Search

Integrating knowledge across different domains is an essential feature of human learning. Learning paradigms such as transfer learning, meta-learning, and multi-task learning reflect the human learning process by exploiting the prior knowledge for new tasks, encouraging faster learning and good generalization for new tasks. This article gives a detailed view of these learning paradigms and their comparative analysis. The weakness of one learning algorithm turns out to be a strength of another, and thus, merging them is a prevalent trait in the literature. Numerous research papers focus on each of these learning paradigms separately and provide a comprehensive overview of them. However, this article reviews research studies that combine (two of) these learning algorithms. This survey describes how these techniques are combined to solve problems in many different fields of research, including computer vision, natural language processing, hyper-spectral imaging, and many more, in a supervised setting only. Based on the knowledge accumulated from the literature, we hypothesize a generic task-agnostic and model-agnostic learning network – an ensemble of meta-learning, transfer learning, and multi-task learning, termed Multi-modal Multi-task Meta Transfer Learning. We also present some open research questions, limitations, and future research directions for this proposed network. The aim of this article is to spark interest among scholars in effectively merging existing learning algorithms with the intention of advancing research in this field. Instead of presenting experimental results, we invite readers to explore and contemplate techniques for merging algorithms while navigating through their limitations.

This paper mainly discusses the influence of organizational social network on enterprise collaborative innovation, as well as the intermediary role of knowledge sharing and the regulatory role of digital construction. Based on social exchange theory and social cognition theory, an integration model of organizational social network influencing enterprise collaborative innovation is constructed. The hierarchical regression analysis was used to sample the high-tech enterprises in China’s Yangtze River Delta. Statistical analysis of 212 questionnaires was performed using SPSS and AMOS. It is found that organizational social network has a significant positive impact on enterprise collaborative innovation. Knowledge sharing plays a partially mediating role in the relationship between the organizational social network and enterprise collaborative innovation. Digital construction positively moderates the relationship between organizational social network and enterprise collaborative innovation. In general, In general, the research findings show that improving the construction of organizational social networks and enhancing employees’ knowledge sharing behavior can improve enterprises’ collaborative innovation ability. In addition, in the context of highly digital construction, the effect of organizational social networks on improving enterprise collaborative innovation is more obvious.

Asking and answering questions are common activities in both the workplace and everyday life. Knowledge-sharing websites have become a popular resource for obtaining instant and searchable answers. However, users of these sites may encounter challenges in acquiring timely and appropriate content from user-provided answers owing to factors such as limited expertise, spam, and time constraints. Identifying trustworthy experts who can provide relevant and reliable answers in knowledge-sharing communities is crucial to overcome this issue. In this study, we propose a solution to the problem of identifying credible experts on knowledge-sharing sites by introducing the CredibleExpertRank algorithm. Our algorithm calculates a CredibleExpert score based on two main factors: activity and credibility. The credibility score is determined by analyzing users’ interactions related to questioning, answering, recommending, and mining users’ opinions, while the activity score reflects the user’s level of participation on the platform. We conducted experiments to evaluate the performance of the CredibleExpertRank algorithm, using user satisfaction measures for answers to given questions. Our findings confirmed that the credible experts identified by our algorithm provided more relevant and timely answers compared to other ordinary users. The timely nature of the credible experts’ answers was due to the reflection of their activity factor, while the superior performance in relevance was attributed to the high recommendation rate of their answers and positive evaluations received from opinion mining results. Our study undertakes an extensive investigation focused on the identification and prioritization of credible experts, revealing their profound advantages in significantly enhancing the overall quality of knowledge-sharing platforms. We proposed the CredibleExpertRank algorithm as a powerful method for effectively identifying trustworthy experts and giving priority to their answers. Through a meticulous process of experimental evaluation, we provide compelling evidence that this approach leads to substantial improvements in both search efficiency and reliability on knowledge-sharing sites. By highlighting the potential benefits derived from the identification of credible experts, our study underscores their pivotal role in elevating the overall performance of knowledge-sharing platforms.

