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arXiv 2025年

作者： LeGrow, Jason T. Morrison, Travis Sikora, Jamie Swanson, Nicolas Department of Mathematics United States Department of Computer Science United States Center for Quantum Information Science and Engineering Virginia Tech BlacksburgVA United States

We propose a modification to the transpiler of a quantum computer to safeguard against side-channel attacks aimed at learning information about a quantum circuit. We demonstrate that if it is feasible to shield a specific subset of gates from side-channel attacks, then it is possible to conceal all information in a quantum circuit by transpiling it into a new circuit whose depth grows linearly, depending on the quantum computer’s architecture. We provide concrete examples of implementing this protection on IBM’s quantum computers, utilizing their virtual gates and editing their transpiler. © 2025, CC BY-NC-ND.

关键词： Quantum electronics

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Vectorial liquid‑crystal holography

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eLight 2024年第1期4卷 251-261页

作者： Ze‑Yu Wang Zhou Zhou Han Zhang Yang Wei Hong‑Guan Yu Wei Hu Wei Chen Hai‑Tao Dai Ling‑Ling Ma Cheng‑Wei Qiu Yan‑Qing Lu National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and ManipulationCollaborative Innovation Center of Advanced MicrostructuresCollege of Engineering and Applied SciencesNanjing UniversityNanjing 210023China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Tianjin Key Laboratory of Low Dimensional Materials Physics and Preparing Technology School of ScienceTianjin UniversityTianjin 300072China

Vectorial optics with fine inhomogeneous polarization control are highly *** have been captivated a promising candidate,but their static post-fabrication geometry largely limits the dynamic *** crystal(LC)is usually employed as an additional index-changing layer together with ***,most of the reported LCs only impart a varying but uniform phase on top of that from the metasurface,which we term“scalar”LC ***,we pixelate a single-layer LC to display versatile and tunable vectorial holography,in which the polarization and amplitude could be arbitrarily and independently controlled at varying spatial ***,the subtle and vectorial LC-holography highlights the broadband and electrically-switchable *** vectorial LC holography reveals significant opportunities for advanced cryptography,super-resolution imaging,and many other applications.

关键词： Liquid crystals Vectorial optics Holography

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Blockchain Based Secure Interoperable Framework for the Internet of Medical Things 16th

Blockchain Based Secure Interoperable Framework for the Inte...

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16th International Conference on Information Technology and Applications, ICITA 2022

作者： Rafique, Wajid Shah, Babar Hakak, Saqib Khan, Maqbool Anwar, Sajid Department of Computer Science and Operations Research University of Montreal Quebec Canada Center of Excellence in IT Institute of Management Sciences Peshawar Pakistan Faculty of Computer Science Canadian Institute for Cybersecurity University of New Brunswick Fredericton Canada Pak-Austria Fachhochschule Institute of Applied Sciences and Technology Haripur Pakistan Software Competence Center Hagenberg Vienna Austria College of Information Technology Zayed University Academic United Arab Emirates

ISBN: (纸本)9789811993305

Internet of Medical Things (IoMT) has revolutionized the way medical infrastructure has been managed in the past. Multiple platforms in IoMT have disparate communication standards, data format requirements, and access policies, which produce immense overhead during data transfer among these platforms. In order to provide seamless healthcare services using IoMT, interoperability concerns of heterogeneous devices need to be addressed. Smart contracts using blockchain provide a secure communication for distributed objects to interact in a secure way. We propose a Blockchain-based Secure Interoperable Framework (BSIIoMT) using smart contracts for secure communication in IoMT. We present components, workflow, and design considerations of the BSIIoMT framework to show the feasibility of using edge-enabled blockchain for secure interoperability in IoMT. The BSIIoMT framework is an ongoing project where we present the framework and its components in this research where further results and evaluation will be presented in future. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Trust-aware Cloudlet Federation Model for Secure Service Selection

Trust-aware Cloudlet Federation Model for Secure Service Sel...

