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International Conference on SmartRail, Traffic, and Transportation engineering, ICSTTE 2023

作者： Yao, Yuhan Chen, Zhibin Division of Engineering and Computer Science NYU Shanghai Shanghai China

ISBN: (纸本)9789819736812

Many universities, companies, hospitals, and government agencies provide shuttle bus services for their staff’s daily commute. Given that the routes of these shuttle buses are fixed, how to optimize the shuttle bus schedule, i.e., timetable, to reduce the operation costs while maintaining high service satisfaction becomes the biggest challenge facing the shuttle manager. To this end, a spatio-temporal network is first proposed to describe the possible work schedule of shuttle buses. Based on the established network, a mathematical program with an implicit objective function is developed to optimize the shuttle bus schedule. A tailored genetic algorithm is presented and implemented to solve the proposed model efficiently. A numerical example based on the shuttle bus and demand data of New York University Shanghai is presented to illustrate the performance of the proposed model and algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Genetic algorithms

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Single to Diverse Video Generation Using Conditional Diffusion Probabilistic Model 1

Single to Diverse Video Generation Using Conditional Diffusi...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Verma, Ayushi Badal, Tapas Gupta, Siddharth Sinha, Saatvik Gupta, Indrajeet School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science and AI S R University Warangal India

ISBN: (纸本)9798350361780

In the interdisciplinary field of computer vision and deep learning, the generation of video sequences stands as a critical task, with wide-ranging applications from entertainment to surveillance systems. We introduce a novel framework based on the Conditional Diffusion Probabilistic Model (CDPM), featuring significant modifications to the convolution block for handling complex patterns and temporal dynamics. Our model integrates Global Response Normalization (GRN) and Sigmoid Linear Unit (SiLU) activation functions, enhancing its ability to interpret high-dimensional video data. This integration reflects our commitment to advancing video generation technology. The model's effectiveness is validated by its performance on various datasets: achieving an NNFDIV score of 0.57 in the SinGAN-GIF dataset and 0.44 in the HP-VAE-GAN dataset, outperforming existing methods. It also excels in NIQE, RECO, and PSNR metrics, indicative of superior image quality and coherence. These results collectively highlight the model's robustness in diverse video generation, marking a notable advancement in computer vision and deep learning. © 2024 IEEE.

关键词： Chemical activation

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CAML: Cryptographic-Based Cloud Security for Healthcare Data with Machine Learning Technique

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Recent Patents on engineering 2025年第2期19卷 26-40页

作者： M.H, Chaithra Vagdevi, S. Department of Computer Science and Engineering REVA University & Research Scholar Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Department of AI & ML City Engineering College Karnataka Bangalore India

Background: The Cloud model is one of the most realistic frameworks with a vast range of social networking interactions. In medical data, security is a major constraint as it incorporates information about the patients. The cloud environment subjected to mobility and openness is exposed to security issues and limits authorization levels for data transmission. Objective: This patent paper aims to propose a security model for attack prevention within the healthcare environment. Methods: The proposed Cryptographic Attribute-based Machine Learning (CAML) scheme incorporates three stages. Initially, the homomorphic encryption escrow is performed for secure data transmission in the cloud. Secondly, the information of the users is evaluated based on the consideration of users' authorization. The authorization process for the users is carried out with the attribute-based ECC technique. Finally, the ML model with the classifier is applied for the detection and classification of attacks in the medical network. Results: The detected attack is computed and processed with the CNN model. Simulation analysis is performed for the proposed CAML with conventional ANN, CNN, and RNN models. The simulation analysis of proposed CAML achieves a higher accuracy of 0.96 while conventional SVM, RF, and DT achieve an accuracy of 0.82, 0.89 and 0.93, respectively. Conclusion: With the analysis, it is concluded that the proposed CAML model achieves higher classification accuracy for attack detection and prevention in the cloud computing environment. © 2025 Bentham science Publishers.

关键词： Cloud security

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On Traffic Prediction With Knowledge-Driven Spatial–Temporal Graph Convolutional Network aided by Selected Attention Mechanism

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 369-380页

作者： Qian, Yuwen Qiu, Tianyang Ma, Chuan Ni, Yiyang Yuan, Long Zhou, Xiangwei Li, Jun Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Chongqing University College of Computer Science Chongqing400044 China Jiangsu Second Normal University School of Computer Engineering Nanjing210037 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Louisiana State University Division of Electrical and Computer Engineering Baton RougeLA70803 United States

