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Urban Data Governance: an Interoperability-Based Approach for Monitoring Natural Threats at Different Geographic Scales, Through smart City Platforms 19th

Urban Data Governance: an Interoperability-Based Approach f...

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19th international conference on Critical Information Infrastructures Security, CRITIS 2024

作者： Brutti, Arianna Di Pietro, Antonio Frascella, Angelo Giovinazzi, Sonia Novelli, Cristiano D’Agosta, Gianluca Pizzuti, Stefano Pollino, Maurizio Ferreiro Sistiaga, Ane Buitrago, Saúl Lopez Villamor, Iñigo Murazzo, Simone Revel, Gian Marco ENEA Italian National Agency for New Technologies Energy and Sustainable Economic Development Rome Italy CYPE SOFT SL Alicante Spain Delft University of Technology Delft Netherlands TECNALIA Alicante Spain Live Information System SRL Ancona Italy Università Politecnica delle Marche Ancona Italy

ISBN: (纸本)9783031842597

Monitoring, preventing and managing the impacts induced by extreme natural events requires the use of multiple and different Information Communication technology (ICT) tools and technologies capable of collecting and processing data from various sources, and supporting public stakeholders in the planning and implementation of prompt actions. In this perspective, the availability of platforms able to harmonize, integrate and manage heterogeneous data and to create new knowledge, can constitute a valuable support. This article presents some preliminary results from the EU-funded MULTICLIMACT project, part of which is defining a reference model for customizing and adopting the ENEA smart City Platform to inform on the severity and extent of possible impacts induced by natural threats and on possible resilience strategies. Towards that the smart City Platform enables the interoperability between heterogeneous digital solutions monitoring natural threats at different geographic scales. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： smart city

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From Silicon Valley to Silicon Alley: Investigating the Dynamic Development of Urban Technological Industries

From Silicon Valley to Silicon Alley: Investigating the Dyna...

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2024 international conference on Construction and Real Estate Management: ESG Development in the Construction Industry, ICCREM 2024

作者： Jiang, Lin Li, Xiaoming Wang, Weixi Tang, Xiao Guo, Renzhong Dept. of Construction Management and Real Estate Shenzhen Univ. Shenzhen China Research Institute for Smart Cities Shenzhen Univ Shenzhen China Dept. of Urban Studies and Planning Univ. of Sheffield Sheffield United Kingdom

ISBN: (数字)9780784485910

ISBN: (纸本)9780784485910

Amid rapid technological innovation and knowledge growth, scrutinizing technological industrial development in megacities becomes essential for their future urban industry spatial planning. However, a profound gap persists in understanding the intricate processes and transformative dynamics of high technological industrial evolution in megacities against the backdrop of distinct economic and social environments. This study looks at the dynamic progression of technological industries in Silicon Valley and Silicon Alley in the USA. Case studies and document analysis were employed to offer an in-depth examination of the tech industry's development traits across different eras, focusing on industrial configuration and spatial distribution. The results indicate that the structure of urban technological industries is transitioning from a focused emphasis on innovation to a broader array of technology application fields, exhibiting increasingly complex spatial distribution patterns. Moreover, the findings underscore the necessity of establishing a comprehensive urban innovation and entrepreneurship ecosystem, highlighting the roles of talent development, spatial infrastructure, and innovation policies. Providing a qualitative and expository dimension to urban industrial planning, this study also reveals specific development paths for industrial structure transformation and industrial spatial development in megacities. © ASCE.

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Generating Commonsense Reasoning Questions with Controllable Complexity through Multi-step Structural Composition 31

Generating Commonsense Reasoning Questions with Controllable...

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31st international conference on Computational Linguistics, COLING 2025

作者： Yu, Jianxing Wang, Shiqi Lai, Hanjiang Chen, Wenqing Rao, Yanghui Su, Qinliang Yin, Jian School of Artificial Intelligence Sun Yat-sen University Zhuhai 519082 China School of Computer Science and Engineering Sun Yat-sen University Key Laboratory of Sustainable Tourism Smart Assessment Technology Ministry of Culture and Tourism Pazhou Lab Guangzhou 510330 China

