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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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MEC-Enabled Cooperative Rendering in Metaverse: A Coalition Formation Game Approach

MEC-Enabled Cooperative Rendering in Metaverse: A Coalition ...

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IEEE International Conference on Communications (ICC)

作者： Mengzhen Cheng Zhou Su Yuan Wu Qichao Xu Minghui Dai Dongfeng Fang School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Department of Computer Science and Software Engineering California Polytechnic State University USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Virtual Reality (VR) paves the way to link Meta-verse and the real world, allowing users to enjoy immersive experiences. However, delivering high-quality full spherical VR service within limited rendering energy is a challenge. Mobile edge computing (MEC) is a promising paradigm to provide rendering computation services to users. It is widely held that the rendering of panoramic video presents a significant impediment in the VR system, with disregard for the importance of the data correlation leading to excessive energy consumption caused by repeated rendering. In this paper, we propose a cooperative rendering scheme in mm Wave-enabled wireless networks with MEC via a coalition formation game, among which we focus on the data correlation of the background environment of VR streams. Specifically, we first devise a multiple MEC servers rendering framework, and we formulate an optimization problem to maximize the system utility, which contains energy savings for MEC servers and users' quality of experience (QoE). Then, considering the overlap of the VR streams requested by users in Metaverse, a coalition formation game is employed to model the cooperations among MEC servers, such that the user's QoE is significantly improved. The simulation experiments show that our proposed algorithm is superior to benchmark algorithms in improving the users' QoE and reducing the total energy consumption of MEC servers.

关键词： Energy consumption Correlation Metaverse Wireless networks Games Virtual reality Rendering (computer graphics)

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GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM Deployment

arXiv

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arXiv 2024年

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

The burgeoning size of Large Language Models (LLMs) has led to enhanced capabilities in generating responses, albeit at the expense of increased inference times and elevated resource demands. Existing methods of acceleration, predominantly hinged on knowledge distillation, generally necessitate fine-tuning of considerably large models, posing a challenge for average users. Furthermore, present techniques for expediting inference and reducing costs operate independently. To address these issues, we introduce a novel and intuitive Guidance-based Knowledge Transfer (GKT) framework. This approach leverages a larger LLM as a "teacher" to create guidance prompts, paired with a smaller "student" model to finalize responses. Remarkably, GKT requires no fine-tuning and doesn’t necessitate the teacher and student models to have the same vocabulary, allowing for extensive batch generation to accelerate the process while ensuring user customization. GKT can be seamlessly integrated into cloud-edge collaboration architectures, and is versatile enough for plug-and-play application across various models. When utilizing ChatGPT as teacher model and Llama2-70B as the student model, we can achieve 95.00% of ChatGPT’s performance at 52% of the cost. The results highlight substantial enhancements in accuracy and processing speed on the GSM8K and CSQA datasets, surpassing the performance of using either the student or teacher models in isolation. Our code is publicly available at https://***/Zoeyyao27/GKT © 2024, CC BY.

关键词： Distillation

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Switching Controller Synthesis for Hybrid Systems Against STL Formulas

arXiv

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arXiv 2024年

作者： Su, Han Feng, Shenghua Zhan, Sinong Zhan, Naijun Institute of Software CAS University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Department of Electrical and Computer Engineering Northwestern University United States School of Computer Science Peking University Beijing China

Switching controllers play a pivotal role in directing hybrid systems (HSs) towards the desired objective, embodying a "correct-by-construction" approach to HS design. Identifying these objectives is thus crucial for the synthesis of effective switching controllers. While most of existing works focus on safety and liveness, few of them consider timing constraints. In this paper, we delves into the synthesis of switching controllers for HSs that meet system objectives given by a fragment of STL, which essentially corresponds to a reach-avoid problem with timing constraints. Our approach involves iteratively computing the state sets that can be driven to satisfy the reach-avoid specification with timing constraints. This technique supports to create switching controllers for both constant and non-constant HSs. We validate our method’s soundness, and confirm its relative completeness for a certain subclass of HSs. Experiment results affirms the efficacy of our approach. © 2024, CC BY.

