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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Hongyu Cong, Runmin Li, Hua Xu, Qianqian Huang, Qingming Zhang, Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature;the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24. Copyright 2024 by the author(s)

关键词： Miocene

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Analysis of converting -circuit into -circuit

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EPJ Quantum Technology 2025年第1期12卷 1-14页

作者： Luo, Qing-bin Ding, Lang Yang, Guo-wu Li, Xiao-yu College of Intelligent Systems Science and Engineering Hubei Minzu University Enshi China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big data research Center & School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

A $\mathfrak{C^{*}}$ -circuit, which was proposed in Asiacypt 2022 by Huang and Sun (Advances in cryptology – ASIACRYPT 2022, pp. 614–644, 2022), can directly perform calculations with the existing quantum states, thereby reducing the use of quantum resources in quantum logic synthesis. We theoretically prove how to convert a $\mathfrak{C^{0}}$ -circuit into the corresponding $\mathfrak{C^{*}}$ -circuit through two lemmas and one theorem. The first lemma proves the interchangeability of CNOT gates and NOT gates by using the equivalence of quantum circuits. The second lemma proves that adding CNOT gates to the front of a quantum circuit whose initial states are all $|0\rangle $ s will not change the output states of the circuit. The theorem is used to describe what kind of $\mathfrak{C^{0}}$ -circuit can be transformed into $\mathfrak{C^{*}}$ -circuit, and the correctness of this transformation is proved. Our work will provide a theoretical basis for converting $\mathfrak{C^{0}}$ -circuit to $\mathfrak{C^{*}}$ -circuit. Then applying the theoretical analysis results to the multiplication over $\text{GF}(2^{8})$ , the constructed quantum circuit needs 27 Toffoli gates and 118 CNOT gates, which is 15 fewer Toffoli gates and 43 CNOT gates than the current best result. This shows that the method of constructing quantum circuits by using the conversion of $\mathfrak{C^{0}}$ -circuit to $\mathfrak{C^{*}}$ -circuit is very efficient.

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Multi-Scale Analysis Technique for Low Energy Data Aggregation 5th

Multi-Scale Analysis Technique for Low Energy Data Aggregati...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Alam, Intekhab Krishnamoorthy, Ramkumar Verma, Tushar K. Raghavendra, T.S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

The multiscale evaluation technique is a low-power facts aggregation method using numerous evaluation scales to mix data from several properties. The method is based on multiscale structures, in which more than one scale of facts can be leveraged to advantage notion into centered styles within an individual source or in the course of resources. Multiscale assessment allows an accurate and dependable example of facts in evaluation compared to conventional unmarried-scale techniques. It’s miles beneficial for studying structures with sparse or low-first-rate information, wherein more significance than the facts accumulated is needed to constitute the general device because it needs to be. Furthermore, this technique can detect styles in records from randomly sampled belongings or residences that might be temporally or spatially separated. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： conventional evaluation Multiscale

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Deep learning and ensemble methods for anomaly detection in ICS security

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International Journal of information Technology (Singapore) 2025年第3期17卷 1761-1775页

作者： Hossain, Md. Alamgir Hasan, Tahmid Karovic, jr, Vincent Abdeljaber, Hikmat A. M. Haque, Md. Alimul Ahmad, Sultan Zafar, Aasim Nazeer, Jabeen Mishra, B.K. Department of Computer Science and Engineering State University of Bangladesh South Purbachal Kanchan Dhaka 1461 Bangladesh Department of Computer Science and Engineering Prime University Dhaka Bangladesh Department of Information Systems Faculty of Management Comenius Bratislava Odbojárov 10 Bratislava 25 82005 Slovakia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman 11931 Jordan MEU Research Unit Middle East University Amman 11831 Lebanon Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Computer Science Aligarh Muslim University U.P Aligarh 202002 India P.G. Department of Physics Veer Kunwar Singh University Bihar Ara India