ISBN: (纸本)9798891761964

This paper studies the task of generating commonsense reasoning questions (QG) with desired difficulty levels. Compared to traditional shallow questions that can be solved by simple term matching, ours are more challenging. Our answering process requires reasoning over multiple contextual and commonsense clues. That involves advanced comprehension skills, such as abstract semantics learning and missing knowledge inference. Existing work mostly learns to map the given text into questions, lacking a mechanism to control results with the desired complexity. To address this problem, we propose a novel controllable framework. We first derive contextual and commonsense clues involved in reasoning questions from the text. These clues are used to create simple sub-questions. We then aggregate multiple sub-questions to compose complex ones under the guidance of prior reasoning structures. By iterating this process, we can compose a complex QG task based on a series of smaller and simpler QG subtasks. Each subtask serves as a building block for a larger one. Each composition corresponds to an increase in the reasoning step. Moreover, we design a voting verifier to ensure results' validity from multiple views, including answer consistency, reasoning difficulty, and context correlation. Finally, we can learn the optimal QG model to yield thought-provoking results. Evaluations on two typical datasets validate our method. © 2025 Association for Computational Linguistics.

关键词： Semantics

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22nd international conference on Business Process Management, BPM 2024

22nd International Conference on Business Process Management...

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22nd international conference on Business Process Management, BPM 2024

ISBN: (纸本)9783031786655

The proceedings contain 85 papers. The special focus in this conference is on Business Process Management. The topics include: Interpreted and Confidential Execution of Process Choreographies on a Blockchain;smart Contracts’ Upgradability for Flexible Business Processes;secure Proof Verification Blockchain Patterns;the Cost of Executing Business Processes on Next-Generation Blockchains: The Case of Algorand;BPMS Blockchain technology Soft Integration For Non-tamperable Logging;from Screenshots to Process Models: Improving Activity Identification Through Screen Text;designing the Organizational Reuse Environment - Enabling Citizen Developers to Reuse Process Automation Artifacts;measuring Complexity of Bot Models in Robotic Process Automation;decision-Making in Robotic Process Automation Programming and its Influence on Robotic Process Mining;Democratizing Robotic Process Mining: A Conceptual Framework for User Actions, Tasks, and RPA Bots;Telemedicine and BPM in Cardiac Rehabilitation: A Comprehensive Post-Myocardial Infarction Care Program Case Study;adapting to the Dynamic Nature of Business Processes in the Digital Age;enhancing Business Process Management Through Nature Assessment: Development and Deployment of the Business Process Nature Assessment Tool;adapting to Change: Employees Ambidexterity as a Driver for Operational Adaptability and Organizational Development;Role and Application of OMG Notations in IT: Analysis and Survey on the Effectiveness of Business Notations Among IT Companies in Poland;Extending CMMN for Effective Management of Data in knowledge-Intensive Processes;application of the Flipped Learning Methodology at a Business Process Modelling Course – A Case Study;Project-Based Learning in BPM: Lessons from Flipped Classroom vs. Lecture-Based Instruction;relevance of Topics in Workflow-Based Student Projects;From Design to Evaluation: Students Experiencing the BPM Lifecycle Hands-on;Enabling BPM Competencies Through Real-Life Student Consulting

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Generating Is Believing: Membership Inference Attacks against Retrieval-Augmented Generation

Generating Is Believing: Membership Inference Attacks agains...

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2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Yuying Liu, Gaoyang Wang, Chen Yang, Yang Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Hubei University Wuhan China Hubei Key Laboratory of Smart Internet Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350368741

Retrieval-Augmented Generation (RAG) is a state-of-the-art technique that mitigates issues such as hallucinations and knowledge staleness in Large Language Models (LLMs) by retrieving relevant knowledge from an external database to assist in content generation. Existing research has demonstrated potential privacy risks associated with the LLMs of RAG. However, the privacy risks posed by the integration of an external database, which often contains sensitive data such as medical records or personal identities, have remained largely unexplored. In this paper, we aim to bridge this gap by focusing on membership privacy of RAG's external database, with the aim of determining whether a given sample is part of the RAG's database. Our basic idea is that if a sample is in the external database, it will exhibit a high degree of semantic similarity to the text generated by the RAG system. We present S2MIA, a Membership Inference Attack that utilizes the Semantic Similarity between a given sample and the content generated by the RAG system. With our proposed S2MIA, we demonstrate the potential to breach the membership privacy of the RAG database. Extensive experimental results demonstrate that S2MIA outperforms five existing MIAs, even when the system is protected by three representative defenses. © 2025 IEEE.

