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Having a gatekeeper position in a collaborative network offers firms great potential to gain competitive advantages. However, it is not well understood what kind of collaborations are associated with such a position. Conceptually grounded in social network theory, this study draws on the resource-based view and the relational factors view to investigate which types of collaboration characterize firms that are in a gatekeeper position, which ultimately could improve firm performance in subsequent periods. The empirical analysis utilizes a unique longitudinal data set to examine dynamic network formation. We used a data crawling approach to reconstruct collaboration networks among the 500 largest companies in Germany over nine years and matched these networks with performance data. The results indicate that firms in gatekeeper positions often engage in medium-intensity collaborations and less likely weak-intensity collaborations. Strong-intensity collaborations are not related to the likelihood of being a gatekeeper. Our study further reveals that a firm's knowledge base is an important moderator and that this knowledge base can increase the benefits of having a gatekeeper position in terms of firm performance.

Purpose The purpose of this study is to explore the feasibility of using ChatGPT-based chatbot systems as an alternative to traditional knowledge base-based chatbot systems in library and information centers (LICs). Design/methodology/approach The study used a qualitative research approach to examine the viability of ChatGPT-based chatbot systems in LICs. The researchers conducted an extensive literature review and analyzed data collected from interviews with experts in the field of library and information science. Findings The findings of the study reveal that ChatGPT-based chatbot systems are a viable alternative to traditional knowledge base-based chatbot systems in LICs. ChatGPT has the potential to provide more accurate and personalized responses to user queries, improve the user experience and reduce the workload of library staff. However, there are some limitations to the use of ChatGPT in LICs, such as the need for substantial training data and the risk of perpetuating biases. Originality/value To the best of the authors’ knowledge, this study is one of the first to explore the potential of ChatGPT-based chatbot systems in the context of LICs. The research provides insights into the advantages and limitations of ChatGPT-based chatbots and highlights the possibilities for future use of this technology in the information service industry.

GitHub and GitLab contain many project repositories. Each repository contains many design artifacts and specific project management features. Developers can automate the processes of design and project management with the approach proposed in this paper. We described the knowledge base model and diagnostic analytics method for the solving of design automation and project management tasks. This paper also presents examples of use cases for applying the proposed approach.

DOI: 10.5152/FNJN.2019.18028Aim: The aim of this study is to examinethe structure of the knowledge creation process in nursing science and toinvestigate the dissemination of theoretical knowledge in other disciplines byanalyzing citations and social network data.Method: This exploratory study evaluatedMetadata to find academic publications. Meleis’s Transition Theory was selectedas a case study. A majority of the publications that represented the transitiontheory were assigned as the core of the theory. Forward and backward citationswere used as agents of knowledge linkage to determine the dissemination of thetheory in the field of science. Social network analysis and visualization wereused to depict graphical and structural relations of the research front and theknowledge base.Results: The knowledge base of theTransition Theory was built on 7 different information networks. The mosteffective and vast information network consisted of Meleis, AI as a researcherand the central information network, which is the journal of Nursing Research.Oncology, geriatrics, public health, and psychology in the areas of informationpropagation within the field of nursing were considered as research areas wherethe theoretical framework provided by the theory was rendered functional.Conclusion: In recent years, the use oftheoretical frameworks has become a necessity to assess the changing needs ofthe study of nursing science. The method used in the study can be effectivelyused to analyze the conceptual structures in the nursing education andprofessional application processes and to understand the origins of thesetheories. Further research can help to structure the use of informationalscience-based research designs to understand the connections between theory,clinical practice, and the development of educational contents in nursingfaculties.Cite this article as: Yayla, K. (2019).Understanding the diffusion of theoretical knowledge in nursing: A citationanalysis of Meleis’s Transition Theory. FNJN Florence Nightingale Journal ofNursing, 27(3), 275-283.

Experience shows that Agile project management tools such as Atlassian Jira capture the state of EAS projects by relying solely on expert judgement that is not supported by any knowledge model. Therefore, the assessment of project content against strategic objectives and business domain features are not supported by any tool. This is one of the reasons why Agile project management still does not provide sufficient EAS project delivery results. In order to address this problem, the Enterprise Application Software (EAS) development using Agile project management is summarized in a conceptual model. The model highlights the knowledge used and indicates its nature (empirical or causal digitized). The modified Agile management process we have developed and described in previous works is based on causal knowledge models that supports EAS development and Agile management processes. The purpose of this article is to specify knowledge repository to ensure the Agile management solutions of an EAS project are aligned with strategic goals and business domain causality. It is worth noticing that strategic goals have been identified and specified as capabilities using some enterprise architecture framework (NAF, MODAF, ArchiMate, etc.). The novelty of the proposed method is incorporating the business domain causal knowledge modelling approach into the Agile project management process. The causal knowledge unit is considered as a Management Transaction (MT), which includes closed loop dependence of its components. The modified Agile activity hierarchy (theme, initiative, epic, user story) defines the required content of their mutual interactions. An important new results obtained are the conceptual model of causal knowledge base (KB) and specification of enhanced Agile management tool components: project management database and project state assesment knowledge base. Causal KB includes specification of causal knowledge unit (MT metamodel) and specifications of traditional and causal Agile hierarchy meta-models. These conceptual models define the causal knowledge components necessary to evaluate the state of Agile activities in the EAS development project using intelligent Agile project management tool.

