Tool For Representation Research Articles

Dynamic graph spatial-temporal dependence information extraction for remaining useful life prediction of rolling bearings

As a powerful tool for learning high-dimensional data representation, graph neural networks (GNN) have been applied to predict the remaining useful life (RUL) of rolling bearings. Existing GNN-based RUL prediction methods predominantly rely on constant pre-constructed graphs. However, the degradation of bearings is a dynamic process, and the dependence information between features may change at different moments of degradation. This article introduces a method for RUL prediction based on dynamic graph spatial-temporal dependence information extraction. The raw signal is segmented into multiple periods, and multiple features of each period data are extracted. Then, the correlation coefficient analysis is conducted, and the feature connection graph of each period is constructed based on different analytical results, thereby dynamically mapping the degradation process. The graph data is fed into graph convolutional networks (GCN) to extract spatial dependence between the graph node features in different periods. To make up for the shortcomings of GCN in temporal dependence extraction, the TimesNet module is introduced. TimesNet considers the two-dimensional changes of time series data and can extract the temporal dependence of graph data within and between different time cycles. Experimental results based on the PHM2012 dataset show that the average RUL prediction error of the proposed method is 17.4%, outperforming other comparative methods.

A novel infrared and visible image fusion network based on cross-modality reinforcement and multi-attention fusion strategy

Infrared and visible image fusion focuses on the integration of complementary information, whose fused results with abundant texture details and salient targets can cater for human visual observation well. However, the loss of some important details is still a challenge in the image fusion process. In this paper, a novel method based on cross-modality reinforcement module and multi-attention fusion strategy is proposed to boost the end-to-end CNN backbone. Specifically, a cross-modality architecture is applied to compensate the spectral differences from heterogeneous images; and the multi-scale strip pooling is utilized as a further feature representation tool to model long-rang independencies precisely; then, a detail injection block is devised to fulfill the enhancement requirements of texture-contrast and target intensity; sequentially, a multi-attention fusion module is proposed to integrate features progressively. Extensive comparative experiments are conducted on several datasets to demonstrate the superiority of the proposed method in both quantitative metrics and visual perception, such as the average of visual information fidelity metric based on all the experiment samples reach to 0.964.

Gender Representation of Health Care Professionals in Large Language Model–Generated Stories

With the growing use of large language models (LLMs) in education and health care settings, it is important to ensure that the information they generate is diverse and equitable, to avoid reinforcing or creating stereotypes that may influence the aspirations of upcoming generations. To evaluate the gender representation of LLM-generated stories involving medical doctors, surgeons, and nurses and to investigate the association of varying personality and professional seniority descriptors with the gender proportions for these professions. This is a cross-sectional simulation study of publicly accessible LLMs, accessed from December 2023 to January 2024. GPT-3.5-turbo and GPT-4 (OpenAI), Gemini-pro (Google), and Llama-2-70B-chat (Meta) were prompted to generate 500 stories featuring medical doctors, surgeons, and nurses for a total 6000 stories. A further 43 200 prompts were submitted to the LLMs containing varying descriptors of personality (agreeableness, neuroticism, extraversion, conscientiousness, and openness) and professional seniority. The primary outcome was the gender proportion (she/her vs he/him) within stories generated by LLMs about medical doctors, surgeons, and nurses, through analyzing the pronouns contained within the stories using χ2 analyses. The pronoun proportions for each health care profession were compared with US Census data by descriptive statistics and χ2 tests. In the initial 6000 prompts submitted to the LLMs, 98% of nurses were referred to by she/her pronouns. The representation of she/her for medical doctors ranged from 50% to 84%, and that for surgeons ranged from 36% to 80%. In the 43 200 additional prompts containing personality and seniority descriptors, stories of medical doctors and surgeons with higher agreeableness, openness, and conscientiousness, as well as lower neuroticism, resulted in higher she/her (reduced he/him) representation. For several LLMs, stories focusing on senior medical doctors and surgeons were less likely to be she/her than stories focusing on junior medical doctors and surgeons. This cross-sectional study highlights the need for LLM developers to update their tools for equitable and diverse gender representation in essential health care roles, including medical doctors, surgeons, and nurses. As LLMs become increasingly adopted throughout health care and education, continuous monitoring of these tools is needed to ensure that they reflect a diverse workforce, capable of serving society's needs effectively.

