Pub Date : 2025-03-01Epub Date: 2024-06-20DOI: 10.1016/j.jiixd.2024.06.001
Yu Liu , Caihong Mu , Shanjiao Jiang , Yi Liu
Although the deep-learning method has achieved great success for hyperspectral image (HSI) classification, the few-shot HSI classification deserves sufficient study because it is difficult and expensive to acquire labeled samples. In fact, the meta-learning methods can improve the performance for few-shot HSI classification effectively. However, most of the existing meta-learning methods for HSI classification are supervised, which still heavily rely on the labeled data for meta-training. Moreover, there are many cross-scene classification tasks in the real world, and domain adaptation of unsupervised meta-learning has been ignored for HSI classification so far. To address the above issues, this paper proposes an unsupervised meta-learning method with domain adaptation based on a multi-task reconstruction-classification network (MRCN) for few-shot HSI classification. MRCN does not need any labeled data for meta-training, where the pseudo labels are generated by multiple spectral random sampling and data augmentation. The meta-training of MRCN jointly learns a shared encoding representation for two tasks and domains. On the one hand, we design an encoder-classifier to learn the classification task on the source-domain data. On the other hand, we devise an encoder-decoder to learn the reconstruction task on the target-domain data. The experimental results on four HSI datasets demonstrate that MRCN preforms better than several state-of-the-art methods with only two to five labeled samples per class. To the best of our knowledge, the proposed method is the first unsupervised meta-learning method that considers the domain adaptation for few-shot HSI classification.
{"title":"Unsupervised meta-learning with domain adaptation based on a multi-task reconstruction-classification network for few-shot hyperspectral image classification","authors":"Yu Liu , Caihong Mu , Shanjiao Jiang , Yi Liu","doi":"10.1016/j.jiixd.2024.06.001","DOIUrl":"10.1016/j.jiixd.2024.06.001","url":null,"abstract":"<div><div>Although the deep-learning method has achieved great success for hyperspectral image (HSI) classification, the few-shot HSI classification deserves sufficient study because it is difficult and expensive to acquire labeled samples. In fact, the meta-learning methods can improve the performance for few-shot HSI classification effectively. However, most of the existing meta-learning methods for HSI classification are supervised, which still heavily rely on the labeled data for meta-training. Moreover, there are many cross-scene classification tasks in the real world, and domain adaptation of unsupervised meta-learning has been ignored for HSI classification so far. To address the above issues, this paper proposes an unsupervised meta-learning method with domain adaptation based on a multi-task reconstruction-classification network (MRCN) for few-shot HSI classification. MRCN does not need any labeled data for meta-training, where the pseudo labels are generated by multiple spectral random sampling and data augmentation. The meta-training of MRCN jointly learns a shared encoding representation for two tasks and domains. On the one hand, we design an encoder-classifier to learn the classification task on the source-domain data. On the other hand, we devise an encoder-decoder to learn the reconstruction task on the target-domain data. The experimental results on four HSI datasets demonstrate that MRCN preforms better than several state-of-the-art methods with only two to five labeled samples per class. To the best of our knowledge, the proposed method is the first unsupervised meta-learning method that considers the domain adaptation for few-shot HSI classification.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 2","pages":"Pages 103-112"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01Epub Date: 2023-03-16DOI: 10.1016/j.jiixd.2023.03.001
Linjie Zhang, Jianfeng Ma
The traffic flow prediction is fast becoming a key instrument in the transportation system, which has achieved impressive performance for traffic management. The graph neural network plays a critical role in the development of the traffic network management. However, it is worthwhile mentioning that the complexity of road networks and traffic conditions makes it unable to obtain sufficient spatiotemporal information. In view of capturing precise environment characteristics, the context could have a precise effect on the prediction results while previous methods rarely took this into account. Besides, the nonlinear characteristics of the graph neural network are hard to quantify with fine granularity and to eliminate overfitting. To stack these challenges, in this paper, we present a spatiotemporal graph wavelet neural network to improve the ability of representations. Specifically, we introduce the wavelet transforms into the deep learning model according to the strong nonlinear optimization ability. Furthermore, we dig the location and time patterns to evaluate the temporal dependence and the spatial proximity correlation. In addition, we introduce a historical context attention mechanism giving fine-grained historical context grade evaluation to ease the phenomenon of over-smoothing. The experimental results on real-world datasets show that our work gets considerable results compared with the baseline and start-of-the-art models. Moreover, our work has better learning performance by employing the connection and interaction of graphs.
