Pub Date : 2024-12-30DOI: 10.26599/TST.2023.9010116
Kaili Qi;Minqing Zhang;Fuqiang Di;Chao Jiang
Until now, some reversible data hiding in encrypted images (RDH-EI) schemes based on secret sharing (SIS-RDHEI) still have the problems of not realizing diffusivity and high embedding capacity. Therefore, this paper innovatively proposes a high capacity RDH-EI scheme that combines adaptive most significant bit (MSB) prediction with secret sharing technology. Firstly, adaptive MSB prediction is performed on the original image and cryptographic feedback secret sharing strategy encrypts the spliced pixels to spare embedding space. In the data hiding phase, each encrypted image is sent to a data hider to embed the secret information independently. When $r$ copies of the image carrying the secret text are collected, the original image can be recovered lossless and the secret information can be extracted. Performance evaluation shows that the proposed method in this paper has the diffusivity, reversibility, and separability. The last but the most important, it has higher embedding capacity. For $512 times 515$ grayscale images, the average embedding rate reaches 4.7358 bits per pixel (bpp). Compared to the average embedding rate that can be achieved by the Wang et al.'s SIS-RDHEI scheme, the proposed scheme with (2, 2), (2, 3), (2, 4), (3, 4), and (3, 5)-threshold can increase by 0.7358 bpp, 2.0658 bpp, 2.7358 bpp, 0.7358 bpp, and 1.5358 bpp, respectively.
{"title":"High Capacity Reversible Data Hiding Algorithm in Encrypted Images Based on Image Adaptive MSB Prediction and Secret Sharing","authors":"Kaili Qi;Minqing Zhang;Fuqiang Di;Chao Jiang","doi":"10.26599/TST.2023.9010116","DOIUrl":"https://doi.org/10.26599/TST.2023.9010116","url":null,"abstract":"Until now, some reversible data hiding in encrypted images (RDH-EI) schemes based on secret sharing (SIS-RDHEI) still have the problems of not realizing diffusivity and high embedding capacity. Therefore, this paper innovatively proposes a high capacity RDH-EI scheme that combines adaptive most significant bit (MSB) prediction with secret sharing technology. Firstly, adaptive MSB prediction is performed on the original image and cryptographic feedback secret sharing strategy encrypts the spliced pixels to spare embedding space. In the data hiding phase, each encrypted image is sent to a data hider to embed the secret information independently. When \u0000<tex>$r$</tex>\u0000 copies of the image carrying the secret text are collected, the original image can be recovered lossless and the secret information can be extracted. Performance evaluation shows that the proposed method in this paper has the diffusivity, reversibility, and separability. The last but the most important, it has higher embedding capacity. For \u0000<tex>$512 times 515$</tex>\u0000 grayscale images, the average embedding rate reaches 4.7358 bits per pixel (bpp). Compared to the average embedding rate that can be achieved by the Wang et al.'s SIS-RDHEI scheme, the proposed scheme with (2, 2), (2, 3), (2, 4), (3, 4), and (3, 5)-threshold can increase by 0.7358 bpp, 2.0658 bpp, 2.7358 bpp, 0.7358 bpp, and 1.5358 bpp, respectively.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1139-1156"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817719","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905731","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}
Pub Date : 2024-12-30DOI: 10.26599/TST.2023.9010115
Hongtao Zhang;Qingguo Zhou
Geological hazard risk assessment and severity prediction are of great significance for disaster prevention and mitigation. Traditional methods require a long time to evaluate and rely heavily on human experience. Therefore, based on the key factors affecting landslides, this paper designs a geological disaster prediction model based on Monte Carlo neural network (MCNN). Firstly, based on the weights of evidence method, a correlation analysis was conducted on common factors affecting landslides, and several key factors that have the greatest impact on landslide disasters, including geological lithology, slope gradient, slope type, and rainfall, were identified. Then, based on the monitoring data of Lanzhou City, 18 367 data records were collected and collated to form a dataset. Subsequently, these multiple key influencing factors were used as inputs to train and test the landslide disaster prediction model based on MCNN. After determining the hyperparameters of the model, the training and prediction capabilities of the model were evaluated. Through comparison with several other artificial intelligence models, it was found that the prediction accuracy of the model studied in this paper reached 89%, and the Macro-Precision, Macro-Recall, and Macro-F1 indicators were also higher than other models. The area under curve (AUC) index reached 0.8755, higher than the AUC value based on a single influencing factor in traditional methods. Overall, the method studied in this paper has strong predictive ability and can provide certain decision support for relevant departments.