Matters of assuring functional dependability compliance of digital manufacturing

Aim. To propose a methodological approach to ensuring the functional dependability of industrial facilities using SMART documents in the context of import-independent digital processes. Methods. The evolving applicability of the dependability theory and information systems design has defined the methodological provisions for algorithmising the application of SMART standards for minimising functional faults and failures of industrial processes. Digital models of production lines were examined as tools for machine-recognisable representation of standards. The proposed form of regulatory documents is intended to enable the transition from machine-readable data to machine-understandable content. Findings. The application of the proposed methods of content systematisation in the context of manufacturing process standardisation shows that the classification of process data based on standardised characteristics of digital information exchange processes is one of the key methodological provisions ensuring unambiguous interpretation of regulatory requirements. Specialised projects often use individual provisions rather than complete regulatory documents. In this case, in order to ensure machine-understandability of the developed content, it is proposed using special identifiers, e.g., «paragraph», «graphic object», «table cell». The deployment of such identification tools will allow creating a class of SMART standards that collect into a single document the provisions regarding the delivery of intended results in situations when the operating equipment is diverse. Publications created using intelligent processing of SMART documents are considered as containers of structured and unstructured data that take into account the conditions of particular projects. The existing dynamics of the demand for advanced industrial products often determines the admissibility of its industrial production by various companies with the parallel use of unique technologies. The conditions of such an organisation impose their limitations on the comparability of various process requirements, therefore, it is proposed to harmonise the presentation of the results of managerial, design, and process engineering solutions using the term «manufacturing system asset». The introduction of relative result estimates as part of the digital format is an efficient mechanism for ensuring management consistency. In this context, the importance of a uniform regulatory framework as the foundation for such assessments is growing. As one of the ways of minimising faults and failures of industrial equipment, it is proposed to create a situation centre with a decision support system based on the regulatory data with harmonised manufacturing system asset requirements. An experience of Industry 4.0 regulation is described in the review of the ISA digital manufacturing series of standards. It was shown that the deployment of SMART standards improves the stability of interaction between information management systems and the functional dependability of production lines. The introduction of provisions regulating the use of algorithms for the development, editing, and examination of draft regulatory documents into the content machine-understandability facilities is an important factor in ensuring the functional dependability of products. Conclusions. The presented approach is focused on the methodology for developing standards that take into account requirements from various subject areas. In the context of ongoing import substitution in manufacturing, the paper examines the feasibility of minimizing digital process faults and failures caused by the use of regulatory frameworks that are not equivalent to the provisions of international documents and take into account the specificity of Russian industrial systems.

Two-sided matching theory-based second-hand house transaction evaluation and recommendation by the modified PLC-DEMATEL method

To enhance the two-sided matching efficiency in a multi-source heterogeneous environment, this paper takes the randomness and unstructured features of the online comments into consideration, and proposes a new matching mechanism by introducing the complex information representation tool. Firstly, the concept of the probabilistic linguistic normal cloud (PLNC) model is introduced to preserve information cohesion and characteristic distribution. Next, the basic operation laws and corresponding operators are given. Then, an innovative maximum boundary concept skipping the indirect approach is presented to update the similarity degree and distance measures, also the correlation coefficient. Furthermore, for the multi-indicator systems with interactions, the peer experts are invited to evaluate the relationship between indicators, a modified algorithm based on the Decision-Making and Trial Evaluation Laboratory (DEMATEL) method is applied to obtain the subjective weights of indicators. After that, a whole matching process and a correlation coefficient cluster method-based recommendation algorithm are presented. A case study is provided to illustrate the method, wherein a new indicator system is constructed by analyzing the correlation of multiple indicators based on online linguistic evaluations. The random forest model is combined to obtain the objective weights and balance its reliability. Finally, sensitivity analysis and comparative analysis are employed to validate the effectiveness and applicability.

