlighted,and future research directions were put forward.This study builds a theoretical framework linking transfer elements to outcomes that can serve as a basis for scholars and practitioners to develop effective strategies for KT in the project environment.展开更多
In multi-agent reinforcement learning(MARL),the behaviors of each agent can influence the learning of others,and the agents have to search in an exponentially enlarged joint-action space.Hence,it is challenging for th...In multi-agent reinforcement learning(MARL),the behaviors of each agent can influence the learning of others,and the agents have to search in an exponentially enlarged joint-action space.Hence,it is challenging for the multi-agent teams to explore in the environment.Agents may achieve suboptimal policies and fail to solve some complex tasks.To improve the exploring efficiency as well as the performance of MARL tasks,in this paper,we propose a new approach by transferring the knowledge across tasks.Differently from the traditional MARL algorithms,we first assume that the reward functions can be computed by linear combinations of a shared feature function and a set of taskspecific weights.Then,we define a set of basic MARL tasks in the source domain and pre-train them as the basic knowledge for further use.Finally,once the weights for target tasks are available,it will be easier to get a well-performed policy to explore in the target domain.Hence,the learning process of agents for target tasks is speeded up by taking full use of the basic knowledge that was learned previously.We evaluate the proposed algorithm on two challenging MARL tasks:cooperative boxpushing and non-monotonic predator-prey.The experiment results have demonstrated the improved performance compared with state-of-the-art MARL algorithms.展开更多
Big data knowledge,such as customer demands and consumer preferences,is among the crucial external knowledge that firms need for new product development in the big data environment.Prior research has focused on the pr...Big data knowledge,such as customer demands and consumer preferences,is among the crucial external knowledge that firms need for new product development in the big data environment.Prior research has focused on the profit of big data knowledge providers rather than the profit and pricing schemes of knowledge recipients.This research addresses this theoretical gap and uses theoretical and numerical analysis to compare the profitability of two pricing schemes commonly used by knowledge recipients:subscription pricing and pay-per-use pricing.We find that:(1)the subscription price of big data knowledge has no effect on the optimal time of knowledge transaction in the same pricing scheme,but the usage ratio of the big data knowledge affects the optimal time of knowledge transaction,and the smaller the usage ratio of big data knowledge the earlier the big data knowledge transaction conducts;(2)big data knowledge with a higher update rate can bring greater profits to the firm both in subscription pricing scheme and pay-per-use pricing scheme;(3)a knowledge recipient will choose the knowledge that can bring a higher market share growth rate regardless of what price scheme it adopts,and firms can choose more efficient knowledge in the pay-per-use pricing scheme by adjusting the usage ratio of knowledge usage according to their economic conditions.The model and findings in this paper can help knowledge recipient firms select optimal pricing method and enhance future new product development performance.展开更多
The birth of a child is a pivotal time in the life of a mother,her family and society.The health and well-being of a mother and child at birth largely determines the future health and wellness of the entire family(Wor...The birth of a child is a pivotal time in the life of a mother,her family and society.The health and well-being of a mother and child at birth largely determines the future health and wellness of the entire family(World Health Organization(WHO),2005).Normal birth has enormous benefits for mothers,neonates,families,and societies.The growing supportive evidence for the promotion of normal birth certainly relies on multidisciplinary collaborations to continue spreading knowledge about the advantages of normal birth and enhancing the understanding of how knowledge about normal birth can change society.Knowledge about normal birth varies among different groups of healthcare professionals,and it would be useful to identify how it is clinically translated to become accessible to other professionals and research teams,consumers,the public,significant decision-or policy makers,the industry,funding bodies,and volunteer health teams.展开更多