{"title":"Multi-Influencing Factors Landslide Susceptibility Prediction Model Based on Monte Carlo Neural Network","authors":"Hongtao Zhang;Qingguo Zhou","doi":"10.26599/TST.2023.9010115","DOIUrl":"https://doi.org/10.26599/TST.2023.9010115","url":null,"abstract":"Geological hazard risk assessment and severity prediction are of great significance for disaster prevention and mitigation. Traditional methods require a long time to evaluate and rely heavily on human experience. Therefore, based on the key factors affecting landslides, this paper designs a geological disaster prediction model based on Monte Carlo neural network (MCNN). Firstly, based on the weights of evidence method, a correlation analysis was conducted on common factors affecting landslides, and several key factors that have the greatest impact on landslide disasters, including geological lithology, slope gradient, slope type, and rainfall, were identified. Then, based on the monitoring data of Lanzhou City, 18 367 data records were collected and collated to form a dataset. Subsequently, these multiple key influencing factors were used as inputs to train and test the landslide disaster prediction model based on MCNN. After determining the hyperparameters of the model, the training and prediction capabilities of the model were evaluated. Through comparison with several other artificial intelligence models, it was found that the prediction accuracy of the model studied in this paper reached 89%, and the Macro-Precision, Macro-Recall, and Macro-F1 indicators were also higher than other models. The area under curve (AUC) index reached 0.8755, higher than the AUC value based on a single influencing factor in traditional methods. Overall, the method studied in this paper has strong predictive ability and can provide certain decision support for relevant departments.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1215-1228"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817721","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905732","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}
Multi-label arrhythmias classification is of great significance to the diagnosis of cardiovascular disease, and it is a challenging task as it requires identifying the label subset most related to each instance. In this paper, by integrating a deep residual neural network and auto-encoder, we propose an advanced deep neural network (DNN) framework with unified feature-aware and label embedding to perform multi-label arrhythmias classification involving 30 types of arrhythmias. Firstly, a deep residual neural network is built to extract the complex pathological features from varying-dimensional electrocardiograms (ECGs). Secondly, the mean square error loss is adopted to learn a latent space associating the deep pathological features and the corresponding label data, and then to achieve unified feature-label embedding. Thirdly, the label-correlation aware loss is introduced to optimize the auto-encoder architecture, which enables our model to exploit label-correlation for improved multi-label prediction. Our proposed DNN model can allow end-to-end training and prediction, which can perform feature-aware, label embedding, and label-correlation aware prediction in a unified framework. Finally, our proposed model is evaluated on the currently largest public dataset worldwide, and achieves the challenge metric scores of 0.492, 0.495, and 0.490 on the 12-lead, 3-lead, and all-lead version ECGs, respectively. The performance of our approach outperforms other current state-of-the-art methods in the leave-one-dataset-out cross-validation setting, which demonstrates that our approach has great competitiveness in identifying a wider range of multi-label arrhythmias.
