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A novel quantum visual secret sharing scheme

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arXiv 2023年

作者： Liu, Wenjie Xu, Yinsong Zhang, Maojun Chen, Junxiu Yang, Ching-Nung Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Division Department of Computer Science and Information Engineering National Dong Hwa University Taiwan97441 Taiwan

Inspired by Naor et al.’s visual secret sharing (VSS) scheme, a novel n out of n quantum visual secret sharing (QVSS) scheme is proposed, which consists of two phases: sharing process and recovering process. In the first process, the color information of each pixel from the original secret image is encoded into an n-qubit superposition state by using the strategy of quantum expansion instead of classical pixel expansion, and then these n qubits are distributed as shares to n participants, respectively. During the recovering process, all participants cooperate to collect these n shares of each pixel together, then perform the corresponding measurement on them, and execute the n-qubit XOR operation to recover each pixel of the secret image. The proposed scheme has the advantage of single-pixel parallel processing that is not available in the existing analogous quantum schemes, and perfectly solves the problem that in the classic VSS schemes the recovered image has the loss in resolution. Moreover, its experiment implementation with IBM Q is conducted to demonstrate the practical feasibility. © 2023, CC BY.

关键词： Pixels

All in One Framework for Multimodal Re-identification in the Wild

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arXiv 2024年

作者： Li, He Ye, Mang Zhang, Ming Du, Bo National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Luojia Laboratory Wuhan University Wuhan China Guangzhou Urban Planning Design Survey Research Institute Guangzhou China

In Re-identification (ReID), recent advancements yield noteworthy progress in both unimodal and cross-modal retrieval tasks. However, the challenge persists in developing a unified framework that could effectively handle varying multimodal data, including RGB, infrared, sketches, and textual information. Additionally, the emergence of large-scale models shows promising performance in various vision tasks but the foundation model in ReID is still blank. In response to these challenges, a novel multimodal learning paradigm for ReID is introduced, referred to as All-in-One (AIO), which harnesses a frozen pre-trained big model as an encoder, enabling effective multimodal retrieval without additional fine-tuning. The diverse multimodal data in AIO are seamlessly tokenized into a unified space, allowing the modality-shared frozen encoder to extract identity-consistent features comprehensively across all modalities. Furthermore, a meticulously crafted ensemble of cross-modality heads is designed to guide the learning trajectory. AIO is the first framework to perform all-in-one ReID, encompassing four commonly used modalities. Experiments on cross-modal and multimodal ReID reveal that AIO not only adeptly handles various modal data but also excels in challenging contexts, showcasing exceptional performance in zero-shot and domain generalization scenarios. Code will be available at: https://***/lihe404/AIO. © 2024, CC BY.

关键词： Modal analysis

A Two-Sided Stable Matching Method in Ridesharing

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A Two-Sided Stable Matching Method in Ridesharing

IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

作者： Jingwei Lv Jiannan Hao Shuzhen Yao Huobin Tan School of Computer Science and Engineering Beihang University Beijing China School of Physics Beihang University Beijing China School of Software Beihang University Beijing China

ISBN: (纸本)9781665456579

Many researchers have studied optimization methods for ridesharing. However, the individual interests of passengers and drivers are not considered enough. So we propose a two-sided stable matching method according to the actual preferences of passengers(requesters) and drivers(workers). We also design a pruning algorithm based on Euclidean distance to speed up the matching process. Experiments based on real data show that our method can perform well.

关键词： Cloud computing Roads Optimization methods Euclidean distance Task analysis Intelligent systems Vehicles

SKDLog: Self-Knowledge Distillation-based CNN for Abnormal Log Detection

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SKDLog: Self-Knowledge Distillation-based CNN for Abnormal L...

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Ningning Han Siyang Lu Dongdong Wang Mingquan Wang Xiaoman Tan Xiang Wei School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Computer Science University of Central Florida Orlando FL USA School of Software Engineering Beijing Jiaotong University Beijing China College of Computer and Information Science College of Software Southwest University Chongqing China