This research addresses the escalating threats to industrial control systems by introducing a novel approach that combines deep learning for feature selection with a robust ensemble-based classification technique to enhance anomaly detection. Our method utilizes a tailored autoencoder architecture to efficiently select features, followed by a Random Forest classifier to ensure reliable and generalizable detection of anomalies. Evaluated on the HAI Security Dataset, the proposed approach demonstrates exceptional performance, with significant improvements in detecting anomalous activities in ICS. The results highlight the method’s potential to enhance ICS security by providing a scalable and adaptable framework that evolves with emerging threats. This study not only advances cybersecurity methodologies for industrial systems but also lays the groundwork for future research, emphasizing the need for innovative and effective security solutions in the face of evolving cyber challenges. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anomaly detection in ICS Cyber-resilience in critical infrastructure Deep learning for feature selection Industrial control systems security Random forest classifier for ICS

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Real-Time Object Detection on Edge Devices Using Mobile Neural Networks 2

Real-Time Object Detection on Edge Devices Using Mobile Neur...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Jose Reena, K. Nigam, Charul Kirubasri, G. Jayachitra, S. Aeron, Anurag Suganthi, D. Vistas Department of Computer Science Tamilnadu Chennai India Institute of Information Technology and Management Department of It New Delhi India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Psna College of Engineering and Technology Department of Ece Tamilnadu Dindigul India Dit University Dehradun School of Computing Department of Computer Science and Engineering Dehradun India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science and Engineering Thandalam Chennai India

ISBN: (纸本)9798350306415

In an era when edge computing rapidly evolves, this study addresses the significant difficulty of real-time object identification on resource-constrained edge devices. We provide a unique neural network model for edge scenarios in this paper. Our model covers object recognition system accuracy, computational efficiency, and speed. Object detection methods that need processing are impractical for edge computing. This paper proposes a Mobile Neural Network tailored to the limitations. Pruning, which decreases model size by 30%, and quantization make the model efficient. On several edge devices, the model had an average accuracy of 92% on Dataset 1 and 89% on Dataset 2. The model's 40-millisecond inference time and 2.3-watt power consumption were impressive. It outperformed standard CNN models and edge-optimized algorithms like YOLOv3 and SSD MobileNet under identical conditions. This study shows that the proposed paradigm can revolutionise edge computing real-time object detection. The model is suitable for high-responsiveness, energy-efficient applications. © 2024 IEEE.

关键词： Edge Computing Mobile Neural Networks Optimization Techniques Real-Time Object Detection

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Iterative Refinement for Variational Quantum Linear Solver 1

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International Conference on Data Analytics and Insights, ICDAI 2023

作者： Saito, Yoshiyuki Lee, Xinwei Cai, Dongsheng Shin, Jungpil Asai, Nobuyoshi Computer Science and Engineering University of Aizu Fukushima Aizu-Wakamatsu965-8580 Japan Faculty of Engineering Information and Systems University of Tsukuba Ibaraki Tsukuba305-8577 Japan

ISBN: (数字)9789819938780

ISBN: (纸本)9789819938773

Variational quantum algorithms require many measurements to train parameters in their circuit to solve a given problem. If we need a highly accurate solution, the further number of measurements increases. However, the increase in the number of measurements may cancel out the usefulness of fast quantum computations. In this paper, we propose a method to reduce the number of measurements that depends on the required accuracy in a variational quantum algorithm for a linear system of equations. We also demonstrate its usefulness through numerical experiments using a quantum simulator from the points of measurement numbers and accuracies obtained. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quantum computers

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Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

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computer Modeling in engineering & sciences 2024年第4期139卷 725-739页

作者： Puja S.Prasad Adepu Sree Lakshmi Sandeep Kautish Simar Preet Singh Rajesh Kumar Shrivastava Abdulaziz S.Almazyad Hossam M.Zawbaa Ali Wagdy Mohamed CSE Department Geethanjali College of EngineeringHyderabadTelangana501301India Department of Computer Science LBEF CampusKathmandu44600Nepal School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia CeADAR Ireland’s Centre for AI Technological University DublinDublinD07 EWV4Ireland Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

Pupil dynamics are the important characteristics of face spoofing *** face recognition system is one of the most used biometrics for authenticating individual *** main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,*** proposed model uses pupil characteristics for liveness detection during the authentication *** pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light *** proposed framework consists of two-phase *** the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video *** above measurement is converted into feature space using Kohn and Clynes model-defined *** Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the *** the second phase,we perform the facial recognition ***-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional *** features are scale,rotation,and orientation invariant and are used for recognizing facial *** brute force matching algorithm is used for matching features of two different *** threshold value we considered is 0.08 for good *** analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Err

