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Cost-efficient and skew-aware data scheduling for incremental learning in 5G network

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arXiv 2020年

作者： Pu, Lingjun Yuan, Xinjing Xu, Xiaohang Chen, Xu Zhou, Pan Xu, Jingdong The College of Computer Science Nankai University Tianjin300071 China The State Key Laboratory of Integrated Services Networks Xidian University Xi’an710126 China The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The Pazhou Lab. Guangzhou510335 China The Hubei Engineering Research Center on Big Data Security The School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

To facilitate the emerging applications in 5G networks, mobile network operators will provide many network functions in terms of control and prediction. Recently, they have recognized the power of machine learning (ML) and started to explore its potential to facilitate those network functions. Nevertheless, the current ML models for network functions are often derived in an offline manner, which is inefficient due to the excessive overhead for transmitting a huge volume of dataset to remote ML training clouds and failing to provide the incremental learning capability for the continuous model updating. As an alternative solution, we propose Cocktail, an incremental learning framework within a reference 5G network architecture. To achieve cost efficiency while increasing trained model accuracy, an efficient online data scheduling policy is essential. To this end, we formulate an online data scheduling problem to optimize the framework cost while alleviating the data skew issue caused by the capacity heterogeneity of training workers from the long-term perspective. We exploit the stochastic gradient descent to devise an online asymptotically optimal algorithm, including two optimal policies based on novel graph constructions for skew-aware data collection and data training. Small-scale testbed and large-scale simulations validate the superior performance of our proposed framework. © 2020, CC BY.

关键词： 5G mobile communication systems

Peripheral-free device pairing by randomly switching power

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arXiv 2020年

作者： Shao, Zhijian Weng, Jian Zhang, Yue Wu, Yongdong Li, Ming Weng, Jiasi Luo, Weiqi Yu, Shui College of Informatin Science and Technology College of Cyber security National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Jinan University Guangzhou510632 China University of Technology Sydney UltimoNSW Australia

The popularity of Internet-of-Things (IoT) comes with security concerns. Attacks against wireless communication venues of IoT (e.g., Man-in-the-Middle attacks) have grown at an alarming rate over the past decade. Pairing, which allows the establishment of the secure communicating channels for IoT devices without a prior relationship, is thus a paramount capability. Existing secure pairing protocols require auxiliary equipment/peripheral (e.g., displays, speakers and sensors) to achieve authentication, which is unacceptable for low-priced devices such as smart lamps. This paper studies how to design a peripheral-free secure pairing protocol. Concretely, we design the protocol, termed SwitchPairing, via out-of-box power supplying chargers and on-board clocks, achieving security and economics at the same time. When a user wants to pair two or more devices, he/she connects the pairing devices to the same power source, and presses/releases the switch on/off button several times. Then, the press and release timing can be used to derive symmetric keys. We implement a prototype via two CC2640R2F development boards from Texas Instruments (TI) due to its prevalence. Extensive experiments and user studies are also conducted to benchmark our protocol in terms of efficiency and security. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

MorLog: Morphable Hardware Logging for Atomic Persistence in Non-Volatile Main Memory

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MorLog: Morphable Hardware Logging for Atomic Persistence in...

Annual International Symposium on Computer Architecture, ISCA

作者： Xueliang Wei Dan Feng Wei Tong Jingning Liu Liuqing Ye Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology (School of Computer Science and Technology Huazhong University of Science and Technology) Ministry of Education of China