关键词： Hybrid systems

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Prediction of cutting power and surface quality, and optimization of cutting parameters using new inference system in high-speed milling process

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Advances in Manufacturing 2021年第3期9卷 388-402页

作者： Long-Hua Xu Chuan-Zhen Huang Jia-Hui Niu Jun Wang Han-Lian Liu Xiao-Dan Wang Center for Advanced Jet Engineering Technologies Key Laboratory of High-efficiency and Clean Mechanical Manufacture(Ministry of Education)National Experimental Teaching Demonstration Center for Mechanical Engineering(Shandong University)School of Mechanical EngineeringShandong UniversityJinanPeople’s Republic of China School of Mechanical and Manufacturing Engineering The University of New South Wales(UNSW)SydneyNSW2052Australia Department of Computer Science and Software Engineering Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuPeople’s Republic of China

During the actual high-speed machining process,it is necessary to reduce the energy consumption and improve the machined surface ***,the appropriate prediction models and optimal cutting parameters are difficult to obtain in complex machining ***,a novel intelligent system is proposed for prediction and optimization.A novel adaptive neuro-fuzzy inference system(NANFIS)is proposed to predict the energy consumption and surface *** the NANFIS model,the membership functions of the inputs are expanded into:membership superior and membership *** membership functions are varied based on the machining *** inputs of the NANFIS model are cutting parameters,and the outputs are the machining *** optimization,the optimal cutting parameters are obtained using the improved particle swarm optimization(IPSO)algorithm and NANFIS ***,the IPSO algorithm as a learning algorithm is used to train the NANFIS *** proposed intelligent system is applied to the high-speed milling process of compacted graphite *** experimental results show that the predictions of energy consumption and surface roughness by adopting the NANFIS models are up to 91.2%and 93.4%,*** NANFIS models can predict the energy consumption and surface roughness more accurately compared with other intelligent *** on the IPSO algorithm and NANFIS models,the optimal cutting parameters are obtained and validated to reduce both the cutting power and surface roughness and improve the milling *** is demonstrated that the proposed intelligent system is applicable to actual high-speed milling processes,thereby enabling sustainable and intelligent manufacturing.

关键词： Novel adaptive neuro-fuzzy inference system(NANFIS)model Improved particle swarm optimization(IPSO)algorithm Energy consumption Surface roughness Multiobjective optimization

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Current status and development of CMOS SiPM for scintillator-based radiation detectors toward all-digital sensors[Invited]

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Chinese Optics Letters 2024年第2期22卷 17-27页

作者： Nicola D'Ascenzo 胡文韬劳慧华越轩张博房磊奚道明郑睿邱奥 Emanuele Antonecchia 凌怡清刘雨晴李琰俞航肖鹏谢庆国 Wuhan National Laboratory for Optoelectronics Wuhan 430074China Biomedical Engineering Department Huazhong University of Science and TechnologyWuhan 430074China RAYQUANT Technology Co. Ltd.Ezhou 436044China RAYSOLUTION Healthcare Co. Ltd.Hefei 230000China RAYMEASURE Technology Co. Ltd.Suzhou 215000China RAYCAN Technology Co. Ltd.Suzhou 215000China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China

Modern scintillator-based radiation detectors require silicon photomultipliers(Si PMs)with photon detection efficiency higher than 40%at 420 nm,possibly extended to the vacuum ultraviolet(VUV)region,single-photon time resolution(SPTR)<100 ps,and dark count rate(DCR)<150 kcps/mm^(2).To enable single-photon time stamping,digital electronics and sensitive microcells need to be integrated in the same CMOS substrate,with a readout frame rate higher than 5 MHz for arrays extending over a total area up to 4 mm×4 *** is challenging due to the increasing doping concentrations at low CMOS scales,deep-level carrier generation in shallow trench isolation fabrication,and power consumption,among *** advances at 350 and 110 nm CMOS nodes are benchmarked against available Si PMs obtained in CMOS and commercial customized *** concept of digital multithreshold Si PMs with a single microcell readout is finally reported,proposing a possible direction toward fully digital scintillator-based radiation detectors.

关键词： silicon photomultiplier complementary metal-oxide semiconductor digital SiPM

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Snapshot boosting: a fast ensemble framework for deep neural networks

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science China(Information sciences) 2020年第1期63卷 77-88页

作者： Wentao ZHANG Jiawei JIANG Yingxia SHAO Bin CUI Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications Department of Computer Science Beijing Key Lab of Intelligent Telecommunications Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications Key Lab of High Confidence Software Technologies Department of Computer SciencePeking University

Boosting has been proven to be effective in improving the generalization of machine learning models in many fields. It is capable of getting high-diversity base learners and getting an accurate ensemble model by combining a sufficient number of weak learners. However, it is rarely used in deep learning due to the high training budget of the neural network. Another method named snapshot ensemble can significantly reduce the training budget, but it is hard to balance the tradeoff between training costs and diversity. Inspired by the ideas of snapshot ensemble and boosting, we propose a method named snapshot boosting. A series of operations are performed to get many base models with high diversity and accuracy, such as the use of the validation set, the boosting-based training framework, and the effective ensemble strategy. Last, we evaluate our method on the computer vision(CV) and the natural language processing(NLP) tasks, and the results show that snapshot boosting can get a more balanced trade-off between training expenses and ensemble accuracy than other well-known ensemble methods.

