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The increasing complexity of industrial systems demands more effective and intelligent maintenance approaches to address manufacturing defects arising from faults in multiple asset modules. Traditional digital twin (DT) systems, however, face limitations in interoperability, knowledge sharing, and causal inference. As such, cognitive digital twins (CDTs) can add value by managing a collaborative web of interconnected systems, facilitating advanced cross-domain analysis and dynamic context considerations. This paper introduces a CDT system that leverages industrial knowledge graphs (iKGs) to support maintenance planning and operations. By employing a design structure matrix (DSM) to model dependencies and relationships, a semantic translation approach maps the knowledge into a graph-based representation for reasoning and analysis. An automatic solution generation mechanism, utilising graph sequencing with Louvain and PageRank algorithms, derives feasible solutions, which can be validated via simulation to minimise production disruption impacts. The CDT system can also identify potential disruptions in new product designs, thus enabling preventive actions to be taken. A case study featuring a print production manufacturing line illustrates the CDT system's capabilities in causal inference and solution explainability. The study concludes with a discussion of limitations and future directions, providing valuable guidelines for manufacturers aiming to enhance reactive and predictive maintenance strategies. [ABSTRACT FROM AUTHOR]

Additive Manufacturing (AM) technology offers remarkable flexibility in fabricating products with intricate geometries, presenting unprecedented advantages in material efficiency and speed. The process planning of AM plays a pivotal role in ensuring overall quality and time-efficiency of printed products. This drives engineers and researchers to explore various approaches to achieve optimal AM process solutions. However, numerous challenges persist, particularly in logical relationship reasoning and information representation for complex manufacturing tasks and design requirements. In this study, a novel AM process reasoning method based on relation mining is proposed, leveraging knowledge graph representation and graph neural networks (GNN). An AM knowledge graph is constructed comprising essential process information, followed by implementing RED-GNN to accomplish graph reasoning tasks for parameter recommendation. We then focus on the process planning scenario of lattice structures, a common geometry used for designing products with weight-relief requirements and high sensitivity to process parameters. A series of lattice structure parts are designed and tested using our proposed method, demonstrating strong performance and unveiling new potentials and opportunities in advancing knowledge-based engineering and intelligent manufacturing. [ABSTRACT FROM AUTHOR]

This paper introduces Tensor Visibility Graph-enhanced Attention Networks (TVGeAN), a novel graph autoencoder model specifically designed for MTS learning tasks. The underlying approach of TVGeAN is to combine the power of complex networks in representing time series as graphs with the strengths of Graph Neural Networks (GNNs) in learning from graph data. TVGeAN consists of two new main components: TVG which extend the capabilities of visibility graph algorithms in representing MTSs by converting them into weighted temporal graphs where both the nodes and the edges are tensors. Each node in the TVG represents the MTS observations at a particular time, while the weights of the edges are defined based on the visibility angle algorithm. The second main component of the proposed model is GeAN, a novel graph attention mechanism developed to seamlessly integrate the temporal interactions represented in the nodes and edges of the graphs into the core learning process. GeAN achieves this by using the outer product to quantify the pairwise interactions of nodes and edges at a fine-grained level and a bilinear model to effectively distil the knowledge interwoven in these representations. From an architectural point of view, TVGeAN builds on the autoencoder approach complemented by sparse and variational learning units. The sparse learning unit is used to promote inductive learning in TVGeAN, and the variational learning unit is used to endow TVGeAN with generative capabilities. The performance of the TVGeAN model is extensively evaluated against four widely cited MTS benchmarks for both supervised and unsupervised learning tasks. The results of these evaluations show the high performance of TVGeAN for various MTS learning tasks. In particular, TVGeAN can achieve an average root mean square error of 6.8 for the C-MPASS dataset (i.e., regression learning tasks) and a precision close to one for the SMD, MSL, and SMAP datasets (i.e., anomaly detection learning tasks), which are better results than most published works. [ABSTRACT FROM AUTHOR]

This paper presents a new cognitive agent design approach integrating spatial knowledge representation and reasoning in agent-based modeling dedicated to land use simulations. A deep motivation for our agent-centric contribution is the ever-increasing development of spatially explicit agent simulation platforms. We build on this technological evolution and topology theory to endow the agent with human's spatial representation and reasoning following a Belief–Desire–Intention architecture. A pilot implementation of the methodology with simulation experiments on a hunting model was carried out in GAMA platform to assess agent performances. Simulations display a consistent trend of animal population dynamics and also confirm a high model sensitivity to the integration of spatial knowledge and reasoning in agent-based models of human actor. These results demonstrate a successful implementation and the importance of spatial dimension in the expressive power and the validity of agent-based models. Future research efforts should therefore emphasize on designing human knowledge representation and incorporating learning abilities to improve models efficiency. [ABSTRACT FROM AUTHOR]

The knowledge graph is a structured representation of knowledge, which typically employs a triplet to convey information. However, the long-tail distribution that characterizes most large-scale knowledge bases poses serious challenges regarding entity and relationship information in the long-tail segment, such as data sparsity and incomplete knowledge. To address these challenges and enhance the completeness of the knowledge graph, this paper proposes a novel model for knowledge representation learning in the context of knowledge graph completion. Specifically, the model maps entities to a two-dimensional Minkowski space and defines relationships as rotations. Additionally, the Adam optimizer is employed to optimize the training process. Finally, the model's efficacy is evaluated via link prediction tasks using public datasets FB15k, WN18, FB15k-237, and WB18RR, with results being compared to those derived from classical knowledge representation models. The empirical analysis reveals that the proposed model achieves superior performance across most evaluation metrics. [ABSTRACT FROM AUTHOR]

Knowledge representation learning is to project entities and relations into low-dimensional vector spaces, and efficiently calculate entities, relations, and complex semantic associations between them. However, the translation models that fuse multi-source information fail to specially pay attention to the semantic information of important sentences in the textual descriptions of an entity, which lead to the low quality of embedded representation of textual descriptions, and fail to utilize the primary neighbor of the entity to further constrain the entity representation, resulting in the weak knowledge representation learning ability of the models. To address these issues, we propose a joint knowledge representation learning model of sentence vectors weighting and primary neighbor constraints called SW &NC-Joint. First, the weight distribution of the sentences is calculated according to the frequency of the neighbors in each sentence, and then the textual descriptions are represented by the sentence vectors weighting. Secondly, the concept of neighbor association degree is put forward, and the primary neighbor is filtered out for each entity according to the association degree. Finally, the weighted textual description information and triple structure information are jointly trained, the most crucial thing that the model is constrained by using the distance between the entity and its primary neighbor in the training process, so as to improve the representation learning ability of the model. The link prediction and triple classification experiments are carried out on FB15K and WN18 datasets. The experimental results demonstrate that our model significantly outperforms existing state-of-the-art text-enhanced knowledge representation learning model for link prediction and triple classification tasks. [ABSTRACT FROM AUTHOR]