{"title":"A spatiotemporal graph wavelet neural network for traffic flow prediction","authors":"Linjie Zhang, Jianfeng Ma","doi":"10.1016/j.jiixd.2023.03.001","DOIUrl":"10.1016/j.jiixd.2023.03.001","url":null,"abstract":"<div><div>The traffic flow prediction is fast becoming a key instrument in the transportation system, which has achieved impressive performance for traffic management. The graph neural network plays a critical role in the development of the traffic network management. However, it is worthwhile mentioning that the complexity of road networks and traffic conditions makes it unable to obtain sufficient spatiotemporal information. In view of capturing precise environment characteristics, the context could have a precise effect on the prediction results while previous methods rarely took this into account. Besides, the nonlinear characteristics of the graph neural network are hard to quantify with fine granularity and to eliminate overfitting. To stack these challenges, in this paper, we present a spatiotemporal graph wavelet neural network to improve the ability of representations. Specifically, we introduce the wavelet transforms into the deep learning model according to the strong nonlinear optimization ability. Furthermore, we dig the location and time patterns to evaluate the temporal dependence and the spatial proximity correlation. In addition, we introduce a historical context attention mechanism giving fine-grained historical context grade evaluation to ease the phenomenon of over-smoothing. The experimental results on real-world datasets show that our work gets considerable results compared with the baseline and start-of-the-art models. Moreover, our work has better learning performance by employing the connection and interaction of graphs.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 2","pages":"Pages 173-188"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84523151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01Epub Date: 2024-07-15DOI: 10.1016/j.jiixd.2024.07.002
Shan Gao , Lei Zuo , Xiaofei Lu , Bo Tang
Heterogeneous platforms collaborate to execute tasks through different operational models, resulting in the task allocation problem that incorporates different agent models. In this paper, we address the problem of cooperative target allocation for heterogeneous agent models, where we design the task-agent matching model and the multi-agent routing model. Since the heterogeneity and cooperativity of agent models lead to a coupled allocation problem, we propose a matrix-encoding genetic algorithm (MEGA) to plan reliable allocation schemes. Specifically, an integer matrix encoding is resorted to represent the priority between targets and agents in MEGA and a ranking rule is designed to decode the priority matrix. Based on the proposed encoding-decoding framework, we use the discrete and continuous optimization operators to update the target-agent match pairs and task execution orders. In addition, to adaptively balance the diversity and intensification of the population, a dynamical supplement strategy based on Hamming distance is proposed. This strategy adds individuals with different diversity and fitness at different stages of the optimization process. Finally, simulation experiments show that MEGA algorithm outperforms the conventional target allocation algorithms in the heterogeneous agent scenario.
{"title":"Cooperative target allocation for heterogeneous agent models using a matrix-encoding genetic algorithm","authors":"Shan Gao , Lei Zuo , Xiaofei Lu , Bo Tang","doi":"10.1016/j.jiixd.2024.07.002","DOIUrl":"10.1016/j.jiixd.2024.07.002","url":null,"abstract":"<div><div>Heterogeneous platforms collaborate to execute tasks through different operational models, resulting in the task allocation problem that incorporates different agent models. In this paper, we address the problem of cooperative target allocation for heterogeneous agent models, where we design the task-agent matching model and the multi-agent routing model. Since the heterogeneity and cooperativity of agent models lead to a coupled allocation problem, we propose a matrix-encoding genetic algorithm (MEGA) to plan reliable allocation schemes. Specifically, an integer matrix encoding is resorted to represent the priority between targets and agents in MEGA and a ranking rule is designed to decode the priority matrix. Based on the proposed encoding-decoding framework, we use the discrete and continuous optimization operators to update the target-agent match pairs and task execution orders. In addition, to adaptively balance the diversity and intensification of the population, a dynamical supplement strategy based on Hamming distance is proposed. This strategy adds individuals with different diversity and fitness at different stages of the optimization process. Finally, simulation experiments show that MEGA algorithm outperforms the conventional target allocation algorithms in the heterogeneous agent scenario.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 2","pages":"Pages 154-172"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141698757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01Epub Date: 2024-08-27DOI: 10.1016/j.jiixd.2024.08.002