Intelligent transportation systems grapple with the formidable task of precisely forecasting real-time traffic conditions, where the traffic dynamics exhibit intricacies arising from spatial and temporal dependencies. The urban road network presents a complex web of interconnected roads, where the state of traffic on one road can influence the conditions of others. Moreover, the prediction of traffic conditions necessitates the consideration of diverse temporal factors. Notably, the proximity of a time point to the present moment wields a more substantial impact on subsequent states. In this paper, we propose the knowledge-driven graph convolutional network (KGCN) aided by the gated recurrent unit with a selected attention mechanism (GSAM) to predict traffic flow. In particular, KGCN is employed to capture the correlation of the external knowledge factors for the road and the spatial dependencies, and the gated recurrent unit (GRU) is used to cope with temporal dependence. Furthermore, to improve traffic prediction accuracy, we propose the GRU combined with a selected attention mechanism with Gumble-Max to predict traffic at the temporal dimension, where a selector is chosen to dynamically assign the feature in various time intervals with different weights. Experimental results with real-life data show the proposed KGCN with GSAM can achieve high accuracy in traffic prediction. Compared to the traditional traffic prediction method, the proposed KGCN with GSAM can achieve higher efficacy and robustness when capturing global dynamic temporal dependencies, external knowledge factor correlations, and spatial correlations. © 2023 IEEE.

关键词： Knowledge graph

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Light People:*** Dai,*** Lo,and *** Su-innovators in silicon photonics

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Light(science & Applications) 2024年第12期13卷 2880-2885页

作者： Yating Wan Chenzi Guo Electrical and Computer Engineering the ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology23955-6900 ThuwalSaudi Arabia Changchun Institute of Optics Fine Mechanics and PhysicsChinese Academy of SciencesChangchunChina

In this edition of Light People,we are excited to feature *** Dai(Zhejiang University),*** Su(Shanghai Jiao Tong University),and *** Lo(Advanced Micro Foundry Pte Ltd,Singapore),three prominent researchers shaping the future of silicon *** collaborative work addresses critical issues in silicon photonics,including reducing propagation losses,enlarging the functionalities and enhancing building blocks,integrating efficient laser sources,expanding applications,and pushing the boundaries of optical and electronic *** this interview,we delve into their academic journeys,challenges,and future visions,offering insights into the ongoing evolution of silicon photonics and its potential to transform *** a deeper exploration of their experiences and advice,the full interview is available in the Supplementary material.

关键词： interview offering photonics

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Light People:*** May Lau,newly elected US NAE member in Hong Kong,talks about future of photonics and women in science

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Light(science & Applications) 2024年第4期13卷 602-608页

Editorial Photonics technology remains a driving force in today’s scientific landscape,marked by continuous innovation and crossdisciplinary *** an enlighting conversation with Light:science&Applications,*** May Lau,a pioneer in photonics research,shares her deep insights on the evolution of technologies of LEDs,lasers,challenges of hetero-epitaxy,and the future of micro-LEDs and quantum dot *** honored as a member of the US National Academy of engineering(NAE)for her significant contributions to photonics and electronics using III-V semiconductors on silicon,*** stands out as the sole Hong Kong scholar inducted into the NAE this year,joining 114 new and 21 international *** this exclusive Light People interview,*** shares her journey as a pioneering woman in engineering,her commitment to mentorship and academia,and her perspective on advancing female representation in *** summary provided is distilled from ***’s thoughtful responses during the *** a deeper exploration of ***’s experiences and advice,the full interview is available in the Supplementary material.

关键词： interview lighting driving

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Machine Learning-based Framework for Malware Detection in Critical Infrastructures for Smart Cities

Machine Learning-based Framework for Malware Detection in Cr...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Bhatt, Aditya Dasadiya, Sujal Gohil, Aditya Gupta, Rajesh Kumar Jadav, Nilesh Tanwar, Sudeep Garg, Deepak Nirma University Institute of Technology Department of Computer Science and Engineering Ahmedabad India SR University School of Computer Science and AI Telangana Warangal India

ISBN: (纸本)9798350361537

Technology is an inseparable part of today's world and is extensively used in every conceivable domain to provide a wide range of applications. Technology has many critical infrastructures, which serve the function of delivering crucial services essential for the smooth functioning of smart cities. These infrastructures produce a large amount of data which is extremely sensitive and very potentially catastrophic if it falls into the wrong hands. Various techniques and concepts have been introduced and developed to deal with the detection of malicious data. But with the passing time, malware threats grow in both frequency and complexity, machine learning and artificial intelligence techniques can be combined to enhance malware detection. Many Machine Learning algorithms exist which may be confusing for users. Different algorithms vary in their results, accuracy, and prediction capabilities depending on the data they are trained and tested on. For data produced by critical infrastructures, the prediction must be as accurate as possible. Encouraged by the aforementioned facts, we propose an approach aiming to contrast and compare different machine learning algorithms for malware detection on portable executable files dataset. Different performance metrics are compared for all the considered algorithms, based on which, the best algorithm for critical infrastructure malware detection can be determined. © 2024 IEEE.

