Knowledge Space Research Articles

On delineating forward- and backward-graded knowledge structures from fuzzy skill maps

Forward-graded and backward-graded structures of knowledge are two important classes of knowledge structures. Spoto and Stefanutti (2020) establish necessary and sufficient conditions for skill maps to delineate these structures. We introduce fuzzy skills to describe varying levels of proficiency in skills and extend the theoretical results of Spoto and Stefanutti (2020) for delineating forward- and backward-graded knowledge structures using fuzzy skill maps. The paper establishes necessary and sufficient conditions for fuzzy skill maps to delineate a backward-graded simple closure space, a forward-graded knowledge space, and a forward-graded simple closure space. Furthermore, the competence-based local independence model (CBLIM) with fuzzy skills is introduced and its unidentifiability is discussed.

Историзм как метанарратив: методологические основания гуманитарных наук

The article examines historicism as an interdisciplinary paradigm that constitutes the methodological foundations of the Humanities from the moment of their inception to the present day. The author puts forward and argues the thesis that the Humanities were constituted in the space freed up after the elimination of classical metaphysics, occupying this space along with the historical and social sciences, but taking possession of a very advantageous position within it. Historicism, according to the author, has become a means of radical repression of mechanism both from philosophy and from the space outlined by the Liberal Arts, and its assertion made possible the emergence of a discursive formation in which the Human Sciences developed as a scientific discipline with their own theoretical foundations and methodology not borrowed from the positive sciences, but having a structural commonality with these latter. In the system of the established Humanities, historicism continues to serve as a universal methodological platform, providing these scientific disciplines with scientific legitimacy and allowing them to create sustainable alliances in the space of scientific knowledge.

Heterogeneous graph-based knowledge tracing with spatiotemporal evolution

Knowledge tracing (KT), in which the future performance of students is estimated by tracing their knowledge states based on their responses to exercises, is widely applied in the field of intelligent education. However, existing mainstream KT models explore the importance of knowledge relations but ignore the key role of cognitive factors. According to the knowledge construction theory, the human cognitive system performs both spatial accommodation and temporal assimilation to internalize knowledge. In this paper, we propose an innovative heterogeneous graph-based Knowledge tracing method with spatiotemporal evolution (TSKT), in which knowledge state evolution is traced along both temporal and spatial dimensions. We construct a heterogeneous graph with multiple exercise attributes, including content, concepts, and difficulty, to obtain a knowledge space with richer exercise representations through hierarchical aggregation. We design a spatial updating module in which each interaction updates the current node’s state of the knowledge space and transfers its influence to its neighbors. We also design a temporal updating module to further update the knowledge state through short-term memory enhancing and long-term memory forgetting. Finally, we stack these modules to obtain deeper features by using alternate spatiotemporal updating. Extensive experiments on three datasets reveal the superiority of the proposed method and its variants in terms of future performance prediction.

Reference System Element Identification Atlas – methods and tools to identify references system elements in product engineering

Companies target innovations, successful new products. One major challenge is to increase efficiency and decrease the risk of developing new successful products. We want to reach these goals by improving the reusability of already existing knowledge elements extracted from e.g., already existing (sub-)systems or their documentation. These elements are called reference system elements and are meant to be the starting point for product development projects. Based on a systematic literature review complemented by an expert workshop and analysis of established methods and tools in product engineering, we developed the Reference System Elements Identification Atlas to support the identification of suiting reference system elements. Within the Reference System Elements Identification Atlas, we collected 30 methods and tools to identify reference system elements and allocated them to the various knowledge spaces they search. All 30 methods and tools were grouped in five clusters – creativity methods, data analysis methods, market/competition analysis methods, similarity methods, and trend analysis methods. We observed that methods and tools are hardly related to the identification of reference system elements in literature explicitly. We believe the Reference System Elements Identification Atlas provides valuable support to collect valuable reference system elements as the starting point in product engineering.

Scalable Transfer Evolutionary Optimization: Coping With Big Task Instances.

In today's digital world, we are faced with an explosion of data and models produced and manipulated by numerous large-scale cloud-based applications. Under such settings, existing transfer evolutionary optimization (TrEO) frameworks grapple with simultaneously satisfying two important quality attributes, namely: 1) scalability against a growing number of source tasks and 2) online learning agility against sparsity of relevant sources to the target task of interest. Satisfying these attributes shall facilitate practical deployment of transfer optimization to scenarios with big task instances, while curbing the threat of negative transfer. While applications of existing algorithms are limited to tens of source tasks, in this article, we take a quantum leap forward in enabling more than two orders of magnitude scale-up in the number of tasks; that is, we efficiently handle scenarios beyond 1000 source task instances. We devise a novel TrEO framework comprising two co-evolving species for joint evolutions in the space of source knowledge and in the search space of solutions to the target problem. In particular, co-evolution enables the learned knowledge to be orchestrated on the fly, expediting convergence in the target optimization task. We have conducted an extensive series of experiments across a set of practically motivated discrete and continuous optimization examples comprising a large number of source task instances, of which only a small fraction indicate source-target relatedness. The experimental results show that not only does our proposed framework scale efficiently with a growing number of source tasks but is also effective in capturing relevant knowledge against sparsity of related sources, fulfilling the two salient features of scalability and online learning agility.