{"title":"Advanced Deep Neural Network with Unified Feature-Aware and Label Embedding for Multi-Label Arrhythmias Classification","authors":"Pan Xia;Zhongrui Bai;Yicheng Yao;Lirui Xu;Hao Zhang;Lidong Du;Xianxiang Chen;Qiao Ye;Yusi Zhu;Peng Wang;Xiaoran Li;Guangyun Wang;Zhen Fang","doi":"10.26599/TST.2023.9010162","DOIUrl":"https://doi.org/10.26599/TST.2023.9010162","url":null,"abstract":"Multi-label arrhythmias classification is of great significance to the diagnosis of cardiovascular disease, and it is a challenging task as it requires identifying the label subset most related to each instance. In this paper, by integrating a deep residual neural network and auto-encoder, we propose an advanced deep neural network (DNN) framework with unified feature-aware and label embedding to perform multi-label arrhythmias classification involving 30 types of arrhythmias. Firstly, a deep residual neural network is built to extract the complex pathological features from varying-dimensional electrocardiograms (ECGs). Secondly, the mean square error loss is adopted to learn a latent space associating the deep pathological features and the corresponding label data, and then to achieve unified feature-label embedding. Thirdly, the label-correlation aware loss is introduced to optimize the auto-encoder architecture, which enables our model to exploit label-correlation for improved multi-label prediction. Our proposed DNN model can allow end-to-end training and prediction, which can perform feature-aware, label embedding, and label-correlation aware prediction in a unified framework. Finally, our proposed model is evaluated on the currently largest public dataset worldwide, and achieves the challenge metric scores of 0.492, 0.495, and 0.490 on the 12-lead, 3-lead, and all-lead version ECGs, respectively. The performance of our approach outperforms other current state-of-the-art methods in the leave-one-dataset-out cross-validation setting, which demonstrates that our approach has great competitiveness in identifying a wider range of multi-label arrhythmias.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1251-1269"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817700","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905810","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}
Pub Date : 2024-12-30DOI: 10.26599/TST.2024.9010131
Lei Gao;Liyuan Cui;Shuwen Chen;Lizhen Deng;Xiaokang Wang;Xiaohong Yan;Hu Zhu
Infrared spectroscopy analysis has found widespread applications in various fields due to advancements in technology and industry convergence. To improve the quality and reliability of infrared spectroscopy signals, deconvolution is a crucial preprocessing step. Inspired by the transformer model, we propose an Auto-correlation Multi-head attention Transformer (AMTrans) for infrared spectrum sequence deconvolution. The auto-correlation attention model improves the scaled dot-product attention in the transformer. It utilizes attention mechanism for feature extraction and implements attention computation using the auto-correlation function. The auto-correlation attention model is used to exploit the inherent sequence nature of spectral data and to effectively recovery spectra by capturing auto-correlation patterns in the sequence. The proposed model is trained using supervised learning and demonstrates promising results in infrared spectroscopic restoration. By comparing the experiments with other deconvolution techniques, the experimental results show that the method has excellent deconvolution performance and can effectively recover the texture details of the infrared spectrum.
{"title":"AMTrans: Auto-Correlation Multi-Head Attention Transformer for Infrared Spectral Deconvolution","authors":"Lei Gao;Liyuan Cui;Shuwen Chen;Lizhen Deng;Xiaokang Wang;Xiaohong Yan;Hu Zhu","doi":"10.26599/TST.2024.9010131","DOIUrl":"https://doi.org/10.26599/TST.2024.9010131","url":null,"abstract":"Infrared spectroscopy analysis has found widespread applications in various fields due to advancements in technology and industry convergence. To improve the quality and reliability of infrared spectroscopy signals, deconvolution is a crucial preprocessing step. Inspired by the transformer model, we propose an Auto-correlation Multi-head attention Transformer (AMTrans) for infrared spectrum sequence deconvolution. The auto-correlation attention model improves the scaled dot-product attention in the transformer. It utilizes attention mechanism for feature extraction and implements attention computation using the auto-correlation function. The auto-correlation attention model is used to exploit the inherent sequence nature of spectral data and to effectively recovery spectra by capturing auto-correlation patterns in the sequence. The proposed model is trained using supervised learning and demonstrates promising results in infrared spectroscopic restoration. By comparing the experiments with other deconvolution techniques, the experimental results show that the method has excellent deconvolution performance and can effectively recover the texture details of the infrared spectrum.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1329-1341"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817762","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905911","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}
Pub Date : 2024-12-30DOI: 10.26599/TST.2024.9010025
Sifeng Wang;Shengxiang Li;Anran Li;Zhaoan Dong;Guangshun Li;Chao Yan
Finding more specific subcategories within a larger category is the goal of fine-grained image classification (FGIC), and the key is to find local discriminative regions of visual features. Most existing methods use traditional convolutional operations to achieve fine-grained image classification. However, traditional convolution cannot extract multi-scale features of an image and existing methods are susceptible to interference from image background information. Therefore, to address the above problems, this paper proposes an FGIC model (Attention-PCNN) based on hybrid attention mechanism and pyramidal convolution. The model feeds the multi-scale features extracted by the pyramidal convolutional neural network into two branches capturing global and local information respectively. In particular, a hybrid attention mechanism is added to the branch capturing global information in order to reduce the interference of image background information and make the model pay more attention to the target region with fine-grained features. In addition, the mutual-channel loss (MC-LOSS) is introduced in the local information branch to capture fine-grained features. We evaluated the model on three publicly available datasets CUB-200-2011, Stanford Cars, FGVC-Aircraft, etc. Compared to the state-of-the-art methods, the results show that Attention-PCNN performs better.
