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SSRN 2024年

作者： Chen, Jingxue Deng, Liangjun Qiu, Yao Zhao, Pengbiao Lei, Hang Song, Jingcheng Wang, Xiaopei School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China Institute of Building Information Modeling Ningbo Polytechnic College Zhejiang Ningbo315800 China College of Information Science and Engineering Linyi University Shandong Linyi276000 China Department of Computer Science and Engineering University of California RiversideCA92521 United States

There are lots of sensitive information in medical consumer devices. Since Consumer electronics products in the healthcare mainly focus on health monitoring and personal health management, such as wearable ECG monitoring devices, smart weight scales and so on. It is paramount to ensure the security and reliability of software in healthcare delivery, especially for sensitive medical data of integration with artificial intelligence (AI). Therefore, it is necessitating strict security verification and continuous vigilance against potential software vulnerabilities. WebAssembly, known for its security, portability, and speed, has emerged as a crucial technology for medical device software, significantly impacting the healthcare electronics sector, particularly in AI-generated content. There is a novel machine learning-based approach to address the challenges of symbolic execution in WebAssembly by automatically modeling unknown functions using a fine-tuned large language model (LLM) with 20 billion parameters, enhancing the execution's integrity and efficiency. The method, applicable across multiple programming languages and extendable to WebAssembly-compiled programs, shows promising experimental results in model performance and efficiency. This ensures enhanced security and reliability of software for CE, setting the stage for more flexible WebAssembly standards in medical CE and AI-driven healthcare devices. © 2024, The Authors. All rights reserved.

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UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction Services

arXiv

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arXiv 2023年

作者： Fang, Jiangyi Chen, Liyue Chai, Di Hong, Yayao Xie, Xiuhuai Chen, Longbiao Wang, Leye Key Lab of High Confidence Software Technologies Ministry of Education China Hong Kong University of Science and Technology Hong Kong Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen China School of Informatics Xiamen University Xiamen China School of Computer Science Peking University Beijing China

Spatiotemporal prediction (STP) service is one of the key infrastructure applications in smart cities. Currently, most of the existing STP services are constructed following the workflow of building deep learning (DL) applications while neglecting the importance of domain knowledge and region partition. However, the performance and interpretability of STP are highly related to them. As a result, there is an urgent requirement to develop a thorough and tailored workflow for STP services. To address this gap, we propose a novel workflow including two factors above as intermediate procedures. Based on the workflow, we design and implement an STP toolbox called UCTB (Urban Computing Tool Box) assisting practitioners in the rapid construction of STP services, which can manage multiple spatiotemporal domain knowledge, support various region partition algorithms, and possess state-of-the-art models simultaneously. The relevant code and supporting documents have been open-sourced at https://***/uctb/UCTB. Copyright © 2023, The Authors. All rights reserved.

关键词： Domain Knowledge

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Exploring structure diversity in atomic resolution microscopy with graph neural networks

arXiv

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arXiv 2024年

作者： Luo, Zheng Feng, Ming Gao, Zijian Yu, Jinyang Hu, Liang Wang, Tao Xue, Shenao Zhou, Shen Ouyang, Fangping Feng, Dawei Xu, Kele Wang, Shanshan Department of Materials Hunan Key Laboratory of Mechanism and Technology of Quantum Information College of Aerospace Science and Engineering National University of Defense Technology Changsha410000 China School of Computer State Key Laboratory of Complex & Critical Software Environment National University of Defense Technology Changsha410000 China College of Electronic and Information Engineering Tongji University Shanghai201804 China School of Material Science and Engineering Peking University Beijing100871 China School of Physics Central South University Changsha410083 China College of Science National University of Defense Technology Changsha410000 China School of Advanced Materials Peking University Shenzhen Graduate School Guangdong Shenzhen518055 China

The emergence of deep learning (DL) has provided great opportunities for the high-throughput analysis of atomic-resolution micrographs. However, the DL models trained by image patches in fixed size generally lack efficiency and flexibility when processing micrographs containing diversified atomic configurations. Herein, inspired by the similarity between the atomic structures and graphs, we describe a few-shot learning framework based on an equivariant graph neural network (EGNN) to analyze a library of atomic structures (e.g., vacancies, phases, grain boundaries, doping, etc.), showing significantly promoted robustness and three orders of magnitude reduced computing parameters compared to the image-driven DL models, which is especially evident for those aggregated vacancy lines with flexible lattice distortion. Besides, the intuitiveness of graphs enables quantitative and straightforward extraction of the atomic-scale structural features in batches, thus statistically unveiling the self-assembly dynamics of vacancy lines under electron beam irradiation. A versatile model toolkit is established by integrating EGNN sub-models for single structure recognition to process images involving varied configurations in the form of a task chain, leading to the discovery of novel doping configurations with superior electrocatalytic properties for hydrogen evolution reactions. This work provides a powerful tool to explore structure diversity in a fast, accurate, and intelligent manner. Copyright © 2024, The Authors. All rights reserved.