Dual Adaptive Representation Alignment for Cross-Domain Few-Shot Learning.

Few-shot learning aims to recognize novel queries with limited support samples by learning from base knowledge. Recent progress in this setting assumes that the base knowledge and novel query samples are distributed in the same domains, which are usually infeasible for realistic applications. Toward this issue, we propose to address the cross-domain few-shot learning problem where only extremely few samples are available in target domains. Under this realistic setting, we focus on the fast adaptation capability of meta-learners by proposing an effective dual adaptive representation alignment approach. In our approach, a prototypical feature alignment is first proposed to recalibrate support instances as prototypes and reproject these prototypes with a differentiable closed-form solution. Therefore feature spaces of learned knowledge can be adaptively transformed to query spaces by the cross-instance and cross-prototype relations. Besides the feature alignment, we further present a normalized distribution alignment module, which exploits prior statistics of query samples for solving the covariant shifts among the support and query samples. With these two modules, a progressive meta-learning framework is constructed to perform the fast adaptation with extremely few-shot samples while maintaining its generalization capabilities. Experimental evidence demonstrates our approach achieves new state-of-the-art results on 4 CDFSL benchmarks and 4 fine-grained cross-domain benchmarks.

Scientific portal Geologyscience.ru: current state and prospects of development

The paper discusses the current state of the Portal "Information and analytical environment to support scientific research in geology" (http://geologyscience.ru), as well as a number of its future prospects related to the development of an intelligent digital space of geological knowledge. It is assumed that this space will help to form new processes for generating geological data and knowledge.

Detailing the idea of the design process of hydraulic automation objects

The work is touched to the design methods of the complex technical objects. The reason of the objects complication is the requirement of the dynamical processes coordination because the planed characteristics are needed. The main design stages idea – circuit diagram – simulation – structure are considered. The means of the design stages information representing of traditional approach are the relatively commonly and not consistent. It leads to design time increasing. In this work to structure the design information, coordinate the means of the different stage information representation with each others and detail design process are proposed. To do it the common information space of knowledge of the subject aria are separated on the streams. The streams represent information about the content, uniting, activities and requirements. The design process is represented as the transactions chain. Each transaction is based on the named streams and successively forms the state of the resulting functional stream. These states reflect the readiness levels of the designed object. Due to this way of means representation and detailing of information the coordination between different stages was provided. There are the possibilities to check the stage correctness executing also. As result the iteration reducing and time consumption of the design process are reached. The design process is illustrated by the example.

Inventors, firms and localities: insights into the nexus that forms and alters the evolution of regional knowledge spaces

Abstract The present investigation aims to provide insights into the nexus between firms and localities that ultimately shapes the evolution of regional knowledge spaces. The ‘inventive footprint’ of firms, indicated by the spatial distribution of associated inventors across regional economic settings, provides the foundation to analyse whether firms’ external knowledge sourcing contributes to their own evolution, and by extension to their home region’s technological capabilities. Findings indicate that firms’ innovation efforts beyond their home base are subsequently absorbed at home and thus that knowledge-sourcing activities by multi-locational firms significantly contributes to the evolution of regional knowledge space trajectories.

Water Conflicts in Chile: Have We Learned Anything from Colonial Times?

The number of disputes related to water that reach courts in Chile has increased in the last decades, the topics of these disputes have become more complex, and the current conflict resolution system has not been able to adjust to this situation. This study analyzes colonial texts from water-related conflicts that were addressed at the Royal Hearings in Santiago (1691–1800) and from the Cabildo gatherings (1541–1802), using an adaptation of the Institutional Analysis and Development framework. The research shows a strong institutional system surrounding conflict resolution during colonial times, with nested schemes and empowered figures appointed in leading roles. However, a lack of equity and inclusion of all actors is also visible, reducing its legitimacy. At present, the increasing value of water and a sense of distrust in the institutional system have led to longer and more complex conflict resolution processes. Here, learning from past times about the empowerment of the institutional system for solving water disputes could be useful. An increased support towards initial conflict resolution mechanisms, giving space for local knowledge and generating stronger participation in these initial steps, is a lesson for the future.