{"title":"A Fine-Grained Image Classification Model Based on Hybrid Attention and Pyramidal Convolution","authors":"Sifeng Wang;Shengxiang Li;Anran Li;Zhaoan Dong;Guangshun Li;Chao Yan","doi":"10.26599/TST.2024.9010025","DOIUrl":"https://doi.org/10.26599/TST.2024.9010025","url":null,"abstract":"Finding more specific subcategories within a larger category is the goal of fine-grained image classification (FGIC), and the key is to find local discriminative regions of visual features. Most existing methods use traditional convolutional operations to achieve fine-grained image classification. However, traditional convolution cannot extract multi-scale features of an image and existing methods are susceptible to interference from image background information. Therefore, to address the above problems, this paper proposes an FGIC model (Attention-PCNN) based on hybrid attention mechanism and pyramidal convolution. The model feeds the multi-scale features extracted by the pyramidal convolutional neural network into two branches capturing global and local information respectively. In particular, a hybrid attention mechanism is added to the branch capturing global information in order to reduce the interference of image background information and make the model pay more attention to the target region with fine-grained features. In addition, the mutual-channel loss (MC-LOSS) is introduced in the local information branch to capture fine-grained features. We evaluated the model on three publicly available datasets CUB-200-2011, Stanford Cars, FGVC-Aircraft, etc. Compared to the state-of-the-art methods, the results show that Attention-PCNN performs better.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1283-1293"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817763","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905727","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}
Deep neural networks are commonly used in computer vision tasks, but they are vulnerable to adversarial samples, resulting in poor recognition accuracy. Although traditional algorithms that craft adversarial samples have been effective in attacking classification models, the attacking performance degrades when facing object detection models with more complex structures. To address this issue better, in this paper we first analyze the mechanism of multi-scale feature extraction of object detection models, and then by constructing the object feature-wise attention module and the perturbation extraction module, a novel adversarial sample generation algorithm for attacking detection models is proposed. Specifically, in the first module, based on the multi-scale feature map, we reduce the range of perturbation and improve the stealthiness of adversarial samples by computing the noise distribution in the object region. Then in the second module, we feed the noise distribution into the generative adversarial networks to generate adversarial perturbation with strong attack transferability. By doing so, the proposed approach possesses the ability to better confuse the judgment of detection models. Experiments carried out on the DroneVehicle dataset show that our method is computationally efficient and works well in attacking detection models measured by qualitative analysis and quantitative analysis.