关键词： Large Language Models Membership Inference Attacks Retrieval-Augmented Generation

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An Integrated knowledge Distillation Framework for Battery SOH Estimation

An Integrated Knowledge Distillation Framework for Battery S...

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smart Grid and Green Energy (ICSGGE), international conference on

作者： Yang Li Chaojie Li Borui Zhang Renyou Xie Huadong Mo Jian Huang School of Electrical Engineering and Telecommunication University of New South Wales Sydney Australia School of Systems and Computing University of New South Wales Sydney Australia School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798331542139

ISBN: (纸本)9798331542146

State of health (SOH) estimation plays an important role in Battery Management Systems (BMS), especially in electric vehicles (EVs). However, deploying deep learning-based SOH estimation models in EVs is challenging due to their large computational demands and model sizes. Moreover, battery datasets often contain environmental noise, which affects the accuracy of predictions. We propose an integrated knowledge distillation (KD) framework for model compression. Under this framework, the size of the model is compressed by knowledge distillation. This framework uses the Wasserstein distance to solve the extreme value problems during the knowledge distillation process. Additionally, we refine Wasserstein distance with a Gradient Penalty (G-P) technique. This method controls the gradients of the model outputs within a certain range, which can solve the gradient explosion or disappearance problems. The experimental results indicate the validity of our framework.

关键词： Deep learning Electric potential Accuracy Computational modeling Working environment noise Estimation Predictive models Electric vehicles Stability analysis smart grids

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Harnessing Hydroponic Innovation for Water Management and Plant Growth Optimization: A Comparative Study with Soil-Based Cultivation Method 4th

Harnessing Hydroponic Innovation for Water Management and Pl...

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4th international conference on Electronic Engineering and Renewable Energy Systems, ICEERE 2024

作者： Hamdaoui, Hajar Hamdi, Inass Hsana, Youness AL Kaddouri, Hanae Kouddane, Nour-Eddine Zarrouk, Yassine Laboratory for Improvement of Agricultural Production Biotechnology and Environment Mohammed First University Oujda Morocco Biology and Health Laboratory Faculty of Sciences Ibn Tofail University Kenitra Morocco Information Communication and Knowledge Sciences and Technology Laboratory Western Brittany University Brest France Smart Information and Communication Technology Laboratory Oujda Mohammed First University Oujda Morocco Applied Sciences Research Laboratory Oujda Mohammed First University Oujda Morocco

ISBN: (纸本)9789819606436

In response to mounting challenges posed by climate change, soil infertility, water scarcity, and agricultural pests and diseases, the adoption of innovative cultivation methods becomes imperative. Hydroponics emerges as a promising solution, offering a departure from traditional soil-based approaches by delivering essential nutrients directly to plant roots through nutrient-rich water solutions. This study explores the efficacy of hydroponic systems in addressing water management challenges while leveraging technology to enhance agricultural production. Through a comparative analysis between hydroponic and soil-based cultivation methods, key growth parameters including height, root length, fresh weight, root weight, and leaf number were evaluated for lettuce, basil, and spinach. Additionally, water consumption patterns were assessed to ascertain the water-saving potential of hydroponic farming. Results reveal significant improvements in plant growth and water efficiency in hydroponic systems compared to conventional soil cultivation, with water savings reaching up to 80%, highlighting its remarkable potential for effective water management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hydroponics

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Modal-Centric Insights Into Multimodal Federated Learning for smart Healthcare: A Survey 24th

Modal-Centric Insights Into Multimodal Federated Learning fo...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wang, Di Liu, Wenjian Gao, Longxiang Qu, Y. Neil. Zhang, Hu Shi, Jihong The Faculty of Data Science City University of Macau Macau China Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Medical Integration and Practice Center Shandong University Jinan China

ISBN: (纸本)9789819615476

Federated Learning (FL) has progressed, providing a distributed mechanism where data need not be consolidated, thereby enhancing the privacy and security of sensitive healthcare data. Recent advancements in multimodal machine learning have empowered the development of accurate and robust AI systems in the medical domain, especially within centralized database systems. Multimodal federated learning (MFL) combines federated learning’s distributed nature with the flexibility to work with various data types. This capability is particularly relevant in medical contexts, where healthcare data can range from electronic health records (EHR) to radiology imaging, genomic data, and wearable device information. The key advantage of MFL in medical settings is the ability to create robust models without centralizing sensitive patient information. However, there are no existent research to comprehensively review the application status of MFL in smart healthcare systems from the modal-centric aspect. Motivated by this, we explore the systematization of knowledge about multimodal federated learning on medical data, find the challenges of this area and related solutions. With this research, we hope to provide a systematical review of this under-explored domain and provide insights for forthcoming researchers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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From Concept to Reality: A Systematic Review of Urban Digital Twin and Their Applications in smart Cities 20th

From Concept to Reality: A Systematic Review of Urban Digita...