Peitao Cheng , Xuanjiao Lei , Haoran Chen , Xiumei Wang
As a computer vision task, object detection algorithms can be applied to various real-world scenarios. However, efficient algorithms often come with a large number of parameters and high computational complexity. To meet the demand for high-performance object detection algorithms on mobile devices and embedded devices with limited computational resources, we propose a new lightweight object detection algorithm called DLE-YOLO. Firstly, we design a novel backbone called dual-branch lightweight excitation network (DLEN) for feature extraction, which is mainly constructed by dual-branch lightweight excitation units (DLEU). DLEU is stacked with different numbers of dual-branch lightweight excitation blocks (DLEB), which can extract comprehensive features and integrate information between different channels of features. Secondly, in order to enhance the network to capture key feature information in the regions of interest, the attention model HS-coordinate attention (HS-CA) is introduced into the network. Thirdly, the localization loss utilizes SIoU loss to further optimize the accuracy of the bounding box. Our method achieves a mAP value of 46.0% on the MS-COCO dataset, which is a 2% mAP improvement compared to the baseline YOLOv5-m, while bringing a 19.3% reduction in parameter count and a 12.9% decrease in GFLOPs. Furthermore, our method outperforms some advanced lightweight object detection algorithms, validating the effectiveness of our approach.
{"title":"DLE-YOLO: An efficient object detection algorithm with dual-branch lightweight excitation network","authors":"Peitao Cheng , Xuanjiao Lei , Haoran Chen , Xiumei Wang","doi":"10.1016/j.jiixd.2024.08.002","DOIUrl":"10.1016/j.jiixd.2024.08.002","url":null,"abstract":"<div><div>As a computer vision task, object detection algorithms can be applied to various real-world scenarios. However, efficient algorithms often come with a large number of parameters and high computational complexity. To meet the demand for high-performance object detection algorithms on mobile devices and embedded devices with limited computational resources, we propose a new lightweight object detection algorithm called DLE-YOLO. Firstly, we design a novel backbone called dual-branch lightweight excitation network (DLEN) for feature extraction, which is mainly constructed by dual-branch lightweight excitation units (DLEU). DLEU is stacked with different numbers of dual-branch lightweight excitation blocks (DLEB), which can extract comprehensive features and integrate information between different channels of features. Secondly, in order to enhance the network to capture key feature information in the regions of interest, the attention model HS-coordinate attention (HS-CA) is introduced into the network. Thirdly, the localization loss utilizes SIoU loss to further optimize the accuracy of the bounding box. Our method achieves a mAP value of 46.0% on the MS-COCO dataset, which is a 2% mAP improvement compared to the baseline YOLOv5-m, while bringing a 19.3% reduction in parameter count and a 12.9% decrease in GFLOPs. Furthermore, our method outperforms some advanced lightweight object detection algorithms, validating the effectiveness of our approach.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 2","pages":"Pages 91-102"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-06-27DOI: 10.1016/j.jiixd.2024.06.002
Jun Niu , Peng Liu , Chunhui Huang , Yangming Zhang , Moxuan Zeng , Kuo Shen , Yangzhong Wang , Suyu An , Yulong Shen , Xiaohong Jiang , Jianfeng Ma , He Wang , Gaofei Wu , Anmin Fu , Chunjie Cao , Xiaoyan Zhu , Yuqing Zhang
Membership inference (MI) attacks threaten user privacy through determining if a given data example has been used to train a target model. Existing MI defenses protect the membership privacy through preemptive exclusion of members techniques and knowledge distillation. Unfortunately, using either of these two defenses alone, the defense effect can still offers an unsatisfactory trade-off between membership privacy and utility.