{"title":"Adversarial Attack on Object Detection via Object Feature-Wise Attention and Perturbation Extraction","authors":"Wei Xue;Xiaoyan Xia;Pengcheng Wan;Ping Zhong;Xiao Zheng","doi":"10.26599/TST.2024.9010029","DOIUrl":"https://doi.org/10.26599/TST.2024.9010029","url":null,"abstract":"Deep neural networks are commonly used in computer vision tasks, but they are vulnerable to adversarial samples, resulting in poor recognition accuracy. Although traditional algorithms that craft adversarial samples have been effective in attacking classification models, the attacking performance degrades when facing object detection models with more complex structures. To address this issue better, in this paper we first analyze the mechanism of multi-scale feature extraction of object detection models, and then by constructing the object feature-wise attention module and the perturbation extraction module, a novel adversarial sample generation algorithm for attacking detection models is proposed. Specifically, in the first module, based on the multi-scale feature map, we reduce the range of perturbation and improve the stealthiness of adversarial samples by computing the noise distribution in the object region. Then in the second module, we feed the noise distribution into the generative adversarial networks to generate adversarial perturbation with strong attack transferability. By doing so, the proposed approach possesses the ability to better confuse the judgment of detection models. Experiments carried out on the DroneVehicle dataset show that our method is computationally efficient and works well in attacking detection models measured by qualitative analysis and quantitative analysis.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1174-1189"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817718","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905809","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}
Pub Date : 2024-12-30DOI: 10.26599/TST.2024.9010030
Haiqiang Chen;Yuanbo Liu;Shuping Dang;Qingnian Li;Youming Sun;Xiangcheng Li
Compared to the successive cancellation (SC)-based decoding algorithms, the sphere decoding (SD) algorithm can achieve better performance with reduced computational complexity, especially for short polar codes. In this paper, we propose a new method to construct the binary polar codes with the modified multiplicative repetition (MR)-based matrix. Different from the original construction, we first design a $2times 2 qtext{-ary}$ kernel to guarantee the single-level polarization effect. Then, by replacing the new-designed binary companion matrix, a novel strategy is further developed to enhance the polarization in the bit level, resulting in a better distance property. Finally, the SD-based Monte-Carlo (SDMC) method is used to construct MR-based binary polar codes, while the resulting codes without the butterfly pattern are decoded by the SD algorithm. Simulation results show that the proposed method with the SD algorithm can achieve a maximum performance gain of 0.27 dB compared to the original method with slightly lower complexity.
{"title":"Sphere Decoding for Binary Polar Codes with the Modified Multiplicative Repetition Construction","authors":"Haiqiang Chen;Yuanbo Liu;Shuping Dang;Qingnian Li;Youming Sun;Xiangcheng Li","doi":"10.26599/TST.2024.9010030","DOIUrl":"https://doi.org/10.26599/TST.2024.9010030","url":null,"abstract":"Compared to the successive cancellation (SC)-based decoding algorithms, the sphere decoding (SD) algorithm can achieve better performance with reduced computational complexity, especially for short polar codes. In this paper, we propose a new method to construct the binary polar codes with the modified multiplicative repetition (MR)-based matrix. Different from the original construction, we first design a \u0000<tex>$2times 2 qtext{-ary}$</tex>\u0000 kernel to guarantee the single-level polarization effect. Then, by replacing the new-designed binary companion matrix, a novel strategy is further developed to enhance the polarization in the bit level, resulting in a better distance property. Finally, the SD-based Monte-Carlo (SDMC) method is used to construct MR-based binary polar codes, while the resulting codes without the butterfly pattern are decoded by the SD algorithm. Simulation results show that the proposed method with the SD algorithm can achieve a maximum performance gain of 0.27 dB compared to the original method with slightly lower complexity.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1229-1236"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905814","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}
Pub Date : 2024-12-30DOI: 10.26599/TST.2023.9010161
Siyu Heng;Ting Cheng;Jiaming Song;Zishu He;Luqing Liu;Yuanqing Wang
Adaptive dwell scheduling is essential to achieve full performance for a simultaneous multi-beam radar system. The dwell scheduling for such a radar system considering desired execution time criterion is studied in this paper. The primary objective of this model is to achieve maximum scheduling gain and minimum scheduling cost while adhering to not only time, aperture, and frequency constraints, but also electromagnetic compatibility (EMC) constraint. The dwell scheduling algorithm is proposed to solve the above optimization problem, where several separation points are set on the timeline, so that each separator divides the scheduling interval into two sides. For the two sides, the dual-side time pointers are introduced, which move from the separator to both ends of the scheduling interval. The dwell tasks are analyzed in sequence at each analysis point based on their two-level synthetical priority. These tasks are then executed simultaneously by sharing the whole aperture under various constraints to accomplish multiple tasks concurrently. The above process is respectively conducted at each separator, and the final scheduling result is the one with the minimal cost among all. Simulation results prove that the proposed algorithm can achieve real-time dwell scheduling and outperform the existing algorithms in terms of scheduling performance.
