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Advanced Air Mobility: Innovations, Applications, Challenges, and Future Potential

Journal of Air Transportation 2025年第2期33卷 169-187页

作者： Raza, Waleed Renkhoff, Justus Ogirimah, Olayemi Bawa, Gurvir Kaur Stansbury, Richard S. Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona BeachFL32114 United States Department of Information Systems University of Maryland BaltimoreMD21250 United States

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Private Inference in Quantized Models

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IEEE Transactions on Information Theory 2025年第5期71卷 3957-3973页

作者： Deng, Zirui Ramkumar, Vinayak Bitar, Rawad Raviv, Netanel Washington University in St. Louis Department of Computer Science and Engineering St. LouisMO63130 United States Tel Aviv University Department of Electrical Engineering-Systems Tel Aviv-Yafo6997801 Israel Institute for Communications Engineering Technical University of Munich Munich80333 Germany

A typical setup in many machine learning scenarios involves a server that holds a model and a user that possesses data, and the challenge is to perform inference while safeguarding the privacy of both parties. Private Inference has been extensively explored in recent years, mainly from a cryptographic standpoint via techniques like homomorphic encryption and multiparty computation. These approaches often come with high computational overhead and may degrade the accuracy of the model. In our work, we take a different approach inspired by the Private Information Retrieval literature. We view private inference as the task of retrieving inner products of parameter vectors with the data, a fundamental operation in many machine learning models. We introduce schemes that enable such retrieval of inner products for models with quantized (i.e., restricted to a finite set) weights;such models are extensively used in practice due to a wide range of benefits. In addition, our schemes uncover a fundamental tradeoff between user and server privacy. Our information-theoretic approach is applicable to a wide range of problems and robust in privacy guarantees for both the user and the server. © 1963-2012 IEEE.

关键词： Differential privacy

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Multi-Mode Inflow Prediction for Urban Transportation systems 14th

Multi-Mode Inflow Prediction for Urban Transportation System...

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14th International Conference on Frontier Computing, FC 2024

作者： Hung, Chih-Chieh Hung, Min-Hsien Chang, Jia-Wei Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9789819623945

Traffic congestion poses significant challenges to modern cities, leading to increased energy use, pollution, and long commute times. Optimizing public transit systems and encouraging their use is an effective solution. However, public transit integrates various modes like subways, buses, and shared bikes, requiring transfers between modes. Therefore, understanding the relationships between modes and accurately predicting passenger flows for each mode is crucial. Accurate predictions enable better capacity allocation, scheduling, and seamless transfers between modes, enhancing accessibility and convenience to attract more riders. Existing multi-modal prediction methods typically focus on station-to-region or region-to-region flows, overlooking station-to-station predictions that can better capture inter-modal relationships and provide fine-grained insights for traffic management. This study proposes a multi-modal framework for MRT and YouBike data that employs a spatial-temporal module to effectively model the spatiotemporal dependencies in the data. Evaluations on real-world datasets demonstrate the framework outperforms benchmark methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Traffic congestion

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Using Local Gradient Compensation in Federated Learning for Improving Privacy and Performance 14th

Using Local Gradient Compensation in Federated Learning for ...

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14th International Conference on Frontier Computing, FC 2024

作者： Chang, Jia-Wei Chen, Wei-Cheng Hung, Chih-Chieh Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan

ISBN: (纸本)9789819623945

Federated learning (FL) has emerged as a leading approach for decentralized model training, preserving data privacy by exchanging only model parameters. However, recent studies have exposed vulnerabilities, revealing that attackers can reconstruct private data from shared model parameters, compromising user privacy. To address this, various defense methods have been proposed, with gradient perturbation being a lightweight yet effective strategy. However, it often incurs a trade-off with model performance. In this paper, we introduce a lightweight defense mechanism tailored for Federated Stochastic Gradient Descent (FedSGD). Our method leverages maximum gradient values to gauge noise scale, while also preserving and compensating for previous noise iterations to mitigate performance impacts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A case study of Nifty 50 Index

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IEEE Access 2025年 13卷 87036-87047页

作者： Singh, Priya Jha, Manoj Patel, Harshita Maulana Azad National Institute of Technology Department of Mathematics Bioinformatics & Computer Applications Bhopal 462003 India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

