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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Liu, Yi Cai, Lizhi Chai, Chenchen Ma, Yuan School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai China

ISBN: (纸本)9798350365658

As the application of smart contracts in blockchain technology becomes increasingly widespread, their security issues have emerged as a focal point of both research and practice. Although symbolic execution technology has been extensively applied for the detection of security vulnerabilities in smart contracts, its limitations in terms of execution efficiency and code coverage remain significant. In response to these challenges, this study introduces an innovative method named HoneyToken-DetectorRev, which optimizes traditional symbolic execution through program slicing technology, aiming to enhance the efficiency and effectiveness of smart contract security detection. By selecting ten smart contracts from the Ethereum platform, which vary in business logic and complexity, we conducted an empirical analysis of HoneyToken-DetectorRev. The experimental results demonstrate that this method significantly improves the efficiency of smart contract detection without sacrificing precision. This research not only provides an effective technical approach for the security detection of smart contracts but also offers theoretical and practical guidance for further enhancing the security of blockchain applications. © 2024 IEEE.

关键词： Smart contract

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Self-adaptive label filtering learning for unsupervised domain adaptation

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Frontiers of Computer Science 2023年第1期17卷 225-227页

作者： Qing TIAN Heyang SUN Shun PENG Tinghuai MA School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing 210044China

1 Introduction As an emerging machine learning paradigm,unsupervised domain adaptation(UDA)aims to train an effective model for unlabeled target domain by leveraging knowledge from related but distribution-inconsistent source *** of the existing UDA methods[2]align class-wise distributions resorting to target domain pseudo-labels,for which hard labels may be misguided by misclassifications while soft labels are confusing with trivial noises so that both of them tend to cause frustrating *** overcome such drawbacks,as shown in Fig.1,we propose to achieve UDA by performing self-adaptive label filtering learning(SALFL)from both the statistical and the geometrical perspectives,which filters out the misclassified pseudo-labels to reduce negative ***,the proposed SALFL firstly predicts labels for the target domain instances by graph-based random walking and then filters out those noise labels by self-adaptive learning strategy.

关键词： filtering resort overcome

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Brain magnetic resonance image (MRI) segmentation using multimodal optimization

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Multimedia Tools and Applications 2024年第16期84卷 1-50页

作者： Akan, Taymaz Oskouei, Amin Golzari Alp, Sait Bhuiyan, Mohammad Alfrad Nobel Department of Medicine Louisiana State University Health Sciences Center ShreveportLA71103 United States Software Engineering Department Istanbul Topkapi University Istanbul34020 Turkey Faculty of Information Technology and Computer Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering İstinye University İstanbul Turkey Department of Artificial Intelligence Engineering Trabzon University Trabzon61335 Turkey

One of the highly focused areas in the medical science community is segmenting tumors from brain magnetic resonance imaging (MRI). The diagnosis of malignant tumors at an early stage is necessary to provide treatment for patients. The patient’s prognosis will improve if it is detected early. Medical experts use a manual method of segmentation when making a diagnosis of brain tumors. This study proposes a new approach to simplify and automate this process. In recent research, multi-level segmentation has been widely used in medical image analysis, and the effectiveness and precision of the segmentation method are directly tied to the number of segments used. However, choosing the appropriate number of segments is often left up to the user and is challenging for many segmentation algorithms. The proposed method is a modified version of the 3D Histogram-based segmentation method, which can automatically determine an appropriate number of segments. The general algorithm contains three main steps: The first step is to use a Gaussian filter to smooth the 3D RGB histogram of an image. This eliminates unreliable and non-dominating histogram peaks that are too close together. Next, a multimodal particle swarm optimization method identifies the histogram’s peaks. In the end, pixels are placed in the cluster that best fits their characteristics based on the non-Euclidean distance. The proposed algorithm has been applied to a Cancer Imaging Archive (TCIA) and brain MRI Images for brain Tumor detection dataset. The results of the proposed method are compared with those of three clustering methods: FCM, FCM_FWCW, and FCM_FW. In the comparative analysis of the three algorithms across various MRI slices. Our algorithm consistently demonstrates superior performance. It achieves the top mean rank in all three metrics, indicating its robustness and effectiveness in clustering. The proposed method is effective in experiments, proving its capacity to find the proper clusters. © The Auth