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20th international conference on Computing in Civil and Building Engineering, ICCCBE 2024

作者： Huang, Hao Tsou, Jin Yeu Department of Architecture and Civil Engineering City University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9783031842078

Metropolises comprise six primary systems that serve distinct domains: governance, environment, mobility, economy, living, and society. These systems work together to create a macrosystem that constitutes a city. In contemporary urban planning, smart Cities (SCs) have emerged as a means of effectively managing and integrating critical infrastructure, optimizing resource allocation, coordinating maintenance activities, and addressing complex urban challenges while prioritizing security. Over the past few years, cutting-edge technologies like Wireless Sensor Networks, Building Information Modeling, Internet of Things (IoT), Virtual Reality, Big Data Analytic, Artificial Intelligence (AI), Cloud Computing, Blockchain, and Cyber-Physical Systems have paved the way for intelligent and groundbreaking solutions in a variety of industries. Built on the knowledge gained from developing SCs and implementing Digital Twins (DTs) in other sectors, the Urban Digital Twin (UDT) has rapidly expanded in just a few years. UDT technology utilizes real-time data, simulation, machine learning, and reasoning to provide valuable assistance in decision-making processes. However, more than informatization and digitalization practices are required to embrace UDT fully. There has yet to be a comprehensive analysis of how to apply these features of DTs to manage SCs better. This review paper analyzed 150 papers that focused on DT-supported SCs. We aim to identify the drivers, challenges, and future directions for applying UDT-supported technologies. We systematically investigated the characteristics and mechanisms of DTs, categorized different technologies based on their application stages, and identified the key challenges, benefits, and open issues. The findings inform stakeholders in urban management to establish a foundation for future design, development, and widespread adoption of UDT. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Resource allocation

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Developing Cognitive Abilities in Robots: A Bibliometric Overview of AI and ML Applications

Developing Cognitive Abilities in Robots: A Bibliometric Ove...

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international IoT, Electronics and Mechatronics conference, IEMTRONICS 2024

作者： Obaideen, Khaled AlShabi, Mohammad Gadsden, Stephen Andrew Bio-Sensing and Bio-Sensors Group Smart Automation and Communication Technologies RISE P.O. Box 27272 Sharjah United Arab Emirates Department of Mechanical and Nuclear Engineering University of Sharjah Sharjah27272 United Arab Emirates Department of Mechanical Engineering McMaster University HamiltonONL8S 4L8 Canada

ISBN: (纸本)9789819747832

The domain of cognitive robotics has gone through many technological innovations, majorly enhanced by the adoption of Artificial Intelligence (AI) and Machine Learning (ML) concepts. This study performs comprehensive bibliometric analysis, using tools like VOSviewer and Biblioshiny, to map the development, the existing scene and the future prospects of cognitive robotics studies since 1953. We address how AI and ML expand robotic abilities, marking the shift from early development phase to the period of fast evolution and improvement. The remarkable results show an increasing trend starting from mid- 1980s with a peak in the late 1990s and an exponential growth in the last ten years which highlights the growing impact and the broadening usage in different fields. The paper shows cross functional aspects of cognitive robotics, which combines biological and cognitive approaches with progress of technology. The two main areas of robots cognitive robotics are human–robot interaction and autonomous decision-making. The ability of robots to acquire knowledge, self-adaptability, and robot operation in human-centered environments is highlighted, as well as the practical problems that may come from robot autonomy and superior cognitive capacities. From this bibliometric study, we get key insights concerning the past and present of cognitive robotics which form the basis of future studies. The study reinforces the necessity of a multi-pronged approach in robotics, with progress aligned with the needs of society, to ensure the responsible deployment of cognitive robots. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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