Person re-identification is a prevalent technology deployed on intelligent surveillance.There have been remarkable achievements in person re-identification methods based on the assumption that all person images have a...Person re-identification is a prevalent technology deployed on intelligent surveillance.There have been remarkable achievements in person re-identification methods based on the assumption that all person images have a sufficiently high resolution,yet such models are not applicable to the open world.In real world,the changing distance between pedestrians and the camera renders the resolution of pedestrians captured by the camera inconsistent.When low-resolution(LR)images in the query set are matched with high-resolution(HR)images in the gallery set,it degrades the performance of the pedestrian matching task due to the absent pedestrian critical information in LR images.To address the above issues,we present a dualstream coupling network with wavelet transform(DSCWT)for the cross-resolution person re-identification task.Firstly,we use the multi-resolution analysis principle of wavelet transform to separately process the low-frequency and high-frequency regions of LR images,which is applied to restore the lost detail information of LR images.Then,we devise a residual knowledge constrained loss function that transfers knowledge between the two streams of LR images and HR images for accessing pedestrian invariant features at various resolutions.Extensive qualitative and quantitative experiments across four benchmark datasets verify the superiority of the proposed approach.展开更多
Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-obj...Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-objective evolutionary algorithms(CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking(EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front(PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.展开更多
In this paper, a deep learning-based method is proposed for crowdcountingproblems. Specifically, by utilizing the convolution kernel densitymap, the ground truth is generated dynamically to enhance the featureextracti...In this paper, a deep learning-based method is proposed for crowdcountingproblems. Specifically, by utilizing the convolution kernel densitymap, the ground truth is generated dynamically to enhance the featureextractingability of the generator model. Meanwhile, the “cross stage partial”module is integrated into congested scene recognition network (CSRNet) toobtain a lightweight network model. In addition, to compensate for the accuracydrop owing to the lightweight model, we take advantage of “structuredknowledge transfer” to train the model in an end-to-end manner. It aimsto accelerate the fitting speed and enhance the learning ability of the studentmodel. The crowd-counting system solution for edge computing is alsoproposed and implemented on an embedded device equipped with a neuralprocessing unit. Simulations demonstrate the performance improvement ofthe proposed solution in terms of model size, processing speed and accuracy.The performance on the Venice dataset shows that the mean absolute error(MAE) and the root mean squared error (RMSE) of our model drop by32.63% and 39.18% compared with CSRNet. Meanwhile, the performance onthe ShanghaiTech PartB dataset reveals that the MAE and the RMSE of ourmodel are close to those of CSRNet. Therefore, we provide a novel embeddedplatform system scheme for public safety pre-warning applications.展开更多
Objectives: We introduce a special form of the Generalized Poisson Distribution. The distribution has one parameter, yet it has a variance that is larger than the mean a phenomenon known as “over dispersion”. We dis...Objectives: We introduce a special form of the Generalized Poisson Distribution. The distribution has one parameter, yet it has a variance that is larger than the mean a phenomenon known as “over dispersion”. We discuss potential applications of the distribution as a model of counts, and under the assumption of independence we will perform statistical inference on the ratio of two means, with generalization to testing the homogeneity of several means. Methods: Bayesian methods depend on the choice of the prior distributions of the population parameters. In this paper, we describe a Bayesian approach for estimation and inference on the parameters of several independent Inflated Poisson (IPD) distributions with two possible priors, the first is the reciprocal of the square root of the Poisson parameter and the other is a conjugate Gamma prior. The parameters of Gamma distribution are estimated in the empirical Bayesian framework using the maximum likelihood (ML) solution using nonlinear mixed model (NLMIXED) in SAS. With these priors we construct the highest posterior confidence intervals on the ratio of two IPD parameters and test the homogeneity of several populations. Results: We encountered convergence problem in estimating the hyperparameters of the posterior distribution using the NLMIXED. However, direct maximization of the predictive density produced solutions to the maximum likelihood equations. We apply the methodologies to RNA-SEQ read count data of gene expression values.展开更多
基金supported in part by the Key-Area Research and Development Program of Guangdong Province (2020B010166006)the National Natural Science Foundation of China (61972102)+1 种基金the Guangzhou Science and Technology Plan Project (023A04J1729)the Science and Technology development fund (FDCT),Macao SAR (015/2020/AMJ)。
文摘Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by sampling.However,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of time.To address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain.Mathematical analyses show that DDS avoids repeated sampling during the iteration.With the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly reduced.In addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA algorithms.Extensive experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.
基金supported by the National Natural Science Foundation of China(62103104)the Natural Science Foundation of Jiangsu Province(BK20210215)the China Postdoctoral Science Foundation(2021M690615).
