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Numerous natural dyes were used to dye mediaeval Islamic paper from the tenth to the nineteenth century, using a variety of techniques. The objective of this research is to develop an analytical method using high performance liquid chromatography with diode array detection (HPLC–DAD) to identify natural dyes in forty historical Islamic paper manuscripts from two collections. In addition, novel information was obtained from original treatises containing recipes for dyeing paper and the chronological use of dyes in dyeing mediaeval Islamic paper between the tenth and seventeenth centuries. The results show that thirty-four of the forty manuscripts examined contained dyes. Six natural dyes were detected, including henna, madder, lac, safflower, turmeric, and weld. This is the first study aimed at developing a method for determining various natural dyes in Islamic paper from a wide historical and geographical range in the mediaeval time and to fill significant gaps in the technical and historical knowledge of papermaking. [ABSTRACT FROM AUTHOR]

In October 2021, the Secure Knowledge Management (SKM) Conference convened many of scholars and practitioners to discuss and share ideas about the use and development of AI for SKM and cybersecurity. Over the past half-decade, numerous federal funding agencies such as the National Science Foundation (NSF), National Academies of Science (NAS), and National Security and Technology Council (NSTC) have noted the significant role that Artificial Intelligence (AI)-enabled analytics techniques such as deep learning, machine learning, network science, generative models, reinforcement learning, text analytics, and other techniques could play for Secure Knowledge Management (SKM), and more broadly, cybersecurity. Papers in each theme had a component of AI that was based in one or more of the major themes of extant AI-enabled cybersecurity and SKM research summarized in the previous section. [Extracted from the article]

The current work highlights the evolution in knowledge management for sustainable entrepreneurship research by analyzing the key trends and major concepts. Additionally, the knowledge structures of such research themes were analyzed and mapped. Moreover, this paper seeks to present a research agenda concerning the study subject. It employed an integrated bibliometric approach and systematic review of knowledge management and sustainable entrepreneurship research by conducting two main procedures, namely domain analysis (i.e., key trends and evolution) and knowledge structures analysis (i.e., intellectual, social, and conceptual structure). A total of 233 documents were obtained from Scopus and Web of Science datasets and analyzed using both R 4.1.2 and VOSviewer software. The findings demonstrated that the contributors (i.e., the authors, nations, journals, and institutions) produced a discernible evolution in the body of knowledge on the themes of knowledge management and sustainable business within the designated period. Furthermore, science mapping approaches deeply grasp the social, conceptual, and intellectual structures of such research themes. This current work is considered one of the first attempts to systematically review, analyze, and visualize the scientific productions on knowledge management and sustainable entrepreneurship. The findings of the current work also offer a solid understanding and insights into the potential directions for the research agenda in these disciplines. [ABSTRACT FROM AUTHOR]

This conceptual point-of-view essay focuses on the authors' teaching experiences in undergraduate business education at Brazilian and German Universities. In line with the Scholarship of Teaching and Learning (SoTL) and based on autoethnographical writing, the paper explores the question of how university business education professors can help their students become more prepared for the job market by establishing university–industry partnerships. This discussion fills a particularly pertinent gap regarding knowledge transfer, skill development, and discrepancies between academic expectations in relation to practice-based education, as well as professors' pedagogical sensibility for students' preparation in higher education. Overall, the significance of the research lies in the potential to improve the effectiveness and relevance of business education. This can lead to more effective knowledge transfer and skill development among students. The study concludes by stressing the importance of university–industry collaborations for academics and students. Students might be better prepared for job employment in such situations. [ABSTRACT FROM AUTHOR]

Human–computer interaction, Artificial Intelligence, and the multilingualism of digital culture open up unexpected scenarios in the contemporary design of cultural spaces with the creation of artifacts in which the analogic and digital dimensions come together to enhance the experience. Sensors and devices track user movement in the real world and translate the inputs into commands through hand gestures, speech recognition, head movements, tangible interfaces, or a combination of these elements. Through theorical models, concepts and tools, the paper reports the evolution of "User Experience" applied to personalized enjoyment and use of cultural places. The functional-performance survey of tools and technologies for perception, narrative and augmented interaction revealed models that highlight the diversity and richness of tangible and intangible cultural heritage through new forms of interaction and knowledge transfer. In this direction, new technological tools make it possible to detect, track and evaluate the personalized user experience by processing or producing large amounts of data. Therefore, on one hand the paper explores the limitations dictated by data management and user privacy in using such systems and, on the other, it prefigures new scenarios for amplifying and personalizing the user experience. [ABSTRACT FROM AUTHOR]

Following the knowledge-based and intellectual capital-based view of firms, where knowledge is at the center of corporate strategic management, this paper aims to examine the role of Green Intellectual Capital (GIC) in the environmental and financial dimensions of corporate performance. To do so, a methodological framework was developed to evaluate the progress of GIC and corporate environmental performance by utilizing information and data from sustainability reports. Also, the Return of Equity (ROE) indicator was used to assess corporate financial performance. The proposed methodology was applied in a sample of 80 firms to examine the mediator role of GIC in the relationship between financial and environmental performance. The findings show a positive relationship between GIC (as a result of Green Relational Capital (GRC) and Green Organizational Capital (GOC)) and financial performance (ROE), as well as a significant positive relationship between environmental performance and the components of Green Intellectual Capital. Finally, a nonlinear U-shaped relationship between environmental and financial performance is identified. [ABSTRACT FROM AUTHOR]

