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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lei, Haijun Zhao, Jia Tong, Guanjie Qiu, Xinyun Su, Huaqiang Lei, Baiying Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen University Medical School Shenzhen China

ISBN: (纸本)9798350337488

Multiple Myeloma (MM) is a growing global health concern, and early diagnosis is crucial for effective treatment. Efforts are underway to produce digital pathology tools with human-level intelligence that are efficient, scalable, accessible, and cost-effective. Microscopic images have high resolution, where cells are enormous and dense. Therefore, the annotation process is time-consuming and complex for tasks such as segmentation due to pixel-level marking. In this paper, we design an end-to-end weakly supervised myeloma cell segmentation framework based on point annotation. It can achieve accurate cell segmentation comparable to fully supervised methods while reducing the need for manual annotation, greatly shortening annotation time. Experimental results demonstrate that our method achieves 98% of its fully-supervised performance with only 10 annotated random points per instance, and outperforms the fully-supervised Mask RCNN. © 2023 IEEE.

关键词： Cell Segmentation Point Annotation Weakly-supervised

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Combining Autoencoder with Adaptive Differential Privacy for Federated Collaborative Filtering 28th

Combining Autoencoder with Adaptive Differential Privacy fo...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Ding, Xuanang Li, Guohui Yuan, Ling Zhang, Lu Rong, Qian School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Software Engineering Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031306365

Recommender systems provide users personalized services by collecting and analyzing interaction data, undermining user privacy to a certain extent. In federated recommender systems, users can train models on local devices without uploading raw data. Nevertheless, model updates transmitted between the user and the server are still vulnerable to privacy inference attacks. Several studies adopt differential privacy to obfuscate transmitted updates, but they ignore the privacy sensitivity of recommender model components. The problem is that components closer to the original data are more susceptible to privacy leakage. To address this point, we propose a novel adaptive privacy-preserving method combining autoencoder for federated collaborative filtering, which guarantees privacy meanwhile maintaining high model performance. First, we extend the variational autoencoder (VAE) to federated settings for privacy-preserving recommendations. Additionally, we analyze the privacy risks of the variational autoencoder model in federated collaborative filtering. Subsequently, we propose an adaptive differential privacy method to enhance user privacy further. The key is to allocate less privacy budget for sensitive layers. We apply a metric based on model weights to determine the privacy sensitivity of each layer in the autoencoder. Then we adaptively allocate the privacy budget to the corresponding model layer. Extensive experiments and analysis demonstrate that our method can achieve competitive performance to non-private recommender models meanwhile providing fine-grained privacy protection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Collaborative filtering

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Boosting Adversarial Training with Learnable Distribution

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computers, Materials & Continua 2024年第3期78卷 3247-3265页

作者： Kai Chen Jinwei Wang James Msughter Adeke Guangjie Liu Yuewei Dai School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Intelligent Support Technology for Complex Environments Ministry of EducationNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Nanjing Center for Applied Mathematics Nanjing211135China

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural *** training is one of the most potent methods to defend against adversarial ***,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial *** paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture *** distribution centroid is built to classify samples and constrain the distribution of the sample *** natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the *** proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial *** algorithm gradually increases the accuracy and robustness of the model by scaling ***,the proposed method outputs the predicted labels and the distance between the sample and the distribution *** distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model *** effectiveness of the proposed method is demonstrated through comprehensive experiments.

关键词： Adversarial training feature space learnable distribution distribution centroid

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PatchBert: Continuous Stable Patch Identification for Linux Kernel via Pre-trained Model Fine-tuning 31

PatchBert: Continuous Stable Patch Identification for Linux ...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Liu, Rongkai Shi, Heyuan Zhang, Yongchao Wang, Runzhe Shen, Yuheng Chen, Yuao Luo, Jing Shi, Xiaohai Hu, Chao Jiang, Yu School of Computer Science and Engineering China School of Electronic Information Central South University Changsha China Alibaba Group Beijing China School of Software Tsinghua University Beijing China

ISBN: (纸本)9798350330663

Stable patch identification is crucial in merging patches into stable versions, which helps ensure the stability of the Linux kernel. Although many tools have been proposed to mitigate the manual effort of stable patch identification, challenges still arise because they neglect continuous stable patch tracking and advanced Natural Language Processing (NLP) pre-training techniques. In this paper, in collaboration with developers from the openAnolis Linux operating system distribution community, we present a stable patch identification model called PatchBERT. It utilizes BERT and CodeBERT to capture the semantic patch representation from the commit message and code changes in a patch. We then perform patch classification and output the probability that the patch should be merged into the stable versions. We perform experiments on the dataset used by the previous methods. The experimental results show the superior performance of PatchBERT over state-of-the-art baselines. Additionally, it is common practice to train the model using the latest Linux patches and implement it in a real-world industrial setting. In this exercise, we randomly select 10,000 patches for identification, accurately identifying 8,617 patches and incorrectly identifying 1,383 patches. This practical outcome further confirms the effectiveness and utility of PatchBERT in real-world scenarios. © 2024 IEEE.