Firms’ knowledge base is one of the key antecedents of firm innovation. While extant literatures identify diversification and depth of firms’ knowledge base as the important inputs of firm innovation, coherence of firms’ knowledge base reflecting the relatedness of knowledge base is largely neglected. This article goes a further step and points out that coherence of firms’ knowledge base will co-affect with diversification or depth of firms’ knowledge base to foster firm innovation, because it improves the efficiency of knowledge combination and integration. Moreover, degree centralization of industrial knowledge networks, as a characteristic of industrial knowledge base, moderates the abovementioned relationships by offering technological combination opportunities in the industry and helping firms break cognitive inertia. Based on patent data of 76 global firms from three-dimensional printing, information and communication technology, wind energy, and lithium battery industries during the period of 1993–2013, the analysis results show that coherence of firms’ knowledge base positively moderates the relationship between diversification of firms’ knowledge base and firm innovation. Furthermore, degree centralization of industrial knowledge networks, together with coherence of firms’ knowledge base, strengthens the positive relationship between knowledge diversification and firm innovation. Theoretical and practical suggestions about firms’ knowledge management were discussed.

The object of research in the paper is the source code of a software product written in the Swift programming language. Most programs as a product of a certain project during the life cycle require changes and modifications, which is costly or impossible to implement in the case of poor code quality. One of the options for solving the problem of poor code quality is the timely application of refactoring principles. The existing problem is that implementation of high-level refactoring must be done manually by the developer without the use of automated tools as built-in solutions cannot fulfill the need due to the architectural complexity of the product. To reduce the number of errors made during refactoring, to simplify the process of performing routine actions, it is suggested to use a new software product for refactoring. It works with high-level user commands based on a formalized description of the source code together with a knowledge base containing a description of code entities and their properties (what specific actions can be performed with them). In the work, the refactoring of the source code was carried out using the example of the Swift programming language. The proposed approach of component architecture (knowledge base, software engine) further allows to expand the functionality of the software product to other programming languages. The work was directed to the development of a prototype of a software product using the proposed approach to check and compare the results with other refactoring tools. A command line utility has been developed that accepts a verbal command as an input and outputs the results of processing and analysis of the source code (search for complex structures in the code) or applies the proposed change. As a result of the conducted testing, it was established that the use of the proposed approach allows performing complex refactoring tasks with the help of a simple verbal formalized command. Accomplishing the same task using only the built-in refactoring tools requires significantly more time and effort or is impossible at all.

Over the last decade, service selection and recommendation had been two strongly related service filtering steps. The ever changing services environment, users tastes, as well as the perception and popularity of available services, rise a question regarding the appropriate means to capture and analyze such changes over time. Most service recommendation solutions are static and do not offer a multi-relational modeling of user-service interactions over time. Time is a contextual dimension that has, recently, received a lot of attention, leading to a new class of recommender systems, called time-aware recommender systems. In this work, we propose a service recommendation method that takes advantage of temporal knowledge graphs. As a de facto standard to model multiple and complex interactions between heterogeneous entities, knowledge graphs will serve as a historical knowledge base for our TASR system. We, first, model the user-service interactions over time, by constructing a temporal service knowledge graph (TSKG) that will be later enriched through a completion step. Second, to explore the TSKG and extract top-rated services, we use Convolutional Neural Networks (CNN) to embed the TSKG into a low-dimensional vector space, facilitating then its mining.

The paper deals with issues of designing and creating knowledge bases in the field of nuclear science and technology. The authors present the results of searching for and testing optimal classification and semantic annotation algorithms applied to the textual network content for the convenience of computer-aided filling and updating of scalable semantic repositories (knowledge bases) in the field of nuclear physics and nuclear power engineering and, in the future, for other subject areas, both in Russian and English. The proposed algorithms will provide a methodological and technological basis for creating problem-oriented knowledge bases as artificial intelligence systems, as well as prerequisites for the development of semantic technologies for acquiring new knowledge on the Internet without direct human participation. Testing of the studied machine learning algorithms is carried out by the cross-validation method using corpora of specialized texts. The novelty of the presented study lies in the application of the Pareto optimality principle for multi-criteria evaluation and ranking of the studied algorithms in the absence of a priori information about the comparative significance of the criteria. The project is implemented in accordance with the Semantic Web standards (RDF, OWL, SPARQL, etc.). There are no technological restrictions for integrating the created knowledge bases with third-party data repositories as well as metasearch, library, reference or information and question-answer systems. The proposed software solutions are based on cloud computing using DBaaS and PaaS service models to ensure the scalability of data warehouses and network services. The created software is in the public domain and can be freely replicated.

In this article, we define four technological strategies by analyzing two dimensions in the exploitation versus exploration choice: knowledge base and technical application. The relationships of technological strategies with the quality of invention are investigated. The operationalization of strategies through patent data is provided, using knowledge field combinations as the unit of analysis. Patents filed by worldwide top R&D spending technology hardware and equipment companies are analyzed. A 2 × 2 matrix is suggested, defining four strategies: strengthening, enrichment, resettlement, and venturing. Mixed effects regression models are used to investigate the effects of such strategies on invention quality, providing positive relationships in relation to the first two strategies.

The complex traffic and road environment pose considerable challenges to the accuracy, timeliness, and adaptive ability of connected and autonomous vehicles (CAVs) in making driving decisions. This paper uses vehicle collaboration and integrates the adaptive learning capabilities of machine learning and the interpretation capabilities of expert systems (ESs) in a unified architecture to form a hybrid autonomous driving guidance system, which not only solves the ``bottleneck'' of knowledge acquisition during the construction of expert systems but also solves the ``black box'' phenomenon of machine learning in the decision-making process. First, an autonomous driving strategy based on deep reinforcement learning (DRL) is proposed for CAVs to make decisions and extract corresponding rules. Next, we design an ES knowledge base expansion method including rule extraction, rule sharing, and rule test. Particularly, vehicular blockchain is adopted to ensure user privacy and data security during the rule-sharing process. Third, hybrid autonomous driving guidance combining ES and machine learning is proposed for CAVs to make accurate and efficient decisions in different driving environments. Once the strategy is well trained, it can effectively guide CAVs to cope with the complex traffic environment. Extensive simulations validate the performance of our proposal in terms of decision-making accuracy, effectiveness, and safety.