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Energy, Power, Environment, Control, and Computing (ICEPECC), International Conference on

作者： Vaneeza Shahid Muhammad Ziad Nayyer Usama Ahmed Department of Computer Science GIFT University Gujranwala Pakistan Department of Software Engineering Government College University Faisalabad Faisalabad Pakistan

The introduction of Edge computing has pushed the horizon to the edge of the network and the user’s proximity. The openness of data on the edge raised many issues about the security of data. Although edge computing is an extension of the cloud, besides this, its solutions to handle security threats are different from cloud computing because of its architectural dissimilarities. There is a need for a comprehensive mechanism to ensure the choice of secure service. For this purpose, the combination of trust would be a better approach to deal with the service selection tasks in edge computing. In this paper, a trust-aware cloudlet federation model has been proposed which ultimately helps in the safe service selection of an edge node. Before using a service ‘A’ of any edge node, the trust value of the edge node for service ‘A’ should be calculated, called Service Trust Value (STV). Then the edge nodes with a higher STV for service ‘A’ are filtered. Lastly, the model will select the edge node with a satisfactory value of trust for service ‘A’. This proposed model will be beneficial in secure service selection.

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Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Aymene Berkani, Mohamed Rafik Chouchane, Ammar Himeur, Yassine Abdennebi, Anes Sagiroglu, Seref Amira, Abbes Medea Algeria University Biskra University Center of Barika Laboratory of LI3C Amdoukal Road Barika05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates École de Technologie Supérieure Software and Information Technology Engineering Montréal Canada Analytics Security R&D Center Gazi University Artificial Intelligence and Big Data Ankara Turkey University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331527396

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments. © 2024 IEEE.

关键词： Federated learning

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Dynamic Selection Scheme of LTE-M and 5G for Efficient Maritime Communication Based on Traffic Volume 24

Dynamic Selection Scheme of LTE-M and 5G for Efficient Marit...

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24th Asia-Pacific Network Operations and Management Symposium, APNOMS 2023

作者： Yoon, Mahnsuk Cho, Kyucheol Kwon, Jaeuk Seo, Jihun Lee, Hyunjoo Yun, Jangkyu Gumi Korea Republic of School of Computer Secience and Engineering Software Education Center Kyungpook National University Daegu Korea Republic of Korea Transport Institute Center for Connected & Automated Driving Research Department of The Fourth Industrial Revolution & Transport Sejong-si Korea Republic of Gumi Korea Republic of

ISBN: (纸本)9788995004395

This paper investigates the technology for selecting communication scheme based on the amount of traffic in LTE-M (Marine) and 5G communication environments. Recently, it has been possible to use LTE-M and 5G communications for preventing collisions between ships and obstacles in coastal areas, moving from a focus on terrestrial users. This paper proposes a scheme for efficiently handling both small-scale and large-volume traffic generated from terminals on ships. LTE-M can accommodate more terminals by utilizing the wide coverage of coastal areas, but it can be vulnerable to high-volume video processing for real-time obstacle detection. 5G provides a high data transmission rate and low latency, making it suitable for large data exchanges between ships or real-time services, but its short coverage makes it unsuitable for accommodating a wide area. Therefore, this paper proposes a scheme for dynamically selecting LTE-M or 5G communication schemes according to the amount of data generated by terminals on ships, to transmit data. In this paper, we propose an algorithm to decide which communication scheme, LTE-M or 5G, should be chosen based on the amount and type of traffic. The algorithm dynamically selects the optimal communication scheme considering real-time traffic information and network status. By implementing a simulation environment for coastal areas using the technique proposed in this paper, we compare the scheme where terminals installed on ships selectively use LTE-M and 5G communication schemes with the scheme where two communication schemes are chosen randomly for transmission. The simulation results confirm that the proposed technique can process more data compared to the existing schemes. Copyright 2023 KICS.

关键词： Ships

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Masked Face Recognition using Deep Learning Model 3

Masked Face Recognition using Deep Learning Model

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3rd International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Kumar, Manoj Mann, Rachit Delhi Technological University Department of Computer Science and Engineering Delhi India Delhi Technological University Department of Software Engineering Delhi India

ISBN: (纸本)9781665438117

Face Masks have become part of our day-to-day activities. But these create a problem with the existing face recognition techniques. Existing face recognition techniques vary from simple ML techniques such as SVM, PCA, etc. to state-of-the-art models such as ResNet, VGG, etc. In this paper, we are studying the effects of masked faces on the performance of face recognition techniques. Face recognition itself is divided into face verification and face identification. This study is performed for the face identification task using different deep learning models. In particular, we are studying the pre-trained deep learning models that are trained using transfer learning for face identification tasks. In this study, a custom dataset is used consisting of 65 subjects. The custom dataset is a part of the VGGFace2 dataset. Subject faces are augmented with masks. The dataset doesn't consist the faces without masks. In this study, we are using different popular pre-trained models such as VGG16, InceptionV3, etc. We are re-training those pre-trained models on a custom dataset and analyzing the gathered results. Apart from the pre-trained models, a new model has been proposed for the masked face identification task. © 2021 IEEE.