文摘In this paper,we study scene image recognition with knowledge transfer for drone navigation.We divide navigation scenes into three macro-classes,namely outdoor special scenes(OSSs),the space from indoors to outdoors or from outdoors to indoors transitional scenes(TSs),and others.However,there are difficulties in how to recognize the TSs,to this end,we employ deep convolutional neural network(CNN)based on knowledge transfer,techniques for image augmentation,and fine tuning to solve the issue.Moreover,there is still a novelty detection prob-lem in the classifier,and we use global navigation satellite sys-tems(GNSS)to solve it in the prediction stage.Experiment results show our method,with a pre-trained model and fine tun-ing,can achieve 91.3196%top-1 accuracy on Scenes21 dataset,paving the way for drones to learn to understand the scenes around them autonomously.
基金supported by the National Natural Science Foundation ofChina (Grant No. 71704016,71331008, 71402010)the Natural Science Foundation of HunanProvince (Grant No. 2017JJ2267)+1 种基金the Educational Economy and Financial Research Base ofHunan Province (Grant No. 13JCJA2)the Project of China Scholarship Council forOverseas Studies (201508430121, 201208430233).
文摘In the big data environment, enterprises must constantly assimilate big dataknowledge and private knowledge by multiple knowledge transfers to maintain theircompetitive advantage. The optimal time of knowledge transfer is one of the mostimportant aspects to improve knowledge transfer efficiency. Based on the analysis of thecomplex characteristics of knowledge transfer in the big data environment, multipleknowledge transfers can be divided into two categories. One is the simultaneous transferof various types of knowledge, and the other one is multiple knowledge transfers atdifferent time points. Taking into consideration the influential factors, such as theknowledge type, knowledge structure, knowledge absorptive capacity, knowledge updaterate, discount rate, market share, profit contributions of each type of knowledge, transfercosts, product life cycle and so on, time optimization models of multiple knowledgetransfers in the big data environment are presented by maximizing the total discountedexpected profits (DEPs) of an enterprise. Some simulation experiments have beenperformed to verify the validity of the models, and the models can help enterprisesdetermine the optimal time of multiple knowledge transfer in the big data environment.
基金supported by the National Natural Science Foundation of China,Grant numbers:71974167 and 71573225。
文摘Purpose:This study explores the underlying research topics regarding CRISPR based on the LDA model and figures out trends in knowledge transfer from science to technology in this area over the latest 10 years.Design/methodology/approach:We collected publications on CRISPR between 2011 and2020 from the Web of Science,and traced all the patents citing them from lens.org.15,904 articles and 18,985 patents in total are downloaded and analyzed.The LDA model was applied to identify underlying research topics in related research.In addition,some indicators were introduced to measure the knowledge transfer from research topics of scientific publications to IPC-4 classes of patents.Findings:The emerging research topics on CRISPR were identified and their evolution over time displayed.Furthermore,a big picture of knowledge transition from research topics to technological classes of patents was presented.We found that for all topics on CRISPR,the average first transition year,the ratio of articles cited by patents,the NPR transition rate are respectively 1.08,15.57%,and 1.19,extremely shorter and more intensive than those of general fields.Moreover,the transition patterns are different among research topics.Research limitations:Our research is limited to publications retrieved from the Web of Science and their citing patents indexed in lens.org.A limitation inherent with LDA analysis is in the manual interpretation and labeling of"topics".Practical implications:Our study provides good references for policy-makers on allocating scientific resources and regulating financial budgets to face challenges related to the transformative technology of CRISPR.Originality/value:The LDA model here is applied to topic identification in the area of transformative researches for the first time,as exemplified on CRISPR.Additionally,the dataset of all citing patents in this area helps to provide a full picture to detect the knowledge transition between S&T.
基金supported by the National Natural Science Foundation of China(71472055 71871007)+2 种基金National Social Science Foundation of China(16AZD0006)Heilongjiang Philosophy and Social Science Research Project(19GLB087)the Fundamental Research Funds for the Central Universities(HIT.NSRIF.2019033)
文摘A study on knowledge transfer in a mutli-agent organization is performed by applying the basic principle in physics such as the kinetic theory.Based on the theoretical analysis of the knowledge accumulation process and knowledge transfer attributes,a special type of knowledge field(KF)is introduced and the knowledge diffusion equation(KDE)is developed.The evolution of knowledge potential is modeled by lattice kinetic equation and verified by numerical experiments.The new equation-based modeling developed in this paper is meaningful to simulate and predict the knowledge transfer process in firms.The development of the lattice kinetic model(LKM)for knowledge transfer can contribute to the knowledge management theory,and the managers can also simulate the knowledge accumulation process by using the LKM.