An interpersonal network within a firm serves as a primary knowledge base for organizational innovation. In this article, we propose that intrafirm network connectivity, which is measured by the transitivity of an intrafirm network, has a nonlinear effect on firm innovation performance. Furthermore, on the basis of the literature on environmental contingency, we propose that two dimensions of environmental uncertainty (i.e., environmental munificence and dynamism) moderate the influence of intrafirm network connectivity on firm innovation performance. To examine our hypotheses, we collect the profiles of firms in the pharmaceutical and biotechnology industries from the COMPUSTAT database and patent data from the United States Patent and Trademark Office database for the period of 1991–2012. Our longitudinal study finds an inverted U-shaped relationship between intrafirm network connectivity and firm innovation performance, and such a relationship is moderated by environmental munificence but not by environmental dynamism.

Machine learning approaches have exhibited high potential in power system transient stability assessment (TSA), yet their initial preparation stages of stability knowledge base generation (SKBG) based on time-domain simulations often undergo high computational costs. In fact, how to ease the heavy computational burden of SKBG without sacrificing the reliability is still a significant challenge. To address this problem, this paper develops a semi-supervised ensemble learning (SSEL) framework for reliable SKBG acceleration, without additional need for computing hardware upgrading. In particular, it performs detailed simulations for a minority of cases and fast simulations for the majority ones to reduce the total computation time. Considering the absence of stability status information of those fast simulated cases, an SSEL scheme is systematically designed to reliably label their stability status. Before implementing SSEL, two concise feature descriptors are first introduced to efficiently extract transient features from multiplex system trajectories. Then, all the cases are characterized in a unified feature space, whereby a series of semi-supervised support vector machines are trained in randomly formed subspaces. Afterward, these single machines are systematically combined to derive an enhanced SSEL model, which is able to make reliable and robust labeling decisions. Further, a backtrace strategy is carefully devised for SSEL, so as to maintain the high reliability of SKBG. Test results on the IEEE 39-bus system and the realistic GD Power Grid in South China illustrate the excellent performances of the proposed framework on SKBG acceleration.

Fish classification (FC) is an expansively studied problem in the domains of image segmentation, pattern recognition, and information retrieval. It has been applied in a countless number of domains including target marketing. Meanwhile, governments are obliged to maintain the fish supply and balance between the ecosystem, commercial, agriculture field, marine scientists, and industrial arena of fish including the nutrition and canning factories. The various FC techniques performance is compared relying on the availability of preprocessing and feature extraction methods, the number of extracted features and classification accuracy, the number of fish families/species recognized. This survey also reviewed the use of Databases such as Fish4-Knowledge (F4K), knowledge database, and Global Information System (GIS) on Fishes and other FC databases. The study on preprocessing methods features extraction techniques and classifiers are gathered from recent works to enhance the understanding of the characteristics of preprocessing methods, features extraction techniques, and classifiers to guide future research directions and compensate for current research gaps.

The predictive vessel surveillance is one of the indispensable functional components in intelligent maritime traffic system. Vessel trajectory prediction serves as a prerequisite for collision detection and risk assessment. Perceiving the forthcoming traffic situation in advance helps decide the succeeding actions to mitigate the potential risk. The availability of maritime big data brings great potential to extract vessel movement patterns to support trajectory forecasting. In this paper, a novel vessel trajectory and navigating state prediction methodology is proposed based on AIS data, which synergizes properly designed learning, motion modelling and knowledge base assisted particle filtering processes. The primary contributions of this work also comprise several critical research findings to handle the key challenges in vessel trajectory and navigating state prediction problem, such as the adaptive training window determination for the learning process and effective knowledge storage and searching algorithm intended to reduce the query time of waterway pattern retrieval. The studies for these challenges are still missing in the reported literatures but they are essentially important for improving the prediction accuracy, efficiency and practicality. With the maritime traffic data collected for Singapore water, a thorough evaluation of the prediction performance has been conducted for different navigating scenarios. It is also observed that better prediction outperforms on account of allowing earlier alert in risk detection.

The inconsistency in the hierarchical knowledge base is due to the fact that the assertions (ABoxes) come from several sources with different levels of reliability. We introduce the management of this inconsistency problem to query the inconsistent DL-Lite knowledge bases. Most of the approaches studied in the literature first repair all of the inconsistent assertions in the DL-Lite incoherent knowledge base and then question it. In this article, we proceed directly to a knowledge base query to retrieve an exhaustive list of responses and then, once these responses are retrieved, we repair them. The main contribution of this work therefore consists of studying several response strategies to queries from various data sources using a recurrent function to calculate the rank of coherence which will allow us to manage inconsistencies in the set of responses. We study these strategies from different points of view: productivity, precision, recall, F-measure, Accuracy, G-mean and computational complexity. The experiments carried out, on different corpora, and the analysis of the results, which we carried out, show encouraging results in productivity since our approach is much more productive, providing the greatest number of responses, while remaining the best in time compared to the other studied approaches.

Cloud computing complexity is growing rapidly with the advancements that it is witnessing. It has created a requirement to simplify the process of configuring cloud and re-configuring it when required, it also involves tasks like auto scaling of infrastructure, elastic computing and maintaining the health of the servers. The proposed method introduces a smart cloud management using knowledge base, which models the resources of cloud; it handles service level agreement and its evaluations. The proposed knowledge base supports representational state transfer (REST/RESTful) services to store and manipulate different cloud aspects like type of application, business configuration, and metrics value and its type; it also implements the strategy for efficient resource management for smart clouds. The proposed architecture consists of smart cloud engine (which provides autonomous services, which help to exploit cloud resources for service optimization and to perform service automation), knowledge base (KB) (provide a cloud ontology which will help in the management of resources and provides intelligence to the smart cloud), server and cloud enrolment, designated monitoring tool and moderator. The resulted module is easy to integrate with any of the existing cloud management tool or orchestrator. As It is developed using REST protocol and extensible markup language (XML) language it is also easy to integrate with existing monitoring tool or application programming interface (APIs).

The challenge of defining and cataloging the building blocks of the brain requires a standardized approach to naming neurons and organizing knowledge about their properties. The US Brain Initiative Cell Census Network, Human Cell Atlas, Blue Brain Project, and others are generating vast amounts of data and characterizing large numbers of neurons throughout the nervous system. The neuroscientific literature contains many neuron names (e.g. parvalbumin-positive interneuron or layer 5 pyramidal cell) that are commonly used and generally accepted. However, it is often unclear how such common usage types relate to many evidence-based types that are proposed based on the results of new techniques. Further, comparing different types across labs remains a significant challenge. Here, we propose an interoperable knowledge representation, the Neuron Phenotype Ontology (NPO), that provides a standardized and automatable approach for naming cell types and normalizing their constituent phenotypes using identifiers from community ontologies as a common language. The NPO provides a framework for systematically organizing knowledge about cellular properties and enables interoperability with existing neuron naming schemes. We evaluate the NPO by populating a knowledge base with three independent cortical neuron classifications derived from published data sets that describe neurons according to molecular, morphological, electrophysiological, and synaptic properties. Competency queries to this knowledge base demonstrate that the NPO knowledge model enables interoperability between the three test cases and neuron names commonly used in the literature.

Traditional topic classification usually adopts the closed-world assumption that all the test topics have been seen in training. However, in open dynamic environments, the potential new topics may appear in testing due to the evolution of text data over time. Considering the uncertainty and multi-granularity of dynamic text data, such open topic classification needs to detect unseen topics by mining the boundary region continually, and incrementally update the previous models by knowledge accumulation. To address these challenge issues, this paper introduces a unified framework of three-way multi-granularity learning to open topic classification based on the fusion of three-way decision and granular computing. First, we propose the multilevel granular structure of tasks from the temporal-spatial multi-granularity perspective. Then, we construct an adaptive decision boundary and use the centroids and the corresponding radius to discover unknowns by the reject option. Subsequently, we further explore the unknown topics by three-way enhanced clustering and the uncertain instances will be re-investigated in the next stage. Besides, we design a built-in knowledge base represented as the centroid of each topic to store the topic knowledge. Finally, the experiments are conducted to compare the performances of proposed models and the efficiency of knowledge accumulation with classic models.

In the Oil and Gas industry, the implementation of artificial intelligence techniques gives advantages of better use of existing infrastructure. It provides better future outcomes, which makes it an essential technology in the operations of the industry. In this paper, the implementation of artificial intelligence techniques for good planning, determination of reservoir rock properties, drilling optimization and production facilities are reviewed. With precise knowledge of permeability and porosity, reservoir engineers can develop robust reservoir development plan and manage hydrocarbon recovery effectively. For porosity prediction, wire-line log data and seismic attributes are used in artificial intelligence-based models. Permeability prediction has been discussed by considering the different types of well-log data in the modeling of artificial intelligence techniques to predict single-phase fluid. Solution of specific developed problems during the determination of permeability in the actual experiment is also mentioned. Artificial intelligence techniques are used in the design and accurate analysis of the drilling fluid. It assists in the selection of drill bit and drilling-related problems. If a reservoir cannot produce naturally, we can select a suitable artificial lift and design the production system using Artificial intelligence techniques. For EOR methods, SADA and NAVA knowledge base systems are discussed. Artificial intelligence can help in optimizing production, improve production efficiency and recovery of hydrocarbons.

Managing customer relationships is essential to any successful business, but it can be a challenging and time-consuming process. Sales teams often struggle to keep track of leads, prospects, and customers across various channels and stages of the sales funnel. This can result in missed opportunities, inefficient processes, and ultimately, lost revenue. However, with the right customer management system, businesses can streamline their sales process, improve customer engagement, and increase sales. In this whitepaper, we will explore the benefits of PresearCMS as a customer management system and how it can help businesses overcome common sales challenges. &nbsp

Precision Agriculture, as the future of farming technology, addresses challenges faced by farmers by data-mining of information collected through IoT-enabled crop monitoring infrastructures. The identification of Crop disease is one of the widely studied challenges. Crop diseases cannot be accurately predicted by merely analyzing individual disease causes. This proposal presents a Fuzzy logic-based pest prediction mechanism assisting in beforehand preparedness for potential pest prevention. The experiments are performed for pests in rice and millet crops. The Data-mining over samples collected in a cropping cycle revealed the plausible correlation between temperature, relative humidity, and rainfall with pest breeding. The data collected through IoT monitoring infrastructure is analyzed for the ambient breeding condition of the pest. These conditions are then employed to design the knowledge base of the fuzzy system. More specifically, the linguistic variables of the fuzzy membership function are optimized using a genetic algorithm for close prediction of pest breeding in given environmental conditions. The proposal verified that the weather factors have a strong impact on the occurrence of pests and diseases, and the fuzzy logic-based pest predication through IoT application development services will help farmers to take precautionary measures beforehand.

Nowadays natural language generation (NLG) is used in everything from news reporting and chatbots to social media management. Recent advances in machine learning have made it possible to train NLG systems that seek to achieve human-level performance in text writing and summarisation. In this paper, we propose such a system in the context of Wikipedia and evaluate it with Wikipedia readers and editors. Our solution builds upon the ArticlePlaceholder, a tool used in 14 under-resourced Wikipedia language versions, which displays structured data from the Wikidata knowledge base on empty Wikipedia pages. We train a neural network to generate an introductory sentence from the Wikidata triples shown by the ArticlePlaceholder, and explore how Wikipedia users engage with it. The evaluation, which includes an automatic, a judgement-based, and a task-based component, shows that the summary sentences score well in terms of perceived fluency and appropriateness for Wikipedia, and can help editors bootstrap new articles. It also hints at several potential implications of using NLG solutions in Wikipedia at large, including content quality, trust in technology, and algorithmic transparency.