关键词： Face recognition

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Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems

arXiv

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arXiv 2024年

作者： Zhong, Qihuang Wang, Kang Xu, Ziyang Liu, Juhua Ding, Liang Du, Bo School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China School of Computer Science University of Sydney Sydney2006 Australia

Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks. However, CoT still falls short in dealing with complex math word problems, as it usually suffers from three pitfalls: semantic misunderstanding errors, calculation errors, and step-missing errors. Prior studies involve addressing the calculation errors and step-missing errors, but neglect the semantic misunderstanding errors, which is the major factor limiting the reasoning performance of LLMs. To this end, we propose a simple-yet-effective method, namely Deeply Understanding the Problems (DUP), to improve the LLMs’ math problem-solving ability by addressing semantic misunderstanding errors. The core of our method is to encourage the LLMs to deeply understand the problems and extract the key problem-solving information used for better reasoning. Extensive experiments on 10 diverse reasoning benchmarks show that our DUP method consistently outperforms the other counterparts by a large margin. More encouragingly, DUP achieves a new SOTA result on the GSM8K benchmark, with an accuracy of 97.1% under the zero-shot setting. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Efficient Resource Allocation in Vehicular Networks Using Embedding-Enhanced MAPPO 10

Efficient Resource Allocation in Vehicular Networks Using Em...

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10th IEEE Smart World Congress, SWC 2024

作者： Qiu, Wen Shao, Xun Zhao, Wei Masui, Hiroshi Kitami Institute of Technology Information and Communication Engineering Hokkaido Japan Toyohashi University of Technology Department of Electrical and Electronic Information Engineering Toyohashi Japan Anhui University of Technology School of Computer Science and Technology Anhui China Kitami Institute of Technology Information Processing Center Hokkaido Japan

ISBN: (纸本)9798331520861

With the swift advancement of automotive technology, the need for efficient allocation of resources in V2V (vehicle-to-vehicle) communications has grown significantly. However, the dynamic nature of channels in vehicular networks introduces considerable uncertainty in resource allocation. This research presents a novel approach to addressing these challenges by utilizing multi-agent deep reinforcement learning (MADRL). By leveraging shared resource blocks (RBs) for V2V and vehicle-to-infrastructure (V2I) communications, our approach minimizes interference with V2I links and ensures optimal V2V performance. Unlike traditional methods that select actions sequentially, our approach treats every V2V link as an agent and simultaneously determines all agents' actions to improve coordination and efficiency. To accelerate the training of the multi-agent critic network, we integrate an embedding module to simplify and compress the network input dimensions. Through the use of our embedding multi-agent proximal policy optimization (E-MAPPO) algorithm, the agents collaboratively optimize their transmission strategies to efficiently share spectrum with V2I links. Simulation results show that our method greatly minimizes interference with V2I links while meeting the stringent latency and reliability requirements of V 2 V communications, surpassing baseline approaches. © 2024 IEEE.

关键词： Deep reinforcement learning

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Mobile Based Solution to Weight Loss Planning for Children (with Obesity) in Sri Lanka 3

Mobile Based Solution to Weight Loss Planning for Children (...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Rajapakse, R.M.M.P.K. Mudalige, J.M.A.I. Perera, L.A.D.Y.S. Warakagoda, R.N.A.M.S.C.B. Siriwardana, Samanthi Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Obesity is a condition where there is excess fat in the body, and it is one of the world's most extreme and dangerous dietary diseases. Genetic factors, lack of physical activity, unhealthy eating patterns, or a combination of these factors are the most common causes of obesity. This is important because it influences every part of a child's life. More, in particular, this disorder leads to poor health and negative social standing with perceptions. Nowadays, children are paying keen interest in technology and related devices. Therefore, in this research, we are planning to give a mobile-based solution with a smart band that is used to monitor the child. In this solution, we are mainly focusing on Sri Lankan children with obesity who are aged between 5-10. In our solution, there are four main sections which are, monitoring child activities, recognizing the activities, and getting relevant data, then based on those data and previous activity completion levels, this solution will suggest activities for losing weight, provide specific diet plans for each child considering the health conditions and predict the probability of having main obesity-related diseases by using previously gathered data. © 2021 IEEE.

关键词： Deep learning

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