基金supported by the National Key R&D Program of China (2018AAA0101400)the National Natural Science Foundation of China (62173251+3 种基金61921004U1713209)the Natural Science Foundation of Jiangsu Province of China (BK20202006)the Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control。
文摘In this paper, the reinforcement learning method for cooperative multi-agent systems(MAS) with incremental number of agents is studied. The existing multi-agent reinforcement learning approaches deal with the MAS with a specific number of agents, and can learn well-performed policies. However, if there is an increasing number of agents, the previously learned in may not perform well in the current scenario. The new agents need to learn from scratch to find optimal policies with others,which may slow down the learning speed of the whole team. To solve that problem, in this paper, we propose a new algorithm to take full advantage of the historical knowledge which was learned before, and transfer it from the previous agents to the new agents. Since the previous agents have been trained well in the source environment, they are treated as teacher agents in the target environment. Correspondingly, the new agents are called student agents. To enable the student agents to learn from the teacher agents, we first modify the input nodes of the networks for teacher agents to adapt to the current environment. Then, the teacher agents take the observations of the student agents as input, and output the advised actions and values as supervising information. Finally, the student agents combine the reward from the environment and the supervising information from the teacher agents, and learn the optimal policies with modified loss functions. By taking full advantage of the knowledge of teacher agents, the search space for the student agents will be reduced significantly, which can accelerate the learning speed of the holistic system. The proposed algorithm is verified in some multi-agent simulation environments, and its efficiency has been demonstrated by the experiment results.
文摘In recent years,knowledge management(KM)theory has become an omnipresent and important element of organisational development.It includes processes intended to improve organisational effectiveness and it describes the convergence of people,processes,and systems.However,its application is limited to the development of technology for document repository and sharing.To promote new ways of approaching KM,this paper focuses on four knowledge topics:the use of human capital,social capital,structural capital,and artificial intelligence.Accepting that the four components of KM:people,processes,tools,and organisation,are interdependent,nested,and porous,then getting relevant knowledge to those who need it,when they need it,is critical for knowledge transfer.This paper considers whether the recovery of forgotten knowledge will create value for organisations.It proposes a new holistic framework to enhance the transferability of tacit and implicit knowledge in emergency relief organisations.It considers the application of artificial intelligence in the aid sector as a means of achieving this,and it proposes its use for providing ready-to-use knowledge for decision making in emergencies.Using a quantitative and qualitative research approach,this research resolves several ambiguities in the application of the KM discipline within emergency relief organisations.It found that there is no relationship between the employees’age and their attitude to communicating across organisational boundaries to exchange knowledge,yet age is a factor in the use of organisational social networks as a communication tool.Further,it found little difference in the way employees of various designations comprehend the human,structural,and social capital elements of an organisation,yet the importance,selection,and use of each of these elements is dependent on the employees’designation and/or position in the organisational hierarchy.Finally,it found that age is a key factor in the frequency of changing jobs,which contributes to the loss of tacit and implicit knowledge in aid organisations.This paper concludes by providing recommendations for action within each of the five knowledge sharing dimensions:individual,social,managerial,cultural,and structural.
文摘Knowledge transfer(KT)from the consultant to the client is an important area that needs to be repeatedly addressed and thoroughly understood.The aim of this research was to examine the assumption that client characteristics and consultant competencies play a defining role in the effective transfer of knowledge to the client party.The authors examined the critical aspects and competencies required of the consultant,and the characteristics and attitudes required of the client,which would contribute to a successful transfer of knowledge,through unstructured in-depth interviews and concise questionnaires.Eighty consulting assignments were studied from both the client side and the consultant side.A conceptual model is presented,factor analysis was used to validate the constructs,and partial least squares were used to test the model.The findings showed that the consultants’professionalism,skills,and behavior were significant contributors to KT to the client.Surprisingly,neither the consultant knowledge nor client characteristics had any significance to the KT to the client.
