A Survey on the Applications of Frontier AI, Foundation Models, and Large Language Models to Intelligent Transportation Systems

This survey paper explores the transformative influence of frontier AI, foundation models, and Large Language Models (LLMs) in the realm of Intelligent Transportation Systems (ITS), emphasizing their integral role in advancing transportation intelligence, optimizing traffic management, and contributing to the realization of smart cities. Frontier AI refers to the forefront of AI technology, encompassing the latest advancements, innovations, and experimental techniques in the field, especially AI foundation models and LLMs. Foundation models, like GPT-4, are large, general-purpose AI models that provide a base for a wide range of applications. They are characterized by their versatility and scalability. LLMs are obtained from finetuning foundation models with a specific focus on processing and generating natural language. They excel in tasks like language understanding, text generation, translation, and summarization. By leveraging vast textual data, including traffic reports and social media interactions, LLMs extract critical insights, fostering the evolution of ITS. The survey navigates the dynamic synergy between LLMs and ITS, delving into applications in traffic management, integration into autonomous vehicles, and their role in shaping smart cities. It provides insights into ongoing research, innovations, and emerging trends, aiming to inspire collaboration at the intersection of language, intelligence, and mobility for safer, more efficient, and sustainable transportation systems. The paper further surveys interactions between LLMs and various aspects of ITS, exploring roles in traffic management, facilitating autonomous vehicles, and contributing to smart city development, while addressing challenges brought by frontier AI and foundation models. This paper offers valuable inspiration for future research and innovation in the transformative domain of intelligent transportation.Comment: This paper appears in International Conference on Computer and Applications (ICCA) 202

ARTIFICIAL INTELLIGENCE IN TACKLING CORONAVIRUS AND FUTURE PANDEMICS

SARS-COV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) was initially tested in Wuhan City, China, in December 2019 and had a devastating impact worldwide, exterminating more than 6 million people as of September 2022. It became the biggest worldwide health crisis since the 1918 influenza outbreak. Viruses generally mutate randomly, so predicting how SARS-CoV-2 will transform over the next few months or years and which forms will predominate is impossible. The possibilities for virus mutation, in theory, are practically endless. Enabling researchers to determine which antibodies have the potential to be most effective against existing and future variations could help machine learning to assist in drug discovery. In the COVID-19 pandemic, AI has benefited four key areas: diagnosis, clinical decision-making for public health, virtual assistance, and therapeutic research. This study conducted a discourse analysis and textual evaluation of AI (deep learning and machine learning) concerning the COVID-19 outbreak. Further, this study also discusses the latest inventions that can be very helpful in future pandemic detection. COVID-19 has already changed our lives, and in the future, we might be able to deal with pandemics like this with the help of AI. This review has also emphasized the legal implications of AI in the battle against COVID-19

Applications of Artificial Intelligence in the Treatment of Behavioral and Mental Health Conditions

Introduction Artificial intelligence (AI) is the branch of science that studies and designs intelligent devices. For individuals unfamiliar with artificial intelligence, the concept of intelligent machines may bring up visions of attractive human-like computers or robots, like those described in science fiction. Others may consider AI technology to be mysterious machines limited to research facilities or a technical triumph that will come in the far future. Popular media accounts on the deployment of aerial drones, autonomous autos, or the potential dangers of developing super-intelligent technologies may have raised some broad awareness of the subject

Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services

The Internet of Things (IoT) has made it possible for devices around the world to acquire information and store it, in order to be able to use it at a later stage. However, this potential opportunity is often not exploited because of the excessively big interval between the data collection and the capability to process and analyse it. In this paper, we review the current IoT technologies, approaches and models in order to discover what challenges need to be met to make more sense of data. The main goal of this paper is to review the surveys related to IoT in order to provide well integrated and context aware intelligent services for IoT. Moreover, we present a state-of-the-art of IoT from the context aware perspective that allows the integration of IoT and social networks in the emerging Social Internet of Things (SIoT) term.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) under the granted Project SEQUOIA-UA (Management requirements and methodology for Big Data analytics) TIN2015-63502-C3-3-R, by the University of Alicante, within the program of support for research, under project GRE14-10, and by the Conselleria de Educación, Investigación, Cultura y Deporte, Comunidad Valenciana, Spain, within the program of support for research, under project GV/2016/087. This work has also been partially funded by projects from the Spanish Ministry of Education and Competitivity TIN2015-65100-R and DIIM2.0 (PROMETEOII/2014/001)