System logs are critical to system health diagnosis, especially for big data distributed systems. To detect anomalies in logs, log analysis has become a popular and practical approach. However, with the increasing complexity of system logs in real-world systems, more noisy data are generated. The noise disturbs the regular detection procedure and decreases the detection quality. Hence, it is more difficult to improve detection performance under such uncertain circumstance. To tackle this challenge, we propose SKDLog, a novel and effective log anomaly detection method equipped with self-knowledge distillation. This approach leverages the knowledge from soft labels and refined feature maps through knowledge distillation. Moreover, it helps the detection model better acquire latent information. To better exploit feature maps, an auxiliary self-teacher branch is incorporated into the framework. After the integration, the model achieves performance gain in log anomaly detection. To demonstrate the effectiveness, we compare SKDLog with the state-of-the-art log-based anomaly detection approaches on HDFS and Hadoop Application datasets. Our model outperforms other approaches on both recall and F1-score. Furthermore, we challenge problem solving by performing experiments on an industry dataset and an unstable dataset. The experimental results show that SKDLog more accurately detects abnormal logs from noisy data with high recall and F1-score.

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SLAH: A Siamese Network Vision Tracking Algorithm Using LSTM Adaptive Head

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SLAH: A Siamese Network Vision Tracking Algorithm Using LSTM...

International Conference on Information science and Technology (ICIST)

作者： Zhitao Liu Xiaonan Luo Dong Wang Fang Li School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China College of Mathematics and Informatics College of Software Engineering South China Agricultural Univerrsity Guangzhou China

In the field of single object tracking, conventional methods often rely on correlation filters or visual image processing. However, these approaches typically focus solely on extracting target object features and consider only the position information in the current frame. They lack integration of contextual and spatial-temporal information, which can limit tracking *** method takes inspiration from single object detection and tracking techniques. It combines efficient model-based object detection with the spatial position of the final target in the image. This approach offers significant advantages in reducing tracking drift (deviation from the target object) and improving tracking accuracy (predicting bounding boxes that closely follow the target).To extract spatial information, we utilize a three-layer ResNet and Convolutional Block Attention Module (CBAM) in conjunction with a siamese network. This combination effectively captures the spatial characteristics of the target object. Additionally, we employ an adaptive processing head with an internal structure of Long Short Term Memory (LSTM) to capture temporal information. By integrating spatial and temporal cues, our method achieves more robust and accurate *** comprehensive comparison and ablation experiments across multiple datasets, we have demonstrated notable improvements with our method. It tightly connects the predicted bounding box with the tracking target and effectively combines spatial and temporal information for precise object tracking.

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A Survey of Stance Detection on Social Media: New Directions and Perspectives

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arXiv 2024年

作者： Zhang, Bowen Dai, Genan Niu, Fuqiang Yin, Nan Fan, Xiaomao Wang, Senzhang Cao, Xiaochun Huang, Hu College of Big Data and Internet Shenzhen Technology University Shenzhen518000 China College of Computer Science and Software Shenzhen University Shenzhen518000 China Hong Kong University of Science and Technology 999077 Hong Kong School of Computer Science and Engineering Central South University Changsha410083 China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China Peking University Shenzhen Graduate School Shenzhen518000 China

In modern digital environments, users frequently express opinions on contentious topics, providing a wealth of information on prevailing attitudes. The systematic analysis of these opinions offers valuable insights for decision-making in various sectors, including marketing and politics. As a result, stance detection has emerged as a crucial subfield within affective computing, enabling the automatic detection of user stances in social media conversations and providing a nuanced understanding of public sentiment on complex issues. Recent years have seen a surge of research interest in developing effective stance detection methods, with contributions from multiple communities, including natural language processing, web science, and social computing. This paper provides a comprehensive survey of stance detection techniques on social media, covering task definitions, datasets, approaches, and future works. We review traditional stance detection models, as well as state-of-the-art methods based on large language models, and discuss their strengths and limitations. Our survey highlights the importance of stance detection in understanding public opinion and sentiment, and identifies gaps in current research. We conclude by outlining potential future directions for stance detection on social media, including the need for more robust and generalizable models, and the importance of addressing emerging challenges such as multi-modal stance detection and stance detection in low-resource languages. Copyright © 2024, The Authors. All rights reserved.