Portfolio theory underpins portfolio management, a much-researched yet uncharted field. Stock market prediction is a challenging and essential endeavour in financial research, owing to the nonlinear, volatile, and stochastic characteristics of financial time series data. Conventional statistical techniques often fall short to encapsulate complex interdependencies, resulting in diminished predictive accuracy. This research proposes an ensemble model that integrates Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Temporal Convolutional Networks (TCN) for effective stock market prediction. The Nifty 50 index dataset is utilized for the empirical evidences. Wavelet-based denoising is utilised as a preprocessing measure to mitigate the intrinsic noise in stock market data. The model’s efficacy is assessed utilising error metrics, such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). The five-fold cross-validation is utilized to establish the robustness of the models. Furthermore, we ascertain the statistical significance of performance enhancements by parametric t-tests, including normality assessments via the Shapiro-Wilk test. Moreover, current state-of-the-art models advocates in favour of proposed study. © 2013 IEEE.

关键词： CNN Ensemble learning LSTM Random Forest Statistical Analysis Stock Market TCN

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Adaptive Quantum and Graph-Attentive Security Framework for DoS Attack Mitigation in VANETs 2

Adaptive Quantum and Graph-Attentive Security Framework for ...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Anjum, Asma Rao, P. S. V. Srinivasa Sindhu, M. Bhatt, Pooja Priya, M.R. Maranan, Ramya HKBK College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560045 India Joginpally B R Engineering College Department of Computer Science and Engineering Telangana Hyderabad500075 India R.M.K Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601206 India Parul institute of Engineering and Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology Post Limda Gujarat 391760 India NIE First Grade College Department of Computer Science Karnataka Mysuru570008 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331505745

Vehicular Ad Hoc Networks (VANETs) are considered crucial for real-time vehicle-to-vehicle communication, which in turn enhances the efficiency of traffic and road safety. VANETs are very vulnerable to Denial-of-Service (DoS) attacks, which seriously disrupt communication and degrade network performance. This paper introduces a high-performance neural network-based detection and prevention mechanism that can effectively counter DoS attacks. The proposed method is initiated with the real-time monitoring of the network. Data collection and preprocessing through the Adaptive Self-Guided Loop Filter (ASGLF) are used to filter out noise from the data. The Fast Hybrid Vision Transformer (FHVT) conducts feature extraction for network traffic to effectively preserve the spatial and temporal relationships. Using the Reflecting Equivariant Quantum Neural Network (REQNN) integrated with Heterogeneous Edge-Enhanced Graph Attention Network (HEEGAT) ensures timely anomaly detection accuracy of attacks. Subtraction-Average-Based Optimizer (SAO) optimizes the detection parameters to effectively utilize resources. The experimental results have shown accuracy in the detection of DoS attacks as 99.9% with a PDR of 99.9% and also improved throughput efficiency by 99.8%. These results validate the effectiveness of the proposed model in securing VANETs from DoS attacks, offering uninterrupted network availability and improved vehicular communication security for intelligent transportation systems. © 2025 IEEE.

关键词： Advanced driver assistance systems

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Remote Patient Healthcare Monitoring and Pose Recognition over Video Sensors

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Traitement du Signal 2025年第2期42卷 995-1005页

作者： Alabdullah, Bayan Tayyab, Muhammad Alhasson, Haifa F. Alshammari, Naif S. Jalal, Ahmad Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia Department of Computer Science Air University Islamabad44000 Pakistan Department of Information Technology College of Computer Qassim University Buraydah52571 Saudi Arabia Department of Computer Sciences College of Computer and Information Sciences Majmaah University Majmaah11952 Saudi Arabia Department of Computer Science and Engineering College of Informatics Korea University Seoul02841 Korea Republic of

Considering the fact of increasing population and as a result, the number of patients is constantly increasing the delivery of medical services must be prompt and of good quality. There is no question that any perfect healthcare system should track the given activities, behaviours, schedules, and even general health of the patients. The focus of this study is in utilizing ML and DL in tracing patients and diagnosing ailments. Mounted cameras from different orientations and placements are intended for health monitoring and event surveillance for possible medical issues in the future. Classical ML and more advanced DL methods are applied for analyzing physiological and behavioral patterns to identify health conditions and stress the importance of high accuracy and real-time performance for practical purposes. Initial outcomes show that patient outcomes are enhanced by getting timely medical advice while decreasing hospital-based burdens on staff. Two challenging datasets, ETRI-Activity3D and NTU-RGB-D, are pre-processed to remove unimportant data and reduce computational load. A strong multi-feature description system can extract gradient values, and the process is followed by feature matching and feature optimization. Then, in the last Maximum Entropy Markov Model (MEMM) classifier is used, achieving an accuracy of 97.2% for ETRI-Activity3D, and 98.3% for the NTU-RGB-D dataset. ©2025 The authors.