ISBN: (数字)9781728146614

ISBN: (纸本)9781728146621

Byte-addressable non-volatile memory (NVM) is emerging as an alternative for main memory. Non-volatile main memory (NVMM) systems are required to support atomic persistence and deal with the high overhead of programming NVM cells. To this end, recent studies propose hardware logging and data encoding designs for NVMM systems. However, prior hardware logging designs incur either extra ordering constraints or redundant log data. Moreover, existing data encoding designs are unaware of the characteristics of log data, resulting in writing unnecessary log *** this paper, we propose a morphable hardware logging design (MorLog) that only logs the data necessary for recovery and dynamically selects encoding methods with least write overhead. We observe that (1) only the oldest undo and the newest redo data in each transaction are necessary for recovery, and (2) the log data for clean bits are clean. The first motivates our morphable logging mechanism. This mechanism logs both undo and redo data for the first update to the data in a transaction, and then logs only redo data. Undo data are eagerly written to NVMM to ensure atomicity, while redo data are buffered in a volatile log buffer and L1 caches to write only the newest redo data to NVMM. The second motivates our selective log data encoding mechanism. This mechanism simultaneously encodes log data with different methods, and writes the encoded log data with the least write cost to NVMM. We devise a differential log data compression method to exploit the characteristics of log data. This method directly discards clean bits from log data and compresses remained dirty bits. Our evaluation shows that MorLog improves performance by 72.5%, reduces NVMM write traffic by 41.1%, and decreases NVMM write energy by 49.9% compared with the state-of-the-art design.

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

A Competitive Swarm Algorithm for Image Segmentation Guided by Opposite Fuzzy Entropy

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A Competitive Swarm Algorithm for Image Segmentation Guided ...

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Mohamed Abd Elaziz Ahmed A. Ewees Dalia Yousri Diego Oliva Songfeng Lu Erik Cuevas Hubei Engineering Research Center on Big Data Security School of cyber science and Engineering Huazhong university of Science and Technology Wuhan China Department of Computer Damietta University Egypt Department of Electrical Engineering Fayoum University Fayoum Egypt Depto. de Ciencias Computacionales Universidad de Guadalajara CUCEI Guadalajara Mexico Depto. de Ciencias Computacionales Universidad de Guadalajara CUCEI Guadalajara Mexico

ISBN: (数字)9781728169323

ISBN: (纸本)9781728169330

This paper proposes an alternative multilevel thresholding (MLT) image segmentation method by improving the behavior of the grasshopper optimization algorithm (GOA). This is achieved by using the operators of the sine-cosine algorithm (SCA) to work in a competitive manner with the operators of traditional GOA. This will lead to enhance the quality of the solutions during the updating process that will affect the convergence of the proposed GOASCA towards the global solution. In addition, the proposed GOASCA aims to minimize the difference between the fuzzy entropy and its opposite fuzzy entropy that is used as a fitness function to evaluate the quality of the solution. This objective function gives the GOASCA to explore the whole search space. To assess the quality of the obtained threshold values by GOASCA, a set of eight images are used which have different characteristics. Moreover, the results of GOASCA are compared with a set of well-known MLT image segmentation approaches, and these results have shown the high quality of GOASCA to segmented the image, as well as, shown that the current objective function provides results better than the traditional fuzzy entropy in terms of the performance measures of image segmentation.

关键词： Image segmentation Entropy Optimization Linear programming Fuzzy sets Thresholding (Imaging) Histograms

Secure Physical Layer Communications for Low-Altitude Economy Networking: A Survey

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arXiv 2025年

作者： Cai, Lingyi Wang, Jiacheng Zhang, Ruichen Zhang, Yu Jiang, Tao Niyato, Dusit Wang, Xianbin Jamalipour, Abbas Shen, Xuemin Research Center of 6G Mobile Communications School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada School of Electrical and Computer Engineering University of Sydney Australia Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

The Low-Altitude Economy Networking (LAENet) is emerging as a transformative paradigm that enables an integrated and sophisticated communication infrastructure to support aerial vehicles in carrying out a wide range of economic activities within low-altitude airspace. However, the physical layer communications in the LAENet face growing security threats due to inherent characteristics of aerial communication environments, such as signal broadcast nature and channel openness. These challenges highlight the urgent need for safeguarding communication confidentiality, availability, and integrity. In view of the above, this survey comprehensively reviews existing secure countermeasures for physical layer communication in the LAENet. We explore core methods focusing on anti-eavesdropping and authentication for ensuring communication confidentiality. Subsequently, availability-enhancing techniques are thoroughly discussed for anti-jamming and spoofing defense. Then, we review approaches for safeguarding integrity through anomaly detection and injection protection. Furthermore, we discuss future research directions, emphasizing energy-efficient physical layer security, multi-drone collaboration for secure communication, AI-driven security defense strategy, space-air-ground integrated security architecture, and 6G-enabled secure UAV communication. This survey may provide valuable references and new insights for researchers in the field of secure physical layer communication for the LAENet. Copyright © 2025, The Authors. All rights reserved.