Data releasing and sharing between several fields has became inevitable tendency in the context of big data. Unfortunately, this situation has clearly caused enormous exposure of sensitive and private information. Along with massive privacy breaches, privacy-preservation issues were brought into sharp focus and privacy concerns may prevent people from providing their personal data. To meet the requirements of privacy protection, such a problem has been extensively studied. However, privacy protection of sensitive information should not prevent data users from conducting valid analyses of the released data. We propose a novel algorithm in this paper, named Data Release under Adjustable Privacy-utility Equilibrium (DRAPE), to address this problem. We handle the privacy versus utility tradeoff in the data release problem by breaking sensitive associations among variables while maintaining the correlations of nonsensitive variables. Furthermore, we quantify the impact of the proposed privacy-preserving method in terms of correlation preservation and privacy level, and thereby develop an optimization model to fulfil data privacy and data utility constraints. The proposed approach is not only able to provide a better privacy levels control scheme for data publishers, but also provides personalized service for data requesters with different utility requirements. We conduct experiments on one simulated dataset and two real datasets, and the simulation results show that DRAPE efficiently achieves a guaranteed privacy level while simultaneously effectively preserving data utility. [ABSTRACT FROM AUTHOR]

In Disaster Management (DM), reusing knowledge of best practices from past experiences is envisaged as the best approach for dealing with future disasters. But analysing and modelling processes involved in those experiences is a well-known challenge. But the efficient storage of those processes to allow reuse by others in future DM endeavours is even more challenging and less discussed. Without an efficient process in place, DM knowledge reuse becomes even more remote as the effort incurred gets construed as a hindrance to more pressing activities during the execution of disaster activities. Efficiency has to also be pursued without compromising the effectiveness of the knowledge analysis and reuse. It is important to ensure that knowledge remains meaningful and relevant after it is transformed. This paper presents and validates a DM knowledge analysis framework (DMKAF 2.0) that caters for efficient transformation of DM knowledge intended for reuse. The paper demonstrates that undertaking knowledge transformation and storage in the context of its use is crucial in DM for both, effectiveness and efficiency of the transformation process. Design Science Research methodology guides the research undertaken, by informing enhancements and how the framework is evaluated. A real case study of flood DM from the State Emergency Service of Victoria State Australia is successfully used to validate these enhancements. [ABSTRACT FROM AUTHOR]

In Industry-Academia Collaborations (IAC) both academic, scientific research results and industrial practitioner findings and experiences are produced. Both types of knowledge should be gathered, codified, and disseminated efficiently and effectively. This paper investigates a recent (2014–2017) large-scale IAC R&D&I program case (Need for Speed, N4S) from a learning perspective. It was one of the programs in the Finnish SHOK (Strategic Centres of Science, Technology, and Innovation) system. The theoretical bases are in innovation management, knowledge management, and higher education (university) pedagogy. In the future, IAC projects should be more and more commonplace since major innovations are hardly ever done in isolation, not even by the largest companies. Both intra-organizational and inter-organizational learning networks are increasingly critical success factors. Collaborative learning capabilities will thus be required more often from all the participating parties. Efficient and effective knowledge creation and sharing are underpinning future core competencies. In this paper, we present and evaluate a collaboratively created and publicly shared digital knowledge repository called "Treasure Chest" produced during our case program. The starting point was a jointly created Strategic Research and Innovation Agenda (SRIA), which defined the main research themes and listed motivating research questions to begin with—i.e., intended learning outcomes (ILO). During the 4-year program, our collaborative industry-academia (I-A) learning process produced a range of theoretical and empirical results, which were iteratively collected and packaged into the Treasure Chest repository. Outstandingly, it contained, in addition to traditional research documents, narratives of the industrial learning experiences and more than 100 actionable knowledge items. In conclusion, our vision of the future is that such transparently shared, ambitious, and versatile outcome goals with a continuous integrative collection of the results are keys to effective networked I-A collaboration and learning. In that way, the N4S largely avoided the general problem of often conflicting motives between industrial firms seeking answers and applied solutions to their immediate practical problems and academic researchers aiming at more generalizable knowledge creation and high-quality scientific publications. [ABSTRACT FROM AUTHOR]

Propelled by digitalisation, crowd knowledge (CK) has gained popularity alongside a plurality of related crowd-based concepts (crowdsourcing, wisdom of crowds and collective intelligence), resulting in an inconsistent understanding of the terms and their application. Based on a structured literature review, we conceptualise CK and develop a formal definition, which is then evaluated using knowledge artefacts on different crowd-related platforms and differentiation criteria in relation to participants, context, purpose and process. The paper posits that CK is conceptually different from related concepts, due to its dynamic nature and its instantiation in the form of a CK knowledge artefact which requires a specific context and a unique knowledge-creation process. Furthermore, we discuss how the concept of CK and its associated artefact relates to established knowledge management concepts such as knowledge assets and the flow of CK in the epistemological differentiation of knowledge into tacit and explicit elements. The article contributes to the formal conceptualisation of crowd-based concepts and therefore improves understanding of existing implementations and supports the prudent design of future systems. [ABSTRACT FROM AUTHOR]

This paper identifies practices that can facilitate knowledge transfer in university–industry (U–I) research partnerships by systematically reviewing extant literature. We aim to contribute to the theoretical development in the field of academic engagement and propose that knowledge transfer provides a valuable perspective. We started our review with identifying barriers and facilitators of knowledge transfer. Extant literature identified knowledge differences and differences in goals resulting from different institutional cultures as important barriers to knowledge transfer. They result in ambiguity, problems with knowledge absorption and difficulties with the application of knowledge. Trust, communication, the use of intermediaries and experience are found as facilitators for knowledge transfer that help to resolve the identified barriers. Our analysis offers practical advice for the management of academic engagement. Finally, we identified questions for future research based on inconsistencies in extant research and open questions we encountered during our analysis. [ABSTRACT FROM AUTHOR]