Graph convolutional networks (GCNs) have emerged as powerful tools for handling graph-structured data. Many knowledge graph embedding models leverage GCNs as encoders to learn the relationships between central entities and their neighbors, showing impressive performance in the knowledge graph completion task. However, the incorporation of GCNs increases the computational burden of the model. Moreover, due to the complex graph structure of knowledge graphs, treating all neighboring entities equally will cause the model to lose its ability to capture important information. To address these challenges, we present a lightweight hierarchical graph convolutional network (Light-HGCN). Light-HGCN removes feature transformation and selectively uses nonlinearity activation in standard GCNs to accelerate model convergence. Additionally, Light-HGCN introduces a hierarchical attention mechanism to determine the neighboring weights to capture complex graph structures. Light-HGCN achieves promising performance for the link prediction task across multiple benchmark datasets. Ablation experiments further illustrate the effectiveness of the hierarchical attention mechanism. The analysis of feature transformation and nonlinearity activation in GCNs on the KGC task indicates that lightweight GCNs can enhance computational efficiency while preserving promising performance. [ABSTRACT FROM AUTHOR]

Temporal knowledge graphs (TKGs) are a form of knowledge representation constructed based on the evolution of events at different time points. It provides an additional perspective by extending the temporal dimension for a range of downstream tasks. Given the evolving nature of events, it is essential for TKGs to reason about non-existent or future events. Most of the existing models divide the graph into multiple time snapshots and predict future events by modeling information within and between snapshots. However, since the knowledge graph inherently suffers from missing data and uneven data distribution, this time-based division leads to a drastic reduction in available data within each snapshot, which makes it difficult to learn high-quality representations of entities and relationships. In addition, the contribution of historical information changes over time, distinguishing its importance to the final results when capturing information that evolves over time. In this paper, we introduce CH-TKG (Contrastive Learning and Historical Information Learning for TKG Reasoning) to addresses issues related to data sparseness and the ambiguity of historical information weights. Firstly, we obtain embedding representations of entities and relationships with evolutionary dependencies by R-GCN and GRU. On this foundation, we introduce a novel contrastive learning method to optimize the representation of entities and relationships within individual snapshots of sparse data. Then we utilize self-attention and copy mechanisms to learn the effects of different historical data on the final inference results. We conduct extensive experiments on four datasets, and the experimental results demonstrate the effectiveness of our proposed model with sparse data. [ABSTRACT FROM AUTHOR]

Background: Accurate prediction of compound-protein interaction (CPI) plays a crucial role in drug discovery. Existing data-driven methods aim to learn from the chemical structures of compounds and proteins yet ignore the conceptual knowledge that is the interrelationships among the fundamental elements in the biomedical knowledge graph (KG). Knowledge graphs provide a comprehensive view of entities and relationships beyond individual compounds and proteins. They encompass a wealth of information like pathways, diseases, and biological processes, offering a richer context for CPI prediction. This contextual information can be used to identify indirect interactions, infer potential relationships, and improve prediction accuracy. In real-world applications, the prevalence of knowledge-missing compounds and proteins is a critical barrier for injecting knowledge into data-driven models. Results: Here, we propose BEACON, a data and knowledge dual-driven framework that bridges chemical structure and conceptual knowledge for CPI prediction. The proposed BEACON learns the consistent representations by maximizing the mutual information between chemical structure and conceptual knowledge and predicts the missing representations by minimizing their conditional entropy. BEACON achieves state-of-the-art performance on multiple datasets compared to competing methods, notably with 5.1% and 6.6% performance gain on the BIOSNAP and DrugBank datasets, respectively. Moreover, BEACON is the only approach capable of effectively predicting knowledge representations for knowledge-lacking compounds and proteins. Conclusions: Overall, our work provides a general approach for directly injecting conceptual knowledge to enhance the performance of CPI prediction. [ABSTRACT FROM AUTHOR]

Animals guide their behaviors through internal representations of the world in the brain. We aimed to understand how the macaque brain stores such general world knowledge, focusing on object color knowledge. Three functional magnetic resonance imaging (fMRI) experiments were conducted in macaque monkeys: viewing chromatic and achromatic gratings, viewing grayscale images of their familiar fruits and vegetables (e.g., grayscale strawberry), and viewing true- and false-colored objects (e.g., red strawberry and green strawberry). We observed robust object knowledge representations in the color patches, especially the one located around TEO: the activity patterns could classify grayscale pictures of objects based on their memory color and response patterns in these regions could translate between chromatic grating viewing and grayscale object viewing (e.g., red grating—grayscale images of strawberry), such that classifiers trained by viewing chromatic gratings could successfully classify grayscale object images according to their memory colors. Our results showed direct positive evidence of object color memory in macaque monkeys. These results indicate the perceptually grounded knowledge representation as a conservative memory mechanism and open a new avenue to study this particular (semantic) memory representation with macaque models. How important is language for storing general information about the world? This study shows that memory color shares neural representations with color perception in macaques, indicating that the sensory-derived knowledge representation previously observed in humans is conserved in species without a developed language system. [ABSTRACT FROM AUTHOR]

As a critical business activity in the aircraft life cycle, maintenance processes are highly complex and require multidisciplinary knowledge. Knowledge integration and representation oriented toward aircraft maintenance processes are necessary to improve work efficiency. Nonetheless, conventional approaches lack effective unified management, which obstructs domain knowledge sharing and ultimately impedes maintenance work. In this context, this paper proposes a knowledge representation framework based on the benefits of ontology, which formalizes multidisciplinary knowledge for aircraft maintenance processes. An ontology of aircraft maintenance processes is developed for knowledge conceptualization and reuse. On this basis, a domain knowledge extraction model based on the bidirectional encoder representation from transformers (BERT) is constructed to automatically extract entities and relationships related to maintenance processes. With a series of Semantic Web Rule Language (SWRL) rules, a knowledge reasoning method is proposed based on the aircraft maintenance process ontology to mine hidden knowledge. We evaluate the developed ontology and demonstrate the feasibility and usefulness of the proposed knowledge reasoning method in a case study. The results show that the proposed knowledge representation framework provides an effective knowledge formalization method for complex knowledge in aircraft maintenance processes to support work optimization. [ABSTRACT FROM AUTHOR]

