Tsinghua Science and Technology最新文献

{"title":"From Traces to Packets: Realistic Deep Learning Based Multi-Tab Website Fingerprinting Attacks","authors":"Haoyu Yin;Yingjian Liu;Zhongwen Guo;Yu Wang","doi":"10.26599/TST.2024.9010073","DOIUrl":"https://doi.org/10.26599/TST.2024.9010073","url":null,"abstract":"Recent advancements in deep learning (DL) have introduced new security challenges in the form of side-channel attacks. A prime example is the website fingerprinting attack (WFA), which targets anonymity networks like Tor, enabling attackers to unveil users' protected browsing activities from traffic data. While state-of-the-art WFAs have achieved remarkable results, they often rely on unrealistic single-website assumptions. In this paper, we undertake an exhaustive exploration of multi-tab website fingerprinting attacks (MTWFAs) in more realistic scenarios. We delve into MTWFAs and introduce MTWFA-SEG, a task involving the fine-grained packet-level classification within multi-tab Tor traffic. By employing deep learning models, we reveal their potential to threaten user privacy by discerning visited websites and browsing session timing. We design an improved fully convolutional model for MTWFA-SEG, which are enhanced by both network architecture advances and traffic data instincts. In the evaluations on interlocking browsing datasets, the proposed models achieve remarkable accuracy rates of over 68.6%, 71.8%, and 76.1% in closed, imbalanced open, and balanced open-world settings, respectively. Furthermore, the proposed models exhibit substantial robustness across diverse train-test settings. We further validate our designs in a coarse-grained task, MTWFA-MultiLabel, where they not only achieve state-of-the-art performance but also demonstrate high robustness in challenging situations.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"830-850"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786942","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physical Layer Security for CR-NOMA Network with Cooperative Jamming","authors":"Meiling Li;Peng Xue;Hu Yuan;Yuxing Han","doi":"10.26599/TST.2023.9010128","DOIUrl":"https://doi.org/10.26599/TST.2023.9010128","url":null,"abstract":"Cooperative jamming can effectively combat eavesdropping in physical layer security communication without affecting the legal receiver and improve the security performance of the system. This paper introduces cooperative jamming to cognitive radio (CR) networks with non-orthogonal multiple access (NOMA) technology. The secure performance of the considered CR and NOMA (CR-NOMA) network is evaluated using two modes: non-cooperative jamming and cooperative jamming. In particular, the secrecy outage probabilities (SOPs) of the primary user (PU) in the two modes are derived under Rician fading channels, based on which, the influences of the transmission signal-to-noise ratio (SNR) of secondary users (SUs), the number of SUs, the secrecy rate, and the power allocation coefficient on the SOPs of PU are analyzed thereafter. Both analysis and simulation results show that cooperative jamming effectively prevents eavesdropping behaviour, which reduces the SOP of PU compared to non-cooperative jamming. We also show that the transmission SNR, the number of SUs, the secrecy rate, and the power distribution coefficients greatly influence performance improvement.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"708-720"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786938","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation Method of Motor Coordination Ability in Children Based on Machine Vision","authors":"Yi Lei;Dawei Shu;Miao Yu;Donglin Shi;Jianqiang Li;Yanjie Chen","doi":"10.26599/TST.2024.9010069","DOIUrl":"https://doi.org/10.26599/TST.2024.9010069","url":null,"abstract":"Motor coordination is crucial for preschoolers' development and is a key factor in assessing childhood development. Current diagnostic methods often rely on subjective manual assessments. This paper presents a machine vision-based approach aimed at improving the objectivity and adaptability of assessments. The method proposed involves the extraction of key points from the human skeleton through the utilization of a lightweight pose estimation network, thereby transforming video assessments into evaluations of keypoint sequences. The study uses different methods to handle static and dynamic actions, including regularization and Dynamic Time Warping (DTW) for spatial alignment and temporal discrepancies. A penalty-adjusted single-frame pose similarity method is used to evaluate actions. The lightweight pose estimation model reduces parameters by 85%, uses only 6.6% of the original computational load, and has an average detection missing rate of less than 1%. The average error for static actions is 0.071 with a correlation coefficient of 0.766, and for dynamic actions it is 0.145 with a correlation coefficient of 0.653. These results confirm the proposed method's effectiveness, which includes customized visual components like motion waveform graphs to improve accuracy in pediatric healthcare diagnoses.