Given that the defense method that directly combines these two defenses is still very limited (e.g., the test accuracy of the target model is decreased by about 40% (in our experiments)), in this work, we propose a dual defense (DD) method that includes the preemptive exclusion of high-risk member samples module and the knowledge distillation module, which thwarts the access of the resulting models to the private training data twice to mitigate MI attacks. Our defense method can be divided into two steps: the preemptive exclusion of high-risk member samples (Step 1) and the knowledge distillation to obtain the protected student model (Step 2). We propose three types of exclusions: existing MI attacks-based exclusions, sample distances of members and nonmembers-based exclusions, and mutual information value-based exclusions, to preemptively exclude the high-risk member samples. During the knowledge distillation phase, we add ground-truth labeled data to the reference dataset to decrease the protected student model's dependency on soft labels, aiming to maintain or improve its test accuracy. Extensive evaluation shows that DD significantly outperforms state-of-the-art defenses and offers a better privacy-utility trade-off. For example, DD achieves ∼100% test accuracy improvement over the distillation for membership privacy (DMP) defense for ResNet50 trained on CIFAR100. DD simultaneously achieves the reductions in the attack effectiveness (e.g., the [email protected]%FPR of enhanced MI attacks decreased by 2.10% on the ImageNet dataset, the membership advantage (MA) of risk score-based attacks decreased by 56.30%) and improvements of the target models' test accuracies (e.g., by 42.80% on CIFAR100).
{"title":"Dual defense: Combining preemptive exclusion of members and knowledge distillation to mitigate membership inference attacks","authors":"Jun Niu , Peng Liu , Chunhui Huang , Yangming Zhang , Moxuan Zeng , Kuo Shen , Yangzhong Wang , Suyu An , Yulong Shen , Xiaohong Jiang , Jianfeng Ma , He Wang , Gaofei Wu , Anmin Fu , Chunjie Cao , Xiaoyan Zhu , Yuqing Zhang","doi":"10.1016/j.jiixd.2024.06.002","DOIUrl":"10.1016/j.jiixd.2024.06.002","url":null,"abstract":"<div><div>Membership inference (MI) attacks threaten user privacy through determining if a given data example has been used to train a target model. Existing MI defenses protect the membership privacy through preemptive exclusion of members techniques and knowledge distillation. Unfortunately, using either of these two defenses alone, the defense effect can still offers an unsatisfactory trade-off between membership privacy and utility.</div><div>Given that the defense method that directly combines these two defenses is still very limited (e.g., the test accuracy of the target model is decreased by about 40% (in our experiments)), in this work, we propose a dual defense (DD) method that includes the preemptive exclusion of high-risk member samples module and the knowledge distillation module, which thwarts the access of the resulting models to the private training data twice to mitigate MI attacks. Our defense method can be divided into two steps: the preemptive exclusion of high-risk member samples (Step 1) and the knowledge distillation to obtain the protected student model (Step 2). We propose three types of exclusions: existing MI attacks-based exclusions, sample distances of members and nonmembers-based exclusions, and mutual information value-based exclusions, to preemptively exclude the high-risk member samples. During the knowledge distillation phase, we add ground-truth labeled data to the reference dataset to decrease the protected student model's dependency on soft labels, aiming to maintain or improve its test accuracy. Extensive evaluation shows that DD significantly outperforms state-of-the-art defenses and offers a better privacy-utility trade-off. For example, DD achieves ∼100% test accuracy improvement over the distillation for membership privacy (DMP) defense for ResNet50 trained on CIFAR100. DD simultaneously achieves the reductions in the attack effectiveness (e.g., the [email protected]%FPR of enhanced MI attacks decreased by 2.10% on the ImageNet dataset, the membership advantage (MA) of risk score-based attacks decreased by 56.30%) and improvements of the target models' test accuracies (e.g., by 42.80% on CIFAR100).</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 1","pages":"Pages 68-92"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-07-31DOI: 10.1016/j.jiixd.2024.07.003
Wei Liu , Meng Niu , Yunghsiang S. Han
RSS fingerprint based indoor localization consists of two phases: offline phase and online phase. A RSS fingerprint database constructed at the offline phase may be outdated for online phase, which may significantly degrade the localization performance. Furthermore, maintaining an RSS fingerprint database is a labor intensive and time-consuming task. In this paper, we proposes a robust and iterative indoor localization algorithm based on Wi-Fi RSS fingerprints, referred to as RIFi, which does not need to update the RSS fingerprint database and perform well even if the RSS fingerprint database is outdated. Specifically, we demonstrate that smaller localization area can provides better performance for outdated fingerprint database. Furthermore, we propose an iterative algorithm to determine the smaller localization area. Finally, the K-nearest neighbors (KNN) algorithm is invoked for the determined smaller localization area. Simulation results show that the proposed RIFi algorithm can significantly outperforms the traditional KNN algorithm for outdated RSS fingerprint database, and is more robust.