Knowledge sharing is an activity through which knowledge, namely, information, skills, or expertise, is exchanged between people, friends, families, communities or organizations. Online knowledge sharing activities are flourishing with the advent of social media and digital life. However, despite of the importance of the online knowledge sharing methods and mechanisms, there is not any comprehensive and systematic study about studying and analyzing its important techniques. Therefore, the main aim of this paper, the comprehensive, detailed, and systematic study, and survey of the state-of-the-art knowledge sharing mechanisms in an online environments is provided. Also, this paper presents a systematic literature review (SLR) on the online knowledge sharing literatures up to end of 2015. We identified 348 papers, which are reduced to 251 primary studies through our paper selection process. Also, the broad overview of the literature provides insights into potential areas for further research. By providing state-of-the-art information, this survey will directly support academics and practicing professionals in their understanding of developments in online knowledge sharing mechanisms and techniques. [ABSTRACT FROM AUTHOR]

Earlier software development processes (SDPs), such as waterfall processes, were mainly focused on process steps and did not address people- and product-related issues. Emergence of Software development methodologies (SDM) has created a new paradigm for developing software systems. A SDM is a special kind of technically engineered framework for organizing SDPs; this framework is expected to specify three main interwoven elements, namely people, products, and process. It has since become evident that it is impossible to provide a general-purpose SDM for developing all the various kinds of software systems, and it has thus become essential to construct the most appropriate methodology for the system development situation in hand, a practice commonly called Situational Method Engineering (SME). The problem with existing SME methods is lack of adequate attention to the role of people who might seek or possess valuable knowledge about the project situation. This knowledge can be tacit information that is hidden in the developer's mind, or it might be explicitly available. This paper proposes a knowledge management (KM)-driven and DevOps-based SME method as a new integrated multi-view methodological paradigm that satisfies the need for sharing human experience in engineering SDMs. The method has been proposed by reusing general SME practices and complementing them by embedding appropriate KM and DevOps practices to alleviate the weaknesses of previous SME methods. Furthermore, the proposed method has been evaluated through four case studies and also by conducting a criteria-based comparison with eight prominent SME methods. [ABSTRACT FROM AUTHOR]

Nowadays, none can expect that knowledge about some part of reality will not change. Consequently, a representation of such evolving knowledge (for example, ontologies) also changes. Such changes entail that applications incorporating such knowledge may become compromised and yield wrong results. An example of such an application is ontology alignment which can be informally described as a set of connections between two ontologies. Those connections mark elements from two ontologies that relate to the same parts of reality. In changing one of the corresponding ontologies, such connections may become invalid. One may designate the ontology alignment once again from scratch for altered ontologies. However, such an approach is time and resource-consuming. The paper comprehensively presents our ontology evolution and alignment maintenance framework. It can be used to preserve the validity of ontology alignment using only the analysis of changes introduced to maintained ontologies. The precise definition of ontologies is provided, along with a definition of the ontology change log. A set of algorithms that allow revalidating ontology alignments have been built based on such elements. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the impact of ontology-based knowledge management on improving tax accounting procedures and reducing tax risks, specifically focusing on the income tax system in a developing country. To achieve this objective, a mixed methodology approach was adopted, combining quantitative and qualitative data collection and analysis methods. The data were gathered through document analysis, interviews, and questionnaires administered to stakeholders in the Egyptian Income Tax Authority. The findings of this study hold significance for both academic researchers and accounting practitioners. The results demonstrate numerous advantages associated with the utilization of ontology in tax management. It has the potential to reduce costs in tax advisory services, minimize errors in tax calculations, and enhance tax revenue generation by promoting transparency and accountability. The empirical analysis further confirms a positive correlation between ontology-based knowledge management and improved accountability procedures, as well as a positive correlation with the reduction of tax risks. These statistically significant relationships (r = 0.85, p < 0.01 and r = 0.79, p < 0.01, respectively) provide empirical evidence of the actual effects of implementing ontology-based knowledge management in improving tax accounting procedures and mitigating tax risks. [ABSTRACT FROM AUTHOR]

Today's companies rely heavily on in-company information technology standards (ICITS) to reduce costs, ensure flexibility, and facilitate the planning, implementation, and operation of IT systems. Steering and managing ICITS has proven to be challenging, revealing the need for efficient governance mechanisms. But even though prior research demonstrates the challenges of ICITS, viable advice on how to implement ICITS is scarce. In this paper, we develop an organizational design theory for the management of ICITS based on the framework of organizational control theory. We conducted a critical case study to identify basic goals, constitutive elements, and fundamental mechanisms of a working ICITS management. The resulting design goals and principles were then evaluated and further refined in the light of additional expert interviews. With our work, we wish to extend the body of theoretical knowledge on the management of ICITS and help practitioners master the various challenges occurring in this domain. [ABSTRACT FROM AUTHOR]

A modern enterprise is a heavily wired and networked socio-technical system where multiple components play in symphony to yield a competitive position in the ear of digital economy. While the underling communication and interaction systems facilitate knowledge workers to carry out the enterprise mission and furnish service to the society, there are many other aspects that rather have adverse effects on the productivity of an enterprise and interruption of knowledge workers, which pose a serious scientific challenge. In this paper, we briefly discuss certain challenging aspects of knowledge work and communication processes in networked enterprises that require more profound scientific attention in networked enterprises. This paper introduces the problem, identifies some specific research challenges, and then briefly discusses emerging research that addresses some of these challenges. [ABSTRACT FROM AUTHOR]

