As large language models (LLMs) rapidly evolve into large multimodal models (LMMs), the integration of these technologies into building information modeling (BIM) tasks to enhance work performance is signiLicantly increasing. The use of generative artiLicial intelligence (AI) during the conceptual design phase is particularly becoming a norm in industry and academia. A recent survey by the Royal Institute of British Architects (RIBA) reported that 68% of the responding architects are already using generative AI, such as text-to-image models, for early design visualization While the application of LLMs in BIM tasks beyond the early design phase is still in an early stage, it is foreseeable that BIM systems with natural language interfaces supported by LLMs will supplant BIM tools with traditional user interfaces in the near future. In this paper, we use the term "LLM-augmented BIM" as a general expression to indicate a task or a process of querying, generating, and managing BIM data and/or models via speech or text in natural language. We refer to the former as "speech-to-BIM" and the latter as "text-to-BIM" tasks.

Large Language Models (LLMs) have transformed natural language processing (NLP) by enabling robust text generation and understanding. However, their deployment in sensitive domains like healthcare, finance, and legal services raises critical concerns about privacy and data security. This paper proposes a comprehensive framework for embedding trust mechanisms into LLMs to dynamically control the disclosure of sensitive information. The framework integrates three core components: User Trust Profiling, Information Sensitivity Detection, and Adaptive Output Control. By leveraging techniques such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Named Entity Recognition (NER), contextual analysis, and privacy-preserving methods like differential privacy, the system ensures that sensitive information is disclosed appropriately based on the user's trust level. By focusing on balancing data utility and privacy, the proposed solution offers a novel approach to securely deploying LLMs in high-risk environments. Future work will focus on testing this framework across various domains to evaluate its effectiveness in managing sensitive data while maintaining system efficiency.

In the rapidly evolving field of robotics, integration of commonsense knowledge (CSK) in AI systems is becoming highly crucial to enhance the decision-making capabilities of robots, especially in nextgeneration multipurpose environments. This paper presents Robo-CSK-Organizer, a pioneering system that employs CSK, via a classical knowledge base, to facilitate sophisticated task-based object organization helpful in multipurpose robots. Unlike systems relying solely on deep learning tools such as ChatGPT, our Robo-CSK-Organizer system stands out in various crucial aspects. This includes: (1) its ability to resolve ambiguities and maintain consistency in object placement; (2) its adaptability to diverse task-based classifications; and moreover, (3) its contributions to explainable AI (XAI), consequently helping to foster trust and human-robot collaboration. This system's efficacy is underlined by DETIC (DEtector with Image Classes), an advanced extension of Detectron2 for object identification; BLIP (Bootstrapping Language-Image Pre-training) for context discernment; and most vitally by the adaptation of ConceptNet, a well-grounded commonsense knowledge base for reasoning based on semantic as well as pragmatic knowledge. While we deploy ConceptNet to extract CSK, the process in Robo-CSK-Organizer is generic enough to be replicated with other state-of-the-art knowledge bases. Controlled experiments and real-world applications, synopsized in this paper, make Robo-CSK-Organizer demonstrate superior performance in placing objects in contextually relevant locations, highlighting its clear capacity for commonsense-guided decision-making closer to the thresholds of human cognition. Hence, Robo-CSK-Organizer makes valuable contributions to Robotics and AI.

This paper explores the design and development of a language-based interface for dynamic mission programming of autonomous underwater vehicles (AUVs). The proposed 'Word2Wave' (W2W) framework enables interactive programming and parameter configuration of AUVs for remote subsea missions. The W2W framework includes: (i) a set of novel language rules and command structures for efficient language-to-mission mapping; (ii) a GPT-based prompt engineering module for training data generation; (iii) a small language model (SLM)-based sequence-to-sequence learning pipeline for mission command generation from human speech or text; and (iv) a novel user interface for 2D mission map visualization and human-machine interfacing. The proposed learning pipeline adapts an SLM named T5-Small that can learn language-to-mission mapping from processed language data effectively, providing robust and efficient performance. In addition to a benchmark evaluation with state-of-the-art, we conduct a user interaction study to demonstrate the effectiveness of W2W over commercial AUV programming interfaces. Across participants, W2W-based programming required less than 10% time for mission programming compared to traditional interfaces; it is deemed to be a simpler and more natural paradigm for subsea mission programming with a usability score of 76.25. W2W opens up promising future research opportunities on hands-free AUV mission programming for efficient subsea deployments.

Large Language Models (LLMs) have shown significant challenges in detecting and repairing vulnerable code, particularly when dealing with vulnerabilities involving multiple aspects, such as variables, code flows, and code structures. In this study, we utilize GitHub Copilot as the LLM and focus on buffer overflow vulnerabilities. Our experiments reveal a notable gap in Copilot's abilities when dealing with buffer overflow vulnerabilities, with a 76% vulnerability detection rate but only a 15% vulnerability repair rate. To address this issue, we propose context-aware prompt tuning techniques designed to enhance LLM performance in repairing buffer overflow. By injecting a sequence of domain knowledge about the vulnerability, including various security and code contexts, we demonstrate that Copilot's successful repair rate increases to 63%, representing more than four times the improvement compared to repairs without domain knowledge.