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"633-649"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786931","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wearable Continuous Blood Pressure Monitoring Based on Pulsatile Cycle Volume Adjustment Method","authors":"Pang Wu;Zhongrui Bai;Pan Xia;Lirui Xu;Peng Wang;Xianxiang Chen;Lidong Du;Ziqing Hei;Weifeng Yao;Xiaoran Li;Zhan Zhao;Zhen Fang","doi":"10.26599/TST.2024.9010043","DOIUrl":"https://doi.org/10.26599/TST.2024.9010043","url":null,"abstract":"Accurate and portable Blood Pressure (BP) monitoring is vital for managing cardiovascular diseases. However, existing wearable continuous BP monitoring technologies are often inaccurate and rely on external calibration, limiting their practical application in continuous BP monitoring. To address this challenge, we have developed a Wearable continuous non-invasive BP Monitor (WeBPM) equipped with a finger cuff sensor, capable of monitoring BP continuously and accurately within medical-grade precision. WeBPM integrates advanced finger oscillographic BP measurement technology to provide reliable self-calibration functionality. Moreover, Pulsatile Cycle Volume Adjustment Method (PCVAM) we proposed for the closed-loop phase can continuously track changes in vasomotor tone under a controlled frequency based on pulsatile cycles, thereby enabling continuous BP measurement. In comparative experiments with the Nexfin monitor, WeBPM demonstrates excellent performance in induced dynamic BP experiments, with measurement errors of (-1.4 ± 6.24) mmHg for Systolic BP (SBP) and (-0.82 ± 4.83) mmHg for Diastolic BP (DBP). Additionally, compared to clinical invasive reference measurements, WeBPM's SBP and DBP measurement errors are (-1.74 ± 4.9) mmHg and (0.37 ± 3.28) mmHg, respectively, further proving its outstanding performance. These results highlight WeBPM's potential in personalized health management and remote monitoring, offering a new solution for continuous non-invasive BP monitoring.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"650-669"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786939","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Maximum Vertex (k,ℓ)-Biplex Computation on Bipartite Graphs","authors":"Hongru Zhou;Shengxin Liu;Ruidi Cao","doi":"10.26599/TST.2024.9010009","DOIUrl":"https://doi.org/10.26599/TST.2024.9010009","url":null,"abstract":"Cohesive subgraph search is a fundamental problem in bipartite graph analysis. Given integers \u0000<tex>$k$</tex>\u0000 and ℓ, a (k,ℓ)-biplex is a cohesive structure which requires each vertex to disconnect at most \u0000<tex>$k$</tex>\u0000 or \u0000<tex>$l$</tex>\u0000 vertices in the other side. Computing (k,ℓ)-biplexes has been a popular research topic in recent years and has various applications. However, most existing studies considered the problem of finding (k, ℓ)-biplex with the largest number of edges. In this paper, we instead consider another variant and focus on the maximum vertex (k, ℓ)-biplex problem which aims to search for a (k, ℓ)-biplex with the maximum cardinality. We first show that this problem is Non-deterministic Polynomial-time hard (NP-hard) for any positive integers \u0000<tex>$k$</tex>\u0000 and ℓ while max{k, ℓ} is at least 3. Guided by this negative result, we design an efficient branch-and-bound algorithm with a novel framework. In particular, we introduce a branching strategy based on whether there is a pivot in the current set, with which our proposed algorithm has the time complexity of γ\u0000ⁿ\u0000n\u0000^O(1)\u0000, where γ< 2. In addition, we also apply multiple speed-up techniques and various pruning strategies. Finally, we conduct extensive experiments on various real datasets which demonstrate the efficiency of our proposed algorithm in terms of running time.