Research on innovation management and technology transfer in China is in its early stages and growing rapidly, as technology is transferred to Chinese organizations and the organizations themselves create innovations. The studies to date provide promising theoretical ideas, but specific findings should be regarded as tentative, due to the research designs and methods employed. As with early research on innovation in the West, it is likely that these early studies of innovation in China will have significant impact on innovation research and practice in the future, both in China and throughout the world. [ABSTRACT FROM AUTHOR]

Guest Advisory Editor: R. K. Shyamasundar is a Fellow of IEEE, a Fellow of ACM, Distinguished ACM Speaker, a Distinguished Alumnus of Indian Institute of Science. As the organizers of the SKM-2019 conference we hope that this special issue in Information Systems Frontiers will highlight the current trends in SKM and will inspire more research on the application of AI for SKM. We see an increasing trend of AI-based systems designed to attack other AI systems that are involved in generating advanced insights from the data (e.g., supervised deep learning systems). [Extracted from the article]

Additive Manufacturing (AM) is becoming data-intensive. The ability to identify Data Analytics (DA) opportunities for effective use of AM data becomes a critical factor in the success of AM. To successfully identify high-potential DA opportunities in AM requires a set of distinctive interdisciplinary knowledge. This paper proposes a methodology that enables collaborative knowledge management for identifying and prioritizing DA opportunities in AM. The framework of the proposed methodology has three components: a team of experts, a DA Opportunity Knowledge Base (DOKB), and a prioritization tool. The team of experts provides diverse knowledge that can be used to identify and prioritize DA opportunities. The DOKB, developed by using the Web Ontology Language (OWL), captures diverse knowledge from the experts to identify DA opportunities. The prioritization tool ranks the identified DA opportunities by using the Fuzzy integrated Technique of Order Preference Similarity to the Ideal Solution (Fuzzy-TOPSIS). A case study, in which National Institute of Standards and Technology (NIST) researchers participated, demonstrates our methodology. As a result, 264 DA opportunities for AM's Laser-Powder Bed Fusion (L-PBF) process are identified and prioritized. The prioritized DA opportunities help set a DA direction for L-PBF AM. Our methodology keeps knowledge sharable, reusable, revisable, and extendable. Thus, this methodology can continue to facilitate collaboration within the AM community to identify high potential and high impact DA opportunities in AM. [ABSTRACT FROM AUTHOR]

In this paper, we develop a synthesizing framework for information exchange. In particular, we identify the drivers of both the content of information exchange as well as the choice of the technology used to facilitate information exchange across multiple organizations. Our first driver is the inter-organizational architecture. Unlike the general architectures developed by Williamson [11] and Adler [1], we focus specifically on the effect of architectures on information exchange. We classify the existing architectures in three dimensions, namely, customization, information sharing or trading and closed or open networks. Such a classification enables us to identify particular architectural characteristics that affect content and technology choices. Our second driver refers to the characteristics of the information, the system, the network and the regulatory environment that affect the credibility and usefulness of information exchange for the participants. Our third driver is the incentive structure in the architecture. Only the information that is deemed to be mutually beneficial by all participants will get exchanged. The incentive structure highlights the cost-benefit trade-offs of individual participants. We also place all the five papers accepted for this special issue within this synthesizing framework. Such positioning allows us to identify potential areas for future research and exploration. [ABSTRACT FROM AUTHOR]

The underlying challenges of knowledge management and technology transfer and their respective contributions to the development of society are well-known and widely discussed issues in business management. This paper aims to (1) build on the state-of-the art literature on knowledge management and technology transfer, (2) to identify the most relevant, recent studies, and main researchers working on the field worldwide, and (3) to unveil topics of recent development in the field, as well as overlap the themes. To this end, Web of Science, ScienceDirect and Scopus databases were screened for studies that explored the relevance of knowledge management and technology transfer. This research focused on the most recent studies comprising the periods of 2013–2018, while employing Metodi Ordinatio as a quality ranking method, producing thus 50 studies submitted for further analysis and appraisal. The study sought to analyze the co-occurrence of keywords on the theme, the number of citations, number of publications per country, year and journal, and the overlap between areas. This paper also enabled an analysis of the steps required for knowledge management and their relationship to technology transfer. The results showed that most studies focused on the private sector and on technology transfer for attaining innovation, customer orientation and acquiring technological abilities. The university–industry relationship was also observed, in which the focus is on universities creating and transferring knowledge in order to foment the economic and technological power of industries. The findings can assist public and private companies, non-governmental organizations, university and industry applications in terms of knowledge management and technology transfer. [ABSTRACT FROM AUTHOR]

The literature on healthcare operations and supply chain management has seen unprecedented growth over the past two decades. This paper seeks to advance the body of knowledge on this topic by utilising a topic modelling-based literature review to identify the core topics, examine their dynamic changes, and identify opportunities for further research in the area. Based on an analysis of 571 articles published until 25 January 2022, we identify numerous popular topics of research in the area, including patient waiting time, COVID-19 pandemic, Industry 4.0 technologies, sustainability, risk and resilience, climate change, circular economy, humanitarian logistics, behavioural operations, service-ecosystem, and knowledge management. We reviewed current literature around each topic and offered insights into what aspects of each topic have been studied and what are the recent developments and opportunities for more impactful future research. Doing so, this review help advance the contemporary scholarship on healthcare operations and supply chain management and offers resonant insights for researchers, research students, journal editors, and policymakers in the field. [ABSTRACT FROM AUTHOR]