关键词： Semantics

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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images 41

Tabular Insights, Visual Impacts: Transferring Expertise fro...

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41st International Conference on Machine Learning, ICML 2024

作者： Jiang, Jun-Peng Ye, Han-Jia Wang, Leye Yang, Yang Jiang, Yuan Zhan, De-Chuan School of Artificial Intelligence Nanjing University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Ministry of Education & School of Computer Science Peking University Beijing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Transferring knowledge across diverse data modalities is receiving increasing attention in machine learning. This paper tackles the task of leveraging expert-derived, yet expensive, tabular data to enhance image-based predictions when tabular data is unavailable during inference. The primary challenges stem from the inherent complexity of accurately mapping diverse tabular data to visual contexts, coupled with the necessity to devise distinct strategies for numerical and categorical tabular attributes. We propose CHannel tAbulaR alignment with optiMal tranSport (CHARMS), which establishes an alignment between image channels and tabular attributes, enabling selective knowledge transfer that is pertinent to visual features. Specifically, CHARMS measures similarity distributions across modalities to effectively differentiate and transfer relevant tabular features, with a focus on morphological characteristics, enhancing the capabilities of visual classifiers. By maximizing the mutual information between image channels and tabular features, knowledge from both numerical and categorical tabular attributes are extracted. Experimental results demonstrate that CHARMS not only enhances the performance of image classifiers but also improves their interpretability by effectively utilizing tabular knowledge. Copyright 2024 by the author(s)

关键词： Knowledge transfer

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Discussion on the Influence of Anti-normalization Parameter Selection of BP Neural Network on Mending Seepage Pressure Data of Tailing Dam

Discussion on the Influence of Anti-normalization Parameter ...

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2022 International Joint Conference on Information and Communication engineering, JCICE 2022

作者： Zhu, Hongli Jin, Meiqi Wen, Yumeng Lu, Ling Liaoning University of Science and Technology School of Computer and Software Engineering Department of Software Engineering Liaoning Anshan China Liaoning University of Science and Technology School of Computer and Software Engineering Department of Network Engineering Liaoning Anshan China Liaoning University of Science and Technology School of Computer and Software Engineering Liaoning Anshan China

ISBN: (数字)9781665460675

ISBN: (纸本)9781665460675

The height of Infiltration line of tailings dam is a very important parameter for the safety of tailings reservoir. Infiltration line is the intersection line between the free water surface and the cross section of the dam body, that the free water surface is formed by the infiltration of water from the earth dam to the downstream. When the buried depth of the pressure sensors at each monitoring point could be obtained and the groundwater seepage pressure of the tailings dam could be measured too, the height of the free water surface can be calculated then. The commonly used pressure sensor is vibrating wire sensor. Among the many seepage pressure monitoring points, because of the lines, power supply or some other reasons, it often occurs that one or some points do not collect groundwater seepage pressure data for a period of time. For the missing data, BP neural network can be used to mend the missed data. Of course, the patched data should be marked to distinguish it from the real collected data. The mending method of missing seepage pressure data using BP neural network model is analyzed. The influence of anti-normalization parameters on mending data is analyzed. In the process of anti-normalization, the maximum and minimum values of the output data of the training data set are not put to use. Instead, a sequences is formed with the existing output data of some range before and after the mission data segment. The maximum and minimum values of the sequence are selected to use as the anti-normalization parameters. This will make the patched data values closer to the actual collected values. © 2022 IEEE.