With the widespread application of computer-aided technologies such as CAD and CAM in the manufacturing industry, a growing number of process documents and design documents generate multi-source process knowledge and expert experience. However, due to the diverse and complex representation of process knowledge, there is a need for more effective methods to mine a large amount of multi-source information and to exploit the explicit and implicit relationships between the knowledge contained in process knowledge. Effective knowledge reuse in process planning still needs to be improved. This paper proposes a reinforcement learning approach that combines knowledge graphs and process decision-making activities in process planning to exploit the learning potential of process knowledge graphs. Firstly, a reinforcement learning environment for process planning is introduced to model the process planning decision-making phase as a sequential recommendation of process knowledge. Then, this paper designs in detail the state representation method that combines process decision sequences and potential relationships between processes. This paper also creates the composite reward function that combines the process planning environment. In addition, a new algorithm is proposed for learning the proposed model more efficiently. Experimental results show that the network structure has more accurate recommendation results than other methods. [ABSTRACT FROM AUTHOR]

The editorial discusses the theory, methods, and applications of geospatial big data, which are large datasets with spatial components collected from various sources like satellite imagery, social media, and sensor networks. The editorial highlights the challenges in defining geospatial big data, collecting and analyzing the data, and addressing biases and ethical concerns. It also introduces the special issue that includes seven articles focusing on human activities, social interactions, disasters, health crises, and environmental characteristics using geospatial big data. The editorial concludes by discussing future challenges and opportunities in geospatial big data analytics, emphasizing the importance of privacy, multimodal GeoAI, and scale issues in data collection and analysis. [Extracted from the article]

National curriculum statements found within the Official Recontextualising Field (ORF) provide an insight into how geography as a school subject is conceptualized in a country’s education system. National curricula can shape teachers’ agency in curriculum making and what, how and where children and young people study and learn geography. This paper engages with the lower secondary national geography curriculum for England, Finland and Sweden. We examine the structure and nature of the national geography curricula in each country, before drawing on the threefold arrangement of geographical knowledge as a tool for the analysis of the curricula. Our analysis found that deep and descriptive world knowledge forms the largest proportion of all three national curricula documents, and we argue that this can lead to a potentially limited conceptualization of geographical knowledge and representation of geography. We also suggest that the threefold arrangement could more actively engage with political dimensions when considering futures, and that there should be greater attention paid to the histories and geographies of the discipline (geography) in school geography. [ABSTRACT FROM AUTHOR]

Background: Familiar cardiopathies are genetic disorders that affect the heart. Cardiologists face a significant problem when treating patients suffering from these disorders: most DNA variations are novel (i.e., they have not been classified before). To facilitate the analysis of novel variations, we present CardioGraph, a platform specially designed to support the analysis of novel variations and help determine whether they are relevant for diagnosis. To do this, CardioGraph identifies and annotates the consequence of variations and provides contextual information regarding which heart structures, pathways, and biological processes are potentially affected by those variations. Methods: We conducted our work through three steps. First, we define a data model to support the representation of the heterogeneous information. Second, we instantiate this data model to integrate and represent all the genomics knowledge available for familiar cardiopathies. In this step, we consider genomic data sources and the scientific literature. Third, the design and implementation of the CardioGraph platform. A three-tier structure was used: the database, the backend, and the frontend. Results: Three main results were obtained: the data model, the knowledge base generated with the instantiation of the data model, and the platform itself. The platform code has been included as supplemental material in this manuscript. Besides, an instance is publicly available in the following link: https://genomics-hub.pros.dsic.upv.es:3090. Conclusion: CardioGraph is a platform that supports the analysis of novel variations. Future work will expand the body of knowledge about familiar cardiopathies and include new information about hotspots, functional studies, and previously reported variations. [ABSTRACT FROM AUTHOR]

Natural-language dialog is key for an intuitive human-robot interaction. It can be used not only to express humans' intents but also to communicate instructions for improvement if a robot does not understand a command correctly. It is of great importance to let robots learn from such interaction experiences in an incremental way to allow them to improve their behaviors or avoid mistakes in the future. In this paper, we propose a system to achieve such incremental learning of complex high-level behavior from natural interaction and demonstrate its implementation on a humanoid robot. Our system deploys large language models (LLMs) for high-level orchestration of the robot's behavior based on the idea of enabling the LLM to generate Python statements in an interactive console to invoke both robot perception and action. Human instructions, environment observations, and execution results are fed back to the LLM, thus informing the generation of the next statement. Since an LLM can misunderstand (potentially ambiguous) user instructions, we introduce incremental learning from the interaction, which enables the system to learn from its mistakes. For that purpose, the LLM can call another LLM responsible for code-level improvements in the current interaction based on human feedback. Subsequently, we store the improved interaction in the robot's memory so that it can later be retrieved on semantically similar requests. We integrate the system in the robot cognitive architecture of the humanoid robot ARMAR-6 and evaluate our methods both quantitatively (in simulation) and qualitatively (in simulation and real-world) by demonstrating generalized incrementally learned knowledge. [ABSTRACT FROM AUTHOR]

Background: The global health system remains determined to leverage on every workable opportunity, including artificial intelligence (AI) to provide care that is consistent with patients' needs. Unfortunately, while AI models generally return high accuracy within the trials in which they are trained, their ability to predict and recommend the best course of care for prospective patients is left to chance. Purpose: This review maps evidence between January 1, 2010 to December 31, 2023, on the perceived threats posed by the usage of AI tools in healthcare on patients' rights and safety. Methods: We deployed the guidelines of Tricco et al. to conduct a comprehensive search of current literature from Nature, PubMed, Scopus, ScienceDirect, Dimensions AI, Web of Science, Ebsco Host, ProQuest, JStore, Semantic Scholar, Taylor & Francis, Emeralds, World Health Organisation, and Google Scholar. In all, 80 peer reviewed articles qualified and were included in this study. Results: We report that there is a real chance of unpredictable errors, inadequate policy and regulatory regime in the use of AI technologies in healthcare. Moreover, medical paternalism, increased healthcare cost and disparities in insurance coverage, data security and privacy concerns, and bias and discriminatory services are imminent in the use of AI tools in healthcare. Conclusions: Our findings have some critical implications for achieving the Sustainable Development Goals (SDGs) 3.8, 11.7, and 16. We recommend that national governments should lead in the roll-out of AI tools in their healthcare systems. Also, other key actors in the healthcare industry should contribute to developing policies on the use of AI in healthcare systems. [ABSTRACT FROM AUTHOR]

