Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation

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.

Scene image recognition with knowledge transfer for drone navigation

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.

Progress and Knowledge Transfer from Science to Technology in the Research Frontier of CRISPR Based on the LDA Model

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.

Time Optimization of Multiple Knowledge Transfers in the Big Data Environment

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.

Knowledge transfer in multi-agent reinforcement learning with incremental number of agents

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.

Knowledge exchange:A strategy for open access success at The University of Hong Kong

The University of Hong Kong’s statement on vision now has three themes:1) Research, 2) teaching & learning, and 3) knowledge exchange(KE). KE emphasizes HKU’s desire to interact with its community for a mutual benefit. A new five-year strategic plan(2009-2014) sets out operational priorities and key indicators to enable knowledge exchange at HKU. Chief among these is the establishment of an exchange hub to make HKU researchers and their research products highly visible. The institutional repository of HKU, the HKU Scholars Hub, developed by its University Libraries, has become this KE exchange hub. Now the Hub includes HKU ResearcherPages, featuring the accomplishments of each HKU professoriate staff. HKU’s policy on knowledge exchange and the HKU ResearcherPages have increased the incentive for faculties, departments, and authors to place more items in open access(OA). This paper will discuss what KE is, the benefits it can bring to the university and its reputation, and how it can increase OA deposit.

Transformation and influencing factors of scholarly communication based on knowledge transfer: A case study of science and technology literature

Purpose: The process of scientific literature use can be regarded as that of knowledge transfer. With the help of the knowledge transfer theory and data from scientific literature databases, we explored the behavior of scientific researchers during their scholarly communication, and studied the factors that influenced the behavior of researchers under network environment. Design/methodology/approach: Based on the literature databases of CNKI, Elsevier Science Direct and Springer Link, we used the knowledge transfer theory to construct a model for describing the scholarly communication process, which attempts to find out factors that may influence the communication behavior of researchers. With a focus laid on the absorption behavior of researchers during the knowledge acceptance process, we defined the independent variables of the model and proposed hypotheses on the basis of a comprehensive literature study. Afterwards, college students were invited to participate in a questionnaire survey, which was designed to prove our research model and hypotheses.Findings: Our results showed that during the scholarly communication, it is not the professional knowledge, but the ability and willingness for knowledge acceptance, organizations’ importance and internal atmosphere as well as knowledge authority and relevance that have played a positive significant role in the knowledge transfer performance. In addition, our distance indicators showed that knowledge distance and knowledge transfer performance have significant negative correlations. Research limitations: This study is mainly based on a questionnaire survey of college students, which may limit the generalization of our research results. In addition, more resource types need be considered for further studies.Practical implications: Under network environment, scholarly communication performance based on knowledge transfer theory could greatly contribute to the enrichment of the contentof the knowledge transfer theory, and stretch out the range of the field. In addition, our result could help commercial scientific database providers to learn more about the users’ needs, which would not only benefit both scientific communities and content providers, but also promote scholarly communication effectively. Originality/value: Compared with existing researches which mainly emphasized the model construction of scholarly communication, our study focused the knowledge relevance during the scholarly communication and influence factors that impacted on the performance of knowledge acceptance under the network environment, which could provide helpful guides for further studies.

Modeling the factors that influence knowledge transfer in mergers and acquisitions

This paper constructs a model on the factors that influence knowledge transfer in mergers and acquisitions(M&A) and validates it via questionnaire surveys. Using 125valid collected questionnaires, multiple linear regression analysis and hierarchical regression analysis showed that five out of the ten factors had a positive effect on knowledge transfer effect. The ranking of factor importance, from high to low, was knowledge explicitness, relationship quality, learning intent, advanced transfer activities, and learning capability, which is fairly consistent with positive factors observed in other interorganizational knowledge transfer researches. Our results also showed that one of the control variables(size of acquired firm) had neither a direct or indirect effect on knowledge transfer in M&A. Additionally, our research found that knowledge distance and degree of M&A integration had a positive influence on knowledge transfer effect at the early stage after M&A, but had a negative influence at the late stage. Based on this research, several suggestions for knowledge transfer in M&A are proposed.

Big Data Knowledge Pricing Schemes for Knowledge Recipient Firms

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.

A model for knowledge transfer in a multi-agent organization based on lattice kinetic model

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.

Intra-firm Horizontal Knowledge Transfer Management

Knowledge transfer is widely emphasized as a strategic issue for firm competition. A model for intra-firm horizontal knowledge transfer is proposed to model horizontal knowledge transfer to solve some demerits in current knowledge transfer researches. The concept model of intra-firm horizontal knowledge transfer was described and a framework was provided to define the main components of the transfer process. Horizontal knowledge transfer is that knowledge is transferred from the source to the same hierarchical level recipients as the target. Horizontal knowledge transfer constitutes a strategic area of knowledge management research. However, little is known about the circumstances under which one particular mechanism is the most appropriate. To address these issues, some significant conclusions are drawn concerning knowledge transfer mechanisms in a real-world setting.

Dissemination and Enrichment of Knowledge About Normal Birth to Bring About a Change to Society

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.

Knowledge Transfer Framework for Managing Knowledge in Relief Organisations

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.

Knowledge Transfer and Management Consulting:The Effect of Consultant and Client Characteristics

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.

knowledgeTrans fer

WHEN Xiao Renrong＇s phone rang in the middle of the day. he found the caller beside himself. ＂Please come immediately,＂ the farmer begged. ＂it＇s an emergency.＂

Dual-stream coupling network with wavelet transform for cross-resolution person re-identification

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.

A Deep Learning-Based Crowd Counting Method and System Implementation on Neural Processing Unit Platform

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.

Evolutionary Multitasking With Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization

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.

Empirical Bayesian Approach to Testing Homogeneity of Several Means of Inflated Poisson Distributions (IPD)

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.

Efficient Exploration for Multi-Agent Reinforcement Learning via Transferable Successor Features

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.