Echoes of Madness: Exploring Disability and Mental Illness in Hellblade: Senua’s Sacrifice

Video games are known for many things, but nuanced portrayals of characters with mental illness might not be one of them. This trend, however, has gradually started to shift with games like Hellblade: Senua’s Sacrifice, which aim to convey a genuine experience of mental illness to the player. Through a close reading of different instances in the game, this paper shows how Hellblade complicates the usual sanist ideas seen in most other games by taking an ambiguous stance, using mental illness as a representational tool. Furthermore, it avoids some of the more sensationalist and problematic tropes often employed in such representations, like the supercrip and the Cartesian divide of the body and mind. In order to show this, we have employed Mitchel and Snyder’s concept of narrative prosthesis to demonstrate how the game does not in fact rely on Senua’s disability as an exotic feature of the narrative to hook players in. By combining insights from disability and mad studies, we show how this game is a step in the right direction when it comes to challenging the perceptions of mental illness prevalent in pop culture.

A PCK-mapping approach to show the integration among components of the pedagogical content knowledge of elementary education teacher educators about biodiversity

Biodiversity is a very important topic nowadays, but there is currently a lack of studies on the Pedagogical Content Knowledge (PCK) of this concept. We examined the integration of the PCK components of ten elementary education teacher educators in Argentina through a qualitative, interpretative, and exploratory study, using semi-structured interviews based on the Content Representation tool and the mapping approach. OTS and KSU were most integrated while KA was the least integrated. We discuss the implications of integration between PCK components for biodiversity teaching as a contribution to teacher training and future research.

Expanding Models for Physics Teaching: A Framework for the Integration of Computational Modeling

Teaching computation in science courses can enhance science education, but doing so requires that teachers expand the vision of their discipline beyond the traditional view of science presented in most curricula. This article describes a design-based research (DBR) program that included collaboration among high school teachers and professional development leaders in physics and computer science education. Through three years of professional development and teacher-led development, field testing, and refinement of integrated curricular resources, we have combined instructional modeling practices, physical lab materials, and computer programming activities. One of the outcomes is a co-created framework for the integration of computational modeling into physics that is sensitive to teachers’ interests and expressed needs in addition to learning goals. This framework merges two evidence-based approaches to teaching: Bootstrap:Algebra, a web-based computing curriculum that emphasizes using multiple representations of functions and scaffolds that make the programming process explicit, and Modeling Instruction in physics, an approach that emphasizes the use of conceptual models, modeling practices and representational tools. In doing so, we uncover the need to balance teachers’ visions for integration opportunities with practical instructional needs and emphasize that frameworks for integration need to reflect teachers’ values and goals.

Examining site intervention efficacy and uncertainties with conceptual Bayesian networks: preventing offsite migration of DNAPL and contaminated groundwater.

For contaminated sites, conceptual site models (CSMs) guide the assessment and management of risks, including remediation strategies. Recent research has expanded diagrammatic CSMs with structural causal modeling to develop what are nominally called conceptual Bayesian networks (CBNs) for environmental risk assessment. These CBNs may also be useful for problems of controlling and preventing offsite contaminant migration, especially for sites containing dense nonaqueous phase liquids (DNAPLs). In particular, the CBNs provide greater clarity on the causal relationships between source term, onsite and offsite migration, and remediation effectiveness characterization for contaminated DNAPL sites compared to traditional CSMs. These ideas are demonstrated by the inclusion of modifying variables, causal pathway analysis, and interventions in CBNs. Additionally, several new extensions of the CBN concept are explored including the representation of measurement variables as lines of evidence and alignment with conventional pictorial CSMs for groundwater modeling. Taken as a whole, the CBNs provide a powerful and adaptable knowledge representation tool for remediating subsurface systems contaminated by DNAPL.

