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Artificial Intelligence (CAI), IEEE Conference on

作者： Fan Li Shanshan Feng Yuqi Yan Ching-Hung Lee Yew Soon Ong Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University HKSAR Centre for Frontier AI Research A*STAR School of Public Policy and Administration Xi'an Jiaotong University Xi’an China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Advancements in technology, pilot shortages, and cost pressures are driving a trend towards single-pilot and even remote operations in aviation. Considering the extensive workload and huge risks associated with single-pilot operations, the development of a Virtual Co-Pilot (V-CoP) is expected to be a potential way to ensure aviation safety. This study proposes a V-CoP concept and explores how humans and virtual assistants can effectively collaborate. A preliminary case study is conducted to explore a critical role of V-CoP, namely automated quick procedures searching, using the multimodal large language model (LLM). The LLM-enabled V-CoP integrates the pilot’s instruction and real-time cockpit instrumental data to prompt applicable aviation manuals and operation procedures. The results showed that the LLM-enabled V-CoP achieved high accuracy in situational analysis (90.5%) and effective retrieval of procedure information (86.5%). The proposed V-CoP is expected to provide a foundation for future virtual intelligent assistant development, improve the performance of single pilots, and reduce the risk of human errors in aviation.

关键词： Error analysis Virtual assistants Large language models Instruments Knowledge based systems Manuals Market research

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engineering Casimir interactions with epsilon-near-zero materials

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Physical Review A 2022年第6期105卷 L061501-L061501页

作者： Miguel Camacho Tao Gong Benjamin Spreng Iñigo Liberal Nader Engheta Jeremy N. Munday Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia Pennsylvania 19104 USA Department of Electronics and Electromagnetism Universidad de Sevilla 41012 Seville Spain Department of Electrical and Computer Engineering University of California Davis California 95616 USA Department of Materials Science and Engineering University of California Davis California 95616 USA Department of Electrical and Electronic Engineering Public University of Navarra 31006 Pamplona Spain

In this paper, we theoretically demonstrate the tunability of the Casimir force both in sign and magnitude between parallel plates coated with dispersive materials. We show that this force, existing between uncharged plates, can be tuned by carefully choosing the value of the plasma frequency (i.e., the epsilon-near-zero frequency) of the coating in the neighborhood of the resonance frequency of the cavity. The coating layer enables a continuous variation of the force between four limiting values when a coating is placed on each plate. We explore the consequences of such variation when pairs of electric and magnetic conductors (i.e., low and high impedance surfaces) are used as substrates on either side, showing that this continuous variation results in changes in the sign of the force, leading to both stable and unstable conditions, which could find interesting potential applications in nanomechanics, including nanoparticle tweezing.

关键词： Casimir effect Electromagnetic interactions Light-matter interaction Metamaterials Optical materials & elements

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DataDecon: Data Cleansing Tools for Large Language Model with Efficient Decontamination Techniques

DataDecon: Data Cleansing Tools for Large Language Model wit...

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International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

作者： Sumeth Yuenyong Norapat Buppodom Koravich Sangkaew Konthee Boonmeeprakob Prachya Boonkwan Jillaphat Jaroenkantasima Pitikorn Khlaisamniang Anuruth Lertpiya Apivadee Piyatumrong Peerawat Rojratchadakorn Thaweewat Rugsujarit Teerapol Saengsukhiran Kriangkrai Saetan Isada Sukprapa Thanachot Thavornmongkol Nucharee Thongthungwong Patteera Triamamornwooth Chanon Utupon Kobkrit Viriyayudhakorn Phoochit Witchutanon Sadit Wongprayon Thepchai Supnithi Department of Computer Engineering Faculty of Engineering Mahidol University National Electronics and Computer Technology Center Pathumthani Thailand Artificial Intelligence Asssociation of Thailand Big Data Institute (Public Organization) Bangkok Department of Electrical Engineering Faculty of Engineering Kasetsart University Looloo Technology Faculty of Engineering Kasetsart University Kasikorn Labs Kasikorn Business Technology Group Faculty of Science King Mongkut’s Institute of Technology Ladkrabang School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thammasat University Next AI Solutions Department of Biomedical Engineering Faculty of Engineering Mahidol University Department of Computer Science University of Liverpool Department of Mechanical Engineering King Mongkut’s University of Technology North Bangkok Artificial Intelligence Entrepreneur Association of Thailand Department of Computer Engineering Faculty of Engineering Chulalongkorn University

