The E-learning environment offers greater flexibility compared to face-to-face interactions, allowing for adapting educational content to meet learners' individual needs and abilities through personalization and customization of e-content and the educational process. Despite the advantages of this approach, customizing the learning environment can reduce the costs of tutoring systems for similar learners by utilizing the same content and process for co-like learning groups. Various indicators for grouping learners exist, but many of them are conceptual, uncertain, and subject to change over time. In this article, we propose using the Felder-Silverman model, which is based on learning styles, to group similar learners. Additionally, we model the behaviors and actions of e-learners in a network environment using Fuzzy Set Theory (FST). After identifying the learning styles of the learners, co-like learning groups are formed, and each group receives adaptive content based on their preferences, needs, talents, and abilities. By comparing the results of the experimental and control groups, we determine the effectiveness of the proposed grouping method. In terms of "educational success," the weighted average score of the experimental group is 17.65 out of 20, while the control group achieves a score of 12.6 out of 20. Furthermore, the "educational satisfaction" of the experimental group is 67%, whereas the control group's satisfaction level is 37%.

Research on knowledge graph embeddings has recently evolved into knowledge base embeddings, where the goal is not only to map facts into vector spaces but also constrain the models so that they take into account the relevant conceptual knowledge available. This paper examines recent methods that have been proposed to embed knowledge bases in description logic into vector spaces through the lens of their geometric-based semantics. We identify several relevant theoretical properties, which we draw from the literature and sometimes generalize or unify. We then investigate how concrete embedding methods fit in this theoretical framework.

This study analyzes hybrid AI systems' design patterns and their effectiveness in clinical decision-making using the boxology framework. It categorizes and copares various architectures combining machine learning and rule-based reasoning to provide insights into their structural foundations and healthcare applications. Addressing two main questions, how to categorize these systems againts established design patterns and how to extract insights through comparative analysis, the study uses design patterns from software engineering to understand and optimize healthcare AI systems. Boxology helps identify commonalities and create reusable solutions, enhancing these systems' scalability, reliability, and performance. Five primary architectures are examined: REML, MLRB, RBML, RMLT, and PERML. Each has unique strengths and weaknesses, highlighting the need for tailored approaches in clinical tasks. REML excels in high-accuracy prediction for datasets with limited data; MLRB in handling large datasets and complex data integration; RBML in explainability and trustworthiness; RMLT in managing high-dimensional data; and PERML, though limited in analysis, shows promise in urgent care scenarios. The study introduces four new patterns, creates five abstract categorization patterns, and refines those five further to specific systems. These contributions enhance Boxlogy's taxonomical organization and offer novel approaches to integrating expert knowledge with machine learning. Boxology's structured, modular apporach offers significant advantages in developing and analyzing hybrid AI systems, revealing commonalities, and promoting reusable solutions. In conclusion, this study underscores hybrid AI systems' crucial role in advancing healthcare and Boxology's potential to drive further innovation in AI integration, ultimately improving clinical decision support and patient outcomes.

Legal charge prediction, an essential task in legal AI, seeks to assign accurate charge labels to case descriptions, attracting significant recent interest. Existing methods primarily employ diverse neural network structures for modeling case descriptions directly, failing to effectively leverage multi-source external knowledge. We propose a prompt learning framework-based method that simultaneously leverages multi-source heterogeneous external knowledge from a legal knowledge base, a conversational LLM, and related legal articles. Specifically, we match knowledge snippets in case descriptions via the legal knowledge base and encapsulate them into the input through a hard prompt template. Additionally, we retrieve legal articles related to a given case description through contrastive learning, and then obtain factual elements within the case description through a conversational LLM. We fuse the embedding vectors of soft prompt tokens with the encoding vector of factual elements to achieve knowledge-enhanced model forward inference. Experimental results show that our method achieved state-of-the-art results on CAIL-2018, the largest legal charge prediction dataset, and our method has lower data dependency. Case studies also demonstrate our method's strong interpretability.

This work uses the state-of-the-art language model GPT-3 to offer a novel method of information extraction for knowledge base development. The suggested method attempts to solve the difficulties associated with obtaining relevant entities and relationships from unstructured text in order to extract structured information. We conduct experiments on a huge corpus of text from diverse fields to assess the performance of our suggested technique. The evaluation measures, which are frequently employed in information extraction tasks, include precision, recall, and F1-score. The findings demonstrate that GPT-3 can be used to efficiently and accurately extract pertinent and correct information from text, hence increasing the precision and productivity of knowledge base creation. We also assess how well our suggested approach performs in comparison to the most advanced information extraction techniques already in use. The findings show that by utilizing only a small number of instances in in-context learning, our suggested strategy yields competitive outcomes with notable savings in terms of data annotation and engineering expense. Additionally, we use our proposed method to retrieve Biomedical information, demonstrating its practicality in a real-world setting. All things considered, our suggested method offers a viable way to overcome the difficulties involved in obtaining structured data from unstructured text in order to create knowledge bases. It can greatly increase the precision and effectiveness of information extraction, which is necessary for many applications including chatbots, recommendation engines, and question-answering systems.