This study examines the relationship between virtual team performance and its critical explaining factors, i.e., individual, team, organizational, and technological factors, by exploring the mediating effect of knowledge sharing. For this purpose, a conceptual framework is proposed based on an in-depth Systematic Literature Review (SLR) analysis. Next, data is collected through a survey from 252 IT practitioners working in Pakistan’s small and medium-sized enterprises (SMEs). Statistical tests on collected data are applied in statistical package for social sciences (SPSS) and smart partial least squares (SmartPLS) to evaluate the proposed conceptual framework empirically. Finally, 19 industrial and 5 academic experts verified the framework. There is a consensus among researchers and practitioners regarding the factors that contribute to the explanation of knowledge sharing and virtual team performance. Thus, the current study’s outcome may facilitate the management of software organizations to improvise their knowledge sharing infrastructure, make the development process more efficient, avoid project failure and increase the productivity of virtual teams.

An evolving set of skills that enable DevOps practice adoption in software-intensive Small and Medium Sized Enterprises (SMEs) contexts has emerged over the last decade intending to achieve a change in mentality, skillset, and toolset. This research examines an unexplored aspect of DevOps: business and IT alignment. Despite the widespread use of DevOps principles, very few research have investigated their interrelationships with existing theories. This study presents a novel perspective by including control variables such as firm size, background, and experience within the specific setting of Sri Lanka. The current body of literature extensively examines the practical elements associated with the adoption of DevOps, the development of skills, and the implementation of tools. However, there is a notable deficiency in comprehending the intersection between these fundamental DevOps principles and factors such as company size, individual backgrounds, and diverse experience levels. The primary objective of this study is to address the existing gap in knowledge by conducting a comprehensive investigation of the influence of firm size, background, and experience on the level of alignment between business and IT functions within SMEs in Sri Lanka that have adopted DevOps practices. First, 248 people participated in a survey conducted as exploratory research. The DevOps IT alignment was tested using partial least squares structural equation modelling. Moreover, it supports the creation of a reliable and credible DevOps instrument for scientific research. DevOps is promoted in SMEs to facilitate continuous information and software communication integration across IT departments. DevOps also has a beneficial effect on business-IT alignment. Greater business IT alignment is achieved via improved information exchange and continuous application integration at enterprises that foster DevOps. IT leaders must determine how to strengthen ties across departments that rely on radically different skill sets.

This article assessed the relationship between psychological empowerment and project success through direct and indirect effects of knowledge sharing. The assumptions outlined in this article were built on the self-determination theory (SDT). In this article, two surveys were conducted to test the proposed hypotheses. Data for study 1 were collected from employees (N = 168) working in the Pakistani telecommunication sector, while data for study 2 were gathered from (N = 160) employees working on development sector projects. Time-lagged design was employed to minimize common method bias. As a result, psychological empowerment emerged as a critical factor for the project success both directly and indirectly via knowledge sharing. Hence, this article recommends several theoretical and practical implications for practitioners and academicians. [ABSTRACT FROM AUTHOR]

Green technology innovation (GTI) requires a large capital investment, while the role of these capital investments in promoting GTI needs to be further confirmed. To improve GTI, university-industry alliances (U-Is) in green innovation ecosystems engage in knowledge-sharing behaviors and form different knowledge-sharing strategies based on changes in cooperation modes. Employing a differential game, this study explores the utility of multichannel funding for innovation revenues in different cooperative modes of U-Is and the impact on revenue distributions. This article considers three game models in five cases: Nash noncooperative game with no multichannel funding, Nash noncooperative game with external funding but no government subsidies, Nash noncooperative game with multichannel funding, Stackelberg game, and cooperative game. Solving the game model and applying the numerical analysis results in certain interesting conclusions. Our research finds that, first, in the cooperative game, the strongest willingness to share knowledge occurs in the university-industry alliance, in which the total revenues of both parties reach the Pareto optimum. Second, multichannel funding can serve as an incentive mechanism for enterprises and universities to improve the knowledge-sharing willingness, the GTI level, and the revenues of the two players, while the utility of the multichannel funding is strongest in the cooperative game. In addition, in the Stackelberg game, enterprises share subsidies with universities, which stimulates their willingness to share knowledge, and both parties’ revenues are better than they are in the three cases of noncooperation. Eventually, the revenue-sharing ratio of the enterprise has a smaller threshold, and the university can share more benefits relative to the absence of the multichannel funding, which helps balance the U-I in the green innovation ecosystem. These conclusions make a substantial contribution to the selection of cooperation modes and the formulation of revenues distribution contracts in university-industry alliances.