ISBN: (数字)9798331509910

ISBN: (纸本)9798331509927

Large language models (LLMs) play an important role in modern NLP technology as they are versatile for a wide array of NLP tasks. However, constructing an LLM is challenging due to concealed construction pipelines, the lack of cleansed datasets, and hyperparameter settings, making it almost irreproducible. This paper presents an efficient pipeline for constructing an LLM tailored to a low-to-medium-sourced language with a high level of data contamination and tools to cleanse the dataset. Following our pipeline, we constructed OpenThaiGPT, an LLM for Thai, with only open-sourced datasets such as CC100, OSCAR, and mC4, and achieved the state-of-the-art accuracies on our downstream tasks. Here, we disclosed the data statistics and all hyperparameter settings for reproducibility.

关键词： Accuracy Large language models Decontamination Pipelines Data visualization Data models Natural language processing Reproducibility of results Arrays Contamination

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Socio-Technical Security Modelling and Simulations in Cyber-Physical Systems: Outlook on Knowledge, Perceptions, Practices, Enablers, and Barriers

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IET Cyber-Physical Systems: Theory and Applications 2025年第1期10卷

作者： Ani, Uchenna Daniel Al-Mhiqani, Mohammed Tuptuk, Nilufer Hailes, Stephen McKendrick Watson, Jeremy Daniel School of Computer Science and Mathematics Keele University Keele United Kingdom Department of Computer Science University of Huddersfield Huddersfield United Kingdom Department of Security and Crime Science University College London London United Kingdom Department of Computer Science University College London London United Kingdom Department of Science Technology Engineering and Public Policy University College London London United Kingdom

Socio-Technical Security Modelling and Simulation (STSec-M&S) is a technique used for reasoning and representing security viewpoints that include both the social and technical aspects of a system. It has shown great potential for improving the cybersecurity and resilience of Critical Infrastructure (CI). This study involved a multi-methods approach, consisting of a scoping literature review and a focus group workshop, conducted with stakeholder engagement from critical infrastructure stakeholders to explore their perceptions and practices regarding the use of socio-technical security modelling and simulation. The findings suggest that the current state of knowledge regarding the use and effectiveness of STSec-M&Ss approaches is limited in CI domains. Consequently, there is little application of it in existing CI systems, regardless of its recognised benefits of enabling a better understanding of CI functionalities, security goals, early and more holistic risk identifications and selection of appropriate countermeasures. The benefits of the STSec-M&S approach can be better realised by effective cross-sector communications and collaborations, team partnerships, system and approach sophistication, and better security awareness amongst others. The potential barriers that can impede such benefits include high expense for implementing the technique, low data availability and quality, regulatory compliance, and competency gaps etc. Helpful recommendations include exploring and using realistic data, validating system security models, and exploring new ways of reskilling and upskilling CI stakeholders in socio-technical security-thinking and M&S approaches to enhance cybersecurity and resilience of CIs. © 2025 The Author(s). IET Cyber-Physical Systems: Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Cyber attacks

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High-Resolution Hydrogen Molecule Detection Utilizing a Nanocandle-Based Optical Sensor 16