Current debates on organizational learning distinguish between two distinct and mutually exclusive learning modes: exploration and exploitation. This paper deals with the concept of ambidextrous routines in knowledge management (KM) initiatives. Specifically, drawing on a sample of 1468 MNEs from 24 regions in China, we find that a synergetic combination of explorative and exploitative virtual knowledge is positively associated with MNE performance. In contrast, an imbalance between explorative and exploitative virtual knowledge hurts MNE performance. Furthermore, the effect of imbalanced ambidexterity in virtual knowledge sharing is moderated by the cultural distance in the uncertainty avoidance between the R&D team and the region where the team operates. This paper elaborates on the characteristics of ambidextrous KM initiatives at the micro-level; firms use ambidextrous KM practices to create a learning context, defined by guidelines and methods rather than by a definite purpose. The clear separation of KM initiatives' purpose and their embedded learning routines and methods enables them to be used ambidextrously. Furthermore, this analysis indicates that ambidextrous KM initiatives follow a path characterized by an increasing variety of purposes but a decreasing variety of underlying structures. Consequently, firms create a learning context that can be activated when necessary in ways required in an exploratory and/or in an exploitative mode. [ABSTRACT FROM AUTHOR]

BREEAM Communities (BC) is a neighbourhood sustainability assessment method that has been implemented across the world and has been widely recognised to be a successful tool in supporting the delivery of urban sustainability. The research presented here aimed to contribute to the current literature in sustainability and adaptation through identifying and evaluating the potential that BC has in supporting and promoting climate change adaptation in terms of theoretical and practical governance. Media City, Salford, UK, provided the context for this study having been identified as an exemplar case study and the site of practical implementation of a breadth of community sustainability strategies. This paper presents a comparative analysis of the relationship between established characteristics of adaptive governance (AG) in relation to relevant BC indicators; alongside an evaluation of the perceptions of key actors' as to the adaptive capacity promoted by the application of this tool in practice. This research was informed by interviews with key actors and wider stakeholders in the design, construction and management of the development as well as focus groups with occupants, workers and students in the case study location. Consequently, the need for enhancement of the tool's indicators was identified, and suggestions for their improvement were proposed. Accordingly, BC was found to be a significant tool that holds potential to positively influence community response to climate change adaptation needs. In conclusion, in order to enable effective neighbourhood scale adaptation to climate change, the following themes were identified for further enhancement in the tool: adaptive behaviour and learning, communication, community awareness, knowledge and management. [ABSTRACT FROM AUTHOR]

This research paper assesses complexity in cloud computing adoption, using the context of the local government sector in Australia. The research utilized both cloud computing adoption literature and an Information Systems Complexity Framework to propose a complexity assessment model for cloud computing adoption. A mixed method approach was used in this research. Firstly, we conducted 21 indepth interviews with IT managers in the local governments in Australia to obtain their insights into the complexity of cloud computing adoption. Secondly, a quantitative method is used in which 480 IT staff from 47 local governments responded to an online survey to validate the proposed assessment model. The findings indicate that structural complexity of an organization (i.e., knowledge management), structural complexity of technology (i.e., technology interoperability, and data processing capability), dynamic complexity of an organization (i.e., business operations), and dynamic complexity of technology (i.e., systems integration, IT infrastructure update, and customization resources) are critical complexity aspects to be considered during cloud computing adoption. These findings provide important implications for both researchers and managers that are trying to understand the complexities involved in cloud computing adoption. [ABSTRACT FROM AUTHOR]

In CSCL studies, language is often foregrounded as the primary resource for engaging in collaborative learning, while the body is more often positioned as a secondary resource. There is, however, a growing interest in the body as a resource in learning and collaboration in and outside CSCL. In this paper, we present, analyse, and discuss how two nine-year-old children collaborate through gesturing and moving their bodies around a touchscreen. The pair is working with the concept of scale and area measurement and are in midst of copying their rooms from paper to touchscreen. During this process, the pair engages in a discussion regarding the size of one meter through language, gestures and manipulation of the material resources. The analysis shows two distinct ways of understanding the length of one meter, which primarily are visible through the children's gestures and bodily movements. In the analysis we show how the children dynamically produce body-material resources for communicative and illustrative purposes; moreover, they use body-material resources as a cognitive tool and as a way of shepherding each other. The study forms part of a body of studies analysing and theorizing the body in education, learning, and interaction. We discuss the wider impact of our findings and argue how they may challenge and improve studies relying mainly on a coding and counting approach or automated capture of e.g. gestures. In addition, we provide a detailed multimodal representation of the subtle bodily-material resources, which we argue is a modest contribution to a catalogue of ways of representing and making bodily-material resources visible in CSCL research. [ABSTRACT FROM AUTHOR]

The broad availability of educational data has led to an interest in analyzing useful knowledge to inform policy and practice with regard to education. A data science research methodology is becoming even more important in an educational context. More specifically, this field urgently requires more studies, especially related to outcome measurement and prediction and linking these to specific interventions. Consequently, the purpose of this paper is first to incorporate an appropriate data-analytic thinking framework for pursuing such goals. The well-defined model presented in this work can help ensure the quality of results, contribute to a better understanding of the techniques behind the model, and lead to faster, more reliable, and more manageable knowledge discovery. Second, a case study of social-emotional learning is presented. We hope the issues we have highlighted in this paper help stimulate further research and practice in the use of data science for education. [ABSTRACT FROM AUTHOR]

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To achieve efficient development of high-quality product, manufacturing constraints must be fully taken into account at the early design stage. However, designers lack in-depth knowledge of manufacturing and production. Many time-consuming iterations of design changes are required between designers and manufacturing engineers. In order to minimize this knowledge gap, this paper presents an ontology-based product design framework for manufacturability verification and knowledge reuse to support the sharing and reuse of design and manufacturing knowledge. It aims at providing advices and feedback of restraints of manufacturing processes to the designers during the design process. The proposed framework consists of three major layers which include a foundation layer, a domain layer, and an instance layer. We use the Web Ontology Language (OWL), a standard of ontology representation language, to formalize the foundation layer. It contains the core product model and the standard ISO 10303 AP224 application protocol. The domain layer comprises extensional concepts and relationships for design and manufacturing integration and a rule base for manufacturability verification, which is represented in Semantic Web Rule Language (SWRL). In the instance layer, an inference engine is developed based on ontology and rule inference. It provides recommendations of manufacturability. Two case studies are provided as application examples to demonstrate the effectiveness of the framework. [ABSTRACT FROM AUTHOR]