Exploring meanings of expert and expertise in patient engagement activities: A qualitative analysis of a pan-Canadian survey

In this paper, we engage with claims of expert identities within the field of patient engagement. We do so through analysis of a subset of data collected as part of a 2020 pan-Canadian survey of patient partners. Our analysis is based on 446 qualitative responses to one target question: “Do you think the lived experience you bring to your patient partner role makes you an expert? Please explain in the box below”. Most respondents answered “yes” (n = 253 comments), a sizeable minority answered “no” (n = 161 comments) or declined to answer the target question while still providing comments (n = 32 comments). Through a discursive analysis of the comments, we explore the meanings ascribed to concepts of expert, expertise, and experience. Ultimately, we find nuanced and sometimes contradictory understandings. Thus, dilemmas of expertise in the patient engagement field may not be entirely about claims to specialized knowledge. Instead, discourses seem to be mobilized in response to the thorny, political question: “who is authorized to speak on behalf of patients”? To meaningfully advance the conversation within patient engagement research and practice, we argue for more sociological and political understandings of forms of expertise, objects of expertise, and deployments of expert status in different kinds of knowledge spaces.

К ВОПРОСУ СОЦИАЛЬНО-ФИЛОСОФСКИХ ОСНОВАНИЙ ДУХОВНОГО ПЕРЕФОРМАТИРОВАНИЯ ОБЩЕСТВА И ЛИЧНОСТИ

Актуальность данного исследования состоит в том, что в настоящее время, время масштабных изменений, происходящих, как в пространстве научного познания, так и в геополитическом пространстве, имеет место быть формирование новой картины мира, новой онтологии, с одной стороны, и актуализация проблем всех типов идентичности (от национально-государственной и до уровня индивида, личности, человеческой идентичности, в конечном итоге), с другой стороны. Авторы считают, что эти два процесса взаимосвязаны через новые подходы к пониманию духовности, основанному на новом понимании принципа целостности, всеобщих связях. Поскольку в основе всех типов кризисов, по мнению авторов, а также многих исследователей, лежит духовный кризис, то и духовное переформатирование общества является главной целью построения перспективных моделей будущего. По мнению авторов, «точкой отсчета» в духовной модернизации общества должна стать, в первую очередь, личность как духовно-телесная целостность, реализующая себя, утверждающая себя через феномен интерсубъективности в пространстве культуры. Цель и новизна данного исследования заключается в том, что подобная философская экспликация понятия «личность» как точки отсчета духовной трансформации общества не должна осуществляться на основе постмодернистского индивидуалистического либертализма в противовес новой формирующейся онтологии с ее новым подходом к феномену духовности как практической необходимости, основанному на новом понимании принципа целостности, значении всеобщих связей и единого происхождения объекта и окружающего мира.

Towards a Domain-Neutral Platform for Sustainable Digital Twin Development

In this paper, we propose an abstract domain-neutral architecture for a cognitive digital twin (CDT) and a software platform to develop such CDTs, including machine reasoning capabilities. Sustainable development refers here to an abstract model that enables a holistic view of limiting resources and has an ability to adapt to different application domains while reusing existing resources. The proposed solution allows for a unified abstract representation and the development of a wide range of diverse digital twins, as well as facilitating their interoperability. The abstract architecture consists of a four-layer structure (observation/actuation layer, data management layer, reasoning layer, and simulation layer) with an upper ontology to which the domain ontology of the specific CDT is mapped. The architecture relies on semantic web technologies, including ontology-based reasoning using OWL, and a loosely coupled, component-based service-oriented software architecture. The platform utilizes a microservice architecture that enables separate, loosely coupled services on each layer, message queues to provide asynchronous communication, and possesses cloud technologies to achieve scalability. The proposed approach was validated by implementing a software platform prototype and demonstrating its key features through two dissimilar scenarios. The first scenario demonstrates simple sustainable energy management through IoT systems inside smart buildings, while the second one demonstrates knowledge quality management based on knowledge space theory.

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems.

For non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem. The multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space. The proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels. The formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications.

Conceptualizing the Role of Racial-Ethnic Identity in U.S. Adolescent Social Technology Use and Well-Being.

Adolescent development and wellbeing now involve how the use of social technologies (e.g., social media and other online spaces) impact daily life. Especially during crises such as COVID-19 and persistent injustices, adolescents rely on online spaces for social connectedness and informational knowledge. Psychosocial impacts, both positive and negative, have been found among racial-ethnic minority adolescents. However, the role of racial-ethnic identity on social media use and wellbeing has been understudied. The current study addresses differential associations on social media experiences and mental health (i.e., depressive, online anxiety symptoms) among a diverse group of adolescents (n = 668; ages 10-17; 45.7% non-White). Furthermore, the roles of self-identified racial-ethnic groups, identity importance, exposure to hate messaging, and gender are investigated. Our study found significant moderating effects of racial-ethnic importance, gender, and online hate messaging. Additionally, the moderating role of race-ethnicity reveals a stronger association between greater social media frequency and heightened depressive symptoms among Asian adolescents. Black adolescents showed a significant association between greater social media frequency and decreased online social anxiety. Significant effects of online hate messaging exposure also reveal associations between online behaviors and depression and online social anxiety across adolescents. As social media adoption coincides with identity exploration, this study highlights how racial-ethnic identity and its formation in the digital age is important to understand its association with online interactions that may help or hinder adolescent wellbeing. Future work should continue examining trajectories of identity formation in relation to social media content and differential mental health impacts.