{"title":"RIFi: Robust and iterative indoor localization based on Wi-Fi RSS fingerprints","authors":"Wei Liu , Meng Niu , Yunghsiang S. Han","doi":"10.1016/j.jiixd.2024.07.003","DOIUrl":"10.1016/j.jiixd.2024.07.003","url":null,"abstract":"<div><div>RSS fingerprint based indoor localization consists of two phases: offline phase and online phase. A RSS fingerprint database constructed at the offline phase may be outdated for online phase, which may significantly degrade the localization performance. Furthermore, maintaining an RSS fingerprint database is a labor intensive and time-consuming task. In this paper, we proposes a robust and iterative indoor localization algorithm based on Wi-Fi RSS fingerprints, referred to as RIFi, which does not need to update the RSS fingerprint database and perform well even if the RSS fingerprint database is outdated. Specifically, we demonstrate that smaller localization area can provides better performance for outdated fingerprint database. Furthermore, we propose an iterative algorithm to determine the smaller localization area. Finally, the K-nearest neighbors (KNN) algorithm is invoked for the determined smaller localization area. Simulation results show that the proposed RIFi algorithm can significantly outperforms the traditional KNN algorithm for outdated RSS fingerprint database, and is more robust.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 1","pages":"Pages 1-18"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143149201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-08-30DOI: 10.1016/j.jiixd.2024.08.001
Juan Song , Huixuechun Wang , Jianan Li , Jian Zheng , Zhifu Zhao , Qingshan Li
Skeleton-based sign language recognition (SLR) is a challenging research area mainly due to the fast and complex hand movement. Currently, graph convolution networks (GCNs) have been employed in skeleton-based SLR and achieved remarkable performance. However, existing GCN-based SLR methods suffer from a lack of explicit attention to hand topology which plays an important role in the sign language representation. To address this issue, we propose a novel hand-aware graph convolution network (HA-GCN) to focus on hand topological relationships of skeleton graph. Specifically, a hand-aware graph convolution layer is designed to capture both global body and local hand information, in which two sub-graphs are defined and incorporated to represent hand topology information. In addition, in order to eliminate the over-fitting problem, an adaptive DropGraph is designed in construction of hand-aware graph convolution block to remove the spatial and temporal redundancy in the sign language representation. With the aim to further improve the performance, the joints information, bones, together with their motion information are simultaneously modeled in a multi-stream framework. Extensive experiments on the two open-source datasets, AUTSL and INCLUDE, demonstrate that our proposed algorithm outperforms the state-of-the-art with a significant margin. Our code is available at https://github.com/snorlaxse/HA-SLR-GCN.
{"title":"Hand-aware graph convolution network for skeleton-based sign language recognition","authors":"Juan Song , Huixuechun Wang , Jianan Li , Jian Zheng , Zhifu Zhao , Qingshan Li","doi":"10.1016/j.jiixd.2024.08.001","DOIUrl":"10.1016/j.jiixd.2024.08.001","url":null,"abstract":"<div><div>Skeleton-based sign language recognition (SLR) is a challenging research area mainly due to the fast and complex hand movement. Currently, graph convolution networks (GCNs) have been employed in skeleton-based SLR and achieved remarkable performance. However, existing GCN-based SLR methods suffer from a lack of explicit attention to hand topology which plays an important role in the sign language representation. To address this issue, we propose a novel hand-aware graph convolution network (HA-GCN) to focus on hand topological relationships of skeleton graph. Specifically, a hand-aware graph convolution layer is designed to capture both global body and local hand information, in which two sub-graphs are defined and incorporated to represent hand topology information. In addition, in order to eliminate the over-fitting problem, an adaptive DropGraph is designed in construction of hand-aware graph convolution block to remove the spatial and temporal redundancy in the sign language representation. With the aim to further improve the performance, the joints information, bones, together with their motion information are simultaneously modeled in a multi-stream framework. Extensive experiments on the two open-source datasets, AUTSL and INCLUDE, demonstrate that our proposed algorithm outperforms the state-of-the-art with a significant margin. Our code is available at <span><span>https://github.com/snorlaxse/HA-SLR-GCN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 1","pages":"Pages 36-50"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-07-02DOI: 10.1016/j.jiixd.2024.06.003
Ziyuan Zhang , Ziyu Wang , Kaitai Guo , Yang Zheng , Minghao Dong , Jimin Liang
Previous efforts to boost the performance of brain-computer interfaces (BCIs) have predominantly focused on optimizing algorithms for decoding brain signals. However, the untapped potential of leveraging brain plasticity for optimization remains underexplored. In this study, we enhanced the temporal resolution of the human brain in discriminating visual stimuli by eliminating the attentional blink (AB) through color-salient cognitive training, and we confirmed that the mechanism was an attention-based improvement. Using the rapid serial visual presentation (RSVP)-based BCI, we evaluated the behavioral and electroencephalogram (EEG) decoding performance of subjects before and after cognitive training in high target percentage (with AB) and low target percentage (without AB) surveillance tasks, respectively. The results consistently demonstrated significant improvements in the trained subjects. Further analysis indicated that this improvement was attributed to the cognitively trained brain producing more discriminative EEG. Our work highlights the feasibility of cognitive training as a means of brain enhancement to boost BCI performance.