This paper draws attention to a major issue of Knowledge Management (KM) technology implementation: potential sources of incongruence between KM software and the adopting organization. Using the case of a global consultancy firm, the paper explains KM software-organization incongruences as the consequence of differences between organizational and KM software developer’s knowledge work context and practice. Such differences reflect the differing ‘situated’ knowledge work practices that KM software developers and adopting organizations deliberately acquire and evolve over time. Theoretically, this paper gives a more comprehensive account of how knowledge work practices emerged and were then embedded into the software or the organization. Practically, this study would help make organizations more aware of the factors influencing KM software adoption and implementation, particularly in the case of large firms that are characterized by high-value, text-based content for decision-making such as professional service, R&D, and healthcare organizations. [ABSTRACT FROM AUTHOR]

The interoperability of proof assistants and the integration of their libraries is a highly valued but elusive goal in the field of theorem proving. As a preparatory step, in previous work, we translated the libraries of multiple proof assistants, specifically the ones of Coq, HOL Light, IMPS, Isabelle, Mizar, and PVS into a universal format: OMDoc/MMT. Each translation presented great theoretical, technical, and social challenges, some universal and some system-specific, some solvable and some still open. In this paper, we survey these challenges and compare and evaluate the solutions we chose. We believe similar library translations will be an essential part of any future system interoperability solution, and our experiences will prove valuable to others undertaking such efforts. [ABSTRACT FROM AUTHOR]

Corporate memory (CM) is the total body of data, information and knowledge required to deliver the strategic aims and objectives of an organization. In the current market, the rapidly increasing volume of unstructured documents in the enterprises has brought the challenge of building an autonomic framework to acquire, represent, learn and maintain CM, and efficiently reason from it to aid in knowledge discovery and reuse. The concept of semantic web is being introduced in the enterprises to structure information in a machine readable way and enhance the understandability of the disparate information. Due to the continual popularity of the semantic web, this paper develops a framework for CM management on the semantic web. The proposed approach gleans information from the documents, converts into a semantic web resource using resource description framework (RDF) and RDF Schema and then identifies relations among them using latent semantic analysis technique. The efficacy of the proposed approach is demonstrated through empirical experiments conducted on two case studies. [ABSTRACT FROM AUTHOR]

This paper considers a reveal and systematization procedure of invariance ensuring methods for transformations of physical quantities, signals and information about them based on the developed interdisciplinary approach. The obtained results allow constructing a genericspecific classification of the methods, which reflects their interrelation and, moreover, the origin of their inherent properties. [ABSTRACT FROM AUTHOR]

Keystroke dynamics and mouse movements are effective behavioral biometric modalities for active authentication. However, very little is done on the privacy of collection and transmission of keyboard and mouse data. In this paper, we develop a rule based data sanitization scheme to detect and remove personally identifiable and other sensitive information from the collected data set. Preliminary experiments show that our scheme incurs on average 5.69 % false negative error rate and 0.64 % false positive error rate. We also develop a data transmission scheme using the Extensible Messaging and Presence Protocol (XMPP) to guarantee privacy during transmission. Using these two schemes as a basis, we develop two distinct architectures for providing secure and privacy preserving data processing support for active authentication. These architectures provide flexibility of use depending upon the application environment. [ABSTRACT FROM AUTHOR]

The focus of this paper is on the use-oriented Product Service System (PSS) adopted within the off-highway construction industry. A use-oriented PSS dictates that the product stays in the ownership of the provider and is made available in a different form to the customer (user), and is sometimes shared by a number of users. Extensive research has been carried out into PSS design and strategy, yet there is a research gap relating to the generation, analysis and management of such information and knowledge in a big data environment to support the implementation of a use-oriented PSS. This gap is a substantial barrier to the exploitation of use-oriented PSS research in the commercial world. A roadmap is presented that clarifies the data and structure requirements to enable OEMs to determine and validate their readiness level towards the adoption of a use-oriented PSS driven business model. [ABSTRACT FROM AUTHOR]

Disaster Management (DM) is a complex set of interrelated activities. The activities are often knowledge-intensive and time sensitive. Timely sharing of the required knowledge is critical for DM. For recurring disasters (e.g. floods), developed countries tend to have dedicated document repositories of Disaster Management Plans (DISPLANs) that can be accessed as needs arise. However, accessing the appropriate plan in a timely manner, and sharing activities between plans, often requires significant domain knowledge and intimate understanding of the plans in the first place. This paper introduces an Agent-Based (AB) knowledge analysis method to convert DISPLANs into a collection of knowledge units that can be stored into a unified repository. The repository of DM actions then enables the mixing and matching of knowledge between different plans. The repository is structured as a layered abstraction according to Meta Object Facility (MOF). We use the flood DISPLANs plans used by SES (State Emergency Service), an authoritative DM agency in New South Wales (NSW) State of Australia (hereinafter referred to as SES NSW) to illustrate and give a preliminary validation of the approach. It is illustrated by using displans along the flood-prone Murrumbidgee river in central NSW. [ABSTRACT FROM AUTHOR]