Knowledge management (KM) comprises several processes, and one of the most important is the knowledge sharing activities. The ability of an organization to manage its organizational knowledge, specifically in the context of knowledge sharing, may enhance the organization’s overall performance. Various approaches and technologies have been introduced to assist the process in achieving that target. Ontology as one of the knowledge representation methods has been becoming popular to assist knowledge sharing in the organization. Previous reviews have mainly focused on general KM issues, with little emphasis on the use of ontology in knowledge sharing. Thus, this article reviews several ontology-based KM tools that can support knowledge-sharing activities to provide some insight into future research in this area. Thirteen ontology-based KM tools were reviewed using ten elements’ comparison criteria: the motivation, domain, source of knowledge, type of knowledge, knowledge extraction, knowledge input process, knowledge retrieval process, knowledge sharing technology, source of ontology component, and ontology methodology. The review found that several elements can be further studied to improve KM implementation in the organization, especially on the knowledge sharing dimension. This includes simplifying the knowledge extraction and retrieval process to explore various knowledge domains from implicit knowledge sources. The review’s outcome also includes proposed components and functions of an ideal ontology-based KM tool.

The emergence of Intelligent Connected Vehicles (ICVs) shows great potential for future intelligent traffic systems, enhancing both traffic safety and road efficiency. However, the ICVs relying on data driven perception and driving models face many challenges, such as the lack of comprehensive knowledge to deal with complicated driving context. In this paper, we investigate cooperative knowledge sharing for ICVs. We propose a secure and efficient blockchain based knowledge sharing framework, wherein a distributed learning based scheme is utilized to enhance the efficiency of knowledge sharing and a directed acyclic graph (DAG) system is designed to guarantee the security of shared learning models. To cater for the time-intense demand of highly dynamic vehicular networks, a lightweight DAG is designed to reduce the operation latency in terms of fast consensus and authentication. Moreover, to further enhance model accuracy as well as minimizing bandwidth consumption, an adaptive asynchronous distributed learning (ADL) based scheme is proposed for model uploading and downloading. Experiment results show that the DAG based framework is lightweight and secure, which reduces both chosen and confirmation delay as well as resisting malicious attacks. In addition, the proposed adaptive ADL scheme enhances driving safety related performance compared to several existing algorithms. [ABSTRACT FROM AUTHOR]

The market in which firms compete magnifies the need for buying firms to effectively manage and develop their supply chain. One of the important notions in the management and development of the supply chain is the implementation of supplier development program. Previous extensive literature review has identified various types of activity in the supplier development program, which categorized as low involvement activity to high involvement activity. Further, extensive literatures on supplier development program implementation and its implication on supplier’s capability are also available. However, there is still limited empirical research for investigating the determinants of knowledge sharing effectiveness in supplier development programs. Aimed at bridging that gap, this study attempts to investigating the role of supplier’s organizational culture, information and communication technology (ICT), and perceived behavioral control as the determinants of knowledge sharing effectiveness by mentorship in the supplier development program (SD). A survey questionnaire was employed to collect data from 200 suppliers after conducting mentorship training at the suppliers’ site. The data were analyzed using bootstrap resampling method (with 5,000 resamples). The bootstrap samples are used to estimate the Partial Least Square (PLS) path model. The results of path analysis indicated that all variables are significantly associated with knowledge sharing effectiveness with mentoring mechanism (p-values< 0.05) except collaborative culture to knowledge sharing effectiveness which was insignificant. It was also observed that ICT and perceived behavioral control affected the effectiveness of knowledge sharing by mentorship in the SD program.