关键词： ensemble learning deep learning boosting neural network snapshot ensemble

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Transformer-based Multi-Touch Attribution Model for Large-Scale Digital Advertising Paths

Transformer-based Multi-Touch Attribution Model for Large-Sc...

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computer Applications and Information Technology (ISCAIT), International Symposium on

作者： Jiehua Ren Fuqiang Zhou Miaomiao Zhang Leting Shentu School of Software Tongji University NAMI Laboratory Shanghai China Computer Science Department Zhongtian Experimental School Jinhua China School of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

As digital advertising becomes more complex and widespread, traditional multi-touch attribution models and statistical methods often struggle to achieve high accuracy. While time-series deep learning models offer improvements, they face challenges such as long training times, slow parameter updates, and limited parallel processing. In response, this paper introduces the Transformer-based Multi-Touch Attribution model (TRMTA), which uses the Transformer’s global attention mechanism to enhance attribution accuracy in large-scale digital advertising campaigns. We validate the TRMTA model using nearly 260 million touchpoint data points from splash ads across 10 platforms. The results show that TRMTA outperforms traditional models like LSTM and Logistic Regression (LR). In real-world applications, the TRMTA model has helped advertisers optimize their strategies for the Double Eleven (11.11) shopping festival, resulting in a revenue increase of over $10 \%$.

关键词： Training Logistic regression Accuracy Statistical analysis Computational modeling Transformers Data models Advertising Long short term memory Faces

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Container Scheduling Strategy Based on Image Layer Reuse and Sequential Arrangement in Mobile Edge Computing

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IEEE Transactions on Mobile Computing 2025年

作者： Wu, Haijie Lin, Weiwei Zhang, Haotong Shi, Fang Shen, Wangbo Li, Keqin Zomaya, Albert Y. South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Pengcheng Laboratory Shenzhen518066 China South China University of Technology School of Software Engineering Guangzhou510006 China South China Agricultural University College of Mathematics and Informatics Guangzhou510642 China State University of New York Department of Computer Science New PaltzNY12561 United States University of Sydney School of Computer Science SydneyNSW2006 Australia

In Mobile Edge Computing (MEC) scenarios, computational tasks are popularly deployed using containerization to isolate the runtime environment. To complete the execution of the task, the edge server first pulls the image, then instantiates and runs the container. Since it takes a lot of time for the edge server to download the image from the cloud, image reuse reduces the pulling latency significantly. However, the limited storage capacity of edge servers hinders image reuse. Recent works have enhanced reuse efficiency by leveraging the hierarchical structure of images and caching high-value layers. However, their efficiency remains limited due to the lack of multi-container collaboration. This paper proposes a novel container scheduling strategy based on image layer reuse and sequence arrangement (ILR-SA) for MEC scenarios, which achieves efficient scheduling by collaborating multiple containers. First, containers are greedily deployed into the edge cluster. Then, the execution sequence of containers is modeled as an optimal Hamiltonian path problem, efficiently solved by our proposed decomposition algorithm. Finally, an efficient image layer update strategy is used to achieve layer reuse. We conduct rigorous experiments to demonstrate that our proposed container scheduling strategy reduces the computational task completion time by up to 91.3% compared to existing approaches. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Integrating Exact Simulation into Sweeping for Datapath Combinational Equivalence Checking

Integrating Exact Simulation into Sweeping for Datapath Comb...

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IEEE International Conference on computer-Aided Design

作者： Zhihan Chen Xindi Zhang Yuhang Qian Qiang Xu Shaowei Cai School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Computer Science & Engineering The Chinese University of Hong Kong

In the application of IC design for microprocessors, there are often demands for optimizing the implementation of datapath circuits, on which various arithmetic operations are performed. Combinational equivalence checking (CEC) plays an essential role in ensuring the correctness of design optimization. The most prevalent CEC algorithms are based on SAT sweeping, which utilizes SAT to prove the equivalence of the internal node pairs in topological order, and the equivalent nodes are merged. Datapath circuits usually contain equivalent pairs for which the transitive fan-in cones are small but have a high XOR chain density, and proving such node pairs is very difficult for SAT solvers. An exact probability-based simulation (EPS) is suitable for verifying such pairs, while this method is not suitable for pairs with many primary inputs due to the memory cost. We first reduce the memory cost of EPS and integrate it to improve the SAT sweeping method. Considering the complementary abilities of SAT and EPS, we design an engine selection heuristic to dynamically choose SAT or EPS in the sweeping process, according to XOR chain density. Our method is further improved by reducing unnecessary engine calls by detecting regularity. Experiments on a benchmark suite from industrial datapath circuits show that our method is much faster than the state-of-the-art CEC tool namely ABC ‘&cec’ on nearly all instances, and is more than 100× faster on 30% of the instances, 1000× faster on 12% of the instances.

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