关键词： Grain boundaries

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NFCS: An Efficient Neural Framework for Cold Start Recommendation

NFCS: An Efficient Neural Framework for Cold Start Recommend...

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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Wang Zhou Laixiang Qiu Meijun Duan Amin Ul Haq School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9781665413657

In this article we have illustrated an efficient and effective neural framework referred to as NFCS for cold start recommendation. In this neural network, the average ratings will be as the input, and the missing ratings will be regarded as the dropout rate for the neural network. Therefore, in the output layer ratings will be reconstructed, which could provide an high performance solution for cold start problem. Experimental results also demonstrate that NFCS could outperform state-of-the-art PMF and IRCD-ICS over real life datasets.

关键词： Neural networks Information processing Media

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Modeling Inter-Intra Heterogeneity for Graph Federated Learning

arXiv

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arXiv 2024年

作者： Yu, Wentao Chen, Shuo Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Center for Advanced Intelligence Project RIKEN Japan State Key Laboratory of Complex & Critical Software Environment Beihang University China Jinan University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgraph similarities estimated with the outputs of local models are less reliable, because the final outputs of local models may not comprehensively represent the real distribution of subgraph data. In addition, they ignore the critical intra-heterogeneity which usually exists within each subgraph itself. To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH). For the inter-subgraph relationship, we propose a novel hierarchical variational model to infer the whole distribution of subgraph data in a multi-level form, so that we can accurately characterize the inter-subgraph similarities with the global perspective. For the intra-heterogeneity, we disentangle the subgraph into multiple latent factors and partition the model parameters into multiple parts, where each part corresponds to a single latent factor. Our FedIIH not only properly computes the distribution similarities between subgraphs, but also learns disentangled representations that are robust to irrelevant factors within subgraphs, so that it successfully considers the inter- and intra- heterogeneity simultaneously. Extensive experiments on six homophilic and five heterophilic graph datasets in both non-overlapping and overlapping settings demonstrate the effectiveness of our method when compared with nine state-of-the-art methods. Specifically, FedIIH averagely outperforms the second-best method by a large margin of 5.79% on all heterophilic datasets. Code is available at https://***/blgpb/FedIIH. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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New Quantum Private Comparison Using Hyperentangled GHZ State

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Journal of Quantum Computing 2021年第2期3卷 45-54页

作者： Jerrel Gianni Zhiguo Qu College of International Students Nanjing University of Information Science and TechnologyNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

In this paper,we propose a new protocol designed for quantum private comparison(QPC).This new protocol utilizes the hyperentanglement as the quantum resource and introduces a semi-honest third party(TP)to achieve the *** protocol’s quantum carrier is a hyperentangled three-photon GHZ state in 2 degrees of freedom(DOF),which could have 64 *** TP can decide which combination to use based on the shared key information provided from a quantum key distribution(QKD)*** doing so,the security of the protocol can be improved *** photon technology is also used as another means of security and checks if the transmission in the quantum channel is secure or not before sending the quantum *** proposed protocol is proved to be able to fend off various kinds of eavesdropping *** addition,the new QPC protocol also can compare secret inputs securely and efficiently.

关键词： GHZ state hyperentangled state information security quantum cryptography quantum private comparison

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Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples

arXiv

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arXiv 2022年

作者： Liu, Chengyuan Gan, Leilei Kuang, Kun Wu, Fei School of Software Technology Zhejiang University China College of Computer Science and Technology Zhejiang University China Shanghai Institute for Advanced Study of Zhejiang University China Shanghai AI Laboratory China