Using evolutionary computation algorithms to solve multiple tasks with knowledge sharing is a promising approach. Image feature learning can be considered as a multitask learning problem because different tasks may have a similar feature space. Genetic programming (GP) has been successfully applied to image feature learning for classification. However, most of the existing GP methods solve one task, independently, using sufficient training data. No multitask GP method has been developed for image feature learning. Therefore, this article develops a multitask GP approach to image feature learning for classification with limited training data. Owing to the flexible representation of GP, a new knowledge sharing mechanism based on a new individual representation is developed to allow GP to automatically learn what to share across two tasks and to improve its learning performance. The shared knowledge is encoded as a common tree, which can represent the common/general features of two tasks. With the new individual representation, each task is solved using the features extracted from a common tree and a task-specific tree representing task-specific features. To find the best common and task-specific trees, a new evolutionary search process and fitness functions are developed. The performance of the new approach is examined on six multitask learning problems of 12 image classification datasets with limited training data and compared with 17 competitive methods. The experimental results show that the new approach outperforms these comparison methods in almost all the comparisons. Further analysis reveals that the new approach learns simple yet effective common trees with high effectiveness and transferability. [ABSTRACT FROM AUTHOR]

Roadmapping is a workshop-based tool that draws on diverse sets of knowledge from individuals to create shared knowledge in the form of a roadmap for navigating a strategic landscape. Theoretical explanations of knowledge sharing in roadmapping are underdeveloped, with only limited insights into the relationship between individuals’ knowledge (at the micro level) and organizational-level outcomes. This article uses a multilevel lens to examine knowledge sharing in two roadmapping projects empirically. The analysis identifies five mechanisms that enable or constrain knowledge sharing in roadmapping: 1) a neutral space for face-to-face interactions between individuals that fosters; 2) a positive disposition of individuals to share knowledge, together with (3) imperatives to trigger knowledge contributions. Individually shared knowledge is transformed into collective outcomes via 4) shared meanings and 5) shared commitment to act. The analysis also highlights the important role of the facilitator in relation to knowledge sharing. This article extends the theoretical understanding of individuals and their behavior in the roadmapping process. In doing so, it demonstrates how microlevel actions relate to macrolevel outcomes. Practitioners, including facilitators, can use these insights as they design and deliver roadmapping interventions to improve knowledge sharing and, therefore, better strategic outcomes. [ABSTRACT FROM AUTHOR]

An imposing task for a reinforcement learning agent in an uncertain environment is to expeditiously learn a policy or a sequence of actions, with which it can achieve the desired goal. In this article, we present an incremental model learning scheme to reconstruct the model of a stochastic environment. In the proposed learning scheme, we introduce a clustering algorithm to assimilate the model information and estimate the probability for each state transition. In addition, utilizing the reconstructed model, we present an experience replay strategy to create virtual interactive experiences by incorporating a balance between exploration and exploitation, which greatly accelerates learning and enables planning. Furthermore, we extend the proposed learning scheme for a multiagent framework to decrease the effort required for exploration and to reduce the learning time in a large environment. In this multiagent framework, we introduce a knowledge-sharing algorithm to share the reconstructed model information among the different agents, as needed, and develop a computationally efficient knowledge fusing mechanism to fuse the knowledge acquired using the agents’ own experience with the knowledge received from its teammates. Finally, the simulation results with comparative analysis are provided to demonstrate the efficacy of the proposed methods in the complex learning tasks. [ABSTRACT FROM AUTHOR]