With the advent of robotics and artificial intelligence, the potential for automating tasks within human-centric environments has increased significantly. This is particularly relevant in the retail sector where the demand for efficient operations and the shortage of labor drive the need for rapid advancements in robot-based technologies. Densely packed retail shelves pose unique challenges for robotic manipulation and detection due to limited space and diverse object shapes. Vacuum-based grasping technologies offer a promising solution but face challenges with object shape adaptability. The study proposes a framework for robotic grasping in retail environments, an adaptive vacuum-based grasping solution, and a new evaluation metric—termed grasp shear force resilience—for measuring the effectiveness and stability of the grasp during manipulation. The metric provides insights into how retail objects behave under different manipulation scenarios, allowing for better assessment and optimization of robotic grasping performance. The study's findings demonstrate the adaptive suction cups' ability to successfully handle a wide range of object shapes and sizes, which, in some cases, overcome commercially available solutions, particularly in adaptability. Additionally, the grasp shear force resilience metric highlights the effects of the manipulation process, such as in shear force and shake, on the manipulated object. This offers insights into its interaction with different vacuum cup grasping solutions in retail picking and restocking scenarios. [ABSTRACT FROM AUTHOR]

While large language models learn sound statistical representations of the language and information therein, ontologies are symbolic knowledge representations that can complement the former ideally. Research at this critical intersection relies on datasets that intertwine ontologies and text corpora to enable training and comprehensive benchmarking of neurosymbolic models. We present the MaterioMiner dataset and the linked materials mechanics ontology where ontological concepts from the mechanics of materials domain are associated with textual entities within the literature corpus. Another distinctive feature of the dataset is its eminently fine-grained annotation. Specifically, 179 distinct classes are manually annotated by three raters within four publications, amounting to 2191 entities that were annotated and curated. Conceptual work is presented for the symbolic representation of causal composition-process-microstructure-property relationships. We explore the annotation consistency between the three raters and perform fine-tuning of pre-trained language models to showcase the feasibility of training named entity recognition models. Reusing the dataset can foster training and benchmarking of materials language models, automated ontology construction, and knowledge graph generation from textual data. [ABSTRACT FROM AUTHOR]

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research. [ABSTRACT FROM AUTHOR]

How people represent categories and how those representations change over time is a basic question about human cognition. Previous research has demonstrated that people categorize objects by comparing them to category prototypes in early stages of learning but consider the individual exemplars within each category in later stages. However, these results do not seem consistent with findings in the memory literature showing that it becomes increasingly easier to access representations of general knowledge than representations of specific items over time. Why would one rely more on exemplar-based representations in later stages of categorization when it is more difficult to access these exemplars in memory? To reconcile these incongruities, our study proposed that previous findings on categorization are a result of human participants adapting to a specific experimental environment, in which the probability of encountering an object stays uniform over time. In a more realistic environment, however, one would be less likely to encounter the same object if a long time has passed. Confirming our hypothesis, we demonstrated that under environmental statistics identical to typical categorization experiments the advantage of exemplar-based categorization over prototype-based categorization increases over time, replicating previous research in categorization. In contrast, under realistic environmental statistics simulated by our experiments the advantage of exemplar-based categorization over prototype-based categorization decreases over time. A second set of experiments replicated our results, while additionally demonstrating that human categorization is sensitive to the category structure presented to the participants. These results provide converging evidence that human categorization adapts appropriately to environmental statistics. [ABSTRACT FROM AUTHOR]

In this paper, I investigate the co-production of genetic research and national politics in post-martial law Taiwan. This entails analyzing two co-produced phenomena: the nationalization of biomedicine—in which the national discourse over racial/ethnic categories and ancestral origin increasingly shapes scientists' biomedical research; and the biomedicalization of the nation—in which people in public discourse increasingly use biomedical categories in characterizing national differences and identities. I analyze how the production and representation of scientific knowledge of the ancestral origins and genetic make-up of Taiwan have been embedded in Taiwanese politics. This includes the emergence of a new categorization into four great ethnic groups, multiculturalism, and the assertion of a distinct Taiwanese national identity, particularly in response to the People's Republic of China's claims of common ancestry. I also examine how the scientific findings produced in the lab have spilled out into both Taiwan and China through journals, media, history textbooks, and public disputes since the 1990s and brought about significant social impact. [ABSTRACT FROM AUTHOR]

Around the turn of the 20th century, Keynes and Johnson extended the well-known square of opposition to an octagon of opposition, in order to account for subject negation (e.g., statements like 'all non-S are P'). The main goal of this paper is to study the logical properties of the Keynes-Johnson (KJ) octagons of opposition. In particular, we will discuss three concrete examples of KJ octagons: the original one for subject-negation, a contemporary one from knowledge representation, and a third one (hitherto not yet studied) from deontic logic. We show that these three KJ octagons are all Aristotelian isomorphic, but not all Boolean isomorphic to each other (the first two are representable by bitstrings of length 7, whereas the third one is representable by bitstrings of length 6). These results nicely fit within our ongoing research efforts toward setting up a systematic classification of squares, octagons, and other diagrams of opposition. Finally, obtaining a better theoretical understanding of the KJ octagons allows us to answer some open questions that have arisen in recent applications of these diagrams. [ABSTRACT FROM AUTHOR]

Commonsense Question Answering (CQA) aims to select the correct answers to common knowledge questions. Most existing approaches focus on integrating external knowledge graph (KG) representations with question context representations to facilitate reasoning. However, the approaches cannot effectively select the correct answer due to (i) the incomplete reasoning chains when using knowledge graphs as external knowledge, and (ii) the insufficient understanding of semantic information of the question during the reasoning process. Here we propose a novel model, KA-AGN. First, we utilize a joint representation of dependency parse trees and language models to describe QA pairs. Next, we introduce question semantic information as nodes into a knowledge subgraph and compute the correlations between nodes using adaptive graph networks. Finally, bidirectional attention and graph pruning are employed to update the question representation and the knowledge subgraph representation. To evaluate the performance of our method, we conducted experiments on two widely used benchmark datasets: CommonsenseQA and OpenBookQA. The ablation experiment results demonstrate the effectiveness of the adaptive graph network in enhancing reasoning chains, while showing the ability of the joint representation of dependency parse trees and language models to correctly understand question semantics. Our code is publicly available at https://github.com/agfsghfdhg/KAAGN-main. [ABSTRACT FROM AUTHOR]

Demonstration Environments (DEs) are essential tools for testing and demonstrating new technologies, products, and services, and reducing uncertainties and risks in the innovation process. However, the terminology used to describe these environments is inconsistent, leading to heterogeneity in defining and characterizing them. This makes it difficult to establish a universal understanding of DEs and to differentiate between the different types of DEs, including testbeds, pilot-plants, and living labs. Moreover, existing literature lacks a holistic view of DEs, with studies focusing on specific types of DEs and not offering an integrated perspective on their characteristics and applicability in different contexts. This study proposes an ontology for knowledge representation related to DEs to address this gap. Using an ontology learning approach analyzing 3621 peer-reviewed journal articles, we develop a standardized framework for defining and characterizing DEs, providing a holistic view of these environments. The resulting ontology allows innovation managers and practitioners to select appropriate DEs for achieving their innovation goals, based on the characteristics and capabilities of the specific type of DE. The contributions of this study are significant in advancing the understanding and application of DEs in innovation processes. The proposed ontology provides a standardized approach for defining and characterizing DEs, reducing inconsistencies in terminology and establishing a common understanding of these environments. This enables innovation managers and practitioners to select appropriate DEs for their specific innovation goals, facilitating more efficient and effective innovation processes. Overall, this study provides a valuable resource for researchers, practitioners, and policymakers interested in the effective use of DEs in innovation. [ABSTRACT FROM AUTHOR]