基金supported by National Natural Science Foundation of China(Nos.61871378,U2003111,62122013 and U2001211).
文摘Action recognition and localization in untrimmed videos is important for many applications and have attracted a lot of attention. Since full supervision with frame-level annotation places an overwhelming burden on manual labeling effort, learning with weak video-level supervision becomes a potential solution. In this paper, we propose a novel weakly supervised framework to recognize actions and locate the corresponding frames in untrimmed videos simultaneously. Considering that there are abundant trimmed videos publicly available and well-segmented with semantic descriptions, the instructive knowledge learned on trimmed videos can be fully leveraged to analyze untrimmed videos. We present an effective knowledge transfer strategy based on inter-class semantic relevance. We also take advantage of the self-attention mechanism to obtain a compact video representation, such that the influence of background frames can be effectively eliminated. A learning architecture is designed with twin networks for trimmed and untrimmed videos, to facilitate transferable self-attentive representation learning. Extensive experiments are conducted on three untrimmed benchmark datasets (i.e., THUMOS14, ActivityNet1.3, and MEXaction2), and the experimental results clearly corroborate the efficacy of our method. It is especially encouraging to see that the proposed weakly supervised method even achieves comparable results to some fully supervised methods.
文摘Evolutionary game theory expands into a number of areas that go beyond the biological concept of evolution to include sociology,economics,and business management.Social networks determine definite interactions between individuals in social settings.The common nature of these two broad areas of research generates interest in applying the approaches of evolutionary game theory to social network-based problems.Knowledge transfer that occurs in the process of social interaction improves a company's innovation capability.This paper attempts to explore ways in which networks relate to knowledge transfer on the basis of evolutionary game theory.We offer a simple mathematical model to examine the interaction of knowledge transfer and actor behavior in games of coordination.
基金This work was supported by the National Key Research and Development Program(No.2016YFB0901900)the National Natural Science Foundation of China(No.61733018)The authors would like to thank Prof.Peng Yi for his helpful suggestions.
文摘In this paper,we consider to learn the inherent probability distribution of types via knowledge transfer in a two-player repeated Bayesian game,which is a basic model in network security.In the Bayesian game,the attacker's distribution of types is unknown by the defender and the defender aims to reconstruct the distribution with historical actions.lt is dificult to calculate the distribution of types directly since the distribution is coupled with a prediction function of the attacker in the game model.Thus,we seek help from an interrelated complete-information game,based on the idea of transfer learning.We provide two different methods to estimate the prediction function in difftrent concrete conditions with knowledge transfer.After obtaining the estimated prediction function,the deiender can decouple the inherent distribution and the prediction function in the Bayesian game,and moreover,reconstruct the distribution of the attacker's types.Finally,we give numerical examples to illustrate the effectiveness of our methods.
文摘The importance of knowledge as a strategic asset for organizations has been recognized by both researchers and practitioners.To gain a competitive advantage,firms are required to effectively manage their knowledge resources.The most central activity in managing knowledge is to ensure its transfer within and between organizations.Knowledge transfer(KT)has thus been recognized as a key component of the knowledge management processes.The purpose of this research is to provide a holistic view of the KT barriers and enablers within an organization,from a multilevel and process-based perspectives.We first review the extant literature to identify the key enablers and barriers to KT.Second,we develop a multilevel conceptualization of enablers and barriers that can influence KT at different levels–individual,team/exchange and organization.The proposed model improves current understanding of KT by offering a holistic and integrated view of enablers and barriers.
基金The study is funded by the National Natural Science Foundation of China(Grant Nos.72171048,72101053,and 71771052)the Humanities and Social Science Project of Ministry of Education of China(Grant No.21YJCZH008).