关键词： Social networking (online)

Augmented V2V Radio Fingerprinting With Heterogeneous Federated Scattering Network for Autonomous Vehicle Road Cooperation

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Tiantian Xu, Dongyang Ma, Jing Alharbi, Abdullah Yu, Keping Guizani, Mohsen Leung, Victor C.M Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China Xi'an Jiaotong University School of Information and Communication Engineering Xi'an710049 China Xidian University State Key Laboratory of Integrated Services Networks China King Saud University Computer Science Department Community College Riyadh11437 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan 999041 United Arab Emirates Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen528060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Augmented Internet of vehicle (IoV) network, providing natural infrastructure support for autonomous vehicle road cooperation (VRC), has been deemed as one of key enablers for autonomous VRC while simultaneously facing the challenge of impersonation, tampering or replay attack towards the high layer digital certificate for critical information. In this paper, we propose a heterogeneous scattering fingerprinting network (HSFNet) which amalgamates wavelet scattering and deep learning to achieve lightweight, rapid and reliable physical layer authentication by radio frequency fingerprinting (RFF) which employs the unique inherent hardware impairments within for vehicle-to everything (V2X) communication. Particularly, we first exploit a wavelet scattering network to extract RFF coefficients and eliminate redundancies within heterogeneous datasets. The establishment of a high-information-density datasets facilitates the efficient processing of extensive heterogeneous data, especially for resource-limited vehicles nodes. To improve the interpretability, we combine the multi-scale coefficients of input waveform and shapley values, formulating a complete interpretable framework and completing parameters tuning. Finally, considering heterogeneous applications in practical scenarios, we design a distributed federated learning framework which not only completes distributed training but also protects privacy with the resources-constrained intelligent IoV nodes. Experimental results demonstrate that the proposed scheme maintains excellent performance while utilizing only about 25.7% of original samples, significantly improving identification accuracy. Furthermore, the identification accuracy of federated learning ultimately converges to 98.1%. © 1967-2012 IEEE.

关键词： Deep learning

ISLS: IoT-Based Smart Lighting System for Improving Energy Conservation in Office Buildings

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arXiv 2025年

作者： Obioma, Peace Agbodike, Obinna Chen, Jenhui Wang, Lei Department of Computer Science University of Manchester M139PL United Kingdom Center for Artificial Intelligence in Medicine Chang Gung Memorial Hospital Guishan Dist Taoyuan33375 Taiwan iTrustal Department of Computer Science and Information Engineering Chang Gung University Guishan Dist. Taoyuan33302 Taiwan Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology China

With the Internet of Things (IoT) fostering seamless device-to-human and device-to-device interactions, the domain of intelligent lighting systems have evolved beyond simple occupancy and daylight sensing towards autonomous monitoring and control of power consumption and illuminance levels. To this regard, this paper proposes a new do-it-yourself (DIY) IoT-based method of smart lighting system featuring an illuminance control algorithm. The design involves the integration of occupancy and presence sensors alongside a communication module, to enable real-time wireless interaction and remote monitoring of the system parameters from any location through an end-user application. A constrained optimization problem was formulated to determine the optimal dimming vector for achieving target illuminance at minimal power consumption. The simplex algorithm was used to solve this problem, and the system’s performance was validated through both MATLAB simulations and real-world prototype testing in an indoor office environment. The obtained experimental results demonstrate substantial power savings across multiple user occupancy scenarios, achieving reductions of approx. 80%, 48%, and 26% for one, two, and four user settings, respectively, in comparison to traditional basic lighting installation systems. © 2025, CC BY.

关键词： Constrained optimization

Deep Edge Intelligence: Architecture, Key Features, Enabling Technologies and Challenges

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arXiv 2022年

作者： Abeysekara, Prabath Dong, Hai Qin, A.K. School of Computing Technologies RMIT University Melbourne Australia Computer Science and Software Engineering Swinburne University of Technology Hawthorn Australia

With the breakthroughs in Deep Learning, recent years have witnessed a massive surge in Artificial Intelligence applications and services. Meanwhile, the rapid advances in Mobile Computing and Internet of Things has also given rise to billions of mobile and smart sensing devices connected to the Internet, generating zettabytes of data at the network edge. The opportunity to combine these two domains of technologies to power interconnected devices with intelligence is likely to pave the way for a new wave of technology revolutions. Embracing this technology revolution, in this article, we present a novel computing vision named Deep Edge Intelligence (DEI). DEI employs Deep Learning, Artificial Intelligence, Cloud and Edge Computing, 5G/6G networks, Internet of Things, Microservices, etc. aiming to provision reliable and secure intelligence services to every person and organisation at any place with better user experience. The vision, system architecture, key layers and features of DEI are also detailed. Finally, we reveal the key enabling technologies and research challenges associated with it. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things