High-Resolution Hydrogen Molecule Detection Utilizing a Nano...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Park, Nu-Ri Kim, Min-Joong Lee, Jongsu Seo, Minah Ryu, Yong-Sang Kim, Myung-Ki KU-KIST Graduate School of Converging Science and Technology Korea University Seoul02841 Korea Republic of Korea Institute of Science and Technology Sensor System Research Center Seoul02792 Korea Republic of Northwestern University Department of Electrical Engineering and Computer Science EvanstonIL60208 United States School of Biomedical Engineering Korea University Seoul02481 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02481 Korea Republic of

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen ( ©... 详细信息

ISBN: (纸本)9798350372076

关键词： Nanoclay

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Ultrasound-Coupled Electrocoagulation Based Azo Dye Fading Rate Prediction Using Deep Neural Networks

Ultrasound-Coupled Electrocoagulation Based Azo Dye Fading R...

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International KES Conference on Human Centred Intelligent Systems, KES HCIS 2023

作者： Akoulih, Meryem Tigani, Smail Wakrim, Asmaa Chehri, Abdellah Saadane, Rachid Ghachtouli, Sanae El IME Lab Faculty of Science Hassan 2 University Casablanca20250 Morocco AAIR Lab Digital Engineering and Artificial Intelligence Systems High Private School Casablanca20100 Morocco Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Mathematics and Computer Science Royal Military College KingstonONK7K 7B4 Canada

ISBN: (纸本)9789819934232

This research paper presents a deep learning based predictive model for predicting the azo dye fading rate during electrocoagulation coupled with ultrasound. Electrocoagulation is a commonly used method for the treatment of wastewater contaminated with azo dyes, but the efficiency of this process can be improved by incorporating ultrasound. The proposed deep learning model is trained on a dataset of electrocoagulation experiments with varying parameters such as current density, pH, and treatment time, as well as the presence or absence of ultrasound. The model is able to accurately predict the azo dye removal rate for new sets of electrocoagulation parameters, providing a valuable tool for optimizing the treatment process. The model’s performance is evaluated using standard metrics, and the results demonstrate its effectiveness in predicting azo dye removal rate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Azo dyes

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Anomaly Detection of Patient Data in public Hospital used Internet of Things Sensors

Anomaly Detection of Patient Data in Public Hospital used In...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Evizal Abdul Kadir Lenni Dianna Putri Sri Listia Rosa Apri Siswanto Farhan Assidiqi Muhammad Fikri Evizal Department of Informatics Engineering Universitas Islam Riau Pekanbaru Indonesia Department of Public Health Universitas Prima Indonesia Medan Indonesia Department of Electrical Engineering Telkom University Bandung Indonesia Computer Science and Information Eng National Dong Hwa University Hualien County Taiwan

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Increasing number of patients doing treatment in a public hospital become as issue for the management. Limited number of equipment to detect abnormal patient and support from medical staff. This research aims to detect on the anomaly detection of hospital patient data through the utilization of Internet of Things (IoT) sensors. With the increasing integration of IoT devices in healthcare settings, such as sensors monitoring vital signs like temperature, blood pressure, and heart rate, the need for robust anomaly detection mechanisms becomes imperative. The research explores advanced algorithms and machine learning techniques to identify irregular patterns or outliers in patient data, aiming to enhance the early detection of potential health issues or abnormalities. Leveraging the vast amount of data generated by IoT sensors in hospital environments, the research aims to contribute to the development of more efficient and accurate anomaly detection systems, ultimately improving the quality of patient care and facilitating timely intervention by healthcare professionals. The findings of this research have significant implications for the evolving landscape of healthcare technologies, emphasizing the importance of ensuring data integrity and patient safety in the era of IoT-driven healthcare solutions. Patient data shows in graph to see how the anomaly indication for the patient. Multi sensor with parallel system will develop in future research plan.