A rule reasoning diagram for visual representation and evaluation of belief rule-based systems

Belief rule-based (BRB) systems are usually regarded as much more interpretable than those systems based on black-box techniques. Unfortunately, understanding the reasoning behavior of BRB systems is a complex task, although the use of linguistic variables and rules makes BRB systems have outstanding semantic expressiveness. In this study, a rule reasoning diagram, named RULING, is proposed as a tool for visual representation and exploratory analysis of the BRB systems. RULING describes the BRB system in the form of a network graph based on the interaction between the rules, which allows users to understand the behavior of the BRB system by examining the information represented graphically, and provides an effective means to improve the interpretability of BRB systems. In the meanwhile, some quantitative indicators based on the network structure and rule characteristics are proposed to evaluate the performance of rules and BRB systems. A classification case and a regression case are studied as examples to illustrate the feasibility and effectiveness of RULING. The results indicate that RULING can offer many possibilities for BRB system analysis and evaluation, such as identifying important rules, interpreting rule interactions, detecting inconsistent rules, and guiding the adjustment of the rule base.

L’analisi grafica tra tradizione e innovazione

Through the analysis of a number of studies on graphic analysis carried out starting with Vincenzo Fasolo’s volume, Analisi grafica dei valori architettonici - a dense book that collects his lectures given at the Faculty of Architecture in Rome - an attempt is made to trace a timeline that, by recounting the evolution of this aspect of the discipline of Drawing, can highlight how the use of new technologies has contributed to making graphically evident themes and issues that the studies of the past had largely consol- idated only to remain in oblivion since the debate on “the crisis of architecture” arose between the 1960s and 1970s. The essay intends, without claiming to be exhaustive, to indicate some reference texts that, once reread, may contribute to further considerations on their topicality and fu- ture developments revised in the light of the broad possibilities provided by the digital. In a certain sense, these thematic expansions have already been addressed in some texts, such as in many of the essays in the journal Ikhnos, founded in 2003 by Giuseppe Pagnano or, even more specifically, by Edoardo Dotto in his 2012 volume, Il progetto della Sinagoga di Hurva di Louis I. Kahn. Analisi grafica. Through a diachronic process that investigates some salient moments of graphic analy- sis studies in Italy, the aim is to provide elements of reflection to broaden these themes with the current tools of representation.

Виртуальная реальность и культурное взаимодействие: виртуальные цифровые технологии в продвижении китайской культуры за рубежом

The article describes virtual digital personages as new means of communication and promoting Chinese culture in the global media environment. Virtual digital personages are a new communication tool. They perform various roles, functions, and tasks by connecting individuals with various scenarios and providing feedback on these scenarios. Their experience helps people complete new learning and socialization processes. The author used communicative properties of virtual digital personages to examine their communication tools in practice. The article introduces some cases when virtual personages represented Chinese culture in different scenarios. Virtual personages proved an effective representation and promotion tool that improves the image of Chinese culture abroad. They show that virtual digital technologies are important for cultural communication and possess great potential in promoting cultural values globally. Virtual digital personages play a key role in the digitalization of cultural communication, thus providing new options for the dissemination and preservation of China’s cultural heritage on a global scale.

Space Analysis in Palaeolithic Cave Art: Towards a Multidisciplinary and Integrated Approach

We present a revision of the concept of space in Palaeolithic cave art. Previous research attempting to approach this notion encounters several gaps, which surface on multiple levels: subjectivity, vagueness, restrictions on its conceptualisation, its illustration (two-dimensional description and representation), among others. We reassess the key elements at play, interpretative attempts, and theoretical concepts to gain a better understanding of the notion of space as applied to cave art. This is achieved by examining the historiography not only in prehistory but also in related disciplines, integrating new theoretical approaches and insights from ethnoarchaeology, linguistics, cognitive science, and analytic philosophy. Following this analysis of the existing research, we propose a new method to address the aforementioned issues. This method relies on digital quantitative tools for spatial analysis and three-dimensional representation, such as geographic information systems (GIS) and 3D visualisation. Through the application of these advanced technological tools, we aim to offer a more comprehensive and precise representation of the spatial aspects in Palaeolithic cave art, enabling researchers to analyse and comprehend the complexities of spatial distribution in a systematic and quantitative manner.

