Tushar Gupta and Shirin Bhambhani, Senior Member, IEEE, USA
The exponential growth of the Internet of Things (IoT) has introduced significant challenges in managing scalability, availability, and efficiency. With billions of interconnected devices generating vast amounts of data, traditional frameworks struggle to handle the complex requirements of modern IoT applications. Addressing these challenges is crucial to fully leverage the potential of IoT in various domains, including smart homes, healthcare, and industrial automation. This paper proposes a novel framework for integrating IoT applications with cloud environments to achieve scalability and high availability. The framework is structured into four layers: the device layer leverages Message Queuing Telemetry Transport (MQTT) as a low overhead communication protocol;the edge gateway layer aggregates data using lightweight Kubernetes; the ingestion and cloud infrastructure layer employs Kafka and Apache Spark for ingesting data and data transformation; and the data processing and analytics layer utilizes the cloud infrastructure layer to send the data to databases for data visualization. By leveraging cloud environments, this solution enhances scalability, availability, and overall system robustness. This paper also explores the challenges involved in implementing such an architecture, and provides insights into future advancements in IoT cloud integration.
Internet of Things (IoT), IoT architecture, Cloud Computing.
Tessa E Andersen, Ayanna Marie Avalos, Gaby G. Dagher, and Min Long, Department of Computer Science, Boise State University, Brigham Young University, California State University, Fresno
Large Language Models (LLMs) have seen an increased use across various applications throughout the world, providing more accurate and reliable models for Artificial Intelligence (AI) systems. While powerful, LLMs do not always produce accurate or up-to-date information. Retrieval Augmented Generation (RAG) is one of the solutions that has emerged to help LLMs to give more recent and accurate responses. While RAG has been successful in reducing hallucinations within LLMs, it remains susceptible to inaccurate and maliciously manipulated data. In this paper, we present Distributed-RAG (D-RAG), a novel blockchain-based framework designed to increase the integrity of the RAG system. D-RAG addresses the risks of malicious data by replacing RAG’s traditionally centralized database with communities, each consisting of a database and a permissioned blockchain. The communities are based on different subjects, each containing experts in the field who verify data through a privacy-preserving consensus protocol before it is added to the database. A Retrieval Blockchain is also designed to communicate between the multiple communities. The miners on this Retrieval Blockchain are responsible for retrieving documents from the database for each query and ranking them using an LLM. These rankings are agreed upon, with the top documents being provided to the LLM with the query to generate a response. D-RAG increases the integrity and security of RAG incorporated LLMs.
Blockchain, RAG, LLM, Privacy-Preserving.
Valentin Colliard, Alain Peres, and Vincent Corruble,Sorbonne Universit´e, CNRS, LIP6 Thales LAS France
In this paper, we introduce two deep reinforcement learning approaches designed to tackle the challenges of air defense systems. StarCraft II has been used as a game environment to create attack scenarios where the agent must learn to defend its assets and points of interest against aerial units. Our agent, by estimating a value for each weapon-target pair, shows an ability to be robust in multiple scenarios to truly distinguish and prioritize targets in order to protect its units. These two methods, one using multi- layer perceptrons and the other using the attention mechanism, are compared with rule-based algorithms. Through empirical evaluation, we validate their efficacy in achieving resilient defense trategies across diverse and dynamic environments.
Deep Reinforcement Learning, Weapon-Target Assignment, Simulation
Tessa E Andersen, Ayanna Marie Avalos, Gaby G. Dagher, and Min Long, Department of Computer Science, Boise State University, Brigham Young University, California State University, Fresno
Large Language Models (LLMs) have seen an increased use across various applications throughout the world, providing more accurate and reliable models for Artificial Intelligence (AI) systems. While powerful, LLMs do not always produce accurate or up-to-date information. Retrieval Augmented Generation (RAG) is one of the solutions that has emerged to help LLMs to give more recent and accurate responses. While RAG has been successful in reducing hallucinations within LLMs, it remains susceptible to inaccurate and maliciously manipulated data. In this paper, we present Distributed-RAG (D-RAG), a novel blockchain-based framework designed to increase the integrity of the RAG system. D-RAG addresses the risks of malicious data by replacing RAG’s traditionally centralized database with communities, each consisting of a database and a permissioned blockchain. The communities are based on different subjects, each containing experts in the field who verify data through a privacy-preserving consensus protocol before it is added to the database. A Retrieval Blockchain is also designed to communicate between the multiple communities. The miners on this Retrieval Blockchain are responsible for retrieving documents from the database for each query and ranking them using an LLM. These rankings are agreed upon, with the top documents being provided to the LLM with the query to generate a response. D-RAG increases the integrity and security of RAG incorporated LLMs.
Blockchain, RAG, LLM, Privacy-Preserving.