Lean production systems (LPS) have been adopted by numerous organizations to improve their performance. Many enterprise implement LPS by inviting external consultants and using social media to promote this collaboration. Social media provide a channel for enterprise employee and external consultants to communicate and exchange their respective knowledge as a form of their reaction to others or surrounding (Hines et al. in Am Antiq 23(3):312, 29; Hu et al. in Prod Plan Control 27:550–562, 31; Kipping and Armbrüster in The burden of otherness: limits of consultancy interventions in historical case studies, Buchkapitel, Routledge, 37). Using a combination of content analysis and sentiment analysis, this study comprehensively analyzed the effect of sentiment expression to knowledge interaction taking place on social media. Three major influences could be culled from the paper. The case study implies that, through social media, both consultant and organizational employee could play a more proactive role in the knowledge interaction, and sentiment expression makes it more effective. [ABSTRACT FROM AUTHOR]

This paper provides an overview of some of the issues in knowledge management related to the sharing of knowledge in new product development. Previous research and concepts reported by international researchers and examples of the research projects carried out by the authors will be introduced. The paper first provides an overview of the history and importance of innovation and challenges in manufacturing. Then, the importance of new product development in the sustainable success of manufacturing enterprises in the globalised business operations is discussed. The formalisation and modelling of product development processes will also be introduced. The concept and different definitions of knowledge management by previous researchers are then introduced, with further discussion on knowledge sharing. At this point, the authors' research in knowledge sharing is also introduced. Finally, the trend of using social media and Enterprise 2 technologies in knowledge management and sharing is introduced using the recent research projects of the authors as examples. [ABSTRACT FROM AUTHOR]

Being able to correctly model semantic relatedness between texts, and consequently the concepts represented by these texts, has become an important part of many intelligent information retrieval and knowledge processing systems. The need for such systems is especially evident within the biomedical domain, where the sheer amount of scientific publishing contributes to an information overflow. In this paper we present a novel method to approximate semantic relatedness in domain-focused settings. The approach is an extension to a well-known ESA (Explicit Semantic Analysis) method. Our extension successfully leverages the semantics of a domain-specific document corpus. We present the evaluation of the proposed method on a set of reference datasets, that are a de facto reference standard for the task of approximating biomedical semantic relatedness. The proposed method is evaluated in comparison with other state-of-the-art methods, as well as the baselines established with the original ESA method. The results of the experiments suggest that the proposed method combines the semantics of a general and domain-specific corpora to provide significant improvements over the original method. [ABSTRACT FROM AUTHOR]

Background: Most studies of teacher professional development (PD) do not rigorously test impact on teaching practice and student learning. This makes it difficult to define what is truly 'effective.' The Science Teachers Learning from Lesson Analysis (STeLLA) PD program, in contrast, was studied in a cluster randomized experimental design that examined impact on teaching practice and student learning. The STeLLA video-based PD (VbPD) program demonstrated significant impact, with high effect sizes, on elementary teachers' science teaching practice and their students' learning. Previously published reports provide details about research methods and findings but only broad sketches of the STeLLA program design and implementation. Deeper explorations of the STeLLA design principles can contribute evidence-based knowledge about the features of effective PD and enrich the existing but limited consensus model of effective PD. This article addresses the following questions: Results: Results from rigorous studies of the STeLLA program are summarized in this paper; details are reported elsewhere and included here as supplementary materials. This article is not a standard research results paper but instead describes the design principles guiding the development, implementation, leadership, and scaling up of the STeLLA VbPD program. Conclusions: The authors argue that this set of design principles is powerful for four reasons: 1) its demonstrated impact on teaching practice and student learning, 2) its strong theoretical and research foundations, 3) the stability and usefulness of the design principles as implemented in changing contexts over a 10-year period, and 4) the coherence and interconnectedness of the principles. The STeLLA VbPD design principles contribute to the field by empirically supporting and advancing the existing consensus model of effective PD. Further study can build on this effort to strengthen our understanding of effective PD based on evidence of impact on teaching practice and student learning. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to use mathematical programming, including linear programming, dynamic programming, integer programming and goal programming to verify rule-based knowledge. We investigate both domain independent verification, exploiting the general structure of rules, and domain dependent verification, exploiting structure in the domain. Mathematical programming software is readily available and is very efficient. As a result, verification using mathematical programming can be very efficient at finding errors. Mathematical programming can be used to more than just find errors in knowledge representation. Once an error has been found, mathematical programming can be used to ‘recommend’ an alternative. The recommendation can take into account the previous verified knowledge to mitigate the potential introduction of redundant knowledge and to help guide the choice process. Normally the development of recommendations to fix errors has been ignored in the verification literature, and treated as a separate knowledge acquisition task. Accordingly, this paper also extends the verification effort by providing a recommendation on how to fix errors. [ABSTRACT FROM AUTHOR]

Over the past 20 years, imaging informatics has been driven by the widespread adoption of radiology information and picture archiving and communication and speech recognition systems. These three clinical information systems are commonplace and are intuitive to most radiologists as they replicate familiar paper and film workflow. So what is next? There is a surge of innovation in imaging informatics around advanced workflow, search, electronic medical record aggregation, dashboarding, and analytics tools for quality measures (Nance et al., AJR Am J Roentgenol 200:1064-1070, ). The challenge lies in not having to rebuild the technological wheel for each of these new applications but instead attempt to share common components through open standards and modern development techniques. The next generation of applications will be built with moving parts that work together to satisfy advanced use cases without replicating databases and without requiring fragile, intense synchronization from clinical systems. The purpose of this paper is to identify building blocks that can position a practice to be able to quickly innovate when addressing clinical, educational, and research-related problems. This paper is the result of identifying common components in the construction of over two dozen clinical informatics projects developed at the University of Maryland Radiology Informatics Research Laboratory. The systems outlined are intended as a mere foundation rather than an exhaustive list of possible extensions. [ABSTRACT FROM AUTHOR]