Multigranulation variable-scale fuzzy neighborhood measures and corresponding Choquet-like integrals for feature selection

Choquet-like integrals, a nonlinear fuzzy aggregation function, are diffusely applied in several real problems. However, the corresponding fuzzy measures in them are provided by human intervention and not by data-driven methods. As an effective knowledge representation tool, rough set models in different approximation spaces are established for data processing. Particularly, the multigranulation fuzzy β-covering approximation space has been concerned with only a single scale β∈(0,1]. However, different granulation structures should have different scales. Therefore, establishing a multigranulation approximation space with a variable scale by inducing fuzzy measures through data-driven methods and using corresponding Choquet-like integrals in practical problems is a significant challenge. To address this challenge, the notion of multigranulation variable-scale fuzzy Θ-covering group approximation space is presented here, as well as multigranulation variable-scale fuzzy neighborhood measures in it. Furthermore, Choquet-like integrals with the presented measures are constructed to solve the issue of feature selection (i.e., attribute reduction) under the new approximation spaces. Firstly, the concept of multigranulation variable-scale fuzzy Θ-covering group approximation space is presented, where different fuzzy β-coverings have different scales β∈Θ. Moreover, multigranulation variable-scale fuzzy neighborhood measures in it, as fuzzy measures, are presented. Subsequently, Choquet-like integrals with multigranulation variable-scale fuzzy neighborhood measures are constructed. A novel attribute reduction method under Choquet-like integrals with multigranulation variable-scale fuzzy neighborhood measures is proposed for any decision information table. Finally, the presented method is selected for solving problems of classification and diagnosis of rolling bearing faults. Several public data sets are used to demonstrate the effectiveness and feasibility of the presented methods mentioned above.

Outlier detection for heterogeneous data via fuzzy [formula omitted] covering

Outlier detection is an important research topic in data mining. The existing outlier detection method does not consider ordinal data, such as heterogeneous data. Fuzzy β covering is an information granularity representation tool, which can better fit the distribution of objects in real data and characterize the differences of objects. This paper studies outlier detection for heterogeneous data via fuzzy β covering. Firstly, some new properties of fuzzy β covering are proposed. Then, the fuzzy similarity relation between any two objects in heterogeneous data is defined, and fuzzy information granules are constructed by fuzzy β covering to obtain the new fuzzy approximation accuracy. Next, the outlier factor based on fuzzy β covering is established by integrating the outlier degree and weight function of fuzzy information granules. Moreover, the corresponding algorithm (called FBCOD algorithm) is designed. Finally, the FBCOD algorithm is compared with eight existing outlier detection algorithms on 12 UCI data sets. The experimental results of AUC value, statistical test and F1 measurement show that the FBCOD algorithm has better effectiveness and flexibility than some existing algorithms.

On Intersections of B-Spline Curves

Bézier and B-spline curves are foundational tools for curve representation in computer graphics and computer-aided geometric design, with their intersection computation presenting a fundamental challenge in geometric modeling. This study introduces an innovative algorithm that quickly and effectively resolves intersections between Bézier and B-spline curves. The number of intersections between the two input curves within a specified region is initially determined by applying the resultant of a polynomial system and Sturm’s theorem. Subsequently, the potential region of the intersection is established through the utilization of the pseudo-curvature-based subdivision scheme and the bounding box detection technique. The projected Gauss-Newton method is ultimately employed to efficiently converge to the intersection. The robustness and efficiency of the proposed algorithm are demonstrated through numerical experiments, demonstrating a speedup of 3 to 150 times over traditional methods.

Bridging Theory and Practice: Using Goal Systems to Spark Professional Dialogue and Develop Personal Theories

School-based mentors play a key role in the learning processes of student teachers. Ideally, they facilitate student teachers to scrutinize their approaches and underlying assumptions, and link these to theoretical notions. In this study we investigated how three mentors used a goal-system representation (GSR) tool in their mentoring conversations. The GSR tool is essentially a visual reflection of the student teacher’s personal theory regarding classroom practice. It was developed at our teacher training institute to help our students see the personal relevance of research literature and theory and apply it to their lesson plans, to bridge the gap between educational theory, their vision of good teaching and their educational practice. In three explorative case studies, we show how mentors use the GSR tool and to what extent they support three levels of personal theory development: sharing, investigating and transforming. In all cases, student teachers could relate their practices to theoretical notions, giving access to their mentors for further questioning of their sense-making of the situation. Mentors successfully use these opportunities for personal theory development in various ways. We conclude that the GSR tool functions as a boundary object between theory and practice and between institute-based and school-based teacher education.