{"title":"Boosting brain-computer interface performance through cognitive training: A brain-centric approach","authors":"Ziyuan Zhang , Ziyu Wang , Kaitai Guo , Yang Zheng , Minghao Dong , Jimin Liang","doi":"10.1016/j.jiixd.2024.06.003","DOIUrl":"10.1016/j.jiixd.2024.06.003","url":null,"abstract":"<div><div>Previous efforts to boost the performance of brain-computer interfaces (BCIs) have predominantly focused on optimizing algorithms for decoding brain signals. However, the untapped potential of leveraging brain plasticity for optimization remains underexplored. In this study, we enhanced the temporal resolution of the human brain in discriminating visual stimuli by eliminating the attentional blink (AB) through color-salient cognitive training, and we confirmed that the mechanism was an attention-based improvement. Using the rapid serial visual presentation (RSVP)-based BCI, we evaluated the behavioral and electroencephalogram (EEG) decoding performance of subjects before and after cognitive training in high target percentage (with AB) and low target percentage (without AB) surveillance tasks, respectively. The results consistently demonstrated significant improvements in the trained subjects. Further analysis indicated that this improvement was attributed to the cognitively trained brain producing more discriminative EEG. Our work highlights the feasibility of cognitive training as a means of brain enhancement to boost BCI performance.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 1","pages":"Pages 19-35"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141690095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01Epub Date: 2024-09-18DOI: 10.1016/j.jiixd.2024.09.002
Zhicheng Cao , Weiqiang Zhao , Heng Zhao, Liaojun Pang
Palmprint recognition has attracted considerable attention due to its advantages over other biometric modalities such as fingerprints, in that it is larger in area, richer in information and able to work at a distance. However, the issue of palmprint privacy and security (especially palmprint template protection) remains under-studied. Among the very few research works, most of them only use orientational features of the palmprint with transformation processing, yielding unsatisfactory recognition and protection performance. Thus, this research work proposes a palmprint feature extraction method for palmprint template protection that is fixed-length and ordered in nature, by fusing point features and orientational features. Firstly, dual orientations are extracted and encoded with more accuracy based on the modified finite Radon transform (MFRAT). Then, SURF feature points are extracted and converted to be fixed-length and ordered features. Finally, composite fixed-length ordered features that fuse up the dual orientations and SURF points are transformed using the irreversible transformation of index-of-max (IoM) to generate the revocable palmprint templates. Experiments show that the matching accuracy of the proposed method of fixed-length and ordered point features are superior to all other feature extraction methods on the PolyU and CASIA datasets. It is also demonstrated that the EERs before and after IoM transformation are better than all other representative template protection methods. A thorough security and privacy analysis including brute-force attack, false accept attack, birthday attack, attack via record multiplicity, irreversibility, unlinkability and revocability is also given, which proves that our proposed method has both high performance and security.