{"title":"Adaptive Dwell Scheduling Based on Dual-Side Time Pointers for Simultaneous Multi-Beam Radar","authors":"Siyu Heng;Ting Cheng;Jiaming Song;Zishu He;Luqing Liu;Yuanqing Wang","doi":"10.26599/TST.2023.9010161","DOIUrl":"https://doi.org/10.26599/TST.2023.9010161","url":null,"abstract":"Adaptive dwell scheduling is essential to achieve full performance for a simultaneous multi-beam radar system. The dwell scheduling for such a radar system considering desired execution time criterion is studied in this paper. The primary objective of this model is to achieve maximum scheduling gain and minimum scheduling cost while adhering to not only time, aperture, and frequency constraints, but also electromagnetic compatibility (EMC) constraint. The dwell scheduling algorithm is proposed to solve the above optimization problem, where several separation points are set on the timeline, so that each separator divides the scheduling interval into two sides. For the two sides, the dual-side time pointers are introduced, which move from the separator to both ends of the scheduling interval. The dwell tasks are analyzed in sequence at each analysis point based on their two-level synthetical priority. These tasks are then executed simultaneously by sharing the whole aperture under various constraints to accomplish multiple tasks concurrently. The above process is respectively conducted at each separator, and the final scheduling result is the one with the minimal cost among all. Simulation results prove that the proposed algorithm can achieve real-time dwell scheduling and outperform the existing algorithms in terms of scheduling performance.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1190-1200"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817723","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905812","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}
Pub Date : 2024-12-30DOI: 10.26599/TST.2024.9010123
Amjed A. Ahmed;Mohammad Kamrul Hasan;Ali Alqahtani;Shayla Islam;Bishwajeet Pandey;Leila Rzayeva;Huda Saleh Abbas;Azana Hafizah Mohd Aman;Nayef Alqahtani
Mobile devices within Fifth Generation (5G) networks, typically equipped with Android systems, serve as a bridge to connect digital gadgets such as global positioning system, mobile devices, and wireless routers, which are vital in facilitating end-user communication requirements. However, the security of Android systems has been challenged by the sensitive data involved, leading to vulnerabilities in mobile devices used in 5G networks. These vulnerabilities expose mobile devices to cyber-attacks, primarily resulting from security gaps. Zero-permission apps in Android can exploit these channels to access sensitive information, including user identities, login credentials, and geolocation data. One such attack leverages “zero-permission” sensors like accelerometers and gyroscopes, enabling attackers to gather information about the smartphone's user. This underscores the importance of fortifying mobile devices against potential future attacks. Our research focuses on a new recurrent neural network prediction model, which has proved highly effective for detecting side-channel attacks in mobile devices in 5G networks. We conducted state-of-the-art comparative studies to validate our experimental approach. The results demonstrate that even a small amount of training data can accurately recognize 37.5% of previously unseen user-typed words. Moreover, our tap detection mechanism achieves a 92% accuracy rate, a crucial factor for text inference. These findings have significant practical implications, as they reinforce mobile device security in 5G networks, enhancing user privacy, and data protection.