Miscalibration, the failure to accurately evaluate one's own work relative to others' evaluation, is a common concern in social systems of knowledge creation where participants act as both creators and evaluators. Theories of social norming hold that individual's self-evaluation miscalibration diminishes over multiple iterations of creator-evaluator interactions and shared understanding emerges. This paper explores intersubjectivity and the longitudinal dynamics of miscalibration between creators' and evaluators' assessments in IT-enabled social knowledge creation and refinement systems. Using Latent Growth Modeling, we investigated dynamics of creator's assessments of their own knowledge artifacts compared to peer evaluators' to determine whether miscalibration attenuates over multiple interactions. Contrary to theory, we found that creator's self-assessment miscalibration does not attenuate over repeated interactions. Moreover, depending on the degree of difference, we found self-assessment miscalibration to amplify over time with knowledge artifact creators' diverging farther from their peers' collective opinion. Deeper analysis found no significant evidence of the influence of bias and controversy on miscalibration. Therefore, relying on social norming to correct miscalibration in knowledge creation environments (e.g., social media interactions) may not function as expected. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to identify different knowledge profiles in organizations and to analyze their contribution to innovation. This is done by analyzing how knowledge is integrated within the work processes by groups of workers with different characteristics (i.e. knowledge profiles). The paper uses a combination of qualitative analysis with quantitative analysis. Using Action Research methodology, several semi-structured group interviews were performed and a questionnaire was applied to employees of two organizations. In order to identify the employee's profiles, different groups were found by means of a factorial analysis, which resulted in specific knowledge profiles. The major finding of this research was the identification of several knowledge profiles - Innovators, Integrators, Organizers and Facilitators,. An important conclusion is that integration of knowledge in day-by-day practices can boost innovation. The collection of evidences carried out helped to identify ways of integrating knowledge through the implementation of knowledge sharing routines and promotion of collaborative activities in systematic transfer processes, including problem-solving routines and participation in improvement, quality and maintenance projects. [ABSTRACT FROM AUTHOR]

Business Intelligence (BI) applications have been gradually ported to the Web in search of a global platform for the consumption and publication of data and services. On the Internet, apart from techniques for data/knowledge management, BI Web applications need interfaces with a high level of interoperability (similar to the traditional desktop interfaces) for the visualisation of data/knowledge. In some cases, this has been provided by Rich Internet Applications (RIA). The development of these BI RIAs is a process traditionally performed manually and, given the complexity of the final application, it is a process which might be prone to errors. The application of model-driven engineering techniques can reduce the cost of development and maintenance (in terms of time and resources) of these applications, as they demonstrated by other types of Web applications. In the light of these issues, the paper introduces the S4RIA-B methodology, i.e., a model-driven methodology for the development of RIA as BI Web applications. In order to overcome the limitations of RIA regarding knowledge management from the Web, this paper also presents a new RIA platform for BI, called RI@BI, which extends the functionalities of traditional RIAs by means of Semantic Web technologies and B2B techniques. Finally, we evaluate the whole approach on a case study-the development of a social network site for an enterprise project manager. [ABSTRACT FROM AUTHOR]

Enterprises in today's networked economy face numerous information management challenges, both from a process management perspective as well as a decision support perspective. While there have been significant relevant advances in the areas of business process management as well as decision sciences, several open research issues exist. In this paper, we highlight the following key challenges. First, current process modeling and management techniques lack in providing a seamless integration of decision models and tools in existing business processes, which is critical to achieve organizational objectives. Second, given the dynamic nature of business processes in networked enterprises, process management approaches that enable organizations to react to business process changes in an agile manner are required. Third, current state-of-the-art decision model management techniques are not particularly amenable to distributed settings in networked enterprises, which limits the sharing and reuse of models in different contexts, including their utility within managing business processes. In this paper, we present a framework for decision-enabled dynamic process management that addresses these challenges. The framework builds on computational formalisms, including the structured modeling paradigm for representing decision models, and hierarchical task networks from the artificial intelligence (AI) planning area for process modeling. Within the framework, interleaved process planning (modeling), execution and monitoring for dynamic process management throughout the process lifecycle is proposed. A service-oriented architecture combined with advances from the semantic Web field for model management support within business processes is proposed. [ABSTRACT FROM AUTHOR]

A new type of data mining considers the case where the instances over which induction takes place are intelligent agents who might act strategically to thwart the learner. Instances comprised of humans, companies, or governments all have this capability. One paper calls this adversarial learning and proposes an iterated learning process—much like reinforcement learning—to determine a classifier. The current authors proposed a different approach that uses rational expectation ideas to alter the learner’s problem to directly anticipate possible strategic gaming by agents during the induction process. This paper explores differences between solutions produced by these two approaches on a credit dataset and draws some general insights. [ABSTRACT FROM AUTHOR]

As to how to acquire potentially useful knowledge to aid engineers in problem solving, this paper proposed a platform of problem solving by using the knowledge grid. This paper is mainly concerned with the implementation of problem-solving-oriented knowledge acquisition in the knowledge grid, which is a platform for sharing and managing globally distributed knowledge resources. Based on the knowledge grid, engineers could acquire the knowledge that will be potentially useful and give them some hints or inspiration for problem solving. Some key technologies for the platform’s implementation, such as ontology building for product development, semantic register, semantic parser, and query dispatch, are investigated. At last, a working scenario is employed to illustrate the knowledge acquisition process for problem solving in the product development process. [ABSTRACT FROM AUTHOR]