关键词： Remote patient monitoring

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Towards Securing Water systems Communications from Adversarial Attacks using AI Assurance 5

Towards Securing Water Systems Communications from Adversari...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Yardimci, Mehmet Oguz Batarseh, Feras A. Virginia Tech Department of Computer Science ArlingtonVA United States Virginia Tech Department of Biological Systems Engineering ArlingtonVA United States

ISBN: (纸本)9798331519957

Water systems are increasingly susceptible to cyberattacks due to their reliance on networked communications for monitoring and control. This paper introduces an AI-Assured approach to detect anomalies in water distribution systems communication layers using Long Short-Term Memory models, which are trained, tuned, and validated through four different scenarios targeting disruptions in communication patterns. Using the Secure Water Treatment and anomalies and malicious acts datasets, and by simulating both operational and malicious scenarios, Long Short-Term Memory model is trained on normal data and tested on both regular (idle) and attack situations. The LSTM model employs a sliding time-window technique to capture sequential dependencies in communication data;it identifies anomalies by comparing predicted and actual sequence-significant deviations that signal potential cyber threats. Our scenarios demonstrate an anomaly detection accuracy of 94%, with a false positive rate of 2.5%, indicating the model's capacity to identify both overt and subtle attacks. Further, we evaluate the intentionality of those outliers, whether they are cyber threats or merely a human operator error. The anomalies and malicious acts dataset scenario successfully detects multiple intentional sabotage scenarios, further validating the model's potential in real-world cyber-physical security settings. The findings suggest that integrating AI-Assured, LSTM-based anomaly detection into water systems can enhance security, enabling early threat detection and reducing the overall risk to critical infrastructures communications. © 2025 IEEE.

关键词： Artificial intelligence

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Generalized Two-Point Visual Control Model of Human Steering for Accurate State Estimation1

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ASME Letters in Dynamic systems and Control 2025年第1期5卷 011004页

作者： Mai, Rene E. Sears, Katherine Roessling, Grace Julius, Agung Mishra, Sandipan Department of Mechanical Aerospace and Nuclear Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Department of Cognitive Science Rensselaer Polytechnic Institute TroyNY12180 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle's lateral state. © 2024 by ASME.

关键词： Man machine systems

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"There is a Damn Hello on the Social Media!" Insights into Public Risk Communication in the Digital Age 18th

"There is a Damn Hello on the Social Media!" Insights into P...

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18th IFIP WG 11.12 International Symposium on Human Aspects of Information Security and Assurance, HAISA 2024

作者： Große, Christine Division of Computer Science Electrical and Space Engineering Department of Digital Services and Systems Luleå University of Technology Luleå971 87 Sweden

ISBN: (纸本)9783031725586

The ability to handle threats, such as disinformation, manipulation of public opinion, and disruption of critical supplies, is becoming increasingly important, thus, necessitating, among other strategies, efforts to establish a proper risk communication to the public. This paper addresses the need for more empirical research in this area to contribute to the development of an in-depth understanding of public risk communication that includes information-related threats and cyber issues. The study involves officials of three public organizations entrusted with safety and security in society: the police, the rescue service, and the county administrative board of a county in the middle of Sweden. The results detail the recognition of risks to be communicated, the organization of the communication process, the messages that these actors seek to bring forth, and to whom as well as challenges of public risk communication in the digital era. The findings indicate that information-related and cyber risks are increasingly essential to consider as an additional layer of public communication. Two implications emerged as particularly important: (1) all communication about risks and crises must consider the systemic risk of mis- and disinformation, and (2) tailored communication about the risks interrelated with disinformation should use human-centered, dialogue-based, and moderated approaches. Further research can focus on associated challenges, considering the distribution of responsibilities, inter-organizational information sharing and cooperation, and the possibly stochastic effects on critical (information) infrastructures and, ultimately, societal values. © IFIP International Federation for Information Processing 2025.

关键词： Stochastic systems

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