Remote sensing image scene classification (RSISC) greatly benefits from the use of few-shot learning, as it enables the recognition of novel scenes with only a small amount of labeled data. Most previous works focused on learning representations of prior knowledge with scarce labeled data while ignoring the feasibility of using potential information with large amounts of unlabeled data. In this paper, we introduce a novel semi-supervised few-shot pseudo-label propagation method through the introduction of unlabeled knowledge. This approach utilizes the pseudo-loss property generated by the classifier to indirectly reflect the credibility of pseudo-labeled samples. Therefore, we propose a semi-supervised pseudo-loss confidence metric-based method called a pseudolabel meta-learner (PLML) for RSISC. Specifically, we adopt a pseudoloss estimation module to map the pseudo-labeled data obtained from different tasks to a unified pseudo-loss metric space. Then, the distributions of the pseudolosses with both correct and incorrect pseudolabels are fitted by a semi-supervised beta mixture model (ss-BMM). This model can iteratively select high-quality unlabeled data to enhance the self-training effect of the classifier. Finally, to address the problem of shifting pseudo-loss distributions in remote sensing images, a progressive self-training strategy is proposed to mitigate the cumulative error induced by the classifier. Experimental results demonstrate that our proposed PLML approach outperforms the existing alternatives on the NWPU-RESISC45, AID, and UC Merced datasets. [ABSTRACT FROM AUTHOR]

In the realm of data privacy protection, federated learning aims to collaboratively train a global model. However, heterogeneous data between clients presents challenges, often resulting in slow convergence and inadequate accuracy of the global model. Utilizing shared feature representations alongside customized classifiers for individual clients emerges as a promising personalized solution. Nonetheless, previous research has frequently neglected the integration of global knowledge into local representation learning and the synergy between global and local classifiers, thereby limiting model performance. To tackle these issues, this study proposes a hierarchical optimization method for federated learning with feature alignment and the fusion of classification decisions (FedFCD). FedFCD regularizes the relationship between global and local feature representations to achieve alignment and incorporates decision information from the global classifier, facilitating the late fusion of decision outputs from both global and local classifiers. Additionally, FedFCD employs a hierarchical optimization strategy to flexibly optimize model parameters. Through experiments on the Fashion-MNIST, CIFAR-10 and CIFAR-100 datasets, we demonstrate the effectiveness and superiority of FedFCD. For instance, on the CIFAR-100 dataset, FedFCD exhibited a significant improvement in average test accuracy by 6.83% compared to four outstanding personalized federated learning approaches. Furthermore, extended experiments confirm the robustness of FedFCD across various hyperparameter values. [ABSTRACT FROM AUTHOR]

An oral two-drug regimen (O2DR) in the form of a once-a-day single tablet is now recommended for treatment switching and treatment initiation for HIV. In clinical care, the process of treatment change refers to adaptation issues, both individual and within the care relationship. The study aim is to present the determinants involved in the acceptability of switching to O2DR in the PROBI (Patient-Reported Outcomes BItherapy) qualitative study. The study includes 30 interviews: 15 were conducted with doctors caring for people living with HIV, 15 were conducted with patients who had been offered a change of treatment. A double analysis was carried out: lexicometric analysis to highlight the structuring of the discourse around the change in treatment and a thematic analysis to understand the associated issues more precisely. The results highlighted common concerns with respect to switching to O2DR. Also, the caregiver-patient relationship was a central determinant in treatment switching. Information, knowledge and representations of O2DR are also factors facilitating treatment change and should be taken into account for doctors' and patients' adherence. [ABSTRACT FROM AUTHOR]

Background: Bio-ontologies are keys in structuring complex biological information for effective data integration and knowledge representation. Semantic similarity analysis on bio-ontologies quantitatively assesses the degree of similarity between biological concepts based on the semantics encoded in ontologies. It plays an important role in structured and meaningful interpretations and integration of complex data from multiple biological domains. Results: We present simona, a novel R package for semantic similarity analysis on general bio-ontologies. Simona implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. Moreover, it provides a robust toolbox supporting over 70 methods for semantic similarity analysis. With simona, we conducted a benchmark against current semantic similarity methods. The results demonstrate methods are clustered based on their mathematical methodologies, thus guiding researchers in the selection of appropriate methods. Additionally, we explored annotation-based versus topology-based methods, revealing that semantic similarities solely based on ontology topology can efficiently reveal semantic similarity structures, facilitating analysis on less-studied organisms and other ontologies. Conclusions: Simona offers a versatile interface and efficient implementation for processing, visualization, and semantic similarity analysis on bio-ontologies. We believe that simona will serve as a robust tool for uncovering relationships and enhancing the interoperability of biological knowledge systems. [ABSTRACT FROM AUTHOR]

Knowledge graph embedding (KGE) has been identified as an effective method for link prediction, which involves predicting missing relations or entities based on existing entities or relations. KGE is an important method for implementing knowledge representation and, as such, has been widely used in driving intelligent applications w.r.t. question-answering systems, recommendation systems, and relationship extraction. Models based on convolutional neural networks (CNNs) have achieved good results in link prediction. However, as the coverage areas of knowledge graphs expand, the increasing volume of information significantly limits the performance of these models. This article introduces a triple-attention-based multi-channel CNN model, named ConvAMC, for the KGE task. In the embedding representation module, entities and relations are embedded into a complex space and the embeddings are performed in an alternating pattern. This approach helps in capturing richer semantic information and enhances the expressive power of the model. In the encoding module, a multi-channel approach is employed to extract more comprehensive interaction features. A triple attention mechanism and max pooling layers are used to ensure that interactions between spatial dimensions and output tensors are captured during the subsequent tensor concatenation and reshaping process, which allows preserving local and detailed information. Finally, feature vectors are transformed into prediction targets for embedding through the Hadamard product of feature mapping and reshaping matrices. Extensive experiments were conducted to evaluate the performance of ConvAMC on three benchmark datasets compared with state-of-the-art (SOTA) models, demonstrating that the proposed model outperforms all compared models across all evaluation metrics on two of the datasets, and achieves advanced link prediction results on most evaluation metrics on the third dataset. [ABSTRACT FROM AUTHOR]