关键词： Image segmentation

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A Text Image Watermarking Algorithm Based on Image Enhancement

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Computers, Materials & Continua 2023年第10期77卷 1183-1207页

作者： Baowei Wang Luyao Shen Junhao Zhang Zenghui Xu Neng Wang School of Computer Nanjing University of Information Science and TechnologyCollaborative Innovation Center of Jiangsu Atmospheric Environment and Equipment TechnologyDigital Forensics Engineering Research Center of Digital Forensics Ministry of EducationNanjing210044China School of Software Nanjing University of Information Science and TechnologyNanjing210044China

Digital watermarking technology is adequate for copyright protection and content *** needs to be more research on the watermarking algorithm after printing and *** at the problem that existing anti-print scanning text image watermarking algorithms cannot take into account the invisibility and robustness of the watermark,an anti-print scanning watermarking algorithm suitable for text images is *** algorithm first performs a series of image enhancement preprocessing operations on the printed scanned image to eliminate the interference of incorrect bit information on watermark embedding and then uses a combination of Discrete Wavelet Transform(DWT)-Singular Value Decomposition(SVD)to embed the *** show that the average Normalized Correlation(NC)of the watermark extracted by this algorithm against attacks such as Joint Photographic Experts Group(JPEG)compression,JPEG2000 compression,and print scanning is above ***,the average NC of the watermark extracted after print scanning attacks is greater than 0.964,and the average Bit Error Ratio(BER)is 5.15%.This indicates that this algorithm has strong resistance to various attacks and print scanning attacks and can better take into account the invisibility of the watermark.

关键词： Print-resistant scanning image enhancement DWT SVD embedding intensity

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Recent advances in breathable electronics

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Nano Research 2023年第3期16卷 4130-4142页

作者： Zhuocheng Yan Jian Xiong Bin Wang Min Gao Guangqiang Yin Tao Hu Taisong Pan Xinzhong Wang Yuan Lin Department of Electronic Communication and Technology Shenzhen Institute of Information TechnologyShenzhen 518172China School of Materials and Energy University of Electronic Science and Technology of ChinaChengdu 610054China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Engineering Cooperation on Applied Medicine Research Center University of Electronic Science and Technology of ChinaChengdu 610054China

The successful implementation of bioelectronic devices attached to living organism hinges on a number of material and device characteristics,including not only electrical and mechanical performances to gather physiological signals from living organism thus enabling status monitoring,but also permeability or breathability for gas/nutrient exchange between living organisms and surroundings to ensure minimum perturbation of the intrinsic biological ***,most bioelectronic devices built on planar polymeric substrates,such as polydimethylsiloxane(PDMS),polyurethane(PU),and polyimide(PI),lack efficient gas permeability,which may hinder the emission of volatile compounds from the surface of living organism,affecting the natural metabolism and reducing the comfort of ***,achieving permeability or breathability in bioelectronic devices is a significant ***,the devices made of gas-permeable materials with porous structures,that combine electronic components with daily garments,such as fibric and textile,offer exciting opportunities for breathable *** this review,several types of gas-permeable materials with their synthesis and processing routes are ***,two methods for measuring water vapor transmission rate of materials are discussed in ***,recent progress in the use of gaspermeable materials for the applications of plant-and skin-attached electronics is summarized systematically.

关键词： breathability gas permeability porous sponge foam nano materials plant electronics wearable electronics

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Utilizing high spatiotemporal resolution lightning observation data for thunderstorm identification 9

Utilizing high spatiotemporal resolution lightning observati...

