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11th KES International Conference on Innovation in Medicine and Healthcare, InMed 2023

作者： Yuan, Yiyi Li, Jingbing Liu, Jing Bhatti, Uzair Aslam Chen, Yen-Wei School of Information and Communication Engineering Hainan University Haikou570228 China Research Center for Healthcare Data Science Zhejiang Lab Zhejiang Hangzhou China Graduate School of Information Science and Engineering Ritsumeikan University Shiga Kusatsu525-8577 Japan

ISBN: (纸本)9789819933105

With the development of digital medical imaging technology, the transmission of medical data in the network is becoming more and more frequent. In order to protect patients’ privacy information during transmission, a robust medical watermarking algorithm based on multilevel discrete wavelet transform (M-DWT), Daisy descriptor and discrete cosine transform (DCT) are proposed. The algorithm first performs a three-level DWT transform on the medical image, takes the center point of its low-frequency part LL3 sub-band as the center pixel point of Daisy descriptor, calculates the Daisy descriptor matrix of this point, and performs DCT transformation on it to obtain a 32-bit binary feature vector of the medical image. Then, we associate the feature vector with the watermark to complete the embedding and extraction of the medical watermark. In addition, in order to enhance the security of the watermark information without destroying the medical data information, the watermark is encrypted using the logical mapping, and the zero watermark technology is also adopted. The simulation results show that the proposed algorithm has strong robustness against traditional attacks and geometric attacks. In particular, the algorithm performs better under Gaussian noise attacks and rotation attacks. It also better balances the conflict between the robustness and invisibility of the image watermarking algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Discrete cosine transforms

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Robust Zero Watermarking Algorithm for Medical Images Based on BRISK-FREAK and DCT 11th

Robust Zero Watermarking Algorithm for Medical Images Based ...

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11th KES International Conference on Innovation in Medicine and Healthcare, InMed 2023

作者： Dong, Fangchun Li, Jingbing Liu, Jing Chen, Yen-Wei School of Information and Communication Engineering Hainan University Haikou570228 China Research Center for Healthcare Data Science Zhejiang Lab Zhejiang Hangzhou311121 China Graduate School of Information Science and Engineering Ritsumeikan University Shiga525-8577 Japan

ISBN: (纸本)9789819933105

With the development of mobile Internet technology, the technology in the field of medical image processing is constantly updated and iterated. Digital watermarking technology plays an important role in the field of medical image processing, providing guarantee for the security of medical image information. Aiming at the poor robustness of medical image watermarking algorithms to geometric attacks, a robust zero-watermarking algorithm for medical images based on BRISK-FREAK and discrete cosine transform (DCT) is proposed. First, Binary Robust Invariant Scalable Kepoints (BRISK) was applied to extract the feature points of the medical image. Then, Fast Retina Keypoint (FREAK) was used to describe the extracted feature points and generate a feature descriptor matrix. Further, DCT was performed on the FREAK descriptor matrix, and the low-frequency part was processed via the perceptual hash algorithm to obtain a 32-bit binary feature vector. In order to enhance the security of the watermark information, the logistic mapping algorithm is utilized to encrypt the watermark before embedding the watermark. Finally, with the help of the zero watermarking technology, the proposed algorithm can be embedded and extract the watermark without changing any of the medical image information. Experimental results show that the algorithm has good robustness under some conventional attacks and geometric attacks. Especially under geometric attacks, the performance of the algorithm is excellent. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Discrete cosine transforms

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Machine learning-enabled MIMO-FBMC communication channel parameter estimation in IIoT: A distributed CS approach

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Digital Communications and Networks 2023年第2期9卷 306-312页

作者： Han Wang Fida Hussain Memon Xianpeng Wang Xingwang Li Ning Zhao Kapal Dev College of Physical Science&Engineering Yichun UniversityYichun336000China State Key Laboratoty of Marine Resource Utilization in South China Sea Hainan UniversityHaikou570228China Faculty of Data Science City University of MacaoMacao999078China Physics and Electronic Information Engineering Henan Polytechnic UniversityJiaozuo454000China Department of Mechatronics AMM Lab Jeju National UniversitySouth Korea Department of Electrical Engineering Sukkur IBA UniversityPakistan Department of Institute of Intellligent Systems University of JohannesburgSouth Africa

Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning *** mainly emphasizes the sparsity of the *** this paper,we study an ML-based CS Channel Estimation(CE)method for wireless communications,which plays an important role in Industrial Internet of Things(IIoT)*** the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation(MIMO-FBMC/OQAM)systems,a Distributed Compressed Sensing(DCS)-based CE approach is studied.A distributed sparse adaptive weak selection threshold method is proposed for ***,the correlation between MIMO channels is utilized to represent a joint sparse model,and CE is transformed into a joint sparse signal reconstruction ***,the number of correlation atoms for inner product operation is optimized by weak selection threshold,and sparse signal reconstruction is realized by sparse *** experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit(OMP)method and other traditional DCS methods in the time-frequency dual selective channels.

