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Encoding Dual Semantic Knowledge for Text-Enhanced Cloud Services 10th

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Encoding Dual Semantic Knowledge for Text-Enhanced Cloud Ser...

10th EAI International Conference on Cloud computing, CloudComp 2020

作者： Cui, Shicheng Li, Qianmu Chen, Shu-Ching Hou, Jun Zhang, Hanrui Meng, Shunmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Intelligent Manufacturing Department Wuyi University Jiangmen529020 China School of Computing and Information Sciences Florida International University MiamiFL33199 United States Nanjing Vocational University of Industry Technology Nanjing210023 China

ISBN: (纸本)9783030699918

Topic modeling techniques have been widely applied in many cloud computing applications. However, few of them have tried to discover latent semantic relationships of implicit topics and explicit words to generate a more comprehensive representation for each text. To fully exploit the semantic knowledge for text classification in cloud computing systems, we attempt to encode topic and word features based on their latent relationships. The extracted topical information reorganizes the original textual structures from two aspects: one is that the topic extracted by Latent Dirichlet Allocation (LDA) is viewed as a textual extension;the other is that the topic feature performs as a counterpart modality to the word. This paper proposes a Dual Semantic Embedding (DSE) method, which uses Convolutional Neural Networks (CNNs) to encode the dual semantic features of topics and words from the reorganized semantic structures. Experimental results show that DSE improves the performance of text classification and outperforms the state-of-the-art feature generation baselines on micro- F1 and macro- F1 scores over the real-world text classification datasets. © 2021, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Network coding

Two Enhanced-rate Power Allocation Strategies for Active IRS-assisted Wireless Network

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arXiv 2023年

作者： Cheng, Qiankun Dong, Rongen Cai, Wenlong Liu, Ruiqi Shu, Feng Wang, Jiangzhou The School of Information and Communication Engineering Hainan University Haikou570228 China The School of Information and Communication Engineering Collaborative Innovation Center of Information Technology Hainan University Haikou570228 China The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China The Wireless and Computing Research Institute ZTE Corporation Beijing100029 China The School of Engineering University of Kent CanterburyCT2 7NT United Kingdom

Due to its ability of overcoming the impact of double-fading effect, active intelligent reflecting surface (IRS) has attracted a lot of attention. Unlike passive IRS, active IRS should be supplied by power, thus adjusting power between base station (BS) and IRS having a direct impact on the system rate performance. In this paper, the active IRS-aided network under a total power constraint is modeled with an ability of adjusting power between BS and IRS. Given the transmit beamforming at BS and reflecting beamforming at IRS, the SNR expression is derived to be a function of power allocation (PA) factor, and the optimization of maximizing the SNR is given. Subsequently, two high-performance PA strategies, enhanced multiple random initialization Newton's (EMRIN) and Taylor polynomial approximation (TPA), are proposed. The former is to improve the rate performance of classic Netwon's method to avoid involving a local optimal point by using multiple random initializations. To reduce its high computational complexity, the latter provides a closed-form solution by making use of the first-order Taylor polynomial approximation to the original SNR function. Actually, using TPA, the original optimization problem is transformed into a problem of finding a root for a third-order polynomial. Simulation results are as follows: the first-order TPA of SNR fit its exact expression well, the proposed two PA methods performs much better than fixed PA in accordance with rate, and appoaches exhaustive search as the number of IRS reflecting elements goes to large-scale. Copyright © 2023, The Authors. All rights reserved.

关键词： Polynomial approximation

Privacy Protection Multi-copy Provable Data Possession Supporting Data Reliability 5th

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Privacy Protection Multi-copy Provable Data Possession Suppo...

5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： Zhang, Feng Wen, Bin Yan, Yifei Zeng, Zhaowu Zhou, Wei School of Information Science and Technology Hainan Normal University Haikou571158 China Cloud Computing and Big Data Research Center Hainan Normal University Haikou571158 China Key Laboratory of Data Science and Smart Education Ministry of Education Hainan Normal University Haikou571158 China

ISBN: (纸本)9789819981007

With the development of technology, a massive amount of data continues to emerge, and there is still a strong demand for data storage. Outsourcing data to cloud service providers has become a common practice. Although cloud storage offers the advantage of location-independent data access and reduces burdens such as hardware and software maintenance, this service still faces some challenges in terms of privacy and data reliability. To address this issue, we present Privacy-preserving Multi-copy Provable Data Possession supporting data Reliability (PMPDP-R). Our proposal ensures the integrity, reliability, and freshness of data by designing Timestamp-based Double-layer Merkle Hash Tree (TD-MHT), a novel dynamic structure for multicopy storage. Furthermore, our approach uses random masking techniques to develop unique replica blocks and safeguard privacy. We’ve also ensured data reliability by enabling PMPDP-R to locate damaged data blocks to aid recovery. The experimental analysis proves that our scheme is both practical and effective. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud storage

Identification of Stochastic Gravitational Wave Backgrounds from Cosmic String Using Machine Learning