FROM URBAN LIVING LAB TO URBAN TRANSFORMATION

openQuesta ricerca approfondisce la crescente rilevanza globale del passaggio verso la sostenibilità nelle città, sottolineando le sfide con cui le amministrazioni locali si confrontano. Prendendo spunto dall'esperienza dell'autore in progetti finanziati dall'UE, lo studio si concentra sul Comune di Maia, con l'obiettivo di chiarire gli ostacoli e le strategie essenziali per raggiungere la sostenibilità urbana. Esamina l'iniziativa BaZe-Maia Living Lab, volta alla decarbonizzazione, mettendo in evidenza le complessità nell'attuare soluzioni ispirate ai living lab, soprattutto in considerazione di sfide come l'epidemia di COVID-19. Inoltre, l'alleanza di Maia con il consorzio SPARCS e la creazione del Piano d'Azione per l'Energia Sostenibile (SEAP) evidenziano i loro passi collettivi per modificare le abitudini urbane, aumentare l'efficienza energetica e ridurre al minimo gli sprechi. Questa esplorazione cerca di identificare gli elementi centrali che guidano tali sforzi, mettendo in luce gli ostacoli e suggerendo soluzioni. L'obiettivo principale è rafforzare la diffusione della conoscenza e integrare pratiche sostenibili innovative nel percorso di pianificazione urbana di Maia. La metodologia dello studio si basa su tecniche qualitative come interviste, analisi documentale e l'utilizzo del modello Morgenstadt per valutare le iniziative verdi di Maia, confrontandole con Lipsia. Un'analisi tematica dei documenti del progetto e delle opinioni degli stakeholder indica l'approccio proattivo di Maia alla co-progettazione, al coinvolgimento degli stakeholder, agli esperimenti e all'integrazione degli aspetti legati alla sostenibilità. Tuttavia, emergono difficoltà nell'avviare attività di co-progettazione e nel raggiungere gli obiettivi verdi strutturali. Un'analisi comparativa con Lipsia offre spunti illuminanti riguardo alla cooperazione completa, alla flessibilità, alle strategie finanziarie e alle visioni lungimiranti benefiche per Maia. Le principali raccomandazioni includono l'adozione di regolamenti completi, l'ampliamento dell'interazione degli stakeholder, il potenziamento delle capacità organizzative, il sfruttamento di collaborazioni esterne e risorse, l'istituzione di solide infrastrutture dati e la promozione delle innovazioni tecnologiche. Tuttavia, le difficoltà persistenti derivano dalla compartimentazione dei dipartimenti, dal coinvolgimento limitato e dalla visione della sostenibilità limitata all'ambito digitale. Questa indagine offre una comprensione più approfondita degli elementi diversificati che guidano i cambiamenti urbani verdi e fornisce una guida per superare gli ostacoli nella diffusione dei living lab. Ulteriori studi mirati potrebbero colmare le lacune osservate riguardo alle realizzazioni della co-progettazione e alle ristrutturazioni edilizie. La ricerca chiarisce l'intricato intreccio negli sforzi urbani verdi, sottolineando l'importanza di approcci unificati, coinvolgimento inclusivo degli stakeholder e rafforzamento delle capacità per indurre cambiamenti su vasta scala.This research delves into the escalating global relevance of transitioning towards sustainability in cities, underscoring the hurdles local administrations grapple with. Drawing from the author's involvement in projects funded by the EU, the study centers on the Municipality of Maia, aiming to clarify the obstacles and essential strategies for achieving urban sustainability. It examines the BaZe-Maia Living Lab initiative, which aims for decarbonization, highlighting the intricacies of enacting living lab-inspired solutions, especially given challenges like the COVID-19 outbreak. Additionally, Maia's alliance with the SPARCS consortium and the crafting of the Sustainable Energy Action Plan (SEAP) underscore their collective steps to modify urban habits, boost energy thriftiness, and minimize waste. This exploration seeks to identify the central elements guiding such endeavors, spotlighting impediments and suggesting solutions. The overarching goal is to fortify the dissemination of knowledge and weave groundbreaking sustainable practices into Maia's urban planning path. The methodology of the study hinges on qualitative techniques such as interviews, scrutinizing documents, and using the Morgenstadt blueprint to evaluate Maia's green undertakings, contrasting them with Leipzig. A thematic breakdown of project paperwork and the views of stakeholders indicates Maia's proactive approach to co-design, stakeholder involvement, trials, and integrating sustainability aspects. However, hitches emerge when initiating co-design activities and meeting structural green targets. A side-by-side analysis with Leipzig offers enlightening takeaways about all-encompassing cooperation, flexibility, financial strategies, and forward-thinking visions beneficial for Maia. Principal suggestions encompass the rollout of all-inclusive regulations, amplifying stakeholder interaction, fortifying organizational capabilities, capitalizing on outside collaborations and resources, instituting sturdy data infrastructures, and championing tech advancements. Yet, enduring difficulties arise from departmental silos, limited involvement, and viewing sustainability solely through a digital lens. This investigation offers a deeper understanding of the diverse elements steering green urban shifts and furnishes a blueprint to navigate roadblocks in living lab rollouts. More pinpointed studies might bridge observed voids concerning co-design realizations and building overhauls. The research clarifies the intricate interplay in urban green endeavors, emphasizing the value of unified approaches, inclusive stakeholder involvement, and strengthening capabilities to induce broad-based alterations