Motivation: The question of what combination of attributes drives the adoption of a particular software technology is critical to developers. It determines both those technologies that receive wide support from the community and those which may be abandoned, thus rendering developers' investments worthless. Aim and Context: We model software technology adoption by developers and provide insights on specific technology attributes that are associated with better visibility among alternative technologies. Thus, our findings have practical value for developers seeking to increase the adoption rate of their products. Approach: We leverage social contagion theory and statistical modeling to identify, define, and test empirically measures that are likely to affect software adoption. More specifically, we leverage a large collection of open source repositories to construct a software dependency chain for a specific set of R language source-code files. We formulate logistic regression models, where developers' software library choices are modeled, to investigate the combination of technological attributes that drive adoption among competing data frame (a core concept for a data science languages) implementations in the R language: tidy and data.table. To describe each technology, we quantify key project attributes that might affect adoption (e.g., response times to raised issues, overall deployments, number of open defects, knowledge base) and also characteristics of developers making the selection (performance needs, scale, and their social network). Results: We find that a quick response to raised issues, a larger number of overall deployments, and a larger number of high-score StackExchange questions are associated with higher adoption. Decision makers tend to adopt the technology that is closer to them in the technical dependency network and in author collaborations networks while meeting their performance needs. To gauge the generalizability of the proposed methodology, we investigate the spread of two popular web JavaScript frameworks Angular and React, and discuss the results. Future work: We hope that our methodology encompassing social contagion that captures both rational and irrational preferences and the elucidation of key measures from large collections of version control data provides a general path toward increasing visibility, driving better informed decisions, and producing more sustainable and widely adopted software.

In the second part of our work, to accomplish the task – to develop the knowledge base needed when developing a personal system, provides a comprehensive and in an ideal – a harmonious physical activity, offered: - the basic principles of the formation, development and use of tempering exercise; - possible kinds of positive effects on the body and obtained results possible.

We present Knowledge Rocks, an implementation strategy and guideline for augmenting visualization systems to knowledge-assisted visualization systems, as defined by the KAVA model. Visualization systems become more and more sophisticated. Hence, it is increasingly important to support users with an integrated knowledge base in making constructive choices and drawing the right conclusions. We support the effective reactivation of visualization software resources by augmenting them with knowledge-assistance. To provide a general and yet supportive implementation strategy, we propose an implementation process that bases on an application-agnostic architecture. This architecture is derived from existing knowledge-assisted visualization systems and the KAVA model. Its centerpiece is an ontology that is able to automatically analyze and classify input data, linked to a database to store classified instances. We discuss design decisions and advantages of the KR framework and illustrate its broad area of application in diverse integration possibilities of this architecture into an existing visualization system. In addition, we provide a detailed case study by augmenting an it-security system with knowledge-assistance facilities., Comment: IEEE Vis 2021

In October of 2017 and 2019, Sonoma County California endured historic wildfires and subsequent community trauma. The Sonoma Wildfire Mental Health Collaborative was created to (a) democratize access to evidence-based mental health resources and services for personal recovery and long-term community resilience building, and to (b) measure the reach and efficacy of the strategies employed in order to create a knowledge base to inform disaster response in other communities. Offerings included a mind-body yoga program and training in Skills for Psychological Recovery (SPR) for counselors who wished to provide services to individuals impacted by the wildfires. An evaluation of the mental health strategies employed revealed that (a) the mind-body program was well-received, with a high degree of satisfaction and self-reported benefit among individuals who attended trauma-informed yoga classes and (b) counselors found SPR to be a practical, flexible short-term intervention for individuals in the aftermath of the wildfires and expressed moderate to high levels of intent to use it in practice. Importantly, the evaluation of the 2017 wildfire mental health response was compromised by the Kincade Fire in 2019, in which prolonged mandatory evacuations and power outages impacted response rates. The origin story is shared for how a community collaborative was built. Lessons learned are discussed and recommendations summarized so as to contribute to the science and practice of disaster mental health outreach. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Higher education management problems in delivering 100% of graduates who can satisfy business demands. In industry it is often difficult for qualified graduates to identify the appropriate means to evaluate problem - solving abilities as well as shortcomings in the evaluation of problem solving skills. This is partially due to the lack of an adequate methodology. The purpose of this paper is to provide the appropriate CBR-KBS model for predicting and evaluating the characteristics of the student's dataset so as to comply with the parameters of selection required by the university industry. Machine learning algorithms have been used in these study areas under supervision, uncompleted and uncontrolled; K-Nearest neighbor, Naïve Bayes, Decision Tree, Neural Network, Logistic Regression and Vector Support Machines. The proposed model would allow university management to make easier, more professional, experienced and industry-specific plans for the manufacturing of graduates and graduates who passed the type I and II examinations held by the employment opportunities.

Automatic International Classification of Diseases (ICD) coding is an important task in the future of artificial intelligence healthcare. In recent years, a lot of traditional machine learning-based methods have been proposed, and they achieved good results on this task. However, these traditional machine learning-based methods for automatic ICD coding only focus on the semantic features of clinical notes and ignore the feature extraction of ICD titles that are the descriptions of ICD codes. In this paper, we propose a knowledge attention-based deep learning framework called KAICD for automatic ICD coding. KAICD makes full use of the clinic notes and the ICD titles. The semantic features of clinic notes are extracted by a multi-scale convolutional neural network. For ICD titles, we use attention-based Bidirectional Gated Recurrent Unit (Bi-GRU) to build a knowledge database, which can offer additional information. Depending on input clinic notes, we can use the attention mechanism to obtain different knowledge vectors from the knowledge database where some ICD titles are more relevant to the input clinic notes. Last, we concatenate the knowledge vectors and the semantic features of clinic notes, and use them for the final prediction. KAICD is tested on a public dataset Medical Information Mart for Intensive Care III (MIMIC III); it achieves micro-precision of 0.502, micro-recall of 0.428, and micro-f1 of 0.462, which outperforms other competing methods. Furthermore, the results of the ablation study show that the knowledge database of ICD titles learned by the attention-based Bi-GRU enhances the feature expression and improves the prediction performance.