关键词： Adversarial machine learning

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IoT Network Intrusion Detection with Deep Learning and Voice Alerts 5

IoT Network Intrusion Detection with Deep Learning and Voice...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Dharshiniya, S. Daniel Madan Raja, S. School of Computer Science and Technology Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

The growing complexity and scale of Internet of Things (IoT) networks have made them a prime target for cyber-attacks, necessitating the creation of Intrusion Detection Systems (IDS) to secure confidential data. A key challenge in existing IDS models is that a high loss value can indicate inadequate detection capabilities, potentially leading to missed attacks especially in time-sensitive environments. This study proposes a robust IDS framework by integrating an Autoencoder for feature extraction with Temporal Fusion Transformer (TFT) for attack detection of network intrusion in IoT environments. UNSW-NB15 dataset, was used to assess the model's efficiency. Data preprocessing techniques, including data cleaning and the applying the SMOTE, were employed to ensure balanced dataset representation. The Autoencoder's encoding layer reduces dimensionality to capture essential features and extracted that feature and that as an input to models like the TFT. The Temporal Fusion Transformer (TFT) leverages LSTM encoding, temporal attention, and Gated Residual Networks to capture complex time-series dependencies with the extracted features which help to detect attacks like Fuzzers, Dos, Backdoor, Exploits, Generic, Reconnaissance, Shellcode, Worms. After detecting the attacks, the voice alert is automatically sent to the admin which helps to maintain the robustness. The accuracy achieved by the proposed IDS is 99.06%, with a recall of 98.66%, precision of 91.59%, F1-score of 94.27% and a loss of 0.030 when compared to other traditional algorithms. These results demonstrate the model's effectiveness in identifying various types of cyber-attacks while maintaining a low loss when compared to others. The integration of the voice alert system further enhances the security of the IDS by providing immediate notifications of detected intrusion, thereby facilitating timely responses. © 2024 IEEE.

关键词： Network intrusion

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Anomaly Detection in IoV Can Bus Traffic Using Variational Autoencoder-LSTM with Attention Mechanism 5

Anomaly Detection in IoV Can Bus Traffic Using Variational A...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Pradeep, C. Malar Dhas, Julia Punitha Isravel, Deva Priya School of Computer Science and Technology Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India

ISBN: (纸本)9798331518097

The rapid growth of the Internet of Vehicles (IoV) greatly enhances the communication and data exchange capability between vehicles, which allows the real-time transmission of critical information. The CAN bus is the backbone developed to transmit data between Electronic Control Units in the in-vehicle networks and it is prone to various cyberattacks that can breach its security and integrity. To identify cyberattacks on a CAN bus this paper proposes a deep learning model employing a Variational Autoencoder in conjunction with Long Short-Term Memory (LSTM) that features an integrated attention mechanism. The proposed model leverages the temporal characteristics of CAN bus traffic to detect anomalies by identifying the differences in the reconstruction loss. The CICIoV2024 dataset, containing benign and malicious messages for the CAN bus, was used for the proposed model, which achieved high effectiveness with the accuracy of 99.78%, precision of 99.81%, a recall of 99.32%, and F1 score of 99.56%. The results show that the model is strong in accurately detecting unusual behaviour and offers a flexible solution for improving cybersecurity in IoV systems. © 2024 IEEE.

关键词： Buses

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Dynamic Quantum Federated Learning for UAV-based Autonomous Surveillance

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8158-8170页

作者： Park, Soohyun Son, Seok Bin Jung, Soyi Kim, Joongheon Sookmyung Women's University Division of Computer Science Seoul Korea Republic of Korea University School of Electrical Engineering Seoul Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

In recent years, unmanned aerial vehicles (UAVs) have proven their effectiveness in surveillance due to their superior mobility. By utilizing multiple UAVs with collaborated learning, surveillance of a huge area while consuming minimum resources is possible. However, the performance of UAV collaborative learning systems is still severely limited because the number of parameters in a classical NN is generally very large, which is unsuitable for unstable wireless communication. To address this issue, this paper uses a quantum neural network (QNN), which has large computational capabilities and uses fewer parameters to overcome the problems caused by many parameters. However, it is still difficult to send all the parameters of the QNN to the server under poor channel conditions. Therefore, this paper proposes dynamic quantum federated learning (DQFL), a novel framework designed for UAVs employing quantum computing (QC) and federated learning (FL). The proposed DQFL uses a dynamic quantum neural network (DQNN) with a multi-depth circuit and employs dynamic control of the circuit layer to improve the efficiency of local parameter transmission between UAVs in unstable wireless communication environments. Extensive simulations conducted under real-world autonomous surveillance conditions demonstrate the robustness of DQFL to non-iidness, varying signal-to-noise ratios (SNRs), and poor communication channel conditions in the UAV environment. © 1967-2012 IEEE.

关键词： Federated learning

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