关键词： SIFT pupil CASIA-SURF pupillary light reflex replay attack dataset brute force

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A Secure Communication Protocol for Unmanned Aerial Vehicles

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computers, Materials & Continua 2022年第1期70卷 601-618页

作者： Navid Ali Khan N.Z.Jhanjhi Sarfraz Nawaz Brohi Abdulwahab Ali Almazroi Abdulaleem Ali Almazroi School of Computer Science and Engineering(SCE) Taylor’s University Lake-side CampusSubang Jaya47500Malaysia School of Information and Technology Monash UniversityMalaysia University of Jeddah College of Computing and Information Technology at KhulaisDepartment of Information TechnologyJeddahSaudi Arabia Department of Computer Science Rafha Community CollegeNorthern Border UniversityArar91431Saudi Arabia

Mavlink is a lightweight and most widely used open-source communication protocol used for Unmanned Aerial *** UAVs and autopilot systems support it,and it provides bi-directional communication between the UAV and Ground Control *** communications contain critical information about the UAV status and basic control commands sent from GCS to UAV and UAV to *** order to increase the transfer speed and efficiency,the Mavlink does not encrypt the *** a result,the protocol is vulnerable to various security attacks such as Eavesdropping,GPS Spoofing,and *** this study,we tackle the problem and secure the Mavlink communication *** leveraging the Mavlink packet’s vulnerabilities,this research work introduces an experiment in which,first,the Mavlink packets are compromised in terms of security requirements based on our threat *** results show that the protocol is insecure and the attacks carried out are *** overcomeMavlink security,an additional security layer is added to encrypt and secure the *** encryption technique is proposed that makes the communication between the UAV and GCS *** results show that the Mavlink packets are encrypted using our technique without affecting the performance and *** results are validated in terms of transfer speed,performance,and efficiency compared to the literature solutions such as MAVSec and benchmarked with the original Mavlink *** achieved results have significant improvement over the literature and Mavlink in terms of security.

关键词： Unmanned aerial vehicles mavlink protocol drones security UAVs communication

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Personal and ubiquitous computing, special issue: "Sonic experiences: interaction, connectivity, and multi-sensory technologies"

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Personal and Ubiquitous Computing 2023年第5期27卷 1779-1781页

作者： Turchet, Luca Chamberlain, Alan Kallionpää, Maria Department of Information Engineering and Computer Science University of Trento Trento Italy Mixed Reality Lab School of Computer Science University of Nottingham Nottingham United Kingdom KreativInstitut.OWL Hochschule Für Musik Detmold Detmold Germany

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Convolutional neural network based data interpretable framework for Alzheimer’s treatment planning

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 375-386页

作者： Sazia Parvin Sonia Farhana Nimmy Md Sarwar Kamal Information Technology Melbourne PolytechnicMelbourne VIC 3072Australia Faculty of Economics and Business University of New South WalesSydney ACT 2612Australia School of Computer Science Faculty of Engineering and ITUniversity of Technology SydneySydney NSW 2007Australia

Alzheimer’s disease(AD)is a neurological disorder that predominantly affects the *** the coming years,it is expected to spread rapidly,with limited progress in diagnostic *** machine learning(ML)and artificial intelligence(AI)algorithms have been employed to detect AD using single-modality ***,recent developments in ML have enabled the application of these methods to multiple data sources and input modalities for AD *** this study,we developed a framework that utilizes multimodal data(tabular data,magnetic resonance imaging(MRI)images,and genetic information)to classify *** part of the pre-processing phase,we generated a knowledge graph from the tabular data and MRI *** employed graph neural networks for knowledge graph creation,and region-based convolutional neural network approach for image-to-knowledge graph ***,we integrated various explainable AI(XAI)techniques to interpret and elucidate the prediction outcomes derived from multimodal ***-wise relevance propagation was used to explain the layer-wise outcomes in the MRI *** also incorporated submodular pick local interpretable model-agnostic explanations to interpret the decision-making process based on the tabular data *** expression values play a crucial role in AD *** used a graphical gene tree to identify genes associated with the ***,a dashboard was designed to display XAI outcomes,enabling experts and medical professionals to easily comprehend the predic-tion results.

关键词： Multimodal Region-based convolutional neural network Layer-wise relevance propagation Submodular pick local interpretable model-agnostic explanations Graphical genes tree Alzheimer’s disease

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