关键词： IIoT Machine learning Distributed compressed sensing MIMO-FBMC Channel estimation

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An enhanced genetic algorithm for computation task offloading in MEC scenario

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International Journal of Wireless and Mobile Computing 2023年第2期25卷 118-127页

作者： Zhao, Jiacheng Li, Wenzao Liu, Hantao Yu, Peizhen Li, Hanyun Wen, Zhan School of Communication Engineering Chengdu University of Information Technology Sichuan Chengdu China Chengdu University of Information Technology Sichuan Chengdu China Network and Data Security Key Lab of Sichuan Pro. University of Electronic Science and Technology of China Sichuan Chengdu China Educational Informationisation and Big Data Centre Education Department of Sichuan Province Sichuan Chengdu China

The explosive growth of Internet of Things (IoT) and 5G communication technologies has driven the increasing computing demands for wireless devices. Mobile edge computing in the 5G scenario is a promising solution for energy-efficient and low latency applications. However, due to limited bandwidth, the selection of appropriate computing tasks greatly affects the user experience and system performance. Under the wireless bandwidth constraint, the reasonable choice of offloading objects is an NP-hard problem. The genetic algorithm has a great ability to solve this problem, but the performance of the algorithm varies with different scenarios. This paper proposes a task offloading strategy based on an enhanced genetic algorithm for small-scale computing tasks with an ultra-dense terminal distribution. Numerical experiments show that the convergence speed and optimisation effect of the enhanced genetic algorithm are significantly improved compared to the conventional genetic algorithm. © 2023 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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Layered Structure Aware Dependent Microservice Placement Toward Cost Efficient Edge Clouds 42

Layered Structure Aware Dependent Microservice Placement Tow...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Zeng, Deze Geng, Hongmin Gu, Lin Li, Zhexiong China University of Geosciences School of Computer Science Wuhan China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350334142

Although the containers are featured by light-weightness, it is still resource-consuming to pull and startup a large container image, especially in relatively resource-constrained edge cloud. Fortunately, Docker, as the most widely used container, provides a unique layered architecture that allows the same layer to be shared between microservices so as to lower the deployment cost. Meanwhile, it is highly desirable to deploy dependent microservices of an application together to lower the operation cost. Therefore, the balancing of microservice deployment cost and the operation cost should be considered comprehensively to achieve minimal overall cost of an on-demand application. In this paper, we first formulate this problem into a Quadratic Integer Programming form (QIP) and prove it as a NP-hard problem. We further propose a Randomized Rounding-based Microservice Deployment and Layer Pulling (RR-MDLP) algorithm with low computation complexity and guaranteed approximation ratio. Through extensive experiments, we verify the high efficiency of our algorithm by the fact that it significantly outperforms existing state-of-the-art microservice deployment strategies. © 2023 IEEE.

关键词： Containers

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Can Language Models Serve as Temporal Knowledge Bases?

Can Language Models Serve as Temporal Knowledge Bases?

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Zhao, Ruilin Zhao, Feng Xu, Guandong Zhang, Sixiao Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Data Science and Machine Intelligence Lab University of Technology Sydney Sydney Australia

Recent progress regarding the use of language models (LMs) as knowledge bases (KBs) has shown that language models can act as structured knowledge bases for storing relational facts. However, most existing works only considered the LM-as-KB paradigm in a static setting, which ignores the analysis of temporal dynamics of world knowledge. Furthermore, a basic function of KBs, i.e., the ability to store conflicting information (i.e., 1-N, N-1 and N-M relations), is underexplored. In this paper, we formulate two practical requirements for treating LMs as temporal KBs: (i) the capacity to store temporally-scoped knowledge that contains conflicting information and (ii) the ability to use stored knowledge for temporally-scoped knowledge queries. We introduce a new dataset called LAMA-TK which is aimed at probing temporally-scoped knowledge, and investigate the two above requirements to explore the LM-as-KB paradigm in the temporal domain. On the one hand, experiments show that LMs can memorize millions of temporally-scoped facts with relatively high accuracy and transfer stored knowledge to temporal knowledge queries, thereby expanding the LM-as-KB paradigm to the temporal domain. On the other hand, we show that memorizing conflicting information, which has been neglected by previous works, is still challenging for LMs and hinders the memorization of other unrelated one-to-one relationships. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Hybrid Parallel Inference for Large Model on Heterogeneous Clusters for High Throughput 29

Hybrid Parallel Inference for Large Model on Heterogeneous C...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Xu, Chaonong Kong, Weiming Liu, Min Zhang, Mingming Li, Chao Gong, Luqi Beijing Key Lab of Petroleum Data Mining Beijing China Institute of Intelligent Computing Zhejiang Lab Hangzhou China Bupt School of Computer Science National Pilot Software Engineering School Beijing China