文摘A project is a specific effort to create a unique product,so it is a favorable place for knowledge creation and development.Knowledge can be transferred inside and outside projects and their parent project-based organizations,thus affecting project performance and organizational competitiveness.However,the current research on the elements and outcomes of knowledge transfer(KT)in the project environment lacks completeness and clarity,and that on the different levels of KT is fragmented.This study aims to conduct comprehensive research to determine and link the elements and outcomes of KT in the project environment.The authors systematically analyzed the relevant literature from 2000 to 2021,which showed an increasing publication trend.They divided KT in the project environment into three levels according to the transfer scenario:Intra-project,cross-project,and cross-organizational KT.Five-dimensional transfer elements and two-dimensional transfer outcomes were then identified and analyzed from previous literature.Lastly,the relationships between the transfer elements and outcomes were gathered to create a comprehensive model.Importantly,the knowledge gap in the current literature was highlighted,and future research directions were put forward.This study builds a theoretical framework linking transfer elements to outcomes that can serve as a basis for scholars and practitioners to develop effective strategies for KT in the project environment.
基金the National Key R&D Program of China(2021ZD0112700,2018AAA0101400)the National Natural Science Foundation of China(62173251,61921004,U1713209)the Natural Science Foundation of Jiangsu Province of China(BK20202006)。
文摘In multi-agent reinforcement learning(MARL),the behaviors of each agent can influence the learning of others,and the agents have to search in an exponentially enlarged joint-action space.Hence,it is challenging for the multi-agent teams to explore in the environment.Agents may achieve suboptimal policies and fail to solve some complex tasks.To improve the exploring efficiency as well as the performance of MARL tasks,in this paper,we propose a new approach by transferring the knowledge across tasks.Differently from the traditional MARL algorithms,we first assume that the reward functions can be computed by linear combinations of a shared feature function and a set of taskspecific weights.Then,we define a set of basic MARL tasks in the source domain and pre-train them as the basic knowledge for further use.Finally,once the weights for target tasks are available,it will be easier to get a well-performed policy to explore in the target domain.Hence,the learning process of agents for target tasks is speeded up by taking full use of the basic knowledge that was learned previously.We evaluate the proposed algorithm on two challenging MARL tasks:cooperative boxpushing and non-monotonic predator-prey.The experiment results have demonstrated the improved performance compared with state-of-the-art MARL algorithms.
基金This research was funded by(the National Natural Science Foundation of China)Grant Number(71704016),(the Key Scientific Research Fund of Hunan Provincial Education Department of China)Grant Number(19A006),and(the Enterprise Strategic Management and Investment Decision Research Base of Hunan Province)Grant Number(19qyzd03).
文摘Big data knowledge,such as customer demands and consumer preferences,is among the crucial external knowledge that firms need for new product development in the big data environment.Prior research has focused on the profit of big data knowledge providers rather than the profit and pricing schemes of knowledge recipients.This research addresses this theoretical gap and uses theoretical and numerical analysis to compare the profitability of two pricing schemes commonly used by knowledge recipients:subscription pricing and pay-per-use pricing.We find that:(1)the subscription price of big data knowledge has no effect on the optimal time of knowledge transaction in the same pricing scheme,but the usage ratio of the big data knowledge affects the optimal time of knowledge transaction,and the smaller the usage ratio of big data knowledge the earlier the big data knowledge transaction conducts;(2)big data knowledge with a higher update rate can bring greater profits to the firm both in subscription pricing scheme and pay-per-use pricing scheme;(3)a knowledge recipient will choose the knowledge that can bring a higher market share growth rate regardless of what price scheme it adopts,and firms can choose more efficient knowledge in the pay-per-use pricing scheme by adjusting the usage ratio of knowledge usage according to their economic conditions.The model and findings in this paper can help knowledge recipient firms select optimal pricing method and enhance future new product development performance.
文摘The birth of a child is a pivotal time in the life of a mother,her family and society.The health and well-being of a mother and child at birth largely determines the future health and wellness of the entire family(World Health Organization(WHO),2005).Normal birth has enormous benefits for mothers,neonates,families,and societies.The growing supportive evidence for the promotion of normal birth certainly relies on multidisciplinary collaborations to continue spreading knowledge about the advantages of normal birth and enhancing the understanding of how knowledge about normal birth can change society.Knowledge about normal birth varies among different groups of healthcare professionals,and it would be useful to identify how it is clinically translated to become accessible to other professionals and research teams,consumers,the public,significant decision-or policy makers,the industry,funding bodies,and volunteer health teams.