In this design study, we present IRVINE, a Visual Analytics (VA) system, which facilitates the analysis of acoustic data to detect and understand previously unknown errors in the manufacturing of electrical engines. In serial manufacturing processes, signatures from acoustic data provide valuable information on how the relationship between multiple produced engines serves to detect and understand previously unknown errors. To analyze such signatures, IRVINE leverages interactive clustering and data labeling techniques, allowing users to analyze clusters of engines with similar signatures, drill down to groups of engines, and select an engine of interest. Furthermore, IRVINE allows to assign labels to engines and clusters and annotate the cause of an error in the acoustic raw measurement of an engine. Since labels and annotations represent valuable knowledge, they are conserved in a knowledge database to be available for other stakeholders. We contribute a design study, where we developed IRVINE in four main iterations with engineers from a company in the automotive sector. To validate IRVINE, we conducted a field study with six domain experts. Our results suggest a high usability and usefulness of IRVINE as part of the improvement of a real-world manufacturing process. Specifically, with IRVINE domain experts were able to label and annotate produced electrical engines more than 30% faster.

In reality there are always a large number of complex massive databases. The notion of homomorphism may be a mathematical tool for studying data compression in knowledge bases. This paper investigates a knowledge base in dynamic environments and its data compression with homomorphism, where “dynamic” refers to the fact that the involved information systems need to be updated with time due to the inflow of new information. First, the relationships among knowledge bases, information systems and relation information systems are illustrated. Next, the idea of non-incremental algorithm for data compression with homomorphism and the concept of dynamic knowledge base are introduced. Two incremental algorithms for data compression with homomorphism in dynamic knowledge bases are presented. Finally, an experimental analysis is employed to demonstrate the applications of the non-incremental algorithm and the incremental algorithms for data compression when calculating the knowledge reduction of dynamic knowledge bases.

The article explains such concepts as continuity, pedagogical continuity, and the main provisions for the implementation of the principle of continuity. The analysis of the works of teachers who studied this principle is carried out. The main directions of the principle of continuity in the educational process were also shown, which include: the implementation of continuity through the connection of new educational material with the previous topic; the implementation of continuity through the expansion and deepening of the knowledge base available to students; the implementation of continuity in the assimilation of the new proposed material at a high level, the implementation of continuity in the development, improvement of existing knowledge among students when mastering new knowledge; the implementation of continuity in the process of repeating the educational material, i.e. along with the reproduction of the material passed by comparing and contrasting it. The article shows the ways of implementing the main directions of the principle of continuity.

Bisphenol A (BPA) is one of the most studied and most controversial chemicals used by the food packaging industry, because of its endocrine disruptive properties. Part of the controversy is due to the uncertainty that surrounds the effects of BPA on the endocrine system. Uncertainty includes data gaps, methodological hurdles, incompatibilities between toxicology and endocrinology-based approaches, and so on. In this article, we analyze how uncertainty has been conceptualized and treated. We focus on the European Food Safety Authority assessments of BPA, and study how exposure and hazard assessments have evolved over time, how uncertainty has been analyzed, and how the agency responded to controversies. Results show that in the attempt to reduce knowledge gaps, assessments have become progressively larger, including more references, evidence, and effects. There is a tendency toward greater precisions and specification of results, and toward protocolization of all processes included in the assessment (from literature review, to uncertainty assessments, and public consultation). Yet, the uncertainty has not diminished following the increase in evidence. We argue that the strategy used to reduce uncertainty within risk assessment, namely including more variables, studies, data, and methods, amplifies the uncertainty linked to indeterminacy (as more results increase the fragmentation of the knowledge base due to the open-ended nature of complex issues) and ambiguity (as complexity gives way to multiple nonequivalent interpretations of results). For this reason, it is important to consider different types of uncertainty and how these uncertainties interact with each other. publishedVersion

Employing grounded theory methodology, this article investigates collaborative innovation based on interview and secondary data collected from a technology alliance. The findings reveal that alliance-specific capabilities and knowledge-based capabilities are the two critical drivers. Alliance-specific capabilities include such components as alliance management, mutual learning, and alliance innovation; while knowledge-based capabilities refer to knowledge-centered activities of exploration, exchange, integration, and exploitation. Further, the two types of capabilities interact with each other in creating a technological knowledge base through mobilizing knowledge and in, ultimately, producing technological innovation. Taken together, this article extends the existing literature of technology alliance by providing a knowledge-based, capability-building view in unpacking collaborative innovation. It also provides implications for managers who aim to enhance technological innovation via alliance cooperation.

This study aims to provide various maps of the evolution, current situation and future lines of sustainability indicators for tourism destinations research. The bibliographic analysis of 440 papers (after refinement) published from 1995 to 2021 reveal the existence of three stages over time and an enormous increase during the last eight years. The results suggest the multidisciplinary nature of the subject that has developed from environmental and social areas to scientific and technological areas, and the existence of seven clusters around the knowledge base. The key research topics have evolved from an essentially instrumental approach to the applicability of developments to management and specific perspectives. The seven following topics merit further research: (1) proposals for different types of destination, (2) indicators construction methodologies, (3) fulfilment of SDGs, (4) development of international frameworks, (5) role of host communities, (6) interactions between stakeholders and (7) focus to climate change effect.

Data driven method based on big data and deep model is an effective tool to solve the problem of online power system transient stability assessment. Nevertheless, when the system suffers from some large disturbances such as the changes of topology, there is no proper way for a model to supplement the original knowledge base but to retrain it from scratch. Meanwhile, with the continuous emergence of new scenarios, data management has become a new dilemma reflected in the increase of data accumulation and retraining time. To address these issues, a novel TSA framework with continual learning ability is proposed. First, the improved convolutional neural network-based orthogonal weight modification algorithm is selected as a transient stability predictor. Then, large disturbance is regarded as a new recognition task which can be continuously learned by the predictor. Through the proposed updating process, the original knowledge base can be updated and supplemented only by the data corresponding to new scenario. The test results on two power systems show that the proposed scheme can realize the tradeoff between the integrity of knowledge base and the update speed without losing too much accuracy in a limited capacity.