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9th International Symposium on Advances in Electrical, Electronics, and Computer engineering, ISAEECE 2024

作者： Zhang, Xi Luo, Fei Luo, Qian Zhi, Yaoling Wu, Huali Lu, Bingfu College of Software Engineering Chengdu University of Information Technology Sichuan Chengdu China Guangxi Lightning Protection Center Guangxi China

ISBN: (纸本)9781510683303

Thunderstorm weather has a significant impact on human society, and the precise identification of thunderstorm processes is crucial for thunderstorm weather prediction and the reduction of thunderstorm-related disasters. Existing research on thunderstorm identification methods is mainly divided into two categories: those based on radar detection data and those based on lightning location data. However, current studies suffer from large identification errors and an inability to effectively reflect the shape and boundaries of thunderstorms. To address these issues, this paper proposes a high spatiotemporal resolution thunderstorm identification method based on lightning location data. The method utilizes DBSCAN for identifying thunderstorm clusters and subsequently employs Alpha Shapes to extract boundaries. The proposed method is evaluated through optimal parameter selection and comparative experiments using a lightning location dataset from the Guangxi region. The results indicate that the performance of the new method in thunderstorm identification far surpasses that of existing methods. © 2024 SPIE.

关键词： Lightning Detection and tracking algorithms Picosecond phenomena Reflectivity Radar sensor technology Radar Spatial resolution Absorbance Reflection Seaborgium

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Joint Computing Offloading and Resource Allocation in MEC-Enabled IoT: A Diffusion-Based Reinforcement Learning Approach

Joint Computing Offloading and Resource Allocation in MEC-En...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Cao, Huimin Xiao, Bo MoE Engineering Research Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China

ISBN: (纸本)9781665410205

The integration of the Internet of Things (IoT) with mobile edge computing (MEC) has come out to be a promising solution to address the requirements of high computing capabilities and low latency services, enabling user equipments(UE) to migrate the computation of tasks onto edge servers. This paper focuses on optimizing the performance of MEC-enabled IoT system by formulating a joint computing offloading and resource allocation problem. The objective is to minimize the total delay of the system consisting of multiple servers and multiple users. The denoising network of a diffusion model with capabilities of generation can be trained to obtain optimal solution given the changed environment conditions. Therefore, we propose the diffusion-based deep deterministic policy gradient (DiffDDPG) algorithm which utilizes a diffusion model as the policy to learn optimal decisions jointly. Simulation results exhibits the superior performance of the DiffDDPG algorithm. © 2024 IEEE.

关键词： Computation offloading

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NL2CTL: Automatic Generation of Formal Requirements Specifications via Large Language Models 1

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25th International Conference on Formal engineering Methods, ICFEM 2024

作者： Zhao, Mengyan Tao, Ran Huang, Yanhong Shi, Jianqi Qin, Shengchao Yang, Yang National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai China Hardware/Software Co-Design Technology and Application Engineering Research Center East China Normal University Shanghai China Guangzhou Institute of Technology Xidian University Xi’an China ICTT and ISN Laboratory Xidian University Xi’an China Software Engineering Institute East China Normal University Shanghai China

ISBN: (数字)9789819606177

ISBN: (纸本)9789819606160

Reducing the gap between natural language requirements and precise formal specifications is a critical task in requirements engineering. In recent years, requirement engineering is becoming increasingly complex alongside the growing intricacy of system engineering. Most requirements are expressed in natural language, which can be incomplete and ambiguous. However, formal languages with strict semantics can accurately represent certain temporal logic properties and allow for automated verification and analysis. This often limits the application of verification techniques, as writing formal specifications is a manual, error-prone, and time-consuming task. To address this, this paper proposes a framework that leverages Large Language Models (LLMs) to achieve automated conversion of natural language requirements to Computation Tree Logic (CTL). To address the issue of dataset scarcity, we leveraged the interactive and generative capabilities of LLMs. By constructing a random generation algorithm and utilizing prompt engineering, we generated an NL-CTL dataset using LLMs. The generated dataset was then used to fine-tune the T5-Large model, enhancing its generative capacity. To improve generalization, this paper proposes the use of the GPT-3.5 Atomic Proposition (AP) Recognition method, which eliminates the constraints of using the framework across different domains. A series of experimental evaluations showed that the fine-tuned LLM achieved an accuracy of 46.4%, whereas the LLM with few-shot learning using only prompt engineering achieved only 2% accuracy, demonstrating the feasibility of this approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Semantics