Learners’ autonomous learning is at the heart of modern education, and the convenient network brings new opportunities for it. We notice that learners mainly use the combination of online and offline learning methods to complete the entire autonomous learning process, but most of the existing models cannot effectively describe the complex process of knowledge diffusion under the multinetworks framework. By analyzing the relationship between online learning and offline learning in the autonomous learning, we develop a novel formal model to characterize the dynamic process of knowledge diffusion in the autonomous learning under multiple networks. To guide learners to learn independently and effectively expand the scope of knowledge through hybrid online learning, we then introduce the personalized needs of learners and the guidance of educators and further propose an effective algorithm (ERAA) to allocate educational resources. Through the experiments, we verify the effectiveness of the model and analyze the efficiency of the proposed algorithm. Then, we compare the proposed algorithm with the existing four algorithms and the results show that the proposed algorithm improves performance by 38%. This article provides highly realistic significance for education administrators to analyze the practice, develop autonomous learning strategy and put online educational resources. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is an emerging trend referring to an interconnected network of ubiquitous intelligence. It is a revolution for Internet, computing and communication. Further extensions of IoT, Social Internet of Things (SIoT) provides a platform for people posting messages and photos, sharing knowledge, and connecting with each other. It is effective and efficient for people to manage their interpersonal relations through SIoT, but produces stress and tension issue coincidentally. Therefore, this study explores how users share knowledge through assessment and response under stress cognition. Three demand appraisals and three coping strategies are proposed to discuss user's behaviour on knowledge sharing, and Smart-PLS is used to test the conceptual framework. Results show that self-protection, anxiety, and avoidance increase when members of the community are threatened or injured. Nevertheless, members with high self-efficacy could reduce anxiety production and improve self-protection. Consequently, the purpose of knowledge sharing is achieved. This study discusses users' psychological perspectives when participating in the SIoT. It provides a better understanding of the human activities on the Internet through the SIot. Meanwhile, further prediction of users' behavior of knowledge sharing provides benefits and opportunities for businesses to establish their marketing strategy.

To solve the problems of high time cost caused by the low efficiency of knowledge acquisition, this paper proposes a knowledge service framework based on case set. Three knowledge retrieval methods are designed based on parts keywords, customer orders and manufacturing processes. Additionally, a VSM-based (vetor space model) knowledge recommender method is proposed by using similarity matching algorithm to improve the efficiency of knowledge acquisition and transfer. Finally, application scenarios and evaluation of the proposed approach in a mechanical product design project are given to illustrate the application effect of knowledge service system.

The product development knowledge stored in manufacturing firms can be obtained and re-used to assist the development engineers in new product development and facilitate the use of knowledge resources, which in turn enhances the quality and reduces the time-cost of product innovation. This study presents a knowledge service integrated framework for product development, which is refined from the literature and supported by practice needs. The proposed integrated framework is supported by four pillars of work, namely, how to represent knowledge exactly, the model of knowledge integration, the method of knowledge recommendation, and the mechanism and performance evaluation of knowledge service. A demonstration of a gantry crane design is provided to show the effectiveness and benefit of the proposed integrated framework and technology solutions. Finally, we offer some pointers for future research.

Internet of Vehicles (IoVs) is highly characterized by collaborative environment data sensing, computing and processing. Emerging big data and Artificial Intelligence (AI) technologies show significant advantages and efficiency for knowledge sharing among intelligent vehicles. However, it is challenging to guarantee the security and privacy of knowledge during the sharing process. Moreover, conventional AI-based algorithms cannot work properly in distributed vehicular networks. In this paper, a hierarchical blockchain framework and a hierarchical federated learning algorithm are proposed for knowledge sharing, by which vehicles learn environmental data through machine learning methods and share the learning knowledge with each others. The proposed hierarchical blockchain framework is feasible for the large scale vehicular networks. The hierarchical federated learning algorithm is designed to meet the distributed pattern and privacy requirement of IoVs. Knowledge sharing is then modeled as a trading market process to stimulate sharing behaviours, and the trading process is formulated as a multi-leader and multi-player game. Simulation results show that the proposed hierarchical algorithm can improve the sharing efficiency and learning quality. Furthermore, the blockchain-enabled framework is able to deal with certain malicious attacks effectively. [ABSTRACT FROM AUTHOR]

Due to increased competitive pressure, modern organizations tend to rely on knowledge and its exploitation to sustain a long-term advantage. This calls for a precise understanding of knowledge management (KM) processes and, specifically, how knowledge is created, shared/transferred, acquired, stored/retrieved, and applied throughout an organizational system. However, since the beginning of the new millennium, such KM processes have been deeply affected and molded by the advent of the fourth industrial revolution, also called Industry 4.0, which involves the interconnectedness of machines and their ability to learn and share data autonomously. For this reason, the present article investigates the intellectual structure and trends of KM in Industry 4.0. Bibliometric analysis and a systematic literature review are conducted on a total of 90 relevant articles. The results reveal six clusters of keywords, subsequently explored via a systematic literature review to identify potential stream of this emergent field and future research avenues capable of producing meaningful advances in managerial knowledge of Industry 4.0 and its consequences. [ABSTRACT FROM AUTHOR]