Mommy blog communities have been established by mothers who publish their stories online with the stated purpose to empower themselves and their peers by offering a seemingly unfiltered representation of motherhood. While this benefit has been felt by many readers, there are invisible governing discourses that have prevented such blogs from becoming authentic spaces of resistance to the hegemonic ideals of motherhood. To understand the governing forces placed upon mothers through “mommy blogs,” we explored the question: How is motherhood shaped through narratives (re)presented in mommy blogs? Critical discourse analysis, informed by a governmentality perspective, was used to analyze mommy blog entries to illuminate representations of power and knowledge within the blog entries. The findings suggest that a governmentality perspective is useful in understanding how mommy blogs enact power over mothers using mechanisms of governance through 1) sameness and imperfection, 2) perceived self-compassion, 3) advice and support, and 4) the scientification of motherhood. As more people turn to online communities for support with the experience of mothering, it is important to garner a critical understanding of how mommy blogs continue to influence contemporary experiences of motherhood. [ABSTRACT FROM AUTHOR]

Ontology plays a significant role in AI (Artificial Intelligence) and robotics by providing structured data, reasoning, action understanding, context awareness, knowledge transfer, and semantic learning. The structured framework created by the ontology for knowledge representation is crucial for enabling intelligent behavior in robots. This paper provides a state-of-the-art analysis on the existing ontology approaches and at the same time consolidates the terms in the robotic task planning domain. The major gap identified in the literature is the need to bridge higher-level robotic process management and lower-level robotic control. This gap makes it difficult for operators/non-robotic experts to integrate robots into their production processes as well as evaluate key performance indicators (KPI) of the processes. To fill the gap, the authors propose an ontology for skill-based robotics process planning (ORPP). ORPP not only provides a standardization in the robotic process planning in the agile manufacturing domain but also enables non-robotic experts to design and plan their production processes using an intuitive Process-Task-Skill-Primitive structure to control low-level robotic actions. On the performance level, this structure provides traceability of the KPIs down to the robot control level. [ABSTRACT FROM AUTHOR]

Transformer-based large language models (LLMs) are very suited for biological sequence data, because of analogies to natural language. Complex relationships can be learned, because a concept of "words" can be generated through tokenization. Training the models with masked token prediction, they learn both token sequence identity and larger sequence context. We developed methodology to interrogate model learning, which is both relevant for the interpretability of the model and to evaluate its potential for specific tasks. We used DNABERT, a DNA language model trained on the human genome with overlapping k-mers as tokens. To gain insight into the model′s learning, we interrogated how the model performs predictions, extracted token embeddings, and defined a fine-tuning benchmarking task to predict the next tokens of different sizes without overlaps. This task evaluates foundation models without interrogating specific genome biology, it does not depend on tokenization strategies, vocabulary size, the dictionary, or the number of training parameters. Lastly, there is no leakage of information from token identity into the prediction task, which makes it particularly useful to evaluate the learning of sequence context. We discovered that the model with overlapping k-mers struggles to learn larger sequence context. Instead, the learned embeddings largely represent token sequence. Still, good performance is achieved for genome-biology-inspired fine-tuning tasks. Models with overlapping tokens may be used for tasks where a larger sequence context is of less relevance, but the token sequence directly represents the desired learning features. This emphasizes the need to interrogate knowledge representation in biological LLMs. [ABSTRACT FROM AUTHOR]

What might it mean to reimagine brown‐girl‐as‐failure to brown‐girl‐as‐success? This article draws on findings from an empirical research study of academically successful teenage girls from Aotearoa New Zealand. In this paper we focus on what it means to be an intelligent and successful young brown woman in the context of the contemporary white‐centering of meritocratic success, and the oppressive narrative that brown girls are not bright. Using a creative methodology, Laurel Richardson's collective storying and Patricia Leavy's fiction‐based research, the paper engages in forms of creative analytic practice and new knowledge representation, which prioritize authentic voice and understanding of the young women participants' lived experiences. Collective stories were used in the study to challenge existing public discourses of girls and success, including the white‐centering of such depictions, and to create narratives that participants could identify with, particularly those that were often unspoken but widely experienced. Using collective stories in the study offered a space of resonance with participants who could engage with the stories during the research process and contribute to their (re)storying. The interplay between the theoretics of methodological creativity and the symbolic violence of a colonial positioning of successful girlhood offers a novel contribution to girlhood studies. Through collective storying and a further interweaving of poetic voice, the disruption of the narrative of deficit offers remembering and revalidation of brown success. [ABSTRACT FROM AUTHOR]

Machine learning (ML)‐guided polymer design and synthesis will enable the next‐generation membrane material discovery for CO2 capture. Herein, ML is leveraged to establish a structure‐performance relationship for the eco‐friendly poly(ethylene oxide) (PEO) membrane and guide its design for high‐efficacy CO2/N2 separation. Through a rational fragment representation method and knowledge sharing across membranes fabricated by different methods, the precise prediction of CO2/N2 separation performance for PEO membranes with high Pearson correlation coefficients (0.973 for permeability and 0.875 for selectivity) despite data scarcity is demonstrated. Expertise knowledge and external monomer databases are then utilized in a human‐in‐the‐loop workflow to effectively explore high‐performance PEO membranes in the design space. Several discovered thermally crosslinked PEO membranes achieve CO2/N2 separation performances close to the 2019 Robeson upper bound, which are promising for practical large‐scale carbon capture applications. Model interpretation techniques are employed to provide data‐driven insights into the design of PEO membranes for high‐efficacy CO2/N2 separation. Further life cycle assessment results reveal the outstanding advantage of discovered PEO membranes in terms of environmental friendliness. The work highlights the enormous potential of ML in expediting the discovery of high‐performance carbon capture membrane materials. [ABSTRACT FROM AUTHOR]

Traditionally, the design of an expert system involves acquiring knowledge, in the form of symbolic rules, directly from the expert(s), which is a complex and time‐consuming task. Although expert systems approach is quite old, it is still present, especially where explicit knowledge representation and reasoning, which assure interpretability and explainability, are necessary. Therefore, machine learning methods have been devised to extract rules from data, to facilitate that task. However, those methods are quite inflexible in adapting to the application domain and provide no help in designing the expert system. In this work, we present a framework and corresponding tool, namely ACRES, for semi‐automatically generating expert systems from datasets. ACRES allows for data preprocessing, which helps in structuring knowledge in the form of a tree, called rule hierarchy, which represents (possible) dependencies among data variables and is used for rule formation. This improves interpretability and explainability of the produced systems. We have also designed and evaluated alternative methods for rule extraction from data and for calculation and use of certainty factors, to represent uncertainty; CFs can be dynamically updated. Experimental results on seven well‐known datasets show that the proposed rule extraction methods are comparable to other popular machine learning approaches like decision trees, CART, JRip, PART, Random Forest, and so on, for the classification task. Finally, we give insights on two applications of ACRES. [ABSTRACT FROM AUTHOR]