ISBN: (纸本)9798350330717

In high-throughput intelligent computing scenarios, multi-device parallelism strategies based on data parallelism or pipeline parallelism have been extensively utilized to accelerate large deep neural network model inference. data parallelism offers nearly linear improvement in inference speed, but it is limited by the memory capacity of a single device which constrains the model size. On the other hand, pipeline parallelism can support larger models, but the total communication of the activations among devices is high, which limits the improvement of the inference speed. To address the demand for efficient model inference in high-throughput heterogeneous scenarios, we proposes a hybrid parallelism strategy that combines data parallelism and pipeline parallelism. The strategy involves grouping heterogeneous device clusters and then employing inter-group data parallelism along with intra-group pipeline parallelism. Moreover, we propose an algorithm to find an optimal hybrid parallel inference strategy with maximum throughput. The control variables of the strategy includes the number of groups, group-device assignments and model partition ratios. Our experimental evaluation demonstrates that compared to PipeEdge, a pipeline parallel inference framework for heterogeneous cluster, our strategy can achieve 1.7× -3.4× acceleration in an 8-device heterogeneous cluster without loss of accuracy. © 2023 IEEE.

关键词： data parallelism heterogeneous cluster hybrid parallelism model inference pipeline parallelism

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Efficient Modeling Attack on Multiplexer PUFs via Kronecker Matrix Multiplication

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Wang, Hongfei Wan, Caixue Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

The Physical Unclonable Function (PUF) is valued for its lightweight nature and unique functionality, making it a common choice for securing hardware products requiring authentication and key generation mechanisms. In response to the susceptibility of individual PUFs to modeling attacks, advanced PUF variants have been developed to improve security measures. One notable type in this regard is the multiplexer-based composition of arbiter PUFs, known as MPUF, which aims to meet high reliability and security standards simultaneously. Current research on attacking MPUF encounters challenges such as substantial demands for training CRPs and low success rates. In this work, we propose a novel numerical modeling attack strategy for MPUFs. Using Kronecker products from mathematical perspective, this method precisely describe the MPUF model without using complex network architectures, boosting attack accuracy and overall efficiency. Experiment comparison with state-of-the-art works demonstrates that our method achieves better performance in terms of attack accuracy, robustness, and efficiency. Our method is able to successfully attack a (512, 8)-MPUF in 32.71 minutes with 97.14% accuracy, outperforming all existing attack methods on MPUFs. More, we validate our method through experiments with hardware implementations on FPGAs. The advantages of our method also include the adaptability to be employed to attack other MPUF variations like cMPUF and rMPUF, and the capability to be integrated with an existing attack method for launching efficient attack on MPUFs leveraging reliability information. © 2025 IEEE.

关键词： modeling attack multiplexer-based PUF Physical unclonable function

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An Exploratory Approach to Intelligent Quiz Question Recommendation 26

An Exploratory Approach to Intelligent Quiz Question Recomme...

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26th International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Mao, Kejie Dong, Qiwen Wang, Ye Hong, Daocheng Shanghai Institute of AI Education Lab for AI Education School of Data Science and Engineering ECNU Shanghai200062 China

With the rapid advancement of ICT, the digital transformation on education is greatly accelerating in various applications. As a particularly prominent application of digital education, quiz question recommendation is playing a vital role in precision teaching, smart tutoring, and personalized learning. However, the looming challenge of quiz question recommender for students is to satisfy the question diversity demands for students ZPD (the zone of proximal development) stage dynamically online. Therefore, we propose to formalize quiz question recommendation with a novel approach of reinforcement learning based two-sided recommender system. We develop a recommendation framework RTR (Reinforcement-Learning based Two-sided Recommender Systems) for taking into account the interests of both sides of the system, learning and adapting to those interests in real time, and resulting in more satisfactory recommended content. This established recommendation framework captures question characters and student dynamic preferences by considering the emergence of both sides of the system, and it yields a better learning experience in the context of practical quiz question generation. © 2022 The Authors. Published by Elsevier B.V.

关键词： Reinforcement learning

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Type-Representation Enhanced Event Detection via Structure Encoder 1

Type-Representation Enhanced Event Detection via Structure E...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Feng, Yuming Huang, Linmeng Liang, Haozhe Jin, Xiaolong Guan, Saiping Guo, Jiafeng School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy of Sciences Key Lab of Network Data Science and Technology China Institute of Systems Engineering Academy of Military Science Beijing China

ISBN: (纸本)9798350358780

Event detection, aiming to detect whether a sentence contains the type of events of interest, is one of the important tasks in information Extraction. There exist many methods for event detection, which usually treat event detection as a classification problem. These existing methods suffer from the imbalanced training data of different event types. To relieve this problem, this paper proposes a new Type-Representation-Enhanced Event Detection (TREED) method, which introduce hierarchical event type representations to enhance classification accuracy. Specifically, TREED first models the hierarchical relationship among event types in the dataset;Next, upon interactions between the representations of trigger words and event types, it obtains the new representations of trigger words integrating event type information. Finally, TREED classifies the events based on the obtained more accurate trigger word representations. Experimental results on a commonly used benchmark dataset, ACE-2005, demonstrate that the proposed model can improve both accuracy and recall of the event detection task. © 2023 IEEE.

关键词： Signal encoding

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