关键词： Temperature sensors Hospitals Data integrity Sensor systems Sensors Safety Internet of Things

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PTQ4ADM: Post-Training Quantization for Efficient Text Conditional Audio Diffusion Models

arXiv

引用

arXiv 2024年

作者： Vora, Jayneel Krishnan, Aditya Bouacida, Nader Shankar, Prabhu R.V. Mohapatra, Prasant Department of Computer Science University of California Davis United States Department of Electrical and Computer Engineering University of California Davis United States Department of Public Health Sciences University of California Davis Health United States Department of Computer Science University of South Florida Tampa United States

Denoising diffusion models have emerged as state-of-the-art in generative tasks across image, audio, and video domains, producing high-quality, diverse, and contextually relevant data. However, their broader adoption is limited by high computational costs and large memory footprints. Post-training quantization (PTQ) offers a promising approach to mitigate these challenges by reducing model complexity through low-bandwidth parameters. Yet, direct application of PTQ to diffusion models can degrade synthesis quality due to accumulated quantization noise across multiple denoising steps, particularly in conditional tasks like text-to-audio synthesis. This work introduces PTQ4ADM, a novel framework for quantizing audio diffusion models(ADMs). Our key contributions include (1) a coverage-driven prompt augmentation method and (2) an activation-aware calibration set generation algorithm for text-conditional ADMs. These techniques ensure comprehensive coverage of audio aspects and modalities while preserving synthesis fidelity. We validate our approach on TANGO, Make-An-Audio, and AudioLDM models for text-conditional audio generation. Extensive experiments demonstrate PTQ4ADM’s capability to reduce the model size by up to 70% while achieving synthesis quality metrics comparable to full-precision models( Copyright © 2024, The Authors. All rights reserved.

关键词： Digital elevation model

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Identity-Focused Inference and Extraction Attacks on Diffusion Models

arXiv

引用

arXiv 2024年

The increasing reliance on diffusion models for generating synthetic images has amplified concerns about the unauthorized use of personal data, particularly facial images, in model training. In this paper, we introduce a novel identity inference framework to hold model owners accountable for including individuals’ identities in their training data. Our approach moves beyond traditional membership inference attacks by focusing on identity-level inference, providing a new perspective on data privacy violations. Through comprehensive evaluations on two facial image datasets, Labeled Faces in the Wild (LFW) and CelebA, our experiments demonstrate that the proposed membership inference attack surpasses baseline methods, achieving an attack success rate of up to 89% and an AUC-ROC of 0.91, while the identity inference attack attains 92% on LDM models trained on LFW, and the data extraction attack achieves 91.6% accuracy on DDPMs, validating the effectiveness of our approach across diffusion models. © 2024, CC BY.

关键词： Differential privacy

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Day-ahead dispatch rescheduling for congestion mitigation under optimal power flow

Day-ahead dispatch rescheduling for congestion mitigation un...

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13th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2022)

作者： N.S. Kelepouris A.S. Bouhouras N. Koltsaklis I. Panapakidis C. Tsiatsios G.C. Christoforidis Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Energy & Environmental Policy Laboratory University of Piraeus Piraeus Greece Department of Electrical and Computer Engineering University of Thessaly Volos Greece

In this paper the problem of congestion management in the power grid under the application of the day-ahead dispatch schedule, is faced based on developing a day-ahead guideline regarding the required modifications to the power units' output to yield a more uniform loading to the grid's lines. These modifications result by comparing the dispatch schedule provided by an optimal power flow (OPF) analysis for the grid, under the objective function of power loss minimization, with the schedule for the day-ahead dispatch schedule. The point here is that the solution for the latter approach is based on an economical perspective while the former one on a technical one. The proposed approach is tested on the benchmarked 118 bus system which was modified to simulate the Greek transmission network. The day-ahead schedule is initially applied on the system to depict possible congestion issues via a power flow (PF) analysis. Then, an OPF analysis is applied in order to reschedule the power output of the power units, within their operational limits, aiming at power loss minimization. The results indicate that congestion mitigation in terms of lines' loading below their thermal limit could result under proper and guided alterations for the power units output.

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