The Centre on Persuasive Systems for Wise Adaptive Living (PERSWADE) aims at developing and applying persuasive technologies and system science for social innovation that can help humanity to move toward sustainable, wise, adaptive living. The PERSWADE collaborative knowledge base needs to be designed with the intent to bring together, enrich and logically relate heterogeneous content, such as datasets, scientific literature and any kind of multimedia and social content, to support a participatory approach and help to translate science into action. PERSWADE-CORE, the foundation ontology described in this paper, plays a critical role in this by providing the backbone semantic infrastructure to enable collaboration through efficient data and knowledge integration, sharing and reuse. It also serves the purpose of clarifying and explaining the goals, functions and operations of the Centre. Because of its purpose, PERSWADE-CORE has been designed to be easy-to-use and easy-to-adapt by allowing generic, as well as more specific, relationships among concepts. The PERSWADE approach prioritizes interoperability and relies on the Semantic Web infrastructure. Furthermore, its design is intrinsically aimed at collaborative environments in which ontologies are expected to evolve as a response to users' activity.

In this paper, we explore a specialized type of knowledge, namely, nomadic knowledge, to understand its implications in knowledge sharing. Nomadic knowledge is enacted in a discontinuous pattern with a changing set of actors and further flows on a defined trajectory. This knowledge is quite important but is required sporadically, so it has a varying level of importance for stakeholders at different instances of time. The limited interest of knowledge holders after the creation of this type of knowledge makes its sharing process complex. Furthermore, new sets of actors overloaded with tasks often ignore the knowledge sharing aspect of nomadic knowledge due to the urgency of the tasks at hand. Using a long-term field study, we illustrate practices concerning the sharing of nomadic knowledge, which, we argue, are to date not yet well supported technologically. The objective of this contribution is, therefore, to outline the concept of nomadic knowledge and associated implications, so that respective knowledge sharing practices can be supported by the knowledge management community with appropriate technology. The underlying complexity of knowledge sharing practices highlighted in this paper stresses the need for appropriate technological and social processes to facilitate the sharing of nomadic knowledge.

Nowadays it is commonly accepted that knowledge, as a resource has become increasingly important in the drive toward knowledge-driven innovation in the manufacturing industry, and which is especially critical for the research and development of complex equipment. In China, there are several fields of manufacturing, such as aviation, ship, and electronics. Although each of them has its own professional technology, they also take advantage of the basic common manufacturing technology. Thus, it is meant for sharing manufacturing technology knowledge. In order to promote innovation, a manufacturing technology knowledge retrieval system is developed to make more efficient use of manufacturing technology knowledge. Manufacturing technology knowledge is a phrase with vast meanings, which may include knowledge of different manufacturing process means, machine, and process capabilities or the latest technology developments. In this system, the manufacturing technology knowledge is defined and organized. A knowledge retrieval method based on the weighted fusion of ontology-based semantic extension (OSE) and user-based collaborative filtering (UCF) is proposed and described detailed. On this basis, the prototype system of manufacturing technology knowledge retrieval system is developed. Its architecture and main functions are elaborated. The results of this system facilitate the sharing of manufacturing knowledge, and it is expected to accelerate the process of knowledge transfer, to improve the level of the overall manufacturing technology.

Embracing outside-in open innovation (OI) can result in a plethora of organizational advantages, including improved innovation performance. Although some studies have found that outside-in OI improves innovation performance, others have shown that it has no effect or even a negative effect. This mixed empirical evidence leads to a need to unpack the relationship between outside-in OI and innovation performance, and to examine how certain key mediating variables related to the outside-in OI process can ensure that outside-in OI turns into improved innovation performance. Thus, this paper aims to examine the influence of outside-in OI on innovation performance considering the mediating roles of knowledge sharing and innovation strategy. This paper draws on a cross-industrial sample of 112 firms. Data are analyzed using a set of ordinary-least-squares regression models and the bootstrap procedure. Results show that knowledge sharing and innovation strategy fully mediate the relationship between outside-in OI and innovation performance. [ABSTRACT FROM AUTHOR]