Ontologies play a vital role in organizing and constructing knowledge across various domains, enabling effective knowledge management and sharing. The development of domain-specific ontologies, such as the ONTO-TDM ontology for teaching domain modeling, is essential for providing a comprehensive and standardized representation of knowledge within a given discipline. However, to maximize the usefulness and relevance of such ontologies, it is crucial to automate their population with domain-specific information, reducing manual work and ensuring scalability. This paper presents a novel method for ontology population by extracting and integrating relevant information from diverse sources. The method combines the TextRank algorithm with Word2Vec to enhance keyword extraction, capturing both semantic meaning and textual importance. Keywords are then annotated and used to train a machine learning classifier, which aids in integrating new instances into the ontology. Experiments show that the proposed method achieves a precision of 63.33%, a recall of 61.29% and an F1-score of 62.28%, significantly improving keyword extraction and ontology population accuracy compared to existing methods. This validates the method's effectiveness in semi-automatically extracting relevant instances from diverse data sources, enhancing the efficiency and accuracy of ontology population, and advancing automated knowledge management in domain-specific contexts. [ABSTRACT FROM AUTHOR]

Radical constructivism (RC), introduced by Ernst von Glasersfeld, is an instrumentalist theory of knowledge that challenges the notion of knowledge as a picture or representation of a real, external world. Instead, RC, by integrating ideas from different theoretical fields - such as cybernetics, evolutionary biology, and evolutionary psychology - considers the purpose of the cognitive organism to construct knowledge not to achieve truth, but to construct perceptual and conceptual structures that help to maintain adaptation and cognitive equilibrium. However, the basic question here is, by what character these structures can achieve the purpose. By examining Glasersfeld's writings, this paper argues that the character is a "functional fit", which is based on a fundamental shift in RC, i.e. the shift from "matching" to "fitness". From this point of view, the "fit knowledge" is not due to its match with the external reality, but due to its repeated success in solving a specific problem, it is viable, and by preventing unwanted changes or perturbations, it gives order to the experiential world of the organism and helps to maintain its cognitive equilibrium. This perspective has the potential to create transformation in various fields, from education to ethics and social issues. Therefore, this paper, while examining the basic concepts of radical constructivist epistemology, also deals with its theoretical and practical applications. [ABSTRACT FROM AUTHOR]

Objective: Clinical practice guidelines (CPGs) for Integrated Traditional Chinese and Western Medicine (TCM and WM) are important medical documents used to assist medical decision‐making and are of great significance for standardizing clinical pathways. However, due to the constraints of text format, it is difficult for Integrated TCM and WM CPGs to play a real role in medical practice. In addition, how to standardize the structure and semantic relationships between Integrated TCM and WM CPG knowledge, and realize the construction of computable, sharable and reliable CPGs, remains an urgent issue to be addressed. Therefore, we are proposing an ontology of CPGs for Integrated TCM and WM. Methods: We first initialized domain concepts and relationships to ensure the accuracy of the ontology knowledge structure. We then screened CPGs that meet the standards for Integrated TCM and WM, analyzed and classified the contents, and extracted the common structures. Based on the seven‐step ontology construction method combined with inference‐complement, referring to the representation methods and hierarchical relationships of terms and concepts in MeSH, ICD‐10, SNOMED‐CT, and other ontologies and terminology sets, we formed the concept structure and semantic relationship tables for the ontology. We also achieved the matching and mapping between the ontology and reference ontologies and term sets. Next, we defined the aspects and constraints of properties, selected multiple Integrated TCM and WM CPGs as instances to populate, and used ontology reasoning tools and formulated defined inference rules to reason and extend the ontology. Finally, we evaluated the performance of the ontology. Results: The content of the Integrated TCM and WM CPGs is divided into nine parts: basic information, background, development method, clinical question, recommendation, evidence, conclusion, result, and reason for recommendations. The Integrated TCM and WM CPG ontology has 152 classes and defines 90 object properties and 114 data properties, with a maximum classification depth of 4 layers. The terms of disease, drug and examination item names in the ontology have been standardized. Conclusions: This study proposes an Integrated TCM and WM CPG ontology. The ontology adopts a modular design, which has both sharing and scaling ability, and can express rich guideline knowledge. It provides important support for the semantic processing and computational application of guideline documents. [ABSTRACT FROM AUTHOR]

Event extraction is a complex and challenging task in the field of information extraction. It aims to identify event types, triggers, and argument information from the text. In recent years, overlapping event extraction has attracted the attention of researchers because of its higher challenge and practicability, and some work has carried out in-depth research on overlapping event extraction and achieved remarkable results. But these works (1) ignore the role of ontology knowledge in event extraction; (2) use the same semantic encoding for multi-stage models, lacking consideration for the independent characteristics of extraction tasks such as event types, triggers, and arguments; and (3) face issues in the training process of multi-stage models, such as error cascading and slow convergence. To address the above issues, we propose an ontology-guided and scheduled-sampling approach for overlapping event extraction, termed as OGSS. First, we design a symmetric matrix for event ontology knowledge representation and integrate it into the semantic encoding process, infusing ontology knowledge into event extraction. Second, for extraction targets such as event types, triggers, and arguments, we process the semantic encoding according to the characteristics of each extraction target, obtaining semantic representations tailored for each subtask. Finally, we view multi-stage predictions as sequential outputs of a joint model, using a scheduled sampling strategy between subtasks to effectively mitigate the cascading propagation of errors during training and accelerate model convergence. We conduct extensive experiments on the FewFc event extraction benchmark dataset. The results show that OGSS achieves significant improvements in overlapping event extraction tasks compared to previous methods. [ABSTRACT FROM AUTHOR]

In the realm of artificial intelligence, knowledge graphs (KGs) serve as an essential structured framework, capturing intricate relationships between diverse entities and supporting a broad spectrum of AI applications. Despite their utility, the static characteristic of KGs poses challenges in dynamically evolving information landscapes. This has catalyzed the development of temporal knowledge graphs (TKGs), which introduce a temporal layer to KGs, facilitating the representation of knowledge progression through time. This study zeroes in on the critical task of TKG extrapolation, which is vital for forecasting future occurrences and offering foresight into emerging situations across a variety of fields. Most contemporary approaches to TKG extrapolation are predicated on the symmetrical encoder–decoder paradigm, wherein the processes of representation learning and reasoning are harmoniously intertwined. While the encoder often garners the most attention due to its role in capturing and encoding information, the pivotal role of the decoder, which is often overlooked, is essential for direct inference and the accurate projection of temporal dynamics. To this end, we present the Householder-transformation-based temporal knowledge graph extrapolation (HTKGE) method: a groundbreaking encoder–decoder framework that reimagines the decoder's contribution to TKG extrapolation. Our approach spotlights an adaptive decoder propelled by Householder transformations, which engage dynamically with the temporal encoding from the encoder. This interaction fosters a nuanced comprehension of the TKG's temporal trajectory. Our empirical evaluations across four benchmark TKG datasets substantiate HTKGE's consistent efficacy in TKG extrapolation tasks. [ABSTRACT FROM AUTHOR]