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A Server Anomaly Detection Method Based on EWC and LSTM-GAN 9

A Server Anomaly Detection Method Based on EWC and LSTM-GAN

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9th International Conference on Computer and Communications, ICCC 2023

作者： Yu, Lei Le, Wei Chengdu University of Information Technology School of Software Engineering Chengdu China Chengdu University of Information Technology Sichuan Manufacturing Information Industry Collaborative Innovation Center School of Software Engineering Chengdu China

ISBN: (纸本)9798350317251

In the realm of server operations and maintenance, anomaly data is often characterized by its rarity, evolving over time with the influx of new data. Models require continuous parameter updates to adapt to these shifting data distributions. To address these challenges, this paper introduces a method for detecting anomalous server data based on Adversarial Learning and Elastic Weight Consolidation (EWCAD-GAN). Firstly, this method employs stacked Long Short-Term Memory networks (LSTM) as both the generator and discriminator, using adversarial training principles to obtain an initial model. Secondly, the Elastic Weight Consolidation (EWC) algorithm is applied to fine-tune the model obtained after adversarial training, bolstering the model's ability to remember temporal patterns within time- series data. This approach alleviates the difficulties associated with training models on rare anomalous data and facilitates online learning through the use of the EWC algorithm. Experimental evaluations of this method were conducted on two publicly available datasets for intelligent operations and maintenance anomaly detection. The results clearly demonstrate that EWCAD- GAN outperforms other similar methods, achieving significantly higher anomaly detection accuracy while simultaneously addressing the challenges associated with model training and online learning. © 2023 IEEE.

关键词： Anomaly detection

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Effect of ion solvation energy on electrohydrodynamic behavior of ionic liquid droplets in electrospray thrusters

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Chinese Journal of Aeronautics 2025年第3期38卷 222-233页

作者： Yipeng FAN Chong CHEN Huifeng KANG Guangqing XIA Chang LU Yajie HAN State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China Key Laboratory of Advanced Technology for Aerospace Vehicles of Liaoning Province Dalian University of TechnologyDalian 116024China Collaborative Innovation Center of Micro&Nano Satellites of Hebei Province North China Institute of Aerospace EngineeringLangfang 065000China

The electrospray thruster supplied by ionic liquid is a promising micro-propulsion thruster with small size and precise thrust, which can emit both cations and anions to achieve self-neutralization. In order to further investigate the effect of ion solvation energy on the evaporation of cations and anions from ionic liquid under the action of a uniform electric field, this paper establishes a transient Electrohydrodynamic (EHD) model for free ionic liquid droplets undergoing ion evaporation. The dynamic processes of droplet deformation and ion evaporation are simulated. And the study further focuses on the influence of different ion solvation energies for cations on the droplet morphology and the ion evaporation characteristics at the positively charged end and negatively charged end of the droplet. The results indicate that, when the ion solvation energy for cations is higher than that of anions, it will cause the ion evaporation at the positively charged end of the droplet to lag behind the ion evaporation at the negatively charged end. And the higher the ion solvation energy for the cations, the longer the evaporation lag time at the positively charged end of the droplet, which will lead to a higher peak of surface charge density that can be reached, resulting in a larger evaporation current and sharper droplet stretching deformation. Additionally, the peak surface charge density of the positively charged end of the droplet is linearly related to the ion solvation energy for cations, while the peak surface charge density of the negatively charged end remains almost unchanged and is not significantly affected by the ion solvation energy for cations.

关键词： Ionic liquid Electrospray Electrohydrodynamics Ion solvation energy Ion evaporation

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