Minimizing energy consumption while satisfying the user-specified performance requirement is a primary design objective for mobile devices. To achieve this, off-the-shelf mobile devices are usually equipped with dynamic voltage and frequency scaling (DVFS)-enabled processors to tradeoff performance for energy reduction through online adjustment of operating points. Recently, reinforcement learning algorithms have been widely studied and show great potential for runtime DVFS control because of their adaptability to the changing environment. However, the rapid evolution of hardware and the ever-growing diversity of mobile applications increase the system complexity dramatically and make it hard for the learning agent to quickly obtain an efficient power management policy. To address this challenge, we propose a decentralized collaborative Q-learning (DCQL)-based approach in this article to solve the DVFS control problem of multicore mobile processors. By exchanging learning experiences and knowledge among multiple devices, DCQL increases the convergence rate of the learning algorithm and improves the quality of the derived power management policy. On each device, the 4-phase action selection strategy is applied for efficient exploration of the action space defined by various power settings. Experimental results on realistic applications show that DCQL can achieve 3.4%–8.0% energy reduction over various existing approaches while providing an average of $3.4\times $ speedup over the state-of-the-art individual learning algorithm. We also show that the proposed approach can scale well with the number of cores through clustered DVFS control. [ABSTRACT FROM AUTHOR]

Rapidly evolving embedded applications continuously demand more functionalities and better performance under tight energy and thermal budgets, and maintaining high energy efficiency has become a significant design challenge for mobile devices. Learning-based methods are adaptive to dynamic conditions and show great potential for runtime power management. However, with the ever-increasing complexity of both hardware and software, it is a challenging issue for a learning agent to explore the state-action space sufficiently and quickly find an efficient management policy. In this paper, we propose a reinforcement learning-based multi-device collaborative power management approach to address this issue. Multiple devices with different runtime conditions can acquire related knowledge during the learning process. Efficient knowledge sharing among these devices can potentially accelerate the learning process and improve the quality of the learned policies. We integrate the proposed method with dynamic voltage and frequency scaling on the multicore processors in mobile devices. Experimental results on realistic applications show that the collaborative power management can achieve up to a $7 \times$ speedup and 10% energy reduction compared with state-of-the-art learning-based approaches. [ABSTRACT FROM AUTHOR]

Collaborative development communities and knowledge sharing communities are highly correlated and mutually complementary. The knowledge sharing between these two types of open source communities can be very beneficial to both of them. However, it is a great challenge to automate this process. Current studies mainly focus on knowledge acquisition in one type of community, and few of them have tackle this problem efficiently. In this paper we take Android Issue Tracker and Stack Overflow as a case to study the mutual knowledge sharing between them. We propose an automatic approach by integrating semantic similarity with temporal locality between Android issues and Stack Overflow posts based on the internal citation-graph to reveal the potential associations between them. Our approach explores the internal citations in communities for closely related posts or issues clustering, exploits the rich semantics in fine-grained information of issues and posts for associations building, and leverages the temporal correlations between issues and posts in-depth for associations ranking. Extensive experiments show that the precision of our approach reaches 62.51 percent for top 10 recommendations when recommending Stack Overflow posts to Android issues, and 66.83 percent in reverse. [ABSTRACT FROM AUTHOR]

The present study aims to empiricaly demonstrate the viability and benefits of an awareness-based approach to diversify knowledge between potential learning partners. Groups of four learners studied lesson material on biology. After a knowledge test, the groups were to form collaborative learning dyads. Based on the test, a novel knowledge discrepancy calculation tool discerned group configurations with maximal and minimal discrepancies in knowledge between potential learning partners. In the experimental condition, groups were made aware of the results of this calculation; in the control condition, groups were asked to form dyads absent any additional guidance. The significant majority of groups in the experimental condition formed dyads with maximal knowledge discrepancy between partners. The majority of groups in the control condition formed dyads based on factors unrelated to learning. We anticipated that in dyads with discrepant knowledge, partners will be better able to fill each other's knowledge gaps. Although a significant difference between conditions did not transpire, there was a significant correlation between knowledge discrepancy and learning gain across conditions. The implications of these findings for effective learning group organization are discussed; implementation of the calculation tool in classrooms and MOOCs is proposed. [ABSTRACT FROM AUTHOR]