Human groups of all sizes and kinds engage in deliberation, problem solving, strategizing, decision making, and more generally, cognition. Some groups are large, and that setting presents unique challenges. The small-group setting often involves face-to-face dialogue, but group cognition in the large-group setting typically requires some form of online interaction. New approaches are needed to facilitate the kind of rich communication and information processing that are required for effective, functional cognition in the online setting, especially for groups characterized by thousands to millions of participants who wish to share potentially complex, nuanced, and dynamic perspectives. This concept paper proposes the CogNarr (Cognitive Narrative) ecosystem, which is designed to facilitate functional cognition in the large-group setting. The paper's contribution is a novel vision as to how recent developments in cognitive science, artificial intelligence, natural language processing, and related fields might be scaled and applied to large-group cognition, using an approach that itself promotes further scientific advancement. A key perspective is to view a group as an organism that uses some form of cognitive architecture to sense the world, process information, remember, learn, predict, make decisions, and adapt to changing conditions. The CogNarr ecosystem is designed to serve as a component within that architecture.

Development of artificial intelligence (AI) techniques in medical imaging requires access to large-scale and diverse datasets for training and evaluation. In dermatology, obtaining such datasets remains challenging due to significant variations in patient populations, illumination conditions, and acquisition system characteristics. In this work, we propose S-SYNTH, the first knowledge-based, adaptable open-source skin simulation framework to rapidly generate synthetic skin, 3D models and digitally rendered images, using an anatomically inspired multi-layer, multi-component skin and growing lesion model. The skin model allows for controlled variation in skin appearance, such as skin color, presence of hair, lesion shape, and blood fraction among other parameters. We use this framework to study the effect of possible variations on the development and evaluation of AI models for skin lesion segmentation, and show that results obtained using synthetic data follow similar comparative trends as real dermatologic images, while mitigating biases and limitations from existing datasets including small dataset size, lack of diversity, and underrepresentation.

Acquiring reviewers for academic submissions is a challenging recommendation scenario. Recent graph learning-driven models have made remarkable progress in the field of recommendation, but their performance in the academic reviewer recommendation task may suffer from a significant false negative issue. This arises from the assumption that unobserved edges represent negative samples. In fact, the mechanism of anonymous review results in inadequate exposure of interactions between reviewers and submissions, leading to a higher number of unobserved interactions compared to those caused by reviewers declining to participate. Therefore, investigating how to better comprehend the negative labeling of unobserved interactions in academic reviewer recommendations is a significant challenge. This study aims to tackle the ambiguous nature of unobserved interactions in academic reviewer recommendations. Specifically, we propose an unsupervised Pseudo Neg-Label strategy to enhance graph contrastive learning (GCL) for recommending reviewers for academic submissions, which we call RevGNN. RevGNN utilizes a two-stage encoder structure that encodes both scientific knowledge and behavior using Pseudo Neg-Label to approximate review preference. Extensive experiments on three real-world datasets demonstrate that RevGNN outperforms all baselines across four metrics. Additionally, detailed further analyses confirm the effectiveness of each component in RevGNN.

Despite explainable AI (XAI) has recently become a hot topic and several different approaches have been developed, there is still a widespread belief that it lacks a convincing unifying foundation. On the other hand, over the past centuries, the very concept of explanation has been the subject of extensive philosophical analysis in an attempt to address the fundamental question of "why" in the context of scientific law. However, this discussion has rarely been connected with XAI. This paper tries to fill in this gap and aims to explore the concept of explanation in AI through an epistemological lens. By comparing the historical development of both the philosophy of science and AI, an intriguing picture emerges. Specifically, we show that a gradual progression has independently occurred in both domains from logical-deductive to statistical models of explanation, thereby experiencing in both cases a paradigm shift from deterministic to nondeterministic and probabilistic causality. Interestingly, we also notice that similar concepts have independently emerged in both realms such as, for example, the relation between explanation and understanding and the importance of pragmatic factors. Our study aims to be the first step towards understanding the philosophical underpinnings of the notion of explanation in AI, and we hope that our findings will shed some fresh light on the elusive nature of XAI.

In the domain of rotating machinery, bearings are vulnerable to different mechanical faults, including ball, inner, and outer race faults. Various techniques can be used in condition-based monitoring, from classical signal analysis to deep learning methods. Based on the complex working conditions of rotary machines, multivariate statistical process control charts such as Hotelling's $T^2$ and Squared Prediction Error are useful for providing early warnings. However, these methods are rarely applied to condition monitoring of rotating machinery due to the univariate nature of the datasets. In the present paper, we propose a multivariate statistical process control-based fault detection method that utilizes multivariate data composed of Fourier transform features extracted for fixed-time batches. Our approach makes use of the multidimensional nature of Fourier transform characteristics, which record more detailed information about the machine's status, in an effort to enhance early defect detection and diagnosis. Experiments with varying vibration measurement locations (Fan End, Drive End), fault types (ball, inner, and outer race faults), and motor loads (0-3 horsepower) are used to validate the suggested approach. The outcomes illustrate our method's effectiveness in fault detection and point to possible broader uses in industrial maintenance.