Code generation by Llama 3.1 models, such as Meta's Llama 3.1 405B, represents a significant advancement in the field of artificial intelligence, particularly in natural language processing and programming automation. This paper explores the capabilities and applications of Llama-driven code generation, highlighting its ability to translate natural language prompts into executable code across multiple programming languages. Key features include contextual awareness, multi-language support, and enhanced debugging and optimization functionalities. By examining these aspects, we illustrate how Llama can serve as a versatile tool for developers of all skill levels, improving productivity and efficiency in software development. The potential implications for education, industry, and the future of coding practices are also discussed, underscoring the transformative impact of AI in programming. Experimentation shows that while Llama 3.1 405B performs well with simple algorithmic and data structure based problems, it still struggles with problems on Quantum Computing, Bioinformatics, and Artificial Intelligence.

As AI takes on pivotal roles in essential applications, like self-driving vehicles, healthcare diagnosis, and financial services, it becomes a tempting target for malicious actors [16]. This study aims to comprehensively explore the realm of offensive AI, shedding light on its multifaceted dimensions, the techniques involved, its consequences, and potential future implications. Cyberattacks have surged in both complexity and frequency. This is evidenced by the escalating costs associated with data breaches. In 2022, businesses incurred an average loss of $4.35 million, an increase of $0.11 million from the previous year and a 12.7% rise from 2020 [22]. Moreover, the volume of data breaches has reached historic highs, with approximately 15 million records exposed during the third quarter of 2022. Furthermore, the third quarter of 2022 witnessed an alarming 57,116 distributed denial-of-service (DDoS) attacks [78]. Against this backdrop, understanding and mitigating security risks in machine learning (ML) has emerged as a pivotal aspect of cybersecurity.

The combination of large language models (LLMs), augmented reality (AR), and user interface/user experience (UI/UX) design in therapies for children, especially with disorders like autism spectrum disorder (ASD), is examined in this review study. Three primary areas are covered in this review: how AR can improve social and learning results; how LLMs can help with communication; and how UI/UX design affects how effective these technologies are. Results reveal that while LLMs can provide individualized learning and communication support, AR has demonstrated promise in enhancing social skills, motivation, and attention. For children with ASD, accessible and interesting interventions depend heavily on effective UI/UX design. To optimize the benefits of these technologies in ASD therapies, the study emphasizes the need for additional research to address difficulties related to customization, accessibility, and integration. Keywords: Autism Spectrum Disorder, Large Language Models (LLM), Augmented Reality (AR), Virtual Reality (VR) 1. Introduction Children with autism can benefit greatly from digitally assisted language therapies thanks to augmented reality (AR). Numerous results and insights about the use of augmented reality (AR) as a teaching and pedagogical aid have been reported by educators and researchers [1]. The use of computer technology--particularly augmented reality--in autism spectrum disorder (ASD) therapies has grown as a means of treating or mitigating the symptoms of the disorder. Not just for kids of a certain age or educational level, augmented reality is an entertaining form of technology that facilitates easy interaction and helps kids comprehend and retain information [2]. A neurodevelopmental disorder known as autism spectrum disorder (ASD) is marked by recurring problems with social interaction and communication, as well as a limitation in interests and repetitive activities [3]. It is believed that one in every 100 youngsters worldwide is affected by ASD.

There is a mismatch between psychological and computational studies on emotions. Psychological research aims at explaining and documenting internal mechanisms of these phenomena, while computational work often simplifies them into labels. Many emotion fundamentals remain under-explored in natural language processing, particularly how emotions develop and how people cope with them. To help reduce this gap, we follow theories on coping, and treat emotions as strategies to cope with salient situations (i.e., how people deal with emotion-eliciting events). This approach allows us to investigate the link between emotions and behavior, which also emerges in language. We introduce the task of coping identification, together with a corpus to do so, constructed via role-playing. We find that coping strategies realize in text even though they are challenging to recognize, both for humans and automatic systems trained and prompted on the same task. We thus open up a promising research direction to enhance the capability of models to better capture emotion mechanisms from text.

This study aims to comprehensively review and empirically evaluate the application of multimodal large language models (MLLMs) and Large Vision Models (VLMs) in object detection for transportation systems. In the first fold, we provide a background about the potential benefits of MLLMs in transportation applications and conduct a comprehensive review of current MLLM technologies in previous studies. We highlight their effectiveness and limitations in object detection within various transportation scenarios. The second fold involves providing an overview of the taxonomy of end-to-end object detection in transportation applications and future directions. Building on this, we proposed empirical analysis for testing MLLMs on three real-world transportation problems that include object detection tasks namely, road safety attributes extraction, safety-critical event detection, and visual reasoning of thermal images. Our findings provide a detailed assessment of MLLM performance, uncovering both strengths and areas for improvement. Finally, we discuss practical limitations and challenges of MLLMs in enhancing object detection in transportation, thereby offering a roadmap for future research and development in this critical area.