{"title":"Composite fixed-length ordered features with index-of-max transformation for high-performing and secure palmprint template protection","authors":"Zhicheng Cao , Weiqiang Zhao , Heng Zhao, Liaojun Pang","doi":"10.1016/j.jiixd.2024.09.002","DOIUrl":"10.1016/j.jiixd.2024.09.002","url":null,"abstract":"<div><div>Palmprint recognition has attracted considerable attention due to its advantages over other biometric modalities such as fingerprints, in that it is larger in area, richer in information and able to work at a distance. However, the issue of palmprint privacy and security (especially palmprint template protection) remains under-studied. Among the very few research works, most of them only use orientational features of the palmprint with transformation processing, yielding unsatisfactory recognition and protection performance. Thus, this research work proposes a palmprint feature extraction method for palmprint template protection that is fixed-length and ordered in nature, by fusing point features and orientational features. Firstly, dual orientations are extracted and encoded with more accuracy based on the modified finite Radon transform (MFRAT). Then, SURF feature points are extracted and converted to be fixed-length and ordered features. Finally, composite fixed-length ordered features that fuse up the dual orientations and SURF points are transformed using the irreversible transformation of index-of-max (IoM) to generate the revocable palmprint templates. Experiments show that the matching accuracy of the proposed method of fixed-length and ordered point features are superior to all other feature extraction methods on the PolyU and CASIA datasets. It is also demonstrated that the EERs before and after IoM transformation are better than all other representative template protection methods. A thorough security and privacy analysis including brute-force attack, false accept attack, birthday attack, attack via record multiplicity, irreversibility, unlinkability and revocability is also given, which proves that our proposed method has both high performance and security.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 1","pages":"Pages 51-67"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-01Epub Date: 2024-07-18DOI: 10.1016/j.jiixd.2024.07.001
Chengjun Jiang , Chensi Zhang , Chongwen Huang , Jiaying He , Zhe Zhang , Jianhua Ge
The amplify-and-forward (AF) relay is widely employed owing to its simplicity, while reconfigurable intelligent surface (RIS) technology is envisioned as the next generation of relay technology due to its high energy efficiency. This paper compares these two technologies at the physical layer security (PLS) level for non-orthogonal multiple access (NOMA) with an internal near-end eavesdropper. Specifically, for a fair comparison, both the number of RIS elements and AF relay antennas are set to N, and similar secure transport strategies are utilized for both models to maximize the secrecy rate. Analytical results demonstrate that the PLS performance of RIS-assisted NOMA is better than that of AF relay-assisted NOMA if N reaches a certain threshold. Simulation results verify the correctness of the theoretical analysis.
放大-前向（AF）中继因其简单而被广泛采用，而可重构智能表面（RIS）技术因其高能效而被视为下一代中继技术。本文在物理层安全（PLS）层面对这两种技术进行了比较，它们适用于带有内部近端窃听器的非正交多址接入（NOMA）。具体来说，为了进行公平比较，RIS 元素和 AF 中继天线的数量都设为 N，并且两种模型都采用了类似的安全传输策略，以最大限度地提高保密率。分析结果表明，当 N 达到一定阈值时，RIS 辅助 NOMA 的 PLS 性能优于 AF 中继辅助 NOMA。仿真结果验证了理论分析的正确性。
{"title":"Secure performance comparison for NOMA: Reconfigurable intelligent surface or amplify-and-forward relay?","authors":"Chengjun Jiang , Chensi Zhang , Chongwen Huang , Jiaying He , Zhe Zhang , Jianhua Ge","doi":"10.1016/j.jiixd.2024.07.001","DOIUrl":"10.1016/j.jiixd.2024.07.001","url":null,"abstract":"<div><div>The amplify-and-forward (AF) relay is widely employed owing to its simplicity, while reconfigurable intelligent surface (RIS) technology is envisioned as the next generation of relay technology due to its high energy efficiency. This paper compares these two technologies at the physical layer security (PLS) level for non-orthogonal multiple access (NOMA) with an internal near-end eavesdropper. Specifically, for a fair comparison, both the number of RIS elements and AF relay antennas are set to <em>N</em>, and similar secure transport strategies are utilized for both models to maximize the secrecy rate. Analytical results demonstrate that the PLS performance of RIS-assisted NOMA is better than that of AF relay-assisted NOMA if <em>N</em> reaches a certain threshold. Simulation results verify the correctness of the theoretical analysis.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"2 6","pages":"Pages 514-524"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141851257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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