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arXiv 2025年

作者： Ma, Xianghe Wang, Borui Yang, Nan Li, Jin McCane, Brendan Sun, Mengfei Wu, Jie Zhang, Minghui Meng, Yan Department of Physics Chongqing University Chongqing401331 China Chongqing Key Laboratory for Strongly Coupled Physics Chongqing University Chongqing401331 China Department of Earth and Sciences Southern University of Science and Technology Shenzhen518055 China Department of Electronical Information Science and Technology Xingtai University Xingtai054001 China School of Computing University of Otago Otago9016 New Zealand Department of Physics Southern University of Science and Technology Shenzhen518055 China Department of Physics Xingtai University Xingtai054001 China

Cosmic strings play a crucial role in enhancing our understanding of the fundamental structure and evolution of the universe, unifying our knowledge of cosmology, and potentially unveiling new physical laws and phenomena. The advent and operation of space-based detectors provide an important opportunity for detecting stochastic gravitational wave backgrounds (SGWB) generated by cosmic strings. However, the intricate nature of SGWB poses a formidable challenge in distinguishing its signal from the complex noise by some traditional methods. Therefore, we attempt to identify SGWB based on machine learning. Our findings show that the joint detection of LISA and Taiji significantly outperforms individual detectors, and even in the presence of numerous low signal-to-noise ratio(SNR) signals, the identification accuracy remains exceptionally high with 95%. Although our discussion is based solely on simulated data, the relevant methods can provide data-driven analytical capabilities for future observations of SGWB. Copyright © 2025, The Authors. All rights reserved.

关键词： Gravitational effects

PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust 3D Human Mesh Recovery

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PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust ...

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wendi Yang Zihang Jiang Shang Zhao S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC Suzhou Jiangsu China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

With the recent advancements in single-image-based 3D human pose and shape estimation (3DHPSE), there is a growing amount of works that can achieve good results on standard benchmarks but struggle to yield accurate hu-man mesh in extreme scenarios like occlusion. Previous works propose to leverage 2D poses to help 3D HPSE model improve performance under occlusion, but usually rely on manual design to integrate 2D poses and only aim for specific kinds of occlusion. In this paper, we present PostoMETRO (Pose token enhanced MEsh TRansfOrmer), which integrates 2D pose prior knowledge as tokens into transformers to improve model's performance under occlusion. Using a VQ- VAE-based pose tokenizer; we efficiently represent 2D poses as tokens andfeed them to transformers together with image tokens. Subsequently, these tokens are queried by vertex andJoint tokens to decode 3D coordinates of mesh vertices and human Joints. Our proposed 2D poses integration strategy is manual-design-free and suitable for various kinds of occlusion. Experiments on both standard and occlusion-specific benchmarks demonstrate the effectiveness of PostoMETRO. Code will be made available 1 1 https://***/PostoMETRO/PostoMETRO-Paper.

关键词： Solid modeling Computer vision Three-dimensional displays Shape Estimation Manuals Benchmark testing Transformers Robustness Standards

Exploring Metamorphic Testing for Fake-News Detection Software: A Case Study

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Exploring Metamorphic Testing for Fake-News Detection Softwa...

IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Lin Miao Dave Towey Yingrui Ma Tsong Yueh Chen Zhi Quan Zhou Shanghai Branch Jiangsu Automation Research Institute Shanghai People’s Republic of China School of Computer Science University of Nottingham Ningbo China Zhejiang People’s Republic of China Department of Computer Science and Software Engineering Swinburne University of Technology Hawthorn VIC Australia School of Computing and Information Technology University of Wollongong Wollongong NSW Australia

Concerns have been growing over fake news and its impact. Software that can automatically detect fake news is becoming more popular. However, the accuracy and reliability of such fake-news detection software remains questionable, partly due to a lack of testing and verification. Testing this kind of software may face the oracle problem, which refers to difficulty (or inability) of identifying the correctness of the software’s output in a reasonable amount of time. Metamorphic testing (MT) has a record of effectively alleviating the oracle problem, and has been successfully applied to testing fake-news detection software. This paper reports on a study, extending previous work, exploring the use of MT for fake-news detection software. The study includes new metamorphic relations and additional experimental results and analysis. Some alternative MR-generation approaches are also explored. The study targets software where the output is a real/fake news decision, enhancing the applicability of MT to current fake-news detection software. The paper also explores the impact of the prediction accuracy of the fake-news detection software on the MT process. The study demonstrates the validity and applicability of MT to fake-news detection software. The prediction accuracy of the software has a greater impact on MT experiments with greater changes between the source and follow-up inputs, and less dependence on prediction stability. Some possible factors affecting the experimental results are discussed, and directions for future work are provided.