Pivotal legislators' positions are critical to legislative outcomes, but does this heightened importance in policymaking translate into heightened electoral accountability or voter knowledge? Arguments about clarity of responsibility suggest that pivotal legislators, who are decisive in determining legislative outcomes, may be held to higher standards, while perspectives rooted in electoral incentives for position taking suggest they may not. Two survey experiments show that voters do not respond more strongly to pivotal legislators' votes on policy. Moreover, observational data analysis rejects the expectation that constituents have more knowledge about the votes of pivotal moderate legislators compared to non-pivotal moderate legislators. These results suggest that pivotal legislators face similar, if not lower, accountability for their votes. Combined with the policy-concessions pivotal legislators can secure, these patterns point to the benefits that accrue to pivotal legislators from institutional rules that give them key veto power over policy. [ABSTRACT FROM AUTHOR]

In recent years, machine learning models have been increasingly used to detect security vulnerabilities in software, due to their ability to achieve high performance and lower false positive rates compared to traditional program analysis tools. However, these models often lack the capability to provide a clear explanation for why a program has been flagged as vulnerable, leaving developers with little reasoning to work with. We present a new method which not only identifies the presence of vulnerabilities in a program, but also the specific type of error, considering the whole program rather than just individual functions. Our approach utilizes graph neural networks that employ inter-procedural value flow graphs, and instruction embedding from the LLVM Intermediate Representation, to predict a class. By mapping these classes to the Common Weakness Enumeration list, we provide a clear indication of the security issue found, saving developers valuable time which would otherwise be spent analyzing a binary vulnerable/non-vulnerable label. To evaluate our method's effectiveness, we used two datasets: one containing memory-related errors (out of bound array accesses), and the other a range of vulnerabilities from the Juliet Test Suite, including buffer and integer overflows, format strings, and invalid frees. Our model, implemented using PyTorch and the Gated Graph Sequence Neural Network from Torch-Geometric, achieved a precision of 96.35 and 91.59% on the two datasets, respectively. Compared to common static analysis tools, our method produced roughly half the number of false positives, while identifying approximately three times the number of vulnerable samples. Compared to recent machine learning systems, we achieve similar performance while offering the added benefit of differentiating between classes. Overall, our approach represents a meaningful improvement in software vulnerability detection, providing developers with valuable insights to better secure their code. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) like ChatGPT and Bard have the potential to greatly benefit healthcare, including the field of uveitis care. LLMs can provide accessible advice for patients, helping them make decisions about when to seek medical attention or escalate their treatment. They can also assist in the diagnosis of uveitis, although more research specific to uveitis is needed. LLMs can improve the consultation experience by engaging with patients and providing empathetic responses. Additionally, they can aid in medication management and streamline administrative processes. LLMs have the potential to accelerate research in uveitis by automating tasks such as data analysis and identifying potential drug targets. However, there are ethical considerations to address, including patient autonomy, bias, transparency, and accountability. Efforts should be made to ensure equity and accessibility in the implementation of LLMs, and regulatory frameworks should be developed to ensure responsible use. The future directions of LLMs in uveitis care include integrating them with visual and auditory processing and training them in real-time. While LLMs show promise, more research is needed to develop them with reliability and replicability. [Extracted from the article]

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Formal Concept Analysis (FCA) is an order theory-based methodology employed for concept analysis and construction. Incomplete fuzzy formal context is employed to present the uncertainty or lack of memberships between individuals and attributes. Acceptable implications and necessary implications are two types of implications that assess the validity of knowledge within incomplete formal contexts. On the one hand, attribute exploration approaches within incomplete formal contexts rely on the prior knowledge of experts. On the other hand, in the existing reasoning mechanism for acceptable implications and necessary implications, the bases are inconvenient as they recursively involve the bases of all the completions of the incomplete formal context. Another critical issue is that the inference rules, originally apply to the implications in formal contexts, may yield invalid implications when they are applied to the two types of implications. In this paper, we firstly discretize incomplete fuzzy formal context into incomplete formal context by employing a dual-threshold filter function and then model the incomplete formal context by two specially constructed decision contexts. Next, we re-represent acceptable implications and necessary implications based on decision implications and demonstrate that the inference rules Augmentation and Combination, initially designed for decision implications, are practicable for necessary implications and acceptable implications. Furthermore, we utilize Augmentation, Combination, and another inference rule Reflexivity to jointly define the completeness and non-redundancy for sets of necessary implications and that of acceptable implications. Finally, we establish necessary implication basis and acceptable implication basis, which preserve all the information implied in the two types of implications while simultaneously minimizing the total number of implications. [ABSTRACT FROM AUTHOR]

The knowledge graph emerges as powerful data structures that provide a deep representation and understanding of the knowledge presented in networks. In the pursuit of representation learning of the knowledge graph, entities and relationships undergo an embedding process, where they are mapped onto a vector space with reduced dimensions. These embeddings are progressively used to extract their information for a multitude of tasks in machine learning. Nevertheless, the increase data in knowledge graph has introduced a challenge, especially as knowledge graph embedding now encompass millions of nodes and billions of edges, surpassing the capacities of existing knowledge representation learning systems. In response to these challenge, this paper presents DistKGE, a distributed learning approach of knowledge graph embedding based on a new partitioning technique. In our experimental evaluation, we illustrate that the proposed approach improves the scalability of distributed knowledge graph learning with respect to graph size compared to existing methods in terms of runtime performances in the link prediction task aimed at identifying new links between entities within the knowledge graph. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach for text-independent phone-to-audio alignment based on phoneme recognition, representation learning and knowledge transfer. Our method leverages a self-supervised model (Wav2Vec2) fine-tuned for phoneme recognition using a Connectionist Temporal Classification (CTC) loss, a dimension reduction model and a frame-level phoneme classifier trained using forced-alignment labels (using Montreal Forced Aligner) to produce multi-lingual phonetic representations, thus requiring minimal additional training. We evaluate our model using synthetic native data from the TIMIT dataset and the SCRIBE dataset for American and British English, respectively. Our proposed model outperforms the state-of-the-art (charsiu) in statistical metrics and has applications in language learning and speech processing systems. We leave experiments on other languages for future work but the design of the system makes it easily adaptable to other languages. [ABSTRACT FROM AUTHOR]