{"title":"Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks","authors":"Amjed A. Ahmed;Mohammad Kamrul Hasan;Ali Alqahtani;Shayla Islam;Bishwajeet Pandey;Leila Rzayeva;Huda Saleh Abbas;Azana Hafizah Mohd Aman;Nayef Alqahtani","doi":"10.26599/TST.2024.9010123","DOIUrl":"https://doi.org/10.26599/TST.2024.9010123","url":null,"abstract":"Mobile devices within Fifth Generation (5G) networks, typically equipped with Android systems, serve as a bridge to connect digital gadgets such as global positioning system, mobile devices, and wireless routers, which are vital in facilitating end-user communication requirements. However, the security of Android systems has been challenged by the sensitive data involved, leading to vulnerabilities in mobile devices used in 5G networks. These vulnerabilities expose mobile devices to cyber-attacks, primarily resulting from security gaps. Zero-permission apps in Android can exploit these channels to access sensitive information, including user identities, login credentials, and geolocation data. One such attack leverages “zero-permission” sensors like accelerometers and gyroscopes, enabling attackers to gather information about the smartphone's user. This underscores the importance of fortifying mobile devices against potential future attacks. Our research focuses on a new recurrent neural network prediction model, which has proved highly effective for detecting side-channel attacks in mobile devices in 5G networks. We conducted state-of-the-art comparative studies to validate our experimental approach. The results demonstrate that even a small amount of training data can accurately recognize 37.5% of previously unseen user-typed words. Moreover, our tap detection mechanism achieves a 92% accuracy rate, a crucial factor for text inference. These findings have significant practical implications, as they reinforce mobile device security in 5G networks, enhancing user privacy, and data protection.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1012-1026"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817772","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905844","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}
Few-shot object detection receives much attention with the ability to detect novel class objects using limited annotated data. The transfer learning-based solution becomes popular due to its simple training with good accuracy, however, it is still challenging to enrich the feature diversity during the training process. And fine-grained features are also insufficient for novel class detection. To deal with the problems, this paper proposes a novel few-shot object detection method based on dual-domain feature fusion and patch-level attention. Upon original base domain, an elementary domain with more category-agnostic features is superposed to construct a two-stream backbone, which benefits to enrich the feature diversity. To better integrate various features, a dual-domain feature fusion is designed, where the feature pairs with the same size are complementarily fused to extract more discriminative features. Moreover, a patch-wise feature refinement termed as patch-level attention is presented to mine internal relations among the patches, which enhances the adaptability to novel classes. In addition, a weighted classification loss is given to assist the fine-tuning of the classifier by combining extra features from FPN of the base training model. In this way, the few-shot detection quality to novel class objects is improved. Experiments on PASCAL VOC and MS COCO datasets verify the effectiveness of the method.
{"title":"Few-Shot Object Detection via Dual-Domain Feature Fusion and Patch-Level Attention","authors":"Guangli Ren;Jierui Liu;Mengyao Wang;Peiyu Guan;Zhiqiang Cao;Junzhi Yu","doi":"10.26599/TST.2024.9010031","DOIUrl":"https://doi.org/10.26599/TST.2024.9010031","url":null,"abstract":"Few-shot object detection receives much attention with the ability to detect novel class objects using limited annotated data. The transfer learning-based solution becomes popular due to its simple training with good accuracy, however, it is still challenging to enrich the feature diversity during the training process. And fine-grained features are also insufficient for novel class detection. To deal with the problems, this paper proposes a novel few-shot object detection method based on dual-domain feature fusion and patch-level attention. Upon original base domain, an elementary domain with more category-agnostic features is superposed to construct a two-stream backbone, which benefits to enrich the feature diversity. To better integrate various features, a dual-domain feature fusion is designed, where the feature pairs with the same size are complementarily fused to extract more discriminative features. Moreover, a patch-wise feature refinement termed as patch-level attention is presented to mine internal relations among the patches, which enhances the adaptability to novel classes. In addition, a weighted classification loss is given to assist the fine-tuning of the classifier by combining extra features from FPN of the base training model. In this way, the few-shot detection quality to novel class objects is improved. Experiments on PASCAL VOC and MS COCO datasets verify the effectiveness of the method.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1237-1250"},"PeriodicalIF":6.6,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817768","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905733","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}
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