Yahoo! Answers is an online platform where users can post questions and answer other users’ questions. Our previous work studied the online social network (OSN) of Yahoo! Answers by analyzing information from the profiles (including fans, contacts, and interests) of top contributors and their related users. Rather than using the static profile information from the top-contributor-centered dataset, in this paper, we particularly analyze the actual questioning and answering (Q/A) behaviors of normal users. We build a Q/A network that unidirectionally connects each asker to his/her answerers. We analyze the structural characteristics of the Q/A network, user Q/A activities, and knowledge base of all users. In addition to the observations similar to our previous study, which indicates that the OSN of Yahoo! Answers can reflect user Q/A activities to a certain extent, we additionally observe that: 1) a large portion of users only ask questions without answering others’ questions; 2) users are active in more knowledge categories than those indicated in their profiles; and 3) the knowledge categories of the top-contributor-related users cannot represent those of normal users. Finally, we analyze the characteristics of questions and answers in different knowledge categories. This paper not only provides an understanding of actual Q/A activities of users but also showcases the aspects of Q/A activities that the OSN of Yahoo! Answers can and cannot accurately reflect. Based on the insights gained from this paper, we propose a few methods to help improve the quality of service of Yahoo! Answers. [ABSTRACT FROM PUBLISHER]

Social media networks are dynamic. As such, the order in which network ties develop is an important aspect of the network dynamics. This study proposes a novel dynamic network model, the Nodal Attribute-based Temporal Exponential Random Graph Model (NATERGM) for dynamic network analysis. The proposed model focuses on how the nodal attributes of a network affect the order in which the network ties develop. Temporal patterns in social media networks are modeled based on the nodal attributes of individuals and the time information of network ties. Using social media data collected from a knowledge sharing community, empirical tests were conducted to evaluate the performance of the NATERGM on identifying the temporal patterns and predicting the characteristics of the future networks. Results showed that the NATERGM demonstrated an enhanced pattern testing capability and an increased prediction accuracy of network characteristics compared to benchmark models. The proposed NATERGM model helps explain the roles of nodal attributes in the formation process of dynamic networks. [ABSTRACT FROM PUBLISHER]

A fundamental issue confronting virtual community (VC) managers is how to retain the existing members and promote their continuance in the VC. Being a virtual interaction space in which members are not formally bound to stay, the extent to which members have a sense of social identity, or perceiving themselves as a part of the VC, may be a key to determine their continuance intention in the VC. However, it is important to recognize that social identity is neither a unidimensional concept nor are VCs universal in nature. In this study, we examine two variants of VCs, one focusing on knowledge sharing and another on support/advocacy, and show that different dimensions of social identity matters for the two VCs. In particular, members' sense of cognitive social identity promotes their continuance intention in knowledge-based VCs, whereas it is the emotional social identity that works in support/advocacy VCs. Furthermore, the cultivation of the two dimensions of social identity requires the facilitation of different VC artifacts. Overall, our findings offer a more nuanced understanding of the role of social identity in promoting members' continuance in VCs, and underscore that a naive universal approach of cultivating social identity without considering VC type is not enough; rather, a VC-dependent strategy with differential emphasis on developing different VC artifacts could be more fruitful. [ABSTRACT FROM AUTHOR]

This paper outlines a means to manage electrical competence in an international oil company (IOC), and it begins to explore the connections that may be possible to the industry in general and a possible role for the Petroleum and Chemical Industry Committee (PCIC) in this process. A key objective is to allow new employees in the electrical discipline to add value to the business as quickly and effectively as possible. This sometimes involves the acquisition of new knowledge and behaviors. Some resources and programs are available in the open market, but these are usually not well matched to the specific needs of the industries covered by the oil, gas, and chemical sector, i.e., the focus is more often on utility companies. By sharing the needs of this IOC and the initial response from PCIC Europe, it is hoped to stimulate further thinking and possible alignment on this important subject. [ABSTRACT FROM AUTHOR]