基金supported by the National Natural Science Foundation of China(61471154,61876057)the Key Research and Development Program of Anhui Province-Special Project of Strengthening Science and Technology Police(202004D07020012).
文摘Person re-identification is a prevalent technology deployed on intelligent surveillance.There have been remarkable achievements in person re-identification methods based on the assumption that all person images have a sufficiently high resolution,yet such models are not applicable to the open world.In real world,the changing distance between pedestrians and the camera renders the resolution of pedestrians captured by the camera inconsistent.When low-resolution(LR)images in the query set are matched with high-resolution(HR)images in the gallery set,it degrades the performance of the pedestrian matching task due to the absent pedestrian critical information in LR images.To address the above issues,we present a dualstream coupling network with wavelet transform(DSCWT)for the cross-resolution person re-identification task.Firstly,we use the multi-resolution analysis principle of wavelet transform to separately process the low-frequency and high-frequency regions of LR images,which is applied to restore the lost detail information of LR images.Then,we devise a residual knowledge constrained loss function that transfers knowledge between the two streams of LR images and HR images for accessing pedestrian invariant features at various resolutions.Extensive qualitative and quantitative experiments across four benchmark datasets verify the superiority of the proposed approach.
基金supported in part by the National Natural Science Fund for Outstanding Young Scholars of China (61922072)the National Natural Science Foundation of China (62176238, 61806179, 61876169, 61976237)+2 种基金China Postdoctoral Science Foundation (2020M682347)the Training Program of Young Backbone Teachers in Colleges and Universities in Henan Province (2020GGJS006)Henan Provincial Young Talents Lifting Project (2021HYTP007)。
文摘Constrained multi-objective optimization problems(CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers.To solve CMOPs, constrained multi-objective evolutionary algorithms(CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking(EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front(PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.
文摘In this paper, a deep learning-based method is proposed for crowdcountingproblems. Specifically, by utilizing the convolution kernel densitymap, the ground truth is generated dynamically to enhance the featureextractingability of the generator model. Meanwhile, the “cross stage partial”module is integrated into congested scene recognition network (CSRNet) toobtain a lightweight network model. In addition, to compensate for the accuracydrop owing to the lightweight model, we take advantage of “structuredknowledge transfer” to train the model in an end-to-end manner. It aimsto accelerate the fitting speed and enhance the learning ability of the studentmodel. The crowd-counting system solution for edge computing is alsoproposed and implemented on an embedded device equipped with a neuralprocessing unit. Simulations demonstrate the performance improvement ofthe proposed solution in terms of model size, processing speed and accuracy.The performance on the Venice dataset shows that the mean absolute error(MAE) and the root mean squared error (RMSE) of our model drop by32.63% and 39.18% compared with CSRNet. Meanwhile, the performance onthe ShanghaiTech PartB dataset reveals that the MAE and the RMSE of ourmodel are close to those of CSRNet. Therefore, we provide a novel embeddedplatform system scheme for public safety pre-warning applications.
文摘Objectives: We introduce a special form of the Generalized Poisson Distribution. The distribution has one parameter, yet it has a variance that is larger than the mean a phenomenon known as “over dispersion”. We discuss potential applications of the distribution as a model of counts, and under the assumption of independence we will perform statistical inference on the ratio of two means, with generalization to testing the homogeneity of several means. Methods: Bayesian methods depend on the choice of the prior distributions of the population parameters. In this paper, we describe a Bayesian approach for estimation and inference on the parameters of several independent Inflated Poisson (IPD) distributions with two possible priors, the first is the reciprocal of the square root of the Poisson parameter and the other is a conjugate Gamma prior. The parameters of Gamma distribution are estimated in the empirical Bayesian framework using the maximum likelihood (ML) solution using nonlinear mixed model (NLMIXED) in SAS. With these priors we construct the highest posterior confidence intervals on the ratio of two IPD parameters and test the homogeneity of several populations. Results: We encountered convergence problem in estimating the hyperparameters of the posterior distribution using the NLMIXED. However, direct maximization of the predictive density produced solutions to the maximum likelihood equations. We apply the methodologies to RNA-SEQ read count data of gene expression values.