关键词： Aircraft communication

Identity-aware attribute recognition via real-time distributed inference in mobile edge clouds

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arXiv 2020年

作者： Xu, Zichuan Zhou, Pan Wu, Jiangkai Ren, Jiankang Xia, Qiufen Liang, Huizhi Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Software Dalian University of Technology Dalian China School of Computer Science Dalian University of Technology Dalian China Key Laboratory for Ubiquitous Network Service Software of Liaoning Province International School of Information Science and Engineering Dalian University of Technology Dalian China Department of Computer Science University of Reading Reading United Kingdom

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing

BeLDPC: Bit Errors Aware Adaptive Rate LDPC Codes for 3D TLC NAND Flash Memory

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BeLDPC: Bit Errors Aware Adaptive Rate LDPC Codes for 3D TLC...

Design, Automation and Test in Europe Conference and Exhibition

作者： Meng Zhang Fei Wu Qin Yu Weihua Liu Lanlan Cui Yahui Zhao Changsheng Xie Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of data storage systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783981926347

ISBN: (纸本)9781728144689

Three-dimensional (3D) NAND flash memory has high capacity and cell storage density by using the multi-bit technology and vertical stack architecture, but degrading data reliability due to high raw bit error rates (RBER) caused by program/erase (P/E) cycles and retention periods. Low-density parity-check (LDPC) codes become more popular error-correcting technologies to improve data reliability due to strong error correction capability, but introducing more decoding iterations at higher RBER. To reduce decoding iterations, this paper proposes BeLDPC: bit errors aware adaptive rate LDPC codes for 3D triple-level cell (TLC) NAND flash memory. Firstly, bit error characteristics in 3D charge trap TLC NAND flash memory are studied on a real FPGA testing platform, including asymmetric bit flipping and temporal locality of bit errors. Then, based on these characteristics, a high-efficiency LDPC code is designed. Experimental results show BeLDPC can reduce decoding iterations under different P/E cycles and retention periods.

关键词： Iterative decoding Three-dimensional displays Error analysis Reliability Encoding Computer architecture

SEGIVE: A Practical Framework of Secure GPU Execution in Virtualization Environment

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SEGIVE: A Practical Framework of Secure GPU Execution in Vir...

IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Ziyang Wang Fangyu Zheng Jingqiang Lin Guang Fan Jiankuo Dong State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Data Assurance and Communication Security Research Center CAS Beijing China School of Cyber Security University of Science and Technology of China Hefei School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728198293

ISBN: (纸本)9781728198309

With the advancement of processor technology, general-purpose GPUs have become popular parallel computing accelerators in the cloud. However, designed for graphics rendering and high-performance computing, GPUs are born without sound security mechanisms. Consequently, the GPU-based service in the cloud is vulnerable to attacks from the potentially compromised guest OS as large amounts of sensitive code and data are offloaded directly to the unprotected *** this paper, we propose SEGIVE, a practical framework of secure GPU execution in the virtualization environment, which protects offloaded device code and data from disclosure or tampering by malicious guest OSes through the full life cycle of security-critical GPU applications. First, SEGIVE secures all the traffic transferred to GPUs with Intel SGX technology, including the users' sensitive data and GPU binaries. Second, with various memory isolation mechanisms, SEGIVE enhances security in multi-user execution scenarios by sharing a GPU among multiple workloads, which avoids underutilization of device resources. Besides, SEGIVE requires no modifications to application source codes, the GPU architecture, or I/O interconnection to fulfill security principles, and thus almost all prevailing GPU-based applications can easily benefit from SEGIVE with little porting effort. We have implemented SEGIVE with KVM-QEMU on off-the-shelf NVIDIA GPUs and CPUs. Evaluation results show that with security-enhances, the performance of SEGIVE prototype is still competitive to the native execution on compute-intensive applications, especially for the public-key cryptography algorithm.

关键词： Cloud computing Graphics processing units Computer architecture Public key cryptography Software security Virtualization