关键词： Groundwater

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Design of an Efficient and Provable Secure Key Exchange Protocol for HTTP Cookies

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computers, Materials & Continua 2024年第7期80卷 263-280页

作者： Waseem Akram Khalid Mahmood Hafiz Burhan ul Haq Muhammad Asif Shehzad Ashraf Chaudhry Taeshik Shon Graduate School of Engineering Science and Technology National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Department of Computer Science Lahore Garrison UniversityLahore54920Pakistan Department of Computer Science and Information Technology College of EngineeringAbu Dhabi UniversityAbu Dhabi69911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbul34398Turkey Department of Cybersecurity Ajou UniversitySuwon16499Republic of Korea

Cookies are considered a fundamental means of web application services for authenticating various Hypertext Transfer Protocol(HTTP)requests andmaintains the states of clients’information over the *** cookies are exploited to carry client patterns observed by a *** client patterns facilitate the particular client’s future visit to the corresponding ***,security and privacy are the primary concerns owing to the value of information over public channels and the storage of client information on the *** protocols have been introduced that maintain HTTP cookies,but many of those fail to achieve the required security,or require a lot of resource *** this article,we have introduced a lightweight Elliptic Curve Cryptographic(ECC)based protocol for authenticating client and server transactions to maintain the privacy and security of HTTP *** proposed protocol uses a secret key embedded within a *** proposed protocol ismore efficient and lightweight than related protocols because of its reduced computation,storage,and communication ***,the analysis presented in this paper confirms that proposed protocol resists various known attacks.

关键词： Cookies authentication protocol impersonation attack ECC

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REORDER++: Enhanced Randomized Real-Time Scheduling Strategy Against Side-Channel Attacks

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IEEE Transactions on Network science and engineering 2023年第6期10卷 3253-3266页

作者： Ren, Jiankang Wang, Zheng Lin, Chi Obaidat, Mohammad S. Xie, Hongrui Zhu, Haihui Liu, Chunxiao Wang, Kaiwen Tan, Guozhen School of Computer Science and Technology Dalian116024 China Dalian University of Technology School of Software Dalian116024 China University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Amity University Honorary Distinguished Professor Noida201301 India

Embedded real-time systems are widely adopted in safety-critical domains such as aircrafts, automobiles and space vehicles. Unfortunately, with the sharp rise in the use of common-off-the-shelf components in systems and the drive towards remote communication through untrusted networks, such as WiFi, radio or cellular, the security is increasingly becoming the key consideration in real-time system design. In particular, the real-time system is vulnerable to side-channel attacks from the external networks, which attempt to infer the timing of task execution by exploiting the system deterministic execution patterns. In this article, we present an enhanced online randomized scheduling strategy (named REORDER++), which breaks the deterministic task execution pattern of systems by random priority inversions at run-time to counteract the timing side-channel attacks in dynamic-priority real-time systems. In order to realize the feasible priority inversions under real-time constraint, we propose an online priority inversion test to increase the opportunity of tasks' priority inversions by judging the feasibility of tasks' priority inversions at run-time. Owing to such online priority inversion test, REORDER++ can generate highly randomized schedule of real-time tasks to mitigate the side-channel attack vulnerability. Experiments with synthesized task sets show that REORDER++ significantly outperforms the existing approaches in terms of schedule randomness. © 2013 IEEE.

关键词： Real time systems

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Analysis of the Spatio-Temporal Characteristics of Winter Surface Urban Heat Island:A Case Study in Beijing,China

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Journal of Earth science 2024年第5期35卷 1640-1653页

作者： Shanshan Lu Fujiang Liu Yunshuang Ye Jiayu Tang Peng Li Weihua Lin Yan Guo Ruqiang Ma Jun Wang School of Geography and Information Engineering China University of GeosciencesWuhan 430074China AutoNavi Software Co. Ltd.Beijing 100102China School of Computer Science China University of GeosciencesWuhan 430078China Yantai Geological Survey of Coastal Resources Center Yantai 264000China

This study reveals the temporal and spatial evolution characteristics of the winter nighttime urban heat island(UHI)effect in the case of Beijing,*** land surface temperature(LST)is retrieved by radiative transfer equation by using the remote sensing data from Landsat ETM+/OLI_TIRS from 2007 to 2017 for the winter nighttime period,and LST is then divided by the mean-standard deviation method into different levels of thermal landscapes.A combination of the migration calculation of gravity center and multi-directional profile analysis is used to study the directional differentiation characteristics of LST and the migratory characteristics of the gravity center of ***,the overall temporal and spatial evolution characteristics of winter nighttime surface urban heat island(SUHI)in Beijing are studied,and the possible reasons for the changes are *** show that Beijing's UHI effect first increased and subsequently decreased from 2007 to *** winter heat island in the urban area developed from low-density agglomeration to high-density agglomeration to lowdensity ***,the high-level thermal landscapes migrated to the southwest along with the city center of gravity,and the expansion rate is fastest in the southwest,which is directly linked to the changes in the urban construction ***,the overall spatial distribution of winter nighttime LST is high in the east and south and low in the west and north,and is influenced by topography,land cover,urbanization,anthropogenic heat,and other factors as well.

关键词： land surface temperature urban heat island spatio-temporal characteristics winter environmentgeology

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