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"569-584"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786929","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DPN: Dynamics Priori Networks for Radiology Report Generation","authors":"Bokai Yang;Hongyang Lei;Huazhen Huang;Xinxin Han;Yunpeng Cai","doi":"10.26599/TST.2023.9010134","DOIUrl":"https://doi.org/10.26599/TST.2023.9010134","url":null,"abstract":"Radiology report generation is of significant importance. Unlike standard image captioning tasks, radiology report generation faces more pronounced visual and textual biases due to constrained data availability, making it increasingly reliant on prior knowledge in this context. In this paper, we introduce a radiology report generation network termed Dynamics Priori Networks (DPN), which leverages a dynamic knowledge graph and prior knowledge. Concretely, we establish an adaptable graph network and harness both medical domain knowledge and expert insights to enhance the model's intelligence. Notably, we introduce an image-text contrastive module and an image-text matching module to enhance the quality of the generated results. Our method is evaluated on two widely available datasets: X-ray collection from Indiana University (IU X-ray) and Medical Information Mart for Intensive Care, Chest X-Ray (MIMIC-CXR), where it demonstrates superior performance, particularly excelling in critical metrics.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"600-609"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786932","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Power of Second Opportunity: Dynamic Pricing with Second Chance","authors":"Chensheng Ma;Shaojie Tang;Zhao Zhang","doi":"10.26599/TST.2023.9010108","DOIUrl":"https://doi.org/10.26599/TST.2023.9010108","url":null,"abstract":"In this paper, we consider the following dynamic pricing problem. Suppose the market price \u0000<tex>$v_{t}$</tex>\u0000 of an item arriving at time \u0000<tex>$t$</tex>\u0000 is determined by \u0000<tex>$v_{t}=pmb{theta}^{mathrm{T}}pmb{x}_{t}$</tex>\u0000, where \u0000<tex>$pmb{x}_{t}$</tex>\u0000 is the feature vector of that item and \u0000<tex>$pmb{theta}$</tex>\u0000 is an unknown vector parameter. The seller has to post prices without knowing \u0000<tex>$pmb{theta}$</tex>\u0000 such that the total regret in time span \u0000<tex>$T$</tex>\u0000 is minimized. Considering real-world scenarios in which people may negotiate prices, we propose a model called Second Chance Pricing, in which a seller has a second opportunity to post a price after the first offer is declined. Theoretical analysis shows that a second chance of pricing results in a total regret between \u0000<tex>$o(frac{ln T}{nln n}+frac{1}{n})$</tex>\u0000 and \u0000<tex>$O(n^{2}ln T)$</tex>\u0000, where \u0000<tex>$n$</tex>\u0000 is the dimension of the feature space. Experiments on both synthetic data and real data demonstrate significant benefits brought about by the second chance where the regret is only 13% of that of one chance.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"543-560"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786952","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Data Distributions Based on Jensen-Shannon Divergence for Federated Learning","authors":"Zhiyao Hu;Dongsheng Li;Ke Yang;Ying Xu;Baoyun Peng","doi":"10.26599/TST.2023.9010091","DOIUrl":"https://doi.org/10.26599/TST.2023.9010091","url":null,"abstract":"In current federated learning frameworks, a central server randomly selects a small number of clients to train local models at the beginning of each global iteration. Since clients' local data are non-dependent and identically distributed, partial local models are not consistent with the global model. Existing studies employ model cleaning methods to find inconsistent local models. Model cleaning methods measure the cosine similarity between local models and the global model. The inconsistent local model is cleaned out and will not be aggregated for the next global model. However, model cleaning methods incur negative effects such as large computation overheads and limited updates. In this paper, we propose a data distribution optimization method, called federated distribution optimization (FedDO), aiming to overcome the shortcomings of model cleaning methods. FedDO calculates the gradient of the Jensen-Shannon divergence to decrease the discrepancy between selected clients' data distribution and the overall data distribution. We test our method on the multi-classification regression model, the multi-layer perceptron, and the convolutional neural network model on a handwritten digital image dataset. Compared with model cleaning methods, FedDO improves the training accuracy by 1.8%, 2.6%, and 5.6%, respectively.