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Towards Communication-Efficient Adversarial Federated Learning for Robust Edge Intelligence

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arXiv 2025年

作者： Qiao, Yu Adhikary, Apurba Le, Huy Q. Huh, Eui-Nam Han, Zhu Hong, Choong Seon School of Computing Kyung Hee University Yongin-si17104 Korea Republic of Department of Computer Science and Engineering School of Computing Kyung Hee University Yongin-si17104 Korea Republic of Department of Information and Communication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

Federated learning (FL) has gained significant attention for enabling decentralized training on edge networks without exposing raw data. However, FL models remain susceptible to adversarial attacks and performance degradation in non-IID data settings, thus posing challenges to both robustness and accuracy. This paper aims to achieve communication-efficient adversarial federated learning (AFL) by leveraging a pre-trained model to enhance both robustness and accuracy under adversarial attacks and non-IID challenges in AFL. By leveraging the concise knowledge embedded in the class probabilities from a pre-trained model for both clean and adversarial images, we propose a pre-trained model-guided adversarial federated learning (PM-AFL) framework. This framework integrates vanilla and adversarial mixture knowledge distillation to effectively balance accuracy and robustness while promoting local models to learn from diverse data. Specifically, for clean accuracy, we adopt a dual distillation strategy where the class probabilities of randomly paired images, and their blended versions are aligned between the teacher model and the local models. For adversarial robustness, we employ a similar distillation approach but replace clean samples on the local side with adversarial examples. Moreover, by considering the bias between local and global models, we also incorporate a consistency regularization term to ensure that local adversarial predictions stay aligned with their corresponding global clean ones. These strategies collectively enable local models to absorb diverse knowledge from the teacher model while maintaining close alignment with the global model, thereby mitigating overfitting to local optima and enhancing the generalization of the global model. Experiments demonstrate that the PM-AFL-based framework not only significantly outperforms other methods but also maintains communication efficiency. © 2025, CC BY-NC-ND.

关键词： Federated learning

Scalable Constant-Time Logical Gates for Large-Scale Quantum Computation Using Window-Based Correlated Decoding

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arXiv 2024年

作者： Zhang, Jiaxuan Chen, Zhao-Yun Li, Jia-Ning Wei, Tian-Hao Liu, Huan-Yu Zhuang, Xi-Ning Li, Qing-Song Wu, Yu-Chun Guo, Guo-Ping Key Laboratory of Quantum Information Chinese Academy of Sciences School of Physics University of Science and Technology of China Anhui Hefei230026 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China Origin Quantum Computing Hefei Anhui230026 China

Large-scale quantum computation requires to be performed in the fault-tolerant manner. One crucial challenge of fault-tolerant quantum computing (FTQC) is reducing the overhead of implementing logical gates. Recently work proposed correlated decoding and "algorithmic fault tolerance" to achieve constant-time logical gates that enables universal quantum computation. However, for circuits involving mid-circuit measurements and feedback, the previous scheme for constant-time logical gates is incompatible with window-based decoding, which is a scalable approach for handling large-scale circuits. In this work, we propose an architecture that employs delayed fixup circuits and window-based correlated decoding, realizing scalable constant-time logical gates. This design significantly reduces both the frequency and duration of decoding, while maintaining support for constant-time and universal logical gates across a broad class of quantum codes. More importantly, by spatial parallelism of windows, this architecture well adapts to time-optimal FTQC, making it particularly useful for large-scale quantum computation. Using Shor’s algorithm as an example, we explore the application of our architecture and reveals the promising potential of using constant-time logical gates to perform large-scale quantum computation with acceptable overhead on physical systems like ion traps. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

FDRL Approach for Association and Resource Allocation in Multi-UAV Air-To-Ground IoMT Network

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TechRxiv

TechRxiv 2022年

作者： Mohammed, Abegaz Erbad, Aiman Nahom, Hayla Albaseer, Abdullatif Abdallah, Mohammed Guizani, Mohsen Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Abu Dhabi United Arab Emirates

In 6G networks, unmanned aerial vehicles (UAVs) can serve as aerial flying base stations (AFBS) with aerial mobile edge computing (AMEC) server capabilities. AFBS is an increasingly popular solution for delivering time-sensitive applications, extending network coverage, and assisting ground base stations in the healthcare systems for remote areas with limited infrastructure. Furthermore, the UAVs are deployed in the healthcare system to support the Internet of medical things (IoMT) devices in data collection, medical equipment distribution, and providing smart services. However, ensuring the privacy and security of patients’ data with the limited UAV resources is a major challenge. In this paper, we present a federated deep reinforcement learning framework for resource allocation in UAV-enabled healthcare systems, where IoMT devices send their trained model parameters without transmitting sensitive raw data to the AMEC server. In the proposed framework, the IoMT device is associated with AFBS based on the quality of the data and its demand in order to maximize learning efficiency and accuracy. This work aims to minimize the computation costs of the IoMT devices while considering UAV resources and the fairness of UAV coverage. Simulation results prove that our proposed algorithm outperforms other baseline algorithms in learning accuracy and computational cost. © 2022, CC BY.

关键词： Resource allocation