Active shooting (AS) violence poses a serious threat to public safety. Most importantly, this number is increasing at an alarming rate over the last few years. Unfortunately, the knowledge base on attack detection and civilian response strategy under an AS incident are very limited. Such a lack of understanding hinders the development of effective training modules, response, and mitigation strategies (for both civilians and law enforcement officers) for an event that is highly unpredictable and unfolds quickly (typically in less than 5 min). To fill this gap in the literature, this article proposes an innovative mathematical model which captures several important features (e.g., capacity of the facility and individual choices, heterogeneity of individual behavioral and choice sets, restriction on choice sets depending on the location of the shooter and facility orientation, and many others) which are essential for appropriately characterizing and analyzing the response strategy for civilians under an AS exposed environment. We demonstrate the applicability of the proposed model by implementing the effectiveness of the RUN.HIDE.FIGHT. program in an academic environment. Results indicate that cognitive delay, the initial distribution of the civilians, and building configurations (e.g., location and number of entrances/exits) play a significant role in the safety of civilians under an AS incident.

How information literate are the Google generation, and what information literacy skills do they bring to university? For university libraries, understanding student prior knowledge provides a foundation on which to introduce appropriate learning activities during the first year. In 2009, in response to a new pedagogical model in health sciences, La Trobe University Library measured and analysed the entry-level information literacy skills of first year health science students. The data was gathered during the first week of semester and 1,029 responses were collected. This paper examines the results of the survey and its implications for programs that broaden and build on students’ existing knowledge base.

In the field of assembly automation with industrial robots the research on the application of artificial intelligence techniques is getting increasing importance. This paper describes the hierarchical structure of a knowledge—based sensory-controlled robot assembly system under development which is capable to plan and execute assembly tasks under real—time requirements. The hybrid knowledge representation scheme combining the rule—based and object—oriented approach to represent the assembly domain—specific knowledge is discussed. Furtheron, the knowledge processing concept based upon the representation scheme is explained. A first prototype of the system has been implemented in a real robotic test—bed. Several peg/hole part mating sequences validated the capability of the system to execute assembly tasks in an uncertain environment using sensory information.

Living with Parkinson's Disease introduces a range of significant challenges into one's daily life. While medical interventions exist to overcome some of these challenges, patient self-care techniques often form an essential complement to the treatments recommended by medical doctors. Knowledge on these self-care techniques often originates from those living with Parkinson's themselves or their close caregivers, as they have the knowledge and experience required to assess self-care techniques. This so-called ‘patient knowledge’ is usually exchanged in peer meetings or discussion forums. Although vital to the Parkinson's Disease community, this information is often difficult to access due to its unstructured format and the difficulty of navigating through online forums. We present an online tool that allows for contributing, assessing, and finally discovering Parkinson's Disease self-care techniques. The custom discovery tool was populated with self-care knowledge by over 300 people with Parkinson's and dozens of their carers, spanning areas such as daily well-being and using assistive equipment. Then, we invited patients to explore the discover features in a smaller scale trial. While well-received, our deployment highlighted several challenges that we further discuss in this paper. Overall, our study contributes to crowdsourced digital health solutions and provides both design and research implications to this challenging domain with a vulnerable user group.

Big data is massive and heterogeneous, along with the rapid increase in data quantity, and the diversification of user access, traditional database, and access control methods can no longer meet the requirements of big data storage and flexible access control. To solve this problem, an entity relationship completion and authority management method is proposed. By combining the weighted graph convolutional neural network and the attention mechanism, a knowledge base completion model is given. On this basis, the authority management model is formally defined and the process of multilevel trust access control is designed. The effectiveness of the proposed method is verified by experiments, and the authority management of knowledge base is more fine-grained and more secure.

One of the algorithms stored in natural intelligence is the writing of Arabic numerals in Indonesian words. Algorithms in naturals intelligence are not easy to find. This problem gave us an idea to create artificial intelligence that tries to mimic natural intelligence algorithms. The proposed algorithm for building artificial intelligence is an R-Z rule-based system. This rule-based system contains a knowledge base of R-Z rules and a knowledge base of facts. In the knowledge base, the R-Z rule provides the R rule and the Z rule, while the facts knowledge base provides facts in the form of a definite standard number and an affix word. R-Z rule-based system for reasoning writing Arabic numerals in Indonesian words uses forward chaining. Artificial intelligence designs that mimic naturals intelligence in writing numbers in Indonesian words were made in C using Borland C++ 5.02 software. The experimental results show that by applying the R's rule of seven rules and Z's of twenty-five rules, the R-Z rule-based system can write Arabic numerals in Indonesian words from Arabic numerals "0" to Arabic numerals "9999999". For example, to write the Arabic number "10" in Indonesian words, the R-Z rule-based system starts with the R2 rule. Rule R2 takes action on Z3 to create new facts about Arabic numerals in the Indonesian word, namely "SEPULUH."

An organized knowledge base can facilitate the exploration of existing knowledge and the detection of emerging topics in a domain. Knowledge about and around Geographic Information Science and its associated system technologies (GIS) is complex, extensive and emerging rapidly. Taking the challenge, we built a GIS knowledge graph (GIS-KG) by (1) merging existing GIS bodies of knowledge to create a hierarchical ontology and then (2) applying deep-learning methods to map GIS publications to the ontology. We conducted several experiments on information retrieval to evaluate the novelty and effectiveness of the GIS-KG. Results showed the robust support of GIS-KG for knowledge search of existing GIS topics and potential to explore emerging research themes.