Modern Architectures of Intelligent Tutoring Systems Based on Integrated Expert Systems: Features of the Approach to the Automated Formation of the Ontological Space of Knowledge and Skills of Students

Modern Architectures of Intelligent Tutoring Systems Based on Integrated Expert Systems: Features of the Approach to the Automated Formation of the Ontological Space of Knowledge and Skills of Students

Bridging technologies in the regional knowledge space: measurement and evolution

Bridging technologies in the regional knowledge space: measurement and evolution

Smart cities and urban dimensions to ensure sustainable development: a bibliometric analysis

PurposeThe multidimensional complexity of urban settlements is increasing and the problem of spaces and territories brought to the scale of smart cities is a critical global issue. This study aims to analyse the scientific production in the Web of Science (WoS) on the relationship between smart cities and the eight urban dimensions defined by the World Economic Forum (WEF) in the period 1990 to 2021, in order to establish which countries lead the knowledge related to the search for sustainable living conditions for people and how this knowledge contributes to improving stakeholders' decision-making.Design/methodology/approachThe methodological steps followed in the study were: (1) Identification and selection of keywords. (2) Design and application of an algorithm to identify these selected keywords in titles, abstracts and keywords using WoS terms to contrast them. (3) Data processing was performed from Journal Citation Report (JCR) journals during the year 2022.FindingsThis study identified the authors, institutions and countries that publish the most globally on the topic of Smart Cities. The acceleration in the integration of new technologies and their impact on population conglomerates and their relationship with urban dimensions were also analysed. The evidence found indicates that the USA and China are leading in this field.Originality/valueThis bibliometric study was designed to analyse a knowledge space not addressed in the scientific literature referred to the relationship between the concept of smart cities and the urban dimensions established by the WEF, the identification of new technologies that are converging to promote developments of new ways of managing urban dimensions and propose new knowledge spaces.

Parameter Estimation of KST-IRT Model under Local Dependence

A mantra often repeated in the introductory material to psychometrics and Item Response Theory (IRT) is that a Rasch model is a probabilistic version of a Guttman scale. The idea comes from the observation that a sigmoidal item response function provides a probabilistic version of the characteristic function that models an item response in the Guttman scale. It appears, however, more difficult to reconcile the assumption of local independence, which traditionally accompanies the Rasch model, with the item dependence existing in a Guttman scale. In recent work, an alternative probabilistic version of a Guttman scale was proposed, combining Knowledge Space Theory (KST) with IRT modeling, here referred to as KST-IRT. The present work has, therefore, a two-fold aim. Firstly, the estimation of the parameters involved in KST-IRT models is discussed. More in detail, two estimation methods based on the Expectation Maximization (EM) procedure are suggested, i.e., Marginal Maximum Likelihood (MML) and Gibbs sampling, and are compared on the basis of simulation studies. Secondly, for a Guttman scale, the estimates of the KST-IRT models are compared with those of the traditional combination of the Rasch model plus local independence under the interchange of the data generation processes. Results show that the KST-IRT approach might be more effective in capturing local dependence as it appears to be more robust under misspecification of the data generation process, but it comes with the price of an increased number of parameters.

Cultivating Trauma-Informed Educational Practices for Students with Refugee Backgrounds

Abstract Schools provide spaces for academic knowledge while also addressing children’s social and emotional developmental needs. Because of the holistic development occurring within the education system, it is important to have an awareness of students’ experiences and challenges both inside and outside of the classroom. Students with refugee backgrounds represent diverse cultures, languages, and experiences but often share some common challenges and traumas. While trauma can impact students’ ability to learn and develop, classroom expectations, procedures, and rhythms can be adjusted to be trauma informed. Trauma-informed educational practices allow for a universal precautionary model for teachers and school staff to neutralize the environment for possible retraumatization, contributing to the creation of better educational experiences for students with refugee backgrounds as well as the rest of the student body. The aim of this article is to provide primary and secondary school staff—including teachers, administrators, and social workers—with practical information about trauma-informed educational practices to help them address the specific and unique needs of students with refugee backgrounds. Authors include examples of how school social workers, teachers, staff, and administrators can think about trauma-informed practices in the context of education and students with refugee backgrounds.