Graphormer based contrastive learning for recommendation

In recent years, Graph Neural Networks (GNNs) have become a powerful tool for graph representation in recommendation systems. Graphormer integrates its graph structure into a Transformer to provide state-of-the-art performance for graph prediction tasks. Meanwhile, self-supervised contrastive learning has succeeded in processing highly sparse data. Despite these successes, most graph contrastive methods perform random enhancement, such as node/edge perturbation, on a user-item interaction graph, or perform heuristic enhancement techniques, such as user clustering, to generate a new contrastive view. These methods cannot preserve the inherent semantic structure well and are susceptible to noise. In this paper, we propose a Graphormer-based graph contrastive learning method, GO-GCL, which reduces the influence of noise while retaining the inherent semantic structure to some extent, and improves the versatility and robustness of the recommendation based on contrastive learning. The model is represented by Graphormer and then uses singular value decomposition for contrastive enhancement. The experiments on Yelp datasets show that our model has a significant improvement over existing GCL-based approaches. In-depth analyses show the superiority and robustness of GO-GCL in data sparsity and prevalence bias.

A detailed study of resampling algorithms for cyberattack classification in engineering applications.

The evolution of engineering applications is highly relevant in the context of protecting industrial systems. As industries are increasingly interconnected, the need for robust cybersecurity measures becomes paramount. Engineering informatics not only provides tools for knowledge representation and extraction but also affords a comprehensive spectrum of developing sophisticated cybersecurity solutions. However, safeguarding industrial systems poses a unique challenge due to the inherent heterogeneity of data within these environments. Together with this problem, it's crucial to acknowledge that datasets that simulate real cyberattacks within these diverse environments exhibit a high imbalance, often skewed towards certain types of traffics. This study proposes a system for addressing class imbalance in cybersecurity. To do this, three oversampling (SMOTE, Borderline1-SMOTE, and ADASYN) and five undersampling (random undersampling, cluster centroids, NearMiss, repeated edited nearest neighbor, and Tomek Links) methods are tested. Particularly, these balancing algorithms are used to generate one-vs-rest binary models and to develop a two-stage classification system. By doing so, this study aims to enhance the efficacy of cybersecurity measures ensuring a more comprehensive understanding and defense against the diverse range of threats encountered in industrial environments. Experimental results demonstrates the effectiveness of proposed system for cyberattack detection and classification among nine widely known cyberattacks.

Quaternion matrix completion using untrained quaternion convolutional neural network for color image inpainting

The use of quaternions as a novel tool for color image representation has yielded impressive results in color image processing. By considering the color image as a unified entity rather than separate color space components, quaternions can effectively exploit the strong correlation among the RGB channels, leading to enhanced performance. Especially, color image inpainting tasks are highly beneficial from the application of quaternion matrix completion techniques, in recent years. However, existing quaternion matrix completion methods suffer from two major drawbacks. First, it can be difficult to choose a regularizer that captures the common characteristics of natural images, and sometimes the regularizer that is chosen based on empirical evidence may not be the optimal or efficient option. Second, the optimization process of quaternion matrix completion models is quite challenging because of the non-commutativity of quaternion multiplication. To address the two drawbacks of the existing quaternion matrix completion approaches mentioned above, this paper tends to use an untrained quaternion convolutional neural network (QCNN) to directly generate the completed quaternion matrix. This approach replaces the explicit regularization term in the quaternion matrix completion model with an implicit prior that is learned by the QCNN. Extensive quantitative and qualitative evaluations demonstrate the superiority of the proposed method for color image inpainting compared with some existing quaternion-based and tensor-based methods.