Wikidata, a community-curated knowledge base related to Wikipedia, affects our access to information, wielding more power than many realize. Seeing an opportunity for improving access to knowledge and promoting their collections, libraries, archives, and other cultural heritage institutions have been experimenting with Wikidata in various ways. One burgeoning area of activity is in Wikidata’s scholarly citation data, but that participation has largely concentrated in the sciences, leaving a gap in its theological and religious studies coverage. This presentation demonstrates how this gap matters to theological libraries and shows how you can contribute to efforts to fill it, even in small ways. Delving into Wikidata can be intimidating, but it doesn’t have to be. As long as you have basic computer skills, you can find a way to participate.

Despite a rich knowledge base about infants' social learning and studies observing social referencing in other species in food contexts, we know surprisingly little about social learning about food among human infants. This gap in the literature is particularly surprising considering that feeding unfamiliar foods to infants is a very common experience as infants begin to eat solid foods. The present study examines whether parental social modeling influences infants' willingness to accept unfamiliar foods. In two Zoom sessions, parents will be asked to feed unfamiliar foods to their 6- to 24-month-old infants (different unfamiliar foods in each session). In both sessions, infants' food acceptance and rejection will be measured. In the first session, parents will be asked to do what they would typically do; spontaneous social modeling will be recorded. In the second session, parents will be instructed to model eating the unfamiliar food. We will examine associations between infants' willingness to eat unfamiliar foods, parent social modeling, and extant parent and infant characteristics.

Cardiac arrhythmias are not life-threatening straight away but can cause serious heart-related complications if not medically handled appropriately. An electrocardiogram (ECG) captures the heart’s electric activity and has widespread usage due to its easy deployment and non-invasive aspect. Arrhythmia classification through manual analysis of electrocardiogram (ECG) is troublesome, tedious, and prone to human errors that can lead to serious repercussions. Hence, it’s a more effective alternative to deploy computational techniques to automatically perform the classification. Traditional techniques are data-driven and require an immense amount of data to train and then perform identification. This paper presents an ontology-driven knowledge model to automatically diagnose arrhythmias based on the patient’s sensor-based ECG data. The proposed approach models the arrhythmia domain knowledge and the conceptual relationships relevant to classification of heartbeat into corresponding cardiac arrhythmias and to facilitate decision making with respect to the patients. The newly developed arrhythmia ontology consists of three different modules, each semantically annotating a different aspect of the arrhythmia detection process. A SWRL (Semantic Web Rule Language) based ontology classifier performs classification of patient’s ECG data into corresponding cardiac arrhythmia types. The constructed knowledge base is ontologically aligned with some benchmarked top-level ontologies that promotes the semantic interoperability across multiple domains. The resultant ontological model is validated with a real-world ECG dataset and compared with the existing approaches showing higher precision rate and comparable performance. The developed model establishes a standardized ontology, that promotes the exchange and shareability of consensual domain knowledge about arrhythmic conditions, supports information retrieval and knowledge discovery.

Improving beverage patterns is important for promoting the health of children and adolescents. Food preferences can begin developing at a young age; thus, children and adolescents should be provided healthy beverages in accordance with current recommendations. The purpose of this review is to identify and summarize existing beverage recommendations for children and adolescents. This review also discusses the health impacts of beverages and how beverage consumption has changed among children and adolescents over time. The mean number of calories that children and adolescents are consuming from beverages on a given day has declined in recent years. However, sugar-sweetened beverages (SSB) are the primary source of added sugars in this population. Therefore, SSB consumption remains a concern, and reducing the intake of SSB is still considered a top priority for helping to improve dietary quality. This review uniquely compiles recommendations into one convenient table for the entire age range of 0–19 years and includes recommendations that can be more adequately individualized. Beverage categories where beverage recommendations from authoritative sources are lacking have also been identified. Potential methods that can be implemented to improve beverage patterns and current public policy recommendations have been discussed to provide actionable steps that public health professionals can utilize in practice. The comprehensive table of beverage recommendations could be useful for professionals promoting the health of children and adolescents in research, clinical, and public health settings.

Word embedding models have recently shown some capability to encode hierarchical information that exists in textual data. However, such models do not explicitly encode the hierarchical structure that exists among words. In this work, we propose a method to learn hierarchical word embeddings (HWEs) in a specific order to encode the hierarchical information of a knowledge base (KB) in a vector space. To learn the word embeddings, our proposed method considers not only the hypernym relations that exist between words in a KB but also contextual information in a text corpus. The experimental results on various applications, such as supervised and unsupervised hypernymy detection, graded lexical entailment prediction, hierarchical path prediction, and word reconstruction tasks, show the ability of the proposed method to encode the hierarchy. Moreover, the proposed method outperforms previously proposed methods for learning nonspecialised, hypernym-specific, and hierarchical word embeddings on multiple benchmarks.

There is widespread agreement that student teachers need to construct an integrated knowledge base across multiple domains. This study examined the contributions of intraindividual factors of self-regulated learning to explaining student teachers’ (a) integration of knowledge across topics and domains (i.e., integrative learning) and (b) disjointed processing of potentially domain-specific learning content (i.e., separative learning). The factors considered were study approaches; cognitive, metacognitive, and resource-related learning strategy use; epistemological and pedagogical beliefs; and career choice motivation. The study applied a cross-sectional survey design and examined separative and integrative learning in N = 103 student teachers by way of multiple regression analyses with backward eliminations. A key finding is that deep and strategic study approaches and certain cognitive learning strategies contributed significantly to explaining integrative learning in student teachers. Epistemological and pedagogical beliefs were not able to predict integrative learning. Regarding separative learning, the study identified the surface study approach, specific epistemological and pedagogical beliefs, and the “usefulness” motive for career choice as positive predictors and critical thinking as a negative predictor. The study demonstrates differences in how integrative and separative learning are shaped by distinct intraindividual factors. Implications are discussed with regard to student teachers’ self-regulated learning and pre-service teacher education.