Dirk Friedenberger, Lukas Pirl, Arne Boockmeyer, and Andreas Polze,Hasso-Plattner Institute University of Potsdam,Potsdam, Germany
Model-based systems engineering can be one of the key enablers for developing increasingly complex systems. Although the topic is actively developed in academia and industry, a holistic approach that is based on openly available tools and that considers all phases of the development life cycle is yet to be established. Addressing this, we propose a new approach in ontology-based systems engineering. We introduce micro models for modeling and use transformation to simplify the usability of the models. The micro models are loosely coupled, domain-specific, and result in an overall composite model. This decomposition of the overall model reduces complexity, offers reusability of models, and allows fast itera- tions when designing systems. Using the transformations, assets, such as source code, documentation, or simulations, can be created from the models. The prospects for automation can support agile development processes. To ensure accessibility, we have based our work on open source resources only. As an evaluation, we have used our approach to verify, develop, test, and simulate the Train Dispatcher in the Cloud (ZLiC), a cloud-based approach to digitalize the German Zugleitbetrieb. The prototype is being used to derive the requirements for a productive system.
Model-based systems engineering, ontology-based systems engineering, railway.
Emmanuel Udekwe and Chux Gervase Iwu, Economic and Management Sciences, University of the Western Cape, South Africa
The responsiveness of Human Resource Information Systems (HRIS) as alternative enablers that assist several sectors in achieving competitiveness is significant. The necessity for HR performance in the healthcare sector often leads to several research conducts. Surprisingly, the advantage of HRIS in healthcare continues to attract HR practitioners and researchers in that category. However, they are yet to determine how HRIS could influence health workforce sustainability in the sector. For this reason, the study intends to highlight the reasons that deprive HRIS influence in the healthcare sector in the Western Cape (WC) of South Africa (SA). Data collected from four public healthcare in the WC were subjected to mixed-model research methods involving qualitative and quantitative scrutiny. Purposively selected employees were involved in the study, interviews (forty-one) were conducted, and questionnaires (forty-six) were collated. The study identified poor organisational structures, absence of resistance to change, deficiency of upgraded HRIS and Automated Information Systems (ISs), and deficiency of knowledge, and awareness of HRIS and manual HR practices amongst others. Ethics and approvals were granted by the public healthcare management of SA and the affiliated institution. The study concluded with procedures that influence HRIS through knowledge, awareness, improvement and automation of technology and infrastructures, and availability of funds. These conclusions are significant in addressing the limitations associated with workforce sustainability in the healthcare sector through HRIS. Recommendations are further indicated in the study.
HRIS, Human Resource Information System, Manual HR, Healthcare Sector, Workforce Sustenance.
Hector Benitez Ventura1, and Yashraj patel2, 1Michigan Medicine, University of Michigan, Ann Arbor MI, USA, 2Department of Computer Science, University of Michigan, Ann Arbor MI, USA
Introduction: Nursing is currently burdened by excessive documentation tasks, reducing the time available for direct patient care. This white paper explores the potential of ambient AI to alleviate these burdens through technology designed to integrate seamlessly into nursing workflows. Ambient AI in Healthcare: Ambient AI refers to background technology that helps streamline healthcare processes, such as documentation, without disrupting clinical workflows. Developing a nursing-specific ambient AI involves choosing either an off-the-shelf or proprietary AI model, adding agent capabilities, fine-tuning it using relevant data, and validating its performance through internal testing, prospective validation, and compliance with regulatory standards. EHR Infrastructure and Interoperability: Integrating ambient AI requires compatibility with electronic health record (EHR) systems. Enhanced interoperability between EHRs using technologies like FHIR APIs allows smoother data exchange, enabling AI to provide insights directly within clinical workflows. AI Procurement Process: Implementing AI solutions in healthcare involves a structured procurement process, including budget cycle considerations, a bidding process, compliance checks, pilot programs, and contract negotiations to ensure that AI solutions meet clinical, regulatory, and operational needs. Workflow Implementation: Successful implementation of AI technology in nursing requires prospective validation, careful integration into existing workflows, ongoing monitoring, and continuous updates. This process ensures that the solution is adopted effectively, supports clinical needs, and evolves over time. Future of Nursing Ambient AI: The future of ambient AI in nursing is promising, focusing on reducing the documentation burden, improving clinical workflows, and advancing patient-centered care. However, only the most effective and well-implemented solutions will be able to meet the evolving challenges of healthcare delivery.
Artificial Intelligence, Healthcare Informatics, Ambient Technology, Nursing Workflows.
Emily Chang and Nada Basit, Department of Computer Science, School of Engineering and Applied Science, University of Virginia, Charlottesville, Virginia, United States of America
Low-resource languages lack the data necessary to completely outright train a model, making crosslingual transfer a potential solution. Even under this methodology, data scarcity remains an issue. We seek to identify a lower limit to the amount of data required to perform cross-lingual transfer learning, namely the smallest vocabulary size needed to create a sentence embedding space. Using various widelyspoken languages as proxies for low-resource languages, we discover that the relationship between a sentence embeddings vocabulary size and performance is logarithmic with performance plateauing at a vocabulary size of 25,000. However, this relationship cannot be replicated across all languages, and most low-resource languages lack this level of documentation. In establishing this lower-bound, we can better assess whether a low-resource language has enough documentation to support the creation of a sentence embedding and language model.
Cross-lingual Transfer, Low-Resource Language, & Sentence Embedding Space.
