Diabetes is a worldwide health issue affecting millions of people. Machine learning methods have shown promising results in improving diabetes prediction, particularly through the analysis of diverse data types, namely gene expression data. While gene expression data can provide valuable insights, challenges arise from the fact that the sample sizes in expression datasets are usually limited, and the data from different datasets with different gene expressions cannot be easily combined. This work proposes a novel approach to address these challenges by integrating multiple gene expression datasets and domain-specific knowledge using knowledge graphs, a unique tool for biomedical data integration. KG embedding methods are then employed to generate vector representations, serving as inputs for a classifier. Experiments demonstrated the efficacy of our approach, revealing improvements in diabetes prediction when integrating multiple gene expression datasets and domain-specific knowledge about protein functions and interactions.

To enhance language models' cultural awareness, we design a generalizable pipeline to construct cultural knowledge bases from different online communities on a massive scale. With the pipeline, we construct CultureBank, a knowledge base built upon users' self-narratives with 12K cultural descriptors sourced from TikTok and 11K from Reddit. Unlike previous cultural knowledge resources, CultureBank contains diverse views on cultural descriptors to allow flexible interpretation of cultural knowledge, and contextualized cultural scenarios to help grounded evaluation. With CultureBank, we evaluate different LLMs' cultural awareness, and identify areas for improvement. We also fine-tune a language model on CultureBank: experiments show that it achieves better performances on two downstream cultural tasks in a zero-shot setting. Finally, we offer recommendations based on our findings for future culturally aware language technologies. The project page is https://culturebank.github.io . The code and model is at https://github.com/SALT-NLP/CultureBank . The released CultureBank dataset is at https://huggingface.co/datasets/SALT-NLP/CultureBank .

The thesis develops a view of design in a concept formation framework and outlines a language to describe both the object of the design and the process of designing. The unknown object at the outset of the design work may be seen as an unknown concept that the designer is to define. Throughout the process, she develops a description of this object by relating it to known concepts. The search stops when the designer is satisfied that the design specification is complete enough to satisfy the requirements from it once built. It is then a collection of propositions that all contribute towards defining the design object - a collection of sentences describing relationships between the object and known concepts. Also, the design process itself may be described by relating known concepts - by organizing known abilities into particular patterns of activation, or mobilization. In view of the demands posed to a language to use in this concept formation process, the framework of a Design Process Language (DPL) is developed. The basis for the language are linguistic categories that act as classes of relations used to combine concepts, containing relations used for describing process and object within the same general system, with some relations being process specific, others being object specific, and with the bulk being used both for process and object description. Another outcome is the distinction of modal relations, or relations describing futurity, possibility, willingness, hypothetical events, and the like. The design process almost always includes aspects such as these, and it is thus necessary for a language facilitating design process description to support such relationships to be constructed. The DPL is argued to be a foundation whereupon to build a language that can be used for enabling computers to be more useful - act more intelligently - in the design process.

In the last year or so (and going back many decades) there has been extensive claims by major computational scientists, engineers, and others that AGI (artificial general intelligence) is 5 or 10 years away, but without a scintilla of scientific evidence, for a broad body of these claims: Computers will become conscious, have a "theory of mind," think and reason, will become more intelligent than humans, and so on. But the claims are science fiction, not science. This article reviews evidence for the following three (3) propositions using extensive body of scientific research and related sources from the cognitive and neurosciences; evolutionary evidence; linguistics; data science; comparative psychology; self-driving cars, and robotics; and the learning sciences.

In this work, we propose a novel tree-based explanation technique, PEACH (Pretrained-embedding Explanation Across Contextual and Hierarchical Structure), that can explain how text-based documents are classified by using any pretrained contextual embeddings in a tree-based human-interpretable manner. Note that PEACH can adopt any contextual embeddings of the PLMs as a training input for the decision tree. Using the proposed PEACH, we perform a comprehensive analysis of several contextual embeddings on nine different NLP text classification benchmarks. This analysis demonstrates the flexibility of the model by applying several PLM contextual embeddings, its attribute selections, scaling, and clustering methods. Furthermore, we show the utility of explanations by visualising the feature selection and important trend of text classification via human-interpretable word-cloud-based trees, which clearly identify model mistakes and assist in dataset debugging. Besides interpretability, PEACH outperforms or is similar to those from pretrained models.

A variety of forms of artificial intelligence systems have been developed. Two well-known techniques are neural networks and rule-fact expert systems. The former can be trained from presented data while the latter is typically developed by human domain experts. A combined implementation that uses gradient descent to train a rule-fact expert system has been previously proposed. A related system type, the Blackboard Architecture, adds an actualization capability to expert systems. This paper proposes and evaluates the incorporation of a defensible-style gradient descent training capability into the Blackboard Architecture. It also introduces the use of activation functions for defensible artificial intelligence systems and implements and evaluates a new best path-based training algorithm.

Fault diagnosis in marine diesel engines is vital for maritime safety and operational efficiency.These engines are integral to marine vessels, and their reliable performance is crucial for safenavigation. Swift identification and resolution of faults are essential to prevent breakdowns,enhance safety, and reduce the risk of catastrophic failures at sea. Proactive fault diagnosisfacilitates timely maintenance, minimizes downtime, and ensures the overall reliability andlongevity of marine diesel engines. This paper explores the importance of fault diagnosis,emphasizing subsystems, common faults, and recent advancements in data-driven approachesfor effective marine diesel engine maintenance

Resolving coreference and bridging relations in chemical patents is important for better understanding the precise chemical process, where chemical domain knowledge is very critical. We proposed an approach incorporating external knowledge into a multi-task learning model for both coreference and bridging resolution in the chemical domain. The results show that integrating external knowledge can benefit both chemical coreference and bridging resolution.

This paper presents an improved system based on our prior work, designed to create explanations for autonomous robot actions during Human-Robot Interaction (HRI). Previously, we developed a system that used Large Language Models (LLMs) to interpret logs and produce natural language explanations. In this study, we expand our approach by incorporating Vision-Language Models (VLMs), enabling the system to analyze textual logs with the added context of visual input. This method allows for generating explanations that combine data from the robot's logs and the images it captures. We tested this enhanced system on a basic navigation task where the robot needs to avoid a human obstacle. The findings from this preliminary study indicate that adding visual interpretation improves our system's explanations by precisely identifying obstacles and increasing the accuracy of the explanations provided.

We analyze knowledge-based visual question answering, for which given a question, the models need to ground it into the visual modality and retrieve the relevant knowledge from a given large knowledge base (KB) to be able to answer. Our analysis has two folds, one based on designing neural architectures and training them from scratch, and another based on large pre-trained language models (LLMs). Our research questions are: 1) Can we effectively augment models by explicit supervised retrieval of the relevant KB information to solve the KB-VQA problem? 2) How do task-specific and LLM-based models perform in the integration of visual and external knowledge, and multi-hop reasoning over both sources of information? 3) Is the implicit knowledge of LLMs sufficient for KB-VQA and to what extent it can replace the explicit KB? Our results demonstrate the positive impact of empowering task-specific and LLM models with supervised external and visual knowledge retrieval models. Our findings show that though LLMs are stronger in 1-hop reasoning, they suffer in 2-hop reasoning in comparison with our fine-tuned NN model even if the relevant information from both modalities is available to the model. Moreover, we observed that LLM models outperform the NN model for KB-related questions which confirms the effectiveness of implicit knowledge in LLMs however, they do not alleviate the need for external KB.