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"670-681"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Acute Lymphoblastic Leukemia Using a Novel Bone Marrow Image Segmentation","authors":"M. Anline Rejula;Ben M JebIn;Ravi Selvakumar;S. Amutha;Eberlein George","doi":"10.26599/TST.2023.9010099","DOIUrl":"https://doi.org/10.26599/TST.2023.9010099","url":null,"abstract":"In our study, we present a novel method for automating the segmentation and classification of bone marrow images to distinguish between normal and Acute Lymphoblastic Leukaemia (ALL). Built upon existing segmentation techniques, our approach enhances the dual threshold segmentation process, optimizing the isolation of nucleus and cytoplasm components. This is achieved by adapting threshold values based on image characteristics, resulting in superior segmentation outcomes compared to previous methods. To address challenges, such as noise and incomplete white blood cells, we employ mathematical morphology and median filtering techniques. These methods effectively denoise the images and remove incomplete cells, leading to cleaner and more precise segmentation. Additionally, we propose a unique feature extraction method using a hybrid discrete wavelet transform, capturing both spatial and frequency information. This allows for the extraction of highly discriminative features from segmented images, enhancing the reliability of classification. For classification purposes, we utilize an improved Adaptive Neuro-Fuzzy Inference System (ANFIS) that leverages the extracted features. Our enhanced classification algorithm surpasses traditional methods, ensuring accurate identification of acute lymphoblastic leukaemia. Our innovation lies in the comprehensive integration of segmentation techniques, advanced denoising methods, novel feature extraction, and improved classification. Through extensive evaluation on bone marrow samples from the Acute Lymphoblastic Leukemia Image DataBase (ALL-IDB) for Image Processing database using MATLAB 10.0, our method demonstrates outstanding classification accuracy. The segmentation accuracy for various cell types, including Band cells (96%), Metamyelocyte (99%), Myeloblast (96%), N. myelocyte (97%), N. promyelocyte (97%), and Neutrophil cells (98%), further underscores the potential of our approach as a high-quality tool for ALL diagnosis.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"610-623"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786930","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Layered Temporal Spatial Graph Attention Reinforcement Learning for Multiplex Networked Industrial Chains Energy Management","authors":"Yuanshuang Jiang;Kai Di;Xingyu Wu;Zhongjian Hu;Fulin Chen;Pan Li;Yichuan Jiang","doi":"10.26599/TST.2023.9010111","DOIUrl":"https://doi.org/10.26599/TST.2023.9010111","url":null,"abstract":"Demand response has recently become an essential means for businesses to reduce production costs in industrial chains. Meanwhile, the current industrial chain structure has also become increasingly complex, forming new characteristics of multiplex networked industrial chains. Fluctuations in real-time electricity prices in demand response propagate through the coupling and cascading relationships within and among these network layers, resulting in negative impacts on the overall energy management cost. However, existing demand response methods based on reinforcement learning typically focus only on individual agents without considering the influence of dynamic factors on intra and inter-network relationships. This paper proposes a Layered Temporal Spatial Graph Attention (LTSGA) reinforcement learning algorithm suitable for demand response in multiplex networked industrial chains to address this issue. The algorithm first uses Long Short-Term Memory (LSTM) to learn the dynamic temporal characteristics of electricity prices for decision-making. Then, LTSGA incorporates a layered spatial graph attention model to evaluate the impact of dynamic factors on the complex multiplex networked industrial chain structure. Experiments demonstrate that the proposed LTSGA approach effectively characterizes the influence of dynamic factors on intra- and inter-network relationships within the multiplex industrial chain, enhancing convergence speed and algorithm performance compared with existing state-of-the-art algorithms.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"528-542"},"PeriodicalIF":6.6,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10786953","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}