The aim of Logic2Text is to generate controllable and faithful texts conditioned on tables and logical forms, which not only requires a deep understanding of the tables and logical forms, but also warrants symbolic reasoning over the tables. State-of-the-art methods based on pre-trained models have achieved remarkable performance on the standard test dataset. However, we question whether these methods really learn how to perform logical reasoning, rather than just relying on the spurious correlations between the headers of the tables and operators of the logical form. To verify this hypothesis, we manually construct a set of counterfactual samples, which modify the original logical forms to generate counterfactual logical forms with rarely co-occurred table headers and logical operators. SOTA methods give much worse results on these counterfactual samples compared with the results on the original test dataset, which verifies our hypothesis. To deal with this problem, we firstly analyze this bias from a causal perspective, based on which we propose two approaches to reduce the model’s reliance on the shortcut. The first one incorporates the hierarchical structure of the logical forms into the model. The second one exploits automatically generated counterfactual data for training. Automatic and manual experimental results on the original test dataset and the counterfactual dataset show that our method is effective to alleviate the spurious correlation. Our work points out the weakness of previous methods and takes a further step toward developing Logic2Text models with real logical reasoning ability. Copyright © 2022, The Authors. All rights reserved.

关键词： Statistical tests

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Efficiently Verifying Mutually Exclusive Inconsistency of Requirements for Embedded Systems

Efficiently Verifying Mutually Exclusive Inconsistency of Re...

引用

International Symposium on System and software Reliability (ISSSR)

作者： Xiaohong Chen Kun Ruan Zhi Jin Junhu Li Lingling Zheng Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China School of Software Jilin University Changchun China School of Computer Science and Technology Peking University Beijing China Southwest China Research Institute of Electronic Equipment Chengdu China

Formal methods have demonstrated tremendous potential for verifying requirements consistency, particularly for safety-critical systems. In our previous research, we included environment characteristics in the scope of formal specification verification for embedded systems and proposed a requirement consistency verification method based on an environment model. However, we found that this method was not sufficiently efficient. To address this issue, this paper defines this environment model introduced inconsistency as mutually exclusive inconsistency, based on its representation characteristics, and proposes an efficient static verification approach for embedded systems. We reduce the consistency verification problem to the traversal of a graph. To support the proposed approach, we implement a requirement consistency verification tool, called Env-Consistency, and apply it to industrial cases. To illustrate the advantages of this approach, several sets of comparative experiments are designed, which demonstrates our approach is both effective and efficient.

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A Revocable Fast and Lightweight Parallel Encryption Scheme for IIoT

引用

IEEE Internet of Things Journal 2025年

作者： Lu, Junze Hu, Chunqiang Chen, Jiajun Xia, Hui Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education China Chongqing University School of Big Data and Software Engineering Chongqing 400030 China Ocean University of China College of Computer Science and Technology China

As the Industrial Internet of Things (IIoT) expands, the number of stakeholders increases. Many entities require significant amounts of data from industrial devices. These data streams improve information sharing and optimize the industrial chain. However, IIoT’s enormous data volumes pose challenges to traditional encryption methods, which are unable to meet efficiency and energy consumption requirements. Thus, a new, efficient encryption algorithm is essential for managing data flows among IIoT subscribers. In this paper, we propose a fast, low-energy, high-security encryption scheme to manage multi-entity data streams. First, we introduce a novel encryption scheme based on the Subset Sum Problem (SSP), which improves energy efficiency and speed. Second, to meet subscription requirements, we employ attribute-based encryption (ABE) for key forwarding. Finally, we handle subscription revocations with device key updates. The device owner utilizes their secret value to generate an identity proof with a key update request. © 2014 IEEE.

关键词： attribute based encryption data sharing Industrial Internet of Things Subset sum problem

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Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms

arXiv

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arXiv 2023年

作者： Bian, Chao Zhou, Yawen Li, Miqing Qian, Chao National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Artificial Intelligence Nanjing University Nanjing210023 China School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

Evolutionary algorithms (EAs) have been widely and successfully applied to solve multi-objective optimization problems, due to their nature of population-based search. Population update, a key component in multi-objective EAs (MOEAs), is usually performed in a greedy, deterministic manner. That is, the next-generation population is formed by selecting the best solutions from the current population and newly-generated solutions (irrespective of the selection criteria used such as Pareto dominance, crowdedness and indicators). In this paper, we analytically present that stochastic population update can be beneficial for the search of MOEAs. Specifically, we prove that the expected running time of two well-established MOEAs, SMS-EMOA and NSGA-II, for solving two bi-objective problems, OneJumpZeroJump and bi-objective RealRoyalRoad, can be exponentially decreased if replacing its deterministic population update mechanism by a stochastic one. Empirical studies also verify the effectiveness of the proposed population update method. This work is an attempt to show the benefit of introducing randomness into the population update of MOEAs. Its positive results, which might hold more generally, should encourage the exploration of developing new MOEAs in the area. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization algorithms

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