Ensuring high quality public safety data in participatory crowdsourcing used as a smart city initiative

The increase in urbanisation is making the management of city resources a difficult task. Data collected through observations of the city surroundings can be used to improve decision-making in terms of manage city resources. However, the data collected must be of quality in order to ensure that effective and efficient decisions are made. This study is focused on improving emergency and non-emergency services (city resources) by using Participatory Crowdsourcing as a data collection method (collect public safety data) utilising voice technology in the form of an advanced IVR system known as the Spoken Web. The study illustrates how Participatory Crowdsourcing can be used as a Smart City initiative by illustrating what is required to contribute to the Smart City, and developing a roadmap in the form of a model to assist decision-making when selecting the optimal Crowdsourcing initiative. A Public Safety Data Quality criteria was also developed to assess and identify the problems affecting Data Quality. This study is guided by the Design Science methodology and utilises two driving theories: the characteristics of a Smart City, and Wang and Strong’s (1996) Data Quality Framework. Five Critical Success Factors were developed to ensure high quality public safety data is collected through Participatory Crowdsourcing utilising voice technologies. These Critical Success Factors include: Relevant Public Safety Data, Public Safety Reporting Instructions, Public Safety Data Interpretation and Presentation Format, Public Safety Data Integrity and Security, and Simple Participatory Crowdsourcing System Setup

A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors.The ability of BC can help in offering decentralized and secure data storage, while CV allows machines to learn and understand visual data. This integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The effort includes how BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics using BC. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed

A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom

Multimodal medical data fusion has emerged as a transformative approach in smart healthcare, enabling a comprehensive understanding of patient health and personalized treatment plans. In this paper, a journey from data to information to knowledge to wisdom (DIKW) is explored through multimodal fusion for smart healthcare. We present a comprehensive review of multimodal medical data fusion focused on the integration of various data modalities. The review explores different approaches such as feature selection, rule-based systems, machine learning, deep learning, and natural language processing, for fusing and analyzing multimodal data. This paper also highlights the challenges associated with multimodal fusion in healthcare. By synthesizing the reviewed frameworks and theories, it proposes a generic framework for multimodal medical data fusion that aligns with the DIKW model. Moreover, it discusses future directions related to the four pillars of healthcare: Predictive, Preventive, Personalized, and Participatory approaches. The components of the comprehensive survey presented in this paper form the foundation for more successful implementation of multimodal fusion in smart healthcare. Our findings can guide researchers and practitioners in leveraging the power of multimodal fusion with the state-of-the-art approaches to revolutionize healthcare and improve patient outcomes.Comment: This work has been submitted to the ELSEVIER for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl